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#!/usr/bin/env python
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet)
"""
import argparse
import logging
import numpy as np
import torch
from transformers import (
CTRLLMHeadModel,
CTRLTokenizer,
GPT2LMHeadModel,
GPT2Tokenizer,
OpenAIGPTLMHeadModel,
OpenAIGPTTokenizer,
TransfoXLLMHeadModel,
TransfoXLTokenizer,
XLMTokenizer,
XLMWithLMHeadModel,
XLNetLMHeadModel,
XLNetTokenizer,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger = logging.getLogger(__name__)
MAX_LENGTH = int(10000) # Hardcoded max length to avoid infinite loop
MODEL_CLASSES = {
"gpt2": (GPT2LMHeadModel, GPT2Tokenizer),
"ctrl": (CTRLLMHeadModel, CTRLTokenizer),
"openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
"xlnet": (XLNetLMHeadModel, XLNetTokenizer),
"transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
"xlm": (XLMWithLMHeadModel, XLMTokenizer),
}
# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
PREFIX = """In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
def set_seed(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
#
# Functions to prepare models' input
#
def prepare_ctrl_input(args, _, tokenizer, prompt_text):
if args.temperature > 0.7:
logger.info("CTRL typically works better with lower temperatures (and lower top_k).")
encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False)
if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()):
logger.info("WARNING! You are not starting your generation from a control code so you won't get good results")
return prompt_text
def prepare_xlm_input(args, model, tokenizer, prompt_text):
# kwargs = {"language": None, "mask_token_id": None}
# Set the language
use_lang_emb = hasattr(model.config, "use_lang_emb") and model.config.use_lang_emb
if hasattr(model.config, "lang2id") and use_lang_emb:
available_languages = model.config.lang2id.keys()
if args.xlm_language in available_languages:
language = args.xlm_language
else:
language = None
while language not in available_languages:
language = input("Using XLM. Select language in " + str(list(available_languages)) + " >>> ")
model.config.lang_id = model.config.lang2id[language]
# kwargs["language"] = tokenizer.lang2id[language]
# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
# XLM masked-language modeling (MLM) models need masked token
# is_xlm_mlm = "mlm" in args.model_name_or_path
# if is_xlm_mlm:
# kwargs["mask_token_id"] = tokenizer.mask_token_id
return prompt_text
def prepare_xlnet_input(args, _, tokenizer, prompt_text):
prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
prompt_text = prefix + prompt_text
return prompt_text
def prepare_transfoxl_input(args, _, tokenizer, prompt_text):
prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
prompt_text = prefix + prompt_text
return prompt_text
PREPROCESSING_FUNCTIONS = {
"ctrl": prepare_ctrl_input,
"xlm": prepare_xlm_input,
"xlnet": prepare_xlnet_input,
"transfo-xl": prepare_transfoxl_input,
}
def adjust_length_to_model(length, max_sequence_length):
if length < 0 and max_sequence_length > 0:
length = max_sequence_length
elif 0 < max_sequence_length < length:
length = max_sequence_length # No generation bigger than model size
elif length < 0:
length = MAX_LENGTH # avoid infinite loop
return length
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument("--prompt", type=str, default="")
parser.add_argument("--length", type=int, default=20)
parser.add_argument("--stop_token", type=str, default=None, help="Token at which text generation is stopped")
parser.add_argument(
"--temperature",
type=float,
default=1.0,
help="temperature of 1.0 has no effect, lower tend toward greedy sampling",
)
parser.add_argument(
"--repetition_penalty", type=float, default=1.0, help="primarily useful for CTRL model; in that case, use 1.2"
)
parser.add_argument("--k", type=int, default=0)
parser.add_argument("--p", type=float, default=0.9)
parser.add_argument("--prefix", type=str, default="", help="Text added prior to input.")
parser.add_argument("--padding_text", type=str, default="", help="Deprecated, the use of `--prefix` is preferred.")
parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.")
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
parser.add_argument("--num_return_sequences", type=int, default=1, help="The number of samples to generate.")
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
args = parser.parse_args()
args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
logger.warning(
"device: %s, n_gpu: %s, 16-bits training: %s",
args.device,
args.n_gpu,
args.fp16,
)
set_seed(args)
# Initialize the model and tokenizer
try:
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
except KeyError:
raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)")
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
model = model_class.from_pretrained(args.model_name_or_path)
model.to(args.device)
if args.fp16:
model.half()
args.length = adjust_length_to_model(args.length, max_sequence_length=model.config.max_position_embeddings)
logger.info(args)
prompt_text = args.prompt if args.prompt else input("Model prompt >>> ")
# Different models need different input formatting and/or extra arguments
requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys()
if requires_preprocessing:
prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text)
if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
tokenizer_kwargs = {"add_space_before_punct_symbol": True}
else:
tokenizer_kwargs = {}
encoded_prompt = tokenizer.encode(
preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", **tokenizer_kwargs
)
else:
prefix = args.prefix if args.prefix else args.padding_text
encoded_prompt = tokenizer.encode(prefix + prompt_text, add_special_tokens=False, return_tensors="pt")
encoded_prompt = encoded_prompt.to(args.device)
if encoded_prompt.size()[-1] == 0:
input_ids = None
else:
input_ids = encoded_prompt
output_sequences = model.generate(
input_ids=input_ids,
max_length=args.length + len(encoded_prompt[0]),
temperature=args.temperature,
top_k=args.k,
top_p=args.p,
repetition_penalty=args.repetition_penalty,
do_sample=True,
num_return_sequences=args.num_return_sequences,
)
# Remove the batch dimension when returning multiple sequences
if len(output_sequences.shape) > 2:
output_sequences.squeeze_()
generated_sequences = []
for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
print("=== GENERATED SEQUENCE {} ===".format(generated_sequence_idx + 1))
generated_sequence = generated_sequence.tolist()
# Decode text
text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
# Remove all text after the stop token
text = text[: text.find(args.stop_token) if args.stop_token else None]
# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
total_sequence = (
prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
)
generated_sequences.append(total_sequence)
print(total_sequence)
return generated_sequences
if __name__ == "__main__":
main()
|
AdaMix/examples/text-generation/run_generation.py/0
|
{
"file_path": "AdaMix/examples/text-generation/run_generation.py",
"repo_id": "AdaMix",
"token_count": 4137
}
| 43 |
## 🔥 Model cards now live inside each huggingface.co model repo 🔥
For consistency, ease of use and scalability, `README.md` model cards now live directly inside each model repo on the HuggingFace model hub.
### How to update a model card
You can directly update a model card inside any model repo you have **write access** to, i.e.:
- a model under your username namespace
- a model under any organization you are a part of.
You can either:
- update it, commit and push using your usual git workflow (command line, GUI, etc.)
- or edit it directly from the website's UI.
**What if you want to create or update a model card for a model you don't have write access to?**
In that case, given that we don't have a Pull request system yet on huggingface.co (🤯),
you can open an issue here, post the card's content, and tag the model author(s) and/or the Hugging Face team.
We might implement a more seamless process at some point, so your early feedback is precious!
Please let us know of any suggestion.
### What happened to the model cards here?
We migrated every model card from the repo to its corresponding huggingface.co model repo. Individual commits were preserved, and they link back to the original commit on GitHub.
|
AdaMix/model_cards/README.md/0
|
{
"file_path": "AdaMix/model_cards/README.md",
"repo_id": "AdaMix",
"token_count": 309
}
| 44 |
#!/usr/bin/env bash
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script evals the following fsmt models
# it covers:
# - facebook/wmt19-ru-en
# - facebook/wmt19-en-ru
# - facebook/wmt19-de-en
# - facebook/wmt19-en-de
# this script needs to be run from the top level of the transformers repo
if [ ! -d "src/transformers" ]; then
echo "Error: This script needs to be run from the top of the transformers repo"
exit 1
fi
# In these scripts you may have to lower BS if you get CUDA OOM (or increase it if you have a large GPU)
### a short estimate version for quick testing ###
export PAIR=en-ru
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=8
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src | head -10 > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref | head -10 > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
### Normal eval ###
# ru-en
export PAIR=ru-en
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=50
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
# (target BLEU: 41.3 http://matrix.statmt.org/matrix/output/1907?run_id=6937)
# en-ru
export PAIR=en-ru
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=50
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
# (target BLEU: 36.4 http://matrix.statmt.org/matrix/output/1914?score_id=37605)
# en-de
export PAIR=en-de
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
# (target BLEU: 43.1 http://matrix.statmt.org/matrix/output/1909?run_id=6862)
# de-en
export PAIR=de-en
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=50
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
# (target BLEU: 42.3 http://matrix.statmt.org/matrix/output/1902?run_id=6750)
### Searching hparams eval ###
# en-ru
export PAIR=ru-en
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=32
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
CUDA_VISIBLE_DEVICES="0" PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1"
# en-ru
export PAIR=en-ru
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=16
mkdir -p $DATA_DIR
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
CUDA_VISIBLE_DEVICES="0" PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5:8:11:15 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1 early_stopping=true:false"
# en-de
export PAIR=en-de
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=16
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
CUDA_VISIBLE_DEVICES="1" PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5:8:11:15 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1 early_stopping=true:false"
# de-en
export PAIR=de-en
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=16
mkdir -p $DATA_DIR
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
CUDA_VISIBLE_DEVICES="1" PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5:8:11:15 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1 early_stopping=true:false"
|
AdaMix/scripts/fsmt/eval-facebook-wmt19.sh/0
|
{
"file_path": "AdaMix/scripts/fsmt/eval-facebook-wmt19.sh",
"repo_id": "AdaMix",
"token_count": 2623
}
| 45 |
#!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def main():
parser = ArgumentParser("Transformers CLI tool", usage="transformers-cli <command> [<args>]")
commands_parser = parser.add_subparsers(help="transformers-cli command helpers")
# Register commands
ConvertCommand.register_subcommand(commands_parser)
DownloadCommand.register_subcommand(commands_parser)
EnvironmentCommand.register_subcommand(commands_parser)
RunCommand.register_subcommand(commands_parser)
ServeCommand.register_subcommand(commands_parser)
UserCommands.register_subcommand(commands_parser)
AddNewModelCommand.register_subcommand(commands_parser)
LfsCommands.register_subcommand(commands_parser)
# Let's go
args = parser.parse_args()
if not hasattr(args, "func"):
parser.print_help()
exit(1)
# Run
service = args.func(args)
service.run()
if __name__ == "__main__":
main()
|
AdaMix/src/transformers/commands/transformers_cli.py/0
|
{
"file_path": "AdaMix/src/transformers/commands/transformers_cli.py",
"repo_id": "AdaMix",
"token_count": 553
}
| 46 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from ...file_utils import is_sklearn_available, requires_sklearn
if is_sklearn_available():
from sklearn.metrics import f1_score, matthews_corrcoef
from scipy.stats import pearsonr, spearmanr
DEPRECATION_WARNING = (
"This metric will be removed from the library soon, metrics should be handled with the 🤗 Datasets "
"library. You can have a look at this example script for pointers: "
"https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_glue.py"
)
def simple_accuracy(preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_sklearn(simple_accuracy)
return (preds == labels).mean()
def acc_and_f1(preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_sklearn(acc_and_f1)
acc = simple_accuracy(preds, labels)
f1 = f1_score(y_true=labels, y_pred=preds)
return {
"acc": acc,
"f1": f1,
"acc_and_f1": (acc + f1) / 2,
}
def pearson_and_spearman(preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_sklearn(pearson_and_spearman)
pearson_corr = pearsonr(preds, labels)[0]
spearman_corr = spearmanr(preds, labels)[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def glue_compute_metrics(task_name, preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_sklearn(glue_compute_metrics)
assert len(preds) == len(labels), f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}"
if task_name == "cola":
return {"mcc": matthews_corrcoef(labels, preds)}
elif task_name == "sst-2":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mrpc":
return acc_and_f1(preds, labels)
elif task_name == "sts-b":
return pearson_and_spearman(preds, labels)
elif task_name == "qqp":
return acc_and_f1(preds, labels)
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(preds, labels)}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(preds, labels)}
elif task_name == "qnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "rte":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "wnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "hans":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)
def xnli_compute_metrics(task_name, preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_sklearn(xnli_compute_metrics)
assert len(preds) == len(labels), f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}"
if task_name == "xnli":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)
|
AdaMix/src/transformers/data/metrics/__init__.py/0
|
{
"file_path": "AdaMix/src/transformers/data/metrics/__init__.py",
"repo_id": "AdaMix",
"token_count": 1426
}
| 47 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Sequence feature extraction class for common feature extrcactors to preprocess sequences.
"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .file_utils import (
PaddingStrategy,
TensorType,
_is_tensorflow,
_is_torch,
is_tf_available,
is_torch_available,
to_py_obj,
)
from .utils import logging
logger = logging.get_logger(__name__)
class SequenceFeatureExtractor(FeatureExtractionMixin):
"""
This is a general feature extraction class for speech recognition.
Args:
feature_size (:obj:`int`):
The feature dimension of the extracted features.
sampling_rate (:obj:`int`):
The sampling rate at which the audio files should be digitalized expressed in Hertz per second (Hz).
padding_value (:obj:`float`):
The value that is used to fill the padding values / vectors.
"""
def __init__(self, feature_size: int, sampling_rate: int, padding_value: float, **kwargs):
self.feature_size = feature_size
self.sampling_rate = sampling_rate
self.padding_value = padding_value
self.padding_side = kwargs.pop("padding_side", "right")
self.return_attention_mask = kwargs.pop("return_attention_mask", True)
# Additional attributes without default values
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error(f"Can't set {key} with value {value} for {self}")
raise err
def pad(
self,
processed_features: Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
],
padding: Union[bool, str, PaddingStrategy] = True,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
) -> BatchFeature:
"""
Pad input values / input vectors or a batch of input values / input vectors up to predefined length or to the
max sequence length in the batch.
Padding side (left/right) padding values are defined at the feature extractor level (with
``self.padding_side``, ``self.padding_value``)
.. note::
If the ``processed_features`` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors,
the result will use the same type unless you provide a different tensor type with ``return_tensors``. In
the case of PyTorch tensors, you will lose the specific device of your tensors however.
Args:
processed_features (:class:`~transformers.BatchFeature`, list of :class:`~transformers.BatchFeature`, :obj:`Dict[str, List[float]]`, :obj:`Dict[str, List[List[float]]` or :obj:`List[Dict[str, List[float]]]`):
Processed inputs. Can represent one input (:class:`~transformers.BatchFeature` or :obj:`Dict[str,
List[float]]`) or a batch of input values / vectors (list of :class:`~transformers.BatchFeature`,
`Dict[str, List[List[float]]]` or `List[Dict[str, List[float]]]`) so you can use this method during
preprocessing as well as in a PyTorch Dataloader collate function.
Instead of :obj:`List[float]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow
tensors), see the note above for the return type.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
single sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
>= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
return_attention_mask (:obj:`bool`, `optional`):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific feature_extractor's default.
`What are attention masks? <../glossary.html#attention-mask>`__
return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
If set, will return tensors instead of list of python integers. Acceptable values are:
* :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
* :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
* :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
"""
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(processed_features, (list, tuple)) and isinstance(processed_features[0], (dict, BatchFeature)):
processed_features = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of :class:`~transformers.BatchFeature` or list of :class:`~transformers.BatchFeature` to this method"
f"that includes {self.model_input_names[0]}, but you provided {list(processed_features.keys())}"
)
required_input = processed_features[self.model_input_names[0]]
return_attention_mask = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if not required_input:
if return_attention_mask:
processed_features["attention_mask"] = []
return processed_features
# If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
first_element = required_input[0]
if isinstance(first_element, (list, tuple)):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
index = 0
while len(required_input[index]) == 0:
index += 1
if index < len(required_input):
first_element = required_input[index][0]
# At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
if not isinstance(first_element, (float, int, list, tuple)):
if is_tf_available() and _is_tensorflow(first_element):
return_tensors = "tf" if return_tensors is None else return_tensors
elif is_torch_available() and _is_torch(first_element):
return_tensors = "pt" if return_tensors is None else return_tensors
elif isinstance(first_element, np.ndarray):
return_tensors = "np" if return_tensors is None else return_tensors
else:
raise ValueError(
f"type of {first_element} unknown: {type(first_element)}. "
f"Should be one of a python, numpy, pytorch or tensorflow object."
)
for key, value in processed_features.items():
processed_features[key] = to_py_obj(value)
# Convert padding_strategy in PaddingStrategy
padding_strategy, max_length, _ = self._get_padding_strategies(padding=padding, max_length=max_length)
required_input = processed_features[self.model_input_names[0]]
if required_input and not isinstance(required_input[0], (list, tuple)):
processed_features = self._pad(
processed_features,
max_length=max_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
return BatchFeature(processed_features, tensor_type=return_tensors)
batch_size = len(required_input)
assert all(
len(v) == batch_size for v in processed_features.values()
), "Some items in the output dictionary have a different batch size than others."
if padding_strategy == PaddingStrategy.LONGEST:
max_length = max(len(inputs) for inputs in required_input)
padding_strategy = PaddingStrategy.MAX_LENGTH
batch_outputs = {}
for i in range(batch_size):
inputs = dict((k, v[i]) for k, v in processed_features.items())
outputs = self._pad(
inputs,
max_length=max_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
return BatchFeature(batch_outputs, tensor_type=return_tensors)
def _pad(
self,
processed_features: Union[Dict[str, List[float]], BatchFeature],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad inputs (on left/right and up to predefined length or max length in the batch)
Args:
processed_features: Dictionary of input values (`List[float]`) / input vectors (`List[List[float]]`) or batch of inputs values (`List[List[int]]`) / input vectors (`List[List[List[int]]]`)
max_length: maximum length of the returned list and optionally padding length (see below)
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The feature_extractor padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
>= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
required_input = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
if needs_to_be_padded:
difference = max_length - len(required_input)
padding_vector = self.feature_size * [self.padding_value] if self.feature_size > 1 else self.padding_value
if self.padding_side == "right":
if return_attention_mask:
processed_features["attention_mask"] = [1] * len(required_input) + [0] * difference
processed_features[self.model_input_names[0]] = required_input + [
padding_vector for _ in range(difference)
]
elif self.padding_side == "left":
if return_attention_mask:
processed_features["attention_mask"] = [0] * difference + [1] * len(required_input)
processed_features[self.model_input_names[0]] = [
padding_vector for _ in range(difference)
] + required_input
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
elif return_attention_mask and "attention_mask" not in processed_features:
processed_features["attention_mask"] = [1] * len(required_input)
return processed_features
def _get_padding_strategies(self, padding=False, max_length=None, pad_to_multiple_of=None, **kwargs):
"""
Find the correct padding strategy
"""
# Get padding strategy
if padding is not False:
if padding is True:
padding_strategy = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(padding, PaddingStrategy):
padding_strategy = PaddingStrategy(padding)
elif isinstance(padding, PaddingStrategy):
padding_strategy = padding
else:
padding_strategy = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that" f" max_length is defined"
)
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. "
"Please select a value to use as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."
)
return padding_strategy, max_length, kwargs
|
AdaMix/src/transformers/feature_extraction_sequence_utils.py/0
|
{
"file_path": "AdaMix/src/transformers/feature_extraction_sequence_utils.py",
"repo_id": "AdaMix",
"token_count": 6638
}
| 48 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF general model utils."""
import functools
import inspect
import os
import re
import warnings
from typing import Dict, List, Optional, Union
import h5py
import numpy as np
import tensorflow as tf
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.saving import hdf5_format
from .configuration_utils import PretrainedConfig
from .file_utils import (
DUMMY_INPUTS,
TF2_WEIGHTS_NAME,
WEIGHTS_NAME,
ModelOutput,
cached_path,
hf_bucket_url,
is_offline_mode,
is_remote_url,
)
from .generation_tf_utils import TFGenerationMixin
from .tokenization_utils_base import BatchEncoding
from .utils import logging
logger = logging.get_logger(__name__)
tf_logger = tf.get_logger()
TFModelInputType = Union[
List[tf.Tensor], List[np.ndarray], Dict[str, tf.Tensor], Dict[str, np.ndarray], np.ndarray, tf.Tensor
]
class TFModelUtilsMixin:
"""
A few utilities for :obj:`tf.keras.Model`, to be used as a mixin.
"""
def num_parameters(self, only_trainable: bool = False) -> int:
"""
Get the number of (optionally, trainable) parameters in the model.
Args:
only_trainable (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to return only the number of trainable parameters
Returns:
:obj:`int`: The number of parameters.
"""
if only_trainable:
return int(sum(np.prod(w.shape.as_list()) for w in self.trainable_variables))
else:
return self.count_params()
def keras_serializable(cls):
"""
Decorate a Keras Layer class to support Keras serialization.
This is done by:
1. Adding a :obj:`transformers_config` dict to the Keras config dictionary in :obj:`get_config` (called by Keras at
serialization time.
2. Wrapping :obj:`__init__` to accept that :obj:`transformers_config` dict (passed by Keras at deserialization
time) and convert it to a config object for the actual layer initializer.
3. Registering the class as a custom object in Keras (if the Tensorflow version supports this), so that it does not
need to be supplied in :obj:`custom_objects` in the call to :obj:`tf.keras.models.load_model`.
Args:
cls (a :obj:`tf.keras.layers.Layers subclass`):
Typically a :obj:`TF.MainLayer` class in this project, in general must accept a :obj:`config` argument to
its initializer.
Returns:
The same class object, with modifications for Keras deserialization.
"""
initializer = cls.__init__
config_class = getattr(cls, "config_class", None)
if config_class is None:
raise AttributeError("Must set `config_class` to use @keras_serializable")
@functools.wraps(initializer)
def wrapped_init(self, *args, **kwargs):
config = args[0] if args and isinstance(args[0], PretrainedConfig) else kwargs.pop("config", None)
if isinstance(config, dict):
config = config_class.from_dict(config)
initializer(self, config, *args, **kwargs)
elif isinstance(config, PretrainedConfig):
if len(args) > 0:
initializer(self, *args, **kwargs)
else:
initializer(self, config, *args, **kwargs)
else:
raise ValueError("Must pass either `config` (PretrainedConfig) or `config` (dict)")
self._config = config
self._kwargs = kwargs
cls.__init__ = wrapped_init
if not hasattr(cls, "get_config"):
raise TypeError("Only use @keras_serializable on tf.keras.layers.Layer subclasses")
if hasattr(cls.get_config, "_is_default"):
def get_config(self):
cfg = super(cls, self).get_config()
cfg["config"] = self._config.to_dict()
cfg.update(self._kwargs)
return cfg
cls.get_config = get_config
cls._keras_serializable = True
if hasattr(tf.keras.utils, "register_keras_serializable"):
cls = tf.keras.utils.register_keras_serializable()(cls)
return cls
class TFCausalLanguageModelingLoss:
"""
Loss function suitable for causal language modeling (CLM), that is, the task of guessing the next token.
.. note::
Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
"""
def compute_loss(self, labels, logits):
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE
)
# make sure only labels that are not equal to -100 affect the loss
active_loss = tf.not_equal(tf.reshape(labels, (-1,)), -100)
reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss)
return loss_fn(labels, reduced_logits)
class TFQuestionAnsweringLoss:
"""
Loss function suitable for question answering.
"""
def compute_loss(self, labels, logits):
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE
)
start_loss = loss_fn(labels["start_position"], logits[0])
end_loss = loss_fn(labels["end_position"], logits[1])
return (start_loss + end_loss) / 2.0
class TFTokenClassificationLoss:
"""
Loss function suitable for token classification.
.. note::
Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
"""
def compute_loss(self, labels, logits):
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE
)
# make sure only labels that are not equal to -100
# are taken into account as loss
if tf.math.reduce_any(labels == -1):
warnings.warn("Using `-1` to mask the loss for the token is deprecated. Please use `-100` instead.")
active_loss = tf.reshape(labels, (-1,)) != -1
else:
active_loss = tf.reshape(labels, (-1,)) != -100
reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss)
return loss_fn(labels, reduced_logits)
class TFSequenceClassificationLoss:
"""
Loss function suitable for sequence classification.
"""
def compute_loss(self, labels, logits):
if len(shape_list(logits)) == 1 or shape_list(logits)[1] == 1:
loss_fn = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE)
else:
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE
)
return loss_fn(labels, logits)
class TFMultipleChoiceLoss(TFSequenceClassificationLoss):
"""Loss function suitable for multiple choice tasks."""
class TFMaskedLanguageModelingLoss(TFCausalLanguageModelingLoss):
"""
Loss function suitable for masked language modeling (MLM), that is, the task of guessing the masked tokens.
.. note::
Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
"""
class TFNextSentencePredictionLoss:
"""
Loss function suitable for next sentence prediction (NSP), that is, the task of guessing the next sentence.
.. note::
Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
"""
def compute_loss(self, labels, logits):
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE
)
# make sure only labels that are not equal to -100
# are taken into account as loss
next_sentence_active_loss = tf.not_equal(tf.reshape(labels, (-1,)), -100)
next_sentence_reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, 2)), next_sentence_active_loss)
next_sentence_label = tf.boolean_mask(tf.reshape(labels, (-1,)), next_sentence_active_loss)
return loss_fn(next_sentence_label, next_sentence_reduced_logits)
def booleans_processing(config, **kwargs):
"""
Process the input booleans of each model in order to be sure they are compliant with the execution mode (eager or
graph)
Args:
config (:class:`~transformers.PretrainedConfig`):
The config of the running model.
**kwargs:
The boolean parameters
Returns:
A dictionary with the proper values for each boolean
"""
final_booleans = {}
if tf.executing_eagerly():
final_booleans["output_attentions"] = (
kwargs["output_attentions"] if kwargs["output_attentions"] is not None else config.output_attentions
)
final_booleans["output_hidden_states"] = (
kwargs["output_hidden_states"]
if kwargs["output_hidden_states"] is not None
else config.output_hidden_states
)
final_booleans["return_dict"] = (
kwargs["return_dict"] if kwargs["return_dict"] is not None else config.return_dict
)
if "use_cache" in kwargs:
final_booleans["use_cache"] = kwargs["use_cache"] if kwargs["use_cache"] is not None else config.use_cache
else:
if (
kwargs["output_attentions"] is not None
or kwargs["output_hidden_states"] is not None
or ("use_cache" in kwargs and kwargs["use_cache"] is not None)
):
tf_logger.warn(
"The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model."
"They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`)."
)
final_booleans["output_attentions"] = config.output_attentions
final_booleans["output_hidden_states"] = config.output_hidden_states
if kwargs["return_dict"] is not None:
tf_logger.warn("The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.")
final_booleans["return_dict"] = True
if "use_cache" in kwargs:
final_booleans["use_cache"] = config.use_cache
return final_booleans
def input_processing(func, config, input_ids, **kwargs):
"""
Process the input of each TensorFlow model including the booleans. In case of a list of symbolic inputs, each input
has to be named accordingly to the parameters name, i.e. `input_ids = tf.keras.Input(shape=(128,), dtype='int32',
name="input_ids")` otherwise the order of the tensors will not be guaranteed during the training.
Args:
func (:obj:`callable`):
The callable function of the TensorFlow model.
config (:class:`~transformers.PretrainedConfig`):
The config of the running model.
**kwargs:
The inputs of the model.
Returns:
Two lists, one for the missing layers, and another one for the unexpected layers.
"""
signature = dict(inspect.signature(func).parameters)
signature.pop("kwargs", None)
signature.pop("self", None)
parameter_names = list(signature.keys())
output = {}
allowed_types = (tf.Tensor, bool, int, ModelOutput, tuple, list, dict, np.ndarray)
if "inputs" in kwargs["kwargs_call"]:
warnings.warn(
"The `inputs` argument is deprecated and will be removed in a future version, use `input_ids` instead.",
FutureWarning,
)
output["input_ids"] = kwargs["kwargs_call"].pop("inputs")
if "decoder_cached_states" in kwargs["kwargs_call"]:
warnings.warn(
"The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
output["past_key_values"] = kwargs["kwargs_call"].pop("decoder_cached_states")
if len(kwargs["kwargs_call"]) > 0:
raise ValueError(
f"The following keyword arguments are not supported by this model: {list(kwargs['kwargs_call'].keys())}."
)
kwargs.pop("kwargs_call")
for k, v in kwargs.items():
if isinstance(v, allowed_types) or v is None:
output[k] = v
else:
raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")
if isinstance(input_ids, (tuple, list)):
for i, input in enumerate(input_ids):
# EagerTensors don't allow to use the .name property so we check for a real Tensor
if type(input) == tf.Tensor:
# Tensor names have always the pattern `name:id` then we check only the
# `name` part
tensor_name = input.name.split(":")[0]
if tensor_name in parameter_names:
output[tensor_name] = input
else:
output[parameter_names[i]] = input
elif isinstance(input, allowed_types) or input is None:
output[parameter_names[i]] = input
else:
raise ValueError(
f"Data of type {type(input)} is not allowed only {allowed_types} is accepted for {parameter_names[i]}."
)
elif isinstance(input_ids, (dict, BatchEncoding)):
if "inputs" in input_ids:
warnings.warn(
"The `inputs` argument is deprecated and will be removed in a future version, use `input_ids` instead.",
FutureWarning,
)
output["input_ids"] = input_ids.pop("inputs")
if "decoder_cached_states" in input_ids:
warnings.warn(
"The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
output["past_key_values"] = input_ids.pop("decoder_cached_states")
for k, v in dict(input_ids).items():
if isinstance(v, allowed_types) or v is None:
output[k] = v
elif k not in parameter_names and "args" not in parameter_names:
logger.warn(
f"The parameter {k} does not belongs to the parameter list {parameter_names} and will be ignored."
)
continue
else:
raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")
else:
if isinstance(input_ids, tf.Tensor) or input_ids is None:
output[parameter_names[0]] = input_ids
else:
raise ValueError(
f"Data of type {type(input_ids)} is not allowed only {allowed_types} is accepted for {parameter_names[0]}."
)
for name in parameter_names:
if name not in list(output.keys()) and name != "args":
output[name] = kwargs.pop(name, signature[name].default)
# When creating a SavedModel TF calls the method with LayerCall.__call__(args, **kwargs)
# So to respect the proper output we have to add this exception
if "args" in output:
if output["args"] is not None and type(output["args"]) == tf.Tensor:
tensor_name = output["args"].name.split(":")[0]
output[tensor_name] = output["args"]
else:
# `args` in this case is always the first parameter, then `input_ids`
output["input_ids"] = output["args"]
del output["args"]
if "kwargs" in output:
del output["kwargs"]
boolean_dict = {
k: v
for k, v in output.items()
if k in ["return_dict", "output_attentions", "output_hidden_states", "use_cache"]
}
output.update(
booleans_processing(
config=config,
**boolean_dict,
)
)
return output
def load_tf_weights(model, resolved_archive_file, _prefix=None):
"""
Detect missing and unexpected layers and load the TF weights accordingly to their names and shapes.
Args:
model (:obj:`tf.keras.models.Model`):
The model to load the weights into.
resolved_archive_file (:obj:`str`):
The location of the H5 file.
Returns:
Two lists, one for the missing layers, and another one for the unexpected layers.
"""
missing_layers = []
unexpected_layers = []
# Read the H5 file
with h5py.File(resolved_archive_file, "r") as f:
# Retrieve the name of each layer from the H5 file
saved_h5_model_layers_name = set(hdf5_format.load_attributes_from_hdf5_group(f, "layer_names"))
# Find the missing layers from the high level list of layers
missing_layers = list(set([layer.name for layer in model.layers]) - saved_h5_model_layers_name)
# Find the unexpected layers from the high level list of layers
unexpected_layers = list(saved_h5_model_layers_name - set([layer.name for layer in model.layers]))
saved_weight_names_set = set()
symbolic_weights_names = set()
weight_value_tuples = []
# Compute missing and unexpected sub layers
# Store the weights in list of tuples that looks like [(weight_object, value_of_weight),...]
for layer in model.layers:
# if layer_name from the H5 file belongs to the layers from the instantiated model
if layer.name in saved_h5_model_layers_name:
# Get the H5 layer object from its name
h5_layer_object = f[layer.name]
# Get all the weights as a list from the layer object
symbolic_weights = layer.trainable_weights + layer.non_trainable_weights
saved_weights = {}
# Create a dict from the H5 saved model that looks like {"weight_name": weight_value}
# And a set with only the names
for weight_name in hdf5_format.load_attributes_from_hdf5_group(h5_layer_object, "weight_names"):
# TF names always start with the model name so we ignore it
name = "/".join(weight_name.split("/")[1:])
if _prefix is not None:
name = _prefix + "/" + name
saved_weights[name] = np.asarray(h5_layer_object[weight_name])
# Add the updated name to the final list for computing missing/unexpected values
saved_weight_names_set.add(name)
# Loop over each weights from the instantiated model and compare with the weights from the H5 file
for symbolic_weight in symbolic_weights:
# TF names always start with the model name so we ignore it
if _prefix is not None:
delimeter = len(_prefix.split("/"))
symbolic_weight_name = "/".join(
symbolic_weight.name.split("/")[:delimeter]
+ symbolic_weight.name.split("/")[delimeter + 1 :]
)
else:
symbolic_weight_name = "/".join(symbolic_weight.name.split("/")[1:])
# here we check if the current weight is among the weights from the H5 file
# If yes, get the weight_value of the corresponding weight from the H5 file
# If not, make the value to None
saved_weight_value = saved_weights.get(symbolic_weight_name, None)
# Add the updated name to the final list for computing missing/unexpected values
symbolic_weights_names.add(symbolic_weight_name)
# If the current weight is found
if saved_weight_value is not None:
# Check if the shape of the current weight and the one from the H5 file are different
if K.int_shape(symbolic_weight) != saved_weight_value.shape:
# If yes we reshape the weight from the H5 file accordingly to the current weight
# If the two shapes are not compatible we raise an issue
try:
array = np.reshape(saved_weight_value, K.int_shape(symbolic_weight))
except AssertionError as e:
e.args += (K.int_shape(symbolic_weight), saved_weight_value.shape)
raise e
else:
array = saved_weight_value
# We create the tuple that will be loaded and add it to the final list
weight_value_tuples.append((symbolic_weight, array))
# Load all the weights
K.batch_set_value(weight_value_tuples)
# Compute the missing and unexpected layers
missing_layers.extend(list(symbolic_weights_names - saved_weight_names_set))
unexpected_layers.extend(list(saved_weight_names_set - symbolic_weights_names))
return missing_layers, unexpected_layers
def init_copy_embeddings(old_embeddings, new_num_tokens):
r"""
This function aims to reduce the embeddings in case new_num_tokens < old_num_tokens or to pad with -1 in case
new_num_tokens > old_num_tokens. A mask is also computed in order to know which weight in the embeddings should be
kept or not. Example:
- if new_num_tokens=5 and old_num_tokens=4 and old_embeddings=[w1,w2,w3,w4]
- mask=[True,True,True,True,False] and current_weights=[w1,w2,w3,w4,-1]
- if new_num_tokens=4 and old_num_tokens=5 and old_embeddings=[w1,w2,w3,w4,w5]
- mask=[True,True,True,True] and current_weights=[w1,w2,w3,w4]
"""
old_num_tokens, old_embedding_dim = shape_list(old_embeddings)
size_diff = new_num_tokens - old_num_tokens
# initialize new embeddings
# Copy token embeddings from the previous ones
if tf.math.greater(size_diff, 0):
# if the new size is greater than the old one, we extend the current embeddings with a padding until getting new size
# and we create a mask to properly identify the padded values and be replaced by the values of the newly created
# embeddings
current_weights = tf.pad(
old_embeddings.value(), tf.convert_to_tensor([[0, size_diff], [0, 0]]), constant_values=-1
)
num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
mask = tf.fill(tf.convert_to_tensor([num_tokens_to_copy, 1]), True)
mask = tf.pad(mask, tf.convert_to_tensor([[0, size_diff], [0, 0]]), constant_values=False)
else:
# if the new size if lower than the old one, we take the current embeddings until the new size
current_weights = tf.slice(
old_embeddings.value(),
tf.convert_to_tensor([0, 0]),
tf.convert_to_tensor([new_num_tokens, old_embedding_dim]),
)
mask = tf.fill(tf.convert_to_tensor([new_num_tokens, 1]), True)
return mask, current_weights
class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
r"""
Base class for all TF models.
:class:`~transformers.TFPreTrainedModel` takes care of storing the configuration of the models and handles methods
for loading, downloading and saving models as well as a few methods common to all models to:
* resize the input embeddings,
* prune heads in the self-attention heads.
Class attributes (overridden by derived classes):
- **config_class** (:class:`~transformers.PretrainedConfig`) -- A subclass of
:class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
- **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in
derived classes of the same architecture adding modules on top of the base model.
"""
config_class = None
base_model_prefix = ""
# a list of re pattern of tensor names to ignore from the model when loading the model weights
# (and avoid unnecessary warnings).
_keys_to_ignore_on_load_missing = None
# a list of re pattern of tensor names to ignore from the weights when loading the model weights
# (and avoid unnecessary warnings).
_keys_to_ignore_on_load_unexpected = None
_requires_load_weight_prefix = False
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network.
Returns:
:obj:`Dict[str, tf.Tensor]`: The dummy inputs.
"""
return {
"input_ids": tf.constant(DUMMY_INPUTS),
}
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
if not isinstance(config, PretrainedConfig):
raise ValueError(
"Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
"To create a model from a pretrained model use "
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
self.__class__.__name__, self.__class__.__name__
)
)
# Save config and origin of the pretrained weights if given in model
self.config = config
self.name_or_path = config.name_or_path
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs):
"""
Method used for serving the model.
Args:
inputs (:obj:`Dict[str, tf.Tensor]`):
The input of the saved model as a dictionnary of tensors.
"""
output = self.call(inputs)
return self.serving_output(output)
def serving_output(output):
"""
Prepare the output of the saved model. Each model must implement this function.
Args:
output (:obj:`~transformers.TFBaseModelOutput`):
The output returned by the model.
"""
raise NotImplementedError
def get_input_embeddings(self) -> tf.keras.layers.Layer:
"""
Returns the model's input embeddings layer.
Returns:
:obj:`tf.Variable`: The embeddings layer mapping vocabulary to hidden states.
"""
main_layer = getattr(self, self.base_model_prefix, self)
if main_layer is not self:
return main_layer.get_input_embeddings()
else:
raise NotImplementedError
def set_input_embeddings(self, value):
"""
Set model's input embeddings
Args:
value (:obj:`tf.Variable`):
The new weights mapping hidden states to vocabulary.
"""
main_layer = getattr(self, self.base_model_prefix)
if main_layer is None:
raise NotImplementedError("The model does not implements the base_model_prefix attribute.")
try:
main_layer.set_input_embeddings(value)
except AttributeError:
logger.info("Building the model")
self(self.dummy_inputs)
main_layer.set_input_embeddings(value)
def get_output_embeddings(self) -> Union[None, tf.keras.layers.Layer]:
"""
Returns the model's output embeddings
Returns:
:obj:`tf.Variable`: The new weights mapping vocabulary to hidden states.
"""
if self.get_lm_head() is not None:
lm_head = self.get_lm_head()
return lm_head.get_output_embeddings()
return None # Overwrite for models with output embeddings
def set_output_embeddings(self, value):
"""
Set model's output embeddings
Args:
value (:obj:`tf.Variable`):
The new weights mapping hidden states to vocabulary.
"""
if self.get_lm_head() is not None:
lm_head = self.get_lm_head()
try:
lm_head.set_output_embeddings(value)
except AttributeError:
logger.info("Building the model")
self(self.dummy_inputs)
lm_head.set_output_embeddings(value)
def get_output_layer_with_bias(self) -> Union[None, tf.keras.layers.Layer]:
"""
Get the layer that handles a bias attribute in case the model has an LM head with weights tied to the
embeddings
Return:
:obj:`tf.keras.layers.Layer`: The layer that handles the bias, None if not an LM model.
"""
warnings.warn(
"The method get_output_layer_with_bias is deprecated. Please use `get_lm_head` instead.", FutureWarning
)
return self.get_lm_head()
def get_prefix_bias_name(self) -> Union[None, str]:
"""
Get the concatenated _prefix name of the bias from the model name to the parent layer
Return:
:obj:`str`: The _prefix name of the bias.
"""
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return None
def get_bias(self) -> Union[None, Dict[str, tf.Variable]]:
"""
Dict of bias attached to an LM head. The key represents the name of the bias attribute.
Return:
:obj:`tf.Variable`: The weights representing the bias, None if not an LM model.
"""
if self.get_lm_head() is not None:
lm_head = self.get_lm_head()
try:
return lm_head.get_bias()
except AttributeError:
self(self.dummy_inputs)
return lm_head.get_bias()
return None
def set_bias(self, value):
"""
Set all the bias in the LM head.
Args:
value (:obj:`Dict[tf.Variable]`):
All the new bias attached to an LM head.
"""
if self.get_lm_head() is not None:
lm_head = self.get_lm_head()
try:
lm_head.set_bias(value)
except AttributeError:
self(self.dummy_inputs)
lm_head.set_bias(value)
def get_lm_head(self) -> tf.keras.layers.Layer:
"""
The LM Head layer. This method must be overwritten by all the models that have a lm head.
Return:
:obj:`tf.keras.layers.Layer`: The LM head layer if the model has one, None if not.
"""
return None
def resize_token_embeddings(self, new_num_tokens=None) -> tf.Variable:
"""
Resizes input token embeddings matrix of the model if :obj:`new_num_tokens != config.vocab_size`.
Takes care of tying weights embeddings afterwards if the model class has a :obj:`tie_weights()` method.
Arguments:
new_num_tokens (:obj:`int`, `optional`):
The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
vectors at the end. Reducing the size will remove vectors from the end. If not provided or :obj:`None`,
just returns a pointer to the input tokens :obj:`tf.Variable` module of the model without doing
anything.
Return:
:obj:`tf.Variable`: Pointer to the input tokens Embeddings Module of the model.
"""
if new_num_tokens is None or new_num_tokens == self.config.vocab_size:
return self._get_word_embedding_weight(self.get_input_embeddings())
model_embeds = self._resize_token_embeddings(new_num_tokens)
# Update base model and current model config
self.config.vocab_size = new_num_tokens
return model_embeds
def _get_word_embedding_weight(model, embedding_layer):
embeds = getattr(embedding_layer, "weight", None)
if embeds is not None:
return embeds
embeds = getattr(embedding_layer, "decoder", None)
if embeds is not None:
return embeds
# The reason why the attributes don't exist might be
# because the model is not built, so retry getting
# the argument after building the model
model(model.dummy_inputs)
embeds = getattr(embedding_layer, "weight", None)
if embeds is not None:
return embeds
embeds = getattr(embedding_layer, "decoder", None)
if embeds is not None:
return embeds
return None
def _resize_token_embeddings(self, new_num_tokens):
old_embeddings = self._get_word_embedding_weight(self.get_input_embeddings())
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
# if word embeddings are not tied, make sure that lm head bias is resized as well
if self.get_bias() is not None:
old_lm_head_bias = self.get_bias()
new_lm_head_bias = self._get_resized_lm_head_bias(old_lm_head_bias, new_num_tokens)
self.set_bias(new_lm_head_bias)
# if word embeddings are not tied, make sure that lm head decoder is resized as well
if self.get_output_embeddings() is not None:
old_lm_head_decoder = self._get_word_embedding_weight(self.get_output_embeddings())
new_lm_head_decoder = self._get_resized_lm_head_decoder(old_lm_head_decoder, new_num_tokens)
self.set_output_embeddings(new_lm_head_decoder)
self.set_input_embeddings(new_embeddings)
return self.get_input_embeddings()
def _get_resized_lm_head_bias(self, old_lm_head_bias, new_num_tokens):
"""
Build a resized bias from the old ones. Increasing the size will add newly initialized vectors at the end.
Reducing the size will remove vectors from the end
Args:
old_lm_head_bias (:obj:`tf.Variable`):
Old lm head bias to be resized.
new_num_tokens (:obj:`int`, `optional`):
New number of tokens in the linear matrix.
Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
vectors from the end. If not provided or :obj:`None`, just returns None
Return:
:obj:`tf.Variable`: Pointer to the resized bias.
"""
new_lm_head_bias = {}
for attr, weight in old_lm_head_bias.items():
first_dim, old_num_tokens = (None, shape_list(weight)[0]) if tf.rank(weight) == 1 else shape_list(weight)
size_diff = new_num_tokens - old_num_tokens
final_shape = [new_num_tokens] if first_dim is None else [first_dim, new_num_tokens]
# initialize new bias
if tf.math.greater(size_diff, 0):
padding_shape = [[0, size_diff]] if first_dim is None else [[0, 0], [0, size_diff]]
current_bias = tf.pad(weight.value(), tf.convert_to_tensor(padding_shape), constant_values=-1)
num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
mask_shape = [num_tokens_to_copy] if first_dim is None else [1, num_tokens_to_copy]
bias_mask = tf.fill(tf.convert_to_tensor(mask_shape), True)
bias_mask = tf.pad(bias_mask, tf.convert_to_tensor(padding_shape), constant_values=False)
else:
slice_from = [0] if first_dim is None else [0, 0]
current_bias = tf.slice(
weight.value(), tf.convert_to_tensor(slice_from), tf.convert_to_tensor(final_shape)
)
bias_mask = tf.fill(tf.convert_to_tensor(final_shape), True)
new_bias = self.add_weight(
shape=final_shape,
initializer="zeros",
trainable=True,
name=weight.name.split(":")[0],
)
init_bias = tf.where(bias_mask, current_bias, new_bias.value())
new_bias.assign(init_bias)
new_lm_head_bias[attr] = new_bias
return new_lm_head_bias
def _get_resized_lm_head_decoder(self, old_lm_head_decoder, new_num_tokens):
"""
Build a resized decoder from the old ones. Increasing the size will add newly initialized vectors at the end.
Reducing the size will remove vectors from the end
Args:
old_lm_head_decoder (:obj:`tf.Variable`):
Old lm head decoder to be resized.
new_num_tokens (:obj:`int`, `optional`):
New number of tokens in the linear matrix.
Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
vectors from the end. If not provided or :obj:`None`, just returns None
Return:
:obj:`tf.Variable`: Pointer to the resized decoder or None if the output embeddings are differents of the
input ones.
"""
new_lm_head_decoder = old_lm_head_decoder
is_input_output_equals = tf.reduce_any(
self._get_word_embedding_weight(self.get_input_embeddings()) == old_lm_head_decoder
)
if old_lm_head_decoder is not None and not is_input_output_equals:
old_embedding_dim = shape_list(old_lm_head_decoder)[1]
decoder_mask, current_decoder = init_copy_embeddings(old_lm_head_decoder, new_num_tokens)
new_lm_head_decoder = self.add_weight(
shape=(new_num_tokens, old_embedding_dim),
initializer="zeros",
trainable=True,
name=old_lm_head_decoder.name.split(":")[0],
)
init_decoder = tf.where(decoder_mask, current_decoder, new_lm_head_decoder.value())
new_lm_head_decoder.assign(init_decoder)
return new_lm_head_decoder
def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None) -> tf.Variable:
"""
Build a resized Embedding weights from a provided token Embedding weights. Increasing the size will add newly
initialized vectors at the end. Reducing the size will remove vectors from the end
Args:
old_embeddings (:obj:`tf.Variable`):
Old embeddings to be resized.
new_num_tokens (:obj:`int`, `optional`):
New number of tokens in the embedding matrix.
Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
vectors from the end. If not provided or :obj:`None`, just returns a pointer to the input tokens
:obj:`tf.Variable`` module of the model without doing anything.
Return:
:obj:`tf.Variable`: Pointer to the resized Embedding Module or the old Embedding Module if
:obj:`new_num_tokens` is :obj:`None`
"""
old_embedding_dim = shape_list(old_embeddings)[1]
init_range = getattr(self.config, "initializer_range", 0.02)
embeddings_mask, current_embeddings = init_copy_embeddings(old_embeddings, new_num_tokens)
new_embeddings = self.add_weight(
name=old_embeddings.name.split(":")[0],
shape=[new_num_tokens, old_embedding_dim],
initializer=get_initializer(init_range),
dtype=tf.float32,
)
init_embeddings = tf.where(embeddings_mask, current_embeddings, new_embeddings.value())
new_embeddings.assign(init_embeddings)
return new_embeddings
def prune_heads(self, heads_to_prune):
"""
Prunes heads of the base model.
Arguments:
heads_to_prune (:obj:`Dict[int, List[int]]`):
Dictionary with keys being selected layer indices (:obj:`int`) and associated values being the list of
heads to prune in said layer (list of :obj:`int`). For instance {1: [0, 2], 2: [2, 3]} will prune heads
0 and 2 on layer 1 and heads 2 and 3 on layer 2.
"""
raise NotImplementedError
def save_pretrained(self, save_directory, saved_model=False, version=1):
"""
Save a model and its configuration file to a directory, so that it can be re-loaded using the
:func:`~transformers.TFPreTrainedModel.from_pretrained` class method.
Arguments:
save_directory (:obj:`str`):
Directory to which to save. Will be created if it doesn't exist.
saved_model (:obj:`bool`, `optional`, defaults to :obj:`False`):
If the model has to be saved in saved model format as well or not.
version (:obj:`int`, `optional`, defaults to 1):
The version of the saved model. A saved model needs to be versioned in order to be properly loaded by
TensorFlow Serving as detailed in the official documentation
https://www.tensorflow.org/tfx/serving/serving_basic
"""
if os.path.isfile(save_directory):
logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
return
os.makedirs(save_directory, exist_ok=True)
if saved_model:
saved_model_dir = os.path.join(save_directory, "saved_model", str(version))
self.save(saved_model_dir, include_optimizer=False, signatures=self.serving)
logger.info(f"Saved model created in {saved_model_dir}")
# Save configuration file
self.config.save_pretrained(save_directory)
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(save_directory, TF2_WEIGHTS_NAME)
self.save_weights(output_model_file)
logger.info("Model weights saved in {}".format(output_model_file))
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Instantiate a pretrained TF 2.0 model from a pre-trained model configuration.
The warning `Weights from XXX not initialized from pretrained model` means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.
The warning `Weights from XXX not used in YYY` means that the layer XXX is not used by YYY, therefore those
weights are discarded.
Parameters:
pretrained_model_name_or_path (:obj:`str`, `optional`):
Can be either:
- A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
a user or organization name, like ``dbmdz/bert-base-german-cased``.
- A path to a `directory` containing model weights saved using
:func:`~transformers.TFPreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
- A path or url to a `PyTorch state_dict save file` (e.g, ``./pt_model/pytorch_model.bin``). In
this case, ``from_pt`` should be set to :obj:`True` and a configuration object should be provided
as ``config`` argument. This loading path is slower than converting the PyTorch model in a
TensorFlow model using the provided conversion scripts and loading the TensorFlow model
afterwards.
- :obj:`None` if you are both providing the configuration and state dictionary (resp. with keyword
arguments ``config`` and ``state_dict``).
model_args (sequence of positional arguments, `optional`):
All remaning positional arguments will be passed to the underlying model's ``__init__`` method.
config (:obj:`Union[PretrainedConfig, str]`, `optional`):
Can be either:
- an instance of a class derived from :class:`~transformers.PretrainedConfig`,
- a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained`.
Configuration for the model to use instead of an automatically loaded configuation. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the `model id` string of a pretrained
model).
- The model was saved using :func:`~transformers.TFPreTrainedModel.save_pretrained` and is reloaded
by supplying the save directory.
- The model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a
configuration JSON file named `config.json` is found in the directory.
from_pt: (:obj:`bool`, `optional`, defaults to :obj:`False`):
Load the model weights from a PyTorch state_dict save file (see docstring of
``pretrained_model_name_or_path`` argument).
cache_dir (:obj:`str`, `optional`):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies: (:obj:`Dict[str, str], `optional`):
A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to only look at local files (e.g., not try doanloading the model).
use_auth_token (:obj:`str` or `bool`, `optional`):
The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
identifier allowed by git.
mirror(:obj:`str`, `optional`, defaults to :obj:`None`):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.
kwargs (remaining dictionary of keyword arguments, `optional`):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
:obj:`output_attentions=True`). Behaves differently depending on whether a ``config`` is provided or
automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
attribute will be passed to the underlying model's ``__init__`` function.
.. note::
Passing :obj:`use_auth_token=True` is required when you want to use a private model.
Examples::
>>> from transformers import BertConfig, TFBertModel
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFBertModel.from_pretrained('bert-base-uncased')
>>> # Model was saved using `save_pretrained('./test/saved_model/')` (for example purposes, not runnable).
>>> model = TFBertModel.from_pretrained('./test/saved_model/')
>>> # Update configuration during loading.
>>> model = TFBertModel.from_pretrained('bert-base-uncased', output_attentions=True)
>>> assert model.config.output_attentions == True
>>> # Loading from a Pytorch model file instead of a TensorFlow checkpoint (slower, for example purposes, not runnable).
>>> config = BertConfig.from_json_file('./pt_model/my_pt_model_config.json')
>>> model = TFBertModel.from_pretrained('./pt_model/my_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
cache_dir = kwargs.pop("cache_dir", None)
from_pt = kwargs.pop("from_pt", False)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
output_loading_info = kwargs.pop("output_loading_info", False)
local_files_only = kwargs.pop("local_files_only", False)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
mirror = kwargs.pop("mirror", None)
load_weight_prefix = kwargs.pop("load_weight_prefix", None)
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
# Load config if we don't provide a configuration
if not isinstance(config, PretrainedConfig):
config_path = config if config is not None else pretrained_model_name_or_path
config, model_kwargs = cls.config_class.from_pretrained(
config_path,
*model_args,
cache_dir=cache_dir,
return_unused_kwargs=True,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
**kwargs,
)
else:
model_kwargs = kwargs
# Load model
if pretrained_model_name_or_path is not None:
if os.path.isdir(pretrained_model_name_or_path):
if from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
# Load from a PyTorch checkpoint in priority if from_pt
archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
elif os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)):
# Load from a TF 2.0 checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)
else:
raise EnvironmentError(
"Error no file named {} found in directory {} or `from_pt` set to False".format(
[WEIGHTS_NAME, TF2_WEIGHTS_NAME], pretrained_model_name_or_path
)
)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
archive_file = pretrained_model_name_or_path
elif os.path.isfile(pretrained_model_name_or_path + ".index"):
archive_file = pretrained_model_name_or_path + ".index"
else:
archive_file = hf_bucket_url(
pretrained_model_name_or_path,
filename=(WEIGHTS_NAME if from_pt else TF2_WEIGHTS_NAME),
revision=revision,
mirror=mirror,
)
try:
# Load from URL or cache if already cached
resolved_archive_file = cached_path(
archive_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
)
except EnvironmentError as err:
logger.error(err)
msg = (
f"Can't load weights for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a file named one of {TF2_WEIGHTS_NAME}, {WEIGHTS_NAME}.\n\n"
)
raise EnvironmentError(msg)
if resolved_archive_file == archive_file:
logger.info("loading weights file {}".format(archive_file))
else:
logger.info("loading weights file {} from cache at {}".format(archive_file, resolved_archive_file))
else:
resolved_archive_file = None
config.name_or_path = pretrained_model_name_or_path
# composed models, *e.g.* TFRag, require special treatment when it comes to loading
# pre-trained weights.
if cls._requires_load_weight_prefix and model_kwargs.get("name") is not None:
model_kwargs["load_weight_prefix"] = load_weight_prefix + "/" + model_kwargs.get("name")
# Instantiate model.
model = cls(config, *model_args, **model_kwargs)
if from_pt:
from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
# Load from a PyTorch checkpoint
return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True)
# we might need to extend the variable scope for composite models
if load_weight_prefix is not None:
with tf.compat.v1.variable_scope(load_weight_prefix):
model(model.dummy_inputs) # build the network with dummy inputs
else:
model(model.dummy_inputs) # build the network with dummy inputs
assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file)
# 'by_name' allow us to do transfer learning by skipping/adding layers
# see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1339-L1357
try:
missing_keys, unexpected_keys = load_tf_weights(model, resolved_archive_file, load_weight_prefix)
except OSError:
raise OSError(
"Unable to load weights from h5 file. "
"If you tried to load a TF 2.0 model from a PyTorch checkpoint, please set from_pt=True. "
)
model(model.dummy_inputs) # Make sure restore ops are run
if cls._keys_to_ignore_on_load_missing is not None:
for pat in cls._keys_to_ignore_on_load_missing:
missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
if cls._keys_to_ignore_on_load_unexpected is not None:
for pat in cls._keys_to_ignore_on_load_unexpected:
unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
if len(unexpected_keys) > 0:
logger.warning(
f"Some layers from the model checkpoint at {pretrained_model_name_or_path} were not used when "
f"initializing {model.__class__.__name__}: {unexpected_keys}\n"
f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task "
f"or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n"
f"- This IS NOT expected if you are initializing {model.__class__.__name__} from the checkpoint of a model that you expect "
f"to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
)
else:
logger.warning(f"All model checkpoint layers were used when initializing {model.__class__.__name__}.\n")
if len(missing_keys) > 0:
logger.warning(
f"Some layers of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} "
f"and are newly initialized: {missing_keys}\n"
f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
)
else:
logger.warning(
f"All the layers of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\n"
f"If your task is similar to the task the model of the checkpoint was trained on, "
f"you can already use {model.__class__.__name__} for predictions without further training."
)
if output_loading_info:
loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys}
return model, loading_info
return model
class TFConv1D(tf.keras.layers.Layer):
"""
1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).
Basically works like a linear layer but the weights are transposed.
Args:
nf (:obj:`int`):
The number of output features.
nx (:obj:`int`):
The number of input features.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation to use to initialize the weights.
kwargs:
Additional keyword arguments passed along to the :obj:`__init__` of :obj:`tf.keras.layers.Layer`.
"""
def __init__(self, nf, nx, initializer_range=0.02, **kwargs):
super().__init__(**kwargs)
self.nf = nf
self.nx = nx
self.initializer_range = initializer_range
def build(self, input_shape):
self.weight = self.add_weight(
"weight", shape=[self.nx, self.nf], initializer=get_initializer(self.initializer_range)
)
self.bias = self.add_weight("bias", shape=[1, self.nf], initializer=tf.zeros_initializer())
def call(self, x):
bz, sl = shape_list(x)[:2]
x = tf.reshape(x, [-1, self.nx])
x = tf.matmul(x, self.weight) + self.bias
x = tf.reshape(x, [bz, sl, self.nf])
return x
class TFSharedEmbeddings(tf.keras.layers.Layer):
r"""
Construct shared token embeddings.
The weights of the embedding layer is usually shared with the weights of the linear decoder when doing language
modeling.
Args:
vocab_size (:obj:`int`):
The size of the vocabulary, e.g., the number of unique tokens.
hidden_size (:obj:`int`):
The size of the embedding vectors.
initializer_range (:obj:`float`, `optional`):
The standard deviation to use when initializing the weights. If no value is provided, it will default to
:math:`1/\sqrt{hidden\_size}`.
kwargs:
Additional keyword arguments passed along to the :obj:`__init__` of :obj:`tf.keras.layers.Layer`.
"""
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: Optional[float] = None, **kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.initializer_range = hidden_size ** -0.5 if initializer_range is None else initializer_range
def build(self, input_shape):
"""
Build shared token embedding layer Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
self.weight = self.add_weight(
"weight", shape=[self.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range)
)
super().build(input_shape)
def get_config(self):
config = {
"vocab_size": self.vocab_size,
"hidden_size": self.hidden_size,
"initializer_range": self.initializer_range,
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
def call(self, inputs: tf.Tensor, mode: str = "embedding") -> tf.Tensor:
"""
Get token embeddings of inputs or decode final hidden state.
Args:
inputs (:obj:`tf.Tensor`):
In embedding mode, should be an int64 tensor with shape :obj:`[batch_size, length]`.
In linear mode, should be a float tensor with shape :obj:`[batch_size, length, hidden_size]`.
mode (:obj:`str`, defaults to :obj:`"embedding"`):
A valid value is either :obj:`"embedding"` or :obj:`"linear"`, the first one indicates that the layer
should be used as an embedding layer, the second one that the layer should be used as a linear decoder.
Returns:
:obj:`tf.Tensor`: In embedding mode, the output is a float32 embedding tensor, with shape
:obj:`[batch_size, length, embedding_size]`.
In linear mode, the output is a float32 with shape :obj:`[batch_size, length, vocab_size]`.
Raises:
ValueError: if :obj:`mode` is not valid.
Shared weights logic is adapted from `here
<https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24>`__.
"""
if mode == "embedding":
return self._embedding(inputs)
elif mode == "linear":
return self._linear(inputs)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, input_ids):
"""Applies embedding based on inputs tensor."""
return tf.gather(self.weight, input_ids)
def _linear(self, inputs):
"""
Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [..., hidden_size]
Returns:
float32 tensor with shape [..., vocab_size].
"""
first_dims = shape_list(inputs)[:-1]
x = tf.reshape(inputs, [-1, self.hidden_size])
logits = tf.matmul(x, self.weight, transpose_b=True)
return tf.reshape(logits, first_dims + [self.vocab_size])
class TFSequenceSummary(tf.keras.layers.Layer):
"""
Compute a single vector summary of a sequence hidden states.
Args:
config (:class:`~transformers.PretrainedConfig`):
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
config class of your model for the default values it uses):
- **summary_type** (:obj:`str`) -- The method to use to make this summary. Accepted values are:
- :obj:`"last"` -- Take the last token hidden state (like XLNet)
- :obj:`"first"` -- Take the first token hidden state (like Bert)
- :obj:`"mean"` -- Take the mean of all tokens hidden states
- :obj:`"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2)
- :obj:`"attn"` -- Not implemented now, use multi-head attention
- **summary_use_proj** (:obj:`bool`) -- Add a projection after the vector extraction.
- **summary_proj_to_labels** (:obj:`bool`) -- If :obj:`True`, the projection outputs to
:obj:`config.num_labels` classes (otherwise to :obj:`config.hidden_size`).
- **summary_activation** (:obj:`Optional[str]`) -- Set to :obj:`"tanh"` to add a tanh activation to the
output, another string or :obj:`None` will add no activation.
- **summary_first_dropout** (:obj:`float`) -- Optional dropout probability before the projection and
activation.
- **summary_last_dropout** (:obj:`float`)-- Optional dropout probability after the projection and
activation.
initializer_range (:obj:`float`, defaults to 0.02): The standard deviation to use to initialize the weights.
kwargs:
Additional keyword arguments passed along to the :obj:`__init__` of :obj:`tf.keras.layers.Layer`.
"""
def __init__(self, config: PretrainedConfig, initializer_range: float = 0.02, **kwargs):
super().__init__(**kwargs)
self.summary_type = config.summary_type if hasattr(config, "summary_use_proj") else "last"
if self.summary_type == "attn":
# We should use a standard multi-head attention module with absolute positional embedding for that.
# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
# We can probably just use the multi-head attention module of PyTorch >=1.1.0
raise NotImplementedError
self.has_summary = hasattr(config, "summary_use_proj") and config.summary_use_proj
if self.has_summary:
if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
num_classes = config.num_labels
else:
num_classes = config.hidden_size
self.summary = tf.keras.layers.Dense(
num_classes, kernel_initializer=get_initializer(initializer_range), name="summary"
)
self.has_activation = hasattr(config, "summary_activation") and config.summary_activation == "tanh"
if self.has_activation:
self.activation = tf.keras.activations.tanh
self.has_first_dropout = hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0
if self.has_first_dropout:
self.first_dropout = tf.keras.layers.Dropout(config.summary_first_dropout)
self.has_last_dropout = hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0
if self.has_last_dropout:
self.last_dropout = tf.keras.layers.Dropout(config.summary_last_dropout)
def call(self, inputs, cls_index=None, training=False):
if not isinstance(inputs, (dict, tuple, list)):
hidden_states = inputs
elif isinstance(inputs, (tuple, list)):
hidden_states = inputs[0]
cls_index = inputs[1] if len(inputs) > 1 else None
assert len(inputs) <= 2, "Too many inputs."
else:
hidden_states = inputs.get("hidden_states")
cls_index = inputs.get("cls_index", None)
if self.summary_type == "last":
output = hidden_states[:, -1]
elif self.summary_type == "first":
output = hidden_states[:, 0]
elif self.summary_type == "mean":
output = tf.reduce_mean(hidden_states, axis=1)
elif self.summary_type == "cls_index":
hidden_shape = shape_list(hidden_states) # e.g. [batch, num choices, seq length, hidden dims]
if cls_index is None:
cls_index = tf.fill(
hidden_shape[:-2], hidden_shape[-2] - 1
) # A tensor full of shape [batch] or [batch, num choices] full of sequence length
cls_shape = shape_list(cls_index)
if len(cls_shape) <= len(hidden_shape) - 2:
cls_index = tf.expand_dims(cls_index, axis=-1)
# else:
# cls_index = cls_index[..., tf.newaxis]
# cls_index = cls_index.expand((-1,) * (cls_index.dim()-1) + (hidden_states.size(-1),))
# shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
output = tf.gather(hidden_states, cls_index, batch_dims=len(hidden_shape) - 2)
output = tf.squeeze(
output, axis=len(hidden_shape) - 2
) # shape of output: (batch, num choices, hidden_size)
elif self.summary_type == "attn":
raise NotImplementedError
if self.has_first_dropout:
output = self.first_dropout(output, training=training)
if self.has_summary:
output = self.summary(output)
if self.has_activation:
output = self.activation(output)
if self.has_last_dropout:
output = self.last_dropout(output, training=training)
return output
def shape_list(tensor: tf.Tensor) -> List[int]:
"""
Deal with dynamic shape in tensorflow cleanly.
Args:
tensor (:obj:`tf.Tensor`): The tensor we want the shape of.
Returns:
:obj:`List[int]`: The shape of the tensor as a list.
"""
dynamic = tf.shape(tensor)
if tensor.shape == tf.TensorShape(None):
return dynamic
static = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(static)]
def get_initializer(initializer_range: float = 0.02) -> tf.initializers.TruncatedNormal:
"""
Creates a :obj:`tf.initializers.TruncatedNormal` with the given range.
Args:
initializer_range (`float`, defaults to 0.02): Standard deviation of the initializer range.
Returns:
:obj:`tf.initializers.TruncatedNormal`: The truncated normal initializer.
"""
return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)
class TFWrappedEmbeddings:
"""
this class wraps a the TFSharedEmbeddingTokens layer into a python 'no-keras-layer' class to avoid problem with
weight restoring. Also it makes sure that the layer is called from the correct scope to avoid problem with
saving/storing the correct weights
"""
def __init__(self, layer, abs_scope_name=None):
self._layer = layer
self._abs_scope_name = abs_scope_name
def call(self, inputs, mode="embedding"):
if self._abs_scope_name is None:
return self._layer.call(inputs, mode)
# if an abs scope name is given to the embedding variable, call variable from absolute scope
with tf.compat.v1.variable_scope(self._abs_scope_name, auxiliary_name_scope=False) as abs_scope_name:
with tf.name_scope(abs_scope_name.original_name_scope):
return self._layer.call(inputs, mode)
def __call__(self, inputs, mode="embedding"):
if self._abs_scope_name is None:
return self._layer(inputs, mode)
# if an abs scope name is given to the embedding variable, call variable from absolute scope
with tf.compat.v1.variable_scope(self._abs_scope_name, auxiliary_name_scope=False) as abs_scope_name:
with tf.name_scope(abs_scope_name.original_name_scope):
return self._layer(inputs, mode)
|
AdaMix/src/transformers/modeling_tf_utils.py/0
|
{
"file_path": "AdaMix/src/transformers/modeling_tf_utils.py",
"repo_id": "AdaMix",
"token_count": 31708
}
| 49 |
# coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 ALBERT model. """
import math
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (
MULTIPLE_CHOICE_DUMMY_INPUTS,
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPooling,
TFMaskedLMOutput,
TFMultipleChoiceModelOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
TFMaskedLanguageModelingLoss,
TFModelInputType,
TFMultipleChoiceLoss,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
TFSequenceClassificationLoss,
TFTokenClassificationLoss,
get_initializer,
input_processing,
keras_serializable,
shape_list,
)
from ...utils import logging
from .configuration_albert import AlbertConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "albert-base-v2"
_CONFIG_FOR_DOC = "AlbertConfig"
_TOKENIZER_FOR_DOC = "AlbertTokenizer"
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"albert-base-v1",
"albert-large-v1",
"albert-xlarge-v1",
"albert-xxlarge-v1",
"albert-base-v2",
"albert-large-v2",
"albert-xlarge-v2",
"albert-xxlarge-v2",
# See all ALBERT models at https://huggingface.co/models?filter=albert
]
class TFAlbertPreTrainingLoss:
"""
Loss function suitable for ALBERT pretraining, that is, the task of pretraining a language model by combining SOP +
MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
"""
def compute_loss(self, labels: tf.Tensor, logits: tf.Tensor) -> tf.Tensor:
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE
)
# make sure only labels that are not equal to -100
# are taken into account as loss
masked_lm_active_loss = tf.not_equal(tf.reshape(tensor=labels["labels"], shape=(-1,)), -100)
masked_lm_reduced_logits = tf.boolean_mask(
tensor=tf.reshape(tensor=logits[0], shape=(-1, shape_list(logits[0])[2])),
mask=masked_lm_active_loss,
)
masked_lm_labels = tf.boolean_mask(
tensor=tf.reshape(tensor=labels["labels"], shape=(-1,)), mask=masked_lm_active_loss
)
sentence_order_active_loss = tf.not_equal(tf.reshape(tensor=labels["sentence_order_label"], shape=(-1,)), -100)
sentence_order_reduced_logits = tf.boolean_mask(
tensor=tf.reshape(tensor=logits[1], shape=(-1, 2)), mask=sentence_order_active_loss
)
sentence_order_label = tf.boolean_mask(
tensor=tf.reshape(tensor=labels["sentence_order_label"], shape=(-1,)), mask=sentence_order_active_loss
)
masked_lm_loss = loss_fn(y_true=masked_lm_labels, y_pred=masked_lm_reduced_logits)
sentence_order_loss = loss_fn(y_true=sentence_order_label, y_pred=sentence_order_reduced_logits)
masked_lm_loss = tf.reshape(tensor=masked_lm_loss, shape=(-1, shape_list(sentence_order_loss)[0]))
masked_lm_loss = tf.reduce_mean(input_tensor=masked_lm_loss, axis=0)
return masked_lm_loss + sentence_order_loss
class TFAlbertEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.type_vocab_size = config.type_vocab_size
self.embedding_size = config.embedding_size
self.max_position_embeddings = config.max_position_embeddings
self.initializer_range = config.initializer_range
self.embeddings_sum = tf.keras.layers.Add()
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def build(self, input_shape: tf.TensorShape):
with tf.name_scope("word_embeddings"):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.type_vocab_size, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
super().build(input_shape)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
"""
assert not (input_ids is None and inputs_embeds is None)
if input_ids is not None:
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
input_shape = shape_list(inputs_embeds)[:-1]
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
final_embeddings = self.LayerNorm(inputs=final_embeddings)
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
return final_embeddings
class TFAlbertAttention(tf.keras.layers.Layer):
""" Contains the complete attention sublayer, including both dropouts and layer norm. """
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number "
f"of attention heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.output_attentions = config.output_attentions
self.query = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
self.value = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
# Two different dropout probabilities; see https://github.com/google-research/albert/blob/master/modeling.py#L971-L993
self.attention_dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
self.output_dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(input_tensor)[0]
mixed_query_layer = self.query(inputs=input_tensor)
mixed_key_layer = self.key(inputs=input_tensor)
mixed_value_layer = self.value(inputs=input_tensor)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFAlbertModel call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
attention_probs = tf.nn.softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.attention_dropout(inputs=attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
context_layer = tf.matmul(attention_probs, value_layer)
context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
context_layer = tf.reshape(tensor=context_layer, shape=(batch_size, -1, self.all_head_size))
self_outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
hidden_states = self_outputs[0]
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.output_dropout(inputs=hidden_states, training=training)
attention_output = self.LayerNorm(inputs=hidden_states + input_tensor)
# add attentions if we output them
outputs = (attention_output,) + self_outputs[1:]
return outputs
class TFAlbertLayer(tf.keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFAlbertAttention(config, name="attention")
self.ffn = tf.keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="ffn"
)
if isinstance(config.hidden_act, str):
self.activation = get_tf_activation(config.hidden_act)
else:
self.activation = config.hidden_act
self.ffn_output = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="ffn_output"
)
self.full_layer_layer_norm = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name="full_layer_layer_norm"
)
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
attention_outputs = self.attention(
input_tensor=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
training=training,
)
ffn_output = self.ffn(inputs=attention_outputs[0])
ffn_output = self.activation(ffn_output)
ffn_output = self.ffn_output(inputs=ffn_output)
ffn_output = self.dropout(inputs=ffn_output, training=training)
hidden_states = self.full_layer_layer_norm(inputs=ffn_output + attention_outputs[0])
# add attentions if we output them
outputs = (hidden_states,) + attention_outputs[1:]
return outputs
class TFAlbertLayerGroup(tf.keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.albert_layers = [
TFAlbertLayer(config, name="albert_layers_._{}".format(i)) for i in range(config.inner_group_num)
]
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
training: bool = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
layer_hidden_states = () if output_hidden_states else None
layer_attentions = () if output_attentions else None
for layer_index, albert_layer in enumerate(self.albert_layers):
if output_hidden_states:
layer_hidden_states = layer_hidden_states + (hidden_states,)
layer_output = albert_layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[layer_index],
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_output[0]
if output_attentions:
layer_attentions = layer_attentions + (layer_output[1],)
# Add last layer
if output_hidden_states:
layer_hidden_states = layer_hidden_states + (hidden_states,)
return tuple(v for v in [hidden_states, layer_hidden_states, layer_attentions] if v is not None)
class TFAlbertTransformer(tf.keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.num_hidden_layers = config.num_hidden_layers
self.num_hidden_groups = config.num_hidden_groups
# Number of layers in a hidden group
self.layers_per_group = int(config.num_hidden_layers / config.num_hidden_groups)
self.embedding_hidden_mapping_in = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="embedding_hidden_mapping_in",
)
self.albert_layer_groups = [
TFAlbertLayerGroup(config, name="albert_layer_groups_._{}".format(i))
for i in range(config.num_hidden_groups)
]
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
hidden_states = self.embedding_hidden_mapping_in(inputs=hidden_states)
all_attentions = () if output_attentions else None
all_hidden_states = (hidden_states,) if output_hidden_states else None
for i in range(self.num_hidden_layers):
# Index of the hidden group
group_idx = int(i / (self.num_hidden_layers / self.num_hidden_groups))
layer_group_output = self.albert_layer_groups[group_idx](
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[group_idx * self.layers_per_group : (group_idx + 1) * self.layers_per_group],
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training,
)
hidden_states = layer_group_output[0]
if output_attentions:
all_attentions = all_attentions + layer_group_output[-1]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
class TFAlbertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = AlbertConfig
base_model_prefix = "albert"
class TFAlbertMLMHead(tf.keras.layers.Layer):
def __init__(self, config: AlbertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.embedding_size = config.embedding_size
self.dense = tf.keras.layers.Dense(
config.embedding_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.activation = get_tf_activation(config.hidden_act)
else:
self.activation = config.hidden_act
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = input_embeddings
def build(self, input_shape: tf.TensorShape):
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
self.decoder_bias = self.add_weight(
shape=(self.vocab_size,), initializer="zeros", trainable=True, name="decoder/bias"
)
super().build(input_shape)
def get_output_embeddings(self) -> tf.keras.layers.Layer:
return self.decoder
def set_output_embeddings(self, value: tf.Variable):
self.decoder.weight = value
self.decoder.vocab_size = shape_list(value)[0]
def get_bias(self) -> Dict[str, tf.Variable]:
return {"bias": self.bias, "decoder_bias": self.decoder_bias}
def set_bias(self, value: tf.Variable):
self.bias = value["bias"]
self.decoder_bias = value["decoder_bias"]
self.vocab_size = shape_list(value["bias"])[0]
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.LayerNorm(inputs=hidden_states)
seq_length = shape_list(tensor=hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size])
hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.decoder_bias)
return hidden_states
@keras_serializable
class TFAlbertMainLayer(tf.keras.layers.Layer):
config_class = AlbertConfig
def __init__(self, config: AlbertConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFAlbertEmbeddings(config, name="embeddings")
self.encoder = TFAlbertTransformer(config, name="encoder")
self.pooler = (
tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="pooler",
)
if add_pooling_layer
else None
)
def get_input_embeddings(self) -> tf.keras.layers.Layer:
return self.embeddings
def set_input_embeddings(self, value: tf.Variable):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
raise NotImplementedError
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
**kwargs,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif inputs["input_ids"] is not None:
input_shape = shape_list(inputs["input_ids"])
elif inputs["inputs_embeds"] is not None:
input_shape = shape_list(inputs["inputs_embeds"])[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill(dims=input_shape, value=1)
if inputs["token_type_ids"] is None:
inputs["token_type_ids"] = tf.fill(dims=input_shape, value=0)
embedding_output = self.embeddings(
input_ids=inputs["input_ids"],
position_ids=inputs["position_ids"],
token_type_ids=inputs["token_type_ids"],
inputs_embeds=inputs["inputs_embeds"],
training=inputs["training"],
)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = tf.reshape(inputs["attention_mask"], (input_shape[0], 1, 1, input_shape[1]))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if inputs["head_mask"] is not None:
raise NotImplementedError
else:
inputs["head_mask"] = [None] * self.config.num_hidden_layers
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=extended_attention_mask,
head_mask=inputs["head_mask"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(inputs=sequence_output[:, 0]) if self.pooler is not None else None
if not inputs["return_dict"]:
return (
sequence_output,
pooled_output,
) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@dataclass
class TFAlbertForPreTrainingOutput(ModelOutput):
"""
Output type of :class:`~transformers.TFAlbertForPreTraining`.
Args:
prediction_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
sop_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: tf.Tensor = None
prediction_logits: tf.Tensor = None
sop_logits: tf.Tensor = None
hidden_states: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[tf.Tensor]] = None
ALBERT_START_DOCSTRING = r"""
This model inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading or saving, resizing the input
embeddings, pruning heads etc.)
This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
and behavior.
.. note::
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
the tensors in the first argument of the model call function: :obj:`model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
the first positional argument :
- a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
:obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Args:
config (:class:`~transformers.AlbertConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
weights.
"""
ALBERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`~transformers.AlbertTokenizer`. See
:func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
1]``:
- 0 corresponds to a `sentence A` token,
- 1 corresponds to a `sentence B` token.
`What are token type IDs? <../glossary.html#token-type-ids>`_
position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
config.max_position_embeddings - 1]``.
`What are position IDs? <../glossary.html#position-ids>`_
head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (:obj:`tf.Tensor` of shape :obj:`({0}, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare Albert Model transformer outputting raw hidden-states without any specific head on top.",
ALBERT_START_DOCSTRING,
)
class TFAlbertModel(TFAlbertPreTrainedModel):
def __init__(self, config: AlbertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.albert = TFAlbertMainLayer(config, name="albert")
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
outputs = self.albert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
return outputs
# Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output
def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutputWithPooling(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=hs,
attentions=attns,
)
@add_start_docstrings(
"""
Albert Model with two heads on top for pretraining: a `masked language modeling` head and a `sentence order
prediction` (classification) head.
""",
ALBERT_START_DOCSTRING,
)
class TFAlbertForPreTraining(TFAlbertPreTrainedModel, TFAlbertPreTrainingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"predictions.decoder.weight"]
def __init__(self, config: AlbertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.albert = TFAlbertMainLayer(config, name="albert")
self.predictions = TFAlbertMLMHead(config, input_embeddings=self.albert.embeddings, name="predictions")
self.sop_classifier = TFAlbertSOPHead(config, name="sop_classifier")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.predictions
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
sentence_order_label: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFAlbertForPreTrainingOutput, Tuple[tf.Tensor]]:
r"""
Return:
Example::
>>> import tensorflow as tf
>>> from transformers import AlbertTokenizer, TFAlbertForPreTraining
>>> tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
>>> model = TFAlbertForPreTraining.from_pretrained('albert-base-v2')
>>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
>>> outputs = model(input_ids)
>>> prediction_logits = outputs.prediction_logits
>>> sop_logits = outputs.sop_logits
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
sentence_order_label=sentence_order_label,
training=training,
kwargs_call=kwargs,
)
outputs = self.albert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output, pooled_output = outputs[:2]
prediction_scores = self.predictions(hidden_states=sequence_output)
sop_scores = self.sop_classifier(pooled_output=pooled_output, training=inputs["training"])
total_loss = None
if inputs["labels"] is not None and inputs["sentence_order_label"] is not None:
d_labels = {"labels": inputs["labels"]}
d_labels["sentence_order_label"] = inputs["sentence_order_label"]
total_loss = self.compute_loss(labels=d_labels, logits=(prediction_scores, sop_scores))
if not inputs["return_dict"]:
output = (prediction_scores, sop_scores) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return TFAlbertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
sop_logits=sop_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFAlbertForPreTrainingOutput) -> TFAlbertForPreTrainingOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFAlbertForPreTrainingOutput(
prediction_logits=output.prediction_logits,
sop_logits=output.sop_logits,
hidden_states=hs,
attentions=attns,
)
class TFAlbertSOPHead(tf.keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.dropout = tf.keras.layers.Dropout(rate=config.classifier_dropout_prob)
self.classifier = tf.keras.layers.Dense(
units=config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="classifier",
)
def call(self, pooled_output: tf.Tensor, training: bool) -> tf.Tensor:
dropout_pooled_output = self.dropout(inputs=pooled_output, training=training)
logits = self.classifier(inputs=dropout_pooled_output)
return logits
@add_start_docstrings("""Albert Model with a `language modeling` head on top. """, ALBERT_START_DOCSTRING)
class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions.decoder.weight"]
def __init__(self, config: AlbertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert")
self.predictions = TFAlbertMLMHead(config, input_embeddings=self.albert.embeddings, name="predictions")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.predictions
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.albert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = outputs[0]
prediction_scores = self.predictions(hidden_states=sequence_output, training=inputs["training"])
loss = (
None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=prediction_scores)
)
if not inputs["return_dict"]:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output
def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
ALBERT_START_DOCSTRING,
)
class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"predictions"]
_keys_to_ignore_on_load_missing = [r"dropout"]
def __init__(self, config: AlbertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.albert = TFAlbertMainLayer(config, name="albert")
self.dropout = tf.keras.layers.Dropout(rate=config.classifier_dropout_prob)
self.classifier = tf.keras.layers.Dense(
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in ``[0, ...,
config.num_labels - 1]``. If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.albert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
pooled_output = outputs[1]
pooled_output = self.dropout(inputs=pooled_output, training=inputs["training"])
logits = self.classifier(inputs=pooled_output)
loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=logits)
if not inputs["return_dict"]:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Albert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
ALBERT_START_DOCSTRING,
)
class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
_keys_to_ignore_on_load_missing = [r"dropout"]
def __init__(self, config: AlbertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
1]``.
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.albert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=return_dict,
training=inputs["training"],
)
sequence_output = outputs[0]
sequence_output = self.dropout(inputs=sequence_output, training=inputs["training"])
logits = self.classifier(inputs=sequence_output)
loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=logits)
if not inputs["return_dict"]:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output
def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Albert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
ALBERT_START_DOCSTRING,
)
class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
def __init__(self, config: AlbertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert")
self.qa_outputs = tf.keras.layers.Dense(
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r"""
start_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
end_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
start_positions=start_positions,
end_positions=end_positions,
training=training,
kwargs_call=kwargs,
)
outputs = self.albert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = outputs[0]
logits = self.qa_outputs(inputs=sequence_output)
start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
start_logits = tf.squeeze(input=start_logits, axis=-1)
end_logits = tf.squeeze(input=end_logits, axis=-1)
loss = None
if inputs["start_positions"] is not None and inputs["end_positions"] is not None:
labels = {"start_position": inputs["start_positions"]}
labels["end_position"] = inputs["end_positions"]
loss = self.compute_loss(labels=labels, logits=(start_logits, end_logits))
if not inputs["return_dict"]:
output = (start_logits, end_logits) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output
def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFQuestionAnsweringModelOutput(
start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
)
@add_start_docstrings(
"""
Albert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
ALBERT_START_DOCSTRING,
)
class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
_keys_to_ignore_on_load_missing = [r"dropout"]
def __init__(self, config: AlbertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.albert = TFAlbertMainLayer(config, name="albert")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
:obj:`input_ids` above)
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None:
num_choices = shape_list(inputs["input_ids"])[1]
seq_length = shape_list(inputs["input_ids"])[2]
else:
num_choices = shape_list(inputs["inputs_embeds"])[1]
seq_length = shape_list(inputs["inputs_embeds"])[2]
flat_input_ids = tf.reshape(inputs["input_ids"], (-1, seq_length)) if inputs["input_ids"] is not None else None
flat_attention_mask = (
tf.reshape(tensor=inputs["attention_mask"], shape=(-1, seq_length))
if inputs["attention_mask"] is not None
else None
)
flat_token_type_ids = (
tf.reshape(tensor=inputs["token_type_ids"], shape=(-1, seq_length))
if inputs["token_type_ids"] is not None
else None
)
flat_position_ids = (
tf.reshape(tensor=position_ids, shape=(-1, seq_length)) if position_ids is not None else None
)
flat_inputs_embeds = (
tf.reshape(tensor=inputs["inputs_embeds"], shape=(-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
if inputs["inputs_embeds"] is not None
else None
)
outputs = self.albert(
input_ids=flat_input_ids,
attention_mask=flat_attention_mask,
token_type_ids=flat_token_type_ids,
position_ids=flat_position_ids,
head_mask=inputs["head_mask"],
inputs_embeds=flat_inputs_embeds,
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
pooled_output = outputs[1]
pooled_output = self.dropout(inputs=pooled_output, training=inputs["training"])
logits = self.classifier(inputs=pooled_output)
reshaped_logits = tf.reshape(tensor=logits, shape=(-1, num_choices))
loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=reshaped_logits)
if not inputs["return_dict"]:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFMultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None, None), tf.int32, name="token_type_ids"),
}
]
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving
def serving(self, inputs: Dict[str, tf.Tensor]) -> TFMultipleChoiceModelOutput:
output = self.call(input_ids=inputs)
return self.serving_output(output)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output
def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
|
AdaMix/src/transformers/models/albert/modeling_tf_albert.py/0
|
{
"file_path": "AdaMix/src/transformers/models/albert/modeling_tf_albert.py",
"repo_id": "AdaMix",
"token_count": 30002
}
| 50 |
# coding=utf-8
# Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization class for BlenderbotSmall."""
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
},
"merges_file": {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
},
"tokenizer_config_file": {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot_small-90M": 512}
def get_pairs(word):
"""
Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
pairs = set(pairs)
return pairs
class BlenderbotSmallTokenizer(PreTrainedTokenizer):
"""
Constructs a Blenderbot-90M tokenizer based on BPE (Byte-Pair-Encoding)
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
Users should refer to the superclass for more information regarding methods.
Args:
vocab_file (:obj:`str`):
File containing the vocabulary.
merges_file (:obj:`str`):
Path to the merges file.
bos_token (:obj:`str`, `optional`, defaults to :obj:`"__start__"`):
The beginning of sentence token.
eos_token (:obj:`str`, `optional`, defaults to :obj:`"__end__"`):
The end of sentence token.
unk_token (:obj:`str`, `optional`, defaults to :obj:`"__unk__"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (:obj:`str`, `optional`, defaults to :obj:`"__pad__"`):
The token used for padding, for example when batching sequences of different lengths.
**kwargs
Additional keyword arguments passed along to :class:`~transformers.PreTrainedTokenizer`
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
merges_file,
bos_token="__start__",
eos_token="__end__",
unk_token="__unk__",
pad_token="__null__",
**kwargs
):
super().__init__(unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, **kwargs)
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
merges = merges_handle.read().split("\n")[1:-1]
merges = [tuple(merge.split()) for merge in merges]
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
@property
def vocab_size(self) -> int:
return len(self.encoder)
def get_vocab(self) -> Dict:
return dict(self.encoder, **self.added_tokens_encoder)
def bpe(self, token: str) -> str:
if token in self.cache:
return self.cache[token]
token = re.sub("([.,!?()])", r" \1", token)
token = re.sub("(')", r" \1 ", token)
token = re.sub(r"\s{2,}", " ", token)
if "\n" in token:
token = token.replace("\n", " __newln__")
tokens = token.split(" ")
words = []
for token in tokens:
if not len(token):
continue
token = token.lower()
word = tuple(token)
word = tuple(list(word[:-1]) + [word[-1] + "</w>"])
pairs = get_pairs(word)
if not pairs:
words.append(token)
continue
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except ValueError:
new_word.extend(word[i:])
break
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = "@@ ".join(word)
word = word[:-4]
self.cache[token] = word
words.append(word)
return " ".join(words)
def _tokenize(self, text: str) -> List[str]:
""" Split a string into tokens using BPE."""
split_tokens = []
words = re.findall(r"\S+\n?", text)
for token in words:
split_tokens.extend([t for t in self.bpe(token).split(" ")])
return split_tokens
def _convert_token_to_id(self, token: str) -> int:
""" Converts a token to an id using the vocab. """
token = token.lower()
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
""" Converts a sequence of tokens in a single string. """
out_string = " ".join(tokens).replace("@@ ", "").strip()
return out_string
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
merge_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
"Saving vocabulary to {}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(merge_file)
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return vocab_file, merge_file
|
AdaMix/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py/0
|
{
"file_path": "AdaMix/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py",
"repo_id": "AdaMix",
"token_count": 4044
}
| 51 |
# coding=utf-8
# Copyright 2018 Salesforce and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch CTRL model."""
from typing import Tuple
import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
from ...modeling_utils import Conv1D, PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import logging
from .configuration_ctrl import CTRLConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "ctrl"
_CONFIG_FOR_DOC = "CTRLConfig"
_TOKENIZER_FOR_DOC = "CTRLTokenizer"
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = [
"ctrl"
# See all CTRL models at https://huggingface.co/models?filter=ctrl
]
def angle_defn(pos, i, d_model_size):
angle_rates = 1 / torch.pow(10000, (2 * (i // 2)) / d_model_size)
return pos * angle_rates
def positional_encoding(position, d_model_size, dtype):
# create the sinusoidal pattern for the positional encoding
angle_rads = angle_defn(
torch.arange(position, dtype=dtype).unsqueeze(1),
torch.arange(d_model_size, dtype=dtype).unsqueeze(0),
d_model_size,
)
sines = torch.sin(angle_rads[:, 0::2])
cosines = torch.cos(angle_rads[:, 1::2])
pos_encoding = torch.cat([sines, cosines], dim=-1)
return pos_encoding
def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=None):
# calculate attention
matmul_qk = torch.matmul(q, k.permute(0, 1, 3, 2))
dk = k.shape[-1]
scaled_attention_logits = matmul_qk / np.sqrt(dk)
if mask is not None:
nd, ns = scaled_attention_logits.size(-2), scaled_attention_logits.size(-1)
scaled_attention_logits += mask[ns - nd : ns, :ns] * -1e4
if attention_mask is not None:
# Apply the attention mask
scaled_attention_logits = scaled_attention_logits + attention_mask
attention_weights = torch.softmax(scaled_attention_logits, dim=-1)
# Mask heads if we want to
if head_mask is not None:
attention_weights = attention_weights * head_mask
output = torch.matmul(attention_weights, v)
return output, attention_weights
class MultiHeadAttention(torch.nn.Module):
def __init__(self, d_model_size, num_heads):
super().__init__()
self.num_heads = num_heads
self.d_model_size = d_model_size
self.depth = int(d_model_size / self.num_heads)
self.Wq = torch.nn.Linear(d_model_size, d_model_size)
self.Wk = torch.nn.Linear(d_model_size, d_model_size)
self.Wv = torch.nn.Linear(d_model_size, d_model_size)
self.dense = torch.nn.Linear(d_model_size, d_model_size)
self.pruned_heads = set()
def prune_heads(self, heads):
attention_head_size = self.d_model_size // self.num_heads
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, attention_head_size, self.pruned_heads)
# Prune linear layers
self.Wq = prune_linear_layer(self.Wq, index)
self.Wk = prune_linear_layer(self.Wk, index)
self.Wv = prune_linear_layer(self.Wv, index)
self.dense = prune_linear_layer(self.dense, index, dim=1)
# Update hyper params
self.num_heads = self.num_heads - len(heads)
self.d_model_size = attention_head_size * self.num_heads
self.pruned_heads = self.pruned_heads.union(heads)
def split_into_heads(self, x, batch_size):
x = x.reshape(batch_size, -1, self.num_heads, self.depth)
return x.permute([0, 2, 1, 3])
def forward(
self,
v,
k,
q,
mask,
layer_past=None,
attention_mask=None,
head_mask=None,
use_cache=False,
output_attentions=False,
):
batch_size = q.shape[0]
q = self.Wq(q)
k = self.Wk(k)
v = self.Wv(v)
q = self.split_into_heads(q, batch_size)
k = self.split_into_heads(k, batch_size)
v = self.split_into_heads(v, batch_size)
if layer_past is not None:
past_key, past_value = layer_past[0], layer_past[1]
k = torch.cat((past_key, k), dim=-2)
v = torch.cat((past_value, v), dim=-2)
if use_cache is True:
present = torch.stack((k, v))
else:
present = (None,)
output = scaled_dot_product_attention(q, k, v, mask, attention_mask, head_mask)
scaled_attention = output[0].permute([0, 2, 1, 3])
attn = output[1]
original_size_attention = scaled_attention.reshape(batch_size, -1, self.d_model_size)
output = self.dense(original_size_attention)
outputs = (output, present)
if output_attentions:
outputs = outputs + (attn,)
return outputs
def point_wise_feed_forward_network(d_model_size, dff):
return torch.nn.Sequential(torch.nn.Linear(d_model_size, dff), torch.nn.ReLU(), torch.nn.Linear(dff, d_model_size))
class EncoderLayer(torch.nn.Module):
def __init__(self, d_model_size, num_heads, dff, rate=0.1):
super().__init__()
self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads)
self.ffn = point_wise_feed_forward_network(d_model_size, dff)
self.layernorm1 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
self.layernorm2 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
self.dropout1 = torch.nn.Dropout(rate)
self.dropout2 = torch.nn.Dropout(rate)
def forward(
self, x, mask, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False
):
normed = self.layernorm1(x)
attn_outputs = self.multi_head_attention(
normed,
normed,
normed,
mask,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0]
attn_output = self.dropout1(attn_output)
out1 = x + attn_output
out2 = self.layernorm2(out1)
ffn_output = self.ffn(out2)
ffn_output = self.dropout2(ffn_output)
out2 = out1 + ffn_output
outputs = (out2,) + attn_outputs[1:]
return outputs
class CTRLPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CTRLConfig
base_model_prefix = "transformer"
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear, Conv1D)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
CTRL_START_DOCSTRING = r"""
This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
pruning heads etc.)
This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.
Parameters:
config (:class:`~transformers.CTRLConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
weights.
"""
CTRL_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
:obj:`input_ids_length` = ``sequence_length`` if :obj:`past_key_values` is ``None`` else
``past_key_values[0].shape[-2]`` (``sequence_length`` of input past key value states). Indices of input
sequence tokens in the vocabulary.
If :obj:`past_key_values` is used, only input IDs that do not have their past calculated should be passed
as ``input_ids``.
Indices can be obtained using :class:`~transformers.CTRLTokenizer`. See
:meth:`transformers.PreTrainedTokenizer.__call__` and :meth:`transformers.PreTrainedTokenizer.encode` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
past_key_values (:obj:`Tuple[Tuple[torch.FloatTensor]]` of length :obj:`config.n_layers`):
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
:obj:`past_key_values` output below). Can be used to speed up sequential decoding. The ``input_ids`` which
have their past given to this model should not be passed as input ids as they have already been computed.
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
1]``:
- 0 corresponds to a `sentence A` token,
- 1 corresponds to a `sentence B` token.
`What are token type IDs? <../glossary.html#token-type-ids>`_
position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
config.max_position_embeddings - 1]``.
`What are position IDs? <../glossary.html#position-ids>`_
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
"""
@add_start_docstrings(
"The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.",
CTRL_START_DOCSTRING,
)
class CTRLModel(CTRLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.d_model_size = config.n_embd
self.num_layers = config.n_layer
self.pos_encoding = positional_encoding(config.n_positions, self.d_model_size, torch.float)
self.w = nn.Embedding(config.vocab_size, config.n_embd)
self.dropout = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList(
[EncoderLayer(config.n_embd, config.n_head, config.dff, config.resid_pdrop) for _ in range(config.n_layer)]
)
self.layernorm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.init_weights()
def get_input_embeddings(self):
return self.w
def set_input_embeddings(self, new_embeddings):
self.w = new_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
for layer, heads in heads_to_prune.items():
self.h[layer].multi_head_attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
assert batch_size > 0, "batch_size has to be defined and > 0"
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
token_type_embeds = self.w(token_type_ids)
token_type_embeds *= np.sqrt(self.d_model_size)
else:
token_type_embeds = 0
position_ids = position_ids.view(-1, input_shape[-1])
if inputs_embeds is None:
inputs_embeds = self.w(input_ids)
# inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
seq_len = input_shape[-1]
mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(inputs_embeds.device)
inputs_embeds *= np.sqrt(self.d_model_size)
pos_embeds = self.pos_encoding[position_ids, :].to(inputs_embeds.device)
hidden_states = inputs_embeds + pos_embeds + token_type_embeds
hidden_states = self.dropout(hidden_states)
presents = () if use_cache else None
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, (h, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = h(
hidden_states,
mask,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states, present = outputs[:2]
if use_cache is True:
presents = presents + (present,)
if output_attentions:
all_attentions += (outputs[2],)
hidden_states = self.layernorm(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
@add_start_docstrings(
"""
The CTRL Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
CTRL_START_DOCSTRING,
)
class CTRLLMHeadModel(CTRLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = CTRLModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=True)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(self, input_ids, past=None, use_cache=None, **kwargs):
# only last token for inputs_ids if past is defined in kwargs
if past:
input_ids = input_ids[:, -1].unsqueeze(-1)
return {"input_ids": input_ids, "past_key_values": past, "use_cache": use_cache}
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@staticmethod
def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the :obj:`past_key_values` cache if
:meth:`~transformers.PretrainedModel.beam_search` or :meth:`~transformers.PretrainedModel.beam_sample` is
called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
"""
return tuple(
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past
)
@add_start_docstrings(
"""
The CTRL Model transformer with a sequence classification head on top (linear layer).
:class:`~transformers.CTRLForSequenceClassification` uses the last token in order to do the classification, as
other causal models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the
position of the last token. If a :obj:`pad_token_id` is defined in the configuration, it finds the last token that
is not a padding token in each row. If no :obj:`pad_token_id` is defined, it simply takes the last value in each
row of the batch. Since it cannot guess the padding tokens when :obj:`inputs_embeds` are passed instead of
:obj:`input_ids`, it does the same (take the last value in each row of the batch).
""",
CTRL_START_DOCSTRING,
)
class CTRLForSequenceClassification(CTRLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = CTRLModel(config)
self.classifier = nn.Linear(config.n_embd, self.num_labels, bias=False)
self.init_weights()
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.classifier(hidden_states)
if input_ids is not None:
batch_size, sequence_length = input_ids.shape[:2]
else:
batch_size, sequence_length = inputs_embeds.shape[:2]
assert (
self.config.pad_token_id is not None or batch_size == 1
), "Cannot handle batch sizes > 1 if no padding token is defined."
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
else:
sequence_lengths = -1
logger.warning(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
f"unexpected if using padding tokens in conjuction with `inputs_embeds.`"
)
pooled_logits = logits[range(batch_size), sequence_lengths]
loss = None
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(pooled_logits.view(-1), labels.to(self.dtype).view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (pooled_logits,) + transformer_outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=pooled_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
|
AdaMix/src/transformers/models/ctrl/modeling_ctrl.py/0
|
{
"file_path": "AdaMix/src/transformers/models/ctrl/modeling_ctrl.py",
"repo_id": "AdaMix",
"token_count": 12620
}
| 52 |
# coding=utf-8
# Copyright 2020, Microsoft and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" DeBERTa-v2 model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
"microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
"microsoft/deberta-v2-xlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json",
"microsoft/deberta-v2-xxlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json",
}
class DebertaV2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.DebertaV2Model`. It is used
to instantiate a DeBERTa-v2 model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the DeBERTa
`microsoft/deberta-v2-xlarge <https://huggingface.co/microsoft/deberta-base>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Arguments:
vocab_size (:obj:`int`, `optional`, defaults to 128100):
Vocabulary size of the DeBERTa-v2 model. Defines the number of different tokens that can be represented by
the :obj:`inputs_ids` passed when calling :class:`~transformers.DebertaV2Model`.
hidden_size (:obj:`int`, `optional`, defaults to 1536):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, `optional`, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, `optional`, defaults to 24):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, `optional`, defaults to 6144):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"`, :obj:`"gelu"`, :obj:`"tanh"`, :obj:`"gelu_fast"`,
:obj:`"mish"`, :obj:`"linear"`, :obj:`"sigmoid"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, `optional`, defaults to 0):
The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.DebertaModel` or
:class:`~transformers.TFDebertaModel`.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-7):
The epsilon used by the layer normalization layers.
relative_attention (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether use relative position encoding.
max_relative_positions (:obj:`int`, `optional`, defaults to -1):
The range of relative positions :obj:`[-max_position_embeddings, max_position_embeddings]`. Use the same
value as :obj:`max_position_embeddings`.
pad_token_id (:obj:`int`, `optional`, defaults to 0):
The value used to pad input_ids.
position_biased_input (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether add absolute position embedding to content embedding.
pos_att_type (:obj:`List[str]`, `optional`):
The type of relative position attention, it can be a combination of :obj:`["p2c", "c2p", "p2p"]`, e.g.
:obj:`["p2c"]`, :obj:`["p2c", "c2p"]`, :obj:`["p2c", "c2p", 'p2p"]`.
layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
The epsilon used by the layer normalization layers.
cls_dropout (:obj:`float`, `optional`):
cls dropout.
apply_lora (:obj:`bool`, `optional`):
apply Lora.
lora_alpha (:obj:`int`, `optional`):
lora alpha.
lora_r (:obj:`int`, `optional`):
lora r.
rdrop_loss_wgt (:obj:`float`, `optional`, defaults to 0):
rdrop loss weight.
"""
model_type = "deberta-v2"
def __init__(
self,
vocab_size=128100,
hidden_size=1536,
num_hidden_layers=24,
num_attention_heads=24,
intermediate_size=6144,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=0,
initializer_range=0.02,
layer_norm_eps=1e-7,
relative_attention=False,
max_relative_positions=-1,
pad_token_id=0,
position_biased_input=True,
pos_att_type=None,
pooler_dropout=0,
pooler_hidden_act="gelu",
cls_dropout=None,
apply_lora=False,
lora_alpha=None,
lora_r=None,
reg_loss_wgt=0.0,
masking_prob=0.0,
cls_token_id=1,
sep_token_id=2,
unk_token_id=3,
**kwargs
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.relative_attention = relative_attention
self.max_relative_positions = max_relative_positions
self.pad_token_id = pad_token_id
self.position_biased_input = position_biased_input
# Backwards compatibility
if type(pos_att_type) == str:
pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")]
self.pos_att_type = pos_att_type
self.vocab_size = vocab_size
self.layer_norm_eps = layer_norm_eps
self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size)
self.pooler_dropout = pooler_dropout
self.pooler_hidden_act = pooler_hidden_act
self.cls_dropout = cls_dropout
self.apply_lora = apply_lora
self.lora_alpha = lora_alpha
self.lora_r = lora_r
self.reg_loss_wgt = reg_loss_wgt
self.masking_prob = masking_prob
self.cls_token_id = cls_token_id
self.sep_token_id = sep_token_id
self.unk_token_id = unk_token_id
|
AdaMix/src/transformers/models/deberta_v2/configuration_deberta_v2.py/0
|
{
"file_path": "AdaMix/src/transformers/models/deberta_v2/configuration_deberta_v2.py",
"repo_id": "AdaMix",
"token_count": 3471
}
| 53 |
# coding=utf-8
# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Original implementation: https://github.com/pytorch/fairseq/tree/master/examples/wmt19
# Authors:
# - @alexeib Alexei Baevski
# - @edunov Sergey Edunov
# - @michaelauli Michael Auli
# - @myleott Myle Ott
# - @nng555 Nathan Ng
# - David Grangier
# - Kyra Yee
#
# Paper: Facebook FAIR's WMT19 News Translation Task Submission https://arxiv.org/abs/1907.06616
#
"""PyTorch Fairseq model, ported from https://github.com/pytorch/fairseq/tree/master/examples/wmt19"""
import math
import random
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss, LayerNorm
from ...activations import ACT2FN
from ...file_utils import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_fsmt import FSMTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/wmt19-ru-en"
_CONFIG_FOR_DOC = "FSMTConfig"
_TOKENIZER_FOR_DOC = "FSMTTokenizer"
# See all FSMT models at https://huggingface.co/models?filter=fsmt
# Porting notes:
# this one is modeled after BartModel*
#
# Currently only translation (fairseq also has weights for LM)
#
# fairseq provides weights for ru-en, en-ru and de-en, en-de pairs. All have been ported.
# - ru-en, en-ru use asymmetric vocab
# - de-en, en-de use a merged single vocab (but the code works as if they are separate)
#
# Differences with Bart:
# - not using bos token
# - 2 separate vocabs (src and target)
# - embed weights aren't tied
# - uses a model Ensemble (but that part isn't ported/implemented yet) - so we
# aren't getting as good of a BLEU score
# - uses a projection layer at the end of the decoder
# - doesn't use final_logits_bias
# - beam search: stops as soon as num_beams == len(hypos) (whereas transformers
# is not satisfied there and will continue searching until the next cycles
# aren't promising something better), comparing BLEU scores - the transformers
# algorithm is slightly superior, therefore using the latter. But if you want
# to match fairseq outputs, you need to pass ``early_stopping=True`` to ``generate()``.
#
# SinusoidalPositionalEmbedding is slightly different from Bart's - generates
# different embeddings. This implementation is copied verbatim from fairseq with
# some small changes to make it work here.
#
# Other changes:
# - doesn't support use_cache as Bart's version does
#
#
# FSMTConfig changes with BartConfig
#
# Differences with BART:
# - src/tgt vocabs aren't shared
# - token embeddings aren't shared
# - needs a language pair
# - scale_embedding are True
#
# some unused args were removed too
#
#
# TODO:
# - port model ensemble (fs uses 4 model checkpoints)
# - solve beam search discrepancies
# docstyle-ignore
"""
Here is how to compare BLEU scores against fairseq implementation:
# en-ru
export PAIR=en-ru
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=50
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
# (fairseq BLEU: 36.4 http://matrix.statmt.org/matrix/output/1914?score_id=37605)
# ru-en
export PAIR=ru-en
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=50
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
# (fairseq BLEU: 41.3 http://matrix.statmt.org/matrix/output/1907?run_id=6937)
# de-en
export PAIR=de-en
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=50
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
# (fairseq BLEU: 42.3 http://matrix.statmt.org/matrix/output/1902?run_id=6750)
# en-de
export PAIR=en-de
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
# (fairseq BLEU: 43.1 http://matrix.statmt.org/matrix/output/1909?run_id=6862)
"""
FSMT_START_DOCSTRING = r"""
This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
pruning heads etc.)
This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.
Parameters:
config (:class:`~transformers.FSMTConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
weights.
"""
FSMT_GENERATION_EXAMPLE = r"""
Translation example::
from transformers import FSMTTokenizer, FSMTForConditionalGeneration
mname = "facebook/wmt19-ru-en"
model = FSMTForConditionalGeneration.from_pretrained(mname)
tokenizer = FSMTTokenizer.from_pretrained(mname)
src_text = "Машинное обучение - это здорово, не так ли?"
input_ids = tokenizer.encode(src_text, return_tensors='pt')
outputs = model.generate(input_ids, num_beams=5, num_return_sequences=3)
for i, output in enumerate(outputs):
decoded = tokenizer.decode(output, skip_special_tokens=True)
print(f"{i}: {decoded})
# 1: Machine learning is great, isn't it? ...
"""
FSMT_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
IIndices can be obtained using :class:`~transformers.FSTMTokenizer`. See
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Provide for translation and summarization training. By default, the model will create this tensor by
shifting the input_ids right, following the paper.
decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
also be used by default. If you want to change padding behavior, you should read
:func:`modeling_fstm._prepare_fstm_decoder_inputs` and modify. See diagram 1 in the paper for more info on
the default strategy
head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the heas is **masked**.
decoder_head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (:obj:`Tuple(torch.FloatTensor)`, `optional`):
Tuple consists of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`:
:obj:`attentions`) :obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)` is a
sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
the decoder.
past_key_values (:obj:`Tuple(torch.FloatTensor)` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
"""
def invert_mask(attention_mask):
"""Turns 1->0, 0->1, False->True, True-> False"""
assert attention_mask.dim() == 2
return attention_mask.eq(0)
def triu_onnx(x, diagonal=0):
l = x.shape[0]
arange = torch.arange(l, device=x.device)
mask = arange.expand(l, l)
arange = arange.unsqueeze(-1)
if diagonal:
arange = arange + diagonal
mask = mask >= arange
return x.masked_fill(mask == 0, 0)
def _prepare_fsmt_decoder_inputs(
config,
input_ids,
decoder_input_ids=None,
decoder_padding_mask=None,
causal_mask_dtype=torch.float32,
):
"""
Prepare masks that ignore padding tokens in the decoder and a causal mask for the decoder if none are provided.
This mimics the default behavior in fairseq. To override it pass in masks. Note: this is not called during
generation
"""
pad_token_id = config.pad_token_id
if decoder_input_ids is None:
decoder_input_ids = shift_tokens_right(input_ids, pad_token_id)
bsz, tgt_len = decoder_input_ids.size()
if decoder_padding_mask is None:
decoder_padding_mask = make_padding_mask(decoder_input_ids, pad_token_id)
else:
decoder_padding_mask = invert_mask(decoder_padding_mask)
causal_mask = triu_onnx(fill_with_neg_inf(torch.zeros(tgt_len, tgt_len)), 1).to(
dtype=causal_mask_dtype, device=decoder_input_ids.device
)
return decoder_input_ids, decoder_padding_mask, causal_mask
class PretrainedFSMTModel(PreTrainedModel):
config_class = FSMTConfig
base_model_prefix = "model"
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, SinusoidalPositionalEmbedding):
pass
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
}
return dummy_inputs
def _make_linear_from_emb(emb):
vocab_size, emb_size = emb.weight.shape
lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
lin_layer.weight.data = emb.weight.data
return lin_layer
# Helper Functions, mostly for making masks
def _check_shapes(shape_1, shape2):
if shape_1 != shape2:
raise AssertionError("shape mismatch: {} != {}".format(shape_1, shape2))
def shift_tokens_right(input_ids, pad_token_id):
"""Shift input ids one token to the right, and wrap the last non pad token (usually <eos>)."""
prev_output_tokens = input_ids.clone()
index_of_eos = (input_ids.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
prev_output_tokens[:, 0] = input_ids.gather(1, index_of_eos).squeeze()
prev_output_tokens[:, 1:] = input_ids[:, :-1]
return prev_output_tokens
def make_padding_mask(input_ids, padding_idx=1):
"""True for pad tokens"""
padding_mask = input_ids.eq(padding_idx)
if not padding_mask.any():
padding_mask = None
return padding_mask
# Helper Modules
class EncoderLayer(nn.Module):
def __init__(self, config: FSMTConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = Attention(self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout)
self.self_attn_layer_norm = LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim)
def forward(self, x, encoder_padding_mask, layer_head_mask, output_attentions=False):
"""
Args:
x (:obj:`torch.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (:obj:`torch.ByteTensor`): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
for t_tgt, t_src is excluded (or masked out), =0 means it is
included in attention
layer_head_mask (:obj:`torch.FloatTensor`): mask for attention heads in a given layer of size
`(config.encoder_attention_heads,)`.
Returns:
encoded output of shape `(seq_len, batch, embed_dim)`
"""
residual = x
x, attn_weights = self.self_attn(
query=x,
key=x,
key_padding_mask=encoder_padding_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.self_attn_layer_norm(x)
residual = x
x = self.activation_fn(self.fc1(x))
x = F.dropout(x, p=self.activation_dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.final_layer_norm(x)
return x, attn_weights
class FSMTEncoder(nn.Module):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
:class:`EncoderLayer`.
Args:
config: FSMTConfig
"""
def __init__(self, config: FSMTConfig, embed_tokens):
super().__init__()
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
self.padding_idx = embed_tokens.padding_idx
self.embed_tokens = embed_tokens
embed_dim = embed_tokens.embedding_dim
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
self.embed_positions = SinusoidalPositionalEmbedding(
config.max_position_embeddings + self.padding_idx + 1, embed_dim, self.padding_idx
)
self.layers = nn.ModuleList(
[EncoderLayer(config) for _ in range(config.encoder_layers)]
) # type: List[EncoderLayer]
def forward(
self,
input_ids,
attention_mask=None,
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
"""
Args:
input_ids (:obj:`torch.LongTensor`): tokens in the source language of shape
`(batch, src_len)`
attention_mask (:obj:`torch.LongTensor`): indicating which indices are padding tokens
head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the heas is **masked**.
Returns:
BaseModelOutput or Tuple comprised of:
- **x** (:obj:`torch.Tensor`): the last encoder layer's output of shape `(src_len, batch, embed_dim)`
- **encoder_states** (:obj:`Tuple(torch.FloatTensor`)): all intermediate hidden states of shape
`(src_len, batch, embed_dim)`. Only populated if *output_hidden_states:* is True.
- **all_attentions** (:obj:`Tuple(torch.FloatTensor`)): Attention weights for each layer.
During training might not be of length n_layers because of layer dropout.
"""
# check attention mask and invert
if attention_mask is not None:
attention_mask = invert_mask(attention_mask)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_ids)
x = inputs_embeds + embed_pos
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layers)
), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
x = x.transpose(0, 1) # T x B x C -> B x T x C
encoder_states += (x,)
x = x.transpose(0, 1) # B x T x C -> T x B x C
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop): # skip the layer
attn = None
else:
x, attn = encoder_layer(
x,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
if output_attentions:
all_attentions = all_attentions + (attn,)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if output_hidden_states:
encoder_states += (x,)
if not return_dict:
return tuple(v for v in [x, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(last_hidden_state=x, hidden_states=encoder_states, attentions=all_attentions)
class DecoderLayer(nn.Module):
def __init__(self, config: FSMTConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = Attention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = LayerNorm(self.embed_dim)
self.encoder_attn = Attention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
encoder_decoder_attention=True,
)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim)
def forward(
self,
x,
encoder_hidden_states,
encoder_attn_mask=None,
layer_state=None,
causal_mask=None,
layer_head_mask=None,
encoder_layer_head_mask=None,
decoder_padding_mask=None,
output_attentions=False,
):
residual = x
if layer_state is None:
layer_state = {}
# Self Attention
x, self_attn_weights = self.self_attn(
query=x,
key=x,
layer_state=layer_state, # adds keys to layer state
key_padding_mask=decoder_padding_mask,
attn_mask=causal_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.self_attn_layer_norm(x)
# Cross attention
residual = x
assert self.encoder_attn.cache_key != self.self_attn.cache_key
x, cross_attn_weights = self.encoder_attn(
query=x,
key=encoder_hidden_states,
key_padding_mask=encoder_attn_mask,
layer_state=layer_state, # mutates layer state
layer_head_mask=encoder_layer_head_mask,
output_attentions=output_attentions,
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.encoder_attn_layer_norm(x)
# Fully Connected
residual = x
x = self.activation_fn(self.fc1(x))
x = F.dropout(x, p=self.activation_dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.final_layer_norm(x)
return (
x,
self_attn_weights,
layer_state,
cross_attn_weights,
) # layer_state = cache for decoding
class FSMTDecoder(nn.Module):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`DecoderLayer`
Args:
config: FSMTConfig
embed_tokens (torch.nn.Embedding): output embedding
"""
def __init__(self, config: FSMTConfig, embed_tokens: nn.Embedding):
super().__init__()
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = embed_tokens.padding_idx
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.embed_tokens = embed_tokens
embed_dim = embed_tokens.embedding_dim
self.embed_positions = SinusoidalPositionalEmbedding(
config.max_position_embeddings + self.padding_idx + 1, embed_dim, self.padding_idx
)
self.layers = nn.ModuleList(
[DecoderLayer(config) for _ in range(config.decoder_layers)]
) # type: List[DecoderLayer]
self.output_projection = nn.Linear(
self.embed_tokens.weight.shape[1],
self.embed_tokens.weight.shape[0],
bias=False,
)
self.output_projection.weight = self.embed_tokens.weight
def forward(
self,
input_ids,
encoder_hidden_states,
encoder_padding_mask,
decoder_padding_mask,
decoder_causal_mask,
head_mask=None,
encoder_head_mask=None,
past_key_values=None,
use_cache=False,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
"""
Includes several features from "Jointly Learning to Align and Translate with Transformer Models" (Garg et al.,
EMNLP 2019).
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch, tgt_len)`):
previous decoder outputs for teacher forcing
encoder_hidden_states: output from the encoder, used for
encoder-side attention
encoder_padding_mask: for ignoring pad tokens
past_key_values (dict or None): dictionary used for storing state during generation
head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the heas is **masked**.
encoder_head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention
on hidden heads. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the heas is **masked**.
Returns:
BaseModelOutputWithPast or tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- the cache
- hidden states
- attentions
"""
# check attention mask and invert
if encoder_padding_mask is not None:
encoder_padding_mask = invert_mask(encoder_padding_mask)
# embed positions
positions = self.embed_positions(input_ids) # , use_cache=use_cache)
if use_cache:
input_ids = input_ids[:, -1:]
positions = positions[:, -1:] # happens after we embed them
# assert input_ids.ne(self.padding_idx).any()
x = self.embed_tokens(input_ids) * self.embed_scale
x += positions
x = F.dropout(x, p=self.dropout, training=self.training)
# Convert to FSMT output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
x = x.transpose(0, 1)
encoder_hidden_states = encoder_hidden_states.transpose(0, 1)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attns = () if output_attentions else None
next_decoder_cache = []
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layers)
), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
x = x.transpose(0, 1)
all_hidden_states += (x,)
x = x.transpose(0, 1)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop):
continue
layer_state = past_key_values[idx] if past_key_values is not None else None
x, layer_self_attn, layer_past, layer_cross_attn = decoder_layer(
x,
encoder_hidden_states,
encoder_attn_mask=encoder_padding_mask,
decoder_padding_mask=decoder_padding_mask,
layer_state=layer_state,
causal_mask=decoder_causal_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
encoder_layer_head_mask=(encoder_head_mask[idx] if encoder_head_mask is not None else None),
output_attentions=output_attentions,
)
if use_cache:
next_decoder_cache.append(layer_past.copy())
if output_attentions:
all_self_attns += (layer_self_attn,)
all_cross_attns += (layer_cross_attn,)
# add hidden states from the last decoder layer
if output_hidden_states:
x = x.transpose(0, 1)
all_hidden_states += (x,)
x = x.transpose(0, 1)
# Convert to standard output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
x = x.transpose(0, 1)
encoder_hidden_states = encoder_hidden_states.transpose(0, 1)
x = self.output_projection(x)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v for v in [x, next_cache, all_hidden_states, all_self_attns, all_cross_attns] if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=x,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attns,
)
def _reorder_buffer(attn_cache, new_order):
for k, input_buffer_k in attn_cache.items():
if input_buffer_k is not None:
attn_cache[k] = input_buffer_k.index_select(0, new_order)
return attn_cache
class Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim,
num_heads,
dropout=0.0,
bias=True,
encoder_decoder_attention=False, # otherwise self_attention
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.encoder_decoder_attention = encoder_decoder_attention
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.cache_key = "encoder_decoder" if self.encoder_decoder_attention else "self"
def _shape(self, tensor, seq_len, bsz):
return tensor.contiguous().view(seq_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
def forward(
self,
query,
key: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
layer_state: Optional[Dict[str, Optional[Tensor]]] = None,
attn_mask: Optional[Tensor] = None,
layer_head_mask: Optional[Tensor] = None,
output_attentions=False,
) -> Tuple[Tensor, Optional[Tensor]]:
"""Input shape: Time(SeqLen) x Batch x Channel"""
static_kv: bool = self.encoder_decoder_attention
tgt_len, bsz, embed_dim = query.size()
assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim]
# get here for encoder decoder cause of static_kv
if layer_state is not None: # reuse k,v and encoder_padding_mask
saved_state = layer_state.get(self.cache_key, {})
if "prev_key" in saved_state and static_kv:
# previous time steps are cached - no need to recompute key and value if they are static
key = None
else:
saved_state = None
layer_state = {}
q = self.q_proj(query) * self.scaling
if static_kv:
if key is None:
k = v = None
else:
k = self.k_proj(key)
v = self.v_proj(key)
else:
k = self.k_proj(query)
v = self.v_proj(query)
q = self._shape(q, tgt_len, bsz)
if k is not None:
k = self._shape(k, -1, bsz)
if v is not None:
v = self._shape(v, -1, bsz)
if saved_state is not None:
k, v, key_padding_mask = self._use_saved_state(k, v, saved_state, key_padding_mask, static_kv, bsz)
# Update cache
layer_state[self.cache_key] = {
"prev_key": k.view(bsz, self.num_heads, -1, self.head_dim),
"prev_value": v.view(bsz, self.num_heads, -1, self.head_dim),
"prev_key_padding_mask": key_padding_mask if not static_kv else None,
}
assert k is not None
src_len = k.size(1)
attn_weights = torch.bmm(q, k.transpose(1, 2))
assert attn_weights.size() == (bsz * self.num_heads, tgt_len, src_len)
if attn_mask is not None:
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
# This is part of a workaround to get around fork/join parallelism not supporting Optional types.
if key_padding_mask is not None and key_padding_mask.dim() == 0:
key_padding_mask = None
assert key_padding_mask is None or key_padding_mask.size()[:2] == (
bsz,
src_len,
)
if key_padding_mask is not None: # don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
reshaped = key_padding_mask.unsqueeze(1).unsqueeze(2)
attn_weights = attn_weights.masked_fill(reshaped, float("-inf"))
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = F.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
assert layer_head_mask.size() == (
self.num_heads,
), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# make sure that attn_weights are included in graph
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = F.dropout(
attn_weights,
p=self.dropout,
training=self.training,
)
assert v is not None
attn_output = torch.bmm(attn_probs, v)
assert attn_output.size() == (bsz * self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
def _use_saved_state(self, k, v, saved_state, key_padding_mask, static_kv, bsz):
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
if "prev_key" in saved_state:
_prev_key = saved_state["prev_key"]
assert _prev_key is not None
prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
k = prev_key
else:
assert k is not None
k = torch.cat([prev_key, k], dim=1)
if "prev_value" in saved_state:
_prev_value = saved_state["prev_value"]
assert _prev_value is not None
prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
v = prev_value
else:
assert v is not None
v = torch.cat([prev_value, v], dim=1)
assert k is not None and v is not None
prev_key_padding_mask: Optional[Tensor] = saved_state.get("prev_key_padding_mask", None)
if prev_key_padding_mask is not None:
if static_kv:
new_key_padding_mask = prev_key_padding_mask
else:
new_key_padding_mask = torch.cat([prev_key_padding_mask, key_padding_mask], dim=1)
else:
new_key_padding_mask = key_padding_mask
return k, v, new_key_padding_mask
def fill_with_neg_inf(t):
"""FP16-compatible function that fills a input_ids with -inf."""
return t.float().fill_(float("-inf")).type_as(t)
# Public API
def _get_shape(t):
return getattr(t, "shape", None)
@add_start_docstrings(
"The bare FSMT Model outputting raw hidden-states without any specific head on top.",
FSMT_START_DOCSTRING,
)
class FSMTModel(PretrainedFSMTModel):
def __init__(self, config: FSMTConfig):
super().__init__(config)
padding_idx = config.pad_token_id
encoder_embed_tokens = nn.Embedding(config.src_vocab_size, config.d_model, padding_idx)
decoder_embed_tokens = nn.Embedding(config.tgt_vocab_size, config.d_model, padding_idx)
self.encoder = FSMTEncoder(config, encoder_embed_tokens)
self.decoder = FSMTDecoder(config, decoder_embed_tokens)
self.init_weights()
@add_start_docstrings_to_model_forward(FSMT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
encoder_outputs: Optional[Tuple] = None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
if decoder_input_ids is None:
use_cache = False
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# make masks if user doesn't supply
if not use_cache:
decoder_input_ids, decoder_padding_mask, causal_mask = _prepare_fsmt_decoder_inputs(
self.config,
input_ids,
decoder_input_ids=decoder_input_ids,
decoder_padding_mask=decoder_attention_mask,
causal_mask_dtype=self.decoder.embed_tokens.weight.dtype,
)
else:
decoder_padding_mask, causal_mask = None, None
assert decoder_input_ids is not None
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=False
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
decoder_input_ids,
encoder_outputs[0],
attention_mask,
decoder_padding_mask,
decoder_causal_mask=causal_mask,
head_mask=decoder_head_mask,
encoder_head_mask=head_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
def get_input_embeddings(self):
return self.encoder.embed_tokens
def set_input_embeddings(self, value):
self.encoder.embed_tokens = value
def get_output_embeddings(self):
return self.decoder.embed_tokens
def set_output_embeddings(self, value):
self.decoder.embed_tokens = value
@add_start_docstrings(
"The FSMT Model with a language modeling head. Can be used for summarization.", FSMT_START_DOCSTRING
)
class FSMTForConditionalGeneration(PretrainedFSMTModel):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
"model.encoder.embed_positions.weight",
"model.decoder.embed_positions.weight",
]
_keys_to_ignore_on_save = [
"model.encoder.embed_positions.weight",
"model.decoder.embed_positions.weight",
]
def __init__(self, config: FSMTConfig):
super().__init__(config)
base_model = FSMTModel(config)
self.model = base_model
def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens)
self.model.encoder.embed_tokens = new_embeddings
new_embeddings = super().resize_token_embeddings(new_num_tokens)
self.model.decoder.embed_tokens = new_embeddings
# XXX: this is not quite correct, as we have 2 different `new_embeddings`, and
# only one return value is expected. Needs to be redesigned in the core to support dual dicts
raise NotImplementedError("this method needs re-thinking for models with 2 separate dictionaries")
return new_embeddings
@add_start_docstrings_to_model_forward(FSMT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(FSMT_GENERATION_EXAMPLE)
def forward(
self,
input_ids,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
encoder_outputs=None,
past_key_values=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should either be in ``[0, ...,
config.vocab_size]`` or -100 (see ``input_ids`` docstring). Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``.
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = outputs[0]
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# TODO(SS): do we need to ignore pad tokens in labels?
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.tgt_vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_inputs_for_generation(
self, decoder_input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
):
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id)
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = []
for layer_past in past:
# get the correct batch idx from decoder layer's batch dim for cross and self-attn
layer_past_new = {
attn_key: _reorder_buffer(attn_cache, beam_idx) for attn_key, attn_cache in layer_past.items()
}
reordered_past.append(layer_past_new)
return reordered_past
def get_encoder(self):
return self.model.encoder
def get_output_embeddings(self):
return self.model.decoder.embed_tokens
class SinusoidalPositionalEmbedding(nn.Embedding):
"""
This module produces sinusoidal positional embeddings of any length.
We don't want to save the weight of this embedding since it's not trained (deterministic) and it can be huge.
Padding symbols are ignored.
These embeddings get automatically extended in forward if more positions is needed.
"""
def __init__(self, num_positions, embedding_dim, padding_idx):
self.make_weight(num_positions, embedding_dim, padding_idx)
def make_weight(self, num_positions, embedding_dim, padding_idx):
weight = self.get_embedding(num_positions, embedding_dim, padding_idx)
if not hasattr(self, "weight"):
# in ___init__
super().__init__(num_positions, embedding_dim, padding_idx, _weight=weight)
else:
# in forward
weight = weight.to(self.weight.device)
self.weight = nn.Parameter(weight)
self.weight.detach_()
self.weight.requires_grad = False
@staticmethod
def get_embedding(num_embeddings, embedding_dim, padding_idx):
"""
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
"Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb
@staticmethod
def make_positions(tensor, padding_idx: int):
"""
Replace non-padding symbols with their position numbers.
Position numbers begin at padding_idx+1. Padding symbols are ignored.
"""
# The series of casts and type-conversions here are carefully
# balanced to both work with ONNX export and XLA. In particular XLA
# prefers ints, cumsum defaults to output longs, and ONNX doesn't know
# how to handle the dtype kwarg in cumsum.
mask = tensor.ne(padding_idx).int()
return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx
def forward(
self,
input,
incremental_state: Optional[Any] = None,
timestep: Optional[Tensor] = None,
):
"""Input is expected to be of size [bsz x seqlen]."""
bsz, seq_len = input.shape[:2]
max_pos = self.padding_idx + 1 + seq_len
if max_pos > self.weight.size(0):
# expand embeddings if needed
self.make_weight(max_pos, self.embedding_dim, self.padding_idx)
positions = self.make_positions(input, self.padding_idx)
return super().forward(positions)
|
AdaMix/src/transformers/models/fsmt/modeling_fsmt.py/0
|
{
"file_path": "AdaMix/src/transformers/models/fsmt/modeling_fsmt.py",
"repo_id": "AdaMix",
"token_count": 23494
}
| 54 |
# coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for model LayoutLM."""
from ...utils import logging
from ..bert.tokenization_bert import BertTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/layoutlm-base-uncased": "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt",
"microsoft/layoutlm-large-uncased": "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"microsoft/layoutlm-base-uncased": 512,
"microsoft/layoutlm-large-uncased": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"microsoft/layoutlm-base-uncased": {"do_lower_case": True},
"microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}
class LayoutLMTokenizer(BertTokenizer):
r"""
Constructs a LayoutLM tokenizer.
:class:`~transformers.LayoutLMTokenizer is identical to :class:`~transformers.BertTokenizer` and runs end-to-end
tokenization: punctuation splitting + wordpiece.
Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
AdaMix/src/transformers/models/layoutlm/tokenization_layoutlm.py/0
|
{
"file_path": "AdaMix/src/transformers/models/layoutlm/tokenization_layoutlm.py",
"repo_id": "AdaMix",
"token_count": 730
}
| 55 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert LXMERT checkpoint."""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
# Initialise PyTorch model
config = LxmertConfig.from_json_file(config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = LxmertForPreTraining(config)
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(model, config, tf_checkpoint_path)
# Save pytorch-model
print("Save PyTorch model to {}".format(pytorch_dump_path))
torch.save(model.state_dict(), pytorch_dump_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \n"
"This specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
|
AdaMix/src/transformers/models/lxmert/convert_lxmert_original_tf_checkpoint_to_pytorch.py/0
|
{
"file_path": "AdaMix/src/transformers/models/lxmert/convert_lxmert_original_tf_checkpoint_to_pytorch.py",
"repo_id": "AdaMix",
"token_count": 735
}
| 56 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch MPNet model. """
import math
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from ...activations import ACT2FN, gelu
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPooling,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import logging
from .configuration_mpnet import MPNetConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "microsoft/mpnet-base"
_CONFIG_FOR_DOC = "MPNetConfig"
_TOKENIZER_FOR_DOC = "MPNetTokenizer"
MPNET_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/mpnet-base",
]
class MPNetPreTrainedModel(PreTrainedModel):
config_class = MPNetConfig
pretrained_model_archive_map = MPNET_PRETRAINED_MODEL_ARCHIVE_LIST
base_model_prefix = "mpnet"
def _init_weights(self, module):
""" Initialize the weights """
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
class MPNetEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
self.padding_idx = 1
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, **kwargs):
if position_ids is None:
if input_ids is not None:
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
embeddings = inputs_embeds + position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape)
class MPNetSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads)
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.q = nn.Linear(config.hidden_size, self.all_head_size)
self.k = nn.Linear(config.hidden_size, self.all_head_size)
self.v = nn.Linear(config.hidden_size, self.all_head_size)
self.o = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
position_bias=None,
output_attentions=False,
**kwargs,
):
q = self.q(hidden_states)
k = self.k(hidden_states)
v = self.v(hidden_states)
q = self.transpose_for_scores(q)
k = self.transpose_for_scores(k)
v = self.transpose_for_scores(v)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(q, k.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Apply relative position embedding (precomputed in MPNetEncoder) if provided.
if position_bias is not None:
attention_scores += position_bias
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
attention_probs = self.dropout(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
c = torch.matmul(attention_probs, v)
c = c.permute(0, 2, 1, 3).contiguous()
new_c_shape = c.size()[:-2] + (self.all_head_size,)
c = c.view(*new_c_shape)
o = self.o(c)
outputs = (o, attention_probs) if output_attentions else (o,)
return outputs
class MPNetAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.attn = MPNetSelfAttention(config)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attn.num_attention_heads, self.attn.attention_head_size, self.pruned_heads
)
self.attn.q = prune_linear_layer(self.attn.q, index)
self.attn.k = prune_linear_layer(self.attn.k, index)
self.attn.v = prune_linear_layer(self.attn.v, index)
self.attn.o = prune_linear_layer(self.attn.o, index, dim=1)
self.attn.num_attention_heads = self.attn.num_attention_heads - len(heads)
self.attn.all_head_size = self.attn.attention_head_size * self.attn.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
position_bias=None,
output_attentions=False,
**kwargs,
):
self_outputs = self.attn(
hidden_states,
attention_mask,
head_mask,
position_bias,
output_attentions=output_attentions,
)
attention_output = self.LayerNorm(self.dropout(self_outputs[0]) + hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class MPNetIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput
class MPNetOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class MPNetLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = MPNetAttention(config)
self.intermediate = MPNetIntermediate(config)
self.output = MPNetOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
position_bias=None,
output_attentions=False,
**kwargs,
):
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
position_bias=position_bias,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
outputs = (layer_output,) + outputs
return outputs
class MPNetEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.n_heads = config.num_attention_heads
self.layer = nn.ModuleList([MPNetLayer(config) for _ in range(config.num_hidden_layers)])
self.relative_attention_bias = nn.Embedding(config.relative_attention_num_buckets, self.n_heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
**kwargs,
):
position_bias = self.compute_position_bias(hidden_states)
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states,
attention_mask,
head_mask[i],
position_bias,
output_attentions=output_attentions,
**kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
def compute_position_bias(self, x, position_ids=None, num_buckets=32):
bsz, qlen, klen = x.size(0), x.size(1), x.size(1)
if position_ids is not None:
context_position = position_ids[:, :, None]
memory_position = position_ids[:, None, :]
else:
context_position = torch.arange(qlen, dtype=torch.long)[:, None]
memory_position = torch.arange(klen, dtype=torch.long)[None, :]
relative_position = memory_position - context_position
rp_bucket = self.relative_position_bucket(relative_position, num_buckets=num_buckets)
rp_bucket = rp_bucket.to(x.device)
values = self.relative_attention_bias(rp_bucket)
values = values.permute([2, 0, 1]).unsqueeze(0)
values = values.expand((bsz, -1, qlen, klen)).contiguous()
return values
@staticmethod
def relative_position_bucket(relative_position, num_buckets=32, max_distance=128):
ret = 0
n = -relative_position
num_buckets //= 2
ret += (n < 0).to(torch.long) * num_buckets
n = torch.abs(n)
max_exact = num_buckets // 2
is_small = n < max_exact
val_if_large = max_exact + (
torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
).to(torch.long)
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
ret += torch.where(is_small, n, val_if_large)
return ret
# Copied from transformers.models.bert.modeling_bert.BertPooler
class MPNetPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
MPNET_START_DOCSTRING = r"""
This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
pruning heads etc.)
This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.
Parameters:
config (:class:`~transformers.MPNetConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
weights.
"""
MPNET_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`transformers.MPNetTokenizer`. See
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
config.max_position_embeddings - 1]``.
`What are position IDs? <../glossary.html#position-ids>`_
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
"""
@add_start_docstrings(
"The bare MPNet Model transformer outputting raw hidden-states without any specific head on top.",
MPNET_START_DOCSTRING,
)
class MPNetModel(MPNetPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = MPNetEmbeddings(config)
self.encoder = MPNetEncoder(config)
self.pooler = MPNetPooler(config) if add_pooling_layer else None
self.init_weights()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, inputs_embeds=inputs_embeds)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class MPNetForMaskedLM(MPNetPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config):
super().__init__(config)
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.lm_head = MPNetLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class MPNetLMHead(nn.Module):
"""MPNet Head for masked and permuted language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, features, **kwargs):
x = self.dense(features)
x = gelu(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = self.decoder(x)
return x
@add_start_docstrings(
"""
MPNet Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
MPNET_START_DOCSTRING,
)
class MPNetForSequenceClassification(MPNetPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.classifier = MPNetClassificationHead(config)
self.init_weights()
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
MPNet Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
MPNET_START_DOCSTRING,
)
class MPNetForMultipleChoice(MPNetPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config):
super().__init__(config)
self.mpnet = MPNetModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
self.init_weights()
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
:obj:`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
flat_inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.mpnet(
flat_input_ids,
position_ids=flat_position_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
MPNet Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
MPNET_START_DOCSTRING,
)
class MPNetForTokenClassification(MPNetPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
1]``.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class MPNetClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to BERT's [CLS] token)
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@add_start_docstrings(
"""
MPNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
MPNET_START_DOCSTRING,
)
class MPNetForQuestionAnswering(MPNetPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def create_position_ids_from_input_ids(input_ids, padding_idx):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`. :param torch.Tensor x: :return torch.Tensor:
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
return incremental_indices.long() + padding_idx
|
AdaMix/src/transformers/models/mpnet/modeling_mpnet.py/0
|
{
"file_path": "AdaMix/src/transformers/models/mpnet/modeling_mpnet.py",
"repo_id": "AdaMix",
"token_count": 17881
}
| 57 |
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...file_utils import (
_BaseLazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig"],
"tokenization_roberta": ["RobertaTokenizer"],
}
if is_tokenizers_available():
_import_structure["tokenization_roberta_fast"] = ["RobertaTokenizerFast"]
if is_torch_available():
_import_structure["modeling_roberta"] = [
"ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaForCausalLM",
"RobertaForMaskedLM",
"RobertaForMultipleChoice",
"RobertaForQuestionAnswering",
"RobertaForSequenceClassification",
"RobertaForTokenClassification",
"RobertaModel",
]
if is_tf_available():
_import_structure["modeling_tf_roberta"] = [
"TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaForMaskedLM",
"TFRobertaForMultipleChoice",
"TFRobertaForQuestionAnswering",
"TFRobertaForSequenceClassification",
"TFRobertaForTokenClassification",
"TFRobertaMainLayer",
"TFRobertaModel",
"TFRobertaPreTrainedModel",
]
if is_flax_available():
_import_structure["modeling_flax_roberta"] = ["FlaxRobertaModel"]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
from .tokenization_roberta import RobertaTokenizer
if is_tokenizers_available():
from .tokenization_roberta_fast import RobertaTokenizerFast
if is_torch_available():
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
)
if is_tf_available():
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
if is_flax_available():
from .modeling_flax_roberta import FlaxRobertaModel
else:
import importlib
import os
import sys
class _LazyModule(_BaseLazyModule):
"""
Module class that surfaces all objects but only performs associated imports when the objects are requested.
"""
__file__ = globals()["__file__"]
__path__ = [os.path.dirname(__file__)]
def _get_module(self, module_name: str):
return importlib.import_module("." + module_name, self.__name__)
sys.modules[__name__] = _LazyModule(__name__, _import_structure)
|
AdaMix/src/transformers/models/roberta/__init__.py/0
|
{
"file_path": "AdaMix/src/transformers/models/roberta/__init__.py",
"repo_id": "AdaMix",
"token_count": 1565
}
| 58 |
# coding=utf-8
# Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for SqueezeBERT."""
from ...utils import logging
from ..bert.tokenization_bert import BertTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"squeezebert/squeezebert-uncased": "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt",
"squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
"squeezebert/squeezebert-mnli-headless": "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"squeezebert/squeezebert-uncased": 512,
"squeezebert/squeezebert-mnli": 512,
"squeezebert/squeezebert-mnli-headless": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"squeezebert/squeezebert-uncased": {"do_lower_case": True},
"squeezebert/squeezebert-mnli": {"do_lower_case": True},
"squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}
class SqueezeBertTokenizer(BertTokenizer):
r"""
Constructs a SqueezeBert tokenizer.
:class:`~transformers.SqueezeBertTokenizer is identical to :class:`~transformers.BertTokenizer` and runs end-to-end
tokenization: punctuation splitting + wordpiece.
Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
|
AdaMix/src/transformers/models/squeezebert/tokenization_squeezebert.py/0
|
{
"file_path": "AdaMix/src/transformers/models/squeezebert/tokenization_squeezebert.py",
"repo_id": "AdaMix",
"token_count": 876
}
| 59 |
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A TF 2.0 Adaptive Softmax for Transformer XL model.
"""
import tensorflow as tf
from ...modeling_tf_utils import shape_list
class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
def __init__(self, vocab_size, d_embed, d_proj, cutoffs, div_val=1, keep_order=False, **kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [vocab_size]
self.cutoff_ends = [0] + self.cutoffs
self.div_val = div_val
self.shortlist_size = self.cutoffs[0]
self.n_clusters = len(self.cutoffs) - 1
self.head_size = self.shortlist_size + self.n_clusters
self.keep_order = keep_order
self.out_layers = []
self.out_projs = []
def build(self, input_shape):
if self.n_clusters > 0:
self.cluster_weight = self.add_weight(
shape=(self.n_clusters, self.d_embed), initializer="zeros", trainable=True, name="cluster_weight"
)
self.cluster_bias = self.add_weight(
shape=(self.n_clusters,), initializer="zeros", trainable=True, name="cluster_bias"
)
if self.div_val == 1:
for i in range(len(self.cutoffs)):
if self.d_proj != self.d_embed:
weight = self.add_weight(
shape=(self.d_embed, self.d_proj),
initializer="zeros",
trainable=True,
name="out_projs_._{}".format(i),
)
self.out_projs.append(weight)
else:
self.out_projs.append(None)
weight = self.add_weight(
shape=(
self.vocab_size,
self.d_embed,
),
initializer="zeros",
trainable=True,
name="out_layers_._{}_._weight".format(i),
)
bias = self.add_weight(
shape=(self.vocab_size,),
initializer="zeros",
trainable=True,
name="out_layers_._{}_._bias".format(i),
)
self.out_layers.append((weight, bias))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
d_emb_i = self.d_embed // (self.div_val ** i)
weight = self.add_weight(
shape=(d_emb_i, self.d_proj), initializer="zeros", trainable=True, name="out_projs_._{}".format(i)
)
self.out_projs.append(weight)
weight = self.add_weight(
shape=(
r_idx - l_idx,
d_emb_i,
),
initializer="zeros",
trainable=True,
name="out_layers_._{}_._weight".format(i),
)
bias = self.add_weight(
shape=(r_idx - l_idx,),
initializer="zeros",
trainable=True,
name="out_layers_._{}_._bias".format(i),
)
self.out_layers.append((weight, bias))
super().build(input_shape)
@staticmethod
def _logit(x, W, b, proj=None):
y = x
if proj is not None:
y = tf.einsum("ibd,ed->ibe", y, proj)
return tf.einsum("ibd,nd->ibn", y, W) + b
@staticmethod
def _gather_logprob(logprob, target):
lp_size = shape_list(logprob)
r = tf.range(lp_size[0])
idx = tf.stack([r, target], 1)
return tf.gather_nd(logprob, idx)
def call(self, hidden, target, return_mean=True, training=False):
head_logprob = 0
if self.n_clusters == 0:
output = self._logit(hidden, self.out_layers[0][0], self.out_layers[0][1], self.out_projs[0])
if target is not None:
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output)
out = tf.nn.log_softmax(output, axis=-1)
else:
hidden_sizes = shape_list(hidden)
out = []
loss = tf.zeros(hidden_sizes[:2])
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
mask = (target >= l_idx) & (target < r_idx)
mask_idx = tf.where(mask)
cur_target = tf.boolean_mask(target, mask) - l_idx
if self.div_val == 1:
cur_W = self.out_layers[0][0][l_idx:r_idx]
cur_b = self.out_layers[0][1][l_idx:r_idx]
else:
cur_W = self.out_layers[i][0]
cur_b = self.out_layers[i][1]
if i == 0:
cur_W = tf.concat([cur_W, self.cluster_weight], 0)
cur_b = tf.concat([cur_b, self.cluster_bias], 0)
head_logit = self._logit(hidden, cur_W, cur_b, self.out_projs[0])
head_logprob = tf.nn.log_softmax(head_logit)
out.append(head_logprob[..., : self.cutoffs[0]])
if target is not None:
cur_head_logprob = tf.boolean_mask(head_logprob, mask)
cur_logprob = self._gather_logprob(cur_head_logprob, cur_target)
else:
tail_logit = self._logit(hidden, cur_W, cur_b, self.out_projs[i])
tail_logprob = tf.nn.log_softmax(tail_logit)
cluster_prob_idx = self.cutoffs[0] + i - 1 # No probability for the head cluster
logprob_i = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(logprob_i)
if target is not None:
cur_head_logprob = tf.boolean_mask(head_logprob, mask)
cur_tail_logprob = tf.boolean_mask(tail_logprob, mask)
cur_logprob = self._gather_logprob(cur_tail_logprob, cur_target)
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(mask_idx, -cur_logprob, shape_list(loss))
out = tf.concat(out, axis=-1)
if target is not None:
if return_mean:
loss = tf.reduce_mean(loss)
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(loss)
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(loss, name=self.name, aggregation="mean" if return_mean else "")
return out
|
AdaMix/src/transformers/models/transfo_xl/modeling_tf_transfo_xl_utilities.py/0
|
{
"file_path": "AdaMix/src/transformers/models/transfo_xl/modeling_tf_transfo_xl_utilities.py",
"repo_id": "AdaMix",
"token_count": 4236
}
| 60 |
import collections
import numpy as np
from ..file_utils import add_end_docstrings, is_torch_available, requires_pandas
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Pipeline, PipelineException
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING
class TableQuestionAnsweringArgumentHandler(ArgumentHandler):
"""
Handles arguments for the TableQuestionAnsweringPipeline
"""
def __call__(self, table=None, query=None, sequential=False, padding=True, truncation=True):
# Returns tqa_pipeline_inputs of shape:
# [
# {"table": pd.DataFrame, "query": List[str]},
# ...,
# {"table": pd.DataFrame, "query" : List[str]}
# ]
requires_pandas(self)
import pandas as pd
if table is None:
raise ValueError("Keyword argument `table` cannot be None.")
elif query is None:
if isinstance(table, dict) and table.get("query") is not None and table.get("table") is not None:
tqa_pipeline_inputs = [table]
elif isinstance(table, list) and len(table) > 0:
if not all(isinstance(d, dict) for d in table):
raise ValueError(
f"Keyword argument `table` should be a list of dict, but is {(type(d) for d in table)}"
)
if table[0].get("query") is not None and table[0].get("table") is not None:
tqa_pipeline_inputs = table
else:
raise ValueError(
f"If keyword argument `table` is a list of dictionaries, each dictionary should have a `table` "
f"and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."
)
else:
raise ValueError(
f"Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
f"is {type(table)})"
)
else:
tqa_pipeline_inputs = [{"table": table, "query": query}]
for tqa_pipeline_input in tqa_pipeline_inputs:
if not isinstance(tqa_pipeline_input["table"], pd.DataFrame):
if tqa_pipeline_input["table"] is None:
raise ValueError("Table cannot be None.")
tqa_pipeline_input["table"] = pd.DataFrame(tqa_pipeline_input["table"])
return tqa_pipeline_inputs, sequential, padding, truncation
@add_end_docstrings(PIPELINE_INIT_ARGS)
class TableQuestionAnsweringPipeline(Pipeline):
"""
Table Question Answering pipeline using a :obj:`ModelForTableQuestionAnswering`. This pipeline is only available in
PyTorch.
This tabular question answering pipeline can currently be loaded from :func:`~transformers.pipeline` using the
following task identifier: :obj:`"table-question-answering"`.
The models that this pipeline can use are models that have been fine-tuned on a tabular question answering task.
See the up-to-date list of available models on `huggingface.co/models
<https://huggingface.co/models?filter=table-question-answering>`__.
"""
default_input_names = "table,query"
def __init__(self, args_parser=TableQuestionAnsweringArgumentHandler(), *args, **kwargs):
super().__init__(*args, **kwargs)
self._args_parser = args_parser
if self.framework == "tf":
raise ValueError("The TableQuestionAnsweringPipeline is only available in PyTorch.")
self.check_model_type(MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING)
self.aggregate = bool(getattr(self.model.config, "aggregation_labels")) and bool(
getattr(self.model.config, "num_aggregation_labels")
)
def batch_inference(self, **inputs):
with torch.no_grad():
return self.model(**inputs)
def sequential_inference(self, **inputs):
"""
Inference used for models that need to process sequences in a sequential fashion, like the SQA models which
handle conversational query related to a table.
"""
with torch.no_grad():
all_logits = []
all_aggregations = []
prev_answers = None
batch_size = inputs["input_ids"].shape[0]
input_ids = inputs["input_ids"].to(self.device)
attention_mask = inputs["attention_mask"].to(self.device)
token_type_ids = inputs["token_type_ids"].to(self.device)
token_type_ids_example = None
for index in range(batch_size):
# If sequences have already been processed, the token type IDs will be created according to the previous
# answer.
if prev_answers is not None:
prev_labels_example = token_type_ids_example[:, 3] # shape (seq_len,)
model_labels = np.zeros_like(prev_labels_example.cpu().numpy()) # shape (seq_len,)
token_type_ids_example = token_type_ids[index] # shape (seq_len, 7)
for i in range(model_labels.shape[0]):
segment_id = token_type_ids_example[:, 0].tolist()[i]
col_id = token_type_ids_example[:, 1].tolist()[i] - 1
row_id = token_type_ids_example[:, 2].tolist()[i] - 1
if row_id >= 0 and col_id >= 0 and segment_id == 1:
model_labels[i] = int(prev_answers[(col_id, row_id)])
token_type_ids_example[:, 3] = torch.from_numpy(model_labels).type(torch.long).to(self.device)
input_ids_example = input_ids[index]
attention_mask_example = attention_mask[index] # shape (seq_len,)
token_type_ids_example = token_type_ids[index] # shape (seq_len, 7)
outputs = self.model(
input_ids=input_ids_example.unsqueeze(0),
attention_mask=attention_mask_example.unsqueeze(0),
token_type_ids=token_type_ids_example.unsqueeze(0),
)
logits = outputs.logits
if self.aggregate:
all_aggregations.append(outputs.logits_aggregation)
all_logits.append(logits)
dist_per_token = torch.distributions.Bernoulli(logits=logits)
probabilities = dist_per_token.probs * attention_mask_example.type(torch.float32).to(
dist_per_token.probs.device
)
coords_to_probs = collections.defaultdict(list)
for i, p in enumerate(probabilities.squeeze().tolist()):
segment_id = token_type_ids_example[:, 0].tolist()[i]
col = token_type_ids_example[:, 1].tolist()[i] - 1
row = token_type_ids_example[:, 2].tolist()[i] - 1
if col >= 0 and row >= 0 and segment_id == 1:
coords_to_probs[(col, row)].append(p)
prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}
logits_batch = torch.cat(tuple(all_logits), 0)
return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(all_aggregations), 0))
def __call__(self, *args, **kwargs):
r"""
Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below:
- ``pipeline(table, query)``
- ``pipeline(table, [query])``
- ``pipeline(table=table, query=query)``
- ``pipeline(table=table, query=[query])``
- ``pipeline({"table": table, "query": query})``
- ``pipeline({"table": table, "query": [query]})``
- ``pipeline([{"table": table, "query": query}, {"table": table, "query": query}])``
The :obj:`table` argument should be a dict or a DataFrame built from that dict, containing the whole table:
Example::
data = {
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
}
This dictionary can be passed in as such, or can be converted to a pandas DataFrame:
Example::
import pandas as pd
table = pd.DataFrame.from_dict(data)
Args:
table (:obj:`pd.DataFrame` or :obj:`Dict`):
Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values.
See above for an example of dictionary.
query (:obj:`str` or :obj:`List[str]`):
Query or list of queries that will be sent to the model alongside the table.
sequential (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
inference to be done sequentially to extract relations within sequences, given their conversational
nature.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`False`):
Activates and controls padding. Accepts the following values:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
single sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
truncation (:obj:`bool`, :obj:`str` or :class:`~transformers.TapasTruncationStrategy`, `optional`, defaults to :obj:`False`):
Activates and controls truncation. Accepts the following values:
* :obj:`True` or :obj:`'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument
:obj:`max_length` or to the maximum acceptable input length for the model if that argument is not
provided. This will truncate row by row, removing rows from the table.
* :obj:`False` or :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with
sequence lengths greater than the model maximum admissible input size).
Return:
A dictionary or a list of dictionaries containing results: Each result is a dictionary with the following
keys:
- **answer** (:obj:`str`) -- The answer of the query given the table. If there is an aggregator, the answer
will be preceded by :obj:`AGGREGATOR >`.
- **coordinates** (:obj:`List[Tuple[int, int]]`) -- Coordinates of the cells of the answers.
- **cells** (:obj:`List[str]`) -- List of strings made up of the answer cell values.
- **aggregator** (:obj:`str`) -- If the model has an aggregator, this returns the aggregator.
"""
pipeline_inputs, sequential, padding, truncation = self._args_parser(*args, **kwargs)
batched_answers = []
for pipeline_input in pipeline_inputs:
table, query = pipeline_input["table"], pipeline_input["query"]
if table.empty:
raise ValueError("table is empty")
if not query:
raise ValueError("query is empty")
inputs = self.tokenizer(
table, query, return_tensors=self.framework, truncation="drop_rows_to_fit", padding=padding
)
outputs = self.sequential_inference(**inputs) if sequential else self.batch_inference(**inputs)
if self.aggregate:
logits, logits_agg = outputs[:2]
predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits.detach(), logits_agg)
answer_coordinates_batch, agg_predictions = predictions
aggregators = {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(agg_predictions)}
no_agg_label_index = self.model.config.no_aggregation_label_index
aggregators_prefix = {
i: aggregators[i] + " > " for i, pred in enumerate(agg_predictions) if pred != no_agg_label_index
}
else:
logits = outputs[0]
predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits.detach())
answer_coordinates_batch = predictions[0]
aggregators = {}
aggregators_prefix = {}
answers = []
for index, coordinates in enumerate(answer_coordinates_batch):
cells = [table.iat[coordinate] for coordinate in coordinates]
aggregator = aggregators.get(index, "")
aggregator_prefix = aggregators_prefix.get(index, "")
answer = {
"answer": aggregator_prefix + ", ".join(cells),
"coordinates": coordinates,
"cells": [table.iat[coordinate] for coordinate in coordinates],
}
if aggregator:
answer["aggregator"] = aggregator
answers.append(answer)
if len(answer) == 0:
raise PipelineException("Empty answer")
batched_answers.append(answers if len(answers) > 1 else answers[0])
return batched_answers if len(batched_answers) > 1 else batched_answers[0]
|
AdaMix/src/transformers/pipelines/table_question_answering.py/0
|
{
"file_path": "AdaMix/src/transformers/pipelines/table_question_answering.py",
"repo_id": "AdaMix",
"token_count": 6349
}
| 61 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from packaging import version
from torch import nn
from torch.utils.data.dataset import Dataset
from .trainer import Trainer
from .trainer_utils import PredictionOutput
from .utils import logging
if version.parse(torch.__version__) >= version.parse("1.6"):
from torch.cuda.amp import autocast
logger = logging.get_logger(__name__)
class Seq2SeqTrainer(Trainer):
def evaluate(
self,
eval_dataset: Optional[Dataset] = None,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
max_length: Optional[int] = None,
num_beams: Optional[int] = None,
) -> Dict[str, float]:
"""
Run evaluation and returns metrics.
The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init :obj:`compute_metrics` argument).
You can also subclass and override this method to inject custom behavior.
Args:
eval_dataset (:obj:`Dataset`, `optional`):
Pass a dataset if you wish to override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`,
columns not accepted by the ``model.forward()`` method are automatically removed. It must implement the
:obj:`__len__` method.
ignore_keys (:obj:`List[str]`, `optional`):
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.
metric_key_prefix (:obj:`str`, `optional`, defaults to :obj:`"eval"`):
An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
"eval_bleu" if the prefix is ``"eval"`` (default)
max_length (:obj:`int`, `optional`):
The maximum target length to use when predicting with the generate method.
num_beams (:obj:`int`, `optional`):
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.
Returns:
A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
dictionary also contains the epoch number which comes from the training state.
"""
self._max_length = max_length
self._num_beams = num_beams
return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
def predict(
self,
test_dataset: Dataset,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
max_length: Optional[int] = None,
num_beams: Optional[int] = None,
) -> PredictionOutput:
"""
Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in :obj:`evaluate()`.
Args:
test_dataset (:obj:`Dataset`):
Dataset to run the predictions on. If it is an :obj:`datasets.Dataset`, columns not accepted by the
``model.forward()`` method are automatically removed. Has to implement the method :obj:`__len__`
ignore_keys (:obj:`List[str]`, `optional`):
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.
metric_key_prefix (:obj:`str`, `optional`, defaults to :obj:`"eval"`):
An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
"eval_bleu" if the prefix is ``"eval"`` (default)
max_length (:obj:`int`, `optional`):
The maximum target length to use when predicting with the generate method.
num_beams (:obj:`int`, `optional`):
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.
.. note::
If your predictions or labels have different sequence lengths (for instance because you're doing dynamic
padding in a token classification task) the predictions will be padded (on the right) to allow for
concatenation into one array. The padding index is -100.
Returns: `NamedTuple` A namedtuple with the following keys:
- predictions (:obj:`np.ndarray`): The predictions on :obj:`test_dataset`.
- label_ids (:obj:`np.ndarray`, `optional`): The labels (if the dataset contained some).
- metrics (:obj:`Dict[str, float]`, `optional`): The potential dictionary of metrics (if the dataset
contained labels).
"""
self._max_length = max_length
self._num_beams = num_beams
return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
def prediction_step(
self,
model: nn.Module,
inputs: Dict[str, Union[torch.Tensor, Any]],
prediction_loss_only: bool,
ignore_keys: Optional[List[str]] = None,
) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
"""
Perform an evaluation step on :obj:`model` using obj:`inputs`.
Subclass and override to inject custom behavior.
Args:
model (:obj:`nn.Module`):
The model to evaluate.
inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
The inputs and targets of the model.
The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument :obj:`labels`. Check your model's documentation for all accepted arguments.
prediction_loss_only (:obj:`bool`):
Whether or not to return the loss only.
Return:
Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and
labels (each being optional).
"""
if not self.args.predict_with_generate or prediction_loss_only:
return super().prediction_step(
model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
)
has_labels = "labels" in inputs
inputs = self._prepare_inputs(inputs)
gen_kwargs = {
"max_length": self._max_length if self._max_length is not None else self.model.config.max_length,
"num_beams": self._num_beams if self._num_beams is not None else self.model.config.num_beams,
}
generated_tokens = self.model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
**gen_kwargs,
)
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])
with torch.no_grad():
if self.use_amp:
with autocast():
outputs = model(**inputs)
else:
outputs = model(**inputs)
if has_labels:
if self.label_smoother is not None:
loss = self.label_smoother(outputs, inputs["labels"]).mean().detach()
else:
loss = (outputs["loss"] if isinstance(outputs, dict) else outputs[0]).mean().detach()
else:
loss = None
if self.args.prediction_loss_only:
return (loss, None, None)
labels = inputs["labels"]
if labels.shape[-1] < gen_kwargs["max_length"]:
labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"])
return (loss, generated_tokens, labels)
def _pad_tensors_to_max_len(self, tensor, max_length):
if self.tokenizer is None:
raise ValueError(
f"Tensor need to be padded to `max_length={max_length}` but no tokenzier was passed when creating "
"this `Trainer`. Make sure to create your `Trainer` with the appropriate tokenizer."
)
# If PAD token is not defined at least EOS token has to be defined
pad_token_id = (
self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
)
padded_tensor = pad_token_id * torch.ones(
(tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device
)
padded_tensor[:, : tensor.shape[-1]] = tensor
return padded_tensor
|
AdaMix/src/transformers/trainer_seq2seq.py/0
|
{
"file_path": "AdaMix/src/transformers/trainer_seq2seq.py",
"repo_id": "AdaMix",
"token_count": 3917
}
| 62 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..file_utils import requires_sentencepiece
class AlbertTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class BarthezTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class BertGenerationTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class CamembertTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class DebertaV2Tokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class M2M100Tokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class MarianTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class MBart50Tokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class MBartTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class MT5Tokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class PegasusTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class ReformerTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class Speech2TextProcessor:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
class Speech2TextTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class T5Tokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class XLMProphetNetTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class XLMRobertaTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
class XLNetTokenizer:
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self)
|
AdaMix/src/transformers/utils/dummy_sentencepiece_objects.py/0
|
{
"file_path": "AdaMix/src/transformers/utils/dummy_sentencepiece_objects.py",
"repo_id": "AdaMix",
"token_count": 1518
}
| 63 |
{
"modelname": "{{cookiecutter.modelname}}",
"uppercase_modelname": "{{cookiecutter.uppercase_modelname}}",
"lowercase_modelname": "{{cookiecutter.lowercase_modelname}}",
"camelcase_modelname": "{{cookiecutter.camelcase_modelname}}",
"authors": "{{cookiecutter.authors}}",
"checkpoint_identifier": "{{cookiecutter.checkpoint_identifier}}",
"tokenizer_type": "{{cookiecutter.tokenizer_type}}",
"generate_tensorflow_and_pytorch": "{{cookiecutter.generate_tensorflow_and_pytorch}}",
"is_encoder_decoder_model": ["True", "False"]
}
|
AdaMix/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/configuration.json/0
|
{
"file_path": "AdaMix/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/configuration.json",
"repo_id": "AdaMix",
"token_count": 201
}
| 64 |
{
"modelname": "TemplateBI",
"uppercase_modelname": "TEMPLATE_BI",
"lowercase_modelname": "template_bi",
"camelcase_modelname": "TemplateBi",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "bi-brand-new-bert-base-cased",
"tokenizer_type": "Standalone",
"generate_tensorflow_and_pytorch": "PyTorch & TensorFlow",
"is_encoder_decoder_model": "False"
}
|
AdaMix/templates/adding_a_new_model/tests/standalone.json/0
|
{
"file_path": "AdaMix/templates/adding_a_new_model/tests/standalone.json",
"repo_id": "AdaMix",
"token_count": 149
}
| 65 |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from unittest.mock import patch
from transformers.testing_utils import CaptureStd
class CLITest(unittest.TestCase):
@patch("sys.argv", ["fakeprogrampath", "env"])
def test_cli_env(self):
# test transformers-cli env
import transformers.commands.transformers_cli
with CaptureStd() as cs:
transformers.commands.transformers_cli.main()
assert "Python version" in cs.out
assert "Platform" in cs.out
assert "Using distributed or parallel set-up in script?" in cs.out
|
AdaMix/tests/test_cli.py/0
|
{
"file_path": "AdaMix/tests/test_cli.py",
"repo_id": "AdaMix",
"token_count": 375
}
| 66 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv
class HfArgumentParserTest(unittest.TestCase):
def test_set_level(self):
logger = logging.get_logger()
# the current default level is logging.WARNING
level_origin = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity())
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity())
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity())
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity())
# restore to the original level
logging.set_verbosity(level_origin)
def test_integration(self):
level_origin = logging.get_verbosity()
logger = logging.get_logger("transformers.models.bart.tokenization_bart")
msg = "Testing 1, 2, 3"
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(logger) as cl:
logger.warn(msg)
self.assertEqual(cl.out, msg + "\n")
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(logger) as cl:
logger.warn(msg)
self.assertEqual(cl.out, "")
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(logger) as cl:
logger.warning(msg)
self.assertEqual(cl.out, msg + "\n")
# restore to the original level
logging.set_verbosity(level_origin)
@mockenv(TRANSFORMERS_VERBOSITY="error")
def test_env_override(self):
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
_ = logging.get_logger("transformers.models.bart.tokenization_bart")
env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None)
env_level = logging.log_levels[env_level_str]
current_level = logging.get_verbosity()
self.assertEqual(
env_level,
current_level,
f"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}",
)
# restore to the original level
os.environ["TRANSFORMERS_VERBOSITY"] = ""
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY="super-error")
def test_env_invalid_override(self):
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
logger = logging.logging.getLogger()
with CaptureLogger(logger) as cl:
# this action activates the env var
logging.get_logger("transformers.models.bart.tokenization_bart")
self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error", cl.out)
# no need to restore as nothing was changed
|
AdaMix/tests/test_logging.py/0
|
{
"file_path": "AdaMix/tests/test_logging.py",
"repo_id": "AdaMix",
"token_count": 1534
}
| 67 |
# coding=utf-8
# Copyright 2018 LXMERT Authors, The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
LxmertConfig,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
)
from transformers.models.lxmert.modeling_lxmert import LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST
class LxmertModelTester:
"""You can also import this e.g from .test_modeling_bart import BartModelTester """
def __init__(
self,
parent,
vocab_size=300,
hidden_size=28,
num_attention_heads=2,
num_labels=2,
intermediate_size=64,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
num_qa_labels=30,
num_object_labels=16,
num_attr_labels=4,
num_visual_features=10,
l_layers=2,
x_layers=1,
r_layers=1,
visual_feat_dim=128,
visual_pos_dim=4,
visual_loss_normalizer=6.67,
seq_length=20,
batch_size=4,
is_training=True,
task_matched=True,
task_mask_lm=True,
task_obj_predict=True,
task_qa=True,
visual_obj_loss=True,
visual_attr_loss=True,
visual_feat_loss=True,
use_token_type_ids=True,
use_lang_mask=True,
output_attentions=False,
output_hidden_states=False,
scope=None,
):
self.parent = parent
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_labels = num_labels
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.pad_token_id = pad_token_id
self.num_qa_labels = num_qa_labels
self.num_object_labels = num_object_labels
self.num_attr_labels = num_attr_labels
self.l_layers = l_layers
self.x_layers = x_layers
self.r_layers = r_layers
self.visual_feat_dim = visual_feat_dim
self.visual_pos_dim = visual_pos_dim
self.visual_loss_normalizer = visual_loss_normalizer
self.seq_length = seq_length
self.batch_size = batch_size
self.is_training = is_training
self.use_lang_mask = use_lang_mask
self.task_matched = task_matched
self.task_mask_lm = task_mask_lm
self.task_obj_predict = task_obj_predict
self.task_qa = task_qa
self.visual_obj_loss = visual_obj_loss
self.visual_attr_loss = visual_attr_loss
self.visual_feat_loss = visual_feat_loss
self.num_visual_features = num_visual_features
self.use_token_type_ids = use_token_type_ids
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.scope = scope
self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
def prepare_config_and_inputs(self):
output_attentions = self.output_attentions
input_ids = ids_tensor([self.batch_size, self.seq_length], vocab_size=self.vocab_size)
visual_feats = torch.rand(self.batch_size, self.num_visual_features, self.visual_feat_dim, device=torch_device)
bounding_boxes = torch.rand(self.batch_size, self.num_visual_features, 4, device=torch_device)
input_mask = None
if self.use_lang_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
obj_labels = None
if self.task_obj_predict:
obj_labels = {}
if self.visual_attr_loss and self.task_obj_predict:
obj_labels["attr"] = (
ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels),
ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels),
)
if self.visual_feat_loss and self.task_obj_predict:
obj_labels["feat"] = (
ids_tensor(
[self.batch_size, self.num_visual_features, self.visual_feat_dim], self.num_visual_features
),
ids_tensor([self.batch_size, self.num_visual_features], self.num_visual_features),
)
if self.visual_obj_loss and self.task_obj_predict:
obj_labels["obj"] = (
ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels),
ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels),
)
ans = None
if self.task_qa:
ans = ids_tensor([self.batch_size], self.num_qa_labels)
masked_lm_labels = None
if self.task_mask_lm:
masked_lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
matched_label = None
if self.task_matched:
matched_label = ids_tensor([self.batch_size], self.num_labels)
config = LxmertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_attention_heads=self.num_attention_heads,
num_labels=self.num_labels,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
layer_norm_eps=self.layer_norm_eps,
pad_token_id=self.pad_token_id,
num_qa_labels=self.num_qa_labels,
num_object_labels=self.num_object_labels,
num_attr_labels=self.num_attr_labels,
l_layers=self.l_layers,
x_layers=self.x_layers,
r_layers=self.r_layers,
visual_feat_dim=self.visual_feat_dim,
visual_pos_dim=self.visual_pos_dim,
visual_loss_normalizer=self.visual_loss_normalizer,
task_matched=self.task_matched,
task_mask_lm=self.task_mask_lm,
task_obj_predict=self.task_obj_predict,
task_qa=self.task_qa,
visual_obj_loss=self.visual_obj_loss,
visual_attr_loss=self.visual_attr_loss,
visual_feat_loss=self.visual_feat_loss,
output_attentions=self.output_attentions,
output_hidden_states=self.output_hidden_states,
)
return (
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
)
def create_and_check_lxmert_model(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
model = LxmertModel(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=output_attentions,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=not output_attentions,
)
result = model(input_ids, visual_feats, bounding_boxes, return_dict=False)
result = model(input_ids, visual_feats, bounding_boxes, return_dict=True)
self.parent.assertEqual(result.language_output.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(
result.vision_output.shape, (self.batch_size, self.num_visual_features, self.hidden_size)
)
self.parent.assertEqual(result.pooled_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_lxmert_for_question_answering(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
model = LxmertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
labels=ans,
output_attentions=output_attentions,
)
result = model(input_ids, visual_feats, bounding_boxes, labels=ans)
result = model(
input_ids,
visual_feats,
bounding_boxes,
labels=ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=output_attentions,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
labels=ans,
output_attentions=not output_attentions,
)
self.parent.assertEqual(result.question_answering_score.shape, (self.batch_size, self.num_qa_labels))
def create_and_check_lxmert_for_pretraining(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
model = LxmertForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
obj_labels=obj_labels,
matched_label=matched_label,
ans=ans,
output_attentions=output_attentions,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
output_attentions=not output_attentions,
return_dict=False,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
obj_labels=obj_labels,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
matched_label=matched_label,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=ans,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
obj_labels=obj_labels,
matched_label=matched_label,
ans=ans,
output_attentions=not output_attentions,
)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def resize_lxmert_num_qa_labels(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
start_labels = config.num_qa_labels
num_large_labels = config.num_qa_labels * 2
num_small_labels = int(config.num_qa_labels * 2)
less_labels_ans = ids_tensor([self.batch_size], num_small_labels)
more_labels_ans = ids_tensor([self.batch_size], num_large_labels)
model_pretrain = LxmertForPreTraining(config=config).to(torch_device)
model_qa = LxmertForQuestionAnswering(config=config).to(torch_device)
config.num_labels = num_small_labels
end_labels = config.num_labels
result_pretrain = model_pretrain(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=ans,
)
result_qa = model_qa(
input_ids,
visual_feats,
bounding_boxes,
labels=ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
)
model_pretrain.resize_num_qa_labels(num_small_labels)
model_qa.resize_num_qa_labels(num_small_labels)
result_pretrain_less = model_pretrain(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=less_labels_ans,
)
result_qa_less = model_qa(
input_ids,
visual_feats,
bounding_boxes,
labels=less_labels_ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
)
model_pretrain.resize_num_qa_labels(num_large_labels)
model_qa.resize_num_qa_labels(num_large_labels)
result_pretrain_more = model_pretrain(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=more_labels_ans,
)
result_qa_more = model_qa(
input_ids,
visual_feats,
bounding_boxes,
labels=more_labels_ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
)
model_qa_labels = model_qa.num_qa_labels
self.parent.assertNotEqual(start_labels, end_labels)
self.parent.assertNotEqual(model_qa_labels, start_labels)
self.parent.assertEqual(result_qa.question_answering_score.shape, (self.batch_size, start_labels))
self.parent.assertEqual(result_pretrain.question_answering_score.shape, (self.batch_size, start_labels))
self.parent.assertEqual(result_qa_less.question_answering_score.shape, (self.batch_size, num_small_labels))
self.parent.assertEqual(
result_pretrain_less.question_answering_score.shape, (self.batch_size, num_small_labels)
)
self.parent.assertEqual(result_qa_more.question_answering_score.shape, (self.batch_size, num_large_labels))
self.parent.assertEqual(
result_pretrain_more.question_answering_score.shape, (self.batch_size, num_large_labels)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"visual_feats": visual_feats,
"visual_pos": bounding_boxes,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (LxmertModel, LxmertForPreTraining, LxmertForQuestionAnswering) if is_torch_available() else ()
test_head_masking = False
test_pruning = False
test_torchscript = False
# overwrite function because qa models takes different input label shape
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if return_labels:
if model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class in MODEL_FOR_PRETRAINING_MAPPING.values():
# special case for models like BERT that use multi-loss training for PreTraining
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = LxmertModelTester(self)
self.config_tester = ConfigTester(self, config_class=LxmertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_lxmert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lxmert_model(*config_and_inputs)
def test_lxmert_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lxmert_for_question_answering(*config_and_inputs)
def test_lxmert_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lxmert_for_pretraining(*config_and_inputs)
def test_lxmert_question_answering_labels_resize(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.resize_lxmert_num_qa_labels(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = LxmertModel.from_pretrained(model_name)
model.to(torch_device)
self.assertIsNotNone(model)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
seq_len = getattr(self.model_tester, "seq_length", None)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
attention_shapes = [
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
[
self.model_tester.num_attention_heads,
self.model_tester.num_visual_features,
self.model_tester.num_visual_features,
],
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
]
for attention, attention_shape in zip(attentions, attention_shapes):
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
# 2 hidden states were added
self.assertEqual(out_len + 2, len(outputs))
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
attention_shapes = [
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
[
self.model_tester.num_attention_heads,
self.model_tester.num_visual_features,
self.model_tester.num_visual_features,
],
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
]
for attention, attention_shape in zip(attentions, attention_shapes):
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
language_hidden_states, vision_hidden_states = outputs[-2], outputs[-1]
self.assertEqual(len(language_hidden_states), self.model_tester.num_hidden_layers["language"] + 1)
self.assertEqual(len(vision_hidden_states), self.model_tester.num_hidden_layers["vision"] + 1)
seq_length = self.model_tester.seq_length
num_visual_features = self.model_tester.num_visual_features
self.assertListEqual(
list(language_hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
self.assertListEqual(
list(vision_hidden_states[0].shape[-2:]),
[num_visual_features, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
hidden_states_lang = outputs.language_hidden_states[0]
attentions_lang = outputs.language_attentions[0]
hidden_states_vision = outputs.vision_hidden_states[0]
attentions_vision = outputs.vision_attentions[0]
hidden_states_lang.retain_grad()
attentions_lang.retain_grad()
hidden_states_vision.retain_grad()
attentions_vision.retain_grad()
outputs.language_output.flatten()[0].backward(retain_graph=True)
outputs.vision_output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states_lang.grad)
self.assertIsNotNone(attentions_vision.grad)
self.assertIsNotNone(hidden_states_vision.grad)
self.assertIsNotNone(attentions_vision.grad)
|
AdaMix/tests/test_modeling_lxmert.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_lxmert.py",
"repo_id": "AdaMix",
"token_count": 13691
}
| 68 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import unittest
import numpy as np
import pandas as pd
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
is_torch_available,
)
from transformers.file_utils import cached_property
from transformers.testing_utils import require_scatter, require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
)
from transformers.models.tapas.modeling_tapas import (
IndexMap,
ProductIndexMap,
flatten,
gather,
range_index_map,
reduce_max,
reduce_mean,
reduce_sum,
)
class TapasModelTester:
"""You can also import this e.g from .test_modeling_tapas import TapasModelTester """
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
max_position_embeddings=512,
type_vocab_sizes=[3, 256, 256, 2, 256, 256, 10],
type_sequence_label_size=2,
positive_weight=10.0,
num_aggregation_labels=4,
num_labels=2,
aggregation_loss_importance=0.8,
use_answer_as_supervision=True,
answer_loss_importance=0.001,
use_normalized_answer_loss=False,
huber_loss_delta=25.0,
temperature=1.0,
agg_temperature=1.0,
use_gumbel_for_cells=False,
use_gumbel_for_agg=False,
average_approximation_function="ratio",
cell_selection_preference=0.5,
answer_loss_cutoff=100,
max_num_rows=64,
max_num_columns=32,
average_logits_per_cell=True,
select_one_column=True,
allow_empty_column_selection=False,
init_cell_selection_weights_to_zero=False,
reset_position_index_per_cell=True,
disable_per_token_loss=False,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.max_position_embeddings = max_position_embeddings
self.type_vocab_sizes = type_vocab_sizes
self.type_sequence_label_size = type_sequence_label_size
self.positive_weight = positive_weight
self.num_aggregation_labels = num_aggregation_labels
self.num_labels = num_labels
self.aggregation_loss_importance = aggregation_loss_importance
self.use_answer_as_supervision = use_answer_as_supervision
self.answer_loss_importance = answer_loss_importance
self.use_normalized_answer_loss = use_normalized_answer_loss
self.huber_loss_delta = huber_loss_delta
self.temperature = temperature
self.agg_temperature = agg_temperature
self.use_gumbel_for_cells = use_gumbel_for_cells
self.use_gumbel_for_agg = use_gumbel_for_agg
self.average_approximation_function = average_approximation_function
self.cell_selection_preference = cell_selection_preference
self.answer_loss_cutoff = answer_loss_cutoff
self.max_num_rows = max_num_rows
self.max_num_columns = max_num_columns
self.average_logits_per_cell = average_logits_per_cell
self.select_one_column = select_one_column
self.allow_empty_column_selection = allow_empty_column_selection
self.init_cell_selection_weights_to_zero = init_cell_selection_weights_to_zero
self.reset_position_index_per_cell = reset_position_index_per_cell
self.disable_per_token_loss = disable_per_token_loss
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).to(torch_device)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length]).to(torch_device)
token_type_ids = []
for type_vocab_size in self.type_vocab_sizes:
token_type_ids.append(ids_tensor(shape=[self.batch_size, self.seq_length], vocab_size=type_vocab_size))
token_type_ids = torch.stack(token_type_ids, dim=2).to(torch_device)
sequence_labels = None
token_labels = None
labels = None
numeric_values = None
numeric_values_scale = None
float_answer = None
aggregation_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size).to(torch_device)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels).to(torch_device)
labels = ids_tensor([self.batch_size, self.seq_length], vocab_size=2).to(torch_device)
numeric_values = floats_tensor([self.batch_size, self.seq_length]).to(torch_device)
numeric_values_scale = floats_tensor([self.batch_size, self.seq_length]).to(torch_device)
float_answer = floats_tensor([self.batch_size]).to(torch_device)
aggregation_labels = ids_tensor([self.batch_size], self.num_aggregation_labels).to(torch_device)
config = TapasConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_sizes=self.type_vocab_sizes,
initializer_range=self.initializer_range,
positive_weight=self.positive_weight,
num_aggregation_labels=self.num_aggregation_labels,
num_labels=self.num_labels,
aggregation_loss_importance=self.aggregation_loss_importance,
use_answer_as_supervision=self.use_answer_as_supervision,
answer_loss_importance=self.answer_loss_importance,
use_normalized_answer_loss=self.use_normalized_answer_loss,
huber_loss_delta=self.huber_loss_delta,
temperature=self.temperature,
agg_temperature=self.agg_temperature,
use_gumbel_for_cells=self.use_gumbel_for_cells,
use_gumbel_for_agg=self.use_gumbel_for_agg,
average_approximation_function=self.average_approximation_function,
cell_selection_preference=self.cell_selection_preference,
answer_loss_cutoff=self.answer_loss_cutoff,
max_num_rows=self.max_num_rows,
max_num_columns=self.max_num_columns,
average_logits_per_cell=self.average_logits_per_cell,
select_one_column=self.select_one_column,
allow_empty_column_selection=self.allow_empty_column_selection,
init_cell_selection_weights_to_zero=self.init_cell_selection_weights_to_zero,
reset_position_index_per_cell=self.reset_position_index_per_cell,
disable_per_token_loss=self.disable_per_token_loss,
)
return (
config,
input_ids,
input_mask,
token_type_ids,
sequence_labels,
token_labels,
labels,
numeric_values,
numeric_values_scale,
float_answer,
aggregation_labels,
)
def create_and_check_model(
self,
config,
input_ids,
input_mask,
token_type_ids,
sequence_labels,
token_labels,
labels,
numeric_values,
numeric_values_scale,
float_answer,
aggregation_labels,
):
model = TapasModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_masked_lm(
self,
config,
input_ids,
input_mask,
token_type_ids,
sequence_labels,
token_labels,
labels,
numeric_values,
numeric_values_scale,
float_answer,
aggregation_labels,
):
model = TapasForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_question_answering(
self,
config,
input_ids,
input_mask,
token_type_ids,
sequence_labels,
token_labels,
labels,
numeric_values,
numeric_values_scale,
float_answer,
aggregation_labels,
):
# inference: without aggregation head (SQA). Model only returns logits
sqa_config = copy.copy(config)
sqa_config.num_aggregation_labels = 0
sqa_config.use_answer_as_supervision = False
model = TapasForQuestionAnswering(config=sqa_config)
model.to(torch_device)
model.eval()
result = model(
input_ids=input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
# inference: with aggregation head (WTQ, WikiSQL-supervised). Model returns logits and aggregation logits
model = TapasForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids=input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.logits_aggregation.shape, (self.batch_size, self.num_aggregation_labels))
# training: can happen in 3 main ways
# case 1: conversational (SQA)
model = TapasForQuestionAnswering(config=sqa_config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=labels,
)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
# case 2: weak supervision for aggregation (WTQ)
model = TapasForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids=input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=labels,
numeric_values=numeric_values,
numeric_values_scale=numeric_values_scale,
float_answer=float_answer,
)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.logits_aggregation.shape, (self.batch_size, self.num_aggregation_labels))
# case 3: strong supervision for aggregation (WikiSQL-supervised)
wikisql_config = copy.copy(config)
wikisql_config.use_answer_as_supervision = False
model = TapasForQuestionAnswering(config=wikisql_config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=labels,
aggregation_labels=aggregation_labels,
)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.logits_aggregation.shape, (self.batch_size, self.num_aggregation_labels))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
input_mask,
token_type_ids,
sequence_labels,
token_labels,
labels,
numeric_values,
numeric_values_scale,
float_answer,
aggregation_labels,
):
config.num_labels = self.num_labels
model = TapasForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
token_type_ids,
sequence_labels,
token_labels,
labels,
numeric_values,
numeric_values_scale,
float_answer,
aggregation_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
@require_scatter
class TapasModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
TapasModel,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
)
if is_torch_available()
else None
)
test_pruning = False
test_torchscript = False
test_resize_embeddings = True
test_head_masking = False
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
inputs_dict = {
k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
if isinstance(v, torch.Tensor) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
elif model_class in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.values():
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
inputs_dict["aggregation_labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
inputs_dict["numeric_values"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length),
dtype=torch.float,
device=torch_device,
)
inputs_dict["numeric_values_scale"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length),
dtype=torch.float,
device=torch_device,
)
inputs_dict["float_answer"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.float, device=torch_device
)
elif model_class in [
*MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values(),
*MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.values(),
]:
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class in [
*MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.values(),
*MODEL_FOR_CAUSAL_LM_MAPPING.values(),
*MODEL_FOR_MASKED_LM_MAPPING.values(),
*MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.values(),
]:
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = TapasModelTester(self)
self.config_tester = ConfigTester(self, config_class=TapasConfig, dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def prepare_tapas_single_inputs_for_inference():
# Here we prepare a single table-question pair to test TAPAS inference on:
data = {
"Footballer": ["Lionel Messi", "Cristiano Ronaldo"],
"Age": ["33", "35"],
}
queries = "Which footballer is 33 years old?"
table = pd.DataFrame.from_dict(data)
return table, queries
def prepare_tapas_batch_inputs_for_inference():
# Here we prepare a batch of 2 table-question pairs to test TAPAS inference on:
data = {
"Footballer": ["Lionel Messi", "Cristiano Ronaldo"],
"Age": ["33", "35"],
"Number of goals": ["712", "750"],
}
queries = ["Which footballer is 33 years old?", "How many goals does Ronaldo have?"]
table = pd.DataFrame.from_dict(data)
return table, queries
def prepare_tapas_batch_inputs_for_training():
# Here we prepare a DIFFERENT batch of 2 table-question pairs to test TAPAS training on:
data = {
"Footballer": ["Lionel Messi", "Cristiano Ronaldo"],
"Age": ["33", "35"],
"Number of goals": ["712", "750"],
}
queries = ["Which footballer is 33 years old?", "What's the total number of goals?"]
table = pd.DataFrame.from_dict(data)
answer_coordinates = [[(0, 0)], [(0, 2), (1, 2)]]
answer_text = [["Lionel Messi"], ["1462"]]
float_answer = [float("NaN"), float("1462")]
return table, queries, answer_coordinates, answer_text, float_answer
@require_torch
@require_scatter
class TapasModelIntegrationTest(unittest.TestCase):
@cached_property
def default_tokenizer(self):
return TapasTokenizer.from_pretrained("google/tapas-base-finetuned-wtq")
@slow
def test_inference_no_head(self):
# ideally we want to test this with the weights of tapas_inter_masklm_base_reset,
# but since it's not straightforward to do this with the TF 1 implementation, we test it with
# the weights of the WTQ base model (i.e. tapas_wtq_wikisql_sqa_inter_masklm_base_reset)
model = TapasModel.from_pretrained("google/tapas-base-finetuned-wtq").to(torch_device)
tokenizer = self.default_tokenizer
table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
outputs = model(**inputs)
# test the sequence output
expected_slice = torch.tensor(
[
[
[-0.141581565, -0.599805772, 0.747186482],
[-0.143664181, -0.602008104, 0.749218345],
[-0.15169853, -0.603363097, 0.741370678],
]
],
device=torch_device,
)
self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :3, :3], expected_slice, atol=0.0005))
# test the pooled output
expected_slice = torch.tensor([[0.987518311, -0.970520139, -0.994303405]], device=torch_device)
self.assertTrue(torch.allclose(outputs.pooler_output[:, :3], expected_slice, atol=0.0005))
@unittest.skip(reason="Model not available yet")
def test_inference_masked_lm(self):
pass
# TapasForQuestionAnswering has 3 possible ways of being fine-tuned:
# - conversational set-up (SQA)
# - weak supervision for aggregation (WTQ, WikiSQL)
# - strong supervision for aggregation (WikiSQL-supervised)
# We test all of them:
@slow
def test_inference_question_answering_head_conversational(self):
# note that google/tapas-base-finetuned-sqa should correspond to tapas_sqa_inter_masklm_base_reset
model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-sqa").to(torch_device)
tokenizer = self.default_tokenizer
table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
outputs = model(**inputs)
# test the logits
logits = outputs.logits
expected_shape = torch.Size((1, 21))
self.assertEqual(logits.shape, expected_shape)
expected_tensor = torch.tensor(
[
[
-9997.22461,
-9997.22461,
-9997.22461,
-9997.22461,
-9997.22461,
-9997.22461,
-9997.22461,
-9997.22461,
-9997.22461,
-16.2628059,
-10004.082,
15.4330549,
15.4330549,
15.4330549,
-9990.42,
-16.3270779,
-16.3270779,
-16.3270779,
-16.3270779,
-16.3270779,
-10004.8506,
]
],
device=torch_device,
)
self.assertTrue(torch.allclose(logits, expected_tensor, atol=0.015))
@slow
def test_inference_question_answering_head_conversational_absolute_embeddings(self):
# note that google/tapas-small-finetuned-sqa should correspond to tapas_sqa_inter_masklm_small_reset
# however here we test the version with absolute position embeddings
model = TapasForQuestionAnswering.from_pretrained("google/tapas-small-finetuned-sqa", revision="no_reset").to(
torch_device
)
tokenizer = self.default_tokenizer
table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
outputs = model(**inputs)
# test the logits
logits = outputs.logits
expected_shape = torch.Size((1, 21))
self.assertEqual(logits.shape, expected_shape)
expected_tensor = torch.tensor(
[
[
-10014.7793,
-10014.7793,
-10014.7793,
-10014.7793,
-10014.7793,
-10014.7793,
-10014.7793,
-10014.7793,
-10014.7793,
-18.8419304,
-10018.0391,
17.7848816,
17.7848816,
17.7848816,
-9981.02832,
-16.4005489,
-16.4005489,
-16.4005489,
-16.4005489,
-16.4005489,
-10013.4736,
]
],
device=torch_device,
)
self.assertTrue(torch.allclose(logits, expected_tensor, atol=0.01))
@slow
def test_inference_question_answering_head_weak_supervision(self):
# note that google/tapas-base-finetuned-wtq should correspond to tapas_wtq_wikisql_sqa_inter_masklm_base_reset
model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq").to(torch_device)
tokenizer = self.default_tokenizer
# let's test on a batch
table, queries = prepare_tapas_batch_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt")
inputs_on_device = {k: v.to(torch_device) for k, v in inputs.items()}
outputs = model(**inputs_on_device)
# test the logits
logits = outputs.logits
expected_shape = torch.Size((2, 28))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor(
[
[-160.375504, -160.375504, -160.375504, -10072.3965, -10070.9414, -10094.9736],
[-9861.6123, -9861.6123, -9861.6123, -9861.6123, -9891.01172, 146.600677],
],
device=torch_device,
)
self.assertTrue(torch.allclose(logits[:, -6:], expected_slice, atol=0.4))
# test the aggregation logits
logits_aggregation = outputs.logits_aggregation
expected_shape = torch.Size((2, 4))
self.assertEqual(logits_aggregation.shape, expected_shape)
expected_tensor = torch.tensor(
[[18.8545208, -9.76614857, -6.3128891, -2.93525243], [-4.05782509, 40.0351, -5.35329962, 23.3978653]],
device=torch_device,
)
self.assertTrue(torch.allclose(logits_aggregation, expected_tensor, atol=0.001))
# test the predicted answer coordinates and aggregation indices
EXPECTED_PREDICTED_ANSWER_COORDINATES = [[(0, 0)], [(1, 2)]]
EXPECTED_PREDICTED_AGGREGATION_INDICES = [0, 1]
predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
inputs, outputs.logits.detach().cpu(), outputs.logits_aggregation.detach().cpu()
)
self.assertEqual(EXPECTED_PREDICTED_ANSWER_COORDINATES, predicted_answer_coordinates)
self.assertEqual(EXPECTED_PREDICTED_AGGREGATION_INDICES, predicted_aggregation_indices)
@slow
def test_training_question_answering_head_weak_supervision(self):
# note that google/tapas-base-finetuned-wtq should correspond to tapas_wtq_wikisql_sqa_inter_masklm_base_reset
model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq").to(torch_device)
model.to(torch_device)
# normally we should put the model in training mode but it's a pain to do this with the TF 1 implementation
tokenizer = self.default_tokenizer
# let's test on a batch
table, queries, answer_coordinates, answer_text, float_answer = prepare_tapas_batch_inputs_for_training()
inputs = tokenizer(
table=table,
queries=queries,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
padding="longest",
return_tensors="pt",
)
# prepare data (created by the tokenizer) and move to torch_device
input_ids = inputs["input_ids"].to(torch_device)
attention_mask = inputs["attention_mask"].to(torch_device)
token_type_ids = inputs["token_type_ids"].to(torch_device)
labels = inputs["labels"].to(torch_device)
numeric_values = inputs["numeric_values"].to(torch_device)
numeric_values_scale = inputs["numeric_values_scale"].to(torch_device)
# the answer should be prepared by the user
float_answer = torch.FloatTensor(float_answer).to(torch_device)
# forward pass to get loss + logits:
outputs = model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
labels=labels,
numeric_values=numeric_values,
numeric_values_scale=numeric_values_scale,
float_answer=float_answer,
)
# test the loss
loss = outputs.loss
expected_loss = torch.tensor(3.3527612686157227e-08, device=torch_device)
self.assertTrue(torch.allclose(loss, expected_loss, atol=1e-6))
# test the logits on the first example
logits = outputs.logits
expected_shape = torch.Size((2, 29))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor(
[
-160.0156,
-160.0156,
-160.0156,
-160.0156,
-160.0156,
-10072.2266,
-10070.8896,
-10092.6006,
-10092.6006,
],
device=torch_device,
)
self.assertTrue(torch.allclose(logits[0, -9:], expected_slice, atol=1e-6))
# test the aggregation logits on the second example
logits_aggregation = outputs.logits_aggregation
expected_shape = torch.Size((2, 4))
self.assertEqual(logits_aggregation.shape, expected_shape)
expected_slice = torch.tensor([-4.0538, 40.0304, -5.3554, 23.3965], device=torch_device)
self.assertTrue(torch.allclose(logits_aggregation[1, -4:], expected_slice, atol=1e-4))
@slow
def test_inference_question_answering_head_strong_supervision(self):
# note that google/tapas-base-finetuned-wikisql-supervised should correspond to tapas_wikisql_sqa_inter_masklm_base_reset
model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wikisql-supervised").to(
torch_device
)
tokenizer = self.default_tokenizer
table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
outputs = model(**inputs)
# test the logits
logits = outputs.logits
expected_shape = torch.Size((1, 21))
self.assertEqual(logits.shape, expected_shape)
expected_tensor = torch.tensor(
[
[
-10011.1084,
-10011.1084,
-10011.1084,
-10011.1084,
-10011.1084,
-10011.1084,
-10011.1084,
-10011.1084,
-10011.1084,
-18.6185989,
-10008.7969,
17.6355762,
17.6355762,
17.6355762,
-10002.4404,
-18.7111301,
-18.7111301,
-18.7111301,
-18.7111301,
-18.7111301,
-10007.0977,
]
],
device=torch_device,
)
self.assertTrue(torch.allclose(logits, expected_tensor, atol=0.02))
# test the aggregation logits
logits_aggregation = outputs.logits_aggregation
expected_shape = torch.Size((1, 4))
self.assertEqual(logits_aggregation.shape, expected_shape)
expected_tensor = torch.tensor(
[[16.5659733, -3.06624889, -2.34152961, -0.970244825]], device=torch_device
) # PyTorch model outputs [[16.5679, -3.0668, -2.3442, -0.9674]]
self.assertTrue(torch.allclose(logits_aggregation, expected_tensor, atol=0.003))
@slow
def test_inference_classification_head(self):
# note that google/tapas-base-finetuned-tabfact should correspond to tapas_tabfact_inter_masklm_base_reset
model = TapasForSequenceClassification.from_pretrained("google/tapas-base-finetuned-tabfact").to(torch_device)
tokenizer = self.default_tokenizer
table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
outputs = model(**inputs)
# test the classification logits
logits = outputs.logits
expected_shape = torch.Size((1, 2))
self.assertEqual(logits.shape, expected_shape)
expected_tensor = torch.tensor(
[[0.795137286, 9.5572]], device=torch_device
) # Note that the PyTorch model outputs [[0.8057, 9.5281]]
self.assertTrue(torch.allclose(outputs.logits, expected_tensor, atol=0.05))
# Below: tests for Tapas utilities which are defined in modeling_tapas.py.
# These are based on segmented_tensor_test.py of the original implementation.
# URL: https://github.com/google-research/tapas/blob/master/tapas/models/segmented_tensor_test.py
@require_scatter
class TapasUtilitiesTest(unittest.TestCase):
def _prepare_tables(self):
"""Prepares two tables, both with three distinct rows.
The first table has two columns:
1.0, 2.0 | 3.0
2.0, 0.0 | 1.0
1.0, 3.0 | 4.0
The second table has three columns:
1.0 | 2.0 | 3.0
2.0 | 0.0 | 1.0
1.0 | 3.0 | 4.0
Returns:
SegmentedTensors with the tables.
"""
values = torch.tensor(
[
[[1.0, 2.0, 3.0], [2.0, 0.0, 1.0], [1.0, 3.0, 4.0]],
[[1.0, 2.0, 3.0], [2.0, 0.0, 1.0], [1.0, 3.0, 4.0]],
]
)
row_index = IndexMap(
indices=torch.tensor(
[
[[0, 0, 0], [1, 1, 1], [2, 2, 2]],
[[0, 0, 0], [1, 1, 1], [2, 2, 2]],
]
),
num_segments=3,
batch_dims=1,
)
col_index = IndexMap(
indices=torch.tensor(
[
[[0, 0, 1], [0, 0, 1], [0, 0, 1]],
[[0, 1, 2], [0, 1, 2], [0, 1, 2]],
]
),
num_segments=3,
batch_dims=1,
)
return values, row_index, col_index
def test_product_index(self):
_, row_index, col_index = self._prepare_tables()
cell_index = ProductIndexMap(row_index, col_index)
row_index_proj = cell_index.project_outer(cell_index)
col_index_proj = cell_index.project_inner(cell_index)
ind = cell_index.indices
self.assertEqual(cell_index.num_segments, 9)
# Projections should give back the original indices.
# we use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
np.testing.assert_array_equal(row_index.indices.numpy(), row_index_proj.indices.numpy())
self.assertEqual(row_index.num_segments, row_index_proj.num_segments)
self.assertEqual(row_index.batch_dims, row_index_proj.batch_dims)
# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
np.testing.assert_array_equal(col_index.indices.numpy(), col_index_proj.indices.numpy())
self.assertEqual(col_index.batch_dims, col_index_proj.batch_dims)
# The first and second "column" are identified in the first table.
for i in range(3):
self.assertEqual(ind[0, i, 0], ind[0, i, 1])
self.assertNotEqual(ind[0, i, 0], ind[0, i, 2])
# All rows are distinct in the first table.
for i, i_2 in zip(range(3), range(3)):
for j, j_2 in zip(range(3), range(3)):
if i != i_2 and j != j_2:
self.assertNotEqual(ind[0, i, j], ind[0, i_2, j_2])
# All cells are distinct in the second table.
for i, i_2 in zip(range(3), range(3)):
for j, j_2 in zip(range(3), range(3)):
if i != i_2 or j != j_2:
self.assertNotEqual(ind[1, i, j], ind[1, i_2, j_2])
def test_flatten(self):
_, row_index, col_index = self._prepare_tables()
row_index_flat = flatten(row_index)
col_index_flat = flatten(col_index)
shape = [3, 4, 5]
batched_index = IndexMap(indices=torch.zeros(shape).type(torch.LongTensor), num_segments=1, batch_dims=3)
batched_index_flat = flatten(batched_index)
# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
np.testing.assert_array_equal(
row_index_flat.indices.numpy(), [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5]
)
np.testing.assert_array_equal(
col_index_flat.indices.numpy(), [0, 0, 1, 0, 0, 1, 0, 0, 1, 3, 4, 5, 3, 4, 5, 3, 4, 5]
)
self.assertEqual(batched_index_flat.num_segments.numpy(), np.prod(shape))
np.testing.assert_array_equal(batched_index_flat.indices.numpy(), range(np.prod(shape)))
def test_range_index_map(self):
batch_shape = [3, 4]
num_segments = 5
index = range_index_map(batch_shape, num_segments)
self.assertEqual(num_segments, index.num_segments)
self.assertEqual(2, index.batch_dims)
indices = index.indices
# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
np.testing.assert_array_equal(list(indices.size()), [3, 4, 5])
for i in range(batch_shape[0]):
for j in range(batch_shape[1]):
# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
np.testing.assert_array_equal(indices[i, j, :].numpy(), range(num_segments))
def test_reduce_sum(self):
values, row_index, col_index = self._prepare_tables()
cell_index = ProductIndexMap(row_index, col_index)
row_sum, _ = reduce_sum(values, row_index)
col_sum, _ = reduce_sum(values, col_index)
cell_sum, _ = reduce_sum(values, cell_index)
# We use np.testing.assert_allclose rather than Tensorflow's assertAllClose
np.testing.assert_allclose(row_sum.numpy(), [[6.0, 3.0, 8.0], [6.0, 3.0, 8.0]])
np.testing.assert_allclose(col_sum.numpy(), [[9.0, 8.0, 0.0], [4.0, 5.0, 8.0]])
np.testing.assert_allclose(
cell_sum.numpy(),
[[3.0, 3.0, 0.0, 2.0, 1.0, 0.0, 4.0, 4.0, 0.0], [1.0, 2.0, 3.0, 2.0, 0.0, 1.0, 1.0, 3.0, 4.0]],
)
def test_reduce_mean(self):
values, row_index, col_index = self._prepare_tables()
cell_index = ProductIndexMap(row_index, col_index)
row_mean, _ = reduce_mean(values, row_index)
col_mean, _ = reduce_mean(values, col_index)
cell_mean, _ = reduce_mean(values, cell_index)
# We use np.testing.assert_allclose rather than Tensorflow's assertAllClose
np.testing.assert_allclose(
row_mean.numpy(), [[6.0 / 3.0, 3.0 / 3.0, 8.0 / 3.0], [6.0 / 3.0, 3.0 / 3.0, 8.0 / 3.0]]
)
np.testing.assert_allclose(col_mean.numpy(), [[9.0 / 6.0, 8.0 / 3.0, 0.0], [4.0 / 3.0, 5.0 / 3.0, 8.0 / 3.0]])
np.testing.assert_allclose(
cell_mean.numpy(),
[
[3.0 / 2.0, 3.0, 0.0, 2.0 / 2.0, 1.0, 0.0, 4.0 / 2.0, 4.0, 0.0],
[1.0, 2.0, 3.0, 2.0, 0.0, 1.0, 1.0, 3.0, 4.0],
],
)
def test_reduce_max(self):
values = torch.as_tensor([2.0, 1.0, 0.0, 3.0])
index = IndexMap(indices=torch.as_tensor([0, 1, 0, 1]), num_segments=2)
maximum, _ = reduce_max(values, index)
# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
np.testing.assert_array_equal(maximum.numpy(), [2, 3])
def test_reduce_sum_vectorized(self):
values = torch.as_tensor([[1.0, 2.0, 3.0], [2.0, 3.0, 4.0], [3.0, 4.0, 5.0]])
index = IndexMap(indices=torch.as_tensor([0, 0, 1]), num_segments=2, batch_dims=0)
sums, new_index = reduce_sum(values, index)
# We use np.testing.assert_allclose rather than Tensorflow's assertAllClose
np.testing.assert_allclose(sums.numpy(), [[3.0, 5.0, 7.0], [3.0, 4.0, 5.0]])
# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
np.testing.assert_array_equal(new_index.indices.numpy(), [0, 1])
np.testing.assert_array_equal(new_index.num_segments.numpy(), 2)
np.testing.assert_array_equal(new_index.batch_dims, 0)
def test_gather(self):
values, row_index, col_index = self._prepare_tables()
cell_index = ProductIndexMap(row_index, col_index)
# Compute sums and then gather. The result should have the same shape as
# the original table and each element should contain the sum the values in
# its cell.
sums, _ = reduce_sum(values, cell_index)
cell_sum = gather(sums, cell_index)
assert cell_sum.size() == values.size()
# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
np.testing.assert_allclose(
cell_sum.numpy(),
[[[3.0, 3.0, 3.0], [2.0, 2.0, 1.0], [4.0, 4.0, 4.0]], [[1.0, 2.0, 3.0], [2.0, 0.0, 1.0], [1.0, 3.0, 4.0]]],
)
def test_gather_vectorized(self):
values = torch.as_tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
index = IndexMap(indices=torch.as_tensor([[0, 1], [1, 0]]), num_segments=2, batch_dims=1)
result = gather(values, index)
# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
np.testing.assert_array_equal(result.numpy(), [[[1, 2], [3, 4]], [[7, 8], [5, 6]]])
|
AdaMix/tests/test_modeling_tapas.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_tapas.py",
"repo_id": "AdaMix",
"token_count": 21540
}
| 69 |
# coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class TFFunnelModelTester:
"""You can also import this e.g, from .test_modeling_funnel import FunnelModelTester """
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
block_sizes=[1, 1, 2],
num_decoder_layers=1,
d_model=32,
n_head=4,
d_head=8,
d_inner=37,
hidden_act="gelu_new",
hidden_dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
max_position_embeddings=512,
type_vocab_size=3,
num_labels=3,
num_choices=4,
scope=None,
base=False,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.block_sizes = block_sizes
self.num_decoder_layers = num_decoder_layers
self.d_model = d_model
self.n_head = n_head
self.d_head = d_head
self.d_inner = d_inner
self.hidden_act = hidden_act
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = 2
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
# Used in the tests to check the size of the first attention layer
self.num_attention_heads = n_head
# Used in the tests to check the size of the first hidden state
self.hidden_size = self.d_model
# Used in the tests to check the number of output hidden states/attentions
self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
self.expected_num_hidden_layers = self.num_hidden_layers + 2
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = FunnelConfig(
vocab_size=self.vocab_size,
block_sizes=self.block_sizes,
num_decoder_layers=self.num_decoder_layers,
d_model=self.d_model,
n_head=self.n_head,
d_head=self.d_head,
d_inner=self.d_inner,
hidden_act=self.hidden_act,
hidden_dropout=self.hidden_dropout,
attention_dropout=self.attention_dropout,
activation_dropout=self.activation_dropout,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = TFFunnelModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
config.truncate_seq = False
model = TFFunnelModel(config=config)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
config.separate_cls = False
model = TFFunnelModel(config=config)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
def create_and_check_base_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = TFFunnelBaseModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
config.truncate_seq = False
model = TFFunnelBaseModel(config=config)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
config.separate_cls = False
model = TFFunnelBaseModel(config=config)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
def create_and_check_for_pretraining(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = TFFunnelForPreTraining(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_masked_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = TFFunnelForMaskedLM(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.num_labels = self.num_labels
model = TFFunnelForSequenceClassification(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.num_choices = self.num_choices
model = TFFunnelForMultipleChoice(config=config)
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.num_labels = self.num_labels
model = TFFunnelForTokenClassification(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = TFFunnelForQuestionAnswering(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TFFunnelModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFFunnelModelTester(self)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_saved_model_creation(self):
# This test is too long (>30sec) and makes fail the CI
pass
@require_tf
class TFFunnelBaseModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFFunnelModelTester(self, base=True)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_base_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_saved_model_creation(self):
# This test is too long (>30sec) and makes fail the CI
pass
|
AdaMix/tests/test_modeling_tf_funnel.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_tf_funnel.py",
"repo_id": "AdaMix",
"token_count": 6594
}
| 70 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class TFTransfoXLModelTester:
def __init__(
self,
parent,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.mem_len = 30
self.key_length = self.seq_length + self.mem_len
self.clamp_len = 15
self.is_training = True
self.use_labels = True
self.vocab_size = 99
self.cutoffs = [10, 50, 80]
self.hidden_size = 32
self.d_embed = 32
self.num_attention_heads = 4
self.d_head = 8
self.d_inner = 128
self.div_val = 2
self.num_hidden_layers = 5
self.scope = None
self.seed = 1
self.eos_token_id = 0
self.num_labels = 3
self.pad_token_id = self.vocab_size - 1
self.init_range = 0.01
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = TransfoXLConfig(
vocab_size=self.vocab_size,
mem_len=self.mem_len,
clamp_len=self.clamp_len,
cutoffs=self.cutoffs,
d_model=self.hidden_size,
d_embed=self.d_embed,
n_head=self.num_attention_heads,
d_head=self.d_head,
d_inner=self.d_inner,
div_val=self.div_val,
n_layer=self.num_hidden_layers,
eos_token_id=self.eos_token_id,
pad_token_id=self.vocab_size - 1,
init_range=self.init_range,
num_labels=self.num_labels,
)
return (config, input_ids_1, input_ids_2, lm_labels)
def set_seed(self):
random.seed(self.seed)
tf.random.set_seed(self.seed)
def create_and_check_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
model = TFTransfoXLModel(config)
hidden_states_1, mems_1 = model(input_ids_1).to_tuple()
inputs = {"input_ids": input_ids_2, "mems": mems_1}
hidden_states_2, mems_2 = model(inputs).to_tuple()
self.parent.assertEqual(hidden_states_1.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(hidden_states_2.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_1],
[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
self.parent.assertListEqual(
[mem.shape for mem in mems_2],
[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
model = TFTransfoXLLMHeadModel(config)
lm_logits_1, mems_1 = model(input_ids_1).to_tuple()
inputs = {"input_ids": input_ids_1, "labels": lm_labels}
_, mems_1 = model(inputs).to_tuple()
lm_logits_2, mems_2 = model([input_ids_2, mems_1]).to_tuple()
inputs = {"input_ids": input_ids_1, "mems": mems_1, "labels": lm_labels}
_, mems_2 = model(inputs).to_tuple()
self.parent.assertEqual(lm_logits_1.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_1],
[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
self.parent.assertEqual(lm_logits_2.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_2],
[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_transfo_xl_for_sequence_classification(self, config, input_ids_1, input_ids_2, lm_labels):
model = TFTransfoXLForSequenceClassification(config)
result = model(input_ids_1)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
inputs_dict = {"input_ids": input_ids_1}
return config, inputs_dict
@require_tf
class TFTransfoXLModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
all_generative_model_classes = () if is_tf_available() else ()
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
test_resize_embeddings = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFTransfoXLModelTester(self)
self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_transfo_xl_model(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*config_and_inputs)
def test_transfo_xl_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*config_and_inputs)
def test_transfo_xl_sequence_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*config_and_inputs)
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
list_other_models_with_output_ebd = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
model = model_class(config)
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
if model_class in list_other_models_with_output_ebd:
x = model.get_output_embeddings()
assert isinstance(x, tf.keras.layers.Layer)
name = model.get_bias()
assert name is None
else:
x = model.get_output_embeddings()
assert x is None
name = model.get_bias()
assert name is None
def test_xla_mode(self):
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def test_model_from_pretrained(self):
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFTransfoXLModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_tf
class TFTransfoXLModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_transfo_xl_wt103(self):
model = TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
input_ids = tf.convert_to_tensor(
[
[
33,
1297,
2,
1,
1009,
4,
1109,
11739,
4762,
358,
5,
25,
245,
22,
1706,
17,
20098,
5,
3215,
21,
37,
1110,
3,
13,
1041,
4,
24,
603,
490,
2,
71477,
20098,
104447,
2,
20961,
1,
2604,
4,
1,
329,
3,
6224,
831,
16002,
2,
8,
603,
78967,
29546,
23,
803,
20,
25,
416,
5,
8,
232,
4,
277,
6,
1855,
4601,
3,
29546,
54,
8,
3609,
5,
57211,
49,
4,
1,
277,
18,
8,
1755,
15691,
3,
341,
25,
416,
693,
42573,
71,
17,
401,
94,
31,
17919,
2,
29546,
7873,
18,
1,
435,
23,
11011,
755,
5,
5167,
3,
7983,
98,
84,
2,
29546,
3267,
8,
3609,
4,
1,
4865,
1075,
2,
6087,
71,
6,
346,
8,
5854,
3,
29546,
824,
1400,
1868,
2,
19,
160,
2,
311,
8,
5496,
2,
20920,
17,
25,
15097,
3,
24,
24,
0,
]
],
dtype=tf.int32,
)
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
expected_output_ids = [
33,
1297,
2,
1,
1009,
4,
1109,
11739,
4762,
358,
5,
25,
245,
22,
1706,
17,
20098,
5,
3215,
21,
37,
1110,
3,
13,
1041,
4,
24,
603,
490,
2,
71477,
20098,
104447,
2,
20961,
1,
2604,
4,
1,
329,
3,
6224,
831,
16002,
2,
8,
603,
78967,
29546,
23,
803,
20,
25,
416,
5,
8,
232,
4,
277,
6,
1855,
4601,
3,
29546,
54,
8,
3609,
5,
57211,
49,
4,
1,
277,
18,
8,
1755,
15691,
3,
341,
25,
416,
693,
42573,
71,
17,
401,
94,
31,
17919,
2,
29546,
7873,
18,
1,
435,
23,
11011,
755,
5,
5167,
3,
7983,
98,
84,
2,
29546,
3267,
8,
3609,
4,
1,
4865,
1075,
2,
6087,
71,
6,
346,
8,
5854,
3,
29546,
824,
1400,
1868,
2,
19,
160,
2,
311,
8,
5496,
2,
20920,
17,
25,
15097,
3,
24,
24,
0,
33,
1,
1857,
2,
1,
1009,
4,
1109,
11739,
4762,
358,
5,
25,
245,
28,
1110,
3,
13,
1041,
4,
24,
603,
490,
2,
71477,
20098,
104447,
2,
20961,
1,
2604,
4,
1,
329,
3,
0,
]
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
output_ids = model.generate(input_ids, max_length=200, do_sample=False)
self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
|
AdaMix/tests/test_modeling_tf_transfo_xl.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_tf_transfo_xl.py",
"repo_id": "AdaMix",
"token_count": 10349
}
| 71 |
# coding=utf-8
# Copyright 2019 Hugging Face inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_tokenizers, slow
from .test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class AlbertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = AlbertTokenizer
rust_tokenizer_class = AlbertTokenizerFast
test_rust_tokenizer = True
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = AlbertTokenizer(SAMPLE_VOCAB)
tokenizer.save_pretrained(self.tmpdirname)
def get_input_output_texts(self, tokenizer):
input_text = "this is a test"
output_text = "this is a test"
return input_text, output_text
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence = "I was born in 92000, and this is falsé."
