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# 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.
"""Tokenization classes for MPNet."""
import collections
import os
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"microsoft/mpnet-base": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"microsoft/mpnet-base": {"do_lower_case": True},
}
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as reader:
tokens = reader.readlines()
for index, token in enumerate(tokens):
token = token.rstrip("\n")
vocab[token] = index
return vocab
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
class MPNetTokenizer(PreTrainedTokenizer):
"""
This tokenizer inherits from :class:`~transformers.BertTokenizer` which contains most of the methods. Users should
refer to the superclass for more information regarding methods.
Args:
vocab_file (:obj:`str`):
Path to the vocabulary file.
do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to do basic tokenization before WordPiece.
never_split (:obj:`Iterable`, `optional`):
Collection of tokens which will never be split during tokenization. Only has an effect when
:obj:`do_basic_tokenize=True`
bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the :obj:`cls_token`.
eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
The end of sequence token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the end of
sequence. The token used is the :obj:`sep_token`.
sep_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
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.
mask_token (:obj:`str`, `optional`, defaults to :obj:`"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this `issue
<https://github.com/huggingface/transformers/issues/328>`__).
strip_accents: (:obj:`bool`, `optional`):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for :obj:`lowercase` (as in the original BERT).
"""
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
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="[UNK]",
pad_token="<pad>",
mask_token="<mask>",
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kwargs,
)
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
@property
def do_lower_case(self):
return self.basic_tokenizer.do_lower_case
@property
def vocab_size(self):
return len(self.vocab)
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text):
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
# If the token is part of the never_split set
if token in self.basic_tokenizer.never_split:
split_tokens.append(token)
else:
split_tokens += self.wordpiece_tokenizer.tokenize(token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
return self.vocab.get(token, self.vocab.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.ids_to_tokens.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A MPNet sequence has the following format:
- single sequence: ``<s> X </s>``
- pair of sequences: ``<s> A </s></s> B </s>``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` methods.
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True if the token list is already formatted with special tokens for the model
Returns:
:obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formated with special tokens for the model."
)
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. MPNet does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
else:
vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
"Saving vocabulary to {}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!".format(vocab_file)
)
index = token_index
writer.write(token + "\n")
index += 1
return (vocab_file,)
# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
class BasicTokenizer(object):
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to lowercase the input when tokenizing.
never_split (:obj:`Iterable`, `optional`):
Collection of tokens which will never be split during tokenization. Only has an effect when
:obj:`do_basic_tokenize=True`
tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this `issue
<https://github.com/huggingface/transformers/issues/328>`__).
strip_accents: (:obj:`bool`, `optional`):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for :obj:`lowercase` (as in the original BERT).
"""
def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(never_split)
self.tokenize_chinese_chars = tokenize_chinese_chars
self.strip_accents = strip_accents
def tokenize(self, text, never_split=None):
"""
Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
WordPieceTokenizer.
Args:
**never_split**: (`optional`) list of str
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
:func:`PreTrainedTokenizer.tokenize`) List of token not to split.
"""
# union() returns a new set by concatenating the two sets.
never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
if self.tokenize_chinese_chars:
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if token not in never_split:
if self.do_lower_case:
token = token.lower()
if self.strip_accents is not False:
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if never_split is not None and text in never_split:
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x20000 and cp <= 0x2A6DF) #
or (cp >= 0x2A700 and cp <= 0x2B73F) #
or (cp >= 0x2B740 and cp <= 0x2B81F) #
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
class WordpieceTokenizer(object):
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
tokenization using the given vocabulary.
For example, :obj:`input = "unaffable"` wil return as output :obj:`["un", "##aff", "##able"]`.
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BasicTokenizer`.
Returns:
A list of wordpiece tokens.
"""
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
|
AdaMix/src/transformers/models/mpnet/tokenization_mpnet.py/0
|
{
"file_path": "AdaMix/src/transformers/models/mpnet/tokenization_mpnet.py",
"repo_id": "AdaMix",
"token_count": 9987
}
| 61 |
# 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 OpenAI GPT checkpoint."""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):
# Construct model
if openai_config_file == "":
config = OpenAIGPTConfig()
else:
config = OpenAIGPTConfig.from_json_file(openai_config_file)
model = OpenAIGPTModel(config)
# Load weights from numpy
load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path)
# Save pytorch-model
pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
torch.save(model.state_dict(), pytorch_weights_dump_path)
print("Save configuration file to {}".format(pytorch_config_dump_path))
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--openai_checkpoint_folder_path",
default=None,
type=str,
required=True,
help="Path to the TensorFlow checkpoint path.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--openai_config_file",
default="",
type=str,
help="An optional config json file corresponding to the pre-trained OpenAI model. \n"
"This specifies the model architecture.",
)
args = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
|
AdaMix/src/transformers/models/openai/convert_openai_original_tf_checkpoint_to_pytorch.py/0
|
{
"file_path": "AdaMix/src/transformers/models/openai/convert_openai_original_tf_checkpoint_to_pytorch.py",
"repo_id": "AdaMix",
"token_count": 969
}
| 62 |
# coding=utf-8
# Copyright 2020 Google 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 class for model PEGASUS."""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
PegasusTokenizer = None
logger = logging.get_logger(__name__)
SPIECE_UNDERLINE = "▁"
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
"tokenizer_file": {
"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"google/pegasus-xsum": 512,
}
class PegasusTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" PEGASUS tokenizer (backed by HuggingFace's `tokenizers` library). Based on `Unigram
<https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models>`__.
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main
methods. Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (:obj:`str`):
`SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
contains the vocabulary necessary to instantiate a tokenizer.
pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
The end of sequence token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the end of
sequence. The token used is the :obj:`sep_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.
mask_token (:obj:`str`, `optional`, defaults to :obj:`"<mask_2>"`):
The token used for masking single token values. This is the token used when training this model with masked
language modeling (MLM). This is the token that the PEGASUS encoder will try to predict during pretraining.
It corresponds to `[MASK2]` in `PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive
Summarization <https://arxiv.org/pdf/1912.08777.pdf>`__.
mask_token_sent (:obj:`str`, `optional`, defaults to :obj:`"<mask_1>"`):
The token used for masking whole target sentences. This is the token used when training this model with gap
sentences generation (GSG). This is the sentence that the PEGASUS decoder will try to predict during
pretraining. It corresponds to `[MASK1]` in `PEGASUS: Pre-training with Extracted Gap-sentences for
Abstractive Summarization <https://arxiv.org/pdf/1912.08777.pdf>`__.
additional_special_tokens (:obj:`List[str]`, `optional`):
Additional special tokens used by the tokenizer. If no additional_special_tokens are provided <mask_2> and
<unk_2, ..., unk_102> are used as additional special tokens corresponding to the `original PEGASUS
tokenizer
<https://github.com/google-research/pegasus/blob/939830367bcf411193d2b5eca2f2f90f3f9260ca/pegasus/ops/pretrain_parsing_ops.cc#L66>`__
that uses the tokens 2 - 104 only for pretraining
"""
offset = 103 # entries 2-104 are only used for pretraining
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = PegasusTokenizer
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
tokenizer_file=None,
pad_token="<pad>",
eos_token="</s>",
unk_token="<unk>",
mask_token="<mask_2>",
mask_token_sent="<mask_1>",
additional_special_tokens=None,
**kwargs
):
if additional_special_tokens is not None:
assert isinstance(
additional_special_tokens, list
), f"additional_special_tokens should be of type {type(list)}, but is {type(additional_special_tokens)}"
additional_special_tokens_extended = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f"<unk_{i}>" for i in range(len(additional_special_tokens_extended), self.offset - 1)
]
if len(set(additional_special_tokens_extended)) != len(additional_special_tokens_extended):
raise ValueError(
f"Please make sure that the provided additional_special_tokens do not contain an incorrectly shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."
)
additional_special_tokens = additional_special_tokens_extended
else:
additional_special_tokens = [mask_token_sent]
additional_special_tokens += [f"<unk_{i}>" for i in range(2, self.offset)]
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
pad_token=pad_token,
eos_token=eos_token,
unk_token=unk_token,
mask_token=mask_token,
mask_token_sent=mask_token_sent,
additional_special_tokens=additional_special_tokens,
**kwargs,
)
self.vocab_file = vocab_file
def _special_token_mask(self, seq):
all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
assert all_special_ids == set(
range(len(self.additional_special_tokens) + 3)
), f"There should be 3 special tokens: mask_token, pad_token, and eos_token + {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}"
return [1 if x in all_special_ids else 0 for x in seq]
def get_special_tokens_mask(
self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""Get list where entries are [1] if a token is [eos] or [pad] else 0."""
if already_has_special_tokens:
return self._special_token_mask(token_ids_0)
elif token_ids_1 is None:
return self._special_token_mask(token_ids_0) + [1]
else:
return self._special_token_mask(token_ids_0 + token_ids_1) + [1]
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""
Build model inputs from a sequence by adding eos to the end. no bos token is added to the front.
- single sequence: ``X </s>``
- pair of sequences: ``A B </s>`` (not intended use)
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
if token_ids_1 is None:
return token_ids_0 + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_0 + token_ids_1 + [self.eos_token_id]
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
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
|
AdaMix/src/transformers/models/pegasus/tokenization_pegasus_fast.py/0
|
{
"file_path": "AdaMix/src/transformers/models/pegasus/tokenization_pegasus_fast.py",
"repo_id": "AdaMix",
"token_count": 3907
}
| 63 |
# 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.
"""Convert Reformer checkpoint."""
import argparse
import pickle
import numpy as np
import torch
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def set_param(torch_layer, weight, bias=None):
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, "{} layer.weight does not match".format(torch_layer)
torch_layer.weight = torch.nn.Parameter(weight)
if bias is not None:
assert torch_layer.bias.shape == bias.shape, "{} layer.bias does not match".format(torch_layer)
torch_layer.bias = torch.nn.Parameter(bias)
def set_layer_weights_in_torch_lsh(weights, torch_layer, hidden_size):
# set torch weights for 1-to-1 comparison
np_query_key = np.asarray(weights[0])
np_value = np.asarray(weights[1])
np_dense = np.asarray(weights[2])
set_param(
torch_layer.self_attention.query_key,
torch.tensor(np_query_key).transpose(1, 2).contiguous().view(-1, hidden_size),
)
set_param(
torch_layer.self_attention.value,
torch.tensor(np_value).transpose(1, 2).contiguous().view(-1, hidden_size),
)
set_param(
torch_layer.output.dense,
torch.tensor(np_dense).view(-1, hidden_size).contiguous().transpose(0, 1),
)
def set_layer_weights_in_torch_local(weights, torch_layer, hidden_size):
# set torch weights for 1-to-1 comparison
np_query = np.asarray(weights[0])
np_key = np.asarray(weights[1])
np_value = np.asarray(weights[2])
np_dense = np.asarray(weights[3])
set_param(
torch_layer.self_attention.query,
torch.tensor(np_query).transpose(1, 2).contiguous().view(-1, hidden_size),
)
set_param(
torch_layer.self_attention.key,
torch.tensor(np_key).transpose(1, 2).contiguous().view(-1, hidden_size),
)
set_param(
torch_layer.self_attention.value,
torch.tensor(np_value).transpose(1, 2).contiguous().view(-1, hidden_size),
)
set_param(
torch_layer.output.dense,
torch.tensor(np_dense).view(-1, hidden_size).contiguous().transpose(0, 1),
)
def set_block_weights_in_torch(weights, torch_block, hidden_size):
# layernorm 1
layer_norm_1 = weights[0][0][0]
layer_norm_1_weight = np.asarray(layer_norm_1[0])
layer_norm_1_bias = np.asarray(layer_norm_1[1])
set_param(
torch_block.attention.layer_norm,
torch.tensor(layer_norm_1_weight),
torch.tensor(layer_norm_1_bias),
)
# lsh weights + output
attn_weights = weights[0][1]
if len(attn_weights) < 4:
set_layer_weights_in_torch_lsh(attn_weights, torch_block.attention, hidden_size)
else:
set_layer_weights_in_torch_local(attn_weights, torch_block.attention, hidden_size)
# intermediate weighs
intermediate_weights = weights[2][0][1][2]
# Chunked Feed Forward
if len(intermediate_weights) == 4:
intermediate_weights = intermediate_weights[2]
# layernorm 2
layer_norm_2_weight = np.asarray(intermediate_weights[0][0])
layer_norm_2_bias = np.asarray(intermediate_weights[0][1])
set_param(
torch_block.feed_forward.layer_norm,
torch.tensor(layer_norm_2_weight),
torch.tensor(layer_norm_2_bias),
)
# intermediate dense
inter_dense_weight = np.asarray(intermediate_weights[1][0])
inter_dense_bias = np.asarray(intermediate_weights[1][1])
set_param(
torch_block.feed_forward.dense.dense,
torch.tensor(inter_dense_weight).transpose(0, 1).contiguous(),
torch.tensor(inter_dense_bias),
)
# intermediate out
out_dense_weight = np.asarray(intermediate_weights[4][0])
out_dense_bias = np.asarray(intermediate_weights[4][1])
set_param(
torch_block.feed_forward.output.dense,
torch.tensor(out_dense_weight).transpose(0, 1).contiguous(),
torch.tensor(out_dense_bias),
)
def set_model_weights_in_torch(weights, torch_model, hidden_size):
# reformer model
torch_model_reformer = torch_model.reformer
# word embeds
word_embeddings = np.asarray(weights[1])
set_param(
torch_model_reformer.embeddings.word_embeddings,
torch.tensor(word_embeddings),
)
if isinstance(weights[3], tuple):
position_embeddings = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights)):
emb_weights = np.asarray(weights[3][emb_idx][0])
assert position_embeddings.weights[emb_idx].shape == emb_weights.shape, "{} emb does not match".format(
position_embeddings[emb_idx]
)
position_embeddings.weights[emb_idx] = torch.nn.Parameter(torch.tensor(emb_weights))
trax_layer_weights = weights[5]
assert len(torch_model_reformer.encoder.layers) * 4 == len(
trax_layer_weights
), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers):
block_weights = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(block_weights, layer, hidden_size)
# output layer norm
layer_norm_out_weight = np.asarray(weights[7][0])
layer_norm_out_bias = np.asarray(weights[7][1])
set_param(
torch_model_reformer.encoder.layer_norm,
torch.tensor(layer_norm_out_weight),
torch.tensor(layer_norm_out_bias),
)
# output embeddings
output_embed_weights = np.asarray(weights[9][0])
output_embed_bias = np.asarray(weights[9][1])
set_param(
torch_model.lm_head.decoder,
torch.tensor(output_embed_weights).transpose(0, 1).contiguous(),
torch.tensor(output_embed_bias),
)
def convert_trax_checkpoint_to_pytorch(trax_model_pkl_path, config_file, pytorch_dump_path):
# Initialise PyTorch model
config = ReformerConfig.from_json_file(config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = ReformerModelWithLMHead(config)
with open(trax_model_pkl_path, "rb") as f:
model_weights = pickle.load(f)["weights"]
set_model_weights_in_torch(model_weights, model, config.hidden_size)
# 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(
"--trax_model_pkl_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 Reformer 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_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
|
AdaMix/src/transformers/models/reformer/convert_reformer_trax_checkpoint_to_pytorch.py/0
|
{
"file_path": "AdaMix/src/transformers/models/reformer/convert_reformer_trax_checkpoint_to_pytorch.py",
"repo_id": "AdaMix",
"token_count": 3189
}
| 64 |
# coding=utf-8
# Copyright 2021 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.
""" Speech2Text model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/s2t-small-librispeech-asr": "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json",
# See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
class Speech2TextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.Speech2TextModel`. It is used
to instantiate an Speech2Text 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 Speech2Text
`facebook/s2t-small-librispeech-asr <https://huggingface.co/facebook/s2t-small-librispeech-asr>`__ 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.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 50265):
Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by
the :obj:`inputs_ids` passed when calling :class:`~transformers.Speech2TextModel`
d_model (:obj:`int`, `optional`, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (:obj:`int`, `optional`, defaults to 12):
Number of encoder layers.
decoder_layers (:obj:`int`, `optional`, defaults to 12):
Number of decoder layers.
encoder_attention_heads (:obj:`int`, `optional`, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (:obj:`int`, `optional`, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (:obj:`str` or :obj:`function`, `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"` and :obj:`"gelu_new"` are supported.
dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout ratio for classifier.
init_std (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
The LayerDrop probability for the encoder. See the `LayerDrop paper <see
https://arxiv.org/abs/1909.11556>`__ for more details.
decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
The LayerDrop probability for the decoder. See the `LayerDrop paper <see
https://arxiv.org/abs/1909.11556>`__ for more details.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not the model should return the last key/values attentions (not used by all models).
max_source_positions (:obj:`int`, `optional`, defaults to 6000):
The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
max_target_positions: (:obj:`int`, `optional`, defaults to 1024):
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).
num_conv_layers (:obj:`int`, `optional`, defaults to 2):
Number of 1D convolutional layers in the conv module.
conv_kernel_sizes (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(5, 5)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the conv module. The length
of :obj:`conv_kernel_sizes` has to match :obj:`num_conv_layers`.
conv_channels (:obj:`int`, `optional`, defaults to 1024):
An integer defining the number of output channels of each convolution layers except the final one in the
conv module.
input_feat_per_channel (:obj:`int`, `optional`, defaults to 80):
An integer specifying the size of feature vector. This is also the dimentions of log-mel filter-bank
features.
input_channels (:obj:`int`, `optional`, defaults to 1):
An integer specifying number of input channels of the input feature vector.
Example::
>>> from transformers import Speech2TextModel, Speech2TextConfig
>>> # Initializing a Speech2Text s2t_transformer_s style configuration
>>> configuration = Speech2TextConfig()
>>> # Initializing a model from the s2t_transformer_s style configuration
>>> model = Speech2TextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "speech_to_text"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=10000,
encoder_layers=12,
encoder_ffn_dim=2048,
encoder_attention_heads=4,
decoder_layers=6,
decoder_ffn_dim=2048,
decoder_attention_heads=4,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
use_cache=True,
is_encoder_decoder=True,
activation_function="relu",
d_model=256,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
decoder_start_token_id=2,
classifier_dropout=0.0,
scale_embedding=True,
gradient_checkpointing=False,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
max_source_positions=6000,
max_target_positions=1024,
num_conv_layers=2,
conv_kernel_sizes=(5, 5),
conv_channels=1024,
input_feat_per_channel=80,
input_channels=1,
**kwargs
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.gradient_checkpointing = gradient_checkpointing
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions
self.num_conv_layers = num_conv_layers
self.conv_kernel_sizes = list(conv_kernel_sizes)
self.conv_channels = conv_channels
self.input_feat_per_channel = input_feat_per_channel
self.input_channels = input_channels
if len(self.conv_kernel_sizes) != self.num_conv_layers:
raise ValueError(
"Configuration for convolutional module is incorrect."
"It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers`"
f"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`,"
f"`config.num_conv_layers = {self.num_conv_layers}`."
)
@property
def num_attention_heads(self) -> int:
return self.encoder_attention_heads
@property
def hidden_size(self) -> int:
return self.d_model
|
AdaMix/src/transformers/models/speech_to_text/configuration_speech_to_text.py/0
|
{
"file_path": "AdaMix/src/transformers/models/speech_to_text/configuration_speech_to_text.py",
"repo_id": "AdaMix",
"token_count": 3954
}
| 65 |
# 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.
"""
Utilities for PyTorch Transformer XL model. Directly adapted from https://github.com/kimiyoung/transformer-xl.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
# CUDA_MAJOR = int(torch.version.cuda.split('.')[0])
# CUDA_MINOR = int(torch.version.cuda.split('.')[1])
class ProjectedAdaptiveLogSoftmax(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, keep_order=False):
super().__init__()
self.n_token = n_token
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [n_token]
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
if self.n_clusters > 0:
self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed))
self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters))
self.out_layers = nn.ModuleList()
self.out_projs = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs)):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed)))
else:
self.out_projs.append(None)
self.out_layers.append(nn.Linear(d_embed, n_token))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
d_emb_i = d_embed // (div_val ** i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_emb_i)))
self.out_layers.append(nn.Linear(d_emb_i, r_idx - l_idx))
self.keep_order = keep_order
def _compute_logit(self, hidden, weight, bias, proj):
if proj is None:
logit = F.linear(hidden, weight, bias=bias)
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
proj_hid = F.linear(hidden, proj.t().contiguous())
logit = F.linear(proj_hid, weight, bias=bias)
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def forward(self, hidden, labels=None, keep_order=False):
"""
Params:
hidden :: [len*bsz x d_proj]
labels :: [len*bsz
Return:
if labels is None: out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabulary else: out ::
[(len-1)*bsz] Negative log likelihood. We could replace this implementation by the native PyTorch one if
theirs had an option to set bias on all clusters in the native one. here:
https://github.com/pytorch/pytorch/blob/dbe6a7a9ff1a364a8706bf5df58a1ca96d2fd9da/torch/nn/modules/adaptive.py#L138
"""
if labels is not None:
# Shift so that tokens < n predict n
hidden = hidden[..., :-1, :].contiguous()
labels = labels[..., 1:].contiguous()
hidden = hidden.view(-1, hidden.size(-1))
labels = labels.view(-1)
if hidden.size(0) != labels.size(0):
raise RuntimeError("Input and labels should have the same size " "in the batch dimension.")
else:
hidden = hidden.view(-1, hidden.size(-1))
if self.n_clusters == 0:
logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0])
if labels is not None:
out = -F.log_softmax(logit, dim=-1).gather(1, labels.unsqueeze(1)).squeeze(1)
else:
out = F.log_softmax(logit, dim=-1)
else:
# construct weights and biases
weights, biases = [], []
for i in range(len(self.cutoffs)):
if self.div_val == 1:
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
weight_i = self.out_layers[0].weight[l_idx:r_idx]
bias_i = self.out_layers[0].bias[l_idx:r_idx]
else:
weight_i = self.out_layers[i].weight
bias_i = self.out_layers[i].bias
if i == 0:
weight_i = torch.cat([weight_i, self.cluster_weight], dim=0)
bias_i = torch.cat([bias_i, self.cluster_bias], dim=0)
weights.append(weight_i)
biases.append(bias_i)
head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0]
head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)
head_logprob = F.log_softmax(head_logit, dim=1)
if labels is None:
out = hidden.new_empty((head_logit.size(0), self.n_token))
else:
out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device)
offset = 0
cutoff_values = [0] + self.cutoffs
for i in range(len(cutoff_values) - 1):
l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
mask_i = (labels >= l_idx) & (labels < r_idx)
indices_i = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
target_i = labels.index_select(0, indices_i) - l_idx
head_logprob_i = head_logprob.index_select(0, indices_i)
hidden_i = hidden.index_select(0, indices_i)
else:
hidden_i = hidden
if i == 0:
if labels is not None:
logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1)
else:
out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]]
else:
weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i]
tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i)
tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
cluster_prob_idx = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
logprob_i = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1, target_i[:, None]
).squeeze(1)
else:
logprob_i = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
out[:, l_idx:r_idx] = logprob_i
if labels is not None:
if (hasattr(self, "keep_order") and self.keep_order) or keep_order:
out.index_copy_(0, indices_i, -logprob_i)
else:
out[offset : offset + logprob_i.size(0)].copy_(-logprob_i)
offset += logprob_i.size(0)
return out
def log_prob(self, hidden):
r"""
Computes log probabilities for all :math:`n\_classes` From:
https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/adaptive.p
Args:
hidden (Tensor): a minibatch of example
Returns:
log-probabilities of for each class :math:`c` in range :math:`0 <= c <= n\_classes`, where
:math:`n\_classes` is a parameter passed to ``AdaptiveLogSoftmaxWithLoss`` constructor. Shape:
- Input: :math:`(N, in\_features)`
- Output: :math:`(N, n\_classes)`
"""
if self.n_clusters == 0:
logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0])
return F.log_softmax(logit, dim=-1)
else:
# construct weights and biases
weights, biases = [], []
for i in range(len(self.cutoffs)):
if self.div_val == 1:
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
weight_i = self.out_layers[0].weight[l_idx:r_idx]
bias_i = self.out_layers[0].bias[l_idx:r_idx]
else:
weight_i = self.out_layers[i].weight
bias_i = self.out_layers[i].bias
if i == 0:
weight_i = torch.cat([weight_i, self.cluster_weight], dim=0)
bias_i = torch.cat([bias_i, self.cluster_bias], dim=0)
weights.append(weight_i)
biases.append(bias_i)
head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0]
head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)
out = hidden.new_empty((head_logit.size(0), self.n_token))
head_logprob = F.log_softmax(head_logit, dim=1)
cutoff_values = [0] + self.cutoffs
for i in range(len(cutoff_values) - 1):
start_idx, stop_idx = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]]
else:
weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i]
tail_logit_i = self._compute_logit(hidden, weight_i, bias_i, proj_i)
tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
logprob_i = head_logprob[:, -i] + tail_logprob_i
out[:, start_idx, stop_idx] = logprob_i
return out
|
AdaMix/src/transformers/models/transfo_xl/modeling_transfo_xl_utilities.py/0
|
{
"file_path": "AdaMix/src/transformers/models/transfo_xl/modeling_transfo_xl_utilities.py",
"repo_id": "AdaMix",
"token_count": 5567
}
| 66 |
# 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.
""" XLNet configuration """
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class XLNetConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a :class:`~transformers.XLNetModel` or a
:class:`~transformers.TFXLNetModel`. It is used to instantiate a XLNet 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 `xlnet-large-cased <https://huggingface.co/xlnet-large-cased>`__ 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.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 32000):
Vocabulary size of the XLNet model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.XLNetModel` or
:class:`~transformers.TFXLNetModel`.
d_model (:obj:`int`, `optional`, defaults to 1024):
Dimensionality of the encoder layers and the pooler layer.
n_layer (:obj:`int`, `optional`, defaults to 24):
Number of hidden layers in the Transformer encoder.
n_head (:obj:`int`, `optional`, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
d_inner (:obj:`int`, `optional`, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
ff_activation (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the If string, :obj:`"gelu"`, :obj:`"relu"`,
:obj:`"silu"` and :obj:`"gelu_new"` are supported.
untie_r (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to untie relative position biases
attn_type (:obj:`str`, `optional`, defaults to :obj:`"bi"`):
The attention type used by the model. Set :obj:`"bi"` for XLNet, :obj:`"uni"` for Transformer-XL.
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-12):
The epsilon used by the layer normalization layers.
dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
mem_len (:obj:`int` or :obj:`None`, `optional`):
The number of tokens to cache. The key/value pairs that have already been pre-computed in a previous
forward pass won't be re-computed. See the `quickstart
<https://huggingface.co/transformers/quickstart.html#using-the-past>`__ for more information.
reuse_len (:obj:`int`, `optional`):
The number of tokens in the current batch to be cached and reused in the future.
bi_data (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use bidirectional input pipeline. Usually set to :obj:`True` during pretraining and
:obj:`False` during finetuning.
clamp_len (:obj:`int`, `optional`, defaults to -1):
Clamp all relative distances larger than clamp_len. Setting this attribute to -1 means no clamping.
same_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the same attention length for each token.
summary_type (:obj:`str`, `optional`, defaults to "last"):
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
Has to be one of the following options:
- :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 (like GPT/GPT-2).
- :obj:`"attn"`: Not implemented now, use multi-head attention.
summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`):
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
Whether or not to add a projection after the vector extraction.
summary_activation (:obj:`str`, `optional`):
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
Pass :obj:`"tanh"` for a tanh activation to the output, any other value will result in no activation.
summary_proj_to_labels (:obj:`boo`, `optional`, defaults to :obj:`True`):
Used in the sequence classification and multiple choice models.
Whether the projection outputs should have :obj:`config.num_labels` or :obj:`config.hidden_size` classes.
summary_last_dropout (:obj:`float`, `optional`, defaults to 0.1):
Used in the sequence classification and multiple choice models.
The dropout ratio to be used after the projection and activation.
start_n_top (:obj:`int`, `optional`, defaults to 5):
Used in the SQuAD evaluation script.
end_n_top (:obj:`int`, `optional`, defaults to 5):
Used in the SQuAD evaluation script.
use_mems_eval (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not the model should make use of the recurrent memory mechanism in evaluation mode.
use_mems_train (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the model should make use of the recurrent memory mechanism in train mode.
.. note::
For pretraining, it is recommended to set ``use_mems_train`` to :obj:`True`. For fine-tuning, it is
recommended to set ``use_mems_train`` to :obj:`False` as discussed `here
<https://github.com/zihangdai/xlnet/issues/41#issuecomment-505102587>`__. If ``use_mems_train`` is set
to :obj:`True`, one has to make sure that the train batches are correctly pre-processed, `e.g.`
:obj:`batch_1 = [[This line is], [This is the]]` and :obj:`batch_2 = [[ the first line], [ second
line]]` and that all batches are of equal size.
Examples::
>>> from transformers import XLNetConfig, XLNetModel
>>> # Initializing a XLNet configuration
>>> configuration = XLNetConfig()
>>> # Initializing a model from the configuration
>>> model = XLNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "xlnet"
keys_to_ignore_at_inference = ["mems"]
def __init__(
self,
vocab_size=32000,
d_model=1024,
n_layer=24,
n_head=16,
d_inner=4096,
ff_activation="gelu",
untie_r=True,
attn_type="bi",
initializer_range=0.02,
layer_norm_eps=1e-12,
dropout=0.1,
mem_len=512,
reuse_len=None,
use_mems_eval=True,
use_mems_train=False,
bi_data=False,
clamp_len=-1,
same_length=False,
summary_type="last",
summary_use_proj=True,
summary_activation="tanh",
summary_last_dropout=0.1,
start_n_top=5,
end_n_top=5,
pad_token_id=5,
bos_token_id=1,
eos_token_id=2,
**kwargs
):
"""Constructs XLNetConfig."""
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.d_model = d_model
self.n_layer = n_layer
self.n_head = n_head
assert d_model % n_head == 0
if "d_head" in kwargs:
assert (
kwargs["d_head"] == d_model // n_head
), f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"
self.d_head = d_model // n_head
self.ff_activation = ff_activation
self.d_inner = d_inner
self.untie_r = untie_r
self.attn_type = attn_type
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.dropout = dropout
self.mem_len = mem_len
self.reuse_len = reuse_len
self.bi_data = bi_data
self.clamp_len = clamp_len
self.same_length = same_length
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_last_dropout = summary_last_dropout
self.start_n_top = start_n_top
self.end_n_top = end_n_top
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.eos_token_id = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval` instead.",
FutureWarning,
)
use_mems_eval = kwargs["use_cache"]
self.use_mems_eval = use_mems_eval
self.use_mems_train = use_mems_train
@property
def max_position_embeddings(self):
return -1
@property
def n_token(self): # Backward compatibility
return self.vocab_size
@n_token.setter
def n_token(self, value): # Backward compatibility
self.vocab_size = value
@property
def hidden_size(self):
return self.d_model
@property
def num_attention_heads(self):
return self.n_head
@property
def num_hidden_layers(self):
return self.n_layer
|
AdaMix/src/transformers/models/xlnet/configuration_xlnet.py/0
|
{
"file_path": "AdaMix/src/transformers/models/xlnet/configuration_xlnet.py",
"repo_id": "AdaMix",
"token_count": 4533
}
| 67 |
import numpy as np
from ..file_utils import add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
@add_end_docstrings(
PIPELINE_INIT_ARGS,
r"""
return_all_scores (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to return all prediction scores or just the one of the predicted class.
""",
)
class TextClassificationPipeline(Pipeline):
"""
Text classification pipeline using any :obj:`ModelForSequenceClassification`. See the `sequence classification
examples <../task_summary.html#sequence-classification>`__ for more information.
This text classification pipeline can currently be loaded from :func:`~transformers.pipeline` using the following
task identifier: :obj:`"sentiment-analysis"` (for classifying sequences according to positive or negative
sentiments).
If multiple classification labels are available (:obj:`model.config.num_labels >= 2`), the pipeline will run a
softmax over the results. If there is a single label, the pipeline will run a sigmoid over the result.
The models that this pipeline can use are models that have been fine-tuned on a sequence classification task. See
the up-to-date list of available models on `huggingface.co/models
<https://huggingface.co/models?filter=text-classification>`__.
"""
def __init__(self, return_all_scores: bool = False, **kwargs):
super().__init__(**kwargs)
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
)
self.return_all_scores = return_all_scores
def __call__(self, *args, **kwargs):
"""
Classify the text(s) given as inputs.
Args:
args (:obj:`str` or :obj:`List[str]`):
One or several texts (or one list of prompts) to classify.
Return:
A list or a list of list of :obj:`dict`: Each result comes as list of dictionaries with the following keys:
- **label** (:obj:`str`) -- The label predicted.
- **score** (:obj:`float`) -- The corresponding probability.
If ``self.return_all_scores=True``, one such dictionary is returned per label.
"""
outputs = super().__call__(*args, **kwargs)
if self.model.config.num_labels == 1:
scores = 1.0 / (1.0 + np.exp(-outputs))
else:
scores = np.exp(outputs) / np.exp(outputs).sum(-1, keepdims=True)
if self.return_all_scores:
return [
[{"label": self.model.config.id2label[i], "score": score.item()} for i, score in enumerate(item)]
for item in scores
]
else:
return [
{"label": self.model.config.id2label[item.argmax()], "score": item.max().item()} for item in scores
]
|
AdaMix/src/transformers/pipelines/text_classification.py/0
|
{
"file_path": "AdaMix/src/transformers/pipelines/text_classification.py",
"repo_id": "AdaMix",
"token_count": 1270
}
| 68 |
# coding=utf-8
# Copyright 2020-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.
"""
Utilities for the Trainer and TFTrainer class. Should be independent from PyTorch and TensorFlow.
"""
import copy
import gc
import inspect
import os
import random
import re
import time
import tracemalloc
from typing import Any, Dict, NamedTuple, Optional, Tuple, Union
import numpy as np
from .file_utils import (
ExplicitEnum,
is_sagemaker_distributed_available,
is_tf_available,
is_torch_available,
is_torch_cuda_available,
is_torch_tpu_available,
)
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
def set_seed(seed: int):
"""
Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if
installed).
Args:
seed (:obj:`int`): The seed to set.
"""
os.environ['PYTHONHASHSEED'] = str(seed)
os.environ['CUBLAS_WORKSPACE_CONFIG']=':16:8'
random.seed(seed)
np.random.seed(seed)
if is_torch_available():
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.cuda.manual_seed(seed)
# ^^ safe to call this function even if cuda is not available
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# torch.backends.cudnn.enabled = False
if is_tf_available():
tf.random.set_seed(seed)
class EvalPrediction(NamedTuple):
"""
Evaluation output (always contains labels), to be used to compute metrics.
Parameters:
predictions (:obj:`np.ndarray`): Predictions of the model.
label_ids (:obj:`np.ndarray`): Targets to be matched.
"""
predictions: Union[np.ndarray, Tuple[np.ndarray]]
label_ids: np.ndarray
class PredictionOutput(NamedTuple):
predictions: Union[np.ndarray, Tuple[np.ndarray]]
label_ids: Optional[np.ndarray]
metrics: Optional[Dict[str, float]]
class TrainOutput(NamedTuple):
global_step: int
training_loss: float
metrics: Dict[str, float]
PREFIX_CHECKPOINT_DIR = "checkpoint"
_re_checkpoint = re.compile(r"^" + PREFIX_CHECKPOINT_DIR + r"\-(\d+)$")
def get_last_checkpoint(folder):
content = os.listdir(folder)
checkpoints = [
path
for path in content
if _re_checkpoint.search(path) is not None and os.path.isdir(os.path.join(folder, path))
]
if len(checkpoints) == 0:
return
return os.path.join(folder, max(checkpoints, key=lambda x: int(_re_checkpoint.search(x).groups()[0])))
class IntervalStrategy(ExplicitEnum):
NO = "no"
STEPS = "steps"
EPOCH = "epoch"
class EvaluationStrategy(ExplicitEnum):
NO = "no"
STEPS = "steps"
EPOCH = "epoch"
class BestRun(NamedTuple):
"""
The best run found by an hyperparameter search (see :class:`~transformers.Trainer.hyperparameter_search`).
Parameters:
run_id (:obj:`str`):
The id of the best run (if models were saved, the corresponding checkpoint will be in the folder ending
with run-{run_id}).
objective (:obj:`float`):
The objective that was obtained for this run.
hyperparameters (:obj:`Dict[str, Any]`):
The hyperparameters picked to get this run.
"""
run_id: str
objective: float
hyperparameters: Dict[str, Any]
def default_compute_objective(metrics: Dict[str, float]) -> float:
"""
The default objective to maximize/minimize when doing an hyperparameter search. It is the evaluation loss if no
metrics are provided to the :class:`~transformers.Trainer`, the sum of all metrics otherwise.
Args:
metrics (:obj:`Dict[str, float]`): The metrics returned by the evaluate method.
Return:
:obj:`float`: The objective to minimize or maximize
"""
metrics = copy.deepcopy(metrics)
loss = metrics.pop("eval_loss", None)
_ = metrics.pop("epoch", None)
# Remove speed metrics
speed_metrics = [m for m in metrics.keys() if m.endswith("_runtime") or m.endswith("_samples_per_second")]
for sm in speed_metrics:
_ = metrics.pop(sm, None)
return loss if len(metrics) == 0 else sum(metrics.values())
def default_hp_space_optuna(trial) -> Dict[str, float]:
from .integrations import is_optuna_available
assert is_optuna_available(), "This function needs Optuna installed: `pip install optuna`"
return {
"learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
"num_train_epochs": trial.suggest_int("num_train_epochs", 1, 5),
"seed": trial.suggest_int("seed", 1, 40),
"per_device_train_batch_size": trial.suggest_categorical("per_device_train_batch_size", [4, 8, 16, 32, 64]),
}
def default_hp_space_ray(trial) -> Dict[str, float]:
from .integrations import is_ray_tune_available
assert is_ray_tune_available(), "This function needs ray installed: `pip " "install ray[tune]`"
from ray import tune
return {
"learning_rate": tune.loguniform(1e-6, 1e-4),
"num_train_epochs": tune.choice(list(range(1, 6))),
"seed": tune.uniform(1, 40),
"per_device_train_batch_size": tune.choice([4, 8, 16, 32, 64]),
}
class HPSearchBackend(ExplicitEnum):
OPTUNA = "optuna"
RAY = "ray"
default_hp_space = {
HPSearchBackend.OPTUNA: default_hp_space_optuna,
HPSearchBackend.RAY: default_hp_space_ray,
}
def is_main_process(local_rank):
"""
Whether or not the current process is the local process, based on `xm.get_ordinal()` (for TPUs) first, then on
`local_rank`.
"""
if is_torch_tpu_available():
import torch_xla.core.xla_model as xm
return xm.get_ordinal() == 0
return local_rank in [-1, 0]
def total_processes_number(local_rank):
"""
Return the number of processes launched in parallel. Works with `torch.distributed` and TPUs.
"""
if is_torch_tpu_available():
import torch_xla.core.xla_model as xm
return xm.xrt_world_size()
elif is_sagemaker_distributed_available():
import smdistributed.dataparallel.torch.distributed as dist
return dist.get_world_size()
elif local_rank != -1 and is_torch_available():
import torch
return torch.distributed.get_world_size()
return 1
def speed_metrics(split, start_time, num_samples=None):
"""
Measure and return speed performance metrics.
This function requires a time snapshot `start_time` before the operation to be measured starts and this function
should be run immediately after the operation to be measured has completed.
Args:
- split: name to prefix metric (like train, eval, test...)
- start_time: operation start time
- num_samples: number of samples processed
"""
runtime = time.time() - start_time
result = {f"{split}_runtime": round(runtime, 4)}
if num_samples is not None:
samples_per_second = 1 / (runtime / num_samples)
result[f"{split}_samples_per_second"] = round(samples_per_second, 3)
return result
class SchedulerType(ExplicitEnum):
LINEAR = "linear"
COSINE = "cosine"
COSINE_WITH_RESTARTS = "cosine_with_restarts"
POLYNOMIAL = "polynomial"
CONSTANT = "constant"
CONSTANT_WITH_WARMUP = "constant_with_warmup"
class TrainerMemoryTracker:
"""
A helper class that tracks cpu and gpu memory.
When a stage completes, it can pass metrics dict to update with the memory metrics gathered during this stage.
Example ::
self._memory_tracker = TrainerMemoryTracker(self.args.skip_memory_metrics)
self._memory_tracker.start()
code ...
metrics = {"train_runtime": 10.5}
self._memory_tracker.stop_and_update_metrics(metrics)
At the moment gpu tracking is only for pytorch, but can be extended to support tensorflow.
Understanding the reports:
- ``*_alloc_delta`` - is the difference in the used/allocated memory counter between the end and the start of the
stage - it can be negative if a function released more memory than it allocated.
- ``*_peaked_delta`` - is any extra memory that was consumed and then freed - relative to the current allocated
memory counter - it is never negative.
So when you look at the metrics of any stage you add up ``alloc_delta`` + ``peaked_delta`` and you know how much
memory was needed to complete that stage.
The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the
main process does the bulk of work, but it could be not quite so if model parallel is used and then other gpus may
use a different amount of gpu RAM. Perhaps in the future this tracker will evolve to measure those too.
Note that this tracker doesn't account for memory allocations outside of :class:`~transformers.Trainer`'s
``__init__``, ``train``, ``evaluate`` and ``predict`` calls.
Because ``evaluation`` calls may happen during ``train``, we can't handle nested invocations because
``torch.cuda.max_memory_allocated`` is a single counter, so if it gets reset by a nested eval call, ``train``'s
tracker will report incorrect info. If this `pytorch issue <https://github.com/pytorch/pytorch/issues/16266>`__
gets resolved it will be possible to change this class to be re-entrant. Until then we will only track the outer
level of ``train``, ``evaluate`` and ``predict`` methods. Which means that if ``eval`` is called during ``train``,
it's the latter that will account for its memory usage and that of the former.
This also means that if any other tool that is used along the :class:`~transformers.Trainer` calls
``torch.cuda.reset_peak_memory_stats``, the gpu peak memory stats could be invalid. And the
:class:`~transformers.Trainer` will disrupt the normal behavior of any such tools that rely on calling
``torch.cuda.reset_peak_memory_stats`` themselves.
"""
# map trainer methods to metrics prefix
stages = {
"__init__": "init",
"train": "train",
"evaluate": "eval",
"predict": "test",
}
def __init__(self, skip_memory_metrics=False):
if is_torch_cuda_available():
import torch
self.torch = torch
self.gpu = {}
else:
self.torch = None
self.cur_stage = None
self.cpu = {}
self.init_reported = False
self.skip_memory_metrics = skip_memory_metrics
def derive_stage(self):
""" derives the stage/caller name automatically """
caller = inspect.currentframe().f_back.f_back.f_code.co_name
if caller in self.stages:
return self.stages[caller]
else:
raise ValueError(
f"was called from {caller}, but only expect to be called from one of {self.stages.keys()}"
)
def start(self):
""" start tracking for the caller's stage """
if self.skip_memory_metrics:
return
stage = self.derive_stage()
# deal with nested calls of eval during train - simply ignore those
if self.cur_stage is not None and self.cur_stage != stage:
return
self.cur_stage = stage
if self.torch is not None:
self.torch.cuda.reset_peak_memory_stats()
self.torch.cuda.empty_cache()
gc.collect()
# gpu
if self.torch is not None:
self.gpu[self.cur_stage] = {}
self.gpu[self.cur_stage]["alloc"] = self.torch.cuda.memory_allocated()
self.gpu[self.cur_stage]["peaked"] = 0
# cpu
self.cpu[self.cur_stage] = {}
tracemalloc.start()
def stop(self, stage):
""" stop tracking for the passed stage """
# deal with nested calls of eval during train - simply ignore those
if self.cur_stage is not None and self.cur_stage != stage:
return
if self.torch is not None:
self.torch.cuda.empty_cache()
gc.collect()
# gpu
if self.torch is not None:
mem_cur = self.torch.cuda.memory_allocated()
# this is the difference between the start and the end allocated memory
self.gpu[self.cur_stage]["alloc"] = mem_cur - self.gpu[self.cur_stage]["alloc"] # can be negative
# this is the difference if any between the start and the peak
self.gpu[self.cur_stage]["peaked"] = max(0, self.torch.cuda.max_memory_allocated() - mem_cur)
# cpu
cpu_mem_used_delta, cpu_mem_used_peak = tracemalloc.get_traced_memory()
tracemalloc.stop() # reset accounting
self.cpu[self.cur_stage]["alloc"] = cpu_mem_used_delta # can be negative
self.cpu[self.cur_stage]["peaked"] = max(0, cpu_mem_used_peak - cpu_mem_used_delta)
# reset - cycle finished
self.cur_stage = None
def update_metrics(self, stage, metrics):
""" stop tracking for the passed stage """
if self.skip_memory_metrics:
return
# deal with nested calls of eval during train - simply ignore those
if self.cur_stage is not None and self.cur_stage != stage:
return
# since we don't have a way to return init metrics, we push them into the first of train/val/predict
stages = [stage]
if not self.init_reported:
stages.insert(0, "init")
self.init_reported = True
for stage in stages:
for t in ["alloc", "peaked"]:
if stage in self.cpu and t in self.cpu[stage]:
metrics[f"{stage}_mem_cpu_{t}_delta"] = self.cpu[stage][t]
if self.torch is not None and stage in self.gpu and t in self.gpu[stage]:
metrics[f"{stage}_mem_gpu_{t}_delta"] = self.gpu[stage][t]
def stop_and_update_metrics(self, metrics=None):
""" combine stop + update in one call for simpler code """
if self.skip_memory_metrics:
return
stage = self.derive_stage()
self.stop(stage)
# init doesn't have metrics to update so we just save that data for later stages to retrieve
if metrics is not None:
self.update_metrics(stage, metrics)
def denumpify_detensorize(metrics):
"""
Recursively calls `.item()` on the element of the dictionary passed
"""
if isinstance(metrics, (list, tuple)):
return type(metrics)(denumpify_detensorize(m) for m in metrics)
elif isinstance(metrics, dict):
return type(metrics)({k: denumpify_detensorize(v) for k, v in metrics.items()})
elif isinstance(metrics, np.generic):
return metrics.item()
elif is_torch_available() and isinstance(metrics, torch.Tensor) and metrics.numel() == 1:
return metrics.item()
return metrics
class ShardedDDPOption(ExplicitEnum):
SIMPLE = "simple"
ZERO_DP_2 = "zero_dp_2"
ZERO_DP_3 = "zero_dp_3"
OFFLOAD = "offload"
AUTO_WRAP = "auto_wrap"
|
AdaMix/src/transformers/trainer_utils.py/0
|
{
"file_path": "AdaMix/src/transformers/trainer_utils.py",
"repo_id": "AdaMix",
"token_count": 6081
}
| 69 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..file_utils import requires_tokenizers
class AlbertTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class BartTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class BarthezTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class BertTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class CamembertTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class ConvBertTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class DistilBertTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class DPRContextEncoderTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class DPRQuestionEncoderTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class DPRReaderTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class ElectraTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class FunnelTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class GPT2TokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class HerbertTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class LayoutLMTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class LEDTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class LongformerTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class LxmertTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class MBart50TokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class MBartTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class MobileBertTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class MPNetTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class MT5TokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class OpenAIGPTTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class PegasusTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class ReformerTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class RetriBertTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class RobertaTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class SqueezeBertTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class T5TokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class XLMRobertaTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class XLNetTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
class PreTrainedTokenizerFast:
def __init__(self, *args, **kwargs):
requires_tokenizers(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tokenizers(self)
SLOW_TO_FAST_CONVERTERS = None
def convert_slow_tokenizer(*args, **kwargs):
requires_tokenizers(convert_slow_tokenizer)
|
AdaMix/src/transformers/utils/dummy_tokenizers_objects.py/0
|
{
"file_path": "AdaMix/src/transformers/utils/dummy_tokenizers_objects.py",
"repo_id": "AdaMix",
"token_count": 2840
}
| 70 |
# coding=utf-8
# Copyright 2021 {{cookiecutter.authors}} 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.
""" TF 2.0 {{cookiecutter.modelname}} model. """
{% if cookiecutter.is_encoder_decoder_model == "False" %}
import math
from typing import Any, 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,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
)
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPooling,
TFCausalLMOutput,
TFMaskedLMOutput,
TFMultipleChoiceModelOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFMaskedLanguageModelingLoss,
TFModelInputType,
TFMultipleChoiceLoss,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
TFSequenceClassificationLoss,
TFSequenceSummary,
TFTokenClassificationLoss,
get_initializer,
input_processing,
keras_serializable,
shape_list,
)
from ...utils import logging
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config"
_TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer"
TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST = [
"{{cookiecutter.checkpoint_identifier}}",
# See all {{cookiecutter.modelname}} models at https://huggingface.co/models?filter={{cookiecutter.lowercase_modelname}}
]
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.type_vocab_size = config.type_vocab_size
self.hidden_size = config.hidden_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.hidden_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.hidden_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.hidden_size],
initializer=get_initializer(self.initializer_range),
)
super().build(input_shape)
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
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}SelfAttention(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **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.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.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_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,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(inputs=hidden_states)
mixed_key_layer = self.key(inputs=hidden_states)
mixed_value_layer = self.value(inputs=hidden_states)
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 TF{{cookiecutter.camelcase_modelname}}Model 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.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)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
return outputs
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}SelfOutput(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
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")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Attention(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.self_attention = TF{{cookiecutter.camelcase_modelname}}SelfAttention(config, name="self")
self.dense_output = TF{{cookiecutter.camelcase_modelname}}SelfOutput(config, name="output")
def prune_heads(self, heads):
raise NotImplementedError
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
self_outputs = self.self_attention(
hidden_states=input_tensor,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
training=training,
)
attention_output = self.dense_output(
hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Intermediate(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
else:
self.intermediate_act_fn = config.hidden_act
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Output(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
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")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
class TF{{cookiecutter.camelcase_modelname}}Layer(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.attention = TF{{cookiecutter.camelcase_modelname}}Attention(config, name="attention")
self.intermediate = TF{{cookiecutter.camelcase_modelname}}Intermediate(config, name="intermediate")
self.bert_output = TF{{cookiecutter.camelcase_modelname}}Output(config, name="output")
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,
)
attention_output = attention_outputs[0]
intermediate_output = self.intermediate(hidden_states=attention_output)
layer_output = self.bert_output(hidden_states=intermediate_output, input_tensor=attention_output, training=training)
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.layer = [TF{{cookiecutter.camelcase_modelname}}Layer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
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]]:
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=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[i],
output_attentions=output_attentions,
training=training,
)
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 TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
if isinstance(config.hidden_act, str):
self.transform_act_fn = get_tf_activation(config.hidden_act)
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(inputs=hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
self.transform = TF{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(config, name="transform")
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape: tf.TensorShape):
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
super().build(input_shape)
def get_output_embeddings(self) -> tf.keras.layers.Layer:
return self.input_embeddings
def set_output_embeddings(self, value: tf.Variable):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self) -> Dict[str, tf.Variable]:
return {"bias": self.bias}
def set_bias(self, value: tf.Variable):
self.bias = value["bias"]
self.vocab_size = shape_list(value["bias"])[0]
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.transform(hidden_states=hidden_states)
seq_length = shape_list(hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.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.bias)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}MLMHead(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
prediction_scores = self.predictions(hidden_states=sequence_output)
return prediction_scores
@keras_serializable
class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
config_class = {{cookiecutter.camelcase_modelname}}Config
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TF{{cookiecutter.camelcase_modelname}}Embeddings(config, name="embeddings")
self.encoder = TF{{cookiecutter.camelcase_modelname}}Encoder(config, name="encoder")
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings
def get_input_embeddings(self) -> tf.keras.layers.Layer:
return self.embeddings
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings
def set_input_embeddings(self, value: tf.Variable):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads
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[TFBaseModelOutput, 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]
if not inputs["return_dict"]:
return (
sequence_output,
) + encoder_outputs[1:]
return TFBaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
"""An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = {{cookiecutter.camelcase_modelname}}Config
base_model_prefix = "{{cookiecutter.lowercase_modelname}}"
{{cookiecutter.uppercase_modelname}}_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.{{cookiecutter.camelcase_modelname}}Config`): 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.
"""
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`np.ndarray`, :obj:`tf.Tensor`, :obj:`List[tf.Tensor]` :obj:`Dict[str, tf.Tensor]` or :obj:`Dict[str, np.ndarray]` and each example must have the shape :obj:`({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`~transformers.BertTokenizer`. See
:func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`np.ndarray` 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:`np.ndarray` 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:`np.ndarray` 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:`np.ndarray` 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:`np.ndarray` or :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 {{cookiecutter.modelname}} Model transformer outputing raw hidden-states without any specific head on top.",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="{{cookiecutter.checkpoint_identifier}}",
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.{{cookiecutter.lowercase_modelname}}(
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.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
def serving_output(self, output: TFBaseModelOutput) -> TFBaseModelOutput:
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 TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
@add_start_docstrings("""{{cookiecutter.modelname}} Model with a `language modeling` head on top. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING)
class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.warning(
"If you want to use `TF{{cookiecutter.camelcase_modelname}}ForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, input_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.mlm.predictions
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="{{cookiecutter.checkpoint_identifier}}",
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` or :obj:`np.ndarray` 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.{{cookiecutter.lowercase_modelname}}(
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.mlm(sequence_output=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(
"""{{cookiecutter.modelname}} Model with a `language modeling` head on top for CLM fine-tuning. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
)
class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if not config.is_decoder:
logger.warning("If you want to use `TF{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`")
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, input_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.mlm.predictions
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="{{cookiecutter.checkpoint_identifier}}",
output_type=TFCausalLMOutput,
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[TFCausalLMOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
config.vocab_size - 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.{{cookiecutter.lowercase_modelname}}(
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.mlm(sequence_output=sequence_output, training=inputs["training"])
loss = None
if inputs["labels"] is not None:
# shift labels to the left and cut last logit token
logits = logits[:, :-1]
labels = inputs["labels"][:, 1:]
loss = self.compute_loss(labels=labels, logits=logits)
if not inputs["return_dict"]:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFCausalLMOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output
def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
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 TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
class TF{{cookiecutter.camelcase_modelname}}ClassificationHead(tf.keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.out_proj = tf.keras.layers.Dense(
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
)
if isinstance(config.hidden_act, str):
self.classifier_act_fn = get_tf_activation(config.hidden_act)
else:
self.classifier_act_fn = config.hidden_act
def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = hidden_states[:, 0, :] # take <s> token (equiv. to [CLS])
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.classifier_act_fn(hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.out_proj(hidden_states)
return hidden_states
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model transformer with a sequence classification/regression head on top
e.g., for GLUE tasks. """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
self.classifier = TF{{cookiecutter.camelcase_modelname}}ClassificationHead(config, name="classifier")
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="{{cookiecutter.checkpoint_identifier}}",
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` or :obj:`np.ndarray` 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).
"""
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.{{cookiecutter.lowercase_modelname}}(
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"],
)
logits = self.classifier(hidden_states=outputs[0], training=inputs["training"])
loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=logits)
if not inputs["return_dict"]:
output = (logits,) + outputs[1:]
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(
"""{{cookiecutter.modelname}} 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. """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
self.sequence_summary = TFSequenceSummary(
config, config.initializer_range, name="sequence_summary"
)
self.classifier = tf.keras.layers.Dense(
units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
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({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="{{cookiecutter.checkpoint_identifier}}",
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` or :obj:`np.ndarray` 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(tensor=inputs["input_ids"], shape=(-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=inputs["position_ids"], shape=(-1, seq_length))
if inputs["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.{{cookiecutter.lowercase_modelname}}(
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"],
)
logits = self.sequence_summary(inputs=outputs[0], training=inputs["training"])
logits = self.classifier(inputs=logits)
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[1:]
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)
@add_start_docstrings(
"""{{cookiecutter.modelname}} 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. """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
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({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="{{cookiecutter.checkpoint_identifier}}",
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` or :obj:`np.ndarray` 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.{{cookiecutter.lowercase_modelname}}(
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]
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[1:]
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(
"""{{cookiecutter.modelname}} 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`). """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
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({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="{{cookiecutter.checkpoint_identifier}}",
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` or :obj:`np.ndarray` 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` or :obj:`np.ndarray` 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.{{cookiecutter.lowercase_modelname}}(
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
)
{% else %}
import random
from typing import Dict, Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPast,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
# Public API
from ...modeling_tf_utils import (
DUMMY_INPUTS,
TFPreTrainedModel,
TFSharedEmbeddings,
TFWrappedEmbeddings,
input_processing,
keras_serializable,
shape_list,
)
from ...utils import logging
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config"
_TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer"
LARGE_NEGATIVE = -1e8
def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
shifted_input_ids = tf.roll(input_ids, 1, axis=-1)
start_tokens = tf.fill((shape_list(shifted_input_ids)[0], 1), decoder_start_token_id)
shifted_input_ids = tf.concat([start_tokens, shifted_input_ids[:, 1:]], -1)
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids = tf.where(
shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids
)
if tf.executing_eagerly():
# "Verify that `labels` has only positive values and -100"
assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0))
# Make sure the assertion op is called by wrapping the result in an identity no-op
with tf.control_dependencies([assert_gte0]):
shifted_input_ids = tf.identity(shifted_input_ids)
return shifted_input_ids
def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
mask_cond = tf.range(shape_list(mask)[-1])
mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
if past_key_values_length > 0:
mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None, past_key_values_length: int = 0):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
src_len = shape_list(mask)[1]
tgt_len = tgt_len if tgt_len is not None else src_len
one_cst = tf.constant(1.0)
mask = tf.cast(mask, dtype=one_cst.dtype)
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
return (one_cst - expanded_mask) * LARGE_NEGATIVE
class TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(TFSharedEmbeddings):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
super().__init__(num_embeddings, embedding_dim, **kwargs)
def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
"""Input is expected to be of size [bsz x seqlen]."""
bsz, seq_len = input_shape[:2]
positions = tf.range(
past_key_values_length, seq_len + past_key_values_length, delta=1, name="range"
)
return super().call(positions)
class TF{{cookiecutter.camelcase_modelname}}Attention(tf.keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = tf.keras.layers.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.is_decoder = is_decoder
self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
def call(
self,
hidden_states: tf.Tensor,
key_value_states: Optional[tf.Tensor] = None,
past_key_value: Optional[Tuple[Tuple[tf.Tensor]]] = None,
attention_mask: Optional[tf.Tensor] = None,
training=False,
) -> Tuple[tf.Tensor, Optional[tf.Tensor]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = shape_list(hidden_states)
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = tf.concat([past_key_value[0], key_states], axis=2)
value_states = tf.concat([past_key_value[1], value_states], axis=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
key_states = tf.reshape(key_states, proj_shape)
value_states = tf.reshape(value_states, proj_shape)
src_len = shape_list(key_states)[1]
attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
# The tf.debugging asserts are not compliant with XLA then they
# have to be disabled in other modes than eager.
if tf.executing_eagerly():
tf.debugging.assert_equal(
shape_list(attn_weights),
[bsz * self.num_heads, tgt_len, src_len],
message=f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {shape_list(attn_weights)}",
)
if attention_mask is not None:
# The tf.debugging asserts are not compliant with XLA then they
# have to be disabled in other modes than eager.
if tf.executing_eagerly():
tf.debugging.assert_equal(
shape_list(attention_mask),
[bsz, 1, tgt_len, src_len],
message=f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(attention_mask)}",
)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_weights = tf.nn.softmax(attn_weights, axis=-1)
attn_probs = self.dropout(attn_weights, training=training)
attn_output = tf.matmul(attn_probs, value_states)
# The tf.debugging asserts are not compliant with XLA then they
# have to be disabled in other modes than eager.
if tf.executing_eagerly():
tf.debugging.assert_equal(
shape_list(attn_output),
[bsz * self.num_heads, tgt_len, self.head_dim],
message=f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {shape_list(attn_output)}",
)
attn_output = tf.transpose(
tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
)
attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
attn_output = self.out_proj(attn_output)
attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
return attn_output, attn_weights, past_key_value
class TF{{cookiecutter.camelcase_modelname}}EncoderLayer(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TF{{cookiecutter.camelcase_modelname}}Attention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
)
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation_fn = get_tf_activation(config.activation_function)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, training=False):
"""
Args:
hidden_states (:obj:`tf.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (:obj:`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
"""
residual = hidden_states
hidden_states, self_attn_weights, _ = self.self_attn(
hidden_states=hidden_states, attention_mask=attention_mask
)
# The tf.debugging asserts are not compliant with XLA then they
# have to be disabled in other modes than eager.
if tf.executing_eagerly():
tf.debugging.assert_equal(
shape_list(hidden_states),
shape_list(residual),
message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
return hidden_states, self_attn_weights
class TF{{cookiecutter.camelcase_modelname}}DecoderLayer(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TF{{cookiecutter.camelcase_modelname}}Attention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
name="self_attn",
is_decoder=True,
)
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation_fn = get_tf_activation(config.activation_function)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.encoder_attn = TF{{cookiecutter.camelcase_modelname}}Attention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
name="encoder_attn",
is_decoder=True,
)
self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
def call(
self,
hidden_states,
attention_mask: Optional[tf.Tensor] = None,
encoder_hidden_states: Optional[tf.Tensor] = None,
encoder_attention_mask: Optional[tf.Tensor] = None,
past_key_value: Optional[Tuple[tf.Tensor]] = None,
training=False,
) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
"""
Args:
hidden_states (:obj:`tf.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (:obj:`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (:obj:`tf.Tensor`): cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_attention_mask (:obj:`tf.Tensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
past_key_value (:obj:`Tuple(tf.Tensor)`): cached past key and value projection states
"""
residual = hidden_states
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_present_key_value = None
if encoder_hidden_states is not None:
residual = hidden_states
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, _, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
past_key_value=cross_attn_past_key_value,
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
return (
hidden_states,
self_attn_weights,
present_key_value,
)
class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
config_class = {{cookiecutter.camelcase_modelname}}Config
base_model_prefix = "model"
@property
def dummy_inputs(self):
pad_token = 1
input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32)
decoder_input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32)
dummy_inputs = {
"decoder_input_ids": decoder_input_ids,
"attention_mask": tf.math.not_equal(input_ids, pad_token),
"input_ids": input_ids,
}
return dummy_inputs
@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"),
"decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
"decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
}
]
)
# Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.serving
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
{{cookiecutter.uppercase_modelname}}_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(input_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.{{cookiecutter.camelcase_modelname}}Config`): 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.TFPreTrainedModel.from_pretrained` method to load the
model weights.
"""
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`tf.Tensor` of shape :obj:`({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`~transformers.{{cookiecutter.camelcase_modelname}}Tokenizer`. See
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (: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>`__
decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using :class:`~transformers.{{cookiecutter.camelcase_modelname}}Tokenizer`. See
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
{{cookiecutter.camelcase_modelname}} uses the :obj:`eos_token_id` as the starting token for
:obj:`decoder_input_ids` generation. If :obj:`past_key_values` is used, optionally only the last
:obj:`decoder_input_ids` have to be input (see :obj:`past_key_values`).
For translation and summarization training, :obj:`decoder_input_ids` should be provided. If no
:obj:`decoder_input_ids` is provided, the model will create this tensor by shifting the :obj:`input_ids` to
the right for denoising pre-training following the paper.
decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
encoder_outputs (:obj:`tf.FloatTensor`, `optional`):
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
of shape :obj:`(batch_size, sequence_length, hidden_size)` is a sequence of
past_key_values (:obj:`Tuple[Tuple[tf.Tensor]]` of length :obj:`config.n_layers`)
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`). Set to :obj:`False` during training, :obj:`True` during generation
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).
"""
@keras_serializable
class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
config_class = {{cookiecutter.camelcase_modelname}}Config
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
:class:`TF{{cookiecutter.camelcase_modelname}}EncoderLayer`.
Args:
config: {{cookiecutter.camelcase_modelname}}Config
"""
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[TFSharedEmbeddings] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.layerdrop = config.encoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
self.embed_tokens = embed_tokens
self.embed_positions = TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.layers = [TF{{cookiecutter.camelcase_modelname}}EncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
def get_embed_tokens(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
"""
Args:
input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using :class:`~transformers.{{cookiecutter.camelcase_modelname}}Tokenizer`. See
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
for details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`tf.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>`__
inputs_embeds (:obj:`tf.Tensor` 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.
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).
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_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["inputs_embeds"] is None:
inputs_embeds = self.embed_tokens(inputs["input_ids"]) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs["inputs_embeds"] + embed_pos
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = self.dropout(hidden_states, training=inputs["training"])
# check attention mask and invert
if inputs["attention_mask"] is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
inputs["attention_mask"] = _expand_mask(inputs["attention_mask"])
encoder_states = () if inputs["output_hidden_states"] else None
all_attentions = () if inputs["output_attentions"] else None
# encoder layers
for encoder_layer in self.layers:
if inputs["output_hidden_states"]:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if inputs["training"] and (dropout_probability < self.layerdrop): # skip the layer
continue
hidden_states, attn = encoder_layer(hidden_states, inputs["attention_mask"])
if inputs["output_attentions"]:
all_attentions += (attn,)
if inputs["output_hidden_states"]:
encoder_states = encoder_states + (hidden_states,)
if not inputs["return_dict"]:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
@keras_serializable
class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
config_class = {{cookiecutter.camelcase_modelname}}Config
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`TF{{cookiecutter.camelcase_modelname}}DecoderLayer`
Args:
config: {{cookiecutter.camelcase_modelname}}Config
embed_tokens: output embedding
"""
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[TFSharedEmbeddings] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.padding_idx = config.pad_token_id
self.embed_tokens = embed_tokens
self.layerdrop = config.decoder_layerdrop
self.embed_positions = TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
self.layers = [TF{{cookiecutter.camelcase_modelname}}DecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
self.dropout = tf.keras.layers.Dropout(config.dropout)
def get_embed_tokens(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
r"""
Args:
input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using :class:`~transformers.{{cookiecutter.camelcase_modelname}}Tokenizer`. See
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
for details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`tf.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>`__
encoder_hidden_states (:obj:`tf.Tensor` of shape :obj:`(batch_size, encoder_sequence_length, hidden_size)`, `optional`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, encoder_sequence_length)`, `optional`):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. 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>`__
past_key_values (:obj:`Tuple[Tuple[tf.Tensor]]` of length :obj:`config.n_layers` with each tuple having 2 tuples each of which has 2 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)`.
inputs_embeds (:obj:`tf.Tensor` 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.
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).
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
use_cache=use_cache,
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 decoder_input_ids and decoder_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 decoder_input_ids or decoder_inputs_embeds")
past_key_values_length = (
shape_list(inputs["past_key_values"][0][0])[2] if inputs["past_key_values"] is not None else 0
)
# embed positions
positions = self.embed_positions(input_shape, past_key_values_length)
if inputs["inputs_embeds"] is None:
inputs["inputs_embeds"] = self.embed_tokens(inputs["input_ids"])
hidden_states = inputs["inputs_embeds"]
inputs["attention_mask"], combined_attention_mask = self.compute_combined_attns_mask(
inputs, input_shape, past_key_values_length
)
if inputs["encoder_hidden_states"] is not None and inputs["encoder_attention_mask"] is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
inputs["encoder_attention_mask"] = _expand_mask(inputs["encoder_attention_mask"], tgt_len=input_shape[-1])
hidden_states = self.layernorm_embedding(hidden_states + positions)
hidden_states = self.dropout(hidden_states, training=inputs["training"])
# decoder layers
all_hidden_states = () if inputs["output_hidden_states"] else None
all_self_attns = () if inputs["output_attentions"] else None
present_key_values = () if inputs["use_cache"] else None
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if inputs["output_hidden_states"]:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if inputs["training"] and (dropout_probability < self.layerdrop):
continue
past_key_value = inputs["past_key_values"][idx] if inputs["past_key_values"] is not None else None
hidden_states, layer_self_attn, present_key_value = decoder_layer(
hidden_states,
attention_mask=combined_attention_mask,
encoder_hidden_states=inputs["encoder_hidden_states"],
encoder_attention_mask=inputs["encoder_attention_mask"],
past_key_value=past_key_value,
)
if inputs["use_cache"]:
present_key_values += (present_key_value,)
if inputs["output_attentions"]:
all_self_attns += (layer_self_attn,)
if inputs["output_hidden_states"]:
all_hidden_states += (hidden_states,)
if inputs["output_attentions"]:
all_self_attns = list(all_self_attns)
if inputs["use_cache"]:
present_key_values = (inputs["encoder_hidden_states"], present_key_values)
if not inputs["return_dict"]:
return hidden_states, present_key_values, all_hidden_states, all_self_attns
else:
return TFBaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=present_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
@tf.function
def compute_combined_attns_mask(self, inputs, input_shape, past_key_values_length):
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
else:
combined_attention_mask = _expand_mask(
tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
)
if inputs["attention_mask"] is None and inputs["input_ids"] is not None and input_shape[-1] > 1:
attention_mask = tf.cast(
tf.math.not_equal(inputs["input_ids"], self.config.pad_token_id), inputs["input_ids"].dtype
)
attention_mask = tf.concat(
[
tf.ones((input_shape[0], past_key_values_length), dtype=attention_mask.dtype),
attention_mask,
],
axis=-1,
)
else:
attention_mask = tf.ones((input_shape[0], input_shape[1] + past_key_values_length))
return attention_mask, combined_attention_mask
@keras_serializable
class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
config_class = {{cookiecutter.camelcase_modelname}}Config
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared")
with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
embed_tokens.vocab_size = self.shared.vocab_size
embed_tokens.hidden_size = self.shared.hidden_size
self.encoder = TF{{cookiecutter.camelcase_modelname}}Encoder(config, embed_tokens, name="encoder")
self.decoder = TF{{cookiecutter.camelcase_modelname}}Decoder(config, embed_tokens, name="decoder")
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared.weight = new_embeddings
self.shared.vocab_size = self.shared.weight.shape[0]
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
self.encoder.set_embed_tokens(embed_tokens)
self.decoder.set_embed_tokens(embed_tokens)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs
):
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
if inputs["decoder_input_ids"] is None and inputs["decoder_inputs_embeds"] is None:
inputs["use_cache"] = False
if inputs["encoder_outputs"] is None:
inputs["encoder_outputs"] = self.encoder(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_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"],
)
# If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
elif inputs["return_dict"] and not isinstance(inputs["encoder_outputs"], TFBaseModelOutput):
inputs["encoder_outputs"] = TFBaseModelOutput(
last_hidden_state=inputs["encoder_outputs"][0],
hidden_states=inputs["encoder_outputs"][1] if len(inputs["encoder_outputs"]) > 1 else None,
attentions=inputs["encoder_outputs"][2] if len(inputs["encoder_outputs"]) > 2 else None,
)
# If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
elif not inputs["return_dict"] and not isinstance(inputs["encoder_outputs"], tuple):
inputs["encoder_outputs"] = inputs["encoder_outputs"].to_tuple()
decoder_outputs = self.decoder(
inputs["decoder_input_ids"],
attention_mask=inputs["decoder_attention_mask"],
encoder_hidden_states=inputs["encoder_outputs"][0],
encoder_attention_mask=inputs["attention_mask"],
past_key_values=inputs["past_key_values"],
inputs_embeds=inputs["decoder_inputs_embeds"],
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
if not inputs["return_dict"]:
return decoder_outputs + inputs["encoder_outputs"]
return TFSeq2SeqModelOutput(
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,
encoder_last_hidden_state=inputs["encoder_outputs"].last_hidden_state,
encoder_hidden_states=inputs["encoder_outputs"].hidden_states,
encoder_attentions=inputs["encoder_outputs"].attentions,
)
@add_start_docstrings(
"The bare {{cookiecutter.uppercase_modelname}} Model outputting raw hidden-states without any specific head on top.",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
return self.model.decoder
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="{{cookiecutter.checkpoint_identifier}}",
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs
):
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
outputs = self.model(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
decoder_input_ids=inputs["decoder_input_ids"],
decoder_attention_mask=inputs["decoder_attention_mask"],
encoder_outputs=inputs["encoder_outputs"],
past_key_values=inputs["past_key_values"],
inputs_embeds=inputs["inputs_embeds"],
decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
use_cache=inputs["use_cache"],
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.bart.modeling_tf_bart.TFBartModel.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqModelOutput(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
@add_start_docstrings(
"The {{cookiecutter.uppercase_modelname}} Model with a language modeling head. Can be used for summarization.",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
_keys_to_ignore_on_load_unexpected = [
r"model.encoder.embed_tokens.weight",
r"model.decoder.embed_tokens.weight",
]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="model")
self.model._set_save_spec(inputs=self.serving.input_signature)
self.use_cache = config.use_cache
# final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
self.final_logits_bias = self.add_weight(
name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
)
def get_decoder(self):
return self.model.decoder
def get_encoder(self):
return self.model.encoder
def get_bias(self):
return {"final_logits_bias": self.final_logits_bias}
def set_bias(self, value):
self.final_logits_bias = value["final_logits_bias"]
def get_output_embeddings(self):
return self.get_input_embeddings()
def set_output_embeddings(self, value):
self.set_input_embeddings(value)
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs: Optional[TFBaseModelOutput] = None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
**kwargs,
):
"""
Returns:
Examples::
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> import tensorflow as tf
>>> mname = '{{cookiecutter.checkpoint_identifier}}'
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained(mname)
>>> TXT = "My friends are <mask> but they eat too many carbs."
>>> model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained(mname)
>>> batch = tokenizer([TXT], return_tensors='tf')
>>> logits = model(inputs=batch.input_ids).logits
>>> probs = tf.nn.softmax(logits[0])
>>> # probs[5] is associated with the mask token
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
if inputs["labels"] is not None:
inputs["use_cache"] = False
if inputs["decoder_input_ids"] is None:
inputs["decoder_input_ids"] = shift_tokens_right(
inputs["labels"], self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
decoder_input_ids=inputs["decoder_input_ids"],
encoder_outputs=inputs["encoder_outputs"],
decoder_attention_mask=inputs["decoder_attention_mask"],
past_key_values=inputs["past_key_values"],
inputs_embeds=inputs["inputs_embeds"],
decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"]
)
lm_logits = self.model.shared(outputs[0], mode="linear")
lm_logits = lm_logits + self.final_logits_bias
masked_lm_loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], lm_logits)
if not inputs["return_dict"]:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return TFSeq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values, # index 1 of d outputs
decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs
decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs
encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs
encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out
encoder_attentions=outputs.encoder_attentions, # 2 of e out
)
# Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
def prepare_inputs_for_generation(self, decoder_input_ids, past, attention_mask, use_cache, **kwargs) -> Dict:
assert past is not None and len(past) in {1, 2}, f"past has to be an iterable of length 1,2 got {past}"
if len(past) == 1:
assert isinstance(past[0], tf.Tensor), f"`past[0]` has to be of type `tf.Tensor`, but is {type(past[0])}"
encoder_outputs = TFBaseModelOutput(last_hidden_state=past[0])
past_key_values = None
else:
assert (
len(past) == 2
), "`past` has to be of length 2 with the encoder_outputs at the first position and past_key_values at the second position."
encoder_outputs, past_key_values = past
if isinstance(encoder_outputs, tuple):
assert isinstance(
encoder_outputs[0], tf.Tensor
), f"`encoder_outputs[0]` has to be of type `tf.Tensor`, but is {type(encoder_outputs[0])}"
encoder_outputs = TFBaseModelOutput(last_hidden_state=encoder_outputs[0])
elif isinstance(encoder_outputs, tf.Tensor):
encoder_outputs = TFBaseModelOutput(last_hidden_state=encoder_outputs)
assert (
past_key_values
), f"decoder cached states must be truthy. got {past_key_values} from the 2nd element of past"
decoder_input_ids = decoder_input_ids[:, -1:]
assert isinstance(
encoder_outputs, TFBaseModelOutput
), f"encoder_outputs should be a TFBaseModelOutput, Instead got {type(encoder_outputs)}."
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
@staticmethod
def _reorder_cache(past, beam_idx):
if len(past) == 1:
return past
past_key_values = past[1]
reordered_past = ()
for layer_past_key_values in past_key_values:
reordered_past += (
tuple(tf.gather(layer_past_key_value, beam_idx) for layer_past_key_value in layer_past_key_values[:2]) + layer_past_key_values[2:],
)
return (past[0], reordered_past)
def compute_loss(self, labels, logits):
"""CrossEntropyLoss that ignores pad tokens"""
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True,
reduction=tf.keras.losses.Reduction.NONE,
)
melted_labels = tf.reshape(labels, (-1,))
active_loss = tf.not_equal(melted_labels, self.config.pad_token_id)
reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
labels = tf.boolean_mask(melted_labels, active_loss)
return loss_fn(labels, reduced_logits)
{% endif -%}
|
AdaMix/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py/0
|
{
"file_path": "AdaMix/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py",
"repo_id": "AdaMix",
"token_count": 57328
}
| 71 |
{
"modelname": "NewTFENCDEC",
"uppercase_modelname": "NEW_TF_ENC_DEC",
"lowercase_modelname": "new_tf_enc_dec",
"camelcase_modelname": "NewTFEncDec",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "new-tf-enc-dec-base",
"tokenizer_type": "Based on BART",
"generate_tensorflow_and_pytorch": "TensorFlow",
"is_encoder_decoder_model": "True"
}
|
AdaMix/templates/adding_a_new_model/tests/tf-seq-2-seq-bart-tokenizer.json/0
|
{
"file_path": "AdaMix/templates/adding_a_new_model/tests/tf-seq-2-seq-bart-tokenizer.json",
"repo_id": "AdaMix",
"token_count": 152
}
| 72 |
# coding=utf-8
# Copyright 2019 HuggingFace 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 json
import os
import tempfile
class ConfigTester(object):
def __init__(self, parent, config_class=None, **kwargs):
self.parent = parent
self.config_class = config_class
self.inputs_dict = kwargs
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "vocab_size"))
self.parent.assertTrue(hasattr(config, "hidden_size"))
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
self.parent.assertTrue(hasattr(config, "num_hidden_layers"))
def create_and_test_config_to_json_string(self):
config = self.config_class(**self.inputs_dict)
obj = json.loads(config.to_json_string())
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key], value)
def create_and_test_config_to_json_file(self):
config_first = self.config_class(**self.inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "config.json")
config_first.to_json_file(json_file_path)
config_second = self.config_class.from_json_file(json_file_path)
self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
def create_and_test_config_from_and_save_pretrained(self):
config_first = self.config_class(**self.inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(tmpdirname)
config_second = self.config_class.from_pretrained(tmpdirname)
self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
def create_and_test_config_with_num_labels(self):
config = self.config_class(**self.inputs_dict, num_labels=5)
self.parent.assertEqual(len(config.id2label), 5)
self.parent.assertEqual(len(config.label2id), 5)
config.num_labels = 3
self.parent.assertEqual(len(config.id2label), 3)
self.parent.assertEqual(len(config.label2id), 3)
def check_config_can_be_init_without_params(self):
if self.config_class.is_composition:
return
config = self.config_class()
self.parent.assertIsNotNone(config)
def run_common_tests(self):
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
|
AdaMix/tests/test_configuration_common.py/0
|
{
"file_path": "AdaMix/tests/test_configuration_common.py",
"repo_id": "AdaMix",
"token_count": 1289
}
| 73 |
# coding=utf-8
# Copyright 2020 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 unittest
from dataclasses import dataclass
from typing import Optional
from transformers.file_utils import ModelOutput
@dataclass
class ModelOutputTest(ModelOutput):
a: float
b: Optional[float] = None
c: Optional[float] = None
class ModelOutputTester(unittest.TestCase):
def test_get_attributes(self):
x = ModelOutputTest(a=30)
self.assertEqual(x.a, 30)
self.assertIsNone(x.b)
self.assertIsNone(x.c)
with self.assertRaises(AttributeError):
_ = x.d
def test_index_with_ints_and_slices(self):
x = ModelOutputTest(a=30, b=10)
self.assertEqual(x[0], 30)
self.assertEqual(x[1], 10)
self.assertEqual(x[:2], (30, 10))
self.assertEqual(x[:], (30, 10))
x = ModelOutputTest(a=30, c=10)
self.assertEqual(x[0], 30)
self.assertEqual(x[1], 10)
self.assertEqual(x[:2], (30, 10))
self.assertEqual(x[:], (30, 10))
def test_index_with_strings(self):
x = ModelOutputTest(a=30, b=10)
self.assertEqual(x["a"], 30)
self.assertEqual(x["b"], 10)
with self.assertRaises(KeyError):
_ = x["c"]
x = ModelOutputTest(a=30, c=10)
self.assertEqual(x["a"], 30)
self.assertEqual(x["c"], 10)
with self.assertRaises(KeyError):
_ = x["b"]
def test_dict_like_properties(self):
x = ModelOutputTest(a=30)
self.assertEqual(list(x.keys()), ["a"])
self.assertEqual(list(x.values()), [30])
self.assertEqual(list(x.items()), [("a", 30)])
self.assertEqual(list(x), ["a"])
x = ModelOutputTest(a=30, b=10)
self.assertEqual(list(x.keys()), ["a", "b"])
self.assertEqual(list(x.values()), [30, 10])
self.assertEqual(list(x.items()), [("a", 30), ("b", 10)])
self.assertEqual(list(x), ["a", "b"])
x = ModelOutputTest(a=30, c=10)
self.assertEqual(list(x.keys()), ["a", "c"])
self.assertEqual(list(x.values()), [30, 10])
self.assertEqual(list(x.items()), [("a", 30), ("c", 10)])
self.assertEqual(list(x), ["a", "c"])
with self.assertRaises(Exception):
x = x.update({"d": 20})
with self.assertRaises(Exception):
del x["a"]
with self.assertRaises(Exception):
_ = x.pop("a")
with self.assertRaises(Exception):
_ = x.setdefault("d", 32)
def test_set_attributes(self):
x = ModelOutputTest(a=30)
x.a = 10
self.assertEqual(x.a, 10)
self.assertEqual(x["a"], 10)
def test_set_keys(self):
x = ModelOutputTest(a=30)
x["a"] = 10
self.assertEqual(x.a, 10)
self.assertEqual(x["a"], 10)
|
AdaMix/tests/test_model_output.py/0
|
{
"file_path": "AdaMix/tests/test_model_output.py",
"repo_id": "AdaMix",
"token_count": 1546
}
| 74 |
# coding=utf-8
# Copyright 2020 Huggingface
#
# 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 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, random_attention_mask
if is_torch_available():
import torch
from transformers import DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
from transformers.models.dpr.modeling_dpr import (
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class DPRModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=False,
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,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
projection_dim=0,
):
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.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.projection_dim = projection_dim
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 = random_attention_mask([self.batch_size, self.seq_length])
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 = DPRConfig(
projection_dim=self.projection_dim,
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_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_context_encoder(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = DPRContextEncoder(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.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
def create_and_check_question_encoder(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = DPRQuestionEncoder(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.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
def create_and_check_reader(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = DPRReader(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
)
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))
self.parent.assertEqual(result.relevance_logits.shape, (self.batch_size,))
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}
return config, inputs_dict
@require_torch
class DPRModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
)
if is_torch_available()
else ()
)
test_resize_embeddings = False
test_missing_keys = False # why?
test_pruning = False
test_head_masking = False
def setUp(self):
self.model_tester = DPRModelTester(self)
self.config_tester = ConfigTester(self, config_class=DPRConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_context_encoder_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_context_encoder(*config_and_inputs)
def test_question_encoder_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_question_encoder(*config_and_inputs)
def test_reader_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reader(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DPRContextEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DPRContextEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DPRQuestionEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DPRReader.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class DPRModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base", return_dict=False)
model.to(torch_device)
input_ids = torch.tensor(
[[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]], dtype=torch.long, device=torch_device
) # [CLS] hello, is my dog cute? [SEP]
output = model(input_ids)[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
expected_slice = torch.tensor(
[
[
0.03236253,
0.12753335,
0.16818509,
0.00279786,
0.3896933,
0.24264945,
0.2178971,
-0.02335227,
-0.08481959,
-0.14324117,
]
],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output[:, :10], expected_slice, atol=1e-4))
@slow
def test_reader_inference(self):
tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base")
model.to(torch_device)
encoded_inputs = tokenizer(
questions="What is love ?",
titles="Haddaway",
texts="What Is Love is a song recorded by the artist Haddaway",
padding=True,
return_tensors="pt",
)
encoded_inputs.to(torch_device)
outputs = model(**encoded_inputs)
# compare the actual values for a slice.
expected_start_logits = torch.tensor(
[[-10.3005, -10.7765, -11.4872, -11.6841, -11.9312, -10.3002, -9.8544, -11.7378, -12.0821, -10.2975]],
dtype=torch.float,
device=torch_device,
)
expected_end_logits = torch.tensor(
[[-11.0684, -11.7041, -11.5397, -10.3465, -10.8791, -6.8443, -11.9959, -11.0364, -10.0096, -6.8405]],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(outputs.start_logits[:, :10], expected_start_logits, atol=1e-4))
self.assertTrue(torch.allclose(outputs.end_logits[:, :10], expected_end_logits, atol=1e-4))
|
AdaMix/tests/test_modeling_dpr.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_dpr.py",
"repo_id": "AdaMix",
"token_count": 5190
}
| 75 |
# coding=utf-8
# Copyright 2021, 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.
""" Testing suite for the PyTorch Marian model. """
import tempfile
import unittest
from transformers import is_torch_available
from transformers.file_utils import cached_property
from transformers.hf_api import HfApi
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
from .test_generation_utils import GenerationTesterMixin
from .test_modeling_common import ModelTesterMixin, ids_tensor
if is_torch_available():
import torch
from transformers import (
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
MarianConfig,
MarianModel,
MarianMTModel,
TranslationPipeline,
)
from transformers.models.marian.convert_marian_to_pytorch import (
ORG_NAME,
convert_hf_name_to_opus_name,
convert_opus_name_to_hf_name,
)
from transformers.models.marian.modeling_marian import (
MarianDecoder,
MarianEncoder,
MarianForCausalLM,
shift_tokens_right,
)
def prepare_marian_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_torch
class MarianModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
decoder_start_token_id=3,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
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.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.decoder_start_token_id = decoder_start_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
3,
)
input_ids[:, -1] = self.eos_token_id # Eos Token
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = MarianConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
)
inputs_dict = prepare_marian_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = MarianModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
head_mask = inputs_dict["head_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = MarianModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = MarianEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = MarianDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class MarianModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (MarianModel, MarianMTModel) if is_torch_available() else ()
all_generative_model_classes = (MarianMTModel,) if is_torch_available() else ()
is_encoder_decoder = True
test_pruning = False
test_missing_keys = False
def setUp(self):
self.model_tester = MarianModelTester(self)
self.config_tester = ConfigTester(self, config_class=MarianConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = MarianMTModel(config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def assert_tensors_close(a, b, atol=1e-12, prefix=""):
"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if torch.allclose(a, b, atol=atol):
return True
raise
except Exception:
pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
if a.numel() > 100:
msg = f"tensor values are {pct_different:.1%} percent different."
else:
msg = f"{a} != {b}"
if prefix:
msg = prefix + ": " + msg
raise AssertionError(msg)
def _long_tensor(tok_lst):
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
class ModelManagementTests(unittest.TestCase):
@slow
@require_torch
def test_model_names(self):
model_list = HfApi().model_list()
model_ids = [x.modelId for x in model_list if x.modelId.startswith(ORG_NAME)]
bad_model_ids = [mid for mid in model_ids if "+" in model_ids]
self.assertListEqual([], bad_model_ids)
self.assertGreater(len(model_ids), 500)
@require_torch
@require_sentencepiece
@require_tokenizers
class MarianIntegrationTest(unittest.TestCase):
src = "en"
tgt = "de"
src_text = [
"I am a small frog.",
"Now I can forget the 100 words of german that I know.",
"Tom asked his teacher for advice.",
"That's how I would do it.",
"Tom really admired Mary's courage.",
"Turn around and close your eyes.",
]
expected_text = [
"Ich bin ein kleiner Frosch.",
"Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
"Tom bat seinen Lehrer um Rat.",
"So würde ich das machen.",
"Tom bewunderte Marias Mut wirklich.",
"Drehen Sie sich um und schließen Sie die Augen.",
]
# ^^ actual C++ output differs slightly: (1) des Deutschen removed, (2) ""-> "O", (3) tun -> machen
@classmethod
def setUpClass(cls) -> None:
cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
return cls
@cached_property
def tokenizer(self):
return AutoTokenizer.from_pretrained(self.model_name)
@property
def eos_token_id(self) -> int:
return self.tokenizer.eos_token_id
@cached_property
def model(self):
model: MarianMTModel = AutoModelWithLMHead.from_pretrained(self.model_name).to(torch_device)
c = model.config
self.assertListEqual(c.bad_words_ids, [[c.pad_token_id]])
self.assertEqual(c.max_length, 512)
self.assertEqual(c.decoder_start_token_id, c.pad_token_id)
if torch_device == "cuda":
return model.half()
else:
return model
def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
generated_words = self.translate_src_text(**tokenizer_kwargs)
self.assertListEqual(self.expected_text, generated_words)
def translate_src_text(self, **tokenizer_kwargs):
model_inputs = self.tokenizer(self.src_text, padding=True, return_tensors="pt", **tokenizer_kwargs).to(
torch_device
)
self.assertEqual(self.model.device, model_inputs.input_ids.device)
generated_ids = self.model.generate(
model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2, max_length=128
)
generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
return generated_words
@require_sentencepiece
@require_tokenizers
class TestMarian_EN_DE_More(MarianIntegrationTest):
@slow
def test_forward(self):
src, tgt = ["I am a small frog"], ["Ich bin ein kleiner Frosch."]
expected_ids = [38, 121, 14, 697, 38848, 0]
model_inputs = self.tokenizer(src, return_tensors="pt").to(torch_device)
with self.tokenizer.as_target_tokenizer():
targets = self.tokenizer(tgt, return_tensors="pt")
model_inputs["labels"] = targets["input_ids"].to(torch_device)
self.assertListEqual(expected_ids, model_inputs.input_ids[0].tolist())
desired_keys = {
"input_ids",
"attention_mask",
"labels",
}
self.assertSetEqual(desired_keys, set(model_inputs.keys()))
model_inputs["decoder_input_ids"] = shift_tokens_right(
model_inputs.labels, self.tokenizer.pad_token_id, self.model.config.decoder_start_token_id
)
model_inputs["return_dict"] = True
model_inputs["use_cache"] = False
with torch.no_grad():
outputs = self.model(**model_inputs)
max_indices = outputs.logits.argmax(-1)
self.tokenizer.batch_decode(max_indices)
def test_unk_support(self):
t = self.tokenizer
ids = t(["||"], return_tensors="pt").to(torch_device).input_ids[0].tolist()
expected = [t.unk_token_id, t.unk_token_id, t.eos_token_id]
self.assertEqual(expected, ids)
def test_pad_not_split(self):
input_ids_w_pad = self.tokenizer(["I am a small frog <pad>"], return_tensors="pt").input_ids[0].tolist()
expected_w_pad = [38, 121, 14, 697, 38848, self.tokenizer.pad_token_id, 0] # pad
self.assertListEqual(expected_w_pad, input_ids_w_pad)
@slow
def test_batch_generation_en_de(self):
self._assert_generated_batch_equal_expected()
def test_auto_config(self):
config = AutoConfig.from_pretrained(self.model_name)
self.assertIsInstance(config, MarianConfig)
@require_sentencepiece
@require_tokenizers
class TestMarian_EN_FR(MarianIntegrationTest):
src = "en"
tgt = "fr"
src_text = [
"I am a small frog.",
"Now I can forget the 100 words of german that I know.",
]
expected_text = [
"Je suis une petite grenouille.",
"Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
]
@slow
def test_batch_generation_en_fr(self):
self._assert_generated_batch_equal_expected()
@require_sentencepiece
@require_tokenizers
class TestMarian_FR_EN(MarianIntegrationTest):
src = "fr"
tgt = "en"
src_text = [
"Donnez moi le micro.",
"Tom et Mary étaient assis à une table.", # Accents
]
expected_text = [
"Give me the microphone.",
"Tom and Mary were sitting at a table.",
]
@slow
def test_batch_generation_fr_en(self):
self._assert_generated_batch_equal_expected()
@require_sentencepiece
@require_tokenizers
class TestMarian_RU_FR(MarianIntegrationTest):
src = "ru"
tgt = "fr"
src_text = ["Он показал мне рукопись своей новой пьесы."]
expected_text = ["Il m'a montré le manuscrit de sa nouvelle pièce."]
@slow
def test_batch_generation_ru_fr(self):
self._assert_generated_batch_equal_expected()
@require_sentencepiece
@require_tokenizers
class TestMarian_MT_EN(MarianIntegrationTest):
"""Cover low resource/high perplexity setting. This breaks without adjust_logits_generation overwritten"""
src = "mt"
tgt = "en"
src_text = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
expected_text = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
@slow
def test_batch_generation_mt_en(self):
self._assert_generated_batch_equal_expected()
@require_sentencepiece
@require_tokenizers
class TestMarian_en_zh(MarianIntegrationTest):
src = "en"
tgt = "zh"
src_text = ["My name is Wolfgang and I live in Berlin"]
expected_text = ["我叫沃尔夫冈 我住在柏林"]
@slow
def test_batch_generation_eng_zho(self):
self._assert_generated_batch_equal_expected()
@require_sentencepiece
@require_tokenizers
class TestMarian_en_ROMANCE(MarianIntegrationTest):
"""Multilingual on target side."""
src = "en"
tgt = "ROMANCE"
src_text = [
">>fr<< Don't spend so much time watching TV.",
">>pt<< Your message has been sent.",
">>es<< He's two years older than me.",
]
expected_text = [
"Ne passez pas autant de temps à regarder la télé.",
"A sua mensagem foi enviada.",
"Es dos años más viejo que yo.",
]
@slow
def test_batch_generation_en_ROMANCE_multi(self):
self._assert_generated_batch_equal_expected()
@slow
def test_pipeline(self):
device = 0 if torch_device == "cuda" else -1
pipeline = TranslationPipeline(self.model, self.tokenizer, framework="pt", device=device)
output = pipeline(self.src_text)
self.assertEqual(self.expected_text, [x["translation_text"] for x in output])
@require_torch
class TestConversionUtils(unittest.TestCase):
def test_renaming_multilingual(self):
old_names = [
"opus-mt-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi",
"opus-mt-cmn+cn-fi", # no group
"opus-mt-en-de", # standard name
"opus-mt-en-de", # standard name
]
expected = ["opus-mt-ZH-fi", "opus-mt-cmn_cn-fi", "opus-mt-en-de", "opus-mt-en-de"]
self.assertListEqual(expected, [convert_opus_name_to_hf_name(x) for x in old_names])
def test_undoing_renaming(self):
hf_names = ["opus-mt-ZH-fi", "opus-mt-cmn_cn-fi", "opus-mt-en-de", "opus-mt-en-de"]
converted_opus_names = [convert_hf_name_to_opus_name(x) for x in hf_names]
expected_opus_names = [
"cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi",
"cmn+cn-fi",
"en-de", # standard name
"en-de",
]
self.assertListEqual(expected_opus_names, converted_opus_names)
class MarianStandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=4,
encoder_attention_heads=4,
decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = MarianConfig(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = MarianDecoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = MarianDecoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=attn_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class MarianStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (MarianDecoder, MarianForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (MarianForCausalLM,) if is_torch_available() else ()
test_pruning = False
is_encoder_decoder = False
def setUp(
self,
):
self.model_tester = MarianStandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=MarianConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_attn_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
return
|
AdaMix/tests/test_modeling_marian.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_marian.py",
"repo_id": "AdaMix",
"token_count": 12859
}
| 76 |
# 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 unittest
from transformers import is_tf_available
from transformers.testing_utils import DUMMY_UNKWOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_tf, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPT2Config,
T5Config,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeq2SeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPT2LMHeadModel,
TFRobertaForMaskedLM,
TFT5ForConditionalGeneration,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
TF_MODEL_WITH_LM_HEAD_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
@require_tf
class TFAutoModelTest(unittest.TestCase):
@slow
def test_model_from_pretrained(self):
import h5py
self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModel.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertModel)
@slow
def test_model_for_pretraining_from_pretrained(self):
import h5py
self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForPreTraining.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForPreTraining)
@slow
def test_model_for_causal_lm(self):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, GPT2Config)
model = TFAutoModelForCausalLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFGPT2LMHeadModel)
@slow
def test_lmhead_model_from_pretrained(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelWithLMHead.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
@slow
def test_model_for_masked_lm(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForMaskedLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
@slow
def test_model_for_encoder_decoder_lm(self):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, T5Config)
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFT5ForConditionalGeneration)
@slow
def test_sequence_classification_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForSequenceClassification)
@slow
def test_question_answering_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForQuestionAnswering)
def test_from_pretrained_identifier(self):
model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
self.assertIsInstance(model, TFBertForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
def test_from_identifier_from_model_type(self):
model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKWOWN_IDENTIFIER)
self.assertIsInstance(model, TFRobertaForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
def test_parents_and_children_in_mappings(self):
# Test that the children are placed before the parents in the mappings, as the `instanceof` will be triggered
# by the parents and will return the wrong configuration type when using auto models
mappings = (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_WITH_LM_HEAD_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
)
for mapping in mappings:
mapping = tuple(mapping.items())
for index, (child_config, child_model) in enumerate(mapping[1:]):
for parent_config, parent_model in mapping[: index + 1]:
with self.subTest(
msg="Testing if {} is child of {}".format(child_config.__name__, parent_config.__name__)
):
self.assertFalse(issubclass(child_config, parent_config))
self.assertFalse(issubclass(child_model, parent_model))
|
AdaMix/tests/test_modeling_tf_auto.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_tf_auto.py",
"repo_id": "AdaMix",
"token_count": 3864
}
| 77 |
# coding=utf-8
# Copyright Iz Beltagy, Matthew E. Peters, Arman Cohan 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.
import unittest
from transformers import LEDConfig, 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 TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class TFLEDModelTester:
config_cls = LEDConfig
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
attention_window=4,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
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_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.attention_window = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
self.key_length = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
self.encoder_seq_length = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def prepare_config_and_inputs_for_common(self):
input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
input_ids = tf.concat([input_ids, eos_tensor], axis=1)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.config_cls(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_ids=[2],
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.pad_token_id,
attention_window=self.attention_window,
**self.config_updates,
)
inputs_dict = prepare_led_inputs_dict(config, input_ids, decoder_input_ids)
global_attention_mask = tf.concat(
[tf.zeros_like(input_ids)[:, :-1], tf.ones_like(input_ids)[:, -1:]],
axis=-1,
)
inputs_dict["global_attention_mask"] = global_attention_mask
return config, inputs_dict
def check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = TFLEDModel(config=config).get_decoder()
input_ids = inputs_dict["input_ids"]
input_ids = input_ids[:1, :]
attention_mask = inputs_dict["attention_mask"][:1, :]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
past_key_values = past_key_values[1]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def prepare_led_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
):
if attention_mask is None:
attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8)
if decoder_attention_mask is None:
decoder_attention_mask = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8),
],
axis=-1,
)
if head_mask is None:
head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class TFLEDModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
all_generative_model_classes = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
is_encoder_decoder = True
test_pruning = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFLEDModelTester(self)
self.config_tester = ConfigTester(self, config_class=LEDConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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 self.all_generative_model_classes:
x = model.get_output_embeddings()
assert isinstance(x, tf.keras.layers.Layer)
name = model.get_bias()
assert isinstance(name, dict)
for k, v in name.items():
assert isinstance(v, tf.Variable)
else:
x = model.get_output_embeddings()
assert x is None
name = model.get_bias()
assert name is None
def test_resize_token_embeddings(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(model, embedding_layer):
if hasattr(embedding_layer, "weight"):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model(model.dummy_inputs)
if hasattr(embedding_layer, "weight"):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10, None]:
# build the embeddings
model = model_class(config=config)
old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
old_final_logits_bias = model.get_bias()
# reshape the embeddings
model.resize_token_embeddings(size)
new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
new_final_logits_bias = model.get_bias()
# check that the resized embeddings size matches the desired size.
assert_size = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0], assert_size)
# check that weights remain the same after resizing
models_equal = True
for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()):
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
models_equal = False
self.assertTrue(models_equal)
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0], assert_size)
models_equal = True
for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()):
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
models_equal = False
self.assertTrue(models_equal)
if old_final_logits_bias is not None and new_final_logits_bias is not None:
old_final_logits_bias = old_final_logits_bias["final_logits_bias"]
new_final_logits_bias = new_final_logits_bias["final_logits_bias"]
self.assertEqual(new_final_logits_bias.shape[0], 1)
self.assertEqual(new_final_logits_bias.shape[1], assert_size)
models_equal = True
for old, new in zip(old_final_logits_bias.value(), new_final_logits_bias.value()):
for p1, p2 in zip(old, new):
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
models_equal = False
self.assertTrue(models_equal)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
inputs_dict["global_attention_mask"] = tf.zeros_like(inputs_dict["attention_mask"])
num_global_attn_indices = 2
inputs_dict["global_attention_mask"] = tf.where(
tf.range(self.model_tester.seq_length)[None, :] < num_global_attn_indices,
1,
inputs_dict["global_attention_mask"],
)
config.return_dict = True
seq_length = self.model_tester.seq_length
encoder_seq_length = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(outputs):
decoder_attentions = outputs.decoder_attentions
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_length, seq_length],
)
def check_encoder_attentions_output(outputs):
attentions = [t.numpy() for t in outputs.encoder_attentions]
global_attentions = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertEqual(len(global_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, seq_length],
)
self.assertListEqual(
list(global_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices],
)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["use_cache"] = False
config.output_hidden_states = False
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
out_len = len(outputs)
self.assertEqual(config.output_hidden_states, False)
check_encoder_attentions_output(outputs)
if self.is_encoder_decoder:
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(config.output_hidden_states, False)
check_decoder_attentions_output(outputs)
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(config.output_hidden_states, False)
check_encoder_attentions_output(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
config.output_hidden_states = True
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
self.assertEqual(model.config.output_hidden_states, True)
check_encoder_attentions_output(outputs)
def test_xla_mode(self):
# TODO JP: Make LED XLA compliant
pass
def test_saved_model_creation(self):
# This test is too long (>30sec) and makes fail the CI
pass
def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
"""If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if tf.debugging.assert_near(a, b, atol=atol):
return True
raise
except Exception:
msg = "{} != {}".format(a, b)
if prefix:
msg = prefix + ": " + msg
raise AssertionError(msg)
def _long_tensor(tok_lst):
return tf.constant(tok_lst, dtype=tf.int32)
TOLERANCE = 1e-4
@slow
@require_tf
class TFLEDModelIntegrationTest(unittest.TestCase):
def test_inference_no_head(self):
model = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384").led
# change to intended input here
input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids)
output = model(**inputs_dict)[0]
expected_shape = (1, 1024, 768)
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]],
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)
def test_inference_with_head(self):
model = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384")
# change to intended input here
input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids)
output = model(**inputs_dict)[0]
expected_shape = (1, 1024, model.config.vocab_size)
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]],
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)
|
AdaMix/tests/test_modeling_tf_led.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_tf_led.py",
"repo_id": "AdaMix",
"token_count": 8634
}
| 78 |
# 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 unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class TFFlaubertModelIntegrationTest(unittest.TestCase):
@slow
def test_output_embeds_base_model(self):
model = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base")
features = {
"input_ids": tf.convert_to_tensor([[0, 2646, 10269, 83, 99942, 2]], dtype=tf.int32), # "My dog is cute"
"attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]], dtype=tf.int32),
}
output = model(features)["last_hidden_state"]
expected_shape = tf.TensorShape((1, 6, 768))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = tf.convert_to_tensor(
[
[
[0.0681762, 0.10894451, 0.06772504],
[-0.06423668, 0.02366615, 0.04329344],
[-0.06057295, 0.09974135, -0.00070584],
]
],
dtype=tf.float32,
)
self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
|
AdaMix/tests/test_modeling_tf_xlm_roberta.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_tf_xlm_roberta.py",
"repo_id": "AdaMix",
"token_count": 842
}
| 79 |
# 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 unittest
from transformers import AutoTokenizer, is_torch_available, pipeline
from transformers.pipelines import Pipeline, TokenClassificationArgumentHandler
from transformers.testing_utils import require_tf, require_torch, slow
from .test_pipelines_common import CustomInputPipelineCommonMixin
if is_torch_available():
import numpy as np
VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]]
class NerPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
pipeline_task = "ner"
small_models = [
"sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
] # Default model - Models tested without the @slow decorator
large_models = [] # Models tested with the @slow decorator
def _test_pipeline(self, nlp: Pipeline):
output_keys = {"entity", "word", "score", "start", "end"}
if nlp.grouped_entities:
output_keys = {"entity_group", "word", "score", "start", "end"}
ungrouped_ner_inputs = [
[
{
"entity": "B-PER",
"index": 1,
"score": 0.9994944930076599,
"is_subword": False,
"word": "Cons",
"start": 0,
"end": 4,
},
{
"entity": "B-PER",
"index": 2,
"score": 0.8025449514389038,
"is_subword": True,
"word": "##uelo",
"start": 4,
"end": 8,
},
{
"entity": "I-PER",
"index": 3,
"score": 0.9993102550506592,
"is_subword": False,
"word": "Ara",
"start": 9,
"end": 11,
},
{
"entity": "I-PER",
"index": 4,
"score": 0.9993743896484375,
"is_subword": True,
"word": "##új",
"start": 11,
"end": 13,
},
{
"entity": "I-PER",
"index": 5,
"score": 0.9992871880531311,
"is_subword": True,
"word": "##o",
"start": 13,
"end": 14,
},
{
"entity": "I-PER",
"index": 6,
"score": 0.9993029236793518,
"is_subword": False,
"word": "No",
"start": 15,
"end": 17,
},
{
"entity": "I-PER",
"index": 7,
"score": 0.9981776475906372,
"is_subword": True,
"word": "##guera",
"start": 17,
"end": 22,
},
{
"entity": "B-PER",
"index": 15,
"score": 0.9998136162757874,
"is_subword": False,
"word": "Andrés",
"start": 23,
"end": 28,
},
{
"entity": "I-PER",
"index": 16,
"score": 0.999740719795227,
"is_subword": False,
"word": "Pas",
"start": 29,
"end": 32,
},
{
"entity": "I-PER",
"index": 17,
"score": 0.9997414350509644,
"is_subword": True,
"word": "##tran",
"start": 32,
"end": 36,
},
{
"entity": "I-PER",
"index": 18,
"score": 0.9996136426925659,
"is_subword": True,
"word": "##a",
"start": 36,
"end": 37,
},
{
"entity": "B-ORG",
"index": 28,
"score": 0.9989739060401917,
"is_subword": False,
"word": "Far",
"start": 39,
"end": 42,
},
{
"entity": "I-ORG",
"index": 29,
"score": 0.7188422083854675,
"is_subword": True,
"word": "##c",
"start": 42,
"end": 43,
},
],
[
{
"entity": "I-PER",
"index": 1,
"score": 0.9968166351318359,
"is_subword": False,
"word": "En",
"start": 0,
"end": 2,
},
{
"entity": "I-PER",
"index": 2,
"score": 0.9957635998725891,
"is_subword": True,
"word": "##zo",
"start": 2,
"end": 4,
},
{
"entity": "I-ORG",
"index": 7,
"score": 0.9986497163772583,
"is_subword": False,
"word": "UN",
"start": 11,
"end": 13,
},
],
]
expected_grouped_ner_results = [
[
{
"entity_group": "PER",
"score": 0.999369223912557,
"word": "Consuelo Araújo Noguera",
"start": 0,
"end": 22,
},
{
"entity_group": "PER",
"score": 0.9997771680355072,
"word": "Andrés Pastrana",
"start": 23,
"end": 37,
},
{"entity_group": "ORG", "score": 0.9989739060401917, "word": "Farc", "start": 39, "end": 43},
],
[
{"entity_group": "PER", "score": 0.9968166351318359, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN", "start": 11, "end": 13},
],
]
expected_grouped_ner_results_w_subword = [
[
{"entity_group": "PER", "score": 0.9994944930076599, "word": "Cons", "start": 0, "end": 4},
{
"entity_group": "PER",
"score": 0.9663328925768534,
"word": "##uelo Araújo Noguera",
"start": 4,
"end": 22,
},
{
"entity_group": "PER",
"score": 0.9997273534536362,
"word": "Andrés Pastrana",
"start": 23,
"end": 37,
},
{"entity_group": "ORG", "score": 0.8589080572128296, "word": "Farc", "start": 39, "end": 43},
],
[
{"entity_group": "PER", "score": 0.9962901175022125, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN", "start": 11, "end": 13},
],
]
self.assertIsNotNone(nlp)
mono_result = nlp(VALID_INPUTS[0])
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], (dict, list))
if isinstance(mono_result[0], list):
mono_result = mono_result[0]
for key in output_keys:
self.assertIn(key, mono_result[0])
multi_result = [nlp(input) for input in VALID_INPUTS]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
if isinstance(multi_result[0], list):
multi_result = multi_result[0]
for result in multi_result:
for key in output_keys:
self.assertIn(key, result)
if nlp.grouped_entities:
if nlp.ignore_subwords:
for ungrouped_input, grouped_result in zip(ungrouped_ner_inputs, expected_grouped_ner_results):
self.assertEqual(nlp.group_entities(ungrouped_input), grouped_result)
else:
for ungrouped_input, grouped_result in zip(
ungrouped_ner_inputs, expected_grouped_ner_results_w_subword
):
self.assertEqual(nlp.group_entities(ungrouped_input), grouped_result)
@require_tf
def test_tf_only(self):
model_name = "Narsil/small" # This model only has a TensorFlow version
# We test that if we don't specificy framework='tf', it gets detected automatically
nlp = pipeline(task="ner", model=model_name)
self._test_pipeline(nlp)
@require_tf
def test_tf_defaults(self):
for model_name in self.small_models:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
nlp = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="tf")
self._test_pipeline(nlp)
@require_tf
def test_tf_small_ignore_subwords_available_for_fast_tokenizers(self):
for model_name in self.small_models:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
nlp = pipeline(
task="ner",
model=model_name,
tokenizer=tokenizer,
framework="tf",
grouped_entities=True,
ignore_subwords=True,
)
self._test_pipeline(nlp)
for model_name in self.small_models:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
nlp = pipeline(
task="ner",
model=model_name,
tokenizer=tokenizer,
framework="tf",
grouped_entities=True,
ignore_subwords=False,
)
self._test_pipeline(nlp)
@require_torch
def test_pt_ignore_subwords_slow_tokenizer_raises(self):
for model_name in self.small_models:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
with self.assertRaises(ValueError):
pipeline(task="ner", model=model_name, tokenizer=tokenizer, ignore_subwords=True, use_fast=False)
@require_torch
def test_pt_defaults_slow_tokenizer(self):
for model_name in self.small_models:
tokenizer = AutoTokenizer.from_pretrained(model_name)
nlp = pipeline(task="ner", model=model_name, tokenizer=tokenizer)
self._test_pipeline(nlp)
@require_torch
def test_pt_defaults(self):
for model_name in self.small_models:
nlp = pipeline(task="ner", model=model_name)
self._test_pipeline(nlp)
@slow
@require_torch
def test_simple(self):
nlp = pipeline(task="ner", model="dslim/bert-base-NER", grouped_entities=True)
sentence = "Hello Sarah Jessica Parker who Jessica lives in New York"
sentence2 = "This is a simple test"
output = nlp(sentence)
def simplify(output):
if isinstance(output, (list, tuple)):
return [simplify(item) for item in output]
elif isinstance(output, dict):
return {simplify(k): simplify(v) for k, v in output.items()}
elif isinstance(output, (str, int, np.int64)):
return output
elif isinstance(output, float):
return round(output, 3)
else:
raise Exception(f"Cannot handle {type(output)}")
output_ = simplify(output)
self.assertEqual(
output_,
[
{
"entity_group": "PER",
"score": 0.996,
"word": "Sarah Jessica Parker",
"start": 6,
"end": 26,
},
{"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38},
{"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56},
],
)
output = nlp([sentence, sentence2])
output_ = simplify(output)
self.assertEqual(
output_,
[
[
{"entity_group": "PER", "score": 0.996, "word": "Sarah Jessica Parker", "start": 6, "end": 26},
{"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38},
{"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56},
],
[],
],
)
@require_torch
def test_pt_small_ignore_subwords_available_for_fast_tokenizers(self):
for model_name in self.small_models:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
nlp = pipeline(
task="ner", model=model_name, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=True
)
self._test_pipeline(nlp)
for model_name in self.small_models:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
nlp = pipeline(
task="ner", model=model_name, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=False
)
self._test_pipeline(nlp)
class TokenClassificationArgumentHandlerTestCase(unittest.TestCase):
def setUp(self):
self.args_parser = TokenClassificationArgumentHandler()
def test_simple(self):
string = "This is a simple input"
inputs, offset_mapping = self.args_parser(string)
self.assertEqual(inputs, [string])
self.assertEqual(offset_mapping, None)
inputs, offset_mapping = self.args_parser([string, string])
self.assertEqual(inputs, [string, string])
self.assertEqual(offset_mapping, None)
inputs, offset_mapping = self.args_parser(string, offset_mapping=[(0, 1), (1, 2)])
self.assertEqual(inputs, [string])
self.assertEqual(offset_mapping, [[(0, 1), (1, 2)]])
inputs, offset_mapping = self.args_parser(
[string, string], offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]]
)
self.assertEqual(inputs, [string, string])
self.assertEqual(offset_mapping, [[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
def test_errors(self):
string = "This is a simple input"
# 2 sentences, 1 offset_mapping, args
with self.assertRaises(TypeError):
self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)]])
# 2 sentences, 1 offset_mapping, args
with self.assertRaises(TypeError):
self.args_parser(string, string, offset_mapping=[(0, 1), (1, 2)])
# 2 sentences, 1 offset_mapping, input_list
with self.assertRaises(ValueError):
self.args_parser([string, string], offset_mapping=[[(0, 1), (1, 2)]])
# 2 sentences, 1 offset_mapping, input_list
with self.assertRaises(ValueError):
self.args_parser([string, string], offset_mapping=[(0, 1), (1, 2)])
# 1 sentences, 2 offset_mapping
with self.assertRaises(ValueError):
self.args_parser(string, offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
# 0 sentences, 1 offset_mapping
with self.assertRaises(TypeError):
self.args_parser(offset_mapping=[[(0, 1), (1, 2)]])
|
AdaMix/tests/test_pipelines_ner.py/0
|
{
"file_path": "AdaMix/tests/test_pipelines_ner.py",
"repo_id": "AdaMix",
"token_count": 9492
}
| 80 |
# 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 import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.file_utils import cached_property
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from .test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class TestTokenizationBart(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = BartTokenizer
rust_tokenizer_class = BartTokenizerFast
test_rust_tokenizer = True
from_pretrained_filter = filter_roberta_detectors
# from_pretrained_kwargs = {'add_prefix_space': True}
def setUp(self):
super().setUp()
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
self.special_tokens_map = {"unk_token": "<unk>"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
def get_rust_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
return "lower newer", "lower newer"
@cached_property
def default_tokenizer(self):
return BartTokenizer.from_pretrained("facebook/bart-large")
@cached_property
def default_tokenizer_fast(self):
return BartTokenizerFast.from_pretrained("facebook/bart-large")
@require_torch
def test_prepare_batch(self):
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
expected_src_tokens = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
batch = tokenizer(src_text, max_length=len(expected_src_tokens), padding=True, return_tensors="pt")
self.assertIsInstance(batch, BatchEncoding)
self.assertEqual((2, 9), batch.input_ids.shape)
self.assertEqual((2, 9), batch.attention_mask.shape)
result = batch.input_ids.tolist()[0]
self.assertListEqual(expected_src_tokens, result)
# Test that special tokens are reset
@require_torch
def test_prepare_batch_empty_target_text(self):
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
batch = tokenizer(src_text, padding=True, return_tensors="pt")
# check if input_ids are returned and no labels
self.assertIn("input_ids", batch)
self.assertIn("attention_mask", batch)
self.assertNotIn("labels", batch)
self.assertNotIn("decoder_attention_mask", batch)
@require_torch
def test_as_target_tokenizer_target_length(self):
tgt_text = [
"Summary of the text.",
"Another summary.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
with tokenizer.as_target_tokenizer():
targets = tokenizer(tgt_text, max_length=32, padding="max_length", return_tensors="pt")
self.assertEqual(32, targets["input_ids"].shape[1])
@require_torch
def test_prepare_batch_not_longer_than_maxlen(self):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
batch = tokenizer(
["I am a small frog" * 1024, "I am a small frog"], padding=True, truncation=True, return_tensors="pt"
)
self.assertIsInstance(batch, BatchEncoding)
self.assertEqual(batch.input_ids.shape, (2, 1024))
@require_torch
def test_special_tokens(self):
src_text = ["A long paragraph for summarization."]
tgt_text = [
"Summary of the text.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
inputs = tokenizer(src_text, return_tensors="pt")
with tokenizer.as_target_tokenizer():
targets = tokenizer(tgt_text, return_tensors="pt")
input_ids = inputs["input_ids"]
labels = targets["input_ids"]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())
def test_pretokenized_inputs(self):
pass
def test_embeded_special_tokens(self):
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)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
sentence = "A, <mask> AllenNLP sentence."
tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]),
sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),
)
tokens_r_str = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
self.assertSequenceEqual(tokens_r["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
self.assertSequenceEqual(
tokens_p_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
)
self.assertSequenceEqual(
tokens_r_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
)
|
AdaMix/tests/test_tokenization_bart.py/0
|
{
"file_path": "AdaMix/tests/test_tokenization_bart.py",
"repo_id": "AdaMix",
"token_count": 3748
}
| 81 |
# 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 import GPT2Tokenizer, GPT2TokenizerFast
from transformers.models.gpt2.tokenization_gpt2 import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from .test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = GPT2Tokenizer
rust_tokenizer_class = GPT2TokenizerFast
test_rust_tokenizer = True
from_pretrained_kwargs = {"add_prefix_space": True}
test_seq2seq = False
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",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
self.special_tokens_map = {"unk_token": "<unk>"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return GPT2Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_rust_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return GPT2TokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
input_text = "lower newer"
output_text = "lower newer"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = GPT2Tokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
text = "lower newer"
bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
tokens = tokenizer.tokenize(text, add_prefix_space=True)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + [tokenizer.unk_token]
input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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(add_prefix_space=True)
sequence = "lower newer"
# Testing tokenization
tokens = tokenizer.tokenize(sequence, add_prefix_space=True)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
# Testing conversion to ids without special tokens
ids = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
# Testing conversion to ids with special tokens
rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True)
ids = tokenizer.encode(sequence, add_prefix_space=True)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
# Testing the unknown token
input_tokens = tokens + [rust_tokenizer.unk_token]
input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def test_pretokenized_inputs(self, *args, **kwargs):
# It's very difficult to mix/test pretokenization with byte-level
# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
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
|
AdaMix/tests/test_tokenization_gpt2.py/0
|
{
"file_path": "AdaMix/tests/test_tokenization_gpt2.py",
"repo_id": "AdaMix",
"token_count": 3241
}
| 82 |
#!/usr/bin/env python3
# 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.
"""Tests for the Blenderbot small tokenizer."""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from .test_tokenization_common import TokenizerTesterMixin
class BlenderbotSmallTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = BlenderbotSmallTokenizer
def setUp(self):
super().setUp()
vocab = ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
self.special_tokens_map = {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
input_text = "adapt act apte"
output_text = "adapt act apte"
return input_text, output_text
def test_full_blenderbot_small_tokenizer(self):
tokenizer = BlenderbotSmallTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
text = "adapt act apte"
bpe_tokens = ["adapt", "act", "ap@@", "te"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
input_bpe_tokens = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def test_special_tokens_small_tok(self):
tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
assert tok("sam").input_ids == [1384]
src_text = "I am a small frog."
encoded = tok([src_text], padding=False, truncation=False)["input_ids"]
decoded = tok.batch_decode(encoded, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def test_empty_word_small_tok(self):
tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
src_text = "I am a small frog ."
src_text_dot = "."
encoded = tok(src_text)["input_ids"]
encoded_dot = tok(src_text_dot)["input_ids"]
assert encoded[-1] == encoded_dot[0]
|
AdaMix/tests/test_tokenization_small_blenderbot.py/0
|
{
"file_path": "AdaMix/tests/test_tokenization_small_blenderbot.py",
"repo_id": "AdaMix",
"token_count": 1479
}
| 83 |
# 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 test is meant to be run in on an instance with TPUs like this:
#
# python examples/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#
import sys
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
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
def main():
parser = HfArgumentParser((TrainingArguments,))
sys.argv += ["--output_dir", "./examples"]
training_args = parser.parse_args_into_dataclasses()[0]
logger.warning(
"Process rank: %s, device: %s, tpu_num_cores: %s",
training_args.local_rank,
training_args.device,
training_args.tpu_num_cores,
)
# 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 [1001, 256, 15]:
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
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
logger.info("🔥 All distributed tests successful")
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
|
AdaMix/tests/test_trainer_tpu.py/0
|
{
"file_path": "AdaMix/tests/test_trainer_tpu.py",
"repo_id": "AdaMix",
"token_count": 1668
}
| 84 |
## Race Monitoring
As a drone is navigating the racetrack, the race progress is streamed to a local log file, which can be found at `ADRL/Saved/Logs/RaceLogs/[TimeStamp]_[Level]_tier_[Tier#]_[Race#].log`.
This log is updated with data such as the current odometry of the drone, number of gates passed/missed, timestamps etc.
It may be useful to continually process this log file on the client side. The log file's data is formatted as per the following snippet:
```
A odometry_XYZRPY (75.000,-200.000,2882.102,0.000,0.000,90.000)
A gates_passed 0
A gates_missed 0
A collision_count 0
A time 8
A penalty 0
A disqualified 0
A finished 0
```
We have an [example python script](../scripts/logging/log_monitor.py) to help you analyze the log file. This script also demonstrates how one can automate starting of a new race upon the completion of a previous one, which may be useful for training scenarios.
|
AirSim-Drone-Racing-Lab/docs/race_monitoring.md/0
|
{
"file_path": "AirSim-Drone-Racing-Lab/docs/race_monitoring.md",
"repo_id": "AirSim-Drone-Racing-Lab",
"token_count": 271
}
| 85 |
from __future__ import division
import numpy as np
import random
import math
import time
import airsimdroneracingvae.types
import airsimdroneracingvae.utils
from airsimdroneracingvae.types import Pose, Vector3r, Quaternionr
from airsimdroneracingvae.utils import to_eularian_angles, to_quaternion
def MoveCheckeredGates(client):
gate_names_sorted = sorted(client.simListSceneObjects("Gate.*"))
pose_far = Pose(Vector3r(0,0,1), Quaternionr())
for gate in gate_names_sorted:
client.simSetObjectPose(gate, pose_far)
# time.sleep(0.05)
def AllGatesDestroyer(client):
for gate_object in client.simListSceneObjects(".*[Gg]ate.*"):
client.simDestroyObject(gate_object)
time.sleep(0.05)
def RedGateSpawner(client, num_gates, noise_amp):
gate_poses=[]
for idx in range(num_gates):
noise = (np.random.random()-0.5)*noise_amp
pose = Pose(Vector3r(10+idx*9, noise*5.0, 10.0), Quaternionr(0.0, 0.0, 0.707, 0.707))
client.simSpawnObject("gate_"+str(idx), "RedGate16x16", pose, 1.5)
gate_poses.append(pose)
time.sleep(0.05)
return gate_poses
def RedGateSpawnerCircle(client, num_gates, radius, radius_noise, height_range, track_offset=[0, 0, 0]):
track = generate_gate_poses(num_gates=num_gates, race_course_radius=radius, radius_noise=radius_noise, height_range=height_range, direction=0, offset=track_offset)
for idx in range(num_gates):
# client.simSpawnObject("gate_" + str(idx), "RedGate16x16", track[idx], 1.5)
client.simSpawnObject("gate_" + str(idx), "RedGate16x16", track[idx], 0.75)
time.sleep(0.05)
def RedGateSpawnerTrack(client, num_gates, radius, radius_noise, height_range, num_ignore = 0, track_offset=[0, 0, 0]):
offset_0 = [sum(x) for x in zip(track_offset, [radius, 0, 0])]
track_0 = generate_gate_poses(num_gates=num_gates, race_course_radius=radius, radius_noise=radius_noise, height_range=height_range, direction=0, offset=offset_0)
offset_1 = [sum(x) for x in zip(track_offset, [-radius, 0, 0])]
track_1 = generate_gate_poses(num_gates=num_gates, race_course_radius=radius, radius_noise=radius_noise,
height_range=height_range, direction=0, offset=offset_1)
list_to_ignore_0 = [0, 1, 7]
for idx in range(num_gates):
if idx not in list_to_ignore_0:
client.simSpawnObject("gate_" + str(idx) + "track_0", "RedGate16x16", track_0[idx], 0.75)
time.sleep(0.05)
list_to_ignore_1 = [3, 4, 5]
for idx in range(num_gates):
if idx not in list_to_ignore_1:
client.simSpawnObject("gate_" + str(idx) + "track_1", "RedGate16x16", track_1[idx], 0.75)
time.sleep(0.05)
def generate_gate_poses(num_gates, race_course_radius, radius_noise, height_range, direction, offset=[0,0,0], type_of_segment="circle"):
if type_of_segment == "circle":
(x_t, y_t, z_t) = tuple([generate_circle(i, num_gates, race_course_radius, radius_noise, direction) for i in range(3)])
# airsimdroneracingvae.Vector3r((x_t[t_i][0] - x_t[0][0]), (y_t[t_i][0] - y_t[0][0]), random.uniform(height_range[0], height_range[1])), \
gate_poses = [airsimdroneracingvae.Pose(airsimdroneracingvae.Vector3r((x_t[t_i][0]+offset[0]),
(y_t[t_i][0]+offset[1]),
random.uniform(height_range[0], height_range[1])+offset[2]),
quaternionFromUnitGradient(x_t[t_i][1], y_t[t_i][1], z_t[t_i][1]))
for t_i in range(num_gates)]
# elif type_of_segment == "cubic":
return gate_poses
def quaternionFromUnitGradient(dx_dt, dy_dt, dz_dt):
default_gate_facing_vector = type("", (), dict(x=0, y=1, z=0))()
r0 = default_gate_facing_vector
q = airsimdroneracingvae.Quaternionr(
r0.y * dz_dt - r0.z * dy_dt,
r0.z * dx_dt - r0.x * dz_dt,
r0.x * dy_dt - r0.y * dx_dt,
math.sqrt((r0.x**2 + r0.y**2 + r0.z**2) * (dx_dt**2 + dy_dt**2 + dz_dt**2)) + (r0.x * dx_dt + r0.y * dy_dt + r0.z * dz_dt)
)
# Normalize
length = q.get_length()
if (length == 0.0):
q.w_val = 1.0
else:
q.w_val /= length
q.x_val /= length
q.y_val /= length
q.z_val /= length
return q
def generate_circle(i, num_gates, race_course_radius, radius_amp, direction):
ts = [t / (num_gates) for t in range(0, num_gates)]
samples = [0 for t in ts]
derivatives = [0 for t in ts]
min_radius = race_course_radius + radius_amp
max_radius = race_course_radius - radius_amp
max_radius_delta = 5.0
radius_list = [random.uniform(min_radius, max_radius) for t in ts]
# not a circle, but hey it's random-ish. and the wrong derivative actually make the track challenging
# come back again later.
if i == 0:
for (idx, t) in enumerate(ts):
radius = radius_list[idx]
if idx > 0:
radius = np.clip(radius, radius_list[idx-1] - max_radius_delta, radius_list[idx-1] + max_radius_delta)
radius = np.clip(radius, 0.0, radius)
if direction == 0:
samples[idx] = radius * math.cos(2.*math.pi * t)
derivatives[idx] = radius * -math.sin(2.*math.pi * t)
else:
samples[idx] = radius * math.sin(2. * math.pi * t)
derivatives[idx] = radius * -math.cos(2. * math.pi * t)
elif i == 1:
for (idx, t) in enumerate(ts):
radius = radius_list[idx]
if idx > 0:
radius = np.clip(radius, radius_list[idx-1] - max_radius_delta, radius_list[idx-1] + max_radius_delta)
radius = np.clip(radius, 0.0, radius)
if direction == 0:
samples[idx] = radius * math.sin(2.*math.pi * t)
derivatives[idx] = radius * math.cos(2.*math.pi * t)
else:
samples[idx] = radius * math.cos(2. * math.pi * t)
derivatives[idx] = radius * math.sin(2. * math.pi * t)
else:
for (idx, t) in enumerate(ts):
samples[idx] = 0.
derivatives[idx] = 0.
return list(zip(samples, derivatives))
|
AirSim-Drone-Racing-VAE-Imitation/racing_utils/trajectory_utils.py/0
|
{
"file_path": "AirSim-Drone-Racing-VAE-Imitation/racing_utils/trajectory_utils.py",
"repo_id": "AirSim-Drone-Racing-VAE-Imitation",
"token_count": 3056
}
| 86 |
from argparse import ArgumentParser
import airsimneurips as airsim
import cv2
import threading
import time
import utils
import numpy as np
import math
# drone_name should match the name in ~/Document/AirSim/settings.json
class BaselineRacer(object):
def __init__(self, drone_name = "drone_1", viz_traj=True, viz_traj_color_rgba=[1.0, 0.0, 0.0, 1.0], viz_image_cv2=True):
self.drone_name = drone_name
self.gate_poses_ground_truth = None
self.viz_image_cv2 = viz_image_cv2
self.viz_traj = viz_traj
self.viz_traj_color_rgba = viz_traj_color_rgba
self.airsim_client = airsim.MultirotorClient()
self.airsim_client.confirmConnection()
# we need two airsim MultirotorClient objects because the comm lib we use (rpclib) is not thread safe
# so we poll images in a thread using one airsim MultirotorClient object
# and use another airsim MultirotorClient for querying state commands
self.airsim_client_images = airsim.MultirotorClient()
self.airsim_client_images.confirmConnection()
self.airsim_client_odom = airsim.MultirotorClient()
self.airsim_client_odom.confirmConnection()
self.level_name = None
self.image_callback_thread = threading.Thread(target=self.repeat_timer_image_callback, args=(self.image_callback, 0.03))
self.odometry_callback_thread = threading.Thread(target=self.repeat_timer_odometry_callback, args=(self.odometry_callback, 0.02))
self.is_image_thread_active = False
self.is_odometry_thread_active = False
self.MAX_NUMBER_OF_GETOBJECTPOSE_TRIALS = 10 # see https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing/issues/38
# loads desired level
def load_level(self, level_name, sleep_sec = 2.0):
self.level_name = level_name
self.airsim_client.simLoadLevel(self.level_name)
self.airsim_client.confirmConnection() # failsafe
time.sleep(sleep_sec) # let the environment load completely
# Starts an instance of a race in your given level, if valid
def start_race(self, tier=3):
self.airsim_client.simStartRace(tier)
# Resets a current race: moves players to start positions, timer and penalties reset
def reset_race(self):
self.airsim_client.simResetRace()
# arms drone, enable APIs, set default traj tracker gains
def initialize_drone(self):
self.airsim_client.enableApiControl(vehicle_name=self.drone_name)
self.airsim_client.arm(vehicle_name=self.drone_name)
# set default values for trajectory tracker gains
traj_tracker_gains = airsim.TrajectoryTrackerGains(kp_cross_track = 5.0, kd_cross_track = 0.0,
kp_vel_cross_track = 3.0, kd_vel_cross_track = 0.0,
kp_along_track = 0.4, kd_along_track = 0.0,
kp_vel_along_track = 0.04, kd_vel_along_track = 0.0,
kp_z_track = 2.0, kd_z_track = 0.0,
kp_vel_z = 0.4, kd_vel_z = 0.0,
kp_yaw = 3.0, kd_yaw = 0.1)
self.airsim_client.setTrajectoryTrackerGains(traj_tracker_gains, vehicle_name=self.drone_name)
time.sleep(0.2)
def takeoffAsync(self):
self.airsim_client.takeoffAsync().join()
# like takeoffAsync(), but with moveOnSpline()
def takeoff_with_moveOnSpline(self, takeoff_height = 1.0):
start_position = self.airsim_client.simGetVehiclePose(vehicle_name=self.drone_name).position
takeoff_waypoint = airsim.Vector3r(start_position.x_val, start_position.y_val, start_position.z_val-takeoff_height)
self.airsim_client.moveOnSplineAsync([takeoff_waypoint], vel_max=15.0, acc_max=5.0, add_position_constraint=True, add_velocity_constraint=False,
add_acceleration_constraint=False, viz_traj=self.viz_traj, viz_traj_color_rgba=self.viz_traj_color_rgba, vehicle_name=self.drone_name).join()
# stores gate ground truth poses as a list of airsim.Pose() objects in self.gate_poses_ground_truth
def get_ground_truth_gate_poses(self):
gate_names_sorted_bad = sorted(self.airsim_client.simListSceneObjects("Gate.*"))
# gate_names_sorted_bad is of the form `GateN_GARBAGE`. for example:
# ['Gate0', 'Gate10_21', 'Gate11_23', 'Gate1_3', 'Gate2_5', 'Gate3_7', 'Gate4_9', 'Gate5_11', 'Gate6_13', 'Gate7_15', 'Gate8_17', 'Gate9_19']
# we sort them by their ibdex of occurence along the race track(N), and ignore the unreal garbage number after the underscore(GARBAGE)
gate_indices_bad = [int(gate_name.split('_')[0][4:]) for gate_name in gate_names_sorted_bad]
gate_indices_correct = sorted(range(len(gate_indices_bad)), key=lambda k: gate_indices_bad[k])
gate_names_sorted = [gate_names_sorted_bad[gate_idx] for gate_idx in gate_indices_correct]
self.gate_poses_ground_truth = []
for gate_name in gate_names_sorted:
curr_pose = self.airsim_client.simGetObjectPose(gate_name)
counter = 0
while (math.isnan(curr_pose.position.x_val) or math.isnan(curr_pose.position.y_val) or math.isnan(curr_pose.position.z_val)) and (counter < self.MAX_NUMBER_OF_GETOBJECTPOSE_TRIALS):
print(f"DEBUG: {gate_name} position is nan, retrying...")
counter += 1
curr_pose = self.airsim_client.simGetObjectPose(gate_name)
assert not math.isnan(curr_pose.position.x_val), f"ERROR: {gate_name} curr_pose.position.x_val is still {curr_pose.position.x_val} after {counter} trials"
assert not math.isnan(curr_pose.position.y_val), f"ERROR: {gate_name} curr_pose.position.y_val is still {curr_pose.position.y_val} after {counter} trials"
assert not math.isnan(curr_pose.position.z_val), f"ERROR: {gate_name} curr_pose.position.z_val is still {curr_pose.position.z_val} after {counter} trials"
self.gate_poses_ground_truth.append(curr_pose)
# 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(self, airsim_quat, scale = 1.0):
import numpy as np
# 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:
return airsim.Vector3r(0.0, 1.0, 0.0)
q *= np.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]
return airsim.Vector3r(scale * gate_facing_vector[0], scale * gate_facing_vector[1], scale * gate_facing_vector[2])
def fly_through_all_gates_one_by_one_with_moveOnSpline(self):
if self.level_name == "Building99_Hard":
vel_max = 5.0
acc_max = 2.0
if self.level_name in ["Soccer_Field_Medium", "Soccer_Field_Easy", "ZhangJiaJie_Medium"] :
vel_max = 10.0
acc_max = 5.0
return self.airsim_client.moveOnSplineAsync([gate_pose.position], vel_max=vel_max, acc_max=acc_max,
add_position_constraint=True, add_velocity_constraint=False, add_acceleration_constraint=False, viz_traj=self.viz_traj, viz_traj_color_rgba=self.viz_traj_color_rgba, vehicle_name=self.drone_name)
def fly_through_all_gates_at_once_with_moveOnSpline(self):
if self.level_name in ["Soccer_Field_Medium", "Soccer_Field_Easy", "ZhangJiaJie_Medium", "Qualifier_Tier_1", "Qualifier_Tier_2", "Qualifier_Tier_3", "Final_Tier_1", "Final_Tier_2", "Final_Tier_3"] :
vel_max = 30.0
acc_max = 15.0
if self.level_name == "Building99_Hard":
vel_max = 4.0
acc_max = 1.0
return self.airsim_client.moveOnSplineAsync([gate_pose.position for gate_pose in self.gate_poses_ground_truth], vel_max=vel_max, acc_max=acc_max,
add_position_constraint=True, add_velocity_constraint=False, add_acceleration_constraint=False, viz_traj=self.viz_traj, viz_traj_color_rgba=self.viz_traj_color_rgba, vehicle_name=self.drone_name)
def fly_through_all_gates_one_by_one_with_moveOnSplineVelConstraints(self):
add_velocity_constraint = True
add_acceleration_constraint = False
if self.level_name in ["Soccer_Field_Medium", "Soccer_Field_Easy"] :
vel_max = 15.0
acc_max = 3.0
speed_through_gate = 2.5
if self.level_name == "ZhangJiaJie_Medium":
vel_max = 10.0
acc_max = 3.0
speed_through_gate = 1.0
if self.level_name == "Building99_Hard":
vel_max = 2.0
acc_max = 0.5
speed_through_gate = 0.5
add_velocity_constraint = False
# scale param scales the gate facing vector by desired speed.
return self.airsim_client.moveOnSplineVelConstraintsAsync([gate_pose.position],
[self.get_gate_facing_vector_from_quaternion(gate_pose.orientation, scale = speed_through_gate)],
vel_max=vel_max, acc_max=acc_max,
add_position_constraint=True, add_velocity_constraint=add_velocity_constraint, add_acceleration_constraint=add_acceleration_constraint,
viz_traj=self.viz_traj, viz_traj_color_rgba=self.viz_traj_color_rgba, vehicle_name=self.drone_name)
def fly_through_all_gates_at_once_with_moveOnSplineVelConstraints(self):
if self.level_name in ["Soccer_Field_Easy", "Soccer_Field_Medium", "ZhangJiaJie_Medium"]:
vel_max = 15.0
acc_max = 7.5
speed_through_gate = 2.5
if self.level_name == "Building99_Hard":
vel_max = 5.0
acc_max = 2.0
speed_through_gate = 1.0
return self.airsim_client.moveOnSplineVelConstraintsAsync([gate_pose.position for gate_pose in self.gate_poses_ground_truth],
[self.get_gate_facing_vector_from_quaternion(gate_pose.orientation, scale = speed_through_gate) for gate_pose in self.gate_poses_ground_truth],
vel_max=vel_max, acc_max=acc_max,
add_position_constraint=True, add_velocity_constraint=True, add_acceleration_constraint=False,
viz_traj=self.viz_traj, viz_traj_color_rgba=self.viz_traj_color_rgba, vehicle_name=self.drone_name)
def image_callback(self):
# get uncompressed fpv cam image
request = [airsim.ImageRequest("fpv_cam", airsim.ImageType.Scene, False, False)]
response = self.airsim_client_images.simGetImages(request)
img_rgb_1d = np.fromstring(response[0].image_data_uint8, dtype=np.uint8)
img_rgb = img_rgb_1d.reshape(response[0].height, response[0].width, 3)
if self.viz_image_cv2:
cv2.imshow("img_rgb", img_rgb)
cv2.waitKey(1)
def odometry_callback(self):
# get uncompressed fpv cam image
drone_state = self.airsim_client_odom.getMultirotorState()
# in world frame:
position = drone_state.kinematics_estimated.position
orientation = drone_state.kinematics_estimated.orientation
linear_velocity = drone_state.kinematics_estimated.linear_velocity
angular_velocity = drone_state.kinematics_estimated.angular_velocity
# call task() method every "period" seconds.
def repeat_timer_image_callback(self, task, period):
while self.is_image_thread_active:
task()
time.sleep(period)
def repeat_timer_odometry_callback(self, task, period):
while self.is_odometry_thread_active:
task()
time.sleep(period)
def start_image_callback_thread(self):
if not self.is_image_thread_active:
self.is_image_thread_active = True
self.image_callback_thread.start()
print("Started image callback thread")
def stop_image_callback_thread(self):
if self.is_image_thread_active:
self.is_image_thread_active = False
self.image_callback_thread.join()
print("Stopped image callback thread.")
def start_odometry_callback_thread(self):
if not self.is_odometry_thread_active:
self.is_odometry_thread_active = True
self.odometry_callback_thread.start()
print("Started odometry callback thread")
def stop_odometry_callback_thread(self):
if self.is_odometry_thread_active:
self.is_odometry_thread_active = False
self.odometry_callback_thread.join()
print("Stopped odometry callback thread.")
def main(args):
# ensure you have generated the neurips planning settings file by running python generate_settings_file.py
baseline_racer = BaselineRacer(drone_name="drone_1", viz_traj=args.viz_traj, viz_traj_color_rgba=[1.0, 1.0, 0.0, 1.0], viz_image_cv2=args.viz_image_cv2)
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)
baseline_racer.initialize_drone()
baseline_racer.takeoff_with_moveOnSpline()
baseline_racer.get_ground_truth_gate_poses()
baseline_racer.start_image_callback_thread()
baseline_racer.start_odometry_callback_thread()
if args.planning_baseline_type == "all_gates_at_once" :
if args.planning_and_control_api == "moveOnSpline":
baseline_racer.fly_through_all_gates_at_once_with_moveOnSpline().join()
if args.planning_and_control_api == "moveOnSplineVelConstraints":
baseline_racer.fly_through_all_gates_at_once_with_moveOnSplineVelConstraints().join()
if args.planning_baseline_type == "all_gates_one_by_one":
if args.planning_and_control_api == "moveOnSpline":
baseline_racer.fly_through_all_gates_one_by_one_with_moveOnSpline().join()
if args.planning_and_control_api == "moveOnSplineVelConstraints":
baseline_racer.fly_through_all_gates_one_by_one_with_moveOnSplineVelConstraints().join()
# Comment out the following if you observe the python script exiting prematurely, and resetting the race
baseline_racer.stop_image_callback_thread()
baseline_racer.stop_odometry_callback_thread()
baseline_racer.reset_race()
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('--level_name', type=str, choices=["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"], default="ZhangJiaJie_Medium")
parser.add_argument('--planning_baseline_type', type=str, choices=["all_gates_at_once","all_gates_one_by_one"], default="all_gates_at_once")
parser.add_argument('--planning_and_control_api', type=str, choices=["moveOnSpline", "moveOnSplineVelConstraints"], default="moveOnSpline")
parser.add_argument('--enable_viz_traj', dest='viz_traj', action='store_true', default=False)
parser.add_argument('--enable_viz_image_cv2', dest='viz_image_cv2', action='store_true', default=False)
parser.add_argument('--race_tier', type=int, choices=[1,2,3], default=1)
args = parser.parse_args()
main(args)
|
AirSim-NeurIPS2019-Drone-Racing/baselines/baseline_racer.py/0
|
{
"file_path": "AirSim-NeurIPS2019-Drone-Racing/baselines/baseline_racer.py",
"repo_id": "AirSim-NeurIPS2019-Drone-Racing",
"token_count": 7400
}
| 87 |
import airsimneurips
import threading
import time
class ReproduceResetRaceCondition():
def __init__(self, drone_name = "drone_1"):
self.airsim_client = airsimneurips.MultirotorClient()
self.airsim_client_2 = airsimneurips.MultirotorClient()
self.airsim_client_3 = airsimneurips.MultirotorClient()
self.drone_name = drone_name
self.is_thread_active = False
self.thread_reset = threading.Thread(target=self.repeat_timer, args=(self.reset, 0.05))
self.thread_reset_race = threading.Thread(target=self.repeat_timer, args=(self.reset_race, 0.03))
self.thread_reset_and_reset_race = threading.Thread(target=self.repeat_timer, args=(self.reset_and_reset_race, 0.09))
self.is_thread_active = False
def repeat_timer(self, callback, period):
while self.is_thread_active:
callback()
time.sleep(period)
def load_level(self, level_name, sleep_sec = 2.0):
self.level_name = level_name
self.airsim_client.simLoadLevel(self.level_name)
self.airsim_client.confirmConnection() # failsafe
time.sleep(sleep_sec) # let the environment load completely
def reset(self):
print(time.time(), 'called reset')
self.airsim_client.reset()
def reset_race(self):
print(time.time(), 'called simResetRace')
self.airsim_client_2.simResetRace()
def reset_and_reset_race(self):
print(time.time(), 'called reset, followed by simResetRace')
self.airsim_client_3.reset()
self.airsim_client_3.simResetRace()
def start_race(self, tier):
print(time.time(), 'called start race')
self.airsim_client.simStartRace(tier)
def initialize_drone(self):
self.airsim_client.enableApiControl(vehicle_name=self.drone_name)
self.airsim_client.arm(vehicle_name=self.drone_name)
# set default values for trajectory tracker gains
traj_tracker_gains = airsimneurips.TrajectoryTrackerGains(kp_cross_track = 5.0, kd_cross_track = 0.0,
kp_vel_cross_track = 3.0, kd_vel_cross_track = 0.0,
kp_along_track = 0.4, kd_along_track = 0.0,
kp_vel_along_track = 0.04, kd_vel_along_track = 0.0,
kp_z_track = 2.0, kd_z_track = 0.0,
kp_vel_z = 0.4, kd_vel_z = 0.0,
kp_yaw = 3.0, kd_yaw = 0.1)
self.airsim_client.setTrajectoryTrackerGains(traj_tracker_gains, vehicle_name=self.drone_name)
time.sleep(0.2)
def start_threads(self):
if not self.is_thread_active:
self.is_thread_active = True
self.thread_reset.start()
self.thread_reset_race.start()
self.thread_reset_and_reset_race.start()
print("Started threads")
def stop_threads(self):
if self.is_thread_active:
self.is_thread_active = False
self.thread_reset.join()
self.thread_reset_race.join()
self.thread_reset_and_reset_race.join()
print("Stopped threads.")
if __name__ == "__main__":
reproducer = ReproduceResetRaceCondition('drone_1')
reproducer.load_level('Qualifier_Tier_1')
reproducer.initialize_drone()
reproducer.start_race(3)
time.sleep(5)
reproducer.start_threads()
time.sleep(3600)
reproducer.stop_threads()
|
AirSim-NeurIPS2019-Drone-Racing/tests/test_reset.py/0
|
{
"file_path": "AirSim-NeurIPS2019-Drone-Racing/tests/test_reset.py",
"repo_id": "AirSim-NeurIPS2019-Drone-Racing",
"token_count": 1810
}
| 88 |
# Contributing
Please read the [Contributing](https://microsoft.github.io/AzureTRE/contributing/) guidelines in the documentation site.
|
AzureTRE/CONTRIBUTING.md/0
|
{
"file_path": "AzureTRE/CONTRIBUTING.md",
"repo_id": "AzureTRE",
"token_count": 34
}
| 89 |
# PYTHON
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
|
AzureTRE/api_app/.dockerignore/0
|
{
"file_path": "AzureTRE/api_app/.dockerignore",
"repo_id": "AzureTRE",
"token_count": 743
}
| 90 |
from typing import List
import uuid
from pydantic import parse_obj_as
from db.errors import EntityDoesNotExist
from db.repositories.base import BaseRepository
from core import config
from models.domain.resource import Resource, ResourceHistoryItem
from services.logging import logger
class ResourceHistoryRepository(BaseRepository):
@classmethod
async def create(cls):
cls = ResourceHistoryRepository()
await super().create(config.STATE_STORE_RESOURCES_HISTORY_CONTAINER)
return cls
@staticmethod
def is_valid_uuid(resourceId):
try:
uuid.UUID(str(resourceId))
except ValueError:
raise ValueError("Resource Id should be a valid GUID")
def resource_history_query(self, resourceId: str):
logger.debug("Validate sanity of resourceId")
self.is_valid_uuid(resourceId)
return f'SELECT * FROM c WHERE c.resourceId = "{resourceId}"'
async def get_resource_history_by_resource_id(self, resource_id: str) -> List[ResourceHistoryItem]:
query = self.resource_history_query(resource_id)
try:
logger.info(f"Fetching history for resource {resource_id}")
resource_history_items = await self.query(query=query)
logger.debug(f"Got {len(resource_history_items)} history items for resource {resource_id}")
except EntityDoesNotExist:
logger.info(f"No history for resource {resource_id}")
resource_history_items = []
return parse_obj_as(List[ResourceHistoryItem], resource_history_items)
async def create_resource_history_item(self, resource: Resource) -> ResourceHistoryItem:
logger.info(f"Creating a new history item for resource {resource.id}")
resource_history_item_id = str(uuid.uuid4())
resource_history_item = ResourceHistoryItem(
id=resource_history_item_id,
resourceId=resource.id,
isEnabled=resource.isEnabled,
properties=resource.properties,
resourceVersion=resource.resourceVersion,
updatedWhen=resource.updatedWhen,
user=resource.user,
templateVersion=resource.templateVersion
)
logger.info(f"Saving history item for {resource.id}")
try:
await self.save_item(resource_history_item)
except Exception:
logger.exception(f"Failed saving history item for {resource.id}")
raise
return resource_history_item
|
AzureTRE/api_app/db/repositories/resources_history.py/0
|
{
"file_path": "AzureTRE/api_app/db/repositories/resources_history.py",
"repo_id": "AzureTRE",
"token_count": 973
}
| 91 |
from datetime import datetime, timedelta, date
from typing import List, Optional
from pydantic import BaseModel
from enum import Enum
import random
import uuid
class GranularityEnum(str, Enum):
daily = "Daily"
none = "None"
class CurrencyEnum(str, Enum):
USD = "USD"
ILS = "ILS"
def generate_cost_row_dict_example(granularity: GranularityEnum, currency: CurrencyEnum):
return dict({
"cost": random.uniform(0, 365), "currency": currency, "date":
(datetime.today() - timedelta(
days=-1 * random.randint(0, 1000))).date() if granularity == GranularityEnum.daily else None
})
def generate_cost_item_dict_example(name: str, granularity: GranularityEnum):
cost_item_dict = dict(
id=str(uuid.uuid4()),
name=name,
costs=[generate_cost_row_dict_example(granularity, CurrencyEnum.USD),
generate_cost_row_dict_example(granularity, CurrencyEnum.ILS)]
)
if granularity == GranularityEnum.daily:
cost_item_dict["costs"].append(generate_cost_row_dict_example(granularity, CurrencyEnum.USD))
cost_item_dict["costs"].append(generate_cost_row_dict_example(granularity, CurrencyEnum.USD))
cost_item_dict["costs"].append(generate_cost_row_dict_example(granularity, CurrencyEnum.ILS))
return cost_item_dict
def generate_cost_report_dict_example(granularity: GranularityEnum):
cost_report = dict(
core_services=[generate_cost_row_dict_example(granularity, CurrencyEnum.USD)],
shared_services=[generate_cost_item_dict_example("Gitea", granularity),
generate_cost_item_dict_example("Nexus", granularity),
generate_cost_item_dict_example("Firewall", granularity)],
workspaces=[generate_cost_item_dict_example("Workspace 1", granularity),
generate_cost_item_dict_example("Workspace 2", granularity),
generate_cost_item_dict_example("Workspace 3", granularity)]
)
if granularity == GranularityEnum.daily:
cost_report["core_services"].append(generate_cost_row_dict_example(granularity, CurrencyEnum.USD))
cost_report["core_services"].append(generate_cost_row_dict_example(granularity, CurrencyEnum.ILS))
return cost_report
def generate_workspace_service_cost_report_dict_example(name: str, granularity: GranularityEnum):
cost_report = dict(
id=str(uuid.uuid4()),
name=name,
costs=[generate_cost_row_dict_example(granularity, CurrencyEnum.USD)],
user_resources=[generate_cost_item_dict_example("VM1", granularity),
generate_cost_item_dict_example("VM2", granularity),
generate_cost_item_dict_example("VM3", granularity)]
)
if granularity == GranularityEnum.daily:
cost_report["costs"].append(generate_cost_row_dict_example(granularity, CurrencyEnum.USD))
cost_report["costs"].append(generate_cost_row_dict_example(granularity, CurrencyEnum.ILS))
return cost_report
def generate_workspace_cost_report_dict_example(name: str, granularity: GranularityEnum):
cost_report = dict(
id=str(uuid.uuid4()),
name=name,
costs=[generate_cost_row_dict_example(granularity, CurrencyEnum.USD)],
workspace_services=[generate_workspace_service_cost_report_dict_example("Guacamole", granularity)]
)
if granularity == GranularityEnum.daily:
cost_report["costs"].append(generate_cost_row_dict_example(granularity, CurrencyEnum.USD))
cost_report["costs"].append(generate_cost_row_dict_example(granularity, CurrencyEnum.ILS))
return cost_report
class CostRow(BaseModel):
cost: float
currency: str
date: Optional[date]
class CostItem(BaseModel):
id: str
name: str
costs: List[CostRow]
class CostReport(BaseModel):
core_services: List[CostRow]
shared_services: List[CostItem]
workspaces: List[CostItem]
class WorkspaceServiceCostItem(CostItem):
user_resources: List[CostItem]
class WorkspaceCostReport(CostItem):
workspace_services: List[WorkspaceServiceCostItem]
|
AzureTRE/api_app/models/domain/costs.py/0
|
{
"file_path": "AzureTRE/api_app/models/domain/costs.py",
"repo_id": "AzureTRE",
"token_count": 1628
}
| 92 |
from datetime import datetime
from pydantic import BaseModel
class Pong(BaseModel):
message: str
time: datetime
|
AzureTRE/api_app/models/schemas/health.py/0
|
{
"file_path": "AzureTRE/api_app/models/schemas/health.py",
"repo_id": "AzureTRE",
"token_count": 40
}
| 93 |
from typing import Union
from resources import strings
from models.domain.resource_template import PipelineStep
from models.domain.resource import Resource
def substitute_properties(template_step: PipelineStep, primary_resource: Resource, primary_parent_workspace: Resource, primary_parent_workspace_svc: Resource, resource_to_update: Resource) -> dict:
properties = {}
parent_ws_dict = {}
parent_ws_svc_dict = {}
primary_resource_dict = primary_resource.dict()
if primary_parent_workspace is not None:
parent_ws_dict = primary_parent_workspace.dict()
if primary_parent_workspace_svc is not None:
parent_ws_svc_dict = primary_parent_workspace_svc.dict()
if template_step is None or template_step.properties is None:
return properties
for prop in template_step.properties:
val = prop.value
if isinstance(prop.value, dict):
val = recurse_object(prop.value, primary_resource_dict, parent_ws_dict, parent_ws_svc_dict)
if prop.type == 'array':
if prop.name in resource_to_update.properties:
existing_arr = resource_to_update.properties[prop.name]
else:
existing_arr = []
if prop.arraySubstitutionAction == 'overwrite':
existing_arr = [val]
if prop.arraySubstitutionAction == 'append':
existing_arr.append(val)
if prop.arraySubstitutionAction == 'remove':
item_index = find_item_index(existing_arr, prop.arrayMatchField, val)
if item_index > -1:
del existing_arr[item_index]
if prop.arraySubstitutionAction == 'replace':
item_index = find_item_index(existing_arr, prop.arrayMatchField, val)
if item_index > -1:
existing_arr[item_index] = val
else:
existing_arr.append(val)
properties[prop.name] = existing_arr
else:
properties[prop.name] = val
else:
val = substitute_value(val, primary_resource_dict, parent_ws_dict, parent_ws_svc_dict)
properties[prop.name] = val
return properties
def find_item_index(array: list, arrayMatchField: str, val: dict) -> int:
for i in range(0, len(array)):
if array[i][arrayMatchField] == val[arrayMatchField]:
return i
return -1
def recurse_object(obj: dict, resource_dict: dict, parent_ws_dict: dict, parent_ws_svc_dict: dict) -> dict:
for prop in obj:
if isinstance(obj[prop], list):
for i in range(0, len(obj[prop])):
if isinstance(obj[prop][i], list) or isinstance(obj[prop][i], dict):
obj[prop][i] = recurse_object(obj[prop][i], resource_dict, parent_ws_dict, parent_ws_svc_dict)
else:
obj[prop][i] = substitute_value(obj[prop][i], resource_dict, parent_ws_dict, parent_ws_svc_dict)
if isinstance(obj[prop], dict):
obj[prop] = recurse_object(obj[prop], resource_dict, parent_ws_dict, parent_ws_svc_dict)
else:
obj[prop] = substitute_value(obj[prop], resource_dict, parent_ws_dict, parent_ws_svc_dict)
return obj
def substitute_value(val: str, primary_resource_dict: dict, primary_parent_ws_dict: dict, primary_parent_ws_svc_dict: dict) -> Union[dict, list, str]:
if "{{" not in val:
return val
if primary_resource_dict is None:
raise Exception("primary_resource_dict cannot be None")
primary_resource_type = primary_resource_dict["resourceType"]
val = val.replace("{{ ", "{{").replace(" }}", "}}")
# if the value being substituted in is a simple type, we can return it in the string, to allow for concatenation
# like "This was deployed by {{ resource.id }}"
# else if the value being injected in is a dict/list - we shouldn't try to concatenate that, we'll return the true value and drop any surrounding text
# extract the tokens to replace
tokens = []
parts = val.split("{{")
for p in parts:
if len(p) > 0 and "}}" in p:
t = p[0:p.index("}}")]
tokens.append(t)
dict_to_use = None
for t in tokens:
# t = "{resource[.parent][.parent].properties.prop_1"
path_tokens = t.split(".")
# decide on which dictionary to use (parents support)
# how many parents levels do we have (0- current resource, 1-direct parent, 2-skip level parent, 3-invalid)
hierarchy_level = 0
for i in range(3, 0, -1):
if len(path_tokens) > i:
if path_tokens[i] == "parent":
hierarchy_level += 1
# sanity
if primary_resource_type == strings.USER_RESOURCE and hierarchy_level > 2:
raise ValueError(f"parent.parent.parent is invalid for a resource of type '{strings.USER_RESOURCE}'")
elif primary_resource_type == strings.RESOURCE_TYPE_WORKSPACE_SERVICE and hierarchy_level > 1:
raise ValueError(f"parent.parent is invalid for a resource of type '{strings.RESOURCE_TYPE_WORKSPACE_SERVICE}'")
elif primary_resource_type == strings.RESOURCE_TYPE_WORKSPACE and hierarchy_level > 0:
raise ValueError(f"parent is invalid for a resource of type '{strings.RESOURCE_TYPE_WORKSPACE}'")
elif primary_resource_type == strings.RESOURCE_TYPE_SHARED_SERVICE and hierarchy_level > 0:
raise ValueError(f"parent is invalid for a resource of type '{strings.RESOURCE_TYPE_SHARED_SERVICE}'")
if hierarchy_level == 2:
if primary_resource_type == strings.USER_RESOURCE:
dict_to_use = primary_parent_ws_dict
del path_tokens[2]
del path_tokens[1]
elif hierarchy_level == 1:
if primary_resource_type == strings.USER_RESOURCE:
dict_to_use = primary_parent_ws_svc_dict
elif primary_resource_type == strings.RESOURCE_TYPE_WORKSPACE_SERVICE:
dict_to_use = primary_parent_ws_dict
del path_tokens[1]
else:
dict_to_use = primary_resource_dict
prop_to_get = dict_to_use
for i in range(1, len(path_tokens)):
# instead of failing, if the value is not found, return empty string. Used for backward compatability
if path_tokens[i] not in prop_to_get:
return ""
prop_to_get = prop_to_get[path_tokens[i]]
# if the value to inject is actually an object / list - just return it, else replace the value in the string
if isinstance(prop_to_get, dict) or isinstance(prop_to_get, list):
return prop_to_get
else:
val = val.replace("{{" + t + "}}", str(prop_to_get))
return val
|
AzureTRE/api_app/service_bus/substitutions.py/0
|
{
"file_path": "AzureTRE/api_app/service_bus/substitutions.py",
"repo_id": "AzureTRE",
"token_count": 2987
}
| 94 |
import json
import pytest
from mock import patch
from pydantic import parse_obj_as
from starlette import status
from services.authentication import get_current_admin_user, get_current_tre_user_or_tre_admin
from models.domain.resource import ResourceType
from resources import strings
from db.errors import DuplicateEntity, EntityDoesNotExist, InvalidInput, UnableToAccessDatabase
from models.domain.resource_template import ResourceTemplate, CustomAction
from models.schemas.resource_template import ResourceTemplateInformation
from models.schemas.workspace_template import WorkspaceTemplateInResponse
from services.schema_service import enrich_workspace_template
pytestmark = pytest.mark.asyncio
@pytest.fixture
def workspace_template_without_enriching():
def create_workspace_template(template_name: str = "base-workspace-template"):
return ResourceTemplate(
id="a7a7a7bd-7f4e-4a4e-b970-dc86a6b31dfb",
name=template_name,
description="base workspace bundle",
version="0.1.0",
resourceType=ResourceType.Workspace,
current=True,
type="object",
required=[],
properties={},
customActions=[]
)
return create_workspace_template
class TestWorkspaceTemplate:
@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 = {}
# GET /workspace-templates
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_templates_information")
async def test_workspace_templates_returns_template_names_and_descriptions(self, get_template_infos_mock, app, client):
expected_template_infos = [
ResourceTemplateInformation(name="template1", title="template 1", description="description1"),
ResourceTemplateInformation(name="template2", title="template 2", description="description2")
]
get_template_infos_mock.return_value = expected_template_infos
response = await client.get(app.url_path_for(strings.API_GET_WORKSPACE_TEMPLATES))
assert response.status_code == status.HTTP_200_OK
actual_template_infos = response.json()["templates"]
assert len(actual_template_infos) == len(expected_template_infos)
for name in expected_template_infos:
assert name in actual_template_infos
# POST /workspace-templates
async def test_post_does_not_create_a_template_with_bad_payload(self, app, client):
input_data = '{"blah": "blah"}'
response = await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_TEMPLATES), json=input_data)
assert response.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY
# POST /workspace-templates
@patch("api.routes.workspace_templates.ResourceTemplateRepository.create_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_template_by_name_and_version")
async def test_when_updating_current_and_template_not_found_create_one(self, get_name_ver_mock, get_current_mock, create_template_mock, app, client, input_workspace_template, basic_resource_template):
get_name_ver_mock.side_effect = EntityDoesNotExist
get_current_mock.side_effect = EntityDoesNotExist
create_template_mock.return_value = basic_resource_template
response = await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_TEMPLATES), json=input_workspace_template.dict())
assert response.status_code == status.HTTP_201_CREATED
# POST /workspace-templates
@patch("api.routes.workspace_templates.ResourceTemplateRepository.create_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.update_item")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_template_by_name_and_version")
async def test_when_updating_current_and_template_found_update_and_add(self, get_template_by_name_and_version_mock, get_current_template_mock, update_item_mock, create_template_mock, app, client, input_workspace_template, basic_resource_template):
get_template_by_name_and_version_mock.side_effect = EntityDoesNotExist
get_current_template_mock.return_value = basic_resource_template
create_template_mock.return_value = basic_resource_template
response = await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_TEMPLATES), json=input_workspace_template.dict())
updated_current_workspace_template = basic_resource_template
updated_current_workspace_template.current = False
update_item_mock.assert_called_once_with(updated_current_workspace_template.dict())
assert response.status_code == status.HTTP_201_CREATED
# POST /workspace-templates
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_template_by_name_and_version")
async def test_same_name_and_version_template_not_allowed(self, get_template_by_name_and_version_mock, app, client, input_workspace_template):
get_template_by_name_and_version_mock.return_value = ["exists"]
response = await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_TEMPLATES), json=input_workspace_template.dict())
assert response.status_code == status.HTTP_409_CONFLICT
@patch("api.routes.workspace_service_templates.ResourceTemplateRepository.create_and_validate_template", side_effect=InvalidInput)
async def test_creating_a_workspace_template_raises_http_422_if_step_ids_are_duplicated(self, _, client, app, input_workspace_template):
response = await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_TEMPLATES), json=input_workspace_template.dict())
assert response.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY
# GET /workspace-templates/{workspace_template_name}
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
async def test_workspace_templates_by_name_returns_enriched_workspace_template(self, get_current_template_mock, app, client, workspace_template_without_enriching):
template_name = "template1"
get_current_template_mock.return_value = workspace_template_without_enriching(template_name)
response = await client.get(app.url_path_for(strings.API_GET_WORKSPACE_TEMPLATE_BY_NAME, workspace_template_name=template_name))
assert response.status_code == status.HTTP_200_OK
assert response.json()["name"] == 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_workspace_templates_by_name_returns_returns_error_status_based_on_exception(self, get_current_template_mock, exception, expected_status, app, client):
get_current_template_mock.side_effect = exception
response = await client.get(app.url_path_for(strings.API_GET_WORKSPACE_TEMPLATE_BY_NAME, workspace_template_name="tre-workspace-base"))
assert response.status_code == expected_status
@patch("api.routes.workspace_templates.ResourceTemplateRepository.create_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_template_by_name_and_version")
async def test_when_not_updating_current_and_new_registration_current_is_enforced(self, get_name_ver_mock, get_current_mock, create_template_mock, app, client, input_workspace_template, basic_resource_template):
input_workspace_template.current = False
get_name_ver_mock.side_effect = EntityDoesNotExist
get_current_mock.side_effect = EntityDoesNotExist
create_template_mock.return_value = basic_resource_template
response = await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_TEMPLATES), json=input_workspace_template.dict())
assert response.status_code == status.HTTP_201_CREATED
assert json.loads(response.text)["current"]
@patch("api.routes.workspace_templates.ResourceTemplateRepository.create_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_template_by_name_and_version")
async def test_when_creating_template_enriched_template_is_returned(self, get_template_by_name_and_version_mock, get_current_template_mock, create_template_mock, app, client, input_workspace_template, basic_resource_template):
get_template_by_name_and_version_mock.side_effect = EntityDoesNotExist
get_current_template_mock.side_effect = EntityDoesNotExist
create_template_mock.return_value = basic_resource_template
response = await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_TEMPLATES), json=input_workspace_template.dict())
expected_template = parse_obj_as(WorkspaceTemplateInResponse, enrich_workspace_template(basic_resource_template))
assert json.loads(response.text)["required"] == expected_template.dict(exclude_unset=True)["required"]
assert json.loads(response.text)["properties"] == expected_template.dict(exclude_unset=True)["properties"]
@patch("api.routes.workspace_templates.ResourceTemplateRepository.create_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_template_by_name_and_version")
async def test_when_creating_workspace_service_template_custom_actions_is_set(self, get_template_by_name_and_version_mock, get_current_template_mock, create_template_mock, app, client, input_workspace_template, basic_resource_template):
get_template_by_name_and_version_mock.side_effect = EntityDoesNotExist
get_current_template_mock.side_effect = EntityDoesNotExist
basic_resource_template.customActions = [CustomAction(name='my-custom-action', description='This is a test custom action')]
create_template_mock.return_value = basic_resource_template
expected_template = parse_obj_as(WorkspaceTemplateInResponse, enrich_workspace_template(basic_resource_template))
response = await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_TEMPLATES), json=input_workspace_template.dict())
assert json.loads(response.text)["customActions"] == expected_template.dict(exclude_unset=True)["customActions"]
@patch("api.routes.workspace_templates.ResourceTemplateRepository.create_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_template_by_name_and_version")
async def test_when_creating_workspace_service_template_custom_actions_is_not_set(self, get_template_by_name_and_version_mock, get_current_template_mock, create_template_mock, app, client, input_workspace_template, basic_resource_template):
get_template_by_name_and_version_mock.side_effect = EntityDoesNotExist
get_current_template_mock.side_effect = EntityDoesNotExist
basic_resource_template.customActions = []
create_template_mock.return_value = basic_resource_template
input_workspace_template_dict = input_workspace_template.dict()
input_workspace_template_dict.pop("customActions")
response = await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_TEMPLATES), json=input_workspace_template_dict)
assert json.loads(response.text)["customActions"] == []
@patch("api.routes.workspace_templates.ResourceTemplateRepository.create_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_template_by_name_and_version")
async def test_when_creating_workspace_template_workspace_resource_type_is_set(self, get_template_by_name_and_version_mock, get_current_template_mock, create_template_mock, app, client, input_workspace_template, basic_resource_template):
get_template_by_name_and_version_mock.side_effect = EntityDoesNotExist
get_current_template_mock.side_effect = EntityDoesNotExist
create_template_mock.return_value = basic_resource_template
await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_TEMPLATES), json=input_workspace_template.dict())
create_template_mock.assert_called_once_with(input_workspace_template, ResourceType.Workspace, '')
@patch("api.routes.workspace_templates.ResourceTemplateRepository.create_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_current_template")
@patch("api.routes.workspace_templates.ResourceTemplateRepository.get_template_by_name_and_version")
async def test_when_creating_workspace_service_template_service_resource_type_is_set(self, get_template_by_name_and_version_mock, get_current_template_mock, create_template_mock, app, client, input_workspace_template, basic_workspace_service_template):
get_template_by_name_and_version_mock.side_effect = EntityDoesNotExist
get_current_template_mock.side_effect = EntityDoesNotExist
create_template_mock.return_value = basic_workspace_service_template
await client.post(app.url_path_for(strings.API_CREATE_WORKSPACE_SERVICE_TEMPLATES), json=input_workspace_template.dict())
create_template_mock.assert_called_once_with(input_workspace_template, ResourceType.WorkspaceService, '')
|
AzureTRE/api_app/tests_ma/test_api/test_routes/test_workspace_templates.py/0
|
{
"file_path": "AzureTRE/api_app/tests_ma/test_api/test_routes/test_workspace_templates.py",
"repo_id": "AzureTRE",
"token_count": 5074
}
| 95 |
from mock import patch
import pytest
import pytest_asyncio
from db.errors import EntityDoesNotExist
from db.repositories.resources import IS_NOT_DELETED_CLAUSE
from db.repositories.user_resources import UserResourceRepository
from models.domain.resource import ResourceType
from models.domain.user_resource import UserResource
from models.schemas.user_resource import UserResourceInCreate
WORKSPACE_ID = "def000d3-82da-4bfc-b6e9-9a7853ef753e"
SERVICE_ID = "937453d3-82da-4bfc-b6e9-9a7853ef753e"
RESOURCE_ID = "000000d3-82da-4bfc-b6e9-9a7853ef753e"
USER_ID = "abc000d3-82da-4bfc-b6e9-9a7853ef753e"
pytestmark = pytest.mark.asyncio
@pytest.fixture
def basic_user_resource_request():
return UserResourceInCreate(templateName="user-resource-type", properties={"display_name": "test", "description": "test", "tre_id": "test"})
@pytest_asyncio.fixture
async def user_resource_repo():
with patch('api.dependencies.database.Database.get_container_proxy', return_value=None):
user_resource_repo = await UserResourceRepository().create()
yield user_resource_repo
@pytest.fixture
def user_resource():
user_resource = UserResource(
id=RESOURCE_ID,
templateVersion="0.1.0",
properties={},
etag='',
templateName="my-user-resource",
resourcePath="test"
)
return user_resource
@patch('db.repositories.user_resources.UserResourceRepository.validate_input_against_template')
@patch('core.config.TRE_ID', "9876")
async def test_create_user_resource_item_creates_a_user_resource_with_the_right_values(validate_input_mock, user_resource_repo, basic_user_resource_request, basic_user_resource_template):
user_resource_to_create = basic_user_resource_request
validate_input_mock.return_value = basic_user_resource_template
user_resource, _ = await user_resource_repo.create_user_resource_item(user_resource_to_create, WORKSPACE_ID, SERVICE_ID, "parent-service-type", USER_ID, [])
assert user_resource.templateName == basic_user_resource_request.templateName
assert user_resource.resourceType == ResourceType.UserResource
assert user_resource.workspaceId == WORKSPACE_ID
assert user_resource.parentWorkspaceServiceId == SERVICE_ID
assert user_resource.ownerId == USER_ID
assert len(user_resource.properties["tre_id"]) > 0
# need to make sure request doesn't override system param
assert user_resource.properties["tre_id"] != "test"
@patch('db.repositories.user_resources.UserResourceRepository.validate_input_against_template', side_effect=ValueError)
async def test_create_user_resource_item_raises_value_error_if_template_is_invalid(_, user_resource_repo, basic_user_resource_request):
with pytest.raises(ValueError):
await user_resource_repo.create_user_resource_item(basic_user_resource_request, WORKSPACE_ID, SERVICE_ID, "parent-service-type", USER_ID, [])
@patch('db.repositories.user_resources.UserResourceRepository.query', return_value=[])
async def test_get_user_resources_for_workspace_queries_db(query_mock, user_resource_repo):
expected_query = f'SELECT * FROM c WHERE {IS_NOT_DELETED_CLAUSE} AND c.resourceType = "user-resource" AND c.parentWorkspaceServiceId = "{SERVICE_ID}" AND c.workspaceId = "{WORKSPACE_ID}"'
await user_resource_repo.get_user_resources_for_workspace_service(WORKSPACE_ID, SERVICE_ID)
query_mock.assert_called_once_with(query=expected_query)
@patch('db.repositories.user_resources.UserResourceRepository.query')
async def test_get_user_resource_returns_resource_if_found(query_mock, user_resource_repo, user_resource):
query_mock.return_value = [user_resource.dict()]
actual_resource = await user_resource_repo.get_user_resource_by_id(WORKSPACE_ID, SERVICE_ID, RESOURCE_ID)
assert actual_resource == user_resource
@patch('db.repositories.user_resources.UserResourceRepository.query')
async def test_get_user_resource_by_id_queries_db(query_mock, user_resource_repo, user_resource):
query_mock.return_value = [user_resource.dict()]
expected_query = f'SELECT * FROM c WHERE c.resourceType = "user-resource" AND c.parentWorkspaceServiceId = "{SERVICE_ID}" AND c.workspaceId = "{WORKSPACE_ID}" AND c.id = "{RESOURCE_ID}"'
await user_resource_repo.get_user_resource_by_id(WORKSPACE_ID, SERVICE_ID, RESOURCE_ID)
query_mock.assert_called_once_with(query=expected_query)
@patch('db.repositories.user_resources.UserResourceRepository.query', return_value=[])
async def test_get_user_resource_by_id_raises_entity_does_not_exist_if_not_found(_, user_resource_repo):
with pytest.raises(EntityDoesNotExist):
await user_resource_repo.get_user_resource_by_id(WORKSPACE_ID, SERVICE_ID, RESOURCE_ID)
|
AzureTRE/api_app/tests_ma/test_db/test_repositories/test_user_resource_repository.py/0
|
{
"file_path": "AzureTRE/api_app/tests_ma/test_db/test_repositories/test_user_resource_repository.py",
"repo_id": "AzureTRE",
"token_count": 1684
}
| 96 |
from unittest.mock import AsyncMock
from mock import patch
import pytest
from models.domain.costs import GranularityEnum
from models.domain.shared_service import SharedService, ResourceType
from models.domain.user_resource import UserResource
from models.domain.workspace import Workspace
from models.domain.workspace_service import WorkspaceService
from services.cost_service import CostService, SubscriptionNotSupported
from datetime import date, datetime, timedelta
from azure.mgmt.costmanagement.models import QueryResult, TimeframeType, QueryDefinition, QueryColumn
from azure.core.exceptions import ResourceNotFoundError
pytestmark = pytest.mark.asyncio
@pytest.fixture(autouse=True)
def clear_lru_cache():
CostService.cache_clear()
yield
CostService.cache_clear()
@patch('db.repositories.workspaces.WorkspaceRepository')
@patch('db.repositories.shared_services.SharedServiceRepository')
@patch('services.cost_service.CostManagementClient')
# CostService is lru_cached which creates a wrapper method
@patch('services.cost_service.CostService.__wrapped__.get_resource_groups_by_tag')
async def test_query_tre_costs_with_granularity_none_returns_correct_cost_report(get_resource_groups_by_tag_mock, client_mock,
shared_service_repo_mock, workspace_repo_mock):
client_mock.return_value.query.usage.return_value = __get_cost_management_query_result()
__set_shared_service_repo_mock_return_value(shared_service_repo_mock)
__set_workspace_repo_mock_get_active_workspaces_return_value(workspace_repo_mock)
__set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock)
cost_service = CostService()
cost_report = await cost_service.query_tre_costs(
"guy22", GranularityEnum.none, datetime.now(), datetime.now(), workspace_repo_mock, shared_service_repo_mock)
assert len(cost_report.core_services) == 1
assert cost_report.core_services[0].cost == 37.6
assert cost_report.core_services[0].date is None
assert len(cost_report.shared_services) == 2
assert cost_report.shared_services[0].id == "848e8eb5-0df6-4d0f-9162-afd9a3fa0631"
assert cost_report.shared_services[0].name == "Shared service tre-shared-service-firewall"
assert len(cost_report.shared_services[0].costs) == 1
assert cost_report.shared_services[0].costs[0].cost == 6.8
assert cost_report.shared_services[1].id == "f16d0324-9027-4448-b69b-2d48d925e6c0"
assert cost_report.shared_services[1].name == "Shared service tre-shared-service-gitea"
assert len(cost_report.shared_services[1].costs) == 1
assert cost_report.shared_services[1].costs[0].cost == 4.8
assert len(cost_report.workspaces) == 2
assert cost_report.workspaces[0].id == "19b7ce24-aa35-438c-adf6-37e6762911a6"
assert cost_report.workspaces[0].name == "the workspace display name1"
assert len(cost_report.workspaces[0].costs) == 1
assert cost_report.workspaces[0].costs[0].cost == 1.8
assert cost_report.workspaces[1].id == "d680d6b7-d1d9-411c-9101-0793da980c81"
assert cost_report.workspaces[1].name == "the workspace display name2"
assert len(cost_report.workspaces[1].costs) == 2
assert cost_report.workspaces[1].costs[0].cost == 5.8
assert cost_report.workspaces[1].costs[0].currency == "ILS"
assert cost_report.workspaces[1].costs[1].cost == 2.8
assert cost_report.workspaces[1].costs[1].currency == "USD"
@patch('db.repositories.workspaces.WorkspaceRepository')
@patch('db.repositories.shared_services.SharedServiceRepository')
@patch('services.cost_service.CostManagementClient')
# CostService is lru_cached which creates a wrapper method
@patch('services.cost_service.CostService.__wrapped__.get_resource_groups_by_tag')
async def test_query_tre_costs_with_granularity_daily_returns_correct_cost_report(
get_resource_groups_by_tag_mock, client_mock, shared_service_repo_mock, workspace_repo_mock):
client_mock.return_value.query.usage.return_value = __set_cost_management_client_mock_query_result()
__set_shared_service_repo_mock_return_value(shared_service_repo_mock)
__set_workspace_repo_mock_get_active_workspaces_return_value(workspace_repo_mock)
__set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock)
cost_service = CostService()
cost_report = await cost_service.query_tre_costs(
"guy22", GranularityEnum.daily, datetime.now(), datetime.now(), workspace_repo_mock, shared_service_repo_mock)
assert len(cost_report.core_services) == 3
assert cost_report.core_services[0].cost == 31.6
assert cost_report.core_services[0].date == date(2022, 5, 1)
assert len(cost_report.shared_services) == 2
assert cost_report.shared_services[0].id == "848e8eb5-0df6-4d0f-9162-afd9a3fa0631"
assert cost_report.shared_services[0].name == "Shared service tre-shared-service-firewall"
assert len(cost_report.shared_services[0].costs) == 3
assert cost_report.shared_services[0].costs[0].cost == 3.8
assert cost_report.shared_services[0].costs[0].date == date(2022, 5, 1)
assert cost_report.shared_services[0].costs[1].cost == 4.8
assert cost_report.shared_services[0].costs[1].date == date(2022, 5, 2)
assert cost_report.shared_services[0].costs[2].cost == 5.8
assert cost_report.shared_services[0].costs[2].date == date(2022, 5, 3)
assert cost_report.shared_services[1].id == "f16d0324-9027-4448-b69b-2d48d925e6c0"
assert cost_report.shared_services[1].name == "Shared service tre-shared-service-gitea"
assert len(cost_report.shared_services[1].costs) == 3
assert cost_report.shared_services[1].costs[0].cost == 2.8
assert cost_report.shared_services[1].costs[0].date == date(2022, 5, 1)
assert cost_report.shared_services[1].costs[1].cost == 3.8
assert cost_report.shared_services[1].costs[1].date == date(2022, 5, 2)
assert cost_report.shared_services[1].costs[2].cost == 4.8
assert cost_report.shared_services[1].costs[2].date == date(2022, 5, 3)
assert len(cost_report.workspaces) == 2
assert cost_report.workspaces[0].id == "19b7ce24-aa35-438c-adf6-37e6762911a6"
assert cost_report.workspaces[0].name == "the workspace display name1"
assert len(cost_report.workspaces[0].costs) == 3
assert cost_report.workspaces[0].costs[0].cost == 1.8
assert cost_report.workspaces[0].costs[0].date == date(2022, 5, 1)
assert cost_report.workspaces[0].costs[1].cost == 2.8
assert cost_report.workspaces[0].costs[1].date == date(2022, 5, 2)
assert cost_report.workspaces[0].costs[2].cost == 3.8
assert cost_report.workspaces[0].costs[2].date == date(2022, 5, 3)
assert cost_report.workspaces[1].id == "d680d6b7-d1d9-411c-9101-0793da980c81"
assert cost_report.workspaces[1].name == "the workspace display name2"
assert len(cost_report.workspaces[1].costs) == 4
assert cost_report.workspaces[1].costs[0].cost == 4.8
assert cost_report.workspaces[1].costs[0].date == date(2022, 5, 1)
assert cost_report.workspaces[1].costs[1].cost == 5.8
assert cost_report.workspaces[1].costs[1].date == date(2022, 5, 2)
assert cost_report.workspaces[1].costs[2].cost == 16.8
assert cost_report.workspaces[1].costs[2].date == date(2022, 5, 3)
assert cost_report.workspaces[1].costs[2].currency == "ILS"
assert cost_report.workspaces[1].costs[3].cost == 6.8
assert cost_report.workspaces[1].costs[3].date == date(2022, 5, 3)
assert cost_report.workspaces[1].costs[3].currency == "USD"
def __set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock):
get_resource_groups_by_tag_mock.return_value = {
'rg-guy22': '"tre_id":"guy22"',
'rg-guy22-ws-11a6': '"tre_workspace_id":"19b7ce24-aa35-438c-adf6-37e6762911a6"',
'rg-guy22-ws-0c81': '"tre_workspace_id":"d680d6b7-d1d9-411c-9101-0793da980c81"'
}
def __get_daily_cost_management_query_result():
query_result = QueryResult()
query_result.rows = [
[31.6, 20220501, '"tre_core_service_id":"guy22"', 'USD'],
[32.6, 20220502, '"tre_core_service_id":"guy22"', 'USD'],
[33.6, 20220503, '"tre_core_service_id":"guy22"', 'USD'],
[44.5, 20220501, '"tre_id":"guy22"', 'USD'],
[44.5, 20220502, '"tre_id":"guy22"', 'USD'],
[44.5, 20220503, '"tre_id":"guy22"', 'USD'],
[12.5, 20220503, '"tre_id":"guy22"', 'ILS'],
[3.8, 20220501, '"tre_shared_service_id":"848e8eb5-0df6-4d0f-9162-afd9a3fa0631"', 'USD'],
[4.8, 20220502, '"tre_shared_service_id":"848e8eb5-0df6-4d0f-9162-afd9a3fa0631"', 'USD'],
[5.8, 20220503, '"tre_shared_service_id":"848e8eb5-0df6-4d0f-9162-afd9a3fa0631"', 'USD'],
[2.8, 20220501, '"tre_shared_service_id":"f16d0324-9027-4448-b69b-2d48d925e6c0"', 'USD'],
[3.8, 20220502, '"tre_shared_service_id":"f16d0324-9027-4448-b69b-2d48d925e6c0"', 'USD'],
[4.8, 20220503, '"tre_shared_service_id":"f16d0324-9027-4448-b69b-2d48d925e6c0"', 'USD'],
[1.8, 20220501, '"tre_workspace_id":"19b7ce24-aa35-438c-adf6-37e6762911a6"', 'USD'],
[2.8, 20220502, '"tre_workspace_id":"19b7ce24-aa35-438c-adf6-37e6762911a6"', 'USD'],
[3.8, 20220503, '"tre_workspace_id":"19b7ce24-aa35-438c-adf6-37e6762911a6"', 'USD'],
[4.8, 20220501, '"tre_workspace_id":"d680d6b7-d1d9-411c-9101-0793da980c81"', 'USD'],
[5.8, 20220502, '"tre_workspace_id":"d680d6b7-d1d9-411c-9101-0793da980c81"', 'USD'],
[6.8, 20220503, '"tre_workspace_id":"d680d6b7-d1d9-411c-9101-0793da980c81"', 'USD'],
]
return query_result
@patch('db.repositories.workspaces.WorkspaceRepository')
@patch('db.repositories.shared_services.SharedServiceRepository')
@patch('services.cost_service.CostManagementClient')
# CostService is lru_cached which creates a wrapper method
@patch('services.cost_service.CostService.__wrapped__.get_resource_groups_by_tag')
async def test_query_tre_costs_with_granularity_none_and_missing_costs_data_returns_empty_cost_report(get_resource_groups_by_tag_mock,
client_mock,
shared_service_repo_mock,
workspace_repo_mock):
query_result = QueryResult()
query_result.rows = [
]
query_result.columns = [QueryColumn(name="PreTaxCost", type="Number"),
QueryColumn(name="ResourceGroup", type="String"),
QueryColumn(name="Tag", type="String"),
QueryColumn(name="Currency", type="String")]
client_mock.return_value.query.usage.return_value = query_result
__set_shared_service_repo_mock_return_value(shared_service_repo_mock)
__set_workspace_repo_mock_get_active_workspaces_return_value(workspace_repo_mock)
__set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock)
cost_service = CostService()
cost_report = await cost_service.query_tre_costs(
"guy22", GranularityEnum.none, datetime.now(), datetime.now(), workspace_repo_mock, shared_service_repo_mock)
assert len(cost_report.core_services) == 0
assert len(cost_report.shared_services) == 2
assert len(cost_report.shared_services[0].costs) == 0
assert len(cost_report.shared_services[1].costs) == 0
assert len(cost_report.workspaces) == 2
assert len(cost_report.workspaces[0].costs) == 0
assert len(cost_report.workspaces[1].costs) == 0
@patch('db.repositories.workspaces.WorkspaceRepository')
@patch('db.repositories.shared_services.SharedServiceRepository')
@patch('services.cost_service.CostManagementClient')
# CostService is lru_cached which creates a wrapper method
@patch('services.cost_service.CostService.__wrapped__.get_resource_groups_by_tag')
async def test_query_tre_costs_for_unsupported_subscription_raises_subscription_not_supported_exception(get_resource_groups_by_tag_mock,
client_mock,
shared_service_repo_mock,
workspace_repo_mock):
client_mock.return_value.query.usage.side_effect = ResourceNotFoundError({
"error": {
"code": "NotFound",
"message": "Given subscription xxx doesn't have valid WebDirect/AIRS offer type. (Request ID: 12daa3b6-8a53-4759-97ba-511ece1ac95b)"
}
})
__set_shared_service_repo_mock_return_value(shared_service_repo_mock)
__set_workspace_repo_mock_get_active_workspaces_return_value(workspace_repo_mock)
__set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock)
cost_service = CostService()
with pytest.raises(SubscriptionNotSupported):
await cost_service.query_tre_costs(
"guy22", GranularityEnum.none, datetime.now(), datetime.now(), workspace_repo_mock, shared_service_repo_mock)
@patch('db.repositories.workspaces.WorkspaceRepository')
@patch('db.repositories.shared_services.SharedServiceRepository')
@patch('services.cost_service.CostManagementClient')
# CostService is lru_cached which creates a wrapper method
@patch('services.cost_service.CostService.__wrapped__.get_resource_groups_by_tag')
async def test_query_tre_costs_with_granularity_daily_and_missing_costs_data_returns_empty_cost_report(get_resource_groups_by_tag_mock,
client_mock,
shared_service_repo_mock,
workspace_repo_mock):
query_result = QueryResult()
query_result.rows = [
]
query_result.columns = [QueryColumn(name="PreTaxCost", type="Number"),
QueryColumn(name="UsageDate", type="DateTime"),
QueryColumn(name="ResourceGroup", type="String"),
QueryColumn(name="Tag", type="String"),
QueryColumn(name="Currency", type="String")]
client_mock.return_value.query.usage.return_value = query_result
__set_shared_service_repo_mock_return_value(shared_service_repo_mock)
__set_workspace_repo_mock_get_active_workspaces_return_value(workspace_repo_mock)
__set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock)
cost_service = CostService()
cost_report = await cost_service.query_tre_costs(
"guy22", GranularityEnum.daily, datetime.now(), datetime.now(), workspace_repo_mock, shared_service_repo_mock)
assert len(cost_report.core_services) == 0
assert len(cost_report.shared_services) == 2
assert len(cost_report.shared_services[0].costs) == 0
assert len(cost_report.shared_services[1].costs) == 0
assert len(cost_report.workspaces) == 2
assert len(cost_report.workspaces[0].costs) == 0
assert len(cost_report.workspaces[1].costs) == 0
@patch('db.repositories.workspaces.WorkspaceRepository')
@patch('db.repositories.shared_services.SharedServiceRepository')
@patch('services.cost_service.CostManagementClient')
# CostService is lru_cached which creates a wrapper method
@patch('services.cost_service.CostService.__wrapped__.get_resource_groups_by_tag')
async def test_query_tre_costs_with_granularity_none_and_display_name_data_returns_template_name_in_cost_report(get_resource_groups_by_tag_mock,
client_mock,
shared_service_repo_mock,
workspace_repo_mock):
client_mock.return_value.query.usage.return_value = __get_cost_management_query_result()
__set_shared_service_repo_mock_return_value_without_display_name(shared_service_repo_mock)
__set_workspace_repo_mock_get_active_workspaces_return_value_without_display_name(workspace_repo_mock)
__set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock)
cost_service = CostService()
cost_report = await cost_service.query_tre_costs(
"guy22", GranularityEnum.none, datetime.now(), datetime.now(), workspace_repo_mock, shared_service_repo_mock)
assert len(cost_report.core_services) == 1
assert cost_report.core_services[0].cost == 37.6
assert cost_report.core_services[0].date is None
assert len(cost_report.shared_services) == 2
assert cost_report.shared_services[0].id == "848e8eb5-0df6-4d0f-9162-afd9a3fa0631"
assert cost_report.shared_services[0].name == "tre-shared-service-firewall"
assert len(cost_report.shared_services[0].costs) == 1
assert cost_report.shared_services[0].costs[0].cost == 6.8
assert cost_report.shared_services[1].id == "f16d0324-9027-4448-b69b-2d48d925e6c0"
assert cost_report.shared_services[1].name == "tre-shared-service-gitea"
assert len(cost_report.shared_services[1].costs) == 1
assert cost_report.shared_services[1].costs[0].cost == 4.8
assert len(cost_report.workspaces) == 2
assert cost_report.workspaces[0].id == "19b7ce24-aa35-438c-adf6-37e6762911a6"
assert cost_report.workspaces[0].name == "tre-workspace-base"
assert len(cost_report.workspaces[0].costs) == 1
assert cost_report.workspaces[0].costs[0].cost == 1.8
assert cost_report.workspaces[1].id == "d680d6b7-d1d9-411c-9101-0793da980c81"
assert cost_report.workspaces[1].name == "tre-workspace-base"
assert cost_report.workspaces[1].costs[0].cost == 5.8
assert cost_report.workspaces[1].costs[0].currency == "ILS"
assert cost_report.workspaces[1].costs[1].cost == 2.8
assert cost_report.workspaces[1].costs[1].currency == "USD"
@pytest.mark.parametrize("from_date,to_date", [(None, datetime.now()), (datetime.now(), None), (None, None)])
@patch('db.repositories.workspaces.WorkspaceRepository')
@patch('db.repositories.shared_services.SharedServiceRepository')
@patch('services.cost_service.CostManagementClient')
# CostService is lru_cached which creates a wrapper method
@patch('services.cost_service.CostService.__wrapped__.get_resource_groups_by_tag')
async def test_query_tre_costs_with_dates_set_as_none_calls_client_with_month_to_date(get_resource_groups_by_tag_mock,
client_mock, shared_service_repo_mock,
workspace_repo_mock, from_date,
to_date):
__set_shared_service_repo_mock_return_value(shared_service_repo_mock)
__set_workspace_repo_mock_get_active_workspaces_return_value(workspace_repo_mock)
__set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock)
cost_service = CostService()
CostService.cache_clear()
await cost_service.query_tre_costs(
"guy22", GranularityEnum.none, from_date, to_date, workspace_repo_mock, shared_service_repo_mock)
query_definition: QueryDefinition = client_mock.return_value.query.usage.call_args_list[0][0][1]
assert query_definition.timeframe == TimeframeType.MONTH_TO_DATE
@patch('db.repositories.workspaces.WorkspaceRepository')
@patch('db.repositories.shared_services.SharedServiceRepository')
@patch('services.cost_service.CostManagementClient')
# CostService is lru_cached which creates a wrapper method
@patch('services.cost_service.CostService.__wrapped__.get_resource_groups_by_tag')
async def test_query_tre_costs_with_dates_set_as_none_calls_client_with_custom_dates(get_resource_groups_by_tag_mock,
client_mock, shared_service_repo_mock,
workspace_repo_mock):
__set_shared_service_repo_mock_return_value(shared_service_repo_mock)
__set_workspace_repo_mock_get_active_workspaces_return_value(workspace_repo_mock)
__set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock)
from_date = datetime.now() - timedelta(days=10)
to_date = datetime.now()
cost_service = CostService()
await cost_service.query_tre_costs(
"guy22", GranularityEnum.none, from_date, to_date, workspace_repo_mock, shared_service_repo_mock)
query_definition: QueryDefinition = client_mock.return_value.query.usage.call_args_list[0][0][1]
assert query_definition.timeframe == TimeframeType.CUSTOM
assert query_definition.time_period.from_property == from_date
assert query_definition.time_period.to == to_date
def __set_workspace_repo_mock_get_active_workspaces_return_value(workspace_repo_mock):
workspace_repo_mock.get_active_workspaces = AsyncMock(return_value=[
Workspace(id='19b7ce24-aa35-438c-adf6-37e6762911a6', templateName='tre-workspace-base',
resourceType=ResourceType.Workspace, templateVersion="1", _etag="x",
properties={'display_name': 'the workspace display name1'}),
Workspace(id='d680d6b7-d1d9-411c-9101-0793da980c81', templateName='tre-workspace-base',
resourceType=ResourceType.Workspace, templateVersion="1", _etag="x",
properties={'display_name': 'the workspace display name2'})
])
def __set_workspace_repo_mock_get_active_workspaces_return_value_without_display_name(workspace_repo_mock):
workspace_repo_mock.get_active_workspaces = AsyncMock(return_value=[
Workspace(id='19b7ce24-aa35-438c-adf6-37e6762911a6', templateName='tre-workspace-base',
resourceType=ResourceType.Workspace, templateVersion="1", _etag="x"),
Workspace(id='d680d6b7-d1d9-411c-9101-0793da980c81', templateName='tre-workspace-base',
resourceType=ResourceType.Workspace, templateVersion="1", _etag="x")
])
def __set_shared_service_repo_mock_return_value(shared_service_repo_mock):
shared_service_repo_mock.get_active_shared_services = AsyncMock(return_value=[
SharedService(id='848e8eb5-0df6-4d0f-9162-afd9a3fa0631', resourceType=ResourceType.SharedService,
templateName="tre-shared-service-firewall", templateVersion="1", _etag="x",
properties={'display_name': 'Shared service tre-shared-service-firewall'}),
SharedService(id='f16d0324-9027-4448-b69b-2d48d925e6c0', resourceType=ResourceType.SharedService,
templateName="tre-shared-service-gitea", templateVersion="1", _etag="x",
properties={'display_name': 'Shared service tre-shared-service-gitea'})
])
def __set_shared_service_repo_mock_return_value_without_display_name(shared_service_repo_mock):
shared_service_repo_mock.get_active_shared_services = AsyncMock(return_value=[
SharedService(id='848e8eb5-0df6-4d0f-9162-afd9a3fa0631', resourceType=ResourceType.SharedService,
templateName="tre-shared-service-firewall", templateVersion="1", _etag="x"),
SharedService(id='f16d0324-9027-4448-b69b-2d48d925e6c0', resourceType=ResourceType.SharedService,
templateName="tre-shared-service-gitea", templateVersion="1", _etag="x")
])
def __set_workspace_repo_mock_get_workspace_by_id_return_value(workspace_repo_mock):
workspace_repo_mock.get_workspace_by_id = AsyncMock(return_value=Workspace(id='19b7ce24-aa35-438c-adf6-37e6762911a6',
templateName='tre-workspace-base',
resourceType=ResourceType.Workspace,
templateVersion="1", _etag="x",
properties={
'display_name': "workspace 1"}))
def __set_workspace_service_repo_mock_return_value(workspace_service_repo_mock):
workspace_service_repo_mock.get_active_workspace_services_for_workspace = AsyncMock(return_value=[
WorkspaceService(id='f8cac589-c497-4896-9fac-58e65685a20c', resourceType=ResourceType.WorkspaceService,
templateName="tre-service-guacamole", templateVersion="1", _etag="x",
properties={'display_name': 'Guacamole'}),
WorkspaceService(id='9ad6e5d8-0bef-4b9f-91d6-ae33884883a1', resourceType=ResourceType.WorkspaceService,
templateName="tre-service-azureml", templateVersion="1", _etag="x",
properties={'display_name': 'Azure ML'})
])
def __set_user_resource_repo_mock_return_value(user_resource_repo_mock):
# each time 'get_user_resources_for_workspace_service' is called it will return
# the next sub-array
user_resource_repo_mock.get_user_resources_for_workspace_service = AsyncMock(side_effect=[
[
UserResource(id='09ed3e6e-fee5-41d0-937e-89644575e78c', resourceType=ResourceType.UserResource,
templateName="tre-user_resource_guacamole_vm", templateVersion="1", _etag="x",
properties={'display_name': 'VM1'}),
UserResource(id='8ce4a294-95ae-45a9-8d48-6525ce84eb5a', resourceType=ResourceType.UserResource,
templateName="tre-user_resource_guacamole_vm", templateVersion="1", _etag="x",
properties={'display_name': 'VM2'})
],
[
UserResource(id='6ede6dc0-a1e1-40bd-92d7-3b3adcbec66d', resourceType=ResourceType.UserResource,
templateName="tre-user_resource_compute_instance", templateVersion="1", _etag="x",
properties={'display_name': 'Compute Instance 1'}),
UserResource(id='915760d8-cf09-4cdb-b73b-815e6bfaef6f', resourceType=ResourceType.UserResource,
templateName="tre-user_resource_compute_instance", templateVersion="1", _etag="x",
properties={'display_name': 'Compute Instance 2'})
]
])
@patch('db.repositories.user_resources.UserResourceRepository')
@patch('db.repositories.workspace_services.WorkspaceServiceRepository')
@patch('db.repositories.workspaces.WorkspaceRepository')
@patch('services.cost_service.CostManagementClient')
# CostService is lru_cached which creates a wrapper method
@patch('services.cost_service.CostService.__wrapped__.get_resource_groups_by_tag')
async def test_query_tre_workspace_costs_with_granularity_none_returns_correct_workspace_cost_report(get_resource_groups_by_tag_mock,
client_mock,
workspace_repo_mock,
workspace_services_repo_mock,
user_resource_repo_mock):
client_mock.return_value.query.usage.return_value = __get_cost_management_query_result()
__set_workspace_repo_mock_get_workspace_by_id_return_value(workspace_repo_mock)
__set_workspace_service_repo_mock_return_value(workspace_services_repo_mock)
__set_user_resource_repo_mock_return_value(user_resource_repo_mock)
__set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock)
cost_service = CostService()
workspace_cost_report = await cost_service.query_tre_workspace_costs(
"19b7ce24-aa35-438c-adf6-37e6762911a6", GranularityEnum.none, datetime.now(), datetime.now(),
workspace_repo_mock,
workspace_services_repo_mock, user_resource_repo_mock)
assert workspace_cost_report.id == "19b7ce24-aa35-438c-adf6-37e6762911a6"
assert workspace_cost_report.name == "workspace 1"
assert len(workspace_cost_report.workspace_services) == 2
assert workspace_cost_report.workspace_services[0].id == "f8cac589-c497-4896-9fac-58e65685a20c"
assert workspace_cost_report.workspace_services[0].name == "Guacamole"
assert len(workspace_cost_report.workspace_services[0].costs) == 1
assert workspace_cost_report.workspace_services[0].costs[0].cost == 6.6
assert len(workspace_cost_report.workspace_services[0].user_resources) == 2
assert workspace_cost_report.workspace_services[0].user_resources[0].id == "09ed3e6e-fee5-41d0-937e-89644575e78c"
assert workspace_cost_report.workspace_services[0].user_resources[0].name == "VM1"
assert len(workspace_cost_report.workspace_services[0].user_resources[0].costs) == 1
assert workspace_cost_report.workspace_services[0].user_resources[0].costs[0].cost == 1.3
assert workspace_cost_report.workspace_services[0].user_resources[1].id == "8ce4a294-95ae-45a9-8d48-6525ce84eb5a"
assert workspace_cost_report.workspace_services[0].user_resources[1].name == "VM2"
assert len(workspace_cost_report.workspace_services[0].user_resources[1].costs) == 1
assert workspace_cost_report.workspace_services[0].user_resources[1].costs[0].cost == 2.3
assert workspace_cost_report.workspace_services[1].id == "9ad6e5d8-0bef-4b9f-91d6-ae33884883a1"
assert workspace_cost_report.workspace_services[1].name == "Azure ML"
assert len(workspace_cost_report.workspace_services[1].costs) == 1
assert workspace_cost_report.workspace_services[1].costs[0].cost == 9.3
assert len(workspace_cost_report.workspace_services[1].user_resources) == 2
assert workspace_cost_report.workspace_services[1].user_resources[0].id == "6ede6dc0-a1e1-40bd-92d7-3b3adcbec66d"
assert workspace_cost_report.workspace_services[1].user_resources[0].name == "Compute Instance 1"
assert len(workspace_cost_report.workspace_services[1].user_resources[0].costs) == 1
assert workspace_cost_report.workspace_services[1].user_resources[0].costs[0].cost == 5.2
assert workspace_cost_report.workspace_services[1].user_resources[1].id == "915760d8-cf09-4cdb-b73b-815e6bfaef6f"
assert workspace_cost_report.workspace_services[1].user_resources[1].name == "Compute Instance 2"
assert len(workspace_cost_report.workspace_services[1].user_resources[1].costs) == 1
assert workspace_cost_report.workspace_services[1].user_resources[1].costs[0].cost == 4.1
@patch('db.repositories.user_resources.UserResourceRepository')
@patch('db.repositories.workspace_services.WorkspaceServiceRepository')
@patch('db.repositories.workspaces.WorkspaceRepository')
@patch('services.cost_service.CostManagementClient')
# CostService is lru_cached which creates a wrapper method
@patch('services.cost_service.CostService.__wrapped__.get_resource_groups_by_tag')
async def test_query_tre_workspace_costs_with_granularity_daily_returns_correct_workspace_cost_report(get_resource_groups_by_tag_mock,
client_mock,
workspace_repo_mock,
workspace_services_repo_mock,
user_resource_repo_mock):
client_mock.return_value.query.usage.return_value = __set_cost_management_client_mock_query_result()
__set_workspace_repo_mock_get_workspace_by_id_return_value(workspace_repo_mock)
__set_workspace_service_repo_mock_return_value(workspace_services_repo_mock)
__set_user_resource_repo_mock_return_value(user_resource_repo_mock)
__set_resource_group_by_tag_return_value(get_resource_groups_by_tag_mock)
cost_service = CostService()
workspace_cost_report = await cost_service.query_tre_workspace_costs(
"19b7ce24-aa35-438c-adf6-37e6762911a6", GranularityEnum.daily, datetime.now(), datetime.now(),
workspace_repo_mock,
workspace_services_repo_mock, user_resource_repo_mock)
assert workspace_cost_report.id == "19b7ce24-aa35-438c-adf6-37e6762911a6"
assert workspace_cost_report.name == "workspace 1"
assert len(workspace_cost_report.workspace_services) == 2
assert workspace_cost_report.workspace_services[0].id == "f8cac589-c497-4896-9fac-58e65685a20c"
assert workspace_cost_report.workspace_services[0].name == "Guacamole"
assert len(workspace_cost_report.workspace_services[0].costs) == 3
assert workspace_cost_report.workspace_services[0].costs[0].cost == 14.8
assert len(workspace_cost_report.workspace_services[0].user_resources) == 2
assert workspace_cost_report.workspace_services[0].user_resources[0].id == "09ed3e6e-fee5-41d0-937e-89644575e78c"
assert workspace_cost_report.workspace_services[0].user_resources[0].name == "VM1"
assert len(workspace_cost_report.workspace_services[0].user_resources[0].costs) == 4
assert workspace_cost_report.workspace_services[0].user_resources[0].costs[0].cost == 114.8
assert workspace_cost_report.workspace_services[0].user_resources[0].costs[1].cost == 115.8
assert workspace_cost_report.workspace_services[0].user_resources[0].costs[2].cost == 216.8
assert workspace_cost_report.workspace_services[0].user_resources[0].costs[2].currency == "ILS"
assert workspace_cost_report.workspace_services[0].user_resources[0].costs[3].cost == 116.8
assert workspace_cost_report.workspace_services[0].user_resources[0].costs[3].currency == "USD"
assert workspace_cost_report.workspace_services[0].user_resources[1].id == "8ce4a294-95ae-45a9-8d48-6525ce84eb5a"
assert workspace_cost_report.workspace_services[0].user_resources[1].name == "VM2"
assert len(workspace_cost_report.workspace_services[0].user_resources[1].costs) == 3
assert workspace_cost_report.workspace_services[0].user_resources[1].costs[0].cost == 164.8
assert workspace_cost_report.workspace_services[1].id == "9ad6e5d8-0bef-4b9f-91d6-ae33884883a1"
assert workspace_cost_report.workspace_services[1].name == "Azure ML"
assert len(workspace_cost_report.workspace_services[1].costs) == 3
assert workspace_cost_report.workspace_services[1].costs[0].cost == 24.8
assert len(workspace_cost_report.workspace_services[1].user_resources) == 2
assert workspace_cost_report.workspace_services[1].user_resources[0].id == "6ede6dc0-a1e1-40bd-92d7-3b3adcbec66d"
assert workspace_cost_report.workspace_services[1].user_resources[0].name == "Compute Instance 1"
assert len(workspace_cost_report.workspace_services[1].user_resources[0].costs) == 3
assert workspace_cost_report.workspace_services[1].user_resources[0].costs[0].cost == 164.8
assert workspace_cost_report.workspace_services[1].user_resources[1].id == "915760d8-cf09-4cdb-b73b-815e6bfaef6f"
assert workspace_cost_report.workspace_services[1].user_resources[1].name == "Compute Instance 2"
assert len(workspace_cost_report.workspace_services[1].user_resources[1].costs) == 3
assert workspace_cost_report.workspace_services[1].user_resources[1].costs[0].cost == 168.8
def __get_cost_management_query_result():
query_result = QueryResult()
query_result.rows = [
[37.6, 'rg-guy22', '"tre_core_service_id":"guy22"', 'USD'],
[44.5, 'rg-guy22', '"tre_id":"guy22"', 'USD'],
[6.8, 'rg-guy22', '"tre_shared_service_id":"848e8eb5-0df6-4d0f-9162-afd9a3fa0631"', 'USD'],
[4.8, 'rg-guy22', '"tre_shared_service_id":"f16d0324-9027-4448-b69b-2d48d925e6c0"', 'USD'],
[1.8, 'rg-guy22-ws-11a6', '"tre_workspace_id":"19b7ce24-aa35-438c-adf6-37e6762911a6"', 'USD'],
[2.8, 'rg-guy22-ws-0c81', '"tre_workspace_id":"d680d6b7-d1d9-411c-9101-0793da980c81"', 'USD'],
[5.8, 'rg-guy22-ws-0c81', '"tre_workspace_id":"d680d6b7-d1d9-411c-9101-0793da980c81"', 'ILS'],
[6.6, 'rg-guy22-ws-11a6', '"tre_workspace_service_id":"f8cac589-c497-4896-9fac-58e65685a20c"', 'USD'],
[9.3, 'rg-guy22-ws-0c81', '"tre_workspace_service_id":"9ad6e5d8-0bef-4b9f-91d6-ae33884883a1"', 'USD'],
[1.3, 'rg-guy22-ws-11a6', '"tre_user_resource_id":"09ed3e6e-fee5-41d0-937e-89644575e78c"', 'USD'],
[2.3, 'rg-guy22-ws-0c81', '"tre_user_resource_id":"8ce4a294-95ae-45a9-8d48-6525ce84eb5a"', 'USD'],
[5.2, 'rg-guy22-ws-11a6', '"tre_user_resource_id":"6ede6dc0-a1e1-40bd-92d7-3b3adcbec66d"', 'USD'],
[4.1, 'rg-guy22-ws-11a6', '"tre_user_resource_id":"915760d8-cf09-4cdb-b73b-815e6bfaef6f"', 'USD'],
]
query_result.columns = [QueryColumn(name="PreTaxCost", type="Number"),
QueryColumn(name="ResourceGroup", type="String"),
QueryColumn(name="Tag", type="String"),
QueryColumn(name="Currency", type="String")]
return query_result
def __set_cost_management_client_mock_query_result():
query_result = QueryResult()
query_result.rows = [
[31.6, 20220501, 'rg-guy22', '"tre_core_service_id":"guy22"', 'USD'],
[32.6, 20220502, 'rg-guy22', '"tre_core_service_id":"guy22"', 'USD'],
[33.6, 20220503, 'rg-guy22', '"tre_core_service_id":"guy22"', 'USD'],
[44.5, 20220501, 'rg-guy22', '"tre_id":"guy22"', 'USD'],
[44.5, 20220502, 'rg-guy22', '"tre_id":"guy22"', 'USD'],
[44.5, 20220503, 'rg-guy22', '"tre_id":"guy22"', 'USD'],
[3.8, 20220501, 'rg-guy22', '"tre_shared_service_id":"848e8eb5-0df6-4d0f-9162-afd9a3fa0631"', 'USD'],
[4.8, 20220502, 'rg-guy22', '"tre_shared_service_id":"848e8eb5-0df6-4d0f-9162-afd9a3fa0631"', 'USD'],
[5.8, 20220503, 'rg-guy22', '"tre_shared_service_id":"848e8eb5-0df6-4d0f-9162-afd9a3fa0631"', 'USD'],
[2.8, 20220501, 'rg-guy22', '"tre_shared_service_id":"f16d0324-9027-4448-b69b-2d48d925e6c0"', 'USD'],
[3.8, 20220502, 'rg-guy22', '"tre_shared_service_id":"f16d0324-9027-4448-b69b-2d48d925e6c0"', 'USD'],
[4.8, 20220503, 'rg-guy22', '"tre_shared_service_id":"f16d0324-9027-4448-b69b-2d48d925e6c0"', 'USD'],
[1.8, 20220501, 'rg-guy22-ws-11a6', '"tre_workspace_id":"19b7ce24-aa35-438c-adf6-37e6762911a6"', 'USD'],
[2.8, 20220502, 'rg-guy22-ws-11a6', '"tre_workspace_id":"19b7ce24-aa35-438c-adf6-37e6762911a6"', 'USD'],
[3.8, 20220503, 'rg-guy22-ws-11a6', '"tre_workspace_id":"19b7ce24-aa35-438c-adf6-37e6762911a6"', 'USD'],
[4.8, 20220501, 'rg-guy22-ws-0c81', '"tre_workspace_id":"d680d6b7-d1d9-411c-9101-0793da980c81"', 'USD'],
[5.8, 20220502, 'rg-guy22-ws-0c81', '"tre_workspace_id":"d680d6b7-d1d9-411c-9101-0793da980c81"', 'USD'],
[6.8, 20220503, 'rg-guy22-ws-0c81', '"tre_workspace_id":"d680d6b7-d1d9-411c-9101-0793da980c81"', 'USD'],
[16.8, 20220503, 'rg-guy22-ws-0c81', '"tre_workspace_id":"d680d6b7-d1d9-411c-9101-0793da980c81"', 'ILS'],
[14.8, 20220501, 'rg-guy22-ws-11a6', '"tre_workspace_service_id":"f8cac589-c497-4896-9fac-58e65685a20c"', 'USD'],
[15.8, 20220502, 'rg-guy22-ws-11a6', '"tre_workspace_service_id":"f8cac589-c497-4896-9fac-58e65685a20c"', 'USD'],
[16.8, 20220503, 'rg-guy22-ws-11a6', '"tre_workspace_service_id":"f8cac589-c497-4896-9fac-58e65685a20c"', 'USD'],
[24.8, 20220501, 'rg-guy22-ws-0c81', '"tre_workspace_service_id":"9ad6e5d8-0bef-4b9f-91d6-ae33884883a1"', 'USD'],
[25.8, 20220502, 'rg-guy22-ws-0c81', '"tre_workspace_service_id":"9ad6e5d8-0bef-4b9f-91d6-ae33884883a1"', 'USD'],
[26.8, 20220503, 'rg-guy22-ws-0c81', '"tre_workspace_service_id":"9ad6e5d8-0bef-4b9f-91d6-ae33884883a1"', 'USD'],
[114.8, 20220501, 'rg-guy22-ws-11a6', '"tre_user_resource_id":"09ed3e6e-fee5-41d0-937e-89644575e78c"', 'USD'],
[115.8, 20220502, 'rg-guy22-ws-11a6', '"tre_user_resource_id":"09ed3e6e-fee5-41d0-937e-89644575e78c"', 'USD'],
[116.8, 20220503, 'rg-guy22-ws-11a6', '"tre_user_resource_id":"09ed3e6e-fee5-41d0-937e-89644575e78c"', 'USD'],
[216.8, 20220503, 'rg-guy22-ws-11a6', '"tre_user_resource_id":"09ed3e6e-fee5-41d0-937e-89644575e78c"', 'ILS'],
[164.8, 20220501, 'rg-guy22-ws-0c81', '"tre_user_resource_id":"8ce4a294-95ae-45a9-8d48-6525ce84eb5a"', 'USD'],
[165.8, 20220502, 'rg-guy22-ws-0c81', '"tre_user_resource_id":"8ce4a294-95ae-45a9-8d48-6525ce84eb5a"', 'USD'],
[166.8, 20220503, 'rg-guy22-ws-0c81', '"tre_user_resource_id":"8ce4a294-95ae-45a9-8d48-6525ce84eb5a"', 'USD'],
[164.8, 20220501, 'rg-guy22-ws-0c81', '"tre_user_resource_id":"6ede6dc0-a1e1-40bd-92d7-3b3adcbec66d"', 'USD'],
[165.8, 20220502, 'rg-guy22-ws-0c81', '"tre_user_resource_id":"6ede6dc0-a1e1-40bd-92d7-3b3adcbec66d"', 'USD'],
[166.8, 20220503, 'rg-guy22-ws-0c81', '"tre_user_resource_id":"6ede6dc0-a1e1-40bd-92d7-3b3adcbec66d"', 'USD'],
[168.8, 20220501, 'rg-guy22-ws-0c81', '"tre_user_resource_id":"915760d8-cf09-4cdb-b73b-815e6bfaef6f"', 'USD'],
[168.8, 20220502, 'rg-guy22-ws-0c81', '"tre_user_resource_id":"915760d8-cf09-4cdb-b73b-815e6bfaef6f"', 'USD'],
[168.8, 20220503, 'rg-guy22-ws-0c81', '"tre_user_resource_id":"915760d8-cf09-4cdb-b73b-815e6bfaef6f"', 'USD']
]
query_result.columns = [QueryColumn(name="PreTaxCost", type="Number"),
QueryColumn(name="UsageDate", type="DateTime"),
QueryColumn(name="ResourceGroup", type="String"),
QueryColumn(name="Tag", type="String"),
QueryColumn(name="Currency", type="String")]
return query_result
|
AzureTRE/api_app/tests_ma/test_services/test_cost_service.py/0
|
{
"file_path": "AzureTRE/api_app/tests_ma/test_services/test_cost_service.py",
"repo_id": "AzureTRE",
"token_count": 19926
}
| 97 |
#!/bin/bash
set -e
echo "Cleaning build/dist folders..."
rm -rf build
rm -rf dist
echo "Running build..."
pip install build
python -m build
echo "Done."
|
AzureTRE/cli/scripts/build.sh/0
|
{
"file_path": "AzureTRE/cli/scripts/build.sh",
"repo_id": "AzureTRE",
"token_count": 53
}
| 98 |
import json
import logging
import click
from tre.api_client import ApiClient
from tre.output import output, output_option, query_option
@click.group(name="shared-service-templates", help="List shared-service-templates ")
def shared_service_templates():
pass
@click.command(name="list", help="List shared-service-templates")
@output_option()
@query_option()
def shared_service_templates_list(output_format, query):
log = logging.getLogger(__name__)
client = ApiClient.get_api_client_from_config()
response = client.call_api(
log,
'GET',
'/api/shared-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 shared 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 shared_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/shared-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
shared_service_templates.add_command(shared_service_templates_list)
shared_service_templates.add_command(shared_service_templates_create)
|
AzureTRE/cli/tre/commands/shared_service_templates/shared_service_templates.py/0
|
{
"file_path": "AzureTRE/cli/tre/commands/shared_service_templates/shared_service_templates.py",
"repo_id": "AzureTRE",
"token_count": 641
}
| 99 |
import click
class WorkspaceTemplateContext(object):
def __init__(self, template_name: str):
self.template_name = template_name
pass_workspace_template_context = click.make_pass_decorator(WorkspaceTemplateContext)
|
AzureTRE/cli/tre/commands/workspace_templates/contexts.py/0
|
{
"file_path": "AzureTRE/cli/tre/commands/workspace_templates/contexts.py",
"repo_id": "AzureTRE",
"token_count": 73
}
| 100 |
data "azurerm_container_registry" "mgmt_acr" {
name = var.mgmt_acr_name
resource_group_name = var.mgmt_resource_group_name
}
resource "azurerm_user_assigned_identity" "airlock_id" {
resource_group_name = var.resource_group_name
location = var.location
name = "id-airlock-${var.tre_id}"
tags = var.tre_core_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_role_assignment" "acrpull_role" {
scope = data.azurerm_container_registry.mgmt_acr.id
role_definition_name = "AcrPull"
principal_id = azurerm_user_assigned_identity.airlock_id.principal_id
}
resource "azurerm_role_assignment" "servicebus_sender" {
scope = var.airlock_servicebus.id
role_definition_name = "Azure Service Bus Data Sender"
principal_id = azurerm_user_assigned_identity.airlock_id.principal_id
}
resource "azurerm_role_assignment" "servicebus_receiver" {
scope = var.airlock_servicebus.id
role_definition_name = "Azure Service Bus Data Receiver"
principal_id = azurerm_user_assigned_identity.airlock_id.principal_id
}
resource "azurerm_role_assignment" "eventgrid_data_sender" {
scope = azurerm_eventgrid_topic.status_changed.id
role_definition_name = "EventGrid Data Sender"
principal_id = var.api_principal_id
}
resource "azurerm_role_assignment" "eventgrid_data_sender_notification" {
scope = azurerm_eventgrid_topic.airlock_notification.id
role_definition_name = "EventGrid Data Sender"
principal_id = var.api_principal_id
}
resource "azurerm_role_assignment" "airlock_blob_data_contributor" {
count = length(local.airlock_sa_blob_data_contributor)
scope = local.airlock_sa_blob_data_contributor[count.index]
role_definition_name = "Storage Blob Data Contributor"
principal_id = azurerm_user_assigned_identity.airlock_id.principal_id
}
# This might be considered redundent since we give Virtual Machine Contributor
# at the subscription level, but best to be explicit.
resource "azurerm_role_assignment" "api_sa_data_contributor" {
count = length(local.api_sa_data_contributor)
scope = local.api_sa_data_contributor[count.index]
role_definition_name = "Storage Blob Data Contributor"
principal_id = var.api_principal_id
}
|
AzureTRE/core/terraform/airlock/identity.tf/0
|
{
"file_path": "AzureTRE/core/terraform/airlock/identity.tf",
"repo_id": "AzureTRE",
"token_count": 1029
}
| 101 |
variable "tre_id" {
type = string
}
variable "location" {
type = string
}
variable "resource_group_name" {
type = string
}
variable "app_gw_subnet" {
type = string
}
variable "shared_subnet" {
type = string
}
variable "api_fqdn" {
type = string
}
variable "keyvault_id" {
type = string
}
variable "static_web_dns_zone_id" {
type = string
}
variable "log_analytics_workspace_id" {
type = string
}
|
AzureTRE/core/terraform/appgateway/variables.tf/0
|
{
"file_path": "AzureTRE/core/terraform/appgateway/variables.tf",
"repo_id": "AzureTRE",
"token_count": 158
}
| 102 |
resource "azurerm_key_vault" "kv" {
name = "kv-${var.tre_id}"
tenant_id = data.azurerm_client_config.current.tenant_id
location = azurerm_resource_group.core.location
resource_group_name = azurerm_resource_group.core.name
sku_name = "standard"
purge_protection_enabled = true
tags = local.tre_core_tags
lifecycle { ignore_changes = [access_policy, tags] }
}
resource "azurerm_key_vault_access_policy" "deployer" {
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
key_permissions = ["Get", "List", "Update", "Create", "Import", "Delete", "Recover"]
secret_permissions = ["Get", "List", "Set", "Delete", "Purge", "Recover"]
certificate_permissions = ["Get", "List", "Update", "Create", "Import", "Delete", "Purge", "Recover"]
storage_permissions = ["Get", "List", "Update", "Delete"]
}
resource "azurerm_key_vault_access_policy" "managed_identity" {
key_vault_id = azurerm_key_vault.kv.id
tenant_id = azurerm_user_assigned_identity.id.tenant_id
object_id = azurerm_user_assigned_identity.id.principal_id
key_permissions = ["Get", "List", ]
secret_permissions = ["Get", "List", ]
certificate_permissions = ["Get", "List", ]
}
data "azurerm_private_dns_zone" "vaultcore" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.vaultcore.azure.net"]
resource_group_name = azurerm_resource_group.core.name
depends_on = [
module.network,
]
}
resource "azurerm_private_endpoint" "kvpe" {
name = "pe-kv-${var.tre_id}"
location = azurerm_resource_group.core.location
resource_group_name = azurerm_resource_group.core.name
subnet_id = module.network.shared_subnet_id
tags = local.tre_core_tags
lifecycle { ignore_changes = [tags] }
private_dns_zone_group {
name = "private-dns-zone-group"
private_dns_zone_ids = [data.azurerm_private_dns_zone.vaultcore.id]
}
private_service_connection {
name = "psc-kv-${var.tre_id}"
private_connection_resource_id = azurerm_key_vault.kv.id
is_manual_connection = false
subresource_names = ["Vault"]
}
}
resource "azurerm_key_vault_secret" "api_client_id" {
name = "api-client-id"
value = var.api_client_id
key_vault_id = azurerm_key_vault.kv.id
tags = local.tre_core_tags
depends_on = [
azurerm_key_vault_access_policy.deployer
]
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_key_vault_secret" "api_client_secret" {
name = "api-client-secret"
value = var.api_client_secret
key_vault_id = azurerm_key_vault.kv.id
tags = local.tre_core_tags
depends_on = [
azurerm_key_vault_access_policy.deployer
]
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_key_vault_secret" "auth_tenant_id" {
name = "auth-tenant-id"
value = var.aad_tenant_id
key_vault_id = azurerm_key_vault.kv.id
tags = local.tre_core_tags
depends_on = [
azurerm_key_vault_access_policy.deployer
]
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_key_vault_secret" "application_admin_client_id" {
name = "application-admin-client-id"
value = var.application_admin_client_id
key_vault_id = azurerm_key_vault.kv.id
tags = local.tre_core_tags
depends_on = [
azurerm_key_vault_access_policy.deployer
]
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_key_vault_secret" "application_admin_client_secret" {
name = "application-admin-client-secret"
value = var.application_admin_client_secret
key_vault_id = azurerm_key_vault.kv.id
tags = local.tre_core_tags
depends_on = [
azurerm_key_vault_access_policy.deployer
]
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_monitor_diagnostic_setting" "kv" {
name = "diagnostics-kv-${var.tre_id}"
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
}
lifecycle { ignore_changes = [log_analytics_destination_type] }
}
|
AzureTRE/core/terraform/keyvault.tf/0
|
{
"file_path": "AzureTRE/core/terraform/keyvault.tf",
"repo_id": "AzureTRE",
"token_count": 2060
}
| 103 |
locals {
version = replace(replace(replace(data.local_file.version.content, "__version__ = \"", ""), "\"", ""), "\n", "")
tre_core_tags = {
tre_id = var.tre_id
tre_core_service_id = var.tre_id
}
azure_environment = lookup({
"public" = "AzureCloud"
"usgovernment" = "AzureUSGovernment"
}, var.arm_environment, "AzureCloud")
}
|
AzureTRE/core/terraform/resource_processor/vmss_porter/locals.tf/0
|
{
"file_path": "AzureTRE/core/terraform/resource_processor/vmss_porter/locals.tf",
"repo_id": "AzureTRE",
"token_count": 157
}
| 104 |
#!/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 a workspace TRE. You would typically have one of these per workspace
for a security boundary.
You must be logged in using Azure CLI with sufficient privileges to modify Azure Active Directory to run this script.
Usage: $0 [--admin-consent]
Options:
-n,--name Required. The prefix for the app (registration) names e.g., "TRE".
-u,--ux-clientid Required. The client ID of the UX must be provided.
-y,--application-admin-clientid Required. The client ID of the Application Administrator that will be able to update this application.
e.g. updating a redirect URI.
-a,--admin-consent Optional, but recommended. Grants admin consent for the app registrations, when this flag is set.
Requires directory admin privileges to the Azure AD in question.
-z,--automation-clientid Optional, the client ID of the automation account can be added to the TRE workspace.
-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
if [[ $(az account list --only-show-errors -o json | jq 'length') -eq 0 ]]; then
echo "Please run az login -t <tenant> --allow-no-subscriptions"
exit 1
fi
# Get the directory that this script is in
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
declare resetPassword=0
declare spPassword=""
declare grantAdminConsent=0
declare currentUserId=""
declare uxClientId=""
declare msGraphUri=""
declare appName=""
declare automationClientId=""
declare applicationAdminClientId=""
declare applicationAdminObjectId=""
# Initialize parameters specified from command line
while [[ $# -gt 0 ]]; do
case "$1" in
-n|--name)
appName=$2
shift 2
;;
-a|--admin-consent)
grantAdminConsent=1
shift 1
;;
-y|--application-admin-clientid)
applicationAdminClientId=$2
shift 2
;;
-u|--ux-clientid)
uxClientId=$2
shift 2
;;
-z|--automation-clientid)
automationClientId=$2
shift 2
;;
-r|--reset-password)
resetPassword=$2
shift 2
;;
*)
echo "Invalid option: $1."
show_usage
;;
esac
done
###################################
# CHECK INCOMMING PARAMETERS #
###################################
if [[ -z "$appName" ]]; then
echo "Please specify the application name." 1>&2
show_usage
fi
appName="$appName API"
if [[ -z "$applicationAdminClientId" ]]; then
echo "Please specify the client id of the Application Admin." 1>&2
show_usage
fi
applicationAdminObjectId=$(az ad sp show --id "${applicationAdminClientId}" --query id -o tsv --only-show-errors)
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 a Workspace Application 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 "${DIR}/grant_admin_consent.sh"
# shellcheck disable=SC1091
source "${DIR}/wait_for_new_app_registration.sh"
# shellcheck disable=SC1091
source "${DIR}/create_or_update_service_principal.sh"
# shellcheck disable=SC1091
source "${DIR}/get_msgraph_access.sh"
# shellcheck disable=SC1091
source "${DIR}/update_resource_access.sh"
# Default of new UUIDs
researcherRoleId=$(cat /proc/sys/kernel/random/uuid)
ownerRoleId=$(cat /proc/sys/kernel/random/uuid)
airlockManagerRoleId=$(cat /proc/sys/kernel/random/uuid)
userImpersonationScopeId=$(cat /proc/sys/kernel/random/uuid)
appObjectId=""
# Get an existing object if its been created before.
existingApp=$(get_existing_app --name "${appName}") || null
if [ -n "${existingApp}" ]; then
appObjectId=$(echo "${existingApp}" | jq -r '.id')
researcherRoleId=$(echo "$existingApp" | jq -r '.appRoles[] | select(.value == "WorkspaceResearcher").id')
ownerRoleId=$(echo "$existingApp" | jq -r '.appRoles[] | select(.value == "WorkspaceOwner").id')
airlockManagerRoleId=$(echo "$existingApp" | jq -r '.appRoles[] | select(.value == "AirlockManager").id')
userImpersonationScopeId=$(echo "$existingApp" | jq -r '.api.oauth2PermissionScopes[] | select(.value == "user_impersonation").id')
if [[ -z "${researcherRoleId}" ]]; then researcherRoleId=$(cat /proc/sys/kernel/random/uuid); fi
if [[ -z "${ownerRoleId}" ]]; then ownerRoleId=$(cat /proc/sys/kernel/random/uuid); fi
if [[ -z "${airlockManagerRoleId}" ]]; then airlockManagerRoleId=$(cat /proc/sys/kernel/random/uuid); fi
if [[ -z "${userImpersonationScopeId}" ]]; then userImpersonationScopeId=$(cat /proc/sys/kernel/random/uuid); fi
fi
# Get the Required Resource Scope/Role
msGraphAppId="00000003-0000-0000-c000-000000000000"
msGraphObjectId=$(az ad sp show --id ${msGraphAppId} --query "id" --output tsv --only-show-errors)
msGraphEmailScopeId="64a6cdd6-aab1-4aaf-94b8-3cc8405e90d0"
msGraphOpenIdScopeId="37f7f235-527c-4136-accd-4a02d197296e"
msGraphProfileScopeId="14dad69e-099b-42c9-810b-d002981feec1"
appDefinition=$(jq -c . << JSON
{
"displayName": "${appName}",
"api": {
"requestedAccessTokenVersion": 2,
"oauth2PermissionScopes": [
{
"adminConsentDescription": "Allow the app to access the Workspace API on behalf of the signed-in user.",
"adminConsentDisplayName": "Access the Workspace API on behalf of signed-in user",
"id": "${userImpersonationScopeId}",
"isEnabled": true,
"type": "User",
"userConsentDescription": "Allow the app to access the Workspace API on your behalf.",
"userConsentDisplayName": "Access the Workspace API",
"value": "user_impersonation"
}
]
},
"appRoles": [
{
"id": "${ownerRoleId}",
"allowedMemberTypes": [ "User", "Application" ],
"description": "Provides workspace owners access to the Workspace.",
"displayName": "Workspace Owner",
"isEnabled": true,
"origin": "Application",
"value": "WorkspaceOwner"
},
{
"id": "${researcherRoleId}",
"allowedMemberTypes": [ "User", "Application" ],
"description": "Provides researchers access to the Workspace.",
"displayName": "Workspace Researcher",
"isEnabled": true,
"origin": "Application",
"value": "WorkspaceResearcher"
},
{
"id": "${airlockManagerRoleId}",
"allowedMemberTypes": [ "User", "Application" ],
"description": "Provides airlock managers access to the Workspace and ability to review airlock requests",
"displayName": "Airlock Manager",
"isEnabled": true,
"origin": "Application",
"value": "AirlockManager"
}],
"signInAudience": "AzureADMyOrg",
"requiredResourceAccess": [
{
"resourceAppId": "${msGraphAppId}",
"resourceAccess": [
{
"id": "${msGraphEmailScopeId}",
"type": "Scope"
},
{
"id": "${msGraphOpenIdScopeId}",
"type": "Scope"
},
{
"id": "${msGraphProfileScopeId}",
"type": "Scope"
}
]
}
],
"web":{
"implicitGrantSettings":{
"enableIdTokenIssuance":true,
"enableAccessTokenIssuance":true
}
},
"optionalClaims": {
"idToken": [
{
"name": "ipaddr",
"source": null,
"essential": false,
"additionalProperties": []
},
{
"name": "email",
"source": null,
"essential": false,
"additionalProperties": []
}
],
"accessToken": [],
"saml2Token": []
}
}
JSON
)
# Is the Workspace app already registered?
if [[ -n ${appObjectId} ]]; then
echo "Updating \"${appName}\" with ObjectId \"${appObjectId}\"."
az rest --method PATCH --uri "${msGraphUri}/applications/${appObjectId}" --headers Content-Type=application/json --body "${appDefinition}"
workspaceAppId=$(az ad app show --id "${appObjectId}" --query "appId" --output tsv --only-show-errors)
echo "Workspace app registration with AppId \"${workspaceAppId}\" updated."
else
echo "Creating \"${appName}\" app registration."
workspaceAppId=$(az rest --method POST --uri "${msGraphUri}/applications" --headers Content-Type=application/json --body "${appDefinition}" --output tsv --query "appId")
# Poll until the app registration is found in the listing.
wait_for_new_app_registration "${workspaceAppId}"
# Update to set the identifier URI.
az ad app update --id "${workspaceAppId}" --identifier-uris "api://${workspaceAppId}" --only-show-errors
fi
# Make the current user an owner of the application.
az ad app owner add --id "${workspaceAppId}" --owner-object-id "$currentUserId" --only-show-errors
az ad app owner add --id "${workspaceAppId}" --owner-object-id "$applicationAdminObjectId" --only-show-errors
# Create a Service Principal for the app.
spPassword=$(create_or_update_service_principal "${workspaceAppId}" "${resetPassword}")
workspaceSpId=$(az ad sp list --filter "appId eq '${workspaceAppId}'" --query '[0].id' --output tsv --only-show-errors)
# needed to make the API permissions change effective, this must be done after SP creation...
echo
echo "Running 'az ad app permission grant' to make changes effective."
az ad app permission grant --id "$workspaceSpId" --api "$msGraphObjectId" --scope "email openid profile" --only-show-errors
# The UX (which was created as part of the API) needs to also have access to this Workspace
echo "Searching for existing UX application (${uxClientId})."
existingUXApp=$(get_existing_app --id "${uxClientId}")
uxObjectId=$(echo "${existingUXApp}" | jq -r .id)
# This is the new API Access we require.
uxWorkspaceAccess=$(jq -c .requiredResourceAccess << JSON
{
"requiredResourceAccess": [
{
"resourceAccess": [
{
"id": "${userImpersonationScopeId}",
"type": "Scope"
}
],
"resourceAppId": "${workspaceAppId}"
}
]
}
JSON
)
# Utility function to add the required permissions.
update_resource_access "$msGraphUri" "${uxObjectId}" "${workspaceAppId}" "${uxWorkspaceAccess}"
echo "Grant UX delegated access '${appName}' (Client ID ${uxClientId})"
az ad app permission grant --id "${uxClientId}" --api "${workspaceAppId}" --scope "user_impersonation" --only-show-errors
if [[ -n ${automationClientId} ]]; then
echo "Searching for existing Automation application (${automationClientId})."
existingAutomationApp=$(get_existing_app --id "${automationClientId}")
automationAppObjectId=$(echo "${existingAutomationApp}" | jq -r .id)
automationAppName=$(echo "${existingAutomationApp}" | jq -r .displayName)
echo "Found '${automationAppName}' with ObjectId: '${automationAppObjectId}'"
# This is the new API Access we require.
automationWorkspaceAccess=$(jq -c .requiredResourceAccess << JSON
{
"requiredResourceAccess": [
{
"resourceAccess": [
{
"id": "${userImpersonationScopeId}",
"type": "Scope"
},
{
"id": "${ownerRoleId}",
"type": "Role"
},
{
"id": "${researcherRoleId}",
"type": "Role"
},
{
"id": "${airlockManagerRoleId}",
"type": "Role"
}
],
"resourceAppId": "${workspaceAppId}"
}
]
}
JSON
)
# Utility function to add the required permissions.
update_resource_access "$msGraphUri" "${automationAppObjectId}" "${workspaceAppId}" "${automationWorkspaceAccess}"
# Grant admin consent for the delegated workspace scopes
if [[ $grantAdminConsent -eq 1 ]]; then
echo "Granting admin consent for \"${automationAppName}\" (Client ID ${automationClientId})"
automationSpId=$(az ad sp list --filter "appId eq '${automationClientId}'" --query '[0].id' --output tsv --only-show-errors)
echo "Found Service Principal \"$automationSpId\" for \"${automationAppName}\"."
grant_admin_consent "${automationSpId}" "${workspaceSpId}" "${ownerRoleId}"
grant_admin_consent "${automationSpId}" "${workspaceSpId}" "${airlockManagerRoleId}"
grant_admin_consent "${automationSpId}" "${workspaceSpId}" "${researcherRoleId}"
az ad app permission grant --id "$automationSpId" --api "$workspaceAppId" --scope "user_impersonation" --only-show-errors
fi
fi
# Set outputs in configuration file
yq -i ".authentication.workspace_api_client_id |= \"${workspaceAppId}\"" config.yaml
yq -i ".authentication.workspace_api_client_secret |= \"${spPassword}\"" config.yaml
echo "workspace_api_client_id=\"${workspaceAppId}\""
echo "workspace_api_client_secret=\"${spPassword}\""
if [[ $grantAdminConsent -eq 0 ]]; then
echo "NOTE: Make sure the API permissions of the app registrations have admin consent granted."
echo "Run this script with flag -a to grant admin consent or configure the registrations in Azure Portal."
echo "See APP REGISTRATIONS in documentation for more information."
fi
|
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|
{
"file_path": "AzureTRE/devops/scripts/aad/create_workspace_application.sh",
"repo_id": "AzureTRE",
"token_count": 5706
}
| 105 |
#!/bin/bash
set -euo pipefail
# Use this for debug only
# set -o xtrace
: "${AAD_TENANT_ID?'You have not set your aad_tenant_id in ./config.yaml'}"
# Get the directory that this script is in
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
CHANGED_TENANT=0
LOGGED_IN_TENANT_ID=$(az account show --query tenantId -o tsv)
if [ "${LOGGED_IN_TENANT_ID}" != "${AAD_TENANT_ID}" ]; then
echo "Attempting to sign you onto ${AAD_TENANT_ID} to setup Azure Active Directory assets."
# First we need to login to the AAD tenant (as it is different to the subscription tenant)
az login --tenant "${AAD_TENANT_ID}" --allow-no-subscriptions --use-device-code
CHANGED_TENANT=1
fi
RESET_PASSWORDS=1
if [ "${RESET_AAD_PASSWORDS:-}" == false ]; then
RESET_PASSWORDS=0
fi
APPLICATION_PERMISSION="Application.ReadWrite.OwnedBy"
if [ "${AUTO_WORKSPACE_APP_REGISTRATION:-}" == true ]; then
APPLICATION_PERMISSION="Application.ReadWrite.All,Directory.Read.All"
fi
if [ "${AUTO_WORKSPACE_GROUP_CREATION:-}" == true ]; then
APPLICATION_PERMISSION="Application.ReadWrite.All,Directory.Read.All,Group.ReadWrite.All"
fi
# Create the identity that is able to administer other applications
"$DIR/aad/create_application_administrator.sh" \
--name "${TRE_ID}" \
--admin-consent \
--application-permission "${APPLICATION_PERMISSION}" \
--reset-password $RESET_PASSWORDS
# Create the identity that is able to automate the testing
"$DIR/aad/create_automation_administrator.sh" \
--name "${TRE_ID}" \
--reset-password $RESET_PASSWORDS
# Load the new values back in because
# we need TEST_ACCOUNT_CLIENT_ID
# shellcheck disable=SC1091
. "$DIR/load_and_validate_env.sh"
# Then register an App for the TRE Core.
"$DIR/aad/create_api_application.sh" \
--name "${TRE_ID}" \
--tre-url "${TRE_URL}" \
--admin-consent --automation-clientid "${TEST_ACCOUNT_CLIENT_ID}" \
--reset-password $RESET_PASSWORDS
if [ "${AUTO_WORKSPACE_APP_REGISTRATION:=false}" == false ]; then
# Load the new values back in
# This is because we want the SWAGGER_UI_CLIENT_ID
# shellcheck disable=SC1091
. "$DIR/load_and_validate_env.sh"
"$DIR/aad/create_workspace_application.sh" \
--name "${TRE_ID} - workspace 1" \
--admin-consent \
--ux-clientid "${SWAGGER_UI_CLIENT_ID}" \
--automation-clientid "${TEST_ACCOUNT_CLIENT_ID}" \
--application-admin-clientid "${APPLICATION_ADMIN_CLIENT_ID}" \
--reset-password $RESET_PASSWORDS
fi
if [ "${CHANGED_TENANT}" -ne 0 ]; then
echo "Attempting to sign you back into ${LOGGED_IN_TENANT_ID}."
# Log back into the tenant the user started on.
az login --tenant "${LOGGED_IN_TENANT_ID}" --allow-no-subscriptions
fi
|
AzureTRE/devops/scripts/create_aad_assets.sh/0
|
{
"file_path": "AzureTRE/devops/scripts/create_aad_assets.sh",
"repo_id": "AzureTRE",
"token_count": 1027
}
| 106 |
#!/bin/bash
set -e
# By default the docker.sock file is not associated with docker group on codespaces or macOS
# which causes a permission issue when docker is run without sudo.
if ! docker ps > /dev/null 2>&1; then
echo "docker ps failed, setting docker.sock permissions"
sudo chgrp docker /var/run/docker.sock
sudo chmod g+rw /var/run/docker.sock
fi
|
AzureTRE/devops/scripts/set_docker_sock_permission.sh/0
|
{
"file_path": "AzureTRE/devops/scripts/set_docker_sock_permission.sh",
"repo_id": "AzureTRE",
"token_count": 113
}
| 107 |
# Azure TRE Architecture
The Azure Trusted Research Environment (TRE) consists of multiple components, all encapsulated in networks with restricted ingress- & egress traffic.
There is one network for the core components and one network per Workspace.
All traffic has to be explicitly allowed by the Application Gateway or the Firewall.
[](../assets/archtecture-overview.png)
The Azure resources outside the network boundries of the Azure TRE are Microsoft Entra ID, Microsoft Graph and TRE Management. TRE Management are resources used during deployment.
The Azure TRE core plane consists of two groups of components:
- API & Composition Service
- Shared Services
The TRE API is a service that users can interact with to request changes to workspaces e.g., to create, update, delete workspaces and workspace services inside each workspace. The Composition Service is doing the actual work of mutating the state of each Workspace including the Workspace Services.
Ingress/egress components governs all inbound and outbound traffic from the public Internet to and from Azure TRE including the Workspaces. The Firewall Service is managing the egress rules of the Firewall.
Shared Services are services available to all Workspaces. **Source Mirror** can mirror source repositories such as GitHub, but only allowing read-access, hence data from a Workspace cannot be pushed to a source repository.
**Package Mirror** is also a read-only front for developer/researcher application package services like NPM, PyPI, and NuGet and operating system application package services like apt-get and Windows Package Manager (winget).
## Azure Resources
The following diagram shows the Azure components deployed as part of a typical TRE deployment. The exact configuration will vary depending on the specific deployment.
[](../assets/architecture-azure.png)
For a full breakdown of Azure Resources see [Azure TRE Resources Breakdown](tre-resources-breakdown.md)
## Composition Service
The Composition Service is responsible for managing and mutating Workspaces and Workspace Services. It consists of multiple components:
| Component Name | Responsibility / Description |
| --- | --- |
| [TRE API](../tre-developers/api.md) | An API responsible for performing all operations on Workspaces and managing Workspace Templates. |
| Configuration Store | Keeping the state of Workspaces and Workspace Templates. The store uses [Cosmos DB (SQL)](https://docs.microsoft.com/en-us/azure/cosmos-db/introduction). |
| Service Bus | [Azure Service Bus](https://docs.microsoft.com/en-us/azure/service-bus-messaging/service-bus-messaging-overview) responsible for reliable delivery of messages between components. |
| [Resource Processor](../tre-developers/resource-processor.md) | Responsible for starting the process of mutating a Workspace via a Workspace Template. |
A Workspace is an instance of a Workspace Template. A Workspace Template is implemented as a [Porter](https://porter.sh/) bundle - read more about [Authoring workspaces templates](../tre-workspace-authors/authoring-workspace-templates.md).
A Porter bundle is a fully encapsulated versioned bundle with everything needed (binaries, scripts, IaC templates etc.) to provision an instance of Workspace Template.
To automate Porter it needs a place to live in Azure TRE. The home chosen for Porter to run was a Linux virtual machine. This Azure TRE component encompassing Porter and its dependencies is called **Resource Processor**.
[](../assets/resource-processor-overview.jpg)
<!-- markdownlint-disable MD013 -->
During the deployment of Resource Processor itself it is given the credentials of a managed identity with the privileges to modify and deploy resources to the subscription associated with the Azure TRE instance. Resource Processor then uses these credentials to receive and send Service Bus messages, authorizes Porter to access its state (stored in Cosmos-MongoDB) and deploy bundles.
<!-- markdownlint-enable MD013 -->
The logic in Resource Processor is written in Python. The Resource Processor implementation is located in [`resource_processor` folder](https://github.com/microsoft/AzureTRE/blob/main/resource_processor/) of the repository.
The [TRE Administrator](user-roles.md#tre-administrator) can register a Porter bundle that will be used to provision instances of bundle (template).
This requires:
1. The Porter bundle to be pushed to the Azure Container Registry (ACR).
1. Registering the Template through the API.
Details on how to [register a Template](../tre-admins/registering-templates.md).
## Provisioning a Workspace
[](../assets/composition-service.png)
The flow to provision a Workspace is as follows (the flow is the same for all kinds of mutations to a Workspace):
1. TRE Admin sends an HTTP request to the TRE API to create a new Workspace. The request contains information like the name of the Workspace, the Workspace Template to use, and the parameters required for the Workspace Template (Workspace Templates can expose the parameters via a JSON Schema ).
1. The API saves the desired state of the Workspace in the Configuration Store.
1. The API sends a command message with the Workspace Template reference and parameters to the `workspacequeue`.
```JSON
{
"action": "install",
"id": "base",
"name": "BaseWorkspaceTemplate",
"version": "1.0",
"parameters": {
"param1": "value1"
}
}
```
1. The Resource Processor picks up the new message from the service bus queue.
1. The Resource Processor processes the command by executing the Porter bundle (the implementation of a Workspace Template).
```bash
# simplified for readability
porter <action> --reference <ACR name>.azurecr.io/bundles/<name>:<version> --params key=value --cred <credentials set name>
# Example
porter install --reference msfttreacr.azurecr.io/bundles/BaseWorkspaceTemplate:1.0 --params param1=value1 --cred arm_auth
```
Deployments are carried out against the Azure Subscription using a User Assigned Managed Identity. The `arm_auth_local_debugging.json` tells Porter where the credential information can be found and for the Resource Processor they are set as environment variables.
Porter bundle actions are required to be idempotent, so if a deployment fails, the Resource Processor can retry.
1. The Porter Docker bundle is pulled from the Azure Container Registry (ACR) and executed.
1. The Porter bundle executes against Azure Resource Manager to provision Azure resources. Any kind of infrastructure of code frameworks like ARM, Terraform, or Pulumi can be used or scripted via PowerShell or Azure CLI.
1. Porter stores state (like outputs) in Cosmos-MongoDB.
1. The Resource Processor sends events to the `deploymentstatus` queue on status changes and informs if the deployment succeeded or failed.
1. The API receives the status of the Porter bundle execution.
1. The API updates the status of the Porter bundle execution in the Configuration Store.
|
AzureTRE/docs/azure-tre-overview/architecture.md/0
|
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}
| 108 |
# The API Identity
## Name
The API Identity is typically called `<TRE_ID> API` within the Microsoft Entra ID Portal.
## Purpose
This identity's credentials are stored in the `core` Key Vault and mandatory for the running of the Trusted Research Environment (TRE). It is required for the API Application, hosted in Azure App Service, to authenticate to Microsoft Entra ID and authorize the various operations.
## Application Roles
| Display name | Description | Allowed member types | Value |
| ------------ | ----------- | -------------------- | ----- |
| TRE Administrators | Provides resource administrator access to the TRE. | Users/Groups,Applications | `TREAdmin` |
| TRE Users | Provides access to the TRE application. | Users/Groups,Applications | `TREUser` |
## Microsoft Graph Permissions
| Name | Type* | Admin consent required | TRE usage |
| --- | -- | -----| --------- |
| Directory.Read.All | Application | Yes | Allows the app to read directory objects (roles/permissions) in your organization's directory, such as roles and permissions, without a signed-in user. |
| User.Read.All | Application | Yes | Allows the app to read user profiles without a signed in user to check that the user has permissions to execute an action e.g., to view workspaces. See `/api_app/services/aad_authentication.py`. |
|email|Delegated|No|Used to read the user's email address when creating TRE resources|
|openid|Delegated|No|Allows users to sign in to the app with their work or school accounts and allows the app to see basic user profile information.|
|profile|Delegated|No|Used to read the user's profile when creating TRE resources|
'*' See the difference between [delegated and application permission](https://docs.microsoft.com/graph/auth/auth-concepts#delegated-and-application-permissions) types. See [Microsoft Graph permissions reference](https://docs.microsoft.com/graph/permissions-reference) for more details.
## Clients
This identity should only be used by the API Application.
## How to create
Example on how to run the script:
```bash
./devops/scripts/aad/create_api_application.sh \
--name <TRE_ID> \
--tre-url "https://<TRE_ID>.<LOCATION>.cloudapp.azure.com" \
--admin-consent \
--automation-clientid <TEST_ACCOUNT_CLIENT_ID>
```
Below is a sample where `TRE_ID` has value `mytre`:
```bash
./devops/scripts/aad/create_api_application.sh --name mytre --admin-consent \
--tre-url "https://mytre_6.westeurope.cloudapp.azure.com" --automation-clientid 176c2f5d-xxxx-xxxx-xxxx-68a5c30f354d
```
| Argument | Description |
| -------- | ----------- |
| `--name` | The prefix of the name of the app registrations. `TRE` will give you `TRE API`. |
| `--tre-url` | Used to construct auth redirection URLs for the UI and Swagger app. Use the values of the [environment variables](../environment-variables.md) `TRE_ID` and `LOCATION` in the URL. Reply URL for the localhost, `http://localhost:8000/api/docs/oauth2-redirect`, will be added by default. |
| `--admin-consent` | Grants admin consent for the app registrations. This is required for them to function properly, but requires Microsoft Entra ID admin privileges. |
| `--automation-clientid` | This is an optional parameter but will grant TREAdmin permission to the Service Principal of the Automation Admin.|
| `--reset-password` | Optional, default is 0. When run in a headless fashion, 1 is passed in to always reset the password. |
!!! caution
The script will create an app password (client secret) for the **TRE API** app and the **Automation App** and write them to `/config.yaml` file. These values are only shown once, if you lose them, the script will create new secrets if run again.
You can create an automation account which will aid your development flow, if you don't want to do this you can omit the `--automation-clientid` switch.
You can run the script without the `--admin-consent` and ask your admin to grant consent. If you don't have permissions and just want to create a development environment then skip this step and see the steps in the "Using a separate Microsoft Entra ID tenant) below.
## Environment Variables
| Variable | Description | Location |
| -------- | ----------- | -------- |
|API_CLIENT_ID|The Client Id|`./config.yaml`|
|API_CLIENT_SECRET|The client secret|`./config.yaml`|
## Comments
The **TRE API** app registration requires no redirect URLs defined. From a security standpoint, public client flows should not be allowed. As the identity of the client application cannot be verified (see the image below taken from app registration authentication blade in Azure Portal).
|
AzureTRE/docs/tre-admins/identities/api.md/0
|
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"repo_id": "AzureTRE",
"token_count": 1282
}
| 109 |
# Pre-deployment steps
!!! info
See [Environment variables](../environment-variables.md) for full details of the deployment related variables.
## Set environment configuration variables of shared management resources
1. In this part we will setup configuration variables in `config.yaml` file for the shared management infrastructure which is used to support the deployment of one or more Azure TRE instances.
2. Provide the values for the following variables:
| Variable | Description |
| -------- | ----------- |
| `location` | The [Azure location (region)](https://azure.microsoft.com/global-infrastructure/geographies/#geographies) for all resources. E.g., `westeurope` |
| `mgmt_resource_group_name` | The shared resource group for all management resources, including the storage account. |
| `mgmt_storage_account_name` | The name of the storage account to hold the Terraform state and other deployment artifacts. |
| `acr_name` | A globally unique name for the [Azure Container Registry (ACR)](https://docs.microsoft.com/azure/container-registry/) that will be created to store deployment images. |
| `arm_subscription_id` | The Azure subscription ID for all resources. |
!!! tip
To retrieve your Azure subscription ID, use the `az` command line interface available in the development container. In the terminal window in Visual Studio Code, type `az login` followed by `az account show` to see your default subscription. Please refer to `az account -help` for further details on how to change your active subscription.
The rest of the variables can have their default values. You should now have a management section in the `config.yaml` file that looks similar to the one below:
```plaintext
management:
location: westeurope
mgmt_resource_group_name: aztremgmt
mgmt_storage_account_name: aztremgmt
terraform_state_container_name: tfstate
acr_name: aztreacr
# Azure Resource Manager credentials used for CI/CD pipelines
arm_subscription_id: 12...54e
# If you want to override the currently signed in credentials
# You would do this if running commands like `make terraform-install DIR=./templates/workspaces/base`
# arm_tenant_id: __CHANGE_ME__
# arm_client_id: __CHANGE_ME__
# arm_client_secret: __CHANGE_ME__
```
3. If you want to disable the built-in web UI (`./ui`) ensure you set `deploy_ui=false` under tre defaults section in the `config.yaml` file.
## Next steps
* [Deploying Azure TRE](manual-deployment.md)
|
AzureTRE/docs/tre-admins/setup-instructions/manual-pre-deployment-steps.md/0
|
{
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}
| 110 |
# Letsencrypt
Certain components of the TRE require the aquisition of a certificate via Letsencrypt to ensure secure HTTPS connections.
In order to aquire these certificates, there must be a public facing endpoint which can be reached by Letsencrypt.
As TREs are secured environments with very few publicly facing points, additional resources are required to ensure the certificate can be provisioned for the correct domain.
The additional resources are as followed:
1. Public IP provisioned in the same location as the web app that the certificate is intended for; this will also have a domain label which matches the web app name.
1. Storage Account with a static web app.
1. Application gateway to route traffic from the Public IP to the static web app
The following diagram illustrated the flow of data between the resources:
```mermaid
flowchart RL
subgraph .dev Container
direction TB
A(letsencrypt process runs)
end
subgraph External
direction TB
B[letsencrypt authority]
end
subgraph TRE
subgraph Core VNet
C[Public IP <br/> Domain Label: < web-app-name > <br/> Endpoint: < web-app-name >.< location >.cloudapp.net]
subgraph Storage Account
D[SA Static Site]
end
end
subgraph VNet
E[Key Vault <br/> kv-< tre_id >]
subgraph VM
F[Web App]
end
G[Private DNS Zone < web-app-name >.< location >.cloudapp.net]
end
end
A --> |1. Request to | B
B --> |2. Attempts to hit | C
C --> |3. App Gateway routes | D
D --> |4. Responds | C
C --> |5. Responds | B
B --> |6. Acquires certificate | A
A --> |7. Stores Certificate | E
F --> |8. Pulls Certificate | E
```
|
AzureTRE/docs/tre-developers/letsencrypt.md/0
|
{
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"repo_id": "AzureTRE",
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}
| 111 |
# Azure Databricks workspace service bundle
See: [https://azure.microsoft.com/en-us/products/databricks/](https://azure.microsoft.com/en-us/products/databricks/)
This service along with Azure Databricks Private Authentication Shared Service installs the following resources into an existing virtual network within the workspace:
This service uses a JSON file to store the various network endpoints required by Databricks to function.
If you hit networking related issues when deploying or using Databricks, please ensure this file [https://github.com/microsoft/AzureTRE/blob/main/templates/workspace_services/databricks/terraform/databricks-udr.json](https://github.com/microsoft/AzureTRE/blob/main/templates/workspace_services/databricks/terraform/databricks-udr.json) contains the approprate settings for the region you are using.
The required settings for each region can be extracted from this document: [https://learn.microsoft.com/azure/databricks/resources/supported-regions](https://learn.microsoft.com/azure/databricks/resources/supported-regions).
## Properties
- `is_exposed_externally` - If `True`, the Azure Databricks workspace is accessible from outside of the workspace virtual network. If `False` use a Guacamole VM and copy the `connection_uri` to access Databricks workspace.
## Prerequisites
- [A base workspace bundle installed](../workspaces/base.md)
- An Azure Databricks Private Authentication Shared Service deployed - required for authenticating to an Azure Databricks workspace.
## References
- Databricks workspace service and authentication shared service deployed according to simplified deployment, for more information see: [Enable Azure Private Link as a simplified deployment](https://learn.microsoft.com/en-us/azure/databricks/administration-guide/cloud-configurations/azure/private-link-simplified)
|
AzureTRE/docs/tre-templates/workspace-services/databricks.md/0
|
{
"file_path": "AzureTRE/docs/tre-templates/workspace-services/databricks.md",
"repo_id": "AzureTRE",
"token_count": 498
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| 112 |
# Airlock Troubleshooting
## Users cannot create Review VMs
If a user sees an error when creating Review VMs, this most likely means that the configuration isn't correct.
Double-check that all GUIDs don't have any symbols missing, and the names of templates are correct.
[](../assets/using-review-vm-errors.png)
## Files do not appear in Review Data folder on the VM
If the Review Data folder is empty, it's likely because the review VM can't connect to the storage account. Import requests must be reviewed using a VM inside the workspace, and export requests must be reviewed using a VM outside the workspace.
For imports ensure that the `airlock-import-review` workspace template is being used and configured in the airlock configuration for the workspace.
## Airlock request does not move through the workflow as expected
If the Airlock request does not move through the workflow as expected, it's likely an issue with the Azure Function that processes airlock requests. This function is deployed as part of the TRE, and can be found in the Azure Portal under the name `func-airlock-processor-<tre_id>`.
To troubleshoot, view the function invocations starting with the StatusChangedQueue Trigger, then the other functions as shown in the image below:
[](../assets/airlock_functions.png)
Look for errors in the function invocations in the same time frame that the airlock request was created. Even if the function executed successfully, there may still be errors within the function invocation details. Invocations that take longer can also be a sign of an issue. For example:
[](../assets/airlock_functions_error.png)
If this error should have been handled please create an issue on the GitHub repository for the Azure TRE.
|
AzureTRE/docs/troubleshooting-faq/airlock-troubleshooting.md/0
|
{
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"repo_id": "AzureTRE",
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| 113 |
# Reviewing Airlock Requests
This document is intended for a user assuming the Airlock Manager role in the workspace.
It explains how to review Airlock Requests, both for data import and data export.
## Accessing Airlock Requests
Airlock Requests page can be found under Airlock menu for the relevant workspace.
The view allows to select requests "Awaiting my review" to quickly get to requests that need to be reviewed for this workspace. Other filters are also available by clicking on the column name.
Only requests that are in `in_review` state can be reviewed.
[](../../assets/using-tre/airlock-requests.png)
## Reviewing a request
> The following steps are the same for import and export request types.
Request page is available by double-clicking on one of the requests.
This gives an overview of the request, including information on who created the request, the title and business justification of the request, when it was created, in what workspace, and type of the request (import or export).
[](../../assets/using-tre/view-airlock-request.png)
Reviewing menu is available by clicking on Review button at the bottom.
[](../../assets/using-tre/review-request-create-vm.png)
This screen offers an option to create a VM from which the imported or exported data can be reviewed. To create a VM, click on "Create" button. The display will show that the VM is "Awaiting Deployment", and then "Deploying".
[](../../assets/using-tre/review-request-vm-being-deployed.png)
After several minutes, the display will show that the VM is "Running", and a "View Data" button will become available. When it's clicked, a Guacamole session is opened in a new tab.
[](../../assets/using-tre/review-request-vm-deployed.png)
On the VM, the data to review will be readily available on Desktop, in ReviewData folder.
[](../../assets/using-tre/review-request-inside-vm.png)
[](../../assets/using-tre/review-request-view-data.png)
Back on the request display on the UI, there is a button "Proceed to review" which will show a dialogue for providing an explanation for the request decision. It must be filled in before the request can be either approved or rejected.
[](../../assets/using-tre/review-request-decision.png)
After the decision is submitted, TRE will automatically start deletion of the VM that was created to review this request.
|
AzureTRE/docs/using-tre/tre-for-research/review-airlock-request.md/0
|
{
"file_path": "AzureTRE/docs/using-tre/tre-for-research/review-airlock-request.md",
"repo_id": "AzureTRE",
"token_count": 804
}
| 114 |
PONG = "pong"
API_HEALTH = "/api/health"
API_WORKSPACE_TEMPLATES = "/api/workspace-templates"
API_WORKSPACES = "/api/workspaces"
API_WORKSPACE_SERVICE_TEMPLATES = "/api/workspace-service-templates"
API_SHARED_SERVICE_TEMPLATES = "/api/shared-service-templates"
API_SHARED_SERVICES = "/api/shared-services"
API_WORKSPACE_SERVICES = "workspace-services"
API_USER_RESOURCES = "user-resources"
BASE_WORKSPACE = "tre-workspace-base"
UNRESTRICTED_WORKSPACE = "tre-workspace-unrestricted"
AIRLOCK_IMPORT_REVIEW_WORKSPACE = "tre-workspace-airlock-import-review"
AZUREML_SERVICE = "tre-service-azureml"
GUACAMOLE_SERVICE = "tre-service-guacamole"
GITEA_SERVICE = "tre-workspace-service-gitea"
MLFLOW_SERVICE = "tre-service-mlflow"
MYSQL_SERVICE = "tre-workspace-service-mysql"
HEALTH_SERVICE = "tre-workspace-service-health"
FIREWALL_SHARED_SERVICE = "tre-shared-service-firewall"
GITEA_SHARED_SERVICE = "tre-shared-service-gitea"
NEXUS_SHARED_SERVICE = "tre-shared-service-sonatype-nexus"
AIRLOCK_NOTIFIER_SHARED_SERVICE = "tre-shared-service-airlock-notifier"
CERTS_SHARED_SERVICE = "tre-shared-service-certs"
ADMIN_VM_SHARED_SERVICE = "tre-shared-service-admin-vm"
CYCLECLOUD_SHARED_SERVICE = "tre-shared-service-cyclecloud"
GUACAMOLE_WINDOWS_USER_RESOURCE = "tre-service-guacamole-windowsvm"
GUACAMOLE_LINUX_USER_RESOURCE = "tre-service-guacamole-linuxvm"
TEST_WORKSPACE_SERVICE_TEMPLATE = "e2e-test-workspace-service"
# Resource Status
RESOURCE_STATUS_AWAITING_DEPLOYMENT = "awaiting_deployment"
RESOURCE_STATUS_DEPLOYING = "deploying"
RESOURCE_STATUS_DEPLOYED = "deployed"
RESOURCE_STATUS_DEPLOYMENT_FAILED = "deployment_failed"
RESOURCE_STATUS_AWAITING_DELETION = "awaiting_deletion"
RESOURCE_STATUS_DELETING = "deleting"
RESOURCE_STATUS_DELETED = "deleted"
RESOURCE_STATUS_DELETING_FAILED = "deleting_failed"
RESOURCE_STATUS_AWAITING_UPDATE = "awaiting_update"
RESOURCE_STATUS_UPDATING = "updating"
RESOURCE_STATUS_UPDATED = "updated"
RESOURCE_STATUS_UPDATING_FAILED = "updating_failed"
# Resource Action Status
RESOURCE_STATUS_AWAITING_ACTION = "awaiting_action"
RESOURCE_ACTION_STATUS_INVOKING = "invoking_action"
RESOURCE_ACTION_STATUS_SUCCEEDED = "action_succeeded"
RESOURCE_ACTION_STATUS_FAILED = "action_failed"
# Pipeline (multi-step) deployments
RESOURCE_ACTION_STATUS_PIPELINE_RUNNING = "pipeline_running"
RESOURCE_ACTION_STATUS_PIPELINE_FAILED = "pipeline_failed"
RESOURCE_ACTION_STATUS_PIPELINE_SUCCEEDED = "pipeline_succeeded"
|
AzureTRE/e2e_tests/resources/strings.py/0
|
{
"file_path": "AzureTRE/e2e_tests/resources/strings.py",
"repo_id": "AzureTRE",
"token_count": 997
}
| 115 |
import logging
import os
import re
from opentelemetry.instrumentation.logging import LoggingInstrumentor
from opentelemetry import trace
from azure.monitor.opentelemetry import configure_azure_monitor
UNWANTED_LOGGERS = [
"azure.core.pipeline.policies.http_logging_policy",
"azure.eventhub._eventprocessor.event_processor",
# suppressing, have support case open
"azure.servicebus._pyamqp.aio._session_async",
"azure.identity.aio._credentials.managed_identity",
"azure.identity.aio._credentials.environment",
"azure.identity.aio._internal.get_token_mixin",
"azure.identity.aio._internal.decorators",
"azure.identity.aio._credentials.chained",
"azure.identity",
"msal.token_cache",
# Remove these once the following PR is merged:
# https://github.com/Azure/azure-sdk-for-python/pull/30832
# Issue: https://github.com/microsoft/AzureTRE/issues/3766
"azure.servicebus._pyamqp.aio._session_async"
]
LOGGERS_FOR_ERRORS_ONLY = [
"uamqp",
"uamqp.authentication.cbs_auth_async",
"uamqp.async_ops.client_async",
"uamqp.async_ops.connection_async",
"uamqp.async_ops",
"uamqp.authentication",
"uamqp.c_uamqp",
"uamqp.connection",
"uamqp.receiver",
"uamqp.async_ops.session_async",
"uamqp.sender",
"uamqp.client",
"azure.servicebus.aio._base_handler_async",
"azure.monitor.opentelemetry.exporter.export._base",
"azure.servicebus.aio._base_handler_async",
"azure.servicebus._pyamqp.aio._connection_async",
"azure.servicebus._pyamqp.aio._link_async",
"opentelemetry.attributes",
"azure.servicebus._pyamqp.aio._management_link_async",
"azure.servicebus._pyamqp.aio._cbs_async",
"azure.servicebus._pyamqp.aio._client_async"
]
logger = logging.getLogger("azuretre_resource_processor")
tracer = trace.get_tracer("azuretre_resource_processor")
def configure_loggers():
for logger_name in LOGGERS_FOR_ERRORS_ONLY:
logging.getLogger(logger_name).setLevel(logging.ERROR)
for logger_name in UNWANTED_LOGGERS:
logging.getLogger(logger_name).setLevel(logging.CRITICAL)
def initialize_logging() -> logging.Logger:
configure_loggers()
logging_level = os.environ.get("LOGGING_LEVEL", "INFO")
if logging_level == "INFO":
logging_level = logging.INFO
elif logging_level == "DEBUG":
logging_level = logging.DEBUG
elif logging_level == "WARNING":
logging_level = logging.WARNING
elif logging_level == "ERROR":
logging_level = logging.ERROR
if os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING"):
configure_azure_monitor(
logger_name="azuretre_resource_processor",
instrumentation_options={
"azure_sdk": {"enabled": False},
"flask": {"enabled": False},
"django": {"enabled": False},
"fastapi": {"enabled": True},
"psycopg2": {"enabled": False},
}
)
LoggingInstrumentor().instrument(
set_logging_format=True,
log_level=logging_level,
tracer_provider=tracer._real_tracer
)
return logger
def shell_output_logger(console_output: str, prefix_item: str, logging_level: int):
if not console_output:
logger.debug("shell console output is empty.")
return
if (logging_level != logging.INFO
and console_output.startswith("Unable to find image '")
and "' locally" in console_output):
console_output = console_output.strip()
console_output = re.sub(r"Unable to find image '.*' locally", '', console_output)
if console_output.startswith("\n"):
console_output = console_output[1:]
logger.debug("Image not present locally, removing text from console output.")
logging_level = logging.INFO
if (logging_level != logging.INFO
and len(console_output) < 34
and "execution completed successfully!" in console_output):
logging_level = logging.INFO
console_output = console_output.strip()
if console_output:
logger.log(logging_level, f"{prefix_item} {console_output}")
|
AzureTRE/resource_processor/shared/logging.py/0
|
{
"file_path": "AzureTRE/resource_processor/shared/logging.py",
"repo_id": "AzureTRE",
"token_count": 1768
}
| 116 |
---
schemaVersion: 1.0.0
name: tre-shared-service-admin-vm
version: 0.4.3
description: "An admin vm shared service"
dockerfile: Dockerfile.tmpl
registry: azuretre
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:
- name: tre_id
type: string
description: "The ID of the parent TRE instance e.g., mytre-dev-3142"
- name: id
type: string
description: "Resource ID"
- name: tfstate_resource_group_name
type: string
description: "Resource group containing the Terraform state storage account"
- name: tfstate_storage_account_name
type: string
description: "The name of the Terraform state storage account"
- name: tfstate_container_name
type: string
default: "tfstate"
description: "The name of the Terraform state storage container"
- name: arm_use_msi
env: ARM_USE_MSI
type: boolean
default: false
- name: arm_environment
env: ARM_ENVIRONMENT
type: string
default: "public"
- name: admin_jumpbox_vm_sku
env: ADMIN_JUMPBOX_VM_SKU
type: string
default: Standard_B2s
mixins:
- terraform:
clientVersion: 1.3.6
install:
- terraform:
description: "Deploy shared service"
vars:
tre_id: ${ bundle.parameters.tre_id }
tre_resource_id: ${ bundle.parameters.id }
admin_jumpbox_vm_sku: ${ bundle.parameters.admin_jumpbox_vm_sku }
backendConfig:
resource_group_name: ${ bundle.parameters.tfstate_resource_group_name }
storage_account_name: ${ bundle.parameters.tfstate_storage_account_name }
container_name: ${ bundle.parameters.tfstate_container_name }
key: ${ bundle.parameters.tre_id }-shared-service-admin-vm
upgrade:
- terraform:
description: "Upgrade shared service"
vars:
tre_id: ${ bundle.parameters.tre_id }
tre_resource_id: ${ bundle.parameters.id }
admin_jumpbox_vm_sku: ${ bundle.parameters.admin_jumpbox_vm_sku }
backendConfig:
resource_group_name: ${ bundle.parameters.tfstate_resource_group_name }
storage_account_name: ${ bundle.parameters.tfstate_storage_account_name }
container_name: ${ bundle.parameters.tfstate_container_name }
key: ${ bundle.parameters.tre_id }-shared-service-admin-vm
uninstall:
- terraform:
description: "Tear down shared service"
vars:
tre_id: ${ bundle.parameters.tre_id }
tre_resource_id: ${ bundle.parameters.id }
admin_jumpbox_vm_sku: ${ bundle.parameters.admin_jumpbox_vm_sku }
backendConfig:
resource_group_name: ${ bundle.parameters.tfstate_resource_group_name }
storage_account_name: ${ bundle.parameters.tfstate_storage_account_name }
container_name: ${ bundle.parameters.tfstate_container_name }
key: ${ bundle.parameters.tre_id }-shared-service-admin-vm
|
AzureTRE/templates/shared_services/admin-vm/porter.yaml/0
|
{
"file_path": "AzureTRE/templates/shared_services/admin-vm/porter.yaml",
"repo_id": "AzureTRE",
"token_count": 1194
}
| 117 |
{
"smtp_from_email": {
"type": "String",
"value": "@appsetting('smtp_from_email')"
},
"tre_url": {
"type": "String",
"value": "@appsetting('tre_url')"
}
}
|
AzureTRE/templates/shared_services/airlock_notifier/app/parameters.json/0
|
{
"file_path": "AzureTRE/templates/shared_services/airlock_notifier/app/parameters.json",
"repo_id": "AzureTRE",
"token_count": 84
}
| 118 |
# syntax=docker/dockerfile-upstream:1.4.0
FROM --platform=linux/amd64 python:3.8-slim-bullseye
# PORTER_INIT
SHELL ["/bin/bash", "-o", "pipefail", "-c"]
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
# Install jq
RUN --mount=type=cache,target=/var/cache/apt --mount=type=cache,target=/var/lib/apt \
apt-get update \
&& apt-get install -y --no-install-recommends jq
# Install Certbot
RUN --mount=type=cache,target=/var/cache/apt --mount=type=cache,target=/var/lib/apt \
apt-get update \
&& apt-get install -y --no-install-recommends libaugeas0 \
&& python3 -m venv /opt/certbot/ \
&& /opt/certbot/bin/pip install --no-cache-dir --upgrade pip \
&& /opt/certbot/bin/pip install --no-cache-dir certbot
# PORTER_MIXINS
# Use the BUNDLE_DIR build argument to copy files into the bundle
COPY --link . ${BUNDLE_DIR}/
|
AzureTRE/templates/shared_services/certs/Dockerfile.tmpl/0
|
{
"file_path": "AzureTRE/templates/shared_services/certs/Dockerfile.tmpl",
"repo_id": "AzureTRE",
"token_count": 378
}
| 119 |
# GUID to identify the shared service
ID=
|
AzureTRE/templates/shared_services/cyclecloud/.env.sample/0
|
{
"file_path": "AzureTRE/templates/shared_services/cyclecloud/.env.sample",
"repo_id": "AzureTRE",
"token_count": 12
}
| 120 |
{
"schemaType": "ParameterSet",
"schemaVersion": "1.0.1",
"namespace": "",
"name": "tre-shared-service-databricks-private-auth",
"parameters": [
{
"name": "id",
"source": {
"env": "ID"
}
},
{
"name": "tre_id",
"source": {
"env": "TRE_ID"
}
},
{
"name": "tfstate_container_name",
"source": {
"env": "TERRAFORM_STATE_CONTAINER_NAME"
}
},
{
"name": "tfstate_resource_group_name",
"source": {
"env": "MGMT_RESOURCE_GROUP_NAME"
}
},
{
"name": "tfstate_storage_account_name",
"source": {
"env": "MGMT_STORAGE_ACCOUNT_NAME"
}
},
{
"name": "arm_environment",
"source": {
"env": "ARM_ENVIRONMENT"
}
}
]
}
|
AzureTRE/templates/shared_services/databricks-auth/parameters.json/0
|
{
"file_path": "AzureTRE/templates/shared_services/databricks-auth/parameters.json",
"repo_id": "AzureTRE",
"token_count": 445
}
| 121 |
---
schemaVersion: 1.0.0
name: tre-shared-service-firewall
version: 1.1.7
description: "An Azure TRE Firewall shared service"
dockerfile: Dockerfile.tmpl
registry: azuretre
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:
- name: tre_id
type: string
description: "The ID of the parent TRE instance e.g., mytre-dev-3142"
- name: id
type: string
description: "Resource ID"
- name: tfstate_resource_group_name
type: string
description: "Resource group containing the Terraform state storage account"
- name: tfstate_storage_account_name
type: string
description: "The name of the Terraform state storage account"
- name: tfstate_container_name
type: string
default: "tfstate"
description: "The name of the Terraform state storage container"
- name: arm_use_msi
env: ARM_USE_MSI
type: boolean
default: false
- name: rule_collections
type: string
default: "W10=" # b64 for []
description: "Application rule collection array"
- name: network_rule_collections
type: string
default: "W10=" # b64 for []
description: "Network rule collection array"
- name: sku_tier
type: string
default: Standard
description: The firewall and its policy SKU tier
- name: microsoft_graph_fqdn
type: string
default: "graph.microsoft.com"
- name: arm_environment
type: string
mixins:
- terraform:
clientVersion: 1.4.5
install:
- terraform:
description: "Deploy shared service"
vars:
tre_id: ${ bundle.parameters.tre_id }
tre_resource_id: ${ bundle.parameters.id }
api_driven_rule_collections_b64: ${ bundle.parameters.rule_collections }
api_driven_network_rule_collections_b64: ${ bundle.parameters.network_rule_collections }
sku_tier: ${ bundle.parameters.sku_tier }
microsoft_graph_fqdn: ${ bundle.parameters.microsoft_graph_fqdn }
backendConfig:
resource_group_name: ${ bundle.parameters.tfstate_resource_group_name }
storage_account_name: ${ bundle.parameters.tfstate_storage_account_name }
container_name: ${ bundle.parameters.tfstate_container_name }
key: ${ bundle.parameters.tre_id }-shared-service-firewall
upgrade:
- terraform:
description: "Upgrade shared service"
vars:
tre_id: ${ bundle.parameters.tre_id }
tre_resource_id: ${ bundle.parameters.id }
api_driven_rule_collections_b64: ${ bundle.parameters.rule_collections }
api_driven_network_rule_collections_b64: ${ bundle.parameters.network_rule_collections }
sku_tier: ${ bundle.parameters.sku_tier }
microsoft_graph_fqdn: ${ bundle.parameters.microsoft_graph_fqdn }
backendConfig:
resource_group_name: ${ bundle.parameters.tfstate_resource_group_name }
storage_account_name: ${ bundle.parameters.tfstate_storage_account_name }
container_name: ${ bundle.parameters.tfstate_container_name }
key: ${ bundle.parameters.tre_id }-shared-service-firewall
uninstall:
- terraform:
description: "Tear down shared service"
vars:
tre_id: ${ bundle.parameters.tre_id }
tre_resource_id: ${ bundle.parameters.id }
api_driven_rule_collections_b64: ${ bundle.parameters.rule_collections }
api_driven_network_rule_collections_b64: ${ bundle.parameters.network_rule_collections }
sku_tier: ${ bundle.parameters.sku_tier }
microsoft_graph_fqdn: ${ bundle.parameters.microsoft_graph_fqdn }
backendConfig:
resource_group_name: ${ bundle.parameters.tfstate_resource_group_name }
storage_account_name: ${ bundle.parameters.tfstate_storage_account_name }
container_name: ${ bundle.parameters.tfstate_container_name }
key: ${ bundle.parameters.tre_id }-shared-service-firewall
|
AzureTRE/templates/shared_services/firewall/porter.yaml/0
|
{
"file_path": "AzureTRE/templates/shared_services/firewall/porter.yaml",
"repo_id": "AzureTRE",
"token_count": 1541
}
| 122 |
{
"name": "microsoft-keys",
"online": true,
"storage": {
"blobStoreName": "default",
"strictContentTypeValidation": false,
"write_policy": "ALLOW"
},
"proxy": {
"remoteUrl": "https://packages.microsoft.com/keys/",
"contentMaxAge": 1440,
"metadataMaxAge": 1440
},
"negativeCache": {
"enabled": true,
"timeToLive": 1440
},
"httpClient": {
"blocked": false,
"autoBlock": false,
"connection": {
"retries": 0,
"userAgentSuffix": "string",
"timeout": 60,
"enableCircularRedirects": false,
"enableCookies": false,
"useTrustStore": false
}
},
"baseType": "raw",
"repoType": "proxy"
}
|
AzureTRE/templates/shared_services/sonatype-nexus-vm/scripts/nexus_repos_config/microsoft_keys_proxy_conf.json/0
|
{
"file_path": "AzureTRE/templates/shared_services/sonatype-nexus-vm/scripts/nexus_repos_config/microsoft_keys_proxy_conf.json",
"repo_id": "AzureTRE",
"token_count": 339
}
| 123 |
resource "azurerm_network_interface" "nexus" {
name = "nic-nexus-${var.tre_id}"
location = data.azurerm_resource_group.rg.location
resource_group_name = local.core_resource_group_name
tags = local.tre_shared_service_tags
ip_configuration {
name = "primary"
subnet_id = data.azurerm_subnet.shared.id
private_ip_address_allocation = "Dynamic"
}
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_private_dns_zone_virtual_network_link" "nexus_core_vnet" {
name = "nexuslink-core"
resource_group_name = local.core_resource_group_name
private_dns_zone_name = data.azurerm_private_dns_zone.nexus.name
virtual_network_id = data.azurerm_virtual_network.core.id
tags = local.tre_shared_service_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_private_dns_a_record" "nexus_vm" {
name = "@"
zone_name = data.azurerm_private_dns_zone.nexus.name
resource_group_name = local.core_resource_group_name
ttl = 300
records = [azurerm_linux_virtual_machine.nexus.private_ip_address]
tags = local.tre_shared_service_tags
lifecycle { ignore_changes = [tags] }
}
resource "random_password" "nexus_vm_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 "random_password" "nexus_admin_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_key_vault_secret" "nexus_vm_password" {
name = "nexus-vm-password"
value = random_password.nexus_vm_password.result
key_vault_id = data.azurerm_key_vault.kv.id
tags = local.tre_shared_service_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_key_vault_secret" "nexus_admin_password" {
name = "nexus-admin-password"
value = random_password.nexus_admin_password.result
key_vault_id = data.azurerm_key_vault.kv.id
tags = local.tre_shared_service_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_user_assigned_identity" "nexus_msi" {
name = "id-nexus-${var.tre_id}"
location = data.azurerm_resource_group.rg.location
resource_group_name = local.core_resource_group_name
tags = local.tre_shared_service_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_key_vault_access_policy" "nexus_msi" {
key_vault_id = data.azurerm_key_vault.kv.id
tenant_id = azurerm_user_assigned_identity.nexus_msi.tenant_id
object_id = azurerm_user_assigned_identity.nexus_msi.principal_id
secret_permissions = ["Get", "List"]
}
resource "azurerm_linux_virtual_machine" "nexus" {
name = "nexus-${var.tre_id}"
resource_group_name = local.core_resource_group_name
location = data.azurerm_resource_group.rg.location
network_interface_ids = [azurerm_network_interface.nexus.id]
size = "Standard_B2s"
disable_password_authentication = false
admin_username = "adminuser"
admin_password = random_password.nexus_vm_password.result
tags = local.tre_shared_service_tags
custom_data = data.template_cloudinit_config.nexus_config.rendered
lifecycle { ignore_changes = [tags] }
source_image_reference {
publisher = "Canonical"
offer = "0001-com-ubuntu-server-jammy"
sku = "22_04-lts-gen2"
version = "latest"
}
os_disk {
name = "osdisk-nexus-${var.tre_id}"
caching = "ReadWrite"
storage_account_type = "Standard_LRS"
disk_size_gb = 64
}
identity {
type = "UserAssigned"
identity_ids = [azurerm_user_assigned_identity.nexus_msi.id]
}
boot_diagnostics {
storage_account_uri = data.azurerm_storage_account.nexus.primary_blob_endpoint
}
depends_on = [
azurerm_key_vault_access_policy.nexus_msi
]
connection {
type = "ssh"
host = azurerm_network_interface.nexus.private_ip_address
user = "adminuser"
password = random_password.nexus_vm_password.result
agent = false
timeout = "10m"
}
}
data "template_cloudinit_config" "nexus_config" {
gzip = true
base64_encode = true
part {
# Ref: https://cloudinit.readthedocs.io/en/latest/reference/merging.html
# Important: merge_type must be defined on each part, contrary to what cloud-init docs say about a "stack" aproach
merge_type = "list(append)+dict(no_replace,recurse_list)+str()"
content_type = "text/cloud-config"
content = data.template_file.nexus_bootstrapping.rendered
}
part {
content_type = "text/cloud-config"
merge_type = "list(append)+dict(no_replace,recurse_list)+str()"
content = jsonencode({
write_files = [
for file in fileset("${path.module}/../scripts/nexus_repos_config", "*") : {
content = file("${path.module}/../scripts/nexus_repos_config/${file}")
path = "/etc/nexus-data/scripts/nexus_repos_config/${file}"
permissions = "0744"
}
]
})
}
part {
content_type = "text/cloud-config"
merge_type = "list(append)+dict(no_replace,recurse_list)+str()"
content = jsonencode({
write_files = [
{
content = file("${path.module}/../scripts/configure_nexus_repos.sh")
path = "/etc/nexus-data/scripts/configure_nexus_repos.sh"
permissions = "0744"
},
{
content = file("${path.module}/../scripts/nexus_realms_config.json")
path = "/etc/nexus-data/scripts/nexus_realms_config.json"
permissions = "0744"
},
{
content = data.template_file.configure_nexus_ssl.rendered
path = "/etc/cron.daily/configure_nexus_ssl"
permissions = "0755"
},
{
content = "nexus.skipDefaultRepositories=true\n"
path = "/etc/nexus-data/etc/nexus.properties"
permissions = "0755"
},
{
content = file("${path.module}/../scripts/reset_nexus_password.sh")
path = "/etc/nexus-data/scripts/reset_nexus_password.sh"
permissions = "0744"
},
{
content = file("${path.module}/../scripts/deploy_nexus_container.sh")
path = "/etc/nexus-data/scripts/deploy_nexus_container.sh"
permissions = "0744"
}
]
})
}
}
data "template_file" "nexus_bootstrapping" {
template = file("${path.module}/cloud-config.yaml")
vars = {
NEXUS_ADMIN_PASSWORD = random_password.nexus_admin_password.result
}
}
data "template_file" "configure_nexus_ssl" {
template = file("${path.module}/../scripts/configure_nexus_ssl.sh")
vars = {
MSI_ID = azurerm_user_assigned_identity.nexus_msi.id
VAULT_NAME = data.azurerm_key_vault.kv.name
SSL_CERT_NAME = data.azurerm_key_vault_certificate.nexus_cert.name
}
}
resource "azurerm_virtual_machine_extension" "keyvault" {
virtual_machine_id = azurerm_linux_virtual_machine.nexus.id
name = "${azurerm_linux_virtual_machine.nexus.name}-KeyVault"
publisher = "Microsoft.Azure.KeyVault"
type = "KeyVaultForLinux"
type_handler_version = "2.0"
auto_upgrade_minor_version = true
tags = local.tre_shared_service_tags
settings = jsonencode({
"secretsManagementSettings" : {
"pollingIntervalInS" : "3600",
"requireInitialSync" : true,
"observedCertificates" : [
data.azurerm_key_vault_certificate.nexus_cert.versionless_secret_id
]
}
"authenticationSettings" : {
"msiEndpoint" : "http://169.254.169.254/metadata/identity",
"msiClientId" : azurerm_user_assigned_identity.nexus_msi.client_id
}
})
lifecycle { ignore_changes = [tags] }
}
|
AzureTRE/templates/shared_services/sonatype-nexus-vm/terraform/vm.tf/0
|
{
"file_path": "AzureTRE/templates/shared_services/sonatype-nexus-vm/terraform/vm.tf",
"repo_id": "AzureTRE",
"token_count": 3924
}
| 124 |
output "azureml_workspace_name" {
value = azurerm_machine_learning_workspace.aml_workspace.name
}
output "azureml_acr_id" {
value = azurerm_container_registry.acr.id
}
output "azureml_storage_account_id" {
value = azurerm_storage_account.aml.id
}
output "aml_fqdn" {
value = regex("(?:(?P<scheme>[^:/?#]+):)?(?://(?P<fqdn>[^/?#:]*))?", module.terraform_azurerm_environment_configuration.aml_studio_endpoint).fqdn
}
output "connection_uri" {
value = format("%s/?wsid=%s&tid=%s", module.terraform_azurerm_environment_configuration.aml_studio_endpoint, azurerm_machine_learning_workspace.aml_workspace.id, var.arm_tenant_id)
}
output "workspace_address_spaces" {
value = data.azurerm_virtual_network.ws.address_space
}
output "aml_subnet_address_prefixes" {
value = azurerm_subnet.aml.address_prefixes
}
data "azurerm_network_service_tags" "storage_tag" {
location = azurerm_storage_account.aml.location
service = "Storage"
location_filter = azurerm_storage_account.aml.location
}
data "azurerm_network_service_tags" "mcr_tag" {
location = azurerm_storage_account.aml.location
service = "MicrosoftContainerRegistry"
location_filter = azurerm_storage_account.aml.location
}
data "azurerm_network_service_tags" "batch_tag" {
location = azurerm_storage_account.aml.location
service = "BatchNodeManagement"
location_filter = azurerm_storage_account.aml.location
}
output "storage_tag" {
value = data.azurerm_network_service_tags.storage_tag.id
}
output "mcr_tag" {
value = data.azurerm_network_service_tags.mcr_tag.id
}
output "batch_tag" {
value = data.azurerm_network_service_tags.batch_tag.id
}
|
AzureTRE/templates/workspace_services/azureml/terraform/outputs.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/azureml/terraform/outputs.tf",
"repo_id": "AzureTRE",
"token_count": 663
}
| 125 |
terraform {
required_providers {
azurerm = {
source = "hashicorp/azurerm"
version = "=3.37.0"
}
azapi = {
source = "Azure/azapi"
version = "=1.1.0"
}
}
backend "azurerm" {}
}
provider "azurerm" {
features {
}
}
provider "azapi" {}
|
AzureTRE/templates/workspace_services/azureml/user_resources/aml_compute/terraform/providers.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/azureml/user_resources/aml_compute/terraform/providers.tf",
"repo_id": "AzureTRE",
"token_count": 147
}
| 126 |
output "databricks_workspace_name" {
value = azurerm_databricks_workspace.databricks.name
}
output "connection_uri" {
value = "https://${azurerm_databricks_workspace.databricks.workspace_url}/aad/auth?has=&Workspace=${data.azurerm_subscription.current.id}/resourceGroups/${local.resource_group_name}/providers/Microsoft.Databricks/workspaces/${local.databricks_workspace_name}&WorkspaceResourceGroupUri=${data.azurerm_subscription.current.id}/resourceGroups/${local.managed_resource_group_name}&l=en-us"
}
output "databricks_storage_account_name" {
value = azurerm_databricks_workspace.databricks.custom_parameters[0].storage_account_name
}
output "dbfs_blob_storage_domain" {
value = replace("<stgacc>.blob.core.windows.net", "<stgacc>", azurerm_databricks_workspace.databricks.custom_parameters[0].storage_account_name)
}
output "log_blob_storage_domains" {
value = local.map_location_url_config[module.azure_region.location_cli].logBlobStorageDomains
}
output "artifact_blob_storage_domains" {
value = setunion(local.map_location_url_config[module.azure_region.location_cli].artifactBlobStoragePrimaryDomains, local.map_location_url_config[module.azure_region.location_cli].artifactBlobStorageSecondaryDomains)
}
output "workspace_address_spaces" {
value = data.azurerm_virtual_network.ws.address_space
}
output "databricks_address_prefixes" {
value = setunion(azurerm_subnet.container.address_prefixes, azurerm_subnet.host.address_prefixes)
}
# convert list of metastore domains to ip addresses
data "dns_a_record_set" "metastore_addresses" {
for_each = toset(local.map_location_url_config[module.azure_region.location_cli].metastoreDomains)
host = each.key
}
output "metastore_addresses" {
value = setunion(flatten([for addr in data.dns_a_record_set.metastore_addresses : addr.addrs]))
}
# convert list of event hub endpoint domains to ip addresses
data "dns_a_record_set" "event_hub_endpoint_addresses" {
for_each = toset(local.map_location_url_config[module.azure_region.location_cli].eventHubEndpointDomains)
host = each.key
}
output "event_hub_endpoint_addresses" {
value = setunion(flatten([for addr in data.dns_a_record_set.event_hub_endpoint_addresses : addr.addrs]))
}
|
AzureTRE/templates/workspace_services/databricks/terraform/outputs.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/databricks/terraform/outputs.tf",
"repo_id": "AzureTRE",
"token_count": 799
}
| 127 |
#!/bin/bash
# 1. Remove any previously run failed flag
# 2. Run maven, but capture the exit code so we always succeed
# 3. Output a file if the tests are not successful.
rm -f /target/surefire-reports/guacamole_package_failed
pytest_result=$(mvn package)
if [ $? != 0 ]; then
touch /target/surefire-reports/guacamole_package_failed
fi
|
AzureTRE/templates/workspace_services/guacamole/guacamole-server/docker/maven_package_and_exit_succesfully.sh/0
|
{
"file_path": "AzureTRE/templates/workspace_services/guacamole/guacamole-server/docker/maven_package_and_exit_succesfully.sh",
"repo_id": "AzureTRE",
"token_count": 111
}
| 128 |
/*
* 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.connection;
import org.apache.guacamole.GuacamoleException;
import org.apache.guacamole.auth.azuretre.AzureTREAuthenticationProvider;
import org.apache.guacamole.auth.azuretre.user.AzureTREAuthenticatedUser;
import org.apache.guacamole.net.auth.Connection;
import org.apache.guacamole.protocol.GuacamoleConfiguration;
import org.json.JSONArray;
import org.json.JSONObject;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.time.Duration;
import java.util.Map;
import java.util.TreeMap;
public class ConnectionService {
/**
* Logger for this class.