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
def test_full_tokenizer(self):
tokenizer = AlbertTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁this", "▁is", "▁a", "▁test"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [48, 25, 21, 1289])
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens, ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(ids, [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."],
)
def test_sequence_builders(self):
tokenizer = AlbertTokenizer(SAMPLE_VOCAB)
text = tokenizer.encode("sequence builders")
text_2 = tokenizer.encode("multi-sequence build")
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [
tokenizer.sep_token_id
]
@slow
def test_tokenizer_integration(self):
tokenizer_classes = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class)
for tokenizer_class in tokenizer_classes:
tokenizer = tokenizer_class.from_pretrained("albert-base-v2")
sequences = [
"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
"ALBERT incorporates two parameter reduction techniques",
"The first one is a factorized embedding parameterization. By decomposing the large vocabulary embedding matrix into two small matrices, we separate the size of the hidden layers from the size of vocabulary embedding.", # noqa: E231
]
encoding = tokenizer(sequences, padding=True)
decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]]
# fmt: off
expected_encoding = {
'input_ids': [
[2, 2953, 45, 21, 13, 10601, 11502, 26, 1119, 8, 8542, 3762, 69, 2477, 16, 816, 18667, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231
[2, 2953, 13760, 81, 18906, 5895, 4212, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231
[2, 14, 64, 53, 25, 21, 3932, 1333, 11911, 69, 3258, 18906, 1829, 9, 34, 121, 960, 14717, 14, 370, 18630, 11911, 69, 3258, 8187, 77, 81, 284, 24849, 15, 95, 1725, 14, 1072, 16, 14, 3689, 9124, 37, 14, 1072, 16, 18630, 11911, 69, 3258, 9, 3]], # noqa: E231
'token_type_ids': [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # noqa: E231
'attention_mask': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] # noqa: E231
]
}
expected_decoded_sequence = [
"albert: a lite bert for self-supervised learning of language representations",
'albert incorporates two parameter reduction techniques',
'the first one is a factorized embedding parameterization. by decomposing the large vocabulary embedding matrix into two small matrices, we separate the size of the hidden layers from the size of vocabulary embedding.' # noqa: E231
]
# fmt: on
self.assertDictEqual(encoding.data, expected_encoding)
for expected, decoded in zip(expected_decoded_sequence, decoded_sequences):
self.assertEqual(expected, decoded)
|
AdaMix/tests/test_tokenization_albert.py/0
|
{
"file_path": "AdaMix/tests/test_tokenization_albert.py",
"repo_id": "AdaMix",
"token_count": 3513
}
| 72 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers.file_utils import cached_property
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from .test_tokenization_common import TokenizerTesterMixin
# using a different tiny model than the one used for default params defined in init to ensure proper testing
FSMT_TINY2 = "stas/tiny-wmt19-en-ru"
class FSMTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = FSMTTokenizer
def setUp(self):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
self.langs = ["en", "ru"]
config = {
"langs": self.langs,
"src_vocab_size": 10,
"tgt_vocab_size": 20,
}
self.src_vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["src_vocab_file"])
self.tgt_vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["tgt_vocab_file"])
config_file = os.path.join(self.tmpdirname, "tokenizer_config.json")
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.src_vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(self.tgt_vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(self.merges_file, "w") as fp:
fp.write("\n".join(merges))
with open(config_file, "w") as fp:
fp.write(json.dumps(config))
@cached_property
def tokenizer_ru_en(self):
return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en")
@cached_property
def tokenizer_en_ru(self):
return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru")
def test_online_tokenizer_config(self):
"""this just tests that the online tokenizer files get correctly fetched and
loaded via its tokenizer_config.json and it's not slow so it's run by normal CI
"""
tokenizer = FSMTTokenizer.from_pretrained(FSMT_TINY2)
self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang], ["en", "ru"])
self.assertEqual(tokenizer.src_vocab_size, 21)
self.assertEqual(tokenizer.tgt_vocab_size, 21)
def test_full_tokenizer(self):
""" Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
tokenizer = FSMTTokenizer(self.langs, self.src_vocab_file, self.tgt_vocab_file, self.merges_file)
text = "lower"
bpe_tokens = ["low", "er</w>"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + ["<unk>"]
input_bpe_tokens = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
@slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_ru_en
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == text + [2]
assert encoded_pair == text + [2] + text_2 + [2]
@slow
def test_match_encode_decode(self):
tokenizer_enc = self.tokenizer_en_ru
tokenizer_dec = self.tokenizer_ru_en
targets = [
[
"Here's a little song I wrote. Don't worry, be happy.",
[2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 5, 26, 6445, 7, 2],
],
["This is it. No more. I'm done!", [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
]
# if data needs to be recreated or added, run:
# import torch
# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")
for src_text, tgt_input_ids in targets:
encoded_ids = tokenizer_enc.encode(src_text, return_tensors=None)
self.assertListEqual(encoded_ids, tgt_input_ids)
# and decode backward, using the reversed languages model
decoded_text = tokenizer_dec.decode(encoded_ids, skip_special_tokens=True)
self.assertEqual(decoded_text, src_text)
@slow
def test_tokenizer_lower(self):
tokenizer = FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en", do_lower_case=True)
tokens = tokenizer.tokenize("USA is United States of America")
expected = ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
self.assertListEqual(tokens, expected)
@unittest.skip("FSMTConfig.__init__ requires non-optional args")
def test_torch_encode_plus_sent_to_model(self):
pass
@unittest.skip("FSMTConfig.__init__ requires non-optional args")
def test_np_encode_plus_sent_to_model(self):
pass
|
AdaMix/tests/test_tokenization_fsmt.py/0
|
{
"file_path": "AdaMix/tests/test_tokenization_fsmt.py",
"repo_id": "AdaMix",
"token_count": 2935
}
| 73 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.file_utils import cached_property
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from .test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class ReformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = ReformerTokenizer
rust_tokenizer_class = ReformerTokenizerFast
test_rust_tokenizer = True
test_seq2seq = False
def setUp(self):
super().setUp()
tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence = "I was born in 92000, and this is falsé."
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
def test_padding(self, max_length=15):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest("{} ({})".format(tokenizer.__class__.__name__, pretrained_name)):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Simple input
s = "This is a simple input"
s2 = ["This is a simple input 1", "This is a simple input 2"]
p = ("This is a simple input", "This is a pair")
p2 = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
s2,
max_length=max_length,
padding="max_length",
)
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
p2,
max_length=max_length,
padding="max_length",
)
# tokenizer has no padding token
def test_padding_different_model_input_name(self):
pass
def test_full_tokenizer(self):
tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[285, 46, 10, 170, 382],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
@cached_property
def big_tokenizer(self):
return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
@slow
def test_tokenization_base_easy_symbols(self):
symbols = "Hello World!"
original_tokenizer_encodings = [126, 32, 262, 152, 38, 72, 287]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@slow
def test_tokenization_base_hard_symbols(self):
symbols = 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
original_tokenizer_encodings = [
108,
265,
24,
111,
4,
258,
156,
35,
28,
275,
3,
259,
297,
260,
84,
4,
35,
110,
44,
8,
259,
91,
268,
21,
11,
209,
274,
109,
266,
277,
117,
86,
93,
315,
258,
278,
258,
277,
258,
0,
258,
288,
258,
319,
258,
0,
258,
0,
258,
0,
258,
0,
258,
287,
258,
315,
258,
289,
258,
278,
99,
269,
266,
262,
8,
259,
241,
4,
217,
230,
268,
266,
55,
168,
106,
75,
193,
266,
223,
27,
49,
26,
282,
25,
264,
299,
19,
26,
0,
258,
277,
117,
86,
93,
176,
183,
270,
11,
262,
42,
61,
265,
]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@require_torch
@slow
def test_torch_encode_plus_sent_to_model(self):
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt")
batch_encoded_sequence = self.big_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
config = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
config.axial_pos_shape = encoded_sequence["input_ids"].shape
model = ReformerModel(config)
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**encoded_sequence)
model(**batch_encoded_sequence)
|
AdaMix/tests/test_tokenization_reformer.py/0
|
{
"file_path": "AdaMix/tests/test_tokenization_reformer.py",
"repo_id": "AdaMix",
"token_count": 5489
}
| 74 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from transformers.utils import logging
logger = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data.dataset import Dataset
from transformers import Trainer
class DummyDataset(Dataset):
def __init__(self, length: int = 101):
self.length = length
def __len__(self):
return self.length
def __getitem__(self, i) -> int:
return i
class DummyDataCollator:
def __call__(self, features):
return {"input_ids": torch.tensor(features), "labels": torch.tensor(features)}
class DummyModel(nn.Module):
def __init__(self):
super().__init__()
# Add some (unused) params otherwise DDP will complain.
self.fc = nn.Linear(120, 80)
def forward(self, input_ids, labels=None):
if labels is not None:
return torch.tensor(0.0, device=input_ids.device), input_ids
else:
return input_ids
class TestTrainerDistributed(TestCasePlus):
@require_torch_multi_gpu
def test_trainer(self):
distributed_args = f"""
-m torch.distributed.launch
--nproc_per_node={torch.cuda.device_count()}
{self.test_file_dir}/test_trainer_distributed.py
""".split()
output_dir = self.get_auto_remove_tmp_dir()
args = f"--output_dir {output_dir}".split()
cmd = [sys.executable] + distributed_args + args
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.launch --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
parser = HfArgumentParser((TrainingArguments,))
training_args = parser.parse_args_into_dataclasses()[0]
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
training_args.local_rank != -1,
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
dataset = DummyDataset(dataset_length)
def compute_metrics(p: EvalPrediction) -> Dict:
sequential = list(range(len(dataset)))
success = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"Predictions and/or labels do not match expected results:\n - predictions: "
f"{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}"
)
return {"success": success}
trainer = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
metrics = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
p = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["eval_success"] is not True:
logger.error(p.metrics)
exit(1)
trainer.args.eval_accumulation_steps = 2
metrics = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
p = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["eval_success"] is not True:
logger.error(p.metrics)
exit(1)
trainer.args.eval_accumulation_steps = None
|
AdaMix/tests/test_trainer_distributed.py/0
|
{
"file_path": "AdaMix/tests/test_trainer_distributed.py",
"repo_id": "AdaMix",
"token_count": 2069
}
| 75 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
import sys
from slack_sdk import WebClient
def handle_test_results(test_results):
expressions = test_results.split(" ")
failed = 0
success = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
time_spent = expressions[-2] if "=" in expressions[-1] else expressions[-1]
for i, expression in enumerate(expressions):
if "failed" in expression:
failed += int(expressions[i - 1])
if "passed" in expression:
success += int(expressions[i - 1])
return failed, success, time_spent
def format_for_slack(total_results, results, scheduled: bool):
print(results)
header = {
"type": "header",
"text": {
"type": "plain_text",
"text": "🤗 Results of the scheduled tests, March 11, 2021." if scheduled else "🤗 Self-push results",
"emoji": True,
},
}
total = (
{
"type": "section",
"fields": [
{"type": "mrkdwn", "text": f"*Failures:*\n❌ {total_results['failed']} failures."},
{"type": "mrkdwn", "text": f"*Passed:*\n✅ {total_results['success']} tests passed."},
],
}
if total_results["failed"] > 0
else {
"type": "section",
"fields": [{"type": "mrkdwn", "text": f"*Congrats!*\nAll {total_results['success']} tests pass."}],
}
)
blocks = [header, total]
if total_results["failed"] > 0:
for key, result in results.items():
print(key, result)
blocks.append({"type": "header", "text": {"type": "plain_text", "text": key, "emoji": True}})
blocks.append(
{
"type": "section",
"fields": [
{
"type": "mrkdwn",
"text": f"*Results:*\n{result['failed']} failed, {result['success']} passed.",
},
{"type": "mrkdwn", "text": f"*Time spent:*\n{result['time_spent']}"},
],
}
)
else:
for key, result in results.items():
blocks.append(
{"type": "section", "fields": [{"type": "mrkdwn", "text": f"*{key}*\n{result['time_spent']}."}]}
)
footer = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": "<https://github.com/huggingface/transformers/actions/workflows/self-scheduled.yml|View on GitHub>"
if scheduled
else "<https://github.com/huggingface/transformers/actions/workflows/self-push.yml|View on GitHub>",
},
}
blocks.append(footer)
blocks = {"blocks": blocks}
return blocks
if __name__ == "__main__":
scheduled = sys.argv[1] == "scheduled"
if scheduled:
# The scheduled run has several artifacts for each job.
file_paths = {
"TF Single GPU": {
"common": "run_all_tests_tf_gpu_test_reports/tests_tf_gpu_[].txt",
"pipeline": "run_all_tests_tf_gpu_test_reports/tests_tf_pipeline_gpu_[].txt",
},
"Torch Single GPU": {
"common": "run_all_tests_torch_gpu_test_reports/tests_torch_gpu_[].txt",
"pipeline": "run_all_tests_torch_gpu_test_reports/tests_torch_pipeline_gpu_[].txt",
"examples": "run_all_tests_torch_gpu_test_reports/examples_torch_gpu_[].txt",
},
"TF Multi GPU": {
"common": "run_all_tests_tf_multi_gpu_test_reports/tests_tf_multi_gpu_[].txt",
"pipeline": "run_all_tests_tf_multi_gpu_test_reports/tests_tf_pipeline_multi_gpu_[].txt",
},
"Torch Multi GPU": {
"common": "run_all_tests_torch_multi_gpu_test_reports/tests_torch_multi_gpu_[].txt",
"pipeline": "run_all_tests_torch_multi_gpu_test_reports/tests_torch_pipeline_multi_gpu_[].txt",
},
}
else:
file_paths = {
"TF Single GPU": {"common": "run_all_tests_tf_gpu_test_reports/tests_tf_gpu_[].txt"},
"Torch Single GPU": {"common": "run_all_tests_torch_gpu_test_reports/tests_torch_gpu_[].txt"},
"TF Multi GPU": {"common": "run_all_tests_tf_multi_gpu_test_reports/tests_tf_multi_gpu_[].txt"},
"Torch Multi GPU": {"common": "run_all_tests_torch_multi_gpu_test_reports/tests_torch_multi_gpu_[].txt"},
}
client = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
channel_id = os.environ["CI_SLACK_CHANNEL_ID"]
try:
results = {}
for job, file_dict in file_paths.items():
# Single return value for failed/success across steps of a same job
results[job] = {"failed": 0, "success": 0, "time_spent": "", "failures": ""}
for key, file_path in file_dict.items():
with open(file_path.replace("[]", "stats")) as f:
failed, success, time_spent = handle_test_results(f.read())
results[job]["failed"] += failed
results[job]["success"] += success
results[job]["time_spent"] += time_spent[1:-1] + ", "
with open(file_path.replace("[]", "summary_short")) as f:
for line in f:
if re.search("FAILED", line):
results[job]["failures"] += line
# Remove the trailing ", "
results[job]["time_spent"] = results[job]["time_spent"][:-2]
test_results_keys = ["failed", "success"]
total = {"failed": 0, "success": 0}
for job, job_result in results.items():
for result_key in test_results_keys:
total[result_key] += job_result[result_key]
to_be_sent_to_slack = format_for_slack(total, results, scheduled)
result = client.chat_postMessage(
channel=channel_id,
blocks=to_be_sent_to_slack["blocks"],
)
for job, job_result in results.items():
if len(job_result["failures"]):
client.chat_postMessage(
channel=channel_id, text=f"{job}\n{job_result['failures']}", thread_ts=result["ts"]
)
except Exception as e:
# Voluntarily catch every exception and send it to Slack.
raise Exception(f"Setup error: no artifacts were found. Error: {e}") from e
|
AdaMix/utils/notification_service.py/0
|
{
"file_path": "AdaMix/utils/notification_service.py",
"repo_id": "AdaMix",
"token_count": 3412
}
| 76 |
## Baselines
### moveOnSpline
- Plan and move on a minimum jerk trajectory using ground truth poses of gates:
- Generate an AirSim settings.json file (same as the one provided in releases)
```shell
$ cd baselines;
$ python generate_settings_file.py
```
- Start the AirSim Neurips binary, [as explained above](#running)
- Run the code!
```shell
$ python baseline_racer.py \
--enable_viz_traj \
--enable_viz_image_cv2 \
--planning_baseline_type all_gates_at_once \
--planning_and_control_api moveOnSpline \
--level_name ZhangJiaJie_Medium \
--race_tier 1
```
Usage is:
```shell
$ python baselines/baseline_racer.py -h
usage: baseline_racer.py [-h]
[--level_name {Soccer_Field_Easy,Soccer_Field_Medium,ZhangJiaJie_Medium,Building99_Hard,Qualifier_Tier_1,Qualifier_Tier_2,Qualifier_Tier_3,Final_Tier_1,Final_Tier_2,Final_Tier_3}]
[--planning_baseline_type {all_gates_at_once,all_gates_one_by_one}]
[--planning_and_control_api {moveOnSpline,moveOnSplineVelConstraints}]
[--enable_viz_traj] [--enable_viz_image_cv2]
[--race_tier {1,2,3}]
```
|
AirSim-Drone-Racing-Lab/docs/baselines.md/0
|
{
"file_path": "AirSim-Drone-Racing-Lab/docs/baselines.md",
"repo_id": "AirSim-Drone-Racing-Lab",
"token_count": 476
}
| 77 |
import os
import sys
from scipy.spatial.transform import Rotation
import math
from airsimdroneracingvae.utils import to_eularian_angles, to_quaternion
import numpy as np
from airsimdroneracingvae.types import Pose, Vector3r, Quaternionr
def interp_vector(a, b, n):
delta = (b-a)/(n-1)
list_vecs = []
for i in range(n):
new_vec = a+delta*i
list_vecs.append(new_vec)
return np.asarray(list_vecs)
def randomQuadPose(x_range, y_range, z_range, yaw_range, pitch_range, roll_range):
x = randomSample(x_range)
y = randomSample(y_range)
z = randomSample(z_range)
yaw = randomSample(yaw_range)
pitch = randomSample(pitch_range)
roll = randomSample(roll_range)
q = Rotation.from_euler('ZYX', [yaw, pitch, roll]) # capital letters denote intrinsic rotation (lower case would be extrinsic)
q = q.as_quat()
t_o_b = Vector3r(x,y,z)
q_o_b = Quaternionr(q[0], q[1], q[2], q[3])
return Pose(t_o_b, q_o_b), yaw
def randomSample(value_range):
return (value_range[1] - value_range[0])*np.random.random() + value_range[0]
def randomGatePose(p_o_b, phi_base, r_range, cam_fov, correction):
gate_ok = False
while not gate_ok:
# create translation of gate
r = randomSample(r_range)
alpha = cam_fov/180.0*np.pi/2.0 # alpha is half of fov angle
theta_range = [-alpha, alpha]
theta = randomSample(theta_range)
# need to make projection on geodesic curve! not equal FOV in theta and psi
alpha_prime = np.arctan(np.cos(np.abs(theta)))
psi_range = [-alpha_prime, alpha_prime]
psi_range = [x * correction for x in psi_range]
psi = randomSample(psi_range) + np.pi/2.0
# get relative vector in the base frame coordinates
t_b_g_body = polarTranslation(r, theta, psi)
# transform relative vector from base frame to the world frame
t_b_g = convert_t_body_2_world(t_b_g_body, p_o_b.orientation)
# get the gate coord in world coordinates from origin
t_o_g = p_o_b.position + t_b_g
# check if gate is at least half outside the ground
if t_o_g.z_val >= 0.0:
continue
# create rotation of gate
eps = 0 # np.pi/15.0
phi_rel_range = [-np.pi + eps, 0 - eps]
phi_rel = randomSample(phi_rel_range)
phi_quad_ref = get_yaw_base(p_o_b)
phi_gate = phi_quad_ref + phi_rel
rot_gate = Rotation.from_euler('ZYX', [phi_gate, 0, 0])
q = rot_gate.as_quat()
p_o_g = Pose(t_o_g, Quaternionr(q[0], q[1], q[2], q[3]))
return p_o_g, r, theta, psi, phi_rel
def debugRelativeOrientation(p_o_b, p_o_g, phi_rel):
phi_quad_ref = get_yaw_base(p_o_b)
phi_gate = phi_quad_ref + phi_rel
rot_gate = Rotation.from_euler('ZYX', [phi_gate, 0, 0])
q = rot_gate.as_quat()
p_o_g = Pose(p_o_g.position, Quaternionr(q[0], q[1], q[2], q[3]))
return p_o_g
def debugGatePoses(p_o_b, r, theta, psi):
# get relative vector in the base frame coordinates
t_b_g_body = polarTranslation(r, theta, psi)
# transform relative vector from base frame to the world frame
t_b_g = convert_t_body_2_world(t_b_g_body, p_o_b.orientation)
# get the gate coord in world coordinates from origin
t_o_g = p_o_b.position + t_b_g
# check if gate is at least half outside the ground
# create rotation of gate
phi_quad_ref = np.arctan2(p_o_b.position.y_val, p_o_b.position.x_val)
phi_rel = np.pi/2
phi_gate = phi_quad_ref + phi_rel
rot_gate = Rotation.from_euler('ZYX', [phi_gate, 0, 0])
q = rot_gate.as_quat()
p_o_g = Pose(t_o_g, Quaternionr(q[0], q[1], q[2], q[3]))
return p_o_g, r, theta, psi, phi_rel
def polarTranslation(r, theta, psi):
# follow math convention for polar coordinates
# r: radius
# theta: azimuth (horizontal)
# psi: vertical
x = r * np.cos(theta) * np.sin(psi)
y = r * np.sin(theta) * np.sin(psi)
z = r * np.cos(psi)
return Vector3r(x, y, z)
def convert_t_body_2_world(t_body, q_o_b):
rotation = Rotation.from_quat([q_o_b.x_val, q_o_b.y_val, q_o_b.z_val, q_o_b.w_val])
t_body_np = [t_body.x_val, t_body.y_val, t_body.z_val]
t_world_np = rotation.apply(t_body_np)
t_world = Vector3r(t_world_np[0], t_world_np[1], t_world_np[2])
return t_world
def get_yaw_base(p_o_b):
q_o_b = p_o_b.orientation
rotation = Rotation.from_quat([q_o_b.x_val, q_o_b.y_val, q_o_b.z_val, q_o_b.w_val])
euler_angles = rotation.as_euler('ZYX')
return euler_angles[0]
# this is utility function to get a velocity constraint which can be passed to moveOnSplineVelConstraints()
# the "scale" parameter scales the gate facing vector accordingly, thereby dictating the speed of the velocity constraint
def get_gate_facing_vector_from_quaternion(airsim_quat, direction, scale=1.0,):
# convert gate quaternion to rotation matrix.
# ref: https://en.wikipedia.org/wiki/Rotation_matrix#Quaternion; https://www.lfd.uci.edu/~gohlke/code/transformations.py.html
q = np.array([airsim_quat.w_val, airsim_quat.x_val, airsim_quat.y_val, airsim_quat.z_val], dtype=np.float64)
n = np.dot(q, q)
if n < np.finfo(float).eps:
if direction == 0:
return airsimdroneracingvae.Vector3r(0.0, 1.0, 0.0)
else:
return airsimdroneracingvae.Vector3r(0.0, -1.0, 0.0)
q *= math.sqrt(2.0 / n)
q = np.outer(q, q)
rotation_matrix = np.array([[1.0-q[2, 2]-q[3, 3], q[1, 2]-q[3, 0], q[1, 3]+q[2, 0]],
[ q[1, 2]+q[3, 0], 1.0-q[1, 1]-q[3, 3], q[2, 3]-q[1, 0]],
[ q[1, 3]-q[2, 0], q[2, 3]+q[1, 0], 1.0-q[1, 1]-q[2, 2]]])
gate_facing_vector = rotation_matrix[:,1]
if direction == 0:
return airsimdroneracingvae.Vector3r(scale * gate_facing_vector[0], scale * gate_facing_vector[1], scale * gate_facing_vector[2])
else:
return airsimdroneracingvae.Vector3r(-scale * gate_facing_vector[0], -scale * gate_facing_vector[1], scale * gate_facing_vector[2])
def getGatePoseWorld(p_o_b, r, theta, psi, phi_rel):
# get relative vector in the base frame coordinates
t_b_g_body = polarTranslation(r, theta, psi)
# transform relative vector from base frame to the world frame
t_b_g = convert_t_body_2_world(t_b_g_body, p_o_b.orientation)
# get the gate coord in world coordinates from origin
t_o_g = p_o_b.position + t_b_g
# create rotation of gate
phi_quad_ref = get_yaw_base(p_o_b)
phi_gate = phi_quad_ref + phi_rel
rot_gate = Rotation.from_euler('ZYX', [phi_gate, 0, 0])
q = rot_gate.as_quat()
p_o_g = Pose(t_o_g, Quaternionr(q[0], q[1], q[2], q[3]))
return p_o_g
|
AirSim-Drone-Racing-VAE-Imitation/racing_utils/geom_utils.py/0
|
{
"file_path": "AirSim-Drone-Racing-VAE-Imitation/racing_utils/geom_utils.py",
"repo_id": "AirSim-Drone-Racing-VAE-Imitation",
"token_count": 3170
}
| 78 |
# Game of Drones: A NeurIPS 2019 Competition
**Note: This repository is not being maintained any more. Please use [AirSim Drone Racing Lab](https://github.com/microsoft/AirSim-Drone-Racing-Lab).**
## Quickstart
- [Website](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/)
- [Register](https://www.microsoft.com/en-us/research/academic-program/game-of-drones-competition-at-neurips-2019/)
- [Competition guidelines](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/blob/master/docs/competition_guidelines.md)
- [Linux and Windows Binaries](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases)
- [Python API](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html), [airsimneurips PyPI package](https://pypi.org/project/airsimneurips/)
<img src="https://github.com/madratman/airsim_neurips_gifs/blob/master/imgs/neurips_b99_3_drones.gif?raw=true" width="285"> <img src="https://github.com/madratman/airsim_neurips_gifs/blob/master/imgs/neurips_soccer_field_8_drones.gif?raw=true" width="285"> <img src="https://github.com/madratman/airsim_neurips_gifs/blob/master/imgs/neurips_zhangjiajie_4_drones.gif?raw=true" width="285">
Note: If you use this repository in your research, please cite our pre-print, [AirSim Drone Racing Lab](https://arxiv.org/abs/2003.05654).
```
@article{madaan2020airsim,
title={AirSim Drone Racing Lab},
author={Madaan, Ratnesh and Gyde, Nicholas and Vemprala, Sai and Brown, Matthew and Nagami, Keiko and Taubner, Tim and Cristofalo, Eric and Scaramuzza, Davide and Schwager, Mac and Kapoor, Ashish},
journal={arXiv preprint arXiv:2003.05654},
year={2020}
}
```
### Downloading and running AirSim Binaries
#### Downloading
- Final round binaries and environments (v1.1)
- tl;dr:
- [Linux] Use the [download_final_round_binaries.sh](download_final_round_binaries.sh) script
- Long version:
- Download the v1.1 [Linux](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/tag/v1.1-linux) or [Windows](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/tag/v1.1-windows) `AirSim.zip`, and unzip it.
- Download your qualifier environments (shipped in pakfiles) - `Final_Tier_1_and_Tier_2.pak` and ` Final_Tier_3.pak`.
- Move the environment pakfiles into `AirSim/AirSimExe/Content/Paks`.
- Download and move the `settings.json` file to `~/Documents/AirSim/settings.json`.
- Use `airsimneurips` >= 1.2.0
- Qualifier binaries and environments (v1.0)
- tl;dr:
- [Linux] Use the [download_qualification_binaries.sh](download_qualification_binaries.sh) script
- Long version:
- Download the v1.0 [Linux](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/tag/v1.0-linux) or [Windows](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/tag/v1.0-windows) `AirSim.zip`, and unzip it.
- Download your qualifier environments (shipped in pakfiles) - `Qual_Tier_1_and_Tier_3.pak` and ` Qual_Tier_2.pak`.
- Move the environment pakfiles into `AirSim/AirSimExe/Content/Paks`.
- Download and move the `settings.json` file to `~/Documents/AirSim/settings.json`.
- Training binaries and environments (v0.3):
- tl;dr:
- [Linux] Use the [download_training_binaries.sh](download_training_binaries.sh) script
- Long version:
- Download the v0.3 [Linux](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/tag/v0.3.0-linux) or [Windows](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/tag/v0.3.0) `AirSim.zip`, and unzip it.
- Download training environments (shipped in pakfiles) - `Soccer_Field.pak`, `ZhangJiaJie.pak`, and `Building99.pak`.
- Move the environment pakfiles into `AirSim/AirSimExe/Content/Paks`.
- Download and move the `settings.json` file to `~/Documents/AirSim/settings.json`.
Notes:
- `Source code (zip)` or `Source code (tar.gz)` might not be up-to-date with the master branch of this repository. It can be lagging by `n commits to master since this release`, specified on the released page.
For the code on this repository, it's best to just `git clone`.
- List of disabled APIs in qualification binaries: The following APIs on the server side in the qualification binaries. You should see an error message pop up in the terminal message when you call these. They do work in the training binaries:
- `simSetObjectPose`
- `simSetVehiclePose`
- `simSetObjectScale`
- `simGetObjectScale`
- `simSetSegmentationObjectID`
- `simGetSegmentationObjectID`
- `simPause`
- `simContinueForTime`
#### Running
- Linux
- Open a terminal window, `cd` to `AirSim_Training/` or `AirSim_Qualification` directory, and enter the following command:
```
./AirSimExe.sh -windowed -opengl4
```
- Running headless (with rendering of images enabled):
```
DISPLAY= ./AirSimExe.sh -opengl4
```
- To disable rendering completely for training planning and / or control policies, you can use:
```
-./AirSimExe.sh -nullrhi
```
Note that `simGetImages` will not work with this option.
- To increase speed of `simGetImages` / increase speed of Unreal Engine's game thread;
- Add the `"ViewMode": "NoDisplay"` to your settings.json file, or use [this file](https://gist.github.com/madratman/5fadbb08f65e9c0187ccc1f5090fc086) directly.
This disables rendering in the main viewport camera.
Then run the binary with the following options.
```
./AirSimExe.sh -windowed -NoVSync -BENCHMARK
```
You can also use the Unreal console commands `Stat FPS`, `Stat UnitGraph`, `r.VSync`, `t.maxFPS`. See [Issue #111](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/issues/111) for more details.
- Windows
- Navigate to the `AirSim/` directory, and double-click `run.bat` (or `AirSimExe.exe -windowed`)
## Docker
- Prerequisites:
- Install [docker-ce](https://docs.docker.com/install/linux/docker-ce/ubuntu/).
- Complete the desired [post-installation steps for linux](https://docs.docker.com/install/linux/linux-postinstall/) after installing docker.
At the minimum, the page tells you how torun docker without root, and other useful setup options.
- Install [nvidia-docker2](https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)).
- Dockerfile:
We provide a sample [dockerfile](docker/Dockerfile) you can modify.
It downloads the training and qualification binaries automatically, and installs the python client.
By default, it uses Ubuntu 18.04 and CUDA 10.0 with OpenGL, and is build on top of [nvidia/cudagl:10.0-devel-ubuntu18.04](https://hub.docker.com/r/nvidia/cudagl).
This can be changed of course, as explained in the following section.
- Building the docker image:
You can use [build_docker_image.py](docker/build_docker_image.py) to build the dockerfile above (or your own custom one)
**Usage** (with default arguments)
```shell
cd docker/;
python3 build_docker_image.py \
--dockerfile Dockerfile \
--base_image nvidia/cudagl:10.0-devel-ubuntu18.04 \
-- target_image airsim_neurips:10.0-devel-ubuntu18.04
```
- Running the docker image:
See [docker/run_docker_image.sh](docker/run_docker_image.sh) to run the docker image:
**Usage**
- for running default image, training binaries, in windowed mode:
`$ ./run_docker_image.sh "" training`
- for running default image, qualification binaries, in windowed mode:
`$ ./run_docker_image.sh "" qualification`
- for running default image, training binaries, in headless mode:
`$ ./run_docker_image.sh "" training headless`
- for running default image, qualification binaries, in headless mode:
`$ ./run_docker_image.sh "" qualification headless`
- for running a custom image in windowed mode, pass in you image name and tag:
`$ ./run_docker_image.sh DOCKER_IMAGE_NAME:TAG`
- for running a custom image in headless mode, pass in you image name and tag, followed by "headless":
`$ ./run_docker_image.sh DOCKER_IMAGE_NAME:TAG headless`
## AirSim API
- To control your drone and get information from the environment, you will need the `airsimneurips` API, which is accessible via Python.
We recommend you used python >= 3.6. Python 2.7 will go [out of support soon](https://pythonclock.org/)
- To install the Python API, do a :
```
pip install airsimneurips
```
- See [quick overview of the API](#quick-api-overview) below
- The API is documented at [airsimneurips API doc](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html)
- Resources
- Going through both open and closed issues in this repository might answer some of your questions. The search bar on top left can prove useful.
- [AirSim upstream API](https://microsoft.github.io/AirSim/docs/apis/) and [examples](https://github.com/microsoft/AirSim/tree/master/PythonClient) can also be of use. However, please note that the main AirSim repo's API is not used in the competition (there's some overlap and some differences), however is a good learning resource.
## Submitting Results and Leaderboard - Qualification Round
- For the qualification round, we have one race track for each tier. The relevant binaries (v1.0) are available for [linux](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/tag/v1.0-linux) and [windows](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/tag/v1.0-windows)
- Tier 1: This is in the Soccer Field environment.
THe race track is in the `Qual_Tier_1_and_Tier_3.pak` pakfile
- Tier 2: This is in the ZhangJiaJie environment.
The race track is in the `Qual_Tier_2.pak` pakfile.
- Tier 3: This is again in the Soccer Field environment.
The race track is in the `Qual_Tier_1_and_Tier_3.pak` pakfile.
- How to generate logfiles for each tier:
- Loading level and starting race:
- Please update your airsimneurips pythonclient (should be >=1.0.0).
- Calling `simStartRace(race_tier=1, 2, or 3)` generates the appropriate log files.
- Tier 1:
```python
airsim_client.simLoadLevel('Qualifier_Tier_1')
airsim_client.simStartRace(1)
```
- Tier 2:
```python
airsim_client.simLoadLevel('Qualifier_Tier_2')
airsim_client.simStartRace(2)
```
- Tier 3:
```python
airsim_client.simLoadLevel('Qualifier_Tier_3')
airsim_client.simStartRace(3)
```
- As Tier 2 focuses on perception and Tier 3 focuses on both perception and planning, note that `simGetObjectPose` returns noisy gate poses, after `simStartRace(2)` and `simStartRace(3)` is called.
- As soon as `simStartRace(1)` or `simStartRace(3)` is called, `drone_2` (MSR opponent racer) will start flying.
- See `baseline_racer.py` for sample code. The previous bullet points are being called in wrapper functions in the following snippet in `baseline_racer.py`:
```python
baseline_racer.load_level(args.level_name)
if args.level_name == "Qualifier_Tier_1":
args.race_tier = 1
if args.level_name == "Qualifier_Tier_2":
args.race_tier = 2
if args.level_name == "Qualifier_Tier_3":
args.race_tier = 3
baseline_racer.start_race(args.race_tier)
```
- To submit your results to the leaderboard:
- Navigate to the [submission site](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/upload.html), enter your team name in the proper field, and upload any number of [race logs](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/blob/master/docs/competition_guidelines.md#race-monitoring).
It's ok to make a submission for as little as a single track and/or a single tier.
You can find race logs inside of `AirSimExe/Saved/Logs/RaceLogs` in your downloaded binary folder.
Please read [the race monitoring section](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/blob/master/docs/competition_guidelines.md#race-monitoring) in the competition guidelines for more details.
- The leaderboard will publish the results of a drone that is named `drone_1` (call [`generate_settings_file.py`](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/blob/master/baselines/generate_settings_file.py) to generate an AirSim settings file, as done for the `baseline_racer` below.
- Please submit a PDF file in the `report` section to help us verify the honesty of your submission for the Nov 21st deadline. Please summarize your approach for all tiers you make a submission for, with appropriate citations. The report PDF size should not exceed 10 MB, and should be a maximum of 4 pages in length. We leave the exact format of the report to your descrition, but the [IEEE template](https://ras.papercept.net/conferences/support/tex.php) is a good choice.
- We have emailed you a private key, which should be entered in the `Team ID` field. This helps us verify it was your team who indeed made the submission.
- The [leaderboard](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/leaderboard.html) is updated once per day at 2100 PST.
If you do not see your results after 24 hours, please [email us](mailto:[email protected]) with your team name and submitted log files.
## Submitting Results and Leaderboard - Final Round
- For the final round, we have one race track for each tier. The relevant binaries (v1.1) are available for [linux](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/tag/v1.1-linux) and [windows](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/tag/v1.1-windows)
- Tier 1: This is in the Soccer Field environment.
THe race track is in the `Final_Tier_1_and_Tier_2.pak` pakfile
- Tier 2: This is in the Soccer Field environment.
The race track is in the `Final_Tier_1_and_Tier_2.pak` pakfile.
- Tier 3: This is again in the ZhangJiaJie environment.
The race track is in the `Final_Tier_3.pak` pakfile.
- How to generate logfiles for each tier:
- Loading level and starting race:
- Please update your airsimneurips pythonclient (should be >=1.2.0).
- Calling `simStartRace(race_tier=1, 2, or 3)` generates the appropriate log files. You can only run `tier N` races in `Final_Tier_N` levels.
- Tier 1:
```python
airsim_client.simLoadLevel('Final_Tier_1')
airsim_client.simStartRace(tier=1)
```
- Tier 2:
```python
airsim_client.simLoadLevel('Final_Tier_2')
airsim_client.simStartRace(tier=2)
```
- Tier 3:
```python
airsim_client.simLoadLevel('Final_Tier_3')
airsim_client.simStartRace(tier=3)
```
- As Tier 2 focuses on perception and Tier 3 focuses on both perception and planning, note that `simGetObjectPose` returns noisy gate poses.
- As soon as `simStartRace(tier=1)` or `simStartRace(tier=3)` is called, `drone_2` (MSR opponent racer) will start flying.
- See `baseline_racer.py` for sample code. The previous bullet points are being called in wrapper functions in the following snippet in `baseline_racer.py`:
```python
baseline_racer.load_level(args.level_name)
baseline_racer.start_race(args.race_tier)
```
- To submit your results to the final leaderboard:
- Navigate to the [submission site](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/upload.html), enter your team name in the proper field, and upload any number of [race logs](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/blob/master/docs/competition_guidelines.md#race-monitoring).
It's ok to make a submission for as little as a single track and/or a single tier.
You can find race logs inside of `AirSimExe/Saved/Logs/RaceLogs` in your downloaded binary folder.
Please read [the race monitoring section](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/blob/master/docs/competition_guidelines.md#race-monitoring) in the competition guidelines for more details.
- The leaderboard will publish the results of a drone that is named `drone_1` (call [`generate_settings_file.py`](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/blob/master/baselines/generate_settings_file.py) to generate an AirSim settings file, as done for the `baseline_racer` below.
- Please submit a PDF file in the `report` section to help us verify the honesty of your submission by the Dec 5th, 2359 PST deadline. Please summarize your approach for all tiers you make a submission for, with appropriate citations. The report PDF size should not exceed 10 MB, and should be a maximum of 6 pages in length. We leave the exact format of the report to your descrition, but the [IEEE template](https://ras.papercept.net/conferences/support/tex.php) is a good choice.
- We have emailed you a private key, which should be entered in the `Team ID` field. This helps us verify it was your team who indeed made the submission.
- The [final leaderboard](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/leaderboard_final.html) is updated once per day at 2100 PST.
If you do not see your results after 24 hours, please [email us](mailto:[email protected]) with your team name and submitted log files.
## Sample code
- Plan and move on a minimum jerk trajectory using ground truth poses of gates:
- Generate an AirSim settings.json file (same as the one provided in releases)
```shell
$ cd baselines;
$ python generate_settings_file.py
```
- Start the AirSim Neurips binary, [as explained above](#running)
- Run the code!
```shell
$ python baseline_racer.py \
--enable_viz_traj \
--enable_viz_image_cv2 \
--planning_baseline_type all_gates_at_once \
--planning_and_control_api moveOnSpline \
--level_name ZhangJiaJie_Medium \
--race_tier 1
```
Usage is:
```shell
$ python baselines/baseline_racer.py -h
usage: baseline_racer.py [-h]
[--level_name {Soccer_Field_Easy,Soccer_Field_Medium,ZhangJiaJie_Medium,Building99_Hard,Qualifier_Tier_1,Qualifier_Tier_2,Qualifier_Tier_3,Final_Tier_1,Final_Tier_2,Final_Tier_3}]
[--planning_baseline_type {all_gates_at_once,all_gates_one_by_one}]
[--planning_and_control_api {moveOnSpline,moveOnSplineVelConstraints}]
[--enable_viz_traj] [--enable_viz_image_cv2]
[--race_tier {1,2,3}]
```
- Plan a Game Theoretic Plan (GTP) trajectory for an ego drone based on an estimate of the opponent drone's behavior.
- Generate an AirSim settings.json file
```shell
$ cd baselines;
$ python generate_settings_file.py
```
- Start the AirSim Neurips binary, [as explained above](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing#running)
- Run the GTP code!
```shell
$ python baseline_racer_gtp.py \
--blocking_behavior \
--plot_gtp \
--enable_viz_traj \
--level_name Qualifier_Tier_1
```
- This method is an Iterative Best Response (IBR) trajectory planning technique. In IBR, first the trajectories of both drones are initialized as straight down the track at maximum speed (to win the game!). The opponent trajectory is then held constant while we solve for the ego trajectory via Model Predictive Control (MPC) optimization (details in [gtp.py](baselines/gtp.py)). Then, we hold the ego trajectory constant and solve for a guess of the opponent's trajectory in the same fashion. If after some iterations, the solution convereges (i.e., the resulting trajectories stop changing), we have reached a Nash equilibrium over the space of trajectories. That is to say, either agents can not unilaterally change their trajectory to increase their own performance. This implementation is a heuristic based on the original method proposed in the paper below ([PDF here](https://arxiv.org/abs/1801.02302)).
- R. Spica, D. Falanga, E. Cristofalo, E. Montijano, D. Scaramuzza, and M. Schwager, "A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing", in the Proccedings of Robotics: Science and Systems (RSS), 2018.
## Quick API overview
We added some new APIs (marked with 💚) to [AirSim](https://github.com/Microsoft/Airsim) for the NeurIPS competition binaries.
#### Loading Unreal Engine environments
- [`simLoadLevel(level_name)`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simLoadLevel) 💚
Possible values for `level_name` are:
- `"Soccer_Field_Easy"`, `"Soccer_Field_Medium"`, `"ZhangJiaJie_Medium"`, `"Building99_Hard"` in the training binaries (`v0.3`).
- `"Qualification_Tier_1"`, `"Qualification_Tier_2"`, `"Qualification_Tier_3"` in the qualification binaries (`v1.0`).
- `"Final_Tier_1"`, `"Final_Tier_2"`, `"Final_Tier_3"` in the final round binaries (`v1.1`).
Before trying this, please ensure you've downloaded the corresponding training (`v0.3`) / qualifier (`v1.0`) / final round (`v1.0`) binaries, [as described above](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing#downloading-airsimexe-and-unreal-environments)
- UI Menu
- Press `F10` to toggle the level menu
- Click your desired level. (Note: the UI lists all the pakfiles in the `AirSim/AirSimExe/Content/Paks` directory. Ensure you downloaded the pakfile, if you are not able to see a particular environment)
#### Race APIs:
- Start a race:
[`simStartRace(tier=1/2/3)`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simStartRace) 💚
- Reset race:
[`simResetRace()`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simResetRace) 💚
- Check if racer is disqualified:
[`simIsRacerDisqualified()`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simIsRacerDisqualified) 💚
- Get index of last gate passed:
[`simGetLastGatePassed()`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simGetLastGatePassed) 💚
- Disable generation of logfiles by race APIs:
[`simDisableRaceLog`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simDisableRaceLog) 💚
#### Lower level control APIs:
- FPV like Angle rate setpoint APIs:
- [`moveByAngleRatesThrottleAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveByAngleRatesThrottleAsync) 💚
- [`moveByAngleRatesZAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveByAngleRatesZAsync) 💚 (stabilizes altitude)
- Angle setpoint APIs:
- [`moveByRollPitchYawThrottleAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveByRollPitchYawThrottleAsync) 💚
- [`moveByRollPitchYawZAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveByRollPitchYawZAsync) 💚 (stabilizes altitude)
- RollPitchYawrate setpoint APIs:
- [`moveByRollPitchYawrateThrottleAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveByRollPitchYawrateThrottleAsync) 💚
- [`moveByRollPitchYawrateZAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveByRollPitchYawrateZAsync) 💚 (stabilizes altitude)
#### Medium level control APIs:
- Velocity setpoints
- [`moveByVelocityAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveByVelocityAsync)
- [`moveByVelocityZAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveByVelocityZAsync) (stabilizes altitude)
- Position setpoints
- [`moveToPosition`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveToPositionAsync)
- [`moveOnPath`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveOnPathAsync)
- [`moveToZAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveToZAsync)
#### High level control APIs:
- Minimum jerk trajectory planning (using [ethz-asl/mav_trajectory_generation](https://github.com/ethz-asl/mav_trajectory_generation)), and trajectory tracking (using a pure pursuit like controller minimizing position and velocity errors), with position setpoints.
Optionally use the `*lookahead*` parameters to start new trajectory from a point sampled `n` seconds ahead for trajectory being tracked currently.
- [`moveOnSplineAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveOnSplineAsync) 💚
- Minimum jerk trajectory planning (using [ethz-asl/mav_trajectory_generation](https://github.com/ethz-asl/mav_trajectory_generation)), and trajectory tracking (using a pure pursuit like controller minimizing position and velocity errors), with position setpoints and corresponding velocity constraints. Useful for making a drone go through a gate waypoint, while obeying speed and direction constraints.
Optionally use the `*lookahead*` parameters to start new trajectory from a point sampled `n` seconds ahead for trajectory being tracked currently.
- [`moveOnSplineVelConstraintsAsync`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.moveOnSplineVelConstraintsAsync) 💚
- Clear and stop following current trajectory.
- [`clearTrajectory`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.clearTrajectory) 💚
#### Gain setter APIs:
- [`setAngleRateControllerGains`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.setAngleRateControllerGains) 💚
- [`setAngleLevelControllerGains`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.setAngleLevelControllerGains) 💚
- [`setVelocityControllerGains`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.setVelocityControllerGains) 💚
- [`setPositionControllerGains`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.setPositionControllerGains) 💚
- [`setTrajectoryTrackerGains`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.setTrajectoryTrackerGains) 💚
#### APIs to help generate gate detection datasets:
- Object pose setter and getter:
- [`simSetObjectPose`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simSetObjectPose)
- [`simGetObjectPose`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simGetObjectPose)
- Object scale setter and getter:
- [`simSetObjectScale`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simSetObjectScale) 💚
- [`simGetObjectScale`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simGetObjectScale) 💚
- Object segmentation ID setter and getter:
- [`simGetSegmentationObjectID`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simGetSegmentationObjectID)
- [`simSetSegmentationObjectID`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simSetSegmentationObjectID)
- Listing all the objects in the scene:
- [`simListSceneObjects`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simListSceneObjects) 💚
- Gate specific APIs:
- [`simGetNominalGateInnerDimensions`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simGetNominalGateInnerDimensions) 💚
- [`simGetNominalGateOuterDimensions`](https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing/api.html#airsimneurips.client.MultirotorClient.simGetNominalGateOuterDimensions) 💚
## Questions
Please open a Github Issue on **this** repository (not [AirSim](https://github.com/microsoft/AirSim)) for any technical questions w.r.t. the Neurips competition.
|
AirSim-NeurIPS2019-Drone-Racing/README.md/0
|
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"file_path": "AirSim-NeurIPS2019-Drone-Racing/README.md",
"repo_id": "AirSim-NeurIPS2019-Drone-Racing",
"token_count": 9711
}
| 79 |
#!/bin/bash
wget -c https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/download/v0.3.0-linux/AirSim.zip;
mkdir -p /home/$USER/Documents/AirSim;
unzip AirSim.zip;
mv AirSim AirSim_Training;
wget --directory-prefix=AirSim_Training/AirSimExe/Content/Paks -c https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/download/v0.3.0-linux/Building99.pak;
wget --directory-prefix=AirSim_Training/AirSimExe/Content/Paks -c https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/download/v0.3.0-linux/Soccer_Field.pak;
wget --directory-prefix=AirSim_Training/AirSimExe/Content/Paks -c https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/download/v0.3.0-linux/ZhangJiaJie.pak;
wget --directory-prefix=/home/$USER/Documents/AirSim -c https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/releases/download/v1.0-linux/settings.json;
rm AirSim.zip;
|
AirSim-NeurIPS2019-Drone-Racing/download_training_binaries.sh/0
|
{
"file_path": "AirSim-NeurIPS2019-Drone-Racing/download_training_binaries.sh",
"repo_id": "AirSim-NeurIPS2019-Drone-Racing",
"token_count": 348
}
| 80 |
# See here for image contents: https://github.com/microsoft/vscode-dev-containers/tree/v0.166.1/containers/python-3/.devcontainer/base.Dockerfile
# [Choice] Python version: 3, 3.9, 3.8, 3.7, 3.6
ARG VARIANT="3.8"
ARG TARGETPLATFORM="linux/amd64"
FROM --platform="${TARGETPLATFORM}" mcr.microsoft.com/vscode/devcontainers/python:dev-${VARIANT}-bullseye
# This will be set to true when running in VSCode
ARG INTERACTIVE="false"
ARG USER_UID=1000
ARG USERNAME=vscode
# make user ID match user ID on host machine
RUN usermod --uid $USER_UID $USERNAME
SHELL ["/bin/bash", "-o", "pipefail", "-c"]
# Set env for tracking that we're running in a devcontainer
ENV DEVCONTAINER=true
# Install Node.js for GH actions tests and UI
ARG NODE_VERSION="lts/*"
RUN su $USERNAME -c "umask 0002 && . /usr/local/share/nvm/nvm.sh && nvm install ${NODE_VERSION} 2>&1"
# Install terraform
ARG TERRAFORM_VERSION="1.4.5"
COPY .devcontainer/scripts/terraform.sh /tmp/
RUN bash /tmp/terraform.sh "${TERRAFORM_VERSION}" /usr/bin
ARG DOCKER_GROUP_ID
COPY .devcontainer/scripts/docker-client.sh /tmp/
RUN /tmp/docker-client.sh $USERNAME
# Install Docker
RUN apt-get update && apt-get install -y ca-certificates curl gnupg lsb-release --no-install-recommends \
&& curl -fsSL https://download.docker.com/linux/debian/gpg | gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg \
&& echo "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/debian $(lsb_release -cs) stable" \
| tee /etc/apt/sources.list.d/docker.list > /dev/null \
&& apt-get update && apt-get install -y docker-ce="5:24.0.0-1~debian.11~bullseye" docker-ce-cli="5:24.0.0-1~debian.11~bullseye" docker-compose-plugin="2.21.0-1~debian.11~bullseye" containerd.io="1.6.24-1" docker-buildx-plugin --no-install-recommends \
&& apt-get clean -y && rm -rf /var/lib/apt/lists/*
# Install Certbot
RUN if [ "${INTERACTIVE}" = "true" ]; then \
apt-get update && apt-get install -y libaugeas0 --no-install-recommends \
&& python3 -m venv /opt/certbot/ \
&& /opt/certbot/bin/pip install --no-cache-dir --upgrade pip \
&& /opt/certbot/bin/pip install --no-cache-dir certbot \
&& apt-get clean -y && rm -rf /var/lib/apt/lists/* ; fi
ARG PORTER_HOME_V1=/home/$USERNAME/.porter/
ARG PORTER_VERSION=v1.0.15
ARG PORTER_TERRAFORM_MIXIN_VERSION=v1.0.2
ARG PORTER_AZ_MIXIN_VERSION=v1.0.1
ARG PORTER_AZURE_PLUGIN_VERSION=v1.2.0
COPY .devcontainer/scripts/porter-v1.sh /tmp/
RUN export PORTER_VERSION=${PORTER_VERSION} \
PORTER_TERRAFORM_MIXIN_VERSION=${PORTER_TERRAFORM_MIXIN_VERSION} \
PORTER_AZ_MIXIN_VERSION=${PORTER_AZ_MIXIN_VERSION} \
PORTER_AZURE_PLUGIN_VERSION=${PORTER_AZURE_PLUGIN_VERSION} \
PORTER_HOME=${PORTER_HOME_V1} \
&& /tmp/porter-v1.sh
ENV PATH ${PORTER_HOME_V1}:$PATH
# Install requirements
ARG PIP_VERSION=23.3.1
RUN pip3 --no-cache-dir install pip==${PIP_VERSION} && pip3 config set global.disable-pip-version-check true
COPY ["requirements.txt", "/tmp/pip-tmp/" ]
COPY ["api_app/requirements.txt", "api_app/requirements-dev.txt", "/tmp/pip-tmp/api_app/" ]
COPY ["resource_processor/vmss_porter/requirements.txt", "/tmp/pip-tmp/resource_processor/vmss_porter/" ]
COPY ["docs/requirements.txt", "/tmp/pip-tmp/docs/"]
COPY ["e2e_tests/requirements.txt", "/tmp/pip-tmp/e2e_tests/"]
COPY ["airlock_processor/requirements.txt", "/tmp/pip-tmp/airlock_processor/"]
RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt
# Install azure-cli
ARG AZURE_CLI_VERSION=2.57.0-1~bullseye
COPY .devcontainer/scripts/azure-cli.sh /tmp/
RUN export AZURE_CLI_VERSION=${AZURE_CLI_VERSION} \
&& /tmp/azure-cli.sh
ARG YQ_VERSION="v4.33.3"
RUN curl -L --fail -o /usr/local/bin/yq "https://github.com/mikefarah/yq/releases/download/${YQ_VERSION}/yq_linux_amd64" \
&& chmod +x /usr/local/bin/yq
ARG PAJV_VERSION="1.2.0"
RUN npm install -g pajv@${PAJV_VERSION}
# Install git - required for terraform's git modules
RUN if [ "${INTERACTIVE}" = "false" ]; then \
apt-get update && apt-get install --no-install-recommends -y git \
&& apt-get clean -y && rm -rf /var/lib/apt/lists/* ; fi
USER $USERNAME
# Save command line history
RUN echo "export HISTFILE=$HOME/commandhistory/.bash_history" >> "$HOME/.bashrc" \
&& echo "export PROMPT_COMMAND='history -a'" >> "$HOME/.bashrc" \
&& mkdir -p "$HOME/commandhistory" \
&& touch "$HOME/commandhistory/.bash_history"
# Install github-cli
COPY ./.devcontainer/scripts/gh.sh /tmp/
RUN if [ "${INTERACTIVE}" = "true" ]; then /tmp/gh.sh; fi
# Install tre-cli
COPY ./cli /tmp/cli
WORKDIR /tmp/cli
RUN make install-cli && echo -e "\n# Set up tre completion\nsource <(_TRE_COMPLETE=bash_source tre)" >> ~/.bashrc
# Build x86-64 docker images by default
ENV DOCKER_DEFAULT_PLATFORM=amd64
|
AzureTRE/.devcontainer/Dockerfile/0
|
{
"file_path": "AzureTRE/.devcontainer/Dockerfile",
"repo_id": "AzureTRE",
"token_count": 1961
}
| 81 |
<!-- markdownlint-disable MD041 -->
## 0.18.0 (Unreleased)
**BREAKING CHANGES & MIGRATIONS**:
FEATURES:
ENHANCEMENTS:
BUG FIXES:
* Update to Resource Processor Image, now using Ubuntu 22.04 (jammy). Part of ([#3523](https://github.com/microsoft/AzureTRE/issues/3523))
* Remove TLS1.0/1.1 support from Application Gateway
COMPONENTS:
## 0.17.0
**BREAKING CHANGES & MIGRATIONS**:
* Update terraform MySQL resources to MySQL Flexible resources to fix depricating recources. ([#3892](https://github.com/microsoft/AzureTRE/pull/3892)) - Migration to new version of Gitea and MySQL, needs to be carried out manually, details to be included in a later release.
ENHANCEMENTS:
* Switch from OpenCensus to OpenTelemetry for logging ([#3762](https://github.com/microsoft/AzureTRE/pull/3762))
* Extend PowerShell auto start script to start core VMs ([#3811](https://github.com/microsoft/AzureTRE/issues/3811))
* Use managed identity for API connection to CosmosDB ([#345](https://github.com/microsoft/AzureTRE/issues/345))
* Switch to Structured Firewall Logs ([#3816](https://github.com/microsoft/AzureTRE/pull/3816))
* Support for building core and workspace service bundles on arm64 platforms ([#3823](https://github.com/microsoft/AzureTRE/issues/3823))
BUG FIXES:
* Fix issue with workspace menu not working correctly([#3819](https://github.com/microsoft/AzureTRE/issues/3819))
* Fix issue with connect button showing when no uri([#3820](https://github.com/microsoft/AzureTRE/issues/3820))
* Fix user resource upgrade validation: use the parent_service_template_name instead of the parent_resource_id. ([#3824](https://github.com/microsoft/AzureTRE/issues/3824))
* Airlock: Creating an import/export request causes a routing error ([#3830](https://github.com/microsoft/AzureTRE/issues/3830))
* Fix registration of templates with no 'authorizedRoles' or 'required' defined ([#3849](https://github.com/microsoft/AzureTRE/pull/3849))
* Update terraform for services bus to move network rules into namespace resource to avoid depreciation warning, and update setup_local_debugging.sh to use network_rule_sets ([#3858](https://github.com/microsoft/AzureTRE/pull/3858))
* Update terraform MySQL resources to MySQL Flexible resources to fix depricating recources. ([#3892](https://github.com/microsoft/AzureTRE/pull/3892))
* Fix issue with firewall failing to deploy on a new TRE deploy ([#3775](https://github.com/microsoft/AzureTRE/issues/3775))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.9.6 |
| ui | 0.5.21 |
| tre-service-guacamole-linuxvm | 0.6.9 |
| tre-service-guacamole-import-reviewvm | 0.2.8 |
| tre-service-guacamole-export-reviewvm | 0.1.8 |
| tre-service-guacamole-windowsvm | 0.7.9 |
| tre-service-guacamole | 0.10.6 |
| tre-service-databricks | 1.0.3 |
| tre-service-mlflow | 0.7.7 |
| tre-service-innereye | 0.6.4 |
| tre-workspace-service-ohdsi | 0.2.4 |
| tre-workspace-service-gitea | 1.0.1 |
| tre-workspace-service-mysql | 1.0.1 |
| tre-user-resource-aml-compute-instance | 0.5.7 |
| tre-service-azureml | 0.8.10 |
| tre-workspace-service-health | 0.2.5 |
| tre-workspace-airlock-import-review | 0.12.16 |
| tre-workspace-unrestricted | 0.11.4 |
| tre-workspace-base | 1.5.3 |
| tre-shared-service-cyclecloud | 0.5.5 |
| tre-shared-service-databricks-private-auth | 0.1.5 |
| tre-shared-service-sonatype-nexus | 2.8.13 |
| tre-shared-service-admin-vm | 0.4.3 |
| tre-shared-service-firewall | 1.1.7 |
| tre-shared-service-gitea | 1.0.1 |
| tre-shared-service-certs | 0.5.1 |
| tre-shared-service-airlock-notifier | 0.9.0 |
## 0.16.0 (December 1, 2023)
**BREAKING CHANGES & MIGRATIONS**:
To resolve the Airlock import issue described in ([#3767](https://github.com/microsoft/AzureTRE/pull/3767)), the new airlock import review template will need to be registered using `make workspace_bundle BUNDLE=airlock-import-review`. Any existing airlock import review workspaces will need to be upgraded.
Once you have upgraded the import review workspaces, delete the private endpoint, named `pe-stg-import-inprogress-blob-*` in the core resource group, and then run `make deploy-core` to reinstate the private endpoint and DNS records.
ENHANCEMENTS:
* Security updates aligning to Dependabot, MS Defender for Cloud and Synk ([#3796](https://github.com/microsoft/AzureTRE/issues/3796))
BUG FIXES:
* Fix issue where updates fail as read only is not configured consistently on schema fields ([#3691](https://github.com/microsoft/AzureTRE/issues/3691))
* When getting available address spaces allow those allocated to deleted workspaces to be reassigned ([#3691](https://github.com/microsoft/AzureTRE/issues/3691))
* Update Python packages, and fix breaking changes ([#3764](https://github.com/microsoft/AzureTRE/issues/3764))
* Enabling support for more than 20 users/groups in Workspace API ([#3759](https://github.com/microsoft/AzureTRE/pull/3759 ))
* Airlock Import Review workspace uses dedicated DNS zone to prevent conflict with core ([#3767](https://github.com/microsoft/AzureTRE/pull/3767))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.9.0 |
| ui | 0.5.17 |
| tre-workspace-base | 1.5.3 |
| tre-workspace-unrestricted | 0.11.4 |
| tre-workspace-airlock-import-review | 0.12.16 |
| tre-service-mlflow | 0.7.7 |
| tre-workspace-service-health | 0.2.5 |
| tre-service-databricks | 1.0.3 |
| tre-service-innereye | 0.6.4 |
| tre-workspace-service-gitea | 0.8.7 |
| tre-workspace-service-mysql | 0.4.5 |
| tre-workspace-service-ohdsi | 0.2.4 |
| tre-service-guacamole-linuxvm | 0.6.9 |
| tre-service-guacamole-export-reviewvm | 0.1.8 |
| tre-service-guacamole-windowsvm | 0.7.9 |
| tre-service-guacamole-import-reviewvm | 0.2.8 |
| tre-service-guacamole | 0.10.6 |
| tre-user-resource-aml-compute-instance | 0.5.7 |
| tre-service-azureml | 0.8.10 |
| tre-shared-service-cyclecloud | 0.5.5 |
| tre-shared-service-databricks-private-auth | 0.1.5 |
| tre-shared-service-gitea | 0.6.10 |
| tre-shared-service-airlock-notifier | 0.9.0 |
| tre-shared-service-admin-vm | 0.4.3 |
| tre-shared-service-certs | 0.5.1 |
| tre-shared-service-sonatype-nexus | 2.8.13 |
| tre-shared-service-firewall | 1.1.5 |
## 0.15.2 (October 24, 2023)
BUG FIXES:
* Remove .sh extension from nexus renewal script so CRON job executes ([#3742](https://github.com/microsoft/AzureTRE/issues/3742))
* Upgrade porter version to v1.0.15 and on error getting porter outputs return dict ([#3744](https://github.com/microsoft/AzureTRE/issues/3744))
* Fix notifications displaying workspace name rather than actual resource ([#3746](https://github.com/microsoft/AzureTRE/issues/3746))
* Fix SecuredByRole fails if app roles are not loaded ([#3752](https://github.com/microsoft/AzureTRE/issues/3752))
* Fix workspace not loading fails if operation or history roles are not loaded ([#3755](https://github.com/microsoft/AzureTRE/issues/3755))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.8.9 |
| ui | 0.5.15 |
| tre-workspace-base | 1.5.0 |
| tre-workspace-unrestricted | 0.11.1 |
| tre-workspace-airlock-import-review | 0.12.7 |
| tre-service-mlflow | 0.7.7 |
| tre-workspace-service-health | 0.2.5 |
| tre-service-databricks | 1.0.3 |
| tre-service-innereye | 0.6.4 |
| tre-workspace-service-gitea | 0.8.7 |
| tre-workspace-service-mysql | 0.4.5 |
| tre-workspace-service-ohdsi | 0.2.4 |
| tre-service-guacamole-linuxvm | 0.6.9 |
| tre-service-guacamole-export-reviewvm | 0.1.8 |
| tre-service-guacamole-windowsvm | 0.7.9 |
| tre-service-guacamole-import-reviewvm | 0.2.8 |
| tre-service-guacamole | 0.10.5 |
| tre-user-resource-aml-compute-instance | 0.5.7 |
| tre-service-azureml | 0.8.10 |
| tre-shared-service-cyclecloud | 0.5.5 |
| tre-shared-service-databricks-private-auth | 0.1.5 |
| tre-shared-service-gitea | 0.6.10 |
| tre-shared-service-airlock-notifier | 0.9.0 |
| tre-shared-service-admin-vm | 0.4.3 |
| tre-shared-service-certs | 0.5.1 |
| tre-shared-service-sonatype-nexus | 2.8.13 |
| tre-shared-service-firewall | 1.1.5 |
## 0.15.1 (October 12, 2023)
BUG FIXES:
* SecuredByRole failing if roles are null ([#3740](https://github.com/microsoft/AzureTRE/issues/3740 ))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.8.9 |
| ui | 0.5.11 |
| tre-workspace-base | 1.5.0 |
| tre-workspace-unrestricted | 0.11.1 |
| tre-workspace-airlock-import-review | 0.12.7 |
| tre-service-mlflow | 0.7.7 |
| tre-workspace-service-health | 0.2.5 |
| tre-service-databricks | 1.0.3 |
| tre-service-innereye | 0.6.4 |
| tre-workspace-service-gitea | 0.8.7 |
| tre-workspace-service-mysql | 0.4.5 |
| tre-workspace-service-ohdsi | 0.2.4 |
| tre-service-guacamole-linuxvm | 0.6.9 |
| tre-service-guacamole-export-reviewvm | 0.1.8 |
| tre-service-guacamole-windowsvm | 0.7.9 |
| tre-service-guacamole-import-reviewvm | 0.2.8 |
| tre-service-guacamole | 0.10.5 |
| tre-user-resource-aml-compute-instance | 0.5.7 |
| tre-service-azureml | 0.8.10 |
| tre-shared-service-cyclecloud | 0.5.5 |
| tre-shared-service-databricks-private-auth | 0.1.5 |
| tre-shared-service-gitea | 0.6.10 |
| tre-shared-service-airlock-notifier | 0.9.0 |
| tre-shared-service-admin-vm | 0.4.3 |
| tre-shared-service-certs | 0.5.1 |
| tre-shared-service-sonatype-nexus | 2.8.12 |
| tre-shared-service-firewall | 1.1.5 |
## 0.15.0 (October 10, 2023)
FEATURES:
ENHANCEMENTS:
* Reduce logging noise ([#2135](https://github.com/microsoft/AzureTRE/issues/2135))
* Update workspace template to use Terraform's AzureRM 3.73 ([#3715](https://github.com/microsoft/AzureTRE/pull/3715))
* Enable cost tags for workspace services and user resources ([#2932](https://github.com/microsoft/AzureTRE/issues/2932))
BUG FIXES:
* Upgrade unresticted and airlock base template versions due to diagnostic settings retention period being depreciated ([#3704](https://github.com/microsoft/AzureTRE/pull/3704))
* Enable TRE Admins to view workspace details when don't have a workspace role ([#2363](https://github.com/microsoft/AzureTRE/issues/2363))
* Fix shared services list return restricted resource for admins causing issues with updates ([#3716](https://github.com/microsoft/AzureTRE/issues/3716))
* Fix grey box appearing on resource card when costs are not available. ([#3254](https://github.com/microsoft/AzureTRE/issues/3254))
* Fix notification panel not passing the workspace scope id to the API hence UI not updating ([#3353](https://github.com/microsoft/AzureTRE/issues/3353))
* Fix issue with cost tags not displaying correctly for some user roles ([#3721](https://github.com/microsoft/AzureTRE/issues/3721))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.8.9 |
| tre-workspace-base | 1.5.0 |
| tre-workspace-unrestricted | 0.11.1 |
| tre-workspace-airlock-import-review | 0.12.7 |
| tre-service-mlflow | 0.7.7 |
| tre-workspace-service-health | 0.2.5 |
| tre-service-databricks | 1.0.3 |
| tre-service-innereye | 0.6.4 |
| tre-workspace-service-gitea | 0.8.7 |
| tre-workspace-service-mysql | 0.4.5 |
| tre-workspace-service-ohdsi | 0.2.4 |
| tre-service-guacamole-linuxvm | 0.6.9 |
| tre-service-guacamole-export-reviewvm | 0.1.8 |
| tre-service-guacamole-windowsvm | 0.7.9 |
| tre-service-guacamole-import-reviewvm | 0.2.8 |
| tre-service-guacamole | 0.10.5 |
| tre-user-resource-aml-compute-instance | 0.5.7 |
| tre-service-azureml | 0.8.10 |
| tre-shared-service-cyclecloud | 0.5.5 |
| tre-shared-service-databricks-private-auth | 0.1.5 |
| tre-shared-service-gitea | 0.6.10 |
| tre-shared-service-airlock-notifier | 0.9.0 |
| tre-shared-service-admin-vm | 0.4.3 |
| tre-shared-service-certs | 0.5.1 |
| tre-shared-service-sonatype-nexus | 2.8.12 |
| tre-shared-service-firewall | 1.1.5 |
## 0.14.1 (September 1, 2023)
BUG FIXES:
* Fix firewall config related to Nexus so that `pypi.org` is added to the allow-list ([#3694](https://github.com/microsoft/AzureTRE/issues/3694))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.8.6 |
| tre-workspace-base | 1.4.7 |
| tre-workspace-unrestricted | 0.10.4 |
| tre-workspace-airlock-import-review | 0.11.6 |
| tre-service-mlflow | 0.7.5 |
| tre-workspace-service-health | 0.2.4 |
| tre-service-databricks | 1.0.3 |
| tre-service-innereye | 0.6.4 |
| tre-workspace-service-gitea | 0.8.5 |
| tre-workspace-service-mysql | 0.4.4 |
| tre-workspace-service-ohdsi | 0.2.3 |
| tre-service-guacamole-linuxvm | 0.6.8 |
| tre-service-guacamole-export-reviewvm | 0.1.7 |
| tre-service-guacamole-windowsvm | 0.7.8 |
| tre-service-guacamole-import-reviewvm | 0.2.7 |
| tre-service-guacamole | 0.10.4 |
| tre-user-resource-aml-compute-instance | 0.5.7 |
| tre-service-azureml | 0.8.10 |
| tre-shared-service-cyclecloud | 0.5.4 |
| tre-shared-service-databricks-private-auth | 0.1.5 |
| tre-shared-service-gitea | 0.6.5 |
| tre-shared-service-airlock-notifier | 0.9.0 |
| tre-shared-service-admin-vm | 0.4.3 |
| tre-shared-service-certs | 0.5.1 |
| tre-shared-service-sonatype-nexus | 2.8.11 |
| tre-shared-service-firewall | 1.1.4 |
## 0.14.0 (August 25, 2023)
ENHANCEMENTS:
* Change Guacamole username claim to `preferred_username`, so email not required ([#3539](https://github.com/microsoft/AzureTRE/issues/3539))
* Upgrade Ubuntu version for Sonatype Nexus VM to 22.04 LTS ([#3523](https://github.com/microsoft/AzureTRE/issues/3523))
BUG FIXES:
* Add temporary workaround for when id with last 4 chars exists ([#3667](https://github.com/microsoft/AzureTRE/pull/3667))
* Apply missing lifecycle blocks. ([#3670](https://github.com/microsoft/AzureTRE/issues/3670))
* Outputs of type boolean are stored as strings ([#3655](https://github.com/microsoft/AzureTRE/pulls/3655))
* Add dependency on firewall deployment to rule collection ([#3672](https://github.com/microsoft/AzureTRE/pulls/3672))
* Check docker return code in set docker sock permissions file ([#3674](https://github.com/microsoft/AzureTRE/pulls/3674))
* Increase reliability of Nexus deployment ([[#3642](https://github.com/microsoft/AzureTRE/issues/3642))
* Add firewall rule to allow airlock to download functions runtime ([#3682](https://github.com/microsoft/AzureTRE/pull/3682))
* Update dev container so doesn't try to create new group with clashing ID, only updates user ID ([#3682](https://github.com/microsoft/AzureTRE/pull/3682))
* Remove diagnostic settings retention period as has been depreciated ([#3682](https://github.com/microsoft/AzureTRE/pull/3682))
* Added missing region entries in `databricks-udr.json` ([[#3688](https://github.com/microsoft/AzureTRE/pull/3688))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.8.6 |
| tre-workspace-base | 1.4.7 |
| tre-workspace-unrestricted | 0.10.4 |
| tre-workspace-airlock-import-review | 0.11.6 |
| tre-service-mlflow | 0.7.5 |
| tre-workspace-service-health | 0.2.4 |
| tre-service-databricks | 1.0.3 |
| tre-service-innereye | 0.6.4 |
| tre-workspace-service-gitea | 0.8.5 |
| tre-workspace-service-mysql | 0.4.4 |
| tre-workspace-service-ohdsi | 0.2.3 |
| tre-service-guacamole-linuxvm | 0.6.8 |
| tre-service-guacamole-export-reviewvm | 0.1.7 |
| tre-service-guacamole-windowsvm | 0.7.8 |
| tre-service-guacamole-import-reviewvm | 0.2.7 |
| tre-service-guacamole | 0.10.4 |
| tre-user-resource-aml-compute-instance | 0.5.7 |
| tre-service-azureml | 0.8.10 |
| tre-shared-service-cyclecloud | 0.5.4 |
| tre-shared-service-databricks-private-auth | 0.1.5 |
| tre-shared-service-gitea | 0.6.5 |
| tre-shared-service-airlock-notifier | 0.9.0 |
| tre-shared-service-admin-vm | 0.4.3 |
| tre-shared-service-certs | 0.5.1 |
| tre-shared-service-sonatype-nexus | 2.8.10 |
| tre-shared-service-firewall | 1.1.4 |
## 0.13.0 (August 9, 2023)
BUG FIXES:
* Custom actions fail on resources with a pipeline ([#3646](https://github.com/microsoft/AzureTRE/issues/3646))
* Fix ability to debug resource processor locally ([#3426](https://github.com/microsoft/AzureTRE/issues/4426))
* Upgrade airlock and unrestricted workspaces to base workspace version 0.12.0 ([#3659](https://github.com/microsoft/AzureTRE/pull/3659))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.8.3 |
| tre-workspace-base | 1.4.4 |
| tre-workspace-unrestricted | 0.10.2 |
| tre-workspace-airlock-import-review | 0.11.2 |
| tre-service-mlflow | 0.7.2 |
| tre-workspace-service-health | 0.2.1 |
| tre-service-databricks | 1.0.0 |
| tre-service-innereye | 0.6.1 |
| tre-workspace-service-gitea | 0.8.2 |
| tre-workspace-service-mysql | 0.4.1 |
| tre-workspace-service-ohdsi | 0.2.0 |
| tre-service-guacamole-linuxvm | 0.6.5 |
| tre-service-guacamole-export-reviewvm | 0.1.4 |
| tre-service-guacamole-windowsvm | 0.7.5 |
| tre-service-guacamole-import-reviewvm | 0.2.4 |
| tre-service-guacamole | 0.9.4 |
| tre-user-resource-aml-compute-instance | 0.5.4 |
| tre-service-azureml | 0.8.7 |
| tre-shared-service-cyclecloud | 0.5.1 |
| tre-shared-service-databricks-private-auth | 0.1.2 |
| tre-shared-service-gitea | 0.6.2 |
| tre-shared-service-airlock-notifier | 0.9.0 |
| tre-shared-service-admin-vm | 0.4.0 |
| tre-shared-service-certs | 0.5.1 |
| tre-shared-service-sonatype-nexus | 2.5.3 |
| tre-shared-service-firewall | 1.1.1 |
## 0.12.0 (July 27, 2023)
FEATURES:
* OHDSI workspace service ([#3562](https://github.com/microsoft/AzureTRE/issues/3562))
ENHANCEMENTS:
* Workspace networking peering sync is handled natively by Terraform ([#3534](https://github.com/microsoft/AzureTRE/issues/3534))
* Use SMTP built in connector vs API connector in Airlock Notifier ([#3572](https://github.com/microsoft/AzureTRE/issues/3572))
* Update Guacamole dependencies ([#3602](https://github.com/microsoft/AzureTRE/issues/3602))
BUG FIXES:
* Nexus might fail to deploy due to wrong identity used in key-vault extension ([#3492](https://github.com/microsoft/AzureTRE/issues/3492))
* Airlock notifier needs SCM basic-auth enabled to install ([#3509](https://github.com/microsoft/AzureTRE/issues/3509))
* Databricks fails to deploy in East US ([#3515](https://github.com/microsoft/AzureTRE/issues/3515))
* `load_env.sh` is able to use an equal `=` sign in values ([#3535](https://github.com/microsoft/AzureTRE/issues/3535))
* Make AML route names unique ([#3546](https://github.com/microsoft/AzureTRE/issues/3546))
* Azure ML connection URI is an object, not string ([#3486](https://github.com/microsoft/AzureTRE/issues/3486))
* Update key in Linux VM deploy script ([#3434](https://github.com/microsoft/AzureTRE/issues/3434))
* Add missing `azure_environment` porter parameters ([#3549](https://github.com/microsoft/AzureTRE/issues/3549))
* Fix airlock_notifier not getting the right smtp password ([#3561](https://github.com/microsoft/AzureTRE/issues/3561))
* Fix issue when deleting failed resources gives no steps ([#3567](https://github.com/microsoft/AzureTRE/issues/3567))
* Fix airlock_notifier not getting the right smtp password ([#3565](https://github.com/microsoft/AzureTRE/issues/3565))
* Fix issues with networking dependencies and AMPLS deployment ([#3433](https://github.com/microsoft/AzureTRE/issues/3433))
* Update CLI install method to fix dependency issue ([#3601](https://github.com/microsoft/AzureTRE/issues/3601))
* Update Databricks UDRs for west europe and switch to DFS private endpoint. ([[#3582](https://github.com/microsoft/AzureTRE/issues/3582))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.8.2 |
| tre-workspace-base | 1.4.4 |
| tre-workspace-airlock-import-review | 0.10.1 |
| tre-workspace-unrestricted | 0.9.0 |
| tre-workspace-service-gitea | 0.8.1 |
| tre-service-guacamole | 0.9.3 |
| tre-service-guacamole-windowsvm | 0.7.5 |
| tre-service-guacamole-import-reviewvm | 0.2.4 |
| tre-service-guacamole-linuxvm | 0.6.5 |
| tre-service-guacamole-export-reviewvm | 0.1.4 |
| tre-workspace-service-health | 0.2.1 |
| tre-workspace-service-ohdsi | 0.2.0 |
| tre-service-azureml | 0.8.7 |
| tre-user-resource-aml-compute-instance | 0.5.4 |
| tre-service-mlflow | 0.7.1 |
| tre-service-databricks | 1.0.0 |
| tre-workspace-service-mysql | 0.4.1 |
| tre-service-innereye | 0.6.1 |
| tre-shared-service-cyclecloud | 0.5.1 |
| tre-shared-service-airlock-notifier | 0.9.0 |
| tre-shared-service-gitea | 0.6.1 |
| tre-shared-service-certs | 0.5.0 |
| tre-shared-service-databricks-private-auth | 0.1.1 |
| tre-shared-service-admin-vm | 0.4.0 |
| tre-shared-service-sonatype-nexus | 2.5.2 |
| tre-shared-service-firewall | 1.1.1 |
## 0.11.0 (April 24, 2023)
ENHANCEMENTS:
* Update Guacamole to version 1.5.1 ([#3443](https://github.com/microsoft/AzureTRE/issues/3443))
* Popup to copy internally accessible URLs ([#3420](https://github.com/microsoft/AzureTRE/issues/3420))
BUG FIXES:
* AML workspace service fails to install and puts firewall into failed state ([#3448](https://github.com/microsoft/AzureTRE/issues/3448))
* Nexus fails to install due to `az login` and firewall rules ([#3453](https://github.com/microsoft/AzureTRE/issues/3453))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.8.1 |
| tre-workspace-base | 1.2.3 |
| tre-workspace-unrestricted | 0.9.0 |
| tre-workspace-airlock-import-review | 0.10.1 |
| tre-service-mlflow | 0.7.1 |
| tre-workspace-service-health | 0.2.1 |
| tre-service-databricks | 0.2.1 |
| tre-service-innereye | 0.6.1 |
| tre-workspace-service-gitea | 0.8.1 |
| tre-workspace-service-mysql | 0.4.1 |
| tre-service-guacamole-linuxvm | 0.6.5 |
| tre-service-guacamole-export-reviewvm | 0.1.4 |
| tre-service-guacamole-windowsvm | 0.7.4 |
| tre-service-guacamole-import-reviewvm | 0.2.4 |
| tre-service-guacamole | 0.9.0 |
| tre-user-resource-aml-compute-instance | 0.5.4 |
| tre-service-azureml | 0.8.2 |
| tre-shared-service-cyclecloud | 0.5.1 |
| tre-shared-service-databricks-private-auth | 0.1.1 |
| tre-shared-service-gitea | 0.6.1 |
| tre-shared-service-airlock-notifier | 0.5.0 |
| tre-shared-service-admin-vm | 0.4.0 |
| tre-shared-service-certs | 0.5.0 |
| tre-shared-service-sonatype-nexus | 2.5.0 |
| tre-shared-service-firewall | 1.1.1 |
## 0.10.0 (April 16, 2023)
**BREAKING CHANGES & MIGRATIONS**:
* A migration for OperationSteps in Operation objects was added ([#3358](https://github.com/microsoft/AzureTRE/pull/3358))
* Some Github _secrets_ have moved to be _environment variables_ - `LOCATION` and a few optional others will need to be redefined as listed [here](https://microsoft.github.io/AzureTRE/latest/tre-admins/setup-instructions/cicd-pre-deployment-steps/#configure-core-variables) ([#3084](https://github.com/microsoft/AzureTRE/pull/3084))
FEATURES:
* (UI) Added upgrade button to resources that have pending template upgrades ([#3387](https://github.com/microsoft/AzureTRE/pull/3387))
* Enable deployment to Azure US Government Cloud ([#3128](https://github.com/microsoft/AzureTRE/issues/3128))
ENHANCEMENTS:
* Added 'availableUpgrades' field to Resources in GET/GET all Resources endpoints. The field indicates whether there are template versions that a resource can be upgraded to [#3234](https://github.com/microsoft/AzureTRE/pull/3234)
* Update Porter (1.0.11), Docker (23.0.3), Terraform (1.4.5) ([#3430](https://github.com/microsoft/AzureTRE/issues/3430))
* Build, publish and register Databricks bundles in workflow ([#3447](https://github.com/microsoft/AzureTRE/issues/3447))
BUG FIXES:
* Fix ENABLE_SWAGGER configuration being ignored in CI ([#3355](https://github.com/microsoft/AzureTRE/pull/3355))
* Set yq output format when reading a json file ([#3441](https://github.com/microsoft/AzureTRE/pull/3441))
* Set `{}` as the workflow default for `RP_BUNDLE_VALUES` parameter ([#3444](https://github.com/microsoft/AzureTRE/pull/3444))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.5.1 |
| core | 0.8.1 |
| tre-shared-service-admin-vm | 0.4.0 |
| tre-shared-service-airlock-notifier | 0.5.0 |
| tre-shared-service-certs | 0.5.0 |
| tre-shared-service-cyclecloud | 0.5.1 |
| tre-shared-service-databricks-private-auth | 0.1.1 |
| tre-shared-service-firewall | 1.1.0 |
| tre-shared-service-gitea | 0.6.1 |
| tre-shared-service-sonatype-nexus | 2.4.0 |
| tre-service-azureml | 0.8.1 |
| tre-user-resource-aml-compute-instance | 0.5.4 |
| tre-service-databricks | 0.2.1 |
| tre-workspace-service-gitea | 0.8.1 |
| tre-service-guacamole | 0.8.4 |
| tre-service-guacamole-export-reviewvm | 0.1.4 |
| tre-service-guacamole-import-reviewvm | 0.2.4 |
| tre-service-guacamole-linuxvm | 0.6.5 |
| tre-service-guacamole-windowsvm | 0.7.4 |
| tre-workspace-service-health | 0.2.1 |
| tre-service-innereye | 0.6.1 |
| tre-service-mlflow | 0.7.1 |
| tre-workspace-service-mysql | 0.4.1 |
| tre-workspace-airlock-import-review | 0.10.1 |
| tre-workspace-base | 1.2.3 |
| tre-workspace-unrestricted | 0.9.0 |
## 0.9.0 (February 9, 2023)
**BREAKING CHANGES & MIGRATIONS**:
* Move to Azure **Firewall Policy** ([#3107](https://github.com/microsoft/AzureTRE/pull/3107)). This is a major version for the firewall shared service and will fail to automatically upgrade. You should follow these steps to complete it:
1. Let the system try to do the upgrade (via CI or `make all`). It will fail but it's fine since now we have the new version published and registered.
2. Make a temporary network change with either of the following options:
* Azure Portal: find your TRE resource group and select the route table resource (named `rt-YOUR_TRE_ID`).
In the overview screen, find the `ResourceProcessorSubnet` (should be last in the subnet list), click on the `...` and select `Dissociate`.
* Azure CLI:
```shell
az network vnet subnet update --resource-group rg-YOUR_TRE_ID --vnet-name vnet-YOUR_TRE_ID --name ResourceProcessorSubnet --remove routeTable
```
4. Issue a patch API request to `force-update` the firewall to its new version.
One way to accomplish this is with the Swagger endpoint (/api/docs).
If this endpoint is not working in your deployment - include `enable_swagger` in your `config.yaml` (see the sample file), or temporarily activate it via the API resource on azure (named `api-YOUR_TRE-ID`) -> Configuration -> `ENABLE_SWAGGER` item.
:warning: Any custom rules you have added manually will be **lost** and you'll need to add them back after the upgrade has been completed.
FEATURES:
* Add Azure Databricks as workspace service ([#1857](https://github.com/microsoft/AzureTRE/pull/1857))
* (UI) Added the option to upload/download files to airlock requests via Azure CLI ([#3196](https://github.com/microsoft/AzureTRE/pull/3196))
ENHANCEMENTS:
* Add support for referencing IP Groups from the Core Resource Group in firewall rules created via the pipeline ([#3089](https://github.com/microsoft/AzureTRE/pull/3089))
* Support for _Azure Firewall Basic_ SKU ([#3107](https://github.com/microsoft/AzureTRE/pull/3107)). This SKU doesn't support deallocation and for most non 24/7 scenarios will be more expensive than the Standard SKU.
* Update Azure Machine Learning Workspace Service to support "no public IP" compute. This is a full rework so upgrades of existing Azure ML Workspace Service deployments are not supported. Requires `v0.8.0` or later of the TRE project. ([#3052](https://github.com/microsoft/AzureTRE/pull/3052))
* Move non-core DNS zones out of the network module to reduce dependencies ([#3119](https://github.com/microsoft/AzureTRE/pull/3119))
* Review VMs are being cleaned up when an Airlock request is canceled ([#3130](https://github.com/microsoft/AzureTRE/pull/3130))
* Sample queries to investigate logs of the core TRE applications ([#3151](https://github.com/microsoft/AzureTRE/pull/3151))
* Remove support of docker-in-docker for templates/bundles ([#3180](https://github.com/microsoft/AzureTRE/pull/3180))
* API runs with gunicorn and uvicorn workers (as recommended) ([#3178](https://github.com/microsoft/AzureTRE/pull/3178))
* Upgrade core components and key templates to Terraform AzureRM ([#3185](https://github.com/microsoft/AzureTRE/pull/3185))
BUG FIXES:
* Reauth CLI if TRE endpoint has changed ([#3137](https://github.com/microsoft/AzureTRE/pull/3137))
* Added Migration for Airlock requests that were created prior to version 0.5.0 ([#3152](https://github.com/microsoft/AzureTRE/pull/3152))
* Temporarily use the remote bundle for `check-params` target ([#3149](https://github.com/microsoft/AzureTRE/pull/3149))
* Workspace module dependency to resolve _AnotherOperationInProgress_ errors ([#3194](https://github.com/microsoft/AzureTRE/pull/3194))
* Skip Certs shared service E2E on Friday & Saturday due to LetsEncrypt limits ([#3203](https://github.com/microsoft/AzureTRE/pull/3203))
* Create Workspace AppInsights via AzAPI provider due to an issue with AzureRM ([#3207](https://github.com/microsoft/AzureTRE/pull/3207))
* 'Workspace Owner' is now able to access Airlock request's SAS URL even if the request is not in review ([#3208](https://github.com/microsoft/AzureTRE/pull/3208))
* Ignore changes in log_analytics_destination_type to prevent redundant updates ([#3217](https://github.com/microsoft/AzureTRE/pull/3217))
* Add Databricks private authentication shared service for SSO ([#3201](https://github.com/microsoft/AzureTRE/pull/3201))
* Remove auth private endpoint from databricks workspace service ([3199](https://github.com/microsoft/AzureTRE/pull/3199))
* Fix DNS conflict in airlock-review workspace that could make the entire airlock module inoperable ([#3215](https://github.com/microsoft/AzureTRE/pull/3215))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.4.5 |
| core | 0.7.4 |
| tre-shared-service-admin-vm | 0.3.0 |
| tre-shared-service-airlock-notifier | 0.4.0 |
| tre-shared-service-certs | 0.4.0 |
| tre-shared-service-cyclecloud | 0.4.0 |
| tre-shared-service-firewall | 1.0.0 |
| tre-shared-service-gitea | 0.5.0 |
| tre-shared-service-sonatype-nexus | 2.3.0 |
| tre-service-azureml | 0.7.26 |
| tre-user-resource-aml-compute-instance | 0.5.3 |
| tre-service-databricks | 0.1.72 |
| tre-workspace-service-gitea | 0.7.0 |
| tre-service-guacamole | 0.7.1 |
| tre-service-guacamole-export-reviewvm | 0.1.2 |
| tre-service-guacamole-import-reviewvm | 0.2.2 |
| tre-service-guacamole-linuxvm | 0.6.2 |
| tre-service-guacamole-windowsvm | 0.7.2 |
| tre-workspace-service-health | 0.1.1 |
| tre-service-innereye | 0.5.0 |
| tre-service-mlflow | 0.6.4 |
| tre-workspace-service-mysql | 0.3.3 |
| tre-workspace-airlock-import-review | 0.8.1 |
| tre-workspace-base | 1.1.0 |
| tre-workspace-unrestricted | 0.8.1 |
## 0.8.0 (January 15, 2023)
**BREAKING CHANGES & MIGRATIONS**:
* The model for `reviewUserResources` in airlock requests has changed from being a list to a dictionary. A migration has been added to update your existing requests automatically; please make sure you run the migrations as part of updating your API and UI.
* Note that any in-flight requests that have review resources deployed will show `UNKNOWN[i]` for the user key of that resource and in the UI users will be prompted to deploy a new resource. [#2883](https://github.com/microsoft/AzureTRE/pull/2883)
* Env files consolidation ([#2944](https://github.com/microsoft/AzureTRE/pull/2944)) - The files /templates/core/.env, /devops/.env, /devops/auth.env are no longer used. The settings and configuration that they contain has been consolidated into a single file config.yaml that lives in the root folder of the project.
Use the script devops/scripts/env_to_yaml_config.sh to migrate /templates/core/.env, /devops/.env, and /devops/auth.env to the new config.yaml file.
* Upgrade to Porter v1 ([#3014](https://github.com/microsoft/AzureTRE/pull/3014)). You should upgrade all custom template definitions and rebuild them.
FEATURES:
* Support review VMs for multiple reviewers for each airlock request [#2883](https://github.com/microsoft/AzureTRE/pull/2883)
* Add Azure Health Data Services as workspace services [#3051](https://github.com/microsoft/AzureTRE/pull/3051)
ENHANCEMENTS:
* Remove Porter's Docker mixin as it's not in use ([#2889](https://github.com/microsoft/AzureTRE/pull/2889))
* Enable properties defined within the API to be overridden by the bundle template - enables default values to be set. ([#2576](https://github.com/microsoft/AzureTRE/pull/2576))
* Support template version update ([#2908](https://github.com/microsoft/AzureTRE/pull/2908))
* Update docker base images to bullseye ([#2946](https://github.com/microsoft/AzureTRE/pull/2946)
* Support updating the firewall when installing via makefile/CICD ([#2942](https://github.com/microsoft/AzureTRE/pull/2942))
* Add the ability for workspace services to request additional address spaces from a workspace ([#2902](https://github.com/microsoft/AzureTRE/pull/2902))
* Airlock processor function and api app service work with http2
* Added the option to disable Swagger ([#2981](https://github.com/microsoft/AzureTRE/pull/2981))
* Serverless CosmosDB for new deployments to reduce cost ([#3029](https://github.com/microsoft/AzureTRE/pull/3029))
* Adding disable_download and disable_upload properties for guacamole ([#2967](https://github.com/microsoft/AzureTRE/pull/2967))
* Upgrade Guacamole dependencies ([#3053](https://github.com/microsoft/AzureTRE/pull/3053))
* Lint TRE cost tags per entity type (workspace, shared service, etc.) ([#3061](https://github.com/microsoft/AzureTRE/pull/3061))
* Validate required secrets have value ([#3073](https://github.com/microsoft/AzureTRE/pull/3073))
* Airlock processor unit-tests uses pytest ([#3026](https://github.com/microsoft/AzureTRE/pull/3026))
BUG FIXES:
* Private endpoints for AppInsights are now provisioning successfully and consistently ([#2841](https://github.com/microsoft/AzureTRE/pull/2841))
* Enable upgrade step of base workspace ([#2899](https://github.com/microsoft/AzureTRE/pull/2899))
* Fix get shared service by template name to filter by active service only ([#2947](https://github.com/microsoft/AzureTRE/pull/2947))
* Fix untagged cost reporting reader role assignment ([#2951](https://github.com/microsoft/AzureTRE/pull/2951))
* Remove Guacamole's firewall rule on uninstall ([#2958](https://github.com/microsoft/AzureTRE/pull/2958))
* Fix KeyVault purge error on MLFlow uninstall ([#3082](https://github.com/microsoft/AzureTRE/pull/3082))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.4.4 |
| core | 0.5.2 |
| tre-shared-service-admin-vm | 0.3.0 |
| tre-shared-service-airlock-notifier | 0.3.0 |
| tre-shared-service-certs | 0.3.1 |
| tre-shared-service-cyclecloud | 0.4.0 |
| tre-shared-service-firewall | 0.7.0 |
| tre-shared-service-gitea | 0.5.0 |
| tre-shared-service-sonatype-nexus | 2.3.0 |
| tre-service-azureml | 0.6.0 |
| tre-user-resource-aml-compute-instance | 0.5.0 |
| tre-workspace-service-gitea | 0.7.0 |
| tre-service-guacamole | 0.7.0 |
| tre-service-guacamole-export-reviewvm | 0.1.0 |
| tre-service-guacamole-import-reviewvm | 0.2.0 |
| tre-service-guacamole-linuxvm | 0.6.1 |
| tre-service-guacamole-windowsvm | 0.6.0 |
| tre-workspace-service-health | 0.1.0 |
| tre-service-innereye | 0.5.0 |
| tre-service-mlflow | 0.6.0 |
| tre-workspace-service-mysql | 0.3.1 |
| tre-workspace-airlock-import-review | 0.6.0 |
| tre-workspace-base | 0.8.1 |
| tre-workspace-unrestricted | 0.6.0 |
## 0.7.0 (November 17, 2022)
**BREAKING CHANGES & MIGRATIONS**:
* The airlock request object has changed. Make sure you have ran the DB migration step after deploying the new API image and UI (which runs automatically in `make all`/`make tre-deploy` but can be manually invoked with `make db-migrate`) so that existing requests in your DB are migrated to the new model.
* Also the model for creating new airlock requests with the API has changed slightly; this is updated in the UI and CLI but if you have written custom tools ensure you POST to `/requests` with the following model:
```json
{
"type": "'import' or 'export'",
"title": "a request title",
"businessJustification": "some business justification"
}
```
* Fields in AirlockNotification event have changed without backward compatibility. If Airlock Notifier shared service is deployed, it needs to be re-deployed. Any other consumers of AirlockNotification event need to be updated. For more details, see [#2798](https://github.com/microsoft/AzureTRE/pull/2798)
FEATURES:
* Display workspace and shared services total costs for admin role in UI [#2738](https://github.com/microsoft/AzureTRE/pull/2772)
* Automatically validate all resources have tre_id tag via TFLint [#2774](https://github.com/microsoft/AzureTRE/pull/2774)
* Add metadata endpoint and simplify `tre` CLI login (also adds API version to UI) (#2794)
* Support workspaces with multiple address spaces [#2808](https://github.com/microsoft/AzureTRE/pull/2808)
* Updated resource card in UI with visual improvements, disabled state badge and resource ID in info popout ([#2846](https://github.com/microsoft/AzureTRE/pull/2846))
* Add health information for backend services to UI info popout in footer ([#2846](https://github.com/microsoft/AzureTRE/pull/2846))
ENHANCEMENTS:
* Renamed several airlock fields to make them more descriptive and added a createdBy field. Included migration for backwards compatibility [#2779](https://github.com/microsoft/AzureTRE/pull/2779)
* Show error message when Review VMs are not configured in the current workspace
* CLI: Add missing endpoints and minor bug fixes ([#2784](https://github.com/microsoft/AzureTRE/pull/2784))
* Airlock Notifier: Provide a link to request in the UI in the email ([#2754](https://github.com/microsoft/AzureTRE/pull/2754))
* Add additional fields for Airlock Notification event ([#2798](https://github.com/microsoft/AzureTRE/pull/2798))
* Fail firewall database migration if there's no firewall deployed ([#2792](https://github.com/microsoft/AzureTRE/pull/2792))
* Added optional parameter to allow a client to retrieve a template by name and version ([#2802](https://github.com/microsoft/AzureTRE/pull/2802))
* Added support for `allOf` usage in Resource Templates - both across the API and the UI. This allows a template author to specify certain fields as being conditionally present / conditionally required, and means we can tidy up some of the resource creation forms substantially ([#2795](https://github.com/microsoft/AzureTRE/pull/2795)).
* As part of the above change, the `auto_create` string passed to the `client_id` field in each Workspace template has now moved to an `auth_type` enum field, where the user can select the authentication type from a dropdown.
* Adds extra dns zones and links into core network ([#2828](https://github.com/microsoft/AzureTRE/pull/2828)).
* Add UI version to its footer card ([#2849](https://github.com/microsoft/AzureTRE/pull/2849)).
* Use `log_category_types` in `azurerm_monitor_diagnostic_categories` to remove deprecation warning ([#2855](https://github.com/microsoft/AzureTRE/pull/2855)).
* Gitea workspace bundle has a number of updates as detailed in PR ([#2862](https://github.com/microsoft/AzureTRE/pull/2862)).
BUG FIXES:
* Show the correct createdBy value for airlock requests in UI and in API queries ([#2779](https://github.com/microsoft/AzureTRE/pull/2779))
* Fix deployment of Airlock Notifier ([#2745](https://github.com/microsoft/AzureTRE/pull/2745))
* Fix Nexus bootstrapping firewall race condition ([#2811](https://github.com/microsoft/AzureTRE/pull/2811))
* Handle unsupported azure subscriptions in cost reporting ([#2823](https://github.com/microsoft/AzureTRE/pull/2823))
* Redact secrets in conditional or nested properties ([#2854](https://github.com/microsoft/AzureTRE/pull/2854))
* Fix missing ID parameter in Certs bundle ([#2841](https://github.com/microsoft/AzureTRE/pull/2841))
* Fix ML Flow deployment issues and update version ([#2865](https://github.com/microsoft/AzureTRE/pull/2865))
* Handle 429 TooManyRequests and 503 ServiceUnavailable which might return from Azure Cost Management in TRE Cost API ([#2835](https://github.com/microsoft/AzureTRE/issues/2835))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.4.2 |
| core | 0.4.43 |
| tre-workspace-base | 0.5.1 |
| tre-workspace-unrestricted | 0.5.0 |
| tre-workspace-airlock-import-review | 0.5.0 |
| tre-service-mlflow | 0.4.0 |
| tre-service-innereye | 0.4.0 |
| tre-workspace-service-gitea | 0.6.0 |
| tre-workspace-service-mysql | 0.2.0 |
| tre-service-guacamole-linuxvm | 0.5.2 |
| tre-service-guacamole-export-reviewvm | 0.0.6 |
| tre-service-guacamole-windowsvm | 0.5.2 |
| tre-service-guacamole-import-reviewvm | 0.1.3 |
| tre-service-guacamole | 0.5.0 |
| tre-user-resource-aml-compute-instance | 0.4.1 |
| tre-service-azureml | 0.5.6 |
| tre-shared-service-cyclecloud | 0.3.0 |
| tre-shared-service-gitea | 0.4.0 |
| tre-shared-service-airlock-notifier | 0.2.3 |
| tre-shared-service-admin-vm | 0.2.0 |
| tre-shared-service-certs | 0.2.2 |
| tre-shared-service-sonatype-nexus | 2.2.3 |
| tre-shared-service-firewall | 0.6.2 |
## 0.6.0 (October 24, 2022)
FEATURES:
* Added filtering and sorting to Airlock UI ([#2511](https://github.com/microsoft/AzureTRE/pull/2730))
* Added title field to Airlock requests ([#2503](https://github.com/microsoft/AzureTRE/pull/2731))
* New Create Review VM functionality for Airlock Reviews ([#2738](https://github.com/microsoft/AzureTRE/pull/2759) & [#2737](https://github.com/microsoft/AzureTRE/pull/2740))
ENHANCEMENTS:
* Add cran support to nexus, open port 80 for the workspace nsg and update the firewall config to allow let's encrypt CRLs ([#2694](https://github.com/microsoft/AzureTRE/pull/2694))
* Upgrade GitHub Actions versions ([#2731](https://github.com/microsoft/AzureTRE/pull/2744))
* Install TRE CLI inside the devcontainer image (rather than via a post-create step) ([#2757](https://github.com/microsoft/AzureTRE/pull/2757))
* Upgrade Terraform to 1.3.2 ([#2758](https://github.com/microsoft/AzureTRE/pull/2758))
* `tre` CLI: added `raw` output option, improved `airlock-requests` handling, more consistent exit codes on error, added examples to CLI README.md
BUG FIXES:
* Pin Porter's plugin/mixin versions used ([#2762](https://github.com/microsoft/AzureTRE/pull/2762))
* Fix issues with AML workspace service deployment ([#2768](https://github.com/microsoft/AzureTRE/pull/2768))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.4.2 |
| core | 0.4.37 |
| tre-workspace-base | 0.4.2 |
| tre-workspace-unrestricted | 0.2.0 |
| tre-workspace-airlock-import-review | 0.4.0 |
| tre-service-mlflow | 0.4.0 |
| tre-service-innereye | 0.4.0 |
| tre-workspace-service-gitea | 0.5.0 |
| tre-workspace-service-mysql | 0.2.0 |
| tre-service-guacamole-linuxvm | 0.5.2 |
| tre-service-guacamole-export-reviewvm | 0.0.6 |
| tre-service-guacamole-windowsvm | 0.5.2 |
| tre-service-guacamole-import-reviewvm | 0.1.3 |
| tre-service-guacamole | 0.5.0 |
| tre-user-resource-aml-compute-instance | 0.4.1 |
| tre-service-azureml | 0.5.6 |
| tre-shared-service-cyclecloud | 0.3.0 |
| tre-shared-service-gitea | 0.4.0 |
| tre-shared-service-airlock-notifier | 0.2.2 |
| tre-shared-service-admin-vm | 0.2.0 |
| tre-shared-service-certs | 0.2.0 |
| tre-shared-service-sonatype-nexus | 2.2.2 |
| tre-shared-service-firewall | 0.6.1 |
## 0.5.1 (October 12, 2022)
BUG FIXES:
* Fix shared service 409 installation issue when in status other than deployed ([#2725](https://github.com/microsoft/AzureTRE/pull/2725))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.4.2 |
| core | 0.4.36 |
| tre-workspace-base | 0.4.0 |
| tre-workspace-unrestricted | 0.2.0 |
| tre-workspace-airlock-import-review | 0.4.0 |
| tre-service-mlflow | 0.4.0 |
| tre-service-innereye | 0.4.0 |
| tre-workspace-service-gitea | 0.5.0 |
| tre-workspace-service-mysql | 0.2.0 |
| tre-service-guacamole-linuxvm | 0.5.1 |
| tre-service-guacamole-export-reviewvm | 0.0.4 |
| tre-service-guacamole-windowsvm | 0.5.1 |
| tre-service-guacamole-import-reviewvm | 0.1.1 |
| tre-service-guacamole | 0.5.0 |
| tre-user-resource-aml-compute-instance | 0.4.1 |
| tre-service-azureml | 0.5.1 |
| tre-shared-service-cyclecloud | 0.3.0 |
| tre-shared-service-gitea | 0.4.0 |
| tre-shared-service-airlock-notifier | 0.2.0 |
| tre-shared-service-admin-vm | 0.2.0 |
| tre-shared-service-certs | 0.2.0 |
| tre-shared-service-sonatype-nexus | 2.2.0 |
| tre-shared-service-firewall | 0.6.1 |
## 0.5.0 (October 10, 2022)
**BREAKING CHANGES & MIGRATIONS**:
* GitHub Actions deployments use a single ACR instead of two. GitHub secrets might need updating, see PR for details. ([#2654](https://github.com/microsoft/AzureTRE/pull/2654))
* Align GitHub Action secret names. Existing GitHub environments must be updated, see PR for details. ([#2655](https://github.com/microsoft/AzureTRE/pull/2655))
* Add workspace creator as an owner of the workspace enterprise application ([#2627](https://github.com/microsoft/AzureTRE/pull/2627)). **Migration** if the `AUTO_WORKSPACE_APP_REGISTRATION` is set, the `Directory.Read.All` MS Graph API permission permission needs granting to the Application Registration identified by `APPLICATION_ADMIN_CLIENT_ID`.
* Add support for setting AppService plan SKU in GitHub Actions. Previous environment variable names of `API_APP_SERVICE_PLAN_SKU_SIZE` and `APP_SERVICE_PLAN_SKU` have been renamed to `CORE_APP_SERVICE_PLAN_SKU` and `WORKSPACE_APP_SERVICE_PLAN_SKU` ([#2684](https://github.com/microsoft/AzureTRE/pull/2684))
* Reworked how status update messages are handled by the API, to enforce ordering and run the queue subscription in a dedicated thread. Since sessions are now enabled for the status update queue, a `tre-deploy` is required, which will re-create the queue. ([#2700](https://github.com/microsoft/AzureTRE/pull/2700))
* Guacamole user-resource templates have been updated. VM SKU and image details are now specified in `porter.yaml`. See `README.md` in the guacamole `user-resources` folder for details.
* `deploy_shared_services.sh` now uses the `tre` CLI. Ensure that your CI/CD environment installs the CLI (`(cd cli && make install-cli)`)
* UI: Moved from React Context API to React-Redux (with Redux Toolkit) to manage the global operations (notifications) state
FEATURES:
* Add Import Review Workspace ([#2498](https://github.com/microsoft/AzureTRE/issues/2498))
* Restrict resource templates to specific roles ([#2600](https://github.com/microsoft/AzureTRE/issues/2600))
* Import review user resource template ([#2601](https://github.com/microsoft/AzureTRE/issues/2601))
* Export review user resource template ([#2602](https://github.com/microsoft/AzureTRE/issues/2602))
* Airlock Manager can use user resources ([#2499](https://github.com/microsoft/AzureTRE/issues/2499))
* Users only see templates they are authorized to use ([#2640](https://github.com/microsoft/AzureTRE/issues/2640))
* Guacamole user-resource templates now have support for custom VM images from image galleries ([#2634](https://github.com/microsoft/AzureTRE/pull/2634))
* Add initial `tre` CLI ([2537](https://github.com/microsoft/AzureTRE/pull/2537))
ENHANCEMENTS:
* Cancelling an Airlock request triggers deletion of the request container and files ([#2584](https://github.com/microsoft/AzureTRE/pull/2584))
* Airlock requests with status "blocked_by_scan" have the reason for being blocked by the malware scanner in the status_message field ([#2666](https://github.com/microsoft/AzureTRE/pull/2666))
* Move admin-vm from core to a shared service ([#2624](https://github.com/microsoft/AzureTRE/pull/2624))
* Remove obsolete docker environment variables ([#2675](https://github.com/microsoft/AzureTRE/pull/2675))
* Using Porter's Terraform mixin 1.0.0-rc.1 where mirror in done internally ([#2677](https://github.com/microsoft/AzureTRE/pull/2677))
* Airlock function internal storage is accessed with private endpoints ([#2679](https://github.com/microsoft/AzureTRE/pull/2679))
BUG FIXES:
* Resource processor error on deploying user-resource: TypeError: 'NoneType' object is not iterable ([#2569](https://github.com/microsoft/AzureTRE/issues/2569))
* Update Porter and Terraform mixin versions ([#2639](https://github.com/microsoft/AzureTRE/issues/2639))
* Airlock Manager should have permissions to get SAS token ([#2502](https://github.com/microsoft/AzureTRE/issues/2502))
* Terraform unmarshal errors in `migrate.sh` ([#2673](https://github.com/microsoft/AzureTRE/issues/2673))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.4.2 |
| core | 0.4.36 |
| porter-hello | 0.1.0 |
| tre-workspace-base | 0.4.0 |
| tre-workspace-unrestricted | 0.2.0 |
| tre-workspace-airlock-import-review | 0.4.0 |
| tre-service-mlflow | 0.4.0 |
| tre-service-innereye | 0.4.0 |
| tre-workspace-service-gitea | 0.5.0 |
| tre-workspace-service-mysql | 0.2.0 |
| tre-service-guacamole-linuxvm | 0.5.1 |
| tre-service-guacamole-export-reviewvm | 0.0.4 |
| tre-service-guacamole-windowsvm | 0.5.1 |
| tre-service-guacamole-import-reviewvm | 0.1.1 |
| tre-service-guacamole | 0.5.0 |
| tre-user-resource-aml-compute-instance | 0.4.1 |
| tre-service-azureml | 0.5.1 |
| tre-shared-service-cyclecloud | 0.3.0 |
| tre-shared-service-gitea | 0.4.0 |
| tre-shared-service-airlock-notifier | 0.2.0 |
| tre-shared-service-admin-vm | 0.2.0 |
| tre-shared-service-certs | 0.2.0 |
| tre-shared-service-sonatype-nexus | 2.2.0 |
| tre-shared-service-firewall | 0.6.1 |
## 0.4.3 (September 12, 2022)
**BREAKING CHANGES & MIGRATIONS**:
* Remove support for Nexus V1 ([#2580](https://github.com/microsoft/AzureTRE/pull/2580)). Please migrate to the newer version as described [here](https://microsoft.github.io/AzureTRE/tre-admins/setup-instructions/configuring-shared-services/).
FEATURES:
*
ENHANCEMENTS:
* Adding Log Analytics & Antimalware VM extensions ([#2520](https://github.com/microsoft/AzureTRE/pull/2520))
* Block anonymous access to 2 storage accounts ([#2524](https://github.com/microsoft/AzureTRE/pull/2524))
* Gitea shared service support app-service standard SKUs ([#2523](https://github.com/microsoft/AzureTRE/pull/2523))
* Keyvault diagnostic settings in base workspace ([#2521](https://github.com/microsoft/AzureTRE/pull/2521))
* Airlock requests contain a field with information about the files that were submitted ([#2504](https://github.com/microsoft/AzureTRE/pull/2504))
* UI - Operations and notifications stability improvements ([[#2530](https://github.com/microsoft/AzureTRE/pull/2530))
* UI - Initial implementation of Workspace Airlock Request View ([#2512](https://github.com/microsoft/AzureTRE/pull/2512))
* Add ability to automatically create Azure AD groups for each application role. Requires API version 0.4.30 or later ([#2532](https://github.com/microsoft/AzureTRE/pull/2532))
* Add `is_exposed_externally` option to Azure ML Workspace Service ([#2548](https://github.com/microsoft/AzureTRE/pull2548))
* Azure ML workspace service assigns Azure ML Data Scientist role to Workspace Researchers ([#2539](https://github.com/microsoft/AzureTRE/pull/2539))
* UI is deployed by default ([#2554](https://github.com/microsoft/AzureTRE/pull/2554))
* Remove manual/makefile option to install Gitea/Nexus ([#2573](https://github.com/microsoft/AzureTRE/pull/2573))
* Exact Terraform provider versions in bundles ([#2579](https://github.com/microsoft/AzureTRE/pull/2579))
* Stabilize E2E tests by issuing the access token prior using it, hence, reducing the change of expired token ([#2572](https://github.com/microsoft/AzureTRE/pull/2572))
BUG FIXES:
* API health check is also returned by accessing the root path at / ([#2469](https://github.com/microsoft/AzureTRE/pull/2469))
* Temporary disable AppInsight's private endpoint in base workspace ([#2543](https://github.com/microsoft/AzureTRE/pull/2543))
* Resource Processor execution optimization (`porter show`) for long-standing services ([#2542](https://github.com/microsoft/AzureTRE/pull/2542))
* Move AML Compute deployment to use AzApi Terraform Provider ([#2555](https://github.com/microsoft/AzureTRE/pull/2555))
* Invalid token exceptions in the API app are caught, throwing 401 instead of 500 Internal server error ([#2572](https://github.com/microsoft/AzureTRE/pull/2572))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.4.0 |
| core | 0.4.23 |
| tre-workspace-base | 0.3.28 |
| tre-workspace-unrestricted | 0.1.9 |
| tre-service-mlflow | 0.3.7 |
| tre-service-innereye | 0.3.5 |
| tre-workspace-service-gitea | 0.3.8 |
| tre-workspace-service-mysql | 0.1.2 |
| tre-service-guacamole-linuxvm | 0.4.14 |
| tre-service-guacamole-windowsvm | 0.4.8 |
| tre-service-guacamole | 0.4.5 |
| tre-user-resource-aml-compute-instance | 0.3.2 |
| tre-service-azureml | 0.4.8 |
| tre-shared-service-cyclecloud | 0.2.6 |
| tre-shared-service-gitea | 0.3.14 |
| tre-shared-service-airlock-notifier | 0.1.2 |
| tre-shared-service-certs | 0.1.3 |
| tre-shared-service-sonatype-nexus | 2.1.6 |
| tre-shared-service-firewall | 0.4.3 |
## 0.4.2 (August 23, 2022)
**BREAKING CHANGES & MIGRATIONS**:
* API identity is only assigned Virtual Machine Contributor on the workspace level ([#2398](https://github.com/microsoft/AzureTRE/pull/2398)). Review the PR for migration steps.
FEATURES:
* MySQL workspace service ([#2476](https://github.com/microsoft/AzureTRE/pull/2476))
ENHANCEMENTS:
* 'CreationTime' field was added to Airlock requests ([#2432](https://github.com/microsoft/AzureTRE/pull/2432))
* Bundles mirror Terraform plugins when built ([#2446](https://github.com/microsoft/AzureTRE/pull/2446))
* 'Get all Airlock requests' endpoint supports filtering ([#2433](https://github.com/microsoft/AzureTRE/pull/2433))
* API uses user delegation key when generating SAS token for airlock requests ([#2460](https://github.com/microsoft/AzureTRE/pull/2460))
* Longer docker caching in Resource Processor ([#2486](https://github.com/microsoft/AzureTRE/pull/2486))
* Remove AppInsights Profiler support in base workspace bundle and deploy with native Terraform resources ([#2478](https://github.com/microsoft/AzureTRE/pull/2478))
BUG FIXES:
* Azure monitor resourced provided by Terraform and don't allow ingestion over internet ([#2375](https://github.com/microsoft/AzureTRE/pull/2375))
* Enable route table on the Airlock Processor subnet ([#2414](https://github.com/microsoft/AzureTRE/pull/2414))
* Support for _Standard_ app service plan SKUs ([#2415](https://github.com/microsoft/AzureTRE/pull/2415))
* Fix Azure ML Workspace deletion ([#2452](https://github.com/microsoft/AzureTRE/pull/2452))
* Get all pages in MS Graph queries ([#2492](https://github.com/microsoft/AzureTRE/pull/2492))
COMPONENTS:
| name | version |
| ----- | ----- |
| devops | 0.4.0 |
| core | 0.4.18 |
| tre-workspace-base | 0.3.25 |
| tre-service-mlflow | 0.3.5 |
| tre-service-innereye | 0.3.3 |
| tre-workspace-service-gitea | 0.3.6 |
| tre-workspace-service-mysql | 0.1.0 |
| tre-service-guacamole-linuxvm | 0.4.11 |
| tre-service-guacamole-windowsvm | 0.4.4 |
| tre-service-guacamole | 0.4.3 |
| tre-user-resource-aml-compute-instance | 0.3.1 |
| tre-service-azureml | 0.4.3 |
| tre-shared-service-cyclecloud | 0.2.4 |
| tre-shared-service-gitea | 0.3.11 |
| tre-shared-service-airlock-notifier | 0.1.0 |
| tre-shared-service-certs | 0.1.2 |
| tre-shared-service-sonatype-nexus | 2.1.4 |
| tre-shared-service-firewall | 0.4.2 |
| tre-shared-service-nexus | 0.3.6 |
## 0.4.1 (August 03, 2022)
**BREAKING CHANGES & MIGRATIONS**:
* Guacamole workspace service configures firewall requirements with deployment pipeline ([#2371](https://github.com/microsoft/AzureTRE/pull/2371)). **Migration** is manual - update the templateVersion of `tre-shared-service-firewall` in Cosmos to `0.4.0` in order to use this capability.
* Workspace now has an AirlockManager role that has the permissions to review airlock requests ([#2349](https://github.com/microsoft/AzureTRE/pull/2349)).
FEATURES:
*
ENHANCEMENTS:
* Guacamole logs are sent to Application Insights ([#2376](https://github.com/microsoft/AzureTRE/pull/2376))
* `make tre-start/stop` run in parallel which saves ~5 minutes ([#2394](https://github.com/microsoft/AzureTRE/pull/2394))
* Airlock requests that fail move to status "Failed" ([#2268](https://github.com/microsoft/AzureTRE/pull/2395))
BUG FIXES:
* Airlock processor creates SAS tokens with _user delegated key_ ([#2382](https://github.com/microsoft/AzureTRE/pull/2382))
* Script updates to work with deployment repo structure ([#2385](https://github.com/microsoft/AzureTRE/pull/2385))
## 0.4.0 (July 27, 2022)
FEATURES:
* Cost reporting APIs
* Airlock - data import/export
* UI
* Nexus v2 to support Docker repositories
* Auto create application registration when creating a base workspace
* Centrally manage the firewall share service state to enable other services to ask for rule changes
Many more enhancements are listed on the [release page](https://github.com/microsoft/AzureTRE/releases/tag/v0.4)
|
AzureTRE/CHANGELOG.md/0
|
{
"file_path": "AzureTRE/CHANGELOG.md",
"repo_id": "AzureTRE",
"token_count": 20098
}
| 82 |
# To enable ssh & remote debugging on app service change the base image to the one below
# FROM mcr.microsoft.com/azure-functions/python:4-python3.8-appservice as base
FROM mcr.microsoft.com/azure-functions/python:4-python3.8-slim as base
COPY requirements.txt /
RUN pip install --no-cache-dir -r /requirements.txt
FROM base as test
COPY requirements-dev.txt /
RUN pip install --no-cache-dir -r /requirements-dev.txt
WORKDIR /app
COPY . .
RUN /app/run_tests_and_exit_succesfully.sh
FROM scratch as test-results
COPY --from=test /test-results/* /
FROM base as runtime
ENV AzureWebJobsScriptRoot=/home/site/wwwroot \
AzureFunctionsJobHost__Logging__Console__IsEnabled=true
COPY . /home/site/wwwroot
|
AzureTRE/airlock_processor/Dockerfile/0
|
{
"file_path": "AzureTRE/airlock_processor/Dockerfile",
"repo_id": "AzureTRE",
"token_count": 250
}
| 83 |
from mock import patch, MagicMock
from DataDeletionTrigger import delete_blob_and_container_if_last_blob
from shared_code.blob_operations import get_storage_endpoint_suffix
class TestDataDeletionTrigger():
@patch("DataDeletionTrigger.BlobServiceClient")
def test_delete_blob_and_container_if_last_blob_deletes_container(self, mock_blob_service_client):
blob_url = f"https://stalimextest.blob.{get_storage_endpoint_suffix()}/c144728c-3c69-4a58-afec-48c2ec8bfd45/test_dataset.txt"
mock_blob_service_client().get_container_client().list_blobs = MagicMock(return_value=["blob"])
delete_blob_and_container_if_last_blob(blob_url)
mock_blob_service_client().get_container_client().delete_container.assert_called_once()
@patch("DataDeletionTrigger.BlobServiceClient")
def test_delete_blob_and_container_if_last_blob_doesnt_delete_container(self, mock_blob_service_client):
blob_url = f"https://stalimextest.blob.{get_storage_endpoint_suffix()}/c144728c-3c69-4a58-afec-48c2ec8bfd45/test_dataset.txt"
mock_blob_service_client().get_container_client().list_blobs = MagicMock(return_value=["blob1", "blob2"])
delete_blob_and_container_if_last_blob(blob_url)
mock_blob_service_client().get_container_client().delete_container.assert_not_called()
@patch("DataDeletionTrigger.BlobServiceClient")
def test_delete_blob_and_container_if_last_blob_deletes_container_if_no_blob_specified(self, mock_blob_service_client):
blob_url = f"https://stalimextest.blob.{get_storage_endpoint_suffix()}/c144728c-3c69-4a58-afec-48c2ec8bfd45/"
delete_blob_and_container_if_last_blob(blob_url)
mock_blob_service_client().get_container_client().delete_container.assert_called_once()
|
AzureTRE/airlock_processor/tests/test_data_deletion_trigger.py/0
|
{
"file_path": "AzureTRE/airlock_processor/tests/test_data_deletion_trigger.py",
"repo_id": "AzureTRE",
"token_count": 725
}
| 84 |
from typing import Union
from fastapi.exceptions import RequestValidationError
from fastapi.openapi.constants import REF_PREFIX
from fastapi.openapi.utils import validation_error_response_definition
from pydantic import ValidationError
from fastapi import Request, status
from fastapi.responses import PlainTextResponse
def http422_error_handler(_: Request, exception: Union[RequestValidationError, ValidationError]) -> PlainTextResponse:
return PlainTextResponse(str(exception), status_code=status.HTTP_422_UNPROCESSABLE_ENTITY)
validation_error_response_definition["properties"] = {
"errors": {
"title": "Errors",
"type": "array",
"items": {"$ref": "{0}ValidationError".format(REF_PREFIX)},
},
}
|
AzureTRE/api_app/api/errors/validation_error.py/0
|
{
"file_path": "AzureTRE/api_app/api/errors/validation_error.py",
"repo_id": "AzureTRE",
"token_count": 235
}
| 85 |
from typing import Optional
from fastapi import APIRouter, Depends, HTTPException, status
from pydantic import parse_obj_as
from api.helpers import get_repository
from db.errors import EntityVersionExist, InvalidInput
from db.repositories.resource_templates import ResourceTemplateRepository
from models.domain.resource import ResourceType
from models.schemas.resource_template import ResourceTemplateInResponse, ResourceTemplateInformationInList
from models.schemas.workspace_template import WorkspaceTemplateInCreate, WorkspaceTemplateInResponse
from resources import strings
from services.authentication import get_current_admin_user
from api.routes.resource_helpers import get_template
workspace_templates_admin_router = APIRouter(dependencies=[Depends(get_current_admin_user)])
@workspace_templates_admin_router.get("/workspace-templates", response_model=ResourceTemplateInformationInList, name=strings.API_GET_WORKSPACE_TEMPLATES)
async def get_workspace_templates(authorized_only: bool = False, template_repo=Depends(get_repository(ResourceTemplateRepository)), user=Depends(get_current_admin_user)) -> ResourceTemplateInformationInList:
templates_infos = await template_repo.get_templates_information(ResourceType.Workspace, user.roles if authorized_only else None)
return ResourceTemplateInformationInList(templates=templates_infos)
@workspace_templates_admin_router.get("/workspace-templates/{workspace_template_name}", response_model=WorkspaceTemplateInResponse, name=strings.API_GET_WORKSPACE_TEMPLATE_BY_NAME, response_model_exclude_none=True)
async def get_workspace_template(workspace_template_name: str, is_update: bool = False, version: Optional[str] = None, template_repo=Depends(get_repository(ResourceTemplateRepository))) -> WorkspaceTemplateInResponse:
template = await get_template(workspace_template_name, template_repo, ResourceType.Workspace, is_update=is_update, version=version)
return parse_obj_as(WorkspaceTemplateInResponse, template)
@workspace_templates_admin_router.post("/workspace-templates", status_code=status.HTTP_201_CREATED, response_model=WorkspaceTemplateInResponse, response_model_exclude_none=True, name=strings.API_CREATE_WORKSPACE_TEMPLATES)
async def register_workspace_template(template_input: WorkspaceTemplateInCreate, template_repo=Depends(get_repository(ResourceTemplateRepository))) -> ResourceTemplateInResponse:
try:
return await template_repo.create_and_validate_template(template_input, ResourceType.Workspace)
except EntityVersionExist:
raise HTTPException(status_code=status.HTTP_409_CONFLICT, detail=strings.WORKSPACE_TEMPLATE_VERSION_EXISTS)
except InvalidInput as e:
raise HTTPException(status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, detail=str(e))
|
AzureTRE/api_app/api/routes/workspace_templates.py/0
|
{
"file_path": "AzureTRE/api_app/api/routes/workspace_templates.py",
"repo_id": "AzureTRE",
"token_count": 828
}
| 86 |
import uuid
from typing import List, Optional, Union
from pydantic import parse_obj_as
from core import config
from db.errors import DuplicateEntity, EntityDoesNotExist, EntityVersionExist, InvalidInput
from db.repositories.base import BaseRepository
from models.domain.resource import ResourceType
from models.domain.resource_template import ResourceTemplate
from models.domain.user_resource_template import UserResourceTemplate
from models.schemas.resource_template import ResourceTemplateInCreate, ResourceTemplateInformation
from services.schema_service import enrich_shared_service_template, enrich_workspace_template, enrich_workspace_service_template, enrich_user_resource_template
class ResourceTemplateRepository(BaseRepository):
@classmethod
async def create(cls):
cls = ResourceTemplateRepository()
await super().create(config.STATE_STORE_RESOURCE_TEMPLATES_CONTAINER)
return cls
@staticmethod
def _template_by_name_query(name: str, resource_type: ResourceType) -> str:
return f'SELECT * FROM c WHERE c.resourceType = "{resource_type}" AND c.name = "{name}"'
@staticmethod
def enrich_template(template: ResourceTemplate, is_update: bool = False) -> dict:
if template.resourceType == ResourceType.Workspace:
return enrich_workspace_template(template, is_update=is_update)
elif template.resourceType == ResourceType.WorkspaceService:
return enrich_workspace_service_template(template, is_update=is_update)
elif template.resourceType == ResourceType.SharedService:
return enrich_shared_service_template(template, is_update=is_update)
else:
return enrich_user_resource_template(template, is_update=is_update)
async def get_templates_information(self, resource_type: ResourceType, user_roles: Optional[List[str]] = None, parent_service_name: str = "") -> List[ResourceTemplateInformation]:
"""
Returns name/title/description for all current resource_type templates
:param user_roles: If set, only return templates that the user is authorized to use.
template.authorizedRoles should contain at least one of user_roles
"""
query = f'SELECT c.name, c.title, c.description, c.authorizedRoles FROM c WHERE c.resourceType = "{resource_type}" AND c.current = true'
if resource_type == ResourceType.UserResource:
query += f' AND c.parentWorkspaceService = "{parent_service_name}"'
template_infos = await self.query(query=query)
templates = [parse_obj_as(ResourceTemplateInformation, info) for info in template_infos]
if not user_roles:
return templates
# User can view template if they have at least one of authorizedRoles
return [t for t in templates if not t.authorizedRoles or len(set(t.authorizedRoles).intersection(set(user_roles))) > 0]
async def get_current_template(self, template_name: str, resource_type: ResourceType, parent_service_name: str = "") -> Union[ResourceTemplate, UserResourceTemplate]:
"""
Returns full template for the current version of the 'template_name' template
"""
query = self._template_by_name_query(template_name, resource_type) + ' AND c.current = true'
if resource_type == ResourceType.UserResource:
query += f' AND c.parentWorkspaceService = "{parent_service_name}"'
templates = await self.query(query=query)
if len(templates) == 0:
raise EntityDoesNotExist
if len(templates) > 1:
raise DuplicateEntity
if resource_type == ResourceType.UserResource:
return parse_obj_as(UserResourceTemplate, templates[0])
else:
return parse_obj_as(ResourceTemplate, templates[0])
async def get_template_by_name_and_version(self, name: str, version: str, resource_type: ResourceType, parent_service_name: Optional[str] = None) -> Union[ResourceTemplate, UserResourceTemplate]:
"""
Returns full template for the 'resource_type' template defined by 'template_name' and 'version'
For UserResource templates, you also need to pass in 'parent_service_name' as a parameter
"""
query = self._template_by_name_query(name, resource_type) + f' AND c.version = "{version}"'
# If querying for a user resource, we also need to add the parentWorkspaceService (name) to the query
if resource_type == ResourceType.UserResource:
if parent_service_name:
query += f' AND c.parentWorkspaceService = "{parent_service_name}"'
else:
raise Exception("When getting a UserResource template, you must pass in a 'parent_service_name'")
# Execute the query and handle results
templates = await self.query(query=query)
if len(templates) != 1:
raise EntityDoesNotExist
if resource_type == ResourceType.UserResource:
return parse_obj_as(UserResourceTemplate, templates[0])
else:
return parse_obj_as(ResourceTemplate, templates[0])
async def get_all_template_versions(self, template_name: str) -> List[str]:
query = 'SELECT VALUE c.version FROM c where c.name = @template_name'
parameters = [{"name": "@template_name", "value": template_name}]
versions = await self.query(query=query, parameters=parameters)
return versions
async def create_template(self, template_input: ResourceTemplateInCreate, resource_type: ResourceType, parent_service_name: str = "") -> Union[ResourceTemplate, UserResourceTemplate]:
"""
creates a template based on the input (workspace and workspace-services template)
"""
template = {
"id": str(uuid.uuid4()),
"name": template_input.name,
"title": template_input.json_schema["title"],
"description": template_input.json_schema["description"],
"version": template_input.version,
"resourceType": resource_type,
"current": template_input.current,
"required": template_input.json_schema.get("required", []),
"authorizedRoles": template_input.json_schema.get("authorizedRoles", []),
"properties": template_input.json_schema["properties"],
"customActions": template_input.customActions
}
if "uiSchema" in template_input.json_schema:
template["uiSchema"] = template_input.json_schema["uiSchema"]
if "pipeline" in template_input.json_schema:
pipeline = template_input.json_schema["pipeline"]
self._validate_pipeline_has_unique_step_ids(pipeline)
template["pipeline"] = pipeline
if "allOf" in template_input.json_schema:
template["allOf"] = template_input.json_schema["allOf"]
if resource_type == ResourceType.UserResource:
template["parentWorkspaceService"] = parent_service_name
template = parse_obj_as(UserResourceTemplate, template)
else:
template = parse_obj_as(ResourceTemplate, template)
await self.save_item(template)
return template
async def create_and_validate_template(self, template_input: ResourceTemplateInCreate, resource_type: ResourceType, workspace_service_template_name: str = "") -> dict:
"""
Validates that we don't have a version conflict
Updates the current version for the template
Saves to the database and returns the enriched template
"""
try:
template = await self.get_template_by_name_and_version(template_input.name, template_input.version, resource_type, workspace_service_template_name)
if template:
raise EntityVersionExist
except EntityDoesNotExist:
try:
template = await self.get_current_template(template_input.name, resource_type, workspace_service_template_name)
if template_input.current:
template.current = False
await self.update_item(template)
except EntityDoesNotExist:
# first registration
template_input.current = True # For first time registration, template is always marked current
created_template = await self.create_template(template_input, resource_type, workspace_service_template_name)
return self.enrich_template(created_template)
def _validate_pipeline_has_unique_step_ids(self, pipeline):
if pipeline is None:
return
step_ids = []
for action in pipeline:
num_of_main_steps = 0
for step in pipeline[action]:
step_id = step["stepId"]
if step_id == "main":
num_of_main_steps += 1
if step_id in step_ids or num_of_main_steps > 1:
raise InvalidInput(f"Invalid template - duplicate stepIds are not allowed. stepId: {step_id}")
if step_id != "main":
step_ids.append(step_id)
|
AzureTRE/api_app/db/repositories/resource_templates.py/0
|
{
"file_path": "AzureTRE/api_app/db/repositories/resource_templates.py",
"repo_id": "AzureTRE",
"token_count": 3478
}
| 87 |
from collections import namedtuple
from typing import List
from pydantic import BaseModel, Field
RoleAssignment = namedtuple("RoleAssignment", "resource_id, role_id")
class User(BaseModel):
id: str
name: str
email: str
roles: List[str] = Field([])
roleAssignments: List[RoleAssignment] = Field([])
|
AzureTRE/api_app/models/domain/authentication.py/0
|
{
"file_path": "AzureTRE/api_app/models/domain/authentication.py",
"repo_id": "AzureTRE",
"token_count": 108
}
| 88 |
from pydantic import BaseModel
def get_sample_airlock_request_container_url(container_url: str) -> dict:
return {
"containerUrl": container_url
}
class AirlockRequestTokenInResponse(BaseModel):
containerUrl: str
class Config:
schema_extra = {
"example": {
"container_url": get_sample_airlock_request_container_url("container_url")
}
}
|
AzureTRE/api_app/models/schemas/airlock_request_url.py/0
|
{
"file_path": "AzureTRE/api_app/models/schemas/airlock_request_url.py",
"repo_id": "AzureTRE",
"token_count": 176
}
| 89 |
from models.domain.resource import ResourceType
from models.domain.resource_template import CustomAction, ResourceTemplate, Property
from models.schemas.resource_template import ResourceTemplateInCreate, ResourceTemplateInResponse
def get_sample_workspace_template_object(template_name: str = "tre-workspace-base") -> ResourceTemplate:
return ResourceTemplate(
id="a7a7a7bd-7f4e-4a4e-b970-dc86a6b31dfb",
name=template_name,
title="Workspace",
description="base workspace bundle",
version="0.1.0",
resourceType=ResourceType.Workspace,
current=True,
type="object",
required=["display_name", "description", "client_id"],
properties={
"display_name": Property(type="string"),
"description": Property(type="string"),
"client_id": Property(type="string"),
"client_secret": Property(type="string"),
"address_space_size": Property(
type="string",
default="small",
description="This can have a value of small, medium, large or custom. If you specify custom, then you need to specify a VNet address space in 'address_space' (e.g. 10.2.1.0/24)")
},
customActions=[
CustomAction()
]
)
def get_sample_workspace_template_in_response() -> dict:
workspace_template = get_sample_workspace_template_object().dict()
workspace_template["system_properties"] = {
"tre_id": Property(type="string"),
"workspace_id": Property(type="string"),
"azure_location": Property(type="string"),
}
return workspace_template
class WorkspaceTemplateInCreate(ResourceTemplateInCreate):
class Config:
schema_extra = {
"example": {
"name": "my-tre-workspace",
"version": "0.0.1",
"current": "true",
"json_schema": {
"$schema": "http://json-schema.org/draft-07/schema",
"$id": "https://github.com/microsoft/AzureTRE/templates/workspaces/myworkspace/workspace.json",
"type": "object",
"title": "My Workspace Template",
"description": "This is a test workspace template schema",
"required": [
"vm_size",
"no_of_vms"
],
"authorizedRoles": [],
"properties": {
"display_name": {
"type": "string",
"title": "Name for the workspace",
"description": "The name of the workspace to be displayed to users"
},
"description": {
"type": "string",
"title": "Description of the workspace",
"description": "Description of the workspace"
},
"address_space_size": {
"type": "string",
"title": "Address space size",
"description": "Network address size (small, medium, large or custom) to be used by the workspace"
},
"address_space": {
"type": "string",
"title": "Address space",
"description": "Network address space to be used by the workspace if address_space_size is custom"
}
}
},
"customActions": [
{
"name": "disable",
"description": "Deallocates resources"
}
]
}
}
class WorkspaceTemplateInResponse(ResourceTemplateInResponse):
class Config:
schema_extra = {
"example": get_sample_workspace_template_in_response()
}
|
AzureTRE/api_app/models/schemas/workspace_template.py/0
|
{
"file_path": "AzureTRE/api_app/models/schemas/workspace_template.py",
"repo_id": "AzureTRE",
"token_count": 2097
}
| 90 |
from azure.servicebus import ServiceBusMessage
from azure.servicebus.aio import ServiceBusClient
from pydantic import parse_obj_as
from db.repositories.resources_history import ResourceHistoryRepository
from service_bus.substitutions import substitute_properties
from models.domain.resource_template import PipelineStep
from models.domain.operation import OperationStep
from models.domain.resource import Resource, ResourceType
from db.repositories.resource_templates import ResourceTemplateRepository
from models.domain.authentication import User
from models.schemas.resource import ResourcePatch
from db.repositories.resources import ResourceRepository
from core import config, credentials
from services.logging import logger
from azure.cosmos.exceptions import CosmosAccessConditionFailedError
async def _send_message(message: ServiceBusMessage, queue: str):
"""
Sends the given message to the given queue in the Service Bus.
:param message: The message to send.
:type message: ServiceBusMessage
:param queue: The Service Bus queue to send the message to.
:type queue: str
"""
async with credentials.get_credential_async_context() as credential:
service_bus_client = ServiceBusClient(config.SERVICE_BUS_FULLY_QUALIFIED_NAMESPACE, credential)
async with service_bus_client:
sender = service_bus_client.get_queue_sender(queue_name=queue)
async with sender:
await sender.send_messages(message)
async def send_deployment_message(content, correlation_id, session_id, action):
resource_request_message = ServiceBusMessage(body=content, correlation_id=correlation_id, session_id=session_id)
logger.info(f"Sending resource request message with correlation ID {resource_request_message.correlation_id}, action: {action}")
await _send_message(resource_request_message, config.SERVICE_BUS_RESOURCE_REQUEST_QUEUE)
async def update_resource_for_step(operation_step: OperationStep, resource_repo: ResourceRepository, resource_template_repo: ResourceTemplateRepository, resource_history_repo: ResourceHistoryRepository, root_resource: Resource, step_resource: Resource, resource_to_update_id: str, primary_action: str, user: User) -> Resource:
# step_resource is the resource instance where the step was defined. e.g. 'add firewall rule' step defined in Guacamole template -> the step_resource is the Guacamole ws service.
# root_resource is theresource on which the user chose to update, i.e. the top most resource in cascaded action or the same resource in a non-cascaded action.
if step_resource is None:
step_resource = await resource_repo.get_resource_by_id(operation_step.sourceTemplateResourceId)
# If we are handling the root resource, we can leverage the given resource which has non redacted properties
if root_resource is not None and root_resource.id == step_resource.id:
step_resource = root_resource
step_resource_parent_service_name = ""
step_resource_parent_workspace = None
step_resource_parent_workspace_service = None
if step_resource.resourceType == ResourceType.UserResource:
step_resource_parent_workspace_service = await resource_repo.get_resource_by_id(step_resource.parentWorkspaceServiceId)
step_resource_parent_service_name = step_resource_parent_workspace_service.templateName
step_resource_parent_workspace = await resource_repo.get_resource_by_id(step_resource.workspaceId)
if step_resource.resourceType == ResourceType.WorkspaceService:
step_resource_parent_workspace = await resource_repo.get_resource_by_id(step_resource.workspaceId)
parent_template = await resource_template_repo.get_template_by_name_and_version(step_resource.templateName, step_resource.templateVersion, step_resource.resourceType, step_resource_parent_service_name)
# if there are no pipelines, or custom action, no need to continue with substitutions.
if not parent_template.pipeline:
return step_resource
parent_template_pipeline_dict = parent_template.pipeline.dict()
# if action not defined as a pipeline, custom action, no need to continue with substitutions.
if primary_action not in parent_template_pipeline_dict:
return step_resource
pipeline_primary_action = parent_template_pipeline_dict[primary_action]
is_first_main_step = pipeline_primary_action and len(pipeline_primary_action) == 1 and pipeline_primary_action[0]['stepId'] == 'main'
if not pipeline_primary_action or is_first_main_step:
return step_resource
# get the template step
template_step = None
for step in parent_template_pipeline_dict[primary_action]:
if step["stepId"] == operation_step.templateStepId:
template_step = parse_obj_as(PipelineStep, step)
break
if template_step is None:
raise Exception(f"Cannot find step with id of {operation_step.templateStepId} in template {step_resource.templateName} for action {primary_action}")
resource_to_send = await try_update_with_retries(
num_retries=3,
attempt_count=0,
resource_repo=resource_repo,
resource_template_repo=resource_template_repo,
resource_history_repo=resource_history_repo,
user=user,
resource_to_update_id=resource_to_update_id,
template_step=template_step,
primary_resource=step_resource,
primary_parent_workspace=step_resource_parent_workspace,
primary_parent_workspace_svc=step_resource_parent_workspace_service
)
return resource_to_send
async def try_update_with_retries(num_retries: int, attempt_count: int, resource_repo: ResourceRepository, resource_template_repo: ResourceTemplateRepository, resource_history_repo: ResourceHistoryRepository, user: User, resource_to_update_id: str, template_step: PipelineStep, primary_resource: Resource, primary_parent_workspace: Resource = None, primary_parent_workspace_svc: Resource = None) -> Resource:
try:
return await try_patch(
resource_repo=resource_repo,
resource_template_repo=resource_template_repo,
resource_history_repo=resource_history_repo,
user=user,
resource_to_update_id=resource_to_update_id,
template_step=template_step,
primary_resource=primary_resource,
primary_parent_workspace=primary_parent_workspace,
primary_parent_workspace_svc=primary_parent_workspace_svc
)
except CosmosAccessConditionFailedError as e:
logger.warning(f"Etag mismatch for {resource_to_update_id}. Retrying.")
if attempt_count < num_retries:
await try_update_with_retries(
num_retries=num_retries,
attempt_count=(attempt_count + 1),
resource_repo=resource_repo,
resource_template_repo=resource_template_repo,
resource_history_repo=resource_history_repo,
user=user,
resource_to_update_id=resource_to_update_id,
template_step=template_step,
primary_resource=primary_resource,
primary_parent_workspace=primary_parent_workspace,
primary_parent_workspace_svc=primary_parent_workspace_svc
)
else:
raise e
async def try_patch(resource_repo: ResourceRepository, resource_template_repo: ResourceTemplateRepository, resource_history_repo: ResourceHistoryRepository, user: User, resource_to_update_id: str, template_step: PipelineStep, primary_resource: Resource, primary_parent_workspace: Resource, primary_parent_workspace_svc: Resource) -> Resource:
resource_to_update = await resource_repo.get_resource_by_id(resource_to_update_id)
# substitute values into new property bag for update
properties = substitute_properties(template_step, primary_resource, primary_parent_workspace, primary_parent_workspace_svc, resource_to_update)
# get the template for the resource to upgrade
parent_service_name = ""
if resource_to_update.resourceType == ResourceType.UserResource:
parent_service_name = primary_parent_workspace_svc.templateName
resource_template_to_send = await resource_template_repo.get_template_by_name_and_version(resource_to_update.templateName, resource_to_update.templateVersion, resource_to_update.resourceType, parent_service_name)
# create the patch
patch = ResourcePatch(
properties=properties
)
# validate and submit the patch
resource_to_send, _ = await resource_repo.patch_resource(
resource=resource_to_update,
resource_patch=patch,
resource_template=resource_template_to_send,
etag=resource_to_update.etag,
resource_template_repo=resource_template_repo,
resource_history_repo=resource_history_repo,
user=user)
return resource_to_send
|
AzureTRE/api_app/service_bus/helpers.py/0
|
{
"file_path": "AzureTRE/api_app/service_bus/helpers.py",
"repo_id": "AzureTRE",
"token_count": 3091
}
| 91 |
# import os
# import sys
# TEST_DIR = os.path.dirname(os.path.abspath(__file__))
# PROJECT_DIR = os.path.abspath(os.path.join(TEST_DIR, os.pardir, 'api'))
# sys.path.insert(0, PROJECT_DIR)
|
AzureTRE/api_app/tests_ma/test_api/__init__.py/0
|
{
"file_path": "AzureTRE/api_app/tests_ma/test_api/__init__.py",
"repo_id": "AzureTRE",
"token_count": 84
}
| 92 |
import json
import pytest
from mock import patch
from starlette import status
from services.authentication import get_current_admin_user, get_current_tre_user_or_tre_admin
from db.errors import DuplicateEntity, EntityDoesNotExist, EntityVersionExist, InvalidInput, UnableToAccessDatabase
from models.domain.resource import ResourceType
from models.domain.user_resource_template import UserResourceTemplate
from models.schemas.resource_template import ResourceTemplateInformation
from resources import strings
pytestmark = pytest.mark.asyncio
@pytest.fixture
def user_resource_template_without_enriching():
def create_user_resource_template(template_name: str = "vm-resource-template", parent_service: str = "guacamole-service"):
return UserResourceTemplate(
id="a7a7a7bd-7f4e-4a4e-b970-dc86a6b31dfb",
name=template_name,
description="vm-bundle",
version="0.1.0",
resourceType=ResourceType.UserResource,
current=True,
type="object",
required=[],
properties={},
actions=[],
parentWorkspaceService=parent_service
)
return create_user_resource_template
class TestUserResourceTemplatesRequiringAdminRights:
@pytest.fixture(autouse=True, scope='class')
def _prepare(self, app, admin_user):
app.dependency_overrides[get_current_tre_user_or_tre_admin] = admin_user
app.dependency_overrides[get_current_admin_user] = admin_user
yield
app.dependency_overrides = {}
# POST /workspace-service-templates/{service_template_name}/user-resource-templates
@patch("api.dependencies.workspace_service_templates.ResourceTemplateRepository.get_current_template", side_effect=EntityDoesNotExist)
async def test_creating_user_resource_template_raises_404_if_service_template_does_not_exist(self, _, input_user_resource_template, app, client):
parent_workspace_service_name = "some_template_name"
response = await client.post(app.url_path_for(strings.API_CREATE_USER_RESOURCE_TEMPLATES, service_template_name=parent_workspace_service_name), json=input_user_resource_template.dict())
assert response.status_code == status.HTTP_404_NOT_FOUND
# POST /workspace-service-templates/{template_name}/user-resource-templates
@patch("api.routes.workspace_service_templates.ResourceTemplateRepository.create_and_validate_template")
@patch("api.dependencies.workspace_service_templates.ResourceTemplateRepository.get_current_template")
async def test_when_creating_user_resource_template_it_is_returned_as_expected(self, get_current_template_mock, create_template_mock, app, client, input_user_resource_template, basic_workspace_service_template, user_resource_template_in_response):
get_current_template_mock.return_value = basic_workspace_service_template
parent_workspace_service_name = "guacamole"
user_resource_template_in_response.parentWorkspaceService = parent_workspace_service_name
create_template_mock.return_value = user_resource_template_in_response
response = await client.post(app.url_path_for(strings.API_CREATE_USER_RESOURCE_TEMPLATES, service_template_name=parent_workspace_service_name), json=input_user_resource_template.dict())
assert json.loads(response.text)["resourceType"] == ResourceType.UserResource
assert json.loads(response.text)["parentWorkspaceService"] == parent_workspace_service_name
assert json.loads(response.text)["name"] == user_resource_template_in_response.name
# POST /workspace-service-templates/{template_name}/user-resource-templates
@patch("api.routes.workspace_service_templates.ResourceTemplateRepository.create_and_validate_template")
@patch("api.dependencies.workspace_service_templates.ResourceTemplateRepository.get_current_template")
async def test_when_creating_user_resource_template_enriched_service_template_is_returned(self, get_current_template_mock, create_template_mock, app, client, input_user_resource_template, basic_workspace_service_template, user_resource_template_in_response):
get_current_template_mock.return_value = basic_workspace_service_template
parent_workspace_service_name = "guacamole"
user_resource_template_in_response.parentWorkspaceService = parent_workspace_service_name
create_template_mock.return_value = user_resource_template_in_response
expected_template = user_resource_template_in_response
response = await client.post(app.url_path_for(strings.API_CREATE_USER_RESOURCE_TEMPLATES, service_template_name=parent_workspace_service_name), json=input_user_resource_template.dict())
assert json.loads(response.text)["properties"] == expected_template.properties
assert json.loads(response.text)["required"] == expected_template.required
# POST /workspace-service-templates/{template_name}/user-resource-templates
@patch("api.routes.workspace_service_templates.ResourceTemplateRepository.create_and_validate_template")
@patch("api.dependencies.workspace_service_templates.ResourceTemplateRepository.get_current_template")
async def test_when_creating_user_resource_template_returns_409_if_version_exists(self, get_current_template_mock, create_user_resource_template_mock, app, client, input_user_resource_template, basic_workspace_service_template, user_resource_template_in_response):
get_current_template_mock.return_value = basic_workspace_service_template
parent_workspace_service_name = "guacamole"
create_user_resource_template_mock.side_effect = EntityVersionExist
response = await client.post(app.url_path_for(strings.API_CREATE_USER_RESOURCE_TEMPLATES, service_template_name=parent_workspace_service_name), json=input_user_resource_template.dict())
assert response.status_code == status.HTTP_409_CONFLICT
@patch("api.routes.workspace_service_templates.ResourceTemplateRepository.create_and_validate_template", side_effect=InvalidInput)
@patch("api.dependencies.workspace_service_templates.ResourceTemplateRepository.get_current_template")
async def test_creating_a_user_resource_template_raises_http_422_if_step_ids_are_duplicated(self, _, __, client, app, input_user_resource_template):
response = await client.post(app.url_path_for(strings.API_CREATE_USER_RESOURCE_TEMPLATES, service_template_name="guacamole"), json=input_user_resource_template.dict())
assert response.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY
class TestUserResourceTemplatesNotRequiringAdminRights:
@pytest.fixture(autouse=True, scope='class')
def _prepare(self, app, researcher_user):
app.dependency_overrides[get_current_tre_user_or_tre_admin] = researcher_user
yield
app.dependency_overrides = {}
@patch("api.routes.workspace_service_templates.ResourceTemplateRepository.get_templates_information")
async def test_get_user_resource_templates_returns_template_names_and_description(self, get_templates_information_mock, app, client):
expected_templates = [
ResourceTemplateInformation(name="template1", title="template 1", description="description1"),
ResourceTemplateInformation(name="template2", title="template 2", description="description2")
]
get_templates_information_mock.return_value = expected_templates
response = await client.get(app.url_path_for(strings.API_GET_USER_RESOURCE_TEMPLATES, service_template_name="parent_service_name"))
assert response.status_code == status.HTTP_200_OK
actual_templates = response.json()["templates"]
assert len(actual_templates) == len(expected_templates)
for template in expected_templates:
assert template in actual_templates
# GET /workspace-service-templates/{service_template_name}/user-resource-templates/{user_resource_template_name}
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
async def test_user_resource_templates_by_name_returns_enriched_user_resource_template(self, get_current_template_mock, app, client, user_resource_template_without_enriching):
service_template_name = "guacamole-service"
user_resource_template_name = "vm-resource"
get_current_template_mock.return_value = user_resource_template_without_enriching(user_resource_template_name, service_template_name)
response = await client.get(app.url_path_for(strings.API_GET_USER_RESOURCE_TEMPLATE_BY_NAME, service_template_name=service_template_name, user_resource_template_name=user_resource_template_name))
assert response.status_code == status.HTTP_200_OK
assert response.json()["name"] == user_resource_template_name
assert "description" in response.json()["required"]
@pytest.mark.parametrize("exception, expected_status", [
(EntityDoesNotExist, status.HTTP_404_NOT_FOUND),
(DuplicateEntity, status.HTTP_500_INTERNAL_SERVER_ERROR),
(UnableToAccessDatabase, status.HTTP_503_SERVICE_UNAVAILABLE)
])
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
async def test_get_user_resource_templates_by_name_returns_returns_error_status_based_on_exception(self, get_current_template_mock, exception, expected_status, app, client):
service_template_name = "guacamole-service"
user_resource_template_name = "vm-resource"
get_current_template_mock.side_effect = exception
response = await client.get(app.url_path_for(strings.API_GET_USER_RESOURCE_TEMPLATE_BY_NAME, service_template_name=service_template_name, user_resource_template_name=user_resource_template_name))
assert response.status_code == expected_status
|
AzureTRE/api_app/tests_ma/test_api/test_routes/test_user_resource_templates.py/0
|
{
"file_path": "AzureTRE/api_app/tests_ma/test_api/test_routes/test_user_resource_templates.py",
"repo_id": "AzureTRE",
"token_count": 3428
}
| 93 |
from unittest.mock import AsyncMock
import pytest
import pytest_asyncio
from mock import patch
from db.errors import DuplicateEntity, EntityDoesNotExist
from db.repositories.shared_services import SharedServiceRepository
from db.repositories.operations import OperationRepository
from models.domain.shared_service import SharedService
from models.domain.resource import ResourceType
from models.schemas.shared_service import SharedServiceInCreate
pytestmark = pytest.mark.asyncio
SHARED_SERVICE_ID = "000000d3-82da-4bfc-b6e9-9a7853ef753e"
@pytest_asyncio.fixture
async def shared_service_repo():
with patch('api.dependencies.database.Database.get_container_proxy', return_value=AsyncMock()):
shared_service_repo = await SharedServiceRepository().create()
yield shared_service_repo
@pytest_asyncio.fixture
async def operations_repo():
with patch('api.dependencies.database.Database.get_container_proxy', return_value=None):
operations_repo = await OperationRepository().create()
yield operations_repo
@pytest.fixture
def shared_service():
shared_service = SharedService(
id=SHARED_SERVICE_ID,
templateVersion="0.1.0",
etag='',
properties={},
templateName="my-shared-service",
resourcePath="test"
)
return shared_service
@pytest.fixture
def basic_shared_service_request():
return SharedServiceInCreate(
templateName="my-shared-service",
properties={
"display_name": "test",
"description": "test",
"tre_id": "test"
})
async def test_get_shared_service_by_id_raises_if_does_not_exist(shared_service_repo):
shared_service_repo.query = AsyncMock(return_value=[])
with pytest.raises(EntityDoesNotExist):
await shared_service_repo.get_shared_service_by_id(SHARED_SERVICE_ID)
async def test_get_active_shared_services_for_shared_queries_db(shared_service_repo):
shared_service_repo.query = AsyncMock(return_value=[])
await shared_service_repo.get_active_shared_services()
shared_service_repo.query.assert_called_once_with(query=SharedServiceRepository.active_shared_services_query())
@patch('db.repositories.shared_services.SharedServiceRepository.validate_input_against_template')
@patch('core.config.TRE_ID', "1234")
async def test_create_shared_service_item_creates_a_shared_with_the_right_values(validate_input_mock, shared_service_repo, basic_shared_service_request, basic_shared_service_template):
shared_service_repo.query = AsyncMock(return_value=[])
shared_service_to_create = basic_shared_service_request
validate_input_mock.return_value = basic_shared_service_template
shared_service, _ = await shared_service_repo.create_shared_service_item(shared_service_to_create, [])
assert shared_service.templateName == basic_shared_service_request.templateName
assert shared_service.resourceType == ResourceType.SharedService
# We expect tre_id to be overriden in the shared service created
assert shared_service.properties["tre_id"] != shared_service_to_create.properties["tre_id"]
assert shared_service.properties["tre_id"] == "1234"
@patch('db.repositories.shared_services.SharedServiceRepository.validate_input_against_template')
@patch('core.config.TRE_ID', "1234")
async def test_create_shared_service_item_with_the_same_name_twice_fails(validate_input_mock, shared_service_repo, basic_shared_service_request, basic_shared_service_template):
shared_service_repo.query = AsyncMock(return_value=[])
validate_input_mock.return_value = basic_shared_service_template
shared_service, _ = await shared_service_repo.create_shared_service_item(basic_shared_service_request, [])
await shared_service_repo.save_item(shared_service)
shared_service_repo.query = AsyncMock()
shared_service_repo.query.return_value = [shared_service.__dict__]
with pytest.raises(DuplicateEntity):
shared_service = await shared_service_repo.create_shared_service_item(basic_shared_service_request, [])
@patch('db.repositories.shared_services.SharedServiceRepository.validate_input_against_template', side_effect=ValueError)
async def test_create_shared_item_raises_value_error_if_template_is_invalid(_, shared_service_repo, basic_shared_service_request):
shared_service_repo.query = AsyncMock(return_value=[])
shared_service_to_create = basic_shared_service_request
with pytest.raises(ValueError):
await shared_service_repo.create_shared_service_item(shared_service_to_create, [])
|
AzureTRE/api_app/tests_ma/test_db/test_repositories/test_shared_service_repository.py/0
|
{
"file_path": "AzureTRE/api_app/tests_ma/test_db/test_repositories/test_shared_service_repository.py",
"repo_id": "AzureTRE",
"token_count": 1606
}
| 94 |
from mock import patch
from services import azure_resource_status
from azure.mgmt.compute.models import VirtualMachineInstanceView, InstanceViewStatus
@patch("services.azure_resource_status.get_azure_vm_instance_view")
def test_get_azure_resource_status__correct_status_returned_for_vm(get_vm_instance_view_mock):
status1 = InstanceViewStatus(code="ProvisioningState/succeeded", level="Info", display_status="Provisioning succeeded")
status2 = InstanceViewStatus(code="PowerState/Running", level="Info", display_status="Running")
virtual_machine_instance_view_mock: VirtualMachineInstanceView = VirtualMachineInstanceView(statuses=[status1, status2])
get_vm_instance_view_mock.return_value = virtual_machine_instance_view_mock
vm_status = azure_resource_status.get_azure_resource_status('/subscriptions/subscription_id/resourceGroups/resource_group_name/providers/Microsoft.Compute/virtualMachines/vm_name')
assert vm_status == {'powerState': 'Running'}
def test_get_azure_resource_status__empty_status_returned_unknown():
vm_status = azure_resource_status.get_azure_resource_status('/subscriptions/subscription_id/resourceGroups/resource_group_name/providers/Microsoft.Unknown/resourceType/name')
assert vm_status == {}
|
AzureTRE/api_app/tests_ma/test_services/test_azure_resource_status.py/0
|
{
"file_path": "AzureTRE/api_app/tests_ma/test_services/test_azure_resource_status.py",
"repo_id": "AzureTRE",
"token_count": 393
}
| 95 |
import click
class SharedServiceTemplateContext(object):
def __init__(self, template_name: str):
self.template_name = template_name
pass_shared_service_template_context = click.make_pass_decorator(SharedServiceTemplateContext)
|
AzureTRE/cli/tre/commands/shared_service_templates/contexts.py/0
|
{
"file_path": "AzureTRE/cli/tre/commands/shared_service_templates/contexts.py",
"repo_id": "AzureTRE",
"token_count": 75
}
| 96 |
import json
import logging
import click
from tre.api_client import ApiClient
from tre.output import output, output_option, query_option
@click.group(name="workspace-service-templates", help="List workspace-service-templates ")
def workspace_service_templates():
pass
@click.command(name="list", help="List workspace-service-templates")
@output_option()
@query_option()
def workspace_service_templates_list(output_format, query):
log = logging.getLogger(__name__)
client = ApiClient.get_api_client_from_config()
response = client.call_api(
log,
'GET',
'/api/workspace-service-templates',
)
output(response, output_format=output_format, query=query, default_table_query=r"templates[].{name:name, title: title, description:description}")
@click.command(name="new", help="Register a new workspace service template")
@click.option('--definition', help='JSON definition for the template', required=False)
@click.option('--definition-file', help='File containing JSON definition for the template', required=False, type=click.File("r"))
@output_option()
@query_option()
def workspace_service_templates_create(definition, definition_file, output_format, query):
log = logging.getLogger(__name__)
if definition is None:
if definition_file is None:
raise click.UsageError('Please specify either a definition or a definition file')
definition = definition_file.read()
definition_dict = json.loads(definition)
client = ApiClient.get_api_client_from_config()
click.echo("Registering template...", err=True)
response = client.call_api(log, 'POST', '/api/workspace-service-templates', json_data=definition_dict)
output(response, output_format=output_format, query=query, default_table_query=r"{id: id, name:name, title: title, description:description}")
return response.text
workspace_service_templates.add_command(workspace_service_templates_list)
workspace_service_templates.add_command(workspace_service_templates_create)
|
AzureTRE/cli/tre/commands/workspace_service_templates/workspace_service_templates.py/0
|
{
"file_path": "AzureTRE/cli/tre/commands/workspace_service_templates/workspace_service_templates.py",
"repo_id": "AzureTRE",
"token_count": 648
}
| 97 |
import logging
import click
from tre.commands.operation import get_operation_id_completion, operation_show
from tre.output import output_option, query_option
from tre.api_client import ApiClient
from .contexts import pass_workspace_service_operation_context, WorkspaceServiceOperationContext
def operation_id_completion(ctx: click.Context, param: click.Parameter, incomplete: str):
log = logging.getLogger(__name__)
parent_ctx = ctx.parent
workspace_service_id = parent_ctx.params["workspace_service_id"]
parent2_ctx = parent_ctx.parent
workspace_id = parent2_ctx.params["workspace_id"]
list_url = f'/api/workspaces/{workspace_id}/workspace-services/{workspace_service_id}/operations'
client = ApiClient.get_api_client_from_config()
workspace_scope = client.get_workspace_scope(log, workspace_id)
return get_operation_id_completion(ctx, log, list_url, param, incomplete, scope_id=workspace_scope)
@click.group(name="operation", invoke_without_command=True, help="Perform actions on an operation")
@click.argument('operation_id', required=True, type=click.UUID, shell_complete=operation_id_completion)
@click.pass_context
def workspace_service_operation(ctx: click.Context, operation_id) -> None:
ctx.obj = WorkspaceServiceOperationContext.add_operation_id_to_context_obj(ctx, operation_id)
@click.command(name="show", help="Workspace-service operation")
@click.option('--no-wait',
help="If an operation is in progress, do not wait for it to complete",
flag_value=True,
default=False)
@output_option()
@query_option()
@pass_workspace_service_operation_context
def workspace_service_operation_show(workspace_service_operation_context: WorkspaceServiceOperationContext, no_wait, output_format, query, suppress_output: bool = False) -> None:
log = logging.getLogger(__name__)
workspace_id = workspace_service_operation_context.workspace_id
if workspace_id is None:
raise click.UsageError('Missing workspace ID')
workspace_service_id = workspace_service_operation_context.workspace_service_id
if workspace_service_id is None:
raise click.UsageError('Missing workspace-service ID')
operation_id = workspace_service_operation_context.operation_id
if operation_id is None:
raise click.UsageError('Missing operation ID')
client = ApiClient.get_api_client_from_config()
workspace_scope = client.get_workspace_scope(log, workspace_id)
operation_url = f'/api/workspaces/{workspace_id}/workspace-services/{workspace_service_id}/operations/{operation_id}'
operation_show(log, operation_url, no_wait, output_format, query, suppress_output, scope_id=workspace_scope)
workspace_service_operation.add_command(workspace_service_operation_show)
|
AzureTRE/cli/tre/commands/workspaces/workspace_services/operation.py/0
|
{
"file_path": "AzureTRE/cli/tre/commands/workspaces/workspace_services/operation.py",
"repo_id": "AzureTRE",
"token_count": 909
}
| 98 |
data "local_file" "airlock_processor_version" {
filename = "${path.root}/../../airlock_processor/_version.py"
}
locals {
version = replace(replace(replace(data.local_file.airlock_processor_version.content, "__version__ = \"", ""), "\"", ""), "\n", "")
}
resource "azurerm_service_plan" "airlock_plan" {
name = "plan-airlock-${var.tre_id}"
resource_group_name = var.resource_group_name
location = var.location
os_type = "Linux"
sku_name = var.airlock_app_service_plan_sku
tags = var.tre_core_tags
worker_count = 1
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_storage_account" "sa_airlock_processor_func_app" {
name = local.airlock_function_sa_name
resource_group_name = var.resource_group_name
location = var.location
account_tier = "Standard"
account_replication_type = "LRS"
allow_nested_items_to_be_public = false
tags = var.tre_core_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_linux_function_app" "airlock_function_app" {
name = local.airlock_function_app_name
resource_group_name = var.resource_group_name
location = var.location
https_only = true
virtual_network_subnet_id = var.airlock_processor_subnet_id
service_plan_id = azurerm_service_plan.airlock_plan.id
storage_account_name = azurerm_storage_account.sa_airlock_processor_func_app.name
# consider moving to a managed identity here
storage_account_access_key = azurerm_storage_account.sa_airlock_processor_func_app.primary_access_key
tags = var.tre_core_tags
identity {
type = "UserAssigned"
identity_ids = [azurerm_user_assigned_identity.airlock_id.id]
}
app_settings = {
"SB_CONNECTION_STRING" = var.airlock_servicebus.default_primary_connection_string
"BLOB_CREATED_TOPIC_NAME" = azurerm_servicebus_topic.blob_created.name
"TOPIC_SUBSCRIPTION_NAME" = azurerm_servicebus_subscription.airlock_processor.name
"EVENT_GRID_STEP_RESULT_TOPIC_URI_SETTING" = azurerm_eventgrid_topic.step_result.endpoint
"EVENT_GRID_STEP_RESULT_TOPIC_KEY_SETTING" = azurerm_eventgrid_topic.step_result.primary_access_key
"EVENT_GRID_DATA_DELETION_TOPIC_URI_SETTING" = azurerm_eventgrid_topic.data_deletion.endpoint
"EVENT_GRID_DATA_DELETION_TOPIC_KEY_SETTING" = azurerm_eventgrid_topic.data_deletion.primary_access_key
"WEBSITES_ENABLE_APP_SERVICE_STORAGE" = false
"AIRLOCK_STATUS_CHANGED_QUEUE_NAME" = local.status_changed_queue_name
"AIRLOCK_SCAN_RESULT_QUEUE_NAME" = local.scan_result_queue_name
"AIRLOCK_DATA_DELETION_QUEUE_NAME" = local.data_deletion_queue_name
"ENABLE_MALWARE_SCANNING" = var.enable_malware_scanning
"ARM_ENVIRONMENT" = var.arm_environment
"MANAGED_IDENTITY_CLIENT_ID" = azurerm_user_assigned_identity.airlock_id.client_id
"TRE_ID" = var.tre_id
"WEBSITE_CONTENTOVERVNET" = 1
"STORAGE_ENDPOINT_SUFFIX" = module.terraform_azurerm_environment_configuration.storage_suffix
}
site_config {
http2_enabled = true
always_on = true
container_registry_managed_identity_client_id = azurerm_user_assigned_identity.airlock_id.client_id
container_registry_use_managed_identity = true
vnet_route_all_enabled = true
ftps_state = "Disabled"
application_stack {
docker {
registry_url = var.docker_registry_server
image_name = var.airlock_processor_image_repository
image_tag = local.version
}
}
# This is added automatically (by Azure?) when the equivalent is set in app_settings.
# Setting it here to save TF from updating on every apply.
application_insights_connection_string = var.applicationinsights_connection_string
}
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_monitor_diagnostic_setting" "airlock_function_app" {
name = "diagnostics-airlock-function-${var.tre_id}"
target_resource_id = azurerm_linux_function_app.airlock_function_app.id
log_analytics_workspace_id = var.log_analytics_workspace_id
enabled_log {
category = "FunctionAppLogs"
}
metric {
category = "AllMetrics"
enabled = true
}
lifecycle { ignore_changes = [log_analytics_destination_type] }
}
resource "azurerm_private_endpoint" "function_storage" {
for_each = {
Blob = var.blob_core_dns_zone_id
File = var.file_core_dns_zone_id
Queue = var.queue_core_dns_zone_id
Table = var.table_core_dns_zone_id
}
name = "pe-${local.airlock_function_sa_name}-${lower(each.key)}"
location = var.location
resource_group_name = var.resource_group_name
subnet_id = var.airlock_storage_subnet_id
tags = var.tre_core_tags
lifecycle { ignore_changes = [tags] }
private_dns_zone_group {
name = "private-dns-zone-group-${local.airlock_function_sa_name}"
private_dns_zone_ids = [each.value]
}
private_service_connection {
name = "psc-${local.airlock_function_sa_name}"
private_connection_resource_id = azurerm_storage_account.sa_airlock_processor_func_app.id
is_manual_connection = false
subresource_names = [each.key]
}
}
|
AzureTRE/core/terraform/airlock/airlock_processor.tf/0
|
{
"file_path": "AzureTRE/core/terraform/airlock/airlock_processor.tf",
"repo_id": "AzureTRE",
"token_count": 2718
}
| 99 |
output "app_gateway_fqdn" {
value = azurerm_public_ip.appgwpip.fqdn
}
output "app_gateway_name" {
value = azurerm_application_gateway.agw.name
}
output "static_web_storage" {
value = azurerm_storage_account.staticweb.name
}
|
AzureTRE/core/terraform/appgateway/outputs.tf/0
|
{
"file_path": "AzureTRE/core/terraform/appgateway/outputs.tf",
"repo_id": "AzureTRE",
"token_count": 95
}
| 100 |
---
# cloud-config
package_upgrade: true
apt:
sources:
docker.list:
source: deb [arch=amd64]
https://download.docker.com/linux/ubuntu $RELEASE stable
keyid: 9DC858229FC7DD38854AE2D88D81803C0EBFCD88
keyserver: hkp://keyserver.ubuntu.com:80
azure-cli.list:
source: deb [arch=amd64]
https://packages.microsoft.com/repos/azure-cli/ $RELEASE main
keyid: BC528686B50D79E339D3721CEB3E94ADBE1229CF
keyserver: hkp://keyserver.ubuntu.com:80
packages:
- docker-ce
- docker-ce-cli
- containerd.io
- docker-compose
- azure-cli
- gnupg2
- pass
# create the docker group
groups:
- docker
# add default auto created user to docker group
system_info:
default_user:
groups: [docker]
write_files:
- path: .env
content: |
REGISTRY_SERVER=${docker_registry_server}
TERRAFORM_STATE_CONTAINER_NAME=${terraform_state_container_name}
MGMT_RESOURCE_GROUP_NAME=${mgmt_resource_group_name}
MGMT_STORAGE_ACCOUNT_NAME=${mgmt_storage_account_name}
SERVICE_BUS_DEPLOYMENT_STATUS_UPDATE_QUEUE=${service_bus_deployment_status_update_queue}
SERVICE_BUS_RESOURCE_REQUEST_QUEUE=${service_bus_resource_request_queue}
SERVICE_BUS_FULLY_QUALIFIED_NAMESPACE=${service_bus_namespace}
VMSS_MSI_ID=${vmss_msi_id}
# the following line makes sure the right msi will be used if multiple are available on the VM
AZURE_CLIENT_ID=${vmss_msi_id}
AZURE_SUBSCRIPTION_ID=${arm_subscription_id}
ARM_CLIENT_ID=${vmss_msi_id}
AZURE_TENANT_ID=${arm_tenant_id}
ARM_USE_MSI=true
APPLICATIONINSIGHTS_CONNECTION_STRING=${app_insights_connection_string}
NUMBER_PROCESSES=${resource_processor_number_processes_per_instance}
KEY_VAULT_NAME=${key_vault_name}
KEY_VAULT_URL=${key_vault_url}
ARM_ENVIRONMENT=${arm_environment}
AZURE_ENVIRONMENT=${azure_environment}
AAD_AUTHORITY_URL=${aad_authority_url}
MICROSOFT_GRAPH_FQDN=${microsoft_graph_fqdn}
OTEL_RESOURCE_ATTRIBUTES=service.name=resource_processor,service.version=${resource_processor_vmss_porter_image_tag}
OTEL_EXPERIMENTAL_RESOURCE_DETECTORS=azure_vm
LOGGING_LEVEL=${logging_level}
${rp_bundle_values}
- path: /etc/cron.hourly/docker-prune
# An hourly cron job to have docker free disk space. Running this frquently
# since disk might get full fast, but we prune only when free space is low.
content: |
#!/bin/bash
set -o errexit
used_percent=$(df / --output=pcent | tail -1 | sed 's/[^0-9]//g')
echo "Used disk space percent: $${used_percent}"
if (( used_percent > 75 )); then
echo "Free space too low, pruning..."
docker system prune -f
fi
permissions: '0755'
runcmd:
# Those are useful live debug commands. Check the docs for details:
# (https://microsoft.github.io/AzureTRE/troubleshooting-faq/troubleshooting-rp/#Logs)
- printf '\nalias dlf="docker logs --since 1m --follow"' >> /etc/bash.bashrc
- printf '\nalias dlf1='\''dlf $(docker ps -q | head -n 1)'\''' >> /etc/bash.bashrc
- >
printf '\nalias rpstatus='\''tmux new-session -d "watch docker ps"; \
tmux split-window -p 100 -v "docker logs --since 1m --follow resource_processor1"; \
tmux split-window -v -p 90; \
tmux -2 attach-session -d'\''\n' >> /etc/bash.bashrc
- export DEBIAN_FRONTEND=noninteractive
- az cloud set --name ${azure_environment}
- az login --identity -u ${vmss_msi_id}
- az acr login --name ${docker_registry_server}
- docker run -d -p 8080:8080 -v /var/run/docker.sock:/var/run/docker.sock
--restart always --env-file .env
--name resource_processor1
--log-driver local
${docker_registry_server}/${resource_processor_vmss_porter_image_repository}:${resource_processor_vmss_porter_image_tag}
|
AzureTRE/core/terraform/resource_processor/vmss_porter/cloud-config.yaml/0
|
{
"file_path": "AzureTRE/core/terraform/resource_processor/vmss_porter/cloud-config.yaml",
"repo_id": "AzureTRE",
"token_count": 1614
}
| 101 |
#!/bin/bash
set -euo pipefail
# Use this for debug only
# set -o xtrace
# AZURE_CORE_OUTPUT=jsonc # force CLI output to JSON for the script (user can still change default for interactive usage in the dev container)
function show_usage()
{
cat << USAGE
Utility script for creating an automation administrator for TRE. This is optional and is used when you
want to run the E2E tests locally or automatically register bundles in the TRE.
You must be logged in using Azure CLI with sufficient privileges to modify Azure Active Directory to run this script.
Usage: $0 --name "mytre" [--admin-consent]
Options:
-n,--name Required. The prefix for the app (registration) names e.g., "TRE".
-r,--reset-password Optional, switch to automatically reset the password. Default 0
USAGE
exit 2
}
if ! command -v az &> /dev/null; then
echo "This script requires Azure CLI" 1>&2
exit 1
fi
# Get the directory that this script is in
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
declare resetPassword=0
declare currentUserId=""
declare msGraphUri=""
declare appName=""
# Initialize parameters specified from command line
while [[ $# -gt 0 ]]; do
case "$1" in
-n|--name)
appName=$2
shift 2
;;
-r|--reset-password)
resetPassword=$2
shift 2
;;
*)
echo "Invalid option: $1."
show_usage
;;
esac
done
###################################
# CHECK INCOMMING PARAMETERS #
###################################
if [[ $(az account list --only-show-errors -o json | jq 'length') -eq 0 ]]; then
echo "Please run az login -t <tenant> --allow-no-subscriptions"
exit 1
fi
if [[ -z "$appName" ]]; then
echo "Please specify the application name" 1>&2
show_usage
fi
appName="$appName Automation Admin"
currentUserId=$(az ad signed-in-user show --query 'id' --output tsv --only-show-errors)
msGraphUri="$(az cloud show --query endpoints.microsoftGraphResourceId --output tsv)/v1.0"
tenant=$(az rest -m get -u "${msGraphUri}/domains" -o json | jq -r '.value[] | select(.isDefault == true) | .id')
echo -e "\e[96mCreating the Automation Admin in the \"${tenant}\" Azure AD tenant.\e[0m"
# Load in helper functions
# shellcheck disable=SC1091
source "${DIR}/get_existing_app.sh"
# shellcheck disable=SC1091
source "${DIR}/wait_for_new_app_registration.sh"
# shellcheck disable=SC1091
source "${DIR}/create_or_update_service_principal.sh"
# Get an existing object if it's been created before.
appObjectId=""
appId=""
existingApp=$(get_existing_app --name "${appName}") || null
if [ -n "${existingApp}" ]; then
appObjectId=$(echo "${existingApp}" | jq -r '.id')
appId=$(echo "${existingApp}" | jq -r .appId)
fi
automationApp=$(jq -c . << JSON
{
"displayName": "${appName}",
"api": {
"requestedAccessTokenVersion": 2
},
"signInAudience": "AzureADMyOrg"
}
JSON
)
# Is the app already registered?
if [[ -n ${appObjectId} ]]; then
echo "\"${appName}\" already exists. Skipping creation."
else
echo "\"${appName}\" doesn't exist - creating..."
appId=$(az rest --method POST --uri "${msGraphUri}/applications" --headers Content-Type=application/json --body "${automationApp}" --output tsv --query "appId")
# Poll until the app registration is found in the listing.
wait_for_new_app_registration "${appId}"
fi
# Make the current user an owner of the application.
az ad app owner add --id "${appId}" --owner-object-id "$currentUserId" --only-show-errors
# Create a Service Principal for the app.
spPassword=$(create_or_update_service_principal "${appId}" "${resetPassword}")
# Set outputs in configuration file
yq -i ".authentication.test_account_client_id |= \"${appId}\"" config.yaml
yq -i ".authentication.test_account_client_secret |= \"${spPassword}\"" config.yaml
echo "test_account_client_id=\"${appId}\""
echo "test_account_client_secret=\"${spPassword}\""
|
AzureTRE/devops/scripts/aad/create_automation_administrator.sh/0
|
{
"file_path": "AzureTRE/devops/scripts/aad/create_automation_administrator.sh",
"repo_id": "AzureTRE",
"token_count": 1454
}
| 102 |
#!/bin/bash
set -o errexit
set -o pipefail
set -o nounset
# set -o xtrace
if [[ -z ${TRE_ID:-} ]]; then
echo "TRE_ID environment variable must be set."
exit 1
fi
core_rg_name="rg-${TRE_ID}"
fw_name="fw-${TRE_ID}"
agw_name="agw-$TRE_ID"
# if the resource group doesn't exist, no need to continue this script.
# most likely this is an automated execution before calling make tre-deploy.
if [[ $(az group list --output json --query "[?name=='${core_rg_name}'] | length(@)") == 0 ]]; then
echo "TRE resource group doesn't exist. Exiting..."
exit 0
fi
az config set extension.use_dynamic_install=yes_without_prompt
az --version
if [[ "$1" == *"start"* ]]; then
if [[ $(az network firewall list --output json --query "[?resourceGroup=='${core_rg_name}'&&name=='${fw_name}'] | length(@)") != 0 ]]; then
CURRENT_PUBLIC_IP=$(az network firewall ip-config list -f "${fw_name}" -g "${core_rg_name}" --query "[0].publicIpAddress" -o tsv)
if [ -z "$CURRENT_PUBLIC_IP" ]; then
echo "Starting Firewall - creating ip-config"
az network firewall ip-config create -f "${fw_name}" -g "${core_rg_name}" -n "fw-ip-configuration" --public-ip-address "pip-${fw_name}" --vnet-name "vnet-$TRE_ID" > /dev/null &
else
echo "Firewall ip-config already exists"
fi
fi
if [[ $(az network application-gateway list --output json --query "[?resourceGroup=='${core_rg_name}'&&name=='${agw_name}'&&operationalState=='Stopped'] | length(@)") != 0 ]]; then
echo "Starting Application Gateway"
az network application-gateway start -g "${core_rg_name}" -n "${agw_name}" &
else
echo "Application Gateway already running"
fi
az mysql server list --resource-group "${core_rg_name}" --query "[?userVisibleState=='Stopped'].name" -o tsv |
while read -r mysql_name; do
echo "Starting MySQL ${mysql_name}"
az mysql server start --resource-group "${core_rg_name}" --name "${mysql_name}" &
done
az vmss list --resource-group "${core_rg_name}" --query "[].name" -o tsv |
while read -r vmss_name; do
if [[ "$(az vmss list-instances --resource-group "${core_rg_name}" --name "${vmss_name}" --expand instanceView --output json | \
jq 'select(.[].instanceView.statuses[].code=="PowerState/deallocated") | length')" -gt 0 ]]; then
echo "Starting VMSS ${vmss_name}"
az vmss start --resource-group "${core_rg_name}" --name "${vmss_name}" &
fi
done
az vm list -d --resource-group "${core_rg_name}" --query "[?powerState!='VM running'].name" -o tsv |
while read -r vm_name; do
echo "Starting VM ${vm_name}"
az vm start --resource-group "${core_rg_name}" --name "${vm_name}" &
done
# We don't start workspace VMs despite maybe stopping them because we don't know if they need to be on.
elif [[ "$1" == *"stop"* ]]; then
if [[ $(az network firewall list --output json --query "[?resourceGroup=='${core_rg_name}'&&name=='${fw_name}'] | length(@)") != 0 ]]; then
fw_sku=$(az network firewall show -n "${fw_name}" -g "${core_rg_name}" --query "sku.tier" -o tsv)
IPCONFIG_NAME=$(az network firewall ip-config list -f "${fw_name}" -g "${core_rg_name}" --query "[0].name" -o tsv)
if [ -n "$IPCONFIG_NAME" ] && [ "${fw_sku}" != "Basic" ]; then
echo "Deleting Firewall ip-config: $IPCONFIG_NAME"
az network firewall ip-config delete -f "${fw_name}" -n "$IPCONFIG_NAME" -g "${core_rg_name}" &
else
echo "No Firewall ip-config found or SKU (${fw_sku}) doesn't allow deallocation"
fi
fi
if [[ $(az network application-gateway list --output json --query "[?resourceGroup=='${core_rg_name}'&&name=='${agw_name}'&&operationalState=='Running'] | length(@)") != 0 ]]; then
echo "Stopping Application Gateway"
az network application-gateway stop -g "${core_rg_name}" -n "${agw_name}" &
else
echo "Application Gateway already stopped"
fi
az mysql server list --resource-group "${core_rg_name}" --query "[?userVisibleState=='Ready'].name" -o tsv |
while read -r mysql_name; do
echo "Stopping MySQL ${mysql_name}"
az mysql server stop --resource-group "${core_rg_name}" --name "${mysql_name}" &
done
az vmss list --resource-group "${core_rg_name}" --query "[].name" -o tsv |
while read -r vmss_name; do
if [[ "$(az vmss list-instances --resource-group "${core_rg_name}" --name "${vmss_name}" --expand instanceView --output json | \
jq 'select(.[].instanceView.statuses[].code=="PowerState/running") | length')" -gt 0 ]]; then
echo "Deallocating VMSS ${vmss_name}"
az vmss deallocate --resource-group "${core_rg_name}" --name "${vmss_name}" &
fi
done
az vm list -d --resource-group "${core_rg_name}" --query "[?powerState=='VM running'].name" -o tsv |
while read -r vm_name; do
echo "Deallocating VM ${vm_name}"
az vm deallocate --resource-group "${core_rg_name}" --name "${vm_name}" &
done
# deallocating all VMs in workspaces
# RG is in uppercase here (which is odd). Checking both cases for future compatability.
az vm list -d --query "[?(starts_with(resourceGroup,'${core_rg_name}-ws') || starts_with(resourceGroup,'${core_rg_name^^}-WS')) && powerState=='VM running'][name, resourceGroup]" -o tsv |
while read -r vm_name rg_name; do
echo "Deallocating VM ${vm_name} in ${rg_name}"
az vm deallocate --resource-group "${rg_name}" --name "${vm_name}" &
done
fi
# for some reason the vm/vmss commands aren't considered as 'jobs', but this will still work in most cases
# since firewall/appgw will take much longer to complete their change.
echo "Waiting for all jobs to finish..."
jobs
wait
# Report final FW status
FW_STATE="Stopped"
if [[ $(az network firewall list --output json --query "[?resourceGroup=='${core_rg_name}'&&name=='${fw_name}'] | length(@)") != 0 ]]; then
PUBLIC_IP=$(az network firewall ip-config list -f "${fw_name}" -g "${core_rg_name}" --query "[0].publicIpAddress" -o tsv)
if [ -n "$PUBLIC_IP" ]; then
FW_STATE="Running"
fi
fi
# Report final AGW status
AGW_STATE=$(az network application-gateway list --query "[?resourceGroup=='${core_rg_name}'&&name=='${agw_name}'].operationalState | [0]" -o tsv)
echo -e "\n\e[34m»»» 🔨 \e[96mTRE Status for $TRE_ID\e[0m"
echo -e "\e[34m»»» • \e[96mFirewall: \e[33m$FW_STATE\e[0m"
echo -e "\e[34m»»» • \e[96mApplication Gateway: \e[33m$AGW_STATE\e[0m\n"
|
AzureTRE/devops/scripts/control_tre.sh/0
|
{
"file_path": "AzureTRE/devops/scripts/control_tre.sh",
"repo_id": "AzureTRE",
"token_count": 2377
}
| 103 |
#!/bin/bash
# This script register a bundle with the TRE API. It relies on the bundle
# pre-existing in the remote repository (i.e. has been publish beforehand).
set -o errexit
set -o pipefail
# Uncomment this line to see each command for debugging (careful: this will show secrets!)
# set -o xtrace
function usage() {
cat <<USAGE
Usage: $0 [-c --current] [-i --insecure]
Options:
-r, --acr-name Azure Container Registry Name
-t, --bundle-type Bundle type: workspace, workspace_service, user_resource or shared_service
-w, --workspace-service-name The template name of the user resource (if registering a user_resource)
-c, --current Make this the currently deployed version of this template
-v, --verify Verify registration with the API
--dry-run Don't submit the template to the API, just output the payload
USAGE
exit 1
}
# if no arguments are provided, return usage function
if [ $# -eq 0 ]; then
usage # run usage function
fi
current="false"
verify="false"
dry_run="false"
while [ "$1" != "" ]; do
case $1 in
-r | --acr-name)
shift
acr_name=$1
;;
-t | --bundle-type)
shift
case $1 in
workspace)
;;
workspace_service)
;;
user_resource)
;;
shared_service)
;;
*)
echo "Bundle type must be workspace, workspace_service, shared_service or user_resource, not $1"
exit 1
esac
bundle_type=$1
;;
-w | --workspace-service-name)
shift
workspace_service_name=$1
;;
-c| --current)
current="true"
;;
-v| --verify)
verify="true"
;;
--dry-run)
dry_run="true"
;;
*)
echo "Unexpected argument: '$1'"
usage
;;
esac
if [[ -z "$2" ]]; then
# if no more args then stop processing
break
fi
shift # remove the current value for `$1` and use the next
done
# done with processing args and can set this
set -o nounset
if [[ -z ${acr_name:-} ]]; then
echo -e "No Azure Container Registry name provided\n"
usage
fi
if [[ -z ${bundle_type:-} ]]; then
echo -e "No bundle type provided\n"
usage
fi
acr_domain_suffix=$(az cloud show --query suffixes.acrLoginServerEndpoint --output tsv)
explain_json=$(porter explain --reference "${acr_name}${acr_domain_suffix}"/"$(yq eval '.name' porter.yaml)":v"$(yq eval '.version' porter.yaml)" -o json)
payload=$(echo "${explain_json}" | jq --argfile json_schema template_schema.json --arg current "${current}" --arg bundle_type "${bundle_type}" '. + {"json_schema": $json_schema, "resourceType": $bundle_type, "current": $current}')
if [ "${dry_run}" == "true" ]; then
echo "--dry-run specified - automatic bundle registration disabled. Use the script output to self-register. "
echo "See documentation for more details: https://microsoft.github.io/AzureTRE/tre-admins/registering-templates/"
echo "${payload}" | jq --color-output .
exit 1
fi
if [ "${bundle_type}" == "user_resource" ] && [ -z "${workspace_service_name:-}" ]; then
echo -e "You must supply a workspace service_name name if you would like to automatically register the user_resource bundle\n"
echo "${payload}" | jq --color-output .
usage
fi
template_name=$(yq eval '.name' porter.yaml)
template_version=$(yq eval '.version' porter.yaml)
function get_template() {
case "${bundle_type}" in
("workspace") get_result=$(tre workspace-template "$template_name" show --output json) || echo ;;
("workspace_service") get_result=$(tre workspace-service-template "$template_name" show --output json) || echo ;;
("user_resource") get_result=$(tre workspace-service-template "${workspace_service_name}" user-resource-template "$template_name" show --output json) || echo;;
("shared_service") get_result=$(tre shared-service-template "$template_name" show --output json) || echo ;;
esac
echo "$get_result"
}
get_result=$(get_template)
if [[ -n "$(echo "$get_result" | jq -r .id)" ]]; then
# 'id' was returned - so we successfully got the template from the API. Now check the version
if [[ "$(echo "$get_result" | jq -r .version)" == "$template_version" ]]; then
echo "Template with this version already exists"
exit
fi
else
error_code=$(echo "$get_result" | jq -r .status_code)
# 404 Not Found error at this point is fine => we want to continue to register the template
# For other errors, show the error and exit with non-zero result
if [[ "$error_code" != "404" ]]; then
echo "Error checking for existing template: $get_result"
exit 1
fi
fi
# If we got here then register the template - CLI exits with non-zero result on error
case "${bundle_type}" in
("workspace") tre workspace-templates new --definition "${payload}" ;;
("workspace_service") tre workspace-service-templates new --definition "${payload}" ;;
("user_resource") tre workspace-service-template "${workspace_service_name}" user-resource-templates new --definition "${payload}" ;;
("shared_service") tre shared-service-templates new --definition "${payload}";;
esac
if [[ "${verify}" = "true" ]]; then
# Check that the template got registered
get_result=$(get_template)
if [[ -z "$(echo "$get_result" | jq -r .id)" ]]; then
echo "Error checking for template after registering: $get_result"
exit 1
fi
fi
|
AzureTRE/devops/scripts/register_bundle_with_api.sh/0
|
{
"file_path": "AzureTRE/devops/scripts/register_bundle_with_api.sh",
"repo_id": "AzureTRE",
"token_count": 2059
}
| 104 |
# Configuring Airlock Review feature
Airlock Review feature allows to setup a process for manually reviewing Airlock requests. When using this feature, Airlock Manager (a role with privileges of reviewing Airlock requests) is able to create Review User Resource (VM) and use it to review the data from.
For information on Airlock feature, please refer to the [overview page](../azure-tre-overview/airlock.md).
For documentation on how to review an Airlock request, please refer to the [user guide](../using-tre/tre-for-research/review-airlock-request.md).
## Pre-requisites
The feature is configured on a per Research Workspace basis. Different Research Workspaces need to be configured separately, although a single Airlock Import Workspace can be reused for all of them.
Research Workspace can only be configured after it has been deployed, and the template must be of version 0.5.0 or later.
Airlock must be enabled in the Research Workspace.
To configure the feature, the following prerequisites need to be fulfilled:
1. [Airlock Import Workspace](../tre-templates/workspaces/airlock-import-review.md) need to be deployed (once per TRE).
1. [Guacamole Workspace Service](../tre-templates/workspace-services/guacamole.md) need to be deployed in Airlock Import Workspace from the previous step.
1. [Template for import review VM](../tre-templates/user-resources/import-reviewvm.md) needs to be installed in the TRE, or a custom template if used.
1. [Guacamole Workspace Service](../tre-templates/workspace-services/guacamole.md) need to be deployed in Research Workspace.
1. [Template for export review VM](../tre-templates/user-resources/export-reviewvm.md) needs to be installed in the TRE, or a custom template if used.
## Configuring Airlock VM for Research Workspace
Navigate to Research Workspace in the UI, and click "Update". You will see a check box "Configure Review VMs".
[](../assets/configure-review-vm.png)
You then will be able to input the values as follows:
1. For `Import Review Workspace ID`, use the GUID of the Import Review workspace from step 1.
1. For `Import Review Workspace Service ID`, use the GUID of the Workspace Service from step 2.
1. For `Import Review VM User Resource Template Name`, unless you have built a custom template for this, you should use `tre-service-guacamole-import-reviewvm` which is the name of the standard template used for Import Reviews from step 3.
1. For `Export Review Workspace Service ID`, use the GUID of the Workspace Service deployed into the Research Workspace from step 4.
1. For `Export Review Vm User Resource Template Name`, unless you have built a custom template for this, you should use `tre-service-guacamole-export-reviewvm` which is the name of the standard template used for Import Reviews from step 5.
Once you're done, click Submit.
Verify that the configuration is working by creating Review VMs for existing import export and export requests (configuration is not verified on update).
For troubleshooting guidance please review [the airlock troubleshooting FAQ](../troubleshooting-faq/airlock-troubleshooting.md)
|
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|
{
"file_path": "AzureTRE/docs/tre-admins/configure-airlock-review.md",
"repo_id": "AzureTRE",
"token_count": 813
}
| 105 |
# Installing workspace service and user resource
## Publish and register a workspace service template
We will use the [Guacamole workspace service bundle](../../tre-templates/workspace-services/guacamole.md) for the purposes of this tutorial; a template that provides Virtual Desktop functionality allowing the deployment of VMs for users. These steps can be repeated for any workspace service template depending on the functionalities required.
1. Run:
```cmd
make workspace_service_bundle BUNDLE=guacamole
```
## Publish and register a user resource template
The Guacamole workspace service also has user resources: the VMs that researchers will deploy. These steps can be repeated for any user resource template.
1. Run:
```cmd
make user_resource_bundle BUNDLE=guacamole-azure-windowsvm WORKSPACE_SERVICE=guacamole
```
## Creating a workspace service
Now that we have published and registered both workspace service and user resource bundles we can use the workspace API to create a workspace service in our workspace.
1. Navigate to the Swagger UI at `https://<azure_tre_fqdn>/api/workspaces/<workspace_id>/docs` . Where `<workspace_id>` is the workspace ID of the workspace created in the previous step.
!!! info
All routes are auth protected. Click the green **Authorize** button to receive a token for Swagger client.
2. Log into the Swagger UI by clicking `Authorize`, then `Authorize` again. You will be redirected to the login page.
!!! info
You need to log in with a user with assigned the WorkspaceOwner role in the app regsitration used when deploying your workspace.
3. Once logged in, click `Try it out` on the `POST` `/api/workspaces/<workspace_id>/workspace-services` operation.
4. Enter the workspace_id in the `workspace_id` field.
5. Paste the following payload json into the `Request body` field. Then click `Execute`. Review the server response.
```json
{
"templateName": "tre-service-guacamole",
"properties": {
"display_name": "Virtual Desktop",
"description": "Create virtual desktops for running research workloads",
"is_exposed_externally": true,
"guac_disable_copy": true,
"guac_disable_paste": true
}
}
```
The API will return an `operation` object with a `Location` header to query the operation status, as well as the `resourceId` and `resourcePath` properties to query the resource under creation. Record this ID for later use.
You can also follow the progress in Azure portal as various resources come up.
!!! info
There is currently a bug where the redirect URI isn't automatically set up correctly in the Workspace API app registration.
Until this is fixed, you need to head to the app registration in the Azure portal, click on **Add a redirect URI** > **Add a platform** > **Web** > then paste in the Guacamole URI in the redirect URI box.
You can find this in the Guacamole app service properties and append `/oauth2/callback` to the end - it should look like this: `https://guacamole-{TRE_ID}-ws-XXXX-svc-XXXX.azurewebsites.net/oauth2/callback/`). Finally, make sure you check the **ID tokens** checkbox and click **Configure**.
## Creating a user resource
Once the workspace service has been created, we can use the workspace API to create a user resource in our workspace.
!!! caution
Before deploying Guacamole user resources, you will want to make sure you have a Nexus shared service deployed in the workspace so that your VMs can access package repositories through a proxy (as they can't access public repositories directly). See [Configuring shared services](./configuring-shared-services.md).
1. Navigate to the Swagger UI at `https://<azure_tre_fqdn>/api/workspaces/<workspace_id>/docs` . Where `<workspace_id>` is the workspace ID of your workspace.
1. Click `Try it out` on the `POST` `/api/workspaces/<workspace_id>/workspace-services/<service_id>/user_resources` operation. Where `<workspace_id>` and `<service_id>` are the workspace ID of your workspace and workspace service ID of your workspace service.
1. Enter the workspace ID and workspace service id in the `workspace_id` and `service_id` fields.
1. Paste the following payload json into the `Request body` field, then click `Execute`. Review the server response.
```json
{
"templateName": "tre-service-guacamole-windowsvm",
"properties": {
"display_name": "My VM",
"description": "Will be using this VM for my research",
"os_image": "Server 2019 Data Science VM",
"nexus_version": "V2"
}
}
```
> Note: You can also specify "Windows 10" in "os_image" for a standard Windows 10 image.
The API will return an `operation` object with a `Location` header to query the operation status, as well as the `resourceId` and `resourcePath` properties to query the resource under creation.
You can also follow the progress in Azure portal as various resources come up. Once deployment has completed you can connect to the user resource using the `connection_uri` property returned by the API.
|
AzureTRE/docs/tre-admins/setup-instructions/installing-workspace-service-and-user-resource.md/0
|
{
"file_path": "AzureTRE/docs/tre-admins/setup-instructions/installing-workspace-service-and-user-resource.md",
"repo_id": "AzureTRE",
"token_count": 1472
}
| 106 |
# End-to-end (E2E) tests
## Prerequisites
1. Authentication and Authorization configuration set up as noted [here](../tre-admins/auth.md)
1. An Azure Tre deployed environment.
## Registering bundles to run End-to-end tests
End-to-end tests depend on certain bundles to be registered within the TRE API.
When End-to-end tests run in CI, they are registered as a prerequisite to running tests.
When running tests locally, use the `prepare-for-e2e` Makefile target:
```cmd
make prepare-for-e2e
```
## Debugging the End-to-End tests
Use the "Run and Debug" panel within Visual Studio Code, select "E2E Extended", "E2E Smoke" or "E2E Performance" in the drop down box and click play.
- This will copy `config.yaml` settings to `/workspaces/AzureTRE/e2e_tests/.env` for you which supplies your authentciation details
- This will also use `/workspaces/AzureTRE/core/private.env` file for other values.
|
AzureTRE/docs/tre-developers/end-to-end-tests.md/0
|
{
"file_path": "AzureTRE/docs/tre-developers/end-to-end-tests.md",
"repo_id": "AzureTRE",
"token_count": 273
}
| 107 |
# Guacamole User Resource Service bundle (Windows)
This is a User Resource Service template. It defines a VM to be used by TRE Airlock Managers with [Guacamole server](https://guacamole.apache.org/).
It blocks all inbound traffic to the internet and allows only RDP connections from within the vnet.
When deployed in an Airlock Import Review workspace, it has access to the Airlock Import In-Progress storage account outside of the workspace. For more information about Airlock, see [overview page](../../azure-tre-overview/airlock.md).
Data that needs to be reviewed will be downloaded onto the VM during VM creation, and available on Desktop.
It can be only deployed by an Airlock Manager.
## Prerequisites
- [An Airlock Import workspace bundle installed](../workspaces/airlock-import-review.md)
- [A guacamole workspace service bundle installed in that workspace](../workspace-services/guacamole.md)
|
AzureTRE/docs/tre-templates/user-resources/import-reviewvm.md/0
|
{
"file_path": "AzureTRE/docs/tre-templates/user-resources/import-reviewvm.md",
"repo_id": "AzureTRE",
"token_count": 231
}
| 108 |
# Authoring templates
Azure TRE workspaces, workspace services, shared services, and user resources are [Porter](https://porter.sh/) bundles. Porter bundles are based on [Cloud Native Application Bundles (CNAB)](https://cnab.io/).
Authors are free to choose the technology stack for provisioning resources (e.g., ARM templates, Terraform etc.), but the Azure TRE framework sets certain requirements for the bundle manifests, which specify the credentials, input and output parameters, deployment actions among other things.
This document describes the requirements, and the process to author a template.
!!! tip
Use [the base workspace bundle](../tre-templates/workspaces/base.md) as reference or as the basis for the new bundle.
To create a bundle from scratch follow the Porter [Quickstart Guide](https://porter.sh/quickstart/) ([`porter create` CLI command](https://porter.sh/cli/porter_create/) will generate a new bundle in the current directory).
Read more about Porter in [Resource Processor doc](../tre-developers/resource-processor.md#porter).
## Prerequisites
* [Docker installed](https://docs.docker.com/get-docker/)
* [Porter installed](https://porter.sh/install)
* Azure TRE instance deployed to test against
## Workspace bundle manifest
The manifest of a workspace bundle is the `porter.yaml` file (see [Author Bundles in Porter documentation](https://porter.sh/author-bundles/)). This section describes the mandatory credentials, input and output parameters of a TRE workspace bundle.
### Credentials
A workspace bundle requires the following [credentials](https://porter.sh/author-bundles/#credentials) to provision resources in Azure:
* [Azure tenant ID](https://learn.microsoft.com/en-us/entra/fundamentals/how-to-find-tenant)
* Azure subscription ID
* The client ID of a [service principal](https://learn.microsoft.com/en-us/entra/identity-platform/app-objects-and-service-principals?tabs=browser) with privileges to provision resources
* The client secret (password) of a service principal
The credentials are provided as environment variables by the deployment runner. The bundle author must use the following environment variable names:
```bash
ARM_TENANT_ID
ARM_SUBSCRIPTION_ID
ARM_CLIENT_ID
ARM_CLIENT_SECRET
```
The names of the Porter credentials (`name` field in `porter.yaml`) can be freely chosen by the author.
Example:
```yaml
credentials:
- name: azure_tenant_id
env: ARM_TENANT_ID
- name: azure_subscription_id
env: ARM_SUBSCRIPTION_ID
- name: azure_client_id
env: ARM_CLIENT_ID
- name: azure_client_secret
env: ARM_CLIENT_SECRET
```
### Parameters
This section describes the mandatory [(input) parameters](https://porter.sh/author-bundles/#parameters) of a workspace bundle manifest.
| <div style="width:120px">Parameter</div> | Type | Description | Example value |
| --------- | ---- | ----------- | ------------- |
| `tre_id` | string | Unique ID of for the TRE instance. | `tre-dev-42` |
| `workspace_id` | string | Unique 4-character long, alphanumeric workspace ID. | `0a9e` |
| `azure_location` | string | Azure location (region) to deploy the workspace resource to. | `westeurope` |
| `address_space` | string | VNet address space for the workspace services. | `10.2.1.0/24` |
`tre_id` can be found in the resource names of the Azure TRE instance; for example the resource group name of the Azure TRE instance based on the example in the above table would be "`rg-tre-dev-42`".
Similarly to `tre_id`, `workspace_id` is used in the resource names of the workspace. The resource group name of the workspace must be of form "`rg-<tre_id>-ws-<workspace_id>`", for example: "`rg-tre-dev-42-ws-0a9e`".
All the values for the required parameters will be provided by the deployment runner.
Any **custom parameters** are picked up by Azure TRE API and will be queried from the user deploying the workspace bundle. Custom parameters should also be defined in the `template_schema.json` file at the root of the bundle. This file follows the [JSON schema standard](http://json-schema.org/) and can be used by a user interface to generate a UI for the user to input the parameters.
### Output
!!! todo
After a workspace with virtual machines is implemented this section can be written based on that. ([Outputs in Porter documentation](https://porter.sh/author-bundles/#outputs) to be linked here too.)
### Actions
The required actions are the main two of CNAB spec:
* `install` - Deploys/repairs the workspace Azure resources, and must be **idempotent**
* `uninstall` - Tears down (deletes) the Azure resources of the workspace and its services
## Workspace service bundle manifests
Workspace service bundles are generated in the same way as workspace bundles.
The mandatory parameters for workspace services are:
| Parameter | Type | Description | Example value |
| --------- | ---- | ----------- | ------------- |
| `tre_id` | string | Unique ID of for the TRE instance. | `tre-dev-42` |
| `workspace_id` | string | Unique 4-character long, alphanumeric workspace ID. | `0a9e` |
### Workpace services requiring additional address sapces
Some workspace services may require additional address spaces to be provisioned. This may be as they need advanced network security groups, route tables or delegated subnets.
To request an additional address space, the workspace service bundle must define an `address_space` parameter in the `porter.yaml` file. The value of this parameter will be provided by API to the resource processor.
The size of the `address_space` will default to `/24`, however other sizes can be requested by including an `address_space_size` as part of the workspace service template.
## User resource bundle manifests
User Resource bundles are generated in the same way as workspace bundles and workspace services bundles.
The main difference is that a workspace service type needs to be supplied when registering a user resource template, as it only applies to a given workspace service.
The mandatory parameters for User Resources are:
| Parameter | Type | Description | Example value |
| --------- | ---- | ----------- | ------------- |
| `tre_id` | string | Unique ID of for the TRE instance. | `tre-dev-42` |
| `workspace_id` | string | Unique 4-character long, alphanumeric workspace ID. | `0a9e` |
## Azure Resources Tagging
TRE Cost Reporting is based on Azure tagging to be able to generate cost report for core services, shared services, workspace, workspace services and user resources.
Templates authors need to make sure that underling Azure resources are tagged with the relevent tags, for more information see [cost reporting](../azure-tre-overview/cost-reporting.md#azure-resources-tagging):
## Versioning
Workspace versions are the bundle versions specified in [the metadata](https://porter.sh/author-bundles/#bundle-metadata). The bundle versions should match the image tags in the container registry (see [Publishing workspace bundle](#publishing-workspace-bundle)).
Bundle versions should follow [Semantic Versioning](https://semver.org/), given a version number **MAJOR.MINOR.PATCH**, increment the:
1. **MAJOR** version when you make a breaking change, potential data loss, changes that don't easily/automatically upgrade, or significant changes which require someone to review what has changed and take some appropriate action, or functionality of the component has significantly changed and users might need training.
2. **MINOR** version when you add minor functionality which can be automatically upgraded.
3. **PATCH** version when you make backward-compatible bug or typo fixes.
For resource version upgrades see [Upgrading Resources Version](../tre-admins/upgrading-resources.md).
## Publishing workspace bundle
See [Registering workspace templates](../tre-admins/registering-templates.md).
## Manual Deployment
!!! caution
Resources should be deployed using the API (i.e. through the Swagger UI as described in the [setup instructions](../tre-admins/setup-instructions/installing-base-workspace.md)). Only deploy manually for development/testing purposes.
1. Create a copy of the Porter bundle's environment settings from `/templates/<scope>/.env.sample` with the name `.env` and update the variables with the appropriate values.
1. Build and deploy the Porter bundle
```cmd
make bundle-build DIR=./templates/<scope>/<bundle_name>
make bundle-publish DIR=./templates/<scope>/<bundle_name>
make bundle-register DIR=./templates/<scope>/<bundle_name> BUNDLE_TYPE=<scope>
```
|
AzureTRE/docs/tre-workspace-authors/authoring-workspace-templates.md/0
|
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| 109 |
# Importing and exporting data with Airlock
This guide will take you through the process of importing data into a TRE workspace, and exporting data from a workspace to the outside world, using the Airlock feature.
The Airlock feature is intended for ad-hoc use when you need to bring in and export out files that you need for your research. It ensures that when you import or export this data all the appropriate approvals and procedures configured by your organisation take place.
You can read more about the Airlock feature in the [Airlock documentation](../../azure-tre-overview/airlock.md).
## Importing data to a workspace
To bring in external data to a secure TRE workspace so you can use it for your research, follow the steps outlined below.
### Step 1: Create a draft import request
1. Open your TRE UI and navigate to the workspace you wish to import data into
1. Navigate to the Airlock tab (in the left-hand menu)
1. Click *Create new* and select *Import*
1. Fill in a suitable **Title** for your request (make this short but descriptive to help you and others identify it in a list of many other requests)
1. Provide a **Business Justification** for bringing the data into the workspace (this will be used to help your organisation's data stewards decide whether to approve or reject the request)
1. Click *Create* when ready. This will create your draft request and allow you to proceed with adding the data you'd like to import
[](../../assets/create-draft-request.png)
### Step 2: Add data to your import request
1. The request you've just created should pop up automatically; however, you can return to it at any time within the Airlock page by finding it in the list of requests. (Use the *My requests* quick filter to find it more easily)
2. Click *Generate* in the **Files** section to generate a Storage SAS URL to use for uploading your data.
[](../../assets/get-request-storage-link.png)
3. Copy the URL and use it to upload your data to the Azure Storage account. You can use several tools for this that accept SAS URLs, such as the Azure Storage Explorer, or the Azure CLI, depending on your preference.
- To use Storage Explorer, follow [this guide](https://learn.microsoft.com/en-us/azure/vs-azure-tools-storage-manage-with-storage-explorer?tabs=macos)
- With the Azure CLI, you can run `az storage blob upload -f /path/to/file --blob-url SAS_URL`. [More info](https://learn.microsoft.com/en-us/cli/azure/storage/blob?view=azure-cli-latest#az-storage-blob-upload)
!!! warning
Airlock only supports a single file per request. If you need to import multiple files, please zip them before uploading to the request's storage container.
4. Once you've uploaded your data, head back to the TRE UI and click *Submit* on your draft request. This will submit your request for approval.
### Step 3: Get your approved data
The request will be in an *In Review* state until it is either approved or rejected by your Airlock Manager(s) manually or by an automated workflow (depending on your organisation's specific configuration).
!!! note
Your organisation may have the Airlock Notifier service configured which will send email notifications when your request has been approved/rejected, or you may have another mechanism in place. Please check with your TRE administrator.
If the request is rejected, your data will be deleted and your request will move into a *Rejected* state. You will be able to see feedback in the **Reviews** section on why your request was rejected so you can create a new request that addresses any concerns.
If your request is approved, you can follow the below steps to get your data from within your workspace:
1. Head back to your Airlock request in the TRE UI. You should find that it is now in an *Approved* state and ready for you to get your data. You can also see the notes from the reviewer in the **Reviews** section.
[](../../assets/get-request-download-link.png)
2. Click *Generate* in the **Files** section to generate another Storage SAS URL which you'll use for downloading your data.
3. Paste this link into your Workspace VM (or whichever workspace resource you're wanting to access the data from). Like before, use your preferred tool to access the data using the SAS URL, but this time to download the data.
- With the Azure CLI, you can use `az storage blob download --file /path/to/write/to --blob-url SAS_URL`. [More info](https://docs.microsoft.com/en-us/cli/azure/storage/blob?view=azure-cli-latest#az-storage-blob-download)
!!! tip
If you are using a Workspace VM that uses one of the standard TRE Data Science VM images, you will likely have both Storage Explorer and the Azure CLI pre-installed.