*/
private static final Logger LOGGER = LoggerFactory.getLogger(ConnectionService.class);
public static Map<String, Connection> getConnections(final AzureTREAuthenticatedUser user)
throws GuacamoleException {
final Map<String, Connection> connections = new TreeMap<>();
final Map<String, GuacamoleConfiguration> configs = getConfigurations(user);
for (final Map.Entry<String, GuacamoleConfiguration> config : configs.entrySet()) {
final Connection connection =
new TokenInjectingConnection(
config.getValue().getParameter("display_name"),
config.getKey(),
config.getValue(),
true);
connection.setParentIdentifier(AzureTREAuthenticationProvider.ROOT_CONNECTION_GROUP);
connections.putIfAbsent(config.getKey(), connection);
}
return connections;
}
private static Map<String, GuacamoleConfiguration> getConfigurations(final AzureTREAuthenticatedUser user)
throws GuacamoleException {
final Map<String, GuacamoleConfiguration> configs = new TreeMap<>();
if (user != null) {
try {
final JSONArray vmsJsonArray = getVMsFromProjectAPI(user);
for (int i = 0; i < vmsJsonArray.length(); i++) {
final GuacamoleConfiguration config = new GuacamoleConfiguration();
final JSONObject vmJsonObject = vmsJsonArray.getJSONObject(i);
final JSONObject templateParameters = (JSONObject) vmJsonObject.get("properties");
if (templateParameters.has("hostname") && templateParameters.has("ip")) {
final String azureResourceId = templateParameters.getString("hostname");
final String ip = templateParameters.getString("ip");
final String displayName = templateParameters.getString("display_name");
setConfig(config, azureResourceId, ip, displayName);
LOGGER.info("Adding a VM, ID: {} IP: {}, Name:{}", azureResourceId, ip, displayName);
configs.putIfAbsent(templateParameters.getString("hostname"), config);
} else {
LOGGER.info("Missing ip or hostname, skipping...");
}
}
} catch (final Exception ex) {
LOGGER.error("Exception getting VMs", ex);
throw new GuacamoleException("Exception getting VMs: " + ex.getMessage());
}
}
return configs;
}
private static void setConfig(
final GuacamoleConfiguration config,
final String azureResourceId,
final String ip,
final String displayName) {
config.setProtocol("rdp");
config.setParameter("hostname", ip);
config.setParameter("display_name", displayName);
config.setParameter("resize-method", "display-update");
config.setParameter("azure-resource-id", azureResourceId);
config.setParameter("port", "3389");
config.setParameter("ignore-cert", "true");
config.setParameter("disable-copy", System.getenv("GUAC_DISABLE_COPY"));
config.setParameter("disable-paste", System.getenv("GUAC_DISABLE_PASTE"));
config.setParameter("enable-drive", System.getenv("GUAC_ENABLE_DRIVE"));
config.setParameter("drive-name", System.getenv("GUAC_DRIVE_NAME"));
config.setParameter("drive-path", System.getenv("GUAC_DRIVE_PATH"));
config.setParameter("disable-download", System.getenv("GUAC_DISABLE_DOWNLOAD"));
config.setParameter("disable-upload", System.getenv("GUAC_DISABLE_UPLOAD"));
}
private static JSONArray getVMsFromProjectAPI(final AzureTREAuthenticatedUser user) throws GuacamoleException {
final JSONArray virtualMachines;
final String url = String.format("%s/api/workspaces/%s/workspace-services/%s/user-resources",
System.getenv("API_URL"), System.getenv("WORKSPACE_ID"), System.getenv("SERVICE_ID"));
final var client = HttpClient.newHttpClient();
final var request = HttpRequest.newBuilder(URI.create(url))
.header("Accept", "application/json")
.header("Authorization", "Bearer " + user.getAccessToken())
.timeout(Duration.ofSeconds(5))
.build();
final HttpResponse<String> response;
try {
response = client.send(request, HttpResponse.BodyHandlers.ofString());
} catch (final IOException | InterruptedException ex) {
LOGGER.error("Connection failed", ex);
throw new GuacamoleException("Connection failed: " + ex.getMessage());
}
var statusCode = response.statusCode();
var resBody = response.body();
if (statusCode >= 300) {
var errorMsg = "Failed getting VMs with status code. statusCode: " + statusCode;
LOGGER.error(errorMsg);
if (!resBody.isBlank()) {
LOGGER.error("response: " + resBody);
}
throw new GuacamoleException(errorMsg);
}
LOGGER.debug("Got VMs list");
if (!resBody.isBlank()) {
final JSONObject result = new JSONObject(resBody);
virtualMachines = result.getJSONArray("userResources");
} else {
LOGGER.debug("Got an empty response");
virtualMachines = new JSONArray();
}
return virtualMachines;
}
}
|
AzureTRE/templates/workspace_services/guacamole/guacamole-server/guacamole-auth-azure/src/main/java/org/apache/guacamole/auth/azuretre/connection/ConnectionService.java/0
|
{
"file_path": "AzureTRE/templates/workspace_services/guacamole/guacamole-server/guacamole-auth-azure/src/main/java/org/apache/guacamole/auth/azuretre/connection/ConnectionService.java",
"repo_id": "AzureTRE",
"token_count": 2866
}
| 129 |
/**
*
*/
package org.apache.guacamole.auth.azuretre.user;
|
AzureTRE/templates/workspace_services/guacamole/guacamole-server/guacamole-auth-azure/src/test/java/org/apache/guacamole/auth/azuretre/user/package-info.java/0
|
{
"file_path": "AzureTRE/templates/workspace_services/guacamole/guacamole-server/guacamole-auth-azure/src/test/java/org/apache/guacamole/auth/azuretre/user/package-info.java",
"repo_id": "AzureTRE",
"token_count": 25
}
| 130 |
# GUID to identify the workspace
WORKSPACE_ID=__CHANGE_ME__
# Unique identifier of the parent Guacamole service
PARENT_SERVICE_ID=__CHANGE_ME__
|
AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-import-reviewvm/.env.sample/0
|
{
"file_path": "AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-import-reviewvm/.env.sample",
"repo_id": "AzureTRE",
"token_count": 49
}
| 131 |
# Azure Provider source and version being used
terraform {
required_providers {
azurerm = {
source = "hashicorp/azurerm"
version = "=3.41.0"
}
template = {
source = "hashicorp/template"
version = "=2.2.0"
}
random = {
source = "hashicorp/random"
version = "=3.4.3"
}
}
backend "azurerm" {
}
}
provider "azurerm" {
features {
key_vault {
# Don't purge on destroy (this would fail due to purge protection being enabled on keyvault)
purge_soft_delete_on_destroy = false
purge_soft_deleted_secrets_on_destroy = false
purge_soft_deleted_certificates_on_destroy = false
purge_soft_deleted_keys_on_destroy = false
# When recreating an environment, recover any previously soft deleted secrets - set to true by default
recover_soft_deleted_key_vaults = true
recover_soft_deleted_secrets = true
recover_soft_deleted_certificates = true
recover_soft_deleted_keys = true
}
}
}
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
}
|
AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-linuxvm/terraform/main.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-linuxvm/terraform/main.tf",
"repo_id": "AzureTRE",
"token_count": 940
}
| 132 |
output "healthcare_workspace_id" {
value = azurerm_healthcare_workspace.healthcare_workspace.id
}
output "fhir_url" {
value = var.deploy_fhir ? "https://hs${local.service_resource_name_suffix}-fhir${local.service_resource_name_suffix}.fhir.azurehealthcareapis.com" : ""
}
output "dicom_url" {
value = var.deploy_dicom ? "https://hs${local.service_resource_name_suffix}-dicom${local.service_resource_name_suffix}.dicom.azurehealthcareapis.com" : ""
}
output "workspace_address_space" {
value = jsonencode(data.azurerm_virtual_network.ws.address_space)
}
|
AzureTRE/templates/workspace_services/health-services/terraform/outputs.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/health-services/terraform/outputs.tf",
"repo_id": "AzureTRE",
"token_count": 219
}
| 133 |
data "azurerm_firewall" "fw" {
name = "fw-${var.tre_id}"
resource_group_name = "rg-${var.tre_id}"
}
locals {
allowedInnerEyeURLs = ["*.anaconda.com", "*.anaconda.org", "binstar-cio-packages-prod.s3.amazonaws.com", "*pythonhosted.org", "github-cloud.githubusercontent.com", "azure.archive.ubuntu.com", "packagecloud.io"]
}
data "azurerm_client_config" "current" {}
resource "null_resource" "az_login_sp" {
count = var.arm_use_msi == true ? 0 : 1
provisioner "local-exec" {
command = "az login --service-principal --username ${var.arm_client_id} --password ${var.arm_client_secret} --tenant ${var.arm_tenant_id}"
}
triggers = {
timestamp = timestamp()
}
}
resource "null_resource" "az_login_msi" {
count = var.arm_use_msi == true ? 1 : 0
provisioner "local-exec" {
command = "az login --identity -u '${data.azurerm_client_config.current.client_id}'"
}
triggers = {
timestamp = timestamp()
}
}
data "external" "rule_priorities" {
program = ["bash", "-c", "./get_firewall_priorities.sh"]
query = {
firewall_name = data.azurerm_firewall.fw.name
resource_group_name = data.azurerm_firewall.fw.resource_group_name
collection_name_suffix = "${local.service_resource_name_suffix}-aml"
}
depends_on = [
null_resource.az_login_sp,
null_resource.az_login_msi
]
}
resource "azurerm_firewall_application_rule_collection" "innereyeapprulecollection" {
name = "arc-${local.service_resource_name_suffix}-aml"
azure_firewall_name = data.azurerm_firewall.fw.name
resource_group_name = data.azurerm_firewall.fw.resource_group_name
priority = data.external.rule_priorities.result.application_rule_priority
action = "Allow"
rule {
name = "allowInnerEyerelated"
source_addresses = data.azurerm_virtual_network.ws.address_space
target_fqdns = local.allowedInnerEyeURLs
protocol {
port = "443"
type = "Https"
}
protocol {
port = "80"
type = "Http"
}
}
}
|
AzureTRE/templates/workspace_services/innereye/terraform/firewall.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/innereye/terraform/firewall.tf",
"repo_id": "AzureTRE",
"token_count": 845
}
| 134 |
---
schemaVersion: 1.0.0
name: tre-workspace-service-ohdsi
version: 0.2.4
description: "An OHDSI workspace service"
registry: azuretre
dockerfile: Dockerfile.tmpl
custom:
dialects:
"Azure Synapse": "synapse"
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:
- name: workspace_id
type: string
- name: tre_id
type: string
- name: address_space
type: string
description: "Address space for PostgreSQL's subnet"
- name: id
type: string
description: "An Id for this installation"
env: id
- name: tfstate_resource_group_name
type: string
description: "Resource group containing the Terraform state storage account"
- name: tfstate_storage_account_name
type: string
description: "The name of the Terraform state storage account"
- name: tfstate_container_name
env: tfstate_container_name
type: string
default: "tfstate"
description: "The name of the Terraform state storage container"
- name: arm_use_msi
env: ARM_USE_MSI
type: boolean
default: false
- name: arm_environment
type: string
- name: azure_environment
type: string
description: "Used by Azure CLI to set the Azure environment"
# parameters for configuring the data source
- name: configure_data_source
type: boolean
default: false
- name: data_source_config
type: string
default: ""
- name: data_source_daimons
type: string
default: ""
mixins:
- terraform:
clientVersion: 1.4.6
- az:
clientVersion: 2.37.0
- exec
outputs:
- name: connection_uri
type: string
applyTo:
- install
- upgrade
- name: webapi_uri
type: string
applyTo:
- install
- upgrade
- name: authentication_callback_uri
type: string
applyTo:
- install
- upgrade
- name: is_exposed_externally
type: boolean
applyTo:
- install
- upgrade
install:
- az:
description: "Set Azure Cloud Environment"
arguments:
- cloud
- set
flags:
name: ${ bundle.parameters.azure_environment }
- az:
description: "Login to Azure"
arguments:
- login
flags:
identity:
username: ${ bundle.credentials.azure_client_id }
- terraform:
description: "Deploy OHDSI workspace service"
vars:
workspace_id: ${ bundle.parameters.workspace_id }
tre_id: ${ bundle.parameters.tre_id }
tre_resource_id: ${ bundle.parameters.id }
address_space: ${ bundle.parameters.address_space }
arm_environment: ${ bundle.parameters.arm_environment }
configure_data_source: ${ bundle.parameters.configure_data_source }
data_source_config: ${ bundle.parameters.data_source_config }
data_source_daimons: ${ bundle.parameters.data_source_daimons }
backendConfig:
resource_group_name: ${ bundle.parameters.tfstate_resource_group_name }
storage_account_name: ${ bundle.parameters.tfstate_storage_account_name }
container_name: ${ bundle.parameters.tfstate_container_name }
key: tre-workspace-service-ohdsi-${ bundle.parameters.id }
outputs:
- name: connection_uri
- name: webapi_uri
- name: authentication_callback_uri
- name: is_exposed_externally
- exec:
description: "Init Synapse Schemas"
command: ./synapse_runner.sh
envs:
DATA_SOURCE_CONFIG: ${ bundle.parameters.data_source_config }
DATA_SOURCE_DIAMONS: ${ bundle.parameters.data_source_daimons }
TRE_RESOURCE_ID: ${ bundle.parameters.id }
SCRIPT_PATH: sql/init_synapse_schemas.sql
upgrade:
- az:
description: "Set Azure Cloud Environment"
arguments:
- cloud
- set
flags:
name: ${ bundle.parameters.azure_environment }
- az:
description: "Login to Azure"
arguments:
- login
flags:
identity:
username: ${ bundle.credentials.azure_client_id }
- terraform:
description: "Upgrade shared service"
vars:
workspace_id: ${ bundle.parameters.workspace_id }
tre_id: ${ bundle.parameters.tre_id }
tre_resource_id: ${ bundle.parameters.id }
address_space: ${ bundle.parameters.address_space }
arm_environment: ${ bundle.parameters.arm_environment }
configure_data_source: ${ bundle.parameters.configure_data_source }
data_source_config: ${ bundle.parameters.data_source_config }
data_source_daimons: ${ bundle.parameters.data_source_daimons }
backendConfig:
resource_group_name: ${ bundle.parameters.tfstate_resource_group_name }
storage_account_name: ${ bundle.parameters.tfstate_storage_account_name }
container_name: ${ bundle.parameters.tfstate_container_name }
key: tre-workspace-service-ohdsi-${ bundle.parameters.id }
outputs:
- name: connection_uri
- name: webapi_uri
- name: authentication_callback_uri
- name: is_exposed_externally
uninstall:
- terraform:
description: "Tear down OHDSI workspace service"
vars:
workspace_id: ${ bundle.parameters.workspace_id }
tre_id: ${ bundle.parameters.tre_id }
tre_resource_id: ${ bundle.parameters.id }
address_space: ${ bundle.parameters.address_space }
arm_environment: ${ bundle.parameters.arm_environment }
configure_data_source: ${ bundle.parameters.configure_data_source }
data_source_config: ${ bundle.parameters.data_source_config }
data_source_daimons: ${ bundle.parameters.data_source_daimons }
backendConfig:
resource_group_name: ${ bundle.parameters.tfstate_resource_group_name }
storage_account_name: ${ bundle.parameters.tfstate_storage_account_name }
container_name: ${ bundle.parameters.tfstate_container_name }
key: tre-workspace-service-ohdsi-${ bundle.parameters.id }
- exec:
description: "Drop Synapse Schemas"
command: ./synapse_runner.sh
envs:
DATA_SOURCE_CONFIG: ${ bundle.parameters.data_source_config }
DATA_SOURCE_DIAMONS: ${ bundle.parameters.data_source_daimons }
TRE_RESOURCE_ID: ${ bundle.parameters.id }
SCRIPT_PATH: sql/drop_synapse_schemas.sql
|
AzureTRE/templates/workspace_services/ohdsi/porter.yaml/0
|
{
"file_path": "AzureTRE/templates/workspace_services/ohdsi/porter.yaml",
"repo_id": "AzureTRE",
"token_count": 2600
}
| 135 |
resource "azurerm_storage_share" "atlas_ui" {
name = local.atlas_ui_storage_share_name
storage_account_name = data.azurerm_storage_account.stg.name
quota = 1
}
resource "local_file" "config_local" {
content = templatefile("${path.module}/config_local.tftpl", { OHDSI_WEBAPI_URL = local.ohdsi_webapi_url })
filename = local.config_local_file_path
}
resource "azurerm_storage_share_file" "config_local" {
name = "config-local.js"
storage_share_id = azurerm_storage_share.atlas_ui.id
source = local.config_local_file_path
depends_on = [
local_file.config_local
]
}
resource "azurerm_linux_web_app" "atlas_ui" {
name = local.atlas_ui_name
location = data.azurerm_resource_group.ws.location
resource_group_name = data.azurerm_resource_group.ws.name
virtual_network_subnet_id = data.azurerm_subnet.web_app.id
service_plan_id = data.azurerm_service_plan.workspace.id
https_only = true
client_affinity_enabled = false
site_config {
always_on = false
ftps_state = "Disabled"
application_stack {
docker_image = "index.docker.io/${local.atlas_ui_docker_image_name}"
docker_image_tag = local.atlas_ui_docker_image_tag
}
}
storage_account {
access_key = data.azurerm_storage_account.stg.primary_access_key
account_name = data.azurerm_storage_account.stg.name
name = "ui-storage-${local.service_suffix}"
share_name = local.atlas_ui_storage_share_name
type = "AzureFiles"
mount_path = local.atlas_ui_mount_path
}
app_settings = {
WEBSITES_ENABLE_APP_SERVICE_STORAGE = false
WEBSITES_PORT = "8080"
}
logs {
application_logs {
file_system_level = "Information"
}
http_logs {
file_system {
retention_in_days = 7
retention_in_mb = 100
}
}
}
depends_on = [
azurerm_storage_share_file.config_local,
]
tags = local.tre_workspace_service_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_private_endpoint" "atlas_ui_private_endpoint" {
name = "pe-${azurerm_linux_web_app.atlas_ui.name}"
location = data.azurerm_resource_group.ws.location
resource_group_name = data.azurerm_resource_group.ws.name
subnet_id = data.azurerm_subnet.services.id
tags = local.tre_workspace_service_tags
private_service_connection {
private_connection_resource_id = azurerm_linux_web_app.atlas_ui.id
name = "psc-${azurerm_linux_web_app.atlas_ui.name}"
subresource_names = ["sites"]
is_manual_connection = false
}
private_dns_zone_group {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.azurewebsites.net"]
private_dns_zone_ids = [data.azurerm_private_dns_zone.azurewebsites.id]
}
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_monitor_diagnostic_setting" "atlas_ui" {
name = azurerm_linux_web_app.atlas_ui.name
target_resource_id = azurerm_linux_web_app.atlas_ui.id
log_analytics_workspace_id = data.azurerm_log_analytics_workspace.workspace.id
dynamic "enabled_log" {
for_each = local.atals_ui_log_analytics_categories
content {
category = enabled_log.value
}
}
metric {
category = "AllMetrics"
enabled = true
}
}
|
AzureTRE/templates/workspace_services/ohdsi/terraform/atlas_ui.tf/0
|
{
"file_path": "AzureTRE/templates/workspace_services/ohdsi/terraform/atlas_ui.tf",
"repo_id": "AzureTRE",
"token_count": 1593
}
| 136 |
# System topics
# Below we assign a SYSTEM-assigned identity for the topics. note that a user-assigned identity will not work.
resource "azurerm_eventgrid_system_topic" "import_approved_blob_created" {
name = local.import_approved_sys_topic_name
location = var.location
resource_group_name = var.ws_resource_group_name
source_arm_resource_id = azurerm_storage_account.sa_import_approved.id
topic_type = "Microsoft.Storage.StorageAccounts"
identity {
type = "SystemAssigned"
}
tags = merge(
var.tre_workspace_tags,
{
Publishers = "airlock;approved-import-sa"
}
)
depends_on = [
azurerm_storage_account.sa_import_approved
]
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_role_assignment" "servicebus_sender_import_approved_blob_created" {
scope = data.azurerm_servicebus_namespace.airlock_sb.id
role_definition_name = "Azure Service Bus Data Sender"
principal_id = azurerm_eventgrid_system_topic.import_approved_blob_created.identity[0].principal_id
depends_on = [
azurerm_eventgrid_system_topic.import_approved_blob_created
]
}
resource "azurerm_eventgrid_system_topic" "export_inprogress_blob_created" {
name = local.export_inprogress_sys_topic_name
location = var.location
resource_group_name = var.ws_resource_group_name
source_arm_resource_id = azurerm_storage_account.sa_export_inprogress.id
topic_type = "Microsoft.Storage.StorageAccounts"
tags = merge(
var.tre_workspace_tags,
{
Publishers = "airlock;inprogress-export-sa"
}
)
identity {
type = "SystemAssigned"
}
depends_on = [
azurerm_storage_account.sa_export_inprogress,
]
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_role_assignment" "servicebus_sender_export_inprogress_blob_created" {
scope = data.azurerm_servicebus_namespace.airlock_sb.id
role_definition_name = "Azure Service Bus Data Sender"
principal_id = azurerm_eventgrid_system_topic.export_inprogress_blob_created.identity[0].principal_id
depends_on = [
azurerm_eventgrid_system_topic.export_inprogress_blob_created
]
}
resource "azurerm_eventgrid_system_topic" "export_rejected_blob_created" {
name = local.export_rejected_sys_topic_name
location = var.location
resource_group_name = var.ws_resource_group_name
source_arm_resource_id = azurerm_storage_account.sa_export_rejected.id
topic_type = "Microsoft.Storage.StorageAccounts"
tags = merge(
var.tre_workspace_tags,
{
Publishers = "airlock;rejected-export-sa"
}
)
identity {
type = "SystemAssigned"
}
depends_on = [
azurerm_storage_account.sa_export_rejected,
]
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_role_assignment" "servicebus_sender_export_rejected_blob_created" {
scope = data.azurerm_servicebus_namespace.airlock_sb.id
role_definition_name = "Azure Service Bus Data Sender"
principal_id = azurerm_eventgrid_system_topic.export_rejected_blob_created.identity[0].principal_id
depends_on = [
azurerm_eventgrid_system_topic.export_rejected_blob_created
]
}
resource "azurerm_eventgrid_system_topic" "export_blocked_blob_created" {
name = local.export_blocked_sys_topic_name
location = var.location
resource_group_name = var.ws_resource_group_name
source_arm_resource_id = azurerm_storage_account.sa_export_blocked.id
topic_type = "Microsoft.Storage.StorageAccounts"
tags = merge(
var.tre_workspace_tags,
{
Publishers = "airlock;export-blocked-sa"
}
)
identity {
type = "SystemAssigned"
}
depends_on = [
azurerm_storage_account.sa_export_blocked,
]
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_role_assignment" "servicebus_sender_export_blocked_blob_created" {
scope = data.azurerm_servicebus_namespace.airlock_sb.id
role_definition_name = "Azure Service Bus Data Sender"
principal_id = azurerm_eventgrid_system_topic.export_blocked_blob_created.identity[0].principal_id
depends_on = [
azurerm_eventgrid_system_topic.export_blocked_blob_created
]
}
## Subscriptions
resource "azurerm_eventgrid_event_subscription" "import_approved_blob_created" {
name = "import-approved-blob-created-${var.short_workspace_id}"
scope = azurerm_storage_account.sa_import_approved.id
service_bus_topic_endpoint_id = data.azurerm_servicebus_topic.blob_created.id
delivery_identity {
type = "SystemAssigned"
}
depends_on = [
azurerm_eventgrid_system_topic.import_approved_blob_created,
azurerm_role_assignment.servicebus_sender_import_approved_blob_created
]
}
resource "azurerm_eventgrid_event_subscription" "export_inprogress_blob_created" {
name = "export-inprogress-blob-created-${var.short_workspace_id}"
scope = azurerm_storage_account.sa_export_inprogress.id
service_bus_topic_endpoint_id = data.azurerm_servicebus_topic.blob_created.id
delivery_identity {
type = "SystemAssigned"
}
depends_on = [
azurerm_eventgrid_system_topic.export_inprogress_blob_created,
azurerm_role_assignment.servicebus_sender_export_inprogress_blob_created
]
}
resource "azurerm_eventgrid_event_subscription" "export_rejected_blob_created" {
name = "export-rejected-blob-created-${var.short_workspace_id}"
scope = azurerm_storage_account.sa_export_rejected.id
service_bus_topic_endpoint_id = data.azurerm_servicebus_topic.blob_created.id
delivery_identity {
type = "SystemAssigned"
}
depends_on = [
azurerm_eventgrid_system_topic.export_rejected_blob_created,
azurerm_role_assignment.servicebus_sender_export_rejected_blob_created
]
}
resource "azurerm_eventgrid_event_subscription" "export_blocked_blob_created" {
name = "export-blocked-blob-created-${var.short_workspace_id}"
scope = azurerm_storage_account.sa_export_blocked.id
service_bus_topic_endpoint_id = data.azurerm_servicebus_topic.blob_created.id
delivery_identity {
type = "SystemAssigned"
}
depends_on = [
azurerm_eventgrid_system_topic.export_blocked_blob_created,
azurerm_role_assignment.servicebus_sender_export_blocked_blob_created
]
}
|
AzureTRE/templates/workspaces/base/terraform/airlock/eventgrid_topics.tf/0
|
{
"file_path": "AzureTRE/templates/workspaces/base/terraform/airlock/eventgrid_topics.tf",
"repo_id": "AzureTRE",
"token_count": 2519
}
| 137 |
data "azurerm_virtual_network" "core" {
name = local.core_vnet
resource_group_name = local.core_resource_group_name
}
data "azurerm_subnet" "core_webapps" {
name = "WebAppSubnet"
virtual_network_name = "vnet-${var.tre_id}"
resource_group_name = "rg-${var.tre_id}"
}
data "azurerm_subnet" "shared" {
resource_group_name = local.core_resource_group_name
virtual_network_name = local.core_vnet
name = "SharedSubnet"
}
data "azurerm_subnet" "bastion" {
resource_group_name = local.core_resource_group_name
virtual_network_name = local.core_vnet
name = "AzureBastionSubnet"
}
data "azurerm_subnet" "resourceprocessor" {
resource_group_name = local.core_resource_group_name
virtual_network_name = local.core_vnet
name = "ResourceProcessorSubnet"
}
data "azurerm_subnet" "airlockprocessor" {
resource_group_name = local.core_resource_group_name
virtual_network_name = local.core_vnet
name = "AirlockProcessorSubnet"
}
data "azurerm_route_table" "rt" {
name = "rt-${var.tre_id}"
resource_group_name = local.core_resource_group_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_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 "azurerm_private_dns_zone" "blobcore" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.blob.core.windows.net"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "dfscore" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.dfs.core.windows.net"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "vaultcore" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.vaultcore.azure.net"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "azurecr" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.azurecr.io"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "azureml" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.api.azureml.ms"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "azuremlcert" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.cert.api.azureml.ms"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "notebooks" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.notebooks.azure.net"]
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" "postgres" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.postgres.database.azure.com"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_public_ip" "app_gateway_ip" {
name = "pip-agw-${var.tre_id}"
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "nexus" {
name = "nexus-${data.azurerm_public_ip.app_gateway_ip.fqdn}"
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "health" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.azurehealthcareapis.com"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "dicom" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.dicom.azurehealthcareapis.com"]
resource_group_name = local.core_resource_group_name
}
data "azurerm_private_dns_zone" "databricks" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.azuredatabricks.net"]
resource_group_name = local.core_resource_group_name
}
|
AzureTRE/templates/workspaces/base/terraform/network/data.tf/0
|
{
"file_path": "AzureTRE/templates/workspaces/base/terraform/network/data.tf",
"repo_id": "AzureTRE",
"token_count": 1929
}
| 138 |
import React from 'react';
import { render, screen } from '@testing-library/react';
import { App } from './App';
it('renders "Welcome to Your Fluent UI App"', () => {
render(<App />);
const linkElement = screen.getByText(/Welcome to Your Fluent UI App/i);
expect(linkElement).toBeInTheDocument();
});
|
AzureTRE/ui/app/src/App.test.tsx/0
|
{
"file_path": "AzureTRE/ui/app/src/App.test.tsx",
"repo_id": "AzureTRE",
"token_count": 98
}
| 139 |
import { MessageBar, MessageBarType } from "@fluentui/react";
import React from "react";
interface ErrorState {
hasError: boolean
}
export class GenericErrorBoundary extends React.Component<any, ErrorState, any> {
constructor(props: any) {
super(props);
this.state = { hasError: false };
}
static getDerivedStateFromError(error: any) {
// Update state so the next render will show the fallback UI.
return { hasError: true };
}
componentDidCatch(error: any, errorInfo: any) {
// You can also log the error to an error reporting service
console.error('UNHANDLED EXCEPTION', error, errorInfo);
}
render() {
if (this.state.hasError) {
return (
<MessageBar
messageBarType={MessageBarType.error}
isMultiline={false}
>
<h3>Uh oh!</h3>
<p>This area encountered an error that we can't recover from. Please check your configuration and refresh. <br/>
Further debugging details can be found in the browser console.</p>
</MessageBar>
);
}
return this.props.children;
}
}
|
AzureTRE/ui/app/src/components/shared/GenericErrorBoundary.tsx/0
|
{
"file_path": "AzureTRE/ui/app/src/components/shared/GenericErrorBoundary.tsx",
"repo_id": "AzureTRE",
"token_count": 403
}
| 140 |
import React, { useContext, useEffect, useState } from 'react';
import { Resource } from '../../models/resource';
import { ResourceCardList } from '../shared/ResourceCardList';
import { PrimaryButton, Stack } from '@fluentui/react';
import { ResourceType } from '../../models/resourceType';
import { SharedService } from '../../models/sharedService';
import { HttpMethod, useAuthApiCall } from '../../hooks/useAuthApiCall';
import { ApiEndpoint } from '../../models/apiEndpoints';
import { CreateUpdateResourceContext } from '../../contexts/CreateUpdateResourceContext';
import { RoleName } from '../../models/roleNames';
import { SecuredByRole } from './SecuredByRole';
interface SharedServiceProps{
readonly?: boolean
}
export const SharedServices: React.FunctionComponent<SharedServiceProps> = (props: SharedServiceProps) => {
const createFormCtx = useContext(CreateUpdateResourceContext);
const [sharedServices, setSharedServices] = useState([] as Array<SharedService>);
const apiCall = useAuthApiCall();
useEffect(() => {
const getSharedServices = async () => {
const ss = (await apiCall(ApiEndpoint.SharedServices, HttpMethod.Get)).sharedServices;
setSharedServices(ss);
}
getSharedServices();
}, [apiCall]);
const updateSharedService = (ss: SharedService) => {
let ssList = [...sharedServices];
let i = ssList.findIndex((f: SharedService) => f.id === ss.id);
ssList.splice(i, 1, ss);
setSharedServices(ssList);
};
const removeSharedService = (ss: SharedService) => {
let ssList = [...sharedServices];
let i = ssList.findIndex((f: SharedService) => f.id === ss.id);
ssList.splice(i, 1);
setSharedServices(ssList);
};
const addSharedService = (ss: SharedService) => {
let ssList = [...sharedServices];
ssList.push(ss);
setSharedServices(ssList);
}
return (
<>
<Stack className="tre-panel">
<Stack.Item>
<Stack horizontal horizontalAlign="space-between">
<h1>Shared Services</h1>
{
!props.readonly &&
<SecuredByRole allowedAppRoles={[RoleName.TREAdmin]} element={
<PrimaryButton iconProps={{ iconName: 'Add' }} text="Create new" onClick={() => {
createFormCtx.openCreateForm({
resourceType: ResourceType.SharedService,
onAdd: (r: Resource) => addSharedService(r as SharedService)
})
}} />
} />
}
</Stack>
</Stack.Item>
<Stack.Item>
<ResourceCardList
resources={sharedServices}
updateResource={(r: Resource) => updateSharedService(r as SharedService)}
removeResource={(r: Resource) => removeSharedService(r as SharedService)}
emptyText="This TRE has no shared services."
readonly={props.readonly} />
</Stack.Item>
</Stack>
</>
);
};
|
AzureTRE/ui/app/src/components/shared/SharedServices.tsx/0
|
{
"file_path": "AzureTRE/ui/app/src/components/shared/SharedServices.tsx",
"repo_id": "AzureTRE",
"token_count": 1168
}
| 141 |
{
"id": "ba02d504-47d5-412d-b192-b75d17c65009",
"resourceId": "8b6e42a0-e236-46ae-9541-01b462e4b468",
"resourcePath": "/workspaces/1e800001-7385-46a1-9f6d-490a6201ea01/workspace-services/8c70974a-5f66-4ae9-9502-7a54e9e0bb86/user-resources/8b6e42a0-e236-46ae-9541-01b462e4b468",
"resourceVersion": 0,
"status": "pipeline_failed",
"action": "install",
"message": "Pipeline deployment completed successfully",
"createdWhen": 1650653543.343581,
"updatedWhen": 1650653543.343581,
"user": {
"id": "7f9756c3-7925-4b78-a10b-83927ab9c008",
"name": "[email protected] Lopes de Almeida",
"email": "",
"roles": [
"WorkspaceOwner"
],
"roleAssignments": []
},
"steps": [
{
"stepId": "6d2d7eb7-984e-4330-bd3c-c7ec98658402",
"stepTitle": "Update the firewall the first time",
"resourceId": "ea079a34-ec53-43e0-9454-84531c96599e",
"resourceTemplateName": "tre-shared-service-firewall",
"resourceType": "shared-service",
"resourceAction": "upgrade",
"status": "action_succeeded",
"message": "ea079a34-ec53-43e0-9454-84531c96599e: upgrade action completed successfully.",
"updatedWhen": 1650898800.932936
},
{
"stepId": "main",
"stepTitle": "Main step for 8b6e42a0-e236-46ae-9541-01b462e4b468",
"resourceId": "8b6e42a0-e236-46ae-9541-01b462e4b4686",
"resourceTemplateName": "tre-service-dev-vm",
"resourceType": "user-resource",
"resourceAction": "install",
"status": "deployment_failed",
"message": "8b6e42a0-e236-46ae-9541-01b462e4b468: install action completed successfully.",
"updatedWhen": 1650899841.316169
},
{
"stepId": "2fe8a6a7-2c27-4c49-8773-127df8a48b4e",
"stepTitle": "Update the firewall the second time",
"resourceId": "ea079a34-ec53-43e0-9454-84531c96599e",
"resourceTemplateName": "tre-shared-service-firewall",
"resourceType": "shared-service",
"resourceAction": "upgrade",
"status": "awaiting_deployment",
"message": "ea079a34-ec53-43e0-9454-84531c96599e: upgrade action in progress",
"updatedWhen": 1650899911.042068
}
]
}
|
AzureTRE/ui/app/src/components/shared/notifications/dummyOpSteps.json/0
|
{
"file_path": "AzureTRE/ui/app/src/components/shared/notifications/dummyOpSteps.json",
"repo_id": "AzureTRE",
"token_count": 1271
}
| 142 |
import { AuthenticationResult, InteractionRequiredAuthError } from "@azure/msal-browser";
import { useMsal, useAccount } from "@azure/msal-react";
import { useCallback } from "react";
import { APIError } from "../models/exceptions";
import config from "../config.json";
export enum ResultType {
JSON = "JSON",
Text = "Text",
None = "None"
}
export enum HttpMethod {
Get = "GET",
Post = "POST",
Patch = "PATCH",
Delete = "DELETE"
}
export const useAuthApiCall = () => {
const { instance, accounts } = useMsal();
const account = useAccount(accounts[0] || {});
const parseJwt = (token: string) => {
var base64Url = token.split('.')[1];
var base64 = base64Url.replace(/-/g, '+').replace(/_/g, '/');
var jsonPayload = decodeURIComponent(atob(base64).split('').map(function (c) {
return '%' + ('00' + c.charCodeAt(0).toString(16)).slice(-2);
}).join(''));
return JSON.parse(jsonPayload);
}
return useCallback(async (
endpoint: string,
method: HttpMethod,
workspaceApplicationIdURI?: string,
body?: any,
resultType?: ResultType,
setRoles?: (roles: Array<string>) => void,
tokenOnly?: boolean,
etag?: string) => {
config.debug && console.log("API call", {
endpoint: endpoint,
method: method,
workspaceApplicationIdURI: workspaceApplicationIdURI,
body: body,
resultType: resultType,
tokenOnly: tokenOnly,
etag: etag
});
if (!account) {
console.error("No account object found, please refresh.");
return;
}
const applicationIdURI = workspaceApplicationIdURI || config.treApplicationId;
let tokenResponse = {} as AuthenticationResult;
let tokenRequest = {
scopes: [`${applicationIdURI}/user_impersonation`],
account: account
}
// try and get a token silently. at times this might throw an InteractionRequiredAuthError - if so give the user a popup to click
try {
tokenResponse = await instance.acquireTokenSilent(tokenRequest);
} catch (err) {
console.warn("Unable to get a token silently", err);
if (err instanceof InteractionRequiredAuthError) {
tokenResponse = await instance.acquireTokenPopup(tokenRequest);
}
}
config.debug && console.log("Token Response", tokenResponse);
if (!tokenResponse) {
console.error("Token could not be retrieved, please refresh.");
return;
}
// caller can pass a function to allow us to set the roles to use for RBAC
if (setRoles) {
let decodedToken = parseJwt(tokenResponse.accessToken);
config.debug && console.log("Decoded token", decodedToken);
setRoles(decodedToken.roles);
}
// we might just want the token to get the roles.
if (tokenOnly) return;
// trim first slash if we're given one
if (endpoint[0] === "/") endpoint = endpoint.substring(1);
// default to JSON unless otherwise told
resultType = resultType || ResultType.JSON;
config.debug && console.log(`Calling ${method} on authenticated api: ${endpoint}`);
// set the headers for auth + http method
const opts: RequestInit = {
mode: "cors",
headers: {
Authorization: `Bearer ${tokenResponse.accessToken}`,
'Content-Type': 'application/json',
'etag': etag ? etag : ""
},
method: method
}
// add a body if we're given one
if (body) opts.body = JSON.stringify(body);
let resp;
try {
resp = await fetch(`${config.treUrl}/${endpoint}`, opts);
} catch (err: any) {
let e = err as APIError;
e.name = 'API call failure';
e.message = 'Unable to make call to API Backend';
e.endpoint = `${config.treUrl}/${endpoint}`;
throw e;
}
if (!resp.ok) {
let e = new APIError();
e.message = await resp.text();
e.status = resp.status;
e.endpoint = endpoint;
throw e;
}
try {
switch (resultType) {
case ResultType.Text:
let text = await resp.text();
config.debug && console.log(text);
return text;
case ResultType.JSON:
let json = await resp.json();
config.debug && console.log(json);
return json
case ResultType.None:
return;
}
} catch (err: any) {
let e = err as APIError;
e.name = "Error with response data";
throw e;
}
}, [account, instance]);
}
|
AzureTRE/ui/app/src/hooks/useAuthApiCall.ts/0
|
{
"file_path": "AzureTRE/ui/app/src/hooks/useAuthApiCall.ts",
"repo_id": "AzureTRE",
"token_count": 1700
}
| 143 |
import { Resource } from "./resource";
export interface UserResource extends Resource {
parentWorkspaceServiceId: string
}
|
AzureTRE/ui/app/src/models/userResource.ts/0
|
{
"file_path": "AzureTRE/ui/app/src/models/userResource.ts",
"repo_id": "AzureTRE",
"token_count": 31
}
| 144 |
t i 278188134
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in to</w> 2717331
k s</w> 2714762
id ence</w> 2710504
k n 2710177
2 . 2705369
f u 2703932
c es</w> 2701491
m il 2700245
SU L 2697847
m ar 2694237
a in</w> 2693081
form ation</w> 2690933
RE SUL 2690905
ac c 2676678
o ph 2667074
b lo 2664599
RESUL TS</w> 2663809
de tec 2658622
if ic</w> 2640246
it ed</w> 2637187
c y 2636398
c h</w> 2634798
u e</w> 2627487
dec re 2616907
cl e</w> 2616136
enc y</w> 2615072
Th ese</w> 2613466
y n 2608472
ra di 2604761
rec ep 2600675
pres ent</w> 2587353
A C 2587328
ut e</w> 2577956
b o 2576015
c yt 2575466
os ph 2572827
tic al</w> 2569770
ur al</w> 2566646
pro vid 2564802
vi v 2558819
c al 2552536
w ell</w> 2552392
s ol 2544602
g re 2537984
m ech 2535911
d es 2532398
char acter 2529105
ol ec 2528116
tr a 2513774
e g 2508789
e tic</w> 2505784
c ases</w> 2490281
grou ps</w> 2488097
u tion</w> 2484664
ch il 2483131
func tion</w> 2478541
um b 2478150
im pro 2476987
gen e</w> 2472300
k in 2470070
spec ific</w> 2468790
T I 2465217
high er</w> 2459400
oc yt 2454941
si ons</w> 2450418
TH O 2449075
on ly</w> 2445128
resp ec 2440483
o ver 2439232
ME THO 2439143
de l</w> 2428914
con tr 2426123
1 9 2413199
uc le 2413088
v alu 2408794
respon se</w> 2408115
an s</w> 2400485
1 ;</w> 2400170
	 1;</w> 2400170
en z 2396328
blo od</w> 2395607
typ e</w> 2390436
m ents</w> 2385888
obser ved</w> 2379629
f l 2378944
tis su 2372462
a il 2371036
3 ;</w> 2368840
	 3;</w> 2368840
ec ted</w> 2367692
y si 2353029
ti v 2351218
gro w 2347906
ro s 2346162
th ree</w> 2346149
ing s</w> 2344746
com m 2339349
s ti 2338749
gen er 2337989
hy dro 2334443
l if 2328230
reg i 2326639
u p 2324163
er g 2323258
at ory</w> 2318658
m p 2304769
f lu 2304545
im port 2304366
en g 2302204
mo del</w> 2301943
y l</w> 2297324
de mon 2296090
demon str 2295387
a d</w> 2289823
fol low 2280885
v ing</w> 2275489
D NA</w> 2270757
nor mal</w> 2267652
p op 2264333
i um</w> 2262062
METHO DS</w> 2260964
d ru 2260563
suc h</w> 2253999
sy stem</w> 2253616
wh o</w> 2241541
a th 2238321
on g 2228658
s o 2226723
at ure</w> 2220447
ot al</w> 2218454
1 2</w> 2208528
in c 2201533
heal th</w> 2198758
u p</w> 2194512
ro le</w> 2190346
h ist 2190312
N D</w> 2189960
tec h 2188445
s tim 2187275
ren t</w> 2179717
fac tors</w> 2175263
an d 2172908
par tic 2172834
tic s</w> 2169100
depend ent</w> 2161965
wh en</w> 2160939
b r 2157725
c are</w> 2154863
n on</w> 2150838
ph osph 2141090
C h 2140704
ac id</w> 2137518
5 0</w> 2132670
hy per 2130597
mul ti 2120603
f ib 2117950
H owever</w> 2117532
th rou 2115959
m ut 2115703
m y 2115622
e th 2115003
v as 2114450
com bin 2114178
o ver</w> 2112354
I I</w> 2111246
chil d 2111178
fir st</w> 2110395
z ed</w> 2110256
y s 2107293
ic i 2100296
kn ow 2100053
pr im 2099392
at s</w> 2096902
ent ly</w> 2095315
con di 2091964
ac t</w> 2089713
ex amin 2089588
d el 2088973
pro g 2082589
th ose</w> 2079599
it r 2079540
tre ated</w> 2078836
in al</w> 2078476
inv ol 2075891
for e</w> 2074339
chang es</w> 2073335
m olec 2072161
tain ed</w> 2071572
ph ysi 2062309
2 0</w> 2060986
pro duc 2060541
ass ess 2059043
n umb 2055169
mech anis 2054586
R NA</w> 2050869
con t 2050122
ar d</w> 2048996
s ul 2046855
lev el</w> 2043723
di vid 2041010
po st 2036157
g en</w> 2034956
3 . 2033589
R O 2033537
t y</w> 2030074
as s</w> 2029885
t otal</w> 2028508
m is 2028422
er y</w> 2026978
incre ase</w> 2025458
re n</w> 2020651
or t</w> 2017437
per im 2016380
it al</w> 2015813
ab l 2015736
no sis</w> 2011847
T o</w> 2010914
m emb 2006799
r ang 2005712
cl u 2005424
st em 2004890
od y</w> 2004544
si ble</w> 2003047
ad di 2000661
c ular</w> 2000598
m us 1994590
re vie 1992937
l arg 1992596
l ess</w> 1992280
deter min 1990282
x im 1988261
ab ol 1987978
ati onal</w> 1983926
respec tively</w> 1969760
or g 1966917
o re 1966653
car di 1961914
meth od</w> 1961816
develop ment</w> 1959651
tr i 1959335
l im 1950478
u res</w> 1946968
o sis</w> 1946525
ep ti 1944219
pre dic 1937440
f er 1935787
co un 1929094
grow th</w> 1927117
ag es</w> 1926241
is h 1926146
th s</w> 1925617
ne w</w> 1919714
ter n 1915333
b ody</w> 1913977
tum or</w> 1912054
p er</w> 1910406
perform ed</w> 1900960
a re 1888462
ne uro 1882090
ic ally</w> 1874868
m ac 1873932
od s</w> 1873740
acti ve</w> 1864905
am ong</w> 1863746
a i 1863484
pl ac 1861034
out com 1859393
me dic 1858839
9 5</w> 1858629
os p 1858205
co uld</w> 1856077
E C 1853200
pot ential</w> 1851535
w om 1850845
O N</w> 1849959
child ren</w> 1843812
t em 1841016
s elec 1839349
f requ 1837974
sen si 1836535
cr ib 1836414
di rec 1832591
sub jec 1826948
fac tor</w> 1823647
p at 1821729
en ted</w> 1818232
i v 1815160
y ing</w> 1813236
ma in 1812045
m i 1808128
al ph 1805830
0 1</w> 1803024
l am 1802683
low er</w> 1800702
i ed</w> 1799056
me an</w> 1797999
ph en 1797581
an e 1793813
c ap 1788199
k ed</w> 1787145
d ed</w> 1786557
ter s</w> 1784161
f t</w> 1780097
s ity</w> 1779584
con si 1777720
numb er</w> 1776218
1 5</w> 1772554
ri p 1771877
o us 1769019
with in</w> 1768977
si mil 1765147
c in 1761104
import ant</w> 1760133
C on 1759624
m g</w> 1758783
repor ted</w> 1751044
c ase</w> 1748643
ex perim 1744735
sy stem 1744294
reg ul 1742906
h em 1739928
throu gh</w> 1737507
th ou 1734866
recep tor</w> 1732911
includ ing</w> 1732589
b re 1731852
res s</w> 1730545
d ings</w> 1729964
ad e</w> 1727548
ear ly</w> 1727387
om e 1725526
bi o 1722477
u r</w> 1720889
id s</w> 1715751
is ol 1714611
b ra 1713727
bin ding</w> 1711580
di sc 1709878
iti es</w> 1706414
n on 1700903
rel ati 1695904
y c 1693355
4 . 1690029
am ine</w> 1684351
te st 1681538
we e 1681198
ti l 1679630
re ve 1677328
ex pos 1674670
n ucle 1672409
ograph y</w> 1672169
wom en</w> 1671411
v ol 1669641
n ec 1665052
p ec 1664308
e x</w> 1663986
with out</w> 1663083
ON S</w> 1662978
s ed</w> 1662033
posi tive</w> 1660689
y ear</w> 1660076
A n 1656210
3 0</w> 1656065
tissu e</w> 1655221
r ats</w> 1647190
com par 1647028
mon ths</w> 1646456
n os 1643635
I t</w> 1641334
ag ing</w> 1640978
prote ins</w> 1640053
l ym 1638762
enz ym 1633705
di d</w> 1632577
met abol 1632515
t est</w> 1632009
e ther</w> 1630838
ot yp 1630695
u b 1629077
ol d</w> 1628999
A T 1628362
mic e</w> 1628097
identi fied</w> 1627175
ti on 1625784
ap p 1624555
col l 1624288
a si 1622847
v es</w> 1622020
d ays</w> 1621866
ni qu 1618102
m it 1618058
t ly</w> 1616310
des crib 1614928
fac e</w> 1609526
g o 1608169
the sis</w> 1608164
t arg 1607705
ar ter 1607363
alph a</w> 1605895
it er 1605061
ha vi 1601994
e ach</w> 1595786
ch rom 1591857
S t 1589296
d o 1586939
tiv ity</w> 1586851
i red</w> 1586027
C I</w> 1583690
wor k</w> 1578292
d ue</w> 1575401
C D 1573826
ic e</w> 1572032
th ere</w> 1571039
o id</w> 1566815
d ose</w> 1566291
in divid 1566074
e u 1564434
sugg est</w> 1563778
memb ran 1563683
ing le</w> 1563643
ch r 1563515
g u 1561946
tech niqu 1561577
ogen e 1561305
sc op 1561217
c al</w> 1560292
inhib it 1558604
bet a</w> 1557744
pl es</w> 1554962
min i 1553270
ma j 1552172
g h 1551106
diag nosis</w> 1550191
z e</w> 1549559
n ing</w> 1548170
ver y</w> 1546716
hy po 1546109
per c 1544686
CONCLUSI ONS</w> 1544531
w il 1543139
re sist 1540096
tu res</w> 1536486
oper ative</w> 1533505
c at 1532697
ti s</w> 1530654
fin dings</w> 1530552
or der</w> 1529281
s k 1529122
sy mp 1528920
vir us</w> 1526888
thou gh</w> 1525533
oc cur 1519190
et y</w> 1517947
ar di 1516022
infec tion</w> 1513581
inf lu 1513507
on d 1513125
fu l</w> 1512344
acti ons</w> 1511193
ous ly</w> 1510501
fic ation</w> 1507761
i p</w> 1507665
be havi 1503492
sur viv 1497137
al e</w> 1496414
5 . 1496339
pres ence</w> 1486820
or al</w> 1483653
ur ther</w> 1480035
is e</w> 1479091
dru g</w> 1478998
a x 1478845
l l 1477823
in iti 1477474
C om 1477009
bra in</w> 1476549
b ec 1474613
ser um</w> 1474331
f il 1474228
m ot 1473541
ad ul 1473314
it es</w> 1472520
vas cular</w> 1471474
prim ary</w> 1471283
wh ile</w> 1469403
for m</w> 1467042
diag nos 1465212
a sis</w> 1464163
in t 1462609
surg ery</w> 1458965
w ay</w> 1457221
p ri 1457008
ati s 1455067
s ingle</w> 1452084
ex pl 1451098
or e</w> 1447263
re duced</w> 1446310
ox id 1446009
i l</w> 1445863
simil ar</w> 1439984
f em 1438982
l ab 1436922
1 4</w> 1436222
P ro 1435108
en h 1435028
activ ation</w> 1434888
ent ation</w> 1434508
differen ces</w> 1432195
pop ulation</w> 1432057
ex trac 1430230
s ing</w> 1425969
A s</w> 1422887
h osp 1420793
ros s</w> 1419588
al ed</w> 1418804
ad mini 1418746
know n</w> 1416636
ev idence</w> 1414360
r ati 1412030
in tr 1411096
pol y 1410754
ear ch</w> 1408783
d ay</w> 1406878
inf lam 1403434
mor ph 1402367
spec ies</w> 1402167
sup por 1401840
ob tained</w> 1401288
at ely</w> 1400445
P C 1399861
plas ma</w> 1398231
ac y</w> 1396999
l ine</w> 1395722
b acter 1394165
2 5</w> 1393399
a u 1393110
gen es</w> 1391427
6 . 1390849
pre vi 1390197
studi ed</w> 1388025
j u 1387304
f o 1387166
ab out</w> 1386315
revie w</w> 1385398
a ut 1384640
1 1</w> 1384143
f am 1380806
differen ti 1378559
l at 1377758
rec ei 1375114
stim ul 1374906
decre ased</w> 1372738
un der</w> 1372240
en e 1371612
s ome</w> 1370979
d en 1369561
is m</w> 1366595
sy ch 1362055
v is 1359914
measu red</w> 1359453
condi tions</w> 1355926
er i 1352960
ro ph 1352589
p ul 1351531
Th ere</w> 1351389
ish ed</w> 1351097
ne ur 1349160
t al</w> 1348190
K G 1347673
e qu 1347481
g s</w> 1346529
B AC 1344679
l ong</w> 1342430
m ental</w> 1340154
cl es</w> 1338632
1 6</w> 1335840
7 . 1331039
ter m</w> 1330216
symp tom 1328196
RO U 1328098
concentr ation</w> 1326993
follow ing</w> 1326766
2 4</w> 1325764
k ers</w> 1325288
BAC KG 1324689
BACKG ROU 1324532
s el 1323894
comm on</w> 1322996
j ur 1322954
p an 1322876
BACKGROU ND</w> 1322769
p ur 1321974
sho uld</w> 1321451
lym ph 1318242
am e 1318191
ar ti 1318127
li ver</w> 1317485
ec t</w> 1317053
compl et 1316058
addi tion</w> 1315375
concentr ations</w> 1314055
as si 1311843
ac ute</w> 1311497
n er 1310773
v enti 1310186
a z 1309333
m all</w> 1308545
gl uc 1307838
1 8</w> 1307806
I V</w> 1305125
d es</w> 1302507
develop ed</w> 1301519
cel l 1300931
itr o</w> 1297112
tran sc 1293787
ic s</w> 1293720
1 3</w> 1292898
repor t</w> 1289954
l ac 1289720
R es 1289318
reve aled</w> 1289237
rang e</w> 1288534
st ate</w> 1288271
peri od</w> 1287268
im pl 1286361
cor rel 1285497
chem ical</w> 1284330
a di 1283899
re duc 1283497
g e</w> 1283460
in te 1281974
on d</w> 1280159
ex c 1279509
surviv al</w> 1276850
C A 1276827
subjec ts</w> 1276069
ar t</w> 1275881
v ement</w> 1275684
b one</w> 1274768
on sh 1273560
1 0 1269843
f ul 1268221
E x 1268184
elec tr 1267869
the y</w> 1265187
ain s</w> 1259466
P A 1258082
co h 1257718
chang e</w> 1257668
ne g 1257277
an im 1256267
investig ated</w> 1255591
demonstr ated</w> 1254813
di ab 1253087
character is 1252813
d rom 1251109
ch ol 1250505
res sion</w> 1249732
elec tro 1249336
sho w</w> 1246366
c op 1244772
di str 1243273
chr onic</w> 1241589
in jur 1241444
effec tive</w> 1241352
compl ic 1239507
s mall</w> 1237493
ag ement</w> 1237075
proc ed 1235224
ven tion</w> 1235163
v itro</w> 1235119
st ress</w> 1233045
anc ed</w> 1231932
ig n 1231842
f our</w> 1231200
st er 1230508
ocyt es</w> 1228119
m ine</w> 1227255
molec ular</w> 1226941
ei ther</w> 1225408
enc es</w> 1224422
struc ture</w> 1224217
est er 1223597
includ ed</w> 1222650
pr acti 1221961
p sych 1221511
m en 1220148
ad v 1218414
res earch</w> 1218394
we ight</w> 1217452
k e 1216858
es ting</w> 1216614
a c</w> 1215134
press ure</w> 1214444
press ed</w> 1214425
membran e</w> 1208073
sam ples</w> 1206326
larg e</w> 1205899
un d</w> 1205478
CONCLUSI ON</w> 1205468
t ox 1203675
relati onsh 1203113
c er 1201540
yn am 1200116
maj or</w> 1199314
sugg est 1198245
re si 1197942
pa in</w> 1197629
4 0</w> 1194751
r ates</w> 1194703
compl ex</w> 1193549
k g</w> 1191800
examin ed</w> 1191621
func tional</w> 1191578
an y</w> 1190201
O B 1189583
car cin 1187894
sur face</w> 1186605
lo b 1185283
ol e</w> 1184804
w ater</w> 1183292
eff ici 1181410
c ir 1180446
ox y 1179842
ain st</w> 1179403
J EC 1178997
evalu ated</w> 1174786
oc c 1173341
I L</w> 1172361
M e 1169068
em ia</w> 1168911
ab s 1166478
determin ed</w> 1166432
proc ess 1166069
b ility</w> 1166052
W h 1165063
con tin 1164627
meth ods</w> 1164622
pro duction</w> 1164440
8 . 1163886
pro b 1162688
appro ach</w> 1162314
ag ainst</w> 1162169
o f 1161998
i tion</w> 1161423
der s</w> 1160998
g a 1158587
di es</w> 1157453
p u 1154059
se t</w> 1153457
par ti 1153441
O ur</w> 1152256
deter mine</w> 1150547
ph ase</w> 1150394
ren al</w> 1149530
ve l</w> 1148242
TI V 1148052
syn drom 1146743
n ess</w> 1146704
d r 1143059
ant i</w> 1142291
f urther</w> 1140855
1 00</w> 1140571
transc rip 1138134
g ra 1137964
partic ip 1137275
pro per 1136524
el d</w> 1135098
l os 1134282
es tim 1132286
OB JEC 1132116
A c 1131997
l un 1131863
qu ality</w> 1130571
vir on 1130480
po in 1127724
m in</w> 1127707
cur rent</w> 1126872
st and 1126744
us es</w> 1126362
OBJEC TIV 1126309
disc us 1123206
qu anti 1122911
be fore</w> 1122442
ap plic 1119476
s ch 1118498
ar ly</w> 1118055
distr ib 1117984
re le 1117531
valu es</w> 1115154
con duc 1114073
E R 1113351
er c 1111556
mus cle</w> 1110904
ta ke</w> 1110295
g t;</w> 1110084
& gt;</w> 1110084
in formation</w> 1109575
ver se</w> 1108673
sam e</w> 1108250
v s</w> 1105932
1 7</w> 1105380
op ath 1104723
c an 1104134
b as 1103381
r at</w> 1102491
tain ing</w> 1101341
symptom s</w> 1101337
ome tr 1101215
D i 1100888
a e</w> 1099837
gre ater</w> 1097180
ic ity</w> 1096199
si de</w> 1094153
f ree</w> 1093713
heal th 1093429
is t</w> 1092932
lif e</w> 1091176
ain ing</w> 1090490
li ke</w> 1090421
str ation</w> 1090003
h or 1089748
proc ess</w> 1085748
mo di 1082719
posi tion</w> 1082696
wh ether</w> 1079204
f ail 1078099
en ing</w> 1077990
de m 1077874
wil l</w> 1077037
o therapy</w> 1075808
a use</w> 1074118
en viron 1073600
6 0</w> 1073579
contr ib 1073378
vari ous</w> 1073360
provid e</w> 1071747
at us</w> 1069233
spec tive</w> 1068737
show n</w> 1067909
under st 1067256
el ial</w> 1067057
f y</w> 1066484
t ality</w> 1065389
neg ative</w> 1064205
in jec 1062509
ane ous</w> 1062186
as ing</w> 1061914
f oc 1060741
di st 1057029
S C 1055838
f low</w> 1053789
respon ses</w> 1053649
man agement</w> 1051976
av ail 1051254
con sist 1050866
A d 1050665
expos ure</w> 1050581
e al</w> 1049264
ell ular</w> 1049255
ex peri 1049021
im p 1048558
ta in</w> 1048283
si ze</w> 1048210
tic ally</w> 1042495
at eg 1042329
p epti 1041204
fol low</w> 1040334
surg ical</w> 1038463
olog ic</w> 1036576
comp on 1036370
ur ation</w> 1035945
detec ted</w> 1035731
ul es</w> 1035684
de fic 1035522
cyt o 1033419
rati o</w> 1032716
v al</w> 1030410
0.0 5</w> 1027055
h om 1025808
en s</w> 1024514
invol ved</w> 1023832
mechanis ms</w> 1023750
syndrom e</w> 1023550
pres ented</w> 1022990
enzym e</w> 1019490
wh ere 1018006
pos sible</w> 1017391
wee ks</w> 1017203
f eren 1016769
multi ple</w> 1016512
suc c 1015323
sev eral</w> 1015040
om ic</w> 1014576
l ong 1014126
en e</w> 1012056
no vel</w> 1011399
mor tality</w> 1011233
og ni 1010935
proper ties</w> 1008757
rec ur 1006951
ren ce</w> 1006842
tin al</w> 1006348
clu sion</w> 1006320
at or</w> 1005148
m uc 1003710
sel f</w> 1002657
o th 1002353
u k 1001013
outcom es</w> 998239
pro tec 997374
T w 995874
ag ed</w> 993808
at ures</w> 993762
com mun 993608
O n 993511
im aging</w> 992620
b il 991508
lun g</w> 990978
perform ance</w> 989351
differen ce</w> 989338
de fin 988052
ll ed</w> 987839
t ur 984370
resist ance</w> 983690
therap eu 982900
man y</w> 982331
de red</w> 982106
assess ed</w> 979737
measu re 979617
flu o 979316
medic al</w> 978638
li p 978327
t en</w> 977808
A L 975737
regi on</w> 974254
car b 973767
tom y</w> 973620
he art</w> 973493
s cre 972716
n an 972467
associ ation</w> 971706
rec or 971513
n al</w> 971062
re duction</w> 970896
at r 970552
et es</w> 970437
contro ls</w> 968855
str ateg 968717
s al 967244
I N 967050
con fir 965455
los s</w> 964672
mod els</w> 963565
i ly</w> 962167
S T 961981
viv o</w> 959592
mechanis m</w> 959410
immun e</w> 957957
at h</w> 957615
l ight</w> 955956
where as</w> 955933
o ur 955816
c entr 954444
1 9</w> 954144
org an 953705
un d 953632
est abl 953104
d u 953056
y st 951829
en d</w> 950176
sti tu 950151
et al</w> 949838
injur y</w> 949775
par ame 949618
al ized</w> 949588
s ite</w> 949551
ar mac 948695
E n 948267
m ed</w> 947965
de f 947802
le uk 947589
I m 945513
lif er 945442
isol ated</w> 945257
gr ad 945172
h el 945069
pre gn 944840
pos e</w> 944043
ad en 943872
N e 943290
C T</w> 943283
o st 942815
ec tion</w> 941179
typ es</w> 940861
in duc 940540
bo dies</w> 940427
yl ation</w> 938947
ogene sis</w> 938620
e p</w> 938430
sequ ence</w> 937902
mat ory</w> 937555
medi ated</w> 936962
P ati 935493
frequ ency</w> 934395
anc y</w> 933770
ograph ic</w> 931605
p ap 931103
m ag 929999
sev ere</w> 929527
a ro 929462
ang i 927156
oc ardi 926934
anc es</w> 926909
ac et 926122
2 1</w> 925992
C O 924287
G F</w> 923841
in sul 922672
tu b 922538
pro lifer 921739
H e 920621
m ass</w> 920498
y ro 918223
at al</w> 917487
analy zed</w> 917071
bl oc 915730
o ch 915325
em ents</w> 914880
com po 914024
is h</w> 913907
v entr 912057
cy cl 911670
res c 909961
be ing</w> 909541
H IV</w> 909166
am p 908912
A M 906743
sensi tivity</w> 906461
b ac 906208
st age</w> 906129
t ural</w> 904231
evalu ate</w> 903844
ran dom 903507
tran sp 902923
c ere 902412
i e 902189
val ence</w> 901850
techniqu e</w> 901122
us e 900306
a ir 900111
ass ay</w> 899931
contin u 899723
a ver 898562
inhib ition</w> 898342
mon ary</w> 898309
em s</w> 897655
ic es</w> 897647
therapeu tic</w> 896623
su s</w> 896091
cal c 895614
fu sion</w> 895270
reg ulation</w> 895125
suppor t</w> 894451
iz e</w> 894447
s mo 894266
t end 894044
li k 893415
pos ed</w> 892841
valu e</w> 892830
contr ast</w> 891841
are a</w> 891223
y r 890141
characteris tics</w> 890008
relationsh ip</w> 887736
iti s</w> 887728
A n</w> 887253
s us 887065
dise ases</w> 886574
der i 885930
d ys 884805
w a 884551
fam ily</w> 884001
ti n</w> 883488
gen etic</w> 882905
tem per 882156
i an</w> 881288
in ten 881052
i on</w> 880898
vi a</w> 880861
vol um 880694
ag on 879896
Al l</w> 878426
0.0 0 877823
ogen ic</w> 877388
al ization</w> 877157
a m</w> 876389
cl assi 875978
gr am 875914
9 . 875811
pe a 875590
ic ular</w> 875417
sec re 875383
ter min 875106
an n 871367
inflam matory</w> 871185
et er</w> 867137
le ft</w> 866977
w ays</w> 866590
ph ot 865501
hosp ital</w> 865116
gluc ose</w> 863823
parame ters</w> 863473
st atis 863384
decre ase</w> 862562
individ u 862310
8 0</w> 862264
S A</w> 860645
le sions</w> 859081
outcom e</w> 859005
con struc 858515
M S</w> 858234
ec ts</w> 858012
combin ation</w> 857839
met ast 857133
detec tion</w> 857117
Al though</w> 856920
pu bl 856508
de x</w> 856306
al ing</w> 856272
o t</w> 856184
d os 856016
ain ed</w> 855036
erg y</w> 853761
ser v 853399
health y</w> 852802
in ary</w> 852753
ma in</w> 852163
mis sion</w> 851727
re action</w> 851525
ex p 850962
bre ast</w> 850211
inc idence</w> 849934
ic h 849404
oc ial</w> 848621
A I 847621
l og 847537
admini stration</w> 846445
ic ul 846056
pos t</w> 845197
P re 844940
pro f 844784
is on</w> 844473
con taining</w> 844450
cell ular</w> 844325
0.0 01</w> 843838
e ding</w> 843203
me tr 842747
es c 841336
s af 841227
fi ve</w> 840611
U n 840328
trans pl 840060
ist s</w> 839779
A D 839602
indic ate</w> 839010
mal e</w> 838679
system s</w> 837946
re tro 836138
R e 835900
st atus</w> 835729
ca used</w> 834571
ab il 834016
l as 832930
th rom 831668
k er</w> 830320
h owever</w> 830160
ad ing</w> 829858
vi ties</w> 829551
requ ired</w> 829315
it ation</w> 828906
e ph 828760
describ ed</w> 828754
ic acy</w> 828021
ab ly</w> 827432
deri ved</w> 826614
he pati 824803
cardi ac</w> 823764
b ene 823044
ten sion</w> 821883
rele ase</w> 820983
consi dered</w> 819664
ac cur 819656
an ding</w> 819602
ca use</w> 819419
distrib ution</w> 818709
or ig 817911
s ites</w> 817852
n it 817829
test ed</w> 817523
i di 817163
evalu ation</w> 815646
l ig 815530
si m 813933
follow ed</w> 813374
inte gr 812463
or ders</w> 811677
g e 811499
tum ors</w> 811457
per s 809995
op tim 809598
s y</w> 809566
si g 809432
recep tors</w> 809117
or ing</w> 808161
previ ously</w> 806530
o di 805747
high ly</w> 805477
ag g 804467
avail able</w> 803519
the si 803002
spec ific 801619
os om 800938
sec ond</w> 800895
st s</w> 800577
M R 800291
anim als</w> 799233
syn thesis</w> 798909
A f 798434
pro mot 798273
otyp e</w> 798067
ul ts</w> 797276
R ec 796907
carcin oma</w> 796709
ph armac 795996
dis tin 795278
en s 794647
resul t</w> 794487
s oci 793404
A B 793133
9 0</w> 792207
F or</w> 792101
targ et</w> 792080
20 0 791996
I C 791735
i ev 791579
l iter 791479
scop y</w> 790279
in es</w> 790086
identi fy</w> 789495
conduc ted</w> 789279
H ere</w> 788841
th ro 788719
pro duced</w> 788566
correl ation</w> 788552
immun o 787832
A ND</w> 787045
investig ate</w> 787005
insul in</w> 786779
e ts</w> 785800
m RNA</w> 784788
OBJECTIV E</w> 784604
direc t</w> 783832
on ary</w> 783247
el ev 782790
complic ations</w> 782155
if ied</w> 782107
p tion</w> 781908
Pati ents</w> 781815
kin ase</w> 781070
ex hib 780481
l es</w> 780377
ai m</w> 779526
O R</w> 779346
recei ved</w> 779179
experim ental</w> 779009
amin o</w> 778549
ap pl 778156
in ation</w> 777432
bec ause</w> 777143
ra m</w> 776622
del i 776514
tin e</w> 775639
res t</w> 774530
defic i 774297
ul e</w> 774007
Af ter</w> 773170
ma xim 773027
m er 772229
m l</w> 772118
C a 771615
o ste 771364
ass ess</w> 771329
influ ence</w> 771146
dru gs</w> 771082
ex pressed</w> 770852
b ro 770049
o s</w> 769607
all eng 768839
ot ox 768330
P O 768274
co ur 767673
2 2</w> 766944
atr ic</w> 766532
ep ith 765112
imp act</w> 764973
fe atures</w> 764813
gl yc 763889
eff icacy</w> 763479
C R 763234
mat er 761860
anti bodies</w> 761765
op to 760257
de ath</w> 760050
impro ved</w> 759861
diab etes</w> 759301
bet ter</w> 758830
gen eral</w> 758197
assess ment</w> 758084
E ff 757823
r h 757290
tran si 757250
n at 757080
ri al</w> 756976
el ine</w> 756833
u ted</w> 756687
stand ard</w> 756295
t oc 754889
car ri 754762
y s</w> 754550
d ynam 753727
aly sis</w> 753624
stim ulation</w> 753203
ho w</w> 753030
A s 752403
int es 751543
i f</w> 751278
con f 750369
he ter 750057
su ff 749503
pre valence</w> 749478
adul t</w> 749167
in trac 748284
s in 747996
individu als</w> 747448
on es</w> 746810
at ten 746487
c ri 746162
iv en</w> 746156
d am 745985
hydro x 745846
ocyt e</w> 745672
volum e</w> 745444
mon it 745370
d ow 745330
Res ults</w> 745154
str ic 744227
u re 744137
aff ected</w> 743868
m m</w> 742792
fail ure</w> 741903
hor mon 741847
gener ation</w> 741265
path way</w> 740984
ch alleng 740813
le d 740585
7 0</w> 740002
i ally</w> 739099
the tic</w> 738753
fi eld</w> 738578
un i 738202
ap opto 737910
sk in</w> 737313
lik ely</w> 736626
h al 736085
I g 735938
h er 735105
l eng 734763
tim es</w> 733098
k en</w> 732954
im pa 732858
ro sis</w> 731859
e y 731635
si onal</w> 730706
pl o 730662
S E</w> 730235
o ff 730125
H C 729330
sub st 729270
t le</w> 729151
ati n</w> 729010
arter y</w> 728887
ol ic</w> 727592
pol ym 726806
acti vities</w> 725298
physi cal</w> 725022
sam ple</w> 724585
sur ve 724210
ant ation</w> 723479
fr ac 723373
ol y 723294
g am 722342
inv a 722123
cont ent</w> 721728
un ds</w> 721307
anti body</w> 720583
1 , 720408
ul ations</w> 720318
th en</w> 718734
A R 717885
at ors</w> 717835
liter ature</w> 717684
A r 717597
h ep 715962
u til 715918
incre asing</w> 715666
id ity</w> 715010
a im 713730
par t</w> 713426
tissu es</w> 713348
in dependent</w> 712975
D E 712785
pat tern 712551
sensi tive</w> 712484
over all</w> 712252
si mul 712200
fluo resc 712021
w o 711812
indic ated</w> 710559
1 2 710476
trans f 710265
r ing</w> 710100
pul monary</w> 708710
en ergy</w> 708200
ox ide</w> 707892
A P 707561
dis orders</w> 707557
r y 706729
pl at 706160
c ross</w> 705836
b el 704614
pl ay</w> 704492
ev en</w> 704158
rel ative</w> 704147
feren ce</w> 704049
radi ation</w> 703899
individ ual</w> 703692
s l 703195
m en</w> 703085
ad ju 702956
inter action</w> 702578
dom in 702129
inter vention</w> 701298
analy ses</w> 701183
o m</w> 701063
v acc 700459
si x</w> 700224
in dex</w> 699129
ol s</w> 699048
wh ere</w> 698997
neur ons</w> 698937
w ent</w> 698821
o p</w> 695714
y oun 695492
ol ution</w> 694922
1 3 694767
t ation</w> 694467
fem ale</w> 693723
cor related</w> 693631
cal cul 693103
temper ature</w> 692881
of ten</w> 692633
u sion</w> 692496
mal ign 692113
mo use</w> 691598
ter n</w> 691552
sequ ences</w> 691377
den sity</w> 690884
tu red</w> 689597
ne ed</w> 688805
is ed</w> 688673
sig n</w> 688661
led ge</w> 688183
or b 688146
contro lled</w> 686995
character ized</w> 686798
le ast</w> 684293
m ade</w> 683872
proced ure</w> 683440
vir al</w> 683300
complet e</w> 682861
o red</w> 682187
br al</w> 680305
at eral</w> 680256
impro ve</w> 680182
under went</w> 679976
sc ore</w> 679008
o tic</w> 678480
b ri 678159
appro xim 677733
l y 677644
19 9 677556
ver sus</w> 677515
i sion</w> 676860
R N 676680
N o</w> 675913
so ur 675386
i de 674047
iz ing</w> 673970
aver age</w> 673934
particip ants</w> 672904
pat tern</w> 672340
ud e</w> 672196
the re 671714
diagnos tic</w> 671531
am oun 671353
o ti 670335
poin t</w> 670139
1 4 670031
gh t</w> 669608
str ains</w> 668029
ro p 667881
p rec 667230
pattern s</w> 666478
N F</w> 666156
n et 666082
demonstr ate</w> 665584
se par 665210
ke y</w> 665020
bi ological</w> 664524
initi al</w> 664441
enh anced</w> 663968
ste red</w> 663482
cul ture</w> 662834
B M 662558
b or 662185
ob jec 661799
s co 661782
I II</w> 661520
P s</w> 661461
b ur 661422
ven ess</w> 661200
te red</w> 660407
ad he 659772
enc ed</w> 659446
techniqu es</w> 658944
regi ons</w> 658745
ati vely</w> 657974
ir atory</w> 657187
obser v 656728
succ ess 656709
reg ar 656054
P h 655858
E S</w> 653879
occur red</w> 653422
cor ding</w> 652980
g iven</w> 652957
test s</w> 652337
m ight</w> 651984
ag ents</w> 651846
i . 651822
n er</w> 651432
appl ied</w> 651158
phosph or 650961
2 8</w> 650880
om as</w> 649959
abl es</w> 649485
centr al</w> 649166
combin ed</w> 648945
cor onary</w> 648533
S D</w> 648486
P A</w> 648210
an ds</w> 648069
re mo 647883
A t</w> 647342
id ne 647285
pot enti 647209
cl os 647009
r ight</w> 646818
mo der 646808
u ally</w> 646566
adul ts</w> 646493
end oth 646448
ur ther 645665
s ocial</w> 645645
al es</w> 644827
si l 644162
ful ly</w> 644148
urther more</w> 642458
are as</w> 641717
ond ary</w> 641375
ev ents</w> 641221
mi x 640623
al low 640055
tr ial</w> 639980
pa ren 639902
2 3</w> 639892
su m 639719
activ ated</w> 638643
ex erc 638598
ach iev 638499
min im 638045
t ol 638042
pap er</w> 637378
3 5</w> 636679
inhibit or</w> 636580
gre es</w> 636312
leng th</w> 635709
str y</w> 634604
P D</w> 634491
loc al</w> 634328
on t 634280
behavi or</w> 633321
re pe 633220
sign aling</w> 632857
inhib ited</w> 632824
struc tural</w> 632736
e duc 632678
know ledge</w> 632324
process es</w> 632321
th al 632274
gu id 631887
w ide</w> 631847
b ir 629597
Tw o</w> 629549
p H</w> 629210
5 3</w> 628950
se en</w> 628733
PC R</w> 628733
ut es</w> 628558
pepti de</w> 627765
ay ed</w> 627742
coll ected</w> 627742
lim ited</w> 627565
infec ted</w> 627170
qu e</w> 626957
discus sed</w> 626110
de grees</w> 625832
prolifer ation</w> 625680
och ond 625443
spec tr 625431
lin es</w> 624674
struc tures</w> 624098
erg ic</w> 623888
F urthermore</w> 623284
anti gen</w> 623259
ventr icular</w> 623197
the m</w> 623166
ex t</w> 622495
peri ph 622366
un it</w> 622296
w id 621745
s em 621718
spec tro 620714
practi ce</w> 620015
7 5</w> 619995
sign ed</w> 619426
sh ort</w> 619381
di men 619156
in e 618264
rec ent</w> 618193
compo unds</w> 618108
par t 617829
wo uld</w> 617633
compar ison</w> 617276
ch ain</w> 617229
ch arg 616692
l it 616582
G F 616442
ne ed 615779
resul ted</w> 615461
use ful</w> 615388
cor rec 615161
go od</w> 615102
ul tr 614551
inter actions</w> 614450
er ing</w> 614354
th yro 614183
. 0 614050
fol d</w> 613630
reg ression</w> 613393
se g 613385
c ogni 612634
or ption</w> 612120
D is 611719
U sing</w> 611465
re main 611402
plas m 611377
k idne 610033
form s</w> 609328
intes tinal</w> 608894
tr aining</w> 608552
s it 608107
confir med</w> 608012
oph ag 607820
ut ure</w> 607391
revie w 607279
m el 606947
cr iter 606914
suggest ed</w> 606759
tech n 606518
P T 606407
au th 605899
ec tomy</w> 605577
hist ory</w> 605328
- - 605276
mag ne 605216
d uration</w> 604729
c i 604131
m ation</w> 603952
s w 603762
bacter ial</w> 603257
incre ases</w> 603074
defin ed</w> 602275
tri als</w> 601060
0.0 1</w> 601003
m ig 599616
ner ve</w> 599463
de v 599159
mo l</w> 598890
re pres 598832
is tic</w> 597473
o vascular</w> 597411
injec tion</w> 596655
n ur 596389
P E 596075
measu res</w> 596047
su per 595844
medi an</w> 595004
an e</w> 594865
ac ross</w> 594058
ch o 594053
applic ation</w> 593315
st re 593297
l or 593286
underst anding</w> 592790
t op 592721
bas eline</w> 592657
an al 592434
ast ro 592431
a k 592219
ra y</w> 592122
c ou 592067
pan cre 591885
d oc 591766
suggest s</w> 591349
scre ening</w> 591051
calc ium</w> 590511
experim ents</w> 590386
e b 590290
fib r 590139
sco res</w> 590004
selec ted</w> 589955
o in 589776
ur ine</w> 589721
t ow 589355
ul ated</w> 588999
nec ess 588789
al coh 587858
e red</w> 586853
ar ds</w> 586497
establ ished</w> 586495
S ur 586232
l ay 585434
pa ir</w> 585148
t able</w> 585137
ch ed</w> 584385
P ar 583950
re comm 583139
im plic 582848
approxim ately</w> 582845
P L 582701
os cop 582534
ver sity</w> 581773
d ra 581149
0 00</w> 580810
om y</w> 580746
pro posed</w> 580631
metabol ism</w> 580529
C o 580414
str ain</w> 580206
S E 580039
ur s</w> 579988
t ory</w> 579489
dam age</w> 579126
sub sequ 578551
acc um 578291
4 5</w> 577718
P S</w> 577622
previ ous</w> 577132
sub str 576062
sugg esting</w> 575845
Th us</w> 575833
D e 575506
bi om 574968
func tions</w> 574837
consi der 574310
ep t</w> 573505
n u 573220
2 7</w> 572019
pro por 571981
sec ondary</w> 571862
p ow 571697
ter i 571501
nor mal 571497
esti on 571002
On e</w> 570990
emb ry 570335
exerc ise</w> 569870
ga str 569349
mut ations</w> 569348
differenti ation</w> 568618
periph eral</w> 568509
diagnos ed</w> 568175
my ocardi 568154
He al 568153
4 8</w> 567879
gl ut 567836
h ar 567752
st ro 567692
og en</w> 567066
cap ac 566778
sc ale</w> 566713
con clud 565666
id es</w> 565623
dis order</w> 565252
measure ments</w> 564685
ad ap 564601
al tern 564593
examin ation</w> 564514
l l</w> 564280
apopto sis</w> 564249
quanti t 563529
2 6</w> 563038
r im 563018
A P</w> 562880
is chem 562587
1 5 562502
C lin 561760
arti cle</w> 561732
o v 560931
rel ev 560797
pop ulations</w> 559855
form ed</w> 559630
pregn ancy</w> 559554
d o</w> 558662
pro gram 557632
post operative</w> 557369
st able</w> 557332
gl y 557050
C T 556178
vis ual</w> 555919
metabol ic</w> 555301
ac ids</w> 554771
es pec 554766
dom ain</w> 554386
y i 553790
ess ential</w> 553730
espec ially</w> 553242
al one</w> 553119
3 1</w> 552835
Th e 552327
d ou 552305
3 6</w> 551947
p ite</w> 551530
1 6 551452
d on 551399
r are</w> 551331
mit ochond 551223
compon ents</w> 551168
provid es</w> 550704
ai ly</w> 550271
3 2</w> 550051
cri tical</w> 549863
s et 549735
c t</w> 549586
medi ate</w> 548626
infec tions</w> 548483
venti onal</w> 548315
1 8 548219
do es</w> 547603
Wh en</w> 546390
sol ution</w> 544473
de sign</w> 541733
ic ro 541600
t esting</w> 541599
le ad</w> 541529
dic al</w> 541515
th us</w> 541506
T R 541391
se x</w> 540794
enc ing</w> 540742
b u 540537
b asis</w> 540412
bi op 540014
oxy gen</w> 539744
a or 539659
requ i 539657
compl ex 539224
cour se</w> 538405
experi ence</w> 538194
hosp it 537758
c u 537642
lip id</w> 537544
transcrip tion</w> 537532
B i 536946
or i 536842
sti ll</w> 536752
oc y 536611
cogni tive</w> 536496
fr ag 536445
b al 536331
ad s</w> 536201
con ver 536132
ul arly</w> 536048
ac cording</w> 535580
le ar 535575
el ing</w> 535420
r ine</w> 535253
criter ia</w> 534742
ent er</w> 534522
effici ent</w> 534477
fac il 534196
prog ression</w> 533991
tra um 533930
or atory</w> 533042
ho urs</w> 532785
v it 532651
m id 532079
re fl 532078
resist ant</w> 532020
aff ect</w> 531855
sign al</w> 531758
ser ies</w> 531667
sen s 531198
is o 530878
electr on</w> 530124
strateg ies</w> 529931
st ability</w> 529893
Q u 529463
on set</w> 529019
impro vement</w> 528942
wee k</w> 528232
rap id</w> 527353
cl ear</w> 527171
am pl 526935
in tra 526820
rec over 526724
g lob 526660
de g 526418
resi d 526329
cor respon 526254
diff icul 526181
addi tional</w> 526131
th i 526013
estion n 525855
resp iratory</w> 525600
A D</w> 525433
ut aneous</w> 524691
di g 524114
I F 523646
cy cle</w> 523629
m es 523450
meth yl 523360
path ways</w> 522930
sc i 522790
gam ma</w> 522745
appro pri 522702
pro bl 522606
eri or</w> 522604
or ity</w> 522009
ne l</w> 521272
scop ic</w> 521223
em plo 520776
re ma 520704
ol der</w> 520528
cl er 520177
d aily</w> 520153
rema ins</w> 519563
in si 518348
p i 518143
kidne y</w> 518033
coh ort</w> 517884
ve red</w> 517746
up take</w> 517690
inhibit ors</w> 517526
us t</w> 517423
chol ester 517350
S P 517187
M R</w> 516988
M eth 516700
o ple</w> 516483
describ e</w> 516305
f uture</w> 515876
reduc e</w> 514607
t ent</w> 514179
d ri 513912
st ed</w> 513096
tol er 512980
ific ation</w> 512870
abs ence</w> 512613
str ong 512404
gr ade</w> 511968
deli very</w> 511733
chem otherapy</w> 511732
M D 511725
flu id</w> 511193
ten sive</w> 510916
pro mis 510639
commun ity</w> 510355
fo od</w> 509963
rel i 509529
B P</w> 509308
my el 508825
re ver 508676
al though</w> 508617
produc ts</w> 508415
sp inal</w> 508188
molec ules</w> 508007
plac ement</w> 507344
dos es</w> 507335
lin ked</w> 506882
m am 506653
r e</w> 506409
3 7</w> 506208
consist ent</w> 505908
t as 505376
c ost</w> 504910
id ine</w> 504615
3 8</w> 504220
O f</w> 504213
3 4</w> 504205
og n 504204
provid ed</w> 504177
ant agon 504054
ther s</w> 503983
recomm end 503977
ach es</w> 503763
in duction</w> 502976
g i 502735
ac e</w> 502428
W ith</w> 502353
po or</w> 502228
t a</w> 502170
ac coun 502140
random ized</w> 500914
U R 500773
d at 500580
import ance</w> 500468
de grad 500412
6 5</w> 500350
medi um</w> 500007
b ial</w> 499797
loc ation</w> 499651
epith elial</w> 499242
b ar 498619
A T</w> 498466
expos ed</w> 498339
is su 498288
s pl 498082
lin ear</w> 497888
3 3</w> 497882
Heal th</w> 497470
O S</w> 497202
ar ch</w> 496850
inter val</w> 496530
im ens</w> 496494
proced ures</w> 496387
om er 496258
deg ree</w> 496216
2 9</w> 495719
er y 495611
sol u 495514
C s</w> 495406
ol esc 495163
pr e</w> 494977
de pression</w> 494826
admini stered</w> 494821
hyper tension</w> 494808
youn g</w> 494405
ogen ous</w> 494227
s le 493395
v ent</w> 493350
g ran 493095
di et</w> 493024
nucle ar</w> 493021
O n</w> 492391
conc er 492121
th ir 492063
hormon e</w> 491680
am ide</w> 491324
en ting</w> 491048
l ation</w> 490363
l ying</w> 490343
o w</w> 489731
re at 489346
environ ment</w> 489231
og ether</w> 489108
regul ated</w> 488791
resul ting</w> 488413
pri or</w> 488294
plat el 487653
A N 487353
B oth</w> 486523
MR I</w> 486427
en co 486278
Ch in 485972
capac ity</w> 485753
ren g 485588
elev ated</w> 485382
ultr as 485159
condi tion</w> 485116
gro und</w> 485006
physi ological</w> 484917
transp ort</w> 484726
probl ems</w> 484392
di um</w> 484343
mar kers</w> 484221
recover y</w> 483955
compon ent</w> 483743
in tro 483194
ib le</w> 482965
nat ural</w> 482768
o si 481874
pre par 481830
in ity</w> 480715
mech an 479651
e ds</w> 479640
ic an</w> 479184
inflam mation</w> 479016
hep at 478985
n ot 478947
A S</w> 478212
T ran 477347
pe ople</w> 476876
hepati c</w> 476814
1 7 476800
i ting</w> 476800
pos si 476363
il s</w> 476359
C S 476323
c o</w> 475688
Th ere 475342
defici ency</w> 475199
M ore 474807
er ity</w> 474696
achiev ed</w> 474565
trans fer 474540
R T</w> 474372
G en 474220
o z 474181
it on 474177
os ome</w> 473811
ta ken</w> 473612
h ost</w> 473571
C H 473540
arter ial</w> 473227
suff ici 472833
r ation</w> 472743
secre tion</w> 472442
More over</w> 472199
r ib 472159
tox icity</w> 471935
teri or</w> 471214
al ter 471189
a er 470794
p ut</w> 470778
In ter 469657
partic ularly</w> 469360
inva sive</w> 469249
mut ation</w> 469086
cat aly 468795
mon th</w> 468545
p en 468507
ast s</w> 468281
saf ety</w> 467329
op en</w> 467173
pur pose</w> 466883
ne um 466618
magne tic</w> 466452
l ed</w> 466409
cr yst 466351
t e</w> 465934
s om 465860
endoth elial</w> 465281
chrom at 464836
st reng 464689
S er 464389
mar k 464296
fic ial</w> 464111
si a</w> 463576
gastr ic</w> 463323
cardi ovascular</w> 463148
system ic</w> 463092
sup pl 462280
vari ables</w> 462276
o ids</w> 462214
fi x 461959
4 2</w> 461786
tr ig 461730
de n</w> 461708
myocardi al</w> 461686
termin al</w> 461666
necess ary</w> 461554
en ter 461373
modi fied</w> 461099
stro ke</w> 460964
mac roph 460913
stimul ated</w> 460736
val ent</w> 460620
e ight</w> 460334
ad verse</w> 460223
cl ass</w> 460220
c epti 460126
con ventional</w> 459820
ch lor 459764
ip s</w> 459478
anti bio 458806
appropri ate</w> 458723
phosph ate</w> 458363
19 8 458349
polym er 457856
0. 5</w> 457585
cere bral</w> 457317
or ies</w> 457176
mot or</w> 457125
RN As</w> 457080
S H</w> 456992
ex amine</w> 456941
s ex 456755
am in</w> 456637
G rou 456526
e tics</w> 456035
ill ary</w> 455961
coun tr 455602
al g 455577
vie w</w> 455392
need ed</w> 455347
specific ity</w> 455137
statis tically</w> 454906
yc in</w> 454607
pro toc 454511
transf er</w> 454255
transpl antation</w> 454240
ear ance</w> 454218
am eter</w> 453810
f un 453774
b ul 453713
org anis 453680
ol ar</w> 453650
There fore</w> 453441
l ast</w> 453237
su mp 452694
St ud 452519
col i</w> 452035
quantit ative</w> 451799
selec tive</w> 451689
wh ole</w> 451651
et ed</w> 451462
eri als</w> 451376
m em 451122
recor ded</w> 451071
p al 450814
re stric 450781
pl ant</w> 450539
l ate</w> 450237
prog ram</w> 450014
poly morph 449555
M M 449544
lac k</w> 449497
ne w 449496
fib ro 448877
on ucle 448797
ar c 448584
e tion</w> 448084
j unc 447956
ca uses</w> 447942
fluoresc ence</w> 447416
j oin 447285
F in 446887
b enz 446843
cl e 446589
monit oring</w> 446583
at t 446361
mod ul 446343
includ e</w> 446230
ti veness</w> 446220
opath y</w> 446067
throm b 445945
direc tly</w> 445741
ab dom 445712
fe w</w> 445699
fi t</w> 445617
fam il 445199
val id 445033
tem por 444897
vari ation</w> 444745
d ate</w> 444641
og lob 444507
em entation</w> 444491
0. 9 444419
gen ic</w> 444166
eph al 444013
he ad</w> 443848
cholester ol</w> 443832
c m</w> 443831
aim ed</w> 443503
high est</w> 443492
carri ed</w> 442953
ad olesc 442764
4 4</w> 442684
intr av 442593
un its</w> 442510
sev erity</w> 442346
al k 441850
l a 441505
diab etic</w> 441494
venti ons</w> 441120
calcul ated</w> 441005
sus cepti 440733
as p 440568
partic ular</w> 440414
P D 440331
sul f 440228
up on</w> 440006
there fore</w> 439609
TI ON</w> 439463
lat eral</w> 439444
an terior</w> 439317
devel op</w> 439124
bacter ia</w> 438990
ur b 438981
regar ding</w> 438851
ri x</w> 438802
an ning</w> 438547
H R</w> 438414
identi fication</w> 438158
rec ogn 437951
tre m 437593
distin ct</w> 437528
p ig 437283
dou ble</w> 437178
ati c 436948
ac qu 436625
on omic</w> 435452
estim ated</w> 435400
2 , 435348
ic ation</w> 434989
cor por 433072
re pair</w> 433070
sim ple</w> 432860
meas ure</w> 432130
z ation</w> 432006
anim al</w> 431975
ir e</w> 431850
c um 431796
malign ant</w> 431519
ho od</w> 431436
tion ing</w> 431399
inf ants</w> 431292
te x</w> 431254
e .</w> 430603
u ter 430310
ad d 429938
li qu 429882
so dium</w> 429564
res sive</w> 429545
tion ally</w> 429473
Ca 2</w> 429263
under lying</w> 429234
res on 428846
amoun t</w> 428674
m an</w> 428317
or d 427850
otyp es</w> 427710
id al</w> 427559
ter ms</w> 427369
qu estionn 427041
A mon 426673
ro w</w> 426594
S T</w> 426062
sev en</w> 426038
P R 426017
alcoh ol</w> 425323
ro gen</w> 424839
plic ation</w> 424573
t ogether</w> 424459
osom al</w> 424171
relev ant</w> 424141
environ mental</w> 423948
gen s</w> 423915
om y 423353
r ab 423281
in ated</w> 423215
appro aches</w> 423094
treat ments</w> 422998
C N 422968
d op 422849
V E 422777
pl ing</w> 422751
enzym es</w> 422751
f at</w> 422741
H y 422657
ag ent</w> 422641
D uring</w> 422529
as pec 422441
x i 422099
dys function</w> 422012
ar r 421485
ent y</w> 421407
0. 8 421256
ometr ic</w> 421052
str ong</w> 420940
metr y</w> 420913
ne o 420393
oper ation</w> 420351
g li 420091
sle ep</w> 419775
per fusion</w> 419760
se p 419118
ac ts</w> 419014
th ic 418981
rec tal</w> 418885
Amon g</w> 418446
mut ant</w> 418226
M ul 418154
maxim um</w> 418134
rel ation</w> 418132
ha ving</w> 418002
l ec 417948
p neum 417695
f at 417491
lab oratory</w> 417489
4 3</w> 417275
review ed</w> 417216
surve y</w> 417200
arti cles</w> 416502
n g</w> 416362
develop ing</w> 416358
res h 416124
re al</w> 415975
ri tis</w> 415940
relati vely</w> 415920
occ ur</w> 415651
muc h</w> 415528
i. e.</w> 415522
b est</w> 415142
moder ate</w> 414688
i stry</w> 414684
D L</w> 414534
ad ren 414369
ar ch 413771
a qu 413689
E D</w> 413610
intrac ellular</w> 413573
S ev 413030
exhib ited</w> 412949
measure ment</w> 412502
C A</w> 412276
indic ating</w> 412229
pa rent</w> 412074
ip p 412047
accur acy</w> 411722
pre vention</w> 411697
os a</w> 411636
dow n</w> 411603
gen ome</w> 411436
v ical</w> 411008
app ears</w> 410949
Clin ical</w> 410937
process ing</w> 410766
con st 410412
ph yl 410362
de signed</w> 410342
inter ventions</w> 409831
strateg y</w> 409750
5 5</w> 409621
Grou p</w> 409047
c rip 408995
o od</w> 408960
stud ents</w> 408949
cur ren 408799
air e</w> 408763
comp are</w> 408740
le m</w> 408366
le ading</w> 408228
emplo y 408190
mal es</w> 407852
subsequ ent</w> 407747
s atis 407686
adv anced</w> 407560
micro scopy</w> 407426
frequ ently</w> 407283
el i 407229
ab normal 407208
acc ep 407041
altern ative</w> 407023
en sive</w> 406839
bir th</w> 406757
pre ven 406432
sump tion</w> 406407
educ ation</w> 406276
di atric</w> 406260
7 2</w> 406096
pea k</w> 405600
C l 405589
hy bri 405348
U S</w> 404814
ometr y</w> 404612
hal f</w> 404542
m L</w> 404520
re x 403910
invol vement</w> 403874
p ac 403873
an ol</w> 403384
fem ales</w> 403279
inten sity</w> 402899
c y</w> 402799
bac k</w> 402773
in take</w> 402635
t i</w> 402461
vi sion</w> 402421
contrib ute</w> 402420
it ary</w> 402416
dimen sional</w> 402390
vie w 402371
effec tiveness</w> 402309
s pati 402276
v en</w> 402175
tr ation</w> 401844
olog ically</w> 401784
main ly</w> 401731
facil it 401601
a k</w> 401295
n s</w> 401285
rec ently</w> 401269
thesi zed</w> 401132
al ong</w> 401127
P er 401051
sp e 401026
el ed</w> 401021
A b 400659
L A</w> 400454
gener ated</w> 400314
pro spective</w> 400299
mat rix</w> 400278
enc ies</w> 400083
hel p</w> 400030
me ans</w> 399899
accum ulation</w> 399790
lit tle</w> 399712
eb o</w> 399706
on tal</w> 399146
lear ning</w> 398764
impa ir 398652
S S</w> 398395
net work</w> 398107
ex tent</w> 398099
oc k</w> 398014
sh or 397910
ru p 397899
ax is</w> 397893
op o 397820
inhibit ory</w> 397663
sour ce</w> 397431
perc ent 397332
R eg 397280
es ophag 397276
A mer 397208
S ub 397015
der iv 396822
fr action</w> 396669
loc ated</w> 396482
sh if 396056
oti de</w> 395694
SI G 395543
re construc 395470
ne u 395381
s al</w> 395359
tu al</w> 395349
poin ts</w> 395103
ell a</w> 394640
conf idence</w> 394588
vari ate</w> 394187
asi c</w> 394000
to ol</w> 393916
P UR 393707
at tr 393620
e ting</w> 393368
L I 393158
charg e</w> 393109
vari able</w> 392961
carb on</w> 392740
rou s</w> 392660
a de 392637
auth ors</w> 392516
s per 392425
PUR PO 392056
wil d</w> 391286
go ing</w> 391280
ur inary</w> 391225
- 1</w> 390928
mater ial</w> 390926
th em 390596
sh ap 390399
ep tion</w> 390366
mel an 389829
coll ag 389825
A p 389775
objec tive</w> 389746
aff inity</w> 389663
plac ebo</w> 389358
ot rop 389322
2. 5</w> 389202
am e</w> 389128
O R 389039
PURPO SE</w> 389024
im ages</w> 388957
h and</w> 388492
D ata</w> 388333
O 2</w> 387585
b le 387346
subst anti 387340
ol ig 387246
mem ory</w> 387099
thir d</w> 387064
in corpor 387040
selec tion</w> 387002
effici ency</w> 386972
ner v 386711
C a</w> 386433
inc ub 386097
an es 386050
hist o 385550
oph il 385478
3 9</w> 385330
adv ant 384964
og s</w> 384924
A u 384733
as th 384684
prob lem</w> 384659
ab o 384614
per sist 384399
regul atory</w> 384167
re st 384156
gn os 384027
com position</w> 383908
P C</w> 383738
ar b 383727
et ary</w> 383279
sy m 382888
w all</w> 382712
B y</w> 382648
mit ted</w> 382217
no w</w> 381901
thyro id</w> 381797
cor tex</w> 381708
com mon 381621
re tin 381587
hypo thesis</w> 381512
sin ce</w> 381013
v ar 380863
mat ure</w> 380524
e ti 380498
ass ays</w> 380346
ic a</w> 379922
mitochond rial</w> 379664
s qu 379338
show s</w> 379276
t y 378817
res ection</w> 378763
inter nal</w> 378563
4 1</w> 378398
C ol 378374
throu gh 378183
kn ess</w> 377522
cul tures</w> 377509
reson ance</w> 377259
le sion</w> 377054
g astro 376896
or ation</w> 376752
ig n</w> 376672
trac t</w> 376484
phosphor ylation</w> 376401
Stud y</w> 376392
ing ly</w> 376245
repor ts</w> 376144
or ph 375973
pro fil 375649
es tion</w> 375539
A C</w> 375363
isol ates</w> 375313
u tive</w> 375310
ne on 375119
Th ree</w> 375112
f av 374688
ste p</w> 374662
um er 374562
ade qu 374432
liqu id</w> 374394
recur rence</w> 374239
predic ted</w> 373652
fat ty</w> 373612
em ic</w> 373577
re produc 373464
oxid ative</w> 373293
f etal</w> 373140
applic ations</w> 372928
correspon ding</w> 372789
ar ity</w> 372540
meth yl</w> 372526
remain ed</w> 372427
6 7</w> 372208
all el 372164
ou th</w> 372153
k ground</w> 372117
N or 371998
ha em 371772
investig ation</w> 371632
col on 371598
hem at 371306
p yr 371266
l ater</w> 371123
trans mission</w> 371075
tend ed</w> 370866
oc l 370544
dem ic</w> 370539
C or 370515
res olution</w> 370356
L e 369939
se ud 369772
protec tive</w> 369724
optim al</w> 369532
syn ap 369488
mor b 369145
aor tic</w> 368923
ati ves</w> 368780
a ir</w> 368725
re actions</w> 368598
hydrox y 368411
aut o 368366
asi a</w> 368352
mechan ical</w> 368022
organ ic</w> 367728
substr ate</w> 367596
d le</w> 367594
S i 367587
r al</w> 367534
con nec 367330
ar th 367108
em er 366279
sp ont 366214
cat eg 366154
nu tri 366106
ex trem 366037
p h</w> 365773
4 6</w> 365269
st ages</w> 365215
lac t 365159
T r 364801
pro xim 364626
en ous</w> 364576
g el</w> 364444
ch ing</w> 364397
wor l 364080
dat ab 364001
lay er</w> 363932
st er</w> 363886
mi R</w> 363865
ER I 363582
a b</w> 363273
el etal</w> 363273
clin ically</w> 362924
an at 362903
exp ected</w> 362684
prog nosis</w> 362567
uni que</w> 362535
com e</w> 362500
I I 362212
abdom inal</w> 362148
plas tic</w> 362015
pro state</w> 361529
al b 361444
atten tion</w> 360892
ch an 360879
mus t</w> 360679
T reat 360620
vari ety</w> 360513
b ound</w> 360441
es ity</w> 360418
ad der</w> 360352
complex es</w> 360265
4 9</w> 359922
hy th 359722
cer vical</w> 359410
gnos tic</w> 359316
ap pe 359179
de ta 359064
emer gen 358920
chil d</w> 358898
publ ic</w> 358895
un known</w> 358874
smo king</w> 358871
larg er</w> 358693
pl ants</w> 358602
i on 358480
ell ing</w> 358028
pres sive</w> 357802
5 6</w> 357531
ful l</w> 357393
respon sible</w> 357370
S C</w> 357367
G l 357238
induc e</w> 356886
parti al</w> 356748
sch o 356709
der ly</w> 356408
prof ile</w> 356370
y g 356203
tom ography</w> 356084
prof es 355865
streng th</w> 355853
retro spective</w> 355785
ar yn 355719
P V</w> 355676
B ased</w> 355632
spec imens</w> 355550
5 2</w> 355475
b ase</w> 355299
ul in</w> 355275
as ym 355258
vit amin</w> 355158
cor e</w> 354954
biop sy</w> 354750
w ar 354740
fun g 353522
degrad ation</w> 353145
ap pea 353122
M A</w> 353097
ari ly</w> 353029
f ying</w> 352784
appea red</w> 352774
re as 352478
4 7</w> 352275
chromat ography</w> 352163
agg reg 352108
serv ices</w> 351923
perc ent</w> 351720
er ro 351577
dynam ics</w> 351347
cyt otox 351186
platel et</w> 351023
ester n</w> 350960
extrac ellular</w> 350854
um in 350723
li po 350658
ir on</w> 350365
H B 349918
1. 5</w> 349910
abo ve</w> 349810
produc t</w> 349770
strong ly</w> 349743
cul tured</w> 349566
s at 349362
tro ph 349110
or ts</w> 348753
ne ural</w> 348739
oxid ation</w> 348530
l ap 348442
success ful</w> 348228
m entation</w> 348168
st aining</w> 347978
mo tion</w> 347646
S N 347582
re ad 347470
common ly</w> 347242
F or 346929
as c 346831
percent age</w> 346638
propor tion</w> 346462
re ference</w> 346283
maj ority</w> 345974
comp ut 345787
enh anc 345750
under going</w> 345737
recei ving</w> 345649
i ous</w> 345610
tern al</w> 345525
Treat ment</w> 345333
continu ous</w> 345290
SIG N</w> 345179
C I 344706
M AT 344575
con sumption</w> 344006
eth yl 343922
6 4</w> 343843
em o 343818
di sp 343700
Di ff 343558
sign s</w> 343351
countr ies</w> 343214
z ing</w> 343091
resi du 343045
mater nal</w> 342943
signific ance</w> 342860
5 4</w> 342805
in ce</w> 342775
long er</w> 342773
M o 342560
o vari 342480
ven ous</w> 342463
publ ished</w> 342210
or rh 341825
up per</w> 341587
e m</w> 341556
cer tain</w> 341522
produc e</w> 341468
simul t 341304
DE SIGN</w> 341279
s in</w> 341151
OBJECTIV ES</w> 341007
ra ther</w> 340961
on ch 340878
d y 340830
AT P</w> 340674
di tional</w> 340385
las er</w> 339986
em erg 339888
S h 339753
chem istry</w> 339470
mig ration</w> 339392
T NF</w> 339365
dynam ic</w> 339348
f ish</w> 339311
gly co 339214
F urther</w> 339169
20 0</w> 338750
st ained</w> 338589
ex am 338140
pre domin 338124
remo val</w> 338112
t ren 338109
6 2</w> 338008
pl ays</w> 337963
pro st 337683
al tered</w> 337626
ti t 337617
Com par 337565
lymph ocytes</w> 337547
doc um 337432
An alysis</w> 337082
an xi 337008
S im 336912
ide mi 336665
ec ting</w> 336656
E N 336425
k er 336413
Mul ti 336244
t or 336182
divid ed</w> 336155
p on 335696
5 1</w> 335681
mat erials</w> 335354
The y</w> 335228
pl asia</w> 335182
os ine</w> 335120
ob struc 335012
residu es</w> 334938
H g</w> 334901
v ation</w> 334868
st ar 334812
post erior</w> 334565
bas al</w> 334531
met al</w> 334517
ti co 334501
pepti des</w> 334337
Meth ods</w> 334146
atten u 334044
M I 334019
pl ast 333974
satis fac 333942
cor tical</w> 333848
pre pared</w> 333806
hepati tis</w> 333620
chan nel</w> 333480
press ing</w> 333439
O b 333309
ane ously</w> 333203
pow er</w> 333088
bl adder</w> 333086
D es 333011
inf arc 333005
re active</w> 332841
, 000</w> 332808
fibro bl 332793
S pec 332706
S y 332398
M y 331893
neur onal</w> 331765
th or 331762
con trac 331665
0. 1</w> 331514
chrom osome</w> 331330
pl ed</w> 331291
el ium</w> 331204
tempor al</w> 331050
pre vent</w> 330824
occur s</w> 330822
foc us</w> 330759
a w 330365
el derly</w> 330341
man if 330320
neuro log 330111
mar ker</w> 330042
S p 329962
inf usion</w> 329933
pro jec 329882
ogni tion</w> 329855
promot er</w> 329855
morb idity</w> 329848
tas k</w> 329712
rec ip 329709
ex tr 329456
im age</w> 329292
rec ru 329184
om es</w> 329091
collag en</w> 328626
abnormal ities</w> 328329
al lo 328020
pur ified</w> 327798
hum ans</w> 327643
emb er</w> 327562
os yn 327482
ob ic</w> 327465
pancre atic</w> 326761
for ce</w> 326547
C B 326545
tub er 326506
0 0 326492
MAT ERI 326479
mat ched</w> 326170
b en 326157
6 3</w> 325990
s on</w> 325956
0. 7 325841
invol ving</w> 325486
e . 325380
micro M</w> 325214
n ational</w> 324927
C M 324500
cler osis</w> 324440
E R</w> 324113
mil d</w> 324024
op e</w> 323897
gen ase</w> 323863
y et</w> 323773
ol ip 323633
ar cin 323598
ac char 323543
gen ital</w> 323355
fl ex 323037
g er</w> 322968
potenti ally</w> 322861
re combin 322840
ver s</w> 322810
Im mun 322790
phen otype</w> 322778
ag re 322382
ti al</w> 322348
cont act</w> 322340
6 8</w> 322335
t on</w> 322239
pro gnostic</w> 322180
s ph 322012
tem p 321904
k es</w> 321824
membran es</w> 321800
th resh 321692
or ith 321569
gra ft</w> 321521
ap h 321489
fer red</w> 321315
con sec 321287
Wh ile</w> 321120
si vely</w> 320900
plasm ic</w> 320854
path ogenesis</w> 320768
B ac 320724
an th 320603
H D</w> 320586
S O 320515
mix ed</w> 320364
do m</w> 320308
spati al</w> 320259
el ines</w> 320146
AL S</w> 320125
pr inc 320060
5 8</w> 319996
A A</w> 319909
ob esity</w> 319770
man ner</w> 319700
c anc 319480
acet yl 319427
ju g 319385
aspec ts</w> 319302
d enti 319238
ab normal</w> 319236
heter ogene 319193
complet ed</w> 319168
bar ri 318875
n ed</w> 318750
molec ule</w> 318721
n ature</w> 318698
sub stitu 318671
differenti al</w> 318455
system atic</w> 318332
plast y</w> 317901
inter pre 317706
res our 317631
ro w 317454
ve si 317332
ar ing</w> 317062
cor d</w> 317036
it able</w> 316911
ro les</w> 316676
abs orption</w> 316218
v ul 315733
reduc ing</w> 315614
in ser 315351
w ard</w> 315319
F rom</w> 315269
gen der</w> 315090
relationsh ips</w> 314964
join t</w> 314931
g est 314831
til ity</w> 314708
P I 314570
alter ations</w> 314462
fe eding</w> 314422
ach ed</w> 314315
bec ome</w> 314294
5 7</w> 314213
v ements</w> 314168
as al</w> 314000
n ine</w> 313999
V I 313896
ic le</w> 313848
B e 313715
implic ations</w> 313501
mar row</w> 313427
od ynam 313371
frequ ent</w> 313211
ar com 313165
questionn aire</w> 313157
us ually</w> 313112
AM P</w> 312947
te en</w> 312905
arth ritis</w> 312898
di etary</w> 312603
kin etics</w> 312587
nucle us</w> 312541
dis co 312345
S tr 312229
h ere</w> 311885
throm bo 311849
bene fit</w> 311840
suffici ent</w> 311193
N C 311126
ser ved</w> 310982
sec tion</w> 310976
pro long 310915
P G 310722
lo ro 310698
traum a</w> 310597
us al</w> 310509
ass es</w> 310303
appe ar</w> 310250
M T 310232
wh ite</w> 310195
6 6</w> 310186
metast asis</w> 310125
plac e</w> 309982
ey e</w> 309872
5 9</w> 309865
attr ib 309775
M et 309656
alg orith 309591
challeng e</w> 309563
M ost</w> 309467
protoc ol</w> 309378
sp ace</w> 309279
hypo x 309211
curren tly</w> 309207
U C 309034
D M</w> 309025
determin ation</w> 308981
predic tive</w> 308803
tum our</w> 308688
N a</w> 308681
biom ar 308619
chang ed</w> 308592
difficul t</w> 308319
pre h 308245
orig in</w> 308185
con clusion</w> 308165
wid ely</w> 308103
S P</w> 308074
character ization</w> 307921
classi fication</w> 307792
ie ve</w> 307727
o ve 307671
conclud e</w> 307543
om en 307428
is ting</w> 307379
ery thro 307336
A R</w> 307334
v ity</w> 307234
dow n 307139
li ving</w> 307015
lym ph</w> 306918
ep id 306751
osom es</w> 306630
app ing</w> 306608
of f</w> 306441
f ed</w> 306432
L i 306334
A L</w> 306286
inte rest</w> 306284
v ec 306182
str um 306119
con stitu 306078
m M</w> 306019
id ly</w> 306000
detec t</w> 305726
ar s</w> 305577
ipp oc 305392
H R 305272
medic ine</w> 305142
em ph 304900
transi tion</w> 304895
parti cles</w> 304892
& amp 304685
& ;</w> 304685
in d 304606
ar ization</w> 304357
N O</w> 304313
y cl 304202
olog ous</w> 304193
it ud 304190
olog ies</w> 304125
ne ut 304014
8 5</w> 303977
lo ad</w> 303832
acc ess</w> 303796
psych i 303548
sec tional</w> 303326
l ys 303268
glob al</w> 303226
sex ual</w> 302967
d or 302697
is ing</w> 302443
IN T 302270
promis ing</w> 302262
P r 302214
ar m</w> 302009
acti n</w> 301832
O ver 301651
if or 301389
end oscop 301024
plac ed</w> 300992
ultras ound</w> 300962
ippoc amp 300939
ev ol 300405
tiv es</w> 300253
th ym 300191
pl us</w> 300090
str i 300088
su peri 300058
polymer ase</w> 299839
el ements</w> 299743
protec tion</w> 299639
pot ent</w> 299619
g raph 299512
macroph ages</w> 299278
tr yp 299197
i i</w> 299084
n umer 298964
D ec 298927
t el 298702
or th 298638
E uro 298619
at temp 298531
tow ard</w> 298406
id ent</w> 298329
en ro 298259
cont ext</w> 298209
rec ognition</w> 298196
ne ph 298187
ev olution</w> 298069
st abil 297875
ster one</w> 297790
impa ired</w> 297595
s ha 297517
l yc 297377
ex ternal</w> 297346
we igh 297323
s ess 297254
wor ks</w> 297078
success fully</w> 297064
impair ment</w> 296978
adju sted</w> 296969
ach ing</w> 296910
spec i 296783
thesi a</w> 296706
sti t 296664
t ob 296607
le ads</w> 296510
ovari an</w> 296470
A fr 296337
complet ely</w> 296270
d ogs</w> 296147
consec utive</w> 296146
n m</w> 295871
O H</w> 295793
hy dr 295786
spec tively</w> 295741
E M 295729
il ep 295723
g .</w> 295641
behavi oral</w> 295618
di ameter</w> 295568
medi a</w> 295510
g er 295410
vacc ine</w> 295314
C C</w> 295260
b ile</w> 295260
pos ite</w> 295257
occ us</w> 295191
sol id</w> 295090
ul a</w> 294932
neu tr 294889
recur rent</w> 294587
ci ans</w> 294524
aro und</w> 294473
6 1</w> 294238
predic t</w> 294081
si on 293952
anxi ety</w> 293421
vir uses</w> 293361
sus p 293297
de x 293215
ap s</w> 293165
1. 0 293094
org an</w> 292940
requi res</w> 292872
recombin ant</w> 292810
ti l</w> 292789
occur rence</w> 292729
f al 292477
co ag 292271
k ine</w> 292234
const ant</w> 292195
in d</w> 292082
ne ar</w> 291825
e. g.</w> 291704
ma king</w> 291701
comp an 291628
U ni 291511
Diff eren 291510
statis tical</w> 291452
b ut 291425
can di 291383
memb ers</w> 291305
Des pite</w> 291221
Ne w</w> 291179
ec onomic</w> 291094
c utaneous</w> 291013
S L 290920
as h 290771
yi el 290765
dom ains</w> 290601
7 3</w> 290350
sequ encing</w> 290197
kin es</w> 290149
C D</w> 289910
h ex 289900
gener ally</w> 289641
kin etic</w> 289442
al ine</w> 289371
iton eal</w> 289128
e ous</w> 289079
rap idly</w> 288984
oxid ase</w> 288900
sh e 288792
vas cul 288773
3 H</w> 288694
h ippocamp 288672
H osp 288644
SC s</w> 288584
c enter</w> 288582
infarc tion</w> 288207
o id 288175
nan op 288139
compar able</w> 288117
sur fac 287954
he at</w> 287895
clu sions</w> 287673
ver te 287536
g in 287464
through out</w> 287271
radi o 287108
qu al 287019
asth ma</w> 286827
set ting</w> 286818
H igh</w> 286739
6 9</w> 286624
Fin ally</w> 286532
p y 286510
k ne 286436
IF N</w> 286308
stre am</w> 286061
el im 285804
bio chemical</w> 285759
o ked</w> 285674
m ia</w> 285502
compo und</w> 285351
in strum 285122
ac ting</w> 285093
lig and</w> 285049
psych ological</w> 284794
C P</w> 284649
H A</w> 284567
Eff ects</w> 284520
F A</w> 284485
ac r 284457
ent ary</w> 284418
mod eling</w> 284396
spectr um</w> 284294
ad j 284209
dev ice</w> 284131
oc k 283623
re main</w> 283608
ischem ia</w> 283447
C l</w> 283302
disc rim 283143
ne eds</w> 283135
metast atic</w> 283121
profil es</w> 282951
it u 282856
anti gens</w> 282727
canc ers</w> 282629
solu ble</w> 282607
saf e</w> 282604
R A</w> 282477
O r 282449
1 0. 282404
ran ial</w> 282390
v ag 282341
adequ ate</w> 282162
C S</w> 282059
P hy 282055
P M 281932
injur ies</w> 281926
r s</w> 281618
associ ations</w> 281618
od ds</w> 281608
expl ore</w> 281429
targ ets</w> 281282
pe diatric</w> 281261
dra w 280984
accur ate</w> 280945
el ic 280831
P I</w> 280782
ocl onal</w> 280765
oc ular</w> 280632
i ons</w> 280597
ex isting</w> 280523
l er</w> 280359
dec ision</w> 280270
frac ture</w> 280147
respec t</w> 280105
r he 279851
form ing</w> 279655
ey es</w> 279505
guid elines</w> 279440
develop mental</w> 279319
yl ated</w> 279312
D I 279170
br onch 278968
st ates</w> 278906
7 8</w> 278734
ed ly</w> 278697
diff er</w> 278651
leuk emia</w> 278393
tuber cul 278386
re tinal</w> 278318
m as 278312
M icro 278290
thal am 278168
characteris tic</w> 278133
f its</w> 278111
ra z 277820
p tom 277719
S yn 277716
n itr 277712
anc ies</w> 277461
mar ked</w> 277400
cl on 277383
ischem ic</w> 277370
vari ability</w> 277137
dist al</w> 277137
Eff ect</w> 277125
chr on 277100
fac ial</w> 277061
D a</w> 277013
ep ilep 276954
il i 276933
def ects</w> 276799
thor ac 276731
P K 276690
il l</w> 276644
qu in 276547
nec k</w> 276484
experi enced</w> 276195
C an 276165
comp uted</w> 276073
B ec 276053
ble eding</w> 275996
famil ies</w> 275904
ep is 275899
organis ms</w> 275792
ro ot</w> 275784
transfer ase</w> 275771
tic ular</w> 275598
al low</w> 275570
re present</w> 275557
targ eted</w> 275521
pro p 275500
lab eled</w> 275481
wh at</w> 275389
o rectal</w> 275233
pneum on 275146
s es 274987
program s</w> 274876
co sts</w> 274740
min utes</w> 274687
sign als</w> 274556
not ed</w> 274373
an other</w> 274133
h ol 274070
on c 274044
S M 274002
manif est 273986
therap ies</w> 273830
fib rosis</w> 273544
ro ti 273427
ch ic 273329
aryn g 273318
pres entation</w> 273311
toler ance</w> 273307
c ed</w> 273131
b on 273098
1 1. 273014
pa ir 273010
nec rosis</w> 272983
allow s</w> 272959
S R 272885
V A</w> 272884
il ed</w> 272884
sub unit</w> 272848
p ast</w> 272778
thic kness</w> 272681
path ological</w> 272622
T L 272512
specific ally</w> 272444
oxid ant</w> 272340
intrav enous</w> 272250
superi or</w> 272129
am s</w> 271938
al most</w> 271932
adhe sion</w> 271771
A F 271714
phen omen 271642
com pe 271591
7 1</w> 271568
d ental</w> 271552
st en 271483
p ut 271166
b asic</w> 270982
extrac tion</w> 270959
h ab 270917
nerv ous</w> 270596
morph ology</w> 270564
S ince</w> 270333
phot o 270258
spont aneous</w> 270214
gram s</w> 269866
ich ia</w> 269864
fin ding</w> 269766
suscepti bility</w> 269715
com preh 269693
ic in</w> 269651
inhib it</w> 269547
di an</w> 269172
pres er 268947
E C</w> 268921
bi t</w> 268886
prob ably</w> 268765
wa ve</w> 268614
syn th 268565
az ole</w> 268517
observ ations</w> 268372
employ ed</w> 268280
B C</w> 268124
soci ation</w> 267949
con jug 267859
int act</w> 267592
ab und 267522
rati os</w> 267513
Tw enty</w> 267474
N S 267465
CS F</w> 267419
pres s</w> 267392
con tained</w> 267255
lap aro 267249
recommend ed</w> 267194
s mal 267137
l igh 267093
ap parent</w> 267061
numb ers</w> 266938
l and 266891
J ap 266858
i ve</w> 266697
CD 4</w> 266639
tic ip 266532
ar y 266509
prepar ation</w> 266495
rang ed</w> 266494
7 4</w> 266399
tain s</w> 265833
accoun t</w> 265792
ac k</w> 265783
r ich 265768
at er 265681
cont amin 265585
ver s 265531
cir cul 265231
bor n</w> 265212
sit u</w> 265190
g as</w> 264958
com pr 264690
oc arcin 264586
at rial</w> 264441
ogen etic</w> 264376
mo vement</w> 264357
ther mal</w> 264297
ane ur 264075
oly tic</w> 264051
H C</w> 264020
Chin ese</w> 264003
ch ann 264002
n ic 263974
uc id 263309
C P 263265
ec tive</w> 263225
tow ards</w> 263185
Amer ican</w> 263147
atic ally</w> 263142
C R</w> 263084
ep idemi 263003
di versity</w> 262968
n am 262967
adolesc ents</w> 262943
the ory</w> 262920
mid dle</w> 262840
end ogenous</w> 262612
techn ology</w> 262608
indic ates</w> 262602
ove rex 262578
gra f 262480
t un 262458
ear ing</w> 262411
gre at 262381
mo de</w> 262323
ther n</w> 262250
R A 262229
le t</w> 262188
aneur ys 262114
em ented</w> 262053
pol ic 261937
mam m 261882
enc ephal 261785
mic r 261693
as sist 261565
bal ance</w> 261462
cel er 261367
on in</w> 261324
op tical</w> 261247
H um 261243
en ic</w> 261039
metr ic</w> 260971
in stitu 260896
M P</w> 260833
don or</w> 260719
o il</w> 260628
discus s</w> 260387
3 , 260297
medic ation</w> 260204
c en 260190
vol un 260137
sour ces</w> 260092
des pite</w> 260062
y lo 260020
succ ess</w> 259873
tra ditional</w> 259861
per me 259831
se arch</w> 259621
bo v 259595
en ed</w> 259577
7 6</w> 259563
a ine</w> 259532
ac u 259474
E p 259262
yi eld</w> 259195
fin al</w> 259165
reconstruc tion</w> 259123
loc alization</w> 259060
g on 258749
T P</w> 258599
to m</w> 258561
a ther 258540
glut am 258333
s on 258223
underst and</w> 258047
esophag eal</w> 258036
depend ently</w> 258035
e str 257924
leuk in</w> 257910
sit u 257829
m ing</w> 257765
transp or 257469
ty ros 257428
prolong ed</w> 257177
eth anol</w> 257169
recogn ized</w> 257123
bloc k</w> 257111
ca th 257087
vari ants</w> 256847
ch ym 256782
op i 256679
gl and</w> 256616
meth od 256514
un til</w> 256440
9 6</w> 256284
cal e</w> 256192
al le 256048
C ancer</w> 255773
pre operative</w> 255560
8 8</w> 255531
nur sing</w> 255455
P F 255438
E sch 255399
emergen cy</w> 255392
electro ph 255296
An ti 255266
cyt es</w> 255257
mon oclonal</w> 255199
Hosp ital</w> 255198
c all 255180
del ayed</w> 255142
oph ysi 255010
H T</w> 254955
cr uc 254932
ob l 254924
Ad di 254921
ma ke</w> 254851
physi cians</w> 254825
immun os 254815
chann els</w> 254734
7 9</w> 254706
Esch er 254627
extrac t</w> 254213
requ ire</w> 254188
l and</w> 254071
7 7</w> 254011
estim ate</w> 254000
Escher ichia</w> 253957
k Da</w> 253926
D A</w> 253920
olec ular</w> 253728
defici ent</w> 253663
random ly</w> 253564
S I</w> 253112
- 0. 252948
oc om 252796
analy sed</w> 252757
syn thesized</w> 252676
re ly</w> 252651
issu es</w> 252623
so il</w> 252598
immuno histo 252439
par as 252378
maxim al</w> 252318
ang li 252151
em is 252110
bene fits</w> 252110
tr im 252051
en si 251974
neut roph 251889
ib r 251772
im en</w> 251734
ten ance</w> 251658
fr ame 251501
proxim al</w> 251475
e di 251419
targ eting</w> 251349
des crip 251259
Ch il 251207
fib ers</w> 251197
an tim 251068
1 3. 250915
cl ose</w> 250868
frac tures</w> 250801
stimul i</w> 250733
our s</w> 250633
end ing</w> 250528
enh ance</w> 250493
tox ic</w> 250485
regi onal</w> 250444
3 0 250371
p 53</w> 250207
Un ited</w> 250202
kne e</w> 250182
h ome</w> 250180
an ted</w> 249978
3 D</w> 249908
app earance</w> 249897
on ate</w> 249893
ac compan 249804
experim ent</w> 249770
inter fer 249689
ar thro 249624
sch iz 249489
um in</w> 249392
oc or 249304
D M 249287
no de</w> 249171
Th ir 249115
Over all</w> 249102
R el 249038
ep s</w> 248983
hy p 248957
inva sion</w> 248776
met a</w> 248755
re plication</w> 248701
F ol 248654
hem orrh 248563
Ig G</w> 248551
R ev 248414
complic ation</w> 248336
re placement</w> 248315
gen omic</w> 248278
y e 248205
a p</w> 248203
recur s 247787
g al 247752
us ual</w> 247742
bil ateral</w> 247586
pl ayed</w> 247512
an u 247424
spectro scopy</w> 247178
fin d</w> 247090
ei ved</w> 247074
in ate</w> 246907
oph ren 246823
distin gu 246791
T A 246773
wh ose</w> 246697
nucle otide</w> 246644
inter mediate</w> 246618
8 6</w> 246599
se iz 246598
ve in</w> 246493
p el 246372
antibio tic</w> 246246
scho ol</w> 246239
ac celer 246064
requi re 245973
ul ate</w> 245861
ang u 245564
cho ice</w> 245441
no d 245341
syn thetic</w> 245299
oglob in</w> 245269
ang le</w> 245246
d one</w> 245234
T 1</w> 245192
m ic</w> 245187
es tions</w> 245171
ex pan 245169
classi fied</w> 245119
MATERI ALS</w> 244907
antagon ist</w> 244888
ap hy 244838
f ast</w> 244821
mut ants</w> 244737
se ems</w> 244731
foc al</w> 244721
sk eletal</w> 244695
T B 244598
avail ability</w> 244413
P E</w> 244381
D s</w> 244017
ev ent</w> 243762
jec tion</w> 243565
depend ence</w> 243461
mil k</w> 243399
oc rine</w> 243362
F e 243277
acti c</w> 243235
os al</w> 243193
uc h</w> 243137
cur ve</w> 242960
ph il 242900
p recurs 242855
N ational</w> 242809
psychi atric</w> 242736
agre ement</w> 242682
injec ted</w> 242654
av age</w> 242639
ass emb 242475
e ase</w> 242343
at ase</w> 242292
ev ery</w> 242235
typ ical</w> 242098
ex cre 242092
5 0 242029
an a 241954
chol ine</w> 241907
provid ing</w> 241880
equ i 241862
rel ap 241792
P las 241703
co ord 241524
neon atal</w> 241378
initi ation</w> 241361
itud inal</w> 241326
ear li 241139
ar in</w> 241099
reli able</w> 240924
possi bility</w> 240907
ben ign</w> 240841
Bec ause</w> 240775
t ase</w> 240728
am b 240702
id en 240579
n or</w> 240357
path ology</w> 240297
st or 240283
prog ressive</w> 240119
f ore 239994
aut om 239896
grow ing</w> 239876
ell ar</w> 239819
av es</w> 239660
transi ent</w> 239660
E lec 239470
dist ance</w> 239467
anal og 239340
H LA</w> 239004
S ign 238956
oper atively</w> 238891
el ucid 238876
B r 238701
cell ent</w> 238699
b ly</w> 238647
BM I</w> 238589
F ac 238538
physi c 238445
pregn ant</w> 238389
ad ded</w> 238350
lymph oma</w> 238337
mo bil 238307
ex change</w> 238274
co ding</w> 238273
micro grams</w> 238221
ari a</w> 238198
conclud ed</w> 238176
rel ax 238139
main tenance</w> 238098
is ation</w> 238041
rou tine</w> 238027
su itable</w> 237996
metabol ites</w> 237928
recip i 237817
it us</w> 237798
Rec ent</w> 237779
g radi 237634
a vi 237589
pres enting</w> 237450
ex tensive</w> 237294
sh ock</w> 237151
9 3</w> 237085
mul ti</w> 236949
inf il 236895
un clear</w> 236607
transpl ant</w> 236605
hydro gen</w> 236564
h und 236477
C F 236343
mark edly</w> 236226
v als</w> 236194
electroph ore 236193
ir radiation</w> 236147
epis od 236076
T E 235978
repe ated</w> 235966
ill ness</w> 235938
ti ous</w> 235924
an ine</w> 235911
z es</w> 235906
g ain</w> 235752
thresh old</w> 235705
sens ory</w> 235692
cyt os 235606
ex ist 235506
1. 0</w> 235483
ch loro 235393
re f 235370
arcom a</w> 235293
R S</w> 235050
w al 234942
fail ed</w> 234886
transcrip tional</w> 234770
ot omy</w> 234687
se ts</w> 234673
1 2. 234648
it ative</w> 234591
observ ation</w> 234531
enro lled</w> 234349
hy d 234304
R ep 234146
r ich</w> 234076
gre at</w> 233999
O F</w> 233983
wor king</w> 233967
ver sion</w> 233956
oly sis</w> 233658
bov ine</w> 233583
ra dical</w> 233409
ess els</w> 233384
path y</w> 233363
abil ities</w> 233329
8 4</w> 233184
D ev 232872
vari ed</w> 232806
ha z 232805
5 00</w> 232682
anu ary</w> 232482
st ly</w> 232415
pro s 232263
tin gs</w> 232210
J anuary</w> 232081
ac t 232013
tyros ine</w> 231810
M an 231737
prim arily</w> 231705
enco ding</w> 231608
h ip</w> 231598
hospit als</w> 231415
k ary 231355
gastro intestinal</w> 231347
loc alized</w> 231291
ri a</w> 231267
phosph olip 231231
al is</w> 231161
port un 231144
tur n</w> 231081
dis charge</w> 231066
ogen s</w> 231034
val ve</w> 230939
gran ul 230857
og ra 230799
cycl ic</w> 230624
dop amine</w> 230563
c ros 230554
predic tion</w> 230527
T A</w> 230516
smo oth</w> 230364
v essels</w> 230359
a rom 230157
ex cellent</w> 230143
compe ti 230080
col on</w> 230077
8 3</w> 230058
c ent 229940
smal ler</w> 229866
ar t 229844
ran dom</w> 229833
iden tical</w> 229791
w ound</w> 229768
synap tic</w> 229648
Hum an</w> 229632
occur ring</w> 229543
cou pled</w> 229401
metast ases</w> 229264
D O 229252
peri ods</w> 229199
show ing</w> 229185
aim s</w> 229170
minim al</w> 229163
fib er</w> 229031
commun ication</w> 229006
col orectal</w> 228975
parti ally</w> 228910
ster oid</w> 228796
s ou 228780
pol yp 228773
schiz ophren 228746
u tions</w> 228702
k et 228617
8 2</w> 228507
C ur 228140
induc es</w> 228092
roti d</w> 227963
challeng es</w> 227928
it ory</w> 227815
vi ous</w> 227815
cyto kines</w> 227791
child hood</w> 227767
pro pose</w> 227660
di ed</w> 227640
main tained</w> 227500
amoun ts</w> 227326
to ols</w> 227252
heal ing</w> 227240
gen e 227069
p seud 227067
dev ices</w> 226972
bel ow</w> 226920
fol l 226732
Pro te 226708
o thers</w> 226700
acqu ired</w> 226660
l ing</w> 226613
P 1</w> 226585
oc ity</w> 226559
L PS</w> 226485
inter vals</w> 226388
On ly</w> 226308
g o</w> 226261
mal ian</w> 225997
ch es</w> 225930
m urine</w> 225857
subjec t</w> 225845
serv ice</w> 225797
trans l 225755
odynam ic</w> 225727
0.00 01</w> 225652
pl ate</w> 225613
1 4. 225416
sup pression</w> 225307
deriv atives</w> 225229
F r 225153
si an</w> 225151
promot e</w> 225077
behavi ors</w> 225053
shif t</w> 225044
In cre 225043
est er</w> 225031
ris e</w> 224975
impl ementation</w> 224937
yl ase</w> 224818
Me dic 224802
pro be</w> 224788
h abil 224648
de hydro 224555
W estern</w> 224532
or ly</w> 224516
S ec 224291
tubercul osis</w> 224211
L DL</w> 224105
Th er 223971
g angli 223951
de ep</w> 223870
conver sion</w> 223793
2 50</w> 223784
exc ept</w> 223719
health care</w> 223697
nanop articles</w> 223654
r ho 223641
view s</w> 223527
ly sis</w> 223476
w ise</w> 223446
dist urb 223369
tre at</w> 223306
C Y 223293
9 8</w> 223281
frame work</w> 223170
G S</w> 223127
contro ll 223062
vi si 223053
incub ation</w> 223038
hist ological</w> 222764
alle l</w> 222756
extrac ted</w> 222341
in oc 222224
St ates</w> 222073
b ular</w> 221975
diff usion</w> 221965
ad s 221931
wor kers</w> 221782
n ative</w> 221777
gra m</w> 221765
inter leukin</w> 221719
ar ies</w> 221693
er r 221689
epid er 221660
sur ro 221464
9 2</w> 221416
res ting</w> 221373
o k</w> 221173
T a 221153
cor ne 221080
mus cles</w> 220969
er ic</w> 220910
a ud 220706
vel ocity</w> 220665
es sive</w> 220649
adju v 220586
e f 220559
datab ase</w> 220543
de pl 220499
domin ant</w> 220239
asp ir 220238
Sev eral</w> 220219
dr in 220193
op portun 220151
b and</w> 220097
recor ds</w> 219949
emb ol 219835
S V</w> 219674
en rich 219589
fil m</w> 219476
ati tis</w> 219468
inten sive</w> 219390
9 9</w> 219347
clos ely</w> 219106
E valu 219025
he re 218967
GF R</w> 218929
f ar</w> 218878
ill us</w> 218877
E E 218859
i b</w> 218765
an a</w> 218688
in du 218667
en ia</w> 218468
res ol 218419
fem oral</w> 218413
foc used</w> 218199
ten tion</w> 218177
8 1</w> 218147
Chin a</w> 218133
direc ted</w> 218046
oz yg 217993
trans formation</w> 217988
em p 217968
issu e</w> 217820
lim b</w> 217818
pers on 217700
equ al</w> 217619
shap e</w> 217584
produc ing</w> 217522
m arg 217496
ex ten 217490
re verse</w> 217366
pl ex</w> 217351
ent ally</w> 217278
P V 217141
arter ies</w> 217132
vari ations</w> 217116
res sed</w> 217089
ocy tic</w> 217028
pers ons</w> 216941
so ft</w> 216836
lat ter</w> 216811
pro mo 216735
0. 2</w> 216682
Con tro 216580
ach ieve</w> 216570
mat ter</w> 216540
p in 216528
paren ts</w> 216415
e a</w> 216332
ow n</w> 216276
st ers</w> 216079
impl antation</w> 216076
as ting</w> 215898
ex pressing</w> 215864
ser ious</w> 215854
p ed</w> 215833
air way</w> 215639
ill in</w> 215625
s low</w> 215588
hyper tensive</w> 215573
o ro 215521
vi ol 215479
hybri di 215463
hund red</w> 215418
analy ze</w> 215378
anti oxidant</w> 215352
seg ment</w> 215291
S cale</w> 215270
P H</w> 215252
ser a</w> 215228
A S 215219
F our</w> 215185
prote in 215161
extrac ts</w> 215130
decre ases</w> 215115
chlor ide</w> 215076
c ent</w> 215036
t al 215017
il ls</w> 214947
in dependently</w> 214943
E 2</w> 214869
mam malian</w> 214857
organ ization</w> 214769
melan oma</w> 214742
1 1 214673
radi otherapy</w> 214613
fix ed</w> 214590
I R</w> 214581
8 9</w> 214548
nur ses</w> 214518
compar ing</w> 214447
N H 214363
C al 214304
ow el</w> 214299
ath s</w> 214286
if y</w> 214274
as tic</w> 214237
D L 214058
tic ul 213978
the ore 213863
con genital</w> 213816
pul se</w> 213803
fluo ro 213794
8 7</w> 213779
ac in</w> 213773
Ex perim 213734
n a 213732
ad mission</w> 213717
rab bit</w> 213646
fibrobl asts</w> 213633
P R</w> 213631
C r 213467
def ect</w> 213405
igh tly</w> 213290
Ne uro 213254
influ enced</w> 213219
it ations</w> 213188
comm erc 213184
practi ces</w> 213183
G C</w> 213171
C ell</w> 213170
uter ine</w> 213031
9 4</w> 213018
ye ast</w> 212897
20 14</w> 212767
rec tomy</w> 212744
ocarcin oma</w> 212639
Sur g 212630
perc eived</w> 212418
o ts</w> 212405
U D 212324
chrom e</w> 212293
remain ing</w> 212159
H ist 212008
B S</w> 211887
ab or 211868
algorith m</w> 211853
expl ored</w> 211821
net works</w> 211819
iz es</w> 211746
t ant</w> 211593
neurolog ical</w> 211548
larg ely</w> 211541
aff ecting</w> 211526
E T</w> 211525
meth ylation</w> 211476
morph ological</w> 211462
z in 211303
ati on 211299
om al 211232
B 1</w> 211001
modi fication</w> 210925
ex clud 210911
H z</w> 210851
op a 210816
est rogen</w> 210786
mag nit 210728
N G</w> 210706
habil itation</w> 210566
In f 210425
HC V</w> 210419
20 15</w> 210354
A 1</w> 210337
ex tended</w> 210273
3 00</w> 210199
fr ontal</w> 210166
gl omer 210116
res er 210065
solu tions</w> 209990
G A</w> 209947
par allel</w> 209927
compreh ensive</w> 209858
impro ving</w> 209835
dem ographic</w> 209720
sam pling</w> 209701
om at 209655
underst ood</w> 209604
A t 209586
pro ved</w> 209483
cruc ial</w> 209450
con tains</w> 209369
differenti ated</w> 209353
exhib it</w> 209335
alb umin</w> 209315
gen otype</w> 209311
m er</w> 209141
wor k 208888
s ting</w> 208780
e de 208463
T 3</w> 208439
behavi our</w> 208249
n ia</w> 208239
il lu 208211
an ese</w> 208183
di verse</w> 208179
stit ute</w> 208124
wh om</w> 208120
earli er</w> 208042
otyp ic</w> 208016
ad op 207964
sum mar 207949
worl d</w> 207938
f ill 207725
repres ents</w> 207683
1 20</w> 207638
stimul us</w> 207606
ris ks</w> 207525
chem o 207439
I M 207418
Tran s 207301
enhanc ement</w> 207290
cy s 207150
S ome</w> 207092
epti de</w> 207068
tox in</w> 206914
electr onic</w> 206903
antibio tics</w> 206887
substanti al</w> 206880
cir cum 206873
surg e 206837
d a</w> 206809
ab r 206661
conc ept</w> 206654
9 7</w> 206576
sl ightly</w> 206492
ob ese</w> 206488
col um 206459
h ome 206454
t ac 206378
pre g 206303
osyn thesis</w> 206276
c DNA</w> 206211
new ly</w> 206200
fe ed 206156
Com pared</w> 206152
Uni versity</w> 206096
Me an</w> 206025
sci enti 206015
4 , 205834
stor age</w> 205750
con tain</w> 205714
spectro metry</w> 205499
predic tors</w> 205497
establ ish</w> 205482
Con clusion</w> 205468
sec tions</w> 205364
P ase</w> 205258
agon ist</w> 205252
mod ulation</w> 205229
high ligh 205201
enti a</w> 205090
posi tively</w> 205058
meas uring</w> 205028
ad es</w> 205017
i od 205011
le th 204981
satisfac tion</w> 204973
ac ter</w> 204918
m m 204775
con served</w> 204728
ar ray</w> 204656
contrib ution</w> 204492
glyc er 204472
Me dical</w> 204441
se l</w> 204438
fl av 204363
tr iti 204325
immun ity</w> 204119
sp re 204034
cyto kine</w> 204019
G AB 203949
detec table</w> 203865
and ed</w> 203844
9 1</w> 203766
S tu 203764
us ers</w> 203758
su stained</w> 203753
e tr 203740
reve al</w> 203694
U T 203609
sal ine</w> 203464
bene ficial</w> 203408
expl ained</w> 203382
olog ists</w> 203334
1 5. 203189
u es</w> 203166
c ad 203111
ex er 203095
L C</w> 203078
yl ate</w> 202935
E LI 202854
impl ant</w> 202732
N a 202713
dec line</w> 202625
t age</w> 202546
accompan ied</w> 202495
it ude</w> 202453
RO D 202412
G H</w> 202352
p il 202207
add ress</w> 202200
ste re 202105
k age</w> 201917
Ch ar 201909
1. 2</w> 201891
icro bial</w> 201862
ca re 201848
log istic</w> 201726
stand ardi 201676
aff ects</w> 201674
T GF</w> 201434
im er</w> 201384
ac ted</w> 201301
de part 201296
oc clusion</w> 201206
eng ine 201169
N P</w> 201112
org ans</w> 201048
A 2</w> 201029
bu il 200997
de hy 200952
D R 200951
sc anning</w> 200919
H 2 200879
describ es</w> 200814
0. 3</w> 200805
T yp 200592
regul ate</w> 200549
itu itary</w> 200549
adjuv ant</w> 200507
el op 200427
sup pressed</w> 200355
cl earance</w> 200299
can not</w> 200297
con sequences</w> 200139
vari ant</w> 200109
st om 200101
C C 200090
ROD UC 200052
traum atic</w> 200003
di ver 199936
multi variate</w> 199924
in ter</w> 199865
phen yl 199855
immun ore 199819
sy st 199773
em bl 199759
HC C</w> 199574
electr ical</w> 199511
lim it</w> 199498
re activity</w> 199325
influ enz 199278
INT RODUC 199278
f a 199247
o virus</w> 199102
S u 199092
long itudinal</w> 199016
frag ment</w> 198925
electr ic</w> 198860
ch est</w> 198853
no v 198833
20 16</w> 198783
integr ated</w> 198737
consist ed</w> 198734
20 13</w> 198711
S M</w> 198703
F unc 198652
del ta</w> 198624
b s</w> 198619
INTRODUC TION</w> 198593
allow ed</w> 198590
i pl 198571
h am 198471
ne t</w> 198468
roph y</w> 198309
ast er</w> 198285
effec tively</w> 198232
acet ate</w> 198193
S al 198180
po orly</w> 198151
PA R 198044
otrop ic</w> 198030
adhe rence</w> 198008
Dev elop 197983
O p 197952
oph osph 197876
U V</w> 197712
P P</w> 197659
I CA 197605
assemb ly</w> 197528
P ol 197471
acchar ide</w> 197459
Stu dies</w> 197420
sten osis</w> 197409
r ig 197180
de vi 197179
coun t</w> 197167
magnit ude</w> 197148
al ities</w> 197141
a emia</w> 197131
nor m 197110
fr actions</w> 197065
el ement</w> 197015
persist ent</w> 196979
press or</w> 196876
ol ds</w> 196875
estim ates</w> 196749
d ual</w> 196740
ath i 196728
low ing</w> 196710
E B 196702
bur den</w> 196679
respon si 196669
fac t</w> 196622
fix ation</w> 196613
ampl itude</w> 196596
gh ts</w> 196574
Bac kground</w> 196536
t ance</w> 196313
co ver 196224
nit rogen</w> 196214
st ay</w> 196209
par ts</w> 196161
ici ans</w> 196108
ens in</w> 196087
pro phyl 196073
biomar kers</w> 195989
d ation</w> 195950
ob tain</w> 195934
an dro 195907
N F 195897
cl early</w> 195892
out put</w> 195742
ell itus</w> 195725
ib ility</w> 195681
cir c 195679
In te 195678
advant ages</w> 195641
P l 195554
separ ation</w> 195487
pl e 195456
erro r</w> 195443
inhib its</w> 195373
se as 195353
A F</w> 195283
h our</w> 195196
substr ates</w> 195105
rang ing</w> 195085
de si 195065
ap er 194909
suppor ted</w> 194906
nutri tion</w> 194885
e f</w> 194752
conc ep 194704
dep th</w> 194488
gener ative</w> 194464
D el 194455
Addi tionally</w> 194404
qu estions</w> 194392
al dehy 194370
7. 5</w> 194270
pres ents</w> 194230
con serv 194173
fluoresc ent</w> 194004
volun te 193975
ri um</w> 193946
an k 193927
polymorph ism</w> 193906
com it 193835
a wa 193831
exam ple</w> 193807
infec tious</w> 193739
pair s</w> 193719
expl ain</w> 193701
vis u 193676
sh ip</w> 193483
n asal</w> 193477
clin ic</w> 193337
rhe um 193317
epith elium</w> 193285
go al</w> 193233
ev oked</w> 193150
valid ated</w> 193004
N K</w> 192973
ca rotid</w> 192950
subst ance</w> 192939
ede ma</w> 192880
C O</w> 192840
pers onal</w> 192800
H I 192771
c os 192647
junc tion</w> 192387
is es</w> 192328
as signed</w> 192200
sen se</w> 192093
PE T</w> 192081
l angu 191908
cap able</w> 191846
end ocrine</w> 191798
ex pec 191757
car bo 191752
O c 191749
ost asis</w> 191683
func tioning</w> 191629
1 6. 191546
docum ented</w> 191517
A G 191482
determin ing</w> 191449
reproduc tive</w> 191359
symp tom</w> 191309
le ts</w> 191306
pel v 191239
thorac ic</w> 191173
si c</w> 191166
micro bial</w> 191159
medi ately</w> 191126
b owel</w> 191106
Sim il 191082
As sess 191022
ill ance</w> 190945
ather os 190902
princ i 190901
pol y</w> 190767
O ut 190607
resid ual</w> 190507
mo thers</w> 190410
g ent</w> 190249
facilit ate</w> 190211
av y</w> 190050
orig inal</w> 190041
W or 190039
embry onic</w> 190009
per itoneal</w> 189977
bet a 189845
Euro pe 189832
cor d 189765
sper m</w> 189751
o res</w> 189680
intro duced</w> 189605
ul c 189561
ul monary</w> 189551
comput er</w> 189549
min or</w> 189472
prolifer ative</w> 189469
S ch 189461
Chil d 189362
sensi ti 189344
C V 189302
restric ted</w> 189294
di a</w> 189248
en al</w> 189168
pol ar</w> 189167
al ve 189127
reg ular</w> 189106
aden osine</w> 189100
assist ed</w> 189091
anal ge 189068
k ill 189066
continu ed</w> 188973
Ser um</w> 188914
sal t</w> 188901
surfac es</w> 188831
a id</w> 188619
no ise</w> 188568
Compar ison</w> 188547
A V 188537
f it 188532
ir ing</w> 188526
de aths</w> 188411
Par ticip 188386
syst olic</w> 188366
correl ations</w> 188329
b yp 188328
medi al</w> 188326
at tit 188234
le a 188202
h r</w> 188122
resour ces</w> 188106
u preg 188099
illu str 188096
N O 188051
car r 188013
R R</w> 187978
anes thesia</w> 187973
p ituitary</w> 187917
flu x</w> 187878
ph ag 187844
confir m</w> 187839
C E 187835
am ous</w> 187767
compar t 187686
h is</w> 187646
circul ating</w> 187610
aer obic</w> 187607
hypox ia</w> 187506
t am 187478
re habilitation</w> 187465
aryng eal</w> 187451
cryst al</w> 187449
pharmac o 187362
m ol 187357
emis sion</w> 187287
is ms</w> 187230
phosph atase</w> 187174
M a 187166
AC T</w> 187053
ma x</w> 186945
R adi 186940
dis played</w> 186810
thou ght</w> 186801
Ch ang 186793
call ed</w> 186709
he aring</w> 186678
chym al</w> 186621
vec tor</w> 186574
m ellitus</w> 186562
frag ments</w> 186534
lymph ocyte</w> 186532
del etion</w> 186518
C F</w> 186505
youn ger</w> 186505
Phy si 186463
es sel</w> 186414
reli ability</w> 186400
insi c</w> 186384
Au str 186283
Evalu ation</w> 186111
A M</w> 186051
cl im 185974
ELI SA</w> 185951
ach ment</w> 185922
ad mitted</w> 185893
fibr ill 185786
p ing</w> 185768
fung al</w> 185761
sulf ate</w> 185759
4 0 185753
s p</w> 185744
au g 185737
m im 185701
ur ia</w> 185689
yt o 185627
os in</w> 185579
allel e</w> 185572
on e 185539
I s</w> 185452
T C</w> 185433
ser ve</w> 185265
si es</w> 185189
val idity</w> 185118
S R</w> 185101
ocyt osis</w> 185063
susp ected</w> 185047
regul ating</w> 185005
di alysis</w> 185003
f und 184706
r ac 184669
symptom atic</w> 184608
pl anning</w> 184606
excre tion</w> 184510
H E 184486
emo tional</w> 184447
set tings</w> 184316
d yl 184253
endoscop ic</w> 184042
loc us</w> 183906
he imer</w> 183896
cataly tic</w> 183859
investig ations</w> 183782
g ir 183733
Europe an</w> 183725
z one</w> 183717
pig s</w> 183716
gener ate</w> 183684
eti ology</w> 183650
f et 183545
Al z 183526
cath eter</w> 183478
ot or</w> 183427
co efficient</w> 183375
util ization</w> 183366
he ight</w> 183330
car til 183282
schizophren ia</w> 183184
mo ti 183145
hybridi zation</w> 183137
possi bly</w> 183090
clu ster</w> 183028
ech ocardi 182988
ul f 182975
recommend ations</w> 182970
cytotox ic</w> 182968
dem entia</w> 182898
tren d</w> 182888
bi ology</w> 182834
end ed</w> 182799
trac he 182741
L D</w> 182682
20 12</w> 182663
ta king</w> 182571
og ram</w> 182560
repres ent 182481
bacter ium</w> 182456
remo ved</w> 182455
muc osal</w> 182440
0.0 2</w> 182436
av oid 182415
B et 182414
P rim 182286
posi tions</w> 182254
1. 1</w> 182175
i si 182080
l ar 182058
plas m</w> 182033
iz ations</w> 182029
pl er</w> 181931
st anding</w> 181920
mac ro 181917
A g</w> 181832
f re 181748
Alz heimer</w> 181737
cor tico 181472
sol ute</w> 181425
cre atin 181417
zin c</w> 181416
B D</w> 181159
per f 181109
am ent</w> 181068
c AMP</w> 180933
muc osa</w> 180904
isol ation</w> 180847
tu be</w> 180726
ec al</w> 180678
tre ating</w> 180614
carb ox 180396
aqu eous</w> 180390
character ize</w> 180362
P ost 180361
al ign 180354
re tention</w> 180310
20 00</w> 180288
sw it 180276
qual itative</w> 180253
umb ar</w> 180055
rever sible</w> 180047
deta iled</w> 180044
angi ography</w> 180031
ul tra 179980
vacc ination</w> 179913
dehydro genase</w> 179883
nit ro 179865
PL C</w> 179807
0. 4</w> 179745
venti lation</w> 179700
hist opath 179599
in clusion</w> 179474
controll ing</w> 179469
il ateral</w> 179463
electrophore sis</w> 179420
fic ations</w> 179402
r hyth 179354
el ds</w> 179315
recei ve</w> 179184
at ter 179170
T B</w> 179144
subjec ted</w> 178998
neph ro 178983
pharmac ological</w> 178909
frequ encies</w> 178884
gradi ent</w> 178770
1. 3</w> 178708
bloc ked</w> 178705
intr a</w> 178689
parti cle</w> 178678
op ic</w> 178654
no des</w> 178635
I GF</w> 178588
M E</w> 178498
d ents</w> 178487
D N 178441
hybri d</w> 178418
demonstr ates</w> 178398
per form</w> 178251
synth ase</w> 178224
del ay</w> 178146
D ep 178107
e asi 178105
bro ad</w> 178064
d end 178039
cyto chrome</w> 178029
vari ance</w> 177918
M on 177894
simult aneously</w> 177846
con form 177803
correc t</w> 177791
o ic</w> 177706
v ate</w> 177697
consider able</w> 177690
im mediately</w> 177569
pre feren 177529
separ ate</w> 177522
var ying</w> 177350
potenti als</w> 177282
fe ature</w> 177266
su ic 177230
ot ensin</w> 177163
Dis ease</w> 177124
ste ine</w> 176997
langu age</w> 176963
P e 176961
integr ation</w> 176921
Thir ty</w> 176920
1. 4</w> 176744
L P</w> 176736
Sign ific 176673
im e</w> 176671
p ic 176651
fav or 176651
Res earch</w> 176621
D op 176597
af f</w> 176560
wom an</w> 176485
in sec 176362
em entary</w> 176355
elec tive</w> 176329
barri er</w> 176266
sc an</w> 176265
re ached</w> 176225
ev er 176225
spe ed</w> 176209
obstruc tion</w> 176139
gen it 176091
um p</w> 176027
N MR</w> 176026
am ph 176012
feed back</w> 175965
urb an</w> 175926
lu tion</w> 175917
u tion 175901
el ess</w> 175895
blo t</w> 175889
experi ences</w> 175887
bl ind</w> 175875
T H</w> 175864
ec h</w> 175782
b its</w> 175743
b ond</w> 175732
C E</w> 175681
D C</w> 175663
assess ing</w> 175637
cryst all 175586
bac kground</w> 175520
opath ic</w> 175464
u ting</w> 175446
ro om</w> 175415
duc t</w> 175351
U C</w> 175321
r ural</w> 175284
inf ant</w> 175265
pres crib 175261
speci al 175206
interfer on</w> 175082
1 50</w> 175017
ac ent</w> 175004
3. 5</w> 174962
z ym 174917
aggreg ation</w> 174885
sp i 174828
ec tin</w> 174827
pr in 174800
ptom atic</w> 174723
spec ial</w> 174689
oc aine</w> 174685
compar ative</w> 174641
B lo 174625
dri ven</w> 174621
reduc es</w> 174588
Ex pression</w> 174577
alph a 174471
embry os</w> 174470
perc eption</w> 174401
bi as</w> 174366
C ardi 174345
coun ter 174299
er b 174296
om ical</w> 174258
numer ous</w> 174178
f ru 174100
n e</w> 174031
G u 174022
antim icrobial</w> 174017
V ari 173989
nucle i</w> 173985
byp ass</w> 173982
P a 173940
ud es</w> 173910
A K 173896
ish ing</w> 173769
MD A</w> 173708
di ast 173672
spectr a</w> 173616
er a</w> 173585
4 00</w> 173492
D R</w> 173409
et h</w> 173390
bu ff 173221
M ore</w> 173086
path ophysi 173080
m ented</w> 173042
r in 173019
physic ian</w> 172978
cy cles</w> 172963
In hib 172934
he avy</w> 172916
C G</w> 172906
seg ments</w> 172858
de oxy 172827
prob ability</w> 172811
ex ist</w> 172781
apopto tic</w> 172738
equi valent</w> 172672
tum ours</w> 172538
tiv ation</w> 172532
tion ed</w> 172532
I V 172453
arr hyth 172434
mix ture</w> 172421
M any</w> 172405
includ es</w> 172392
assi um</w> 172382
exist ence</w> 172349
hypo thalam 172346
retro spectively</w> 172318
nitr ic</w> 172300
lipo protein</w> 172269
athi one</w> 172250
invol ves</w> 172126
C L</w> 172101
wh e 172071
phenomen on</w> 172060
de generation</w> 172049
il ib 172032
men ing 171996
ast oma</w> 171935
st ment</w> 171854
F e</w> 171837
heterogene ity</w> 171747
prepar ations</w> 171707
n ar 171662
ch ec 171614
L H</w> 171476
rele ased</w> 171452
sk ills</w> 171448
I denti 171404
dor sal</w> 171391
B L</w> 171373
cycl o 171362
glutam ate</w> 171356
os in 171341
C M</w> 171315
it ol</w> 171267
2. 0</w> 171237
R s</w> 171066
complic ated</w> 171054
aldehy de</w> 171050
el ls</w> 170999
preven ted</w> 170980
vi ability</w> 170929
d ase</w> 170911
comp et 170883
program m 170688
L V</w> 170675
subsequ ently</w> 170650
L E 170637
ca using</w> 170603
t on 170593
v essel</w> 170574
cou pling</w> 170487
ul l 170213
Me as 170200
b earing</w> 170152
um ab</w> 170141
prog ress</w> 170125
at um</w> 170107
F ive</w> 170106
v o 170096
s . 170056
low est</w> 170033
AI M</w> 170019
N I 169953
repor ting</w> 169934
restric tion</w> 169869
C u</w> 169838
phen yl</w> 169757
U l 169747
cys tic</w> 169610
pi g</w> 169538
attenu ated</w> 169520
so f 169517
adj acent</w> 169513
ul l</w> 169500
theore tical</w> 169495
lo ading</w> 169486
az ol 169485
Austr al 169464
sal iv 169442
sim ulation</w> 169383
dis per 169365
cyto plasmic</w> 169309
fi elds</w> 169306
triti onal</w> 169306
an omal 169265
Particip ants</w> 169094
g et 169025
u bi 168980
dis ability</w> 168960
circ ulation</w> 168910
ster oids</w> 168788
eth n 168766
implic ated</w> 168640
Afr ican</w> 168599
perc ep 168547
m 2</w> 168511
mon o 168507
on as</w> 168491
im mediate</w> 168421
f ic</w> 168395
si des</w> 168355
in a</w> 168341
or i</w> 168128
T 2</w> 168085
influenz a</w> 168071
alve olar</w> 167874
E arly</w> 167815
polic y</w> 167814
identi fying</w> 167795
polym er</w> 167793
0. 6</w> 167782
Dec ember</w> 167766
vers ely</w> 167714
.0 01</w> 167579
r s 167553
reg ard</w> 167515
hyper troph 167442
lin e 167383
acc o</w> 167357
distrib uted</w> 167341
od on 167278
eff orts</w> 167199
CY P 167131
aden ocarcinoma</w> 167118
th re 167105
su b</w> 166997
m os 166959
cor tis 166904
respec tive</w> 166883
H PV</w> 166817
cere b 166813
Di ag 166800
0. 8</w> 166789
un treated</w> 166756
man ip 166719
a x</w> 166683
st atic</w> 166664
recru ited</w> 166645
st rom 166631
am ylo 166585
micro g</w> 166577
volunte ers</w> 166560
squ amous</w> 166416
minim um</w> 166406
Typ e</w> 166399
di min 166383
1 8. 166305
initi ally</w> 166301
hemorrh age</w> 166273
lim itations</w> 166237
rou gh</w> 166233
tra its</w> 166066
p ine</w> 166045
es tive</w> 165997
re perfusion</w> 165962
de pressive</w> 165956
st aff</w> 165925
f ever</w> 165908
me t</w> 165906
et ro 165850
aphy loc 165822
tic ity</w> 165771
lu e</w> 165652
o plasty</w> 165639
re ferred</w> 165576
Con clusions</w> 165569
vesi cles</w> 165558
direc tion</w> 165361
op tic</w> 165290
immun oglob 165056
F I 165012
construc ted</w> 164993
B M</w> 164923
esc ence</w> 164892
cartil age</w> 164807
1. 6</w> 164788
adap tation</w> 164769
c ess 164710
st eps</w> 164690
laparo scopic</w> 164656
G R 164653
au re 164593
ro t 164519
In v 164475
ex per 164431
al ter</w> 164262
com posed</w> 164199
ine a</w> 164165
enti re</w> 164139
om ics</w> 164121
rab bits</w> 164093
K 1</w> 164092
profes sional</w> 164074
auto immune</w> 164010
sin us</w> 163943
on ce</w> 163891
cir rho 163887
bin d</w> 163780
b ic 163774
monit ored</w> 163717
com posite</w> 163686
mitochond ria</w> 163664
2 -</w> 163603
phosph ati 163562
arg in 163252
dis cre 163240
o g</w> 163148
cen ters</w> 163121
concer ning</w> 163038
M in 163021
disc ipl 163021
struc tured</w> 163009
ar rang 163008
elev ation</w> 162973
p ene 162958
b ran 162956
S outh</w> 162924
ta x 162894
ad ed</w> 162893
clos ed</w> 162863
eth yl</w> 162823
ai l</w> 162813
sequ ently</w> 162786
evalu ating</w> 162782
le p 162753
o sterone</w> 162700
asi bility</w> 162648
Ac ute</w> 162469
pot assium</w> 162465
spl een</w> 162407
D P</w> 162296
w er</w> 162276
sat ur 162257
Char acter 162237
expan sion</w> 162231
coll ection</w> 162172
shor ter</w> 162154
con comit 162150
pol arization</w> 162116
put ative</w> 162102
smo kers</w> 162060
simult aneous</w> 162051
predomin antly</w> 162031
tin g 161945
s chem 161893
bec ame</w> 161879
read y</w> 161847
p us</w> 161788
f ing 161780
in ner</w> 161750
mo bility</w> 161737
adv ances</w> 161723
standardi zed</w> 161721
adi ol</w> 161663
it ment</w> 161596
inser tion</w> 161565
sil ic 161552
1. 8</w> 161440
there by</w> 161378
injec tions</w> 161377
consi sting</w> 161363
respon sive</w> 161358
SE T 161339
epilep sy</w> 161311
reg imen</w> 161180
G er 161142
overex pression</w> 161119
suscepti ble</w> 161084
k in</w> 161064
3. 3</w> 161038
ad renal</w> 161014
H M 160976
recipi ents</w> 160947
cle avage</w> 160938
A b</w> 160775
ph ases</w> 160763
i ance</w> 160682
te tr 160681
carcin omas</w> 160529
ad y</w> 160510
k ly</w> 160430
R is 160407
surro un 160386
oscop y</w> 160311
TI NG</w> 160307
S S 160237
relax ation</w> 160108
don ors</w> 160087
depend ing</w> 160084
br id 159987
T s</w> 159956
pl atin</w> 159891
be d 159872
E ach</w> 159821
lig ands</w> 159807
opi oid</w> 159707
Ch r 159641
AI DS</w> 159618
ex tension</w> 159576
ST R 159483
F ir 159473
ca vity</w> 159450
1. 7</w> 159338
ati g 159295
end ometr 159262
neg atively</w> 159254
p ly</w> 159248
Blo od</w> 159213
contrac tion</w> 159071
pa ired</w> 158977
chol in 158969
l um 158955
idi c</w> 158752
episod es</w> 158684
g ut</w> 158669
pl an 158659
fr acti 158659
extrem ely</w> 158658
depend s</w> 158650
glut athione</w> 158597
rec o 158519
diast olic</w> 158518
nan o 158489
compl ement</w> 158465
ro b 158394
G I 158369
D 1</w> 158281
pre treatment</w> 158220
res ear 158111
practi cal</w> 158048
cl asses</w> 157986
as ingly</w> 157873
ren ess</w> 157856
glyc os 157829
mat uration</w> 157768
am mon 157700
angi otensin</w> 157669
p ass 157664
util ized</w> 157643
v in 157631
m apping</w> 157566
tas ks</w> 157548
i ent</w> 157508
ep it 157497
ic illin</w> 157466
oc ellular</w> 157401
Fol lowing</w> 157378
polymorph isms</w> 157337
le g 157274
cytotox icity</w> 157237
grow n</w> 157158
allow ing</w> 157157
EE G</w> 157136
P 2</w> 157098
path ogens</w> 157095
ser ot 157065
otox in</w> 156946
Child ren</w> 156930
ro se</w> 156927
sp ine</w> 156923
f lo 156862
reco vered</w> 156781
re t 156738
4. 5</w> 156707
plasm id</w> 156698
def ine</w> 156673
clos ure</w> 156625
VE GF</w> 156620
util ity</w> 156602
I f</w> 156522
surve illance</w> 156502
re generation</w> 156457
lo op</w> 156455
d wide</w> 156450
cur ves</w> 156442
A g 156333
path ogenic</w> 156319
de position</w> 156303
F if 156263
M D</w> 156240
S uch</w> 156201
consi der</w> 156187
guid ed</w> 156153
valid ation</w> 156125
20 10</w> 156103
pr o</w> 156001
al ways</w> 155976
man di 155963
os por 155951
incorpor ation</w> 155923
dr y</w> 155902
emph asi 155821
sp ring</w> 155807
en se</w> 155773
coun ts</w> 155712
asym ptomatic</w> 155671
s ome 155652
P S 155630
insi ghts</w> 155600
flu or 155583
sub units</w> 155572
CO 2</w> 155557
arter i 155541
O x 155536
res s 155511
AT Pase</w> 155432
atig ue</w> 155431
trans genic</w> 155426
y le</w> 155399
l amin 155374
20 17</w> 155372
th in</w> 155325
separ ated</w> 155309
refl ect</w> 155294
oper ating</w> 155276
M T</w> 155253
v ic 155237
nic o 155232
col or</w> 155227
p yl 155220
As sociation</w> 155211
ab use</w> 155198
precurs or</w> 155156
drin king</w> 155156
0. 9</w> 155136
cardi omy 155083
A G</w> 154978
enzym atic</w> 154924
si c 154817
me d 154814
A m 154774
favor able</w> 154772
i onic</w> 154681
sc ales</w> 154657
tw are</w> 154649
tw ice</w> 154612
ch ains</w> 154603
Assess ment</w> 154587
lay ers</w> 154586
M ar 154491
E m 154484
onucle ar</w> 154415
candi date</w> 154315
thrombo sis</w> 154313
vol tage</w> 154305
H F</w> 154246
os c 154155
pre lim 154144
macroph age</w> 154140
p p 154137
METHO D</w> 154128
correc tion</w> 154100
Prim ary</w> 154097
ester one</w> 154080
R I 154056
mo stly</w> 154020
temper atures</w> 154018
t an 153965
2 D</w> 153860
ast om 153749
ol e 153696
incre asingly</w> 153598
Ther ap 153538
ST UD 153447
hyd rate</w> 153429
STUD Y</w> 153393
immunos up 153298
L 1</w> 153295
resid ents</w> 153265
om eg 153222
ru ption</w> 153150
pa thetic</w> 153089
in st 153037
inter face</w> 153003
m u 152998
r in</w> 152907
in direct</w> 152869
requ iring</w> 152738
aure us</w> 152678
i etic</w> 152660
ubi qu 152652
ot ting</w> 152629
du od 152601
inf ra 152569
ren e</w> 152501
le x</w> 152495
dr am 152473
suppl ementation</w> 152415
l umbar</w> 152340
fil ms</w> 152324
valu able</w> 152310
adap tive</w> 152302
ana es 152298
P ur 152246
CN S</w> 152201
dec i 152155
agg ressive</w> 152142
defic its</w> 152064
vir ul 152007
predic ting</w> 151991
str ati 151976
f s</w> 151930
H S</w> 151918
or adi 151912
inf erior</w> 151870
par ameter</w> 151854
main tain</w> 151853
sen sus</w> 151843
perme ability</w> 151735
2. 2</w> 151708
commun ities</w> 151700
qu estion</w> 151635
te eth</w> 151603
gi ve</w> 151576
si ties</w> 151533
g over 151494
Pati ent</w> 151435
m ach 151428
1 7. 151386
S V 151384
en coun 151344
categ ories</w> 151277
worl dwide</w> 151275
indic es</w> 151150
attrib uted</w> 151094
pos es</w> 151078
scienti fic</w> 151077
te tra 151060
om s</w> 150992
loc ations</w> 150922
col d</w> 150868
Str uc 150862
ne arly</w> 150828
test osterone</w> 150805
ene sis</w> 150778
ori entation</w> 150765
on dro 150568
b p</w> 150560
hem oglobin</w> 150533
profes sion 150511
ar rest</w> 150382
pr on 150372
Sy stem 150360
coag ulation</w> 150341
P O</w> 150319
influ ences</w> 150318
N OS</w> 150221
ri tic</w> 150206
relap se</w> 150170
ig ible</w> 150168
con sum 150124
Pre vious</w> 150055
bi osynthesis</w> 149920
e w 149770
oc al</w> 149746
20 11</w> 149723
h i 149698
m mol</w> 149685
dec om 149683
cell ul 149682
T ra 149675
impl ants</w> 149633
ac e 149614
erro rs</w> 149606
oti des</w> 149576
predic tor</w> 149543
se e 149504
prote ase</w> 149486
aud itory</w> 149463
require ments</w> 149459
mo vements</w> 149456
CD 8</w> 149432
cl ones</w> 149403
manifest ations</w> 149364
ilib rium</w> 149276
emerg ing</w> 149267
op tions</w> 149218
Ob jec 149156
regul ates</w> 149153
su g 149148
cl ar 149109
g old</w> 149084
unc er 148992
oper ated</w> 148926
L ong</w> 148799
nu tritional</w> 148787
tob acco</w> 148759
n M</w> 148740
repe at</w> 148725
spre ad</w> 148699
E D 148690
PA TI 148659
The ir</w> 148653
exc ess</w> 148645
resi due</w> 148598
R h 148565
mm Hg</w> 148553
abl ation</w> 148552
ve h 148547
impro vements</w> 148540
s clerosis</w> 148512
t ations</w> 148418
li ve</w> 148328
exc it 148274
prelim inary</w> 148263
re mission</w> 148253
comp ens 148251
SET TING</w> 148236
2 5 148234
10 00</w> 148210
pelv ic</w> 148202
om ers</w> 148195
C ar 148181
P T</w> 148123
T R</w> 148108
sep sis</w> 148096
N L 148046
ari ties</w> 148036
o side</w> 147985
pneumon ia</w> 147960
colum n</w> 147951
PATI EN 147898
corne al</w> 147854
fi st 147776
fo ot</w> 147759
sof tware</w> 147741
M I</w> 147693
Jap anese</w> 147617
ga p</w> 147597
ell ed</w> 147579
2. 1</w> 147572
ag land 147547
ag ne 147519
estr adiol</w> 147509
h and 147423
hippocamp al</w> 147352
10 0 147331
advant age</w> 147323
pl ex 147280
0. 7</w> 147226
pl an</w> 147217
struc tion</w> 147215
spectr al</w> 147183
in ating</w> 147073
is ons</w> 147060
fe asibility</w> 146957
lin ed</w> 146899
great ly</w> 146859
C ase</w> 146856
Sur ve 146850
G 1</w> 146753
p s 146741
p ren 146700
ex press</w> 146663
ful ness</w> 146612
d ine</w> 146554
pati onal</w> 146536
estim ation</w> 146523
easi ly</w> 146451
Ris k</w> 146448
occ u 146412
O P</w> 146368
acqu isi 146359
li on</w> 146331
cyt e</w> 146298
hydro ph 146255
ud g 146221
es es</w> 146215
ifor m</w> 146205
W e 146201
4 50</w> 146174
M olecular</w> 146113
re plic 146035
lim its</w> 146028
ti le</w> 145983
de sign 145931
f abr 145925
S ep 145910
om onas</w> 145904
intr insic</w> 145895
aneurys m</w> 145868
simul ations</w> 145854
ol ys 145737
H O</w> 145710
all ergic</w> 145697
vi de 145617
stimul ating</w> 145548
decre asing</w> 145488
E vid 145455
kn ock 145445
su med</w> 145443
scop e</w> 145395
ant um</w> 145345
U se</w> 145314
activ ator</w> 145310
lac king</w> 145304
lyc er 145250
al in</w> 145224
pl ay 145146
opo ietic</w> 145098
promo ting</w> 145033
reas ons</w> 144975
ox y</w> 144957
i ther</w> 144930
gre en</w> 144915
id osis</w> 144914
cop per</w> 144838
tren ds</w> 144832
a que</w> 144824
rheum at 144819
n ers</w> 144774
fund am 144743
clu sters</w> 144708
re ticul 144677
off er</w> 144566
Dop pler</w> 144555
In tr 144551
lip ids</w> 144527
rever sed</w> 144513
evol ution 144510
0.0 3</w> 144471
intro duction</w> 144467
2. 3</w> 144465
induc ing</w> 144421
v ents</w> 144376
depart ment</w> 144370
h er</w> 144309
educ ational</w> 144286
opa usal</w> 144261
lact ate</w> 144238
hom ogene 144158
ab solute</w> 144137
we ak</w> 144118
he ad 144112
D F 144023
per spective</w> 143828
with draw 143828
activ ating</w> 143820
ed ed</w> 143808
vag inal</w> 143757
sch ed 143741
asp ase</w> 143561
antagon ists</w> 143549
ethyl ene</w> 143542
f ro 143536
inter views</w> 143522
un usual</w> 143478
19 . 143455
examin ations</w> 143415
O A</w> 143324
trans lation</w> 143323
loc i</w> 143310
prog esterone</w> 143280
Chang es</w> 143268
ul ative</w> 143190
se di 143176
dis rup 143173
entr y</w> 143173
argin ine</w> 143094
medic ations</w> 143045
hormon es</w> 142953
home ostasis</w> 142939
y r</w> 142901
method ology</w> 142872
ax ial</w> 142789
gu ide</w> 142736
r un 142597
D H</w> 142573
Plas ma</w> 142489
IC U</w> 142412
oun d 142328
adren ergic</w> 142277
D et 142259
malign ancy</w> 142197
osc ill 142165
RO S</w> 142147
s plic 142146
gu inea</w> 142121
A ut 142116
gest ation</w> 142089
c ut 142081
indic ators</w> 142058
ie f</w> 142046
Develop ment</w> 142011
p ent 142001
ep i 141982
tro p 141978
deli vered</w> 141950
re frac 141943
Chr onic</w> 141899
M od 141892
fl ic 141887
correl ate</w> 141879
r icul 141872
ve get 141860
N T</w> 141793
seiz ures</w> 141791
resol ved</w> 141779
barri ers</w> 141766
phen otypes</w> 141685
challeng ing</w> 141625
l ess 141622
en chymal</w> 141607
tran s</w> 141509
Evid ence</w> 141468
micro bi 141443
dra in 141431
in form 141424
C K 141415
MM P</w> 141360
lin k</w> 141350
hypo thesized</w> 141296
datab ases</w> 141205
ow s</w> 141185
rob ust</w> 141151
ip al</w> 141144
thromb in</w> 141103
cap illary</w> 141079
SC C</w> 141078
can n 141062
b is 141052
S e 141045
ste ady</w> 141043
Hy per 141013
anat omical</w> 140996
ob acter 140965
under taken</w> 140930
ol i 140886
c ephal 140858
T ri 140852
ain ts</w> 140831
all ing</w> 140808
fe a 140794
F 1</w> 140592
Al so</w> 140562
dis section</w> 140471
tr ue</w> 140463
top ic</w> 140409
W il 140391
L O 140340
end o 140297
intr am 140285
commerc ial</w> 140174
cap sul 140159
di stress</w> 140154
2. 4</w> 140131
re mark 140128
glyco protein</w> 140090
yiel ded</w> 140073
n one</w> 140053
are t 140014
ra ised</w> 140013
path ogen</w> 139991
ch ers</w> 139967
haz ard</w> 139949
N on 139945
biop sies</w> 139937
Rev ie 139928
cere bro 139920
classi cal</w> 139895
analy tical</w> 139893
6 0 139889
Or g 139880
CL C</w> 139866
subst ances</w> 139832
prec ip 139821
le uc 139750
ox in</w> 139738
P G</w> 139704
n ext</w> 139666
chrom atin</w> 139660
og r 139651
C H</w> 139604
i tive</w> 139588
al ready</w> 139542
particip ation</w> 139521
f atigue</w> 139348
fe wer</w> 139331
in activation</w> 139313
in come</w> 139215
Bi o 139133
ga ve</w> 139028
graf ts</w> 139008
disco very</w> 138907
Si x</w> 138898
P seud 138867
ig aret 138854
lab eling</w> 138839
acquisi tion</w> 138810
diff ered</w> 138806
Rec ently</w> 138786
micro scopic</w> 138744
p toc 138737
charg ed</w> 138708
en sis</w> 138698
arti ficial</w> 138650
compl iance</w> 138620
P M</w> 138605
fl ap</w> 138597
profession als</w> 138556
heterogene ous</w> 138514
m ary</w> 138501
2 3 138483
P ath 138444
eph rine</w> 138439
gest ational</w> 138434
ozyg ous</w> 138434
al anine</w> 138418
di arr 138391
initi ated</w> 138357
Multi ple</w> 138320
c is 138319
typ ically</w> 138297
ev ident</w> 138224
pol ar 138184
amin o 138178
n ose</w> 138106
re in 138074
e in</w> 138007
environ ments</w> 137944
in put</w> 137942
it ems</w> 137931
be y 137905
contrib utes</w> 137873
G E</w> 137859
fil tration</w> 137856
L ow</w> 137803
insi ght</w> 137716
al izing</w> 137648
N o 137639
Fir st</w> 137606
dis play</w> 137605
Par kin 137605
men opausal</w> 137591
og ly 137576
us cular</w> 137570
reve als</w> 137532
n atal</w> 137518
it ec 137503
om ycin</w> 137480
un t</w> 137396
neutr al</w> 137289
provid ers</w> 137272
B R 137262
awa reness</w> 137246
bey ond</w> 137218
electro de</w> 137150
func tion 137140
cont ents</w> 137131
re fer 137110
practi tion 137066
concomit ant</w> 137062
ophil a</w> 137021
de generative</w> 137009
conf ig 136999
T um 136990
H em 136951
ca regi 136898
adju stment</w> 136894
le y</w> 136832
os y 136796
protoc ols</w> 136740
se em</w> 136734
hep arin</w> 136702
dis m</w> 136685
sc aff 136667
mut ag 136579
N TS</w> 136517
G 2</w> 136488
hippocamp us</w> 136406
respon d</w> 136380
su pr 136327
mul tic 136121
review s</w> 136077
con sensus</w> 136073
aut ophag 136044
responsi veness</w> 135947
C are</w> 135932
m atic</w> 135872
soci o 135872
paren tal</w> 135812
I r 135808
con sul 135801
depl etion</w> 135788
mal aria</w> 135753
weigh ted</w> 135737
ampl ification</w> 135724
pers on</w> 135663
I ts</w> 135642
HB V</w> 135606
em por 135470
ads orption</w> 135374
oc cal</w> 135349
G y</w> 135332
b ot 135310
ou ter</w> 135300
H PLC</w> 135236
high light</w> 135135
interpre tation</w> 135078
roph ic</w> 135058
O L</w> 135020
ne ither</w> 135015
determin ants</w> 135015
d yst 135014
tr ained</w> 135003
O ther</w> 134974
detec ting</w> 134929
GAB A</w> 134922
H DL</w> 134883
in os 134849
exc e 134845
an astom 134817
cir cu 134803
S tre 134763
ogra ft</w> 134715
l ibr 134594
inhib iting</w> 134591
AB STR 134561
ub er 134547
l ens</w> 134530
platel ets</w> 134510
acc es 134481
clin icians</w> 134441
immuno deficiency</w> 134441
d less</w> 134430
epider mal</w> 134391
en abl 134385
P H 134376
relev ance</w> 134282
re tri 134258
at yp 134198
it self</w> 134176
Qu anti 134173
t es 134169
por tion</w> 134152
modi fications</w> 134120
incub ated</w> 134101
S te 134070
pre valent</w> 134046
1. 9</w> 134034
ABSTR ACT</w> 134027
impro ves</w> 134021
contro ver 133975
AI MS</w> 133970
m ess 133964
she ep</w> 133958
hospit alization</w> 133927
di l 133882
W OR 133774
atic s</w> 133760
d ates</w> 133730
under go</w> 133682
com pression</w> 133482
U S 133481
ot a</w> 133436
N E 133435
intro duc 133359
syn erg 133282
sou ght</w> 133224
preven ting</w> 133209
N S</w> 133098
prost agland 133071
b al</w> 133020
ti ary</w> 132997
immunoglob ulin</w> 132984
cr y 132965
M AP 132963
5. 5</w> 132941
hydro lysis</w> 132932
S up 132905
av oid</w> 132847
pro ven</w> 132814
ch ondro 132766
scre ened</w> 132765
ig en 132721
D I</w> 132715
diff use</w> 132657
PATIEN TS</w> 132641
For ty</w> 132639
D ru 132544
prec ise</w> 132521
fibr in 132503
N an 132460
F s</w> 132446
clon ed</w> 132387
ha ir</w> 132366
W ith 132294
simil arity</w> 132182
N AD 132107
re ading</w> 132107
attit udes</w> 132103
bre ak 132016
pyl ori</w> 132005
ex clu 131986
B A</w> 131972
G a 131947
lu tin 131921
ma x 131903
enrich ed</w> 131838
2. 6</w> 131801
z o 131776
co ated</w> 131741
un ilateral</w> 131716
creatin ine</w> 131678
st ent</w> 131617
we a 131617
om er</w> 131553
l ost</w> 131551
colon y</w> 131525
por t</w> 131511
en able</w> 131491
inf er 131483
Si x 131471
arch itec 131422
C entr 131351
L ev 131345
port al</w> 131303
A I</w> 131295
ure a</w> 131275
dig ital</w> 131267
yl ic</w> 131265
op tion</w> 131188
bac k 131133
adi ly</w> 131111
rec i 131103
refrac tory</w> 131056
ic ans</w> 131052
pol l 131032
B raz 130952
need le</w> 130836
S oci 130829
embry o</w> 130823
gon ad 130815
N Ps</w> 130789
AT ED</w> 130788
ocor tico 130771
sp ir 130765
perc utaneous</w> 130764
ul t</w> 130729
exclud ed</w> 130727
U NC 130720
ra ys</w> 130707
s a 130703
N on</w> 130697
memb er</w> 130682
agon ists</w> 130669
coag ul 130660
ex ogenous</w> 130626
tend ing</w> 130606
pre d 130599
sub types</w> 130594
identi ty</w> 130511
com pati 130504
P eri 130491
correl ates</w> 130477
.0 0 130475
combin ations</w> 130425
li pos 130387
per for 130361
observ ational</w> 130296
plic ations</w> 130278
pil ot</w> 130198
im mobil 130108
x en 130103
μ g</w> 130051
dat as 130037
transcrip ts</w> 130013
T otal</w> 129991
en sion</w> 129901
ar ach 129873
3. 0</w> 129872
c am 129869
ne a</w> 129857
ic les</w> 129836
A B</w> 129818
G T 129811
impl emented</w> 129811
In tern 129789
cortis ol</w> 129785
medi ates</w> 129762
trans location</w> 129746
an ing</w> 129744
norm ally</w> 129737
I C</w> 129729
P oly 129715
A SU 129693
techn ical</w> 129647
co he 129622
p ure</w> 129620
M ech 129608
bil i 129563
is ot 129562
abund ance</w> 129447
c ut</w> 129386
C ells</w> 129347
hom ologous</w> 129328
vis its</w> 129289
c it 129272
S F</w> 129267
amin es</w> 129197
off ers</w> 129162
f all</w> 129081
le aves</w> 129063
ch amb 129055
Parkin son</w> 128998
k ade</w> 128968
os es</w> 128941
pl aque</w> 128877
AC E</w> 128798
inter sti 128722
secre tory</w> 128711
morph ine</w> 128684
P sych 128683
2. 8</w> 128665
gen otypes</w> 128652
lim iting</w> 128641
2. 7</w> 128637
S H 128614
promot es</w> 128589
lo be</w> 128553
WOR DS</w> 128544
ME ASU 128521
on to</w> 128520
spe ech</w> 128495
com es</w> 128467
be am</w> 128432
abs orb 128422
ste ad</w> 128404
reduc tase</w> 128366
pro min 128363
lu te 128353
M at 128336
auth or</w> 128301
tr ic 128269
im id 128241
fundam ental</w> 128236
con stric 128214
obstruc tive</w> 128197
ta il</w> 128082
pro genit 128072
subjec tive</w> 128037
ex ac 127991
re presented</w> 127933
sp ac 127922
ser ine</w> 127914
e j 127880
surviv ors</w> 127781
c igaret 127753
e osin 127725
dimen sions</w> 127723
dos age</w> 127675
enter ic</w> 127653
Signific ant</w> 127635
cover age</w> 127534
ai res</w> 127508
chang ing</w> 127504
Prote in</w> 127485
lea f</w> 127435
un changed</w> 127305
consist s</w> 127291
V ir 127262
pre ad</w> 127249
bre a 127238
as sive</w> 127101
substanti ally</w> 127097
exc ision</w> 127084
surroun ding</w> 127079
suppor ting</w> 127010
di ets</w> 127009
Contro l</w> 127009
pres sions</w> 126974
5 7 126965
suppor ts</w> 126954
itr al</w> 126929
ex ists</w> 126903
h is 126863
ac a 126859
Z n</w> 126745
si zes</w> 126742
pres um 126688
trig lycer 126666
L L 126554
gli al</w> 126541
prob es</w> 126540
ath yro 126519
adolesc ent</w> 126434
hab it 126423
ou gh</w> 126417
ur ity</w> 126415
w estern</w> 126384
chem ically</w> 126359
f ine</w> 126332
n oc 126321
sub set</w> 126320
consist ently</w> 126287
od e</w> 126251
c aspase</w> 126248
cre ated</w> 126238
accep table</w> 126228
bel ong 126187
c l</w> 126174
TR UNC 126155
TRUNC ATED</w> 126141
Com bin 126127
r it 126121
iti n</w> 126114
fo ur 126111
micr o</w> 126049
simul ated</w> 125981
concer n</w> 125974
c ine</w> 125973
trans duction</w> 125945
qu ad 125905
re tur 125887
b lue</w> 125884
M ut 125819
elic ited</w> 125813
fibrill ation</w> 125722
agne tic</w> 125713
9 9 125638
sp in 125518
trans fusion</w> 125458
indic ations</w> 125439
concer ns</w> 125421
evolution ary</w> 125417
emp ir 125394
satur ated</w> 125393
M 1</w> 125389
app ed</w> 125368
resour ce</w> 125324
particip ated</w> 125322
cirrho sis</w> 125314
pres crip 125285
F lu 125243
Ar ab 125175
Res pon 125167
un e</w> 125151
op es</w> 125099
ati le</w> 125051
chrom osomal</w> 125050
T E</w> 125027
A E</w> 125012
E V 124998
6. 5</w> 124997
S el 124973
ra rely</w> 124960
phosphati dyl 124929
agg lutin 124920
pre term</w> 124893
as h</w> 124727
contr acti 124673
pron oun 124592
epidemi ological</w> 124565
D 2</w> 124445
ran e</w> 124422
metabol ite</w> 124400
es pread</w> 124372
r a</w> 124333
oper ations</w> 124330
pol ys 124264
occu pational</w> 124260
sper mat 124235
cy steine</w> 124163
G L 124112
z e 124111
activ ate</w> 124107
trans formed</w> 124069
fal se</w> 124024
5. 0</w> 123999
reg imens</w> 123995
rheumat oid</w> 123986
intrac ranial</w> 123970
equ ilibrium</w> 123969
eli hood</w> 123932
fea sible</w> 123923
be gin 123895
devi ation</w> 123876
te am</w> 123846
in ess</w> 123843
o ing</w> 123835
d ar 123804
intes tine</w> 123789
on ing</w> 123705
sc ans</w> 123634
ca the 123541
a uc 123470
s or 123433
sp in</w> 123411
syn chron 123407
inc id 123390
qu antum</w> 123359
complex ity</w> 123295
AR T</w> 123292
b ed</w> 123235
inter national</w> 123219
re jection</w> 123214
Ph armac 123199
elim ination</w> 123199
som at 123165
gen us</w> 123156
as one</w> 123072
enco ded</w> 123055
mes enchymal</w> 122996
mo tility</w> 122986
retri ev 122972
sci ence</w> 122919
3. 2</w> 122912
sym pathetic</w> 122899
Cur rent</w> 122899
eff lu 122865
ti zed</w> 122810
ec ological</w> 122810
att achment</w> 122801
correc ted</w> 122729
ting ly</w> 122718
sol vent</w> 122713
projec t</w> 122695
infil tration</w> 122674
gir ls</w> 122658
P 3</w> 122612
adi pose</w> 122607
th eses</w> 122543
con str 122485
occ up 122484
dec ades</w> 122455
an emia</w> 122442
oph thal 122437
sup ply</w> 122428
ogen icity</w> 122418
AI N</w> 122415
i dism</w> 122398
abs ent</w> 122396
in stability</w> 122356
su res</w> 122307
M O 122202
al ize</w> 122202
allel es</w> 122170
down stream</w> 122044
0.0 4</w> 122034
hydrox y</w> 121985
pronoun ced</w> 121971
indic ator</w> 121926
paras ite</w> 121915
app a</w> 121906
per man 121846
odi c</w> 121836
t es</w> 121779
ay er</w> 121774
enti ally</w> 121748
carri ers</w> 121674
m id</w> 121654
s ar 121649
ester ase</w> 121619
Bet ween</w> 121612
2 7 121600
con n 121560
Sy stem</w> 121503
Revie w</w> 121479
us h 121475
ep tive</w> 121470
ol ol</w> 121442
techn ologies</w> 121384
G s</w> 121380
te aching</w> 121380
CO PD</w> 121314
sub cutaneous</w> 121295
I R 121276
hepat ocellular</w> 121258
aor ta</w> 121230
alk aline</w> 121217
deci sions</w> 121200
f asting</w> 121196
is ch 121109
situ ation</w> 121103
min eral</w> 121100
tim ing</w> 121035
per in 121023
out patient</w> 121021
bin ds</w> 121020
der mal</w> 121014
ab err 121012
tox ic 120967
H L</w> 120950
bl ack</w> 120934
bili ary</w> 120930
ag e 120929
pl ane</w> 120905
prophyl axis</w> 120762
os ocial</w> 120719
.0 5</w> 120674
et e</w> 120620
serot onin</w> 120571
categ or 120544
3. 1</w> 120533
I L 120517
res sing</w> 120517
K ore 120492
lo aded</w> 120465
res h</w> 120462
St aphyloc 120444
lik elihood</w> 120403
vacc ines</w> 120391
vul ner 120375
as cular</w> 120293
atheros clerosis</w> 120226
res tingly</w> 120163
atin gs</w> 120133
Ig E</w> 120124
og lyc 120116
ph y 120074
begin ning</w> 120059
difficul ties</w> 120048
v ary</w> 120035
carri er</w> 120030
oun ds</w> 120004
M al 119918
Intern ational</w> 119917
2 4 119777
W om 119737
3 -</w> 119723
ann ual</w> 119713
star ting</w> 119712
li th 119694
1 25</w> 119680
chrom osomes</w> 119645
veh icle</w> 119633
H T 119629
nam ely</w> 119621
Identi fication</w> 119606
m T 119548
st art</w> 119529
Un der</w> 119524
r ated</w> 119520
pow er 119492
cyt ometry</w> 119491
mo ther</w> 119425
re ach</w> 119421
f ar 119405
attemp t</w> 119390
discrim ination</w> 119387
ast er 119267
Func tional</w> 119263
5 , 119260
H F 119257
cy an 119247
wid espread</w> 119220
inter act</w> 119197
to ph 119184
p ump</w> 119161
vis ed</w> 119138
tu ally</w> 119131
G M</w> 119117
G re 119109
1 A</w> 119098
discipl inary</w> 119078
amylo id</w> 119050
S W 119045
ap ical</w> 119022
diagnos es</w> 118990
man aged</w> 118947
monit or</w> 118880
Surg ical</w> 118830
Pre dic 118816
li es</w> 118814
ar is</w> 118777
bloc kade</w> 118739
2. 9</w> 118731
i ae</w> 118730
ter tiary</w> 118704
Immun o 118647
om orph 118646
resear chers</w> 118602
B ar 118596
Incre ased</w> 118588
j udg 118584
ess es</w> 118560
k eletal</w> 118545
neutroph ils</w> 118491
p es 118461
glycer ol</w> 118437
scre en</w> 118417
an tic 118415
dep ri 118400
m ent 118360
adi g 118354
som atic</w> 118345
angi ogenesis</w> 118333
ro tation</w> 118313
form ations</w> 118237
D ros 118204
engine ering</w> 118064
respon dents</w> 118049
P B 118048
hepat ocytes</w> 118046
intersti tial</w> 118021
hydroph obic</w> 118005
trans mit 117986
verte bral</w> 117964
neutroph il</w> 117942
regar dless</w> 117939
carbo hydrate</w> 117923
tac hy 117769
200 9</w> 117759
selec tively</w> 117711
ultras on 117706
H a 117677
modul ate</w> 117646
be g 117576
volum es</w> 117552
E GFR</w> 117551
Jap an</w> 117520
My co 117518
differenti ally</w> 117509
pancre as</w> 117487
intra operative</w> 117372
T e 117366
ir radi 117359
mil lion</w> 117342
recru itment</w> 117325
cis platin</w> 117315
R 1</w> 117309
emerg ed</w> 117222
hist amine</w> 117220
inter n 117201
Ac cording</w> 117195
pro spectively</w> 117156
c ro 117138
invol ve</w> 117134
uc tu 117084
Dros ophila</w> 117049
Wom en</w> 117008
ot es</w> 116941
S 1</w> 116873
leth al</w> 116847
am ycin</w> 116837
P o 116824
con flic 116822
satisfac tory</w> 116808
O ver</w> 116807
In dex</w> 116720
sens or</w> 116720
main taining</w> 116644
as y</w> 116616
quanti fied</w> 116586
plat form</w> 116586
Afr ica</w> 116548
odi alysis</w> 116546
ref lex</w> 116544
phen otypic</w> 116514
P L</w> 116502
t uring</w> 116500
immunohisto chemistry</w> 116482
idi opathic</w> 116475
organis m</w> 116445
competi tive</w> 116442
ath ing</w> 116411
gl auc 116401
res ses</w> 116386
ens ure</w> 116383
b ol 116382
str ial</w> 116267
bre ak</w> 116252
phil ic</w> 116235
atyp ical</w> 116219
post natal</w> 116211
for ces</w> 116179
M HC</w> 116103
iv es</w> 116062
ma kes</w> 116052
gl ands</w> 116017
transpor ter</w> 116000
h old</w> 115989
v .</w> 115934
J une</w> 115923
m itral</w> 115905
part um</w> 115898
A E 115826
Sub jec 115772
Ig M</w> 115741
CR P</w> 115715
princi ples</w> 115693
ur g 115663
f low 115609
ul y</w> 115601
pro per</w> 115581
e ating</w> 115554
quanti fication</w> 115473
I P</w> 115461
fer til 115434
Elec tr 115408
great est</w> 115338
suff ering</w> 115326
ses sions</w> 115305
g all 115248
sup pressor</w> 115239
transf ected</w> 115238
end s</w> 115181
mamm ary</w> 115139
secre ted</w> 115134
U p 115130
represent ative</w> 115103
3. 6</w> 115093
H ep 115084
hist ologic</w> 115059
at rophy</w> 115039
l ist</w> 115030
sus pen 115030
ong oing</w> 114966
out side</w> 114962
cardi a</w> 114881
cl onal</w> 114877
follow s</w> 114802
r ice</w> 114786
T 4</w> 114786
construc t</w> 114783
con sequence</w> 114758
perform ing</w> 114753
sol ub 114740
. S 114674
y ed</w> 114654
C 1</w> 114629
lin king</w> 114583
effici ents</w> 114552
ann ed</w> 114538
aca demic</w> 114515
V I</w> 114484
3. 8</w> 114461
par adig 114460
ol ed</w> 114440
sc en 114356
le g</w> 114351
ma p</w> 114350
6. 7</w> 114293
R H</w> 114284
mis sions</w> 114255
sub group</w> 114222
2 6 114184
H igh 114159
sha red</w> 114154
lun gs</w> 114094
echocardi ography</w> 114074
assess ments</w> 114073
S O</w> 113984
sequ ential</w> 113982
abol ic</w> 113950
M AIN</w> 113944
H ere 113940
inter ference</w> 113928
bacter i 113922
endometr ial</w> 113814
immunohisto chemical</w> 113810
elucid ate</w> 113782
infil tr 113759
lab el</w> 113756
In dia</w> 113713
pre ferred</w> 113642
accur ately</w> 113612
CR C</w> 113577
ha pl 113550
p g</w> 113549
ta kes</w> 113472
Staphyloc occus</w> 113462
contamin ation</w> 113458
stimul ate</w> 113423
new born</w> 113415
z er 113389
de press 113370
B re 113360
4. 7</w> 113328
mon ol 113323
c ation</w> 113305
questionn aires</w> 113291
integr ity</w> 113274
effici ently</w> 113265
ventr al</w> 113250
u g 113230
re modeling</w> 113195
regul ator</w> 113184
eth asone</w> 113173
malign ancies</w> 113116
withdraw al</w> 113105
on line</w> 113036
se ed</w> 113013
mon ella</w> 112968
ograph s</w> 112948
S F 112939
C o</w> 112917
pro ton</w> 112914
con g 112912
ul der</w> 112881
P ot 112857
lin kage</w> 112857
t ext</w> 112837
em bed 112769
incorpor ated</w> 112723
ulc er</w> 112718
cat ech 112682
unc ture</w> 112679
G r 112672
ac ous 112641
surge ons</w> 112635
ac tual</w> 112631
S ci 112603
Inte restingly</w> 112595
St atis 112551
enhanc ing</w> 112502
biomar ker</w> 112416
ox idi 112362
produc es</w> 112308
termin ation</w> 112302
super oxide</w> 112293
v a</w> 112283
drain age</w> 112257
vi sible</w> 112236
st o 112209
adi p 112208
der m 112143
abund ant</w> 112136
phosphor ylated</w> 112131
deter i 112112
s we 112043
fl uctu 112035
f aster</w> 111995
cum ulative</w> 111987
et on</w> 111976
ho use 111939
Gen etic</w> 111914
U K</w> 111906
ro ute</w> 111828
prec ision</w> 111827
co efficients</w> 111824
larg est</w> 111824
expl an 111803
ogr ams</w> 111800
b rom 111796
h e</w> 111764
lo x 111758
S equ 111724
y ces</w> 111684
MEASU RE 111684
con formation</w> 111667
an ch 111655
M L 111630
compar isons</w> 111629
sum mary</w> 111590
path ologic</w> 111588
f atal</w> 111518
b ands</w> 111499
oc in</w> 111496
C V</w> 111461
hem is 111457
sub type</w> 111448
gener alized</w> 111447
substitu tion</w> 111427
op enia</w> 111414
ure th 111408
c ement</w> 111406
pan el</w> 111399
hypertroph y</w> 111364
im etr 111316
iev ed</w> 111286
Ch em 111273
stom ach</w> 111257
o x</w> 111233
t us</w> 111175
strom al</w> 111157
B acter 111117
az ine</w> 111080
f resh</w> 111046
toler ated</w> 111043
ic k</w> 111038
del e 111036
om otor</w> 111009
vi able</w> 110911
bro ad 110844
repres enting</w> 110825
inos itol</w> 110818
ul tim 110812
2 A</w> 110791
d ly</w> 110771
induc ible</w> 110762
hy pot 110759
ner ves</w> 110703
distin c 110690
v ital</w> 110680
ra dic 110661
D G</w> 110650
las tic</w> 110625
deriv ative</w> 110611
promin ent</w> 110592
abol ished</w> 110580
id ae</w> 110573
suic ide</w> 110559
compl ementary</w> 110549
demonstr ating</w> 110518
T M 110500
random ised</w> 110492
N A 110474
respon ded</w> 110471
pr an 110421
omer ic</w> 110358
over view</w> 110351
or a</w> 110325
f f</w> 110251
countr y</w> 110232
re adily</w> 110231
glomer ular</w> 110230
der ing</w> 110197
en cour 110181
n ever</w> 110162
ob acter</w> 110136
ra in</w> 110125
consider ation</w> 110099
R ole</w> 110095
promot ed</w> 109967
C X 109965
pre mature</w> 109955
oc ca 109944
Di ab 109924
wid th</w> 109917
Dru g</w> 109906
eg g</w> 109889
hist one</w> 109879
4. 2</w> 109876
r ace</w> 109865
ne igh 109855
enh ances</w> 109853
dis sem 109843
bri ef</w> 109779
mi RNAs</w> 109747
g ing 109743
str and</w> 109717
distrib utions</w> 109714
i x</w> 109700
paren tly</w> 109668
7 0 109653
autom ated</w> 109649
ch im 109626
par athyro 109589
n ight</w> 109566
v an 109528
hem odynamic</w> 109520
th reat 109518
I tal 109492
de pressed</w> 109489
selec tivity</w> 109474
oste opo 109461
sur vi 109442
osi tion</w> 109432
ach e</w> 109421
grad u 109418
t ability</w> 109404
k appa</w> 109386
for ward</w> 109378
to oth</w> 109368
impl anted</w> 109351
predomin ant</w> 109319
dimin ished</w> 109307
descrip tion</w> 109289
eff ort</w> 109279
A V</w> 109269
ol amine</w> 109249
wor se</w> 109247
myel oid</w> 109243
lo g</w> 109225
polyp eptide</w> 109208
endoth elium</w> 109189
vascul arization</w> 109167
mod alities</w> 109153
calcul ations</w> 109152
stud ying</w> 109142
neo plastic</w> 109106
pe di 109104
t ative</w> 109067
D 3</w> 109060
a udi 109031
shap ed</w> 109028
rup ture</w> 108999
dis ruption</w> 108944
2 8 108905
ul atory</w> 108839
fil am 108815
pro t 108810
dis c</w> 108764
dend ritic</w> 108743
defic it</w> 108729
ec tions</w> 108718
Sal monella</w> 108707
quanti fy</w> 108690
th eless</w> 108684
no ver</w> 108631
Ig A</w> 108623
ever theless</w> 108602
mandi bular</w> 108592
S ym 108556
si al</w> 108541
gener ating</w> 108532
tr ic</w> 108498
sti ff 108468
tre e</w> 108454
T G</w> 108413
pe d 108407
cy st</w> 108406
micro som 108398
I A</w> 108396
p assive</w> 108359
j ust</w> 108318
n mol</w> 108317
stand ards</w> 108317
3. 4</w> 108311
tis m</w> 108310
otox icity</w> 108308
in her 108274
ol ym 108273
F our 108208
1 -</w> 108206
pre ference</w> 108199
ker at 108189
di al</w> 108163
b er 108154
al c 108150
p e</w> 108115
f ts</w> 108107
mon ocytes</w> 108104
I s 108097
ic ardi 108055
ish ment</w> 107994
exac erb 107989
ev ent 107977
s arcoma</w> 107973
As soci 107922
oxy gen 107896
Qu ality</w> 107855
L ab 107782
t ab 107771
8. 5</w> 107754
add ressed</w> 107748
s ent</w> 107744
cat tle</w> 107703
op sis</w> 107702
V al 107658
autophag y</w> 107634
yiel ds</w> 107631
contr al 107550
Un der 107513
os ing</w> 107508
substitu ted</w> 107471
protec ted</w> 107469
pregn ancies</w> 107412
c ats</w> 107396
infra red</w> 107390
squ are</w> 107383
tryp sin</w> 107329
l d</w> 107327
particip ate</w> 107325
ur on 107306
comput ational</w> 107300
ter al</w> 107283
I mp 107274
vas o 107234
co vered</w> 107230
Nor th</w> 107227
sc ap 107213
con sci 107212
NS CLC</w> 107202
E 1</w> 107192
disco vered</w> 107160
fol ding</w> 107028
ca ud 106965
cyto plasm</w> 106964
immun ization</w> 106961
wal king</w> 106961
percep tions</w> 106953
Org an 106936
TI ONS</w> 106859
en erg 106847
instrum ent</w> 106806
mod ality</w> 106792
expl o 106791
fac tory</w> 106729
tend on</w> 106704
iso forms</w> 106654
H O 106634
gen ce</w> 106578
S ingle</w> 106574
mo bile</w> 106509
N ot 106503
ke ys</w> 106500
min er 106499
trac tion</w> 106488
virul ence</w> 106317
physi ology</w> 106291
w in 106279
process ed</w> 106242
oste o 106242
pot ency</w> 106237
ech n 106232
bo ys</w> 106227
nutri ent</w> 106222
CA D</w> 106208
man u 106186
j o 106178
compr ised</w> 106138
m om 106129
perman ent</w> 106124
ne gl 106103
In flu 106095
plac ental</w> 106076
A k 106054
consider ably</w> 106043
ar ticular</w> 106030
mar k</w> 106027
dis sociation</w> 105963
ab sc 105949
Hy dro 105895
wa vel 105861
an ical</w> 105846
repe ti 105807
nur se</w> 105785
no vi 105783
4. 4</w> 105737
accoun ted</w> 105731
i m</w> 105722
per oxidase</w> 105578
f ur 105574
gene tically</w> 105562
il e 105549
4. 3</w> 105544
4. 0</w> 105520
gu an 105515
200 8</w> 105491
Co x</w> 105451
gluc ocortico 105432
u ous</w> 105431
bloc king</w> 105368
post operatively</w> 105339
form ulation</w> 105336
por cine</w> 105313
ver tical</w> 105274
princ ipal</w> 105269
pos sess 105255
gl ass</w> 105244
cri tically</w> 105212
pathophysi ology</w> 105208
propor tional</w> 105173
reduc tions</w> 105168
if er 105160
radi ographic</w> 105154
f el 105150
kin ases</w> 105076
experim entally</w> 105067
- -</w> 105050
t um</w> 105017
us c 105006
carr ying</w> 104965
hemat opoietic</w> 104938
3. 7</w> 104916
Fif ty</w> 104864
condi tioning</w> 104862
si um</w> 104846
ul ating</w> 104799
off spring</w> 104776
AD P</w> 104640
micro tub 104635
foc using</w> 104596
M arch</w> 104575
M C</w> 104562
neuro pathy</w> 104556
P N</w> 104513
tur nover</w> 104482
v acu 104465
intr aper 104414
immun ological</w> 104407
immuno assay</w> 104401
re combination</w> 104390
over come</w> 104378
hemis ph 104362
W T</w> 104336
pyr id 104335
seiz ure</w> 104319
c ocaine</w> 104288
inf u 104249
to p</w> 104185
Pseud omonas</w> 104176
bi di 104170
se e</w> 104167
consider ing</w> 104150
ic king</w> 104035
H D 104027
acu ity</w> 104026
e res</w> 104024
E F</w> 103994
M ed</w> 103989
star ted</w> 103970
f if 103968
transl ational</w> 103947
peri od 103928
re turn</w> 103913
a virus</w> 103857
scen ari 103800
der mat 103766
lat ency</w> 103741
PK C</w> 103736
el ong 103716
sub groups</w> 103697
A ge</w> 103692
my co 103652
pres sures</w> 103583
κ B</w> 103566
Elec tro 103560
12 5 103559
ec tom 103522
min ute</w> 103466
el igible</w> 103441
fin ed</w> 103440
dep end</w> 103429
20 18</w> 103425
pan ic</w> 103425
promis e</w> 103418
2 9 103412
o ocytes</w> 103386
adap ted</w> 103349
pren atal</w> 103342
ther mo 103333
P res 103284
acous tic</w> 103246
at o</w> 103242
A UC</w> 103230
H b 103230
for ced</w> 103230
ret ar 103201
E GF</w> 103191
bloc ks</w> 103179
sul ph 103168
ex on</w> 103133
fibrobl ast</w> 103130
orb ent</w> 103126
immunore activity</w> 103124
e asy</w> 103104
4. 1</w> 103065
prostagland in</w> 103050
P u 103010
Inhib ition</w> 103002
visi t</w> 102993
defin ition</w> 102971
R F 102966
Di rec 102966
ventr icle</w> 102957
osi tive</w> 102954
Objec tive</w> 102950
os us</w> 102945
ste in</w> 102921
mod es</w> 102861
protec t</w> 102855
ap parently</w> 102817
c ran 102812
oz o 102796
S B 102768
tr aff 102768
sha m</w> 102762
moder n</w> 102758
v ae</w> 102741
un affected</w> 102737
prop ag 102732
on ed</w> 102728
pap ill 102714
I D</w> 102668
E lev 102658
radi ological</w> 102635
met allo 102589
R etro 102515
re tained</w> 102502
to ok</w> 102502
Com pl 102501
be an</w> 102500
ad a</w> 102482
T T</w> 102441
0. 0</w> 102441
situ ations</w> 102411
ec on 102410
in suffici 102407
pro mp 102384
mod ulated</w> 102377
chamb er</w> 102368
equ ation</w> 102346
I sol 102338
SE M</w> 102328
ac idic</w> 102327
ju red</w> 102274
sem i</w> 102228
in ally</w> 102227
contral ateral</w> 102179
dy e</w> 102175
embol ism</w> 102174
alter ation</w> 102151
contin ue</w> 102148
4. 6</w> 102124
encoun tered</w> 102118
p . 102059
de form 102050
k b</w> 102032
fe ed</w> 102019
AT A</w> 102007
T en</w> 101988
In iti 101984
preven tive</w> 101955
manu fac 101952
hyper plasia</w> 101942
B o 101899
view ed</w> 101859
recor ding</w> 101843
if e</w> 101839
av ing</w> 101838
fill ed</w> 101822
w ast 101816
eu tical</w> 101780
os ens 101767
az ep 101754
H e</w> 101746
Surve y</w> 101738
N C</w> 101733
M B 101724
bon ds</w> 101712
can al</w> 101687
imp acts</w> 101635
ch olec 101615
syndrom es</w> 101570
or ally</w> 101513
g el 101510
C y 101488
c is</w> 101473
dec ade</w> 101427
id ation</w> 101378
S ol 101375
regi stered</w> 101304
I D 101293
famil ial</w> 101279
- 2</w> 101274
mon onuclear</w> 101227
Sel f</w> 101213
irradi ated</w> 101203
ag ic</w> 101181
he ar 101143
ar ched</w> 101103
ass embl 101103
shor ten 101063
om eter</w> 101042
co d 101040
8 0 101025
H ear 101019
optim ized</w> 101014
power ful</w> 100998
psych osocial</w> 100970
5. 6</w> 100968
ic als</w> 100956
H 2</w> 100936
de pos 100920
row th</w> 100909
ep tor</w> 100907
top ical</w> 100893
acchar ides</w> 100821
institu tion</w> 100769
S A 100728
sign alling</w> 100721
re d 100691
his ti 100676
do x</w> 100662
le ge</w> 100618
est yle</w> 100612
ro ots</w> 100589
practition ers</w> 100576
eu kary 100516
anes the 100490
retin a</w> 100469
ere ly</w> 100452
G ene</w> 100439
d al 100425
P K</w> 100409
cy sts</w> 100382
hom ology</w> 100375
C d 100366
R 2</w> 100267
M or 100263
plas ticity</w> 100260
c as 100249
contrib uting</w> 100220
E P</w> 100219
isol ate</w> 100211
aut ologous</w> 100203
fr on 100186
P erc 100181
gen cy</w> 100167
C d</w> 100125
rou gh 100119
1 80</w> 100118
i asis</w> 100112
in jured</w> 100098
Dep art 100092
ri l</w> 100076
ff e 100067
conform ational</w> 100061
in n 100001
pre disp 99990
wee kly</w> 99980
conn ected</w> 99957
zym e</w> 99902
programm e</w> 99896
fracti on 99858
whe at</w> 99831
N s</w> 99819
et a</w> 99812
0.00 5</w> 99793
sen sing</w> 99772
z en</w> 99771
ch ro 99765
cop y</w> 99760
sug ar</w> 99760
aff eren 99759
i er</w> 99752
Fac tors</w> 99741
R P</w> 99711
Ta ken</w> 99663
se arched</w> 99639
ul cer 99632
L ym 99627
compart ment</w> 99606
C enter</w> 99586
prescrib ed</w> 99581
B 2</w> 99556
int ell 99468
can ine</w> 99467
Soci ety</w> 99464
P F</w> 99463
B D 99443
glauc oma</w> 99432
hormon al</w> 99390
phosph o 99372
brea ks</w> 99360
stre t 99339
highligh ts</w> 99310
EC G</w> 99309
M ed 99306
anat omy</w> 99296
an thro 99258
With in</w> 99244
s n 99241
facil ities</w> 99215
epti des</w> 99207
el astic</w> 99169
exhib its</w> 99157
ran k</w> 99145
el ve</w> 99131
w ash 98987
op ulmonary</w> 98981
b ound 98963
D ue</w> 98959
ac ks</w> 98916
ath l 98902
ac etic</w> 98886
bronch ial</w> 98862
discus sion</w> 98846
neur on</w> 98842
atter ing</w> 98800
buff er</w> 98785
Qu estionn 98784
recor d</w> 98777
st one</w> 98770
hor iz 98748
bidi ties</w> 98725
pro l 98702
sho ulder</w> 98677
w k</w> 98653
saliv ary</w> 98618
form er</w> 98613
ex act</w> 98606
candi dates</w> 98575
em ul 98571
bal lo 98562
arrang ement</w> 98548
reticul um</w> 98508
lab elled</w> 98475
4. 8</w> 98469
tur ally</w> 98464
phospholip id</w> 98459
reas on</w> 98442
Im pro 98405
st ain 98389
5. 7</w> 98346
arom atic</w> 98343
cat ar 98333
phen ol</w> 98326
en larg 98324
dig estion</w> 98307
c ows</w> 98293
rel ations</w> 98292
do g</w> 98287
radi al</w> 98279
m olar</w> 98273
accep ted</w> 98260
po ol</w> 98239
s low 98231
V er 98162
ocom pati 98130
in side</w> 98127
3 5 98114
ex pressions</w> 98099
tra it</w> 98082
clin ic 98045
en in</w> 98034
contrib uted</w> 98034
indu stry</w> 98030
per m 97991
d ur 97983
diarr he 97922
C r</w> 97885
m ers</w> 97831
ex ec 97819
acr yl 97808
differenti ate</w> 97795
ucle ar</w> 97726
fr ame</w> 97715
cap ture</w> 97672
fist ula</w> 97665
clim ate</w> 97662
Character ization</w> 97662
architec ture</w> 97654
os arcoma</w> 97623
u gin 97622
electro des</w> 97619
sev erely</w> 97610
bre athing</w> 97540
T est</w> 97529
g one</w> 97517
em a</w> 97515
or dered</w> 97434
am ethasone</w> 97406
g ate</w> 97382
rou ght</w> 97381
ap o 97351
J uly</w> 97347
deta il</w> 97341
dem and</w> 97338
su d 97337
con duction</w> 97336
9. 5</w> 97314
H P</w> 97304
bio tic</w> 97300
difficul ty</w> 97263
M ay</w> 97254
us ting</w> 97254
phyl ogenetic</w> 97250
el in</w> 97239
un related</w> 97227
aer ugin 97216
EC T</w> 97188
cre ate</w> 97180
guid ance</w> 97180
oxy genase</w> 97175
d in 97121
rele asing</w> 97098
omat ous</w> 97066
erythro cytes</w> 97047
hist or 97011
D IN 97007
stiff ness</w> 96961
pur ification</w> 96951
nat ur 96951
tub ular</w> 96943
S w 96924
strong er</w> 96917
lar vae</w> 96912
com for 96903
sc attering</w> 96903
disturb ances</w> 96863
as ked</w> 96860
L V 96855
di vision</w> 96840
CM V</w> 96813
m RNAs</w> 96799
Can di 96781
P ub 96773
aerugin osa</w> 96771
P RO 96766
7. 4</w> 96760
h n</w> 96736
duc ts</w> 96730
per tur 96729
pancre atitis</w> 96722
aster n</w> 96693
amin ation</w> 96687
combin ing</w> 96660
vide o</w> 96562
fre e 96560
exc essive</w> 96490
Here in</w> 96474
ple ural</w> 96470
g els</w> 96458
cul tural</w> 96432
M s</w> 96363
cord ance</w> 96359
M ac 96354
H is 96349
am bul 96341
F l 96327
N E</w> 96276
c asc 96272
hospit alized</w> 96254
retur ned</w> 96201
otyp ing</w> 96196
opportun ities</w> 96086
require ment</w> 96073
T F 96027
Man agement</w> 96021
pal li 96001
en ables</w> 95978
person ality</w> 95976
med ull 95975
re dox</w> 95954
moti f</w> 95934
C 3</w> 95932
connec tivity</w> 95931
SL E</w> 95882
f ies</w> 95878
vi br 95825
trache al</w> 95807
l ands</w> 95805
acetyl choline</w> 95801
neon ates</w> 95773
andro gen</w> 95718
graph y</w> 95711
Be havi 95657
M g 95656
2 2 95653
war ran 95652
con current</w> 95614
G P</w> 95610
amin ergic</w> 95606
VI D</w> 95591
Meas ure 95591
5. 2</w> 95573
nico tine</w> 95561
ej un 95534
L in 95524
s ess</w> 95503
w s</w> 95498
S ou 95479
curren ts</w> 95467
o idal</w> 95458
dis placement</w> 95442
N one</w> 95387
ca dian</w> 95363
exc itation</w> 95352
coord ination</w> 95335
ker atin 95325
in ic</w> 95147
gly col 95129
un common</w> 95122
met als</w> 95098
lymph oid</w> 95080
on ia</w> 95075
i. v.</w> 95066
ge red</w> 95054
chic ken</w> 95044
o es</w> 95036
wor ds</w> 95022
ses sion</w> 95000
e gg 94963
3. 9</w> 94955
lig ament</w> 94953
5. 3</w> 94939
bor ne</w> 94936
st aging</w> 94913
ren in</w> 94902
U .S 94898
sph er 94891
fac es</w> 94883
In c 94878
pneumon iae</w> 94875
kin in</w> 94868
com b 94847
O UT 94808
am bi 94808
e y</w> 94771
indic ation</w> 94756
P SA</w> 94745
bre eding</w> 94722
optim ization</w> 94693
cellul ose</w> 94683
ub icin</w> 94625
200 7</w> 94625
ca us 94599
sur ance</w> 94582
obl astoma</w> 94579
cholin ergic</w> 94566
log ical</w> 94558
n em 94512
pl ates</w> 94505
spont aneously</w> 94493
Differen t</w> 94493
super ficial</w> 94465
k ap 94443
t ex 94404
200 5</w> 94401
fo ods</w> 94385
suppl emented</w> 94376
P an 94375
sp p</w> 94331
leuk ocyte</w> 94321
Ph ot 94311
cereb ellar</w> 94293
ger m</w> 94256
nod ules</w> 94255
it em</w> 94230
ther mia</w> 94226
mus cular</w> 94214
OUT CO 94202
Depart ment</w> 94181
ace ae</w> 94165
S even</w> 94148
dys plasia</w> 94092
idi um</w> 94068
system atically</w> 94045
er ia</w> 94039
0.00 2</w> 94024
inc ident</w> 93985
sat uration</w> 93985
mat ch</w> 93958
tes ticular</w> 93958
1 10</w> 93953
Im port 93950
retro viral</w> 93922
categ ory</w> 93915
de m</w> 93912
per oxide</w> 93910
exclu sively</w> 93875
Mo del</w> 93873
yt e</w> 93840
ophil ic</w> 93794
weigh ts</w> 93787
surge on</w> 93756
effec tor</w> 93706
rest ored</w> 93702
analog ues</w> 93671
insuffici ency</w> 93650
R T 93643
A pri 93619
concep ts</w> 93586
to o</w> 93574
line age</w> 93573
E igh 93564
as part 93564
A 1 93548
E l 93545
est ed</w> 93527
hist ology</w> 93511
T ime</w> 93509
Z n 93476
dec lined</w> 93476
empir ical</w> 93469
in hal 93461
ili ary</w> 93451
ol es</w> 93436
clar ify</w> 93424
arb ox 93388
Apri l</w> 93351
N 1</w> 93336
ob lastic</w> 93332
eng er</w> 93324
flex ible</w> 93283
in stead</w> 93237
S 2</w> 93228
vec tors</w> 93214
foll icular</w> 93169
over weight</w> 93160
aspir ation</w> 93158
plas ms</w> 93128
G SH</w> 93092
tain ty</w> 93074
anti tumor</w> 93070
Ad mini 93058
anti depress 93053
equ ally</w> 93031
at omic</w> 93001
vas odi 92994
fru it</w> 92986
G iven</w> 92984
rhyth m</w> 92981
n t</w> 92974
INT ER 92967
hel p 92960
En gl 92953
r ag 92938
cr yp 92918
fil ter</w> 92907
T i 92857
ev id 92812
refer ral</w> 92798
bel ieved</w> 92797
chem icals</w> 92753
re flux</w> 92737
Simil ar</w> 92725
embed ded</w> 92712
analog ue</w> 92695
E ight</w> 92670
t esti 92663
op posite</w> 92642
p es</w> 92635
olog ist</w> 92625
suc rose</w> 92624
algorith ms</w> 92622
r RNA</w> 92617
14 C</w> 92592
libr ary</w> 92562
conf oun 92545
ti g 92529
N ur 92514
s light</w> 92509
K n 92505
j ejun 92479
20 01</w> 92476
d l</w> 92463
S we 92439
n c 92349
head ache</w> 92331
fit ness</w> 92319
pro v 92276
kap pa 92275
grad ually</w> 92269
orb ital</w> 92264
acceler ated</w> 92263
com orb 92258
L T</w> 92254
aneurys ms</w> 92247
enc ap 92233
li pop 92231
stabil ization</w> 92222
Subjec ts</w> 92200
cy st 92195
mar ine</w> 92150
t one</w> 92099
am p</w> 92096
an k</w> 92082
ps ori 92016
Ad v 92006
bi polar</w> 91985
acet yl</w> 91948
appro ved</w> 91917
G I</w> 91914
R F</w> 91902
199 9</w> 91899
condi tioned</w> 91881
den se</w> 91877
util izing</w> 91877
fer tility</w> 91863
ju ven 91833
col itis</w> 91819
sh ear</w> 91816
sy novi 91813
tend ency</w> 91811
Euro pe</w> 91782
objec t</w> 91760
anomal ies</w> 91749
Engl ish</w> 91741
g ated</w> 91734
19 90</w> 91726
cataly zed</w> 91713
surve ys</w> 91703
us ti 91693
con ven 91690
use fulness</w> 91618
under gone</w> 91594
Hear t</w> 91564
quin ol 91544
kidne ys</w> 91540
L S</w> 91519
c at</w> 91497
ex tra 91480
Com mun 91472
pol i 91453
lep tin</w> 91449
c ef 91436
M SCs</w> 91433
ax el</w> 91420
b ab 91358
ery them 91331
Tum or</w> 91323
200 6</w> 91319
am ni 91317
alk al 91302
emph asis</w> 91302
Ul tr 91290
expos ures</w> 91259
un g</w> 91258
ethn ic</w> 91224
de man 91223
tub ation</w> 91219
G n 91198
2 8. 91185
sw elling</w> 91178
hear ts</w> 91165
ab in 91135
omer ase</w> 91116
arthro plasty</w> 91073
9 0 91055
conserv ative</w> 91046
ob i 91039
help ful</w> 91028
R a 91024
contin ence</w> 91020
lu pus</w> 90958
an al</w> 90936
cul tiv 90879
controver sial</w> 90854
at oms</w> 90852
Centr al</w> 90847
osyn thetic</w> 90813
O C</w> 90810
mus cul 90797
wavel eng 90790
N MDA</w> 90786
Experim ental</w> 90779
En h 90759
perf used</w> 90740
distingu ish</w> 90734
C K</w> 90720
di hydro 90653
loc ally</w> 90625
V is 90619
dri ving</w> 90617
period ontal</w> 90615
C oun 90585
Syn thesis</w> 90584
6. 2</w> 90572
199 8</w> 90566
immuno fluorescence</w> 90544
6 00</w> 90536
ga it</w> 90536
CA M</w> 90461
ir ation</w> 90443
D em 90432
statis tics</w> 90415
om ing</w> 90414
sh are</w> 90411
bir ds</w> 90397
C ro 90393
trans membrane</w> 90392
Diag nos 90391
olys accharide</w> 90379
col onic</w> 90352
Out comes</w> 90345
esc ent</w> 90336
V T</w> 90322
compati ble</w> 90271
5. 8</w> 90268
repor ter</w> 90263
at onin</w> 90245
pat ch</w> 90220
DI SC 90195
sel ves</w> 90174
refl ected</w> 90172
mi RNA</w> 90161
6. 3</w> 90158
spec tives</w> 90124
ch le 90113
0.00 0 90108
lif estyle</w> 90100
in active</w> 90086
upreg ulated</w> 90060
ide al</w> 90057
BM D</w> 90046
align ment</w> 90029
Surg ery</w> 89995
peri operative</w> 89981
dar k</w> 89921
Q o 89901
5. 4</w> 89897
N evertheless</w> 89893
e an</w> 89870
F am 89852
adren aline</w> 89801
conjug ated</w> 89775
vi r</w> 89757
fin anc 89747
pharmaco kinetic</w> 89747
anti viral</w> 89724
cess ation</w> 89718
Tran sc 89714
er arch 89700
l uc 89696
L A 89679
Co A</w> 89670
B G</w> 89621
antic ancer</w> 89606
Quanti tative</w> 89600
SI ON</w> 89580
ster ing</w> 89504
mo od</w> 89431
hypothalam ic</w> 89420
four th</w> 89413
cl one</w> 89394
U r 89377
gover n 89369
sc al 89336
pos sess</w> 89319
6. 4</w> 89291
ure tic</w> 89272
trig gered</w> 89256
opath ological</w> 89252
dom es 89204
S D 89192
tum orig 89182
d ent 89132
ac ch 89120
H 1</w> 89106
s a</w> 89104
opportun ity</w> 89055
inten se</w> 89014
M AP</w> 89008
Simil arly</w> 88971
recor dings</w> 88969
ocy an 88960
vis c 88958
ep igen 88945
Austral ia</w> 88879
el eton</w> 88859
ST AT 88854
dex amethasone</w> 88847
o ural</w> 88841
fl ur 88813
MAP K</w> 88807
S c 88777
Au g 88760
glob ulin</w> 88702
w aves</w> 88697
G ol 88642
preser vation</w> 88639
ion ine</w> 88633
res embl 88632
op ia</w> 88614
press es</w> 88611
centr e</w> 88605
us age</w> 88591
analge sia</w> 88580
In divid 88573
SN Ps</w> 88559
T o 88553
off ered</w> 88503
Questionn aire</w> 88501
plac ent 88496
C yt 88489
ex ual</w> 88470
att ached</w> 88462
ballo on</w> 88453
transfer red</w> 88444
TB I</w> 88420
es are 88362
b und 88349
cran i 88300
ag ue</w> 88292
prescrip tion</w> 88270
ist ar</w> 88267
ank le</w> 88243
k le</w> 88231
ell ite</w> 88217
lif e 88199
6. 8</w> 88179
6. 6</w> 88174
or ial</w> 88168
us er</w> 88167
examin ing</w> 88157
par tly</w> 88154
F uture</w> 88091
os ensi 88068
gen omes</w> 88052
por e</w> 88044
mit tent</w> 88044
str ic</w> 88034
b ic</w> 88026
pr is 88025
an ion</w> 88018
th in 88009
Det ection</w> 87991
dos ing</w> 87963
l en 87936
C er 87930
bil ities</w> 87925
S DS</w> 87924
7. 8</w> 87908
aro se</w> 87830
ost omy</w> 87825
omeg al 87821
influ encing</w> 87801
attemp ts</w> 87795
s cler 87767
rib onucle 87747
me al</w> 87692
Mech anis 87684
P P 87672
E r 87671
Six ty</w> 87667
hem odialysis</w> 87662
preser ved</w> 87659
CA 1</w> 87656
paradig m</w> 87632
M V 87627
exam ples</w> 87616
An aly 87591
investig ating</w> 87577
duod enal</w> 87564
olig onucle 87560
os en</w> 87541
neo plasms</w> 87540
b a 87508
in sufficient</w> 87496
I S</w> 87463
en vel 87457
C och 87428
gli oma</w> 87424
v ast 87420
cytos olic</w> 87418
Ak t</w> 87409
coh orts</w> 87393
7. 1</w> 87378
esare an</w> 87363
exten sively</w> 87328
v ill 87307
F O 87294
G ram</w> 87288
po oled</w> 87282
di methyl 87274
qu it 87273
19 95</w> 87259
medi ators</w> 87239
op ening</w> 87233
V i 87224
A sian</w> 87222
precurs ors</w> 87216
y outh</w> 87200
Sep t 87182
ag o</w> 87155
us h</w> 87152
ag ain</w> 87093
199 7</w> 87086
le ad 87054
acryl amide</w> 87052
socio economic</w> 87051
remark able</w> 87016
alcoh olic</w> 86945
neu rom 86930
lab or 86861
St ate</w> 86858
en ium</w> 86837
T 2 86796
res tim 86795
func tionally</w> 86795
diff r 86783
de b 86774
Bac illus</w> 86770
id ase</w> 86764
epidemi ology</w> 86762
W istar</w> 86745
immun otherapy</w> 86724
l a</w> 86717
at om</w> 86712
ob ut 86705
F IN 86680
tra jec 86668
ic ations</w> 86663
L .</w> 86659
re mia</w> 86636
ogen es</w> 86615
ol factory</w> 86609
up stream</w> 86596
AD HD</w> 86575
CO VID</w> 86572
R an 86560
C ap 86522
pros thesis</w> 86455
dist ant</w> 86452
I T 86439
Qo L</w> 86435
G O</w> 86413
uni versity</w> 86382
homogene ous</w> 86357
them selves</w> 86342
mig r 86319
an aerobic</w> 86301
Differen ces</w> 86296
CO X</w> 86292
em ed</w> 86284
R am 86281
F SH</w> 86281
bo x</w> 86281
ampl ified</w> 86278
Sept ember</w> 86264
go als</w> 86253
aspec t</w> 86238
ultrason ography</w> 86237
gangli on</w> 86216
st a 86195
s ound</w> 86190
od g 86143
m osph 86117
st ress 86104
Pub Med</w> 86096
DISC US 86079
threat ening</w> 86027
6. 0</w> 86003
dram atically</w> 85998
assi st</w> 85971
N ov 85957
Wor ld</w> 85936
mon keys</w> 85929
ax ons</w> 85926
pharmaco kinetics</w> 85924
0. 25</w> 85923
mix tures</w> 85913
200 2</w> 85893
low ering</w> 85892
125 I</w> 85876
osteopo rosis</w> 85869
tan dem</w> 85857
a emic</w> 85846
val id</w> 85838
instrum ents</w> 85819
M i 85809
C 57 85793
le ak 85784
electro l 85784
bec omes</w> 85731
physi ologic</w> 85726
def ense</w> 85714
ch osen</w> 85700
transcrip t</w> 85697
pred n 85694
extr a</w> 85692
M A 85691
ser ial</w> 85639
inn ate</w> 85638
4 5 85632
DISCUS SION</w> 85627
hel ix</w> 85611
Admini stration</w> 85585
x ia</w> 85573
CV D</w> 85564
qu ite</w> 85562
flex ion</w> 85535
os mo 85532
P U 85508
U.S .</w> 85507
hydr ation</w> 85486
Imp act</w> 85470
En ter 85443
obl asts</w> 85443
biom ass</w> 85430
emergen ce</w> 85396
protein ase</w> 85368
exp anded</w> 85356
ish es</w> 85355
M 2</w> 85332
tob er</w> 85326
cir cadian</w> 85324
res in</w> 85318
ograph ical</w> 85310
glomer ul 85304
appl ying</w> 85295
Con tr 85278
ati cal</w> 85271
adj usting</w> 85259
simil arly</w> 85248
contamin ated</w> 85242
ogene ic</w> 85214
C 2</w> 85201
T arg 85198
ammon ium</w> 85181
en ess</w> 85168
B one</w> 85154
L a</w> 85131
immunore active</w> 85115
anal og</w> 85089
st ored</w> 85082
anc h</w> 85074
T re 85066
exp on 85051
Oc tober</w> 85039
graph ene</w> 85021
an tic</w> 85014
caregi vers</w> 85006
shif ts</w> 84996
id opsis</w> 84989
spec imen</w> 84985
in verse</w> 84954
ent ations</w> 84954
ic ated</w> 84935
proper ty</w> 84932
ver sal</w> 84928
te trac 84922
rel ief</w> 84920
egg s</w> 84905
an g</w> 84899
avi an</w> 84876
pp m</w> 84869
f ash 84863
B rain</w> 84809
man ual</w> 84786
sol ved</w> 84781
M g</w> 84778
def ective</w> 84778
PE G</w> 84768
se c</w> 84761
cigaret te</w> 84724
m ium</w> 84717
ra ine</w> 84701
g yn 84700
lys ine</w> 84698
r ic 84687
no re 84666
5. 1</w> 84653
H odg 84635
config uration</w> 84631
through put</w> 84619
199 6</w> 84604
progenit or</w> 84587
si bility</w> 84489
ov ary</w> 84476
termin us</w> 84465
histopath ological</w> 84429
ar sen 84422
Arab idopsis</w> 84371
some times</w> 84358
ptoc occus</w> 84344
my o 84326
nov o</w> 84320
ta il 84295
bur n</w> 84285
ther m 84274
compl etion</w> 84270
diarrhe a</w> 84263
In stitute</w> 84255
ec osy 84253
nucle otides</w> 84243
7. 2</w> 84223
chec k 84221
at ch</w> 84217
clu sive</w> 84178
in tim 84170
fung i</w> 84141
No vel</w> 84137
gli a</w> 84136
clin ics</w> 84120
t ap 84111
natur ally</w> 84105
anat omic</w> 84096
bl ing</w> 84088
B cl</w> 84084
x y 84067
occ i</w> 84053
M O</w> 84040
microbi ota</w> 83996
Therap y</w> 83967
descrip tive</w> 83962
zer o</w> 83948
Me dian</w> 83941
mod elling</w> 83927
w t</w> 83893
the size</w> 83857
on yl</w> 83844
in complete</w> 83839
ain t</w> 83825
cardiomy opathy</w> 83804
conduc tance</w> 83778
compr ising</w> 83760
am eli 83752
SO D</w> 83746
ti tis</w> 83738
Medic ine</w> 83735
G MP</w> 83697
inter view</w> 83696
B B</w> 83646
graf ting</w> 83635
2 9. 83628
ation ally</w> 83591
schem e</w> 83584
un iform</w> 83568
seas on</w> 83547
aqu es</w> 83537
cont ex 83532
psych otic</w> 83497
n as 83481
acc ess 83448
predic tions</w> 83418
G T</w> 83417
ch i 83413
contrib utions</w> 83407
over l 83394
1 a</w> 83380
m um 83345
ob last</w> 83303
see king</w> 83280
visi ae</w> 83269
ing u 83258
op sy</w> 83229
sud den</w> 83208
mat ching</w> 83181
R I</w> 83180
Hodg kin</w> 83172
F GF</w> 83165
er o 83089
fro zen</w> 83085
cere visiae</w> 83072
sc oring</w> 83064
viol ence</w> 83045
4. 9</w> 83042
micro scope</w> 83025
T SH</w> 83024
CI PA 83011
a red</w> 83009
on ectin</w> 82997
St and 82992
anthro p 82960
s me 82957
scho ols</w> 82931
v esti 82923
mi as</w> 82915
neurolog ic</w> 82892
Th en</w> 82881
ro bo 82871
200 4</w> 82863
depri vation</w> 82831
bl ast 82826
a w</w> 82774
ere bral</w> 82764
otox ic</w> 82760
7. 3</w> 82757
un able</w> 82753
Nor thern</w> 82746
const ants</w> 82736
Na Cl</w> 82726
aspart ate</w> 82724
gran ules</w> 82723
leuk ocytes</w> 82719
PT SD</w> 82675
cal ib 82663
erythro cyte</w> 82661
T ur 82616
C at 82614
bl in 82609
R B 82559
7. 0</w> 82531
otrop in</w> 82519
pl oid</w> 82512
Spec ific</w> 82496
H b</w> 82474
ph yto 82460
o ther 82443
construc tion</w> 82407
A SD</w> 82404
ma them 82400
e jection</w> 82373
part ners</w> 82369
1 40</w> 82344
C ross</w> 82337
ph age</w> 82308
magne sium</w> 82299
T F</w> 82294
fib res</w> 82269
ti p</w> 82258
f ri 82256
W HO</w> 82208
CK D</w> 82197
B AL 82195
cros s 82184
ost atin</w> 82175
P N 82167
F F 82162
cycl ase</w> 82149
d ox 82144
strati fied</w> 82114
6. 1</w> 82107
M M</w> 82082
H ence</w> 82078
rap h</w> 82038
co chle 82008
las ting</w> 82004
cycl in</w> 81997
athl etes</w> 81996
myocardi um</w> 81995
as sumed</w> 81977
wast e</w> 81961
An ti</w> 81932
s ing 81928
pre -</w> 81928
H SV</w> 81926
Braz il</w> 81911
respon ders</w> 81890
aw are</w> 81855
pu ts</w> 81852
I U</w> 81835
fash ion</w> 81743
de t 81741
a u</w> 81713
S K 81712
discus ses</w> 81685
pin ephrine</w> 81655
fing er</w> 81636
aspir in</w> 81604
cu es</w> 81592
C ul 81585
minim ally</w> 81582
equ ip 81578
ynam ic</w> 81550
tr unc 81549
anis h</w> 81519
di c</w> 81514
EC s</w> 81509
lab or</w> 81507
bul k</w> 81487
MATERI AL</w> 81474
numer ical</w> 81464
th o 81459
2. 0 81456
dem ia</w> 81453
j usti 81432
mamm als</w> 81415
competi tion</w> 81414
iz umab</w> 81405
lig ation</w> 81398
dex tr 81360
v ent 81357
FIN DIN 81317
sh ar 81274
Candi da</w> 81265
M V</w> 81262
G R</w> 81252
glyc ine</w> 81228
trans mitted</w> 81223
M B</w> 81211
- 3</w> 81160
ur ic</w> 81157
pri vate</w> 81122
P a</w> 81112
n al 81094
ifor m 81073
N in 81070
rest oration</w> 81056
B ur 81045
kappa B</w> 81031
p to 81015
T er 81010
coun sel 80999
O ral</w> 80974
ver ified</w> 80969
st ac 80944
si fication</w> 80944
trig g 80935
g yr 80927
E M</w> 80922
electro chemical</w> 80920
ar ising</w> 80914
addi tive</w> 80876
B A 80872
Q OL</w> 80855
mach ine</w> 80846
ol er 80829
sil ver</w> 80828
thresh olds</w> 80825
FINDIN GS</w> 80807
in formed</w> 80792
trac king</w> 80758
contracti le</w> 80741
enco des</w> 80728
Ac tivity</w> 80718
cler otic</w> 80685
m ast</w> 80680
op s</w> 80672
me an 80634
ac yl 80612
in adequate</w> 80601
T ox 80593
lev i 80590
im mature</w> 80576
EB V</w> 80573
OUTCO ME</w> 80570
efflu x</w> 80538
ph asic</w> 80537
gal act 80509
g p 80473
R ap 80463
f ear</w> 80447
polic ies</w> 80421
et amine</w> 80392
c ranial</w> 80389
su m</w> 80379
medi ating</w> 80365
AC S</w> 80334
immun op 80332
p .</w> 80325
Compar ative</w> 80305
est ers</w> 80301
or ubicin</w> 80292
AC TH</w> 80286
Sur viv 80277
troph ic</w> 80259
consist ency</w> 80246
7. 6</w> 80225
AM L</w> 80219
enrich ment</w> 80202
ol in</w> 80196
Myco bacterium</w> 80178
5. 9</w> 80161
indu strial</w> 80142
op in 80129
ist er</w> 80128
h eld</w> 80125
pol lution</w> 80123
interpre ted</w> 80113
continu ously</w> 80111
adren oc 80089
d L</w> 80086
a dic 80076
ti um</w> 80019
prost atic</w> 80018
cir cular</w> 80015
optim um</w> 80010
cop ing</w> 80001
- 5</w> 79988
1 30</w> 79982
quantit atively</w> 79968
b if 79958
In formation</w> 79900
bl otting</w> 79898
fal ls</w> 79894
ti bial</w> 79892
flu ids</w> 79888
umb il 79885
G rowth</w> 79874
s ac 79842
design ated</w> 79842
paras ites</w> 79836
u tri 79821
P RE 79772
en anti 79755
m enti 79741
Ph osph 79716
ch el 79715
aut onomic</w> 79697
C u 79694
anti bacterial</w> 79669
w ards</w> 79658
epigen etic</w> 79649
m aps</w> 79638
arom yces</w> 79636
pl anned</w> 79627
at mosph 79623
lam p 79611
g es</w> 79573
separ ately</w> 79573
ell es</w> 79553
hypox ic</w> 79550
conserv ation</w> 79541
lox acin</w> 79518
per oxidation</w> 79434
fill ing</w> 79389
gene sis</w> 79385
sh ell</w> 79368
O VA</w> 79366
Ex pos 79359
H I</w> 79340
polym ers</w> 79312
de ed</w> 79300
bi a</w> 79272
N T 79269
an ium</w> 79250
j our 79243
max illary</w> 79235
applic able</w> 79233
an o 79215
aut osomal</w> 79167
L s</w> 79165
cerebro spinal</w> 79158
br anch</w> 79155
te e</w> 79152
Fin dings</w> 79117
promis ed</w> 79112
persist ence</w> 79098
e es</w> 79097
ger min 79096
tri ple</w> 79085
Can ada</w> 79073
moder ately</w> 79060
p uber 79050
t RNA</w> 79018
inte resting</w> 79010
gi ant</w> 78981
py ru 78980
. 01</w> 78964
stit utes</w> 78960
g ri 78956
x yl 78935
en ough</w> 78931
ho sts</w> 78877
en demic</w> 78873
ex clusion</w> 78873
la p</w> 78866
achiev ing</w> 78849
g ives</w> 78831
AC h 78811
sh e</w> 78791
inter mittent</w> 78787
al ised</w> 78771
Ph ase</w> 78717
ver bal</w> 78702
sc in 78702
transpl anted</w> 78698
ut able</w> 78685
G D</w> 78684
a ur 78664
pur poses</w> 78662
epi demic</w> 78626
acch aromyces</w> 78607
200 3</w> 78594
my osin</w> 78574
199 4</w> 78562
elucid ated</w> 78559
per spectives</w> 78553
leak age</w> 78549
S am 78516
m ill 78497
ec lamp 78494
R o 78473
super nat 78402
re vision</w> 78398
testi s</w> 78396
f os 78353
in patient</w> 78349
pro motion</w> 78343
fet uses</w> 78330
7. 7</w> 78325
ost atic</w> 78324
recomm end</w> 78306
manip ulation</w> 78295
ta u</w> 78290
ac yl</w> 78271
post partum</w> 78264
abnormal ity</w> 78262
M ic 78260
rou tin 78254
bon ding</w> 78239
haem at 78228
umin escence</w> 78202
on ally</w> 78187
Retro spective</w> 78185
edi c</w> 78159
ad y 78157
iv al</w> 78157
T N 78126
if ug 78123
bio film</w> 78108
mas sive</w> 78101
par i 78080
t ary</w> 78068
accum ulated</w> 78062
non invasive</w> 78048
sc ar 78036
micro organisms</w> 78025
behavi oural</w> 78025
SI S</w> 78015
cryst als</w> 78015
correc tly</w> 78011
Statis tical</w> 78007
4 -</w> 77971
exp ensive</w> 77962
ambul atory</w> 77960
C ir 77957
S ocial</w> 77956
1 01</w> 77937
anaes thesia</w> 77931
ca usal</w> 77911
s s</w> 77884
H ER 77852
repres entation</w> 77851
ti ally</w> 77850
6. 9</w> 77823
C N</w> 77789
ad dic 77789
cour ses</w> 77772
ho use</w> 77759
des c 77743
con vul 77731
li p</w> 77729
ip si 77723
K A</w> 77720
diffr action</w> 77699
os yl 77691
Coch rane</w> 77686
os an</w> 77661
c ally</w> 77654
suspen sion</w> 77638
integr in</w> 77628
tachy cardia</w> 77617
8. 3</w> 77607
iod ine</w> 77597
N R 77586
He pati 77559
Pro spective</w> 77538
pharmac eutical</w> 77538
Con si 77525
GF P</w> 77515
adop ted</w> 77509
sil ica</w> 77451
horiz ontal</w> 77448
est ro 77421
ori ented</w> 77420
7 , 77403
inoc ulated</w> 77392
ass ayed</w> 77386
splic ing</w> 77367
m on</w> 77362
ol ing</w> 77362
In tra 77354
dis h</w> 77344
admini str 77341
S pr 77334
or bidities</w> 77322
episod e</w> 77322
Spec ific 77321
Pre valence</w> 77284
rang es</w> 77255
al levi 77239
F i 77233
foc uses</w> 77227
ar rays</w> 77208
de struction</w> 77205
less er</w> 77193
constric tion</w> 77182
respon ding</w> 77173
I GF 77158
w at 77122
i k 77119
M ex 77114
lat ent</w> 77096
contr actions</w> 77095
M ater 77082
c ure</w> 77041
life time</w> 77030
filam ents</w> 76996
g ing</w> 76993
PT H</w> 76985
ant en 76976
spl en 76976
inser ted</w> 76976
De pression</w> 76975
w ing</w> 76955
viol et</w> 76953
lat or</w> 76938
extrem ity</w> 76909
transf ection</w> 76899
lipop olysaccharide</w> 76858
c row 76855
CT s</w> 76819
buil ding</w> 76771
defin itive</w> 76769
ed ge</w> 76766
phot on</w> 76756
r ings</w> 76736
nore pinephrine</w> 76723
D D 76717
wh y</w> 76708
ren tly</w> 76702
nit rate</w> 76695
at ories</w> 76688
ocyt o 76677
join ts</w> 76668
men str 76655
disturb ance</w> 76654
L arg 76651
3 2 76647
empor al</w> 76646
ro tic</w> 76631
ag on</w> 76631
astro cytes</w> 76602
ici an</w> 76585
po rous</w> 76572
tin ib</w> 76552
U SA</w> 76549
M n</w> 76532
pea ks</w> 76528
ocompati bility</w> 76500
eng agement</w> 76489
profil ing</w> 76486
surg ically</w> 76476
se d 76474
capsul e</w> 76473
μ m</w> 76465
analy zing</w> 76441
conver ted</w> 76410
perin atal</w> 76406
se a</w> 76395
recipi ent</w> 76387
part ner</w> 76384
multic enter</w> 76368
P ain</w> 76345
sequ enced</w> 76341
me et</w> 76323
sup press</w> 76321
sep tic</w> 76310
form ulations</w> 76275
connec tion</w> 76273
in nov 76272
inter acting</w> 76262
y el 76254
p ep 76245
h ind</w> 76218
i e</w> 76213
activ ates</w> 76198
examin es</w> 76180
ish man 76180
in nerv 76156
non linear</w> 76154
cr ude</w> 76117
m asses</w> 76102
h in 76097
recogn ize</w> 76090
omy el 76078
prol actin</w> 76072
cap ability</w> 76058
lat ation</w> 76047
A N</w> 76043
- induced</w> 76039
Diab etes</w> 76036
on ine</w> 76034
tryp toph 76024
Inv estig 76012
objec tives</w> 75990
suff ered</w> 75962
hem e</w> 75960
ome dical</w> 75947
deta ils</w> 75945
oxygen ation</w> 75940
re aching</w> 75918
fore ign</w> 75888
hi erarch 75884
Physi cal</w> 75865
deteri oration</w> 75855
Im aging</w> 75850
Pro g 75847
ve ter 75830
end it 75812
str anded</w> 75811
aden oma</w> 75803
PI 3 75794
pre vents</w> 75793
aph rag 75774
if ying</w> 75773
fr uc 75768
S cre 75755
sat ellite</w> 75743
Cardi ac</w> 75732
financ ial</w> 75723
phenomen a</w> 75705
ag ricul 75694
discre te</w> 75679
G lu 75677
M it 75677
co ver</w> 75636
palli ative</w> 75636
Aug ust</w> 75619
un k</w> 75613
Stre ptococcus</w> 75575
pene tr 75563
M orph 75548
s -- 75542
di lution</w> 75536
reas on 75523
N i</w> 75518
an us</w> 75510
g i</w> 75505
ell um</w> 75480
evalu ations</w> 75419
pap illary</w> 75416
stri atum</w> 75398
PAR TI 75366
Influ ence</w> 75365
synerg istic</w> 75349
cod on</w> 75333
S e</w> 75318
consider ations</w> 75317
pic ture</w> 75300
p yro 75299
G al 75296
A no 75292
app reci 75283
air y</w> 75268
regul ators</w> 75262
O ste 75237
mit otic</w> 75235
valid ate</w> 75228
establ ishment</w> 75212
H ow</w> 75203
oph yl 75184
post menopausal</w> 75155
enti ty</w> 75140
id ed</w> 75121
endoscop y</w> 75118
pul ses</w> 75106
anastom osis</w> 75094
un stable</w> 75086
oscop ic</w> 75082
umbil ical</w> 75077
embol ization</w> 75020
extrem e</w> 74987
cholec yst 74983
seg reg 74961
ch ir 74949
ru n</w> 74947
P b</w> 74944
L im 74939
u red</w> 74911
illustr ate</w> 74909
re placed</w> 74902
mel atonin</w> 74898
cat al 74895
selec t</w> 74889
PARTI CIPA 74888
comfor t</w> 74859
per ox 74793
R enal</w> 74788
an ticip 74784
n ig 74715
h u 74689
modul ating</w> 74684
enabl ed</w> 74678
ob vious</w> 74667
in surance</w> 74665
do or</w> 74651
anti genic</w> 74650
mac ular</w> 74646
i ar</w> 74589
expl oration</w> 74572
ast ed</w> 74563
sim pl 74550
iton e 74530
- 6</w> 74525
PARTICIPA NTS</w> 74518
con stitute</w> 74484
stud ent</w> 74456
per g 74455
thyro idism</w> 74450
st e</w> 74439
discre p 74420
te m</w> 74415
Under standing</w> 74404
sp or 74375
NC E</w> 74362
aggreg ates</w> 74355
determin ant</w> 74346
discrim in 74344
F em 74309
ge ometry</w> 74308
histo chemical</w> 74306
esophag us</w> 74299
cat enin</w> 74285
at ri 74275
t onic</w> 74272
mer cur 74260
bl astoma</w> 74255
ing estion</w> 74238
Rep ort</w> 74234
C 3 74223
pene tration</w> 74212
ogra fts</w> 74199
sist ance</w> 74184
odynam ics</w> 74150
arrhyth mias</w> 74140
trans forming</w> 74128
ow ing</w> 74089
trans duc 74083
Ad ditional</w> 74073
pari etal</w> 74069
neuro degenerative</w> 74028
vi ously</w> 74024
lum en</w> 74022
Ser v 74018
oxidi zed</w> 73989
ER K</w> 73955
direc tions</w> 73945
meth ionine</w> 73938
intra ocular</w> 73930
all erg 73926
stem s</w> 73920
stri atal</w> 73917
radi ographs</w> 73915
rib osomal</w> 73910
benz ene</w> 73906
E th 73903
t ants</w> 73899
attrib utable</w> 73865
eosin oph 73864
l id 73859
ent ory</w> 73850
F S</w> 73841
th ood</w> 73834
com orbidities</w> 73834
r ule</w> 73818
V it 73814
pros thetic</w> 73813
Diag nosis</w> 73811
radic als</w> 73808
n a</w> 73799
Gn RH</w> 73791
princi ple</w> 73787
R i 73779
dis ulf 73755
ob ste 73741
cont acts</w> 73721
m V</w> 73709
h aled</w> 73706
menti oned</w> 73648
di mer</w> 73647
myel oma</w> 73640
carcin ogenesis</w> 73614
fu r</w> 73598
pe er</w> 73580
al umin 73569
SN P</w> 73564
st at 73545
fol ate</w> 73522
attenu ation</w> 73469
sl ices</w> 73458
ho t</w> 73453
ulc ers</w> 73449
at trac 73440
we ak 73435
ar ise</w> 73420
ent an 73407
P ost</w> 73404
sep tal</w> 73355
3 8 73309
Li ver</w> 73295
E G 73286
dro p</w> 73281
cor tic 73275
AR S</w> 73270
gangli a</w> 73268
modi fy</w> 73248
equ ations</w> 73247
P op 73244
trig ger</w> 73232
fibrin ogen</w> 73224
asym metric</w> 73217
om al</w> 73184
M S 73181
equip ment</w> 73173
ap tic</w> 73172
K a 73155
eth ical</w> 73148
k el</w> 73142
epilep tic</w> 73119
oc ul 73117
N P 73111
ac tor</w> 73094
ac compl 73091
199 2</w> 73090
En do 73062
hypothalam us</w> 73062
ang les</w> 73039
ol ab 73026
199 3</w> 73022
car ies</w> 73009
6 , 73002
chromat ographic</w> 73002
sh unt</w> 72951
pes tic 72943
analge sic</w> 72910
am ic</w> 72866
m o</w> 72836
e -</w> 72819
other wise</w> 72809
op res 72799
en op 72794
bec oming</w> 72791
1 60</w> 72774
form ula</w> 72765
hom ogen 72765
C57 BL</w> 72746
upreg ulation</w> 72725
pyru vate</w> 72702
ser op 72678
iso form</w> 72678
lith ium</w> 72633
pol e</w> 72618
r atings</w> 72612
juven ile</w> 72608
app ly</w> 72603
d itary</w> 72590
PC s</w> 72576
o rous</w> 72569
pertur b 72558
ie fs</w> 72556
cent ur 72541
sc av 72540
S uc 72538
anesthe tized</w> 72538
dop aminergic</w> 72523
exer t</w> 72523
immunos orbent</w> 72503
calc ulation</w> 72502
T PA</w> 72488
d ent</w> 72470
B ri 72467
catar act</w> 72412
stimul ates</w> 72405
summar ize</w> 72355
10 5</w> 72347
ameli or 72338
Syn drom 72336
t od 72312
aff ective</w> 72299
con den 72293
Hy po 72285
lam b 72280
Expos ure</w> 72254
G e 72230
N 2</w> 72230
b is</w> 72223
run ning</w> 72211
gi ving</w> 72203
ti zation</w> 72182
bo w</w> 72182
T D</w> 72157
pre clinical</w> 72153
attr active</w> 72138
ca ro 72135
v om 72121
avoid ance</w> 72113
P os 72101
knock down</w> 72100
PA P</w> 72054
f ig 72030
A l</w> 71995
N ine</w> 71980
kin e 71975
ap par 71963
ultim ately</w> 71957
st ain</w> 71940
Op tim 71936
t ose</w> 71934
Sh ort</w> 71929
Direc t</w> 71908
surfac tant</w> 71897
ro und</w> 71889
i pati 71879
exp endit 71876
ot rophic</w> 71872
end ocardi 71868
me at</w> 71861
F T</w> 71845
ot actic</w> 71836
out break</w> 71832
epit opes</w> 71820
direc tional</w> 71818
i ii</w> 71801
dri ve</w> 71796
ore r</w> 71780
draw n</w> 71766
si x 71726
wor d</w> 71722
Ran dom 71720
con junc 71696
N et 71688
T NF 71679
1 β</w> 71677
electro cardi 71658
h ence</w> 71656
cardi opulmonary</w> 71634
ograph ically</w> 71606
dec ay</w> 71600
E ven</w> 71592
Ep idemi 71578
bl ic</w> 71567
pr one</w> 71540
yt es</w> 71539
pl ant 71536
Ap plication</w> 71525
ang ina</w> 71510
p ip 71504
in form</w> 71501
Pr acti 71484
N either</w> 71476
Th rough</w> 71442
visu alization</w> 71439
usc itation</w> 71439
at ro 71419
8. 6</w> 71418
attemp ted</w> 71404
ox if 71386
continu ation</w> 71380
int ended</w> 71373
sub sets</w> 71369
dy sp 71356
g ained</w> 71334
immunosup pressive</w> 71332
speci alized</w> 71330
k al 71325
Sh e</w> 71314
Z e 71266
institu tions</w> 71264
Gen er 71247
resid ent</w> 71246
epid ural</w> 71244
bel ieve</w> 71242
R E</w> 71226
P or 71222
at tending</w> 71207
flur ane</w> 71205
mig raine</w> 71167
distr ic 71163
ve ins</w> 71157
evol ved</w> 71140
pro ve</w> 71132
trim ester</w> 71122
opres sin</w> 71109
visc eral</w> 71097
on eph 71084
A ff 71076
st ones</w> 71053
- 4</w> 71018
ca ffe 70990
att ack</w> 70981
p it 70958
min ogen</w> 70956
epit ope</w> 70952
ch e 70940
qu ar 70930
thromb ocyt 70929
M us 70917
H is</w> 70888
ham ster</w> 70887
clon ing</w> 70875
w ounds</w> 70842
bloc kers</w> 70832
all ogeneic</w> 70825
B as 70821
al do 70820
hist ologically</w> 70816
warran ted</w> 70808
b rate</w> 70805
u n</w> 70800
ch ori 70792
osteo arthritis</w> 70784
obser ver</w> 70774
thym idine</w> 70732
trac e</w> 70718
ipsi lateral</w> 70718
K i</w> 70709
mis sing</w> 70696
R D</w> 70692
0.00 3</w> 70666
tor ial</w> 70642
heter o 70629
cho ol</w> 70623
m outh</w> 70615
br ady 70615
casc ade</w> 70583
fluor ide</w> 70563
m u</w> 70562
f ecal</w> 70551
Diagnos tic</w> 70533
xim ab</w> 70529
or ic</w> 70524
exten d</w> 70518
ome tri 70512
adv anc 70511
c a</w> 70483
L ife</w> 70471
in versely</w> 70468
all ergy</w> 70465
c ity</w> 70459
sam pled</w> 70449
8. 2</w> 70434
hydrox ylase</w> 70425
er t</w> 70422
sar co 70403
nephro pathy</w> 70402
los ses</w> 70401
histor ical</w> 70381
s ular</w> 70377
resp iration</w> 70377
An im 70372
var ies</w> 70353
mo tiv 70345
7. 9</w> 70330
ac il</w> 70327
In dian</w> 70290
F U</w> 70277
t ot 70270
vast atin</w> 70267
L ac 70264
D F</w> 70247
facilit ated</w> 70244
ab ine</w> 70226
l ity</w> 70208
ur ance</w> 70200
Ab s</w> 70171
re mot 70166
verte br 70152
ipati ng</w> 70123
Wh at</w> 70105
ol l 70101
all ograft</w> 70088
propor tions</w> 70087
rh iz 70078
atheros clerotic</w> 70051
ar ct</w> 70050
smo ke</w> 70046
ay ers</w> 70038
Ultr as 70025
swit ch</w> 70016
F L 69990
in continence</w> 69990
end ometri 69983
Pro gram</w> 69975
M agnetic</w> 69968
ep ing</w> 69966
Inte gr 69931
polymer ization</w> 69926
S il 69924
quanti ty</w> 69921
calcul ate</w> 69921
e pa 69920
U V 69904
venti l 69886
L ip 69871
e us</w> 69849
S accharomyces</w> 69846
excit atory</w> 69833
Austral ian</w> 69814
oc occal</w> 69796
preferen ces</w> 69789
endo plasmic</w> 69783
adhe sive</w> 69776
G lob 69774
ec es</w> 69772
con junction</w> 69757
glyc ogen</w> 69745
prophyl actic</w> 69734
F DG</w> 69714
n ull</w> 69700
r y</w> 69698
criter ion</w> 69696
normal ized</w> 69659
l et 69638
Vari ous</w> 69612
uncer tainty</w> 69612
cogni tion</w> 69609
inc ision</w> 69582
Ap pro 69580
develop ments</w> 69572
con clusions</w> 69571
transmit ter</w> 69556
lam y 69550
charg es</w> 69547
B s</w> 69530
ma st 69518
igh ts</w> 69511
trans verse</w> 69509
di oxide</w> 69502
h ard</w> 69501
amph etamine</w> 69490
Sec ondary</w> 69472
Gen eral</w> 69468
ion ization</w> 69466
f resh 69459
odon tic</w> 69459
struc turally</w> 69450
acces sible</w> 69443
im ide</w> 69439
res sively</w> 69438
objec ts</w> 69436
ph thal 69433
inoc ulation</w> 69418
p ation</w> 69404
doc tors</w> 69394
psych o 69391
anti coagul 69390
tryptoph an</w> 69372
counsel ing</w> 69367
M il 69339
fl am 69336
lim bs</w> 69331
engine ered</w> 69305
PI3 K</w> 69280
tw enty</w> 69274
em atic</w> 69257
obser ve</w> 69253
H A 69242
T M</w> 69216
P 450</w> 69213
rib ed</w> 69200
S ensi 69196
colon ization</w> 69195
frag mentation</w> 69190
del ine 69164
ar ms</w> 69148
slow ly</w> 69141
B W</w> 69129
wa ve 69108
P at 69107
pro line</w> 69105
bro w 69093
appar atus</w> 69070
Rec om 69045
199 1</w> 69038
simil arities</w> 69028
cardi o 69026
M el 69022
con e</w> 69012
fib rom 69010
al isation</w> 68994
CH D</w> 68986
ophthal m 68982
par asi 68976
elim inated</w> 68906
susp ici 68893
m iti 68856
D at 68848
reproduc ibility</w> 68848
tic a</w> 68833
I N</w> 68832
organ ized</w> 68831
routin ely</w> 68823
8. 8</w> 68779
acc id 68778
it ated</w> 68773
P W 68769
System atic</w> 68766
th y</w> 68752
Multi variate</w> 68728
ycl ine</w> 68727
ol ateral</w> 68725
p et 68715
envel ope</w> 68712
labor atories</w> 68697
od ec 68690
er adic 68689
In trac 68650
her nia</w> 68643
f en 68641
mal formations</w> 68619
refl ects</w> 68615
exc eption</w> 68613
belong ing</w> 68610
absc ess</w> 68569
ch i</w> 68514
phosphor us</w> 68502
F ib 68499
in organic</w> 68492
lipos omes</w> 68478
Struc tural</w> 68472
ag ar</w> 68460
Ger many</w> 68458
EM G</w> 68457
em enting</w> 68456
m entally</w> 68448
knock out</w> 68426
S tro 68423
ris t</w> 68423
g ed</w> 68420
disper sion</w> 68407
bal anced</w> 68397
am id 68365
placent a</w> 68363
An gi 68360
mon ocyte</w> 68346
medi as 68342
ad junc 68332
ess entially</w> 68323
epa m</w> 68302
clu stering</w> 68298
D aw 68275
hor ses</w> 68270
construc ts</w> 68265
pos ites</w> 68250
Ram an</w> 68248
relati ves</w> 68241
T rial</w> 68239
F eb 68223
Ac tivation</w> 68222
multi disciplinary</w> 68216
om od 68206
bio availability</w> 68202
describ ing</w> 68201
over lap</w> 68193
cohe rence</w> 68172
reproduc ible</w> 68163
in ase</w> 68142
F ood</w> 68140
f used</w> 68118
emplo ying</w> 68113
ide a</w> 68112
har d 68105
in sp 68093
A1 c</w> 68089
z er</w> 68088
Bre ast</w> 68071
S mall</w> 68067
po is 68056
m t 68054
ob arb 68054
od es</w> 68051
el ast 68039
mit ogen</w> 68036
Syndrom e</w> 68024
R ats</w> 68011
seas onal</w> 68008
hy dra 67989
d airy</w> 67982
preferen tially</w> 67975
exper t</w> 67968
f ate</w> 67935
Sec ond</w> 67929
survi ved</w> 67915
l ic 67912
igh t 67905
F C</w> 67898
predic ts</w> 67896
dist or 67894
1, 2</w> 67894
sl ope</w> 67892
I Q 67878
as cor 67860
mathem atical</w> 67851
ter med</w> 67850
pro po 67833
ul ed</w> 67827
In tro 67808
insec t</w> 67765
aug mented</w> 67757
ru ary</w> 67752
eclamp sia</w> 67752
l nc 67744
var ic 67724
Fac tor</w> 67705
Gl uc 67676
Associ ated</w> 67674
P ulmonary</w> 67653
G TP</w> 67643
fi er</w> 67633
eukary otic</w> 67631
br ac 67624
dam aged</w> 67618
am yg 67607
vom iting</w> 67600
ti ters</w> 67599
regar ded</w> 67596
Feb ruary</w> 67581
her in</w> 67572
bor ns</w> 67563
fertil ization</w> 67561
c e 67560
mos quit 67560
glyc emic</w> 67548
0. 6 67547
ax onal</w> 67512
M G</w> 67487
Nov ember</w> 67484
den sities</w> 67478
influ x</w> 67466
st arch</w> 67461
so y 67449
mut ase</w> 67437
o k 67419
lim itation</w> 67399
desi r 67366
har b 67354
fibr onectin</w> 67350
di aphrag 67344
re al 67342
De sign</w> 67313
8. 0</w> 67304
immobil ized</w> 67302
waveleng th</w> 67296
fl ight</w> 67262
adul thood</w> 67231
hom ozygous</w> 67225
aden omas</w> 67216
Respon se</w> 67205
P ac 67198
dro pl 67197
sp ar 67196
t ome</w> 67193
hel ical</w> 67185
fet us</w> 67182
F V 67177
lat er 67173
ec topic</w> 67172
rever sal</w> 67163
L ung</w> 67154
s lower</w> 67151
o yl</w> 67142
8. 9</w> 67126
C ycl 67103
anal ogs</w> 67103
c . 67101
fac ility</w> 67100
mes enteric</w> 67065
Ad ult</w> 67056
O T</w> 67053
micro vascular</w> 67052
jo b</w> 67047
aver aged</w> 67046
ab str 67024
tem plate</w> 67016
dimen sion</w> 66990
stabil ized</w> 66990
f asc 66983
plas minogen</w> 66965
v ap 66961
icardi al</w> 66958
carr y</w> 66954
Rel ati 66947
Amer ica</w> 66946
P 4</w> 66935
distingu ished</w> 66886
wa ste 66863
T 1 66859
ad missions</w> 66859
cathe ter 66855
immun ized</w> 66845
in he 66840
employ ment</w> 66823
id in</w> 66814
meth anol</w> 66802
in tubation</w> 66784
conver ting</w> 66784
compl aints</w> 66782
parathyro id</w> 66780
h all 66774
ox al 66734
gradu ate</w> 66733
scin ti 66701
oph en 66694
ogni tive</w> 66694
sens ors</w> 66690
introduc e</w> 66670
transi tions</w> 66634
nan ot 66633
cho ro 66594
o ides</w> 66588
compet ence</w> 66579
se arch 66568
glucocortico id</w> 66568
proc e 66554
explan ation</w> 66552
PR L</w> 66534
leg al</w> 66530
arach id 66519
micro environment</w> 66503
3 4 66493
CYP 2 66493
po orer</w> 66492
all ic</w> 66489
T CR</w> 66479
H s</w> 66478
IC 50</w> 66475
bel iefs</w> 66471
co ating</w> 66460
gal ac 66451
foll icles</w> 66428
G ra 66418
col lo 66402
psori asis</w> 66402
transl ated</w> 66401
S af 66384
aut on 66374
IN E</w> 66370
inher ited</w> 66369
immunop recip 66331
amyg dal 66331
highligh ted</w> 66328
den at 66296
T issue</w> 66295
phospholip ase</w> 66289
fer mentation</w> 66274
ad ding</w> 66264
mercur y</w> 66250
Daw ley</w> 66244
ari um</w> 66243
leuc ine</w> 66222
m ir 66211
ap ine</w> 66198
her pes</w> 66196
Tw elve</w> 66193
regi stration</w> 66177
nan oc 66169
re production</w> 66154
cl amp</w> 66153
2 a</w> 66113
domes tic</w> 66111
ep sil 66104
Relati onsh 66103
intraper itoneal</w> 66102
si RNA</w> 66076
Spr ague</w> 66075
ul ty</w> 66071
un g 66063
High er</w> 66049
sensiti zation</w> 66029
atin um</w> 66026
ph ant 66022
tr is 66019
re a</w> 66013
um inal</w> 66004
k y</w> 65994
MEASURE S</w> 65992
method ological</w> 65991
plan ar</w> 65984
derm atitis</w> 65980
res uscitation</w> 65975
uni versal</w> 65975
hydro ly 65962
inf arct</w> 65948
datas et</w> 65945
manifest ation</w> 65935
mut ated</w> 65933
se eds</w> 65928
Pot ential</w> 65904
saliv a</w> 65903
desi red</w> 65889
peri odic</w> 65887
osmo tic</w> 65885
f oun 65860
co de</w> 65859
them es</w> 65857
spl enic</w> 65850
8 00</w> 65844
res ected</w> 65842
end otoxin</w> 65804
s s 65800
circu it</w> 65790
mark et</w> 65762
P B</w> 65758
5 p</w> 65706
m us</w> 65704
ac tin 65695
8. 4</w> 65681
z ones</w> 65679
azep ine</w> 65672
fer ri 65654
trans form</w> 65653
mo ving</w> 65635
e diatric</w> 65628
po protein</w> 65619
hom olog 65612
AL L</w> 65597
nucle ic</w> 65547
con fined</w> 65534
vesti bular</w> 65528
si zed</w> 65495
i. p.</w> 65490
wor sen 65463
com ing</w> 65445
mutag enesis</w> 65443
I BD</w> 65438
ph on 65420
di az 65414
0. 5 65411
P BM 65401
E N</w> 65397
junc tions</w> 65386
B in 65370
conduc t</w> 65367
quin one</w> 65361
Gol gi</w> 65344
ame ters</w> 65340
repeti tive</w> 65331
an sw 65322
ong ue</w> 65318
S r 65279
micro gram</w> 65275
et c</w> 65261
ech anical</w> 65242
app en 65242
calib ration</w> 65239
bil ir 65218
sub mitted</w> 65214
establ ishing</w> 65197
C s 65196
triglycer ide</w> 65176
H u 65174
precip itation</w> 65155
al izations</w> 65132
P lat 65128
lin ks</w> 65128
pl ans</w> 65120
in struc 65118
b atter 65117
Post operative</w> 65107
cortico steroids</w> 65104
sequ ent</w> 65091
ab ec 65087
synth et 65067
care ful</w> 65062
hol ds</w> 65023
la w</w> 65019
al i 65014
B a 64985
pre treated</w> 64974
poly acrylamide</w> 64973
pig ment</w> 64967
mening itis</w> 64966
Experim ent</w> 64963
micro array</w> 64961
acti vely</w> 64956
flav on 64940
comp uter 64937
st ep 64932
enti onal</w> 64929
fo rest</w> 64919
li e</w> 64909
C B</w> 64903
elong ation</w> 64902
inhal ation</w> 64894
minim ize</w> 64861
R V</w> 64859
bi omedical</w> 64856
in ium</w> 64855
gen etics</w> 64848
Cro hn</w> 64829
1 H</w> 64812
4, 5</w> 64805
I F</w> 64800
su stain 64797
F 2</w> 64780
nam ed</w> 64779
pro found</w> 64768
assembl ed</w> 64768
20 19</w> 64767
depl eted</w> 64758
gluc agon</w> 64748
verte brate</w> 64739
detec tor</w> 64724
ectom ized</w> 64723
ambi ent</w> 64706
it ting</w> 64704
commerc ially</w> 64697
cycl ing</w> 64669
H2 O2</w> 64638
T ogether</w> 64635
tr unk</w> 64606
cur ative</w> 64606
spermat ozo 64580
Reg i 64579
ograph ics</w> 64578
ter a</w> 64565
flex ibility</w> 64564
vulner able</w> 64563
dram atic</w> 64551
ang ular</w> 64541
S tim 64534
l aryngeal</w> 64531
sco red</w> 64511
thrombo tic</w> 64484
par a 64465
ili ac</w> 64460
ophil ia</w> 64457
Con tin 64432
aberr ant</w> 64432
B I</w> 64431
9. 2</w> 64423
hyper sensitivity</w> 64410
O l 64408
K 2</w> 64404
glyco l</w> 64382
hemorrh agic</w> 64372
8. 1</w> 64360
angi oplasty</w> 64349
NAD PH</w> 64323
Sev enty</w> 64320
kin ds</w> 64316
re fr 64309
li um</w> 64296
marg in</w> 64274
Ph en 64267
b red</w> 64262
cord ingly</w> 64253
tr abec 64249
nar row</w> 64249
re constitu 64246
su ture</w> 64243
rati on 64227
pac ing</w> 64222
en ously</w> 64221
Ca uc 64211
ar ri 64210
er ies</w> 64208
w w 64204
fib rous</w> 64172
b uc 64153
hypo theses</w> 64150
entr icular</w> 64141
ogen e</w> 64102
aer os 64095
ophosph ate</w> 64083
scaff old</w> 64070
Con sequently</w> 64065
Per form 64063
as m</w> 64058
fluctu ations</w> 64053
duod en 64052
ren ces</w> 64043
M ale</w> 64038
Transc rip 64007
prob able</w> 64001
marg inal</w> 63961
i ents</w> 63958
em ental</w> 63955
coll abor 63955
promo ters</w> 63948
2, 3</w> 63942
repe at 63938
repe ats</w> 63925
Differen tial</w> 63924
mechanis tic</w> 63914
partic ipating</w> 63900
SC T</w> 63890
Sou thern</w> 63889
cycl ospor 63879
optim ize</w> 63872
u si 63855
use a</w> 63838
menstr ual</w> 63820
ex plic 63815
beta 1</w> 63811
0. 2 63808
bol us</w> 63802
dox orubicin</w> 63799
integr al</w> 63798
glut amine</w> 63795
Eff icacy</w> 63791
8. 7</w> 63757
overl apping</w> 63756
ge ographic</w> 63734
MI C</w> 63720
0.0 6</w> 63701
datas ets</w> 63690
Specific ally</w> 63679
un saturated</w> 63678
16 S</w> 63670
commun ic 63648
estim ating</w> 63643
ud ge</w> 63639
low ered</w> 63636
C arb 63634
sph eres</w> 63620
B ay 63616
pre frontal</w> 63600
y o 63588
qu i 63585
crystall ine</w> 63575
olig os 63574
E S 63573
3 3 63572
as sum 63570
D en 63557
kill ing</w> 63557
scaff olds</w> 63538
di s</w> 63530
h aps</w> 63527
i ety</w> 63513
Gen ome</w> 63510
arrang ements</w> 63465
continu es</w> 63449
consci ous</w> 63435
autom atic</w> 63423
sil encing</w> 63420
caro ten 63413
mo id</w> 63408
prop yl 63377
P 2 63371
house hold</w> 63351
In jur 63349
l t</w> 63345
us p 63343
x anth 63339
infer tility</w> 63329
dele tions</w> 63272
dom eth 63269
glyc emia</w> 63267
cereb ellum</w> 63253
connec tions</w> 63235
vis cos 63228
VE N 63215
ophosph amide</w> 63213
rec all</w> 63209
el as 63200
M odi 63192
arti le</w> 63177
evid enced</w> 63175
I S 63151
Ar g</w> 63143
pat h</w> 63140
soci o</w> 63140
C op 63109
intrav enously</w> 63107
new borns</w> 63097
ic o</w> 63094
RO C</w> 63093
centur y</w> 63092
posi tioning</w> 63089
h t 63081
dis mutase</w> 63081
ren ch</w> 63079
erythem at 63064
os ity</w> 63058
L M 63049
9. 4</w> 63028
9. 6</w> 63005
pal sy</w> 63004
Ch rom 62995
bi ologically</w> 62983
fron t</w> 62979
colon ies</w> 62975
pro to 62967
ab oration</w> 62958
non specific</w> 62957
re l</w> 62938
bloc ker</w> 62904
play ing</w> 62865
connec tive</w> 62844
ow er</w> 62839
O CT</w> 62837
d i</w> 62834
mo iety</w> 62821
persist ed</w> 62820
S m 62808
cad herin</w> 62801
M ice</w> 62793
AR Y</w> 62790
thym us</w> 62787
gro ss</w> 62760
person nel</w> 62759
dometh acin</w> 62757
9. 3</w> 62731
CA T</w> 62726
retin opathy</w> 62718
ul us</w> 62713
synovi al</w> 62709
dis charged</w> 62705
pl aques</w> 62702
qu artile</w> 62672
L a 62661
prog ressively</w> 62655
heter ozygous</w> 62655
I T</w> 62641
man age</w> 62641
on ds</w> 62634
se emed</w> 62628
Res p 62623
os keletal</w> 62615
ph al 62592
ig u 62587
H V</w> 62558
hy gi 62557
s or</w> 62527
In flam 62526
1 11</w> 62519
p 38</w> 62519
pro vision</w> 62518
fac ts</w> 62496
l ib 62489
E A</w> 62474
Cl assi 62467
circum st 62455
haz ards</w> 62438
refl ecting</w> 62427
str a 62425
remot e</w> 62389
em a 62350
In sul 62350
con sumed</w> 62344
do w</w> 62328
pran olol</w> 62326
si ded</w> 62308
D ay</w> 62301
bre ast 62284
B ut</w> 62282
rel im 62275
ur acil</w> 62274
pro pi 62268
accep tance</w> 62238
hypot ension</w> 62237
bra fish</w> 62227
deli ver 62226
er ous</w> 62211
vic tim 62209
angi ogenic</w> 62205
deman ds</w> 62186
opath ology</w> 62185
Can adi 62177
i res</w> 62175
PG E2</w> 62175
moti fs</w> 62156
bio active</w> 62152
kill ed</w> 62136
cent res</w> 62124
ch it 62113
M Y 62112
propag ation</w> 62110
disulf ide</w> 62106
Perform ance</w> 62103
P ri 62092
conjug ates</w> 62088
neurom uscular</w> 62082
Surviv al</w> 62074
mon ic</w> 62061
x in</w> 62059
b ones</w> 62056
desc ending</w> 62048
A U 62018
anti retroviral</w> 62015
bri ef 61992
pro pen 61988
constr aints</w> 61987
sist ent</w> 61982
u sive</w> 61952
brid ge</w> 61948
categor ized</w> 61942
dist ances</w> 61930
swit ching</w> 61929
ob taining</w> 61904
B T</w> 61881
desir able</w> 61880
retar dation</w> 61871
Ma j 61864
D Cs</w> 61853
L eu 61824
oxid ants</w> 61818
foc i</w> 61817
poll ut 61786
TL R 61767
re acted</w> 61760
cyt ology</w> 61759
G V 61747
ha ir 61730
INTER VEN 61729
T L</w> 61719
sc at 61698
prin ting</w> 61671
10 2</w> 61670
retro grade</w> 61664
lip i 61640
Kore an</w> 61561
en or 61550
S HR</w> 61546
pris ingly</w> 61544
st op 61537
n om 61536
ar ial</w> 61527
chic k 61520
here ditary</w> 61506
Al l 61495
PA R</w> 61488
in ous</w> 61486
S K</w> 61480
abor tion</w> 61452
fabr icated</w> 61439
anes thetic</w> 61433
presum ably</w> 61425
pl eg 61407
aryn x</w> 61404
E s</w> 61402
BAL B</w> 61402
9. 1</w> 61380
cad mium</w> 61379
Lym ph 61379
ech o</w> 61377
pap ers</w> 61369
sp ray</w> 61364
cop e</w> 61358
d odec 61357
I OP</w> 61350
r ating</w> 61348
fil ament</w> 61337
reser vo 61329
prote olytic</w> 61319
at osis</w> 61307
ph orb 61305
Cauc asi 61295
fu sions</w> 61278
Larg e</w> 61264
r ing 61238
waste water</w> 61221
c ing</w> 61214
cur ricul 61211
sem i 61211
fin ally</w> 61206
rein for 61195
lin ing</w> 61193
Scre ening</w> 61193
mor tem</w> 61177
F c</w> 61169
p up 61162
CA SE</w> 61153
pyr idine</w> 61147
d ust</w> 61143
S a 61139
vir tual</w> 61137
0. 3 61135
scre w</w> 61130
L oc 61112
we t</w> 61103
mar ks</w> 61084
de ep 61076
C p 61074
ol ine</w> 61069
perfor ation</w> 61069
multi variable</w> 61041
V e 61032
seg mental</w> 61017
tin s</w> 61013
F o 61001
Th ose</w> 60999
fr am 60981
demonstr ation</w> 60972
vas opressin</w> 60971
facilit ates</w> 60967
s acr 60946
Ex amination</w> 60940
challeng ed</w> 60905
ped ance</w> 60905
wal ls</w> 60900
V ol 60897
plasm ids</w> 60897
res orption</w> 60885
posi tivity</w> 60872
p iv 60851
T U 60832
up uncture</w> 60831
hyp oglyc 60820
st atin</w> 60816
am el</w> 60801
Ger man</w> 60800
so ils</w> 60787
he dr 60762
feren ces</w> 60748
CD 3</w> 60745
L ong 60744
mo un 60720
cop ies</w> 60714
Con versely</w> 60713
M et</w> 60702
Y oun 60697
cle ft</w> 60687
au gh 60682
l actic</w> 60677
sched ule</w> 60670
nat ri 60645
compart ments</w> 60642
adequ ately</w> 60637
Ex c 60635
b ases</w> 60619
ultra structural</w> 60614
pl atinum</w> 60612
C ognitive</w> 60597
N at 60596
ex in</w> 60585
F rench</w> 60573
198 8</w> 60570
S B</w> 60567
f ell</w> 60536
N H</w> 60536
conjug ate</w> 60536
is let</w> 60519
M id 60516
phorb ol</w> 60507
N CT 60505
thic k 60499
198 9</w> 60492
St ress</w> 60469
win dow</w> 60466
f MRI</w> 60458
y a</w> 60451
ogly can</w> 60439
neuro endocrine</w> 60423
id a</w> 60411
Pre vention</w> 60388
he ating</w> 60379
poll en</w> 60360
re vascularization</w> 60352
te ach 60332
ul ates</w> 60325
M ental</w> 60304
b order</w> 60294
oth i 60293
k now</w> 60291
inter acts</w> 60291
bur st</w> 60289
ubiqu itin</w> 60283
g aps</w> 60273
ot roph 60273
lit axel</w> 60260
Wh ite</w> 60252
1, 3</w> 60226
b ud 60219
Intro duction</w> 60218
K O</w> 60217
or ities</w> 60215
diff ers</w> 60212
prim ers</w> 60209
ous ness</w> 60189
ic idal</w> 60187
transpor ters</w> 60179
mim ic</w> 60177
di latation</w> 60168
centr ifug 60152
mess enger</w> 60150
T2 DM</w> 60121
We b</w> 60118
in ates</w> 60108
B ody</w> 60107
na usea</w> 60099
viscos ity</w> 60093
ap nea</w> 60091
eg ative</w> 60091
classi c</w> 60082
V AS</w> 60071
constitu ents</w> 60066
D er 60058
Mat erials</w> 60047
p mol</w> 60014
qu ic 60009
m Ab</w> 59997
L M</w> 59991
ati onic</w> 59986
ru b 59986
onc ology</w> 59971
kin d</w> 59959
spermatozo a</w> 59944
sph inc 59930
i otic</w> 59928
projec tion</w> 59919
shorten ing</w> 59910
P CI</w> 59887
alb icans</w> 59887
publ ications</w> 59859
Glob al</w> 59847
cor ro 59845
caffe ine</w> 59845
produc tivity</w> 59843
inf used</w> 59841
Cur rently</w> 59820
H2 O</w> 59819
au tism</w> 59811
an ter 59800
T c</w> 59787
1, 000</w> 59773
aut opsy</w> 59771
haem orrh 59764
ultra violet</w> 59740
suc cin 59729
trop ical</w> 59716
w rit 59712
u ed</w> 59707
mic rom 59705
lymph atic</w> 59698
St age</w> 59683
or imetr 59668
d ated</w> 59653
phospholip ids</w> 59652
con sent</w> 59630
nor adrenaline</w> 59626
leng ths</w> 59613
st resses</w> 59601
hy al 59577
Incre asing</w> 59573
cal ves</w> 59568
Organ ization</w> 59564
fibr in</w> 59559
ten d</w> 59555
un de 59554
nico tin 59553
form in</w> 59536
exper ts</w> 59527
ag it 59503
H er 59495
phen olic</w> 59486
in dex 59485
m apped</w> 59478
N -</w> 59469
W est</w> 59462
ul in 59460
er ship</w> 59458
ur ium</w> 59455
rex ate</w> 59440
pharmac y</w> 59431
event ually</w> 59426
mobil ization</w> 59424
T n 59416
I a</w> 59410
rec essive</w> 59407
tempor ary</w> 59389
Cal c 59389
lymph omas</w> 59388
amin ase</w> 59383
ra w</w> 59366
grad ed</w> 59360
hab its</w> 59353
H 3</w> 59337
electrol yte</w> 59326
lo t</w> 59285
R CC</w> 59277
T ai 59269
solub ility</w> 59232
Be sides</w> 59230
st al 59229
L o 59206
mum ol</w> 59206
vacc inated</w> 59200
Combin ed</w> 59199
oc at 59185
ke ts</w> 59183
ot opic</w> 59177
me eting</w> 59176
therapeu tics</w> 59174
fin ite</w> 59169
calc ification</w> 59163
encap sul 59156
uni variate</w> 59152
or p 59149
modul ates</w> 59130
ty ph 59103
en tered</w> 59098
C 4</w> 59087
gro w</w> 59086
organ izations</w> 59080
3 7 59075
ati onary</w> 59070
Dat ab 59068
electro physiological</w> 59067
de signs</w> 59058
ot e</w> 59042
W nt</w> 59036
H or 59034
isot ope</w> 59032
dis appeared</w> 59026
He ter 59025
SP ECT</w> 59009
H ol 59004
D u 59001
cle a 58997
mat urity</w> 58990
dis soci 58957
aug mentation</w> 58947
modul in</w> 58931
o king</w> 58919
mean ing 58917
myel in</w> 58911
F P</w> 58905
AL T</w> 58895
cover ing</w> 58891
uncer tain</w> 58891
amp ut 58872
R AS</w> 58867
re str 58833
aldo sterone</w> 58823
e sian</w> 58811
B or 58778
fav our 58767
AB E 58763
f ly</w> 58754
10 3</w> 58752
vesi cle</w> 58750
accoun ting</w> 58741
Out come</w> 58738
agglutin in</w> 58723
1 st</w> 58714
hand ling</w> 58706
av ig 58696
man aging</w> 58693
S i</w> 58683
kill er</w> 58683
ten si 58682
diag nose</w> 58681
sp ot</w> 58679
E stim 58671
μ M</w> 58664
typ ing</w> 58654
polar ized</w> 58652
impair ments</w> 58636
ro d</w> 58632
Ha em 58630
sp ent</w> 58624
ads orb 58624
retriev al</w> 58617
R CTs</w> 58611
lamb da</w> 58606
we ar</w> 58598
lys osomal</w> 58588
b all</w> 58574
ul t 58569
er n 58565
1 2.5</w> 58562
In duced</w> 58555
ic ac 58551
bound ary</w> 58551
Reg ulation</w> 58537
10 4</w> 58500
synthet ase</w> 58500
Sci ence</w> 58483
Insul in</w> 58469
play ers</w> 58468
continu ing</w> 58455
1 α</w> 58449
pow der</w> 58432
il lo 58424
it ance</w> 58413
T I</w> 58405
CD 34</w> 58387
sul fur</w> 58382
fi able</w> 58379
gluc uron 58379
f ol</w> 58362
et te</w> 58351
sugg estive</w> 58350
triglycer ides</w> 58336
sp ite</w> 58329
ub es</w> 58327
provid er</w> 58325
volun tary</w> 58321
n es 58314
D ATA</w> 58298
caus ative</w> 58273
c ot 58261
β 1</w> 58248
bran ches</w> 58244
B u 58240
termin als</w> 58236
ir reversible</w> 58231
cycl ophosphamide</w> 58206
comm it 58187
sec onds</w> 58174
D SM</w> 58166
P al 58165
land scap 58160
accoun ts</w> 58152
gradi ents</w> 58147
foll icle</w> 58144
beg an</w> 58140
scenari os</w> 58137
f ra 58133
y ment</w> 58129
0.00 4</w> 58123
H elic 58122
encephal opathy</w> 58118
CE A</w> 58091
9. 7</w> 58077
HER 2</w> 58069
en es</w> 58040
defin ing</w> 58027
an tr 58015
P ositive</w> 58010
path ic</w> 58008
sub cellular</w> 58004
k ept</w> 57988
t ongue</w> 57987
Pe diatric</w> 57985
li ves</w> 57962
H SP 57941
a un 57936
pass age</w> 57935
3 p</w> 57932
H al 57929
ar d 57927
tit anium</w> 57922
adv ance</w> 57919
ot rexate</w> 57902
blin ded</w> 57899
si al 57891
Ital y</w> 57870
fibr e</w> 57864
immun ocom 57860
E mer 57858
D E</w> 57857
m is</w> 57856
N i 57856
care fully</w> 57845
S core</w> 57842
am orph 57831
in activated</w> 57823
Canadi an</w> 57814
sti g 57803
y tic</w> 57802
yr in</w> 57797
il ian</w> 57788
EC M</w> 57788
nor thern</w> 57771
H S 57755
char acter</w> 57755
sou thern</w> 57753
so ver</w> 57747
Therap eu 57714
inter mediates</w> 57712
ucle otide</w> 57675
d B</w> 57634
dis sec 57618
in stem</w> 57609
conflic t</w> 57602
n aph 57600
mod est</w> 57593
expec tations</w> 57583
9. 8</w> 57573
3 6 57564
S eph 57554
Rap id</w> 57539
os s</w> 57537
ap oli 57529
medi ator</w> 57529
ammon ia</w> 57529
ub in</w> 57510
rel ating</w> 57507
st ock</w> 57504
ic on 57504
Compl ete</w> 57497
CB F</w> 57495
am idal</w> 57489
prescrib ing</w> 57473
spl it</w> 57469
IC D</w> 57457
shor t 57456
no tic 57454
E duc 57443
har v 57438
ethn icity</w> 57436
glycos ylation</w> 57425
analy se</w> 57422
se dation</w> 57414
her ni 57404
individu ally</w> 57398
cit rate</w> 57398
uter us</w> 57397
dem ographics</w> 57390
Commun ity</w> 57389
spin dle</w> 57376
tend s</w> 57370
p in</w> 57359
V s</w> 57357
100 ,000</w> 57354
I g</w> 57340
c itr 57337
ser ves</w> 57336
concentr ated</w> 57294
a ren 57285
M ei 57257
spectro scopic</w> 57253
ag ers</w> 57241
alg ia</w> 57238
coord inated</w> 57238
t us 57198
ann ot 57194
at ology</w> 57182
docum ent</w> 57179
M emb 57174
ging ival</w> 57171
12 3</w> 57155
att acks</w> 57153
G P 57151
abol ism</w> 57149
nutri ents</w> 57149
His panic</w> 57141
ocy cl 57119