## Exporting data from a workspace
Exporting data from a secure TRE workspace to the outside world involves similar steps to Import, but with a couple of key differences. Follow these steps:
1. Open your TRE UI and navigate to the workspace you wish to export data from
2. Navigate to the Airlock tab (in the left-hand menu) and click *Create new*, then select *Export*
3. Fill in a suitable **Title** and **Business Justification** for the request then hit *Create*
4. Once the draft request pop-out opens, click *Generate* in the **Files** section to generate a Storage SAS URL to use for uploading your data.
5. You now need to head into your Workspace VM/resource containing the data you wish to export, and paste in the SAS URL you've just generated. Use your preferred storage tool to upload the data to the request container. See Step 2 in the [Importing data](#importing-data-to-a-workspace) section for more details on using these tools
6. Once you've uploaded your data, head back to the TRE UI in your host and click *Submit* on your draft request. This will submit your request for approval.
7. Like in Step 3 of [Importing data](#importing-data-to-a-workspace), your request will be in an *In Review* state until it's either approved or rejected by your organisation's approval workflow.
8. Once it's approved, head back to your request in the UI and click *Generate* a second time to get a download link.
9. In your host, you can use this link with your tool of choice to download the data from the request container.
## How to Contribute to our Documentation
[Contribute to Documentation](https://microsoft.github.io/AzureTRE/coming-soon/)
|
AzureTRE/docs/using-tre/tre-for-research/importing-exporting-data-airlock.md/0
|
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| 110 |
import logging
import backoff
from httpx import TimeoutException
from e2e_tests.helpers import get_full_endpoint
from e2e_tests.resources import strings
LOGGER = logging.getLogger(__name__)
async def delete_done(client, operation_endpoint, headers):
delete_terminal_states = [strings.RESOURCE_STATUS_DELETED, strings.RESOURCE_STATUS_DELETING_FAILED]
deployment_status, message, operation_steps = await check_deployment(client, operation_endpoint, headers)
return (True, deployment_status, message, operation_steps) if deployment_status in delete_terminal_states else (False, deployment_status, message, operation_steps)
async def install_done(client, operation_endpoint, headers):
install_terminal_states = [strings.RESOURCE_STATUS_DEPLOYED, strings.RESOURCE_STATUS_DEPLOYMENT_FAILED]
deployment_status, message, operation_steps = await check_deployment(client, operation_endpoint, headers)
return (True, deployment_status, message, operation_steps) if deployment_status in install_terminal_states else (False, deployment_status, message, operation_steps)
async def patch_done(client, operation_endpoint, headers):
install_terminal_states = [strings.RESOURCE_STATUS_UPDATED, strings.RESOURCE_STATUS_UPDATING_FAILED]
deployment_status, message, operation_steps = await check_deployment(client, operation_endpoint, headers)
return (True, deployment_status, message, operation_steps) if deployment_status in install_terminal_states else (False, deployment_status, message, operation_steps)
@backoff.on_exception(backoff.constant,
TimeoutException, # catching all timeout types (Connection, Read, etc.)
max_time=90)
async def check_deployment(client, operation_endpoint, headers):
full_endpoint = get_full_endpoint(operation_endpoint)
response = await client.get(full_endpoint, headers=headers, timeout=5.0)
if response.status_code == 200:
response_json = response.json()
deployment_status = response_json["operation"]["status"]
message = response_json["operation"]["message"]
operation_steps = stringify_operation_steps(response_json["operation"]["steps"])
return deployment_status, message, operation_steps
else:
LOGGER.error(f"Non 200 response in check_deployment: {response.status_code}")
LOGGER.error(f"Full response: {response}")
raise Exception("Non 200 response in check_deployment")
def stringify_operation_steps(steps):
string = ''
for i, step in enumerate(steps, 1):
string += f'Step {i}: {step["stepTitle"]}\n'
string += f'{step["message"]}\n\n'
return string
|
AzureTRE/e2e_tests/resources/deployment.py/0
|
{
"file_path": "AzureTRE/e2e_tests/resources/deployment.py",
"repo_id": "AzureTRE",
"token_count": 891
}
| 111 |
{% extends "base.html" %}
{% block outdated %}
You're not viewing the latest version.
<a href="{{ '../' ~ base_url }}">
<strong>Click here to go to latest.</strong>
</a>
</script>
{% endblock %}
{% block analytics %}
<script type="text/javascript">
(function(c,l,a,r,i,t,y){
c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)};
t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i;
y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);
})(window, document, "clarity", "script", "7gescazz1m");
</script>
{% endblock %}
|
AzureTRE/mkdocs-overrides/main.html/0
|
{
"file_path": "AzureTRE/mkdocs-overrides/main.html",
"repo_id": "AzureTRE",
"token_count": 252
}
| 112 |
# syntax=docker/dockerfile-upstream:1.4.0
FROM --platform=linux/amd64 debian:bullseye-slim
# PORTER_INIT
# PORTER_MIXINS
# Use the BUNDLE_DIR build argument to copy files into the bundle
COPY --link . ${BUNDLE_DIR}//
|
AzureTRE/templates/shared_services/admin-vm/Dockerfile.tmpl/0
|
{
"file_path": "AzureTRE/templates/shared_services/admin-vm/Dockerfile.tmpl",
"repo_id": "AzureTRE",
"token_count": 89
}
| 113 |
{
"serviceProviderConnections": {
"serviceBus": {
"parameterValues": {
"connectionString": "@appsetting('serviceBus_connectionString')"
},
"serviceProvider": {
"id": "/serviceProviders/serviceBus"
},
"displayName": "core-service-bus"
},
"Smtp": {
"displayName": "smtp",
"parameterValues": {
"enableSSL": "@appsetting('smtp_server_enable_ssl')",
"port": "@appsetting('smtp_server_port')",
"password": "@appsetting('smtp_password')",
"serverAddress": "@appsetting('smtp_server_address')",
"username": "@appsetting('smtp_username')"
},
"serviceProvider": {
"id": "/serviceProviders/Smtp"
}
}
},
"managedApiConnections": {
"smtp": {
"api": {
"id": "/subscriptions/@appsetting('subscription')/providers/Microsoft.Web/locations/westeurope/managedApis/smtp"
},
"connection": {
"id": "/subscriptions/@appsetting('subscription')/resourceGroups/@appsetting('resource_group')/providers/Microsoft.Web/connections/smtp"
},
"authentication": {
"type": "ManagedServiceIdentity"
},
"connectionRuntimeUrl": "@appsetting('smtp_connection_runtime_url')"
}
}
}
|
AzureTRE/templates/shared_services/airlock_notifier/app/connections.json/0
|
{
"file_path": "AzureTRE/templates/shared_services/airlock_notifier/app/connections.json",
"repo_id": "AzureTRE",
"token_count": 542
}
| 114 |
variable "tre_id" {
type = string
}
variable "domain_prefix" {
type = string
}
variable "cert_name" {
type = string
}
variable "tre_resource_id" {
type = string
description = "Resource ID"
}
|
AzureTRE/templates/shared_services/certs/terraform/variables.tf/0
|
{
"file_path": "AzureTRE/templates/shared_services/certs/terraform/variables.tf",
"repo_id": "AzureTRE",
"token_count": 80
}
| 115 |
ID=__CHANGE_ME__
AZURE_LOCATION=__CHANGE_ME__
|
AzureTRE/templates/shared_services/databricks-auth/.env.sample/0
|
{
"file_path": "AzureTRE/templates/shared_services/databricks-auth/.env.sample",
"repo_id": "AzureTRE",
"token_count": 22
}
| 116 |
output "nexus_allowed_fqdns_list" {
value = jsonencode(local.nexus_allowed_fqdns_list)
}
output "workspace_vm_allowed_fqdns_list" {
value = jsonencode(local.workspace_vm_allowed_fqdns_list)
}
output "private_ip_addresses" {
value = jsonencode(azurerm_network_interface.nexus.private_ip_addresses)
}
output "connection_uri" {
value = "https://${data.azurerm_private_dns_zone.nexus.name}"
}
output "is_exposed_externally" {
value = false
}
|
AzureTRE/templates/shared_services/sonatype-nexus-vm/terraform/outputs.tf/0
|
{
"file_path": "AzureTRE/templates/shared_services/sonatype-nexus-vm/terraform/outputs.tf",
"repo_id": "AzureTRE",
"token_count": 178
}
| 117 |
resource "azurerm_machine_learning_workspace" "aml_workspace" {
name = local.workspace_name
resource_group_name = data.azurerm_resource_group.ws.name
location = data.azurerm_resource_group.ws.location
application_insights_id = azurerm_application_insights.ai.id
container_registry_id = azurerm_container_registry.acr.id
friendly_name = var.display_name
description = var.description
high_business_impact = true
key_vault_id = data.azurerm_key_vault.ws.id
public_network_access_enabled = var.is_exposed_externally ? true : false
storage_account_id = azurerm_storage_account.aml.id
tags = local.tre_workspace_service_tags
identity {
type = "SystemAssigned"
}
lifecycle {
ignore_changes = [
tags,
image_build_compute_name,
public_access_behind_virtual_network_enabled
]
}
}
resource "azurerm_private_endpoint" "mlpe" {
name = "mlpe-${local.service_resource_name_suffix}"
location = data.azurerm_resource_group.ws.location
resource_group_name = data.azurerm_resource_group.ws.name
subnet_id = azurerm_subnet.aml.id
tags = local.tre_workspace_service_tags
lifecycle { ignore_changes = [tags] }
private_dns_zone_group {
name = "private-dns-zone-group"
private_dns_zone_ids = [data.azurerm_private_dns_zone.azureml.id, data.azurerm_private_dns_zone.notebooks.id, data.azurerm_private_dns_zone.azuremlcert.id]
}
private_service_connection {
name = "mlpesc-${local.service_resource_name_suffix}"
private_connection_resource_id = azurerm_machine_learning_workspace.aml_workspace.id
is_manual_connection = false
subresource_names = ["amlworkspace"]
}
depends_on = [
azurerm_subnet_network_security_group_association.aml,
azapi_resource.aml_service_endpoint_policy
]
}
|
AzureTRE/templates/workspace_services/azureml/terraform/main.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/azureml/terraform/main.tf",
"repo_id": "AzureTRE",
"token_count": 945
}
| 118 |
#!/bin/bash
set -o errexit
set -o pipefail
set -o nounset
export TF_LOG=""
terraform init -input=false -backend=true -reconfigure \
-backend-config="resource_group_name=${TF_VAR_mgmt_resource_group_name?}" \
-backend-config="storage_account_name=${TF_VAR_mgmt_storage_account_name?}" \
-backend-config="container_name=${TF_VAR_terraform_state_container_name?}" \
-backend-config="key=tre-user-resource-aml-compute-instance-${TF_VAR_id?}"
terraform plan
terraform apply -auto-approve
|
AzureTRE/templates/workspace_services/azureml/user_resources/aml_compute/terraform/deploy.sh/0
|
{
"file_path": "AzureTRE/templates/workspace_services/azureml/user_resources/aml_compute/terraform/deploy.sh",
"repo_id": "AzureTRE",
"token_count": 199
}
| 119 |
resource "azurerm_databricks_workspace" "databricks" {
name = local.databricks_workspace_name
resource_group_name = data.azurerm_resource_group.ws.name
location = data.azurerm_resource_group.ws.location
sku = "premium"
managed_resource_group_name = local.managed_resource_group_name
infrastructure_encryption_enabled = true
public_network_access_enabled = var.is_exposed_externally
network_security_group_rules_required = "NoAzureDatabricksRules"
tags = local.tre_workspace_service_tags
lifecycle { ignore_changes = [tags] }
custom_parameters {
no_public_ip = true
public_subnet_name = azurerm_subnet.host.name
private_subnet_name = azurerm_subnet.container.name
virtual_network_id = data.azurerm_virtual_network.ws.id
public_subnet_network_security_group_association_id = azurerm_subnet_network_security_group_association.host.id
private_subnet_network_security_group_association_id = azurerm_subnet_network_security_group_association.container.id
storage_account_name = local.storage_name
}
depends_on = [
azurerm_subnet_network_security_group_association.host,
azurerm_subnet_network_security_group_association.container
]
}
|
AzureTRE/templates/workspace_services/databricks/terraform/main.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/databricks/terraform/main.tf",
"repo_id": "AzureTRE",
"token_count": 754
}
| 120 |
data "azurerm_resource_group" "ws" {
name = "rg-${var.tre_id}-ws-${local.short_workspace_id}"
}
data "azurerm_virtual_network" "ws" {
name = "vnet-${var.tre_id}-ws-${local.short_workspace_id}"
resource_group_name = "rg-${var.tre_id}-ws-${local.short_workspace_id}"
}
data "azurerm_subnet" "web_apps" {
name = "WebAppsSubnet"
virtual_network_name = data.azurerm_virtual_network.ws.name
resource_group_name = data.azurerm_virtual_network.ws.resource_group_name
}
data "azurerm_subnet" "services" {
name = "ServicesSubnet"
virtual_network_name = data.azurerm_virtual_network.ws.name
resource_group_name = data.azurerm_resource_group.ws.name
}
data "azurerm_private_dns_zone" "azurewebsites" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.azurewebsites.net"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_container_registry" "mgmt_acr" {
name = var.mgmt_acr_name
resource_group_name = var.mgmt_resource_group_name
}
data "azurerm_log_analytics_workspace" "tre" {
name = "log-${var.tre_id}"
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "mysql" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.mysql.database.azure.com"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "filecore" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.file.core.windows.net"]
resource_group_name = local.core_resource_group_name
}
data "local_file" "version" {
filename = "${path.module}/../version.txt"
}
data "azurerm_key_vault" "ws" {
name = local.keyvault_name
resource_group_name = data.azurerm_resource_group.ws.name
}
data "azurerm_key_vault_secret" "aad_tenant_id" {
name = "auth-tenant-id"
key_vault_id = data.azurerm_key_vault.ws.id
}
data "azurerm_key_vault_secret" "client_id" {
name = "workspace-client-id"
key_vault_id = data.azurerm_key_vault.ws.id
}
data "azurerm_key_vault_secret" "client_secret" {
name = "workspace-client-secret"
key_vault_id = data.azurerm_key_vault.ws.id
}
data "azurerm_monitor_diagnostic_categories" "gitea" {
resource_id = azurerm_linux_web_app.gitea.id
depends_on = [
azurerm_linux_web_app.gitea,
]
}
|
AzureTRE/templates/workspace_services/gitea/terraform/data.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/gitea/terraform/data.tf",
"repo_id": "AzureTRE",
"token_count": 1090
}
| 121 |
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package org.apache.guacamole.auth.azuretre;
import com.auth0.jwk.Jwk;
import com.auth0.jwk.UrlJwkProvider;
import com.auth0.jwt.JWT;
import com.auth0.jwt.JWTVerifier;
import com.auth0.jwt.algorithms.Algorithm;
import com.auth0.jwt.interfaces.Claim;
import com.auth0.jwt.interfaces.DecodedJWT;
import org.apache.guacamole.net.auth.credentials.CredentialsInfo;
import org.apache.guacamole.net.auth.credentials.GuacamoleInvalidCredentialsException;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.security.interfaces.RSAPublicKey;
import java.util.List;
public class AuthenticationProviderService {
private static final Logger LOGGER = LoggerFactory.getLogger(AzureTREAuthenticationProvider.class);
public void validateToken(final String token, final UrlJwkProvider jwkProvider)
throws GuacamoleInvalidCredentialsException {
try {
if (System.getenv("AUDIENCE").length() == 0) {
throw new Exception("AUDIENCE is not provided");
}
if (System.getenv("ISSUER").length() == 0) {
throw new Exception("ISSUER is not provided");
}
final Jwk jwk = jwkProvider.get(JWT.decode(token).getKeyId());
final Algorithm algorithm = Algorithm.RSA256((RSAPublicKey) jwk.getPublicKey(), null);
final JWTVerifier verifier = JWT.require(algorithm)
.withAudience(System.getenv("AUDIENCE"))
.withClaimPresence("roles")
.withIssuer(System.getenv("ISSUER"))
.build();
final DecodedJWT jwt = verifier.verify(token);
// Since we verify we have the correct Audience we validate the token if at least one role is present, no
// matter which one.
final Claim roles = jwt.getClaim("roles");
if (roles == null || roles.isNull() || roles.asArray(Object.class).length == 0) {
throw new GuacamoleInvalidCredentialsException(
"Token must contain a 'roles' claim", CredentialsInfo.USERNAME_PASSWORD);
}
List<String> rolesList = roles.asList(String.class);
if (rolesList.stream().noneMatch(x -> x.equalsIgnoreCase("WorkspaceOwner")
|| x.equalsIgnoreCase("WorkspaceResearcher")
|| x.equalsIgnoreCase("AirlockManager"))) {
throw new GuacamoleInvalidCredentialsException(
"User must have a workspace owner or workspace researcher or Airlock Manager role",
CredentialsInfo.USERNAME_PASSWORD);
}
} catch (final Exception ex) {
LOGGER.error("Could not validate token", ex);
throw new GuacamoleInvalidCredentialsException(
"Could not validate token:" + ex.getMessage(),
CredentialsInfo.USERNAME_PASSWORD);
}
}
}
|
AzureTRE/templates/workspace_services/guacamole/guacamole-server/guacamole-auth-azure/src/main/java/org/apache/guacamole/auth/azuretre/AuthenticationProviderService.java/0
|
{
"file_path": "AzureTRE/templates/workspace_services/guacamole/guacamole-server/guacamole-auth-azure/src/main/java/org/apache/guacamole/auth/azuretre/AuthenticationProviderService.java",
"repo_id": "AzureTRE",
"token_count": 1454
}
| 122 |
/**
*
*/
package org.apache.guacamole.auth.azuretre;
|
AzureTRE/templates/workspace_services/guacamole/guacamole-server/guacamole-auth-azure/src/test/java/org/apache/guacamole/auth/azuretre/package-info.java/0
|
{
"file_path": "AzureTRE/templates/workspace_services/guacamole/guacamole-server/guacamole-auth-azure/src/test/java/org/apache/guacamole/auth/azuretre/package-info.java",
"repo_id": "AzureTRE",
"token_count": 23
}
| 123 |
resource "azurerm_network_interface" "internal" {
name = "internal-nic-${local.service_resource_name_suffix}"
location = data.azurerm_resource_group.ws.location
resource_group_name = data.azurerm_resource_group.ws.name
tags = local.tre_user_resources_tags
ip_configuration {
name = "primary"
subnet_id = data.azurerm_subnet.services.id
private_ip_address_allocation = "Dynamic"
}
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_network_security_group" "vm_nsg" {
name = "vm-nsg-${local.service_resource_name_suffix}"
location = data.azurerm_resource_group.ws.location
resource_group_name = data.azurerm_resource_group.ws.name
tags = local.tre_user_resources_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_network_security_rule" "allow_outbound_airlock_exip_storage_pe" {
access = "Allow"
# Should this be a list?
destination_address_prefixes = [for pe in data.azurerm_private_endpoint_connection.airlock_export_inprogress_pe.private_service_connection : pe.private_ip_address]
destination_port_range = "*"
direction = "Outbound"
name = "allow-airlock-exip-storage-pe"
network_security_group_name = azurerm_network_security_group.vm_nsg.name
priority = 101
protocol = "*"
resource_group_name = data.azurerm_resource_group.ws.name
source_address_prefixes = azurerm_windows_virtual_machine.windowsvm.private_ip_addresses
source_port_range = "*"
}
// Outbound traffic gets routed to the firewall
resource "azurerm_network_security_rule" "allow_outbound_to_internet" {
access = "Allow"
destination_address_prefix = "INTERNET"
destination_port_range = "443"
direction = "Outbound"
name = "to-internet"
network_security_group_name = azurerm_network_security_group.vm_nsg.name
priority = 120
protocol = "Tcp"
resource_group_name = data.azurerm_resource_group.ws.name
source_address_prefixes = azurerm_windows_virtual_machine.windowsvm.private_ip_addresses
source_port_range = "*"
}
resource "azurerm_network_security_rule" "allow_outbound_webapps_to_vm" {
access = "Allow"
destination_port_ranges = [
"80",
"443",
"445",
"3306",
"3389",
"5432",
]
destination_address_prefixes = azurerm_windows_virtual_machine.windowsvm.private_ip_addresses
source_address_prefixes = data.azurerm_subnet.webapps.address_prefixes
direction = "Outbound"
name = "outbound-from-webapps-to-vm"
network_security_group_name = azurerm_network_security_group.vm_nsg.name
priority = 140
protocol = "Tcp"
resource_group_name = data.azurerm_resource_group.ws.name
source_port_range = "*"
}
resource "azurerm_network_security_rule" "deny_outbound_override" {
access = "Deny"
destination_address_prefix = "*"
destination_port_range = "*"
direction = "Outbound"
name = "deny-outbound-override"
network_security_group_name = azurerm_network_security_group.vm_nsg.name
priority = 4096
protocol = "*"
resource_group_name = data.azurerm_resource_group.ws.name
source_address_prefixes = azurerm_windows_virtual_machine.windowsvm.private_ip_addresses
source_port_range = "*"
}
resource "azurerm_network_interface_security_group_association" "nsg_association" {
network_interface_id = azurerm_network_interface.internal.id
network_security_group_id = azurerm_network_security_group.vm_nsg.id
}
resource "random_string" "username" {
length = 4
upper = true
lower = true
numeric = true
min_numeric = 1
min_lower = 1
special = false
}
resource "random_password" "password" {
length = 16
lower = true
min_lower = 1
upper = true
min_upper = 1
numeric = true
min_numeric = 1
special = true
min_special = 1
override_special = "_%@"
}
resource "azurerm_windows_virtual_machine" "windowsvm" {
name = local.vm_name
location = data.azurerm_resource_group.ws.location
resource_group_name = data.azurerm_resource_group.ws.name
network_interface_ids = [azurerm_network_interface.internal.id]
size = local.vm_sizes[var.vm_size]
allow_extension_operations = true
admin_username = random_string.username.result
admin_password = random_password.password.result
custom_data = base64encode(data.template_file.download_review_data_script.rendered)
# set source_image_id/reference depending on the config for the selected image
source_image_id = local.selected_image_source_id
dynamic "source_image_reference" {
for_each = local.selected_image_source_refs
content {
publisher = source_image_reference.value["publisher"]
offer = source_image_reference.value["offer"]
sku = source_image_reference.value["sku"]
version = source_image_reference.value["version"]
}
}
os_disk {
name = "osdisk-${local.vm_name}"
caching = "ReadWrite"
storage_account_type = "Standard_LRS"
}
identity {
type = "SystemAssigned"
}
tags = local.tre_user_resources_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_virtual_machine_extension" "config_script" {
name = "${azurerm_windows_virtual_machine.windowsvm.name}-vmextension"
virtual_machine_id = azurerm_windows_virtual_machine.windowsvm.id
publisher = "Microsoft.Compute"
type = "CustomScriptExtension"
type_handler_version = "1.10"
tags = local.tre_user_resources_tags
protected_settings = <<PROT
{
"commandToExecute": "powershell -ExecutionPolicy Unrestricted -NoProfile -NonInteractive -command \"cp c:/azuredata/customdata.bin c:/azuredata/configure.ps1; c:/azuredata/configure.ps1 \""
}
PROT
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_key_vault_secret" "windowsvm_password" {
name = "${local.vm_name}-admin-credentials"
value = "${random_string.username.result}\n${random_password.password.result}"
key_vault_id = data.azurerm_key_vault.ws.id
tags = local.tre_user_resources_tags
lifecycle { ignore_changes = [tags] }
}
data "template_file" "download_review_data_script" {
template = file("${path.module}/download_review_data.ps1")
vars = {
airlock_request_sas_url = var.airlock_request_sas_url
}
}
|
AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-export-reviewvm/terraform/windowsvm.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-export-reviewvm/terraform/windowsvm.tf",
"repo_id": "AzureTRE",
"token_count": 3077
}
| 124 |
resource "azurerm_network_interface" "internal" {
name = "internal-nic-${local.service_resource_name_suffix}"
location = data.azurerm_resource_group.ws.location
resource_group_name = data.azurerm_resource_group.ws.name
tags = local.tre_user_resources_tags
ip_configuration {
name = "primary"
subnet_id = data.azurerm_subnet.services.id
private_ip_address_allocation = "Dynamic"
}
lifecycle { ignore_changes = [tags] }
}
resource "random_string" "username" {
length = 4
upper = true
lower = true
numeric = true
min_numeric = 1
min_lower = 1
special = false
}
resource "random_password" "password" {
length = 16
lower = true
min_lower = 1
upper = true
min_upper = 1
numeric = true
min_numeric = 1
special = true
min_special = 1
override_special = "_%@"
}
resource "azurerm_linux_virtual_machine" "linuxvm" {
name = local.vm_name
location = data.azurerm_resource_group.ws.location
resource_group_name = data.azurerm_resource_group.ws.name
network_interface_ids = [azurerm_network_interface.internal.id]
size = local.vm_sizes[var.vm_size]
disable_password_authentication = false
admin_username = random_string.username.result
admin_password = random_password.password.result
custom_data = data.template_cloudinit_config.config.rendered
# set source_image_id/reference depending on the config for the selected image
source_image_id = local.selected_image_source_id
dynamic "source_image_reference" {
for_each = local.selected_image_source_refs
content {
publisher = source_image_reference.value["publisher"]
offer = source_image_reference.value["offer"]
sku = source_image_reference.value["sku"]
version = source_image_reference.value["version"]
}
}
os_disk {
name = "osdisk-${local.vm_name}"
caching = "ReadWrite"
storage_account_type = "Standard_LRS"
}
identity {
type = "SystemAssigned"
}
tags = local.tre_user_resources_tags
lifecycle { ignore_changes = [tags] }
}
data "template_cloudinit_config" "config" {
gzip = true
base64_encode = true
part {
content_type = "text/x-shellscript"
content = data.template_file.get_apt_keys.rendered
}
part {
content_type = "text/cloud-config"
content = data.template_file.apt_sources_config.rendered
}
part {
content_type = "text/x-shellscript"
content = data.template_file.pypi_sources_config.rendered
}
part {
content_type = "text/x-shellscript"
content = data.template_file.vm_config.rendered
}
}
data "template_file" "vm_config" {
template = file("${path.module}/vm_config.sh")
vars = {
INSTALL_UI = local.selected_image.install_ui ? 1 : 0
SHARED_STORAGE_ACCESS = tobool(var.shared_storage_access) ? 1 : 0
STORAGE_ACCOUNT_NAME = data.azurerm_storage_account.stg.name
STORAGE_ACCOUNT_KEY = data.azurerm_storage_account.stg.primary_access_key
HTTP_ENDPOINT = data.azurerm_storage_account.stg.primary_file_endpoint
FILESHARE_NAME = var.shared_storage_access ? data.azurerm_storage_share.shared_storage[0].name : ""
NEXUS_PROXY_URL = local.nexus_proxy_url
CONDA_CONFIG = local.selected_image.conda_config ? 1 : 0
}
}
data "template_file" "get_apt_keys" {
template = file("${path.module}/get_apt_keys.sh")
vars = {
NEXUS_PROXY_URL = local.nexus_proxy_url
}
}
data "template_file" "pypi_sources_config" {
template = file("${path.module}/pypi_sources_config.sh")
vars = {
nexus_proxy_url = local.nexus_proxy_url
}
}
data "template_file" "apt_sources_config" {
template = file("${path.module}/apt_sources_config.yml")
vars = {
nexus_proxy_url = local.nexus_proxy_url
}
}
resource "azurerm_key_vault_secret" "linuxvm_password" {
name = local.vm_password_secret_name
value = "${random_string.username.result}\n${random_password.password.result}"
key_vault_id = data.azurerm_key_vault.ws.id
tags = local.tre_user_resources_tags
lifecycle { ignore_changes = [tags] }
}
data "azurerm_storage_account" "stg" {
name = local.storage_name
resource_group_name = data.azurerm_resource_group.ws.name
}
data "azurerm_storage_share" "shared_storage" {
count = var.shared_storage_access ? 1 : 0
name = var.shared_storage_name
storage_account_name = data.azurerm_storage_account.stg.name
}
|
AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-linuxvm/terraform/linuxvm.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-linuxvm/terraform/linuxvm.tf",
"repo_id": "AzureTRE",
"token_count": 2096
}
| 125 |
data "azurerm_resource_group" "ws" {
name = "rg-${var.tre_id}-ws-${local.short_workspace_id}"
}
data "azurerm_resource_group" "core" {
name = "rg-${var.tre_id}"
}
data "azurerm_virtual_network" "ws" {
name = "vnet-${var.tre_id}-ws-${local.short_workspace_id}"
resource_group_name = data.azurerm_resource_group.ws.name
}
data "azurerm_subnet" "services" {
name = "ServicesSubnet"
virtual_network_name = data.azurerm_virtual_network.ws.name
resource_group_name = data.azurerm_resource_group.ws.name
}
data "azurerm_key_vault" "ws" {
name = local.keyvault_name
resource_group_name = data.azurerm_resource_group.ws.name
}
data "azurerm_linux_web_app" "guacamole" {
name = "guacamole-${var.tre_id}-ws-${local.short_workspace_id}-svc-${local.short_parent_id}"
resource_group_name = data.azurerm_resource_group.ws.name
}
data "azurerm_public_ip" "app_gateway_ip" {
name = "pip-agw-${var.tre_id}"
resource_group_name = data.azurerm_resource_group.core.name
}
data "azurerm_storage_account" "stg" {
name = local.storage_name
resource_group_name = data.azurerm_resource_group.ws.name
}
data "azurerm_storage_share" "shared_storage" {
count = var.shared_storage_access ? 1 : 0
name = var.shared_storage_name
storage_account_name = data.azurerm_storage_account.stg.name
}
|
AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-windowsvm/terraform/data.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-windowsvm/terraform/data.tf",
"repo_id": "AzureTRE",
"token_count": 651
}
| 126 |
locals {
short_service_id = substr(var.tre_resource_id, -4, -1)
short_workspace_id = substr(var.workspace_id, -4, -1)
aad_tenant_id = data.azurerm_key_vault_secret.aad_tenant_id.value
workspace_resource_name_suffix = "${var.tre_id}-ws-${local.short_workspace_id}"
keyvault_name = lower("kv-${substr(local.workspace_resource_name_suffix, -20, -1)}")
service_resource_name_suffix = "${local.short_workspace_id}svc${local.short_service_id}"
authority = "${var.aad_authority_url}/${local.aad_tenant_id}"
core_resource_group_name = "rg-${var.tre_id}"
workspace_service_tags = {
tre_id = var.tre_id
tre_workspace_id = var.workspace_id
tre_workspace_service_id = var.tre_resource_id
}
}
|
AzureTRE/templates/workspace_services/health-services/terraform/locals.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/health-services/terraform/locals.tf",
"repo_id": "AzureTRE",
"token_count": 412
}
| 127 |
data "local_file" "deploypl_compute_cluster" {
filename = "${path.module}/nopipcompute/deploypl_compute_cluster.json"
}
# need to add existing VNET
resource "azurerm_resource_group_template_deployment" "deploy_compute_cluster" {
name = "dpl-${local.service_resource_name_suffix}_deploy_compute_cluster"
resource_group_name = data.azurerm_resource_group.ws.name
tags = local.tre_workspace_service_tags
template_content = data.local_file.deploypl_compute_cluster.content
# these key-value pairs are passed into the ARM Template's `parameters` block
parameters_content = jsonencode({
"vnet_name" = {
value = data.azurerm_virtual_network.ws.name
},
"location" = {
value = data.azurerm_resource_group.ws.location
},
"workspace_name" = {
value = local.aml_workspace_name
},
"cluster_name" = {
value = local.aml_compute_cluster_name
},
"subnet_name" = {
value = data.azurerm_subnet.services.name
},
"admin_username" = {
value = "azureuser"
},
"admin_user_password" = {
"value" = "DONOTMERGE"
},
"vm_size_sku" = {
"value" = "Standard_D4_v2"
},
"min_node_count" = {
"value" = 0
},
"max_node_count" = {
"value" = 1
}
})
deployment_mode = "Incremental"
lifecycle { ignore_changes = [tags] }
}
data "azurerm_container_registry" "aml" {
name = local.azureml_acr_name
resource_group_name = data.azurerm_resource_group.ws.name
}
resource "azurerm_role_assignment" "compute_cluster_acr_pull" {
scope = data.azurerm_container_registry.aml.id
role_definition_name = "AcrPull"
principal_id = jsondecode(azurerm_resource_group_template_deployment.deploy_compute_cluster.output_content).cluster_principal_id.value
}
|
AzureTRE/templates/workspace_services/innereye/terraform/compute.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/innereye/terraform/compute.tf",
"repo_id": "AzureTRE",
"token_count": 801
}
| 128 |
# This file will be auto populated by Terraform during deployment
|
AzureTRE/templates/workspace_services/mlflow/mlflow-vm-config/linux/config.sh/0
|
{
"file_path": "AzureTRE/templates/workspace_services/mlflow/mlflow-vm-config/linux/config.sh",
"repo_id": "AzureTRE",
"token_count": 13
}
| 129 |
# OHDSI Workspace Service
## IMPORTANT
- This workspace service does not work "out of the box". It requires additional networking configuration to work properly.
- Currently the only CDM data source supported by the workspace service is Azure Synapse.
Further details are provided in the [documentation](https://microsoft.github.io/AzureTRE/latest/tre-templates/workspace-services/ohdsi/).
|
AzureTRE/templates/workspace_services/ohdsi/README.md/0
|
{
"file_path": "AzureTRE/templates/workspace_services/ohdsi/README.md",
"repo_id": "AzureTRE",
"token_count": 98
}
| 130 |
resource "random_password" "postgres_admin_password" {
length = 32
special = false
}
resource "random_password" "postgres_webapi_admin_password" {
length = 32
special = false
}
resource "random_password" "postgres_webapi_app_password" {
length = 32
special = false
}
resource "azurerm_key_vault_secret" "postgres_admin_password" {
name = "postgres-admin-password-${local.short_service_id}"
key_vault_id = data.azurerm_key_vault.ws.id
value = random_password.postgres_admin_password.result
tags = local.tre_workspace_service_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_key_vault_secret" "postgres_webapi_admin_password" {
name = "ohdsi-admin-password-${local.short_service_id}"
key_vault_id = data.azurerm_key_vault.ws.id
value = random_password.postgres_webapi_admin_password.result
tags = local.tre_workspace_service_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_key_vault_secret" "postgres_webapi_app_password" {
name = "ohdsi-app-password-${local.short_service_id}"
key_vault_id = data.azurerm_key_vault.ws.id
value = random_password.postgres_webapi_app_password.result
tags = local.tre_workspace_service_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_network_security_group" "postgres" {
name = "nsg-psql-${local.service_suffix}"
resource_group_name = data.azurerm_resource_group.ws.name
location = data.azurerm_resource_group.ws.location
tags = local.tre_workspace_service_tags
lifecycle { ignore_changes = [tags] }
security_rule {
name = "AllowWebAppsToPostgres"
priority = 100
direction = "Inbound"
access = "Allow"
protocol = "Tcp"
source_port_range = "*"
destination_port_range = "5432"
source_address_prefixes = [data.azurerm_subnet.web_app.address_prefix]
destination_address_prefixes = azurerm_subnet.postgres.address_prefixes
}
security_rule {
name = "AllowResourceProcessorToPostgres"
priority = 101
direction = "Inbound"
access = "Allow"
protocol = "Tcp"
source_port_range = "*"
destination_port_range = "5432"
source_address_prefixes = [data.azurerm_subnet.resource_processor.address_prefix]
destination_address_prefixes = azurerm_subnet.postgres.address_prefixes
}
security_rule {
name = "DenyInboundOverride"
priority = 4096
direction = "Inbound"
access = "Deny"
protocol = "*"
source_port_range = "*"
destination_port_range = "*"
source_address_prefix = "*"
destination_address_prefix = "*"
}
security_rule {
name = "DenyOutboundOverride"
priority = 4096
direction = "Outbound"
access = "Deny"
protocol = "*"
source_port_range = "*"
destination_port_range = "*"
source_address_prefix = "*"
destination_address_prefix = "*"
}
}
resource "azurerm_subnet" "postgres" {
name = "PostgreSQLSubnet${local.short_service_id}"
virtual_network_name = data.azurerm_virtual_network.ws.name
resource_group_name = data.azurerm_resource_group.ws.name
address_prefixes = [var.address_space]
delegation {
name = "psql-delegation"
service_delegation {
name = "Microsoft.DBforPostgreSQL/flexibleServers"
actions = ["Microsoft.Network/virtualNetworks/subnets/join/action"]
}
}
}
resource "azurerm_subnet_network_security_group_association" "postgres" {
subnet_id = azurerm_subnet.postgres.id
network_security_group_id = azurerm_network_security_group.postgres.id
}
resource "terraform_data" "postgres_core_dns_link" {
provisioner "local-exec" {
environment = {
RESOURCE_GROUP = local.core_resource_group_name
DNS_ZONE_NAME = data.azurerm_private_dns_zone.postgres.name
VNET = data.azurerm_virtual_network.core.name
}
command = "../scripts/postgres_dns_link.sh"
}
}
resource "terraform_data" "postgres_subnet_wait" {
provisioner "local-exec" {
command = "sleep 30"
}
depends_on = [
azurerm_subnet.postgres,
azurerm_subnet_network_security_group_association.postgres,
terraform_data.postgres_core_dns_link
]
}
resource "azurerm_postgresql_flexible_server" "postgres" {
name = "psql-server-${local.service_suffix}"
resource_group_name = data.azurerm_resource_group.ws.name
location = data.azurerm_resource_group.ws.location
delegated_subnet_id = azurerm_subnet.postgres.id
private_dns_zone_id = data.azurerm_private_dns_zone.postgres.id
sku_name = var.postgres_sku
version = local.postgres_version
administrator_login = local.postgres_admin_username
administrator_password = azurerm_key_vault_secret.postgres_admin_password.value
storage_mb = var.postgres_storage_size_in_mb
zone = "1"
tags = local.tre_workspace_service_tags
timeouts {
# If this doesn't complete in a realistic time, no point in waiting the full/default 60m
create = "15m"
}
depends_on = [
terraform_data.postgres_subnet_wait,
]
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_postgresql_flexible_server_database" "db" {
name = local.postgres_webapi_database_name
server_id = azurerm_postgresql_flexible_server.postgres.id
charset = "utf8"
collation = "en_US.utf8"
}
resource "azurerm_monitor_diagnostic_setting" "postgres" {
name = azurerm_postgresql_flexible_server.postgres.name
target_resource_id = azurerm_postgresql_flexible_server.postgres.id
log_analytics_workspace_id = data.azurerm_log_analytics_workspace.workspace.id
dynamic "enabled_log" {
for_each = local.postgres_server_log_analytics_categories
content {
category = enabled_log.value
}
}
metric {
category = "AllMetrics"
enabled = true
}
}
resource "terraform_data" "deployment_ohdsi_webapi_init" {
triggers_replace = {
postgres_database_id = azurerm_postgresql_flexible_server_database.db.id
}
provisioner "local-exec" {
environment = {
MAIN_CONNECTION_STRING = "host=${azurerm_postgresql_flexible_server.postgres.fqdn} port=5432 dbname=${local.postgres_webapi_database_name} user=${local.postgres_admin_username} password=${azurerm_key_vault_secret.postgres_admin_password.value} sslmode=require"
OHDSI_ADMIN_CONNECTION_STRING = "host=${azurerm_postgresql_flexible_server.postgres.fqdn} port=5432 dbname=${local.postgres_webapi_database_name} user=${local.postgres_webapi_admin_username} password=${azurerm_key_vault_secret.postgres_webapi_admin_password.value} sslmode=require"
DATABASE_NAME = local.postgres_webapi_database_name
SCHEMA_NAME = local.postgres_schema_name
OHDSI_ADMIN_PASSWORD = azurerm_key_vault_secret.postgres_webapi_admin_password.value
OHDSI_APP_PASSWORD = azurerm_key_vault_secret.postgres_webapi_app_password.value
OHDSI_APP_USERNAME = local.postgres_webapi_app_username
OHDSI_ADMIN_USERNAME = local.postgres_webapi_admin_username
OHDSI_ADMIN_ROLE = local.postgres_webapi_admin_role
OHDSI_APP_ROLE = local.postgres_webapi_app_role
}
command = "sleep 60 && ../scripts/atlas_db_init.sh"
}
depends_on = [
terraform_data.postgres_core_dns_link,
azurerm_subnet_network_security_group_association.postgres
]
}
|
AzureTRE/templates/workspace_services/ohdsi/terraform/atlas_database.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/ohdsi/terraform/atlas_database.tf",
"repo_id": "AzureTRE",
"token_count": 3631
}
| 131 |
# syntax=docker/dockerfile-upstream:1.4.0
FROM --platform=linux/amd64 debian:bullseye-slim
# PORTER_INIT
RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
# Git is required for terraform_azurerm_environment_configuration
RUN --mount=type=cache,target=/var/cache/apt --mount=type=cache,target=/var/lib/apt \
apt-get update && apt-get install -y git jq curl ca-certificates patch --no-install-recommends
ARG AZURE_TRE_VERSION="0.15.2"
WORKDIR ${BUNDLE_DIR}
# Copy all files from base workspace (note: some of them will be overwritten with the following COPY command)
RUN curl -o azuretre.tar.gz -L "https://github.com/microsoft/AzureTRE/archive/refs/tags/v${AZURE_TRE_VERSION}.tar.gz" \
&& tar -xzf azuretre.tar.gz "AzureTRE-${AZURE_TRE_VERSION}/templates/workspaces/base" --strip-components=4 --skip-old-files \
&& rm -rf azuretre.tar.gz
# Copy and change the file extension of .terraform file to .tf
COPY ./terraform/import_review_resources.terraform "${BUNDLE_DIR}"/terraform/import_review_resources.tf
# HACK: PR #3769: Remove once base workspace includes this change
COPY ./terraform/network_output.terraform "${BUNDLE_DIR}"/terraform/network/temp_output.tf
# PORTER_MIXINS
# Use the BUNDLE_DIR build argument to copy files into the bundle
COPY --link . ${BUNDLE_DIR}/
|
AzureTRE/templates/workspaces/airlock-import-review/Dockerfile.tmpl/0
|
{
"file_path": "AzureTRE/templates/workspaces/airlock-import-review/Dockerfile.tmpl",
"repo_id": "AzureTRE",
"token_count": 514
}
| 132 |
variable "key_vault_id" {
type = string
}
variable "workspace_resource_name_suffix" {
type = string
}
variable "workspace_owner_object_id" {
type = string
}
variable "tre_workspace_tags" {
type = map(string)
}
variable "aad_redirect_uris_b64" {
type = string # list of objects like [{"name": "my uri 1", "value": "https://..."}, {}]
}
variable "create_aad_groups" {
type = string
}
|
AzureTRE/templates/workspaces/base/terraform/aad/variables.tf/0
|
{
"file_path": "AzureTRE/templates/workspaces/base/terraform/aad/variables.tf",
"repo_id": "AzureTRE",
"token_count": 146
}
| 133 |
data "azurerm_client_config" "current" {}
resource "azurerm_key_vault" "kv" {
name = local.keyvault_name
location = azurerm_resource_group.ws.location
resource_group_name = azurerm_resource_group.ws.name
sku_name = "standard"
purge_protection_enabled = true
tenant_id = data.azurerm_client_config.current.tenant_id
tags = local.tre_workspace_tags
network_acls {
bypass = "AzureServices"
default_action = var.enable_local_debugging ? "Allow" : "Deny"
}
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_private_endpoint" "kvpe" {
name = "kvpe-${local.workspace_resource_name_suffix}"
location = azurerm_resource_group.ws.location
resource_group_name = azurerm_resource_group.ws.name
subnet_id = module.network.services_subnet_id
tags = local.tre_workspace_tags
depends_on = [
module.network,
]
lifecycle { ignore_changes = [tags] }
private_dns_zone_group {
name = "private-dns-zone-group"
private_dns_zone_ids = [module.network.vaultcore_zone_id]
}
private_service_connection {
name = "kvpescv-${local.workspace_resource_name_suffix}"
private_connection_resource_id = azurerm_key_vault.kv.id
is_manual_connection = false
subresource_names = ["Vault"]
}
}
resource "azurerm_monitor_diagnostic_setting" "kv" {
name = "diag-${local.keyvault_name}"
target_resource_id = azurerm_key_vault.kv.id
log_analytics_workspace_id = module.azure_monitor.log_analytics_workspace_id
dynamic "enabled_log" {
for_each = ["AuditEvent", "AzurePolicyEvaluationDetails"]
content {
category = enabled_log.value
}
}
metric {
category = "AllMetrics"
enabled = true
}
}
data "azurerm_user_assigned_identity" "resource_processor_vmss_id" {
name = "id-vmss-${var.tre_id}"
resource_group_name = "rg-${var.tre_id}"
}
resource "azurerm_key_vault_access_policy" "resource_processor" {
key_vault_id = azurerm_key_vault.kv.id
tenant_id = data.azurerm_user_assigned_identity.resource_processor_vmss_id.tenant_id
object_id = data.azurerm_user_assigned_identity.resource_processor_vmss_id.principal_id
secret_permissions = ["Get", "List", "Set", "Delete", "Purge", "Recover"]
}
# If running the terraform locally
resource "azurerm_key_vault_access_policy" "deployer" {
count = var.enable_local_debugging ? 1 : 0
key_vault_id = azurerm_key_vault.kv.id
tenant_id = data.azurerm_client_config.current.tenant_id
object_id = data.azurerm_client_config.current.object_id
secret_permissions = ["Get", "List", "Set", "Delete", "Purge", "Recover"]
}
resource "terraform_data" "wait_for_dns_vault" {
provisioner "local-exec" {
command = "bash -c \"sleep 120s\""
on_failure = fail
}
triggers_replace = [
azurerm_private_endpoint.kvpe.private_service_connection[0].private_ip_address # only wait on new/changed private IP address
]
depends_on = [azurerm_private_endpoint.kvpe]
}
resource "azurerm_key_vault_secret" "aad_tenant_id" {
name = "auth-tenant-id"
value = var.auth_tenant_id
key_vault_id = azurerm_key_vault.kv.id
tags = local.tre_workspace_tags
depends_on = [
azurerm_key_vault_access_policy.deployer,
azurerm_key_vault_access_policy.resource_processor,
terraform_data.wait_for_dns_vault
]
lifecycle { ignore_changes = [tags] }
}
# This secret only gets written if Terraform is not responsible for
# registering the AAD Application
resource "azurerm_key_vault_secret" "client_id" {
name = "workspace-client-id"
value = var.client_id
key_vault_id = azurerm_key_vault.kv.id
count = var.register_aad_application ? 0 : 1
tags = local.tre_workspace_tags
depends_on = [
azurerm_key_vault_access_policy.deployer,
azurerm_key_vault_access_policy.resource_processor,
terraform_data.wait_for_dns_vault
]
lifecycle { ignore_changes = [tags] }
}
data "azurerm_key_vault_secret" "client_secret" {
count = var.client_secret == local.redacted_senstive_value ? 1 : 0
name = "workspace-client-secret"
key_vault_id = azurerm_key_vault.kv.id
}
# This secret only gets written if Terraform is not responsible for
# registering the AAD Application
resource "azurerm_key_vault_secret" "client_secret" {
name = "workspace-client-secret"
value = var.client_secret == local.redacted_senstive_value ? data.azurerm_key_vault_secret.client_secret[0].value : var.client_secret
key_vault_id = azurerm_key_vault.kv.id
count = var.register_aad_application ? 0 : 1
tags = local.tre_workspace_tags
depends_on = [
azurerm_key_vault_access_policy.deployer,
azurerm_key_vault_access_policy.resource_processor,
terraform_data.wait_for_dns_vault
]
lifecycle { ignore_changes = [tags] }
}
|
AzureTRE/templates/workspaces/base/terraform/keyvault.tf/0
|
{
"file_path": "AzureTRE/templates/workspaces/base/terraform/keyvault.tf",
"repo_id": "AzureTRE",
"token_count": 2191
}
| 134 |
#!/bin/bash
set -o errexit
set -o pipefail
# Uncomment this line to see each command for debugging (careful: this will show secrets!)
# set -o xtrace
function usage() {
cat <<USAGE
Usage: $0 --workspace-api-client-id some_guid --aad-redirect-uris-b64 json_array_of_urls_in_base64 --register-aad-application false
Options:
--workspace-api-client-id The workspace api AAD application registration client Id
--aad-redirect-uris-b64 The allowed redirect urls for the application
--register-aad-application This script runs only if this value is set to false
USAGE
exit 1
}
# if no arguments are provided, return usage function
if [ $# -eq 0 ]; then
usage # run usage function
fi
while [ "$1" != "" ]; do
case $1 in
--workspace-api-client-id)
shift
workspace_api_client_id=$1
;;
--aad-redirect-uris-b64)
shift
aad_redirect_uris_b64=$1
;;
--register-aad-application)
shift
register_aad_application=$1
;;
*)
echo "Unexpected argument: '$1'"
usage
;;
esac
if [[ -z "$2" ]]; then
# if no more args then stop processing
break
fi
shift # remove the current value for `$1` and use the next
done
# done with processing args and can set this
set -o nounset
az cloud set --name "$AZURE_ENVIRONMENT"
if [ "${register_aad_application}" != "false" ]; then
echo "This script can only run when auto-aad is disabled but got value of: ${register_aad_application}. Exiting..."
exit 0
fi
az ad app show --id "${workspace_api_client_id}" --query web.redirectUris --only-show-errors | jq -r '. | join(" ")'
echo "urls:"
echo "${aad_redirect_uris_b64}"
echo "end of urls."
# web-redirect-uris param doesn't like any type of quotes, hence jq -r
# decode the string and read as json, then take just the values inside the object, concat lines into a space-separated
# single line, trim end.
updated_uris=$(echo "${aad_redirect_uris_b64}" | base64 --decode | jq -r '.[].value' | tr '\n' ' ' | sed 's/ *$//g')
if [ -z "${updated_uris}" ]; then
# the azure cli command doesn't accept empty strings, so using a dummy value which will be overwriten next time
updated_uris="http://localhost:8080/dummy"
fi
echo "Going to update application: ${workspace_api_client_id} with URIs: '${updated_uris}'"
# web-redirect-uris param doesn't like any type of quotes
# shellcheck disable=SC2086
az ad app update --id "${workspace_api_client_id}" --web-redirect-uris ${updated_uris} --only-show-errors
|
AzureTRE/templates/workspaces/base/update_redirect_urls.sh/0
|
{
"file_path": "AzureTRE/templates/workspaces/base/update_redirect_urls.sh",
"repo_id": "AzureTRE",
"token_count": 982
}
| 135 |
{
"short_name": "React App",
"name": "Create React App Sample",
"icons": [
{
"src": "favicon.ico",
"sizes": "64x64 32x32 24x24 16x16",
"type": "image/x-icon"
}
],
"start_url": ".",
"display": "standalone",
"theme_color": "#000000",
"background_color": "#ffffff"
}
|
AzureTRE/ui/app/public/manifest.json/0
|
{
"file_path": "AzureTRE/ui/app/public/manifest.json",
"repo_id": "AzureTRE",
"token_count": 141
}
| 136 |
import { MessageBar, MessageBarType, Link as FluentLink, Icon, } from '@fluentui/react';
import React, { useState } from 'react';
import { APIError } from '../../models/exceptions';
interface ExceptionLayoutProps {
e: APIError
}
export const ExceptionLayout: React.FunctionComponent<ExceptionLayoutProps> = (props: ExceptionLayoutProps) => {
const [showDetails, setShowDetails] = useState(false);
switch (props.e.status) {
case 403:
return (
<MessageBar
messageBarType={MessageBarType.error}
isMultiline={true}
>
<h3>Access Denied</h3>
<h4>{props.e.userMessage}</h4>
<p>{props.e.message}</p>
<p>Attempted resource: {props.e.endpoint}</p>
</MessageBar>
);
default:
return (
<MessageBar
messageBarType={MessageBarType.error}
isMultiline={true}
>
<h3>{props.e.userMessage}</h3>
<p>{props.e.message}</p><br />
<FluentLink title={showDetails ? 'Show less' : 'Show more'} href="#" onClick={() => { setShowDetails(!showDetails) }} style={{ position: 'relative', top: '2px', paddingLeft: 0 }}>
{
showDetails ?
<><Icon iconName='ChevronUp' aria-label='Expand Details' /> {'Hide Details'}</> :
<><Icon iconName='ChevronDown' aria-label='Collapse Details' /> {'Show Details'} </>
}
</FluentLink>
{
showDetails &&
<>
<table style={{ border: '1px solid #666', width: '100%', padding: 10, marginTop: 15 }}>
<tbody>
<tr>
<td><b>Endpoint</b></td>
<td>{props.e.endpoint}</td>
</tr>
<tr>
<td><b>Status Code</b></td>
<td>{props.e.status || '(none)'}</td>
</tr>
<tr>
<td><b>Stack Trace</b></td>
<td>{props.e.stack}</td>
</tr>
<tr>
<td><b>Exception</b></td>
<td>{props.e.exception}</td>
</tr>
</tbody>
</table>
</>
}
</MessageBar>
)
}
};
|
AzureTRE/ui/app/src/components/shared/ExceptionLayout.tsx/0
|
{
"file_path": "AzureTRE/ui/app/src/components/shared/ExceptionLayout.tsx",
"repo_id": "AzureTRE",
"token_count": 1288
}
| 137 |
import React, { useContext, useEffect, useState } from 'react';
import { WorkspaceContext } from '../../contexts/WorkspaceContext';
import { AppRolesContext } from '../../contexts/AppRolesContext';
import { MessageBar, MessageBarType } from '@fluentui/react';
import { ApiEndpoint } from '../../models/apiEndpoints';
import { HttpMethod, ResultType, useAuthApiCall } from '../../hooks/useAuthApiCall';
interface SecuredByRoleProps {
element: JSX.Element,
allowedAppRoles?: Array<string>,
allowedWorkspaceRoles?: Array<string>,
workspaceId?: string,
errorString?: String;
}
// Check if the user roles match any of the roles we are given - if they do, show the element, if not, don't
export const SecuredByRole: React.FunctionComponent<SecuredByRoleProps> = (props: SecuredByRoleProps) => {
const apiCall = useAuthApiCall();
const appRoles = useContext(AppRolesContext);
const workspaceCtx = useContext(WorkspaceContext);
const [workspaceRoles, setRoles] = useState([] as Array<string>);
useEffect(() => {
const getWorkspaceRoles = async () => {
if (!workspaceCtx.workspace.id && props.workspaceId !== "") {
let r = [] as Array<string>;
let workspaceAuth = (await apiCall(`${ApiEndpoint.Workspaces}/${props.workspaceId}/scopeid`, HttpMethod.Get)).workspaceAuth;
if (workspaceAuth) {
await apiCall(`${ApiEndpoint.Workspaces}/${props.workspaceId}`, HttpMethod.Get, workspaceAuth.scopeId,
undefined, ResultType.JSON, (roles: Array<string>) => {
r = roles;
}, true);
}
setRoles(r);
}
};
if (workspaceCtx.roles.length === 0 && props.workspaceId !== undefined) {
getWorkspaceRoles();
}
else {
setRoles(workspaceCtx.roles);
}
}, [apiCall, workspaceCtx.workspace.id, props.workspaceId, workspaceCtx.roles]);
return (
(workspaceRoles?.some(x => props.allowedWorkspaceRoles?.includes(x)) || appRoles?.roles?.some(x => props.allowedAppRoles?.includes(x)))
? props.element
: (props.errorString && (workspaceRoles.length > 0 || appRoles.roles.length > 0)
? <MessageBar messageBarType={MessageBarType.error} isMultiline={true}>
<h3>Access Denied</h3>
<p>{props.errorString}</p>
</MessageBar>
: null)
);
};
|
AzureTRE/ui/app/src/components/shared/SecuredByRole.tsx/0
|
{
"file_path": "AzureTRE/ui/app/src/components/shared/SecuredByRole.tsx",
"repo_id": "AzureTRE",
"token_count": 911
}
| 138 |
import React from 'react';
import { useInterval } from './useInterval';
import { HttpMethod, useAuthApiCall } from '../../../hooks/useAuthApiCall';
import { ApiEndpoint } from '../../../models/apiEndpoints';
import { TRENotification } from '../../../models/treNotification';
import { Operation } from '../../../models/operation';
import config from '../../../config.json';
interface NotificationPollerProps {
notification: TRENotification,
updateOperation: (n: Operation) => void
}
export const NotificationPoller: React.FunctionComponent<NotificationPollerProps> = (props: NotificationPollerProps) => {
const apiCall = useAuthApiCall();
useInterval(async () => {
try {
let op = (await apiCall(`${props.notification.operation.resourcePath}/${ApiEndpoint.Operations}/${props.notification.operation.id}`,
HttpMethod.Get, props.notification.workspace ? props.notification.workspace.properties.scope_id: null)).operation as Operation;
// check if any fields have changed - ie the json is any different. we don't care _what_ has changed, just that something has
if (JSON.stringify(op) !== JSON.stringify(props.notification.operation)) {
props.notification.operation = op;
props.updateOperation(op);
}
} catch (e: any) {
// likely that the user no longer has access to the operation due to a role change
config.debug && console.log(`Operation ${props.notification.operation.id} for ${props.notification.operation.resourcePath} cqnnot be retrieved`);
}
}, config.pollingDelayMilliseconds);
return (<></>);
};
|
AzureTRE/ui/app/src/components/shared/notifications/NotificationPoller.tsx/0
|
{
"file_path": "AzureTRE/ui/app/src/components/shared/notifications/NotificationPoller.tsx",
"repo_id": "AzureTRE",
"token_count": 514
}
| 139 |
import React from "react";
import { Workspace } from "../models/workspace";
import { CostResource } from "../models/costs";
export const WorkspaceContext = React.createContext({
roles: [] as Array<string>,
costs: [] as Array<CostResource>,
setCosts: (costs: Array<CostResource>) => { },
setRoles: (roles: Array<string>) => { },
setWorkspace: (w: Workspace) => { },
workspace: {} as Workspace,
workspaceApplicationIdURI: "" as string
});
|
AzureTRE/ui/app/src/contexts/WorkspaceContext.ts/0
|
{
"file_path": "AzureTRE/ui/app/src/contexts/WorkspaceContext.ts",
"repo_id": "AzureTRE",
"token_count": 140
}
| 140 |
import { Operation } from "./operation";
import { Resource } from "./resource";
import { Workspace } from "./workspace";
export interface TRENotification {
operation: Operation,
resource: Resource,
workspace?: Workspace
}
|
AzureTRE/ui/app/src/models/treNotification.ts/0
|
{
"file_path": "AzureTRE/ui/app/src/models/treNotification.ts",
"repo_id": "AzureTRE",
"token_count": 71
}
| 141 |
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v er</w> 8872069
c an</w> 8862357
tw o</w> 8842746
a tes</w> 8830892
re e</w> 8805856
m s</w> 8798079
d s</w> 8791964
m an 8762878
F i 8737515
resul ts</w> 8720681
ul d</w> 8716094
pa th 8684305
c ur 8676942
c ri 8628396
p ol 8610220
om e</w> 8599155
ur ing</w> 8574158
b oth</w> 8546825
se qu 8506512
i ti 8505246
os t</w> 8496036
de vel 8439531
d u 8436530
di se 8394831
ec tion</w> 8381611
ther ap 8325291
de p 8307376
d om 8299063
l in 8291211
d ec 8280356
am pl 8217574
og en 8199085
b le</w> 8163911
os ph 8163407
Fi g</w> 8134167
i a</w> 8118915
o ther</w> 8106034
anc er</w> 8084822
su gg 8083615
ob ser 8031909
tr ac 8025106
h igh 8014068
o l</w> 7984799
d ata</w> 7978647
t um 7978607
the ir</w> 7969515
igh t</w> 7967755
re qu 7912633
ti onal</w> 7885649
ac teri 7877363
analy sis</w> 7875112
c ul 7833098
sy n 7802107
em ent</w> 7771685
ig h</w> 7765120
re por 7751455
im mun 7745709
vel s</w> 7731915
a v 7715409
ay s</w> 7708327
h um 7685770
it e</w> 7676345
cl in 7655245
ic i 7651251
d el 7646020
le vels</w> 7632827
devel op 7631088
ne u 7612798
re si 7608738
po si 7608428
con c 7601483
ph osph 7596730
t ors</w> 7590280
vi r 7587499
F ig 7582533
pa red</w> 7542671
s uc 7534184
ac h</w> 7529739
associ ated</w> 7525022
D NA</w> 7508850
mic ro 7469767
s ed</w> 7467521
g r 7420083
d r 7418814
ch ang 7408150
in clud 7388727
an ts</w> 7380565
su p 7371035
si m 7357868
ter min 7328371
pl as 7301188
sy ste 7273826
i p 7264844
t ure</w> 7254397
el l</w> 7248073
id enti 7229501
ab ility</w> 7220652
pro c 7205230
w ith 7200799
h igh</w> 7200159
dep end 7188605
ec h 7183584
fi r 7169144
me t 7166225
A l 7160728
gro up</w> 7159783
c ancer</w> 7155005
u b 7150395
es t</w> 7146915
w e 7145175
duc tion</w> 7124613
gen e</w> 7119513
R NA</w> 7114017
uc le 7102583
ant ly</w> 7098659
s el 7088813
m b 7078007
ev er</w> 7067958
gen er 7053213
st ra 7045157
on g</w> 7035844
ac tion</w> 7026896
al l 7023697
d uring</w> 7016942
id e</w> 7009101
d er 7003897
ch em 6975360
ecti ve</w> 6942848
O N 6934290
te c 6922596
in ing</w> 6885182
ha d</w> 6836475
k e</w> 6818815
d ed</w> 6813644
olog ical</w> 6813348
z ed</w> 6800778
f ound</w> 6783564
de f 6782519
s en 6779795
stu dies</w> 6754122
typ e</w> 6743593
ic ation</w> 6735537
incre ased</w> 6663459
dise ase</w> 6638001
ow ever</w> 6634961
per form 6628034
in to</w> 6622581
in dic 6612282
a ff 6608895
t om 6602015
n i 6599606
me as 6592708
es ti 6548293
n or 6500181
k in 6495105
hum an</w> 6477384
k n 6475018
ex per 6474620
iz ed</w> 6452269
f r 6415993
a in</w> 6413958
d ro 6382007
bin ding</w> 6371035
po ten 6334273
fi ed</w> 6328853
in duced</w> 6314292
th ro 6311148
ve l</w> 6308629
an is 6303773
m al 6301103
tim e</w> 6237713
d es 6231616
m at 6228169
com pared</w> 6226927
effec t</w> 6223302
contro l</w> 6212927
si ve</w> 6206307
s ec 6202627
differen t</w> 6171945
iz ation</w> 6154475
b o 6148045
ol ec 6146551
prote ins</w> 6136412
i al</w> 6119381
rec ep 6099518
if ic</w> 6097438
clin ical</w> 6089976
il e</w> 6079726
oc y 6075028
d er</w> 6060485
n o</w> 6041177
form ation</w> 6037573
S I 6002017
A T 5999304
effec ts</w> 5995677
e re 5983101
obser ved</w> 5976253
signific antly</w> 5967590
ent al</w> 5966604
por t 5960863
e i 5960558
w ell</w> 5951576
und er 5930223
ap pro 5928906
s es</w> 5927463
show ed</w> 5923696
con si 5910226
ati c</w> 5902531
de tec 5877668
st s</w> 5851689
s ol 5842579
signific ant</w> 5832972
am in 5814613
spec ific</w> 5808472
k ing</w> 5769498
conc entr 5765733
gro w 5758832
C L 5757896
reg ul 5736560
ti al</w> 5735398
func tion</w> 5731334
vi v 5725492
m ech 5721898
rel ated</w> 5709297
h o 5695973
Th ese</w> 5678479
mic e</w> 5667960
s ti 5644835
ac c 5636645
com p 5628518
olog y</w> 5622605
depend ent</w> 5605485
f in 5588139
ow n</w> 5552643
A C 5552371
therap y</w> 5534971
f e 5512609
ch ar 5506762
m al</w> 5501038
on ly</w> 5489931
ecti vely</w> 5469982
sh o 5465861
T o</w> 5465564
el l 5459956
b r 5449096
me mb 5441004
ri s 5429723
m ost</w> 5422471
m ar 5411732
ti s 5411155
re a 5392104
n ucle 5362228
m a</w> 5358849
w or 5358024
b ased</w> 5349056
sc ri 5335139
ro le</w> 5334456
og ra 5329861
e tic</w> 5324569
suc h</w> 5317298
um b 5290175
ear s</w> 5285187
t arg 5272098
meas u 5271811
ad di 5257807
tic al</w> 5255780
en z 5254627
tr a 5244453
ul ti 5243631
he al 5224172
s o 5212901
m olec 5205917
b it 5200818
dec re 5194369
k s</w> 5192807
respon se</w> 5171542
fol low 5170651
c h</w> 5156441
inhi bi 5151173
reg i 5151017
s ampl 5139298
f lu 5137634
esti g 5118568
ev alu 5117584
ten t</w> 5114147
o ver 5096585
Fig ure</w> 5092483
c al 5068434
de l</w> 5066014
compl e 5056236
thro ug 5051980
de mon 5046612
f u 5045559
activ ation</w> 5035939
con ta 5017053
p r 5013844
C D 4994044
ou g 4993175
inv estig 4974512
y n 4973607
gen es</w> 4969514
mech anis 4955427
ph y 4953919
h ed</w> 4950437
pri m 4946495
ti es</w> 4920188
im port 4918011
H owever</w> 4891025
&apos ; 4889503
v en 4888008
S U 4885453
I I</w> 4884490
per i 4879361
des cri 4878104
ec t</w> 4875961
o us 4866647
l y 4861503
tum or</w> 4849995
pres ent</w> 4848593
el y</w> 4843463
hy dro 4843311
for e</w> 4841730
com bin 4833285
t ot 4831133
v al 4817363
perform ed</w> 4810661
c es</w> 4795925
mo del</w> 4782969
ro s 4781824
T S</w> 4778425
ph en 4772943
vi e 4771341
y l</w> 4768540
ut e</w> 4766527
en g 4765572
il l 4758022
g en</w> 4734981
ec ts</w> 4731663
D S</w> 4728849
th ree</w> 4727956
tis su 4717980
ris k</w> 4698205
inv ol 4697230
a d</w> 4694990
demon str 4690036
exper im 4687272
l if 4686614
ur al</w> 4664859
v alu 4664383
ent ly</w> 4662639
f l 4659917
P C 4657444
ep ti 4655386
w il 4654469
di ag 4646302
n umb 4645725
pl e</w> 4644960
m i 4638212
s ing</w> 4631971
in te 4630309
el ec 4628819
m it 4627308
scri p 4623576
m ents</w> 4622757
tre ated</w> 4620701
al ity</w> 4612577
yl ation</w> 4606244
' s</w> 4604836
char acteri 4586445
b lo 4581232
high er</w> 4574421
r ate</w> 4572010
C h 4568097
pre vi 4561635
grow th</w> 4546269
ter n 4545369
co uld</w> 4539726
ogra ph 4538182
wh en</w> 4535020
inhi bit 4527166
pro vid 4521684
a il 4518403
i ed</w> 4512348
at ure</w> 4511187
show n</w> 4500086
ou t 4499445
ac t</w> 4481583
A s</w> 4481264
om a</w> 4478281
od y</w> 4476640
di rec 4474558
a te 4474067
t ly</w> 4471839
e ther</w> 4470881
ra di 4464860
ous ly</w> 4461846
an s</w> 4459471
path w 4452490
ter s</w> 4436295
o ver</w> 4424922
v it 4414122
con di 4400371
ar d</w> 4399986
ac id</w> 4396172
tran scrip 4388108
er g 4371469
sim il 4361660
&apos ;</w> 4349282
at ory</w> 4346625
b ody</w> 4333122
b ed</w> 4319843
syste m</w> 4314113
pro duc 4309607
y ears</w> 4292783
ing s</w> 4291704
t e</w> 4287152
pati ent</w> 4285948
fir st</w> 4283068
e ach</w> 4279501
par tic 4274217
s tim 4272211
up s</w> 4272037
m ulti 4267496
si ble</w> 4263755
ot yp 4263723
th oug 4258230
memb ran 4257370
di st 4249176
enc y</w> 4248815
throug h</w> 4239360
e d 4235693
de termin 4226523
o per 4217911
te m 4208588
n on</w> 4185960
in j 4159250
acti ve</w> 4127883
nor mal</w> 4126615
incre ase</w> 4122964
C A 4120902
ta ined</w> 4118364
m ac 4117252
pro g 4116222
H O 4106676
C ON 4102704
le vel</w> 4099393
resp ectively</w> 4088056
p op 4084937
pl ic 4083991
c i 4078957
recep tor</w> 4073945
fac tors</w> 4071431
e u 4070498
gro ups</w> 4063434
m y 4060834
k ed</w> 4047022
i red</w> 4040553
hi st 4031190
l arg 4026282
trans f 4022668
ren t</w> 4013571
sur g 4009087
m is 4004504
includ ing</w> 3999953
phy si 3997522
ur ther</w> 3995013
e st 3990161
ab ol 3987549
c ases</w> 3980867
si ons</w> 3979370
numb er</w> 3978461
U SI 3972468
repor ted</w> 3972426
w a 3964571
CL USI 3961846
CON CLUSI 3957799
v ing</w> 3953812
as s</w> 3949379
t y</w> 3948917
an d 3947547
im pro 3939134
ant i</w> 3939012
SU L 3926841
develop ment</w> 3917720
ag es</w> 3917671
te ro 3912729
chang es</w> 3912616
tic s</w> 3905832
RE SUL 3895795
elec tro 3893573
on d 3892538
ati onal</w> 3885454
o re 3879943
G F 3874150
ab l 3871559
l l 3860074
x im 3856031
te ch 3850438
identi fied</w> 3836627
com m 3823883
c le</w> 3821230
s ity</w> 3813399
e ar</w> 3810704
resi st 3808787
or t</w> 3808233
with in</w> 3806463
s ul 3802865
import ant</w> 3797912
RESUL TS</w> 3793238
th ose</w> 3785248
ch rom 3784102
r ang 3776717
acti ons</w> 3770116
du e</w> 3767233
thoug h</w> 3766834
blo od</w> 3758902
fac tor</w> 3755783
v es</w> 3752907
b l 3748863
di vid 3748805
e th 3748353
or g 3728491
pro mo 3727862
poten tial</w> 3725781
neu ro 3711235
id s</w> 3705050
p er</w> 3704548
tot al</w> 3701993
l im 3695598
ver y</w> 3695101
ch il 3689282
enz ym 3686802
al ing</w> 3684381
o ph 3670971
c ap 3668458
sen si 3668364
pre dic 3655698
R O 3653929
en ted</w> 3649703
y c 3647793
comple x</w> 3644062
l am 3641858
v ity</w> 3636864
z e</w> 3635287
co un 3619011
u r</w> 3614681
am e</w> 3608516
phosph or 3605037
M ET 3594040
g u 3588239
s ing 3584330
a th 3579953
bi o 3576440
e l</w> 3573856
v as 3567623
for m</w> 3562262
m p 3553414
res s</w> 3549246
b ra 3536206
te st</w> 3532833
m in</w> 3531027
l a 3529262
simil ar</w> 3528816
vir us</w> 3523958
pres ence</w> 3519340
MET HO 3505404
d o 3500487
di d</w> 3489863
pl ac 3485987
re ve 3485062
addi tion</w> 3484026
l ess</w> 3482649
E R 3473823
I t</w> 3472333
r o</w> 3470277
ne w</w> 3467688
E C 3466978
be t 3466700
ex am 3464398
fi b 3461263
fac e</w> 3457847
re le 3455107
s m 3454230
pres sion</w> 3451196
u al</w> 3449495
hy per 3445143
sugg es 3441338
st a 3438214
pol y 3431104
com par 3425376
re duced</w> 3425125
membran e</w> 3422151
no sis</w> 3421967
g o 3421629
is ol 3420162
kn own</w> 3402290
m us 3395368
ass ess 3388351
ad e</w> 3384515
posi tive</w> 3378742
ic ally</w> 3374033
ti on 3373561
cel l 3371528
f il 3368695
low er</w> 3361722
tu res</w> 3360168
the sis</w> 3359150
u g</w> 3358188
b ec 3355898
A n 3355387
meth od</w> 3346383
f am 3341596
ap p 3341303
d es</w> 3330425
in iti 3329026
p ur 3327844
h om 3316989
P ro 3314356
f requ 3308756
en h 3294856
l ine</w> 3287856
a ut 3286063
c le 3284947
C on 3281025
v en</w> 3277015
i ly</w> 3275783
pl em 3274532
c ular</w> 3263218
descri bed</w> 3259970
ex trac 3256797
met abol 3256378
m g</w> 3252195
in al</w> 3247542
u tion</w> 3238881
wh o</w> 3238274
N D</w> 3226039
te s 3221017
c are</w> 3220024
f urther</w> 3216292
e sis</w> 3214511
e vid 3212665
METHO DS</w> 3199575
se ver 3198365
l ab 3197511
sugg est</w> 3194025
und er</w> 3184816
sing le</w> 3181081
i l</w> 3180173
car di 3176489
tissu e</w> 3164491
R e 3161219
n on 3157055
heal th</w> 3152839
hy po 3135680
d ings</w> 3134717
chil d 3126443
ic e</w> 3124441
th ere</w> 3119648
inf ection</w> 3101046
a re 3098992
am ong</w> 3098223
re vie 3097473
ob tained</w> 3097366
c r 3097288
O N</w> 3097198
wh ile</w> 3095326
f ication</w> 3094942
at s</w> 3094670
ex pos 3088727
c in 3084695
sur viv 3082778
sequ enc 3080653
a si 3072588
with out</w> 3069622
th s</w> 3068035
mo del 3062115
dr ug</w> 3054595
sign aling</w> 3052911
sub j 3052645
mut ations</w> 3051261
F or</w> 3048684
v ol 3044003
sc op 3041335
sup por 3038401
a in 3033279
previ ously</w> 3033168
ar ly</w> 3032636
s ome</w> 3029575
pro b 3029059
conta ining</w> 3028507
ma j 3024894
p at 3023546
re n</w> 3014798
condi tions</w> 3011156
s ite</w> 3001458
h em 2998305
in divid 2990389
y s 2983197
dom ain</w> 2977590
A P 2966286
prim ary</w> 2964386
c ase</w> 2960442
con tr 2952703
sampl es</w> 2949458
an e 2949353
rel ati 2947248
struc ture</w> 2944184
mo d 2944139
is hed</w> 2938391
ei ther</w> 2938335
f t</w> 2932689
ex pl 2926327
ex p 2922211
p it 2917051
ag ing</w> 2914203
re ma 2912905
an n 2911816
col l 2904356
am e 2903645
medi ated</w> 2901905
follow ing</w> 2900979
inf lam 2897605
o ch 2896955
ter m</w> 2896903
mut ant</w> 2895334
al ed</w> 2890370
neu ron 2888629
der i 2888368
res c 2885526
ici ent</w> 2883282
e qu 2883064
od s</w> 2881404
as si 2880376
H I 2877094
st ate</w> 2873473
concentr ation</w> 2864885
molec ular</w> 2864747
inf lu 2863339
m M</w> 2860915
on g 2853869
a x 2853467
th en</w> 2851861
ol d</w> 2851369
we e 2851313
on d</w> 2850165
ear ly</w> 2848560
wh ether</w> 2848238
d ays</w> 2841912
u res</w> 2836783
me an</w> 2836187
or e</w> 2833449
me dic 2828288
ab s 2823370
l ym 2822370
f ul 2820060
vit ro</w> 2819863
l ac 2819166
I L</w> 2816504
d en 2816115
fin dings</w> 2815074
demonstr ated</w> 2808674
ne g 2800749
ac h 2798005
mor ph 2797736
ri c</w> 2797668
sequ ence</w> 2789805
b ility</w> 2786109
S C 2777579
mar k 2776077
out com 2774594
s k 2774300
bra in</w> 2773925
er y</w> 2772733
cy to 2770259
a use</w> 2765431
po in 2765198
ser um</w> 2762009
r ati 2761101
m ic</w> 2760049
flu o 2753027
oc cur 2751764
targ e 2749541
p epti 2748389
or der</w> 2745037
iti es</w> 2743329
ma in 2742167
the y</w> 2741619
stim ul 2740700
C om 2740275
sho w</w> 2740114
C T 2732777
R N 2730235
s ame</w> 2717040
u sion</w> 2709907
reg ulation</w> 2709660
child ren</w> 2706157
b acteri 2701933
ph ase</w> 2700375
decre ased</w> 2699361
li ke</w> 2696328
sti tu 2694791
surviv al</w> 2692990
pathw ay</w> 2690943
w om 2686221
evid ence</w> 2685807
y ing</w> 2684784
l os 2678777
un d</w> 2678329
differen ces</w> 2676554
g i 2675702
l ig 2674302
b re 2674091
determin ed</w> 2673552
ain st</w> 2672038
h t</w> 2667794
A M 2667454
ex c 2664996
d ose</w> 2664359
measu red</w> 2660656
is m</w> 2658309
func tional</w> 2655621
pres s 2651031
ros s</w> 2650201
mo di 2650184
qu e</w> 2650021
ag ainst</w> 2649509
sm all</w> 2648331
S A</w> 2646364
ro ph 2644890
ter m 2640155
se t</w> 2639275
ate ly</w> 2636064
W h 2636020
regi on</w> 2635341
b ri 2633963
in duc 2633302
et y</w> 2632595
reve aled</w> 2632435
ati s 2630237
cl es</w> 2622796
differen ti 2611667
c al</w> 2608050
inhibi tion</w> 2603355
spec ies</w> 2598827
te g 2595035
consi s 2593184
k e 2591297
an y</w> 2589365
proc ess 2586580
ha vi 2585499
invol ved</w> 2583787
ear ch</w> 2581641
sur face</w> 2580607
ograph y</w> 2578853
di sc 2576532
con tri 2575807
concentr ations</w> 2571460
te red</w> 2565894
ti s</w> 2564366
t ox 2562894
pop ulation</w> 2558449
mon ths</w> 2557035
E x 2553752
us es</w> 2552917
an im 2547327
ter n</w> 2546895
anc ed</w> 2546820
is e</w> 2544618
pl ication</w> 2542544
con fir 2541361
expres sed</w> 2536099
chang e</w> 2535431
re duc 2530649
po st 2528613
m l</w> 2526568
fu l</w> 2525807
PC R</w> 2524215
gl uc 2520541
bo dies</w> 2518928
i o 2518366
ent ation</w> 2510439
a u 2509620
ol e</w> 2506416
c at 2504359
op to 2502544
targ et</w> 2499931
n os 2499818
at or</w> 2498520
detec ted</w> 2498171
ox id 2495982
ell ular</w> 2495699
f o 2493694
ig n 2488476
di ed</w> 2486963
sever al</w> 2486590
kin ase</w> 2486230
a z 2479960
cell ular</w> 2479024
al e</w> 2478638
resist ance</w> 2478046
ocy tes</w> 2477987
ic s</w> 2477887
M e 2477631
inter action</w> 2476978
S t 2476969
d ay</w> 2471308
ap opto 2469132
per c 2468148
pro duction</w> 2465194
qu anti 2462375
be havi 2456447
chem ical</w> 2456328
os pit 2454574
mechanis m</w> 2450268
mo use</w> 2449636
res sion</w> 2447005
O ur</w> 2446050
h el 2445405
viv o</w> 2445281
plas ma</w> 2444732
wom en</w> 2437413
ul es</w> 2437394
ON S</w> 2432405
i str 2432102
posi tion</w> 2430990
ch ron 2427519
G F</w> 2426573
ass ay</w> 2425708
n ec 2419622
A L 2415088
ver se</w> 2410176
wh ere 2407551
ti vity</w> 2397731
o f 2397710
st ress</w> 2394611
term ine</w> 2389121
lif er 2386105
l ong</w> 2385112
al ph 2382936
larg e</w> 2381990
de termine</w> 2380366
rang e</w> 2377043
phosphor ylation</w> 2376965
f ree</w> 2375780
if ied</w> 2375076
y ear</w> 2374556
or d 2369865
b loc 2365624
man y</w> 2365131
los s</w> 2364544
cor rel 2362572
si de</w> 2361531
ab out</w> 2360355
vas cular</w> 2360209
t al</w> 2357191
si tes</w> 2349713
neg ative</w> 2349020
ad ul 2346614
ox y 2342268
pro lifer 2339863
C I</w> 2338838
m ental</w> 2335332
comm on</w> 2329654
anti body</w> 2324795
inc ub 2323425
f our</w> 2323341
li k 2322638
mut ation</w> 2318292
f er 2317044
en e 2315039
ecti ons</w> 2313047
gr ad 2311324
diag nosis</w> 2309695
s oci 2307724
b ac 2304523
ad min 2301542
sho uld</w> 2301365
A d 2298783
ac y</w> 2296995
am ine</w> 2296554
r ats</w> 2295086
tech ni 2294254
al ization</w> 2293961
yn am 2293760
g ra 2293173
o th 2292195
fam ily</w> 2291295
os om 2290323
com po 2289688
analy zed</w> 2289526
or al</w> 2289310
exam ined</w> 2287551
mechanis ms</w> 2284989
maj or</w> 2281686
valu es</w> 2278956
p ul 2274849
li ver</w> 2270802
in tr 2267078
wa ter</w> 2266326
A c 2263090
requ ired</w> 2261219
I m 2257881
g re 2254709
multi ple</w> 2253679
ar di 2253138
ri al</w> 2243812
ar t</w> 2242027
wil d</w> 2241774
r u 2241466
wil l</w> 2240467
where as</w> 2239787
di m 2238986
g e</w> 2237404
otyp e</w> 2235842
sy mp 2234540
contr ast</w> 2231141
b one</w> 2230834
lym ph 2227233
I N 2224750
vir al</w> 2223895
el ial</w> 2222766
CONCLUSI ONS</w> 2221376
ag g 2220727
ic ity</w> 2219019
ri bu 2217300
f oc 2216051
proc ess</w> 2212975
amin o</w> 2210851
vi a</w> 2209008
M E 2205953
be fore</w> 2205221
f em 2203881
z ation</w> 2197076
l un 2194724
ac et 2192846
R P 2192692
ogen esis</w> 2192185
c op 2191985
st and 2191799
con struc 2188066
oc c 2187551
m RNA</w> 2187346
li p 2187155
C O 2186963
Th ere</w> 2180105
ad v 2175457
investig ated</w> 2172964
resi du 2169651
comp on 2168513
diag nos 2166870
wor k</w> 2166552
develop ed</w> 2166354
si ze</w> 2166340
at h</w> 2164130
c an 2162313
n it 2159441
dist ribu 2158092
gen etic</w> 2156400
l as 2155734
fluo resc 2154874
A D 2152911
c er 2151516
vari ous</w> 2144741
experim ents</w> 2141000
stu died</w> 2140066
pro per 2139901
av ail 2137059
repor t</w> 2135659
om ic</w> 2135139
v ement</w> 2134813
co h 2134329
rec ei 2134013
f y</w> 2131840
transcrip tion</w> 2130976
w o 2129663
appro ach</w> 2126541
enzym e</w> 2125335
lin es</w> 2122903
immun e</w> 2122365
tr y</w> 2121475
ch ol 2120008
Al l</w> 2118803
m ed</w> 2118424
e ro 2115613
pos sible</w> 2115340
v enti 2111737
a e</w> 2105216
si g 2104984
prote c 2103348
model s</w> 2102539
vi ron 2102082
b as 2100254
is t</w> 2100224
t ur 2098587
O n 2096165
al ized</w> 2094821
o ti 2094484
f eren 2091625
anti bodies</w> 2091296
D i 2090982
alph a</w> 2090407
d rom 2089824
tran sp 2089228
R es 2086764
oper ative</w> 2083998
c ir 2083625
i p</w> 2082757
under st 2081256
C R 2080222
tic ally</w> 2079529
therap eu 2078060
a c</w> 2077756
typ es</w> 2076926
tum ors</w> 2076502
surg ery</w> 2075615
tu b 2075501
tem per 2074572
eff ective</w> 2073775
ici ency</w> 2069523
le ad 2068007
H e 2061566
ab ly</w> 2061504
gl yc 2056901
bet a</w> 2056868
er ase</w> 2053838
cur rent</w> 2049965
respon ses</w> 2049777
r ap 2049311
revie w</w> 2048703
ch o 2048502
Al though</w> 2046321
associ ation</w> 2042415
ra tes</w> 2042229
cy cl 2042216
lik ely</w> 2041550
as p 2038315
re duction</w> 2037503
n er 2036480
on sh 2035865
provid e</w> 2035704
v ation</w> 2035057
car cin 2030213
och ond 2030171
lo b 2027150
u re 2026571
a di 2024972
ac ute</w> 2022519
combin ation</w> 2021918
p rec 2021833
p H</w> 2019512
A f 2018943
bre ast</w> 2018765
te x 2017777
o id</w> 2016896
includ ed</w> 2014170
en viron 2013637
en d</w> 2007346
re action</w> 2003246
ul e</w> 1999959
as t 1999770
no vel</w> 1999565
n at 1997862
ex ten 1997758
g rea 1996794
ati n</w> 1994732
el d</w> 1992463
on ic</w> 1991297
v al</w> 1991109
i um</w> 1990474
res earch</w> 1990270
y st 1990206
we ight</w> 1989593
G T 1984211
a k 1982128
di ab 1978273
de red</w> 1977421
chron ic</w> 1975215
characteri s 1974739
H C 1972289
con tin 1971531
as ing</w> 1968517
est abl 1965396
evalu ated</w> 1963453
id ence</w> 1961333
h ospit 1960468
am p 1955127
im p 1954613
st ro 1952557
car b 1951875
l ight</w> 1950729
at us</w> 1949267
ta ke</w> 1949149
sc re 1949124
i ted</w> 1945651
wh ere</w> 1944666
inhibit or</w> 1944651
symp tom 1942936
Af ter</w> 1941543
ad j 1939666
resul t</w> 1939376
spec ific 1938423
relati onsh 1936787
syn drom 1936245
deri ved</w> 1933829
recep tors</w> 1933340
f ail 1931497
bec ause</w> 1929825
peri od</w> 1929578
fu sion</w> 1927571
rati o</w> 1927537
enti al</w> 1926951
re pres 1926601
S 1</w> 1926259
und s</w> 1925531
fol d</w> 1924161
pl e 1923954
hi bi 1922694
lun g</w> 1921716
in ten 1921605
meth ods</w> 1919488
ab il 1918187
high ly</w> 1915041
t ural</w> 1914956
ub l 1913915
a ro 1912435
neuron s</w> 1912269
B AC 1909719
ogen ic</w> 1906846
EC TI 1905146
tes ted</w> 1902489
subj ects</w> 1901922
N e 1901396
mit ochond 1901109
te n</w> 1897929
el s</w> 1897509
activ ated</w> 1897185
S P 1895616
follow ed</w> 1895442
in formation</w> 1893206
isol ated</w> 1892421
contro ls</w> 1891227
K G 1889667
con duc 1887811
u k 1886279
inj ur 1886058
HI V</w> 1883722
N F</w> 1882946
inte gr 1876758
org an 1872220
mus cle</w> 1872140
p ec 1870869
mat ory</w> 1870637
m ight</w> 1867439
os te 1865100
ff er</w> 1864799
expos ure</w> 1864075
T able</w> 1863310
A R 1862718
ta in</w> 1860946
A r 1860764
ter al</w> 1857521
em ia</w> 1856647
therapeu tic</w> 1856579
h ar 1855059
proper ties</w> 1853319
the re 1852926
differen ce</w> 1852301
sequenc es</w> 1851742
m uc 1851686
k g</w> 1847658
h or 1847626
ven tion</w> 1844775
M S</w> 1844760
inter actions</w> 1841315
ur ation</w> 1838963
O B 1838515
sec re 1837552
dis tin 1836314
l es</w> 1835551
pos ed</w> 1834604
n ess</w> 1834122
en s</w> 1833617
o therapy</w> 1829050
sugges ting</w> 1827094
en e</w> 1826933
he tero 1826906
termin al</w> 1826715
l ong 1823862
E n 1823110
wo uld</w> 1822617
I F 1821915
is h</w> 1821740
pr acti 1820008
s us 1819611
Th us</w> 1819156
or ig 1817220
RO U 1816393
ad en 1811604
s al 1809229
ro sis</w> 1809082
o p</w> 1808188
in ce</w> 1806040
n ing</w> 1804064
BAC KG 1804013
BACKG ROU 1803858
BACKGROU ND</w> 1801813
grea ter</w> 1800924
in dependent</w> 1799771
at ures</w> 1799114
p sy 1797660
stra teg 1795509
residu es</w> 1793397
ag ement</w> 1792231
T w 1789768
de ath</w> 1789034
pa in</w> 1785403
i f</w> 1782825
o sis</w> 1782029
f low</w> 1781167
op ath 1780740
l eng 1780470
sel f</w> 1780354
d am 1779852
a ins</w> 1779798
n an 1778930
m m</w> 1777673
c re 1775051
inflam matory</w> 1774393
su s</w> 1771108
apopto sis</w> 1770764
indic ate</w> 1770355
l og 1769742
u e</w> 1769685
ic ations</w> 1767867
sensi tivity</w> 1766555
n a 1764350
valu e</w> 1763301
C a 1763087
direc t</w> 1763062
ag on 1762978
tri c</w> 1762666
ren al</w> 1759399
pe a 1758921
I C 1757527
par ame 1757311
lif e</w> 1756165
qu ality</w> 1755854
g ly</w> 1753594
pathw ays</w> 1753336
press ure</w> 1752060
decre ase</w> 1748871
medi um</w> 1747006
car ri 1746237
be ing</w> 1745457
ul ations</w> 1744571
ane ous</w> 1744209
h owever</w> 1743898
assess ed</w> 1742866
og ni 1740541
u t</w> 1737786
D e 1737667
ur s</w> 1737579
b ro 1736364
tran si 1735521
suc c 1735364
CONCLUSI ON</w> 1734995
ag ed</w> 1734482
m ass</w> 1733781
temper ature</w> 1728331
P T 1728167
o s</w> 1726175
o ve 1725560
ph ot 1725171
indic ated</w> 1722282
gluc ose</w> 1722270
in sul 1722090
le uk 1721826
partic ip 1721714
b il 1720239
c o</w> 1719653
measu re 1717414
et al</w> 1715836
en co 1714858
re mo 1714478
erg y</w> 1713214
regul ated</w> 1712254
disc us 1710177
d own</w> 1709336
syndrom e</w> 1703232
it ation</w> 1701632
r at</w> 1700230
en ing</w> 1698629
res t</w> 1698298
pl ay</w> 1695643
poin t</w> 1694268
ac cor 1694001
P 1</w> 1693816
O R</w> 1692738
ac hi 1692709
et e</w> 1692305
suppor t</w> 1690030
im aging</w> 1688871
p ubl 1688567
wee ks</w> 1688302
t oc 1687815
n er</w> 1687740
st age</w> 1685681
C C 1685662
frequ ency</w> 1685503
r ic 1682505
nucle ar</w> 1682068
g e 1681243
prolifer ation</w> 1680404
cal cul 1677594
tin al</w> 1677538
sampl e</w> 1677361
pepti de</w> 1676631
S up 1675525
ri g 1674744
st atis 1674743
syn thesis</w> 1673261
anc es</w> 1673260
ll ed</w> 1673124
heal th 1671593
om e 1671288
R ec 1669025
a ver 1668129
avail able</w> 1667781
dise ases</w> 1667400
ex hibi 1666423
an ds</w> 1663846
OB J 1663174
pos t</w> 1662672
tex t</w> 1662210
m il 1660797
ECTI V 1660627
vol um 1660217
sec ond</w> 1659350
immun o 1651729
OBJ ECTIV 1651308
gener ation</w> 1651023
ul ated</w> 1649931
regi ons</w> 1649756
inhibit ors</w> 1645000
rele ase</w> 1644966
ur ther 1643626
oph ag 1642564
detec tion</w> 1639696
tu red</w> 1638948
urther more</w> 1638723
in trac 1638405
ca used</w> 1637815
P s</w> 1636510
o m</w> 1636147
at ten 1635267
M D 1634454
m ag 1632788
pol ym 1625770
mi x 1625274
stra in</w> 1624391
B i 1624354
rel ative</w> 1623443
s l 1622129
K 1</w> 1622103
es tim 1620237
se v 1620068
C .</w> 1619589
B S</w> 1619121
stra ins</w> 1618811
tim es</w> 1618309
acc um 1618297
inf ected</w> 1617873
symptom s</w> 1611443
cl assi 1610750
d ys 1610662
in es</w> 1610141
ti l 1609628
compl ex 1609171
a sis</w> 1607879
cul ture</w> 1606287
er al</w> 1605530
rec or 1605117
oc k 1600277
yl ated</w> 1599806
olog ic</w> 1599735
d ynam 1597666
un i 1596776
ev en</w> 1595622
F urthermore</w> 1595422
mor t 1594531
A G 1593410
ug s</w> 1592929
A P</w> 1592274
le ast</w> 1591561
the si 1591389
m er 1590884
form s</w> 1589440
cl usion</w> 1588189
consi dered</w> 1588125
ep ith 1587857
C o 1587166
identi fy</w> 1586930
vi ties</w> 1585563
ent ary</w> 1584183
ste m</w> 1583939
de grad 1583453
C A</w> 1582344
distribu tion</w> 1582280
stand ard</w> 1580238
ph ar 1580205
leng th</w> 1579714
A n</w> 1577531
cat aly 1575039
p 5</w> 1573562
str ic 1573195
an ding</w> 1573180
A I 1572038
P D 1572027
re x 1571999
aff ected</w> 1571678
b el 1571359
entr al</w> 1568937
am oun 1568211
y s</w> 1567408
cy cle</w> 1567327
c ere 1567097
iz e</w> 1566226
mut ants</w> 1566089
is on</w> 1565936
experim ental</w> 1565829
anim als</w> 1565816
I g 1564535
teri al</w> 1564500
er c 1564168
ca use</w> 1561633
ke y</w> 1560714
ar tic 1560329
individ u 1560084
pa ir</w> 1559273
anc y</w> 1557222
fr ag 1555700
analy ses</w> 1554894
func tions</w> 1554721
in e 1554179
p i 1553323
struc tures</w> 1552690
P re 1551929
em ents</w> 1551841
se par 1549948
o red</w> 1548608
confir med</w> 1546289
E S</w> 1545592
at ors</w> 1545358
exper i 1545140
scop y</w> 1543887
sign al</w> 1543411
contin u 1543284
GF P</w> 1542581
es c 1542488
tissu es</w> 1541302
ren ce</w> 1540971
pl ied</w> 1539819
he art</w> 1539659
abs ence</w> 1539466
fol low</w> 1538937
A s 1537476
str ate</w> 1534427
dr ugs</w> 1534067
sev ere</w> 1533447
pres ented</w> 1531203
or ing</w> 1530704
pos e</w> 1530600
e sts</w> 1526991
syste ms</w> 1523816
g as 1522737
P I 1521650
dom in 1521528
enh anced</w> 1521088
d ri 1520555
par t</w> 1518913
o t</w> 1517134
ho w</w> 1515337
sp ective</w> 1514309
p y 1513697
previ ous</w> 1512086
pro duced</w> 1508825
ro n</w> 1507420
consis tent</w> 1503579
og ether</w> 1502335
st atus</w> 1501233
pl ant</w> 1500987
un it</w> 1500016
w as 1499468
man agement</w> 1498404
are a</w> 1498092
E R</w> 1496648
sol ution</w> 1496472
al low 1496163
B M 1495444
w ay</w> 1492852
contri bu 1492487
th us</w> 1491273
on es</w> 1491175
ti ally</w> 1491039
P L 1490072
differenti ation</w> 1489540
su ff 1489332
fi ve</w> 1488738
op tim 1488400
i an</w> 1488318
p tion</w> 1487690
perform ance</w> 1487485
I II</w> 1486739
istr ation</w> 1486415
com mun 1484200
A D</w> 1483960
si l 1480669
h er 1480402
compo unds</w> 1480264
em br 1479422
mort ality</w> 1475881
ir e</w> 1473777
do es</w> 1472939
injur y</w> 1472650
ow n 1471561
eff ic 1471383
m ig 1471141
insul in</w> 1470987
feren ce</w> 1467211
sensi tive</w> 1466882
form ed</w> 1465077
con t 1464536
stim ulation</w> 1463932
promo ter</w> 1461657
S e 1461189
pro f 1461150
embr y 1459829
characteri zed</w> 1457424
del i 1456912
ti n</w> 1453794
r ing</w> 1450895
us e 1450497
ra y</w> 1450464
d ge</w> 1450143
C l 1447719
M R 1447451
d os 1447150
del e 1446877
sugg ests</w> 1445341
surg ical</w> 1444927
phar mac 1443424
S N 1438903
medic al</w> 1437585
e y 1436496
con f 1436459
ic es</w> 1436358
en ergy</w> 1434079
os ome</w> 1433696
p ap 1432574
addi tional</w> 1432379
P D</w> 1432130
relationsh ip</w> 1432129
parame ters</w> 1430609
individ ual</w> 1430173
ay ed</w> 1429222
im pa 1428680
bu ffer</w> 1428224
RN As</w> 1428041
b ene 1427005
el ev 1426679
den sity</w> 1426176
ol y 1425778
vi su 1425681
e red</w> 1425130
m el 1424093
accor ding</w> 1423436
cor respon 1423165
i sts</w> 1423040
prob le 1422908
i di 1422159
as se 1421821
p p 1421477
ro gen</w> 1421027
is o 1418019
outcom es</w> 1417815
S T</w> 1416695
s mo 1415406
r e</w> 1413011
mis sion</w> 1412370
i de 1411433
c en 1411314
P A</w> 1410689
ond ary</w> 1410681
pl o 1408457
investig ate</w> 1408066
re ad 1407907
characteris tics</w> 1407857
sugges ted</w> 1407570
sub sequ 1407376
h al 1407121
in de 1406600
t op 1404208
evalu ate</w> 1404096
ess ential</w> 1403261
acti vities</w> 1402555
pre val 1402197
direc tly</w> 1401444
in duction</w> 1401344
ran dom 1401242
psy ch 1401196
demonstr ate</w> 1401125
resul ted</w> 1400669
all el 1399772
ag ents</w> 1399128
molec ules</w> 1398765
C 1</w> 1397159
fluoresc ence</w> 1396032
r up 1394882
correl ation</w> 1394194
imp act</w> 1392525
compl ete</w> 1391478
i m</w> 1390136
ol ution</w> 1390009
inv a 1389826
rec ent</w> 1389065
incub ated</w> 1388428
e tes</w> 1388269
tom y</w> 1387299
ograph ic</w> 1386974
ord ers</w> 1386706
ocy te</w> 1385825
cri tical</w> 1385313
ma xim 1384711
re pe 1384625
v ec 1383457
i . 1381355
mon it 1380978
hydro x 1378314
d o</w> 1376670
bi ological</w> 1376475
c y</w> 1376453
dam age</w> 1376049
ac ids</w> 1375742
resul ting</w> 1375577
gener al</w> 1375544
e p</w> 1375415
over all</w> 1374717
is h 1373354
D E 1373194
le ft</w> 1371877
volum e</w> 1371562
t le</w> 1370779
pre gn 1370555
oti de</w> 1370515
influ ence</w> 1370506
gener ated</w> 1370000
pl ant 1369111
Th ere 1368791
- 1</w> 1368272
struc tural</w> 1364401
effic acy</w> 1364224
coll ected</w> 1363478
H ere</w> 1363398
in ity</w> 1361491
an e</w> 1360583
gr am 1359307
con ser 1358816
mo l</w> 1357656
ti cles</w> 1356850
ogen ous</w> 1356661
d own 1355789
s or 1355034
in tro 1354953
incre asing</w> 1354715
re tro 1352463
t ation</w> 1352258
spec tr 1350930
appro xim 1350324
ch ain</w> 1349474
process es</w> 1349322
outcom e</w> 1348355
v acc 1348019
e ding</w> 1347699
health y</w> 1347132
le sions</w> 1346014
tw or 1345386
admin istration</w> 1344045
spec tro 1343396
fe atures</w> 1343370
U ni 1342300
sub strate</w> 1342113
k ers</w> 1341580
gl ut 1341379
ev ents</w> 1340205
tech n 1338834
cardi ac</w> 1338829
oc ardi 1336608
ful ly</w> 1336208
m am 1333434
bet ter</w> 1333090
y ro 1331542
W T</w> 1330732
up on</w> 1329806
carcin oma</w> 1329165
ly s 1328202
prog ression</w> 1327876
d ou 1327782
ne e 1327200
plic ations</w> 1326260
individu als</w> 1324733
m L</w> 1324664
al though</w> 1324345
rea m</w> 1324148
l ed</w> 1323972
C H 1322846
R T</w> 1322815
A t</w> 1321563
si s 1321244
sub st 1319135
c ross</w> 1319065
distin c 1318809
mon ary</w> 1318332
There fore</w> 1318224
th rom 1318169
Sup plem 1318056
ver sity</w> 1317999
o ve</w> 1317165
in ary</w> 1315782
C s</w> 1314269
P K 1314114
su m 1313928
B 1</w> 1312311
he pati 1311885
plas m 1310912
su per 1309889
cy t 1308777
conduc ted</w> 1308577
inhibi ted</w> 1307516
ass ess</w> 1307473
ati vely</w> 1307071
cl os 1305815
N O 1303663
ove rex 1303142
s ch 1302903
all eng 1301865
se en</w> 1301093
ad he 1299526
cr yst 1299272
resist ant</w> 1298456
b ound</w> 1298274
t ol 1296097
initi al</w> 1295739
yc in</w> 1295636
ver sus</w> 1294804
the tic</w> 1294383
techni que</w> 1293779
t able</w> 1293467
ol s</w> 1293182
ass ays</w> 1293072
s af 1292707
ro p 1291882
sel ected</w> 1291210
P ati 1290789
ac cur 1290596
di um</w> 1290582
M ore 1290530
cl us 1287918
fi eld</w> 1287302
sec ondary</w> 1287095
expres sing</w> 1286449
st ron 1286175
P S</w> 1285315
M S 1284577
phosph ate</w> 1283668
min im 1282875
mitochond rial</w> 1282655
More over</w> 1282259
l l</w> 1280177
st ability</w> 1279482
inte s 1276166
adul t</w> 1275677
s .</w> 1274377
trans l 1272944
mal e</w> 1272231
al one</w> 1271013
h ep 1270354
pos si 1269825
gen ome</w> 1269621
st ream</w> 1269293
aver age</w> 1269115
sta ining</w> 1268172
v ant</w> 1267666
teri or</w> 1267428
evalu ation</w> 1265993
impro ved</w> 1265769
le t</w> 1265424
P h 1264670
ac ts</w> 1264654
def ined</w> 1264040
P E 1263991
ch alleng 1263629
aff ect</w> 1263134
s w 1263008
nor mal 1262939
ne l</w> 1261834
inter n 1261292
s y</w> 1260898
sup plem 1259119
inc idence</w> 1258939
pat tern</w> 1258083
tern al</w> 1257948
combin ed</w> 1255232
con tent</w> 1253858
ific ation</w> 1253346
ul t 1253286
R 1</w> 1253061
indic ating</w> 1252251
el ine</w> 1252199
a m</w> 1251636
pat tern 1251516
radi ation</w> 1251165
qu es</w> 1250195
C a</w> 1249596
B P</w> 1247748
ol ic</w> 1247101
ang i 1246790
i on</w> 1246527
A ND</w> 1246258
so ur 1246229
ic ular</w> 1245503
m ade</w> 1241669
ud e</w> 1240701
phen otype</w> 1238634
s on</w> 1237029
ne twor 1232900
cor related</w> 1232461
physi cal</w> 1230753
iti s</w> 1230674
medi ate</w> 1230128
of ten</w> 1229645
t ogether</w> 1229562
mal ign 1229173
A S 1228115
sk in</w> 1227092
complex es</w> 1226505
amin ation</w> 1225932
On e</w> 1225829
st able</w> 1225171
incre ases</w> 1223971
ap plied</w> 1222848
diab etes</w> 1222657
ch arg 1222130
F 1</w> 1222000
metabol ism</w> 1221426
de sign 1221348
nec ess 1221028
c ou 1220572
en s 1220382
ab ove</w> 1220302
ti li 1219937
si x</w> 1219815
tin e</w> 1218953
hor mon 1218760
at al</w> 1218187
compar ison</w> 1217547
val id 1217045
U n 1217005
degrad ation</w> 1215176
b ur 1214894
en si 1212680
establ ished</w> 1212520
au th 1212455
ev ed</w> 1211774
G en 1211323
the m</w> 1211017
end oth 1210750
R I 1210475
d oc 1210445
fail ure</w> 1210035
requ i 1208699
lin ked</w> 1208531
A 1</w> 1208484
ad ap 1206401
hospit al</w> 1205035
N a 1204945
bacteri al</w> 1204612
c entral</w> 1204407
recei ved</w> 1204273
m ation</w> 1204067
gi ven</w> 1204033
dis orders</w> 1203009
compon ents</w> 1200001
rec ur 1199327
there fore</w> 1198656
id es</w> 1197595
y i 1197593
sh ort</w> 1196266
H E 1193153
lab el 1193109
w ide</w> 1191809
ep t</w> 1191504
lim ited</w> 1190611
dom ains</w> 1189068
u ally</w> 1188369
aly sis</w> 1188288
mo ti 1187860
M R</w> 1186610
se g 1185055
provid ed</w> 1184899
con j 1184027
C l</w> 1183909
ol ig 1183753
t ow 1182834
μ g</w> 1182598
ter y</w> 1182047
L e 1182023
d a 1181079
li ter 1181013
le ad</w> 1180883
mig r 1179336
ho urs</w> 1176567
l it 1176148
bi qu 1175904
con ver 1174342
osom al</w> 1174308
aff inity</w> 1174072
k en</w> 1173419
al tern 1172893
sti ll</w> 1172879
T 1</w> 1170963
contro lled</w> 1170477
pa ren 1169986
g li 1168960
ow le 1167730
D 1</w> 1167379
sol u 1166879
p anc 1165893
lip id</w> 1165827
o ur 1165087
ar ds</w> 1164643
g a 1164348
OBJECTIV E</w> 1164348
re li 1163573
gl y 1163442
plant ation</w> 1162401
gen e 1162084
gen ic</w> 1161996
sig n</w> 1161737
approxim ately</w> 1161623
h ost</w> 1160953
r h 1160914
K 2</w> 1160651
2 A</w> 1159303
ne ed</w> 1156868
om et 1155136
pro posed</w> 1154426
tri al</w> 1154061
at ric</w> 1153220
tri als</w> 1151773
preval ence</w> 1151388
s co 1151037
tion ally</w> 1150888
proc ed 1150762
iz ing</w> 1150096
assess ment</w> 1149363
AT P</w> 1149349
enc es</w> 1148091
pattern s</w> 1147983
ro x 1147330
re plication</w> 1146554
ci um</w> 1146114
di g 1145006
ac ross</w> 1144982
un its</w> 1142569
v entr 1139826
w id 1138818
b or 1138689
p ite</w> 1137986
sc ore</w> 1136871
CD 4</w> 1136435
o w</w> 1136279
ro us</w> 1136022
succ ess 1135031
rig ht</w> 1134815
N o</w> 1131012
Wh en</w> 1130169
stra tes</w> 1130153
M A</w> 1129056
intrac ellular</w> 1128524
. 0</w> 1128408
og n 1128239
abl es</w> 1126709
eff icient</w> 1126195
ra m</w> 1125028
calcul ated</w> 1124310
i ally</w> 1123064
lab or 1122978
E ff 1122816
p 1</w> 1122513
produc ts</w> 1121650
fac il 1121058
accum ulation</w> 1118807
pr e</w> 1118399
tr as 1118136
id ne 1117979
A b 1117603
anti gen</w> 1117571
underst anding</w> 1117353
met ast 1117273
v ent</w> 1116998
otyp es</w> 1116923
d on 1116543
fi x 1116001
pro toc 1115811
loc ation</w> 1115780
p s</w> 1115629
an al 1115355
ur se</w> 1114530
te ly</w> 1114115
v s.</w> 1113149
A A 1111955
ut ure</w> 1111615
S O 1111172
il i 1110086
distinc t</w> 1109673
y e 1108802
n m</w> 1108078
par t 1107545
acti n</w> 1107129
m en</w> 1106191
C S 1105939
owle dge</w> 1104596
metabol ic</w> 1104541
O n</w> 1103888
P 2</w> 1103850
am ide</w> 1103735
peri ph 1103246
m es 1102781
in ation</w> 1102495
re fl 1101962
epith elial</w> 1101675
physi ological</w> 1100237
u biqu 1099929
GF R</w> 1098819
V E 1096879
rema ins</w> 1096372
a im</w> 1095887
es tern</w> 1095720
ad ded</w> 1095383
enzym es</w> 1095301
L I 1095161
s it 1095028
b its</w> 1094877
p an 1094627
G 1</w> 1094352
dou ble</w> 1094184
quanti t 1094083
Tw o</w> 1093942
pop ulations</w> 1093197
p en 1093163
cor rec 1092201
rec ru 1091699
meth yl 1091313
ex ampl 1090630
behavi or</w> 1089955
ur ine</w> 1089723
asse mb 1089600
mod ul 1088470
exp ected</w> 1088006
l ay 1087078
pl ants</w> 1085962
om as</w> 1085934
fem ale</w> 1085768
inj ection</w> 1085750
sup pres 1085580
pur ified</w> 1085530
og en</w> 1085314
M C 1084197
ch ed</w> 1084136
m id 1083962
transp ort</w> 1082799
U sing</w> 1082463
contri b 1082338
cap ac 1081942
S .</w> 1081622
syn ap 1081534
inde x</w> 1081105
rec ip 1080282
1 A</w> 1080271
loc al</w> 1079441
e al</w> 1079280
He al 1078586
T r 1078243
d at 1077375
proced ure</w> 1076731
compl ications</w> 1076636
cou pl 1076126
l or 1075591
vari ants</w> 1075013
sur ve 1074931
carri ed</w> 1073865
S im 1073842
er ing</w> 1073598
n s</w> 1073416
S E 1073103
ta il 1072764
Fig . 1072328
er ic</w> 1072162
impro ve</w> 1071845
col i</w> 1071131
induc e</w> 1070722
us t</w> 1068831
re pair</w> 1068174
cal cium</w> 1068021
Q u 1067588
te sts</w> 1066455
liter ature</w> 1066032
ob j 1065413
scre ening</w> 1064887
fr ac 1064184
reg ar 1063789
mo der 1063655
lead ing</w> 1063277
s in</w> 1063026
ecti ng</w> 1061605
rec ently</w> 1061202
al ter 1061086
k idne 1060777
oc k</w> 1060131
L 1</w> 1059485
panc re 1057878
H A</w> 1057795
Pati ents</w> 1057689
pri or</w> 1057598
ul arly</w> 1057279
ven ess</w> 1057253
th al 1056471
ph yl 1056336
M P</w> 1054031
st ri 1052746
y oun 1052654
mac roph 1052329
hyper ten 1051646
enc ed</w> 1050492
pul monary</w> 1049719
mag ne 1049038
G G 1048748
revie w 1048304
ful l</w> 1046951
regul atory</w> 1046562
C N 1045820
P R 1045154
a b</w> 1044712
artic le</w> 1043173
S E</w> 1043101
e .</w> 1043098
O R 1040154
consi der 1039970
L i 1038544
modi fied</w> 1037267
transcrip tional</w> 1037242
provid es</w> 1037063
co ron 1036528
ut ri 1035461
occur red</w> 1035295
electro n</w> 1035159
M T 1035137
re ver 1034044
my el 1032506
tol er 1032307
correspon ding</w> 1032116
fib ro 1031235
p ow 1031224
ron e</w> 1031223
kn owledge</w> 1028859
stro ng</w> 1028426
A C</w> 1027946
ma in</w> 1027478
pro mis 1027235
transf ected</w> 1026919
mark ers</w> 1026425
A T</w> 1025743
pe rox 1025093
H D 1024688
medi an</w> 1024277
d ate</w> 1023412
M y 1022103
vir uses</w> 1021805
measure ments</w> 1021486
bl as 1020594
al es</w> 1019863
r ab 1019542
lac k</w> 1019255
go od</w> 1018661
mel an 1018386
re stric 1018259
ag ent</w> 1017894
lig and</w> 1017885
k er</w> 1017785
bi om 1017543
loc ated</w> 1017503
are as</w> 1015474
cul tured</w> 1013833
F in 1013093
t y 1011592
N S 1011415
sta ined</w> 1011124
gu id 1010056
nucle otide</w> 1009604
dic al</w> 1009429
b ar 1009232
A R</w> 1008980
pro por 1007979
intes tinal</w> 1007819
Th e 1007650
ar tery</w> 1007183
cyto plas 1007144
or ity</w> 1007028
sub stitu 1006939
g lob 1006402
it al</w> 1006093
oxy gen</w> 1005370
ir atory</w> 1004601
In te 1004541
vie w</w> 1004275
per s 1003955
specific ity</w> 1002801
is ed</w> 1002579
br al</w> 1002127
s om 1001858
erg ic</w> 1001038
ap plication</w> 999993
gr ade</w> 998782
ic ro 998672
ac qu 998378
up take</w> 998150
b asis</w> 997191
poly morph 996756
techni ques</w> 995718
loc alization</w> 995621
expos ed</w> 995204
is tic</w> 994911
environ ment</w> 994555
use ful</w> 994485
T ran 994368
diagnos tic</w> 994221
elev ated</w> 993994
cri teri 993968
show s</w> 993774
el ls</w> 993458
on ed</w> 993296
D is 992095
partic ular</w> 991342
appro pri 990949
t ory</w> 989616
d ra 989549
S ur 989371
contrib ute</w> 988507
th yro 988442
import ance</w> 987499
st ar 987252
c u 987008
includ e</w> 986649
w ent</w> 986139
ch an 985829
inter vention</w> 985630
osom es</w> 984597
μ M</w> 983850
amoun t</w> 983367
obser v 982180
b al 979863
tes ting</w> 979049
proc e 978358
sel ective</w> 978109
on ucle 977763
pro gram 977237
Ad di 977113
ser ies</w> 976980
tox icity</w> 976749
bi r 976262
bas eline</w> 976189
necess ary</w> 975645
T C 975207
c epti 975183
vec tor</w> 974707
Supplem entary</w> 974354
M 1</w> 974077
hy bri 973939
u tili 973643
inflam mation</w> 973456
periph eral</w> 973429
i. e.</w> 973387
predic ted</w> 973220
B oth</w> 971666
de m 971488
rap id</w> 971094
ac coun 970490
m an</w> 969869
g am 969755
S D</w> 969479
S er 969258
- - 968572
kn ock 968349
stimul ated</w> 967807
thesi zed</w> 967227
chem otherapy</w> 967172
st abil 966320
rec ogn 966083
ste p</w> 965701
compon ent</w> 965554
ph il 964832
em b 964162
a im 963236
es pec 963081
particip ants</w> 961790
ic ul 961652
reduc e</w> 961567
pap er</w> 961108
o ff 960971
cl ass</w> 960550
espec ially</w> 960223
m ary</w> 959312
bac k 958776
de sign</w> 958459
reg ression</w> 958073
p ac 956043
exhibi ted</w> 955591
con stitu 954786
g s</w> 954532
in d 954329
ach es</w> 953475
com pe 953072
sh if 952897
ant agon 952337
conser ved</w> 952050
an ol</w> 951873
SC s</w> 951655
g el</w> 951168
targe ting</w> 950721
under went</w> 950599
on t 950036
wh ole</w> 949116
practi ce</w> 949084
se t 948682
em plo 948486
m en 948144
z ing</w> 947149
O 2</w> 947111
sus cepti 947093
B ec 946219
on set</w> 945801
chan nel</w> 945432
mi R</w> 945141
g ran 944753
ul ts</w> 943100
transf er</w> 942861
re plac 942586
b rea 942416
sc ale</w> 941399
ne w 941325
sul f 940842
pre pared</w> 940578
op o 939792
def iciency</w> 939583
A S</w> 939081
targe ts</w> 938992
i ble</w> 938889
z y 938231
is tered</w> 937802
ri b 937710
inf ections</w> 937471
anim al</w> 937419
o vascular</w> 937326
in ser 937024
S 2</w> 936865
lin ear</w> 936638
endoth elial</w> 936392
adul ts</w> 935940
capac ity</w> 935596
re combin 935362
tero l</w> 933750
sequenc ing</w> 933686
muc h</w> 933155
hist ory</w> 932990
f uture</w> 932886
o tic</w> 930970
discus sed</w> 930816
p ut</w> 930647
t a</w> 929719
P er 929396
secre tion</w> 929359
c ent 927621
cur ren 925498
cl u 925293
sco res</w> 925169
s ince</w> 924606
inhibit ory</w> 924572
ser v 924243
A N 924169
c m</w> 923112
chol es 921581
ye ast</w> 921387
al coh 921213
pl ays</w> 921096
abs or 920851
c ogni 920247
end ed</w> 920059
conj ug 919495
C lin 917979
so dium</w> 917499
subsequ ent</w> 916971
G l 916856
facil it 916471
bacteri a</w> 916220
strateg ies</w> 914842
re comm 914770
coron ary</w> 913956
e . 913376
ly ing</w> 913186
B e 912724
lo ad 912526
e duc 912492
agg reg 912457
aut ophag 912455
ex is 912016
o v 911524
con n 911321
at t 911315
op en</w> 910830
d uration</w> 910567
th i 910378
ri x</w> 910296
soci al</w> 908597
r ine</w> 908131
C ol 908029
O f</w> 907088
e ph 906935
migr ation</w> 906568
ar ing</w> 906481
G ro 905968
wee k</w> 905887
se e</w> 905873
co urse</w> 904625
cyt os 904581
partic ularly</w> 904354
kidne y</w> 903530
deli very</w> 903408
app ears</w> 902789
tem por 902305
nat ural</w> 901052
u ro 900917
is su 899196
pl ate 899056
con clud 898256
molec ule</w> 898217
I I 897979
al g 897556
fr action</w> 897257
o plas 896822
oxid ative</w> 896134
H R</w> 896101
or ies</w> 895368
A 2</w> 894565
de g 893986
cor e</w> 893188
measu res</w> 892832
P ar 891297
coh ort</w> 890897
exampl e</w> 890594
respon sible</w> 890294
neuron al</w> 890160
pepti des</w> 889840
cul tures</w> 888927
a teral</w> 888610
c anc 887224
occ ur</w> 887157
in a</w> 886882
man ner</w> 886235
sub unit</w> 885754
rele vant</w> 885172
M I 884241
eff iciency</w> 884142
ati c 883933
gro und</w> 883570
ventr icular</w> 883498
inten sity</w> 883123
sel ection</w> 882419
res tin 882282
dis order</w> 882107
T re 881796
fi t</w> 881688
l ast</w> 881325
Res ults</w> 880872
gre es</w> 880753
f as 880486
ex t</w> 880342
identi fication</w> 880022
appropri ate</w> 879885
tra ining</w> 879837
end ogenous</w> 879359
f ici 878054
de v 877520
po or</w> 877500
relati vely</w> 877431
quantit ative</w> 877168
stron gly</w> 876944
P C</w> 876376
n utri 875430
ad d 875321
ic an</w> 874301
poin ts</w> 874076
pre par 873803
produc t</w> 873700
ol ar</w> 872979
ca uses</w> 872867
venti onal</w> 872540
or i 872331
al ong</w> 872221
chrom at 871820
achi eved</w> 871716
ex erc 870457
acti ng</w> 868489
criteri a</w> 867788
un g 867081
deg ree</w> 866953
ner ve</w> 866730
al k 866542
extrac ellular</w> 865600
P BS</w> 865544
h ere</w> 865476
A p 865417
o di 865334
comp u 864245
S C</w> 863957
E .</w> 863288
dele tion</w> 862779
t as 862680
j o 862426
ag en</w> 862211
exam ine</w> 862108
til ity</w> 861698
inc or 861673
sen s 861402
hypo thesis</w> 860999
fe w</w> 860970
pro fil 860527
ro w</w> 860205
rec over 860105
me try</w> 860029
dis rup 859918
th ir 859903
pl ate</w> 859508
de n</w> 859459
mat ure</w> 859396
targe ted</w> 858749
col on 858100
e g 857794
de grees</w> 857738
ta ken</w> 855614
C y 855373
lead s</w> 855084
process ing</w> 855016
l ate</w> 854253
r are</w> 854060
nee ded</w> 853711
se e 853353
mo tor</w> 852285
H 2 852073
ch lor 851250
condi tion</w> 850852
t on 850664
t on</w> 850206
micro scopy</w> 849143
se ro 848228
ati n 847706
con st 847398
altern ative</w> 847369
M a 847276
H 1</w> 846140
restin gly</w> 845779
M T</w> 845101
is chem 845027
g .</w> 844947
S h 844213
ex amination</w> 843804
bi op 842706
T ri 842373
Wh ile</w> 842200
o st 841448
ro les</w> 840137
diagnos ed</w> 839989
ul in</w> 839933
Heal th</w> 839873
li qu 839152
label ed</w> 839086
tot ox 839065
suff icient</w> 838918
pa rent</w> 838705
du res</w> 838493
gen omic</w> 838189
3 A</w> 838120
F L 838035
dos es</w> 837380
U SA</w> 837075
def icient</w> 835939
A mer 835864
publ ished</w> 835771
ann ing</w> 835751
v s</w> 835655
strateg y</w> 834777
e b 834388
org anis 834051
ar ch 833667
inhibi t</w> 832836
E GFR</w> 832377
P U 831731
an other</w> 830720
prog ram</w> 830668
sc i 830579
de ple 830494
mat rix</w> 830276
om es</w> 829338
re actions</w> 829333
T T 829302
ac e</w> 828765
av age</w> 828498
e tics</w> 827938
my ocardi 827580
se x</w> 826858
b enz 825957
resp iratory</w> 825707
l at 825659
visu al</w> 824509
C ell</w> 824490
vari ation</w> 824027
im ens</w> 823944
subst anti 823893
pregn ancy</w> 823888
go ing</w> 823293
dis co 823040
bin d</w> 822638
ul tras 822334
high est</w> 822140
tr ation</w> 821414
ar ity</w> 820440
recombin ant</w> 820336
e. g.</w> 820233
D ata</w> 820174
al tered</w> 820065
plasm id</w> 819926
IF N</w> 819870
bi t</w> 819862
hal f</w> 819356
B D</w> 818659
S H</w> 817387
scop ic</w> 817202
conc er 816726
aut o 816600
networ k</w> 815542
i st 815096
op e</w> 815020
res h 814995
appro aches</w> 814435
perc ent 814422
s pl 813985
I V</w> 813649
fin al</w> 813240
E N 813065
statis tically</w> 812764
throug h 812266
D T 811983
Inte restingly</w> 811635
B io 811344
meth ylation</w> 811297
uni que</w> 811273
da ily</w> 810684
ra ther</w> 810250
design ed</w> 809857
ut aneous</w> 809402
i sion</w> 808499
in t</w> 807857
ar c 807735
ti vely</w> 807710
under lying</w> 807199
le ar 806473
id ity</w> 805327
cogni tive</w> 805168
hep at 804522
incor por 804041
er y 804000
hormon e</w> 803834
C ells</w> 803721
w ard</w> 802591
p al 801971
nucle us</w> 801810
estim ated</w> 801681
B y</w> 800761
mark er</w> 800248
es ted</w> 800176
regul ate</w> 800125
i b</w> 799712
devel op</w> 799652
a de 799599
proce dures</w> 799027
sp inal</w> 798689
ari ly</w> 798662
ri f 797578
no w</w> 797573
fo od</w> 797033
tw are</w> 796748
W ith</w> 796498
impro vement</w> 796202
fici al</w> 795698
ren g 795678
m atic</w> 795519
overex pression</w> 794740
ox ide</w> 794219
S y 794044
bas al</w> 793815
ic a</w> 793613
main ly</w> 793437
auth ors</w> 791615
experi ence</w> 791514
occur s</w> 791414
R s</w> 791170
H B 791038
en ting</w> 790243
h yp 790211
R P</w> 790114
S S</w> 789706
Pro te 789699
sign als</w> 789664
pea k</w> 789513
Sim il 789499
fam il 789493
bi al</w> 789147
E 1</w> 788879
choles terol</w> 788795
inva sive</w> 788544
u tes</w> 788458
meas ure</w> 788264
pro state</w> 788023
olog ies</w> 787692
compo und</w> 787362
i x</w> 786915
en ric 786521
deri v 786491
h and</w> 785848
canc ers</w> 785840
C C</w> 785692
Fig ure 785684
im ages</w> 785099
re st 784711
recover y</w> 784686
D N 784664
p 2</w> 784574
treat ments</w> 784342
as ta 783259
descri be</w> 783016
S ub 782674
cy totox 780255
bac k</w> 780105
recor ded</w> 780067
chrom osome</w> 779510
ex tent</w> 779329
memb ers</w> 779252
re al</w> 778870
admin istered</w> 778860
F urther</w> 778694
domin ant</w> 778566
anti bio 778216
me mor 778062
b ul 777578
c um 777258
produc e</w> 777201
medi a</w> 777074
a or 776738
protoc ol</w> 776693
vi sion</w> 776679
inter val</w> 776588
la teral</w> 776450
istr y</w> 775971
res sive</w> 775178
it on 774584
repor ts</w> 774520
es tion</w> 774454
exerc ise</w> 773427
val ent</w> 772950
meth yl</w> 771837
poten t</w> 771567
L D 771490
ma terial</w> 770656
ch ann 770600
O F</w> 770528
la ter</w> 770486
o od</w> 770470
signific ance</w> 770363
he ad</w> 769747
st reng 769029
b est</w> 768825
syste mic</w> 768641
te x</w> 768518
er r 768470
membran es</w> 768452
f av 768011
ve red</w> 767815
ros ine</w> 767433
os cop 767254
am in</w> 766863
H 2</w> 766756
i e 766487
def ects</w> 765771
specific ally</w> 765675
di lu 765212
hepati c</w> 765018
in tra 764930
mech an 763459
S ince</w> 763086
teri als</w> 762966
P P 762920
di et</w> 762897
H y 761848
n ur 761594
flu id</w> 761421
dys function</w> 761242
ol der</w> 761100
U S</w> 761035
om y 760793
magne tic</w> 760577
I P</w> 760176
asi c</w> 759889
b on 759743
pro xim 759725
dynam ics</w> 759634
A mon 759545
O H</w> 759519
random ized</w> 759187
so f 759148
sour ce</w> 758902
incub ation</w> 758569
lit tle</w> 758247
esti on 757597
un known</w> 757508
R 2</w> 757359
maxim um</w> 757218
was hed</w> 757078
e ight</w> 756531
W estern</w> 756140
trans plantation</w> 755173
pre g 755113
re as 754607
f at 754135
E 2</w> 754104
re ference</w> 753956
C Y 753883
S M 753474
observ ations</w> 753327
d y 752321
c DNA</w> 752302
malign ant</w> 752287
res olution</w> 751440
hem at 750588
develop ing</w> 750289
c ost</w> 750126
comp are</w> 749669
ter ms</w> 749319
Bec ause</w> 749196
ve si 748654
ta il</w> 748590
zy g 748099
tion ing</w> 747858
ap pe 747790
youn g</w> 747396
sh or 747073
H P 746761
con ventional</w> 746435
larg er</w> 746273
liqu id</w> 746262
chann els</w> 746192
si c</w> 746132
tre m 745697
tra um 745247
vari ety</w> 745106
invol vement</w> 744911
su mp 744887
C M 744306
Amon g</w> 744141
allel e</w> 744043
T NF</w> 743723
assemb ly</w> 743420
met asta 743219
o ple</w> 743027
Fin ally</w> 742520
carb on</w> 742228
commun ity</w> 742143
transp or 741701
Im mun 739683
i ron</w> 739426
environ mental</w> 739355
H z</w> 739289
S ig 739014
G E</w> 738694
cardi ovascular</w> 738618
protec tive</w> 738603
labor atory</w> 738545
L A</w> 738222
post operative</w> 737832
g er 737365
gen s</w> 736988
re verse</w> 736888
om y</w> 736423
f at</w> 736091
ar ch</w> 735947
dat ab 735744
ing ly</w> 735721
ogni tion</w> 735552
me ans</w> 734885
saf ety</w> 734738
hyperten sion</w> 734738
of f</w> 734375
hy dr 734252
y et</w> 734110
ax is</w> 734095
N F 733407
o vari 733279
enco ding</w> 733181
proble ms</w> 732596
vari ant</w> 732567
adv anced</w> 732384
ad verse</w> 732315
st ages</w> 731981
m as 731799
impa ir 731609
maj ority</w> 730312
resi due</w> 730075
tro ph 729940
rec tal</w> 729408
b ase</w> 728712
isol ates</w> 728643
sc le 728507
St ud 728045
macroph ages</w> 727977
acc ep 727968
adv ant 727795
cle ar</w> 726438
ter ing</w> 725792
si a</w> 725516
review ed</w> 725340
tri gg 725270
i on 725167
M 2</w> 724423
D es 724196
ha ving</w> 724161
In ter 724136
can di 723592
T A 723292
vari able</w> 723046
hum ans</w> 723020
p ig 722846
prof ile</w> 722837
memor y</w> 722151
C I 722137
frequ ently</w> 722019
S L 721619
charg e</w> 721416
on c 721143
D a</w> 719899
ri a</w> 719722
diff icul 719424
k ine</w> 718428
oc l 718393
ab und 718356
MR I</w> 718156
y ed</w> 718012
sh ap 717778
enh anc 717738
O S</w> 717559
de pression</w> 717484
pl an 717277
detec t</w> 716490
estion na 716287
mon th</w> 716120
gas tric</w> 716031
os yn 715551
sim ple</w> 715318
possi bility</w> 715130
C F 714743
atten u 714590
ste ro 713767
re p 713506
con text</w> 713359
app ing</w> 713340
percent age</w> 713148
ho od</w> 712875
opath y</w> 712837
intern al</w> 712801
res on 712595
d op 712409
a k</w> 711722
gam ma</w> 711322
u preg 711258
A t 711183
chrom atin</w> 711170
rema ined</w> 710940
sof tware</w> 710643
s al</w> 710034
cer tain</w> 709830
eph al 709346
se ven</w> 709201
polym erase</w> 708969
sp ati 708532
ol esc 708293
down stream</w> 707917
regar ding</w> 707549
se ud 707351
AM P</w> 707351
lo op</w> 707104
kin es</w> 707082
inte rest</w> 707049
f etal</w> 707010
& amp 706878
& ;</w> 706878
hel p</w> 706829
D I 706540
dim ensi 706535
vari ables</w> 705892
al le 705824
f ish</w> 705794
poten tially</w> 705308
og lob 704644
ac hed</w> 704605
experim ent</w> 704536
grow n</w> 704489
t ase</w> 704411
monit oring</w> 704242
to sis</w> 704231
chem istry</w> 703858
fix ed</w> 703401
id al</w> 703130
um in 702934
ati ves</w> 702711
comple tely</w> 702302
im plic 702255
thir d</w> 702184
ch ing</w> 701890
enc ing</w> 701637
sis tent</w> 701223
G 2</w> 700967
mix ed</w> 700239
ly sis</w> 700065
metasta sis</w> 699982
ag re 699943
s le 699641
tr yp 699595
trans mission</w> 699461
cataly tic</w> 699406
C d 698644
adj u 698323
or th 698192
fac t</w> 697261
solu ble</w> 696936
κ B</w> 696835
conta ins</w> 696491
impa ired</w> 696066
pre vent</w> 696060
D uring</w> 695341
mal ian</w> 695304
os a</w> 694687
ag en 693889
alter ations</w> 693869
ty rosine</w> 693868
fin ding</w> 693816
P O 693636
apopto tic</w> 692852
transi tion</w> 692623
phosphor ylated</w> 692338
w ar 692240
er ro 692163
promo te</w> 691977
r am 691804
throm b 691467
A k 690997
an a 690710
T H 690671
neu tr 690542
measure ment</w> 690230
rec ognition</w> 690227
1 B</w> 689222
ul ate</w> 688921
th ic 688727
techn ology</w> 688123
pe ople</w> 688000
Uni versity</w> 687672
e ous</w> 687635
S ta 686862
hist o 686845
pl us</w> 686505
B R 686450
m T 685963
el etal</w> 685659
s ex 685639
mus t</w> 685493
ro om</w> 685414
M eth 685352
p 3</w> 684516
pur pose</w> 684332
cle avage</w> 683748
frag ment</w> 683720
l ing</w> 683361
rab bit</w> 683224
moder ate</w> 682909
k es</w> 682863
cere bral</w> 682796
long er</w> 682349
stro ke</w> 681508
lay er</w> 681488
ab normal 681474
investig ation</w> 681270
A .</w> 681191
T R</w> 680920
es si 680327
sub strates</w> 679828
f ying</w> 679283
pro p 679089
r al</w> 679055
b le 679047
f ung 678777
s qu 678551
diab etic</w> 678411
pl ayed</w> 678033
fat ty</w> 677699
sever ity</w> 677073
adj us 676756
dynam ic</w> 676717
par tial</w> 676327
chromat ography</w> 676107
P R</w> 675237
pol ar 675080
olog ically</w> 674981
P M 674873
requi res</w> 674697
H S 674188
at tr 673780
asp ase</w> 673453
com mon 673301
dimensi onal</w> 673301
A G</w> 673187
Sig ma</w> 673012
ar y 672811
m ia</w> 672346
S ci 672333
Clin ical</w> 671875
C 2</w> 671691
alcoh ol</w> 670987
B ased</w> 670026
i ety</w> 669841
ti l</w> 669790
M AP 669606
cor tex</w> 669187
A K 668978
to ol</w> 667925
a ir 667440
al b 667434
4 A</w> 667380
myocardi al</w> 667252
se em 667068
ap s</w> 666840
sump tion</w> 666770
ar ticles</w> 666430
el i 665515
met al</w> 665461
metast atic</w> 665085
ev ol 665065
min utes</w> 665043
V A</w> 664668
autophag y</w> 664522
e di 664470
se p 664467
n ic</w> 664372
par ticles</w> 664211
ill ary</w> 664206
R A</w> 663823
pre vention</w> 662729
re ly</w> 662590
P F 662497
obser vation</w> 662446
in ated</w> 662288
P r 662108
S pec 661599
mam malian</w> 660704
hypo x 660586
path ogenesis</w> 660500
TI ON</w> 660249
appe ar</w> 660033
el ements</w> 658438
c as 658430
B C 658182
le ts</w> 658036
ap pea 657877
sp e 657817
E l 657807
con form 657784
indic ates</w> 657778
s per 657702
appea red</w> 657402
ri tis</w> 657318
ten ce</w> 656887
vie w 656871
conta ined</w> 656789
optim al</w> 656163
enc ies</w> 655940
C or 655892
re present</w> 655673
oc amp 655467
ec t 655178
com position</w> 655145
sen se</w> 655018
P G 654915
T 3</w> 654796
al low</w> 654532
blas ts</w> 654424
ar ray</w> 654311
ep tion</w> 654275
p h</w> 654100
ad s</w> 653810
fer red</w> 653356
synap tic</w> 653355
rel ation</w> 653093
3 D</w> 652766
b ut 652340
s plic 652320
gly co 651886
T E 651877
s in 651662
H C</w> 651454
ca teg 651189
A A</w> 651063
curren tly</w> 651034
anal og 650309
b p</w> 649992
N or 649987
intr av 649740
it ary</w> 649398
ip s</w> 649345
organ ic</w> 649200
ar terial</w> 649034
B 2</w> 648362
kin etics</w> 648098
pa ir 648056
eff ici 647959
de tail 647876
ne um 647806
ecti vity</w> 646827
pre ven 645552
remo val</w> 645462
pl ates</w> 644882
co ding</w> 644666
ad olesc 644008
N K</w> 643656
over n 643632
i i</w> 643230
lo ro 642886
The y</w> 642884
ab dom 642569
differen tial</w> 642175
re active</w> 642121
tu al</w> 641953
mit ted</w> 641934
induc es</w> 641498
2 B</w> 641296
pancre atic</w> 640941
thyro id</w> 640828
ot rop 640585
model ing</w> 640244
in sig 640162
bloc k</w> 639650
tow ard</w> 639502
invol ving</w> 639475
sim ult 639470
le ar</w> 639384
ecti veness</w> 639076
n ic 638980
ne ural</w> 638315
oc ar 638131
ne ut 637817
em erg 637547
fem ales</w> 637465
ic le</w> 637002
hi pp 636923
T a 636663
v ical</w> 636644
P .</w> 636241
mal es</w> 636194
M o 636120
n ature</w> 636049
sp ont 635728
n M</w> 635463
at tri 635251
obj ective</w> 635153
gener ate</w> 634991
rif ug 634989
s ph 634732
Tre at 634539
k Da</w> 634463
N ot 634447
5 A</w> 634419
si vely</w> 634224
per me 634218
p ut 634184
col um 633723
adhe sion</w> 633587
S DS</w> 633048
an terior</w> 632963
an g</w> 632888
P I</w> 632813
tempor al</w> 632800
statis tical</w> 632520
protec tion</w> 632474
e f 632465
l ar 632437
numb ers</w> 632431
asi a</w> 632417
main tained</w> 632334
b en 632256
wh at</w> 632174
olec ular</w> 632122
B r 632032
C G 631645
tum our</w> 631552
aro und</w> 631400
ecti n</w> 631349
hetero gene 631216
hipp ocamp 631095
S ign 630976
las er</w> 630790
i ting</w> 630773
I R 630709
ane ously</w> 630669
i tes</w> 630356
N 1</w> 630164
H 3</w> 630156
foc al</w> 630134
Ig G</w> 630072
physi ci 629854
y lo 629603
chang ed</w> 628951
Th er 627956
id ine</w> 627945
P l 627690
ent y</w> 627484
or ts</w> 627308
u fac 627271
yi el 626948
th resh 626394
er cul 626306
te l 626191
at ase</w> 626172
li po 626150
hydrox y 626124
gn os 625951
th em 625830
com e</w> 625465
conf idence</w> 625324
reduc ing</w> 625232
H D</w> 624553
proble m</w> 624315
co ord 623945
gen otype</w> 623712
inj ected</w> 623470
g er</w> 623231
P 3</w> 623192
cont act</w> 623088
i ous</w> 622651
w all</w> 622648
propor tion</w> 622319
sol id</w> 622022
common ly</w> 622009
re f 621721
G N</w> 621357
an es 621163
v ar 620397
Th ree</w> 620341
ex trem 620095
op recip 619945
n t</w> 619811
exp an 619783
si RNA</w> 619776
accur acy</w> 619704
compar able</w> 619695
ar r 619665
ne ar</w> 618919
ot ox 618822
suppor ted</w> 618624
t am 618022
P ri 617717
man ufac 617416
bir th</w> 617177
g al 617161
hydro gen</w> 616600
vari ate</w> 616577
B C</w> 616306
CD 1</w> 616079
sy m 616078
r s 616018
con clusion</w> 615902
I T 615648
ocl onal</w> 615643
k er 615516
w ise</w> 615469
kn ess</w> 615150
melan oma</w> 614922
do m</w> 614743
m ing</w> 614154
el im 614074
im plem 614004
An alysis</w> 613800
ell a</w> 613699
hom olog 613565
prog nosis</w> 613253
M D</w> 613208
μ l</w> 613189
inter pre 612330
cor tical</w> 612278
w ays</w> 612128
in t 611679
p el 611439
es ity</w> 611044
trac t</w> 610953
st ates</w> 610862
venti ons</w> 610519
per fusion</w> 610162
moti f</w> 610033
F or 610017
s ection</w> 609985
c all 609820
therap ies</w> 609776
Treat ment</w> 609388
id ly</w> 609083
t or 608987
reson ance</w> 608911
inf ants</w> 608893
F s</w> 608779
bel ow</w> 608434
on omic</w> 608349
show ing</w> 608301
il s</w> 608120
chem o 607882
a ir</w> 607575
clin ically</w> 607567
ac u 607558
pro be</w> 607533
omet ric</w> 607191
as ym 606858
const ant</w> 606832
a qu 606416
equ i 605843
b ly</w> 605327
C ar 605112
form ing</w> 604802
bec ome</w> 604313
for ce</w> 604038
er ization</w> 603721
c ros 603670
am b 603462
syn thesized</w> 603401
cytoplas mic</w> 603113
ex tr 602975
in take</w> 602960
az ole</w> 602932
cy tom 602828
t ren 602557
eb o</w> 602524
M .</w> 602220
cryst al</w> 602010
ass es</w> 601683
b idity</w> 601562
log ical</w> 601500
ob las 601464
ig n</w> 601203
ver sion</w> 600958
profil es</w> 600318
di ame 600285
oper ation</w> 600087
S p 599991
educ ation</w> 599847
ot ting</w> 599274
ap plications</w> 598938
re produc 597994
hist one</w> 597778
bin ds</w> 597107
eff ectiveness</w> 597079
bio chemical</w> 596766
through out</w> 596751
phot o 596721
glob al</w> 596592
acet yl 596290
loc alized</w> 596215
surve y</w> 595821
sub units</w> 595482
no ted</w> 595458
an th 595062
inva sion</w> 594715
glut am 594584
as th 594455
gener ally</w> 594443
n ext</w> 594254
ovari an</w> 594207
mar row</w> 594025
un til</w> 593749
Se ver 593682
poten ti 593315
initi ation</w> 593315
ver s</w> 593286
bo v 593200
lear ning</w> 592858
re construc 592404
G i 592114
eth anol</w> 592066
y r 591730
gen ase</w> 591266
pr inc 591143
c aspase</w> 591068
asp ects</w> 590842
vi ous</w> 590756
In de 590684
aim ed</w> 590300
soci ation</w> 590236
AL S</w> 589715
tem p 589412
im age</w> 589327
plac ebo</w> 589304
fl ex 589207
Gro up</w> 588926
coupl ed</w> 588510
c ad 588371
mitochond ria</w> 588361
pro j 588356
bar ri 588280
extrac ted</w> 588211
T 2</w> 587759
N a</w> 587380
gas tro 586951
vi si 586921
transf erase</w> 586574
success ful</w> 586478
gnos tic</w> 586311
suscepti bility</w> 585935
se ts</w> 585602
N eu 584950
conta in</w> 584608
coll agen</w> 584522
determin ation</w> 584457
biom ar 584389
z es</w> 584305
pow er</w> 584235
vi ding</w> 584106
Di ff 584102
ic hi 584101
regul ating</w> 583919
iti n</w> 583798
knock down</w> 583703
ul l</w> 583247
qu estionna 583204
sle ep</w> 583189
E B 583069
ev olution</w> 582875
continu ous</w> 582770
ab or 582245
ur s 582014
rema in</w> 581829
an ine</w> 581791
up per</w> 581701
N I 581611
ol ip 581544
rap idly</w> 581475
de hy 581293
zyg ous</w> 581245
orig in</w> 581034
inf arc 581022
es ophag 580845
vacc ine</w> 580845
al most</w> 580803
doc um 580776
Ne w</w> 580402
P ase</w> 580371
O SE</w> 580127
vit amin</w> 579947
transf ection</w> 579886
mat ched</w> 579795
leuk emia</w> 579455
i ons</w> 578904
D 2</w> 578676
ur b 578578
ou th</w> 578540
modi fication</w> 578512
at om 578302
regul ates</w> 578283
or ith 578260
M O 578092
h ex 577931
prote ase</w> 577866
cy tes</w> 577865
Des pite</w> 577808
extrac ts</w> 577691
N O</w> 577133
Na Cl</w> 577119
mo de</w> 577094
inter act</w> 577086
spectr um</w> 576645
n e</w> 576642
pro spective</w> 576424
e ts</w> 576374
ad ren 576251
spec imens</w> 576136
eth yl 576029
transf er 575994
ar th 575793
M ost</w> 575752
le sion</w> 575744
o g</w> 575323
P 4</w> 575248
F ro 575129
V 1</w> 574838
exhibi t</w> 574755
2 D</w> 574750
inhi bits</w> 574699
recur rence</w> 574625
activ ate</w> 574152
p neum 574129
com pri 574090
blo t</w> 574090
PU RP 573896
s atis 573877
e ti 573652
morph ology</w> 573585
fluoresc ent</w> 573465
foc us</w> 573197
cent rifug 573071
streng th</w> 572867
F e 572775
O ver 572596
RO S</w> 572581
S I</w> 572429
n am 572385
res ection</w> 571962
C B 571826
bene fit</w> 571787
et ary</w> 571570
prec urs 571355
inter ventions</w> 570925
is sion</w> 570684
s po 570444
sti t 570288
famil ies</w> 570216
T R 570148
s ections</w> 570075
allow s</w> 569893
characteri zation</w> 569749
mix ture</w> 569643
L .</w> 569547
oxid ation</w> 569196
mon oclonal</w> 569044
h in 569033
cr uc 568335
PURP OSE</w> 568232
M icro 568037
ap parent</w> 567825
he at</w> 567817
sh e 567728
em er 567717
u tive</w> 567497
sm all 567298
al dehy 567008
cl ose</w> 566680
Ak t</w> 566610
cer vical</w> 566469
confir m</w> 566393
uc id 566371
toler ance</w> 566338
- 0</w> 566131
back ground</w> 566079
wor l 565612
promis ing</w> 565599
wid ely</w> 565555
ep ide 565376
r ich</w> 565319
plate let</w> 565153
a w 565115
H um 564906
mechan ical</w> 564715
M L 564334
enh ance</w> 564319
ten ance</w> 564137
abnormal ities</w> 564122
remo ved</w> 564025
t t 564001
difficul t</w> 563929
press or</w> 563877
F A</w> 563790
atic ally</w> 563787
pe di 563546
direc ted</w> 563512
or ph 563207
em ic</w> 563042
S ch 562992
amoun ts</w> 562796
car r 562734
us ually</w> 562235
fibro blasts</w> 562201
ner v 562115
alg orith 562096
ade qu 561861
om en 561593
N ational</w> 561399
str um 561306
ul a</w> 561301
yi eld</w> 561207
en ter</w> 560807
divid ed</w> 560519
sp ace</w> 560481
N MR</w> 560394
ne on 560216
Res earch</w> 560186
ab normal</w> 560156
agre ement</w> 560125
S ec 559929
it able</w> 559798
M ulti 559790
ad der</w> 559626
des pite</w> 559618
ar teri 559090
kin etic</w> 559072
develop mental</w> 558730
wh ite</w> 558475
al .</w> 558315
t an 558312
stit ute</w> 558214
m entation</w> 557886
shif t</w> 557740
r s</w> 557720
dis played</w> 557671
inter mediate</w> 557012
enti f 556606
immun oprecip 556502
ear li 555701
man if 555576
ur inary</w> 555211
ha em 554896
challeng e</w> 554869
en sive</w> 554341
smo king</w> 554213
el ines</w> 554076
C 3</w> 553792
sup pression</w> 553745
as tro 553601
hom e 553430
hy th 553341
Com par 553059
fib ri 552908
prim ers</w> 552450
absor ption</w> 551964
g ain</w> 551893
atten tion</w> 551749
ep id 551242
A L</w> 551227
A u 550946
spati al</w> 550898
id en 550758
pos ite</w> 550758
emer gen 550566
pro long 550537
res ting</w> 550241
ob esity</w> 549828
par tially</w> 549664
ery thro 549386
oth ers</w> 549348
stud ents</w> 549320
re plic 549319
M P 549236
D r 549068
pl asia</w> 549042
ar com 548843
w w 548806
cyto kines</w> 548726
e very</w> 548659
ill in</w> 548516
comp an 548467
el ium</w> 548452
bov ine</w> 548193
requ ire</w> 547727
tri es</w> 547641
Tran s 547359
G er 547326
Sever al</w> 547229
trans genic</w> 547092
lig ands</w> 546946
mil d</w> 546877
C D</w> 546242
L E 546237
J ap 545867
ER I 545636
u me 545311
depend ently</w> 545297
publ ic</w> 544631
transl ation</w> 544307
tur n</w> 544248
u es</w> 544153
b er 544137
Fro m</w> 543967
K O</w> 543663
C S</w> 543285
C al 543282
can not</w> 543093
bloc ked</w> 542874
f ications</w> 542803
accoun t</w> 542713
μ m</w> 542553
con sumption</w> 542476
medic ine</w> 541793
ar ti 541758
S m 541670
th ym 541068
ac char 541021
z o 540856
cle arly</w> 540825
os ine</w> 540805
ati on 540545
main tenance</w> 540174
F 2</w> 540057
frag ments</w> 539977
ST AT 539953
don or</w> 539654
chil d</w> 539330
lymph ocytes</w> 539058
l ap 538781
N E 537912
allow ed</w> 537884
sus p 537699
detec table</w> 537556
nan op 537271
ec tomy</w> 537234
m urine</w> 537092
entif ic</w> 536689
ma terials</w> 536660
adjus ted</w> 536628
pro gnostic</w> 536591
G L 536478
h ol 536430
f al 536356
supplem ented</w> 535979
D A</w> 535825
k ary 535428
repor ter</w> 535265
pr o</w> 535196
b asic</w> 535026
. 1</w> 535004
phen otypes</w> 534999
g radi 534770
n ative</w> 534590
G C</w> 534536
acqu ired</w> 534534
S 3</w> 534481
ma tes</w> 534245
N P</w> 534068
aor tic</w> 533652
biop sy</w> 533597
fac es</w> 533515
d ine</w> 533305
embry os</w> 533178
prob ably</w> 532888
recei ving</w> 532755
a vi 532566
extrac t</w> 532171
d or 532161
if or 532118
tow ards</w> 532035
ic in</w> 531772
extrac tion</w> 531718
p on 531607
l igh 531587
resp ect</w> 531411
ex clud 531321
S R 531186
t ment</w> 530941
sta sis</w> 530730
U V</w> 530709
kin ases</w> 530563
S P</w> 529787
ro u 529663
subj ected</w> 529569
it ment</w> 529516
M AT 529097
underst and</w> 528847
predic t</w> 528772
emplo yed</w> 528646
o si 528586
electro ph 528466
b and</w> 528322
path ological</w> 528211
ac k</w> 528120
resi d 527510
success fully</w> 527461
syn thetic</w> 527275
fail ed</w> 527216
im plications</w> 527162
di sp 527104
3 B</w> 526786
lo ad</w> 526659
T GF</w> 526381
F ol 526313
j unc 525737
al ign 525682
ar m</w> 525672
acc ess</w> 525597
implic ated</w> 525373
re r</w> 525301
uc h</w> 524472
s at 524347
gen ital</w> 524341
D P</w> 524210
k age</w> 524097
u ter 523956
hepati tis</w> 523816
al lo 523726
if y</w> 523695
s cho 523118
CD 8</w> 523110
int act</w> 522565
el ucid 522346
D U 522075
iden tical</w> 522043
D O 521910
hom o 521804
frequ ent</w> 521614
SI GN</w> 521514
under going</w> 521325
requi re 521222
tr uc 520881
ex change</w> 520872
Addi tionally</w> 520619
scop e</w> 520468
st ly</w> 520292
at temp 519996
d le</w> 519892
wh ose</w> 519483
HC V</w> 519168
ver te 518942
S yn 518704
embry onic</w> 518635
struc tion</w> 518435
pos terior</w> 518408
H T</w> 518330
o rectal</w> 518318
th or 518304
O r 518263
s on 518049
T ech 517945
aldehy de</w> 517723
prim arily</w> 517714
ch ec 517570
def ect</w> 517559
ma ternal</w> 517457
diame ter</w> 517432
ari es</w> 517322
CS F</w> 517273
perc ent</w> 517233
distin gu 517061
deple tion</w> 516971
ach ing</w> 516694
d on</w> 516356
fin d</w> 516289
mar ked</w> 515979
d enti 515966
b ron 515643
t un 515463
em o 515206
there by</w> 515194
Ca 2</w> 515141
recogn ized</w> 515048
tub ercul 515008
clus ter</w> 514980
characteris tic</w> 514860
or ation</w> 514848
ogen etic</w> 514475
M A 514365
d one</w> 514360
re tinal</w> 513734
conclud e</w> 513710
ocy tosis</w> 513284
cal c 512770
app ear 512659
dist ance</w> 512626
en ding</w> 512545
expres s</w> 512352
recru itment</w> 512256
cl oned</w> 512177
PI 3 512176
MD A</w> 512124
v o 511844
abdom inal</w> 511717
i tin 511606
ta ining</w> 511490
B .</w> 511431
an x 511382
diff er</w> 511261
v ul 511118
le th 511075
organis ms</w> 510801
seem s</w> 510649
g o</w> 510601
tox ic</w> 510408
impair ment</w> 510385
C an 510260
bl adder</w> 510256
throm bo 510199
der ly</w> 509952
transi ent</w> 509716
oly tic</w> 509715
in oc 509707
unc lear</w> 509584
compe ti 509018
pro gen 508952
co ver 508756
cor d</w> 508663
ma ke</w> 508507
set ting</w> 508430
ma king</w> 508161
per im 508085
to m</w> 507823
coun tries</w> 507645
vi ability</w> 507559
construc t</w> 507548
esc ence</w> 507337
B M</w> 507095
ev ent</w> 506900
C ancer</w> 506860
L PS</w> 506612
cen ter</w> 506575
influ enz 506494
C E 506056
predic tive</w> 505992
s ess 505901
l ation</w> 505807
pre h 505457
y cl 504952
manufac tu 504907
plac e</w> 504537
1 a</w> 504479
posi tions</w> 504241
trans formation</w> 504173
ey e</w> 503932
CD 3</w> 503923
relationsh ips</w> 503903
F P</w> 503806
b ond</w> 503695
lym ph</w> 503596
classi fication</w> 503533
bi osyn 503518
prepar ation</w> 503248
DE SIGN</w> 503103
ne ph 503075
res ol 502649
oxid ant</w> 502565
rati os</w> 502461
Amer ican</w> 502277
te en</w> 502061
cyto kine</w> 501795
plac ed</w> 501673
ch loro 501566
k it</w> 501515
a rom 501502
cy ste 501456
iz es</w> 501357
Stud y</w> 501278
tom ography</w> 501006
n as 500996
f ar</w> 500941
es sive</w> 500856
ph ob 500240
alle l</w> 500183
Hum an</w> 500154
qu in 499773
t agg 499713
A fr 499223
termin us</w> 498721
org an</w> 498684
f s</w> 498645
radi o 498624
n ine</w> 498577
ar rang 498479
lac t 498433
- 2</w> 498179
fib rosis</w> 498052
datab ase</w> 497864
cruc ial</w> 497803
id ase</w> 497722
phosph atase</w> 497662
lac king</w> 497639
mo bil 497478
G A</w> 496976
bal ance</w> 496591
overex pres 496468
i pl 496264
N T 495511
colum n</w> 495139
p u 495035
b ic</w> 494865
um ab</w> 494676
Diff eren 494653
M an 494636
In vit 494350
Rec ent</w> 494251
we igh 494176
ch ym 494088
expl ore</w> 493911
v ements</w> 493777
S u 493505
e f</w> 493234
electroph ore 493222
cl ones</w> 493120
dra w 492958
M et 492886
fe eding</w> 492844
fr actions</w> 492638
path ology</w> 492536
tas k</w> 492524
d end 492503
con sec 492383
op tical</w> 492381
exc ept</w> 492297
Invit rogen</w> 492254
small er</w> 492184
N L 492031
li ving</w> 491920
rea d</w> 491870
serv ices</w> 491863
tom a</w> 491837
activ ating</w> 491825
ome try</w> 491742
l and</w> 491617
H ospit 491604
An ti 491600
associ ations</w> 490933
arth ritis</w> 490652
minim al</w> 490547
subsequ ently</w> 490072
larg ely</w> 489948
put ative</w> 489897
ocar cin 489868
occ us</w> 489866
loc us</w> 489750
ex ternal</w> 489729
C E</w> 489422
e ve</w> 489414
or rh 489341
ch es</w> 489145
il ep 489079
tra di 488974
se arch</w> 488803
col on</w> 488785
I R</w> 488676
en ed</w> 488574
cir cul 488281
e try</w> 488276
vas cul 488215
as sign 488044
re t 487853
D M</w> 487736
n ed</w> 487672
f ed</w> 487506
def in 487429
intrav en 487292
ex on</w> 487044
expl a 486207
Tri s</w> 486099
OBJECTIV ES</w> 485880
earli er</w> 485136
pro viding</w> 485087
depend ence</w> 484634
comple ted</w> 484573
P hy 484306
phen omen 484222
be g 484057
spec i 483963
gen der</w> 483915
H 3 483738
pres sive</w> 483586
igh tly</w> 483390
dist al</w> 483208
scle rosis</w> 483140
o stasis</w> 482815
vari ability</w> 482545
mo vement</w> 482472
r ac 482467
t ob 482296
res our 482176
sw it 482150
oti des</w> 482031
C X 481758
repres ents</w> 481671
hom ogen 481173
neut roph 481087
D s</w> 480969
ult i</w> 480951
n or</w> 480728
PA R 480718
H igh</w> 480680
av es</w> 480632
F ir 480555
sel y</w> 480460
spont aneous</w> 480237
T RA 480135
pre domin 479883
ven ous</w> 479489
l uc 479360
fluo ro 479346
T B 479073
retro spective</w> 479018
recur rent</w> 478875
yl ate</w> 478826
guid elines</w> 478714
behavi oral</w> 478447
di verse</w> 478368
b ile</w> 478320
M I</w> 478287
c it 478280
thresh old</w> 477989
on ing</w> 477954
c utaneous</w> 477810
d og 477709
ple x</w> 477647
expl ain</w> 477627
Over all</w> 476790
di versity</w> 476779
sl ightly</w> 476777
S i 476755
a the 476753
deriv atives</w> 476672
m er</w> 476581
hydro phob 476460
par allel</w> 476398
repe ated</w> 476232
i atric</w> 476195
gl and</w> 476090
il l</w> 475816
contribu tion</w> 475630
H R 475586
conjug ated</w> 475530
el derly</w> 475368
VE GF</w> 475289
exis ting</w> 475202
1 C</w> 474894
st atic</w> 474829
analy sed</w> 474737
G AB 474300
el ess</w> 474159
con trac 474119
ac compan 474076
mon om 473799
lat ter</w> 473661
ac cel 473540
ep it 473427
L 2</w> 473247
M ut 473124
S S 473112
D R 473086
immuno histo 472999
in strum 472944
ro ot</w> 472525
ch ic 472401
me tric</w> 472284
conver sion</w> 472160
typ ical</w> 471871
mor bidity</w> 471700
con formation</w> 471576
on to</w> 471309
exten sive</w> 471249
stimul i</w> 471022
all ing</w> 470997
ot al</w> 470990
pan el</w> 470684
G S</w> 470559
T ak 470471
z in 470414
H g</w> 470334
call ed</w> 470266
lu e</w> 470106
c l</w> 469965
scri min 469787
anti gens</w> 469747
cur ve</w> 469529
di scrimin 469407
lin e 469370
ther mal</w> 469179
ro ug 469160
Prote in</w> 468586
differenti ated</w> 468404
occur rence</w> 468287
attr s</w> 467936
CO 2</w> 467930
p seud 467896
in dependently</w> 467879
m im 467805
ER K</w> 467680
A M</w> 467604
nerv ous</w> 467175
s ecti 466981
inf il 466845
py ri 466577
S tu 466431
di etary</w> 466361
sit u</w> 466313
L a</w> 466283
D ec 466192
rema ining</w> 466112
ed ly</w> 465887
E r 465859
str and</w> 465594
f it 465435
exten ded</w> 465419
spectro scopy</w> 465346
spectr a</w> 465103
Tech n 465084
sur ro 464368
tr ical</w> 464328
us al</w> 464240
sequ ently</w> 464107
proxim al</w> 464105
ynam ic</w> 463898
ig en 463789
f ts</w> 463744
ther n</w> 463598
tagg ed</w> 463572
li br 463552
avail ability</w> 463329
ume rous</w> 462897
M ed</w> 462753
fro n 462672
ul f 462646
L C</w> 462516
ampl ification</w> 462516
B L</w> 462290
ocy tic</w> 462258
C r 462199
manufactu rer</w> 461907
inf er 461668
b lin 461606
phen yl 461535
intro duced</w> 461384
dri ven</w> 461308
aff ects</w> 461225
nee ds</w> 461146
re tin 461116
it ud 461114
pres er 460973
ep ilep 460916
su peri 460868
nec rosis</w> 460799
B ri 460668
otyp ic</w> 460487
compl em 460441
pol y</w> 460270
C 5</w> 460033
inf usion</w> 459518
ero l</w> 459372
immun ity</w> 459080
di an</w> 458916
gra ft</w> 458582
reve al</w> 458529
R el 458409
grow ing</w> 458402
separ ated</w> 458127
influenz a</w> 457911
foc used</w> 457746
super nat 457476
ll ing</w> 457428
jo int</w> 457391
C P 457190
en ro 457147
co ag 457129
PI3 K</w> 456679
organ ization</w> 456598
iton eal</w> 456524
replac ement</w> 456497
satis fac 456352
prolong ed</w> 456314
modi fications</w> 456170
i v 456045
pair s</w> 455963
enric hed</w> 455941
thoug ht</w> 455882
he re 455794
suppres sed</w> 455732
mut ated</w> 455299
g as</w> 454769
f a 454739
P las 454476
ran dom</w> 454265
sex ual</w> 454217
classi fied</w> 454131
H LA</w> 454100
low ing</w> 454024
C P</w> 453838
f its</w> 453598
inter fe 453560
E C</w> 453354
consec utive</w> 453318
sk eletal</w> 453093
nit rogen</w> 452851
al ine</w> 452763
4 B</w> 452651
accur ate</w> 452539
col orectal</w> 452348
syste matic</w> 452309
transcrip ts</w> 452231
C ur 452106
por e</w> 451956
S ome</w> 451944
promo tes</w> 451902
A 3</w> 451770
mark edly</w> 451689
ubiqu itin</w> 451361
r in</w> 451143
clos ely</w> 451132
trans location</w> 451014
mon o 451011
agon ist</w> 450967
f ast</w> 450854
sal ine</w> 450821
def ic 450813
M O</w> 450718
Ex perim 450660
the ory</w> 450414
el ement</w> 450208
g on 450023
cap able</w> 449860
b es</w> 449611
asth ma</w> 449570
equ al</w> 449339
de vi 449193
ichi a</w> 448996
ampl ified</w> 448878
ob tain</w> 448798
sour ces</w> 448608
p ast</w> 448396
Me dical</w> 448124
mod ulation</w> 447884
ex er 447511
phil a</w> 447428
3 H</w> 447131
is ms</w> 446999
A U 446761
r he 446741
ph ag 446638
com preh 446549
ca ten 446376
in sic</w> 446284
R ep 446204
oc rine</w> 446198
ra z 446154
spectro metry</w> 445922
ar rest</w> 445882
accel er 445642
6 A</w> 445559
D S 445175
Ch in 445133
P ub 445069
cy cles</w> 444975
is om 444860
ac r 444412
hybri di 444328
ogen s</w> 444252
micro tub 444028
overn ight</w> 443854
MAT ERI 443775
o il</w> 443757
h ab 443708
ste ps</w> 443192
Hospit al</w> 442840
eng ine 442768
mid dle</w> 442539
ser ine</w> 442457
pres entation</w> 441959
t age</w> 441893
o k</w> 441803
MAP K</w> 441689
er a</w> 441620
2 C</w> 441439
F lu 441377
p ass 441366
no v 441338
a red</w> 441302
ir radiation</w> 441122
prof essi 440906
hel ix</w> 440890
aff ecting</w> 440859
oph il 440835
Ch ina</w> 440581
P K</w> 440575
Eff ect</w> 439822
em p 439819
CY P 439808
ti g 439800
d ers</w> 439746
um in</w> 439482
E LI 439408
ten cy</w> 439336
program s</w> 439296
pol ar</w> 439109
maxim al</w> 439103
if erase</w> 439085
oste rone</w> 438964
har v 438816
dev ice</w> 438532
pl a 438448
facilit ate</w> 438296
thesi a</w> 438286
Bi os 438203
issu e</w> 437799
est rogen</w> 437726
in activation</w> 437616
qu al 437596
possi bly</w> 437429
del ayed</w> 437157
immun os 437115
entr y</w> 437066
oc or 437057
fil am 436787
O b 436719
decre ases</w> 436532
nec k</w> 436451
pneum on 436243
oly sis</w> 436230
sit u 436141
il lu 435757
lymph oma</w> 435457
wa ve</w> 435442
em ph 435323
attri bu 435057
sign s</w> 434916
m 2</w> 434908
epid er 434574
Chin ese</w> 434522
estim ate</w> 434521
thic kness</w> 434414
construc ted</w> 434227
h ts</w> 433991
str y</w> 433985
candi date</w> 433942
ne oplas 433693
carb ox 433690
predic tion</w> 433644
bor n</w> 433602
appear ance</w> 432939
metabol ites</w> 432814
Uni ted</w> 432760
ar in</w> 432510
arg in 432496
j u 432469
thal am 432337
D C</w> 432306
tox in</w> 432175
bi ology</w> 431975
glyc os 431912
acet ate</w> 431655
pedi atric</w> 431635
an k</w> 431430
regul ator</w> 431136
aggreg ation</w> 431031
D ro 430901
d ance</w> 430860
rel ap 430761
CL C</w> 430758
antagon ist</w> 430713
D R</w> 430423
ten tion</w> 430364
occur ring</w> 430234
dim er</w> 430185
t al 430098
de hydro 430050
li on</w> 429757
p ed</w> 429744
enc ephal 429638
PL C</w> 429577
Stu dies</w> 429439
dend ri 429053
pro pose</w> 429041
ad sor 428865
cycl o 428463
A b</w> 428451
opo i 428392
i si 428125
res sed</w> 427990
sh ock</w> 427730
G ST</w> 427621
prog ressive</w> 426930
tw ice</w> 426785
o ked</w> 426535
dr am 426533
m ulti</w> 426525
prim er</w> 426523
polymorph ism</w> 426447
3 C</w> 426373
or age</w> 426087
compu ted</w> 426060
P ol 426052
questionna ire</w> 425988
mes en 425963
mil k</w> 425875
tubercul osis</w> 425866
so il</w> 425848
fil m</w> 425813
ro w 425695
hypox ia</w> 425647
Fir st</w> 425410
l and 425368
gradi ent</w> 425360
m ul 425303
CA 1</w> 425303
ischem ia</w> 425213
traum a</w> 424911
anc ies</w> 424748
nor m 424709
an tic 424388
underst ood</w> 424175
allel es</w> 424148
pres cri 424147
E sch 424050
inter face</w> 423987
ta x 423958
fac ial</w> 423948
construc ts</w> 423728
al ter</w> 423594
g est 423548
s low</w> 423451
Re g 423329
col d</w> 423216
od ds</w> 423161
anx iety</w> 423025
or ly</w> 422908
add ress</w> 422878
tro p 422861
in stitu 422808
ro b 422808
produc ing</w> 422734
plac ement</w> 422676
posi tively</w> 422603
Inde ed</w> 422392
adequ ate</w> 422346
random ly</w> 422053
Pub Med</w> 421999
c AMP</w> 421630
resp ective</w> 421596
wor ds</w> 421425
fi br 421383
Esch er 421332
re z</w> 421142
cl onal</w> 420856
th re 420828
in ate</w> 420762
chlor ide</w> 420624
issu es</w> 420498
meth od 420462
metast ases</w> 420432
su itable</w> 420380
fl av 420377
min or</w> 420341
coun ter 420229
experi enced</w> 419996
no de</w> 419962
Escher ichia</w> 419787
compar ing</w> 419733
em ission</w> 419413
U T 419363
recomm ended</w> 419221
im en</w> 419201
substanti al</w> 419197
ic illin</w> 419070
an a</w> 419023
caten in</w> 418723
dog s</w> 418647
superi or</w> 418646
an tim 418603
chrom e</w> 418374
hydrophob ic</w> 418324
fib ers</w> 418303
u ary</w> 418279
nucle i</w> 418207
ecti ous</w> 418190
ca the 418075
syn th 418061
ultras ound</w> 417960
aut om 417877
G C 417722
equ ili 417640
CD 2</w> 417533
dec ision</w> 417391
am ylo 417302
a f</w> 417071
ischem ic</w> 417048
ca ro 416857
f und 416815
mut agen 416793
detail ed</w> 416748
P E</w> 416719
saf e</w> 416632
ari a</w> 416409
os in</w> 416296
transfer red</w> 416117
prote in 416014
appro ved</w> 415890
nanop articles</w> 415813
iso forms</w> 415734
M M 415645
s a 415618
medi ately</w> 415519
L O 415497
medi ates</w> 415086
to ols</w> 415041
monit ored</w> 414887
ent ally</w> 414774
ic king</w> 414763
du al</w> 414489
de generation</w> 414351
quanti fied</w> 414212
z e 413851
expla ined</w> 413850
mo tion</w> 413686
analy ze</w> 413474
og ly 413260
M on 413165
i ent</w> 413058
diff usion</w> 412872
pa ired</w> 412850
read y</w> 412780
ath i 412734
phosph olip 412696
bu il 412668
load ing</w> 412642
sal t</w> 412556
cycl ic</w> 412003
1 H</w> 411752
succ ess</w> 411629
nur sing</w> 411471
establ ish</w> 411419
d ly</w> 411314
subj ect</w> 411194
d a</w> 411066
i te 410954
t ant</w> 410842
aro se</w> 410818
E uro 410694
restric ted</w> 410343
inser tion</w> 410222
1 α</w> 410198
st rep 410107
ran ial</w> 410106
ad op 410070
Ex pression</w> 410068
G lu 409966
tr aff 409939
ex ci 409911
infarc tion</w> 409686
S al 409530
high ligh 409416
RA S</w> 409354
I N</w> 409256
eff ectively</w> 409162
a x</w> 408943
E ach</w> 408904
ec onomic</w> 408675
F r 408163
N A 408035
ent rez</w> 408030
seg ment</w> 408017
v ag 407854
end oscop 407787
de x 407786
zin c</w> 407721
A g 407486
di ver 407407
ar s</w> 407234
di a</w> 407191
valid ated</w> 407133
vari ations</w> 407070
ang le</w> 407019
teri c</w> 406866
SN Ps</w> 406532
expl ored</w> 406348
IN T 406228
epide mi 406197
p tom 406137
M ore</w> 406071
ic h 405985
sp ectively</w> 405967
new ly</w> 405845
promo ting</w> 405792
om er 405614
ey es</w> 405543
S K 405484
prote as 405473
restric tion</w> 405449
solu tions</w> 405201
k et 405179
F O 405099
antibio tic</w> 404878
k in</w> 404877
is ation</w> 404732
st ance</w> 404516
On ly</w> 404382
d ental</w> 404282
defin i 404230
achi eve</w> 404221
lim it</w> 403949
conform ational</w> 403929
por t</w> 403901
se iz 403848
oc om 403711
prolifer ative</w> 403674
tradi tional</w> 403597
M E</w> 403255
E N</w> 403195
us ual</w> 403129
fe ed 403064
trans formed</w> 403057
up stream</w> 403054
4 C</w> 403012
invol ves</w> 402995
ser ve</w> 402752
arcom a</w> 402655
Simil arly</w> 402553
co sts</w> 402475
an dro 402297
pre feren 402289
I f</w> 402205
β 1</w> 402160
ri se</w> 402098
Th ir 402016
physi ology</w> 401967
h r</w> 401961
shap e</w> 401518
pol yp 401514
Me dic 401337
oc ular</w> 401210
activ ator</w> 401200
ac ent</w> 401152
networ ks</w> 401009
ac ted</w> 400971
1 b</w> 400785
ste ms</w> 400700
gre at</w> 400597
pur ch 400456
A β</w> 400329
rel ax 400209
M B</w> 400191
Tw enty</w> 400163
trans duction</w> 400042
home ostasis</w> 399543
polymorph isms</w> 399536
cycl in</w> 399503
P o 399109
epti de</w> 399059
read ing</w> 399053
enti re</w> 398996
O p 398783
si an</w> 398774
R T 398521
h our</w> 398448
clus ters</w> 398439
cell ent</w> 398398
Cl 2</w> 398398
sum mar 398348
El ec 398307
un treated</w> 398279
ste ad</w> 398252
glutam ate</w> 398115
gener ative</w> 398025
z ym 397997
fe ature</w> 397735
equi valent</w> 397689
M ar 397591
ra dical</w> 397543
rele ased</w> 397524
ch ains</w> 397516
sch iz 397423
gre en</w> 397280
PA GE</w> 397229
Eff ects</w> 397213
p . 397174
path ogenic</w> 397127
bron ch 397117
abil ities</w> 397116
h tt 397101
separ ate</w> 396999
c ent</w> 396867
G u 396729
cytoplas m</w> 396702
injur ies</w> 396680
p ly</w> 396565
I D</w> 396348
v als</w> 396249
cytotox ic</w> 396058
k ne 395979
Ac cor 395697
dis play</w> 395380
w t</w> 395249
leuk in</w> 395226
induc ing</w> 395099
cle ar 394954
anti oxidant</w> 394921
H ER 394777
enzym atic</w> 394622
con sequences</w> 394287
T P 394245
orig inal</w> 394146
H S</w> 393985
ELI SA</w> 393946
repres ent 393907
M in 393855
C O</w> 393854
inhibi ting</w> 393854
athe ro 393773
D L 393637
fib er</w> 393630
thor ac 393248
ti d</w> 393168
V i 393135
algorith m</w> 393071
reduc es</w> 393069
mat uration</w> 392877
am ycin</w> 392731
In cre 392479
m mol</w> 392351
ble eding</w> 392338
sensi ti 392332
adj acent</w> 392162
no d 391804
cyste ine</w> 391726
on ce</w> 391636
f used</w> 391625
bene fits</w> 391567
pl atin</w> 391527
separ ation</w> 391491
mag nit 391358
p 6</w> 391221
ess els</w> 391089
te tr 390789
tre at</w> 390629
ell ing</w> 390407
micro scope</w> 390345
S V</w> 390316
ten tly</w> 390311
bro ad</w> 390189
meas uring</w> 390080
progen it 390055
secti onal</w> 390040
H O</w> 389971
pul se</w> 389920
so phila</w> 389886
M olecular</w> 389862
un ding</w> 389762
M g 389684
x en 389668
ubiqu itin 389577
for ward</w> 389530
BM I</w> 389487
oph ren 389337
n umerous</w> 389304
N G</w> 389160
w ound</w> 388969
ca using</w> 388964
u tion 388601
od on 388540
aden ocarcin 388318
psych ological</w> 388230
n ull</w> 388098
G I 388062
ac t 387826
tin ib</w> 387699
intraven ous</w> 387575
psy cho 387481
erro r</w> 387368
disco very</w> 387026
ab err 386980
includ es</w> 386967
I GF</w> 386944
Neu ro 386704
cho ice</w> 386596
z one</w> 386555
vari ed</w> 386547
N T</w> 386491
S 4</w> 386451
im mediately</w> 386450
2 a</w> 386419
N 2</w> 386348
port un 385858
ve h 385786
enhanc ement</w> 385619
Dro sophila</w> 385500
k ill 385458
ex cellent</w> 385438
contribu tes</w> 385363
yp e</w> 385266
perox ide</w> 385225
electrophore sis</w> 385053
v essels</w> 384926
ur ys 384528
hybri d</w> 384503
tin gs</w> 384494
F e</w> 384446
f ore 384334
vesi cles</w> 384325
ul um</w> 384319
par ts</w> 384175
discus s</w> 384021
F ac 383978
de oxy 383899
HC C</w> 383884
plas tic</w> 383583
fr ame 383573
sign alling</w> 383564
luc iferase</w> 383034
bloc king</w> 383005
po orly</w> 383003
H ist 382877
w al 382806
acti c</w> 382776
Im port 382546
Meth ods</w> 382342
frac ture</w> 382262
AT Pase</w> 381848
ma x</w> 381845
clear ance</w> 381803
A F</w> 381786
M Y 381708
Con tro 381703
sens ory</w> 381558
m o</w> 381477
te tra 381373
prec ip 381348
accompan ied</w> 381173
yl ase</w> 380868
Tak en</w> 380697
h und 380644
coupl ing</w> 380585
I s</w> 380474
Pre vious</w> 380364
org ans</w> 380256
vol tage</w> 380196
es sion</w> 380130
neg atively</w> 379826
O c 379690
fol l 379687
integr ated</w> 379632
od e</w> 379588
D 3</w> 379415
met a</w> 379410
co ated</w> 379342
inter vals</w> 379316
ver sely</w> 379283
rang ing</w> 379128
adolesc ents</w> 379061
com posed</w> 379049
Fol lowing</w> 379022
initi ally</w> 378935
ach ment</w> 378737
V s</w> 378730
end omet 378710
res us 378021
respon sive</w> 377996
inf ectious</w> 377877
gra ph 377859
biosyn thesis</w> 377734
P a 377710
ti t 377576
trans plant</w> 376950
esophag eal</w> 376830
de si 376684
pol ic 376526
5 B</w> 376251
el u 376219
ve in</w> 375964
path y</w> 375758
reli able</w> 375464
vir ul 375264
vol un 375219
Rec ently</w> 375186
fol ding</w> 375174
app ed</w> 375097
nit ro 375036
reconstruc tion</w> 374907
phen yl</w> 374844
pharmac ological</w> 374842
sc anning</w> 374774
re ached</w> 374764
enco ded</w> 374734
bene ficial</w> 374685
ang io 374387
S el 374113
D H</w> 374109
H M 374084
it ations</w> 374033
G r 374027
substitu tion</w> 373706
l um 373511
b ot 373454
frequ encies</w> 372741
es tions</w> 372575
vel op 372340
mT OR</w> 372251
barri er</w> 372220
pl at 372116
Euro pe 372107
ang u 372067
pres enting</w> 372030
load ed</w> 371979
medic ation</w> 371966
ill us</w> 371824
Simil ar</w> 371689
J an 371587
conc ept</w> 371454
w in 371431
determin ing</w> 371362
re combination</w> 371141
athi one</w> 370979
recomm end 370820
ex ist</w> 370688
abund ance</w> 370677
clu sions</w> 370643
s . 370587
neon atal</w> 370572
partic le</w> 370510
op portun 370493
D el 370307
cor tic 370285
su stained</w> 370282
st rom 370270
alb umin</w> 370260
oglob in</w> 370158
H is</w> 370151
hem orrh 370151
DT A</w> 370092
S r 370068
chym al</w> 369957
oug h</w> 369956
contro lling</w> 369915
the ore 369902
integr ation</w> 369812
be aring</w> 369774
micro M</w> 369564
E T</w> 369499
tu mo 369462
allow ing</w> 369438
lap aro 369414
R S</w> 369335
isol ation</w> 369251
glyc erol</w> 369104
sen sus</w> 369031
be ads</w> 369000
morph ological</w> 368976
hist ological</w> 368890
h ome</w> 368889
transl ational</w> 368817
lin k</w> 368810
A 5</w> 368770
Com pared</w> 368663
main tain</w> 368572
hi p</w> 368537
g ent</w> 368196
precurs or</w> 368040
commun ication</w> 367937
gran ul 367872
c is 367465
g ang 367380
B o 367322
B ac 367055
smo oth</w> 367045
regi onal</w> 366997
ef ly</w> 366925
magnit ude</w> 366910
tr unc 366877
di min 366827
S te 366709
at rial</w> 366649
d ase</w> 366423
Sta tes</w> 366214
is ing</w> 366191
influ enced</w> 366121
memb er</w> 366065
sulf ate</w> 366013
psych iatric</w> 365909
expan sion</w> 365853
loc i</w> 365825
qu estion</w> 365800
al ize</w> 365743
fibri ll 365696
to p</w> 365554
purch ased</w> 365497
ane urys 365424
vi ol 365398
p ic 365192
cl one</w> 364979
om ics</w> 364938
H .</w> 364774
ep is 364675
plasm ids</w> 364431
dop amine</w> 364364
physici ans</w> 364257
cal e</w> 364255
al ready</w> 364187
tu be</w> 364158
attenu ated</w> 363922
si des</w> 363896
rob ust</w> 363893
Ch il 363859
o ids</w> 363834
schiz ophren 363720
com it 363606
ben ign</w> 363511
wor king</w> 363411
olig om 363262
antibio tics</w> 363226
synth ase</w> 363201
sub set</w> 362943
S uch</w> 362926
se min 362828
W il 362650
cytotox icity</w> 362578
p il 362534
N AD 362500
it ude</w> 362372
k 1</w> 362320
flu x</w> 362289
st orage</w> 362127
li es</w> 362106
tel om 362062
a ine</w> 362046
child hood</w> 362020
L if 361962
b s</w> 361732
ne o 361467
carr ying</w> 361457
predomin antly</w> 361395
He La</w> 361175
al i 360819
it ol</w> 360644
im er</w> 360539
peri ods</w> 360429
exten sion</w> 360404
scho ol</w> 360376
in struc 360253
E D</w> 360219
repe at</w> 359948
v ate</w> 359916
intr insic</w> 359726
ha z 359640
1 p</w> 359615
HC l</w> 359390
r in 359359
vec tors</w> 359283
T .</w> 359215
en ia</w> 359209
ad y</w> 359038
j ection</w> 359035
M MP</w> 358836
In c</w> 358826
re activity</w> 358815
E DTA</w> 358810
so ft</w> 358629
k b</w> 358608
S er</w> 358487
be d 358305
residu al</w> 358149
i bility</w> 358088
homolog ous</w> 358045
7 A</w> 357988
adi p 357908
st om 357878
tran s</w> 357801
fung al</w> 357744
Europe an</w> 357632
gra m</w> 357606
mat ter</w> 357251
effici ently</w> 357184
od es</w> 357153
circul ating</w> 357139
ic ated</w> 357116
S oci 356977
E M 356852
rang ed</w> 356804
L T 356753
roph y</w> 356734
su b</w> 356590
gen otypes</w> 356548
biomar kers</w> 356514
ei ved</w> 356481
re tic 356447
tion ed</w> 356418
gastro intestinal</w> 356395
ell ar</w> 356302
in n 356205
for c 356089
sc aff 356008
sim ulations</w> 355972
i ents</w> 355819
f ill 355752
compl ement</w> 355650
docum ented</w> 355631
a ph 355563
recru ited</w> 355355
y r</w> 355194
scre en</w> 355082
ac idi 355007
al anine</w> 354869
b lue</w> 354835
inter acts</w> 354697
n ia</w> 354541
B D 354510
f ing 354498
conclud ed</w> 354421
demon strates</w> 354369
Gi ven</w> 354360
inc id 354306
assign ed</w> 354299
rou tine</w> 354295
CN S</w> 354284
oc ity</w> 354050
adju vant</w> 353879
ap e 353843
bil ateral</w> 353843
re ferred</w> 353819
B lo 353812
RN A 353799
LD L</w> 353431
splic ing</w> 353248
ampl itude</w> 353201
ati tis</w> 352934
mac ro 352927
H F 352840
av y</w> 352825
r a</w> 352704
chol ine</w> 352658
simult aneously</w> 352617
compreh ensive</w> 352549
S c 352470
label ing</w> 352196
refl ect</w> 352126
identi ty</w> 351990
en ic</w> 351884
plan ted</w> 351865
characteri ze</w> 351840
W or 351714
an ese</w> 351663
F I 351640
res er 351614
d yl 351609
effec tor</w> 351606
sur faces</w> 351582
ta u</w> 351333
typ ically</w> 351285
A m 351217
R h 351194
frac tures</w> 351020
it self</w> 350968
Addi tional</w> 350818
heal ing</w> 350803
transcrip t</w> 350736
dep ending</w> 350731
sampl ing</w> 350602
rever sible</w> 350584
G a 350564
v ents</w> 350448
comp ens 350397
de ep</w> 350346
aqu eous</w> 350288
epith elium</w> 350283
O ther</w> 350237
f lo 350183
sup pressor</w> 350092
H L</w> 350053
hypo thesized</w> 349986
e ase</w> 349941
A B</w> 349851
tren d</w> 349735
enro lled</w> 349706
cir cum 349699
hybridi zation</w> 349617
stimul us</w> 349576
it us</w> 349551
G ol 349422
depend s</w> 349337
in du 349308
mus cles</w> 348891
polar ization</w> 348885
os al</w> 348880
we ak</w> 348815
emergen cy</w> 348649
S R</w> 348591
process ed</w> 348475
dec line</w> 348465
exc ess</w> 348275
re tention</w> 348072
3 a</w> 347536
bri um</w> 347515
A F 347478
r ho 347470
M any</w> 347281
consis ting</w> 347022
feed back</w> 346988
flu or 346807
se l</w> 346750
challeng es</w> 346732
tic ity</w> 346705
al is</w> 346702
PE T</w> 346589
ne t</w> 346537
itud inal</w> 346464
cop y</w> 346418
o virus</w> 346317
2 -</w> 346294
in ner</w> 346222
behavi ors</w> 346172
neuro logical</w> 346074
dehydro genase</w> 345906
sp i 345862
emb er</w> 345724
charg ed</w> 345703
ar thro 345569
heterogene ity</w> 345541
manif est 345296
F our</w> 345290
kne e</w> 345188
cell ul 345097
knock out</w> 344687
K D</w> 344664
car bo 344598
I V 344549
a ud 344543
top ic</w> 344441
ex amin 344281
b ing</w> 344256
a ug 344222
G AT 344006
prob ability</w> 343791
continu ed</w> 343769
it ated</w> 343445
preven ted</w> 343327
ne arly</w> 343145
S V 343096
C R</w> 342996
cyto chrome</w> 342938
glyc er 342696
bacteri um</w> 342463
- 3</w> 342336
simil arity</w> 342145
l an 342118
utili zed</w> 342107
per sistent</w> 342031
clos ed</w> 342024
E p 342003
angi ogenesis</w> 341718
vari ance</w> 341673
con sensus</w> 341644
pre operative</w> 341640
paren ts</w> 341636
C u</w> 341621
ir ation</w> 341490
con genital</w> 341453
sp or 341337
Ch ang 341244
fav or 341194
suscepti ble</w> 341124
inter leukin</w> 340986
phosph ati 340960
frame work</w> 340840
si ties</w> 340813
sol ute</w> 340729
vel ocity</w> 340609
establ ish 340574
D M 340556
in ning</w> 340487
ill ness</w> 340461
ol ds</w> 340281
disrup tion</w> 340237
incorpor ation</w> 340178
sol vent</w> 340131
en se</w> 340127
del ay</w> 340004
paren tal</w> 339884
NS CLC</w> 339683
w estern</w> 339488
S H 339470
coun t</w> 339381
d ar 339372
ME M</w> 339347
f re 339333
B cl</w> 339327
resol ved</w> 339298
glut athione</w> 339198
exc re 339056
o id 338885
coll ection</w> 338758
ser a</w> 338662
bur den</w> 338656
qu estions</w> 338632
u red</w> 338451
im plantation</w> 338411
b ran 338343
air way</w> 338177
h am 338133
prob es</w> 337680
ch amb 337624
co efficient</w> 337611
I D 337539
occ up 337426
advant age</w> 337107
CA M</w> 336983
i od 336976
R as</w> 336897
B s</w> 336878
per form</w> 336774
A g</w> 336757
ro se</w> 336697
compl ication</w> 336640
lys ine</w> 336593
cytom etry</w> 336449
lys ates</w> 336394
normal ized</w> 336380
suppor ting</w> 336322
sil encing</w> 336263
follow s</w> 336228
I L 336168
opoi etic</w> 336167
ten ts</w> 336026
TH E</w> 335987
f ig 335747
bl otting</w> 335711
eth yl</w> 335622
r it 335422
electro nic</w> 335406
T otal</w> 335387
ob ese</w> 335356
cardi omy 335335
radi otherapy</w> 335330
t i</w> 335329
el ong 335317
end ocrine</w> 335122
in os 335065
es ter</w> 334959
µ g</w> 334941
IC 5</w> 334765
veh icle</w> 334606
wor kers</w> 334581
gu e</w> 334386
L abor 334301
distribu ted</w> 334297
m os 334268
Fig ures</w> 334026
dep th</w> 333761
dr in 333670
tumo urs</w> 333659
NO S</w> 333652
cy s 333478
C M</w> 333456
sat ur 333436
consis ted</w> 333365
ero bic</w> 333332
pregn ant</w> 333295
U p 333228
view s</w> 333223
e asi 332979
nan o 332758
pro ved</w> 332700
po t 332613
sub types</w> 332236
AC T 332192
pers on 332152
c ulation</w> 332090
F 3</w> 332084
dilu tion</w> 332063
Ger many</w> 332063
d ox 332042
am mon 331828
Cd c 331745
1 D</w> 331730
ad es</w> 331556
ro tic</w> 331449
v ance</w> 331325
p ene 331319
otrop ic</w> 331313
p um 331289
ac e 331084
N R 330990
hippocamp al</w> 330976
cur ves</w> 330971
attribu ted</w> 330945
hospit als</w> 330908
norm ally</w> 330737