Eigh ty</w> 57108
regi stry</w> 57104
D D</w> 57092
J o 57088
im pedance</w> 57088
fo ot 57085
l ign 57081
T echn 57081
remo ve</w> 57057
cochle ar</w> 57043
Pur pose</w> 57034
E ast</w> 56998
end ovascular</w> 56988
ch lo 56979
br ach 56972
prote as 56968
exhib iting</w> 56953
N ig 56943
pul sed</w> 56943
A u</w> 56940
reg urg 56876
ax illary</w> 56876
seg mentation</w> 56867
diff ering</w> 56855
P en 56832
Com pu 56831
chir al</w> 56818
LL ED</w> 56799
m ang 56795
0. 75</w> 56791
W here 56789
r as</w> 56777
TA TION</w> 56752
buil t</w> 56749
catal ase</w> 56749
os se 56743
omyel itis</w> 56736
En viron 56732
afferen t</w> 56722
in ephrine</w> 56721
bir ths</w> 56709
c ations</w> 56703
gyr us</w> 56692
ter ing</w> 56667
consul tation</w> 56664
sex es</w> 56657
r am 56641
thre at</w> 56612
per haps</w> 56607
mod al</w> 56603
Import antly</w> 56575
U NL 56573
ro x 56571
lec tin</w> 56570
Fam ily</w> 56570
sym metry</w> 56568
inter viewed</w> 56567
E X 56548
form al</w> 56523
programm es</w> 56523
dyst rophy</w> 56523
ABE LLED</w> 56518
mic rom</w> 56517
M n 56516
sp oradi 56515
sporadi c</w> 56510
UNL ABELLED</w> 56508
incorpor ating</w> 56501
w inter</w> 56491
Re si 56487
Hb A1c</w> 56456
lum inal</w> 56447
all er 56446
innov ative</w> 56427
cathe ters</w> 56423
mac rom 56391
ock et</w> 56390
stimul atory</w> 56386
tw in</w> 56358
Fol low</w> 56344
fir ing</w> 56343
pen icillin</w> 56336
Co h 56305
id ing</w> 56286
emo tion</w> 56283
at ectomy</w> 56265
2, 4</w> 56265
deform ity</w> 56261
re sted</w> 56252
s ati 56246
o ocyte</w> 56240
spi ke</w> 56235
it ability</w> 56225
we b</w> 56225
ze brafish</w> 56216
microtub ule</w> 56215
conduc ting</w> 56210
tic k</w> 56199
om et 56184
am o 56175
S CI</w> 56160
ifer ase</w> 56158
mT OR</w> 56142
Where as</w> 56135
ti ce</w> 56129
pop ular</w> 56128
s ation</w> 56123
enti n</w> 56121
M c 56110
z ol 56064
0. 15</w> 56060
Amer icans</w> 56053
t ories</w> 56047
conduc tivity</w> 56041
computer ized</w> 56039
Sev ere</w> 56015
th elial</w> 56013
in puts</w> 56012
nan os 56007
thic k</w> 56007
Kn ow 56007
ti de</w> 55997
soy bean</w> 55992
Meth od</w> 55988
reason able</w> 55972
circum ference</w> 55968
program ming</w> 55936
hum oral</w> 55898
BD NF</w> 55888
Tai w 55871
F R</w> 55870
En d 55861
S le 55853
lymph ocytic</w> 55847
as sign 55837
bra ins</w> 55834
en amel</w> 55827
circumst ances</w> 55819
lip ase</w> 55815
b asi 55811
pro inflammatory</w> 55804
i or</w> 55795
lymph o 55790
pa ediatric</w> 55783
st ationary</w> 55781
bi ologic</w> 55775
od op 55768
transc ribed</w> 55765
end point</w> 55759
at opic</w> 55752
particip ant</w> 55748
Pro tec 55746
medi ation</w> 55745
tr ich 55739
polymorph ic</w> 55736
add ressing</w> 55718
vascul ature</w> 55717
v ig 55710
IQ R</w> 55705
a i</w> 55686
Random ized</w> 55684
L F</w> 55678
Lev el</w> 55675
modi fying</w> 55661
o protein</w> 55643
ur gent</w> 55638
tic us</w> 55635
com mod 55634
benz odi 55629
l avage</w> 55622
sex ually</w> 55619
K m</w> 55605
modi um</w> 55598
shif ted</w> 55597
ang er</w> 55590
eff icac 55581
tre es</w> 55580
omeg a</w> 55569
pro pranolol</w> 55568
hydro l 55563
sedi ment</w> 55555
antidepress ant</w> 55555
0. 4 55535
k eleton</w> 55529
it ro 55528
pac k 55527
e tin</w> 55520
auto antibodies</w> 55519
mon key</w> 55516
enabl ing</w> 55514
depos its</w> 55483
cen tered</w> 55477
Inf ection</w> 55468
ir respective</w> 55463
met er</w> 55459
C lo 55455
agit tal</w> 55454
projec tions</w> 55447
on a</w> 55443
defin ite</w> 55432
di mer 55430
CF U</w> 55430
pi per 55429
He La</w> 55425
parame tric</w> 55417
ti gh 55413
ush ing</w> 55395
rel ate</w> 55394
pois oning</w> 55361
ul as</w> 55360
re m 55353
198 7</w> 55336
occ ip 55332
am il</w> 55331
tin c 55322
institu tional</w> 55321
solub il 55317
bin ary</w> 55312
fl an 55301
gra in</w> 55268
Inv entory</w> 55257
govern ment</w> 55234
min ority</w> 55232
pre operatively</w> 55229
th ly</w> 55222
ht t 55215
oscill ations</w> 55212
arach no 55199
retriev ed</w> 55190
suppor tive</w> 55186
quanti ties</w> 55183
recei ver</w> 55182
be hind</w> 55179
AM I</w> 55176
in domethacin</w> 55170
aff or 55169
Pr o</w> 55169
3, 4</w> 55165
ubiqu it 55146
polyp eptides</w> 55135
k it</w> 55123
yl in 55119
me tic</w> 55114
nod al</w> 55094
eff usion</w> 55086
prop yl</w> 55070
ac idosis</w> 55068
cataly st</w> 55061
Ir an</w> 55033
0 2</w> 55018
L it 55014
gall bladder</w> 54995
asym metry</w> 54976
line ages</w> 54972
5 -</w> 54958
gran ule</w> 54952
spe a 54943
e ties</w> 54941
pseud o 54929
eng ue</w> 54928
cros sover</w> 54920
B al 54919
af ter 54919
trac er</w> 54916
s arcom 54908
A 4</w> 54903
per si 54890
isol one</w> 54884
Know ledge</w> 54883
f entan 54881
ra ise</w> 54881
obacter ia</w> 54872
adv oc 54859
ar yl</w> 54856
aly tic</w> 54850
raz ole</w> 54840
gra y</w> 54829
soci ety</w> 54821
co valent</w> 54818
En dos 54810
est s</w> 54808
absorb ed</w> 54801
fem ur</w> 54800
bre ath</w> 54794
al ga 54785
sal v 54766
ec ology</w> 54763
lo ok</w> 54760
circul atory</w> 54757
car inic</w> 54753
si dase</w> 54742
Medic are</w> 54742
propen sity</w> 54741
Struc ture</w> 54720
L y 54712
chic k</w> 54704
aden ine</w> 54691
plex us</w> 54689
eph one</w> 54684
fracti onal</w> 54681
F A 54651
ill ing</w> 54631
sensiti zed</w> 54627
tumorig enesis</w> 54617
writ ten</w> 54611
Lev els</w> 54601
cholin esterase</w> 54598
0.0 8</w> 54597
M es 54591
C CR 54573
microsom al</w> 54573
ro c 54569
sk ull</w> 54549
sequ el 54543
h CG</w> 54534
fil l</w> 54531
radio activity</w> 54489
har ve 54486
brief ly</w> 54477
Bin ding</w> 54475
Cal ifor 54455
el e</w> 54453
inhe rent</w> 54453
Tran si 54447
vertebr ates</w> 54446
bra instem</w> 54436
Ka plan</w> 54409
CC K</w> 54398
counter parts</w> 54377
sion ally</w> 54360
f ail</w> 54347
reci proc 54346
cytos ol</w> 54344
amygdal a</w> 54342
ME DL 54331
sub til 54330
iv ac 54311
encephal itis</w> 54311
hel per</w> 54304
itr on</w> 54302
par um</w> 54279
com ycin</w> 54270
tox ins</w> 54268
multi drug</w> 54255
fal ci 54242
Bi om 54234
allerg en</w> 54233
anti hypertensive</w> 54232
S Y 54227
constitu tive</w> 54224
L E</w> 54194
ac illus</w> 54194
fac torial</w> 54187
1 B</w> 54186
enh ancer</w> 54164
- ATPase</w> 54161
ac ycl 54161
19 80</w> 54143
Four ier</w> 54134
chick ens</w> 54131
p ass</w> 54125
wea kness</w> 54113
gluc os 54104
N GF</w> 54098
TR I 54096
- dependent</w> 54076
AN OVA</w> 54076
aden ovirus</w> 54057
P PAR 54052
stric t</w> 54045
lar val</w> 54045
spher ical</w> 54041
sp ots</w> 54021
H E</w> 54004
neo plasia</w> 54004
h ole</w> 53991
r o</w> 53990
MR SA</w> 53989
MEDL INE</w> 53986
Mg 2</w> 53983
sim plex</w> 53979
Le u</w> 53973
R BC</w> 53972
le an</w> 53969
R SV</w> 53954
am ylase</w> 53949
Plas modium</w> 53942
thrombocyt openia</w> 53935
sh ed</w> 53934
in effective</w> 53918
sen escence</w> 53903
publ ication</w> 53903
intell ig 53903
alk yl</w> 53897
infu l</w> 53886
C yto 53882
meth ane</w> 53873
anti fungal</w> 53857
c ra 53855
p al</w> 53851
Kore a</w> 53845
se al</w> 53844
al and</w> 53835
surve yed</w> 53833
o resc 53828
S o 53821
g ar 53821
iz er</w> 53806
erythemat osus</w> 53799
re ward</w> 53780
PA H</w> 53776
pa inful</w> 53774
prim ing</w> 53763
fl ag 53754
prec eding</w> 53730
G o 53729
clin ician</w> 53729
0.0 7</w> 53717
fic i 53697
down regulated</w> 53682
lipo proteins</w> 53680
vol atile</w> 53679
l ings</w> 53674
ox yt 53673
immun ocyto 53671
mo tivation</w> 53653
ureth ral</w> 53653
falci parum</w> 53653
Datab ase</w> 53650
mon otherapy</w> 53646
chit osan</w> 53645
an os 53635
olec ules</w> 53629
mat r 53609
prote ases</w> 53603
onc ogene</w> 53603
brow n</w> 53592
consum ing</w> 53577
cholester ol 53566
somat ostatin</w> 53565
un likely</w> 53559
se ed 53541
Al tern 53539
te dly</w> 53525
p um 53519
Vis ual</w> 53510
ph one</w> 53498
es cap 53497
guid eline</w> 53493
on cho 53491
D A 53489
al og 53486
ie sis</w> 53475
DN As</w> 53453
ad ec 53452
struc tive</w> 53439
iton in</w> 53439
Califor nia</w> 53439
AK I</w> 53427
Tra um 53414
H 3 53400
GI ST 53399
ac upuncture</w> 53369
ma ize</w> 53359
F GF 53358
pac litaxel</w> 53356
e o 53341
ferri tin</w> 53322
co sis</w> 53319
oph aryngeal</w> 53315
synap ses</w> 53298
adip ocytes</w> 53286
i as</w> 53284
od end 53280
path ologies</w> 53272
S er</w> 53255
identi fies</w> 53255
sh aring</w> 53254
l o</w> 53251
Therapeu tic</w> 53237
10 6</w> 53228
lo ads</w> 53219
ab ro 53216
Ex peri 53205
ell ul 53198
o ate</w> 53197
ph yt 53192
A sia</w> 53180
Ap proxim 53168
P art</w> 53165
pro pan 53163
un expected</w> 53161
war f 53156
HI F</w> 53156
chron ically</w> 53145
N AD</w> 53134
198 5</w> 53128
emb le</w> 53126
Ac c 53111
sacr ific 53103
par ac 53086
cor n</w> 53084
all ed</w> 53081
ti ter</w> 53079
radi us</w> 53079
dic ated</w> 53068
monol ayer</w> 53055
sol ar</w> 53053
9. 0</w> 53048
oc t 53047
tub ules</w> 53044
plac ing</w> 53018
peri odon 53013
behavi ours</w> 53010
Le w 53010
sp ong 53006
summar izes</w> 53004
remark ably</w> 53000
M CF</w> 52997
Cor relation</w> 52992
co il</w> 52991
cou gh</w> 52991
Th rom 52990
AC h</w> 52988
Contro lled</w> 52985
Ar ter 52983
phen y 52972
exp iratory</w> 52968
coun ting</w> 52967
proced ural</w> 52962
Pop ulation</w> 52936
m ating</w> 52935
li ved</w> 52928
is lets</w> 52912
lear ned</w> 52912
thromb us</w> 52884
V R</w> 52883
co des</w> 52872
nor mot 52865
glyco proteins</w> 52856
al located</w> 52847
ep inephrine</w> 52846
ICA M</w> 52846
12 6</w> 52839
n ar</w> 52836
10 8</w> 52836
immun ost 52815
X enop 52804
ther mod 52796
ICA L</w> 52784
Mei er</w> 52782
oste oc 52770
hy ste 52768
S G</w> 52767
hemisph ere</w> 52749
lymph aden 52748
S elec 52720
oc occus</w> 52714
illustr ated</w> 52713
intr on</w> 52709
e tine</w> 52701
4 8 52701
L R</w> 52692
a way</w> 52687
oc yst 52685
li vers</w> 52682
re pression</w> 52672
c esarean</w> 52669
car ni 52646
c GMP</w> 52638
asc ites</w> 52638
G G</w> 52635
it ating</w> 52635
if t</w> 52634
Saf ety</w> 52634
N umer 52626
agricul tural</w> 52611
in ver 52598
ro dent</w> 52596
Ne ur 52594
S s</w> 52582
here in</w> 52577
Cl ass</w> 52574
in haled</w> 52566
o venous</w> 52564
Experim ents</w> 52561
epsil on</w> 52559
ax on</w> 52554
w el 52548
lac tation</w> 52544
i tions</w> 52539
coll aboration</w> 52522
Lab oratory</w> 52517
down regulation</w> 52511
ed ull 52503
sum mer</w> 52485
predn isolone</w> 52484
o der 52482
degrad ed</w> 52481
ol a</w> 52468
rig id</w> 52460
rest or 52455
ar ate</w> 52451
Mor tality</w> 52426
sp orts</w> 52423
Ze aland</w> 52410
L DH</w> 52409
natri uretic</w> 52408
sp ond 52399
Chil d</w> 52387
the ories</w> 52353
microsom es</w> 52343
percep tual</w> 52342
Ad olesc 52341
de polarization</w> 52336
Xenop us</w> 52334
Sequ ence</w> 52321
epti dase</w> 52318
on ym 52311
et ting</w> 52306
mar ri 52304
Pa ren 52298
fif th</w> 52291
cal modulin</w> 52287
a stric</w> 52278
Net work</w> 52269
inter f 52253
im ag 52251
vir tually</w> 52248
vol u 52245
mix ing</w> 52240
G ly</w> 52237
initi ate</w> 52224
B R</w> 52206
ne y</w> 52199
cal f</w> 52197
bilir ubin</w> 52184
W he 52182
oler ance</w> 52174
grad ing</w> 52168
po real</w> 52166
Col lege</w> 52164
P MA</w> 52145
str ies</w> 52145
suffici ently</w> 52134
indic ative</w> 52131
f um 52129
meaning ful</w> 52126
sc ar</w> 52120
N K 52102
L ocal</w> 52101
tub ulin</w> 52101
pl ast</w> 52099
catheter ization</w> 52092
caud al</w> 52057
nor mo 52042
A den 52037
Im plications</w> 52029
diagnos ing</w> 52028
meth otrexate</w> 52026
Ex trac 52015
0 5</w> 52013
regul arly</w> 52013
T V</w> 52012
PF S</w> 52007
in appropriate</w> 51996
adolesc ence</w> 51977
tub es</w> 51942
extr ap 51942
cortico sterone</w> 51940
attit ude</w> 51936
histi dine</w> 51932
dil ated</w> 51911
14 5</w> 51895
polys accharide</w> 51891
od al</w> 51884
program med</w> 51884
trans l</w> 51880
emis sions</w> 51879
Helic obacter</w> 51878
9. 9</w> 51877
ut ely</w> 51872
fl ora</w> 51865
agre ed</w> 51863
neutr alizing</w> 51862
fol ded</w> 51861
sw ine</w> 51861
ster ic</w> 51844
al ters</w> 51841
F as</w> 51839
EM T</w> 51836
ir rig 51827
TH E</w> 51820
fl aps</w> 51817
adrenoc eptor</w> 51812
he avi 51801
cor pus</w> 51797
SP R</w> 51779
expendit ure</w> 51770
sp ort</w> 51757
o in</w> 51754
L I</w> 51753
oxif en</w> 51749
mach in 51746
depart ments</w> 51734
my ocytes</w> 51729
interf ere</w> 51723
co oling</w> 51721
O 3</w> 51720
Ab out</w> 51716
elic it</w> 51689
pos ing</w> 51687
I M</w> 51683
hydro chloride</w> 51681
re agents</w> 51657
pac ema 51647
pal mit 51641
o is 51626
M ont 51621
Met a</w> 51620
lipi demia</w> 51620
A 3</w> 51617
c ationic</w> 51596
1 beta</w> 51591
quic kly</w> 51590
expon ential</w> 51585
immunosup pression</w> 51584
y rate</w> 51560
deli ver</w> 51552
Reg ar 51514
As perg 51511
opath ies</w> 51497
strong est</w> 51483
O M</w> 51472
har m 51466
anti sense</w> 51464
yel low</w> 51463
ot om 51460
arachno id</w> 51455
c ru 51452
muscul oskeletal</w> 51444
Electr on</w> 51435
arachid onic</w> 51433
fruc tose</w> 51430
s un 51429
198 6</w> 51417
nem at 51411
S em 51401
r ational</w> 51384
log ic</w> 51374
coll ap 51373
inten sities</w> 51370
ati te</w> 51349
Ma xim 51348
o -</w> 51321
PA F</w> 51316
ven om</w> 51299
ex ha 51295
concer ned</w> 51292
discrim inate</w> 51282
possi bilities</w> 51277
sp an</w> 51263
exce eded</w> 51256
l in</w> 51255
cerebro vascular</w> 51248
grad es</w> 51245
dig estive</w> 51241
3 A</w> 51233
characteris ed</w> 51228
dissem inated</w> 51215
st ents</w> 51202
1 b</w> 51197
ing ual</w> 51191
endoth elin</w> 51179
wor m</w> 51167
Y or 51157
k il 51142
un iform 51142
s orption</w> 51132
p all 51110
dis plays</w> 51099
PC A</w> 51096
neo plasm</w> 51089
scav eng 51083
omegal ovirus</w> 51079
strati fication</w> 51072
expl oratory</w> 51070
favour able</w> 51050
Gl u</w> 51040
il st</w> 51030
access ory</w> 51015
tr ain 51014
Initi al</w> 51009
ver ify</w> 51008
decom pression</w> 51000
sp ut 50981
L ap 50979
B el 50977
sup presses</w> 50975
CR F</w> 50966
ophysi cal</w> 50963
silic on</w> 50962
aden ylate</w> 50958
Cor onary</w> 50954
summar ized</w> 50952
impl ement</w> 50951
ti ght</w> 50942
insul in 50916
cur v 50915
applic ability</w> 50909
Injur y</w> 50908
Be fore</w> 50899
IC C</w> 50889
thym ic</w> 50887
ren ew 50885
accompl ished</w> 50885
endometri osis</w> 50882
CT L</w> 50878
PF C</w> 50873
alk yl 50816
elim inate</w> 50812
traff ic</w> 50800
P t</w> 50789
ta ste</w> 50786
there after</w> 50786
Mater nal</w> 50783
bi ased</w> 50774
dis location</w> 50765
pres entations</w> 50754
e k</w> 50743
initi s</w> 50741
duc tal</w> 50738
is tics</w> 50734
S ER 50726
ac commod 50725
w rist</w> 50720
osens ory</w> 50705
intra uterine</w> 50702
propo fol</w> 50693
- 7</w> 50689
phenyl alanine</w> 50688
allel ic</w> 50680
one ur 50668
Fr ance</w> 50666
protec ts</w> 50665
pat ency</w> 50664
r ules</w> 50660
NS AI 50650
w ire</w> 50649
ti a</w> 50645
keratin ocytes</w> 50643
break down</w> 50639
comorb idity</w> 50638
poly clonal</w> 50636
1 15</w> 50631
o tri 50630
paren teral</w> 50614
cyclo hex 50599
Sur face</w> 50598
K in 50589
dri vers</w> 50586
phant om</w> 50580
ser ological</w> 50575
di lu 50572
en comp 50571
con focal</w> 50558
nan o</w> 50554
P re</w> 50553
thalam ic</w> 50535
medic inal</w> 50517
De termination</w> 50516
electrophore tic</w> 50516
co chemical</w> 50515
hemat oma</w> 50510
ail s</w> 50504
luc iferase</w> 50498
es thetic</w> 50485
T em 50483
neut rop 50478
eri atric</w> 50469
exer ted</w> 50452
e au</w> 50447
reconstruc ted</w> 50447
hom ocy 50435
sl udge</w> 50434
unde restim 50431
n esses</w> 50429
perox is 50428
resc ue</w> 50426
AP C</w> 50415
spec ified</w> 50414
b ar</w> 50407
rh in 50399
D ys 50394
radio active</w> 50386
tel ephone</w> 50383
ad d</w> 50382
dro p 50372
int ox 50367
Addi tion</w> 50354
de plo 50349
neigh b 50348
Elev en</w> 50345
S BP</w> 50340
1 R</w> 50334
tr ip 50303
gonad otropin</w> 50299
immun omod 50296
BM P</w> 50281
partic ulate</w> 50273
Reg istry</w> 50273
grou ped</w> 50248
ti d 50240
cre ating</w> 50229
F re 50213
stre ptoc 50208
loc omotor</w> 50205
habit at</w> 50193
I K 50186
dis continuation</w> 50181
Sim ult 50170
amni otic</w> 50169
sec tor</w> 50168
id ol</w> 50165
erythro po 50155
brom ide</w> 50150
micro glia</w> 50149
ole ic</w> 50140
B O 50136
regurg itation</w> 50129
O ut</w> 50122
Pre gn 50120
fabr ication</w> 50119
pathophysi ological</w> 50106
y ph 50102
C he 50100
L og 50079
h ands</w> 50070
anti serum</w> 50070
ocardi al</w> 50063
saf ely</w> 50060
fore arm</w> 50059
SU MM 50056
D N</w> 50045
Vit amin</w> 50032
a ic</w> 50029
transfer rin</w> 49996
mon thly</w> 49976
confir ming</w> 49965
cro p</w> 49965
nar row 49930
neuro blastoma</w> 49929
u als</w> 49917
experi encing</w> 49916
fung us</w> 49902
p 21</w> 49899
vulner ability</w> 49896
O s 49895
e able</w> 49889
n if 49886
percent ages</w> 49880
oc clud 49876
to sis</w> 49873
sel enium</w> 49871
Flu oresc 49860
rest ore</w> 49853
breast feeding</w> 49853
cont empor 49852
om avirus</w> 49848
emb olic</w> 49842
N AF 49840
ge ographical</w> 49831
alter ing</w> 49831
abdom en</w> 49828
ulcer ative</w> 49808
sequel ae</w> 49805
sph ing 49800
Pre operative</w> 49795
un ion</w> 49794
R em 49793
overex pressed</w> 49790
ivac aine</w> 49785
mil l</w> 49771
psych osis</w> 49759
leuk emic</w> 49759
marg ins</w> 49747
R V 49745
or poreal</w> 49743
di methyl</w> 49742
Approxim ately</w> 49734
in equ 49733
as sumption</w> 49703
char t</w> 49697
di i</w> 49679
sem en</w> 49678
bund le</w> 49658
prim er</w> 49643
de formation</w> 49642
i us</w> 49639
E c 49639
ic eptive</w> 49639
Com put 49637
ultras onic</w> 49633
SUMM ARY</w> 49631
tus sis</w> 49621
stri king</w> 49600
traff icking</w> 49586
Rec ur 49585
Ig G 49581
pl ur 49579
Nin ety</w> 49575
piv otal</w> 49570
Asperg illus</w> 49561
granul ocyte</w> 49558
p an</w> 49521
inter ventional</w> 49513
Youn g</w> 49511
B SA</w> 49501
L i</w> 49488
transcrip tase</w> 49475
G i 49472
cl aims</w> 49468
out breaks</w> 49460
der line</w> 49457
Al coh 49439
L 2</w> 49431
dis solved</w> 49429
e rec 49425
exec utive</w> 49414
inf eren 49410
Sub sequently</w> 49397
O SA</w> 49391
cortico steroid</w> 49390
n avig 49386
disrup ted</w> 49380
Ne w 49374
TE M</w> 49363
hydrox yl</w> 49357
exer ts</w> 49357
sol e</w> 49331
13 3</w> 49328
flo or</w> 49313
om ically</w> 49305
matr ices</w> 49294
bo y</w> 49292
ur idine</w> 49287
con sequently</w> 49287
confir ms</w> 49265
F requ 49262
fac ulty</w> 49262
PD GF</w> 49261
v is</w> 49255
s agittal</w> 49250
as ci 49250
differenti ating</w> 49249
lu oro 49248
gran ular</w> 49229
aqu atic</w> 49201
E V</w> 49194
id y</w> 49184
post synaptic</w> 49175
Un like</w> 49175
hierarch ical</w> 49161
ie tin</w> 49157
inter cellular</w> 49128
methyl ated</w> 49117
differen tly</w> 49115
10 7</w> 49107
odi um</w> 49105
l ide</w> 49086
n ude</w> 49083
isch aemic</w> 49082
gu anine</w> 49076
weak ly</w> 49076
vit re 49068
suppl ement</w> 49067
Emer gency</w> 49067
neuro psychological</w> 49049
pri ority</w> 49043
mus carinic</w> 49037
inv ari 49024
chim eric</w> 49024
scinti graphy</w> 49020
PM N</w> 49005
cytos keleton</w> 49004
suff er</w> 49003
osi des</w> 48990
R ob 48973
hom es</w> 48973
Bay esian</w> 48965
S pain</w> 48958
she et</w> 48958
Con centr 48956
R C</w> 48955
gener a</w> 48948
Mut ations</w> 48939
throm ycin</w> 48937
Ital ian</w> 48930
transp os 48918
ox one</w> 48914
special ist</w> 48905
apoli poprotein</w> 48905
D P 48898
th ening</w> 48897
arsen ic</w> 48897
is s 48889
sens or 48886
ai ve</w> 48879
ai ting</w> 48875
s ely</w> 48866
salv age</w> 48864
co bal 48853
ran ts</w> 48850
ac cordance</w> 48844
gen ous</w> 48844
c ylin 48841
am ate</w> 48839
tr if 48831
f er</w> 48828
dec arbox 48817
alcoh ol 48814
S L</w> 48813
AP P</w> 48812
ED TA</w> 48798
in sensitive</w> 48796
Sp anish</w> 48791
o dic 48783
ge ometric</w> 48782
bo ard</w> 48777
T O 48776
is s</w> 48758
15 7</w> 48758
re arrangement</w> 48743
hygi ene</w> 48739
th ous 48735
om o 48733
sw im 48714
br oncho 48711
emphasi ze</w> 48709
At l 48707
mor ning</w> 48699
PA I</w> 48699
i v</w> 48694
oth or 48690
arrhyth mia</w> 48670
te ar</w> 48658
ocor tical</w> 48656
w an 48650
t rou 48640
us sion</w> 48639
ex tending</w> 48625
noc tur 48604
Vir us</w> 48594
investig ators</w> 48567
we aning</w> 48566
i NOS</w> 48563
B NP</w> 48549
du plication</w> 48541
suic idal</w> 48533
obarb ital</w> 48524
b up 48504
aff in</w> 48504
acr ylate</w> 48496
dri ed</w> 48495
CR T</w> 48494
ch ance</w> 48482
the red</w> 48482
Chem ical</w> 48481
protein uria</w> 48480
fraction ated</w> 48479
W ater</w> 48477
nit ros 48476
sal ts</w> 48456
S elective</w> 48455
P relim 48450
Ph e</w> 48446
aberr ations</w> 48439
F low</w> 48435
M F</w> 48426
appen dic 48426
contr ary</w> 48402
chemo kine</w> 48402
ro dents</w> 48401
Maj or</w> 48400
reli ably</w> 48397
O V 48395
N D 48388
lap se</w> 48376
amino transferase</w> 48374
Ac id</w> 48360
Di etary</w> 48353
sc aling</w> 48349
ro utes</w> 48347
ser otype</w> 48336
tra di 48328
conjunc tiv 48322
fibr ils</w> 48315
heter ozyg 48314
fing er 48312
cop ic</w> 48306
recur rences</w> 48306
inten tion</w> 48286
Analy ses</w> 48279
ip ot 48273
cum in</w> 48267
judg ed</w> 48264
NA c</w> 48262
fluoresc ein</w> 48256
hydro gel</w> 48254
it oring</w> 48253
cer ti 48245
iz ational</w> 48239
at ax 48238
satis fied</w> 48233
poly ethylene</w> 48232
initi ating</w> 48230
Al a</w> 48209
hepat ocyte</w> 48204
qu ine</w> 48200
cap abilities</w> 48198
Inter net</w> 48196
hydr o</w> 48182
Prelim inary</w> 48182
adhe rent</w> 48180
st op</w> 48179
Intr av 48177
Fif teen</w> 48170
avoid ed</w> 48159
energ ies</w> 48157
attrib utes</w> 48153
cu ff</w> 48147
infu sions</w> 48143
in directly</w> 48121
col lege</w> 48120
lamin a</w> 48110
Tr yp 48102
dis abilities</w> 48092
opin ion</w> 48090
creatin e</w> 48071
ker atin</w> 48042
prin ted</w> 48034
cop olym 48032
V ascular</w> 48031
confoun ding</w> 48023
plo idy</w> 48010
sti ll 48007
ap on 48004
C arcin 48001
asc ending</w> 47998
hapl otype</w> 47981
dis solution</w> 47968
end oc 47947
H B</w> 47933
Relationsh ip</w> 47924
be ads</w> 47914
medic ines</w> 47910
A ca 47909
oligonucle otide</w> 47908
D esc 47904
inter rup 47896
some what</w> 47890
re di 47889
accep tor</w> 47883
ap atite</w> 47874
qu en 47865
encapsul ated</w> 47855
substitu tions</w> 47852
as k</w> 47847
Measure ment</w> 47846
denti n</w> 47843
m e</w> 47840
facilit ating</w> 47836
phag ia</w> 47826
NAF LD</w> 47822
concep tual</w> 47809
carb oxy 47794
Enh anced</w> 47794
medull ary</w> 47787
SM C</w> 47763
nanop article</w> 47758
phosphatidyl inositol</w> 47746
ap amil</w> 47743
L T 47739
neur ones</w> 47738
Str ateg 47728
ap o</w> 47720
H sp 47713
4 4 47713
hydro philic</w> 47707
Met abolic</w> 47700
b ub 47675
R as</w> 47661
enlarg ement</w> 47653
in o</w> 47652
RN A 47650
Measure ments</w> 47640
ter at 47639
hyste rectomy</w> 47626
oxid es</w> 47605
an es</w> 47596
P ap 47595
sym metric</w> 47591
jec t</w> 47584
thi ol</w> 47584
w elling</w> 47570
il ing</w> 47570
expec t 47568
tax a</w> 47568
chemo therapeutic</w> 47561
os um</w> 47554
mes h</w> 47554
laparo tomy</w> 47550
orig ins</w> 47549
anth rac 47549
i ana</w> 47546
on ical</w> 47543
Nor mal</w> 47541
L D 47527
as i</w> 47527
air ways</w> 47521
Ano ther</w> 47519
oneph ritis</w> 47517
det achment</w> 47511
carb ons</w> 47499
HB s 47484
os elective</w> 47478
te a</w> 47470
br ids</w> 47467
Ob serv 47466
deb ate</w> 47461
mt DNA</w> 47445
A β</w> 47431
MT X</w> 47425
fraction ation</w> 47422
morph ologic</w> 47421
echocardi ographic</w> 47419
T i</w> 47413
dissem ination</w> 47409
contrac eptive</w> 47408
idi d 47397
ster ol</w> 47391
AK T</w> 47391
con sol 47389
TL V</w> 47364
2 B</w> 47353
fl at</w> 47332
Pro f 47331
as par 47324
clea ved</w> 47313
confir mation</w> 47306
out flow</w> 47305
strom a</w> 47299
ep tors</w> 47296
curricul um</w> 47284
in consistent</w> 47281
mom ent</w> 47273
Cardi ovascular</w> 47271
c us</w> 47266
en ol 47261
res ec 47261
13 1</w> 47236
go v</w> 47233
cri sis</w> 47233
de trim 47218
AN CE</w> 47213
Dec re 47208
X 2</w> 47198
radi olab 47189
pul p</w> 47181
lat tice</w> 47150
art an</w> 47149
spre ading</w> 47149
Sensi tivity</w> 47146
wo od</w> 47145
9 , 47142
ad ducts</w> 47130
harve sted</w> 47120
G B</w> 47119
u ve 47117
G ly 47096
st ressed</w> 47092
in us</w> 47081
exc ited</w> 47079
visu alized</w> 47076
orig inating</w> 47072
Consi dering</w> 47067
function ality</w> 47062
h ind 47030
pre p 47023
diver gence</w> 47022
acceler ation</w> 47008
di ameters</w> 47007
S mo 47002
MD R</w> 47001
oph ore</w> 46997
Up on</w> 46984
10 9</w> 46962
radio immunoassay</w> 46960
biom echanical</w> 46951
Col lec 46942
Gl c 46933
yn aptic</w> 46925
SU B 46918
hypertroph ic</w> 46914
ch aper 46891
cou ples</w> 46875
ophyl line</w> 46864
on itr 46862
tim ely</w> 46859
at tend 46853
domin ated</w> 46847
com promised</w> 46841
ser ving</w> 46823
y ran 46805
Lit tle</w> 46805
Com po 46802
enco de</w> 46781
V O 46771
assist ance</w> 46767
ex ons</w> 46765
tail ored</w> 46761
Ac cur 46759
odend ro 46759
Sle ep</w> 46754
periodon titis</w> 46754
normot ensive</w> 46723
B F</w> 46720
f 2</w> 46717
pharmac ologic</w> 46717
den er 46701
b it 46693
tis h</w> 46681
r id 46679
trunc ated</w> 46665
it ors</w> 46663
pos tin 46658
c all</w> 46655
C ri 46653
U N 46648
M SC</w> 46643
D ist 46632
Or th 46624
re agent</w> 46614
id ectomy</w> 46610
ene di 46600
vi x</w> 46585
grad ual</w> 46554
CH O</w> 46554
w est</w> 46551
decom position</w> 46548
ib ution</w> 46527
am en</w> 46518
1 12</w> 46508
ac tually</w> 46485
re act</w> 46479
gir l</w> 46477
ster oidal</w> 46469
immobil ization</w> 46468
ta m</w> 46453
D ou 46442
tot ally</w> 46438
F ail 46436
1, 4</w> 46432
mal formation</w> 46418
my ri 46410
har m</w> 46407
circu its</w> 46403
Stro ke</w> 46403
in ing</w> 46400
occip ital</w> 46382
Lap aro 46382
vi ri 46381
di hydroxy 46380
os per 46378
ti tle</w> 46364
sol vents</w> 46349
wat ers</w> 46337
D ig 46334
um ination</w> 46330
polyp s</w> 46329
refl ection</w> 46319
d rought</w> 46317
hyper glycemia</w> 46309
potenti ation</w> 46308
phag ocytosis</w> 46304
ir regular</w> 46302
r on 46279
up date</w> 46276
orig inally</w> 46272
trajec tories</w> 46272
distric t</w> 46269
sal ic 46264
el eg 46253
L N</w> 46251
AC L</w> 46240
addic tion</w> 46234
anti oxidants</w> 46226
ME NTS</w> 46226
n aive</w> 46214
pros theses</w> 46213
transpl ants</w> 46209
osi tis</w> 46207
fel t</w> 46206
cr im 46201
met formin</w> 46200
v ocal</w> 46199
ak es</w> 46197
am il 46192
b rought</w> 46189
us sian</w> 46181
end points</w> 46180
N r 46163
man n</w> 46160
tod ay</w> 46153
otyp ed</w> 46147
fit ted</w> 46125
Braz ilian</w> 46101
H om 46091
System ic</w> 46090
A min 46078
accum ulate</w> 46068
ot emporal</w> 46064
CA P</w> 46060
B CG</w> 46043
teri ous</w> 46035
at rop 46011
promp t</w> 46008
homogene ity</w> 45995
- 10</w> 45992
Re sistance</w> 45972
X A</w> 45964
ren sic</w> 45952
mon omer</w> 45947
En gland</w> 45939
sig ma</w> 45936
hedr al</w> 45935
oin t</w> 45934
0 3</w> 45913
di lation</w> 45904
sign ature</w> 45903
AT 1</w> 45903
pa id</w> 45897
succ essive</w> 45896
ber g</w> 45883
pur su 45878
deri ve</w> 45877
sched uled</w> 45866
VI P</w> 45864
L ys</w> 45861
retin oic</w> 45861
SIG N 45858
F L</w> 45854
spac es</w> 45854
cholecyst ectomy</w> 45843
inst ances</w> 45841
ac tors</w> 45837
an te 45835
anticoagul ant</w> 45833
contrib ut 45830
AV P</w> 45829
Ac cordingly</w> 45820
bil ayer</w> 45817
onc ogenic</w> 45817
U s</w> 45816
cut off</w> 45815
an ia</w> 45809
og raph</w> 45788
S n 45786
0.9 9</w> 45784
manifest ed</w> 45773
ur able</w> 45771
M CP</w> 45767
agglutin ation</w> 45759
W a 45758
al s. 45756
ger m 45755
oxyt ocin</w> 45755
ch a 45735
conven ient</w> 45735
go es</w> 45733
W I</w> 45731
interfer ing</w> 45721
im mer 45718
z ine</w> 45702
x y</w> 45673
l ex 45671
is ely</w> 45671
J NK</w> 45662
az one</w> 45656
On c 45650
men is 45649
post ulated</w> 45648
12 8</w> 45646
enti rely</w> 45630
impl ementing</w> 45630
C ys</w> 45616
neuro protective</w> 45616
STAT 3</w> 45614
d ural</w> 45607
anti coagulation</w> 45605
Taiw an</w> 45596
in version</w> 45577
predic table</w> 45568
syn thesize</w> 45566
ation wide</w> 45564
omat osis</w> 45564
IV F</w> 45542
atr y</w> 45540
2 40</w> 45535
fail ures</w> 45532
vol ution</w> 45530
transcrip tome</w> 45529
H N 45521
sol ely</w> 45521
scenari o</w> 45516
Pos sible</w> 45514
landscap e</w> 45494
Whe ther</w> 45491
exc ised</w> 45488
epider mis</w> 45484
or in</w> 45478
ing redi 45476
ogen y</w> 45468
rec ycl 45461
lab elling</w> 45452
Pro c 45452
o esophageal</w> 45449
perc enti 45446
SUB JEC 45444
at tended</w> 45426
insec tic 45421
reve aling</w> 45420
off ic 45420
13 5</w> 45417
L oss</w> 45414
del ays</w> 45411
Y et</w> 45408
suppl ements</w> 45406
HBs Ag</w> 45406
ly so 45404
harm ful</w> 45404
1 70</w> 45394
pyr amidal</w> 45392
M K</w> 45387
1 16</w> 45384
tetrac ycline</w> 45384
buil d</w> 45379
lact am 45372
len ses</w> 45361
bor derline</w> 45360
ur a</w> 45351
for tun 45349
fit ting</w> 45343
no v</w> 45341
oph er 45341
br ile</w> 45337
prof en</w> 45334
rel ig 45333
bi o</w> 45333
bas ement</w> 45333
ill a</w> 45332
resi li 45322
st aphyloc 45315
agg ed</w> 45303
le b 45298
in tran 45296
dis ordered</w> 45280
ap art</w> 45272
idid ym 45268
c .</w> 45266
so on</w> 45266
angi ographic</w> 45257
ing e</w> 45256
compens ation</w> 45255
posi tional</w> 45252
GR P</w> 45247
ration ale</w> 45244
el bow</w> 45236
Le ft</w> 45232
tab lets</w> 45228
phosphatidyl choline</w> 45211
0.0 9</w> 45192
lin ear 45188
N AC</w> 45180
sulph ate</w> 45179
van comycin</w> 45163
un like</w> 45152
p i</w> 45143
acet ylation</w> 45142
efficac ious</w> 45137
hel ps</w> 45134
mol ars</w> 45130
contex ts</w> 45114
sol itary</w> 45104
cor on 45099
streng ths</w> 45098
RE GIST 45085
Peri ph 45085
Cl 2</w> 45077
rough ly</w> 45071
eradic ation</w> 45069
T P 45048
dele terious</w> 45025
cl ients</w> 45017
Th 1</w> 45002
c m2</w> 45001
extrem ities</w> 44998
in ability</w> 44992
v or 44985
W S</w> 44978
medull a</w> 44976
tinc tion</w> 44952
t et 44950
i ence</w> 44950
I .</w> 44944
cre ation</w> 44942
micro RNA</w> 44917
g et</w> 44916
al location</w> 44915
suspici on</w> 44914
H TLV</w> 44908
C re 44908
re generative</w> 44908
PA GE</w> 44904
micro mol</w> 44899
F M</w> 44895
produc tive</w> 44889
design ing</w> 44884
ecosy stem</w> 44884
ha m</w> 44877
T K 44867
off ice</w> 44853
ov ine</w> 44841
scop ically</w> 44835
depos ited</w> 44828
ec tual</w> 44816
g ast 44810
distinc tive</w> 44799
tetr am 44795
S N</w> 44790
asth matic</w> 44774
C am 44766
per tussis</w> 44763
e ste 44762
p ocket</w> 44754
RE M</w> 44751
lear n</w> 44750
subtil is</w> 44746
de ad</w> 44731
Bas eline</w> 44731
O per 44725
An g</w> 44723
dop ed</w> 44722
Me di 44717
Suc cess 44707
metr ics</w> 44702
virul ent</w> 44702
t s 44698
d engue</w> 44695
al ive</w> 44695
lid ocaine</w> 44683
contempor ary</w> 44681
nitr ite</w> 44680
me aning</w> 44676
An xi 44659
pan demic</w> 44659
am ong 44649
stret ch</w> 44647
par affin</w> 44631
er ation</w> 44626
ven a</w> 44614
18 7</w> 44612
os yl</w> 44611
A SA</w> 44609
pollut ants</w> 44605
reser ve</w> 44601
inter active</w> 44593
physi cochemical</w> 44593
negl igible</w> 44593
B l 44588
post ural</w> 44584
ultr a</w> 44574
H o 44566
val ves</w> 44557
my c</w> 44553
I U 44549
IC P</w> 44546
spi ro 44545
IC s</w> 44541
batter y</w> 44532
electro static</w> 44529
B lack</w> 44523
4 6 44522
edull ary</w> 44522
20 1</w> 44520
F er 44519
E T 44517
man d 44505
roph yl 44504
vari ates</w> 44502
E ch 44495
R ab 44477
Yor k</w> 44471
y rene</w> 44470
RA TION</w> 44469
Clo str 44462
pos itron</w> 44459
mis sed</w> 44453
ov ulation</w> 44449
cad aver 44449
min d</w> 44448
acc ident</w> 44446
Ur inary</w> 44432
it ch</w> 44430
ab is</w> 44429
mal nutrition</w> 44428
q PCR</w> 44423
N utri 44422
function alized</w> 44422
bi phasic</w> 44420
F EV 44418
N eu 44410
Func tion</w> 44409
Pol ym 44407
defici encies</w> 44406
atmosph eric</w> 44403
1 22</w> 44388
rac ial</w> 44372
hy brids</w> 44367
D own</w> 44366
sump tions</w> 44366
develop s</w> 44364
warf arin</w> 44364
proxim ity</w> 44363
among st</w> 44363
heter ologous</w> 44362
S ES</w> 44360
wid er</w> 44360
i odi 44358
proteas ome</w> 44355
stre pto 44350
sub populations</w> 44343
bl ings</w> 44339
oste otomy</w> 44327
escap e</w> 44325
gener ic</w> 44281
MT T</w> 44272
adv ice</w> 44256
supernat ant</w> 44253
2 R</w> 44251
alb umin 44239
L ight</w> 44235
B C 44224
des ensi 44214
h app 44208
tit ude</w> 44204
pal ate</w> 44188
hypo thermia</w> 44182
sep tum</w> 44181
inf requ 44175
sem antic</w> 44170
M r</w> 44166
diagnos tics</w> 44152
par all 44142
w ri 44140
SUBJEC TS</w> 44137
immunoprecip itation</w> 44134
V P</w> 44131
15 5</w> 44130
L ear 44114
hem odynamics</w> 44114
Tri als</w> 44103
ot ed</w> 44101
dend r 44096
la g</w> 44095
fru its</w> 44092
organ izational</w> 44084
dis abl 44083
dis comfort</w> 44083
IC A</w> 44080
Rel ated</w> 44080
leuk aemia</w> 44064
benz o</w> 44063
nic kel</w> 44054
G BM</w> 44045
us tion</w> 44033
pat ches</w> 44032
es in</w> 44029
opath ologic</w> 44014
prolifer ating</w> 43999
log y</w> 43994
an z 43988
can onical</w> 43985
ul ins</w> 43978
atin ib</w> 43976
r if 43975
lin early</w> 43973
NCT 0 43970
r yst 43960
Isol ation</w> 43957
0. 10</w> 43956
mil itary</w> 43954
arg ue</w> 43947
ic us</w> 43934
ater ally</w> 43925
but yrate</w> 43917
Th 2</w> 43911
sphinc ter</w> 43908
2 b</w> 43903
cu red</w> 43903
po res</w> 43902
g ating</w> 43901
Ch lamy 43885
trigg ers</w> 43878
endocardi tis</w> 43878
en oic</w> 43875
gen tam 43871
ill nesses</w> 43868
qu estion 43864
im urium</w> 43850
possess es</w> 43847
cap ill 43840
sta ke 43834
Rep e 43826
g em 43806
intrav ascular</w> 43806
gam ma 43803
ph i</w> 43796
itud es</w> 43796
necess arily</w> 43793
ic os 43788
N B</w> 43778
cer vix</w> 43774
no tion</w> 43769
util ize</w> 43751
ar ranged</w> 43742
thick ening</w> 43734
microtub ules</w> 43733
F ree</w> 43725
check point</w> 43725
aggreg ate</w> 43723
C as 43713
hydrol ase</w> 43713
special ty</w> 43710
M K 43709
CL IN 43709
pol arity</w> 43703
az ide</w> 43701
p uncture</w> 43697
V III</w> 43695
T op 43692
prec ed 43691
micr ons</w> 43687
In duction</w> 43685
detrim ental</w> 43681
galac to 43675
surg eries</w> 43673
carbox yl</w> 43673
adop tion</w> 43672
ep ididym 43667
MEASURE MENTS</w> 43664
over load</w> 43661
Gl yc 43660
olig odendro 43658
Periph eral</w> 43647
parasi tic</w> 43644
appropri ately</w> 43639
metr ical</w> 43638
yr ic</w> 43637
vi ro 43628
polymer ic</w> 43624
Ar g 43618
fav or</w> 43616
Sam ples</w> 43614
val ently</w> 43608
acces sibility</w> 43596
por ph 43591
0 4</w> 43590
perm its</w> 43585
ec e</w> 43584
A v 43583
correspon ded</w> 43573
onucle ase</w> 43573
syn thesi 43565
ati vity</w> 43562
Caucasi an</w> 43546
trac k</w> 43538
expl oring</w> 43538
st yle</w> 43537
an di 43535
- 8</w> 43533
ene m</w> 43531
e igh 43530
tun nel</w> 43529
sp o 43516
free zing</w> 43516
hyper cholesterol 43489
agg ression</w> 43489
On t 43488
free ze</w> 43480
onitr ile</w> 43461
an tin 43458
AC C</w> 43455
disp arities</w> 43454
Co V</w> 43451
chondro cytes</w> 43451
Bacter ial</w> 43450
Ex pl 43445
ren ol</w> 43442
Im pl 43438
arteri ovenous</w> 43438
lute al</w> 43432
D T</w> 43428
H ar 43423
hypo thyroidism</w> 43420
mening i 43418
sup ra 43413
AN P</w> 43407
gr ass</w> 43404
REGIST RATION</w> 43399
tain ment</w> 43386
is sion</w> 43370
devi ations</w> 43370
flu idic</w> 43367
segreg ation</w> 43365
b atch</w> 43364
progenit ors</w> 43364
vi sions</w> 43336
n ich 43333
k cal</w> 43331
ish able</w> 43328
sed entary</w> 43323
mod ule</w> 43322
0.00 6</w> 43314
L ower</w> 43313
medias tinal</w> 43313
tetra hydro 43306
L L</w> 43303
P ear 43296
M en 43295
hor n</w> 43295
famil iar</w> 43292
com posites</w> 43284
ra is 43272
bab ies</w> 43255
C EN 43248
contracti lity</w> 43243
als. gov</w> 43242
tr ib 43240
k o 43236
T T 43226
altern atives</w> 43226
incre mental</w> 43223
her bal</w> 43219
C ys 43216
epidemi ologic</w> 43213
an il 43209
mes oth 43201
ati zation</w> 43180
oste osarcoma</w> 43179
1 14</w> 43178
de duced</w> 43175
sup pressing</w> 43174
dis appearance</w> 43164
gu n</w> 43162
P s 43156
MD D</w> 43149
gli oblastoma</w> 43148
exist ed</w> 43144
reservo ir</w> 43118
re arrangements</w> 43114
ran ean</w> 43113
paren chym 43113
Del ta 43112
docum entation</w> 43106
cyt omegalovirus</w> 43093
innerv ation</w> 43093
pati al</w> 43084
perm it</w> 43078
O H 43076
noctur nal</w> 43060
pran dial</w> 43057
met allic</w> 43053
Ne ther 43052
neuro transmitter</w> 43050
osse ous</w> 43045
lit er</w> 43031
re ad</w> 43027
eri an</w> 43027
analy tic</w> 43017
In deed</w> 42996
met a 42993
Ex erc 42993
bl ast</w> 42991
S ARS</w> 42982
fil ters</w> 42976
I P 42972
diab etics</w> 42972
Com mit 42970
Clin ic 42969
cigaret tes</w> 42944
ris ing</w> 42941
W M</w> 42938
fu el</w> 42938
carb onyl</w> 42937
out patients</w> 42935
m y</w> 42930
ut ch</w> 42922
As p</w> 42920
hyper activity</w> 42913
p y</w> 42910
im balance</w> 42898
ca emia</w> 42898
mon ophosphate</w> 42896
I sl 42893
or n 42879
expan ding</w> 42874
hypoglyc emia</w> 42874
lo ops</w> 42869
I X</w> 42867
ul ae</w> 42862
T yr</w> 42859
mod eled</w> 42858
yiel ding</w> 42838
dodec yl</w> 42831
sput um</w> 42830
8 , 42825
l umin 42825
on ium</w> 42820
Gu id 42817
sub clinical</w> 42812
di aly 42792
v oc 42789
step wise</w> 42789
ep sin</w> 42781
rel in</w> 42781
nal oxone</w> 42775
Swe den</w> 42770
em inal</w> 42768
sp aring</w> 42765
galact ose</w> 42762
g ains</w> 42758
employ ees</w> 42755
cat alysis</w> 42739
rheum atic</w> 42737
Tran spl 42721
1 24</w> 42720
Grou ps</w> 42718
toler ability</w> 42717
E g 42710
spectro phot 42708
refr active</w> 42707
surviv ing</w> 42704
spati ally</w> 42697
special ists</w> 42690
ampl itudes</w> 42684
vo ice</w> 42676
du plex</w> 42675
F il 42671
pro of</w> 42668
T OF</w> 42659
3 a</w> 42655
FI SH</w> 42653
Hist ological</w> 42632
1 alpha</w> 42630
Di str 42624
amput ation</w> 42618
err y</w> 42615
enti ties</w> 42602
tigh tly</w> 42602
bi osynthetic</w> 42580
A H</w> 42578
N ic 42573
ly tic</w> 42561
L Y 42556
is op 42556
es us</w> 42555
presum ed</w> 42549
h r 42541
pro pri 42537
Classi fication</w> 42533
PC a</w> 42522
si mp 42518
os mol 42514
tam oxifen</w> 42510
gener ates</w> 42506
gra vity</w> 42504
oper oxidase</w> 42501
avoid ing</w> 42500
arb onate</w> 42499
imp ly</w> 42496
amin op 42495
prep are</w> 42495
te ams</w> 42493
plur ipot 42492
toler ant</w> 42490
Sp ont 42486
di genous</w> 42480
catech ol 42479
ro ad</w> 42474
3 T3</w> 42462
O ve 42450
es cal 42450
en ters</w> 42448
as sumptions</w> 42447
chol angi 42446
N ot</w> 42434
A ver 42423
physic ally</w> 42422
M u 42418
lim us</w> 42408
Immuno histochemical</w> 42408
mo ve</w> 42407
s and 42406
defin itions</w> 42400
Mit ochond 42386
Rep or 42385
CX CR 42382
ang e</w> 42377
3 9 42373
M L</w> 42366
Nether lands</w> 42360
Four teen</w> 42354
ath eter</w> 42342
end er</w> 42337
Mex ico</w> 42333
Ul tra 42329
NAD H</w> 42320
0 6</w> 42300
refl ex 42300
Inc idence</w> 42299
h an 42297
acet ab 42296
si blings</w> 42295
Pan cre 42286
L iter 42283
radi ography</w> 42281
Plat el 42281
0. 12</w> 42276
age ing</w> 42275
Pre viously</w> 42271
hy pe 42265
estro gens</w> 42262
con stitutes</w> 42261
Success ful</w> 42259
ul um</w> 42256
inv asi 42248
Anxi ety</w> 42248
Fem ale</w> 42240
P arti 42226
M AL 42215
or um</w> 42212
ad ox 42203
mot oneur 42194
distingu ishable</w> 42186
ile um</w> 42184
do si 42181
ol og</w> 42180
som nia</w> 42178
puber tal</w> 42173
tri phosphate</w> 42172
ingu inal</w> 42170
Long itudinal</w> 42162
M G 42140
Rev er 42135
exp and</w> 42134
198 4</w> 42122
T or 42111
phot osynthetic</w> 42111
T el 42102
micro spheres</w> 42101
insec ts</w> 42092
al ge 42083
el lo 42074
cho ices</w> 42073
Stand ard</w> 42069
inc i 42068
plo ts</w> 42053
enlarg ed</w> 42052
thal ass 42047
cry op 42045
de al</w> 42042
Serv ice</w> 42030
broad er</w> 42027
atin s</w> 42020
ar ized</w> 42017
lac ks</w> 42017
cong ru 42009
vesi cular</w> 42008
Wh ole</w> 42003
selec tin</w> 41987
pestic ides</w> 41982
µ g</w> 41978
on uc 41967
simp ly</w> 41967
ro bu 41963
robo tic</w> 41957
Gr ade</w> 41954
6 7 41943
F F</w> 41932
un necessary</w> 41932
CLIN ICAL</w> 41931
ac ro 41930
isch aemia</w> 41927
nucle oside</w> 41922
edi pine</w> 41922
PC OS</w> 41915
inter play</w> 41906
tu re 41898
M CI</w> 41892
ex tinction</w> 41887
condi tional</w> 41884
compet ent</w> 41876
si d</w> 41868
Com plex</w> 41867
tw ins</w> 41862
necess ity</w> 41858
anti sera</w> 41832
ph o 41830
n itro</w> 41827
S chool</w> 41817
real istic</w> 41813
Cp G</w> 41810
ri t</w> 41808
dextr an</w> 41804
T GF 41802
GV HD</w> 41802
evol ving</w> 41800
H yp 41799
depend ency</w> 41799
reg ime</w> 41789
R PE</w> 41788
hemat ological</w> 41779
-- a</w> 41778
accid ents</w> 41769
G ST</w> 41766
par alysis</w> 41763
emul sion</w> 41762
prec isely</w> 41760
eosinoph ils</w> 41759
ther mic</w> 41757
emphasi zed</w> 41755
G astro 41751
plat eau</w> 41742
fin ement</w> 41733
ent rop 41718
machin ery</w> 41713
thir ty</w> 41712
ver apamil</w> 41706
alga e</w> 41706
Stim ulation</w> 41696
co variates</w> 41695
clinic opathological</w> 41690
Isol ated</w> 41686
obacter ium</w> 41684
im idine</w> 41680
rel ies</w> 41668
f a</w> 41664
Ox y 41664
1. 25</w> 41655
Ol der</w> 41645
tid al</w> 41644
w ater 41641
Cs A</w> 41639
Cor rec 41632
orth op 41632
co ded</w> 41631
v ular</w> 41629
col our</w> 41617
prin t</w> 41616
plac es</w> 41609
0.9 8</w> 41607
D oes</w> 41602
top ics</w> 41600
d s 41595
meth oxy 41594
Compu ted</w> 41593
it or</w> 41590
ac idi 41590
wa ist</w> 41590
1. 00</w> 41575
P i</w> 41561
4 7 41560
R ou 41557
i tin 41553
icon duc 41553
Ultras ound</w> 41549
phosph odi 41547
cap tured</w> 41543
am ol</w> 41541
C ity</w> 41532
dis k</w> 41522
Ove rex 41509
y e</w> 41506
Fe w</w> 41504
histopath ology</w> 41494
integr ating</w> 41492
aller gens</w> 41484
P i 41483
u sions</w> 41480
0 9</w> 41475
il ty</w> 41474
in takes</w> 41470
e i</w> 41468
reser ved</w> 41467
immun ologic</w> 41464
5 49</w> 41459
sy ring 41450
Educ ation</w> 41449
victim s</w> 41442
mir ro 41440
silic one</w> 41415
cataly sts</w> 41402
coll ateral</w> 41395
impl ies</w> 41384
potenti ated</w> 41375
requ is 41369
normal ization</w> 41368
ket amine</w> 41364
electro my 41363
miner alization</w> 41360
pro pos 41353
NP Y</w> 41351
mechan ics</w> 41350
orth odontic</w> 41348
comorb id</w> 41344
sto ol</w> 41330
microbi ome</w> 41329
CH F</w> 41321
eng aged</w> 41318
Le ishman 41312
intell ectual</w> 41306
ost er</w> 41302
Mo der 41300
selec ting</w> 41286
Con sistent</w> 41276
integr ate</w> 41273
b im 41269
obste tric</w> 41254
per iton 41252
Zn O</w> 41250
cardiomy ocytes</w> 41238
s mar 41228
pat ent</w> 41224
le i 41216
Fail ure</w> 41216
S kin</w> 41213
ific ations</w> 41202
gentam icin</w> 41193
bec tomy</w> 41188
hem angi 41186
cas ein</w> 41180
pacema ker</w> 41178
rot ational</w> 41169
inc is 41168
hem olytic</w> 41157
toc opher 41147
gener ations</w> 41145
1 17</w> 41144
exerc ises</w> 41143
anticip ated</w> 41141
Com plications</w> 41135
doc king</w> 41135
Ac et 41130
nec rotic</w> 41119
projec ts</w> 41119
cong estive</w> 41118
sustain able</w> 41110
Dis e 41103
ch ip</w> 41100
v ast</w> 41096
13 2</w> 41095
adsorb ed</w> 41093
As s 41084
flow s</w> 41081
HC l</w> 41079
Mus cle</w> 41070
til es</w> 41067
determin es</w> 41054
represent ations</w> 41049
ev ery 41047
eleg ans</w> 41044
prec eded</w> 41042
neph rectomy</w> 41042
Hepati tis</w> 41035
In c</w> 41034
FEV 1</w> 41033
sedi ments</w> 41021
F DA</w> 41002
anomal y</w> 40992
con cordance</w> 40985
ac in 40984
vul g 40980
prote renol</w> 40971
depend ed</w> 40969
inform atics</w> 40960
compens atory</w> 40956
occa sionally</w> 40955
flan king</w> 40954
ter ic</w> 40950
N ec 40939
radi ologic</w> 40935
st ores</w> 40925
per i</w> 40925
prof loxacin</w> 40925
toxic ities</w> 40912
parenchym a</w> 40908
Nat ural</w> 40905
E 3</w> 40902
o edema</w> 40891
ogly co 40891
Nur sing</w> 40886
Inter action</w> 40882
config ur 40881
ocyst s</w> 40880
fac ing</w> 40876
aud i</w> 40854
Individ ual</w> 40848
she ath</w> 40837
Pre treatment</w> 40835
ke eping</w> 40831
b and 40828
tho rough</w> 40821
pres ynaptic</w> 40811
ma kers</w> 40810
carni tine</w> 40807
exclud ing</w> 40802
Coh ort</w> 40797
otrop y</w> 40796
R b</w> 40791
fortun ately</w> 40787
rup tured</w> 40786
Individ uals</w> 40782
doc tor</w> 40781
s ac</w> 40775
M N</w> 40768
7 00</w> 40762
CR I 40759
penetr ating</w> 40752
Clostr idium</w> 40746
12 9</w> 40742
F E</w> 40741
CA BG</w> 40740
opi oids</w> 40737
hemat ologic</w> 40733
E SR 40730
inf ancy</w> 40728
end urance</w> 40723
0. 95</w> 40716
bon y</w> 40711
Pri or</w> 40709
K D</w> 40704
haem oglobin</w> 40701
disper sed</w> 40701
immunost aining</w> 40694
U p</w> 40692
micro wave</w> 40684
d ol 40682
de tox 40682
B V</w> 40681
cyt ogenetic</w> 40675
pro ton 40674
monol ayers</w> 40665
D im 40650
brach ial</w> 40646
mis match</w> 40641
lamin in</w> 40641
Exerc ise</w> 40639
rh initis</w> 40634
di mers</w> 40624
compe ting</w> 40623
neo adjuvant</w> 40622
k a</w> 40619
resid ence</w> 40617
Met ast 40617
rec tum</w> 40609
Collec tively</w> 40608
An d</w> 40604
Ch ol 40599
AB C</w> 40589
AE s</w> 40561
IGF BP</w> 40556
Rec eptor</w> 40546
os clerosis</w> 40544
survi ve</w> 40540
transi t</w> 40539
oper ate</w> 40531
anaes the 40531
G Ps</w> 40510
tr on</w> 40507
o con 40506
Endos copic</w> 40499
w it 40498
el ia</w> 40496
ox ane</w> 40490
Cr yst 40489
lo oked</w> 40485
A Z 40475
Sym ptom 40469
appro val</w> 40467
N PC</w> 40465
Q T</w> 40461
hapl otypes</w> 40454
Desc rip 40452
up dated</w> 40451
diarr ho 40451
am ides</w> 40444
lymph oblastic</w> 40437
i osis</w> 40427
oci ties</w> 40424
ir r 40410
0.8 5</w> 40409
bronch i 40408
v ar</w> 40406
1 13</w> 40400
Ab str 40399
Elev ated</w> 40398
Resp iratory</w> 40398
ish er</w> 40397
anc ing</w> 40392
pelv is</w> 40388
anten atal</w> 40372
nod ule</w> 40368
Th yro 40366
c ue</w> 40363
gen otyping</w> 40362
re vised</w> 40361
correspon d</w> 40360
worsen ing</w> 40345
n d</w> 40341
x ine</w> 40332
Ad ults</w> 40327
.00 01</w> 40313
aeros ol</w> 40313
L ang 40306
prost atectomy</w> 40304
consci ousness</w> 40299
centrifug ation</w> 40273
under lie</w> 40270
secre ting</w> 40267
R AN 40257
trajec tory</w> 40257
f lies</w> 40250
ed om</w> 40250
R ed</w> 40243
son ography</w> 40243
fluoro uracil</w> 40221
reciproc al</w> 40221
For mul 40220
Mont e</w> 40214
bran ched</w> 40205
AL Y 40202
oper ator</w> 40199
excit ability</w> 40195
X T</w> 40187
aut oradi 40184
Character istics</w> 40175
st och 40171
iter ranean</w> 40167
constitu tively</w> 40159
cul tivation</w> 40153
p unc 40152
en ke 40146
view ing</w> 40143
bic arbonate</w> 40141
4 50 40140
Cl -</w> 40140
3 50</w> 40138
U T</w> 40138
k m</w> 40137
intram uscular</w> 40130
V alu 40120
incorpor ate</w> 40106
sk ill</w> 40099
Six teen</w> 40099
teach ers</w> 40097
TRI AL</w> 40095
M H</w> 40093
vascul itis</w> 40088
S MA</w> 40086
import antly</w> 40084
Ar e</w> 40079
Ob esity</w> 40077
sec urity</w> 40072
le af 40071
thre onine</w> 40059
pre eclampsia</w> 40057
electro encephal 40052
L R 40044
W F</w> 40043
ch er</w> 40041
bl es</w> 40039
pl asts</w> 40032
ju ice</w> 40031
vibr ation</w> 40030
vel ocities</w> 40029
A O 40028
g . 40022
bound aries</w> 40020
Ad ju 40018
sh i 40016
ri m</w> 40011
hep ar 40009
tre ad 39996
metallo proteinase</w> 39993
L VE 39990
de myel 39984
alumin um</w> 39982
M ass</w> 39979
Pu blic</w> 39976
histopath ologic</w> 39973
opa edic</w> 39972
it ate</w> 39968
rhe a</w> 39965
Rel ative</w> 39947
fri end 39942
tub ule</w> 39938
stress ors</w> 39935
stabil izing</w> 39931
not ably</w> 39923
Car lo</w> 39918
ent ous</w> 39915
ill i</w> 39912
per ic 39911
protein ases</w> 39902
conf er</w> 39899
u z 39883
as tig 39883
TR H</w> 39881
PL ICA 39876
Dise ases</w> 39868
thalam us</w> 39862
ing er</w> 39849
sc ann 39841
C erebral</w> 39833
consum ers</w> 39824
compr ises</w> 39822
ing dom</w> 39816
shar p</w> 39813
oc ks</w> 39809
prog ressed</w> 39807
D ynamic</w> 39806
0 7</w> 39805
Spont aneous</w> 39805
n als</w> 39803
ipp ed</w> 39800
proper ly</w> 39787
ec to 39786
CM L</w> 39786
uber cul 39784
I O 39780
B r</w> 39776
ti d</w> 39772
om ere</w> 39771
t ying</w> 39768
12 7</w> 39768
ster ile</w> 39748
- 2 39736
Min im 39715
Cr yp 39712
ag en 39693
age -</w> 39679
collabor ative</w> 39676
eth ics</w> 39674
hyp og 39669
ocy st</w> 39664
ac tu 39662
fro g</w> 39645
remo ving</w> 39642
1 21</w> 39640
ex ud 39639
fe brile</w> 39626
oc ele</w> 39625
chol est 39625
colum ns</w> 39624
n ic</w> 39614
quad r 39614
d ay 39612
P AC 39604
7 5 39600
0.9 6</w> 39600
cam era</w> 39595
an ions</w> 39592
B loc 39586
ere bro 39572
Alcoh ol</w> 39565
cyclo oxygenase</w> 39560
wh ilst</w> 39549
phosph at 39546
resul tant</w> 39541
psych ometric</w> 39522
1 18</w> 39520
X 1</w> 39516
V max</w> 39512
der al</w> 39510
oglyc ans</w> 39496
v on</w> 39495
ur onic</w> 39488
sup pressive</w> 39480
sarco idosis</w> 39480
ari an</w> 39475
sme ar</w> 39474
P t 39467
where by</w> 39464
pon ectin</w> 39452
is omers</w> 39450
0.00 7</w> 39450
highligh ting</w> 39438
simul ate</w> 39437
por tions</w> 39435
ob stac 39426
N uclear</w> 39418
graph ic</w> 39407
S o</w> 39393
intr ad 39391
ham sters</w> 39389
S ex</w> 39386
oc rit</w> 39380
f are</w> 39378
Thir teen</w> 39376
Calc ium</w> 39369
coun ter</w> 39362
Ad he 39357
CA R</w> 39356
ï ve</w> 39355
sem inal</w> 39353
micro satellite</w> 39350
prote olysis</w> 39348
fentan yl</w> 39344
preferen tial</w> 39343
- 1. 39342
usp id</w> 39340
A Q 39338
nan ow 39328
Dis orders</w> 39324
Med iterranean</w> 39314
tun ing</w> 39312
co operation</w> 39309
test es</w> 39283
read mission</w> 39282
M F 39278
a res</w> 39257
papill omavirus</w> 39249
n ame</w> 39239
id el 39232
Re duction</w> 39229
i ate</w> 39226
0.9 7</w> 39224
R eal</w> 39220
arch a 39214
V D</w> 39194
mag n 39192
disturb ed</w> 39188
om ide</w> 39184
sup ine</w> 39182
eng age</w> 39173
po ols</w> 39169
shap es</w> 39161
ecosy stems</w> 39157
fo rensic</w> 39156
a ult</w> 39154
accep tability</w> 39150
- di 39146
t acti 39142
Radi ation</w> 39140
t 1</w> 39138
all op 39132
l aryng 39130
tread mill</w> 39130
ca va</w> 39118
ent um</w> 39112
re per 39103
carb am 39103
Me an 39101
transi ently</w> 39096
Qu al 39094
electr on 39092
enro ll 39092
index es</w> 39088
E d 39085
ac utely</w> 39083
ec ta 39074
os ed</w> 39058
ind ole</w> 39046
omorph ic</w> 39041
aff in 39038
transpor ted</w> 39036
quad ru 39030
il eal</w> 39025
back bone</w> 39016
hyper thermia</w> 39015
multic entr 39015
sub arachnoid</w> 39007
Medic aid</w> 39005
simpl ified</w> 39004
cam pa 39002
AM E</w> 38998
ro ta 38993
B BB</w> 38981
Dis order</w> 38973
Mon itoring</w> 38965
Hep at 38956
hol ders</w> 38942
consi st</w> 38933
co w</w> 38931
B E</w> 38923
scar ce</w> 38917
achiev ement</w> 38916
ox etine</w> 38905
0 8</w> 38902
phen ols</w> 38900
e z 38898
Log istic</w> 38898
deter gent</w> 38897
ynam ics</w> 38892
ana emia</w> 38892
Commit tee</w> 38889
B ax</w> 38882
pre y</w> 38876
P rom 38874
N V</w> 38863
A ch 38860
T MS</w> 38850
Overex pression</w> 38848
is ometric</w> 38847
quanti tation</w> 38840
Ri ver</w> 38840
um e</w> 38836
di e</w> 38834
form at</w> 38827
V acc 38824
Pear son</w> 38815
re place</w> 38810
pyr ro 38800
aro usal</w> 38793
Hep G2</w> 38790
continu ity</w> 38782
neuro pathic</w> 38781
Gl ut 38779
Eff ici 38777
pleg ia</w> 38777
bel ong</w> 38775
L ys 38770
stop ped</w> 38758
coll ecting</w> 38747
dys regulation</w> 38746
Re duc 38744
d yl</w> 38741
t ar 38734
pur ity</w> 38728
infiltr ating</w> 38726
Lac tob 38719
laparo scopy</w> 38715
4 9 38714
TI R</w> 38711
Ep stein</w> 38700
N ext</w> 38692
G LP</w> 38683
paren chymal</w> 38683
sol ving</w> 38680
rhyth ms</w> 38680
b us 38665
coll ect</w> 38663
inher itance</w> 38662
bacteri oph 38659
C RE 38656
el le</w> 38653
hist ocompatibility</w> 38651
corne a</w> 38651
Symptom s</w> 38651
Objec tives</w> 38636
mig rants</w> 38624
meas les</w> 38617
t ag 38612
bl ed</w> 38611
colon oscopy</w> 38611
er ated</w> 38608
z ot 38603
Ac tive</w> 38598
mim icking</w> 38598
2 10</w> 38593
Swe dish</w> 38583
V TE</w> 38577
co res</w> 38570
sub tle</w> 38570
gre w</w> 38565
ti vities</w> 38563
correspon ds</w> 38563
M em 38555
morph ologically</w> 38547
th an 38546
Clin ic</w> 38545
ti se</w> 38542
si veness</w> 38529
neutrop enia</w> 38526
M e</w> 38524
Bar r</w> 38521
b aro 38510
trig eminal</w> 38502
CL L</w> 38496
HR H</w> 38495
mod ules</w> 38486
P y 38485
thir ds</w> 38484
cl ock</w> 38481
gastro enter 38469
G W 38464
organ elles</w> 38458
lyso zyme</w> 38447
b umin</w> 38410
mandi ble</w> 38408
Lew is</w> 38407
gli omas</w> 38406
b er</w> 38401
trabec ular</w> 38395
periton itis</w> 38392
0.00 8</w> 38387
haemorrh age</w> 38372
cap sular</w> 38371
ent h</w> 38370
surro gate</w> 38366
htt p</w> 38331
198 2</w> 38326
ti tr 38324
ol ate</w> 38317
s ulation</w> 38309
opo iesis</w> 38305
neuro imaging</w> 38302
Aver age</w> 38301
Environ mental</w> 38299
atrop ine</w> 38295
co valently</w> 38293
co operative</w> 38286
explic it</w> 38277
ar a</w> 38272
socio demographic</w> 38266
E P 38265
ster il 38261
pa rox 38254
w aiting</w> 38252
accompan ying</w> 38251
work place</w> 38251
ophil us</w> 38250
co at</w> 38240
MM A</w> 38238
ascor bic</w> 38238
anti psychotic</w> 38215
form alin</w> 38213
E E</w> 38211
Respon ses</w> 38208
ub ic</w> 38206
ati s</w> 38200
Ar ti 38198
perform ances</w> 38197
fil es</w> 38185
C lu 38177
overex pressing</w> 38175
cyclospor ine</w> 38162
bi osens 38159
mand atory</w> 38153
choro idal</w> 38148
endometr ium</w> 38142
r d</w> 38139
G D 38131
clu stered</w> 38131
Bri tish</w> 38121
AB A</w> 38111
scal p</w> 38110
Pe ople</w> 38106
gro unds</w> 38105
ubiquit ous</w> 38102
prote omic</w> 38097
RN ase</w> 38097
imid azole</w> 38088
intox ication</w> 38086
Ne on 38085
lab our</w> 38081
Lear ning</w> 38079
0 s</w> 38074
resec table</w> 38074
bac te 38067
ocyt oma</w> 38066
in ve 38063
og aster</w> 38059
prim ed</w> 38059
C x 38058
estr us</w> 38052
is tically</w> 38051
C aro 38046
bot tom</w> 38045
Immun e</w> 38041
a etiology</w> 38040
hyd rates</w> 38037
chec k</w> 38027
diver gent</w> 38023
en osis</w> 38022
g eriatric</w> 38021
curv ature</w> 38021
FO X 38011
abs orp 37995
A ST</w> 37992
reper to 37992
Simult aneous</w> 37991
relap sed</w> 37985
homocy steine</w> 37980
erythro id</w> 37976
Regi onal</w> 37972
muc us</w> 37970
physi ologically</w> 37968
oligonucle otides</w> 37963
pseud o</w> 37957
obl otting</w> 37956
fol i 37944
Impro vement</w> 37941
C ellular</w> 37935
Rec or 37931
h at 37928
ati d</w> 37921
plac ements</w> 37921
it atively</w> 37918
ure teral</w> 37916
tom ato</w> 37901
form ulated</w> 37900
inter faces</w> 37900
discrep ancy</w> 37900
X R</w> 37896
mo ist 37894
O s</w> 37890
b ending</w> 37888
cann abin 37887
P HA</w> 37875
o plas 37874
stig ma</w> 37870
ex tern 37868
arrhyth mic</w> 37868
catech olamine</w> 37864
14 4</w> 37859
libr aries</w> 37853
prot otype</w> 37853
PD T</w> 37846
pre vent 37844
glycos ylated</w> 37844
ca ver 37804
b ing</w> 37792
h id 37786
A c</w> 37784
99 mT 37782
pon in</w> 37780
therap ists</w> 37760
ro limus</w> 37759
D H 37758
13 C</w> 37749
0.9 4</w> 37749
manufac turing</w> 37749
k ling</w> 37744
resol ve</w> 37742
AF P</w> 37739
ultr af 37738
W s</w> 37737
capill aries</w> 37732
ipp ing</w> 37731
sph ere</w> 37729
mosquit o</w> 37725
br ush</w> 37724
D HA</w> 37722
y sis</w> 37719
s av 37718
y mo 37716
atax ia</w> 37715
de oxy</w> 37712
198 3</w> 37712
veget ables</w> 37706
ich i 37703
splic e</w> 37696
2 20</w> 37692
sem in 37690
D utch</w> 37688
cos metic</w> 37686
sem iconduc 37679
ri o</w> 37664
ic ol</w> 37662
hist ories</w> 37656
neuro trophic</w> 37649
A OR</w> 37646
f uc 37641
st enting</w> 37640
oc o 37634
gri p</w> 37634
tom ographic</w> 37631
dise ased</w> 37628
Liter ature</w> 37628
calc itonin</w> 37624
Contin uous</w> 37611
meas urable</w> 37597
qu asi</w> 37596
1 p</w> 37593
cal ls</w> 37590
align ed</w> 37588
Laparo scopic</w> 37586
investig ates</w> 37568
onym ous</w> 37567
pa re 37559
F O</w> 37556
Phy l 37554
coord inate</w> 37553
Nig eria</w> 37547
histo chemistry</w> 37546
te ars</w> 37544
Recom bin 37544
Ad verse</w> 37543
po t</w> 37540
en teen</w> 37537
fibro tic</w> 37537
radi onuc 37535
op ac 37533
min i</w> 37528
bon ded</w> 37525
e e</w> 37514
illustr ates</w> 37508
sedi mentation</w> 37501
rophyl l</w> 37499
T radi 37497
D U 37479
d ness</w> 37478
war m</w> 37470
xen ograft</w> 37465
0.9 3</w> 37464
B P 37462
99 m</w> 37459
lei omy 37456
bi ore 37452
cann abis</w> 37442
guan osine</w> 37438
epith eli 37436
L as 37421
swim ming</w> 37420
o ils</w> 37419
macro scopic</w> 37419
C ases</w> 37406
oper ational</w> 37404
sic kle</w> 37404
fun ding</w> 37401
M er 37387
E W</w> 37382
0.9 2</w> 37380
Anim als</w> 37379
thrombo embolism</w> 37377
op posed</w> 37368
capsul es</w> 37367
id one</w> 37361
Ste m</w> 37358
ic ate</w> 37343
germin ation</w> 37333
adi um</w> 37332
auton om 37329
dis placed</w> 37321
Tra ining</w> 37321
nif edipine</w> 37318
H Y 37313
c ame</w> 37309
bro il 37309
disp ens 37308
ton si 37308
le aving</w> 37301
le ak</w> 37298
reinfor cement</w> 37296
Pro per 37295
13 7</w> 37293
C 6</w> 37284
hospit alizations</w> 37284
answ er</w> 37281
D ose</w> 37280
hypo thesize</w> 37280
extrac orporeal</w> 37279
A x 37276
L OS</w> 37270
hor se</w> 37270
s acc 37268
r on</w> 37267
amph i 37258
A O</w> 37257
Ve ter 37257
V E</w> 37254
c ogn 37252
att entional</w> 37247
see k</w> 37232
antidepress ants</w> 37231
id azole</w> 37222
protec ting</w> 37218
attenu ates</w> 37216
expl ores</w> 37215
un detectable</w> 37209
comb ine</w> 37203
pa in 37197
smal l 37171
Mechanis ms</w> 37171
pre fer 37166
tensi le</w> 37164
CT A</w> 37163
app rais 37141
an i</w> 37131
Leishman ia</w> 37120
pro gen 37118
P d</w> 37113
conden sation</w> 37112
intern alization</w> 37103
intr ap 37095
ti te</w> 37093
g ol 37089
IF ICA 37063
BA SE</w> 37061
Spec tr 37060
manif est</w> 37059
responsi bility</w> 37059
dropl ets</w> 37059
analog ous</w> 37055
na ïve</w> 37054
decarbox ylase</w> 37038
AG E</w> 37035
vitre ous</w> 37035
ocarcin omas</w> 37034
methyl ene</w> 37032
sil ent</w> 37030
2 31</w> 37029
morph ogenesis</w> 37019
ag en</w> 37018
Investig ation</w> 37010
ad min 37001
pharmac ists</w> 37000
un ting</w> 36995
ci profloxacin</w> 36985
de dicated</w> 36975
oste ogenic</w> 36969
em e 36965
K idne 36963
st and</w> 36961
propor tion 36961
Pro ject</w> 36959
not e</w> 36958
ma ze</w> 36946
PLICA TIONS</w> 36946
Pro gnostic</w> 36943
domin ance</w> 36941
si bly</w> 36936
th eta</w> 36933
re ality</w> 36931
sor ting</w> 36930
ser otypes</w> 36923
B ir 36919
cephal us</w> 36915
cer amic</w> 36913
suspen sions</w> 36911
T C 36904
Bi ological</w> 36903
cur cumin</w> 36894
0.8 9</w> 36886
ge ometr 36884
Im proved</w> 36881
0.8 8</w> 36872
trigg ering</w> 36870
jour nals</w> 36857
s en</w> 36849
su red</w> 36847
0.8 3</w> 36845
seed lings</w> 36843
c ities</w> 36841
E PO</w> 36839
compati bility</w> 36835
om yc 36832
conflic ting</w> 36827
preser ving</w> 36824
imp acted</w> 36822
in patients</w> 36820
Sur prisingly</w> 36813
M is 36812
E 2 36811
C amp 36797
expect ancy</w> 36796
st ments</w> 36794
h op 36792
Sr c</w> 36792
somat osensory</w> 36791
U L 36789
ph aryngeal</w> 36787
SIGN IFICA 36784
c il 36783
x es</w> 36772
f ec 36765
tol u 36765
Dou ble</w> 36765
collag en 36764
di ploid</w> 36763
emp tying</w> 36763
1 19</w> 36758
at ur 36753
posi tioned</w> 36743
val vular</w> 36743
her pes 36743
al ism</w> 36738
z i</w> 36728
I B</w> 36724
ser o 36723
Gluc ose</w> 36719
administr ative</w> 36717
in spec 36699
v ated</w> 36697
an olamine</w> 36692
an ore 36685
n ationwide</w> 36684
cytos keletal</w> 36682
D ental</w> 36676
In dic 36665
M AC</w> 36661
Com mon</w> 36661
Spec tro 36660
don ation</w> 36653
ab s</w> 36649
E VI 36648
Re duced</w> 36643
encour age</w> 36641
ot echn 36636
par am 36634
P GF 36632
enter oc 36632
sug ars</w> 36623
ol itis</w> 36622
f arm</w> 36617
P neum 36606
T ech 36579
D om 36578
E L</w> 36572
com promise</w> 36572
paras it 36568
co al</w> 36555
ep ig 36555
trans gene</w> 36553
or iti 36550
ultim ate</w> 36547
en sing</w> 36540
instrum ental</w> 36527
adjunc t</w> 36526
AM PK</w> 36521
ili ated</w> 36515
tom ic</w> 36514
R et 36506
periph ery</w> 36501
L eg 36492
SIGNIFICA NCE</w> 36489
tradi tionally</w> 36488
6 -</w> 36483
iso proterenol</w> 36478
w ell 36473
regul ations</w> 36469
hyp oc 36467
V 1</w> 36464
infec tive</w> 36462
0.9 1</w> 36462
ar te 36445
transduc er</w> 36440
E t 36437
M C 36428
Rec on 36424
g p</w> 36418
or rho 36418
dr ying</w> 36412
conc eption</w> 36410
Pro duction</w> 36408
ti bia</w> 36406
exacerb ation</w> 36400
scienti sts</w> 36397
p H 36393
mon ocyt 36386
0.8 6</w> 36386
at tained</w> 36379
Sp inal</w> 36375
oligos accharides</w> 36373
omo tion</w> 36372
bran ching</w> 36371
cl o 36366
sign atures</w> 36366
modul us</w> 36353
end ocytosis</w> 36352
distingu ishing</w> 36350
P ul 36346
inter personal</w> 36345
gast rectomy</w> 36340
edi ting</w> 36338
co x 36336
el usive</w> 36332
S k 36327
1, 25</w> 36327
glob ally</w> 36327
synchron ous</w> 36325
r DNA</w> 36324
anthrop ometric</w> 36324
M align 36321
ab ain</w> 36319
contr ain 36319
op yran 36318
as cer 36314
ER K1</w> 36312
M N 36309
obser vers</w> 36306
del eted</w> 36306
cu stom 36301
L N 36290
melan ogaster</w> 36290
emo tions</w> 36281
spec ially</w> 36279
micro biological</w> 36275
V ib 36273
Lactob acillus</w> 36273
at rium</w> 36270
Inflam matory</w> 36263
Not ably</w> 36260
al op 36258
bio tin</w> 36258
99mT c</w> 36253
Pro v 36249
practition er</w> 36248
percenti le</w> 36248
heter odi 36241
dysp nea</w> 36238
ar dis 36237
TLR 4</w> 36237
tr ade</w> 36235
Al ter 36234
encour aging</w> 36227
re sts</w> 36222
mit osis</w> 36212
conform ations</w> 36205
arr ative</w> 36204
Pl ant</w> 36198
super family</w> 36197
b es</w> 36189
13 8</w> 36189
diaphrag m</w> 36185
puber ty</w> 36182
lat est</w> 36178
Su per 36171
serop ositive</w> 36165
L ibr 36164
M uc 36164
My ocardial</w> 36161
configur ations</w> 36161
Del ta</w> 36159
activ ators</w> 36158
V L 36157
R ating</w> 36156
0.8 7</w> 36156
in deed</w> 36155
inter quartile</w> 36154
glob in</w> 36153
Mean while</w> 36148
heavi ly</w> 36145
irrig ation</w> 36144
ay ing</w> 36138
contamin ants</w> 36134
deteri or 36132
ir ium</w> 36131
wal k</w> 36129
the ast</w> 36127
s oph 36122
por ted</w> 36121
sti tis</w> 36121
Hepati c</w> 36121
ad ver 36118
habit ats</w> 36112
tra vel 36109
graf ted</w> 36106
shorten ed</w> 36106
trans m 36104
sens ation</w> 36091
h ap 36088
trac ts</w> 36079
avi r</w> 36065
mid line</w> 36056
impa ir</w> 36052
lead ership</w> 36051
t amine</w> 36049
x ed</w> 36049
T ol 36039
dy es</w> 36038
eti ological</w> 36037
G ST 36029
ventil atory</w> 36028
0. 13</w> 36022
0. 11</w> 36020
ri ver</w> 36020
ch ond 36019
re pl 36018
qual itatively</w> 36013
e astern</w> 36008
inform ative</w> 36004
micro RNAs</w> 35985
man agers</w> 35984
K L</w> 35976
Pac ific</w> 35976
prim ates</w> 35975
AB P</w> 35975
dis continued</w> 35973
cul t</w> 35971
comm itment</w> 35966
consum er</w> 35965
S ens 35962
con clusive</w> 35961
the ophylline</w> 35956
discus sions</w> 35951
conjug ation</w> 35951
method ologies</w> 35950
azol am</w> 35939
F V</w> 35936
ast al</w> 35933
AM D</w> 35930
dys phagia</w> 35925
ro str 35919
exper tise</w> 35904
col li 35903
poli tical</w> 35899
er ve</w> 35893
br and</w> 35892
1 0.5</w> 35890
li sts</w> 35878
mang anese</w> 35878
Sub sequent</w> 35874
duoden um</w> 35873
cut aneously</w> 35871
0.7 8</w> 35859
introduc ing</w> 35848
0. 14</w> 35826
5 5 35825
I 2</w> 35824
man nose</w> 35822
iz able</w> 35816
PBM C</w> 35815
A AA</w> 35814
caregi ver</w> 35814
ot axis</w> 35802
o val 35799
pedi cle</w> 35798
PK A</w> 35795
character izing</w> 35790
care er</w> 35777
ob enz 35776
h ope</w> 35773
Practi ce</w> 35771
men op 35770
T G 35766
SM Cs</w> 35758
abs tin 35756
ra ising</w> 35753
iodi de</w> 35753
vin yl</w> 35749
serot on 35748
An g 35741
Inter leukin</w> 35738
ner ship</w> 35737
N egative</w> 35732
AD C</w> 35731
th ecal</w> 35730
collap se</w> 35730
o facial</w> 35723
but yl</w> 35722
prote omics</w> 35704
as certain</w> 35696
Rec over 35696
persi st</w> 35672
c iliary</w> 35669
ari th 35668
intellig ence</w> 35664
- 9</w> 35658
Regar ding</w> 35642
az ol</w> 35638
A er 35637
war ming</w> 35637
hydro carbons</w> 35631
clon idine</w> 35629
path ogenicity</w> 35625
arti facts</w> 35625
vag al</w> 35619
oz one</w> 35610
ventr icul 35609
CP R</w> 35588
sc oli 35585
DF T</w> 35583
sti s</w> 35580
e ur 35578
gn ess</w> 35575
V C</w> 35573
me g 35568
Ga stric</w> 35567
st yrene</w> 35561
EM BASE</w> 35557
pr amine</w> 35556
stoch astic</w> 35546
Th ough</w> 35543
D Q 35539
F H</w> 35536
im entin</w> 35534
Inter vention</w> 35534
Tr ic 35534
O 1</w> 35520
Eigh teen</w> 35519
ant able</w> 35507
Li ke</w> 35499
Behavi or</w> 35493
radi ology</w> 35478
add resses</w> 35472
bur ns</w> 35469
A pop 35464
probl ematic</w> 35461
ke wise</w> 35460
S G 35458
ul ing</w> 35457
we b 35450
PC P</w> 35449
enti nel</w> 35448
reconstitu ted</w> 35447
wea ker</w> 35444
omer ization</w> 35443
we d 35437
P PI</w> 35434
sel en 35424
acyl glycerol</w> 35414
thin king</w> 35413
rh esus</w> 35407
Ox id 35399
thermod ynamic</w> 35394
Con genital</w> 35364
CD K 35348
PT EN</w> 35347
0.8 4</w> 35345
cer amide</w> 35339
n in 35335
En g 35327
L ig 35326
comp ul 35322
C IN 35317
tel omerase</w> 35316
sl ice</w> 35311
a sive</w> 35302
IM PLICATIONS</w> 35302
amin idase</w> 35297
pneum ococcal</w> 35297
post ure</w> 35295
U A</w> 35294
G al</w> 35293
altern ate</w> 35291
nan ocom 35288
b est 35274
neuro toxicity</w> 35272
plat forms</w> 35267
gyn ec 35267
an ionic</w> 35265
PL A</w> 35265
volu metric</w> 35265
ER T</w> 35264
l ingual</w> 35258
op re 35249
B 3</w> 35248
0. 16</w> 35246
exclu sive</w> 35240
foun dation</w> 35240
mun ic 35231
C GRP</w> 35227
germ line</w> 35218
dep ic 35213
INTERVEN TIONS</w> 35207
un complicated</w> 35205
sch ist 35201
E st 35198
ocom ial</w> 35196
m ari 35191
exc ep 35188
dis charges</w> 35184
Bio chemical</w> 35176
em pl 35170
M DS</w> 35165
AL P</w> 35157
bio tics</w> 35152
resili ence</w> 35141
spar se</w> 35133
Ad ap 35131
cath epsin</w> 35130
fresh water</w> 35130
perturb ation</w> 35127
im es</w> 35126
v ic</w> 35115
di ol</w> 35115
bl unt</w> 35115
des orption</w> 35114
ne sted</w> 35112
sub cutaneously</w> 35107
m 3</w> 35106
19 0</w> 35104
C CL 35093
RE VI 35091
po int 35088
transfer ases</w> 35077
bl ind 35074
0. 35</w> 35072
ul nar</w> 35060
comp act</w> 35060
adap t</w> 35057
associ ate</w> 35044
scre ws</w> 35044
G am 35043
re pressor</w> 35043
w anted</w> 35040
ar yl 35037
ra ises</w> 35030
S audi</w> 35024
glucocortico ids</w> 35024
vaso constriction</w> 35023
gro und 35019
ey el 35018
house holds</w> 35006
instrum entation</w> 34984
capac ities</w> 34981
p m</w> 34966
nod ular</w> 34964
0.8 2</w> 34963
star vation</w> 34963
di chloro 34962
itone ally</w> 34962
pa rotid</w> 34958
Nr f2</w> 34957
I Q</w> 34948
R at</w> 34945
SA H</w> 34943
chem oradi 34941
pac ked</w> 34940
affin ities</w> 34939
Formul a</w> 34933
CRI SPR</w> 34929
v ox 34928
un favorable</w> 34928
f air 34924
R he 34924
ra in 34917
gastr in</w> 34908
mit ting</w> 34907
Col l 34906
Pharmac o 34900
Mul ti</w> 34899
S . 34897
g lu 34894
er nal</w> 34892
ail ed</w> 34892
perf ect</w> 34892
0. 90</w> 34886
pair ing</w> 34886
prescrip tions</w> 34885
B L 34882
attenu ate</w> 34878
pro spec 34870
sa icin</w> 34863
. .</w> 34862
com positions</w> 34851
in soluble</w> 34846
le ishman 34841
re activation</w> 34840
cohe rent</w> 34840
is land</w> 34838
NH 2</w> 34836
at orial</w> 34832
individu alized</w> 34823
fre edom</w> 34823
cro ps</w> 34817
ov aries</w> 34815
carcin ogenic</w> 34810
le pro 34808
nicotin ic</w> 34807
si ella</w> 34805
ste el</w> 34802
cyt osine</w> 34801
mic elles</w> 34800
pass ed</w> 34800
Adv anced</w> 34799
bl asts</w> 34795
198 1</w> 34794
tra vel</w> 34793
augh ter</w> 34793
DE NCE</w> 34792
diaz epam</w> 34790
far ms</w> 34787
gl and 34785
pen ile</w> 34767
P lac 34763
al titude</w> 34762
sulf on 34757
H 4</w> 34756
b FGF</w> 34756
tic ated</w> 34755
co factor</w> 34750
pac kage</w> 34739
re alized</w> 34736
sci atic</w> 34736
in somnia</w> 34734
compr ise</w> 34733
be ats</w> 34731
homogen ates</w> 34715
un ded</w> 34713
cobal t</w> 34710
fig ures</w> 34708
tax onomic</w> 34704
TR A 34703
mos aic</w> 34702
rem n 34702
nas opharyngeal</w> 34700
ot ted</w> 34694
prim ate</w> 34691
inver ted</w> 34684
degrad able</w> 34682
techn ological</w> 34678
A po 34676
es sion</w> 34675
ic um</w> 34674
0. 45</w> 34674
0. 80</w> 34669
equ ine</w> 34664
prov ince</w> 34664
SC D</w> 34661
mas k</w> 34658
plex es</w> 34651
Stud ents</w> 34648
neuro peptide</w> 34636
met ac 34635
sacrific ed</w> 34635
hem i 34631
bacter icidal</w> 34631
A 549</w> 34625
Q U 34622
ox o</w> 34614
mar ital</w> 34614
tor que</w> 34610
pu ps</w> 34608
en sur 34607
CX CL 34605
brady kinin</w> 34601
F D</w> 34600
vis ited</w> 34593
oper idol</w> 34592
og rou 34587
eu tic 34585
GAB A 34585
epti n</w> 34584
Numer ous</w> 34584
Nutri tion</w> 34574
ic ide</w> 34571
fos sa</w> 34569
pa rous</w> 34557
Kidne y</w> 34551
k D</w> 34548
un controlled</w> 34548
adi ponectin</w> 34547
cultiv ars</w> 34544
neigh bor 34543
E L 34539
trem or</w> 34537
P SC</w> 34535
lin ker</w> 34529
addi tionally</w> 34529
brac hy 34529
exclud e</w> 34528
K en 34523
br ing</w> 34522
is one</w> 34516
D ic 34510
du plic 34502
Platel et</w> 34501
omegal y</w> 34500
c d 34487
L ate</w> 34486
be ef</w> 34482
tocopher ol</w> 34482
Sup pl 34480
p ter 34477
fore brain</w> 34472
V S</w> 34469
an ic</w> 34469
sme ars</w> 34469
osp ec 34452
pl ank 34448
K S</w> 34442
di thi 34442
requis ite</w> 34441
c rest</w> 34437
carbo hydrates</w> 34433
Not ch</w> 34426
red und 34424
determin ations</w> 34417
classi fy</w> 34411
Associ ations</w> 34404
13 9</w> 34403
mal on 34402
b ite</w> 34399
PA C</w> 34395
orig inated</w> 34395
cirrho tic</w> 34389
0.7 6</w> 34387
pri oriti 34383
pro tr 34378
ph alin</w> 34378
tel omere</w> 34378
notic ed</w> 34377
st ance</w> 34372
di aldehyde</w> 34370
0.8 1</w> 34370
broad ly</w> 34356
possess ed</w> 34355
1. 01</w> 34349
go ats</w> 34348
convul s 34342
sp ores</w> 34341
pac king</w> 34334
ill ed</w> 34331
j e</w> 34327
an um</w> 34323
su ited</w> 34316
E ar 34315
ari o</w> 34315
carb onate</w> 34314
par ity</w> 34312
EVI DENCE</w> 34306
leg is 34305
Un it</w> 34304
Heal th 34303
acqu ire</w> 34296
sub stitute</w> 34292
silic o</w> 34291
ob lig 34290
she ets</w> 34287
um en</w> 34274
tro ponin</w> 34274
diarrho ea</w> 34268
2, 2</w> 34258
b ever 34248
meth an 34245
K Cl</w> 34244
Eff ective</w> 34240
eutic als</w> 34240
L G 34235
ti tration</w> 34234
D V</w> 34232
ol k</w> 34232
s. c.</w> 34232
S ar 34231
K ingdom</w> 34224
acceler ate</w> 34223
hydro gels</w> 34222
linear ity</w> 34222
person alized</w> 34220
di ure 34209
Th at</w> 34194
n ev 34187
sol ve</w> 34173
A gg 34171
10. 0</w> 34170
ou tw 34169
inst ance</w> 34157
Behavi oral</w> 34150
sto ichi 34138
it on</w> 34137
un infected</w> 34134
pl ain</w> 34134
lnc RNAs</w> 34134
lam bs</w> 34126
scaveng ing</w> 34120
o ter 34118
prolong ation</w> 34108
N u 34107
10 ,000</w> 34106
sp anning</w> 34093
Libr ary</w> 34092
menop ause</w> 34092
clos er</w> 34086
a ided</w> 34084
ubercul osis</w> 34082
E astern</w> 34079
n ail</w> 34077
junc tional</w> 34070
supernat ants</w> 34066
an aphy 34054
14 2</w> 34054
RA F</w> 34051
tex ture</w> 34050
Lip id</w> 34049
Eff ec 34040
dri ver</w> 34038
vap or</w> 34038
chro mo 34037
super vised</w> 34033
li ve 34030
buc cal</w> 34030
M en</w> 34023
sp as 34023
z z 34022
ill umination</w> 34021
si vity</w> 34013
1.0 2</w> 34008
fac ed</w> 33999
f loc 33997
F isher</w> 33996
En ergy</w> 33993
progen y</w> 33991
fir e</w> 33977
bif ur 33977
assign ment</w> 33967
a str 33962
Tem per 33961
Experi ence</w> 33959
In vol 33957
o ventricular</w> 33956
Leu k 33954
amorph ous</w> 33954
insp iratory</w> 33953
T g</w> 33952
O D</w> 33951
0.7 7</w> 33936
schizophren ic</w> 33934
conjunctiv al</w> 33931
pro lapse</w> 33922
Heal thy</w> 33922
H el 33915
E B</w> 33912
ben ch 33909
L ist 33908
D BP</w> 33908
Ac tiv 33907
distinc tion</w> 33897
un differentiated</w> 33896
HR V</w> 33879
min ing</w> 33878
s ent 33876
Met S</w> 33870
muc in</w> 33868
day time</w> 33861
vulg aris</w> 33856
collo idal</w> 33850
fi ers</w> 33847
nanot ubes</w> 33846
radi ologists</w> 33837
bro mo 33837
sh el 33835
intr al 33830
immun ogenicity</w> 33823
Proper ties</w> 33823
dis pers 33817
ie ties</w> 33812
Appro ach</w> 33810
de structive</w> 33804
Parti al</w> 33804
xanth ine</w> 33801
pa res</w> 33800
13 4</w> 33793
Qual itative</w> 33793
f lower</w> 33791
t ym 33789
othor ax</w> 33786
tel e 33778
til t</w> 33773
atro l</w> 33773
sti pation</w> 33769
R G 33764
16 5</w> 33759
monocyt ogenes</w> 33751
ou pling</w> 33749
re p 33742
ane u 33735
new er</w> 33734
allo y</w> 33731
vacu um</w> 33730
resid ential</w> 33725
sub optimal</w> 33721
pot ato</w> 33721
stabil ize</w> 33720
ca ution</w> 33719
tab let</w> 33717
3 44</w> 33714
K d</w> 33705
ubiqu itin 33695
11. 2</w> 33692
High ly</w> 33692
afferen ts</w> 33685
erc etin</w> 33678
SP SS</w> 33677
interpre t</w> 33676
transi tional</w> 33663
replic ated</w> 33655
B 6</w> 33644
Rh o</w> 33638
In stead</w> 33636
amin s</w> 33620
rel la</w> 33616
deep er</w> 33612
V II</w> 33607
vers atile</w> 33605
mutag enic</w> 33599
fe et</w> 33598
er up 33595
mamm ography</w> 33592
Pro state</w> 33591
For mation</w> 33589
Un fortunately</w> 33585
chlor o</w> 33567
B ro 33565
pul s 33561
recommend ation</w> 33552
oste oblasts</w> 33547
Carcin oma</w> 33547
over t</w> 33538
dendr ites</w> 33536
flav in</w> 33534
ver atrol</w> 33522
0.7 4</w> 33520
r ic</w> 33513
out line</w> 33513
On ce</w> 33512
hepat oma</w> 33510
Lin ear</w> 33510
per icardial</w> 33506
galacto sidase</w> 33498
be at</w> 33493
Memb rane</w> 33467
V F</w> 33457
intraper itoneally</w> 33451
in ations</w> 33449
schem es</w> 33449
stal k</w> 33442
ord inary</w> 33440
acetab ular</w> 33438
trans fusions</w> 33431
de stro 33427
bi os 33421
aden ocarcinomas</w> 33418
concentr ate</w> 33417
fo s</w> 33407
recycl ing</w> 33405
re tain</w> 33399
it an</w> 33398
RE T</w> 33398
loc omotion</w> 33390
my ot 33387
2 93</w> 33380
F I</w> 33379
j aw</w> 33379
disp osition</w> 33379
S TI 33373
manufac ture 33363
flavon oids</w> 33357
lute in 33356
re th 33344
Psych i 33342
zot ocin</w> 33337
B 4</w> 33328
gel atin</w> 33327
mal e 33326
mic s</w> 33319
sk eleton</w> 33316
moist ure</w> 33315
hepat ectomy</w> 33310
plant ar</w> 33308
H GF</w> 33302
adap tations</w> 33299
0.7 1</w> 33292
st ated</w> 33290
0.7 9</w> 33290
d a 33289
W al 33275
0. 18</w> 33274
enter ing</w> 33261
catechol amines</w> 33257
G ran 33252
op y</w> 33252
phil is</w> 33248
l ich 33247
ACh E</w> 33246
post prandial</w> 33241
L u 33240
se q</w> 33239
os exual</w> 33231
hel ped</w> 33216
ur nal</w> 33210
F R 33207
repeat edly</w> 33207
ho using</w> 33206
hol ding</w> 33205
o chemical</w> 33204
kne es</w> 33203
ag gra 33199
F os</w> 33190
psychi atry</w> 33190
F K 33188
swe red</w> 33185
K ey</w> 33183
wor ms</w> 33181
el ution</w> 33176
dic ine</w> 33175
1 P</w> 33171
arg um 33163
gro o 33160
stake holders</w> 33157
14 3</w> 33147
granul osa</w> 33147
vi tis</w> 33145
ang l 33137
methyl transferase</w> 33135
alpha -</w> 33128
tr apping</w> 33124
w . 33122
confoun ders</w> 33122
ith elial</w> 33119
h il 33116
multi plex</w> 33115
depri ved</w> 33114
cruc iate</w> 33110
chemo kines</w> 33106
A z 33102
U til 33096
lactam ase</w> 33076
entrop y</w> 33068
sin us 33059
symptom atology</w> 33038
ou abain</w> 33037
Dem ographic</w> 33034
1 75</w> 33032
out lined</w> 33030
extr av 33029
LO G 33027
0. 65</w> 33025
preser ve</w> 33020
enke phalin</w> 33014
le gs</w> 33013
av en 33008
jug ular</w> 33008
replic ate</w> 32998
iz ers</w> 32996
l ay</w> 32994
B N</w> 32994
pharmac ology</w> 32983
co astal</w> 32971
mim ic 32965
in expensive</w> 32962
hom olog</w> 32962
uniform ly</w> 32957
SE N 32956
earli est</w> 32952
0.7 3</w> 32949
bacte remia</w> 32949
le ph 32948
quen ching</w> 32943
et een</w> 32939
dig esti 32934
occup ied</w> 32928
con tra 32926
in er 32921
Combin ation</w> 32919
Is ra 32918
an swered</w> 32917
tw elve</w> 32909
am yl 32908
BR CA1</w> 32900
Rep u 32893
approxim ate</w> 32892
susp ended</w> 32892
dec id 32886
C G 32885
re actor</w> 32884
de ox 32878
for d</w> 32877
autom atically</w> 32877
Carb on</w> 32873
g au 32871
wash ing</w> 32868
tic ks</w> 32866
rest enosis</w> 32865
anis otropy</w> 32859
14 7</w> 32857
app end 32854
bro th</w> 32854
j aun 32853
benz yl</w> 32847
l enti 32842
S AR</w> 32827
od ing</w> 32826
prote ome</w> 32826
py rene</w> 32822
ec ond</w> 32820
1.0 3</w> 32819
rel ates</w> 32815
syn cy 32813
post traumatic</w> 32809
gon dii</w> 32802
Distr ibution</w> 32802
f air</w> 32796
carri age</w> 32796
hydro cephalus</w> 32791
Em base</w> 32791
di ce</w> 32784
OR s</w> 32781
F N</w> 32778
medic ally</w> 32778
hum eral</w> 32768
Prov ince</w> 32767
C 5</w> 32762
MR T</w> 32759
consol idation</w> 32759
in door</w> 32756
rib ose</w> 32755
constr ained</w> 32754
r at 32749
dent ate</w> 32748
mis sense</w> 32740
usi tis</w> 32735
car ing</w> 32734
hy pop 32727
PT P</w> 32726
oc ar 32705
per mitted</w> 32705
F ox 32701
scat tered</w> 32700
1 53</w> 32697
Ch i</w> 32697
de aling</w> 32696
stand ardis 32693
lo l</w> 32684
K Y</w> 32681
ad duct</w> 32681
do t</w> 32680
ev ity</w> 32672
mon omers</w> 32665
c ass 32664
AC s</w> 32662
deliver ing</w> 32661
H L 32658
0. 17</w> 32658
An tim 32651
bio films</w> 32650
r ights</w> 32647
con v 32646
specific ities</w> 32645
ag encies</w> 32644
do ts</w> 32644
anomal ous</w> 32639
intram olecular</w> 32634
B K</w> 32633
os urg 32629
int olerance</w> 32629
cap top 32625
S exual</w> 32623
ac id 32602
e icos 32596
disc ord 32581
i ans</w> 32579
Au th 32573
carbox ylic</w> 32571
enroll ment</w> 32570
10 , 32568
C 2 32567
Stud ent</w> 32562
continu um</w> 32552
ys in</w> 32548
orthop edic</w> 32547
app earing</w> 32544
sho ot</w> 32540
tra p</w> 32533
dis advantages</w> 32529
refl ec 32525
bab y</w> 32514
K leb 32512
0. 50</w> 32512
amp ing</w> 32509
w et 32503
IC H</w> 32501
plic ity</w> 32499
oper on</w> 32498
y go 32495
cultiv ated</w> 32491
Compar ing</w> 32476
mu sic</w> 32471
or ch 32467
rom ycin</w> 32467
st ably</w> 32461
ex chang 32461
emph ys 32456
pri v 32454
search ing</w> 32454
F c 32452
ag arose</w> 32452
System s</w> 32449
in dependence</w> 32447
liter acy</w> 32438
s orb 32434
Hyper tension</w> 32433
rec over</w> 32432
H un 32431
radio frequency</w> 32431
sk olin</w> 32422
cut ting</w> 32418
A or 32417
re operation</w> 32417
CD D</w> 32417
re test</w> 32416
phosphodi esterase</w> 32416
onch ial</w> 32415
met aph 32413
fa ec 32401
fre ely</w> 32401
scoli osis</w> 32401
13 6</w> 32392
obste tr 32383
reperto ire</w> 32380
yl es</w> 32374
id us</w> 32371
robu st 32371
c ing 32370
intra hepatic</w> 32368
s elling</w> 32366
itone um</w> 32365
sho t</w> 32361
cit abine</w> 32357
elev ations</w> 32356
lig am 32355
modul ators</w> 32350
coll ective</w> 32349
F E 32345
I BS</w> 32343
ero sion</w> 32340
ys mal</w> 32334
mast ectomy</w> 32324
beg in</w> 32321
expl aining</w> 32320
conven ience</w> 32320
pec ul 32315
J un</w> 32311
under goes</w> 32310
HR P</w> 32306
rif amp 32297
Throm b 32282
Pre par 32274
ultr y</w> 32274
it er</w> 32273
ari atric</w> 32271
CD 44</w> 32264
I ll 32263
immun oblotting</w> 32263
explo ited</w> 32263
retro peritoneal</w> 32256
pig lets</w> 32255
c s</w> 32251
1.0 5</w> 32244
zym es</w> 32242
aggreg ated</w> 32239
sh rin 32235
etr y</w> 32230
gland ular</w> 32224
lif es 32222
cortis one</w> 32217
ophthal mic</w> 32207
g rap 32201
o ks</w> 32198
psych opathology</w> 32196
R B</w> 32195
Oxid ative</w> 32186
out growth</w> 32178
arb itr 32177
Aut om 32170
li tis</w> 32168
ex tends</w> 32167
em ma</w> 32164
V A 32163
nit us</w> 32154
ar ises</w> 32150
pur ch 32150
amylo idosis</w> 32146
equ iv 32143
occa sions</w> 32142
2 nd</w> 32127
P PV</w> 32124
com pares</w> 32120
ob sc 32109
cel i 32108
Op en</w> 32103
4 A</w> 32087
u be</w> 32087
De f 32087
c age</w> 32086
cul ating</w> 32086
el abor 32083
thyro xine</w> 32083
V al</w> 32081
gonad al</w> 32080
glomerul onephritis</w> 32080
Mo use</w> 32069
An n 32065
De termin 32063
char ts</w> 32054
mut ational</w> 32045
neutr alization</w> 32045
predn isone</w> 32044
un explained</w> 32039
T ow 32032
1, 0 32029
n ous</w> 32028
attrac ted</w> 32025
typh imurium</w> 32014
p ir 32013
con ve 32006
initi atives</w> 32005
chlo rophyll</w> 32003
K E 31998
lact ose</w> 31997
oc cult</w> 31991
search es</w> 31987
approxim ation</w> 31985
micro fluidic</w> 31982
radi o</w> 31981
Anim al</w> 31981
mel ting</w> 31980
0.00 9</w> 31979
5 6 31975
Con t 31973
5 62</w> 31972
15 4</w> 31971
thal iana</w> 31969
0. 20</w> 31967
Kleb siella</w> 31963
know led 31955
chem otactic</w> 31954
e NOS</w> 31953
in yl</w> 31953
desensi tization</w> 31952
electr ons</w> 31950
ud ine</w> 31943
s i</w> 31938
dur ations</w> 31936
1.0 4</w> 31935
o pro 31932
mechan ically</w> 31931
dissec ted</w> 31929
dig it 31918
otox ins</w> 31914
top ois 31913
ph er 31908
C an</w> 31906
re fined</w> 31901
bund les</w> 31901
bup ivacaine</w> 31901
eosinoph il</w> 31900
audi t</w> 31881
II b</w> 31874
sugg estions</w> 31873
opportun istic</w> 31871
c ast</w> 31866
Perc utaneous</w> 31850
impair s</w> 31847
m .</w> 31840
li poly 31840
gam e</w> 31840
per mis 31837
BM T</w> 31834
fram es</w> 31832
lay ered</w> 31821
CB T</w> 31817
lat ex</w> 31811
chloro plast</w> 31811
el ings</w> 31806
oste ocl 31806
di electric</w> 31801
Gen es</w> 31801
Sev erity</w> 31800
marri ed</w> 31790
dispens able</w> 31780
per s</w> 31775
resid ency</w> 31773
Physi cians</w> 31769
ribonucle ic</w> 31764
induc er</w> 31762
ti fication</w> 31760
expl ains</w> 31760
14 1</w> 31758
be red</w> 31754
blo od 31754
distor tion</w> 31753
Wh it 31752
rib osome</w> 31750
mon omeric</w> 31749
dis closed</w> 31748
- based</w> 31746
am phen 31741
rever sibly</w> 31740
N ucle 31737
til l</w> 31730
Pat tern 31729
apprais al</w> 31721
facil itation</w> 31719
de hydration</w> 31714
C SA</w> 31713
sho p</w> 31712
at e 31710
h n 31707
F TIR</w> 31706
o yl 31704
om ial</w> 31704
D ual</w> 31698
yn chron 31694
kin je</w> 31691
or us</w> 31689
c im 31682
cot ton</w> 31680
lea ve</w> 31679
for ty</w> 31678
O 4</w> 31673
uve itis</w> 31671
aim ing</w> 31667
Intrav enous</w> 31667
fra ilty</w> 31662
Mech anical</w> 31661
3 rd</w> 31657
DF S</w> 31656
tw o 31653
lnc RNA</w> 31653
' t</w> 31650
s .</w> 31649
modul ator</w> 31648
anore xia</w> 31648
au x 31642
B 12</w> 31640
gastr itis</w> 31640
m erg 31637
tes tinal</w> 31628
glycol ysis</w> 31628
A im</w> 31621
combin es</w> 31619
3, 5</w> 31617
Signific antly</w> 31616
cal orimetr 31615
Reg ister</w> 31614
hal operidol</w> 31608
meth icillin</w> 31607
tryp tamine</w> 31604
ESR D</w> 31593
C 1 31592
DO X</w> 31592
oti tis</w> 31591
Mitochond rial</w> 31590
K s</w> 31582
este em</w> 31579
obl ast 31577
di ethyl 31574
Las er</w> 31574
Meas ures</w> 31573
ar abin 31572
ol us</w> 31567
N R</w> 31563
Ph il 31560
ox yl 31552
0. 67</w> 31550
Pregn ancy</w> 31546
ventil ated</w> 31537
del ta 31530
0.0 25</w> 31529
M Hz</w> 31526
F ri 31520
Repu blic</w> 31520
ac clim 31519
recep tive</w> 31518
oth ane</w> 31515
thym ocytes</w> 31514
dec lines</w> 31512
e ve</w> 31511
Recover y</w> 31511
transmit ters</w> 31504
E thi 31503
mul tin 31502
bir th 31501
dra w</w> 31494
polys accharides</w> 31492
chori onic</w> 31486
t os 31483
iso electric</w> 31478
re paired</w> 31476
Ar thro 31476
mo ieties</w> 31466
veter inary</w> 31462
friend ly</w> 31462
benzodi azepine</w> 31459
P ress 31446
GABA ergic</w> 31446
umb er</w> 31443
Pur kinje</w> 31443
0. 72</w> 31437
8 6 31435
fund us</w> 31426
nich e</w> 31426
Onc ology</w> 31425
T ext</w> 31419
lac ked</w> 31419
15 2</w> 31411
par a</w> 31409
v imentin</w> 31406
ic ile</w> 31404
y olk</w> 31403
arthro scopic</w> 31402
M CA</w> 31400
theore tically</w> 31399
S odium</w> 31396
NSAI Ds</w> 31396
effici encies</w> 31391
ver sions</w> 31387
14 6</w> 31377
one y</w> 31376
amphen icol</w> 31375
pro poses</w> 31374
cle an</w> 31369
excre ted</w> 31368
post mortem</w> 31367
ging i 31367
B ilateral</w> 31360
cor onal</w> 31360
Recombin ant</w> 31360
1 1.5</w> 31359
F us 31357
li st 31353
stric tly</w> 31350
polymorph onuclear</w> 31345
po pl 31344
e ae</w> 31342
M W 31330
PR P</w> 31323
ev ac 31322
C ost</w> 31317
II a</w> 31316
R ho 31315
B CL</w> 31309
synap t 31309
cap saicin</w> 31298
osensi tivity</w> 31295
crow n</w> 31294
lit ter</w> 31292
wel fare</w> 31285
5 th</w> 31283
emerg ent</w> 31283
ad versely</w> 31278
pa uc 31270
n arrative</w> 31266
roti zing</w> 31262
par kin 31261
docum ents</w> 31260
Surve illance</w> 31259
chem il 31251
albumin uria</w> 31249
ca vities</w> 31248
sic s</w> 31247
Epidemi ology</w> 31245
antagon ism</w> 31241
expec tedly</w> 31241
ch ap 31240
fif ty</w> 31238
under graduate</w> 31232
mi x</w> 31225
atri oventricular</w> 31224
Clinic al 31220
or ylation</w> 31216
qu ick</w> 31216
k Hz</w> 31207
stra ight</w> 31206
Aut o 31198
str ips</w> 31196
Nur ses</w> 31195
rap amycin</w> 31193
erythropo ietin</w> 31181
p ens 31179
pur ine</w> 31178
co incid 31177
chloro quine</w> 31177
meth oxy</w> 31176
chrom ium</w> 31175
TI MP</w> 31170
Con ventional</w> 31168
pyr imidine</w> 31167
ME T</w> 31162
im otor</w> 31158
0. 22</w> 31156
E US</w> 31148
ail ing</w> 31147
Con di 31142
Nan op 31132
in qu 31131
veget ation</w> 31131
abdom y 31130
not able</w> 31125
pig mented</w> 31112
F 4</w> 31109
lo os 31104
I schem 31101
T c 31098
e g</w> 31097
perme able</w> 31095
di uretic</w> 31092
pl ot</w> 31089
imid azol 31086
ap ex</w> 31074
ep tic</w> 31073
Tr p</w> 31073
two fold</w> 31069
S ho 31068
di ch 31064
hum idity</w> 31063
at resi 31056
all ografts</w> 31052
sion ed</w> 31051
prim itive</w> 31046
de polar 31042
A K</w> 31041
lys ates</w> 31040
C ry 31038
NH S</w> 31038
Recom m 31038
poly po 31037
Med line</w> 31034
Neon atal</w> 31034
ww w. 31024
character s</w> 31022
Centr e</w> 31000
M Pa</w> 30999
tri pl 30998
D anish</w> 30991
con dyl 30991
0. 70</w> 30989
Com preh 30986
D on 30978
B AL</w> 30978
ove restim 30966
evac izumab</w> 30965
sarcom as</w> 30947
reser vation</w> 30942
H U 30937
multi factorial</w> 30934
chic ks</w> 30934
it a</w> 30933
CP B</w> 30931
electro magnetic</w> 30930
B PD</w> 30929
ag ree</w> 30923
ac ic</w> 30912
suppl ied</w> 30901
tubercul ous</w> 30900
pi x 30891
j et</w> 30890
cyt ometric</w> 30890
s er</w> 30882
dis playing</w> 30878
rota virus</w> 30874
me ga 30870
phag ocytic</w> 30858
navig ation</w> 30855
an in</w> 30854
0. 33</w> 30849
PA Hs</w> 30847
cl av 30844
idel ity</w> 30842
Inter actions</w> 30839
n out</w> 30837
t agged</w> 30835
oc ap 30831
paren ting</w> 30831
contra dic 30830
every day</w> 30828
AR DS</w> 30821
resembl ing</w> 30820
meth acrylate</w> 30818
E s 30807
inter facial</w> 30799
parathyro idism</w> 30798
Q TL</w> 30778
immunoglob ulins</w> 30771
I HC</w> 30756
Diab etic</w> 30751
atin e</w> 30748
contex tual</w> 30737
neuro degeneration</w> 30733
spac er</w> 30730
HM G 30729
carb on 30725
atis m</w> 30724
pl au 30722
op en 30715
wor ked</w> 30709
col le 30707
off ering</w> 30707
16 8</w> 30706
Spec ial</w> 30706
elast ase</w> 30701
vent ro 30693
TH E 30690
O K 30689
ou ts</w> 30687
mor al</w> 30687
Po is 30684
clar ified</w> 30681
del irium</w> 30678
Serv ices</w> 30678
Ne ural</w> 30677
Fr ac 30677
L p</w> 30676
Frequ ency</w> 30675
spir al</w> 30670
d welling</w> 30668
ac ia</w> 30662
thromb oxane</w> 30660
anastom otic</w> 30660
wri ting</w> 30657
d re 30650
altern ating</w> 30647
F 3</w> 30644
analy tes</w> 30642
energ etic</w> 30642
sist er</w> 30634
nos ocomial</w> 30630
CI S</w> 30628
bi ogenesis</w> 30626
th y 30621
re min 30617
supr am 30615
pal p 30613
drop ped</w> 30610
CI s</w> 30606
me als</w> 30599
en ses</w> 30597
infer red</w> 30594
5 a</w> 30593
glutam ic</w> 30588
anti le</w> 30584
exten sor</w> 30578
N AME</w> 30575
intra -</w> 30575
co st 30573
harv esting</w> 30569
m ur 30559
con stipation</w> 30555
long evity</w> 30550
st atins</w> 30548
mit ogenic</w> 30546
intrac erebral</w> 30544
cho ose</w> 30542
L U 30540
influenz ae</w> 30538
acet one</w> 30525
ob r 30524
1 D</w> 30523
P ak 30523
AP S</w> 30515
NH L</w> 30512
po ultry</w> 30506
discrep ancies</w> 30506
O CD</w> 30496
CP T</w> 30496
V L</w> 30492
carri es</w> 30484
sub cortical</w> 30480
0. 21</w> 30476
hydroxy apatite</w> 30475
micro bes</w> 30474
atmosph ere</w> 30473
orn ith 30469
Stre ptom 30467
Fluoresc ence</w> 30466
Hosp it 30464
th ec 30461
centr ic</w> 30453
Cri tical</w> 30453
hydro carbon</w> 30451
d ang 30448
sy philis</w> 30445
diff icile</w> 30442
br uc 30439
d in</w> 30438
nig ra</w> 30438
veter ans</w> 30435
poin ted</w> 30434
pluripot ent</w> 30434
HR QoL</w> 30420
vis ually</w> 30417
secre tions</w> 30415
AP 1</w> 30405
phosph on 30395
collagen ase</w> 30395
me iotic</w> 30394
belong s</w> 30388
sit ting</w> 30385
oc clusive</w> 30383
si fied</w> 30382
impl ying</w> 30381
G li 30379
trou t</w> 30365
d ressing</w> 30363
Anti bodies</w> 30357
Al ph 30356
inferen ce</w> 30355
cent ro 30348
dextr in</w> 30348
car c 30346
eli oma</w> 30345
nor ms</w> 30344
leuk otri 30337
maxim ize</w> 30334
oph en</w> 30326
ann ually</w> 30326
D o</w> 30325
lob es</w> 30321
vasodi lation</w> 30319
oste oblast</w> 30317
Vib rio</w> 30317
fo am</w> 30315
di chro 30314
in otropic</w> 30311
ob ia</w> 30305
hel ic 30304
Guid elines</w> 30303
Dis c 30291
th igh</w> 30287
thi o 30280
es h</w> 30279
l asted</w> 30275
eosin ophilic</w> 30275
MI P</w> 30272
D .</w> 30268
fa ecal</w> 30266
0. 24</w> 30265
an hydr 30261
sal inity</w> 30258
p raz 30252
B CR</w> 30252
l ers</w> 30249
extr insic</w> 30248
k et</w> 30247
DE S</w> 30245
pestic ide</w> 30236
neuro genic</w> 30235
phot ocat 30233
itu ximab</w> 30233
regar ds</w> 30232
Th r</w> 30231
m ass 30230
ph atic</w> 30229
Press ure</w> 30229
er mal</w> 30224
atresi a</w> 30224
Pro vid 30221
T ren 30219
H G 30217
ap ha 30213
H G</w> 30211
parkin son 30211
Che m</w> 30202
ca p</w> 30199
psych otherapy</w> 30199
REVI EW</w> 30193
epith elia</w> 30191
gen omics</w> 30188
degrad ing</w> 30188
ox azole</w> 30185
V O2</w> 30183
ist an</w> 30176
fil tering</w> 30168
g ases</w> 30166
V LDL</w> 30163
ad ine</w> 30158
Health care</w> 30158
R M</w> 30157
olig o 30157
sw allowing</w> 30156
obut yric</w> 30143
ke ep</w> 30136
strepto zotocin</w> 30136
VE L</w> 30130
form aldehyde</w> 30127
trache a</w> 30127
Val idation</w> 30124
Sw iss</w> 30123
pos ity</w> 30122
efflu ent</w> 30109
im pul 30105
R ac 30099
flex or</w> 30098
umb ens</w> 30097
tr ain</w> 30095
f eces</w> 30094
em ent 30090
col oc 30086
Thyro id</w> 30086
Th i 30084
idi a</w> 30080
gra ins</w> 30069
UT R</w> 30064
top ography</w> 30063
W eight</w> 30062
Ir on</w> 30062
Nor way</w> 30060
li que</w> 30058
Tech niqu 30056
incre ment</w> 30055
co enzyme</w> 30049
Differen ti 30046
ser ous</w> 30045
ter re 30039
Sc ot 30032
prob ing</w> 30030
scann er</w> 30030
descrip tions</w> 30029
dropl et</w> 30027
r ater</w> 30023
in bred</w> 30022
35 S</w> 30022
R u 30020
B PA</w> 30020
embry ogenesis</w> 30017
Modi fied</w> 30017
recogn ised</w> 30013
tor sion</w> 30012
Sup port</w> 30005
2 1 29999
T O</w> 29998
bas olateral</w> 29997
ox ia</w> 29995
AI S</w> 29995
emphasi zes</w> 29979
retar ded</w> 29979
sequ e 29976
calcul ating</w> 29976
F etal</w> 29972
amp icillin</w> 29969
IF N 29969
neutr on</w> 29962
robust ness</w> 29961
morph ometric</w> 29955
ho c</w> 29942
R elev 29938
adop t</w> 29935
g us</w> 29930
dispers al</w> 29930
im pe 29922
bi g</w> 29913
D r 29911
pa y</w> 29902
il ic</w> 29897
cle an 29896
Targ eting</w> 29893
thromb olysis</w> 29890
T 2D</w> 29889
Mex ican</w> 29888
corro bor 29888
R ot 29885
feren tial</w> 29882
res ections</w> 29880
0. 19</w> 29880
con ti 29879
ass ing</w> 29879
ti dine</w> 29875
su itability</w> 29869
sav ings</w> 29869
af en 29865
uter o</w> 29862
Mid dle</w> 29859
S ea</w> 29857
in ed</w> 29857
D aily</w> 29855
a f</w> 29852
predisp osing</w> 29852
M UC 29850
0. 32</w> 29849
cal cin 29849
sub population</w> 29845
expl ants</w> 29843
s m 29838
V 2</w> 29837
Ont ario</w> 29837
z eta</w> 29832
H i 29830
Q RS</w> 29828
A .</w> 29825
immunomod ulatory</w> 29821
Phot o 29817
predisp osition</w> 29815
fair ly</w> 29815
D 4</w> 29814
T KA</w> 29812
hydrox ylation</w> 29812
EM I</w> 29807
b i</w> 29800
micr on 29794
0. 23</w> 29789
cros sing</w> 29788
HF D</w> 29780
bil aterally</w> 29778
C U 29777
migr ant</w> 29775
T S 29764
R emo 29763
harb oring</w> 29761
ev ap 29759
acetyl transferase</w> 29744
PT C</w> 29742
lin gness</w> 29741
Strateg ies</w> 29738
mes ang 29737
5 A</w> 29734
Sev enteen</w> 29734
E ry 29732
perturb ations</w> 29720
LE V 29718
s aving</w> 29713
d ying</w> 29709
an ase</w> 29707
Gen omic</w> 29705
an der</w> 29702
my c 29697
G DM</w> 29696
hemat ocrit</w> 29696
vari eties</w> 29694
Sch iz 29694
sh ips</w> 29686
fe elings</w> 29678
l ar</w> 29677
appendic itis</w> 29676
M otor</w> 29673
j apon 29671
chem otaxis</w> 29671
19 79</w> 29670
2 C</w> 29668
ch air</w> 29666
abstin ence</w> 29666
g ation</w> 29665
propi onate</w> 29663
jaun dice</w> 29661
pig mentation</w> 29658
M ain</w> 29650
y lob 29643
ell ate</w> 29635
0. 55</w> 29634
brachy therapy</w> 29628
my x 29625
M etal</w> 29623
1.0 6</w> 29619
anos oma</w> 29617
9 00</w> 29614
ro ll 29611
ati zed</w> 29606
0. 68</w> 29606
Ac t</w> 29603
EC TION</w> 29601
Concentr ations</w> 29589
perc ei 29586
tric uspid</w> 29583
dissoci ated</w> 29583
CF TR</w> 29582
an cre 29581
correspon d 29580
L 4</w> 29579
f t 29566
k 1</w> 29565
conf ers</w> 29564
Chlamy dia</w> 29559
fail s</w> 29557
Seph arose</w> 29553
easi er</w> 29544
dos ages</w> 29540
es h 29538
iti vely</w> 29535
Pancre atic</w> 29535
A PO 29529
0.0 00</w> 29525
dilu ted</w> 29517
0. 66</w> 29516
end onuclease</w> 29515
PC NA</w> 29515
li sted</w> 29512
ulin um</w> 29506
prostagland ins</w> 29501
Mod els</w> 29500
desi re</w> 29497
exce eding</w> 29494
xen ografts</w> 29489
em it 29484
per icardi 29480
effec tors</w> 29479
cl ient</w> 29475
V ic 29474
advanc ement</w> 29469
est rous</w> 29468
eti ologies</w> 29468
en icity</w> 29465
N GS</w> 29463
multi modal</w> 29463
profes sion</w> 29457
rec re 29448
ore duc 29448
orth opaedic</w> 29448
epis odic</w> 29448
ascor b 29447
diver ticul 29446
cataly zes</w> 29444
it ates</w> 29442
os elec 29441
chlor inated</w> 29436
alg inate</w> 29434
sulf ide</w> 29428
Effec tiveness</w> 29425
ca emic</w> 29422
AR CH</w> 29420
ass urance</w> 29415
d t</w> 29413
andi bular</w> 29403
po l</w> 29400
I so 29399
P il 29398
neighb oring</w> 29396
veget able</w> 29395
a vid 29394
el emental</w> 29394
D VT</w> 29387
hyper parathyroidism</w> 29387
and a</w> 29380
Tryp anosoma</w> 29377
kary otype</w> 29375
od ont 29372
ke to</w> 29372
RF LP</w> 29372
mim ics</w> 29371
Cer vical</w> 29371
O d 29363
cap sid</w> 29359
sensor imotor</w> 29351
B er 29342
on idazole</w> 29338
dener vation</w> 29334
sensiti vities</w> 29329
bri de 29328
Tr ac 29324
E v 29320
T esting</w> 29319
stret ching</w> 29319
E PR</w> 29315
ul ic</w> 29309
o roph 29306
er ine</w> 29304
disc s</w> 29301
T om 29299
Ti O2</w> 29298
as best 29293
16 3</w> 29292
7 9 29290
es -- 29289
po uch</w> 29284
ga thered</w> 29284
kine sia</w> 29280
comp any</w> 29278
0 , 29276
low -</w> 29276
N it 29273
et axel</w> 29273
Op tical</w> 29273
m sec</w> 29272
radiolab eled</w> 29267
ta z 29264
RE LEV 29262
M SM</w> 29257
O ff 29241
phosph ates</w> 29237
14 8</w> 29226
orth og 29217
Dep end 29212
predomin ance</w> 29208
0. 63</w> 29207
sig moid</w> 29202
tr y</w> 29199
ta urine</w> 29192
ut roph 29190
Malign ant</w> 29189
t ent 29188
transc ranial</w> 29185
Cd c 29184
vaso active</w> 29182
K er 29181
integr ative</w> 29181
spea king</w> 29179
substanti a</w> 29174
quar ter</w> 29168
C ES</w> 29164
cl usal</w> 29160
tic ation</w> 29158
evalu ates</w> 29158
restor ations</w> 29156
Coun ty</w> 29154
conver gence</w> 29152
ocy stis</w> 29150
Adv ances</w> 29150
15 1</w> 29148
In ternal</w> 29141
et opo 29137
flu xes</w> 29136
o plasma</w> 29134
ju ana</w> 29133
hemisph eric</w> 29131
l acti 29128
for skolin</w> 29125
occup ation</w> 29124
encap sulation</w> 29118
0. 56</w> 29117
cre te</w> 29115
RELEV ANCE</w> 29114
e GFR</w> 29113
20 5</w> 29113
tr ust</w> 29112
psych ology</w> 29110
loc alize</w> 29109
chol era</w> 29104
gen otyped</w> 29102
cy tic</w> 29092
MM C</w> 29092
a rent</w> 29090
quanti fying</w> 29090
controver sy</w> 29088
in distinguishable</w> 29084
ex port</w> 29080
D BS</w> 29077
ery thromycin</w> 29077
Mod ulation</w> 29071
C . 29069
tri ed</w> 29069
N itr 29067
0. 69</w> 29063
ob viously</w> 29063
Streptom yces</w> 29063
bacterioph age</w> 29062
el lip 29055
h on 29050
R CT</w> 29050
rein forced</w> 29047
s outh</w> 29043
immun ogenic</w> 29041
Impro ving</w> 29033
cru zi</w> 29022
B en 29020
caud ate</w> 29019
ch ew 29018
em ias</w> 29013
succin ate</w> 29006
antagon ized</w> 29002
Reg ion</w> 28999
D oc 28982
immunocom promised</w> 28982
18 F</w> 28978
di urnal</w> 28975
T X</w> 28974
wil d 28974
or ide</w> 28973
vit amins</w> 28971
For m</w> 28971
sh ed 28968
har monic</w> 28968
mid w 28967
32 P</w> 28964
alpha 1</w> 28962
Pattern s</w> 28959
wed ge</w> 28957
T ex 28955
etopo side</w> 28954
fr ont 28953
a OR</w> 28947
0. 28</w> 28934
op eptide</w> 28933
chol ic</w> 28926
0. 64</w> 28924
osi dase</w> 28924
lig ase</w> 28923
ylob acter</w> 28922
E qu 28921
sub acute</w> 28917
par v 28916
anaes thetic</w> 28911
im bur 28907
y ros 28905
phot osynthesis</w> 28905
micr on</w> 28889
s ounds</w> 28883
intr ag 28883
G raph 28881
u ght</w> 28880
15 6</w> 28875
coll ec 28871
fresh ly</w> 28866
k ill</w> 28864
0. 26</w> 28864
C on</w> 28861
pattern ing</w> 28861
un ted</w> 28860
eng ra 28860
ec k 28857
proto zo 28853
omat erials</w> 28845
s entinel</w> 28843
Intrac ellular</w> 28843
H PA</w> 28841
ad one</w> 28839
ycl ic</w> 28837
ew es</w> 28835
aden o 28832
d .</w> 28825
ke ting</w> 28824
3 60</w> 28821
i o</w> 28814
Anti body</w> 28812
nano structures</w> 28810
poly cystic</w> 28807
discrimin ant</w> 28805
RE S</w> 28803
Th ail 28802
poly unsaturated</w> 28794
anti platelet</w> 28782
pro thrombin</w> 28776
ingredi ents</w> 28775
hal othane</w> 28771
Inter ventions</w> 28771
Hem at 28764
Phyl ogenetic</w> 28760
im etic</w> 28757
multi focal</w> 28757
Optim al</w> 28756
bi ocompatibility</w> 28749
g lo 28748
cal ibr 28746
stress ful</w> 28745
Sm ad 28745
Sw it 28744
Arter ial</w> 28739
herpes virus</w> 28735
Thail and</w> 28734
perme ation</w> 28728
do ing</w> 28727
mul tim 28721
Atl antic</w> 28719
mo sis</w> 28717
Em erg 28714
B RAF</w> 28713
lys osomes</w> 28713
red ness</w> 28709
man eu 28705
transp osition</w> 28700
bo ok</w> 28696
adequ acy</w> 28692
par adox 28690
Transi ent</w> 28689
attend ance</w> 28688
ash es</w> 28681
parti tion</w> 28678
as on</w> 28675
antin oc 28675
Sch wan 28673
explan ations</w> 28673
uni qu 28669
allevi ate</w> 28669
me ters</w> 28662
photo receptor</w> 28662
int enti 28659
In t 28658
autonom ous</w> 28657
Th or 28653
ACh R</w> 28653
SE ARCH</w> 28645
elas ticity</w> 28644
astro cyt 28642
re fu 28640
M am 28638
i tic</w> 28637
re ferences</w> 28635
gen tly</w> 28630
ket o 28620
noc iceptive</w> 28619
ap point 28614
MAL DI</w> 28610
St atus</w> 28604
LE VEL</w> 28599
cu st 28597
0. 62</w> 28591
po ver 28589
emerg e</w> 28589
nemat ode</w> 28580
Maxim um</w> 28577
interrup ted</w> 28577
5 3 28573
soph is 28573
lam ellar</w> 28572
topois omerase</w> 28569
opo rous</w> 28566
oph armac 28565
U ro 28562
mess ages</w> 28562
he al</w> 28561
. ..</w> 28560
W I 28560
electrocardi ogram</w> 28558
inter cal 28555
olig omers</w> 28553
iso flurane</w> 28550
amin ophen</w> 28548
filam entous</w> 28548
pea ked</w> 28547
tur ned</w> 28542
occlud ed</w> 28540
6 6 28536
con sequent</w> 28534
I ran 28522
PC L</w> 28518
bi ases</w> 28516
ol ive</w> 28514
ensur ing</w> 28508
h ips</w> 28494
A di 28491
prob abilities</w> 28482
coron avirus</w> 28481
M .</w> 28475
reconstruc tions</w> 28473
parti tioning</w> 28465
recogn izing</w> 28464
py raz 28457
ulcer ation</w> 28457
absc esses</w> 28455
5 9 28447
h a</w> 28446
in one</w> 28442
pre v 28442
5 8 28441
1 A1</w> 28439
M ag 28436
p 16</w> 28431
J oint</w> 28431
2, 6</w> 28427
oscop e</w> 28426
insp ired</w> 28426
V as 28421
7 7 28419
n ary</w> 28417
po d 28416
ear man</w> 28416
te g 28413
M o</w> 28412
bar ley</w> 28411
g raph</w> 28407
b cl</w> 28407
D B 28402
granul ocytes</w> 28399
secre te</w> 28398
auton omy</w> 28396
as e 28391
pe ers</w> 28387
Bi ol</w> 28387
bio informatics</w> 28384
qu ercetin</w> 28379
le tic</w> 28379
promp ted</w> 28375
G h 28372
adi posity</w> 28366
ou red</w> 28364
lat encies</w> 28358
cr is 28352
q RT</w> 28350
4 3 28348
fil tered</w> 28348
Child hood</w> 28348
ocyan ate</w> 28347
emphys ema</w> 28336
H SCT</w> 28334
tacti le</w> 28329
R 3</w> 28328
diaphrag matic</w> 28327
atro genic</w> 28326
en te 28324
ach ol</w> 28323
seroton ergic</w> 28319
chlor amphenicol</w> 28318
be ams</w> 28311
compreh ension</w> 28311
multi functional</w> 28310
acc umbens</w> 28307
al im 28305
Comput er</w> 28305
dev oid</w> 28296
fol ds</w> 28295
sy l 28293
implic ation</w> 28285
Repe ated</w> 28283
RO M</w> 28281
19 5</w> 28280
CP P</w> 28277
In dependent</w> 28272
la b</w> 28269
bride ment</w> 28269
pre clud 28249
in ward</w> 28248
en o 28248
Correc tion</w> 28248
tin nitus</w> 28238
malon dialdehyde</w> 28238
poin ting</w> 28237
leph rine</w> 28236
nan om 28235
Apop tosis</w> 28235
peri odic 28234
muc ous</w> 28231
ultra structure</w> 28231
dou b 28230
alkal oids</w> 28224
pa st 28221
br y 28219
ide as</w> 28217
ch ymo 28214
auto immunity</w> 28214
M U 28210
gu ar 28209
R ates</w> 28204
Schwan n</w> 28202
S AS</w> 28201
bel ief</w> 28198
E I</w> 28189
ing ested</w> 28186
B 7</w> 28185
plan es</w> 28185
lutein izing</w> 28183
un successful</w> 28180
ap p</w> 28179
re distribution</w> 28177
beta 2</w> 28177
incid ences</w> 28175
mess age</w> 28174
Ag ing</w> 28172
n ish</w> 28171
infec tivity</w> 28171
g al</w> 28169
b ath</w> 28169
Ab normal</w> 28167
eukary otes</w> 28165
em atics</w> 28161
A . 28155
V O</w> 28154
En d</w> 28150
str ands</w> 28140
f idelity</w> 28139
constitu ent</w> 28139
neph ritis</w> 28136
minim izing</w> 28134
ig uous</w> 28133
broncho alveolar</w> 28130
Hy bri 28129
ma ster</w> 28128
fist ulas</w> 28105
hem agglutinin</w> 28103
mas ked</w> 28101
kn esses</w> 28100
at or 28099
am ox 28095
adhe sions</w> 28095
Electr onic</w> 28094
it um</w> 28093
sci ences</w> 28093
F ibr 28087
anc ient</w> 28081
E SI</w> 28072
L TP</w> 28068
c DNAs</w> 28066
E u 28056
E PA</w> 28056
Compreh ensive</w> 28051
reconstruc tive</w> 28050
pl eth 28048
List eria</w> 28048
C 18</w> 28045
ornith ine</w> 28044
a ids</w> 28036
cat abolism</w> 28036
G ene 28035
0. 36</w> 28034
frame works</w> 28032
T N</w> 28028
ion izing</w> 28028
lact one</w> 28026
con du 28025
x ic</w> 28023
bir d</w> 28012
L C 28008
IC S</w> 28005
impl antable</w> 28001
m ates</w> 27996
Uni variate</w> 27995
jejun al</w> 27995
Bet a</w> 27992
un less</w> 27990
shif ting</w> 27989
vill i</w> 27982
re pressed</w> 27975
swit ched</w> 27971
adrenoc eptors</w> 27971
mon oc 27967
phosphor yl 27964
Sp earman</w> 27958
opi ate</w> 27953
im per 27950
f en</w> 27949
fici ency</w> 27948
ro pol 27945
CD 11 27943
ab iotic</w> 27942
par tur 27942
ov sk 27938
dichro ism</w> 27936
Wil ey</w> 27932
ol inium</w> 27929
if ies</w> 27929
ster oid 27927
adju stments</w> 27927
14 9</w> 27923
glu con 27918
H 1 27916
H P 27911
pin eal</w> 27911
p ra 27909
lab ile</w> 27906
s ement</w> 27904
EC MO</w> 27899
ine ural</w> 27898
rostr al</w> 27897
neuro lep 27894
glucos amine</w> 27894
ac on 27893
19 2</w> 27886
ar one</w> 27878
ascorb ate</w> 27868
orb idity</w> 27867
Al most</w> 27863
0. 27</w> 27861
Oxy gen</w> 27859
p 1</w> 27858
repeti tion</w> 27858
comb at</w> 27855
ob lique</w> 27852
anti gen 27850
G ood</w> 27846
brid ging</w> 27844
cing ulate</w> 27842
el es</w> 27839
glycos amin 27831
gener al 27828
B I 27825
4 2 27822
trim eth 27819
g ad 27817
awa ke</w> 27808
dis si 27804
con flu 27802
Recur rent</w> 27801
rib osomes</w> 27791
encoun ter</w> 27791
thalass emia</w> 27789
lifes pan</w> 27787
deliver ies</w> 27780
A d</w> 27777
D HE 27774
ov ulatory</w> 27772
de uter 27770
Thir d</w> 27765
Endo thelial</w> 27765
0. 30</w> 27764
parall eled</w> 27762
reflex es</w> 27762
inter disciplinary</w> 27758
reg imes</w> 27757
seas ons</w> 27755
restric tions</w> 27753
vibr ational</w> 27745
ej ac 27744
0. 38</w> 27742
Abstr act 27742
1. 73</w> 27731
int ent</w> 27724
17 4</w> 27721
neuro developmental</w> 27719
lact ating</w> 27718
0. 48</w> 27715
im ine</w> 27713
wil lingness</w> 27713
Y AG</w> 27711
Tre g</w> 27711
fo ve 27704
metabol ized</w> 27703
osom iasis</w> 27703
pro kary 27697
immun ob 27696
O G</w> 27695
intra epithelial</w> 27693
rig orous</w> 27693
squ ares</w> 27692
eth eless</w> 27685
heal ed</w> 27681
sched ules</w> 27674
od or</w> 27673
emplo y</w> 27666
L 3</w> 27665
osurg ery</w> 27660
oin osi 27653
F As</w> 27644
neo vascularization</w> 27641
the tical</w> 27639
micro glial</w> 27636
sen ior</w> 27635
pen dic 27635
sym path 27634
Traum a</w> 27633
di z 27630
eff eren 27629
ag eous</w> 27623
top ological</w> 27623
an ide</w> 27621
el ig 27620
coun selling</w> 27617
pol lin 27615
de sis</w> 27607
SV 40</w> 27604
nanoc ryst 27602
nanos cale</w> 27597
S 3</w> 27595
morb id</w> 27593
F oc 27592
blind ness</w> 27583
0. 58</w> 27582
neuro genesis</w> 27579
od ine</w> 27555
NI H</w> 27555
vesi cal</w> 27554
at l 27549
cl ade</w> 27549
Ac cum 27549
0. 42</w> 27543
inter fe 27542
re imbur 27534
work load</w> 27533
par s</w> 27532
relig ious</w> 27529
Descrip tive</w> 27525
shed ding</w> 27525
su tures</w> 27519
solubil ized</w> 27517
multicentr e</w> 27516
detox ification</w> 27515
u ps</w> 27513
P le 27512
D CS</w> 27511
no vation</w> 27510
ex empl 27507
hyper insulin 27504
An t 27504
afen ib</w> 27499
dic ation</w> 27498
0. 34</w> 27497
ovari ectomized</w> 27497
deform ities</w> 27495
se a 27493
here sis</w> 27493
Predic tion</w> 27490
acetyl cholinesterase</w> 27485
or h 27483
lepro sy</w> 27471
d an 27470
Camp ylobacter</w> 27469
stere otactic</w> 27464
us crip 27461
1.0 7</w> 27457
tolu ene</w> 27457
r ad 27452
de acetyl 27449
18 5</w> 27448
Fam il 27444
homolog ue</w> 27444
c ep 27434
oc clusal</w> 27429
ar tic 27427
her bic 27427
caroten e</w> 27427
Abstract Text</w> 27426
19 75</w> 27424
B row 27420
RNA i</w> 27420
celi ac</w> 27415
shor tly</w> 27411
ste atosis</w> 27409
amb iguous</w> 27409
bio diversity</w> 27404
vol ution 27404
IN G</w> 27402
L BP</w> 27400
allo steric</w> 27400
mosquit oes</w> 27398
S um 27383
T im 27383
Ch alleng 27380
00 0 27378
M BP</w> 27376
top ology</w> 27373
0. 61</w> 27372
venti lator</w> 27369
Temper ature</w> 27369
S pl 27367
S he 27358
Reg ression</w> 27358
1.0 8</w> 27357
L ack</w> 27355
um b</w> 27352
pec tor 27346
flo x 27335
optim izing</w> 27334
ME S</w> 27332
G ender</w> 27331
im posed</w> 27331
av alin</w> 27331
sub scales</w> 27330
bul b</w> 27328
hypo phy 27326
veget ative</w> 27324
quinol ine</w> 27316
mo ved</w> 27314
train ees</w> 27309
iter pen 27308
id ate</w> 27306
AT P 27306
gene ic</w> 27305
R e</w> 27302
be sides</w> 27297
pho to</w> 27297
II Ia</w> 27295
S 100 27293
yl amine</w> 27292
granul omatous</w> 27288
mesang ial</w> 27288
el vic</w> 27285
at ar 27277
non coding</w> 27275
g ian</w> 27274
bur g</w> 27273
miti gate</w> 27271
HR s</w> 27257
gem citabine</w> 27252
Ac tin 27251
spi kes</w> 27251
sym metrical</w> 27246
ti me 27241
Kin etic</w> 27241
w ine</w> 27239
in clusions</w> 27239
0. 54</w> 27237
k it 27235
En zyme</w> 27235
trif luoro 27234
ap heresis</w> 27228
M ob 27223
el uted</w> 27223
st ation</w> 27222
RI A</w> 27217
fac tion</w> 27216
ari ans</w> 27212
Re action</w> 27212
Compar isons</w> 27209
ir rit 27203
crystall ization</w> 27201
in still 27197
icul us</w> 27197
A A 27193
ter ol</w> 27192
manip ulated</w> 27192
un event 27184
0. 52</w> 27180
0. 60</w> 27169
d ac 27167
pop ul 27165
Mal ay 27162
restr aint</w> 27161
hex a 27159
chap ter</w> 27157
dam aging</w> 27155
au to</w> 27145
Pur ification</w> 27144
Prote ins</w> 27140
hypercholesterol emia</w> 27134
advant ageous</w> 27130
as simil 27128
hu ge</w> 27124
al bin 27113
in digenous</w> 27112
lo oking</w> 27109
op yr 27107
Inc ub 27106
13. 5</w> 27102
at able</w> 27101
THE SIS</w> 27101
f uro 27100
t ate</w> 27098
chic ine</w> 27095
ob liter 27090
perform s</w> 27087
con currently</w> 27086
wor ker</w> 27086
enz a</w> 27086
11. 1</w> 27084
oste omyelitis</w> 27082
AL A</w> 27073
hum an 27068
s angu 27066
ex osomes</w> 27059
prim e</w> 27059
exce ed</w> 27059
inter neurons</w> 27058
coll ections</w> 27058
Fi eld</w> 27058
SS c</w> 27055
buff ered</w> 27048
at erials</w> 27047
odop sin</w> 27043
pat ernal</w> 27041
Clinical Tri 27034
Morph ological</w> 27030
ar um</w> 27028
constitu ted</w> 27021
Decre ased</w> 27019
ol ith 27016
fol ic</w> 27016
it eal</w> 27011
cou ple</w> 27006
Rh od 27006
N IR</w> 27005
Mechanis m</w> 27004
inte rested</w> 27003
bio assay</w> 27002
termin ated</w> 27001
0. 31</w> 27000
ground water</w> 26991
terre strial</w> 26990
li thi 26984
idi tis</w> 26978
dig oxin</w> 26978
osp asm</w> 26977
anti convuls 26975
trim ethyl 26974
C ho 26971
TH A</w> 26969
mid azolam</w> 26966
S AM 26965
Consi der 26965
M ig 26964
acet onitrile</w> 26955
Ig G1</w> 26953
Ga ussian</w> 26953
dis til 26950
ST EMI</w> 26947
streptoc occi</w> 26947
imetr y</w> 26945
Chang e</w> 26941
om b 26940
trac hom 26940
co ffe 26938
er i</w> 26937
osph ere</w> 26937
AM PA</w> 26926
pre requisite</w> 26925
co stly</w> 26923
tol i</w> 26922
extrap ol 26921
man uscrip 26920
ER D</w> 26920
.0 2</w> 26919
sial ic</w> 26916
b an 26914
set t 26911
ch al 26906
ri amycin</w> 26906
cephal ospor 26906
cle aning</w> 26905
projec ted</w> 26904
PC C</w> 26900
Develop mental</w> 26899
pl u 26898
pedi atr 26893
tic um</w> 26891
yo u</w> 26891
a esthetic</w> 26889
il es</w> 26882
In tensive</w> 26882
cre d 26881
clos ing</w> 26879
De ep</w> 26877
t ude</w> 26875
amb igu 26861
exacerb ations</w> 26859
DM SO</w> 26855
c ited</w> 26851
less ons</w> 26845
cap ital</w> 26844
super vision</w> 26842
fr ic 26841
possess ing</w> 26841
distr action</w> 26839
fa ther</w> 26834
migr atory</w> 26834
SO 4</w> 26826
3 20</w> 26824
mirro r</w> 26823
id o</w> 26821
17 7</w> 26818
i ens</w> 26816
w ann 26816
im mor 26816
consul tations</w> 26816
oxal ate</w> 26815
Veter ans</w> 26814
restric tive</w> 26811
pack aging</w> 26806
harv est</w> 26799
standardis ed</w> 26799
0. 53</w> 26794
0. 43</w> 26793
res emble</w> 26787
anth ocyan 26786
W BC</w> 26781
sic k</w> 26779
b evacizumab</w> 26776
tryp an 26774
CA L</w> 26762
se w 26760
B E 26754
pare sis</w> 26750
M an</w> 26749
Recomm end 26746
small est</w> 26744
19 78</w> 26735
rh abdomy 26732
transpor tation</w> 26726
ER G</w> 26722
GT Pase</w> 26718
TRA IL</w> 26718
intrac table</w> 26711
0. 40</w> 26710
der m</w> 26709
CI N</w> 26709
gradu ates</w> 26696
0. 46</w> 26695
lys ate</w> 26690
Iran ian</w> 26685
AN ALY 26683
Effici ent</w> 26683
en a</w> 26679
ab rup 26671
i aceae</w> 26668
puls atile</w> 26668
chaper one</w> 26653
b ariatric</w> 26646
retin ol</w> 26644
yto in</w> 26644
assum e</w> 26643
antagon istic</w> 26638
adjunc tive</w> 26634
intr at 26632
dev ast 26625
Antim icrobial</w> 26625
Aor tic</w> 26624
G d</w> 26623
thrombo embolic</w> 26622
0. 44</w> 26619
in significant</w> 26614
crib ed</w> 26608
0. 57</w> 26596
un translated</w> 26594
S patial</w> 26590
an ne 26585
E SR</w> 26583
is omer</w> 26580
Smo king</w> 26577
the astern</w> 26572
gen otoxic</w> 26570
13. 3</w> 26570
H H</w> 26563
Tradi tional</w> 26560
Tur key</w> 26552
is in</w> 26551
Mod eling</w> 26550
radionuc lide</w> 26548
I OL</w> 26546
olip id</w> 26544
derm is</w> 26543
TL R</w> 26541
reth ral</w> 26540
ra ft</w> 26539
is oc 26538
canc erous</w> 26538
s an 26537
pat ellar</w> 26537
analge sics</w> 26537
synchron ization</w> 26533
com pressive</w> 26532
tem po 26532
PD A</w> 26532
ure ter 26528
infrequ ent</w> 26528
nec rotizing</w> 26525
review ers</w> 26524
m ite</w> 26520
no ea</w> 26520
ag ulation</w> 26518
adip ocyte</w> 26513
He ad</w> 26512
nephro tic</w> 26512
fibrill ar</w> 26510
PA S</w> 26509
liqu ids</w> 26504
cyt ological</w> 26501
disrup t</w> 26500
lipo xy 26498
Sh ig 26493
alge sia</w> 26492
calorimetr y</w> 26492
W KY</w> 26487
oc ation</w> 26485
chamb ers</w> 26485
S SR 26484
P DE 26480
bot t 26475
0. 59</w> 26474
In testinal</w> 26473
G BS</w> 26471
0. 37</w> 26471
orph in</w> 26470
LVE F</w> 26470
six th</w> 26465
erythem a</w> 26456
Per son 26455
l ingu 26454
off set</w> 26454
w are</w> 26452
PA D</w> 26451
i ously</w> 26446
my opia</w> 26446
reconstitu tion</w> 26446
acin ar</w> 26445
dig ested</w> 26442
leth ality</w> 26438
C le 26436
it real</w> 26435
pe a</w> 26435
t an</w> 26434
0. 29</w> 26431
Ch i 26431
oc utaneous</w> 26427
ME NT</w> 26427
micro structure</w> 26424
tris omy</w> 26423
INTERVEN TION</w> 26422
l av 26421
Nin eteen</w> 26421
loc ate</w> 26415
sequ entially</w> 26412
ke y 26407
electro spray</w> 26405
oder ma</w> 26403
port able</w> 26398
guid ing</w> 26397
Ab dom 26396
s ate</w> 26392
re jec 26390
trac ing</w> 26385
sic kness</w> 26385
asbest os</w> 26384
mono amine</w> 26378
conduc tive</w> 26373
robo t</w> 26368
neur ite</w> 26366
R eli 26365
erec tile</w> 26365
deox yn 26363
PC 12</w> 26362
bi ophysical</w> 26360
captop ril</w> 26360
20 20</w> 26357
Ser toli</w> 26356
rab ies</w> 26351
H SA</w> 26350
Per s 26345
My el 26339
W C</w> 26336
PT X</w> 26333
ic il 26327
me gal 26326
V ery</w> 26324
UD P</w> 26324
dent ure</w> 26322
norm ative</w> 26319
con ferred</w> 26317
lead ers</w> 26317
nor th</w> 26315
pheny lephrine</w> 26314
inf antile</w> 26308
mega kary 26302
Remo val</w> 26299
P sy 26298
consi ders</w> 26295
ol im 26294
immunocyto chemistry</w> 26294
hem olysis</w> 26290
advanc ing</w> 26290
Pois son</w> 26287
trachom atis</w> 26285
12 , 26282
UT I</w> 26277
CRE B</w> 26277
anti proliferative</w> 26270
Rhe um 26268
in ol</w> 26266
ogen ously</w> 26266
TI ON 26265
negl ected</w> 26261
flox acin</w> 26260
coun ted</w> 26259
th aw 26258
remain der</w> 26256
chec ked</w> 26255
O P 26251
cont our</w> 26246
AN G</w> 26246
o b</w> 26242
ER P</w> 26242
arri val</w> 26242
over night</w> 26241
bi otechn 26240
CA S</w> 26240
gin sen 26239
Alter ations</w> 26239
r umin 26236
SI R 26234
intra thecal</w> 26234
z yg 26231
M ain 26230
h s</w> 26226
b ach</w> 26218
ob ium</w> 26218
acr ylic</w> 26217
hard ness</w> 26215
adren al 26214
hel ping</w> 26213
alcoh ols</w> 26212
O vari 26210
D ip 26209
Man n</w> 26205
2, 5</w> 26197
ac ral</w> 26192
In si 26191
A my 26188
hydrox ide</w> 26183
denti sts</w> 26179
at uration</w> 26175
air s</w> 26166
contrac eption</w> 26165
son ographic</w> 26165
coffe e</w> 26163
O X 26159
cyan ide</w> 26154
Amin o</w> 26147
b . 26146
0. 47</w> 26144
I E</w> 26142
con azole</w> 26136
il is</w> 26136
surroun ded</w> 26132
ple as 26131
s aph 26125
B PH</w> 26125
myel o 26124
im possible</w> 26123
yn e</w> 26123
S can 26119
ir is</w> 26113
ClinicalTri als.gov</w> 26113
od ys 26110
immunocom pet 26106
on y</w> 26104
thor ax</w> 26100
granul oma</w> 26100
B is 26097
C PAP</w> 26096
end or 26092
0. 39</w> 26091
f asci 26090
H CO 26088
pep tic</w> 26088
opin ions</w> 26087
PL A2</w> 26086
P BS</w> 26076
an ni 26075
S ing 26073
en oid</w> 26070
hyper calc 26068
param agnetic</w> 26064
if ers</w> 26063
parox ysmal</w> 26059
tran ce</w> 26051
tal k</w> 26051
absorb ance</w> 26047
ascer tained</w> 26043
6 5 26042
phot osensi 26042
Vi et 26042
eo chrom 26042
0. 51</w> 26041
is lands</w> 26034
0. 41</w> 26031
infer tile</w> 26026
bre ed</w> 26025
Cir culating</w> 26023
og lo 26019
AP D</w> 26015
crani ofacial</w> 26015
standardi zation</w> 26012
Spec ies</w> 26009
relap sing</w> 26009
pr ice</w> 26008
v oid 25995
off s</w> 25994
Pe ak</w> 25987
in ogen</w> 25983
ed ges</w> 25980
bar ic</w> 25979
val pro 25976
e uc 25973
ex it</w> 25973
macrom olecules</w> 25972
hom ozyg 25971
tet anus</w> 25969
An terior</w> 25967
ap ril</w> 25965
Paren ts</w> 25962
15 8</w> 25960
A rea</w> 25958
Dis ability</w> 25958
of ur 25955
C ha 25953
ureth ane</w> 25951
orig inate</w> 25950
mid brain</w> 25949
hall mark</w> 25948
P Y 25946
A trial</w> 25945
mo tions</w> 25944
or ange</w> 25943
sist ant</w> 25940
py el 25937
ca ve 25934
GF AP</w> 25934
su stain</w> 25930
transp arent</w> 25927
Adolesc ents</w> 25919
o estrogen</w> 25911
belong ed</w> 25911
hydr ated</w> 25905
syn geneic</w> 25903
az epam</w> 25901
immer sion</w> 25901
min us</w> 25900
R u</w> 25894
m Abs</w> 25893
se where</w> 25891
ch or 25885
pe ti 25884
tw itch</w> 25875
mari juana</w> 25871
ac o</w> 25870
co vers</w> 25870
D ynamics</w> 25868
sym bi 25866
sha ft</w> 25863
man nit 25856
yl transferase</w> 25855
16 6</w> 25855
sophis ticated</w> 25851
osteoc last</w> 25850
R D 25849
PE C</w> 25845
elig ibility</w> 25842
f alling</w> 25830
entr ic</w> 25818
ag inal</w> 25814
iter ative</w> 25813
2 80</w> 25810
Poly morph 25808
vi an</w> 25805
TE XT</w> 25805
Predic tors</w> 25804
doc etaxel</w> 25803
ga ze</w> 25801
S ite</w> 25800
Identi fying</w> 25800
no ur 25797
0. 49</w> 25797
yl yl</w> 25792
res veratrol</w> 25791
ro ds</w> 25790
splen ectomy</w> 25788
de bridement</w> 25786
prote oglycan</w> 25778
tin a</w> 25777
visu alize</w> 25777
Cas 9</w> 25774
ten sor</w> 25770
r onate</w> 25768
equ ipped</w> 25765
17 2</w> 25765
E MS</w> 25764
thous and</w> 25758
1.0 9</w> 25756
50 6</w> 25751
CD C</w> 25750
Ex tr 25749
fibr illary</w> 25749
RESUL T</w> 25743
sub mucosal</w> 25742
tun n 25742
on asal</w> 25739
meth adone</w> 25738
it ly</w> 25737
th ing</w> 25730
amin oglyco 25730
mon o</w> 25725
di g</w> 25719
pauc ity</w> 25716
16 4</w> 25714
M PO</w> 25711
az o 25711
20 6</w> 25708
hypo plasia</w> 25701
he ads</w> 25700
R os 25699
T K</w> 25699
elev en</w> 25695
so y</w> 25689
AR C</w> 25685
CXCR 4</w> 25684
KE Y</w> 25680
MA O</w> 25676
cl oz 25673
B ene 25672
CO S</w> 25672
t ons</w> 25669
thorac otomy</w> 25667
eyel id</w> 25664
Assess ing</w> 25659
lign in</w> 25655
re organization</w> 25653
encour aged</w> 25653
INTER PRE 25648
Mac roph 25646
jejun um</w> 25646
Al together</w> 25642
infiltr ate</w> 25642
fe el</w> 25640
A sp 25639
M 3</w> 25638
implic it</w> 25636
flow ering</w> 25636
my ometr 25633
phosph oinosi 25630
op tera</w> 25629
Sa har 25628
ap tam 25627
carb achol</w> 25623
Abdom inal</w> 25620
M Ab</w> 25609
ventr icles</w> 25605
enter al</w> 25602
sand w 25601
live stock</w> 25597
As th 25596
wash ed</w> 25593
T ask</w> 25587
mannit ol</w> 25585
er as</w> 25583
sup er</w> 25577
fac ile</w> 25576
af lat 25576
a vir 25573
B ank</w> 25554
mater n 25549
Rh iz 25548
leishman iasis</w> 25541
melan omas</w> 25540
ti m</w> 25537
cal oric</w> 25531
prevent able</w> 25524
CD s</w> 25522
RN P</w> 25519
str ated</w> 25517
nucle ation</w> 25511
w ind</w> 25510
b erry</w> 25508
16 2</w> 25507
S us 25506
PC B</w> 25504
st ore</w> 25502
l arynx</w> 25501
lab els</w> 25501
pe red</w> 25495
copolym er</w> 25494
a a</w> 25493
gre y</w> 25492
ket one</w> 25481
ad vis 25480
S en 25479
Str ain</w> 25474
dys lipidemia</w> 25473
instruc tions</w> 25473
gh relin</w> 25472
fa thers</w> 25466
My c</w> 25461
F abr 25460
ol uminescence</w> 25457
scat ter</w> 25454
wor th</w> 25450
compan ies</w> 25445
H M</w> 25444
saf er</w> 25443
read ers</w> 25443
Clin ically</w> 25441
Radi o 25440
e IF 25433
compens ated</w> 25432
wash out</w> 25431
Ethi opia</w> 25430
caver nous</w> 25429
bi og 25427
mar keting</w> 25427
neuro psychiatric</w> 25421
2 30</w> 25419
AR F</w> 25418
synap se</w> 25416
negl ect</w> 25415
mon oxide</w> 25414
in struction</w> 25412
vi d</w> 25411
hel ices</w> 25407
appe tite</w> 25405
F as 25404
pres chool</w> 25404
Whit ney</w> 25404
tic ism</w> 25402
H PV 25394
u tively</w> 25393
hypo thetical</w> 25387
mut ual</w> 25385
S j 25384
go at</w> 25382
el sewhere</w> 25372
C ong 25370
auth en 25370
hydroph ob 25370
2 O</w> 25366
am y 25366
D eli 25364
inv ited</w> 25360
PRE SEN 25356
dynam ical</w> 25353
un identified</w> 25352
son i</w> 25345
ro oms</w> 25343
nucle ase</w> 25340
vic inity</w> 25340
INTERPRE TATION</w> 25340
s r 25338
sal mon</w> 25336
bac ks</w> 25336
gau ge</w> 25330
L ep 25329
correc ts</w> 25325
rough ness</w> 25322
basi lar</w> 25321
ch lamy 25316
he ated</w> 25313
lik re 25313
10. 3</w> 25310
Targ eted</w> 25310
C enters</w> 25309
An es 25307
pi ece</w> 25306
pit ch</w> 25306
M P 25305
Li kewise</w> 25304
vasodi lator</w> 25304
at ally</w> 25302
s h</w> 25300
Pro long 25299
mat ches</w> 25298
ri b</w> 25286
elim inating</w> 25286
acet aminophen</w> 25283
fer ro 25281
ST Z</w> 25281
n ess 25278
c c</w> 25276
ar rested</w> 25270
c ocul 25269
cl ot</w> 25269
ver ification</w> 25269
Intra operative</w> 25267
oglo bin 25264
C 3H</w> 25258
ble omycin</w> 25257
en sed</w> 25255
pa w</w> 25253
alcohol ism</w> 25253
Extrac ellular</w> 25249
Mut ation</w> 25247
motoneur ons</w> 25246
y er</w> 25244
ic o 25244
appro ached</w> 25244
spermat ogenesis</w> 25241
adj ust</w> 25237
D G 25235
chrom atic</w> 25234
car pal</w> 25231
rom andibular</w> 25231
G a</w> 25229
k Pa</w> 25225
op ent 25225
z ers</w> 25222
fav ored</w> 25219
eti dine</w> 25217
tr u 25214
Mem ory</w> 25213
jour nal</w> 25203
hall uc 25196
um s</w> 25195
ropol itan</w> 25190
C .</w> 25181
op ened</w> 25178
be ds</w> 25178
devast ating</w> 25178
K i 25174
H RT</w> 25169
igh ten 25166
Cr iter 25166
e ast</w> 25163
di valent</w> 25163
sp ore</w> 25163
re ticular</w> 25162
aspar ag 25162
fe eling</w> 25156
De ath</w> 25151
Prepar ation</w> 25151
veh icles</w> 25150
Haem ophilus</w> 25150
Pro cess 25148
inter molecular</w> 25147
S eg 25143
con es</w> 25141
chol ate</w> 25134
ep icardial</w> 25133
Li qu 25130
ard ous</w> 25129
K ne 25128
gre en 25128
hy ph 25126
osyl transferase</w> 25124
m ar</w> 25119
xim e</w> 25116
p A</w> 25111
Eg yp 25110
infra structure</w> 25103
propos al</w> 25103
l umb 25101
P b 25098
tin ent</w> 25097
electr ically</w> 25095
orthog onal</w> 25091
Gra ves</w> 25089
st ains</w> 25087
br ight</w> 25082
ox ic</w> 25081
ca v 25078
Pol y</w> 25076
her oin</w> 25075
Sch ist 25075
cadaver ic</w> 25067
Incub ation</w> 25066
uro thelial</w> 25064
be gun</w> 25057
ac ne</w> 25056
A AV</w> 25050
AL K</w> 25042
CON TEXT</w> 25037
amo eb 25036
di pine</w> 25031
amph oter 25031
co atings</w> 25030
em itting</w> 25027
pre conditioning</w> 25025
spl itting</w> 25023
w r 25022
inf est 25020
sol eus</w> 25017
fer tile</w> 25012
am a</w> 25007
al be 25004
Sci ences</w> 25004
ophag y</w> 25001
res sions</w> 24999
Sym p 24999
Pu bl 24999
20 4</w> 24997
vaso constric 24997
Den mark</w> 24992
we an 24989
CN Ts</w> 24989
ti ps</w> 24988
preced ented</w> 24988
PRESEN TATION</w> 24988
E . 24983
her bi 24983
cyclospor in</w> 24982
16. 7</w> 24980
pri orities</w> 24977
ven tive</w> 24976
disco ver 24976
omy cosis</w> 24975
intim al</w> 24974
reas oning</w> 24967
graph s</w> 24967
underestim ated</w> 24966
trans formations</w> 24962
on ec 24959
Sur ge 24959
narrow ing</w> 24958
divid ing</w> 24956
bre eds</w> 24954
or rhea</w> 24953
15 9</w> 24952
plau sible</w> 24952
dis aster</w> 24950
evol ve</w> 24949
neighbor hood</w> 24941
Vir al</w> 24939
neigh bo 24936
deplo yment</w> 24936
DR G</w> 24935
C ateg 24931
u a</w> 24931
hy brid 24931
PAR P</w> 24928
pri on</w> 24924
s ap 24914
H U</w> 24909
yr onine</w> 24907
UV B</w> 24907
disp arity</w> 24903
NL R</w> 24903
Protec tion</w> 24901
kin ematics</w> 24900
ANALY SIS</w> 24898
medias tin 24896
aug ment</w> 24893
M Ps</w> 24890
peroxis ome</w> 24890
F u 24889
T X 24888
ou d</w> 24882
er ate</w> 24878
it ely</w> 24877
Un til</w> 24876
L 5</w> 24874
L at 24873
di a 24867
Gre ater</w> 24867
evalu able</w> 24865
Sim ple</w> 24863
connec ting</w> 24858
NE L</w> 24858
compar atively</w> 24857
cho osing</w> 24856
per t</w> 24851
stric ture</w> 24849
HD AC 24845
ello sis</w> 24841
18 6</w> 24840
oscill atory</w> 24840
util izes</w> 24834
hid den</w> 24833
cros sed</w> 24831
yl ene</w> 24829
F NA</w> 24826
Symp tom</w> 24825
L ey 24823
haem odynamic</w> 24817
th west</w> 24816
fic ially</w> 24815
renew al</w> 24813
as in</w> 24811
set up</w> 24808
br is</w> 24804
2, 000</w> 24804
bul l 24797
eti ologic</w> 24794
wh ites</w> 24792
hem ostasis</w> 24791
c amp 24790
beg ins</w> 24789
clinic opathologic</w> 24789
denat uration</w> 24789
ren dered</w> 24786
os oph 24785
t oral</w> 24784
consec utively</w> 24781
suppl emental</w> 24778
mag net 24775
particip ates</w> 24772
Psych ological</w> 24769
trans form 24766
carbox y</w> 24759
rot ator</w> 24759
st omy</w> 24753
S l 24752
sl ides</w> 24752
onc ogenes</w> 24750
W D</w> 24749
Ser ial</w> 24749
Hor mon 24738
ectom ies</w> 24736
m Glu 24731
CD 14</w> 24730
L atin</w> 24729
Im age</w> 24725
arom atase</w> 24723
M ad 24716
CD 40</w> 24710
carbo platin</w> 24709
Chem otherapy</w> 24708
J our 24707
distric ts</w> 24707
abro gated</w> 24707
LOG Y</w> 24705
m ist 24704
1. 11</w> 24699
inve stment</w> 24697
P rec 24693
can als</w> 24690
ag onal</w> 24685
fing ers</w> 24683
e sia</w> 24681
sp ic 24676
sur prisingly</w> 24676
zo on 24669
ful filled</w> 24667
fibrin olytic</w> 24666
1. 12</w> 24665
L ond 24660
R ight</w> 24658
orth otopic</w> 24656
inflam ed</w> 24654
cl op 24653
far mers</w> 24650
on co 24647
ion ophore</w> 24645
10. 2</w> 24645
m A</w> 24643
Emerg ing</w> 24640
resembl es</w> 24637
de tom 24635
ros ity</w> 24633
inser t</w> 24631
pover ty</w> 24620
fail ing</w> 24613
EC 50</w> 24612
A qu 24606
St at 24606
lipoxy genase</w> 24603
G H 24600
justi fied</w> 24598
tun able</w> 24595
S an</w> 24594
r ituximab</w> 24594
im i 24594
plasm on</w> 24591
ros ome</w> 24588
ol one</w> 24587
amphoter icin</w> 24586
pneum othorax</w> 24580
inspec tion</w> 24580
ca st 24579
spong e</w> 24577
assum ing</w> 24573
t ou 24572
Ep id 24570
as tically</w> 24567
Transpl antation</w> 24564
S TS</w> 24563
la ws</w> 24563
e tra 24561
-- -- 24560
isot opic</w> 24558
detom idine</w> 24551
Tol l</w> 24550
A ir 24547
ti an</w> 24541
3 3.3</w> 24540
inter ven 24540
Br d 24539
um oral</w> 24538
E U</w> 24537
AD L</w> 24535
Res on 24533
S W</w> 24530
20 2</w> 24528
sh ut 24525
R h</w> 24518
chemil uminescence</w> 24515
r als</w> 24509
not es</w> 24501
Q I</w> 24499
st aged</w> 24499
IM T</w> 24498
cloz apine</w> 24498
dis proportion 24497
1. 10</w> 24497
andro gens</w> 24497
Combin ing</w> 24496
n ationally</w> 24487
nec rop 24487
15 00</w> 24481
cl eral</w> 24479
brady cardia</w> 24479
L ess</w> 24478
P 450 24478
disabl ed</w> 24476
H CT</w> 24474
compul sive</w> 24469
or age</w> 24467
n yst 24466
Ex tensive</w> 24462
k ain 24461
antr al</w> 24458
Myco plasma</w> 24457
synerg istically</w> 24455
ocycl ic</w> 24455
AF M</w> 24445
o flurane</w> 24436
1. 15</w> 24426
spati otemporal</w> 24418
Gre en</w> 24417
Epidemi ological</w> 24416
IgG 4</w> 24406
di pole</w> 24397
o estradiol</w> 24396
an yl</w> 24383
spectro meter</w> 24381
Recommend ations</w> 24378
acidi fication</w> 24373
hol low</w> 24372
im pression</w> 24371
l ati 24370
ag enesis</w> 24370
injec ting</w> 24369
psycho tics</w> 24369
str atum</w> 24367
carcin ogen</w> 24366
Por tu 24365
19 9</w> 24363
ME K</w> 24360
N G 24358
AD A</w> 24358
18 2</w> 24357
cl ass 24355
ist ering</w> 24350
paradig ms</w> 24347
lipos ome</w> 24346
. The</w> 24345
14. 5</w> 24339
brid ges</w> 24334
conver t</w> 24328
dro ps</w> 24323
random ization</w> 24321
arbitr ary</w> 24319
s and</w> 24318
trans duced</w> 24316
SI I</w> 24305
pic tures</w> 24296
yros ine</w> 24295
16 1</w> 24293
rich ness</w> 24292
porph yrin</w> 24291
ib u 24287
ot ec 24286
Glc NAc</w> 24284
ind ol 24274
fig ure</w> 24272
prot ons</w> 24270
dyst roph 24266
gen i 24265
choro id</w> 24261
parac rine</w> 24261
HC s</w> 24260
S qu 24257
micro circulation</w> 24257
techn ically</w> 24256
relap ses</w> 24256
14. 3</w> 24254
iod oth 24251
Gener ation</w> 24248
bur nout</w> 24247
Psy c 24244
H ong</w> 24241
ko v</w> 24241
or ship</w> 24238
rel ess</w> 24237
permis sive</w> 24235
c f 24229
h ir 24222
ol i</w> 24222
T yr 24221
T emporal</w> 24221
k el 24219
Sahar an</w> 24219
h exam 24211
P SCs</w> 24208
ad esh</w> 24208
syn cope</w> 24208
groo ve</w> 24208
we ed</w> 24206
V P 24199
be e</w> 24197
m ole</w> 24190
ti tre 24188
simul ating</w> 24187
O B</w> 24185
pa d</w> 24185
c ies</w> 24184
Inf ants</w> 24183
compens ate</w> 24179
pur pur 24175
mesoth elioma</w> 24172
tr apped</w> 24169
it rate</w> 24168
Kne e</w> 24167
di morph 24166
therap ist</w> 24163
saph enous</w> 24163
in sem 24161
I VI 24158
Intr a</w> 24158
H3 K 24155
h ot 24152
r umen</w> 24151
Pro b 24151
neon ate</w> 24148
in ergic</w> 24146
18 8</w> 24146
cyt ologic</w> 24145
b anding</w> 24140
Lond on</w> 24137
equi val 24136
amin obutyric</w> 24133
deline ate</w> 24133
M b</w> 24126
lin oleic</w> 24125
likre in</w> 24122
hy per</w> 24119
NL R 24116
et tes</w> 24114
mer cap 24114
congru ent</w> 24114
V H</w> 24113
PBM Cs</w> 24111
g as 24103
Ley dig</w> 24101
Q Ds</w> 24100
19 77</w> 24098
in in</w> 24094
For ce</w> 24094
G 6 24092
glomerul i</w> 24088
hel min 24086
TN M</w> 24085
am ing</w> 24084
tripl et</w> 24084
uz umab</w> 24078
E ye</w> 24076
T h</w> 24076
d b</w> 24075
nerv osa</w> 24074
AT CC</w> 24073
de vised</w> 24069
if f</w> 24066
bra e</w> 24066
Sign aling</w> 24064
fri ends</w> 24055
4 a</w> 24053
un altered</w> 24053
ore sistance</w> 24048
disco ver</w> 24047
herni ation</w> 24047
main tains</w> 24045
estr al</w> 24045
shrin kage</w> 24042
an oid</w> 24041
ax es</w> 24038
v an</w> 24037
St ar 24037
un precedented</w> 24036
can cell 24036
cl otting</w> 24035
Con cer 24035
Fin land</w> 24032
assemb lies</w> 24031
sp .</w> 24027
id ene</w> 24024
10. 6</w> 24023
ure mic</w> 24022
pr ur 24019
N U 24018
tempor ally</w> 24015
He at</w> 24014
thin k</w> 24014
RB Cs</w> 24014
i atrogenic</w> 24013
AC R</w> 24013
ater ial</w> 24008
1. 13</w> 24007
D TPA</w> 24005
plas min</w> 24005
2 60</w> 23998
over dose</w> 23997
Ap pl 23996
Tr iton</w> 23989
B U 23988
sur pr 23987
muc inous</w> 23982
µ m</w> 23981
LI F</w> 23981
ag ues</w> 23980
a dex</w> 23979
CYP 3 23979
O lig 23977
10. 4</w> 23976
thyro idectomy</w> 23972
re acting</w> 23962
er h 23957
sarco plasmic</w> 23952
PD E</w> 23949
1. 14</w> 23948
troph oblast</w> 23947
cy ste 23941
0 01</w> 23940
2 D3</w> 23937
nan omaterials</w> 23935
incid ental</w> 23933
in i</w> 23926
cust om</w> 23924
10. 8</w> 23918
x 2</w> 23916
electrol ytes</w> 23916
TU NEL</w> 23913
k ish</w> 23910
0.0 15</w> 23908
18 3</w> 23901
Coun c 23901
H er</w> 23898
im aged</w> 23896
t ness</w> 23895
ec ia</w> 23895
k ingly</w> 23894
li ons</w> 23894
to uch</w> 23894
ens emble</w> 23894
per idone</w> 23891
sha res</w> 23890
La ke</w> 23888
pharmac odynamic</w> 23887
myel inated</w> 23886
re modelling</w> 23880
ir con 23878
rec tus</w> 23877
sec ond 23871
syn ucle 23869
dou bling</w> 23861
re placing</w> 23859
cylin dr 23857
CT X</w> 23854
dimer ic</w> 23854
20 3</w> 23852
in ia</w> 23848
bio degradable</w> 23847
emp ty</w> 23847
mut ans</w> 23845
autoradi ography</w> 23845
un folding</w> 23843
N L</w> 23835
a ver</w> 23831
19 76</w> 23827
PA T</w> 23827
F ull</w> 23826
phot odynamic</w> 23824
cat ast 23823
- deoxy 23822
mi an</w> 23820
def ence</w> 23819
substitu ents</w> 23813
A sc 23812
G 3</w> 23811
azep ines</w> 23811
biosens or</w> 23811
of loxacin</w> 23810
gri d</w> 23807
publ ic 23803
rim al</w> 23801
ses sive</w> 23800
Mi xed</w> 23800
16 7</w> 23798
Invol vement</w> 23798
orimetr ic</w> 23793
lac tam</w> 23791
B lue</w> 23789
si bling</w> 23786
aver aging</w> 23785
ar by</w> 23778
transf ers</w> 23777
ep razole</w> 23775
extr usion</w> 23774
SR S</w> 23772
B asal</w> 23768
resc ued</w> 23767
ing ton</w> 23765
rhyth mic</w> 23757
y ch 23750
ul ph 23750
join ing</w> 23750
G E 23746
f lex</w> 23734
specific ation</w> 23734
tac rolimus</w> 23734
ran ked</w> 23732
discipl ines</w> 23728
Wil le 23726
strateg ic</w> 23724
SC ID</w> 23722
legis lation</w> 23722
IN FO</w> 23721
ip olar</w> 23720
of en 23720
19 70</w> 23717
P ren 23715
or bit</w> 23715
al a</w> 23714
7 50</w> 23713
Del ayed</w> 23713
hyal uron 23713
sol ids</w> 23709
rub ber</w> 23709
8 01</w> 23707
den s</w> 23707
un ic 23703
haz ardous</w> 23703
popl iteal</w> 23703
six ty</w> 23702
r ash</w> 23700
At tention</w> 23700
Cr on 23698
reg ister</w> 23695
2 70</w> 23689
TH P</w> 23687
Impl ementation</w> 23683
su n</w> 23674
K L 23668
c ock 23666
tur b 23666
CEN T</w> 23666
Pol and</w> 23665
K 562</w> 23664
f im 23663
an o</w> 23660
Col orectal</w> 23660
16 9</w> 23658
N B 23655
l ut 23652
6 9 23652
ev acu 23646
is othi 23644
K ong</w> 23643
but yr 23641
associ ates</w> 23640
phen ytoin</w> 23635
sem ide</w> 23635
W ar 23624
kin d 23624
CN T</w> 23620
ta g</w> 23619
Wille brand</w> 23618
gover ning</w> 23616
amph ib 23615
ti i</w> 23613
anti arrhythmic</w> 23608
fluor o</w> 23606
p 65</w> 23604
asth enia</w> 23603
d ating</w> 23601
metallo proteinases</w> 23601
deoxy glucose</w> 23592
CA 2</w> 23591
Di et</w> 23587
Pro gram 23582
di am 23578
di one</w> 23578
Est abl 23576
AD H</w> 23572
re aches</w> 23570
uniqu ely</w> 23570
dec lining</w> 23569
t z</w> 23566
speci ation</w> 23561
moti le</w> 23561
stra ight 23558
The ore 23557
A 5</w> 23555
elucid ation</w> 23552
cre ates</w> 23551
interpre ting</w> 23551
anch ored</w> 23550
qui escent</w> 23549
HM G</w> 23548
post er 23547
phosph onate</w> 23543
am el 23541
per pendic 23541
pl ei 23537
moun ted</w> 23536
b ig 23532
Disc ussion</w> 23529
H 7</w> 23523
in clin 23521
8 -</w> 23518
PC T</w> 23518
enanti omers</w> 23514
A TION</w> 23512
X a</w> 23511
equi valence</w> 23509
w i 23506
loc king</w> 23505
f ishes</w> 23501
for amen</w> 23499
PO RT</w> 23499
alc in</w> 23499
coord inates</w> 23498
om eth 23496
neuro cognitive</w> 23495
4 th</w> 23493
instill ation</w> 23492
N I</w> 23491
plank ton</w> 23491
adv ent</w> 23486
corro sion</w> 23478
Counc il</w> 23474
W H</w> 23473
f eline</w> 23470
pharmac otherapy</w> 23467
Det ailed</w> 23467
de bris</w> 23466
ep tives</w> 23463
An at 23463
b arium</w> 23461
de er</w> 23461
is ser 23460
check list</w> 23460
objec tively</w> 23457
sinus oidal</w> 23456
W ol 23454
c ari 23451
isop ren 23449
Ti O</w> 23448
y cle</w> 23447
op ter 23446
anc e 23446
2, 3, 23446
naph thal 23443
Traum atic</w> 23442
gynec ologic</w> 23441
tem plates</w> 23439
RE CENT</w> 23437
hol es</w> 23436
electrocardi ographic</w> 23435
ac a</w> 23428
MR S</w> 23428
semiconduc tor</w> 23427
w arr 23426
my opathy</w> 23422
N Y 23417
lo bectomy</w> 23417
arteri oles</w> 23417
CR H</w> 23404
AD AM 23402
R R 23401
drin kers</w> 23397
HA ART</w> 23394
membran ous</w> 23388
Pro lifer 23388
Organ ic</w> 23385
AR E</w> 23383
ti fied</w> 23380
situ ated</w> 23374
comp uting</w> 23368
Wor king</w> 23368
Y ear</w> 23367
amelior ated</w> 23367
in novation</w> 23366
C e 23363
Lim ited</w> 23363
statis tic</w> 23356
tun ed</w> 23355
os sification</w> 23352
aut ocrine</w> 23348
X RD</w> 23343
Alph a</w> 23336
imper ative</w> 23336
waveleng ths</w> 23335
emul sions</w> 23332
broil er</w> 23329
A ir</w> 23327
F at 23325
orb it 23322
Techn ology</w> 23316
myco bacterial</w> 23315
neur o</w> 23312
ser ov 23311
chromo ph 23310
su perim 23309
bot ulinum</w> 23308
2 H</w> 23307
pro gnos 23301
multi dimensional</w> 23301
annot ation</w> 23295
bur sts</w> 23294
morph ogenetic</w> 23293
im pulse</w> 23289
S ter 23283
albe it</w> 23278
phen obarbital</w> 23269
psori atic</w> 23262
digesti bility</w> 23262
mem bered</w> 23260
bifur cation</w> 23260
vag in 23258
flavon oid</w> 23254
heterozyg osity</w> 23252
or dering</w> 23251
phyl ogeny</w> 23250
resembl ed</w> 23250
analy zer</w> 23248
superi ority</w> 23248
cris tine</w> 23248
H amil 23247
naph th 23236
1 C</w> 23232
S cal 23227
pro biotic</w> 23227
micr onucle 23227
mas king</w> 23223
tern ary</w> 23222
Nan o 23219
in cur 23218
col chicine</w> 23218
neuro fibrom 23218
resi ding</w> 23214
Ac ross</w> 23214
Gl n</w> 23210
sor tium</w> 23210
ide ation</w> 23207
D O</w> 23202
pres ently</w> 23202
Hal f</w> 23197
mimic ked</w> 23197
Tum ors</w> 23190
me detomidine</w> 23188
U g 23187
our ce</w> 23185
10. 7</w> 23183
elong ated</w> 23178
hyper thyroidism</w> 23174
re programming</w> 23172
Scan ning</w> 23172
alg al</w> 23171
neuro transmitters</w> 23170
C CA</w> 23168
Asth ma</w> 23168
con fusion</w> 23163
per tinent</w> 23163
Tr ich 23160
staphyloc occal</w> 23160
vacu oles</w> 23159
pow ered</w> 23157
PU FA</w> 23154
Fib ro 23154
Biom ar 23154
cer tainty</w> 23153
G DP</w> 23152
pul sion</w> 23152
L if 23151
Sol id</w> 23151
occup ancy</w> 23150
bov is</w> 23148
meta plasia</w> 23144
pe l</w> 23142
z oster</w> 23140
concep tu 23139
Mic ha 23139
de emed</w> 23136
Accur ate</w> 23135
vis co 23130
A beta</w> 23125
E volution</w> 23123
iti dis</w> 23123
on i 23119
ã o</w> 23118
ab normally</w> 23118
po rosity</w> 23118
un met</w> 23115
Inflam mation</w> 23115
warr ant</w> 23114
E .</w> 23105
dri ves</w> 23103
ic tal</w> 23100
1 1, 23097
With out</w> 23092
cre am</w> 23090
x ation</w> 23088
orph ine</w> 23088
carb ap 23086
ygo tic</w> 23086
nanow ires</w> 23081
mod ular</w> 23077
one uro 23071
qu it</w> 23069
immunocompet ent</w> 23060
dic s</w> 23059
tend ons</w> 23056
j i 23054
comput ation</w> 23054
ome res</w> 23053
path ogenetic</w> 23043
neuro transmission</w> 23043
scar ring</w> 23041
li sten 23040
s tive</w> 23038
g ates</w> 23038
conver gent</w> 23038
si l</w> 23036
ser a 23035
sub traction</w> 23035
integr ins</w> 23035
Oc cu 23033
gly can</w> 23032
contrac eptives</w> 23030
Ne isser 23028
Selec tion</w> 23027
phil osoph 23026
og u 23023
t osp 23020
har mon 23019
Ne o 23018
ric eps</w> 23017
ure ter</w> 23016
shap ing</w> 23016
Psychi atric</w> 23012
coun ty</w> 23005
AL E</w> 23000
affor ded</w> 23000
f mol</w> 22997
m entary</w> 22997
- 3 22995
stat ements</w> 22985
wa ke</w> 22984
ori entations</w> 22984
sp oro 22982
S ud 22980
s phen 22978
- 2. 22970
ac illin</w> 22970
K r 22968
kal likrein</w> 22965
Compo unds</w> 22965
c up</w> 22962
inf lation</w> 22962
rh o</w> 22960
titre s</w> 22959
19 6</w> 22958
immunocyto chemical</w> 22953
compl aint</w> 22952
sera dish</w> 22952
yl ine</w> 22951
re ared</w> 22949
oly b 22948
biom aterials</w> 22948
pl ating</w> 22947
efferen t</w> 22947
P PAR</w> 22945
m i</w> 22945
Pro toc 22945
wor thy</w> 22944
verte brae</w> 22937
sch wann 22932
l ass</w> 22929
andro st 22928
metr onidazole</w> 22924
SP E</w> 22924
occa sional</w> 22923
g aining</w> 22920
bil ayers</w> 22920
equiv ocal</w> 22920
hair pin</w> 22919
cod ons</w> 22915
prov oked</w> 22915
Fus arium</w> 22911
sin uses</w> 22908
bub ble</w> 22908
Proc ed 22907
ud ed</w> 22906
0.0 0</w> 22906
18 4</w> 22905
intran asal</w> 22903
l em 22902
u ality</w> 22900
ac k 22900
abil istic</w> 22900
L HRH</w> 22897
anticoagul ants</w> 22897
synchron ized</w> 22896
suppl ementary</w> 22893
de af 22890
II A</w> 22884
icro bi 22883
oligos accharide</w> 22882
phosph o</w> 22880
Compo und</w> 22878
at rophic</w> 22877
qu ater 22877
commit ted</w> 22875
AR s</w> 22874
METHO DO 22866
S CLC</w> 22865
end ym 22864
concomit antly</w> 22864
ter r 22863
amelior ate</w> 22863
di ode</w> 22862
17 6</w> 22857
m ate</w> 22855
int entional</w> 22853
infec ting</w> 22847
N erve</w> 22846
erh ans</w> 22846
Inter view</w> 22844
princ ip 22844
CB CT</w> 22843
Sam ple</w> 22841
Arab ia</w> 22837
od or 22829
phot ographs</w> 22829
clim atic</w> 22829
inser tions</w> 22827
at ed 22825
initi ative</w> 22825
17 1</w> 22821
op enic</w> 22820
G O 22819
Rec ognition</w> 22817
ac o 22813
gastro esophageal</w> 22811
18 1</w> 22803
lim bic</w> 22801
23 187</w> 22799
Sequ encing</w> 22798
tum oral</w> 22796
Mat rix</w> 22794
Immuno histochemistry</w> 22794
fluctu ation</w> 22794
precip itated</w> 22793
rec ap 22792
expec t</w> 22790
tin ct</w> 22786
osteopo rotic</w> 22786
di phosphate</w> 22777
contr asting</w> 22776
ep ist 22773
birth weight</w> 22772
G PI</w> 22767
The ory</w> 22767
lymphaden opathy</w> 22764
trans ection</w> 22761
neuro surgical</w> 22759
refer rals</w> 22759
ma z 22756
germin al</w> 22756
conc an 22755
sk y</w> 22753
abstr act</w> 22752
Vari ation</w> 22750
compl eting</w> 22749
common est</w> 22749
Clin icians</w> 22748
gre n</w> 22747
Ste re 22747
co existence</w> 22746
en ucle 22743
T Y 22739
short -</w> 22739
on egative</w> 22734
tho roughly</w> 22729
1. 16</w> 22728
t ospor 22727
alkal i</w> 22724
M ICs</w> 22722
anesthe tics</w> 22718
el ite</w> 22716
streng thening</w> 22715
6 8 22714
id er</w> 22714
gen ation</w> 22712
endo tracheal</w> 22709
b at 22705
e osin</w> 22702
stat ement</w> 22702
11. 4</w> 22701
il ance</w> 22698
H K</w> 22693
sac ral</w> 22693
0.0 12</w> 22691
OR F</w> 22689
bronch itis</w> 22689
Po or</w> 22687
C are 22677
flu oxetine</w> 22673
F OR</w> 22672
hydroly zed</w> 22672
A 2 22671
p ene</w> 22671
migr ating</w> 22671
G le 22670
P f 22668
colle agues</w> 22668
uc e</w> 22665
trop in</w> 22665
albin o</w> 22661
famil i 22660
Neisser ia</w> 22660
dimer ization</w> 22657
invari ant</w> 22657
ti az 22655
thi o</w> 22654
mo x 22650
streptoc occal</w> 22650
I PV</w> 22649
P eptide</w> 22648
sub family</w> 22644
6 J</w> 22642
eu than 22642
dou bled</w> 22640
PL GA</w> 22636
J AK 22632
di phenyl 22632
s aid</w> 22631
b lu 22628
anc estral</w> 22626
AD R</w> 22620
infiltr ates</w> 22618
Path way</w> 22616
2 12</w> 22612
recogn izes</w> 22607
work shop</w> 22602
inter vertebral</w> 22601
oxyl in</w> 22600
- mediated</w> 22598
R ate</w> 22597
I G 22594
cardi over 22593
rig idity</w> 22589
b ib 22588
ot ax 22587
unevent ful</w> 22586
accum ulating</w> 22585
al izes</w> 22583
distor ted</w> 22583
ag ency</w> 22580
re ti 22579
olip ids</w> 22579
lead er</w> 22579
Integr ated</w> 22576
vig orous</w> 22571
loc k</w> 22570
encephal ic</w> 22569
Ca P</w> 22568
ibu profen</w> 22567
H en 22565
af il</w> 22563
immun o</w> 22562
d yn 22560
d osis</w> 22559
brea king</w> 22555
al li 22551
pre menopausal</w> 22549
R ib 22547
bed side</w> 22547
hy ster 22546
compl ementation</w> 22543
F lor 22537
ana plastic</w> 22537
thous ands</w> 22534
fav our</w> 22533
Al k 22531
PI s</w> 22531
meth amphetamine</w> 22525
ex ocytosis</w> 22524
E SCC</w> 22523
osy stem</w> 22522
hypot ensive</w> 22521
par asym 22518
chol er 22514
h im 22513
illo facial</w> 22503
Lang erhans</w> 22502
A -</w> 22496
S AP</w> 22495
ir s</w> 22495
tis tic</w> 22491
man ually</w> 22490
phosphor ylase</w> 22490
phthal ate</w> 22490
Adolesc ent</w> 22490
dec ided</w> 22488
sy l</w> 22484
anti psychotics</w> 22481
Isl and</w> 22480
D 6</w> 22476
ente sis</w> 22475
sew age</w> 22474
anti epileptic</w> 22472
Pres ence</w> 22472
invasi veness</w> 22472
dyst onia</w> 22465
inter ruption</w> 22464
intim a</w> 22461
trac ted</w> 22459
bio chemistry</w> 22458
ec ules</w> 22453
benz oate</w> 22452
B angl 22450
p r</w> 22445
PK C 22442
re absorption</w> 22440
bio activity</w> 22439
Re tin 22436
osmol ality</w> 22435
G G 22433
M PA</w> 22433
peri oste 22433
lithi asis</w> 22433
cul turally</w> 22431
om i 22429
supram olecular</w> 22428
2 25</w> 22427
a . 22427
Altern ative</w> 22427
sc anned</w> 22426
ham pered</w> 22423
iodoth yronine</w> 22423
Ken ya</w> 22422
de fibrill 22419
S to 22417
carcin ogens</w> 22417
commit tee</w> 22412
op ancre 22411
transc atheter</w> 22410
Anti biotic</w> 22406
st ature</w> 22404
anch or</w> 22404
w ash</w> 22402
suspici ous</w> 22401
streng then</w> 22397
bo ost</w> 22396
pher om 22395
log arith 22394
n ings</w> 22389
out lines</w> 22389
ome ters</w> 22389
hepat otoxicity</w> 22387
ac ea</w> 22386
A HL</w> 22385
Th 17</w> 22385
cou mar 22385
h sp 22382
Seph adex</w> 22382
AC P</w> 22380
11. 8</w> 22377
Ta king</w> 22377
od om 22373
Prolong ed</w> 22372
Go og 22369
viol ent</w> 22367
dis closure</w> 22363
as cribed</w> 22361
e zing</w> 22359
modul atory</w> 22358
v able</w> 22357
E AE</w> 22357
di visions</w> 22357
dex tro 22357
elas tin</w> 22354
y litis</w> 22352
E ss 22350
glycol ytic</w> 22348
lec tins</w> 22346
HN SCC</w> 22346
let ter</w> 22344
Up per</w> 22341
salic ylic</w> 22341
obr onchial</w> 22339
Min i</w> 22337
ear ances</w> 22335
addi tives</w> 22331
im port</w> 22330
dop ing</w> 22326
mil dly</w> 22325
Ovari an</w> 22322
work force</w> 22315
discrim inating</w> 22314
ureth ra</w> 22310
imag ery</w> 22308
nephro toxicity</w> 22307
prospec ts</w> 22305
calc ified</w> 22303
cl avian</w> 22301
cyclohex imide</w> 22297
leth anolamine</w> 22296
op ens</w> 22290
perf ec 22285
seque stration</w> 22281
hyp ogly 22276
con ference</w> 22272
xen obi 22269
de grade</w> 22267
M W</w> 22258
11. 3</w> 22258
ph eochrom 22247
dr astically</w> 22246
Tren ds</w> 22246
co ar 22243
Occu pational</w> 22241
ost at</w> 22239
Li ving</w> 22239
to ur 22237
CR S</w> 22237
O -</w> 22236
toxic ological</w> 22236
oc ephal 22235
c ist 22234
PC Bs</w> 22231
pepti dase</w> 22230
Wor k</w> 22230
econ omically</w> 22229
L DL 22227
outw ard</w> 22227
min d 22226
te en 22225
at ography</w> 22224
fo etal</w> 22223
my ogenic</w> 22222
her ds</w> 22218
METHODO LOGY</w> 22216
W B</w> 22212
asc ent</w> 22211
judg ment</w> 22209
K RAS</w> 22205
18. 5</w> 22205
m . 22204
r ams</w> 22202
oglob ulin</w> 22197
In jection</w> 22195
gluc an</w> 22194
S ph 22192
al t</w> 22192
Tox oplasma</w> 22190
CD DP</w> 22189
decom pens 22184
CYP2 C 22183
tri age</w> 22182
gl ot 22180
le aching</w> 22179
00 2</w> 22179
emph ig 22179
compreh en 22176
L AD</w> 22172
re form</w> 22169
G yn 22168
weigh ing</w> 22167
convul sive</w> 22167
ph ages</w> 22166
Ch lor 22166
L ith 22165
oste ogenesis</w> 22163
F FA</w> 22159
c c 22155
com b</w> 22153
R ati 22152
cyclo dextrin</w> 22152
B P1</w> 22150
rest oring</w> 22146
read er</w> 22146
tumorig enic</w> 22146
er vical</w> 22145
epi phy 22145
on omy</w> 22143
concan avalin</w> 22142
S etting</w> 22138
p p</w> 22137
fib ros 22136
intrav itreal</w> 22134
propyl ene</w> 22132
Hun ting 22131
1. 17</w> 22130
K ir 22129
pr us 22128
tetrac h 22128
mill im 22127
U R</w> 22124
intr ons</w> 22124
ab domin 22116
row ing</w> 22114
ubiquitin ation</w> 22112
irr itation</w> 22109
PG E</w> 22108
esophag itis</w> 22107
ye asts</w> 22107
O 157</w> 22105
ac knowled 22104
SE L 22104
1 0.1</w> 22103
fun ded</w> 22102
id og 22099
oscill ation</w> 22095
w ar</w> 22093
17 3</w> 22090
judg ments</w> 22088
glut ar 22083
perme abil 22081
out let</w> 22080
osteocl asts</w> 22077
CN V</w> 22076
Recon struction</w> 22075
19 74</w> 22074
S on 22071
ak i</w> 22066
h ens</w> 22061
se al 22058
odop a</w> 22058
y -- 22057
x 1</w> 22057
str ing</w> 22057
re ds</w> 22055
in dispensable</w> 22054
ic eption</w> 22053
g um</w> 22049
viri ons</w> 22049
K ar 22047
os oma</w> 22046
atr ac 22045
sp asm</w> 22044
der ma</w> 22042
prepar ing</w> 22042
ur ate</w> 22041
h an</w> 22039
re traction</w> 22036
col o 22034
answ ers</w> 22031
esti n</w> 22028
ca stration</w> 22027
Mil d</w> 22024
CL A</w> 22020
DR B1</w> 22019
S pe 22016
fer ment 22014
remn ant</w> 22013
trans activation</w> 22012
mig rate</w> 22011
crystall ographic</w> 22011
intram edullary</w> 22011
auth orities</w> 22010
intrav entricular</w> 22004
wor st</w> 22001
Ter m</w> 21997
vit rectomy</w> 21995
ap eptide</w> 21994
lit ers</w> 21992
VE GF 21988
J u 21983
Trans fer</w> 21983
ter t</w> 21974
C ost 21971
f asted</w> 21966
co iled</w> 21961
2 4. 21954
Goog le</w> 21948
is o</w> 21947
contrac ted</w> 21947
ER CP</w> 21942
har bo 21942
AP s</w> 21933
macrom olecular</w> 21932
P TH 21931
Enter ococcus</w> 21930
read ings</w> 21927
eosin ophilia</w> 21927
prus side</w> 21927
inequ alities</w> 21925
at o 21923
p a</w> 21919
bu d</w> 21919
17 8</w> 21919
var ices</w> 21918
fore sts</w> 21914
perin eal</w> 21912
di ap 21910
pa yment</w> 21909
Sim ulation</w> 21908
restor ative</w> 21906
mem ories</w> 21904
motiv ated</w> 21904
Se q</w> 21903
obstac les</w> 21901
h us 21899
thromb olytic</w> 21897
ac i 21895
regi stries</w> 21893
gad olinium</w> 21893
acycl in</w> 21885
ang itis</w> 21884
otyp ically</w> 21883
myri state</w> 21882
P BL</w> 21881
tr i</w> 21877
if era</w> 21872
s na 21871
over looked</w> 21868
obacter iaceae</w> 21860
smo ked</w> 21859
surfac tants</w> 21858
anc ement</w> 21855
hydrophob icity</w> 21855
si aly 21851
ul line</w> 21851
aer ob 21844
herbi v 21844
F ast</w> 21842
T u 21841
hyper lipidemia</w> 21841
Ex p 21840
con crete</w> 21838
Lab el</w> 21836
DE X</w> 21835
en yl</w> 21834
Isra el</w> 21833
b inge</w> 21832
O SCC</w> 21830
lipo philic</w> 21828
be et 21826
G AD</w> 21823
straight forward</w> 21822
TP 53</w> 21820
inflam mas 21819
DO I</w> 21819
Pharmac ological</w> 21818
Ech ocardi 21817
reimbur sement</w> 21817
S AM</w> 21813
t etro 21812
ser ogrou 21809
w earing</w> 21806
peri vascular</w> 21805
s pro 21802
mom ents</w> 21802
manip ulations</w> 21801
D DM</w> 21799
sw abs</w> 21796
cord ant</w> 21796
centr ally</w> 21795
Liqu id</w> 21795
am enable</w> 21790
Ca M</w> 21790
ü ll 21788
eng aging</w> 21784
T ES</w> 21782
sev oflurane</w> 21782
counter part</w> 21782
l iz 21781
re finement</w> 21781
out door</w> 21778
ket t 21774
disp osal</w> 21770
nor adrenergic</w> 21769
flow ers</w> 21767
k yph 21756
nour ished</w> 21756
sp ra 21755
D BA</w> 21752
y og 21751
pul l</w> 21751
V 3</w> 21747
ep iness</w> 21746
inter medi 21744
ur gently</w> 21740
acet amol</w> 21740
lingu istic</w> 21740
Reson ance</w> 21740
Multi variable</w> 21739
N early</w> 21733
cataly ze</w> 21733
in correct</w> 21731
at ergic</w> 21731
2 56</w> 21730
dr ift</w> 21727
A cin 21723
b ank</w> 21722
infec t</w> 21722
end orphin</w> 21721
act one</w> 21721
her d</w> 21720
D ri 21717
R us 21714
N d</w> 21714
educ ators</w> 21714
tub al</w> 21712
vin cristine</w> 21710
repeat ability</w> 21709
cent rom 21707
u ation</w> 21705
myocardi tis</w> 21705
idog rel</w> 21705
d am</w> 21703
CD 25</w> 21700
SC N</w> 21696
6 4 21694
arter itis</w> 21694
A m</w> 21693
j i</w> 21693
h oney</w> 21692
vascul arized</w> 21689
le sioned</w> 21687
R are</w> 21686
1. 18</w> 21686
it ual</w> 21685
Ther mal</w> 21683
leukotri ene</w> 21682
appreci ated</w> 21681
am ne 21680
detec tors</w> 21680
en ol</w> 21678
per taining</w> 21676
campa ign</w> 21670
ab an</w> 21669
in completely</w> 21666
au xin</w> 21666
20 9</w> 21660
meningi oma</w> 21660
Pak istan</w> 21660
tiaz em</w> 21660
Criter ia</w> 21658
re uptake</w> 21657
frag ile</w> 21656
10. 9</w> 21656
imetr ic</w> 21656
el ementary</w> 21655
Differenti ation</w> 21655
leaf let</w> 21654
el uting</w> 21651
de fl 21649
j ing</w> 21647
ecta sia</w> 21644
CA 3</w> 21641
Con nec 21639
poly cyclic</w> 21638
back grounds</w> 21636
glut en</w> 21633
aldehy des</w> 21633
mon os 21631
D SS</w> 21630
A h 21629
o kinin</w> 21629
ig enic</w> 21626
om n 21624
aly zed</w> 21624
3 β</w> 21623
c nem 21622
FV C</w> 21622
19 8</w> 21618
l axis</w> 21612
faec alis</w> 21610
c 2</w> 21607
AC A</w> 21606
M ass 21605
ep endym 21603
my cel 21602
adren ocortical</w> 21602
ip ple</w> 21601
E F 21600
agricul ture</w> 21600
review ing</w> 21594
di version</w> 21593
sc ars</w> 21589
B enz 21582
mon oph 21578
sub scale</w> 21578
ell ae</w> 21571
TI s</w> 21570
P he 21568
con dom</w> 21567
inform al</w> 21565
Em bry 21561
ST AT</w> 21559
Ch ic 21558
qual ified</w> 21558
inc enti 21556
otec an</w> 21555
a g</w> 21554
gluc osidase</w> 21553
intrac or 21553
ab ut 21552
11. 6</w> 21551
Accur acy</w> 21545
pre existing</w> 21543
fer ric</w> 21541
classi fications</w> 21539
7, 8</w> 21538
re generating</w> 21537
ur tic 21537
confir matory</w> 21536
lith o 21532
Mac ro 21527
prost acyclin</w> 21524
fasc ia</w> 21523
tr in 21521
ox al</w> 21513
Ap plications</w> 21513
ly ase</w> 21508
melan in</w> 21502
anis otropic</w> 21501
I MRT</w> 21500
o .</w> 21496
rid ge</w> 21496
l ot 21495
ox ib</w> 21494
fe deral</w> 21494
get ting</w> 21491
Inter fer 21488
Non etheless</w> 21488
exec ution</w> 21488
ogene tically</w> 21487
ad el 21485
n s 21481
k its</w> 21477
benz yl 21469
ag er</w> 21467
perpendic ular</w> 21467
ch in</w> 21466
f in</w> 21465
on ide</w> 21465
p etro 21464
sub divided</w> 21464
arg ued</w> 21463
retin oblastoma</w> 21461
hor seradish</w> 21460
E PS</w> 21453
Pr P</w> 21452
S per 21451
19 7</w> 21448
M SI</w> 21447
C ON</w> 21445
h un 21442
stitu tion</w> 21442
smar t 21442
TN BC</w> 21441
Der mat 21441
trans aminase</w> 21438
Micro bial</w> 21435
cop enia</w> 21433
ardi ac</w> 21432
head aches</w> 21432
DN ase</w> 21431
Valu e</w> 21431
me iosis</w> 21429
Al tered</w> 21426
B asic</w> 21424
Re ference</w> 21424
T uberculosis</w> 21423
R est 21422
ex am</w> 21422
vin ces</w> 21421
sym b 21420
tonsi ll 21419
ti vitis</w> 21418
gen otypic</w> 21416
F ab</w> 21413
vi al</w> 21413
serop re 21412
long est</w> 21410
Cl oning</w> 21407
iev ing</w> 21406
nicotin amide</w> 21404
inte rests</w> 21402
Re tinal</w> 21402
5 4 21400
tig mine</w> 21397
defin es</w> 21392
them atic</w> 21391
hab di 21391
Mater ial</w> 21387
enc h</w> 21385
quinol ones</w> 21383
in c</w> 21380
press ors</w> 21378
lat ors</w> 21378
Gastro intestinal</w> 21377
public ly</w> 21375
il tration</w> 21374
arbox ylic</w> 21370
Dig ital</w> 21369
ospec ific</w> 21369
ogen in</w> 21368
mit omycin</w> 21368
bor on</w> 21363
om atic</w> 21362
op on 21361
encoun ters</w> 21361
MM Ps</w> 21360
fluoro scopy</w> 21359
Bri ef</w> 21358
introduc es</w> 21357
maxim ally</w> 21353
l an 21350
tain ties</w> 21350
scav enger</w> 21346
Tex as</w> 21345
Contr ary</w> 21343
ut y</w> 21341
Uni on</w> 21341
ith romycin</w> 21337
contin ent</w> 21337
inter acted</w> 21336
di hydro</w> 21335
Psyc INFO</w> 21335
pol yn 21334
percei ve</w> 21333
im migrants</w> 21332
leng thening</w> 21330
lac tin 21322
P PD</w> 21320
si mian</w> 21319
b ad</w> 21317
ref ers</w> 21317
sc ene</w> 21316
dang erous</w> 21314
Ne ph 21312
s lowing</w> 21310
ref er</w> 21310
palp able</w> 21308
h i</w> 21307
11. 7</w> 21307
spac ing</w> 21307
d ases</w> 21304
cl aim</w> 21301
L atin 21297
id omide</w> 21297
E G</w> 21296
un covered</w> 21294
en tion</w> 21293
immunohisto chemically</w> 21289
EV s</w> 21286
kn ock</w> 21283
nitro prusside</w> 21282
endocardi al</w> 21278
Trans mission</w> 21270
metaph ase</w> 21270
Nanop articles</w> 21270
equival ents</w> 21268
stro kes</w> 21265
pum ps</w> 21265
di cl 21264
er us</w> 21263
vin yl 21263
in conclusive</w> 21261
soci eties</w> 21261
De fin 21259
miner als</w> 21254
S cop 21253
W ales</w> 21253
1. 21</w> 21250
Ar gen 21249
blast ocyst</w> 21242
encomp assing</w> 21241
drin k</w> 21240
tempo romandibular</w> 21240
casc ades</w> 21239
Cryst al</w> 21238
lay ing</w> 21235
S ha 21233
Deli very</w> 21233
val is</w> 21231
ech an 21230
persist s</w> 21227
Repor ting</w> 21221
lin k 21220
Prof ile</w> 21216
us ate</w> 21214
sc ru 21214
ear th 21212
Del etion</w> 21212
wave form</w> 21212
la ev 21210
cry o 21209
synucle in</w> 21209
ow ad 21205
p 2</w> 21204
Rou tine</w> 21204
Eng ine 21203
l ides</w> 21202
H an</w> 21200
Jour nal</w> 21196
K I</w> 21195
marg inally</w> 21194
al istic</w> 21190
ocompati ble</w> 21189
ID DM</w> 21188
ometr ically</w> 21186
com a</w> 21185
ren ic</w> 21185
K u 21182
cylindr ical</w> 21181
R ad 21179
accid ental</w> 21177
T CDD</w> 21174
AP A</w> 21172
v ice</w> 21171
anastom oses</w> 21171
la ke</w> 21167
Ac tion</w> 21166
extrav as 21166
thre e 21160
B ra 21156
Develop ing</w> 21155
f ission</w> 21154
chymo trypsin</w> 21152
owad ays</w> 21148
brom o</w> 21144
CB 1</w> 21138
quad riceps</w> 21135
c ar</w> 21131
M uch</w> 21127
onc ological</w> 21126
ap ple</w> 21125
sandw ich</w> 21125
dam s</w> 21123
syn ergy</w> 21122
μ mol</w> 21121
G el</w> 21118
HO MA</w> 21118
sh allow</w> 21117
immor t 21117
P h</w> 21116
tr ate</w> 21116
compl i 21114
D WI</w> 21110
PR A</w> 21110
Cron bach</w> 21109
20 7</w> 21100
sugg estion</w> 21100
R N</w> 21098
polypo sis</w> 21098
insul t</w> 21095
furo semide</w> 21095
fer mented</w> 21092
g t</w> 21086
17 9</w> 21086
b ine</w> 21083
oc clusions</w> 21078
lymphaden ectomy</w> 21072
prolifer ator</w> 21066
cnem ius</w> 21065
H ome</w> 21062
hand le</w> 21061
ST I</w> 21060
al th</w> 21058
1 c</w> 21056
alcohol ics</w> 21053
chemoradi otherapy</w> 21049
oz ym 21047
anil ine</w> 21047
sustain ability</w> 21045
dosi metry</w> 21044
tetra zol 21041
fluo roph 21037
pass ing</w> 21034
de als</w> 21032
In duc 21032
res um 21030
ra inf 21027
calc ifications</w> 21027
chem opre 21025
at eness</w> 21024
ell ularly</w> 21024
Pren atal</w> 21021
Cor relations</w> 21020
to oth 21019
arth ritic</w> 21018
R M 21017
classi fier</w> 21014
home ostatic</w> 21013
oselec tivity</w> 21012
path ologically</w> 21011
m atism</w> 21010
disrup ting</w> 21010
PE P</w> 21005
contribut or</w> 21005
environ mentally</w> 21003
B ay</w> 20998
Ac qu 20995
Physi ological</w> 20993
eff usions</w> 20988
prin ts</w> 20987
HC MV</w> 20983
gangli onic</w> 20983
Gle ason</w> 20978
li ximab</w> 20975
k a 20973
complex ed</w> 20968
Transcrip tion</w> 20967
tro chan 20964
work flow</w> 20962
ad o</w> 20959
b an</w> 20957
mor bidities</w> 20954
ex agg 20951
15. 5</w> 20951
ran king</w> 20950
li t</w> 20947
Es ophag 20944
void ing</w> 20943
er ul 20942
educ ated</w> 20941
t PA</w> 20940
OV X</w> 20936
w ires</w> 20932
un resectable</w> 20928
El derly</w> 20927
bac illi</w> 20924
p om 20921
Tran sl 20921
si ans</w> 20919
H ip</w> 20918
In di 20917
deman ding</w> 20916
CD I</w> 20910
sh ad 20909
re as</w> 20908
ill es</w> 20908
E stro 20904
vox el</w> 20904
l uminescence</w> 20897
compet ency</w> 20897
19 4</w> 20896
hyper algesia</w> 20895
phosphat ases</w> 20894
ti go</w> 20890
Hunting ton</w> 20888
op eptides</w> 20887
unc tional</w> 20887
R o</w> 20884
ear able</w> 20884
rh od 20884
CY P</w> 20881
W all 20874
war ning</w> 20874
met ropolitan</w> 20871
gastroenter itis</w> 20871
at ous</w> 20869
hydro dynamic</w> 20867
sub clavian</w> 20866
cim etidine</w> 20866
9 37</w> 20865
18 9</w> 20863
hab it</w> 20862
chew ing</w> 20862
us cul 20861
se eded</w> 20860
assemb le</w> 20860
r ide</w> 20858
oc ene</w> 20855
iss ure</w> 20855
P 3 20854
ho g</w> 20853
hydroxy tryptamine</w> 20852
uro kinase</w> 20851
19 1</w> 20844
follow up</w> 20844
ff er</w> 20842
Met abol 20842
N ative</w> 20841
T ib 20841
tran su 20841
compl ained</w> 20839
man soni</w> 20839
parasym pathetic</w> 20837
1 7.5</w> 20836
MR A</w> 20836
pro lactin 20835
rec tom 20834
cyto keratin</w> 20832
explic itly</w> 20832
men ting</w> 20831
incorpor ates</w> 20829
A 23187</w> 20827
L IN 20825
ar k 20825
bl y 20822
immuno assays</w> 20822
EC D</w> 20818
H1 N1</w> 20816
ten s</w> 20813
DE Gs</w> 20807
an ut</w> 20805
anti thrombin</w> 20800
tub ul 20800
myel operoxidase</w> 20799
l ose</w> 20796
three fold</w> 20793
C aco</w> 20791
TH C</w> 20790
VE C</w> 20790
cin nam 20787
K e 20784
lipos omal</w> 20784
om eprazole</w> 20783
gon orrho 20783
modi fiable</w> 20781
surg e</w> 20779
0.0 13</w> 20777
a ke</w> 20776
onec rosis</w> 20776
0.0 16</w> 20774
conjunc tivitis</w> 20772
meningi omas</w> 20772
in habit 20771
avir in</w> 20771
fe at 20768
S GA</w> 20763
l eton</w> 20762
n ex 20761
av a</w> 20758
I SO</w> 20756
glyc ans</w> 20756
12. 3</w> 20752
hard ware</w> 20752
Surge ons</w> 20751
re ment</w> 20750
ep ide 20750
o kinase</w> 20749
ab er 20749
intenti ons</w> 20747
transpos on</w> 20745
hyper polarization</w> 20741
end arte 20736
ev o 20735
Meth yl 20735
habdi tis</w> 20732
calibr ated</w> 20730
organis ation</w> 20729
expec tation</w> 20726
haemat ological</w> 20724
motiv ational</w> 20722
Import ance</w> 20718
In corpor 20713
collo id</w> 20710
olys in</w> 20706
warran ts</w> 20706
man n 20703
HE V</w> 20701
st ellate</w> 20700
Aca demic</w> 20700
anaesthe tized</w> 20700
os up 20699
phosphoinosi tide</w> 20698
ne bul 20694
mig rated</w> 20693
bus iness</w> 20693
pharmac ist</w> 20690
gel atin 20688
par ap 20685
Adhe rence</w> 20682
G ERD</w> 20679
Qu e 20678
loos ening</w> 20676
inter course</w> 20672
reg ressions</w> 20669
mu g</w> 20669
abstr acts</w> 20669
smo oth 20667
c r</w> 20665
ell er</w> 20662
PD AC</w> 20658
Sec re 20658
reproduc e</w> 20652
il le</w> 20651
ser ologic</w> 20651
0.0 14</w> 20651
1. 23</w> 20649
por ts</w> 20645
ti ometry</w> 20642
IK V</w> 20642
ser onegative</w> 20641
Inf ec 20639
Tox icity</w> 20639
E IA</w> 20634
19 3</w> 20633
z ircon 20629
k J</w> 20626
Fl ex 20624
o u</w> 20621
1. 19</w> 20621
hydra ulic</w> 20619
terr itory</w> 20618
met atar 20614
spe eds</w> 20613
bronch oscopy</w> 20613
esc ens</w> 20611
AS 1</w> 20609
Or ig 20608
d oxin</w> 20607
colli sion</w> 20607
p assi 20606
bio tin 20606
ather ogenic</w> 20604
manuscrip t</w> 20599
reflec tance</w> 20598
T ob 20597
gluc opyran 20597
Inf ections</w> 20596
Met abolism</w> 20594
appreci able</w> 20594
Im paired</w> 20590
sec ure</w> 20588
re produced</w> 20587
sk illed</w> 20586
air borne</w> 20583
j ump</w> 20582
bil lion</w> 20578
12. 8</w> 20578
er ization</w> 20575
S ul 20572
V CAM</w> 20566
deaf ness</w> 20564
endoc rin 20563
R L</w> 20562
rot ating</w> 20561
Dru gs</w> 20554
g in</w> 20550
12. 2</w> 20550
K T</w> 20549
th enium</w> 20547
Swit zer 20546
To ol</w> 20544
non diabetic</w> 20539
E con 20537
F a 20537
bl unted</w> 20530
blo ts</w> 20529
I b</w> 20527
2 2. 20525
M PS</w> 20525
oper able</w> 20525
thro at</w> 20525
trans late</w> 20522
c oc 20520
diver sification</w> 20520
Wil cox 20518
Stand ardi 20514
Nitr ic</w> 20512
itr y</w> 20510
extra hepatic</w> 20505
ol aryng 20504
carbam azepine</w> 20504
H ER</w> 20501
13. 2</w> 20501
in sured</w> 20497
re feren 20497
batter ies</w> 20496
un a</w> 20495
seropre valence</w> 20494
lac to 20491
denti stry</w> 20489
characteris ation</w> 20484
nit rous</w> 20483
I AP</w> 20482
B on 20481
dys functions</w> 20481
pecul iar</w> 20479
ker atitis</w> 20477
loc ked</w> 20474
cost al</w> 20474
Quanti fication</w> 20470
sulf oxide</w> 20468
um n</w> 20467
hyal uronic</w> 20467
scre ens</w> 20466
admin istering</w> 20463
ro t</w> 20462
ev ening</w> 20462
Switzer land</w> 20461
Auth ors</w> 20459
haem odialysis</w> 20458
Arter y</w> 20456
In s</w> 20455
p est</w> 20454
B ul 20454
Meth od 20454
tryp tic</w> 20454
NI DDM</w> 20452
spec ulate</w> 20450
multi level</w> 20450
2 alpha</w> 20448
Inhib itors</w> 20448
soci etal</w> 20445
discord ant</w> 20443
inter net</w> 20442
identi fiable</w> 20442
lib itum</w> 20442
exacerb ated</w> 20437
hyper cap 20436
Contr ast</w> 20434
as perg 20428
1. 20</w> 20427
W ash 20426
es el</w> 20426
uc tal</w> 20426
atri sts</w> 20422
in -</w> 20419
y algia</w> 20418
Initi ally</w> 20417
H cy</w> 20416
neuro toxic</w> 20411
DHE A</w> 20411
cl amping</w> 20408
St ability</w> 20404
con gen 20403
question ed</w> 20401
analy te</w> 20399
ru g 20398
Re pair</w> 20398
.0 3</w> 20393
3 7.5</w> 20390
in ae</w> 20384
de tr 20381
Protec tive</w> 20381
oper ators</w> 20380
Acc ess</w> 20377
dys functional</w> 20376
Targ et</w> 20375
glycer in</w> 20371
be ha 20370
i ensis</w> 20368
comprehen sively</w> 20367
13. 6</w> 20366
P it 20365
K v 20364
blood stream</w> 20363
dur able</w> 20361
Wilcox on</w> 20361
c iti 20358
hist ones</w> 20356
SC F</w> 20356
Im pair 20355
cont acted</w> 20354
al ert</w> 20352
en al 20351
col orimetric</w> 20350
ec z 20349
spir ation</w> 20349
cross linking</w> 20349
kine sis</w> 20347
oder m</w> 20347
A im 20346
ca esarean</w> 20344
5 D</w> 20341
H SC</w> 20340
m E 20338
G ro 20336
C ob 20335
MI S</w> 20334
physi otherapy</w> 20333
exc iting</w> 20332
icrobi als</w> 20332
li er</w> 20331
flav one</w> 20327
ev en 20323
TR PV 20323
ang eal</w> 20321
7 3 20319
ere b 20318
pul l 20317
chemo attrac 20317
pol es</w> 20316
insul a</w> 20316
2 3. 20314
worsen ed</w> 20313
Str ong</w> 20311
myco bacteria</w> 20307
Per sistent</w> 20303
T CM</w> 20300
CO L 20300
proce eds</w> 20300
ligam ents</w> 20300
exp ense</w> 20297
V PA</w> 20296
B AT</w> 20296
R AD 20294
pig e 20293
glutam atergic</w> 20293
pro vo 20291
C 4 20290
per ovsk 20289
at tainment</w> 20284
Hist opathological</w> 20282
hy pos 20279
indu stries</w> 20278
Syn thetic</w> 20277
ad ds</w> 20276
recover ies</w> 20275
DM D</w> 20275
quinol one</w> 20272
SH 2</w> 20271
annot ated</w> 20271
benzodi azepines</w> 20268
ost om 20263
zin ess</w> 20263
R S 20262
gastro cnemius</w> 20262
conflic ts</w> 20261
2 22</w> 20259
in ant</w> 20258
carc ass</w> 20256
D end 20255
hund reds</w> 20255
omorph ine</w> 20255
l ated</w> 20254
han dic 20254
K C</w> 20248
aneurys mal</w> 20247
adver tis 20246
const antly</w> 20243
12. 1</w> 20243
T TP</w> 20241
enor m 20236
prote oglycans</w> 20235
princip ally</w> 20235
T ax 20233
anth in</w> 20232
ing ent</w> 20231
ste adily</w> 20227
aw s</w> 20225
Con comit 20221
fer rom 20215
pro long</w> 20214
dise qu 20213
sep tic 20211
Mul tic 20211
in tu 20210
vol tam 20208
R es</w> 20206
sec tors</w> 20206
ighten ed</w> 20205
L S 20203
epididym al</w> 20203
insectic ide</w> 20202
anticonvuls ant</w> 20200
sten oses</w> 20199
t rough</w> 20198
ag mus</w> 20189
op rim</w> 20187
L and 20182
neuro physiological</w> 20181
D B</w> 20180
is omerase</w> 20180
Sup pression</w> 20179
e ip 20178
C ore</w> 20177
allo ys</w> 20177
econ omy</w> 20176
fum ig 20176
be ar</w> 20175
val ine</w> 20175
exchang er</w> 20175
plas monic</w> 20173
constr aint</w> 20172
fric tion</w> 20172
1. 22</w> 20171
fibrin olysis</w> 20171
sea water</w> 20171
lin s</w> 20170
Tur kish</w> 20169
radi ograph</w> 20168
otroph in</w> 20166
v itr 20165
mm 2</w> 20161
interpre tations</w> 20159
galact os 20158
2 14</w> 20154
a es 20154
R O</w> 20154
Tric ho 20154
D eri 20153
dam ages</w> 20153
co variance</w> 20151
for aging</w> 20149
co m</w> 20149
draw ing</w> 20148
te es</w> 20145
amox icillin</w> 20145
Fat ty</w> 20145
h rs</w> 20144
re taining</w> 20144
gr as 20144
obut amine</w> 20142
SE P</w> 20130
di p 20129
condu it</w> 20129
form ulas</w> 20127
PT CA</w> 20126
c ulation</w> 20125
k 2</w> 20125
dis infection</w> 20123
choler ae</w> 20119
tetrazol ium</w> 20119
I on</w> 20118
ery th 20117
PT A</w> 20116
fer rin</w> 20115
Hybri d</w> 20111
compet e</w> 20110
excep tional</w> 20108
12. 6</w> 20105
ser iously</w> 20102
Electr ical</w> 20100
cortic es</w> 20095
tit udes</w> 20092
5 000</w> 20091
DO PA</w> 20083
ba g</w> 20082
steril ization</w> 20073
wa it</w> 20072
oval bumin</w> 20072
CB D</w> 20071
incid ents</w> 20071
C ushing</w> 20070
Y 1</w> 20070
un ra 20068
expendit ures</w> 20066
di phenyl</w> 20064
et obacter</w> 20064
l ax 20063
E Q</w> 20063
f ection</w> 20061
rel and</w> 20061
En dom 20061
Dep ending</w> 20061
B AC</w> 20059
az o</w> 20054
e ased</w> 20051
Par tic 20047
stac king</w> 20044
uncer tainties</w> 20042
z ona</w> 20041
hyp no 20040
r al 20034
if er</w> 20034
CO OH</w> 20032
din ucleotide</w> 20030
engra ft 20026
enorm ous</w> 20026
lev odopa</w> 20021
Mon oclonal</w> 20019
ultrason ographic</w> 20019
laev is</w> 20017
u pr 20016
micro arrays</w> 20014
d oll 20006
exha ustion</w> 20002
F X 19999
po d</w> 19998
circu itry</w> 19996
pro vinces</w> 19994
gener ator</w> 19993
Lym e</w> 19992
20 8</w> 19991
ph os 19990
et ching</w> 19989
a 1</w> 19985
v ac 19983
de aminase</w> 19983
physic s</w> 19978
st ations</w> 19976
sti n</w> 19976
purpur a</w> 19968
M apping</w> 19967
- N</w> 19966
schist osomiasis</w> 19963
bur g 19961
dop a</w> 19959
correspond ence</w> 19959
splic ed</w> 19955
combin atorial</w> 19953
prob abilistic</w> 19951
arthro scopy</w> 19947
Ac tivities</w> 19945
r uled</w> 19941
6 2 19941
Is su 19939
ff in</w> 19937
sp ines</w> 19936
Mel an 19933
eli ac</w> 19932
absorp tiometry</w> 19932
anthrop ogenic</w> 19931
Techn ical</w> 19930
ester ol</w> 19928
HMG B1</w> 19927
Ca enor 19926
l ic</w> 19924
LI MI 19923
engraft ment</w> 19922
tr id 19920
Com pon 19917
cass ette</w> 19917
w illing</w> 19914
redund ant</w> 19914
respon der</w> 19912
id ated</w> 19909
9 8 19908
ut ter</w> 19908
cle ared</w> 19907
p 27</w> 19906
diure tics</w> 19903
carcin oid</w> 19901
St .</w> 19897
recre ational</w> 19893
p I</w> 19889
equ ilib 19889
N N 19882
metabol omics</w> 19880
Path ology</w> 19878
inf er</w> 19877
19 73</w> 19873
quater nary</w> 19872
clu es</w> 19869
withdraw n</w> 19867
Re habilitation</w> 19863
Enter obacteriaceae</w> 19862
coar se</w> 19862
iso zymes</w> 19859
4, 4</w> 19859
D am 19857
trem end 19855
resol ving</w> 19854
Path ological</w> 19853
Inv asive</w> 19852
poly p</w> 19849
por ation</w> 19846
C yp 19844
gly caemic</w> 19844
h anded</w> 19842
ofen ac</w> 19842
bronch odi 19841
poly styrene</w> 19840
ic it</w> 19839
f 1</w> 19836
intim ate</w> 19834
In don 19829
sh rim 19828
CS C</w> 19828
r 2</w> 19826
FI TC</w> 19826
F v</w> 19825
Caenor habditis</w> 19825
wh el 19824
1. 24</w> 19824
mac aques</w> 19823
Ep ithelial</w> 19823
phot ore 19822
Incre ases</w> 19822
HR QOL</w> 19820
Av ail 19819
0.0 11</w> 19818
Heter ogene 19816
a eg 19814
succ e 19814
HC A</w> 19814
im pregn 19813
Nor we 19808
allevi ated</w> 19805
empir ically</w> 19804
deoxyn ucle 19804
in t</w> 19801
ST R</w> 19801
st yles</w> 19800
Acin etobacter</w> 19799
par av 19796
AN S</w> 19796
SO UR 19796
2 23</w> 19795
escal ation</w> 19790
b abo 19788
en ever</w> 19786
hydro cortisone</w> 19785
Bar ri 19785
12. 0</w> 19783
stric tures</w> 19781
sc rip 19780
Modi fication</w> 19780
K B</w> 19779
6 3 19778
2 21</w> 19775
s chol 19773
de bil 19768
din itro 19768
sa w</w> 19765
bever ages</w> 19763
Le sions</w> 19761
or g</w> 19757
suc tion</w> 19753
p un 19752
n ation</w> 19749
work up</w> 19747
varic ella</w> 19747
refu ge 19747
d y</w> 19746
di asis</w> 19745
sub dural</w> 19741
Pro bl 19741
A ther 19739
lu ted</w> 19739
chondro itin</w> 19736
end o</w> 19731
special ties</w> 19729
z ole</w> 19728
P ig 19727
hat ching</w> 19727
coun ties</w> 19726
Mar kov</w> 19726
in tub 19724
Be ing</w> 19723
cannabin oid</w> 19720
Gener ally</w> 19715
pal atal</w> 19714
n am</w> 19711
E 7</w> 19711
am iod 19710
bi directional</w> 19710
3 B</w> 19709
R y 19709
p emphig 19704
in ev 19695
p. o.</w> 19693
omet abolic</w> 19686
Ultra structural</w> 19685
cryop reservation</w> 19685
e tings</w> 19684
fo rec 19681
ter rit 19680
mus h 19679
Sep ar 19675
air flow</w> 19672
ed es</w> 19671
th umb</w> 19668
contrac ture</w> 19668
1, 1</w> 19666
tur bul 19662
ing ing</w> 19661
St able</w> 19660
chloro form</w> 19659
V enous</w> 19657
minim ized</w> 19657
ex ocrine</w> 19656
I reland</w> 19653
Z inc</w> 19651
su staining</w> 19650
d G</w> 19647
ori e</w> 19646
cyst ectomy</w> 19646
c Tn 19645
fo ster</w> 19645
bub bles</w> 19641
l es 19640
eme dicine</w> 19638
prolifer ate</w> 19637
hep t 19635
amiod arone</w> 19634
ogene tics</w> 19619
T od 19614
th ened</w> 19614
path s</w> 19613
or ific 19612
ou rea</w> 19612
ep ic 19612
prim or 19609
V ar 19608
las ers</w> 19606
S SI</w> 19604
p ts</w> 19604
poly amine</w> 19603
oc ta 19602
psych omotor</w> 19602
sup posed</w> 19599
induc tive</w> 19598
glucuron ide</w> 19597
H HV</w> 19595
sin usitis</w> 19595
volunte er</w> 19595
ifer ous</w> 19594
c et 19593
4 80</w> 19593
rh am 19593
IC E</w> 19591
ex presses</w> 19589
dynam ically</w> 19586
conserv atively</w> 19586
ch in 19581
Influ enza</w> 19581
sing leton</w> 19579
Hydro xy 19578
AN CA</w> 19575
proton ated</w> 19573
Loc alization</w> 19572
practi cing</w> 19570
synchron y</w> 19570
L act 19569
Hist ologic</w> 19567
11. 9</w> 19566
T CP</w> 19561
cre di 19557
Moder ate</w> 19557
conti gu 19557
ri es</w> 19554
F oun 19553
immunob lot</w> 19552
bud ding</w> 19551
en de 19550
analy sing</w> 19550
Perc eived</w> 19550
w alled</w> 19547
ta iled</w> 19547
V DR</w> 19545
G las 19539
th western</w> 19539
PM Ns</w> 19538
dist ension</w> 19537
aspar tic</w> 19537
G L</w> 19536
hyper plastic</w> 19536
gingi valis</w> 19534
Ob ste 19529
g est</w> 19526
n u</w> 19525
G AP</w> 19525
p ie 19523
persist ently</w> 19519
therm ally</w> 19516
p tive</w> 19511
dicl ofenac</w> 19507
uni or</w> 19505
AT 2</w> 19502
synthesi zing</w> 19502
S in 19501
inflammas ome</w> 19500
methyl prednisolone</w> 19498
shar ply</w> 19498
g m</w> 19497
cad aver</w> 19496
cul turing</w> 19493
em in 19492
cor al</w> 19492
soci ally</w> 19492
myel odys 19492
reconstruc t</w> 19492
St reng 19491
arti fact</w> 19491
Nu tritional</w> 19491
Ne utroph 19489
fel low 19488
metast asi 19486
cil ia</w> 19484
ent gen 19482
SC P</w> 19482
NO D</w> 19478
aflat oxin</w> 19478
Schist osoma</w> 19477
eip t</w> 19477
he ifers</w> 19475
V entricular</w> 19473
Ch est</w> 19469
C MR</w> 19468
jejun i</w> 19465
path ies</w> 19463
hemat uria</w> 19456
osp inal</w> 19454
at en 19452
Exc ept</w> 19451
antim icrobials</w> 19444
MT 1</w> 19442
D AT</w> 19441
RE PORT</w> 19437
PA M</w> 19437
fl our</w> 19435
k an 19434
function alization</w> 19432
As n</w> 19431
mas titis</w> 19431
rel ia</w> 19429
spectro metric</w> 19428
rifamp icin</w> 19426
mar king</w> 19425
Brd U</w> 19424
ret t</w> 19422
estro genic</w> 19421
CT C</w> 19420
erup tion</w> 19419
path ologists</w> 19418
oroph aryngeal</w> 19418
cholest asis</w> 19414
N MD 19411
us ability</w> 19410
che ese</w> 19408
S po 19405
flu conazole</w> 19404
ther ing</w> 19403
Pl us</w> 19403
W L</w> 19402
N ep 19399
c m 19397
ac ul 19396
anaphy laxis</w> 19393
Theore tical</w> 19391
ar c</w> 19390
practi cally</w> 19390
plas mac 19388
h older</w> 19386
4 D</w> 19386
H SP</w> 19385
evid ences</w> 19384
X 3</w> 19383
Gre at</w> 19382
C utaneous</w> 19379
at om 19378
im printed</w> 19378
inhabit ants</w> 19378
ster nal</w> 19376
vers a</w> 19374
X Y</w> 19373
d otoxin</w> 19372
thou ghts</w> 19372
dil tiazem</w> 19370
emphasi zing</w> 19369
T AC</w> 19359
max illa</w> 19358
anne aling</w> 19357
ast ly</w> 19356
mis use</w> 19356
NA SH</w> 19355
m ant 19354
Deri ved</w> 19350
out puts</w> 19346
Cul ture</w> 19344
A gency</w> 19341
12. 7</w> 19341
poly phenols</w> 19338
fer ring</w> 19337
cycl ine</w> 19337
Leg ion 19333
str ip</w> 19332
pleg ic</w> 19331
auto antibody</w> 19330
acetyl ated</w> 19329
ir a</w> 19327
pred ator</w> 19327
Rec ei 19326
Br uc 19326
co inc 19321
Medi ated</w> 19320
ST s</w> 19319
glyco sides</w> 19318
d uty</w> 19315
Angi otensin</w> 19313
3 000</w> 19312
mit es</w> 19312
compli ant</w> 19311
ol ia</w> 19310
non steroidal</w> 19309
omy ces</w> 19309
VO 2 19309
fac et</w> 19308
he d 19305
sulf hydr 19300
I di 19298
GL UT 19298
glyc ation</w> 19296
is ure</w> 19293
E mp 19282
ris ky</w> 19282
TA TIONS</w> 19282
a ural</w> 19279
arthro sis</w> 19278
palmit ate</w> 19278
an ser 19276
ne arby</w> 19274
need les</w> 19273
scho ol 19273
communic ating</w> 19273
A II</w> 19271
exac tly</w> 19270
lig ated</w> 19268
ann ular</w> 19266
under pin 19264
chol angitis</w> 19260
disequ ilibrium</w> 19260
ne o</w> 19259
fac eted</w> 19259
but yl 19259
L PL</w> 19257
distinc tly</w> 19256
ie u</w> 19252
dep ths</w> 19251
us or</w> 19250
inhib in</w> 19244
NLR P3</w> 19244
a pro 19241
lympho proliferative</w> 19240
og ran 19239
nyst agmus</w> 19236
C um 19235
benz oic</w> 19235
tym pan 19235
AC TI 19234
W ell</w> 19233
sub mandibular</w> 19228
S atis 19227
cat abolic</w> 19227
dig it</w> 19226
Sp 1</w> 19226
cy tidine</w> 19224
tic es</w> 19223
re aring</w> 19222
shor tage</w> 19218
ten sin</w> 19217
A part</w> 19214
favor ably</w> 19213
phot onic</w> 19212
Inf ra 19211
gp 120</w> 19210
thi one 19209
14 , 19205
un recognized</w> 19202
vas ospasm</w> 19202
S pi 19199
or ization</w> 19198
af luoro 19192
lo ts</w> 19191
on t</w> 19187
Neuro logical</w> 19187
H a</w> 19185
D CM</w> 19183
pr ag 19182
electron ics</w> 19181
T yph 19178
sul fam 19178
pi eces</w> 19177
H V 19176
og luc 19175
E 6</w> 19174
f urthermore</w> 19173
W A</w> 19173
clop idogrel</w> 19169
dys plastic</w> 19168
qual ities</w> 19166
pel lets</w> 19165
Bo ard</w> 19162
TNF alpha</w> 19161
go w</w> 19156
denat ured</w> 19154
Bacter ia</w> 19152
hy pon 19151
Predic ting</w> 19150
nemat odes</w> 19147
1. 26</w> 19145
intern alized</w> 19139
toc occus</w> 19138
on ad 19134
predisp ose</w> 19133
o vid 19131
T es 19131
ch ose</w> 19131
ju x 19129
dor m 19129
fir m</w> 19128
discus sing</w> 19127
Norwe gian</w> 19125
rec ti 19123
al er 19121
Wor k 19119
swe et</w> 19119
m ography</w> 19117
c uc 19117
be es</w> 19117
obsc ure</w> 19115
fet oprotein</w> 19114
let ters</w> 19114
Pol ic 19111
pup il</w> 19109
anes thesi 19104
land marks</w> 19104
er ase</w> 19103
reg s</w> 19102
Post erior</w> 19102
inoc ulum</w> 19101
I PF</w> 19100
Re active</w> 19100
obarb it 19096
osteoc alcin</w> 19095
cogn ate</w> 19095
geni stein</w> 19094
Den sity</w> 19091
CIN AHL</w> 19091
t ogenic</w> 19085
SM D</w> 19085
sp astic</w> 19083
glycol ip 19082
er in</w> 19080
ex ome</w> 19080
Metast atic</w> 19079
pector is</w> 19079
otom ies</w> 19077
PT B</w> 19076
alop ecia</w> 19076
O ri 19075
fo od 19075
I H</w> 19072
Di strict</w> 19072
cor rhiz 19071
immun ophen 19070
all o</w> 19070
n ut</w> 19069
C PA</w> 19068
sti l 19067
U TP</w> 19065
al ia</w> 19065
ser ology</w> 19065
TM J</w> 19063
paradox ical</w> 19063
un satisfactory</w> 19062
requ ested</w> 19062
G 4</w> 19061
ri d</w> 19061
Ra ther</w> 19061
kil ob 19058
stimul ator</w> 19057
ap illary</w> 19056
pyro phosphate</w> 19056
H ost</w> 19055
descrip tors</w> 19055
al azine</w> 19053
miner alized</w> 19053
Categ ory</w> 19053
R om 19052
s n</w> 19046
S ig 19046
implic ate</w> 19046
pip eline</w> 19044
og astric</w> 19041
un ts</w> 19038
bro ken</w> 19036
D SA</w> 19034
S n</w> 19031
end os 19031
fer i</w> 19031
D an 19028
0.0 17</w> 19026
pa ste</w> 19023
1, 6</w> 19022
Estim ation</w> 19021
evap oration</w> 19021
N PV</w> 19020
swe at</w> 19019
NSAI D</w> 19017
ro unds</w> 19015
satur able</w> 19013
AP I</w> 19012
ith in</w> 19010
R and 19007
hyper tonic</w> 19005
a 2</w> 19004
Optim ization</w> 19004
Glas gow</w> 19004
M t 19001
Rel ease</w> 19000
di ph 18999
V M</w> 18994
TL C</w> 18994
24 h</w> 18994
I RS</w> 18993
ul ator</w> 18993
GW AS</w> 18993
retur ning</w> 18992
simul ator</w> 18991
as phy 18990
per itoneum</w> 18990
I . 18989
alpha 2</w> 18988
d aughter</w> 18987
16. 5</w> 18986
rib o 18985
Appro pri 18982
wee k 18979
Te aching</w> 18978
in ii</w> 18972
13 , 18969
ot on 18968
Fr ame 18968
Fr ag 18965
Sequ ential</w> 18963
pix el</w> 18958
endarte rectomy</w> 18957
U rine</w> 18954
CO D</w> 18954
trac ers</w> 18952
trich loro 18952
coincid ed</w> 18951
proce ed</w> 18950
ac ies</w> 18949
bl acks</w> 18947
hol istic</w> 18947
dia stere 18946
y stitis</w> 18945
hex idine</w> 18945
Sci enti 18942
BM SCs</w> 18939
tr ap 18938
op posing</w> 18933
ong side</w> 18932
1. 27</w> 18931
orph an</w> 18927
er sin 18926
metabol ically</w> 18925
t ables</w> 18924
12. 9</w> 18924
re r</w> 18923
F B 18922
gen ders</w> 18920
ap e</w> 18915
eur in</w> 18914
oxy cycline</w> 18913
Evalu ating</w> 18913
incenti ves</w> 18913
thyro iditis</w> 18912
invasi vely</w> 18911
CM C</w> 18910
kin ematic</w> 18905
2 51</w> 18904
correl ating</w> 18903
GT T</w> 18901
me etings</w> 18897
ep oxide</w> 18896
par acetamol</w> 18896
AT M</w> 18889
En doc 18886
Ug anda</w> 18885
bac ter</w> 18882
controll able</w> 18882
c ubic</w> 18880
Prof es 18876
C 6 18873
concentr ates</w> 18873
es sively</w> 18871
co ils</w> 18870
circum ferential</w> 18866
prefer able</w> 18866
co existing</w> 18862
anc est 18862
Tom ography</w> 18861
T at</w> 18857
f ted</w> 18856
t ann 18856
com pressed</w> 18856
aff ord 18855
ster ols</w> 18854
Q L</w> 18852
N 3</w> 18851
3 75</w> 18849
associ ative</w> 18848
Fin nish</w> 18847
hin dered</w> 18846
encephal omyelitis</w> 18845
Maxim al</w> 18845
an y 18840
ion ized</w> 18840
calcul i</w> 18839
pre maturity</w> 18838
V ide 18836
L B</w> 18835
Cer tain</w> 18835
cann ula</w> 18832
transcrip tionally</w> 18829
elucid ating</w> 18829
sl ide</w> 18828
den sit 18827
Bangl adesh</w> 18825
ne al</w> 18820
In clusion</w> 18819
RA TION 18819
pan ze 18818
elic its</w> 18815
S r</w> 18814
h ur 18813
ho used</w> 18808
astig matism</w> 18805
oxidi zing</w> 18802
Am pl 18801
AI R</w> 18799
C ou 18798
carbox ylate</w> 18796
Ischem ic</w> 18796
wea ther</w> 18794
thyro tropin</w> 18789
30 8</w> 18789
keratin ocyte</w> 18788
B ot 18787
no xi 18785
numer ically</w> 18784
H N</w> 18781
refl ective</w> 18780
Depend ent</w> 18780
k B</w> 18779
li d</w> 18779
stre p 18777
lip id 18775
munic ipal</w> 18775
cent red</w> 18773
dec or 18772
ther to</w> 18767
mal ate</w> 18767
produc ers</w> 18766
amil oride</w> 18765
RAN KL</w> 18764
13. 1</w> 18763
8 7 18759
incre ments</w> 18757
1. 28</w> 18756
condyl ar</w> 18755
separ ating</w> 18752
explo it</w> 18751
c enti 18749
Col om 18748
xyl ose</w> 18747
HY PO 18746
Ge org 18745
Hydro gen</w> 18742
W B 18738
ar i</w> 18736
ti bi 18735
Ear th</w> 18733
deg ran 18731
Bar rett</w> 18731
dra in</w> 18730
P SS</w> 18727
F ine</w> 18727
rib avirin</w> 18724
morph ologies</w> 18723
intes tin 18723
12. 4</w> 18722
ribonucle ase</w> 18719
a e 18718
CA A</w> 18718
Flor ida</w> 18715
gener alization</w> 18714
teach er</w> 18711
n evertheless</w> 18709
edi atr 18709
termin i</w> 18705
Re stric 18703
thromb i</w> 18701
ha i</w> 18700
osi tivity</w> 18698
incis ors</w> 18697
T ERT</w> 18687
Sing ap 18687
trache ostomy</w> 18678
agg res 18676
de phosphorylation</w> 18674
orph yrin</w> 18674
My o 18673
institu tion 18673
photo receptors</w> 18671
hepar an</w> 18671
rel ational</w> 18670
a sion</w> 18668
trans dermal</w> 18666
FA K</w> 18666
B ab 18664
LIMI TATIONS</w> 18663
C i</w> 18658
rub ella</w> 18658
H UV 18656
event ual</w> 18654
astro cyte</w> 18653
v ol</w> 18650
Infec tious</w> 18650
az id</w> 18648
or b</w> 18647
GAB AA</w> 18647
stre ptom 18640
n ial</w> 18638
dow s</w> 18636
sub sp</w> 18634
requ est</w> 18633
Viet nam</w> 18631
cholecyst okinin</w> 18630
R FA</w> 18629
spond ylo 18627
agg ing</w> 18626
sus pec 18623
demyel inating</w> 18622
plei otropic</w> 18621
13. 8</w> 18620
O pi 18616
categor ical</w> 18611
refer ring</w> 18610
Er b 18608
at oid</w> 18606
Jo hn</w> 18605
micro particles</w> 18603
PO 2</w> 18602
arcom as</w> 18602
PE I</w> 18600
ellul ose</w> 18600
develop mentally</w> 18599
ad riamycin</w> 18596
di d 18594
RATION ALE</w> 18593
M . 18590
11. 0</w> 18590
re acts</w> 18589
determin ate</w> 18589
bench mark</w> 18588
1. 35</w> 18587
d est</w> 18583
hypox emia</w> 18583
evolution arily</w> 18582
inter ior</w> 18580
lamin ar</w> 18580
gr ating</w> 18579
inv entory</w> 18578
un predictable</w> 18577
Con serv 18577
sati va</w> 18576
un cover</w> 18574
em pathy</w> 18574
lev er</w> 18573
h it</w> 18572
E 4</w> 18572
met amorph 18571
con cordant</w> 18569
CD 28</w> 18566
Ter min 18565
granul omas</w> 18564
syncy tial</w> 18563
ak a</w> 18560
cal pain</w> 18557
mm 3</w> 18557
sc rap 18556
bin ocular</w> 18551
ab is 18549
my ocl 18549
dor feri</w> 18547
g ar</w> 18543
S ulf 18542
uni vers 18540
pe anut</w> 18536
call osum</w> 18536
E co 18535
chlor ine</w> 18535
sle eping</w> 18534
IV C</w> 18534
instrum ented</w> 18533
slow ed</w> 18533
p ell 18531
comfor table</w> 18531
menis cus</w> 18529
fl ash</w> 18528
candi diasis</w> 18527
deacetyl ase</w> 18524
conden sed</w> 18519
neuro science</w> 18517
achiev es</w> 18517
SC E</w> 18516
recru it</w> 18516
cochle a</w> 18511
sing let</w> 18511
oph ores</w> 18510
altern atively</w> 18510
dimin ish</w> 18510
MM SE</w> 18509
ated ness</w> 18507
6 6.7</w> 18505
en thal 18505
micro dialysis</w> 18504
3 30</w> 18503
homolog ues</w> 18503
emul sification</w> 18502
y al</w> 18499
pe t</w> 18499
ch ief</w> 18498
pro to</w> 18497
ear th</w> 18497
M IN 18494
hex ane</w> 18494
anti thrombotic</w> 18493
disper sive</w> 18490
P yr 18485
ound ly</w> 18484
syn onymous</w> 18483
Sch iff</w> 18483
qu er 18480
cycl ization</w> 18480
4, 6</w> 18478
B ei 18476
itud inally</w> 18475
ob sessive</w> 18474
oc arb 18473
epide mics</w> 18473
den itr 18472
harb or</w> 18466
amy otrophic</w> 18466
ab ra 18460
gam bling</w> 18458
um ps</w> 18457
sc ro 18457
viri on</w> 18457
insem ination</w> 18457
t DCS</w> 18453
pro xy</w> 18453
H g 18451
ess ness</w> 18450
MY C</w> 18450
inter ro 18449
Pil ot</w> 18449
induc ers</w> 18448
di esel</w> 18447
con spic 18444
lac Z</w> 18444
weigh ed</w> 18444
SL N</w> 18444
3 b</w> 18441
1 I</w> 18432
burg dorferi</w> 18430
M Abs</w> 18429
Observ ational</w> 18428
cl aimed</w> 18427
ac cordingly</w> 18425
0.0 18</w> 18423
ec centric</w> 18421
reason ably</w> 18421
cardiomy ocyte</w> 18419
immun ologically</w> 18416
y es</w> 18411
t amp 18409
w ells</w> 18406
cer vic 18405
oxid oreduc 18402
deline ated</w> 18401
collec tively</w> 18401
Enh ancement</w> 18400
hem ostatic</w> 18399
transcrip tomic</w> 18397
colon ized</w> 18396
extravas ation</w> 18396
staphyloc occi</w> 18394
ec k</w> 18389
2 15</w> 18388
homolog s</w> 18388
PR ACTI 18386
us ly</w> 18384
CA G</w> 18384
glutar aldehyde</w> 18382
popul arity</w> 18377
fat ality</w> 18376
zoon otic</w> 18375
propan e</w> 18373
im ported</w> 18367
C FA</w> 18366
k i</w> 18365
re act 18361
p t</w> 18359
F B</w> 18357
sal ient</w> 18355
sel dom</w> 18353
PG s</w> 18353
Foun dation</w> 18353
A edes</w> 18351
10 th</w> 18351
dys regulated</w> 18349
concentr ic</w> 18346
phosphati dy 18343
ipl atin</w> 18340
gyn a 18340
apos i</w> 18339
br a</w> 18335
Statis tically</w> 18335
tr aps</w> 18334
sl iding</w> 18334
noxi ous</w> 18331
run s</w> 18329
mid ine</w> 18328
HI T</w> 18327
communic ate</w> 18326
stoichi ometry</w> 18325
s ows</w> 18324
un folded</w> 18321
pro x 18319
sim vastatin</w> 18318
upr ight</w> 18318
tel omeric</w> 18317
Bacter i 18316
hist omorph 18311
Thor acic</w> 18311
H an 18310
T anz 18309
SN R</w> 18308
avi um</w> 18307
I le</w> 18304
diz ziness</w> 18304
E PI</w> 18303
B CL 18303
Ery thro 18302
des cent</w> 18295
syndrom ic</w> 18295
vacc inia</w> 18287
we alth</w> 18285
Ep ilep 18283
T1 D</w> 18283
sy n</w> 18282
duc tus</w> 18281
yr s</w> 18280
c 1</w> 18279
D HT</w> 18276
to e</w> 18276
ag s</w> 18273
PG I2</w> 18272
tetr a</w> 18268
cholec ystitis</w> 18268
shel f</w> 18268
hind limb</w> 18267
back ward</w> 18266
tap e</w> 18266
M üll 18262
2 13</w> 18261
A X</w> 18260
demyel ination</w> 18258
fib ril</w> 18256
deterior ated</w> 18256
K aposi</w> 18255
im atinib</w> 18254
ox es</w> 18253
PC S</w> 18253
alkal oid</w> 18253
U I</w> 18252
Rem ark 18252
M AC 18250
T 7</w> 18250
T CA</w> 18250
D Q</w> 18248
dem y</w> 18247
discrimin ative</w> 18247
Malay sia</w> 18247
d d 18245
illo sis</w> 18244
st aff 18240
pig ments</w> 18240
M AT</w> 18237
litho trip 18237
bio degradation</w> 18236
lac rimal</w> 18235
conv inc 18235
care rs</w> 18232
N ET</w> 18228
Spec i 18228
m olyb 18227
Ch eck 18227
thermo philic</w> 18226
advoc ated</w> 18225
tetr adec 18224
neur aminidase</w> 18219
Expl oring</w> 18219
SC A</w> 18218
b y 18217
trans locations</w> 18217
Sj ö 18217
m ati 18214
post treatment</w> 18212
hemat opoiesis</w> 18209
disabl ing</w> 18204
TE E</w> 18201
over whel 18198
D S 18195
hypon at 18194
Hypo xia</w> 18191
D TI</w> 18188
facilit ators</w> 18185
seg mented</w> 18182
ap omorphine</w> 18180
pu rely</w> 18180
hom odi 18180
ex changes</w> 18179
An y</w> 18176
Sus cepti 18175
abl ative</w> 18171
AD M</w> 18171
ur ic 18167
dialy sate</w> 18167
tre otide</w> 18166
n t 18165
NAD P</w> 18164
Relationsh ips</w> 18164
M os 18163
antim al 18163
represent atives</w> 18161
Predic tive</w> 18160
lipoly sis</w> 18156
ren orphine</w> 18154
ma zine</w> 18154
guid es</w> 18148
M oun 18147
under took</w> 18147
appropri ateness</w> 18145
A NF</w> 18144
Comput ational</w> 18142
au tistic</w> 18139
temper ate</w> 18139
N et</w> 18138
bi an</w> 18136
put amen</w> 18135
reservo irs</w> 18133
el iness</w> 18128
prof oundly</w> 18126
op rine</w> 18122
E BP</w> 18121
con tain 18119
1. 33</w> 18118
thin ning</w> 18117
G old</w> 18116
oligodendro cytes</w> 18114
nam es</w> 18113
Condi tions</w> 18113
pent obarbital</w> 18110
M LP</w> 18108
attrib ute</w> 18106
J ur 18102
grou ping</w> 18101
un responsive</w> 18099
M SH</w> 18098
0.000 5</w> 18098
N M</w> 18096
str um</w> 18095
1 --</w> 18093
radi osurgery</w> 18092
helic ase</w> 18090
Dis co 18087
Autom ated</w> 18086
C SCs</w> 18079
os tigmine</w> 18079
my oglobin</w> 18079
stain less</w> 18076
sal mon 18074
Proc ess</w> 18074
ox ins</w> 18072
frag mented</w> 18072
Prog ression</w> 18070
is otropic</w> 18069
2 16</w> 18068
nanocryst als</w> 18068
Person ality</w> 18068
PA A</w> 18064
occu pati 18063
Cl 4</w> 18062
- . 18055
yr in 18054
I SS</w> 18053
F MD</w> 18051
13. 4</w> 18050
di hedral</w> 18049
ic ol 18048
cryp t</w> 18048
occ idi 18047
A ra 18044
be ad</w> 18039
O X</w> 18032
gastr o</w> 18031
E A 18028
arti ficially</w> 18027
inc eption</w> 18022
vill ages</w> 18021
alk ylation</w> 18020
drin ks</w> 18020
Probl ems</w> 18020
pleth ys 18019
he ightened</w> 18017
rel ieve</w> 18010
ver tigo</w> 18008
1. 5 18007
melan ocytes</w> 18007
w asting</w> 18006
al o</w> 18004
ox amine</w> 18002
Scop us</w> 18001
6 A</w> 18000
si ed</w> 18000
SO C</w> 17999
gra zing</w> 17997
psych otropic</w> 17995
Em plo 17994
dor sol 17993
bene fici 17991
inequ ality</w> 17991
Challeng es</w> 17991
Chol esterol</w> 17990
Insi ghts</w> 17988
link ages</w> 17985
l ac</w> 17983
P MMA</w> 17981
An dro 17979
ampl ify</w> 17979
R Y 17976
an abolic</w> 17973
T en 17972
sulf ated</w> 17972
tric ho 17972
CD 45 17971
Bi o</w> 17970
justi ce</w> 17969
thre ats</w> 17967
re generated</w> 17965
complic ating</w> 17963
CT P</w> 17963
2 35</w> 17960
infarc ts</w> 17960
Throm bo 17960
ch ar</w> 17958
M MR</w> 17955
c eption</w> 17951
tur ning</w> 17951
spi ked</w> 17951
des m 17950
H am 17949
der ang 17948
Be h 17946
onc ologic</w> 17942
discipl ine</w> 17939
Ach illes</w> 17938
I TS</w> 17935
apha sia</w> 17935
shi el 17934
row s</w> 17933
raph e</w> 17932
26 4.7</w> 17932
Famil ial</w> 17932
H and</w> 17928
viro logical</w> 17926
15. 4</w> 17925
T d 17924
pot ently</w> 17924
Ultras on 17924
pharmac euticals</w> 17922
vit ell 17921
correc ting</w> 17919
compet encies</w> 17917
F AS</w> 17916
intr igu 17912
myel ination</w> 17911
ro l 17908
IC SI</w> 17906
relax ed</w> 17903
Neur onal</w> 17903
trimeth oprim</w> 17901
oph ytes</w> 17900
w ant</w> 17899
prob ands</w> 17897
8 5 17896
y z 17894
A TI 17892
re mediation</w> 17891
leuk o 17891
8 9 17889
assess es</w> 17889
oin tim 17889
O cular</w> 17887
iso enzyme</w> 17885
T ACE</w> 17884
iv a</w> 17882
Activ ated</w> 17878
Mes enchymal</w> 17876
M ay 17875
ereb ellar</w> 17872
chim panze 17870
contr asts</w> 17869
organ elle</w> 17869
optim ally</w> 17869
ici ting</w> 17868
Bir th</w> 17864
en umer 17862
s cleral</w> 17861
co oking</w> 17859
potenti ate</w> 17856
re alize</w> 17855
rec eipt</w> 17855
ac rom 17852
un tary</w> 17851
W is 17850
et us</w> 17850
archa e 17850
reduc tive</w> 17848
cos tim 17847
Bet ter</w> 17847
Brow n</w> 17847
chloro plasts</w> 17846
Sjö gren</w> 17846
A reas</w> 17844
un il 17844
stre ams</w> 17840
Up take</w> 17840
encephal on</w> 17835
mil ieu</w> 17834
abrup t</w> 17834
E pig 17833
rel atedness</w> 17831
sub total</w> 17831
oc ean</w> 17826
embol i</w> 17825
toxic ology</w> 17824
B le 17823
TNF α</w> 17822
K a</w> 17820
lex ical</w> 17820
transu rethral</w> 17819
D SC</w> 17810
Ra f</w> 17807
aff iliated</w> 17804
4 B</w> 17800
neuro peptides</w> 17799
Ar th 17799
injec table</w> 17798
1 200</w> 17796
homogen ate</w> 17792
athi oprine</w> 17790
hem o 17787
R GD</w> 17783
respon ds</w> 17782
insi cally</w> 17781
ecz ema</w> 17781
ex y</w> 17780
Dem entia</w> 17779
pit falls</w> 17776
antinoc iceptive</w> 17772
ati co 17767
MT s</w> 17766
contigu ous</w> 17766
met agen 17765
exce eds</w> 17765
cho w</w> 17764
sev enty</w> 17762
green house</w> 17759
iform is</w> 17757
AL I</w> 17755
shrim p</w> 17753
ex foli 17751
1. 32</w> 17750
Rever se</w> 17749
d obutamine</w> 17747
trans loc 17747
S 4</w> 17746
tr ast 17746
end op 17746
obser ving</w> 17746
ad here</w> 17745
L ateral</w> 17744
H 5 17744
hy n 17743
cong estion</w> 17742
Repor ted</w> 17741
A SC</w> 17740
M ach 17740
R ed 17739
un diagnosed</w> 17739
insectic ides</w> 17739
pen is</w> 17737
CF S</w> 17734
dev oted</w> 17731
smar t</w> 17730
D ow 17728
sle epiness</w> 17725
ec um</w> 17723
hierarch y</w> 17723
cholangi ocarcinoma</w> 17721
Au tism</w> 17718
str ip 17717
iso enzymes</w> 17717
con to 17716
R a</w> 17715
ip enem</w> 17714
oplas mosis</w> 17714
manufacture rs</w> 17713
y our</w> 17712
echan ics</w> 17712
a h 17709
Protoc ol</w> 17709
squ ared</w> 17708
trans thoracic</w> 17707
SER S</w> 17707
G as</w> 17702
Caucasi ans</w> 17702
des tin 17701
M PTP</w> 17700
F G</w> 17700
car p</w> 17690
ir al</w> 17687
pheochrom ocytoma</w> 17687
Ro ot</w> 17686
y ne 17685
ser s</w> 17685
ox azol 17682
RF S</w> 17681
mut ually</w> 17680
pair wise</w> 17677
c uring</w> 17675
sim plicity</w> 17671
pol luted</w> 17669
dra ft</w> 17669
Commun ication</w> 17668
IC G</w> 17667
un labeled</w> 17665
Reli ability</w> 17663
am plic 17660
util isation</w> 17660
n ascent</w> 17658
hum erus</w> 17657
e V</w> 17656
del ib 17655
cardio respiratory</w> 17654
radi ologist</w> 17653
14. 2</w> 17652
econ omical</w> 17648
hemisph eres</w> 17646
Interfer on</w> 17645
termin ally</w> 17644
14. 6</w> 17643
S tri 17642
ig no 17642
vill ous</w> 17642
ym ic</w> 17638
titr e</w> 17636
re ads</w> 17635
recur red</w> 17635
carbox ylase</w> 17634
un usually</w> 17630
aden ylyl</w> 17630
embed ding</w> 17629
G SK 17628
In s 17626
DE AE</w> 17626
obacter ial</w> 17626
In strum 17623
Tob acco</w> 17622
micron utri 17621
imi pramine</w> 17621
lin a</w> 17619
anxi ous</w> 17618
metabol izing</w> 17613
ren der</w> 17610
perf usate</w> 17610
cal orie</w> 17609
distil led</w> 17608
CB A</w> 17607
.0 4</w> 17605
end azole</w> 17603
ochond ral</w> 17602
iti dine</w> 17596
stop ping</w> 17596
GT P 17595
photocat alytic</w> 17593
list ening</w> 17592
2 S</w> 17590
sl opes</w> 17590
DI C</w> 17588
Impair ment</w> 17588
detr usor</w> 17587
copolym ers</w> 17583
M TH 17582
Cor tical</w> 17582
c ian</w> 17580
ER S</w> 17580
Ar c 17580
ge hog</w> 17579
substitu ent</w> 17579
anser in</w> 17577
m ou 17576
hi therto</w> 17574
O ld</w> 17572
har dly</w> 17569
system ically</w> 17568
rele ases</w> 17568
Hist ologically</w> 17567
2 11</w> 17566
str ate</w> 17566
sp on 17563
max illofacial</w> 17562
electr o</w> 17558
Y outh</w> 17557
g out</w> 17556
T RE 17556
L OH</w> 17554
star ts</w> 17553
geometr ies</w> 17553
ol actone</w> 17552
lys ed</w> 17543
long itudinally</w> 17542
ill icit</w> 17540
quadr ant</w> 17540
justi fy</w> 17539
Recur rence</w> 17538
U re 17536
Vari ations</w> 17534
partur ition</w> 17533
micro globulin</w> 17532
organ izing</w> 17532
S ide</w> 17531
Q Tc</w> 17529
chondro cyte</w> 17529
promis es</w> 17528
corne as</w> 17528
pass ages</w> 17528
argum ents</w> 17526
o regional</w> 17524
Phosph orylation</w> 17523
Bei jing</w> 17520
dent ures</w> 17519
N M 17518
a rest</w> 17515
C re</w> 17515
Dim en 17515
H SCs</w> 17510
rec t 17508
ful min 17507
acceler ates</w> 17507
G CS</w> 17506
SIR T1</w> 17506
bol a</w> 17505
intral uminal</w> 17502
S ir 17501
MI F</w> 17501
sil k</w> 17500
calc ane 17495
X X</w> 17494
op ost 17486
Mic ros 17485
1. 29</w> 17483
3 40</w> 17480
obstac le</w> 17480
C CN 17479
ris es</w> 17479
al ongside</w> 17478
oun ded</w> 17478
umin escent</w> 17478
cros ses</w> 17478
assimil ation</w> 17478
HUV ECs</w> 17476
3 T</w> 17475
re tin</w> 17475
ance stry</w> 17472
lich en</w> 17470
offic ial</w> 17468
m d 17467
read missions</w> 17467
merg ed</w> 17467
ign s</w> 17466
I TP</w> 17465
visi ting</w> 17465
W H 17464
read iness</w> 17464
bic eps</w> 17463
diph ther 17463
di x</w> 17462
6 a</w> 17459
ori ent 17459
ocortico id</w> 17457
gover ned</w> 17455
den afil</w> 17454
recap it 17454
X R 17453
Ex ternal</w> 17451
hybridi zed</w> 17450
kal emia</w> 17448
occ er</w> 17444
vag ina</w> 17444
Te tra 17444
c ic 17443
her itability</w> 17443
199 0s</w> 17438
RE SEARCH</w> 17432
hom o 17432
mo tional</w> 17431
asth m 17431
HB e 17431
Meas uring</w> 17431
restr ained</w> 17431
trast uzumab</w> 17431
aren cy</w> 17429
posi tives</w> 17426
C 7</w> 17425
bi ocompatible</w> 17422
pro biotics</w> 17416
reson ances</w> 17415
yr ight</w> 17414
5, 6</w> 17414
ann exin</w> 17413
r arity</w> 17412
li a</w> 17412
Par ameters</w> 17410
accum ulates</w> 17409
drain ing</w> 17408
en su 17407
13. 7</w> 17407
2 18</w> 17406
transf used</w> 17406
mes h 17406
ali phatic</w> 17406
T regs</w> 17405
sna ke</w> 17405
g ang 17404
l on 17404
catech ol</w> 17403
1. 31</w> 17401
G PR 17399
G over 17398
end odontic</w> 17398
ester ified</w> 17396
well being</w> 17396
K ine 17393
b 1</w> 17391
detec ts</w> 17389
SEL ECTION</w> 17389
esophag ectomy</w> 17388
Estro gen</w> 17388
le isure</w> 17386
HSP 70</w> 17386
el is</w> 17385
j unior</w> 17384
B V 17380
lob ular</w> 17378
mu si 17377
deplo yed</w> 17377
f all 17376
L TR</w> 17375
rel ieved</w> 17374
per it 17373
sulfhydr yl</w> 17372
2 26</w> 17370
chel ating</w> 17370
initi ates</w> 17366
cl ades</w> 17365
alog ue</w> 17365
ren dering</w> 17364
surpr ising</w> 17357
os si 17355
tre hal 17355
hum or</w> 17353
praz osin</w> 17351
2 5. 17349
ver sities</w> 17349
part nership</w> 17349
S cat 17346
conc ussion</w> 17345
ES BL</w> 17342
spir itual</w> 17341
I B 17340
Incre ase</w> 17340
P ic 17339
I l 17337
un resolved</w> 17337
top ically</w> 17337
initi o</w> 17334
Pl ants</w> 17333
P VA</w> 17330
Org an</w> 17330
J A</w> 17327
f ile</w> 17326
ex tubation</w> 17324
ME D</w> 17324
Rho A</w> 17323
retro virus</w> 17322
fur an</w> 17322
hypercalc emia</w> 17322
en em 17321
Idi opathic</w> 17320
K yo 17318
autophag ic</w> 17316
CL P</w> 17314
caroten oids</w> 17314
v WF</w> 17311
Shig ella</w> 17311
Oper ative</w> 17309
it abine</w> 17305
child birth</w> 17302
Dec ision</w> 17302
beta -</w> 17302
D Y 17299
ro entgen 17297
14. 8</w> 17297
x aban</w> 17295
c ab 17293
ir y</w> 17293
lo id</w> 17291
sur al</w> 17291
bas oph 17291
h os 17289
dec eased</w> 17289
deb ated</w> 17288
Top ical</w> 17285
un selected</w> 17284
so red</w> 17284
scler osing</w> 17284
myel opathy</w> 17277
D e</w> 17274
exagg erated</w> 17274
HYPO THESIS</w> 17274
phosphon ates</w> 17273
fl oral</w> 17270
ont ology</w> 17269
comp ute</w> 17265
persi sting</w> 17265
In surance</w> 17264
reson ant</w> 17262
ul ose</w> 17261
victim ization</w> 17261
L astly</w> 17259
Signific ance</w> 17258
In clud 17257
ben th 17251
ch itin</w> 17247
abor tions</w> 17241
ul ous</w> 17240
gyn ecological</w> 17239
splen ocytes</w> 17238
15. 6</w> 17235
osper mia</w> 17235
z in</w> 17234
Recor d</w> 17234
rip ening</w> 17231
phen ol 17230
PW V</w> 17228
H SD</w> 17226
C los 17226
b ru 17226
mes oporous</w> 17224
SOUR CES</w> 17224
D or 17223
Bacter oides</w> 17222
D ays</w> 17221
S 6</w> 17220
m ec 17220
C ad 17218
AL Y</w> 17217
ma ffin</w> 17215
R ic 17214
di acylglycerol</w> 17213
tre x 17213
mol l 17213
bib li 17213
Reduc ing</w> 17212
cen sus</w> 17211
Oste o 17211
propi onic</w> 17211
Diff usion</w> 17210
aneu ploidy</w> 17210
p ing 17208
-- the</w> 17205
cann ulation</w> 17205
me rely</w> 17204
P . 17199
m eri 17199
4 E</w> 17199
ne tium</w> 17199
H IF 17197
Ag ainst</w> 17197
ob iliary</w> 17196
loc alisation</w> 17195
pH i</w> 17195
ne u</w> 17190
in o 17189
vasoconstric tor</w> 17187
1. 30</w> 17185
00 00 17185
G M1</w> 17184
gam es</w> 17181
il in</w> 17180
o ting</w> 17179
mo re 17179
ant ec 17179
iso ther 17179
nan oro 17178
er ly</w> 17176
chrom es</w> 17174
Tr ig 17173
an oic</w> 17170
fl atten 17169
ta p</w> 17168
end ers</w> 17166
yl osing</w> 17166
1, 3 17163
hypoglyc emic</w> 17163
expon entially</w> 17158
entr ap 17156
rh odopsin</w> 17156
pharmac ologically</w> 17155
17 beta</w> 17155
sho ots</w> 17150
a h</w> 17148
m as</w> 17146
Inhib itory</w> 17145
prokary otic</w> 17145
rejec ted</w> 17145
30 4</w> 17140
F al 17138
adrenal ectomy</w> 17138
enzym atically</w> 17133
ch en 17132
des er 17131
biom olecules</w> 17131
in activating</w> 17130
ersin ia</w> 17130
Dis tinct</w> 17129
Peri odic 17129
dorsol ateral</w> 17129
um ping</w> 17126
certi fied</w> 17126
am i</w> 17124
M AS</w> 17123
c t 17120
prur itus</w> 17118
pre incubation</w> 17111
1. 34</w> 17111
trig lyc 17110
acro megal 17110
impul sivity</w> 17108
herni as</w> 17105
O V</w> 17104
lo ose</w> 17104
- beta</w> 17103
fos sil</w> 17100
Kyo to</w> 17098
ul aris</w> 17097
B ovine</w> 17096
accommod ate</w> 17096
on ade</w> 17095
demonstr able</w> 17094
neuro inflammation</w> 17089
sub species</w> 17083
establ ishes</w> 17079
og lut 17071
met ab 17068
sensor ineural</w> 17068
y early</w> 17067
electromy ography</w> 17067
CCR 5</w> 17064
blast ocysts</w> 17062
ar ine</w> 17061
leuc ocyte</w> 17060
est u 17058
anth ine</w> 17058
spond ylitis</w> 17058
ip 1</w> 17054
F S 17053
lo fen</w> 17052
P ER 17051
prob ed</w> 17051
pollut ant</w> 17051
A 6</w> 17048
Ev ans</w> 17048
play er</w> 17046
replic ative</w> 17045
bud s</w> 17043
AB O</w> 17041
ED 50</w> 17041
cryp tic</w> 17039
mach ines</w> 17038
Col um 17037
sero conversion</w> 17036
mT OR 17034
crim inal</w> 17034
ar abine</w> 17033
ob ac 17033
IC Us</w> 17032
ag itation</w> 17029
in ert</w> 17027
acet aldehyde</w> 17024
T e</w> 17023
ul ary</w> 17022
photo chemical</w> 17022
P oint</w> 17020
polys omn 17020
B i</w> 17019
T it 17018
t ar</w> 17016
Fr anc 17016
ore doxin</w> 17014
- 20</w> 17012
call us</w> 17012
P SP</w> 17010
pneumon itis</w> 17010
leuc ocytes</w> 17010
c v</w> 17009
bis phosphate</w> 17009
ff s</w> 17008
pre fer</w> 17008
pal m</w> 17006
opost erior</w> 17005
L -</w> 17001
k at</w> 17001
ure sis</w> 17001
Th ai</w> 16997
3 R</w> 16996
SE A</w> 16996
microm olar</w> 16996
deoxy uridine</w> 16992
neuro vascular</w> 16991
where in</w> 16991
si ght</w> 16986
destro yed</w> 16985
tr al</w> 16984
urtic aria</w> 16984
ecto dermal</w> 16983
- alpha</w> 16979
IN R</w> 16979
Od ds</w> 16978
aber ration</w> 16977
tremend ous</w> 16977
modi fies</w> 16974
ad ol</w> 16973
thorac oscopic</w> 16973
olig omeric</w> 16971
t z 16970
ik a</w> 16969
2 O3</w> 16963
Singap ore</w> 16960
D XA</w> 16958
lit ter 16956
them e</w> 16955
baro reflex</w> 16955
deriv ation</w> 16953
appro aching</w> 16952
re polarization</w> 16951
pursu it</w> 16949
observ able</w> 16947
198 0s</w> 16947
asp inal</w> 16946
polym y 16944
dos ed</w> 16942
H DL 16941
res ur 16940
br ings</w> 16939
sor afenib</w> 16936
construc ting</w> 16934
Hol stein</w> 16934
1. 36</w> 16930
L uc 16928
m U</w> 16928
ax one</w> 16926
ch us</w> 16922
opath ogenic</w> 16921
os acral</w> 16920
Tre ated</w> 16917
c ence</w> 16914
di ary</w> 16912
G SK</w> 16907
NF 1</w> 16907
14. 7</w> 16906
acon azole</w> 16906
PM L</w> 16904
vascul ar 16903
od a</w> 16902
co al 16901
thermo phil 16898
Sub st 16897
stimul ant</w> 16895
wh enever</w> 16893
enter o 16892
.00 2</w> 16891
deriv atization</w> 16890
Pr EP</w> 16890
E x</w> 16889
is th 16889
inter observer</w> 16889
nanocom posite</w> 16888
ho ods</w> 16887
epider mi 16887
evacu ation</w> 16887
anti oxidative</w> 16884
2 6. 16880
cur rence</w> 16879
BM C</w> 16878
Reg ulatory</w> 16877
ocor tex</w> 16877
ab and 16875
n ul 16874
later ally</w> 16873
B u</w> 16872
6 00 16871
ba um 16871
uniform ity</w> 16871
wave forms</w> 16870
- 4 16868
lev er 16867
bur ning</w> 16866
apo E</w> 16866
c k</w> 16864
campa igns</w> 16863
for amin 16861
contamin ant</w> 16861
mutag enicity</w> 16856
ac eous</w> 16855
commun ications</w> 16855
2 54</w> 16852
at ria</w> 16851
tri iodothyronine</w> 16851
aven ues</w> 16851
cir cle</w> 16849
d oxycycline</w> 16846
com ments</w> 16846
oco agulation</w> 16844
T ests</w> 16842
t ful</w> 16841
neuro troph 16841
am er</w> 16839
co oper 16838
htt ps</w> 16832
Pharmaco kinetics</w> 16830
Inf ant</w> 16826
O t 16825
or is</w> 16825
as tine</w> 16823
Wash ington</w> 16823
an odine</w> 16822
nec ro 16822
1, 4,5</w> 16822
adop ting</w> 16821
C ros 16820
sp ending</w> 16820
HBe Ag</w> 16820
fra il</w> 16819
entrap ment</w> 16819
HC M</w> 16817
un wanted</w> 16816
DL BCL</w> 16816
acc ept</w> 16814
In ten 16813
Clu ster</w> 16813
et etra 16807
RA GE</w> 16805
interfe res</w> 16803
epidermi dis</w> 16799
Medic ation</w> 16798
PE G 16797
fertil ized</w> 16797
os ylation</w> 16796
B ey 16795
SV R</w> 16795
rec an 16794
RO P</w> 16793
di pl 16785
1 A2</w> 16783
hem ic 16783
b 2</w> 16782
Cycl ic</w> 16782
debil itating</w> 16781
Com mission</w> 16779
lu x</w> 16777
Cryp tospor 16776
chemoattrac tant</w> 16776
nucle olar</w> 16774
perfor ated</w> 16774
aden omatous</w> 16768
smart phone</w> 16767
HT 1A</w> 16762
ari thromycin</w> 16761
car cer 16761
up ward</w> 16758
immort alized</w> 16758
S ten 16752
dic ations</w> 16752
µ M</w> 16751
oph ytic</w> 16751
1, 4 16751
M ur 16750
in k</w> 16748
glucon e 16747
E bola</w> 16745
d ap 16743
Y ersinia</w> 16743
tyros inase</w> 16743
W P</w> 16741
An gel 16741
nanot ube</w> 16741
py re 16739
pro hib 16737
chond ral</w> 16737
appro x</w> 16735
ST D</w> 16733
5 50</w> 16729
MEASU RE</w> 16729
inf low</w> 16728
transm ural</w> 16726
extr am 16725
ole ate</w> 16725
Ch loro 16723
hypo thermic</w> 16722
udg et</w> 16718
conflu ent</w> 16718
retin oid</w> 16717
Th r 16716
PPAR γ</w> 16716
fl ame</w> 16713
ent a 16710
co ast</w> 16709
E Ds</w> 16708
A sians</w> 16707
Hepat ocellular</w> 16705
Ass ay</w> 16703
out standing</w> 16701
erc ept</w> 16700
o hex 16698
tur ns</w> 16698
Initi ative</w> 16698
7 -</w> 16696
3 90</w> 16695
flu idity</w> 16695
ad ate</w> 16694
M CA 16693
prog ressing</w> 16693
isch er</w> 16690
b erg 16689
te re 16688
Sign al</w> 16688
con finement</w> 16684
ati de</w> 16680
os a 16680
TI A</w> 16679
Bi oin 16679
ac ylation</w> 16678
un stimulated</w> 16677
inst ant 16676
hemangi oma</w> 16676
ker nel</w> 16675
z a</w> 16672
aggres siveness</w> 16672
rain bow</w> 16671
un expectedly</w> 16670
13. 9</w> 16667
ad vised</w> 16664
PV N</w> 16664
resi de</w> 16663
Schiz ophren 16663
Ap p 16661
Rati o</w> 16661
S oil</w> 16659
quantit ated</w> 16659
pro BNP</w> 16658
Aca demy</w> 16655
chlor hexidine</w> 16654
calcin eurin</w> 16654
HB s</w> 16652
Dis crim 16651
as ynchron 16650
cyt olytic</w> 16649
Disco very</w> 16649
atten d</w> 16648
ingredi ent</w> 16648
oste oblastic</w> 16647
emplo ys</w> 16645
SI L</w> 16644
1. 45</w> 16642
d anger</w> 16641
intrigu ing</w> 16641
un desirable</w> 16640
ep oxy</w> 16640
anti inflammatory</w> 16640
g avage</w> 16637
EX P 16637
pre pubertal</w> 16636
alumin ium</w> 16636
aeg yp 16634
emit ted</w> 16633
val gus</w> 16627
psychi atrists</w> 16627
phosphatidyl serine</w> 16622
dener vated</w> 16622
gran ulation</w> 16620
Endom e 16620
ul o</w> 16618
cardi ogenic</w> 16614
CO M 16614
in emia</w> 16611
as eptic</w> 16611
si ves</w> 16610
superim posed</w> 16610
metac ar 16607
y i</w> 16605
Wh ere</w> 16605
glutam yl</w> 16605
ul trac 16601
14. 1</w> 16601
F N 16600
pro mazine</w> 16594
infrequ ently</w> 16593
pig ment 16592
spin ning</w> 16590
re volution 16588
depolar izing</w> 16588
u PA</w> 16587
1, 5</w> 16586
Tran sport</w> 16585
incis or</w> 16583
dys kinesia</w> 16580
bal ances</w> 16578
cryop reserved</w> 16576
M AR</w> 16574
en jo 16574
sub maximal</w> 16574
Cop per</w> 16573
olec ule</w> 16571
thi azide</w> 16571
Perc ep 16571
sc aled</w> 16569
discover ies</w> 16569
un modified</w> 16567
nor ing</w> 16567
contradic tory</w> 16567
c ages</w> 16566
Rand om</w> 16565
streptom ycin</w> 16563
dy le</w> 16562
ol ite</w> 16559
pri ori</w> 16559
anz apine</w> 16557
dec on 16554
Ex amin 16554
heter osexual</w> 16552
F at</w> 16551
nitro glycerin</w> 16551
R ay 16547
6 2.5</w> 16545
7 4 16539
o tics</w> 16538
am ole</w> 16538
e ens</w> 16534
itu ation</w> 16534
sk i</w> 16533
Anti oxidant</w> 16533
ul f</w> 16532
enhanc ers</w> 16532
z om 16531
19 72</w> 16530
my asthenia</w> 16530
oph ene</w> 16527
od ed</w> 16524
over coming</w> 16524
Pr inc 16524
Sec ond 16524
pherom one</w> 16523
compl emented</w> 16521
cur ved</w> 16521
integr ates</w> 16521
Gh ana</w> 16521
Pro phyl 16520
tri axone</w> 16519
13 1I</w> 16518
umin a</w> 16518
trans esophageal</w> 16517
Fl av 16514
analy zes</w> 16513
end ings</w> 16507
il ia</w> 16506
neurolep tic</w> 16506
b are</w> 16504
Util ization</w> 16504
anth rene</w> 16501
Techniqu e</w> 16501
c iv 16499
high -</w> 16499
el iciting</w> 16497
transi ents</w> 16497
2 0.0</w> 16493
K up 16485
is omerization</w> 16483
cros stalk</w> 16483
F ung 16481
Import ant</w> 16481
lacti s</w> 16480
dur ability</w> 16479
hydra z 16479
No .</w> 16478
AB L</w> 16476
se eding</w> 16474
om on 16471
lic ensed</w> 16470
con fron 16468
N O2</w> 16467
3, 3</w> 16463
hype re 16463
affor d</w> 16460
Bey ond</w> 16458
Al g 16456
sub class</w> 16454
N l 16452
transp arency</w> 16450
transl uminal</w> 16442
a plastic</w> 16438
ne arest</w> 16438
par an 16438
di pyrid 16437
regul arity</w> 16435
matern ity</w> 16433
C CT</w> 16432
sh unting</w> 16432
denat uring</w> 16432
mind fulness</w> 16432
15. 3</w> 16431
H ir 16430
tensi ves</w> 16430
sacc ades</w> 16430
more over</w> 16429
Valu es</w> 16428
prev ailing</w> 16428
cl au 16427
Kn ock 16427
Anti gen</w> 16426
eth ane</w> 16425
pepti d 16425
struc turing</w> 16424
leuk emias</w> 16424
eigh ty</w> 16419
res ins</w> 16414
N ear</w> 16409
at ech 16407
nar co 16407
med ul 16407
hex agonal</w> 16406
Et OH</w> 16405
7 th</w> 16401
S ize</w> 16401
N 0</w> 16401
1. 37</w> 16400
l . 16395
pto sis</w> 16394
ex tran 16393
chol ed 16392
emergen cies</w> 16390
L PA</w> 16388
clos est</w> 16388
le gi 16387
yto plasmic</w> 16387
Fe atures</w> 16386
per fluoro 16384
sh unts</w> 16384
ol ide</w> 16381
low ers</w> 16381
pro drug</w> 16378
hydroxy vitamin</w> 16378
ple omorphic</w> 16378
neuro protection</w> 16377
15. 2</w> 16375
ocor tic 16374
caroten oid</w> 16374
valu ed</w> 16372
W R</w> 16370
dil emma</w> 16369
band width</w> 16369
Car ib 16368
Resp ond 16367
o vi 16364
eth an 16364
Hist ory</w> 16362
proton ation</w> 16362
str ingent</w> 16361
innerv ated</w> 16361
Spectr um</w> 16361
r is</w> 16360
A wa 16359
P ON 16359
a -</w> 16358
nanocom posites</w> 16358
asym metr 16357
spas ticity</w> 16357
tri als.gov</w> 16356
phosphatidy lethanolamine</w> 16356
T l</w> 16355
CS S</w> 16355
vacu olar</w> 16354
M SA</w> 16353
Relev ance</w> 16348
fa eces</w> 16347
30 2</w> 16346
n ight 16341
bar k</w> 16341
ang ulation</w> 16339
unc oupling</w> 16339
posi t</w> 16336
thione in</w> 16336
fif teen</w> 16331
sar col 16329
er ness</w> 16327
fram esh 16325
Gam ma</w> 16325
less ness</w> 16321
activ in</w> 16316
mon ocytic</w> 16315
propri oc 16314
ow l</w> 16313
PL D</w> 16313
presum ptive</w> 16313
L O</w> 16310
toler ate</w> 16309
ili ation</w> 16309
aper ture</w> 16309
dis advantage</w> 16308
doll ars</w> 16307
myel ogenous</w> 16305
Eth anol</w> 16305
pall adium</w> 16305
H 4 16304
on ality</w> 16303
SC H</w> 16301
End ogenous</w> 16300
intrac lass</w> 16299
PV P</w> 16298
ul ent</w> 16297
acqu iring</w> 16297
ach oline</w> 16296
sent ence</w> 16291
anni i</w> 16291
contribut ors</w> 16288
14. 4</w> 16286
lo vir</w> 16282
A maz 16279
defin itely</w> 16278
accommod ation</w> 16277
R un 16276
1. 43</w> 16276
Ad ren 16276
Periodic als</w> 16274
rainf all</w> 16269
coagul ant</w> 16268
mediastin um</w> 16267
gra vis</w> 16266
an ode</w> 16265
nano fibers</w> 16264
Bor relia</w> 16263
2 90</w> 16262
los artan</w> 16260
re fin 16256
is oni 16255
inter phase</w> 16254
Argen tina</w> 16254
otyp ical</w> 16252
par ag 16250
spea kers</w> 16250
1. 38</w> 16248
propri a</w> 16248
un explored</w> 16246
viv ax</w> 16245
vag otomy</w> 16245
spectrophot ometric</w> 16245
st ocks</w> 16243
p M</w> 16242
Flu oro 16240
st ag 16239
0.0 19</w> 16239
neuro chemical</w> 16239
frac tured</w> 16239
gluc oside</w> 16236
anch oring</w> 16236
bo oks</w> 16234
TL R2</w> 16229
1. 75</w> 16228
weigh ting</w> 16228
marri age</w> 16228
hypogly caemia</w> 16226
CA C</w> 16225
R inger</w> 16224
munic ip 16223
pl asi 16221
bas in</w> 16218
thi amine</w> 16217
silic ate</w> 16217
Indic ations</w> 16216
obstruc ted</w> 16214
fo vir</w> 16213
O PN</w> 16212
Pur ified</w> 16212
x 10</w> 16211
D y 16210
carb onic</w> 16210
actin omyc 16209
bio chemically</w> 16208
t yline</w> 16205
inf liximab</w> 16205
fir stly</w> 16204
M ol 16200
15. 8</w> 16200
W eb 16198
actu arial</w> 16198
o y 16196
pattern ed</w> 16194
poli ovirus</w> 16193
Com peti 16190
rib ozym 16190
D -</w> 16188
CD11 b</w> 16188
ant a</w> 16187
dec ide</w> 16184
N asal</w> 16181
S chol 16180
trac ed</w> 16179
Frame work</w> 16178
micro surgical</w> 16177
my oblasts</w> 16176
E HR</w> 16173
acceler ating</w> 16173
wild life</w> 16172
spac ed</w> 16171
S -</w> 16168
E ST</w> 16167
N ICU</w> 16166
AT R</w> 16166
lys ophosph 16165
Fas L</w> 16165
ab duction</w> 16164
mer its</w> 16163
super vis 16163
gly caemia</w> 16163
swe et 16161
explan atory</w> 16159
PGF 2</w> 16158
par amount</w> 16157
ot onic</w> 16156
G N 16155
yl choline</w> 16155
ac cred 16153
az athioprine</w> 16153
4 000</w> 16152
ma il</w> 16150
oly tica</w> 16148
nes ota</w> 16148
poly aden 16145
Transcrip tional</w> 16144
e der</w> 16141
D PP</w> 16137
G PC 16134
molec ularly</w> 16134
Re placement</w> 16131
sar copenia</w> 16128
15 , 16127
retin itis</w> 16125
pe g 16124
gonorrho eae</w> 16122
T f 16119
con dyle</w> 16119
anthrac ene</w> 16119
op olym 16117
25 -</w> 16113
rom a</w> 16112
Adju v 16111
photosensi ti 16110
Vol ume</w> 16109
s wa 16108
Peri operative</w> 16108
7 8 16107
D NP</w> 16107
K it</w> 16107
hyper tensives</w> 16107
arc tion</w> 16107
re pairs</w> 16105
counter act</w> 16105
Bi och 16104
Min istry</w> 16103
is y</w> 16102
ultraf iltration</w> 16098
F RET</w> 16096
y ou 16095
olip in</w> 16095
dyst rophic</w> 16094
B ut 16093
vol ati 16091
deline ation</w> 16090
hemat oxylin</w> 16089
web site</w> 16088
R U 16087
ym ents</w> 16086
fo uling</w> 16086
re placements</w> 16085
Mn 2</w> 16083
P BC</w> 16081
C 8</w> 16081
chrom ic</w> 16081
pseud ot 16080
ac idity</w> 16079
mo unt</w> 16078
cryst al 16078
G N</w> 16077
ir relevant</w> 16077
non smokers</w> 16076
flu ency</w> 16075
ul i</w> 16074
Im mediate</w> 16073
hosp ice</w> 16071
T PO</w> 16070
conclud es</w> 16070
Nec k</w> 16070
F atigue</w> 16066
uro logical</w> 16066
S ox 16065
AC H</w> 16065
male imide</w> 16065
g il 16063
4 86</w> 16063
sed ative</w> 16060
hal ogen 16059
opro lol</w> 16058
C b 16057
cephal ic</w> 16053
Ano ph 16053
mon y</w> 16052
L ED</w> 16050
P ho 16050
L t 16049
APO E</w> 16049
R II</w> 16047
ho u</w> 16042
Subjec tive</w> 16042
myelodys plastic</w> 16042
pl ated</w> 16041
le in</w> 16041
AB R</w> 16041
pa redness</w> 16040
vag us</w> 16039
de his 16038
oc kets</w> 16037
menis cal</w> 16036
f as 16035
ul opathy</w> 16035
tag s</w> 16034
S J 16033
om el 16033
fove al</w> 16033
infest ation</w> 16033
Nic o 16032
tamp onade</w> 16031
B Ps</w> 16030
P 5</w> 16029
run ners</w> 16029
s c</w> 16026
w earable</w> 16026
Bruc ella</w> 16026
C i 16024
glob ular</w> 16020
sig mo 16019
D il 16018
D ar 16017
M ACE</w> 16017
ot a 16017
baum annii</w> 16017
actin omycin</w> 16015
DD T</w> 16012
res orb 16010
There after</w> 16009
PV R</w> 16009
orific e</w> 16009
grad ation</w> 16007
p ush</w> 16006
mis diagnosed</w> 16004
mar c 16000
influ ential</w> 16000
op sies</w> 15997
fu si 15997
stri kingly</w> 15997
m g. 15995
Z V</w> 15992
replic ating</w> 15991
Tanz ania</w> 15989
un biased</w> 15987
ep sia</w> 15984
crystall in</w> 15984
p 3</w> 15982
intra operatively</w> 15982
P ent 15981
triglyc eri 15981
L FA</w> 15980
st oma</w> 15980
p ins</w> 15979
Scot land</w> 15977
Econ omic</w> 15974
Ex am 15973
asthm atics</w> 15973
o prost 15972
T MP</w> 15971
complex ation</w> 15971
comb ustion</w> 15969
T SP</w> 15967
apo B</w> 15966
Coll agen</w> 15965
- 3. 15964
N Z 15962
bup renorphine</w> 15962
Hospit als</w> 15962
manufac tured</w> 15960
suppl ies</w> 15958
Prog ressive</w> 15957
Rep orts</w> 15954
on a 15953
loc alised</w> 15950
al loc 15949
C our 15947
iso zyme</w> 15947
F U 15944
asperg illosis</w> 15944
N ucleotide</w> 15943
O phthal 15942
pred ators</w> 15941
o embryonic</w> 15939
form atics</w> 15939
be have</w> 15939
acti on 15937
cardi ology</w> 15936
lam p</w> 15935
L am 15933
sp ous 15933
mi R 15933
dig iti 15931
B a</w> 15930
access ed</w> 15930
ph en</w> 15929
kerat oplasty</w> 15928
anhydr ase</w> 15926
cadaver s</w> 15925
I AA</w> 15923
C ath 15922
ph in</w> 15922
vi z</w> 15922
suc kling</w> 15922
loc oregional</w> 15919
radi olig 15917
E GFP</w> 15916
enter otoxin</w> 15915
biore actor</w> 15915
pro fit</w> 15914
no vel 15914
elast ography</w> 15914
heterozyg otes</w> 15911
l anth 15910
2 24</w> 15909
2 17</w> 15909
pum ping</w> 15907
bio transformation</w> 15906
habit ual</w> 15903
Sol u 15903
S keletal</w> 15902
Aim s</w> 15902
y . 15901
arch e</w> 15900
LO D</w> 15900
absorb able</w> 15900
authen tic</w> 15900
mant le</w> 15899
inf eri 15897
classi fying</w> 15897
caregi ving</w> 15896
ure t 15892
listen ers</w> 15892
J .</w> 15890
rac es</w> 15890
he el</w> 15889
denti tion</w> 15889
comm is 15886
pan els</w> 15886
morph ometry</w> 15885
appl ies</w> 15885
top ographic</w> 15877
Br assi 15876
bac ul 15875
c ept</w> 15874
PL S</w> 15872
roll ing</w> 15872
pre dation</w> 15871
andro sterone</w> 15871
aptam er</w> 15871
tri ad</w> 15870
pel let</w> 15870
ag inous</w> 15863
glut aminase</w> 15858
T x</w> 15854
architec tures</w> 15850
iv ary</w> 15849
SI V</w> 15848
dich otom 15848
Ess ential</w> 15847
manip ulate</w> 15846
wean ed</w> 15845
end ocytic</w> 15842
Q A</w> 15840
arg on</w> 15839
bul bar</w> 15837
my ocyte</w> 15836
II B</w> 15835
T ak 15834
trac es</w> 15833
pertur bed</w> 15829
degran ulation</w> 15829
capac itance</w> 15828
SP 1</w> 15827
disrup ts</w> 15827
Transi tion</w> 15826
Altern atively</w> 15826
glyc ated</w> 15825
ol dest</w> 15823
col ic</w> 15823
hospit alisation</w> 15822
prec au 15819
cardi ometabolic</w> 15818
G GT</w> 15817
corrhiz al</w> 15817
2 7. 15815
cor p 15814
o plastic</w> 15813
p ace</w> 15813
Ch ag 15813
F ischer</w> 15811
CYP3 A4</w> 15808
form erly</w> 15807
lip tin</w> 15807
carr age 15807
scler oderma</w> 15807
NE Ts</w> 15806
N umber</w> 15805
G o</w> 15803
nucle osome</w> 15801
in compatible</w> 15800
frac tal</w> 15798
notic eable</w> 15798
Com posite</w> 15797
dex medetomidine</w> 15797
mom entum</w> 15797
phen otypically</w> 15795
sen ile</w> 15793
12 4 15793
in vertebrates</w> 15791
ro l</w> 15791
arthro desis</w> 15790
enal april</w> 15789
isoni azid</w> 15788
energ etics</w> 15787
oxal iplatin</w> 15787
un ding</w> 15786
fib rate</w> 15786
cor ds</w> 15785
adap ting</w> 15782
astrocyt oma</w> 15779
si der 15776
guan idine</w> 15776
en th 15775
al ty</w> 15774
AM s</w> 15774
Ro yal</w> 15774
Regar dless</w> 15773
K re 15769
ads orbent</w> 15766
deep ly</w> 15761
V iv 15760
st ump</w> 15756
frag ility</w> 15755
hil ar</w> 15754
dihydroxy vitamin</w> 15753
c asp 15752
Radi otherapy</w> 15746
9 5 15745
discrim inated</w> 15744
Cum ulative</w> 15744
oxidoreduc tase</w> 15743
ph ones</w> 15741
crystall ography</w> 15741
S SA</w> 15740
cholester yl</w> 15740
heter ocyclic</w> 15739
ir id 15737
C z 15736
en chym 15734
arteri ography</w> 15733
isot onic</w> 15732
Mo thers</w> 15730
EG CG</w> 15730
P SD</w> 15729
P ower</w> 15729
Qu antum</w> 15727
iod inated</w> 15726
Scienti fic</w> 15726
K et 15725
16 , 15725
ator vastatin</w> 15724
bi variate</w> 15721
phal angeal</w> 15721
e ze</w> 15720
p ill 15720
memb ership</w> 15719
orth ostatic</w> 15719
se em 15718
ne ocortex</w> 15717
ograph ies</w> 15716
EN T</w> 15716
geometr ical</w> 15716
eng th</w> 15712
trex one</w> 15711
om us</w> 15708
TL s</w> 15708
1. 42</w> 15707
Fe deral</w> 15707
ag le</w> 15705
oc treotide</w> 15705
dystroph in</w> 15705
acet amide</w> 15702
adop tive</w> 15702
dissi pation</w> 15702
A gro 15693
50 ,000</w> 15692
mac ro</w> 15691
K CN 15690
mono -</w> 15690
C 5 15689
N TR</w> 15687
Z ika</w> 15687
18. 2</w> 15687
perovsk ite</w> 15687
acclim ation</w> 15684
Respond ents</w> 15681
L ar 15679
AD s</w> 15679
NO 3</w> 15679
phon ological</w> 15679
Selec ted</w> 15679
Appropri ate</w> 15677
epididym is</w> 15676
Pharmaco kinetic</w> 15673
3 22</w> 15671
at oxin</w> 15670
ph arynx</w> 15669
J D</w> 15666
Gra ft</w> 15666
n adi 15663
re war 15661
al ist</w> 15660
Flu id</w> 15656
LV H</w> 15656
sev enth</w> 15655
tos econd</w> 15653
tel emedicine</w> 15652
el ong</w> 15650
unc h</w> 15650
M Cs</w> 15645
V an 15645
tax onomy</w> 15644
CA PD</w> 15642
anti trypsin</w> 15641
gener ational</w> 15641
E H</w> 15639
P SI</w> 15637
u ti 15637
connec t</w> 15637
2 2.5</w> 15635
ore sis</w> 15634
iod o 15634
sub si 15633
mu st 15632
stabil izes</w> 15630
op tically</w> 15628
dam ycin</w> 15628
3 01</w> 15625
prag matic</w> 15624
z ens</w> 15623
str aw</w> 15623
expos ing</w> 15623
mini ature</w> 15623
Anoph eles</w> 15623
sr c</w> 15620
Suscepti bility</w> 15617
E specially</w> 15616
N Cs</w> 15616
Ta q 15616
incid entally</w> 15615
per oxides</w> 15614
im migrant</w> 15611
M LC</w> 15609
70 s</w> 15609
f an</w> 15608
fil ari 15607
sul cus</w> 15604
crow ns</w> 15604
d m 15602
ac hy 15602
pathophysi ologic</w> 15601
manufacture r</w> 15601
ar tem 15598
anat omically</w> 15598
ta ils</w> 15597
str abis 15596
midw ives</w> 15596
om ethyl</w> 15595
b yst 15594
solubil ization</w> 15594
L G</w> 15593
gen otoxicity</w> 15593
vide os</w> 15593
down ward</w> 15588
beta 3</w> 15588
or o</w> 15586
hem ophilia</w> 15585
kin et 15583
Cryptospor idium</w> 15583
Ab s 15582
stere o 15582
bene ath</w> 15581
ann abin 15578
intr insically</w> 15577
D X 15576
Micro RNAs</w> 15576
alkyl ating</w> 15576
om ic 15575
thi azol 15575
Biomar kers</w> 15574
T 3 15573
ne ointim 15569
pre hospital</w> 15567
gangli oside</w> 15567
if i 15566
enter ica</w> 15566
Graph ene</w> 15566
it ter</w> 15562
Gran ul 15562
G AT 15561
SI s</w> 15561
amid o</w> 15560
ur us</w> 15559
sh RNA</w> 15558
therapeu tically</w> 15557
Dop amine</w> 15553
FV III</w> 15553
elim inates</w> 15552
Awa reness</w> 15552
com promising</w> 15551
Caro lina</w> 15547
17. 6</w> 15545
or ized</w> 15544
odont ogenic</w> 15544
G AG</w> 15541
iso propyl 15541
M OF</w> 15539
Stand ards</w> 15539
fu els</w> 15538
cylin der</w> 15534
id um</w> 15533
deoxy cholic</w> 15532
amp ed</w> 15530
ble aching</w> 15530
a ks</w> 15528
sh ocks</w> 15528
pol ice</w> 15528
h es 15526
visi bility</w> 15526
myel ocytic</w> 15525
responsi bilities</w> 15525
5 1 15523
Man ual</w> 15523
inc ap 15519
PC D</w> 15519
venti onally</w> 15517
V SMC</w> 15515
TR s</w> 15515
de methylation</w> 15514
ow ed</w> 15514
supr ac 15513
crani otomy</w> 15512
ru thenium</w> 15511
G 0</w> 15508
mer ic</w> 15507
cl ath 15506
epitheli oid</w> 15505
tin y</w> 15504
Int ell 15503
ep il 15497
rest ores</w> 15493
famili arity</w> 15493
dis locations</w> 15492
P ol</w> 15491
be ating</w> 15488
quadr atic</w> 15487
break through</w> 15485
appen dix</w> 15485
So ft</w> 15481
omen cl 15480
micro flora</w> 15479
attemp ting</w> 15475
innov ations</w> 15474
gl it 15473
radi osensi 15473
A ud 15472
impl ied</w> 15471
Caro tid</w> 15470
class room</w> 15470
zom ib</w> 15470
o protective</w> 15469
is obut 15469
tr uly</w> 15468
di hydroxy</w> 15467
contr asted</w> 15465
10 -</w> 15463
phot ons</w> 15462
thym ocyte</w> 15462
Jur kat</w> 15461
K inase</w> 15460
on ts</w> 15460
At ten 15460
ho ur 15458
f ene 15453
es . 15453
edi ble</w> 15453
sec tioned</w> 15451
ore active</w> 15451
aur icular</w> 15451
t ones</w> 15450
ta ught</w> 15448
am enor 15442
CD 45</w> 15440
instruc ted</w> 15439
neph ron</w> 15438
Arti ficial</w> 15438
bal ancing</w> 15437
polymer ases</w> 15437
SN AP</w> 15437
P IN 15436
oph yte</w> 15436
estr one</w> 15436
In stitu 15432
O DC</w> 15431
sist ed</w> 15431
Con sensus</w> 15429
im printing</w> 15428
LO X</w> 15425
f ish 15424
phil icity</w> 15424
hom in 15422
mar kets</w> 15419
fl are</w> 15417
re fraction</w> 15415
Pan el</w> 15415
Ste p</w> 15413
tunn eling</w> 15412
ann ulus</w> 15408
convul sions</w> 15408
bronchi olitis</w> 15408
our ces</w> 15407
olym ph</w> 15407
igno red</w> 15407
chem o</w> 15406
r P</w> 15404
R as 15403
G all 15402
coll iculus</w> 15402
am bly 15396
ag r 15394
direc ting</w> 15394
epilep ticus</w> 15394
opa que</w> 15394
quar ters</w> 15393
1. 39</w> 15392
13. 0</w> 15392
B ond</w> 15391
termin ology</w> 15391
Fe asibility</w> 15391
pr ick</w> 15390
ak ia</w> 15389
cl ay</w> 15388
F AD</w> 15386
practi ced</w> 15385
inv ading</w> 15383
ME C</w> 15383
L angu 15382
def ensive</w> 15380
PR O</w> 15375
PAC AP</w> 15374
re alization</w> 15370
col ectomy</w> 15369
parkinson ism</w> 15368
P ap</w> 15366
arc tic</w> 15365
Legion ella</w> 15365
mes oderm</w> 15364
In digenous</w> 15363
benefici aries</w> 15362
Sub stance</w> 15361
thyro globulin</w> 15360
Bi opsy</w> 15359
t ably</w> 15358
ure ase</w> 15356
d h 15350
PG E1</w> 15350
r atic</w> 15349
S RE 15346
E O</w> 15346
Frac ture</w> 15344
M H 15343
comm entary</w> 15343
ant ations</w> 15341
tel angi 15340
promp tly</w> 15340
TA M</w> 15339
icul ate</w> 15337
r TMS</w> 15336
myco sis</w> 15335
electromy ographic</w> 15333
anch orage</w> 15332
appoint ment</w> 15332
at oma</w> 15331
v s.</w> 15329
Kup ffer</w> 15329
dermat ology</w> 15328
ur gency</w> 15327
Pa O2</w> 15324
U VA</w> 15323
an onymous</w> 15321
aer ial</w> 15320
spiro metry</w> 15320
bre vi 15318
fentan il</w> 15317
ap ps</w> 15315
re bound</w> 15313
P .</w> 15309
C max</w> 15309
ry anodine</w> 15309
acycl o 15308
lithotrip sy</w> 15308
oneph rosis</w> 15302
tend erness</w> 15298
E motional</w> 15296
O I</w> 15294
av ul 15293
ness ed</w> 15293
M CT</w> 15292
s occer</w> 15287
Q C</w> 15287
cl arithromycin</w> 15287
sper mine</w> 15284
spher oids</w> 15283
b orders</w> 15282
Investig ations</w> 15282
Pro st 15281
N CI</w> 15280
ill umin 15280
16. 3</w> 15280
fumig atus</w> 15280
T D 15276
hydro thermal</w> 15276
ocyan in</w> 15276
im a</w> 15275
W A 15270
serov ar</w> 15265
nor m</w> 15264
Micro RNA</w> 15263
Gu inea</w> 15263
transm it</w> 15263
R MS</w> 15261
V D 15260
ph renic</w> 15260
ad venti 15260
inten sified</w> 15260
c ecal</w> 15259
qu ail</w> 15259
w rap 15258
pot encies</w> 15255
CC 1</w> 15255
spi king</w> 15255
flag ell 15255
parav entricular</w> 15254
Carib bean</w> 15254
qu oti 15253
at traction</w> 15250
ent ate</w> 15248
CH OP</w> 15248
PO D</w> 15247
pac k</w> 15247
fav oring</w> 15246
I O</w> 15244
ti sts</w> 15243
sim pler</w> 15243
Process ing</w> 15243
exer tion</w> 15240
intrac erebro 15238
dil ute</w> 15238
micro liters</w> 15237
correc tions</w> 15236
G A 15233
W ide</w> 15233
ore tic</w> 15233
cortic otropin</w> 15233
salic ylate</w> 15233
spl anch 15232
hyper glycemic</w> 15228
time -</w> 15228
an one</w> 15222
inci sions</w> 15221
od u 15220
Appl ying</w> 15220
un determined</w> 15219
aegyp ti</w> 15219
un ified</w> 15218
car inii</w> 15218
Th y</w> 15217
rhe ological</w> 15215
th en 15209
2 39</w> 15207
diverticul um</w> 15207
z able</w> 15206
cephal y</w> 15205
in carcer 15203
us able</w> 15203
citr ic</w> 15202
f ast 15201
yst olic</w> 15196
Pres ent</w> 15196
G ATA</w> 15194
S ta 15194
β 2</w> 15193
RO I</w> 15193
draw backs</w> 15193
DO C</w> 15193
aux iliary</w> 15192
Remark ably</w> 15191
I CI</w> 15187
on us</w> 15187
stress or</w> 15186
Aden osine</w> 15185