ori entation</w> 330727
activ ates</w> 330627
fron tal</w> 330562
exis tence</w> 330542
sc at 330402
cor ne 330356
h ment</w> 330280
htt p</w> 330202
P V 329965
evol ution 329783
inter acting</w> 329576
acchar ide</w> 329466
P har 329461
esti mates</w> 329347
c entr 329077
minim um</w> 328905
tre ating</w> 328767
modul ate</w> 328747
lim b</w> 328746
seg ments</w> 328729
D .</w> 328721
gl omer 328685
def ine</w> 328658
he imer</w> 328459
per sis 328415
arch i 328339
syn erg 328262
Al z 328252
cl asses</w> 328236
i ther</w> 328128
a p</w> 328124
respon si 328077
mo stly</w> 327879
id ation</w> 327848
insig hts</w> 327808
inten sive</w> 327670
as tic</w> 327664
in clusion</w> 327613
Ar ab 327402
man ip 327391
under go</w> 327328
behavi our</w> 327322
pharmac o 327148
mo bility</w> 327147
ex clu 327088
H sp 327075
ca re 327022
bl ast</w> 326803
c oc 326791
is ons</w> 326746
shor ter</w> 326741
reg ard</w> 326671
E GF</w> 326623
P P</w> 326564
in ts</w> 326485
Con sistent</w> 326474
transpor ter</w> 326448
phosph o 326406
dat as 326278
integr in</w> 326123
AK T</w> 326116
upreg ulated</w> 325993
assi s 325804
val ve</w> 325786
pro s 325641
eu kary 325623
var ying</w> 325529
serv ice</w> 325503
al izing</w> 325397
sim ulation</w> 325286
ron ic</w> 325275
dis per 325243
hund red</w> 325173
di ol</w> 325140
identi fying</w> 324877
qual it 324751
R R</w> 324663
G E 324633
s ome 324623
demonstr ating</w> 324413
an ded</w> 324395
MATERI ALS</w> 324341
he avy</w> 324090
ti no 324077
Al so</w> 324066
µ M</w> 324005
dev ices</w> 323972
Import antly</w> 323943
phenomen on</w> 323708
isi tion</w> 323577
hydro lysis</w> 323549
moti fs</w> 323503
harv ested</w> 323382
st o 323300
mam m 323030
path o 322972
go al</w> 322958
out put</w> 322949
A 4</w> 322907
oper atively</w> 322831
A V 322766
ste re 322683
uter ine</w> 322647
In f 322542
differenti ally</w> 322393
ne ur 322320
Alz heimer</w> 322306
trans membrane</w> 322260
in sec 322257
M il 322235
om al 322175
prog ress</w> 322152
investig ations</w> 322085
h is</w> 322007
correc t</w> 321974
dilu ted</w> 321929
assi um</w> 321910
bi as</w> 321875
vi s 321811
op a 321603
p as 321558
ath s</w> 321262
HP V</w> 321214
Ch ar 321209
P e 321168
ant a</w> 321110
acet ylation</w> 321039
tum orig 321034
SN P</w> 321002
Qu anti 320981
Supplem ental</w> 320803
V ari 320550
correl ations</w> 320467
ac in</w> 320400
dis charge</w> 320359
hom ology</w> 320323
bo x</w> 320292
impro ving</w> 320171
th ers</w> 320123
no des</w> 319935
vacc ination</w> 319906
olig onucle 319723
g i</w> 319669
ph ases</w> 319626
li ve</w> 319591
HB V</w> 319552
ove red</w> 319419
Elec tro 319238
poten cy</w> 319177
ra ys</w> 319169
d g 319000
protoc ols</w> 318856
fix ation</w> 318762
ser ious</w> 318627
no ise</w> 318578
mit otic</w> 318459
gi ve</w> 318362
icro bial</w> 318307
Sur g 318297
H ep 318276
au re 318266
h ing</w> 318198
macroph age</w> 318072
am ph 318016
path ogens</w> 317937
argin ine</w> 317888
a wa 317859
immun ore 317700
enti a</w> 317611
at omic</w> 317555
fr acti 317534
cri p 317434
or i</w> 317354
low est</w> 317353
st art</w> 317143
U l 317115
sh ip</w> 317037
dim en 316935
E valu 316636
h abil 316615
J .</w> 316345
Jan uary</w> 316333
t 1</w> 316182
adenocarcin oma</w> 316164
M s</w> 315958
De velop 315934
w er</w> 315851
elec tric</w> 315805
dis sociation</w> 315723
libr ary</w> 315648
adap tive</w> 315638
E E 315636
se as 315611
nur ses</w> 315583
aden osine</w> 315581
in form 315571
v acu 315487
at ly</w> 315487
G s</w> 315477
m apping</w> 315127
rec overed</w> 315102
T A</w> 315097
tem plate</w> 314956
initi ated</w> 314874
worl d</w> 314863
Ch em 314848
pl ying</w> 314714
path ogen</w> 314681
s s 314496
multi variate</w> 314480
interfe ron</w> 314384
CR C</w> 314297
om ycin</w> 314093
el ic 313884
m ented</w> 313758
W nt</w> 313741
ar d 313422
som atic</w> 313391
func tioning</w> 313363
ta in 313237
el ds</w> 313234
sil ic 313107
ste ady</w> 313025
sum mary</w> 313007
ill ance</w> 312861
Immun o 312849
repres ented</w> 312817
gre atly</w> 312725
spe ed</w> 312723
Sign ific 312719
pl ay 312712
par as 312703
A no 312652
m RNAs</w> 312514
de position</w> 312464
ris ks</w> 312358
implem entation</w> 312344
si es</w> 312211
comp ut 312185
recor ds</w> 312126
micro bial</w> 312051
set tings</w> 312007
ver th 311995
evid ent</w> 311842
advant ages</w> 311837
verth eless</w> 311813
con sequence</w> 311778
E 3</w> 311728
P H 311659
pres um 311611
o x</w> 311553
T e 311518
C V 311453
sel ectivity</w> 311445
no t 311374
par asi 311347
classi cal</w> 311238
e de 311227
def ective</w> 311158
am ous</w> 311095
on ate</w> 310955
cere b 310873
n ar 310859
op sis</w> 310801
stand ardi 310710
enti ally</w> 310667
ou ter</w> 310617
rhe um 310582
induc ible</w> 310522
amylo id</w> 310502
eff orts</w> 310452
consider able</w> 310435
m u 310296
ow el</w> 310123
co don</w> 309926
emb ol 309912
me g 309879
T 4</w> 309819
b ular</w> 309667
W e 309650
Lif e</w> 309624
ur ia</w> 309603
o side</w> 309516
i k 309469
immun ob 309385
es sel</w> 309374
U K</w> 309240
con comit 309236
pers ons</w> 309127
secre ted</w> 309045
b yp 308888
ir ing</w> 308864
N C</w> 308834
wor k 308823
T ype</w> 308725
ro t 308567
at um</w> 308477
od ynamic</w> 308425
valid ation</w> 308238
enric hment</w> 308199
lip ids</w> 307918
le p 307909
lim iting</w> 307899
sol ved</w> 307897
expl an 307802
direc tion</w> 307778
P artic 307666
gram s</w> 307650
k ly</w> 307626
l a</w> 307625
predic tors</w> 307578
t ac 307440
u itary</w> 307333
in active</w> 307312
c ut 307280
reg ular</w> 307241
D ep 307063
M K 307057
od er 307051
chrom osomal</w> 307029
ag e 307012
over l 306877
N on 306726
cl ar 306711
pur ification</w> 306624
R adi 306544
A K</w> 306471
dr ich</w> 306342
H PLC</w> 306340
e in</w> 306311
insig ht</w> 306243
ta king</w> 306222
C o</w> 306157
pot assium</w> 305821
mutagen esis</w> 305749
acr yl 305405
ni b</w> 305174
n oc 305155
l in</w> 305084
O 1</w> 305035
lin king</w> 304960
acqu isition</w> 304940
ex ogenous</w> 304939
Di ag 304933
U se</w> 304893
es te 304843
H is 304808
auto immune</w> 304805
X 1</w> 304741
ev oked</w> 304735
im e</w> 304722
ne ither</w> 304627
phen otypic</w> 304464
ha pl 304381
si c 304344
In stitute</w> 304327
lim itations</w> 304200
reproduc tive</w> 304079
b ands</w> 304030
gli al</w> 303972
chec k 303804
D D 303782
ep igen 303759
mo tility</w> 303723
duc t</w> 303718
ch im 303567
T M 303374
S K</w> 303307
res h</w> 303211
de generative</w> 302955
be ta 302891
cytos k 302801
C K</w> 302777
favor able</w> 302715
T F 302665
U D 302518
h is 302482
l ost</w> 302472
ish er</w> 302419
proteas ome</w> 302273
compar ative</w> 302030
aim s</w> 301967
preven ting</w> 301561
schizophren ia</w> 301519
pl ex 301516
polym er</w> 301507
in direct</w> 301360
sper m</w> 301210
Jap an</w> 301149
hyper troph 301077
re modeling</w> 301046
Al drich</w> 301011
rele vance</w> 300988
coun ts</w> 300974
cytos olic</w> 300960
G ene</w> 300943
PK C</w> 300919
exc it 300809
dr y</w> 300773
Me an</w> 300706
dendri tic</w> 300649
elec trical</w> 300608
F unc 300570
b ic 300540
func tion 300524
wh e 300441
clin ic</w> 300383
suppor ts</w> 300356
sp read</w> 300325
in put</w> 300179
pres ents</w> 300126
B L 300042
star ting</w> 299997
v ated</w> 299957
adap tation</w> 299901
HD AC 299837
we ak 299815
mi RNAs</w> 299608
sc an</w> 299418
plas ty</w> 299369
c ut</w> 299256
abs ent</w> 298998
C F</w> 298977
inte resting</w> 298858
pro phyl 298750
fr u 298706
sus pen 298658
arteri es</w> 298606
rec tomy</w> 298498
assis ted</w> 298437
et ric</w> 298423
l is 298384
colon y</w> 298335
conc ep 298250
aure us</w> 298213
regul ators</w> 298198
E L 298093
My c</w> 297941
immunoprecip itation</w> 297904
S Y 297852
p ing</w> 297793
t te</w> 297648
st anding</w> 297615
intr a</w> 297615
R ab 297606
exclud ed</w> 297571
Col l 297525
al ve 297500
habil itation</w> 297338
n ational</w> 297253
ten ing</w> 297232
i onic</w> 297209
m ol 297149
har b 297093
oug ht</w> 297020
S W 296915
I P 296889
health care</w> 296883
combin ations</w> 296705
HI F</w> 296643
loc ations</w> 296623
potenti als</w> 296609
olog ists</w> 296602
stero id</w> 296536
centrifug ation</w> 296365
Sci entific</w> 296177
integr ity</w> 296159
perc eived</w> 295979
prec ise</w> 295886
don ors</w> 295862
practi ces</w> 295846
sub type</w> 295715
s chem 295555
end s</w> 295491
dor sal</w> 295255
quanti fication</w> 295091
C H</w> 295080
log istic</w> 295045
ic k</w> 295003
a er 294821
e IF 294794
cis platin</w> 294792
P ur 294781
traff icking</w> 294772
spl een</w> 294744
mam mary</w> 294552
AD P</w> 294519
N o 294374
recommend ations</w> 294357
en tero 294241
descri bes</w> 294192
bu ff 294186
at ories</w> 294179
ge al</w> 294159
bec ame</w> 294061
. 2</w> 294011
G H</w> 293839
m arg 293799
Pri mary</w> 293651
myel oid</w> 293561
mi RNA</w> 293483
wh om</w> 293420
v essel</w> 293366
pro st 293281
1 β</w> 293257
carri ers</w> 293128
se m 293126
hyperten sive</w> 293090
ca vity</w> 293066
cryst all 292951
por tion</w> 292890
yiel ded</w> 292807
e as 292715
le y</w> 292654
agg ressive</w> 292550
Compar ison</w> 292518
cre ated</w> 292472
su g 292463
upreg ulation</w> 292461
re mark 292377
S anta</w> 292373
be y 292302
Phy si 292270
Afr ican</w> 292220
An im 292205
promo ters</w> 292199
pers onal</w> 292157
dem ographic</w> 292087
spec ial</w> 292085
fi elds</w> 292076
neutr al</w> 291808
intro duction</w> 291698
us ers</w> 291589
m ell 291566
ogra ft</w> 291405
S equ 291383
B ar 291379
n ers</w> 291229
R 3</w> 291180
sel ectively</w> 291091
S cale</w> 290760
stabil ization</w> 290722
ot omy</w> 290682
do x</w> 290672
il ls</w> 290621
al ities</w> 290570
t ance</w> 290517
av oid 290517
le uc 290437
el et 290423
ten sin</w> 290278
iso form</w> 290136
long itudinal</w> 290105
Ser um</w> 290076
ol ed</w> 290019
B A</w> 289982
it ory</w> 289813
bey ond</w> 289595
n one</w> 289555
perme ability</w> 289540
chrom osomes</w> 289475
correl ate</w> 289438
temper atures</w> 289412
her in</w> 289271
S F 289177
he ight</w> 289163
respon d</w> 289092
ten sion</w> 289051
de aths</w> 288864
st ored</w> 288852
nas al</w> 288821
az ol 288794
he aring</w> 288760
theore tical</w> 288752
ede ma</w> 288747
supernat ant</w> 288730
1 c</w> 288720
per itoneal</w> 288638
Au str 288578
g els</w> 288543
pit uitary</w> 288537
re arrang 288460
re pression</w> 288408
z en</w> 288356
instruc tions</w> 288266
SC C</w> 288187
prepar ations</w> 287969
equili brium</w> 287839
fem oral</w> 287781
eti ology</w> 287766
C enter</w> 287694
substanti ally</w> 287621
pro min 287442
ch e 287267
tr ue</w> 287250
al ways</w> 287148
ul c 287105
per tur 286984
nutri tion</w> 286978
S M</w> 286908
ad mission</w> 286720
yn geal</w> 286626
cr y 286564
cop per</w> 286524
traum atic</w> 286497
h er</w> 286491
parame ter</w> 286447
T i 286427
L a 286078
en al</w> 286024
G re 285886
dem entia</w> 285487
l ined</w> 285479
correc tion</w> 285471
m e</w> 285425
b a 285197
carcin omas</w> 285171
consi sts</w> 284895
F isher</w> 284854
utili zation</w> 284747
cl im 284713
hippocamp us</w> 284672
d ates</w> 284624
di alysis</w> 284604
simult aneous</w> 284574
medi al</w> 284449
represent ative</w> 284378
extrem ely</w> 284362
ot or</w> 284340
EC s</w> 284236
AM L</w> 284194
m m 284188
T C</w> 284105
virul ence</w> 283950
gl ass</w> 283603
r hyth 283598
D MS 283538
T B</w> 283535
compu ter</w> 283439
in stead</w> 283340
bon ds</w> 283240
H 4</w> 283163
youn ger</w> 283140
di ly</w> 283109
cy te</w> 283096
cir culation</w> 283087
resour ces</w> 283029
I C</w> 282977
nic o 282816
St atis 282813
r y</w> 282726
ch on 282698
adhe rence</w> 282691
re generation</w> 282610
recei ve</w> 282593
scre ened</w> 282588
rec i 282585
correl ates</w> 282540
Y 1</w> 282366
vi c</w> 282304
lymph ocyte</w> 282232
i res</w> 282226
1 R</w> 282084
rever sed</w> 282072
anes thesia</w> 282019
phosph o</w> 281984
ari ties</w> 281926
reve als</w> 281864
bel ie 281817
i asis</w> 281794
ecti ves</w> 281768
N P 281762
col or</w> 281680
S ampl 281622
T um 281532
amin o 281446
po ol</w> 281300
antim icrobial</w> 281256
influ ences</w> 281189
squ amous</w> 281023
abund ant</w> 280992
C TION</w> 280971
interpre tation</w> 280969
O x 280957
op io 280810
assess ing</w> 280770
C L</w> 280747
RA F</w> 280674
enh ances</w> 280656
pro per</w> 280654
opath ic</w> 280654
er b 280466
ens ure</w> 280273
mesen chymal</w> 280254
line age</w> 280245
a emia</w> 280224
th in</w> 280208
bel ong 280154
fr ame</w> 280140
im mobil 280105
ati nib</w> 280012
expl o 279940
T O 279881
pre lim 279752
s ought</w> 279622
G R 279611
surg e 279611
im plant</w> 279435
car til 279328
f resh</w> 279169
m t 279156
E P 279148
g et 279043
volun te 278972
Z n</w> 278779
le aves</w> 278751
sy st 278708
ap er 278664
agon ists</w> 278622
archi tec 278566
fund am 278544
de part 278505
i or</w> 278455
b owel</w> 278408
Austr al 278372
stimul ate</w> 278370
decre asing</w> 278233
Ac ti 278196
GT P</w> 278132
d ents</w> 278097
N S</w> 278064
fil ms</w> 278003
S truc 277858
pos es</w> 277810
D F 277763
os c 277692
G R</w> 277647
anti viral</w> 277596
B SA</w> 277504
st res 277473
em bed 277454
muc osal</w> 277385
lo g</w> 277242
ren e</w> 277180
5 C</w> 277113
p .</w> 277111
r un 277085
STAT 3</w> 277063
elev ation</w> 277022
satisfac tion</w> 276926
lay ers</w> 276886
im id 276791
easi ly</w> 276750
ici ans</w> 276612
T NF 276541
inn ate</w> 276537
L H</w> 276510
tu ally</w> 276469
an o 276464
op es</w> 276450
ap plic 276431
epigen etic</w> 276375
Fi ve</w> 276333
conf ig 276302
symp tom</w> 276291
l angu 276288
epider mal</w> 276281
compl icated</w> 276244
caro tid</w> 276083
h e</w> 276037
st ment</w> 276011
acc o</w> 276008
HER 2</w> 275785
en able</w> 275757
thorac ic</w> 275756
tra its</w> 275708
c am 275506
homo zygous</w> 275399
is es</w> 275384
leth al</w> 275329
cen tro 275178
fl ic 275163
ec h</w> 275158
pig s</w> 275143
main taining</w> 275136
replac ed</w> 275068
DMS O</w> 275006
defic its</w> 274988
adv ances</w> 274939
F GF 274912
tub ulin</w> 274709
al li 274704
disco vered</w> 274683
H u 274644
P oly 274547
nucle otides</w> 274475
oc clusion</w> 274457
ste nosis</w> 274330
id opsis</w> 274294
ogra m</w> 274270
kin son</w> 274161
nit ric</w> 274131
bloc ks</w> 274083
C ul 274050
reg imen</w> 274045
hist opath 273949
mar k</w> 273921
iz ations</w> 273839
te e</w> 273832
Gol gi</w> 273792
dist urb 273778
N K 273758
su med</w> 273740
V ir 273626
mell itus</w> 273614
m id</w> 273608
men ing 273603
gli a</w> 273597
a id</w> 273580
st ay</w> 273551
symptom atic</w> 273394
an k 273362
b is 273329
B P1</w> 273315
cre atin 273285
Z e 273251
emerg ing</w> 273005
valid ity</w> 272995
ic ans</w> 272839
Arab idopsis</w> 272816
consi der</w> 272769
vi sc 272745
T ogether</w> 272742
fil ter</w> 272673
intro duc 272618
w . 272494
deple ted</w> 272491
cle a 272447
no te</w> 272349
F 4</w> 272343
Evalu ation</w> 272304
Sr c</w> 272284
byp ass</w> 272203
ag ain</w> 272129
K 5</w> 272090
att achment</w> 272090
S ET 272017
d in 272007
ga p</w> 271968
C p 271795
see m</w> 271758
mo un 271602
im mediate</w> 271536
hetero zygous</w> 271447
L V</w> 271441
av oid</w> 271312
epilep sy</w> 271223
hemat opoietic</w> 271119
Sy stem</w> 271094
plat form</w> 271073
neuro n</w> 270979
in stability</w> 270964
muc osa</w> 270858
S 6</w> 270838
T RI 270597
C ru 270566
invol ve</w> 270480
immun oglob 270453
T o 270443
anal ge 270378
cad herin</w> 270366
In v 270265
ma p</w> 270227
susp ected</w> 270208
cere visi 270187
Blo od</w> 270183
re dox</w> 270140
er ical</w> 270045
li pos 269990
AP P</w> 269879
C are</w> 269847
S A 269825
mo thers</w> 269776
sp ind 269710
on as</w> 269691
K ey 269602
abl ation</w> 269600
en abl 269597
dy e</w> 269511
heterogene ous</w> 269506
ul ating</w> 269420
M HC</w> 269384
micro g</w> 269381
Chil d 269380
cerevisi ae</w> 269343
A ut 269340
u tility</w> 269300
prelim inary</w> 269277
achi ev 269225
N Ps</w> 269116
H F</w> 268995
ab solute</w> 268946
ac ter</w> 268923
simil arly</w> 268854
me t</w> 268696
eff lu 268638
ol e 268548
ul monary</w> 268546
hypo thalam 268504
erro rs</w> 268376
inj ections</w> 268348
G ra 268333
Phar mac 268326
immunos up 268163
un able</w> 268101
no unc 268086
TI NG</w> 268057
ant um</w> 268047
reli ability</w> 268011
sol ub 267907
relax ation</w> 267835
sequenc ed</w> 267735
perox idase</w> 267691
ar b 267676
relap se</w> 267665
surro unding</w> 267619
v ic 267618
ww w. 267558
C ardi 267524
Bri efly</w> 267506
categ ories</w> 267465
g ir 267464
ay er</w> 267427
ch ro 267317
M c 267249
curren ts</w> 267227
phyl oc 267149
k ade</w> 267049
enco des</w> 267031
g old</w> 267028
ch est</w> 267026
M C</w> 266917
subst ance</w> 266851
junc tion</w> 266850
chamb er</w> 266830
adsor ption</w> 266829
gener ating</w> 266806
In tern 266658
re tino 266648
ra ised</w> 266574
ne ed 266521
ure a</w> 266486
Ne vertheless</w> 266475
fe wer</w> 266338
orig in 266298
H T 266290
no ver</w> 266249
compar isons</w> 266233
op ic</w> 266216
res ear 266160
sp ine</w> 266103
al in</w> 266090
d yst 266067
S F</w> 266002
ri le</w> 265900
antagon ists</w> 265634
U L 265570
H a 265268
S tre 265223
l ess 265211
re habilitation</w> 265193
S outh</w> 265181
clos ure</w> 265178
l amin 265049
rea dily</w> 264991
As sess 264956
evalu ating</w> 264947
I K 264729
on ical</w> 264248
Y 2</w> 264139
g ated</w> 264111
pl anning</w> 264093
struc tive</w> 264072
stimul ating</w> 264009
require ment</w> 263953
or b 263948
Un der</w> 263864
reas ons</w> 263834
ec al</w> 263770
Ab s</w> 263723
spectr al</w> 263696
μ L</w> 263507
j ust</w> 263503
phil ic</w> 263375
f et 263359
METHO D</w> 263293
struc tured</w> 263283
sens or</w> 263266
s ac 263242
ga ve</w> 263219
f ever</w> 263191
tryp sin</w> 263120
D MEM</w> 263010
trac he 262937
en erg 262916
cys tic</w> 262824
e x</w> 262778
el ici 262756
syst olic</w> 262739
vi able</w> 262611
inf ant</w> 262516
E arly</w> 262458
m enti 262448
an omal 262331
AI DS</w> 262281
G al 262219
oxid ase</w> 262086
K it</w> 262078
epit ope</w> 262046
P PAR 261898
h and 261885
Ano ther</w> 261856
dar k</w> 261823
pl ement</w> 261703
RO DU 261679
Cru z</w> 261645
3 T</w> 261609
cover age</w> 261514
gly col 261498
3 -</w> 261465
t RNA</w> 261409
lim its</w> 261367
hydr ate</w> 261345
pl an</w> 261297
it on</w> 261266
str ati 261241
Key words</w> 261069
physici an</w> 261057
evolution ary</w> 260955
ubiquitin ation</w> 260842
func tionally</w> 260771
f ile</w> 260604
out side</w> 260566
valu able</w> 260560
abol ished</w> 260557
res em 260471
lipo protein</w> 260466
ti co 260460
RNA i</w> 260407
c is</w> 260319
Me as 260173
alve olar</w> 260104
pre treatment</w> 260037
recip ients</w> 260007
T ra 259989
surve illance</w> 259989
C re</w> 259869
As sociation</w> 259862
emo tional</w> 259813
s ent</w> 259688
cont amin 259557
PT EN</w> 259534
osc ill 259465
o ts</w> 259358
elet on</w> 259248
ma kes</w> 259239
o ther 259225
K 4</w> 259205
l ist</w> 259184
F BS</w> 259100
p 4</w> 259031
nounc ed</w> 258980
sp ring</w> 258955
gu t</w> 258751
su ic 258678
c os 258629
N CT 258525
qualit ative</w> 258411
supplem ental</w> 258273
arr hyth 258229
S ing 258224
sh ared</w> 258109
ch os 258088
pla que</w> 258063
ptom atic</w> 258051
cen ters</w> 257949
concomit ant</w> 257930
calcul ations</w> 257927
tur nover</w> 257909
embry o</w> 257894
f 2</w> 257872
Th en</w> 257864
repor ting</w> 257818
micro array</w> 257774
phosphati dyl 257690
inter ference</w> 257685
microtub ule</w> 257651
emph asi 257620
ap ical</w> 257589
me d 257466
ow s</w> 257431
ad renal</w> 257396
ock et</w> 257362
es ter 257328
roug h</w> 257274
RODU CTION</w> 257230
mon ol 257168
stra diol</w> 257077
acidi c</w> 257069
2 H</w> 257035
6 B</w> 256968
gu ide</w> 256939
b at 256890
I denti 256868
ex act</w> 256835
consis tently</w> 256755
concer ning</w> 256734
excre tion</w> 256716
X 2</w> 256687
ex ists</w> 256668
vi ri 256554
s 1</w> 256517
er o</w> 256511
erc ial</w> 256479
g 1</w> 256462
comm ercial</w> 256404
require ments</w> 256311
C as 256290
J NK</w> 256208
blin d</w> 256147
re tained</w> 256138
p yl 256125
ox idi 256125
at tit 256036
os in 255964
re ach</w> 255884
nam ely</w> 255872
M V</w> 255856
ic als</w> 255752
re frac 255690
ol ys 255625
can onical</w> 255334
as ingly</w> 255286
me ta 255231
dig estion</w> 255039
langu age</w> 255001
ex ac 254996
ucle ar</w> 254933
c ess 254882
cor tis 254852
N ext</w> 254725
si zes</w> 254664
INT RODUCTION</w> 254652
a e 254633
ud es</w> 254558
v a</w> 254487
in her 254469
intr am 254447
deli vered</w> 254253
Contro l</w> 254193
analy tical</w> 254066
T ab 253957
ogen icity</w> 253946
hi bits</w> 253901
H2 O2</w> 253864
R R 253820
re tri 253748
un ifor 253724
te st 253705
CM V</w> 253637
H I</w> 253464
I .</w> 253189
r ural</w> 253154
ethyl ene</w> 253132
M it 252993
P B 252975
with draw 252943
perc eption</w> 252901
le g 252865
ver ified</w> 252862
re agent</w> 252854
AM PK</w> 252852
Partic ip 252850
ne igh 252786
der ing</w> 252776
compar tment</w> 252737
crip t</w> 252710
micro grams</w> 252632
qu ad 252473
supplem entation</w> 252337
f 1</w> 252297
ech ocardi 252245
glyco protein</w> 252214
re in 252181
in ter</w> 252073
vi sible</w> 252057
neph ro 251989
auth or</w> 251928
urb an</w> 251908
immuno fluorescence</w> 251866
sal iv 251811
R 4</w> 251778
gen omes</w> 251753
cathe ter</w> 251680
T M</w> 251675
este rone</w> 251628
cartil age</w> 251603
FL AG</w> 251592
com posite</w> 251480
n c 251362
promo ted</w> 251271
gene tically</w> 251196
N s</w> 251151
sp ac 251140
p ure</w> 251130
il ateral</w> 250879
electro de</w> 250808
substitu ted</w> 250796
ta g</w> 250676
Bios ci 250608
smo kers</w> 250601
sh am</w> 250574
d wide</w> 250455
di vision</w> 250356
ig ible</w> 250333
align ment</w> 250316
he ad 250256
cir cu 250235
Thir ty</w> 250066
complem entary</w> 250051
compar t 249920
alph a 249914
lin kage</w> 249774
de m</w> 249745
dimin ished</w> 249737
colon ies</w> 249695
inos itol</w> 249549
D Cs</w> 249521
pro nounced</w> 249496
S w 249452
thromb in</w> 249406
lact ate</w> 249367
om at 249350
complex ity</w> 249336
dis h</w> 249323
satur ated</w> 249121
str anded</w> 249100
Intern ational</w> 249006
dis ability</w> 248991
V I 248965
bloc kade</w> 248961
Ph osph 248892
oper ating</w> 248862
2 b</w> 248829
yp ical</w> 248688
wa vel 248669
practi cal</w> 248428
aggreg ates</w> 248358
T K 248341
e ting</w> 248326
incre asingly</w> 248176
glut amine</w> 248133
d s 247995
speci al 247965
suppres s</w> 247638
AI M</w> 247599
pi g</w> 247514
R B 247499
com pati 247458
P ath 247408
sp in</w> 247391
promin ent</w> 247327
trigg ered</w> 247323
endoscop ic</w> 247322
g ate</w> 247287
umb ar</w> 247220
adi pose</w> 246962
diff use</w> 246891
o ffer</w> 246890
Multi ple</w> 246889
C 4</w> 246787
experi ences</w> 246751
M at 246747
T G 246701
Co A</w> 246595
M et</w> 246511
Techn ologies</w> 246471
R I</w> 246444
ch ape 246431
u n</w> 246423
strom al</w> 246408
p ended</w> 246334
unc er 246313
W ith 246185
menti oned</w> 246181
en coun 246141
un usual</w> 246130
devi ation</w> 246041
b rom 246019
tr ic 245967
neutroph ils</w> 245858
Ac ute</w> 245796
sero ton 245753
termin ation</w> 245746
fundam ental</w> 245726
ad a</w> 245700
protec ted</w> 245700
ag ne 245654
cir rho 245638
acryl amide</w> 245607
H SP 245487
standardi zed</w> 245388
del ta</w> 245378
is ot 245357
nat al</w> 245311
dec i 245153
exc itation</w> 245146
fil tration</w> 245107
an atom 245079
NCT 0</w> 245076
venti lation</w> 245063
atic s</w> 245001
log ic</w> 244993
prop ag 244962
predic tor</w> 244923
E GFP</w> 244910
fro zen</w> 244859
S D 244812
contrac tion</w> 244780
polic y</w> 244759
suc rose</w> 244739
R .</w> 244655
requ iring</w> 244595
poly clonal</w> 244515
The ir</w> 244494
op tic</w> 244465
Dis ease</w> 244455
tob acco</w> 244400
e y</w> 244372
M M</w> 244325
f ur 244232
S ep 244224
fal se</w> 244223
phen ol</w> 244211
angi ography</w> 244132
op tions</w> 244112
ha ir</w> 244015
hepat ocytes</w> 243952
conj unc 243927
R ho 243914
MC F</w> 243479
af f</w> 243437
atom s</w> 243437
hormon es</w> 243331
N r 243325
wom an</w> 243301
resus pended</w> 243232
repres enting</w> 243182
re perfusion</w> 243042
de pri 243039
V al 243016
sh RNA</w> 242962
reduc tase</w> 242907
sing ly</w> 242885
pl er</w> 242838
op posite</w> 242805
cul ti 242789
p ren 242775
ex hibits</w> 242668
thrombo sis</w> 242632
pres sed</w> 242631
c ade</w> 242455
cellul ose</w> 242421
progenit or</w> 242393
es es</w> 242369
or dered</w> 242259
mil lion</w> 242242
high light</w> 242124
ob struction</w> 242094
O ut 241892
worl dwide</w> 241773
br ation</w> 241702
dim ers</w> 241500
volum es</w> 241449
tumorig en 241447
diff ic 241402
mit tee</w> 241373
arti ficial</w> 241371
M g</w> 241267
G ly 241194
pro ton</w> 241188
n al</w> 241110
ad mitted</w> 241080
vacc ines</w> 241075
le af</w> 241043
Bi o</w> 241021
embed ded</w> 241012
Jap anese</w> 240935
sh are</w> 240917
de pressive</w> 240915
techn ical</w> 240909
PC s</w> 240848
on line</w> 240813
0 A</w> 240794
c 1</w> 240747
immunohisto chemistry</w> 240574
e th</w> 240442
plate lets</w> 240397
4 E</w> 240396
vascul ar 240280
gli oma</w> 240147
chang ing</w> 240093
p ocket</w> 240077
method ology</w> 240066
D G</w> 240036
ST UD 239987
re d 239892
princ ip 239857
particip ate</w> 239790
environ ments</w> 239727
STUD Y</w> 239725
plas ticity</w> 239718
umin escence</w> 239701
obser ve</w> 239663
ul t</w> 239485
L D</w> 239416
GAB A</w> 239260
pos sess 239244
ma tely</w> 239239
par a 239229
incorpor ated</w> 239201
deriv ative</w> 239168
ac etic</w> 239155
enhanc ing</w> 239092
sci entific</w> 239088
elucid ate</w> 239031
coag ulation</w> 238842
0 s</w> 238803
refrac tory</w> 238803
resc ue</w> 238769
1 -</w> 238735
fol ded</w> 238654
hemorrh age</w> 238585
dram atically</w> 238579
chon dro 238505
guid ed</w> 238504
exc e 238421
y le</w> 238408
paras ite</w> 238370
rest ored</w> 238308
sk ills</w> 238251
x 1</w> 238249
con focal</w> 238199
predic ting</w> 238058
biop sies</w> 238033
v an 238007
rab bits</w> 237985
ox y</w> 237975
c ation</w> 237951
volunte ers</w> 237873
H em 237832
competi tive</w> 237831
manifest ations</w> 237828
il ed</w> 237796
G M</w> 237784
AP 1</w> 237777
ul ative</w> 237728
r RNA</w> 237726
his ti 237721
cap illary</w> 237702
chol in 237689
overexpres sed</w> 237619
AR T</w> 237537
carri er</w> 237495
oc ellular</w> 237451
t os 237368
c at</w> 237327
P sy 237215
dec om 237187
Tr iton</w> 237181
H ence</w> 236941
y ces</w> 236862
Accor ding</w> 236849
f ab 236842
contro ver 236723
cont acts</w> 236619
esc ent</w> 236563
con stra 236487
D op 236472
perc ep 236472
malign ancies</w> 236355
secre tory</w> 236340
bro ad 236314
hep arin</w> 236311
4 a</w> 236262
compl i 236232
mal aria</w> 236203
id osis</w> 236134
str ation</w> 236050
bra fish</w> 236042
per spective</w> 236034
seiz ures</w> 235982
ECTI ON</w> 235980
mo vements</w> 235868
HE K2</w> 235864
infil tration</w> 235858
cal i 235818
ir radi 235809
professi onal</w> 235744
z er</w> 235663
st aff</w> 235653
F ox 235646
de mic</w> 235487
B I 235485
di ast 235455
ell ed</w> 235445
e stradiol</w> 235393
B R</w> 235371
la p</w> 235352
test osterone</w> 235329
visu alized</w> 235269
d ingly</w> 235202
nov o</w> 235183
ch ers</w> 235032
drin king</w> 235003
isol ate</w> 234856
metabol ite</w> 234771
Ther mo</w> 234519
com or 234350
pea ks</w> 234348
op s</w> 234228
me ter</w> 234112
sc ales</w> 234070
E 2 234057
G 4</w> 234027
prog esterone</w> 234011
monit or</w> 233942
d less</w> 233929
ve get 233855
P seud 233816
th io 233679
inser ted</w> 233675
chem ically</w> 233573
pro ven</w> 233502
determin ants</w> 233499
astro cytes</w> 233453
as h 233429
glyc ine</w> 233408
ti le</w> 233351
S 5</w> 233330
s n 233306
Heal th 233290
bit al</w> 233289
impro vements</w> 233195
M al 233186
laparo scopic</w> 233135
ine a</w> 233038
o ocytes</w> 232973
fas ter</w> 232918
ud g 232830
N M</w> 232825
- 4</w> 232797
Dec ember</w> 232774
Ar g</w> 232677
ati gue</w> 232562
onc ogen 232504
Not ably</w> 232401
FO X 232340
elim ination</w> 232280
wh y</w> 232267
ass ayed</w> 232233
over come</w> 232177
F l 232166
datab ases</w> 232155
un changed</w> 232136
regar dless</w> 232135
Tab le 232124
con flic 232029
tren ds</w> 231996
ab use</w> 231973
D 5</w> 231967
optim ized</w> 231877
asi bility</w> 231857
fo ur 231738
arom atic</w> 231716
r p 231641
sequ ential</w> 231596
con tents</w> 231555
dele tions</w> 231548
L ong</w> 231526
Ch IP</w> 231525
con sum 231481
AP C</w> 231433
mon ocytes</w> 231357
detec ting</w> 231302
8 A</w> 231241
l ane</w> 231210
bri ef</w> 231204
alter ation</w> 231136
F E 231003
syn chron 230998
5 a</w> 230969
an ch 230938
av ing</w> 230905
AC S</w> 230846
p ent 230832
co he 230786
ru n</w> 230775
malign ancy</w> 230720
cas cade</w> 230692
f ine</w> 230615
estim ation</w> 230573
v ary</w> 230559
en sis</w> 230551
F if 230495
E MT</w> 230486
V I</w> 230483
S ev 230386
responsi veness</w> 230272
HS V</w> 230257
in side</w> 230149
l ens</w> 230126
G O</w> 230062
substitu tions</w> 230047
P M</w> 229949
n ose</w> 229896
correc ted</w> 229805
G y</w> 229799
K a 229750
lun gs</w> 229690
v oc 229680
de tail</w> 229669
intes tine</w> 229661
ocor tico 229504
brea k 229487
equ ip 229464
A NO 229459
eli hood</w> 229320
diff ered</w> 229304
R och 229299
ER K1</w> 229142
retro spectively</w> 229122
PK A</w> 229105
tas ks</w> 229101
un affected</w> 229071
disc ipl 228864
swit ch</w> 228705
all ergic</w> 228686
syn the 228675
infil tr 228660
chos en</w> 228615
disc re 228606
Chang es</w> 228578
fibro blast</w> 228574
R i 228549
O A</w> 228525
E u 228444
I CA 228347
T U 228341
tur ally</w> 228330
hem oglobin</w> 228309
candi dates</w> 228274
rheum at 228240
pos s 228207
check point</w> 228173
pre d 228104
onucle ar</w> 228069
elici ted</w> 228004
other wise</w> 227985
i er</w> 227953
m ess 227927
protec t</w> 227919
zym e</w> 227894
cre ate</w> 227882
stron ger</w> 227855
e res</w> 227792
trans mit 227766
foc i</w> 227645
m ent 227616
beg inning</w> 227601
qu antum</w> 227579
asym ptomatic</w> 227424
p unc 227413
dep end</w> 227388
P G</w> 227384
D I</w> 227320
P ost 227211
tox ic 227162
lo x 227158
nutri ent</w> 227148
biomar ker</w> 227140
epis odes</w> 227107
go at</w> 227101
re mission</w> 227078
F o 227040
ar rays</w> 227000
ion ine</w> 226987
Pati ent</w> 226972
RE S</w> 226928
la g</w> 226879
hab it 226867
v ae</w> 226856
challeng ing</w> 226738
sin us</w> 226709
ly sed</w> 226523
co vered</w> 226510
preval ent</w> 226476
sch ed 226429
CR P</w> 226307
n um 226306
quanti fy</w> 226301
tur ned</w> 226255
mac hin 226128
ev ent 226064
G o 225993
HD L</w> 225910
Ther ap 225786
on ally</w> 225708
c e 225671
angio tensin</w> 225636
trigg er</w> 225568
reas on</w> 225528
Or g 225488
equ ation</w> 225341
cap ture</w> 225333
W a 225269
yiel ds</w> 225242
techn ologies</w> 225191
exten sively</w> 225138
B RAF</w> 225135
spind le</w> 225134
andro gen</w> 225065
P res 225015
exclu sively</w> 224998
fo ot</w> 224844
Particip ants</w> 224823
wavel eng 224785
Par kinson</w> 224781
dis ulf 224773
M or 224729
pl ated</w> 224708
I s 224701
p ine</w> 224660
ax ial</w> 224651
oper ated</w> 224643
contribu ting</w> 224635
R ap 224621
e ded</w> 224534
tri glycer 224378
u ed</w> 224358
ou thern</w> 224311
N E</w> 224237
A v 224224
pre mature</w> 224171
E vid 224125
produc es</w> 224105
pre ad</w> 224085
ol in</w> 223932
emerg ed</w> 223915
micro scopic</w> 223906
otyp ing</w> 223870
M i 223838
J o 223775
on going</w> 223774
Child ren</w> 223768
PA TI 223746
vi r</w> 223742
proj ect</w> 223717
lab el</w> 223651
pow er 223573
G A 223457
com promis 223457
M ac 223407
am s</w> 223389
s ati 223382
sc en 223279
e ds</w> 223200
fo und 223198
indic es</w> 223086
Re gi 223018
experim entally</w> 222867
opa usal</w> 222808
lik elihood</w> 222590
pneumon ia</w> 222473
PA R</w> 222428
opio id</w> 222374
v in 222360
particip ation</w> 222314
down regulation</w> 222303
V 2</w> 222283
sen sing</w> 222276
as h</w> 222269
be am</w> 222174
tan dem</w> 222163
larg est</w> 222086
tri ple</w> 222014
I ts</w> 221979
Gen etic</w> 221957
N ot</w> 221934
medi ating</w> 221912
pyl ori</w> 221859
pre dominant</w> 221786
immunoglob ulin</w> 221722
T RP 221647
elong ation</w> 221635
conn ected</w> 221600
dis section</w> 221551
Char acteri 221480
Develop ment</w> 221429
adju stment</w> 221402
- 5</w> 221324
le x</w> 221308
dos age</w> 221299
ten d 221257
den at 221232
exci sion</w> 221175
R ad 221131
fracti on 221101
c ann 221078
acet yl</w> 221066
ph rine</w> 221000
detail s</w> 220905
ten ded</w> 220890
E s</w> 220803
bri dge</w> 220785
trunc ated</w> 220771
tim ing</w> 220747
on yl</w> 220737
bl ack</w> 220693
brea ks</w> 220626
v .</w> 220586
om onas</w> 220563
ad ding</w> 220559
pen icillin</w> 220530
form aldehyde</w> 220474
pol l 220473
blas toma</w> 220453
ro tation</w> 220442
di arr 220425
remo ve</w> 220380
R E</w> 220341
aberr ant</w> 220318
comput ational</w> 220099
CO X</w> 220066
pre vents</w> 220036
impro ves</w> 220027
ub icin</w> 219988
es pread</w> 219920
cap sul 219915
mod ulated</w> 219897
add ressed</w> 219886
organis m</w> 219874
nutri tional</w> 219847
repe ats</w> 219720
bo und 219676
c ally</w> 219660
bl ing</w> 219603
s a</w> 219522
seg reg 219371
s es 219332
eas y</w> 219310
reg imens</w> 219249
contribu ted</w> 219243
diast olic</w> 219204
f f</w> 219201
overexpres sing</w> 219143
D 4</w> 219108
ubiqu it 219090
olog ous</w> 219059
ar t 219036
se ed</w> 219026
PATI EN 218972
brea k</w> 218942
part ners</w> 218909
pati onal</w> 218887
onc ogenic</w> 218725
ram s</w> 218719
op tion</w> 218660
T G</w> 218607
c ephal 218587
haz ard</w> 218576
me an 218569
review s</w> 218538
is s</w> 218536
lu tin 218462
fl uc 218385
subst ances</w> 218345
paren tly</w> 218338
p in</w> 218303
alk aline</w> 218215
man di 218184
sep sis</w> 218128
c ine</w> 218114
du od 218103
g el 218012
Com bin 217967
ze brafish</w> 217920
clea ved</w> 217878
infer ior</w> 217873
R H</w> 217773
implem ented</w> 217723
in osa</w> 217631
Incre ased</w> 217603
antic ancer</w> 217369
commun ities</w> 217311
poss ess</w> 217280
p es 217277
m V</w> 217240
R a 217238
O L</w> 217080
In hibi 217080
aud itory</w> 217077
indic ator</w> 217015
att ached</w> 217012
fe a 217010
corne al</w> 216993
P 5</w> 216943
TC R</w> 216942
Statis tical</w> 216867
A E</w> 216806
pre ference</w> 216749
sur vi 216637
ag arose</w> 216617
I M 216563
mamm als</w> 216540
col le 216510
tr a</w> 216494
L ys 216477
y stems</w> 216471
cataly zed</w> 216457
ran k</w> 216449
adi g 216407
ul atory</w> 216338
7 BL</w> 216310
amin es</w> 216285
In stitu 216252
transpor ters</w> 216128
C ase</w> 216083
mat ch</w> 216080
L ow</w> 215976
c d 215934
A I</w> 215855
di c</w> 215853
ann ed</w> 215804
situ ation</w> 215657
architec ture</w> 215650
weigh ted</w> 215646
t ative</w> 215617
SE M</w> 215607
Evid ence</w> 215600
an emia</w> 215484
need le</w> 215445
S ix</w> 215314
h aps</w> 215301
distribu tions</w> 215230
sim ulated</w> 215154
over lap</w> 215153
lo be</w> 215109
s low 215090
scri bed</w> 215062
ide a</w> 215056
hydrox y</w> 214912
mo ther</w> 214900
for ces</w> 214859
cat al 214817
pum p</w> 214565
N ov 214529
anti tumor</w> 214475
inf ra 214466
el eg 214457
see ded</w> 214259
in st 214242
R is 214235
B B</w> 214228
vi de 214204
ro ute</w> 214191
famil ial</w> 214191
dig ital</w> 214178
3 b</w> 214116
G SH</w> 214098
ther mo 214040
m Ab</w> 213845
barri ers</w> 213842
Cur rent</w> 213838
us cular</w> 213798
engine ering</w> 213745
otox in</w> 213690
mul tic 213668
p 7</w> 213653
hem e</w> 213619
lar vae</w> 213594
os ity</w> 213551
vag inal</w> 213545
N on</w> 213529
ron s</w> 213494
tre e</w> 213462
men opausal</w> 213418
C ys</w> 213380
on in</w> 213275
Experim ental</w> 213267
descri p 213250
S ol 213227
J un 213200
te eth</w> 213148
cl oning</w> 213148
dec ades</w> 213103
meth anol</w> 213088
K s</w> 213062
indic ations</w> 212934
fe asibility</w> 212931
U S 212860
meth ylated</w> 212853
P i 212836
4 D</w> 212818
profil ing</w> 212816
retro viral</w> 212799
r ice</w> 212766
w ing</w> 212649
oc cal</w> 212561
ta kes</w> 212559
neoplas tic</w> 212547
Sampl es</w> 212527
I A</w> 212478
M ice</w> 212448
off ers</w> 212409
to ph 212401
resid ents</w> 212312
Assess ment</w> 212280
Hy per 212215
ic les</w> 212212
D 6</w> 212187
machin ery</w> 212120
wid espread</w> 212063
ol ab 211998
B ra 211850
q PCR</w> 211836
im plants</w> 211826
tec tion</w> 211798
trac tion</w> 211742
sper mat 211712
l umbar</w> 211535
fig ure</w> 211517
Ch ronic</w> 211456
constitu tive</w> 211344
perform ing</w> 211250
bel i 211240
mic ro</w> 211223
at om</w> 211218
wid th</w> 211077
medic ations</w> 211044
SET TING</w> 211025
fluc tu 210983
B raz 210939
f atigue</w> 210918
as ked</w> 210839
ren ess</w> 210827
pri singly</w> 210826
ap o 210780
athero sclerosis</w> 210758
C G</w> 210750
retin a</w> 210750
pa thetic</w> 210748
ag ar</w> 210723
su peroxide</w> 210588
dis m</w> 210538
f ar 210459
toler ated</w> 210450
cere bro 210428
lan es</w> 210327
L i</w> 210300
as tom 210288
par adig 210268
explan ation</w> 210211
ex in</w> 210119
pancre as</w> 210108
BR CA1</w> 210072
ana es 210071
i ae</w> 209999
aer ug 209963
pel vic</w> 209900
coord ination</w> 209889
K 3</w> 209812
gang li 209764
occ u 209751
F c</w> 209721
ne ver</w> 209592
G G</w> 209556
indic ators</w> 209546
Sur ve 209462
categ or 209459
N TS</w> 209417
ul l 209331
suspen sion</w> 209315
Spec ific 209304
In formation</w> 209293
as ter</w> 209279
stud ying</w> 209130
post natal</w> 209096
ti zed</w> 209036
G P 209021
In iti 208933
ser ved</w> 208905
sub group</w> 208878
P ES</w> 208820
ol og</w> 208782
mo des</w> 208780
ti ary</w> 208778
eukary otic</w> 208758
am p</w> 208730
bi a</w> 208665
contr acti 208618
aerug inosa</w> 208618
fit ness</w> 208534
fi st 208505
ur y</w> 208413
D ue</w> 208372
P 2 208292
tumorigen esis</w> 208256
read s</w> 208235
ran e</w> 208227
ot a</w> 208167
ap e</w> 208158
AC h 208145
Sec ond</w> 208000
Roch e</w> 207959
P RO 207918
lin ker</w> 207900
ester ase</w> 207843
neutroph il</w> 207719
met allo 207661
ANO VA</w> 207568
sub cellular</w> 207494
CX CR 207370
gen ce</w> 207234
E F</w> 207194
PE G</w> 207071
anatom ical</w> 207065
agg lutin 207008
E le 206987
bec omes</w> 206818
Z n 206802
under taken</w> 206734
ell es</w> 206727
adren ergic</w> 206664
hel ical</w> 206640
cryst als</w> 206610
EE G</w> 206606
po x 206564
H P</w> 206555
K n 206511
to o</w> 206461
ful ness</w> 206453
T GF 206433
C u 206414
aneurys m</w> 206390
I GF 206374
se ve 206371
po oled</w> 206323
scen ari 206281
centrifug ed</w> 206254
l otting</w> 206207
expan ded</w> 206143
fl an 205929
compri sed</w> 205833
L in 205820
vas t 205804
os tero 205782
pl ane</w> 205680
scat tering</w> 205612
ag land 205571
I tal 205486
ap ol 205398
az ine</w> 205344
U TR</w> 205314
sub cutaneous</w> 205262
Plas ma</w> 205218
irradi ated</w> 205174
O P</w> 205154
epti des</w> 205145
at rophy</w> 205100
Flu o 205085
gu an 204935
adap ted</w> 204928
6 C</w> 204890
L y 204841
phyl ogenetic</w> 204811
tec tomy</w> 204758
dis solved</w> 204741
Sci ence</w> 204646
ipl es</w> 204619
ma x 204566
she ep</w> 204534
effici ents</w> 204520
low ering</w> 204498
retic ulum</w> 204446
b al</w> 204436
ob vious</w> 204397
m olar</w> 204309
dig ested</w> 204208
fea sible</w> 204130
T F</w> 204088
rib osomal</w> 204086
roug h 204080
polym erization</w> 204032
te le 204021
immuno deficiency</w> 204002
fibr in 203989
instrum ent</w> 203841
g ri 203799
differenti ate</w> 203776
Sign aling</w> 203662
shap ed</w> 203608
stand ards</w> 203586
clus tering</w> 203580
sti s</w> 203501
mon ella</w> 203453
compli ance</w> 203448
sup ply</w> 203436
st ret 203364
through put</w> 203347
neuro degenerative</w> 203343
star ted</w> 203316
yl ic</w> 203292
R o 203278
concer ns</w> 203243
vul ner 203234
B ur 203199
lig ase</w> 203198
C N</w> 203090
if t</w> 203088
oblas ts</w> 203062
consider ing</w> 203025
seve rely</w> 202898
B 3</w> 202861
precurs ors</w> 202807
fing er</w> 202736
peri odon 202684
inde ed</w> 202618
v entral</w> 202597
venti ve</w> 202595
C 6</w> 202548
th eses</w> 202489
def ense</w> 202398
CL 1</w> 202378
micro environment</w> 202374
Al ex 202368
os es</w> 202303
agne tic</w> 202303
sci ence</w> 202232
conver ted</w> 202206
concer n</w> 202141
constitu tively</w> 202096
at ypical</w> 202003
squ are</w> 201987
oscop y</w> 201925
in suff 201889
My co 201888
fl ap</w> 201815
m osph 201716
examin ations</w> 201651
l ands</w> 201563
e osin 201459
T yr</w> 201425
beli eved</w> 201380
vi sed</w> 201257
lys osomal</w> 201234
dimen sions</w> 201229
ug ht</w> 201190
M od 201181
Dr ug</w> 201164
resear chers</w> 201162
gen esis</w> 201135
S ac 201124
distingu ish</w> 201054
un related</w> 201043
my osin</w> 201015
gen us</w> 201009
overl apping</w> 200985
ap parently</w> 200940
s tent</w> 200776
hi on</w> 200749
inter sti 200715
Signific ant</w> 200660
cont amination</w> 200629
N i</w> 200525
gon ad 200516
ulti es</w> 200468
co efficients</w> 200459
R G 200453
xim ab</w> 200447
duc ts</w> 200444
hypertroph y</w> 200402
professi on 200399
ra rely</w> 200342
fas ting</w> 200333
h n</w> 200316
c DN 200295
assemb led</w> 200258
descri ption</w> 200171
engine ered</w> 200111
M B 199986
conser vation</w> 199960
Inhibi tion</w> 199869
disulf ide</w> 199865
chem icals</w> 199647
L ys</w> 199643
flu ence</w> 199598
ammon ium</w> 199589
p g</w> 199585
N V</w> 199550
t uring</w> 199529
depart ment</w> 199508
sc ans</w> 199448
immunohisto chemical</w> 199442
e j 199399
sug ar</w> 199372
concentr ated</w> 199369
exampl es</w> 199281
inoc ulated</w> 199210
M ech 199204
ite ms</w> 199034
Soci ety</w> 199021
s light</w> 198991
ab ine</w> 198984
rap amycin</w> 198961
pre ferred</w> 198930
preferen tially</w> 198919
hepat ocellular</w> 198887
Mg Cl2</w> 198887
doc king</w> 198861
li th 198816
B u 198737
rs 1</w> 198726
An ti</w> 198705
th ing</w> 198691
immun ization</w> 198672
di l 198600
mT OR 198565
autom ated</w> 198487
attemp t</w> 198478
sen escence</w> 198421
as one</w> 198419
K Cl</w> 198236
form ations</w> 198197
plic ate</w> 198173
acc es 198078
M U 197956
mix tures</w> 197915
gest ation</w> 197908
efflu x</w> 197905
behavi o 197899
C CR 197872
modul ating</w> 197849
gu inea</w> 197836
I S 197770
per for 197746
cy an 197700
ger m</w> 197678
lep tin</w> 197623
princip al</w> 197610
For ty</w> 197594
intr aper 197591
T E</w> 197565
Sal monella</w> 197534
se d 197522
ex ons</w> 197495
on om 197424
roph ic</w> 197414
pat ch</w> 197342
mm Hg</w> 197323
accor dance</w> 197286
accur ately</w> 197283
RN ase</w> 197232
gl ands</w> 197210
prog res 197189
su res</w> 197179
ac tual</w> 197169
com es</w> 197156
P S 197111
Al tern 197095
vel ope</w> 197055
is ch 196985
O ver</w> 196944
N AD</w> 196885
vi ously</w> 196810
fill ed</w> 196743
v ac 196725
tic ular</w> 196712
exac erb 196682
res in</w> 196627
U . 196566
dri ve</w> 196546
AI MS</w> 196511
col oc 196424
term ed</w> 196422
m is</w> 196374
dist ress</w> 196295
. 3</w> 196268
rheumat oid</w> 196260
polyp eptide</w> 196077
ab in 196062
power ful</w> 196026
flex ible</w> 195810
distingu ish 195746
PATIEN TS</w> 195733
K ore 195678
tra ined</w> 195675
part ner</w> 195671
accum ulated</w> 195651
con sul 195589
qu i 195538
grea test</w> 195535
C RE 195483
f all</w> 195483
Bio technology</w> 195409
re fer 195361
el ing</w> 195349
carbo hydrate</w> 195339
coag ul 195329
nat ur 195265
recogn ize</w> 195197
F S</w> 195192
ra dic 195131
Su per 195107
educ ational</w> 194969
tran scribed</w> 194952
K .</w> 194933
pil ot</w> 194925
man us 194922
ph age</w> 194844
dri ving</w> 194786
p ip 194722
Sta phyloc 194696
phen e</w> 194692
R ac 194642
H igh 194614
S tr 194592
consider ably</w> 194587
w an 194536
Fig s</w> 194465
per son</w> 194440
Con versely</w> 194431
pro mp 194391
ber g</w> 194382
ri um</w> 194381
ME A 194361
consider ation</w> 194320
or ption</w> 194232
N MDA</w> 194164
down regulated</w> 194163
reduc tions</w> 194142
creatin ine</w> 194077
partic i 194003
as al</w> 193981
op ening</w> 193961
C V</w> 193928
en velope</w> 193920
endomet rial</w> 193891
i at 193882
ox if 193872
exp on 193862
glycos ylation</w> 193805
ker atin 193790
st ac 193673
eg g</w> 193626
abol ic</w> 193625
troph ic</w> 193470
epidemi ological</w> 193458
lig ation</w> 193412
filam ents</w> 193368
val ence</w> 193297
A X</w> 193295
tetr am 193232
combin ing</w> 193179
prec ision</w> 193116
col labor 193114
ru vate</w> 193103
t ab 193086
Func tional</w> 193052
per man 193029
pre clinical</w> 193027
lo t</w> 193023
ax ons</w> 193015
resour ce</w> 193012
qu ite</w> 192946
cortis ol</w> 192913
stabil ized</w> 192886
ag o</w> 192844
meth ionine</w> 192831
withdraw al</w> 192755
eff ort</w> 192719
os por 192655
RP 1</w> 192640
or ial</w> 192639
dim erization</w> 192613
ogen e 192611
en o 192592
my co 192586
discrimin ation</w> 192557
IT C</w> 192473
eleg ans</w> 192456
M SCs</w> 192412
F T</w> 192336
un t</w> 192325
Si x 192309
practi tion 192276
ex clusion</w> 192263
gra fts</w> 192260
F GF</w> 192239
P V</w> 192164
or ubicin</w> 192148
R am 192128
ca dian</w> 192087
Ig M</w> 191975
to ok</w> 191875
MEA SU 191831
h old</w> 191766
l d</w> 191689
glycol y 191657
s p</w> 191654
Th 1</w> 191654
ne gl 191653
particip ated</w> 191587
M AP</w> 191580
vit al</w> 191488
cirrho sis</w> 191469
P t 191467
competi tion</w> 191446
par am 191387
Sing le</w> 191354
inter views</w> 191311
cytos ol</w> 191302
mo iety</w> 191249
lu te 191222
asp ir 191206
mo bile</w> 191111
intern ational</w> 191063
si al</w> 190998
medi ators</w> 190985
mechanis tic</w> 190959
dis semin 190947
meg a</w> 190822
accep table</w> 190761
AC E</w> 190750
su m</w> 190746
P L</w> 190686
Dop pler</w> 190666
U N 190653
CO PD</w> 190502
un g</w> 190499
b und 190490
H ow</w> 190487
ann ual</w> 190451
ter tiary</w> 190413
An aly 190386
at mosph 190351
const ants</w> 189991
om a 189883
ulti mately</w> 189859
j udg 189838
ho use 189797
α 1</w> 189788
gest ational</w> 189715
a uc 189677
in come</w> 189667
clin icians</w> 189661
ocy an 189655
Ris k</w> 189654
ab ro 189643
seroton in</w> 189557
cop ies</w> 189504
BD NF</w> 189474
princ iples</w> 189392
dec ay</w> 189335
- 6</w> 189323
compart ments</w> 189299
E M</w> 189237
id in</w> 189196
Di rec 189188
recor ding</w> 189171
design ated</w> 189167
bil i 189141
condi tioned</w> 189104
h ence</w> 189095
z umab</w> 189034
G AP 189027
per ic 188960
Tum or</w> 188950
ig are 188906
s b 188878
presum ably</w> 188831
discus sion</w> 188817
elu ted</w> 188796
loc al 188769
E P</w> 188747
morph ine</w> 188723
min ute</w> 188702
G e 188666
IC U</w> 188659
com pression</w> 188625
new born</w> 188578
Nor th</w> 188555
illu str 188503
Not ch</w> 188495
st atin</w> 188470
el ective</w> 188440
ca us 188365
ol ym 188347
Cy cl 188321
AL L</w> 188305
gluc ocortico 188294
exc essive</w> 188285
Ig E</w> 188285
ero us</w> 188279
g em 188191
ob acteri 188186
b ud 188184
CYP 2 188184
an ion</w> 187984
Th r</w> 187969
ti p</w> 187939
epit opes</w> 187848
i ve</w> 187823
Ph ot 187690
man n</w> 187678
con den 187650
en ables</w> 187640
P ro</w> 187600
G n 187586
pr in 187544
acceler ated</w> 187529
gra f 187396
deci sions</w> 187394
repe ti 187355
expres sions</w> 187324
ec topic</w> 187308
N an 187230
min eral</w> 187185
scaff old</w> 187174
micro bi 187145
la tency</w> 187110
bio tic</w> 187043
ess entially</w> 186994
H e</w> 186971
Medic ine</w> 186966
n em 186929
L 3</w> 186798
Re si 186779
oste oc 186777
re constitu 186727
F our 186718
R D</w> 186716
chic ken</w> 186716
Afr ica</w> 186633
shif ts</w> 186598
A x 186539
re jection</w> 186533
fre e 186510
Biosci ences</w> 186507
bul k</w> 186496
confir ming</w> 186491
Z h 186457
on ine</w> 186423
syndrom es</w> 186379
buff ered</w> 186334
poly acrylamide</w> 186213
B re 186203
1 Δ</w> 186200
s ar 186173
sp .</w> 186147
profession als</w> 186144
ul y</w> 186141
ur a</w> 186059
synerg istic</w> 186056
K RAS</w> 186055
tom ycin</w> 186011
ob structive</w> 185929
contribu tions</w> 185928
condi tioning</w> 185901
G BM</w> 185859
i dism</w> 185789
port al</w> 185776
L oc 185650
emp ir 185633
se q</w> 185576
V er 185496
soci o 185454
ne a</w> 185364
F c 185297
F L</w> 185295
k 2</w> 185199
ogly c 185175
Res pon 185152
bot tom</w> 185129
t s 185065
V 3</w> 185050
fas hion</w> 185050
5 D</w> 185008
Quanti tative</w> 185006
l enti 184964
S an</w> 184961
pro line</w> 184941
s arcoma</w> 184911
6 S</w> 184861
uni ver 184855
ate ll 184852
infra red</w> 184773
manus cript</w> 184749
Surg ical</w> 184728
With in</w> 184721
perc utaneous</w> 184617
S o 184615
diffic ulties</w> 184607
con stric 184515
AI N</w> 184514
tis m</w> 184473
V 6</w> 184465
micro glia</w> 184420
PA RP</w> 184404
forc ed</w> 184399
m apped</w> 184360
attit udes</w> 184292
en ough</w> 184284
fibrill ation</w> 184253
o es</w> 184213
prost agland 184091
ar ach 184078
amin ergic</w> 184070
dram atic</w> 184049
eu tical</w> 183952
Al a</w> 183938
intersti tial</w> 183883
form er</w> 183832
G T</w> 183760
n mol</w> 183756
adolesc ent</w> 183742
cytosk eleton</w> 183732
C AT 183718
end ocytosis</w> 183710
equ ally</w> 183684
ca regi 183652
K L 183592
ur ity</w> 183538
E S 183521
O s 183518
di methyl 183439
Dep ar 183414
stimul ates</w> 183365
col itis</w> 183360
eng er</w> 183298
M G</w> 183296
pro pi 183269
ER T</w> 183260
awa reness</w> 183253
cr yp 183252
cum ulative</w> 183188
SP R</w> 183187
Specific ally</w> 183186
path ologic</w> 183175
Staphyloc occus</w> 183153
S ECTION</w> 183109
A ff 183080
oph thal 183037
ep tive</w> 183032
x yl 183031
CA R</w> 182957
in formed</w> 182935
trans fusion</w> 182927
O C</w> 182916
P ol</w> 182915
i e</w> 182884
4 -</w> 182841
eth asone</w> 182805
hospit alization</w> 182786
provid ers</w> 182771
F C</w> 182721
prophyl axis</w> 182690
exc eption</w> 182667
spe ech</w> 182594
oplas mic</w> 182583
ha ir 182556
mit osis</w> 182551
proper ty</w> 182512
refl ected</w> 182510
ol es</w> 182502
coh orts</w> 182455
ensi ti 182422
un likely</w> 182362
TI ONS</w> 182317
el low</w> 182308
si um</w> 182304
occu pational</w> 182222
ynam ics</w> 182195
po f 182152
st ably</w> 182124
intrac ranial</w> 182122
my o 182113
propor tional</w> 182105
bra ins</w> 182099
Q 1</w> 181992
T s</w> 181976
cri tically</w> 181932
u ous</w> 181871
dri ed</w> 181846
un ilateral</w> 181800
compri sing</w> 181769
ser ial</w> 181759
y ment</w> 181708
P BM 181684
a way</w> 181646
wor se</w> 181646
enh ancer</w> 181643
separ ately</w> 181616
lab elled</w> 181609
r ule</w> 181570
surviv ors</w> 181553
ath yro 181529
an astom 181514
st op</w> 181419
optim ization</w> 181384
sign ature</w> 181361
re agents</w> 181318
ol ine</w> 181311
ro ots</w> 181282
g p1</w> 181245
T ime</w> 181213
intro n</w> 181194
monom er</w> 181120
in activated</w> 181108
or g</w> 181100
Depar tment</w> 181077
R ad</w> 181030
bil ities</w> 181023
sub groups</w> 181013
vis its</w> 181010
Q i 180980
m 1</w> 180968
l en 180942
ap tic</w> 180934
lymph oid</w> 180908
ec ological</w> 180894
transl ated</w> 180861
absor b 180835
Co x</w> 180786
a a</w> 180784
Ap plied</w> 180694
per m 180650
L ac 180603
pertur b 180485
al d 180434
ili ary</w> 180431
r action</w> 180327
mod est</w> 180304
contin ue</w> 180193
elucid ated</w> 180179
oc ca 180129
fer tili 180124
gener alized</w> 180103
phosphor yl 180094
M n</w> 180089
pas sive</w> 180075
fi es</w> 180040
per haps</w> 180005
parasi tes</w> 179966
ob last</w> 179957
edi atric</w> 179936
angi ogenic</w> 179921
par ti 179907
H ear 179901
f ate</w> 179891
Diag nos 179883
compati ble</w> 179876
G MP</w> 179870
st ain</w> 179835
disrup ted</w> 179828
enco ur 179822
es in</w> 179806
fluor ide</w> 179793
CA D</w> 179755
m ers</w> 179752
G 3</w> 179750
D F</w> 179655
dec ade</w> 179644
pl ies</w> 179520
I T</w> 179467
err y</w> 179404
py ruvate</w> 179367
ine phrine</w> 179274
p an</w> 179251
os ens 179205
om otor</w> 179078
H A 179070
defini tion</w> 178876
e a</w> 178847
form ulation</w> 178844
accep ted</w> 178821
remark able</w> 178792
Health care</w> 178768
ar is</w> 178734
ex ec 178696
lis ted</w> 178683
Identi fication</w> 178678
pre term</w> 178625
dist ant</w> 178534
T 7</w> 178505
b 1</w> 178428
oxy gen 178423
sat uration</w> 178423
as cular</w> 178376
U C</w> 178316
exer t</w> 178300
ocy to 178298
trans duc 178293
ER α</w> 178282
min er 178279
back bone</w> 178200
analog ues</w> 178185
sem i</w> 178175
idi opathic</w> 178160
di al</w> 178113
im planted</w> 178018
con sent</w> 178003
e m</w> 177972
ch el 177968
f atal</w> 177918
pp m</w> 177882
NAD PH</w> 177863
H2 O</w> 177779
te am</w> 177726
S we 177696
gir ls</w> 177660
di ets</w> 177479
in sufficient</w> 177411
config uration</w> 177373
Com mittee</w> 177351
my c</w> 177332
valid ate</w> 177324
br ate</w> 177241
f os 177220
vascular ization</w> 177216
adi c</w> 177126
stom ach</w> 177096
U V 177078
ac yl</w> 177077
syste m 177066
hel ic 177056
z ol 177041
as part 177007
N .</w> 176990
gl auc 176982
Y FP</w> 176893
co de</w> 176893
og rams</w> 176844
SI R 176839
IF N 176806
com b 176805
EB V</w> 176761
bon ding</w> 176750
tig h 176637
M V 176571
c a</w> 176509
B a 176502
natur ally</w> 176493
dis placement</w> 176483
te trac 176475
L T</w> 176471
µ l</w> 176470
P T</w> 176414
observ ational</w> 176405
immunore activity</w> 176367
Anim al</w> 176357
d ness</w> 176342
ome ter</w> 176329
cir cadian</w> 176296
ax onal</w> 176287
protein ase</w> 176268
fac tory</w> 176263
ca tech 176230
vesi cle</w> 176224
ed ge</w> 176213
onucle ase</w> 176213
microtub ules</w> 176212
Con si 176188
de press 176044
I sol 176025
subj ective</w> 176006
splic e</w> 175991
ME Fs</w> 175971
conser v 175964
ess es</w> 175943
ogra fts</w> 175928
anal og</w> 175865
mt DNA</w> 175847
enc ap 175824
AT M</w> 175818
off spring</w> 175784
aor ta</w> 175722
9 A</w> 175690
p us</w> 175645
predic tions</w> 175639
AU C</w> 175633
sl ices</w> 175613
all er 175569
indic ation</w> 175319
a erobic</w> 175264
statis tics</w> 175252
Sm ad 175197
extr a</w> 175162
exam ining</w> 175160
M ic 175151
ing i 175127
bili ary</w> 175117
patho physiology</w> 175115
emergen ce</w> 175082
EC M</w> 175077
dele ted</w> 175021
E th 175002
he ar 174960
view ed</w> 174846
HE PES</w> 174759
ar sen 174606
leuc ine</w> 174510
3 β</w> 174500
defic it</w> 174450
IN G</w> 174434
v inc 174381
was hing</w> 174381
par tly</w> 174369
S ym 174361
se c</w> 174345
or a</w> 174274
nam ed</w> 174230
trans gene</w> 174222
oblas toma</w> 174218
utili zing</w> 174209
I r 174194
oxidi zed</w> 174138
Rh o</w> 174136
F uture</w> 174109
lif e 174091
DT T</w> 174057
N one</w> 174051
oper ations</w> 174045
par atus</w> 174007
E ven</w> 173981
app a</w> 173919
ger m 173885
en larg 173844
for d</w> 173833
highligh ts</w> 173716
Cp G</w> 173633
categ ory</w> 173622
F U</w> 173506
Wh at</w> 173477
en op 173460
asym metric</w> 173364
wee kly</w> 173311
me io 173309
c igare 173297
insuff iciency</w> 173246
immun otherapy</w> 173243
dis c</w> 173223
depri vation</w> 173220
b ol 173204
t us</w> 173183
m om 173170
In tr 173162
F 5</w> 173119
C d</w> 173080
C s 173065
es ting</w> 173018
Gl u</w> 173005
reas on 172991
Pseud omonas</w> 172983
prote olytic</w> 172956
L S</w> 172940
h all 172908
ti ters</w> 172890
accoun ted</w> 172876
oci al</w> 172868
sel ves</w> 172830
evol ved</w> 172815
el in</w> 172799
F 7</w> 172797
e gg 172792
ero x 172767
ur i 172697
Jun e</w> 172678
p sor 172630
cortic ostero 172582
Dr .</w> 172530
Car l 172475
junc tions</w> 172464
n urse</w> 172437
pl oid</w> 172426
over view</w> 172419
clar ify</w> 172411
acchar ides</w> 172395
O 3</w> 172296
a 1</w> 172286
ep ing</w> 172239
D E</w> 172197
hypox ic</w> 172095
L u 172092
er arch 172054
cros s 171944
te aching</w> 171930
Techn ology</w> 171924
recor dings</w> 171918
H ar 171880
as sumed</w> 171876
per in 171804
phospholip id</w> 171800
aut osomal</w> 171783
intern alization</w> 171751
Up on</w> 171735
immun ological</w> 171709
transi ently</w> 171683
er ci 171665
g all 171656
SL E</w> 171656
war ran 171600
attr active</w> 171599
gal ac 171581
inter mediates</w> 171440
fr am 171333
degrad ed</w> 171332
l up 171316
ac k 171236
carr y</w> 171220
exp ect 171170
respon ded</w> 171163
ti um</w> 171159
acces sible</w> 171131
W om 171128
endoth elium</w> 171064
cereb ellar</w> 170952
ro l</w> 170947
om orph 170876
ax el</w> 170847
B in 170797
R F</w> 170741
t ome</w> 170699
atell ite</w> 170681
z ero</w> 170674
struc turally</w> 170671
datas et</w> 170627
PK C 170619
anc h</w> 170614
f if 170603
ff e 170571
suff ering</w> 170523
C er 170493
L R</w> 170304
dam aged</w> 170286
p ub 170218
M 3</w> 170180
i ii</w> 170101
Gl c 170075
sym pathetic</w> 170072
m aps</w> 170041
R SV</w> 169991
lig am 169949
co ver</w> 169891
g overn 169881
anes the 169794
tra it</w> 169784
As s 169782
In c 169715
Re view</w> 169692
d 1</w> 169603
tec h</w> 169580
oste opo 169572
lys ate</w> 169514
hem is 169504
physi ologic</w> 169501
mon onuclear</w> 169417
in ess</w> 169413
P SA</w> 169393
coc aine</w> 169362
diagnos es</w> 169318
ren tly</w> 169298
immobil ized</w> 169283
y o 169243
Sub j 169234
co ur 169213
foc using</w> 169158
Labor atory</w> 169158
oxy genase</w> 169138
ar ise</w> 169080
k ept</w> 169013
prote ases</w> 168984
gro up 168972
waveleng th</w> 168928
pro spectively</w> 168892
t um</w> 168865
In dia</w> 168820
r ite</w> 168787
construc tion</w> 168782
ste in</w> 168763
im ide</w> 168708
ide al</w> 168705
no tion</w> 168701
G .</w> 168688
B 4</w> 168671
G CT 168647
gro w</w> 168631
ph thal 168622
sc oring</w> 168604
sco red</w> 168584
strep tomycin</w> 168553
o res</w> 168539
supernat ants</w> 168492
ro und</w> 168405
E 6</w> 168373
Hy dro 168364
T g</w> 168338
H o 168294
mar ks</w> 168282
K in 168197
De tection</w> 168194
es tive</w> 168191
p s 168178
p ep 168118
hist ology</w> 168012
cat tle</w> 168006
neuro pathy</w> 167955
coun sel 167925
ap paratus</w> 167899
oste o 167896
frag mentation</w> 167822
per f 167819
X R</w> 167773
carcin ogenesis</w> 167721
le g</w> 167717
am y 167713
rop e</w> 167704
adip ocytes</w> 167694
sul ph 167682
r ag 167671
es cap 167669
verte bral</w> 167542
assess ments</w> 167430
sur fac 167422
imp acts</w> 167422
Con sequently</w> 167398
R an 167372
Can di 167370
si onal</w> 167367
otox icity</w> 167303
on it 167279
bi polar</w> 167276
hist ologic</w> 167255
M AIN</w> 167244
P y 167167
gen cy</w> 167147
se di 167139
uc h 167138
som at 167124
lo ops</w> 167112
hyper plasia</w> 167082
ac ade 167040
investig ating</w> 167036
H b 167026
ol amine</w> 167026
SI S</w> 167023
seiz ure</w> 167008
B AL 166997
BC R</w> 166975
H ere 166922
s lower</w> 166917
C r</w> 166887
S a 166866
inten se</w> 166828
O 4</w> 166807
H3 K 166794
suic ide</w> 166783
Mut ations</w> 166761
G I</w> 166741
grad ually</w> 166719
Org an 166715
dos ing</w> 166676
avi an</w> 166674
facilit ates</w> 166638
sel ect</w> 166634
CA T</w> 166634
mis sense</w> 166543
neutr alizing</w> 166498
T en</w> 166484
Pro mega</w> 166471
ophil ic</w> 166469
Un der 166425
in verse</w> 166415
in complete</w> 166349
h n 166322
or adi 166322
coord in 166154
S k 166118
Can ada</w> 166098
dis soci 166034
ar ched</w> 166015
in stance</w> 165924
w k</w> 165923
Wom en</w> 165915
glomer ular</w> 165910
CF TR</w> 165900
e ating</w> 165863
ul ates</w> 165863
ulc er</w> 165855
ation ally</w> 165822
C U 165719
met als</w> 165710
os arcoma</w> 165693
clin ic 165637
ow ing</w> 165635
exten d</w> 165632
discipl inary</w> 165631
Stud ent</w> 165569
sti ff 165542
sh a 165541
in ic</w> 165527
fol ate</w> 165523
therapeu tics</w> 165516
ch arom 165514
addi tive</w> 165511
Ph ase</w> 165443
synap ses</w> 165438
nor mo 165362
Be tween</w> 165336
toc occus</w> 165314
di hydro 165274
PC 1</w> 165253
f lies</w> 165210
bri dg 165198
P N 165182
guid ance</w> 165167
th o 165141
them selves</w> 165134
ur g 165052
st ock</w> 165006
H er 164988
coun ted</w> 164957
fung i</w> 164905
K i</w> 164879
diff raction</w> 164867
refl ects</w> 164836
por cine</w> 164800
mis sing</w> 164747
as sum 164696
or ally</w> 164620
lit axel</w> 164571
con stitute</w> 164568
ir al</w> 164549
acc id 164548
cy st</w> 164514
out patient</w> 164436
plac ental</w> 164344
li e</w> 164334
de posi 164300
moder n</w> 164256
ach e</w> 164246
bre eding</w> 164240
dro p</w> 164235
rup ture</w> 164214
immunob lotting</w> 164148
tub ular</w> 164118
establish ment</w> 164007
T arg 163989
im mature</w> 163980
res ted</w> 163972
G P</w> 163961
NI H</w> 163827
hypo the 163808
As p</w> 163638
B ank</w> 163637
7 B</w> 163624
G ram</w> 163594
Eu rope</w> 163535
Sub sequently</w> 163471
ke ys</w> 163469
be an</w> 163461
L o 163448
ser ves</w> 163307
cryst alli 163274
hor iz 163269
sub sets</w> 163258
four th</w> 163249
Inde x</w> 163244
qu it 163218
Qi agen</w> 163183
2 α</w> 163148
de teri 163120
ver tical</w> 163107
SO D</w> 163095
pneumon iae</w> 163087
perman ent</w> 163065
Pre dic 163050
S l 163044
asp ect</w> 163033
AT A</w> 163030
on ds</w> 162946
charom yces</w> 162908
se arched</w> 162827
E d 162810
Ar g 162802
desi red</w> 162745
Syn thesis</w> 162688
gal act 162675
odi alysis</w> 162663
pap ill 162642
ox in</w> 162621
opportun ity</w> 162602
ac ous 162548
man aged</w> 162507
thre onine</w> 162495
er t</w> 162376
if ying</w> 162376
ograph s</w> 162367
ex port</w> 162335
Le u</w> 162298
F V 162283
T N 162200
nucle ic</w> 162198
A c</w> 162167
calcul ate</w> 162160
ri p 162151
i ple</w> 162088
ultras on 162082
hist amine</w> 162080
L eu 162015
D H 161948
L V 161894
n 1</w> 161821
cl amp</w> 161814
diarr he 161791
iz umab</w> 161752
sequ ent</w> 161751
Labor atories</w> 161661
paradig m</w> 161660
end oplasmic</w> 161595
S tro 161589
form in</w> 161572
loc ally</w> 161572
bio film</w> 161563
5 -</w> 161546
ham s 161529
st al 161511
tail ed</w> 161486
satisfac tory</w> 161470
trans duced</w> 161432
anti sense</w> 161412
b ab 161410
plas mic</w> 161399
r ing 161384
rec ycl 161365
ag ic</w> 161344
ay ers</w> 161343
C CT 161248
fru it</w> 161167
con current</w> 161150
electro des</w> 161143
G ene 161113
analog ue</w> 161102
N ur 161095
ses sions</w> 161089
G AG 161084
controver sial</w> 161076
F am 161047
ab sc 161032
ner ves</w> 161014
tra j 161004
im pe 161001
tac hy 160937
BM P</w> 160911
te ll 160872
oligonucle otide</w> 160824
os us</w> 160797
algorith ms</w> 160792
lin ks</w> 160701
r ace</w> 160690
β 2</w> 160670
leuk ocyte</w> 160649
kn ock</w> 160594
ger min 160590
ho use</w> 160589
s we 160364
L P</w> 160347
am ethasone</w> 160299
A N</w> 160263
w ri 160201
histi dine</w> 160107
t one</w> 160071
kill ing</w> 160065
2 R</w> 159954
radi al</w> 159936
th rea 159878
analy zing</w> 159864
ca th 159798
filam ent</w> 159781
verte bra 159759
summar ized</w> 159642
3 p</w> 159613
tryp toph 159608
T ur 159605
whe at</w> 159536
comor bi 159528
p i</w> 159512
sil ica</w> 159491
G W 159489
B I</w> 159479
Tran scrip 159444
C K 159437
hear ts</w> 159432
Ig A</w> 159400
part um</w> 159373
g ating</w> 159288
Rec om 159273
Di ab 159190
nico tine</w> 159190
ace ae</w> 159185
analog s</w> 159149
il e 159146
L M 159132
incid ent</w> 159110
situ ations</w> 159079
con e</w> 159070
ap plying</w> 159060
can ine</w> 159059
applic able</w> 159029
pla ques</w> 159020
P or 159018
V .</w> 159015
inj ured</w> 159013
pro ne</w> 158972
acc ess 158907
cen ding</w> 158906
respon dents</w> 158904
mit ral</w> 158890
oxif en</w> 158826
om ing</w> 158800
ob acter</w> 158794
ten ed</w> 158787
hydro ly 158769
hormon al</w> 158763
mis sions</w> 158754
N i 158749
mim ic</w> 158596
p es</w> 158558
no vi 158558
Austral ia</w> 158516
mac h 158494
preser ved</w> 158445
au di 158405
pac k 158386
phy to 158349
less er</w> 158346
r ated</w> 158340
acade mic</w> 158321
grad u 158314
bronch ial</w> 158284
rele asing</w> 158273
Per i 158174
Accor dingly</w> 158168
in ti 158105
PM C</w> 158100
zy go 158098
conduc tance</w> 158081
ul der</w> 158080
to oth</w> 158035
dox orubicin</w> 158034
x 2</w> 158012
g one</w> 158007
aspart ate</w> 157956
alk al 157936
benz o 157917
f el 157901
C ro 157888
electro ly 157874
fe ed</w> 157844
1 E</w> 157806
R b</w> 157772
T T</w> 157765
ca ud 157736
lup us</w> 157714
eth nic</w> 157703
app reci 157676
echocardi ography</w> 157648
in age</w> 157617
an at 157599
tempor ary</w> 157557
sl ides</w> 157531
antic ip 157520
coun try</w> 157480
A sian</w> 157447
micro som 157445
D AP 157406
B ax</w> 157373
do g</w> 157358
princ iple</w> 157355
viol et</w> 157353
hist or 157329
tim ate</w> 157320
in ability</w> 157223
br anch</w> 157184
am id 157140
c ran 157106
telom ere</w> 157047
cy sts</w> 157035
der mat 156985
aut ologous</w> 156976
harb oring</w> 156968
par athyro 156874
k el</w> 156848
d i</w> 156841
hemis ph 156773
qu en 156725
all os 156721
Meas ure 156712
b ases</w> 156660
F F 156657
in o</w> 156607
mod alities</w> 156602
em ul 156572
questionna ires</w> 156562
l ity</w> 156553
b is</w> 156538
den se</w> 156456
os mo 156412
ron ectin</w> 156396
M el 156340
con tex 156320
D D</w> 156246
we a 156236
re turn</w> 156211
E pi 156210
Ad min 156210
play ing</w> 156159
b ate</w> 156143
polar ized</w> 156112
kidne ys</w> 156093
si dase</w> 156006
atin um</w> 155906
bor ne</w> 155863
de t 155845
PF S</w> 155841
inf u 155812
w s</w> 155802
wal king</w> 155769
schem e</w> 155698
m ast</w> 155673
ax on</w> 155642
initi ate</w> 155614
pharmaco kinetic</w> 155612
in directly</w> 155588
am eli 155564
mus cular</w> 155521
psych os 155483
chim eric</w> 155483
Acti vation</w> 155384
Differen t</w> 155369
res sing</w> 155357
mar ine</w> 155339
Se ph 155337
oph ar 155241
et a</w> 155232
ycl ine</w> 155221
N -</w> 155216
P os 155210
g yr 155196
T H</w> 155187
cardi a</w> 155147
t onic</w> 155129
a u</w> 155109
od ec 155096
oblas tic</w> 155055
ath le 155033
spor adic</w> 154982
st op 154978
difficul ty</w> 154970
intra operative</w> 154928
weigh ts</w> 154901
sh ar 154762
cap si 154737
hin d</w> 154669
F 6</w> 154659
trans planted</w> 154578
pre ventive</w> 154571
pl ete</w> 154569
Ch rom 154567
I S</w> 154557
persis tence</w> 154495
hydr ation</w> 154482
Re tro 154445
oligonucle otides</w> 154427
c ies</w> 154389
ME K</w> 154325
random ised</w> 154317
datas ets</w> 154272
St and 154263
c in</w> 154259
system atically</w> 154199
enco de</w> 154186
D N</w> 154164
accep tor</w> 154113
egg s</w> 154091
5 b</w> 154077
Gen ome</w> 154041
gran ules</w> 154027
li p</w> 154025
Mil li 153996
dex tr 153955
indu stry</w> 153936
inter f 153932
CH O</w> 153884
pass age</w> 153884
tend ency</w> 153862
B acteri 153842
immunoprecip itated</w> 153835
on ia</w> 153816
Qu ality</w> 153811
hel p 153750
simil arities</w> 153676
ju ven 153610
Nr f2</w> 153574
ref lex</w> 153517
Coll ectively</w> 153506
inher ited</w> 153505
model ling</w> 153482
S pr 153474
MI C</w> 153471
unifor m</w> 153440
resp iration</w> 153395
star vation</w> 153395
ol ol</w> 153374
re turned</w> 153363
resc ued</w> 153349
d ent</w> 153330
te m</w> 153328
stero ids</w> 153306
j us 153301
el astic</w> 153272
ep i 153267
attemp ts</w> 153242
lu tion</w> 153213
mod ality</w> 153208
V T</w> 153201
TP 5</w> 153168
In divid 153159
L C3</w> 153151
flex ibility</w> 153070
sup plement</w> 153067
rest oration</w> 153062
W here 153055
d al 153049
o tes</w> 153039
interf ere</w> 153019
spont aneously</w> 153016
k appa</w> 152983
bo ys</w> 152955
re l</w> 152927
conn ection</w> 152913
Sy ste 152907
Differen ces</w> 152856
log y</w> 152848
obj ect</w> 152827
C B</w> 152785
saliv ary</w> 152737
AB C</w> 152733
ex tra 152728
fu r</w> 152690
foll icular</w> 152638
F .</w> 152612
pren atal</w> 152582
pu ts</w> 152480
id ed</w> 152428
hom olog</w> 152428
rel ations</w> 152421
C ap 152368
T er 152352
A sp 152310
2 p</w> 152200
us age</w> 152193
re pressor</w> 152156
anti depress 152136
opportun ities</w> 152122
H RP</w> 152107
c asse 152103
G ly</w> 152078
S . 152073
at aly 152063
on ym 152052
dem and</w> 152032
cu es</w> 151993
influ encing</w> 151991
modi fy</w> 151936
Fif ty</w> 151912
R ole</w> 151887
buil ding</w> 151864
arb ox 151808
bl ed</w> 151803
conform ations</w> 151781
o k 151745
comple tion</w> 151735
dra inage</w> 151718
r as</w> 151650
contr al 151565
inten sities</w> 151556
par aff 151550
A AT 151544
stiff ness</w> 151544
Where as</w> 151500
embol ism</w> 151414
M emb 151406
visi t</w> 151372
fu sions</w> 151370
3 T3</w> 151353
an thro 151325
encoun tered</w> 151319
F AK</w> 151271
bir ds</w> 151201
c ats</w> 151192
v is</w> 151129
li pop 151088
n a</w> 151069
A Z 151060
gene tics</w> 151042
co valent</w> 151034
M arch</w> 151023
Bios ystems</w> 151023
ar ising</w> 151018
germ line</w> 150991
Ad v 150989
help ful</w> 150949
ot ted</w> 150939
A V</w> 150904
biom ass</w> 150848
ten sive</w> 150827
sh ell</w> 150825
recip ient</w> 150824
el igible</w> 150789
de struction</w> 150772
accum ulate</w> 150742
CR I 150717
determin ant</w> 150705
gi ves</w> 150701
pancre atitis</w> 150696
associ ate</w> 150692
transi tions</w> 150681
ten don</w> 150680
glauc oma</w> 150672
neigh b 150662
B er 150625
program me</w> 150584
man ual</w> 150576
s s</w> 150560
ure th 150559
op y 150494
recor d</w> 150476
attenu ation</w> 150470
artic ular</w> 150448
ro d</w> 150431
h es</w> 150423
suppres ses</w> 150387
M n 150333
Cas 9</w> 150306
pan ic</w> 150304
cr ude</w> 150302
modul ates</w> 150298
ep tor</w> 150270
s atellite</w> 150249
cap ability</w> 150203
x ed</w> 150154
pac litaxel</w> 150131
C am 150115
erythro cytes</w> 150063
elim inated</w> 150028
comm erci 149999
St ate</w> 149923
pro inflammatory</w> 149895
T est</w> 149873
allel ic</w> 149844
re aching</w> 149842
Fluo resc 149714
ion ization</w> 149713
surge ons</w> 149713
myel oma</w> 149711
RN P</w> 149705
M ay</w> 149702
- 7</w> 149691
inoc ulation</w> 149679
el ve</w> 149676
or bital</w> 149637
top ical</w> 149611
highligh ted</w> 149606
py ro 149588
o ro 149559
Psy ch 149541
con sci 149498
inte ll 149481
ca usal</w> 149476
polym ers</w> 149449
dys plasia</w> 149439
Stre p 149413
calc ulation</w> 149412
ventr icle</w> 149381
verte brate</w> 149344
ho sts</w> 149325
. 4</w> 149311
program ming</w> 149293
empir ical</w> 149272
st one</w> 149262
E m 149208
an ical</w> 149201
lim itation</w> 149200
pal mit 149122
m ill 149049
ti ce</w> 149011
g est</w> 148981
col onic</w> 148959
event ually</w> 148886
acous tic</w> 148880
cir cular</w> 148875
fer tility</w> 148863
N 3</w> 148857
Me di 148827
oncogen e</w> 148810
m on</w> 148794
pres sures</w> 148788
lum inal</w> 148752
ver ify</w> 148739
electro chemical</w> 148713
Milli pore</w> 148698
mandi bular</w> 148688
some times</w> 148646
d ur 148637
A ge</w> 148626
homogen eous</w> 148607
AL K</w> 148571
hel ices</w> 148541
β 4</w> 148537
est yle</w> 148537
hi erarch 148519
enabl ed</w> 148514
om al</w> 148502
mod ule</w> 148463
nod ules</w> 148436
sh ear</w> 148431
lipos omes</w> 148382
T yr 148377
dex amethasone</w> 148370
trigg ers</w> 148369
q 2</w> 148364
am bi 148311
S il 148297
mTOR C1</w> 148279
de pressed</w> 148237
sc av 148218
e ter</w> 148209
evid enced</w> 148170
precip itation</w> 148095
cycl ing</w> 148079
sor ting</w> 148073
l ing 148066
I mp 148049
manip ulation</w> 148022
ac compl 147977
interpre ted</w> 147959
ang er</w> 147938
olog ist</w> 147919
repres entation</w> 147904
NA c</w> 147891
- -</w> 147871
k ap 147869
L ym 147850
co il</w> 147841
stabil ize</w> 147802
C ri 147758
con duction</w> 147751
r 1</w> 147714
ca m</w> 147711
individu ally</w> 147700
fil tered</w> 147682
R ed</w> 147678
tryptoph an</w> 147675
Wor ld</w> 147620
d ent 147520
pregn ancies</w> 147500
ov ary</w> 147431
l er</w> 147406
f low 147397
e an</w> 147391
tri gu 147349
T L</w> 147344
T n 147340
Ma terials</w> 147337
AS D</w> 147306
progenit ors</w> 147298
trans mitted</w> 147296
le ak 147287
ur ac 147282
s or</w> 147279
continu ously</w> 147276
life time</w> 147258
over weight</w> 147229
n ig 147196
percep tions</w> 147173
tigh tly</w> 147152
0 S</w> 147109
rec essive</w> 147108
G SK 147092
acti vely</w> 147077
stabil izing</w> 147042
hel d</w> 147015
W HO</w> 147001
influ x</w> 147001
5 p</w> 146993
s en</w> 146983
re new 146977
a i</w> 146967
su d 146901
s lip 146864
pro l 146858
expos ures</w> 146837
sim pl 146823
facil ities</w> 146823
C he 146820
cit rate</w> 146818
ana erobic</w> 146813
I 1</w> 146776
ell um</w> 146742
dop aminergic</w> 146736
ta ins</w> 146734
r y 146731
pro ve</w> 146635
discre te</w> 146635
C ys 146618
der mal</w> 146577
activ ators</w> 146576
en ium</w> 146453
proxim ity</w> 146392
r ings</w> 146356
c c 146213
concep ts</w> 146212
T ox 146161
st rial</w> 146143
ac yl 146102
6 a</w> 146079
spo t</w> 146075
en anti 146068
D en 146054
tin c 146012
G GT 145989
con ven 145979
polym er 145922
pri l</w> 145881
C EN 145873
c ro 145852
Un like</w> 145816
fin ally</w> 145769
p in 145734
u ximab</w> 145718
- 8</w> 145680
radi ographic</w> 145637
coord inated</w> 145585
shif ted</w> 145522
uncer tain 145510
cardiomy opathy</w> 145475
do w</w> 145433
rou tin 145413
mat ching</w> 145316
N H 145281
leuk ocytes</w> 145218
ar ms</w> 145211
perme abil 145208
ol factory</w> 145132
Av ail 145104
la tent</w> 145061
M Hz</w> 145018
slow ly</w> 145015
L ip 145009
vast atin</w> 144912
chape rone</w> 144908
acu ity</w> 144818
lum en</w> 144771
Im aging</w> 144736
spo ts</w> 144733
Ph e</w> 144728
un published</w> 144707
tub es</w> 144675
U. S.</w> 144666
vide o</w> 144661
at osis</w> 144649
dec lined</w> 144619
J uly</w> 144612
S 1 144598
belie ve</w> 144577
red und 144543
x in</w> 144527
b if 144507
medi ator</w> 144503
cho ol</w> 144498
conn ectivity</w> 144491
P erc 144476
w ards</w> 144438
MY C</w> 144435
L s</w> 144390
reg istered</w> 144389
facilit ated</w> 144388
chemo kine</w> 144369
cap tured</w> 144298
T P</w> 144295
contral ateral</w> 144183
ent ations</w> 144116
G N 144100
v ol</w> 144085
ery them 144070
ho t</w> 144066
vas o 144009
fl ur 143922
S n 143762
otox ic</w> 143754
non specific</w> 143746
opath ological</w> 143689
t ability</w> 143670
un stable</w> 143659
C entral</w> 143639
pal li 143634
k le</w> 143608
pur poses</w> 143606
lac ks</w> 143598
F lag</w> 143583
dom es 143545
posi tioning</w> 143540
Be sides</w> 143529
pene tr 143480
I ll 143450
Bac illus</w> 143426
prescri bed</w> 143397
speci alized</w> 143375
k y</w> 143365
az ep 143357
Gro w 143351
Alex a</w> 143350
P N</w> 143286
quantit atively</w> 143263
t ose</w> 143231
W al 143221
ul cer 143202
conserv ative</w> 143200
de form 143196
hem odynamic</w> 143170
st aging</w> 143157
pharmaco kinetics</w> 143132
neoplas ms</w> 143101
G AL 143081
moder ately</w> 143079
ej un 143061
is ter</w> 143029
M AL 143026
2 E</w> 142949
ost atic</w> 142930
under gone</w> 142895
te dly</w> 142867
urac il</w> 142836
in he 142820
ocom pati 142799
promo tion</w> 142798
os s</w> 142785
direc tional</w> 142744
L ou 142729
illustr ated</w> 142703
Bin ding</w> 142703
emp ty</w> 142698
trans forming</w> 142692
tran sc 142636
bio tin</w> 142625
institu tion</w> 142602
tig ht</w> 142579
anat omy</w> 142562
conf er</w> 142545
pre treated</w> 142519
C AC 142517
ge ometry</w> 142516
ag ue</w> 142510
GAP DH</w> 142505
ang les</w> 142466
bi ogenesis</w> 142433
I U</w> 142423
H ol 142388
C x 142376
mit ogen</w> 142362
kap pa 142347
te stis</w> 142339
resid ent</w> 142322
edi ting</w> 142283
f resh 142275
Er b 142270
c ows</w> 142244
poly s 142237
ocy top 142171
Admin istration</w> 142165
5 S</w> 142123
in ver 142106
origin ally</w> 142093
W he 142036
fi er</w> 141975
cat ar 141969
xen ograft</w> 141967
U r 141955
A ug 141938
transp os 141938
win dow</w> 141937
sur ance</w> 141913
O T</w> 141902
Surve y</w> 141885
Pro gram</w> 141879
E V 141866
S even</w> 141859
T issue</w> 141826
B as 141826
dr al</w> 141815
X enop 141813
but able</w> 141807
I g</w> 141804
L 4</w> 141802
ple ural</w> 141775
E 4</w> 141688
E c 141685
cholin ergic</w> 141675
organ ized</w> 141667
Nor thern</w> 141661
d itary</w> 141655
Mech anis 141614
sw elling</w> 141610
fer ing</w> 141592
bro w 141587
acetyl choline</w> 141541
T op 141531
extrem e</w> 141513
pro found</w> 141497
Hear t</w> 141421
line ages</w> 141397
prostagland in</w> 141331
pic ture</w> 141317
chem ot 141310
i P 141307
magne sium</w> 141289
Sac charomyces</w> 141279
SO D1</w> 141167
te mb 141144
al og 141136
den sities</w> 141108
ensi tivity</w> 141029
hi z 141028
diarrhe a</w> 140973
immuno assay</w> 140971
S 7</w> 140968
odi c</w> 140943
at onin</w> 140939
GT Pase</w> 140934
t ations</w> 140903
clim ate</w> 140871
disturb ances</w> 140857
Xenop us</w> 140853
aver aged</w> 140831
Rac 1</w> 140819
ther m 140813
se ed 140788
form ula</w> 140758
DI SC 140754
ac ks</w> 140745
sil ver</w> 140706
n ight</w> 140688
rati on 140670
Sur viv 140663
ri s</w> 140654
l ic 140618
G SE 140609
W I</w> 140599
Ul tras 140585
attri butable</w> 140551
Grow th</w> 140537
alb icans</w> 140533
ther mia</w> 140430
en ib</w> 140376
escap e</w> 140342
BM D</w> 140327
rib osome</w> 140305
E CT</w> 140304
tes ticular</w> 140300
pul ses</w> 140268
tr ics</w> 140243
N at 140176
AT CC</w> 140160
O UT 140155
V it 140119
me 3</w> 140089
graph y</w> 140074
pl atinum</w> 140038
ultras truc 140000
mi x</w> 139980
radi ological</w> 139955
psychos ocial</w> 139865
un like</w> 139847
N D 139777
P RE 139745
effec tors</w> 139738
M N</w> 139609
o zo 139608
us h</w> 139590
ori ented</w> 139588
libr aries</w> 139534
fo ods</w> 139508
or im 139500
os ing</w> 139489
CRE B</w> 139453
adjus ting</w> 139384
respon ders</w> 139379
fin anc 139368
m ir 139337
belong ing</w> 139336
se arch 139332
see ds</w> 139321
trac e</w> 139299
conjug ate</w> 139298
per fused</w> 139295
n om 139264
conjunc tion</w> 139242
am o 139237
olys accharide</w> 139216
eng agement</w> 139140
tam oxifen</w> 139116
insec t</w> 139114
K m</w> 139107
detec tor</w> 139101
mut ational</w> 139091
D at 139050
de man 139047
indic ative</w> 139035
pol arity</w> 139022
es ters</w> 139011
por ation</w> 139003
ti ter</w> 138994
Al l 138985
can al</w> 138979
kin d</w> 138950
centr e</w> 138942
rang es</w> 138923
um ina</w> 138921
d odec 138887
E ight</w> 138865
phot on</w> 138865
d l</w> 138823
S TI 138783
H N 138759
EC G</w> 138727
con clusions</w> 138697
dist ances</w> 138694
stri atum</w> 138677
j ejun 138666
id ae</w> 138602
no tic 138598
electro static</w> 138583
gi ving</w> 138570
ul tra 138563
y ellow</w> 138521
ent a</w> 138518
s ession</w> 138515
di ties</w> 138512
rit ten</w> 138499
ac tor</w> 138495
v 1</w> 138471
stric t</w> 138456
cul tural</w> 138407
sy novi 138398
Inte gr 138398
trac king</w> 138379
inter fering</w> 138362
adop ted</w> 138352
ph one</w> 138276
person ality</w> 138244
OUT CO 138240
L Y2</w> 138178
R 5</w> 138167
she et</w> 138148
pharmac eutical</w> 138128
H s</w> 138123
N ucle 138098
w aves</w> 138064
mus cul 138012
fib ronectin</w> 137956
Inv estig 137956
flan king</w> 137949
anomal ies</w> 137907
hypothalam ic</w> 137879
Candi da</w> 137856
B 6</w> 137845
visu alization</w> 137845
omat ous</w> 137826
imid azole</w> 137816
rhyth m</w> 137791
venti l 137769
assi st</w> 137764
T em 137717
w ells</w> 137713
A pril</w> 137666
glomer ul 137659
aff eren 137577
practition ers</w> 137571
in hal 137561
com ing</w> 137561
SI ON</w> 137537
fist ula</w> 137530
und ed</w> 137509
Mo del</w> 137499
ull ary</w> 137480
preser vation</w> 137464
FRE T</w> 137451
M ex 137439
conjug ates</w> 137383
sho ulder</w> 137347
P en 137305
ME T</w> 137236
en or 137229
R em 137186
yn aptic</w> 137151
F IN 137094
brea thing</w> 137090
Myco bacterium</w> 137087
anis h</w> 137078
H od 137061
align ed</w> 137054
mi um</w> 137040
att ack</w> 137023
M us 137016
s ation</w> 137014
S ER 136993
py r 136958
swit ching</w> 136947
L ab 136933
a dic 136850
dis s 136825
W ang</w> 136817
Spec ific</w> 136806
cor ro 136772
ph o 136724
mas sive</w> 136716
spl en 136688
T ol 136682
A ST</w> 136681
head ache</w> 136678
Sel f</w> 136657
re flux</w> 136651
pl uri 136596
op ulmonary</w> 136546
D em 136541
sampl ed</w> 136525
respon ding</w> 136488
excit atory</w> 136477
us er</w> 136436
ch i 136343
E A</w> 136336
an not 136311
ra ine</w> 136278
P an 136269
val id</w> 136266
ob ut 136231
L B</w> 136229
commerci ally</w> 136190
Man agement</w> 136182
trache al</w> 136160
post operatively</w> 136141
tox ins</w> 136125
PPAR γ</w> 136122
Gn RH</w> 136070
4 b</w> 136058
f ly</w> 136046
D 8</w> 136032
non linear</w> 136020
run ning</w> 136020
c s</w> 136019
β 3</w> 136009
e str 136002
co ch 135988
Rho A</w> 135974
S tra 135970
id ing</w> 135940
consider ations</w> 135900
sc al 135888
CD K 135762
F ITC</w> 135748
spec imen</w> 135744
S L</w> 135700
mix ing</w> 135700
ag ues</w> 135640
off ered</w> 135632
neu rom 135615
IC D</w> 135594
solub ility</w> 135575
H V</w> 135560
num erical</w> 135481
rest ore</w> 135478
hydrox yl</w> 135473
wor ks</w> 135437
mo od</w> 135431
teri ous</w> 135402
de ter 135392
osteopo rosis</w> 135380
ati cal</w> 135345
J A 135332
En h 135319
os y 135309
emph asis</w> 135300
minim ize</w> 135260
dis plays</w> 135250
am bul 135198
attemp ted</w> 135176
os yl 135168
tr ically</w> 135162
k now</w> 135138
alle vi 135133
ogene ic</w> 135132
ot ub 135121
glyc ogen</w> 135034
las ting</w> 135024
INT ER 134985
Se q</w> 134956
f l</w> 134904
ne y</w> 134900
ameli or 134884
Hsp 9</w> 134869
Sur prisingly</w> 134853
cess ation</w> 134848
stri king</w> 134817
mos quit 134804
l nc 134791
Avail able</w> 134788
en comp 134762
super ficial</w> 134732
Pr P</w> 134703
Com po 134702
radic als</w> 134626
cataly st</w> 134604
bal anced</w> 134604
ol ing</w> 134570
R ob 134526
sur pri 134463
D ic 134443
refl ecting</w> 134418
strati fied</w> 134401
contamin ated</w> 134384
vi g 134362
ch olec 134343
un common</w> 134325
AC C</w> 134285
reason able</w> 134263
on tal</w> 134237
As soci 134229
RP E</w> 134221
sc inti 134140
ite m</w> 134130
thresh olds</w> 134086
Fr ance</w> 134086
vari es</w> 134055
com for 134049
integr al</w> 134017
ubl ic</w> 134014
an ne 134012
ep s</w> 134006
transmit ter</w> 134006
rib onucle 133996
Gra ph 133950
CT 1</w> 133937
lif estyle</w> 133924
W est</w> 133902
kin in</w> 133856
H 1 133790
epidemi ology</w> 133771
bal lo 133730
noc ic 133724
illu strate</w> 133718
ish man 133713
ti de</w> 133703
nit rate</w> 133691
lim us</w> 133671
compe tent</w> 133656
Gen er 133637
g ingi 133623
challeng ed</w> 133613
B T</w> 133598
pene tration</w> 133567
T 2 133563
C TL 133515
leng ths</w> 133501
fe ar</w> 133462
hy d 133454
H b</w> 133442
f ri 133429
tor ial</w> 133381
neon ates</w> 133380
se a</w> 133356
g yn 133341
Compar ative</w> 133335
d L</w> 133305
pel let</w> 133295
indu strial</w> 133292
colle agues</w> 133260
l ings</w> 133257
V E</w> 133245
propag ation</w> 133242
v ill 133235
Cy to 133196
dele terious</w> 133182
Hod g 133148
con sequently</w> 133136
TL R4</w> 133128
use fulness</w> 133117
immunosup pressive</w> 133086
clu sive</w> 133065
A AG 133038
Qu estionna 133029
En gl 133024
Br d 133006
IN E</w> 133002
temb er</w> 132986
ad dic 132924
hapl otype</w> 132905
Bra in</w> 132898
it ance</w> 132893
MT T</w> 132888
Braz il</w> 132878
der m 132866
deposi ted</w> 132818
sec onds</w> 132815
exhibi ting</w> 132815
mon keys</w> 132808
anti genic</w> 132801
ir y</w> 132786
aff or 132754
S s</w> 132657
flav on 132639
E t 132638
PA P</w> 132634
A pp 132581
c il 132574
Sep tember</w> 132573
Here in</w> 132566
iton e 132554
stri atal</w> 132524
oun ding</w> 132523
un expected</w> 132499
pro to 132481
seas on</w> 132479
ograph ical</w> 132476
R B</w> 132472
Me dian</w> 132460
quanti ty</w> 132457
del ine 132392
CL 2</w> 132383
contin ence</w> 132363
mit tent</w> 132345
elim inate</w> 132302
a ter 132292
direc tions</w> 132290
BC L</w> 132243
E igh 132212
Gen eral</w> 132202
syn ucle 132198
ur ic</w> 132176
St .</w> 132114
predic ts</w> 132107
chemo therapeutic</w> 132099
per g 132020
v il 132017
contracti le</w> 131986
trans l</w> 131971
condi tional</w> 131960
suc cin 131939
coun ting</w> 131931
hospit alized</w> 131929
surve ys</w> 131901
P f 131899
exclud e</w> 131880
ke wise</w> 131817
7 C</w> 131802
tob er</w> 131760
sp ectives</w> 131722
A . 131667
si fication</w> 131650
a to</w> 131646
AT 1</w> 131637
pi v 131634
aspir in</w> 131631
alter ing</w> 131618
absorb ance</w> 131600
c 2</w> 131587
prop yl</w> 131543
E co 131496
compromis ed</w> 131469
S e</w> 131462
sl ope</w> 131449
H om 131436
te no 131385
rh od 131374
an al</w> 131371
immun ized</w> 131356
u ron 131355
Com mun 131318
Oc tober</w> 131301
B one</w> 131296
rever sal</w> 131205
S outhern</w> 131197
CM L</w> 131180
labor atories</w> 131177
CV D</w> 131141
solub il 131140
Cal ifor 131139
pat h</w> 131129
i. v.</w> 131079
emplo ying</w> 131070
F T 131033
CA P</w> 131032
cle ft</w> 131008
eth n 130994
oplas ty</w> 130976
otyp ed</w> 130954
ak er</w> 130954
mon ocyte</w> 130952
bi ologically</w> 130939
susp ended</w> 130938
achiev ing</w> 130862
s c</w> 130857
gang lion</w> 130841
SIR T1</w> 130784
recycl ing</w> 130774
O D</w> 130765
TRA IL</w> 130744
lox acin</w> 130738
Fac tors</w> 130732
paraff in</w> 130730
SP 1</w> 130677
ma them 130654
routin ely</w> 130654
colon ization</w> 130617
CXCR 4</w> 130591
counter parts</w> 130558
P F</w> 130550
consis tency</w> 130488
fl ag 130447
optim um</w> 130367
ot e</w> 130366
aden ine</w> 130348
disrup t</w> 130262
F DG</w> 130258
od end 130221
dim eric</w> 130211
P b</w> 130208
Characteri zation</w> 130201
gli oblastoma</w> 130191
PC NA</w> 130188
x es</w> 130138
helic ase</w> 130116
aneurys ms</w> 130098
Sci ences</w> 130087
bi ased</w> 130080
refer ral</w> 130069
i ana</w> 130068
ra w</w> 130065
id a</w> 130064
io di 130055
establish ing</w> 130015
el ed</w> 129976
asp iration</w> 129964
anti bacterial</w> 129955
segreg ation</w> 129939
M u 129869
disper sion</w> 129834
c ock 129801
ic o</w> 129789
scle rotic</w> 129767
mid ine</w> 129745
R CC</w> 129734
te re 129710
H el 129670
S 6 129627
Ca uc 129619
lith ium</w> 129616
Engl ish</w> 129533
P 6</w> 129522
glob ulin</w> 129522
PD B</w> 129504
C KD</w> 129494
y a</w> 129490
Hep G2</w> 129452
bl es</w> 129441
N H</w> 129384
Subj ects</w> 129370
amph i 129336
Califor nia</w> 129332
trans form</w> 129329
pl et</w> 129309
retri ev 129201
surge on</w> 129184
D IN 129175
Immuno histo 129173
c . 129150
Prote ins</w> 129132
P osi 129126
tern ary</w> 129100
at trac 129087
Struc tural</w> 129007
D er 128989
F re 128947
Ab cam</w> 128933
inter rup 128924
- induced</w> 128921
casse tte</w> 128861
I BD</w> 128853
cycl ase</w> 128836
CA 2</w> 128825
diff ers</w> 128801
eth ical</w> 128781
benz ene</w> 128768
correc tly</w> 128747
modi fying</w> 128726
histopath ological</w> 128714
instrum ents</w> 128710
AD HD</w> 128705
minim ally</w> 128693
cam era</w> 128685
form ulations</w> 128655
draw n</w> 128590
omy el 128589
con tra 128558
os an</w> 128550
Be havi 128469
le t 128465
Hodg kin</w> 128453
sens ors</w> 128433
est ro 128411
Th rough</w> 128399
r ub 128390
RP MI</w> 128376
En do 128369
onym ous</w> 128368
thym idine</w> 128346
moun ted</w> 128332
Gre en</w> 128274
aden ovirus</w> 128271
II I 128247
x y 128246
oligom ers</w> 128229
q RT</w> 128226
s ound</w> 128184
qu ic 128153
ex ual</w> 128131
sub mitted</w> 128058
introduc e</w> 128046
M K</w> 128038
e tin</w> 127961
P B</w> 127961
enti n</w> 127930
c itation</w> 127928
ot ri 127928
du plication</w> 127914
af enib</w> 127875
periodon tal</w> 127849
Ca MK 127833
L ev 127829
allos teric</w> 127794
Tre g</w> 127782
Altern atively</w> 127762
P at 127760
bec oming</w> 127757
v or 127756
P ases</w> 127730
Surg ery</w> 127720
occ i</w> 127710
ug h 127687
m asses</w> 127647
M G 127628
T y 127611
ec es</w> 127604
pac kage</w> 127589
th ood</w> 127579
de ad</w> 127562
iod ine</w> 127560
ren in</w> 127502
yn g 127496
F SH</w> 127495
transcrip tase</w> 127482
ish ing</w> 127464
telom erase</w> 127449
onit rile</w> 127399
le a 127375
E F 127373
3 G</w> 127372
haem at 127312
PD GF</w> 127307
keratin ocytes</w> 127302
go als</w> 127267
phot osyn 127237
go es</w> 127207
fa il</w> 127198
ost atin</w> 127197
promis e</w> 127194
am ni 127190
cataly sis</w> 127162
as par 127161
n es 127138
rearrang ements</w> 127134
immuno staining</w> 127099
E D 127057
vas odi 127036
ligam ent</w> 127015
repres sed</w> 126975
charg es</w> 126942
psycho tic</w> 126941
Im pro 126939
L as 126915
ec tor</w> 126913
V e 126882
defini tive</w> 126875
hetero log 126874
S cre 126858
Sy stems</w> 126838
du plex</w> 126827
correspon ds</w> 126819
reser vo 126800
lam p 126762
R α</w> 126711
app ly</w> 126702
CI PA 126691
go v</w> 126674
exp ec 126673
v ice</w> 126668
me et</w> 126642
me al</w> 126622
gen otyping</w> 126610
expon ential</w> 126608
spl it</w> 126539
pred n 126515
fore ign</w> 126502
si RNAs</w> 126488
ou pl 126469
rp m</w> 126468
C hi 126443
Cdc 4</w> 126442
i. p.</w> 126385
horiz ontal</w> 126374
Cur rently</w> 126362
pur ity</w> 126351
AR E</w> 126346
leak age</w> 126305
ga ined</w> 126298
fab ric 126259
Ram an</w> 126232
bur n</w> 126230
TE M</w> 126224
nar row</w> 126195
was te</w> 126194
ga it</w> 126172
micro organisms</w> 126166
bio availability</w> 126134
X 4</w> 126132
ri es</w> 126132
H B</w> 126123
classi c</w> 126112
univer sal</w> 126101
Mo use</w> 126075
prescri ption</w> 126061
H or 126042
in versely</w> 126021
mi as</w> 126020
il ical</w> 125996
monom eric</w> 125970
met formin</w> 125933
N CE</w> 125932
sim ply</w> 125929
at ax 125908
flu ids</w> 125846
E 7</w> 125777
F as</w> 125769
j our 125767
B T 125763
buil t</w> 125719
igh t 125705
ph asic</w> 125694
Trans f 125693
ox al 125678
alcoh olic</w> 125677
si zed</w> 125673
import antly</w> 125672
kappa B</w> 125638
sh e</w> 125603
- dependent</w> 125534
e -</w> 125514
duod enal</w> 125383
pos ing</w> 125315
c row 125296
posi tivity</w> 125287
threa tening</w> 125286
phenomen a</w> 125281
Cauc asi 125261
anesthe tized</w> 125257
Whe ther</w> 125237
some what</w> 125155
po l</w> 125125
L N 125114
com mod 125103
pre -</w> 125102
P ac 125084
ure tic</w> 124991
sud den</w> 124990
tic a</w> 124975
prim ing</w> 124858
BAL B</w> 124847
ha m</w> 124833
po rous</w> 124829
cal f</w> 124784
ish es</w> 124778
h u 124763
ome dical</w> 124759
C ross</w> 124733
7 D</w> 124703
rearrang ement</w> 124700
En gland</w> 124664
fr uc 124615
T SH</w> 124599
L im 124599
S uc 124586
L arg 124570
sph ing 124531
es are 124525
initi ating</w> 124508
F DA</w> 124481
Pro g 124474
R C</w> 124471
ec tom 124468
fac ility</w> 124410
R V</w> 124408
isom erase</w> 124344
A l</w> 124333
pol e</w> 124329
fin ement</w> 124319
J un</w> 124305
aug mented</w> 124296
scaff olds</w> 124264
Bac k 124255
TNF α</w> 124235
see king</w> 124227
vertebra tes</w> 124221
gra phene</w> 124210
Chem ical</w> 124203
M ei 124120
exp ensive</w> 124110
equ ations</w> 124109
ar ate</w> 124078
super family</w> 124075
ation ary</w> 124073
fro nt</w> 124061
hem odialysis</w> 124061
feren ces</w> 124037
C och 124003
B am 123995
ogen es</w> 123966
disp ens 123962
I HC</w> 123949
fal ls</w> 123904
warran ted</w> 123885
PT H</w> 123859
c ure</w> 123795
ac tually</w> 123788
3 E</w> 123766
caus ative</w> 123751
DIN GS</w> 123737
epide mic</w> 123729
N GF</w> 123725
pati ng</w> 123721
pub er 123720
S em 123671
St at 123664
glucocortico id</w> 123617
I Q 123612
v esti 123609
Con clusion</w> 123603
monom ers</w> 123587
galac to 123533
h ard</w> 123524
analge sia</w> 123502
Sec ondary</w> 123499
c ef 123488
nico tin 123482
Under standing</w> 123479
g h 123381
Experim ents</w> 123375
O ve 123369
spec ified</w> 123324
sul fur</w> 123321
an ing</w> 123263
CF U</w> 123260
lam y 123241
ank le</w> 123229
FIN DINGS</w> 123208
m ating</w> 123207
ist ar</w> 123203
B O 123178
Col le 123176
A d</w> 123143
reli ef</w> 123128
brom ide</w> 123113
ther mod 123110
cereb ellum</w> 123075
m es</w> 123061
expl oration</w> 123057
stres ses</w> 123036
An n 122986
prote olysis</w> 122982
esare an</w> 122956
H i 122932
w ritten</w> 122929
a ren 122920
Ap proxim 122892
OR F</w> 122886
B E 122883
Lou is</w> 122874
hy dra 122842
De f 122840
capsul e</w> 122815
lab or</w> 122798
immunore active</w> 122774
fibri ls</w> 122757
in sp 122751
sub til 122734
fir ing</w> 122733
Vari ous</w> 122730
C ir 122707
LE D</w> 122700
myocardi um</w> 122672
cl on 122622
sp ray</w> 122613
p A</w> 122557
C HI 122513
al ters</w> 122481
criteri on</w> 122473
glyco l</w> 122425
Ph en 122406
perox idation</w> 122403
k a</w> 122388
cali bration</w> 122376
Amer ica</w> 122340
ke eping</w> 122328
reci proc 122309
p mol</w> 122303
CRI SPR</w> 122296
AA A</w> 122295
def ining</w> 122248
con cor 122194
et c</w> 122181
F er 122173
α 2</w> 122169
re stra 122165
ten d</w> 122158
3 R</w> 122081
ore r</w> 122079
post synaptic</w> 122069
q 1</w> 122062
gi ant</w> 122060
reproduc ible</w> 122047
Q o 122037
N either</w> 122036
an sw 122017
co ating</w> 121985
tub ation</w> 121956
transcrip tome</w> 121955
inter view</w> 121928
Direc t</w> 121921
id el 121900
fill ing</w> 121828
v an</w> 121807
Six ty</w> 121795
juven ile</w> 121768
arthro plasty</w> 121728
uni versity</w> 121672
regar ded</w> 121644
di s</w> 121630
y outh</w> 121621
Ove rex 121615
Ob j 121608
peri operative</w> 121606
ti tis</w> 121604
acc ession</w> 121601
co factor</w> 121580
p ass</w> 121578
DS B</w> 121558
ï ve</w> 121555
a ero 121514
Com pu 121484
cyclo hex 121444
D u 121426
pro tot 121417
vid in</w> 121417
erythro cyte</w> 121386
B el 121380
cum in</w> 121380
synucle in</w> 121310
accoun ts</w> 121309
ici ently</w> 121293
pri vate</w> 121274
k cal</w> 121255
scho ols</w> 121249
rough ly</w> 121241
5 I</w> 121211
fluctu ations</w> 121180
AA V</w> 121163
clin ics</w> 121150
W istar</w> 121138
intraper itoneal</w> 121123
op in 121117
br ated</w> 121111
b red</w> 121110
R os 121108
ret ard 121095
socio economic</w> 121075
as ts</w> 121063
ere bral</w> 121057
neutr alization</w> 121055
fit ting</w> 121038
se mi 121016
hydro l 121013
energ ies</w> 121012
facilit ating</w> 121009
tail s</w> 120981
mo ving</w> 120973
µ m</w> 120953
ec lamp 120949
elic it</w> 120930
. org</w> 120918
L A 120902
fit ted</w> 120870
op sy</w> 120855
f um 120854
s me 120853
sph eres</w> 120840
Sequ ence</w> 120840
bin ary</w> 120827
TL R</w> 120807
Pre viously</w> 120750
autophag ic</w> 120740
graf ting</w> 120740
c ranial</w> 120736
aw are</w> 120728
program med</w> 120709
n in</w> 120707
ve ter 120689
pol lution</w> 120672
de bri 120669
ste rile</w> 120651
Sm all</w> 120642
bloc kers</w> 120622
Rad 5</w> 120608
mach ine</w> 120607
c us 120596
blo ts</w> 120563
X 3</w> 120560
pro ton 120557
c t 120548
Consi dering</w> 120494
sc ar 120488
M orph 120479
publ ication</w> 120472
Ta i 120437
P ost</w> 120406
pre disp 120366
capsi d</w> 120362
Diag nosis</w> 120298
g lu 120280
ati onic</w> 120277
pro bed</w> 120264
ly so 120263
sac rif 120262
en demic</w> 120228
G D</w> 120223
descri bing</w> 120199
Q OL</w> 120187
opath ies</w> 120127
en di 120117
li um</w> 120071
monol ayer</w> 120000
anthro p 119987
ap o</w> 119962
distingu ished</w> 119958
ker at 119942
in ating</w> 119934
- 9</w> 119911
ad ing</w> 119896
cerebro spinal</w> 119895
orb ent</w> 119892
suppres sing</w> 119885
OUTCO ME</w> 119867
as cor 119855
P O</w> 119836
qu arti 119836
here ditary</w> 119820
SU MO</w> 119815
phosphor us</w> 119782
in adequate</w> 119777
bio tin 119748
gran ule</w> 119739
pros thesis</w> 119712
H am 119698
spectro phot 119689
DISC US 119676
er adic 119671
surfac tant</w> 119666
S B</w> 119665
pyri dine</w> 119587
all ed</w> 119583
CL L</w> 119582
L L 119581
dist or 119577
sensiti zation</w> 119545
visc eral</w> 119545
aden oma</w> 119521
demonstr ation</w> 119515
v ap 119475
I 2</w> 119446
pos ure</w> 119418
A 6</w> 119399
an atomic</w> 119393
h 1</w> 119361
hyper sensitivity</w> 119354
end o 119336
av en 119335
ca ffe 119322
cur y</w> 119294
HE K</w> 119259
MATERI AL</w> 119233
desi r 119229
n ude</w> 119227
compl ain 119223
po is 119212
thromb ocytop 119172
as ci 119152
pap illary</w> 119142
F em 119141
propor tions</w> 119103
de ep 119101
he ating</w> 119081
weak ly</w> 119066
Tw een</w> 119041
ss DNA</w> 119038
sta in 119034
entr e</w> 118985
sym metry</w> 118969
ar a</w> 118968
oxid ants</w> 118954
bound ary</w> 118912
ro dent</w> 118891
an yl</w> 118883
adul thood</w> 118881
mel atonin</w> 118878
Questionna ire</w> 118876
DISCUS SION</w> 118876
behavio ural</w> 118856
as tric</w> 118851
ill a</w> 118836
ag on</w> 118818
Tr p</w> 118817
et amine</w> 118787
sh ed</w> 118774
st ationary</w> 118741
c ot 118738
los ses</w> 118713
si al 118707
stres s 118698
g p</w> 118680
phosphatidyl inositol</w> 118674
psor iasis</w> 118663
piv otal</w> 118629
si x 118619
T u 118566
neuro logic</w> 118535
lipop olysaccharide</w> 118521
foc uses</w> 118516
H Y 118503
i o</w> 118500
na il</w> 118495
Re al</w> 118480
ballo on</w> 118479
repeti tive</w> 118475
circu it</w> 118473
inter mittent</w> 118444
admin istr 118439
par i 118376
z z 118346
F ren 118340
osmo tic</w> 118329
ACT H</w> 118299
I F</w> 118290
P MA</w> 118272
vascul ature</w> 118269
replic ate</w> 118267
glycoly sis</w> 118238
viol ence</w> 118237
ul ed</w> 118219
it ting</w> 118215
E SI</w> 118214
In tro 118182
gangli a</w> 118135
cigare tte</w> 118124
Strep tococcus</w> 118122
constra ints</w> 118120
pl anned</w> 118105
g ift</w> 118100
at r 118061
m entally</w> 118059
Epi de 118052
NAD H</w> 118050
cardiomy ocytes</w> 118049
V 5</w> 118030
Po ten 118015
He pati 117997
dis position</w> 117994
C re 117969
F ood</w> 117969
phag ocytosis</w> 117942
phospholip ids</w> 117942
initi es</w> 117921
mor tem</w> 117907
otrop in</w> 117906
I M</w> 117858
kill ed</w> 117843
function ality</w> 117817
protec ts</w> 117816
gu anine</w> 117771
tun ately</w> 117771
seem ed</w> 117755
A u</w> 117741
adhe sive</w> 117732
TB I</w> 117722
leuk emic</w> 117717
th in 117701
land sc 117676
dis ordered</w> 117673
inten ded</w> 117589
eosin oph 117549
M en 117529
c ogn 117521
equip ped</w> 117517
pres ynaptic</w> 117515
conf ined</w> 117501
b er</w> 117402
amb ig 117399
Approxim ately</w> 117386
epilep tic</w> 117383
reproduc ibility</w> 117355
flex ion</w> 117351
wor d</w> 117310
pro pen 117285
Ze iss</w> 117263
carb onyl</w> 117223
H al 117192
p d 117187
Qo L</w> 117183
lar val</w> 117169
dep ic 117149
ug e</w> 117131
C CA 117103
neuro blastoma</w> 117103
G AC 117085
ac commod 117085
Ass ay</w> 117062
In fluence</w> 117052
fluoresc ein</w> 117036
K d</w> 117004
Phosph or 117002
O ral</w> 116969
financ ial</w> 116963
j a 116937
x anth 116936
L Y 116924
ram idal</w> 116919
AR S</w> 116913
hypothalam us</w> 116906
7 a</w> 116896
pro static</w> 116882
a emic</w> 116873
in organic</w> 116871
la ter 116871
d in</w> 116843
enabl ing</w> 116798
re al 116778
C oun 116753
im aged</w> 116753
str ands</w> 116744
c arg 116716
Con clusions</w> 116698
retro grade</w> 116671
ca p</w> 116657
Ac tivity</w> 116656
thic k</w> 116645
hydrox ylase</w> 116595
supplem entary</w> 116568
B ut</w> 116552
l id 116530
O ste 116508
ro bo 116508
trans activation</w> 116493
neu mo 116489
pow der</w> 116478
fer mentation</w> 116476
Epide mi 116476
r ad 116456
end osomes</w> 116454
pp ed</w> 116430
post menopausal</w> 116411
mer ic</w> 116388
chem il 116373
ambi ent</w> 116365
met all 116351
relati ves</w> 116325
A po 116254
E mb 116206
gener ates</w> 116204
con vul 116184
hypo theses</w> 116157
do or</w> 116154
fib rom 116132
o ocyte</w> 116106
abnormal ity</w> 116102
pes tic 116035
para formaldehyde</w> 116002
umb ilical</w> 115987
an ten 115966
rs 2</w> 115956
end it 115930
S N</w> 115928
S G</w> 115902
per si 115885
M F 115874
flur ane</w> 115853
t ary</w> 115841
quin ol 115822
En tero 115807
HD AC</w> 115806
d ons</w> 115795
per spectives</w> 115756
ar yl</w> 115732
mac ular</w> 115730
P t</w> 115717
vol ati 115707
na ïve</w> 115677
s -- 115674
ch ip</w> 115672
C 8</w> 115665
cover ing</w> 115659
fibrin ogen</w> 115633
modul in</w> 115625
E V</w> 115621
retriev al</w> 115559
om od 115550
inform atics</w> 115549
Le w 115546
exer ts</w> 115523
ca ve 115504
s ten 115487
Ill umina</w> 115472
phy se 115438
tu zumab</w> 115433
odend ro 115427
stud ent</w> 115425
AR Y</w> 115417
M odi 115394
6 D</w> 115338
la w</w> 115334
slip s</w> 115231
un k</w> 115223
ultrason ography</w> 115221
N it 115217
phen y 115191
mig raine</w> 115191
sp an</w> 115171
Diagnos tic</w> 115130
op sin</w> 115116
an i</w> 115114
ric ul 115090
H 7</w> 115070
possi bilities</w> 115067
leth ality</w> 115063
plo ts</w> 115026
U b 115022
Gl uc 115004
thyro idism</w> 114918
N HE 114898
chromat ographic</w> 114882
V ol 114866
B ay 114804
ur in</w> 114791
FI SH</w> 114786
ti bial</w> 114748
th y</w> 114739
A GT 114724
P op 114724
ir reversible</w> 114711
biosyn thetic</w> 114705
On ce</w> 114701
care fully</w> 114646
lys osomes</w> 114645
B al 114635
microbi ota</w> 114633
par ac 114630
mut ase</w> 114630
Gi b 114629
SO 4</w> 114612
stron gest</w> 114609
seas onal</w> 114589
os k 114587
oth i 114587
im atinib</w> 114581
di aph 114558
replic ates</w> 114534
ot rophic</w> 114461
loc alize</w> 114444
C B1</w> 114398
endomet ri 114385
im plies</w> 114370
plan ar</w> 114361
Overex pression</w> 114357
organ elles</w> 114343
spec ulate</w> 114319
PT P</w> 114310
quic kly</w> 114303
wa ve 114272
smo ke</w> 114252
peri odic</w> 114228
de acetyl 114218
trans loc 114197
fav or</w> 114177
F ACS</w> 114162
PG E2</w> 114159
cytosk eletal</w> 114152
ap ine</w> 114135
pos tin 114103
Ex c 114102
p al</w> 114082
In trac 114080
exc ised</w> 114080
K E 114076
cut off</w> 114058
sp reading</w> 114021
dimen sion</w> 113993
am ic</w> 113990
de polarization</w> 113988
aut onomic</w> 113981
obj ectives</w> 113969
survi ved</w> 113939
G lob 113918
acet ylated</w> 113917
Diab etes</w> 113916
ver bal</w> 113905
un de 113887
Fren ch</w> 113868
0 -</w> 113834
e jection</w> 113811
Ital y</w> 113774
mon otherapy</w> 113726
jo ints</w> 113661
obj ects</w> 113656
A AC 113629
fib res</w> 113573
fac ts</w> 113553
stimul atory</w> 113513
all ogeneic</w> 113505
d yl</w> 113496
athero sclerotic</w> 113494
Pr acti 113489
1 A1</w> 113470
fal ci 113469
heterolog ous</w> 113463
institu tional</w> 113425
Anim als</w> 113420
recomm end</w> 113417
Recom bin 113396
endi x</w> 113384
prolifer ating</w> 113377
in nov 113375
iodi de</w> 113372
esophag us</w> 113369
remark ably</w> 113354
pig ment</w> 113349
fl ight</w> 113323
conn ections</w> 113277
plac enta</w> 113269
fer ri 113243
multic enter</w> 113219
oder m</w> 113213
U b</w> 113209
ide mia</w> 113197
continu ation</w> 113179
palli ative</w> 113162
hy poten 113157
PT SD</w> 113146
re production</w> 113137
vesi cular</w> 113117
isot ope</w> 113067
G V 113036
ex plic 113019
M ed 112977
adren aline</w> 112974
ag ricul 112972
di methyl</w> 112967
par um</w> 112885
constric tion</w> 112873
ab str 112868
evalu ations</w> 112866
Cardi ac</w> 112865
polic ies</w> 112849
S chol 112833
Aug ust</w> 112824
caregi vers</w> 112814
prote omic</w> 112786
re a</w> 112778
Ger man</w> 112764
M F</w> 112738
im mer 112715
re vision</w> 112711
M L</w> 112696
physi cally</w> 112670
x ia</w> 112657
m iti 112645
non invasive</w> 112643
b ar</w> 112616
oxygen ation</w> 112606
neuro degeneration</w> 112576
ten e</w> 112570
Bre ast</w> 112567
dri ver</w> 112546
rel im 112542
stop ped</w> 112532
GT A</w> 112499
orig ins</w> 112494
Seph arose</w> 112493
l ate 112475
it ate</w> 112473
H2 AX</w> 112445
S MA</w> 112441
PA I</w> 112415
P 7</w> 112408
epis ode</w> 112403
Respon se</w> 112352
S i</w> 112348
is let</w> 112330
mobil ization</w> 112327
S B 112325
fil ters</w> 112317
g dal 112302
Fo und 112299
correspon d</w> 112288
discus ses</w> 112285
gas tr 112268
model ed</w> 112265
CC 1</w> 112263
clus tered</w> 112259
di aly 112257
in surance</w> 112170
res tim 112129
bur st</w> 112118
immunos orbent</w> 112114
R af</w> 112032
En v</w> 112027
DS Bs</w> 112025
Op tim 112020
L ung</w> 111994
ensiti zation</w> 111983
B ro 111981
H U 111943
sign atures</w> 111920
possess es</w> 111919
essi bility</w> 111918
enti rely</w> 111909
G D 111903
we b</w> 111897
accoun ting</w> 111874
perin atal</w> 111869
col lo 111841
falci parum</w> 111840
disturb ance</w> 111833
bac ter</w> 111831
thi ol</w> 111829
cent ury</w> 111819
nutri ents</w> 111818
M 4</w> 111815
ad y 111786
fertili zation</w> 111760
c ations</w> 111754
N B</w> 111752
uncertain ty</w> 111736
lymph omas</w> 111729
read er</w> 111721
e pox 111719
nephro pathy</w> 111716
MS C</w> 111713
im port</w> 111712
otub es</w> 111703
low ered</w> 111700
appro val</w> 111687
v ar</w> 111676
prop yl 111673
di oxide</w> 111672
coll ap 111664
ocompati bility</w> 111660
aden omas</w> 111659
ic ate</w> 111599
con g 111584
M IC 111580
is lets</w> 111580
Therap y</w> 111580
P al 111577
ob ste 111567
marg inal</w> 111559
ind ole</w> 111547
histor ical</w> 111538
aff inities</w> 111534
T ERT</w> 111499
pl ast</w> 111469
glyco proteins</w> 111468
K 6</w> 111417
conjug ation</w> 111372
prob able</w> 111366
ali quo 111361
quin one</w> 111335
normal ization</w> 111328
me at</w> 111326
sp ar 111326
PC Rs</w> 111323
exci ted</w> 111305
analog ous</w> 111289
MD M2</w> 111264
Li ver</w> 111258
inhe rent</w> 111257
Austral ian</w> 111213
Flu or</w> 111200
dispens able</w> 111193
continu es</w> 111187
conver ting</w> 111184
Sh ort</w> 111172
partici pating</w> 111172
im ag 111142
path ic</w> 111141
hol ds</w> 111138
ocy ste 111130
be hind</w> 111126
preferen ces</w> 111117
max illary</w> 111079
all ergy</w> 111077
ab ec 111068
optim ize</w> 111061
g ic</w> 111049
ow er</w> 111036
Ka plan</w> 111036
t ag 111027
sol ely</w> 110993
AM D</w> 110990
un saturated</w> 110948
lymph atic</w> 110940
avoid ance</w> 110918
amin ase</w> 110873
phospholip ase</w> 110873
desir able</w> 110866
ec table</w> 110865
oc occal</w> 110865
precip itated</w> 110850
L oss</w> 110842
j ect</w> 110840
spectro scopic</w> 110837
En d 110807
sep tal</w> 110801
Ap plication</w> 110796
L F</w> 110786
po ols</w> 110754
develop ments</w> 110749
m outh</w> 110746
di az 110705
Back ground</w> 110700
PAR TI 110696
Colle ge</w> 110691
F B 110689
in tim 110647
Ga g</w> 110618
Table 1</w> 110603
ati ns</w> 110602
cre ating</w> 110569
VE GF 110568
Sh e</w> 110568
scenari o</w> 110567
pos its</w> 110558
li vers</w> 110532
C ity</w> 110529
descrip tive</w> 110519
athle tes</w> 110500
cop ing</w> 110494
PC a</w> 110488
sep tic</w> 110482
mac rom 110472
polymorph ic</w> 110461
v ince</w> 110452
fraction ation</w> 110437
exp endit 110399
xen ografts</w> 110399
progres sively</w> 110398
v im 110392
deteri oration</w> 110360
CA L</w> 110355
nem at 110355
H 5</w> 110350
L E</w> 110348
beg an</w> 110348
ici an</w> 110334
olig odendro 110329
ost omy</w> 110328
Soci al</w> 110319
Fac tor</w> 110312
st ate 110311
oc in</w> 110307
n al 110273
G 5</w> 110246
Zh ang</w> 110229
po orer</w> 110214
retard ation</w> 110202
CX CL1</w> 110197
LD H</w> 110170
S O</w> 110151
In stead</w> 110145
C 2 110135
b 2</w> 110098
H GF</w> 110076
U 2</w> 110070
Vi su 110069
op ia</w> 110062
F K 110059
immun ocom 110052
PC I</w> 110034
R enal</w> 110021
S ir 110015
si ded</w> 109933
anti retroviral</w> 109900
Ca M</w> 109900
val ently</w> 109892
pari etal</w> 109855
g a</w> 109838
adv anc 109796
au tism</w> 109784
here in</w> 109774
min ogen</w> 109712
it ates</w> 109711
wo unds</w> 109686
in soluble</w> 109678
surg ically</w> 109629
vir tually</w> 109593
lin ing</w> 109582
li ved</w> 109525
was te 109524
W S</w> 109496
progen y</w> 109485
tri mes 109468
In tra 109466
top o 109437
epti n</w> 109419
myel in</w> 109414
bloc ker</w> 109407
el as 109396
transfer ases</w> 109388
bran ches</w> 109386
Imp act</w> 109382
ris m</w> 109377
Ca P</w> 109357
olig os 109351
foll icles</w> 109306
teri a</w> 109302
amy gdal 109294
N B 109289
arrang ement</w> 109265
0 E</w> 109254
thym us</w> 109228
DAP I</w> 109226
op res 109187
p y</w> 109167
C at 109165
mer cap 109157
ob taining</w> 109143
sp anning</w> 109142
spl enic</w> 109135
compe tence</w> 109098
EC 5</w> 109037
Ex posure</w> 109028
pi per 108999
CH D</w> 108951
acc essibility</w> 108898
o king</w> 108882
agglutin in</w> 108882
Physi cal</w> 108876
el le</w> 108875
phot oc 108834
le v 108829
Rep ort</w> 108803
catar act</w> 108803
cour ses</w> 108789
suff ered</w> 108777
h aled</w> 108756
Com plete</w> 108716
T OF</w> 108703
ang ular</w> 108637
in ase</w> 108632
M id 108604
AT R</w> 108567
ambul atory</w> 108565
duc tal</w> 108554
conflic t</w> 108530
bio active</w> 108467
b ones</w> 108465
diss ected</w> 108445
kin et 108441
m us</w> 108433
vacc inated</w> 108433
AD AM 108405
Im ages</w> 108400
High er</w> 108385
imp ly</w> 108351
hyper glyc 108348
categor ized</w> 108337
as k</w> 108285
rig id</w> 108243
oc t 108218
sto ichi 108159
mathem atical</w> 108154
med ul 108136
yiel ding</w> 108109
Found ation</w> 108109
ic ed</w> 108107
in sensitive</w> 108071
summar ize</w> 108055
ver at 108052
gro ss</w> 108033
equip ment</w> 108031
gradi ents</w> 108025
og le</w> 108013
stal k</w> 107993
inc ision</w> 107969
propen sity</w> 107964
posi tioned</w> 107949
neuro protective</w> 107941
M BP</w> 107939
distinguish able</w> 107938
constitu ents</w> 107905
reconstitu ted</w> 107901
s arcom 107898
PR L</w> 107895
E g 107894
abol ism</w> 107893
N uclear</w> 107887
A Q 107878
z ones</w> 107857
re acted</w> 107843
PARTI CIPA 107836
tri plicate</w> 107835
t ants</w> 107812
infarc t</w> 107788
enti ty</w> 107775
tetrac ycline</w> 107760
ob ar 107759
men str 107747
my ri 107670
bur g</w> 107635
vi br 107633
arrhyth mias</w> 107625
P IN 107610
1 L</w> 107596
contr ary</w> 107593
ll ation</w> 107592
st ances</w> 107552
me eting</w> 107520
de tri 107518
catal ase</w> 107512
ulc ers</w> 107509
la b</w> 107508
edi c</w> 107493
vulner able</w> 107451
encap sul 107450
ag le</w> 107442
post partum</w> 107435
E qu 107432
Go ogle</w> 107403
In dian</w> 107393
weak er</w> 107372
el ution</w> 107363
hams ter</w> 107354
ip si 107333
lim bs</w> 107325
T RE 107322
prophyl actic</w> 107316
Wh ite</w> 107297
F low</w> 107281
anaes thesia</w> 107278
HM G 107274
trans verse</w> 107266
suff iciently</w> 107257
ad d</w> 107242
as sumption</w> 107231
sol e</w> 107226
di sh 107217
where by</w> 107207
k i</w> 107192
D ay</w> 107170
confir ms</w> 107166
PARTICIPA NTS</w> 107157
ter a</w> 107156
F u 107133
V P</w> 107114
out break</w> 107113
hair pin</w> 107105
strom a</w> 107098
reve aling</w> 107089
ch i</w> 107081
a 2</w> 107018
for tunately</w> 106996
electro physiological</w> 106969
phen olic</w> 106961
necess arily</w> 106956
e ted</w> 106936
press ors</w> 106910
pan els</w> 106893
T c</w> 106884
Mit ochond 106839
T DP</w> 106820
pl ot</w> 106763
ph on 106762
colum ns</w> 106760
D aw 106741
extrem ity</w> 106739
poll en</w> 106729
G al</w> 106720
mer cury</w> 106717
surpri sing</w> 106709
cros sed</w> 106704
sugges tive</w> 106699
fet uses</w> 106657
derm atitis</w> 106573
C 9</w> 106561
Th r 106561
U 1</w> 106529
n gs</w> 106518
CT s</w> 106489
all ograft</w> 106478
sati le</w> 106463
t od 106447
effic ac 106434
c ity</w> 106409
No vel</w> 106403
PI K 106324
Cl ass</w> 106315
mal formations</w> 106308
or in</w> 106303
analge sic</w> 106303
investig ators</w> 106257
un less</w> 106249
L it 106236
ing estion</w> 106236
end point</w> 106234
Gl ut 106234
ste ri 106228
de phosphorylation</w> 106215
termin als</w> 106203
suppres sive</w> 106184
od ynamics</w> 106175
but y 106165
chemo kines</w> 106126
I X</w> 106125
im pair</w> 106087
i tive</w> 106083
os p 106049
cho ro 106045
dyst rophy</w> 106044
occup ancy</w> 106040
CT D</w> 105993
thrombo tic</w> 105980
spi ke</w> 105971
e w 105945
l ectin</w> 105924
manifest ation</w> 105911
E SCs</w> 105900
CD 5</w> 105826
Differen tial</w> 105816
a ic</w> 105764
radi olab 105753
ch it 105739
Mei er</w> 105728
el se 105722
alk yl</w> 105720
hospit al 105713
compens atory</w> 105697
ophag y</w> 105688
examin es</w> 105688
Pro spective</w> 105672
v om 105669
ra ise</w> 105658
an es</w> 105653
aggreg ate</w> 105650
c ationic</w> 105640
ti fied</w> 105640
kin ds</w> 105619
oph yl 105603
seed lings</w> 105599
brow n</w> 105593
Pre vention</w> 105581
em is 105575
shor tening</w> 105543
o protein</w> 105538
blin ded</w> 105521
Pre valence</w> 105515
electroly te</w> 105513
m Abs</w> 105508
ir respective</w> 105484
th an 105483
diff ering</w> 105447
bl ast 105442
In sul 105373
aberr ations</w> 105362
tw enty</w> 105349
cogni tion</w> 105339
dis mutase</w> 105334
m ine</w> 105312
Y or 105311
I CAM</w> 105300
i po 105279
T at</w> 105278
ro bu 105259
Dat ab 105258
O X 105256
p cDN 105243
2 c</w> 105238
Be fore</w> 105218
A ca 105213
in puts</w> 105197
differen tly</w> 105187
oper ate</w> 105163
N Y</w> 105153
quanti ties</w> 105147
Ch en</w> 105145
associ ates</w> 105133
adhe rent</w> 105127
B or 105124
ro c 105050
star ch</w> 105018
hand ling</w> 105017
Ch erry</w> 104979
os ter</w> 104961
conduc t</w> 104892
meth ane</w> 104881
na ive</w> 104873
remo ving</w> 104863
G SK</w> 104843
hypothe size</w> 104836
phenyl alanine</w> 104818
An gi 104788
0 th</w> 104775
Nov ember</w> 104770
param etric</w> 104757
endoscop y</w> 104749
pseud o 104745
P ad</w> 104739
viri ons</w> 104719
ud ge</w> 104704
ari um</w> 104701
morph ogenesis</w> 104688
Ne w 104687
Li ke</w> 104671
accompl ished</w> 104662
U B 104647
pul sed</w> 104638
crystall ine</w> 104608
o z 104606
multi drug</w> 104586
pre v 104573
i NOS</w> 104566
az one</w> 104560
in patient</w> 104543
D A 104507
as e 104498
r hiz 104484
lin ear 104481
T ric 104478
lo ok</w> 104437
eclamp sia</w> 104436
Tai w 104420
Out comes</w> 104397
O l 104392
Th ose</w> 104386
Cro hn</w> 104385
teri zed</w> 104345
micro vascular</w> 104343
GAB A 104331
disc rep 104325
voc al</w> 104320
co des</w> 104301
eg o</w> 104299
am orph 104293
char acter</w> 104259
deli ver</w> 104246
prin ting</w> 104233
bo w</w> 104211
MD R</w> 104190
tre es</w> 104158
electro cardi 104156
ing er</w> 104153
cros so 104151
silic o</w> 104143
lat ation</w> 104117
end ocardi 104114
care ful</w> 104112
ur ance</w> 104087
S af 104085
N ine</w> 104050
fo ot 104044
CT L</w> 104033
MR SA</w> 104025
ve ins</w> 104024
subtil is</w> 104023
leg al</w> 104015
pro tr 104004
dys regulation</w> 103973
galacto sidase</w> 103973
tw o 103966
adap tor</w> 103964
k B</w> 103959
Cr yst 103952
rati ngs</w> 103949
bud ding</w> 103943
modi um</w> 103926
amph etamine</w> 103926
Can adi 103918
k y 103892
o v</w> 103884
mess enger</w> 103878
Brd U</w> 103853
5 E</w> 103852
ent ory</w> 103842
domes tic</w> 103832
De pression</w> 103831
contr actions</w> 103821
un detectable</w> 103798
V al</w> 103786
ch iral</w> 103758
pe er</w> 103738
inver ted</w> 103707
tr abec 103703
N ic 103697
hist ologically</w> 103688
. 5</w> 103665
O CT</w> 103658
ti tration</w> 103651
C b 103647
hydro philic</w> 103646
plas minogen</w> 103644
eukary otes</w> 103625
galact ose</w> 103599
Incre asing</w> 103595
S tim 103586
S ox 103585
Con tin 103532
carbox yl</w> 103518
om ain</w> 103501
c aries</w> 103446
cen tered</w> 103419
res ected</w> 103414
il ing</w> 103408
f ecal</w> 103407
doc tors</w> 103402
Cl assi 103394
grad ed</w> 103375
Sup por 103358
de posits</w> 103352
h ole</w> 103321
comfor t</w> 103302
monol ayers</w> 103298
zygo tes</w> 103286
counsel ing</w> 103285
M T1</w> 103279
bil ayer</w> 103263
nucle oside</w> 103215
methyl transferase</w> 103211
tachy cardia</w> 103201
atmosph ere</w> 103191
ograph ically</w> 103185
Coch rane</w> 103184
incorpor ating</w> 103176
i as</w> 103150
dy sp 103148
id y</w> 103145
R u 103141
S chool</w> 103122
typ ing</w> 103119
anti fungal</w> 103116
ple ts</w> 103109
publ ications</w> 103079
AI D</w> 103067
cardi o 103066
R K 103038
pre frontal</w> 103024
G CA 102990
Wh ole</w> 102983
ammon ia</w> 102983
cohe rence</w> 102978
is ely</w> 102972
Fin dings</w> 102958
institu tions</w> 102899
not ably</w> 102897
ti tr 102889
adv ance</w> 102845
in continence</w> 102828
fil es</w> 102805
so y 102801
triglycer ide</w> 102800
n t 102791
N utri 102781
k al 102779
P ear 102770
ti tan 102770
trigg ering</w> 102707
else where</w> 102705
MC P</w> 102681
gre w</w> 102677
hist ones</w> 102676
9 a</w> 102663
B BB</w> 102630
p neumo 102627
ur able</w> 102617
Ag il 102615
anti coagul 102612
retri eved</w> 102607
R y 102598
ti ves</w> 102582
B W</w> 102579
sy n</w> 102576
lap se</w> 102572
ro dents</w> 102557
Res p 102550
Ex amination</w> 102546
my ocytes</w> 102507
chic k 102504
unc ture</w> 102502
al c 102498
fam ili 102497
Initi al</w> 102487
sk i</w> 102481
w et</w> 102458
ul ty</w> 102447
proj ection</w> 102428
utili ze</w> 102417
ar ding</w> 102382
P 0</w> 102377
ge ographic</w> 102366
hemorrh agic</w> 102341
Li kewise</w> 102332
hepat ocyte</w> 102318
mo ve</w> 102296
ser a 102295
her pes</w> 102282
survi ve</w> 102282
av 1</w> 102274
ch lo 102272
Multi variate</w> 102271
Agil ent</w> 102268
mod ules</w> 102255
pro pri 102253
pe ro 102228
h i</w> 102214
per mis 102209
i b 102208
ol a</w> 102208
en tered</w> 102178
PC A</w> 102167
ul us</w> 102158
C le 102155
coord inate</w> 102140
3 F</w> 102101
so ils</w> 102101
path ologies</w> 102079
g et</w> 102069
p wise</w> 102044
X L</w> 102031
da iry</w> 102028
or ic</w> 101999
phosphor ylate</w> 101996
exper t</w> 101987
sera dish</w> 101982
Ne twor 101978
pros thetic</w> 101950
autom atic</w> 101911
tub ules</w> 101902
oc ks</w> 101898
yn x</w> 101847
mark et</w> 101836
Retro spective</w> 101832
V S</w> 101779
anastom osis</w> 101772
co iled</w> 101765
inf ectivity</w> 101755
scav eng 101741
Combin ed</w> 101739
O M</w> 101729
hor ses</w> 101697
eth eless</w> 101692
ta m</w> 101689
exer ted</w> 101658
cal modulin</w> 101647
post ulated</w> 101645
embol ization</w> 101642
on omy</w> 101635
8 S</w> 101622
B 7</w> 101615
synthe tase</w> 101610
Th 2</w> 101583
synap se</w> 101582
pul l</w> 101564
dish es</w> 101559
inter play</w> 101531
osteo arthritis</w> 101508
M er 101495
P i</w> 101471
Tr yp 101464
y e</w> 101441
8 a</w> 101437
adren oc 101415
nan os 101409
li ter</w> 101382
ur ium</w> 101377
rap h</w> 101359
wor ms</w> 101331
radi us</w> 101329
wil d 101326
sar co 101308
Anti bodies</w> 101306
r ational</w> 101273
c GMP</w> 101271
under goes</w> 101266
sp ent</w> 101258
erythem at 101254
A 1c</w> 101247
group ed</w> 101240
intra ocular</w> 101228
F A 101198
s g 101180
alk yl 101177
lif es 101143
cop e</w> 101138
trimes ter</w> 101136
fl am 101129
d ated</w> 101125
mening itis</w> 101095
S 8</w> 101094
mis match</w> 101092
coun ter</w> 101092
yl in</w> 101091
pi x 101090
sensiti zed</w> 101081
neurom uscular</w> 101076
hel per</w> 101068
Plas modium</w> 101043
bund le</w> 101004
anc re 101003
en es</w> 100997
conjunc ti 100963
fo rest</w> 100960
onc ology</w> 100926
lat tice</w> 100898
g over 100895
wor sen 100875
persis ted</w> 100858
qu ar 100855
pre p 100765
rein forc 100763
transf err 100734
1 st</w> 100731
aff ective</w> 100715
ap art</w> 100700
MT s</w> 100689
fraction ated</w> 100680
PP 2A</w> 100678
K e 100675
ophthal m 100667
her nia</w> 100665
periph ery</w> 100634
G AP</w> 100627
pel lets</w> 100608
in form</w> 100595
gluc uron 100586
diver gence</w> 100571
plo idy</w> 100570
B r</w> 100555
uncer tain</w> 100545
gly can</w> 100508
verat rol</w> 100505
pat ches</w> 100497
RESUL T</w> 100467
Schol ar</w> 100446
ari al</w> 100424
vir tual</w> 100401
ect amine</w> 100401
H1 N1</w> 100398
M d 100384
ep sin</w> 100366
P ain</w> 100355
g al</w> 100355
sched ule</w> 100354
produc tive</w> 100353
Di st 100353
prol actin</w> 100327
pap ers</w> 100317
D a 100311
µ L</w> 100305
D P 100301
RA D5</w> 100299
opath ology</w> 100270
bel ong</w> 100257
percent ages</w> 100222
Pi er 100209
rom ycin</w> 100199
Organ ization</w> 100187
p ation</w> 100132
play ers</w> 100127
SM C</w> 100084
in ous</w> 100077
con sumed</w> 100036
F as 100003
k ingly</w> 99984
g aps</w> 99964
spectro meter</w> 99944
G DP</w> 99941
ero n</w> 99939
deter gent</w> 99931
mon ic</w> 99927
foll icle</w> 99926
f ission</w> 99906
ur idine</w> 99884
traff ic</w> 99878
P ulmonary</w> 99853
e able</w> 99845
wal ls</w> 99841
ron es</w> 99835
N 4</w> 99831
corticostero ids</w> 99823
th ous 99816
1 G</w> 99815
diaph rag 99811
olec ules</w> 99807
eng ue</w> 99794
gli omas</w> 99785
lac tam 99772
Insul in</w> 99772
transpor ted</w> 99751
Experim ent</w> 99740
bil ir 99739
ex ha 99712
N AC</w> 99704
micro m</w> 99703
assi stance</w> 99702
lam b 99647
waste water</w> 99634
m 3</w> 99608
ethn icity</w> 99608
E duc 99596
dodec yl</w> 99593
detri mental</w> 99590
Ad ult</w> 99553
AB A</w> 99545
b order</w> 99533
S o</w> 99533
no rep 99531
ex tinc 99520
PA H</w> 99513
kill er</w> 99503
form alin</w> 99487
ph al 99486
On c 99480
s ular</w> 99463
CD 6</w> 99455
S HR</w> 99449
cur cumin</w> 99444
poll ut 99441
T ub 99430
Graph Pad</w> 99429
pon ectin</w> 99417
Tran si 99413
load s</w> 99413
intraven ously</w> 99401
V L 99379
compri ses</w> 99361
ardi al</w> 99357
deli ver 99352
PA 1</w> 99309
marg in</w> 99262
acet onitrile</w> 99261
G ST 99237
prec eding</w> 99222
homogen e 99201
AS P</w> 99170
u si 99133
Im age 99132
Cul ture</w> 99129
om ide</w> 99121
conduc ting</w> 99120
sh unt</w> 99103
ultra violet</w> 99102
L ig 99097
di e</w> 99073
bi ologic</w> 99067
pl au 99062
O V 99059
At g 99052
glycos ylated</w> 99051
inv ari 99047
sph erical</w> 99038
ly tic</w> 99015
P art</w> 99000
me tic</w> 98988
s r 98983
co valently</w> 98983
M DS</w> 98977
neuro endocrine</w> 98975
com mit 98964
beta 1</w> 98960
br uary</w> 98952
hydro chloride</w> 98949
he avi 98935
be it</w> 98929
sy ring 98917
N PC</w> 98914
psych iat 98898
Y oun 98875
cl ock</w> 98870
comorbi dities</w> 98866
M ad 98854
RO C</w> 98849
M ale</w> 98846
L uc 98843
bri efly</w> 98838
sub tle</w> 98816
In hi 98805
co dons</w> 98799
oscill ations</w> 98791
E ast</w> 98778
De termination</w> 98774
L I</w> 98741
sp ite</w> 98714
mean ing 98694
Fe bruary</w> 98691
norep inephrine</w> 98690
v ast</w> 98647
oc clud 98620
reli es</w> 98605
ri bo 98604
was h</w> 98600
Recombin ant</w> 98568
cit abine</w> 98546
pois oning</w> 98519
Pri or</w> 98514
Surviv al</w> 98475
G li 98474
mat ric 98421
cer amide</w> 98421
Cdc 2</w> 98410
discrimin ate</w> 98403
it ability</w> 98401
hapl otypes</w> 98387
erythro id</w> 98383
ip 1</w> 98377
homolog s</w> 98345
Fluoresc ence</w> 98327
br ady 98326
em a</w> 98283
Spr ague</w> 98277
Kore a</w> 98276
plex es</w> 98272
cock tail</w> 98270
L og 98262
py ramidal</w> 98242
ration ale</w> 98242
os yl</w> 98240
ul in 98235
prote ome</w> 98225
Ac c 98207
palmit o 98202
gra y</w> 98186
al umin 98183
m Glu 98166
fin ite</w> 98153
identi fies</w> 98151
wor m</w> 98127
ald osterone</w> 98126
Electro n</w> 98110
confir mation</w> 98108
meth oxy 98102
gener ations</w> 98025
kinet och 98017
ipsi lateral</w> 98015
uni variate</w> 98006
Gen omic</w> 97971
rib ose</w> 97959
C HO 97954
var ic 97941
access ory</w> 97907
qu a 97902
E Z 97900
cur ative</w> 97889
vom iting</w> 97879
ph ant 97872
res orption</w> 97846
N M 97829
gluc agon</w> 97824
Daw ley</w> 97796
h ind 97787
quarti le</w> 97787
m Cherry</w> 97775
Larg e</w> 97765
a ult</w> 97741
L U 97717
meio tic</w> 97715
h r 97703
estim ating</w> 97703
po res</w> 97690
remo te</w> 97685
D ul 97673
pro of</w> 97670
i v</w> 97652
neighb oring</w> 97647
unifor m 97645
saliv a</w> 97643
Ne ther 97636
M agnetic</w> 97621
aut onom 97618
r yst 97611
recru it</w> 97593
out growth</w> 97591
Amer icans</w> 97551
phosph at 97541
EB P 97539
regi stration</w> 97499
impair ments</w> 97479
N U 97478
lig ated</w> 97472
transferr in</w> 97458
fil l</w> 97427
bi omedical</w> 97420
gradu ate</w> 97420
p up 97390
absc ess</w> 97390
deman ds</w> 97374
H CT1</w> 97372
ST AT</w> 97372
AL T</w> 97367
odon tic</w> 97360
ar ded</w> 97355
sw ine</w> 97353
L 5</w> 97339
use a</w> 97337
inhal ation</w> 97331
JA K2</w> 97326
I sl 97315
Bam HI</w> 97308
D ig 97305
TL R 97295
an o</w> 97284
as tern</w> 97259
arach id 97240
Therap eu 97205
atten ding</w> 97204
w it 97201
nor thern</w> 97184
r ant</w> 97181
ma ize</w> 97181
exten ding</w> 97154
adj unc 97134
Datab ase</w> 97125
Y AP</w> 97123
caffe ine</w> 97117
drom e</w> 97109
under lie</w> 97094
radi ographs</w> 97087
st re 97084
fet us</w> 97083
st ated</w> 97047
bron cho 97023
Canadi an</w> 97016
V p 97015
pl ans</w> 97003
potenti ation</w> 97000
compe ting</w> 96987
epith el 96962
resus citation</w> 96953
Scre ening</w> 96885
azep ine</w> 96874
ad ducts</w> 96871
H3 K2</w> 96839
osy stem</w> 96833
path ogenicity</w> 96832
pri or 96823
Ma j 96813
cardi opulmonary</w> 96806
prec ed 96802
anticip ated</w> 96793
polyp eptides</w> 96741
m u</w> 96740
B V</w> 96700
6 b</w> 96681
medi as 96676
ap on 96660
P rism</w> 96659
arsen ic</w> 96658
tr ich 96642
phosph amide</w> 96637
rec all</w> 96635
Li pof 96634
bro mo 96608
posi tes</w> 96607
ic idal</w> 96598
conduc tivity</w> 96589
il st</w> 96579
renew al</w> 96544
Fam ily</w> 96525
phosphoryl ates</w> 96524
hierarch ical</w> 96515
ific ations</w> 96505
break down</w> 96499
the red</w> 96489
pair ing</w> 96454
hum oral</w> 96439
C 1 96418
Tw elve</w> 96387
M is 96351
Im age</w> 96336
ar bon 96324
tin g 96320
M an</w> 96303
VE N 96297
prec isely</w> 96280
8 B</w> 96272
haz ards</w> 96268
In j 96263
method ological</w> 96258
Struc ture</w> 96258
MEASU RES</w> 96246
E B</w> 96243
T g 96204
inv asi 96204
He tero 96198
hy gi 96182
jo b</w> 96163
cycl ospor 96161
M . 96151
im plying</w> 96151
sedi ment</w> 96141
fl at</w> 96116
negl igible</w> 96094
proj ections</w> 96078
fabric ated</w> 96066
J ack 96048
Fig. 1</w> 96045
K i 95993
re activation</w> 95959
Nether lands</w> 95941
rec ap 95924
el em 95903
od al</w> 95902
rel ate</w> 95888
Glob al</w> 95887
NL S</w> 95881
qu ine</w> 95862
ER G</w> 95852
S G 95846
ar k 95842
proper ly</w> 95807
pro t 95784
ro limus</w> 95770
S of 95769
stret ch</w> 95768
eg al 95761
e sian</w> 95747
cy st 95744
continu ing</w> 95732
circum stances</w> 95730
Hsp 7</w> 95729
encephal itis</w> 95686
im por 95662
parathyro id</w> 95644
tr unk</w> 95628
fi able</w> 95626
L N</w> 95586
bor ns</w> 95559
z a</w> 95545
corro bor 95544
wo od</w> 95539
in o 95509
nan o</w> 95509
emis sions</w> 95481
reciproc al</w> 95475
anth rac 95467
scenari os</w> 95426
erythro po 95407
Inhi bit 95377
i ter 95373
al beit</w> 95369
C i</w> 95351
broad ly</w> 95335
s ure</w> 95332
bran ching</w> 95285
differenti ating</w> 95282
emplo yment</w> 95255
t tes</w> 95245
um inal</w> 95242
ang e</w> 95225
ren ces</w> 95215
ch ance</w> 95119
conn ective</w> 95091
nucle osome</w> 95057
Posi tive</w> 95055
sh ut 95038
re tain</w> 95034
ul tr 95026
4 F</w> 94993
h on 94983
epid ural</w> 94983
ub in</w> 94979
ver age</w> 94972
pos it 94917
crosso ver</w> 94905
CS C</w> 94902
replic ated</w> 94890
d B</w> 94865
ang ina</w> 94859
Pl us</w> 94858
Intro duction</w> 94847
cad mium</w> 94841
r as 94831
ri l</w> 94831
sym metric</w> 94821
-- a</w> 94802
F il 94794
sol vents</w> 94779
adequ ately</w> 94779
exclu sive</w> 94726
li ves</w> 94695
S 9</w> 94689
se dim 94688
PM L</w> 94678
H elic 94670
C BP</w> 94660
T CT 94659
omyel itis</w> 94652
phy rin</w> 94646
p t 94616
D ou 94615
bat ch</w> 94610
end osomal</w> 94606
re programming</w> 94599
mir ro 94596
DO X</w> 94589
belie fs</w> 94557
wan ted</w> 94538
prote omics</w> 94531
resul tant</w> 94512
st ones</w> 94502
vi ro 94500
A sia</w> 94476
DN ase</w> 94461
landsc ape</w> 94416
retin opathy</w> 94398
toler ant</w> 94397
Rel ati 94388
inser t</w> 94378
us sian</w> 94374
Ca Cl2</w> 94360
As perg 94360
infer tility</w> 94345
Four ier</w> 94337
H L 94298
cas cad 94287
buil d</w> 94273
addi tionally</w> 94255
oscop ic</w> 94190
diver gent</w> 94157
F H</w> 94116
prep are</w> 94116
es h 94089
analy se</w> 94051
form at</w> 94045
h t 94036
b asi 94007
Institu tes</w> 93978
ron ary</w> 93974
spermat ozo 93965
gyr us</w> 93964
auto antibodies</w> 93956
gr ap 93943
ophil ia</w> 93943
lip ase</w> 93941
uter us</w> 93926
syn onymous</w> 93919
vesti bular</w> 93907
th elial</w> 93834
bi o</w> 93832
electrophore tic</w> 93803
V D 93793
Rel ative</w> 93791
contrib ut 93779
en teric</w> 93764
C op 93761
hom ocyste 93745
encephal opathy</w> 93741
epider mis</w> 93739
design s</w> 93732
hel ps</w> 93729
mesen teric</w> 93724
s er</w> 93681
nanop article</w> 93679
DS M</w> 93635
synovi al</w> 93634
CO S</w> 93626
devi ations</w> 93623
mo id</w> 93603
c tomy</w> 93591
K CN 93568
perturb ation</w> 93557
TGF β</w> 93546
t ate</w> 93528
lar yngeal</w> 93528
sal ts</w> 93527
P ap 93519
F F</w> 93512
C lo 93506
B en 93503
c f 93501
integr ins</w> 93479
vas opres 93466
Di ego</w> 93461
su ture</w> 93456
e k</w> 93450
oligom erization</w> 93400
A β4</w> 93391
tra p</w> 93389
end otoxin</w> 93386
ti a</w> 93363
lymph ocytic</w> 93310
correspon ded</w> 93309
L C 93305
in effective</w> 93304
ap tam 93300
characteri sed</w> 93244
ho c</w> 93197
nucle ase</w> 93190
annot ated</w> 93189
m ock</w> 93176
Amer sham</w> 93152
visc osity</w> 93140
ech anical</w> 93125
susp ici 93122
hor seradish</w> 93115
s outhern</w> 93108
neo plasia</w> 93105
war f 93098
con flu 93093
abor tion</w> 93085
lifes pan</w> 93071
dis appeared</w> 93031
o ted</w> 93024
impair s</w> 93024
thrombocytop enia</w> 92998
H s 92996
L ong 92992
F V</w> 92992
μ mol</w> 92992
in i</w> 92944
b uc 92904
or atory</w> 92899
8 C</w> 92889
toxic ities</w> 92884
hyp oglyc 92881
ex it</w> 92868
person nel</w> 92856
1 F</w> 92855
reservo ir</w> 92842
fas c 92837
t ap 92815
N H2</w> 92812
an no 92809
contex ts</w> 92807
R FP</w> 92778
T el 92764
neut rop 92753
no table</w> 92747
ra f 92729
carg o</w> 92729
bra in 92722
Dul bec 92714
Pro ject</w> 92690
radio active</w> 92687
nic kel</w> 92680
sis ter</w> 92676
B on 92665
pal sy</w> 92661
Addi tion</w> 92655
c all</w> 92653
triglycer ides</w> 92645
discrep ancy</w> 92645
T2 DM</w> 92643
G PCR</w> 92639
matric es</w> 92626
sh aring</w> 92603
P W 92599
tetra hydro 92587
B A 92568
antidepress ant</w> 92563
wea kness</w> 92556
Relati onsh 92538
H 6</w> 92520
multi disciplinary</w> 92511
C t 92503
sor ption</w> 92490
co oling</w> 92487
cDN As</w> 92456
in tran 92439
dig estive</w> 92438
broad er</w> 92431
AS E</w> 92424
asym metry</w> 92421
Le e</w> 92419
A den 92412
na usea</w> 92409
ut ation</w> 92406
ra ises</w> 92402
mas ter</w> 92384
um ination</w> 92354
isom ers</w> 92348
h exam 92329
pa m</w> 92321
amygdal a</w> 92302
Y et</w> 92296
conden sation</w> 92256
G L</w> 92255
trac er</w> 92248
EM G</w> 92242
ke ts</w> 92239
s i</w> 92234
fif th</w> 92222
T i</w> 92200
im posed</w> 92179
min ority</w> 92176
LT P</w> 92171
di i</w> 92164
fib rous</w> 92131
at ology</w> 92120
immunob lot</w> 92120
tri phosphate</w> 92081
E ph 92072
Dulbec co</w> 92069
rever si 92067
immun omod 92053
mon key</w> 92050
cr u 92041
physi ologically</w> 91963
orth olog 91956
determin es</w> 91913
om s</w> 91888
d ae</w> 91878
MC F7</w> 91876
fav our 91862
cent res</w> 91848
strati fication</w> 91848
pri on</w> 91830
R BC</w> 91810
tic k</w> 91808
en am 91806
anti serum</w> 91784
Sub sequent</w> 91780
5 th</w> 91761
calc ification</w> 91743
iz er</w> 91741
T or 91704
hydr ated</w> 91688
is ite</w> 91673
G CC 91639
cur ric 91627
S pain</w> 91606
preferen tial</w> 91605
r ules</w> 91580
soy bean</w> 91573
rel ating</w> 91560
cyt ology</w> 91510
Pear son</w> 91509
permeabil ized</w> 91495
bo ard</w> 91464
complem entation</w> 91459
Co h 91433
loc omotor</w> 91411
Yor k</w> 91409
seg mental</w> 91400
deri ve</w> 91382
inter cellular</w> 91357
sp ong 91344
K u 91322
mag n 91315
trop ical</w> 91311
Un fortunately</w> 91304
il lo 91284
s h</w> 91274
abro gated</w> 91259
occup ied</w> 91229
bro th</w> 91192
ici encies</w> 91190
coordin ates</w> 91180
Ex trac 91179
N S1</w> 91158
bl ings</w> 91074
chick ens</w> 91063
dissemin ation</w> 91057
Jack son</w> 91013
produc tivity</w> 91010
house hold</w> 90989
Asperg illus</w> 90965
STAT 1</w> 90964
rex ate</w> 90960
N MD 90923
plic ity</w> 90915
opoi esis</w> 90913
4 G</w> 90883
N R</w> 90876
tic us</w> 90815
st ressed</w> 90812
crystalli zation</w> 90810
tr al</w> 90792
trans form 90779
immun ocyto 90769
V en 90748
ro utes</w> 90744
raz ole</w> 90738
e us</w> 90720
ati vity</w> 90715
neuro transmitter</w> 90708
to id</w> 90707
sb ad</w> 90702
L RR 90694
Sev enty</w> 90691
z ine</w> 90682
Poten tial</w> 90669
co operative</w> 90617
Rap id</w> 90615
6 -</w> 90606
in ety</w> 90561
d ust</w> 90558
biotin ylated</w> 90533
CE A</w> 90530
Ran dom 90522
se mic 90496
dam aging</w> 90495
recogn izes</w> 90490
en ol 90478
ad missions</w> 90470
conf ers</w> 90467
ect ories</w> 90427
e NOS</w> 90404
docum ent</w> 90384
Carl sbad</w> 90374
ad ec 90358
ge ometric</w> 90355
aug mentation</w> 90330
de tox 90328
add ressing</w> 90319
exp and</w> 90312
cl ath 90293
fig ures</w> 90284
cholec y 90280
IK K 90271
perox is 90268
p all 90262
er ship</w> 90251
Le vels</w> 90250
SP ECT</w> 90247
accep tance</w> 90238
ome res</w> 90236
chemil uminescence</w> 90225
inher itance</w> 90221
re fr 90220
belong s</w> 90189
ac tors</w> 90175
patho physiological</w> 90170
anne aling</w> 90138
U PR</w> 90126
A pop 90124
chit osan</w> 90109
up uncture</w> 90107
IR S</w> 90078
shor t 90056
HF R</w> 90028
in tubation</w> 90015
hydrol ase</w> 90008
bre ast 89975
R F 89962
min i</w> 89958
cogn ate</w> 89957
synthe size</w> 89928
H ip 89914
anes thetic</w> 89895
A uro 89883
CR T</w> 89865
Ta u</w> 89863
sacrif iced</w> 89856
W 2</w> 89847
aut opsy</w> 89837
new borns</w> 89823
Y .</w> 89821
cl ade</w> 89814
sero type</w> 89813
r ating</w> 89803
Eco RI</w> 89786
My o 89760
G α 89742
tw in</w> 89730
K ir 89715
Gen Bank</w> 89702
menstr ual</w> 89702
E GTA</w> 89700
micro glial</w> 89698
lac Z</w> 89697
det achment</w> 89694
S AM</w> 89691
In flam 89685
PBM Cs</w> 89666
sal v 89653
particip ant</w> 89637
F AN 89635
Sw it 89617
ech o</w> 89597
there after</w> 89593
po d 89590
eth ics</w> 89590
modul ators</w> 89588
ep sil 89583
in ium</w> 89565
osteoc las 89560
osi des</w> 89525
ran ts</w> 89523
ot rexate</w> 89494
gluc os 89494
i ate</w> 89486
cop olym 89484
A ng</w> 89480
App endix</w> 89470
c yl 89464
Reg ulation</w> 89423
sc aling</w> 89407
lenti viral</w> 89403
pur su 89393
pcDN A3</w> 89370
re per 89366
op posed</w> 89347
pro vision</w> 89343
Institu tional</w> 89304
duod en 89301
arg ue</w> 89294
gen otyped</w> 89293
V R</w> 89274
B ody</w> 89232
R N</w> 89217
fibr in</w> 89206
mat urity</w> 89200
GT Pases</w> 89197
thic k 89172
soci o</w> 89145
2 F</w> 89134
M as 89130
M es 89110
virul ent</w> 89104
zyg osity</w> 89102
vi sions</w> 89094
am ides</w> 89085
bu b 89080
ol l 89059
circum ference</w> 89053
at orial</w> 89033
sil ent</w> 89029
C SCs</w> 89023
pol i 89014
J ur 89009
exclud ing</w> 88991
en trop 88984
man age</w> 88981
br ac 88976
Kore an</w> 88952
radio activity</w> 88947
re act</w> 88932
F ri 88930
excit ability</w> 88905
intram olecular</w> 88903
dri vers</w> 88901
M ental</w> 88891
le aving</w> 88879
Ab out</w> 88875
inter faces</w> 88874
ds RNA</w> 88874
Con tr 88872
he dral</w> 88871
dox in</w> 88867
TL V</w> 88862
M t 88858
H 5 88847
fung us</w> 88831
radi os 88826
alg ia</w> 88815
complain ts</w> 88797
T CA</w> 88784
cyclo phosphamide</w> 88775
epti dase</w> 88766
N J</w> 88757
dendri tes</w> 88750
sp as 88740
op eron</w> 88719
chec ked</w> 88716
thre at</w> 88708
N GS</w> 88700
ph orb 88690
c .</w> 88686
carb ons</w> 88683
lo tinib</w> 88678
AC h</w> 88658
P Y 88623
G 0</w> 88602
h app 88561
hypertroph ic</w> 88544
b all</w> 88532
t us 88519
absor bed</w> 88509
inflam mas 88505
de bate</w> 88491
SC I</w> 88479
idel ity</w> 88451
Syn drome</w> 88450
d w 88429
Mut ation</w> 88429
win ter</w> 88421
ferri tin</w> 88401
adi ponectin</w> 88357
cataly zes</w> 88348
introduc ing</w> 88324
er ia</w> 88320
Auro ra</w> 88288
co bal 88282
sph ero 88268
B loc 88262
go rous</w> 88256
har m 88250
- ATPase</w> 88236
E agle</w> 88232
D ATA</w> 88231
PT C</w> 88210
meaning ful</w> 88199
ograph ics</w> 88188
8 F</w> 88186
fing er 88180
C 7</w> 88164
ag it 88154
att acks</w> 88148
an te 88110
ch ori 88104
Ha em 88099
suppor tive</w> 88067
origin ating</w> 88067
P re</w> 88058
Car b 88042
cataly sts</w> 88039
pass ed</w> 88036
con ferred</w> 88032
ili ac</w> 88023
mes h</w> 88017
ent ric</w> 88014
ver sions</w> 88013
B J 88003
a ur 87995
wor th</w> 87988
c p 87986
us hing</w> 87986
summar izes</w> 87958
INTER VEN 87936
Lym ph 87927
En viron 87905
m ang 87901
def iciencies</w> 87874
gem citabine</w> 87872
ker atin</w> 87869
in appropriate</w> 87852
P relim 87848
I AP</w> 87841
expect ations</w> 87821
f ell</w> 87806
consum ing</w> 87785
TN BC</w> 87772
retino ic</w> 87758
erythemat osus</w> 87720
y er</w> 87674
domin ated</w> 87673
spati ally</w> 87655
gro unds</w> 87614
H U</w> 87612
ty ph 87603
cover slips</w> 87600
dis playing</w> 87594
micro RNA</w> 87591
oc aine</w> 87553
PF C</w> 87533
carb oxy 87531
brain stem</w> 87518
in der</w> 87499
thym ic</w> 87490
ubiquit ous</w> 87464
ar rested</w> 87451
phorb ol</w> 87448
M e</w> 87442
vic tim 87416
Caucasi an</w> 87412
obacteri a</w> 87397
en ters</w> 87390
og aster</w> 87389
pack aging</w> 87383
scre ens</w> 87377
coch lear</w> 87371
im mort 87363
an tero 87353
prim ed</w> 87351
er ies</w> 87350
Th at</w> 87343
D na 87328
BR CA2</w> 87326
Pier ce</w> 87324
f MRI</w> 87322
ten cies</w> 87318
Prelim inary</w> 87311
R ats</w> 87309
reper to 87307
judg ed</w> 87304
volati le</w> 87297
L t 87294
p 8</w> 87286
u als</w> 87281
Post operative</w> 87281
gen ous</w> 87274
wor thy</w> 87259
an il 87250
E mer 87248
Inf ection</w> 87243
AB EL 87241
A min 87238
ipo protein</w> 87236
com ycin</w> 87229
inser tions</w> 87216
function alized</w> 87197
neur ite</w> 87195
Suppor ting</w> 87193
extinc tion</w> 87189
AS H</w> 87177
M ass</w> 87158
os el 87158
pp ing</w> 87155
form al</w> 87145
re di 87141
4 R</w> 87117
r DNA</w> 87099
spermatozo a</w> 87093
gra in</w> 87073
ent ous</w> 87071
ser ver</w> 87053
clar ified</w> 87049
F R</w> 87040
bol us</w> 86996
ABEL LED</w> 86964
trac k</w> 86961
sub populations</w> 86939
ti fication</w> 86938
e tax 86923
fac torial</w> 86913
MT X</w> 86912
in trigu 86881
chron ically</w> 86879
CP T</w> 86873
ton gue</w> 86869
N u 86865
consi st</w> 86865
Vir us</w> 86855
U NL 86832
compri se</w> 86815
cascad es</w> 86814
it ating</w> 86807
n id 86791
ultrastruc tural</w> 86786
idi c</w> 86782
Wh it 86744
PIK 3 86743
e pam</w> 86742
nod al</w> 86738
impe dance</w> 86737
L L</w> 86714
e tine</w> 86712
fruc tose</w> 86693
ie ties</w> 86652
kin e 86651
syn thesi 86650
re finement</w> 86647
RE T</w> 86642
S AM 86625
lear ned</w> 86615
conf ounding</w> 86611
bro ught</w> 86590
UNL ABELLED</w> 86587
clos er</w> 86579
S H2</w> 86570
ec ology</w> 86556
nar row 86554
et ter</w> 86547
K A</w> 86541
Lipof ectamine</w> 86539
them es</w> 86531
L is 86507
decom position</w> 86497
r ication</w> 86483
mel ting</w> 86473
des cending</w> 86466
l umin 86464
traj ectories</w> 86444
silic on</w> 86432
cer tain 86428
pro pan 86414
G PCRs</w> 86408
permis sive</w> 86408
stri es</w> 86386
A h 86374
recap it 86374
fracti onal</w> 86366
ap 1</w> 86348
i ri 86346
car ni 86340
homolog ue</w> 86334
L ight</w> 86323
medi ation</w> 86317
osk eletal</w> 86317
c ra 86309
i us</w> 86303
n ame</w> 86298
plas ts</w> 86295
His panic</w> 86263
p B 86260
ic on</w> 86257
lamin in</w> 86234
inf used</w> 86220
cytos ine</w> 86208
oste oblasts</w> 86205
coll agen 86196
sp p</w> 86191
k at</w> 86181
compens ation</w> 86180
Q TL</w> 86161
ie tin</w> 86154
Ig G1</w> 86142
titan ium</w> 86138
etax el</w> 86131
re ward</w> 86130
al and</w> 86122
d 2</w> 86104
polys accharide</w> 86018
RP 3</w> 85997
TK I</w> 85994
T K</w> 85977
Stand ard</w> 85970
AG E</w> 85957
scre w</w> 85950
S tri 85938
cal cin 85917
redund ant</w> 85911
di ploid</w> 85888
photo receptor</w> 85878
iton in</w> 85875
O s</w> 85862
phosphat ases</w> 85847
cros sing</w> 85843
l ec 85830
T BS</w> 85820
CT C</w> 85790
plau sible</w> 85783
circu its</w> 85782
e ties</w> 85780
glyc ans</w> 85773
vacu um</w> 85763
E L</w> 85762
Isol ation</w> 85761
is tically</w> 85751
NE L</w> 85745
highligh ting</w> 85730
re vascularization</w> 85703
GF AP</w> 85669
am ate</w> 85652
chec k</w> 85650
pac ing</w> 85644
in consistent</w> 85641
Taiw an</w> 85625
On t 85622
. 6</w> 85616
ne in</w> 85607
ar ri 85595
disper sed</w> 85593
s and 85592
ass ing</w> 85591
acqu ire</w> 85589
recei ver</w> 85587
hor se</w> 85587
reconstruc ted</w> 85563
Nor mal</w> 85560
sol ar</w> 85548
2 S</w> 85544
S BP</w> 85535
dec arbox 85535
haem orrh 85535
nat ri 85518
1 beta</w> 85517
sp p.</w> 85512
er in</w> 85507
I OP</w> 85498
CO 3</w> 85498
sph ere</w> 85484
P S1</w> 85469
immunosup pression</w> 85469
nec rotic</w> 85458
constitu tes</w> 85453
egal ovirus</w> 85449
bran ched</w> 85447
fi tinib</w> 85442
anti oxidants</w> 85403
vulner ability</w> 85402
ic acy</w> 85399
az ide</w> 85396
organ izations</w> 85385
program mes</w> 85369
over load</w> 85366
al ised</w> 85360
B ir 85359
s un 85358
om o 85357
ACh R</w> 85357
co m</w> 85351
m erg 85347
end oc 85347
ann er</w> 85332
S p1</w> 85319
atax ia</w> 85298
ref ined</w> 85292
Ma xim 85291
Pro f 85276
V SV</w> 85258
ic us</w> 85258
FL D</w> 85257
succ essive</w> 85217
termin i</w> 85211
Sof tware</w> 85210
teri zation</w> 85199
H SCs</w> 85195
P ho 85187
el ast 85187
7 F</w> 85183
inst ances</w> 85176
distinc tive</w> 85171
be vac 85168
itud es</w> 85146
end ocytic</w> 85145
SU MO 85119
hall mark</w> 85078
tis h</w> 85072
perm it</w> 85071
pharmac ologic</w> 85059
volun tary</w> 85017
Com plex</w> 84971
CA R 84970
cap abilities</w> 84952
Gen es</w> 84936
sil enced</w> 84932
F ab 84930
ap nea</w> 84923
exce eded</w> 84919
Bi ol</w> 84903
gal y</w> 84874
re m 84860
th ank</w> 84858
Li u</w> 84858
efficac ious</w> 84844
encapsul ated</w> 84832
applic ability</w> 84829
g ar 84822
yo u</w> 84817
bevac izumab</w> 84815
end onuclease</w> 84810
pres s</w> 84807
chic k</w> 84781
protec ting</w> 84772
altern ate</w> 84771
multi variable</w> 84762
surviv ing</w> 84738
h ands</w> 84723
K S</w> 84718
free zing</w> 84712
it a</w> 84702
CA G</w> 84693
refl ection</w> 84677
depend ency</w> 84653
c et 84645
ac idosis</w> 84634
fibr e</w> 84618
ur gent</w> 84597
insul in 84594
le an</w> 84586
he ated</w> 84584
infiltr ating</w> 84507
C yp 84486
r at 84483
reli ably</w> 84464
b im 84463
Me an 84459
pof ol</w> 84456
pa tes</w> 84455
regul arly</w> 84448
clath rin</w> 84417
denat uration</w> 84407
innov ative</w> 84404
en g</w> 84382
perfor ation</w> 84380
sor ted</w> 84377
mom ent</w> 84371
her pes 84357
co sis</w> 84353
O E</w> 84320
dist ur 84315
tran ce</w> 84292
Thir d</w> 84281
cut aneously</w> 84278
mim icking</w> 84263
wa ters</w> 84261
re min 84257
af ter 84246
Gl n</w> 84245
dilu tions</w> 84237
reg s</w> 84232
in ia</w> 84222
Ital ian</w> 84218
hy alu 84206
grad ual</w> 84194
MS .</w> 84193
An d</w> 84173
melan ogaster</w> 84171
d t</w> 84167
ing dom</w> 84162
posi tional</w> 84157
par ad 84126
visu alize</w> 84101
V D</w> 84092
pl ating</w> 84078
te ach 84069
di viding</w> 84066
Bi ological</w> 84065
splic ed</w> 84049
G U 84031
b in</w> 84028
in spec 84019
sim plex</w> 84019
flav in</w> 84013
AP 2</w> 84008
cil ia</w> 84001
ugh ter</w> 84001
su ffer</w> 83992
M W 83987
L M</w> 83983
effici encies</w> 83961
infu l</w> 83955
m y</w> 83924
P d</w> 83917
b ath</w> 83895
chondro cytes</w> 83889
L S 83884
Del e 83883
exper ts</w> 83879
is op 83865
col li 83834
C . 83826
el e</w> 83825
ampl ify</w> 83807
CCR 5</w> 83807
P he 83796
CD 9</w> 83794
enlarg ed</w> 83787
Ze aland</w> 83777
- di 83770
St age</w> 83767
ectom ized</w> 83763
dextr an</w> 83743
thor ax</w> 83734
An g 83723
w i 83720
Eff icacy</w> 83706
PT P 83685
choles tero 83664
gen tam 83644
acet one</w> 83634
dis charged</w> 83623
re vised</w> 83608
res veratrol</w> 83601
l actic</w> 83575
P MS 83569
im balance</w> 83566
Sp anish</w> 83564
meta phase</w> 83558
M MR</w> 83554
B lue</w> 83547
Hb A1c</w> 83511
th ly</w> 83497
dis solution</w> 83486
chro mo 83483
M N 83472
RA P</w> 83472
lip idemia</w> 83472
hypoten sion</w> 83469
medic inal</w> 83454
sex ually</w> 83452
surro gate</w> 83449
In di 83439
u sions</w> 83429
hab its</w> 83429
retic ul 83428
coloc alization</w> 83414
ampl itudes</w> 83395
pa ediatric</w> 83374
am a</w> 83370
mo ieties</w> 83361
PC 3</w> 83351
suspen sions</w> 83350
ous ness</w> 83342
hy ste 83341
V P1</w> 83329
C ellular</w> 83301
U R 83296
pop ular</w> 83295
Networ k</w> 83293
ter pen 83288
tem plates</w> 83283
ven om</w> 83271
H H</w> 83262
U SP 83260
re sts</w> 83260
sp ir 83243
asci tes</w> 83242
Al together</w> 83241
bir ths</w> 83207
or n 83204
v on</w> 83150
sl udge</w> 83126
bi osens 83113
lo oked</w> 83110
L ap 83094
T X</w> 83092
sk y</w> 83092
R he 83091
H K</w> 83085
R as 83082
F N</w> 83079
expec tedly</w> 83079
sk ull</w> 83061
GW AS</w> 83054
B NP</w> 83037
promp ted</w> 83035
an os 83025
on eph 83013
consci ous</w> 83013
sub cutaneously</w> 83001
ic ol</w> 82990
he te 82989
R ING</w> 82981
N ig 82973
fac ing</w> 82972
del ays</w> 82952
S core</w> 82927
Analy ses</w> 82923
sti g 82915
man aging</w> 82914
enam el</w> 82877
ocor tical</w> 82865
PBM C</w> 82852
um p</w> 82844
Lu c</w> 82838
z er 82836
sel ecting</w> 82816
thermod ynamic</w> 82781
di latation</w> 82779
un infected</w> 82779
TU NEL</w> 82776
si blings</w> 82770
AB L</w> 82751
replac e</w> 82747
regi stry</w> 82734
In strum 82725
CB F</w> 82721
H TLV</w> 82718
vol ution</w> 82711
d .</w> 82688
ten si 82688
bound aries</w> 82685
GL P</w> 82683
B 5</w> 82680
D an 82672
aut oradi 82660
Therapeu tic</w> 82640
F CS</w> 82630
perc enti 82628
lamin a</w> 82628
throm ycin</w> 82621
nan oc 82614
design ing</w> 82612
con sol 82611
Loc al</w> 82605
k an 82602
collabor ation</w> 82594
emphasi ze</w> 82586
elu sive</w> 82585
br achi 82581
estro gens</w> 82578
pharmac y</w> 82574
mic ron 82564
Obj ective</w> 82537
Lit tle</w> 82512
T O</w> 82494
c ed</w> 82485
B row 82480
dop ed</w> 82474
hyperglyc emia</w> 82429
mod al</w> 82397
adip ocyte</w> 82395
p 9</w> 82388
K I</w> 82386
dissoci ated</w> 82385
AN CE</w> 82367
EC TS</w> 82366
adsor bed</w> 82338
expendit ure</w> 82328
io sis</w> 82311
kn oc 82301
idi um</w> 82294
Ab l</w> 82273
so on</w> 82248
inf eren 82244
SC F</w> 82235
at opic</w> 82217
d al</w> 82215
lipo proteins</w> 82215
at ri 82199
m .</w> 82193
pseud o</w> 82188
ac ycl 82171
f a</w> 82157
sen sor 82138
lac ked</w> 82135
sed ation</w> 82135
TI R</w> 82131
g es</w> 82126
d engue</w> 82124
inter acted</w> 82097
seg mentation</w> 82095
ak i</w> 82091
insec tic 82076
Pt d 82075
angio plasty</w> 82073
j ug 82071
ax illary</w> 82063
sex es</w> 82056
otrop y</w> 82053
ot opic</w> 82047
B l 82024
L ang 81993
ogni tive</w> 81964
ole ic</w> 81940
W .</w> 81925
Non etheless</w> 81918
sum mer</w> 81905
p H 81903
n d</w> 81902
ol ateral</w> 81875
Ex peri 81868
st och 81862
e ast</w> 81847
Swe den</w> 81827
grad es</w> 81817
pac king</w> 81798
exha us 81795
O lig 81791
cath epsin</w> 81781
S ES</w> 81760
m . 81750
H un 81744
acti n 81740
tel omeres</w> 81734
Youn g</w> 81725
ho u</w> 81722
om ib</w> 81704
mercap to 81696
3 S</w> 81691
T D</w> 81687
supplem ents</w> 81679
Q C</w> 81666
F X 81651
in osi 81636
H G</w> 81635
Th rom 81627
Bi om 81614
sel enium</w> 81613
Jur kat</w> 81599
micro satellite</w> 81589
si onally</w> 81581
W F</w> 81572
Bay esian</w> 81550
P ediatric</w> 81542
eng aged</w> 81537
co at</w> 81507
AR F</w> 81507
no id</w> 81506
es cal 81504
We b</w> 81498
cur v 81471
cholin esterase</w> 81462
os elective</w> 81461
CR F</w> 81455
Helic obacter</w> 81448
chape rones</w> 81426
poly ethylene</w> 81409
CYP 3 81390
appropri ately</w> 81380
T b 81370
fi ers</w> 81368
cor pus</w> 81360
sp ut 81349
entr icular</w> 81340
hetero dimer</w> 81321
gu n</w> 81308
agre ed</w> 81298
l avage</w> 81275
IC C</w> 81266
consul tation</w> 81254
W 1</w> 81242
aller gen</w> 81242
T ables</w> 81229
tro n</w> 81229
soci ety</w> 81228
DE NV</w> 81213
D HA</w> 81206
As n</w> 81202
HB s 81179
oph ore</w> 81177
dom eth 81173
roph yl 81173
dy nein</w> 81167
Sur face</w> 81165
V alu 81162
ec e</w> 81159
ine al</w> 81158
Per form 81120
sw im 81109
S H3</w> 81102
ste pwise</w> 81100
Sev ere</w> 81095
ataly tic</w> 81080
dro plets</w> 81077
gro ove</w> 81055
arbon ate</w> 81050
protein uria</w> 81043
nitro cellulose</w> 81033
Dec re 81012
addic tion</w> 81007
er ated</w> 80992
enanti om 80986
end ovascular</w> 80979
ogen y</w> 80968
P 8</w> 80960
st om</w> 80960
hemisph ere</w> 80959
fem ur</w> 80955
GL U 80948
Individ ual</w> 80935
Ti O2</w> 80932
guid eline</w> 80926
ob enz 80923
in distinguishable</w> 80914
mim ics</w> 80899
circul atory</w> 80898
S ma 80882
thal iana</w> 80875
mean ing</w> 80868
ocy t 80847
glob in</w> 80838
C 3 80831
avi r</w> 80831
Glc NAc</w> 80822
oncogen es</w> 80797
oma virus</w> 80787
T rial</w> 80785
salv age</w> 80772
li sts</w> 80766
diag nose</w> 80759
Nat ural</w> 80755
rel in</w> 80753
on e 80745
w el 80729
transcrip tionally</w> 80726
in clusions</w> 80719
fresh ly</w> 80718
c asp 80713
notic ed</w> 80709
L K 80708
Sim ult 80708
oste osarcoma</w> 80692
modul ator</w> 80691
h rs</w> 80687
partici pates</w> 80682
P it 80672
dis location</w> 80669
aden o 80669
co chemical</w> 80665
Ta q 80655
cal ves</w> 80640
Con centr 80617
lamb da</w> 80615
govern ment</w> 80608
Medic are</w> 80605
explo ited</w> 80599
meth otrexate</w> 80592
ing e</w> 80577
Cor relation</w> 80565
ha i</w> 80564
mic rom 80549
P P1</w> 80533
fibro tic</w> 80522
p R 80519
AR s</w> 80513
c ement</w> 80511
ag ers</w> 80505
free ze</w> 80489
row s</w> 80483
P in 80475
et te</w> 80424
ubiquitin ated</w> 80423
dometh acin</w> 80418
ell ul 80412
SM Cs</w> 80407
quen ching</w> 80406
favour able</w> 80404
a res</w> 80394
V AS</w> 80387
di str 80385
ul as</w> 80378
habit at</w> 80363
manif ested</w> 80359
m ati 80351
DF T</w> 80349
plex us</w> 80323
mes oth 80316
shar p</w> 80314
Ch lamy 80301
ul tra</w> 80281
FL P</w> 80277
perme able</w> 80270
integr ate</w> 80254
Commun ity</w> 80228
incorpor ate</w> 80220
E E</w> 80218
dis continuation</w> 80215
Sequ encing</w> 80212
wid er</w> 80208
resol ve</w> 80191
provid er</w> 80190
C amp 80182
HC MV</w> 80174
st y 80164
caud al</w> 80154
nit ros 80153
mis sed</w> 80142
streng th 80137
secre tase</w> 80130
g ains</w> 80120
R CTs</w> 80115
relap sed</w> 80114
end points</w> 80106
man ually</w> 80105
f en 80087
comp act</w> 80069
c k</w> 80053
co res</w> 80025
EC L</w> 80022
tub ule</w> 80022
hemat ologic</w> 80014
expl oring</w> 80010
EZ H2</w> 80009
im urium</w> 80005
B E</w> 79996
adv oc 79996
z ol</w> 79984
MA b</w> 79982
eff usion</w> 79976
S le 79947
ex osomes</w> 79921
w ire</w> 79884
n 2</w> 79869
art an</w> 79852
EP O</w> 79845
Ph il 79844
Bacteri al</w> 79838
concor dance</w> 79830
x y</w> 79819
b us 79799
car inic</w> 79797
we ar</w> 79793
cycl ine</w> 79777
ta vidin</w> 79771
tras tuzumab</w> 79727
p om 79719
m ble</w> 79714
occ ip 79714
out breaks</w> 79707
agit tal</w> 79696
rib osomes</w> 79675
her ni 79650
b i</w> 79642
F M</w> 79639
fl ora</w> 79639
requ isite</w> 79629
dissemin ated</w> 79629
buty rate</w> 79629
T PA</w> 79621
0 a</w> 79620
II a</w> 79606
me tro 79591
presum ed</w> 79583
fu el</w> 79574
auth or 79562
d h 79556
up date</w> 79555
sh i 79528
Mean while</w> 79522
Image J</w> 79511
GABA ergic</w> 79510
immobil ization</w> 79506
ing ton</w> 79493
l 1</w> 79492
it or</w> 79461
intrigu ing</w> 79457
tal k</w> 79450
stric tly</w> 79431
dil ated</w> 79430
ch ose</w> 79426
re f</w> 79422
afferen t</w> 79409
Le ica</w> 79403
bio tics</w> 79387
hydr o</w> 79381
s ter 79368
CA SE</w> 79347
asse mble</w> 79344
eng age</w> 79308
diame ters</w> 79307
6 E</w> 79303
ac upuncture</w> 79298
exten ds</w> 79298
Pol ym 79275
defin ite</w> 79274
sp ond 79262
atmosph eric</w> 79225
Chang e</w> 79209
develop s</w> 79205
cor n</w> 79197
aggreg ated</w> 79188
attenu ate</w> 79186
scle ro 79182
ge ographical</w> 79146
de plo 79136
Eigh ty</w> 79135
bilir ubin</w> 79122
SY BR</w> 79120
Phosphor ylation</w> 79106
pluri potent</w> 79102
adop t</w> 79087
prog ressed</w> 79073
mac a 79060
Fol low</w> 79057
induc er</w> 79051
pro vo 79041
ocy cl 79041
behavio urs</w> 79020
tot ally</w> 79016
Rem ark 79015
quanti tation</w> 79005
am ong 78993
vasopres sin</w> 78991
au to</w> 78972
gran ular</w> 78962
er lotinib</w> 78961
acceler ation</w> 78940
perturb ations</w> 78936
am ylase</w> 78916
sub family</w> 78905
isol one</w> 78873
4 H</w> 78849
H SC</w> 78849
CaMK II</w> 78848
dem ographics</w> 78841
ra ising</w> 78840
fram es</w> 78832
tas te</w> 78829
mul tim 78820
Syste matic</w> 78800
vim entin</w> 78789
Ri ver</w> 78778
In duction</w> 78777
ing u 78772
Vit amin</w> 78757
oxy cycline</w> 78752
T regs</w> 78738
Measure ment</w> 78731
intr at 78729
N CI</w> 78728
hep ar 78698
sph inc 78685
agricul tural</w> 78683
inflammas ome</w> 78670
S ite</w> 78666
doc etaxel</w> 78658
LN CaP</w> 78654
n ar</w> 78646
illu strates</w> 78621
s 2</w> 78600
c iliary</w> 78590
among st</w> 78573
Perform ance</w> 78571
PA F</w> 78560
as i</w> 78542
adolesc ence</w> 78536
es us</w> 78524
kin esis</w> 78518
pa inful</w> 78479
Nur sing</w> 78469
op e 78459
co operation</w> 78459
mic elles</w> 78455
te a</w> 78452
po t</w> 78443
prescri bing</w> 78438
tumorig enic</w> 78434
Q 2</w> 78430
emplo ye 78398
f ra 78393
in tox 78379
CL IN 78365
in one</w> 78359
Rep or 78357
IQ R</w> 78339
aw a</w> 78326
pyri midine</w> 78320
C 0</w> 78304
e i</w> 78292
nocic eptive</w> 78292
i .</w> 78290
Gib co</w> 78287
ll er</w> 78271
BC G</w> 78270
Chil d</w> 78249
homogene ity</w> 78228
S mo 78185
ran olol</w> 78169
Ox y 78168
bon ded</w> 78163
dy es</w> 78163
z omib</w> 78160
ox ylin</w> 78158
endometri osis</w> 78156
por tions</w> 78122
gener ic</w> 78110
stabil izes</w> 78097
expl oratory</w> 78093
cortic osterone</w> 78080
surve yed</w> 78079
top ology</w> 78071
Gu id 78056
c ame</w> 78041
il ian</w> 78039
polym eric</w> 78025
over t</w> 78006
ome ga</w> 77990
sal ic 77982
CC K</w> 77975
harm ful</w> 77959
hemat ological</w> 77944
b ox 77934
inter viewed</w> 77933
cl aims</w> 77929
CH 3</w> 77921
sequ el 77920
T f 77917
osens ory</w> 77908
tol u 77901
exp anding</w> 77897
k o</w> 77886
viri on</w> 77885
Y 3</w> 77882
amino transferase</w> 77870
benz odi 77861
metallo proteinase</w> 77860
AT G</w> 77809
G RA 77808
HBs Ag</w> 77808
tun ing</w> 77807
SV 4</w> 77799
lymph o 77795
PO 4</w> 77786
microsom es</w> 77783
ox o</w> 77775
MAL DI</w> 77774
attenu ates</w> 77766
ot roph 77742
config ur 77739
bic arbonate</w> 77725
the ories</w> 77724
expl ains</w> 77716
Measure ments</w> 77715
ou tw 77710
prin ted</w> 77706
carri es</w> 77698
Q U 77691
s tents</w> 77682
R e</w> 77682
cre ation</w> 77679
nic he</w> 77668
V III</w> 77662
thromb us</w> 77655
ec tual</w> 77644
microsom al</w> 77637
g ed</w> 77627
e ver 77607
tod ay</w> 77598
NA FLD</w> 77594
man nose</w> 77582
sup plied</w> 77580
pac ked</w> 77549
two fold</w> 77549
ifor m</w> 77544
h at 77522
cle ared</w> 77521
lac tation</w> 77519
myel ination</w> 77512
acr ylate</w> 77509
dis k</w> 77413
tom ato</w> 77413
Compo und</w> 77386
S AR</w> 77378
granul ocyte</w> 77355
FGF 2</w> 77348
3 c</w> 77336
sti r 77325
reg ime</w> 77318
am il</w> 77315
S ero 77313
migr ating</w> 77304
perf ect</w> 77298
GSK 3β</w> 77296
κ B 77255
de formation</w> 77253
F os</w> 77234
inform ative</w> 77229
ev ap 77228
j e</w> 77223
me trical</w> 77222
pr int</w> 77216
reg urg 77214
sto res</w> 77212
oste oblast</w> 77192
ogen ously</w> 77191
St ress</w> 77161
exp ect</w> 77155
un differentiated</w> 77154
ner vation</w> 77154
h an 77135
bre ath</w> 77106
dri ves</w> 77101
K le 77094
bac ter 77094
at able</w> 77085
D T</w> 77067
mon e</w> 77060
germin ation</w> 77060
os um</w> 77049
f idelity</w> 77047
proteas omal</w> 77047
D ys 77040
O ut</w> 77040
AM I</w> 77031
gingi val</w> 77029
gener a</w> 77028
termin ally</w> 77026
harb or</w> 77021
chloro form</w> 77020
phosph odi 77008
z i</w> 76996
hybri ds</w> 76986
Pa ren 76979
in haled</w> 76948
atin gs</w> 76940
aden ylate</w> 76936
AD C</w> 76932
D on 76930
au x 76928
ot om 76926
pro pos 76886
pac em 76882
an ions</w> 76880
sl ice</w> 76847
infer red</w> 76838
autonom ous</w> 76838
par alysis</w> 76836
moti vation</w> 76830
pre y</w> 76812
local izes</w> 76812
g ia</w> 76810
S RE 76805
. .</w> 76792
ec tory</w> 76770
Ac et 76759
dis ti 76758
U 8</w> 76754
en ter 76753
creatin e</w> 76752
re generative</w> 76747
integr ating</w> 76738
conven ient</w> 76720
compu terized</w> 76716
pre clud 76715
Visu al</w> 76704
pyr ro 76695
lab elling</w> 76680
nit rite</w> 76629
sc ar</w> 76627
h CG</w> 76594
et ting</w> 76585
K ar 76582
dic ated</w> 76576
zym es</w> 76575
in version</w> 76563
PD AC</w> 76557
D ev 76544
or ated</w> 76516
PA S</w> 76506
sup ra 76500
grad ing</w> 76500
me tri 76494
R 6</w> 76481
con azole</w> 76476
Pre par 76475
enlarg ement</w> 76470
os per 76454
lymph aden 76439
insec ts</w> 76426
Not ch 76420
coupl es</w> 76382
M H</w> 76375
re paired</w> 76369
cran i 76365
sel ectin</w> 76355
Le ishman 76318
perm its</w> 76307
vi x</w> 76300
tom as</w> 76299
Del ta 76299
ucle otide</w> 76296
d ol 76292
Man n</w> 76272
c us</w> 76258
c ough</w> 76254
neuro genesis</w> 76253
D own</w> 76252
C GG 76251
F ab</w> 76242
flo or</w> 76229
Inv entory</w> 76207
k D</w> 76203
har bo 76197
PP I</w> 76184
H E</w> 76182
Q T</w> 76181
sig ma</w> 76177
T ex 76140
out lined</w> 76123
tax a</w> 76122
sec tor</w> 76116
pri ority</w> 76103
D o 76095
Cx 4</w> 76072
l ide</w> 76070
Saf ety</w> 76064
Physi ci 76058
toler ability</w> 76051
Cl on 76034
electro ns</w> 76007
co ded</w> 75977
Reg arding</w> 75977
et opo 75965
der line</w> 75962
mat ches</w> 75960
neoplas m</w> 75958
T an 75950
de duced</w> 75945
ex ome</w> 75943
gen tly</w> 75942
cerebro vascular</w> 75926
plat forms</w> 75919
ge fitinib</w> 75903
Y 4</w> 75902
dou b 75901
el abor 75897
diagnos tics</w> 75887
ro ta 75881
beg in</w> 75865
sur prisingly</w> 75857
k il 75849
Th ough</w> 75816
aqu atic</w> 75814
ic os 75796
bas ement</w> 75784
mor ning</w> 75782
cataly ze</w> 75780
pl otted</w> 75773
M ur 75769
for k</w> 75767
Pl ate 75757
M G1</w> 75756
co x 75726
cann abin 75720
cathe ters</w> 75711
sh ed 75706
Kn owledge</w> 75703
ulcer ative</w> 75691
ost at</w> 75688
percep tual</w> 75682
phen ols</w> 75679
polyp s</w> 75667
avoid ed</w> 75658
er ation</w> 75643
B K</w> 75642
2 G</w> 75639
V max</w> 75634
air ways</w> 75632
M am 75621
RP A</w> 75594
tus sis</w> 75580
pl i 75574
debri s</w> 75570
P ublic</w> 75556
our ce</w> 75555
B L2</w> 75550
Fig. 2</w> 75541
Educ ation</w> 75540
un folded</w> 75521
emo tion</w> 75510
transcrip tom 75471
Che m</w> 75468
G Y</w> 75465
bur g 75453
C at</w> 75450
mar ri 75448
reperto ire</w> 75426
anno tation</w> 75421
ir regular</w> 75415
chemot axis</w> 75414
topo isomerase</w> 75407
d d 75405
conflic ting</w> 75396
vari ates</w> 75391
C t</w> 75390
RI G</w> 75386
sulf on 75385
HF D</w> 75343
concer ned</w> 75329
meas urable</w> 75322
SP SS</w> 75312
contracti lity</w> 75307
Cs A</w> 75305
des ensitization</w> 75284
predn isolone</w> 75284
gall bladder</w> 75281
min d</w> 75272
immun ogenicity</w> 75269
AT P 75267
ec k</w> 75265
Lew is</w> 75252
rec over</w> 75227
attribu tes</w> 75209
gr ant</w> 75185
etopo side</w> 75163
E SC</w> 75161
e z 75160
anti hypertensive</w> 75153
nas cent</w> 75137
Suc cess 75131
eradic ation</w> 75131
l ex 75127
ter at 75107
us ly</w> 75098
t 2</w> 75094
E 5</w> 75080
f uc 75075
TT P</w> 75072
C ore</w> 75060
V ascular</w> 75055
ocy st</w> 75054
phant om</w> 75031
De sign</w> 75023
v 2</w> 75003
neuro toxicity</w> 74999
Or th 74998
carb onate</w> 74997
warf arin</w> 74991
eu tic 74986
stero idal</w> 74968
rig el</w> 74967
exec utive</w> 74960
enti ties</w> 74958
u ve 74948
B F</w> 74947
ter ran 74928
expres ses</w> 74925
disrup ting</w> 74924
pro pofol</w> 74919
2 P</w> 74879
evol ving</w> 74879
S in 74867
l i</w> 74851
partic ulate</w> 74851
sump tions</w> 74849
M 5</w> 74847
tr i</w> 74839
tun nel</w> 74838
termin ated</w> 74822
O lym 74814
ge o</w> 74813
hydro gel</w> 74809
lyso zyme</w> 74807
AM PA</w> 74805
telom eric</w> 74796
clin ician</w> 74777
TF II 74776
or um</w> 74766
spo res</w> 74756
thalam ic</w> 74751
hy ph 74749
an cho 74747
ep ic 74739
hum an 74739
replac ing</w> 74733
MS I</w> 74732
deacetyl ase</w> 74725
D oes</w> 74704
gentam icin</w> 74702
lit ter 74689
ICA L</w> 74682
u ted</w> 74681
Out come</w> 74675
Δ 1</w> 74669
metall ic</w> 74654
f unding</w> 74652
answ er</w> 74635
V 4</w> 74634
F v</w> 74633
ati te</w> 74609
neutrop enia</w> 74604
tes tes</w> 74601
phosphatidyl choline</w> 74598
se man 74595
natri uretic</w> 74586
E . 74575
cyt ogenetic</w> 74575
S ph 74562
H yp 74560
n esses</w> 74548
Pro ce 74535
bi os 74504
collap se</w> 74484
MO I</w> 74454
en dic 74449
marg ins</w> 74444
5 F</w> 74435
P a</w> 74432
amp icillin</w> 74432
exis ted</w> 74426
un folding</w> 74410
K im</w> 74403
comor bidity</w> 74394
agen esis</w> 74389
erc etin</w> 74363
experi encing</w> 74354
cri sis</w> 74352
mon thly</w> 74342
sti n</w> 74340
eutic als</w> 74340
D 7</w> 74327
ds DNA</w> 74322
p K 74313
cytom egalovirus</w> 74301
I U 74296
avoid ing</w> 74295
Cr y 74275
NHE J</w> 74275
deform ity</w> 74270
re ferences</w> 74266
C ognitive</w> 74261
plate au</w> 74257
inter molecular</w> 74236
V O 74219
homocyste ine</w> 74217
SR C</w> 74203
LRR K2</w> 74193
Wa ter</w> 74191
Sel ective</w> 74189
sp ort</w> 74179
ros ol</w> 74159
assign ment</w> 74156
ac illus</w> 74136
Ac tive</w> 74136
Maj or</w> 74132
regul ations</w> 74123
om eth 74120
assum e</w> 74120
K IT</w> 74113
R ev 74111
O .</w> 74099
cas ein</w> 74082
traj ectory</w> 74064
Clo stri 74060
apol ipoprotein</w> 74056
arrhyth mia</w> 74049
I L1</w> 74039
spi ro 74039
an ionic</w> 74035
ir rig 74023
secre ting</w> 74021
initi s</w> 74016
psycho sis</w> 74003
ro ds</w> 74000
ophar yngeal</w> 73994
migr atory</w> 73989
chim er 73986
G B</w> 73983
az ol</w> 73981
heavi ly</w> 73981
X R 73978
Re verse</w> 73964
inf ect</w> 73962
obser ver</w> 73923
fl aps</w> 73904
arachid onic</w> 73895
miner alization</w> 73886
lin early</w> 73859
res tin</w> 73847
ari th 73847
GC G</w> 73840
neuro trophic</w> 73822
hem i 73819
PD GF 73816
G EN 73810
t ality</w> 73801
im plement</w> 73789
cu stom</w> 73777
F requ 73774
hom es</w> 73771
mor bi 73742
rest or 73726
MR T</w> 73709
hyper activity</w> 73707
Peri ph 73704
ill umination</w> 73701
Min i</w> 73700
phosph ates</w> 73694
posit ron</w> 73685
sequ entially</w> 73683
0 B</w> 73676
Remark ably</w> 73674
V HL</w> 73659
oligom eric</w> 73646
Inter action</w> 73645
age ing</w> 73636
d yn 73630
CC A</w> 73630
Enh anced</w> 73603
NF κB</w> 73599
vil le</w> 73594
repres s</w> 73593
ev ity</w> 73579
incub ating</w> 73579
micro gram</w> 73570
proj ects</w> 73565
compens ate</w> 73550
N umerous</w> 73535
PM N</w> 73531
si veness</w> 73513
methyl ene</w> 73512
c m2</w> 73511
mosquit o</w> 73438
al s. 73434
is land</w> 73433
w est</w> 73424
star ved</w> 73424
leuk aemia</w> 73414
En dos 73412
S ha 73399
tele phone</w> 73397
char t</w> 73387
cal ls</w> 73367
fail s</w> 73359
ultras onic</w> 73350
ar ises</w> 73342
comb ine</w> 73339
r ation</w> 73331
1 2 73324
tr o</w> 73315
IC P</w> 73307
4 ;</w> 73306
&# 12 73306
 4;</w> 73306
compromis e</w> 73300
van comycin</w> 73280
kin dly</w> 73261
ME DL 73258
S chem 73249
on a</w> 73238
exc ep 73232
streng ths</w> 73205
Hy po 73200
eu than 73191
U 2 73185
rot ational</w> 73179
V ector</w> 73152
morph ologic</w> 73152
Rec ur 73151
F ree</w> 73150
CLIN ICAL</w> 73142
o venous</w> 73140
S ensitivity</w> 73124
pres entations</w> 73122
FL T3</w> 73120
W M</w> 73118
2 L</w> 73102
A 9</w> 73102
SC ID</w> 73091
e rec 73076
parasi tic</w> 73053
sha res</w> 73039
An x 73028
wh ilst</w> 73027
Mon te</w> 73020
nor adrenaline</w> 73015
saf ely</w> 72989
hygi ene</w> 72980
I B</w> 72973
R ac</w> 72963
radiolab eled</w> 72962
she ets</w> 72940
physi cochemical</w> 72935
sero logical</w> 72907
fru its</w> 72904
5 α</w> 72899
PG C</w> 72890
sci enti 72879
gr u 72866
1 d</w> 72864
N HS</w> 72864
TL R2</w> 72864
u til 72835
Z 1</w> 72823
litter mates</w> 72821
paren chym 72820
contra dic 72811
ak a</w> 72809
fab rication</w> 72798
meio sis</w> 72782
c age</w> 72781
r d</w> 72780
contr as 72779
R V 72773
ton eu 72759
xen o 72759
SU MM 72756
fol ds</w> 72749
Carl o</w> 72749
MEDL INE</w> 72749
sub cloned</w> 72729
ec ta 72727
al located</w> 72723
A Es</w> 72721
muscul oskeletal</w> 72696
cro p</w> 72681
ch er</w> 72660
5 Y</w> 72647
mos aic</w> 72646
E2 F</w> 72646
tail ored</w> 72642
TA TION</w> 72641
abdom en</w> 72639
Li br 72636
enhanc ers</w> 72635
D im 72629
a king</w> 72628
var s</w> 72628
proced ural</w> 72605
4 c</w> 72584
e ases</w> 72550
I RES</w> 72545
transduc er</w> 72545
Fc γ 72541
6 J</w> 72536
Ma ternal</w> 72515
ou th 72510
Al coh 72508
B G</w> 72500
og raph</w> 72499
Met abolic</w> 72494
glycoly tic</w> 72493
ov ine</w> 72490
gonad otropin</w> 72484
curv ature</w> 72484
ut ch</w> 72475
IGF BP</w> 72469
strep toc 72466
Ch ol 72465
rel ig 72459
intra uterine</w> 72436
N N 72433
Braz ilian</w> 72428
as cending</w> 72426
Mitochond rial</w> 72426
. 7</w> 72403
SUMM ARY</w> 72401
tim ely</w> 72398
C aro 72394
acceler ate</w> 72387
V II</w> 72380
PK R</w> 72369
di chloro 72357
C ha 72337
micro wave</w> 72337
expla ining</w> 72336
Immunohisto chemical</w> 72335
Dele tion</w> 72329
I le</w> 72324
Hist ological</w> 72315
diagnos ing</w> 72304
sta phyloc 72297
dis appearance</w> 72255
d oxycycline</w> 72241
D oc 72238
k ill</w> 72227
intern alized</w> 72220
K ingdom</w> 72207
protein ases</w> 72195
special ist</w> 72188
7 b</w> 72171
ill i</w> 72160
ep ig 72127
U U 72126
sedim entation</w> 72121
techn ological</w> 72118
Regi stry</w> 72117
an olamine</w> 72113
ay ing</w> 72107
des orption</w> 72091
le af 72086
ch a 72084
GV HD</w> 72080
U p</w> 72070
ancho red</w> 72069
vit re 72034
ME NTS</w> 72031
ol ate</w> 72025
hor n</w> 72022
ul i 72018
id ene</w> 72018
bi phasic</w> 72008
nucle ation</w> 72003
In s 71994
Tra um 71985
sp orts</w> 71980
mono phosphate</w> 71979
tab lets</w> 71975
2 O</w> 71973
adv ice</w> 71966
ene m</w> 71918
A 7</w> 71913
depend ed</w> 71913
pre operatively</w> 71912
refr active</w> 71911
hem olytic</w> 71893
ag er</w> 71892
C GT 71886
depart ments</w> 71869
corticostero id</w> 71861
culti vation</w> 71859
c esarean</w> 71858
intell ectual</w> 71858
culti vated</w> 71854
p tive</w> 71852
mus carinic</w> 71850
Whit ney</w> 71849
Mic ha 71836
crystall ographic</w> 71832
pal ate</w> 71829
on azole</w> 71804
TC GA</w> 71803
T 5</w> 71798
L TR</w> 71797
concep tual</w> 71783
non sense</w> 71780
CH 2</w> 71779
as sumptions</w> 71762
lear n</w> 71762
somat ostatin</w> 71753
comp ete</w> 71750
itone ally</w> 71737
Inter net</w> 71707
sor afenib</w> 71704
bo y</w> 71700
Lt d</w> 71689
PD Z</w> 71679
Cam bridge</w> 71677
oneph ritis</w> 71648
R en 71647
di lation</w> 71646
N ik 71632
satis fied</w> 71626
O B</w> 71611
C K1</w> 71609
predic table</w> 71606
an eu 71604
ma ze</w> 71591
paren teral</w> 71587
ou ts</w> 71565
ati d</w> 71563
Ser vice</w> 71559
recogn izing</w> 71539
F AD</w> 71537
ti dine</w> 71530
trans plants</w> 71524
unc il</w> 71503
cap ill 71484
X X 71481
breast feeding</w> 71467
pollut ants</w> 71454
f ent 71441
photosyn thetic</w> 71438
n if 71426
2 Δ</w> 71425
mon ocyt 71420
Str at 71411
Ele ven</w> 71409
clinic opathological</w> 71393
gen otoxic</w> 71383
Meth od</w> 71378
flu idic</w> 71373
ali ve</w> 71371
c ue</w> 71366
phospho inosi 71366
sequel ae</w> 71364
we aning</w> 71355
cer vix</w> 71355
search es</w> 71336
re mia</w> 71324
Periph eral</w> 71314
oc ul 71311
energ etic</w> 71301
stero l</w> 71296
ogly co 71290
F oc 71287
benz o</w> 71285
NS 3</w> 71281
PD T</w> 71275
Mor tality</w> 71257
op re 71252
we b 71244
Ar e</w> 71234
ex em 71223
sug ars</w> 71214
neo adjuvant</w> 71208
K o 71207
I κB 71204
Ele vated</w> 71184
phot os 71176
e urin</w> 71170
filam entous</w> 71170
fram esh 71165
A H</w> 71163
toc in</w> 71142
emb olic</w> 71132
se men</w> 71131
monocyt ogenes</w> 71130
H3K 4 71122
O D6</w> 71115
pattern ing</w> 71110
E ar 71106
sub population</w> 71103
PA C</w> 71091
disc s</w> 71090
G 9</w> 71084
in domethacin</w> 71066
ST E 71066
Associ ated</w> 71066
A E 71060
ocarcin oma</w> 71059
Characteri s 71052
DR G</w> 71041
sep tum</w> 71024
Bio chemical</w> 71011
sc ram 71010
analy zer</w> 71010
plac es</w> 70996
U DP</w> 70989
co variates</w> 70982
shor tened</w> 70977
endoth elin</w> 70976
ul ins</w> 70969
v able</w> 70968
hy dri 70968
j ing</w> 70966
shed ding</w> 70926
MI M</w> 70920
AB I</w> 70919
T SC 70889
quad ru 70879
opin ion</w> 70872
cri bed</w> 70846
Pop ulation</w> 70836
ar yl 70834
inten tion</w> 70823
dis abilities</w> 70818
alg ae</w> 70816
intell ig 70812
accompan ying</w> 70802
ben ding</w> 70790
Cl us 70774
multi plex</w> 70769
Success ful</w> 70769
C K2</w> 70765
us p 70762
reconstitu tion</w> 70740
neuro psychological</w> 70726
caro teno 70726
characteri zing</w> 70725
Dou ble</w> 70721
W nt 70714
view ing</w> 70713
cathe terization</w> 70701
Ac cur 70699
P u 70692
gel atin</w> 70690
mil l</w> 70684
manif est</w> 70677
omat osis</w> 70676
guan osine</w> 70670
T im 70663
mon o</w> 70663
ureth ral</w> 70661
Spec tro 70660
phar yngeal</w> 70647
carbox ylic</w> 70646
pur ine</w> 70645
bri le</w> 70645
se mb 70620
kin son 70620
homogen ates</w> 70595
commit ment</w> 70590
R un 70586
A TION</w> 70585
e 1</w> 70564
al ge 70554
medic ines</w> 70547
ag ene</w> 70542
am en</w> 70538
micro RNAs</w> 70530
necess ity</w> 70526
de al</w> 70516
Dis eases</w> 70515
Pre gn 70509
nec ro 70508
ic al 70506
PC OS</w> 70504
C entre</w> 70501
m A</w> 70483
solubil ized</w> 70473
h a</w> 70467
inf ancy</w> 70466
A 2 70463
Bri tish</w> 70457
Cardi ovascular</w> 70446
oste ogenic</w> 70425
Leishman ia</w> 70413
O per 70412
noc tur 70412
ket amine</w> 70391
TH P</w> 70378
but yl</w> 70371
inter neurons</w> 70364
min us</w> 70356
wild type</w> 70353
sulph ate</w> 70351
prof en</w> 70343
meth oxy</w> 70335
ri de</w> 70331
adhe sions</w> 70311
MD CK</w> 70304
pul p</w> 70303
s agittal</w> 70299
de oxy</w> 70299
ec onom 70287
bor derline</w> 70283
ob stac 70279
os se 70273
ensi tive</w> 70270
D Y 70269
Wil d</w> 70265
occa sionally</w> 70259
ri m</w> 70242
R ock 70230
grap h</w> 70230
tic ated</w> 70212
ing redi 70212
B N</w> 70211
po real</w> 70200
af il 70194
lys osome</w> 70193
uni on</w> 70190
approxim ate</w> 70188
HN SCC</w> 70154
ar restin</w> 70139
y n</w> 70138
bi ore 70128
ME S</w> 70101
Ser 1</w> 70101
am phen 70100
ocardi al</w> 70090
Phy l 70083
ogly can</w> 70081
o chemical</w> 70076
prec eded</w> 70068
pro kary 70058
j i</w> 70057
scop ically</w> 70045
vin yl 70039
sedi ments</w> 70025
h id 70022
long evity</w> 70016
poin ted</w> 70001
earli est</w> 69999
med ullary</w> 69998
Cl 3</w> 69986
embry ogenesis</w> 69985
Fif teen</w> 69979
sem ble</w> 69973
in equ 69970
5 R</w> 69960
N inety</w> 69949
c ub 69930
arach noid</w> 69925
strep tavidin</w> 69911
D ar 69891
d ym 69888
exp iratory</w> 69879
complem ented</w> 69879
k Hz</w> 69876
chamb ers</w> 69865
alcoh ol 69862
it ted</w> 69822
ME NT</w> 69819
mal e 69815
opath ologic</w> 69815
adenocarcin omas</w> 69815
anti sera</w> 69813
sero types</w> 69809
mo toneu 69803
con found 69790
fail ures</w> 69777
extrem ities</w> 69768
B AC</w> 69750
emul sion</w> 69750
ri t</w> 69742
th y 69740
an der</w> 69734
bu d</w> 69734
ar abin 69733
1 S</w> 69729
lymph oblastic</w> 69724
ste ric</w> 69716
ol us</w> 69715
te zomib</w> 69714
inter active</w> 69713
M em 69695
U AS</w> 69691
op eptide</w> 69690
F ail 69679
stoch astic</w> 69668
epsil on</w> 69666
sc anned</w> 69653
PC P</w> 69653
erox idase</w> 69615
Dis cus 69584
PT G</w> 69579
tr apping</w> 69575
d p 69574
Re duced</w> 69574
fil tering</w> 69570
K en 69550
parenchym a</w> 69525
benz yl</w> 69524
anticoagul ant</w> 69521
casp ases</w> 69521
col our</w> 69517
t t</w> 69511
Lev el</w> 69501
osi tis</w> 69499
tetram er</w> 69493
ed om</w> 69492
potenti ated</w> 69482
Gr ade</w> 69482
as certain</w> 69472
TK Is</w> 69471
A CT</w> 69467
ep inephrine</w> 69454
fo s</w> 69451
extr insic</w> 69447
i tion</w> 69446
F LI 69446
b and 69432
br ing</w> 69432
C CL2</w> 69430
ri tic</w> 69419
ch or 69403
lnc RNAs</w> 69401
iz ational</w> 69397
Apop tosis</w> 69395
it ro 69388
His 6</w> 69371
ad duct</w> 69364
E ch 69359
ith elial</w> 69355
ser ving</w> 69349
l as</w> 69340
amphen icol</w> 69331
prop ranolol</w> 69326
ri fam 69318
con ti 69305
constra ined</w> 69298
terran ean</w> 69286
PL A</w> 69283
pri mates</w> 69282
vap or</w> 69272
id ectomy</w> 69270
L ower</w> 69262
Rep e 69260
thym ocytes</w> 69252
catal og</w> 69248
es thetic</w> 69231
specific ities</w> 69228
persi st</w> 69227
mal formation</w> 69217
Rec eptor</w> 69214
M AR 69208
P ak 69199
simpl ified</w> 69196
A SA</w> 69194
melan omas</w> 69193
transpos on</w> 69192
Ne g 69187
oupl ing</w> 69186
be ad</w> 69182
fer ring</w> 69182
R S 69180
Mex ico</w> 69174
cal orim 69169
ren sic</w> 69166
chol angi 69164
poin ting</w> 69159
depic ted</w> 69144
G O 69126
ar bit 69126
mercapto ethanol</w> 69121
F D</w> 69115
rin sed</w> 69103
S anger</w> 69096
chlo rophyl 69093
micro arrays</w> 69088
mig rate</w> 69056
colle ge</w> 69051
deoxy chol 69050
W ar 69042
Swe dish</w> 69027
kin je</w> 69020
C ac 69002
fluo roph 68978
p ran 68966
transi t</w> 68954
adap t</w> 68942
prof loxacin</w> 68925
un favorable</w> 68910
conn ecting</w> 68909
intraper itoneally</w> 68898
isch aemic</w> 68886
hyp oc 68882
inter ventional</w> 68880
AK I</w> 68879
Le ft</w> 68877
ple gia</w> 68872
con temporary</w> 68854
M 0</w> 68850
fac ulty</w> 68848
hyd rates</w> 68843
inf requ 68839
g ation</w> 68827
Stre ptom 68822
p ups</w> 68817
ep tors</w> 68815
uniform ly</w> 68799
distur bed</w> 68787
cl ients</w> 68784
ro unds</w> 68783
di hydroxy 68768
du plicate</w> 68755
4 S</w> 68747
len ses</w> 68744
centro some</w> 68742
8 -</w> 68739
alb umin 68735
carbox y</w> 68731
do ts</w> 68720
Co ronary</w> 68699
PIK3 CA</w> 68681
n ested</w> 68678
tho rough</w> 68668
Quanti fication</w> 68650
regurg itation</w> 68649
rheum atic</w> 68645
entrop y</w> 68645
resid ence</w> 68587
antagon ism</w> 68574
con sequent</w> 68567
st and</w> 68545
le ak</w> 68537
GE F</w> 68535
spac es</w> 68514
ocyan ate</w> 68513
NL RP3</w> 68511
CEN P</w> 68508
Sp ont 68496
di thio 68491
b an 68464
P ER 68460
E v 68460
Emer gency</w> 68458
obar bital</w> 68450
Syste mic</w> 68447
trunc ation</w> 68436
F ull</w> 68432
pa id</w> 68431
app endic 68420
m p</w> 68414
n u 68414
ex ocytosis</w> 68412
configur ations</w> 68411
prim e</w> 68410
MEASU RE 68405
dro ught</w> 68401
oper ator</w> 68401
Cor rec 68396
defini tions</w> 68393
R 0</w> 68386
P el 68386
secre te</w> 68385
Pur kinje</w> 68378
dev oid</w> 68376
P as 68367
Fem ale</w> 68365
scaveng ing</w> 68356
b ent</w> 68349
flavon oids</w> 68337
ic ating</w> 68332
ap atite</w> 68330
sp aring</w> 68321
ul timate</w> 68305
E GF 68294
Tol l</w> 68292
shap es</w> 68286
ST s</w> 68280
4 h</w> 68261
f are</w> 68245
o idal</w> 68243
agg ression</w> 68228
T X 68219
agglutin ation</w> 68206
reser ve</w> 68195
amni otic</w> 68195
tri fluoro 68190
obacteri um</w> 68185
4 p</w> 68173
coll ecting</w> 68159
syn cy 68149
Di etary</w> 68147
eosinoph ils</w> 68139
en sur 68115
Cd k 68107
spac er</w> 68107
El mer</w> 68104
off ice</w> 68070
recur rences</w> 68054
ep idi 68050
6 R</w> 68027
P SD</w> 68002
cu red</w> 67992
M b</w> 67968
Pos sible</w> 67968
od omain</w> 67961
ther ing</w> 67956
K r 67946
ovari es</w> 67944
R MS 67938
In s</w> 67934
infu sions</w> 67932
orig inated</w> 67929
ron ate</w> 67925
m ere</w> 67921
B lack</w> 67912
bio physical</w> 67909
hyste rectomy</w> 67908
C AB 67905
Ac id</w> 67899
gir l</w> 67898
bat tery</w> 67893
possess ed</w> 67873
Clostri dium</w> 67865
rhyth ms</w> 67853
Bas eline</w> 67833
ec k 67819
per tussis</w> 67819
N ano 67810
pro sta 67801
was hes</w> 67797
z ole</w> 67787
on duc 67779
cytom etric</w> 67779
is te 67776
anis otropy</w> 67773
Swit zer 67747
al location</w> 67737
wri st</w> 67728
mil itary</w> 67725
Fig. 3</w> 67715
fl ash</w> 67714
sp in 67681
bacteri oph 67674
exem pl 67662
graph s</w> 67651
stress ors</w> 67642
der al</w> 67632
biom echanical</w> 67628
man d 67614
micro mol</w> 67613
M CI</w> 67606
pan demic</w> 67603
Switzer land</w> 67595
O SA</w> 67594
de dicated</w> 67581
hi t</w> 67576
Met a</w> 67562
ell er</w> 67546
ris ing</w> 67541
neuro pathic</w> 67536
hypo thermia</w> 67532
evolution arily</w> 67530
Cr yp 67527
di strict</w> 67522
auto immunity</w> 67517
mutagen ic</w> 67515
Signific antly</w> 67495
pestic ides</w> 67489
sp ines</w> 67484
rac ial</w> 67484
ocy sts</w> 67471
agg res 67471
punc ture</w> 67467
fluoro uracil</w> 67425
GAT A</w> 67414
T AC 67412
hypo thyroidism</w> 67407
trac ted</w> 67391
real istic</w> 67391
me trics</w> 67383
S he 67363
Cycl in</w> 67353
calcul ating</w> 67351
Vir al</w> 67340
Tr ac 67336
micro spheres</w> 67332
d T</w> 67327
C IN 67307
ke ep</w> 67300
op reser 67296
analy tic</w> 67280
um e</w> 67270
B U 67267
Pro bes</w> 67259
epithel ia</w> 67256
par allel 67243
chlor o</w> 67243
famili ar</w> 67241
di one</w> 67233
pel le 67218
vic inity</w> 67208
Ther mo 67200
Co uncil</w> 67195
F As</w> 67190
gr ass</w> 67182
main tains</w> 67180
H ind 67172
k m</w> 67172
homolog ues</w> 67154
wa ist</w> 67153
conver t</w> 67136
g ht</w> 67135
bri o</w> 67134
dis on</w> 67117
up dated</w> 67117
Pl ant</w> 67112
1 alpha</w> 67104
hydrox ylation</w> 67100
G am 67086
R ic 67084
te worthy</w> 67080
Zn O</w> 67079
aspar ag 67076
tw ins</w> 67068
fi g</w> 67060
dise ased</w> 67055
br uc 67054
denti n</w> 67046
elong ated</w> 67038
c n 67030
Resi stance</w> 67028
fel t</w> 67027
mang anese</w> 67025
bridg es</w> 67019
scinti graphy</w> 67015
end er</w> 66999
nit ro</w> 66997
interpre t</w> 66996
degrad ing</w> 66983
P PAR</w> 66980
percenti le</w> 66964
bi um</w> 66959
H h</w> 66955
har m</w> 66943
or poreal</w> 66932
Olym pus</w> 66920
ill ing</w> 66912
tur ning</w> 66884
' t</w> 66882
S AS</w> 66879
disrup ts</w> 66879
arbit r 66878
R p 66873
ag ree</w> 66870
ascor bic</w> 66827
g ol 66825
C ho 66823
fore brain</w> 66813
hol ding</w> 66800
pa tency</w> 66775
encour aging</w> 66772
sched uled</w> 66769
altern atives</w> 66742
occip ital</w> 66736
S en 66733
G AG</w> 66728
Con di 66727
G 6 66723
H an 66714
doc tor</w> 66709
anch or</w> 66708
mas k</w> 66702
T c 66691
mechan ics</w> 66677
hemat oma</w> 66671
IR F</w> 66658
Stra teg 66657
carni tine</w> 66654
AP S</w> 66651
ra ft</w> 66646
be red</w> 66637
MD D</w> 66634
cal pain</w> 66624
ket o 66620
an in</w> 66619
Aut o 66618
bas olateral</w> 66613
su ited</w> 66601
dis placed</w> 66582
C e 66553
ST 1</w> 66547
t ar 66545
dis abl 66531
out flow</w> 66530
eno ic</w> 66516
. com</w> 66508
vul g 66508
e stration</w> 66503
SH V</w> 66497
gen omics</w> 66492
N AM 66481
cy tic</w> 66458
Pre operative</w> 66458
e igh 66456
con gru 66456
k man</w> 66455
h ot 66449
adrenoc eptor</w> 66445
C P2</w> 66444
glob ally</w> 66439
phag ia</w> 66420
re organization</w> 66416
oci ties</w> 66413
sial ic</w> 66405
morph ologically</w> 66399
ten s</w> 66396
acu tely</w> 66394
i ens</w> 66383
curric ulum</w> 66379
V L</w> 66353
G AD 66350
E PR</w> 66339
explic it</w> 66333
Relationsh ip</w> 66325
immort alized</w> 66323
M ass 66312
imp acted</w> 66291
trac ts</w> 66289
S um 66286
hyp er</w> 66282
decom pression</w> 66264
Clin ic</w> 66259
do t</w> 66237
Hy bri 66234
g ing</w> 66230
Schem e</w> 66220
bio informatics</w> 66207
a pro 66196
c rest</w> 66195
Coll ection</w> 66195
glyc emic</w> 66191
sol ving</w> 66186
AL P</w> 66186
suic idal</w> 66182
equili brated</w> 66154
th aw 66139
ili ated</w> 66134
glu con 66131
ap amil</w> 66125
emphasi zed</w> 66114
op en 66111
invasi veness</w> 66111
tit ude</w> 66109
P d 66108
re ds</w> 66106
hum idity</w> 66105
N CB 66098
homogen ized</w> 66092
qui escent</w> 66088
ob lig 66069
fore arm</w> 66064
d an 66059
cholestero le 66050
b umin</w> 66040
Im plications</w> 66030
in ward</w> 66020
glutam ic</w> 66018
re distribution</w> 66010
Uni ver 66005
V C</w> 65992
contrac eptive</w> 65989
macro scopic</w> 65968
SU BJ 65966
de grade</w> 65964
resem bles</w> 65957
H G 65950
dent ate</w> 65947
6 G</w> 65939
P HO 65936
attit ude</w> 65920
prim ate</w> 65906
omorph ic</w> 65904
on ium</w> 65889
hum idi 65878
Gal 4</w> 65869
S1 P</w> 65865
be sides</w> 65834
eng raf 65833
Au th 65833
Adv anced</w> 65808
m g 65802
she ath</w> 65801
x L</w> 65798
SI GN 65796
den er 65796
bridg ing</w> 65794
Random ized</w> 65775
c c</w> 65768
epidemi ologic</w> 65765
jo ining</w> 65743
Strat agene</w> 65743
DN As</w> 65732
vel ocities</w> 65730
un translated</w> 65728
dec ided</w> 65720
M Ps</w> 65713
specific ation</w> 65704
resem bling</w> 65695
ac in 65686
epidi dym 65683
Kn ock 65671
Investig ation</w> 65670
Min im 65668
le i 65665
cop ic</w> 65664
D B 65654
T SA</w> 65630
R UN 65625
T om 65619
histopath ology</w> 65613
Memb rane</w> 65613
h p 65596
g in 65584
ri dine</w> 65583
yl transferase</w> 65580
Sh ang 65580
chloro plast</w> 65576
M AC</w> 65571
IV F</w> 65570
htt ps</w> 65567
ref er</w> 65563
en semble</w> 65523
VE GFR</w> 65521
ph i</w> 65511
Onc ology</w> 65507
V acc 65482
ob viously</w> 65481
J AK</w> 65472
st atins</w> 65467
incre mental</w> 65461
conflu ent</w> 65444
sk olin</w> 65441
penetr ating</w> 65425
CC D</w> 65419
ati zation</w> 65410
loc omo 65408
plac ing</w> 65369
Al ph 65357
E stim 65355
l uminescence</w> 65348
f us 65343
phag ocytic</w> 65336
rh esus</w> 65335
protot ype</w> 65335
bund les</w> 65328
ul ae</w> 65327
s ap 65324
sub stitute</w> 65317
mening i 65309
Pur ification</w> 65307
mo ved</w> 65289
cho ices</w> 65280
bl unt</w> 65278
transform ants</w> 65278
replic ative</w> 65246
P ax 65220
r 2</w> 65206
P SC</w> 65193
O VA</w> 65188
implem enting</w> 65187
ov a</w> 65186
K I 65171
de stro 65166
te ar</w> 65158
SM AD 65156
Contro lled</w> 65154
i ence</w> 65144
ana phase</w> 65127
pa tent</w> 65098
trop ic</w> 65090
st yle</w> 65082
I RE 65081
o chrom 65078
sput um</w> 65067
PT X</w> 65066
swit ched</w> 65064
pre disposition</w> 65063
Ma terial</w> 65059
in nervation</w> 65042
hyp ere 65035
echocardi ographic</w> 65032
pl ei 65023
immer sion</w> 65019
o esophageal</w> 65007
sim ulate</w> 65007
f air 65006
D L</w> 64995
K ey</w> 64987
IF ICA 64968
o ic</w> 64966
panc ies</w> 64957
ion izing</w> 64944
Lac tob 64938
acetyl transferase</w> 64934
G astro 64927
hypoglyc emia</w> 64907
coll ect</w> 64905
leuk em 64901
ne o</w> 64900
0 K</w> 64895
U GT 64892
qualit atively</w> 64888
pharmac ology</w> 64885
an ia</w> 64879
oder mal</w> 64872
ste ctomy</w> 64856
val ves</w> 64844
R ou 64840
i 1</w> 64819
gam ma 64816
aut ocrine</w> 64799
S pe 64794
trabec ular</w> 64787
e GFP</w> 64775
s ulation</w> 64773
Classi fication</w> 64767
Transcrip tion</w> 64756
hetero chromatin</w> 64752
v ox 64735
P 9</w> 64720
swim ming</w> 64717
ari an</w> 64708
as tically</w> 64701
mid line</w> 64694
Pur ified</w> 64692
ic um</w> 64684
M uc 64678
P ul 64678
it ative</w> 64673
ot y 64672
form ulated</w> 64655
deep er</w> 64652
Cal cium</w> 64649
anti proliferative</w> 64639
pass ages</w> 64634
periodon titis</w> 64634
an us</w> 64613
war m</w> 64603
Con sis 64601
Spont aneous</w> 64601
h ope</w> 64599
un paired</w> 64599
y our</w> 64586
S ar 64582
K 7</w> 64581
disp arities</w> 64579
docum entation</w> 64578
person alized</w> 64564
co al</w> 64543
a o</w> 64542
th ec 64526
ate dly</w> 64526
AG A</w> 64524
bronch i 64507
anc ing</w> 64506
Anti body</w> 64506
t z</w> 64499
E st 64499
Stim ulation</w> 64495
M oder 64494
Mg 2</w> 64490
sulf oxide</w> 64485
hel ped</w> 64456
chloro quine</w> 64454
c r</w> 64451
T AP</w> 64451
encomp assing</w> 64443
Car cin 64441
do si 64436
Con genital</w> 64434
meth an 64433
V DR</w> 64429
trans m 64425
pon in</w> 64423
F us 64419
stain able</w> 64419
Lap aro 64400
qu ercetin</w> 64399
robu st 64399
gra vity</w> 64397
immun ogenic</w> 64389
m argin 64375
ec osystem</w> 64374
un controlled</w> 64356
li poly 64348
cle rosis</w> 64346
I Q</w> 64341
medul la</w> 64341
te can</w> 64340
ol uminescence</w> 64339
eli oma</w> 64327
HMG B1</w> 64326
thre itol</w> 64313
calcin eurin</w> 64297
promp t</w> 64278
distinguish ing</w> 64277
H CT</w> 64255
bio films</w> 64252
TA M</w> 64246
seman tic</w> 64238
con fluence</w> 64230
depri ved</w> 64230
ca ution</w> 64228
mim ic 64220
Ob serv 64217
Gluc ose</w> 64217
cit r 64215
Isol ated</w> 64194
accum ulates</w> 64188
T MS</w> 64186
Ad olesc 64176
represent ations</w> 64172
MEASURE MENTS</w> 64170
el bow</w> 64166
y mo 64163
auto phosphorylation</w> 64129
id ol</w> 64125
fol i 64123
par a</w> 64121
per i</w> 64115
Y ang</w> 64114
CD D</w> 64107
parac rine</w> 64088
d b</w> 64087
3 L</w> 64064
un labeled</w> 64060
ov ulation</w> 64057
Ur inary</w> 64054
glucocortico ids</w> 64053
cataly tically</w> 64044
Hip p 64037
method ologies</w> 64031
R M</w> 64018
ul ator</w> 64014
was h 64014
un covered</w> 64011
res ses</w> 63997
com mittee</w> 63994
super vised</w> 63992
surviv in</w> 63965
cad aver 63965
F 8</w> 63964
Radi ation</w> 63961
Q I 63954
sequ estration</w> 63954
sub clinical</w> 63946
Fe w</w> 63943
discus sions</w> 63926
her bal</w> 63925
cu ff</w> 63924
un identified</w> 63923
sol itary</w> 63915
AC A</w> 63898
AA G</w> 63896
ven a</w> 63884
capac ities</w> 63880
FE V1</w> 63878
SP s</w> 63874
CTL A</w> 63874
ater ally</w> 63866
cyclo oxygenase</w> 63860
recru its</w> 63849
cyclohex imide</w> 63844
me m 63842
SN ARE</w> 63841
ill nesses</w> 63836
anti coagulation</w> 63834
ann exin</w> 63817
adi um</w> 63807
H M</w> 63797
PL A2</w> 63794
stoichi ometry</w> 63792
SC D</w> 63785
endocardi tis</w> 63779
gon dii</w> 63773
D utch</w> 63761
als. gov</w> 63754
fav ored</w> 63735
found ation</w> 63735
resem ble</w> 63726
AV P</w> 63712
Re duction</w> 63705
ar te 63704
im umab</w> 63700
ari o</w> 63697
Biosci ence</w> 63695
sty rene</w> 63689
or ange</w> 63683
Hepati tis</w> 63668
asth matic</w> 63658
steri li 63652
H ER</w> 63644
V IP</w> 63637
ti ps</w> 63616
re actor</w> 63604
o vi 63602
sor tium</w> 63593
H5 N1</w> 63591
emerg e</w> 63588
immun ologic</w> 63582
AS C</w> 63582
Ir an</w> 63581
arti facts</w> 63577
Medi terranean</w> 63576
polar izing</w> 63570
For m 63570
preser ve</w> 63567
Shang hai</w> 63562
S kin</w> 63558
ren ol</w> 63551
S po 63546
MI P</w> 63540
ca emia</w> 63526
Gro ups</w> 63518
mis folded</w> 63511
kin em 63508
amorph ous</w> 63505
F E</w> 63494
MP O</w> 63494
dr ying</w> 63487
eIF 4E</w> 63477
opy ran 63475
NP Y</w> 63468
prosta tectomy</w> 63466
semic onduc 63463
cou ple</w> 63460
vacu oles</w> 63449
pa ro 63439
search ing</w> 63437
P RI 63426
alumin um</w> 63418
canc erous</w> 63409
chlor amphenicol</w> 63386
s A</w> 63380
op y</w> 63379
Tex as</w> 63378
ever y 63366
P ancre 63352
TM Z</w> 63335
ingu inal</w> 63333
A verage</w> 63319
D ual</w> 63319
polymer ases</w> 63304
wa ter 63299
I l 63298
d ac 63283
0 M</w> 63282
lay ered</w> 63279
om in 63269
tri bu 63262
su red</w> 63261
cam pa 63259
oglyc ans</w> 63245
tag s</w> 63243
ti te</w> 63236
approxim ation</w> 63235
Is ra 63218
thick ening</w> 63213
distinc tion</w> 63206
pros theses</w> 63206
puber tal</w> 63202
VE C</w> 63201
en tering</w> 63191
toc oph 63181
unde restim 63173
intrav ascular</w> 63159
ci profloxacin</w> 63134
back grounds</w> 63109
il in</w> 63106
multi functional</w> 63093
atten ded</w> 63070
thalam us</w> 63053
mit ting</w> 63043
5 G</w> 63042
con clusive</w> 63039
7 R</w> 63022
gen it 63019
ju ice</w> 63004
G h 62990
SUBJ ECTS</w> 62987
o u</w> 62949
h its</w> 62943
he igh 62939
cobal t</w> 62931
instrum ental</w> 62914
na ph 62911
Tri als</w> 62888
ky o</w> 62888
ec ond</w> 62879
bri ght</w> 62867
C RP 62865
sc anner</w> 62862
b FGF</w> 62843
N K1</w> 62835
Bo ard</w> 62833
hyp og 62832
di chro 62821
autom atically</w> 62813
hem agglutinin</w> 62812
2 d</w> 62807
rec tum</w> 62807
ra is 62805
altern atively</w> 62800
A z 62792
vasodi l 62791
migr ated</w> 62788
ver satile</w> 62786
TI MP</w> 62779
pom be</w> 62771
F g 62768
olig o</w> 62768
synchron ized</w> 62756
P RA 62755
ph atic</w> 62747
Del ta</w> 62736
T W 62724
Inc idence</w> 62717
con es</w> 62705
Ann exin</w> 62701
nan otubes</w> 62678
ed ullary</w> 62676
arg um 62664
organ izational</w> 62660
V ero</w> 62644
reversi bly</w> 62636
electro spray</w> 62631
domin ance</w> 62614
ae rosol</w> 62605
orth og 62604
kin esin</w> 62594
ron ch 62586
ex chang 62582
CI S</w> 62579
papill omavirus</w> 62577
oxy tocin</w> 62575
nan om 62572
ter in</w> 62563
ox etine</w> 62561
cyto kinesis</w> 62530
ene di 62522
ex tern 62521
sol ve</w> 62503
succin ate</w> 62493
RAG E</w> 62490
S ho 62489
H N</w> 62487
adop tion</w> 62482
un di 62441
min i 62434
u ation</w> 62430
ak ary 62426
ch in</w> 62424
bre vi 62403
accid ent</w> 62386
strep to 62385
an ni 62372
nod ule</w> 62372
Sw iss</w> 62354
Environ mental</w> 62350
re rs</w> 62349
ome galy</w> 62348
IC A</w> 62344
state ment</w> 62338
iz able</w> 62336
preser ving</w> 62336
iP SCs</w> 62313
special ty</w> 62306
osteoc last</w> 62302
com positions</w> 62295
ob sc 62284
denat ured</w> 62283
ten ess</w> 62279
Ve ter 62252
prob ing</w> 62249
Discus sion</w> 62246
acyl glycerol</w> 62243
vin yl</w> 62238
repe atedly</w> 62227
ter rit 62221
Stro ke</w> 62212
carbo hydrates</w> 62211
SIGN IFICA 62204
P il 62182
mal nutrition</w> 62176
PP V</w> 62176
capill aries</w> 62172
g ates</w> 62167
Immun ob 62162
F I</w> 62159
N F1</w> 62149
cle an</w> 62136
synerg istically</w> 62131
Com mon</w> 62119
post ural</w> 62117
contamin ants</w> 62114
I PTG</w> 62110
al isation</w> 62103
To kyo</w> 62100
dro plet</w> 62098
qu ick</w> 62097
fe brile</w> 62093
lid ocaine</w> 62084
compati bility</w> 62074
muc us</w> 62073
T GT 62066
intro ns</w> 62059
sh rin 62057
AC L</w> 62034
synchron ous</w> 62032
if ies</w> 62031
refl ex 62025
vo ice</w> 62020
Ep stein</w> 62009
Figure 1</w> 62006
Bar r</w> 62000
dis pers 61985
semin al</w> 61983
Neg ative</w> 61983
D ental</w> 61981
M cl</w> 61973
pre eclampsia</w> 61973
co w</w> 61959
G ran 61953
iP SC</w> 61953
amin idase</w> 61951
T Y 61926
carb on 61919
aliquo ts</w> 61918
ul ing</w> 61906
DE 3</w> 61906
hybridi zed</w> 61904
EC D</w> 61888
responsi bility</w> 61886
pran dial</w> 61874
s ac</w> 61868
nucle osomes</w> 61868
erc ise</w> 61868
ent rifug 61860
qu asi</w> 61856
Sc ot 61833
V ic 61829
cohe rent</w> 61804
som es</w> 61793
Pac ific</w> 61785
detox ification</w> 61785
d ural</w> 61772
ure ly</w> 61772
P h</w> 61771
t ness</w> 61767
In duced</w> 61762
ecti ns</w> 61748
SIGNIFICA NCE</w> 61747
K L</w> 61734
HSP 9</w> 61727
assum ing</w> 61719
rit uximab</w> 61716
st ments</w> 61700
B MD 61697
sulf ide</w> 61689
ho used</w> 61683
qu ely</w> 61679
per s</w> 61668
umb er</w> 61664
sel en 61658
ACh E</w> 61655
graph ic</w> 61654
cet uximab</w> 61653
V ie 61652
com posites</w> 61652
HP V1</w> 61651
NK L</w> 61640
proble matic</w> 61634
du plic 61620
sl ide</w> 61617
D X 61613
J . 61606
ed ings</w> 61600
PL C 61599
M o</w> 61594
oligos accharides</w> 61585
noctur nal</w> 61579
loc k</w> 61564
un c</w> 61562
y metri 61554
0 b</w> 61552
SH P</w> 61549
scat tered</w> 61545
corne a</w> 61545
C an</w> 61542
nec rop 61534
fro g</w> 61532
T N</w> 61525
vibr ational</w> 61517
M ill 61514
age -</w> 61503
arteri ovenous</w> 61498
ab s</w> 61495
Re action</w> 61489
assemb lies</w> 61486
P HA</w> 61450
intox ication</w> 61420
dithio threitol</w> 61418
D AT</w> 61411
blo od 61404
per iton 61403
im plication</w> 61392
cortis one</w> 61382
G 7</w> 61370
un necessary</w> 61368
Y 7</w> 61367
anti psychotic</w> 61366
is lands</w> 61362
S Q 61348
col lateral</w> 61348
Prepar ation</w> 61344
pre requisite</w> 61341
Libr ary</w> 61339
sup er</w> 61332
GAL 4</w> 61321
C ases</w> 61304
h an</w> 61299
on uc 61297
ste atosis</w> 61297
IκB α</w> 61296
PMS F</w> 61293
locomo tion</w> 61291
HSP 7</w> 61284
hepat oma</w> 61278
En g 61272
st on</w> 61264
9 C</w> 61262
Pre treatment</w> 61250
meas les</w> 61244
My D8</w> 61227
pu romycin</w> 61207
sub optimal</w> 61205
Fail ure</w> 61193
ent um</w> 61191
medias tinal</w> 61190
D Q 61180
junc tional</w> 61179
se tu 61177
P ic 61165
isom er</w> 61164
mol ars</w> 61163
Not e</w> 61158
auth en 61155
NCB I</w> 61155
nicotin ic</w> 61153
E W</w> 61146
A di 61145
prior iti 61138
D ynamic</w> 61111
id ative</w> 61102
vi bration</w> 61092
kinetoch ore</w> 61087
s na 61083
thir ds</w> 61079
tri gem 61073
prot ons</w> 61062
fi l</w> 61049
syn ergy</w> 61043
ra ph 61039
mic ally</w> 61023
Lactob acillus</w> 61012
til l</w> 61011
co incid 61005
Ac t</w> 61004
men is 61001
. 8</w> 60996
peroxis ome</w> 60987
Individ uals</w> 60985
dis comfort</w> 60982
sme ar</w> 60974
en ess</w> 60973
CA S</w> 60972
counter part</w> 60967
9 m</w> 60964
te tan 60964
pneum ococcal</w> 60953
sa icin</w> 60945
st ocks</w> 60941
inter cal 60937
ume tric</w> 60925
O V</w> 60922
post translational</w> 60913
ud ine</w> 60911
es teri 60904
ad ed</w> 60904
D e</w> 60901
arrang ed</w> 60889
A RE 60878
cl amp 60862
esti n</w> 60862
end or 60849
che m</w> 60845
MC L</w> 60829
mu mol</w> 60821
Leu k 60813
gran ulation</w> 60804
laparo tomy</w> 60803
CA A</w> 60802
res ectable</w> 60798
cl ient</w> 60796
n ation 60791
fir e</w> 60788
rhod opsin</w> 60787
fre ely</w> 60777
veget ables</w> 60761
re alized</w> 60756
dis continued</w> 60729
graf ted</w> 60725
Cb l</w> 60723
nod ular</w> 60718
intram uscular</w> 60716
G PI</w> 60713
W D</w> 60671
tradi tionally</w> 60666
0 mg</w> 60653
M ag 60651
ymetri x</w> 60646
tra vel 60640
o b</w> 60637
thir ty</w> 60610
pix el</w> 60609
d ying</w> 60602
co oled</w> 60597
R 7</w> 60594
T S 60578
neuron es</w> 60568
ch al 60530
en teri 60529
CT A</w> 60523
aller gens</w> 60523
Mat rigel</w> 60521
accid ents</w> 60521
im etic</w> 60516
M 6</w> 60508
hist ocompatibility</w> 60507
P le 60505
disc ord 60500
SA H</w> 60495
inte rested</w> 60493
ra fts</w> 60491
worsen ing</w> 60488
wri ting</w> 60487
ron al</w> 60477
ty ros 60468
H AT</w> 60464
neph rectomy</w> 60459
robust ness</w> 60453
0 L</w> 60452
T 1 60449
X ho 60442
AR 1</w> 60440
In trigu 60436
antagon istic</w> 60432
angi ographic</w> 60423
special ists</w> 60422
H SA</w> 60416
cyclospor ine</w> 60398
scienti sts</w> 60388
da ughter</w> 60387
po ul 60386
e osin</w> 60385
in activating</w> 60361
Rh od 60353
A gg 60346
lea ve</w> 60336
combin atorial</w> 60328
collo idal</w> 60326
anomal ous</w> 60313
G B 60310
endomet rium</w> 60310
AB P</w> 60306
y i</w> 60298
Lip id</w> 60284
cot ton</w> 60273
tin s</w> 60272
muc in</w> 60272
ly l</w> 60267
al op 60263
inspec tion</w> 60260
Ob esity</w> 60246
appear ing</w> 60245
sc rap 60235
AP s</w> 60235
im a</w> 60227
on co 60218
D B</w> 60212
comorbi d</w> 60202
H 4 60201
Aff ymetrix</w> 60181
in ally</w> 60178
ran ked</w> 60174
pelle ted</w> 60167
d ay 60162
fre edom</w> 60162
ocy toma</w> 60159
radi ography</w> 60159
cry opreser 60157
Immun e</w> 60151
wal k</w> 60138
IFN γ</w> 60133
vacu ole</w> 60126
Sle ep</w> 60124
CHO P</w> 60115
harv esting</w> 60114
Cy t 60099
sha king</w> 60096
ab is</w> 60082
C aspase</w> 60075
m t</w> 60070
ond on</w> 60067
fibrill ar</w> 60065
Fig. 4</w> 60055
acidi fication</w> 60053
Four teen</w> 60052
sk eleton</w> 60040
propor tion 60040
coll ective</w> 60033
is othi 60017
2 K</w> 60007
op posing</w> 59999
swe red</w> 59969
chlorophyl l</w> 59968
cholecy stectomy</w> 59964
oste otomy</w> 59953
E SR 59951
Ad he 59949
Eth ics</w> 59943
on i 59940
collabor ative</w> 59939
de hydration</w> 59929
um b</w> 59917
ro ad</w> 59914
R u</w> 59910
co oper 59910
G PR 59907
O G</w> 59900
te ams</w> 59893
8 R</w> 59885
pacem aker</w> 59878
initi ates</w> 59872
denat uring</w> 59872
chol est 59862
hydro carbons</w> 59861
L IN 59841
Th yro 59839
ercul osis</w> 59836
a 3</w> 59834
theore tically</w> 59812
Transi ent</w> 59811
Table 2</w> 59803
yn e</w> 59800
Re f</w> 59783
nemat ode</w> 59771
T Fs</w> 59762
vacu olar</w> 59754
so ph 59753
cle an 59750
ste m 59748
un ting</w> 59747
Inj ury</w> 59739
ana emia</w> 59733
Consis tently</w> 59727
rhe a</w> 59726
gri d</w> 59725
l li 59710
PD A</w> 59705
NO D</w> 59701
fa ec 59701
anc estr 59700
gro und 59690
intr ad 59690
D BP</w> 59683
uni quely</w> 59670
synap t 59664
pa rox 59663
tri mer</w> 59663
TA L</w> 59642
a g</w> 59637
plan k 59631
sed entary</w> 59630
f er</w> 59618
Resp iratory</w> 59612
F DR</w> 59584
L VE 59573
F R 59567
brachi al</w> 59563
spec ially</w> 59562
nan ow 59555
F TIR</w> 59544
I a</w> 59543
tri meric</w> 59525
out patients</w> 59521
sto ol</w> 59516
su stain</w> 59514
trans position</w> 59489
eIF 2α</w> 59481
AN P</w> 59460
Na OH</w> 59459
op enia</w> 59435
ne ar 59431
linear ity</w> 59431
rh initis</w> 59429
Sy n</w> 59423
ton si 59417
sho t</w> 59403
an swered</w> 59397
Regi onal</w> 59396
Co V</w> 59390
nan ocom 59382
in takes</w> 59377
RA NKL</w> 59376
um oral</w> 59371
Simult aneous</w> 59362
Fr anc 59358
dis assembly</w> 59357
G Ps</w> 59350
PL ICA 59348
top ics</w> 59348
ri ver</w> 59343
ed ges</w> 59336
RA D</w> 59335
ere bro 59331
pa d</w> 59324
poten tly</w> 59321
. gov</w> 59313
puber ty</w> 59311
bor tezomib</w> 59298
organ elle</w> 59294
disc er 59291
rs 3</w> 59289
ox one</w> 59288
prim itive</w> 59272
o ils</w> 59270
classi fy</w> 59269
expl ants</w> 59266
histopath ologic</w> 59263
D ox</w> 59251
ol k</w> 59251
O ff 59245
V ps 59236
Op en</w> 59232
k its</w> 59229
or ac 59220
c em 59219
me ters</w> 59209
rel la</w> 59201
F OR</w> 59196
wa iting</w> 59192
catech ol 59180
G 8</w> 59162
antidepress ants</w> 59158
G el</w> 59157
ex ud 59149
possess ing</w> 59145
am ins</w> 59144
enti nel</w> 59129
suspici on</w> 59129
Ap pro 59123
h TERT</w> 59121
ad ver 59115
cat abolism</w> 59115
I so 59113
4 th</w> 59103
Y east</w> 59102
sh el 59091
St ar 59081
Isl and</w> 59074
ile um</w> 59072
PI P2</w> 59064
o val 59062
ti me 59062
Mus cle</w> 59061
dr astically</w> 59050
herpes virus</w> 59048
ch ap 59046
3 d</w> 59034
amp utation</w> 59028
glyc ero 59025
for skolin</w> 59020
II A</w> 59006
f lower</w> 59002
For mation</w> 58996
Ch ic 58987
A 8</w> 58965
postin f 58959
k cat</w> 58933
tr apped</w> 58928
EM B 58924
UT P</w> 58923
Ga ussian</w> 58922
don ation</w> 58919
at tained</w> 58907
meg akary 58888
T OR 58880
br a</w> 58878
ER S</w> 58875
psychiat ry</w> 58872
extr apol 58871
victim s</w> 58869
ti dal</w> 58859
ben ch 58852
ll ment</w> 58847
maxim ize</w> 58840
e uc 58835
discre pancies</w> 58827
flo x</w> 58824
Ch k1</w> 58814
tryp sin 58809
Al ter 58807
hem odynamics</w> 58806
he pa 58798
orth odontic</w> 58796
oc ap 58790
employe es</w> 58789
0 C</w> 58777
isch aemia</w> 58768
Ac cum 58764
shif ting</w> 58763
S CLC</w> 58758
ing ent</w> 58750
Sh ig 58750
tur ns</w> 58744
end urance</w> 58743
moti v 58741
im ine</w> 58736
vascul itis</w> 58732
disti lled</w> 58712
late st</w> 58709
carbo platin</w> 58705
lam in</w> 58701
carbox ylate</w> 58699
scar ce</w> 58698
re ality</w> 58689
Intrigu ingly</w> 58678
tetra zol 58677
O X</w> 58660
spar se</w> 58659
L R 58657
pati al</w> 58656
Streptom yces</w> 58655
Dist ribu 58650
con ve 58649
Nik on</w> 58641
ancestr al</w> 58641
bar s</w> 58632
ol i 58630
v in</w> 58628
coc ul 58625
au xin</w> 58621
M ul 58619
lnc RNA</w> 58612
diarr ho 58610
z i 58595
De tailed</w> 58591
D V</w> 58580
B ei 58579
ili brium</w> 58576
HS CT</w> 58574
C ell 58568
Transf ection</w> 58561
punc ta</w> 58557
hn RNP</w> 58546
B PA</w> 58520
CO OH</w> 58518
TRP V1</w> 58515
cre ates</w> 58509
sand w 58502
sho ot</w> 58500
ou red</w> 58495
pa tell 58479
fund ed</w> 58478
5 c</w> 58470
. 9</w> 58468
prote renol</w> 58457
home ostatic</w> 58455
CN V</w> 58439
H en 58438
am ol</w> 58434
bab ies</w> 58433
D ose</w> 58428
en sion</w> 58424
lob es</w> 58406
lo ts</w> 58405
hydri de</w> 58399
resi li 58398
Ma dison</w> 58396
ol i</w> 58391
myel o 58391
K V</w> 58390
K 8</w> 58388
incub ator</w> 58386
ET s</w> 58384
E2 F1</w> 58382
A mb 58376
non coding</w> 58368
consol idation</w> 58368
oper ational</w> 58363
Rep ublic</w> 58356
pro spec 58347
parti tioning</w> 58344
pig mentation</w> 58343
X .</w> 58341
Pro ble 58341
ind ol 58338
o yl</w> 58335
co enzyme</w> 58328
lei omy 58316
2 nd</w> 58311
l ay</w> 58310
an ase</w> 58308
plant able</w> 58297
complex ed</w> 58294
ti se</w> 58292
no teworthy</w> 58288
pi vac 58287
electro poration</w> 58279
c amp 58278
tr ade</w> 58269
at or 58264
humidi fied</w> 58258
kn owle 58255
achiev ement</w> 58252
R at</w> 58251
dra w</w> 58247
li er</w> 58245
A do 58243
diab etics</w> 58232
W B</w> 58216
flow s</w> 58212
lig ases</w> 58203
in er</w> 58195
CH 1</w> 58192
MM Ps</w> 58189
ME F</w> 58187
den sit 58183
normo tensive</w> 58172
cross linking</w> 58162
yl amine</w> 58161
see k</w> 58153
HI F 58144
ta to</w> 58141
ophyl line</w> 58130
o ks</w> 58129
Ch i</w> 58127
ear man</w> 58123
h s</w> 58115
tolu ene</w> 58112
cros stalk</w> 58107
ambig u 58104
H 9</w> 58101
som nia</w> 58100
a virus</w> 58095
W ol 58081
L ar 58080
ML L</w> 58080
ug h</w> 58074
magn ification</w> 58073
un ted</w> 58072
CD C</w> 58062
obste tric</w> 58050
ver apamil</w> 58048
di electric</w> 58043
se rous</w> 58043
Re duc 58039
HDAC 1</w> 58038
M ig 58032
beg ins</w> 58032
AL Y 58026
RE M</w> 58018
CD K</w> 57998
Compar ing</w> 57992
the ast</w> 57989
TL C</w> 57987
H r 57983
hem angi 57975
Hep at 57974
stere ot 57971
dro pped</w> 57965
accum ulating</w> 57964
Sch wan 57961
CDK N 57955
C CL 57951
Mon it 57949
cong estive</w> 57947
visi ted</w> 57928
pivac aine</w> 57927
Com p 57925
hemat oxylin</w> 57920
Com plications</w> 57918
framesh ift</w> 57915
e g</w> 57909
Xho I</w> 57906
Six teen</w> 57905
mut ually</w> 57898
br and</w> 57890
inc is 57886
tra vel</w> 57857
ag eous</w> 57856
recru iting</w> 57843
fo rensic</w> 57841
CD 7</w> 57829
immunomod ulatory</w> 57815
fri end 57812
Intrac ellular</w> 57809
In dic 57806
ambig uous</w> 57803
anc e 57799
promis ed</w> 57787
anomal y</w> 57785
ma kers</w> 57782
imid azol 57782
Pro duction</w> 57767
v ular</w> 57764
analy tes</w> 57761
T HE 57758
astro cytom 57746
Hind III</w> 57725
un bound</w> 57701
pertur bed</w> 57695
K SHV</w> 57690
poul try</w> 57664
V TE</w> 57657
id one</w> 57654
conver gence</w> 57654
squ ares</w> 57650
propi dium</w> 57644
M 7</w> 57640
it ch</w> 57640
pho to</w> 57633
mand atory</w> 57630
umin escent</w> 57625
A F1</w> 57620
m d 57615
P b 57608
cut ting</w> 57605
ph ero 57596
CO L 57594
mig rants</w> 57585
DN A 57561
fum ig 57556
hex a 57552
capsul es</w> 57552
carcin ogenic</w> 57547
yn chron 57538
g p4</w> 57536
as in</w> 57525
in dependence</w> 57524
haem oglobin</w> 57515
al ism</w> 57511
perform ances</w> 57504
rup tured</w> 57494
peri o 57477
parad ox 57474
I G 57462
SE N 57457
A S1</w> 57455
arch ae 57450
l acti 57449
K er 57420
W A</w> 57420
N ox 57411
G ri 57411
lactam ase</w> 57378
b ig 57368
CYP3 A4</w> 57364
sphinc ter</w> 57354
9 S</w> 57353
robo tic</w> 57341
le ishman 57331
autophag osome</w> 57331
Sign al</w> 57329
d n 57325
inde xes</w> 57321
8 b</w> 57318
ron i</w> 57317
ho g</w> 57312
Mar k 57311
decarbox ylase</w> 57310
Ex pl 57293
D O</w> 57292
deliver ing</w> 57291
silic one</w> 57283
AB C 57269
Rec or 57259
CAB G</w> 57256
aro usal</w> 57255
extr av 57255
ater gic</w> 57255
incor rec 57248
Contin uous</w> 57244
H CO3</w> 57242
exerc ises</w> 57238
mosquit oes</w> 57237
T 6</w> 57235
explan ations</w> 57234
t acti 57224
habit ats</w> 57223
A r</w> 57212
J 1</w> 57201
F AC 57194
rop s</w> 57193
7 E</w> 57182
Dis orders</w> 57173
trigem inal</w> 57173
DL BCL</w> 57167
periton itis</w> 57166
ab 1</w> 57164
equ ine</w> 57164
o zone</w> 57140
Bu ffer</w> 57140
H UV 57137
hetero dimers</w> 57136
F 0</w> 57134
l ar</w> 57125
CA 3</w> 57124
astro cyte</w> 57118
requ est</w> 57113
in in</w> 57111
PV DF</w> 57109
Sp earman</w> 57101
LO GY</w> 57100
anaes the 57096
D CM</w> 57091
Practi ce</w> 57087
h ap 57084
B is 57082
ac id 57079
an di 57051
cu stom 57044
Ex tended</w> 57035
post mortem</w> 57032
Inc ub 57023
D o</w> 57022
py rene</w> 57019
el lip 57014
p ET 57007
MU C1</w> 57000
physe al</w> 56997
lact one</w> 56979
ir r 56970
hyper thermia</w> 56963
Pr inc 56955
advant ageous</w> 56954
No .</w> 56954
di valent</w> 56949
ti tle</w> 56947
CI N</w> 56943
os ystems</w> 56942
reinforc ement</w> 56923
con idi 56919
g 2</w> 56918
sy l 56916
poly po 56912
M IN 56901
Ad verse</w> 56901
Alcoh ol</w> 56890
transmit ters</w> 56888
t ases</w> 56883
ac tu 56846
Modi fied</w> 56844
coll ectively</w> 56843
ish ment</w> 56843
E astern</w> 56833
photo receptors</w> 56832
n s 56830
tro ponin</w> 56821
di tis</w> 56818
Guid elines</w> 56814
t 3</w> 56811
sten ting</w> 56809
ubiquit ylation</w> 56785
Ki 6</w> 56783
Se x</w> 56765
CH F</w> 56763
lute al</w> 56763
f arm</w> 56760
paren chymal</w> 56751
ar ization</w> 56748
D 9</w> 56746
C erebral</w> 56743
anti le</w> 56740
mit ogenic</w> 56739
coloc alized</w> 56732
continu ity</w> 56721
no v</w> 56716
L ondon</w> 56706
nig ra</w> 56702
de b 56696
chem oradi 56696
radi ologic</w> 56695
veget ative</w> 56694
iso flurane</w> 56688
val ents</w> 56674
hetero zygosity</w> 56666
T AT 56662
J our 56650
M align 56646
def ault</w> 56626
h op 56625
surro unded</w> 56611
ill ed</w> 56610
mo tions</w> 56587
elim inating</w> 56580
ST S</w> 56576
v i</w> 56574
su mo 56571
n als</w> 56559
choro idal</w> 56547
c occus</w> 56541
ir a</w> 56541
L 6</w> 56539
Schwan n</w> 56538
sic kle</w> 56535
R U 56516
an um</w> 56516
visu ally</w> 56514
in sti 56509
de x</w> 56508
cry o 56503
sup pressors</w> 56499
En ergy</w> 56497
determin ations</w> 56492
G AA 56486
orth otopic</w> 56472
co activator</w> 56455
conc eption</w> 56448
x ine</w> 56443
aden ylation</w> 56436
T radi 56435
amylo idosis</w> 56428
fair ly</w> 56417
sup ram 56416
pel vis</w> 56415
P Y</w> 56402
PI s</w> 56396
inf ective</w> 56394
ash es</w> 56376
leg is 56360
num er 56359
culti vars</w> 56357
colon oscopy</w> 56356
L ate</w> 56355
radi o</w> 56354
CA C</w> 56354
TI C</w> 56347
Emb ry 56345
in tolerance</w> 56343
sensiti vities</w> 56336
bacter icidal</w> 56331
rel ates</w> 56320
in ations</w> 56309
exacerb ation</w> 56309
sk ill</w> 56297
X P</w> 56286
AI Ds</w> 56286
ac ro 56276
T 2D</w> 56271
PA Hs</w> 56271
1 . 56268
expect ancy</w> 56262
inter phase</w> 56256
str atum</w> 56252
si de 56239
compens ated</w> 56228
knoc ked</w> 56228
Co ul 56221
b y 56215
Ne on 56213
su stainable</w> 56211
substitu ents</w> 56210
olig o 56208
enor hab 56189
acet ab 56185
modul us</w> 56184
Sm ith</w> 56182
Log istic</w> 56179
M oun 56175
mic s</w> 56163
resi de</w> 56163
schem es</w> 56151
eas tern</w> 56150
Monit oring</w> 56150
lab our</w> 56141
a etiology</w> 56137
Long itudinal</w> 56133
opo dia</w> 56124
om atic</w> 56122
po tato</w> 56121
co transfected</w> 56099
an ore 56087
quanti fying</w> 56087
AF M</w> 56076
D DR</w> 56075
5 K</w> 56074
d ence</w> 56074
dro ps</w> 56071
OR Fs</w> 56069
autoradi ography</w> 56055
consum ers</w> 56052
inter facial</w> 56047
emplo y</w> 56045
utili zes</w> 56034
sc ol 56032
sec urity</w> 56023
sch ist 56020
in struction</w> 56017
degrad able</w> 56006
or rho 56002
B P2</w> 56000
Er y 55996
K idne 55990
thrombo embolism</w> 55975
perme ation</w> 55961
ca va</w> 55950
E B1</w> 55946
RI PA</w> 55944
S hi 55938
min ing</w> 55936
concentr ate</w> 55934
prob abilities</w> 55933
d as 55932
C la 55931
mas ked</w> 55931
oc rit</w> 55928
mechan ically</w> 55899
f lowering</w> 55895
P eptide</w> 55894
diffic ile</w> 55894
T 8</w> 55892
neigh bor 55886
ocyan in</w> 55882
spo t 55881
b p 55879
S ul 55875
per mitted</w> 55875
G RP 55868
opio ids</w> 55864
C PR</w> 55858
3 rd</w> 55856
C ES</w> 55833
sp ore</w> 55833
electro my 55828
DE Gs</w> 55825
diaphrag m</w> 55808
S an 55806
s. c.</w> 55804
orim etric</w> 55788
son ication</w> 55785
exac tly</w> 55781
H an</w> 55780
D G 55775
dispers al</w> 55755
Stat 3</w> 55751
dys regulated</w> 55749
aptam er</w> 55745
disco ver 55743
neuro imaging</w> 55735
f un 55728
Clon tech</w> 55728
op eroxidase</w> 55722
prolong ation</w> 55720
m ari 55719
fibrill ary</w> 55715
mandi ble</w> 55713
radio immunoassay</w> 55694
Func tion</w> 55691
enro llment</w> 55689
ensur ing</w> 55686
nicotin amide</w> 55681
c .1</w> 55679
h inge</w> 55670
dec id 55667
distor tion</w> 55662
Phyl ogenetic</w> 55661
Characteris tics</w> 55652
en ses</w> 55641
F O</w> 55640
ophil us</w> 55603
P c 55591
F ast</w> 55590
collagen ase</w> 55583
conc ei 55574
comp any</w> 55571
lar yng 55569
dim er 55541
p tions</w> 55535
Tum ors</w> 55534
I E</w> 55530
ord inary</w> 55529
fing ers</w> 55516
adap tations</w> 55508
fer re 55506
sarco idosis</w> 55506
st ant</w> 55501
orig inate</w> 55494
restric tive</w> 55472
- based</w> 55457
pa res</w> 55456
ec osystems</w> 55449
waveleng ths</w> 55449
D BD</w> 55442
phil is</w> 55441
ran es</w> 55428
na vig 55428
4 d</w> 55422
pair wise</w> 55422
tetrazol ium</w> 55421
micro fluidic</w> 55419
gri p</w> 55416
bon y</w> 55414
sci atic</w> 55390
men op 55376
id omide</w> 55351
chemot actic</w> 55346
Stri kingly</w> 55337
vitre ous</w> 55335
run s</w> 55327
R ed 55325
consci ousness</w> 55325
Ser 2</w> 55322
D n 55320
er i</w> 55317
de methylation</w> 55315
arrhyth mic</w> 55312
Nutri tion</w> 55309
st ore</w> 55307
f is 55305
MA O</w> 55293
Luc iferase</w> 55285
pul l 55275
spec ulated</w> 55273
cap saicin</w> 55272
recommend ation</w> 55269
le aching</w> 55265
tre ad 55262
altern ating</w> 55260
ox ane</w> 55255
A FP</w> 55252
PC C</w> 55251
F U 55249
un modified</w> 55249
H ong</w> 55239
stac king</w> 55222
k a 55201
Jo hn 55183
phosphoinosi tide</w> 55176
pe dic 55175
lu c</w> 55161
V H</w> 55160
Thir teen</w> 55158
lo os 55155
ag gra 55154
continu um</w> 55141
mo ist 55137
dec o 55137
hypothe tical</w> 55134
p p</w> 55127
hist ories</w> 55125
electro encephal 55117
Pro vince</w> 55115
E VI 55102
at rop 55095
p U 55091
arom atase</w> 55091
combin es</w> 55087
Compo unds</w> 55086
de methyl 55079
STAT 5</w> 55078
J A</w> 55067
alkal oids</w> 55063
micro bes</w> 55062
HUV ECs</w> 55051
granul osa</w> 55045
R us 55030
ER B 55029
stret ching</w> 55028
glutam atergic</w> 55027
rem n 55026
er mal</w> 55024
proton ated</w> 55009
Medic aid</w> 54997
Bei jing</w> 54992
polys accharides</w> 54990
le pro 54988
CF P</w> 54982
9 b</w> 54980
N TR</w> 54976
PI P</w> 54974
T BP</w> 54968
facil itation</w> 54968
se y</w> 54960
top ically</w> 54959
pe l</w> 54958
Amin o</w> 54956
ner ship</w> 54954
B cl 54952
B a</w> 54949
cigare ttes</w> 54946
sic ally</w> 54928
Rel ated</w> 54923
PE C</w> 54921
d NT 54917
cap sular</w> 54913
ti bia</w> 54912
CT P</w> 54912
Ste m</w> 54906
Po is 54898
CT Cs</w> 54895
reduc tive</w> 54882
L ear 54877
In flu 54865
in bred</w> 54859
biosens or</w> 54854
veter inary</w> 54849
om ial</w> 54840
z ers</w> 54835
hyper cholesterole 54833
s angu 54826
hydro carbon</w> 54799
orth op 54799
p a</w> 54798
DF S</w> 54796
G CG 54791
v ices</w> 54789
bif ur 54786
ren dered</w> 54784
d ant</w> 54781
tempor ally</w> 54781
PK D</w> 54778
j apon 54777
dextr in</w> 54771
work place</w> 54761
cri min 54755
Par tial</w> 54749
the ta</w> 54739
implic ate</w> 54733
CON T 54728
bac tere 54727
RO M</w> 54724
l ose</w> 54722
Me dium</w> 54710
W s</w> 54704
pea ked</w> 54703
it orial</w> 54685
ger iatric</w> 54667
tho roughly</w> 54663
S or 54662
radi onuc 54654
app rais 54647
Ox idative</w> 54647
cephal us</w> 54634
obser vers</w> 54631
stra ight</w> 54623
fibri l</w> 54622
near by</w> 54621
X 5</w> 54620
ast rectomy</w> 54617
CG RP</w> 54617
R 8</w> 54598
gh relin</w> 54593
prev ent 54588
ul trac 54582
R et 54565
G M1</w> 54565
Pro c 54563
cru zi</w> 54555
M W</w> 54554
Anx iety</w> 54553
SE T</w> 54551
d re 54549
GAB AA</w> 54549
β 2 54548
and a</w> 54538
exper tise</w> 54534
Par k</w> 54531
g in</w> 54529
clos ing</w> 54526
a ided</w> 54524
t ude</w> 54522
ste ll 54516
Lin ear</w> 54516
os mol 54512
P . 54509
M Abs</w> 54502
. ..</w> 54500
new er</w> 54500
vol umetric</w> 54499
W NV</w> 54493
4 T</w> 54490
Endos copic</w> 54482
dis ed</w> 54477
tri plet</w> 54474
teach ers</w> 54474
hydro gels</w> 54473
Combin ation</w> 54466
D r</w> 54465
remin is 54453
har monic</w> 54446
br ush</w> 54440
anc ient</w> 54435
ch ymo 54432
Ca enorhab 54430
lo sis</w> 54419
r atic</w> 54418
I 3</w> 54406
N av1</w> 54400
haemorrh age</w> 54394
Md m2</w> 54393
ID H1</w> 54391
- mediated</w> 54382
gyn ec 54380
N Ac 54379
C CT</w> 54378
cul t</w> 54374
Im proved</w> 54362
M ol 54349
re th 54343
NI R</w> 54341
par kinson 54338
L in</w> 54337
T I</w> 54334
outw ard</w> 54326
L PA</w> 54325
Ne f</w> 54320
psych ometric</w> 54317
NS AIDs</w> 54315
o temporal</w> 54308
ket one</w> 54308
atten tional</w> 54302
5 H</w> 54280
practition er</w> 54274
manufac turing</w> 54272
6 F</w> 54271
R emo 54262
Ultras ound</w> 54252
se eding</w> 54248
Spec ial</w> 54243
en velop 54242
vulg aris</w> 54242
per itone 54241
setu p</w> 54237
mal on 54233
SN AP</w> 54233
E Na 54232
lo oking</w> 54232
mirro r</w> 54224
er ve</w> 54223
H3K 9 54217
R L</w> 54200
Th i 54200
f p</w> 54195
estr us</w> 54177
Den mark</w> 54166
Tra ining</w> 54160
B H3</w> 54154
e V</w> 54152
F ts 54151
gl and 54135
ac clim 54133
obut y 54127
eti ological</w> 54124
ati s</w> 54123
CY P1</w> 54122
dichro ism</w> 54118
i ang</w> 54115
I O 54113
encour age</w> 54113
oc occus</w> 54107
surg eries</w> 54105
opa edic</w> 54105
Caenorhab ditis</w> 54098
trem or</w> 54096
Immun oprecip 54093
Re v</w> 54092
Taq Man</w> 54089
T AT</w> 54085
NMD AR</w> 54085
inte rests</w> 54080
lipos ome</w> 54075
H ill</w> 54072
M r</w> 54064
cer amic</w> 54057
AR C</w> 54050
Pro state</w> 54046
kan amycin</w> 54036
ca vities</w> 54034
s av 54031
inter ro 54019
anten atal</w> 54011
add resses</w> 54008
h il 54004
proce ed</w> 53997
Gr ant</w> 53990
ail s</w> 53973
a . 53965
ip ping</w> 53958
IK K</w> 53955
Bio tech</w> 53948
M ulti</w> 53944
tab let</w> 53932
moti le</w> 53926
p. i.</w> 53925
M ec 53920
review ers</w> 53916
ca ver 53890
is one</w> 53880
hund reds</w> 53878
L ep 53871
is oc 53857
be ha 53844
sec t</w> 53844
ana phyl 53832
F US</w> 53823
L etter</w> 53811
reg ards</w> 53809
attrac ted</w> 53804
k ling</w> 53800
V F</w> 53798
aud i</w> 53795
y olk</w> 53792
H2 B</w> 53787
ol ding</w> 53784
usp id</w> 53782
f ec 53779
im es</w> 53775
metabol ized</w> 53769
rema inder</w> 53765
R ot 53756
hams ters</w> 53749
CT X</w> 53710
is o</w> 53707
s acc 53705
as tal</w> 53689
is otype</w> 53686
Initi ally</w> 53684
Hepati c</w> 53684
elem ental</w> 53677
R h</w> 53666
V 7</w> 53661
pec uli 53661
glut ar 53661
stero id 53660
CL s</w> 53658
H PA</w> 53657
th ecal</w> 53649
ore mentioned</w> 53649
il eal</w> 53638
HDAC s</w> 53613
id er</w> 53584
ey el 53581
K B</w> 53580
Instrum ents</w> 53572
cl o 53571
Sym ptom 53568
S6 K</w> 53564
E I</w> 53557
Qu al 53555
RE VI 53551
parti tion</w> 53551
U 0</w> 53538
form ic</w> 53532
fresh water</w> 53531
dyst roph 53529
So dium</w> 53529
pluri potency</w> 53523
m C</w> 53518
E AE</w> 53517
prolifer ate</w> 53501
O rig 53492
En zyme</w> 53488
SY 5Y</w> 53485
Impro vement</w> 53481
Col um 53476
N L</w> 53474
dur ations</w> 53470
en sing</w> 53467
osteoclas ts</w> 53466
edi pine</w> 53460
Ad d 53455
ortholog s</w> 53453
af orementioned</w> 53441
NT A</w> 53435
T GG 53433
sc Fv</w> 53433
m ur 53427
t ying</w> 53425
truc ture</w> 53423
P rec 53418
alcoh ols</w> 53400
PA M</w> 53389
a sive</w> 53388
autophag osomes</w> 53385
scal p</w> 53382
tri ed</w> 53379
bur ns</w> 53375
en te 53365
AT 2</w> 53361
RG D</w> 53352
R FLP</w> 53347
sarcom as</w> 53342
interfe res</w> 53338
cl op 53337
T AL 53335
Sh h</w> 53325
un altered</w> 53317
TC C</w> 53309
ADAM 1</w> 53307
Ad ap 53305
he ads</w> 53295
en sed</w> 53292
si ella</w> 53288
tom ographic</w> 53276
thin king</w> 53273
H LH</w> 53265
pro vince</w> 53263
quin oline</w> 53241
mun ici 53236
ent h</w> 53232
Knock down</w> 53223
S AP</w> 53218
sub arachnoid</w> 53214
tw elve</w> 53211
Un it</w> 53210
ori entations</w> 53205
re modelling</w> 53200
Fox p3</w> 53195
atin e</w> 53191
pos ity</w> 53180
c rops</w> 53177
o 1</w> 53173
re aches</w> 53169
be st 53169
xanth ine</w> 53162
si vity</w> 53145
tread mill</w> 53139
sens ation</w> 53137
fa ther</w> 53133
k A</w> 53131
dou bling</w> 53124
hemat opoiesis</w> 53124
Ta king</w> 53118
m ut</w> 53111
nas opharyngeal</w> 53108
fac ed</w> 53107
Mechanis ms</w> 53100
si aly 53092
H T2</w> 53089
propos al</w> 53083
andro gens</w> 53059
R . 53058
em physe 53058
og l 53056
pec ific</w> 53052
small est</w> 53047
ure teral</w> 53040
dec lines</w> 53036
p n 53020
cos metic</w> 53015
J 2</w> 53012
io tic</w> 53011
st ation</w> 53006
ph e 52999
diver tic 52990
nucle olar</w> 52987
ga uge</w> 52984
co factors</w> 52983
NE T</w> 52981
cho ose</w> 52973
fer ro 52968
O m 52966
vit amins</w> 52961
S ARS</w> 52957
fe et</w> 52956
plei otropic</w> 52941
ill umin 52939
ic o 52926
L OS</w> 52917
h 2</w> 52912
CC s</w> 52911
I AV</w> 52901
Pe ople</w> 52892
C ros 52891
pa re 52890
W all 52888
phosphodi esterase</w> 52883
Targ eting</w> 52881
le gs</w> 52880
Inter leukin</w> 52874
stra ight 52873
far ms</w> 52870
kin d 52869
bro ken</w> 52866
Am pl 52866
laparo scopy</w> 52865
al titude</w> 52862
neuro peptide</w> 52861
ti i</w> 52855
ME K1</w> 52842
GL UT 52838
D . 52837
somat osensory</w> 52829
T AC</w> 52824
inferen ce</w> 52820
V PA</w> 52810
ad o</w> 52806
D ES</w> 52800
acin ar</w> 52797
catech olamine</w> 52787
d y</w> 52779
B AL</w> 52775
war ming</w> 52773
ter p 52769
elev ations</w> 52769
Ra ther</w> 52767
D SS</w> 52765
H 2A</w> 52763
N HL</w> 52753
intra -</w> 52742
c ing 52727
Ont ario</w> 52725
in ates</w> 52712
bo ost</w> 52707
Rec on 52699
M aster</w> 52694
tryp an 52694
duoden um</w> 52687
b a</w> 52685
carri age</w> 52679
E GCG</w> 52676
P lac 52671
orthog onal</w> 52658
thal asse 52654
align ments</w> 52648
S 1A</w> 52647
UV B</w> 52644
comp ul 52643
intr ap 52642
Sampl e</w> 52641
Pharmac o 52637
fent anyl</w> 52633
G AS</w> 52624
multi plicity</w> 52619
op ac 52618
af t</w> 52617
sta ke 52616
ther mic</w> 52605
M Cs</w> 52602
chimer as</w> 52591
elucid ation</w> 52578
S to 52569
affor ded</w> 52558
RO CK</w> 52555
menop ause</w> 52555
scat ter</w> 52540
inser ts</w> 52538
iste in</w> 52536
abs tin 52534
bi an</w> 52528
v 3</w> 52521
iso proterenol</w> 52520
repres ses</w> 52519
phosph on 52517
aug ment</w> 52513
neo vascularization</w> 52511
easi er</w> 52499
evol ve</w> 52494
biom a 52489
chromo ph 52484
c ep 52475
CY P</w> 52468
cros ses</w> 52457
dysp nea</w> 52454
radi ology</w> 52451
substanti a</w> 52423
in di 52420
constitu ent</w> 52419
3 BP1</w> 52414
U2 OS</w> 52410
C SA</w> 52398
ti zing</w> 52397
r amine</w> 52387
pol es</w> 52378
Ex ercise</w> 52374
mid brain</w> 52371
NT D</w> 52371
CD K4</w> 52364
jour nals</w> 52363
d amp 52346
poli tical</w> 52341
or f 52339
Com pe 52334
E Vs</w> 52333
immunocom promised</w> 52331
log arith 52312
Re actions</w> 52307
bec tomy</w> 52301
coron al</w> 52295
CR 1</w> 52292
chim era</w> 52284
ric h 52278
w ro 52268
Coh ort</w> 52267
RB Cs</w> 52264
in dispensable</w> 52257
PA RP1</w> 52254
E ss 52244
flu xes</w> 52243
PLICA TIONS</w> 52243
micro biological</w> 52242
pl en 52240
ste el</w> 52239
vir in</w> 52233
hyper methylation</w> 52220
ab ain</w> 52213
perform s</w> 52198
tocoph erol</w> 52198
tax onomic</w> 52194
J ak 52193
W at 52191
individu alized</w> 52186
deline ate</w> 52182
Co om 52158
cran io 52153
dig it 52146
gonad al</w> 52138
li ve 52135
B asic</w> 52133
b us</w> 52131
lead ership</w> 52125
ER β</w> 52117
sugges tions</w> 52113
zyg ote</w> 52107
B ub 52100
inter quartile</w> 52086
t 4</w> 52082
U A</w> 52072
di ethyl 52056
ex por 52052
plasm on</w> 52043
G old</w> 52036
frag mented</w> 52032
Rec over 52029
co expression</w> 52027
di as 52020
enc h</w> 52012
encoun ter</w> 52012
oc ele</w> 52009
- UTR</w> 52008
aim ing</w> 52007
ed u</w> 52003
le a</w> 51998
L OH</w> 51994
Laparo scopic</w> 51994
OR s</w> 51990
Met S</w> 51986
com pares</w> 51985
hospital izations</w> 51976
Gene tics</w> 51969
ic ide</w> 51963
On line</w> 51961
explo res</w> 51953
M erc 51940
α -</w> 51927
V P 51924
glob ular</w> 51924
ju x 51924
pe red</w> 51913
sti tis</w> 51906
s u</w> 51887
th am</w> 51884
si fied</w> 51875
contex tual</w> 51866
um ps</w> 51858
c ities</w> 51839
soph is 51838
di aldehyde</w> 51832
assi e</w> 51830
pi eces</w> 51829
calc itonin</w> 51821
V ery</w> 51812
TL Rs</w> 51811
Rab 1</w> 51804
son ography</w> 51803
calorim etry</w> 51802
cave olin</w> 51801
cul turing</w> 51791
ac ters</w> 51779
del ta 51778
IC H</w> 51776
intim al</w> 51774
crow n</w> 51756
osse ous</w> 51749
hydrophob icity</w> 51738
shor tly</w> 51734
ot ocin</w> 51730
meth icillin</w> 51727
stere o 51717
ENa C</w> 51713
Op tical</w> 51712
I le 51700
RE GI 51698
Phot o 51698
Brow n</w> 51689
merg ed</w> 51687
AC 1</w> 51685
plant ar</w> 51685
Cac o</w> 51682
bi g</w> 51676
app end 51673
colli sion</w> 51664
di visions</w> 51657
W u</w> 51656
fol ic</w> 51651
be ef</w> 51642
Vi brio</w> 51632
nal oxone</w> 51630
is ogenic</w> 51625
res or 51616
te nosis</w> 51614
E BP</w> 51609
op eptidase</w> 51609
lo l</w> 51609
val ine</w> 51609
Erb B2</w> 51605
mul tin 51599
accommod ate</w> 51582
resid ential</w> 51572
enteri tis</w> 51571
U s</w> 51570
care er</w> 51564
im possible</w> 51558
lenti virus</w> 51557
S on 51556
Ser vices</w> 51551
w et 51542
son icated</w> 51539
administr ative</w> 51538
ho using</w> 51531
E x</w> 51527
controver sy</w> 51527
Las tly</w> 51520
it an</w> 51513
U M</w> 51503
ple tion</w> 51503
a w</w> 51502
phen otyp 51502
carb am 51496
brac hy 51491
dys functional</w> 51490
R L 51483
investig ates</w> 51483
lit ter</w> 51472
pro lapse</w> 51471
gi um</w> 51468
encap sulation</w> 51459
T 3 51456
therap ists</w> 51453
MIC s</w> 51452
in nerv 51437
kary otype</w> 51436
my oblasts</w> 51426
is ometric</w> 51425
AR CH</w> 51409
ad ate</w> 51401
d l 51395
de structive</w> 51392
irrig ation</w> 51392
transi tional</w> 51389
lact ose</w> 51385
clea ve</w> 51365
a sion</w> 51361
in ve 51358
T ow 51356
consum er</w> 51354
exce eding</w> 51350
sho p</w> 51346
S us 51339
prob and</w> 51332
manip ulated</w> 51327
o plasma</w> 51315
harv est</w> 51312
Kidne y</w> 51306
proton ation</w> 51286
str ingent</w> 51271
stig ma</w> 51271
Inter actions</w> 51265
out line</w> 51262
HO X 51261
ipl atin</w> 51256
An der 51255
do ing</w> 51247
disco ver</w> 51242
alg inate</w> 51240
Pri mers</w> 51239
d 3</w> 51232
confound ers</w> 51231
W C</w> 51230
ac ia</w> 51222
ke to</w> 51219
as cer 51215
1 B1</w> 51209
spi kes</w> 51209
ge hog</w> 51207
T h</w> 51205
Epidemi ology</w> 51200
Wh ere</w> 51188
aspar tic</w> 51188
op eptides</w> 51175
I G</w> 51172
in ol</w> 51171
lab ile</w> 51169
CH 2 51163
ling ual</w> 51152
bro il 51147
convul s 51138
pig mented</w> 51135
in yl</w> 51129
tubercul ous</w> 51126
adjunc t</w> 51123
1 A2</w> 51117
op ened</w> 51104
ug g 51103
CA GG 51091
invari ant</w> 51084
exce ed</w> 51075
reminis cent</w> 51067
SI I</w> 51057
din ucleotide</w> 51057
Compu ted</w> 51056
rel oc 51046
Lis teria</w> 51044
BL M</w> 51034
pa ternal</w> 51028
for ks</w> 51020
ero sion</w> 51019
ap eptide</w> 51018
1 Δ 51017
uter o</w> 51012
X 7</w> 51009
ja w</w> 51001
til t</w> 51000
arbitr ary</w> 50994
Ta q</w> 50984
pum ps</w> 50977
F ran 50972
repres sive</w> 50968
g astrectomy</w> 50956
inf ecting</w> 50951
PD E</w> 50924
Sch iz 50924
RP 2</w> 50918
erythropo ietin</w> 50917
bot t 50915
lead er</w> 50898
Con tribu 50897
Nig eria</w> 50895
anch oring</w> 50891
top ological</w> 50886
neut ron</w> 50885
T yp 50882
3 K</w> 50880
opportun istic</w> 50877
immun o</w> 50865
ape x</w> 50854
en a</w> 50848
k el 50847
ter t</w> 50847
ot u 50846
tic ks</w> 50843
Tem per 50840
8 G</w> 50835
ur nal</w> 50835
L u</w> 50834
H cy</w> 50832
ocom ial</w> 50818
osi dase</w> 50812
C X</w> 50810
bac ul 50810
ret arded</w> 50809
adi posity</w> 50806
Ho ech 50798
allevi ate</w> 50794
DN MT 50792
un successful</w> 50789
in sically</w> 50784
LI F</w> 50784
tr um</w> 50778
GT T</w> 50768
multi factorial</w> 50764
ath ers</w> 50764
SI V</w> 50745
Pol y</w> 50737
on t</w> 50735
immunocyto chemistry</w> 50731
if e</w> 50726
str al</w> 50722
melan ocytes</w> 50717
trans locations</w> 50702
G le 50698
perio dic 50696
V ide 50693
7 me3</w> 50686
ESR D</w> 50686
post ure</w> 50684
abdom y 50678
er up 50671
thin k</w> 50657
inter personal</w> 50656
N Y 50654
ci ted</w> 50654
tensi le</w> 50653
sti pation</w> 50651
K M</w> 50639
un explained</w> 50637
bacterioph age</w> 50637
ros ity</w> 50636
in expensive</w> 50630
Sp inal</w> 50630
LK B1</w> 50627
rest oring</w> 50608
thous ands</w> 50597
lab els</w> 50596
stri ps</w> 50595
bar ley</w> 50594
dis charges</w> 50588
Plate let</w> 50585
Tr kB</w> 50583
Ol der</w> 50583
fer roni</w> 50581
ad versely</w> 50579
F 9</w> 50576
SC N</w> 50558
in er 50554
adj ust</w> 50547
proto zo 50547
man agers</w> 50546
Z IK 50543
Develop mental</w> 50540
com b</w> 50537
fil in</w> 50536
c ing</w> 50534
minim izing</w> 50529
My ocardial</w> 50523
M ol</w> 50518
hydrox ide</w> 50516
h sp 50508
PI 3</w> 50503
Symptom s</w> 50503
LO X</w> 50492
STRA TION</w> 50491
HR V</w> 50486
Eigh teen</w> 50481
integr ative</w> 50453
iso propyl</w> 50449
2 M</w> 50448
bur ied</w> 50446
ascor bate</w> 50443
P K1</w> 50442
val vular</w> 50434
postinf ection</w> 50430
C BD</w> 50409
ph ages</w> 50402
dialy zed</w> 50401
Y am 50396
neuro genic</w> 50384
anthrop ometric</w> 50384
Nur ses</w> 50380
con ferring</w> 50377
n ish</w> 50376
Immunohisto chemistry</w> 50373
F etal</w> 50370
nanoc ryst 50364
L am 50363
T m</w> 50362
trans formations</w> 50355
Distribu tion</w> 50355
lin k 50350
microm olar</w> 50350
my ogenic</w> 50342
flan ked</w> 50342
pi ece</w> 50338
B B 50332
pre fer 50324
CC N 50318
param agnetic</w> 50316
extrac orporeal</w> 50315
HM G</w> 50309
sta ins</w> 50308
T ME 50302
con currently</w> 50302
Hun ting 50300
Bon ferroni</w> 50297
olip id</w> 50286
d ases</w> 50284
di phenyl 50284
instrum entation</w> 50284
L AM 50271
tor que</w> 50270
u ality</w> 50261
sup ine</w> 50258
moist ure</w> 50255
par ity</w> 50241
D HE 50239
mod ular</w> 50229
rota virus</w> 50226
clos est</w> 50223
Notch 1</w> 50215
G Cs</w> 50206
IN S</w> 50197
A O</w> 50187
cul ating</w> 50185
all op 50183
sic s</w> 50158
D anish</w> 50150
pa rous</w> 50144
Experi ence</w> 50142
E X</w> 50139
propi onate</w> 50138
bab y</w> 50137
L BD</w> 50134
Sma d</w> 50130
eu ro 50127
comb at</w> 50126
pa uc 50109
quen ched</w> 50103
it ly</w> 50095
gen otypic</w> 50093
id og 50088
resc ence</w> 50087
tum oral</w> 50077
K IF 50074
TA G</w> 50064
AI S</w> 50057
X RD</w> 50056
off ering</w> 50046
electro n 50036
albumin uria</w> 50035
ter o</w> 50033
T MD</w> 50018
co astal</w> 50006
op ens</w> 50002
photosyn thesis</w> 49998
mo x 49992
P neum 49988
K DM 49986
flag ell 49974
an ide</w> 49971
M oti 49965
Kin etic</w> 49964
Incub ation</w> 49961
sing le 49958
am enable</w> 49954
High ly</w> 49951
Sol u 49946
B ey 49943
leng th 49938
M AC 49937
b aro 49933
T SS</w> 49918
li ber 49906
U 9</w> 49904
dis si 49903
cou mar 49903
un win 49901
orn ith 49899
dec l 49893
descrip tions</w> 49893
ann ually</w> 49888
sci ences</w> 49887
equi valents</w> 49887
ma ph 49882
Physici ans</w> 49860
At g1</w> 49857
S ections</w> 49856
diarrho ea</w> 49854
tic le</w> 49853
glucos amine</w> 49852
N up 49847
I BS</w> 49845
s pro 49836
T ren 49831
Kle b 49821
IC s</w> 49813
pro apoptotic</w> 49810
U T</w> 49809
iso dic</w> 49801
neuro transmission</w> 49791
rhod amine</w> 49788
t k</w> 49786
intr al 49777
appreci ated</w> 49776
tri methyl 49768
met ac 49767
Eff ective</w> 49765
Hem at 49764
mamm ography</w> 49762
yl yl</w> 49755
house holds</w> 49755
La ke</w> 49748
R ating</w> 49742
hol o 49739
h l</w> 49735
c ast</w> 49730
ak o</w> 49727
jus ti 49726
m ec 49717
Bec kman</w> 49717
ere sis</w> 49716
trans located</w> 49709
h l 49707
PE I</w> 49702
MM C</w> 49692
tex ture</w> 49689
gover ning</w> 49685
S W</w> 49675
predn isone</w> 49675
ten ds</w> 49674
off set</w> 49673
pip ette</w> 49666
tr ain</w> 49656
E PA</w> 49655
anc est 49653
7 -</w> 49648
chap ter</w> 49647
te ars</w> 49646
cann abis</w> 49644
inj ecting</w> 49643
or is</w> 49640
fo am</w> 49639
SE ARCH</w> 49631
ver sa</w> 49628
moun t</w> 49623
Inflam matory</w> 49623
TR H</w> 49616
in door</w> 49611
e GFR</w> 49610
sy philis</w> 49605
a ined</w> 49595
trac es</w> 49595
Fi bro 49590
W IT 49585
RI P</w> 49575
ak es</w> 49573
orth olog</w> 49569
Pro mo 49565
A gain</w> 49560
p l</w> 49558
let ter</w> 49555
at o 49544
p r</w> 49538
ery thromycin</w> 49536
ail and</w> 49535
isom al</w> 49530
gland ular</w> 49520
pl eth 49515
HR H</w> 49515
idog rel</w> 49500
L G 49498
adip ogenesis</w> 49493
sequ es 49492
M ix</w> 49489
sero positive</w> 49487
straight forward</w> 49486
z eta</w> 49485
ti ation</w> 49483
ren der</w> 49483
P SI</w> 49469
mess age</w> 49463
N ER</w> 49460
as simil 49455
g ad 49453
sp iral</w> 49450
ell ularly</w> 49445
g us</w> 49444
apol is</w> 49443
M CA</w> 49433
sin us 49431
Las er</w> 49427
sme ars</w> 49426
rib os 49404
Plas mid</w> 49395
post prandial</w> 49394
Par kin</w> 49390
PF A</w> 49389
cardiomy ocyte</w> 49389
ap es</w> 49386
tr in</w> 49384
ond ly</w> 49382
efflu ent</w> 49378
dom a</w> 49376
Jour nal</w> 49372
H NF 49367
Pro gnostic</w> 49357
macrom olecules</w> 49353
s entinel</w> 49348
Cl -</w> 49346
intra hepatic</w> 49342
p ra 49338
tr ate</w> 49331
K ong</w> 49326
IR F3</w> 49317
AA T</w> 49316
q 3</w> 49311
AG CT 49301
A β1</w> 49296
G a</w> 49288
sym metrical</w> 49263
mut ual</w> 49261
d ang 49258
emphasi zes</w> 49253
vacc inia</w> 49246
S cript</w> 49234
nation wide</w> 49229
W o 49224
U tili 49220
dic ular</w> 49218
ear ing</w> 49218
ut roph 49216
p M 49209
otox ins</w> 49206
Wil cox 49198
ar um</w> 49192
Y 5</w> 49189
di phosphate</w> 49189
8 D</w> 49180
azol am</w> 49176
D HT</w> 49174
9 mT 49165
dex tro 49163
sit ting</w> 49162
sal inity</w> 49160
ZIK V</w> 49150
Wil li 49148
radio frequency</w> 49146
caregi ver</w> 49146
TH F</w> 49140
V O</w> 49138
B ER</w> 49132
cyste ines</w> 49128
b ank</w> 49127
pe tro 49127
at rium</w> 49125
gran ts</w> 49104
SS c</w> 49097
Cri tical</w> 49085
f air</w> 49077
tun ed</w> 49076
Decre ased</w> 49070
pl ain</w> 49069
M 8</w> 49066
mit ophagy</w> 49065
Hoech st</w> 49065
Radi o 49042
u a</w> 49041
re tains</w> 49037
belong ed</w> 49031
ubiquit ously</w> 49020
a rent</w> 49014
Compar isons</w> 49006
un complicated</w> 48999
Nor way</w> 48999
proce eds</w> 48990
retro virus</w> 48985
N Q 48983
ac ylation</w> 48982
r ash</w> 48972
bar rel</w> 48963
i ously</w> 48960
ie u</w> 48954
buc cal</w> 48952
fumig atus</w> 48951
col y 48946
CC 2</w> 48940
ail ing</w> 48939
ter icin</w> 48938
gener al 48935
K C</w> 48928
S t</w> 48917
bio chem</w> 48914
P ubl 48913
eosinoph il</w> 48912
exci ting</w> 48910
I ron</w> 48905
ang li 48904
ind ows</w> 48899
end osome</w> 48897
T V</w> 48888
ir s</w> 48885
in us</w> 48877
Wilcox on</w> 48877
a 4</w> 48876
PIN K1</w> 48872
anch orage</w> 48868
s n</w> 48864
dis closed</w> 48854
pene trance</w> 48852
Com preh 48848
uni tinib</w> 48846
as cribed</w> 48845
phy sis</w> 48842
G astric</w> 48838
RN easy</w> 48834
manip ulations</w> 48834
G d</w> 48832
parallel ed</w> 48827
bir d</w> 48825
interrup ted</w> 48824
inc enti 48821
dis advantages</w> 48818
C P1</w> 48816
t z 48806
po s</w> 48806
hol ders</w> 48802
bir th 48795
α 3</w> 48788
glomerul onephritis</w> 48788
Kleb siella</w> 48782
Fam il 48779
my x 48777
Ah R</w> 48770
is tering</w> 48768
Kin ase</w> 48766
thre e 48762
li tis</w> 48756
RI P1</w> 48754
b ite</w> 48749
ocy stis</w> 48749
emp tying</w> 48749
scram bled</w> 48748
Inter view</w> 48747
b ach</w> 48742
ab normally</w> 48742
or che 48735
incre ment</w> 48717
inf antile</w> 48711
un ra 48705
no vation</w> 48704
microbi ome</w> 48699
por phyrin</w> 48698
G 6</w> 48691
Th ailand</w> 48686
go ats</w> 48686
ycl ic</w> 48681
er ae</w> 48678
n ation</w> 48674
paro tid</w> 48674
socio demographic</w> 48668
ben th 48667
w ine</w> 48666
vi tis</w> 48638
intro nic</w> 48637
engraf tment</w> 48634
od ys 48629
atis m</w> 48622
cirrho tic</w> 48616
F ar 48613
emo tions</w> 48613
spac ing</w> 48605
granul ocytes</w> 48598
it abine</w> 48593
C RA 48592
s cl 48582
dynam in</w> 48580
ver ification</w> 48570
in somnia</w> 48566
ine es</w> 48564
am i</w> 48561
day time</w> 48558
per pen 48554
cent ric</w> 48553
0 g</w> 48551
c m 48551
ag encies</w> 48549
D own 48540
su itability</w> 48539
crystall ography</w> 48530
Cal biochem</w> 48528
Ig G 48526
ultrac entrifug 48525
char acters</w> 48524
Bur k 48522
ri gorous</w> 48519
atrop ine</w> 48511
oligom er</w> 48509
AT C</w> 48490
t amine</w> 48486
my opathy</w> 48485
R a</w> 48478
ri fication</w> 48478
elu ting</w> 48478
o phy 48476
bi ases</w> 48465
Mex ican</w> 48464
5 N</w> 48458
r uled</w> 48455
hyp op 48451
thous and</w> 48448
ore doxin</w> 48434
SP E</w> 48429
Cl ick</w> 48428
chic ine</w> 48425
A O 48415
NAD P</w> 48411
fluor o</w> 48411
usi tis</w> 48409
IM PLICATIONS</w> 48406
tryp tic</w> 48401
- GG 48398
R II</w> 48394
re taining</w> 48386
pedic le</w> 48384
x ic</w> 48383
Un til</w> 48380
Respon ses</w> 48380
anos oma</w> 48379
P ten</w> 48376
in ergic</w> 48373
ore duc 48354
replic ating</w> 48353
id o</w> 48352
MP a</w> 48345
meth acrylate</w> 48341
adju stments</w> 48341
osph ere</w> 48339
pro poses</w> 48334
sh ips</w> 48326
res tenosis</w> 48324
in patients</w> 48321
gi an</w> 48313
CO M 48309
conjuncti val</w> 48309
Behavi oral</w> 48308
contro ll 48307
Dis rup 48302
S et 48284
v um</w> 48283
7 T</w> 48270
Re agent</w> 48265
MR P</w> 48263
ni h 48245
ox ib</w> 48243
I RA 48235
bo iled</w> 48235
aver aging</w> 48228
de aling</w> 48221
s mar 48219
im olar</w> 48213
optim izing</w> 48212
g ases</w> 48203
B P 48200
physi cs</w> 48197
opo rous</w> 48193
ME C</w> 48191
Ar g1</w> 48186
PC 2</w> 48183
publ ic 48179
spl itting</w> 48171
gl it 48142
thi o</w> 48141
cry o</w> 48133
statis tic</w> 48130
D ynamics</w> 48129
un biased</w> 48126
r al 48112
di ch 48110
HS P</w> 48103
stir red</w> 48102
sy mb 48101
ash i</w> 48098
Am bi 48094
Associ ations</w> 48092
m i</w> 48091
vi al</w> 48090
Aca dem 48090
In ternal</w> 48087
cat as 48086
II b</w> 48086
contribut or</w> 48085
T 0</w> 48071
6 H</w> 48067
bactere mia</w> 48066
impor ted</w> 48065
Ac qu 48059
Consi der 48053
gn ess</w> 48048
scre ws</w> 48046
- deoxy 48042
Memb ranes</w> 48042
tor sion</w> 48016
DE NCE</w> 48014
histo chemical</w> 48011
gra ins</w> 47998
Na 2 47992
Dis co 47989
chrom ium</w> 47978
Se a</w> 47962
hydro cephalus</w> 47957
be at</w> 47953
concomit antly</w> 47953
Tryp anosoma</w> 47938
P GF 47935
AM s</w> 47932
chlor inated</w> 47920
sulf onyl</w> 47919
inflam ed</w> 47918
trac ing</w> 47916
cent ri 47916
un cover</w> 47913
elast ase</w> 47911
gluc an</w> 47907
sa w</w> 47907
Malign ant</w> 47905
chol era</w> 47897
r A</w> 47892
neuro developmental</w> 47882
predisp osing</w> 47882
im ip 47878
es h</w> 47872
TI s</w> 47867
scol iosis</w> 47866
CP P</w> 47855
propag ated</w> 47850
Br ad 47849
coh esin</w> 47847
Wal tham</w> 47844
cel i 47843
medic ally</w> 47840
where in</w> 47836
Smad 3</w> 47831
reconstruc tions</w> 47829
wel fare</w> 47829
Adv ances</w> 47824
PT B</w> 47823
cor por 47819
di pole</w> 47814
di ure 47810
Hy pox 47807
pepti dase</w> 47801
d d</w> 47796
M ay 47793
recogn ised</w> 47790
F AS</w> 47783
MT V</w> 47771
prolifer ator</w> 47770
Maxim um</w> 47766
E G</w> 47762
d b 47760
iter ative</w> 47759
accep tability</w> 47746
Col on 47737
ph ox</w> 47731
immunoprecip itates</w> 47724
transl ate</w> 47708
mon os 47702
precip itate</w> 47700
spermat ogenesis</w> 47695
A my 47682
ou abain</w> 47679
or bit 47673
mic rons</w> 47673
three fold</w> 47673
HC s</w> 47672
TI ON 47662
SUMO ylation</w> 47652
co vers</w> 47640
blin dness</w> 47638
D K 47634
di sequ 47632
se ment</w> 47631
Val idation</w> 47620
fer tile</w> 47617
ref ers</w> 47616
bil ayers</w> 47612
ab iotic</w> 47605
predomin ance</w> 47605
en ke 47600
trans fusions</w> 47594
1 I</w> 47588
peric ardial</w> 47575
st al</w> 47566
undi ce</w> 47564
synchron ization</w> 47562
E G 47561
os clerosis</w> 47556
Behavi or</w> 47555
ati zed</w> 47554
oc cult</w> 47549
1 P</w> 47547
2 O3</w> 47538
T ax 47537
od or</w> 47535
d A</w> 47531
1 s</w> 47524
os ed</w> 47522
chrom atic</w> 47521
I P3</w> 47519
eosin ophilic</w> 47519
be ams</w> 47517
her bic 47505
sugges tion</w> 47500
m ids</w> 47499
cl av 47498
dys phagia</w> 47496
def ence</w> 47493
9 B</w> 47485
nif edipine</w> 47480
mar ital</w> 47464
x 3</w> 47463
veget ation</w> 47460
in activate</w> 47459
pharmac ists</w> 47455
H op 47447
sophis ticated</w> 47443
L ack</w> 47439
p G 47437
def ines</w> 47428
hid den</w> 47425
nano structures</w> 47419
N 6</w> 47402
H aving</w> 47395
pestic ide</w> 47391
M MS</w> 47389
enz a</w> 47387
isot opic</w> 47386
se a 47385
S U</w> 47374
M ü 47373
ventil atory</w> 47370
at ability</w> 47369
R b 47343
2 h</w> 47335
Cor poration</w> 47335
prescri ptions</w> 47331
INTERVEN TIONS</w> 47331
B eta</w> 47324
yl es</w> 47318
M PTP</w> 47315
De termin 47315
conven ience</w> 47314
B ilateral</w> 47307
z otocin</w> 47304
hydro gen 47304
pen ile</w> 47302
s ors</w> 47295
C i 47295
knowle dg 47279
if f</w> 47278
DE X</w> 47274
PC L</w> 47271
NS 5A</w> 47270
T CR 47269
R O</w> 47257
qua ternary</w> 47244
oper idol</w> 47243
emerg ent</w> 47242
pa y</w> 47235
A part</w> 47231
initi atives</w> 47223
nih .gov</w> 47212
rs 7</w> 47206
pyro phosphate</w> 47197
pres sing</w> 47189
e e</w> 47179
V as 47179
brom o</w> 47159
col orimetric</w> 47153
EMB ASE</w> 47149
p e</w> 47145
anore xia</w> 47145
pe d 47131
co expressed</w> 47116
Coom assie</w> 47112
G LI 47111
fas ci 47111
roph ar 47098
l ich 47097
char ts</w> 47092
t ym 47086
ogen in</w> 47083
Acti vated</w> 47083
od ont 47081
PL GA</w> 47077
Integr ated</w> 47063
c b 47061
W W 47053
f loc 47053
im per 47053
psycho therapy</w> 47052
fu ro 47049
affor d</w> 47046
off ici 47044
ble omycin</w> 47038
fici ally</w> 47032
AC P</w> 47030
chemo attrac 47026
tri tis</w> 47006
be gun</w> 47002
set t 46993
ulin um</w> 46987
J C</w> 46985
E MS</w> 46982
las ted</w> 46981
l o</w> 46979
induc ers</w> 46977
quar ter</w> 46967
PD 1</w> 46962
TR O</w> 46962
Lear ning</w> 46951
3 α</w> 46948
oligodendro cytes</w> 46942
fail ing</w> 46938
allo y</w> 46938
constitu ted</w> 46937
F a 46920
de af 46919
cyan ide</w> 46909
coll ections</w> 46907
o ventricular</w> 46900
uve itis</w> 46900
ine teen</w> 46898
metallo proteinases</w> 46886
wash out</w> 46882
emphyse ma</w> 46880
X 6</w> 46878
dynam ically</w> 46876
redund ancy</w> 46875
il ty</w> 46873
read mission</w> 46871
hydroly zed</w> 46868
c Tn 46867
en thal 46864
Regi on</w> 46862
γ 2</w> 46860
ro genic</w> 46849
pip eline</w> 46840
fos sa</w> 46837
B ost 46835
N 9</w> 46830
Sup port</w> 46824
decl are</w> 46824
wor ked</w> 46823
morph ogenetic</w> 46820
des at 46819
influenz ae</w> 46815
poly cystic</w> 46811
intr insically</w> 46808
post traumatic</w> 46807
TA Z</w> 46804
H 8</w> 46792
Ma x</w> 46791
brea king</w> 46788
ye asts</w> 46785
Ge org 46785
REGI STRATION</w> 46783
IP F</w> 46781
A ra 46779
Ub c 46779
ophar mac 46763
B PD</w> 46760
macrom olecular</w> 46753
vari eties</w> 46742
Hep es</w> 46738
Health y</w> 46738
tic ation</w> 46732
Re ference</w> 46732
yr in</w> 46730
Reg ister</w> 46722
9mT c</w> 46716
St able</w> 46712
occa sions</w> 46710
implic ating</w> 46708
inf er</w> 46698
Fig. 5</w> 46694
u ing</w> 46678
N I</w> 46675
AL A</w> 46675
CD Cl3</w> 46670
TT R</w> 46665
tol i</w> 46661
u ting</w> 46651
Remo val</w> 46646
Ham il 46644
cy tidine</w> 46638
apprais al</w> 46632
amin oglyco 46630
anti apoptotic</w> 46621
REVI EW</w> 46613
vol ution 46585
Chlamy dia</w> 46574
paras it 46570
r t 46548
kine sia</w> 46548
hepa tectomy</w> 46540
pro vinc 46538
preced ented</w> 46529
Con t 46527
brady kinin</w> 46526
tin a</w> 46524
dos ages</w> 46524
CRP C</w> 46521
sen escent</w> 46516
TL R3</w> 46514
rib a 46509
ari atric</w> 46506
maca ques</w> 46504
intellig ence</w> 46501
ly ase</w> 46495
mas king</w> 46494
PA T</w> 46493
oti tis</w> 46489
glutar aldehyde</w> 46487
sub cortical</w> 46484
oxal iplatin</w> 46484
o edema</w> 46480
ome trically</w> 46456
T et 46454
moun ting</w> 46454
is in</w> 46451
sandw ich</w> 46451
sc ap 46450
repeti tion</w> 46450
py raz 46441
docum ents</w> 46434
tun n 46432
retino l</w> 46432
ul nar</w> 46423
ophthal mic</w> 46420
neuro protection</w> 46419
ta urine</w> 46417
EM SA</w> 46415
chem opre 46411
exacerb ated</w> 46410
f en</w> 46406
An tim 46406
um en</w> 46394
PA D</w> 46388
k J</w> 46386
T esting</w> 46384
RN AP</w> 46383
ylo bacter</w> 46382
stress ful</w> 46380
D un 46375
copolym er</w> 46375
intra epithelial</w> 46373
schizophren ic</w> 46373
S am 46371
ect ants</w> 46369
f eces</w> 46366
bo ok</w> 46354
n AChR</w> 46350
immunoglob ulins</w> 46350
Pois son</w> 46333
an ox 46331
CN Ts</w> 46330
proj ected</w> 46326
E 8</w> 46324
P ep 46323
pl es</w> 46319
con gen 46317
ma tern 46316
quantit ated</w> 46313
CC C</w> 46310
pa w</w> 46306
O ri 46303
Hyper tension</w> 46298
lam ellar</w> 46292
s outh</w> 46288
six th</w> 46288
vag al</w> 46286
radi ologists</w> 46281
bul b</w> 46281
E U</w> 46278
F G</w> 46277
An t 46265
liqu ids</w> 46265
intrac erebral</w> 46260
ance stry</w> 46260
de regulation</w> 46256
V AD</w> 46253
riba virin</w> 46246
K v1</w> 46239
car ing</w> 46238
W ash 46237
lipo philic</w> 46232
MG MT</w> 46225
In dependent</w> 46223
ascer tained</w> 46214
B AT</w> 46207
F B</w> 46207
vaso constriction</w> 46207
id us</w> 46201
ar i</w> 46195
de stabilization</w> 46193
disequ ilibrium</w> 46193
AD A</w> 46189
neigh bo 46185
H er</w> 46179
Li ter 46175
lute in 46167
iz ers</w> 46166
rig hts</w> 46163
R D 46162
em ed</w> 46155
CI s</w> 46151
cali brated</w> 46140
re j 46134
rein forced</w> 46127
fund us</w> 46123
ul ic</w> 46122
reg imes</w> 46121
dor m 46116
multi focal</w> 46110
pri s 46105
neph ritis</w> 46104
ep isodic</w> 46096
afferen ts</w> 46094
fif ty</w> 46090
con stipation</w> 46080
harbo red</w> 46078
Al most</w> 46072
SW I</w> 46069
alge sia</w> 46063
ffe e</w> 46058
caro tene</w> 46055
A SP 46052
pa x 46045
Pri mer</w> 46045
cos tim 46044
myco bacterial</w> 46044
Rab bit</w> 46039
De ath</w> 46033
fac ile</w> 46029
ref ly</w> 46029
ha plo 46012
scav enger</w> 46007
anti platelet</w> 45991
cr e</w> 45982
off s</w> 45980
Neon atal</w> 45975
par alog 45970
car c 45963
pic tures</w> 45962
Bost on</w> 45960
com mitted</w> 45959
hel ping</w> 45959
ob struc 45958
co inc 45945
flavon oid</w> 45944
Pancre atic</w> 45943
stand ar 45927
trache a</w> 45919
F AD 45910
W is 45909
nanom olar</w> 45903
so y</w> 45896
compar atively</w> 45895
anti gen 45894
b cl</w> 45892
rh in 45892
electro magnetic</w> 45885
P ERK</w> 45884
S 2A</w> 45883
advanc ement</w> 45883
CYP2 D6</w> 45875
G ood</w> 45868
ide as</w> 45868
In vol 45860
Concentr ations</w> 45860
li ters</w> 45857
sy l</w> 45857
RT I</w> 45857
CN T</w> 45854
infiltr ate</w> 45850
SE C</w> 45848
h uge</w> 45846
Min istry</w> 45843
Ab normal</w> 45841
Fr ag 45834
U ro 45830
li t</w> 45830
Mat rix</w> 45827
less ons</w> 45826
amph o 45817
V ANCE</w> 45815
conden sed</w> 45814
Ab str 45809
aldehy des</w> 45805
long est</w> 45800
Con ventional</w> 45793
0 mM</w> 45791
li bit 45788
O CD</w> 45787
GG T</w> 45785
S c</w> 45784
mo sis</w> 45778
dri ft</w> 45776
kne es</w> 45774
Intr aven 45770
splen ocytes</w> 45761
NO T 45754
intim a</w> 45753
ys mal</w> 45751
Clin ical 45747
centro mere</w> 45746
margin ally</w> 45736
at resi 45726
oph en</w> 45726
Pe ak</w> 45723
2 e</w> 45716
Z O</w> 45714
Pro lifer 45714
br u 45712
nit us</w> 45712
catechol amines</w> 45712
p GE 45710
Mid dle</w> 45707
ST R</w> 45706
Er k</w> 45706
restra int</w> 45695
Mac roph 45689
libit um</w> 45678
inoc ulum</w> 45676
veget able</w> 45667
HA ART</w> 45667
jug ular</w> 45663
u PA</w> 45659
AB T</w> 45654
lan tic</w> 45654
NF AT</w> 45653
Hu ang</w> 45644
perin uclear</w> 45644
e ae</w> 45641
M 9</w> 45641
Surve illance</w> 45631
Hunting ton</w> 45631
g ame</w> 45630
occlud ed</w> 45622
ga thered</w> 45621
Dem ographic</w> 45596
membran ous</w> 45585
for ty</w> 45583
re 1</w> 45576
A -</w> 45574
ab rup 45572
n u</w> 45571
diaz epam</w> 45569
I b</w> 45563
st omy</w> 45559
i bu 45555
FGF R1</w> 45555
ist an</w> 45552
En d</w> 45549
B . 45545
ad ds</w> 45535
f ic 45533
ornith ine</w> 45533
hall marks</w> 45526
C Q</w> 45525
weigh ed</w> 45524
L 9</w> 45516
t an</w> 45511
shrin kage</w> 45509
Merc k</w> 45493
od azole</w> 45487
or ide</w> 45486
gen u 45486
N b 45484
Jo int</w> 45484
it y 45480
syring e</w> 45480
V an 45476
Immuno fluorescence</w> 45471
ad one</w> 45463
n an</w> 45461
cl aud 45457
Sy k</w> 45453
ne al</w> 45450
T am 45447
ultrastruc ture</w> 45445
oxid es</w> 45444
b om 45443
el es</w> 45440
bio chemistry</w> 45440
M en</w> 45439
situ ated</w> 45435
rest rial</w> 45434
N ec 45425
lig nin</w> 45418
RE LE 45416
hyper insulin 45414
un stimulated</w> 45413
te thered</w> 45406
mil dly</w> 45405
hum eral</w> 45402
loc ked</w> 45399
S exual</w> 45396
po pl 45394
de u 45387
mes ang 45386
hypo plasia</w> 45384
CT G</w> 45374
keratin ocyte</w> 45367
plan es</w> 45363
ag onal</w> 45358
gen istein</w> 45355
BM T</w> 45354
migr ant</w> 45353
p GL 45352
to tic</w> 45350
EVI DENCE</w> 45347
pig lets</w> 45344
Con cer 45335
Hist one</w> 45334
alpha 1</w> 45329
Gener ally</w> 45328
s la 45323
ne vertheless</w> 45307
ev is</w> 45303
SD F</w> 45303
seroton ergic</w> 45300
ET S</w> 45295
thalasse mia</w> 45295
CD K1</w> 45290
A U</w> 45288
navig ation</w> 45285
M PA</w> 45284
minim ized</w> 45281
cranio facial</w> 45276
re test</w> 45274
im pul 45265
in completely</w> 45260
Ex tensive</w> 45257
Compreh ensive</w> 45254
sal mon</w> 45250
gas tritis</w> 45248
resi des</w> 45247
secre tions</w> 45247
euthan ized</w> 45246
audi t</w> 45243
CC AT 45241
dec on 45237
0 R</w> 45233
ad ine</w> 45227
h ur 45223
pol lin 45222
au er</w> 45220
hetero zygotes</w> 45217
Recover y</w> 45210
sp ic 45209
Pregn ancy</w> 45209
sc aled</w> 45208
p f 45207
P SCs</w> 45205
psych opathology</w> 45205
clu es</w> 45204
ass urance</w> 45200
ham pered</w> 45198
Targ eted</w> 45198
m oly 45195
mo de 45195
D HFR</w> 45194
s an 45192
PU FA</w> 45191
o -</w> 45187
uch i</w> 45185
ap en 45177
typh imurium</w> 45174
spe aking</w> 45171
oc kets</w> 45168
cycl op 45162
ST 2</w> 45161
re feren 45149
late x</w> 45146
Rec A</w> 45144
Na F</w> 45138
re pressors</w> 45135
cytom eter</w> 45130
andro ste 45125
cyl in 45122
Em erg 45121
in qu 45117
immunocom pe 45112
Mut ant</w> 45107
glit azone</w> 45106
inter ven 45105
id ate</w> 45103
CD DP</w> 45102
Figure 2</w> 45100
val pro 45096
re produced</w> 45088
ren dering</w> 45087
resid ency</w> 45085
counter act</w> 45085
pass ing</w> 45084
le m 45083
Thyro id</w> 45083
star ts</w> 45080
N ature</w> 45075
crystalli zed</w> 45067
lam bs</w> 45058
C ost</w> 45052
hex ane</w> 45048
mil ieu</w> 45047
Gener ation</w> 45037
O k 45034
friend ly</w> 45026
tri s 45023
phero mone</w> 45020
Δ Δ 45007
zygo tic</w> 45005
N SCs</w> 45003
P ot 45003
A or 44998
re pul 44995
W T1</w> 44993
amelior ate</w> 44991
E 9</w> 44985
top ography</w> 44985
d am</w> 44984
osyl transferase</w> 44982
M r 44978
prof ession</w> 44977
6 c</w> 44966
pd f</w> 44966
G CN 44960
de emed</w> 44959
sub traction</w> 44959
resi ding</w> 44953
ag itation</w> 44952
L p</w> 44943
Ser toli</w> 44939
asparag ine</w> 44938
Recom m 44933
peritone um</w> 44932
sic k</w> 44931
d 4</w> 44928
accor dingly</w> 44921
der ma</w> 44916
isom erization</w> 44916
un precedented</w> 44911
ly can</w> 44910
M ain 44909
BAC E1</w> 44898
recombin ase</w> 44896
lac tam</w> 44894
poly unsaturated</w> 44889
j et</w> 44882
occup ation</w> 44873
n ous</w> 44867
rec re 44866
re produce</w> 44863
M ann 44848
sched ules</w> 44847
parathyro idism</w> 44843
TC P</w> 44841
VEGF R2</w> 44835
M os 44824
D yn 44819
K l 44813
buff ers</w> 44813
y z 44799
R v 44796
symptom atology</w> 44796
BL AST</w> 44794
read ers</w> 44789
Tu key</w> 44783
as c 44781
ground water</w> 44780
pro thrombin</w> 44775
as on</w> 44773
pharmac odynamic</w> 44772
phyl ogeny</w> 44772
y le 44766
GSK 3</w> 44760
ogen etics</w> 44755
el is</w> 44744
ph os 44735
cas tration</w> 44734
orthop edic</w> 44732
ion ized</w> 44727
gene ic</w> 44725
teri um</w> 44722
g lo 44720
be ats</w> 44720
Uni on</w> 44719
f im 44710
liter acy</w> 44703
clop idogrel</w> 44701
ronch ial</w> 44694
st ar</w> 44692
MS M</w> 44689
com ments</w> 44687
ja undice</w> 44680
V LDL</w> 44677
gastr in</w> 44676
Tur key</w> 44669
Diagnos tics</w> 44668
tern s</w> 44664
TRI AL</w> 44659
conflic ts</w> 44648
CD K2</w> 44647
Tran sl 44645
gre y</w> 44639
PE P</w> 44636
uro thelial</w> 44634
AG C</w> 44626
n 3</w> 44625
P M2</w> 44619
re traction</w> 44617
lepro sy</w> 44616
resem bled</w> 44610
spati otemporal</w> 44606
m ant 44595
γ H2AX</w> 44595
in efficient</w> 44595
W BC</w> 44589
play er</w> 44589
rot ating</w> 44589
vi e</w> 44576
WIT H</w> 44572
ST Z</w> 44569
di am 44568
ren ders</w> 44564
MC C</w> 44564
transf ers</w> 44558
Academ y</w> 44557
dynam ical</w> 44551
ax es</w> 44543
E q</w> 44540
rig idity</w> 44536
Isra el</w> 44533
a ids</w> 44532
sp un</w> 44523
Combin ing</w> 44512
di urnal</w> 44509
peric ardi 44505
5 d</w> 44503
transpor tation</w> 44500
ME N 44494
Wh y</w> 44488
de regulated</w> 44487
atresi a</w> 44482
M ob 44475
man nit 44474
W OR 44468
ser ially</w> 44466
mimic ked</w> 44466
modul atory</w> 44457
Camp ylobacter</w> 44457
transp arent</w> 44450
ana plastic</w> 44445
B ol 44428
ABC G2</w> 44427
Sc i</w> 44423
my otubes</w> 44408
retro peritoneal</w> 44402
acetab ular</w> 44402
bil aterally</w> 44394
X IAP</w> 44390
te es</w> 44389
cing ulate</w> 44384
IN K 44383
Cl p 44378
all ografts</w> 44377
Ag ing</w> 44374
Tem por 44373
pac e</w> 44359
S ens 44354
At lantic</w> 44352
T OR</w> 44351
chori onic</w> 44345
supram olecular</w> 44345
Coun ty</w> 44343
Path ology</w> 44342
O Ac</w> 44335
IN TRO</w> 44330
Smad 2</w> 44328
Aut ophagy</w> 44326
thyro xine</w> 44317
stri kingly</w> 44316
the ophylline</w> 44314
With out</w> 44314
E ST</w> 44308
perpen dicular</w> 44300
AM 1</w> 44298
marri ed</w> 44297
every day</w> 44277
D J</w> 44265
Intraven ous</w> 44265
ris tine</w> 44259
substitu ent</w> 44259
la evis</w> 44257
germin al</w> 44257
B ang 44256
A li 44248
ol ive</w> 44247
RELE VANCE</w> 44242
AA C</w> 44239
restric tions</w> 44230
y cle</w> 44229
mor al</w> 44228
. edu</w> 44222
pres ently</w> 44222
Perc utaneous</w> 44221
hem olysis</w> 44220
N ak 44218
sl iding</w> 44215
fa ecal</w> 44215
mas tectomy</w> 44205
PPAR α</w> 44200
excre ted</w> 44196
trac hom 44195
PR P</w> 44192
D y 44191
guid ing</w> 44190
na phthal 44176
Ta x</w> 44175
cl ades</w> 44172
cruc iate</w> 44171
si l</w> 44167
Signific ance</w> 44165
an il</w> 44162
des tin 44162
local isation</w> 44161
in significant</w> 44158
h os 44156
G AA</w> 44156
si ves</w> 44154
iso electric</w> 44154
tri som 44149
d 6</w> 44144
or dering</w> 44137
rh abdomy 44136
ab e 44135
Q Ds</w> 44130
AB CB1</w> 44130
tic um</w> 44130
Hu R</w> 44124
PC B</w> 44121
restric t</w> 44121
tro ut</w> 44117
amelior ated</w> 44117
n ess 44115
im otor</w> 44098
f r</w> 44094
ol ith 44093
oupl ed</w> 44085
obuty ric</w> 44085
The ore 44084
I on</w> 44083
m ole</w> 44079
D BS</w> 44078
con ference</w> 44078
phosph onate</w> 44075
dw elling</w> 44074
mannit ol</w> 44072
z ip 44071
recep tive</w> 44070
Frequ ency</w> 44068
si on 44062
io id</w> 44060
nos ocomial</w> 44059
Endo thelial</w> 44058
in terior</w> 44054
Bi m</w> 44050
ir o</w> 44043
ev en 44029
osom iasis</w> 44028
public ly</w> 44028
CD s</w> 44023
psych ology</w> 44021
O CT 44017
de acetylation</w> 44008
TA K1</w> 44008
gu ar 44005
Poly morph 44001
Targ et</w> 43998
Str ain</w> 43996
Extrac ellular</w> 43990
TL D</w> 43988
hi zo 43988
far nes 43988
fluctu ation</w> 43987
umb ens</w> 43985
posi tives</w> 43984
paradig ms</w> 43983
contras ting</w> 43952
N MD</w> 43948
si bling</w> 43948
E SC 43939
John son</w> 43938
SRE BP</w> 43932
flo x 43928
elas ticity</w> 43928
ak ers</w> 43923
my c 43914
tryp tamine</w> 43914
al bin 43912
olab s</w> 43885
S can</w> 43884
circu it 43884
thi oredoxin</w> 43880
CL A</w> 43878
Stud ents</w> 43872
cycl ization</w> 43868
my cel 43862
ali phatic</w> 43862
Re ver 43859
R CT</w> 43855
CP B</w> 43854
retino blastoma</w> 43852
retin as</w> 43840
T D 43838
neuro fibrom 43835
For m</w> 43833
bac ks</w> 43832
L BP</w> 43825
neuro psychiatric</w> 43824
L ab</w> 43820
Dis order</w> 43813
vill i</w> 43810
hydroxy apatite</w> 43806
fo under</w> 43804
Jo hn</w> 43801
off ic 43798
seas ons</w> 43798
ML H1</w> 43796
ari ans</w> 43795
compan ies</w> 43791
fe at 43784
S pi 43781
rele ases</w> 43777
A OR</w> 43768
T GA</w> 43768
Fas L</w> 43762
Ad ults</w> 43757
L 8</w> 43755
is co</w> 43753
chol erae</w> 43751
In tes 43750
mesoth elioma</w> 43748
adeno viral</w> 43744
b erg 43738
deoxychol ate</w> 43737
hyalu ron 43736
Ser 4</w> 43735
t g 43733
tin in</w> 43715
Wor k</w> 43714
tre s</w> 43711
Ter min 43710
gre en 43709
S f 43706
osel ectivity</w> 43706
hol low</w> 43703
Qual itative</w> 43693
es -- 43676
H3K4 me3</w> 43664
4 L</w> 43660
insp iratory</w> 43659
sic kness</w> 43645
W R</w> 43644
Vi et 43644
ev ing</w> 43637
correl ating</w> 43637
PD E 43636
potenti ate</w> 43633
F b 43629
urg ery</w> 43629
bl unted</w> 43628
cho w</w> 43625
orph an</w> 43624
Ptd Ins</w> 43624
mu sic</w> 43622
abstin ence</w> 43620
ti ous</w> 43616
p et 43615
chemo resistance</w> 43611
transcriptom ic</w> 43608
di ode</w> 43606
ap p</w> 43606
xyl ose</w> 43598
G BS</w> 43592
leaf let</w> 43583
W 3</w> 43577
con vinc 43576
bro aden 43571
Dep ending</w> 43553
ca emic</w> 43551
Lac Z</w> 43550
Bi olabs</w> 43544
si r 43534
B asal</w> 43533
bi directional</w> 43532
rh o</w> 43532
od ine</w> 43531
y ran 43528
ti les</w> 43528
inter genic</w> 43523
L RP 43521
appreci able</w> 43521
MD R1</w> 43520
Compu ter</w> 43518
as best 43515
palmit ate</w> 43512
K Y</w> 43505
h ang 43501
el ings</w> 43499
neuro toxic</w> 43496
HU VEC</w> 43493
ine ural</w> 43482
si bility</w> 43477
S AP 43474
W el 43468
H -</w> 43465
itin ib</w> 43465
ventil ated</w> 43464
ter restrial</w> 43460
cont our</w> 43454
perc ei 43450
spectro metric</w> 43449
modi fier</w> 43445
s lowing</w> 43440
ã o</w> 43440
de vast 43431
sphero ids</w> 43431
Alter ations</w> 43429
ingredi ents</w> 43426
abol ish</w> 43424
spectrophot ometer</w> 43424
C 5 43422
NAM E</w> 43419
fer ment 43396
C on</w> 43390
AD H</w> 43388
Rhe um 43388
miti gate</w> 43387
Optim al</w> 43385
an dr 43384
V ar 43381
orac ic</w> 43379
nan oma 43367
GI ST</w> 43367
pa in 43366
ch air</w> 43352
ic k 43351
il o 43349
f in</w> 43340
spong e</w> 43326
li thi 43324
Impro ving</w> 43321
Morph ological</w> 43321
Pro tection</w> 43320
ML V</w> 43319
strepto zotocin</w> 43317
com mis 43316
isothi ocyanate</w> 43313
ER K2</w> 43311
nor ms</w> 43310
kn esses</w> 43309
tac rolimus</w> 43303
th igh</w> 43296
ma z 43296
f urthermore</w> 43294
P s 43292
mon oc 43290
M uch</w> 43286
erythropo i 43284
d G</w> 43277
plem entation</w> 43271
t RNAs</w> 43267
Ser ial</w> 43267
MI F</w> 43258
mTOR C2</w> 43256
est rous</w> 43254
elic its</w> 43238
dig esti 43235
W hi 43234
ti er</w> 43232
ep tic</w> 43230
oxid oreduc 43229
polypo sis</w> 43229
por phy 43224
end o</w> 43220
on ality</w> 43215
bul ky</w> 43214
secti oned</w> 43199
nemat odes</w> 43199
me als</w> 43198
AN S</w> 43196
AC s</w> 43188
dener vation</w> 43188
slow ed</w> 43182
H2 S</w> 43176
cryp tic</w> 43173
rs 4</w> 43169
respon ds</w> 43167
F lor 43161
oc ta 43154
MT HFR</w> 43145
hal operidol</w> 43144
om er</w> 43139
deaf ness</w> 43133
der mis</w> 43123
terp ene</w> 43123
co stly</w> 43119
leuk otri 43111
AD R</w> 43110
V CAM</w> 43106
he al</w> 43105
trachom atis</w> 43104
arg ued</w> 43103
nanos cale</w> 43101
sp ra 43096
NS C</w> 43092
pre conditioning</w> 43087
bre ed</w> 43079
oty rosine</w> 43075
PD I</w> 43071
ful filled</w> 43070
ampl icon</w> 43066
S B2</w> 43063
op a</w> 43058
oscill ation</w> 43058
mem bered</w> 43056
S F1</w> 43048
ad el 43048
desi re</w> 43046
paren ting</w> 43045
me 2</w> 43042
este em</w> 43041
PI C</w> 43038
W NT</w> 43031
Pro tec 43027
N 5</w> 43016
evalu able</w> 43002
Wash ington</w> 43000
c im 42996
PL D</w> 42994
AT RA</w> 42991
ph le 42989
ac knowledg 42986
os ac 42986
NO 3</w> 42983
Recur rent</w> 42983
extr usion</w> 42979
A sh 42974
res ections</w> 42974
ass ed</w> 42965
U 3</w> 42963
U 6</w> 42956
T ET 42953
Un expectedly</w> 42952
omet ries</w> 42947
human ized</w> 42947
motoneu rons</w> 42944
w age</w> 42942
insp ired</w> 42940
bre eds</w> 42930
t oral</w> 42929
ten sor</w> 42928
R BD</w> 42924
M otor</w> 42917
ribonucle ic</w> 42917
c idin</w> 42916
resili ence</w> 42913
ne u</w> 42904
li que</w> 42904
di uretic</w> 42897
omen cl 42896
evid ences</w> 42880
atri oventricular</w> 42873
A SC 42869
dur able</w> 42865
cannabin oid</w> 42863
p L 42854
s aving</w> 42851
e icos 42850
detec ts</w> 42848
D eli 42847
glycos amin 42838
but yl 42837
C2 C1</w> 42837
A si 42835
T NM</w> 42831
veter ans</w> 42830
fer ric</w> 42828
X S</w> 42827
J s</w> 42818
CB T</w> 42816
cyt ological</w> 42813
amo eb 42813
no tic</w> 42808
cat abolic</w> 42805
prokary otic</w> 42803
mess ages</w> 42802
Fin land</w> 42799
b ute</w> 42790
neph rotic</w> 42783
hyper parathyroidism</w> 42772
I L2</w> 42771
S j 42770
analy te</w> 42767
A sc 42763
blood stream</w> 42757
dys lipidemia</w> 42751
flow ers</w> 42751
adop ts</w> 42750
elucid ating</w> 42747
Alph a</w> 42745
Bi ology</w> 42741
sig moid</w> 42733
b . 42730
M ax 42729
conver gent</w> 42720
neuro transmitters</w> 42716
al th</w> 42709
OR R</w> 42707
Sim ple</w> 42702
ex onuclease</w> 42701
o rophar 42700
RA L</w> 42695
my cin</w> 42684
athe ter</w> 42682
α 4</w> 42680
micro plate</w> 42680
reas oned</w> 42680
Cul tures</w> 42672
pedi atr 42671
jour nal</w> 42666
el even</w> 42663
oc clusive</w> 42661
link ages</w> 42654
Br uc 42650
u sive</w> 42641
explo it</w> 42641
Al k 42630
appe tite</w> 42629
G AT</w> 42625
arr ative</w> 42624
bi oc 42619
S ud 42618
inf ras 42618
A HR</w> 42615
gin sen 42611
replic on</w> 42610
Oxy gen</w> 42609
Electro nic</w> 42605
syn geneic</w> 42603
F rac 42599
sh RNAs</w> 42596
b erry</w> 42593
am yl 42587
bi ocompatibility</w> 42579
isop ren 42578
hemat ocrit</w> 42565
ecta sia</w> 42564
ampho tericin</w> 42560
B at 42547
ke y 42545
ra in 42542
bri gh 42538
C are 42533
acryl ic</w> 42524
D AG</w> 42513
X 9</w> 42509
GA AG 42505
TRA F6</w> 42500
p M</w> 42497
A ch 42495
aneu ploidy</w> 42473
N ag 42461
malon dialdehyde</w> 42446
S audi</w> 42445
streng then</w> 42441
bio assay</w> 42439
P ON 42433
im bur 42431
R X 42430
R ight</w> 42425
ER D</w> 42400
GLUT 4</w> 42398
mono amine</w> 42391
S 1B</w> 42388
melan in</w> 42387
A qu 42385
T ATA</w> 42378
em itting</w> 42377
bio activity</w> 42374
Per s 42365
veh icles</w> 42357
in tent</w> 42348
con du 42348
reson ances</w> 42348
C hip</w> 42346
og e 42343
osyn thesis</w> 42343
5 T</w> 42342
low -</w> 42322
Sev en 42315
V a 42313
su n</w> 42299
AR DS</w> 42299
- end</w> 42295
bo os 42295
ur an 42291
box es</w> 42288
eti ologies</w> 42282
vasodi lation</w> 42281
PF U</w> 42279
it rate</w> 42278
2 T</w> 42273
a fil</w> 42273
ch lamy 42266
expec tation</w> 42257
st or 42245
col chicine</w> 42245
7 p</w> 42244
ven tro 42244
nucle oti 42242
chic ks</w> 42235
benzodi azepine</w> 42234
L 7</w> 42233
electrocardi ogram</w> 42216
Fi eld</w> 42215
X ba 42204
interpre ting</w> 42204
concer t</w> 42202
M PS</w> 42200
AI R</w> 42197
underestim ated</w> 42195
a h</w> 42179
Q RS</w> 42175
Con focal</w> 42175
N ES</w> 42170
Med line</w> 42168
under graduate</w> 42158
troph oblast</w> 42155
nor th</w> 42153
L F 42151
li d</w> 42150
An a 42148
Meth yl 42148
hydro dynamic</w> 42141
ulcer ation</w> 42140
H2 PO4</w> 42137
ba it</w> 42127
MAP Ks</w> 42126
CR E</w> 42122
deli rium</w> 42120
m ar</w> 42116
W eight</w> 42114
as ks</w> 42114
la id</w> 42113
J ol 42111
devast ating</w> 42104
he im</w> 42101
hapl oid</w> 42100
I MT</w> 42097
to uch</w> 42093
tun able</w> 42089
appro ached</w> 42085
chro no 42081
clon idine</w> 42081
ch itin</w> 42071
k ins</w> 42069
MR S</w> 42069
po uch</w> 42065
R om 42064
comp elling</w> 42064
m entary</w> 42063
L LC</w> 42062
M un 42060
conver ts</w> 42056
us h 42055
Sever ity</w> 42055
igen in</w> 42055
ard less</w> 42054
7 S</w> 42050
noc odazole</w> 42046
f alling</w> 42044
k V</w> 42043
tr ust</w> 42032
prostagland ins</w> 42030
D am 42028
consi ders</w> 42019
prob ands</w> 42015
7 G</w> 42006
mesang ial</w> 41999
pri mor 41998
IC E</w> 41995
Correc tion</w> 41992
modi fiers</w> 41990
end el 41981
ra ter</w> 41973
initi o</w> 41970
Et OH</w> 41964
Mech anical</w> 41961
Ep id 41955
das atinib</w> 41955
om eric</w> 41951
sc rat 41948
PA A</w> 41947
war rant</w> 41945
as y</w> 41943
sensor imotor</w> 41939
pres sions</w> 41930
cho osing</w> 41927
frag ile</w> 41927
AP OB 41925
celi ac</w> 41925
hypercholesterole mia</w> 41923
frame works</w> 41921
s lip</w> 41920
Reg ardless</w> 41916
manufactu rers</w> 41910
ACh Rs</w> 41907
scrip t</w> 41901
Repe ated</w> 41899
ar abine</w> 41894
advanc ing</w> 41890
Dev ices</w> 41889
SP 6</w> 41884
sub acute</w> 41881
occa sional</w> 41868
Inter vention</w> 41863
C el 41860
Col labor 41859
sing let</w> 41854
f 4</w> 41852
TLR 9</w> 41852
Pol and</w> 41848
beta 2</w> 41848
morph ometric</w> 41845
3 I</w> 41843
c ow 41843
Form ula</w> 41838
ucle ated</w> 41837
pro to</w> 41836
sh aft</w> 41831
Altern ative</w> 41827
ogl ut 41822
insul t</w> 41821
belie f</w> 41820
1 M</w> 41814
k ain 41810
exacerb ations</w> 41810
infrequ ent</w> 41810
ran king</w> 41797
blas tic</w> 41796
dop ing</w> 41794
be ar</w> 41791
AT S</w> 41789
otu be</w> 41785
ri ers</w> 41779
Pharmac ological</w> 41778
cyclo dextrin</w> 41777
hol es</w> 41771
rest ores</w> 41765
1 e</w> 41764
amin ophen</w> 41756
ass or 41754
trans genes</w> 41754
He at</w> 41754
des cent</w> 41747
os terol</w> 41738
vol tam 41738
tetan us</w> 41737
appendic itis</w> 41735
c 3</w> 41722
o estrogen</w> 41719
Eg yp 41719
TL s</w> 41716
Ne ural</w> 41715
ogene tically</w> 41712
γ 1</w> 41698
if ier</w> 41696
mode stly</w> 41693
pep tic</w> 41691
so unds</w> 41687
antagon ized</w> 41681
IC S</w> 41679
contr asts</w> 41676
K re 41674
const antly</w> 41674
quinol one</w> 41667
unc tional</w> 41658
ir rit 41657
ab brevi 41651
ou d</w> 41638
V ision</w> 41635
w ell 41631
exten sor</w> 41630
h pi</w> 41624
are ly</w> 41623
G US</w> 41621
ro stral</w> 41617
4 V</w> 41616
compu ting</w> 41615
Temper ature</w> 41615
speci ation</w> 41612
random ization</w> 41604
A m</w> 41597
Sox 2</w> 41593
il ic</w> 41591
wor ker</w> 41585
dyst onia</w> 41580
ob liter 41568
de activation</w> 41567
ace a</w> 41563
psor i 41562
glucon e 41562
evalu ates</w> 41560
eth idium</w> 41556
Carb on</w> 41551
re form</w> 41543
pauc ity</w> 41541
bub ble</w> 41540
s unitinib</w> 41531
arri val</w> 41531
negl ected</w> 41528
V an</w> 41518
V O2</w> 41514
PD K1</w> 41513
ti m</w> 41506
perf ec 41498
Clin ically</w> 41486
PL P</w> 41485
trich loro 41485
caud ate</w> 41480
IS O</w> 41478
m s 41476
bi . 41470
observ able</w> 41470
Ig G4</w> 41464
R G</w> 41459
di g</w> 41455
ery th 41454
III a</w> 41454
m ate</w> 41450
andi bular</w> 41443
reflex es</w> 41442
cap ping</w> 41439
O t 41435
ad here</w> 41429
punc tate</w> 41427
discrimin ant</w> 41425
um s</w> 41424
top yran 41421
C PAP</w> 41408
anastom otic</w> 41401
O K 41398
standardi zation</w> 41396
I κB</w> 41395
Tyr 1</w> 41391
Ty ph 41391
Mod els</w> 41386
Con sortium</w> 41378
S TING</w> 41375
n l 41374
ventr icul 41371
con dyl 41366
tub er 41366
S R1</w> 41364
intran asal</w> 41358
2 V</w> 41352
ing ested</w> 41350
alpha -</w> 41345
H D1</w> 41344
benzo ate</w> 41337
CR H</w> 41335
E 1A</w> 41334
gen d</w> 41334
B ru 41331
spo ro 41331
sim ulating</w> 41330
inf est 41328
flav one</w> 41326
H a</w> 41320
cooper ativity</w> 41320
T CDD</w> 41319
thromb olysis</w> 41318
a vid 41309
NO 2</w> 41308
D M1</w> 41307
syndrom ic</w> 41306
Da ily</w> 41302
morbi d</w> 41296
L . 41288
acet amide</w> 41285
leishman iasis</w> 41285
smo oth 41279
implic it</w> 41279
e z</w> 41269
E US</w> 41267
per tin 41261
C av 41260
V M</w> 41256
tr in 41252
ho p</w> 41249
l ad 41242
ac a</w> 41228
vi ted</w> 41226
he ro 41224
O -</w> 41221
brachy therapy</w> 41219
establish es</w> 41216
persi sts</w> 41213
acceler ates</w> 41212
re operation</w> 41210
rab ies</w> 41210
constra int</w> 41209
S ou 41208
mon oxide</w> 41207
intrac table</w> 41204
cle aves</w> 41202
po d</w> 41189
z ona</w> 41179
er ian</w> 41177
My el 41176
red ness</w> 41173
anil ine</w> 41172
choro id</w> 41168
acc umbens</w> 41155
er ine</w> 41147
Bacteri a</w> 41136
broncho alveolar</w> 41135
fr ic 41130
sig ht</w> 41130
fo ve 41129
sw allowing</w> 41129
Seven teen</w> 41129
ph alin</w> 41121
overl apped</w> 41117
Pro long 41110
Identi fying</w> 41106
incorrec t</w> 41104
B Ps</w> 41103
neur ites</w> 41102
TL S</w> 41095
explic itly</w> 41093
C enters</w> 41092
li ximab</w> 41090
O d 41087
S CT</w> 41077
ant o 41076
saf er</w> 41076
ir ino 41075
Pro gram 41064
homogen ate</w> 41058
metabol ically</w> 41057
Influ enza</w> 41056
af 1</w> 41047
ut amide</w> 41042
s oma</w> 41040
DM F</w> 41039
micro n</w> 41036
nid azole</w> 41035
lam p</w> 41031
oxal ate</w> 41023
c ili 41019
2 D3</w> 41014
TC F</w> 41008
ap poin 40999
VE L</w> 40997
cal oric</w> 40992
y ch 40991
G on 40991
CONT EXT</w> 40988
multi modal</w> 40984
l ati 40983
N BD</w> 40981
fe elings</w> 40981
nic k</w> 40974
Path way</w> 40971
clinic opathologic</w> 40958
aut onomy</w> 40956
sol ids</w> 40955
EBP β</w> 40955
Stra ins</w> 40954
pre existing</w> 40952
chrom atid</w> 40951
incid ences</w> 40947
immunocompe tent</w> 40945
C α</w> 40942
B CA</w> 40941
enzym atically</w> 40939
po ver 40938
Diab etic</w> 40938
sav ings</w> 40933
teri es</w> 40928
idi tis</w> 40910
introduc es</w> 40909
SO S</w> 40908
Contr ary</w> 40903
Uni variate</w> 40897
iod o 40897
Strateg ies</w> 40896
Bec lin</w> 40888
log ists</w> 40887
chymo trypsin</w> 40886
M TS</w> 40885
Mal ay 40881
corrobor ated</w> 40878
R al 40877
ur o</w> 40874
ventr icles</w> 40874
peri plasmic</w> 40868
anx i 40868
ch ie 40861
w at 40856
homo dimer</w> 40855
alk ylation</w> 40851
s and</w> 40847
de aminase</w> 40842
FV III</w> 40839
it real</w> 40836
al cin</w> 40836
su tures</w> 40836
cle aning</w> 40832
L ess</w> 40825
O T 40823
omencl ature</w> 40815
loc ate</w> 40810
male imide</w> 40804
psycho tics</w> 40803
w t 40801
K v 40800
dimin ish</w> 40800
i tic</w> 40797
Plas mids</w> 40796
compreh ension</w> 40796
irino tecan</w> 40791
te l</w> 40790
Chrom atin</w> 40790
Liter ature</w> 40790
burg h</w> 40784
organ o 40782
he d 40778
c ages</w> 40775
disp arity</w> 40771
her bi 40770
te thering</w> 40765
exec ution</w> 40765
per t</w> 40761
A beta</w> 40760
oxidi zing</w> 40760
c up</w> 40747
myel inated</w> 40747
oscill atory</w> 40747
W H</w> 40743
Por tu 40739
Chic ago</w> 40728
R d 40724
B PH</w> 40715
K ras</w> 40709
co filin</w> 40705
we dge</w> 40704
TSC 2</w> 40704
SS B</w> 40702
AG 1</w> 40701
. The</w> 40700
warran ts</w> 40698
certain ly</w> 40698
tri ad</w> 40697
muc inous</w> 40696
scaff olding</w> 40685
Valu es</w> 40685
poly vinyl 40682
spor ulation</w> 40670
u an</w> 40669
counsel ling</w> 40660
g B</w> 40658
U biqu 40656
co ffee</w> 40654
AP OE</w> 40654
X Y</w> 40653
HR QoL</w> 40649
fluoroph ore</w> 40632
Intes tinal</w> 40632
cylin dr 40631
Disco very</w> 40624
pe a</w> 40623
clus al</w> 40623
e der</w> 40622
D VT</w> 40619
O vari 40619
Z 2</w> 40618
Com put 40616
T 9</w> 40610
rhyth mic</w> 40608
interrup tion</w> 40598
Mechanis m</w> 40591
circuit ry</w> 40589
E stro 40586
fe el</w> 40586
Ad ju 40585
Xba I</w> 40580
pur pur 40576
flex or</w> 40560
tin nitus</w> 40549
L O</w> 40544
plas min</w> 40541
read ings</w> 40525
neutr alized</w> 40523
inv ading</w> 40520
fo etal</w> 40513
C 6 40512
adequ acy</w> 40504
D 0</w> 40501
restor ations</w> 40501
D s 40500
p ast 40493
val is</w> 40490
tacti le</w> 40488
HC M</w> 40483
E SR</w> 40482
G AL</w> 40479
faec alis</w> 40473
oscop e</w> 40471
HE V</w> 40469
Ti O</w> 40457
N . 40452
win d</w> 40450
Q 3</w> 40438
Cryst al</w> 40437
Qu e 40432
it z</w> 40430
compreh ensi 40430
STE MI</w> 40426
up eptin</w> 40425
w ar</w> 40420
myel inating</w> 40420
electroly tes</w> 40419
S CA 40415
PC D</w> 40415
B H</w> 40412
cohe sion</w> 40411
Pl k1</w> 40409
hy gro 40405
Vp r</w> 40404
w a</w> 40400
Tox oplasma</w> 40398
acetyl cholinesterase</w> 40397
INTERVEN TION</w> 40396
found ly</w> 40387
disc arded</w> 40385
neighbor hood</w> 40381
R 9</w> 40380
the astern</w> 40380
Clinical Tri 40380
Rel A</w> 40370
X u</w> 40367
ol one</w> 40365
X a</w> 40354
inter medi 40336
Epidemi ological</w> 40331
el ig 40330
ome dic 40330
sti l 40327
N 0</w> 40324
NP V</w> 40324
RP s</w> 40321
Cer vical</w> 40310
y ri 40306
PK B</w> 40304
relap sing</w> 40298
diver sification</w> 40295
igh ts</w> 40293
C TI 40292
m sec</w> 40292
us tion</w> 40290
di thi 40288
encour aged</w> 40288
Mod ulation</w> 40286
Fac ility</w> 40283
bur sts</w> 40277
indi genous</w> 40275
om yc 40273
mim etic</w> 40261
H ox 40259
Mod eling</w> 40257
pr ice</w> 40255
resol ving</w> 40251
conduc tive</w> 40247
re acting</w> 40246
H K 40244
M MTV</w> 40238
E PS</w> 40235
pro foundly</w> 40234
oligos accharide</w> 40233
W eb 40229
cle av 40229
hyp erox 40227
METHO DO 40223
a eg 40219
z apine</w> 40218
adv ent</w> 40218
bacul ovirus</w> 40217
S SC</w> 40213
Ar thro 40213
6 K</w> 40211
O PN</w> 40211
ox o 40211
el losis</w> 40207
G ar 40196
ing ing</w> 40189
muc ous</w> 40183
semiconduc tor</w> 40183
stereot actic</w> 40183
edi ted</w> 40175
B m 40162
morph ologies</w> 40162
Smo king</w> 40159
B ul 40155
ovari ectomized</w> 40154
Des crip 40150
tric uspid</w> 40150
ol inium</w> 40147
ul ous</w> 40146
H ir 40139
super vision</w> 40139
RI F</w> 40134
son i</w> 40124
End ogenous</w> 40122
fl ank</w> 40120
p ins</w> 40112
cephal ospor 40109
CA GT 40108
de sis</w> 40103
M BL</w> 40102
cl ot</w> 40097
ic ons</w> 40096
direc ting</w> 40096
l ated</w> 40095
Δ 2</w> 40094
iso ther 40091
w ann 40090
initi ator</w> 40090
Pro per 40090
di morph 40088
granul oma</w> 40084
on ella</w> 40082
h s 40079
V P2</w> 40076
stake holders</w> 40074
prepar ing</w> 40063
G S 40057
flox acin</w> 40057
M J 40054
re imbur 40053
co operate</w> 40050
p raz 40049
y 1</w> 40047
absc esses</w> 40046
D ip 40038
deform ities</w> 40038
R XR</w> 40029
mon as</w> 40027
lipos omal</w> 40024
essi vely</w> 40018
assi vely</w> 40013
te tro 40010
Sep ar 40004
trisom y</w> 39999
CF A</w> 39995
wh el 39993
st at</w> 39984
c g 39982
te c</w> 39981
fer rom 39976
meth adone</w> 39974
Mac ro 39974
C ol</w> 39966
intrat umoral</w> 39966
α v 39949
ag ens</w> 39949
deoxy glucose</w> 39949
Eff ectiveness</w> 39943
4 P</w> 39940
anti convuls 39937
har d 39934
Ambi on</w> 39929
M AG 39928
quad rup 39927
an n</w> 39924
ont ology</w> 39924
n yst 39923
fluoresc ently</w> 39921
SI RT 39920
shap ing</w> 39919
leukem ias</w> 39918
legis lation</w> 39917
CT CT 39910
Emerg ing</w> 39908
TB ST</w> 39906
neuro troph 39904
NF AT 39904
separ ating</w> 39897
CO R</w> 39881
im plantable</w> 39879
P ERI 39878
sumo ylation</w> 39878
DR 5</w> 39876
es thesia</w> 39875
viri dae</w> 39867
inter disciplinary</w> 39865
carcin ogen</w> 39859
em m 39857
ud ed</w> 39857
pow ered</w> 39855
pe ers</w> 39849
E ye</w> 39847
N ative</w> 39845
H RT</w> 39835
J ones</w> 39833
ob s</w> 39831
glomerul i</w> 39830
erec tile</w> 39828
G CC</w> 39824
sub scales</w> 39821
granul omatous</w> 39817
lin gness</w> 39813
a rest</w> 39811
ograph ed</w> 39809
lip id 39806
benzo ic</w> 39801
Ar terial</w> 39799
F oster</w> 39791
dis proportion 39791
n .</w> 39789
ra p</w> 39783
Sub stitu 39782
Me OH</w> 39778
cap ital</w> 39777
cl aim</w> 39774
AN G</w> 39774
sen ior</w> 39773
intra thecal</w> 39771
s ter</w> 39768
Myco plasma</w> 39767
orth opaedic</w> 39760
Mel an 39759
homo zygotes</w> 39757
A ri 39755
no tes</w> 39752
ventil ator</w> 39750
polymorph onuclear</w> 39748
reas oning</w> 39747
par ag 39742
H il 39737
ac y 39737
LVE F</w> 39733
R ates</w> 39730
vi gorous</w> 39729
PA K</w> 39728
coordin ating</w> 39725
b lu 39720
k Pa</w> 39717
T RP</w> 39716
coly tic</w> 39710
ER P</w> 39696
as tig 39691
man eu 39688
Tur k 39686
pr ints</w> 39682
L ey 39681
GRP 7</w> 39673
spot ted</w> 39670
S nail</w> 39665
A SI 39662
AD CC</w> 39662
relig ious</w> 39661
propyl ene</w> 39658
C 3H</w> 39657
Wil le 39653
o ides</w> 39650
atten dance</w> 39650
ch ase</w> 39642
am ox 39641
Ass ays</w> 39637
bu pivacaine</w> 39636
hemisph eric</w> 39629
ej ac 39626
DR B1</w> 39621
Gen omics</w> 39616
B u</w> 39603
dou bled</w> 39600
benz yl 39596
exp ense</w> 39589
fist ulas</w> 39588
fav ors</w> 39586
L atin</w> 39585
Sc anning</w> 39585
robu stly</w> 39585
al a</w> 39574
hel min 39564
an abolic</w> 39556
contrac eption</w> 39556
poly phenols</w> 39555
st a</w> 39553
igen ic</w> 39544
K an 39539
H ercul 39536
qu id</w> 39534
P B1</w> 39532
fas ted</w> 39531
oth ane</w> 39527
lox P</w> 39520
AC V</w> 39518
ol ar 39517
t ogenesis</w> 39516
c on</w> 39512
o ke</w> 39512
av a</w> 39511
X X</w> 39509
M MP 39508
mal ate</w> 39507
ca u 39504
congru ent</w> 39504
la ws</w> 39501
- binding</w> 39489
an ze 39489
Ac tion</w> 39488
pro drug</w> 39485
u tively</w> 39483
co atings</w> 39475
la tencies</w> 39472
det ached</w> 39469
evap oration</w> 39467
flu conazole</w> 39466
N RAS</w> 39459
extrav as 39455
Child hood</w> 39448
aggres siveness</w> 39445
parox ysmal</w> 39445
N 7</w> 39442
imp anze 39442
S ection</w> 39441
spi ked</w> 39441
Dr ugs</w> 39434
F S 39431
tic ism</w> 39426
str ing</w> 39421
Carcin oma</w> 39421
BR CA</w> 39418
in k</w> 39410
phenotyp ically</w> 39409
diaphrag matic</w> 39405
B ak</w> 39391
techn ically</w> 39389
D BA</w> 39388
CC AC 39388
thaw ed</w> 39386
i x 39385
METHODO LOGY</w> 39385
f eline</w> 39383
har an</w> 39382
escal ation</w> 39381
Eff icient</w> 39379
Cd k1</w> 39378
erythem a</w> 39378
t man 39368
M CA 39367
cl oz 39359
A gency</w> 39358
r po 39348
O DN</w> 39339
sub divided</w> 39328
ul ph 39327
y al</w> 39312
An y</w> 39309
if ers</w> 39308
leth anolamine</w> 39307
M etal</w> 39286
at tain 39281
loc king</w> 39280
me rely</w> 39277
Wor king</w> 39275
comput ation</w> 39271
conti gu 39271
Proce dures</w> 39268
s ins</w> 39262
L G</w> 39262
IT D</w> 39253
Clus ter</w> 39253
hyper sensitive</w> 39236
p I</w> 39234
es terol</w> 39234
govern ed</w> 39233
im printed</w> 39227
tom ies</w> 39226
A gro 39223
lead ers</w> 39222
transc ranial</w> 39221
ut y</w> 39218
pal p 39217
Cri teria</w> 39215
un a</w> 39213
gl ab 39206
olip ids</w> 39204
activ in</w> 39198
F at 39189
mark eting</w> 39188
function alization</w> 39187
Dic kinson</w> 39187
rom a</w> 39186
Sel ection</w> 39183
R Y 39178
be have</w> 39177
ER M</w> 39170
Ac tin</w> 39170
par tur 39167
di es 39166
ar se</w> 39160
LT D</w> 39155
i vir</w> 39151
bruc ei</w> 39151
staphyloc occal</w> 39150
medul lo 39149
don key</w> 39144
ic ol 39142
my omet 39141
I TS</w> 39139
acti on 39134
STI M1</w> 39126
Reg ression</w> 39121
v us</w> 39120
lay ing</w> 39117
in tu 39116
G DM</w> 39109
VL Ps</w> 39108
neuro inflammation</w> 39102
AP A</w> 39101
anes ulf 39101
asbest os</w> 39096
Fus arium</w> 39094
ov ulatory</w> 39092
Per son 39086
Ther mal</w> 39085
Pres sure</w> 39081
A rea</w> 39079
ec ia</w> 39079
p ockets</w> 39069
He ad</w> 39069
ect ance</w> 39060
robo t</w> 39057
inform atic</w> 39056
Trans genic</w> 39045
enke phalin</w> 39041
G as 39035
ri amycin</w> 39035
lipoly sis</w> 39033
Jol la</w> 39031
Cir culating</w> 39020
ew es</w> 39016
neur o</w> 39012
un resolved</w> 39011
og al 39008
retino id</w> 39007
analge sics</w> 39006
par s</w> 39004
jejun i</w> 39004
BM SCs</w> 38998
Sol id</w> 38994
G CT</w> 38992
CR S</w> 38985
TT X</w> 38985
ur ate</w> 38977
pro long</w> 38973
Sch ist 38966
s ally</w> 38965
spir it 38960
anaes thetic</w> 38958
1 X</w> 38952
pit ch</w> 38945
sa id</w> 38944
b ariatric</w> 38943
NK T</w> 38940
mes oderm</w> 38936
For ce</w> 38935
claud in</w> 38934
fav our</w> 38931
E MD</w> 38928
den s</w> 38926
di phenyl</w> 38925
phosph otyrosine</w> 38925
v sk 38919
permeabil ization</w> 38915
Predic tion</w> 38914
pre incubated</w> 38908
des m 38906
ach ol</w> 38906
Cy totox 38906
fav oring</w> 38900
ac ne</w> 38894
hi ps</w> 38887
E SCC</w> 38882
lar ynx</w> 38879
l ectins</w> 38878
C ad 38876
De x</w> 38876
arg on</w> 38871
ox azole</w> 38869
imper ative</w> 38865
FT D</w> 38859
O ra 38857
spind les</w> 38848
F FA</w> 38847
bio sis</w> 38847
β γ</w> 38843
PC Bs</w> 38843
unc oupling</w> 38833
fac tion</w> 38828
al t</w> 38827
MC F1</w> 38827
chemoattrac tant</w> 38825
HO MA</w> 38822
Cl oning</w> 38820
la x 38818
vox el</w> 38818
contradic tory</w> 38815
rich ness</w> 38815
distor ted</w> 38814
og u 38811
im plied</w> 38798
deliver ies</w> 38795
- N</w> 38793
6 th</w> 38790
I schem 38788
ul line</w> 38785
thrombo embolic</w> 38781
incorpor ates</w> 38779
Y AG</w> 38777
hepat otoxicity</w> 38774
C ob 38773
y to 38772
Cy 3</w> 38770
C ox 38769
path s</w> 38766
myri sto 38765
D end 38759
rough ness</w> 38755
vinc ristine</w> 38753
distr action</w> 38753
SR F</w> 38746
l us 38744
U NC</w> 38738
repor ters</w> 38728
T es 38725
per idone</w> 38725
hom ing</w> 38717
reg ister</w> 38716
az a</w> 38716
form ans</w> 38710
M AD 38707
b anding</w> 38703
G rea 38688
Ren illa</w> 38688
le si 38687
enantiom ers</w> 38681
sim vastatin</w> 38677
dias tere 38670
V is 38667
Bi och 38666
R ab</w> 38658
myco bacteria</w> 38656
os sification</w> 38653
Proble ms</w> 38643
lact ating</w> 38640
ju ana</w> 38639
h 7</w> 38634
li a</w> 38632
plas m</w> 38631
al izes</w> 38628
SS R</w> 38626
ti zation</w> 38622
DI C</w> 38617
isol euc 38616
zo on 38611
peri vascular</w> 38609
arthro scopic</w> 38606
quinol ones</w> 38606
N PCs</w> 38603
rh am 38595
bov is</w> 38593
V WF</w> 38591
qu estion 38591
Su n</w> 38585
necro tizing</w> 38584
r umin 38580
pe g 38578
Ab dom 38578
S har 38575
endel ian</w> 38575
Hal f</w> 38573
Fluo ro 38572
k en 38568
ga ining</w> 38564
spac ed</w> 38563
concor dant</w> 38562
b ine</w> 38560
hem ostasis</w> 38559
S pl 38558
b ad</w> 38555
Ar ray</w> 38555
u k</w> 38552
ple ens</w> 38549
oste omyelitis</w> 38548
GD NF</w> 38548
plank ton</w> 38548
Micro array</w> 38544
photoc atalytic</w> 38541
morph ol 38540
tem po 38539
A 0</w> 38532
G yn 38531
denti stry</w> 38531
Rap 1</w> 38528
c ab 38525
energ etics</w> 38521
amin obenz 38517
7 th</w> 38516
provo ked</w> 38508
princip ally</w> 38505
Cdc 1</w> 38502
propan ol</w> 38491
st aged</w> 38487
alcohol ism</w> 38487
cl of 38486
consec utively</w> 38486
rifam p 38484
Colum bia</w> 38478
surfac tants</w> 38475
y g 38474
a j 38473
C z 38466
standar dised</w> 38464
C en 38460
L on 38446
en sures</w> 38446
am er</w> 38443
z ations</w> 38435
hin dered</w> 38426
be e</w> 38425
en um 38424
FB X 38417
T Z 38415
shut tle</w> 38412
rifamp icin</w> 38410
CEN T</w> 38409
epox ide</w> 38409
As th 38405
H . 38401
CD I</w> 38396
jus tified</w> 38396
de hydrated</w> 38395
house keeping</w> 38392
hall uc 38392
esti oned</w> 38391
my opia</w> 38391
Au stri 38391
dig oxin</w> 38384
le upeptin</w> 38382
C BS</w> 38376
B ot 38375
arti fact</w> 38371
a ver</w> 38370
cir cle</w> 38370
D l 38369
CA F</w> 38367
Glc NAc 38362
disabl ed</w> 38358
zo tinib</w> 38357
pertin ent</w> 38352
satur ating</w> 38349
DO PA</w> 38346
notic eable</w> 38346
G em 38344
PKC δ</w> 38344
Recomm end 38344
P ER</w> 38343
fel low 38340
an oid</w> 38337
S 3A</w> 38329
th enium</w> 38328
U g 38326
G ender</w> 38325
n r 38323
os pas 38313
TH C</w> 38313
qu estioned</w> 38310
oste ogenesis</w> 38308
C as</w> 38301
U RA 38290
hand le</w> 38288
tu be 38287
T MA</w> 38277
MS E</w> 38277
alg al</w> 38274
lutein izing</w> 38273
ple as 38269
MR C</w> 38269
t ogenic</w> 38268
s wa 38266
lin a</w> 38261
E thi 38259
PD H</w> 38254
therapeu tically</w> 38247
pop ul 38243
ri dge</w> 38242
Veter ans</w> 38242
end ym 38241
S HI 38239
R od 38238
T GN</w> 38236
Exc ept</w> 38235
Syn thetic</w> 38234
c 4</w> 38233
i bly</w> 38232
5 L</w> 38227
B ene 38227
O SCC</w> 38226
inc ap 38221
poly adenylation</w> 38217
at ar 38213
Cy 5</w> 38211
M SA</w> 38208
eyel id</w> 38207
adjunc tive</w> 38201
feren tial</w> 38200
alkal i</w> 38199
E H</w> 38198
bud s</w> 38193
work load</w> 38191
re generating</w> 38190
t A</w> 38187
Wa als</w> 38187
N H4</w> 38186
bit ol</w> 38183
ob lique</w> 38181
ol ide</w> 38176
over production</w> 38175
Tech ni 38175
thromb oxane</w> 38169
Ex ampl 38168
R MS</w> 38167
meta zo 38167
py el 38166
oplas mosis</w> 38166
Est abl 38163
N ASH</w> 38152
centr ally</w> 38146
en su 38145
Sec re 38145
scinti llation</w> 38142
pec tor 38140
bio diversity</w> 38139
LD LR</w> 38137
ex agg 38135
RI A</w> 38135
Hercul es</w> 38130
CH X</w> 38127
A cin 38120
ome trical</w> 38120
exhaus tion</w> 38117
T Y</w> 38116
apro tinin</w> 38116
SO C</w> 38115
upreg ulate</w> 38100
unwin ding</w> 38096
no ur 38094
live stock</w> 38090
Observ ations</w> 38088
Lim ited</w> 38087
lipo xy 38072
no ea</w> 38068
mut ans</w> 38068
PR C2</w> 38068
su ite</w> 38067
sh allow</w> 38052
Pro toc 38049
Inf ectious</w> 38048
no x 38047
discord ant</w> 38046
P r</w> 38045
α 7</w> 38043
Stro ng</w> 38039
th yl 38032
col o 38027
MP P</w> 38027
un expectedly</w> 38014
crystall in</w> 38008
oc utaneous</w> 38006
l 2</w> 37999
flu oxetine</w> 37999
hero in</w> 37995
PO RT</w> 37992
neo formans</w> 37987
L Q 37982
C p</w> 37977
LE VEL</w> 37976
P XR</w> 37973
L PL</w> 37973
vaso active</w> 37972
D MD</w> 37970
elim inates</w> 37970
I . 37967
E pig 37966
bar ic</w> 37965
Ander son</w> 37964
mid azolam</w> 37961
Ri b 37956
discrimin ating</w> 37954
infiltr ates</w> 37953
gluc osidase</w> 37950
dec lining</w> 37949
trop ism</w> 37947
ampl icons</w> 37939
HDAC 6</w> 37925
Dr p1</w> 37924
chem os 37923
aper ture</w> 37912
molec ularly</w> 37909
ClinicalTri als.gov</w> 37908
Met ast 37907
pax illin</w> 37901
F yn</w> 37900
i ris</w> 37899
re uptake</w> 37894
hyper algesia</w> 37891
anti psychotics</w> 37890
web site</w> 37889
s ate</w> 37887
HS F1</w> 37874
D ra 37872
ly ophil 37867
re acts</w> 37861
Vide o</w> 37859
B enz 37855
prote oglycan</w> 37855
Chem otherapy</w> 37849
o kinase</w> 37847
Mon oclonal</w> 37845
r an</w> 37844
Y PD</w> 37838
er sin 37835
cortic es</w> 37835
proce eded</w> 37833
pyri din 37826
olys in</w> 37825
ar rows</w> 37822
devi sed</w> 37817
ne arest</w> 37815
tax in</w> 37815
8 th</w> 37814
Oc cu 37814
cl otting</w> 37812
sub sp.</w> 37797
Cor p.</w> 37797
tre hal 37787
Sec ondly</w> 37787
inve stment</w> 37787
asth enia</w> 37769
tic es</w> 37761
S J 37757
ex changed</w> 37751
EC 1</w> 37749
K R</w> 37745
pepti dog 37742
T 1D</w> 37739
deplo yment</w> 37739
anis otropic</w> 37736
Ser 3</w> 37736
icul us</w> 37726
stell ate</w> 37722
R eli 37718
dis closure</w> 37716
4 K</w> 37697
classi fier</w> 37695
ti ll 37693
phosphoryl ating</w> 37689
Caucasi ans</w> 37687
B CL 37685
f ishes</w> 37683
T ru 37681
poly styrene</w> 37681
ong side</w> 37672
T od 37671
H Q</w> 37669
res um 37662
Gle ason</w> 37662
SR S</w> 37655
l ot 37654
bio degradable</w> 37654
equi valence</w> 37654
Re f 37651
or h 37649
opin ions</w> 37647
maxim ally</w> 37646
Sur ge 37644
Mill er</w> 37643
adop tive</w> 37640
answ ers</w> 37638
chloro plasts</w> 37623
port able</w> 37616
F GFR</w> 37609
st ature</w> 37605
pharmac ologically</w> 37605
act one</w> 37605
wh ites</w> 37604
reconstruc tive</w> 37603
cyto keratin</w> 37602
bio chemically</w> 37596
H V 37591
concei vable</w> 37590
os oph 37588
go tes</w> 37585
b 3</w> 37574
ber t</w> 37572
Wille brand</w> 37570
blast ocyst</w> 37567
transi ents</w> 37563
pro gnos 37560
RE CENT</w> 37556
am el 37555
antim al 37555
enter ica</w> 37555
M AT</w> 37554
W AT</w> 37553
af lat 37553
re ally</w> 37550
enor m 37549
P BL</w> 37546
d ating</w> 37545
obser ving</w> 37543
bot ulinum</w> 37543
an ic</w> 37529
Me CP2</w> 37528
expon entially</w> 37527
ch impanze 37520
dr astic</w> 37509
n arrative</w> 37508
ag inal</w> 37507
strength ening</w> 37507
NR F2</w> 37503
infiltr ated</w> 37501
ann abin 37497
od or 37491
dis sect</w> 37489
fi refly</w> 37487
Y 6</w> 37486
gradu ates</w> 37486
tub ul 37484
nam es</w> 37480
relax ed</w> 37479
rea red</w> 37474
TRA P</w> 37472
co existence</w> 37470
SN s</w> 37470
PR O</w> 37470
CX CL 37466
of loxacin</w> 37461
ret t</w> 37460
NH 4 37458
nucle ophilic</w> 37447
En doc 37447
pare sis</w> 37447
ol itis</w> 37442
tric ho 37442
un characterized</w> 37438
RT K</w> 37434
Coul ter</w> 37427
x A</w> 37426
sg RNA</w> 37419
du plications</w> 37416
awa ke</w> 37416
G ab 37414
F al 37413
Assess ing</w> 37413
Me ta 37412
B RA 37411
mac y 37411
be ds</w> 37406
P -</w> 37404
dent ure</w> 37404
oder ma</w> 37397
den oted</w> 37391
SO X2</w> 37388
I PA</w> 37387
pul satile</w> 37382
wil lingness</w> 37380
in otropic</w> 37378
W i 37376
hete rom 37374
bo ron</w> 37366
an y 37365
m r 37359
f ted</w> 37359
reth ral</w> 37354
in semin 37350
norm ative</w> 37348
relap ses</w> 37343
B Y</w> 37341
Se g 37341
mo re 37337
Grea ter</w> 37335
U R</w> 37334
Cer tain</w> 37334
S patial</w> 37332
Ed itorial</w> 37332
K al 37329
SW 4</w> 37329
ochrom ocytoma</w> 37320
sub tracted</w> 37318
ro oms</w> 37316
heal ed</w> 37315
sign ed</w> 37311
Abdom inal</w> 37310
con fusion</w> 37308
n out</w> 37302
CO D</w> 37302
op on 37301
De pletion</w> 37294
our ces</w> 37294
MS 2</w> 37287
end ogenously</w> 37286
PA s</w> 37285
shut t 37285
AN ALY 37284
Dig ital</w> 37283
log 1</w> 37274
U SP1</w> 37273
event ual</w> 37272
over whel 37271
wi ves</w> 37268
cloz apine</w> 37267
acet aminophen</w> 37263
M ain</w> 37256
ac eous</w> 37252
Ch alleng 37251
9 D</w> 37246
Bec ton</w> 37245
T ob 37243
B 3 37242
et ri 37240
jejun al</w> 37240
Ley dig</w> 37237
s ou 37234
pres chool</w> 37231
te t</w> 37229
dic s</w> 37227
metabol izing</w> 37225
descrip tors</w> 37222
gingi valis</w> 37220
Tradi tional</w> 37219
-- the</w> 37218
identi fiable</w> 37217
li z 37214
elig ibility</w> 37214
or bit</w> 37213
ore active</w> 37203
po x</w> 37202
TK A</w> 37200
sati lity</w> 37198
streptoc occi</w> 37197
emul sions</w> 37194
S g 37189
RMS D</w> 37188
a rone</w> 37181
hyper polarization</w> 37181
ym ic</w> 37180
w r 37178
cryp t</w> 37177
abstr act</w> 37177
all ic</w> 37175
m l 37170
organ izing</w> 37169
ri b</w> 37168
p ens 37163
C ST</w> 37155
under lies</w> 37155
duplic ated</w> 37153
amy otrophic</w> 37151
vesi cal</w> 37150
swit ches</w> 37149
ard ous</w> 37145
narrow ing</w> 37142
astro cytic</w> 37139
conn ect</w> 37138
diag ram</w> 37137
ex osome</w> 37136
multi dimensional</w> 37130
p ineal</w> 37128
de ment</w> 37127
Not I</w> 37126
Vari ation</w> 37126
mil li 37122
psori atic</w> 37121
carcin ogens</w> 37103
aero sol 37102
T ask</w> 37097
AG AT 37090
discover ies</w> 37089
negl ect</w> 37088
retic ular</w> 37086
multic entre</w> 37083
retro viruses</w> 37082
T et</w> 37081
exchang er</w> 37077
cylindr ical</w> 37076
expos ing</w> 37075
B AX</w> 37068
L ei 37068
dam ages</w> 37063
acceler ating</w> 37061
Car l</w> 37054
Meas ures</w> 37047
estro genic</w> 37045
strateg ic</w> 37037
mom ents</w> 37035
Cor ning</w> 37032
CHI KV</w> 37029
Valu e</w> 37029
attri bute</w> 37017
N ineteen</w> 37006
enorm ous</w> 37003
d pi</w> 36997
ga ze</w> 36996
aux iliary</w> 36996
et ts</w> 36991
path ologically</w> 36991
H Be 36990
is s 36987
fib ros 36985
centro somes</w> 36984
identi ties</w> 36983
In tensive</w> 36981
prospec ts</w> 36979
Shig ella</w> 36974
ain t</w> 36973
ens in</w> 36970
aptam ers</w> 36969
elec trically</w> 36967
arbox ylic</w> 36965
ling u 36963
paradox ical</w> 36963
Gly c 36962
lu ted</w> 36960
RO I</w> 36960
B i</w> 36959
transf ections</w> 36958
Pat terns</w> 36957
gluc opyran 36955
1 T</w> 36954
st alled</w> 36953
is ser 36951
withdraw n</w> 36941
vi an</w> 36940
Polym erase</w> 36937
infer tile</w> 36933
di hydro</w> 36930
HD A</w> 36928
C1 q</w> 36928
N 8</w> 36927
en v</w> 36915
rs 6</w> 36914
reason ably</w> 36914
E O 36912
develop mentally</w> 36907
apo E</w> 36905
d n</w> 36888
elas tin</w> 36887
gynec ologic</w> 36884
oplas tic</w> 36882
har dness</w> 36879
su staining</w> 36869
t PA</w> 36858
posi um</w> 36848
ect odomain</w> 36840
thi azol 36839
cap ped</w> 36835
rec ti 36833
3 Δ</w> 36832
l ides</w> 36832
ST I</w> 36827
harb ors</w> 36826
myel odys 36825
Bac tero 36824
ospas m</w> 36824
har dly</w> 36820
phosphor ylase</w> 36818
oc clusal</w> 36811
F ile</w> 36797
Ak t1</w> 36795
fur an</w> 36792
n NOS</w> 36790
air s</w> 36789
H3 N2</w> 36787
intes tin 36786
F lo 36784
assign ments</w> 36781
B 8</w> 36776
GG AT 36768
chel ating</w> 36768
lin oleic</w> 36764
C HC 36760
eng aging</w> 36759
comple ting</w> 36749
ten one</w> 36746
pix els</w> 36743
hep cidin</w> 36741
rel and</w> 36737
f mol</w> 36733
S AC</w> 36731
phot ographs</w> 36726
ite al</w> 36726
kinetoch ores</w> 36726
ultracentrifug ation</w> 36720
im etry</w> 36715
ch in 36714
EBP α</w> 36707
myri state</w> 36705
predisp ose</w> 36699
Con n 36698
initi ative</w> 36697
rot ator</w> 36697
U .</w> 36694
dis aster</w> 36694
N ar 36693
mar king</w> 36682
DN MT1</w> 36676
KE GG</w> 36674
Ar gen 36672
Wor k 36671
ff in</w> 36666
bil lion</w> 36662
An gel 36660
C ON</w> 36656
sol eus</w> 36648
Z in 36644
tman nin</w> 36639
Figure 3</w> 36632
UL 3</w> 36629
H 9 36628
multi plex 36626
equi vocal</w> 36626
phot odynamic</w> 36621
AG T</w> 36621
compul sive</w> 36617
pol io 36616
N early</w> 36615
cd c2</w> 36615
insectic ide</w> 36613
Occu pational</w> 36611
Der mat 36609
f athers</w> 36608
sphing osine</w> 36608
tra inees</w> 36604
L e</w> 36603
K 9</w> 36598
psychiat ri 36597
vig il 36595
Prolong ed</w> 36592
pK a</w> 36591
am il 36586
har mon 36585
en teral</w> 36573
CD C2</w> 36570
TI M</w> 36569
ibu profen</w> 36562
IF Ns</w> 36560
pen ta 36560
arch a 36560
SV R</w> 36550
obstac les</w> 36547
B ill 36546
adren ocortical</w> 36545
modi fies</w> 36542
ophil in</w> 36541
pol itan</w> 36540
R and 36539
distinc tly</w> 36538
lu x 36534
LO D</w> 36532
A ir 36526
campa ign</w> 36526
exempl ified</w> 36524
salic ylic</w> 36516
8 E</w> 36511
AT F4</w> 36511
las ers</w> 36511
pro biotic</w> 36509
SS G</w> 36503
ic it</w> 36501
Pres ence</w> 36500
dehydro gen 36495
at ally</w> 36493
Vie w</w> 36493
bifur cation</w> 36493
We in 36491
ureth ane</w> 36485
commun ic 36480
viro logical</w> 36480
De ep</w> 36470
o to</w> 36464
B CL2</w> 36464
wor st</w> 36454
wa ke</w> 36453
P ren 36451
sw abs</w> 36448
moti vated</w> 36435
D ri 36434
abol ishes</w> 36431
denti sts</w> 36431
Psych iatric</w> 36420
chem o</w> 36418
Sa haran</w> 36418
S NF</w> 36417
op iate</w> 36417
thi amine</w> 36417
DHE A</w> 36415
trem end 36413
remo ves</w> 36400
TC S</w> 36398
Ar c 36394
it t</w> 36392
S SA</w> 36391
H ome</w> 36390
her itability</w> 36389
fl oral</w> 36387
CM s</w> 36385
pul led</w> 36384
ach us 36372
Entero coccus</w> 36364
categor ical</w> 36362
I OL</w> 36358
F el 36357
K yo 36356
poly amine</w> 36354
in ev 36353
arrang ements</w> 36347
path ogenetic</w> 36342
Di et</w> 36341
ga g</w> 36338
Fluoresc ent</w> 36335
Hipp o</w> 36332
peric y 36331
g n</w> 36330
eti ologic</w> 36328
J ose</w> 36327
di ph 36324
y ers</w> 36318
coincid ed</w> 36315
mono -</w> 36305
c ari 36304
p t</w> 36301
un wanted</w> 36299
glyco sides</w> 36299
er ly</w> 36298
J u 36290
lacti s</w> 36290
micron ucle 36280
co t</w> 36276
V SMCs</w> 36272
In tra</w> 36272
O mp 36270
Angi o 36270
Mass achus 36268
Aor tic</w> 36267
MS H</w> 36265
consul tations</w> 36263
un recognized</w> 36261
Ma p</w> 36260
ir relevant</w> 36256
palmito ylation</w> 36256
agen ic</w> 36253
po rosity</w> 36248
refl ectance</w> 36241
B AF 36238
pharmac otherapy</w> 36237
R elev 36236
X i 36232
mus h 36231
ba um 36231
h un 36224
G PI 36220
V R 36219
meta plasia</w> 36219
M endelian</w> 36218
Pharmac ia</w> 36206
restra ined</w> 36206
contigu ous</w> 36191
classi fications</w> 36185
CG H</w> 36183
Oc t4</w> 36183
splen ectomy</w> 36183
I MRT</w> 36182
sup posed</w> 36181
pass aged</w> 36178
stero ls</w> 36176
addi tives</w> 36169
TT C</w> 36164
K T</w> 36162
AD L</w> 36159
top ril</w> 36159
Dep end 36153
tetram eric</w> 36149
Q TLs</w> 36145
t ables</w> 36142
ron ine</w> 36141
sp inning</w> 36139
Disrup tion</w> 36138
1 K</w> 36137
Ne isser 36137
la ke</w> 36131
radionuc lide</w> 36129
dys functions</w> 36128
get ting</w> 36125
jejun um</w> 36124
ancest or</w> 36107
ove restim 36106
D AF</w> 36104
az o</w> 36104
tos por 36102
p EGFP</w> 36100
interfe ro 36097
encephal ic</w> 36096
er h 36094
albin o</w> 36092
infras tructure</w> 36090
P eptides</w> 36088
St atus</w> 36081
stro kes</w> 36081
titr ated</w> 36078
pheny le 36071
succ e 36065
K G</w> 36063
in alis</w> 36063
cub ic</w> 36057
work flow</w> 36055
Micha elis</w> 36051
G CA</w> 36049
hi r 36048
F AP</w> 36046
yl ine</w> 36046
Massachus etts</w> 36040
isoleuc ine</w> 36035
amylo id 36032
aliquo t</w> 36032
s pleens</w> 36030
M AS</w> 36030
E volution</w> 36029
At las</w> 36029
sim ian</w> 36026
isch er</w> 36026
A ir</w> 36025
in vited</w> 36024
Pro ducts</w> 36020
if era</w> 36017
DT PA</w> 36017
Erb B</w> 36014
Mt b</w> 36013
AP O 36007
den it 36006
anesthe tics</w> 36004
b 5</w> 36002
ap ple</w> 36000
oph o 35998
Pro cess 35998
W P</w> 35989
M a</w> 35988
N KG 35982
di pine</w> 35978
in 1</w> 35974
IKK β</w> 35971
H 7 35970
H HV</w> 35970
splic e 35969
ec z 35968
HBe Ag</w> 35967
Spec ies</w> 35965
parti te</w> 35964
hyper thyroidism</w> 35961
squ ared</w> 35961
Is su 35958
adren al 35956
Bey ond</w> 35951
e ased</w> 35945
There after</w> 35944
abil istic</w> 35940
Gol d 35939
hal othane</w> 35938
fri ends</w> 35935
phal ang 35932
Auth ors</w> 35929
seem ingly</w> 35928
com pressive</w> 35925
eff eren 35922
N umber</w> 35919
H2 A. 35917
ML C</w> 35916
rom andibular</w> 35914
SP 2</w> 35910
sulf ated</w> 35905
vi remia</w> 35891
emphasi zing</w> 35891
afil tration</w> 35890
men ting</w> 35887
linear ized</w> 35887
caver nous</w> 35887
caroteno id</w> 35883
S lo 35875
FF PE</w> 35874
3 P</w> 35873
Tren ds</w> 35862
AC R</w> 35861
W I 35859
poly cyclic</w> 35849
F NA</w> 35843
yl on</w> 35837
T FA</w> 35836
tro ugh</w> 35831
se wage</w> 35827
mit omycin</w> 35827
P g 35819
Se e</w> 35814
comprehensi vely</w> 35813
obsc ure</w> 35812
persis tently</w> 35811
th umb</w> 35806
adi ly</w> 35803
Cy tos 35799
T al 35793
d ap 35786
D istr 35785
tr us 35785
memor ies</w> 35782
INTER PRE 35780
im pression</w> 35768
SO CS 35764
Wil son</w> 35760
nanow ires</w> 35759
ro set 35755
peptidog lycan</w> 35755
I reland</w> 35751
M OF</w> 35749
PA Rs</w> 35749
Tub erculosis</w> 35747
qu ery</w> 35743
T s 35739
B MS</w> 35738
tr uly</w> 35735
de granulation</w> 35733
PC SK 35731
clon ogenic</w> 35731
hepar an</w> 35731
super imposed</w> 35729
c i</w> 35726
Traum a</w> 35723
T en 35711
IL 6</w> 35706
8 α</w> 35705
s son</w> 35704
t ul 35703
Dis ability</w> 35695
le ment</w> 35692
lim bic</w> 35690
ess or</w> 35688
hom os 35683
alkyl ating</w> 35679
weigh ing</w> 35674
detec tors</w> 35670
b arely</w> 35669
Psych ological</w> 35667
co ils</w> 35662
os up 35660
sh ri 35657
chondro cyte</w> 35657
pneumo thorax</w> 35657
en ol</w> 35652
overl aps</w> 35649
art z</w> 35646
B Z 35645
compart ment 35639
il es</w> 35637
syste mically</w> 35636
more over</w> 35636
sim plicity</w> 35620
p CMV</w> 35613
op i 35613
ir con 35611
ple gic</w> 35606
ac o 35600
shi el 35598
Z NF 35590
bloc kage</w> 35589
n ip 35581
tetrac h 35581
d m 35579
Hu h7</w> 35577
g avage</w> 35576
access ed</w> 35567
syn cope</w> 35562
out puts</w> 35560
Inter fe 35557
Q S</w> 35556
synthesi zing</w> 35551
h us 35549
path ologists</w> 35549
characteris ation</w> 35543
doc ked</w> 35543
T ro 35542
call ing</w> 35542
o on</w> 35540
Apo E</w> 35534
Ne ph 35533
micro structure</w> 35532
amin obutyric</w> 35532
Cy tom 35531
Paren ts</w> 35528
Inhibit or</w> 35525
attenu ating</w> 35520
fe eling</w> 35519
exce eds</w> 35519
b an</w> 35517
prop ane</w> 35516
J am 35511
Mark ov</w> 35511
L X 35505
recover ies</w> 35501
sub mucosal</w> 35496
hp f</w> 35496
G mb 35495
ch r 35490
xim e</w> 35490
incre ments</w> 35488
log ically</w> 35486
Emb ase</w> 35484
ou rea</w> 35481
rub ber</w> 35481
spe eds</w> 35478
Flor ida</w> 35477
ur gently</w> 35472
os ulf 35471
ut ter</w> 35470
L Y</w> 35469
L ow 35469
Neisser ia</w> 35467
Res ource</w> 35460
B ay</w> 35457
Trans mission</w> 35456
gal ectin</w> 35449
arti ficially</w> 35447
kinem atics</w> 35442
Trans plantation</w> 35436
NL R</w> 35436
tis tic</w> 35431
disp osal</w> 35430
neuro lep 35423
- independent</w> 35418
co arse</w> 35415
nanoma terials</w> 35412
H u</w> 35406
Res on 35405
la c</w> 35403
conta in 35399
under lined</w> 35398
superi ority</w> 35398
chromoph ore</w> 35396
trans aminase</w> 35393
fac iens</w> 35387
7 β</w> 35384
E mp 35384
toxic ological</w> 35378
neur aminidase</w> 35372
we ed</w> 35363
I ran 35360
nucle olus</w> 35359
Micro scopy</w> 35359
9 th</w> 35358
Ch loro 35352
U 5</w> 35351
meth amphetamine</w> 35349
actin omycin</w> 35342
protr usions</w> 35341
K OH</w> 35339
Descrip tive</w> 35339
PA 2</w> 35338
T SP</w> 35334
na m</w> 35330
D avi 35328
L ati 35325
in novation</w> 35321
segreg ated</w> 35319
id ent</w> 35316
ket t 35313
ure mic</w> 35310
thi ols</w> 35310
Pin 1</w> 35305
glycos yl 35299
it ter</w> 35297
UT I</w> 35295
hierarch y</w> 35295
bl ank</w> 35294
un event 35290
il is</w> 35284
a a 35282
on ec 35281
D CS</w> 35279
pyri mid 35279
authen tic</w> 35277
di a 35276
1 N</w> 35275
ch us</w> 35272
arg in</w> 35267
arg ues</w> 35265
neon ate</w> 35265
ti gotes</w> 35264
W RN</w> 35262
Ex traction</w> 35259
Z V</w> 35250
deline ated</w> 35250
R ag 35247
transf ectants</w> 35247
fe deral</w> 35246
Gra ves</w> 35244
gon orrho 35243
N ed 35242
heigh tened</w> 35239
tri mers</w> 35238
ion ophore</w> 35236
ev ero 35234
Haem ophilus</w> 35234
Ste re 35233
ure tero 35232
Transf er</w> 35232
allevi ated</w> 35228
INTERPRE TATION</w> 35227
emph ig 35222
pri v 35221
Antim icrobial</w> 35220
Z hou</w> 35213
sch wann 35211
S ource</w> 35208
bis phosphate</w> 35206
us k 35204
CP E</w> 35198
Inter ventions</w> 35197
Aut om 35197
pover ty</w> 35186
I AA</w> 35185
prevent able</w> 35176
o flurane</w> 35175
Del ayed</w> 35175
environ mentally</w> 35171
cir c 35159
patell ar</w> 35159
fra ilty</w> 35158
nocic eption</w> 35157
sea water</w> 35156
l av 35154
re absorption</w> 35153
glyc ation</w> 35153
oval bumin</w> 35152
in timate</w> 35151
prior ities</w> 35151
S m</w> 35149
in ert</w> 35149
ro t</w> 35148
br ings</w> 35145
incid ental</w> 35145
lithi asis</w> 35142
hyp ometh 35140
aden ylyl</w> 35139
GC CT 35139
al og</w> 35138
mo ves</w> 35124
SC N 35124
RE s</w> 35123
bif unctional</w> 35118
GT GT 35117
SE L 35117
sinus oidal</w> 35112
8 T</w> 35111
bl otted</w> 35104
gu ides</w> 35102
Sym p 35102
mal tose</w> 35101
PT 1</w> 35100
plac ements</w> 35095
z oster</w> 35094
recapit ulate</w> 35094
sarco plasmic</w> 35093
over coming</w> 35091
od a</w> 35086
over looked</w> 35085
7 K</w> 35084
J D</w> 35084
PK cs</w> 35079
S n</w> 35078
if er</w> 35078
tri s</w> 35077
vertebra e</w> 35077
AP L</w> 35076
sa phen 35075
cadaver ic</w> 35073
pre menopausal</w> 35071
epti ves</w> 35070
Te tra 35068
dystroph in</w> 35067
l agg 35066
r um</w> 35062
bo iling</w> 35062
volum ab</w> 35061
T on 35055
av alin</w> 35051
Ovari an</w> 35049
ot onic</w> 35047
se aled</w> 35043
OV X</w> 35033
repres sing</w> 35032
La ter</w> 35032
M ich 35031
phenyle phrine</w> 35030
Al tered</w> 35022
Modi fication</w> 35017
mon ocytic</w> 35016
thyro idectomy</w> 35013
U P</w> 35012
comp ute</w> 35012
Cl ar 35003
Comput ational</w> 34997
S cal 34994
haz ardous</w> 34994
ed es</w> 34993
carb achol</w> 34990
evero limus</w> 34990
b li 34989
Immunoprecip itation</w> 34989
oxidoreduc tase</w> 34989
Wal ker</w> 34988
reg res 34986
ta u 34981
can als</w> 34978
empir ically</w> 34978
M VA</w> 34977
ex ogenously</w> 34975
Kyo to</w> 34968
ad er</w> 34967
manip ulate</w> 34967
z o</w> 34964
neuro cognitive</w> 34960
S r</w> 34958
exten sions</w> 34958
met ab 34951
peculi ar</w> 34946
anti epileptic</w> 34939
t g</w> 34937
Con comit 34935
enc ia</w> 34932
Ev ans</w> 34928
tr aps</w> 34926
insul ts</w> 34921
ade x</w> 34909
respon der</w> 34896
ep razole</w> 34895
PM Ns</w> 34894
6 p</w> 34891
at rophic</w> 34891
ol ase</w> 34890
Sup pression</w> 34881
excep tional</w> 34877
Per kin</w> 34876
pre incubation</w> 34874
FV C</w> 34867
Asth ma</w> 34865
form ate</w> 34864
du plexes</w> 34863
sp 1</w> 34862
y el 34861
in conclusive</w> 34857
hydro cortisone</w> 34856
af lu 34850
s wee 34846
therm ally</w> 34843
L AT 34842
dig ree</w> 34839
CHI P</w> 34835
0 G</w> 34834
lin kers</w> 34834
l 4</w> 34832
MA X</w> 34829
shar ply</w> 34822
M X</w> 34814
iso zymes</w> 34809
al ongside</w> 34802
N d</w> 34801
Symp tom</w> 34796
0 p</w> 34789
topyran oside</w> 34788
S GA</w> 34787
length ening</w> 34786
C W</w> 34784
gen tle</w> 34784
py re 34782
C PA</w> 34780
immuno stained</w> 34779
so red</w> 34777
anni i</w> 34775
archa eal</w> 34775
Li quid</w> 34772
Ch lor 34770
Up per</w> 34769
Ann ual</w> 34767
pro xy</w> 34765
AS 2</w> 34762
un usually</w> 34761
pri ori</w> 34761
tw itch</w> 34759
fibrin olytic</w> 34758
medias tin 34757
col ored</w> 34754
Mi xed</w> 34754
cont acted</w> 34749
ge ometries</w> 34748
o .</w> 34743
d C</w> 34737
un resectable</w> 34736
ste adily</w> 34732
S6 K1</w> 34731
Run x2</w> 34731
P p 34721
p Rb</w> 34719
en yl</w> 34717
S un 34716
Ex o 34695
J AK 34694
minim ization</w> 34694
centro meric</w> 34693
cyclospor in</w> 34687
di stric 34686
Y u</w> 34679
R BP</w> 34678
re in</w> 34662
ev asion</w> 34654
mil der</w> 34654
Typh imurium</w> 34642
Gmb H</w> 34642
excep tions</w> 34634
group ing</w> 34620
ere b 34617
Gl c</w> 34616
it ors</w> 34615
post ulate</w> 34613
PI3 K 34609
V if</w> 34607
D ye</w> 34603
repe atability</w> 34598
l es 34596
Ultras truc 34594
seg mented</w> 34589
Ne o 34588
7 c</w> 34587
sati va</w> 34587
Hypox ia</w> 34574
Exampl es</w> 34574
NI S</w> 34563
correspon dence</w> 34561
dextro se</w> 34555
ep er</w> 34549
hyper lipidemia</w> 34540
fibr ate</w> 34539
P NA</w> 34532
pn I</w> 34531
Proper ties</w> 34530
a i 34528
Q 6</w> 34526
2 N</w> 34522
Neu rons</w> 34518
op tera</w> 34515
adap ter</w> 34515
TC M</w> 34506
adrenoc eptors</w> 34499
lipoxy genase</w> 34499
bub bles</w> 34498
antim icro 34497
Per kin 34496
fl avi 34492
2 R 34489
ti tres</w> 34489
Nan og</w> 34488
solu tely</w> 34487
Recommend ations</w> 34475
Rec ognition</w> 34472
Inflam mation</w> 34470
sc h</w> 34469
can cell 34469
ver ages</w> 34467
hon ey</w> 34466
conidi a</w> 34466
son ographic</w> 34464
let ters</w> 34464
distric ts</w> 34459
Mam malian</w> 34458
In jection</w> 34457
G at 34455
un met</w> 34450
Y ersin 34449
contrac eptives</w> 34449
intrav itreal</w> 34447
Ch ri 34442
Th r1</w> 34440
is otropic</w> 34439
oth y 34438
s essive</w> 34427
L RR</w> 34424
The ory</w> 34422
guan idine</w> 34421
organis ation</w> 34416
fac eted</w> 34414
Le gi 34410
overexpres s</w> 34409
R p</w> 34408
TA A</w> 34408
Ca v</w> 34407
ig an</w> 34406
gastro esophageal</w> 34406
multi cellular</w> 34404
Bel gium</w> 34403
S olution</w> 34402
encoun ters</w> 34402
car pal</w> 34401
or yz 34400
qual ified</w> 34397
ac ros 34396
progres ses</w> 34389
elem entary</w> 34389
g ren</w> 34387
Univer sal</w> 34380
Differen tiation</w> 34373
ric eps</w> 34372
ri mal</w> 34369
auto antibody</w> 34369
min g 34366
p T 34360
os oma</w> 34360
Loc alization</w> 34360
HY PO 34358
perox isomal</w> 34354
RN Ps</w> 34347
Physi ological</w> 34346
in ized</w> 34344
baum annii</w> 34344
drin kers</w> 34337
GLU T1</w> 34333
Accur ate</w> 34332
sc ru 34331
RE ST</w> 34323
ate dness</w> 34320
PD X</w> 34316
c ream</w> 34311
Mit o 34307
erythropoi esis</w> 34300
som n 34295
D SC</w> 34292
dra ft</w> 34283
inser ting</w> 34282
in -</w> 34278
deep ly</w> 34277
8 K</w> 34270
p age</w> 34270
AN T</w> 34259
abund antly</w> 34257
toler ate</w> 34256
streptoc occal</w> 34255
B ut 34252
oc alization</w> 34252
ren s</w> 34252
CH 3 34252
h im 34248
tel angi 34246
D or 34229
w alled</w> 34229
polym y 34225
we alth</w> 34223
R est 34218
oste oblastic</w> 34217
O H 34200
F K5</w> 34195
em itted</w> 34195
need les</w> 34193
gener ator</w> 34188
adip ogenic</w> 34183
n ationally</w> 34182
0 c</w> 34181
over dose</w> 34179
m m2</w> 34178
mari juana</w> 34178
A mp 34176
le u 34175
9 E</w> 34174
fl asks</w> 34173
remn ant</w> 34173
concer ted</w> 34168
GE O</w> 34168
phil osoph 34166
FGF 1</w> 34166
caroteno ids</w> 34161
phosphatidyl serine</w> 34154
AL E</w> 34152
therap ist</w> 34149
basi lar</w> 34149
ANALY SIS</w> 34145
PR MT 34144
C um 34140
e at</w> 34140
down loaded</w> 34139
V SMC</w> 34132
path ies</w> 34128
bed side</w> 34128
Predic tors</w> 34128
an tral</w> 34125
on colytic</w> 34124
phil us</w> 34124
r ata</w> 34120
G AD</w> 34119
W L</w> 34117
as perg 34117
orph ine</w> 34115
prote oglycans</w> 34114
vi sco 34113
M P2</w> 34110
am ent</w> 34110
tagg ing</w> 34110
AT G1</w> 34108
Po int</w> 34107
bronch itis</w> 34104
inte restingly</w> 34102
ear th 34099
qu e 34097
Fir stly</w> 34097
Pan el</w> 34097
blast ocysts</w> 34097
micro particles</w> 34094
bo ot 34094
call osum</w> 34091
n es</w> 34086
E con 34086
bac illus</w> 34084
Nucle otide</w> 34084
AT Pases</w> 34083
Wil ey</w> 34076
Per haps</w> 34075
et obacter</w> 34074
phthal ate</w> 34073
Col orectal</w> 34069
de fibrill 34067
interpre tations</w> 34063
at ous</w> 34061
divid e</w> 34057
cre di 34053
H ard 34042
T ACE</w> 34038
- O</w> 34030
y es</w> 34029
ul ans</w> 34026
ol ae</w> 34023
Ep ithelial</w> 34023
palp able</w> 34021
R ay 34020
chlor ine</w> 34019
c nem 34016
re ti 34015
stoichi ometric</w> 34015
short -</w> 34004
G6 PD</w> 34004
hydroly tic</w> 34000
in ostat</w> 33996
am ing</w> 33993
be et 33988
T x</w> 33982
b are</w> 33982
hyp ogly 33981
e me 33980
normal ize</w> 33975
dang erous</w> 33975
we an 33974
GI CAL</w> 33974
Lew y</w> 33973
micro circulation</w> 33972
y litis</w> 33971
de polarizing</w> 33971
flag ellar</w> 33961
antagon ize</w> 33958
Per io 33951
az id</w> 33950
i ter</w> 33946
Turk ish</w> 33946
st at 33945
ure ter</w> 33945
absor p 33945
deman ding</w> 33944
A trial</w> 33939
oc ene</w> 33932
even ly</w> 33932
j ump</w> 33931
moly b 33929
N Z 33927
och on 33927
fore sts</w> 33926
SI V 33921
cross linked</w> 33918
athero genic</w> 33917
ox azol 33915
c uc 33914
cri zotinib</w> 33911
cy t</w> 33905
b on</w> 33904
mo tors</w> 33904
progres sing</w> 33904
SL C2</w> 33903
Yersin ia</w> 33899
pod ocytes</w> 33898
m ite</w> 33895
L ith 33894
x x 33892
mis folding</w> 33892
S 2B</w> 33885
W ales</w> 33885
optim ally</w> 33880
analy sing</w> 33879
im eter</w> 33877
Fig. 6</w> 33876
Famil ial</w> 33876
MS H2</w> 33871
Sing ap 33868
at ography</w> 33863
convul sive</w> 33862
el ite</w> 33859
PP P</w> 33852
D KO</w> 33849
ra inf 33849
7 L</w> 33848
Coh en</w> 33844
ab solutely</w> 33842
Caro lina</w> 33842
Sch iff</w> 33841
arteri tis</w> 33841
abstr acts</w> 33840
w indows</w> 33838
HC A</w> 33835
AL 1</w> 33833
re mediation</w> 33827
erh ans</w> 33826
Q I</w> 33824
tom y 33823
sub domain</w> 33817
Fc R 33808
Fcγ RI 33808
AM PK 33806
copolym ers</w> 33806
draw ing</w> 33805
Ul tra</w> 33805
3 x 33801
Im paired</w> 33801
Meth od 33801
phosphati dy 33801
a h 33799
inte gra 33799
pe an 33794
reservo irs</w> 33793
k o 33790
R ati 33789
co variance</w> 33788
eosin ophilia</w> 33787
rot ated</w> 33786
ic tal</w> 33785
confir matory</w> 33784
bac illi</w> 33783
P la 33781
synchron y</w> 33777
Nan op 33777
h anded</w> 33773
nerv osa</w> 33773
favor ably</w> 33771
ev acu 33770
ac illin</w> 33768
MD V</w> 33768
elici ting</w> 33765
TP O</w> 33762
AL D 33761
dend rite</w> 33760
vo iding</w> 33759
de generate</w> 33757
SM AR 33757
Intra operative</w> 33753
vasodil ator</w> 33742
rel ess</w> 33734
Ac ross</w> 33730
my ocyte</w> 33726
D DM</w> 33715
rib onuclease</w> 33715
gad olinium</w> 33714
aven ues</w> 33711
apo A</w> 33707
R ub 33701
admin istering</w> 33698
Acin etobacter</w> 33698
six ty</w> 33690
Ste p</w> 33689
T . 33686
n omenclature</w> 33676
p urely</w> 33673
emm li</w> 33666
Ke ap1</w> 33665
onc ogenesis</w> 33663
obstac le</w> 33662
glycer aldehyde</w> 33659
stem ness</w> 33653
debri dement</w> 33649
pen ic 33642
pack aged</w> 33642
azep ines</w> 33642
a esthetic</w> 33640
S -</w> 33628
densit ometry</w> 33623
here after</w> 33619
Eng ine 33615
L FA</w> 33611
phil icity</w> 33610
p J 33600
bacteri aceae</w> 33598
MC M</w> 33598
under took</w> 33595
Z F 33594
catech ol</w> 33593
under score</w> 33592
ep im 33588
promis cu 33585
In du 33583
ocy anine</w> 33579
integra tes</w> 33576
PCSK 9</w> 33570
en de 33566
Proce edings</w> 33559
Tempor al</w> 33559
Brad ford</w> 33557
P B2</w> 33555
alkal oid</w> 33555
V o 33554
in compatible</w> 33552
concep tu 33552
U 7</w> 33544
SC P</w> 33542
2 . 33541
F ischer</w> 33541