sy ll 15183
Prolifer ation</w> 15181
exist ent</w> 15180
taz idi 15180
school children</w> 15178
W at 15175
hon ey 15175
mening ococcal</w> 15173
reinfor cing</w> 15173
ph i 15172
li ps</w> 15172
SD F</w> 15171
D uration</w> 15170
plasm as</w> 15170
rel ied</w> 15167
T ATA</w> 15166
ser ially</w> 15166
imp ing 15164
7 6 15162
dec el 15157
sent ences</w> 15154
E lig 15151
in determinate</w> 15151
IC R</w> 15150
Concentr ation</w> 15149
b at</w> 15148
in ization</w> 15148
az a</w> 15148
her bs</w> 15147
pre optic</w> 15146
Neur ons</w> 15146
2 32</w> 15145
CD 20</w> 15145
Pregn ant</w> 15143
in efficient</w> 15139
Per in 15137
Rev ised</w> 15135
T MD</w> 15134
prog resses</w> 15134
PATIEN T</w> 15134
P -</w> 15132
extern ally</w> 15130
sn ail</w> 15129
controll er</w> 15127
duc tor</w> 15126
hom eless</w> 15126
bac eous</w> 15125
la un 15124
le um</w> 15122
fulmin ant</w> 15122
e ing</w> 15119
tim ed</w> 15118
comp iled</w> 15117
Hist or 15117
phag ocytes</w> 15115
dis satisfaction</w> 15114
main stay</w> 15114
Ag NPs</w> 15112
S ma 15110
ulin emia</w> 15108
CS T</w> 15107
De fic 15104
dig ree</w> 15104
stom atitis</w> 15104
Ab original</w> 15102
re ally</w> 15100
hemorrh ages</w> 15099
Min nesota</w> 15098
ac y 15096
yn u 15096
sp rin 15093
2 p</w> 15092
plas tics</w> 15092
j a</w> 15091
H earing</w> 15090
SC S</w> 15090
oc alization</w> 15085
speci alization</w> 15085
n ap 15084
Th rough 15084
haemorrh agic</w> 15084
F asting</w> 15082
vi l</w> 15082
attenu ating</w> 15081
Adjuv ant</w> 15081
SV M</w> 15080
2 75</w> 15078
R ussian</w> 15075
aminoglyco side</w> 15075
ic lovir</w> 15074
te bral</w> 15074
post ulate</w> 15074
Integr ation</w> 15074
v as</w> 15072
BO LD</w> 15071
Esophag eal</w> 15068
A CL 15067
en sures</w> 15062
Ex trem 15061
l ich</w> 15060
Ac celer 15059
hydrox yl 15059
n id 15058
C CI</w> 15054
wa x</w> 15052
but anol</w> 15051
H ung 15050
2 2.2</w> 15049
5 S</w> 15049
av oids</w> 15045
non sense</w> 15045
pul sive</w> 15044
oly ticus</w> 15043
acti onal</w> 15036
1. 40</w> 15036
U 937</w> 15035
hydroly tic</w> 15035
zz y</w> 15035
join ed</w> 15030
IU GR</w> 15029
Ill umina</w> 15029
plasmac yt 15029
glucuron idase</w> 15028
def ault</w> 15026
Hamil ton</w> 15026
bi phenyl</w> 15025
sex uality</w> 15025
C 12</w> 15024
- independent</w> 15021
eth oxazole</w> 15021
vill age</w> 15021
bur gh</w> 15020
NY HA</w> 15019
multi plex 15017
E DS</w> 15016
con sen 15016
our inary</w> 15016
V ZV</w> 15012
po ds</w> 15011
AL D</w> 15011
ig o</w> 15010
os tim 15009
anti phospholipid</w> 15009
O wing</w> 15008
anter oposterior</w> 15007
ovid uc 15007
ca sts</w> 15005
an thus</w> 15004
ox ime</w> 15001
col ored</w> 15001
ti er</w> 15000
go iter</w> 14999
cy stitis</w> 14997
vacu ol 14996
cr ic 14995
coord in 14992
VA TS</w> 14990
precip itate</w> 14989
TE A</w> 14988
Agg reg 14988
def enses</w> 14987
Col labor 14986
po x</w> 14985
16. 6</w> 14985
tazidi me</w> 14984
ME NT 14982
break age</w> 14982
ri an</w> 14981
2 M</w> 14980
the tically</w> 14980
man ure</w> 14978
A th 14977
Pol ish</w> 14977
C erebro 14976
B if 14976
DN A 14976
cyan obacteria</w> 14974
hyper baric</w> 14973
thi oredoxin</w> 14972
Cx 43</w> 14972
I b 14971
HU VEC</w> 14970
ribo flavin</w> 14970
otroph s</w> 14968
Solu ble</w> 14968
Chag as</w> 14964
epider moid</w> 14961
audi o</w> 14958
nom inal</w> 14957
od y 14955
poly neuropathy</w> 14952
anxi olytic</w> 14952
aphy seal</w> 14951
op tics</w> 14950
an oids</w> 14949
categor ization</w> 14948
B H</w> 14947
her itable</w> 14947
14. 9</w> 14947
Numer ical</w> 14942
I LD</w> 14941
syring e</w> 14941
papill oma</w> 14940
C CD</w> 14939
3 10</w> 14939
st ays</w> 14939
V SMCs</w> 14937
plasi as</w> 14937
str u 14936
ic am</w> 14935
E ph 14934
ac ial</w> 14933
whe y</w> 14933
exud ate</w> 14932
g lobe</w> 14930
ast atin</w> 14929
S 6 14928
dis continuous</w> 14927
tain ly</w> 14927
y le 14926
HD AC</w> 14925
D ra 14923
PK D</w> 14922
Sens ory</w> 14922
M as 14921
sk ew 14921
under score</w> 14919
naphthal ene</w> 14919
H LH</w> 14918
col lim 14918
Gi ant</w> 14918
S CA 14917
4 20</w> 14917
UR E</w> 14917
Observ ations</w> 14917
s lit</w> 14916
t ad 14916
defibrill ator</w> 14916
ris peridone</w> 14915
ket ones</w> 14915
ep im 14912
tox oplasmosis</w> 14912
PE EP</w> 14911
15 N</w> 14911
o esophagus</w> 14909
3 ' 14908
ST N</w> 14907
log ists</w> 14905
EG TA</w> 14905
cultiv ar</w> 14904
aminop yr 14904
sper midine</w> 14903
L TB 14901
6 th</w> 14901
R K 14901
meth acholine</w> 14899
achiev able</w> 14899
aver sive</w> 14898
bul lying</w> 14898
A de 14897
multi plication</w> 14896
mes enchym 14894
aut umn</w> 14893
trans mis 14890
ac anth 14889
CD 19</w> 14888
ont ogeny</w> 14886
char d</w> 14885
wa ke 14884
Statis tics</w> 14883
Accum ulation</w> 14882
mil lions</w> 14881
A neur 14880
shor test</w> 14880
pepti dyl</w> 14878
peroxis omal</w> 14878
investig ator</w> 14876
form e</w> 14873
sol utes</w> 14873
per mitting</w> 14870
Micro bi 14868
micro biology</w> 14866
Ser a</w> 14865
phot osystem</w> 14864
l akes</w> 14861
u ate</w> 14861
15. 7</w> 14861
op r 14859
bruc ei</w> 14858
maneu ver</w> 14858
1, 5 14855
ginsen g</w> 14855
20 ,000</w> 14854
a viruses</w> 14853
re t</w> 14853
at tain</w> 14852
Colum bia</w> 14850
can al 14846
projec ting</w> 14845
19. 5</w> 14844
Gover n 14844
anti diabetic</w> 14843
Ob structive</w> 14842
19 71</w> 14840
view point</w> 14839
hypo thyroid</w> 14837
deoxy ribonucleic</w> 14837
2 66</w> 14835
Chrom osome</w> 14834
bott len 14834
n ipple</w> 14833
g ay</w> 14833
hydroxy butyrate</w> 14833
Ca regi 14828
U P</w> 14826
b li 14825
IN S</w> 14823
PI P</w> 14823
L et 14822
oc rip 14822
neuro biological</w> 14822
os patial</w> 14820
seed ling</w> 14818
oll en</w> 14817
fac iens</w> 14816
iter pene</w> 14815
Re sistant</w> 14815
P ros 14814
ri st 14814
v um</w> 14813
th ri 14813
the tics</w> 14813
Cont act</w> 14813
0.000 2</w> 14812
question able</w> 14808
adap tor</w> 14807
Tod ay</w> 14807
ph or 14806
an no 14805
initi ator</w> 14805
Langu age</w> 14805
thro p 14801
c uret 14800
3 65</w> 14799
F ar 14797
angi opathy</w> 14797
micro scopically</w> 14796
ga g</w> 14796
AP L</w> 14795
gro unded</w> 14794
mac aque</w> 14793
i. d.</w> 14790
Engine ering</w> 14790
replic ates</w> 14789
H aving</w> 14788
relax ant</w> 14787
k en 14785
sim us</w> 14785
mechan o 14785
st ory</w> 14784
en cl 14783
enro l 14783
Prog ress</w> 14782
cycl o</w> 14781
Separ ation</w> 14781
His pan 14780
recan alization</w> 14779
surviv in</w> 14778
S m</w> 14777
Z IKV</w> 14777
2 19</w> 14775
Co ord 14775
BC VA</w> 14772
yn itr 14771
aut ograft</w> 14771
can e</w> 14770
tetro dotoxin</w> 14769
8 7.5</w> 14768
J ew 14768
al fa</w> 14765
acknowled ged</w> 14762
nom ogram</w> 14761
Moder n</w> 14760
dysp epsia</w> 14759
2 28</w> 14757
oc occi</w> 14756
17 , 14756
C Cl4</w> 14755
Su b</w> 14755
moder ated</w> 14754
adhe red</w> 14753
itr e</w> 14752
antimal arial</w> 14752
herbic ide</w> 14750
aeros ols</w> 14749
contrain dications</w> 14749
ac rine</w> 14748
thal lium</w> 14748
G AP 14747
odi pine</w> 14744
im ipenem</w> 14743
in um</w> 14742
w a</w> 14740
micro scop 14738
gl asses</w> 14735
BAL F</w> 14735
con ferring</w> 14734
At t 14734
Bec k</w> 14733
st osis</w> 14732
part ner 14732
cr ack</w> 14731
AD E</w> 14731
Fem ales</w> 14729
t ases</w> 14725
sh ops</w> 14725
Tow ard</w> 14725
kain ate</w> 14725
mos a 14724
anti neoplastic</w> 14719
ed ent 14719
tran sist 14716
oxid ases</w> 14715
- 0.0 14714
Q D</w> 14714
priv acy</w> 14714
W W 14712
for c 14712
p anc 14711
cl oud</w> 14710
identi ties</w> 14710
amelior ates</w> 14710
adi c</w> 14709
VO2 max</w> 14709
y led 14706
poly amines</w> 14706
Ki 67</w> 14701
vi remia</w> 14699
1. 41</w> 14699
Con tent</w> 14698
PRO CE 14697
le sional</w> 14696
Schizophren ia</w> 14695
le -</w> 14694
L ow 14693
miti gation</w> 14693
br um</w> 14692
diag ram</w> 14688
val ences</w> 14687
bre ad</w> 14687
au d</w> 14685
hyper oxia</w> 14683
Sou theast</w> 14683
bur dens</w> 14680
indu stri 14680
bel t</w> 14678
mobil ized</w> 14678
non human</w> 14677
ma res</w> 14676
tris phosphate</w> 14675
c p 14671
ib ly</w> 14670
pep sin</w> 14669
visco elastic</w> 14669
JAK 2</w> 14669
W ound</w> 14668
isol euc 14668
cer ul 14668
sen escent</w> 14667
end osomes</w> 14662
T am 14661
U ter 14661
mic ellar</w> 14661
is co</w> 14659
h en</w> 14658
C1 q</w> 14657
micro injection</w> 14656
histor ically</w> 14654
Contro ll 14652
even ly</w> 14647
PG C</w> 14646
15. 0</w> 14646
v illus</w> 14645
comput ationally</w> 14644
Se as 14644
c emented</w> 14643
ocrip tine</w> 14643
lu xation</w> 14642
grap e</w> 14642
C MS</w> 14641
pancre atico 14640
tric yclic</w> 14639
V itro</w> 14635
exerc ised</w> 14635
antec ed 14635
igh ting</w> 14634
over lying</w> 14634
Q S</w> 14632
pre defined</w> 14632
nanow ire</w> 14632
spond yl 14631
asparag ine</w> 14631
multi faceted</w> 14630
competi tively</w> 14630
D 4 14628
gonad otrop 14627
G ulf</w> 14626
SL C 14625
form ans</w> 14624
wi reless</w> 14624
form ate</w> 14622
papill a</w> 14621
argum ent</w> 14621
hom ing</w> 14620
Cor neal</w> 14618
3 25</w> 14616
CH 3</w> 14614
terat oma</w> 14614
B T 14613
cephalospor ins</w> 14613
pp b</w> 14610
A SC 14609
enedi one</w> 14608
ma iled</w> 14607
1. 44</w> 14607
perioste al</w> 14607
6 50</w> 14606
Bu il 14606
certi fic 14606
R G</w> 14604
ta x</w> 14602
rein force</w> 14602
complet eness</w> 14601
ush ed</w> 14601
aor t 14600
lu br 14599
P ES</w> 14597
16. 2</w> 14597
dend rim 14597
histi ocytosis</w> 14597
Adap tive</w> 14596
H aw 14593
Indon esia</w> 14593
penetr ate</w> 14592
mon ey</w> 14591
- like</w> 14589
dipyrid amole</w> 14589
in ite</w> 14587
Bl adder</w> 14587
D o 14586
anti al</w> 14586
amphi philic</w> 14586
atl as</w> 14586
di oxin</w> 14584
b udget</w> 14581
oc entesis</w> 14581
As sisted</w> 14581
r amycin</w> 14579
C TS</w> 14579
mid gut</w> 14579
lac un 14578
chro maffin</w> 14577
Pat tern</w> 14577
fim bri 14577
advanc ements</w> 14575
multi cellular</w> 14574
PL P</w> 14574
Pers onal</w> 14574
m g 14571
gen u 14571
CD 2</w> 14571
MD M2</w> 14570
under lies</w> 14566
de methyl 14565
G HRH</w> 14564
heterodi mer</w> 14564
mass age</w> 14564
ferrom agnetic</w> 14563
P 9</w> 14562
15. 1</w> 14562
consum e</w> 14562
flo ating</w> 14561
Arth ritis</w> 14561
2 38</w> 14560
my opic</w> 14560
M 0</w> 14556
P NA</w> 14554
car go</w> 14554
ub ular</w> 14554
E SC</w> 14553
for cement</w> 14549
20 th</w> 14549
seem ingly</w> 14548
M ill 14547
non significant</w> 14544
ra e</w> 14543
f le 14542
cef triaxone</w> 14542
st en</w> 14540
On line</w> 14540
mal treatment</w> 14538
19 66</w> 14537
tran sep 14533
Fe eding</w> 14531
sor ted</w> 14530
el ian</w> 14526
AN C</w> 14525
d . 14523
d 1</w> 14523
M CL</w> 14523
5 ' 14522
Second ly</w> 14521
ho uses</w> 14520
Vari ability</w> 14519
apo A</w> 14519
restric t</w> 14517
F inal</w> 14514
col pos 14512
win dows</w> 14510
photo period</w> 14507
3 C</w> 14504
immunosup pressed</w> 14504
al ol</w> 14501
U SP 14499
sym biotic</w> 14499
c ach 14498
crow ding</w> 14498
2 68</w> 14496
Un ilateral</w> 14495
Der iv 14495
MT A</w> 14494
arteri olar</w> 14494
CYP2 D6</w> 14494
medic ated</w> 14490
At temp 14490
Ex pert</w> 14489
vic tim</w> 14489
Latin o</w> 14487
ap es</w> 14486
extrac ting</w> 14486
T AP</w> 14484
O PG</w> 14484
F usion</w> 14483
myot ubes</w> 14483
E SS</w> 14481
am er 14481
M ax 14480
ar ic</w> 14480
c. v.</w> 14478
H ill</w> 14477
Gre ece</w> 14475
fl un 14474
.00 5</w> 14470
N em 14468
B CC</w> 14468
Eff orts</w> 14465
hyp om 14464
whe el 14464
aminop eptidase</w> 14464
1. 50</w> 14463
coll ater 14463
gr ass 14462
AP T</w> 14460
gen ases</w> 14458
d ae</w> 14457
Cal cul 14457
doub t</w> 14457
De pressive</w> 14456
enucle ation</w> 14456
ophthalm itis</w> 14455
mod elled</w> 14454
modi fier</w> 14453
dis ks</w> 14452
admin ister</w> 14452
den tine</w> 14451
immuno fluorescent</w> 14451
ca strated</w> 14449
E ating</w> 14446
explo iting</w> 14445
sphing osine</w> 14444
L ei 14441
met oprolol</w> 14441
vig ilance</w> 14441
ecta sis</w> 14441
ad nex 14440
Dow n 14439
ter pen 14438
g ill</w> 14436
Mil k</w> 14436
acyclo vir</w> 14433
f us 14431
athl ete</w> 14430
30 5</w> 14429
rot ations</w> 14427
Aud itory</w> 14427
Y ears</w> 14425
be ans</w> 14425
char coal</w> 14425
oxygen ated</w> 14425
Ex tended</w> 14424
halluc inations</w> 14421
ore mentioned</w> 14419
phant oms</w> 14419
Con sec 14418
steroid ogenesis</w> 14418
mercap to 14418
M agne 14416
c asting</w> 14414
yr amine</w> 14414
0.0 23</w> 14413
Rev e 14413
B CS</w> 14412
gam m 14412
Ann ual</w> 14412
tri tion</w> 14411
norm als</w> 14411
Physi cian</w> 14410
lif t</w> 14409
vasodi latation</w> 14405
m c 14404
ogr ade</w> 14404
trop ic</w> 14403
bif unctional</w> 14403
in vertebrate</w> 14402
ob s</w> 14402
potenti ates</w> 14402
chromoph ore</w> 14401
langu ages</w> 14399
Dip tera</w> 14399
synapt osomes</w> 14393
glit azone</w> 14393
2 45</w> 14391
M ales</w> 14391
adjuv ants</w> 14391
iz ability</w> 14389
to id</w> 14389
zircon ia</w> 14389
orch estr 14387
Satis faction</w> 14387
end otox 14386
int us 14386
pro top 14384
2 42</w> 14381
- positive</w> 14380
aly st</w> 14380
Ma king</w> 14380
nin ety</w> 14380
E wing</w> 14379
vertebr a</w> 14379
Ass emb 14379
En zym 14375
buff ers</w> 14375
access ing</w> 14375
1. 56</w> 14374
Ra dical</w> 14374
biotin ylated</w> 14370
Inter pre 14368
fix ing</w> 14367
univers ally</w> 14366
spectrophot ometry</w> 14365
dic t 14364
ut h</w> 14363
PT FE</w> 14363
af orementioned</w> 14362
0.0 21</w> 14360
glycosamin oglycans</w> 14360
d aun 14355
art z</w> 14354
S AA</w> 14353
duplic ated</w> 14353
s ol</w> 14349
ith iasis</w> 14349
omy osin</w> 14347
thrombocyt openic</w> 14347
fac ets</w> 14346
12 3 14345
communic able</w> 14344
diphther ia</w> 14344
de feren 14342
Ta u</w> 14342
periodic ally</w> 14342
on ella</w> 14341
embry onal</w> 14338
1. 46</w> 14337
ol anzapine</w> 14333
I HD</w> 14330
S hor 14329
pro vi 14329
15 0 14328
la id</w> 14328
nam ing</w> 14328
trehal ose</w> 14327
congen ers</w> 14326
co incident</w> 14324
A H 14323
aff ir 14322
Pu er 14322
P 300</w> 14319
irr itable</w> 14319
anth ra 14318
E X</w> 14317
hydroxy proline</w> 14316
S GL 14315
B 16</w> 14315
k al</w> 14314
rever sibility</w> 14314
Obste tr 14314
neo formans</w> 14313
hypercap nia</w> 14312
F ra 14311
yst rophy</w> 14311
ref ine</w> 14311
chrom atid</w> 14310
op son 14309
uc cin 14308
ath letic</w> 14308
Wh y</w> 14308
valpro ate</w> 14306
pharmac ies</w> 14305
pseudo aneurysm</w> 14305
T etr 14304
ventro lateral</w> 14304
8 8 14303
sal but 14303
aw a</w> 14301
th yl 14300
el i</w> 14300
et ast 14299
Estim ates</w> 14298
ge ographically</w> 14297
scap ular</w> 14297
ol ding</w> 14295
In ter</w> 14295
Net works</w> 14295
four fold</w> 14294
re actors</w> 14293
on eal</w> 14290
separ ations</w> 14286
Transpl ant</w> 14286
ow nership</w> 14285
benz o 14284
olig omerization</w> 14284
biosens ors</w> 14284
s apon 14283
sem ic 14283
st ar</w> 14282
scop olamine</w> 14279
def ec 14279
parkinson ian</w> 14278
prepar ative</w> 14275
bic ycle</w> 14275
salbut amol</w> 14274
divid e</w> 14273
mis e</w> 14273
os ulf 14271
ver m 14271
1. 47</w> 14268
lacto ferrin</w> 14267
Res ear 14266
Coh en</w> 14265
Kine tics</w> 14265
Vis u 14264
Prog nosis</w> 14264
pyrro lid 14262
N EC</w> 14261
st u 14258
neg ativity</w> 14258
magne tization</w> 14258
1. 48</w> 14257
photo electron</w> 14257
v oid</w> 14255
un healthy</w> 14255
hom ogenous</w> 14254
si s 14252
manip ulating</w> 14252
brid ged</w> 14252
lif elong</w> 14250
tempor arily</w> 14250
μ L</w> 14248
Str ains</w> 14247
li ability</w> 14245
T MA</w> 14244
A HI</w> 14243
splanch nic</w> 14242
den um</w> 14241
Treat ments</w> 14241
Paren tal</w> 14241
Le ad</w> 14240
intus sus 14239
eng ers</w> 14238
fibrom yalgia</w> 14238
0.0 24</w> 14237
sub stitutes</w> 14236
S b</w> 14235
Hsp 70</w> 14235
chen ne</w> 14235
ip ient</w> 14234
arg ues</w> 14231
transcrip tom 14231
ensi tive</w> 14231
instant aneous</w> 14231
28. 6</w> 14230
oun ding</w> 14222
throm bectomy</w> 14222
intrat umoral</w> 14217
ex tant</w> 14216
phosph og 14216
pa y 14215
0.0 28</w> 14213
1. 67</w> 14213
com ings</w> 14212
Hispan ics</w> 14212
Ter tiary</w> 14209
de af</w> 14208
typ ed</w> 14208
Com m 14208
Res ection</w> 14206
SY N 14206
Q 1</w> 14203
is otype</w> 14202
2 52</w> 14201
onc ologists</w> 14201
far ming</w> 14201
PO RT 14200
break fast</w> 14200
p eren 14199
dro w 14197
prov ocation</w> 14197
PL E</w> 14194
Zn 2</w> 14193
depic ted</w> 14193
G B 14192
G Cs</w> 14192
E O 14192
clin es</w> 14192
br ushing</w> 14192
v in</w> 14191
micro vessels</w> 14188
aut ogenous</w> 14188
ste ep 14187
pyrid yl</w> 14187
electro physiologic</w> 14185
y -</w> 14184
PM C</w> 14184
re tains</w> 14183
ag al 14183
MR P</w> 14182
serogrou p</w> 14182
Ac anth 14181
poly chlorinated</w> 14179
G AL 14178
ur inol</w> 14178
id ov 14177
necrop sy</w> 14177
fabr icate</w> 14176
PRACTI CE</w> 14176
dehis cence</w> 14174
ME P</w> 14173
reli ance</w> 14173
diure sis</w> 14173
equ ity</w> 14169
dimen sion 14169
M CC</w> 14168
her b</w> 14168
pyr imid 14168
co oled</w> 14166
sw ollen</w> 14166
C la 14165
mat ous</w> 14163
cancell ous</w> 14161
cellul arity</w> 14154
Wil d</w> 14154
OH DA</w> 14154
arteri osus</w> 14153
tym panic</w> 14153
diff usi 14151
alg a</w> 14149
- 5 14147
fu zzy</w> 14147
ocor tin</w> 14147
hyd atid</w> 14146
multi layer</w> 14142
iso propyl</w> 14142
stabil ities</w> 14142
in activity</w> 14141
- O</w> 14140
N Ac 14139
ap er</w> 14137
In stitutes</w> 14135
astro cytic</w> 14135
quadru pole</w> 14134
cogn itively</w> 14134
adeno viral</w> 14133
peptid oglycan</w> 14133
BR CA2</w> 14131
bacteri ological</w> 14130
p age</w> 14128
pl ings</w> 14127
Nep al</w> 14127
isopren aline</w> 14126
un n 14122
isothi ocyanate</w> 14122
join tly</w> 14120
S v</w> 14119
3 beta</w> 14119
Q ALY</w> 14117
Suc c 14117
an oxic</w> 14113
Don or</w> 14113
Au NPs</w> 14112
2 48</w> 14110
thic knesses</w> 14110
Ab err 14109
E hr 14108
Res olution</w> 14108
Pres entation</w> 14106
comp elling</w> 14105
Wil ms</w> 14105
schwann oma</w> 14105
ren ders</w> 14104
prob and</w> 14103
in stitute</w> 14101
AM S</w> 14101
PO 4</w> 14101
D T 14100
st ear 14100
CR 1</w> 14100
K 3</w> 14099
ar cu 14098
hypo thesi 14098
omencl ature</w> 14098
In du 14093
Fac il 14093
magn ification</w> 14093
oc e 14092
az ithromycin</w> 14089
benz oyl</w> 14085
Intrac ranial</w> 14085
bever age</w> 14085
Standardi zed</w> 14085
ch ances</w> 14084
cardi otoxicity</w> 14084
2 34</w> 14083
g lue</w> 14083
b ats</w> 14083
cycl opent 14083
AV R</w> 14083
tion ality</w> 14082
Rus sia</w> 14082
over growth</w> 14081
cyst atin</w> 14081
o val</w> 14080
pharmac odynamics</w> 14080
C is 14079
1. 55</w> 14078
proce eded</w> 14078
At titudes</w> 14077
DE NV</w> 14073
Experi ences</w> 14073
sacc ade</w> 14073
3 70</w> 14072
h GH</w> 14072
un bound</w> 14071
bot tle</w> 14071
hair y</w> 14071
trochan teric</w> 14068
anten na</w> 14067
Dys function</w> 14067
end osomal</w> 14066
Du chenne</w> 14065
Concomit ant</w> 14065
micro l</w> 14064
exten sions</w> 14064
17. 2</w> 14063
Rac 1</w> 14063
hyaluron an</w> 14062
I G</w> 14061
Util izing</w> 14061
scal able</w> 14058
bul ky</w> 14057
- free</w> 14054
mul tiv 14054
Un expectedly</w> 14052
17 A</w> 14051
Exam ples</w> 14048
R L 14045
ardi a</w> 14045
U GT 14044
hyponat remia</w> 14043
F ocal</w> 14042
S AD</w> 14041
it z</w> 14041
Contro ls</w> 14041
Vic tor 14041
A HR</w> 14040
are ly</w> 14040
n est</w> 14037
microm eter</w> 14037
c ements</w> 14035
extrac ranial</w> 14034
ib encl 14033
fec und 14032
Q TLs</w> 14029
A gr 14026
it ably</w> 14026
2 43</w> 14023
NI RS</w> 14021
trabec ul 14018
asphy xia</w> 14018
con sin</w> 14017
multi plicity</w> 14013
Al lo 14010
am ik 14008
oc cosis</w> 14007
vir tu 14007
lif ting</w> 14006
bur ned</w> 14005
A U</w> 14003
S cores</w> 14003
wid ths</w> 14002
Hem odynamic</w> 14002
ent orial</w> 14001
secre tin</w> 14001
thi ols</w> 13999
carcin oembryonic</w> 13997
hydra zine</w> 13997
om icro 13994
graph ical</w> 13993
nanos he 13993
go ti 13992
O DN</w> 13991
D CIS</w> 13990
ol or</w> 13990
clin damycin</w> 13990
PA G</w> 13986
en an</w> 13985
B ase</w> 13983
if os 13982
Col or</w> 13982
allo dyn 13982
T SA</w> 13981
ne goti 13981
PTH rP</w> 13981
Fac ial</w> 13980
Fluoresc ent</w> 13980
f issure</w> 13977
3 1P</w> 13977
Pt d 13977
w ish</w> 13976
ME G</w> 13976
IN H</w> 13976
ly ophil 13976
CT D</w> 13976
ty ly</w> 13976
PI C</w> 13976
metallo thionein</w> 13976
Polym erase</w> 13976
N 2O</w> 13975
denti st</w> 13975
omer ular</w> 13974
avoid able</w> 13974
w etting</w> 13973
ibencl amide</w> 13970
2 3.5</w> 13967
ca ve</w> 13967
omal acia</w> 13967
pleas ant</w> 13967
st asis</w> 13966
Radi ographic</w> 13966
di sh 13964
AT G</w> 13963
transduc ers</w> 13959
M ich 13955
broil ers</w> 13955
micro organism</w> 13953
tri acylglycerol</w> 13953
swa b</w> 13953
2 27</w> 13951
pre paredness</w> 13948
aper itoneal</w> 13948
0.0 22</w> 13946
T AT</w> 13945
PE R</w> 13942
cr ash</w> 13941
16. 4</w> 13940
mou th 13939
Rou x</w> 13938
trac ked</w> 13937
g n</w> 13934
erb al</w> 13934
Co operative</w> 13931
co oked</w> 13930
Im ages</w> 13930
spor ulation</w> 13930
pe digree</w> 13927
hydr ide</w> 13927
Col on 13926
gro in</w> 13925
certi fication</w> 13925
ST M</w> 13924
A gre 13923
G6 PD</w> 13921
Endo th 13918
C aspase</w> 13914
entr ifug 13913
od ular</w> 13912
16. 1</w> 13912
vid in</w> 13911
ig an</w> 13909
lob ar</w> 13909
Soci o 13908
elabor ate</w> 13908
8 3.3</w> 13906
clinic al 13906
M CS</w> 13905
H SS</w> 13901
Wis consin</w> 13901
b om 13899
Dis ruption</w> 13899
see ks</w> 13898
Al a 13896
Consec utive</w> 13896
st on</w> 13894
non alcoholic</w> 13894
cardio protective</w> 13893
resc ine</w> 13892
Environ ment</w> 13892
chron ological</w> 13890
fir m 13889
interven ing</w> 13888
nadi r</w> 13886
Gener alized</w> 13885
Gen Bank</w> 13883
Aut ophagy</w> 13883
inter connected</w> 13882
D U</w> 13881
A mp 13877
suspec t</w> 13877
threat ened</w> 13876
ecto derm</w> 13876
Recei ver</w> 13876
car d</w> 13874
sw im</w> 13871
De gradation</w> 13869
Syn erg 13869
0.0 26</w> 13867
asym pto 13867
i. c.v.</w> 13864
PD s</w> 13864
assign ments</w> 13863
pec tin</w> 13862
asym metrical</w> 13862
Micha elis</w> 13862
abund ances</w> 13861
exha us 13860
orth olog 13859
IR I</w> 13857
SI R</w> 13856
Hem orrh 13856
dar kness</w> 13856
heter otopic</w> 13855
con tract</w> 13853
man ia</w> 13852
1. 54</w> 13852
re conc 13851
cr ime</w> 13851
flav or</w> 13849
lea ks</w> 13846
A r</w> 13845
o int 13845
Egyp t</w> 13844
O 2-</w> 13843
A typical</w> 13838
direc tors</w> 13838
extr as 13838
3 80</w> 13834
modi fiers</w> 13834
cyto chal 13834
eu thyroid</w> 13832
2 36</w> 13828
rhod amine</w> 13827
17. 4</w> 13826
ex ia</w> 13825
gonad otrophin</w> 13825
y o</w> 13824
C Cs</w> 13824
cryp toc 13824
SS G</w> 13823
en trance</w> 13822
Cys tic</w> 13820
non pregnant</w> 13818
PI D</w> 13818
2 29</w> 13817
P J 13817
pur ities</w> 13817
och lor 13817
por in</w> 13816
cin ti 13816
land mark</w> 13815
ol ence</w> 13814
ad ox</w> 13813
pat ella</w> 13813
ker t</w> 13813
Tow ards</w> 13813
2 5.0</w> 13811
e at</w> 13809
xim al</w> 13808
off enders</w> 13808
butyr yl</w> 13808
ran itidine</w> 13803
fer rous</w> 13803
lymph edema</w> 13803
B ol 13802
iv udine</w> 13801
AV M</w> 13801
N ar 13798
evo ke</w> 13798
R .</w> 13797
T ru 13797
sten otic</w> 13797
Oc currence</w> 13797
voltam metry</w> 13795
ti r 13794
S lo 13793
Spec imens</w> 13793
X 4</w> 13791
E vents</w> 13790
orig inates</w> 13790
imping ement</w> 13790
y n</w> 13789
E TS</w> 13789
B K 13789
s acchar 13787
graph ite</w> 13786
R ural</w> 13783
B max</w> 13783
gol den</w> 13783
gal ectin</w> 13782
Sep sis</w> 13782
ser ver</w> 13780
is tin</w> 13779
poly cy 13777
visc ous</w> 13775
g ab 13774
all yl</w> 13772
epi androsterone</w> 13771
Nur se</w> 13771
hol o 13770
Ver sus</w> 13770
de generate</w> 13769
oct adec 13769
2 55</w> 13768
predomin ated</w> 13768
accep tors</w> 13767
t led</w> 13766
duplic ate</w> 13766
Ex traction</w> 13765
ocon azole</w> 13764
f ound 13763
ar tom 13763
form ulate</w> 13763
en ox 13762
Partic ularly</w> 13762
isot opes</w> 13759
invari ably</w> 13759
ot str 13758
lec ithin</w> 13756
atic a</w> 13754
H AV</w> 13753
O 6</w> 13753
ter ized</w> 13752
T PN</w> 13749
18. 8</w> 13749
Intr am 13749
Revie ws</w> 13745
treat able</w> 13743
se y</w> 13742
disper se</w> 13742
amik acin</w> 13742
Q 10</w> 13741
red oxin</w> 13740
hour ly</w> 13740
ass ault</w> 13737
ag end 13736
l or</w> 13733
An throp 13732
sup pur 13730
Mic ro</w> 13730
Vacc ination</w> 13730
Re v</w> 13728
lab yrin 13727
monit ors</w> 13727
T rop 13717
SP C</w> 13716
pac ed</w> 13714
Dr .</w> 13714
O m 13713
hem agglutination</w> 13712
ple uro 13709
mE q</w> 13709
app earances</w> 13708
1. 49</w> 13707
Mo vement</w> 13707
Dic ty 13706
n NOS</w> 13702
ac ity</w> 13702
ps i</w> 13702
micro villi</w> 13698
N ile</w> 13696
co eliac</w> 13696
Con current</w> 13696
AF 1</w> 13694
per pe 13693
mo s</w> 13693
2 65</w> 13691
pon tine</w> 13691
F ore 13688
ol umbar</w> 13687
adren ocortic 13687
n is</w> 13685
Nl m 13685
bo di 13683
eth anolamine</w> 13683
Nlm Category</w> 13683
k ynu 13681
0.0 27</w> 13681
ynitr ite</w> 13680
T GA</w> 13679
neurofibrom atosis</w> 13677
pa di 13676
hall marks</w> 13676
E Z 13675
em mal</w> 13675
estim ations</w> 13675
manufac ture</w> 13674
Macroph ages</w> 13674
D DP</w> 13673
semi quantitative</w> 13673
14. 0</w> 13671
genit ourinary</w> 13671
multi tude</w> 13670
pyel onephritis</w> 13670
C ent 13669
Strateg y</w> 13669
Hi erarch 13667
aco emulsification</w> 13663
go t</w> 13662
dur a</w> 13661
alop ram</w> 13659
Di versity</w> 13658
Dam age</w> 13657
alog y</w> 13656
ach ments</w> 13655
unc ou 13654
Re as 13654
NH 3</w> 13654
advoc ate</w> 13653
fo res 13652
lic ted</w> 13651
biom echanics</w> 13649
intrac ytoplasmic</w> 13646
ati dyl 13645
0.0 35</w> 13645
13 0 13645
Sub group</w> 13645
segreg ated</w> 13645
implic ating</w> 13644
Leuk emia</w> 13643
pie zo 13643
r uminal</w> 13641
sho ul 13640
TK I</w> 13640
G AD 13639
5 ,000</w> 13639
kerat osis</w> 13639
sub classes</w> 13638
RT I</w> 13638
oblig ate</w> 13638
c ili 13637
disp arate</w> 13637
P in 13635
Dev ice</w> 13635
activ ations</w> 13634
conc eived</w> 13633
mush room</w> 13633
finger print</w> 13632
synth eses</w> 13631
par ty</w> 13629
sp un</w> 13628
index ed</w> 13628
novel ty</w> 13627
wake fulness</w> 13627
hem oglobin 13624
du plications</w> 13624
endoscop e</w> 13623
G AS</w> 13622
fib er 13620
15. 9</w> 13620
ank ylosing</w> 13620
Air way</w> 13619
Foc us</w> 13618
cardi o</w> 13616
recover ing</w> 13616
MTH FR</w> 13615
ds RNA</w> 13614
nic k</w> 13613
AM A</w> 13612
B B 13611
OUTCO MES</w> 13611
sac ro 13611
Ex pan 13610
vascul arity</w> 13609
enanti omer</w> 13607
r CBF</w> 13605
el et 13605
arc tation</w> 13604
tr ying</w> 13603
I MP</w> 13602
ud in</w> 13602
cath ode</w> 13602
caus ality</w> 13602
Ade qu 13602
P d 13601
S t</w> 13601
predic tability</w> 13601
C BP</w> 13600
ass er 13599
CT Cs</w> 13599
F ul 13597
cos me 13597
Check list</w> 13597
bl end</w> 13596
contrac ting</w> 13596
serop ositivity</w> 13593
E ur 13592
Vacc ine</w> 13592
Osteo arthritis</w> 13592
A NA</w> 13591
re oxygenation</w> 13591
T CR 13590
auto regulation</w> 13590
Un usual</w> 13589
jejun ostomy</w> 13588
f ting</w> 13587
Res ting</w> 13587
ob let</w> 13585
pro ficiency</w> 13584
post s</w> 13584
tel omeres</w> 13583
intern ally</w> 13583
urb an 13580
over production</w> 13579
lumin ance</w> 13579
P ag 13577
S 1P</w> 13576
dr astic</w> 13576
SO D1</w> 13576
o yst 13575
am adol</w> 13573
rec ir 13573
nucle osides</w> 13572
arthro pathy</w> 13572
neuro surgery</w> 13570
muc ositis</w> 13570
4 R</w> 13569
hetero chromatin</w> 13569
fibros arcoma</w> 13568
C U</w> 13567
V en 13567
nanom olar</w> 13567
PC V</w> 13565
cl arity</w> 13564
Vide o</w> 13564
2 64</w> 13563
0.0 10</w> 13563
bac lofen</w> 13563
e thers</w> 13559
in accurate</w> 13558
exc epti 13558
HC G</w> 13558
aten olol</w> 13558
IVI G</w> 13557
deuter ium</w> 13556
neuro toxin</w> 13554
TL E</w> 13554
h s 13553
ERI A</w> 13553
emb ran 13552
den sely</w> 13552
n es</w> 13551
co us 13551
cer tainly</w> 13551
fluctu ating</w> 13551
ds DNA</w> 13551
power ment</w> 13550
dextro se</w> 13550
Tr p 13549
n im 13547
Epidemi ologic</w> 13545
S U</w> 13544
nal trexone</w> 13543
Ex ogenous</w> 13542
short comings</w> 13542
chemoradi ation</w> 13542
te zomib</w> 13540
M atr 13539
trap ez 13538
N N</w> 13537
bar rel</w> 13537
gam et 13535
- 12</w> 13531
ure ly</w> 13531
av al 13530
Del phi</w> 13530
pro collagen</w> 13526
ure thro 13526
Techniqu es</w> 13526
protozo an</w> 13526
ch y</w> 13524
casp ases</w> 13524
L ater</w> 13523
Sho ulder</w> 13523
ent olamine</w> 13522
plas tin</w> 13521
cef tazidime</w> 13520
OR Fs</w> 13515
cra ving</w> 13515
erythropo iesis</w> 13515
transform ants</w> 13513
ist ers</w> 13510
Fe der 13509
wor th 13507
E MR</w> 13506
V ie 13504
diam ond</w> 13503
immer sed</w> 13501
propag ated</w> 13500
em powerment</w> 13496
1, 6 13496
cap ping</w> 13495
Mal aria</w> 13495
conver ts</w> 13493
g ig 13492
scro tal</w> 13491
Sy rian</w> 13489
con ventionally</w> 13487
D AS</w> 13486
4 31</w> 13485
Sh ang 13485
hybrid oma</w> 13485
medul lo 13484
AT RA</w> 13483
aspec ific</w> 13482
T an 13481
O T 13480
immun operoxidase</w> 13480
Publ ished</w> 13479
trans vaginal</w> 13478
av ascular</w> 13478
as aki</w> 13477
NH 4</w> 13476
2 α</w> 13475
VE P</w> 13475
govern ance</w> 13470
explo itation</w> 13469
synovi tis</w> 13466
S earch</w> 13465
encomp asses</w> 13461
enol ase</w> 13461
S SR</w> 13460
9 6 13459
in clusive</w> 13457
o to</w> 13455
Portu gu 13454
sa ve</w> 13453
cl ip</w> 13452
17. 8</w> 13450
Gre ek</w> 13449
heavi er</w> 13449
intrag astric</w> 13449
Larg er</w> 13448
magnit udes</w> 13447
e uro 13443
C W</w> 13443
N ature</w> 13443
hyper triglyceri 13443
3 15</w> 13441
opo ietin</w> 13441
an tine</w> 13438
ipl eg 13436
Ben ign</w> 13436
cat ch 13435
angi oma</w> 13435
phenomen ological</w> 13434
non surgical</w> 13432
Atl as</w> 13430
inferen ces</w> 13429
mesenchym e</w> 13429
Nit rogen</w> 13428
1. 52</w> 13427
ell ites</w> 13427
gover n</w> 13426
sorb itol</w> 13426
D na 13425
V AD</w> 13425
H op 13424
ic ill 13424
an orectal</w> 13422
Re productive</w> 13422
Elec troph 13422
2 47</w> 13421
g 1</w> 13421
Con fir 13421
ra d</w> 13420
ut amide</w> 13419
Ex isting</w> 13419
9 7 13417
arcu ate</w> 13417
mic ity</w> 13416
mis carriage</w> 13410
advant aged</w> 13410
oc h</w> 13409
ophosph orylation</w> 13409
palli ation</w> 13409
Br it 13408
antr um</w> 13406
Relev ant</w> 13404
L ou 13403
W ar</w> 13402
Inhib itor</w> 13400
chro tron</w> 13399
T Y</w> 13398
key words</w> 13397
mi tic</w> 13396
neur algia</w> 13395
30 3</w> 13395
LTB 4</w> 13394
or yz 13392
electroencephal ogram</w> 13392
ach loro 13390
alle y</w> 13390
Epilep sy</w> 13388
Polym er</w> 13387
shut tle</w> 13387
G el 13386
op olysaccharide</w> 13386
b ench</w> 13385
immunosup press 13384
To k 13383
Knock down</w> 13383
e de</w> 13382
Indi ans</w> 13382
an oxia</w> 13381
Immun ization</w> 13381
opac ity</w> 13379
oligodendro cyte</w> 13377
post translational</w> 13375
EC S</w> 13375
Heterogene ity</w> 13374
fo relim 13373
resear ches</w> 13373
iz ine</w> 13372
ip ients</w> 13372
ut ation</w> 13369
1. 65</w> 13368
vul var</w> 13366
mo de 13365
bronch us</w> 13365
G est 13364
1. 53</w> 13364
Princ ipal</w> 13364
mer it</w> 13362
far nes 13362
varic eal</w> 13361
E SD</w> 13358
ec tor</w> 13358
ass a</w> 13358
S can</w> 13357
Ep o</w> 13355
isoleuc ine</w> 13355
ic ides</w> 13349
es ulf 13349
ocul omotor</w> 13349
B DI</w> 13347
dis placements</w> 13347
Abs ence</w> 13347
mic ally</w> 13346
ap sular</w> 13345
h ang 13344
chem oresistance</w> 13344
perfor ations</w> 13342
character izes</w> 13341
Trans genic</w> 13340
elabor ated</w> 13339
Th ym 13337
fi ve 13337
to ol 13335
ste ers</w> 13333
2 0.5</w> 13332
em an 13332
uron ium</w> 13332
maxim a</w> 13330
androst enedione</w> 13330
hapl oid</w> 13327
you ths</w> 13327
J er 13326
ass or 13326
ad er</w> 13325
inten si 13325
11 C</w> 13325
custom ized</w> 13325
clath rin</w> 13325
bloc kage</w> 13324
insec urity</w> 13324
gall stones</w> 13324
Z P</w> 13323
fib ular</w> 13323
emerg es</w> 13323
chim eras</w> 13322
bacul ovirus</w> 13322
Viv o</w> 13322
PG A</w> 13320
un planned</w> 13319
mes o 13319
trim eric</w> 13319
ancest or</w> 13318
multi pl 13317
Endo vascular</w> 13317
Care ful</w> 13317
dys lex 13316
bl astine</w> 13315
Spectr al</w> 13315
recru iting</w> 13313
curricul a</w> 13313
idov udine</w> 13313
b a</w> 13312
ta uro 13311
res or 13310
R AR 13308
M urine</w> 13308
rad on</w> 13308
inter specific</w> 13306
mon onucle 13304
colo strum</w> 13304
Portugu ese</w> 13302
ro ll</w> 13297
tech netium</w> 13297
cer am 13296
invol untary</w> 13295
catast rophic</w> 13295
F lo 13294
for age</w> 13294
Appro aches</w> 13294
Phen otypic</w> 13294
- 11</w> 13293
Ox ide</w> 13291
2 98</w> 13289
chaper ones</w> 13289
b ites</w> 13288
infarc ted</w> 13288
gl enoid</w> 13287
e -- 13283
K o 13283
L C3</w> 13280
a C</w> 13280
LN CaP</w> 13280
broad band</w> 13279
g ium</w> 13277
met e 13276
appreci ation</w> 13276
epsil on 13275
O GTT</w> 13272
Colom bia</w> 13272
Main tenance</w> 13271
dec ont 13270
P U</w> 13269
M CF 13269
squ ir 13269
ligh ting</w> 13269
3 00 13267
flag ellar</w> 13266
kyph osis</w> 13265
2 33</w> 13264
2 41</w> 13264
R SA</w> 13263
iso thermal</w> 13263
ep sy</w> 13262
spec ify</w> 13262
mal to 13262
des atur 13262
ju n</w> 13262
biotechn ology</w> 13262
coumar in</w> 13261
ir in 13259
Th al 13258
Al t 13257
Barri ers</w> 13257
VE GFR</w> 13256
Li po 13252
PC O2</w> 13250
at trition</w> 13249
yl line</w> 13249
av icular</w> 13249
S MS</w> 13248
appoint ments</w> 13246
bi osis</w> 13245
. com</w> 13244
sle eve</w> 13242
FK 506</w> 13242
uter i</w> 13239
PM 2.5</w> 13238
Adju sted</w> 13238
infiltr ated</w> 13235
lumb osacral</w> 13235
mil der</w> 13234
roc k</w> 13231
ag liptin</w> 13229
cal ving</w> 13228
fibrobl astic</w> 13228
streng thened</w> 13227
MT C</w> 13227
cap tive</w> 13226
- binding</w> 13224
I P3</w> 13221
- activated</w> 13221
1. 51</w> 13221
AB I</w> 13220
bull ous</w> 13220
qu artz</w> 13219
oth re 13219
S Q</w> 13218
C Ps</w> 13216
R ub 13216
R LS</w> 13215
biom aterial</w> 13215
star tle</w> 13215
fem tosecond</w> 13214
correc tive</w> 13214
e ters</w> 13213
ospor a</w> 13213
osper m</w> 13212
Kre bs</w> 13212
E very</w> 13211
He ad 13211
Polic y</w> 13211
u ing</w> 13210
17. 3</w> 13209
perman ently</w> 13209
preced es</w> 13206
ch yl 13205
Determin ants</w> 13205
igen in</w> 13202
web sites</w> 13202
Multic enter</w> 13202
ion toph 13199
star ved</w> 13198
Sum mary</w> 13198
wea knesses</w> 13193
aly sts</w> 13192
strabis mus</w> 13191
2 61</w> 13190
phen oxy 13190
MD P</w> 13190
coagul opathy</w> 13190
u ters</w> 13189
vasodi l 13189
m it</w> 13188
al -</w> 13188
um es</w> 13186
CS P</w> 13186
mim etic</w> 13186
insul ts</w> 13185
stom atal</w> 13185
oblast omas</w> 13184
Vir tual</w> 13183
ten ing</w> 13181
sin ensis</w> 13181
conc ei 13179
Li kert</w> 13177
oler ant</w> 13175
Memb ers</w> 13175
am it 13174
Assemb ly</w> 13174
In direct</w> 13173
u bin 13172
Phy to 13171
os able</w> 13170
oper itoneum</w> 13167
con tu 13166
od en 13163
Immun ocyto 13160
osa hexa 13158
C lim 13155
under weight</w> 13155
ac rosome</w> 13153
DIN G</w> 13153
Schol ar</w> 13153
rap e</w> 13152
comm ens 13152
transc utaneous</w> 13152
propan ol</w> 13152
immun ology</w> 13151
triti ated</w> 13151
retin as</w> 13150
Pen icil 13150
P 6</w> 13149
te thered</w> 13147
Cop yright</w> 13147
T g 13146
hem odi 13146
CT LA</w> 13145
allerg ies</w> 13145
d on</w> 13144
J AK</w> 13144
intub ated</w> 13144
finger printing</w> 13142
bronch opulmonary</w> 13141
0.000 01</w> 13141
wavel et</w> 13140
16. 8</w> 13139
tw o-</w> 13138
radiolig and</w> 13138
Ch ile</w> 13137
absorp tive</w> 13135
excepti onally</w> 13133
gran d 13132
em etic</w> 13131
SN s</w> 13130
H .</w> 13129
MM F</w> 13128
op ed</w> 13127
tow n</w> 13127
M end 13125
omy elin</w> 13124
ome dicine</w> 13122
Hsp 90</w> 13122
aspir ates</w> 13121
P elvic</w> 13120
pyl oric</w> 13120
precau tions</w> 13120
par at 13119
rac emic</w> 13119
yl us</w> 13117
plu g</w> 13117
ar id</w> 13111
Par t 13111
an tee</w> 13110
sun light</w> 13110
stil b 13110
ca pro 13108
hydr onephrosis</w> 13108
TA G</w> 13108
cys tine</w> 13108
hyper polar 13107
elev ision</w> 13107
40 2</w> 13105
conspic uous</w> 13105
P PS</w> 13102
in sular</w> 13102
dem ented</w> 13102
smo ker</w> 13101
T 0</w> 13100
cru st 13100
ter tile</w> 13099
ax anthin</w> 13095
CM s</w> 13095
Agro bacterium</w> 13095
allop ian</w> 13094
be c</w> 13093
thaw ed</w> 13093
cross linked</w> 13092
sp ider</w> 13091
S omat 13090
rumin ants</w> 13090
K .</w> 13089
Meth yl</w> 13089
micro L</w> 13088
excep tions</w> 13088
cl ick</w> 13087
clon ogenic</w> 13086
intrac ardiac</w> 13085
PD MS</w> 13083
resear cher</w> 13083
LV AD</w> 13081
citr us</w> 13080
G e</w> 13077
physi co</w> 13077
TR P</w> 13077
SM I</w> 13077
fist ulae</w> 13076
glot tic</w> 13075
Ca MK 13074
hyper sensitive</w> 13072
un adjusted</w> 13070
E SW 13068
GL U 13068
append ectomy</w> 13067
mic elle</w> 13065
phen e</w> 13063
catar acts</w> 13062
deferen s</w> 13062
19 70s</w> 13060
2 46</w> 13059
Rel ation</w> 13059
pr ices</w> 13058
l is 13056
gangli osides</w> 13056
corrobor ated</w> 13056
Post operatively</w> 13054
Sym ptomatic</w> 13053
tetram er</w> 13053
S ão</w> 13052
in spiration</w> 13051
en tive</w> 13050
ocar pine</w> 13049
surviv orship</w> 13048
intrad ermal</w> 13048
H p</w> 13047
spec ulated</w> 13047
pi one 13047
thick ened</w> 13047
2 1.3</w> 13044
sub lethal</w> 13044
Incorpor ation</w> 13044
hib ern 13043
3 3. 13042
PM I</w> 13042
hab ituation</w> 13042
hu b</w> 13042
hyperinsulin emia</w> 13042
17. 1</w> 13041
Hormon e</w> 13041
Impro vements</w> 13040
ex cur 13039
PT T</w> 13037
e worthy</w> 13036
cast le</w> 13036
2 37</w> 13035
M ong 13035
a king</w> 13034
partner ships</w> 13034
or ative</w> 13033
guan ylate</w> 13033
yl on</w> 13031
pow ders</w> 13031
E2 F</w> 13031
c i</w> 13028
fluor ine</w> 13028
am end 13027
glycosamin oglycan</w> 13027
S OS</w> 13026
ti da</w> 13026
de fibrillation</w> 13026
fal fa</w> 13026
all ine</w> 13022
19. 4</w> 13021
oper ates</w> 13020
drom ic</w> 13020
dys morph 13019
B est</w> 13018
oste onecrosis</w> 13017
F ran 13015
an aerob 13015
CH 2</w> 13015
MV D</w> 13014
L F 13013
Prost agland 13013
AV F</w> 13011
per v 13010
quar tiles</w> 13010
dom s</w> 13009
wave guide</w> 13008
colum nar</w> 13006
Fir stly</w> 13004
un ipolar</w> 13003
ethyl enedi 13003
if ier</w> 13002
thaw ing</w> 13002
C p</w> 13000
z on 13000
refl ections</w> 12999
Pro ton</w> 12995
fluoro scopic</w> 12995
oblast oid</w> 12994
four teen</w> 12993
absorb ing</w> 12992
padi as</w> 12991
2 58</w> 12989
por k</w> 12987
17. 9</w> 12986
W G</w> 12985
run off</w> 12984
P ep 12982
. - 12982
grow s</w> 12982
Brassi ca</w> 12981
H ex 12980
G in 12979
tr ains</w> 12979
Micro scopic</w> 12979
T est 12978
S clerosis</w> 12976
uro genital</w> 12976
Lt d</w> 12972
i .</w> 12971
D CA</w> 12969
palmit oyl</w> 12969
pe de 12968
cytochal asin</w> 12968
cap turing</w> 12967
2 500</w> 12966
reti rement</w> 12965
H -</w> 12964
osi c</w> 12963
disproportion ately</w> 12963
TI C</w> 12961
accompl ish</w> 12959
F 5</w> 12958
ER α</w> 12957
shiel ding</w> 12955
Pa ulo</w> 12952
ch ips</w> 12949
intra oral</w> 12948
I r</w> 12947
erb B</w> 12947
ush es</w> 12947
cu ti 12946
ful le 12944
necess itating</w> 12944
b ears</w> 12942
K 4</w> 12942
accur acies</w> 12942
Diff icul 12942
guar antee</w> 12941
ass ed</w> 12940
abil is</w> 12937
nev us</w> 12937
Avail able</w> 12937
lin ol 12936
Con jug 12935
bul ls</w> 12935
Neph ro 12935
cere al</w> 12934
Ur ban</w> 12934
inci sional</w> 12933
P ACT</w> 12932
thym ine</w> 12932
co factors</w> 12931
PF GE</w> 12930
intrac ellularly</w> 12928
sh ells</w> 12926
ach i 12926
olys accharides</w> 12925
olig o</w> 12924
exacerb ate</w> 12924
cere us</w> 12922
h sa</w> 12920
o sterol</w> 12919
conf identi 12919
pac hy 12916
end ophthalmitis</w> 12915
osom atic</w> 12914
S har 12913
L e</w> 12911
ham artom 12911
vagin alis</w> 12911
j as 12910
RN FL</w> 12907
amne sia</w> 12906
Immuno fluorescence</w> 12905
Cor yne 12904
ji ang</w> 12903
hypox anthine</w> 12902
m old</w> 12901
op ty 12901
resi st</w> 12900
Pal li 12900
los ing</w> 12899
L umbar</w> 12898
U D</w> 12897
t ut 12896
employ ee</w> 12896
bo oster</w> 12894
ster on 12894
practi c 12894
2 1.4</w> 12893
glyco side</w> 12893
bim odal</w> 12893
sp ind 12892
G J 12891
3 42</w> 12891
nul li 12891
c itations</w> 12890
thir teen</w> 12890
pneum ophila</w> 12890
ocon us</w> 12889
arg in</w> 12887
Con struction</w> 12887
te ach</w> 12886
cry o</w> 12886
ic ism</w> 12885
18 S</w> 12882
Pa ste 12882
t -</w> 12880
whe ezing</w> 12879
whe el</w> 12876
y anine</w> 12875
ex haled</w> 12875
ine -</w> 12875
requ ests</w> 12873
Con sortium</w> 12873
MD MA</w> 12873
iz o 12872
sion ing</w> 12871
spl eens</w> 12870
no on</w> 12869
hy stere 12869
AM H</w> 12867
rever sing</w> 12867
ile us</w> 12867
uni versities</w> 12866
spectr in</w> 12863
con sangu 12862
spro uting</w> 12862
PS Ps</w> 12861
g all</w> 12860
de posit</w> 12860
framesh ift</w> 12860
ed ings</w> 12858
path ologist</w> 12857
sulf onate</w> 12857
territ ories</w> 12856
U niqu 12851
cat ch</w> 12851
angi o 12851
re tic 12850
character ise</w> 12847
impregn ated</w> 12846
B N 12845
co t</w> 12844
od enal</w> 12844
produc er</w> 12843
EC P</w> 12843
V o 12842
on ite</w> 12842
ot olaryng 12842
collater als</w> 12839
cataly tically</w> 12838
GD NF</w> 12838
ota xime</w> 12838
j e 12837
co variate</w> 12837
bilir ubin 12837
il eg 12836
Pul se</w> 12836
s ingly</w> 12833
E I 12833
hyper responsiveness</w> 12833
Kr us 12833
enthal py</w> 12833
N OR</w> 12832
mis matched</w> 12832
lear ners</w> 12832
per oneal</w> 12830
fl at 12830
nev i</w> 12830
ra fts</w> 12829
shoul ders</w> 12829
os arcomas</w> 12827
DM N</w> 12826
Wall is</w> 12826
ox imetry</w> 12825
DP PH</w> 12825
X L</w> 12824
Cor d</w> 12824
LD 50</w> 12822
amoeb a</w> 12822
fecund ity</w> 12822
Oste opo 12820
ALY s</w> 12820
g ib 12819
M ent 12818
CL s</w> 12817
1, 2,4</w> 12816
L ength</w> 12815
c M</w> 12813
ER Ps</w> 12813
aqu at</w> 12813
moun tain</w> 12813
stri ated</w> 12812
des aturation</w> 12811
Not ch 12811
1 G</w> 12810
wast es</w> 12809
be re 12808
ju dic 12807
ogu an 12806
inter individual</w> 12805
CR IT 12805
2 63</w> 12804
S mar 12804
bul im 12804
16. 9</w> 12803
atyp ia</w> 12802
concer t</w> 12801
g un 12800
re arranged</w> 12800
1. 58</w> 12800
Ar ch 12799
2 44</w> 12797
mul tis 12792
him bine</w> 12791
Ex p</w> 12790
16. 0</w> 12790
Sim ulations</w> 12788
h um</w> 12786
E FS</w> 12786
thic ker</w> 12783
a o</w> 12782
18. 6</w> 12780
intu itive</w> 12780
5 Y</w> 12775
trans located</w> 12775
hex yl</w> 12775
put rescine</w> 12775
re habil 12773
ag u 12773
co erul 12773
AZ T</w> 12773
b ach 12772
A 9</w> 12771
y clines</w> 12771
ast ers</w> 12770
I on 12766
I RR</w> 12766
Mich igan</w> 12766
1. 57</w> 12764
Acc ep 12763
pain less</w> 12763
r il 12762
K 3 12762
N SE</w> 12761
De ficiency</w> 12761
homozyg otes</w> 12761
poster olateral</w> 12761
Infra red</w> 12761
M AR 12759
cr ashes</w> 12758
conto urs</w> 12758
ind welling</w> 12757
S AT</w> 12754
hyper methylation</w> 12754
19. 2</w> 12754
TT X</w> 12752
c resc 12750
prot amine</w> 12750
L AB</w> 12749
harbo red</w> 12749
HB G</w> 12748
orth o</w> 12747
bio distribution</w> 12746
petro leum</w> 12745
Analy tical</w> 12742
P Q</w> 12741
CRIT ERIA</w> 12740
peri ventricular</w> 12739
F ont 12738
. 1</w> 12737
F 344</w> 12737
qui et</w> 12737
t ul 12735
clinical trials.gov</w> 12734
Collabor ative</w> 12733
l i</w> 12732
50 8</w> 12732
swit ches</w> 12732
M ath 12730
vi til 12730
AC HE</w> 12730
mis oprost 12730
w as 12728
de activation</w> 12728
met ach 12728
vul us</w> 12727
gas eous</w> 12727
hem ipleg 12725
long us</w> 12725
acyl glycer 12724
Mig ration</w> 12723
pu er 12722
gu est</w> 12720
porph yr 12720
Mel atonin</w> 12719
opty sis</w> 12719
I 1</w> 12718
brom ocriptine</w> 12718
he ights</w> 12715
repe ating</w> 12714
centro id</w> 12714
itr aconazole</w> 12713
AD T</w> 12712
ano ate</w> 12712
ou tre 12711
micro albuminuria</w> 12710
Ab normal 12707
enanti oselective</w> 12706
P c</w> 12705
x er 12705
str adiol</w> 12705
buff ering</w> 12705
Gu ang 12704
ob ox</w> 12703
40 5</w> 12702
break points</w> 12700
contin gent</w> 12699
ar bo 12698
misoprost ol</w> 12698
qu asi 12697
inferi ority</w> 12697
sil denafil</w> 12696
Spectro scopy</w> 12695
p en</w> 12692
clav ul 12692
har ms</w> 12691
12 10</w> 12689
Ather osclerosis</w> 12689
miner al 12688
LA K</w> 12686
myometr ial</w> 12686
pre test</w> 12685
I le 12684
C 5a</w> 12683
40 L</w> 12683
chi ro 12683
alga m</w> 12682
irin otecan</w> 12682
H 9 12680
Vari ables</w> 12680
Opi oid</w> 12678
NK T</w> 12677
polymer ized</w> 12676
addi tions</w> 12675
electroencephal ography</w> 12675
el essness</w> 12673
ic k 12673
chlamy dial</w> 12673
dimorph ism</w> 12672
er ally</w> 12671
gen iculate</w> 12671
collag enous</w> 12671
Acqu ired</w> 12671
s oma</w> 12670
- K</w> 12670
mel t</w> 12670
2 57</w> 12669
dy ads</w> 12669
Br onch 12669
adi po 12668
sulfam ethoxazole</w> 12667
uro spor 12666
2 1.1</w> 12665
hydrox ylated</w> 12665
anthrac ycline</w> 12665
lo dipine</w> 12664
cyto static</w> 12662
am algam</w> 12661
isol ating</w> 12660
oc ked</w> 12657
t uration</w> 12656
Q SAR</w> 12656
acces sions</w> 12656
E r</w> 12655
fluor inated</w> 12655
intracor onary</w> 12654
ov a</w> 12651
Gi ardia</w> 12651
V WF</w> 12646
F AP</w> 12646
Inter views</w> 12644
adop ts</w> 12644
New castle</w> 12644
E SCs</w> 12643
Dimen sional</w> 12641
determin istic</w> 12638
olec tomy</w> 12637
splen omegaly</w> 12636
ep ir 12635
sp ur 12634
Psych osocial</w> 12634
A RE 12632
dou ble 12632
del aying</w> 12631
Fo rest</w> 12631
opath ogenesis</w> 12630
auth ority</w> 12630
Prote us</w> 12630
remn ants</w> 12629
de dness</w> 12628
GTP ases</w> 12628
man ic</w> 12627
I PC</w> 12626
inter professional</w> 12625
Sc i</w> 12625
DM BA</w> 12624
metatar sal</w> 12624
ai red</w> 12623
TRPV 1</w> 12623
s tic 12622
c m3</w> 12622
5 0.0</w> 12621
Am bul 12621
myometr ium</w> 12620
vas omotor</w> 12619
Off ice</w> 12619
oglut arate</w> 12619
staff ing</w> 12617
B O</w> 12616
transpor ting</w> 12616
Ther mo 12616
22 q 12616
Program me</w> 12616
phen otyping</w> 12615
teg mental</w> 12614
un favourable</w> 12613
mut h</w> 12613
ti des</w> 12608
18. 4</w> 12608
ac ellular</w> 12606
L eb 12604
Meas ure</w> 12603
b us</w> 12602
carcin ogenicity</w> 12602
mTOR C1</w> 12602
cl omi 12601
ap noea</w> 12600
tic atory</w> 12600
J ak 12598
1, 7 12598
CD 56</w> 12596
N AA</w> 12595
phyl ogenetically</w> 12595
cooper ativity</w> 12594
Minim al</w> 12593
M BC</w> 12592
concep tions</w> 12591
2 73</w> 12590
H arr 12588
gu il 12588
acetyl cysteine</w> 12587
PO P</w> 12586
eme sis</w> 12586
tend er</w> 12585
Frac tures</w> 12585
S .</w> 12584
Fac ulty</w> 12583
Metast asis</w> 12582
rifamp in</w> 12582
o ocysts</w> 12581
l pr</w> 12581
supra ventricular</w> 12580
kit t</w> 12580
g oblet</w> 12579
dis course</w> 12577
phen idate</w> 12577
anticip ate</w> 12577
poly urethane</w> 12576
ac company</w> 12575
leiomy oma</w> 12575
b ell</w> 12573
architec tural</w> 12573
hypoc alc 12573
Shang hai</w> 12573
h olog 12572
ari us</w> 12572
ME M</w> 12572
R och 12571
Pneum ocystis</w> 12571
past ure</w> 12571
ot ens 12569
acromegal y</w> 12569
R AR</w> 12568
es onide</w> 12568
GLUT 4</w> 12567
ophyl l 12566
lo d 12565
in ulin</w> 12564
ap enta 12563
Mor ris</w> 12560
Ear lier</w> 12560
lox ane</w> 12559
occa sion</w> 12559
offic ers</w> 12559
synerg ism</w> 12558
rec alled</w> 12557
CD H</w> 12555
japon ica</w> 12555
vol ta 12552
F EV</w> 12551
Bel gium</w> 12551
outre ach</w> 12549
ane oplastic</w> 12548
dy ne 12546
AE Ds</w> 12546
artic ulation</w> 12546
ona dism</w> 12546
inser ts</w> 12545
U M</w> 12544
18. 3</w> 12544
S AP 12543
M PP</w> 12542
ML R</w> 12542
vacu ole</w> 12541
dal tons</w> 12541
lor dosis</w> 12539
pre implantation</w> 12538
var us</w> 12538
quoti ent</w> 12536
hydro peroxide</w> 12533
neuro pathies</w> 12533
mal tose</w> 12532
inc entive</w> 12531
re par 12530
res us 12530
Sil ver</w> 12530
DM F</w> 12529
kn ife</w> 12525
Rheum atoid</w> 12524
CH B</w> 12523
B 27</w> 12522
ox o 12522
intussus ception</w> 12522
B 3 12519
Sem i</w> 12519
orh inal</w> 12519
J am 12518
fibrom a</w> 12515
br inging</w> 12514
as ka</w> 12513
sl augh 12513
Ul tim 12510
s ay</w> 12509
c us 12508
form ats</w> 12508
Lym ph</w> 12508
bis -</w> 12507
sn ails</w> 12506
In patient</w> 12505
lep tosp 12502
Tum our</w> 12502
m W</w> 12500
inter pol 12500
recei ves</w> 12499
fi re 12498
ocycl ine</w> 12498
p ill</w> 12496
bur ied</w> 12496
SH V</w> 12495
Epig en 12495
E MA</w> 12494
I SR 12490
doc yanine</w> 12490
im purities</w> 12489
ph ys 12489
Seas onal</w> 12489
fum arate</w> 12488
avid in</w> 12487
bio char</w> 12485
must ard</w> 12482
o oph 12481
nar rati 12481
pa yments</w> 12480
Rev ision</w> 12480
spermat og 12480
Bal ance</w> 12480
lo zin</w> 12478
carrage enan</w> 12478
allodyn ia</w> 12478
R U</w> 12476
disrup tive</w> 12475
ent orhinal</w> 12474
Epid ermal</w> 12474
2 1.5</w> 12473
inten sively</w> 12472
Aer omonas</w> 12471
litter mates</w> 12471
gly cin 12470
SI K</w> 12468
T CC</w> 12467
comp uters</w> 12466
pal pation</w> 12465
Arc tic</w> 12465
ri vers</w> 12464
ato hepatitis</w> 12464
MD CK</w> 12463
II IA</w> 12463
semin iferous</w> 12463
L os</w> 12462
CB Z</w> 12462
α 1</w> 12460
i. m.</w> 12460
chol este 12459
endoscop ically</w> 12458
su ite</w> 12456
mal nourished</w> 12456
MD R1</w> 12456
B ost 12455
pro myelocytic</w> 12455
can opy</w> 12455
9 9. 12453
50 s</w> 12453
Cyto kine</w> 12453
gr ac 12452
proto plasts</w> 12450
aqu educ 12448
inclin ation</w> 12446
TR AP</w> 12445
oscill ator</w> 12444
Bur kitt</w> 12443
Jo hn 12443
redund ancy</w> 12443
O SAS</w> 12441
vin blastine</w> 12441
ur anium</w> 12440
break point</w> 12439
radi osensitivity</w> 12438
Ab sorption</w> 12437
sporo zo 12437
C yst 12436
recip itation</w> 12436
HE K 12436
marg in 12435
E vent</w> 12433
Ox idation</w> 12433
si le</w> 12429
electro physiology</w> 12429
Ser ies</w> 12429
ph acoemulsification</w> 12426
post surgical</w> 12425
Streng th</w> 12424
k d</w> 12423
min ated</w> 12423
av al</w> 12423
Lew y</w> 12422
PS 1</w> 12421
Resi dual</w> 12420
forc eps</w> 12420
1. 3 12417
compart ment 12416
thyl ak 12415
Mob ile</w> 12413
enzym ic</w> 12412
not omy</w> 12412
DE P</w> 12411
T1 DM</w> 12411
nanoro ds</w> 12411
steroid ogenic</w> 12410
peri stal 12409
cys tic 12409
19. 6</w> 12407
coord inating</w> 12405
hype remia</w> 12405
oc tan 12403
non selective</w> 12402
ant oin</w> 12401
radi ologically</w> 12400
E osin 12399
re generate</w> 12398
F und 12397
ab used</w> 12397
ob acillus</w> 12397
ampl ing</w> 12397
sub lingual</w> 12395
administr ations</w> 12395
ful fill</w> 12394
haem olytic</w> 12394
immunoprecip itated</w> 12394
PV C</w> 12393
sol di 12392
infarc tions</w> 12390
in ning</w> 12389
anes ulf 12389
gl abr 12387
AG Es</w> 12387
ab ulary</w> 12386
parv um</w> 12383
Aberr ant</w> 12381
W i 12380
foc us 12380
in gens</w> 12379
gl ibenclamide</w> 12379
EB P 12379
inter im</w> 12376
phosph ot 12376
remin is 12375
30 9</w> 12374
E H 12373
min g 12372
ox i 12371
Wil li 12371
sh ire</w> 12370
D . 12368
D CE</w> 12366
T x 12366
diffusi vity</w> 12366
anc iclovir</w> 12365
O il</w> 12363
sl aughter</w> 12363
R SD</w> 12362
bio assays</w> 12362
circum flex</w> 12362
cav itation</w> 12362
AT L</w> 12361
M IC 12360
ver tically</w> 12359
Ch E</w> 12359
ten sions</w> 12358
P r</w> 12356
Agre ement</w> 12356
EG G</w> 12355
Path ways</w> 12354
vir gin</w> 12353
rec alc 12351
transfer ring</w> 12351
V AP</w> 12350
7 20</w> 12349
H az 12348
assi gn</w> 12348
b omb 12347
men arche</w> 12347
Org anis 12346
epti form</w> 12345
thi ophene</w> 12345
vitil igo</w> 12344
mening eal</w> 12343
.00 3</w> 12341
Penicil lium</w> 12341
PR S</w> 12338
Radi ological</w> 12338
reser pine</w> 12337
non union</w> 12336
GI ST</w> 12336
0.0 29</w> 12334
hi m</w> 12334
F TD</w> 12333
lign oc 12333
pon der 12332
pancre atectomy</w> 12331
L ine</w> 12330
acc ru 12330
util ised</w> 12330
chel ator</w> 12329
K P</w> 12327
quin ones</w> 12327
cor ner 12326
afford able</w> 12325
e ders</w> 12324
thi op 12324
curet tage</w> 12324
Cycl in</w> 12322
S O2</w> 12321
Specific ity</w> 12320
b un 12319
ann ul 12318
PD H</w> 12318
oro usly</w> 12316
C old</w> 12315
lymph oblastoid</w> 12315
Diff use</w> 12315
convinc ing</w> 12315
ph ia</w> 12313
E MENT</w> 12312
R . 12311
fi tinib</w> 12311
placent as</w> 12311
tod d 12309
B Z 12308
Con version</w> 12306
hypo perfusion</w> 12306
advoc acy</w> 12306
disc er 12305
17. 7</w> 12304
tent atively</w> 12304
A β 12301
ultrac entrifug 12301
2 88</w> 12300
atech in</w> 12300
neurolog y</w> 12299
cave olin</w> 12299
rop ic</w> 12298
Ch emo 12297
pl otted</w> 12296
30 6</w> 12296
RAN TES</w> 12294
in compatibility</w> 12293
fuc ose</w> 12291
osahexa enoic</w> 12291
F ish</w> 12287
aug ments</w> 12287
Lig and</w> 12286
2 49</w> 12285
aff e 12284
MUC 1</w> 12284
entr al</w> 12283
earth qu 12283
18 , 12280
W E</w> 12279
o viruses</w> 12278
clim bing</w> 12278
2 62</w> 12276
thym oma</w> 12276
pre determined</w> 12275
wr ong</w> 12274
s ins</w> 12272
P SII</w> 12272
sub retinal</w> 12272
Perc eption</w> 12272
b pm</w> 12271
pal mitic</w> 12271
T ro 12265
le ar</w> 12265
assum es</w> 12265
b athing</w> 12264
mes enter 12264
2 7.5</w> 12263
Fib rosis</w> 12262
sc ribed</w> 12261
end ang 12259
II IB</w> 12259
D ex</w> 12258
pur ple</w> 12257
muscul ature</w> 12257
3 11</w> 12254
ur chin</w> 12254
u ximab</w> 12253
Coryne bacterium</w> 12253
or d</w> 12252
glucone ogenesis</w> 12252
ç et</w> 12250
PD R</w> 12250
Echocardi ography</w> 12249
Mechanis tically</w> 12248
Contr ibution</w> 12248
hom osexual</w> 12246
in activate</w> 12245
c Gy</w> 12242
er v 12241
itu it 12240
issu ed</w> 12239
C PS</w> 12238
Pre ventive</w> 12238
Hol ter</w> 12238
landscap es</w> 12238
spous es</w> 12238
B IS</w> 12237
AC D</w> 12237
pur ulent</w> 12237
Resear chers</w> 12236
restric ting</w> 12234
rhiz osphere</w> 12234
2 53</w> 12233
Eth ics</w> 12233
S olution</w> 12232
hy aline</w> 12232
administr ators</w> 12232
Gene tics</w> 12230
inqu iry</w> 12229
centrom ere</w> 12229
acyl transferase</w> 12228
Ischem ia</w> 12228
tr is</w> 12227
ester ases</w> 12225
edent ulous</w> 12224
cap it 12223
nucle oti 12222
no stosis</w> 12219
An .</w> 12219
Par as 12219
mis leading</w> 12218
p K</w> 12217
C Y</w> 12216
DQ B1</w> 12216
biom imetic</w> 12214
g ents</w> 12212
intra thoracic</w> 12212
foot ball</w> 12212
G PCR</w> 12211
MR C</w> 12210
sil enced</w> 12210
ec tic</w> 12208
odynam ically</w> 12208
Observ ation</w> 12208
multin ucle 12208
Lith ium</w> 12208
Con sumption</w> 12207
4 32</w> 12206
ri ol</w> 12204
hydroxy steroid</w> 12204
sphing omyelin</w> 12203
propos als</w> 12203
TA VI</w> 12202
amphib ian</w> 12202
un defined</w> 12201
Re actions</w> 12201
citi zens</w> 12200
e igen 12199
Mi R</w> 12199
ym ph 12198
bo otstr 12198
gli osis</w> 12198
im olar</w> 12197
imp acting</w> 12197
osin usitis</w> 12197
Surve ys</w> 12197
fel low</w> 12197
Bost on</w> 12197
2 71</w> 12196
depress ant</w> 12196
T DP</w> 12195
phot ocoagulation</w> 12194
occup ying</w> 12194
cha otic</w> 12194
AN ES</w> 12193
Beh çet</w> 12193
K ing</w> 12191
olig omer</w> 12191
obi ology</w> 12191
pediatr icians</w> 12190
en ing 12188
an ate</w> 12187
1. 63</w> 12186
Per spective</w> 12186
neur ites</w> 12185
in operable</w> 12184
mal adaptive</w> 12184
Practi cal</w> 12184
stoichi ometric</w> 12184
bi olog 12183
Dem on 12182
Pre clinical</w> 12181
Cyto chrome</w> 12181
dec iding</w> 12180
S HBG</w> 12179
perox ynitrite</w> 12179
L H 12178
cartil aginous</w> 12177
H eli 12176
M U</w> 12176
ph entolamine</w> 12176
ocat alytic</w> 12176
l op 12175
mag lob 12174
Cor tico 12172
Im plant</w> 12171
ap entin</w> 12170
H ippocamp 12169
TC GA</w> 12169
Pro ducts</w> 12168
rever ses</w> 12168
u an</w> 12167
c idin</w> 12166
ovari ectomy</w> 12166
physi o 12165
atr an</w> 12164
3 6. 12163
align ments</w> 12163
en v</w> 12162
wh it 12162
ru it</w> 12162
concer ted</w> 12162
verm ectin</w> 12161
IM PORT 12160
protr usion</w> 12158
S or 12157
s lip 12156
b outs</w> 12156
dis infec 12156
bl ank</w> 12153
prog estin</w> 12153
mode stly</w> 12153
nanot echn 12151
phal an</w> 12151
eg o</w> 12150
tetradec ano 12149
ec ule</w> 12148
phot oc 12148
lam ivudine</w> 12147
gr atings</w> 12146
l ement</w> 12145
mark eted</w> 12145
hap ten</w> 12145
org hum</w> 12144
pyro lysis</w> 12144
ster ility</w> 12143
stre am 12143
d ress 12140
deci sive</w> 12140
Ap gar</w> 12139
guan id 12139
S MR</w> 12138
mat ters</w> 12138
G ross</w> 12136
B owel</w> 12136
aver sion</w> 12136
rhabdomy osarcoma</w> 12136
D W</w> 12135
post graduate</w> 12135
paran asal</w> 12135
H ind 12134
SC L</w> 12134
pren atally</w> 12134
Dist al</w> 12134
inter costal</w> 12132
ob a</w> 12130
1. 66</w> 12130
N Q 12129
fundam entally</w> 12129
cohe sion</w> 12129
Er ro 12129
bu terol</w> 12128
clau dication</w> 12128
sol vation</w> 12126
overestim ated</w> 12126
th emia</w> 12124
dosi metric</w> 12124
T .</w> 12121
LA SIK</w> 12120
per fr 12118
osup pression</w> 12118
2 1st</w> 12117
cataly sed</w> 12117
lys osome</w> 12115
1 20 12114
intra vesical</w> 12114
Simult aneously</w> 12113
phen olate</w> 12112
anc ers</w> 12110
ur ve 12109
fam ide</w> 12108
P As</w> 12107
em py 12107
0.0 36</w> 12107
bo ost 12107
he art 12106
pre valences</w> 12105
Ch lo 12105
anc erous</w> 12103
toxic osis</w> 12103
discrimin atory</w> 12103
satisfac tor 12102
ophysi ological</w> 12102
Aneur ys 12100
rom e</w> 12096
HO X 12096
2 72</w> 12095
organis ations</w> 12095
neutr alized</w> 12095
at opy</w> 12093
en one</w> 12088
cl oth 12088
AP ACHE</w> 12088
re positioning</w> 12087
Car bo 12087
T 8</w> 12085
K aw 12085
De pl 12085
edi tion</w> 12085
sulfon yl 12084
1. 64</w> 12082
Injur ies</w> 12082
pr ison</w> 12080
multi component</w> 12079
PA L</w> 12079
T olerance</w> 12078
ig ence</w> 12077
Im mediately</w> 12077
F it 12075
is inin</w> 12074
bin omial</w> 12071
sal ping 12069
ultra thin</w> 12069
rot ated</w> 12068
mono valent</w> 12067
passi vely</w> 12067
plethys mography</w> 12067
Squ amous</w> 12066
man ometry</w> 12063
CD 5</w> 12063
inten sification</w> 12062
MD s</w> 12062
S Q 12061
ca dic</w> 12061
bi oc 12059
rem if 12058
tri angular</w> 12057
mit ogens</w> 12057
pen sion</w> 12057
sk elet 12056
z idovudine</w> 12055
chlor promazine</w> 12055
thorac olumbar</w> 12054
exc i 12053
AD Rs</w> 12052
thrombo plastin</w> 12052
man no 12051
or ib 12045
vesti bul 12045
Star ting</w> 12045
G PS</w> 12044
tin ine</w> 12044
reg ressed</w> 12043
tri vial</w> 12043
excre tory</w> 12042
urospor ine</w> 12042
con val 12041
- stimulated</w> 12040
0.0 32</w> 12040
Krus kal</w> 12039
my o</w> 12038
Uter ine</w> 12038
E l</w> 12036
olim us</w> 12034
peri or</w> 12032
phot oluminescence</w> 12032
manifest s</w> 12032
conjunctiv a</w> 12032
ten o 12031
Suppl ementation</w> 12031
com positional</w> 12030
ventro medial</w> 12029
C el 12028
dis sections</w> 12026
tur key</w> 12026
Le uc 12025
neuro pathological</w> 12024
HCO 3-</w> 12024
k V</w> 12023
di benzo</w> 12023
RE E</w> 12023
resp ir 12021
adel phia</w> 12020
ros ette</w> 12019
bodi ly</w> 12019
rel ying</w> 12018
cap ita</w> 12018
propag ating</w> 12018
sam ine</w> 12017
circum c 12015
Ps ori 12015
Con f 12014
O ry 12013
ure ment</w> 12013
M ari 12010
pati ent 12008
electro poration</w> 12007
2 3.3</w> 12006
4 S</w> 12006
blo c</w> 12006
fav ors</w> 12006
reticul ocyte</w> 12006
metamorph osis</w> 12006
Mac aca</w> 12005
Consider able</w> 12004
t elevision</w> 12001
L Q</w> 11999
tin ase</w> 11999
tain able</w> 11999
chel ation</w> 11999
st ands</w> 11998
Hep arin</w> 11997
L Y</w> 11996
3 5. 11996
Val idity</w> 11996
septic emia</w> 11994
o v</w> 11993
F OR 11991
th inner</w> 11990
piezo electric</w> 11990
s lip</w> 11989
K I 11989
uc ulline</w> 11989
sup pressors</w> 11989
p ubic</w> 11988
con serving</w> 11988
par adox</w> 11988
un trained</w> 11987
aggra vated</w> 11986
sti r 11985
vi bri 11984
uro dynamic</w> 11984
mono oxygenase</w> 11984
prec ede</w> 11981
anter ograde</w> 11981
lit azone</w> 11980
un available</w> 11979
acet am</w> 11979
Elig ible</w> 11979
sal va</w> 11978
opon tin</w> 11978
lif lozin</w> 11977
Particip ation</w> 11976
carbox yp 11976
r uc 11973
adju stable</w> 11973
r ust</w> 11972
toler able</w> 11972
Com plexes</w> 11970
Cz ech</w> 11969
Mar kers</w> 11968
transf ec 11967
.0 00 11967
volu tional</w> 11966
Mach ine</w> 11966
teen agers</w> 11965
Ultrason ography</w> 11965
2 3.1</w> 11964
cal ories</w> 11964
histopath ologically</w> 11964
obliter ation</w> 11964
F UN 11963
n ations</w> 11961
le ach 11961
M eg 11959
py ogenes</w> 11958
wheel chair</w> 11958
phospho protein</w> 11957
ton ian</w> 11954
Font an</w> 11954
O mp 11953
ex ogenously</w> 11953
S ham</w> 11952
P Y</w> 11950
Phil adelphia</w> 11949
exhaus tive</w> 11949
us et 11948
ha y</w> 11947
institu ted</w> 11947
re visions</w> 11945
at e-</w> 11945
ph aryng 11945
2 67</w> 11944
0.0 45</w> 11942
C ef 11941
suppl ying</w> 11940
cardiover ter</w> 11940
m 1</w> 11939
EXP ERI 11939
X I</w> 11938
mo ves</w> 11936
neur itis</w> 11936
ic ola</w> 11935
tubercul in</w> 11935
2 59</w> 11933
n omenclature</w> 11933
al falfa</w> 11933
later alis</w> 11933
n AChR</w> 11932
mal absorption</w> 11932
stre et</w> 11931
Po oled</w> 11931
ello femoral</w> 11930
str is</w> 11928
as sion</w> 11927
spec kle</w> 11927
S ystolic</w> 11924
Immun ological</w> 11924
SY 5Y</w> 11924
hyn chus</w> 11923
os ep 11920
tumorig enicity</w> 11920
V HL</w> 11919
resi stive</w> 11919
B land</w> 11917
Su stained</w> 11917
Her pes</w> 11917
MD CT</w> 11916
gall stone</w> 11916
philosoph y</w> 11916
angi ograms</w> 11915
six teen</w> 11915
opr amide</w> 11915
entan il</w> 11913
Controll ing</w> 11912
satisfactor ily</w> 11912
L . 11911
fl ank</w> 11911
60 s</w> 11910
coagul ase</w> 11910
Tr is</w> 11908
an ian</w> 11907
inter genic</w> 11907
broncho constriction</w> 11906
su turing</w> 11904
rec ession</w> 11904
inter ing</w> 11904
B e</w> 11903
Di vision</w> 11903
H 5</w> 11902
intr atrac 11901
pheny ls</w> 11901
k r 11900
hypo plastic</w> 11900
2 85</w> 11899
PD I</w> 11897
amin obenz 11896
IMPORT ANCE</w> 11896
Dom ain</w> 11894
priv ileg 11894
acceler ometer</w> 11893
Al ong</w> 11892
3 4. 11891
do ors</w> 11891
3 2. 11890
c on</w> 11890
pom be</w> 11889
Alg orith 11889
Phy s</w> 11887
pyrid inium</w> 11887
wit nessed</w> 11887
Melan oma</w> 11887
In activation</w> 11885
poly phenol</w> 11885
TB ARS</w> 11885
Contr ib 11885
L ess 11884
fl utter</w> 11884
k o</w> 11883
ic on</w> 11883
ster notomy</w> 11882
acr idine</w> 11882
PA 1</w> 11881
ne ocortical</w> 11880
R 0</w> 11879
SU V</w> 11879
gar lic</w> 11879
Isl ands</w> 11879
O x</w> 11878
parti te</w> 11877
r ons</w> 11876
Dend ritic</w> 11876
s ong</w> 11875
intern alizing</w> 11875
br an</w> 11874
PA N</w> 11874
GPC Rs</w> 11874
wa king</w> 11872
stret ched</w> 11872
CH S</w> 11871
Aff airs</w> 11871
p ons</w> 11870
N W</w> 11870
Müll er</w> 11868
f usc 11867
Inf arction</w> 11867
Auto immune</w> 11867
ta vidin</w> 11865
sens ations</w> 11865
spati o</w> 11865
inc ial</w> 11863
Su icide</w> 11862
Con ference</w> 11861
CH C</w> 11860
yog a</w> 11860
L CA</w> 11859
ad or</w> 11859
stitu ted</w> 11858
othre itol</w> 11857
F ACS</w> 11856
gl os 11856
post transplant</w> 11856
Cul tured</w> 11853
Th in</w> 11852
inhe rently</w> 11852
Enter obacter</w> 11848
R CA</w> 11847
gastro stomy</w> 11847
organ ize</w> 11846
angi ogram</w> 11846
plas tid</w> 11845
Electro chemical</w> 11844
Determin ing</w> 11844
radi ographically</w> 11843
r IL</w> 11840
bi um</w> 11839
nitro phenyl</w> 11839
2 95</w> 11838
Ch IP</w> 11838
Trans forming</w> 11838
L eptin</w> 11836
oint ment</w> 11836
Pa CO2</w> 11835
cef otaxime</w> 11835
osmol arity</w> 11833
Buil ding</w> 11833
Enzym atic</w> 11833
expan ds</w> 11832
Mal aw 11832
broad ening</w> 11832
Per itoneal</w> 11831
tob ramycin</w> 11831
anter olateral</w> 11831
Uniqu e</w> 11831
Y AP</w> 11830
di ous</w> 11830
mal occlusion</w> 11830
corne um</w> 11830
X anth 11829
ch oc 11827
compart mental</w> 11827
Lymph oma</w> 11827
arch es</w> 11825
9 -</w> 11824
d A</w> 11823
8 th</w> 11822
1. 62</w> 11821
sum mation</w> 11821
P 7</w> 11819
war d 11818
supr a</w> 11818
W el 11815
I x 11814
Uni versal</w> 11814
intern ationally</w> 11814
1, 8 11813
Gl c</w> 11813
f ats</w> 11812
enol pyruvate</w> 11812
x i</w> 11811
2 76</w> 11810
u ated</w> 11810
Ga g</w> 11810
Profes sional</w> 11810
perfr ingens</w> 11810
re wards</w> 11807
L ag 11806
mat ured</w> 11806
ge ts</w> 11806
r an</w> 11805
PO C</w> 11805
refuge es</w> 11805
byst ander</w> 11805
lin ic</w> 11803
multi forme</w> 11803
achiev ements</w> 11803
bi ogenic</w> 11801
quin ine</w> 11801
Com p 11800
s 1</w> 11797
V enti 11797
Ec topic</w> 11797
Minim ally</w> 11797
z ip 11796
N K1</w> 11795
T TR</w> 11795
P orph 11794
relax ing</w> 11792
Alt man</w> 11792
gra vit 11791
pseud om 11790
MD V</w> 11789
el min 11788
ost elium</w> 11788
PE F</w> 11788
ocl opramide</w> 11788
A udi 11785
y fish</w> 11784
P ow 11784
l one</w> 11784
V TA</w> 11783
profes sions</w> 11783
Z r</w> 11782
rel ieving</w> 11782
des min</w> 11782
disp osable</w> 11780
SB RT</w> 11780
it ted</w> 11779
pro tracted</w> 11779
MR L</w> 11779
disper sions</w> 11779
glycos yl 11779
Sto kes</w> 11779
OR R</w> 11778
retin oids</w> 11778
Chang ing</w> 11778
pro active</w> 11777
d P</w> 11776
S ource</w> 11775
vers ati 11775
fill er</w> 11774
Wil son</w> 11774
re mitting</w> 11772
3 93</w> 11771
ir o</w> 11771
bre ad 11771
Struc tured</w> 11770
ur ing 11769
F 6</w> 11768
B MS</w> 11768
se ated</w> 11768
h inge</w> 11767
In sec 11767
macro lide</w> 11766
S an 11765
B m 11764
molyb denum</w> 11762
controver sies</w> 11761
G EN 11760
19 60</w> 11760
R -</w> 11757
ero sive</w> 11757
dehydro epiandrosterone</w> 11756
Typh imurium</w> 11756
C e</w> 11754
minim ization</w> 11753
para plegia</w> 11753
b oxes</w> 11752
30 7</w> 11752
obstetr ics</w> 11752
Meth ylation</w> 11750
administr ated</w> 11749
Par allel</w> 11748
Gol d 11748
telangi ectasia</w> 11748
Ver o</w> 11747
abol ish</w> 11746
neuro tensin</w> 11745
ss DNA</w> 11745
str ipping</w> 11743
E ti 11742
Ah R</w> 11742
CB S</w> 11740
inv aded</w> 11738
Y el 11737
18. 9</w> 11737
V alley</w> 11736
ec top 11736
Bi ology</w> 11736
cyto kinesis</w> 11735
co arctation</w> 11733
0.0 31</w> 11733
DM AR 11733
ach uset 11732
tri azole</w> 11732
T Fs</w> 11731
at ellite</w> 11731
ac ar 11731
renew ed</w> 11730
bronchi ectasis</w> 11729
B il 11728
J i 11727
hypog onadism</w> 11727
May o</w> 11726
1. 4 11724
PA O</w> 11723
HCO 3</w> 11723
ns 0</w> 11721
hepat obiliary</w> 11719
thermophil us</w> 11719
clinic o</w> 11718
Cost s</w> 11717
S. D.</w> 11715
T a</w> 11714
ow ners</w> 11714
hydro static</w> 11714
child bearing</w> 11714
18. 7</w> 11713
neuro filament</w> 11712
ometr ics</w> 11712
S 100</w> 11711
B CI</w> 11711
nanoc arri 11711
dilu tions</w> 11710
al asia</w> 11709
inf inite</w> 11709
ten e</w> 11708
eth oxy 11708
transep ithelial</w> 11708
os ting</w> 11707
achuset ts</w> 11707
2 74</w> 11705
S till</w> 11705
Inf ected</w> 11705
ref used</w> 11703
p ockets</w> 11702
immuno chemical</w> 11700
SP T</w> 11700
pericardi tis</w> 11700
duc k</w> 11699
S DH</w> 11698
un intended</w> 11697
Amy loid</w> 11697
MCA O</w> 11696
virtu e</w> 11694
Sp ace</w> 11691
pericardi um</w> 11691
pre mat 11690
Al ber 11690
Conserv ative</w> 11690
cl earing</w> 11688
extern alizing</w> 11688
ste ep</w> 11687
phot ography</w> 11687
Anat omical</w> 11687
econ omics</w> 11686
epig astric</w> 11686
D AP</w> 11685
ester ification</w> 11685
HE TE</w> 11684
loc alizes</w> 11683
Mass achusetts</w> 11681
hydroxy dopamine</w> 11680
quin idine</w> 11680
im eter</w> 11679
1. 85</w> 11678
fasc icul 11677
car trid 11676
m ex 11673
18. 1</w> 11673
estim ator</w> 11672
lamin ae</w> 11671
T W 11670
rip tyline</w> 11670
C PC</w> 11669
expos e</w> 11669
permeabil ized</w> 11668
p 24</w> 11667
0.0 37</w> 11667
Ble eding</w> 11667
p it</w> 11666
Str ong 11666
bet aine</w> 11665
shor ten</w> 11665
6 19</w> 11664
Sm ith</w> 11664
van adate</w> 11662
str ata</w> 11661
echocardi ogram</w> 11661
19 69</w> 11660
chr ys 11660
comp action</w> 11659
vol vulus</w> 11659
pal mar</w> 11659
epil eptiform</w> 11659
Me V</w> 11658
amoun ted</w> 11658
juven iles</w> 11658
lu tide</w> 11657
sat ellites</w> 11657
MV PA</w> 11657
m ated</w> 11656
Par k</w> 11655
Ara b</w> 11655
enter itis</w> 11653
ultraf ast</w> 11653
ul inic</w> 11652
Neutroph il</w> 11652
post eri 11651
mas ticatory</w> 11650
R APD</w> 11649
un protected</w> 11649
sto ols</w> 11649
ylo xy</w> 11648
andro genic</w> 11647
sensiti zing</w> 11647
cat abol 11645
Feder ation</w> 11644
sa ved</w> 11642
E TH 11641
ox ine</w> 11640
dos e-</w> 11640
Wh o</w> 11638
bar bit 11638
chori o 11636
melan ocytic</w> 11635
ab users</w> 11634
hyper uric 11634
neutrop enic</w> 11634
Adap tation</w> 11634
new s</w> 11633
pecul i 11633
intr as 11631
40 7</w> 11631
mini atur 11627
ath ymic</w> 11626
sk inf 11626
preser v 11626
Abnormal ities</w> 11626
Gre en 11625
Dicty ostelium</w> 11625
A SCs</w> 11624
ende av 11623
cyto chromes</w> 11622
al arm</w> 11621
con idia</w> 11619
c em 11618
O v 11617
hem opoietic</w> 11617
gon ads</w> 11615
synthesi sed</w> 11615
uc k</w> 11613
CYP 1A1</w> 11612
FO L 11611
non toxic</w> 11609
aqu ap 11609
Hierarch ical</w> 11609
Fo ot</w> 11608
end ogenously</w> 11607
ac king</w> 11606
bran ch 11606
Apo E</w> 11606
V .</w> 11604
docum enting</w> 11604
maneu vers</w> 11602
Compon ent</w> 11602
Attemp ts</w> 11601
ar is 11600
Emp ir 11600
deter gents</w> 11599
pen icill 11598
B 19</w> 11597
1. 59</w> 11597
Spe ech</w> 11597
mol es</w> 11596
B arr 11594
resist ances</w> 11593
at ever</w> 11590
1 10 11588
tr ies</w> 11587
B . 11586
HPV 16</w> 11586
Educ ational</w> 11584
b id 11583
gro ss 11583
inev itable</w> 11583
ses qu 11582
function alities</w> 11582
4 60</w> 11581
5 00 11580
od ystrophy</w> 11580
PR IS 11580
bre ath 11579
raz ine</w> 11578
versati lity</w> 11576
TA VR</w> 11575
ste atohepatitis</w> 11574
I MA</w> 11573
oviduc t</w> 11573
coerul eus</w> 11573
B 6 11572
nig er</w> 11572
U 1</w> 11571
den ly</w> 11571
fertil izer</w> 11570
M ec 11569
U se 11569
sp ora</w> 11568
&# 124 11568
| ;</w> 11568
gre l</w> 11567
hex adec 11567
emin ence</w> 11567
2 0.8</w> 11566
AC T 11564
Sper m</w> 11563
keratin s</w> 11562
0.0 38</w> 11560
CR E</w> 11560
agend a</w> 11560
but yric</w> 11559
se aling</w> 11558
Lim itations</w> 11556
m PFC</w> 11555
Lang mu 11555
ome dial</w> 11552
Oc ean</w> 11550
under stand 11549
fe eds</w> 11549
disc ern 11549
Non invasive</w> 11548
myo fibroblasts</w> 11548
Pag et</w> 11548
ic ing</w> 11547
ath ion</w> 11547
necess itates</w> 11546
im pressive</w> 11545
Govern ment</w> 11545
a -- 11543
In fusion</w> 11543
ne ovascular</w> 11541
anc illary</w> 11541
f ight</w> 11537
TK Is</w> 11537
g A</w> 11536
0.0 34</w> 11536
Re fl 11536
hom os 11534
charg ing</w> 11532
ACh Rs</w> 11532
Cycl o 11532
kilob ase</w> 11531
W nt 11530
are r</w> 11530
19. 3</w> 11529
EC C</w> 11528
2 7.3</w> 11527
Ad ding</w> 11525
recommend s</w> 11524
amb ulation</w> 11524
F BS</w> 11523
ic ating</w> 11523
s u</w> 11522
CM E</w> 11521
periodic ity</w> 11521
W O 11520
non responders</w> 11520
fac ul 11519
T b 11518
scler otherapy</w> 11518
K IT</w> 11517
tend encies</w> 11514
R UN 11513
vir idae</w> 11513
ce ased</w> 11513
t . 11512
GP x</w> 11510
4 10</w> 11509
for th</w> 11509
sin ogen</w> 11509
CL S</w> 11508
N itro 11507
youn gest</w> 11507
tan k</w> 11507
A J 11505
E OC</w> 11504
lo s</w> 11504
parall els</w> 11504
conve x</w> 11504
T EN 11501
yo himbine</w> 11501
T EC</w> 11500
ch able</w> 11500
k ins</w> 11499
F LI 11499
1. 60</w> 11499
Val ve</w> 11499
viro logic</w> 11499
us en</w> 11497
agre ements</w> 11497
ubiquit ously</w> 11497
micro liter</w> 11496
inst all 11496
vor tex</w> 11496
Cong o</w> 11495
ar ticul 11494
1. 61</w> 11494
T UR 11493
rel ay</w> 11493
In jec 11492
B ud 11490
Mut ant</w> 11490
Struc tures</w> 11487
hystere sis</w> 11487
tr amadol</w> 11486
T al 11485
linol enic</w> 11485
2 4.5</w> 11484
Glut athione</w> 11484
en etetra 11483
micro vessel</w> 11481
erg ometer</w> 11481
ure teric</w> 11481
se aled</w> 11480
Wh ilst</w> 11479
phyto hemagglutinin</w> 11479
r t</w> 11478
Fung al</w> 11478
G CT</w> 11476
gross ly</w> 11476
electro retin 11475
hir su 11475
chemopre ventive</w> 11475
asymmetr ies</w> 11475
uni directional</w> 11474
l ou 11473
et ch</w> 11473
mas cul 11473
cach exia</w> 11473
P aired</w> 11472
t RNAs</w> 11472
ol gus</w> 11472
dis advantaged</w> 11472
Con text</w> 11472
Puer to</w> 11472
A SI 11464
CN N</w> 11464
in breeding</w> 11463
dress ings</w> 11463
S As</w> 11462
gal anin</w> 11461
fro gs</w> 11461
s way</w> 11460
lor ide</w> 11459
G ab 11458
CM P</w> 11458
plas ties</w> 11457
nano structured</w> 11457
Ox ford</w> 11456
hin der</w> 11456
mill is 11455
Adequ ate</w> 11455
form yl</w> 11454
p ine 11453
kerat oconus</w> 11453
p end 11452
ab ies</w> 11452
pil ocarpine</w> 11450
3 8.5</w> 11449
occur rences</w> 11449
abund antly</w> 11449
P PA</w> 11447
lo tinib</w> 11445
per ip 11444
P NS</w> 11443
tail or</w> 11443
ab sol 11442
hybri dis 11442
Mend elian</w> 11442
t les</w> 11441
erb erine</w> 11441
enteroc occi</w> 11441
cobal amin</w> 11440
lon eliness</w> 11440
peri apical</w> 11438
a j 11437
w .</w> 11436
pheny leth 11436
Langmu ir</w> 11433
agon istic</w> 11432
enteroc olitis</w> 11432
dyne in</w> 11431
Ad sorption</w> 11430
odi a</w> 11430
AP 2</w> 11430
H AM</w> 11429
AT E</w> 11429
sud denly</w> 11429
MV C</w> 11429
Cryp tococcus</w> 11428
antigen icity</w> 11427
W T1</w> 11425
colli sions</w> 11424
disc ectomy</w> 11422
mesoth elial</w> 11422
Subst antial</w> 11420
Su pr 11419
Aut ologous</w> 11419
H PC</w> 11418
IN K 11418
intracerebro ventricular</w> 11418
mg. kg</w> 11417
equ imolar</w> 11416
CD 38</w> 11415
phosph ine</w> 11413
troph oblastic</w> 11413
eigh th</w> 11412
s ors</w> 11410
centrifug al</w> 11409
normo xic</w> 11409
1 S</w> 11406
s ure</w> 11406
Li pos 11406
19. 8</w> 11405
5 B</w> 11403
i ol</w> 11402
HI P</w> 11402
rac em 11402
assembl ages</w> 11402
nanoshe ets</w> 11402
P TS</w> 11401
muc o 11400
lax ity</w> 11399
C av 11397
home obox</w> 11396
A ED</w> 11395
R oles</w> 11395
B atter 11395
ser osal</w> 11395
aneu ploid</w> 11395
1. 68</w> 11394
endor sed</w> 11394
precip itating</w> 11392
Through out</w> 11392
iz z 11391
al ex 11390
os tin</w> 11389
spec ts</w> 11389
Out patient</w> 11388
vibr ations</w> 11388
B LM</w> 11387
Sequ ences</w> 11387
ron utri 11387
omy ositis</w> 11386
PPAR gamma</w> 11386
assembl ing</w> 11385
S et 11383
be ings</w> 11383
scin til 11383
C itr 11382
Wa ve</w> 11382
adip ogenesis</w> 11381
ch ord 11379
me sial</w> 11375
nucle ophilic</w> 11375
Ps A</w> 11375
bruc ellosis</w> 11374
A UC 11373
oci ous</w> 11373
extrapol ation</w> 11371
crystal lin 11371
occupati ons</w> 11370
cuti cle</w> 11370
corpor a</w> 11367
aur a</w> 11367
O CT 11366
conver s 11365
Def ects</w> 11365
un acceptable</w> 11364
2 77</w> 11363
CO P</w> 11363
flav ones</w> 11363
EC A</w> 11362
Anes thesia</w> 11361
On set</w> 11360
beet le</w> 11360
leaf lets</w> 11359
Bioin formatics</w> 11359
un detected</w> 11358
glauc omatous</w> 11358
- monophosphate</w> 11357
dihydro testosterone</w> 11354
ynchron ization</w> 11354
mening itidis</w> 11353
SB s</w> 11353
O F 11352
E u</w> 11351
prox en</w> 11351
D ON</w> 11350
de ad 11348
b out</w> 11347
Sensi tive</w> 11347
se at</w> 11345
K M</w> 11342
resi des</w> 11342
Cy clos 11342
h TERT</w> 11340
DE s</w> 11338
Micro scopy</w> 11338
ulf an</w> 11338
cl ipping</w> 11337
Min or</w> 11337
incub ating</w> 11336
Palli ative</w> 11335
practi c</w> 11334
Estim ated</w> 11334
Pers ons</w> 11334
y x</w> 11333
P el 11333
K appa</w> 11333
CN P</w> 11333
narrati ves</w> 11333
tric eps</w> 11331
11 1 11330
uscul arly</w> 11328
p ads</w> 11327
allevi ating</w> 11327
Ri o</w> 11327
er ism</w> 11326
FUN DING</w> 11326
di oxygenase</w> 11325
acul ture</w> 11324
2 6.7</w> 11323
commens al</w> 11322
aro xaban</w> 11321
MR s</w> 11319
G ut</w> 11318
d n 11318
inv ag 11318
19 68</w> 11318
th uring 11317
for tified</w> 11315
hydrol ases</w> 11314
benth ic</w> 11314
ad in</w> 11313
ameli oration</w> 11313
peren nial</w> 11312
M AG 11311
3 7. 11310
azol es</w> 11310
c iliated</w> 11309
PO MC</w> 11308
Perin atal</w> 11307
PRIS MA</w> 11307
cap ped</w> 11306
2 1.2</w> 11304
metab otropic</w> 11304
ur ge</w> 11303
classi fiers</w> 11303
RE D</w> 11302
Scal es</w> 11301
nich es</w> 11300
th ings</w> 11299
ul ant</w> 11299
B SI</w> 11297
ocyt openia</w> 11297
lympho kine</w> 11297
l ing 11296
PC 3</w> 11296
aspir ate</w> 11296
PP 2A</w> 11296
E pi 11295
dy nor 11295
0.0 33</w> 11294
overl apped</w> 11294
bor tezomib</w> 11293
hydroxy urea</w> 11293
ri v 11292
my corrhizal</w> 11291
Wa als</w> 11291
vol t 11289
recru its</w> 11289
CM T</w> 11289
Eco RI</w> 11289
know ing</w> 11287
H2 S</w> 11287
consul tant</w> 11287
M 4</w> 11286
pup ils</w> 11285
Com position</w> 11282
logarith mic</w> 11282
reg rowth</w> 11281
dissec ting</w> 11281
an am 11280
ad ders</w> 11280
ero sions</w> 11279
2 69</w> 11278
3 98</w> 11278
Ab solute</w> 11278
E PCs</w> 11277
pre formed</w> 11275
urg ical</w> 11273
primor dial</w> 11273
C GH</w> 11272
um -</w> 11272
pro ves</w> 11270
Gu ided</w> 11269
CO LL 11268
bl ur 11267
Aqu eous</w> 11267
L ES</w> 11263
pursu ed</w> 11263
ple ura</w> 11262
as matic</w> 11261
Sp A</w> 11260
l ice</w> 11259
ultracentrifug ation</w> 11259
constitu ting</w> 11258
deci ph 11257
hypno tic</w> 11257
tol l</w> 11256
empir ic</w> 11255
ul ators</w> 11254
18 0 11254
induc tively</w> 11253
stere oselective</w> 11253
ter in</w> 11251
GR F</w> 11251
Comm erc 11250
T able</w> 11249
bat ches</w> 11248
fem oris</w> 11247
archa eal</w> 11246
tex ts</w> 11245
t at 11242
ide ally</w> 11241
astrocyt omas</w> 11241
reser ves</w> 11239
tonsill ectomy</w> 11238
Per spectives</w> 11236
Ob ese</w> 11236
se tron</w> 11235
met all 11234
inst ar</w> 11234
un ve 11232
eng ine</w> 11232
he ur 11230
anti angiogenic</w> 11230
angi otens 11229
CO M</w> 11229
dig its</w> 11229
rect angular</w> 11229
g anciclovir</w> 11227
ri tical</w> 11226
vide ot 11226
Val salva</w> 11226
Haw ai 11226
od al 11224
8 5.7</w> 11223
wor n</w> 11222
vascular isation</w> 11221
aut ophosphorylation</w> 11220
xenobi otic</w> 11218
tour niqu 11218
oz ol 11217
Typ ical</w> 11217
cont ag 11216
SS B</w> 11216
MD I</w> 11215
not eworthy</w> 11215
Ill ness</w> 11215
allop urinol</w> 11213
neighbo uring</w> 11210
legi tim 11210
Gly co 11209
X PS</w> 11208
on om 11208
di peptide</w> 11207
de polarized</w> 11207
Electro physiological</w> 11207
Exc essive</w> 11206
I SI</w> 11204
AR I</w> 11203
obarbit uric</w> 11203
M d 11202
pol is</w> 11202
immun isation</w> 11201
col ors</w> 11200
mamm ographic</w> 11200
oblig atory</w> 11200
parv ovirus</w> 11199
6 R</w> 11198
ma y 11198
ke V</w> 11197
thuring iensis</w> 11197
Mechanis tic</w> 11196
bi phenyls</w> 11195
H ym 11194
co exist</w> 11194
Resi dents</w> 11194
elev ate</w> 11193
ation -- 11193
M PV</w> 11192
aver ages</w> 11192
mel phalan</w> 11192
avid ity</w> 11192
2 78</w> 11190
2 86</w> 11190
sub luxation</w> 11190
calc it 11190
lap s</w> 11190
CD 18</w> 11189
diff us 11188
nucle oli</w> 11188
ST A</w> 11188
3 8. 11187
4 40</w> 11187
D exam 11187
inv ade</w> 11187
1. 72</w> 11187
strep tavidin</w> 11187
N ow</w> 11186
ec oxib</w> 11186
p asses</w> 11185
aryn go 11185
RA DS</w> 11185
intram uscularly</w> 11185
Fr am 11184
deser ves</w> 11184
7 1.4</w> 11182
Di ver 11182
kinet och 11182
W GA</w> 11181
hist ogram</w> 11181
hemat omas</w> 11181
- C</w> 11180
au er</w> 11180
SR C</w> 11180
L ib 11179
PD GF 11176
im mobility</w> 11173
og old</w> 11173
ath ic</w> 11173
periph erally</w> 11173
rs 10 11173
We e 11173
A e.</w> 11172
Sub cutaneous</w> 11172
Tox ic 11171
CH 2 11170
granul omatosis</w> 11170
s els</w> 11169
ac ros 11167
circ RNAs</w> 11166
det ached</w> 11165
B lin 11163
An th 11163
exempl ified</w> 11163
galactos amine</w> 11162
etr on</w> 11161
Epigen etic</w> 11161
alumin a</w> 11160
U . 11159
tri am 11159
rat ers</w> 11158
dis similar</w> 11156
Brit ain</w> 11156
decompens ated</w> 11155
tun e</w> 11154
C SE</w> 11153
Ver sion</w> 11153
hypertriglyceri demia</w> 11151
A stro 11150
Nic oti 11150
inf ested</w> 11149
pos itory</w> 11149
cy nom 11148
sacrific e</w> 11148
empy ema</w> 11148
2, 0 11147
trunc ation</w> 11147
gen in</w> 11146
Con sist 11146
e sional</w> 11144
19. 7</w> 11143
am lodipine</w> 11142
CD 16</w> 11141
A spir 11140
C ot 11139
k ening</w> 11139
no isy</w> 11139
met am 11138
Hist one</w> 11138
Isol ates</w> 11138
myri sto 11137
cholangi opancre 11136
2 96</w> 11135
ne sts</w> 11135
pa ste 11135
syn chrotron</w> 11135
relax ations</w> 11135
appreci ably</w> 11135
Macroph age</w> 11135
D SB</w> 11134
2 6.5</w> 11132
zym osan</w> 11132
narco tic</w> 11131
inter -</w> 11130
en forcement</w> 11129
CD 40L</w> 11129
ambul ance</w> 11129
anti cholinergic</w> 11128
exp ired</w> 11128
ben z</w> 11127
distor tions</w> 11124
CR PC</w> 11123
rhin osinusitis</w> 11123
electr icity</w> 11122
P PP</w> 11121
m t</w> 11121
di bular</w> 11120
Ar tem 11120
ket oconazole</w> 11119
L up 11118
consul ted</w> 11117
aldo steron 11117
bud esonide</w> 11117
Se iz 11115
IR F</w> 11115
ooph o 11115
de pot</w> 11113
Reg ular</w> 11113
C CC</w> 11112
1. 69</w> 11112
ay a</w> 11111
compan ion</w> 11110
Up date</w> 11110
A bl 11109
9 80 11109
co iling</w> 11109
VO Cs</w> 11109
S ib 11107
ion omer</w> 11106
os sy 11105
Gal NAc</w> 11105
3 12</w> 11104
T NP</w> 11104
bis phosphonates</w> 11104
sec tomy</w> 11103
recur ring</w> 11103
cre ative</w> 11101
og litazone</w> 11100
Bioch em</w> 11100
gil ts</w> 11100
R AP</w> 11099
di ethyl</w> 11099
co incidence</w> 11096
mat h</w> 11094
P x</w> 11093
un reliable</w> 11093
antagon ize</w> 11093
Syn ech 11092
hyph ae</w> 11092
Z O</w> 11091
CI D</w> 11090
parag angli 11089
di polar</w> 11088
co expression</w> 11088
col istin</w> 11087
pre molars</w> 11086
ather ogenesis</w> 11085
Si O2</w> 11084
IF A</w> 11083
dithi othreitol</w> 11082
og el</w> 11080
anti apoptotic</w> 11078
2 1.7</w> 11077
peri kary 11077
di butyryl</w> 11073
ent on</w> 11073
Here ditary</w> 11073
T Z</w> 11072
Y east</w> 11072
lim e</w> 11072
phot otherapy</w> 11072
anhydr ide</w> 11072
Ju ven 11072
MENT AL</w> 11072
cot yled 11071
purch ase</w> 11071
R 5</w> 11066
affor ds</w> 11066
0.0 48</w> 11065
C igaret 11063
W in 11063
9 3 11063
Rel ap 11063
in let</w> 11062
prog est 11062
um or</w> 11061
Al ex 11061
Depl etion</w> 11061
T AA</w> 11060
my ositis</w> 11060
ter ro 11059
17. 0</w> 11059
SH 3</w> 11058
Effici ency</w> 11058
Bal b</w> 11057
effec ted</w> 11054
SE C</w> 11054
co tinine</w> 11053
st ories</w> 11051
Mam malian</w> 11051
b os 11050
ne ts</w> 11050
rop ivacaine</w> 11049
ham string</w> 11049
vast us</w> 11049
qu is 11048
CD 36</w> 11046
rot ary</w> 11046
un reported</w> 11045
F ecal</w> 11044
grou p 11044
fat alities</w> 11044
offic in 11044
cri tic 11043
ch a</w> 11041
Pro fil 11040
ing ham</w> 11039
CV C</w> 11039
dimin ishes</w> 11039
O TC</w> 11038
fa una</w> 11038
on ian</w> 11037
approxim ated</w> 11037
ne eding</w> 11036
D TH</w> 11035
recombin ants</w> 11034
co de 11032
therapeu tical</w> 11032
Hy al 11032
Morph ology</w> 11032
1. 76</w> 11031
dis ulph 11030
leuk openia</w> 11029
Restric tion</w> 11029
E uc 11027
T ST</w> 11027
A GA</w> 11026
mosa icism</w> 11026
pris on 11025
arith metic</w> 11025
musi cal</w> 11024
Nicoti ana</w> 11023
40 4</w> 11022
Percep tions</w> 11021
par aquat</w> 11019
papill ae</w> 11019
extra ordinary</w> 11018
glyco si 11017
in atus</w> 11016
on ych 11016
inten sely</w> 11015
G ib 11013
nucle ocap 11012
rewar ding</w> 11011
laun ched</w> 11011
S ed 11010
He avy</w> 11010
t ang 11009
Ch arg 11009
az ines</w> 11007
O k 11005
mac ula</w> 11005
under line</w> 11004
R DS</w> 11003
un familiar</w> 11003
tel em 11003
19. 1</w> 11003
rip tan</w> 11002
Expl oratory</w> 11002
2 2.7</w> 11001
7 T</w> 11001
M ann 11001
rom atic</w> 11001
Ag ents</w> 11000
cas u 10998
Dexam ethasone</w> 10998
j obs</w> 10997
Cul tures</w> 10997
bic uculline</w> 10996
sin k</w> 10994
29 4 10993
incub ations</w> 10992
PR F</w> 10991
z umab</w> 10989
opac ities</w> 10989
L an 10988
PA N 10988
benz othi 10988
trop omyosin</w> 10988
Recor ds</w> 10988
PU FAs</w> 10986
N SCs</w> 10985
T uni 10985
2. 25</w> 10985
auto fluorescence</w> 10985
retro viruses</w> 10984
un exposed</w> 10982
PC M</w> 10982
Na F</w> 10982
e ut 10980
el ap 10980
micro algae</w> 10980
y ls</w> 10979
ap sig 10979
Per fusion</w> 10977
polysomn ography</w> 10977
monoph y 10976
oryz ae</w> 10974
1. 78</w> 10973
finger prints</w> 10973
Concer ning</w> 10973
DO M</w> 10972
lab oration</w> 10971
call ing</w> 10971
ob tur 10970
CD 133</w> 10970
m ock</w> 10969
cyto protective</w> 10969
na ked</w> 10969
pup illary</w> 10968
encephal y</w> 10967
photo thermal</w> 10966
human ized</w> 10965
night time</w> 10965
pter yg 10963
NMD AR</w> 10963
cu ts</w> 10962
pack ages</w> 10961
yn es</w> 10960
st ut 10958
as king</w> 10958
visu ospatial</w> 10958
T un 10957
post test</w> 10957
feat uring</w> 10956
G la 10955
forelim b</w> 10955
2 2.3</w> 10954
mon ocular</w> 10954
ble ph 10954
uc ker</w> 10953
LM WH</w> 10953
n . 10952
nas oph 10952
varic ose</w> 10952
peri plasmic</w> 10950
cf u</w> 10950
2 82</w> 10947
pall idum</w> 10947
de fer 10943
is ogenic</w> 10942
oz ygotic</w> 10942
later alization</w> 10942
t k</w> 10941
Cis platin</w> 10941
P MS</w> 10940
An der 10938
S 9</w> 10937
D 88</w> 10937
tr iterpen 10937
phag ocyt 10937
log 10</w> 10936
fluoro deoxyglucose</w> 10936
S cler 10934
5 40</w> 10934
it ant</w> 10934
strepto kinase</w> 10934
S ca 10933
fluoro quinolones</w> 10933
E lim 10932
on ase</w> 10932
dimin ution</w> 10931
f is 10930
ind ole 10930
so as</w> 10929
cocul ture</w> 10929
cy r 10928
organ ophosph 10928
cyan o 10928
ag lutide</w> 10927
ph r 10926
neuro behavioral</w> 10926
sal es</w> 10926
t ently</w> 10925
TH F</w> 10923
FF R</w> 10922
un suitable</w> 10920
frag ilis</w> 10920
valid ating</w> 10919
predisp osed</w> 10917
C -</w> 10916
T ub 10916
amput ations</w> 10915
lactam ases</w> 10915
s onic</w> 10914
Z h 10914
hospit alised</w> 10913
zol id</w> 10913
re tr 10911
bio feedback</w> 10911
fore head</w> 10910
to thec 10909
Cha in</w> 10909
tim olol</w> 10908
eti r 10908
ight ness</w> 10908
non parametric</w> 10907
T m</w> 10905
J ord 10905
SP M</w> 10905
AD D</w> 10904
F ever</w> 10903
Ambul atory</w> 10903
T -</w> 10902
appl iance</w> 10902
impair ing</w> 10901
PL L</w> 10900
cra b</w> 10900
rup tures</w> 10898
mal le 10897
V P1</w> 10896
basoph ils</w> 10896
R v 10895
anthocyan in</w> 10895
Fi g</w> 10894
on ous</w> 10893
bio inform 10893
xenobi otics</w> 10893
In ser 10892
CD 31</w> 10892
n ails</w> 10891
ap i 10891
homocy ste 10890
s ine</w> 10888
G ar 10888
acter ium</w> 10888
v imetric</w> 10887
b on</w> 10887
it arian</w> 10887
fun do 10887
beha ved</w> 10887
2 83</w> 10884
CO MT</w> 10884
pene trance</w> 10884
STAT 1</w> 10884
intestin es</w> 10884
vacc inations</w> 10883
SO X 10883
autoradi ographic</w> 10883
D PAT</w> 10882
consist encies</w> 10882
N SC</w> 10881
entr apped</w> 10881
Tri ple</w> 10881
weak ened</w> 10881
M PI</w> 10880
V SV</w> 10879
F CM</w> 10878
under way</w> 10878
U su 10877
ti zing</w> 10877
per ine 10877
pos tis 10876
exec uted</w> 10876
B 5</w> 10875
12 th</w> 10874
Sp ir 10874
trop ism</w> 10872
stimul ants</w> 10870
fur n 10870
histor ic</w> 10870
en ig 10869
0.0 46</w> 10868
AN N</w> 10867
ozol omide</w> 10867
Kaw asaki</w> 10866
H T2</w> 10865
un employment</w> 10865
foun der</w> 10865
complic ate</w> 10864
icon azole</w> 10864
Inten sity</w> 10863
con son 10861
pluripot ency</w> 10861
trans gender</w> 10859
art um</w> 10859
babo ons</w> 10859
absol utely</w> 10859
S anger</w> 10857
t actic</w> 10857
F 0</w> 10856
U E</w> 10856
CP E</w> 10856
immunos tim 10856
Gu ill 10855
Hb A</w> 10855
tun g 10854
discord ance</w> 10853
clo ac 10852
al ar 10851
Ch AT</w> 10851
Mar ked</w> 10851
B omb 10850
biotechn ological</w> 10850
n owadays</w> 10847
E di 10847
igen es</w> 10847
2 1.6</w> 10846
r um</w> 10846
phyto plankton</w> 10846
extrapol ated</w> 10846
fast est</w> 10845
2 99</w> 10844
ph le 10842
herbic ides</w> 10841
C ann 10840
al o 10839
lamin ectomy</w> 10839
sil age</w> 10838
d T</w> 10837
quantit ate</w> 10837
Op portun 10837
sc Fv</w> 10835
pre malignant</w> 10834
ici ous</w> 10833
tetr afluoro 10832
Polymorph isms</w> 10832
al ists</w> 10831
N oc 10830
ner i</w> 10830
ir reversibly</w> 10829
Cyt otoxicity</w> 10829
H ed 10828
genit alia</w> 10828
A tomic</w> 10827
lenti viral</w> 10827
1. 74</w> 10825
exam s</w> 10825
Mi x 10825
osse o 10824
recalc itr 10824
ven ting</w> 10823
intram ural</w> 10823
Portu gal</w> 10823
CH 3 10820
helmin th</w> 10820
hepat ocarcin 10819
p c 10817
M BL</w> 10817
Ve si 10817
hypos padias</w> 10817
V V</w> 10815
sper mati 10815
pyrid oxal</w> 10814
Sch ed 10813
conf ident</w> 10812
5, 5</w> 10812
expl ant</w> 10811
COLL ECTION</w> 10810
un aware</w> 10808
MT B</w> 10808
G i</w> 10807
tail oring</w> 10807
examin er</w> 10806
othor acic</w> 10806
dec la 10805
fasci itis</w> 10805
classi cally</w> 10804
- cyclic</w> 10802
overwhel ming</w> 10802
collag ens</w> 10801
To oth</w> 10801
am ant 10800
particip atory</w> 10800
but ter 10800
sist ence</w> 10799
amer ic 10799
S SE 10798
cr ush</w> 10798
1. 95</w> 10797
D RE 10796
Chi ari</w> 10796
al is 10795
van adium</w> 10795
apsig argin</w> 10795
V ision</w> 10794
di op 10794
hemangi omas</w> 10794
si loxane</w> 10792
ster n 10791
regul arities</w> 10790
illustr ating</w> 10790
clar ification</w> 10790
TM Z</w> 10790
refr ig 10790
spind les</w> 10790
sp aw 10789
conform al</w> 10789
tibi alis</w> 10789
pemphig us</w> 10787
M ST</w> 10786
d f</w> 10784
P ax 10783
us s</w> 10783
4 70</w> 10782
dis assembly</w> 10782
can is</w> 10781
floc ks</w> 10781
S Z</w> 10780
f lowing</w> 10779
thal idomide</w> 10779
in sensitivity</w> 10778
Pot enti 10776
Adhe sion</w> 10775
preclud e</w> 10774
ribozym e</w> 10774
PS G</w> 10773
antinoc iception</w> 10772
P 0</w> 10771
il ization</w> 10768
hydroxy ethyl</w> 10768
F ig 10765
IU D</w> 10764
A ph 10763
Investig ating</w> 10760
L AMP</w> 10759
m ogenic</w> 10759
B am 10759
i PSCs</w> 10758
fin ished</w> 10758
disrup tions</w> 10758
perfec tly</w> 10758
G Hz</w> 10757
Minim um</w> 10757
os al 10756
doc osahexaenoic</w> 10755
Bac k</w> 10755
Pro duc 10754
E volution 10752
si rolimus</w> 10750
ot onin</w> 10750
dis agreement</w> 10750
conf used</w> 10750
sp otted</w> 10748
Fol l 10748
ligh ts</w> 10747
N V 10746
ar res 10745
Men ing 10745
1. 71</w> 10744
non operative</w> 10744
B art 10743
con tainment</w> 10743
V ul 10740
no tification</w> 10740
contrain dicated</w> 10740
fluoro metric</w> 10739
on olactone</w> 10738
ob vi 10738
Pa rent</w> 10738
tax on</w> 10738
voc abulary</w> 10738
investig ational</w> 10737
op sin</w> 10736
crystall ized</w> 10736
se af 10734
ver ing</w> 10734
Na OH</w> 10734
nig ro 10734
turbul ence</w> 10734
mis diagnosis</w> 10733
hel ium</w> 10733
O LT</w> 10732
ha plo 10732
bl ends</w> 10732
hype rex 10732
if ery</w> 10731
chloro thiazide</w> 10731
interfe red</w> 10731
hyper glycaemia</w> 10730
PO AG</w> 10729
choleste atoma</w> 10729
HI AA</w> 10727
alim umab</w> 10727
pre ponder 10726
repres s</w> 10726
anth elmin 10725
2 3.8</w> 10724
C PD</w> 10724
bis exual</w> 10724
Bur k 10724
H . 10722
constitu tional</w> 10722
azol amide</w> 10722
abl ated</w> 10721
E 1A</w> 10720
Enh ancing</w> 10718
im pulses</w> 10717
phosphog lycer 10717
si li 10716
concentr ating</w> 10716
perv asive</w> 10715
satis fy</w> 10712
cour t</w> 10711
extrac table</w> 10710
SC R</w> 10710
neighbo ur 10710
ph lo 10709
St abil 10708
trans glutaminase</w> 10707
lo ad 10706
SP L</w> 10706
C ran 10705
hyper prolactin 10705
haem ophilia</w> 10705
radionuc lides</w> 10705
nanotechn ology</w> 10705
crystallin ity</w> 10704
M CD</w> 10703
C CA 10702
PL R</w> 10702
kil odal 10701
2 5.5</w> 10700
2 0.6</w> 10698
sil yl</w> 10698
O K</w> 10697
biop olym 10697
2 81</w> 10696
ur onate</w> 10696
if olia</w> 10696
bin din</w> 10694
electroencephal ographic</w> 10694
oc tion</w> 10693
U s 10692
ist ed</w> 10692
v ign 10691
om as 10690
cuc umber</w> 10690
Re plication</w> 10689
renew able</w> 10689
tele ost</w> 10689
con genit 10688
mum ps</w> 10688
Sal ivary</w> 10687
4 01</w> 10686
n ights</w> 10684
cel ecoxib</w> 10684
mon as</w> 10684
S of 10683
aqu aculture</w> 10681
DO T</w> 10681
gelatin ase</w> 10681
Succ ess</w> 10680
D ogs</w> 10679
S ic 10675
in osine</w> 10675
ra p</w> 10675
neutroph ilic</w> 10675
rati a</w> 10674
2. 00</w> 10673
cl aud 10671
substanti ated</w> 10671
E K</w> 10670
lab ial</w> 10670
anticip ation</w> 10669
yr amidal</w> 10668
d war 10666
oli tica</w> 10664
EZ H2</w> 10664
gol d 10663
leiomy osarcoma</w> 10662
tem s</w> 10661
bene f 10661
spermati ds</w> 10661
M orbidity</w> 10659
hex okinase</w> 10659
neurotroph in</w> 10659
cle fts</w> 10658
autom ation</w> 10656
abut ment</w> 10656
N RS</w> 10655
azol ine</w> 10655
N al 10653
ph ox</w> 10653
R ice</w> 10652
reg isters</w> 10652
lep rae</w> 10652
neighb or</w> 10651
s tick</w> 10650
En v</w> 10650
S omatic</w> 10649
SU D</w> 10649
phot olysis</w> 10649
unc ul 10648
ribonucle otide</w> 10648
educ ate</w> 10647
chec king</w> 10647
eradic ate</w> 10647
C lear 10646
in ized</w> 10646
si RNAs</w> 10646
E PC</w> 10645
ech inoc 10645
H ES</w> 10644
rs 2 10644
3 35</w> 10643
complex ities</w> 10643
prevent ative</w> 10642
ys tic</w> 10641
J IA</w> 10639
decre ment</w> 10639
immuno regulatory</w> 10639
pen icil 10639
Victor ia</w> 10639
ging iva</w> 10638
ó n</w> 10636
prim i 10635
cum ulus</w> 10634
odon tics</w> 10634
nes tic</w> 10634
in noc 10632
nitro samine</w> 10632
occup y</w> 10632
dis integration</w> 10631
op ene</w> 10630
A SP</w> 10629
AM R</w> 10629
anti social</w> 10628
astig otes</w> 10627
ambigu ity</w> 10626
obi ological</w> 10625
op ic 10624
plan us</w> 10618
T yrosine</w> 10617
Trans formation</w> 10617
c ow 10616
dis appear</w> 10616
0.0 39</w> 10615
econ ium</w> 10615
micro structural</w> 10614
ven ules</w> 10614
sil ane</w> 10614
glob us</w> 10614
preser ves</w> 10614
cephalospor in</w> 10614
A AV 10613
ont ally</w> 10611
meg a</w> 10611
2 0.4</w> 10610
ec itabine</w> 10610
ap ed</w> 10610
photo volta 10610
poly propylene</w> 10609
su tured</w> 10608
spin ach</w> 10607
stake holder</w> 10606
1, 9 10605
rectom ized</w> 10605
DD D</w> 10604
C Q</w> 10603
sapon ins</w> 10602
vari ances</w> 10601
80 3</w> 10600
th aw</w> 10599
por cel 10599
imp rec 10599
Ca uses</w> 10598
sac k 10598
Ser otonin</w> 10597
micro vasculature</w> 10596
enteroc olitica</w> 10596
acc ent 10594
h ip 10593
temper ament</w> 10593
calcul us</w> 10593
one -</w> 10593
ge fitinib</w> 10592
adhe sives</w> 10592
mes o</w> 10592
ser ologically</w> 10591
ev al 10590
Sph ing 10590
L en 10589
in adver 10589
oro facial</w> 10588
ogran in</w> 10588
R up 10587
AS M</w> 10587
in arily</w> 10586
ric kett 10585
l ag 10584
Me Hg</w> 10584
MR D</w> 10583
Anes thesi 10582
cal end 10581
carbap enem</w> 10581
col ytic</w> 10580
sol it 10580
ach alasia</w> 10578
Adi pose</w> 10577
Angel es</w> 10576
monol ith 10575
decont amination</w> 10575
D ex 10574
am ps</w> 10572
myocl onus</w> 10571
ch ase</w> 10570
i vermectin</w> 10569
3 84</w> 10569
Con ver 10569
ceram ics</w> 10569
pol yc 10568
ful filling</w> 10568
Co ul 10568
methyl phenidate</w> 10568
Bio chemistry</w> 10568
otox ic 10567
Prophyl actic</w> 10567
H ab 10565
Ar m</w> 10565
Eth ical</w> 10565
M ang 10563
multicentr ic</w> 10562
Sma d</w> 10562
2 4.3</w> 10561
peroxis omes</w> 10561
aph ase</w> 10560
fluoroph ore</w> 10560
V SD</w> 10558
0.0 47</w> 10558
Pl an</w> 10558
asc in</w> 10557
premat urely</w> 10556
H PS</w> 10553
i g</w> 10553
in ear</w> 10552
Cyt otoxic</w> 10551
fl or 10550
Youn ger</w> 10550
post al</w> 10549
oste osynthesis</w> 10549
N 6</w> 10548
N AT</w> 10548
EV AR</w> 10548
T . 10547
sh ade</w> 10547
ab a</w> 10545
caud a</w> 10545
2 92</w> 10544
A PAP</w> 10544
ig nor 10544
wet land</w> 10543
g ut 10541
SW I</w> 10541
5 b</w> 10540
I ON</w> 10540
q 21</w> 10540
b ags</w> 10540
ne omycin</w> 10540
il li 10539
che ap</w> 10539
AT s</w> 10538
magne t</w> 10537
19 65</w> 10536
enti tled</w> 10535
multi ply</w> 10535
ane diol</w> 10535
lev ofloxacin</w> 10534
- 15</w> 10533
eri ous</w> 10533
qu es</w> 10532
over use</w> 10532
ant o 10531
dec oding</w> 10531
g ic</w> 10530
er ations</w> 10527
endo derm</w> 10526
S par 10524
Hist ology</w> 10520
diff u 10519
mil est 10519
ens or</w> 10519
fin ishing</w> 10517
thi obarbituric</w> 10517
it orial</w> 10514
im oto</w> 10514
addic ts</w> 10514
p t 10513
an ting</w> 10513
experim entation</w> 10513
bis phosphonate</w> 10513
artem isinin</w> 10511
com et</w> 10510
inter view 10510
chromat osis</w> 10510
2 79</w> 10509
t oglobin</w> 10509
em mas</w> 10509
xy lem</w> 10509
y m</w> 10508
TB S</w> 10508
C atheter</w> 10507
GAP DH</w> 10506
piper azine</w> 10505
Osteopo rosis</w> 10505
wor sen</w> 10503
hypocalc emia</w> 10503
ud inal</w> 10502
phosph oro 10501
meth rin</w> 10500
tw e 10500
T ME</w> 10499
Pl anning</w> 10499
G ir 10498
- 4. 10498
pap ain</w> 10498
inst alled</w> 10498
food borne</w> 10498
benef ited</w> 10498
4 18</w> 10497
wh is 10494
micro graphs</w> 10494
cle av 10493
bis phenol</w> 10493
e at 10492
P PIs</w> 10491
cl ed</w> 10491
inter body</w> 10491
por ter</w> 10490
uni tinib</w> 10490
Cu 2</w> 10490
avul sion</w> 10490
Scat chard</w> 10489
X II</w> 10488
work shops</w> 10487
Tradi tionally</w> 10487
PC 1</w> 10486
Random ised</w> 10486
l acc 10484
coll ar</w> 10483
AF B1</w> 10483
S 1 10482
i PSC</w> 10479
4 30</w> 10479
satis fying</w> 10479
allo t</w> 10479
vis cer 10478
ER alpha</w> 10477
alph ab 10476
sphen oidal</w> 10476
β 3</w> 10475
micro gravity</w> 10475
GR P 10475
guar ante 10474
A sh 10473
z wit 10472
catast roph 10472
et an</w> 10471
L 1210</w> 10470
des mo 10470
bronch i</w> 10470
CD 68</w> 10469
rever sion</w> 10469
P g 10468
NC S</w> 10468
ym ia</w> 10467
ante grade</w> 10467
Ultim ately</w> 10467
C 9</w> 10466
sati lity</w> 10466
Juven ile</w> 10466
AU D</w> 10462
kin esin</w> 10461
syst ole</w> 10461
radi opharmac 10460
maxim izing</w> 10460
- related</w> 10459
sci sic</w> 10459
plan in</w> 10459
hair s</w> 10459
acycl ic</w> 10458
hyper bilirubin 10457
ca ught</w> 10456
C3 b</w> 10456
Autom atic</w> 10456
calcit riol</w> 10456
aminoglyco sides</w> 10455
b c 10454
cat fish</w> 10454
ul ans</w> 10453
pop ulated</w> 10452
for cing</w> 10451
dr illing</w> 10451
pack aged</w> 10450
2 0.3</w> 10449
P eptides</w> 10448
five fold</w> 10448
or chi 10447
RA W 10447
sp ans</w> 10446
RI P 10446
a etiological</w> 10444
pre load</w> 10444
tw ard</w> 10444
51 Cr</w> 10444
c ecum</w> 10443
re visited</w> 10443
op elvic</w> 10443
fi l</w> 10442
prol yl</w> 10442
Glut amate</w> 10442
AS L</w> 10441
CD 1</w> 10440
H VA</w> 10439
oplas min</w> 10439
uve al</w> 10438
eg al</w> 10436
her tz</w> 10436
Stro op</w> 10436
cra ft</w> 10436
re mic</w> 10435
AI D</w> 10435
scop ing</w> 10434
secre tag 10434
analog y</w> 10434
conn exin</w> 10434
amit riptyline</w> 10433
chem ic</w> 10432
P HC</w> 10431
ow ned</w> 10430
uro logy</w> 10430
ni x</w> 10428
clea ve</w> 10428
endang ered</w> 10428
de stabilization</w> 10427
cann ulated</w> 10427
Practi ces</w> 10427
pall idus</w> 10427
phospho enolpyruvate</w> 10426
dimension ality</w> 10426
nitr ide</w> 10425
spec tac 10424
n ymph 10423
B -</w> 10423
Un its</w> 10423
ejac ulation</w> 10423
met oclopramide</w> 10421
pige ons</w> 10419
Orth opaedic</w> 10417
j udge</w> 10416
crust ace 10416
remo ves</w> 10415
Cap e</w> 10415
Tur ner</w> 10415
op enem</w> 10414
1. 82</w> 10414
de regulation</w> 10412
stac ked</w> 10412
Si O</w> 10411
bread th</w> 10410
amenor rhea</w> 10409
medullo blastoma</w> 10409
P la 10408
gener alised</w> 10408
densit ometry</w> 10408
m oth</w> 10405
el aboration</w> 10405
genu ine</w> 10405
intr icate</w> 10404
cellul ase</w> 10402
IL 6</w> 10398
apenta enoic</w> 10398
cholangiopancre atography</w> 10398
el eph 10397
min ocycline</w> 10397
ho id</w> 10397
pu tida</w> 10397
sta urosporine</w> 10397
abra sion</w> 10397
B ran 10396
qu ery</w> 10395
oste openia</w> 10395
bar s</w> 10395
mass eter</w> 10395
advis able</w> 10395
bomb esin</w> 10395
lumin escent</w> 10394
B ic 10393
de generated</w> 10393
Egyp tian</w> 10393
LIN C 10393
p add 10392
ple ur 10392
D enti 10391
assist ants</w> 10391
am ended</w> 10390
ag liflozin</w> 10390
kin ins</w> 10390
pat ellofemoral</w> 10390
us sions</w> 10389
loc alizing</w> 10389
0.000 3</w> 10389
D TC</w> 10387
unra vel</w> 10387
prec ocious</w> 10386
nitros ourea</w> 10386
choled och 10386
w ol 10385
asympto tic</w> 10385
N as 10383
19. 9</w> 10382
HD V</w> 10382
remif entanil</w> 10381
comm enced</w> 10380
OR S</w> 10380
magnet o 10380
fasc ial</w> 10379
2 6.3</w> 10377
kind ling</w> 10375
govern mental</w> 10373
der ia</w> 10372
CD P</w> 10372
not ch</w> 10372
circum scribed</w> 10372
xen o 10372
amin ol 10371
nu triti 10371
k no 10370
R P 10369
aden osyl 10369
aldosteron ism</w> 10369
CI A</w> 10368
sub chondral</w> 10367
Mitochond ria</w> 10367
termin ate</w> 10365
potenti ating</w> 10365
2 91</w> 10364
CE M</w> 10363
HT N</w> 10362
sun flower</w> 10361
- containing</w> 10360
Su gg 10360
wor ry</w> 10359
NCT 00 10359
equilib ration</w> 10359
EXPERI MENTAL</w> 10358
un characterized</w> 10355
polyaden ylation</w> 10352
v owel</w> 10349
micro sphere</w> 10349
Regi stration</w> 10348
o rest</w> 10347
fil ls</w> 10347
tor sional</w> 10347
18. 0</w> 10347
piper acillin</w> 10347
A OM</w> 10346
G rowing</w> 10346
co activator</w> 10346
acetyl glucosamine</w> 10346
indol ent</w> 10346
corner stone</w> 10345
a plasia</w> 10343
uro logic</w> 10343
cTn I</w> 10341
Nico tine</w> 10341
Pen n 10340
1. 70</w> 10339
AG s</w> 10338
123 I</w> 10338
pos ting</w> 10337
de par 10336
micronucle i</w> 10336
I ris 10335
me 3</w> 10333
sub merged</w> 10333
adnex al</w> 10333
r po 10331
adi ene</w> 10331
secretag og 10331
I GT</w> 10330
sati ety</w> 10329
prefer ably</w> 10328
azol in</w> 10327
micro electrode</w> 10326
financ ing</w> 10326
cynom olgus</w> 10326
S c</w> 10325
C er</w> 10325
impul sive</w> 10325
H NE</w> 10324
t ances</w> 10324
Per u</w> 10324
min orities</w> 10323
bi ting</w> 10323
spong es</w> 10323
2 2.6</w> 10321
Nig erian</w> 10321
after noon</w> 10321
kynu ren 10321
2 89</w> 10320
p Ka</w> 10319
oro logical</w> 10319
2 84</w> 10318
sw ing</w> 10317
Compu terized</w> 10317
polic y 10316
7 A</w> 10314
w in</w> 10313
ab ig 10313
SI DS</w> 10313
AL D 10313
vascul opathy</w> 10313
J anus</w> 10312
in dium</w> 10312
con sortium</w> 10312
Phil ipp 10311
NU MB 10311
incur red</w> 10311
C CS</w> 10310
pa stor 10310
hist olytica</w> 10310
Adv ant 10310
Reas ons</w> 10310
Ric kett 10309
ga ther</w> 10308
abstr acted</w> 10308
institution alized</w> 10308
tor ch 10307
Fabr y</w> 10307
decla red</w> 10307
phosphor ib 10306
coloc alized</w> 10306
di vor 10305
Res ource</w> 10305
Mar ine</w> 10305
en closed</w> 10304
en ema</w> 10304
ass ure</w> 10304
lip oma</w> 10304
Compl iance</w> 10304
dihydro pyridine</w> 10304
dynor phin</w> 10304
AI P</w> 10303
sac cadic</w> 10303
Ap o</w> 10301
rein nerv 10300
catch ment</w> 10300
N owadays</w> 10299
iner tial</w> 10299
TR AF 10298
dimorph ic</w> 10297
x an</w> 10294
exerc ising</w> 10294
hum ic</w> 10293
MI D</w> 10293
mar mos 10291
ex pedi 10290
ad mixture</w> 10290
Cat al 10290
fundo plication</w> 10290
G au 10289
T s 10289
tion ary</w> 10289
tetrach loro 10289
NS 1</w> 10288
St ent</w> 10287
dys ph 10287
reminis cent</w> 10287
M ol</w> 10286
am pull 10286
po oling</w> 10286
dehydro genases</w> 10286
append age</w> 10283
k led</w> 10282
sh ore</w> 10281
K an 10280
Ar gin 10280
opter in</w> 10280
am ib 10279
gingi vitis</w> 10278
costim ulatory</w> 10278
cellul itis</w> 10277
poli o</w> 10277
Ch ondro 10276
ediatr ics</w> 10276
entr ant</w> 10274
gra ined</w> 10274
PD L</w> 10274
tis ed</w> 10273
chimpanze es</w> 10273
ca val</w> 10272
bre asts</w> 10272
cin olone</w> 10270
myelo proliferative</w> 10270
3 45</w> 10269
mot or 10269
than ks</w> 10268
endop eptidase</w> 10267
protop orphyrin</w> 10267
manif esting</w> 10266
Malaw i</w> 10266
Ch it 10264
2 0.7</w> 10262
.00 4</w> 10261
hemi paresis</w> 10261
G V</w> 10260
sym metr 10260
bur sting</w> 10259
tor tu 10259
macro globulin</w> 10258
Cir cular</w> 10258
V eg 10256
sl er</w> 10256
resus cit 10256
p c</w> 10254
Chrom osomal</w> 10254
ambly opia</w> 10254
John son</w> 10254
di amine</w> 10253
s noring</w> 10252
bra sili 10252
under take</w> 10251
decid uous</w> 10251
1. 96</w> 10249
annabin oid</w> 10248
Con form 10247
psych ophysical</w> 10247
excit on</w> 10247
SR P</w> 10245
hr an</w> 10245
p ests</w> 10244
per pet 10242
Cat alyzed</w> 10242
oc ine</w> 10241
glomerul osclerosis</w> 10241
rec to 10240
2 94</w> 10239
ov an 10238
U PR</w> 10237
plas mal 10237
phen anthro 10237
accep ting</w> 10234
Sho uld</w> 10234
fusi form</w> 10233
contin ental</w> 10231
Func tioning</w> 10231
sub surface</w> 10228
tur bin 10228
chlor ite</w> 10228
plex iform</w> 10227
2 4, 10226
sh eds</w> 10226
le ptom 10225
eclamp tic</w> 10225
D engue</w> 10223
p 75</w> 10223
san itation</w> 10221
re presses</w> 10219
Repe at</w> 10218
on idine</w> 10217
chim erism</w> 10217
on eous</w> 10216
Con cep 10214
Con st 10213
Par is</w> 10213
2 97</w> 10212
AB I 10212
hemat ology</w> 10212
supr as 10210
n ylon</w> 10209
Austr ia</w> 10207
4 90</w> 10206
ophthalm ology</w> 10205
anten n 10204
cinti graphy</w> 10204
iso kinetic</w> 10203
prospec t</w> 10203
kan amycin</w> 10203
non fatal</w> 10201
endoth eli 10201
dimin ishing</w> 10201
U tility</w> 10199
1 L</w> 10198
1. 77</w> 10198
u el 10196
he ard</w> 10196
abstr action</w> 10196
Lep tosp 10195
ore tin 10194
off ici 10194
glo ves</w> 10193
Test osterone</w> 10193
Harr is</w> 10193
m IU</w> 10192
og e 10192
na proxen</w> 10192
cell ence</w> 10191
hy oid</w> 10190
form ally</w> 10190
G SSG</w> 10189
ord inate</w> 10188
an ks</w> 10185
si de 10183
V a 10182
ag glomer 10182
thermod ynamics</w> 10181
retur ns</w> 10180
ER O</w> 10179
uro ra</w> 10178
Lymph ocyte</w> 10178
P ut 10177
Q M</w> 10177
re medi 10176
sl ing</w> 10176
mineral ocorticoid</w> 10176
front otemporal</w> 10175
ill ation</w> 10174
war ts</w> 10172
noc iception</w> 10172
compl ement 10168
Chic ago</w> 10168
Burk hol 10167
2 2.1</w> 10166
- 6 10166
2 87</w> 10164
H US</w> 10164
un paired</w> 10164
Isra eli</w> 10164
gyna ecological</w> 10162
C DS</w> 10161
p ic</w> 10161
T rim 10161
0.0 42</w> 10161
phil a</w> 10161
dec o 10160
Establ ishment</w> 10158
bronchodi lator</w> 10157
Jew ish</w> 10157
err oneous</w> 10156
mic tur 10155
Col laboration</w> 10155
CT Ls</w> 10154
transmis sible</w> 10154
moun ting</w> 10153
C oupling</w> 10152
li kewise</w> 10152
Guill ain</w> 10152
1. 86</w> 10151
2 2.4</w> 10150
D AG</w> 10149
RA W</w> 10149
per chlor 10148
CA H</w> 10148
F CS</w> 10147
hydro genation</w> 10147
H and 10146
ore tinal</w> 10145
n us</w> 10144
end onucle 10144
10 A</w> 10144
exp enses</w> 10144
14 0 10144
underestim ate</w> 10142
op ing</w> 10141
micro cephaly</w> 10141
AM Ps</w> 10141
y osin</w> 10140
V ene 10140
bis muth</w> 10139
BU N</w> 10139
P GF</w> 10138
post ures</w> 10138
HC O</w> 10138
Issu es</w> 10138
impe de</w> 10137
l anding</w> 10135
Mon o 10135
cyto chemical</w> 10134
tac kle</w> 10133
2 8.5</w> 10132
k is 10129
L BW</w> 10128
Z im 10128
lo oks</w> 10128
in homogeneous</w> 10127
op razole</w> 10127
extr actions</w> 10127
am y</w> 10126
1. 6 10126
main stream</w> 10126
arsen ite</w> 10126
ar ding</w> 10125
trans rectal</w> 10125
phon on</w> 10124
osph eres</w> 10122
dissec t</w> 10122
ensu ing</w> 10122
al ign</w> 10121
PC E</w> 10121
Rec ru 10121
lati tude</w> 10120
micronucle us</w> 10119
faec ium</w> 10118
sprin t</w> 10118
S it 10117
par aneoplastic</w> 10117
u d</w> 10116
ain ide</w> 10116
vas op 10116
CYP2 E1</w> 10116
2 4.1</w> 10115
3 2.5</w> 10115
Ser ratia</w> 10114
Angi ography</w> 10114
counter acted</w> 10113
Estim ating</w> 10112
scaveng ers</w> 10111
Que bec</w> 10109
augh ters</w> 10108
un fractionated</w> 10107
eth idium</w> 10107
k new</w> 10106
4 3. 10105
L AP</w> 10104
tr uth</w> 10104
Sup por 10104
succ essively</w> 10102
acylglycer ols</w> 10102
Evolution ary</w> 10101
P ituitary</w> 10100
non ionic</w> 10100
transpl antations</w> 10099
gh ing</w> 10098
step ping</w> 10098
pyre thro 10098
0.0 43</w> 10097
resc ence</w> 10097
conver sions</w> 10097
RI F</w> 10096
inter section</w> 10095
haemat opoietic</w> 10095
abol ishes</w> 10094
M ir 10093
expan sions</w> 10092
Rec tal</w> 10091
ylo xy 10091
en yl 10090
Cigaret te</w> 10090
ag enic</w> 10089
tex tile</w> 10089
fruc to 10089
cardio plegia</w> 10089
9 29</w> 10088
efflu ents</w> 10088
im pressions</w> 10087
Endome trial</w> 10086
el ic</w> 10085
tr ium</w> 10084
con doms</w> 10084
Con sul 10084
ori enting</w> 10084
Obstetr ics</w> 10084
ver a</w> 10083
sc arc 10083
LA TION</w> 10083
gonadotrop ins</w> 10083
dorm ancy</w> 10082
em es</w> 10081
Pro ximal</w> 10081
Por cine</w> 10081
terat ogenic</w> 10081
al dol 10080
Ar teri 10079
1. 83</w> 10078
an ders</w> 10077
ec o</w> 10077
iton avir</w> 10077
prolong ing</w> 10077
3 33</w> 10076
b ody 10076
T AS</w> 10075
there of</w> 10075
HE K</w> 10075
buff alo</w> 10075
b arely</w> 10074
che ek</w> 10071
G US</w> 10070
Gen otype</w> 10070
Hy dr 10070
GI P</w> 10070
micro extraction</w> 10067
osph ing 10067
tro pic 10065
glycolip id</w> 10065
s IL</w> 10064
R ana</w> 10064
et an 10064
Con stitu 10064
her ing</w> 10064
ocy anine</w> 10064
decompens ation</w> 10064
succe eded</w> 10063
S AC</w> 10061
di ox 10061
ren s</w> 10061
chem osensitivity</w> 10061
hal ide</w> 10061
ast e</w> 10059
paste ur 10058
cortic ospinal</w> 10057
and able</w> 10056
Gla uc 10056
CA F</w> 10054
ci vil</w> 10054
Sr i</w> 10054
N 4</w> 10053
u ates</w> 10053
K al 10053
k A</w> 10052
0.0 49</w> 10052
aven ue</w> 10052
ste aro 10051
electrocardi ography</w> 10051
U preg 10050
W id 10048
wo od 10048
Immun oglobulin</w> 10048
tetr ap 10048
hybridis ation</w> 10048
U B 10047
All ergic</w> 10047
chondro sarcoma</w> 10046
duc ks</w> 10045
val bumin</w> 10045
CD C 10045
Rever sible</w> 10044
lip ogenesis</w> 10043
PD C</w> 10043
normo xia</w> 10043
prolifer ated</w> 10041
D 5</w> 10038
J H</w> 10038
Con duc 10038
sou theastern</w> 10037
illu sion</w> 10037
ozo ites</w> 10037
in docyanine</w> 10036
trim er</w> 10036
p 21 10035
S p</w> 10034
T on 10034
orp tive</w> 10034
Ann exin</w> 10033
A cr 10032
respec ts</w> 10032
chol angiography</w> 10032
an emic</w> 10030
ed le</w> 10030
hydro gen 10030
ob e 10029
Ch l</w> 10028
disp ensing</w> 10028
Tre ating</w> 10028
on tium</w> 10027
os clerotic</w> 10026
contrac tures</w> 10026
Bloc kade</w> 10025
N TP</w> 10024
epiphy seal</w> 10023
P assive</w> 10022
com ment</w> 10022
ore activity</w> 10022
mon op 10019
amylo id 10017
Cam ero 10016
comput ations</w> 10015
CD 95</w> 10014
etr ic</w> 10014
til l 10013
nucle ated</w> 10013
adip ogenic</w> 10013
P ure</w> 10012
or amic</w> 10011
og al 10011
nan ometer</w> 10011
com pres 10010
fr onto</w> 10010
tourniqu et</w> 10010
br ightness</w> 10009
ger an 10008
en er</w> 10007
Practi tion 10007
B ow 10006
HB c</w> 10006
semi structured</w> 10006
nulli parous</w> 10006
co transporter</w> 10005
miti gated</w> 10005
L epid 10004
q 13</w> 10004
Q s</w> 10003
met azo 10003
intr aspecific</w> 10003
psych ologists</w> 10003
endotox emia</w> 10003
after wards</w> 9999
3 13</w> 9998
c ement 9998
hypo vol 9998
12 9 9998
am idine</w> 9997
F 10</w> 9996
- untranslated</w> 9996
or f 9996
flu ence</w> 9996
pneum atic</w> 9996
neurolep tics</w> 9996
decor ated</w> 9996
lec ture</w> 9995
benz ofur 9994
cet uximab</w> 9994
w rin 9992
posi um</w> 9992
anis m</w> 9992
B .</w> 9991
Impro ve</w> 9989
SE D</w> 9988
NUMB ER</w> 9988
ur in 9987
sal ience</w> 9987
hand led</w> 9987
HF R</w> 9987
ile oc 9987
Ophthal m 9987
SO M</w> 9985
40 3</w> 9985
L LC</w> 9984
gastro duodenal</w> 9984
haemat oma</w> 9984
psycho active</w> 9984
an nel</w> 9983
ress ors</w> 9983
H ep</w> 9982
P ICU</w> 9982
DA SH</w> 9982
myri ad</w> 9982
Yel low</w> 9981
secre tase</w> 9978
propor tionally</w> 9978
T BP</w> 9977
exha ust</w> 9977
r icin</w> 9975
dis close</w> 9975
still birth</w> 9975
brevi ated</w> 9975
associ ating</w> 9974
Questionn aires</w> 9974
For m 9973
Compl ementary</w> 9973
R PA</w> 9971
un corrected</w> 9971
DM I</w> 9971
ATP ases</w> 9971
U F</w> 9969
c aly 9968
muc os 9968
pseud op 9968
tetrach loride</w> 9968
inter viewing</w> 9967
ther mot 9967
Pre -</w> 9967
trabecul ectomy</w> 9967
F ABP</w> 9966
pec tomy</w> 9966
ribonucle oprotein</w> 9966
re tail</w> 9965
trac ks</w> 9965
Tu key</w> 9965
gab apentin</w> 9963
ph obia</w> 9962
sp ared</w> 9962
L ank 9961
dor si</w> 9961
ed rine</w> 9960
Agr icul 9960
b in</w> 9959
ro oted</w> 9959
drain ed</w> 9959
ron i</w> 9959
inv alu 9958
E OR 9957
1. 87</w> 9957
Met formin</w> 9957
Sub stitution</w> 9956
oste olysis</w> 9954
under scores</w> 9952