V V</w> 33540
Dis tinc 33540
aeg yp 33539
ti an</w> 33536
Theore tical</w> 33536
state ments</w> 33527
O 6</w> 33524
per taining</w> 33523
lymphaden opathy</w> 33522
0 α</w> 33520
Al ong</w> 33519
ano ic</w> 33515
Gre at</w> 33513
M apping</w> 33511
work shop</w> 33508
gover n</w> 33506
brea kage</w> 33501
LS D1</w> 33499
qu it</w> 33498
im pulse</w> 33497
i sions</w> 33496
A 3G</w> 33493
omy cosis</w> 33490
it rac 33479
insti llation</w> 33479
transl ationally</w> 33475
inf lation</w> 33467
den afil</w> 33465
tend ons</w> 33465
t ons</w> 33454
transm it</w> 33451
3 N</w> 33448
stric ture</w> 33448
clim atic</w> 33447
c v</w> 33444
pi one 33443
PK M2</w> 33441
oglut arate</w> 33438
SL P</w> 33436
Statis tics</w> 33434
was ting</w> 33429
correc ting</w> 33422
Sub strate</w> 33415
her itable</w> 33410
Invol vement</w> 33410
A gr 33409
con dom</w> 33409
DE N 33406
3 M</w> 33405
g fp</w> 33404
G AC</w> 33404
FOX O1</w> 33403
RE PORT</w> 33402
fil opodia</w> 33402
Fin nish</w> 33401
le ver</w> 33400
lob ular</w> 33399
c s 33398
ynam ically</w> 33396
read out</w> 33396
un selected</w> 33395
tr u 33393
scar ring</w> 33393
chro tron</w> 33387
C max</w> 33366
nanocryst als</w> 33366
pyri dox 33365
P GE</w> 33364
rel atedness</w> 33354
far mers</w> 33350
IC R</w> 33347
ME L</w> 33345
thorac otomy</w> 33343
ab ut 33342
H ed 33341
L SM</w> 33340
re generated</w> 33340
Sequ ences</w> 33336
G i</w> 33333
Con ference</w> 33329
endoc rin 33329
I DH</w> 33325
purpur a</w> 33325
ex changes</w> 33324
del s</w> 33323
osteopo rotic</w> 33322
D RE 33321
Mitochond ria</w> 33319
TI A</w> 33317
C os 33316
ec topically</w> 33316
st anti 33313
Pl an</w> 33310
Bi P</w> 33303
CA AG 33302
pro x 33299
od opa</w> 33295
U PS</w> 33292
embed ding</w> 33288
epic ardial</w> 33285
Con g 33284
Pur pose</w> 33281
b ath 33280
ch ips</w> 33280
B ET</w> 33279
Acc ess</w> 33279
de myelination</w> 33278
decom pens 33277
ac i 33274
anti angiogenic</w> 33274
ur tic 33273
extravas ation</w> 33260
ill es</w> 33259
ag ency</w> 33256
0 D</w> 33255
ar ized</w> 33247
d uty</w> 33238
uro spor 33235
judg ment</w> 33232
ide t</w> 33231
ur ies</w> 33230
qu er 33226
uncertain ties</w> 33221
hom ogenous</w> 33220
EC MO</w> 33218
P v 33216
equili bration</w> 33214
gastro enteritis</w> 33210
econom ically</w> 33210
r als</w> 33206
Lang erhans</w> 33203
di acylglycerol</w> 33199
La emmli</w> 33198
al d</w> 33195
Fl ex 33195
peri oste 33194
si rolimus</w> 33193
pig ments</w> 33189
Ob ste 33186
carbox ylase</w> 33185
immunocyto chemical</w> 33184
Q L</w> 33183
flo ating</w> 33183
nom inal</w> 33183
F usion</w> 33180
Appro ach</w> 33175
K el 33163
DU 1</w> 33163
Li ving</w> 33162
P CT</w> 33161
RN AP 33159
astrocytom a</w> 33157
TB K1</w> 33154
gr ating</w> 33153
P Q</w> 33151
discipl ines</w> 33149
reli eve</w> 33148
cnem ius</w> 33146
metabol omics</w> 33143
pl ug</w> 33142
Inhibit ors</w> 33141
S per 33138
transloc ate</w> 33138
CD H1</w> 33133
9 -</w> 33128
B HK</w> 33127
SM N</w> 33125
meth op 33122
TRI zol</w> 33118
tell a</w> 33116
deu terium</w> 33115
kerat o 33107
chrom es</w> 33103
ar restin 33101
complex ation</w> 33100
p un 33095
SN Vs</w> 33091
GG A</w> 33086
construc ting</w> 33084
P ure</w> 33082
com a</w> 33080
MI A</w> 33077
but yr 33076
c ar</w> 33073
emplo ys</w> 33073
I MP 33071
phosphon ates</w> 33071
Re active</w> 33070
as ynchron 33067
out lines</w> 33067
author ities</w> 33067
im migrants</w> 33066
preced es</w> 33063
refl ective</w> 33060
an ode</w> 33058
resi s 33058
si d</w> 33055
α 5</w> 33053
represent atives</w> 33053
S ulf 33050
ocor tex</w> 33049
C it 33048
P c</w> 33048
compl icating</w> 33048
her ds</w> 33046
iat rogenic</w> 33045
conc eived</w> 33042
re tin</w> 33041
sp on 33040
lic ensed</w> 33037
B gl 33036
EN T</w> 33031
gly ox 33029
hydra z 33029
NO D2</w> 33027
smo ked</w> 33026
myocardi tis</w> 33026
e h 33025
a OR</w> 33025
MC S</w> 33025
qu artz</w> 33022
if erous</w> 33018
Anti biotic</w> 33014
impor tin</w> 33013
cholec yst 33011
en trance</w> 33009
ble aching</w> 33006
C 3b</w> 33003
Perkin Elmer</w> 33000
pall adium</w> 32990
CN Vs</w> 32987
ne omycin</w> 32983
electrocardi ographic</w> 32964
Met abolism</w> 32961
en ever</w> 32958
Lys ates</w> 32954
inten tional</w> 32952
d ressing</w> 32950
cardi over 32946
Com mission</w> 32946
g RNA</w> 32944
capac itance</w> 32944
Z .</w> 32941
par kin</w> 32941
V 8</w> 32939
nephro toxicity</w> 32935
us hed</w> 32933
Zin c</w> 32927
uc e</w> 32924
ank yrin</w> 32920
be l</w> 32914
PR A</w> 32914
reli ed</w> 32913
oligodendro cyte</w> 32911
tonsi ll 32908
lam el 32907
work force</w> 32906
iod othy 32902
Anti gen</w> 32899
ed a</w> 32898
os amine</w> 32896
Re tinal</w> 32896
6 P</w> 32895
te en 32892
Transcrip tional</w> 32891
er us</w> 32888
CM C</w> 32886
KE Y</w> 32883
micro liters</w> 32876
z able</w> 32875
Mem ory</w> 32866
str ated</w> 32862
V ZV</w> 32859
ligh ts</w> 32859
multi tude</w> 32858
ha o</w> 32857
T AG 32854
tetram ers</w> 32852
ro ent 32850
lap atinib</w> 32847
stri p</w> 32842
immer sed</w> 32841
- CT 32838
Contribu tion</w> 32837
form erly</w> 32829
proto plasts</w> 32829
es ulf 32822
sc ene</w> 32822
pepti dyl</w> 32821
bi als</w> 32815
occup y</w> 32815
CTL s</w> 32814
pig ment 32805
sor bitol</w> 32800
ardi ac</w> 32798
capsi ds</w> 32794
accid ental</w> 32791
D ako</w> 32790
biom olecules</w> 32788
c ic 32787
H TS</w> 32786
phy sin</w> 32782
An terior</w> 32780
polymer ized</w> 32779
L an 32774
B K 32768
MA F</w> 32768
Ap propri 32763
pres ent 32760
dNT P</w> 32756
Dic ty 32755
AD s</w> 32754
MR A</w> 32752
pro hibi 32751
sh ad 32744
metro nidazole</w> 32744
AB 1</w> 32743
n i</w> 32742
glycol ip 32733
ol or</w> 32722
deci ph 32715
A ST 32714
solubil ization</w> 32714
I SS</w> 32708
kal lik 32707
ERB B2</w> 32705
ur red</w> 32704
trehal ose</w> 32703
ap sig 32700
Traum atic</w> 32699
polio virus</w> 32698
at traction</w> 32696
Path ological</w> 32690
man soni</w> 32688
Chol esterol</w> 32686
PD 9</w> 32678
G AP1</w> 32674
W ES</w> 32672
brady cardia</w> 32664
electro por 32654
out let</w> 32653
B ovine</w> 32648
dam s</w> 32648
Ab err 32648
3 e</w> 32647
rec t 32646
GA AT 32642
anticoagul ants</w> 32642
P SP</w> 32632
her d</w> 32630
encomp asses</w> 32630
Bar rett</w> 32629
syncy tial</w> 32627
oc h</w> 32624
T it 32618
W 7</w> 32616
rever sion</w> 32615
ac ies</w> 32614
9 R</w> 32612
AC AT 32609
sin uses</w> 32609
acqu iring</w> 32608
u ren 32606
pneumo phila</w> 32605
N H3</w> 32604
Equ al</w> 32602
mesen chym 32599
A HL</w> 32598
E PI</w> 32598
to in</w> 32597
pharmac euticals</w> 32597
for amen</w> 32595
ri d</w> 32590
m W</w> 32582
ori e</w> 32581
break through</w> 32581
dosi metry</w> 32580
F ung 32576
coun ty</w> 32575
lo x</w> 32572
P ig 32570
M p 32569
miner als</w> 32569
Iran ian</w> 32567
M ari 32558
low ers</w> 32553
SE P</w> 32553
Ver sion</w> 32550
hyph al</w> 32549
Q D</w> 32546
zol omide</w> 32546
osteoc alcin</w> 32545
emb rane</w> 32542
anne aled</w> 32542
ari b</w> 32541
K at 32540
de er</w> 32534
M H 32530
ser vation</w> 32529
bioma terials</w> 32519
S et</w> 32517
AL I</w> 32515
cephal y</w> 32510
pe digree</w> 32505
penetr ate</w> 32504
mis e</w> 32503
N t 32499
R ate</w> 32498
S ERT</w> 32498
CR L</w> 32498
hal ogen 32492
ou biqu 32491
chemoradi otherapy</w> 32482
coch lea</w> 32479
trac ers</w> 32476
assimil ation</w> 32471
T HO 32469
Non idet</w> 32469
An dro 32463
rec tus</w> 32458
sl opes</w> 32457
sub class</w> 32453
inter vertebral</w> 32450
saphen ous</w> 32448
HI T</w> 32444
PD GFR</w> 32442
d ines</w> 32436
pheny toin</w> 32430
sub tr 32429
sul fam 32429
Estro gen</w> 32428
ow ad 32423
intr ag 32420
st osis</w> 32419
Neuro logical</w> 32414
pos tero 32412
PL S</w> 32411
fi ve 32409
Nor we 32409
i g</w> 32407
ar c</w> 32406
Hydro gen</w> 32404
F inal</w> 32401
og luc 32400
C ate 32399
H NE</w> 32394
RA R</w> 32393
C ron 32391
din itro 32391
HR MS</w> 32389
re polarization</w> 32385
bench mark</w> 32383
pr ur 32382
CC P</w> 32379
b arium</w> 32374
er ul 32372
Cros s 32372
them e</w> 32368
ex ocrine</w> 32361
Ery thro 32360
aberr ation</w> 32356
GT G</w> 32355
r umen</w> 32353
h es 32348
om icro 32347
on ide</w> 32346
E RAD</w> 32344
A edes</w> 32343
green house</w> 32342
encephal omyelitis</w> 32341
owad ays</w> 32338
sulf hydr 32337
deri vation</w> 32336
L AD</w> 32335
yl ene</w> 32333
Glu N 32331
vascular ized</w> 32328
Bang lad 32327
l ic</w> 32325
re turning</w> 32322
tri age</w> 32319
fir stly</w> 32319
di asis</w> 32316
b ell</w> 32315
ML P</w> 32315
Neu ronal</w> 32312
cyt ologic</w> 32306
stimul ator</w> 32303
co immunoprecipitation</w> 32302
c ast 32301
surg e</w> 32300
CT CF</w> 32300
trop in</w> 32300
as teri 32298
w ant</w> 32293
d . 32288
C ath 32284
AC CT 32279
dra ining</w> 32278
Ar ch 32275
g um</w> 32274
oglob ulin</w> 32274
silic ate</w> 32274
Cor relations</w> 32272
Tox icity</w> 32271
inv ade</w> 32270
Asi ans</w> 32268
inf liximab</w> 32266
Δ C</w> 32260
B ud 32259
FLI P</w> 32257
back ward</w> 32255
yl or</w> 32254
re te</w> 32253
apsig argin</w> 32251
C c 32245
holo enzyme</w> 32244
il ia</w> 32241
avid ity</w> 32230
T HA</w> 32229
B le 32229
phot ons</w> 32229
N Cs</w> 32228
inst ar</w> 32227
morbi dities</w> 32227
Wall is</w> 32226
PRA CTI 32223
op son 32221
shor tage</w> 32218
calc ified</w> 32217
Agro bacterium</w> 32217
9 F</w> 32214
rearrang ed</w> 32214
D har 32208
T SC</w> 32207
G el 32202
e ros 32200
Ken ya</w> 32197
bin ge</w> 32196
H3 T3</w> 32196
ma y 32195
drin k</w> 32195
C g 32193
vaso constric 32191
produc ers</w> 32188
Prolifer ation</w> 32186
tr am 32184
K K 32180
meningi oma</w> 32178
n g 32177
I CI</w> 32175
thermo philic</w> 32170
ther to</w> 32168
un responsive</w> 32167
Ech ocardi 32162
oc ephal 32161
W B 32154
annot ations</w> 32153
pred ator</w> 32151
orph in</w> 32150
PKC α</w> 32150
al o</w> 32147
hyper plastic</w> 32147
Be h 32142
My b</w> 32141
insectic ides</w> 32136
fim bri 32133
H SD</w> 32129
Techn ical</w> 32125
def enses</w> 32111
sol vation</w> 32111
ph allo 32109
regi stries</w> 32107
t .</w> 32105
Ro yal</w> 32104
phen otyping</w> 32101
iti vely</w> 32098
haemat ological</w> 32096
oper ates</w> 32095
tumorigen icity</w> 32095
Cdk 5</w> 32092
ti des</w> 32088
do sis</w> 32088
su be 32087
P X 32086
judg ments</w> 32084
D omain</w> 32083
an one</w> 32081
tem is 32077
us cul 32072
DD T</w> 32071
0 T</w> 32070
Ad 5</w> 32070
IN H</w> 32064
obj ectively</w> 32063
anth ra 32056
G J 32055
Per sistent</w> 32054
hexam er</w> 32052
B id</w> 32045
Cycl o 32038
In ten 32035
un ic 32026
Seph adex</w> 32019
K ne 32018
mes oporous</w> 32018
ta e</w> 32012
B is</w> 32007
sp r 32004
Ne utroph 32000
RA W</w> 31993
neutr alize</w> 31991
on at 31989
ampl ifying</w> 31989
out door</w> 31989
G las 31988
N um 31982
T ER 31978
Col om 31978
BR 3</w> 31978
un explored</w> 31976
glucuron ide</w> 31976
Re stric 31973
for aging</w> 31971
olar yng 31971
PE G 31969
CT GF</w> 31968
CC CT 31962
S 4A</w> 31957
Tran sc 31957
swe et</w> 31950
Er k1</w> 31945
Av r 31945
W KY</w> 31944
associ ative</w> 31939
O PG</w> 31937
J NK1</w> 31936
diph ther 31936
Po or</w> 31935
Gu ang 31932
MD SCs</w> 31929
th ri 31921
ID 5</w> 31920
u PAR</w> 31918
worsen ed</w> 31918
bus iness</w> 31914
neuro surgical</w> 31912
CD S</w> 31911
popl iteal</w> 31910
practi cally</w> 31908
pum ping</w> 31908
ca vit 31907
conc us 31905
erc ept</w> 31903
4 N</w> 31898
vi d</w> 31898
con sangu 31895
E Q</w> 31891
Mar tin</w> 31891
broil er</w> 31891
Mut agenesis</w> 31888
Viet nam</w> 31888
C tr 31884
gli osis</w> 31882
review ing</w> 31879
M ir 31874
Qu ik 31870
f ide</w> 31864
Pa ul 31862
d anger</w> 31860
myel operoxidase</w> 31857
anth ine</w> 31856
pro voc 31855
gastro cnemius</w> 31853
ri ol</w> 31846
c up 31844
P R1</w> 31844
prob abilistic</w> 31844
meningi omas</w> 31844
TLR 7</w> 31843
shor test</w> 31837
al istic</w> 31834
comput ationally</w> 31833
ip il 31828
az o 31827
cycl ins</w> 31827
M f 31824
k d</w> 31823
og lo 31821
viv ax</w> 31820
sub scale</w> 31819
hem o 31819
contribut ors</w> 31818
Off ice</w> 31818
cer tified</w> 31817
acet amol</w> 31814
res timate</w> 31812
lympho proliferative</w> 31812
IP V</w> 31811
immuno fluorescent</w> 31809
MT C</w> 31809
corro sion</w> 31807
ex am</w> 31804
epig ene 31800
tom ic</w> 31799
four fold</w> 31799
wa it</w> 31798
reimbur sement</w> 31793
u res 31792
Te k</w> 31792
al ia</w> 31787
Gre en 31782
agricul ture</w> 31777
dies el</w> 31776
nit ri 31772
quadrup ole</w> 31771
equ imolar</w> 31770
SL N</w> 31767
z ircon 31766
appro aching</w> 31759
cor ds</w> 31757
EX PERI 31745
Pak istan</w> 31745
esophag itis</w> 31743
MA VS</w> 31743
met amorph 31742
a ter</w> 31736
tion ality</w> 31733
SI L</w> 31732
T MP</w> 31727
adju v 31724
CYP2 C1</w> 31724
ac . 31723
In clusion</w> 31723
D il 31718
az epam</w> 31715
ro si 31710
Al g 31709
air borne</w> 31707
pa yment</w> 31698
trac ked</w> 31695
Si O2</w> 31686
exagg erated</w> 31685
P HD</w> 31683
GT TT 31682
tom ized</w> 31679
sev oflurane</w> 31679
numer ically</w> 31678
c KO</w> 31668
l ich</w> 31667
Ess ential</w> 31664
ta p</w> 31660
suspici ous</w> 31659
P ower</w> 31658
st ations</w> 31655
AG EN</w> 31654
sac ral</w> 31653
β 5</w> 31650
ad vis 31646
deoxy uridine</w> 31643
ap ril</w> 31638
i ans</w> 31636
L AC</w> 31636
ig no 31635
ow ed</w> 31635
Add gene</w> 31635
maca que</w> 31633
enthal py</w> 31631
Te aching</w> 31630
ana ero 31626
helic ases</w> 31626
ore sis</w> 31621
R ich 31613
St ar</w> 31611
Me dia</w> 31609
dep ths</w> 31607
eth anolamine</w> 31607
ear th</w> 31605
expor ted</w> 31603
AB L1</w> 31602
urospor ine</w> 31599
olip in</w> 31598
ect oderm</w> 31597
tyros inase</w> 31597
Fu j 31596
band width</w> 31596
intrac ellularly</w> 31594
ogen e</w> 31593
cor al</w> 31592
J U 31589
- -1</w> 31587
antimicro bials</w> 31587
lesi oned</w> 31585
restra ints</w> 31580
quad riceps</w> 31577
G all 31576
ex tras 31576
seed ling</w> 31576
Kr usk 31572
offici al</w> 31571
G il 31569
cl ip 31568
co incident</w> 31568
9 G</w> 31562
D WI</w> 31557
Rou tine</w> 31557
argum ent</w> 31557
replac ements</w> 31555
s k</w> 31554
Micha el</w> 31550
poly p</w> 31549
pro tom 31548
stri s</w> 31548
Mg SO4</w> 31546
PT A</w> 31544
J i 31542
ov an 31542
phot onic</w> 31540
me res</w> 31533
cul turally</w> 31533
RA NT 31532
ap plies</w> 31532
fo res 31531
fer i</w> 31530
WOR DS</w> 31517
Hel sin 31515
Inf ants</w> 31513
Ear lier</w> 31513
sta inability</w> 31512
Sal I</w> 31508
im printing</w> 31506
Q 4</w> 31501
fir m</w> 31497
extr ap 31496
Th omas</w> 31495
Ep o</w> 31495
birth weight</w> 31493
dys plastic</w> 31491
C Y</w> 31490
head aches</w> 31489
eti dine</w> 31486
esc ens</w> 31482
H P1</w> 31481
in vertebrates</w> 31480
Memb ers</w> 31479
deb ated</w> 31479
mon y</w> 31477
ag s</w> 31476
Pl ants</w> 31472
re aring</w> 31467
ger an 31461
the ro 31458
irr itation</w> 31458
Entero bacteriaceae</w> 31450
g y 31449
p est</w> 31447
r ug 31443
NI H3T3</w> 31442
trans ection</w> 31441
ob ac 31440
ref ug 31435
vi l</w> 31433
lu x</w> 31431
IR I</w> 31430
sh ire</w> 31429
sil ane</w> 31429
ys in</w> 31426
ic t</w> 31425
Bru ker</w> 31422
viol ent</w> 31421
mon onucle 31419
Cl one</w> 31418
RANT ES</w> 31418
C U</w> 31416
lamel lipo 31416
S ca 31414
id ated</w> 31413
G K</w> 31411
achiev es</w> 31411
re folding</w> 31410
pl ak 31410
om on 31405
AD M</w> 31405
Develop ing</w> 31405
P PD</w> 31403
ulf ite</w> 31400
B ase</w> 31398
tur bul 31398
P is 31389
arth ritic</w> 31387
Helsin ki</w> 31387
potenti ates</w> 31383
ne ocortex</w> 31374
integr ase</w> 31374
hypog on 31370
territ ory</w> 31370
quad rant</w> 31367
PR E</w> 31364
arteri oles</w> 31358
c ac 31357
gr as 31356
K apos 31353
LC 3B</w> 31353
clamp ing</w> 31350
ag ulation</w> 31344
ro tor</w> 31341
amid o</w> 31340
C er</w> 31339
em ur 31328
lo id</w> 31328
vir tu 31328
K ATP</w> 31327
om itted</w> 31325
Sl ides</w> 31324
PA CAP</w> 31315
haem odynamic</w> 31312
biore actor</w> 31311
cholest asis</w> 31308
C is 31307
Cl ara</w> 31301
assemb l 31301
Norwe gian</w> 31300
elec tro</w> 31293
AP E1</w> 31288
ech an 31287
Par ame 31285
Prog ress</w> 31285
hat ching</w> 31282
Super nat 31281
stom atitis</w> 31280
micro injection</w> 31279
war ning</w> 31276
hetero cyclic</w> 31272
j a</w> 31266
ass a</w> 31264
regres sions</w> 31264
phosph ine</w> 31260
R o</w> 31254
mis matches</w> 31254
onec rosis</w> 31250
fl atten 31247
PHO S</w> 31246
clof en 31246
NS AID</w> 31244
trac ed</w> 31243
dig it</w> 31241
G ERD</w> 31239
I k 31236
A PCs</w> 31234
S p</w> 31232
fibrin olysis</w> 31232
γ -</w> 31231
hyph ae</w> 31230
Pren atal</w> 31221
L ex 31219
tam er</w> 31217
carbam azepine</w> 31215
C utaneous</w> 31212
investig ator</w> 31209
to e</w> 31208
Rep orts</w> 31203
t actin</w> 31201
w ires</w> 31201
restric ting</w> 31201
ori ent 31201
spectrophot ometric</w> 31201
Met abol 31199
cl ass 31198
inequ alities</w> 31193
M ath 31190
compe tency</w> 31190
shri mp</w> 31189
iso thermal</w> 31177
fertili zed</w> 31176
des er 31173
s phen 31171
og ran 31171
CL P</w> 31171
Partic ularly</w> 31171
A TI 31166
nc bi. 31161
encephal on</w> 31160
t ann 31158
H ex 31156
sp astic</w> 31156
fer rin</w> 31153
Gastro intestinal</w> 31149
Bo x</w> 31147
anc ement</w> 31146
cap topril</w> 31143
Compe ti 31143
ma zine</w> 31141
Be ing</w> 31140
incub ations</w> 31139
my ocl 31135
insul a</w> 31135
epide mics</w> 31134
As semb 31133
LI MI 31132
O phthal 31129
6 T</w> 31128
restor ative</w> 31125
wave form</w> 31124
qu asi 31122
ser s</w> 31120
transfer ring</w> 31120
r r 31119
bat teries</w> 31117
dyst rophic</w> 31116
dos ed</w> 31111
assi gn</w> 31108
M OR</w> 31105
percei ve</w> 31105
P ex 31102
aegyp ti</w> 31101
H all</w> 31100
A re 31100
and 2</w> 31095
MP N</w> 31091
is ting</w> 31089
Mor ris</w> 31087
4 T1</w> 31079
lo bectomy</w> 31075
Sim ulation</w> 31073
lamin ar</w> 31072
ob ronchial</w> 31071
n ap 31066
tri m 31065
bon a</w> 31061
NL 4</w> 31060
RA SS 31057
herni ation</w> 31056
S qu 31055
iodothy ronine</w> 31053
fos ter</w> 31047
Z hao</w> 31046
methyl sulfonyl</w> 31045
arcom as</w> 31044
re conc 31042
SC R</w> 31041
spectr in</w> 31037
cl aimed</w> 31036
B ran 31035
fer mented</w> 31035
ph in</w> 31032
ipil imumab</w> 31027
hydrox yl 31026
Singap ore</w> 31026
S 0</w> 31025
p emphig 31019
D om 31019
Rel ease</w> 31015
H Rs</w> 31014
disper sive</w> 31014
GT CT 31011
se al 31008
check points</w> 31004
gen ders</w> 31003
to oth 31002
epididym al</w> 31002
Reson ance</w> 31001
D AB</w> 31000
abund ances</w> 31000
ator vastatin</w> 31000
refer rals</w> 30993
to thec 30991
under way</w> 30987
ba g</w> 30982
clofen ac</w> 30980
B P5</w> 30975
Ta g</w> 30974
sec tors</w> 30972
non diabetic</w> 30969
1 h</w> 30965
di z 30960
pero vsk 30958
homo dimers</w> 30954
dg es</w> 30954
CA AT 30953
I 5</w> 30951
ep iness</w> 30949
B 9</w> 30946
cul min 30946
yn it 30945
SP C</w> 30945
att ach</w> 30945
sero var</w> 30942
soci eties</w> 30941
e tings</w> 30938
i PS</w> 30925
ot on 30923
ob ase</w> 30923
GC AT 30922
n ul 30919
oper ators</w> 30919
hu b</w> 30918
illo facial</w> 30918
dig est</w> 30917
reli eved</w> 30911
l anth 30908
c enti 30903
o urs</w> 30901
eff usions</w> 30901
conjuncti vitis</w> 30900
9 K</w> 30898
re ment</w> 30897
L and 30891
gluc oside</w> 30891
reconstruc t</w> 30888
inter connected</w> 30886
calc ifications</w> 30886
A ero 30883
com ment</w> 30881
out standing</w> 30881
Kapos i</w> 30880
tun e</w> 30879
toxic ology</w> 30878
prepar ative</w> 30877
deriv atization</w> 30866
ca esarean</w> 30865
Har v 30864
d est</w> 30863
per mitting</w> 30856
tremend ous</w> 30855
an odine</w> 30854
ne bul 30852
bridg ed</w> 30851
in habit 30848
mid gut</w> 30848
de tom 30846
poly cy 30842
edi atr 30841
B ow 30840
TA TIONS</w> 30839
reson ant</w> 30838
7 H</w> 30835
Ampl ification</w> 30834
NT s</w> 30833
Sta ining</w> 30823
radi olig 30821
stac ks</w> 30821
ru thenium</w> 30820
O wing</w> 30818
cit ric</w> 30818
L AMP</w> 30815
H ung 30813
com pressed</w> 30810
go ro 30808
omin ant</w> 30808
al ty</w> 30805
local ised</w> 30802
pli ance</w> 30799
9 L</w> 30798
hyalu ronic</w> 30796
U 4</w> 30795
C av1</w> 30794
di ox 30790
cur ved</w> 30789
C ushing</w> 30788
CYP 1A1</w> 30787
dar kness</w> 30786
dor feri</w> 30783
quadru plex</w> 30780
n ex 30779
hab it</w> 30779
Surge ons</w> 30777
Z IP</w> 30776
k 3</w> 30774
IGF 1R</w> 30769
ti ated</w> 30765
Stand ardi 30764
anti arrhythmic</w> 30760
hydrol ases</w> 30760
imag ery</w> 30755
medullo blastoma</w> 30753
l s 30749
o estradiol</w> 30746
Ch ang</w> 30745
ur is 30739
phosphatidy lethanolamine</w> 30739
parkinson ism</w> 30729
Solu ble</w> 30729
myelodys plastic</w> 30727
whe e 30725
schist osomiasis</w> 30721
comp iled</w> 30720
sc ars</w> 30717
TE V</w> 30717
methyl transferases</w> 30710
photo chemical</w> 30710
C MR</w> 30707
dis infection</w> 30704
ker atitis</w> 30703
Figure 4</w> 30700
lit azone</w> 30698
ereb ellar</w> 30697
me trically</w> 30694
om as 30692
protr usion</w> 30692
N ep 30687
stra p</w> 30687
At tention</w> 30685
Smad 4</w> 30685
G SCs</w> 30683
de bil 30682
Clin icians</w> 30680
ocor tic 30676
ide ation</w> 30675
t ably</w> 30674
B 6 30674
normo xic</w> 30671
detom idine</w> 30671
AA V2</w> 30669
rein force</w> 30669
dig iti 30668
F H 30666
ect odermal</w> 30665
KL F4</w> 30665
F AM 30658
end oderm</w> 30651
ker nel</w> 30650
break points</w> 30650
tempo romandibular</w> 30650
plant ations</w> 30645
im pregn 30644
otroph in</w> 30644
form amide</w> 30637
conc an 30637
Cle arly</w> 30637
ab domin 30635
ref ine</w> 30634
si li 30628
nyst ag 30625
HR QOL</w> 30624
pig e 30623
burg dorferi</w> 30623
en oid</w> 30622
ket ones</w> 30621
ti go</w> 30618
pre fer</w> 30618
Gr b2</w> 30616
phen obarbital</w> 30614
dis advantage</w> 30611
go w</w> 30608
Den sity</w> 30607
nl m. 30607
resi st</w> 30601
SO UR 30601
et ching</w> 30600
colon ized</w> 30600
T oc 30595
bi phenyl</w> 30595
GG AG 30595
Initi ative</w> 30594
ros ph 30593
Chrom osome</w> 30592
H g 30591
wor tmannin</w> 30588
AA AT 30584
T β 30581
Myo D</w> 30581
mic elle</w> 30580
ax on 30579
phag ocytes</w> 30578
lo ose</w> 30577
con spic 30576
I H</w> 30575
W ell</w> 30575
bott len 30574
QI AGEN</w> 30572
AT s</w> 30569
Lig and</w> 30567
2 alpha</w> 30563
emur afenib</w> 30563
pre formed</w> 30562
cryopreser vation</w> 30561
di clofenac</w> 30559
acet aldehyde</w> 30558
s ay</w> 30555
cl avian</w> 30552
cin nam 30552
RT Ks</w> 30552
bal ances</w> 30549
han dic 30549
methop rim</w> 30545
RE SEARCH</w> 30544
ten ers</w> 30544
miner alized</w> 30541
St an 30536
ecz ema</w> 30536
endo tracheal</w> 30533
Repor ting</w> 30530
ro lling</w> 30527
e ip 30526
form e</w> 30520
G . 30517
immunohisto chemically</w> 30517
fl ask</w> 30512
po ds</w> 30510
og ro 30509
log 2</w> 30509
tetr a</w> 30509
HS QC</w> 30503
HD R</w> 30502
abl ative</w> 30499
contrac ted</w> 30498
collo id</w> 30498
I B 30497
amil oride</w> 30496
kallik rein</w> 30491
lingu istic</w> 30490
commun icate</w> 30487
visi ting</w> 30485
ev ening</w> 30484
contin ent</w> 30484
c eption</w> 30483
in dels</w> 30483
In duc 30479
SE A</w> 30477
sig mo 30471
a .</w> 30464
trans mis 30463
dis continuous</w> 30463
educ ated</w> 30460
p S 30452
antimal arial</w> 30449
ri er</w> 30448
eth ane</w> 30445
o vir</w> 30444
un desirable</w> 30444
aden omatous</w> 30433
fric tion</w> 30432
ocompati ble</w> 30431
stabil ities</w> 30429
um ent</w> 30427
Pol ic 30427
sk a</w> 30426
Gen otyping</w> 30424
illo sis</w> 30423
. ht 30422
NE DD 30422
B eth 30419
bol a</w> 30419
endocardi al</w> 30419
AC AG 30416
candi diasis</w> 30412
Fat ty</w> 30412
uro kinase</w> 30410
St ability</w> 30408
O HDA</w> 30404
swe at</w> 30402
am ne 30399
ol an 30394
Wa ters</w> 30387
tub al</w> 30382
com promising</w> 30380
pro viral</w> 30377
op t 30377
Tr kA</w> 30377
SA HA</w> 30377
ax in</w> 30375
rec al 30372
thro at</w> 30370
experim entation</w> 30369
refer ring</w> 30369
ni volumab</w> 30368
Hydro xy 30367
common est</w> 30364
pattern ed</w> 30361
Aca demic</w> 30361
op tics</w> 30360
do i</w> 30358
CD R</w> 30356
stom atal</w> 30356
Re present 30352
b r</w> 30337
controll able</w> 30337
Analy zer</w> 30335
per ineal</w> 30331
condu it</w> 30331
GT 1</w> 30329
At temp 30329
requ ested</w> 30325
C los 30323
Adolesc ents</w> 30318
ti bi 30312
centrifug e</w> 30311
Eth ical</w> 30311
D al 30310
comp action</w> 30307
di version</w> 30302
P ut 30301
ide mic</w> 30300
indu stri 30300
Spec i 30298
nucle osides</w> 30292
spro uting</w> 30292
Sel ected</w> 30290
ent ries</w> 30287
ncbi. nlm. 30285
T e</w> 30283
K u</w> 30282
anti thrombin</w> 30281
OX PHOS</w> 30280
A H 30277
pac k</w> 30274
BR D4</w> 30272
nor adrenergic</w> 30270
To ol</w> 30270
ente sis</w> 30270
l ass</w> 30269
sensiti ze</w> 30266
K ine 30261
be es</w> 30261
motiv ational</w> 30261
abl ated</w> 30259
Bri ef</w> 30259
TRI M2</w> 30257
digesti bility</w> 30255
RUN X1</w> 30254
Organ ic</w> 30253
gl ot 30251
isom er 30236
con v 30234
Er ro 30233
DN P</w> 30231
gel atin 30231
myel ocytic</w> 30225
N ile</w> 30220
sterili zation</w> 30218
0 P</w> 30213
ann ular</w> 30208
adop ting</w> 30208
acc ept</w> 30206
extra hepatic</w> 30204
sec ure</w> 30203
glut aminase</w> 30203
accep tors</w> 30203
thoug hts</w> 30202
DO C</w> 30200
C -</w> 30196
P NP</w> 30194
sal ient</w> 30194
com et</w> 30189
s chol 30187
sem ide</w> 30186
Char les</w> 30181
anth ocyan 30175
ow a</w> 30174
anth in</w> 30168
zz y</w> 30168
m c 30165
P rom 30163
met atar 30161
cle aving</w> 30159
induc tive</w> 30158
Sm o</w> 30157
L um 30156
D CIS</w> 30155
prefer able</w> 30155
Ar ti 30154
Com pon 30152
Re habilitation</w> 30149
CY P2</w> 30148
ob ox</w> 30147
RT A</w> 30140
in sured</w> 30139
pal m</w> 30137
m ated</w> 30133
a vidin</w> 30128
f 3</w> 30128
circum vent</w> 30126
eng ers</w> 30124
al ert</w> 30123
de myelinating</w> 30121
p et</w> 30118
SC E</w> 30118
I O</w> 30116
r ins</w> 30104
GG CT 30103
view point</w> 30103
hydra ulic</w> 30101
amox icillin</w> 30100
ser iously</w> 30099
pur ify</w> 30098
up right</w> 30095
ureth ra</w> 30092
Schist osoma</w> 30092
ep endym 30090
bio degradation</w> 30090
dil ute</w> 30090
glu ten</w> 30090
chondro itin</w> 30089
dev oted</w> 30086
LD 5</w> 30084
Glas gow</w> 30084
oc la 30083
TE A</w> 30083
PI D</w> 30082
polyvinyl idene</w> 30080
α 2 30077
pas te</w> 30076
impair ing</w> 30074
mit o 30072
leukotri ene</w> 30070
con sin</w> 30065
anaphyl axis</w> 30065
Psy cho 30064
AM Ps</w> 30063
H ET 30062
Y Y1</w> 30059
psych omotor</w> 30058
T ER</w> 30057
guan id 30057
ab i 30055
Me eting</w> 30055
g out</w> 30053
vigil ance</w> 30053
J e 30050
reinforc ing</w> 30050
hi therto</w> 30044
nan otube</w> 30044
cave olae</w> 30044
st ory</w> 30043
N N</w> 30042
Typ ically</w> 30042
1 B 30041
zip per</w> 30041
end onucle 30040
Krusk al</w> 30040
compli ant</w> 30039
lev odopa</w> 30036
L -</w> 30032
ang itis</w> 30032
e q</w> 30031
m it</w> 30028
propi onic</w> 30023
P k 30021
bul l 30019
sir tu 30019
AC TT 30018
qual ities</w> 30016
pyri dyl</w> 30015
poten cies</w> 30011
Z F</w> 30008
ati de</w> 30005
all erg 30001
Interfe ron</w> 30001
us en</w> 29994
har dw 29994
dissoci ate</w> 29994
MC D</w> 29987
i eld</w> 29986
nucle ocap 29984
ol ia</w> 29979
nox ious</w> 29979
ampl ifier</w> 29978
alop ecia</w> 29977
form ulas</w> 29975
ros ing</w> 29973
gam et 29971
ESC RT</w> 29970
I P1</w> 29967
par asym 29967
bal ancing</w> 29965
coagul ant</w> 29963
TNF alpha</w> 29957
Rab 5</w> 29955
coumar in</w> 29947
t m 29946
be ars</w> 29943
CE M</w> 29943
bi oluminescence</w> 29941
et us</w> 29935
Mich igan</w> 29935
an hydr 29930
p ter 29929
is oni 29927
om eprazole</w> 29926
op olym 29924
D av 29921
anthrop ogenic</w> 29916
E so 29910
contamin ant</w> 29909
P HE 29908
w earing</w> 29904
par ap 29903
arsen ite</w> 29901
transc atheter</w> 29901
certain ty</w> 29900
hex agonal</w> 29899
ncbi.nlm. nih.gov</w> 29898
tin y</w> 29893
univer sally</w> 29893
R are</w> 29888
W E 29888
t ape</w> 29887
Ar f</w> 29886
tig mine</w> 29880
som al</w> 29870
anticonvuls ant</w> 29864
mesenchym e</w> 29864
par all 29860
PA CT</w> 29859
ocy tos 29859
cyt olytic</w> 29858
res ins</w> 29856
Table 3</w> 29856
ab an</w> 29855
sup r 29854
mitochond ri 29854
anti parallel</w> 29853
ro tenone</w> 29851
adhe red</w> 29849
W n 29846
b HLH</w> 29839
al loc 29836
Im plementation</w> 29836
x i 29831
AR P</w> 29830
volunte er</w> 29830
determin ate</w> 29828
but anol</w> 29826
ti ometry</w> 29825
cur ing</w> 29822
iv udine</w> 29821
Δ 3</w> 29820
inter course</w> 29818
educ ators</w> 29818
sho ots</w> 29815
immuno assays</w> 29814
CDKN 2A</w> 29812
terpen es</w> 29811
correc tions</w> 29805
ith romycin</w> 29804
Ch eck 29803
Pro b 29803
Inf ections</w> 29802
Res .</w> 29800
aver ages</w> 29799
vacu ol 29797
Franc isco</w> 29797
goro usly</w> 29797
r . 29792
ph i 29791
Y ear</w> 29787
AZ T</w> 29785
oblig ate</w> 29785
H T1</w> 29780
T ME</w> 29780
BM s</w> 29776
cow or 29773
ct DNA</w> 29770
F b</w> 29769
efferen t</w> 29769
4 e</w> 29768
counter stained</w> 29768
L act 29763
F MD</w> 29762
Q A</w> 29762
G e</w> 29762
k et</w> 29759
rej ected</w> 29759
conver ge</w> 29757
C MS</w> 29754
W ASP</w> 29754
h ens</w> 29753
naph th 29751
le in</w> 29750
iso propyl 29750
syn taxin</w> 29749
orophar yngeal</w> 29749
sero prevalence</w> 29745
oryz ae</w> 29744
bas oph 29742
defini tely</w> 29733
cyto protective</w> 29730
er vical</w> 29729
pro lyl</w> 29727
no ting</w> 29726
intrav entricular</w> 29726
transpor ting</w> 29723
ac idity</w> 29722
pal atal</w> 29718
assemb ling</w> 29717
Ep CAM</w> 29717
Prof essi 29717
erup tion</w> 29716
P 1 29712
mi tes</w> 29711
M EC 29709
s lit</w> 29704
Biom ar 29703
che wing</w> 29701
S b</w> 29697
sym path 29697
au d</w> 29695
valid ating</w> 29694
hyper phosphorylation</w> 29691
oph ores</w> 29690
antero grade</w> 29685
N k 29684
Mil d</w> 29683
anti oxidative</w> 29682
Ly n</w> 29680
prev ailing</w> 29679
co existing</w> 29678
oste lium</w> 29672
stain less</w> 29671
Micro bial</w> 29667
ll a</w> 29661
O G 29657
CB CT</w> 29656
uk i</w> 29654
un structured</w> 29652
glucone ogenesis</w> 29650
APOB EC 29648
carcin oid</w> 29647
trypsin ized</w> 29646
hete ros 29644
phot ob 29640
HI S</w> 29638
fix ing</w> 29638
ow ski</w> 29636
sec ond 29633
Rock ford</w> 29631
intram edullary</w> 29627
Reduc ing</w> 29625
phallo idin</w> 29621
char coal</w> 29619
Na HCO3</w> 29617
mechan o 29615
Bu il 29612
explo iting</w> 29610
sero negative</w> 29608
cholangi ocarcinoma</w> 29608
ar row</w> 29607
ph en</w> 29607
Val encia</w> 29607
ar ic</w> 29600
Cd k2</w> 29600
n AChRs</w> 29594
ur ic 29594
Wis consin</w> 29589
A 2A</w> 29583
T PR</w> 29583
incid ents</w> 29581
e 2</w> 29577
pET 2</w> 29577
P SS</w> 29572
in ae</w> 29572
Z EB1</w> 29571
PA K1</w> 29568
phe ochromocytoma</w> 29568
check list</w> 29566
ch ir 29563
ma il</w> 29563
as phy 29562
ric k</w> 29558
osp ice</w> 29558
dich otom 29558
SER S</w> 29557
BM C</w> 29556
sil k</w> 29553
L enti 29551
Δ N 29551
var ices</w> 29550
GSE 1</w> 29549
palmito yl</w> 29549
organ oids</w> 29544
be verages</w> 29543
E IA</w> 29538
f A</w> 29538
Ultrastruc tural</w> 29531
ass as</w> 29530
SC A</w> 29530
aw s</w> 29529
Thermo Fisher</w> 29529
Em plo 29528
h older</w> 29527
a il</w> 29526
En ric 29525
5 P</w> 29522
argum ents</w> 29522
At ten 29521
zo ites</w> 29520
Mo vie</w> 29516
Q Tc</w> 29514
u l</w> 29513
spi king</w> 29511
che ese</w> 29511
recombin ants</w> 29509
achiev able</w> 29509
mant le</w> 29505
corrobor ate</w> 29501
pursu ed</w> 29498
LM P1</w> 29497
esteri fication</w> 29497
direc ts</w> 29496
h ESCs</w> 29494
C ari 29492
HDAC 3</w> 29488
single ton</w> 29487
SN R</w> 29484
ester ases</w> 29483
pod ocyte</w> 29482
neuro peptides</w> 29480
li kewise</w> 29479
DT I</w> 29478
Import ance</w> 29477
inten tions</w> 29472
Pen n 29471
onc ological</w> 29470
ST N</w> 29464
Accum ulation</w> 29464
eip t</w> 29464
Bloc king</w> 29463
be t</w> 29462
Al ign 29456
6 L</w> 29455
PTP 1B</w> 29451
um n</w> 29450
N em 29444
E specially</w> 29443
allo ys</w> 29443
pac hy 29440
sero logic</w> 29440
Hist opathological</w> 29438
if 1</w> 29434
HT T</w> 29434
lymphaden ectomy</w> 29433
multi level</w> 29432
perfec tly</w> 29432
lar ation</w> 29431
fac et</w> 29430
Throm bo 29429
MI S</w> 29427
beha ved</w> 29427
all ine</w> 29426
W W</w> 29423
plas macy 29422
tem ia</w> 29420
li zumab</w> 29418
URA 3</w> 29417
Willi ams</w> 29416
y sm 29414
Y 9</w> 29414
photo electron</w> 29414
hyper tonic</w> 29408
path ologist</w> 29407
thi azol</w> 29406
hind limb</w> 29405
ND V</w> 29398
Nit ric</w> 29395
unevent ful</w> 29394
later ally</w> 29391
inst ant 29386
por in</w> 29385
inform al</w> 29385
complain t</w> 29385
C 9 29381
Analy tical</w> 29381
stop ping</w> 29380
susp ect</w> 29377
c occi</w> 29372
to ur 29370
munici pal</w> 29367
architec tures</w> 29362
ampl ifications</w> 29361
bur nout</w> 29358
mac ro</w> 29357
ori b 29356
a teness</w> 29355
PA L</w> 29353
Man assas</w> 29353
AA R</w> 29350
V TA</w> 29345
varic ella</w> 29334
oblas tomas</w> 29332
thym ine</w> 29332
model led</w> 29330
sl ur 29326
Fe deral</w> 29325
Ex p 29324
Vari ations</w> 29324
nour ished</w> 29324
EP SCs</w> 29323
Hamil ton</w> 29322
met agen 29318
metro politan</w> 29318
phen ol 29317
AC H</w> 29313
fasc ia</w> 29310
es .</w> 29301
upreg ulates</w> 29300
N erve</w> 29298
S till</w> 29298
4 I</w> 29297
TC CT 29296
Kne e</w> 29295
provinc es</w> 29294
indu stries</w> 29288
G overn 29287
follow up</w> 29287
emerg es</w> 29287
oper able</w> 29285
Accur acy</w> 29285
vit rectomy</w> 29284
gu est</w> 29283
R FA</w> 29276
Dicty ostelium</w> 29274
D AP</w> 29266
aren cy</w> 29264
Re pair</w> 29261
I di 29259
th apsigargin</w> 29259
AB CA1</w> 29256
au tistic</w> 29256
Vic tor 29255
spas m</w> 29254
Asp 1</w> 29250
esteri fied</w> 29248
O 3a</w> 29240
Tob acco</w> 29240
L ck</w> 29239
G o</w> 29239
er ica</w> 29238
Ano ph 29238
F OR 29234
de stabilizing</w> 29228
il le</w> 29228
MC V</w> 29227
be am 29222
manip ulating</w> 29222
S AD</w> 29220
hot spot</w> 29219
MP V</w> 29218
phy tic</w> 29217
nes ota</w> 29215
L AT</w> 29214
c t</w> 29211
peroxis omes</w> 29210
phospho protein</w> 29209
- like</w> 29206
Stand ards</w> 29206
Su z 29205
carc ass</w> 29205
CC S</w> 29203
oc ean</w> 29202
UT Rs</w> 29202
me tre 29197
seques tered</w> 29196
Mü ller</w> 29195
M urine</w> 29192
modi fiable</w> 29188
ad al 29187
bar k</w> 29186
Abstr acts</w> 29186
cl ick</w> 29181
A RN 29180
s ol</w> 29179
fo vir</w> 29178
dec eased</w> 29176
mas titis</w> 29175
thi on 29174
S earch</w> 29173
mobil ized</w> 29170
competi tively</w> 29169
lu g</w> 29166
HT 1A</w> 29162
om ers</w> 29161
Hist ory</w> 29161
B CC</w> 29157
arabin ose</w> 29153
W E</w> 29152
th inning</w> 29151
hem ic 29143
N d 29139
myel ogenous</w> 29139
post treatment</w> 29135
lem ma</w> 29135
En zym 29131
ar ine</w> 29130
s 3</w> 29128
Adolesc ent</w> 29125
non steroidal</w> 29124
CA Fs</w> 29123
M HV</w> 29120
C 4 29111
HB SS</w> 29107
M agne 29104
nystag mus</w> 29102
Incre ases</w> 29094
under p 29093
anticip ate</w> 29092
broaden ing</w> 29090
CB A</w> 29083
fluoro scopy</w> 29082
Sequ ential</w> 29073
Cron bach</w> 29073
R FS</w> 29072
Bruc ella</w> 29067
dop a</w> 29066
in vertebrate</w> 29065
manufac ture</w> 29056
te mo 29053
associ ating</w> 29048
f lex</w> 29047
Elec troph 29045
Mic ro</w> 29045
thym ocyte</w> 29043
F ine</w> 29041
isoni azid</w> 29040
N ETs</w> 29039
R an</w> 29038
if ts</w> 29037
2 I</w> 29032
f 6</w> 29032
Agg reg 29030
yst ander</w> 29029
down regulate</w> 29022
Nutri tional</w> 29018
Sum mary</w> 29018
ris ky</w> 29016
oll en</w> 29014
PT Ms</w> 29007
Lab s</w> 29004
hypoten sive</w> 29002
sp ans</w> 29001
An alog 28997
Q M</w> 28992
st rea 28992
D Q</w> 28990
cent red</w> 28989
RA W2</w> 28987
reversi bility</w> 28984
prof icient</w> 28983
R 1 28977
C PC</w> 28975
v ating</w> 28969
mos a 28969
MEN TAL</w> 28967
IT P</w> 28962
Ab s 28958
De tec 28954
PI N</w> 28952
hetero dimeric</w> 28951
tunn eling</w> 28949
be ating</w> 28942
un predictable</w> 28941
Pro vid 28941
intr as 28941
envelop ed</w> 28939
c d</w> 28938
Immunob lotting</w> 28938
SO 2</w> 28935
Fl av 28934
lys ines</w> 28928
S SI</w> 28921
am idine</w> 28921
he tero</w> 28916
Hist or 28914
R IN 28913
a ks</w> 28913
sulfhydr yl</w> 28913
Autom ated</w> 28910
ES BL</w> 28906
S F2</w> 28905
PC S</w> 28904
R B1</w> 28903
ocortico id</w> 28899
zin ess</w> 28897
anaesthe tized</w> 28897
ataly st</w> 28895
resc ues</w> 28893
pro BNP</w> 28890
temis inin</w> 28890
orth o</w> 28886
Distr ict</w> 28885
D -</w> 28884
ole ate</w> 28883
in ine</w> 28882
leuk o 28880
MI TF</w> 28880
Q 5</w> 28878
PG s</w> 28878
sub families</w> 28877
De tails</w> 28876
sep tic 28874
diure tics</w> 28871
b out 28870
mach ines</w> 28870
comb ustion</w> 28868
comm ens 28867
U L1</w> 28866
destro yed</w> 28865
over growth</w> 28863
temper ate</w> 28860
Li po 28857
phy tes</w> 28851
thyro iditis</w> 28845
hemisph eres</w> 28844
om p 28839
H X 28836
m atism</w> 28836
Hospit als</w> 28835
tran su 28834
P an</w> 28833
on omics</w> 28832
CS P</w> 28830
chal asin</w> 28829
pean ut</w> 28829
S u</w> 28815
M ach 28814
Tr x</w> 28814
strength ened</w> 28813
comm entary</w> 28812
loos ening</w> 28810
me detomidine</w> 28809
mis matched</w> 28807
osp inal</w> 28802
un defined</w> 28798
pro lol</w> 28793
oglo bin 28792
z in</w> 28789
Li ve</w> 28788
Man ual</w> 28788
en th 28787
adjuv ants</w> 28786
H3K 9</w> 28781
Sir t1</w> 28780
N et</w> 28772
mon d</w> 28766
synerg ism</w> 28764
hardw are</w> 28764
K ing</w> 28762
opa que</w> 28760
at oid</w> 28758
IT Y</w> 28754
AL DH</w> 28752
hypo thesi 28747
B erg 28742
hydroxy urea</w> 28741
phot ore 28739
Import ant</w> 28738
pent obarbital</w> 28733
4 Δ</w> 28731
cal orie</w> 28730
de phosphorylated</w> 28724
En dom 28719
logarith mic</w> 28718
Quik Change</w> 28718
L en 28715
in emia</w> 28714
µ mol</w> 28713
m U</w> 28710
inhabit ants</w> 28708
la red</w> 28704
ST D</w> 28699
h ERG</w> 28698
de polar 28697
deplo yed</w> 28694
amid ino</w> 28689
shel f</w> 28688
ur us</w> 28686
tr ated</w> 28684
N VP</w> 28681
RI s</w> 28681
Fig ures 28680
fo od 28678
assum es</w> 28677
fer rous</w> 28672
V LP</w> 28670
hep t 28670
no vel 28668
suc tion</w> 28668
cryopreser ved</w> 28668
h o</w> 28667
chel ator</w> 28667
co activators</w> 28664
un il 28662
ec onomy</w> 28661
sper midine</w> 28655
n ylon</w> 28653
P P2</w> 28653
d h</w> 28651
FGF R3</w> 28650
Statis tically</w> 28649
break point</w> 28649
osmol ality</w> 28649
TRA F2</w> 28642
sensiti zing</w> 28642
PR 2</w> 28640
ferrom agnetic</w> 28640
Th ai</w> 28638
discus sing</w> 28637
K up 28634
bio distribution</w> 28631
Ca T</w> 28630
sim pler</w> 28629
n ut</w> 28627
acknowledg ed</w> 28625
J er 28621
Li Cl</w> 28618
8 p</w> 28614
P ag 28611
NQ O1</w> 28611
k 4</w> 28609
K ol 28608
o viruses</w> 28606
G as</w> 28602
ap ment</w> 28601
grad ation</w> 28599
Meas uring</w> 28598
rip ening</w> 28598
uni or</w> 28590
Nov agen</w> 28588
land marks</w> 28584
C S1</w> 28579
Ul tra 28579
cowor kers</w> 28576
V CP</w> 28575
Pro file</w> 28573
itud inally</w> 28573
um ping</w> 28572
- positive</w> 28568
ER CP</w> 28568
PO MC</w> 28566
ad in</w> 28565
v r 28562
sym bi 28561
hyper calc 28558
HIF 1α</w> 28558
Indic ations</w> 28554
end arte 28553
con finement</w> 28549
pur ple</w> 28547
In don 28546
concentr ates</w> 28543
analog y</w> 28535
LIMI TATIONS</w> 28529
alpha 2</w> 28528
Up state</w> 28527
scho ol 28526
c ept</w> 28520
itin ation</w> 28520
sti ll 28519
phalang eal</w> 28519
co stal</w> 28517
mono valent</w> 28517
cann ula</w> 28513
M K2</w> 28511
TR PA1</w> 28511
CT T</w> 28509
electropor ated</w> 28509
ol actone</w> 28506
de als</w> 28506
Anoph eles</w> 28505
sna ke</w> 28501
origin ates</w> 28500
attain ment</w> 28500
J ac 28498
S RP</w> 28498
cellul arity</w> 28496
photos ensiti 28495
E l</w> 28488
x 4</w> 28485
conc entric</w> 28484
lac to 28483
Cal cul 28479
Chem icals</w> 28477
PV N</w> 28472
res s 28471
CH E 28471
G en</w> 28466
Re placement</w> 28466
sub clavian</w> 28463
s no 28462
ph ia</w> 28462
B N 28459
w illing</w> 28458
drom ic</w> 28456
Ox ford</w> 28456
k head</w> 28455
L at 28455
e igen 28454
l 3</w> 28451
pGL 3</w> 28451
D SA</w> 28450
par aly 28442
AL D</w> 28441
disp arate</w> 28436
H ap 28426
he ifers</w> 28425
fir m 28424
absorp tiometry</w> 28424
bio informatic</w> 28421
ribo flavin</w> 28419
S at 28417
haem odialysis</w> 28417
D i</w> 28416
per it 28415
unc 1</w> 28415
mi sta 28415
ur t</w> 28414
ky ph 28411
ful le 28409
pro mazine</w> 28408
oc clusions</w> 28407
sk illed</w> 28404
Cryst als</w> 28404
k i 28403
st ands</w> 28397
Eso phag 28395
immun olab 28392
an thus</w> 28391
C Ps</w> 28387
Pres ent</w> 28383
zoon otic</w> 28383
stre ams</w> 28381
M MSE</w> 28378
form ally</w> 28377
tetr adec 28375
5 M</w> 28372
b id 28372
es the 28372
C asp 28371
S Y</w> 28367
IN FO</w> 28360
F XR</w> 28356
cyan obacteria</w> 28355
immunoprecip itate</w> 28353
cyste ctomy</w> 28353
arthro sis</w> 28353
vol t 28352
Hybri d</w> 28350
temo zolomide</w> 28348
CO MT</w> 28338
bi valent</w> 28337
oly tica</w> 28337
up ward</w> 28336
ex tran 28335
H3 K4</w> 28333
expos e</w> 28331
CF H</w> 28331
g m</w> 28330
F ra 28329
convinc ing</w> 28327
hybri doma</w> 28319
Throm b 28319
ul f</w> 28317
a A</w> 28313
T ac 28311
bronch oscopy</w> 28311
bor ate</w> 28308
in s 28307
un ts</w> 28307
ethyl enedi 28307
complem ent 28306
m 0</w> 28304
sin usitis</w> 28304
anes thesi 28301
rosi glitazone</w> 28299
Rep ly</w> 28297
aflat oxin</w> 28294
d na 28291
D IV</w> 28287
sle eping</w> 28286
f an</w> 28285
trans dermal</w> 28284
ro ll</w> 28283
RA G</w> 28283
benzodi azepines</w> 28282
ry anodine</w> 28280
Y E 28279
ellul ose</w> 28279
oxygen ated</w> 28278
all o</w> 28274
ME D</w> 28273
MS P</w> 28272
Reg ulatory</w> 28268
neuro physiological</w> 28260
U VA</w> 28257
li b</w> 28257
C lu 28255
S 3B</w> 28254
Dan vers</w> 28250
p. o.</w> 28248
hist omorph 28247
olec ule</w> 28246
benth am 28246
inhe rently</w> 28245
bre ad 28243
wea ther</w> 28236
gonorrho eae</w> 28235
cath ode</w> 28234
Rand om</w> 28234
voltam metry</w> 28231
pl asi 28219
ambigu ously</w> 28218
Con flic 28217
G SSG</w> 28216
V ec 28214
D PP</w> 28212
dissi pation</w> 28209
S v</w> 28207
biosens ors</w> 28207
sen ile</w> 28206
su stainability</w> 28204
P ow 28202
l on 28198
hydrox ylated</w> 28195
SM D</w> 28194
RG Cs</w> 28192
N AT</w> 28191
interven ing</w> 28191
tri methoprim</w> 28190
M P1</w> 28187
hygro mycin</w> 28187
U P 28185
7 A1</w> 28184
II B</w> 28178
MMP 9</w> 28175
mm 3</w> 28174
gener alization</w> 28171
L yn 28169
al er 28169
lig anded</w> 28166
reser ved</w> 28163
cocul ture</w> 28163
electro physiology</w> 28161
M AR</w> 28160
ut h</w> 28160
for th</w> 28158
aneurys mal</w> 28157
Ch k2</w> 28154
Ec topic</w> 28154
sper mati 28150
T AS 28148
Psy c 28148
non human</w> 28146
L B 28145
aqu at</w> 28144
demethyl ase</w> 28143
T f</w> 28141
L ight 28141
eph rin</w> 28141
Mechanis tically</w> 28139
adsor bent</w> 28138
re alize</w> 28130
C B2</w> 28128
C UL 28125
or ship</w> 28122
justi fy</w> 28119
Ul ti 28118
HB s</w> 28113
F RAP</w> 28112
phot ographed</w> 28112
tit udes</w> 28110
I r</w> 28109
ow l</w> 28107
inc h</w> 28107
E bola</w> 28101
part nership</w> 28101
whe el</w> 28099
put amen</w> 28097
B uc 28095
B etter</w> 28095
Z ym 28095
Ar th 28094
CYP2 C9</w> 28090
AB R</w> 28088
conform ers</w> 28087
G CS</w> 28086
R inger</w> 28084
m ass 28083
al ign</w> 28079
D b 28075
ci sion</w> 28067
L m 28066
ultr afiltration</w> 28064
disabl ing</w> 28060
Qu antum</w> 28056
recre ational</w> 28056
presum ptive</w> 28055
Coll agen</w> 28055
PI P3</w> 28054
b orders</w> 28052
sta urosporine</w> 28051
thromb olytic</w> 28051
anastom oses</w> 28048
Gal NAc</w> 28044
ol ite</w> 28043
be c</w> 28042
ell ae</w> 28042
c iti 28041
in si 28040
ath ymic</w> 28039
bentham iana</w> 28036
E z 28034
transduc ers</w> 28033
i ro 28032
Fig. 1A</w> 28031
nom yc 28031
me 1</w> 28029
op ancre 28029
ord able</w> 28028
K P</w> 28023
le um</w> 28023
dou bly</w> 28021
Aberr ant</w> 28021
Estim ation</w> 28016
spec ify</w> 28014
Ar p2</w> 28012
e de</w> 28010
epox y</w> 28008
sim ulator</w> 28007
Br assi 28007
jo in 28006
In activation</w> 28004
GR K2</w> 28000
hyper cap 27999
Fran k 27999
d otoxin</w> 27996
C CI</w> 27991
isot opes</w> 27988
H US</w> 27986
O ld</w> 27983
K pnI</w> 27978
ach ments</w> 27973
op tically</w> 27971
ci di 27969
wro te</w> 27969
y og 27968
ta tes</w> 27968
S PA 27967
ud in</w> 27967
me etings</w> 27962
pol luted</w> 27962
Protec tive</w> 27958
I MP</w> 27953
chro ma 27953
fos sil</w> 27952
CH 4</w> 27951
physi otherapy</w> 27949
j e 27947
amo to</w> 27947
m ug</w> 27945
W in 27945
III B</w> 27944
carb onic</w> 27942
ad riamycin</w> 27941
lo vir</w> 27940
U re 27934
E wing</w> 27930
naphthal ene</w> 27926
ten ces</w> 27925
Ethi opia</w> 27925
mar c 27924
gastr o</w> 27922
b ench</w> 27918
re bound</w> 27916
phenyl indole</w> 27913
FAD D</w> 27909
A cr 27906
ynit rite</w> 27906
E OC</w> 27903
eli ac</w> 27901
re assor 27900
expendit ures</w> 27900
mer its</w> 27899
VO 2 27898
AP I</w> 27896
F low 27894
nanocom posite</w> 27888
par acetamol</w> 27886
acti nomyc 27883
pharmac ist</w> 27882
O I</w> 27880
max illa</w> 27880
w in</w> 27875
bi d</w> 27874
Th oracic</w> 27870
tre otide</w> 27867
Thr 2</w> 27863
hydroxy tryptamine</w> 27860
γ S</w> 27859
co tic</w> 27858
c .2</w> 27852
Moder n</w> 27852
Hist ologic</w> 27851
por ts</w> 27849
term ate</w> 27848
ing ham</w> 27844
F AB 27843
IC G</w> 27840
rub ella</w> 27839
st yles</w> 27838
Ug anda</w> 27833
p ell 27832
Contr ast</w> 27831
stron ic</w> 27829
X BP1</w> 27826
immunob lots</w> 27824
Min nesota</w> 27822
concan avalin</w> 27822
ram ycin</w> 27819
d f</w> 27818
cataly zing</w> 27818
PL E</w> 27817
Ch ag 27816
Ap o</w> 27811
H ost</w> 27808
telangi ectasia</w> 27807
S ug 27804
am iod 27801
struc turing</w> 27796
pollut ant</w> 27795
invasi vely</w> 27795
all yl</w> 27794
AA AG 27792
Sh im 27792
i ol</w> 27787
TRE AT 27786
gu ard</w> 27785
tro chan 27784
aff ili 27782
re volution 27780
st ack</w> 27780
Meth ylation</w> 27777
HYPO THE 27775
AN CA</w> 27773
transloc ates</w> 27769
es da</w> 27768
Be ads</w> 27767
Pit ts 27767
gra zing</w> 27765
commun icating</w> 27765
persis ting</w> 27765
op rine</w> 27763
orth ologous</w> 27760
ful fill</w> 27758
prur itus</w> 27753
sub species</w> 27750
AD T</w> 27750
contro ller</w> 27748
is ure</w> 27746
bur ning</w> 27738
sero logy</w> 27735
parasym pathetic</w> 27733
centro meres</w> 27730
S AR 27726
SA XS</w> 27726
pur ities</w> 27724
fat ality</w> 27724
amphi philic</w> 27724
p c</w> 27723
tos econd</w> 27719
R el</w> 27718
B ed 27718
Up take</w> 27716
circum ferential</w> 27715
bl a 27714
rever sing</w> 27713
im oto</w> 27711
semi quantitative</w> 27706
c p</w> 27704
. 1A</w> 27702
neighb or</w> 27700
titr e</w> 27699
plas tid</w> 27697
Elec trical</w> 27695
Bio informatics</w> 27693
AS L</w> 27692
ex o 27691
Beth esda</w> 27691
AK T1</w> 27690
ec oxib</w> 27689
virtu e</w> 27689
V el 27688
ul aris</w> 27686
ri ses</w> 27686
uc cin 27686
Mul tic 27686
l umb 27684
- free</w> 27682
diss ecting</w> 27682
EB OV</w> 27680
pen sion</w> 27679
disper se</w> 27679
Ear th</w> 27676
ra in</w> 27675
bul bar</w> 27671
anti nociceptive</w> 27669
K ip1</w> 27665
ate lli 27659
om el 27658
Con struction</w> 27655
PL K1</w> 27653
adel phia</w> 27652
myo fibroblasts</w> 27650
N CS</w> 27647
perox ides</w> 27647
flu idity</w> 27646
nit rous</w> 27645
amiod arone</w> 27645
clin es</w> 27644
exacerb ate</w> 27644
pup il</w> 27642
wa x</w> 27636
DE AE</w> 27635
S tero 27632
min s</w> 27631
hy n 27627
sur al</w> 27626
t angles</w> 27620
B rea 27618
necrop tosis</w> 27618
BC S</w> 27617
S AA</w> 27611
di lemma</w> 27610
il lium</w> 27608
alcohol ics</w> 27608
7 beta</w> 27604
al idomide</w> 27603
prost acycl 27603
um umab</w> 27600
wee k 27599
a ked</w> 27598
l one</w> 27597
B ad</w> 27589
co al 27589
Me V</w> 27587
inv entory</w> 27585
soci etal</w> 27585
Moder ate</w> 27583
ca ve</w> 27581
air flow</w> 27581
vor tex 27579
appro x</w> 27576
Argen tina</w> 27574
uniform ity</w> 27572
ST A</w> 27571
soci ally</w> 27571
Fi b 27570
precip itates</w> 27570
b ystander</w> 27569
Kre bs</w> 27566
u ate</w> 27560
S. D.</w> 27556
toler able</w> 27552
oma to</w> 27552
TNF R1</w> 27548
J os 27547
F ore 27544
transp arency</w> 27541
S DH</w> 27538
y ama</w> 27537
micro dialysis</w> 27532
na ked</w> 27529
accompl ish</w> 27529
yr s</w> 27527
RNAP II</w> 27525
or rhea</w> 27524
Ep ilep 27524
Phil adelphia</w> 27524
W A 27522
bas in</w> 27518
Nic o 27517
ob sessive</w> 27516
fl ame</w> 27515
PD F</w> 27510
tris phosphate</w> 27498
er ate</w> 27497
mat ous</w> 27490
buff ering</w> 27487
con to 27485
coun ties</w> 27485
Appropri ate</w> 27485
or ific 27484
hem ophilia</w> 27481
al azine</w> 27480
pic ked</w> 27479
V P3</w> 27478
Glut athione</w> 27478
C MA</w> 27476
al an 27473
3 X</w> 27472
atax in</w> 27467
hem ostatic</w> 27465
V AP</w> 27464
U rine</w> 27463
sero conversion</w> 27461
3 D 27460
Par is</w> 27460
econom ical</w> 27460
igno red</w> 27459
PT Hr 27457
R ussian</w> 27455
pit falls</w> 27455
cyl inder</w> 27452
y algia</w> 27451
Phot os 27448
og litazone</w> 27447
Angio tensin</w> 27442
rever ses</w> 27438
micro globulin</w> 27436
sk ew 27434
van adate</w> 27434
chie f</w> 27433
M ont 27430
RE 1</w> 27430
sr c</w> 27430
AI F</w> 27429
inhi bin</w> 27427
T x 27425
F ron 27419
g D</w> 27418
O cular</w> 27414
obacteri al</w> 27410
dr a</w> 27409
an hydro 27404
ac ylated</w> 27404
invari ably</w> 27404
fum arate</w> 27403
satur able</w> 27402
hizo bium</w> 27401
nucle oli</w> 27398
Ach illes</w> 27395
sub classes</w> 27392
gyn ecological</w> 27392
E sp 27391
Bill erica</w> 27391
L HRH</w> 27390
fu els</w> 27390
Gen omes</w> 27386
Ch est</w> 27385
har a</w> 27384
BR L</w> 27382
A β 27378
neg ativity</w> 27378
Cor tical</w> 27378
ju n</w> 27375
GAL 1</w> 27375
costim ulatory</w> 27374
Hetero gene 27373
IN R</w> 27371
sensor ineural</w> 27371
sper mine</w> 27369
P SII</w> 27368
D U</w> 27367
ay a</w> 27363
Acti vities</w> 27363
ar ro 27361
av ascular</w> 27361
T CC 27358
HER 3</w> 27358
d ab 27357
drin ks</w> 27357
AG S</w> 27356
normo xia</w> 27356
weak ened</w> 27354
Ven ous</w> 27353
PB ST</w> 27350
grow s</w> 27345
on ts</w> 27340
S ize</w> 27338
SV M</w> 27333
RA TION 27332
pr us 27332
rec eipt</w> 27329
athi oprine</w> 27328
ra d</w> 27326
ep il 27326
Lym e</w> 27326
st aff 27324
ti az 27323
to ol 27319
sk ipping</w> 27318
opo ietin</w> 27318
lip ogenesis</w> 27315
Arg 2</w> 27314
end os 27312
agon istic</w> 27312
glutam yl</w> 27312
SEL ECTION</w> 27310
pGE X</w> 27309
ligam ents</w> 27305
staphyloc occi</w> 27303
I 4</w> 27299
inv ag 27291
P gp</w> 27290
V O4</w> 27288
ac ul 27288
Recon struction</w> 27288
ge ometrical</w> 27286
L CA</w> 27284
rel ay</w> 27284
N 2O</w> 27279
sal mon 27279
mal to 27278
long itudinally</w> 27275
sw ollen</w> 27274
iso enzymes</w> 27272
gly caemic</w> 27271
li b 27268
inter net</w> 27265
avi um</w> 27265
si ed</w> 27261
T p 27260
acet o 27260
S hor 27255
pre maturity</w> 27253
Immuno Research</w> 27253
wh enever</w> 27252
Sj ö 27252
rom e</w> 27249
nucle ases</w> 27248
vinc ulin</w> 27247
Exc el</w> 27242
ab is 27239
tothec in</w> 27239
ez rin</w> 27237
micron utri 27232
elabor ate</w> 27232
Ad jus 27231
0 F</w> 27225
AD 1</w> 27225
preclud e</w> 27222
TOR C1</w> 27222
Condi tions</w> 27221
Multi variable</w> 27218
2 B 27217
Pu er 27213
aer ial</w> 27211
cf DNA</w> 27209
3 s</w> 27206
T AR</w> 27206
MY CN</w> 27203
work up</w> 27196
C n 27195
tryp an</w> 27189
stri pped</w> 27188
gonad otrop 27183
var .</w> 27182
prostacycl in</w> 27179
retin itis</w> 27178
AT F</w> 27177
H H 27176
Incre ase</w> 27175
PM MA</w> 27173
home obox</w> 27173
rad 5</w> 27173
jo ined</w> 27170
CR s</w> 27169
2 D 27167
tetram ethyl 27165
Di vision</w> 27164
extr am 27164
Mut ants</w> 27164
Princ ip 27161
un ambiguously</w> 27160
sar col 27160
r us</w> 27159
neuro science</w> 27159
f all 27158
ff s</w> 27158
produc er</w> 27157
anti inflammatory</w> 27156
CD 3 27155
cy statin</w> 27153
thermo philus</w> 27153
Arab ia</w> 27147
ne tt</w> 27143
Protoc ol</w> 27143
ex foli 27142
repor tedly</w> 27141
8 H</w> 27139
at tain</w> 27138
AB O</w> 27137
TR PC 27137
P VA</w> 27135
0 X</w> 27134
PD s</w> 27134
sa ved</w> 27131
lyophil ized</w> 27130
om ic 27128
MD s</w> 27126
f use</w> 27125
Qu ant</w> 27124
car p</w> 27121
Af ter 27121
xyl ene</w> 27121
Metast atic</w> 27121
ec um</w> 27119
st oma</w> 27116
Optim ization</w> 27115
c ag 27109
gl asses</w> 27109
H ip</w> 27108
psych otropic</w> 27104
Ac tin 27102
Star ting</w> 27101
kain ate</w> 27096
c resc 27093
0 A1</w> 27092
pector is</w> 27092
me ro 27090
B ab 27089
bene ath</w> 27088
MR s</w> 27087
CA N</w> 27086
neuro pathological</w> 27083
Malay sia</w> 27083
y ne 27082
teach er</w> 27079
ultr af 27078
ith in</w> 27076
Gu ide</w> 27074
Cys 1</w> 27072
Represent ative</w> 27072
centrifug al</w> 27066
IL K</w> 27066
Maxim al</w> 27063
de polymerization</w> 27059
Tran sport</w> 27057
AT L</w> 27055
mom entum</w> 27055
HYPOTHE SIS</w> 27054
M re1</w> 27053
J H</w> 27052
Sk p2</w> 27051
t man</w> 27049
ph or 27047
par at 27044
deli ber 27043
L ec 27042
Cycl ic</w> 27038
graph ite</w> 27036
MT A</w> 27034
prus side</w> 27034
M CT</w> 27033
W RK 27033
vo res</w> 27024
accel ero 27024
hemat uria</w> 27023
cad aver</w> 27019
lit termate</w> 27017
post transcriptional</w> 27015
p ush</w> 27014
abrup t</w> 27014
Banglad esh</w> 27013
azol es</w> 27012
os a 27010
ni al</w> 27006
y -- 27005
phyl ogenetically</w> 27005
Ha CaT</w> 27002
assess es</w> 26999
practi cing</w> 26998
gen otoxicity</w> 26996
Cryp tospor 26996
PR s</w> 26995
five fold</w> 26995
culti var</w> 26994
anhydr ase</w> 26994
phenyl methylsulfonyl</w> 26993
pneumon itis</w> 26992
SI M</w> 26990
1 A 26987
cur rence</w> 26986
hol istic</w> 26986
is tin</w> 26983
my oglobin</w> 26982
e stu 26978
anti neoplastic</w> 26978
trac ks</w> 26978
UL K1</w> 26971
metre xed</w> 26969
Con sensus</w> 26968
es sions</w> 26966
ge ts</w> 26964
f t 26958
Obj ectives</w> 26957
P HD 26955
P2 Y1</w> 26951
ap er</w> 26950
RO P</w> 26950
mut ating</w> 26947
CS E</w> 26946
asperg illosis</w> 26943
Ch emo 26940
HB x</w> 26938
v WF</w> 26933
coloc alize</w> 26931
fluor ine</w> 26928
ne vi</w> 26924
ho ods</w> 26915
magne tization</w> 26915
obar bit 26915
ultrason ographic</w> 26913
2 q1</w> 26911
Gr and</w> 26910
T omato</w> 26909
c 5</w> 26908
cri ption</w> 26908
attemp ting</w> 26907
parall els</w> 26907
Cycl er</w> 26903
cap turing</w> 26902
insemin ation</w> 26899
mini ature</w> 26892
k at 26890
steri lity</w> 26890
incis ors</w> 26889
sup plies</w> 26886
G βγ</w> 26883
nanocom posites</w> 26882
incenti ves</w> 26881
h sa</w> 26880
ochon dral</w> 26879
d UTP</w> 26878
multi faceted</w> 26878
C TS</w> 26872
P IC 26872
Prote ase</w> 26869
PK 2</w> 26867
BAL F</w> 26866
sc 7</w> 26864
PK 3</w> 26861
Investig ations</w> 26859
Gam ma</w> 26859
atelli tes</w> 26859
wid ths</w> 26858
dis ks</w> 26856
CI D</w> 26855
furo semide</w> 26855
K et 26854
un determined</w> 26850
commun ications</w> 26850
M BC</w> 26843
FOX P3</w> 26842
Con fir 26841
endoc annabin 26841
cel ecoxib</w> 26840
frag ility</w> 26839
calcul i</w> 26837
Legi onella</w> 26835
aug ments</w> 26834
N orth 26833
lam ivudine</w> 26832
cor rhiz 26830
der ang 26830
tym pan 26826
O DC</w> 26825
Macroph ages</w> 26823
un methylated</w> 26821
j as 26819
Scot land</w> 26816
lar s</w> 26812
PW V</w> 26812
dor sol 26809
Separ ation</w> 26808
neph ron</w> 26807
bio transformation</w> 26806
glycer in</w> 26805
ME K 26801
fet oprotein</w> 26801
Distinc t</w> 26800
AS K1</w> 26799
8 h</w> 26795
spond ylitis</w> 26795
Thr 3</w> 26790
Co ord 26785
acces sions</w> 26783
RATION ALE</w> 26783
solu tes</w> 26782
ME P</w> 26776
deple ting</w> 26776
Au NPs</w> 26776
rainf all</w> 26775
multi plication</w> 26774
anti thrombotic</w> 26773
di hedral</w> 26772
4 α</w> 26771
aff iliated</w> 26770
gyn a 26769
bi ocompatible</w> 26766
ign s</w> 26765
hot spots</w> 26765
A J 26760
TP P</w> 26760
radi ograph</w> 26759
IGF 1</w> 26757
nitro phenyl</w> 26755
E s 26754
ev a</w> 26753
ig nor 26747
D CA</w> 26745
hypo tonic</w> 26744
hypoglyc emic</w> 26742
P st 26741
entr apment</w> 26738
R GC</w> 26737
S it 26737
ot rip 26734
ec centric</w> 26733
O MIM</w> 26732
im od</w> 26725
Super dex</w> 26724
catas troph 26724
k is 26723
xeno biotic</w> 26723
Y B</w> 26722
Fig. 2A</w> 26722
gastro entero 26722
μ Ci</w> 26719
mush room</w> 26719
ul sion</w> 26716
contras ted</w> 26713
it ably</w> 26709
sw im</w> 26708
con fron 26702
non synonymous</w> 26702
TC AT 26701
nic hes</w> 26701
le gi 26700
isol ating</w> 26700
HP O4</w> 26698
May o</w> 26698
N od 26696
o kinin</w> 26688
ingredi ent</w> 26687
. 2A</w> 26683
is en</w> 26681
aminoglyco side</w> 26678
ness ed</w> 26676
H tt</w> 26673
accommod ation</w> 26673
v emurafenib</w> 26672
temp ting</w> 26672
ru ff 26671
compl ained</w> 26670
co ast</w> 26668
milli ons</w> 26667
epider mi 26664
periodic ally</w> 26662
u tions</w> 26660
X I</w> 26659
radios urgery</w> 26659
fo rec 26649
Medi ator</w> 26649
PU .1</w> 26648
PA N</w> 26646
II α</w> 26642
hyp onat 26642
ab and 26641
MEASU RE</w> 26638
LI C</w> 26635
NE C</w> 26634
cor tico 26629
un tary</w> 26626
hyp os 26626
osper m</w> 26626
end azole</w> 26625
spectrophot ometry</w> 26625
mo tor 26624
deteri or 26621
p ts</w> 26616
NP M1</w> 26615
ac ity</w> 26613
micro titer</w> 26612
discrimin ative</w> 26612
sev enth</w> 26611
benzo yl</w> 26610
glab rata</w> 26609
conc rete</w> 26607
sil ence</w> 26605
quad ratic</w> 26605
ES R1</w> 26605
0 x</w> 26600
ent ang 26599
chel ation</w> 26596
in coming</w> 26594
A thero 26588
Inv asive</w> 26585
ful min 26580
en berg</w> 26578
al utamide</w> 26578
AI CAR</w> 26578
CS S</w> 26578
institu tion 26578
C PD</w> 26577
Tr ust</w> 26575
kal emia</w> 26575
propri a</w> 26574
cer vic 26572
Ap plications</w> 26570
cir cles</w> 26569
Y A</w> 26568
weigh ting</w> 26568
protot yp 26566
S ide</w> 26562
contrac ture</w> 26562
maxim a</w> 26561
yl osing</w> 26560
lac rimal</w> 26559
MD P</w> 26558
CF S</w> 26558
c RNA</w> 26557
Ex ternal</w> 26551
un satisfactory</w> 26550
ribonucle oprotein</w> 26545
Th rough 26544
Austri a</w> 26544
sen tence</w> 26536
dra in</w> 26536
pH i</w> 26535
Ora i1</w> 26535
Resp ond 26534
Con struc 26533
doub t</w> 26533
gangli oside</w> 26530
ip er</w> 26528
A II</w> 26527
cl ay</w> 26526
K . 26525
av oids</w> 26525
T sc 26524
in i 26521
in teri 26521
E very</w> 26520
qui escence</w> 26520
desat ur 26517
un ve 26516
re v</w> 26507
epidermi dis</w> 26507
bl acks</w> 26506
P BC</w> 26504
oly ticus</w> 26504
endarte rectomy</w> 26501
pursu it</w> 26500
time -</w> 26500
b at</w> 26499
AM PAR</w> 26499
b os 26498
3 O4</w> 26493
In clud 26492
mis use</w> 26491
unc or 26489
sten oses</w> 26483
L ag 26479
Contro ls</w> 26475
E O</w> 26473
yl ates</w> 26465
ulin emia</w> 26464
exc epti 26461
dis ation</w> 26460
am ant 26459
M unc1</w> 26455
MR 1</w> 26453
IKK α</w> 26453
ty ly</w> 26452
A NA</w> 26450
an olic</w> 26450
duc tus</w> 26448
los ing</w> 26447
ec itabine</w> 26446
or ch 26440
al izations</w> 26440
Y op 26439
Lys 1</w> 26439
dimin ishes</w> 26438
Bir th</w> 26438
pos itory</w> 26437
ul ators</w> 26435
electron ics</w> 26435
dic ation</w> 26431
Epig en 26431
vag ina</w> 26427
F und 26425
Tan z 26425
L AM</w> 26424
ste n</w> 26422
br inging</w> 26417
discipl ine</w> 26417
sub dural</w> 26414
spond ylo 26410
c ach 26408
Con stitu 26404
C 5a</w> 26402
Q SAR</w> 26402
B RI 26397
cardi ology</w> 26397
Na V1</w> 26394
O x</w> 26389
alle y</w> 26389
at yp 26385
transduc e</w> 26385
m ast 26382
inde xed</w> 26378
bu r</w> 26377
pred ators</w> 26375
special ties</w> 26374
Pol ish</w> 26373
Ex pan 26371
sub mandibular</w> 26369
trans well</w> 26368
addi tions</w> 26366
Pitts burgh</w> 26365
mu ir</w> 26364
doub let</w> 26362
Sho uld</w> 26362
cyto static</w> 26361
B ond</w> 26359
Inhibit ory</w> 26359
stric tures</w> 26354
exp ands</w> 26352
IF T</w> 26352
aph asia</w> 26351
H es 26350
cop recip 26349
Pl ates</w> 26349
allo dyn 26348
W ell 26346
rel ational</w> 26345
n ilo 26343
pen is</w> 26343
S b 26342
splen omegaly</w> 26341
onco protein</w> 26340
Wein berg</w> 26339
g ate 26337
inj ectable</w> 26337
anx ious</w> 26330
age en 26330
steroid ogenesis</w> 26330
D AS</w> 26328
T ests</w> 26327
in ite</w> 26326
Rec ei 26325
Bioch em</w> 26325
p S</w> 26322
chroma ffin</w> 26320
unc h</w> 26317
I owa</w> 26315
cys tine</w> 26313
oscill ator</w> 26309
fl our</w> 26308
sel dom</w> 26306
en ne</w> 26305
udg et</w> 26305
chol angitis</w> 26303
thaw ing</w> 26303
in ization</w> 26301
granul omas</w> 26301
AR ID 26295
T G2</w> 26292
RE D</w> 26292
dis tension</w> 26292
yn uren 26289
z hou</w> 26286
col ic</w> 26286
conserv atively</w> 26286
adi po 26283
crow ding</w> 26283
par amount</w> 26281
s nail</w> 26280
His 1</w> 26280
dent ures</w> 26280
Im mediately</w> 26278
nitro prusside</w> 26278
mol lus 26276
Ulti mately</w> 26273
Res ear 26272
draw backs</w> 26270
kinem atic</w> 26270
al fa</w> 26266
SI s</w> 26264
methyl prednisolone</w> 26264
gam bling</w> 26261
n is</w> 26260
dys kinesia</w> 26260
pop ulated</w> 26259
py ogenes</w> 26259
PV P</w> 26251
XR CC1</w> 26248
phar ynx</w> 26242
phosphati dic</w> 26241
olym ph</w> 26239
plas monic</w> 26238
lumin escent</w> 26235
och oline</w> 26233
or phyrin</w> 26230
ev oke</w> 26226
res ur 26219
vas ospasm</w> 26218
cor p 26216
BO LD</w> 26216
v oid</w> 26215
J o</w> 26212
conflu ency</w> 26211
CC l4</w> 26210
PTHr P</w> 26210
AG G</w> 26208
iti dine</w> 26202
www. ncbi.nlm.nih.gov</w> 26202
DO I</w> 26201
Evalu ating</w> 26196
o rescence</w> 26195
H ay 26194
bio technology</w> 26193
h en</w> 26192
MT 2</w> 26192
Relationsh ips</w> 26189
LI N</w> 26188
thromb i</w> 26187
anthrac ene</w> 26187
Pas te 26187
2 .1</w> 26183
discrimin ated</w> 26183
Concentr ation</w> 26182
histor ically</w> 26181
bat ches</w> 26180
thre ats</w> 26177
- AT 26176
repe ating</w> 26175
plan ting</w> 26167
ucle ation</w> 26165
ri d 26162
Dhar mac 26162
Mad 2</w> 26161
ill icit</w> 26159
Sjö gren</w> 26154
SI N</w> 26153
hydro thermal</w> 26153
in um</w> 26145
lich en</w> 26144
S cat 26143
L RP</w> 26138
Lang muir</w> 26135
high -</w> 26133
hetero zygote</w> 26132
LY P</w> 26131
sarcol em 26131
pal in 26130
interfe red</w> 26129
bread th</w> 26127
T J</w> 26125
r arity</w> 26124
yto plasmic</w> 26124
Fox O1</w> 26121
vill ages</w> 26121
co oking</w> 26120
DE L</w> 26115
con nec 26114
organ ize</w> 26114
conjug ating</w> 26113
ID O</w> 26110
finger print</w> 26108
Ch o</w> 26105
Adhe rence</w> 26104
Hom o 26103
ion omycin</w> 26102
incap able</w> 26101
L AP</w> 26098
gr ape</w> 26098
canc er 26097
Chag as</w> 26095
LD s</w> 26093
perfor ated</w> 26093
Od ds</w> 26091
MT D</w> 26089
H and</w> 26087
X PS</w> 26084
asp ec 26078
g ents</w> 26077
intr icate</w> 26077
uter i</w> 26070
ST M1</w> 26069
hist ogram</w> 26068
stir ring</w> 26064
reticul ocyte</w> 26062
Ex tr 26058
in tub 26057
ore tic</w> 26053
hetero trimeric</w> 26052
An k 26050
side roph 26048
knoc king</w> 26046
a ten 26041
2 X</w> 26039
cer ti 26039
Sus cepti 26039
Tra p</w> 26038
dermat ology</w> 26036
mic ellar</w> 26035
Ado be</w> 26035
R β</w> 26034
immun ology</w> 26033
scle rosing</w> 26029
LO GICAL</w> 26026
ac ial</w> 26023
Dic er</w> 26023
T SP 26019
dec ide</w> 26018
Aff inity</w> 26018
M Z</w> 26016
amelior ates</w> 26016
hed gehog</w> 26014
el i</w> 26007
oblas toid</w> 26003
OR T 26002
teri orly</w> 26001
down ward</w> 26001
u ts</w> 26000
TGF β1</w> 25999
immuno deficient</w> 25998
T n</w> 25997
f lip 25996
G α</w> 25995
thi op 25994
agre es</w> 25993
Le sions</w> 25992
S 1C</w> 25989
coinc ides</w> 25989
CD H</w> 25987
ob iliary</w> 25985
dur ability</w> 25983
par av 25982
tri azole</w> 25982
omet abolic</w> 25982
call us</w> 25982
conver sely</w> 25981
f m 25979
typ ed</w> 25979
Predic ting</w> 25979
tim ed</w> 25977
cor poration</w> 25972
nor m</w> 25971
tiaz em</w> 25971
o ting</w> 25969
ER CC1</w> 25969
Prof essor</w> 25969
ris peridone</w> 25968
o res 25967
B if 25966
L TB 25966
T CA 25964
cen sus</w> 25963
DE P</w> 25963
illustr ating</w> 25963
neuro fibrillary</w> 25962
l r 25960
lex ical</w> 25959
Rus sia</w> 25959
AI P</w> 25957
ar temisinin</w> 25956
S Q</w> 25955
acclim ation</w> 25953
B ID</w> 25951
os si 25949
o regional</w> 25946
L os</w> 25946
Tow ard</w> 25946
r i</w> 25943
og astric</w> 25938
NKG 2D</w> 25938
ic lovir</w> 25936
salic ylate</w> 25936
cal vari 25932
syl van 25932
tal in</w> 25931
poly amines</w> 25930
T RO 25929
dol lars</w> 25928
cor tactin</w> 25921
termin ator</w> 25921
SH H</w> 25921
NE B</w> 25909
cer ul 25907
campa igns</w> 25906
c M</w> 25903
An atom 25902
vill ous</w> 25899
lea ved</w> 25898
om imetic</w> 25896
home odomain</w> 25894
fron to 25891
od y 25888
anth rene</w> 25887
mat ured</w> 25881
Idi opathic</w> 25877
fur in</w> 25875
neph rosis</w> 25873
novel ty</w> 25873
asym p 25872
graph ical</w> 25871
Har ris</w> 25871
Dharmac on</w> 25871
immun ologically</w> 25868
CG G</w> 25868
deteri orated</w> 25867
li ability</w> 25865
ass er 25861
emul sification</w> 25859
m ex 25857
tis ing</w> 25855
E PCs</w> 25853
7 B 25852
p D 25852
Org an</w> 25852
Mann heim</w> 25852
transf ecting</w> 25850
z ens</w> 25849
en rich</w> 25846
str ata</w> 25846
ET A</w> 25845
Cong ress</w> 25845
IC P0</w> 25844
termin ology</w> 25843
hex idine</w> 25843
instruc ted</w> 25843
onc ologic</w> 25833
αv β3</w> 25833
PRO CE 25832
So ft</w> 25832
debil itating</w> 25832
obut amine</w> 25830
apo B</w> 25827
CH C</w> 25824
8 c</w> 25822
de plete</w> 25821
B cr</w> 25816
V 9</w> 25816
Im pair 25816
um a</w> 25815
Ap plying</w> 25815
archae a</w> 25814
bi bli 25810
P X</w> 25809
intern ally</w> 25806
pro biotics</w> 25805
B AP 25804
U. S. 25804
ocor tin</w> 25801
FAN CD2</w> 25800
chemopre ventive</w> 25797
A pa 25794
stres sor</w> 25792
sn RNP</w> 25785
S outh 25783
Ac cel 25779
F TLD</w> 25777
hyaluron an</w> 25777
extr acting</w> 25775
th en 25774
plasi as</w> 25774
pro st</w> 25773
mon oubiqu 25772
ribonucle otide</w> 25770
impul sivity</w> 25770
fif teen</w> 25757
Prog ressive</w> 25754
meth ox 25752
transpos ons</w> 25751
N urse</w> 25750
DR 4</w> 25746
excepti onally</w> 25744
de uter 25740
diphther ia</w> 25737
is ters</w> 25732
gen in</w> 25731
Ven us</w> 25730
steroid ogenic</w> 25730
no l</w> 25724
Sh c</w> 25724
V eg 25723
P Z 25722
re alization</w> 25719
is obut 25716
C e</w> 25715
trus or</w> 25713
V entricular</w> 25710
urtic aria</w> 25709
R U</w> 25705
el ong</w> 25701
separ ations</w> 25698
implic ates</w> 25698
F at</w> 25695
spermat og 25695
tw e 25690
MA M</w> 25688
ses qui 25686
hon ey 25686
a ural</w> 25685
spas ticity</w> 25682
pro myelocytic</w> 25681
eth oxy 25681
abro gate</w> 25681
T rop 25679
in activates</w> 25679
cle aring</w> 25677
reli ance</w> 25677
Lon za</w> 25673
AS M</w> 25672
F UN 25670
neuro vascular</w> 25664
ti dis</w> 25661
m atics</w> 25660
Figure 5</w> 25659
J MJ 25658
Princip al</w> 25658
th aw</w> 25654
pos omes</w> 25649
SR E</w> 25648
ron to</w> 25646
tun ic 25646
corne as</w> 25643
E u</w> 25642
IV C</w> 25639
Enh ancement</w> 25637
regul in</w> 25636
Prog ression</w> 25636
end ers</w> 25635
sh ik 25634
end orphin</w> 25634
c I 25633
no s</w> 25632
c ence</w> 25631
Top ical</w> 25631
y early</w> 25629
V is</w> 25627
coron avirus</w> 25623
reli a</w> 25617
per fluoro 25608
ch oc 25604
IC SI</w> 25604
stimul ant</w> 25598
H3 K3</w> 25596
mu si 25594
amphi pathic</w> 25594
Bo NT</w> 25591
thio phene</w> 25590
E FS</w> 25589
fl at 25589
synth eses</w> 25586
pred ation</w> 25585
DX A</w> 25584
SOUR CES</w> 25584
adap ting</w> 25583
epith eli 25578
nanos he 25576
sym biotic</w> 25575
Ry R2</w> 25574
PL 1</w> 25573
blas tine</w> 25573
yn es</w> 25569
n est</w> 25567
u ated</w> 25567
ó n</w> 25567
Eff orts</w> 25565
Indi an 25565
dorsol ateral</w> 25564
den sely</w> 25563
PF GE</w> 25562
em in 25560
de polarized</w> 25554
ate chin</w> 25551
enantiom er</w> 25551
Ch im 25550
Vps 3</w> 25550
pro fit</w> 25549
ad ol</w> 25548
FGF R2</w> 25548
PA G</w> 25546
ton ia</w> 25545
1 .1</w> 25543
stanti al</w> 25543
dic t 25542
hydra zine</w> 25542
deacetyl ases</w> 25539
C yc 25535
r t</w> 25535
Sp in</w> 25535
ac rine</w> 25532
par an 25532
aberr antly</w> 25530
F uk 25529
dec lared</w> 25528
p esti 25526
ver tigo</w> 25526
i. d.</w> 25524
sc ro 25522
Δ N</w> 25517
MR D</w> 25517
ac knowledge</w> 25513
LI M</w> 25509
PO D</w> 25507
yr in 25504
6 V</w> 25503
ol dest</w> 25502
competi tor</w> 25502
w n</w> 25501
RA CE</w> 25498
classi fying</w> 25490
nano fibers</w> 25489
De gradation</w> 25488
raph e</w> 25488
ari thromycin</w> 25485
TM J</w> 25482
ra e</w> 25481
E SS</w> 25480
Tric h 25475
S EC 25471
un ified</w> 25470
diz ziness</w> 25469
α 6</w> 25466
RN F1</w> 25466
cholecy stitis</w> 25463
ti da</w> 25461
un diagnosed</w> 25461
PD MS</w> 25461
ab e</w> 25460
perman ently</w> 25457
hom o</w> 25453
comfor table</w> 25451
f ting</w> 25449
pharmac odynamics</w> 25445
F MRP</w> 25444
deline ation</w> 25442
Cari b 25442
L XR</w> 25440
astig matism</w> 25440
rifam pin</w> 25437
infrequ ently</w> 25434
SC S</w> 25432
Commun ication</w> 25432
as eptic</w> 25431
non selective</w> 25431
ip in</w> 25429
inte rob 25429
poly Q</w> 25424
sle epiness</w> 25424
Pro phyl 25422
di ary</w> 25420
re pairs</w> 25418
car tri 25418
El derly</w> 25417
radiolig and</w> 25417
col i 25416
D ays</w> 25415
dro p 25415
unra vel</w> 25414
Ont ology</w> 25410
stri pping</w> 25408
Com ment</w> 25406
isi onal</w> 25405
pull down</w> 25403
RI C</w> 25402
speci alization</w> 25402
cef ta 25401
PK D1</w> 25400
or o</w> 25398
n y</w> 25396
O P 25396
herbic ide</w> 25394
oc o 25393
fo uling</w> 25393
Un usual</w> 25393
alop ram</w> 25393
SC 1</w> 25389
min d 25387
im pressive</w> 25386
pati ent 25385
. 3A</w> 25384
loc alizing</w> 25383
inequ ality</w> 25383
vi tell 25379
spor a</w> 25377
B las 25375
vi ti 25372
hi a</w> 25372
bin ocular</w> 25372
sp ending</w> 25369
los artan</w> 25369
sev enty</w> 25369
re plen 25365
sna p</w> 25363
e ably</w> 25359
T3 SS</w> 25359
Mn SOD</w> 25357
IR B</w> 25354
sor b 25351
Br it 25351
my asthenia</w> 25349
ess ness</w> 25348
micro graphs</w> 25347
spirit ual</w> 25344
Pre clinical</w> 25343
da f</w> 25343
fundam entally</w> 25343
hin dr 25342
Deli very</w> 25341
Om ni 25340
L ine</w> 25339
enor rhea</w> 25338
ob let</w> 25337
Glu 1</w> 25337
care rs</w> 25335
terpen oids</w> 25330
s ting</w> 25329
W GS</w> 25329
deco y</w> 25325
and 3</w> 25322
dom onas</w> 25320
p DCs</w> 25319
Blo t</w> 25318
complement arity</w> 25317
Person ality</w> 25316
cul tur 25314
Hol stein</w> 25314
perox ynitrite</w> 25311
FcR n</w> 25309
ev ade</w> 25308
memb ership</w> 25306
inten sively</w> 25306
a R</w> 25302
α 4 25302
un phosphorylated</w> 25302
pertur b</w> 25301
mechanis tically</w> 25299
Ty rosine</w> 25299
9 T</w> 25295
C MP</w> 25295
AL Y</w> 25294
my o</w> 25292
CR D</w> 25291
ch ord 25288
slur ry</w> 25286
. 1B</w> 25285
p ic</w> 25283
con tract</w> 25281
g . 25280
TT T</w> 25279
t R</w> 25277
interob server</w> 25275
Physici an</w> 25272
T ec 25270
amin opy 25268
Ro ot</w> 25267
lo fen</w> 25265
h ospice</w> 25264
ip ient</w> 25259
arch e</w> 25258
them atic</w> 25258
commens al</w> 25256
Cryptospor idium</w> 25254
tu me 25251
R es</w> 25250
land mark</w> 25250
advoc ated</w> 25249
Dav is</w> 25246
Indian apolis</w> 25238
em an</w> 25237
intu itive</w> 25235
2 Q</w> 25231
NE MO</w> 25231
Gl n 25230
O S 25226
Tum our</w> 25226
Rob er 25226
eth ers</w> 25224
di hydroxy</w> 25220
bi oge 25219
Psyc INFO</w> 25219
ap illary</w> 25215
di oxin</w> 25214
PM F</w> 25213
glyco side</w> 25213
intral uminal</w> 25213
ribo zyme</w> 25212
ribos ylation</w> 25211
penic ill 25209
alumin ium</w> 25207
PR K 25205
og ue</w> 25201
cl arity</w> 25201
Ra w</w> 25199
E D5</w> 25198
des min</w> 25198
sterili zed</w> 25197
Integr ation</w> 25196
def ensive</w> 25193
tam ivir</w> 25192
sclero derma</w> 25192
T AMs</w> 25188
w ish</w> 25186
neurotroph in</w> 25186
cataly sed</w> 25183
F li 25182
le sional</w> 25182
a -</w> 25181
E DS</w> 25181
Pers onal</w> 25173
HE C</w> 25171
infarc ts</w> 25170
roent gen 25169
prec ede</w> 25165
tol l</w> 25165
Hed gehog</w> 25165
anti ne</w> 25162
nucle osomal</w> 25158
rot ations</w> 25157
bis ulfite</w> 25157
CX 3 25155
flag ella</w> 25154
ch ar</w> 25146
g ab 25143
ox ime</w> 25143
cil ium</w> 25143
M J</w> 25141
le tic</w> 25140
L AS</w> 25138
ph y</w> 25137
em bro 25136
Co operative</w> 25131
osph eres</w> 25130
fibros arcoma</w> 25130
t amp 25129
pren orphine</w> 25129
BI O 25128
P om 25126
an onymous</w> 25126
Diff usion</w> 25119
Sou theast</w> 25118
HDAC 2</w> 25116
Proc ess</w> 25115
or us</w> 25113
hum erus</w> 25113
dr 1</w> 25113
hin der</w> 25113
phil ia</w> 25111
appropri ateness</w> 25107
consum e</w> 25106
Z r</w> 25105
lo op 25104
recapit ulated</w> 25104
le isure</w> 25103
M Ns</w> 25097
occu pati 25096
9 H</w> 25092
To ronto</w> 25087
diff usi 25086
Sem a 25084
Cont act</w> 25083
ent on</w> 25082
Mem orial</w> 25082
def initi 25081
Fig. 3A</w> 25081
pro -</w> 25080
disrup tions</w> 25080
manufac tured</w> 25074
star tle</w> 25072
A X 25070
Y 8</w> 25069
NS 2</w> 25068
CE D</w> 25067
Abs ence</w> 25065
F . 25058
ul ose</w> 25058
M yc 25053
ADAM T 25052
p ads</w> 25051
R g 25049
Bi omedical</w> 25049
photo period</w> 25048
petro leum</w> 25044
Concer ning</w> 25043
lip tin</w> 25042
re war 25037
R -</w> 25035
Kup ffer</w> 25034
Eth anol</w> 25033
h olog 25030
bur dens</w> 25027
M ot 25025
shutt ling</w> 25025
cop enia</w> 25024
h ESC</w> 25023
ac anth 25023
tum or 25023
MAP 2</w> 25022
gra vis</w> 25018
di pl 25017
poly protein</w> 25016
influ ential</w> 25014
FL C</w> 25014
acro me 25014
mening ococcal</w> 25008
thyl ak 25007
MI D</w> 25006
Fac ulty</w> 25005
P NS</w> 24998
an tro 24997
on asal</w> 24994
SC H</w> 24992
Br un 24992
GP R1</w> 24992
D m 24991
tax ol</w> 24990
In ser 24989
s apon 24988
Fe der 24986
P2 X7</w> 24986
Pos terior</w> 24984
Hard y</w> 24983
e po 24982
S omatic</w> 24982
g ig 24982
ps i</w> 24980
ag al 24973
H ox</w> 24971
Micro RNAs</w> 24971
N at</w> 24970
diff us 24967
F MN</w> 24966
glycos ylase</w> 24964
mel t</w> 24963
Cop per</w> 24961
D W</w> 24960
cyto chalasin</w> 24960
aflu oro 24958
MD A5</w> 24957
Pre p</w> 24952
K 2 24951
swa b</w> 24949
PD C</w> 24946
V ACV</w> 24944
dele ting</w> 24942
RI D</w> 24937
fluoroph ores</w> 24935
repul sion</w> 24935
PA R1</w> 24933
def l 24933
menis cus</w> 24933
Inf ra 24932
hypercalc emia</w> 24932
im migrant</w> 24929
W G</w> 24928
F AM</w> 24925
B AR</w> 24924
sulf onate</w> 24924
propag ating</w> 24922
lig o</w> 24920
partur ition</w> 24919
Oc t</w> 24911
deter gents</w> 24909
iv a</w> 24907
wave guide</w> 24906
extern ally</w> 24906
P II</w> 24903
trache ostomy</w> 24903
Jo h 24903
Chlamy domonas</w> 24902
T EN 24899
Are as</w> 24899
G cn 24898
in 2</w> 24897
lig noc 24897
HP T</w> 24897
Cd h1</w> 24897
x R</w> 24895
An .</w> 24895
ta x</w> 24893
pr 1</w> 24893
ogal ac 24893
an ser 24887
del aying</w> 24886
anthrac ycline</w> 24886
s 4</w> 24884
ax one</w> 24884
impe de</w> 24884
hum or</w> 24881
TREAT MENT</w> 24880
EXPERI MENTAL</w> 24879
radios ensitivity</w> 24878
Bcl 2</w> 24878
matern ity</w> 24878
epilep ticus</w> 24877
on us</w> 24875
us ability</w> 24873
Resi du 24873
popul arity</w> 24871
PG A</w> 24869
hemangi oma</w> 24868
SOCS 3</w> 24867
homogen ization</w> 24865
cardio protective</w> 24863
omorph ine</w> 24861
ap arib</w> 24858
beta ine</w> 24858
micro domains</w> 24855
clinic al 24855
pres en 24853
A pro 24850
ER E</w> 24850
TE C</w> 24848
Zh u</w> 24847
activ ations</w> 24844
In surance</w> 24842
di peptide</w> 24841
congen ers</w> 24831
par aquat</w> 24825
gon ads</w> 24823
pre defined</w> 24821
F RA 24820
Ed U</w> 24819
mer it</w> 24818
venti onally</w> 24813
CR 2</w> 24811
an am 24810
gly caemia</w> 24810
bo oks</w> 24808
se al</w> 24807
di oxygenase</w> 24804
cTn I</w> 24804
P PI 24802
pre mat 24800
terpen oid</w> 24799
ad vised</w> 24796
disrup tive</w> 24793
CG A</w> 24792
phosph os 24789
IS G1</w> 24789
P PS</w> 24787
ch ond 24787
pp b</w> 24786
pleth ora</w> 24786
degrad es</w> 24785
m ir</w> 24782
chos ocial</w> 24782
s tic 24779
cong estion</w> 24779
ide ally</w> 24776
S GL 24775
str um</w> 24774
fi x</w> 24774
AM C</w> 24773
ine -</w> 24770
B an 24768
G in 24766
deoxychol ic</w> 24763
P J 24762
R n 24760
ver m 24758
W er 24757
T ext</w> 24754
G SC</w> 24753
ic ing</w> 24749
Oper ative</w> 24749
in ii</w> 24748
refl ections</w> 24748
tempor arily</w> 24745
compe tencies</w> 24744
sum ing</w> 24742
arthro scopy</w> 24741
rain bow</w> 24738
g ar</w> 24737
PC M</w> 24732
herni as</w> 24730
RI S</w> 24728
o ronary</w> 24727
ae a</w> 24727
proj ecting</w> 24726
bel t</w> 24725
DT C</w> 24725
PV R</w> 24716
parad ox</w> 24716
instant aneous</w> 24716
volt ages</w> 24716
tri als.gov</w> 24714
arro whe 24712
an oids</w> 24711
fac ets</w> 24711
brigh tness</w> 24711
Intr am 24708
fuc ose</w> 24703
CC 4</w> 24701
pyrro lid 24699
az athioprine</w> 24698
condyl ar</w> 24693
cim etidine</w> 24689
aven ue</w> 24686
n adi 24685
Fig. 1B</w> 24685
Pen ic 24681
P TS</w> 24679
olan zapine</w> 24679
ocytos ed</w> 24679
y clines</w> 24675
S ak 24675
gen ds</w> 24675
iso zyme</w> 24670
ARE s</w> 24670
Zn 2</w> 24667
bim odal</w> 24667
A im</w> 24662
NS 5B</w> 24662
thyro tropin</w> 24662
NM J</w> 24662
AM S</w> 24661
in sensitivity</w> 24659
Vol ume</w> 24659
ple omorphic</w> 24658
tom eter</w> 24657
analy zes</w> 24656
FO L 24654
ex ia</w> 24650
ent ate</w> 24650
ex y</w> 24649
diam ond</w> 24646
multi forme</w> 24644
osper mia</w> 24644
des c 24643
f ene 24642
hyper active</w> 24642
ic ism</w> 24640
hyperox ia</w> 24635
marri age</w> 24630
Im ager</w> 24626
photob leaching</w> 24625
S atis 24622
IgG 2a</w> 24622
under scores</w> 24620
Ag NPs</w> 24619
IFN α</w> 24617
Tod ay</w> 24617
opon tin</w> 24617
CD P</w> 24615
Jan us</w> 24612
i. org</w> 24608
P lat 24602
HD V</w> 24600
explan atory</w> 24598
. 2B</w> 24597
Inte ll 24592
Experi ences</w> 24592
D DP</w> 24588
two -</w> 24586
th o</w> 24585
Meth yl</w> 24585
K um 24581
ker atosis</w> 24579
melan ocyte</w> 24578
ME G</w> 24575
S 5A</w> 24570
if lu 24569
uc her</w> 24565
e es</w> 24557
glyco si 24556
- activated</w> 24550
Ma king</w> 24549
dys morph 24548
c gi</w> 24547
ot a 24545
GG C</w> 24545
em pathy</w> 24539
sap onin</w> 24539
con sen 24536
l adder</w> 24535
GE Fs</w> 24533
M U</w> 24529
leuc ocytes</w> 24529
Aden osine</w> 24529
carr ageen 24525
T CT</w> 24524
recur red</w> 24523
cardi otoxicity</w> 24522
aspir ated</w> 24518
sub total</w> 24517
ati co 24515
Gl o</w> 24513
infest ation</w> 24513
synthesi sed</w> 24512
7 N</w> 24504
MD CT</w> 24504
as m</w> 24503
P 3 24502
cl 1</w> 24502
Engine ering</w> 24501
Phy s</w> 24499
RA B</w> 24496
GT C</w> 24494
U PLC</w> 24489
BD E</w> 24487
th emia</w> 24486
crimin al</w> 24483
re positioning</w> 24481
hyper triglycer 24481
S 4B</w> 24479
trans thoracic</w> 24475
papill oma</w> 24471
bo ur 24470
rig h 24470
E MA</w> 24469
ul i</w> 24466
TE E</w> 24466
question able</w> 24466
radi ologist</w> 24464
echan ics</w> 24460
T H1</w> 24459
multi layer</w> 24459
L r 24458
wor th 24456
dim e</w> 24455
H A1</w> 24451
mon ey</w> 24447
Moun tain</w> 24447
haplo insufficiency</w> 24447
categor ization</w> 24444
SC O 24441
Sil encing</w> 24440
cot yle 24440
lymph oblastoid</w> 24439
ine ous</w> 24439
www. j 24439
D 2O</w> 24437
arc tic</w> 24434
E ts</w> 24432
V K 24431
R X</w> 24430
di ethyl</w> 24430
elev ate</w> 24430
go iter</w> 24429
HA V</w> 24427
y t 24424
en ig 24423
ex tant</w> 24423
go t</w> 24417
child birth</w> 24417
astrocytom as</w> 24414
pe metrexed</w> 24410
hom in 24407
T -</w> 24402
tre x 24402
part ner 24401
hemorrh ages</w> 24399
micro scopically</w> 24397
regul arity</w> 24397
AR D</w> 24396
E hr 24395
neuro chemical</w> 24394
enol ase</w> 24394
omy osin</w> 24392
emergen cies</w> 24390
lis tening</w> 24390
C PS</w> 24387
imp acting</w> 24387
fu si 24386
PC V</w> 24386
ur anyl</w> 24385
M l 24384
max illofacial</w> 24384
M ST</w> 24376
nid ulans</w> 24376
F ul 24375
stra w</w> 24364
bene fici 24362
de trusor</w> 24360
embol i</w> 24359
C over 24355
Hist ologically</w> 24355
synap to 24353
F D 24348
St reng 24348
A SCs</w> 24347
PK G</w> 24347
omy elin</w> 24346
Bgl II</w> 24346
Nic oti 24345
Bactero ides</w> 24339
anti diabetic</w> 24335
n ight 24334
A stro 24334
QI A 24333
I p 24332
inv ad 24330
s ows</w> 24325
CS R</w> 24324
nan ocar 24322
scrat ch</w> 24322
non significant</w> 24321
inten sified</w> 24321
ul ary</w> 24318
Perc eived</w> 24317
t at 24316
la kes</w> 24314
an ian</w> 24307
Syn ech 24305
M s 24304
Tur ner</w> 24304
ex o</w> 24299
aver sive</w> 24299
SQ STM1</w> 24298
mill is 24297
TE Fb</w> 24295
imip ramine</w> 24293
promin ently</w> 24292
0 min</w> 24290
ut lin</w> 24288
om us</w> 24287
ig e</w> 24286
O h 24284
hi m</w> 24282
rom atic</w> 24280
in activity</w> 24277
k ynuren 24276
un available</w> 24276
F it 24275
R BM 24274
acti nin</w> 24274
isot onic</w> 24273
IN 1</w> 24272
In sig 24268
util isation</w> 24268
tax ane</w> 24262
ell i</w> 24259
A 1 24258
D CC</w> 24258
anti trypsin</w> 24257
DL S</w> 24256
Residu es</w> 24254
amyloid ogenic</w> 24253
p en</w> 24251
tox oplasmosis</w> 24251
H 0</w> 24250
fu zzy</w> 24248
mu st 24247
mi R1</w> 24246
R r 24245
adv enti 24245
E ll 24244
la un 24244
Inf ant</w> 24244
Min ne 24243
H in 24242
A de 24239
ocycl ine</w> 24239
morphol ino</w> 24237
re actors</w> 24234
CR M1</w> 24233
Fr ame 24231
U I</w> 24230
pro phase</w> 24229
N AA</w> 24227
man no 24227
GI STs</w> 24226
dialy sate</w> 24223
sup rac 24220
lith otrip 24217
Sk eletal</w> 24217
id um</w> 24213
6 N</w> 24212
epithel ioid</w> 24210
s ternal</w> 24208
Nor mal 24208
n im 24205
conn exin</w> 24204
9 p</w> 24203
tr ying</w> 24203
pu zz 24203
thi ogalac 24202
Dec laration</w> 24195
boot strap</w> 24195
s ine</w> 24194
des al 24192
tax onomy</w> 24192
scl eral</w> 24189
T at 24184
wave forms</w> 24184
Repor ted</w> 24183
dec orated</w> 24180
D ur 24179
hydroxy vitamin</w> 24176
Trans duction</w> 24176
uc tal</w> 24175
erro ne 24174
BI M</w> 24174
Atg 5</w> 24174
myelo proliferative</w> 24174
bi olog 24172
poly ubiquitination</w> 24172
re generate</w> 24171
S Gs</w> 24170
magnit udes</w> 24170
pro top 24167
Ter m</w> 24167
exis tent</w> 24159
Ultras on 24159
- C</w> 24158
C h</w> 24157
S ic 24156
iton avir</w> 24156
Vp u</w> 24156
flav ones</w> 24154
bin omial</w> 24152
fluor inated</w> 24150
calc ane 24149
5 e</w> 24141
Dem entia</w> 24138
abor tions</w> 24135
valu ed</w> 24134
IF IT 24133
hang ing</w> 24131
pack ages</w> 24130
it o</w> 24129
cardi ogenic</w> 24129
Predic tive</w> 24129
inv aded</w> 24126
F la 24125
inter pol 24124
S and 24120
CH S</w> 24120
an tec 24119
sylvan ia</w> 24117
aff ir 24115
pi ri 24115
plac ent 24112
characteri zes</w> 24111
ure ase</w> 24106
cere us</w> 24106
hyperglyc emic</w> 24105
TI S</w> 24104
Carib bean</w> 24101
proteas omes</w> 24100
Foc al</w> 24100
n l</w> 24099
β -</w> 24099
ten t 24096
bac eous</w> 24096
P erox 24091
micro scop 24090
e Bioscience</w> 24085
MM P1</w> 24085
L ateral</w> 24083
se ven 24082
CA 4</w> 24081
manif ests</w> 24080
V iv 24074
tomy osin</w> 24074
en jo 24073
M ON 24071
ev es</w> 24071
AM L1</w> 24068
ER s</w> 24066
Enzym atic</w> 24066
ch ances</w> 24065
perovsk ite</w> 24065
CC 3</w> 24064
H ab 24063
in determinate</w> 24063
PT M</w> 24063
b o</w> 24059
z on 24058
A maz 24056
TT TT 24056
F X</w> 24054
F Z 24054
a ining</w> 24053
co operatively</w> 24052
iso enzyme</w> 24052
h mC</w> 24051
Nicoti ana</w> 24050
zi dime</w> 24047
tri axone</w> 24044
str abis 24042
p K</w> 24041
di fluoride</w> 24041
3 beta</w> 24040
CM E</w> 24038
quin ones</w> 24038
hypox emia</w> 24037
Kine tics</w> 24037
man ure</w> 24034
wro ng</w> 24033
c A</w> 24032
Im mediate</w> 24032
s ort</w> 24028
f 5</w> 24028
beha ves</w> 24025
CH B</w> 24024
4 A1</w> 24023
I on 24023
thi azide</w> 24023
w on 24021
auto fluorescence</w> 24021
plic ates</w> 24019
W 4</w> 24015
A NF</w> 24012
fove al</w> 24012
rac emic</w> 24009
sphero id</w> 24006
comple teness</w> 24005
her bs</w> 24005
hypometh ylation</w> 24004
alli zation</w> 24003
0 N</w> 24002
harbo uring</w> 24002
Pro c</w> 24000
Pro be</w> 24000
Com plem 23999
monit ors</w> 23999
P ros 23997
Reli ability</w> 23997
excit otoxicity</w> 23996
sul fo 23995
IF 1</w> 23993
EV 7</w> 23993
Cyto chrome</w> 23992
PRACTI CE</w> 23988
sub domains</w> 23987
le m</w> 23984
b ats</w> 23982
re vi 23982
perf us 23979
smar t</w> 23979
ti sts</w> 23978
ren ic</w> 23977
diver ged</w> 23977
Pro t</w> 23973
H az 23971
cal ci 23971
constitu ting</w> 23971
ir in</w> 23970
E ti 23969
sphing olipid</w> 23969
aerosol s</w> 23969
oplas m</w> 23968
cann ulation</w> 23966
oct adec 23964
sn ap 23963
embro lizumab</w> 23962
Epid ermal</w> 23960
catar acts</w> 23958
anxi olytic</w> 23958
T b</w> 23956
choles ter 23956
Ex ogenous</w> 23955
join tly</w> 23955
Burk itt</w> 23949
anox ic</w> 23948
p ing 23946
en chymal</w> 23946
mati s</w> 23946
2 J</w> 23945
re act 23945
Rel ation</w> 23944
incis or</w> 23942
BC O</w> 23941
deposi t</w> 23940
Embry os</w> 23939
nucle oph 23937
AM A</w> 23935
SI R</w> 23930
thion ein</w> 23929
cardio respiratory</w> 23927
À 1</w> 23926
iz ability</w> 23926
5 mg</w> 23925
A PA 23925
b ene</w> 23925
sil denafil</w> 23925
ardi a</w> 23919
fem tosecond</w> 23919
itrac onazole</w> 23918
Sf 9</w> 23915
P M1</w> 23914
pi e 23914
urg ical</w> 23914
atic a</w> 23913
yst rophy</w> 23911
Bec k</w> 23910
im purities</w> 23908
-- -- 23908
Bio Rad</w> 23907
Fig. 2B</w> 23905
micro flora</w> 23903
mi tic</w> 23903
prokary otes</w> 23901
fo relim 23900
M ap 23897
Gu inea</w> 23897
patho physiologic</w> 23896
classi cally</w> 23894
Gre ek</w> 23891
Arg 3</w> 23888
Bor relia</w> 23887
j unior</w> 23885
pyre thro 23885
Rati o</w> 23884
apen em 23883
cyt opathic</w> 23878
Ptd Ins 23878
S As</w> 23877
nucle oprotein</w> 23877
ber ine</w> 23877
Men ten</w> 23874
id ases</w> 23873
de af</w> 23870
glyc emia</w> 23870
IU GR</w> 23868
oc occi</w> 23867
GI C</w> 23867
paralog s</w> 23867
hyper phosphorylated</w> 23866
non parametric</w> 23865
O il</w> 23864
transu rethral</w> 23864
AS O</w> 23863
IMP ORT 23861
TG G</w> 23859
phon ological</w> 23859
Paul o</w> 23859
app endix</w> 23855
O v 23852
te k</w> 23852
is ome</w> 23849
viro logic</w> 23848
protozo an</w> 23847
di l</w> 23845
n y 23844
NO X</w> 23844
inc ar 23843
TME M1</w> 23840
G V</w> 23838
ner i</w> 23838
est rone</w> 23835
p ET</w> 23833
His pan 23833
vin blastine</w> 23833
l ers</w> 23830
s. d.</w> 23826
S ti 23825
ed och 23825
hist ograms</w> 23825
CO P</w> 23825
O L 23821
at las</w> 23818
le gends</w> 23818
ren z</w> 23817
Sup pl</w> 23816
hindr ance</w> 23815
ab ra 23811
caus ality</w> 23808
uran ium</w> 23807
P 2A</w> 23805
x ing</w> 23805
Paren tal</w> 23805
custom ized</w> 23803
cephal ic</w> 23800
shiel ding</w> 23800
Penn sylvania</w> 23800
D ON</w> 23795
Practi cal</w> 23793
0 S6K</w> 23791
N ow</w> 23790
xeno biotics</w> 23790
adrenal ectomy</w> 23789
SP S</w> 23788
R hiz 23787
victim ization</w> 23787
CIN AHL</w> 23786
mic ity</w> 23785
Minne apolis</w> 23781
u d</w> 23779
Re plication</w> 23777
exec uted</w> 23777
architec tural</w> 23776
SO CS</w> 23769
terat oma</w> 23768
Ali quo 23767
Bur ling 23760
co variate</w> 23758
Pa p</w> 23758
Fri ed 23757
E YFP</w> 23755
In t 23755
pharmac ophore</w> 23755
tric yclic</w> 23752
Def ects</w> 23751
tunic amycin</w> 23751
asynchron ous</w> 23748
EC S</w> 23746
DM BA</w> 23746
L em 23745
Expl oring</w> 23745
Ch E</w> 23741
pleth ysm 23741
SC L</w> 23738
loc oregional</w> 23737
TC H</w> 23736
ch y</w> 23734
Cum ulative</w> 23734
g oblet</w> 23732
ad ox 23731
dom s</w> 23730
frac tal</w> 23730
Specific ity</w> 23729
bu prenorphine</w> 23728
tran sep 23725
X T</w> 23724
ac rom 23723
stac ked</w> 23721
anatom ically</w> 23719
Gran ul 23719
6 d</w> 23718
form ats</w> 23717
prol actin 23717
definiti vely</w> 23716
hyp om 23712
R SD</w> 23705
ET P</w> 23704
mo tional</w> 23704
gam es</w> 23704
asp inal</w> 23703
Vari ants</w> 23703
Ad ren 23702
Spec imens</w> 23701
tetra acetic</w> 23701
Cz ech</w> 23700
Ne o</w> 23699
Econ omic</w> 23699
Z AP</w> 23697
T d 23695
st ays</w> 23693
N ear</w> 23691
metab otropic</w> 23690
Utili zation</w> 23689
co iling</w> 23686
Ed itor</w> 23686
appreci ation</w> 23686
CL O 23683
mo s</w> 23682
X 8</w> 23681
cap ecitabine</w> 23681
S ab 23675
Wil ms</w> 23674
hydrogen ation</w> 23674
R LS</w> 23673
reproduc ibly</w> 23671
ik a</w> 23665
end a</w> 23664
stu res</w> 23662
Mal aria</w> 23658
Ex p</w> 23654
Gu o</w> 23654
Ned d4</w> 23654
NP M</w> 23651
f le 23649
Trans well</w> 23649
J M</w> 23648
pericy tes</w> 23648
amin opeptidase</w> 23647
S PAR 23645
PRE SEN 23645
CL E</w> 23642
Rab 7</w> 23642
splice osome</w> 23642
B CRP</w> 23639
Ischem ic</w> 23637
D c 23636
Δ G</w> 23636
atten d</w> 23636
p CM 23635
gas eous</w> 23634
simpl est</w> 23627
pep sin</w> 23622
plasm as</w> 23620
thal idomide</w> 23617
f ight</w> 23616
Immuno staining</w> 23616
bronchi olitis</w> 23616
N R1</w> 23612
leuc ocyte</w> 23611
ab 2</w> 23610
. 4A</w> 23609
EGF Rv 23609
3 ' 23608
ag inous</w> 23601
PON 1</w> 23599
substitu tes</w> 23597
allodyn ia</w> 23595
G Y 23594
orth o 23594
fe eder</w> 23592
Pharmaco kinetic</w> 23590
guar an 23589
pre pubertal</w> 23587
pseud om 23587
car d</w> 23586
can al 23586
S ão</w> 23585
bic eps</w> 23585
Res ources</w> 23584
asym metrical</w> 23584
TSC 1</w> 23582
BAF F</w> 23580
so aked</w> 23579
ge ographically</w> 23577
transf used</w> 23575
embry onal</w> 23575
CH E</w> 23573
N av 23569
lo s</w> 23568
Cul tured</w> 23567
MY B</w> 23565
AG Es</w> 23562
In corporation</w> 23561
is th 23560
GT GG 23556
nucle ated</w> 23554
N as 23552
Cyto kine</w> 23550
2 O 23548
immunosup press 23543
au l</w> 23542
enanti oselective</w> 23542
Th ym 23541
PU MA</w> 23541
E PC</w> 23539
E U 23538
fec und 23538
Ap c</w> 23537
occup ying</w> 23534
Ar c</w> 23532
AR I 23531
main stay</w> 23530
B H4</w> 23527
cu ticle</w> 23527
S RP 23524
meth anesulf 23518
rel ying</w> 23516
dec an</w> 23516
instrum ented</w> 23516
pol yc 23514
expan sions</w> 23514
Recur rence</w> 23513
G q</w> 23512
Th y</w> 23511
Hi erarch 23511
P o</w> 23510
sub si 23509
Prec ision</w> 23509
Y outh</w> 23507
Treat ments</w> 23507
NOT CH1</w> 23506
sy lation</w> 23503
overl ay</w> 23502
Egyp t</w> 23502
thyro globulin</w> 23501
SR I</w> 23499
. 3B</w> 23498
Bal b</w> 23497
TF EB</w> 23496
O FF</w> 23495
A AD</w> 23492
Un less</w> 23492
C IP</w> 23491
Ax io 23489
li ers</w> 23484
broncho dil 23484
upreg ulating</w> 23482
As suming</w> 23481
innerv ated</w> 23480
Tre ated</w> 23477
Gre ece</w> 23477
adeno viruses</w> 23477
PR R</w> 23476
dis ulph 23475
he el</w> 23474
rot ary</w> 23474
TO PO</w> 23471
ogly cer 23470
si lox 23465
K Y 23458
electromy ography</w> 23458
D V 23455
gener ational</w> 23455
flo xed</w> 23453
jus tice</w> 23453
6 h</w> 23452
poly somn 23452
Ta ylor</w> 23452
oligo deoxy 23452
me tries</w> 23451
hydroly ze</w> 23448
ann ulus</w> 23447
mark ets</w> 23446
gri ds</w> 23445
correc ts</w> 23443
colli sions</w> 23442
metac ar 23442
IC L</w> 23441
angi opathy</w> 23441
Davi d</w> 23440
hypogly caemia</w> 23440
H p</w> 23439
val gus</w> 23439
read iness</w> 23438
s atellites</w> 23437
me te 23434
M AL</w> 23433
primor dial</w> 23432
PE A</w> 23431
EB N 23431
transduc ing</w> 23430
Attemp ts</w> 23428
bin ders</w> 23426
lif t</w> 23426
war med</w> 23426
ir reversibly</w> 23425
otom ies</w> 23425
SU V</w> 23424
cy ano 23423
Wh ilst</w> 23423
re fraction</w> 23422
denti tion</w> 23421
Mon o 23421
micro meter</w> 23419
MV D</w> 23419
ataly sts</w> 23417
ac. uk</w> 23414
C ag 23413
recei ves</w> 23413
ST AR</w> 23410
GP x</w> 23410
M AGE</w> 23403
chemoradi ation</w> 23403
view er</w> 23401
responsi bilities</w> 23399
multi step</w> 23398
CS D</w> 23391
pr ag 23387
den um</w> 23386
T issues</w> 23385
heteros exual</w> 23383
C JD</w> 23380
B est</w> 23379
cl arithromycin</w> 23378
Aliquo ts</w> 23378
ac tomyosin</w> 23376
ham ar 23375
dec el 23374
IV IG</w> 23372
epididym is</w> 23372
LV H</w> 23368
HDAC i</w> 23366
P PIs</w> 23365
hem in</w> 23365
synth ases</w> 23365
S oil</w> 23363
alg a</w> 23361
Gα q</w> 23361
Trac ker</w> 23360
ome trics</w> 23356
Su b</w> 23354
explo itation</w> 23354
stret ches</w> 23354
aro x 23353
sh ells</w> 23352
chol ate</w> 23351
qua il</w> 23351
B os 23350
bi variate</w> 23350
anten n 23350
are r</w> 23349
ho uses</w> 23348
cry os 23348
lif elong</w> 23344
intrac lass</w> 23344
Gi ant</w> 23336
inf low</w> 23335
eigh ty</w> 23332
Con go</w> 23327
T l</w> 23326
pro n 23322
P U</w> 23319
hair y</w> 23318
Don or</w> 23318
N asal</w> 23317
sex uality</w> 23316
Na 3 23315
non canonical</w> 23314
sulfon yl 23314
K O 23312
visc ous</w> 23312
sn RNA</w> 23312
W as 23310
premat urely</w> 23310
k les</w> 23309
fibro blastic</w> 23308
ecta sis</w> 23306
MD M</w> 23304
ati ds</w> 23302
k it 23301
cf u</w> 23301
Dn mt 23301
eng ine</w> 23298
guaran tee</w> 23293
Adju vant</w> 23290
ud ing</w> 23287
recycl ed</w> 23284
ic lib</w> 23283
EX P</w> 23282
L eptin</w> 23280
HSP 2</w> 23278
there of</w> 23276
BM P4</w> 23276
hypox anthine</w> 23276
sub maximal</w> 23275
pa redness</w> 23273
Eph A2</w> 23273
u ronic</w> 23272
Ad V</w> 23272
dorm ant</w> 23271
CL IP</w> 23270
beta -</w> 23267
ac company</w> 23266
AP D</w> 23266
psychiatri sts</w> 23263
ou ths</w> 23262
op sies</w> 23262
cl oud</w> 23262
Re fl 23261
CC G</w> 23261
For ward</w> 23261
androste rone</w> 23261
gen erous</w> 23260
MM P2</w> 23259
fluctu ating</w> 23251
DE s</w> 23250
ic onazole</w> 23249
C MT</w> 23248
fl un 23244
O HT</w> 23243
encomp ass</w> 23242
tra ins</w> 23241
conn ects</w> 23238
Spec trum</w> 23238
PU FAs</w> 23236
eIF 4G</w> 23236
hypere x 23236
CA RD</w> 23235
hem agglutination</w> 23234
wild life</w> 23232
β2 AR</w> 23232
N de 23230
Et OAc</w> 23230
0 β</w> 23228
rec an 23228
o 2</w> 23226
cat .</w> 23224
Cal c 23224
tume faciens</w> 23221
Cys tic</w> 23220
magne to 23219
j 1</w> 23217
pi oglitazone</w> 23215
Cor tic 23213
V SD</w> 23212
spermat ocytes</w> 23211
ver satility</w> 23209
IMPORT ANCE</w> 23209
chon dral</w> 23207
S mar 23206
infer i 23206
neighbo uring</w> 23205
ST U 23202
cat abol 23200
omy ces</w> 23200
pre p</w> 23199
glucuron idase</w> 23198
co receptor</w> 23196
us s</w> 23194
Col or</w> 23193
2 β</w> 23190
L et 23190
ran i 23188
her b</w> 23188
1 --</w> 23187
S AL 23187
sen tences</w> 23184
dil tiazem</w> 23182
co us 23181
fa ith 23181
NMD ARs</w> 23181
hyp eros 23180
lo tho</w> 23179
ul ent</w> 23178
ME F 23176
habit ual</w> 23174
anth ocyanin</w> 23167
el iness</w> 23164
all er</w> 23164
mer is 23163
N os 23160
e sia</w> 23159
W 5</w> 23159
Cu 2</w> 23157
IL D</w> 23151
ag ro 23150
DP PH</w> 23148
Parame ters</w> 23147
co repressor</w> 23144
Immunob lot</w> 23143
non smokers</w> 23142
col istin</w> 23141
Utili zing</w> 23138
am 1</w> 23137
MF I</w> 23136
phen olate</w> 23135
N og 23133
evacu ation</w> 23132
Melan oma</w> 23130
n A</w> 23128
c iliated</w> 23126
AC D</w> 23126
propag ate</w> 23125
vari ances</w> 23120
B ic 23119
Govern ment</w> 23119
Fig. 4A</w> 23112
B om 23111
quo ti 23110
synapt osomes</w> 23108
W ar</w> 23106
TI Ls</w> 23106
CCR 2</w> 23103
N CC</w> 23098
F ur 23098
is os 23098
D 2 23097
sub lethal</w> 23097
obstruc ted</w> 23097
Dis crimin 23096
hy stere 23095
collap sed</w> 23094
administr ated</w> 23090
necrop sy</w> 23090
g il 23088
Fig. 7</w> 23084
Al u</w> 23083
absor bing</w> 23083
O RA 23080
mul tis 23079
Assemb ly</w> 23079
act ome</w> 23077
ID E</w> 23077
Observ ational</w> 23073
end odontic</w> 23071
idel berg</w> 23071
con us</w> 23069
endor f</w> 23069
der ness</w> 23068
PATIEN T</w> 23068
Bi olog 23065
1 .</w> 23064
bin din</w> 23063
morph ometry</w> 23063
at g 23062
8 L</w> 23059
lic ted</w> 23059
visi bility</w> 23057
am er 23056
oc treotide</w> 23054
ch itin 23053
myel opathy</w> 23053
ne ts</w> 23052
piper azine</w> 23051
imip enem</w> 23049
chemil uminescent</w> 23048
oc tan 23045
o yl 23042
tri iodothyronine</w> 23040
esophag ectomy</w> 23040
min gen</w> 23039
MK P</w> 23038
Sto kes</w> 23037
cholecyst okinin</w> 23033
t -</w> 23029
re visited</w> 23029
B Cs</w> 23028
s meg 23026
Le g 23023
chlor hexidine</w> 23020
oug h 23017
EGFRv III</w> 23016
Cor respon 23010
ecti on 23006
spe akers</w> 23005
ag ly 23002
RA Rα</w> 22999
Aca d</w> 22997
D ox 22995
H p 22993
si d 22993
admin ister</w> 22993
Pil ot</w> 22993
Schiz ophren 22992
6 F1</w> 22987
TB E</w> 22987
mid wives</w> 22985
ech inoc 22984
K un 22982
G ross</w> 22981
Smad 1</w> 22981
sacc ades</w> 22979
endonucle ases</w> 22979
B 0</w> 22976
O GT</w> 22976
di polar</w> 22976
zo ite</w> 22975
g ill</w> 22974
Inter pre 22972
z 1</w> 22971
PG I2</w> 22971
propri oc 22971
2 .</w> 22970
x ero 22968
C SN 22967
GT P 22961
ub a</w> 22952
SM O</w> 22951
SH P2</w> 22947
Challeng es</w> 22944
bene fi 22940
Pro st 22939
prec au 22939
equi v</w> 22938
Mil ten 22936
Tol er 22935
Tanz ania</w> 22935
cat ch</w> 22933
ome ters</w> 22933
acyl transferase</w> 22933
anten na</w> 22933
Or bit 22925
fa eces</w> 22923
famili arity</w> 22922
Orig in</w> 22918
at ria</w> 22917
Tri zol</w> 22915
NH4 Cl</w> 22914
oc e 22913
eng th</w> 22913
acetyl cysteine</w> 22913
bot tle</w> 22913
exhaus tive</w> 22912
gam m 22911
Au tism</w> 22908
Ds Red</w> 22906
amb ly 22904
on ade</w> 22903
ure sis</w> 22902
s outh 22901
occup ies</w> 22900
gran d 22899
vill age</w> 22898
restric ts</w> 22896
- OH</w> 22894
p end 22893
Cy cle</w> 22891
b udget</w> 22890
di -</w> 22890
Accum ulating</w> 22889
t ones</w> 22885
Spec tral</w> 22883
AT T</w> 22882
K K</w> 22879
en al 22878
pro visi 22878
o embryonic</w> 22876
poly chlorinated</w> 22876
och lor 22874
kerat oplasty</w> 22871
OR F1</w> 22867
sun light</w> 22866
cast le</w> 22866
GG G</w> 22864
thrombocytop enic</w> 22864
B FA</w> 22862
S AT</w> 22860
SIR T3</w> 22860
rop tosis</w> 22859
E Ds</w> 22858
glycer o</w> 22857
Tow ards</w> 22856
Gen otype</w> 22854
a 5</w> 22853
ric in</w> 22853
valpro ate</w> 22853
mening iti 22851
mac ul 22850
aut umn</w> 22850
o val</w> 22849
B et 22842
Leuk emia</w> 22840
I PC</w> 22838
In nov 22836
Ber lin</w> 22834
si lyl</w> 22833
uro logical</w> 22833
ylo xy</w> 22833
Con f 22831
sin ensis</w> 22831
micro organism</w> 22828
Re tino 22828
ensu ing</w> 22828
sed ative</w> 22826
INK 4a</w> 22825
MM F</w> 22823
fulmin ant</w> 22823
Re p</w> 22822
micro vessels</w> 22817
d obutamine</w> 22815
α β</w> 22815
end ings</w> 22814
com ings</w> 22813
up -</w> 22813
pheny leth 22812
H o</w> 22811
X anth 22804
occ er</w> 22802
O dys 22799
c m3</w> 22798
gro ss 22796
Chem istry</w> 22796
N CAM</w> 22795
Hispan ics</w> 22792
Sil ver</w> 22791
consangu ineous</w> 22790
F Q</w> 22788
sulfam eth 22788
o ys 22787
col lim 22786
fl are</w> 22785
Dop amine</w> 22784
char d</w> 22780
as ka</w> 22778
casse ttes</w> 22777
flu ency</w> 22776
Micro soft</w> 22775
dis placements</w> 22773
s .1</w> 22772
ST M</w> 22772
hol o</w> 22770
melan ocytic</w> 22768
J Q1</w> 22766
am y</w> 22766
Pharmaco kinetics</w> 22764
SP T</w> 22762
F AS 22759
s arc 22747
Immun ocyto 22747
AU G</w> 22747
Oste o 22743
dorm ancy</w> 22743
conduc tor</w> 22742
qu et</w> 22740
ograph ies</w> 22740
Infra red</w> 22740
TI V 22738
HDAC 4</w> 22738
subtr acting</w> 22736
CL S</w> 22735
lob ar</w> 22735
Epigen etic</w> 22733
e EF 22732
ten din 22730
demonstr able</w> 22729
Mig ration</w> 22729
ad duc 22728
sphing omyelin</w> 22726
par vo 22725
Pa ired</w> 22721
re ovirus</w> 22717
hil ar</w> 22716
essi vity</w> 22715
p ectin</w> 22713
mediastin um</w> 22712
cholester yl</w> 22712
p m 22711
gu il 22710
c ecal</w> 22709
w elling</w> 22709
. kg</w> 22708
Cis platin</w> 22706
smeg matis</w> 22706
ar ming</w> 22704
A As</w> 22703
micro biology</w> 22701
BM P2</w> 22699
Mes enchymal</w> 22698
parkinson ian</w> 22697
F u</w> 22692
my oblast</w> 22691
Phar mingen</w> 22690
smo ker</w> 22688
Num erical</w> 22688
hex yl</w> 22686
mol es</w> 22684
Lati no</w> 22684
ur gency</w> 22683
Le uc 22683
He idelberg</w> 22681
L UC</w> 22678
A e.</w> 22677
dam ycin</w> 22677
tech ne 22675
Co R</w> 22675
PRESEN TATION</w> 22672
duc k</w> 22669
phot oluminescence</w> 22668
Ly so 22668
lac un 22665
foot print</w> 22663
D uch 22662
pre adipocytes</w> 22662
over lying</w> 22661
pep statin</w> 22661
Peri operative</w> 22661
school children</w> 22660
Sim ulations</w> 22659
Der i 22659
op yr 22657
nano rods</w> 22657
BI S</w> 22657
Vacc ination</w> 22656
accor d</w> 22655
FOX M1</w> 22654
NO G</w> 22653
Dip tera</w> 22652
camp tothecin</w> 22650
T ech</w> 22649
de amination</w> 22646
u el 22643
R K</w> 22642
neuro toxin</w> 22642
Suscepti bility</w> 22642
crow ns</w> 22639
- containing</w> 22637
N er 22637
aor t 22632
macro lide</w> 22628
past oris</w> 22628
sensiti zes</w> 22627
v as</w> 22622
heigh ts</w> 22622
osel tamivir</w> 22621
top ographic</w> 22615
TI L</w> 22613
Ad ding</w> 22611
defibrill ator</w> 22611
N C 22610
Ser a</w> 22610
DNMT 3A</w> 22605
feat uring</w> 22604
moder ated</w> 22603
Equ ation</w> 22600
ho ur 22595
T AR 22594
neuro biological</w> 22594
I BM</w> 22593
In direct</w> 22589
g lue</w> 22588
Q 7</w> 22586
Rap amycin</w> 22585
r uc 22582
tr ape 22581
enc er</w> 22578
dos e-</w> 22576
Com mentary</w> 22575
or ization</w> 22574
o y 22573
W ound</w> 22573
methyl cellulose</w> 22572
Ang II</w> 22572
sh unting</w> 22571
PP T</w> 22570
recover ing</w> 22562
E h 22560
cl au 22556
Ex pert</w> 22555
T ypical</w> 22554
wa ke 22554
α 1 22553
Odys sey</w> 22551
intestin es</w> 22550
ep sia</w> 22549
G IR 22547
Cx 3</w> 22546
frac tured</w> 22545
Prote in 22543
ginsen g</w> 22542
me val 22540
A Q</w> 22539
al o 22539
pyro sequencing</w> 22537
A typical</w> 22530
voc ally</w> 22528
e tically</w> 22527
M MA</w> 22524
Le ad</w> 22520
Arti ficial</w> 22520
Program me</w> 22517
thic ker</w> 22512
Phen otypic</w> 22510
ERI A</w> 22509
t own</w> 22507
Rou x</w> 22507
Dec ision</w> 22505
less ness</w> 22504
MP M</w> 22500
O V3</w> 22498
glyc ated</w> 22498
Larg er</w> 22498
Hipp ocamp 22496
transl ating</w> 22495
phosph ocholine</w> 22494
Micro scopic</w> 22492
W O 22490
hypo thermic</w> 22487
ti veness</w> 22486
E sti 22484
en semb 22483
f ox 22482
Ag ainst</w> 22482
landsc apes</w> 22481
an hydride</w> 22480
ont ogeny</w> 22479
He p</w> 22478
flagell in</w> 22478
CS B</w> 22476
H ou 22475
ID DM</w> 22474
life times</w> 22474
exer tion</w> 22469
roc k</w> 22469
myco sis</w> 22468
SO CE</w> 22467
in stitute</w> 22466
lagg ing</w> 22464
M ac</w> 22462
Ab original</w> 22460
pi on</w> 22458
tr y 22457
Fi br 22457
enro l 22455
w etting</w> 22453
Vir uses</w> 22452
promp tly</w> 22450
Substitu tion</w> 22447
ast atin</w> 22446
p CR</w> 22439
fellow ship</w> 22439
Re vised</w> 22438
L aw 22436
poly neuropathy</w> 22436
S id 22432
BMD Ms</w> 22431
in osine</w> 22429
chron ological</w> 22428
ker t</w> 22427
E I 22425
electro philic</w> 22425
AU RK 22421
Stan ford</w> 22421
Nit ro 22420
HB G</w> 22419
im entary</w> 22418
os sy 22417
spiro metry</w> 22417
om i 22415
PR IN 22413
innov ations</w> 22413
hospital isation</w> 22412
tan k</w> 22411
SP 5</w> 22402
4 M</w> 22400
tri vial</w> 22397
adhe rens</w> 22397
de his 22396
cc RCC</w> 22396
discer ni 22395
tetrahydro folate</w> 22394
P EX 22391
ecti vities</w> 22386
elabor ated</w> 22386
F und</w> 22384
CYP2 E1</w> 22383
arthro desis</w> 22382
homo zygosity</w> 22380
R GS</w> 22378
mutagen icity</w> 22378
L H 22373
asth matics</w> 22373
ligh ting</w> 22373
pow ders</w> 22372
p. m.</w> 22366
Ac anth 22360
Gly cine</w> 22360
aqu ap 22359
Hsp 1</w> 22359
nucleocap sid</w> 22358
convul sions</w> 22355
quar ters</w> 22355
clu sively</w> 22354
neuro pathology</w> 22353
A th 22350
termin ate</w> 22350
habit uation</w> 22347
Ser ies</w> 22346
onc ologists</w> 22346
9 V</w> 22342
s onic</w> 22341
Pharmac eutical</w> 22341
pyridin ium</w> 22340
We e 22339
en i 22338
Com plexes</w> 22337
D v 22335
sp A</w> 22335
sa ve</w> 22335
ecti n 22334
Foc us</w> 22333
v illus</w> 22329
chrom ic</w> 22329
Pharmac euticals</w> 22329
syn chrotron</w> 22327
lamellipo dia</w> 22327
orific e</w> 22326
1 f</w> 22324
A HI</w> 22322
HS D1</w> 22320
H3K9 me3</w> 22320
secre tin</w> 22316
Se c</w> 22316
con dy 22315
Hsp 2</w> 22315
TI VE</w> 22314
oc cosis</w> 22314
Concomit ant</w> 22312
protot ypical</w> 22311
bo oster</w> 22310
si zing</w> 22309
prote oly 22308
sacrif ice</w> 22308
ver a</w> 22306
E ither</w> 22304
G arc 22304
LI P</w> 22304
conclud es</w> 22304
ben cl 22304
N co 22301
di m</w> 22301
nov .</w> 22301
ang ulation</w> 22299
Collabor ative</w> 22297
pass enger</w> 22295
Fig. 3B</w> 22290
gangli osides</w> 22289
en tro 22288
compan ion</w> 22288
resear ches</w> 22287
Ku 7</w> 22286
gross ly</w> 22285
TET 2</w> 22280
ad aic</w> 22279
SC Cs</w> 22278
MD MA</w> 22278
r itonavir</w> 22277
og s</w> 22277
ensi tized</w> 22277
concus sion</w> 22272
inter section</w> 22268
B LO 22267
Tric ho 22267
GA GT 22266
ap omorphine</w> 22265
f eno 22263
aff e 22261
haemat opoietic</w> 22260
Process ing</w> 22258
Brassi ca</w> 22258
c f</w> 22255
guan ylate</w> 22255
FK BP5</w> 22252
Impair ment</w> 22249
6 M</w> 22248
D C1</w> 22248
L RP1</w> 22247
Y os 22247
sac ro 22241
w ounding</w> 22239
oxygen ases</w> 22236
pie zo 22235
retino ids</w> 22234
dehydrogen ases</w> 22234
C old</w> 22232
W GA</w> 22230
extrapol ated</w> 22230
- alpha</w> 22227
Nat l</w> 22227
a viruses</w> 22225
S MRT</w> 22225
Ar tery</w> 22224
intrac erebro 22221
un specific</w> 22219
appreci ably</w> 22219
Photos hop</w> 22219
inferen ces</w> 22218
allop ian</w> 22218
stri ated</w> 22217
δ 1</w> 22214
ven ting</w> 22214
Pis cat 22214
nig er</w> 22213
con vol 22212
cy stitis</w> 22212
mono oxygenase</w> 22208
Dem on 22208
hi ber 22207
inc isions</w> 22206
japon ica</w> 22206
bl end</w> 22205
di amidino</w> 22204
F MDV</w> 22201
D uration</w> 22198
sup pur 22198
amp ut 22198
hydroxy ethyl</w> 22196
in compatibility</w> 22195
par g 22195
lu br 22195
bencl amide</w> 22194
PR RSV</w> 22183
irr itable</w> 22183
x 5</w> 22180
p asses</w> 22178
hand led</w> 22176
H awa 22175
iod inated</w> 22175
F an 22174
anti phospholipid</w> 22173
ab duction</w> 22172
cadaver s</w> 22171
M AN 22168
per v 22168
con sortium</w> 22167
finger printing</w> 22167
Harv ard</w> 22165
C W 22164
nam ing</w> 22163
r atum</w> 22162
Inf ected</w> 22161
Comp any</w> 22161
rever ted</w> 22159
Agr icul 22158
P yro 22156
p assively</w> 22156
imp ing 22156
F unding</w> 22154
ra vir</w> 22154
substanti ated</w> 22153
oblig atory</w> 22153
cere br 22149
abro gates</w> 22149
1 mM</w> 22148
poly ke 22148
haemorrh agic</w> 22148
us able</w> 22143
R F1</w> 22142
l or</w> 22141
Insig hts</w> 22140
ici ous</w> 22139
auto regulation</w> 22139
Caro tid</w> 22137
be ans</w> 22136
l otted</w> 22134
ocar b 22132
euthan asia</w> 22132
de z</w> 22131
inti mately</w> 22131
ari ae</w> 22130
W t</w> 22128
mac rop 22127
histo chemistry</w> 22127
vide os</w> 22127
cefta zidime</w> 22126
Radi otherapy</w> 22123
T u</w> 22122
un equal</w> 22119
pol ice</w> 22118
st ump</w> 22117
eradic ate</w> 22115
A gre 22114
interi m</w> 22113
fronto temporal</w> 22112
L amb 22109
Piscat away</w> 22107
Y AP1</w> 22105
rac em 22105
T ip 22104
Medic ation</w> 22104
Se as 22103
pol is</w> 22101
CCL 5</w> 22101
sh ocks</w> 22100
al ist</w> 22097
halluc inations</w> 22097
5 ' 22095
micro cephaly</w> 22095
col ectomy</w> 22092
Chang ing</w> 22091
alk ylated</w> 22087
wean ed</w> 22087
enti t 22085
p m</w> 22084
PR S</w> 22084
ar bo 22083
vag us</w> 22082
ne ocortical</w> 22081
CT 2</w> 22081
yn yl</w> 22076
Surviv in</w> 22074
M ACS</w> 22071
p B</w> 22070
osmol arity</w> 22069
vir ally</w> 22067
P O2</w> 22066
skew ed</w> 22066
NR 2B</w> 22065
our inary</w> 22064
aph eresis</w> 22062
o int 22059
sube p 22054
Main tenance</w> 22050
gr ass 22049
ton ian</w> 22049
o ding</w> 22046
me to 22046
i j 22045
B ron 22045
NE P</w> 22045
u ff 22044
T M1</w> 22043
sub merged</w> 22043
da un 22043
phal an</w> 22043
mill ili 22039
mark eted</w> 22038
stret ched</w> 22038
E r</w> 22034
sul cus</w> 22033
divertic ulum</w> 22033
orh inal</w> 22030
h u</w> 22027
Four th</w> 22026
Acqu ired</w> 22025
cer am 22024
attri tion</w> 22020
neurofibrom atosis</w> 22018
PROCE DU 22018
ri an</w> 22016
B3 LYP</w> 22016
ath ion 22014
trans esophageal</w> 22013
cas ting</w> 22012
aden o</w> 22010
p V 22009
gro n</w> 22008
imag er</w> 22008
Fr actions</w> 22006
pa ins</w> 22004
prof iciency</w> 21999
Milten yi</w> 21999
G m 21996
super im 21995
immunob lotted</w> 21995
e ug 21994
glycosamin oglycans</w> 21994
PO P</w> 21993
epider moid</w> 21993
E lig 21992
and 4</w> 21992
resc ine</w> 21990
Bio chemistry</w> 21990
suc kling</w> 21989
F RT</w> 21988
pal mitic</w> 21987
6 I</w> 21983
proc essivity</w> 21983
hydroxy butyrate</w> 21983
SO M</w> 21981
lis teners</w> 21981
Col or 21976
affor ds</w> 21976
New castle</w> 21975
V alley</w> 21974
mon ogenic</w> 21973
Her 2</w> 21972
poly phenol</w> 21971
phot osystem</w> 21971
obste trics</w> 21971
A ward</w> 21967
MR M</w> 21967
RE C 21963
J 7</w> 21961
I Ps</w> 21961
og old</w> 21961
c .3</w> 21956
edi ble</w> 21955
ET C</w> 21953
trex one</w> 21951
2 s</w> 21949
anc erous</w> 21949
hyperinsulin emia</w> 21949
re visions</w> 21948
Tel om 21947
J EV</w> 21946
multiplex ed</w> 21943
accompan ies</w> 21942
ph ones</w> 21941
non toxic</w> 21937
Q L 21935
run off</w> 21932
beta 3</w> 21930
X II</w> 21929
R Y</w> 21928
cour t</w> 21928
absorb able</w> 21926
colum nar</w> 21925
quarti les</w> 21925
can e</w> 21924
osteoclas togenesis</w> 21920
Mi y 21918
ent orhinal</w> 21916
adap tors</w> 21916
de ubiquitin 21914
Gener alized</w> 21911
ad t</w> 21908
C opy 21906
mon op 21906
fecund ity</w> 21904
Ac t 21901
corrhiz al</w> 21901
Esti mates</w> 21901
o dium</w> 21899
FL U 21895
amoeb a</w> 21894
condy le</w> 21894
min ated</w> 21892
H sc7</w> 21891
PT CA</w> 21890
IRE 1α</w> 21888
miner al 21885
os arcomas</w> 21884
Minim al</w> 21882
op er</w> 21880
Fas ting</w> 21880
genu ine</w> 21877
h um</w> 21876
at oma</w> 21876
electroph ores 21876
Through out</w> 21876
E clip 21874
rhabdomy osarcoma</w> 21874
at t</w> 21872
Par allel</w> 21870
AR 2</w> 21868
cyto sis</w> 21863
Transi tion</w> 21862
E uc 21861
res ti 21861
immunos tim 21854
sh unts</w> 21852
thic knesses</w> 21852
ten torial</w> 21851
phosphor yl</w> 21850
arb or</w> 21849
poly ubiquitin</w> 21847
CS 2</w> 21847
hydroxy proline</w> 21847
ver tically</w> 21846
r TMS</w> 21845
p C</w> 21845
access ing</w> 21844
phosph ate 21839
Hear ing</w> 21839
cryp toc 21836
Blo ts</w> 21834
vor tex</w> 21833
micro villi</w> 21832
Con cep 21832
non essential</w> 21830
Lyn ch</w> 21829
ato hepatitis</w> 21828
ca 1</w> 21822
- beta</w> 21821
on ite</w> 21821
man ia</w> 21820
vir gin</w> 21820
phospho ro 21820
neu rosph 21819
local izations</w> 21819
por ter</w> 21818
evap orated</w> 21816
NI DDM</w> 21815
Here ditary</w> 21815
N Rs</w> 21813
contamin ating</w> 21813
myri ad</w> 21813
carb apenem 21810
sin k</w> 21807
Ch l</w> 21806
cell ence</w> 21806
boos ted</w> 21806
omedic ine</w> 21805
D in 21803
un detected</w> 21800
ar cu 21799
B us 21795
substitu ting</w> 21795
M ong 21794
N ICU</w> 21793
ac ridine</w> 21786
fibrom yalgia</w> 21786
K E</w> 21785
G 9a</w> 21781
firm ly</w> 21781
rs 9</w> 21777
X L1</w> 21776
N is 21774
K 0</w> 21771
In t</w> 21771
RP P</w> 21770
H art 21769
Br ac 21769
abbrevi ations</w> 21769
cer tification</w> 21768
tetra ploid</w> 21768
A ud 21767
i qu 21764
C ot 21761
m t1</w> 21761
phy te</w> 21761
s ell</w> 21759
antero posterior</w> 21759
ic am</w> 21758
Struc tures</w> 21757
p cDNA</w> 21755
. 4B</w> 21752
charg ing</w> 21751
ur inol</w> 21749
S 2C</w> 21745
cu ts</w> 21744
b p1</w> 21743
Inf arc 21742
chlamy dial</w> 21742
bio assays</w> 21741
0 μM</w> 21740
down regulating</w> 21740
AM H</w> 21736
Polic y</w> 21736
i 2</w> 21734
C ra 21732
heavi er</w> 21731
Oc currence</w> 21729
kine tically</w> 21729
th inner</w> 21728
CT R</w> 21727
immunosup pressed</w> 21727
phil es</w> 21726
GSE A</w> 21725
D k 21724
viti ligo</w> 21723
o protective</w> 21722
k u 21720
7 MG</w> 21719
W AF1</w> 21718
roset te</w> 21717
MO s</w> 21716
iv ary</w> 21716
short comings</w> 21716
exerc ised</w> 21715
B AD</w> 21712
F ram 21706
FK BP1</w> 21706
J iang</w> 21705
mat ters</w> 21702
Min or</w> 21701
gi b 21700
Nanop articles</w> 21700
H og 21698
tetro dotoxin</w> 21696
Victor ia</w> 21696
re oxygenation</w> 21693
ME N</w> 21691
S cop 21687
pre malignant</w> 21687
Resear chers</w> 21687
pl ed</w> 21686
pu er 21686
dichloro methane</w> 21685
meningiti dis</w> 21685
s burg</w> 21684
FOX O</w> 21682
AB 2</w> 21679
TRI F</w> 21679
Penic illium</w> 21676
inten si 21674
T Ap 21673
se ros 21673
cephalospor ins</w> 21673
V N</w> 21672
anc iclovir</w> 21672
HP G</w> 21671
de stabilize</w> 21670
tamp onade</w> 21670
us hes</w> 21669
B il 21667
histi ocytosis</w> 21664
trop omyosin</w> 21663
advanc ements</w> 21663
0 nm</w> 21662
in gens</w> 21661
A im 21660
Eg r</w> 21659
ph renic</w> 21656
vasoconstric tor</w> 21654
Extrac ts</w> 21653
az ithromycin</w> 21652
transm ural</w> 21650
t ad 21649
et ch</w> 21649
a dism</w> 21648
2 f</w> 21647
facilit ators</w> 21643
BL s</w> 21641
A bl 21638
ne vus</w> 21638
sub retinal</w> 21638
pe de 21638
Figure 1A</w> 21636
AT E</w> 21635
d c</w> 21634
U ter 21634
cas trated</w> 21634
Tyr 2</w> 21634
thermod ynamics</w> 21633
N oc 21632
LA TION</w> 21629
acti onal</w> 21628
Enric hment</w> 21628
D PP 21627
bran ch 21626
sp ider</w> 21625
con ventionally</w> 21623
OCT 4</w> 21623
Repor ter</w> 21621
2 Δ 21619
discerni ble</w> 21618
F atigue</w> 21617
igen es</w> 21616
der ia</w> 21611
Be ver 21611
visco elastic</w> 21609
al lin 21608
estim ations</w> 21608
chemos ensitivity</w> 21608
l ag 21605
m PFC</w> 21604
co transfection</w> 21604
Suc c 21602
IR E</w> 21598
g ing 21595
dener vated</w> 21595
c yp 21594
N al 21591
end otox 21590
than ks</w> 21590
Buil ding</w> 21589
proce edings</w> 21588
ent i</w> 21586
os able</w> 21585
per fr 21585
gol den</w> 21585
- TT 21584
ic ides</w> 21584
se ph 21583
1 g</w> 21580
Immun ization</w> 21579
edi tion</w> 21579
3 h</w> 21569
L h 21569
T GC</w> 21568
SO X1</w> 21568
ho ok</w> 21564
ca pro 21563
m 4</w> 21562
forc ing</w> 21560
mosa icism</w> 21556
inter individual</w> 21554
unra vel 21554
un adjusted</w> 21552
NY HA</w> 21551
mess engers</w> 21550
M ACE</w> 21546
Proc ed 21546
sh all</w> 21542
afil omycin</w> 21541
I x 21538
H SS</w> 21536
a head</w> 21535
pl ier</w> 21535
NT P</w> 21532
L AS 21530
neuro pathies</w> 21529
F os 21526
Ch ile</w> 21526
IF A</w> 21526
perioste al</w> 21525
ou b 21524
characteri se</w> 21523
lip s</w> 21523
Amy loid</w> 21523
K it 21521
in accurate</w> 21521
V . 21517
Fo rest</w> 21516
m old</w> 21515
dis similar</w> 21515
1 x</w> 21514
LE F</w> 21514
sl er</w> 21512
G ulf</w> 21511
identi fier</w> 21510
TT F</w> 21510
n exin</w> 21509
SC V</w> 21507
cef triaxone</w> 21506
De fin 21502
PH B</w> 21494
illumin ated</w> 21494
ne ovascular</w> 21489
AC AC 21489
pre determined</w> 21488
V P4</w> 21487
re growth</w> 21487
hyper baric</w> 21487
SP B</w> 21486
Portu gu 21485
ex tubation</w> 21483
gro unded</w> 21483
en closed</w> 21481
maph ro 21481
RN S</w> 21479
plo ids</w> 21479
N OR</w> 21477
BO DI 21474
fra il</w> 21474
conspic uous</w> 21474
Lif e 21470
herpes viruses</w> 21470
anox ia</w> 21466
clim bing</w> 21462
uro genital</w> 21459
counter acted</w> 21459
piper idine</w> 21458
Respond ents</w> 21458
o c</w> 21456
exten ts</w> 21451
B t</w> 21450
In ter</w> 21450
domin ate</w> 21450
l pr</w> 21447
ne go 21446
cortic otropin</w> 21446
ation .</w> 21445
es thesi 21444
equi vocally</w> 21444
o is</w> 21443
dihydroxy vitamin</w> 21442
AT G5</w> 21438
inf inite</w> 21437
down regulates</w> 21437
micro vessel</w> 21436
NI K</w> 21432
resist ances</w> 21431
am it 21427
gam bi 21427
hex okinase</w> 21426
SP L</w> 21425
S as 21424
Ax i 21424
eg ress</w> 21422
EP SC</w> 21417
Rab 2</w> 21416
whe y</w> 21415
vir als</w> 21414
compe tes</w> 21413
caregi ving</w> 21413
o ate</w> 21409
Z r 21407
AA GT 21407
hex adec 21405
vic tim</w> 21405
hydro peroxide</w> 21404
Hep arin</w> 21402
Rd Rp</w> 21401
G DH</w> 21400
cogn itively</w> 21400
re t</w> 21399
prog estin</w> 21398
clar ification</w> 21395
Visu alization</w> 21395
cel lo 21394
B -</w> 21393
multi potent</w> 21393
hun ting 21393
Burk hol 21392
function alities</w> 21391
g t</w> 21390
Tak ara</w> 21390
yl choline</w> 21389
hal ogen</w> 21389
happ ens</w> 21385
ank ylosing</w> 21384
MV C</w> 21382
diffus ely</w> 21382
ob a</w> 21381
Ser 5</w> 21381
ol gus</w> 21380
trunc ations</w> 21380
centri ole</w> 21373
appoin tment</w> 21372
I MS</w> 21370
co exist</w> 21367
Ar m</w> 21367
metamorph osis</w> 21366
praz osin</w> 21364
Publ ished</w> 21361
H7 N9</w> 21359
7 M</w> 21356
oxid ases</w> 21356
uc ker</w> 21352
techn ic</w> 21345
V inc 21340
par ty</w> 21340
en sured</w> 21336
A Z</w> 21334
Copy right</w> 21334
qu orum</w> 21333
Org anis 21333
Recor d</w> 21333
phy letic</w> 21331
A us 21330
A go 21329
O M 21329
bl ade</w> 21328
RI N</w> 21326
PR C1</w> 21323
on dro 21321
od ed</w> 21320
nadi r</w> 21318
d war 21317
transi st 21315
p RB</w> 21312
aren sis</w> 21312
att achments</w> 21309
renew ed</w> 21309
Apro pos</w> 21307
IP SCs</w> 21306
catas trophic</w> 21305
O GD</w> 21304
Rad 1</w> 21303
osom atic</w> 21302
mid w 21302
Vit ro</w> 21302
cont acting</w> 21300
hep ta 21299
megakary ocytes</w> 21298
T ST</w> 21289
coupl ings</w> 21289
b ically</w> 21288
deser ves</w> 21287
aur icular</w> 21286
carbox yp 21284
E K</w> 21281
s tern 21280
ker atins</w> 21280
orbit als</w> 21280
yl and</w> 21279
NS S</w> 21279
Wn t1</w> 21279
non alcoholic</w> 21277
phosph opeptide</w> 21273
mis diagnosed</w> 21273
determin istic</w> 21272
ifor mis</w> 21271
syn ten 21269
ospor a</w> 21269
ari us</w> 21268
succ essively</w> 21268
ore xin</w> 21266
fur n 21266
Mn 2</w> 21261
Ex press</w> 21259
herbi v 21258
SP A</w> 21257
phant oms</w> 21256
er ally</w> 21255
Subj ective</w> 21255
pr ick</w> 21253
LA NA</w> 21252
ac king</w> 21250
Kir 6</w> 21250
preser ves</w> 21247
eIF 4 21244
7 d</w> 21243
hyper activation</w> 21242
Bl adder</w> 21242
CH K1</w> 21240
whe el 21239
ad ly</w> 21238
elast ography</w> 21238
o esophagus</w> 21237
denti st</w> 21237
ta ught</w> 21235
distinguish es</w> 21233
CDK 9</w> 21233
ectom ies</w> 21232
acycl ovir</w> 21229
2 A1</w> 21228
p eren 21228
AG 3</w> 21225
asta sis</w> 21223
B ound</w> 21222
r hom 21220
is y</w> 21218
SN S</w> 21217
3 Q</w> 21216
pap ain</w> 21216
TR F2</w> 21215
um es</w> 21212
tri angular</w> 21209
A wa 21208
ano ate</w> 21207
Adap tive</w> 21207
antigen icity</w> 21207
in clin 21206
amin op 21206
LE T</w> 21206
Ca V1</w> 21204
prec oci 21199
Gi ardia</w> 21197
e ing</w> 21196
M ull 21193
Ch eng</w> 21191
dis satisfaction</w> 21190
ocar pine</w> 21190
overwhel ming</w> 21190
myco plasma</w> 21188
broad band</w> 21187
Bol tz 21187
H f 21186
no thing</w> 21183
potenti ating</w> 21183
IC Us</w> 21182
Ob servation</w> 21182
n in 21180
mis leading</w> 21180
Epidemi ologic</w> 21180
anch ors</w> 21177
pre hospital</w> 21176
L og</w> 21174
p ir 21174
util ised</w> 21174
L K</w> 21170
B F 21169
E pp 21169
vibr ations</w> 21168
Determin ing</w> 21168
N W</w> 21167
rad on</w> 21167
id ov 21166
cycl o</w> 21166
multim eric</w> 21166
Simult aneously</w> 21164
interfe rons</w> 21161
Portugu ese</w> 21161
lin ic</w> 21157
chemo therapeutics</w> 21157
incid entally</w> 21157
Indon esia</w> 21154
cach exia</w> 21154
PR 8</w> 21153
tri acylglycerol</w> 21151
D 1 21150
brea d</w> 21149
fur an 21149
micro l</w> 21148
Vi ol 21147
bul l</w> 21146
Frame work</w> 21144
menis cal</w> 21141
F resh</w> 21139
non polar</w> 21139
R ig 21138
Flow Jo</w> 21137
f ates</w> 21136
SO X9</w> 21136
EB s</w> 21136
off enders</w> 21135
Eclip se</w> 21134
avoid able</w> 21133
cryp ts</w> 21133
lacto ferrin</w> 21133
JA K1</w> 21130
osac charomyces</w> 21129
Cryp tococcus</w> 21127
synapto physin</w> 21127
I LI 21126
tw ist</w> 21126
Puer to</w> 21125
Orbit rap</w> 21124
GE M</w> 21123
hunting tin</w> 21123
D CE</w> 21115
Suz uki</w> 21113
zol id</w> 21112
IRE 1</w> 21108
FAC SC 21108
ma iled</w> 21107
mut h</w> 21107
Er ratum</w> 21107
Cyto plasmic</w> 21107
NO E</w> 21106
l op 21104
Feder ation</w> 21104
vi es</w> 21103
non functional</w> 21103
entero toxin</w> 21103
pyr role</w> 21103
DE N</w> 21102
md x</w> 21102
Sph ing 21099
smar t 21098
AT F6</w> 21097
dis locations</w> 21095
mes o</w> 21095
vag otomy</w> 21093
PV C</w> 21093
Duch enne</w> 21093
H2A. Z</w> 21090
form yl</w> 21088
un equivocally</w> 21087
T uni 21082
rhe ological</w> 21081
G α1</w> 21080
Ben ign</w> 21080
A PAP</w> 21078
BD I</w> 21076
s f 21075
Indi genous</w> 21071
H R1</w> 21069
W ri 21068
muc ositis</w> 21067
dimethyl thiazol</w> 21064
syring ae</w> 21064
normal izing</w> 21063
al um</w> 21062
Mo S2</w> 21062
Professi onal</w> 21062
OR C</w> 21060
Re as 21059
contra indications</w> 21059
AV R</w> 21058
abstr action</w> 21058
m ally</w> 21057
log ist</w> 21056
Fem ales</w> 21055
ma res</w> 21052
antec ed 21052
orth ostatic</w> 21051
I d</w> 21044
Dend ritic</w> 21044
AA S</w> 21042
antagon izes</w> 21041
y .</w> 21040
M é 21039
ten derness</w> 21038
out liers</w> 21037
di o</w> 21036
dec ap 21036
micro surgical</w> 21035
anser in</w> 21035
C erebro 21034
Pst I</w> 21033
Ch AT</w> 21032
dp f</w> 21032
bru tinib</w> 21032
con i</w> 21030
g ay</w> 21027
O TU 21027
olys accharides</w> 21027
hom eless</w> 21025
sim us</w> 21024
Fe eding</w> 21024
P m 21023
Rec ru 21023
G W</w> 21022
.ht ml</w> 21022
cy nom 21021
treat able</w> 21021
des mo 21021
th ings</w> 21018
practi ced</w> 21014
ad nex 21013
arteri osus</w> 21013
T ag 21012
kn ife</w> 21012
T MB</w> 21011
verm ectin</w> 21011
D Y</w> 21010
Cytotox icity</w> 21009
ly cop 21008
S yst 21007
Ar a</w> 21007
miti gation</w> 21005
T AD</w> 21002
X s</w> 21002
L CMV</w> 21001
trunc ating</w> 21001
P on 20996
F EN 20996
G PS</w> 20996
AD PKD</w> 20994
pedi grees</w> 20994
γ δ</w> 20993
OX A</w> 20992
dimin ishing</w> 20991
Resi dual</w> 20991
cu ve 20988
retic ulin</w> 20986
in set</w> 20985
sero group</w> 20982
hy stero 20981
Al ber 20981
m CRC</w> 20980
F Y 20980
u bin 20977
scal able</w> 20977
for amin 20976
Pat tern</w> 20976
D enti 20975
HET E</w> 20975
D OR</w> 20974
T TC 20973
Rhe b</w> 20972
ex onic</w> 20971
Prof .</w> 20971
is er</w> 20970
hetero topic</w> 20968
ing ual</w> 20966
H AI</w> 20964
read missions</w> 20964
PT T</w> 20963
cali br 20961
p its</w> 20960
indol ent</w> 20954
inc eption</w> 20952
lab s</w> 20952
U G</w> 20950
ket oglutarate</w> 20949
citr us</w> 20948
numb ering</w> 20947
cardi ometabolic</w> 20946
N am 20944
class room</w> 20941
l acc 20940
rec ir 20939
Supernat ants</w> 20938
S etting</w> 20937
ch e</w> 20936
S par 20934
immun opo 20934
promiscu ous</w> 20934
SAM HD1</w> 20930
Con jug 20929
Ox idation</w> 20927
uni directional</w> 20925
PL L</w> 20924
lab yrin 20922
CE P</w> 20922
ag liptin</w> 20921
cardi o</w> 20919
quin azol 20919
maneu ver</w> 20912
pyrro l 20911
s ary</w> 20909
energ etically</w> 20905
magne t</w> 20902
pu tida</w> 20899
S cores</w> 20894
Anti oxidant</w> 20894
N odal</w> 20893
An thro 20893
Con tent</w> 20893
Tf R</w> 20892
An n</w> 20890
sacc ade</w> 20889
hyp omorphic</w> 20886
0 Q</w> 20885
approxim ated</w> 20884
gon ad</w> 20883
E vents</w> 20882
f lip</w> 20880
her maphro 20880
p Y 20879
PR V</w> 20878
nanow ire</w> 20878
T ie 20877
t ung 20876
see ks</w> 20876
Arc tic</w> 20876
nanoshe ets</w> 20875
em e</w> 20873
end osperm</w> 20872
ope rons</w> 20871
um -</w> 20870
bis phosphonates</w> 20867
L up 20864
cap tive</w> 20863
L 1 20862
B AF</w> 20859
cyclo addition</w> 20857
M others</w> 20856
s acchar 20855
re par 20855
placent as</w> 20855
arcu ate</w> 20854
Hepat ocellular</w> 20853
lactam ases</w> 20852
quantit ate</w> 20851
run ners</w> 20850
Gh ana</w> 20850
P sor 20843
Hierarch ical</w> 20842
schwann oma</w> 20840
S ed 20839
I SH</w> 20838
M RP 20838
AP B</w> 20838
CC M</w> 20837
opath ogenesis</w> 20836
Fig. 5A</w> 20836
parav entricular</w> 20836
ste atohepatitis</w> 20835
L n</w> 20833
cas ts</w> 20833
Cor yne 20833
transl uminal</w> 20832
To ols</w> 20831
ter tile</w> 20829
Ubiqu itin</w> 20829
- untranslated</w> 20828
osa hexa 20828
T ang</w> 20826
anto in</w> 20825
strabis mus</w> 20825
p embrolizumab</w> 20823
S z 20822
O thers</w> 20821
anc illary</w> 20820
sequ ester</w> 20820
X yl 20817
HI P</w> 20816
thorac oscopic</w> 20816
co incidence</w> 20814
pre implantation</w> 20814
dysp epsia</w> 20814
extra ordinary</w> 20812
Cross Ref</w> 20810
me l</w> 20808
electromy ographic</w> 20807
osac ral</w> 20807
bi ose</w> 20806
ther in</w> 20804
deci sive</w> 20804
perfr ingens</w> 20804
at su</w> 20802
con genic</w> 20801
wavel et</w> 20800
M ales</w> 20799
Direc ted</w> 20799
alog y</w> 20799
inv alu 20798
O K</w> 20796
ur chin</w> 20795
ref s</w> 20795
J ew 20794
f using</w> 20794
Sym posium</w> 20792
gastr ulation</w> 20791
butyr yl</w> 20790
col ors</w> 20789
Con current</w> 20784
spermati ds</w> 20780
E 1 20779
phen anthro 20776
Ra dical</w> 20770
remn ants</w> 20769
mac ron 20768
anthrac is</w> 20768
O GTT</w> 20767
s occer</w> 20764
V ac 20764
ann ers</w> 20764
Sem i</w> 20761
invalu able</w> 20761
B O</w> 20760
coll agens</w> 20760
cin ti 20759
4 f</w> 20757
BT K</w> 20757
dis place</w> 20756
Car bo 20752
p tosis</w> 20748
E WS</w> 20748
Gr ad 20748
Pro ton</w> 20745
cat ch 20744
hel ium</w> 20744
Pul se</w> 20744
encephal y</w> 20741
g H</w> 20740
phot olysis</w> 20739
K im 20738
E di 20738
entero cytes</w> 20736
occlud in</w> 20735
satis fy</w> 20733
R IL</w> 20731
ph lo 20730
papill a</w> 20730
W ide</w> 20728
clav ul 20728
HM M</w> 20727
Boltz mann</w> 20727
en ec 20726
HM W</w> 20726
MA N</w> 20724
P aul</w> 20723
Nde I</w> 20723
compl icate</w> 20722
electrophores ed</w> 20722
Fab ry</w> 20720
must ard</w> 20720
chol edoch 20718
po ts</w> 20717
p N 20716
PD L</w> 20716
A tomic</w> 20713
st ag 20712
issu ed</w> 20711
As n 20709
scap ular</w> 20707
in clusive</w> 20703
regul on</w> 20703
sk elet 20703
ap enem</w> 20701
spec kle</w> 20701
cyt arabine</w> 20701
dys lex 20700
MT B</w> 20697
wake fulness</w> 20697
perfus ate</w> 20696
O . 20694
Burkhol deria</w> 20694
A ne 20693
clean ed</w> 20693
n ell</w> 20691
verte bra</w> 20690
Dat as 20690
ferre ts</w> 20690
o . 20686
Radi ological</w> 20686
L Z</w> 20684
ta z 20681
multi valent</w> 20680
1 q2</w> 20679
cr assa</w> 20679
fl ag</w> 20678
H ES</w> 20675
ach oline</w> 20675
dNT Ps</w> 20674
ovan adate</w> 20673
metallo thionein</w> 20672
well being</w> 20671
Omni bus</w> 20670
en ucleation</w> 20669
se aling</w> 20668
Olig onucle 20668
Tax ol</w> 20667
neuro filament</w> 20666
c ure 20664
Care ful</w> 20664
E t</w> 20663
PC K</w> 20663
lep tos 20661
asp i 20660
R it 20659
O f 20655
aden osyl 20655
M sh 20654
ON E</w> 20652
h ang</w> 20649
Un ilateral</w> 20647
H PC</w> 20645
hystere sis</w> 20645
MCA O</w> 20644
RX Rα</w> 20643
M PI</w> 20642
FV B</w> 20642
nul li 20642
rect angular</w> 20641
Res olution</w> 20640
dist antly</w> 20639
At t 20638
C ock 20637
cit ations</w> 20637
S ST</w> 20636
t c 20633
benefici aries</w> 20633
acycl ic</w> 20632
androste ne 20631
ad h 20630
be verage</w> 20630
cobal amin</w> 20630
extr actions</w> 20627
d ATP</w> 20626
D PI</w> 20624
I HD</w> 20623
hi PSC</w> 20623
lif ting</w> 20623
denti ne</w> 20623
un protected</w> 20622
fro gs</w> 20622
neurolep tic</w> 20621
ass ault</w> 20620
contin gent</w> 20620
normal ity</w> 20620
occur rences</w> 20616
put atively</w> 20616
F N 20615
under line</w> 20615
3 f</w> 20614
CH N</w> 20614
sc or 20612
Perc ep 20612
physi co</w> 20609
n inety</w> 20607
sin ogen</w> 20607
Nucle i</w> 20607
Micro RNA</w> 20605
Angel es</w> 20604
hyperten sives</w> 20601
extrac ranial</w> 20600
Syn erg 20598
flav or</w> 20598
N et 20596
resear cher</w> 20596
Cl C</w> 20594
SV Z</w> 20593
odont ogenic</w> 20592
T SHR</w> 20591
am end 20591
diffusi vity</w> 20590
Esophag eal</w> 20587
anhydro us</w> 20587
NF 2</w> 20585
ic ola</w> 20581
en ox 20580
Vacc ine</w> 20578
or ins</w> 20577
Lex A</w> 20577
W H 20575
CXCR 2</w> 20575
priv acy</w> 20572
ro unded</w> 20569
unc oupled</w> 20569
ald olase</w> 20564
baro reflex</w> 20564
LTB 4</w> 20563
co oked</w> 20562
secti oning</w> 20561
abro gation</w> 20561
F icol 20560
argin ase</w> 20560
plas tin</w> 20558
. 5A</w> 20557
8 Q</w> 20556
Ri o</w> 20556
compe ted</w> 20555
k no 20552
tent atively</w> 20552
Y ellow</w> 20551
dam ole</w> 20549
Sens ory</w> 20549
A ge 20548
mi Rs</w> 20548
dre w</w> 20548
super vis 20546
Nan o</w> 20546
hist olytica</w> 20545
perfor in</w> 20545
pre paredness</w> 20544
er as</w> 20543
ubiqu inone</w> 20541
vag inalis</w> 20541
Lin k</w> 20541
bur ned</w> 20539
CYP 1A2</w> 20539
per chlor 20537
Epilep sy</w> 20536
- AG 20535
bacteri ological</w> 20535
macrop in 20534
I APP</w> 20533
Con version</w> 20530
shor ten</w> 20530
ach loro 20529
ation -- 20529
ameli oration</w> 20529
d -</w> 20527
sup plying</w> 20527
re y</w> 20526
PS MA</w> 20526
induc tively</w> 20524
intrad ermal</w> 20524
B VD 20522
ed ent 20522
bac lofen</w> 20522
Ab sorption</w> 20520
F enton</w> 20519
Pre ventive</w> 20519
O reg 20517
ex pedi 20517
foc us 20517
s d</w> 20516
GI T</w> 20514
lux ation</w> 20514
extrapol ation</w> 20512
N eck</w> 20511
ip p 20511
predomin ated</w> 20510
M HC 20509
if erin</w> 20509
B ac</w> 20508
Con sec 20508
AN ES</w> 20506
Mi R</w> 20504
ep i</w> 20502
eth ing</w> 20502
V 0</w> 20500
P SE 20499
ef f</w> 20499
athle tic</w> 20498
op olysaccharide</w> 20497
Hybri di 20496
non structural</w> 20494
Lab eling</w> 20494
antro ne</w> 20494
I f 20492
abil is</w> 20489
fis sure</w> 20488
P od 20487
Syn ap 20487
nig ro 20487
Wat son</w> 20487
t d 20485
break fast</w> 20483
Indi ans</w> 20483
k an</w> 20482
gen ome 20482
sy ll 20479
ultra thin</w> 20476
c aly 20474
assemb les</w> 20472
Hor mone</w> 20472
far ming</w> 20470
isoc itrate</w> 20469
am ik 20467
digiti zed</w> 20467
territ ories</w> 20466
leiomy oma</w> 20464
carb amate</w> 20462
adren ocortic 20462
reli eving</w> 20461
nitro glycerin</w> 20461
icul ate</w> 20460
neur algia</w> 20456
nilo tinib</w> 20454
Ha pl 20452
Q ALY</w> 20451
r b 20449
domin antly</w> 20449
AE Ds</w> 20449
Restric tion</w> 20449
MS S</w> 20448
of ur 20447
y x 20446
col pos 20446
Phy to 20444
SK OV3</w> 20444
S Z</w> 20442
SI K</w> 20440
di as</w> 20438
VO2 max</w> 20438
partner ships</w> 20437
al -</w> 20436
put rescine</w> 20436
Wnt 3a</w> 20436
conve x</w> 20436
multi component</w> 20435
neuro surgery</w> 20434
insul ator</w> 20434
V AM 20433
intra operatively</w> 20433
lithotrip sy</w> 20433
EC P</w> 20432
J a 20431
un healthy</w> 20430
no ro 20430
tr icity</w> 20428
immun od 20427
allerg ies</w> 20427
pro collagen</w> 20425
de ionized</w> 20425
langu ages</w> 20425
Py k2</w> 20425
et an 20423
cr ash</w> 20422
down s</w> 20422
ep sy</w> 20414
I de 20413
lymph edema</w> 20413
thir teen</w> 20411
P ar</w> 20409
forc ement</w> 20406
di pyri 20404
aneu ploid</w> 20404
or tical</w> 20403
stre am 20400
uc ulline</w> 20398
forc eps</w> 20394
L p 20393
wea knesses</w> 20393
multi protein</w> 20392
amin e 20391
Fig. 4B</w> 20390
L angu 20389
resem bl 20389
spond yl 20389
investig ational</w> 20388
vasodi latation</w> 20387
angi oma</w> 20381
otroph s</w> 20381
cl ip</w> 20378
AL DH 20377
UGT 1A1</w> 20377
D h 20376
Tom ography</w> 20376
I EC</w> 20374
K or 20368
Bloc k</w> 20368
crani otomy</w> 20368
mel phalan</w> 20367
necess itating</w> 20367
compartment alization</w> 20366
Na3 VO4</w> 20366
Relev ant</w> 20365
Recei ver</w> 20362
fluoro quinolones</w> 20361
prag matic</w> 20360
cap tures</w> 20359
periodic ity</w> 20358
hyponat remia</w> 20357
O Cs</w> 20356
requ ests</w> 20355
amid o 20355
pleas ant</w> 20352
K H2PO4</w> 20351
vascul arity</w> 20351
Flu id</w> 20351
Ficol l</w> 20349
GST P1</w> 20347
im e 20346
intrac ytoplasmic</w> 20345
ec onds</w> 20344
Rad 2</w> 20342
sp reads</w> 20341
od in</w> 20341
in arily</w> 20339
uve al</w> 20339
compl ements</w> 20338
Par t 20338
Ch r 20334
neuro logy</w> 20334
Chrom osomal</w> 20332
deuter ated</w> 20330
hyd atid</w> 20327
C ip1</w> 20325
ens or</w> 20324
az ines</w> 20323
form ulate</w> 20321
Ex trem 20321
Fi xed</w> 20320
epti form</w> 20320
semin iferous</w> 20320
Environ ment</w> 20316
m st 20315
pa tern 20314
PH Y 20314
F ABP</w> 20310
E MR</w> 20310
x i</w> 20309
me sis</w> 20308
Immun ological</w> 20308
M RP1</w> 20306
K ur 20306
ot t</w> 20306
pol yn 20306
Schizophren ia</w> 20306
Mil k</w> 20305
K R 20304
mis carriage</w> 20303
BR G1</w> 20302
t ar</w> 20297
ol ef 20297
TL 1</w> 20297
an nel</w> 20295
S MS</w> 20293
E HR</w> 20293
athle te</w> 20293
ke V</w> 20291
isother m</w> 20291
k bp</w> 20290
pre optic</w> 20290
CCN D1</w> 20290
en na</w> 20289
mag lob 20285
incar cer 20284
Complem entary</w> 20283
wit teri 20282
sum mation</w> 20280
Depend ent</w> 20279
aper itoneal</w> 20278
matern ally</w> 20275
tw ard</w> 20271
seques tering</w> 20270
nip ple</w> 20268
Sal t</w> 20265
olig os</w> 20262
yl us</w> 20261
CI C</w> 20261
arthro pathy</w> 20261
UL 4</w> 20260
fabric ate</w> 20257
B P3</w> 20253
gall ate</w> 20252
apen ta 20250
J ord 20249
enol pyruvate</w> 20249
h GH</w> 20248
re pairing</w> 20247
AN C</w> 20247
hem iz 20246
ophthalm itis</w> 20245
accur acies</w> 20242
B e</w> 20240
chel ate</w> 20239
S S2</w> 20237
He ad 20237
chlor promazine</w> 20237
Bever ly</w> 20235
A OM</w> 20233
M in</w> 20232
Aff airs</w> 20231
U E</w> 20229
inter specific</w> 20227
threa tened</w> 20226
K a</w> 20223
te bral</w> 20223
g lobe</w> 20222
Bi opsy</w> 20215
cis tern 20214
F ish</w> 20213
Fe 3O4</w> 20212
glycosamin oglycan</w> 20212
ve g 20206
hypogon adism</w> 20206
C ER 20204
duplic ates</w> 20203
am ended</w> 20202
ik is</w> 20201
ES Y</w> 20201
biotin ylation</w> 20201
cra ving</w> 20201
bout ons</w> 20200
ter ro 20199
micro L</w> 20199
hyp o</w> 20199
ob tur 20198
Jak 2</w> 20198
un acceptable</w> 20197
Ex ome</w> 20197
Asp 2</w> 20197
exud ate</w> 20196
medic ated</w> 20195
CG C</w> 20194
P ent 20192
K IR 20192
CT NN 20188
II IA</w> 20186
C CL1</w> 20185
Colom bia</w> 20183
ste ep</w> 20181
Nit rogen</w> 20180
hi o</w> 20178
u it 20177
or bidity</w> 20177
sho ul 20176
aggra vated</w> 20174
blas tomas</w> 20169
carrageen an</w> 20169
AP E</w> 20168
hydra zone</w> 20167
multi ply</w> 20165
hypo plastic</w> 20164
I t 20163
pa use</w> 20163
Pro fil 20163
M OR 20161
chromat osis</w> 20161
K as 20158
Sup er</w> 20158
Sox 9</w> 20158
gr atings</w> 20154
War burg</w> 20151
pter y 20151
advoc ate</w> 20150
em ias</w> 20149
diff u 20149
thick ened</w> 20149
ou l</w> 20148
actu arial</w> 20148
an ate</w> 20147
H tr 20141
BC P</w> 20141
un answered</w> 20140
conf identi 20136
zircon ia</w> 20133
D AV 20130
und oub 20129
cri me</w> 20127
Figure 2A</w> 20126
aspec ific</w> 20126
MD SC</w> 20125
TR UN 20121
expl ant</w> 20120
P PA</w> 20119
recur ring</w> 20119
osup pression</w> 20119
As sis 20117
B x 20114
G lo 20114
complem enting</w> 20113
Electro physiological</w> 20113
re pository</w> 20110
panc rea 20110
PE EP</w> 20108
G Hz</w> 20107
org hum</w> 20107
conf ident</w> 20107
arteri ography</w> 20107
ende av 20107
conto urs</w> 20103
D ING</w> 20102
scop olamine</w> 20101
X en 20098
erg osterol</w> 20098
four teen</w> 20098
T a</w> 20096
g A</w> 20096
phot ography</w> 20096
keto conazole</w> 20095
ur n</w> 20094
carcin oembryonic</w> 20094
metast able</w> 20093
CC L</w> 20092
R FC</w> 20086
le ptom 20086
CCA AT</w> 20086
my opic</w> 20085
nan ob 20083
new s</w> 20083
x is</w> 20082
HC N</w> 20079
Pregn ant</w> 20079
U preg 20075
arc tation</w> 20071
Jew ish</w> 20071
pol yl 20070
silox ane</w> 20070
Tran s</w> 20069
deriv atized</w> 20069
F 1A</w> 20068
SP I</w> 20067
chimpanze es</w> 20066
nitro sylation</w> 20065
heterom eric</w> 20063
no stosis</w> 20062
Hu h</w> 20062
CR Cs</w> 20061
wh is 20059
PT FE</w> 20059
metazo an</w> 20056
Her pes</w> 20054
sn ails</w> 20053
os patial</w> 20052
Gli 1</w> 20052
S HBG</w> 20051
L AB 20051
disproportion ately</w> 20048
clin damycin</w> 20046
ec ules</w> 20045
pyel onephritis</w> 20043
En do</w> 20040
seph arose</w> 20039
2 n</w> 20036
E 0</w> 20036
w ished</w> 20036
un ambiguous</w> 20036
engine er</w> 20036
HL 6</w> 20035
U RE</w> 20034
inf ects</w> 20031
IFN β</w> 20031
disulph ide</w> 20030
oste opontin</w> 20029
is otypes</w> 20026
recan alization</w> 20026
L Ns</w> 20023
MR N</w> 20023
ub s</w> 20022
im mobility</w> 20021
min ocycline</w> 20019
4 A 20018
in breeding</w> 20016
finger prints</w> 20014
dimer ize</w> 20012
squ ir 20011
TRUN CAT 20009
ocul omotor</w> 20008
phosph opeptides</w> 20007
dimorph ism</w> 20004
amik acin</w> 20004
mat rip 20003
hepat ocarcin 20002
ylo xy 19999
RE CI 19997
bi ans</w> 19996
Na H2PO4</w> 19996
methoxy phenyl</w> 19996
aspi rates</w> 19994
rac es</w> 19993
bilir ubin 19993
Com posite</w> 19992
ta ut 19991
Prog nosis</w> 19991
n ings</w> 19990
C ut 19988
U su 19987
af atinib</w> 19987
ur ized</w> 19986
Cor neal</w> 19986
AQ P4</w> 19986
ep ist 19985
pep tone</w> 19985
Cate g 19985
bi ogenic</w> 19984
hypo thyroid</w> 19982
F TC</w> 19981
prolifer ated</w> 19978
oste onecrosis</w> 19977
Tr p5</w> 19977
pancre atico 19977
cover slip</w> 19977
M ERS</w> 19974
phen oxy 19967
cardi olipin</w> 19963
ut z 19958
TT G</w> 19957
clinical trials.gov</w> 19957
if os 19956
gangli onic</w> 19954
inter -</w> 19953
- GT 19949
buty ric</w> 19948
hetero dimerization</w> 19947
DD D</w> 19946
x 6</w> 19944
pos tex 19944
H ow 19941
col later 19940
genit ourinary</w> 19940
phosph or</w> 19939
lea ks</w> 19935
til in</w> 19934
discord ance</w> 19934
F K</w> 19933
carbox amide</w> 19932
acetyl glucosamine</w> 19931
alog ue</w> 19931
phen oc 19930
pro ves</w> 19927
sk e 19922
ophyl l 19922
duc ks</w> 19921
on y</w> 19917
br ushing</w> 19917
ex ins</w> 19916
divertic ul 19915
S S1</w> 19914
omer ular</w> 19913
tetr ap 19913
o prost 19912
SS s</w> 19911
F OS</w> 19910
MC MV</w> 19910
phenomen ological</w> 19909
men tion</w> 19905
decon volution</w> 19905
sac k 19904
Gra ft</w> 19902
Ber n 19902
Q X</w> 19900
was tes</w> 19899
coinc ide</w> 19899
predic tability</w> 19898
per pet 19897
Cur cumin</w> 19894
predisp osed</w> 19890
coll iculus</w> 19889
prolong ing</w> 19883
phosphor ic</w> 19882
pro drugs</w> 19881
Cle avage</w> 19881
if er 19880
unde restimate</w> 19875
mitochondri on</w> 19875
ed itor</w> 19873
TE s</w> 19870
CE AC 19870
G HRH</w> 19869
Vari ant</w> 19867
N AL</w> 19863
p it</w> 19861
T NT</w> 19860
A A1</w> 19858
Minim um</w> 19857
aut ograft</w> 19856
Leu 1</w> 19855
knock outs</w> 19854
Com mer 19853
CB Z</w> 19851
Z ika</w> 19850
direc tors</w> 19850
meto prolol</w> 19850
i re 19849
k h 19849
plas ti 19845
G RP</w> 19844
Ste ady</w> 19842
chimer ism</w> 19842
relax ant</w> 19841
al umina</w> 19840
fil ls</w> 19840
mal onyl</w> 19838
mass age</w> 19838
HE L</w> 19837
aut oreactive</w> 19836
Arth ritis</w> 19836
TC ID5</w> 19835
f .</w> 19833
carb amo 19833
dem ented</w> 19833
ex p</w> 19832
Trans forming</w> 19832
lin ol 19831
wi reless</w> 19831
n els</w> 19829
S ham</w> 19827
meth rin</w> 19826
t DCS</w> 19825
Glut amate</w> 19823
b py</w> 19821
SN O</w> 19821
omal acia</w> 19821
dissoci ates</w> 19819
provo ke</w> 19815
Schiz osaccharomyces</w> 19815
E IF 19814
len alidomide</w> 19814
t ut 19810
tim es 19810
osph ing 19809
Min i 19808
d ura</w> 19806
phosph oglycer 19806
trop ic 19804
op r 19802
GA AC 19801
dehis cence</w> 19801
Hem odynamic</w> 19799
dis organized</w> 19794
NE M</w> 19794
I PS</w> 19793
ap re 19793
ge sts</w> 19793
un e</w> 19790
por k</w> 19788
S lug</w> 19787
CN P</w> 19787
pigment osa</w> 19787
B AP1</w> 19786
quin ine</w> 19785
CI A</w> 19781
sh ops</w> 19780
CA AC 19780
mal treatment</w> 19778
conver sions</w> 19778
moun tain</w> 19778
PA O1</w> 19776
pent am 19776
Consider able</w> 19775
ogen omics</w> 19774
molyb denum</w> 19774
A RA 19773
calc it 19771
Tre ating</w> 19768
Radi ographic</w> 19768
epi androsterone</w> 19768
iso propanol</w> 19767
destin ation</w> 19767
pon tine</w> 19766
inter actors</w> 19765
infarc ted</w> 19765
disp osable</w> 19765
Pa O2</w> 19765
L ES</w> 19763
hsp 7</w> 19763
Wh ich</w> 19762
titr ations</w> 19762
nor th 19761
cancell ous</w> 19761
necess itates</w> 19759
di p 19758
SI P</w> 19756
glut athion 19756
Trans formation</w> 19755
glucon ate</w> 19755
discrimin atory</w> 19754
I AC 19752
B arr 19752
SC 3</w> 19752
profil ed</w> 19751
y -</w> 19750
s ong</w> 19749
disco ur 19747
Fac ial</w> 19745
in sular</w> 19743
calc d</w> 19742
ole oyl</w> 19742
bul lying</w> 19741
non etheless</w> 19740
oc entesis</w> 19739
A ph 19738
Fe atures</w> 19737
zol amide</w> 19737
oxif ene</w> 19736
Oreg on</w> 19736
FT O</w> 19733
un ing</w> 19732
sar copenia</w> 19732
no to 19731
bin der</w> 19731
Standardi zed</w> 19730
overestim ated</w> 19729
reg ressed</w> 19728
lo dipine</w> 19726
dom as</w> 19724
G an 19721
TRUNCAT ED</w> 19721
juven iles</w> 19719
Perio dic 19719
def eren 19718
phosphor ib 19718
ME F2</w> 19718
Figure 6</w> 19718
Sec 1</w> 19718
Sub stance</w> 19717
isother ms</w> 19716
prefer ably</w> 19713
Impro vements</w> 19711
desat uration</w> 19708
rh inos 19707
sarcom ere</w> 19706
bul ls</w> 19705
b end</w> 19704
PE O</w> 19701
ar isen</w> 19697
ow nership</w> 19697
aut ogenous</w> 19696
AV M</w> 19695
distor tions</w> 19695
lli ant</w> 19695
MR L</w> 19694
Tox ic 19693
st 1</w> 19690
g anciclovir</w> 19687
An th 19687
Tri ple</w> 19685
nc RNAs</w> 19683
RB V</w> 19681
Y ield</w> 19680
ig ence</w> 19680
pro ving</w> 19678
E motional</w> 19674
Al tman</w> 19673
fo il</w> 19673
F IV</w> 19672
G pp</w> 19670
CO LL 19670
kn owing</w> 19669
RO N</w> 19669
th ur 19668
Awa reness</w> 19668
hydroxy steroid</w> 19667
disper sions</w> 19666
st -</w> 19665
hy men 19665
Glu A1</w> 19665
transep ithelial</w> 19660
propos als</w> 19659
BC VA</w> 19656
int us 19656
Super Script</w> 19654
ym ents</w> 19653
Wh o</w> 19653
myomet rial</w> 19651
ortholog ue</w> 19647
lycop ene</w> 19647
at tract</w> 19644
Issu e</w> 19644
inten sely</w> 19641
b b</w> 19640
dec ont 19639
CD 4 19639
i. c. 19639
preclud ed</w> 19639
asphy xia</w> 19637
ster ically</w> 19635
atyp ia</w> 19635
tele medicine</w> 19634
vim etric</w> 19633
G RE 19632
if i 19631
intrac ardiac</w> 19631
clinic o</w> 19631
Ch lo 19630
com positional</w> 19629
invol untary</w> 19629
l n</w> 19628
constitu tional</w> 19627
pur inergic</w> 19626
immuno therapies</w> 19626
TE T</w> 19624
cr ack</w> 19623
IS Gs</w> 19623
di ene</w> 19619
complex ities</w> 19619
idov udine</w> 19619
o be 19618
Ve si 19617
Mechanis tic</w> 19616
Epp endorf</w> 19616
0 . 19614
in ogen</w> 19614
ap entin</w> 19614
cere al</w> 19614
M S1</w> 19613
fas test</w> 19613
r ust</w> 19609
et ta</w> 19609
pharmac ies</w> 19609
was aki</w> 19608
Vir g 19607
un intended</w> 19605
alk yne</w> 19604
bioma terial</w> 19601
tele vision</w> 19599
S ap 19598
cleav able</w> 19598
tur key</w> 19597
Diff use</w> 19597
C ame 19596
RI PK1</w> 19596
specific ations</w> 19595
in accessible</w> 19594
phen one</w> 19594
PD R</w> 19594
Tel e 19594
Biomar kers</w> 19593
or ative</w> 19592
synovi tis</w> 19591
- treated</w> 19589
aminoglyco sides</w> 19589
Consec utive</w> 19588
fl u</w> 19587
R up 19585
lumin ance</w> 19584
deco ding</w> 19584
De g 19583
inev itable</w> 19583
is ted</w> 19582
hydr atase</w> 19582
L ess 19580
ot o 19580
ar a 19575
vi als</w> 19574
accum ulations</w> 19574
b oc 19573
Brit ain</w> 19572
R SA</w> 19570
C Z 19569
g un 19568
zym osan</w> 19566
o a</w> 19565
hour ly</w> 19565
H ym 19564
Georg e</w> 19564
AA AC 19562
hypercap nia</w> 19561
scrap ed</w> 19559
par si 19557
impregn ated</w> 19556
pea king</w> 19554
auth ority</w> 19552
Polymorph isms</w> 19549
g onal</w> 19546
Sub group</w> 19545
ex cur 19543
W . 19540
ocl us 19540
R ice</w> 19539
ip ients</w> 19539
CA SP 19539
I BM 19535
tri plicates</w> 19535
poly ethyl 19533
a plastic</w> 19532
sus ception</w> 19532
NS F</w> 19532
B max</w> 19531
Oc ean</w> 19529
c 8</w> 19528
N HER 19524
An esthesia</w> 19524
te ll</w> 19522
ta uro 19522
gra vit 19520
RT T</w> 19520
ari asis</w> 19519
t ments</w> 19515
spl anch 19514
CO M</w> 19513
destro y</w> 19511
dis appear</w> 19510
Me Hg</w> 19510
Intrac ranial</w> 19510
Rob ert</w> 19507
bi tes</w> 19498
p ill 19497
spati o</w> 19495
tab ac 19495
rumin ants</w> 19494
str in 19493
cynom olgus</w> 19493
A CL 19490
osahexa enoic</w> 19489
E ating</w> 19485
men arche</w> 19485
P2 X</w> 19484
SG K1</w> 19484
Si O</w> 19482
o sic</w> 19481
can opy</w> 19481
epsil on 19478
PP 2 19477
po oling</w> 19476
Re productive</w> 19475
Ca the 19474
cem ented</w> 19472
tubercul in</w> 19471
docum enting</w> 19469
Atg 7</w> 19469
compart mental</w> 19467
Quanti tation</w> 19466
GlcNAc ylation</w> 19466
CI MP</w> 19465
st ane</w> 19464
agg ed</w> 19463
author ized</w> 19458
hyp ero 19457
N SE</w> 19456
employe e</w> 19455
ac ellular</w> 19453
wal d</w> 19453
L uria</w> 19452
man nos 19451
gr amin 19449
segreg ating</w> 19449
3 V</w> 19446
olig omer 19446
for khead</w> 19445
pa 1</w> 19445
impe ded</w> 19445
multin ucleated</w> 19443
di benzo</w> 19439
Ab solute</w> 19439
audi o</w> 19439
cannabin oids</w> 19439
p ill</w> 19438
erox ide</w> 19437
ile us</w> 19437
tym panic</w> 19437
K b</w> 19434
Con ver 19431
ventro lateral</w> 19431
fal fa</w> 19430
Vari ability</w> 19430
amphi bian</w> 19429
- β</w> 19427
miti gated</w> 19426
rhiz osphere</w> 19426
prot amine</w> 19426
Coryne bacterium</w> 19426
stereo chemistry</w> 19425
p F</w> 19423
per pe 19419
3 . 19418
sel s</w> 19418
ok a</w> 19418
Color ado</w> 19417
Sub stantial</w> 19416
L 0</w> 19414
CA PD</w> 19414
bull ous</w> 19414
D MA</w> 19411
dr ine</w> 19411
os e 19410
- phosphate</w> 19407
G ir 19403
N el 19402
androstene dione</w> 19402
mim etics</w> 19401
cryst allin 19398
form is</w> 19397
RN A1</w> 19397
teg ravir</w> 19391
elev ating</w> 19391
Th in</w> 19390
Ophthal m 19387
chr ys 19385
micronucle i</w> 19384
CR IT 19382
b ins</w> 19381
Fac il 19380
staff ing</w> 19379
Prost agland 19379
raz ine</w> 19378
E lim 19376
N V 19375
χ 2</w> 19375
Pneum ocystis</w> 19375
mut ator</w> 19374
som ething</w> 19374
thi ol 19374
ex qu 19372
ch yl 19372
ane diol</w> 19371
dro w 19367
se mis 19366
bi allelic</w> 19366
EC A</w> 19361
exer ting</w> 19361
od ystrophy</w> 19360
mal adaptive</w> 19358
advant aged</w> 19357
occa sion</w> 19357
bo ro 19355
ET O</w> 19353
a ura</w> 19352
IC M</w> 19348
PRMT 5</w> 19346
pseudo aneurysm</w> 19345
ex haled</w> 19344
plan in</w> 19344
is 1</w> 19343
Hor mon 19343
basoph ils</w> 19343
bl a</w> 19342
J NK 19341
adju stable</w> 19340
pesti s</w> 19340
Po oled</w> 19339
Langu age</w> 19339
aug menting</w> 19338
Bas el</w> 19338
p ant 19337
hair s</w> 19337
clamp ed</w> 19335
resembl ance</w> 19334
P BD</w> 19330
Sch war 19330
Hop kins</w> 19330
un favourable</w> 19329
ann ul 19329
Endo th 19328
cleav ages</w> 19328
Techni que</w> 19328
NR TIs</w> 19325
rem if 19325
el min 19324
Exc essive</w> 19323
precoci ous</w> 19323
in nam 19322
y ouths</w> 19320
Isl ands</w> 19319
teno fovir</w> 19317
S ant 19316
MS H6</w> 19310
controver sies</w> 19310
prote obacteria</w> 19308
T as 19307
ultraf ast</w> 19307
diag rams</w> 19304
preval ences</w> 19304
sulf ation</w> 19300
ag g</w> 19299
met ach 19297
Ob structive</w> 19297
orth ovanadate</w> 19294
transmis sible</w> 19294
U.S. A.</w> 19293
O CR</w> 19292
f ellow</w> 19291
cellul ase</w> 19291
ambigu ity</w> 19290
o ons</w> 19289
Ac tino 19288
substanti ate</w> 19287
MI T</w> 19285
T1 DM</w> 19285
pheny ls</w> 19284
P y</w> 19283
reti rement</w> 19282
p im 19281
GI BCO</w> 19280
BAP TA</w> 19280
op ic 19279
my ogenesis</w> 19279
intrat rac 19279
Anthro p 19278
gr o</w> 19270
dex medetomidine</w> 19270
epil eptiform</w> 19270
contr acting</w> 19269
Z m 19268
ra c</w> 19268
fol ia</w> 19267
she ds</w> 19265
te ach</w> 19264
for age</w> 19264
consis tencies</w> 19261
n estin</w> 19259
syn decan</w> 19255
equ ity</w> 19255
- α</w> 19254
Ex on</w> 19254
pr ices</w> 19253
au x</w> 19253
nucleoti dyl</w> 19252
prospec t</w> 19251
H AM</w> 19250
GC AC 19246
Fox P3</w> 19246
f ats</w> 19245
F ast 19244
cha otic</w> 19244
bo ards</w> 19243
Ex isting</w> 19243
G M 19241
concentr ating</w> 19240
- CC 19239
Agre ement</w> 19238
de stabilized</w> 19236
periph erally</w> 19235
az in</w> 19233
pyro lysis</w> 19232
RECI ST</w> 19232
sulfameth oxazole</w> 19229
def ec 19227
comput ations</w> 19226
chemopre vention</w> 19225
RO S1</w> 19224
k ing 19219
ri vers</w> 19217
allevi ating</w> 19216
broil ers</w> 19215
tetrach lor 19214
F MF</w> 19213
ST O 19213
Ser ine</w> 19212
andro genic</w> 19211
opac ity</w> 19209
cavit ation</w> 19208
PDGF RA</w> 19207
ph r 19206
nal trexone</w> 19206
desatur ase</w> 19206
7B l</w> 19206
J P</w> 19204
DQ B1</w> 19203
T est 19202
inn oc 19198
herbic ides</w> 19198
b or</w> 19197
Sero ton 19197
Athero sclerosis</w> 19195
CRIT ERIA</w> 19194
un reported</w> 19193
3 B1</w> 19192
fin ished</w> 19192
Nep al</w> 19192
enal april</w> 19191
ti e</w> 19190
wit nessed</w> 19189
Frac ture</w> 19189
d CTP</w> 19185
para plegia</w> 19185
sup ra</w> 19181
ung er</w> 19180
E W 19178
acidi fied</w> 19177
l ice</w> 19171
spec ts</w> 19171
Neu tr 19171
pin k</w> 19171
E MENT</w> 19170
recogn izable</w> 19164
sero positivity</w> 19164
. 5B</w> 19162
Per in 19161
T dT</w> 19156
dimensi onality</w> 19154
sla ughter</w> 19151
mi 1</w> 19149
fluoro deoxyglucose</w> 19148
Air way</w> 19148
Y S</w> 19145
amin ases</w> 19143
z witteri 19142
D ME</w> 19142
amb er</w> 19142
tro ubl 19140
Os aka</w> 19139
neighb ors</w> 19138
gli oblastomas</w> 19131
tox igenic</w> 19130
Gly co 19128
Wnt 5a</w> 19126
con tu 19124
ON O 19123
TB ARS</w> 19122
non fat</w> 19121
AF B1</w> 19120
thiogalac topyranoside</w> 19120
8 N</w> 19117
Portu gal</w> 19113
peg ylated</w> 19113
big ger</w> 19109
odi pine</w> 19108
dis integration</w> 19106
vul var</w> 19106
Su stained</w> 19106
inferi ority</w> 19104
m outh 19103
phen idate</w> 19102
TC EP</w> 19096
Met formin</w> 19093
CTL A4</w> 19090
RN FL</w> 19089
C lim 19086
compu ters</w> 19085
coagul opathy</w> 19085
Def iciency</w> 19085
provoc ation</w> 19084
E las 19083
Mac aca</w> 19083
Seas onal</w> 19083
Figure 3 19082
a PKC</w> 19078
fac ult 19077
entit led</w> 19077
R CA</w> 19074
non responders</w> 19074
0 ng</w> 19073
post transfection</w> 19071
pa dias</w> 19067
Ch ro 19066
T ar 19065
Ca p</w> 19065
Que bec</w> 19065
gli benclamide</w> 19064
E -</w> 19063
py razole</w> 19062
B tk</w> 19061
peri ventricular</w> 19059
patell a</w> 19058
L AR</w> 19055
poly propylene</w> 19055
II b 19055
ME D1</w> 19054
top o</w> 19054
E osin 19053
GC GG 19053
endometri oid</w> 19053
GLI 1</w> 19052
metallo protease</w> 19050
Fi x 19049
antim y 19049
tele ost</w> 19049
conser ving</w> 19045
Physi ology</w> 19043
Psych iat 19043
wh i 19042
0 V</w> 19039
T ES</w> 19038
P ha 19036
myomet rium</w> 19035
or ities</w> 19033
neur itis</w> 19032
isom eric</w> 19031
rec ted</w> 19029
Dam age</w> 19029
Adjus ted</w> 19029
T DF</w> 19026
pur ifying</w> 19025
ane oplastic</w> 19021
vacc inations</w> 19020
inqu iry</w> 19020
MS D</w> 19019
G PC</w> 19018
re activated</w> 19018
ag enda</w> 19018
st ol</w> 19014
Perc ent 19013
whee zing</w> 19012
F ever</w> 19010
und ec 19010
n ym 19009
avi renz</w> 19009
cal ving</w> 19007
mil est 19003
Δ 4</w> 19002
incenti ve</w> 19002
quoti ent</w> 19002
Promo ter</w> 18998
b outs</w> 18996
degrad ative</w> 18996
oc atalytic</w> 18995
P MS</w> 18994
SU R 18994
ra te 18993
cyan o</w> 18993
gyr ase</w> 18992
GAD 6</w> 18991
teg mental</w> 18990
Scre en</w> 18988
Pc G</w> 18988
KCN Q1</w> 18987
undoub tedly</w> 18987
AP 5</w> 18985
CT F</w> 18984
biom imetic</w> 18984
K ra 18983
W it 18983
rs 5</w> 18983
B TB</w> 18982
PA R2</w> 18981
Ch ap 18981
appear ances</w> 18981
depend encies</w> 18979
chec king</w> 18979
hexam ers</w> 18979
h g1</w> 18978
succe eded</w> 18978
STAT 6</w> 18975
TRE M2</w> 18975
ac ini</w> 18973
- 2-</w> 18970
no on</w> 18970
9 c</w> 18969
ju dic 18969
acrome galy</w> 18969
Aud itory</w> 18969
bronch opulmonary</w> 18967
meth acholine</w> 18965
chondro genic</w> 18964
Ade qu 18962
ste aro 18961
condi tionally</w> 18961
atr an</w> 18961
cor ner 18960
pseud otyped</w> 18959
HS L</w> 18957
im ino</w> 18956
E uk 18955
toxic osis</w> 18955
T NP</w> 18954
de pot</w> 18954
RO R 18954
v 4</w> 18951
pro tracted</w> 18950
HC Cs</w> 18949
GLU T</w> 18949
beam line</w> 18948
te t 18947
arteri olar</w> 18946
NS s</w> 18945
RR M</w> 18943
G p 18941
PA L 18941
intercal ated</w> 18941
in let</w> 18940
un tagged</w> 18940
meval onate</w> 18939
par vum</w> 18937
SP M</w> 18936
Cat al 18936
ingi ensis</w> 18935
mesoth elial</w> 18934
Viv o</w> 18934
RA R 18933
lin 1</w> 18933
L ast</w> 18932
od d</w> 18932
tur bidity</w> 18931
ring er</w> 18931
D d 18930
E 4 18930
dis close</w> 18929
di gests</w> 18928
accep ting</w> 18928
do ors</w> 18925
pas ture</w> 18925
5 mM</w> 18924
A XL</w> 18922
ovari ectomy</w> 18920
forelim b</w> 18920
sal but 18916
P DS</w> 18915
ram p</w> 18914
yn ess</w> 18912
B land</w> 18910
R SK</w> 18909
sp aw 18908
propor tionally</w> 18908
ass ure</w> 18905
dict ated</w> 18902
h A</w> 18901
re turns</w> 18901
C ACT 18895
gro in</w> 18895
Figure 3A</w> 18895
pursu e</w> 18895
l an</w> 18894
ten sions</w> 18894
B ar</w> 18893
gra ined</w> 18893
Ba P</w> 18893
an ib</w> 18892
xim al</w> 18892
mis localization</w> 18890
g all</w> 18887
sum med</w> 18887
BL 6</w> 18887
endoscop e</w> 18886
M ess 18881
ac s</w> 18881
Lei den</w> 18881
haem olytic</w> 18880
CYP 3A</w> 18880
denit rification</w> 18878
bic uculline</w> 18876
Ane urys 18874
aden ylated</w> 18873
H PS</w> 18871
CA H</w> 18869
salbut amol</w> 18869
micro albuminuria</w> 18868
A 3A</w> 18867
hydroxy methyl</w> 18867
D sb 18866
lec ithin</w> 18865
ten ding</w> 18864
over laid</w> 18864
bio technological</w> 18864
te ment</w> 18863
immuno therapeutic</w> 18862
valpro ic</w> 18861
neutroph ilic</w> 18858
fibrom a</w> 18858
kyph osis</w> 18856
e I</w> 18854
SM I</w> 18854
lactam s</w> 18853
scre en 18851
Neph ro 18847
athero genesis</w> 18846
Endoc rine</w> 18845
T au 18844
cr ashes</w> 18844
as ters</w> 18837
un suitable</w> 18836
administr ations</w> 18835
bp m</w> 18835
M FS</w> 18834
ch 1</w> 18834
ME DI 18834
L Cs</w> 18831
ath ion</w> 18830
Mo vement</w> 18830
f lowing</w> 18829
2 g</w> 18828
i 3</w> 18826
K od 18826
ve str 18826
aff ordable</w> 18825
bur sting</w> 18824
phon on</w> 18823
SMAD 4</w> 18822
B AR 18820
H i</w> 18816
bi ol 18816
SIRT 6</w> 18816
Adhe sion</w> 18815
all as</w> 18811
absor ptive</w> 18811
intercal ation</w> 18811
ox antrone</w> 18810
parag angli 18809
ano ikis</w> 18808
S LI 18805
CXCR 3</w> 18805
tail or</w> 18799
DO M</w> 18796
B RET</w> 18795
Trans plant</w> 18795
p c 18794
f unds</w> 18794
electro retin 18794
doc toral</w> 18794
acros ome</w> 18794
N G 18792
g ifts</w> 18791
dic tate</w> 18789
TNF R</w> 18789
ag a</w> 18787
meris tem</w> 18785
fist ulae</w> 18784
ag u 18783
C ent 18782
oste ron 18781
Check list</w> 18781
C oc 18780
e IF</w> 18780
tail oring</w> 18780
pac ed</w> 18779
intern alizing</w> 18778
F ont 18776
ar id</w> 18776
inser tional</w> 18776
CA L 18774
Pax 6</w> 18774
ec onomics</w> 18773
o ocysts</w> 18772
hal ves</w> 18771
inst alled</w> 18771
precau tions</w> 18768
vi er</w> 18765
H CO 18764
t 6</w> 18761
Ab bot 18761
O TC</w> 18760
un supervised</w> 18759
ref rig 18759
govern ance</w> 18758
fe eds</w> 18757
classi fiers</w> 18756
n ond 18755
opath ogenic</w> 18755
genu ity</w> 18755
deri ves</w> 18753
slow s</w> 18752
k led</w> 18751
B LE 18747
drop out</w> 18747
bio available</w> 18745
Syn aptic</w> 18745
hal ide</w> 18744
arg uing</w> 18743
rec A</w> 18742
pip et 18741
intermedi ary</w> 18740
R ural</w> 18738
F CM</w> 18737
y za</w> 18735
PA RA 18733
v o</w> 18732
beet le</w> 18732
A HA</w> 18731
sol di 18729
Electro chemical</w> 18728
cin ere 18725
trochan teric</w> 18725
under weight</w> 18724
AC N</w> 18724
LT A</w> 18724
Qu ick</w> 18723
monol ith 18723
func tioned</w> 18720
basi cally</w> 18720
diure sis</w> 18720
dis ap 18719
i.c. v.</w> 18719
fluoro metric</w> 18718
pil ocarpine</w> 18717
bruc ellosis</w> 18716
ensemb les</w> 18716
ni an</w> 18715
Taiw anese</w> 18715
Pag et</w> 18715
α 5 18714
hir su 18713
immunopo si 18713
Mic ros 18711
chor io 18711
po sts</w> 18710
ent anil</w> 18710
reser pine</w> 18710
ther mos 18708
ris h</w> 18707
y 2</w> 18702
b l</w> 18702
organ ogenesis</w> 18701
Veter inary</w> 18701
diff rac 18700
HF E</w> 18699
A ED</w> 18698
odon tics</w> 18698
lys in</w> 18697
ur ge</w> 18695
ID H2</w> 18695
conti gs</w> 18695
Oste opo 18692
IM PACT</w> 18691
ca d</w> 18689
G la 18688
ure ment</w> 18688
FE CT</w> 18687
u od 18686
an orectal</w> 18686
P2 X 18686
H K2</w> 18685
Erb B3</w> 18681
V ER 18677
nano structured</w> 18676
ota xime</w> 18675
G IP</w> 18674
M arg 18673
C MT 18669
blo c</w> 18669
Up date</w> 18664
biom echanics</w> 18661
mening eal</w> 18658
TE D</w> 18657
co eliac</w> 18656
cyto chromes</w> 18656
ML CK</w> 18656
pro insulin</w> 18655
aph id</w> 18655
di amine</w> 18653
v ine</w> 18652
Endo vascular</w> 18651
end ang 18649
Hist ology</w> 18649
lev ofloxacin</w> 18649
phenanthro line</w> 18649
E volution 18647
de par 18646
pro ca 18645
acc ent 18645
Con form 18645
ynchron ization</w> 18645
pericardi tis</w> 18644
correc tive</w> 18643
referen ced</w> 18642
os tigmine</w> 18640
benz imidazole</w> 18640
Fre und</w> 18639
ediatr ics</w> 18639
inter actome</w> 18638
de duc 18637
en esulf 18634
lo oks</w> 18634
sign al 18634
ob ases</w> 18634
In formed</w> 18633
un conventional</w> 18632
NS 5</w> 18632
fulle rene</w> 18632
cap it 18631
PPAR gamma</w> 18631
j iang</w> 18630
it ant</w> 18626
OR F2</w> 18626
parvo virus</w> 18625
erb al</w> 18624
PE F</w> 18623
y . 18622
Heterogene ity</w> 18621
LRP 6</w> 18620
endocannabin oid</w> 18620
aer ation</w> 18619
sulfon amide</w> 18618
H and 18617
deli vers</w> 18617
epist atic</w> 18617
sep tin</w> 18614
nucleoph ile</w> 18613
st ants</w> 18610
B J</w> 18606
immuno regulatory</w> 18606
PRIN CIPA 18606
as exual</w> 18605
tin gu 18605
immunoprecip itations</w> 18604
- specific</w> 18600
Ga ucher</w> 18600
Mil an</w> 18600
hind brain</w> 18599
S 5B</w> 18598
Gly 1</w> 18598
bronch us</w> 18597
muscul ature</w> 18597
S 3C</w> 18595
' S</w> 18593
Neuro spora</w> 18593
ortholog ues</w> 18593
og el</w> 18591
ear um</w> 18591
maxim izing</w> 18591
B ig 18590
b ers</w> 18589
Ubc 9</w> 18589
hydro nephrosis</w> 18588
To oth</w> 18586
continu ally</w> 18585
TRI M 18584
inst all 18584
m RFP</w> 18583
G ACT 18582
AT O</w> 18582
CR 3</w> 18580
perfor ations</w> 18579
cardiover ter</w> 18578
T ree</w> 18576
p E 18576
oint ment</w> 18575
Con serv 18574
imping ement</w> 18571
w ol 18570
At titudes</w> 18570
glycero phosphate</w> 18567
BI A</w> 18566
HSP s</w> 18565
op ically</w> 18561
ethyl maleimide</w> 18561
h MSCs</w> 18560
n . 18560
G ate 18560
od on</w> 18557
HB c</w> 18557
P ip 18556
man ic</w> 18556
collagen ous</w> 18554
peri stal 18552
equ atorial</w> 18552
pub ic</w> 18552
ce ased</w> 18547
PRINCIPA L</w> 18547
L ine 18545
muc o 18545
Chem icon</w> 18544
RT s</w> 18542
urb an 18541
clo ac 18536
TG T</w> 18535
curric ula</w> 18533
Jam es</w> 18533
P er</w> 18532
u x</w> 18531
simpl ify</w> 18527
pro peptide</w> 18526
bar bit 18524
N b</w> 18520
M Z 18520
U se 18520
GC V</w> 18520
fertili zer</w> 18520
sequenc er</w> 18519
Mar yland</w> 18518
Ra ji</w> 18517
boos ting</w> 18517
hypothesi sed</w> 18517
I b 18516
TM PR 18516
compromis es</w> 18515
sh ade</w> 18514
1 a1</w> 18513
ocyto tic</w> 18513
Cdc 3</w> 18511
Pos si 18511
thur ingiensis</w> 18509
an ks</w> 18507
me ant</w> 18507
thym oma</w> 18506
Sh i</w> 18505
laun ched</w> 18505
de dness</w> 18503
bis phosphonate</w> 18503
Phosph o 18503
Sh en</w> 18502
Dev ice</w> 18502
P alli 18501
at opy</w> 18501
achiev ements</w> 18501
MD I</w> 18499
igh ting</w> 18498
inter cept</w> 18498
and 5</w> 18497
coordin ately</w> 18497
sla ugh 18494
TR E</w> 18492
palli ation</w> 18492
O hio</w> 18491
ne sts</w> 18490
B rom 18487
stimul ations</w> 18487
Figure 1B</w> 18487
metatar sal</w> 18487
perv asive</w> 18483
anc ers</w> 18480
imidazol ium</w> 18480
s tick</w> 18479
trans vaginal</w> 18479
D X</w> 18475
Q s</w> 18474
osp ond 18473
Exc ess</w> 18472
y an</w> 18470
guar ante 18470
centro id</w> 18468
ç et</w> 18467
confound ed</w> 18467
R 2 18466
fluoro quinolone</w> 18466
ation -</w> 18465
dendri mers</w> 18463
bo ronic</w> 18462
Ca regi 18460
xy lem</w> 18459
consul tant</w> 18458
oplas min</w> 18456
CG I</w> 18455
after wards</w> 18454
ri gorously</w> 18452
promis es</w> 18451
es onide</w> 18449
mo th</w> 18448
N IRS</w> 18446
fav oured</w> 18446
he art 18445
ide um</w> 18444
d up</w> 18443
electroencephal ogram</w> 18442
IAC UC</w> 18441
cyan obacterial</w> 18440
PO AG</w> 18439
EC C</w> 18438
na vi 18437
hexam eric</w> 18435
phy co 18432
post transplant</w> 18432
Hem orrh 18431
p add 18430
fin ds</w> 18429
advoc acy</w> 18429
Virg inia</w> 18428
deri ving</w> 18427
ber berine</w> 18427
decarbox ylation</w> 18427
te sti 18426
Moti f</w> 18425
cl oth 18423
RO Is</w> 18423
adi ab 18420
Str and</w> 18420
van adium</w> 18418
F ST</w> 18416
hyper responsiveness</w> 18416
pil i</w> 18415
ch illed</w> 18414
voc ab 18414
dac tyly</w> 18414
elabor ation</w> 18412
D R1</w> 18409
Ax l</w> 18408
m ESCs</w> 18407
U s 18407
ell o</w> 18407
gar lic</w> 18407
poly genic</w> 18406
micro environments</w> 18405
proc aspase</w> 18405
d 8</w> 18404
NE W</w> 18402
shoul ders</w> 18402
dist rib 18401
pro of 18400
cor n 18398
Mel atonin</w> 18397
cru st 18397
i. m.</w> 18396
Nco I</w> 18395
CG AG 18394
DU B</w> 18391
entr apped</w> 18390
gonad otrophin</w> 18390
calcit riol</w> 18390
p ons</w> 18389
cocul tured</w> 18388
De pressive</w> 18387
Op ti 18386
Beh çet</w> 18386
N g 18383
Fe asibility</w> 18381
cl ue</w> 18380
ucle otides</w> 18380
pyl oric</w> 18380
palin dromic</w> 18380
Por phy 18379
sub clones</w> 18378
Br ug 18378
W NT 18377
6 e</w> 18375
hem op 18375
al together</w> 18374
oc al</w> 18374
trichloro acetic</w> 18374
Glu R1</w> 18373
RA CK1</w> 18369
SE s</w> 18369
Hs 0</w> 18369
ed ul 18368
A F2</w> 18367
communic able</w> 18367
appe aling</w> 18366
re wards</w> 18365
Erro r</w> 18365
BVD V</w> 18364
neg atives</w> 18363
reser ves</w> 18362
S MR</w> 18361
RA NK</w> 18361
mis s</w> 18361
mas ks</w> 18360
SER CA</w> 18360
Erb B4</w> 18359
H AP</w> 18358
six teen</w> 18356
AT F3</w> 18355
Nk x2</w> 18355
cum ulus</w> 18354
Pe ter 18354
ol aparib</w> 18352
car inii</w> 18352
Lin kage</w> 18351
T AS</w> 18348
dec iding</w> 18348
vol ta 18348
errone ous</w> 18348
agon ism</w> 18347
ure thro 18346
Anatom ical</w> 18346
Hou ston</w> 18346
mes ylate</w> 18344
AA H</w> 18343
em es</w> 18342
as ting</w> 18340
rs 8</w> 18340
OUTCO MES</w> 18339
de an</w> 18338
mid point</w> 18335
Uter ine</w> 18335
haem ophilia</w> 18333
cartil aginous</w> 18333
Uni que</w> 18332
TW I 18331
r n 18330
N BS</w> 18329
PC E</w> 18329
HI V 18328
loos ely</w> 18328
transmit ting</w> 18327
Y ears</w> 18326
SO D2</w> 18324
av al 18321
Fts Z</w> 18320
St art</w> 18319
benth ic</w> 18319
tend encies</w> 18316
es . 18314
lat tices</w> 18313
segreg ate</w> 18313
g L</w> 18310
um or</w> 18308
EB P1</w> 18308
Euk ary 18308
uni polar</w> 18303
sulf onic</w> 18298
F z 18296
schem atic</w> 18296
ap sular</w> 18294
CD C4</w> 18294
conform er</w> 18294
Micro bi 18291
en o</w> 18290
Tradi tionally</w> 18290
Q R</w> 18285
Nucle ic</w> 18285
im etric</w> 18284
Fig. 5B</w> 18282
NT G</w> 18281
manif esting</w> 18278
on ey</w> 18275
ot olaryng 18268
bi phenyls</w> 18268
Instrum ent</w> 18266
ann els</w> 18265
VE P</w> 18263
L PC</w> 18262
Res ection</w> 18262
O SM</w> 18261
piezo electric</w> 18261
Sox 1</w> 18260
Align ment</w> 18260
precip itating</w> 18255
etan ercept</w> 18255
R po 18254
surpri se</w> 18254
Prote us</w> 18253
FE V</w> 18253
jejun ostomy</w> 18253
H ash 18252
efficac ies</w> 18252
t . 18249
mid t</w> 18249
Al a 18248
ou mar 18247
b out</w> 18246
re fin 18246
rec alled</w> 18246
X PC</w> 18245
spong es</w> 18245
proc essive</w> 18244
Gα i</w> 18244
surviv orship</w> 18243
under stand 18241
poly glutamine</w> 18240
pent ose</w> 18240
con cave</w> 18237
intrag astric</w> 18234
Sel ect</w> 18231
N D1</w> 18230
O mega</w> 18230
Glu 2</w> 18230
RUN X2</w> 18230
phyto plankton</w> 18229
ol umbar</w> 18228
qui et</w> 18228
elec tricity</w> 18225
tor sional</w> 18223
A chi 18222
Periodic als</w> 18220
I ra 18219
bronchi ectasis</w> 18219
J IA</w> 18217
trans glutaminase</w> 18216
Ara b</w> 18216
thylak oid</w> 18216
ML R</w> 18215
O SAS</w> 18213
dig itonin</w> 18212
dy ads</w> 18212
aminobenz idine</w> 18212
J N 18209
estim ator</w> 18206
G est 18205
Fan coni</w> 18201
tr it 18199
dipyri damole</w> 18197
am ed</w> 18195
n ations</w> 18193
Ad sorption</w> 18193
crystallin ity</w> 18191
spon sored</w> 18190
T 4 18188
inter ing</w> 18188
C SP 18187
intr agenic</w> 18187
vo rous</w> 18184
faec ium</w> 18180
alk anes</w> 18178
ero sive</w> 18177
C As</w> 18176
a rene</w> 18172
St abil 18171
leaf lets</w> 18170
si b</w> 18169
BODI PY</w> 18168
SC 2</w> 18167
AI s</w> 18167
L INE</w> 18166
recommend s</w> 18166
Go od 18166
R DS</w> 18165
Re agents</w> 18165
japon icum</w> 18165
Di versity</w> 18163
Cl in</w> 18163
coli tica</w> 18163
AR M</w> 18162
lenti viruses</w> 18161
resis tive</w> 18161
ag glomer 18160
K os 18159
de generated</w> 18159
sphing olipids</w> 18159
VEGF A</w> 18159
Mu LV</w> 18158
gl ass 18156
hum ic</w> 18155
A 3 18154
AR V</w> 18154
n ails</w> 18151
e metic</w> 18148
c Gy</w> 18148
aver sion</w> 18148
bic ycle</w> 18148
leak y</w> 18147
dwar f</w> 18147
E PEC</w> 18146
no isy</w> 18146
0 μg</w> 18141
trabec ul 18140
Sym ptomatic</w> 18139
ze in</w> 18137
on ian</w> 18136
thio ester</w> 18133
transcriptom es</w> 18131
m Eq</w> 18130
pon der 18130
i vermectin</w> 18129
thermo stability</w> 18127
ame tes</w> 18120
OR F5</w> 18120
lic ense</w> 18120
Cha in</w> 18118
mirro red</w> 18117
Acqu isition</w> 18117
id o 18116
B s 18114
in operable</w> 18113
end ophthalmitis</w> 18113
BM Ps</w> 18112
L ength</w> 18111
fin ely</w> 18110
hyper uric 18110
MB q</w> 18110
Rev .</w> 18110
Spectro scopy</w> 18110
phospho enolpyruvate</w> 18109
Ze brafish</w> 18108
direc tionality</w> 18106
micro injected</w> 18105
eu thyroid</w> 18103
tri methylation</w> 18102
pul ling</w> 18102
quer ied</w> 18102
erb B</w> 18096
Rheum atoid</w> 18096
neuro p 18095
B SI</w> 18094
AN X 18094
chape ron 18093
T EC 18091
benz othi 18091
lar va</w> 18091
Pel vic</w> 18090
quadru ple</w> 18086
dis inf 18083
mind fulness</w> 18083
D IC 18082
inspec ted</w> 18082
K IR</w> 18080
pal pation</w> 18078
Vir tual</w> 18078
cys tic 18077
sper sed</w> 18075
L MP</w> 18072
ec top 18071
H as 18070
mal arial</w> 18070
hypertriglycer idemia</w> 18070
entero colitica</w> 18069
on wards</w> 18068
SI LAC</w> 18067
thal lium</w> 18067
insp iration</w> 18067
tul arensis</w> 18067
rop enem</w> 18065
Qu in 18064
Abbot t</w> 18064
colon ize</w> 18063
suscepti bilities</w> 18062
OR E</w> 18060
single -</w> 18059
N 1 18052
ap igenin</w> 18052
co infection</w> 18052
anthra x</w> 18052
separ able</w> 18051
IR R</w> 18048
r ics</w> 18047
G ad 18047
S ST 18046
omy ositis</w> 18046
rep ly</w> 18045
sapon ins</w> 18045
id ylate</w> 18044
add ers</w> 18044
IR F7</w> 18044
T AM 18043
IT G 18043
h as 18041
D u</w> 18041
Tw ist</w> 18040
univer sities</w> 18040
S AGA</w> 18038
d well</w> 18036
rap e</w> 18036
Echocardi ography</w> 18036
pseud op 18035
inn ings</w> 18035
GST M1</w> 18035
qua ke</w> 18035
b ody 18034
SP F</w> 18034
Hybridi zation</w> 18033
hemiz yg 18033
E Z</w> 18031
mill ig 18031
helmin th</w> 18031
K on 18029
Por tal</w> 18029
Ar sen 18027
PG E1</w> 18026
in ulin</w> 18025
re hydrated</w> 18025
A ur 18023
S 6A</w> 18023
sm an</w> 18023
fent anil</w> 18022
vor iconazole</w> 18021
hyn chus</w> 18021
Bor de 18020
plank tonic</w> 18018
comm and</w> 18017
tax on</w> 18016
b ZIP</w> 18014
Stre p</w> 18012
radionuc lides</w> 18012
β 6</w> 18011
Sub cutaneous</w> 18010
TRA F3</w> 18010
aqu educ 18009
stre et</w> 18008
j uris 18007
tin ine</w> 18007
cp m</w> 18006
scro tal</w> 18006
p Q 18005
dis course</w> 18004
V o</w> 18003
di ous</w> 18001
OG G1</w> 18000
pl y 17999
Cy t</w> 17999
ro s</w> 17998
el astom 17998
el ap 17997
gr ac 17996
ca ught</w> 17994
Shig a</w> 17994
i k</w> 17993
W id 17993
al ol</w> 17993
CD A</w> 17993
G EN</w> 17992
amne sia</w> 17992
o sts</w> 17991
S ong</w> 17990
St ock</w> 17990
splanch nic</w> 17990
5 Δ</w> 17989
agre ements</w> 17989
min iat 17988
1 V</w> 17987
end op 17987
c ements</w> 17986
ph aco 17985
super position</w> 17985
DN s</w> 17985
Bo eh 17985
ou tre 17984
ly c 17984
Mec p2</w> 17984
ol o</w> 17983
bo dily</w> 17983
H ig 17980
excit on</w> 17980
FA CT</w> 17980
V a</w> 17978
f eld</w> 17978
w .</w> 17978
fac tor 17976
Phosph ate</w> 17975
Aero monas</w> 17975
mit ogens</w> 17974
protom er</w> 17974
pl ug 17973
vas omotor</w> 17973
flatten ed</w> 17973
LA SIK</w> 17972
philosoph y</w> 17972
PA O</w> 17970
oc rip 17970
Bi g</w> 17970
tic ul 17968
myo fibroblast</w> 17967
y fish</w> 17966
Transl ation</w> 17966
p ERK</w> 17965
SE D</w> 17965
arrhyth m 17965
pep per</w> 17964
mal le 17963
- conjugated</w> 17962
Dro p</w> 17962
T op</w> 17961
r uling</w> 17958
CD Ks</w> 17957
assis ting</w> 17957
S cle 17956
diag onal</w> 17956
PT I</w> 17955
D GF</w> 17954
col or 17952
Fre e 17952
al falfa</w> 17950
Contro lling</w> 17950
ac ar 17949
non covalent</w> 17948
RN s</w> 17947
im perfect</w> 17945
Cd k</w> 17944
vul us</w> 17943
Techni ques</w> 17943
Δ F5</w> 17942
AP P 17942
entero colitis</w> 17942
FV IIa</w> 17942
Bo y 17941
electro physiologic</w> 17940
Soci o 17940
Macroph age</w> 17940
en tails</w> 17939
sc aph 17939
eigh th</w> 17939
prox en</w> 17939
colo strum</w> 17938
Path ogenesis</w> 17937
G ap 17936
Rab 3</w> 17936
dimorph ic</w> 17936
GAT A3</w> 17931
dosi metric</w> 17931
dig its</w> 17929
Auto immune</w> 17927
times cale</w> 17925
not ch</w> 17924
ens it 17923
mst adt</w> 17922
re tro</w> 17921
Cal if</w> 17918
Solu tions</w> 17918
hypoc alc 17917
al ic 17916
Fung al</w> 17915
fi re 17913
mal absorption</w> 17913
Per itoneal</w> 17913
Vari ables</w> 17911
P HI 17908
fam ide</w> 17907
St at</w> 17904
gro o 17902
SI FT</w> 17901
non surgical</w> 17901
porphy rins</w> 17901
co erul 17900
ob vi 17900
Prote omic</w> 17900
sp ared</w> 17897
SU Vmax</w> 17897
FOX O3a</w> 17896
o ven</w> 17895
micro liter</w> 17895
pemphig us</w> 17895
F W</w> 17894
O u 17894
an trum</w> 17893
im balances</w> 17887
prec ancerous</w> 17885
Gen et</w> 17885
TG CT 17885
polyke tide</w> 17885
Lim itations</w> 17884
satisfac tor 17883
0 nM</w> 17881
concep tions</w> 17880
selen ite</w> 17880
co ol</w> 17879
Ul tr 17878
pediatr ics</w> 17877
La w</w> 17875
Regi ons</w> 17874
bath ing</w> 17873
em power 17869
termin ating</w> 17869
AS F</w> 17868
Lep tos 17868
SPAR C</w> 17868
H och 17865
F 1 17865
ox imetry</w> 17865
BM AL1</w> 17865
Path ways</w> 17864
foot printing</w> 17864
PT N</w> 17863
resp ir 17862
emb ran 17862
CYP 1B1</w> 17862
ri o</w> 17861
MD 1</w> 17861
idi a</w> 17860
epi stasis</w> 17860
ferre doxin</w> 17859
ste ers</w> 17857
tin opathy</w> 17856
an idine</w> 17855
und es 17855
Sy d 17855
ze olite</w> 17855
A EC</w> 17853
P et 17853
pericardi um</w> 17853
commit tees</w> 17852
USP 2</w> 17849
Adv ant 17847
Inten sity</w> 17847
AC G</w> 17846
SN CA</w> 17846
Gu ill 17846
Ax in</w> 17846
CXCL 8</w> 17845
H ur 17844
append ectomy</w> 17844
exc itable</w> 17843
per methrin</w> 17842
Ar my</w> 17842
PV I</w> 17840
E. coli</w> 17840
Web er</w> 17837
ap noea</w> 17836
typ ic</w> 17834
M PC</w> 17830
CCR 7</w> 17830
pre ponder 17829
Vec tas 17829
M BD</w> 17826
repul sive</w> 17824
ad her 17823
adsor b 17822
V ene 17819
co de 17816
Gro wing</w> 17816
le c</w> 17815
Sy rian</w> 17815
collater als</w> 17815
M ang 17814
SL C1</w> 17814
lo vastatin</w> 17812
quad ri 17812
uncor rected</w> 17812
empower ment</w> 17810
stere oselective</w> 17809
resus cit 17808
G ö 17804
diff usive</w> 17804
sal i 17802
LM W</w> 17802
8 MAPK</w> 17801
2 th</w> 17801
youn gest</w> 17801
conjuncti va</w> 17799
Cardi ology</w> 17797
St ock 17796
rewar ding</w> 17796
ad mixture</w> 17795
ha y</w> 17794
L ind 17792
ti ed</w> 17791
un transfected</w> 17790
tw isted</w> 17789
k not</w> 17787
ot yl</w> 17786
k ov</w> 17785
cle fts</w> 17785
ER Ps</w> 17784
M SNs</w> 17783
Entero bacter</w> 17782
hem odi 17780
F mo 17778
ha em</w> 17777
Ka wasaki</w> 17775
LY 3</w> 17774
nigro striatal</w> 17773
inc isional</w> 17772
ab duc 17771
benefi ted</w> 17771
HS F</w> 17770
dis sections</w> 17769
- K</w> 17768
M C1</w> 17768
RA PD</w> 17768
vi z</w> 17768
gran zyme</w> 17766
Res ting</w> 17764
PR MT1</w> 17764
IGF 2</w> 17764
chel ators</w> 17760
cal ories</w> 17759
Dar mstadt</w> 17758
NN RTI</w> 17757
Pr d 17755
a -- 17754
con clusively</w> 17753
phen oxy</w> 17752
Met astasis</w> 17752
am idation</w> 17750
TP N</w> 17749
DL B</w> 17748
al arm</w> 17747
Pl anning</w> 17746
preclud es</w> 17746
ADAMT S1</w> 17746
AZ A</w> 17745
c able</w> 17744
over comes</w> 17741
B ack</w> 17740
BCL 6</w> 17740
0 A4</w> 17739
ocrip tine</w> 17739
artic ulation</w> 17736
Georg ia</w> 17734
t ances</w> 17733
PR F</w> 17732
AN IM 17732
E po 17730
anis m</w> 17730
AT 1R</w> 17730
sw ing</w> 17730
contain ment</w> 17728
str in</w> 17726
glot tic</w> 17725
quin idine</w> 17723
S v 17722
dyn actin</w> 17722
CD K5</w> 17721
I E1</w> 17720
appoin tments</w> 17720
X A</w> 17719
top enia</w> 17719
ty sis</w> 17719
nano technology</w> 17718
happ en</w> 17712
mac ula</w> 17711
fluoro scopic</w> 17710
p N</w> 17708
har ms</w> 17708
g t 17706
st ut 17705
am alg 17703
mutagen ized</w> 17701
Deri ved</w> 17701
R v</w> 17700
pil us</w> 17698
PKC ζ</w> 17698
tob ramycin</w> 17697
e -- 17695
outre ach</w> 17693
chrom ogenic</w> 17692
polymy xin</w> 17690
L P1</w> 17688
H NF</w> 17684
cri tic 17684
rhinos in 17684
fib ular</w> 17683
ar rests</w> 17681
par ab 17681
HC G</w> 17679
RA C</w> 17678
granul ocytic</w> 17678
Embry onic</w> 17678
Particip ation</w> 17677
typh oid</w> 17677
imp utation</w> 17676
effec ted</w> 17673
St ation</w> 17671
berg er</w> 17670
z idovudine</w> 17669
am lodipine</w> 17668
VP S3</w> 17667
G HR</w> 17665
ab used</w> 17665
sub mission</w> 17662
per mutation</w> 17661
Mar kers</w> 17661
anthocyan ins</w> 17661
Ch er 17659
Spec tra</w> 17659
MO G</w> 17659
dis agreement</w> 17655
Ca uses</w> 17655
centro somal</w> 17655
in omycin</w> 17652
pe ti 17651
hepat obiliary</w> 17651
excre tory</w> 17651
A mic 17650
S K1</w> 17650
bottlen eck</w> 17650
p -</w> 17649
anth elmin 17649
pero neal</w> 17649
r uminal</w> 17648
E SD</w> 17648
Di ver 17647
ald osteron 17646
amalg am</w> 17643
HS PCs</w> 17642
cas u 17640
Jer sey</w> 17639
ear able</w> 17638
corne um</w> 17638
al en</w> 17636
mas cul 17636
H ub 17635
F MS</w> 17635
TT A</w> 17635
intran uclear</w> 17634
septic emia</w> 17634
yl line</w> 17630
gl os 17630
scaveng ers</w> 17629
as er</w> 17628
ion toph 17628
web sites</w> 17628
x . 17627
phar yng 17627
aten olol</w> 17626
cont ag 17624
aren a</w> 17623
therm ogenesis</w> 17622
foot ball</w> 17622
RE E</w> 17620
cap ita</w> 17619
antagon izing</w> 17619
CG CT 17618
Y C</w> 17617
ab ies</w> 17617
iri d 17617
ped unc 17616
Satis faction</w> 17616
ful filling</w> 17614
hy aline</w> 17612
metabol omic</w> 17611
satisfactor ily</w> 17611
glomerul osclerosis</w> 17609
fm k</w> 17609
P ie 17607
in adver 17607
turbul ence</w> 17606
El ucid 17604
naph tho 17604
fl or 17603
Syd ney</w> 17602
D MS</w> 17598
spo uses</w> 17598
Gn R 17597
genit alia</w> 17597
Transl ational</w> 17597
tetra hedral</w> 17596
lumb osacral</w> 17595
radic ul 17594
ol er 17593
comm enced</w> 17593
Bri stol</w> 17591
Ga o</w> 17591
inclin ation</w> 17591
N PC1</w> 17589
presen ilin</w> 17589
g p9</w> 17587
ste notic</w> 17587
L SD</w> 17585
E ur 17584
val e</w> 17584
O Me</w> 17583
PA 3</w> 17583
radi i</w> 17582
Lith ium</w> 17579
m umps</w> 17576
TRP V4</w> 17574
AD MA</w> 17573
IQ GAP1</w> 17573
TFII D</w> 17573
GEN E</w> 17571
assembl ages</w> 17569
- H</w> 17566
Ur ban</w> 17565
ul inic</w> 17563
HE SIS</w> 17563
Assis ted</w> 17563
fluoro genic</w> 17561
Ter tiary</w> 17559
P article</w> 17558
Mut ational</w> 17558
d u</w> 17557
k r 17557
MC H</w> 17557
N 2 17555
Bloc kade</w> 17555
AM PARs</w> 17552
Sch ne 17551
SY NT 17551
Z o 17550
GAG s</w> 17550
I SI</w> 17549
cy r 17549
offic ers</w> 17548
wheel chair</w> 17548
gra te 17546
Sper m</w> 17546
ste pping</w> 17545
hal o</w> 17545
PR Rs</w> 17545
Clin ic 17545
VO Cs</w> 17544
O PC</w> 17543
ser ines</w> 17543
Proble m</w> 17542
G ray</w> 17538
post graduate</w> 17538
visu alizing</w> 17537
ud a</w> 17536
DI M</w> 17536
fil trate</w> 17535
allo x 17535
Ado Met</w> 17535
x ls 17534
Pl an 17532
hemat ology</w> 17531
Hi Seq</w> 17529
Ch arg 17528
PR C</w> 17528
fas cin 17528
SS RIs</w> 17528
f allopian</w> 17523
transc utaneous</w> 17519
ster no 17518
hypo perfusion</w> 17516
stil bene</w> 17516
Sp A</w> 17515
D engue</w> 17513
TI P</w> 17512
Pres entation</w> 17512
SF N</w> 17510
non pregnant</w> 17509
deoxy ribonucleic</w> 17509
surro und</w> 17507
neutrop enic</w> 17507
dis accharide</w> 17505
IR S1</w> 17505
GAB AB</w> 17505
tonsill ectomy</w> 17505
mis oprost 17504
W est 17500
te ther</w> 17500
blas tom 17497
alk enes</w> 17496
3 .</w> 17495
k ar 17495
st alling</w> 17494
inten sification</w> 17493
Bif id 17491
tic atory</w> 17490
NS G</w> 17490
correl ative</w> 17486
UL AR</w> 17485
s m</w> 17484
ovi duct</w> 17484
l inc 17483
discover ing</w> 17483
Dna A</w> 17482
en one</w> 17481
g at 17480
gl enoid</w> 17479
cr ush</w> 17479
K am 17478
om ethyl</w> 17477
av icular</w> 17473
hybri disation</w> 17473
con ical</w> 17472
gall stones</w> 17470
Hippocamp al</w> 17469
BC D</w> 17468
H BP</w> 17467
P ichia</w> 17467
be ings</w> 17464
dimin ution</w> 17464
uc k</w> 17463
xls x</w> 17463
misoprost ol</w> 17463
m Ci</w> 17462
DI AG 17461
underestim ation</w> 17461
chrom atids</w> 17460
SM AD</w> 17456
intern ationally</w> 17454
Vi enna</w> 17454
re mitting</w> 17453
gene tic 17453
ov ani</w> 17453
Fer mi</w> 17453
ad vi 17452
nan ometer</w> 17452
E STs</w> 17451
M ER 17450
RN 1</w> 17450
5 hmC</w> 17448
i brutinib</w> 17448
mineral ocorticoid</w> 17444
L P 17443
histor ic</w> 17443
et rial</w> 17440
Cap acity</w> 17440
Hom o</w> 17439
disap pointing</w> 17439
protr uding</w> 17438
r AAV</w> 17434
relax ing</w> 17434
at ch</w> 17433
carb apenem</w> 17433
Pro per</w> 17431
L AB</w> 17430
edent ulous</w> 17430
CDK 6</w> 17429
COLL ECTION</w> 17428
hap ten</w> 17425
Cys 2</w> 17423
long us</w> 17422
Lys 2</w> 17422
en ing 17421
entero cocci</w> 17420
cl er 17419
tibi alis</w> 17417
O LT</w> 17416
cur ated</w> 17416
Lip ids</w> 17416
E AAT 17415
photoc o 17413
radi ative</w> 17412
H9 N2</w> 17412
peri plasm</w> 17411
PI P 17410
UD CA</w> 17409
VD AC</w> 17409
B IR 17408
N he 17407
as king</w> 17407
wor sen</w> 17407
PDGF Rα</w> 17407
chimpanze e</w> 17407
p C 17406
Te am</w> 17406
isopren aline</w> 17406
pro mas 17405
Estim ated</w> 17404
ogen omic</w> 17403
immun isation</w> 17403
MT P</w> 17402
append age</w> 17402
Neu N</w> 17401
lit ters</w> 17399
pre test</w> 17398
Me yer</w> 17396
abe ads</w> 17396
Z E 17395
pren atally</w> 17394
M O2</w> 17393
glycos yl</w> 17392
em ann</w> 17391
sle eve</w> 17390
em al</w> 17389
Ab brevi 17389
micro extraction</w> 17388
tt age</w> 17387
rip t 17387
exp and 17386
F 2 17385
correspon dingly</w> 17384
Ap parently</w> 17384
gastro stomy</w> 17384
de repression</w> 17382
STU DI 17380
K ap 17379
t B</w> 17378
com pres 17376
ST 3</w> 17376
em py 17376
- monophosphate</w> 17375
K H</w> 17375
oxidi ze</w> 17375
tot ag 17374
- stimulated</w> 17373
if ery</w> 17373
micro vasculature</w> 17373
Ric kett 17372
He avy</w> 17370
O 7</w> 17368
cef otaxime</w> 17368
am yl</w> 17365
ke eper</w> 17365
A EA</w> 17364
un reliable</w> 17364
sel eno 17360
TRP M7</w> 17360
or ph</w> 17359
transpos ase</w> 17353
AD F</w> 17352
Qu est</w> 17351
car d 17350
AD Rs</w> 17350
BD V</w> 17350
parti tioned</w> 17350
S ON</w> 17349
sor bent</w> 17349
ribos wit 17349
Figure 4A</w> 17348
Dna K</w> 17347
FS GS</w> 17345
techne tium</w> 17344
aspir ate</w> 17342
react ants</w> 17339
y ls</w> 17338
edi a</w> 17338
epigene tics</w> 17338
ll o</w> 17336
ec tors</w> 17335
da ugh 17335
rhinosin usitis</w> 17335
Nup 1</w> 17334
ep iflu 17333
d um</w> 17332
anticip ation</w> 17332
J ust</w> 17331
bro ther</w> 17331
H ug 17329
re habil 17329
ALY s</w> 17329
Bio tin</w> 17326
uscul arly</w> 17325
rib bon</w> 17323
L akes</w> 17322
el eph 17321
linol enic</w> 17320
N DR 17318
or ax</w> 17318
Math em 17317
f ade</w> 17316
sci sic</w> 17316
Conserv ative</w> 17315
FT Y7</w> 17314
X O</w> 17311
organis ations</w> 17309
Ju ven 17309
K lotho</w> 17308
deciph er</w> 17308
on e-</w> 17305
alph ab 17301
s d 17300
caus ally</w> 17299
Chri sti 17299
spermatog onia</w> 17299
for tified</w> 17298
histopath ologically</w> 17298
spec ulation</w> 17297
eb a</w> 17295
success es</w> 17293
sl and</w> 17292
chir ality</w> 17292
spor um</w> 17290
Com plement</w> 17289
tube rous</w> 17289
do id</w> 17286
Mal aw 17285
SP OT</w> 17284
p ale</w> 17283
post surgical</w> 17282
AF S</w> 17282
pum ped</w> 17282
Or yza</w> 17281
fl ud 17280
intra thoracic</w> 17280
L SCs</w> 17278
A TIONS</w> 17277
PA N 17277
C ep 17276
im pose</w> 17276
β 1 17274
sub lingual</w> 17273
pri son</w> 17273
pas te 17273
Cir cular</w> 17273
STAT 2</w> 17272
Top o</w> 17271
desi c 17269
Pres um 17269
Lymph oma</w> 17267
Morph ology</w> 17266
rin se</w> 17265
O ff</w> 17263
ano yl</w> 17263
end owed</w> 17262
G or 17261
normal ities</w> 17258
e QTL</w> 17257
I 6</w> 17257
b ags</w> 17257
E 6 17257
Atg 8</w> 17256
Flu or 17255
S cho 17253
vascular isation</w> 17253
D ex 17250
se at</w> 17250
my ositis</w> 17250
extern alizing</w> 17250
tr e</w> 17247
dihydro testosterone</w> 17247
hol m</w> 17246
Evolution ary</w> 17246
Im atinib</w> 17245
val bumin</w> 17242
haem olyticus</w> 17240
rein hard 17238
doc osahexaenoic</w> 17237
biom olecular</w> 17237
Pro p 17236
ad al</w> 17235
P sA</w> 17234
eng ages</w> 17232
tri tiated</w> 17231
LM WH</w> 17231
con val 17229
Clar k</w> 17229
in king</w> 17228
r CBF</w> 17224
g l</w> 17222
satis fying</w> 17221
an tic</w> 17219
AP 4</w> 17219
C ode</w> 17218
it ching</w> 17218
varic eal</w> 17218
Li kert</w> 17216
key words</w> 17216
Su icide</w> 17214
G DI</w> 17213
scrap ie</w> 17213
NR G1</w> 17211
N ST</w> 17209
olec tomy</w> 17209
DO T</w> 17209
paran asal</w> 17208
Gly cos 17205
1 q</w> 17202
T m 17199
pancrea tectomy</w> 17199
plas mal 17197
gyn ecology</w> 17197
em sa</w> 17196
leuk openia</w> 17195
bul ge</w> 17195
tram adol</w> 17195
inf ested</w> 17194
wet land</w> 17194
ni um</w> 17193
Spectro metry</w> 17192
postero lateral</w> 17192
un exposed</w> 17191
Par am 17191
biop olym 17190
st ories</w> 17189
flex neri</w> 17188
ok adaic</w> 17188
al ties</w> 17185
di acetate</w> 17184
ir ubicin</w> 17184
Cy clos 17183
worth while</w> 17183
G r</w> 17182
vestr ant</w> 17180
4 K 17179
Ab normalities</w> 17179
N AG</w> 17178
MP T</w> 17178
inter spersed</w> 17177
Bro ad</w> 17174
u c</w> 17172
O TA</w> 17172
w ing 17169
7 α</w> 17166
k l 17165
bul im 17165
C J</w> 17164
ophthalm ology</w> 17163
Repe at</w> 17163
Gene Chip</w> 17162
Psy chosocial</w> 17158
over represented</w> 17157
carcin ogenicity</w> 17156
ey e 17156
DI SE 17156
ript yline</w> 17155
A y 17154
Pro pi 17154
fe rol</w> 17151
ori enting</w> 17151
carboxyp eptidase</w> 17151
phospho transferase</w> 17150
sh aker</w> 17148
hydro static</w> 17148
Lym ph</w> 17147
arith metic</w> 17145
c G 17138
hydro genase</w> 17135
Re views</w> 17135
GPR 3</w> 17132
ac a 17131
teen agers</w> 17131
NO ESY</w> 17128
per if 17127
lacc ase</w> 17124
Bo c</w> 17123
V ASP</w> 17122
own ers</w> 17122
obliter ation</w> 17120
reinhard tii</w> 17120
AQ P2</w> 17119
mGlu R5</w> 17118
P ey 17117
cd c1</w> 17115
macro cyclic</w> 17114
fur row</w> 17114
B lin 17113
Re vision</w> 17112
AN N</w> 17112
MO Fs</w> 17109
Fal se</w> 17107
AS I</w> 17106
intram uscularly</w> 17106
mimic ry</w> 17105
G d 17104
choles te 17104
After wards</w> 17104
ex in 17103
Aqu eous</w> 17103
norm als</w> 17101
b afilomycin</w> 17099
G 3 17099
preser v 17099
op ter 17095
s ations</w> 17094
j o</w> 17094
phyl um</w> 17093
pa yments</w> 17087
sing ular</w> 17086
end opeptidase</w> 17085
numer ary</w> 17084
floc ks</w> 17083
gambi ae</w> 17083
ure teric</w> 17082
z u</w> 17081
ak in</w> 17080
Sec 6</w> 17079
hymen a</w> 17077
ind ole 17076
sati ety</w> 17076
Sho ulder</w> 17076
C lear</w> 17075
Ex am 17075
NA NOG</w> 17074
de fibrillation</w> 17073
mes odermal</w> 17073
Resi stant</w> 17073
I SR</w> 17071
co arctation</w> 17071
An at 17071
c n</w> 17070
as ally</w> 17069
GP a</w> 17069
mar mos 17065
Bub R1</w> 17065
Fram ingham</w> 17065
vari ably</w> 17064
hyper glycaemia</w> 17064
8 M</w> 17062
re hydration</w> 17061
Ar chi 17061
SYNT HESIS</w> 17061
I -</w> 17060
war ts</w> 17060
PE CAM</w> 17059
GT AT 17058
t ography</w> 17057
Fel low 17057
transpos able</w> 17056
withdraw ing</w> 17054
- related</w> 17053
g 3</w> 17053
LV AD</w> 17053
V AT</w> 17051
intus susception</w> 17051
es cul 17048
SW 6</w> 17048
lic ensing</w> 17048
SF K</w> 17047
k off</w> 17046
Elig ible</w> 17042
K h 17041
Sh p2</w> 17039
Dys function</w> 17037
N Bs</w> 17035
bri a</w> 17035
organ otypic</w> 17035
PG D</w> 17035
immunod ominant</w> 17035
EP SP</w> 17033
p v.</w> 17032
bis pecific</w> 17032
dem ia</w> 17031
lec ture</w> 17029
Strateg y</w> 17028
DIAG NO 17028
Cytotox ic</w> 17026
7 ac</w> 17025
su is</w> 17022
review er</w> 17022
B im 17020
Ry R</w> 17017
pediatr icians</w> 17016
cuc umber</w> 17016
atic ity</w> 17015
intracerebro ventricular</w> 17015
CB M</w> 17014
extrac table</w> 17012
E vent</w> 17009
Transf ected</w> 17009
adiab atic</w> 17006
Ig G2</w> 17005
Dis cre 17004
Figure 2B</w> 17003
glauc omatous</w> 17002
mal function</w> 17001
per il 17000
poly urethane</w> 17000
Determin ants</w> 17000
All ergic</w> 16999
s nor 16998
won dered</w> 16998
Sun ny 16997
Cyp A</w> 16995
le ach 16994
w rin 16992
g st</w> 16991
K PC</w> 16990
varic ose</w> 16988
N T1</w> 16987
sulph ur</w> 16987
el essness</w> 16986
Recru itment</w> 16986
Z f 16985
pur ulent</w> 16985
N IP</w> 16984
HT 2A</w> 16984
f aint</w> 16983
D AC</w> 16982
mat uring</w> 16977
- bi 16976
Sac I</w> 16976
plas ties</w> 16974
V B</w> 16973
protozo a</w> 16973
kil obase</w> 16972
obarbit uric</w> 16971
CK S</w> 16970
I GT</w> 16969
B ou 16969
intro g 16969
can is</w> 16968
Co sta</w> 16968
nucle op 16967
Ig G3</w> 16967
co transporter</w> 16966
pup ils</w> 16966
M PD</w> 16963
AN G 16962
var us</w> 16961
P ere 16960
k ening</w> 16959
ref used</w> 16959
Rever sible</w> 16959
administr ators</w> 16958
Palli ative</w> 16958
coloc alizes</w> 16957
di ap 16956
plant arum</w> 16956
Zh eng</w> 16953
Fib rosis</w> 16952
de paraff 16951
repeti tions</w> 16951
hyper bilirubin 16949
Chem il 16949
tri am 16948
Sup plement</w> 16946
trans forms</w> 16945
Pas sive</w> 16944
bound ed</w> 16943
SP I 16941
S L1</w> 16939
D el</w> 16938
plas tics</w> 16938
micro be</w> 16936
Ch an</w> 16936
hal o 16936
l ou 16935
MC 3T3</w> 16935
Na2 HPO4</w> 16935
vi sit 16934
D SP</w> 16932
process or</w> 16931
BR CT</w> 16931
0 H</w> 16930
Fro zen</w> 16930
phosphoglycer ate</w> 16930
B EC 16928
load ings</w> 16926
ul lin 16925
Biolog icals</w> 16925
ospond in</w> 16925
Sep sis</w> 16924
Spr ing</w> 16924
endor sed</w> 16924
ex 1</w> 16923
tro zole</w> 16923
Compon ent</w> 16923
CO R 16922
U F</w> 16921
SP P</w> 16921
M im 16920
2 x</w> 16919
deferen s</w> 16919
nas ophar 16917
un ity</w> 16916
D re 16912
dis organization</w> 16912
Gi emsa</w> 16912
un trained</w> 16911
At l 16911
Δ 5</w> 16910
Hash imoto</w> 16910
U MP</w> 16909
impro per</w> 16909
dendri mer</w> 16909
sh ore</w> 16908
intra vesical</w> 16900
am ura</w> 16898
te enth</w> 16897
LD A</w> 16894
renew able</w> 16894
d ge 16893
vil eg 16893
myocl onus</w> 16893
un planned</w> 16890
E AC</w> 16887
resi stin</w> 16887
Classi cal</w> 16887
symb ol</w> 16887
v RNA</w> 16886
RP S</w> 16880
shi eld</w> 16880
bu terol</w> 16878
P u</w> 16877
RASS F1A</w> 16876
lim e</w> 16875
glyox al</w> 16875
prevent ative</w> 16874
citi zens</w> 16870
intub ated</w> 16870
ak ic</w> 16869
protec tant</w> 16867
open ings</w> 16867
lipo fus 16864
CEN TRA 16862
PP R</w> 16859
MP L</w> 16859
C m</w> 16858
bur ne 16858
asymp totic</w> 16858
J E</w> 16857
ran ks</w> 16856
m able</w> 16855
L d 16854
av al</w> 16854
bac hia</w> 16854
TR F1</w> 16854
Acc ep 16854
decompens ated</w> 16854
SL C3</w> 16853
O 2-</w> 16852
PR 3</w> 16852
E F2</w> 16851
glucopyran oside</w> 16850
coll ar</w> 16849
ress ings</w> 16848
mon t</w> 16847
AI V</w> 16847
narrow er</w> 16847
glycero ls</w> 16847
cycl ophilin</w> 16846
amino acyl</w> 16845
Stero id</w> 16845
B la 16844
me ets</w> 16843
Ku 8</w> 16843
Osteo arthritis</w> 16843
AI 1</w> 16842
P ND</w> 16839
Cdc 6</w> 16839
Ti ter</w> 16839
he at 16831
t oglobin</w> 16828
hyper methylated</w> 16827
IN T</w> 16825
Fe 2</w> 16823
Appro aches</w> 16819
W N</w> 16818
Neu 5 16816
CTNN B1</w> 16815
H off 16812
on i</w> 16812
allop urinol</w> 16812
f ati 16809
Isra eli</w> 16809
Angi ogenesis</w> 16808
se ated</w> 16806
x l</w> 16805
ocytop enia</w> 16805
t og 16803
pal mar</w> 16803
throm bectomy</w> 16801
u in</w> 16800
bra sili 16800
Poly comb</w> 16800
hapl ogro 16799
biolog ics</w> 16799
ER K5</w> 16798
Res veratrol</w> 16797
Conflic t</w> 16797
HF S</w> 16796
Down regulation</w> 16795
G U</w> 16792
EC F</w> 16792
thrombo plastin</w> 16791
ro ad 16789
S SCP</w> 16788
trans plantations</w> 16788
hex ose</w> 16788
mel i 16787
dehydro epiandrosterone</w> 16786
E b 16785
ann a</w> 16783
V X</w> 16782
HC O</w> 16781
prof essions</w> 16780
Meas ure</w> 16780
spin ach</w> 16779
A ro 16777
don ated</w> 16776
I CT</w> 16774
On going</w> 16774
sirtu in</w> 16774
Auth or</w> 16772
clau dication</w> 16770
pri son 16769
ubiquit ylated</w> 16769
w iring</w> 16767
ch ore 16767
child bearing</w> 16764
minim izes</w> 16763
vascul opathy</w> 16763
rati ves</w> 16762
exc itations</w> 16761
prop eller</w> 16761
Tetra hymena</w> 16761
N P4</w> 16760
Ser 6</w> 16755
j udge</w> 16754
own s</w> 16754
ate -</w> 16753
as cin</w> 16751
dys one</w> 16751
col l</w> 16750
Sol anum</w> 16747
contain ers</w> 16747
daugh ters</w> 16745
fl ushing</w> 16744
NS CL 16744
Fb x 16744
wo od 16742
wo unded</w> 16742
Ben j 16742
sym biosis</w> 16741
op ril</w> 16739
Fig. 6A</w> 16739
indic a</w> 16738
er g</w> 16737
mp 1</w> 16737
crust ace 16737
lax ity</w> 16736
ind welling</w> 16734
plasm atic</w> 16733
Isol ates</w> 16733
later alis</w> 16733
rea u</w> 16731
predomin ately</w> 16731
stimul ants</w> 16729
phyto chemicals</w> 16729
ab at 16728
millili ter</w> 16728
tetr afluoro 16726
CV C</w> 16724
sis tence</w> 16723
PGF 2</w> 16723
bio char</w> 16721
ero sions</w> 16720
transpor ts</w> 16720
an ec 16719
at ment</w> 16718
wr apped</w> 16718
un aware</w> 16715
inter professional</w> 16714
K appa</w> 16713
gl acial</w> 16713
sens ations</w> 16713
F le 16711
en forced</w> 16711
co immunoprecipitated</w> 16711
mal nourished</w> 16710
Bub 1</w> 16709
Perin atal</w> 16709
Bi FC</w> 16708
row ing</w> 16708
sub cloning</w> 16707
un balanced</w> 16706
zz led</w> 16706
vol can 16705
PEG ylated</w> 16705
C entr 16704
GR E</w> 16704
Del phi</w> 16703
S A1</w> 16700
geran yl 16699
compl ic 16698
enum eration</w> 16697
Fmo c</w> 16696
del im 16695
poly somes</w> 16695
photo thermal</w> 16695
bromo domain</w> 16694
d R</w> 16691
tax anes</w> 16691
insec urity</w> 16691
tr uth</w> 16690
ple ura</w> 16690
depress ant</w> 16690
Val 1</w> 16688
Egyp tian</w> 16687
Ap gar</w> 16685
GEN E 16685
Homo zygous</w> 16685
Ak t2</w> 16684
stron g 16682
granul omatosis</w> 16682
electroencephal ography</w> 16682
bis phenol</w> 16681
R et</w> 16680
sal va</w> 16680
line zolid</w> 16679
strati fy</w> 16678
CP M</w> 16677
proteoly tically</w> 16677
o rest</w> 16676
AK O</w> 16676
aquap orin</w> 16676
c ecum</w> 16675
ble b 16673
ca dic</w> 16672
Medi ated</w> 16672
benz ofur 16670
elu ate</w> 16668
t actic</w> 16667
D AD</w> 16667
pl ugs</w> 16667
photoco agulation</w> 16665
W ill 16662
S omat 16661
ther mol 16661
glycolip id</w> 16661
Wel fare</w> 16660
in homogeneous</w> 16655
other mic</w> 16655
en vis 16654
New ly</w> 16653
Se iz 16652
ox LDL</w> 16651
Der iv 16651
Hil den</w> 16651
t le 16650
Mi x 16650
Rock ville</w> 16649
bi partite</w> 16648
troph ozo 16647
bo died</w> 16646
pur o</w> 16646
relax in</w> 16646
Cy ste 16645
CH2 Cl2</w> 16645
L AR 16644
sol idi 16641
lear ners</w> 16640
juris dic 16640
chlor ite</w> 16638
hydroxy dopamine</w> 16638
ur ve 16637
I DA</w> 16636
ventro medial</w> 16636
Histor ically</w> 16635
o opho 16634
rati onally</w> 16633
contin ental</w> 16633
ge o 16632
au ro 16631
A J</w> 16630
Kv 4</w> 16630
Surve ys</w> 16628
c ups</w> 16627
CC R</w> 16626
Phosph o</w> 16625
ul ant</w> 16622
imag inal</w> 16621
Glu A2</w> 16620
mit oxantrone</w> 16619
Sm ur 16619
c 6</w> 16618
S od 16618
hem olymph</w> 16617
B NI 16616
diff using</w> 16616
n ac 16614
w are</w> 16614
X Y 16613
neuro behavioral</w> 16612
che ap</w> 16611
disco ideum</w> 16610
efflu ents</w> 16610
gen ol</w> 16608
conver tase</w> 16607
pyridox al</w> 16607
d P</w> 16606
sel ectable</w> 16606
citr ulline</w> 16606
Neutroph il</w> 16603
neighbo ur 16601
ten in</w> 16599
ve t</w> 16599
emp ferol</w> 16599
metabol ize</w> 16598
immun operoxidase</w> 16597
bo id</w> 16597
ric kett 16596
Ser 7</w> 16594
muscul us</w> 16594
A ACT 16593
J R</w> 16591
Se V</w> 16590
Vari able</w> 16590
SIR T2</w> 16590
den ly</w> 16589
S pace</w> 16588
Educ ational</w> 16588
P CD 16586
aldosteron ism</w> 16582
G c</w> 16581
co tinine</w> 16581
Non invasive</w> 16580
pollin ation</w> 16580
nucle olin</w> 16579
exha ust</w> 16579
cu ed</w> 16578
adal imumab</w> 16577
Burling ame</w> 16577
f u</w> 16573
H ut 16572
MR E</w> 16572
papill ae</w> 16571
Polym er</w> 16570
b am 16568
ar bor 16568
ens ation</w> 16568
sap iens</w> 16567
el ic</w> 16564
CYP3 A5</w> 16564
C atalytic</w> 16563
μ s</w> 16563
cl ed</w> 16563
del icate</w> 16562
B ell</w> 16561
ph asing</w> 16561
bl ends</w> 16559
Wa ve</w> 16559
spectrophot ometrically</w> 16559
Wol bachia</w> 16559
ΔN p6</w> 16558
galact osamine</w> 16557
sho ts</w> 16556
Gl i</w> 16556
Colon ies</w> 16556
E OR 16552
APA CHE</w> 16552
n owadays</w> 16540
gen iculate</w> 16539
recti fying</w> 16538
ALD H2</w> 16538
collo ids</w> 16537
6 L1</w> 16536
SUMO 1</w> 16536
Struc tured</w> 16535
Cag A</w> 16535
pro active</w> 16533
un ate</w> 16533
Di am 16533
oste olysis</w> 16533
DM N</w> 16532
Z im 16531
am pero 16530
deoxy nucleotidyl</w> 16530
Ar ticle</w> 16529
DR E</w> 16529
li zation</w> 16525
br an</w> 16523
Consider ations</w> 16523
glut aryl</w> 16522
stabil isation</w> 16522
ELI SAs</w> 16522
ass aying</w> 16521
R og 16520
ci vil 16518
Sir 2</w> 16517
α IIb 16516
sel f 16516
Fo ot</w> 16516
Inj uries</w> 16516
poli omyelitis</w> 16516
Post operatively</w> 16514
mycel ium</w> 16514
m j 16512
in del</w> 16512
tri um</w> 16512
ro oted</w> 16511
infarc tions</w> 16511
V NTR</w> 16510
peren nial</w> 16510
s as</w> 16509
pain less</w> 16509
Me tro 16508
Sc eI</w> 16507
cra b</w> 16507
dis advantaged</w> 16505
trape z 16505
fa una</w> 16504
m asse 16503
lati tude</w> 16503
D exam 16502
ef avirenz</w> 16501
v end 16500
prolong s</w> 16500
Garc ia</w> 16499
us tine</w> 16498
ni x</w> 16497
perim eter</w> 16496
Hung ary</w> 16496
Hb A</w> 16495
amer ic 16495
RE F</w> 16493
For mal 16493
Vi ability</w> 16493
expon ent</w> 16493
Scop us</w> 16493
t yro 16492
un conjugated</w> 16492
ven s</w> 16492
glucuron idation</w> 16492
S cor 16491
C PE 16490
physi o 16489
L PO</w> 16488
ogra fted</w> 16488
chol ic</w> 16488
but ter 16488
KL F1</w> 16488
am atsu</w> 16487
HC L</w> 16487
g ut 16486
OR S</w> 16486
In genuity</w> 16485
if loxacin</w> 16484
O 8</w> 16483
Demon stration</w> 16483
Un its</w> 16481
Y b</w> 16480
amb ulation</w> 16479
d inger</w> 16478
intra oral</w> 16478
Mari e</w> 16478
resp ects</w> 16476
vit ronectin</w> 16475
cure ttage</w> 16473
hn RN 16472
un familiar</w> 16471
sal es</w> 16471
protop orphyrin</w> 16471
In her 16470
F n</w> 16468
Rob ust</w> 16468
it i</w> 16467
pat chy</w> 16464
L NA</w> 16463
Ar teri 16462
T ot 16461
amin ol 16461
ord inate</w> 16458
SC O</w> 16454
nym ph 16454
pre mise</w> 16453
photo volta 16453
7 I</w> 16451
Cro ati 16451
B Z</w> 16450
Radi ology</w> 16449
An ton 16448
PS G</w> 16448
tur bin 16447
phos ate</w> 16443
I VA</w> 16442
sub surface</w> 16441
cyclohex ane</w> 16441
phy sec 16440
K ri 16438
govern mental</w> 16437
D ax 16435
Ca SR</w> 16434
SN V</w> 16433
b es 16431
F -</w> 16430
ore activity</w> 16429
phag osomes</w> 16427
L F1</w> 16425
meth ion 16425
An d 16425
PA NC</w> 16424
regul arities</w> 16424
ur i</w> 16422
Obste trics</w> 16422
Peri odon 16419
IN 2</w> 16418
GTP γS</w> 16418
sideroph ore</w> 16417
LQ TS</w> 16416
E PI 16415
retri eve</w> 16414
la tently</w> 16413
thermod ynamically</w> 16411
Inter views</w> 16409
su p</w> 16408
Bar riers</w> 16408
H T3</w> 16407
it umumab</w> 16407
remif entanil</w> 16406
ox igenin</w> 16405
S aint</w> 16403
Ct IP</w> 16403
hyper polarized</w> 16402
phosphatidyl glycerol</w> 16402
gall stone</w> 16402
P IF 16401
phaco emulsification</w> 16401
D 2R</w> 16399
P GF</w> 16398
oro logical</w> 16398
em pi 16397
inter costal</w> 16396
compar ably</w> 16396
phot otherapy</w> 16395
Frac tures</w> 16395
F ru 16393
ver sary</w> 16393
rep ell 16393
acetyl ene</w> 16392
Phe 1</w> 16390
gal anin</w> 16389
dy nor 16387
tryp tase</w> 16385
B AK</w> 16384
Bio tec</w> 16384
M A1</w> 16383
X 0</w> 16382
leukem ogenesis</w> 16382
0 W</w> 16380
lipoly tic</w> 16380
D AM 16379
asym metries</w> 16378
parac ellular</w> 16378
phenol ics</w> 16376
Tr p 16375
si t</w> 16374
dg ed</w> 16373
Cul ex</w> 16373
Ry R1</w> 16373
b ons</w> 16372
V ATS</w> 16372
conform al</w> 16371
l anding</w> 16369
some how</w> 16369
coagul ase</w> 16369
- triphosphate</w> 16367
hyaluron idase</w> 16367
Fcγ R</w> 16366
pris m</w> 16364
Frank lin</w> 16364
re ar</w> 16363
inc urred</w> 16363
M i</w> 16361
tar trate</w> 16359
qu ench</w> 16358
invol ution</w> 16358
R oss</w> 16356
L C5</w> 16356
mal occlusion</w> 16356
star ter</w> 16356
Tr CP</w> 16351
sud denly</w> 16351
1 Q</w> 16350
M PR</w> 16350
v a 16350
sol it 16350
FAS N</w> 16350
phosph oc 16349
under take</w> 16347
pow ers</w> 16347
autom ation</w> 16346
cortic ospinal</w> 16344
stere oc 16344
Ig D</w> 16343
M AG</w> 16341
in dium</w> 16341
ten sis</w> 16341
non ionic</w> 16341
ben z</w> 16340
astro glial</w> 16340
thorac olumbar</w> 16340
scrip ts</w> 16339
Pa rent</w> 16339
excit otoxic</w> 16338
Cyto kines</w> 16338
S MC 16337
Be g 16337
resum ption</w> 16337
Font an</w> 16337
5 Q</w> 16335
I rish</w> 16335
anti codon</w> 16335
age able</w> 16335
hyper polarizing</w> 16331
osyl ceramide</w> 16331
M RE1</w> 16330
Am ster 16330
densit ometric</w> 16329
plant a</w> 16328
b ore</w> 16326
Sch ed 16326
y ramidal</w> 16325
K az 16325
ad or</w> 16325
2 Y</w> 16323
F G 16323
ch able</w> 16323
bro minated</w> 16322
Competi tion</w> 16322
liqu or</w> 16321
fat alities</w> 16320
Gate way</w> 16319
Glu R2</w> 16318
de formations</w> 16315
radio iodine</w> 16314
Que en 16314
. 6A</w> 16313
bud esonide</w> 16313
ep sins</w> 16312
tim ulation</w> 16312
ST EM</w> 16309
tropic alis</w> 16308
de g</w> 16307
AT RX</w> 16307
EB NA</w> 16307
uro dynamic</w> 16305
no tation</w> 16304
lor dosis</w> 16304
AS S</w> 16303
don ovani</w> 16303
glob us</w> 16303
Y e 16301
carc asses</w> 16298
phosphati dyl</w> 16297
C ab 16296
inter chang 16295
hom otypic</w> 16292
Con sumption</w> 16292
he i</w> 16291
S top</w> 16290
E 1B</w> 16290
P SF</w> 16288
L SM 16288
STUDI ES</w> 16288
me ropenem</w> 16287
radi opharmac 16287
wa king</w> 16287
Juven ile</w> 16287
ero usly</w> 16285
nes tic</w> 16285
t r</w> 16282
Amb ulatory</w> 16280
mod ulations</w> 16279
diphenyl tetrazolium</w> 16279
P av 16278
D allas</w> 16278
enzym ic</w> 16278
We ek</w> 16278
Amster dam</w> 16277
notic eably</w> 16275
gelatin ase</w> 16275
l end</w> 16272
ε 4</w> 16272
uron ium</w> 16272
own ed</w> 16271
AP 3</w> 16270
am idase</w> 16269
spac ers</w> 16269
Dist al</w> 16269
homogen izer</w> 16267
idi zed</w> 16264
parox etine</w> 16263
DISC 1</w> 16260
BT V</w> 16260
Gib bs</w> 16260
rh ab 16259
neo intimal</w> 16259
analy tically</w> 16257
star s</w> 16257
stor m</w> 16256
d ressings</w> 16255
P x</w> 16255
therapeu tical</w> 16255
T yro 16254
d r</w> 16252
uro logy</w> 16252
Sal v 16252
inc urable</w> 16250
tt 1</w> 16250
night time</w> 16250
op us</w> 16249
B and</w> 16248
RA M</w> 16247
lu tide</w> 16244
pp Gpp</w> 16244
MY O 16244
bis muth</w> 16244
ac tant</w> 16242
Mob ile</w> 16242
gab apentin</w> 16242
im bic</w> 16241
ne eding</w> 16241
Figure 5 16240
sc arc 16239
assi stants</w> 16239
daun orubicin</w> 16239
tetrachlor ide</w> 16238
ethyl amine</w> 16237
EN CE</w> 16236
Z P</w> 16235
nan oclus 16233
echocardi ogram</w> 16233
h oma</w> 16231
chondro sarcoma</w> 16231
p ies</w> 16230
ol itinib</w> 16229
pro virus</w> 16229
hypo tonia</w> 16227
S ell 16225
hyp notic</w> 16224
Datas et</w> 16224
Fg fr 16222
TC E</w> 16221
ro l 16219
gn ath 16215
CB L</w> 16215
abbrevi ated</w> 16215
amelior ating</w> 16213
lanth anide</w> 16213
Ez h2</w> 16213
purch ase</w> 16212
Prophyl actic</w> 16212
os pecific</w> 16211
and amide</w> 16210
TO P</w> 16210
C EB 16209
Gon z 16209
A bo 16208
ic z</w> 16208
mamm ographic</w> 16206
E n</w> 16205
aut ocor 16205
IC F</w> 16205
vesti bul 16205
HNF 4α</w> 16205
sal ping 16204
ME A</w> 16201
P ra 16200
mas ticatory</w> 16200
kerato conus</w> 16200
sen sed</w> 16199
PRO BLE 16198
Vol tage</w> 16198
Mach ine</w> 16197
glutam icum</w> 16196
Conform ational</w> 16195
all ylic</w> 16193
Supplem entation</w> 16193
MO L</w> 16193
otox ic 16192
n el 16191
logarith m</w> 16191
Cd t1</w> 16188
noro virus</w> 16188
cit rant</w> 16187
Par adox 16186
RB Ps</w> 16185
ich thy 16184
Con sul 16183
counter acting</w> 16180
fac t 16179
recep tion</w> 16179
Co sts</w> 16178
w ings</w> 16177
aort as</w> 16177
prim ase</w> 16176
discer n</w> 16176
sg RNAs</w> 16175
AR A</w> 16174
amp s</w> 16173
CO DE</w> 16172
Pr EP</w> 16170
B q</w> 16169
pl ague</w> 16167
chrono usly</w> 16167
Dexam ethasone</w> 16165
prog est 16164
emin ence</w> 16163
inf l 16162
AG AG 16162
Rel ap 16162
m J</w> 16161
po ised</w> 16161
sh uff 16160
anti nociception</w> 16159
minim a</w> 16159
PP i</w> 16159
CD R3</w> 16156
IV M</w> 16156
ach al 16155
I K</w> 16154
AN TI 16154
Aggreg ation</w> 16154
amoun ted</w> 16153
X V</w> 16151
Al li 16151
ur ified</w> 16150
acul ture</w> 16150
cal bindin</w> 16149
stand point</w> 16148
oste openia</w> 16148
her tz</w> 16148
Mo roc 16147
veter in 16147
H ome 16145
L amin 16145
Mar k</w> 16144
ch a</w> 16143
pos ting</w> 16142
fl utter</w> 16142
ran itidine</w> 16141
methyl phenidate</w> 16141
Val idity</w> 16140
supra ventricular</w> 16140
water shed</w> 16139
Upreg ulation</w> 16139
adher ing</w> 16138
na proxen</w> 16137
nom ogram</w> 16137
ed itorial</w> 16136
CC CP</w> 16136
GluN 2B</w> 16136
plex iform</w> 16135
endang ered</w> 16135
8 del</w> 16133
post test</w> 16133
cra ft</w> 16133
Adap tation</w> 16133
b A</w> 16132
cl ipping</w> 16132
Y an</w> 16131
ocyste ine</w> 16131
ER alpha</w> 16130
myelo fibrosis</w> 16130
hop ed</w> 16128
ΔΔ Ct</w> 16128
ig ene</w> 16127
Cal i 16127
GSE 2</w> 16126
reduc tases</w> 16125
topo tecan</w> 16125
Arg inine</w> 16124
Dyn abeads</w> 16124
Par as 16123
- flanking</w> 16122
ni er</w> 16122
un loading</w> 16120
ocyto plasmic</w> 16120
Kod ak</w> 16120
sci entif 16114
h ope 16113
ceram ics</w> 16111
AP N</w> 16109
- labeled</w> 16108
Saf e</w> 16108
L SC</w> 16107
CG AT 16107
ect ant</w> 16106
cyto kinin</w> 16105
Prote omics</w> 16105
destin ed</w> 16104
teri on</w> 16103
S 1D</w> 16101
glycosi dic</w> 16101
feat ured</w> 16098
p icro 16096
enol one</w> 16096
CH AR 16095
L ic 16092
gonadotrop ins</w> 16092
H os 16090
extrem es</w> 16089
Cd S</w> 16088
Stat 1</w> 16087
ST OP</w> 16086
suff ers</w> 16086
Tub ulin</w> 16086
DL D</w> 16085
P2 Y</w> 16085
R im 16084
ophil es</w> 16084
GAT A4</w> 16084
tac kle</w> 16084
L epid 16081
qu est</w> 16080
lig n 16080
don ating</w> 16079
Apop totic</w> 16079
C KO</w> 16078
I DC</w> 16078
immuno chemical</w> 16076
optim ised</w> 16076
ampl e</w> 16073
e sional</w> 16072
phob ic</w> 16072
J S</w> 16068
mGlu R</w> 16064
Def ining</w> 16063
g DNA</w> 16062
rec ession</w> 16062
S HO 16061
decid uous</w> 16060
rin sing</w> 16059
R ip 16058
targ et 16057
flavi virus</w> 16057
S na 16055
Y ap</w> 16054
infiltr ative</w> 16054
W 6</w> 16052
at i</w> 16052
Cl ose</w> 16052
oscill ators</w> 16052
at razine</w> 16051
PT V</w> 16051
TA VI</w> 16050
ogran in</w> 16050
gol d 16049
In patient</w> 16048
intrac oronary</w> 16048
PO C</w> 16048
I SC</w> 16046
Per u</w> 16046
s GC</w> 16045
Rec eptors</w> 16045
empy ema</w> 16045
empi ric</w> 16045
tor tu 16044
M ut</w> 16043
L ist</w> 16043
in cl 16043
Minim ally</w> 16043
Phosph ati 16042
fruc to 16042
intran asally</w> 16042
S Ds</w> 16041
HR E</w> 16039
Coul om 16039
su turing</w> 16038
vit ality</w> 16038
R hin 16035
O range</w> 16035
E pit 16031
-- an</w> 16031
di lol</w> 16030
immun ogold</w> 16030
consul ted</w> 16030
αIIb β3</w> 16030
multi plied</w> 16029
PL E 16029
oro facial</w> 16029
F i</w> 16028
isol e</w> 16028
FA O</w> 16028
sti ff</w> 16027
perme ant</w> 16026
holog raph 16026
nat ally</w> 16025
ot gun</w> 16024
ME 1</w> 16023
Cor d</w> 16023
Alg orith 16023
inv al 16022
decre ment</w> 16021
thi ourea</w> 16020
volati les</w> 16020
sil k 16019
ME N1</w> 16018
P n 16017
pige ons</w> 16017
n ate</w> 16016
B SE</w> 16015
sen ses</w> 16015
Im mobil 16015
Bay es</w> 16015
pall idum</w> 16013
BU N</w> 16013
a den</w> 16012
sulf one</w> 16012
EV AR</w> 16012
Syst olic</w> 16012
immun ogen</w> 16011
PK S</w> 16011
DH 5α</w> 16011
som ata</w> 16010
Poly clonal</w> 16009
wid ening</w> 16008
hypere mia</w> 16007
adh esin</w> 16007
Inte rest</w> 16006
Par kinson 16006
cl ock 16003
may be</w> 16002
sto ols</w> 16001
Tyr 3</w> 16000
Tie 2</w> 16000
TU B 15998
anil ide</w> 15998
PE DF</w> 15997
H arbor</w> 15996
Hel ix</w> 15996
le vel 15995
we ap 15995
pi rosis</w> 15994
tri phenyl 15991
separ ates</w> 15991
Hol ter</w> 15991
Par a 15989
th ol</w> 15987
un fractionated</w> 15987
sialy lated</w> 15985
c. 5</w> 15984
x B</w> 15983
ame tinib</w> 15983
oli posomes</w> 15981
Queen sland</w> 15980
ste ep 15978
HI S3</w> 15977
incorrec tly</w> 15977
On co 15976
normal ised</w> 15976
maph eresis</w> 15976
occupati ons</w> 15976
C ape</w> 15975
c .4</w> 15975
dr yness</w> 15974
sc is 15973
Roch ester</w> 15972
. 1C</w> 15970
bis -</w> 15970
motoneu ron</w> 15970
Man aging</w> 15968
CYP2 B6</w> 15968
per ine 15966
contra indicated</w> 15966
TC D</w> 15964
lipo philicity</w> 15964
rati a</w> 15963
c GAS</w> 15962
micro algae</w> 15961
AI T</w> 15960
GG GG 15960
S end 15959
N ET 15958
F r</w> 15957
G DS</w> 15957
as matic</w> 15957
N Abs</w> 15956
U CB</w> 15956
oc tamer</w> 15956
non union</w> 15955
ero genic</w> 15955
CD T</w> 15954
Il lin 15954
pri vileg 15953
there in</w> 15953
di dn</w> 15952
PP E</w> 15952
enantiom eric</w> 15950
q i</w> 15949
as trin</w> 15949
en forcement</w> 15948
Sm ads</w> 15948
Fol l 15944
de differentiation</w> 15943
ron olactone</w> 15943
az ido</w> 15942
sym posium</w> 15942
Am p</w> 15942
hypos padias</w> 15942
V ul 15937
C oupling</w> 15934
Ar f6</w> 15933
Establ ishment</w> 15931
Seroton in</w> 15930
assi stant</w> 15927
GI S</w> 15927
bromo deoxyuridine</w> 15927
Issu es</w> 15927
Ch it 15926
Sunny vale</w> 15924
recapit ulates</w> 15920
troph oblastic</w> 15918
endoscop ically</w> 15917
P W</w> 15916
IP 3R</w> 15916
arrowhe ads</w> 15915
S low</w> 15914
- associated</w> 15912
se af 15912
M us</w> 15911
me sial</w> 15908
anti social</w> 15908
py ram 15908
Method ology</w> 15908
tran scri 15907
5 alpha</w> 15906
buff alo</w> 15906
coerul eus</w> 15902
P ugh</w> 15901
K if 15901
B aker</w> 15899
SW L</w> 15899
plethysm ography</w> 15899
Ble eding</w> 15898
SR M</w> 15897
asp ing</w> 15896
corner stone</w> 15896
fill er</w> 15895
Alber ta</w> 15895
M el</w> 15893
sp end</w> 15893
ver si 15893
T M2</w> 15892
un liganded</w> 15890
design ation</w> 15890
hepatocarcin ogenesis</w> 15889
pup al</w> 15888
ur ally</w> 15887
OR F3</w> 15887
Sub cellular</w> 15887
sep ta</w> 15887
pall idus</w> 15887
decont amination</w> 15886
op razole</w> 15885
Ac ids</w> 15885
envelop es</w> 15884
thri ve</w> 15884
angio ten 15882
D MI</w> 15881
succ ession</w> 15880
d ome</w> 15878
gover ns</w> 15878
ve dilol</w> 15877
predomin ate</w> 15877
ent e</w> 15876
mor b 15876
pris tine</w> 15876
pe sts</w> 15875
bit ter</w> 15874
Gro EL</w> 15874
cholest atic</w> 15874
Ele ments</w> 15873
Nico tine</w> 15873
tyros ines</w> 15872
def er 15871
Long er</w> 15870
Scal es</w> 15868
cinere a</w> 15868
Char cot</w> 15867
gel ation</w> 15867
ud inal</w> 15866
my corrhizal</w> 15866
Ag ents</w> 15866
endos ym 15864
D ET 15863
radi ologically</w> 15863
NL R 15863
micronucle us</w> 15863
la w 15862
ME LD</w> 15861
Networ ks</w> 15860
micro dissection</w> 15859
enhanc ements</w> 15859
ox yl</w> 15858
dist ally</w> 15858
Illin ois</w> 15858
S clerosis</w> 15856
thi obarbituric</w> 15855
ocla x</w> 15853
multi plic 15852
ambul ance</w> 15852
undes cribed</w> 15852
spec ifying</w> 15850
man ometry</w> 15850
as tigotes</w> 15848
immun izations</w> 15848
Path ologic</w> 15845
OR C1</w> 15843
elu ent</w> 15842
E long 15841
Ther m 15841
exacerb ates</w> 15841
cin olone</w> 15839
ali ke</w> 15839
x olitinib</w> 15838
cross links</w> 15837
All ergy</w> 15837
IC 1</w> 15836
Co vance</w> 15835
cephalospor in</w> 15835
H at 15833
eng ulf 15831
extrac ellularly</w> 15831
hypoc otyl</w> 15831
Sud den</w> 15831
industri alized</w> 15830
S cri 15829
ellip so 15829
am eth 15828
An tic 15828
S BS</w> 15827
under scoring</w> 15827
cap s</w> 15827
hydrox yc 15827
si i</w> 15826
Mar ine</w> 15826
3 g</w> 15825
tim ore</w> 15825
vast us</w> 15825
ist 1</w> 15824
Reg ular</w> 15822
T RA</w> 15821
sun flower</w> 15820
Osteopo rosis</w> 15820
AR I</w> 15819
secre tag 15819
V ill 15818
cyt ogenetics</w> 15818
abor tus</w> 15817
hemangi omas</w> 15817
cuve tte</w> 15817
corpor a</w> 15816
Multic enter</w> 15815
2 m</w> 15814
S SRI</w> 15814
de polymer 15814
xyl azine</w> 15814
Ctr l</w> 15814
super coiled</w> 15813
E asy</w> 15812
Adequ ate</w> 15812
con son 15811
pU C1</w> 15811
G PA</w> 15810
C oupled</w> 15808
mes h 15808
Figure 3B</w> 15808
R KO</w> 15807
ec dysone</w> 15807
P Z</w> 15806
ost om 15806
phyl a</w> 15805
DAP T</w> 15804
trifluoro methyl</w> 15804
unic ellular</w> 15804
te tran 15803
di imide</w> 15803
ec o</w> 15803
Integr in</w> 15803
C y</w> 15801
IL s</w> 15801
micro satellites</w> 15800
ap pliance</w> 15799
tung sten</w> 15798
s way</w> 15797
g ills</w> 15796
CR Y 15796
stro m</w> 15795
Su peri 15795
lo zin</w> 15793
transl ates</w> 15793
N owadays</w> 15792
fore head</w> 15791
ab users</w> 15790
C ow 15789
iod o</w> 15788
BI O</w> 15788
H PA 15787
l af 15787
non competitive</w> 15787
PKC ε</w> 15787
In fusion</w> 15785
Scot t</w> 15785
vig ne 15783
mirro rs</w> 15783
dichotom ous</w> 15782
yn go 15781
Chi ari</w> 15780
T opo 15778
oc ked</w> 15778
ba 1</w> 15778
Ax on</w> 15778
D BT</w> 15776
tan gen 15776
Tn 5</w> 15775
hemizyg ous</w> 15774
te tras 15773
or na 15773
organis mal</w> 15773
allo t</w> 15773
o kes</w> 15771
fem oris</w> 15771
M u</w> 15769
et ti</w> 15769
glycolip ids</w> 15768
ax anthin</w> 15767
Fi bri 15766
N TR 15764
spiro ch 15764
Mon tre 15762
L es 15761
Stat 5</w> 15761
A 1C</w> 15760
Asp irin</w> 15760
V enti 15757
Subj ect</w> 15755
m assively</w> 15754
spec tac 15754
vap our</w> 15752
te therin</w> 15751
hetero chromatic</w> 15751
DR 3</w> 15751
Acc ession</w> 15751
contrac tures</w> 15750
tap ered</w> 15750
multim ers</w> 15749
T BC 15748
Des cription</w> 15748
me ga 15747
C ann 15745
j obs</w> 15744
S ear 15744
p ine 15744
ver tex</w> 15743
Qu estions</w> 15743
radiolab elled</w> 15743
J in</w> 15742
bi tis</w> 15742
Rec Q</w> 15742
sp eptin</w> 15740
SI A</w> 15740
IC P2</w> 15739
lute olin</w> 15739
PR D</w> 15738
abstr acted</w> 15738
umb ers</w> 15736
sigmo idal</w> 15736
guanid inium</w> 15736
obste trical</w> 15735
hete ron 15735
catastroph e</w> 15734
bi zumab</w> 15733
2 B1</w> 15732
conf used</w> 15732
reg isters</w> 15731
re combined</w> 15730
non obese</w> 15730
b rene</w> 15728
ot i</w> 15728
co done</w> 15728
s IL</w> 15726
centri oles</w> 15726
e uch 15725
Ch ondro 15725
L y</w> 15724
glyc opeptides</w> 15724
visu ospatial</w> 15722
st ayed</w> 15720
facult ative</w> 15720
em an 15717
HP R</w> 15717
F uc 15716
resum ed</w> 15716
wor ry</w> 15715
od ynamically</w> 15714
Eff iciency</w> 15714
see ing</w> 15714
Sign als</w> 15714
MIC A</w> 15714
prote omes</w> 15713
2 W</w> 15712
ycl o</w> 15712
CO P1</w> 15711
Eu gene</w> 15711
5 X</w> 15708
adver ti 15708
rein forces</w> 15706
AR B</w> 15703
maneu vers</w> 15701
. 2C</w> 15698
hetero trophic</w> 15698
mam mal</w> 15698
CN TF</w> 15698
narrow ed</w> 15698
end ronate</w> 15696
radi ographically</w> 15696
m Ab 15694
remo vable</w> 15694
P Q 15693
CLO CK</w> 15693
EOR TC</w> 15693
5 g</w> 15692
tric arboxylic</w> 15692
eclamp tic</w> 15692
ex on 15691
Bal timore</w> 15691
N PS</w> 15690
per sic 15689
ion otropic</w> 15687
omen ing 15687
desic cation</w> 15686
HR M</w> 15685
arthro pod</w> 15684
F H1</w> 15683
bur y</w> 15683
apenta enoic</w> 15682
v ent 15681
O PA1</w> 15681
e u</w> 15679
i j</w> 15679
Spe ech</w> 15679
S 4C</w> 15678
galact os 15677
catch ment</w> 15677
Aut ologous</w> 15675
cent uries</w> 15674
BMD Cs</w> 15673
PO A</w> 15672
vide ot 15672
burne tii</w> 15672
C ay 15671
U BE 15671
ep irubicin</w> 15671
bi refr 15671
mill ing</w> 15671
m ons</w> 15670
inf lo 15670
Pl atinum</w> 15670
id azole</w> 15669
cat fish</w> 15669
Pseud o 15669
perc us 15668
Ehr lich</w> 15668
B c 15667
et om 15667
stabil izer</w> 15667
co infected</w> 15664
chemo therapies</w> 15664
phag osome</w> 15664
on ych 15663
Aut omatic</w> 15663
CI P1</w> 15662
distrib ute</w> 15661
V p</w> 15660
kary otypes</w> 15660
DP PC</w> 15658
or d</w> 15657
PL N</w> 15657
Wri ting</w> 15657
L b 15654
ap ed</w> 15654
megakary ocyte</w> 15653
az i</w> 15652
impul sive</w> 15652
cal end 15649
Collabor ation</w> 15648
pol io</w> 15646
cr ic 15646
DE HP</w> 15646
Cor responding</w> 15646
in ones</w> 15644
nit ride</w> 15644
OR F7</w> 15644
weak ening</w> 15644
N GAL</w> 15643
dic arbox 15643
synapt ogenesis</w> 15643
o ogenesis</w> 15641
am enorrhea</w> 15641
archi val</w> 15641
7 Q</w> 15640
m um</w> 15639
standardi ze</w> 15638
N utlin</w> 15637
oper ant</w> 15636
PRI SM</w> 15636
inev itably</w> 15634
T im</w> 15633
ur ization</w> 15633
fasci itis</w> 15633
Mg ATP</w> 15632
mat ri 15627
Hep 3B</w> 15626
phyto hemagglutinin</w> 15624
hi p 15622
ach er</w> 15622
St AR</w> 15621
O HD</w> 15619
Y P</w> 15619
mor i</w> 15619
homo zygote</w> 15619
N ICD</w> 15618
autonom ously</w> 15618
W ood</w> 15617
vol vulus</w> 15617
metasta size</w> 15617
assign ing</w> 15617
lo femoral</w> 15616
Syn ergy</w> 15616
post al</w> 15613
anti -</w> 15611
Cd 2</w> 15611
nit rox 15609
Col on</w> 15609
re ef</w> 15608
miti gating</w> 15608
enig matic</w> 15606
micro sphere</w> 15605
ro ads</w> 15604
Syn chron 15604
Of ten</w> 15604
Mn Cl2</w> 15603
in consistencies</w> 15601
ic ks</w> 15601
gras p</w> 15600
TSP O</w> 15600
- cyclic</w> 15597
DN 1</w> 15597
ak is</w> 15596
lip ogenic</w> 15595
l .</w> 15594
aggreg ating</w> 15592
predisp oses</w> 15592
hem if 15591
scarc ity</w> 15591
conc e 15590
AT X 15590
A K1</w> 15589
har sh</w> 15589
g 0</w> 15587
Figure 2 15587
tuber cle</w> 15587
reg ained</w> 15586
phosphoinosi tides</w> 15586
Z am 15585
gal lium</w> 15585
tan i</w> 15585
homo -</w> 15585
rac e 15583
is ations</w> 15582
if olia</w> 15581
SN AREs</w> 15581
macro autophagy</w> 15581
F im 15580
halogen ated</w> 15580
b esin</w> 15579
pGE M</w> 15579
invad opodia</w> 15579
all ant 15578
hydraz ide</w> 15578
nucle oplasm</w> 15577
NE U 15577
Tip 6</w> 15575
ver ing</w> 15574
cle arer</w> 15574
Sw i 15574
pyrimid ines</w> 15574
ren es</w> 15573
di le 15572
pro coagulant</w> 15572
Alex a 15572
cTn T</w> 15570
NB S1</w> 15567
F W 15566
trifluoro acetic</w> 15565
adhe sives</w> 15563
L ox 15562
de w</w> 15562
pri ons</w> 15559
scen es</w> 15558
Succ ess</w> 15558
bi ologists</w> 15557
I PP</w> 15556
D HS</w> 15556
c anti 15554
S SE 15554
sk inf 15554
sk im</w> 15552
T MT</w> 15551
gener alised</w> 15551
exam s</w> 15551
electroencephal ographic</w> 15550
f s 15549
Out patient</w> 15549
appreci ate</w> 15549
tetrach loro 15549
foll icul 15548
H it 15545
SO CS1</w> 15544
HO T 15542
cal nexin</w> 15541
Man ip 15541
f owl</w> 15540
Co he 15540
4 s</w> 15539
Phosphor ylated</w> 15539
dermat ological</w> 15538
omp son</w> 15537
ga ther</w> 15536
frag ilis</w> 15535
UL 5</w> 15535
ospor ium</w> 15533
Prog esterone</w> 15533
NI C</w> 15532
C igare 15531
C lear 15530
PC 4</w> 15530
Mun ich</w> 15530
enti ne</w> 15529
E ph</w> 15528
prote ostasis</w> 15528
pres sur 15527
ostero ids</w> 15527
W ritten</w> 15526
om ission</w> 15525
ind ers</w> 15525
electron ically</w> 15525
osulf ate</w> 15524
vor inostat</w> 15523
T l 15522
pp 6</w> 15522
immunolab eling</w> 15522
on ous</w> 15521
br ine</w> 15521
p O 15520
percenti les</w> 15520
over hang</w> 15519
GSE 3</w> 15519
accelero meter</w> 15519
BT X</w> 15518
osom ally</w> 15517
ti st</w> 15516
Ch el 15516
main stream</w> 15516
tor ch 15515
n sp 15514
ent om 15514
den otes</w> 15514
P RP 15513
bacterioph ages</w> 15513
D er</w> 15510
eleg ant</w> 15510
homos exual</w> 15510
ach i</w> 15509
sh a</w> 15507
him bine</w> 15506
go od 15504
Val salva</w> 15501
ophyl l</w> 15501
extram edullary</w> 15501
min ent</w> 15498
MO PS</w> 15498
replic a</w> 15498
s CD4</w> 15496
Op ioid</w> 15496
Termin al</w> 15496
TI F</w> 15492
ta h</w> 15492
sterno tomy</w> 15492
B ST</w> 15491
num bered</w> 15491
D ST</w> 15489
Q 8</w> 15489
Montre al</w> 15488
co expressing</w> 15487
car ds</w> 15487
vocab ulary</w> 15487
ra ters</w> 15485
Tam oxifen</w> 15484
entr ain 15480
C ant 15479
p T</w> 15478
re missions</w> 15476
re plete</w> 15472
tri peptide</w> 15471
examin er</w> 15471
C uc 15470
β 7</w> 15470
F BP</w> 15468
AC E2</w> 15468
tem plated</w> 15468
recal citrant</w> 15468
G yr 15467
ack et</w> 15467
totag min</w> 15467
Pro duct</w> 15466
intram ural</w> 15466
L umbar</w> 15465
Relev ance</w> 15465
ten th</w> 15464
tub ing</w> 15464
later alization</w> 15463
Val ve</w> 15462
pho bia</w> 15462
FACSC ali 15462
B ren 15460
CH APS</w> 15459
nar ratives</w> 15459
nc RNA</w> 15459
α5 β1</w> 15459
P S2</w> 15458
I to</w> 15457
G v 15457
phlo em</w> 15456
AD AM</w> 15454
multic entric</w> 15454
kynuren ine</w> 15453
caus ation</w> 15452
Toler ance</w> 15452
E OL</w> 15450
O F 15450
harb our</w> 15450
AV F</w> 15449
R H 15448
ble ph 15447
he dro 15444
IRA K</w> 15444
eIF 2</w> 15443
polyn omial</w> 15443
9 Q</w> 15442
M ature</w> 15442
poly nucleotide</w> 15441
tion ary</w> 15441
E P4</w> 15439
penetr ant</w> 15439
lep rae</w> 15438
ER 1</w> 15437
hospital ised</w> 15437
Sud an</w> 15436
a plasia</w> 15435
pa e</w> 15434
pul mon 15432
leuk ocytosis</w> 15432
MT ase</w> 15431
t apping</w> 15429
Mp s1</w> 15429
pu st 15428
GAD D4</w> 15428
adver tising</w> 15428
ANIM ALS</w> 15428
Z R</w> 15427
le trozole</w> 15427
PI V</w> 15425
Se oul</w> 15425
T Q</w> 15424
institu ted</w> 15424
AP O</w> 15422
m um 15421
liber ated</w> 15421
neuro blasts</w> 15417
Test osterone</w> 15417
it ron</w> 15416
MC 4R</w> 15416
co el 15415
Came ro 15413
temper ament</w> 15412
rup tures</w> 15410
antero lateral</w> 15410
cor ner</w> 15408
hem opoietic</w> 15406
Au di 15405
P J</w> 15404
un coated</w> 15404
EC OG</w> 15404
s orghum</w> 15403
ot rig 15403
Th ompson</w> 15402
neuro tensin</w> 15402
M az 15401
nit ration</w> 15401
R ana</w> 15399
un im 15398
cartri dge</w> 15398
pr M</w> 15397
super numerary</w> 15396
obste tr 15395
st am 15393
th al</w> 15390
Th resh 15390
M y</w> 15389
ch en</w> 15389
penetr ated</w> 15389
M b 15388
P yr 15387
til ted</w> 15387
anaphyl actic</w> 15387
phospho proteins</w> 15385
exce ed 15385
amin ic</w> 15383
Hun ter</w> 15383
cel ain</w> 15382
reson ator</w> 15382
5 HT</w> 15380
e k 15380
N y 15380
Investig ating</w> 15380
zwitteri onic</w> 15380
intr af 15379
rpo B</w> 15378
poly electrolyte</w> 15377
nitros ourea</w> 15377
H J</w> 15375
thermo stable</w> 15374
dg ing</w> 15373
found ed</w> 15373
fuc os 15373
Random ised</w> 15372
ap arin</w> 15370
Ser ratia</w> 15369
S Vs</w> 15368
Reas ons</w> 15368
U CSC</w> 15366
G rants</w> 15365
LIN C0</w> 15365
S plic 15364
Smad 7</w> 15364
g ametes</w> 15363
AP 7</w> 15363
IP 3 15362
myco phenolate</w> 15359
onco proteins</w> 15358
AC F</w> 15357
s essi 15356
ap position</w> 15356
de protonation</w> 15355
N G2</w> 15354
W ei 15354
Ant arctic</w> 15353
M AD</w> 15350
tri angle</w> 15350
N us 15349
g ers</w> 15349
m as</w> 15345
sum m 15345
Spi ro 15345
epti dases</w> 15344
peri apical</w> 15343
cre d 15343
GAB AAR</w> 15342
PC O2</w> 15339
G BMs</w> 15338
non permissive</w> 15337
disp ensing</w> 15337
T U</w> 15336
sub threshold</w> 15335
asi an</w> 15335
. 6B</w> 15333
legis l 15333
benz aldehyde</w> 15332
c ial</w> 15331
N ex 15331
DF G</w> 15331
Cir cadian</w> 15331
aband oned</w> 15331
se baceous</w> 15330
bul king</w> 15330
trim med</w> 15330
on ase</w> 15329
Send ai</w> 15329
Qu ick 15328
S AT 15327
vortex ed</w> 15327
Tox ic</w> 15326
cl ips</w> 15325
gastro duodenal</w> 15324
tric eps</w> 15323
bronch i</w> 15322
F AB</w> 15320
bromo phenol</w> 15320
horiz on</w> 15319
micro electrode</w> 15318
under lines</w> 15318
flav us</w> 15318
Rest oration</w> 15318
mon oph 15317
met am 15317
an emic</w> 15315
brac kets</w> 15314
de formed</w> 15312
characteri zations</w> 15312
v 6</w> 15311
food borne</w> 15311
allevi ates</w> 15310
meth amine</w> 15307
plan us</w> 15305
brea th 15304
fimbri ae</w> 15304
AM R</w> 15303
P WS</w> 15302
Fl t</w> 15302
w 7</w> 15300
circum flex</w> 15300
PT Z</w> 15299
H VA</w> 15295
aqu aculture</w> 15294
Cho i</w> 15294
hydro quinone</w> 15293
circum scribed</w> 15292
nanocar riers</w> 15292
spr int</w> 15290
S ER</w> 15289
no dos 15289
bif ida</w> 15288
Dox orubicin</w> 15287
dy ad</w> 15286
Se at 15285
vul va</w> 15284
hydroly zing</w> 15284
5 f</w> 15283
X C</w> 15282
CY LD</w> 15282
Sig lec</w> 15281
X M</w> 15280
Adi pose</w> 15280
UB E2 15279
M ode</w> 15276
Pot assium</w> 15274
sou theastern</w> 15274
et ched</w> 15273
Exam ining</w> 15273
me gal 15272
M iz 15267
mos sy</w> 15267
u v 15265
S BA</w> 15264
B asi 15263
RA DI 15263
happ y</w> 15263
L CLs</w> 15262
Ago 2</w> 15262
disp ensed</w> 15261
ther mo</w> 15259
Fibro blast</w> 15259
conform ationally</w> 15257
glass y</w> 15257
pl ing</w> 15256
MV PA</w> 15255
U BA</w> 15254
O X4</w> 15254
8 I</w> 15252
ant e</w> 15252
lif lozin</w> 15252
F ar</w> 15250
D ol 15250
su pero 15250
fir st-</w> 15250
brom ocriptine</w> 15250
New man</w> 15250
her ins</w> 15249
D PA</w> 15248
amit riptyline</w> 15248
ut a</w> 15247
opson ized</w> 15247
sup ras 15246
Gra phene</w> 15246
4 Q</w> 15245
TM 3</w> 15245
T an</w> 15243
U SP</w> 15243
fem urs</w> 15243
i dic 15241
fin ishing</w> 15241
Per spectives</w> 15241
mm as</w> 15241
pa ve</w> 15240
propi on</w> 15240
ven oms</w> 15239
particip atory</w> 15239
Tf h</w> 15239
CI T</w> 15238
I DU 15237
o plication</w> 15237
gen erously</w> 15237
N AP</w> 15236
V AC</w> 15235
neighbor hoods</w> 15235
photosensiti zer</w> 15235
cit ability</w> 15234
HR R</w> 15234
FI GO</w> 15234
lute in</w> 15234
ond yl 15232
endotox emia</w> 15232
sp ur 15229
W right</w> 15228
hyper lip 15228
IV US</w> 15227
eIF 4A</w> 15227
TFII H</w> 15227
Univer sit 15224
Mar ked</w> 15223
N CL</w> 15222
T su 15221
oste osynthesis</w> 15219
TO C</w> 15219
- B</w> 15218
CF C</w> 15218
wh ate 15216
Nhe I</w> 15216
synthe tases</w> 15214
hom ologies</w> 15213
k is</w> 15211
pl otting</w> 15211
ano yl 15211
enz alutamide</w> 15210
dec ays</w> 15209
PC AF</w> 15208
whate ver</w> 15208
Chrom atography</w> 15206
escal ating</w> 15206
G RO 15205
lamin ae</w> 15205
man ageable</w> 15204
amino ethyl</w> 15204
ind oles</w> 15203
cre ative</w> 15202
His 2</w> 15202
super conducting</w> 15201
ind entation</w> 15201
Path o 15200
circ RNAs</w> 15200
poly vinyl</w> 15198
cit alopram</w> 15197
sc F 15196
Electroph oretic</w> 15196
polysomn ography</w> 15196
ovi position</w> 15195
in competent</w> 15194
An emia</w> 15194
o rel 15193
B T4</w> 15193
ne trin</w> 15193
PL A2 15193
Abstr act</w> 15193
E gg 15190
eu ronal</w> 15190
altern ation</w> 15190
epiflu orescence</w> 15190
F PG</w> 15189
perme ase</w> 15188
GC R</w> 15186
RE C</w> 15185
p g 15184
Bu ff 15184
Aus tin</w> 15184
st le</w> 15182
Boeh ringer</w> 15182
p CD 15180
An omal 15180
O PCs</w> 15178
ber ries</w> 15178
octa hedral</w> 15178
sta ur 15177
me c</w> 15176
phosphodi ester</w> 15176
0 Δ</w> 15173
lact ones</w> 15173
ph os</w> 15172
il ine</w> 15171
dec or 15170
chor d</w> 15170
par aneoplastic</w> 15169
hermaphro di 15169
se tron</w> 15168
N DM</w> 15167
uro logic</w> 15167
K DR</w> 15166
gu ardi 15165
o h 15164
8 V</w> 15163
arrestin s</w> 15163
m inc 15162
bott les</w> 15162
Le gend</w> 15161
in ding</w> 15160
inc in 15157
AURK A</w> 15156
Perc eption</w> 15155
exud ative</w> 15155
leiomy osarcoma</w> 15153
le tt 15152
opac ities</w> 15152
underp inning</w> 15152
adi ene</w> 15150
ul ting</w> 15147
ra ff 15146
B BS</w> 15145
Ga As</w> 15145
ligh tly</w> 15143
Ob ese</w> 15143
RG S1</w> 15143
Mor bidity</w> 15142
prioriti ze</w> 15142
un itary</w> 15141
P ick</w> 15140
f ishing</w> 15140
bi ting</w> 15140
os h</w> 15139
Princ iples</w> 15138
acc red 15137
USP 7</w> 15137
F ROM</w> 15135
extr uded</w> 15135
k new</w> 15134
Pit uitary</w> 15133
Squ amous</w> 15133
Recor ds</w> 15132
fusi form</w> 15132
post mitotic</w> 15131
op al 15130
esthe tics</w> 15130
marc escens</w> 15130
des ert</w> 15128
contin gency</w> 15128
cal reticulin</w> 15125
As si 15125
L HC 15124
arbitr arily</w> 15124
reloc ation</w> 15124
DT 4</w> 15123
ox ins</w> 15122
O 2 15121
CF SE</w> 15121
G RI 15119
el ected</w> 15119
Guill ain</w> 15118
bro aden</w> 15117
U be 15116
Ph D</w> 15116
Stro op</w> 15115
D as 15114
intrad uctal</w> 15114
Ze a</w> 15113
sh ak 15112
beet les</w> 15111
Andro gen</w> 15111
p idus</w> 15110
al ex 15108
IS G</w> 15108
Per fusion</w> 15107
nar cotic</w> 15106
at alysis</w> 15105
mono zygotic</w> 15103
Cyt ogenetic</w> 15103
AR MS</w> 15099
FO X</w> 15099
5 A1</w> 15098
p k 15097
SU D</w> 15097
C or</w> 15096
C yst 15096
lam ins</w> 15095
Co Q1</w> 15094
E RO 15093
pen ding</w> 15092
Tryp sin</w> 15091
ig er</w> 15090
Al to</w> 15090
B ile</w> 15089
osse o 15089
F ecal</w> 15088
conve y</w> 15088
tel ec 15086
fron to</w> 15086
K g</w> 15085
D DS</w> 15085
Op portun 15085
su tured</w> 15081
HT N</w> 15081
advis able</w> 15081
N estin</w> 15080
B h 15080
P ero 15080
smart phone</w> 15080
at ro 15079
flu ent</w> 15079
L ank 15078
epig astric</w> 15078
PA BP</w> 15077
ejac ulation</w> 15077
C II</w> 15074
cl i 15074
FACSCali bur</w> 15074
fe til</w> 15073
MET H</w> 15072
non fatal</w> 15072
9 X</w> 15071
w art</w> 15070
in tolerant</w> 15070
NS 4B</w> 15070
NHE 1</w> 15070
brasili ensis</w> 15070
F allot</w> 15068
d ul 15067
Bal ance</w> 15067
no tification</w> 15066
s -</w> 15065
distur b</w> 15065
Polymorph ism</w> 15065
un employment</w> 15061
An esthesi 15060
metazo ans</w> 15060
T DI</w> 15059
TC F4</w> 15057
Differen ce</w> 15057
Condi tional</w> 15057
G els</w> 15056
ab scisic</w> 15056
preponder ance</w> 15056
Gl ass</w> 15055
fun nel</w> 15055
entero virus</w> 15054
EF FECT</w> 15054
L un 15053
log is 15052
As n1</w> 15050
ac ked</w> 15049
sl ing</w> 15049
os tin</w> 15048
activ eness</w> 15048
PL T</w> 15048
to uc 15046
dr ugg 15046
UL 9</w> 15046
C CD 15045
Ischem ia</w> 15045
nucle ocytoplasmic</w> 15044
agg rec 15044
S co 15043
ci vil</w> 15043
Con duc 15041
carb ene</w> 15041
defl ection</w> 15040
in ear</w> 15039
6 Q</w> 15037
ful vestrant</w> 15036
synerg ize</w> 15036
Lo op</w> 15035
ambly opia</w> 15034
ST E</w> 15033
B t 15032
HE p</w> 15032
L AK</w> 15031
ot actic</w> 15029
bec k</w> 15029
W indows</w> 15028
p o</w> 15027
electrocardi ography</w> 15027
fe e</w> 15026
GAT A1</w> 15026
con doms</w> 15025
sol in</w> 15025
D ASH</w> 15023
Sch midt</w> 15022
phosphoryl ations</w> 15020
Bifid obacterium</w> 15019
G AS 15015
re orientation</w> 15015
ur ated</w> 15014
anti hist 15014
hydrox ylamine</w> 15014
T H2</w> 15013
oc ol 15012
he l</w> 15012
Ly sis</w> 15012
Mat su 15011
Emp ir 15011
ent olamine</w> 15010
phen azine</w> 15010
H RAS</w> 15006
anti cholinergic</w> 15006
inoc occus</w> 15006
catech in</w> 15006
X e</w> 15005
otrig ine</w> 15005
op terin</w> 15003
chloro thiazide</w> 15003
soldi ers</w> 15003
sta e</w> 15001
P ir 15000
L il 14999
Z 3</w> 14999
id ine 14999
Brd 4</w> 14999
dys ph 14998
SL s</w> 14998
decompens ation</w> 14998
dynor phin</w> 14998
ak ia</w> 14996
MAL T</w> 14996
Gα s</w> 14996
happ ened</w> 14996
after noon</w> 14993
SB RT</w> 14992
pa using</w> 14991
CO X2</w> 14991
IN s</w> 14989
Ka i 14989
Willi am</w> 14988
oste ocytes</w> 14987
PF O</w> 14987
GR F</w> 14987
voc ational</w> 14987
Amic on</w> 14985
B owel</w> 14983
equili bria</w> 14980
Al lo 14978
Fig. 6B</w> 14978
Hor iz 14978
T PS</w> 14977
xyl anase</w> 14977
prim i 14974
ig atran</w> 14973
resol utions</w> 14973
Amon gst</w> 14972
engraf ted</w> 14972
pil in</w> 14969
Lact ate</w> 14968
abra sion</w> 14965
ar tis</w> 14964
CE C</w> 14963
py ran 14960
EP SPs</w> 14960
pli ances</w> 14959
angi ograms</w> 14956
E DC</w> 14955
olys osomes</w> 14954
Fc ε 14954
cus hion</w> 14953
un natural</w> 14952
ron ous</w> 14952
f ers</w> 14949
CO RT</w> 14949
P ast</w> 14948
am acrine</w> 14948
di alogue</w> 14947
p n</w> 14946
V aginal</w> 14945
up dating</w> 14945
prioriti zed</w> 14945
Gro ve</w> 14944
M and 14942
iter ations</w> 14941
explo sion</w> 14939
id ins</w> 14938
dihydro folate</w> 14936
DU Bs</w> 14935
Pa ediatric</w> 14935
sclero therapy</w> 14935
E P3</w> 14934
MI BG</w> 14934
g an 14933
Cr ude</w> 14933
immunoposi tive</w> 14933
In k 14931
multi locus</w> 14931
Ca m</w> 14931
DO 1</w> 14931
GPR 5</w> 14931
oval e</w> 14931
O ther 14929
erg ometer</w> 14928
AD E</w> 14928
citr ullin 14928
PRO TE 14927
fix ative</w> 14926
G BP</w> 14925
di op 14925
ep in</w> 14925
tex ts</w> 14925
educ ate</w> 14925
Immun oglobulin</w> 14924
tra ver 14923
B CA 14922
evid ently</w> 14922
iso flavones</w> 14921
D ensit 14920
po ison</w> 14919
P im</w> 14918
AT CT 14917
musi cal</w> 14917
hypocalc emia</w> 14917
a is</w> 14915
he ard</w> 14915
TE L</w> 14914
eth anolic</w> 14913
pip razole</w> 14913
conduc ive</w> 14912
Nox a</w> 14912
Ultrason ography</w> 14912
Secre tion</w> 14910
tex tile</w> 14908
Ver sus</w> 14907
cen sored</w> 14905
oct yl</w> 14905
profil in</w> 14904
adi one</w> 14903
amphi bians</w> 14903
h rom 14902
Figure 5A</w> 14900
Seat tle</w> 14900
re use</w> 14899
- terminal</w> 14898
Py V</w> 14897
R III</w> 14895
ur istic</w> 14895
reloc alization</w> 14895
ogn ath 14892
PG S</w> 14892
big gest</w> 14892
DO TA</w> 14891
hypo physec 14890
R id 14889
C BC</w> 14888
Nem at 14888
provisi onal</w> 14888
NFAT c1</w> 14887
angi ogram</w> 14886
arom a</w> 14886
Rel atively</w> 14886
addic ts</w> 14886
asi de</w> 14885
isi tions</w> 14884
We i</w> 14883
eu rop 14882
Anti biotics</w> 14881
psycho active</w> 14881
pf u</w> 14881
Ca j 14880
str a</w> 14879
AN K</w> 14879
inter species</w> 14876
Dis semin 14874
L n 14872
inf lated</w> 14872
N RS</w> 14871
glyc opeptide</w> 14871
Joh ns</w> 14871
W ard</w> 14870
I PD</w> 14867
W ong</w> 14867
oc tion</w> 14867
Pers ons</w> 14866
in atus</w> 14865
HC C1</w> 14864
M 2 14863
Al aska</w> 14861
PI AS 14861
IR F4</w> 14861
LE U2</w> 14859
in son</w> 14858
le f 14858
Sal ivary</w> 14857
Sing h</w> 14857
financ ing</w> 14857
inter neuron</w> 14855
CENTRA L</w> 14852
dile mmas</w> 14852
ab l</w> 14849
it arian</w> 14847
exchang eable</w> 14845
ph ased</w> 14844
be rel 14843
rot ate</w> 14843
bab oons</w> 14843
cim ol</w> 14843
groo ves</w> 14843
S cr 14842
IT T</w> 14842
explan ted</w> 14841
adnex al</w> 14841
to l</w> 14840
C ast 14839
D AI</w> 14839
dec ol 14839
- sensitive</w> 14838
b age</w> 14838
tam ers</w> 14838
I v 14837
gen icity</w> 14837
fl on</w> 14837
T rib 14836
Usu ally</w> 14836
c un 14835
inf inity</w> 14834
MA E</w> 14833
ur in 14830
Br agg</w> 14830
g E</w> 14827
cy l</w> 14827
flu or</w> 14827
relax ations</w> 14827
K CC2</w> 14825
N ation 14823
Malaw i</w> 14823
hydro chlor 14822
PT s</w> 14821
LE Ds</w> 14821
dermat ophy 14821
Hamil tonian</w> 14821
capro lactone</w> 14821
Con A</w> 14817
amput ations</w> 14817
R ome</w> 14816
I cel 14816
t 5</w> 14815
ne z</w> 14815
Ros etta</w> 14815
RA T</w> 14814
multi system</w> 14811
W ag 14810
tri valent</w> 14810
so aking</w> 14810
R W 14809
lacti de</w> 14809
ocy tomas</w> 14808
GAP s</w> 14808
re tail</w> 14807
art um</w> 14806
E is 14805
S ensitive</w> 14804
C ETP</w> 14803
piper acillin</w> 14802
earth quake</w> 14801
hem ich 14800
parasi tism</w> 14800
Cp Gs</w> 14800
c ite</w> 14799
con form</w> 14799
Sma I</w> 14799
X L 14798
addic tive</w> 14797
lipid aemia</w> 14797
ram er</w> 14796
str ating</w> 14794
Intr av 14793
Contin ued</w> 14793
Schne ider</w> 14793
pre molars</w> 14791
kin ins</w> 14791
IBM X</w> 14791
AK R1 14790
ensiti zing</w> 14790
Tan dem</w> 14789
si as</w> 14788
inter conversion</w> 14788
an ol 14787
fru it 14787
inter related</w> 14786
quin tile</w> 14786
draw s</w> 14785
liber ation</w> 14785
Lymph ocyte</w> 14783
ten tative</w> 14782
accred itation</w> 14782
pup illary</w> 14780
α B</w> 14778
dissoci ative</w> 14778
flag ellum</w> 14778
H on 14776
counter acts</w> 14775
Mc m2</w> 14775
G P1</w> 14774
Dr y</w> 14774
ro pivacaine</w> 14773
sub genomic</w> 14770
homogene ously</w> 14770
BA Y</w> 14770
er cosis</w> 14769
mis diagnosis</w> 14769
PO L 14769
erythro blasts</w> 14769
G K 14768
entr ant</w> 14768
Sr i</w> 14768
lab ial</w> 14767
gly phosate</w> 14765
seros al</w> 14764
acyl glycerols</w> 14761
Ill ness</w> 14761
myo epithelial</w> 14760
J CV</w> 14759
CA RT</w> 14759
t les</w> 14758
M arti 14758
be er</w> 14757
th rice</w> 14756
pharmac ogenetic</w> 14756
im pressions</w> 14755
Resi dents</w> 14755
autoradi ographic</w> 14755
S RT</w> 14753
ch t</w> 14753
Electro cardi 14753
mon ensin</w> 14752
F AST</w> 14751
PI A</w> 14751
Str ati 14751
later ality</w> 14751
Pen icillin</w> 14750
en ius</w> 14749
PK s</w> 14749
F atal</w> 14748
rel uc 14748
ARID 1A</w> 14748
suppur ative</w> 14748
N 2a</w> 14747
DF O</w> 14746
fasc icul 14746
Pat ch</w> 14743
Interpre tation</w> 14743
N if 14741
vox els</w> 14741
direc tionally</w> 14739
oligom ycin</w> 14739
w earable</w> 14737
fl ashes</w> 14737
P ress</w> 14736
GG GT 14736
hygi enic</w> 14736
oopho rectomy</w> 14736
sal is</w> 14735
bacteri ally</w> 14734
calcul us</w> 14734
nitri l 14733
plas tic 14732
Le af</w> 14732
cohe sive</w> 14732
hydro lysate</w> 14730
che ek</w> 14730
Youn ger</w> 14730
chi ro 14729
disproportion ate</w> 14729
mo fetil</w> 14728
r IL</w> 14726
intr aspecific</w> 14725
IN a</w> 14725
IN TE 14725
ing en</w> 14723
ven ue</w> 14722
Prote obacteria</w> 14722
EM BL</w> 14722
PA RP 14721
sil age</w> 14720
MC R</w> 14719
el er</w> 14718
LI G 14718
PM s</w> 14718
explo sive</w> 14718
Pak 1</w> 14718
ss RNA</w> 14716
in hab 14715
ic ho 14715
mon ocular</w> 14715
M II</w> 14714
ic om 14714
Constitu tive</w> 14712
S ites</w> 14710
V im 14710
eti apine</w> 14710
Mar fan</w> 14710
phot ometric</w> 14709
S SP</w> 14708
arox aban</w> 14708
Mil itary</w> 14707
lip oma</w> 14703
micronutri ent</w> 14703
C ef 14701
W u 14698
SC RI 14698
choleste atoma</w> 14697
el o 14694
par alog</w> 14694
min orities</w> 14693
replic ons</w> 14693
Other wise</w> 14691
D CF</w> 14690
nor theastern</w> 14690
Expl oratory</w> 14689
re volution</w> 14688
n ights</w> 14687
hem olysin</w> 14687
am isole</w> 14686
nulli parous</w> 14686
T ou 14685
cl ou 14684
mer idi 14684
cry ostat</w> 14684
cl onic</w> 14683
mono phyletic</w> 14683
. 3C</w> 14680
AF 4</w> 14680
Nov artis</w> 14680
eryth rin</w> 14679
bour ne</w> 14677
G M0</w> 14676
decid ual</w> 14676
ser pin</w> 14675
fac tual</w> 14674
I DS</w> 14673
bronchodil ator</w> 14673
ma ys</w> 14671
chitin ase</w> 14671
a ke</w> 14670
end ocytosed</w> 14670
RN R</w> 14670
Top 1</w> 14669
i dics</w> 14668
sub luxation</w> 14668
micro fil 14668
out performed</w> 14667
S ports</w> 14666
EE A1</w> 14666
fascin ating</w> 14665
din ucleotides</w> 14664
V ent 14663
SF V</w> 14663
m ad 14661
Pe tri</w> 14661
9 N</w> 14659
bo ar</w> 14659
AD Cs</w> 14659
Elim ination</w> 14659
allant oic</w> 14657
ber ian</w> 14656
F 7 14655
des erve</w> 14655
phy t 14654
war d 14654
Fl p</w> 14654
Do c</w> 14654
over use</w> 14653
institution alized</w> 14653
Psychiat ry</w> 14653
cotyle dons</w> 14651
interro gate</w> 14650
h unger</w> 14649
PE ST</w> 14649
pen alty</w> 14649
E HEC</w> 14648
ven ules</w> 14647
VEGF 1</w> 14646
still birth</w> 14646
broncho constriction</w> 14645
cere als</w> 14644
R ing</w> 14643
Includ ed</w> 14643
E Y 14641
C II 14640
E sc 14640
LM NA</w> 14640
D IP</w> 14636
sub chondral</w> 14636
Al a1</w> 14636
ri v 14633
SP D</w> 14633
hem oglobin 14632
p is 14630
bas ket</w> 14630
E mission</w> 14629
Ji ang 14629
moti ves</w> 14628
R or 14627
intersti tium</w> 14627
terat ogenic</w> 14627
Va v</w> 14623
Super script</w> 14622
ot axin</w> 14621
Physi ol</w> 14621
L SL</w> 14620
tri partite</w> 14620
subtil isin</w> 14620
pyrrol idine</w> 14620
cer amides</w> 14618
Op ti</w> 14617
Correspon dingly</w> 14617
j er 14615
an them 14615
Hir sch 14615
biop sied</w> 14613
fa ult</w> 14613
tour ni 14611
P MT</w> 14610
CX3 CR1</w> 14610
anti virals</w> 14609
est yles</w> 14609
S ugg 14608
d t 14607
DI s</w> 14607
pe tic</w> 14606
AS s</w> 14605
THE RA 14605
isomer ases</w> 14605
vir apine</w> 14604
asp in</w> 14604
Pharmac y</w> 14604
T Z</w> 14601
CO I</w> 14601
Sh an 14601
Borde tella</w> 14601
asparag inase</w> 14599
LAM P1</w> 14599
depolar izations</w> 14599
b 4</w> 14597
CG P</w> 14597
m IU</w> 14595
triam cinolone</w> 14595
st oring</w> 14592
absor b</w> 14592
GC GT 14591
Abl ation</w> 14591
al ene</w> 14590
xen on</w> 14590
i NKT</w> 14589
P OS</w> 14589
sub site</w> 14589
evol ves</w> 14589
characteris tically</w> 14588
ka empferol</w> 14588
re actant</w> 14587
im pulses</w> 14587
fac tant</w> 14587
conduc tances</w> 14587
SM s</w> 14587
amyl ose</w> 14587
High lights</w> 14586
Alter ation</w> 14586
rate rone</w> 14586
Per man 14585
DI S</w> 14585
S SS</w> 14583
organ ochlor 14583
G ill</w> 14582
inter domain</w> 14581
R RT</w> 14580
phen anthrene</w> 14580
M m 14579
z el</w> 14578
K v</w> 14578
os mium</w> 14578
un equivocal</w> 14578
PD D</w> 14578
My x 14578
Vene z 14578
MAP T</w> 14577
ML ST</w> 14577
quin ox 14577
dyst ro 14577
om ni 14576
pachy tene</w> 14576
Infarc tion</w> 14575
bac tam</w> 14574
dysmorph ic</w> 14574
stri pe</w> 14573
Re yn 14572
abut ment</w> 14572
Rec tal</w> 14571
PAL B2</w> 14571
Ig Gs</w> 14568
ont ally</w> 14568
orph ic</w> 14568
calc ein</w> 14567
Enh ancing</w> 14567
gold fish</w> 14567
hemat omas</w> 14565
FL T</w> 14563
PG N</w> 14563
stron tium</w> 14562
cholangi opancre 14562
R at 14561
l is</w> 14561
IC a</w> 14559
arteri osclerosis</w> 14559
oc ulation</w> 14557
irradi ance</w> 14553
myo fibrillar</w> 14553
pro survival</w> 14552
ip ped</w> 14551
conden sin</w> 14551
I BV</w> 14550
wor n</w> 14550
fluo rene</w> 14550
illu sion</w> 14550
Im plant</w> 14549
Hist opathology</w> 14549
TI RF</w> 14548
jo in</w> 14548
α 3 14547
or ia</w> 14545
draw back</w> 14545
Rap tor</w> 14545
strin gency</w> 14545
S ten 14541
re activities</w> 14541
er ti 14541
Dis sociation</w> 14539
flud arabine</w> 14539
H ous 14538
S pot</w> 14537
S FA</w> 14536
exp enses</w> 14536
illumin ate</w> 14536
cath epsins</w> 14535
muc os 14534
Doc king</w> 14534
herbi vores</w> 14532
6 f</w> 14529
disc ol 14529
b w</w> 14528
b all 14525
leth ally</w> 14524
FX S</w> 14522
interpol ation</w> 14521
opr amide</w> 14521
acceler ator</w> 14518
6 α</w> 14517
def ensin</w> 14517
Re ed</w> 14516
elic itation</w> 14516
vide o 14515
STI s</w> 14514
anni versary</w> 14513
hap toglobin</w> 14513
entrain ment</w> 14513
. - 14510
V ER</w> 14509
exud ates</w> 14509
phosphate mia</w> 14509
H R2</w> 14508
photo physical</w> 14508
PO PC</w> 14507
HCO 3-</w> 14507
w af 14506
5 RA</w> 14505
carbox yl 14505
ty ramine</w> 14503
nitros amine</w> 14503
neurop il</w> 14503
second arily</w> 14501
M K1</w> 14497
glyc osphing 14497
RI SC</w> 14497
dystroph ies</w> 14497
R HO 14496
at roph 14496
up dates</w> 14495
thym idylate</w> 14495
otrop h</w> 14494
lam ellae</w> 14493
Sec 2</w> 14493
Y F</w> 14492
CO G</w> 14492
mut agens</w> 14491
T G1</w> 14489
Cor ri 14488
eth oxy</w> 14487
Du ke</w> 14485
L au 14484
0 I</w> 14482
form alism</w> 14482
Dim ensi 14482
CA PS</w> 14481
7 e</w> 14479
AR F6</w> 14479
g ossy 14478
D PC</w> 14477
micro bic 14477
U SE</w> 14476
No tes</w> 14476
ocl opramide</w> 14474
Fox O</w> 14474
transloc ase</w> 14473
gyna ecological</w> 14473
refug ees</w> 14472
X PA</w> 14471
S RA</w> 14470
Con stant</w> 14470
meth y 14468
Estim ating</w> 14468
C Vs</w> 14464
eth i 14464
AL CL</w> 14464
ST EC</w> 14462
fri end</w> 14462
k k 14461
Ar bor</w> 14461
spra ying</w> 14461
las h</w> 14460
Ger iatric</w> 14460
IU D</w> 14460
Bio Legend</w> 14459
rel l</w> 14458
dri lling</w> 14458
SO CI 14458
e e 14457
Ex ec 14457
Lu o</w> 14457
relig ion</w> 14457
obenz ene</w> 14456
peculi arities</w> 14456
Cap illary</w> 14455
morb idly</w> 14455
NO x</w> 14454
exp ired</w> 14453
Sk i</w> 14453
pneum atic</w> 14452
Nig erian</w> 14451
2 C1</w> 14450
I MA</w> 14450
sk i 14448
Mas sive</w> 14448
α S</w> 14446
un desired</w> 14446
Fig. 1C</w> 14446
fo reg 14445
Inser tion</w> 14444
P BM</w> 14442
PTP N1</w> 14441
Transcrip tome</w> 14440
san itation</w> 14440
l n 14439
A pol 14438
mesh work</w> 14438
syst ole</w> 14437
c ART</w> 14436
P BA</w> 14435
HB A</w> 14434
SI DS</w> 14433
so as</w> 14433
t ang 14432
man ager</w> 14431
IP SS</w> 14431
Bar cel 14431
Ho use</w> 14431
s ons</w> 14430
- expressing</w> 14430
A e 14430
morph ants</w> 14430
caud a</w> 14430
Bel gian</w> 14430
Cy an 14429
fund oplication</w> 14427
TF PI</w> 14427
m at</w> 14426
stra ined</w> 14423
dou ble 14423
sulf ite</w> 14423
affili ation</w> 14423
AC 2</w> 14422
thromb ospondin</w> 14422
pro parg 14420
adap tability</w> 14420
dy sen 14420
sp ina</w> 14419
Sero logical</w> 14419
CL D</w> 14418
Cardi omy 14418
ax ine</w> 14416
L MB</w> 14415
pneumon ectomy</w> 14415
P ET 14412
D PAT</w> 14412
al ising</w> 14411
T Hz</w> 14410
j umping</w> 14410
electro st 14410
D CR</w> 14409
spo ken</w> 14409
am work</w> 14408
Pa CO2</w> 14408
SHI P</w> 14408
- AC 14407
resc uing</w> 14407
FOX O3</w> 14407
di ving</w> 14405
hemo chromatosis</w> 14404
polycy themia</w> 14404
Com pliance</w> 14403
tr aline</w> 14400
ap ia</w> 14398
Integr ating</w> 14398
cooper ates</w> 14397
AB S</w> 14396
over active</w> 14396
V SV 14395
ar cane</w> 14395
gran ted</w> 14394
N f1</w> 14393
Endom etrial</w> 14393
BEC N1</w> 14393
R Rs</w> 14392
sle y</w> 14392
glycol ic</w> 14392
ad duction</w> 14390
iodo acetamide</w> 14390
y m</w> 14389
ar ab 14386
En semb 14384
ar ra 14383
Ric tor</w> 14382
T A1</w> 14381
at oes</w> 14381
ble ached</w> 14376
cann ulated</w> 14376
mycel ia</w> 14376
de alt</w> 14375
Light Cycler</w> 14375
M PH</w> 14373
B CI</w> 14373
induc ibility</w> 14373
W D4</w> 14371
alb opic 14371
M t</w> 14370
W ells</w> 14370
fluoresc ens</w> 14370
masse ter</w> 14370
LT Q</w> 14369
My osin</w> 14368
Tr k 14367
rhabdomy olysis</w> 14367
calend ar</w> 14366
min .</w> 14361
micro -</w> 14360
shad ow</w> 14360
a etiological</w> 14359
mes encephalic</w> 14359
competi tors</w> 14359
epi physeal</w> 14358
kind ling</w> 14358
f o</w> 14357
identi fications</w> 14355
B LA</w> 14354
protein emia</w> 14354
B ET 14353
back crossed</w> 14350
oscill ating</w> 14350
hal t</w> 14348
Toc ris</w> 14348
Hormon al</w> 14344
jour ney</w> 14343
dl ers</w> 14342
o frontal</w> 14341
Z Y 14341
iv ac 14341
encomp assed</w> 14341
PB DEs</w> 14340
os ins</w> 14339
Tran sp 14339
AD 2</w> 14337
chloro phenyl</w> 14336
GR N</w> 14336
spra yed</w> 14335
S MI 14334
ar med</w> 14333
de generating</w> 14333
dra ined</w> 14333
Par asi 14332
oug hing</w> 14331
ris on</w> 14329
HR QL</w> 14329
S tem 14327
mill imolar</w> 14327
necess itate</w> 14326
D ogs</w> 14325
cy stin</w> 14325
conta iner</w> 14325
stere otyp 14325
D AA</w> 14324
ham string</w> 14324
Cu O</w> 14324
ex otic</w> 14323
s artan</w> 14322
ion omer</w> 14322
Taq man</w> 14322
str ings</w> 14321
anti tumour</w> 14321
Os m</w> 14321
Less ons</w> 14320
ly se</w> 14319
A p</w> 14317
oth oracic</w> 14317
gingi vitis</w> 14317
sub stratum</w> 14316
GL 2</w> 14315
pun ishment</w> 14315
sis ters</w> 14314
main land</w> 14312
gil ts</w> 14312
HP RT</w> 14311
s. 2</w> 14311
sr A</w> 14310
W SN</w> 14309
under taking</w> 14309
el ders</w> 14308
muc ins</w> 14308
p ira</w> 14306
ML PA</w> 14306
T GAT 14304
doc k</w> 14304
lipos arcoma</w> 14304
4 EBP1</w> 14303
í a</w> 14303
epox y 14302
sett led</w> 14302
ger man 14301
sac cadic</w> 14301
ic idin</w> 14300
ore tinal</w> 14300
PH P</w> 14299
mag na</w> 14296
Dax x</w> 14296
acqu isitions</w> 14295
HB E</w> 14295
psych osomatic</w> 14294
Figure 4B</w> 14294
pseud ot 14294
im migration</w> 14293
- 1-</w> 14290
un even</w> 14290
of uran 14290
o eba</w> 14288
cri ses</w> 14288
An al</w> 14288
O D 14287
rib s</w> 14287
R RID</w> 14286
F AC</w> 14286
land fill</w> 14286
coc cal</w> 14286
neurotroph ins</w> 14286
F m 14285
GC CA 14285
ferre t</w> 14285
El k</w> 14284
psycho physical</w> 14284
pB lu 14284
sal ience</w> 14283
thal ass 14283
è re</w> 14282
pre motor</w> 14282
diarrhe al</w> 14282
K il 14281
phy s</w> 14279
tubul o 14279
Cop enh 14278
BE Z2</w> 14277
P es 14276
di vor 14276
J en 14275
inter viewing</w> 14275
cellul itis</w> 14275
li m</w> 14274
AP V</w> 14273
emp tive</w> 14273
cryp torch 14273
ante grade</w> 14272
pip e</w> 14271
On set</w> 14270
psych o</w> 14270
HS R</w> 14269
J 5</w> 14268
ac ral</w> 14267
se us</w> 14267
opres sin</w> 14267
odor ant</w> 14267
Re tention</w> 14265
tumo ur 14264
Cdk 4</w> 14263
di fluoro 14262
iden tically</w> 14262
snor ing</w> 14262
Cigare tte</w> 14262
trans activator</w> 14260
d U</w> 14259
F AAH</w> 14259
imid yl</w> 14259
A a 14258
Pal o</w> 14258
con strain</w> 14257
explo its</w> 14257
di p</w> 14254
C ER</w> 14253
RT X</w> 14253
dermat ologic</w> 14252
confron ted</w> 14252
K v2</w> 14248
ex osomal</w> 14248
a virulent</w> 14247
L BW</w> 14246
out lier</w> 14245
congru ence</w> 14245
c k 14244
Cryst all 14243
ri l 14242
hair pins</w> 14242
broaden ed</w> 14242
Ne uro</w> 14241
th read</w> 14240
BM E</w> 14240
ED L</w> 14240
K F</w> 14239
PTP N2</w> 14239
r RNAs</w> 14238
loc alities</w> 14237
DD E</w> 14237
yog a</w> 14236
5 q</w> 14235
t ful</w> 14235
Practi tion 14235
N CO 14234
GCN 5</w> 14234
Proced ure</w> 14234
DD B1</w> 14233
replac es</w> 14232
on yl 14231
bacteri ocin</w> 14231
allevi ation</w> 14231
leptom ening 14231
Tg 2</w> 14229
epigene tically</w> 14229
albopic tus</w> 14228
Compu terized</w> 14227
In spec 14226
B mp 14225
pl 1</w> 14225
SP R 14225
spas ms</w> 14224
install ation</w> 14224
o at</w> 14223
ni l</w> 14223
stereot ax 14223
ver ruc 14220
trypan osomes</w> 14220
iner tial</w> 14220
re rio</w> 14219
HER V</w> 14219
P PC</w> 14218
G DC</w> 14217
super critical</w> 14216
nar col 14216
NHE 3</w> 14216
hom opolym 14215
D M2</w> 14214
ly syl</w> 14214
millis econds</w> 14214
action able</w> 14213
uc ent</w> 14212
SI T</w> 14212
A symmetric</w> 14211
P BP</w> 14210
cock ro 14210
ter ically</w> 14207
Y ou</w> 14206
po stures</w> 14206
A spects</w> 14205
SC M</w> 14205
O 5</w> 14204
TM V</w> 14204
alloc atechin</w> 14203
DAV ID</w> 14203
cos me 14201
Lab eled</w> 14201
Synech ocystis</w> 14201
my opathies</w> 14200
opto electronic</w> 14198
PV L</w> 14197
interro gated</w> 14197
of er 14196
non malignant</w> 14195
methyl ene 14195
DT H</w> 14194
om agenesis</w> 14193
mat rigel</w> 14193
Am mon 14192
v ably</w> 14191
MB F</w> 14191
iso flavone</w> 14190
cardio protection</w> 14190
I BC</w> 14189
M Y</w> 14189
Per coll</w> 14189
surviv or</w> 14188
orche strated</w> 14187
Orth opaedic</w> 14186
trac table</w> 14185
Pro 1</w> 14180
verte bro 14180
haemat oma</w> 14180
Calcul ations</w> 14180
av ulsion</w> 14179
FEN 1</w> 14178
- 3-</w> 14177
M ell 14176
SI MS</w> 14176
acc ru 14176
enth usi 14172
Hum ans</w> 14171
Pho s</w> 14170
in avir</w> 14169
Fe wer</w> 14168
M P3</w> 14167
ser y</w> 14167
NR F</w> 14167
coc ryst 14167
et allic</w> 14166
HR S</w> 14165
pregn enolone</w> 14164
gl ing</w> 14163
complic ates</w> 14163
ging iva</w> 14162
s kin 14161
Inter mediate</w> 14160
7 Δ</w> 14159
trans rectal</w> 14159
radio resistance</w> 14157
gang rene</w> 14157
U. K.</w> 14156
ban ks</w> 14156
inte ro 14155
surro gates</w> 14155
Ham amatsu</w> 14155
reg ain</w> 14154
2 i</w> 14152
aden osyl</w> 14152
NH ANES</w> 14152
PA MPs</w> 14151
hind ers</w> 14151
zym ogen</w> 14148
cali ber</w> 14147
card inal</w> 14147
fung in</w> 14146
ocycl ic</w> 14146
U D</w> 14144
FOX A1</w> 14144
catal ogue</w> 14144
in osis</w> 14143
phosphoryl atable</w> 14143
AB D</w> 14142
Bi osynthesis</w> 14142
sh otgun</w> 14141
TL R1</w> 14140
attr activeness</w> 14137
domin ates</w> 14134
horiz ontally</w> 14134
bre asts</w> 14133
AM F</w> 14133
ag er 14131
T HI 14130
j el 14130
cyclop ent 14130
al ists</w> 14128
pro pul 14128
dor si</w> 14128
glo ves</w> 14124
g ym 14123
In corpor 14123
acc redi 14123
teg ument</w> 14123
GR 5</w> 14123
Alk aline</w> 14122
Rel B</w> 14121
eigh teen</w> 14121
end omy 14119
ste llation</w> 14118
app s</w> 14118
Psor iasis</w> 14118
ML 4</w> 14117
Post natal</w> 14117
Abo ve</w> 14117
nano structure</w> 14115
pa tel 14113
pp 1</w> 14112
sphen oidal</w> 14111
proprioc eptive</w> 14111
autocor relation</w> 14110
Por cine</w> 14106
chi 2</w> 14106
Streng th</w> 14105
por celain</w> 14104
S s 14103
b n 14101
lute um</w> 14101
A E1</w> 14100
-- 2</w> 14100
tis ation</w> 14098
orf 7</w> 14098
SO X</w> 14097
Ad ams</w> 14095
UP F1</w> 14095
exerc ising</w> 14094
DL T</w> 14094
Sig ma 14093
pu pae</w> 14093
phyto chemical</w> 14093
Frequ ent</w> 14091
v owel</w> 14090
peri kary 14089
Famil ies</w> 14088
Ab sorb 14086
VI M</w> 14086
re infection</w> 14085
Ex change</w> 14085
Pre -</w> 14085
illu stration</w> 14085
C et 14084
t all</w> 14084
rec to 14084
GP ER</w> 14084
Y ale</w> 14083
lipo f 14083
prec le 14082
met te</w> 14081
anthrac yclines</w> 14080
Per ic 14079
eug lyc 14079
TT CT 14077
Ga i 14077
Meth an 14075
olith iasis</w> 14075
Nov us</w> 14074
vel and</w> 14072
suic ides</w> 14072
LU TS</w> 14071
macron utri 14071
melan ocortin</w> 14069
E P2</w> 14068
re constitute</w> 14068
Conf idence</w> 14068
on ion</w> 14067
Pac litaxel</w> 14067
en em 14065
TB A</w> 14064
Gli oblastoma</w> 14063
Amaz on</w> 14063
B CL1</w> 14061
top ographical</w> 14061
spo use</w> 14061
S her 14060
manif old</w> 14060
scru tiny</w> 14060
ca v 14058
promp ting</w> 14058
rec tification</w> 14056
radic ular</w> 14056
H us 14055
si le</w> 14055
optim isation</w> 14055
W 8</w> 14054
exc imer</w> 14054
BR S</w> 14053
Eco RV</w> 14053
J as 14052
j aws</w> 14052
Z A</w> 14052
Partic ular</w> 14052
accommod ated</w> 14052
R f</w> 14050
AT F2</w> 14050
AT G 14048
micro structural</w> 14047
FF R</w> 14047
nasophar ynx</w> 14047
di e 14045
Figure 4 14045
multi stage</w> 14044
Ed wards</w> 14044
Phosph atase</w> 14043
per ip 14042
tox oid</w> 14042
Fibro blasts</w> 14042
bio energetics</w> 14041
DR S</w> 14040
exec ute</w> 14040
rani bizumab</w> 14040
tri pt 14039
az aki</w> 14039
Transcrip ts</w> 14037
hyper mutation</w> 14036
AD AR1</w> 14036
HC 1</w> 14035
Intr insic</w> 14035
monolith ic</w> 14035
n L</w> 14034
methan olic</w> 14034
N AT2</w> 14033
acc re 14033
bis exual</w> 14032
Well come</w> 14032
w is 14031
micro gravity</w> 14030
aller genic</w> 14029
scram ble</w> 14029
TCR s</w> 14029
hybri domas</w> 14028
F ES</w> 14027
MI B</w> 14026
Lys 3</w> 14025
anticip atory</w> 14025
amp ton</w> 14023
td Tomato</w> 14023
clar ifying</w> 14022
es e 14021
post natally</w> 14019
- GFP</w> 14018
ov orin</w> 14018
Stu dio</w> 14017
lon eliness</w> 14017
Sep arate</w> 14014
Lin da 14013
acclim ated</w> 14013
Characteris tic</w> 14011
n ine 14010
S SU 14010
ach s</w> 14007
L ac</w> 14006
bi ob 14006
Pe ter</w> 14006
bro ok</w> 14005
u b</w> 14004
in amide</w> 14004
al anyl</w> 14004
fasc ial</w> 14003
E qui 14002
un fortunately</w> 14002
L CR</w> 14000
spi ronolactone</w> 13999
V il 13998
Figure 7</w> 13998
Candi date</w> 13997
costim ulation</w> 13997
TRPC 6</w> 13997
optim ism</w> 13995
N AM</w> 13994
D PD</w> 13994
CS M</w> 13994
T Ps</w> 13993
non invasively</w> 13992
rex ia</w> 13991
alk ane</w> 13991
d otal</w> 13990
PE N</w> 13990
Glut amine</w> 13990
elap sed</w> 13990
om ental</w> 13989
RE N</w> 13988
HR F1</w> 13988
prescri be</w> 13988
loop ing</w> 13988
Jord an</w> 13987
on euro 13986
achal asia</w> 13986
tis chem 13984
recti fier</w> 13984
at m</w> 13983
tra in 13983
roph ilic</w> 13982
im men 13981
f atin</w> 13980
pe el</w> 13980
opy ri 13979
m Sv</w> 13978
Q 9</w> 13978
PT Ps</w> 13978
Ph en</w> 13978
Cathe ter</w> 13978
Percent age</w> 13977
psych ologists</w> 13973
Per spective</w> 13971
I m</w> 13969
Q .</w> 13969
del inqu 13969
Fig. 8</w> 13968
Pre venting</w> 13967
metagen omic</w> 13967
ren yl</w> 13966
CH I</w> 13966
toxic ants</w> 13966
troph y</w> 13965
phosph omimetic</w> 13964
Func tioning</w> 13964
com post</w> 13963
mon oton 13963
TNF R2</w> 13962
yo himbine</w> 13962
laryng ectomy</w> 13962
V 1 13961
si ll 13961
ace ta 13961
fru iting</w> 13961
Nur 7</w> 13961
lymph oblasts</w> 13959
od opsin</w> 13958
methyl cytosine</w> 13958
immun ogens</w> 13957
synovi um</w> 13957
C affe 13956
D RO 13956
parasi temia</w> 13956
H3K9 me2</w> 13954
SAP K</w> 13954
H4 K2</w> 13953
Adi po 13952
DE AD</w> 13951
stro l</w> 13948
min ip 13947
at l 13946
aden oid</w> 13946
hetero genous</w> 13945
bar code</w> 13944
Arg 4</w> 13944
galac topyranoside</w> 13943
Tg f 13943
monoph asic</w> 13941
Mer kel</w> 13940
lu k 13939
IF G</w> 13939
ath y</w> 13938
ker nels</w> 13938
tri plex</w> 13936
Sma c</w> 13936
re pl 13935
om ata</w> 13935
ec to 13932
SI D</w> 13932
Radi x</w> 13932
Immobil on</w> 13932
tri oxide</w> 13931
dysp noea</w> 13931
Ex clusion</w> 13930
H am</w> 13929
O DNs</w> 13929
am aged</w> 13928
mo tic</w> 13927
ep ri 13927
De sign 13927
ar athyro 13925
Cs k</w> 13925
MW CNTs</w> 13925
SMAD 3</w> 13925
strea k</w> 13925
Com mon 13924
Prostagland in</w> 13924
exceed ingly</w> 13924
non linearity</w> 13923
saf e 13923
rec tomized</w> 13922
pyrethro id</w> 13922
decom posed</w> 13920
ush ima</w> 13919
Ch al 13918
duc ing</w> 13916
Rickett sia</w> 13916
R ico</w> 13915
E gr 13915
and 6</w> 13915
lett uce</w> 13915
Red ox</w> 13913
perchlor ate</w> 13913
M M.</w> 13912
bl ack 13912
para ox 13912
bund ling</w> 13912
sc anners</w> 13910
mus sels</w> 13910
overestim ation</w> 13910
co vi 13909
transp eptidase</w> 13908
ogu anine</w> 13907
R Q</w> 13906
gro oming</w> 13906
dis appears</w> 13906
pede stri 13906
hem ocytes</w> 13905
los es</w> 13905
oste ochondral</w> 13905
Hist ochemical</w> 13904
mc g</w> 13904
PC O</w> 13903
disturb ing</w> 13902
hypo chlorite</w> 13901
tetan ic</w> 13901
resear ched</w> 13900
DN R</w> 13899
fem oro 13898
DE A</w> 13898
az a 13897
emplo yers</w> 13897
M uk 13896
LT C</w> 13895
Ret t</w> 13895
cis -</w> 13894
cd k 13894
ILI TY</w> 13893
Barcel ona</w> 13893
hydro ps</w> 13892
non operative</w> 13892
b ici 13891
gu ez</w> 13891
TE N</w> 13891
Ne ed 13890
ant al</w> 13887
H end 13886
em oral</w> 13886
chromoph ores</w> 13885
desc end 13885
laf axine</w> 13884
Ga N</w> 13880
K ang</w> 13878
oc al 13876
par valbumin</w> 13876
gu st 13876
p VHL</w> 13875
grad ely</w> 13875
MAL AT1</w> 13875
dr usen</w> 13874
T sa 13873
cri stae</w> 13873
calvari al</w> 13873
epilep togenic</w> 13872
m n 13871
oth yro 13871
Ti bet 13869
arte facts</w> 13869
K LH</w> 13868
macropin ocytosis</w> 13868
ap op 13867
hemi paresis</w> 13866
ch ry 13864
lif estyles</w> 13864
cem ent 13864
mill imeter</w> 13861
Expan ded</w> 13861
A TL 13860
nitro so 13860
gyr A</w> 13860
Re qu 13859
R ay</w> 13858
9 M</w> 13857
ac cultur 13857
Mo ore</w> 13854
viol ations</w> 13853
AM 2</w> 13851
BL AST 13851
Rad 9</w> 13850
E AT</w> 13849
prioriti zation</w> 13849
Venez uel 13849
NO TCH</w> 13848
Pa Ca</w> 13848
B Q</w> 13847
Gu ided</w> 13846
perfor ator</w> 13846
Ur d</w> 13846
resi sti 13845
up 1</w> 13844
PG K</w> 13844
p E</w> 13843
lo v 13843
work shops</w> 13843
mean ings</w> 13842
ep ez 13840
ens en</w> 13840
routin es</w> 13840
Vi ro 13839
sarcolem mal</w> 13839
Ne ur 13838
wet lands</w> 13838
in consistency</w> 13837
patel lofemoral</w> 13837
pol ished</w> 13835
plas mapheresis</w> 13835
chin ensis</w> 13835
compar ator</w> 13833
im pedi 13832
Re in 13832
Ap parent</w> 13832
ataly zed</w> 13832
myc otoxins</w> 13832
ucid a</w> 13831
R oles</w> 13830
Scot tish</w> 13830
T cd 13828
im part</w> 13828
tr ametinib</w> 13826
re ls</w> 13824
CRA C</w> 13824
clock wise</w> 13824
d S</w> 13822
O ATP 13822
asteri de</w> 13822
. ac.uk</w> 13820
CD E</w> 13820
eph e 13820
electro catalytic</w> 13819
dig oxigenin</w> 13819
mem antine</w> 13819
intratrac heal</w> 13819
er tinib</w> 13818
st an 13818
cl ots</w> 13817
bio feedback</w> 13817
thi azole</w> 13817
cad herins</w> 13815
arthro pods</w> 13815
SY N 13815
leach ate</w> 13815
T MR</w> 13814
d um 13813
Neuro logy</w> 13813
f MLP</w> 13812
Ig AN</w> 13812
GG E</w> 13806
Copenh agen</w> 13806
V AR</w> 13803
methanesulf onate</w> 13803
E din 13802
Func tionally</w> 13802
ym ia</w> 13799
lati t 13799
hi PSCs</w> 13798
R ol 13797
il age</w> 13795
micro capsules</w> 13795
elong ating</w> 13794
contra indication</w> 13794
D ot 13793
5 mC</w> 13792
my enteric</w> 13792
Asp 3</w> 13792
RE V</w> 13791
resp ecti 13791
berg hei</w> 13791
FL S</w> 13790
Lys ine</w> 13789
tim olol</w> 13788
Neutroph ils</w> 13788
F MK</w> 13786
ht t</w> 13786
En tam 13785
Cho ice</w> 13785
Colon y</w> 13783
extrap yramidal</w> 13783
ron ium</w> 13782
o A</w> 13781
CE N</w> 13781
NH E</w> 13781
SK F</w> 13780
pione er</w> 13779
Posi tion</w> 13778
chem i 13777
mec onium</w> 13777
TRIM 5α</w> 13777
ub er 13776
en ema</w> 13775
pr ud 13774
CAR DI 13774
C PI</w> 13773
b olic</w> 13773
n ailing</w> 13771
chol angiography</w> 13771
B lac 13770
cri ti 13770
hi eld</w> 13769
CD V</w> 13769
Modi fications</w> 13769
orche strate</w> 13769
good ness</w> 13769
P HC</w> 13768
Pl GF</w> 13767
FI X</w> 13767
homocyste inemia</w> 13766
ine e</w> 13764
homin is</w> 13764
Sim on</w> 13763
stere o</w> 13762
disc ectomy</w> 13761
merg ing</w> 13761
opre gn 13761
w i</w> 13759
imid azo</w> 13759
C al</w> 13754
A gon 13754
EX TRA 13754
L TC 13753
Di alysis</w> 13753
epez il</w> 13753
ir amate</w> 13752
Un treated</w> 13751
mete orological</w> 13751
i ensis</w> 13750
out performs</w> 13750
Rec i 13748
D AKO</w> 13747
te lo 13747
ic ked</w> 13747
revolution ized</w> 13747
sulf atase</w> 13745
hrom osomal</w> 13744
F eng</w> 13743
my ostatin</w> 13743
Run x1</w> 13743
re programmed</w> 13742
sub tropical</w> 13742
M Ω</w> 13741
p H7</w> 13741
D ectin</w> 13740
DI G</w> 13740
ou re 13739
nem atic</w> 13738
- derived</w> 13737
Pro grams</w> 13737
Trich oderma</w> 13737
AD SCs</w> 13736
Cover slips</w> 13736
thi olate</w> 13735
Questionna ires</w> 13735
- negative</w> 13734
P fi 13734
al endronate</w> 13734
replic ase</w> 13734
SC G</w> 13733
RI PK3</w> 13733
CS s</w> 13732
bio accumulation</w> 13730
Qu ad 13730
MEC P2</w> 13730
top ologies</w> 13728
cell s.</w> 13727
para haemolyticus</w> 13727
OR F 13726
o eu 13725
MU T</w> 13725
un ifying</w> 13724
Inter ference</w> 13723
ari etal</w> 13722
ox acillin</w> 13721
U vr 13720
ag i</w> 13720
ord inal</w> 13720
ante l</w> 13720
el ia</w> 13719
an andamide</w> 13717
lamin ectomy</w> 13717
CX C</w> 13716
A AP</w> 13715
Presum ably</w> 13715
2 V6</w> 13711
ten ascin</w> 13710
i z</w> 13707
ec ologically</w> 13707
ll erian</w> 13707
dend ro 13707
Ar gon 13703
Immun otherapy</w> 13703
acetyl transferases</w> 13703
Nano Drop</w> 13702
Entam oeba</w> 13702
S ex 13701
trac ker</w> 13701
HI AA</w> 13700
r. m. 13700
kis speptin</w> 13700
S om 13698
CT V</w> 13694
BM A</w> 13694
err a</w> 13693
F HA</w> 13692
TP MT</w> 13691
Bene fits</w> 13691
hyperbilirubin emia</w> 13691
pan or 13690
ket on 13690
M B2</w> 13688
CYP 5</w> 13688
attr acting</w> 13687
7 V</w> 13686
Hist amine</w> 13685
Coll ins</w> 13685
propri etary</w> 13685
contu sion</w> 13685
co ro 13684
ay ama</w> 13684
AV E</w> 13683
sen i 13682
mig rates</w> 13682
papill omas</w> 13682
Int act</w> 13682
m T</w> 13681
K is 13681
underp innings</w> 13681
AN GP 13680
Per me 13679
macro lides</w> 13679
N SP 13677
ec tic</w> 13677
dyslex ia</w> 13677
pri t</w> 13675
Pe er</w> 13674
I ON</w> 13672
Bi or 13672
fl um 13671
pal ities</w> 13671
shak en</w> 13669
O Cl</w> 13668
dermat omyositis</w> 13668
ming ham</w> 13668
AL K 13667
pacem akers</w> 13667
Mad rid</w> 13664
H NC</w> 13662
AT X</w> 13662
in ose</w> 13659
p assi 13657
ca val</w> 13655
PC Ps</w> 13654
RN A2</w> 13653
0 A8</w> 13652
W AS 13652
W MD</w> 13652
Hum an 13651
commis sural</w> 13651
ogu anidine</w> 13651
b s 13650
LE C</w> 13650
hydroxy phenyl</w> 13649
M ast</w> 13647
RO CK1</w> 13647
ul ls</w> 13646
ethyl ammonium</w> 13646
reconc ile</w> 13646
mic tur 13645
degrad ability</w> 13642
circum cision</w> 13642
dec apping</w> 13641
Tw ist1</w> 13641
immort alization</w> 13641
N MO</w> 13639
U tility</w> 13639
le gend</w> 13639
Whi tes</w> 13639
os pec 13638
SER PIN 13638
U tah</w> 13637
seas onality</w> 13636
Ber tani</w> 13636
en ko</w> 13635
adenosyl methionine</w> 13635
CCR 4</w> 13633
ethn ically</w> 13633
. 4C</w> 13632
ti tin</w> 13631
B SO</w> 13630
3 TC</w> 13629
EN s</w> 13629
rop tic</w> 13628
atom istic</w> 13628
Table 4</w> 13627
B abe 13620
p ann 13620
Cle veland</w> 13620
R hizobium</w> 13619
Ig H</w> 13619
pre load</w> 13617
ch l 13616
hy oid</w> 13616
PT 2</w> 13615
Tes ticular</w> 13615
neuro development</w> 13614
PP O</w> 13613
N ocardi 13612
me dies</w> 13612
Chrom at 13612
di odes</w> 13609
el der</w> 13609
oc ine</w> 13609
ric kets</w> 13609
entrop ic</w> 13609
G BA</w> 13608
Com position</w> 13608
FUN DING</w> 13607
y ang</w> 13606
cros sb 13606
Stud ying</w> 13604
ref usal</w> 13604
Bas in</w> 13603
Gla uc 13601
contribut ory</w> 13598
inter ictal</w> 13597
perox idases</w> 13596
un ya</w> 13595
p DC</w> 13594
n sp1</w> 13593
alve oli</w> 13593
if erol</w> 13591
sp anned</w> 13590
SW 1</w> 13590
covi tine</w> 13590
tonsi l</w> 13589
wr apping</w> 13589
S tent</w> 13586
n aud</w> 13585
strepto kinase</w> 13585
Gai ther 13585
T 6 13583
MD C</w> 13582
Plate lets</w> 13582
bom besin</w> 13582
Mathem atical</w> 13582
Al ve 13580
Ch R2</w> 13579
ori ent</w> 13579
Pro ximal</w> 13578
SER V 13578
xyl an</w> 13577
cholangiopancre atography</w> 13577
fib ula</w> 13576
D f</w> 13575
at uring</w> 13574
mat h</w> 13574
gon ococcal</w> 13572
align ing</w> 13571
P ak</w> 13570
alan ines</w> 13570
SE LE 13569
I TAM</w> 13568
homo dimerization</w> 13568
aren es</w> 13568
P reser 13567
TA AT 13567
Fr action 13564
special ised</w> 13564
ryst alline</w> 13561
penicill amine</w> 13561
Tu be</w> 13559
O LI 13555
alk ynes</w> 13555
E SA</w> 13554
por a</w> 13554
paradox ically</w> 13554
B iliary</w> 13551
es cript</w> 13551
AT 3</w> 13551
FK BP</w> 13550
tangen tial</w> 13550
hs CRP</w> 13547
immunosuppress ant</w> 13547
di substituted</w> 13546
ifos famide</w> 13545
FO A</w> 13544
3 Δ 13542
or chi 13542
ligh ter</w> 13542
conc ise</w> 13540
cyto chemical</w> 13540
SP EC 13540
synap totagmin</w> 13540
Hem oglobin</w> 13539
co administration</w> 13538
Cs Cl</w> 13538
X PF</w> 13537
O AB</w> 13535
iso quinoline</w> 13535
insi pidus</w> 13535
N IV</w> 13534
enter pri 13534
-deoxy uridine</w> 13534
she aths</w> 13533
dic tates</w> 13531
L et</w> 13530
ver b 13530
sub regions</w> 13530
In d 13530
pige on</w> 13529
y an 13527
di butyryl</w> 13527
sched uling</w> 13527
do i.org</w> 13526
Edin burgh</w> 13526
SK BR3</w> 13524
handic apped</w> 13524
cinti graphy</w> 13524
m itis</w> 13523
Ger m 13523
al imentary</w> 13522
ph entolamine</w> 13522
sn akes</w> 13522
T V 13519
electro spun</w> 13518
U B</w> 13515
impe des</w> 13515
den ing</w> 13514
En zymes</w> 13514
dithi ocarb 13514
d TTP</w> 13513
macro globulin</w> 13513
leg ume</w> 13513
aminol ev 13513
G s 13510
ap pliances</w> 13510
aro n</w> 13509
pyrrolid one</w> 13509
histi ocytic</w> 13507
Cdc 5</w> 13506
mus cimol</w> 13505
G OF</w> 13504
carg oes</w> 13504
approxim ations</w> 13503
Fig. 2C</w> 13503
TRP M8</w> 13503
Haz ard</w> 13502
Ph ag 13501
H As</w> 13500
P en</w> 13500
CC N</w> 13500
ure as</w> 13499
Superi or</w> 13499
k s 13497
comit ans</w> 13497
asci tic</w> 13496
E DA</w> 13495
Phar ma</w> 13492
Nel son</w> 13492
sero logically</w> 13491
Glu N1</w> 13490
Ex tra 13489
del ete</w> 13488
dihydro pyridine</w> 13487
los an</w> 13486
doc x</w> 13485
iso prost 13484
9 q</w> 13483
St or 13483
5 s</w> 13481
D HF</w> 13480
Diam ond</w> 13479
gro ve</w> 13478
RNA seq</w> 13477
Camero on</w> 13477
as certain 13476
PL s</w> 13476
D ak 13475
calcin osis</w> 13475
ag in</w> 13473
dr in</w> 13472
SL 3</w> 13472
BR A</w> 13472
v ps 13471
E 2A</w> 13471
tab ulated</w> 13471
Hetero zygous</w> 13471
SE B</w> 13470
r g 13469
kain ic</w> 13468
elev ates</w> 13467
N im 13466
fur a</w> 13466
tom etry</w> 13465
vis tic</w> 13465
TC G</w> 13464
PP G</w> 13464
turbul ent</w> 13464
BM SC</w> 13463
Head ache</w> 13462
cystic ercosis</w> 13462
code ine</w> 13462
flu idics</w> 13461
semiconduc tors</w> 13459
Bed ford</w> 13459
tri tium</w> 13457
poly adenylated</w> 13457
fel dt</w> 13457
GG AC 13456
HMG A2</w> 13456
arsen ate</w> 13455
M NC</w> 13453
E m</w> 13453
neurosph eres</w> 13453
op t</w> 13452
l -</w> 13451
a ire</w> 13451
alle i</w> 13451
cyan obacterium</w> 13451
Work shop</w> 13451
organis ational</w> 13450
omon ocytic</w> 13450
health ier</w> 13448
Fig. 3 13448
sett lement</w> 13448
feno fibrate</w> 13448
Porphy ro 13448
k ills</w> 13446
CV B3</w> 13445
chyl omic 13444
os teric</w> 13443
SP ME</w> 13441
program mable</w> 13441
in forming</w> 13440
ap elin</w> 13440
germin ated</w> 13440
E in 13438
ol is 13438
regi onally</w> 13438
lipo ic</w> 13438
P ad 13436
ar mam 13436
enanti oselectivity</w> 13436
M US 13433
zol ed 13433
depic t</w> 13433
Fellow ship</w> 13433
S ources</w> 13431
M id</w> 13430
cor ti 13429
gate keeper</w> 13429
ON O</w> 13427
N CT</w> 13426
or n</w> 13426
ste am</w> 13426
ev al</w> 13425
he uristic</w> 13424
U pp 13423
Hemat opoietic</w> 13423
ARN T</w> 13423
im purity</w> 13422
ed ers</w> 13422
Ad vis 13422
san itary</w> 13422
HD M</w> 13418
Synerg istic</w> 13418
I Ds</w> 13417
E f 13416
Sens or</w> 13416
Clim ate</w> 13416
stri de</w> 13415
bim olecular</w> 13415
actinomyc e 13415
VD AC1</w> 13412
CG AC 13410
epigen ome</w> 13410
consul ting</w> 13410
Cal mette</w> 13409
equ i</w> 13408
synap sin</w> 13405
Ad renal</w> 13403
proce eding</w> 13403
sens u</w> 13403
am i 13402
mal ic</w> 13402
X PD</w> 13401
C m 13400
D ac 13400
PA C1</w> 13400
diverticul itis</w> 13400
M Vs</w> 13399
inter ferences</w> 13398
P SM</w> 13396
Trac king</w> 13396
com yc 13395
Al bumin</w> 13395
equ als</w> 13395
u tero 13393
ver ifying</w> 13393
Be ad 13393
5 h</w> 13392
chondro genesis</w> 13392
pyri fos</w> 13389
Ob served</w> 13389
SD B</w> 13389
Con cor 13388
diast ole</w> 13388
war dly</w> 13387
Bir mingham</w> 13387
interfero meter</w> 13387
F MR1</w> 13386
tec tum</w> 13386
hydrox yp 13385
Rheum atology</w> 13384
L PP</w> 13383
Ψ m</w> 13382
confidenti ality</w> 13381
c res 13380
Sem i 13378
laryng oscopy</w> 13376
myx oma</w> 13376
branch ial</w> 13375
Co oper</w> 13374
hat ch 13374
Cyste ine</w> 13374
H pa 13373
PDGF Rβ</w> 13373
I MR</w> 13372
c 7</w> 13371
sup rag 13371
S 6B</w> 13370
TBC 1 13370
Use fulness</w> 13369
T C1</w> 13368
sh ell 13367
midw ifery</w> 13367
oumar in</w> 13367
Fer ment 13365
Innov ative</w> 13365
chlamy dia</w> 13364
cloth ing</w> 13364
EC 2</w> 13363
cycl er</w> 13362
P ack 13361
E MS 13359
K MT 13358
form ulae</w> 13358
DE F</w> 13358
ell ings</w> 13357
PO PU 13357
phle bitis</w> 13357
Empir ical</w> 13356
L ich 13355
B lu 13354
B CN 13352
techn icians</w> 13351
SL S</w> 13351
Lenti viral</w> 13351
immunostim ulatory</w> 13351
V av1</w> 13350
f 8</w> 13350
CO S7</w> 13350
Gaither sburg</w> 13350
heteron uclear</w> 13348
ann an</w> 13347
o bium</w> 13346
PL B</w> 13346
icos a 13346
tal ar</w> 13345
tri phosphatase</w> 13344
MN NG</w> 13344
ot or 13343
mox ifloxacin</w> 13343
spur ious</w> 13343
a z</w> 13342
accoun tability</w> 13342
arri ved</w> 13341
Par ac 13340
Sol vent</w> 13340
Compar able</w> 13339
nucleoti dase</w> 13338
hyperuric emia</w> 13338
Vim entin</w> 13338
pseudom allei</w> 13337
SF Ks</w> 13334
NT Ds</w> 13333
f i</w> 13332
ten der</w> 13332
Kv 7</w> 13332
Th al 13331
Al 2O3</w> 13331
Th y1</w> 13330
head space</w> 13330
N p 13329
ag liflozin</w> 13329
res equ 13328
cardiover sion</w> 13328
W NK 13326
dis solving</w> 13326
B eng 13325
CF s</w> 13323
IR F1</w> 13323
amin i</w> 13322
Ir radiation</w> 13322
spaw ning</w> 13322
varic ocele</w> 13321
Throm bin</w> 13321
promas tigotes</w> 13321
Hy gi 13320
il er</w> 13319
v anis 13318
alli ed</w> 13318
digit orum</w> 13318
Acanth amoeba</w> 13318
CB V</w> 13317
pertur bing</w> 13317
succ umb 13316
S 2D</w> 13315
JU N</w> 13315
H A2</w> 13313
A za</w> 13313
P lu 13313
I S1</w> 13312
Fin ding</w> 13311
Pla que</w> 13311
Brow nian</w> 13310
d ong</w> 13308
tem comitans</w> 13308
phyto chrome</w> 13307
Mel bourne</w> 13307
D W 13306
ac onit 13306
ish i</w> 13305
Scle ro 13305
5 . 13304
TI PS</w> 13304
st un 13303
inter mit 13303
omyc etes</w> 13303
In novation</w> 13302
ace us</w> 13302
symb ols</w> 13302
CG T</w> 13301
Sampl ing</w> 13300
swe ating</w> 13300
end obronchial</w> 13299
D ich 13297
fol k</w> 13297
im par 13296
tur tle</w> 13296
Construc ts</w> 13296
plasti ds</w> 13296
ro unding</w> 13295
SMAD 2</w> 13295
J mj 13294
han dedness</w> 13293
c aged</w> 13292
Ensemb l</w> 13292
A round</w> 13291
F resh 13290
no ti 13290
stra w 13290
defec ation</w> 13290
op per</w> 13289
grac ilis</w> 13289
oste olytic</w> 13287
depic ts</w> 13287
pra vastatin</w> 13287
p Y</w> 13285
ond an 13285
CR M</w> 13285
ll um</w> 13283
R ud 13282
re treatment</w> 13282
el vic</w> 13282
Corri gen 13282
Col e 13281
All en</w> 13281
mor ph</w> 13280
Caj al</w> 13280
o plankton</w> 13277
tetra hydro</w> 13277
ar ism</w> 13276
residu als</w> 13276
s-- a</w> 13274
G t 13273
fibrom atosis</w> 13273
fr active</w> 13271
osy nostosis</w> 13270
neuro inflammatory</w> 13269
Sor afenib</w> 13269
gel solin</w> 13268
P 4 13267
ga thering</w> 13267
aggrec an</w> 13267
K len 13266
reg ressive</w> 13266
la den</w> 13266
ol ocalization</w> 13265
de stabilizes</w> 13265
MM s</w> 13265
Scat chard</w> 13264
lymph angiogenesis</w> 13263
ME R</w> 13263
P SD 13261
SU M1</w> 13261
bil s</w> 13261
hypo vol 13260
od ors</w> 13259
gri ef</w> 13259
decid u 13259
m ap 13258
S tern 13258
ble ed</w> 13258
Magne sium</w> 13258
aro s</w> 13257
trim ming</w> 13257
myo fibers</w> 13253
am pull 13251
P olar</w> 13250
co d</w> 13250
unde rex 13250
r yp 13249
il legal</w> 13249
oc ratic</w> 13249
Stock holm</w> 13249
W ako</w> 13248
G un 13246
Z ucker</w> 13246
ox adi 13246
munici palities</w> 13245
glutathion ylation</w> 13245
i osity</w> 13244
A HP</w> 13244
in born</w> 13243
S av 13242
de gron</w> 13242
ag al</w> 13241
Analy zing</w> 13240
A cous 13239
opac ification</w> 13239
U C 13238
G MA</w> 13238
Re x</w> 13238
buil dings</w> 13238
arathyro idism</w> 13236
ag us</w> 13235
AD PR</w> 13235
Man tel</w> 13235
A h</w> 13234
p EF</w> 13234
achi asmatic</w> 13234
re ject</w> 13233
PE M</w> 13233
ind ocyanine</w> 13233
C all 13232
bom bar 13232
N e</w> 13231
er son</w> 13231
homo e 13230
s lab</w> 13229
cathe teri 13229
actinomyce temcomitans</w> 13229
loc ating</w> 13228
L GN</w> 13227
inhal er</w> 13227
fung icide</w> 13226
od ro 13225
conv ection</w> 13225
MAT a</w> 13224
R b1</w> 13223
organ oid</w> 13223
Tri ticum</w> 13223
mar sup 13220
E ri 13217
synthe tically</w> 13217
A id</w> 13215
engulf ment</w> 13215
ther anos 13213
rheumat ology</w> 13213
vi st</w> 13211
cut offs</w> 13210
Cryst allization</w> 13210
D aph 13208
Agg ressive</w> 13208
EBN A1</w> 13208
p Akt</w> 13207
PK Cs</w> 13206
H dac 13205
om entum</w> 13205
r GO</w> 13202
Atl anta</w> 13202
d Cas9</w> 13200
for maz 13200
carboxy methyl</w> 13200
e ism</w> 13199
un conscious</w> 13199
vin orel 13199
UV R</w> 13197
Ham burg</w> 13197
S tom 13196
G II 13196
sti tial</w> 13196
De v</w> 13196
dor si 13195
a v</w> 13194
CM D</w> 13194
oz on 13193
thi os 13192
aminopy ridine</w> 13192
pin point</w> 13191
re atment</w> 13189
- infected</w> 13188
append ages</w> 13188
SU R1</w> 13187
est rel</w> 13186
multim erization</w> 13186
M CT 13185
neuro logically</w> 13185
expos es</w> 13185
HB O</w> 13185
mechan os 13184
ot us</w> 13183
S ome 13182
t old</w> 13181
JAK 3</w> 13181
ul ants</w> 13180
SC I 13180
los sal</w> 13180
iso kinetic</w> 13180
nur sery</w> 13180
oct ane</w> 13180
travel ing</w> 13179
retro mer</w> 13177
Cam bo 13177
phosphoro thio 13177
distinc tions</w> 13176
DD K</w> 13176
provinc ial</w> 13176
arch es</w> 13175
allos terically</w> 13175
H x 13174
hydroxy propyl</w> 13174
Hydro x 13174
strength ens</w> 13174
O OH</w> 13173
PN G 13173
o sta 13172
erythro cytic</w> 13171
D CP</w> 13170
pur ines</w> 13170
grass land</w> 13170
Dys regulation</w> 13168
pe dal</w> 13167
D CD</w> 13166
sup ran 13166
PI M</w> 13166
F AT</w> 13165
lam b</w> 13164
S DM</w> 13163
japon icus</w> 13163
carbo diimide</w> 13162
conven i 13161
deparaff inized</w> 13161
h en 13159
J V</w> 13158
A RP 13157
pos teriorly</w> 13155
un remarkable</w> 13153
respecti vel 13153
We e1</w> 13152
grate ful</w> 13152
b low 13151
all yl 13151
Plac ental</w> 13150
con ferences</w> 13148
trypsin ization</w> 13148
1 r</w> 13147
con stellation</w> 13147
tim a</w> 13146
clu b</w> 13146
be roptic</w> 13145
wo ol</w> 13144
O le 13143
Y es</w> 13143
arri ve</w> 13143
cam eras</w> 13142
G raph</w> 13141
IC V</w> 13141
U L2</w> 13140
AI H</w> 13140
Inter mittent</w> 13140
small pox</w> 13139
stabil izers</w> 13137
Compon ents</w> 13137
C rick</w> 13136
on er</w> 13136
perim etry</w> 13136
T PC</w> 13133
ethylenedi amine 13131
expres sive</w> 13130
En cephal 13130
V erbal</w> 13128
op anib</w> 13127
c. 6</w> 13127
pro metaphase</w> 13126
su it</w> 13126
per col 13126
A mi 13124
pneumo peritoneum</w> 13123
ma 1</w> 13122
diver sified</w> 13122
ur id 13121
G AR</w> 13120
tuber cular</w> 13120
mic utes</w> 13119
re forms</w> 13118
opt ogenetic</w> 13116
a ena</w> 13115
kin umab</w> 13115
a hertz</w> 13113
SP IN 13113
multi modality</w> 13112
Lepid optera</w> 13112
D p 13111
ase 1</w> 13110
Tw in</w> 13109
a war 13108
tri phosphates</w> 13108
em ens</w> 13106
CC 5</w> 13106
hyperex citability</w> 13106
F un 13104
g c 13104
TW E 13104
mo tive</w> 13103
ech in 13103
sw allow</w> 13103
lact obac 13103
arrhythm ogenic</w> 13103
Oper ation</w> 13101
D Z</w> 13100
PD E4</w> 13100
EN U</w> 13100
ascertain ment</w> 13099
G H3</w> 13098
tor iness</w> 13096
PE D</w> 13095
log s</w> 13094
jus tification</w> 13093
he dra</w> 13092
vinorel bine</w> 13092
Medic ines</w> 13091
CA B</w> 13090
conver ging</w> 13090
cul prit</w> 13087
exhaus ted</w> 13087
s ocket</w> 13085
un insured</w> 13085
SM G</w> 13085
NA B</w> 13085
de protonated</w> 13084
isth mus</w> 13084
pepti dom 13083
ru xolitinib</w> 13082
synapt osomal</w> 13082
damp ing</w> 13082
adsorb ents</w> 13082
hydrochlor ic</w> 13082
por tin</w> 13081
lymph atics</w> 13081
IL 3</w> 13081
M MT</w> 13080
car vedilol</w> 13080
ith iasis</w> 13079
nitro so</w> 13079
sup plier</w> 13077
AL F</w> 13077
Paste u 13077
Practi ces</w> 13076
duoden ectomy</w> 13076
y en</w> 13075
Th T</w> 13074
smooth ing</w> 13074
tonsill ar</w> 13074
scop ies</w> 13073
tischem ic</w> 13073
fo als</w> 13072
dox ins</w> 13072
hop ping</w> 13072
P OR</w> 13071
on iae</w> 13069
prost atitis</w> 13069
monoc linic</w> 13068
ab asic</w> 13067
pyri din</w> 13067
Cob b</w> 13066
L inc 13065
E im 13065
hyper variable</w> 13063
Arsen ic</w> 13061
CL R</w> 13060
Pri me</w> 13060
TFII B</w> 13060
psychiatri st</w> 13060
hydrox amic</w> 13058
ill o</w> 13057
rhyth micity</w> 13057
ondan setron</w> 13057
le ver 13056
em atic</w> 13056
GST T1</w> 13056
deform ability</w> 13055
en i</w> 13054
dec entr 13054
are sis</w> 13054
emul si 13054
hem i</w> 13053
rever tant</w> 13053
recogn ise</w> 13053
tel encephalon</w> 13053
MO P</w> 13053
w rite</w> 13052
Sup eroxide</w> 13052
Ther mod 13052
moder ating</w> 13050
B ag 13049
exp iration</w> 13048
NR 2A</w> 13048
cross link</w> 13048
dis asters</w> 13047
H of 13046
m Osm</w> 13046
centr ality</w> 13045
vis m</w> 13045
w 1</w> 13044
sor ter</w> 13044
endo phytic</w> 13044
Dimensi onal</w> 13044
rham n 13043
D K</w> 13041
lymph openia</w> 13041
IN F 13041
M. D.</w> 13040
trans differentiation</w> 13039
ste aric</w> 13037
palmito ylated</w> 13036
Y L</w> 13033
o sim 13032
HE CT</w> 13032
TA F</w> 13032
Percep tions</w> 13032
Tan aka</w> 13030
s ory</w> 13029
AT D</w> 13029
emo tionally</w> 13029
chal cone</w> 13028
und ings</w> 13027
rop ic</w> 13027
att aching</w> 13027
myocl onic</w> 13027
Al a2</w> 13026
Phil ipp 13026
expand able</w> 13026
wave guides</w> 13025
F o</w> 13024
wh ol 13024
E ML4</w> 13023
for tification</w> 13022
thyro idal</w> 13021
Vari an</w> 13021
farnes yl</w> 13021
U L</w> 13020
gen ation</w> 13020
rec ur</w> 13020
natur alistic</w> 13020
intrap artum</w> 13020
govern ments</w> 13019
swee p</w> 13019
P f</w> 13017
m og 13016
trans itory</w> 13016
menstr uation</w> 13016
Inter stitial</w> 13015
am icin</w> 13014
accredi ted</w> 13014
N Ab</w> 13013
Re produc 13013
otrop ia</w> 13013
Sp ir 13013
fron t 13013
B IC</w> 13012
transcriptom ics</w> 13012
sol vated</w> 13011
cyclop edia</w> 13011
proof reading</w> 13011
poly ploid</w> 13010
bu propion</w> 13009
CN C</w> 13009
benz amide</w> 13009
MO 1</w> 13009
s ae</w> 13008
C 3a</w> 13008
Gyn ecology</w> 13008
with stand</w> 13007
tourni quet</w> 13006
m if 13005
qu in</w> 13005
Fl t3</w> 13005
NO X2</w> 13004
N DU 13003
form yl 13003
P ediatrics</w> 13002
d GTP</w> 13001
Corrigen dum</w> 13001
re member</w> 13000
dr ug 13000
astro l</w> 12999
D ock</w> 12998
Wh at 12997
adipo kines</w> 12997
bi l</w> 12995
cu ticular</w> 12995
All ele</w> 12995
EF V</w> 12995
Kum ar</w> 12995
phy ton</w> 12994
N T2</w> 12993
M AA</w> 12993
homo dimeric</w> 12993
lumin ometer</w> 12993
photoc ur 12992
pow dered</w> 12991
nan ospheres</w> 12990
ad an</w> 12989
cry otherapy</w> 12989
RE P</w> 12988
enum erated</w> 12988
F ERM</w> 12987
Ne igh 12987
ens ins</w> 12987
immunomod ulation</w> 12987
U AE</w> 12986
f li 12986
end odermal</w> 12986
HR CT</w> 12986
TT E</w> 12985
Pfi zer</w> 12985
D C3</w> 12984
isopren oid</w> 12984
tumo rous</w> 12982
Spe I</w> 12982
w ines</w> 12981
fis her 12981
in appropriately</w> 12979
trans ver 12979
HO X</w> 12979
- CA 12978
bl adders</w> 12978
Hog 1</w> 12978
ci an</w> 12977
can ines</w> 12977
o dies</w> 12976
od ylate</w> 12976
p O2</w> 12974
O MP</w> 12974
dic arboxylic</w> 12974
jux tam 12974
o o</w> 12973
spin y</w> 12973
mg .kg</w> 12973
Pro viding</w> 12972
bid entate</w> 12972
sc ill 12971
Te tr 12971
Therapeu tics</w> 12971
endoth eli 12969
param yx 12969
Nu PAGE</w> 12969
alge sic</w> 12969
un restricted</w> 12968
Brac hy 12967
N g</w> 12965
NB D1</w> 12965
decel eration</w> 12964
ar cin 12963
Indi ana</w> 12962
E stradiol</w> 12961
G R1</w> 12961
ov o</w> 12960
SY K</w> 12959
Sch r 12957
parag raph</w> 12956
insectic idal</w> 12954
re medies</w> 12953
FE RE 12953
sialy lation</w> 12953
Plas tic</w> 12952
Europe ans</w> 12951
de adly</w> 12950
and B</w> 12950
xen ografted</w> 12950
hydroly zes</w> 12950
Porphyro monas</w> 12950
pr amine</w> 12949
Acti v 12948
heterolog ously</w> 12948
cos mid</w> 12947
B un 12946
Sn f1</w> 12946
re med 12945
G ros 12944
phosphos erine</w> 12944
SE ER</w> 12943
Ox ide</w> 12943
C BM 12942
ur a3</w> 12942
per id 12942
Ex cellent</w> 12942
comp ly</w> 12940
eck strin</w> 12940
R if 12939
v 5</w> 12939
bab oon</w> 12939
GSE 4</w> 12939
ar o</w> 12937
immuno phenotype</w> 12936
quanti fiable</w> 12935
FF M</w> 12935
Emplo ying</w> 12934
R oc 12933
Ar ticles</w> 12933
compens ating</w> 12933
tyro syl</w> 12933
ov aginal</w> 12932
P im 12930
neuro ticism</w> 12930
un covering</w> 12929
CY P4</w> 12927
Hip pel</w> 12927
u M</w> 12925
Alph a 12925
Δ 6</w> 12923
δ 2</w> 12921
Ken ne 12921
trabecul ectomy</w> 12920
andro gen 12919
tur keys</w> 12918
nap us</w> 12918
hyper ventilation</w> 12917
ol ium</w> 12916
ut most</w> 12916
ven om 12916
hal ides</w> 12915
U CP1</w> 12914
am ellar</w> 12914
SM ART</w> 12913
P ene 12912
F ET</w> 12912
ah ashi</w> 12911
4 g</w> 12910
o uring</w> 12908
ent arium</w> 12907
br ace</w> 12907
An kle</w> 12907
Rus sell</w> 12907
te in</w> 12906
awa iting</w> 12906
CAC NA 12905
Rp n1</w> 12905
cur able</w> 12903
chromat ograms</w> 12903
blin ding</w> 12903
symb olic</w> 12903
an odic</w> 12902
Cerebro spinal</w> 12902
L HR</w> 12901
ord inarily</w> 12900
rever tants</w> 12900
macro scopically</w> 12900
diff r 12899
y on</w> 12898
M FC</w> 12896
V anc 12896
Bio chemicals</w> 12896
allox an</w> 12896
r hizo 12895
0 mm</w> 12894
B HI</w> 12894
Abbrevi ations</w> 12893
cl 2</w> 12892
tri methyl</w> 12892
sero vars</w> 12892
byp asses</w> 12891
La tent</w> 12891
Phen yl 12891
magne tically</w> 12890
L CL</w> 12889
end ole 12889
semic ir 12889
OH dG</w> 12889
epri stone</w> 12889
cal ifor 12888
erti b</w> 12888
Rep eti 12887
Initi ation</w> 12887
pa tents</w> 12886
aliquo ted</w> 12885
o V</w> 12884
ub acteri 12884
inter position</w> 12883
pr i</w> 12883
vi brio</w> 12882
hypop nea</w> 12882
w an</w> 12881
W HR</w> 12880
chemo embolization</w> 12880
GCN 2</w> 12880
prognos es</w> 12880
CT E</w> 12879
dal tons</w> 12879
ogran ul 12878
antigen ically</w> 12877
oxal o 12876
schizophren ics</w> 12875
FI T</w> 12875
. This</w> 12874
B SP</w> 12874
screen ings</w> 12874
BM I1</w> 12872
metach ronous</w> 12872
sha ke</w> 12871
CS F 12869
hat ched</w> 12869
qu etiapine</w> 12868
gu ide 12868
Afr icans</w> 12868
ne virapine</w> 12867
Lup us</w> 12867
i TRA 12866
sever ing</w> 12866
Fe 3</w> 12866
CB 1R</w> 12866
consul tants</w> 12866
orth osteric</w> 12865
A ni 12864
pre molar</w> 12864
en nium</w> 12863
micro electrodes</w> 12863
aff in</w> 12863
Lys 4</w> 12863
6 x 12862
tex t 12862
Ca o</w> 12862
male ate</w> 12860
tur tles</w> 12859
Al ab 12858
abi raterone</w> 12858
P ed 12857
K F 12857
mycel ial</w> 12855
mobil ities</w> 12853
5 β</w> 12850
V RE</w> 12849
os econd</w> 12848
6 A1</w> 12847
mal practice</w> 12847
thi ocyanate</w> 12847
organ izer</w> 12846
CV S</w> 12845
tonsi ls</w> 12845
J ar 12844
tri plets</w> 12843
bre vis</w> 12843
LO Q</w> 12843
D if 12842
tetr alogy</w> 12842
leptos pirosis</w> 12842
specific s</w> 12840
electrophore tically</w> 12840
flavi viruses</w> 12840
sphinc tero 12839
MD C1</w> 12838
oxo G</w> 12838
Rod ri 12837
ness ing</w> 12835
cerul oplasmin</w> 12835
spec ulative</w> 12834
myo fibrils</w> 12834
pa c</w> 12833
methyl glutaryl</w> 12833
tod dlers</w> 12833
my b</w> 12832
Figure 1 12832
Y un 12831
alli ance</w> 12831
Cre ER</w> 12831
d les</w> 12829
AU C0</w> 12829
reticul ocytes</w> 12829
T Cs</w> 12828
Go at</w> 12828
ANGP TL 12828
ma ker</w> 12826
vari eg 12826
de phosphorylate</w> 12825
SL C</w> 12825
B Y4</w> 12824
ho ok 12824
Inhibi ting</w> 12824
Babe sia</w> 12823
dra fted</w> 12819
flo od</w> 12819
L AA</w> 12818
6 . 12817
val ency</w> 12817
bran ds</w> 12816
u mo 12815
invari ance</w> 12815
Wo od 12815
SM 2</w> 12814
IT O</w> 12814
C anine</w> 12813
Sca ff 12813
mes o 12811
pan itumumab</w> 12811
inoc erebellar</w> 12809
neur itic</w> 12809
p AKT</w> 12808
bacter aemia</w> 12808
coincid ing</w> 12808
addic ted</w> 12807
RA DS</w> 12806
mir abilis</w> 12806
E RA 12805
dan amycin</w> 12804
di ones</w> 12802
flan ks</w> 12802
opho bia</w> 12802
compar ability</w> 12801
RNA i 12801
inter body</w> 12799
leuc ovorin</w> 12799
ADAM TS</w> 12799
3 x</w> 12797
In form 12796
abor ted</w> 12795
ass o</w> 12794
AA O</w> 12794
B ev 12793
PM E</w> 12793
monoubiqu itination</w> 12793
diffu sible</w> 12793
well ness</w> 12792
she ared</w> 12791
myel osuppression</w> 12790
attribu tion</w> 12789
M WT</w> 12788
N AR</w> 12786
Leptos pira</w> 12785
Ele vation</w> 12784
Barr é</w> 12784
s one</w> 12783
Com ments</w> 12783
inc ipient</w> 12782
Per for 12782
SS M</w> 12782
metacar pal</w> 12782
M ab</w> 12780
Im plantation</w> 12779
pseud ogene</w> 12779
deline ating</w> 12779
RP M</w> 12778
squ ee 12777
ur bit 12776
op exy</w> 12776
pac ks</w> 12776
SF M</w> 12776
Hap Map</w> 12776
sal a</w> 12775
fact ories</w> 12775
lactobac illi</w> 12774
co elic 12773
neuro anatomical</w> 12773
MD MX</w> 12773
rever t</w> 12773
leukotri enes</w> 12772
neurolep tics</w> 12772
GL S</w> 12771
gib berel 12771
8 P</w> 12770
exfoli ation</w> 12770
ess ay</w> 12769
Hist opathologic</w> 12769
ribonucle otides</w> 12769
Ran kin</w> 12768
K y 12766
Phosphati dyl 12766
sirtu ins</w> 12765
α C</w> 12763
N HC</w> 12762
F ox</w> 12762
yl idene</w> 12762
Is ot 12762
scor pion</w> 12762
in growth</w> 12761
β CD</w> 12760
7 P</w> 12759
de pots</w> 12758
dec ayed</w> 12758
ent an</w> 12757
oph ora</w> 12757
succin ic</w> 12757
Yam ag 12757
D 3 12755
em ig 12755
Cr uc 12755
antim ycin</w> 12755
disper sity</w> 12755
CXCR 7</w> 12755
Rev e 12755
plasmacy toid</w> 12754
AB CB 12753
Phot on</w> 12753
Myel oid</w> 12753
PROCEDU RES</w> 12752
f id 12751
ru tin</w> 12751
practi c</w> 12751
Sched ule</w> 12750
P uma</w> 12749
CD 1d</w> 12748
eradic ated</w> 12748
thec a</w> 12748
Bi polar</w> 12747
cataly ses</w> 12747
Loc alized</w> 12746
omod ulin</w> 12746
erec tion</w> 12746
proc urement</w> 12745
AI C</w> 12743
mes ophilic</w> 12743
Top BP1</w> 12742
immuno affinity</w> 12741
Bi ole 12741
photovolta ic</w> 12741
Contro ver 12740
in adequately</w> 12739
sel ections</w> 12739
PE L</w> 12739
db SNP</w> 12739
Philipp ines</w> 12739
ser traline</w> 12738
semin oma</w> 12738
AP PRO 12737
Cr k</w> 12737
tabac um</w> 12737
val vul 12735
hypo physis</w> 12735
deliber ate</w> 12735
Rob inson</w> 12734
Kir 2</w> 12734
car nos 12733
peri od 12733
IC 2</w> 12733
met oclopramide</w> 12732
sug arcane</w> 12732
Mus cul 12732
pro polis</w> 12730
ex changing</w> 12730
micro dissected</w> 12730
H TR 12728
B ronch 12727
Fox M1</w> 12727
hepar anase</w> 12727
lam otrigine</w> 12725
ple ase</w> 12725
Am phi 12725
scinti graphic</w> 12725
eme sis</w> 12725
h ubs</w> 12724
cili ogenesis</w> 12724
G SI</w> 12723
S ene 12722
f ounding</w> 12722
lip ases</w> 12722
at ta</w> 12721
D ER</w> 12720
supr achiasmatic</w> 12720
I COS</w> 12719
oys ter</w> 12719
E pic 12717
Trans fusion</w> 12716
surro undings</w> 12716
N MS</w> 12715
P PH</w> 12715
Diff raction</w> 12715
canal icular</w> 12715
surviv als</w> 12713
contin ual</w> 12713
ox i 12712
hum id</w> 12712
HOT AIR</w> 12712
I c 12711
innerv ating</w> 12711
pal e 12709
extrav ascular</w> 12709
pemphig oid</w> 12709
ess ence</w> 12707
ae us</w> 12705
Xanth omonas</w> 12705
p ality</w> 12704
sh ut</w> 12704
HC F</w> 12704
n ectin</w> 12701
G add 12701
Grad uate</w> 12701
CA 0</w> 12698
pR S3</w> 12698
MA TL 12697
Apa f</w> 12697
rec tomies</w> 12696
out look</w> 12696
PG C 12696
N RTI</w> 12695
mag ic</w> 12695
eno humeral</w> 12694
ral oxifene</w> 12694
nan dez</w> 12693
HDAC 5</w> 12693
cyl inders</w> 12693
Nu RD</w> 12693
at rac 12692
polic y 12692
Mis sis 12692
cas e 12691
Ray naud</w> 12691
Doc um 12690
Wee kly</w> 12690
A ICD</w> 12689
ER C</w> 12688
CE US</w> 12687
reciproc ally</w> 12687
z ia</w> 12686
B IR</w> 12686
tur b 12685
amb uc 12685
ch ester</w> 12684
Guang zhou</w> 12684
to es</w> 12683
En zo</w> 12683
urban ization</w> 12683
multi channel</w> 12682
p G</w> 12681
ur ch</w> 12681
resc ent</w> 12681
suff iciency</w> 12681
biore actors</w> 12679
I PI</w> 12678
P recip 12675
f ears</w> 12675
Benj amini</w> 12675
r c 12674
P am 12673
on ensis</w> 12673
ec ycline</w> 12672
RR M2</w> 12672
M ak 12671
Ser 9</w> 12671
viol ation</w> 12670
ri ding</w> 12669
Tes la</w> 12669
Apol ipoprotein</w> 12669
Se a 12668
Figure 1C</w> 12668
EG R1</w> 12668
R ose</w> 12667
te amwork</w> 12667
no ble</w> 12665
epilep sies</w> 12665
N Ws</w> 12664
R CA 12663
mon es</w> 12663
HO S</w> 12663
Mit ral</w> 12663
ER Rα</w> 12662
poly carbonate</w> 12662
mus sel</w> 12662
abro ad</w> 12662
re feeding</w> 12660
C oma</w> 12659
T t 12659
Reyn olds</w> 12659
pos th 12658
g or 12657
dec orin</w> 12657
Bio analyzer</w> 12657
Tsc 1</w> 12657
nit rification</w> 12656
GC L</w> 12656
enedi amine</w> 12656
CDKN 1A</w> 12656
n us</w> 12655
CS N</w> 12655
caffe ic</w> 12655
Cop ing</w> 12655
g yp 12654
Vectas hield</w> 12654
be i</w> 12653
tec tal</w> 12653
irrit ability</w> 12653
se ments</w> 12652
Immun ology</w> 12652
clav icle</w> 12651
oun ce</w> 12650
off ices</w> 12650
immun op 12649
Mon o</w> 12649
S SI 12648
ung unya</w> 12648
otub erculosis</w> 12648
Aden ovirus</w> 12648
Cell Titer</w> 12648
ptery gium</w> 12648
nitril otri 12648
M ik 12647
im permeable</w> 12647
iTRA Q</w> 12646
n isin</w> 12645
scal ar</w> 12644
Mo tion</w> 12643
faith fully</w> 12642
Eukary otic</w> 12641
U k 12640
Z ip 12640
Se q 12640
eicos apentaenoic</w> 12639
t at</w> 12638
Mam m 12637
am ylin</w> 12636
differenti ates</w> 12636
metabol ome</w> 12635
ec to</w> 12634
micro surgery</w> 12634
s now</w> 12632
apo protein</w> 12632
HC S</w> 12631
n ad 12630
V LA</w> 12630
Ax is</w> 12630
extran odal</w> 12630
mel il 12629
Ser ious</w> 12629
O HP</w> 12628
te dness</w> 12628
regi str 12627
Cay man</w> 12626
at oxin</w> 12625
rati ometric</w> 12625
J 4</w> 12622
mis classification</w> 12622
therm ocycl 12622
subj ectively</w> 12621
K N</w> 12620
enc e 12619
ero id</w> 12619
Path ogenic</w> 12617
oxif ylline</w> 12617
BNI P3</w> 12617
E Ps</w> 12616
hemop tysis</w> 12616
GAT A2</w> 12615
d 5</w> 12613
ML KL</w> 12612
embran ous</w> 12611
dri ll</w> 12610
dab igatran</w> 12609
C f 12606
j in</w> 12606
stimul ators</w> 12605
Rober ts</w> 12605
a esti 12604
AD I</w> 12604
DT s</w> 12604
cac odylate</w> 12603
inter media</w> 12602
omet allic</w> 12602
Ik aros</w> 12602
SL Ns</w> 12601
sin onasal</w> 12601
Ca tenin</w> 12600
PT Cs</w> 12600
re g</w> 12599
sub mucosa</w> 12599
Rich ard</w> 12599
H PAI</w> 12598
H Z</w> 12597
un intentional</w> 12597
Nor theast</w> 12597
TRA NS 12596
Fem oral</w> 12595
coelic olor</w> 12595
las ts</w> 12594
AV Ms</w> 12594
ep isomal</w> 12593
str ö 12593
quad rants</w> 12593
hypercholesterole mic</w> 12593
un labelled</w> 12591
r Hu 12589
sub lines</w> 12589
hydro chlorothiazide</w> 12589
im mobile</w> 12588
dec tomy</w> 12588
inter ventricular</w> 12587
uro lithiasis</w> 12587
w al</w> 12585
I SR 12584
tr ine</w> 12583
ag og 12583
A a</w> 12582
O CH3</w> 12582
on amide</w> 12582
sen d</w> 12582
N 3 12581
Ab use</w> 12581
C ary</w> 12578
Tr x 12578
B lotting</w> 12577
dec h 12575
muc oid</w> 12575
minim ise</w> 12575
Inv asion</w> 12575
intox icated</w> 12575
Typ es</w> 12575
h ill</w> 12574
Intr aperitoneal</w> 12571
clon ality</w> 12570
cl onally</w> 12569
cm H2O</w> 12569
bi og 12568
Bio tin 12568
E P1</w> 12567
hyperos motic</w> 12567
ag r 12565
TRA F</w> 12565
nov a</w> 12565
poly phosphate</w> 12564
roset tes</w> 12564
B PS</w> 12562
Ch annel</w> 12561
neuro logists</w> 12560
PD E5</w> 12560
T DG</w> 12559
ific us</w> 12559
nig ral</w> 12559
offic inalis</w> 12559
flatten ing</w> 12559
p ar</w> 12558
9 P</w> 12557
um i</w> 12557
constitu tion</w> 12557
machin eries</w> 12557
Dan io</w> 12557
Establ ishing</w> 12557
resur facing</w> 12556
H EX 12555
L MA</w> 12554
multiplex ing</w> 12554
transist ors</w> 12554
Morph ine</w> 12552
ab alin</w> 12551
it ters</w> 12550
sub complex</w> 12550
L os 12549
de iod 12549
lev amisole</w> 12549
immun izing</w> 12548
Hel a</w> 12548
nu ts</w> 12547
formaz an</w> 12547
AB TS</w> 12546
hydro philicity</w> 12546
adren als</w> 12545
R HD</w> 12544
armam entarium</w> 12544
peri anal</w> 12543
CT B</w> 12543
P g</w> 12542
con caten 12542
am ental</w> 12542
ile ostomy</w> 12542
NF Ts</w> 12541
myel omonocytic</w> 12539
Pos itron</w> 12538
Dun n</w> 12538
ede matous</w> 12537
ou ver</w> 12535
NT M</w> 12535
pipet tes</w> 12535
- L</w> 12534
con s</w> 12533
ap ing</w> 12533
MAR CKS</w> 12532
rig or</w> 12531
Prote as 12531
im poses</w> 12530
col ostomy</w> 12530
PF OS</w> 12530
ostero id</w> 12530
MC 1R</w> 12529
ampull ary</w> 12529
clos ures</w> 12528
Angi ography</w> 12528
interro gation</w> 12528
ML S</w> 12526
cardi ologists</w> 12525
1 mg</w> 12524
ugg le</w> 12524
CO PII</w> 12523
Cd Se</w> 12521
R oth 12520
but ton</w> 12520
Differen tially</w> 12520
Si emens</w> 12520
Linda u</w> 12520
CF R</w> 12517
apre vir</w> 12517
P Ps</w> 12516
bed ding</w> 12516
por tr 12514
val erate</w> 12514
AP R</w> 12514
MATL AB</w> 12513
M af 12512
gra vid 12511
spl int</w> 12511
HC H</w> 12510
examin ers</w> 12510
sarcom eric</w> 12510
NAM PT</w> 12509
tubulo interstitial</w> 12509
Absorb ance</w> 12509
Prote ome</w> 12508
men thol</w> 12507
3 R 12506
f an 12506
des ensitized</w> 12506
chec ks</w> 12506
strep tococcus</w> 12506
D CT</w> 12505
U mb 12505
phag ocyte</w> 12504
Dun nett</w> 12504
pre ferring</w> 12503
thor a</w> 12500
IP L</w> 12500
mening o 12500
sta xis</w> 12499
yr ase</w> 12499
w on</w> 12498
ax y</w> 12498
angio edema</w> 12498
remed y</w> 12498
In sp 12497
AMPK α</w> 12496
Hom ology</w> 12495
pul satility</w> 12494
Py th 12494
b yl</w> 12492
non randomized</w> 12492
orb able</w> 12492
visit ors</w> 12491
CG D</w> 12490
tri p 12489
dise as 12489
lipo dystrophy</w> 12489
5 V</w> 12488
ta sis</w> 12488
Nor dic</w> 12487
depic ting</w> 12487
allo on</w> 12486
ni acin</w> 12485
ig u 12484
des ulf 12484
PK D2</w> 12484
ocar box 12484
Hoch berg</w> 12484
hypo physi 12483
semi structured</w> 12483
assi sts</w> 12482
G LA</w> 12481
RI P3</w> 12481
ob acillus</w> 12480
encephal ography</w> 12480
dimethyl amino</w> 12480
R x</w> 12479
v ault</w> 12479
coron a</w> 12479
wal ked</w> 12479
PM I</w> 12478
poly some</w> 12477
drom al</w> 12477
Schr ö 12477
6 me3</w> 12476
A symptomatic</w> 12476
ger bils</w> 12476
V CR</w> 12475
N BT</w> 12474
Vanc ouver</w> 12474
Al am 12472
hypo kalemia</w> 12472
leuko encephalopathy</w> 12472
Y in</w> 12470
ven lafaxine</w> 12470
amant adine</w> 12469
u tional</w> 12468
pyri methamine</w> 12468
aph idic 12466
Pharmac ology</w> 12466
P PARs</w> 12465
Promo ting</w> 12465
bic yclic</w> 12464
irrig ated</w> 12463
Sar co 12463
No V</w> 12462
privileg ed</w> 12461
silk worm</w> 12460
oct anol</w> 12459
ich i</w> 12458
Muc osal</w> 12458
glyco conjugates</w> 12457
beta 4</w> 12457
pil ls</w> 12456
He aling</w> 12454
CS L</w> 12454
intra renal</w> 12454
Br ca1</w> 12454
ultra high</w> 12453
x on</w> 12452
G radi 12451
me tic 12451
py ru 12451
RB F</w> 12451
EB M</w> 12450
trans plantable</w> 12449
sw apping</w> 12449
Vps 2</w> 12448
general izability</w> 12446
hetero cycles</w> 12444
Δ p 12442
INK 4A</w> 12442
di peptidyl</w> 12440
AK AP1</w> 12440
Re ading</w> 12439
Ne u</w> 12439
cortic o</w> 12439
TI MI</w> 12438
ym ptomatic</w> 12438
ER T2</w> 12438
fibro genesis</w> 12438
X BP</w> 12437
Amer ic 12436
bro thers</w> 12435
orophar ynx</w> 12435
P ICU</w> 12434
TR PC1</w> 12434
lute us</w> 12434
conform ity</w> 12433
Ex cellence</w> 12431
Fir micutes</w> 12429
ge phyrin</w> 12428
Cys 3</w> 12428
ech oic</w> 12427
t l 12426
S plen 12426
no tice</w> 12426
scaph oid</w> 12426
F it</w> 12425
Bec lin1</w> 12425
view ers</w> 12425
ep h</w> 12424
sc id</w> 12424
En anti 12422
Depend ence</w> 12422
Charg e</w> 12422
L MP 12420
apol ipo 12420
angi ographically</w> 12419
hr an</w> 12419
E ET</w> 12418
G ER</w> 12418
hyper thyroid</w> 12418
C x2</w> 12417
p ond</w> 12417
co repressors</w> 12417
7 Rv</w> 12416
ha in</w> 12416
cy tot 12416
vi et</w> 12415
Fix ation</w> 12415
pre neoplastic</w> 12413
DE C</w> 12412
audi ometry</w> 12412
U CP2</w> 12411
γ t</w> 12411
ET EC</w> 12411
AF C</w> 12411
assembl age</w> 12411
T in 12409
chrom ogranin</w> 12409
in genin</w> 12407
ro of</w> 12407
p I 12406
comm ensur 12406
CB s</w> 12405
5 FU</w> 12404
stain ings</w> 12404
Th io 12403
A side</w> 12401
ad versity</w> 12401
sulfon amides</w> 12401
neuro kinin</w> 12400
injur ious</w> 12400
minc ed</w> 12400
asym metrically</w> 12399
cistern ae</w> 12399
T MDs</w> 12398
CA V</w> 12398
mean while</w> 12398
protom ers</w> 12398
fibro genic</w> 12397
trimethyl ammonium</w> 12397
Put ative</w> 12397
stake holder</w> 12396
nymph s</w> 12396
exclud es</w> 12395
col icin</w> 12394
nitro tyrosine</w> 12394
ef s</w> 12393
sc ant</w> 12391
b un 12389
S HS</w> 12389
Th o 12389
Mec 1</w> 12389
sul for 12388
estim ators</w> 12388
PH Y</w> 12388
Jak ob</w> 12388
micronutri ents</w> 12388
prototyp ic</w> 12388
et z</w> 12387
pseud ogenes</w> 12387
PR K</w> 12386
Con sum 12385
AB 3</w> 12384
Oc ca 12384
piper id 12384
U N</w> 12383
keto acidosis</w> 12383
Tsc 2</w> 12383
G ut</w> 12382
under served</w> 12382
legi timate</w> 12382
Advis ory</w> 12382
intellig ent</w> 12381
AD D</w> 12380
spirit uality</w> 12380
depar ture</w> 12380
V av 12379
amin ated</w> 12379
awa kening</w> 12379
din o 12379
chori ocarcinoma</w> 12379
S 7A</w> 12378
photo induced</w> 12378
Mut S</w> 12378
ev okes</w> 12377
Ga p</w> 12377
focus sed</w> 12376
J ak</w> 12375
put ting</w> 12375
pro posing</w> 12374
f lying</w> 12373
er ations</w> 12372
promin ence</w> 12372
TRP M2</w> 12372
Concomit antly</w> 12372
seal ant</w> 12372
TAp 7</w> 12372
Y . 12370
re pur 12370
PE PCK</w> 12370
TM 6</w> 12370
NK X2</w> 12369
S ASP</w> 12368
v y</w> 12367
Maj ority</w> 12367
raf enib</w> 12367
tis ol</w> 12366
path om 12365
slip page</w> 12365
KE Y 12365
Upp sala</w> 12365
un charged</w> 12363
ifor mes</w> 12362
Intell igence</w> 12360
C ou 12359
CA I</w> 12359
Super Signal</w> 12359
Sn ail 12359
E FF 12358
Le ad 12357
UGT 1 12357
oste otomies</w> 12356
hippocamp i</w> 12356
pR L</w> 12356
syncy tium</w> 12356
S pati 12355
gl um 12355
Bec ker</w> 12355
Endoc rin 12353
re entry</w> 12352
A Y 12351
acti ae</w> 12351
Rec ogn 12351
TA K</w> 12351
L CH</w> 12350
sphing omyel 12350
denit rif 12350
ad amant 12349
dap tomycin</w> 12349
qu eries</w> 12348
id ers</w> 12348
N c 12347
ve y</w> 12346
PT K</w> 12346
PMS 2</w> 12344
D ub 12343
FBX W7</w> 12343
un processed</w> 12342
Fla vi 12342
phy c 12341
en ucleated</w> 12340
E PH 12339
conjug ative</w> 12339
plak ia</w> 12339
For mal</w> 12338
alc iferol</w> 12338
riv aroxaban</w> 12338
flo oding</w> 12336
TA F 12335
zym ography</w> 12335
CHC l3</w> 12335
MS N</w> 12334
Tibet an</w> 12334
N unc</w> 12333
si dec 12333
RA B 12332
TA F1</w> 12331
il a</w> 12330
subep ithelial</w> 12330
n f 12329
d 7</w> 12329
se als</w> 12329
Δ Ψm</w> 12327
Pex 1</w> 12327
TAL EN</w> 12326
icom plex 12326
o otic</w> 12325
K och</w> 12325
N U</w> 12324
strö m</w> 12324
B ED</w> 12323
Tr ace</w> 12323
categor ize</w> 12323
3 A1</w> 12322
lympho kine</w> 12321
ax otomy</w> 12320
HD F</w> 12320
CF 3</w> 12319
ST RU 12318
spe aker</w> 12318
DG K 12317
M -</w> 12316
angi osarcoma</w> 12316
ox yp 12315
- T 12314
z en 12314
el ithiasis</w> 12314
conver sation</w> 12314
Reci proc 12314
O pen 12313
po ro 12312
dis tention</w> 12312
Pre mature</w> 12312
amni ocentesis</w> 12311
L IF 12310
F ur</w> 12310
ol imbic</w> 12310
ot rophy</w> 12309
neuro biology</w> 12308
hypop arathyroidism</w> 12308
D ong</w> 12307
al yp 12307
ag rel 12307
-- in</w> 12306
opo ul 12306
hex os 12306
myelo ablative</w> 12306
op ted</w> 12304
Ex tract</w> 12304
p AC 12303
ad ural</w> 12303
0 Y</w> 12302
Tr k</w> 12302
UT E</w> 12302
en to</w> 12301
RO L</w> 12300
PT D</w> 12300
d p</w> 12298
necess itated</w> 12298
3 .1</w> 12296
g 4</w> 12296
id ality</w> 12296
if ting</w> 12296
in chon 12294
VEGF R1</w> 12294
Acti vating</w> 12293
denat ur 12293
GE P</w> 12292
Wa ter 12292
g ith 12291
DI O</w> 12290
stig mati 12289
myristo ylated</w> 12289
RE R</w> 12288
multi detector</w> 12288
n ica</w> 12287
Bel ow</w> 12287
cholestero la 12287
den ote</w> 12286
TOR C2</w> 12286
POPU LATION</w> 12286
stere otyped</w> 12285
posit ories</w> 12285
S car 12284
Ste vens</w> 12283
diff used</w> 12282
CS I</w> 12282
ET V</w> 12281
con tention</w> 12281
psych ologic</w> 12280
F ura</w> 12279
Pre term</w> 12278
melil oti</w> 12278
phenyl propan 12277
Inc id 12276
s RNAs</w> 12275
S ind 12275
micro circulatory</w> 12275
men adione</w> 12274
Figure 5B</w> 12274
R BS</w> 12273
psych oses</w> 12273
yel low 12272
W y 12271
bro od</w> 12271
parathyro idectomy</w> 12271
S F3</w> 12270
2 G1</w> 12269
Epo R</w> 12268
ys ine</w> 12267
oma viruses</w> 12267
y ear 12266
spe ak</w> 12266
postin ter 12263
interven e</w> 12262
schwann omas</w> 12262
drugg able</w> 12262
eb all</w> 12261
ampl ifies</w> 12260
byp assing</w> 12260
Pin us</w> 12260
par then 12259
pren ylation</w> 12259
C NA</w> 12258
res ynchronization</w> 12258
Cl ones</w> 12258
ret te</w> 12258
Ri bo 12258
un ia</w> 12257
mit oses</w> 12257
uni ons</w> 12255
There by</w> 12255
men in</w> 12255
collabor ate</w> 12255
Q ALYs</w> 12254
Neu rom 12254
judg ement</w> 12253
pa res 12252
mag noc 12252
organis ed</w> 12252
arthro plasties</w> 12252
obac tam</w> 12252
respon dent</w> 12251
opi ates</w> 12251
symbi onts</w> 12251
reproduc ing</w> 12250
X III</w> 12249
p DNA</w> 12248
micro cos 12248
PHD 2</w> 12248
herbiv ore</w> 12248
ri dges</w> 12247
non equilibrium</w> 12247
ain ide</w> 12247
M x1</w> 12246
alk ene</w> 12246
Sc a</w> 12246
motiv ations</w> 12246
Ret ri 12246
transmit tance</w> 12245
AS CT</w> 12244
Me 3</w> 12243
H ot 12242
ph 1</w> 12242
cycl ists</w> 12241
G ord 12240
Blac k 12240
gr inding</w> 12237
o estrus</w> 12235
Tran spor 12235
mas toid</w> 12235
cycl open 12233
interpre table</w> 12233
XR CC4</w> 12233
G ard 12232
Ac cred 12231
ici al</w> 12229
EC E</w> 12229
gener ators</w> 12227
CY Ps</w> 12227
ration alized</w> 12227
Y 2H</w> 12226
EC Gs</w> 12226
ry e</w> 12226
Tr unc 12225
RY GB</w> 12225
Biole gend</w> 12225
B AM</w> 12223
PA CS</w> 12221
Oper ating</w> 12217
Exec utive</w> 12217
Y H</w> 12216
Nutri ent</w> 12216
rota rod</w> 12216
4 .</w> 12215
Commer cial</w> 12215
cler k 12214
at ric 12213
T 1L</w> 12211
VI A</w> 12211
J AM</w> 12210
n uc 12210
SI RS</w> 12210
Se e 12210
ac ri 12209
mort alities</w> 12208
desal ted</w> 12208
t ough 12207
PK M</w> 12207
protein aceous</w> 12207
Clus tering</w> 12207
thyro toxicosis</w> 12206
Pho en 12205
sc ission</w> 12204
MD 2</w> 12203
FO V</w> 12203
col oration</w> 12202
acetyl salicylic</w> 12202
HR G</w> 12201
KE AP1</w> 12201
ra emia</w> 12200
phyl lo 12200
mer cur 12199
iri dium</w> 12199
aden itis</w> 12198
Th om 12197
Gyn ec 12197
re atine</w> 12196
Eim eria</w> 12194
bio energetic</w> 12193
Y uan</w> 12192
cI AP1</w> 12192
T p</w> 12191
run g</w> 12191
LRP 5</w> 12191
ec dy 12190
CH A</w> 12190
Pop ulations</w> 12190
TO P 12189
shell fish</w> 12189
sial idase</w> 12188
X ie</w> 12187
ni k</w> 12187
s od 12186
9 I</w> 12186
AA AA 12185
Accred itation</w> 12185
oc alin</w> 12184
my ogenin</w> 12183
poly omavirus</w> 12182
DY RK 12182
unil aterally</w> 12182
N AL 12181
W O</w> 12181
micro bubbles</w> 12181
Sc and 12181
T SE</w> 12180
V or 12180
synthesi zes</w> 12180
L id 12179
ren ts</w> 12178
bo unds</w> 12177
p ial</w> 12176
Arch aea</w> 12176
Regi stration</w> 12174
fec ture</w> 12173
PU S</w> 12170
te ms</w> 12169
mon osom 12169
Fig. 3C</w> 12169
lav icular</w> 12169
r ance</w> 12168
muc oc 12168
biore mediation</w> 12168
D ot</w> 12166
val ley</w> 12166
fil tr 12166
inver sions</w> 12166
tad poles</w> 12165
P all 12164
ess enti 12164
poly phenolic</w> 12163
ke eps</w> 12162
min is 12160
S SD</w> 12159
sarcolem ma</w> 12158
sl o</w> 12157
PIN 1</w> 12157
caver nos 12157
AR F1</w> 12156
SAM PLE</w> 12155
my otonic</w> 12153
angli onic</w> 12152
organo phosphate</w> 12152
Com mit 12151
anti toxin</w> 12150
CK II</w> 12150
enox aparin</w> 12150
m ural</w> 12149
mut agen</w> 12149
Immunocyto chemical</w> 12149
M ECs</w> 12148
mig ra 12147
Cre utz 12147
ucle ate</w> 12146
PROBLE M</w> 12145
aceta zolamide</w> 12145
h m</w> 12143
4 .1</w> 12142
Cad mium</w> 12141
l A</w> 12140
患 者 12140
dipl opia</w> 12140
D up 12139
CG GT 12139
E4 orf 12139
4 X</w> 12138
in k 12138
Al anine</w> 12138
archi ved</w> 12138
flow metry</w> 12138
erythemat ous</w> 12138
R ange</w> 12137
B ach 12137
recir culation</w> 12137
N CX</w> 12136
hy al 12136
c PLA2</w> 12134
eti opathogenesis</w> 12133
Gcn 5</w> 12133
P MID</w> 12132
f ighting</w> 12131
dys genesis</w> 12131
rh BMP</w> 12131
Rhod amine</w> 12130
eth rin</w> 12129
SR Y</w> 12129
Ru v 12128
an ergy</w> 12127
HE L 12127
ec centr 12126
the at 12126
secre tome</w> 12126
A mo 12125
Sp ine</w> 12125
So viet</w> 12124
ico ides</w> 12122
1 BB</w> 12120
amp u 12120
yl -</w> 12118
AK R</w> 12118
lagg ed</w> 12118
micro dilution</w> 12117
GL I</w> 12116
Correc t</w> 12116
ay ashi</w> 12115
in visible</w> 12114
dihydro chloride</w> 12114
N umb</w> 12112
foli ar</w> 12112
under scored</w> 12111
phen othi 12111
Sec urity</w> 12110
P CDD</w> 12109
ste mic</w> 12109
RA S 12108
IP SC</w> 12108
U S2</w> 12107
phyco erythrin</w> 12107
N RP1</w> 12105
o proteins</w> 12105
G ot 12105
preclud ing</w> 12105
applic ants</w> 12104
Guid eline</w> 12104
micro environmental</w> 12103
und amaged</w> 12102
bulim ia</w> 12102
Sh ank 12101
scru tin 12101
in os</w> 12099
tin ic</w> 12099
can inum</w> 12099
b old</w> 12098
L W</w> 12098
quasi species</w> 12098
al ise</w> 12097
T W</w> 12095
ti ble</w> 12094
ro idal</w> 12094
pres ses</w> 12093
Hen ry</w> 12093
enc ement</w> 12092
hang s</w> 12092
OTU s</w> 12092
ag i 12091
inform ants</w> 12091
un anticipated</w> 12090
EB NA 12090
precle ared</w> 12090
eth anesulf 12089
No ise</w> 12089
u 1</w> 12088
p S2</w> 12088
pa z 12088
multi polar</w> 12088
peri prosthetic</w> 12087
SL O</w> 12086
resus pension</w> 12085
excur sion</w> 12085
9 E1</w> 12084
5 W</w> 12084
P eg 12084
ori us</w> 12084
glucos aminidase</w> 12084
D CX</w> 12081
at ula</w> 12081
SR B</w> 12081
ming ton</w> 12081
resisti vity</w> 12080
de ma</w> 12079
Ca V2</w> 12079
fung icides</w> 12079
S 3D</w> 12078
ret ard</w> 12078
che z</w> 12078
multic opy</w> 12078
Mf n2</w> 12078
Cal culation</w> 12077
At tribu 12075
L 3 12074
Ile 1</w> 12073
Mu eller</w> 12072
str uggle</w> 12071
ill er</w> 12071
Re ader</w> 12071
ch illing</w> 12070
mon t 12070
N t</w> 12069
M m0</w> 12069
cardio plegia</w> 12069
Dec om 12068
cannabin ol</w> 12068
andr an</w> 12068
cha os</w> 12067
Gli 2</w> 12065
porphy ria</w> 12065
H ATs</w> 12064
fil amin</w> 12064
luc iferin</w> 12064
aph rag 12064
ca red</w> 12063
ham per</w> 12063
N PH</w> 12062
k ur 12062
ocortico ids</w> 12062
syring es</w> 12062
pre medication</w> 12061
imp action</w> 12061
en ate</w> 12060
la z 12060
Hes 1</w> 12060
H SC 12059
Sk p1</w> 12059
r ha 12058
chlor pyrifos</w> 12058
FO LF 12058
M ST1</w> 12056
co persic 12056
HC P</w> 12056
toc ida</w> 12056
sym metrically</w> 12056
Guang dong</w> 12056
j an</w> 12055
un pleasant</w> 12055
CA ST</w> 12055
IF I 12055
-- and</w> 12055
JAK 2V6</w> 12055
eli i</w> 12054
extra ocular</w> 12053
P late</w> 12051
od our</w> 12051
S NA</w> 12050
QL Q</w> 12050
civil ian</w> 12050
pos tischemic</w> 12048
BC 0</w> 12048
luk ast</w> 12048
anoyl phorbol</w> 12047
O LOGICAL</w> 12046
G M3</w> 12046
CH R</w> 12046
sub thalamic</w> 12045
bu oy 12044
Fi ber</w> 12044
Ari zona</w> 12043
interfero metry</w> 12043
pyridox ine</w> 12043
I ST</w> 12042
I d1</w> 12041
ut z</w> 12041
CC GG 12041
R ON 12040
squir rel</w> 12040
prison ers</w> 12040
perfec ta</w> 12039
L FS</w> 12038
T ac</w> 12036
re p</w> 12036
Hy p</w> 12036
bim etallic</w> 12036
anthelmin tic</w> 12036
di chloro</w> 12035
AM T</w> 12035
i be</w> 12034
S al</w> 12034
AT G7</w> 12034
GAT 1</w> 12034
an teriorly</w> 12033
aver ine</w> 12033
AC SF</w> 12032
Non linear</w> 12032
1 p1</w> 12031
Pro bing</w> 12031
nego ti 12031
domin ating</w> 12030
Tak ahashi</w> 12030
anteced ent</w> 12029
TI Cs</w> 12028
PA SS</w> 12028
sub sample</w> 12028
NO M</w> 12028
flex ural</w> 12027
PG M</w> 12026
happ iness</w> 12026
Cul tural</w> 12025
Ar ach 12024
consen ted</w> 12024
Py MT</w> 12023
Na N3</w> 12022
n r</w> 12021
mul tocida</w> 12019
tic idin</w> 12018
Pol l 12018
ophthalm ologic</w> 12018
embry on 12017
Wag ner</w> 12017
met al 12016
super coiling</w> 12016
Figure 2C</w> 12015
eu genol</w> 12014
TC 2</w> 12014
Pd x1</w> 12012
exp ulsion</w> 12011
mur m 12010
8 q2</w> 12007
ne dd 12007
ellul osic</w> 12007
Mass on</w> 12007
mon otonic</w> 12006
phenoc op 12006
CM I</w> 12005
A FLP</w> 12004
un classified</w> 12004
prim es</w> 12004
Method ological</w> 12004
TA E</w> 12003
PT E</w> 12002
CII TA</w> 12002
thalass aemia</w> 12002
HI PK2</w> 12001
LE DGF</w> 12001
chie fly</w> 12001
culmin ating</w> 12001
poly ester</w> 12000
Ca i</w> 12000
PN H</w> 11999
t C</w> 11997
Y M</w> 11997
hal lux</w> 11996
Mod elling</w> 11996
bi ographical</w> 11995
gener alize</w> 11994
insulin oma</w> 11993
distr actor</w> 11993
e c</w> 11992
Bra ins</w> 11992
Tn T</w> 11992
Dan i 11991
cocul tures</w> 11991
K om 11990
gall ic</w> 11990
α Syn</w> 11988
at on</w> 11988
gu ai 11988
gener ality</w> 11987
ol ian</w> 11986
hope fully</w> 11986
Ex tra</w> 11982
I sh 11981
Ct BP</w> 11981
PL LA</w> 11980
Bur den</w> 11980
trophozo ites</w> 11980
electro convulsive</w> 11978
Don ald</w> 11977
p ens</w> 11976
imp uted</w> 11976
disulf ides</w> 11976
MD H</w> 11975
a ters</w> 11974
LI D</w> 11974
bar bed</w> 11974
din uclear</w> 11974
l ene</w> 11972
H4 K1</w> 11972
sack ie 11971
multi parous</w> 11970
Am es</w> 11970
ALD H1</w> 11970
E o 11969
re population</w> 11969
digit ally</w> 11969
mil king</w> 11967
trans aminases</w> 11965
eicos anoids</w> 11965
D HP</w> 11964
chann el 11964
Att achment</w> 11964
re myelination</w> 11963
stitu tion</w> 11963
Tr as 11963
NI HSS</w> 11963
ul o 11961
MM PI</w> 11960
weak en</w> 11960
H AN 11959
inj ect</w> 11958
inter s 11956
Nox 4</w> 11956
maglob ulinemia</w> 11956
Rho GEF</w> 11954
mictur ition</w> 11954
AP T</w> 11953
di d 11952
graph ically</w> 11952
interrup tions</w> 11952
K ob 11951
incl ined</w> 11951
pre dil 11950
Re ad 11950
pre diabetes</w> 11949
argin ines</w> 11949
GST s</w> 11949
ton ella</w> 11948
pseud otuberculosis</w> 11948
monop olar</w> 11948
as y 11946
p W 11945
SN 2</w> 11944
gir dle</w> 11943
crani osynostosis</w> 11942
S 5C</w> 11941
ro su 11941
pre ovulatory</w> 11941
IF I1</w> 11941
AG P</w> 11941
insuff iciently</w> 11941
N CR</w> 11940
HS PA 11940
AQ P1</w> 11939
tri azine</w> 11938
gen er</w> 11937
regi on 11937
devi ated</w> 11937
some one</w> 11937
g p 11936
om uc 11936
AD G</w> 11936
4 Fe</w> 11935
ra zin 11935
concep tually</w> 11934
oper ation 11933
pre pulse</w> 11931
recep tor 11931
Par affin</w> 11931
perit umoral</w> 11931
Mull er</w> 11931
Glauc oma</w> 11931
sur amin</w> 11930
multi gene</w> 11930
An dre 11929
Germ line</w> 11929
t arily</w> 11928
S J</w> 11927
H AdV</w> 11925
N AC 11925
k ingdom</w> 11925
Con nec 11925
hydro peroxides</w> 11924
nit rates</w> 11924
ampero metric</w> 11924
h c</w> 11923
Wor kers</w> 11923
bot anical</w> 11922
Tyr 4</w> 11922
O scill 11921
Pe tro 11921
Chri st 11921
oth io 11920
intestin alis</w> 11919
Rit uximab</w> 11918
neighbour hood</w> 11918
T un 11917
six fold</w> 11917
phyl axis</w> 11916
SIV mac 11916
Nb s1</w> 11915
U NI 11913
Histor ical</w> 11912
c ass 11911
ur ans</w> 11909
radi ol 11908
C US</w> 11907
long standing</w> 11906
sa p</w> 11906
quit ting</w> 11906
st ol 11904
Hy clone</w> 11903
Tra it</w> 11903
Indu stry</w> 11903
V IIa</w> 11902
f ish 11902
pepti dases</w> 11902
Bio phys</w> 11901
at taining</w> 11900
wo ven</w> 11900
pare tic</w> 11899
fic ates</w> 11899
mast ocytosis</w> 11899
Z ol 11898
MM N</w> 11898
. 5C</w> 11897
SS V</w> 11897
Men ing 11897
ass e</w> 11896
po fungin</w> 11895
ER 2</w> 11895
haem agglutinin</w> 11895
Flex ible</w> 11894
O PD</w> 11891
b ax</w> 11890
Ex act</w> 11890
da id 11890
dra g</w> 11890
1 γ</w> 11889
nitro phenol</w> 11889
refrac toriness</w> 11889
ne f</w> 11888
CE L</w> 11888
Cat ar 11888
C x</w> 11886
gl iding</w> 11886
EN S</w> 11886
un injured</w> 11884
auto antigen</w> 11883
Perio dic</w> 11883
y o</w> 11882
IN F</w> 11882
N l 11881
aut otrophic</w> 11881
ig s</w> 11880
PSD 9</w> 11880
de hydratase</w> 11879
Fr uc 11879
Te flon</w> 11879
- 1H</w> 11878
PC 9</w> 11878
Zn S</w> 11878
al is 11877
MI CAL</w> 11877
Lif estyle</w> 11877
5 .1</w> 11876
St orage</w> 11876
PL ZF</w> 11876
com posting</w> 11875
s am 11874
n is 11874
X i</w> 11874
Mer cury</w> 11874
gith ub 11874
reconstruc ting</w> 11873
K AP1</w> 11872
un safe</w> 11872
DS F</w> 11871
Integr ative</w> 11871
co erci 11870
are rs</w> 11870
AA V 11869
perox ire 11867
Ub c1</w> 11867
I SO 11866
sub division</w> 11866
AG GT 11866
P ST</w> 11865
sp rings</w> 11865
Vp s1</w> 11864
tub ers</w> 11863
panor amic</w> 11863
AU THO 11862
C9 orf7</w> 11862
mal 1</w> 11861
HSP C</w> 11861
dermat ologists</w> 11861
Astro cytes</w> 11861
G H 11860
mon ate</w> 11860
br it 11860
APPRO ACH</w> 11859
aph th 11858
gun shot</w> 11857
aut ocla 11855
S. E.</w> 11855
repe atable</w> 11854
cari ous</w> 11854
hydro phila</w> 11852
Hsp 4</w> 11852
TMPR SS2</w> 11851
Foc using</w> 11850
P SY 11849
invag ination</w> 11849
ove restimate</w> 11847
0 T1</w> 11846
B ethyl</w> 11846
Asc aris</w> 11846
oc ation</w> 11845
lob ule</w> 11845
Tr p1</w> 11845
DT X</w> 11845
med aka</w> 11845
3' UTR</w> 11845
Re ad</w> 11844
EM F</w> 11844
SCN 5A</w> 11844
dis placing</w> 11843
gl enohumeral</w> 11843
ail ed</w> 11842
Ser 8</w> 11842
desi pramine</w> 11842
Char l 11842
osper mic</w> 11841
repar ative</w> 11841
Me g 11839
ophag ocytic</w> 11839
MI 1</w> 11839
NM Js</w> 11839
handic ap</w> 11839
re mic</w> 11838
res orbable</w> 11837
M PK 11836
c z 11835
tol ide</w> 11835
carbamo yl</w> 11835
olis thesis</w> 11835
don epezil</w> 11834
Acous tic</w> 11834
supra tentorial</w> 11833
IL 4</w> 11832
milest ones</w> 11832
extras ynaptic</w> 11831
B6 . 11831
om agnetic</w> 11830
se as</w> 11829
Chlo rella</w> 11829
ply sia</w> 11829
T DM</w> 11827
T SLP</w> 11827
O HC</w> 11827
st an</w> 11827
grap ev 11827
CG S</w> 11826
oxy sporum</w> 11825
s ox 11824
PL R</w> 11821
W et 11820
H HT</w> 11819
Si Ha</w> 11817
IV Ig</w> 11817
Gv HD</w> 11817
D GGE</w> 11816
Diff ic 11816
J ane 11815
RN ase 11815
PG P</w> 11815
interrup t</w> 11815
ser rated</w> 11813
fron tier</w> 11813
Law rence</w> 11813
TX NIP</w> 11812
cho ol 11811
vacu o</w> 11807
lig ating</w> 11806
HSP 6</w> 11806
reperto ires</w> 11806
bl ight</w> 11805
hal ation</w> 11805
V og 11804
is e 11804
de protection</w> 11803
Ma h 11803
HT P</w> 11803
Aff ected</w> 11803
Ref Seq</w> 11803
nanoclus ters</w> 11803
ind ol</w> 11802
under nutrition</w> 11801
fer til 11801
MK K4</w> 11801
BL I</w> 11801
hyper bolic</w> 11800
H MEC</w> 11799
R Fs</w> 11799
radi ations</w> 11799
centr alized</w> 11799
pro angiogenic</w> 11798
unc ul 11798
hypog lossal</w> 11798
sour i</w> 11797
air y</w> 11797
Sch e 11797
tal us</w> 11797
RR M1</w> 11797
ce sium</w> 11795
BR M</w> 11793
HN PCC</w> 11793
Mob ility</w> 11793
Eti ology</w> 11793
res ume</w> 11792
pro phage</w> 11792
re structuring</w> 11791
s ore</w> 11790
leiomy omas</w> 11790
lipop olysaccharides</w> 11789
- nitro</w> 11788
J 0</w> 11788
Lac tococcus</w> 11787
n p</w> 11786
P ate 11786
F Ps</w> 11786
f ts 11786
sub typing</w> 11785
SI C</w> 11785
PP B</w> 11785
Su ff 11785
Telom erase</w> 11785
ure mia</w> 11784
MAP K1</w> 11784
punc h</w> 11784
ac nes</w> 11783
ST ATI 11783
ON s</w> 11783
TGF B 11783
re staur 11782
Uni Prot</w> 11782
HM C</w> 11781
p ia</w> 11779
min igene</w> 11779
Ti am1</w> 11779
Cit rus</w> 11779
g C</w> 11778
st 2</w> 11778
x ylo 11777
per ineural</w> 11777
Micro systems</w> 11777
O FC</w> 11776
Ex o1</w> 11776
ze bra</w> 11776
Cali bration</w> 11776
DN MT</w> 11775
ana x</w> 11775
leth arg 11775
Prec ise</w> 11775
p inch</w> 11774
Ga it</w> 11774
azo ospermia</w> 11774
K oz 11773
Ultras onic</w> 11773
condu its</w> 11773
R LC</w> 11772
tetr asp 11772
cau tious</w> 11772
B AS 11771
bi ventricular</w> 11771
ith ymia</w> 11771
cri ticism</w> 11771
S teri 11770
Alve olar</w> 11770
F en 11769
Al der</w> 11767
orbit ofrontal</w> 11767
V AL 11766
ay ne</w> 11766
SU S</w> 11766
hydro lysates</w> 11766
Eg 5</w> 11766
al i</w> 11765
IL 8</w> 11765
spong iform</w> 11765
R t 11764
K ro 11764
pa y 11764
Cole optera</w> 11764
W AS</w> 11763
wor e</w> 11762
muc op 11762
AG 2</w> 11762
PL D1</w> 11762
s RNA</w> 11760
Competi tive</w> 11760
Ste pwise</w> 11759
ment oring</w> 11759
pro pox 11758
ut sch 11758
Cry 1 11757
jour nal 11755
FX a</w> 11755
P HT</w> 11754
ill ic</w> 11754
cycl ical</w> 11754
ond o</w> 11752
analy ser</w> 11751
spermat ogenic</w> 11751
Dor sal</w> 11751
graph ics</w> 11750
vortex ing</w> 11750
e ut 11749
cd c4</w> 11749
ti ously</w> 11748
stere ochemical</w> 11748
Ra g</w> 11748
pione ering</w> 11748
escal es</w> 11747
supplem enting</w> 11746
sesqui terpene</w> 11746
PL X4</w> 11745
conveni ently</w> 11745
leg es</w> 11744
F SS</w> 11743
r algia</w> 11743
Nan jing</w> 11743
y amo 11742
ran k 11742
Sc n 11742
hyp nosis</w> 11741
reproduc es</w> 11741
MC U</w> 11740
inos us</w> 11740
m Gy</w> 11739
num eric</w> 11738
hip pur 11737
st oc 11736
under studied</w> 11736
cryst allo 11735
bor reli 11735
isobut yl</w> 11735
fil ariasis</w> 11734
travel ed</w> 11733
R BL</w> 11732
Al b</w> 11730
rec ycle</w> 11729
PK U</w> 11727
stri a</w> 11727
N AT1</w> 11726
TRP 1</w> 11726
Equ ilibrium</w> 11726
AB E</w> 11723
dec itabine</w> 11723
epitheli alization</w> 11723
l om 11722
tt i</w> 11722
Periodon tal</w> 11722
tri azol 11721
RU 4</w> 11721
hetero typic</w> 11720
extrap ulmonary</w> 11720
C ause</w> 11718
B CAA</w> 11718
poly ubiquitinated</w> 11716
Gene tically</w> 11716
he m</w> 11715
En cyclopedia</w> 11715
DE D</w> 11715
TC AC 11715
AK AP</w> 11715
teen age</w> 11715
L AI</w> 11714
am pul 11714
cr 1</w> 11713
detec tability</w> 11712
thy retin</w> 11712
direc tor</w> 11711
MP ER</w> 11711
Hom ogen 11711
C ocaine</w> 11710
T ris 11710
TR X</w> 11710
Ferment as</w> 11710
v ations</w> 11709
found ations</w> 11709
neuro ectodermal</w> 11708
SN AP2</w> 11708
H air</w> 11706
N m</w> 11706
. min</w> 11705
l ays</w> 11705
moun ts</w> 11705
AJ CC</w> 11705
IDU s</w> 11705
man nan</w> 11704
PT X3</w> 11704
dextr ins</w> 11704
inter dependent</w> 11703
What man</w> 11703
Fu ji</w> 11701
oglob ins</w> 11700
ari piprazole</w> 11699
J L</w> 11697
osyl -</w> 11697
tau opathies</w> 11697
dech lor 11697
H erc 11696
ba um</w> 11696
sex -</w> 11695
Notch 3</w> 11695
EGF R 11694
V ig 11693
Ir respective</w> 11692
J ensen</w> 11691
mo vies</w> 11691
In sight</w> 11691
ba ical 11691
E ST 11690
hom omeric</w> 11690
vesti b 11690
wave front</w> 11690
JMJ D 11690
buil ds</w> 11689
convol uted</w> 11689
Ty 1</w> 11688
i als</w> 11687
idi xic</w> 11687
over hangs</w> 11686
co ats</w> 11685
Adi p 11685
F asci 11684
eIF 3</w> 11684
RE SP 11683
ic ities</w> 11682
hom onas</w> 11681
H ib</w> 11680
N RT</w> 11680
U CH</w> 11680
rein statement</w> 11680
rosu vastatin</w> 11680
expect ancies</w> 11679
T cf 11678
resi ded</w> 11677
AU D</w> 11677
Kl f4</w> 11676
aphidic olin</w> 11676
Cad herin</w> 11675
il lin 11674
Res us 11674
Te hran</w> 11674
tele metry</w> 11674
j ury</w> 11673
idi otypic</w> 11673
illustr ative</w> 11672
unil amellar</w> 11672
Denti stry</w> 11670
L ud 11669
lim bal</w> 11668
N DI</w> 11667
F R1</w> 11667
cul lin</w> 11667
pBlu escript</w> 11667
aut opsies</w> 11666
dis lo 11665
benz enesulf 11665
adic ally</w> 11665
re activate</w> 11664
grow ths</w> 11664
cyst oscopy</w> 11663
irrit ant</w> 11663
Transcrip t</w> 11662
cave at</w> 11662
F ort</w> 11661
meta thesis</w> 11661
rin us</w> 11660
elong ate</w> 11660
Bey o 11660
scl era</w> 11660
RA I</w> 11659
Thr 4</w> 11659
picro toxin</w> 11659
Pasteu rella</w> 11659
Pro motion</w> 11658
CP V</w> 11658
ISR CT 11658
B V 11657
ognath ic</w> 11657
ob us</w> 11653
ed o</w> 11653
chron icity</w> 11653
Rel ax 11653
r ual</w> 11652
fer ulic</w> 11652
S por 11651
inf antum</w> 11651
Di verse</w> 11651
lis ting</w> 11651
olef in</w> 11651
2 x 11650
b .</w> 11650
HO MO</w> 11650
Neuro psychological</w> 11650
U CLA</w> 11649
ass uring</w> 11649
HER G</w> 11649
s ong 11648
S H2 11647
dis odium</w> 11647
leg umes</w> 11646
Institu t</w> 11646
end og 11645
mobil ize</w> 11645
be re 11643
arachid onate</w> 11641
B AG3</w> 11639
Cre ative</w> 11639
vit rification</w> 11638
CH K2</w> 11638
quanti fies</w> 11637
trache obronchial</w> 11637
Leu 2</w> 11637
em ma</w> 11634
cephal ometric</w> 11634
J 6</w> 11633
pr uning</w> 11633
mic a</w> 11632
comm encement</w> 11632
LO C</w> 11632
Anat omy</w> 11632
Glu 3</w> 11630
trin itro 11630
F old</w> 11629
angi omy 11629
micro angiopathy</w> 11627
narcol epsy</w> 11627
III b</w> 11626
MB T</w> 11624
en visi 11623
ar ship</w> 11623
tri but 11623
lum enal</w> 11622
H BS</w> 11621
N A3</w> 11621
teri sed</w> 11621
SJ L</w> 11620
O DI</w> 11619
ar us</w> 11619
E SP</w> 11618
sessi le</w> 11618
Creutz feldt</w> 11618
D PN</w> 11616
I pa 11615
RAG 1</w> 11615
mu n</w> 11615
Figure 6 11613
aph thal 11613
st ops</w> 11612
achi an</w> 11610
cd k2</w> 11610
cel y</w> 11609
inform ational</w> 11609
Anti bacterial</w> 11608
yamo ya</w> 11608
Co 2</w> 11607
myristo ylation</w> 11607
D ll 11606
acc rual</w> 11606
A pe 11605
Def ective</w> 11605
dis mal</w> 11604
OK T3</w> 11603
it re</w> 11602
A plysia</w> 11601
tim escales</w> 11601
otom ized</w> 11601
Sha red</w> 11601
ate n</w> 11600
V ital</w> 11599
reassor tant</w> 11597
vasodil atory</w> 11596
M x</w> 11595
tigh ter</w> 11595
MV B</w> 11595
Boy den</w> 11595
W alter</w> 11594
CAR M1</w> 11593
Transc atheter</w> 11593
Fig. 7A</w> 11592
FL AIR</w> 11592
e v</w> 11591
d le 11591
G u</w> 11591
rep tiles</w> 11591
OR F4</w> 11588
Ir vine</w> 11588
PD K</w> 11587
fibro ids</w> 11587
ce iling</w> 11587
ac ta</w> 11586
oun saturated</w> 11586
aspart yl</w> 11585
sp inous</w> 11584
Di els</w> 11583
AR G</w> 11583
Klen ow</w> 11583
SC Z</w> 11582
phot ographic</w> 11582
Li k 11582
advoc ates</w> 11581
pancre at 11580
organochlor ine</w> 11580
B 2 11579
N ob 11578
or ase</w> 11578
Diffic ulties</w> 11578
PL D2</w> 11576
immuno electron</w> 11575
obtur ator</w> 11575
AF 9</w> 11574
I d2</w> 11573
DR Gs</w> 11573
micro filaments</w> 11572
dy sm 11572
raff inose</w> 11572
los a</w> 11571
sulfonyl urea</w> 11571
ff ing</w> 11570
De oxy 11570
Ne wer</w> 11569
sulf uric</w> 11569
in congruent</w> 11568
ap athy</w> 11568
hat ch</w> 11568
air craft</w> 11567
hamar toma</w> 11567
Bom by 11567
Am B</w> 11566
tam ethrin</w> 11565
I 7</w> 11563
si losis</w> 11563
Co urt</w> 11563
pneum ococci</w> 11563
Normal ized</w> 11562
un spliced</w> 11561
V acu 11560
SS U</w> 11560
am az 11559
regul arization</w> 11559
TA VR</w> 11559
Perc ent</w> 11559
cra yfish</w> 11559
e pers</w> 11558
α 6 11558
in gence</w> 11558
S AL</w> 11557
sec ondly</w> 11557
H im 11556
Ni V</w> 11556
asp inatus</w> 11555
plan a</w> 11554
dor some 11554
mesen tery</w> 11554
es ins</w> 11553
ferro electric</w> 11553
ot ation</w> 11552
refl ectivity</w> 11551
MI BI</w> 11551
Immun o</w> 11551
eph rin 11550
Per sistence</w> 11549
- D</w> 11548
ar an</w> 11548
- GCT 11547
W ST</w> 11547
mon etary</w> 11547
non verbal</w> 11547
south western</w> 11547
F M 11546
oxy codone</w> 11546
Tyr 7</w> 11546
b anded</w> 11545
L CM</w> 11545
MI R</w> 11545
care ers</w> 11545
ec ks</w> 11544
loc ality</w> 11544
cr t</w> 11544
pic orna 11544
L gr 11543
CYP 7</w> 11543
coprecip itated</w> 11543
ro ot 11542
stre aming</w> 11542
B AP</w> 11541
re model 11541
ref ining</w> 11541
DK K1</w> 11541
Gest ational</w> 11541
multi scale</w> 11540
paraly zed</w> 11540
2 .2</w> 11539
Y D</w> 11539
activ atable</w> 11539
bio diesel</w> 11539
Ad op 11539
pon ds</w> 11539
L ob 11538
Ne ed</w> 11538
dilu ent</w> 11538
bur sa</w> 11537
multi plying</w> 11536
H W</w> 11535
G D2</w> 11535
Ar n 11534
L GG</w> 11533
fl ick</w> 11533
Sh ock</w> 11533
thre e-</w> 11533
hyd ati 11533
hyperinsulin emic</w> 11532
derang ement</w> 11532
a HUS</w> 11531
ox oglutarate</w> 11531
group ings</w> 11530
Add ressing</w> 11530
multis ite</w> 11530
coag ulated</w> 11529
olym phatic</w> 11529
GCN 4</w> 11529
B j 11528
co b 11528
RS K2</w> 11527
cu tis</w> 11524
tric losan</w> 11524
D os 11523
en imine</w> 11523
Li pos 11523
seven teen</w> 11523
diap ause</w> 11523
y topenia</w> 11522
ter tiles</w> 11522
N Z</w> 11521
A EP</w> 11520
sub urban</w> 11520
I shik 11519
b ow 11519
pr in</w> 11519
io s</w> 11519
macrom olecule</w> 11519
BMD M</w> 11519
le es</w> 11518
Decre ase</w> 11518
Mur ray</w> 11518
Daph nia</w> 11518
apolipo proteins</w> 11518
E yes</w> 11516
Acet yl 11516
syn chronously</w> 11514
Discrimin ation</w> 11514
si ds</w> 11511
anti nib</w> 11510
compl aining</w> 11510
tra inee</w> 11509
L AP 11508
guan ylyl</w> 11508
coch le 11508
t Hcy</w> 11507
transform ant</w> 11507
cal iper</w> 11506
S GC</w> 11504
cont or 11504
PE s</w> 11504
Mar row</w> 11504
Def ense</w> 11504
4 . 11503
un perturbed</w> 11503
rele as 11503
Advant ages</w> 11503
obsc ured</w> 11501
leng thy</w> 11500
predil ection</w> 11499
valu ation</w> 11498
intr ab 11498
j ac 11497
maxim ized</w> 11495
transloc ating</w> 11495
MT F</w> 11493
Sec A</w> 11493
Fr action</w> 11493
an ium</w> 11492
entero pathy</w> 11492
conduc tors</w> 11491
E F1</w> 11490
P ico</w> 11489
po orest</w> 11489
ephe drine</w> 11489
ap ar 11488
Caffe ine</w> 11488
Mas cot</w> 11486
Δ H</w> 11485
ogen ies</w> 11484
B ronchial</w> 11482
leuk aemic</w> 11482
NE FA</w> 11482
colpos copy</w> 11482
Aim s</w> 11481
L Z 11480
ch erry</w> 11480
def ensins</w> 11480
Mis souri</w> 11480
icosa hedral</w> 11480
ath on</w> 11479
Agricul ture</w> 11478
B ChE</w> 11477
aff licted</w> 11476
Eth nic</w> 11476
travel ers</w> 11476
Phoen ix</w> 11476
mo x</w> 11475
SH M</w> 11475
mu M</w> 11475
sel la</w> 11474
CL 3</w> 11474
N B4</w> 11473
U rea</w> 11473
EL F</w> 11473
optim ise</w> 11472
Ix odes</w> 11471
Beyo time</w> 11471
ex terior</w> 11470
rom y 11470
Al b 11470
f icate</w> 11469
Indu strial</w> 11469
- linked</w> 11468
CA E</w> 11468
Austri an</w> 11467
fellow s</w> 11466
G ab1</w> 11465
ph or</w> 11465
IL I</w> 11465
G c 11464
gr asses</w> 11464
cyto reductive</w> 11464
riboswit ch</w> 11464
rib osyl</w> 11463
hand grip</w> 11463
Hug hes</w> 11463
e utroph 11462
inf licted</w> 11461
hepat os 11461
gon i 11461
ag am 11459
resili ent</w> 11459
it tin</w> 11458
- tagged</w> 11457
r yl</w> 11457
inter s</w> 11457
entang lement</w> 11457
Scand in 11457
M CL1</w> 11456
P ik 11456
transf eren 11456
colon isation</w> 11456
spor adically</w> 11456
in competence</w> 11455
Figure 6A</w> 11454
Con text</w> 11453
CC GT 11453
poly pharmacy</w> 11452
Af gh 11452
Cri tically</w> 11452
L AN 11451
EB RT</w> 11451
the or 11450
TP M</w> 11449
ginsen oside</w> 11449
summar ised</w> 11448
hyper secretion</w> 11447
tub er</w> 11447
Paste ur</w> 11447
quinox aline</w> 11446
it um</w> 11445
PA E</w> 11445
mut u 11444
D ock 11443
Ste 1</w> 11443
fab ric</w> 11443
Jos eph</w> 11443
hiber nation</w> 11443
ir regularly</w> 11442
QU ES 11442
ependym al</w> 11441
glyco sidase</w> 11439
NEDD 8</w> 11439
L CN 11438
oc top 11437
ass ment</w> 11437
depend ant</w> 11437
Adju stment</w> 11437
fores kin</w> 11437
tetrafluoro ethylene</w> 11437
Fig. 2 11436
KL F</w> 11436
unc leaved</w> 11435
multi vesicular</w> 11435
Dn mt1</w> 11435
multi organ</w> 11434
Leuk ocyte</w> 11434
b t 11432
log MAR</w> 11432
skinf old</w> 11432
la e</w> 11431
AA F</w> 11430
s lot</w> 11429
ed 1</w> 11429
imid azol</w> 11429
Eph B2</w> 11429
sati vum</w> 11428
ho ff</w> 11427
hom bic</w> 11427
Acti vin</w> 11427
Neuro imaging</w> 11426
po unds</w> 11425
sp 2</w> 11425
lith ography</w> 11425
rhiz a</w> 11424
P ass 11423
pox virus</w> 11423
categor ised</w> 11422
6 Δ</w> 11421
k on</w> 11421
bre eders</w> 11421
Viet nam 11421
certi ficates</w> 11421
M olecules</w> 11420
end om 11420
crow ded</w> 11420
ancest ors</w> 11419
anti ferromagnetic</w> 11418
ocardi tis</w> 11418
G AN 11417
ann u 11417
Axi overt</w> 11417
ox yl 11414
electro genic</w> 11414
off ending</w> 11414
opo res</w> 11414
El ite</w> 11414
R MA</w> 11413
ne g</w> 11413
M NPs</w> 11412
anth ran 11412
I den 11411
un limited</w> 11411
S H3 11410
fi beroptic</w> 11410
por ted</w> 11410
ven e 11410
On cor 11410
ELI SPOT</w> 11410
ribo zymes</w> 11410
LE P</w> 11409
prec o 11408
head ed</w> 11408
N ations</w> 11407
Mu SK</w> 11407
Sen ior</w> 11407
RK IP</w> 11406
VL BW</w> 11406
cryptoc occal</w> 11406
2 O4</w> 11405
J an</w> 11405
pl eckstrin</w> 11404
cl ad 11404
Meas ured</w> 11404
B n 11403
P TION</w> 11403
gr and</w> 11402
RN F4</w> 11402
acy tidine</w> 11402
So c</w> 11401
a ul 11400
F rank</w> 11400
Kn own</w> 11399
l on</w> 11398
V G</w> 11398
SF 3B1</w> 11398
chemi stries</w> 11398
u i</w> 11397
Z Z</w> 11397
H Y</w> 11396
CA V1</w> 11396
decid ua</w> 11396
AI M2</w> 11395
tetradec anoylphorbol</w> 11394
AI RE</w> 11393
MC M2</w> 11393
th onous</w> 11392
on itis</w> 11392
g p3</w> 11391
Δ E</w> 11391
ogra vimetric</w> 11391
V MAT</w> 11390
nitro genase</w> 11389
Bar t 11389
h aptic</w> 11388
B AS</w> 11387
adap table</w> 11387
Coun ter</w> 11387
In take</w> 11386
ub erculosis</w> 11386
DNA J 11386
Dra wing</w> 11386
N L1</w> 11385
u ron</w> 11384
U HRF1</w> 11384
dis solve</w> 11384
CR F0</w> 11384
FB G</w> 11384
scrap ing</w> 11384
glucone ogenic</w> 11384
5 min</w> 11383
EL EC 11383
Fre der 11383
electro mechanical</w> 11381
millig ram</w> 11381
bl ended</w> 11380
hepat oprotective</w> 11380
sn ow 11380
HA DS</w> 11380
audi ence</w> 11379
aqueduc tal</w> 11379
AF B</w> 11378
Py MOL</w> 11378
tolu idine</w> 11378
Electroph oresis</w> 11378
Sel enium</w> 11376
Profil ing</w> 11374
ph ile</w> 11372
hypertroph ied</w> 11372
h 3</w> 11370
schem a</w> 11370
megakary ocytic</w> 11370
Glyc erol</w> 11370
wo ody</w> 11369
lim on 11368
sta ple</w> 11367
bleph aro 11367
F SHR</w> 11366
dissemin ate</w> 11366
ate lec 11365
GF 1</w> 11365
mono hydrate</w> 11365
fo etus</w> 11364
mat ur 11363
radio tracer</w> 11362
Hous e 11362
enop tera</w> 11361
enrol ment</w> 11361
end ostatin</w> 11360
od al 11360
ath ionine</w> 11360
P un 11359
ot axis</w> 11359
hyper coagul 11359
CT TT 11359
adren o 11359
phle bot 11359
Gol den</w> 11358
Pre incubation</w> 11357
olip oma</w> 11356
Z F1</w> 11355
min es</w> 11355
UV C</w> 11355
mGlu R1</w> 11355
den omin 11354
tann ins</w> 11353
f 7</w> 11352
carcin omatosis</w> 11352
Pol o</w> 11352
fluor imetric</w> 11352
sp a</w> 11351
MR V</w> 11351
requi sites</w> 11350
ulf an</w> 11350
Kle in</w> 11349
cyt olysis</w> 11348
roc king</w> 11348
biog as</w> 11348
over flow</w> 11347
situ ational</w> 11346
PIC K1</w> 11346
lacun ar</w> 11346
D uc 11345
ch er 11345
vi gorously</w> 11345
Sta ff</w> 11345
B lacks</w> 11344
D P1</w> 11344
Neu ron</w> 11344
remin der</w> 11344
prog es 11342
AS ES</w> 11342
di ploids</w> 11341
ci ans</w> 11341
Pro j 11341
AS XL1</w> 11340
conver ged</w> 11340
TGF beta</w> 11340
acro lein</w> 11340
con found</w> 11339
MAP 3 11338
cas ei</w> 11338
fer ric 11337
BAC E</w> 11336
Log an</w> 11336
Multi plex</w> 11335
H v 11334
r st</w> 11334
un induced</w> 11334
Hung arian</w> 11334
f ear 11333
ar asi 11332
Rob otic</w> 11332
PNG ase</w> 11332
CO S 11331
V in 11330
dor ff</w> 11330
crip ts</w> 11330
ne st 11329
CT CL</w> 11329
pro dromal</w> 11328
up ta</w> 11328
del ocalization</w> 11328
sphen oid</w> 11328
PL A 11327
FI M</w> 11327
Blas t 11326
g p7</w> 11325
den ied</w> 11325
Rem ote</w> 11325
Contin uing</w> 11325
D PBS</w> 11324
calcane al</w> 11324
Sequ en 11323
1 Cr</w> 11322
epi staxis</w> 11321
discrep ant</w> 11320
T r</w> 11318
glo ve</w> 11318
myc otoxin</w> 11318
AUTHO RS</w> 11318
Lank a</w> 11317
vesi cou 11316
SR BC</w> 11316
Fig. 4C</w> 11315
IFN AR1</w> 11314
body weight</w> 11314
gen .</w> 11313
ER A</w> 11313
immen se</w> 11313
juxtam embrane</w> 11313
reticul o 11312
if lor 11311
HE AL 11311
canti lever</w> 11311
cyste inyl</w> 11310
semicir cular</w> 11309
cell aneous</w> 11308
Y pt 11307
Co expression</w> 11307
azol in</w> 11307
Rac ial</w> 11307
S ons</w> 11306
p ear 11306
val sartan</w> 11306
rho dium</w> 11306
on ward</w> 11304
rhe ology</w> 11304
Echocardi ographic</w> 11304
nov ice</w> 11303
cab bage</w> 11303
Pr x</w> 11302
i ella</w> 11301
im perfecta</w> 11301
op sins</w> 11301
H9 c2</w> 11301
or afenib</w> 11300
sel ects</w> 11300
liter atures</w> 11300
fas cin</w> 11300
arth ralgia</w> 11300
Ultra violet</w> 11300
succumb ed</w> 11300
U NG</w> 11299
HG PS</w> 11299
Pro to 11298
util ising</w> 11298
Phe 2</w> 11297
O W</w> 11296
ip ramine</w> 11296
fructo kinase</w> 11296
vas tly</w> 11295
Met 1</w> 11295
un coating</w> 11294
gluc osin 11294
PD 0</w> 11294
Anthrop ometric</w> 11294
R oth</w> 11293
pericy te</w> 11293
N MP</w> 11291
chem osensory</w> 11291
Fo rensic</w> 11288
communic ative</w> 11287
B os</w> 11286
Stro ng 11285
Lim b</w> 11285
TION AL</w> 11285
is oxazol 11284
dis inhibition</w> 11284
der gar 11284
PG RN</w> 11284
RR V</w> 11282
U CH 11281
yn gi 11281
benzo yl 11281
lipofus cin</w> 11281
som ite</w> 11280
instruc tional</w> 11280
CN As</w> 11279
corrobor ating</w> 11279
Δ 7</w> 11278
mis ci 11278
Micro glia</w> 11278
noto chord</w> 11278
pass aging</w> 11277
Vietnam ese</w> 11277
O w 11276
Radi olab 11276
6 W</w> 11275
bi us</w> 11275
quinol ines</w> 11275
S es 11273
mes ophyll</w> 11273
rt TA</w> 11273
Carbo hydrate</w> 11273
di aries</w> 11272
trunc al</w> 11272
synap tically</w> 11271
Bomby x</w> 11271
hi j 11270
CO L</w> 11270
fove a</w> 11270
den uded</w> 11269
New born</w> 11269
SU I</w> 11266
omyc ete</w> 11266
el se</w> 11265
trans version</w> 11265
hel met</w> 11265
B mal1</w> 11264
sm ell</w> 11264
man oeu 11263
cur ation</w> 11263
gust atory</w> 11262
Mit ch 11261
sh ocked</w> 11260
PA MAM</w> 11259
DM PC</w> 11259
MV P</w> 11259
encapsul ating</w> 11259
spec kles</w> 11258
Re generation</w> 11258
PY Y</w> 11258
M tr 11257
l t</w> 11257
F is 11257
Fluoresc ein</w> 11257
Y Y</w> 11256
de marc 11256
my s 11256
H ot</w> 11255
Z he 11255
tra de 11255
a ward</w> 11254
head ing</w> 11253
manno sidase</w> 11253
Charl son</w> 11253
de bulking</w> 11252
og on 11252
ev an 11252
rel ax</w> 11252
dis continu 11252
compo unded</w> 11252
Dar by</w> 11252
RP S1</w> 11251
mol ten</w> 11251
1A 1A</w> 11251
CAR D1</w> 11250
M UT 11249
PG L</w> 11249
Spectro scopic</w> 11249
oro v</w> 11248
L uminal</w> 11247
dec oction</w> 11247
FGF R4</w> 11247
choc olate</w> 11247
bleb bing</w> 11247
coal escence</w> 11246
D oses</w> 11244
met ane 11244
Fe LV</w> 11244
top iramate</w> 11243
ind olol</w> 11243
let ons</w> 11243
er m</w> 11242
chrom osomally</w> 11242
FI G</w> 11241
on ics</w> 11240
nucle ophiles</w> 11240
quanti le</w> 11240
Im plem 11240
psych opharmac 11240
Sch ul 11240
R RP</w> 11238
L LA</w> 11237
br ushes</w> 11237
biol uminescent</w> 11237
H AE</w> 11236
adren om 11236
Camp bell</w> 11236
NF T</w> 11234
For ced</w> 11234
hos tility</w> 11233
t ata</w> 11232
NR G</w> 11232
d ment</w> 11231
qu antal</w> 11231
trans ected</w> 11231
abdomin is</w> 11231
Pol ys 11230
act yl 11230
rub rum</w> 11230
Sn R 11229
sec ured</w> 11228
DE VD</w> 11228
ss 1</w> 11227
trans migration</w> 11226
slip ped</w> 11226
Nic kel</w> 11226
alb a</w> 11225
bi ota</w> 11223
0 A9</w> 11222
simpl ification</w> 11222
CA U 11221
MAL T1</w> 11221
Cath epsin</w> 11221
8 Y</w> 11220
LL O</w> 11220
Dnmt 3a</w> 11220
icul i</w> 11219
ocul tural</w> 11219
idi zation</w> 11218
sph aero 11218
C li 11217
ari ous</w> 11217
stu ffs</w> 11217
Fin der</w> 11217
We gener</w> 11217
ine x</w> 11216
Par ker</w> 11216
provo kes</w> 11216
U PD 11215
st agg 11215
psych opathological</w> 11215
A erobic</w> 11214
TWE AK</w> 11214
P OR 11213
clos es</w> 11213
foramin al</w> 11213
R ech 11212
micro structures</w> 11212
Con servation</w> 11212
Ar t</w> 11212
Ca ve 11210
devi ant</w> 11210
se maph 11209
mo vie</w> 11209
Gly R</w> 11208
M RP2</w> 11207
ze tim 11206
LX Rα</w> 11206
u ating</w> 11205
han ta 11205
BCN U</w> 11205
od uod 11204
AR R</w> 11203
Over weight</w> 11203
Coulom b</w> 11203
profession alism</w> 11202
SL AM</w> 11201
ifor ms</w> 11201
ble ach</w> 11200
CaMK II 11200
unc ou 11199
Hy Clone</w> 11198
Mi Seq</w> 11198
A RPE</w> 11197
aden omy 11197
Na v</w> 11197
cam pe 11197
SUMO ylated</w> 11197
Moun t</w> 11197
Y p 11195
ot ten</w> 11195
magne tite</w> 11195
J 3</w> 11194
HC Ws</w> 11194
typh i</w> 11194
Nation wide</w> 11192
dimethyl siloxane</w> 11191
numb ness</w> 11190
je op 11190
mon ounsaturated</w> 11189
Trans form 11189
Deriv atives</w> 11189
Ch ol</w> 11188
pathom ech 11188
Ly t</w> 11187
ondyl ar</w> 11186
V As</w> 11185
Ple ist 11184
buil dup</w> 11183
hypo physeal</w> 11182
NK A</w> 11182
BE AS</w> 11182
introg ression</w> 11181
D ominant</w> 11180
atic us</w> 11180
TRA M</w> 11180
Condi tioned</w> 11180
WW OX</w> 11180
T ul 11179
Multi drug</w> 11178
AF 0</w> 11178
SB F</w> 11178
Ver tical</w> 11177
imidazol es</w> 11177
baro receptor</w> 11177
SP N</w> 11176
piper az 11176
Ein stein</w> 11176
hor ns</w> 11175
R ank</w> 11174
trabec ulae</w> 11174
scientif ically</w> 11174
K AP</w> 11173
DI F</w> 11173
TO P1</w> 11173
chrono tropic</w> 11173
hom icide</w> 11172
S. E. 11172
H K1</w> 11171
matrip tase</w> 11170
wet tability</w> 11169
Cytos olic</w> 11168
C ic 11167
R S1</w> 11167
L ayer</w> 11166
ot ron</w> 11166
elu ates</w> 11166
phosphorothio ate</w> 11166
oste oid</w> 11165
hydr alazine</w> 11165
Schrö dinger</w> 11165
uro th 11164
ph ora</w> 11163
transp iration</w> 11163
A le 11162
Anti viral</w> 11162
Per 2</w> 11161
PF 4</w> 11161
TC F7 11160
scar cely</w> 11160
postinter vention</w> 11160
N HP</w> 11159
Mol oney</w> 11159
FL ASH</w> 11158
transc y 11158
omel an 11158
I tem</w> 11155
M L1</w> 11155
j . 11155
em ic 11155
- TG 11154
E ro 11154
ag rin</w> 11153
mGlu Rs</w> 11153
crani oc 11153
al pine</w> 11152
amoeb ae</w> 11152
epig allocatechin</w> 11150
con ate</w> 11149
sul fin 11149
bas ins</w> 11149
Franc is 11149
A H1</w> 11148
V v 11147
et c.</w> 11147
Hapl otype</w> 11147
mif epristone</w> 11147
tradi tion</w> 11146
Fer tility</w> 11146
miniat urized</w> 11146
inf undi 11145
IC ER</w> 11145
chool ers</w> 11145
al oric</w> 11144
ac lop 11144
My ers</w> 11144
PG D2</w> 11144
P anc 11142
th reads</w> 11142
resti mates</w> 11142
o plast</w> 11140
end plate</w> 11140
gr asping</w> 11140
fru stration</w> 11140
helmin ths</w> 11140
rest art</w> 11139
SL I</w> 11139
L ECs</w> 11138
An ten 11138
squ alene</w> 11138
QIA amp</w> 11138
ARE G</w> 11136
floc culation</w> 11136
7 q</w> 11135
F alc 11135
AT AT 11135
Sup p</w> 11135
gel danamycin</w> 11134
labor ious</w> 11133
MO MP</w> 11133
by products</w> 11132
phosphoc reatine</w> 11132
cl ocks</w> 11131
Co urse</w> 11131
fix ator</w> 11131
cas ual</w> 11131
hid rosis</w> 11131
neuro toxins</w> 11130
ar icus</w> 11129
st ath 11129
Investig ators</w> 11129
Ple ase</w> 11129
perpe tr 11129
resol ves</w> 11127
J eff 11126
R CM</w> 11126
A PH</w> 11126
Fig. 5 11126
fati gu 11126
pol ishing</w> 11124
til age</w> 11124
Su c</w> 11124
Op tic</w> 11124
re folded</w> 11123
zetim ibe</w> 11123
ou n</w> 11122
PC V2</w> 11122
anaesthe tics</w> 11122
inchon inic</w> 11122
un suspected</w> 11121
Man chester</w> 11121
Behavi our</w> 11121
auto regulatory</w> 11120
Ishik awa</w> 11120
L SK</w> 11119
sol d</w> 11119
sensiti zer</w> 11119
hand ful</w> 11119
photocur rent</w> 11119
inter group</w> 11118
aqu ifer</w> 11118
networ king</w> 11117
Y AC</w> 11116
in den 11116
BL V</w> 11114
ob tain 11113
Ex tension</w> 11113
ib i</w> 11113
Schwar tz</w> 11113
L om 11112
retro transposons</w> 11112
NO S2</w> 11112
osim ertinib</w> 11111
C cr 11110
multi domain</w> 11110
cr acks</w> 11110
Extrac orporeal</w> 11110
tes ter</w> 11109
MT OR</w> 11109
hes ins</w> 11109
conf using</w> 11108
R SC</w> 11107
orth or 11107
insuff lation</w> 11107
psychos tim 11106
abrup tly</w> 11106
n 4</w> 11105
pa int</w> 11105
chol elithiasis</w> 11105
veterin arians</w> 11105
aclop rid</w> 11105
Ph ase 11103
PO T1</w> 11103
sigmo id 11103
un employed</w> 11102
poly comb</w> 11102
Endoth elin</w> 11102
a ise</w> 11100
L o</w> 11100
pl euro 11100
CD F</w> 11100
affor ding</w> 11100
pd m0</w> 11100
U ra</w> 11099
ger in</w> 11099
provoc ative</w> 11099
mo ist</w> 11098
cy to</w> 11096
AC M</w> 11096
pos tim 11095
trin ucleotide</w> 11095
Zim bab 11095
Se ed</w> 11094
Pak ist 11094
symbi ont</w> 11093
p BR3</w> 11092
MA TION</w> 11092
hypop it 11092
multi layered</w> 11090
par ties</w> 11089
im etics</w> 11088
Cl onal</w> 11088
Tim ing</w> 11088
lit re</w> 11087
pedi cled</w> 11087
Thrombo sis</w> 11087
ub ular</w> 11084
terat omas</w> 11084
aw ed</w> 11083
tar sal</w> 11083
anti nuclear</w> 11081
toph thora</w> 11081
V OC</w> 11080
behavi orally</w> 11079
V on</w> 11078
en vision</w> 11078
de mise</w> 11078
Bar tonella</w> 11078
Fab rication</w> 11078
orh odopsin</w> 11078
un stressed</w> 11077
PF OA</w> 11077
ros covitine</w> 11076
a ison</w> 11075
tic ola</w> 11075
TM 4</w> 11075
sporo zoites</w> 11074
ac izumab</w> 11073
E MCV</w> 11072
stri pes</w> 11072
not ori 11072
rham nose</w> 11072
R SS</w> 11071
th ia</w> 11071
all i</w> 11071
Ad riamycin</w> 11070
PM V</w> 11070
Predic ted</w> 11070
diaph orase</w> 11070
keto profen</w> 11070
h man</w> 11069
cap e</w> 11068
Dna B</w> 11068
sol ani</w> 11067
non homologous</w> 11067
Lo ad</w> 11067
rhin ovirus</w> 11067
HI FU</w> 11065
osi ties</w> 11065
DR C</w> 11065
ferment able</w> 11065
bacteri uria</w> 11064
H BD</w> 11063
Tre pon 11063
cat arr 11062
atelec tasis</w> 11062
th r 11061
hetero duplex</w> 11061
AF 6</w> 11061
Ag arose</w> 11060
Gadd 4</w> 11060
ol iv 11059
exc ipients</w> 11059
M UL 11058
hyper kalemia</w> 11058
gen om 11057
Plat form</w> 11057
taz obactam</w> 11057
Biom echanical</w> 11056
1 In</w> 11055
S LA</w> 11055
end uring</w> 11054
min k</w> 11054
shiel ded</w> 11054
R al</w> 11053
c um</w> 11052
hin dering</w> 11052
Cap ture</w> 11052
G HD</w> 11051
G AF</w> 11051
ag ia</w> 11051
sub telomeric</w> 11051
AR H 11051
isoc ratic</w> 11051
3 A4</w> 11050
em bar 11050
av ement</w> 11050
Edi tion</w> 11050
GSE 6</w> 11049
kil odal 11048
Adi ponectin</w> 11048
under represented</w> 11047
ax ially</w> 11047
thrombo philia</w> 11047
n n 11046
an ova</w> 11046
ob is</w> 11046
ardi um</w> 11046
p v 11045
F av 11044
dodec yl 11044
pec tomy</w> 11042
hepat omegaly</w> 11042
Recombin ation</w> 11042
accultur ation</w> 11042
atis simus</w> 11041
p enc 11040
cry ogenic</w> 11039
Gly 2</w> 11038
sub clone</w> 11037
Br g1</w> 11037
cl ub 11036
aff ection</w> 11036
Ar te 11036
ab a</w> 11035
Syn chrotron</w> 11035
m olysis</w> 11034
aden yl 11034
fluoro chrome</w> 11034
SW S</w> 11033
poly brene</w> 11032
hex yl 11032
º C</w> 11031
CT AB</w> 11031
py roptosis</w> 11031
dorsome dial</w> 11029
Lo Vo</w> 11027
ne ath</w> 11026
Ar f1</w> 11025
Includ ing</w> 11025
brow ser</w> 11024
gravit ational</w> 11024
psych ic</w> 11023
DM H</w> 11023
per ing</w> 11022
TA U</w> 11022
An atomic</w> 11021
iz ine</w> 11020
substanti ve</w> 11020
Nog o</w> 11020
birefr ingence</w> 11020
NS 4A</w> 11019
IV D</w> 11019
Cop y</w> 11019
gluc o 11018
hypometh ylated</w> 11016
credi ble</w> 11016
Scor ing</w> 11016
M Bs</w> 11015
basi di 11015
intra abdominal</w> 11014
Arg 5</w> 11014
iner tia</w> 11014
Oncor hynchus</w> 11014
ar ag 11012
man ners</w> 11012
1 L1</w> 11011
U GT</w> 11010
ar b</w> 11010
h y</w> 11009
de ubiquitination</w> 11009
th es</w> 11008
ste ering</w> 11008
sp ins</w> 11007
aggra vate</w> 11007
out bred</w> 11006
AD V</w> 11006
NO 3-</w> 11006
ingi val</w> 11005
Tuni sia</w> 11005
4 W</w> 11004
innerv ate</w> 11004
S ure 11003
BM V</w> 11003
thalam ocortical</w> 11003
recep tivity</w> 11002
HP P</w> 11002
5 RO</w> 11001
en il</w> 11001
ach andran</w> 11001
PA X3</w> 11000
Med 1</w> 11000
Ophthalm ology</w> 10999
- resistant</w> 10998
lute a</w> 10998
lot tic</w> 10997
eman ating</w> 10997
all ogenic</w> 10996
Neuro logic</w> 10996
Cdc 7</w> 10996
CN R</w> 10994
sing letons</w> 10993
ili ation</w> 10993
mercap top 10993
4 Y</w> 10992
2 F5</w> 10992
S A2</w> 10992
dys arth 10992
DD R1</w> 10992
Morph ometric</w> 10991
seaf ood</w> 10991
pip iens</w> 10990
ethylenediamine tetraacetic</w> 10990
Iden tical</w> 10990
u .</w> 10989
Pres ently</w> 10989
inter hemispheric</w> 10988
Dist urb 10988
Ex clud 10987
ultras ensitive</w> 10987
zz i</w> 10987
awa its</w> 10986
envisi oned</w> 10986
poly arthritis</w> 10985
Han sen</w> 10985
7 B1</w> 10984
I sc 10984
Image Quant</w> 10984
be et</w> 10983
amni on</w> 10983
Frag ment</w> 10983
Cytom egalovirus</w> 10983
N RP</w> 10982
ophthalm ological</w> 10982
S aline</w> 10981
Ve h 10981
paz opanib</w> 10981
G II</w> 10980
fa 1</w> 10980
Mec kel</w> 10980
ferment ative</w> 10980
es 1</w> 10979
aval ent</w> 10979
pur pos 10978
postin jection</w> 10978
F d 10977
F T4</w> 10977
TH ER</w> 10976
pursu ing</w> 10976
My BP</w> 10975
glomerul us</w> 10975
Americ as</w> 10975
AT GL</w> 10974
arch et 10974
t TA</w> 10973
N f 10973
muc ocutaneous</w> 10973
Fe ed 10973
epilep togenesis</w> 10973
pec toral</w> 10972
SER CA 10972
hsp 9</w> 10972
Spl enic</w> 10972
M ood</w> 10971
ic y 10971
hos tile</w> 10971
STATI STI 10971
oc alized</w> 10969
glycos yltransferase</w> 10969
Vi sible</w> 10968
suic idality</w> 10967
F et 10966
M all 10965
k le 10965
ri dae</w> 10965
gen ics</w> 10965
Sta tement</w> 10965
e igh</w> 10963
Pap er</w> 10963
pal boc 10962
Rub isco</w> 10962
Le sion</w> 10961
Dermat ology</w> 10961
adduc tor</w> 10961
N AS</w> 10960
Glyc ogen</w> 10960
m Di 10959
P ath</w> 10959
inter dependence</w> 10959
In put</w> 10959
pur posi 10959
cello biose</w> 10958
B H 10957
Ot ta 10957
orthor hombic</w> 10957
F ear</w> 10956
angioten sinogen</w> 10956
ET B</w> 10955
hem odynamically</w> 10955
kary otyping</w> 10955
S p3</w> 10954
lev ator</w> 10954
FLI 1</w> 10954
B PV</w> 10953
O X1</w> 10953
micro emulsion</w> 10953
H MO</w> 10951
nal idixic</w> 10951
primor dia</w> 10951
un gu 10950
um ann</w> 10950
dermat oses</w> 10950
DHE AS</w> 10950
sp rung</w> 10948
eno yl</w> 10948
f lock</w> 10947
nucle of 10947
Mig raine</w> 10947
b auer</w> 10946
c. 7</w> 10946
dehy de</w> 10945
sympath ectomy</w> 10945
Assess ments</w> 10944
my ot 10943
iter ation</w> 10943
Res ulting</w> 10942
adsor b</w> 10942
HC D</w> 10941
ec ule</w> 10940
kin a</w> 10940
CC CA 10940
metall o</w> 10939
descrip tor</w> 10937
Clus ters</w> 10937
MEK K1</w> 10937
ex ocyst</w> 10936
ru b</w> 10936
CC SD</w> 10936
antihist amines</w> 10934
domes tication</w> 10933
vestib ule</w> 10933
e ta 10932
P RT</w> 10932
L on</w> 10932
Bel ie 10932
end ochondral</w> 10930
att us</w> 10930
Commun ities</w> 10930
R en</w> 10929
ML E</w> 10929
Zn Cl2</w> 10929
cy toc 10928
Pleist ocene</w> 10928
Hawa ii</w> 10927
DIAGNO SIS</w> 10927
L im</w> 10926
G H1</w> 10925
2 SO4</w> 10924
til apia</w> 10924
DI SC</w> 10924
h sp</w> 10923
O CP</w> 10923
nanos econd</w> 10922
refin ements</w> 10922
ble b</w> 10921
demethyl ases</w> 10921
Oligonucle otides</w> 10921
N As</w> 10920
MA B</w> 10920
Poten tially</w> 10920
exc essively</w> 10919
DO PAC</w> 10919
tacti cs</w> 10919
Micro biology</w> 10918
L c 10917
diver si 10916
adrenocortic otropic</w> 10916
moti vate</w> 10915
Phy tophthora</w> 10915
ad zu</w> 10913
pl 2</w> 10912
w ide 10911
ven ues</w> 10911
CF DA</w> 10910
res ort</w> 10909
knock in</w> 10909
RA s</w> 10908
conti g</w> 10908
thiazol idine 10908
equ ality</w> 10907
DE B</w> 10907
HD 2</w> 10907
hu a</w> 10907
S Rs</w> 10906
nephro toxic</w> 10906
o ak</w> 10905
fea ther</w> 10905
MN Cs</w> 10904
SS Bs</w> 10903
oro lac</w> 10903
omi phene</w> 10903
palboc iclib</w> 10903
dithiocarb amate</w> 10902
AT ED</w> 10901
ban ana</w> 10901
EN CODE</w> 10900
vel i 10897
sy r 10897
RA TION</w> 10896
We iss</w> 10896
function als</w> 10896
eyel ids</w> 10896
lam eness</w> 10895
Sim pl 10895
C ond 10894
V m</w> 10894
bul losa</w> 10892
later alized</w> 10892
L S1</w> 10891
inter relationships</w> 10890
CF I</w> 10889
Nd c8</w> 10889
- GAT 10888
G BC</w> 10888
ardi pine</w> 10888
devi ate</w> 10888
Recor dings</w> 10888
Vas cul 10888
whee ze</w> 10887
melan osomes</w> 10885
Rho B</w> 10881
Suc rose</w> 10881
s als</w> 10880
a em 10880
un igenes</w> 10879
her it 10879
Ph age</w> 10878
Alex ander</w> 10878
T R1</w> 10877
rin one</w> 10877
Muscul oskeletal</w> 10877
es sus</w> 10876
protec tin</w> 10876
predomin ates</w> 10876
THERA PY</w> 10876
A DS</w> 10874
Func tions</w> 10874
med itation</w> 10874
Tec an</w> 10874
G M2</w> 10873
us a</w> 10873
NK CC1</w> 10873
RN F8</w> 10872
parat uberculosis</w> 10872
prolactin emia</w> 10872
r h</w> 10871
at uration</w> 10871
flo quine</w> 10870
P G1</w> 10869
as u</w> 10869
tic asone</w> 10869
inter rater</w> 10869
DR M</w> 10869
byp assed</w> 10869
be ige</w> 10868
T end 10867
Elec tric</w> 10867
Ev ent 10867
op ing</w> 10866
Pharmac ologic</w> 10866
TE AD</w> 10865
haem odynamics</w> 10865
consol idated</w> 10865
GRA DE</w> 10865
pli ers</w> 10865
N umbers</w> 10863
tan ib</w> 10863
TRI M3</w> 10863
B SC</w> 10862
inter layer</w> 10862
TF AM</w> 10862
Mit otic</w> 10862
Ad dic 10861
phil um</w> 10861
kind red</w> 10861
ur so 10860
ag ron 10860
non syndromic</w> 10860
STAT EMENT</w> 10860
sedim ented</w> 10860
tetrahydro furan</w> 10860
细 胞 10859
olip idemic</w> 10859
endocrin ology</w> 10859
naphth yl</w> 10859
PGC 1α</w> 10858
Ec ological</w> 10857
u st 10856
T or</w> 10856
suc king</w> 10856
hin ts</w> 10856
sed ated</w> 10854
ile oc 10854
DISE ASE</w> 10854
g luteal</w> 10853
multi loc 10853
pic om 10853
Lipo protein</w> 10853
cerebr um</w> 10853
Bev acizumab</w> 10852
ung in</w> 10851
PRI SMA</w> 10851
syn tactic</w> 10850
facilit ator</w> 10850
Algorith m</w> 10850
AA V9</w> 10849
N ip 10848
op position</w> 10848
norm oglyc 10848
RA D1</w> 10847
denti fr 10847
Su ite</w> 10847
catechol aminergic</w> 10847
MO D</w> 10846
GP T</w> 10844
Or bital</w> 10842
Shim adzu</w> 10841
or f1</w> 10840
oly tics</w> 10840
bul bs</w> 10840
swim mers</w> 10840
insti lled</w> 10840
NZ B</w> 10840
le igh</w> 10839
transf ect</w> 10838
go ides</w> 10837
un solved</w> 10836
liz ards</w> 10836
com man 10834
organo phosphorus</w> 10834
Jane iro</w> 10834
H id 10833
N op 10833
lid ay</w> 10833
N ad 10832
c tt 10831
Coun ting</w> 10831
transloc on</w> 10831
cosme tics</w> 10831
visu alised</w> 10830
Figure 3C</w> 10829
PH AR 10829
tes unate</w> 10828
Lo oking</w> 10828
un vaccinated</w> 10827
xyl itol</w> 10827
Organis ation</w> 10827
leptomening eal</w> 10827
d warf 10826
radic ally</w> 10826
di peptides</w> 10825
In nate</w> 10825
Hem e</w> 10825
under pin</w> 10824
over activity</w> 10824
eth nic 10824
2 E1</w> 10823
v ine 10823
en er</w> 10823
AP Ps 10823
m 6</w> 10822
Re elin</w> 10822
Transf ections</w> 10822
inter dig 10821
bici stronic</w> 10820
Di e 10819
oll is</w> 10819
bi otype</w> 10818
valid ates</w> 10818
Em ph 10818
guil t</w> 10818
DO CA</w> 10817
Fox O3a</w> 10817
- deficient</w> 10816
Over view</w> 10816
Nec ro 10816
oc aly 10815
trans gender</w> 10815
Ex pec 10813
NO X4</w> 10813
controll ers</w> 10813
og els</w> 10812
ne opterin</w> 10812
mono disperse</w> 10812
Yun nan</w> 10812
relati vistic</w> 10811
Ne ut 10810
kil ogram</w> 10810
aesti vum</w> 10810
Y ok 10808
l ends</w> 10807
In tran 10807
Sh and 10807
I E2</w> 10806
knowle dge 10806
r ms 10805
Gen otypes</w> 10805
SN HL</w> 10804
neo intima</w> 10804
spondyl olisthesis</w> 10804
S ae 10803
yl methyl</w> 10803
har dt</w> 10803
purch asing</w> 10803
succin yl</w> 10803
Doc tors</w> 10803
3 alpha</w> 10802
del usions</w> 10802
F ace</w> 10801
tra verse</w> 10801
3x Tg</w> 10801
inter membrane</w> 10800
non reducing</w> 10799
Pey er</w> 10798
front line</w> 10798
st unting</w> 10797
microscop ical</w> 10796
f ast 10793
M X 10792
SM V</w> 10792
nitros ative</w> 10790
W g</w> 10789
ST P</w> 10789
GO LD</w> 10789
under neath</w> 10788
Zhe jiang</w> 10788
op enic</w> 10787
is ep 10786
tap er</w> 10786
homos erine</w> 10786
N B1</w> 10785
L MS</w> 10785
tw isting</w> 10785
bet amethasone</w> 10785
Dv l</w> 10785
ethanesulf onic</w> 10785
ti sed</w> 10784
Nox 2</w> 10784
decon vol 10784
ych nine</w> 10784
nar ingenin</w> 10782
RA C1</w> 10781
pi ride</w> 10781
stom ia</w> 10781
I l1</w> 10780
mo esin</w> 10780
pa yer</w> 10780
MT A1</w> 10779
snap shot</w> 10779
et t</w> 10778
rot um</w> 10778
m ata</w> 10777
e q 10775
Ac upuncture</w> 10775
Co ron 10775
nutri tive</w> 10775
radio chemical</w> 10775
f ins</w> 10774
achi al</w> 10774
experi ential</w> 10774
s. e. 10774
WB RT</w> 10774
labyrin th 10774
Ag ed</w> 10773
pET 1</w> 10772
myx oid</w> 10772
fasci atus</w> 10772
hal is</w> 10771
Ep i</w> 10771
centi le</w> 10771
For tunately</w> 10770
FI P2</w> 10770
D AMPs</w> 10769
arrestin 2</w> 10769
M ayer</w> 10767
S imp 10767
CA LR</w> 10767
Micro scope</w> 10767
bio control</w> 10766
se ous</w> 10765
ED SS</w> 10765
BR C</w> 10764
I α</w> 10763
re entrant</w> 10763
nephro logy</w> 10763
hyp no 10759
r ase</w> 10758
ir i</w> 10758
Paradox ically</w> 10758
Tn I</w> 10757
Hybri doma</w> 10757
the tically</w> 10756
t RN 10755
L SR</w> 10755
and ers</w> 10753
py ogenic</w> 10753
CK 1 10753
tauro cholate</w> 10753
prote oliposomes</w> 10752
H all 10751
ys tic</w> 10751
nano fiber</w> 10751
p STAT3</w> 10750
ap ne 10750
AC I</w> 10749
pyri dines</w> 10748
Haem at 10748
edul lin</w> 10748
race tam</w> 10748
BD D</w> 10747
campe stris</w> 10747
Immun ity</w> 10746
CI MT</w> 10746
En teri 10745
b 7</w> 10744
ra xia</w> 10744
L CAT</w> 10743
PI 4K 10743
understand ings</w> 10741
re bleeding</w> 10740
de phosphorylates</w> 10740
del tamethrin</w> 10740
T PE</w> 10739
accent uated</w> 10739
Mad 1</w> 10738
puzz le</w> 10737
Viol ence</w> 10737
St atins</w> 10736
Sp O2</w> 10736
k iss</w> 10735
dec ellul 10735
carbox ylation</w> 10734
Gli 3</w> 10733
Altern aria</w> 10732
M CT1</w> 10731
K an</w> 10731
Radi al</w> 10731
bombar dment</w> 10731
Optim izing</w> 10730
let t</w> 10729
glyox ylate</w> 10729
al arming</w> 10728
gluc okinase</w> 10728
R s 10727
Sg s1</w> 10727
ROR γt</w> 10727
seni ors</w> 10727
AB CC1</w> 10726
pre concentration</w> 10726
ith ec 10726
3 Y</w> 10725
Al d 10725
rat oxin</w> 10725
promiscu ity</w> 10725
dab rafenib</w> 10725
telo phase</w> 10725
enro lling</w> 10724
acy stin</w> 10724
- 5-</w> 10723
AM PH</w> 10723
angi opoietin</w> 10723
T AP 10722
aden os 10722
som ites</w> 10722
L RH</w> 10720
super capac 10720
MU C5 10720
H X</w> 10718
R tt1</w> 10718
mT BI</w> 10718
Mur phy</w> 10717
F ound</w> 10716
shar k</w> 10716
co di 10714
parasi tized</w> 10714
transferen ce</w> 10714
Otta wa</w> 10714
Re activity</w> 10713
el and</w> 10712
no .</w> 10712
Co ast</w> 10712
rec oll 10711
tricho statin</w> 10711
uroth elium</w> 10711
Sci enti 10710
lec tures</w> 10709
tail ing</w> 10707
p F 10706
PM D</w> 10706
I no 10705
immuno diffusion</w> 10705
Stro mal</w> 10705
Brug ada</w> 10705
d us</w> 10703
V FA</w> 10703
Coord ination</w> 10703
constric ted</w> 10702
sackie virus</w> 10702
E AR</w> 10700
O ST</w> 10700
psych ogenic</w> 10700
SO ME</w> 10700
after load</w> 10700
R ps 10699
B z 10698
Δ 9</w> 10697
Com plex 10696
Knock out</w> 10696
hyper thermic</w> 10695
sub ver 10694
glob ule</w> 10694
hyper polar 10693
Hom ologous</w> 10692
Mito Tracker</w> 10692
L uk 10690
ag awa</w> 10690
ev in</w> 10690
chemo resistant</w> 10690
an ac 10689
novi al</w> 10688
RI T</w> 10687
proxim ate</w> 10687
ble eds</w> 10687
photo acoustic</w> 10687
Acet yl</w> 10687
T SG</w> 10684
Con temporary</w> 10684
P2 R 10684
vasodil ators</w> 10684
perioste um</w> 10684
sph er 10683
cruc ially</w> 10682
H3K 7</w> 10682
pancreatico duodenectomy</w> 10682
5 mm</w> 10681
al izumab</w> 10681
vac ancies</w> 10681
commensur ate</w> 10681
ren ovascular</w> 10680
carb azole</w> 10680
PARP i</w> 10679
ho e</w> 10678
Hym enoptera</w> 10678
inten tionally</w> 10677
amni os</w> 10677
ignor e</w> 10676
nitrilotri acetic</w> 10676
t apos 10675
repl isome</w> 10675
methyl mercury</w> 10674
colon izing</w> 10674
mor tal</w> 10673
chlor ination</w> 10673
Tub ercul 10673
. 7A</w> 10672
PT G 10671
Mo ving</w> 10671
abor tive</w> 10671
Pneum onia</w> 10671
hur dles</w> 10671
f issu 10670
re introduction</w> 10670
af ety</w> 10670
d c 10669
RE G 10668
Del hi</w> 10668
Mad in</w> 10667
C ub 10665
gli adin</w> 10665
Mon ol 10665
Erro rs</w> 10665
B uch 10664
pap ules</w> 10664
m RS</w> 10663
fe es</w> 10663
intern alize</w> 10663
bu vir</w> 10662
modul i</w> 10662
Mo Ab</w> 10662
teno id</w> 10662
ethylenedi amine</w> 10662
d m</w> 10661
retro gradely</w> 10661
Der m 10661
s APP 10660
O vid</w> 10659
ste eper</w> 10659
IP 6</w> 10659
leuc yl</w> 10659
Pan IN</w> 10659
awa it</w> 10658
Por t 10657
V -</w> 10655
con specific</w> 10655
sh i</w> 10655
Ca esarean</w> 10655
ul na</w> 10654
Let t</w> 10654
collim ator</w> 10654
0 h</w> 10653
op ent 10653
DE SCRI 10653
cc cDNA</w> 10653
A2 AR</w> 10652
Z 4</w> 10651
az obenzene</w> 10651
ES O</w> 10651
deform able</w> 10651
Smar t</w> 10651
to ad</w> 10649
Lin 2</w> 10649
P SV</w> 10648
p X 10648
DR B</w> 10648
Lymph ocytes</w> 10648
W HAT</w> 10647
par af 10647
Ha ve</w> 10647
Sim ulated</w> 10646
s PLA2</w> 10645
F CCP</w> 10645
ch s</w> 10645
Ad o</w> 10644
MAP s</w> 10644
strati fying</w> 10644
Aff iliated</w> 10644
u y 10643
imp ending</w> 10643
SV C</w> 10643
Nov a</w> 10643
poli tics</w> 10643
CN 0</w> 10642
lip ocalin</w> 10641
SL 2</w> 10641
spar sely</w> 10641
sphinctero tomy</w> 10641
cas pofungin</w> 10640
tr 1</w> 10639
muscul aris</w> 10639
os pores</w> 10638
ne at</w> 10638
LU MO</w> 10637
I EF</w> 10636
j umps</w> 10636
treat ment 10636
ATP 7B</w> 10636
squ id</w> 10635
At m</w> 10634
qual ification</w> 10633
Sk ills</w> 10633
l ang 10632
S a</w> 10632
Co Cl2</w> 10632
star k</w> 10632
sumo ylated</w> 10632
euglyc emic</w> 10632
Fraction ation</w> 10632
H3K4 me1</w> 10631
Expan sion</w> 10631
Fcε RI</w> 10631
V isi 10630
app ended</w> 10630
RP 4</w> 10630
P ort</w> 10629
nic ked</w> 10629
L OF</w> 10628
osseo integration</w> 10628
P SM 10627
KL F5</w> 10627
ter re 10626
scho oling</w> 10626
dn f</w> 10626
Christi an</w> 10626
cur vil 10625
GT N</w> 10625
add le</w> 10625
1 B2</w> 10624
is ocyanate</w> 10624
tool box</w> 10624
cry oglob 10623
catabol ite</w> 10623
LIN 2</w> 10622
Formal in</w> 10622
R Cs</w> 10621
ble bs</w> 10621
GL T</w> 10621
- methyl 10620
B P4</w> 10620
ten tial</w> 10620
intellig ibility</w> 10620
M IL</w> 10619
im in 10619
clavul anic</w> 10619
m B</w> 10618
mamm ograms</w> 10618
oligodeoxy nucleotides</w> 10618
O O</w> 10617
Ab und 10617
ip id</w> 10616
monos accharide</w> 10616
un restrained</w> 10614
off line</w> 10614
br acket</w> 10613
reli eves</w> 10613
Ris ks</w> 10613
St x 10612
convinc ingly</w> 10611
MP E</w> 10610
tit les</w> 10610
sm 1</w> 10607
til ting</w> 10607
an ual</w> 10606
Di stance</w> 10606
cycl obut 10606
c. 8</w> 10606
l ins</w> 10605
monom ethyl</w> 10605
om ectomy</w> 10604
Li ang</w> 10604
ML D</w> 10604
ocycl o 10604
ed .</w> 10603
renew ing</w> 10603
aler ts</w> 10603
E q 10602
Na OCl</w> 10602
Nor mally</w> 10602
I tems</w> 10601
un regulated</w> 10601
TL R5</w> 10601
Hol liday</w> 10601
perikary a</w> 10601
or nam 10600
SC ORE</w> 10600
acr yl</w> 10600
Bi opsies</w> 10599
ap ride</w> 10598
fluoro meter</w> 10598
nim odipine</w> 10598
PC r</w> 10597
Nan os 10597
Tot ally</w> 10597
S AG</w> 10595
mis artan</w> 10595
gon orrhea</w> 10595
Intr al 10594
FI B</w> 10593
Aden ocarcinoma</w> 10593
IgG 2b</w> 10593
patern ity</w> 10593
Ins P3</w> 10592
PtdIns P</w> 10592
f ect</w> 10591
MUC5 AC</w> 10591
tel es 10590
H aw 10588
si g</w> 10586
pic k</w> 10586
BLO OD</w> 10586
S hel 10585
N BC 10583
dec ond 10583
PG J2</w> 10583
SY N</w> 10583
- bound</w> 10582
Cr k 10582
PV D</w> 10582
Tr x1</w> 10581
sialy l</w> 10581
An notation</w> 10580
ex ti 10579
I DO1</w> 10578
sho e</w> 10578
epi physis</w> 10578
cep acia</w> 10578
s B</w> 10577
fav ouring</w> 10577
re ich</w> 10576
ed y</w> 10576
osteo arthritic</w> 10576
n ard</w> 10575
4 E1</w> 10574
em itter</w> 10574
Ta Ka 10574
D ynam 10572
Trepon ema</w> 10571
Am ph 10570
plex y</w> 10570
meth em 10569
acet onide</w> 10569
B res 10567
B L1</w> 10567
G b 10567
VR C0</w> 10567
arc tica</w> 10566
PPAR β</w> 10566
ometh ionine</w> 10566
stereotax ic</w> 10566
9 A1</w> 10565
escap ed</w> 10565
Rev 1</w> 10565
a ou</w> 10564
Ex AC</w> 10564
g f</w> 10563
out i</w> 10563
Dis charge</w> 10563
ton icity</w> 10562
DD B2</w> 10562
alphab eta</w> 10562
6 Y</w> 10561
V AS 10561
bal ls</w> 10561
glycosyl transferases</w> 10561
APOBEC 3G</w> 10561
astro gliosis</w> 10560
Xi ao</w> 10560
credi bility</w> 10560
estu arine</w> 10560
phosphorib osyltransferase</w> 10560
o 3</w> 10559
P H1</w> 10559
FO G</w> 10559
ten fold</w> 10558
amino acylation</w> 10558
general ist</w> 10558
C ephal 10557
Se q2</w> 10557
read through</w> 10557
sit tac 10557
clear ances</w> 10557
pell ucida</w> 10557
AT TT 10556
benz idine</w> 10556
osup pressive</w> 10556
B C1</w> 10555
Acet ylation</w> 10555
succe ed</w> 10555
compartment alized</w> 10555
straw berry</w> 10555
en atide</w> 10554
IL 1 10554
nocic eptors</w> 10554
tendin ous</w> 10554
- catalyzed</w> 10553
Z FN</w> 10553
os moly 10553
Immun oreactive</w> 10553
pain ting</w> 10553
auto antigens</w> 10552
Tou rette</w> 10552
em ide</w> 10551
GC ase</w> 10551
fus ogenic</w> 10551
deciph ering</w> 10551
Hirsch sprung</w> 10551
c oughing</w> 10550
I SRE</w> 10550
anti porter</w> 10550
Il lu 10550
I MD</w> 10548
P kn 10548
the l</w> 10548
HE MA</w> 10548
Shan non</w> 10548
aconit ase</w> 10548
ard i</w> 10547
SN 1</w> 10546
nas ogastric</w> 10546
tig ecycline</w> 10546
obar ic</w> 10546
liz ard</w> 10546
D esc 10545
hyper cholesterola 10545
F RC</w> 10543
al cium</w> 10543
Du od 10543
KIF 1</w> 10543
auto inhibitory</w> 10542
hex a</w> 10542
Inspec tion</w> 10542
N ICE</w> 10541
encour ages</w> 10541
Peri od</w> 10541
O AS</w> 10539
uro logists</w> 10539
but amide</w> 10539
Gastro entero 10539
fos tering</w> 10538
CK 2α</w> 10538
k awa</w> 10537
Detec ting</w> 10537
s tin 10536
Hy alu 10536
Kenne dy</w> 10536
F N1</w> 10535
Combin ations</w> 10535
PN AS</w> 10535
correl ational</w> 10534
Di stress</w> 10534
op os 10533
ep ine</w> 10532
sa 1</w> 10532
e el</w> 10531
Δ Ct</w> 10531
phosphor amid 10531
gam ete</w> 10531
paraly tic</w> 10531
P air 10530
2 mM</w> 10529
M ö 10528
PF T</w> 10528
E max</w> 10527
est a</w> 10527
methyl xanthine</w> 10527
TaKa Ra</w> 10527
col oured</w> 10526
Hy bond</w> 10526
D EX 10524
con formed</w> 10524
od om 10524
Inter val</w> 10524
pyri d 10524
Py ri 10524
hemi plegia</w> 10524
sacchar in</w> 10524
Van der 10523
manoeu v 10523
MC 3</w> 10522
ul er</w> 10521
Li ber 10521
PO M</w> 10521
a 6</w> 10520
ow itz</w> 10520
Aut opsy</w> 10520
Agricul tural</w> 10520
al ez</w> 10519
pa ying</w> 10519
neut rons</w> 10519
T ERI 10518
influ ent</w> 10518
H TA</w> 10517
G EM 10517
2 L1</w> 10516
J ena</w> 10516
T SB</w> 10516
puzz ling</w> 10516
or ium</w> 10515
CO PI</w> 10515
Ram achandran</w> 10514
catarr halis</w> 10514
. B</w> 10511
Jo b</w> 10511
P MP</w> 10510
ger anyl</w> 10510
O MM</w> 10509
Ide ally</w> 10509
em itters</w> 10508
DL PFC</w> 10508
ED X</w> 10508
question ing</w> 10507
at ted</w> 10505
AT V</w> 10504
Cen tro 10504
prud ent</w> 10504
IC Cs</w> 10503
stut tering</w> 10503
my algia</w> 10502
popl ar</w> 10502
sampl er</w> 10501
my th</w> 10501
e we</w> 10500
aor to 10500
AM G</w> 10499
ph orin</w> 10497
ich rom 10497
intermit tently</w> 10497
a iding</w> 10496
PA X6</w> 10496
SE MA 10496
A bi 10495
z a 10495
AB 0</w> 10495
ER G1</w> 10494
dino flagell 10494
AR Ds</w> 10493
SH G</w> 10492
Sa to</w> 10492
Jiang su</w> 10492
random isation</w> 10491
olys osomal</w> 10491
M st1</w> 10489
OBJ ECT</w> 10489
NS P</w> 10489
BAR D1</w> 10489
am astigotes</w> 10488
Ad . 10488
pro t</w> 10487
Par ticles</w> 10487
derang ements</w> 10487
S olar</w> 10486
di gn 10485
acet amido</w> 10485
Pap an 10485
Ti an 10484
sensiti zers</w> 10483
N om 10481
w d</w> 10481
G OS</w> 10481
ir regularities</w> 10481
epith elioma</w> 10481
ide tes</w> 10481
Consider ation</w> 10481
ectin omycin</w> 10481
U F 10480
fl utamide</w> 10479
chem ists</w> 10478
dor f</w> 10478
Accel erated</w> 10478
USP 4</w> 10477
vulg are</w> 10477
holograph ic</w> 10477
D x</w> 10476
arg u 10476
M NV</w> 10475
p LK 10474
os umab</w> 10474
vol ar</w> 10474
vo ices</w> 10474
Lys 6</w> 10474
syncy tia</w> 10474
anaero bes</w> 10474
Gonz alez</w> 10474
u is</w> 10473
peric entro 10471
治 疗 10470
Ch ung</w> 10470
til us</w> 10470
PL US</w> 10470
neum oniae</w> 10469
jux tapos 10469
st all</w> 10468
spro uts</w> 10468
Cen sus</w> 10468
G AU 10467
co aching</w> 10467
Wer ner</w> 10467
T Rs</w> 10466
re perfused</w> 10466
with standing</w> 10465
retin in</w> 10465
Chromat ographic</w> 10465
DD X3</w> 10463
N C1</w> 10462
S ir</w> 10462
aero bically</w> 10461
Fri zzled</w> 10460
B PI</w> 10459
Vis cer 10459
xanth in</w> 10458
sialy l 10458
Cytos k 10458
Di arr 10457
L DS</w> 10456
Z eb 10456
sub cultured</w> 10456
s. 3</w> 10456
v ora</w> 10455
rap tor</w> 10455
h unting</w> 10452
D AS2</w> 10452
FO P</w> 10452
onit oring</w> 10451
inj ector</w> 10450
De ad</w> 10449
- CG 10448
tran she 10448
mil dew</w> 10448
PER 2</w> 10448
C atalyzed</w> 10447
nit rile</w> 10447
retro transposition</w> 10446
mox azole</w> 10446
tachy arrhythmias</w> 10445
aggra vation</w> 10445
Sam 6</w> 10445
OR F6</w> 10444
voc alizations</w> 10444
anesthesi ologists</w> 10444
MHC II</w> 10444
compul sory</w> 10443
P resident</w> 10442
F CR</w> 10442
Ch r</w> 10442
hem orrho 10442
B TB 10440
aph ane</w> 10440
detox ifying</w> 10440
G t</w> 10439
i sis</w> 10437
b ol</w> 10437
Poly Phen</w> 10437
D IF 10436
anaero bically</w> 10436
R W</w> 10435
f y 10435
meningo encephalitis</w> 10435
color less</w> 10434
reg ation</w> 10433
AM M</w> 10433
cloac ae</w> 10433
v ates</w> 10432
neuro secretory</w> 10432
kil obases</w> 10432
N REM</w> 10431
br 1</w> 10431
inver tase</w> 10431
os buvir</w> 10430
rumin ant</w> 10430
re appraisal</w> 10429
micro beads</w> 10429
Te trac 10429
exqu isite</w> 10429
k ii</w> 10428
β 8</w> 10428
inve stments</w> 10428
R pd 10427
K AT 10427
tic ide</w> 10427
CP Y</w> 10426
Sind bis</w> 10426
s ad 10425
c amph 10425
ter ahertz</w> 10425
ul ence</w> 10424
hyper reactivity</w> 10423
leuk ins</w> 10423
DM C</w> 10422
Phase olus</w> 10422
N ish 10421
ron t 10420
phospho kinase</w> 10420
ram us</w> 10419
LTC 4</w> 10419
U HPLC</w> 10418
sac s</w> 10418
stere oselectivity</w> 10417
mechano transduction</w> 10417
incarcer ated</w> 10417
M xA</w> 10416
chiro practic</w> 10416
K r</w> 10415
phyl ogenies</w> 10415
I onic</w> 10414
re implantation</w> 10414
anti emetic</w> 10414
ico planin</w> 10414
We igh 10413
paragangli oma</w> 10413
ophthal mos</w> 10412
C in 10411
ep iz 10411
str ug 10411
ar resting</w> 10410
me yer</w> 10410
PH Q</w> 10410
SIVmac 2</w> 10410
retino in</w> 10409
F anc 10408
Ob viously</w> 10408
B AG</w> 10407
chemo -</w> 10407
We ak</w> 10407
Nocardi a</w> 10407
SD HB</w> 10405
Ver te 10405
summ aries</w> 10405
fol ates</w> 10404
CP K</w> 10404
responsi vity</w> 10404
RR E</w> 10404
ET V6</w> 10403
inform ant</w> 10403
hyper stimulation</w> 10402
is ocyan 10401
G DF</w> 10400
leg acy</w> 10400
cy s</w> 10399
genome . 10399
B U</w> 10398
E c</w> 10398
sp 5</w> 10398
micro vesicles</w> 10398
electro spinning</w> 10398
Euc li 10398
S onic</w> 10397
cl en 10397
He in 10397
SR SF1</w> 10397
N AP 10396
RA 1</w> 10396
re model</w> 10395
thromb ocytosis</w> 10395
pector alis</w> 10394
G BM 10393
tem plating</w> 10392
Th re 10391
Un ified</w> 10391
Chit osan</w> 10391
AC TIV 10390
Tru e</w> 10390
Pri m 10389
tool kit</w> 10389
tu re 10388
NU MB 10388
illo id</w> 10388
di aminobenzidine</w> 10387
HO Cl</w> 10387
troph oblasts</w> 10387
Trans location</w> 10387
eIF 4F</w> 10387
ul arity</w> 10386
tw enti 10385
Co y</w> 10385
hepta d</w> 10385
W all</w> 10384
az osin</w> 10384
ME s</w> 10384
TP X2</w> 10384
E GL</w> 10383
orient ational</w> 10383
Tβ RII</w> 10383
dystro glycan</w> 10383
Francis ella</w> 10381
B og 10380
ful l 10380
T am</w> 10378
f omycin</w> 10378
fl ushed</w> 10378
mac ros 10378
foc ally</w> 10377
don ations</w> 10377
y x</w> 10376
Concer ns</w> 10376
fi res</w> 10375
pepti dic</w> 10375
9 H1</w> 10374
- bis</w> 10374
7 alpha</w> 10374
dec oupling</w> 10374
Cy A</w> 10374
Anti sense</w> 10374
mononucle osis</w> 10374
P ec 10373
Δ G 10373
og astro 10373
daid zein</w> 10373
unc ulin</w> 10372
EC H</w> 10372
for tu 10371
cock tails</w> 10371
quic ker</w> 10371
Bor tezomib</w> 10371
D et 10370
arabin ofuran 10369
ST 4</w> 10368
F SC</w> 10367
an ases</w> 10367
GSE 5</w> 10367
1 W</w> 10364
A ci 10364
S M1</w> 10364
gluc uronic</w> 10364
preg abalin</w> 10364
echinoc occosis</w> 10364
F RT 10363
p ushing</w> 10363
CU G</w> 10363
ST K1</w> 10362
nucle oid</w> 10362
CA GCT 10362
carboxy fluorescein</w> 10362
leu 2</w> 10362
etom idate</w> 10362
ward ship</w> 10362
PW M</w> 10360
anti diuretic</w> 10359
MI G</w> 10359
rou ting</w> 10359
pati c</w> 10358
ha user</w> 10358
al p 10357
RE BP</w> 10357
biop terin</w> 10357
S 2 10356
nor floxacin</w> 10356
varic osities</w> 10356
resor ptive</w> 10355
N NK</w> 10354
M ond 10354
Electro my 10354
H SE</w> 10352
Q PCR</w> 10352
Man di 10350
deoxy cytidine</w> 10350
Y ki</w> 10349
he t</w> 10349
blin dly</w> 10349
lymph otropic</w> 10348
h MLH1</w> 10347
Com plementation</w> 10347
ty r 10347
TU RP</w> 10347
uit arism</w> 10347
s anc 10346
Inter ventional</w> 10346
Pul sed</w> 10346
NEDD 4</w> 10346
phenyl acetic</w> 10345
Cardi o 10345
Stro n 10345
sp inocerebellar</w> 10344
proxim ally</w> 10344
radicul opathy</w> 10344
M FA</w> 10343
qu ist</w> 10343
non infectious</w> 10343
DYRK 1A</w> 10343
PT OR</w> 10342
CV ID</w> 10342
Plac ebo</w> 10342
hyperlip idemic</w> 10342
re pop 10339
V UR</w> 10338
rp s 10338
o viral</w> 10337
P st</w> 10337
Supernat ant</w> 10336
Fresh ly</w> 10336
Pakist ani</w> 10336
mis alignment</w> 10335
MT R</w> 10335
F ö 10334
D HC</w> 10334
ac ore</w> 10334
EC V</w> 10334
est e</w> 10333
me th</w> 10332
Pr k 10332
oligodendro glial</w> 10332
pentam eric</w> 10332
S HE 10331
Y T</w> 10330
clus terin</w> 10330
Lan gen 10330
p or</w> 10329
pro position</w> 10328
bi opharmac 10327
ecti onal</w> 10326
MS G</w> 10326
blastom eres</w> 10326
AF 3</w> 10325
u plo 10324
ot re 10324
Ex osomes</w> 10324
dri p</w> 10324
W NK1</w> 10320
GT AC 10320
Rhod ococcus</w> 10320
aph ids</w> 10319
tough ness</w> 10319
2 HG</w> 10318
de pressor</w> 10318
TET 1</w> 10317
b ay</w> 10316
P lot</w> 10316
Cd kn 10316
tensi bility</w> 10316
Tra ff 10315
policy makers</w> 10315
W IN</w> 10314
dor sum</w> 10314
camp us</w> 10314
2 Fe</w> 10313
ac onazole</w> 10312
COS MIC</w> 10312
Dis per 10310
envis aged</w> 10309
Y O 10308
PD TC</w> 10308
munici pality</w> 10308
Fore ign</w> 10308
cor als</w> 10307
flex ors</w> 10307
C3 H1</w> 10307
e otaxin</w> 10306
in homogeneity</w> 10306
config ured</w> 10306
North west</w> 10306
sk 1</w> 10305
b. i.d.</w> 10305
si eve</w> 10304
digesti ble</w> 10304
N TCP</w> 10303
E H 10303
uc ency</w> 10303
pre pro 10303
ent als</w> 10303
mi st 10303
agrel or</w> 10303
D D1</w> 10302
dro xy 10302
smooth ly</w> 10300
CL SM</w> 10299
dorsi flexion</w> 10299
An aerobic</w> 10298
HI M</w> 10298
ol ac 10297
ad i</w> 10297
non treated</w> 10297
acqu ires</w> 10297
ER O</w> 10296
weigh t 10296
O CI</w> 10295
decre ments</w> 10295
DNMT 3B</w> 10295
fil arial</w> 10294
scrat ching</w> 10294
Repeti tive</w> 10294
- CCT 10292
M sn 10291
ere rs</w> 10291
bas ophilic</w> 10291
E y 10290
IV T</w> 10290
merg e</w> 10290
MCA K</w> 10290
3 q</w> 10289
be having</w> 10289
Croati a</w> 10289
E SWL</w> 10288
un ruptured</w> 10288
1 i</w> 10287
meta plastic</w> 10287
cholec alciferol</w> 10287
IO Ls</w> 10287
multiplic ative</w> 10286
f sky</w> 10284
G reg 10284
dd PCR</w> 10284
Mc m1</w> 10283
G -</w> 10282
Ray leigh</w> 10282
L amin</w> 10281
en in</w> 10281
M om 10280
intrav ital</w> 10280
Confir mation</w> 10280
R pt 10279
prote gerin</w> 10279
germ plasm</w> 10279
ycl o 10277
e au</w> 10275
EXTRA CTION</w> 10275
trac tor</w> 10274
CR A</w> 10274
Gl omerular</w> 10274
tri gonal</w> 10273
work station</w> 10273
proca ine</w> 10273
S tric 10272
harb oured</w> 10272
lith otomy</w> 10272
sc i.org</w> 10271
Atg 3</w> 10271
Frag ments</w> 10271
chrom ate</w> 10270
Tr i</w> 10270
ration alize</w> 10270
trans activate</w> 10269
ethnic ities</w> 10269
semis yn 10267
inter relationship</w> 10266
ed in</w> 10266
non adherent</w> 10266
amend ment</w> 10266
Ira q</w> 10266
B b</w> 10265
Inter face</w> 10265
Gas 6</w> 10265
Pen insula</w> 10264
1 th</w> 10263
w ob 10263
prost anoid</w> 10263
absc ission</w> 10263
alc ogen 10263
di pping</w> 10262
af ine</w> 10262
GC K</w> 10262
pren ylated</w> 10262
Dif co</w> 10262
stath min</w> 10262
angi itis</w> 10260
Doc ument</w> 10260
regi oselective</w> 10259
ore x 10259
py razin 10259
GAT K</w> 10259
Reproduc ibility</w> 10259
St atic</w> 10258
A ES</w> 10257
av itary</w> 10257
under water</w> 10257
PT U</w> 10257
pati ve</w> 10256
fl otation</w> 10256
Ash ken 10256
ent us</w> 10255
CA SES</w> 10254
Har lan</w> 10254
Intra ocular</w> 10254
educ ating</w> 10253
0 d</w> 10251
impe ding</w> 10251
echinoc and 10251
A bility</w> 10250
N X 10250
L ens</w> 10250
enric h 10250
trypsin ogen</w> 10250
U W</w> 10249
Fe O</w> 10249
Stabil ization</w> 10249
3 ζ</w> 10248
L GE</w> 10248
f ug 10248
as sion</w> 10248
Cy ber 10248
floc cul 10248
Y a 10247
mo terol</w> 10247
neuro epithelial</w> 10247
con otoxin</w> 10246
gen cies</w> 10246
li statin</w> 10245
ocl avicular</w> 10245
Sp in 10245
Rever sal</w> 10245
agal actiae</w> 10245
construc tions</w> 10244
K OR</w> 10243
nor theast</w> 10243
Nur r1</w> 10243
u ble</w> 10242
hybridi zations</w> 10242
V OR</w> 10241
Ac ta</w> 10241
Nic ol 10241
jel ly</w> 10241
theat re</w> 10241
f B</w> 10240
ac al 10240
IRA K1</w> 10240
eicos anoid</w> 10240
ph ere</w> 10239
Caregi vers</w> 10239
glyc ogen 10237
AN 1</w> 10237
His 3</w> 10237
Rodri guez</w> 10237
g om 10236
Rh 1</w> 10235
Fil m</w> 10235
breast fed</w> 10235
skin ned</w> 10235
n m 10234
St one</w> 10234
Bri lliant</w> 10234
inflo rescence</w> 10234
Simp son</w> 10234
pres urgical</w> 10233
ven ography</w> 10233
benzo quinone</w> 10233
mill ime 10233
www.j neuro 10233
resequ encing</w> 10233
C G1</w> 10232
CA RE</w> 10232
hepati ca</w> 10232
coc cidi 10232
cryos ections</w> 10232
macro cycle</w> 10231
PH F</w> 10231
S1 P1</w> 10231
k Vp</w> 10230
F err 10230
capac itation</w> 10228
hydr amnios</w> 10228
d w</w> 10227
del l</w> 10227
Malay sian</w> 10227
unve iled</w> 10227
ph ine</w> 10225
Ech inococcus</w> 10224
PLC γ</w> 10223
toxic ant</w> 10222
B Y 10221
de es</w> 10220
s. org</w> 10220
comb ating</w> 10220
www.jneuro sci.org</w> 10219
Olig 2</w> 10218
4A 4A</w> 10218
amo e 10217
bios ensing</w> 10217
MP G</w> 10216
Brow ser</w> 10216
fun goides</w> 10216
di amide</w> 10215
Mar ker</w> 10215
tetrahydro cannabinol</w> 10215
HC C8</w> 10213
Ca ul 10212
iqu antel</w> 10212
Re qui 10211
pharyng itis</w> 10210
I odine</w> 10209
F AT 10209
SU MO2</w> 10209
construc tive</w> 10209
Decre asing</w> 10209
k ering</w> 10208
trop ics</w> 10208
supran uclear</w> 10208
MW CNT</w> 10207
Sit u</w> 10207
Com orbidity</w> 10206
Sha ker</w> 10206
Hepat ocyte</w> 10205
N CoR</w> 10204
inter tw 10204
Wel ch</w> 10204
SO L</w> 10203
basoph il</w> 10203
short ness</w> 10202
i. t.</w> 10201
p tero 10200
ing es</w> 10200
dim entary</w> 10200
c 9</w> 10199
CCL 3</w> 10199
troubl es 10199
proges tins</w> 10199
mobil izing</w> 10198
Δ F</w> 10197
pl and</w> 10197
acti nic</w> 10197
Mit o</w> 10197
oc ins</w> 10196
ST O</w> 10196
vit ri 10196
Th orac 10195
dys biosis</w> 10195
resti tu 10195
chore a</w> 10195
G om 10194
inf ra</w> 10194
volun tarily</w> 10194
IP T</w> 10194
lin er</w> 10193
E rec 10191
sub structure</w> 10191
sk im 10191
ib ustion</w> 10191
Lu x 10190
Bactero idetes</w> 10190
IC P4</w> 10189
conver sations</w> 10189
hex achloro 10189
blin k</w> 10189
conve yed</w> 10189
bifur c 10189
L or 10188
panc ytopenia</w> 10188
Ther mus</w> 10188
github .com</w> 10187
ni p</w> 10186
cr ushed</w> 10185
Avail ability</w> 10185
V NS</w> 10184
E a</w> 10184
comp assion</w> 10184
sw apped</w> 10184
haem olysis</w> 10184
pe ts</w> 10183
H ad 10182
I so</w> 10182
forec ast</w> 10182
micro centrifuge</w> 10181
m dr1</w> 10180
inter strand</w> 10180
sour ced</w> 10179
theore m</w> 10179
E J</w> 10178
op tional</w> 10178
AL C</w> 10178
mathem atics</w> 10178
Bi ob 10177
kin dergar 10176
Fig. 7B</w> 10175
anec dotal</w> 10174
zyg omatic</w> 10173
nond en 10173
gra vid</w> 10172
OR Y</w> 10172
Socio economic</w> 10172
MT CT</w> 10171
1B 1B</w> 10171
h B 10170
w ick</w> 10170
by product</w> 10170
tm 1 10170
t ach 10169
ir regularity</w> 10169
SF C</w> 10169
un loaded</w> 10168
MY PT1</w> 10166
ip e</w> 10165
sphaero ides</w> 10165
o ural</w> 10164
k ova</w> 10164
CA M1</w> 10164
S 1E</w> 10163
chol anthrene</w> 10163
1 J</w> 10162
k ar</w> 10161
ogen ital</w> 10161
deubiquitin ating</w> 10161
Mo z 10160
s ob 10158
Pop ulus</w> 10158
ol ism</w> 10157
PP 5</w> 10157
se ab 10156
sis .</w> 10156
IR Dye</w> 10156
GC B</w> 10155
stern um</w> 10155
ME Ps</w> 10154
He x</w> 10154
MD L</w> 10154
T n1</w> 10153
D HPG</w> 10153
orb ide</w> 10152
Tric hin 10152
syncy ti 10152
k b1</w> 10151
le man</w> 10151
hyper pigmentation</w> 10151
Ac ro 10151
r umination</w> 10150
Le gal</w> 10150
brow ning</w> 10150
7 p1</w> 10149
oc entric</w> 10149
S RIF</w> 10148
O z 10148
PP M</w> 10148
aut och 10147
p D</w> 10146
Tri gg 10146
diffrac ted</w> 10146
LO AD</w> 10145
pre fers</w> 10144
medi ans</w> 10144
sen te 10144
laparo scopically</w> 10144
intra observer</w> 10143
photoc atalyst</w> 10143
an x</w> 10142
Pro l 10142
PI AS1</w> 10142
butter fly</w> 10142
L eth 10141
mel ittin</w> 10141
Fig. 5C</w> 10141
D B1</w> 10140
ER Ks</w> 10140
Ex o</w> 10140
transloc ator</w> 10140
Cher no 10140
Ar rh 10139
S pa 10138
histomorph ometric</w> 10138
Event ually</w> 10138
A Vs</w> 10137
ri tin</w> 10137
metic ulous</w> 10137
A vo 10136
fl ush</w> 10136
l unch</w> 10134
x ide</w> 10134
wh ite 10134
hyper homocysteinemia</w> 10133
H AD</w> 10132
tro ch 10132
conden sate</w> 10132
NHER F1</w> 10132
condi tion 10131
5 .</w> 10130
ad hesins</w> 10130
mista kes</w> 10130
Implem enting</w> 10130
D yst 10129
Y our</w> 10129
Q i</w> 10128
De b 10128
RP L1</w> 10127
G ag 10126
IR F5</w> 10126
gang ren 10126
P HF 10125
alb endazole</w> 10125
In g 10124
fam ous</w> 10124
HD X</w> 10124
SV D</w> 10124
DT G</w> 10123
SM ase</w> 10123
HP MC</w> 10123
Aut osomal</w> 10123
stereo isomers</w> 10123
tol erogenic</w> 10122
T max</w> 10121
Fc R</w> 10121
Behavi oural</w> 10121
cus p</w> 10121
d I</w> 10120
Figure 4C</w> 10120
Mon ocytes</w> 10120
Beg inning</w> 10120
re flu 10119
ca ul 10119
Papan icol 10119
D 4 10118
sw arming</w> 10118
Si C</w> 10118
tetr apeptide</w> 10118
Pt ch1</w> 10118
b. w.</w> 10118
k go</w> 10117
Wor l 10117
pertur bs</w> 10117
l atissimus</w> 10116
CN B</w> 10116
Trop ical</w> 10114
odom ains</w> 10114
T os 10112
P sp 10112
Me CN</w> 10112
C lock</w> 10111
R f 10111
U t 10111
pre eclamptic</w> 10111
ann e</w> 10111
Sign alling</w> 10111
H3 K1</w> 10111
M arc 10110
L ines</w> 10110
CA 5</w> 10109
CE F</w> 10109
P to</w> 10108
ΔΔ CT</w> 10108
E SE</w> 10107
trans thyretin</w> 10107
N CX 10106
length ened</w> 10106
paralog ous</w> 10106
legisl ative</w> 10106
co transport</w> 10104
P ST 10103
S HIV</w> 10103
cy bri 10103
sensi bility</w> 10103
St ages</w> 10103
stabil ised</w> 10103
STAT s</w> 10103
s at</w> 10102
N PA</w> 10102
sero groups</w> 10102
cass ava</w> 10102
Viscer al</w> 10102
m are</w> 10101
AC ID</w> 10101
polypo id</w> 10101
plic ated</w> 10100
don ia</w> 10100
CYP2 A6</w> 10100
ca erul 10099
electro chemically</w> 10099
C erebellar</w> 10098
r yl 10098
poly tetrafluoroethylene</w> 10098
phyl oge 10098
TE F</w> 10098
patho biology</w> 10098
arb azine</w> 10098
xeno transplantation</w> 10098
ag asc 10097
transfer able</w> 10097
Smo oth</w> 10097
NS W</w> 10096
Ner v 10096
ind ac</w> 10095
har ness</w> 10094
Lig ation</w> 10094
lou dness</w> 10093
SC CHN</w> 10092
call osal</w> 10092
In ner</w> 10091
cockro ach</w> 10091
Reg istered</w> 10089
immuno detection</w> 10088
arabin oside</w> 10088
T err 10087
T KO</w> 10087
gluc ans</w> 10087
AS FV</w> 10087
MC E</w> 10087
pred atory</w> 10086
sa pro 10085
u ity</w> 10084
M ate</w> 10084
A gs</w> 10083
sub fractions</w> 10083
N gu 10082
W heat</w> 10082
bo ars</w> 10082
SR H</w> 10082
WO MAC</w> 10082
Ink 4a</w> 10082
non pathogenic</w> 10081
ox amine</w> 10080
gr in</w> 10080
Cor tisol</w> 10080
CH L</w> 10079
opon tine</w> 10079
ut ter 10078
MI N</w> 10078
A br 10077
O ocytes</w> 10077
ton ometry</w> 10077
Transcrip tase</w> 10077
m 5</w> 10076
G MR</w> 10076
De utsch 10076
aggres sively</w> 10076
scap ularis</w> 10076
R g</w> 10075
Co ur 10075
divertic ula</w> 10075
sequ esters</w> 10074
mat s</w> 10074
Col onic</w> 10074
gastroentero logy</w> 10074
Marti nez</w> 10074
Fi ji</w> 10073
non adherence</w> 10073
AM D3</w> 10073
agg ing</w> 10073
neph rin</w> 10073
Amin o 10073
aux otrophic</w> 10073
conceptu alization</w> 10073
mid body</w> 10072
Hsp 6</w> 10072
t ship</w> 10071
cl ing</w> 10071
counter balanced</w> 10071
Transc ranial</w> 10071
promo tor</w> 10070
infer ring</w> 10070
GR K5</w> 10070
R ap</w> 10069
te ens</w> 10069
dec alc 10069
tur n 10069
osyn aptic</w> 10069
hu i</w> 10069
intral esional</w> 10069
M OB 10068
stri ke</w> 10068
Non specific</w> 10068
Oste oc 10068
slaugh tered</w> 10068
re strain</w> 10067
nan oporous</w> 10066
reassor tment</w> 10066
lip in</w> 10065
sente eism</w> 10065
β R</w> 10064
galac tu 10064
Pow er 10064
Fried man</w> 10064
magnoc ellular</w> 10064
DESCRI PTION</w> 10064
DI DS</w> 10063
m olds</w> 10061
f 1p</w> 10061
ob liqu 10061
acr y 10061
conval escent</w> 10060
keton uria</w> 10060
C 7 10059
bot tom 10059
dimethyl sulfoxide</w> 10058
Cl O</w> 10057
tun ica</w> 10057
N iss 10056
M x 10056
F ad 10056
intrac ortical</w> 10056
PH I</w> 10056
Var ic 10056
py rine</w> 10055
arc is 10055
week end</w> 10055
BM Ms</w> 10054
frac tory</w> 10054
Bul g 10054
os cle 10053
MIN ATION</w> 10053
JN K2</w> 10053
FE P</w> 10052
pent oxifylline</w> 10052
Ech in 10052
Enteri tidis</w> 10052
- nucleotidase</w> 10051
Te x</w> 10051
parap silosis</w> 10051
uccin ate</w> 10051
I l</w> 10050
be agle</w> 10050
direc tives</w> 10050
CF D</w> 10050
les bian</w> 10049
p T1</w> 10048
PC H</w> 10048
HP r</w> 10048
Kel ly</w> 10048
uro graphy</w> 10047
Q ing 10046
TB T</w> 10046
HF F</w> 10046
bic inchoninic</w> 10046
LU AD</w> 10046
VE M</w> 10045
Direc tive</w> 10045
B BR</w> 10044
An ab 10044
Mg O</w> 10044
vacuol ization</w> 10044
Hb F</w> 10043
tam eric</w> 10042
radio immunoprecipitation</w> 10041
CP G</w> 10041
Sph K1</w> 10041
Sor ting</w> 10041
L af 10040
summar izing</w> 10040
umb er 10039
cytotox in</w> 10039
revi sing</w> 10039
or id 10038
Clin ico</w> 10038
CO C</w> 10037
wee ds</w> 10036
parasit oid</w> 10036
exam ic</w> 10035
Im prove</w> 10035
hin olaryng 10035
anticonvuls ants</w> 10035
M NPV</w> 10034
hal ted</w> 10032
super paramagnetic</w> 10032
Ubiqu itination</w> 10032
A bra 10031
Bi omedic 10031
1 ra</w> 10030
comp acta</w> 10030
Chic ken</w> 10030
Pub med</w> 10029
est riol</w> 10028
pap averine</w> 10028
pyri dyl 10028
aero dynamic</w> 10028
PV T</w> 10027
ag m 10026
TN BS</w> 10026
ophar yngi 10026
sou theast</w> 10026
W F 10025
Ra j 10025
5 NTR</w> 10024
m N</w> 10024
amino propyl</w> 10024
inhab iting</w> 10024
A car 10023
Provid ers</w> 10023
ran s</w> 10022
produc tively</w> 10022
E EC</w> 10020
Pres cription</w> 10020
ra rer</w> 10019
PG R</w> 10019
AV AI 10019
Bra ak</w> 10019
Immunocyto chemistry</w> 10019
C 5b</w> 10018
sp ectinomycin</w> 10018
asc ial</w> 10018
S HR 10017
sc rotum</w> 10016
HA Q</w> 10016
Dissemin ated</w> 10016
Brea k 10015
ten er</w> 10014
n esting</w> 10013
Lip opolysaccharide</w> 10013
p sb 10011
D R2</w> 10011
ag nes 10011
Endos copy</w> 10011
endosym bi 10011
D PO 10010
G RIP1</w> 10010
Co ok</w> 10010
traff icked</w> 10010
righ tward</w> 10010
clean up</w> 10009
- one</w> 10008
pro visions</w> 10008
intr usion</w> 10008
DB H</w> 10008
P MR</w> 10007
g RNAs</w> 10007
cyst athionine</w> 10007
F AM1</w> 10006
ubl in</w> 10006
SELE X</w> 10006
opoul os</w> 10006
carbon yls</w> 10004
ML L1</w> 10003
inoc ulate</w> 10003
com min 10002
ne red</w> 10002
Sol ub 10002
Spl een</w> 10002
her petic</w> 10001
estu ary</w> 10001
D ap 10000
nu trac 10000
acti cally</w> 9999
commun icated</w> 9999
inadver tent</w> 9999
Anesthesi ologists</w> 9999
endog lin</w> 9999
9 p2</w> 9998
u fin</w> 9998
G SNO</w> 9998
MT OC</w> 9998
S yl 9997
Fox o1</w> 9997
osteoclas tic</w> 9997
B our 9996
inte dan 9996
fruit ful</w> 9996
af e 9994
micro scale</w> 9994
Phil ips</w> 9994
monosom y</w> 9994
im iqu 9993
Vi br 9993
vac ancy</w> 9993
olith ic</w> 9993
dis continuing</w> 9992
Sh ape</w> 9992
ID T</w> 9992
carboxy ethyl</w> 9992
unve il</w> 9991
Phot odynamic</w> 9990
ar ylation</w> 9989
ech ogenicity</w> 9989
D y</w> 9988
Thresh old</w> 9988
AVAI LAB 9988
is oxazole</w> 9987
des artan</w> 9987
cartri dges</w> 9987
wing ed</w> 9987
adju van 9986
cryp to 9986
Neuro science</w> 9984
To ol 9984
3 J</w> 9983
carg os</w> 9982
mista ken</w> 9980
Fm r1</w> 9980
B right</w> 9978
el ter</w> 9978
angi omas</w> 9978
millis econd</w> 9978
fi red</w> 9977
et on</w> 9977
NF kappaB</w> 9977
J FH1</w> 9976
G ent 9976
symp atric</w> 9976
DI T</w> 9976
hydroly zable</w> 9976
sett ling</w> 9976
mi R3</w> 9975
bra tes</w> 9975
Flu o</w> 9975
ID D</w> 9974
1 Y</w> 9972
CA AX</w> 9972
au str 9972
acet oxy 9972
PS s</w> 9972
PC N</w> 9971
cre w</w> 9971
intedan ib</w> 9971
ma ter</w> 9970
natri uresis</w> 9969
intrap ulmonary</w> 9969
curvil inear</w> 9969
RE G</w> 9968
am an 9968
fro st</w> 9968
RP C</w> 9968
Si a 9968
d N</w> 9967
F n 9967
f und</w> 9967
Se p</w> 9967
idi otype</w> 9967
RV FV</w> 9966
stem ming</w> 9966
h ler</w> 9965
r d1</w> 9965
D AP1</w> 9965
qu il 9965
Ang le</w> 9965
sc i</w> 9964
aminolev ulinic</w> 9964
S DR</w> 9963
in stabilities</w> 9963
Han ks</w> 9963
Telom ere</w> 9962
3 A2</w> 9961
re membered</w> 9961
For ces</w> 9960
R PA 9959
S arcoma</w> 9958
ophil a</w> 9958
Aut oradi 9958
refug ee</w> 9958
recomm ending</w> 9957
F NAC</w> 9956
favour ably</w> 9956
kel ey</w> 9956
S la 9955
F PLC</w> 9953
SV T</w> 9953
ur ines</w> 9952
Aut onomic</w> 9952
plug in</w> 9952
pressur ized</w> 9952
C M1</w> 9951
ET D</w> 9951
digit alis</w> 9951
hystero scopy</w> 9951
F Cs</w> 9950
nin th</w> 9950
offici als</w> 9948
t ardi 9947
Classi c</w> 9947
d j 9946
blo om</w> 9946
Qu ercetin</w> 9946
PB T</w> 9946
Tras tuzumab</w> 9946
ca e</w> 9945
distr actors</w> 9945
Hemat oxylin</w> 9945
percol ation</w> 9945
p ages</w> 9944
e ight 9943
V IR 9943
Mat the 9943
scienti st</w> 9943
sp are</w> 9942
rec ryst 9942
du ties</w> 9942
physi opathology</w> 9942
dra ins</w> 9942
M OS</w> 9941
P TLD</w> 9940
AS R</w> 9940
mex ic 9939
pent apeptide</w> 9938
metallo proteases</w> 9938
O ber 9937
In sec 9937
CD X2</w> 9936
op ol 9934
gro ns</w> 9934
emer in</w> 9934
SL T</w> 9933
inst ant</w> 9933
imiqu imod</w> 9933
di atom</w> 9931
mis cellaneous</w> 9931
entero cyte</w> 9930
pleas ure</w> 9930
discol oration</w> 9930
pent yl 9929
phyc ocyanin</w> 9929
X B</w> 9928
ob u 9927
Reli able</w> 9927
Z 0</w> 9926
CE L 9926
Therap ies</w> 9926
malto philia</w> 9926
R l 9925
PI PK 9925
PO U 9925
Sup 3</w> 9924
manus cripts</w> 9924
hydro lysed</w> 9923
AG O2</w> 9923
ht ful</w> 9923
CEAC AM1</w> 9923
AS Ds</w> 9922
Par athyro 9921
8 W</w> 9920
non cardiac</w> 9920
commis sure</w> 9920
H MR</w> 9918
pic osecond</w> 9918
GD F1</w> 9918
ox itin</w> 9917
gramin earum</w> 9917
NI X</w> 9916
ab 3</w> 9914
C g</w> 9913
devi se</w> 9913
S au 9912
HB c 9912
marmos et</w> 9912
TM 5</w> 9911
corrobor ates</w> 9910
E ur</w> 9909
bu g</w> 9909
MD Ms</w> 9909
diox ins</w> 9908
H2 SO4</w> 9907
un ks</w> 9906
form ative</w> 9906
lob ules</w> 9906
Arab ic</w> 9906
al ar</w> 9905
inter subunit</w> 9905
lymphaden itis</w> 9905
M ock</w> 9904
B S 9903
Snail 1</w> 9903
s intering</w> 9900
umb ell 9900
DE G</w> 9900
poly ploidy</w> 9899
anti mony</w> 9898
bre fel 9898
Zam bia</w> 9898
Bre ast 9897
jux tap 9897
T NC</w> 9896
contrac ts</w> 9896
Davi es</w> 9896
Com mons</w> 9895
P PA 9894
Erythro cyte</w> 9894
P Ph 9893
K U</w> 9893
inv ent 9893
plas ter</w> 9893
plas modi 9893
DD I</w> 9893
faith ful</w> 9893
pR S4</w> 9891
indol yl</w> 9891
oxalo acetate</w> 9891
acti le</w> 9890
extr adural</w> 9889
IFN gamma</w> 9889
n ineteen</w> 9888
b v 9888
NO D1</w> 9888
immunosuppress ants</w> 9888
parsi mon 9888
comm encing</w> 9887
fem in 9887
Gl n1</w> 9887
rein nervation</w> 9887
LAT S1</w> 9887
arbor ization</w> 9887
Chim eric</w> 9886
R ta</w> 9885
cen ten 9885
Neu tral</w> 9885
As n2</w> 9884
secre tes</w> 9883
dehydrogen ation</w> 9883
bio fuel</w> 9882
PP F</w> 9882
CXCR 1</w> 9882
Fig. 4 9881
ruff les</w> 9881
tri mer 9880
vir ine</w> 9880
Pan c</w> 9880
Arrh enius</w> 9880
Bio technologies</w> 9879
agglutin ating</w> 9879
plasmacy toma</w> 9879
man dated</w> 9878
M other</w> 9877
K ent 9877
enc ir 9877
ed itors</w> 9877
lac eration</w> 9877
oct yl 9876
Equi valent</w> 9876
str ychnine</w> 9875
experim enter</w> 9875
crossb red</w> 9875
W ill</w> 9874
fum on 9874
Dna J</w> 9874
Pav lo 9874
ros a</w> 9873
GABA ARs</w> 9873
Phen otype</w> 9872
mononucle otide</w> 9872
pyrethro ids</w> 9872
whis ker</w> 9872
radio isotope</w> 9870
My r</w> 9869
Hem is 9868
CEB PA</w> 9868
Ngu yen</w> 9868
sh earing</w> 9867
dy n</w> 9867
metasta sized</w> 9867
cy an</w> 9866
sphingomyel inase</w> 9866
in v</w> 9865
infest ans</w> 9865
Possi bly</w> 9865
S so 9864
h h</w> 9863
en icillin</w> 9862
ju ices</w> 9862
Ec R</w> 9862
haemat ocrit</w> 9862
phy tase</w> 9861
Au NP</w> 9861
neur aminic</w> 9861
dich otomy</w> 9861
electro kinetic</w> 9858
appe al</w> 9858
Jun g</w> 9858
sh re 9857
emplo yer</w> 9857
grapev ine</w> 9857
n ificus</w> 9856
D III</w> 9856
Car tilage</w> 9856
andr a</w> 9856
fene stration</w> 9856
in wardly</w> 9854
traum atized</w> 9853
Hit achi</w> 9853
CS FV</w> 9852
PI F</w> 9851
Mü llerian</w> 9851
bronch ogenic</w> 9849
AC TION</w> 9848
vesicou re 9847
cr ine</w> 9846
TRA MP</w> 9846
endoc ervical</w> 9846
Hf q</w> 9846
G NPs</w> 9845
per su 9845
ne t 9845
min ers</w> 9844
DF MO</w> 9844
wi g</w> 9844
B mi1</w> 9842
CaMK IIα</w> 9842
NK p4</w> 9840
visc era</w> 9840
7 q2</w> 9839
CA Rs</w> 9839
Con served</w> 9839
phag ocy 9839
hepar inized</w> 9839
pCD NA3</w> 9839
Chro mo 9838
D MR</w> 9837
iat rics</w> 9837
micro cystin</w> 9836
MD T</w> 9835
advis ory</w> 9835
2 AP</w> 9834
CA F1</w> 9834
attain able</w> 9834
radi ally</w> 9833
Graph ical</w> 9833
sid ation</w> 9832
cardiomy opathies</w> 9831
re juven 9830
Bcl 1</w> 9830
TAR G 9830
publ ish</w> 9829
F an</w> 9828
ch am 9828
therm oc 9828
radios ensitization</w> 9828
lat r 9827
odor ants</w> 9827
cu be</w> 9826
birth day</w> 9825
preserv ative</w> 9825
bere avement</w> 9825
U CSF</w> 9824
. 7B</w> 9823
F ang</w> 9823
TI ES</w> 9823
E6 AP</w> 9823
PE X</w> 9822
therm ogenic</w> 9822
Mus 8</w> 9822
g otten</w> 9821
succin imide</w> 9821
deb ates</w> 9821
Veg et 9821
DU SP1</w> 9820
G ang 9819
primi parous</w> 9819
CDK 8</w> 9818
enrich ments</w> 9818
T HR</w> 9817
kin ess</w> 9817
GAG CT 9817
Plate au</w> 9817
Slo v 9817
mucoc iliary</w> 9817
He pa 9815
emic arb 9815
Mam mary</w> 9814
E ver 9813
oste ochond 9813
but ol</w> 9813
cryo protectant</w> 9813
All ogeneic</w> 9812
rhin oplasty</w> 9812
CA TION</w> 9811
lu I</w> 9810
so oner</w> 9810
om ab</w> 9809
dis continue</w> 9809
He ight</w> 9809
perfor ating</w> 9809
dissi p 9809
f . 9808
me floquine</w> 9808
yl ight</w> 9808
bud ded</w> 9808
CP Z</w> 9807
tetra zole</w> 9807
Post traumatic</w> 9807
scaveng e</w> 9807
5 A2</w> 9806
ti vities</w> 9806
op ping</w> 9806
Freder ick</w> 9805
p inn 9804
In viv 9804
S ip 9803
pre transplant</w> 9803
mit o</w> 9803
otyp ically</w> 9803
co s</w> 9802
RN F2</w> 9802
diver ge</w> 9802
dysarth ria</w> 9802
bec lin</w> 9801
phot odi 9801
genit ally</w> 9801
K ary 9800
my osins</w> 9799
sig ma 9799
Induc ible</w> 9799
U TIs</w> 9798
os alic 9798
dro pping</w> 9798
vul nificus</w> 9798
v . 9797
U CP</w> 9797
pres choolers</w> 9797
R HO</w> 9796
oys ters</w> 9796
Dar k</w> 9795
su ms</w> 9794
ram ifications</w> 9794
hydroxy cholesterol</w> 9794
PSE N1</w> 9794
STRA TE 9792
endometri otic</w> 9792
termin alis</w> 9790
fr ataxin</w> 9790
B KV</w> 9788
PA PP</w> 9788
mo urs</w> 9788
Termin ator</w> 9788
enthusi asm</w> 9788
U AS 9787
ur ring</w> 9787
spec ifies</w> 9786
Ste 2</w> 9786
mes hes</w> 9785
UPD RS</w> 9785
dp c</w> 9784
Aneurys m</w> 9784
o abdominal</w> 9783
NP T</w> 9783
Y ang 9782
regi oselectivity</w> 9782
bio equivalence</w> 9782
MS SA</w> 9781
lignoc ellulosic</w> 9780
certi ficate</w> 9779
2 DG</w> 9778
re operations</w> 9778
nan of 9778
H ag 9777
phy s 9777
LD LT</w> 9777
Y sc 9776
am nestic</w> 9776
em odin</w> 9776
inter phalangeal</w> 9776
non transgenic</w> 9776
but aline</w> 9776
Epid ural</w> 9776
P edi 9774
gi ate</w> 9774
cataly se</w> 9774
Pol yp 9774
AVAILAB ILITY</w> 9774
N lr 9773
hyper intense</w> 9773
H c 9772
HI E</w> 9772
cryp togenic</w> 9772
C row 9771
J us 9771
M ND</w> 9771
di en</w> 9771
un repaired</w> 9771
pre mis 9771
AT AC</w> 9771
topo isomerases</w> 9771
o ing</w> 9770
F PP</w> 9770
ge ological</w> 9770
sulfo transferase</w> 9770
remo tely</w> 9769
Ber keley</w> 9769
up coming</w> 9768
B TC</w> 9767
Ne eds</w> 9767
PR LR</w> 9767
dia physeal</w> 9767
ortho phosphate</w> 9767
na ev 9766
HF V</w> 9766
N ap 9765
is ch</w> 9765
Synech ococcus</w> 9764
F B1</w> 9763
Ab 1</w> 9763
TR CN0</w> 9763
Ni 2</w> 9762
Standardi zation</w> 9762
ske w</w> 9762
pyraz ine</w> 9761
Shar p</w> 9761
Erythro po 9761
T SP1</w> 9760
Bat tery</w> 9760
F DC</w> 9759
lu ting</w> 9759
ane th</w> 9758
oxid atively</w> 9758
Medic ago</w> 9758
s sp.</w> 9757
ont ogenetic</w> 9757
O sp 9756
TL A</w> 9756
Re ads</w> 9756
benzofur an</w> 9756
3 W</w> 9755
adul ter 9755
En try</w> 9755
lo ve</w> 9754
bil li 9754
Micro tubule</w> 9753
yt trium</w> 9753
prognos tication</w> 9752
mys tery</w> 9752
tetra d</w> 9751
P Vs</w> 9750
un i</w> 9750
m c</w> 9749
A W 9749
AC 0</w> 9749
Hydro lysis</w> 9749
quinazol ine</w> 9749
Bio conductor</w> 9748
Amy otrophic</w> 9748
st ock 9747
South west</w> 9747
B HT</w> 9746
eth am 9746
initi ators</w> 9746
utili ties</w> 9746
gar den</w> 9746
TX A2</w> 9746
C od 9745
U sed</w> 9745
Sign s</w> 9745
silic osis</w> 9745
Program med</w> 9744
S op 9743
av en</w> 9743
pil ots</w> 9743
Mong olian</w> 9743
E cu 9742
sal am 9742
PF K</w> 9742
Tit anium</w> 9742
advi se</w> 9742
NUMB ER</w> 9742
D omin 9741
S entinel</w> 9740
thi osulfate</w> 9740
nod ulation</w> 9740
Ultras tructure</w> 9740
GIR K</w> 9740
Re viewing</w> 9739
brefel din</w> 9739
gradu ated</w> 9738
Retro grade</w> 9738
criti que</w> 9738
tem sirolimus</w> 9737
spr uce</w> 9737
N ox</w> 9736
U BC 9734
az enil</w> 9734
Fr a</w> 9733
lumin ol</w> 9733
y ard</w> 9732
PA NSS</w> 9732
Den ver</w> 9732
troph in</w> 9731
electrocardi ograms</w> 9731
flu ticasone</w> 9730
back cross</w> 9730
inoc ula</w> 9730
le verage</w> 9729
REC Q 9729
CA PE</w> 9728
GC F</w> 9728
instruc tive</w> 9728
G X</w> 9727
bl urred</w> 9727
Ros a2</w> 9727
Linc ol 9727
Di I</w> 9726
HE T</w> 9726
adel ta</w> 9725
Tβ RI</w> 9725
S can 9724
conj ec 9724
Sc r</w> 9724
aerosol ized</w> 9724
contag ious</w> 9724
C ran 9723
B ayer</w> 9722
Met allo 9722
nom inally</w> 9722
Kol mog 9722
N RPS</w> 9721
acetab ulum</w> 9721
clo stri 9721
urin alysis</w> 9721
L ane</w> 9720
phor ia</w> 9720
1 Ra</w> 9719
consul t</w> 9719
v icious</w> 9718
k land</w> 9718
pr t</w> 9718
rh IL</w> 9718
TH Y</w> 9718
exci sional</w> 9718
uch es</w> 9717
furo xime</w> 9716
Gord on</w> 9716
sno RNA</w> 9715
A vi 9714
am ation</w> 9714
isi ana</w> 9714
sporo zoite</w> 9714
Arthro scopic</w> 9713
iz z 9712
chlor ides</w> 9712
retro transposon</w> 9711
TF II</w> 9711
promp ts</w> 9711
B FU</w> 9710
Clu stal 9710
DI A</w> 9709
hyd antoin</w> 9709
uc ky</w> 9708
che aper</w> 9708
Achi eving</w> 9708
activ ity 9707
fr ug 9707
r 6</w> 9706
E pac 9706
Rober t 9706
m f 9705
per mission</w> 9705
psor alen</w> 9705
T PI</w> 9704
p ex 9704
my r</w> 9704
Pl anc 9704
dal ton</w> 9704
t ant 9702
I z 9702
a q</w> 9702
H3K4 me2</w> 9701
wat ch</w> 9701
y op 9700
double ts</w> 9700
H sp</w> 9699
Dig estion</w> 9699
Func tion 9698
target able</w> 9698
droxy progesterone</w> 9698
M BS</w> 9697
ig mentation</w> 9697
thym ectomy</w> 9697
vulner abilities</w> 9697
equi potent</w> 9695
. 1D</w> 9694
congen er</w> 9694
icho ic</w> 9694
In fr 9693
cri bing</w> 9693
back bones</w> 9693
R luc</w> 9692
AK T2</w> 9692
PAR G</w> 9692
Preser vation</w> 9692
OV CAR</w> 9691
Res erve</w> 9689
adap ters</w> 9689
z onal</w> 9688
Z one</w> 9688
anten nal</w> 9688
trypan osom 9687
carbapenem s</w> 9687
Papanicol aou</w> 9687
SO FA</w> 9685
fet uin</w> 9684
pyr an</w> 9684
ou in</w> 9683
deterior ating</w> 9683
T er</w> 9682
F MT</w> 9682
Re fer 9682
IKK ε</w> 9680
A vian</w> 9679
deoxy guanosine</w> 9679
Nec rosis</w> 9679
and 7</w> 9678
PI T</w> 9678
R ace</w> 9677
M uscular</w> 9677
p up</w> 9676
mathem atically</w> 9676
erc a</w> 9674
tol butamide</w> 9673
CHI LD 9673
H WE</w> 9672
CL U</w> 9672
Br anch</w> 9672
meta phases</w> 9672
detec tably</w> 9671
radi ated</w> 9671
atten dant</w> 9671
general izable</w> 9671
k owski</w> 9670
de struc 9670
O at 9669
psycho analytic</w> 9669
sclero stin</w> 9668
con vic 9667
she ath 9667
Phosphor Imager</w> 9667
O SI</w> 9666
dist ressing</w> 9666
NP R1</w> 9665
en uresis</w> 9664
PI X</w> 9664
erythro leukemia</w> 9664
U S1</w> 9663
Ac tually</w> 9663
L atin 9662
chrom at</w> 9661
HP 1α</w> 9661
circul ate</w> 9661
D ow 9660
ul fi 9660
az acytidine</w> 9659
CM ML</w> 9659
LT F</w> 9659
Targ ets</w> 9659
-deoxy cytidine</w> 9659
D XR</w> 9658
Bi acore</w> 9658
ald ol</w> 9658
monos accharides</w> 9657
philosoph ical</w> 9657
supervis ors</w> 9657
WAS p</w> 9657
cogni tions</w> 9656
min ation</w> 9655
As 2O3</w> 9655
Prote inase</w> 9655
cyclohex yl</w> 9655
8 q</w> 9654
K no 9654
ac er 9654
V i</w> 9653
CL M</w> 9653
fer roc 9653
mo plegia</w> 9652
ful fil</w> 9652
P2X 7R</w> 9652
CB G</w> 9651
cau tiously</w> 9651
H L1</w> 9650
p CAGG 9650
Z OL</w> 9650
si tis</w> 9650
el sen</w> 9650
ate tra 9650
VP g</w> 9650
Cann abis</w> 9650
T6 SS</w> 9650
3 D7</w> 9649
D RA 9649
ç ão</w> 9649
poly A</w> 9649
brom ine</w> 9649
R CS</w> 9647
bir ch</w> 9646
WA VE 9646
F lash</w> 9645
Con sequences</w> 9645
nic ardipine</w> 9644
G ain</w> 9643
II S</w> 9643
Ag Cl</w> 9643
Sil ica</w> 9643
BRI 1</w> 9643
Feed back</w> 9643
P age</w> 9642
fr inge</w> 9642
TC L</w> 9642
Rup ture</w> 9642
SM M</w> 9641
Gal ac 9640
diaz oxide</w> 9640
Challeng e</w> 9640
al dose</w> 9639
SF 6</w> 9639
0 μl</w> 9638
0 .1</w> 9638
MI BC</w> 9638
Tor r</w> 9638
acros omal</w> 9638
anc ial</w> 9637
parsi mony</w> 9637
de afferen 9636
af ungin</w> 9634
phero mones</w> 9634
resor ufin</w> 9634
athe rom 9633
I E 9632
sp ace 9631
bur den 9631
defibrill ators</w> 9631
EMS As</w> 9631
ga wa</w> 9630
Ste wart</w> 9630
rms d</w> 9629
ri ed</w> 9628
PP M 9628
Alab ama</w> 9628
as ug 9627
eu x</w> 9627
quen cher</w> 9627
galacto side</w> 9627
M BD 9626
PHY SI 9626
S 4D</w> 9625
di eti 9625
ab ic 9625
Mod ule</w> 9625
state wide</w> 9625
Lum inex</w> 9625
CX3 CL1</w> 9625
sil encer</w> 9624
E 3s</w> 9623
eng ines</w> 9623
Fr actional</w> 9623
TWI ST1</w> 9623
propul sion</w> 9623
opin avir</w> 9622
a CGH</w> 9621
cin ol</w> 9621
MA DS</w> 9621
collabor ators</w> 9621
S CA1</w> 9620
del toid</w> 9620
NEU RO 9620
st r</w> 9619
TI N</w> 9619
ST C</w> 9619
CI E</w> 9619
T urb 9618
gal van 9618
Falc on</w> 9618
ph ire</w> 9617
N GT</w> 9616
li rag 9616
Bu reau</w> 9616
psycho therapeutic</w> 9615
dang ers</w> 9615
RP E6</w> 9613
We is 9613
Pres enting</w> 9613
domes ticated</w> 9613
s an</w> 9612
δ C</w> 9612
ob long 9612
CH MP 9612
buc kling</w> 9612
PO 1</w> 9611
ar th</w> 9610
oc us</w> 9610
ow els</w> 9610
Down load</w> 9610
2B 2B</w> 9610
Pr ice</w> 9609
Den g</w> 9609
pho on</w> 9609
Cock tail</w> 9609
F NR</w> 9608
IV H</w> 9608
Strep tavidin</w> 9608
Biomar ker</w> 9608
un responsiveness</w> 9607
B alloon</w> 9605
CH RO 9605
i pating</w> 9604
insul ating</w> 9604
Cy 7</w> 9604
dilu ting</w> 9604
CE s</w> 9604
f g 9603
non enzymatic</w> 9603
imp etus</w> 9603
NM MA</w> 9603
AAG CT 9602
lib ercept</w> 9602
entang led</w> 9602
hypothalam o</w> 9601
art ments</w> 9600
cl omiphene</w> 9599
foot prints</w> 9599
N MB</w> 9598
hist ogenesis</w> 9598
MS K1</w> 9598
G pp 9597
arch ing</w> 9597
PRI M 9597
Practition ers</w> 9597
5 x</w> 9596
re venue</w> 9596
un avoidable</w> 9596
sub ventricular</w> 9596
pseud os 9596
Kim ura</w> 9596
il le 9595
mal onic</w> 9595
Chil ean</w> 9595
PA THO 9594
der gic</w> 9594
Arg 7</w> 9594
Zimbab we</w> 9594
Lati nos</w> 9593
s E</w> 9592
t 7</w> 9592
Δ h 9592
ag in 9592
fo x</w> 9592
car s</w> 9591
Morph ologic</w> 9591
GF R 9590
cre ativity</w> 9589
ard t</w> 9589
deterior ate</w> 9589
es n</w> 9588
ve ti 9587
we bs</w> 9587
under graduates</w> 9587
scal pel</w> 9587
W o</w> 9586
ic hed</w> 9586
man grove</w> 9586
AK R 9586
Ery them 9586
jas monate</w> 9586
cryptorch idism</w> 9586
dis integrin</w> 9585
alkal osis</w> 9585
m CRPC</w> 9584
Orth op 9584
TL PR</w> 9583
per idine</w> 9583
Amp C</w> 9583
S.E. M.</w> 9583
2 B4</w> 9582
d it 9582
dop tera</w> 9582
monom orphic</w> 9582
zoled ronic</w> 9582
C eb 9581
K Ca 9581
inde x 9580
Sch ro 9580
FE I</w> 9580
Emph asis</w> 9580
m atings</w> 9579
Identi fied</w> 9579
A W</w> 9578
is obaric</w> 9578
inter leukins</w> 9578
Bio film</w> 9578
lact acystin</w> 9578
dimethyl formamide</w> 9578
grap es</w> 9578
remin ders</w> 9578
cyano acrylate</w> 9578
na i</w> 9577
blas ticidin</w> 9576
D uk 9575
if en 9575
dis counting</w> 9575
tic ut</w> 9574
anthra quinone</w> 9574
Y e</w> 9573
mol . 9573
Ka iser</w> 9573
las tic</w> 9572
flic ker</w> 9572
Pg R</w> 9571
Clos ed</w> 9571
7 X</w> 9569
CO MP</w> 9569
h ida</w> 9568
un disturbed</w> 9568
HP O</w> 9568
pre processing</w> 9567
Be ta 9567
hem ocyanin</w> 9566
ket anserin</w> 9566
rs ter</w> 9565
FGF s</w> 9565
O CT1</w> 9563
umb rella</w> 9563
nan omedicine</w> 9563
Oligonucle otide</w> 9563
Perman ent</w> 9563
de t</w> 9562
op irid 9562
Fig. 1 9562
TCF7 L2</w> 9562
Kolmog orov</w> 9562
lirag lutide</w> 9562
pre morbid</w> 9560
path ogn 9560
investig ative</w> 9560
CR I</w> 9560
PPAR δ</w> 9560
Kar no 9560
terro rism</w> 9560
hemat ogenous</w> 9559
histi ocytes</w> 9559
O AT</w> 9558
ann ulation</w> 9558
ung aro 9558
NE O</w> 9558
HDAC 8</w> 9558
dro ve</w> 9557
ye icos 9557
pen um 9557
transduc in</w> 9557
S cope</w> 9556
AG AC 9556
ket orolac</w> 9556
Quanti fying</w> 9556
impregn ation</w> 9556
b ad 9555
di oxane</w> 9555
obliter ans</w> 9555
2R v1</w> 9555
a ve</w> 9554
phospho rous</w> 9554
N PP</w> 9553
rec idi 9553
Analog ous</w> 9553
ect ability</w> 9552
M organ</w> 9551
sub chronic</w> 9551
bloc k 9551
C us 9550
IM AC</w> 9549
Parkinson ism</w> 9549
s ICAM</w> 9548
CT O</w> 9548
RB E</w> 9548
phy B</w> 9547
fissu res</w> 9547
N le 9546
Yam amoto</w> 9546
s.e. m.</w> 9546
P par 9545
Cir cum 9545
glucopyran osyl</w> 9545
ang inal</w> 9544
SU V3</w> 9544
Dis section</w> 9544
opirid ol</w> 9544
I MC</w> 9543
ati veness</w> 9543
VI SA</w> 9543
wat ching</w> 9543
U SD</w> 9542
trans versions</w> 9542
tetro xide</w> 9542
deduc e</w> 9542
Na2 SO4</w> 9541
X r 9540
fabric ating</w> 9540
reson ators</w> 9539
LO X 9539
L az 9537
hydr in</w> 9537
feed forward</w> 9537
mu til 9537
B lun 9536
super infection</w> 9536
neurom a</w> 9536
Transform ants</w> 9536
2 k</w> 9535
tic in</w> 9535
SH Rs</w> 9535
clerk ship</w> 9535
P SE</w> 9534
T OS</w> 9533
T Ms</w> 9533
W ech 9533
LI S</w> 9533
cour tship</w> 9533
Spo doptera</w> 9533
S no 9532
O DS</w> 9532
om icin</w> 9532
se stam 9532
tre mat 9532
yr role</w> 9532
quin que 9532
Inj ections</w> 9532
Fri end</w> 9532
tri acylglycerols</w> 9531
Bio Tek</w> 9531
PLC γ1</w> 9531
F luc 9530
ph oid</w> 9530
phospho fructokinase</w> 9530
encap sidation</w> 9530
Connec ticut</w> 9530
b Z 9529
synap sis</w> 9529
Lo ren 9529
C esarean</w> 9528
esti bular</w> 9528
pharmac ogenomics</w> 9528
Sl c 9528
sp ill</w> 9527
wel ding</w> 9527
X DR</w> 9526
ma di 9524
Pro t 9524
CN G 9524
Bior ad</w> 9524
phosph onic</w> 9523
NS L</w> 9523
quen ess</w> 9523
ar tesunate</w> 9522
sor tilin</w> 9522
NF K 9522
inflammas omes</w> 9522
LIG HT</w> 9522
Δ 8</w> 9521
c. 9</w> 9521
E ud 9520
ER beta</w> 9520
erb B2</w> 9520
anti estrogen</w> 9518
MET AB 9518
hyper ammon 9518
SE PT 9517
Cor relates</w> 9517
D ing</w> 9516
myristo yl</w> 9516
V HH</w> 9515
sta pled</w> 9515
Fu GENE</w> 9515
Tuni sian</w> 9515
l ability</w> 9514
S K2</w> 9514
ol ated</w> 9514
osy stemic</w> 9514
Ful ly</w> 9513
ore tinopathy</w> 9512
clean sing</w> 9512
Contribu tions</w> 9512
FOR MATION</w> 9512
sestam ibi</w> 9512
T DR</w> 9511
cd k</w> 9511
K au 9510
phos tin</w> 9510
k ip1</w> 9509
man n 9509
IN TS</w> 9509
N TH 9508
S ense</w> 9508
EP OR</w> 9508
tumour igen 9508
tetr agonal</w> 9507
Radio frequency</w> 9507
molyb date</w> 9507
padd y</w> 9507
exti rp 9507
V US</w> 9506
me terol</w> 9506
oti n</w> 9505
Pf am</w> 9505
E ND</w> 9504
HT TLPR</w> 9504
Nucle of 9504
ferment ations</w> 9504
tetram ethyl</w> 9503
peristal tic</w> 9503
Hemorrh age</w> 9502
hermaphrodi tes</w> 9502
S ie 9501
h opes</w> 9500
D PH</w> 9500
sen g</w> 9500
electro chemistry</w> 9500
HC B</w> 9500
gas oline</w> 9500
NO Es</w> 9500
tax onom 9500
Immunob lots</w> 9500
B ox 9498
K NO 9498
idi osyn 9498
ylo sis</w> 9498
Ashken azi</w> 9498
bin in</w> 9497
PKC θ</w> 9497
8 d</w> 9496
ul u</w> 9496
gen sis</w> 9496
ell en</w> 9496
Mon te 9496
In tero 9495
identi fiers</w> 9495
U mu 9494
DO PC</w> 9494
vul val</w> 9494
fruc tos 9494
hexos aminidase</w> 9494
Res cue</w> 9492
astig ote</w> 9492
an abe</w> 9491
tr p</w> 9491
ord ination</w> 9490
SO A</w> 9490
ger bil</w> 9490
osper ms</w> 9490
R ing 9489
no tions</w> 9489
enrol lees</w> 9489
B ax 9488
SO F</w> 9488
ton sil 9488
AR Bs</w> 9487
cro pping</w> 9487
vec tomy</w> 9485
MP K</w> 9485
TI P6</w> 9484
sulf ates</w> 9484
TR I</w> 9484
form ulating</w> 9483
mos s</w> 9483
FOL FOX</w> 9483
ste pped</w> 9482
trans -</w> 9482
ACT B</w> 9482
H d 9481
t owns</w> 9481
hyper fine</w> 9481
sulf ides</w> 9481
dor so 9481
Gluc agon</w> 9481
2 p4</w> 9480
end olymphatic</w> 9480
haem ostasis</w> 9480
I LC</w> 9479
W es 9479
di ce</w> 9479
Sene gal</w> 9479
. 6C</w> 9478
h TR</w> 9478
thiop ental</w> 9478
tro glitazone</w> 9475
Pl ain</w> 9475
Te c</w> 9475
E ED</w> 9474
ti dyl 9474
dis continuity</w> 9474
micro fluidics</w> 9474
dh fr</w> 9474
Proteas ome</w> 9473
N es 9472
Loc ation</w> 9472
par anoid</w> 9471
HC V 9471
Pro pofol</w> 9470
publ ishing</w> 9470
DR .</w> 9470
GCC AT 9469
Arg 6</w> 9468
Need le</w> 9468
icul um</w> 9467
IV S</w> 9466
calorim etric</w> 9466
it ous</w> 9464
MR TF</w> 9464
r .</w> 9463
or ations</w> 9463
Tet R</w> 9462
an onym 9461
AU Cs</w> 9461
autoch thonous</w> 9461
D RA</w> 9460
organ ometallic</w> 9460
Dis tingu 9460
p Ab</w> 9459
az im 9459
succin imidyl</w> 9459
Ste in 9458
pip elines</w> 9458
om ia</w> 9457
calci uria</w> 9457
D MRs</w> 9456
pros th 9456
mo d</w> 9455
micro phthal 9455
V R1</w> 9454
ut ory</w> 9454
bri lliant</w> 9454
Figure 6B</w> 9454
7 SK</w> 9453
Se Met</w> 9453
a PL</w> 9452
G et 9452
OB SERV 9452
CU R</w> 9452
Fil ter</w> 9452
judg ing</w> 9450
Kent ucky</w> 9450
6 q2</w> 9449
ze axanthin</w> 9449
- CAC 9448
ph otic</w> 9448
ou ter 9448
mid -</w> 9448
Gl n3</w> 9448
Psych ometric</w> 9448
W ing 9447
de adenylation</w> 9447
O HCA</w> 9446
ag itated</w> 9446
Fl ag 9446
g hosts</w> 9445
DAM GO</w> 9445
B ail 9444
O GRA 9444
trans sphenoidal</w> 9444
bo t</w> 9444
Chi ral</w> 9444
cf .</w> 9444
pres pec 9443
reg orafenib</w> 9443
glob ulins</w> 9443
benz amidine</w> 9443
Sch u 9443
L CV</w> 9442
Fi bron 9442
AA 0</w> 9442
Mit ogen</w> 9442
Sol itary</w> 9442
RD W</w> 9442
osulf an</w> 9442
O MA</w> 9441
vi z 9440
I d 9439
C hy 9438
le an 9438
te ichoic</w> 9436
Thermod ynamic</w> 9436
Karno fsky</w> 9436
Ph le 9435
NSCL Cs</w> 9435
j ee</w> 9434
sup rap 9434
bac illary</w> 9434
CN N</w> 9434
photo degradation</w> 9434
Cambo dia</w> 9434
w ife</w> 9433
Mer lin</w> 9433
methyl cholanthrene</w> 9432
te icoplanin</w> 9431
RA ST</w> 9431
ch ii</w> 9431
inv oked</w> 9431
permis sible</w> 9431
chaperon in</w> 9431
ab senteeism</w> 9430
ag ran 9430
Be side</w> 9430
wean ling</w> 9430
trich rome</w> 9429
cot reatment</w> 9428
F DP</w> 9427
bre ech</w> 9427
normo thermic</w> 9427
Cytom etry</w> 9427
nic king</w> 9426
Am plic 9426
pe i</w> 9425
MN ase</w> 9425
ruff ling</w> 9425
J B</w> 9424
o S</w> 9424
reduc ible</w> 9424
SL C6 9424
B ak 9423
P aneth</w> 9423
PA MP</w> 9423
sor bed</w> 9423
hypo albumin 9422
hybridi ze</w> 9422
ungaro toxin</w> 9421
R free</w> 9420
B rig 9420
sub gingival</w> 9420
GLU T2</w> 9420
oto acoustic</w> 9420
C ranial</w> 9419
- 4-</w> 9419
conf id 9419
visu alisation</w> 9419
bo o</w> 9418
disti llation</w> 9418
A max 9417
I BA</w> 9417
Hy dr 9417
sandw iched</w> 9417
H AS</w> 9416
po isons</w> 9416
autophag osomal</w> 9416
CV P</w> 9416
forec asting</w> 9416
TP P1</w> 9415
pent amer</w> 9415
Pen g</w> 9415
w ishes</w> 9414
TR β</w> 9414
skelet ons</w> 9414
phy A</w> 9413
fl ares</w> 9413
embr ac 9413
hydrox yn 9413
s acr 9411
L N2</w> 9411
oct apeptide</w> 9411
DQ A1</w> 9411
-- 3</w> 9410
alli um</w> 9410
Down stream</w> 9410
Foll icular</w> 9410
Pre vo 9409
spectro scopies</w> 9409
adop tively</w> 9409
Blo om</w> 9409
Pap ill 9409
re efs</w> 9408
Ex cision</w> 9408
p Tyr</w> 9407
ma c</w> 9407
propi onyl</w> 9407
KCN Q2</w> 9407
Afgh an 9407
ARE AS</w> 9406
in mates</w> 9405
am mary</w> 9405
EC FP</w> 9405
PF 6</w> 9405
schiz o 9405
Resi dent</w> 9405
RUN X3</w> 9405
poss ession</w> 9404
Clinic opathological</w> 9404
6 g</w> 9403
1 q1</w> 9402
Micro biological</w> 9401
Or n 9401
Mon ocyte</w> 9401
head group</w> 9401
Fre y</w> 9401
trypan osome</w> 9401
gossy pol</w> 9401
C f</w> 9400
thromb ocy 9400
VI S</w> 9400
0 δ</w> 9399
J r</w> 9399
ob inostat</w> 9399
hus band</w> 9399
adj o 9398
Ampl ified</w> 9397
Lincol n</w> 9397
- SD</w> 9395
X D</w> 9395
trans endothelial</w> 9395
phosph om 9394
aut op 9394
C se 9393
V d</w> 9393
al tru 9393
pent ag 9393
Al umin 9392
CD G</w> 9392
Radi ologic</w> 9392
epic atechin</w> 9392
Endom etri 9392
I ne 9391
O .1</w> 9391
Ka to</w> 9391
Ram os</w> 9390
band gap</w> 9390
5 Ca</w> 9389
e EF2</w> 9389
Sh ap 9389
fibrill in</w> 9389
Pf u</w> 9389
s ak 9388
re ar 9388
aler tness</w> 9388
pant oth 9388
hypopit uitarism</w> 9388
te stable</w> 9387
ab o 9387
IF s</w> 9387
thromb omodulin</w> 9387
A ER</w> 9386
mid life</w> 9386
flur amine</w> 9386
Wat anabe</w> 9386
for moterol</w> 9385
prob enec 9384
Perox isome</w> 9383
incarcer ation</w> 9383
eleph ant</w> 9383
Calcul ated</w> 9382
Le on 9381
Dr s.</w> 9381
in su 9380
en burg</w> 9379
si re</w> 9379
GluN 2A</w> 9378
Whi te 9377
T abl 9376
tachy cardi 9376
agasc ar</w> 9376
I BDV</w> 9375
d ans</w> 9375
ly sing</w> 9375
Micro wave</w> 9375
van illoid</w> 9375
T ap 9374
En v 9374
filam entation</w> 9374
EV D</w> 9373
Spe ed</w> 9373
Pig s</w> 9373
P HS</w> 9372
bench marks</w> 9372
N NRTIs</w> 9371
O LP</w> 9371
Cul lin</w> 9371
Mor ph</w> 9371
fram ing</w> 9371
C w 9370
addi tivity</w> 9370
ise tron</w> 9370
micro meters</w> 9369
Wech sler</w> 9369
sul piride</w> 9368
HD C</w> 9368
N MN</w> 9366
ST F</w> 9366
b ard</w> 9365
orib onuclease</w> 9365
corp uscular</w> 9364
Pro posed</w> 9362
DH E</w> 9362
Sha w</w> 9362
. 2D</w> 9361
K ab 9361
cl om 9361
summar ise</w> 9361
Am ni 9361
found ers</w> 9361
pd b</w> 9361
Libr aries</w> 9361
ochrom ocytomas</w> 9361
2 N 9360
re percus 9360
ic alin</w> 9358
methyl enedi 9358
on als</w> 9357
bi ocin</w> 9357
adrenal ectomized</w> 9356
Ex plo 9355
bar codes</w> 9355
dign ity</w> 9354
c ables</w> 9352
S pit 9351
posi tely</w> 9351
Me ans</w> 9351
PI 4P</w> 9351
s 8</w> 9350
w ed</w> 9350
ox ic</w> 9350
RNA se</w> 9350
macro vascular</w> 9350
peri aqueductal</w> 9349
TR PC3</w> 9349
fall en</w> 9349
ru dimentary</w> 9348
I ND</w> 9347
as yn 9347
ec e 9347
is on 9347
trans abdominal</w> 9346
Fin ancial</w> 9346
Elucid ation</w> 9346
obtain able</w> 9345
N A1</w> 9344
through s</w> 9344
Hal o 9344
Mad agascar</w> 9343
In domethacin</w> 9342
CR 8</w> 9342
CP F</w> 9342
With draw 9342
restitu tion</w> 9342
Fil ip 9341
Flav on 9341
Afghan istan</w> 9341
M er</w> 9340
Di hydro 9340
C3 d</w> 9340
probenec id</w> 9340
co tics</w> 9339
De vi 9339
pe g</w> 9338
s aw 9337
Cl ad 9337
My HC</w> 9337
Expl oration</w> 9337
S q 9336
SR L</w> 9336
Nat urally</w> 9336
tau opathy</w> 9336
org estrel</w> 9335
HP Vs</w> 9335
ediatr icians</w> 9335
under lining</w> 9334
sig h 9334
compu terised</w> 9334
PG Cs</w> 9334
1 p3</w> 9333
B BP</w> 9332
Gu é 9332
Fund amental</w> 9332
9 Δ</w> 9331
g ap 9331
Amax a</w> 9331
G J</w> 9330
ul izumab</w> 9330
Ni emann</w> 9330
ap press 9329
top ologically</w> 9328
EL L</w> 9328
gly cyr 9327
rad 2</w> 9327
aly l</w> 9325
micro metastases</w> 9325
Ch ron 9325
Or der</w> 9324
HT H</w> 9324
ter ity</w> 9323
Co star</w> 9323
CB F 9323
So y 9323
pyraz ol 9323
fron ts</w> 9321
commis si 9321
P eroxidase</w> 9320
Mell itus</w> 9320
as alazine</w> 9319
soci ocultural</w> 9319
convuls ant</w> 9319
- type</w> 9318
l us</w> 9318
adjuvan ted</w> 9318
E Q 9317
photos ensitivity</w> 9317
metam a 9317
dermat osis</w> 9316
g 5</w> 9315
ly so</w> 9315
tor so</w> 9315
Ge fitinib</w> 9315
spir alis</w> 9315
polyethyl ene 9315
sh ame</w> 9314
eukary ote</w> 9314
L PD</w> 9313
CT Z</w> 9313
t su 9312
g inger</w> 9312
par ities</w> 9312
pig mentary</w> 9312
pin ned</w> 9312
su stains</w> 9310
id able</w> 9310
Pv u 9310
labyrin th</w> 9310
Fö rster</w> 9310
cyto keratins</w> 9309
SP 3</w> 9309
ped agog 9309
trapez ius</w> 9309
feed stock</w> 9308
methan ogenic</w> 9308
ral tegravir</w> 9308
be strol</w> 9307
uc a</w> 9305
tele osts</w> 9305
s ays</w> 9304
A i 9304
Fl u</w> 9304
Wal king</w> 9304
ifi ers</w> 9304
nucle obase</w> 9303
can th 9303
Sp ind 9303
Onc ogene</w> 9303
un myelinated</w> 9302
Le an</w> 9302
CO s</w> 9301
Cor ti</w> 9301
Sm ir 9301
Swe et</w> 9301
It ch</w> 9301
C PT 9300
oper s</w> 9300
CA S 9300
end onasal</w> 9299
Main taining</w> 9299
I P2</w> 9298
Leuc ine</w> 9298
D Z 9297
trans lesion</w> 9297
pi rone</w> 9297
B lim 9296
Res ult</w> 9296
ure ters</w> 9296
En vs</w> 9296
surro unds</w> 9296
pre biotic</w> 9294
nos us</w> 9293
Ar rays</w> 9293
read ed</w> 9293
F ACT 9292
coc oa</w> 9292
BI T</w> 9292
e ters</w> 9291
BD L</w> 9291
DNA se</w> 9290
lich en 9290
S ich 9289
li tig 9289
end ore 9289
to il 9289
K lin 9288
pa yers</w> 9288
T ail 9287
I PMN</w> 9286
K DEL</w> 9286
p EN 9286
pre menstrual</w> 9286
CA Ms</w> 9286
be ac 9285
Incorpor ating</w> 9285
e zetimibe</w> 9284
P ax</w> 9284
os tium</w> 9284
im itation</w> 9284
phy tos 9284
CR Y2</w> 9283
ass er</w> 9282
semiconduc ting</w> 9282
compens ates</w> 9281
e ubacteri 9280
P N1</w> 9280
Mos cow</w> 9280
f C</w> 9279
Co ding</w> 9279
brea ths</w> 9279
tetra ethylammonium</w> 9279
nephro lithiasis</w> 9279
pi role</w> 9278
PL M</w> 9278
Gluc ocorticoid</w> 9278
sub sites</w> 9277
bus ulfan</w> 9277
methion yl</w> 9277
X BP 9276
authen ticated</w> 9276
s oprazole</w> 9275
oscop ies</w> 9275
Bo try 9275
gras tim</w> 9275
STRATE GY</w> 9275
H PD</w> 9274
f e</w> 9274
concaten ated</w> 9274
stiff ening</w> 9273
shuff ling</w> 9273
ocaly x</w> 9273
Auth ority</w> 9271
TZ M</w> 9271
rehabil itative</w> 9271
an em 9270
Lar vae</w> 9269
V MH</w> 9268
5 q1</w> 9267
C v 9267
un specified</w> 9267
HSP 1</w> 9267
interchang eable</w> 9267
ex cellence</w> 9266
Ex amin 9266
Yor k 9266
provo king</w> 9266
C un 9265
sensi tively</w> 9265
conson ant</w> 9265
con ut</w> 9264
Fre edom</w> 9264
tap ering</w> 9264
Gem citabine</w> 9264
transi tioning</w> 9263
MV Bs</w> 9263
congen ita</w> 9263
Hem ip 9262
extrac apsular</w> 9261
CT RL</w> 9261
Ge ographic</w> 9261
ON L</w> 9260
end in</w> 9259
yl ating</w> 9259
sin ess</w> 9259
E μ</w> 9258
me si 9258
PG ES</w> 9258
Cardi opulmonary</w> 9258
poly microbial</w> 9257
RR MS</w> 9257
Ori entation</w> 9257
orient ated</w> 9257
Experim entally</w> 9256
ti ers</w> 9255
res p</w> 9255
CYP 4 9255
HO N</w> 9254
do esn</w> 9254
TR US</w> 9254
odend ritic</w> 9253
Plu ronic</w> 9252
E p</w> 9251
Bor n</w> 9251
APOB EC</w> 9251
prespec ified</w> 9251
5 X 9250
Pate l</w> 9250
S BD</w> 9248
glum ine</w> 9248
new sp 9247
DM ARDs</w> 9246
I ON 9245
F k 9245
D 1R</w> 9245
G PR</w> 9245
s ters</w> 9244
Con taining</w> 9244
dis tensibility</w> 9243
lin ess</w> 9243
pi dem</w> 9243
ethyl ene 9242
E ve 9241
DE K</w> 9241
Thir dly</w> 9241
haplogro up</w> 9241
trac tography</w> 9240
Wil mington</w> 9240
tube ro 9240
WNK 4</w> 9240
NF s</w> 9238
escal ated</w> 9238
PEX 1</w> 9238
ulfi ram</w> 9238
V P6</w> 9236
min ed</w> 9236
Res ol 9236
p SS</w> 9235
ab ove 9235
sul es</w> 9235
Resi d 9235
ulcer ations</w> 9235
ese ed</w> 9235
TF E</w> 9234
Nu MA</w> 9234
mox ibustion</w> 9234
MEF 2C</w> 9234
E BC</w> 9233
ug a</w> 9232
BR CA 9232
SK OV</w> 9232
Mc Coy</w> 9232
G ES</w> 9231
Rho 1</w> 9231
hyalu ronate</w> 9231
CHE K2</w> 9231
Trichin ella</w> 9231
Se 2</w> 9230
rop in</w> 9230
isot opically</w> 9230
penicill ins</w> 9230
magne ts</w> 9229
m AChR</w> 9228
B inary</w> 9228
plas h</w> 9228
BM PR 9228
I APs</w> 9227
sub capsular</w> 9227
Neuro endocrine</w> 9227
Targ et 9227
V IT 9226
omyel ia</w> 9226
Eucli dean</w> 9226
C ulti 9225
FF As</w> 9225
ul ism</w> 9224
res e 9223
PROCEDU RE</w> 9223
citrullin ated</w> 9223
2 q</w> 9222
d ane</w> 9222
At p 9222
rain y</w> 9222
I 8</w> 9221
D ob 9221
il ate</w> 9221
AB s</w> 9221
I 0</w> 9220
Decre ases</w> 9220
U K 9219
cor d 9219
trop ia</w> 9219
any thing</w> 9218
di methoxy</w> 9217
ec ium</w> 9217
ol ites</w> 9217
expon ents</w> 9217
amygdal oid</w> 9217
gamm opathy</w> 9217
suff erers</w> 9216
ophthalm ologists</w> 9216
br in</w> 9215
maxim ization</w> 9215
arri ving</w> 9215
I RIS</w> 9214
ST RE 9214
fe der 9214
weak est</w> 9214
Fibron ectin</w> 9214
B 3B</w> 9213
port osystemic</w> 9213
non phosphorylated</w> 9213
hel icity</w> 9213
sno RNAs</w> 9213
an atase</w> 9212
ST EP</w> 9212
thresh olding</w> 9212
iter atively</w> 9211
BM MCs</w> 9210
ste wardship</w> 9209
poten ce</w> 9209
GG CA 9209
fluoro metry</w> 9208
iontoph oresis</w> 9208
ivac aine</w> 9208
3 q2</w> 9207
ra f</w> 9207
Re ferences</w> 9207
amni onitis</w> 9207
eryth r 9207
Neo adjuvant</w> 9207
sp iders</w> 9206
Sch a 9205
crosso vers</w> 9205
vacuol ation</w> 9205
blo ody</w> 9204
Gluc ocorticoids</w> 9204
A tax 9203
letharg y</w> 9203
HSP G</w> 9202
multin omial</w> 9202
thermo tolerance</w> 9201
GJ B2</w> 9201
der y</w> 9200
damp en</w> 9199
Bal tic</w> 9197
south west</w> 9197
S CD1</w> 9196
Gri ff 9196
S ia</w> 9195
har ve 9195
embry oid</w> 9195
Fo k 9195
ic ida</w> 9194
comp acted</w> 9194
lam prey</w> 9194
TRP C</w> 9194
R ational</w> 9193
biom icro 9193
Ma f</w> 9193
F CG 9192
Ara C</w> 9192
Cav 3</w> 9192
k u</w> 9191
FL X</w> 9191
nonden aturing</w> 9191
HEAL TH</w> 9190
H 6 9189
om onic</w> 9189
rap idity</w> 9189
bil ingual</w> 9189
n au 9188
RA D2</w> 9188
ax illa</w> 9188
uni axial</w> 9188
Identi fier</w> 9188
wash ings</w> 9188
dac arbazine</w> 9188
nedd ylation</w> 9188
O PTN</w> 9187
He J</w> 9187
Dr inking</w> 9187
aer ated</w> 9187
Resi due</w> 9187
DA RPP</w> 9187
PHAR MAC 9186
Shand ong</w> 9186
R on</w> 9185
os h 9185
sep tation</w> 9184
typh us</w> 9184
M BR</w> 9183
Z ur 9182
neuro modulation</w> 9181
AM B</w> 9181
anis omycin</w> 9180
apo E4</w> 9180
H m 9179
B y 9179
mit ogenesis</w> 9179
argu ably</w> 9179
design er</w> 9178
reli ant</w> 9178
SM Y 9178
short ages</w> 9178
Ander sen</w> 9178
os ep 9177
trunc atula</w> 9177
Tc f</w> 9177
L amp 9176
nanop ore</w> 9176
op tom 9175
destro ying</w> 9175
D II</w> 9173
re distributed</w> 9173
ER F</w> 9173
Pro duc 9173
DR D2</w> 9173
HU MAN</w> 9173
c or</w> 9172
w rap</w> 9172
Surviv ors</w> 9172
Reduc tions</w> 9172
di lat 9171
KCN E1</w> 9171
Dna 2</w> 9171
was p</w> 9170
conn exins</w> 9170
- PCR</w> 9169
X LF</w> 9169
PU VA</w> 9169
arra yed</w> 9169
w l</w> 9167
pro neural</w> 9167
ag les</w> 9167
ter us</w> 9166
VE LO 9166
es tic</w> 9165
PA SI</w> 9165
ser o</w> 9165
contin ents</w> 9165
Ig A1</w> 9165
Fin ite</w> 9165
uri tic</w> 9165
TE X</w> 9164
electro philes</w> 9163
coun teri 9163
Leu 3</w> 9163
omet al 9162
K c 9161
W DR 9161
f ly 9161
HI ST 9159
thio uracil</w> 9159
oupl es</w> 9159
concei vably</w> 9159
H PCs</w> 9158
dro pwise</w> 9158
TRI M</w> 9158
m ative</w> 9157
G HB</w> 9157
kin k</w> 9157
Lin ked</w> 9157
Ed man</w> 9157
Hi ro 9157
Chemil uminescent</w> 9157
cyste amine</w> 9156
IFN AR</w> 9156
auto inhibition</w> 9155
Age ing</w> 9155
Relap se</w> 9155
R ich</w> 9154
it ochond 9154
roc ks</w> 9154
1. min</w> 9154
H PI 9153
d ot 9153
aut os 9153
K t</w> 9152
F HR</w> 9152
p soas</w> 9152
sper mi 9152
Cu SO4</w> 9152
acrom ial</w> 9152
clim ates</w> 9151
bottlen ecks</w> 9151
ser i 9150
bin aural</w> 9150
AC EI</w> 9150
atten tive</w> 9150
d auer</w> 9149
p ad 9149
V V 9149
Ex citation</w> 9149
uni queness</w> 9149
intra osseous</w> 9149
ph 2</w> 9148
SN pc</w> 9148
collabor ations</w> 9148
A j 9146
AP TT</w> 9146
Dis soci 9146
Tru Seq</w> 9146
Tsa i</w> 9146
ff y</w> 9145
PI T 9145
Is let</w> 9145
b -</w> 9144
Psych ology</w> 9144
Atten uation</w> 9144
borreli osis</w> 9144
G IV</w> 9143
Is c</w> 9143
South western</w> 9143
alyp tus</w> 9143
por ine</w> 9142
u el</w> 9141
flun omide</w> 9141
for in</w> 9140
pi RNA</w> 9140
Struc turally</w> 9140
Pro vinc 9139
Gib son</w> 9139
VL M</w> 9139
Ral A</w> 9139
gamet ophy 9139
B ris 9137
ex onucle 9137
Con trac 9137
therm ogravimetric</w> 9137
proto plast</w> 9137
transver sal</w> 9137
il ization</w> 9136
CD 8 9136
Per turb 9136
syncyti otroph 9136
C β</w> 9135
Cor p</w> 9135
isop rene</w> 9134
Smar t 9134
arg ine</w> 9132
ML N</w> 9132
ferroc ene</w> 9132
un ited</w> 9131
Pro files</w> 9131
har dening</w> 9130
elic itor</w> 9130
TNF AI 9130
car rot</w> 9128
suprac lavicular</w> 9128
C Z</w> 9127
pin ene</w> 9127
a ute</w> 9126
ch aro 9126
qual ify</w> 9126
COL 1A1</w> 9126
cau tery</w> 9126
O RI 9125
In do</w> 9125
detail ing</w> 9125
el ute</w> 9124
adhe siveness</w> 9124
re vascularisation</w> 9123
ec ula</w> 9123
asp s</w> 9123
obstruc tions</w> 9123
wis dom</w> 9123
ER Y</w> 9122
Rh esus</w> 9122
- tri 9121
sec ular</w> 9121
Bar thel</w> 9121
re assessed</w> 9118
Tyro de</w> 9118
J agg 9117
P GT1</w> 9117
9 d</w> 9116
read mitted</w> 9116
ses hoe</w> 9116
domes tica</w> 9116
proc alc 9115
AI II</w> 9115
Ba 2</w> 9115
electroretin ogram</w> 9115
ion isation</w> 9114
tal king</w> 9114
pyl orus</w> 9114
Ele ment</w> 9114
Post partum</w> 9113
stil bestrol</w> 9113
ser vers</w> 9112
Cg A</w> 9112
B it 9111
E IS</w> 9111
inte in</w> 9111
CH NO</w> 9111
DR F</w> 9111
phy totox 9110
F V1</w> 9109
la unch</w> 9109
Tr p2</w> 9109
ref use</w> 9109
LE F1</w> 9109
Mac ular</w> 9109
Veh icle</w> 9109
Ne k 9108
hist oplasmosis</w> 9107
LT s</w> 9107
at ome</w> 9106
CL I</w> 9105
Post mortem</w> 9105
Shar ma</w> 9105
ol ates</w> 9104
methyl amine</w> 9104
d rank</w> 9103
pe eling</w> 9103
contradic tion</w> 9103
Metast ases</w> 9103
How ard</w> 9103
CO L1</w> 9101
amend ments</w> 9101
S ox</w> 9100
Not withstanding</w> 9100
boot str 9100
Withdraw al</w> 9100
r d 9099
app raised</w> 9099
cyan idin</w> 9099
P BLs</w> 9098
S ut 9098
U BC</w> 9098
ub ic</w> 9098
N ev 9097
ole an</w> 9097
PPAR alpha</w> 9096
soci alization</w> 9095
CH 5</w> 9094
TE G</w> 9094
hydroxyc innam 9093
Sich uan</w> 9093
co st 9092
Ar H</w> 9092
Ple uro 9092
lipof ectamine</w> 9092
z ip</w> 9091
S 7B</w> 9091
Coun sel 9091
t sch 9090
b 6</w> 9090
ol ith</w> 9090
thermo regulation</w> 9090
Lou isiana</w> 9090
MEDI CAL</w> 9090
Over night</w> 9089
phospholip ases</w> 9089
Bow man</w> 9089
dwarf ism</w> 9089
melan ogenesis</w> 9088
Rad 3</w> 9088
micro spor 9087
idi osis</w> 9087
Swit ch</w> 9087
dysen tery</w> 9087
spectro meters</w> 9085
cr RNA</w> 9084
piri llum</w> 9084
read outs</w> 9083
glucos amin 9083
á n 9082
LE V</w> 9082
Moroc co</w> 9082
ec ainide</w> 9081
cl eral</w> 9081
J EOL</w> 9080
meta physeal</w> 9080
lubr ic 9080
ri vas 9079
neo plasias</w> 9079
ucid um</w> 9078
Sen sing</w> 9078
sho es</w> 9077
HD M2</w> 9076
ephal us</w> 9076
Z E</w> 9075
ag 1</w> 9075
cef oxitin</w> 9075
answ ering</w> 9075
stereoc ilia</w> 9075
transcy tosis</w> 9075
Ton B</w> 9074
D RP1</w> 9073
inte ros 9073
SK I</w> 9073
m sh 9071
phenomen ology</w> 9071
W HO 9070
CM OS</w> 9070
synucle in 9070
B SS</w> 9069
G SD</w> 9068
cal retinin</w> 9068
post ulates</w> 9068
BA K1</w> 9068
o rous</w> 9067
O g 9067
evalu ative</w> 9067
Tak ay 9067
G D3</w> 9066
rh ino 9066
fav ours</w> 9066
tachy kinin</w> 9066
quadru plexes</w> 9066
SM X</w> 9065
hetero structures</w> 9064
Lox P</w> 9064
ost oc</w> 9063
Fri e 9063
HOX A9</w> 9061
tibi ae</w> 9061
premis es</w> 9061
for um</w> 9060
produc tions</w> 9060
O PC 9059
long is 9059
arsen ess</w> 9059
Alli ance</w> 9059
R ei 9058
f ural</w> 9058
anti pyrine</w> 9058
orth ognathic</w> 9058
Multi disciplinary</w> 9058
Fac e 9058
ab u 9057
AP RIL</w> 9057
cef azolin</w> 9057
TLR 8</w> 9057
con sor 9056
multi sensory</w> 9056
P lex</w> 9055
U c 9055
FY VE</w> 9055
cel e 9054
az urin</w> 9053
Co ot</w> 9053
accep tably</w> 9053
xen ogeneic</w> 9053
EMB O</w> 9053
PA K4</w> 9051
Blo om 9051
re marks</w> 9050
but adiene</w> 9050
ML N4</w> 9050
assis tive</w> 9050
ultr am 9049
op pon 9048
bas e 9048
NU R 9048
TEN S</w> 9048
carb enicillin</w> 9047
para influenza</w> 9047
Bx PC</w> 9047
pa uses</w> 9046
D MP</w> 9045
Herc eptin</w> 9045
Nup 9</w> 9044
- deoxy</w> 9043
O ME</w> 9043
Ro om</w> 9043
s ot 9042
isot op 9042
Fc gamma 9042
o viridae</w> 9041
kind led</w> 9041
α A</w> 9040
St aging</w> 9040
ur ates</w> 9039
pre requisites</w> 9039
ethyl hexyl</w> 9039
buff y</w> 9039
H sl 9038
ci stronic</w> 9037
Pap illary</w> 9037
un targeted</w> 9036
mal onate</w> 9036
but ane</w> 9034
f ont</w> 9033
ad mixed</w> 9033
stere ospecific</w> 9033
LD HA</w> 9032
S BR</w> 9031
sper matic</w> 9030
Sir tu 9030
kappa B 9029
ATP γS</w> 9029
Biotin ylated</w> 9029
cali x 9028
E vol 9027
PR DM1</w> 9026
pip etting</w> 9026
Cys 4</w> 9026
polyethyl enimine</w> 9026
S d 9025
Z ATION</w> 9025
contor tus</w> 9025
F idelity</w> 9024
ant on</w> 9024
recombin ational</w> 9022
fos fomycin</w> 9022
Streptom ycin</w> 9022
Exp anding</w> 9021
desmo plastic</w> 9021
in ting</w> 9020
Z EN</w> 9019
E NO 9018
mes oc 9018
bif id 9018
war mer</w> 9017
appendic eal</w> 9017
drow ning</w> 9017
pic ol 9016
sideroph ores</w> 9016
Y ao</w> 9015
ET T</w> 9014
Me dial</w> 9014
syr up</w> 9014
H rs</w> 9012
duc t 9012
erc us</w> 9012
EB US</w> 9012
ethn ographic</w> 9012
vitell ogenin</w> 9012
over produced</w> 9011
raz epam</w> 9011
Ple ural</w> 9011
h . 9010
chromat ogram</w> 9010
swee tened</w> 9010
y d 9008
Con c 9008
otrop ical</w> 9008
Arthro plasty</w> 9008
α V 9006
7 BL6</w> 9005
rip e</w> 9005
Neu5 Ac</w> 9005
lip idation</w> 9004
Gli al</w> 9004
microscop es</w> 9004
TAp 6</w> 9004
0 bp</w> 9003
sub sided</w> 9003
ron asal</w> 9003
multi vessel</w> 9003
Frag ile</w> 9003
N NT</w> 9002
MP NST</w> 9002
imid acloprid</w> 9002
thermo regulatory</w> 9002
Beng al</w> 9002
MP F</w> 9001
lep id 9001
is or 9000
on eu 8999
os min</w> 8999
Res er 8999
super fused</w> 8999
t ress</w> 8998
penc il</w> 8998
parsimon ious</w> 8998
t s1</w> 8997
Tr xR</w> 8997
GAD D3</w> 8997
ur a4</w> 8996
glycer ide</w> 8996
Tric homonas</w> 8996
o z</w> 8995
K GF</w> 8995
Δ S</w> 8995
Super mix</w> 8995
a. m.</w> 8995
V ap 8994
di amino</w> 8994
se o 8993
cycl ases</w> 8993
resuscit ated</w> 8993
C F1</w> 8992
eg l</w> 8992
pon atinib</w> 8992
Lgr 5</w> 8992
N PI</w> 8991
b cr</w> 8991
F PR</w> 8991
G 1 8991
us in</w> 8991
port fol 8991
pair ings</w> 8991
dissoci ating</w> 8991
A lo 8990
de tanib</w> 8989
ot otoxicity</w> 8989
prote ch</w> 8989
PG I</w> 8989
ellip tical</w> 8989
N ull</w> 8988
Mar tin 8988
STI C</w> 8988
Eph A4</w> 8988
conidi al</w> 8988
Protein tech</w> 8988
Δ CT</w> 8987
lib eral</w> 8986
ud or</w> 8985
my otube</w> 8985
ting ham</w> 8985
re modeled</w> 8984
sc ul 8984
vec tor 8983
an der 8982
do zen</w> 8982
plasm y</w> 8982
h PSCs</w> 8981
B en</w> 8981
p ud 8981
Con tras 8981
mono chromatic</w> 8981
intermedi us</w> 8981
fear ful</w> 8981
SI RP 8980
cr abs</w> 8980
C es 8979
dys plasias</w> 8979
mush rooms</w> 8979
6 s</w> 8978
Bio Labs</w> 8978
fung als</w> 8978
at ose</w> 8977
post stroke</w> 8977
Prote olytic</w> 8976
J I</w> 8975
U f 8975
Ros en 8975
H AND</w> 8974
S tran 8974
G m</w> 8974
Wol f</w> 8974
ST 8</w> 8973
histi dines</w> 8973
Eg g</w> 8973
R T1</w> 8972
M 1 8972
on ial</w> 8972
e on</w> 8971
IC N</w> 8971
fluoro phenyl</w> 8971
GI 5</w> 8971
Gal ectin</w> 8971
pigment osum</w> 8970
Prophyl axis</w> 8970
pCAGG S</w> 8970
ill ard</w> 8969
ge ochemical</w> 8969
bench marking</w> 8969
conv ective</w> 8969
j i 8968
Hem atology</w> 8968
Kn owing</w> 8968
Sul fol 8968
guarante ed</w> 8968
on orgestrel</w> 8967
bio degradability</w> 8967
thermos ensitive</w> 8967
ver tic 8966
ep ime</w> 8966
glutam yl 8966
hand held</w> 8966
selen omethionine</w> 8966
RNAi MAX</w> 8966
I D1</w> 8965
Msh 2</w> 8965
n ut 8964
pepti dergic</w> 8964
ancre atic</w> 8964
ZE B2</w> 8964
theranos tic</w> 8964
She ar</w> 8963
Tricho phyton</w> 8963
Cherno byl</w> 8963
er man</w> 8962
un conditioned</w> 8962
wa i 8962
K RAB</w> 8961
Anab aena</w> 8961
na b</w> 8960
catech ins</w> 8960
F NAB</w> 8958
wh ale</w> 8958
sub line</w> 8958
sup rab 8958
sacro iliac</w> 8958
Biomedic als</w> 8958
A EC 8957
ter butaline</w> 8957
GPI Ib</w> 8957
for nix</w> 8956
CO UP</w> 8956
con sequential</w> 8955
PR B</w> 8955
phenyl ene</w> 8955
Min eral</w> 8954
cen s 8953
applic ator</w> 8953
Optim ized</w> 8953
hydroxyl ases</w> 8953
S PAK</w> 8952
E vo 8951
shor tens</w> 8951
CE P1</w> 8951
mul atta</w> 8950
Somat ostatin</w> 8950
Y L 8949
PO INTS</w> 8949
orchi ectomy</w> 8949
L f</w> 8948
ex tingu 8948
Neuro basal</w> 8948
cam p</w> 8948
replen ishment</w> 8948
d pp 8947
F on 8947
pos ted</w> 8947
SA N</w> 8947
t TG</w> 8946
ac arb 8946
micro porous</w> 8946
thorac oabdominal</w> 8946
doub tful</w> 8946
herbiv ory</w> 8946
vigne ttes</w> 8946
T ip</w> 8945
thi amin</w> 8945
hepat otoxic</w> 8945
NDR G1</w> 8945
sub nuclear</w> 8944
dimer izes</w> 8944
s aff 8943
im ines</w> 8943
par k</w> 8943
PD Z 8943
Fe 2O3</w> 8943
less en</w> 8943
spec t</w> 8942
ket ogenic</w> 8942
Ras ch</w> 8942
time points</w> 8942
procalc itonin</w> 8942
di deoxy</w> 8941
Cul 3</w> 8941
C. I.</w> 8941
ut aneously</w> 8940
mu d</w> 8940
FAN CJ</w> 8940
Actino bacteria</w> 8940
Erec tile</w> 8940
p ERK1</w> 8939
prolifer ations</w> 8939
Sh ift</w> 8939
carbon ylation</w> 8939
5 i</w> 8938
as e-</w> 8938
hyper acetylation</w> 8938
Soci o</w> 8938
oler acea</w> 8938
micro gram 8937
postin jury</w> 8937
S und 8936
multi photon</w> 8936
non uniform</w> 8936
C9 ORF7</w> 8936
Rho GAP</w> 8935
valvul oplasty</w> 8935
C arr 8934
Rot ter 8934
IFIT M3</w> 8934
mer lin</w> 8933
al em 8932
dig esting</w> 8932
mening es</w> 8932
rophyl l</w> 8932
calcane us</w> 8932
Pere z</w> 8932
Th anks</w> 8930
GI T1</w> 8930
il izumab</w> 8929
erythropoi etic</w> 8929
Rel ations</w> 8928
dum my</w> 8928
pres ymptomatic</w> 8927
sub divisions</w> 8927
nat i</w> 8927
jeop ardi 8927
CO SY</w> 8926
perme ate</w> 8926
isi tely</w> 8926
ignor ing</w> 8926
occupati onally</w> 8926
il ation</w> 8925
In haled</w> 8925
AL G</w> 8925
bar coding</w> 8925
shi vering</w> 8925
referen t</w> 8925
N DP</w> 8924
me glumine</w> 8924
Intro ducing</w> 8924
A to 8923
r ations</w> 8923
anam ivir</w> 8923
pedestri an</w> 8923
S ard 8922
ci ences</w> 8921
Glut 1</w> 8921
Nak amura</w> 8921
t angle</w> 8920
aut olysis</w> 8920
Ul cer 8920
en yl 8919
AB CC 8919
pos us</w> 8919
max ill 8919
epigen omic</w> 8918
pent a</w> 8918
TGF β 8918
diop ters</w> 8918
A o 8917
Q 0</w> 8917
Ne ws</w> 8917
territ orial</w> 8917
Cox 1</w> 8917
rha phy</w> 8917
immun opathology</w> 8916
go ods</w> 8916
Spr inger</w> 8916
unravel ing</w> 8916
pLK O.1</w> 8916
appe ti 8915
wor t</w> 8914
Ar tem 8914
Prom pt</w> 8913
ap raxia</w> 8912
Dro sha</w> 8912
Mis 1</w> 8912
S 6C</w> 8911
te pl 8911
pro thrombotic</w> 8910
sp illo 8910
dis g 8910
mobil isation</w> 8910
Ni O</w> 8910
Dim ethyl 8910
Lu te 8909
thios emicarb 8909
R TS</w> 8908
. 3D</w> 8907
Sem en</w> 8907
un involved</w> 8906
omat oid</w> 8906
Sil icon</w> 8906
D CV</w> 8904
ü r 8904
non -</w> 8904
dicarbox ylate</w> 8904
d ity</w> 8903
T il 8902
L TBI</w> 8902
artic ulate</w> 8902
odro p</w> 8902
H ck</w> 8901
Bio informatic</w> 8901
h IL</w> 8900
Ex pressed</w> 8900
r ach 8899
sul e</w> 8899
SC C 8899
dem ography</w> 8899
his 3</w> 8899
GSE 7</w> 8899
oscle rotic</w> 8899
sub o</w> 8898
cycl idine</w> 8898
Co uld</w> 8898
Cor nell</w> 8898
Cal u</w> 8898
a HR</w> 8897
di phen 8897
Ly 6C</w> 8897
reduc tant</w> 8895
ensi ties</w> 8895
post p 8893
stom ata</w> 8893
SAM s</w> 8893
phthal ocyanine</w> 8892
TRE M</w> 8892
trans arterial</w> 8891
institu te 8891
Ep is 8891
Austr ali 8891
absorb er</w> 8891
descend ants</w> 8891
ri st</w> 8890
histi ocytoma</w> 8890
Sil va</w> 8889
ope tro 8889
gl ed</w> 8888
crani opharyngi 8888
commun is</w> 8887
PM CA</w> 8887
Pax 3</w> 8887
Yos hida</w> 8887
Retro viral</w> 8886
Dem ographics</w> 8886
photo affinity</w> 8885
SW s</w> 8883
awar ded</w> 8883
e Life</w> 8882
sub normal</w> 8882
disinf ectants</w> 8882
hel pl 8881
Bi variate</w> 8881
Pho 8</w> 8881
shel ter</w> 8881
exqu isitely</w> 8881
ER N</w> 8880
un occupied</w> 8879
man s</w> 8879
ylo ad</w> 8878
anteced ents</w> 8878
h SOD1</w> 8877
P ON</w> 8877
HOX A1</w> 8877
lef tward</w> 8877
J K</w> 8876
re assembly</w> 8876
PA Ms</w> 8876
pa yload</w> 8876
T oxin</w> 8875
phenotyp ical</w> 8875
any where</w> 8875
Rotter dam</w> 8875
P recur 8874
CO VA</w> 8874
o o 8873
P x 8873
T CI</w> 8872
poly dispersity</w> 8872
Sal mo</w> 8872
NR K</w> 8872
voltam metric</w> 8872
en venom 8871
for gotten</w> 8871
bo ok 8871
j p</w> 8870
U FH</w> 8870
cir c</w> 8869
og los 8868
no tified</w> 8868
tro uble</w> 8868
Rab bits</w> 8868
pyrro lo</w> 8868
invag inations</w> 8868
Weigh ted</w> 8868
TRI TC</w> 8867
Vacc ines</w> 8867
chemo prophylaxis</w> 8866
menti oning</w> 8866
per n 8865
FF Q</w> 8865
sc 1</w> 8864
G BV</w> 8863
hyper androgen 8863
integr ations</w> 8863
ez ers</w> 8863
di ment</w> 8862
Rep os 8862
anthrop ometry</w> 8862
CG M</w> 8861
Hor seradish</w> 8861
Ol factory</w> 8861
ZNF 2</w> 8861
6x His</w> 8860
eight fold</w> 8860
Sch le 8859
IGF s</w> 8859
robo ts</w> 8859
olys acchar 8858
re new</w> 8857
sten ted</w> 8857
cyto protection</w> 8856
methyl adenine</w> 8856
lay out</w> 8856
dem enti 8856
SW CNTs</w> 8856
epox idation</w> 8856
in d</w> 8855
en ias</w> 8855
under ground</w> 8855
fluo rosis</w> 8855
FI C</w> 8855
Bra in 8855
J O 8854
uch al</w> 8854
8 X</w> 8853
F uch 8853
EX 1</w> 8853
SO 3</w> 8853
re classification</w> 8852
Ra o</w> 8852
Prevo tella</w> 8852
C itation</w> 8851
incub ate</w> 8851
MAP K 8851
A de</w> 8850
TA g</w> 8850
redund antly</w> 8850
bil oba</w> 8849
attribu tions</w> 8849
S ound</w> 8848
p T2</w> 8847
he donia</w> 8847
conj o 8847
agly cone</w> 8847
Duk es</w> 8847
cycl otron</w> 8846
ris en</w> 8845
ven ted</w> 8844
Sy novial</w> 8844
Immun ologic</w> 8844
Gu an 8844
hur dle</w> 8844
h int</w> 8843
pro social</w> 8843
de mineralized</w> 8843
Mech an 8843
praz iquantel</w> 8843
Th orn 8842
posi ted</w> 8840
RB P4</w> 8840
AE G</w> 8840
Fibri llation</w> 8840
con genitally</w> 8838
cl ashes</w> 8838
mas tery</w> 8838
cran ium</w> 8838
Ex tending</w> 8837
gluc anase</w> 8837
V c</w> 8836
Vac A</w> 8836
li i</w> 8835
pathogn omonic</w> 8835
schizo affective</w> 8835
ME 2</w> 8834
atten dees</w> 8834
ophthal moplegia</w> 8834
Vp x</w> 8834
met anide</w> 8833
Ta il</w> 8833
nitrox ide</w> 8833
F n1</w> 8832
me droxyprogesterone</w> 8832
GR K</w> 8832
Ca MK</w> 8830
isothi ocyan 8830
H2A. X</w> 8830
Resus citation</w> 8830
re alizing</w> 8829
don ate</w> 8829
2 H3</w> 8828
E pac</w> 8828
cer cari 8828
Eng agement</w> 8828
X pert</w> 8827
eEF 1A</w> 8827
Alexa Fluor</w> 8827
T el</w> 8826
b ungarotoxin</w> 8826
E OS</w> 8826
post transplantation</w> 8826
Gué rin</w> 8826
z h 8825
D on</w> 8825
For khead</w> 8825
L uminescence</w> 8824
al titudes</w> 8824
st asy</w> 8824
TL 3</w> 8824
gluc osides</w> 8824
Kan sas</w> 8824
p inning</w> 8823
re warming</w> 8823
e h</w> 8822
P NI 8822
hed onic</w> 8822
B lan 8821
High est</w> 8821
vascul ogenesis</w> 8820
ö n 8819
mut ate</w> 8819
ce furoxime</w> 8819
S ore 8818
ano v</w> 8818
Recon stitution</w> 8818
Parathyro id</w> 8818
D C 8817
gl omus</w> 8817
electro lysis</w> 8817
chromat ograph</w> 8817
zircon ium</w> 8817
de arth</w> 8816
over activation</w> 8816
fer roptosis</w> 8816
Lev ine</w> 8816
pe tri</w> 8815
L anc 8814
D AC 8814
scop ing</w> 8813
I ECs</w> 8812
LI R</w> 8811
prepar atory</w> 8811
F lex</w> 8810
el an 8810
bio electrical</w> 8810
pro viruses</w> 8809
Reg ulated</w> 8809
coprecip itation</w> 8809
T GE 8808
CM G</w> 8808
stearo yl</w> 8808
S AGE</w> 8807
tri ol</w> 8807
phenyl enediamine</w> 8807
ten nis</w> 8806
micro deletion</w> 8806
ly A</w> 8806
tin um</w> 8805
Neurom uscular</w> 8805
chor doma</w> 8804
Mun ici 8804
I SM</w> 8803
en tail</w> 8803
Multi level</w> 8803
-bi pyridine</w> 8803
ichthy osis</w> 8803
Pr ad 8802
fa ith</w> 8802
theore tic</w> 8802
schist osome</w> 8802
Retino ic</w> 8802
mercaptop urine</w> 8802
juxtapos ed</w> 8801
B st 8800
p MD 8800
over ride</w> 8800
o estrous</w> 8799
L C1</w> 8799
tom es</w> 8799
pharmac ogenetics</w> 8798
Olig o</w> 8798
Anomal ous</w> 8798
u ates</w> 8797
chloro genic</w> 8797
plic it</w> 8796
Li posomes</w> 8795
snap shots</w> 8795
semisyn thetic</w> 8795
Di xon</w> 8794
cu ronium</w> 8794
D GE</w> 8793
ket ball</w> 8792
No teworthy</w> 8792
STATISTI CAL</w> 8792
P hor 8791
ro lled</w> 8791
endocardi um</w> 8791
Fasci ola</w> 8791
ge stures</w> 8790
crystalli ze</w> 8790
L ope 8789
o h</w> 8788
At mosph 8788
trip tolide</w> 8788
termin ates</w> 8787
non redundant</w> 8787
Anti tumor</w> 8787
Sal via</w> 8787
scap ula</w> 8787
hiber n 8787
C hou</w> 8786
trimethyl silyl</w> 8786
arrowhe ad</w> 8786
7 Cs</w> 8785
per is 8785
reg ma</w> 8785
ail ments</w> 8785
Py ruvate</w> 8785
8 Δ</w> 8784
Neuro sci</w> 8784
Kin esin</w> 8784
e ful</w> 8783
dim entation</w> 8783
0 i</w> 8782
medi ally</w> 8782
og ram 8781
as p</w> 8780
im precise</w> 8780
Pl ant 8780
after ward</w> 8780
gradu ation</w> 8779
- CAT 8778
z ar 8778
per mutations</w> 8778
methyl glyoxal</w> 8778
credi t</w> 8778
C itro 8777
m RNP</w> 8777
T TS</w> 8777
B em 8777
SU N</w> 8777
ga ug 8777
dialy tic</w> 8777
Colum n</w> 8777
s addle</w> 8776
I RT</w> 8776
toph an</w> 8776
ER -</w> 8775
Pro 2</w> 8775
Cl 4</w> 8775
Bot ulinum</w> 8775
ox icam</w> 8774
Wor d</w> 8774
PY D</w> 8774
dap sone</w> 8774
Ge m</w> 8773
t m1</w> 8772
P anx 8772
su matrip 8772
ach lor</w> 8772
peristal sis</w> 8772
M Q</w> 8771
Ro y</w> 8771
ope dics</w> 8770
ch ik 8769
phil in</w> 8769
SB E</w> 8769
axon eme</w> 8769
ul s</w> 8768
hair less</w> 8767
osel ectivities</w> 8767
abstin ent</w> 8767
inter viewer</w> 8766
P hal 8765
de halogen 8765
euro pa 8765
-deoxy guanosine</w> 8765
in coherent</w> 8764
onc ologist</w> 8764
Graph pad</w> 8763
Hart mann</w> 8763
Me 2</w> 8762
Ben nett</w> 8762
Omp A</w> 8762
cl age</w> 8761
bi asis</w> 8761
CC P4</w> 8761
Meth otrexate</w> 8761
transist or</w> 8761
etham butol</w> 8761
e du 8760
d up 8760
UU O</w> 8760
glycosphing olipids</w> 8759
F ID</w> 8758
blo oms</w> 8758
Le h 8758
Tryp an</w> 8758
ha ze</w> 8757
ca ke</w> 8757
athe roma</w> 8756
U sers</w> 8755
S AB 8754
DH HC 8754
palmito yl 8754
Apa I</w> 8754
adverti sements</w> 8754
gal lo 8753
En cour 8752
no vo 8751
dis similarity</w> 8751
Se eds</w> 8751
sumatrip tan</w> 8751
PF D</w> 8750
epox ides</w> 8750
Cle an</w> 8750
Tor res</w> 8750
an i 8749
SE AP</w> 8749
V ia</w> 8747
Inter active</w> 8747
musi cians</w> 8747
hedro n</w> 8747
pro s</w> 8746
conser ve</w> 8746
MO DY</w> 8746
be side</w> 8745
non clinical</w> 8745
Am pli 8745
T SSs</w> 8744
p NL4</w> 8744
atten d 8744
Immun oreactivity</w> 8744
outw eigh</w> 8744
U GU 8743
pe ach</w> 8743
D CFDA</w> 8742
MIC 9</w> 8742
biopolym er</w> 8742
denomin ator</w> 8742
Lope z</w> 8741
Aff ect</w> 8739
photosensiti zers</w> 8739
madi llo</w> 8739
yl lium</w> 8738
AZ D6</w> 8738
Stere otactic</w> 8738
Occa sionally</w> 8738
s C</w> 8737
incre tin</w> 8736
lact oglobulin</w> 8736
Bur n</w> 8736
Pap ua</w> 8736
an ted</w> 8735
di methoxy 8734
trans cellular</w> 8734
Pro 3</w> 8734
acet al</w> 8734
PK I</w> 8734
Y ap1</w> 8733
connec tor</w> 8733
de mineralization</w> 8732
oc erebro 8732
sy no 8732
conceptu alized</w> 8732
spo ilage</w> 8731
cem entum</w> 8730
key hole</w> 8730
p CO2</w> 8729
rom atin</w> 8729
TRI M5</w> 8729
parasit aemia</w> 8729
z ag</w> 8728
Fe ature</w> 8728
ligam entous</w> 8728
glycosyl phosphatidylinositol</w> 8728
mis localized</w> 8727
TM C</w> 8727
Pel lets</w> 8727
Secre ted</w> 8727
bronchi olar</w> 8726
ac ept</w> 8725
broad institute 8725
a B</w> 8724
sign ment</w> 8724
neuro plasticity</w> 8724
discer ned</w> 8724
butyr yl 8724
Opportun ities</w> 8724
3 i</w> 8723
d ream</w> 8723
bronch oscopic</w> 8723
stere ological</w> 8723
ST RI 8722
FI A</w> 8722
thermo electric</w> 8722
-di chloro 8722
lamb lia</w> 8721
CH3 CN</w> 8721
R L1</w> 8720
Figure 5C</w> 8720
TE MPO</w> 8719
e ability</w> 8717
H ec 8716
a P</w> 8716
ip es</w> 8716
e YFP</w> 8715
hepat opancre 8715
omas tia</w> 8715
c umber 8714
fluctu ated</w> 8714
flavon ol</w> 8714
phosph ol 8713
sk ii</w> 8713
leishman ial</w> 8713
nyst atin</w> 8713
r us 8712
tec tomies</w> 8711
Gl a</w> 8711
deple tes</w> 8711
mon te 8710
New ton</w> 8710
z ig 8709
en tae</w> 8709
medic o</w> 8709
Pe protech</w> 8709
Quanti ty</w> 8709
Tyr 5</w> 8709
immun olocalization</w> 8708
osteo protegerin</w> 8708
dichotom ized</w> 8708
PN I</w> 8707
Con currently</w> 8706
CP Ps</w> 8706
S rs2</w> 8705
of ascial</w> 8705
n af 8704
pp i</w> 8704
Ty phoon</w> 8704
limon ene</w> 8704
Recogn izing</w> 8703
K ip 8702
V WR</w> 8702
Coch lear</w> 8702
Categ ory</w> 8702
s q 8701
regi o 8701
rox icam</w> 8701
TF H</w> 8701
L XX 8700
res ampl 8700
Swit ching</w> 8700
repell ent</w> 8700
pseudot umor</w> 8700
o G</w> 8699
tic agrelor</w> 8699
ly -</w> 8699
poly gonal</w> 8699
bioge ochemical</w> 8699
did actic</w> 8699
dis accharides</w> 8698
phy ses</w> 8698
BM P7</w> 8698
ta c</w> 8697
tin amide</w> 8697
bio activities</w> 8697
crystalli zes</w> 8697
Hemip tera</w> 8697
B V2</w> 8696
dis junction</w> 8696
adi azine</w> 8696
Sch mit 8696
calcul ates</w> 8695
pal pe 8695
I MM 8694
recur rently</w> 8693
Per sian</w> 8693
Figure 8</w> 8693
HMG A1</w> 8693
ach able</w> 8692
RP TP 8692
conce aled</w> 8692
K Br</w> 8691
dys synchrony</w> 8691
AS Os</w> 8691
N ck</w> 8689
di ps 8689
Ph usion</w> 8689
Nox 1</w> 8689
kind reds</w> 8689
cercari ae</w> 8689
it al 8687
co ali 8687
CA NC 8687
oth orax</w> 8687
it alopram</w> 8685
co planar</w> 8685
- O-</w> 8684
phal anx</w> 8684
leuko plakia</w> 8684
n ir 8683
SI N 8683
SI NV</w> 8683
LI MK1</w> 8683
cave ats</w> 8682
- GGT 8681
un linked</w> 8681
Imp ul 8681
Rpo S</w> 8681
K aw 8680
inter actor</w> 8679
M olecule</w> 8678
O b</w> 8678
pro fibrotic</w> 8678
direc tive</w> 8678
PL 2</w> 8678
Immuno fluorescent</w> 8677
Ath le 8677
subj ecting</w> 8676
war m 8676
turbin ate</w> 8676
di ols</w> 8675
dis connection</w> 8675
7 f</w> 8674
lymph ovascular</w> 8674
ellip ticity</w> 8674
AM IN 8673
G As</w> 8672
neu ros 8672
NO T</w> 8672
PRA S4</w> 8672
Protoc ols</w> 8672
C incin 8671
Os we 8671
draw ings</w> 8670
Mar shall</w> 8670
DM T1</w> 8670
quin pirole</w> 8669
diver ging</w> 8669
scF vs</w> 8669
immuno phenotypic</w> 8668
mes olimbic</w> 8668
stic ky</w> 8668
Th or 8667
AM E</w> 8667
furn ace</w> 8667
dysm enorrhea</w> 8667
k obs</w> 8666
tro car</w> 8665
SP OP</w> 8665
DD R2</w> 8665
Pur o</w> 8665
C im 8664
DI AB 8664
SS Rs</w> 8664
LO V</w> 8664
TG GT 8663
8 s</w> 8662
A NS 8662
sub specialty</w> 8662
RO R</w> 8662
gra ve</w> 8662
co evolution</w> 8661
hyper prolactinemia</w> 8661
Inc ident</w> 8661
Men i 8661
knowledge able</w> 8660
S MP</w> 8659
SI F</w> 8659
t less</w> 8658
Rhod obacter</w> 8658
O DD</w> 8657
punc tu 8657
TAL ENs</w> 8657
cumber some</w> 8657
R MP</w> 8656
re ten 8656
PD E1</w> 8656
infra renal</w> 8656
col leges</w> 8655
HF pEF</w> 8655
FOLF IRI</w> 8655
plen omegaly</w> 8654
B Ca</w> 8653
techn ics</w> 8653
rhe ic</w> 8653
real m</w> 8653
Inher ited</w> 8653
S usp 8652
eth ynyl</w> 8652
Re ady</w> 8652
Bro mo 8652
waf er</w> 8652
Ce O2</w> 8651
fo et 8650
PI H</w> 8650
Del ay</w> 8650
Cur iously</w> 8649
H MB</w> 8648
e QTLs</w> 8648
L U</w> 8648
V CA</w> 8648
sh inone</w> 8648
sa ke</w> 8648
Gradi ent</w> 8648
polyp ectomy</w> 8647
Mat lab</w> 8647
b B</w> 8646
ban dry</w> 8646
F FT</w> 8645
bl ister</w> 8645
carcin oids</w> 8645
das hed</w> 8645
M N1</w> 8644
phag a</w> 8644
DRE AM</w> 8644
os ite</w> 8643
subtr active</w> 8643
obarbit one</w> 8643
l opinavir</w> 8642
L PV</w> 8642
Y an 8642
In complete</w> 8642
Acet ylcholine</w> 8642
aphthal ene</w> 8642
transhe patic</w> 8642
J ava</w> 8641
hom on 8641
institu tes</w> 8641
innoc uous</w> 8641
D us 8640
cal protectin</w> 8640
dil ator</w> 8640
TE P</w> 8639
sin o 8639
non users</w> 8638
Archi tec 8638
mucos ae</w> 8637
cy clin 8636
granul osus</w> 8636
pul posus</w> 8635
Man if 8634
trans venous</w> 8633
G upta</w> 8632
chel ates</w> 8632
pyro phosphatase</w> 8632
kno b</w> 8632
as ticity</w> 8631
ren z 8630
MC 2</w> 8630
astro zole</w> 8630
occlud ing</w> 8630
5 μM</w> 8629
sis -- 8629
T BM</w> 8628
ech oes</w> 8628
archae on</w> 8628
s F 8627
V H 8627
EX AFS</w> 8627
auto inflammatory</w> 8627
epider molysis</w> 8627
progen ies</w> 8626
Face book</w> 8626
Con cept</w> 8625
sun screen</w> 8625
Worl dwide</w> 8625
monos pecific</w> 8624
Wu han</w> 8624
b ass</w> 8623
Common ly</w> 8623
haem ostatic</w> 8622
per tuzumab</w> 8621
em it</w> 8621
pe o 8621
partic ulates</w> 8621
Ar m 8620
NO XA</w> 8620
embol isation</w> 8620
ti nea</w> 8619
EP M</w> 8619
PA X2</w> 8618
anti fungals</w> 8618
vag ue</w> 8618
B CT</w> 8617
or derly</w> 8617
idi s</w> 8617
DM SA</w> 8617
Lab el</w> 8617
doub ts</w> 8617
pill ar</w> 8617
contradic t</w> 8616
C ord 8614
ag no 8614
t d</w> 8613
U RE 8613
enlarg ing</w> 8613
tetrahydro pyridine</w> 8613
gl as</w> 8612
IT S2</w> 8612
idiosyn cr 8612
M al</w> 8611
bi profen</w> 8611
Ad vance</w> 8611
eccentr icity</w> 8611
fi el 8610
fal sely</w> 8610
TCT P</w> 8610
MIN 6</w> 8610
weap ons</w> 8610
S anti 8609
O MT</w> 8609
Ch 3</w> 8609
cre ep</w> 8609
HS V1</w> 8609
Slo an</w> 8609
M SCV</w> 8608
F SH 8607
ip el 8607
prec or 8607
MG US</w> 8607
Cincin nati</w> 8607
G DI 8606
depolymer izing</w> 8606
K ind 8605
in oma</w> 8605
HE AT</w> 8605
At 1 8605
ERB B3</w> 8605
kel oid</w> 8605
Slo ven 8605
methylene tetrahydrofolate</w> 8605
t n 8604
R 1a</w> 8604
dp p</w> 8604
polymy ositis</w> 8604
CC l</w> 8603
Process es</w> 8603
P anax</w> 8602
in elastic</w> 8602
pig let</w> 8602
E ar</w> 8601
om itting</w> 8601
MO E</w> 8601
aver ted</w> 8600
TP L</w> 8600
shrin king</w> 8600
6 X</w> 8599
SK P2</w> 8599
ZF Ns</w> 8599
- N-</w> 8598
Ro ent 8598
N or</w> 8597
p S1</w> 8597
ra x</w> 8597
cop e 8597
R hyth 8595
li vid 8595
trich um</w> 8595
0 m</w> 8594
lanth anum</w> 8594
flip ped</w> 8594
casu alties</w> 8594
spiroch etes</w> 8594
Attribu tion</w> 8594
cytos ines</w> 8593
SL CO 8593
nitro fur 8593
Indu stries</w> 8593
or able</w> 8592
il io 8592
ma teri 8592
Pe ters</w> 8592
Nu A4</w> 8592
t p 8591
a Syn</w> 8591
nucle atum</w> 8591
Multi dimensional</w> 8591
Rom ania</w> 8591
un married</w> 8590
MR F</w> 8590
fen fluramine</w> 8590
il us</w> 8589
urso deoxycholic</w> 8589
e 3</w> 8588
Tri o</w> 8587
at op 8586
Co valent</w> 8586
thi onine</w> 8586
L SS</w> 8585
HC Ps</w> 8585
le veti 8583
ylo x 8583
electroly tic</w> 8583
c ec 8582
posi t</w> 8582
AQ P</w> 8582
ventricul ography</w> 8582
ace um</w> 8581
Phosph olip 8581
Fal ls</w> 8581
mi m</w> 8580
Ca N</w> 8580
s lowest</w> 8579
dy adic</w> 8579
vaso relaxation</w> 8579
v est</w> 8578
Ex cit 8578
o y</w> 8577
ven o</w> 8577
Fb w7</w> 8577
O st 8576
AU F1</w> 8576
F ire</w> 8575
KEY WORDS</w> 8575
Commit tees</w> 8574
0 e</w> 8573
lymph ocytosis</w> 8573
crystallo id</w> 8573
im posing</w> 8572
tri be</w> 8572
glyc ocalyx</w> 8572
py rin</w> 8572
synten y</w> 8572
am ustine</w> 8571
BM PR2</w> 8571
Clar ke</w> 8571
QIA quick</w> 8571
D AB 8570
att acked</w> 8570
retin yl</w> 8569
bin uclear</w> 8568
oid omycosis</w> 8568
extinc t</w> 8568
Eosin ophilic</w> 8568
N HR</w> 8567
K EN</w> 8567
CT GT 8567
N DF</w> 8566
replic as</w> 8566
postex ercise</w> 8566
Icel and</w> 8566
Takay asu</w> 8566
c ig 8565
gen dered</w> 8565
opo e 8565
Cam el 8565
Tu mours</w> 8565
imag ine</w> 8565
omicro scopy</w> 8565
I M1</w> 8564
p Lys 8564
co operating</w> 8564
ma d</w> 8564
Sy t1</w> 8564
K etamine</w> 8563
mit ogen 8563
SM Z</w> 8563
β 3 8562
ak u</w> 8562
AG 0</w> 8562
Fre ud</w> 8562
con tempor 8561
tren ded</w> 8561
SA HS</w> 8561
pul sation</w> 8560
sit osterol</w> 8560
MAT α</w> 8560
PRIM ARY</w> 8560
to t</w> 8559
Fos B</w> 8559
Mitch ell</w> 8559
e b</w> 8558
un substituted</w> 8558
de gener 8558
RS F1</w> 8557
A go</w> 8556
pri de</w> 8556
bl er</w> 8556
Di ets</w> 8556
rib ulose</w> 8556
Pri vate</w> 8556
t gg 8555
B all 8555
CF Us</w> 8555
neo antigens</w> 8555
fill ers</w> 8555
zz le</w> 8555
phys ostigmine</w> 8555
sal meterol</w> 8554
ct g 8554
L SG</w> 8553
O smo 8553
dis places</w> 8553
to red</w> 8553
Addic tion</w> 8553
ocy tometer</w> 8552
Car bon 8552
pois oned</w> 8552
alex ithymia</w> 8552
twenti eth</w> 8552
a ve 8551
CH T</w> 8551
F FP</w> 8550
PD CD4</w> 8550
Ag RP</w> 8550
co conut</w> 8549
postin oculation</w> 8549
b acc 8548
un transformed</w> 8548
osens or</w> 8548
D DC</w> 8547
O PA</w> 8547
AG GG 8547
AZ D1</w> 8547
fis cal</w> 8547
I MR 8546
CCR 1</w> 8546
c eph 8545
B W 8545
pro states</w> 8545
olef ins</w> 8545
B OS</w> 8544
Squ are</w> 8544
Big Dye</w> 8544
Pl ex 8543
Rich mond</w> 8543
car p 8542
adipo kine</w> 8542
navi gate</w> 8542
min gly</w> 8541
E MB</w> 8540
tel misartan</w> 8540
hydraz yl</w> 8540
leveti racetam</w> 8540
abol ishing</w> 8539
La i</w> 8539
Lig ands</w> 8539
Isot ope</w> 8539
K v3</w> 8538
p H1N1</w> 8538
ph ae 8538
corpor ate</w> 8538
su fentanil</w> 8537
TR M</w> 8537
EM C</w> 8537
PRO M</w> 8537
stag n 8537
af in 8536
aro v</w> 8536
Sin us</w> 8536
Zym ed</w> 8536
Turb o</w> 8536
Fi refly</w> 8535
stitu ted</w> 8535
thio ether</w> 8535
Kir sch 8535
v owels</w> 8534
SP 4</w> 8534
but toc 8534
constric tor</w> 8534
R osa</w> 8533
op positely</w> 8533
DE VELO 8533
bus y</w> 8533
Cla I</w> 8533
guide wire</w> 8533
psycho physiological</w> 8532
Intra thecal</w> 8532
m PTP</w> 8531
I ba1</w> 8531
δ H</w> 8531
an odes</w> 8531
st ann 8531
concentr ator</w> 8531
gre l</w> 8531
r ud 8530
carbapenem ase</w> 8530
aff ord 8529
asth enic</w> 8529
I tk</w> 8528
Y u 8528
immuno electrophoresis</w> 8528
Prol actin</w> 8528
R CCs</w> 8527
re c</w> 8527
war nings</w> 8527
supra optic</w> 8527
hypercholesterola emia</w> 8527
og ues</w> 8526
tan ks</w> 8526
SF T</w> 8526
ö r 8525
orth od 8524
hydrogen ated</w> 8524
Mos quit 8523
fur fural</w> 8522
SEN P1</w> 8522
cement less</w> 8522
C ut</w> 8521
i PTH</w> 8521
W SSV</w> 8521
Cd Te</w> 8521
implem entations</w> 8521
Radio active</w> 8521
sp I</w> 8520
cryptoc occosis</w> 8520
o estrogens</w> 8519
E DCs</w> 8519
go s</w> 8519
epi blast</w> 8518
abut ments</w> 8518
far m 8517
Sa O2</w> 8517
bl ades</w> 8516
Rock y</w> 8516
Seiz ures</w> 8516
sof osbuvir</w> 8515
squ at</w> 8515
Var ying</w> 8515
o facial</w> 8514
acteri c</w> 8514
met ad 8514
bl istering</w> 8514
cop y 8514
c rom 8513
pl europ 8513
em ail</w> 8513
Aβ PP</w> 8513
D extr 8511
V ar</w> 8511
Sch w 8511
Mont gom 8511
N uc 8510
aut ografts</w> 8510
extrac hromosomal</w> 8510
cycl ed</w> 8510
Manip ulation</w> 8510
phot om 8509
haemat oxylin</w> 8509
sul indac</w> 8508
AG O</w> 8508
fore foot</w> 8507
R J</w> 8506
ren uous</w> 8506
non cancerous</w> 8506
altern ately</w> 8506
prototyp es</w> 8506
G PVI</w> 8505
CL SI</w> 8505
CA MK 8505
adrenom edullin</w> 8505
A 5A</w> 8504
Ch eck</w> 8504
decidu alization</w> 8504
G NA 8503
SN B</w> 8503
exchang ers</w> 8503
Prom ising</w> 8503
l b</w> 8502
CI P2A</w> 8502
denti nal</w> 8502
Immuno deficiency</w> 8502
TX B2</w> 8502
Wol ff</w> 8502
FAC Scan</w> 8501
glycosyl ases</w> 8501
b illing</w> 8500
il li 8500
Land au</w> 8500
Citro bacter</w> 8500
U BL</w> 8499
ver mis</w> 8499
LD I</w> 8498
TIV ATION</w> 8498
突 变 8497
cel e</w> 8497
TAL 1</w> 8497
itrac in</w> 8497
Sugg estions</w> 8497
te lling</w> 8496
at adine</w> 8496
de repressed</w> 8496
metasta sizing</w> 8496
sand y</w> 8496
Possi bilities</w> 8496
g ins</w> 8495
Metabol ites</w> 8495
N CD</w> 8494
p if 8494
NL RP1</w> 8494
tele health</w> 8494
SGL T2</w> 8494
sc an 8493
TU G</w> 8493
p ag 8492
intro n 8492
CV R</w> 8492
TX A</w> 8492
ependym oma</w> 8492
Vps 4</w> 8490
E hl 8489
un eus</w> 8489
aqu ine</w> 8489
neutr ality</w> 8488
Life time</w> 8488
X G</w> 8487
Fig. 6 8487
radio resistant</w> 8487
spillo ver</w> 8487
arthro graphy</w> 8485
ribonucle oside</w> 8485
parap aresis</w> 8485
macronutri ent</w> 8485
Not tingham</w> 8484
Man ager</w> 8484
N PR</w> 8483
Y F 8483
NB Ds</w> 8483
arachid on 8483
Gh relin</w> 8483
B CCs</w> 8482
re intervention</w> 8482
ab ac 8482
Man ufac 8482
CRP S</w> 8482
h m 8481
f rank</w> 8481
fin asteride</w> 8481
Hy al 8481
At 5 8481
omening ocele</w> 8481
k ens</w> 8480
D KA</w> 8479
flav oprotein</w> 8479
hirsu tism</w> 8479
physio therapists</w> 8479
Egr 1</w> 8479
C os</w> 8478
Lar yngeal</w> 8478
H S1</w> 8477
abl ate</w> 8477
Ta enia</w> 8477
Vi able</w> 8477
An is 8476
period ate</w> 8476
N GM</w> 8475
on an</w> 8475
pur ging</w> 8475
Po rous</w> 8475
G n</w> 8474
ter ly</w> 8474
fu tile</w> 8474
OT f</w> 8474
baical ein</w> 8474
sw ich</w> 8472
Bir A</w> 8472
non infected</w> 8471
weak ens</w> 8471
oz antinib</w> 8471
R i</w> 8470
bic alutamide</w> 8470
S tained</w> 8468
AD O</w> 8468
hol id 8468
MK K7</w> 8468
gyr i</w> 8468
circ RNA</w> 8468
Hab it 8467
rap eseed</w> 8466
F PKM</w> 8465
nanoc ap 8465
sacc ular</w> 8465
obstetr icians</w> 8465
Breast feeding</w> 8465
interpre tive</w> 8464
TR F</w> 8464
Ta i</w> 8463
Amb ly 8463
tendin opathy</w> 8463
f H</w> 8462
Al bert</w> 8461
Ger m</w> 8461
ge ography</w> 8460
re pletion</w> 8459
HP E</w> 8459
Hyper tensive</w> 8459
DA As</w> 8459
consangu inity</w> 8459
con verse</w> 8458
im practical</w> 8458
dr um</w> 8457
euch romatin</w> 8457
SU V 8456
amid ated</w> 8456
Australi ans</w> 8456
fucos ylated</w> 8455
H VEM</w> 8454
B ands</w> 8454
CM F</w> 8454
potenti ometric</w> 8454
G BD</w> 8453
ther hood</w> 8453
chemo receptor</w> 8453
RG S4</w> 8453
I h</w> 8452
E AL</w> 8452
preced ent</w> 8452
Simpl ified</w> 8452
tri tic 8451
hemi plegic</w> 8451
3 σ</w> 8450
vo ids</w> 8450
Arch ive</w> 8450
repur posing</w> 8450
L ange</w> 8449
Com pression</w> 8449
Jun B</w> 8449
Pax 7</w> 8449
n ography</w> 8448
y anide</w> 8448
Ex ac 8448
AA V8</w> 8448
Crystall ographic</w> 8448
erythr itol</w> 8448
inhibi tions</w> 8446
A mar 8445
quin tiles</w> 8445
oid an</w> 8445
tortu osity</w> 8445
D SE</w> 8444
hi atal</w> 8444
z VAD</w> 8442
k g 8442
C 2B</w> 8440
L HON</w> 8440
fol listatin</w> 8440
myco tic</w> 8440
tricho sis</w> 8440
opter an</w> 8440
res etting</w> 8438
eg er</w> 8438
PP ase</w> 8438
Fil ms</w> 8438
coumar ins</w> 8438
Sm aller</w> 8437
DA XX</w> 8437
hypom agnes 8437
mil i 8436
cresc ent</w> 8436
k son</w> 8435
cor rid 8435
Ar ch</w> 8435
vag inosis</w> 8435
HA p</w> 8435
Ric hard 8435
L AG</w> 8433
non compliance</w> 8433
set tle</w> 8433
Gil bert</w> 8433
trache otomy</w> 8432
R LR</w> 8431
pur p 8431
Exclud ing</w> 8431
J AZ 8430
P it</w> 8430
RN A3</w> 8430
obuty rate</w> 8430
e um</w> 8429
x s</w> 8429
Mc Gill</w> 8429
lyso phosphatidic</w> 8429
Fit z 8429
R d</w> 8428
S aw 8428
te -</w> 8427
proc ured</w> 8427
coc cy 8427
HIF 2α</w> 8427
Sema 3A</w> 8427
N 4 8426
clamp s</w> 8426
tur g 8425
TC CA 8425
Ip swich</w> 8425
Chr ys 8425
O li 8424
ht ml</w> 8424
u zumab</w> 8423
mo ul 8423
co aches</w> 8423
aut ocatalytic</w> 8423
lipoly tica</w> 8423
A str 8422
cy anine</w> 8422
Tr in 8422
hal ophilic</w> 8421
Hawa i 8421
H ud 8420
ear al 8420
p UL3</w> 8419
D FP</w> 8419
pseud ocyst</w> 8419
phot olab 8418
Cd Cl2</w> 8418
Emer gence</w> 8417
crustace an</w> 8417
C ancers</w> 8416
b R</w> 8416
ham pers</w> 8416
oblong ata</w> 8416
mono amines</w> 8415
atetra enoic</w> 8415
U AC 8414
un stained</w> 8414
Glu R</w> 8414
erup tions</w> 8414
exfoli ated</w> 8414
vol es</w> 8413
crystall ins</w> 8413
S MT</w> 8412
diab et 8412
rac ing</w> 8412
Tf R1</w> 8412
L c</w> 8411
g low</w> 8411
SM N2</w> 8411
gamm adelta</w> 8411
Atax ia</w> 8411
b aths</w> 8409
f 1Δ</w> 8409
sep tins</w> 8409
Her pes 8409
accre tion</w> 8409
tetram erization</w> 8408
P Ns</w> 8407
PPAR G</w> 8407
ST RING</w> 8406
VE RED</w> 8406
break throughs</w> 8406
EBN A2</w> 8406
F ing 8405
D MT</w> 8405
de c</w> 8405
transl ations</w> 8405
0 Cas</w> 8404
wan dering</w> 8404
ti bio 8403
trac ings</w> 8403
nec tar</w> 8403
Anti genic</w> 8403
te thers</w> 8402
per idol</w> 8402
Gon ad 8402
polyc rystalline</w> 8402
di methylation</w> 8401
as accharide</w> 8401
har der</w> 8401
Arab ian</w> 8401
Montgom ery</w> 8400
othyro xine</w> 8399
ung s 8398
NOT ES</w> 8398
antimy cotic</w> 8398
as ted</w> 8397
aro tid</w> 8397
SN X1</w> 8397
evap or 8397
r 3</w> 8396
col iforms</w> 8396
non ischemic</w> 8395
lis teri 8395
multim orbidity</w> 8395
impar ted</w> 8395
mut p5</w> 8394
fin er</w> 8394
inte ger</w> 8394
cop ur 8394
aver tebral</w> 8394
quin ol</w> 8394
ot opically</w> 8393
az iri 8393
Richard son</w> 8393
de amidation</w> 8392
pe do</w> 8392
epis tem 8392
Refl ections</w> 8392
ol ization</w> 8391
MAD 2</w> 8391
foreg ut</w> 8391
tumourigen esis</w> 8390
in do</w> 8389
od one</w> 8389
acid ophilus</w> 8389
Publ ication</w> 8389
aci ón</w> 8389
Fron tal</w> 8389
V A 8388
oc eles</w> 8388
sur plus</w> 8387
EB A</w> 8387
Glob ally</w> 8387
hir udin</w> 8387
al amin</w> 8386
ud er</w> 8386
vis fatin</w> 8386
arcis sis 8386
H ei 8385
cl over</w> 8385
vel oc 8385
SE 1</w> 8385
4 J</w> 8384
E uro</w> 8384
EC K</w> 8384
Tol C</w> 8384
north western</w> 8384
im precision</w> 8383
oste omalacia</w> 8383
schol ars</w> 8383
B yp 8382
rec umb 8380
oste oma</w> 8380
GL Y 8380
phag ocyto 8380
Eth yl</w> 8380
devel opers</w> 8379
SU P</w> 8379
LI L 8378
Tai pei</w> 8378
nebul izer</w> 8378
com ig 8377
wid ened</w> 8377
damp ened</w> 8377
Moun ting</w> 8376
hus bands</w> 8376
can desartan</w> 8375
PO L</w> 8375
sh er</w> 8374
complex ing</w> 8374
Ser ver</w> 8374
SN c</w> 8373
encephal omy 8373
chel ated</w> 8373
Dele tions</w> 8373
2 H2O</w> 8372
S ad 8372
iz ard</w> 8372
tin es</w> 8372
Ten nes 8372
a P2</w> 8371
ul osis</w> 8371
artic ulated</w> 8371
b c1</w> 8369
al ar 8369
plac entae</w> 8369
hyper intensity</w> 8369
mel amine</w> 8369
HB 2</w> 8369
Che Y</w> 8369
G y 8368
Diff icul 8368
anti fibrotic</w> 8367
eth iol</w> 8367
hor seshoe</w> 8367
Cor tex</w> 8367
harmon ics</w> 8367
fisher ies</w> 8367
comman ds</w> 8367
Shap iro</w> 8367
N in 8366
g host</w> 8366
lan soprazole</w> 8366
worsen s</w> 8366
n. s.</w> 8366
fid uc 8366
Y ou 8365
HC Q</w> 8365
asta xanthin</w> 8365
PP NP</w> 8365
i os 8364
G ur 8364
Heterogene ous</w> 8364
rheum atism</w> 8363
spot ting</w> 8363
Artem is</w> 8363
exc ise</w> 8362
aor to</w> 8362
Nerv ous</w> 8362
on am</w> 8361
di o 8361
De position</w> 8361
IT S1</w> 8361
Consum er</w> 8361
Y E</w> 8360
bl ers</w> 8360
Dis abilities</w> 8360
scis sile</w> 8360
l p 8358
ap ically</w> 8358
hexa histidine</w> 8358
1 l</w> 8357
or mycosis</w> 8357
cl umps</w> 8356
IN DU 8356
Res in</w> 8356
sit agliptin</w> 8356
H x</w> 8355
es on</w> 8355
man date</w> 8355
shar p 8355
cytot roph 8355
RAG 2</w> 8354
ker a 8354
Fit ness</w> 8354
peroxire doxin</w> 8354
itochond rial</w> 8354
Clp P</w> 8353
N gn 8352
photo products</w> 8351
mess aging</w> 8351
trochan ter</w> 8351
metabol izers</w> 8350
neurosph ere</w> 8350
a tech</w> 8349
sh ig 8349
sulf ox 8349
arte fact</w> 8349
north west</w> 8349
. net</w> 8348
HM F</w> 8348
Tit ration</w> 8348
L NG</w> 8346
ig en</w> 8346
hemodi lution</w> 8345
demarc ated</w> 8345
osyn thetic</w> 8344
Rib osomal</w> 8344
electro acupuncture</w> 8343
exer tional</w> 8343
ba its</w> 8343
DAP K</w> 8343
I SP</w> 8342
I kappaB</w> 8342
W T 8342
bu gs</w> 8342
AT P1</w> 8342
CD R1</w> 8342
C 2A</w> 8341
mi R 8341
circul ated</w> 8341
as hi 8340
zo oplankton</w> 8340
I SC 8339
Extrem e</w> 8339
L P2</w> 8338
absc essus</w> 8338
Meta Morph</w> 8338
pyru vic</w> 8338
9 W</w> 8337
ti ans</w> 8336
des methyl 8336
Smur f1</w> 8336
solit ons</w> 8336
W DR5</w> 8335
icul aris</w> 8335
Lac I</w> 8335
Da hl</w> 8335
x C</w> 8334
G SIS</w> 8334
exten sors</w> 8334
phospho rescence</w> 8334
ASC O</w> 8334
su itably</w> 8333
aut olysosomes</w> 8333
na 1</w> 8333
epit axial</w> 8332
oce anic</w> 8331
m EPSC</w> 8330
oly b 8330
n intedanib</w> 8329
om ethyl 8329
ca vir</w> 8329
Lys M</w> 8329
Fus obacterium</w> 8329
t 0</w> 8328
R ation 8328
strong ylus</w> 8328
1 ---- 8327
Le ague</w> 8327
Kn ock</w> 8327
Y FV</w> 8326
er i 8326
af ric 8326
PL oS</w> 8326
p sittac 8325
|
BioGPT/data/biogpt_large_bpecodes/0
|
{
"file_path": "BioGPT/data/biogpt_large_bpecodes",
"repo_id": "BioGPT",
"token_count": 418071
}
| 142 |
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