sal is</w> 9952
CT GF</w> 9952
penicill amine</w> 9952
Pre term</w> 9951
plasm apheresis</w> 9951
son ication</w> 9950
antr one</w> 9950
En rich 9949
B ile</w> 9948
ti st</w> 9948
Descrip tion</w> 9948
wid ening</w> 9947
M Z</w> 9946
neuro p 9945
enter ocytes</w> 9945
coinc ides</w> 9944
S ST</w> 9943
er cosis</w> 9942
ME L</w> 9942
Fem oral</w> 9941
interfer ons</w> 9940
pro viral</w> 9939
invalu able</w> 9938
tap ered</w> 9936
mo tives</w> 9935
op t 9933
Ste p 9933
P article</w> 9930
G .</w> 9929
yl ates</w> 9929
R ich 9928
remo vable</w> 9928
PT P 9928
haem olyticus</w> 9928
Cur cumin</w> 9928
glycer aldehyde</w> 9928
1. 79</w> 9927
Ar a</w> 9927
AN T</w> 9927
laryng oscopy</w> 9927
We ek</w> 9926
pip ette</w> 9926
de formations</w> 9923
ver b 9923
PR RSV</w> 9923
t apping</w> 9922
spon sored</w> 9922
ox yl</w> 9921
un equal</w> 9920
ther mol 9918
voc ational</w> 9918
ore xin</w> 9917
P CD 9916
J . 9916
opto electronic</w> 9916
spiro ch 9915
heterodi mers</w> 9915
ot o 9914
AD CC</w> 9912
cel lo 9910
stem ness</w> 9910
T DF</w> 9909
rec al 9909
ess or</w> 9909
pediatr ics</w> 9908
scen es</w> 9907
firm ly</w> 9907
V aginal</w> 9906
spo ken</w> 9906
em atical</w> 9904
it ching</w> 9903
3 17</w> 9902
form ic</w> 9902
t elec 9901
hyper ventilation</w> 9900
0.0 41</w> 9899
19. 0</w> 9899
Taiw anese</w> 9899
di -</w> 9898
SH P</w> 9898
drop out</w> 9896
ol is 9895
amplic on</w> 9894
oc al 9893
sev er 9893
odom ain</w> 9893
dermat ological</w> 9892
dist ally</w> 9891
Le af</w> 9891
I CT</w> 9889
conn ex 9888
C CP</w> 9887
Trans fection</w> 9887
industri alized</w> 9887
japon icum</w> 9886
G eriatric</w> 9885
es cul 9885
coloc alization</w> 9885
f allopian</w> 9884
om ies</w> 9884
Ster oid</w> 9884
glabr ata</w> 9884
an teri 9883
supr ag 9882
Flex ible</w> 9882
G K</w> 9881
- 9 9881
hydro ps</w> 9881
ISR CT 9881
angi ographically</w> 9878
cor n 9877
emb rane</w> 9877
Car ri 9876
vir als</w> 9875
spo use</w> 9875
cardiover sion</w> 9875
2 3.4</w> 9874
Burkhol deria</w> 9874
3 38</w> 9873
D un 9872
gen ol</w> 9872
Mo tion</w> 9872
Endoc rine</w> 9872
DE HP</w> 9871
tin in</w> 9870
cap tures</w> 9869
domin ate</w> 9869
cannabin oids</w> 9869
rel s</w> 9868
ST 2</w> 9868
ap arin</w> 9866
em et 9866
immun olab 9866
gal lium</w> 9865
diver ged</w> 9865
w ives</w> 9864
fe e</w> 9864
aller genic</w> 9863
0.0 20</w> 9862
pro coagulant</w> 9861
1. 81</w> 9861
assi sting</w> 9861
carc asses</w> 9861
R RT</w> 9860
omen ing 9860
7 a</w> 9859
U PLC</w> 9859
im umab</w> 9859
Includ ed</w> 9858
foot print</w> 9856
baro receptor</w> 9856
bor ate</w> 9855
simpl ify</w> 9855
repor tedly</w> 9854
ag al</w> 9852
aph id</w> 9852
stimul ations</w> 9851
Path ogenesis</w> 9850
later ality</w> 9850
A As</w> 9849
protein emia</w> 9849
5 01</w> 9848
p S</w> 9847
dal ton</w> 9847
lipi demic</w> 9847
W AT</w> 9844
organis ational</w> 9844
soldi ers</w> 9844
te am 9843
ac ylated</w> 9840
SSR Is</w> 9840
P Z 9839
ophen yl</w> 9838
la w 9837
perin uclear</w> 9837
est yles</w> 9836
1. 84</w> 9835
sl ur 9835
F allot</w> 9834
al ties</w> 9834
deoxy cholate</w> 9834
Fib er</w> 9834
Ar sen 9833
intrad uctal</w> 9833
Synerg istic</w> 9833
histo chemically</w> 9832
PV I</w> 9832
scinti graphic</w> 9832
underestim ation</w> 9831
R BP</w> 9830
idi otypic</w> 9830
Qu in 9830
ali qu 9830
rel uc 9828
TR IM 9828
no thing</w> 9827
in sti 9826
dom onas</w> 9826
sul fo 9826
6 B</w> 9823
G M 9822
fluo rene</w> 9822
oper ant</w> 9821
pat chy</w> 9821
II I 9821
19 50</w> 9820
0. 125</w> 9819
Me dium</w> 9819
vit ality</w> 9817
denitr ification</w> 9817
dil emmas</w> 9816
pos th 9814
disc ol 9813
rin se</w> 9813
I SH</w> 9812
ol uc 9812
PI K3 9812
tetr as 9812
Ultr a</w> 9812
Maj ority</w> 9811
- flanking</w> 9810
porcel ain</w> 9810
tal ar</w> 9809
stor m</w> 9807
Ga N</w> 9807
non obese</w> 9806
epig ene 9806
Institu tional</w> 9806
on itis</w> 9804
tin gu 9804
3 9. 9803
pneumon ectomy</w> 9803
T ou 9802
Path ologic</w> 9802
S SCP</w> 9801
5 12</w> 9801
hex os 9801
provo ke</w> 9801
intr onic</w> 9798
Accum ulating</w> 9797
bi tis</w> 9796
New ly</w> 9796
Clim ate</w> 9795
Pr inci 9794
pix els</w> 9793
R ank 9792
Te hran</w> 9792
mas ks</w> 9790
g ills</w> 9789
is os 9786
gold fish</w> 9786
PC SK 9785
opac ification</w> 9785
asp ing</w> 9784
cholest atic</w> 9784
T CD</w> 9783
T b</w> 9782
an setron</w> 9782
sec tioning</w> 9782
water shed</w> 9782
6 20</w> 9781
conc e 9781
cr ises</w> 9780
ograph ed</w> 9780
laryng ectomy</w> 9780
contain ers</w> 9780
calend ar</w> 9780
F MS</w> 9779
S low</w> 9778
de duc 9778
therm ogenesis</w> 9778
c ub 9777
980 59</w> 9777
Cir cadian</w> 9776
varic ocele</w> 9774
J C</w> 9773
th apsigargin</w> 9773
prec ancerous</w> 9773
Spi ro 9773
H T3</w> 9772
de alt</w> 9770
normal ize</w> 9770
oxif ylline</w> 9770
ud ing</w> 9769
MI BG</w> 9769
Cyt ogenetic</w> 9769
TGF β</w> 9769
s arc 9768
fem in 9768
exp iration</w> 9768
on wards</w> 9767
C 16</w> 9764
TL Rs</w> 9764
abig atran</w> 9764
P ast</w> 9763
bil s</w> 9763
oguan idine</w> 9763
ap ia</w> 9760
2 c</w> 9759
F enton</w> 9759
Con struc 9759
He aling</w> 9759
schizophren ics</w> 9759
un published</w> 9758
bo ards</w> 9757
inhal er</w> 9757
Spectro metry</w> 9757
eno humeral</w> 9757
mc g</w> 9757
An kle</w> 9755
hist ograms</w> 9754
allo on</w> 9754
Pa ediatric</w> 9753
advertis ing</w> 9753
Gen otyping</w> 9752
b all 9751
ey e 9751
Bloc k</w> 9751
Fri ed 9751
Prostagland in</w> 9750
ca uter 9749
g ris 9748
de g</w> 9748
estro l</w> 9747
W NV</w> 9746
3 43</w> 9746
up dating</w> 9746
Vari able</w> 9746
6. 25</w> 9745
ovi position</w> 9744
A AT</w> 9743
CH 4</w> 9742
3 26</w> 9740
N av 9740
ov aginal</w> 9739
TR AL</w> 9739
lute um</w> 9739
Sal t</w> 9738
poly genic</w> 9737
PE A</w> 9737
precip itates</w> 9736
ad an</w> 9734
prescrib e</w> 9733
P W</w> 9732
govern ments</w> 9730
19 , 9729
MT D</w> 9729
lipoly tic</w> 9729
um inated</w> 9728
top ographical</w> 9728
Co he 9727
ped al</w> 9726
1. 89</w> 9725
dou bly</w> 9725
NS 3</w> 9725
Bene fits</w> 9724
2 n</w> 9723
intercal ated</w> 9723
G ri 9722
noc ic 9722
CDK N 9722
prop idium</w> 9721
og els</w> 9720
H ash 9718
al um</w> 9718
oste ochondral</w> 9718
SYN THESIS</w> 9718
1. 80</w> 9717
Bur den</w> 9717
phosphoryl ates</w> 9717
euthan ized</w> 9715
reg ressive</w> 9714
pigment osa</w> 9714
standardi ze</w> 9713
foramin al</w> 9713
re missions</w> 9712
ile ostomy</w> 9712
fav oured</w> 9710
kerat o 9710
glut aryl</w> 9709
L 6</w> 9707
2 4.2</w> 9706
M CH</w> 9706
sphinc ter 9706
obstetr ical</w> 9706
3 73</w> 9705
1. 88</w> 9705
K EGG</w> 9704
C JD</w> 9703
T PP</w> 9703
cho ol 9703
Cardi ology</w> 9702
radiolab elled</w> 9702
fl ushing</w> 9701
Tran sp 9701
D SBs</w> 9700
P v 9699
proprioc eptive</w> 9699
mel i 9698
oopho rectomy</w> 9698
X P</w> 9697
penetr ated</w> 9697
al en</w> 9696
N Ts</w> 9695
ex onuclease</w> 9695
ent ails</w> 9695
sum ing</w> 9695
n ailing</w> 9694
Dis sem 9694
S impl 9692
pa ins</w> 9692
ag A</w> 9691
ve g 9691
ME N</w> 9690
b ore</w> 9689
2 1.8</w> 9688
ond ansetron</w> 9688
Rec eptors</w> 9688
Sens or</w> 9687
pul mon 9686
ML H1</w> 9686
re folding</w> 9685
ine ous</w> 9685
o at</w> 9683
cul min 9683
depend encies</w> 9683
2. 15</w> 9683
Post natal</w> 9683
Chlamy domonas</w> 9683
intrap artum</w> 9683
O f 9682
re ef</w> 9682
Integr ating</w> 9682
Tor onto</w> 9682
carboxyp eptidase</w> 9682
immuno deficient</w> 9681
immun odom 9678
biop sied</w> 9677
IM PACT</w> 9677
cyan obacterial</w> 9677
Ant arctic</w> 9677
am ed</w> 9676
cl amped</w> 9676
gel ation</w> 9676
3 14</w> 9675
br ace</w> 9675
oval e</w> 9674
L RP</w> 9672
transpos ons</w> 9672
syl van 9672
euthan asia</w> 9672
brac kets</w> 9671
riv aroxaban</w> 9671
Ar my</w> 9670
capro lactone</w> 9670
fol ia</w> 9669
tuber cle</w> 9669
typh oid</w> 9669
recep tion</w> 9668
ing rowth</w> 9667
mac ronutri 9667
F D 9666
T 6</w> 9666
Sub strate</w> 9665
My D88</w> 9665
PE O</w> 9664
sulf onyl</w> 9664
unra vel 9664
Aspir in</w> 9663
blin k</w> 9662
CI C</w> 9661
si i</w> 9660
suic ides</w> 9660
biog e 9660
st -</w> 9659
end osperm</w> 9659
s orbent</w> 9658
om ata</w> 9657
pre molar</w> 9656
50 5</w> 9656
quadru plex</w> 9656
monoph asic</w> 9656
co operate</w> 9654
fib ula</w> 9654
di alogue</w> 9653
Mo roc 9653
psych o</w> 9652
dec entr 9651
Amaz on</w> 9649
cre tion</w> 9648
En cephal 9648
si ds</w> 9647
granul ocytic</w> 9646
Impl antation</w> 9646
Fre und</w> 9645
con ferences</w> 9643
re use</w> 9642
AN IM 9642
pal in 9641
p 62</w> 9640
cl ips</w> 9640
analy tically</w> 9640
ambi a</w> 9640
grac ilis</w> 9640
compl aining</w> 9639
Bif id 9638
CYP2C 19</w> 9637
pol yl 9634
see ing</w> 9634
O TA</w> 9633
pursu e</w> 9633
mor tal 9632
i rosis</w> 9630
ut z 9630
satur ating</w> 9630
scarc ity</w> 9630
o estrus</w> 9629
propo s</w> 9629
chondro genic</w> 9628
hed gehog</w> 9627
cu ps</w> 9626
c up 9625
re ven 9625
si ll 9625
tou gh 9625
ad ural</w> 9624
4 88</w> 9623
contrain dication</w> 9623
H AP</w> 9621
t .</w> 9621
reg ained</w> 9621
nor theastern</w> 9619
pep per</w> 9619
activ eness</w> 9618
i eld</w> 9617
p -</w> 9617
kine tically</w> 9617
phosphot yrosine</w> 9617
end obronchial</w> 9616
H ome 9615
or hin 9614
encomp ass</w> 9614
protozo a</w> 9614
I DA</w> 9613
my enteric</w> 9613
repeti tions</w> 9613
arachid onate</w> 9613
osp ond 9611
B is</w> 9610
PC Ps</w> 9610
ependym al</w> 9610
D in 9608
MB q</w> 9606
hop ed</w> 9606
per if 9604
ig el</w> 9604
TE s</w> 9604
1. 92</w> 9603
tox oid</w> 9603
duoden ectomy</w> 9603
Pro mo 9602
HR CT</w> 9602
Extrac ts</w> 9602
hy men 9601
mill il 9601
A HA</w> 9600
car ds</w> 9600
pro insulin</w> 9598
aband oned</w> 9598
H K 9597
F g 9597
un ity</w> 9597
k i 9596
I tems</w> 9596
appl i 9596
gall ate</w> 9596
perc ritical</w> 9594
hand grip</w> 9593
Prog esterone</w> 9591
Issu e</w> 9591
phenol ics</w> 9591
st at</w> 9590
non invasively</w> 9590
Transcrip tome</w> 9590
dish es</w> 9590
H all</w> 9589
5 HT</w> 9589
op exy</w> 9589
ari etal</w> 9589
poly vinyl</w> 9589
Er r 9589
quer ied</w> 9589
- 8 9588
SP F</w> 9588
r 1</w> 9587
elev ating</w> 9587
O DI</w> 9586
un ate</w> 9586
anaphy lactic</w> 9586
Tok yo</w> 9585
od d</w> 9584
H ard 9583
pro ving</w> 9582
ub a</w> 9581
N TG</w> 9580
GI S</w> 9580
deriv atized</w> 9579
m s 9578
qu e 9578
co done</w> 9578
lar va</w> 9578
ho ok</w> 9577
Lin kage</w> 9577
m Health</w> 9576
- treated</w> 9576
Por tal</w> 9576
asci tic</w> 9576
k not</w> 9575
sp end</w> 9575
nucle osomes</w> 9575
distingu ishes</w> 9575
trib ut 9575
D ox 9574
Wor kers</w> 9574
well ness</w> 9574
2 0.2</w> 9573
Hist amine</w> 9573
dysp noea</w> 9571
pollin ation</w> 9570
pres sur 9569
om uc 9568
cyclo addition</w> 9568
cryp torch 9568
si as</w> 9565
o acetate</w> 9564
I PD</w> 9564
ha em</w> 9564
T ween</w> 9563
aden oid</w> 9563
ME LD</w> 9562
valpro ic</w> 9562
3 16</w> 9560
T ME 9560
sw allow</w> 9558
circum vent</w> 9558
diarrhe al</w> 9558
aptam ers</w> 9557
m econium</w> 9555
L PO</w> 9554
K 5</w> 9554
IV US</w> 9554
T ER 9553
miti gating</w> 9553
eigh teen</w> 9553
porphyr ins</w> 9553
M aster</w> 9551
Z o 9551
hal ogen</w> 9551
SE T</w> 9551
tam er</w> 9551
ever olimus</w> 9551
P NP</w> 9550
ech oic</w> 9550
tw isted</w> 9549
Pu gh</w> 9549
diffus ely</w> 9548
le u 9546
collo ids</w> 9546
dac tyly</w> 9545
ib izumab</w> 9544
bl ade</w> 9544
an ec 9543
uro graphy</w> 9543
aph th 9543
intellig ent</w> 9543
ron ous</w> 9543
M é 9542
pat ern 9542
Cat alytic</w> 9542
robu stly</w> 9542
oste opontin</w> 9541
Tes ticular</w> 9541
T n</w> 9540
In nov 9540
up dates</w> 9540
hypot onic</w> 9540
onco protein</w> 9539
extram edullary</w> 9537
8 4 9536
D ry</w> 9536
pow ers</w> 9536
anticip atory</w> 9536
L Ns</w> 9535
E U 9535
T DI</w> 9535
bo ur 9535
carb amate</w> 9535
Radi ology</w> 9535
horiz on</w> 9535
tw ist</w> 9534
identi fier</w> 9531
s ort</w> 9530
ex t 9530
Li ve</w> 9530
Gr ad 9529
arom a</w> 9528
Glyc ine</w> 9527
s kin 9526
PT V</w> 9526
at tract</w> 9524
A cous 9523
hyper thyroid</w> 9523
di ving</w> 9522
bl a 9521
otrop h</w> 9521
Agg ressive</w> 9521
inter position</w> 9520
palmit oyl 9520
Str ati 9519
a head</w> 9518
happ y</w> 9518
P ha 9517
code ine</w> 9517
im ino</w> 9516
ress or</w> 9516
2 1.9</w> 9515
cyt opathic</w> 9515
fac tor 9512
3 D 9511
L AM</w> 9510
dre w</w> 9510
accred itation</w> 9510
aug menting</w> 9509
x ide</w> 9508
de regulated</w> 9508
p its</w> 9507
den ing</w> 9507
PK 1</w> 9507
d C</w> 9506
par g 9506
H2 AX</w> 9505
m es</w> 9504
op us</w> 9504
Prob lem</w> 9504
ifos famide</w> 9504
B iliary</w> 9503
ac tom 9502
Prote omic</w> 9502
in hab 9501
thi ol 9500
G PA</w> 9499
et ched</w> 9499
anthocyan ins</w> 9499
co infection</w> 9498
pe el</w> 9498
dor si 9497
relig ion</w> 9496
melan ocyte</w> 9495
mis s</w> 9493
lam ellae</w> 9492
ser y</w> 9491
bl astic</w> 9491
d aughters</w> 9490
entr ain 9490
chi sis</w> 9490
flag ella</w> 9490
Pharmac eutical</w> 9489
EOR TC</w> 9488
K Y 9487
H ox</w> 9485
phospho choline</w> 9485
fellow ship</w> 9485
4 4. 9484
acin i</w> 9484
mal arial</w> 9483
P ND</w> 9481
hep cidin</w> 9481
intra renal</w> 9481
2 2.8</w> 9478
A ge 9478
G . 9478
P P1</w> 9477
lat tices</w> 9477
CD 30</w> 9476
i ors</w> 9475
c un 9475
pe tic</w> 9475
30 ,000</w> 9475
me ant</w> 9474
Pro per</w> 9474
NH ANES</w> 9474
Te am</w> 9474
tet anic</w> 9474
los sal</w> 9473
tur tle</w> 9473
rhabdomy olysis</w> 9473
p o</w> 9472
6 000</w> 9471
inv al 9470
clav icle</w> 9470
we ap 9469
poly pharmacy</w> 9469
di lol</w> 9468
ev asion</w> 9468
out performed</w> 9468
atic ity</w> 9468
archa ea</w> 9468
F el 9467
40 9</w> 9467
L Q 9466
In take</w> 9466
PW S</w> 9466
3 24</w> 9464
fl avi 9464
quad ri 9464
at rist</w> 9463
isol e</w> 9463
Ste ady</w> 9462
2 5.6</w> 9461
dis asters</w> 9461
P c 9460
eth onium</w> 9460
Fram ingham</w> 9460
3 23</w> 9459
V is</w> 9459
Bon fer 9459
steril ized</w> 9458
neointim al</w> 9458
2 7.8</w> 9457
t ography</w> 9457
in suff 9456
gam bi 9456
O S 9455
lact ams</w> 9455
neighbor hoods</w> 9455
19 60s</w> 9454
transfec tants</w> 9453
STAT EMENT</w> 9452
Me dia</w> 9451
LE Ds</w> 9451
5 20</w> 9450
neuro fibrillary</w> 9450
R XR</w> 9449
dis ap 9449
phospho transferase</w> 9448
ic ks</w> 9447
ver si 9447
ep i</w> 9446
dis cour 9446
esti s</w> 9446
5 7.1</w> 9445
V AT</w> 9445
om itted</w> 9445
contu sion</w> 9445
on yl 9444
fa ult</w> 9442
Perc ent 9442
H DR</w> 9441
r hom 9441
O E</w> 9441
al og</w> 9441
20 , 9441
Micro array</w> 9441
pacema kers</w> 9441
econ ds</w> 9440
D ro 9439
cc RCC</w> 9439
O FF</w> 9438
er lotinib</w> 9437
basi cally</w> 9436
em e</w> 9435
ind olol</w> 9435
diff usive</w> 9433
Par ticular</w> 9433
S ons</w> 9432
aspir ated</w> 9432
bac illus</w> 9429
LU TS</w> 9429
A Y 9428
pos it 9428
employ ers</w> 9428
sug ar 9428
asynchron ous</w> 9428
un supervised</w> 9427
p ant 9426
X S</w> 9426
Th resh 9426
cere als</w> 9426
cle aves</w> 9426
volt ages</w> 9426
D ox</w> 9425
leuk ocytosis</w> 9425
An omal 9424
cou plings</w> 9423
micronutri ent</w> 9423
H Q</w> 9420
metabol omic</w> 9420
oc king</w> 9419
ve dilol</w> 9419
PO A</w> 9419
CEN TRAL</w> 9419
val vul 9418
CA N</w> 9418
W MD</w> 9417
p idus</w> 9416
ali ke</w> 9416
AC I</w> 9415
pancre at 9415
entr ies</w> 9414
PO IN 9414
OR F 9414
P2 X 9413
assist ant</w> 9412
pre incubated</w> 9411
alk anes</w> 9411
SN S</w> 9411
Cyt os 9411
Ba P</w> 9411
aort as</w> 9411
A AC</w> 9409
multi system</w> 9409
P osi 9407
moder ating</w> 9407
P PE</w> 9406
4 4.4</w> 9406
E ither</w> 9406
const ell 9406
peculi arities</w> 9406
idi al</w> 9405
immuno diffusion</w> 9405
urban ization</w> 9405
G CA</w> 9404
pleth ora</w> 9404
Cle ar</w> 9403
L ind 9402
specific ations</w> 9402
GI T</w> 9400
Rob ust</w> 9400
biolog ics</w> 9400
P yro 9399
immunost ained</w> 9399
sc r 9398
mus cim 9398
un balanced</w> 9396
Syn chron 9396
plas tic 9395
mes encephalic</w> 9395
kain ic</w> 9395
pedi grees</w> 9394
a plasma</w> 9393
d amp 9393
in forming</w> 9393
quin tile</w> 9393
FI GO</w> 9393
life times</w> 9393
B rom 9391
de hydr 9391
rib osylation</w> 9391
Ab l</w> 9391
PI A</w> 9390
CE US</w> 9390
Bloc king</w> 9389
tubul o 9389
ethan olic</w> 9389
flow metry</w> 9388
hydra zone</w> 9388
2 0.9</w> 9387
clu sively</w> 9387
mutag ens</w> 9387
som ata</w> 9386
prov incial</w> 9386
mill ig 9386
t agging</w> 9385
et allic</w> 9385
ep ri 9385
HEK 293</w> 9382
5 alpha</w> 9381
ad hering</w> 9381
Cerebro spinal</w> 9381
W o 9379
M AM</w> 9379
hydro genase</w> 9379
SC Cs</w> 9379
Differen ce</w> 9379
out performs</w> 9378
parox etine</w> 9378
GLU T1</w> 9377
ent om 9376
CT R</w> 9376
od on</w> 9375
Cl 3</w> 9375
plant arum</w> 9375
pell ucid 9375
CN Vs</w> 9373
a rene</w> 9372
est rel</w> 9372
exc imer</w> 9372
lipid aemia</w> 9372
X yl 9371
Trac king</w> 9371
recalcitr ant</w> 9371
F ro 9370
eth ing</w> 9370
CD R</w> 9370
2, 7 9370
caus ation</w> 9370
s ocket</w> 9369
U bi 9369
infiltr ative</w> 9369
beet les</w> 9369
defl ection</w> 9368
Per man 9367
CP M</w> 9367
chim era</w> 9367
pip e</w> 9367
astr in</w> 9367
flatten ed</w> 9367
des ert</w> 9365
r as 9364
se baceous</w> 9364
Con focal</w> 9364
An emia</w> 9364
muscim ol</w> 9364
pro apoptotic</w> 9362
deri ving</w> 9362
digit orum</w> 9362
E AC</w> 9361
fe eder</w> 9360
postis chemic</w> 9360
p 38 9359
qu orum</w> 9359
kary otypes</w> 9359
scann ers</w> 9359
Examin ing</w> 9359
ab at 9358
lin kers</w> 9358
aer ation</w> 9358
centrom eric</w> 9358
resear ched</w> 9357
2.0 5</w> 9357
ol ae</w> 9356
Cdc 42</w> 9356
me ticul 9355
CL E</w> 9355
Col on</w> 9355
Ga it</w> 9355
perfor ator</w> 9355
metacar pal</w> 9355
perim etry</w> 9352
40 8</w> 9352
swe ating</w> 9352
o chr 9351
par si 9351
Rab bit</w> 9351
at razine</w> 9350
Wh ites</w> 9349
ad versity</w> 9347
ospor ium</w> 9347
MO Fs</w> 9347
Su perior</w> 9345
confron ted</w> 9345
I d</w> 9344
V erbal</w> 9344
yn ess</w> 9344
Inter sti 9343
argin ase</w> 9342
chir ality</w> 9342
coinc ide</w> 9342
AB C 9341
eb a</w> 9341
cle aving</w> 9340
claud in</w> 9340
resembl ance</w> 9339
D MA</w> 9338
9 4 9337
op al 9337
extrem es</w> 9337
chiro practic</w> 9337
S BS</w> 9336
piper idine</w> 9336
20 1 9335
recogn izable</w> 9335
re pairing</w> 9334
electro spun</w> 9334
A symmetric</w> 9333
mess engers</w> 9333
achi asmatic</w> 9332
per methrin</w> 9331
motiv ations</w> 9331
e ism</w> 9330
- UTR</w> 9330
veter in 9330
3 G</w> 9329
ow a</w> 9329
equ atorial</w> 9329
foun ded</w> 9329
hun ger</w> 9329
p ies</w> 9328
F PG</w> 9328
att ach</w> 9328
3 36</w> 9327
2. 5 9327
HC Cs</w> 9327
r p 9326
SK F</w> 9326
un loading</w> 9325
suff ers</w> 9325
Pot assium</w> 9325
Cy an 9325
LY P</w> 9325
s orghum</w> 9324
lyc opene</w> 9324
histi ocytic</w> 9324
turb idity</w> 9323
hex ose</w> 9322
DR 4</w> 9322
Prec ision</w> 9322
ith ymia</w> 9321
bl a</w> 9321
U m 9320
psych ologic</w> 9320
4 2.9</w> 9318
O Cs</w> 9318
phenanthro line</w> 9318
4 5.5</w> 9317
p ou 9315
megakary ocytes</w> 9315
1 B1</w> 9314
flex ural</w> 9314
verte bro 9314
ur on</w> 9313
hem optysis</w> 9313
An k 9313
triam cinolone</w> 9313
Physi ology</w> 9312
29. 4</w> 9312
beha ves</w> 9312
inf lated</w> 9311
intersti tium</w> 9311
osteocl ast 9311
j umping</w> 9310
Cl ose</w> 9310
Bonfer roni</w> 9310
T SC</w> 9309
Al aska</w> 9308
EC L</w> 9308
Col or 9307
ar athyro 9306
phyto chemical</w> 9306
un described</w> 9305
flan ked</w> 9305
erec tion</w> 9305
Magne sium</w> 9304
mill ing</w> 9303
pul satility</w> 9302
S pati 9301
M AD 9301
Ar ray</w> 9301
Cryst all 9300
ec ologically</w> 9299
Less ons</w> 9299
indic a</w> 9298
R en 9297
ellip so 9297
3 19</w> 9296
E W 9296
C As</w> 9294
3 28</w> 9294
SU Vmax</w> 9294
w ounding</w> 9293
hydro lysate</w> 9293
neuro kinin</w> 9292
PI N</w> 9292
2 4.4</w> 9291
A Q</w> 9291
Hist opathology</w> 9291
gri ef</w> 9291
gang rene</w> 9291
ol . 9290
yn yl</w> 9290
oste otomies</w> 9290
Immun otherapy</w> 9290
exud ative</w> 9289
3 0.0</w> 9288
Alber ta</w> 9288
succ ession</w> 9287
Doc um 9287
X i 9286
phosphor ylate</w> 9285
spor um</w> 9284
Arti cles</w> 9284
Sud an</w> 9284
diverticul itis</w> 9281
Sy d 9280
moll us 9280
inter ictal</w> 9279
micro be</w> 9279
PRO M</w> 9279
S ection</w> 9278
N iss 9278
loc alities</w> 9278
H3 N2</w> 9278
T f</w> 9277
Wh ich</w> 9277
marc escens</w> 9277
id ylate</w> 9275
itu ality</w> 9275
T CS</w> 9274
neurolog ically</w> 9274
- 30</w> 9273
a ked</w> 9272
ro ad 9272
p 63</w> 9268
col ostomy</w> 9268
TI PS</w> 9268
C oma</w> 9267
D 2 9267
aver ine</w> 9267
ze olite</w> 9267
exp andable</w> 9266
7 2 9265
g ym 9265
ocyt omas</w> 9265
Frequ ent</w> 9265
Ampl ification</w> 9265
re hydration</w> 9263
oper ation 9263
Bi g</w> 9263
at entorial</w> 9262
ther mos 9261
annot ations</w> 9261
hap toglobin</w> 9261
Psori asis</w> 9261
an aphase</w> 9260
ifer ol</w> 9260
Long er</w> 9260
conver ge</w> 9258
acr on</w> 9258
Typ es</w> 9258
E TA</w> 9257
scap hoid</w> 9257
gyn ecology</w> 9257
Sud den</w> 9256
EN aC</w> 9254
2 2.9</w> 9253
M SP</w> 9253
lif estyles</w> 9253
spl in 9253
dig est</w> 9252
T CE</w> 9251
im migration</w> 9251
al ising</w> 9250
Fox p3</w> 9250
L AS</w> 9249
cock ro 9249
fac tant</w> 9248
TR PM 9248
aris en</w> 9248
success es</w> 9246
bran ds</w> 9245
Regi stered</w> 9245
skew ed</w> 9245
i o 9244
p 300</w> 9244
diast ole</w> 9243
Peri od 9243
ambigu ously</w> 9243
C GP</w> 9241
centur ies</w> 9241
onc ogenesis</w> 9240
Col e 9239
3 55</w> 9238
p ub 9238
enter pr 9238
earthqu ake</w> 9238
P 8</w> 9237
r itonavir</w> 9237
cry otherapy</w> 9237
sing le-</w> 9237
ic ho 9236
te l</w> 9236
ir one</w> 9235
natur alistic</w> 9235
AT G 9234
L ayer</w> 9233
micro capsules</w> 9233
ore sistant</w> 9231
Nor mal 9230
aldol ase</w> 9228
h its</w> 9227
bif ida</w> 9227
AI T</w> 9226
Dec om 9226
Intersti tial</w> 9226
bo ar</w> 9223
flo od</w> 9223
appli ances</w> 9223
resc rip 9222
subjec tively</w> 9220
Pres enting</w> 9220
amni ocentesis</w> 9220
Interpre tation</w> 9220
dwar f</w> 9220
i qu 9219
4 6. 9218
B t</w> 9218
det ella</w> 9218
gly oxal</w> 9216
ver ruc 9215
13 7 9215
p. i.</w> 9215
nigro striatal</w> 9215
L AA</w> 9214
H ip 9212
G em 9212
sing ular</w> 9212
E t</w> 9211
titr ated</w> 9210
transp eptidase</w> 9209
inter cept</w> 9208
Pro grams</w> 9208
Man ip 9208
Suppl ementary</w> 9208
aden o</w> 9207
pyr role</w> 9207
accoun tability</w> 9206
plank tonic</w> 9205
fem oro 9204
pod ocytes</w> 9204
ger bils</w> 9203
Anti bacterial</w> 9203
exce ed 9203
FL T3</w> 9203
ter y</w> 9202
tetram ethyl 9202
Secre tion</w> 9202
bach ia</w> 9202
4 00 9201
minim izes</w> 9201
spermat ocytes</w> 9201
3 6.4</w> 9200
1. 98</w> 9200
radi ative</w> 9200
2 3.2</w> 9199
L ec 9199
4 b</w> 9199
hal o</w> 9198
C affe 9196
i x 9196
4 5. 9195
si t</w> 9195
lam b</w> 9194
sulph ur</w> 9194
cap ecitabine</w> 9193
g or 9192
Mar fan</w> 9191
Empir ical</w> 9191
ation -</w> 9190
hyp o</w> 9189
stem ic</w> 9188
cist er 9187
bioinform atic</w> 9187
tan gen 9186
- 80</w> 9185
electron ically</w> 9185
H5 N1</w> 9185
o frontal</w> 9184
M J</w> 9183
mod ulations</w> 9183
Famil ies</w> 9183
Lim b</w> 9182
en sured</w> 9180
eg u 9180
ND V</w> 9180
J ones</w> 9179
phos ate</w> 9179
intr af 9178
Ca Cl2</w> 9178
F ES</w> 9177
hem olymph</w> 9177
T ot 9176
resum ption</w> 9176
gras p</w> 9175
W er 9174
AR A</w> 9173
3 18</w> 9172
F MR 9172
cl ots</w> 9171
bin der</w> 9171
im balances</w> 9170
GL UT</w> 9170
P DS</w> 9169
ot us</w> 9169
chel ate</w> 9169
M im 9167
dic tion</w> 9166
E mission</w> 9165
peri prosthetic</w> 9165
lit ters</w> 9165
ad alimumab</w> 9164
d ane</w> 9163
z ein</w> 9163
hydro chlorothiazide</w> 9163
Syd ney</w> 9163
d UTP</w> 9162
1. 7 9162
111 In</w> 9162
cu ed</w> 9161
SP I</w> 9161
chron otropic</w> 9161
A1 C</w> 9161
Cyto kines</w> 9160
Hippocamp al</w> 9160
Conf idence</w> 9160
naph tho 9159
plasm atic</w> 9158
PR IN 9158
8 50</w> 9157
B ipolar</w> 9157
brasili ensis</w> 9157
f ishing</w> 9156
M ellitus</w> 9156
triti um</w> 9156
C en 9155
tri angle</w> 9155
buil dings</w> 9155
arthro plasties</w> 9154
calib er</w> 9154
Bor detella</w> 9154
rectom ies</w> 9154
SM AD 9153
C ER 9152
end ronate</w> 9152
sub threshold</w> 9152
corrobor ate</w> 9152
A SE</w> 9151
H 19</w> 9151
I PS</w> 9150
pre diabetes</w> 9149
V el 9146
PS MA</w> 9146
inf inity</w> 9145
I E 9144
expon ent</w> 9144
RG Cs</w> 9144
benz imidazole</w> 9143
ampull ary</w> 9143
N . 9142
no s</w> 9142
sched uling</w> 9142
N PS</w> 9141
ent a</w> 9141
direc ts</w> 9141
2, 4,6</w> 9141
compl ements</w> 9140
1, 25-</w> 9140
Polymorph ism</w> 9140
fo reg 9139
zz le</w> 9139
myc otoxins</w> 9139
integr ase</w> 9137
l af 9136
T ab 9136
Nan o</w> 9136
Cop ing</w> 9136
2 0.1</w> 9135
yl and</w> 9135
Rel ax 9135
DP PC</w> 9135
mon ozygotic</w> 9134
peric ytes</w> 9134
dec ap 9133
Abl ation</w> 9133
PRO SP 9132
Or b 9131
ad duction</w> 9130
6 30</w> 9129
NI S</w> 9129
SR s</w> 9128
2 3.6</w> 9127
eb all</w> 9127
294 002</w> 9127
sh all</w> 9126
cycl op 9126
cent rosome</w> 9126
re rs</w> 9125
diag rams</w> 9125
fer redoxin</w> 9125
hem in</w> 9125
PT s</w> 9125
mel it 9124
ord inal</w> 9124
pun ishment</w> 9124
multi step</w> 9123
atr ic 9123
Cl ar 9123
PM T</w> 9121
nitro so</w> 9120
deform ability</w> 9120
rep ell 9119
shad ow</w> 9119
otrop ia</w> 9118
cataly zing</w> 9117
sap onin</w> 9117
olis thesis</w> 9117
B F 9115
Ser ological</w> 9115
perpe tr 9115
un conscious</w> 9113
1. 93</w> 9113
40 6</w> 9113
hot spots</w> 9113
sel f 9112
D TT</w> 9111
2.0 4</w> 9111
symbi osis</w> 9111
S et</w> 9110
as ks</w> 9110
meth ion 9110
M ALT</w> 9109
oph obia</w> 9109
transl ating</w> 9109
cystic ercosis</w> 9109
4 35</w> 9108
b am 9108
ti ed</w> 9108
tec tum</w> 9108
pod ocyte</w> 9108
PI 3</w> 9107
continu ally</w> 9106
arbox ylate</w> 9104
extrap yramidal</w> 9104
phosph oc 9103
percenti les</w> 9103
Lat ent</w> 9103
pige on</w> 9103
Al u</w> 9102
rib s</w> 9102
ward ship</w> 9100
H ous 9099
ex o 9098
ven oms</w> 9098
bul king</w> 9098
Tra it</w> 9098
gl ing</w> 9097
pi oglitazone</w> 9097
Cardi omy 9097
municip alities</w> 9097
t angles</w> 9096
Sol anum</w> 9096
selen ite</w> 9096
1. 94</w> 9094
- phosphate</w> 9093
read out</w> 9093
hypothesi sed</w> 9093
fi ref 9092
diag onal</w> 9092
PC O</w> 9092
hybrid omas</w> 9092
a 3</w> 9091
5 60</w> 9091
immuno electrophoresis</w> 9091
pre motor</w> 9090
SO CS 9090
dist ention</w> 9089
desatur ase</w> 9089
draw s</w> 9088
C uc 9087
om ental</w> 9087
mer openem</w> 9087
addic tive</w> 9086
insul ator</w> 9085
DE N</w> 9084
CK 2</w> 9084
cock tail</w> 9083
provi sional</w> 9083
transmit ting</w> 9082
gra vid 9081
Neuro spora</w> 9081
am id</w> 9080
bromo deoxyuridine</w> 9080
Georg ia</w> 9080
2 4.6</w> 9078
1, 500</w> 9078
tap eptide</w> 9078
cm H2O</w> 9078
ni an</w> 9075
R ange</w> 9074
O vid</w> 9074
glycer o</w> 9073
Ehr lich</w> 9073
nasoph arynx</w> 9073
ch ore 9072
SI P</w> 9072
oid ectomy</w> 9072
iod o</w> 9072
Character istic</w> 9072
PCSK 9</w> 9072
cl onic</w> 9071
fo als</w> 9070
nat ally</w> 9070
seas onality</w> 9070
A AS</w> 9069
ar ab 9069
2 9.5</w> 9068
C ause</w> 9068
ill uminated</w> 9068
Cap illary</w> 9067
disproportion ate</w> 9067
dipl opia</w> 9066
ar as 9064
biom olecular</w> 9064
am per 9063
Al ve 9063
mean ings</w> 9063
m eval 9062
pro st</w> 9062
numer ary</w> 9062
oc ally</w> 9060
0.0 44</w> 9060
chemo embolization</w> 9060
d na 9058
P PG</w> 9057
radio iodine</w> 9055
Saf e</w> 9055
mal function</w> 9054
isth mus</w> 9052
P p 9051
Chrom atin</w> 9051
Gest ational</w> 9051
PL T</w> 9050
chyl omic 9049
fulle rene</w> 9049
preponder ance</w> 9049
multi stage</w> 9047
Que ens 9047
ogen omics</w> 9046
tothec in</w> 9046
un specific</w> 9044
j aws</w> 9043
am isole</w> 9043
tom ized</w> 9043
retri eve</w> 9043
ac cul 9042
SR BC</w> 9042
S and 9041
tr aline</w> 9040
me ets</w> 9039
sp ina</w> 9039
tab ac 9039
3 1.5</w> 9038
r Hu 9037
homin is</w> 9037
neuro ticism</w> 9036
ocul tural</w> 9036
CL D</w> 9035
pre medication</w> 9034
IC M</w> 9034
postin ter 9034
ag og 9033
consul tants</w> 9033
Lif estyle</w> 9033
3 ,000</w> 9032
psych osomatic</w> 9032
radic ular</w> 9032
Proced ures</w> 9032
C as</w> 9031
Ex ec 9031
Sh h</w> 9030
L u</w> 9029
D HFR</w> 9029
ac s</w> 9029
ad aic</w> 9029
hor ase</w> 9028
DE SC 9027
bul l</w> 9026
synth ases</w> 9025
Cro ati 9025
mictur ition</w> 9025
Compl ement</w> 9024
mast oid</w> 9024
L TD</w> 9023
Av oid 9023
Hash imoto</w> 9023
t uce</w> 9022
cul in</w> 9022
SE ER</w> 9022
resum ed</w> 9022
f owl</w> 9021
4 25</w> 9021
R K2</w> 9017
Q ALYs</w> 9017
Wol bachia</w> 9015
ph obic</w> 9014
techn icians</w> 9014
ref usal</w> 9014
digiti zed</w> 9014
3 4.5</w> 9013
e fully</w> 9012
re infection</w> 9010
parasi tism</w> 9010
con vol 9009
micro -</w> 9009
MM PI</w> 9009
pu st 9007
oth yro 9007
t mann 9006
ST ATI 9006
L ET</w> 9003
teri onic</w> 9003
exud ates</w> 9003
gl enohumeral</w> 9001
1. 8 9001
mis matches</w> 9001
ho ok 9000
UD CA</w> 9000
dorm ant</w> 8997
12 -</w> 8996
parathyro idectomy</w> 8995
u rella</w> 8994
Pro c</w> 8994
fluoro quinolone</w> 8994
p. m.</w> 8994
Glauc oma</w> 8994
i osity</w> 8993
SU MO</w> 8993
Muc osal</w> 8993
ni volum 8992
nucle olus</w> 8992
polymy xin</w> 8992
comm and</w> 8991
allevi ates</w> 8991
amid o 8991
disulph ide</w> 8990
p is 8989
Pop ulations</w> 8989
suscepti bilities</w> 8987
4 54</w> 8985
b ath 8985
ide um</w> 8985
Ray n 8985
id ality</w> 8984
con volutional</w> 8983
amelior ating</w> 8983
pea king</w> 8982
nucleoti dase</w> 8981
CH X</w> 8979
Bas in</w> 8979
pneum operitoneum</w> 8978
stri ate</w> 8978
S tra 8977
I ra 8977
p f 8977
decid ual</w> 8977
volati les</w> 8977
nivolum ab</w> 8977
H 7 8976
inter related</w> 8976
relax in</w> 8976
MAP Ks</w> 8976
no dos 8975
M un 8971
ST E</w> 8971
IL 2</w> 8971
Head ache</w> 8970
Queens land</w> 8970
ro unded</w> 8969
2 3.7</w> 8968
L y</w> 8968
it i</w> 8967
po ts</w> 8967
chemopre vention</w> 8967
Taq Man</w> 8967
AN G 8966
Tox ic</w> 8966
NG AL</w> 8966
lyophil ized</w> 8966
entrain ment</w> 8966
anti hist 8965
emo tionally</w> 8965
supr achiasmatic</w> 8962
p O2</w> 8960
O sc 8960
over laps</w> 8960
CM I</w> 8960
be er</w> 8958
load ings</w> 8958
conver sely</w> 8956
p 40</w> 8955
applic ants</w> 8955
health ier</w> 8952
at roph 8951
V B</w> 8950
complement arity</w> 8950
Mu LV</w> 8949
Jer sey</w> 8949
b out 8948
V p</w> 8948
Iris h</w> 8946
AC TION</w> 8945
micro circulatory</w> 8944
Demon stration</w> 8944
sh ru 8942
DM 1</w> 8942
ANIM ALS</w> 8942
Gl n 8941
ho p</w> 8940
Elev ation</w> 8940
fin ds</w> 8938
Memb ran 8937
M NC</w> 8936
b ounded</w> 8936
ex otic</w> 8936
bac tam</w> 8936
ond yl 8935
MG MT</w> 8935
millim eter</w> 8935
Intell igence</w> 8935
di odes</w> 8934
symb olic</w> 8934
mar in</w> 8933
accompan ies</w> 8933
C ast 8932
- specific</w> 8932
tem ozolomide</w> 8932
happ iness</w> 8932
ex pressive</w> 8931
por phy 8931
jur is 8931
S BA</w> 8930
I PA</w> 8930
Mo Ab</w> 8930
Morph ine</w> 8930
C Z 8929
under taking</w> 8928
PG D</w> 8928
S RT</w> 8927
el ders</w> 8927
P EC 8926
bl adders</w> 8926
glycol ic</w> 8926
FF M</w> 8926
polyn omial</w> 8926
fi x</w> 8924
PR s</w> 8923
discern ible</w> 8923
n m 8920
st oring</w> 8920
K G</w> 8919
defin itively</w> 8919
explo sive</w> 8919
Mus cul 8919
ox antrone</w> 8918
Sp ine</w> 8918
T SS</w> 8917
i ers</w> 8916
end ophytic</w> 8916
func tioned</w> 8916
synovi um</w> 8916
Matr igel</w> 8916
4 2. 8915
ogn athic</w> 8915
prokary otes</w> 8915
Pit ts 8915
- nitro</w> 8914
Di alysis</w> 8914
bulim ia</w> 8914
3 0.8</w> 8913
X O</w> 8913
3. 75</w> 8913
e dics</w> 8911
pro -</w> 8911
cardi ologists</w> 8911
morb idly</w> 8911
sub tropical</w> 8910
ga thering</w> 8910
edi a</w> 8908
RY GB</w> 8908
j er 8907
implic ates</w> 8907
cloth ing</w> 8907
As suming</w> 8906
degrad ability</w> 8905
menstr uation</w> 8905
D ST</w> 8904
TH z</w> 8904
Hist opathologic</w> 8904
H o</w> 8903
str ide</w> 8903
enol one</w> 8903
adi ab 8900
Aff inity</w> 8899
1. min</w> 8898
ede matous</w> 8898
babo on</w> 8898
L TC</w> 8897
As n 8897
surro und</w> 8896
Ac ids</w> 8895
.S .</w> 8894
Rob otic</w> 8894
4 T</w> 8893
judg ement</w> 8893
wh atever</w> 8892
spas ms</w> 8892
T end 8891
gu st 8891
nur sery</w> 8891
17 0 8891
k les</w> 8890
mon ensin</w> 8890
den oted</w> 8890
attr activeness</w> 8890
lacc ase</w> 8888
D ur 8886
MI A</w> 8886
Ad j 8885
mag na</w> 8885
bro minated</w> 8885
2 5.8</w> 8884
P PT</w> 8884
ta sis</w> 8884
1. 97</w> 8884
50 3</w> 8882
tum or 8881
Com pet 8881
organ ochlor 8880
CaMK II</w> 8879
at elec 8878
defec ation</w> 8877
D eg 8876
O AB</w> 8876
re activities</w> 8874
prop ion</w> 8874
ex o</w> 8873
gl um 8873
examin ers</w> 8873
TIV E</w> 8872
mon t 8871
alim entary</w> 8870
fac tual</w> 8869
main land</w> 8869
multinucle ated</w> 8869
efficac ies</w> 8867
invol ution</w> 8866
dichotom ous</w> 8866
gl ass 8865
fr active</w> 8864
dissoci ative</w> 8864
Rank in</w> 8864
Re produc 8863
cyan o</w> 8862
glycolip ids</w> 8862
abor tus</w> 8858
R od 8857
R ac</w> 8857
de formed</w> 8857
min ent</w> 8856
Al li 8856
epilep togenic</w> 8856
Cul ex</w> 8856
E GF 8855
Typ ically</w> 8855
X en 8854
nar col 8854
Deriv atives</w> 8854
del inqu 8853
B W 8852
Reve als</w> 8852
S ti 8851
conc ise</w> 8851
tur keys</w> 8851
car d 8850
ab scisic</w> 8849
CF C</w> 8848
Ig AN</w> 8847
cler k 8847
alli ed</w> 8847
b ent</w> 8846
m C</w> 8845
invari ance</w> 8845
S PO 8844
3 32</w> 8844
SD B</w> 8844
p CR</w> 8842
tor iness</w> 8841
Franc isco</w> 8841
Hem oglobin</w> 8840
Classi cal</w> 8840
Wal ker</w> 8840
A y 8839
N IV</w> 8839
z hou</w> 8839
SR IF</w> 8839
circumc ision</w> 8839
Camero on</w> 8839
rex ia</w> 8837
N ocardi 8836
suppur ative</w> 8836
tr y 8834
cri st 8833
sh op 8832
t enth</w> 8830
un ambiguously</w> 8830
un intentional</w> 8829
SL P</w> 8829
arathyro idism</w> 8829
acet azolamide</w> 8828
c occus</w> 8827
citr ulline</w> 8827
strati fy</w> 8826
CCL 2</w> 8825
routin es</w> 8824
A urora</w> 8823
heter otrophic</w> 8823
trig ine</w> 8823
SL T</w> 8823
n i</w> 8821
H AI</w> 8821
Dist urb 8821
cist ern 8821
N Y</w> 8820
u tional</w> 8820
arthro pod</w> 8820
Ep is 8819
leptom ening 8818
ox acillin</w> 8817
surviv or</w> 8817
2 5.3</w> 8816
som ething</w> 8816
bit ter</w> 8816
Profil ing</w> 8816
ot er</w> 8815
ang led</w> 8815
2.0 3</w> 8815
e ably</w> 8814
chori ocarcinoma</w> 8814
m ative</w> 8813
su p</w> 8812
Ar cha 8812
S ampling</w> 8811
yl phorbol</w> 8811
19 67</w> 8811
ethyl maleimide</w> 8811
ante l</w> 8811
DESC RIP 8811
tr a</w> 8810
consul ting</w> 8810
is otypes</w> 8809
Rayn aud</w> 8809
CF s</w> 8808
imidazol ium</w> 8807
pan oramic</w> 8806
transpos able</w> 8806
on itoring</w> 8805
adap tability</w> 8805
neuro pathology</w> 8804
ket on 8804
under served</w> 8803
H AT</w> 8802
e IF</w> 8802
decel eration</w> 8802
Lei den</w> 8801
end ole 8800
CYP 1A2</w> 8800
hypercholesterol emic</w> 8799
Rep eti 8798
Ir radiation</w> 8798
M PM</w> 8796
actinomyc et 8796
16 0 8795
IgG 2a</w> 8795
N ationwide</w> 8794
3 1.3</w> 8793
sarcol emmal</w> 8793
w af 8792
cir c</w> 8792
met h</w> 8790
promin ently</w> 8789
leg ume</w> 8786
cryp ts</w> 8786
fimbri ae</w> 8786
caver nos 8785
rin one</w> 8784
ubi quinone</w> 8784
impe ded</w> 8784
Ory za</w> 8784
A poli 8781
micro surgery</w> 8781
Sk ills</w> 8781
MCF 7</w> 8781
8 a</w> 8780
organ ogenesis</w> 8780
sin onasal</w> 8780
elap sed</w> 8780
s artan</w> 8779
a hertz</w> 8779
CA 125</w> 8779
equiv ocally</w> 8779
Gau cher</w> 8778
S olar</w> 8777
aff iliation</w> 8777
f ears</w> 8776
de an</w> 8776
mas tig 8776
s -</w> 8774
1. 00 8774
worth while</w> 8774
in ding</w> 8773
process or</w> 8773
diap horase</w> 8773
todd lers</w> 8772
hem olysin</w> 8771
inser ting</w> 8770
tent ative</w> 8770
lav icular</w> 8769
hyaluron idase</w> 8769
ospond in</w> 8769
ex changed</w> 8767
arch i 8767
ar ded</w> 8766
ace us</w> 8764
Ret ention</w> 8764
install ation</w> 8764
I K</w> 8763
pro drom 8763
midw ifery</w> 8763
amphib ians</w> 8762
ph leb 8760
1. 90</w> 8760
uro lithiasis</w> 8760
stere ois 8759
resorb able</w> 8759
in correctly</w> 8758
ba ic 8758
A 10</w> 8757
F un 8757
CD 44 8757
P rescrip 8755
uni ons</w> 8754
Nor theast</w> 8754
B SE</w> 8753
re treatment</w> 8753
at oes</w> 8753
ic ed</w> 8753
ME N 8753
MM T</w> 8753
Reg ions</w> 8753
Rhiz obium</w> 8753
1. 91</w> 8752
ax on 8752
neurolog ists</w> 8752
Fe wer</w> 8752
pl akia</w> 8750
k cat</w> 8749
- 0.5</w> 8749
1, 3,5</w> 8749
herbiv ores</w> 8749
Hum ans</w> 8748
desi pramine</w> 8748
u in</w> 8746
b 5</w> 8746
ul ants</w> 8745
peri anal</w> 8745
cardi olipin</w> 8745
emul si 8745
m ally</w> 8744
lam in</w> 8743
2.0 2</w> 8742
Pro l 8741
MI BI</w> 8741
alc iferol</w> 8740
pare tic</w> 8740
man o 8739
wo ol</w> 8739
med itation</w> 8739
fiber optic</w> 8739
Meth an 8738
45 Ca</w> 8738
DL S</w> 8736
2 6.6</w> 8735
mar sup 8735
Porph yro 8735
sub epithelial</w> 8734
aphy sis</w> 8734
turbul ent</w> 8734
amin ic</w> 8733
RB F</w> 8733
L em 8732
W u 8732
I BM</w> 8732
sub mucosa</w> 8732
prost atitis</w> 8732
opi ates</w> 8732
osyl ceramide</w> 8732
- 7 8731
el o 8731
optim ism</w> 8731
Qu estions</w> 8730
aquap orin</w> 8730
micro electrodes</w> 8728
AE P</w> 8728
destro y</w> 8728
O HD</w> 8726
otom ized</w> 8726
si r 8725
Hormon al</w> 8725
c oco 8724
therm o</w> 8724
P 53</w> 8723
allo xan</w> 8723
dermat ologic</w> 8723
hygi enic</w> 8723
quen ched</w> 8723
Hung ary</w> 8723
in ella</w> 8722
ep ine</w> 8721
py razole</w> 8721
endometri oid</w> 8720
deoxynucle oti 8719
teno fovir</w> 8719
m ere</w> 8718
cryst allo 8718
St orage</w> 8717
ag us</w> 8715
y co 8714
D DS</w> 8714
intim ately</w> 8714
L amin 8713
irrit ability</w> 8713
F .</w> 8712
K g</w> 8712
Man aging</w> 8711
team work</w> 8711
yr id 8709
glucos aminidase</w> 8708
pl ana</w> 8707
m ell 8706
il age</w> 8706
Bi osynthesis</w> 8706
postin fection</w> 8706
sh ik 8705
50 2</w> 8703
D s 8702
spir onolactone</w> 8701
s unitinib</w> 8699
Syn aptic</w> 8699
extrav ascular</w> 8699
N CAM</w> 8697
immunodom inant</w> 8697
after load</w> 8696
ca ine</w> 8695
pro drugs</w> 8694
stre ptococcus</w> 8694
GR ADE</w> 8694
K K</w> 8693
ul ls</w> 8693
imp utation</w> 8693
Inter mittent</w> 8692
multi modality</w> 8691
Cad mium</w> 8691
3 29</w> 8690
3 0.5</w> 8690
in opathy</w> 8690
AC 1</w> 8690
emp tive</w> 8690
tonsill ar</w> 8690
facul tative</w> 8690
HC Ws</w> 8689
Competi tion</w> 8689
met te</w> 8687
enanti omeric</w> 8687
3 21</w> 8686
pyrro l 8686
isother ms</w> 8686
AV Ms</w> 8685
Pseud o 8685
3 5.7</w> 8684
CO I</w> 8684
ampl ifier</w> 8684
HB O</w> 8684
congru ence</w> 8684
cy pro 8682
cyan obacterium</w> 8682
POIN TS</w> 8682
os ylated</w> 8680
Ch im 8679
Dec i 8678
6, 7</w> 8678
CDK 4</w> 8678
pastor is</w> 8678
Acous tic</w> 8678
glucon ate</w> 8677
spra ying</w> 8676
D rin 8675
pur ify</w> 8675
ophil in</w> 8675
s d</w> 8674
N b</w> 8674
man ag 8674
An tico 8674
q 22</w> 8673
Z r 8673
Af ter 8673
off ices</w> 8673
LI TY</w> 8673
2 5.4</w> 8672
phosphatidyl glycerol</w> 8672
Plac ental</w> 8672
oscill ators</w> 8671
P ip 8670
ur ally</w> 8670
ou ght</w> 8670
Cob b</w> 8670
- associated</w> 8669
il legal</w> 8669
fluoresc ently</w> 8669
1 -- 8668
Y 2</w> 8668
Alter ation</w> 8668
- 13</w> 8667
regi str 8667
HR QL</w> 8666
cari ous</w> 8666
h u</w> 8665
C os 8664
att achments</w> 8663
GST M1</w> 8661
Pharmac y</w> 8660
metagen omic</w> 8660
S PA</w> 8659
b ons</w> 8659
ion omycin</w> 8659
AP N</w> 8658
desi c 8658
mam mal</w> 8657
Conform ational</w> 8657
scr atch</w> 8657
delib erate</w> 8655
c occidi 8654
1. 9 8654
NI HSS</w> 8652
Dist ress</w> 8652
M TS</w> 8651
X X 8651
Mar row</w> 8651
W HR</w> 8650
amin ases</w> 8650
atis simus</w> 8650
Neu rom 8650
hat ch 8650
P PH</w> 8649
L AR</w> 8648
acceler ator</w> 8648
collap sed</w> 8647
pemphig oid</w> 8647
special ised</w> 8645
il o 8644
co aching</w> 8644
upreg ulate</w> 8644
tur tles</w> 8643
transist ors</w> 8643
sugar cane</w> 8643
S SS</w> 8642
2.0 8</w> 8642
tu me 8641
On going</w> 8640
Coll ection</w> 8640
mete orological</w> 8640
phyl um</w> 8639
s k</w> 8637
am pu 8636
per il 8636
Mem orial</w> 8636
DO CA</w> 8635
confoun ded</w> 8635
intratrac heal</w> 8635
- bi 8634
Transc ranial</w> 8634
abdomin is</w> 8634
Exp anded</w> 8634
thylak oid</w> 8634
m Sv</w> 8633
d ul 8633
keto acidosis</w> 8633
ME RS</w> 8632
W R 8631
su is</w> 8631
sh ell 8631
aeros ol 8631
16 00</w> 8630
gon ad</w> 8630
Ig G2</w> 8629
metach ronous</w> 8629
can ines</w> 8628
4 8. 8627
B Q</w> 8627
ax ine</w> 8627
micro gram 8626
c er</w> 8625
equ il 8625
lute us</w> 8625
enumer ation</w> 8625
4 7. 8624
El ucid 8624
sub families</w> 8623
Ag ed</w> 8622
M C3 8621
in adequately</w> 8620
over flow</w> 8620
fibrom atosis</w> 8620
l son</w> 8619
Ad renal</w> 8619
Sched ule</w> 8619
Of ten</w> 8619
tri phosphatase</w> 8618
PE s</w> 8618
halogen ated</w> 8616
Bl ast 8615
x L</w> 8614
V 4</w> 8614
Cran i 8614
mus sels</w> 8612
RN s</w> 8612
Fe ed 8612
sn akes</w> 8612
Cou pled</w> 8612
P X</w> 8611
awa kening</w> 8611
osper mic</w> 8611
Lup us</w> 8611
ver tex</w> 8610
rig orously</w> 8610
wet lands</w> 8610
Immunocyto chemical</w> 8610
perikary a</w> 8610
1 s</w> 8608
conflu ence</w> 8608
Ch ip</w> 8607
glyc opeptides</w> 8606
policy makers</w> 8606
V ector</w> 8605
PROSP ERO</w> 8605
P PC</w> 8604
M assive</w> 8604
min ip 8604
17 β</w> 8602
grass land</w> 8602
hypo chlorite</w> 8601
regi onally</w> 8600
tar trate</w> 8600
hem odynamically</w> 8599
Barr é</w> 8597
STATI STI 8597
form ulae</w> 8596
fil arial</w> 8596
Fer mi</w> 8596
Z ucker</w> 8595
19 th</w> 8595
Mod e</w> 8595
2 6.1</w> 8594
5 4.5</w> 8594
ven om 8593
ber ries</w> 8593
S ex 8592
dyslex ia</w> 8592
phosphoglycer ate</w> 8592
3 41</w> 8591
2 6.8</w> 8589
ag g</w> 8589
asp inatus</w> 8589
skinf old</w> 8589
Char cot</w> 8588
ax y</w> 8587
Ax is</w> 8587
Fabr ication</w> 8587
ext ents</w> 8587
C h</w> 8586
g eal</w> 8586
. org</w> 8585
d -</w> 8585
il ine</w> 8585
4 73</w> 8584
seg ment 8581
handic ap</w> 8581
ir us</w> 8580
eth ox 8580
Transc atheter</w> 8580
2 6.2</w> 8579
meth anesulf 8579
bi valent</w> 8577
CB V</w> 8577
Penn sylvan 8577
ol ium</w> 8576
CC M</w> 8576
hypop nea</w> 8575
MT P</w> 8573
A symptomatic</w> 8572
N os 8572
t yramine</w> 8571
ou b 8571
imp action</w> 8571
lacti de</w> 8571
Color ado</w> 8571
b w</w> 8570
in consistencies</w> 8570
ari ae</w> 8569
Cal mette</w> 8569
ro si 8568
visu alizing</w> 8568
Arthro plasty</w> 8568
Pitts burgh</w> 8568
Sal v 8567
QU ES 8567
Arthro scopic</w> 8567
recir culation</w> 8567
Consist ently</w> 8567
m ural</w> 8566
de pressor</w> 8566
intro g 8566
Sp in</w> 8566
M CV</w> 8565
ar mam 8565
Con sum 8565
y amo 8564
OR E</w> 8564
methyl cellulose</w> 8563
34 a</w> 8563
second arily</w> 8562
En anti 8561
pa res 8560
toph ysin</w> 8560
dermat omyositis</w> 8560
S erious</w> 8559
matern ally</w> 8559
pl ers</w> 8558
op ically</w> 8558
under nutrition</w> 8558
microm etast 8558
2 5.7</w> 8557
T TE</w> 8556
un im 8556
inf l 8555
motoneur on</w> 8555
1, 2, 8554
Radi x</w> 8552
os accharomyces</w> 8551
un equivocally</w> 8550
if loxacin</w> 8550
Ex cellent</w> 8550
pil i</w> 8550
- end</w> 8549
ogen omic</w> 8549
F resh</w> 8548
al d</w> 8548
AI s</w> 8548
Cb l</w> 8548
wor e</w> 8547
R ig 8545
ec t 8545
etan ercept</w> 8545
photosensiti zer</w> 8544
aminol ev 8544
intra abdominal</w> 8543
myx oma</w> 8543
MI T</w> 8542
P gp</w> 8541
9 8. 8541
Br id 8540
Ont ology</w> 8540
seal ant</w> 8540
Method ology</w> 8539
f unds</w> 8538
transduc ing</w> 8538
b py</w> 8535
3 5.5</w> 8534
o ide 8534
is er</w> 8534
separ able</w> 8533
chlamy dia</w> 8533
dis accharide</w> 8531
Ab use</w> 8531
Tr ust</w> 8531
nucleocap sid</w> 8531
P o</w> 8530
F r</w> 8530
O ct</w> 8530
oc eles</w> 8530
cy t</w> 8530
cont acting</w> 8530
dihydro folate</w> 8529
c n 8528
d 2</w> 8528
Re generation</w> 8528
PV s</w> 8528
male ate</w> 8528
scrap ie</w> 8526
d ome</w> 8525
non linearity</w> 8525
trache obronchial</w> 8525
un insured</w> 8524
d otal</w> 8523
conform ers</w> 8523
phosphati dic</w> 8523
Cor on 8522
pir ide</w> 8522
U AE</w> 8521
em ide</w> 8520
L n</w> 8519
apo protein</w> 8519
Math ematical</w> 8519
r A</w> 8518
gn ath 8518
tax ol</w> 8518
tung sten</w> 8518
B ox</w> 8517
3 31</w> 8516
en ec 8516
Angi ogenesis</w> 8516
del im 8515
2.0 7</w> 8515
ad h 8514
han dedness</w> 8514
sphen oid</w> 8514
Zim bab 8514
dec an</w> 8512
hyper active</w> 8512
explo sion</w> 8512
c p</w> 8510
b olic</w> 8510
v us</w> 8509
Pol ys 8509
oxygen ases</w> 8509
aggreg ating</w> 8508
consangu ineous</w> 8508
p 50</w> 8507
Prom otes</w> 8507
ren z</w> 8506
li ties</w> 8506
Pe er</w> 8506
CV S</w> 8505
opro st</w> 8505
semic ir 8505
mur m 8504
Elim ination</w> 8504
comit ans</w> 8503
leukotri enes</w> 8503
re forms</w> 8502
flu i 8502
m TBI</w> 8501
m l. 8501
B ET</w> 8501
appe aling</w> 8501
H ox 8500
CR D4 8500
psychi atrist</w> 8500
in s 8499
per sic 8499
for k</w> 8499
e book</w> 8498
andro gen 8498
6 6. 8497
M r 8497
O XA</w> 8497
om entum</w> 8497
pro peptide</w> 8497
th al</w> 8496
ran ibizumab</w> 8496
Visu alization</w> 8496
co ol</w> 8495
cyste ines</w> 8494
4 2.5</w> 8493
M PC</w> 8493
inter ventricular</w> 8493
col l</w> 8493
lipos arcoma</w> 8493
eicos apentaenoic</w> 8493
excur sion</w> 8493
quin ox 8492
Provid ing</w> 8492
Acanth amoeba</w> 8492
multi parous</w> 8489
angi osarcoma</w> 8489
ESW L</w> 8489
Func tions</w> 8488
neg atives</w> 8487
DM E</w> 8486
immun ogold</w> 8485
CD 86</w> 8485
cr assa</w> 8484
IC F</w> 8484
Aca d</w> 8484
contin gency</w> 8483
Hor iz 8483
A bo 8482
endos ym 8482
G p 8481
ar ism</w> 8481
SSR I</w> 8481
h oma</w> 8480
cam eras</w> 8480
bacterioph ages</w> 8480
Dev ices</w> 8479
phyto chemicals</w> 8479
4 0.0</w> 8478
T ube</w> 8478
el der</w> 8478
auth or 8478
Em bol 8478
dr ill</w> 8477
extran odal</w> 8476
H TS</w> 8475
3 27</w> 8475
ib ration</w> 8475
Const ant</w> 8475
P ichia</w> 8474
tetra ploid</w> 8474
DESCRIP TION</w> 8474
ap position</w> 8473
bi l</w> 8473
PT Z</w> 8473
CN U</w> 8473
hyperuric emia</w> 8473
M itral</w> 8472
semin oma</w> 8472
post transcriptional</w> 8470
incur able</w> 8470
commit tees</w> 8469
S Y</w> 8468
C SD</w> 8468
3 37</w> 8468
mon etary</w> 8468
pyr if 8468
Contin ued</w> 8468
alge sic</w> 8468
ban ks</w> 8468
F an 8467
trans itory</w> 8467
P2 X</w> 8467
Fibro blast</w> 8467
Rickett sia</w> 8467
m itis</w> 8466
acetyl salicylic</w> 8466
transl uc 8466
glucopyran oside</w> 8466
fas cin 8466
ak ic</w> 8465
aut ocor 8464
no ro 8463
CP 2</w> 8462
LE M</w> 8461
Am ph 8461
2.0 6</w> 8461
demethyl ase</w> 8461
te t</w> 8460
CYP2C 9</w> 8460
Cole optera</w> 8460
3 S</w> 8459
aden itis</w> 8459
extra ocular</w> 8459
oreduc tive</w> 8458
Proced ure</w> 8458
ocyt otic</w> 8457
RO CK</w> 8456
sph aer 8456
60 2</w> 8456
underpin ning</w> 8456
S 2 8455
al an 8455
el ithiasis</w> 8455
SI M</w> 8455
Ho ech 8455
Porphyro monas</w> 8455
en esulf 8454
bo osting</w> 8454
metast able</w> 8454
Con taining</w> 8453
Micha el</w> 8453
3 O4</w> 8452
Func tion 8452
Behavi our</w> 8451
yamo ya</w> 8451
M VA</w> 8449
po proteins</w> 8449
dermat ologists</w> 8449
mycel ium</w> 8449
Ch er 8448
exc itable</w> 8448
- triphosphate</w> 8446
anch ors</w> 8446
oxif ene</w> 8446
tmann in</w> 8446
trans genes</w> 8445
ucle otides</w> 8445
perim eter</w> 8443
Promo ting</w> 8443
L AC</w> 8442
sti ff</w> 8442
unil aterally</w> 8442
tom ies</w> 8441
Rel atively</w> 8441
28. 8</w> 8441
Caregi vers</w> 8441
EC Gs</w> 8440
expl anted</w> 8440
thermo stable</w> 8440
isother m</w> 8440
th o</w> 8439
inter mit 8438
hypertroph ied</w> 8438
myo fibrillar</w> 8438
Defin ing</w> 8438
T PS</w> 8437
train ee</w> 8437
E im 8435
Commun ities</w> 8435
heterozyg ote</w> 8435
2 7.6</w> 8434
I -</w> 8434
chrom ogenic</w> 8434
My x 8434
Mod elling</w> 8433
D er</w> 8432
em al</w> 8432
gg ed</w> 8432
Pos itron</w> 8431
u PAR</w> 8430
on ics</w> 8430
Ment en</w> 8430
l ane</w> 8429
Bab esia</w> 8429
bio available</w> 8427
gon i 8427
0.000 4</w> 8427
A 8</w> 8426
S 5</w> 8426
cl earances</w> 8426
Vir gin 8426
hypo phosphat 8425
contribut ory</w> 8425
man ager</w> 8424
10 9 8424
hydroxy methyl</w> 8424
endonucle ases</w> 8424
respon dent</w> 8423
exc itations</w> 8423
op tom 8422
insi pidus</w> 8422
land fill</w> 8422
non competitive</w> 8421
Intr insic</w> 8421
T 4 8420
cal bindin</w> 8420
situ ational</w> 8420
Hir sch 8419
B LA 8418
immuno therapeutic</w> 8418
domin antly</w> 8418
exfoli ation</w> 8418
Notch 1</w> 8418
holog raph 8418
st unting</w> 8417
fur a</w> 8417
Pennsylvan ia</w> 8417
R GC</w> 8416
α 2</w> 8416
ethyl ene 8415
foll icul 8415
Smad 3</w> 8415
Err atum</w> 8415
Period ontal</w> 8415
desic cation</w> 8415
ac cord</w> 8414
pres ent 8414
vis itors</w> 8414
3 39</w> 8413
inoc occus</w> 8413
trans forms</w> 8412
SI T</w> 8411
Intr aperitoneal</w> 8411
kynuren ine</w> 8411
AR 1</w> 8410
P ress</w> 8408
non randomized</w> 8408
sarcom ere</w> 8408
s one</w> 8407
3 48</w> 8407
3 51</w> 8407
fri end</w> 8407
el and</w> 8406
2 2.0</w> 8405
N ag 8405
enhanc ements</w> 8405
Aden ocarcinoma</w> 8405
F ts 8404
gun shot</w> 8404
N b 8403
boost ed</w> 8403
C MA</w> 8402
ing ival</w> 8402
ipp o</w> 8402
star ter</w> 8402
sil ence</w> 8401
fix ator</w> 8400
hypog lossal</w> 8400
bott les</w> 8398
S ensing</w> 8397
B rug 8397
und oub 8397
tis ol</w> 8396
Gli oblastoma</w> 8396
S SC</w> 8395
RAW 264.7</w> 8395
AD SCs</w> 8394
hal ation</w> 8394
Vis c 8394
- terminal</w> 8393
home odomain</w> 8393
flav us</w> 8393
laf axine</w> 8393
numb ness</w> 8392
bis pec 8392
2 6.4</w> 8391
ag ro 8391
sep ta</w> 8391
confidenti ality</w> 8391
n els</w> 8389
F K</w> 8389
st ayed</w> 8389
cycl ists</w> 8387
irradi ance</w> 8387
D AF</w> 8386
M WT</w> 8386
ut most</w> 8386
upreg ulating</w> 8386
K SHV</w> 8385
od in</w> 8385
estim ators</w> 8384
HC P</w> 8383
caus ally</w> 8383
f r</w> 8381
5 30</w> 8381
em comitans</w> 8381
cephal ometric</w> 8381
cl ue</w> 8379
Extrac orporeal</w> 8379
Use fulness</w> 8379
s now</w> 8378
- 40</w> 8378
el ly</w> 8378
diff using</w> 8378
oroph arynx</w> 8378
G C 8377
P ene 8376
omod ulin</w> 8376
squir rel</w> 8376
3 0.4</w> 8375
recycl ed</w> 8375
t arily</w> 8374
aph ro 8374
h p 8373
o is</w> 8373
U N</w> 8373
gu ard</w> 8373
methan olic</w> 8373
ico tin 8373
spermatog onia</w> 8373
e w</w> 8372
Ap parent</w> 8372
9, 10</w> 8372
L MA</w> 8371
tetrafluoro ethylene</w> 8370
2 3.9</w> 8369
H NF 8369
Cor relates</w> 8369
ni um</w> 8368
PO PU 8368
plas mosis</w> 8366
RI S</w> 8366
2 4.7</w> 8365
L SD</w> 8365
29. 6</w> 8365
co erc 8364
flo oding</w> 8364
tetradecano ylphorbol</w> 8363
AI H</w> 8362
necess itate</w> 8362
glycos yl</w> 8362
S PS</w> 8361
mir abilis</w> 8359
l end</w> 8358
d abigatran</w> 8357
1. 99</w> 8357
St an 8357
2 5.9</w> 8356
Anti biotics</w> 8356
ens embl 8355
AP PRO 8355
E -</w> 8354
AD PKD</w> 8353
Tr kB</w> 8352
dithi ocarb 8352
Lact ate</w> 8352
te dness</w> 8351
1 ---- 8350
Em ph 8349
Cap acity</w> 8349
Termin al</w> 8349
BV DV</w> 8347
sper sed</w> 8346
civ ilian</w> 8346
S HS</w> 8344
d l 8344
are rs</w> 8344
Parkin son 8344
isoc itrate</w> 8343
un answered</w> 8342
in competence</w> 8341
AB T</w> 8340
O L 8339
PC V 8339
pyrif os</w> 8339
Hemat opoietic</w> 8337
pro mastig 8336
B IA</w> 8334
Discrim ination</w> 8334
q i</w> 8333
28. 3</w> 8333
multiplex ed</w> 8333
3 9.5</w> 8332
su pers 8332
SI RS</w> 8332
equ i</w> 8332
Ga As</w> 8332
G SE 8331
E AT</w> 8330
CD K</w> 8330
2 6.9</w> 8329
3 61</w> 8329
to tic</w> 8329
actinomycet emcomitans</w> 8329
N AG</w> 8327
alk enes</w> 8327
adren o 8327
Rest oration</w> 8326
plo ids</w> 8325
tax ane</w> 8325
postinter vention</w> 8325
pyr ine</w> 8324
bacteri uria</w> 8324
multi locus</w> 8322
M ac</w> 8321
sulf atase</w> 8321
hy pol 8320
intercal ation</w> 8320
I de 8319
- sensitive</w> 8319
Tricho derma</w> 8319
Chem istry</w> 8318
2 4.8</w> 8317
o eba</w> 8317
R al 8317
O Cl</w> 8317
di ene</w> 8316
un ambiguous</w> 8316
Cd S</w> 8316
Ber lin</w> 8316
n y</w> 8315
reg ain</w> 8315
C2 C12</w> 8315
irrit ant</w> 8315
Fe 2</w> 8313
engine er</w> 8313
fl ashes</w> 8312
myco phenolate</w> 8312
hyph al</w> 8312
lati t 8312
6 b</w> 8311
An tic 8311
hyperbilirubin emia</w> 8310
29. 2</w> 8309
en g</w> 8308
bi ob 8307
conduc tor</w> 8307
Ur d</w> 8307
narrow er</w> 8307
escal ating</w> 8306
Ad riamycin</w> 8305
Prote obacteria</w> 8305
le es</w> 8304
commerc i 8304
p ills</w> 8302
aval ent</w> 8302
N Ws</w> 8301
in ones</w> 8301
min s</w> 8301
AB S</w> 8301
pris tine</w> 8301
as exual</w> 8300
mil king</w> 8300
IP SS</w> 8300
Vene z 8300
hypophy sis</w> 8299
CRD4 20 8298
ent arium</w> 8297
Oc t 8297
No ise</w> 8297
3 6.5</w> 8296
bi ologists</w> 8296
lot tic</w> 8296
fe til</w> 8294
HC T1 8294
Histor ically</w> 8294
re ts</w> 8293
catar rh 8292
form amide</w> 8291
ach ial</w> 8291
thi azol</w> 8290
poly electrolyte</w> 8289
cyst oscopy</w> 8289
R BD</w> 8288
cl ou 8288
fl um 8288
Jo h 8288
viol ations</w> 8287
intermedi ary</w> 8287
m RS</w> 8286
re bleeding</w> 8286
meth y 8286
haem odynamics</w> 8286
ycl o</w> 8286
general izability</w> 8286
cohe sive</w> 8285
fluoroph ores</w> 8285
om ission</w> 8284
re perfused</w> 8283
bio accumulation</w> 8283
CO RT</w> 8281
Chrom atography</w> 8281
a HR</w> 8280
5 32</w> 8280
E h 8280
b end</w> 8280
enth usi 8280
Alve olar</w> 8280
- 100</w> 8279
bre vis</w> 8279
tub ing</w> 8279
str ontium</w> 8278
inter media</w> 8278
pr ud 8278
Transl ational</w> 8278
ov orin</w> 8276
multi potent</w> 8275
S ources</w> 8274
b age</w> 8274
Inter mediate</w> 8274
are sis</w> 8273
hydr alazine</w> 8273
Vari ants</w> 8273
pre ovulatory</w> 8272
hypo physi 8272
lute in</w> 8272
exceed ingly</w> 8272
audi ometry</w> 8271
S FA</w> 8270
et om 8270
Bifid obacterium</w> 8270
ec centr 8269
low s</w> 8269
mat uring</w> 8269
cr acks</w> 8269
Cd 2</w> 8268
har sh</w> 8266
tetr alogy</w> 8266
g st</w> 8265
un pleasant</w> 8265
ste am</w> 8265
p ial</w> 8264
Shig a</w> 8264
un conventional</w> 8262
du plexes</w> 8262
quit ting</w> 8262
bi refr 8261
CD 80</w> 8261
AD MA</w> 8261
Compon ents</w> 8261
S PAR 8260
compar ator</w> 8260
Cor responding</w> 8259
50 4</w> 8258
pl en 8257
te tran 8257
ess ay</w> 8256
dys men 8256
gr amin 8255
semiconduc tors</w> 8255
D eb 8254
ach ian</w> 8254
fo il</w> 8254
vap our</w> 8254
thalass aemia</w> 8253
TI L</w> 8252
epir ubicin</w> 8252
p icro 8251
cur able</w> 8250
sil k 8250
bo iling</w> 8249
aqueduc tal</w> 8249
I tem</w> 8248
Q LQ</w> 8248
sp r 8248
amni on</w> 8248
Mn SOD</w> 8248
substr atum</w> 8247
tuber ous</w> 8247
Anthrop ometric</w> 8246
cri ti 8245
prioriti ze</w> 8245
Paste urella</w> 8245
eti apine</w> 8244
Cho ice</w> 8244
Mt b</w> 8244
fol k</w> 8243
Per ox 8243
L og</w> 8241
over comes</w> 8241
Sil encing</w> 8241
Scot tish</w> 8241
bi oluminescence</w> 8240
inform ants</w> 8239
2 1.0</w> 8238
6 32</w> 8238
encephal ography</w> 8238
overestim ation</w> 8238
D AA</w> 8237
ep in</w> 8237
poly ke 8237
Neuro psychological</w> 8237
G m 8236
V K 8235
reson ator</w> 8235
CB I</w> 8235
an them 8231
aut oreactive</w> 8231
O GD</w> 8230
emb odi 8230
im pose</w> 8229
posteri orly</w> 8229
ER s</w> 8228
sens u</w> 8228
5, 7</w> 8228
s ore</w> 8227
C in 8227
v i</w> 8227
us tine</w> 8227
cy clin 8227
simpl est</w> 8227
vasodi lators</w> 8226
Loc alized</w> 8225
A ro 8224
b erberine</w> 8224
zip per</w> 8224
hydro quinone</w> 8223
allevi ation</w> 8222
A 7</w> 8220
cl ock 8220
chrom ogranin</w> 8220
holo enzyme</w> 8220
b es 8219
B lacks</w> 8217
Philipp ines</w> 8216
bootstr ap</w> 8215
R ing</w> 8214
E n</w> 8214
sn ow 8214
incap able</w> 8214
monolith ic</w> 8214
3 1.6</w> 8213
18 00</w> 8213
prolong s</w> 8213
lo tho</w> 8212
jour ney</w> 8212
p im 8209
termin ating</w> 8209
dys biosis</w> 8208
spl int</w> 8208
agre es</w> 8208
xyl anase</w> 8207
adiab atic</w> 8207
I MS</w> 8206
mot ors</w> 8206
CS M</w> 8206
ig ilance</w> 8205
oph ora</w> 8205
AM F</w> 8204
lo vastatin</w> 8203
nic ardi 8203
pl ague</w> 8202
am yl</w> 8202
let tuce</w> 8200
C t 8199
c itation</w> 8199
hyper coagul 8199
az ido</w> 8199
PE D</w> 8199
innerv ating</w> 8199
asi an</w> 8198
NE P</w> 8198
om imetic</w> 8197
Andro gen</w> 8196
Argin ine</w> 8196
un safe</w> 8195
heter ogenous</w> 8195
asc ial</w> 8195
Hoech st</w> 8195
3 78</w> 8194
heter ocy 8194
lib eration</w> 8194
Al bumin</w> 8193
enter ovirus</w> 8192
o c</w> 8191
pur inergic</w> 8191
ophen one</w> 8191
Red ox</w> 8191
hymen a</w> 8191
A spects</w> 8190
form ative</w> 8190
2 5.2</w> 8189
4 H</w> 8189
proteas omal</w> 8189
sub sample</w> 8188
she aths</w> 8188
Cy A</w> 8188
3 62</w> 8187
T in 8187
Ne f</w> 8187
my oblast</w> 8186
imid azo</w> 8186
M ood</w> 8185
Mut ants</w> 8185
g B</w> 8184
cit alopram</w> 8184
O PD</w> 8183
angi oedema</w> 8183
Behavi ors</w> 8183
Ec ological</w> 8183
reat ment</w> 8182
UC B</w> 8182
thre ad</w> 8181
Cy cle</w> 8181
O O</w> 8180
Tran s</w> 8180
ellul osic</w> 8180
hypop arathyroidism</w> 8180
calcane al</w> 8180
is omer 8179
anteced ent</w> 8179
mesenter y</w> 8179
hepatocarcin ogenesis</w> 8179
H NC</w> 8178
d iterpen 8178
T 5</w> 8178
leg es</w> 8178
tex t 8178
hip pur 8178
3 46</w> 8177
B os 8177
mos sy</w> 8177
obac illi</w> 8177
L DA</w> 8176
m um</w> 8175
mimic ry</w> 8175
osi ties</w> 8174
psych opathological</w> 8173
fabr ic</w> 8173
Myel oid</w> 8173
di imide</w> 8172
cin ere 8172
instruc tional</w> 8172
HCT1 16</w> 8172
k at 8171
til age</w> 8171
supr atentorial</w> 8171
Phosph ate</w> 8171
G v 8170
E stradiol</w> 8170
CR s</w> 8170
zwit terionic</w> 8170
Plac ebo</w> 8169
4 G</w> 8167
ten ascin</w> 8167
c in</w> 8166
val erate</w> 8166
Orig in</w> 8165
3 34</w> 8164
Bi omedical</w> 8164
Vi olence</w> 8164
phosphati dyl</w> 8162
alli ance</w> 8162
M OR</w> 8161
6 40</w> 8160
su percritical</w> 8160
Chit osan</w> 8160
st un 8159
ell i</w> 8159
thyro idal</w> 8158
nano structure</w> 8158
extrap ulmonary</w> 8158
disap pointing</w> 8158
n ong 8157
Virgin ia</w> 8156
U sed</w> 8155
un conjugated</w> 8154
str aw 8154
HC Ps</w> 8154
ir aglutide</w> 8153
DO PAC</w> 8153
PH Q</w> 8153
in omycin</w> 8152
otax in</w> 8152
x ylo 8151
li ers</w> 8151
nitr ates</w> 8151
Electro cardi 8151
Embry onic</w> 8150
micronutri ents</w> 8149
C ocaine</w> 8148
nicardi pine</w> 8147
ap re 8146
Apoli poprotein</w> 8146
- 5. 8145
si zing</w> 8145
extr uded</w> 8145
ensi bility</w> 8145
phyl a</w> 8144
PGF 1</w> 8144
A bility</w> 8143
4 1.7</w> 8143
inter sec 8143
V an</w> 8141
programm able</w> 8141
mano eu 8141
L abor 8139
rop in</w> 8139
CI P</w> 8139
L aw</w> 8138
bl ended</w> 8138
Neuro imaging</w> 8138
p ag 8137
B CP</w> 8136
CD T</w> 8136
En tam 8136
toxic ants</w> 8136
tryp tase</w> 8135
Cul tural</w> 8135
P is 8134
vul va</w> 8134
compar ability</w> 8133
e k 8132
optim ised</w> 8132
I owa</w> 8131
tal us</w> 8129
Biom echanical</w> 8129
b um 8128
STI s</w> 8128
5 5.6</w> 8127
thri ve</w> 8127
spondyl olisthesis</w> 8127
vasodil atory</w> 8127
M MS</w> 8126
tri phenyl 8126
Char lson</w> 8126
line zolid</w> 8126
radic ulopathy</w> 8126
Recru itment</w> 8126
Neu tr 8125
IC Cs</w> 8124
ethyl amine</w> 8124
g y 8123
c s 8122
induc ibility</w> 8122
lenti virus</w> 8122
3 56</w> 8121
Ca T</w> 8121
papill omas</w> 8121
L K 8120
PD B</w> 8120
ro ads</w> 8119
SIGN ED</w> 8119
inter hemispheric</w> 8118
proc urement</w> 8118
Nor dic</w> 8118
Depend ence</w> 8118
Dra wing</w> 8118
hal lux</w> 8117
ro of</w> 8116
od ors</w> 8116
In sp 8116
abro ad</w> 8116
remedi es</w> 8116
en rich</w> 8115
Pro files</w> 8115
WI TH</w> 8114
B q</w> 8113
ori ous</w> 8113
sulf onic</w> 8113
olip oma</w> 8113
intox icated</w> 8113
acclim ated</w> 8113
I SR</w> 8112
del icate</w> 8112
altern ation</w> 8112
ensembl es</w> 8112
am pul 8111
Par ac 8111
rheumat ology</w> 8111
deuter ated</w> 8111
2. 10</w> 8109
super numerary</w> 8108
dor some 8107
Hous e 8107
erythemat ous</w> 8106
Can ine</w> 8105
V 600 8104
cra yfish</w> 8104
W ell 8103
inf un 8103
neutr alize</w> 8103
B3 LYP</w> 8102
photovolta ic</w> 8102
lob ule</w> 8101
win ding</w> 8101
Am mon 8100
rec A</w> 8098
heter o</w> 8098
cd c2</w> 8098
super conducting</w> 8097
Ly t</w> 8097
tangen tial</w> 8097
Entam oeba</w> 8097
C X</w> 8095
AF S</w> 8095
Aer obic</w> 8095
n ate</w> 8094
3 85</w> 8094
neuro anatomical</w> 8091
Mob ility</w> 8091
microscop ical</w> 8091
fic ally</w> 8090
but aline</w> 8090
orbit als</w> 8090
sigmo id 8090
t RN 8089
mu M</w> 8089
aminopyr idine</w> 8089
un employed</w> 8088
0.9 9 8088
armam entarium</w> 8088
Sec urity</w> 8087
ulf ite</w> 8087
hsp 70</w> 8087
ful l 8086
MM N</w> 8086
ati gu 8085
fol iar</w> 8085
conform ity</w> 8085
BR CA</w> 8085
MI M</w> 8084
40 ,000</w> 8084
multim orbidity</w> 8083
d ence</w> 8082
ex osome</w> 8082
electroph ilic</w> 8080
derang ement</w> 8080
AS SIGNED</w> 8078
A PCs</w> 8077
de ma</w> 8077
AP E</w> 8076
rever ted</w> 8076
nid ulans</w> 8076
mercapto ethanol</w> 8076
a den</w> 8075
elic itation</w> 8075
care ers</w> 8075
PH B</w> 8073
R p</w> 8072
par valbumin</w> 8072
exer ting</w> 8072
in accessible</w> 8071
Gyn ecology</w> 8071
revolution ized</w> 8071
con cave</w> 8070
is omeric</w> 8070
aw aiting</w> 8070
arteri osclerosis</w> 8070
hi o</w> 8070
NT M</w> 8069
ren yl</w> 8068
bl ack 8068
N od 8067
ar rests</w> 8067
radi oluc 8067
aor to 8067
2 .</w> 8066
plas ti 8066
Fi xation</w> 8066
Vol tage</w> 8066
D raft</w> 8065
ap ride</w> 8065
irrig ated</w> 8065
DN R</w> 8064
Step wise</w> 8064
vari ably</w> 8063
pec toral</w> 8063
but ton</w> 8062
1 T</w> 8061
clu b</w> 8061
macro scopically</w> 8061
homogene ously</w> 8061
Hemorrh age</w> 8061
under studied</w> 8060
comp assion</w> 8060
bur sa</w> 8060
RA ST</w> 8060
3 58</w> 8059
T y 8059
bi ocat 8059
ML L</w> 8059
I DE 8058
en o</w> 8058
characteris tically</w> 8058
idi otype</w> 8058
GIST s</w> 8058
happ ens</w> 8058
infec ts</w> 8057
wo ven</w> 8057
CT G</w> 8056
mus sel</w> 8055
Bo NT</w> 8055
logarith m</w> 8055
Transl ation</w> 8052
filari asis</w> 8052
transl ationally</w> 8051
apro tinin</w> 8051
Ander son</w> 8051
Rec A</w> 8048
chron icity</w> 8048
CE TP</w> 8048
2. 01</w> 8047
TI MI</w> 8047
bacter aemia</w> 8047
d amping</w> 8046
M ot 8046
60 9</w> 8046
29. 9</w> 8046
gg le</w> 8045
v ille</w> 8044
ass ment</w> 8044
SC M</w> 8044
Tetra hymena</w> 8044
TF II 8043
off ending</w> 8042
var ial</w> 8042
Q X</w> 8041
9 7. 8040
osulf ate</w> 8040
F AB</w> 8039
Provid ers</w> 8039
symb ol</w> 8039
certific ates</w> 8039
2 7.7</w> 8038
K at 8038
am acrine</w> 8038
calc i 8037
necess itated</w> 8037
blast omas</w> 8037
MI B</w> 8036
Ar ach 8035
inclin ed</w> 8035
DL B</w> 8034
G AT</w> 8033
oscill ating</w> 8032
suic idality</w> 8031
P od 8030
carboxy methyl</w> 8030
fellow s</w> 8030
anteri orly</w> 8030
L PC</w> 8029
ap ne 8029
audi ence</w> 8029
Lepid optera</w> 8029
fic ient</w> 8028
react ants</w> 8028
Endoth elin</w> 8028
re pulsion</w> 8027
de w</w> 8027
V NS</w> 8026
lu x 8026
log MAR</w> 8026
F ace</w> 8025
tonsi l</w> 8024
optim isation</w> 8023
deli vers</w> 8023
F uk 8022
im men 8021
mul toc 8020
a ea</w> 8019
R Rs</w> 8019
Algorith m</w> 8019
Charg e</w> 8019
L B 8018
par af 8018
eph edrine</w> 8018
vesi cou 8018
BD E</w> 8018
id o 8017
EM D</w> 8017
Ha pl 8017
L TA</w> 8016
Tran spor 8016
conv ection</w> 8016
discover ing</w> 8016
6 60</w> 8015
5 10</w> 8015
M u</w> 8015
PRIN CIPA 8015
O le 8014
expect ancies</w> 8014
M PH</w> 8013
pr ism</w> 8013
carbox amide</w> 8013
hyd ati 8012
s wee 8011
SAM PLE</w> 8010
heur istic</w> 8010
chool ers</w> 8010
phlo em</w> 8009
out look</w> 8008
aggra v 8008
un equivocal</w> 8007
AR B</w> 8007
-0. 2</w> 8007
S SD</w> 8006
ifor ms</w> 8006
tropic alis</w> 8006
mo fetil</w> 8005
2. 13</w> 8005
CXCL 12</w> 8005
wh ol 8004
Pro duct</w> 8004
Under going</w> 8004
Inter val</w> 8003
2. 14</w> 8002
sex -</w> 8002
arch ival</w> 8002
Rac ial</w> 8002
un even</w> 8001
sub urban</w> 8001
nov ice</w> 8001
valu ation</w> 8000
detec tability</w> 7999
cal ifor 7999
ophyl l</w> 7999
g ate 7998
ren ovascular</w> 7998
ste wardship</w> 7998
go od 7998
C ode</w> 7997
pap averine</w> 7997
justi fication</w> 7997
feat ured</w> 7997
6 1.5</w> 7996
serov ars</w> 7996
i vir</w> 7995
Ex tra 7995
amb er</w> 7995
oro lac</w> 7995
radi i</w> 7994
bo id</w> 7994
Ver tical</w> 7994
air craft</w> 7993
4 23</w> 7992
Ex change</w> 7992
p ens</w> 7989
end owed</w> 7989
depolar izations</w> 7989
fin s</w> 7988
attrib ution</w> 7988
multoc ida</w> 7988
PRINCIPA L</w> 7988
Amon gst</w> 7987
squ e 7987
IgG 3</w> 7987
wild type</w> 7987
V NTR</w> 7985
stand point</w> 7985
p EF</w> 7984
de hydrated</w> 7984
cor ona</w> 7984
Cul ic 7984
D oses</w> 7983
colon ize</w> 7983
chel ators</w> 7983
predisp oses</w> 7982
PD Z</w> 7981
HI V 7981
bottlen eck</w> 7981
Haz ard</w> 7981
branch ial</w> 7981
Dis charge</w> 7980
Inte rest</w> 7980
VL BW</w> 7980
Tib etan</w> 7980
3 88</w> 7979
4 C</w> 7979
predomin ate</w> 7979
pedi cled</w> 7979
S ports</w> 7978
F AB 7978
t all</w> 7977
av ement</w> 7976
DD E</w> 7976
azo ospermia</w> 7975
APPRO ACH</w> 7975
tr il 7973
23 S</w> 7973
P ed 7972
lact obacilli</w> 7972
MB F</w> 7972
F NAC</w> 7971
2. 75</w> 7971
electro convulsive</w> 7971
ket anserin</w> 7971
3 68</w> 7970
igh ted</w> 7970
P edi 7969
S ites</w> 7969
refrac toriness</w> 7969
legis l 7969
schwann omas</w> 7969
W heat</w> 7968
Assess ments</w> 7967
pent ag 7967
post natally</w> 7966
scaff olding</w> 7966
SRE BP</w> 7966
ocl avicular</w> 7965
5 3. 7964
Mon tre 7964
flui dics</w> 7964
ad ductor</w> 7963
UN ASSIGNED</w> 7963
mid point</w> 7961
EM F</w> 7961
2. 12</w> 7960
al ise</w> 7959
arri ved</w> 7958
LY 294002</w> 7958
sett lement</w> 7958
r udi 7957
star s</w> 7956
perfor in</w> 7956
m ir</w> 7955
be rel 7955
CA R 7955
HA DS</w> 7955
micro satellites</w> 7953
des erve</w> 7953
Sh ape</w> 7953
spher oid</w> 7953
bu oy 7952
cerul oplasmin</w> 7952
D PI</w> 7951
Gn R 7951
C US</w> 7949
pri t</w> 7949
H air</w> 7948
T AR</w> 7948
im perfect</w> 7948
PR V</w> 7948
Z ea</w> 7947
bi ventricular</w> 7947
Br agg</w> 7947
Hed gehog</w> 7947
w iring</w> 7946
dec oction</w> 7946
arthro pods</w> 7946
nu ts</w> 7945
ut a</w> 7944
xen on</w> 7944
Plat form</w> 7944
Int act</w> 7944
daun orubicin</w> 7943
A pl 7942
chi 2</w> 7942
gu ide 7941
SO FA</w> 7941
fl u</w> 7939
ment oring</w> 7939
Nat l</w> 7939
ST EM</w> 7938
alk ylated</w> 7938
convers ations</w> 7938
H h</w> 7937
homogen ization</w> 7937
2 4.9</w> 7936
mortal ities</w> 7936
Eim eria</w> 7936
3, 4,5</w> 7933
str ings</w> 7932
obac tam</w> 7932
POPU LATION</w> 7932
olym phatic</w> 7931
pector alis</w> 7931
4 V</w> 7930
J s</w> 7930
al d 7930
arte facts</w> 7930
oste oid</w> 7929
her m 7929
accum ulations</w> 7929
PV L</w> 7929
Fal se</w> 7928
B cl 7927
0.0 30</w> 7927
loc alizations</w> 7927
fil tr 7927
gam etes</w> 7927
gener ators</w> 7925
spea ker</w> 7925
V RE</w> 7924
SI N</w> 7924
osteoclast ogenesis</w> 7924
E o 7923
fir st-</w> 7923
De sign 7923
aff ection</w> 7922
non verbal</w> 7922
PF O</w> 7922
L ati 7921
N RT</w> 7921
ap hor 7921
ster ically</w> 7921
synap tophysin</w> 7921
age able</w> 7921
L ine 7919
hos tility</w> 7919
exam ic</w> 7918
L amb 7917
gl acial</w> 7917
aut opsies</w> 7916
28. 9</w> 7916
anis h 7915
thyro toxicosis</w> 7915
1 d</w> 7914
b anded</w> 7914
al endronate</w> 7914
organ ophosphate</w> 7914
subtil isin</w> 7914
bleph aro 7914
- 2-</w> 7913
col or 7912
mac ul 7912
Tim ing</w> 7912
o plankton</w> 7911
F US</w> 7911
cTn T</w> 7910
O hio</w> 7909
e h 7908
pa ve</w> 7908
ser traline</w> 7908
conduc tances</w> 7908
gly phosate</w> 7908
3 63</w> 7907
p ond</w> 7907
IV M</w> 7907
amni os</w> 7907
interpol ation</w> 7906
abs orb</w> 7905
hal o 7905
Ob served</w> 7905
f o</w> 7904
r us</w> 7903
ang inal</w> 7903
ca vit 7903
ol ian</w> 7902
resi stin</w> 7902
aph y</w> 7902
tachy arrhythmias</w> 7902
refuge e</w> 7901
double t</w> 7901
μ l</w> 7900
la den</w> 7899
Asc aris</w> 7899
degrad es</w> 7897
scintil lation</w> 7897
Abo ve</w> 7896
Res ult</w> 7895
issu res</w> 7895
6 25</w> 7894
ever sion</w> 7894
shell fish</w> 7894
H ib</w> 7893
p 13</w> 7893
ap ath 7893
cy l</w> 7893
3 82</w> 7891
M ess 7891
mesh work</w> 7891
STATISTI CAL</w> 7891
di zation</w> 7890
nucle oprotein</w> 7890
envel oped</w> 7890
sphincter otomy</w> 7890
L yn 7889
osyn aptic</w> 7889
sou thwestern</w> 7889
incarcer ated</w> 7889
P ec 7888
D X</w> 7888
meticul ous</w> 7888
O PC</w> 7885
PK U</w> 7885
l atissimus</w> 7884
inter spersed</w> 7884
cas e 7884
aren a</w> 7883
2 7.2</w> 7882
g luteal</w> 7882
elec ted</w> 7882
top yran 7882
J E</w> 7881
vor iconazole</w> 7880
Ptd Ins</w> 7879
R ome</w> 7878
co expressed</w> 7878
inform ational</w> 7877
angiotens inogen</w> 7877
in coming</w> 7876
prison ers</w> 7876
G ray</w> 7875
e ed</w> 7875
F T 7875
methyl glutaryl</w> 7875
A GS</w> 7874
con genic</w> 7874
Z NF 7873
der gar 7873
hal ves</w> 7873
calcin osis</w> 7873
mit ophagy</w> 7872
neurop il</w> 7872
q 23</w> 7871
Venez uel 7871
4 12</w> 7870
EB RT</w> 7870
gr ant</w> 7869
colpos copy</w> 7868
D a 7867
hot spot</w> 7867
cortic o</w> 7866
pra vastatin</w> 7866
tis ation</w> 7865
Exc ess</w> 7865
-- in</w> 7864
Sec tional</w> 7864
aster ide</w> 7864
T TF</w> 7862
for tification</w> 7862
dec tomy</w> 7862
al pine</w> 7861
ag ly 7861
loc ating</w> 7861
substanti ate</w> 7861
intellig ibility</w> 7861
bl es 7858
Initi ation</w> 7858
N ox 7857
recti fier</w> 7857
l agged</w> 7855
Over weight</w> 7855
We in 7855
P2 Y</w> 7855
Fibr illation</w> 7855
3 52</w> 7854
Ch o</w> 7854
Pre ferred</w> 7854
RD W</w> 7854
Montre al</w> 7854
m assi 7853
form alism</w> 7853
2.0 9</w> 7853
under lines</w> 7852
AM C</w> 7852
repl en 7852
di peptidyl</w> 7851
stear ic</w> 7851
w ines</w> 7849
trans ected</w> 7849
electro mechanical</w> 7849
eti opathogenesis</w> 7849
phy sis</w> 7849
B SP</w> 7847
sub thalamic</w> 7847
big ger</w> 7847
Stri kingly</w> 7847
comp ly</w> 7846
phosphor ic</w> 7846
deoxynucleoti dyl</w> 7846
injur ious</w> 7845
1, 2,3</w> 7844
Le sion</w> 7844
CI T</w> 7844
destin ation</w> 7844
3 96</w> 7843
un ya</w> 7843
ran ks</w> 7843
4 68</w> 7842
ev al</w> 7842
D BT</w> 7841
hypo kalemia</w> 7841
phyl lo 7841
Sep arate</w> 7841
E po 7840
Di verse</w> 7840
dec ol 7839
hyper thermic</w> 7839
para haemolyticus</w> 7839
SC O 7838
flic k</w> 7838
retar d 7838
Morph ometric</w> 7837
y an 7836
hyper reactivity</w> 7836
Sp in 7836
apoli poproteins</w> 7835
v end 7834
M ap</w> 7834
F ear</w> 7833
ab alin</w> 7833
Cyt omegalovirus</w> 7833
gro oming</w> 7832
den uded</w> 7832
Over view</w> 7832
HSP 90</w> 7832
dig ests</w> 7831
hel met</w> 7830
decon volution</w> 7830
3 72</w> 7829
U TIs</w> 7829
rec ur</w> 7828
aden omy 7828
chi tinase</w> 7828
oc iliary</w> 7827
meth ox 7827
id ers</w> 7826
PD D</w> 7826
sc ant</w> 7825
ampl ifying</w> 7825
2 d</w> 7824
en ings</w> 7824
re entrant</w> 7823
lim bal</w> 7823
uro logists</w> 7822
s-- a</w> 7822
LM ICs</w> 7822
pill ar</w> 7822
md x</w> 7821
3 66</w> 7820
adren om 7820
te thering</w> 7819
tum orous</w> 7818
ven e 7817
antin uclear</w> 7817
tonsi ls</w> 7817
pancreatico duodenectomy</w> 7817
l adder</w> 7815
re entry</w> 7814
ocy steine</w> 7814
Qu ad 7814
fec ture</w> 7814
sympath ectomy</w> 7814
N if 7813
max ill 7813
enox aparin</w> 7813
ic idin</w> 7811
ich thy 7811
photo acoustic</w> 7811
clar ifying</w> 7811
V600 E</w> 7811
ov ani</w> 7809
AF F</w> 7809
Nan os 7809
sulfon amide</w> 7809
J 2</w> 7808
Stud ying</w> 7808
amino ethyl</w> 7808
protr usions</w> 7808
weigh t 7807
synapt osomal</w> 7807
M id</w> 7806
eradic ated</w> 7806
Caffe ine</w> 7806
Gen omes</w> 7805
angl ement</w> 7805
teen age</w> 7805
ur so 7804
Method ological</w> 7804
communic ative</w> 7803
3 0.6</w> 7802
in born</w> 7802
SE R</w> 7802
Orth op 7802
H PT</w> 7801
down regulating</w> 7801
Pneum onia</w> 7801
chol anthrene</w> 7800
Schiz osaccharomyces</w> 7800
3 54</w> 7799
cere br 7799
ethyl ammonium</w> 7799
propag ate</w> 7799
Mer kel</w> 7799
ac ked</w> 7798
mon oton 7798
sev enteen</w> 7798
Mil itary</w> 7798
mang ro 7798
anaesthe tics</w> 7798
normal ity</w> 7797
handic apped</w> 7796
til apia</w> 7795
al ene</w> 7794
14 6a</w> 7794
N PH</w> 7793
later alized</w> 7793
Batter y</w> 7793
G EM</w> 7792
un structured</w> 7792
Li poprotein</w> 7792
Vir uses</w> 7792
ED SS</w> 7791
Muscul oskeletal</w> 7791
atelec tasis</w> 7791
5 4. 7790
ma ys</w> 7790
contain er</w> 7790
nymph s</w> 7788
amib i</w> 7788
CO 3</w> 7787
A SP 7786
s ell</w> 7786
CYP 3A</w> 7786
rot um</w> 7786
phen azine</w> 7785
Att achment</w> 7785
f use</w> 7784
Willi ams</w> 7784
spaw ning</w> 7784
Exec utive</w> 7784
manoeu v 7784
stere o</w> 7783
conve y</w> 7783
Mer cur 7782
ch ie 7781
all ant 7781
poly g 7781
1 100</w> 7780
5 80</w> 7780
an icol 7780
eng al</w> 7780
extr adural</w> 7780
iod ination</w> 7780
ap ol 7779
po ro 7778
I VA</w> 7777
Ba 2</w> 7777
condu its</w> 7777
opon tine</w> 7777
f lip</w> 7776
multi valent</w> 7776
chol elithiasis</w> 7776
hom icide</w> 7776
Eth nic</w> 7776
big gest</w> 7776
experi ential</w> 7775
spir ituality</w> 7772
Tes la</w> 7772
nim odipine</w> 7772
9 th</w> 7771
str ained</w> 7771
put atively</w> 7771
homocyste inemia</w> 7771
SC G</w> 7770
olim bic</w> 7769
mitochond ri 7768
mol . 7768
natri uresis</w> 7768
hom ologies</w> 7767
gon ococcal</w> 7767
Meas ured</w> 7767
Vas cul 7767
pent oxifylline</w> 7766
In trig 7765
mes ylate</w> 7765
28. 2</w> 7765
micro angiopathy</w> 7764
RO Is</w> 7764
O ff</w> 7763
enc ycl 7763
CD A</w> 7763
syn tactic</w> 7762
DI s</w> 7762
AR P</w> 7761
re volution</w> 7760
CCA AT</w> 7759
Ex tract</w> 7758
Appl ied</w> 7758
Commerc ial</w> 7758
oph aryn 7757
Impro ves</w> 7757
o h 7756
ure mia</w> 7756
Glc NAc 7756
deriv es</w> 7755
HF pEF</w> 7755
trif lu 7755
vol ar</w> 7754
purch ased</w> 7754
y x 7753
di aries</w> 7753
transcriptom es</w> 7752
re med 7751
sub regions</w> 7751
X e</w> 7750
re inst 7750
novi al</w> 7750
punc tate</w> 7750
S HR 7749
mit oxantrone</w> 7749
glass y</w> 7749
3 81</w> 7748
inter rater</w> 7748
SN ARE</w> 7748
macro cyclic</w> 7748
con i</w> 7747
wal ked</w> 7747
SV C</w> 7747
Multi drug</w> 7746
dendr ite</w> 7746
V in 7745
ofur an 7745
N MMA</w> 7744
organis ed</w> 7744
osy nostosis</w> 7744
nanocarri ers</w> 7744
com post</w> 7743
leach ate</w> 7743
ness ing</w> 7742
A gon 7741
phen anthrene</w> 7741
enop tera</w> 7740
agg rec 7739
Cr uc 7739
Adju stment</w> 7739
prognos es</w> 7739
S f 7738
X pert</w> 7738
ren es</w> 7738
insuff lation</w> 7738
muc ins</w> 7737
to ad</w> 7736
multi channel</w> 7736
Usu ally</w> 7736
5 7. 7734
E J</w> 7734
don ovani</w> 7734
N ations</w> 7733
IC V</w> 7733
micro structures</w> 7732
Per sian</w> 7732
HM W</w> 7732
un itary</w> 7731
immuno therapies</w> 7729
AR M</w> 7729
aqu ine</w> 7729
fove a</w> 7729
i demic</w> 7728
cyt arabine</w> 7728
fl ying</w> 7728
ann els</w> 7728
Sh ock</w> 7728
contamin ating</w> 7728
pa yer</w> 7727
antigen ically</w> 7727
I TC</w> 7725
F asci 7725
stabil izer</w> 7725
dil ection</w> 7725
P u</w> 7724
D OR</w> 7724
Re fer 7724
NL S</w> 7724
Socio economic</w> 7724
surviv als</w> 7723
al anyl</w> 7722
anti emetic</w> 7722
pregn enolone</w> 7722
fibro ids</w> 7722
O HCA</w> 7721
narrow ed</w> 7721
iform es</w> 7720
os pores</w> 7719
AC E2</w> 7719
PS s</w> 7719
lanth anide</w> 7719
3 83</w> 7718
ot gun</w> 7718
rid ges</w> 7718
M arg 7717
inter chang 7717
2. 17</w> 7717
Ex clusion</w> 7717
su it</w> 7716
PC r</w> 7716
some one</w> 7716
tetram eric</w> 7716
pro ficient</w> 7715
alc ogen 7714
3 M</w> 7713
ill ic</w> 7712
AT C</w> 7712
distr actor</w> 7712
ibr ated</w> 7712
Sel enium</w> 7711
v ault</w> 7710
AF B</w> 7710
bacteri ocin</w> 7710
cyclohex ane</w> 7710
F 9</w> 7709
on colytic</w> 7709
og astro 7709
mis folded</w> 7709
poly ester</w> 7709
conver sation</w> 7708
adren als</w> 7708
inter species</w> 7707
ey er</w> 7707
swim mers</w> 7707
wo ody</w> 7706
Mut ational</w> 7706
en il</w> 7705
ven ography</w> 7705
ung unya</w> 7705
stru ggle</w> 7705
oyst er</w> 7705
Lank a</w> 7705
ant al</w> 7704
grad ely</w> 7704
Ig D</w> 7704
O reg 7703
Th o 7703
he i</w> 7703
chr y 7703
mel on</w> 7703
NO x</w> 7703
advoc ates</w> 7703
oc entric</w> 7702
acces sion</w> 7702
O 2 7701
minim ise</w> 7701
chec ks</w> 7701
C EC</w> 7700
sub division</w> 7700
NC C</w> 7700
prot otypical</w> 7700
T XA</w> 7699
8 A</w> 7698
4 11</w> 7698
ME TH</w> 7698
lymph atics</w> 7698
reproduc ibly</w> 7698
interven e</w> 7698
Con flic 7697
ger man 7697
nc RNA</w> 7697
S LI 7696
IC a</w> 7696
Quanti tation</w> 7696
leptosp irosis</w> 7696
Res veratrol</w> 7695
Nan oc 7695
T MT</w> 7692
PON 1</w> 7692
anaerob es</w> 7692
S DM</w> 7691
K ATP</w> 7691
ca red</w> 7691
VL Ps</w> 7691
neighbour hood</w> 7691
B ed 7690
Sar co 7690
G Pa</w> 7689
3 1.8</w> 7689
DN s</w> 7689
trin itro 7689
supr an 7688
myo epithelial</w> 7688
AQ P4</w> 7688
t 2</w> 7687
5 3.3</w> 7687
par allel 7687
5 5. 7686
An ten 7686
promis cu 7686
osyl -</w> 7686
open ings</w> 7686
pre mise</w> 7685
Pre mature</w> 7685
flur amine</w> 7685
MW CNTs</w> 7685
undoub tedly</w> 7685
P an</w> 7683
9 7.5</w> 7683
Pub med</w> 7683
A do 7682
n ation 7681
4 9. 7681
al oric</w> 7681
arabin ose</w> 7681
re feeding</w> 7679
cf DNA</w> 7679
dorsome dial</w> 7679
f atigu 7678
Ch ir 7678
prioriti zation</w> 7678
polypo id</w> 7678
hyperpolar izing</w> 7678
D aph 7677
embran ous</w> 7677
G onad 7676
CH A</w> 7676
aph thal 7675
G ER</w> 7674
oph ile</w> 7674
Path ogenic</w> 7674
aer o 7673
CL 1</w> 7672
Com mon 7672
On cor 7672
emp fer 7672
jur y</w> 7671
inter ferences</w> 7670
AD G</w> 7670
inter group</w> 7669
P X 7667
bet amethasone</w> 7667
PK R</w> 7667
upreg ulates</w> 7667
prefer ring</w> 7667
h 1</w> 7666
29. 3</w> 7666
tough ness</w> 7666
ir amate</w> 7663
2. 35</w> 7663
Echocardi ographic</w> 7663
T issues</w> 7662
restr aints</w> 7661
n um 7660
V LA</w> 7660
alc ium</w> 7660
H art 7659
r umination</w> 7659
W AF1</w> 7659
CM D</w> 7659
Emplo ying</w> 7659
monophy letic</w> 7659
ro ten 7658
ear um</w> 7658
S ear 7657
An atomic</w> 7657
canal icular</w> 7657
5 8.3</w> 7656
AG T</w> 7656
para ox 7655
oc aly 7654
comp eted</w> 7654
T EM 7653
non polar</w> 7653
Down regulation</w> 7653
remin der</w> 7652
D CD</w> 7651
bot anical</w> 7651
catech in</w> 7651
labyrin th 7651
G d 7650
3 47</w> 7650
h aptic</w> 7650
pro polis</w> 7650
per ineural</w> 7650
TL D</w> 7650
P HT</w> 7649
c anti 7649
I onic</w> 7649
V R 7649
Fac ebook</w> 7649
CD 10</w> 7648
crani oc 7648
bro aden</w> 7647
disco ideum</w> 7647
Instrum ent</w> 7647
E PSPs</w> 7646
ther anos 7645
trans well</w> 7645
ath on</w> 7645
az osin</w> 7645
Su peroxide</w> 7645
Surviv ors</w> 7645
G Y 7644
et uses</w> 7643
SR H</w> 7643
un coated</w> 7642
men th 7641
macro lides</w> 7641
cannabin ol</w> 7640
obtur ator</w> 7640
vo rous</w> 7639
spin ous</w> 7639
14 th</w> 7638
nas ogastric</w> 7638
Re ading</w> 7637
ul is</w> 7636
Intr al 7636
2 7.1</w> 7635
S cor 7635
spac ers</w> 7635
28. 1</w> 7635
FI M</w> 7634
osy stemic</w> 7634
-0. 3</w> 7633
oli pos 7633
nanoc lu 7633
repeat able</w> 7633
Ang II</w> 7633
don ia</w> 7632
nephro toxic</w> 7631
Mar yland</w> 7631
Tot ally</w> 7631
M P2</w> 7630
5 71</w> 7629
ap 1</w> 7629
prov oc 7629
oc la 7628
BD V</w> 7628
chie fly</w> 7628
correl ative</w> 7627
oth rom 7627
Establ ishing</w> 7627
D r</w> 7626
di fluoro 7626
PR K</w> 7626
Com orbidity</w> 7625
stimul ators</w> 7625
4 21</w> 7624
2. 11</w> 7624
seque stered</w> 7624
tic ula</w> 7623
ari ous</w> 7623
Tw in</w> 7623
ophysi ology</w> 7623
Arab ic</w> 7623
W all</w> 7622
acetyl ene</w> 7622
scienti fically</w> 7622
pren eo 7622
28. 4</w> 7622
a ve</w> 7621
ed ited</w> 7621
cou ghing</w> 7621
MM P9</w> 7621
ar med</w> 7620
4 48</w> 7619
par ties</w> 7619
chin ensis</w> 7619
rest aur 7618
00 01</w> 7618
AS O</w> 7617
robo ts</w> 7617
attrac ting</w> 7616
knowled ge 7615
- 14</w> 7614
oph ot 7614
cyt ogenetics</w> 7614
glyc opeptide</w> 7614
SR F</w> 7614
promin ence</w> 7614
3 67</w> 7613
curv il 7613
optim ise</w> 7612
restric ts</w> 7612
dendrim ers</w> 7612
idi zed</w> 7611
eli i</w> 7611
stere otyped</w> 7611
St aff</w> 7610
Acet yl 7609
sensiti ze</w> 7608
k head</w> 7607
pre dilection</w> 7607
ra emia</w> 7607
Tri ticum</w> 7607
neuro development</w> 7606
PE M</w> 7606
Fr uc 7606
hind brain</w> 7606
G HD</w> 7605
viol ation</w> 7605
alve oli</w> 7605
Inc id 7605
re operations</w> 7604
Contro ver 7603
ophthalm ologic</w> 7603
correl ational</w> 7602
Re activity</w> 7602
overex press</w> 7601
osensi tive</w> 7601
tit les</w> 7600
Pap anicol 7600
underpin nings</w> 7599
3 74</w> 7598
run g</w> 7598
Stro mal</w> 7598
enc ement</w> 7596
EC F</w> 7596
pharmac ophore</w> 7596
fo etus</w> 7595
Immun op 7595
otroph oblast</w> 7595
S taining</w> 7594
fl ud 7594
deoxynucle otides</w> 7594
sem is 7593
Emph asis</w> 7593
I DH</w> 7592
ch al</w> 7591
em ig 7591
multi scale</w> 7591
CD 23</w> 7591
recti fying</w> 7591
St ages</w> 7590
Lymph ocytes</w> 7590
or tical</w> 7589
pa use</w> 7589
leg umes</w> 7589
Bel gian</w> 7589
astro glial</w> 7588
adsorb ents</w> 7588
sett led</w> 7588
hydr amnios</w> 7587
stereo chemistry</w> 7587
2 L</w> 7586
3 5.3</w> 7586
de afferen 7586
tuber cular</w> 7586
ta ut 7584
TUR P</w> 7584
Anesthesi ologists</w> 7584
2 7.4</w> 7583
P inus</w> 7583
por tr 7583
gr asping</w> 7583
Ab bot 7583
AS s</w> 7583
inform ant</w> 7583
chimpanze e</w> 7583
short ness</w> 7582
in i 7581
til ted</w> 7581
a ou</w> 7580
in ator</w> 7580
dic arboxylic</w> 7580
scru tiny</w> 7580
Me eting</w> 7579
E DL</w> 7578
exchang eable</w> 7578
spec ifying</w> 7575
ME A</w> 7575
EX TR 7575
str ia</w> 7574
In domethacin</w> 7574
dysmorph ic</w> 7574
2. 50</w> 7573
1, 2,3, 7573
epist axis</w> 7572
IF T</w> 7571
decom posed</w> 7571
2 m</w> 7570
peg ylated</w> 7569
os e 7568
Ne igh 7568
Smad 2</w> 7568
U k 7567
Al umin 7567
CA I</w> 7567
G angli 7566
otox igenic</w> 7566
4 0.5</w> 7565
micro cos 7565
osseo integration</w> 7565
nem atic</w> 7564
t yro 7563
dec ellul 7563
lacun ar</w> 7563
D CP</w> 7562
Ne eds</w> 7562
D PP 7561
disc arded</w> 7561
inte restingly</w> 7561
anti pyrine</w> 7560
dy sen 7560
a ison</w> 7559
B H4</w> 7559
ro toxin</w> 7559
psych ogenic</w> 7559
glyco conjugates</w> 7559
hyp nosis</w> 7558
Cr ude</w> 7558
lute a</w> 7558
sacchar in</w> 7558
N erv 7557
psych ic</w> 7557
ID O</w> 7557
2. 16</w> 7556
substitu ting</w> 7556
DI S</w> 7556
semicir cular</w> 7556
3 77</w> 7555
I ON 7555
mat a</w> 7555
pl antation</w> 7554
br it 7554
PM s</w> 7554
depl eting</w> 7554
pin ch</w> 7554
3 6.8</w> 7553
4 3.5</w> 7553
Inser tion</w> 7553
Endos copy</w> 7552
amplic ons</w> 7552
Trans well</w> 7551
y me 7550
- 1-</w> 7550
car vedilol</w> 7550
hyper secretion</w> 7550
reconstruc ting</w> 7550
LOG ICAL</w> 7550
perfec ta</w> 7549
li m</w> 7548
otyp y</w> 7548
B ER</w> 7547
al together</w> 7547
2. 45</w> 7547
BM s</w> 7547
conven i 7547
digesti ble</w> 7547
- H</w> 7546
pon ds</w> 7546
Aggreg ation</w> 7546
V OR</w> 7545
om ectomy</w> 7544
F SS</w> 7542
por a</w> 7542
r amp</w> 7541
e Health</w> 7541
cul prit</w> 7541
.00 6</w> 7541
Aff ect</w> 7541
ophil es</w> 7540
Ha CaT</w> 7540
R equi 7539
S tom 7538
on er</w> 7538
atr e</w> 7538
am idase</w> 7537
rhe ology</w> 7537
F os 7536
inter s</w> 7535
haem agglutinin</w> 7535
resur facing</w> 7535
Y b</w> 7534
Pro be</w> 7534
N 9</w> 7533
ul na</w> 7533
un remarkable</w> 7533
Det ecting</w> 7533
hin dr 7533
Pla que</w> 7532
co d</w> 7531
av itary</w> 7531
IFN γ</w> 7531
5 51</w> 7530
til in</w> 7530
3 1.2</w> 7528
ut um</w> 7528
angi omy 7528
inev itably</w> 7528
ren z 7527
F RA 7526
no ti 7526
cre p 7525
ysi a</w> 7525
pati c</w> 7524
rs 1 7524
vide o 7524
bor reli 7523
a ena</w> 7522
excit otoxicity</w> 7522
tab ulated</w> 7522
immunosuppress ant</w> 7522
metabol ize</w> 7521
Upreg ulation</w> 7521
bin ders</w> 7520
3 0.3</w> 7519
ag am 7519
PS F</w> 7519
glucuron idation</w> 7519
myot onic</w> 7519
dec ayed</w> 7518
Plas tic</w> 7518
3 53</w> 7517
5 8. 7517
lev ator</w> 7517
Ca used</w> 7517
sulf ation</w> 7517
Po rous</w> 7517
did n</w> 7517
Nem at 7517
dist antly</w> 7516
ligh ter</w> 7516
C ol</w> 7515
insti lled</w> 7515
C GI</w> 7514
lab s</w> 7514
ole oyl</w> 7514
cholec alciferol</w> 7514
ram us</w> 7514
vibri o</w> 7514
imp ending</w> 7512
arrhyth mogenic</w> 7512
Li Cl</w> 7511
I cel 7510
mal ic</w> 7509
SW S</w> 7508
Difficul ties</w> 7508
lec tures</w> 7507
reticul ocytes</w> 7507
pum ped</w> 7507
normo thermic</w> 7507
P SM</w> 7506
og o</w> 7506
mig ra 7506
New born</w> 7506
F atal</w> 7505
- α</w> 7505
to es</w> 7505
Ix odes</w> 7505
7 7.8</w> 7504
s ory</w> 7504
leuc ovorin</w> 7504
Ca esarean</w> 7503
Trans fusion</w> 7503
Hydro x 7503
wet tability</w> 7503
3 95</w> 7502
co transport</w> 7502
PIK3 CA</w> 7502
Feed back</w> 7501
r yp 7500
co administration</w> 7500
typh i</w> 7500
E ll 7499
re implantation</w> 7498
fil trate</w> 7498
anesthesi ologists</w> 7498
oct yl</w> 7497
Thrombo sis</w> 7497
Psych ometric</w> 7495
neighb ors</w> 7495
mis classification</w> 7494
bor onic</w> 7494
Arsen ic</w> 7494
un ruptured</w> 7493
hydro lysates</w> 7493
psych oses</w> 7493
Per for 7493
horiz ontally</w> 7493
hyper kalemia</w> 7492
J ac 7491
re pository</w> 7491
scop ies</w> 7491
l itre</w> 7490
stri ol</w> 7490
lipop olysaccharides</w> 7490
homodi mer</w> 7490
p l</w> 7489
loc ality</w> 7489
anil ide</w> 7489
preclud ed</w> 7489
mor i</w> 7488
glyc osphing 7488
immun izations</w> 7487
umb ers</w> 7486
Mo tiv 7486
repar ative</w> 7486
GRP 78</w> 7486
Oncor hynchus</w> 7486
out liers</w> 7485
Ang le</w> 7485
3 2.4</w> 7484
injec t</w> 7484
S Ps</w> 7483
i ation</w> 7482
stabil isation</w> 7482
lib erated</w> 7482
ul ence</w> 7481
kil ogram</w> 7481
c rop 7480
J P</w> 7480
ex pres 7480
SP ME</w> 7480
F Q</w> 7479
Gu ide</w> 7479
kinetoch ore</w> 7478
Zimbab we</w> 7478
intran uclear</w> 7477
hypophy seal</w> 7477
acti le</w> 7476
gastr ulation</w> 7476
travel ing</w> 7476
guil t</w> 7476
ph ased</w> 7475
Par adox 7475
PV T</w> 7473
clock wise</w> 7473
V N</w> 7472
ven lafaxine</w> 7472
py ogenic</w> 7472
4 24</w> 7471
I RT</w> 7471
chlor pyrifos</w> 7471
thiop ental</w> 7471
Dri ven</w> 7470
ret te</w> 7469
3 1.4</w> 7468
- 50</w> 7468
ind oles</w> 7468
om p 7467
oper ons</w> 7467
vol can 7467
6 G</w> 7466
impro per</w> 7466
HM O</w> 7466
keto profen</w> 7466
Rheum atology</w> 7466
F RC</w> 7465
remed y</w> 7465
r iding</w> 7464
pre pro 7464
par alyzed</w> 7464
mening o 7464
Lif e 7464
tauro cholate</w> 7464
Intra ocular</w> 7463
ec ologic</w> 7461
Jord an</w> 7461
op ril</w> 7459
Predic t</w> 7459
R 4</w> 7457
Fer tility</w> 7457
obenz ene</w> 7457
dorsi flexion</w> 7456
is ob 7455
Op tic</w> 7455
3 89</w> 7454
NH 4 7454
Biomar ker</w> 7454
Cour se</w> 7454
ex tir 7453
9 5. 7451
ometr ical</w> 7451
cas ual</w> 7451
CP K</w> 7450
icul i</w> 7449
Su stain 7449
orb ide</w> 7448
Al ign 7447
RE s</w> 7446
je op 7446
lac eration</w> 7445
Consider ations</w> 7445
op neum 7444
icardi um</w> 7444
9 3.3</w> 7443
om ni 7443
ros ettes</w> 7443
edull in</w> 7443
per col 7442
ou l</w> 7442
Phot odynamic</w> 7442
par ab 7441
ortholog s</w> 7441
Ch annel</w> 7440
Ac upuncture</w> 7440
Modi fications</w> 7440
Decre ase</w> 7440
H ur 7439
strip e</w> 7438
str onic</w> 7437
Me dial</w> 7437
ann a</w> 7436
magn ocellular</w> 7436
ca u 7434
6 80</w> 7433
r onic</w> 7432
A AD</w> 7431
if ts</w> 7431
cor ner</w> 7431
leuk em 7431
rein hard 7431
tar sal</w> 7431
correspond ingly</w> 7431
um ent</w> 7430
bu propion</w> 7430
ultra high</w> 7430
Rh in 7430
commis sural</w> 7430
- L</w> 7429
PT G</w> 7429
dor sum</w> 7429
end plate</w> 7428
mon ogenic</w> 7427
CF R</w> 7427
san itary</w> 7427
A propos</w> 7426
n ac 7426
on ion</w> 7425
lam eness</w> 7425
M a</w> 7424
ter butaline</w> 7424
neuro ectodermal</w> 7424
flatten ing</w> 7424
Orb ital</w> 7424
B alloon</w> 7423
8 S</w> 7422
muscul aris</w> 7422
MC3 T3</w> 7422
par then 7421
approxim ations</w> 7421
Per sistence</w> 7420
lipo philicity</w> 7420
S un 7419
si b</w> 7419
Pre incubation</w> 7419
Car tilage</w> 7419
w ing 7418
In t</w> 7418
all ylic</w> 7418
uro xime</w> 7418
c ush 7417
B 10</w> 7417
ST 1</w> 7417
ana x</w> 7417
epitheli alization</w> 7417
5 C</w> 7416
p v</w> 7416
Z one</w> 7416
es thesia</w> 7416
2. 27</w> 7416
SL S</w> 7416
Peri od</w> 7414
Ultras onic</w> 7414
adeno viruses</w> 7414
ker nels</w> 7413
8 8.9</w> 7412
f ighting</w> 7412
gly ox 7412
K ur 7411
omet allic</w> 7411
S b 7410
9 6. 7409
com min 7409
Leptosp ira</w> 7409
B RS</w> 7408
sul piride</w> 7408
random isation</w> 7408
Sol utions</w> 7408
Cd k 7408
3 2.3</w> 7407
man nos 7407
re activated</w> 7406
Inter face</w> 7406
alb opic 7406
re ar</w> 7405
ph e 7405
vitr onectin</w> 7405
rati onally</w> 7404
Metast ases</w> 7404
retic ulin</w> 7404
f ear 7403
3 6.7</w> 7403
4 6.7</w> 7403
om ethyl 7403
N 0 7402
recogn ise</w> 7402
G PI 7401
F BG</w> 7401
FL AIR</w> 7401
2 4.0</w> 7400
f id 7400
F GFR</w> 7400
li pot 7400
Fra ilty</w> 7400
IF G</w> 7399
F T4</w> 7398
- nucleotidase</w> 7398
50 9</w> 7398
surro gates</w> 7398
ry e</w> 7397
IO Ls</w> 7396
on ation</w> 7395
rin us</w> 7395
Psychi atry</w> 7395
happ ened</w> 7394
gravit ational</w> 7393
ag rel 7392
orbit ofrontal</w> 7392
6 3.6</w> 7391
leuk aemic</w> 7391
lev amisole</w> 7390
poly tetrafluoroethylene</w> 7390
erythro cytic</w> 7390
s. d.</w> 7390
tetra hydro</w> 7390
numer ic</w> 7389
3, 0 7389
abor ted</w> 7389
NF κB</w> 7389
scler a</w> 7389
cycl ical</w> 7388
H es 7387
W GS</w> 7387
3 97</w> 7387
scho oling</w> 7387
4 29</w> 7386
ig s</w> 7385
IN T</w> 7385
ig e</w> 7384
atidyl choline</w> 7384
F W</w> 7383
- 0.1</w> 7383
st ack</w> 7383
V MAT</w> 7382
pyrrolid one</w> 7382
hs CRP</w> 7382
3 92</w> 7381
U U 7380
immunop ositive</w> 7380
H s 7379
lam o 7379
Therap ies</w> 7379
posit ories</w> 7378
. A.</w> 7377
cent ros 7377
referen ced</w> 7377
R H 7376
py ran 7376
tubulo interstitial</w> 7376
long standing</w> 7375
immuno electron</w> 7375
25 , 7375
ind entation</w> 7374
Alk aline</w> 7374
B eg 7373
manif old</w> 7373
eri atr 7372
dissoci ate</w> 7372
Guid eline</w> 7372
opson ized</w> 7372
tap er</w> 7371
intrap ulmonary</w> 7370
wean ling</w> 7370
AL F</w> 7369
F ST</w> 7368
parti tioned</w> 7367
saf egu 7366
ophag ocytic</w> 7366
loos ely</w> 7366
LT C4</w> 7365
M and 7364
ge o 7364
DR F</w> 7364
S ab 7363
AC F</w> 7363
resol utions</w> 7363
a is</w> 7362
chor dom 7361
ph a 7360
str ian</w> 7360
2 K</w> 7359
herbiv ore</w> 7359
laparo scopically</w> 7357
mobil ities</w> 7357
actom yosin</w> 7357
tume faciens</w> 7357
h en 7356
d og 7356
specific s</w> 7356
withdraw ing</w> 7356
hyal uronate</w> 7356
6 4. 7354
co infected</w> 7354
over active</w> 7354
electro spinning</w> 7354
Mo S2</w> 7354
Immun ost 7353
avi d</w> 7353
Rem ote</w> 7353
3 0.2</w> 7352
xyl ene</w> 7352
rham n 7352
D PD</w> 7351
M ast</w> 7351
conduc ive</w> 7351
ophthalm ological</w> 7351
ech in 7350
rh ab 7350
candi d 7350
Tre pon 7350
cardio protection</w> 7350
3 1.1</w> 7349
re teral</w> 7349
ta u 7349
Rec ogn 7349
46 619</w> 7349
hamartom a</w> 7349
O u 7348
w ounded</w> 7348
Ther mod 7348
Chr on 7348
Scre en</w> 7348
TGF beta</w> 7348
seaf ood</w> 7348
Papanicol aou</w> 7348
qui escence</w> 7347
I DC</w> 7346
clos ures</w> 7346
Nutri ent</w> 7346
ster ase</w> 7345
oly tics</w> 7345
iso prost 7345
6 8. 7344
N J</w> 7344
perchlor ate</w> 7344
ch ur 7343
e pers</w> 7342
brach i 7342
F X</w> 7341
comm encement</w> 7341
g hosts</w> 7340
v est</w> 7340
min k</w> 7340
devi ant</w> 7340
assembl age</w> 7340
6 3. 7339
cu ticular</w> 7339
Viet nam 7339
Cost a</w> 7339
sub fractions</w> 7338
hum idi 7338
categor ised</w> 7338
4 1.5</w> 7337
nutri tive</w> 7336
az in</w> 7335
acetab ulum</w> 7334
Citr us</w> 7334
albopic tus</w> 7334
thym idylate</w> 7333
mon d</w> 7332
sig ma 7331
T Z 7330
osteo arthritic</w> 7330
un suspected</w> 7329
purch asing</w> 7329
3 57</w> 7328
3 2.1</w> 7326
K ol 7326
-0. 4</w> 7326
3 64</w> 7325
po unds</w> 7325
syn ten 7325
Inter ference</w> 7325
Aneurys m</w> 7325
RI P</w> 7324
29. 7</w> 7324
SC O</w> 7323
sum med</w> 7323
HR S</w> 7323
F ur 7322
Post traumatic</w> 7322
th ia</w> 7321
ain flu 7321
ogrou p</w> 7321
pol yt 7320
SL Ns</w> 7320
pent ose</w> 7320
cry oglob 7320
Veter inary</w> 7320
week end</w> 7320
D AS 7319
reinnerv ation</w> 7319
z ol</w> 7318
methyl cholanthrene</w> 7318
insuffici ently</w> 7318
os molar</w> 7316
ow els</w> 7316
mamm ograms</w> 7316
evol ves</w> 7315
IF Ns</w> 7314
disp ensed</w> 7314
sta ple</w> 7314
Ass ays</w> 7314
pellucid a</w> 7314
osp ir 7313
C AC 7312
B and</w> 7312
inos us</w> 7312
lamo trigine</w> 7312
1 p 7311
fen o 7311
epiphy sis</w> 7311
re ject</w> 7310
anti diuretic</w> 7310
neuro secretory</w> 7310
TF PI</w> 7310
28. 7</w> 7310
decid ua</w> 7310
A str 7309
J ust</w> 7309
men tion</w> 7309
Bi opsies</w> 7309
Hym enoptera</w> 7309
gambi ae</w> 7308
6 5. 7307
tri valent</w> 7307
hyster oscopy</w> 7307
dr um</w> 7306
con ical</w> 7305
cel y</w> 7305
yt openia</w> 7304
cle arer</w> 7304
trunc al</w> 7304
harb ors</w> 7304
cha os</w> 7304
L ast</w> 7303
K b</w> 7303
gu ai 7303
sl ab</w> 7302
HT 29</w> 7302
Analy zing</w> 7301
resili ent</w> 7301
ban ana</w> 7301
equilib ria</w> 7301
PRO B 7300
ure as</w> 7299
Reg ulates</w> 7299
found ations</w> 7299
roten one</w> 7299
er ol</w> 7298
arres tin</w> 7298
R equ 7297
cl en 7297
myelo fibrosis</w> 7297
forec ast</w> 7297
pres urgical</w> 7296
2. 22</w> 7296
y ear 7295
categor ize</w> 7295
dichloro methane</w> 7295
W .</w> 7294
man ageable</w> 7294
sist ers</w> 7294
cal varial</w> 7294
lact ones</w> 7294
immunomod ulation</w> 7294
r GO</w> 7293
con form</w> 7293
SI V 7292
sc ram 7291
on azole</w> 7290
tele health</w> 7290
thromb ospondin</w> 7289
B HK</w> 7288
ab breviated</w> 7288
nitro cellulose</w> 7288
ber t</w> 7288
un ia</w> 7287
hydroxy propyl</w> 7287
permeabil ization</w> 7286
AB CB1</w> 7285
Cyto plasmic</w> 7285
collim ator</w> 7285
3' UTR</w> 7285
TB A</w> 7284
amend ment</w> 7284
understand ings</w> 7284
N KG 7283
dr yness</w> 7283
PR E</w> 7282
Vietnam ese</w> 7282
N BS</w> 7281
mat ur 7281
micro environments</w> 7281
hydrox yp 7281
myo fibrils</w> 7281
asparag inase</w> 7281
myocl onic</w> 7281
Advant ages</w> 7281
Fan coni</w> 7281
re trac 7280
di eti 7280
com posting</w> 7280
PG S</w> 7280
Ath l 7280
autocor relation</w> 7280
t old</w> 7279
d b 7279
mac ros 7279
herm aphro 7279
D RS</w> 7278
whe eze</w> 7278
syring ae</w> 7278
Intrig u 7278
M HV</w> 7277
nan ob 7277
NO T 7277
bom bar 7277
it o</w> 7276
Tel e 7276
sc orp 7275
MV s</w> 7275
Intrigu ingly</w> 7275
em an</w> 7274
SU I</w> 7272
2. 33</w> 7272
officin alis</w> 7272
ap ing</w> 7271
gust atory</w> 7271
4 7.5</w> 7270
MD SCs</w> 7270
Labor atories</w> 7270
bi otype</w> 7269
pin k</w> 7269
sm ell</w> 7269
eu genol</w> 7268
ly syl</w> 7268
ak in</w> 7268
Sim ulated</w> 7268
unic ellular</w> 7268
P et 7267
2 7.9</w> 7266
M yc 7266
19 64</w> 7266
non covalent</w> 7266
liz ards</w> 7266
TI Ls</w> 7265
id ins</w> 7264
under water</w> 7264
wel ding</w> 7264
insectic idal</w> 7264
scorp ion</w> 7264
4 51</w> 7263
u ity</w> 7263
morph ol 7262
Electroph oretic</w> 7262
Prec ise</w> 7261
5 25</w> 7260
is ome</w> 7260
BCL 2</w> 7260
acrom ial</w> 7260
5 6. 7259
ul lin 7259
oc ulation</w> 7259
ane ph 7259
P aro 7258
ac nes</w> 7258
fl on</w> 7258
F 8</w> 7257
PL E 7257
Gl omerular</w> 7257
U PD 7256
per id 7256
the l</w> 7256
termin alis</w> 7256
Oreg on</w> 7256
P y</w> 7255
traum atized</w> 7255
Syn ap 7255
shi eld</w> 7254
MM TV</w> 7253
VEGF R2</w> 7253
T J</w> 7252
ch illing</w> 7252
f using</w> 7251
Y ou 7250
ne opterin</w> 7250
inter relationships</w> 7250
az ocine</w> 7250
3 76</w> 7249
Pl ain</w> 7249
anthelmin tic</w> 7249
M FS</w> 7248
chem i 7248
raz epam</w> 7248
sc rotum</w> 7247
Pro bing</w> 7247
denti fr 7247
Platel ets</w> 7247
A h</w> 7246
phen oxy</w> 7246
non adherence</w> 7246
carcin omatosis</w> 7246
stern um</w> 7246
re member</w> 7245
ut ter 7245
M arti 7244
N 2 7244
at ability</w> 7244
Radi al</w> 7244
acquisi tions</w> 7244
GSK 3β</w> 7244
FI T</w> 7243
Tit anium</w> 7243
biore mediation</w> 7242
e utroph 7241
muc op 7241
Cor tisol</w> 7241
3 1.7</w> 7240
ö r 7240
26 , 7240
po sive</w> 7239
hol m</w> 7239
cylin ders</w> 7239
trabec ulae</w> 7238
Thromb in</w> 7238
GI C</w> 7237
non malignant</w> 7236
los a</w> 7235
porphy ria</w> 7235
G il 7234
G AGs</w> 7234
fluoresc ens</w> 7233
Dis per 7233
Ha ve</w> 7233
hydrox ylamine</w> 7232
ometh ylation</w> 7232
s ong 7231
del usions</w> 7231
Optim izing</w> 7231
prioriti zed</w> 7231
kilodal ton</w> 7231
gener alize</w> 7230
oll is</w> 7230
mut u 7229
Se ed</w> 7229
symbi onts</w> 7229
tachy cardi 7228
meso dermal</w> 7228
disturb ing</w> 7227
5 70</w> 7226
st y 7226
sp rings</w> 7226
den ied</w> 7226
decarbox ylation</w> 7226
forec asting</w> 7226
Princi ples</w> 7226
I PL</w> 7225
M SH2</w> 7225
sem an 7225
Web er</w> 7225
dict ated</w> 7225
Sca ff 7225
L Cs</w> 7224
p enc 7224
O HP</w> 7224
29. 1</w> 7224
m Gy</w> 7223
micro filaments</w> 7223
St ock 7223
constell ation</w> 7222
V G 7221
at taining</w> 7221
pur pos 7221
OK T3</w> 7221
connec ts</w> 7220
protr uding</w> 7219
Mig raine</w> 7219
Me tr 7218
Abbot t</w> 7218
on ensis</w> 7217
n AChRs</w> 7216
transpl antable</w> 7216
motiv ate</w> 7216
TU s</w> 7216
multi plic 7215
p 70</w> 7214
no b 7214
sen iors</w> 7214
heterodi meric</w> 7214
Bol tz 7214
trans abdominal</w> 7213
ogran ul 7213
hydroly ze</w> 7212
rosi glitazone</w> 7212
a eus</w> 7211
omel an 7211
hypot onia</w> 7210
Ultra violet</w> 7210
orh odopsin</w> 7210
S end 7209
F -</w> 7209
cal vari 7209
gp 41</w> 7209
ev ine</w> 7208
hepat oprotective</w> 7208
Aut opsy</w> 7208
R 1 7207
mon grel</w> 7207
Insi ght</w> 7207
hemo chromatosis</w> 7207
Ma x</w> 7206
M PN</w> 7205
cell aneous</w> 7205
cave olae</w> 7205
V FA</w> 7204
sho e</w> 7204
decre ments</w> 7204
mo ul 7203
thym ectomy</w> 7203
remin ders</w> 7203
4 15</w> 7202
N .</w> 7202
orth ologous</w> 7202
Ta x</w> 7202
V UR</w> 7201
prote obacteria</w> 7201
meth em 7201
nano fiber</w> 7201
ventricul ography</w> 7201
pyraz ol 7201
homozyg osity</w> 7201
kilob ases</w> 7201
G ad 7200
Re in 7200
thermo stability</w> 7199
B ID</w> 7198
append ages</w> 7198
en ius</w> 7197
Cat ech 7197
recombin ase</w> 7196
ger bil</w> 7196
Ris ks</w> 7196
Cat ar 7196
meval onate</w> 7196
U SD</w> 7195
em atous</w> 7195
rac ing</w> 7195
E yes</w> 7194
leiomy omas</w> 7193
Cong ress</w> 7193
rh BMP</w> 7192
GL S</w> 7192
IL s</w> 7192
S edi 7191
micro titer</w> 7191
angi omas</w> 7191
in amide</w> 7190
T et 7189
U K 7189
oc ratic</w> 7189
end omy 7189
sub gingival</w> 7189
educ ating</w> 7189
ary otic</w> 7189
Tr act</w> 7188
head space</w> 7187
heter ot 7186
meri stem</w> 7186
PT E</w> 7185
29. 8</w> 7185
no ble</w> 7184
Aut onomic</w> 7183
h MSCs</w> 7182
K un 7182
iso flavones</w> 7182
hydro phila</w> 7181
cardio plegic</w> 7180
centr alized</w> 7179
strong ylus</w> 7179
narcol epsy</w> 7179
d um 7178
constitu tion</w> 7178
u v 7177
in consistency</w> 7177
bi ota</w> 7177
ere rs</w> 7177
dys one</w> 7177
haemat ocrit</w> 7175
yl or</w> 7174
1, 10</w> 7174
AK R</w> 7174
glycos ylase</w> 7174
O OH</w> 7173
2. 18</w> 7172
ov o</w> 7172
CA 19</w> 7171
All ergy</w> 7171
Indu stry</w> 7171
alar ming</w> 7171
angi itis</w> 7170
H ippo</w> 7169
eth i 7169
organ o 7169
surroun dings</w> 7169
rhin oplasty</w> 7169
l om 7168
ur b</w> 7168
3 87</w> 7167
cre w</w> 7167
PM 10</w> 7167
A TS</w> 7166
13 00</w> 7166
fea ther</w> 7166
myc otoxin</w> 7166
D CC</w> 7165
de generating</w> 7165
po ison</w> 7165
Pro pi 7165
read mitted</w> 7165
helmin ths</w> 7165
T rib 7164
ent an</w> 7164
HI E</w> 7164
hemipleg ia</w> 7164
f MLP</w> 7163
amino acyl</w> 7163
homogen ized</w> 7163
omon ocytic</w> 7163
preneo plastic</w> 7163
B CNU</w> 7162
abrup tly</w> 7162
Epid ural</w> 7161
4 55</w> 7160
li b</w> 7160
ric kets</w> 7160
ble ed</w> 7159
coag ulated</w> 7159
2 3.0</w> 7156
AC V</w> 7156
multi loc 7156
ER M</w> 7156
3 49</w> 7155
o proteins</w> 7155
od our</w> 7155
2. 24</w> 7155
Lin ked</w> 7155
D ich 7154
bl ight</w> 7154
ardi ve</w> 7154
PL LA</w> 7154
2 5.1</w> 7153
uc k 7153
lymph oblasts</w> 7153
slur ry</w> 7153
pro caine</w> 7152
Per me 7152
C all 7150
endoc ervical</w> 7150
FK BP 7150
polycy themia</w> 7150
p ale</w> 7149
em sa</w> 7149
Di arr 7149
F n</w> 7148
an ual</w> 7148
ech ogenicity</w> 7148
non equilibrium</w> 7148
lymph otropic</w> 7148
Oper ation</w> 7147
fin ely</w> 7146
catechol aminergic</w> 7146
r uling</w> 7145
ep oxy 7145
suc king</w> 7145
ach s</w> 7144
Mech an 7144
2,3, 7,8</w> 7144
n ica</w> 7143
e tically</w> 7142
N PCs</w> 7142
ar bor 7142
2. 23</w> 7142
TLR 9</w> 7142
megakary ocyte</w> 7142
exci sional</w> 7142
ip id</w> 7141
Hybri dization</w> 7141
ti tu 7140
net working</w> 7140
cef azolin</w> 7140
sed ated</w> 7140
T ER</w> 7139
volun tarily</w> 7139
hang ing</w> 7139
Repeti tive</w> 7139
dis organization</w> 7138
P om 7137
ti e</w> 7137
Con temporary</w> 7137
4 28</w> 7136
B l</w> 7136
B asi 7135
harbo uring</w> 7135
su fentanil</w> 7134
g yp 7133
frac tory</w> 7133
Reli able</w> 7133
fructo kinase</w> 7133
am ellar</w> 7132
Pro pen 7132
clomi phene</w> 7132
a the 7131
AL DH</w> 7131
E ver 7130
ti p 7130
si lo 7130
gen er</w> 7130
stret ches</w> 7130
Pig s</w> 7130
Immun ob 7129
Bay es</w> 7129
o chrom 7128
dis appears</w> 7128
myx oid</w> 7128
de es</w> 7127
T RO 7126
sp rung</w> 7125
lem an</w> 7124
card inal</w> 7124
E B1</w> 7123
mycel ial</w> 7123
C AT 7122
F MF</w> 7122
tu zumab</w> 7122
ore duction</w> 7122
act yl 7122
S Ds</w> 7121
R HD</w> 7121
N MS</w> 7121
F uc 7121
po orest</w> 7121
Se e 7121
good ness</w> 7121
t max</w> 7120
an odic</w> 7120
troph y</w> 7120
flagell in</w> 7120
T u</w> 7119
extrac ellularly</w> 7119
FV IIa</w> 7119
v anish 7118
HT 2A</w> 7118
Abstr act</w> 7118
acti ae</w> 7117
Oc ca 7117
I PP</w> 7116
hyper stimulation</w> 7116
flex ors</w> 7116
spermat ogenic</w> 7116
primi parous</w> 7116
Boltz mann</w> 7116
R b 7115
perioste um</w> 7115
tri oxide</w> 7114
AD F</w> 7114
Pro po 7113
CS I</w> 7113
rep tiles</w> 7113
m Ci</w> 7112
9 5th</w> 7112
W NT</w> 7111
qu etiapine</w> 7111
trim oxazole</w> 7111
ce iling</w> 7111
adrenal ectomized</w> 7111
p soas</w> 7110
ter tiles</w> 7110
pa e</w> 7110
thec a</w> 7110
obarbit one</w> 7110
adrenocortic otropic</w> 7110
FGF 23</w> 7109
oxidi ze</w> 7108
recapit ulate</w> 7108
extrac apsular</w> 7107
Inter ventional</w> 7107
Min i 7107
D ir 7106
as ally</w> 7106
HIF U</w> 7106
N AN 7105
è re</w> 7105
ent anglement</w> 7105
all ogenic</w> 7105
pip razole</w> 7105
Coord ination</w> 7105
S chem 7104
bronch ogenic</w> 7104
arri ve</w> 7104
SET TI 7103
L is 7102
ep er</w> 7102
fung icide</w> 7102
myel osuppression</w> 7101
o uring</w> 7100
if ting</w> 7100
Pul sed</w> 7100
Leuk ocyte</w> 7100
miniatur ized</w> 7100
my ogenesis</w> 7099
az enil</w> 7099
Ph ase 7099
60 3</w> 7099
AG P</w> 7098
T OC</w> 7097
Mg Cl2</w> 7097
voc alizations</w> 7096
Ch el 7095
enucle ated</w> 7095
aler ts</w> 7095
empfer ol</w> 7095
mo tive</w> 7094
soci ocultural</w> 7094
Non linear</w> 7094
cryptoc occal</w> 7094
envel opes</w> 7092
se st 7091
Com pens 7091
spra yed</w> 7091
Nocardi a</w> 7091
w is 7090
Vi sible</w> 7090
lou dness</w> 7090
microgram s. 7090
per idine</w> 7089
sal i 7089
be agle</w> 7088
Per coll</w> 7088
ate tra 7088
tun ica</w> 7087
travel ers</w> 7087
DR E</w> 7086
carbap enem 7085
curvil inear</w> 7085
6 35</w> 7084
ec dy 7084
pres en 7084
DE A</w> 7083
haem olysis</w> 7083
fen fluramine</w> 7083
B TX</w> 7082
er ial</w> 7082
under graduates</w> 7082
thor a</w> 7082
ex pulsion</w> 7081
1 ra</w> 7080
G ap</w> 7080
multi organ</w> 7080
PD 98059</w> 7080
can inum</w> 7079
AR G</w> 7079
millis econds</w> 7078
kin umab</w> 7077
stre ak</w> 7077
dermat oses</w> 7077
biog eo 7077
re ovirus</w> 7076
L aw 7075
3 59</w> 7075
mathem atics</w> 7075
7 5.0</w> 7074
sin ess</w> 7074
SE B</w> 7074
IK K 7074
dor ff</w> 7073
PN I</w> 7073
bil ingual</w> 7072
T OR</w> 7071
gen icity</w> 7071
Stat 3</w> 7071
reinhard tii</w> 7071
r ase</w> 7070
3. 33</w> 7070
PK B</w> 7070
Par athyro 7069
oc top 7068
re appraisal</w> 7067
re atine</w> 7067
dimethyl siloxane</w> 7067
stu ffs</w> 7067
ah l</w> 7065
alk ane</w> 7064
35 80</w> 7064
consen ted</w> 7064
u alized</w> 7063
micro bubbles</w> 7063
2. 26</w> 7063
RA M</w> 7063
Plas mid</w> 7063
pyrid oxine</w> 7063
iqu antel</w> 7063
3 91</w> 7062
as tin 7062
wor tmannin</w> 7060
val ley</w> 7060
scal ar</w> 7060
9 a</w> 7059
inve stments</w> 7059
G U 7058
3 69</w> 7058
mal onyl</w> 7058
Par a 7058
assign ing</w> 7057
trypan osomes</w> 7057
G PC</w> 7056
idi an</w> 7056
PY Y</w> 7056
a em 7055
3. 0 7055
Impl ementing</w> 7054
L AT</w> 7053
Res ources</w> 7053
mor ph</w> 7052
phot ographic</w> 7051
at ta</w> 7050
assi stive</w> 7050
par ities</w> 7049
The il 7049
rhyth micity</w> 7049
M AGE</w> 7048
hyper lipidemic</w> 7048
An aes 7048
neurom a</w> 7048
happ en</w> 7048
7 2.7</w> 7047
- infected</w> 7047
bro ther</w> 7047
util ising</w> 7047
a ken</w> 7046
or nam 7046
im purity</w> 7045
cre ativity</w> 7045
sen te 7045
ST EC</w> 7045
camp tothecin</w> 7045
4 85</w> 7044
if lor 7044
oste olytic</w> 7044
ir regularity</w> 7042
esti bular</w> 7042
Plat eau</w> 7042
endoc annabinoid</w> 7042
Trepon ema</w> 7041
E cu 7040
F AM 7040
monoc linic</w> 7040
immunophen otype</w> 7040
adh esin</w> 7040
Con A</w> 7039
on ym</w> 7038
ox itin</w> 7038
retro gradely</w> 7038
neutr ons</w> 7038
Inhib its</w> 7038
A round</w> 7037
ass uring</w> 7037
Ch ris 7037
arc is 7037
2, 8 7037
Le ad 7036
amygdal oid</w> 7036
slaugh tered</w> 7036
y an</w> 7035
a z</w> 7035
To ols</w> 7034
Tou rette</w> 7034
organochlor ine</w> 7034
3 .</w> 7033
im eth 7033
port osystemic</w> 7033
CT V</w> 7033
she patic</w> 7033
N REM</w> 7032
PR 1</w> 7032
blin ding</w> 7032
ascer tainment</w> 7032
f ates</w> 7031
M CM</w> 7031
acidi fied</w> 7031
uk i</w> 7030
Mac ular</w> 7030
Calcul ations</w> 7030
foot printing</w> 7029
ondyl ar</w> 7029
ris m</w> 7028
Cor tex</w> 7028
se als</w> 7027
bin aural</w> 7027
Su ff 7027
oct ane</w> 7027
TR A</w> 7026
oretin opathy</w> 7026
X III</w> 7025
den om 7025
phot ob 7025
Neuro logic</w> 7025
supr ap 7025
scar cely</w> 7025
Roch e</w> 7025
u st 7024
om b</w> 7024
pil ots</w> 7024
TX B2</w> 7024
L ens</w> 7023
bout ons</w> 7023
SETTI NGS</w> 7023
val sartan</w> 7022
Phen yl 7021
Ne ed</w> 7020
enter opathy</w> 7020
benz oyl 7020
Hamil tonian</w> 7020
LDL R</w> 7020
serogrou ps</w> 7020
Fin ding</w> 7019
Nor thwest</w> 7019
tachy kinin</w> 7019
biog as</w> 7019
enrol lees</w> 7019
F IV</w> 7018
id ine 7018
ip in</w> 7018
G U</w> 7017
m J</w> 7017
t A</w> 7016
I TT</w> 7016
ox LDL</w> 7016
carb ene</w> 7016
P av 7015
trans sphenoidal</w> 7015
Re present 7015
cri ticism</w> 7015
IV H</w> 7015
Tor r</w> 7015
f atin</w> 7014
str ug 7014
pre morbid</w> 7014
liter atures</w> 7014
Propen sity</w> 7014
Mercur y</w> 7012
centr i 7010
oscop ies</w> 7010
Practition ers</w> 7010
oc idal</w> 7009
taz obactam</w> 7009
n l</w> 7008
3 8.9</w> 7008
me dro 7008
PB DEs</w> 7008
45 Ca2</w> 7008
LINC 00 7008
Br un 7007
H2 A</w> 7007
3 4.8</w> 7006
rig or</w> 7006
pteryg ium</w> 7006
conval escent</w> 7005
Prescrip tion</w> 7005
Send ai</w> 7005
Y AC</w> 7004
Cu O</w> 7004
nan ospheres</w> 7003
bur ne 7003
Georg e</w> 7003
k bp</w> 7002
neuro biology</w> 7002
Sc and 7002
Sem i 7002
ico planin</w> 7002
3 7.8</w> 7001
Bi oph 7001
tradi tion</w> 7001
m olysis</w> 7000
3 4.4</w> 7000
PA E</w> 7000
do id</w> 6999
complic ates</w> 6998
50 7</w> 6998
sente eism</w> 6998
3 79</w> 6997
go ides</w> 6997
diver ticula</w> 6997
eicos anoids</w> 6997
c able</w> 6996
M ind 6996
ff ing</w> 6996
opharyn gi 6996
C ox 6994
3 2.7</w> 6994
l ays</w> 6994
x A</w> 6994
Dis section</w> 6994
mes ophilic</w> 6994
SP P</w> 6994
hydroxy phenyl</w> 6994
body weight</w> 6994
- 60</w> 6993
pa ying</w> 6993
enc er</w> 6993
In tim 6993
sest amibi</w> 6993
in congruent</w> 6992
con ut</w> 6992
Gi emsa</w> 6992
im perfecta</w> 6991
constric tor</w> 6991
Expan sion</w> 6991
d well</w> 6990
O thers</w> 6990
histi ocytes</w> 6990
H NF</w> 6989
en flurane</w> 6989
retin oin</w> 6989
EP SP</w> 6989
re pressing</w> 6988
iso quinoline</w> 6988
IR E</w> 6988
Der m 6988
ex ped 6987
Ad op 6987
SR M</w> 6987
HF S</w> 6987
i als</w> 6986
di alytic</w> 6986
liz ard</w> 6986
CI MT</w> 6985
mess aging</w> 6985
dermat osis</w> 6985
Resi d 6985
illumin ate</w> 6985
M ature</w> 6983
Ex cit 6983
CO R</w> 6983
mono hydrate</w> 6983
WO MAC</w> 6983
9 1.7</w> 6982
el astom 6982
qu est</w> 6981
stat e 6981
antihist amines</w> 6981
sit tac 6980
C GS</w> 6979
norm oglyc 6979
un restricted</w> 6978
CS s</w> 6978
apath y</w> 6978
5 3.8</w> 6977
com promises</w> 6977
iner tia</w> 6977
J V</w> 6976
kin dergar 6976
hid rosis</w> 6976
Immun ity</w> 6974
Includ ing</w> 6974
s ob 6973
M AN 6973
4 97</w> 6972
micro dilution</w> 6972
dra ins</w> 6972
z ygotic</w> 6971
skin ned</w> 6971
s tivity</w> 6970
3 2.6</w> 6970
cyt oreductive</w> 6970
bre eders</w> 6970
non structural</w> 6970
orchi ectomy</w> 6970
Malay sian</w> 6969
Fil ms</w> 6968
H BP</w> 6967
C W 6967
yl -</w> 6967
character izations</w> 6967
iso flavone</w> 6967
amphi pathic</w> 6967
M 5</w> 6966
hydro peroxides</w> 6966
groo ves</w> 6966
ol ith</w> 6965
ib acterium</w> 6965
ob liqu 6965
suffici ency</w> 6965
sulf ite</w> 6965
PF OS</w> 6965
3 94</w> 6964
T err 6964
anti tumour</w> 6964
Wal king</w> 6963
a ise</w> 6962
aren es</w> 6962
rol izumab</w> 6962
P ST</w> 6961
o on</w> 6961
Sign s</w> 6961
incarcer ation</w> 6961
bere avement</w> 6961
convol uted</w> 6961
c ART</w> 6960
ot alol</w> 6960
emb rolizumab</w> 6960
alloc atechin</w> 6960
Percent age</w> 6960
3 4.6</w> 6959
design ation</w> 6959
D AD</w> 6958
Neuro endocrine</w> 6958
spre ads</w> 6958
Physi ol</w> 6958
evid ently</w> 6958
D MS</w> 6957
clu b 6957
se as</w> 6956
tom ycin</w> 6956
transmit tance</w> 6956
house keeping</w> 6956
millim olar</w> 6956
10 0.0</w> 6955
organophosph orus</w> 6955
3 3.5</w> 6954
B 8</w> 6954
inc ipient</w> 6954
Re tri 6954
M olecules</w> 6953
the atre</w> 6953
Al b 6953
5 2.5</w> 6952
F ET</w> 6952
intra observer</w> 6952
plasmacyt oma</w> 6951
syring es</w> 6950
cyr rhiz 6950
5 9. 6949
de polymerization</w> 6949
co operatively</w> 6949
br ushes</w> 6949
agon ism</w> 6949
CA RT</w> 6948
t ata</w> 6947
ost s</w> 6947
Pre v 6947
60 5</w> 6947
An alog 6946
2, 6 6946
avi renz</w> 6946
glycer ophosph 6946
illustr ative</w> 6946
FS GS</w> 6946
my opathies</w> 6945
distrib ute</w> 6945
ket orolac</w> 6945
B ell</w> 6944
los an</w> 6943
im pedi 6942
Behavi oural</w> 6942
segreg ating</w> 6942
She ar</w> 6942
4 22</w> 6941
AT D</w> 6941
hist ogenesis</w> 6940
dist ressing</w> 6940
Four th</w> 6940
sn ap 6940
clerk ship</w> 6940
J an</w> 6939
thi ocyanate</w> 6939
wrap ped</w> 6939
Veg et 6939
D AI</w> 6938
mutag en</w> 6938
ID Us</w> 6938
S SP</w> 6937
Ad dressing</w> 6937
B erg 6936
gr inding</w> 6936
b. w.</w> 6936
s ella</w> 6935
T SC 6935
micro dissection</w> 6935
R Ts</w> 6934
ur y</w> 6933
An aerobic</w> 6933
metabol ome</w> 6933
aggra vation</w> 6933
p 15</w> 6932
α -</w> 6932
L ab</w> 6931
3 6.6</w> 6931
recep tivity</w> 6931
hydro chlor 6931
protec tin</w> 6931
mes oc 6931
P SV</w> 6930
intral esional</w> 6930
me si 6929
SM N</w> 6929
municip ality</w> 6929
a esti 6928
O mega</w> 6928
thi ourea</w> 6928
c res 6927
quad rants</w> 6927
hyperinsulin emic</w> 6927
anthra x</w> 6927
SL s</w> 6926
E2 F1</w> 6926
Reproduc ibility</w> 6926
prodrom al</w> 6926
g ossy 6925
trip eptide</w> 6925
Induc es</w> 6925
inf licted</w> 6923
Prom pt</w> 6923
G pp</w> 6922
r pm</w> 6922
sc rat 6921
bo ro 6921
Tax ol</w> 6921
Dox orubicin</w> 6921
I odine</w> 6920
rex ed</w> 6920
5 11</w> 6919
Br ad 6919
compul sory</w> 6919
odor ant</w> 6919
res p</w> 6918
compartment alization</w> 6918
conceptu alization</w> 6917
regul in</w> 6916
behavi orally</w> 6916
leuc yl</w> 6916
intra osseous</w> 6915
ep ez 6914
impl em 6913
trim ers</w> 6913
Oper ating</w> 6913
E gg 6912
th es</w> 6912
ir regularly</w> 6912
id ines</w> 6911
2. 19</w> 6911
1, 8</w> 6911
orth ognathic</w> 6911
50 , 6911
eyel ids</w> 6911
smooth ing</w> 6911
d R</w> 6909
immuno affinity</w> 6909
CT O</w> 6909
hem orrho 6908
Ad V</w> 6908
Neo adjuvant</w> 6908
r algia</w> 6907
om ain</w> 6906
ox icam</w> 6906
Process es</w> 6906
cau tious</w> 6906
burne tii</w> 6906
SC N 6905
plu gs</w> 6905
at ers</w> 6904
DR 3</w> 6904
K O 6903
tub er</w> 6903
3 0.7</w> 6902
app raised</w> 6902
adul ter 6902
Radio frequency</w> 6902
allant oic</w> 6902
- 2,5</w> 6901
B ED</w> 6901
del toid</w> 6900
bro thers</w> 6900
aly p 6899
Sub cellular</w> 6899
dialy zed</w> 6899
4 ,000</w> 6898
F ar</w> 6898
im od</w> 6898
lith ography</w> 6898
parat uberculosis</w> 6898
clavul anic</w> 6898
Hirsch sprung</w> 6898
L RR</w> 6897
os itol</w> 6897
pos tim 6897
Pre venting</w> 6897
AB CA1</w> 6897
exacerb ates</w> 6897
EXTR ACTION</w> 6897
un vaccinated</w> 6896
ket oglutarate</w> 6896
4 74</w> 6895
O LP</w> 6895
un injured</w> 6895
ocortico ids</w> 6895
Peri odic</w> 6895
Jo b</w> 6895
Sn ail</w> 6895
cass ava</w> 6895
sarcol emma</w> 6895
stig m 6894
muc oid</w> 6893
phot ophysical</w> 6893
cross bred</w> 6893
F MT</w> 6892
In her 6892
tri x</w> 6891
4 34</w> 6890
bas ket</w> 6890
Competi tive</w> 6890
e striol</w> 6889
ar us</w> 6889
plast er</w> 6889
w ings</w> 6888
ok adaic</w> 6888
DM T</w> 6887
ex ported</w> 6886
2. 38</w> 6886
RO D</w> 6886
Bac k 6886
xy progesterone</w> 6886
Concer ns</w> 6886
agglutin ating</w> 6885
vox els</w> 6885
vo ices</w> 6884
D W 6883
omyc etes</w> 6883
P all 6882
fa ith 6882
.00 7</w> 6882
dextr ins</w> 6882
fascin ating</w> 6882
alb endazole</w> 6881
scru tin 6881
veterin arians</w> 6881
ep ib 6880
cus p</w> 6880
G AL</w> 6879
PL s</w> 6879
Na OCl</w> 6879
favour ably</w> 6879
parac ellular</w> 6879
sphing olipid</w> 6879
h l</w> 6878
lu g</w> 6878
regul arization</w> 6878
oxy codone</w> 6878
Tw itter</w> 6878
15 th</w> 6878
trans ference</w> 6877
micro injected</w> 6877
SI D</w> 6877
ket ball</w> 6877
appreci ate</w> 6877
ABC G2</w> 6877
focus sed</w> 6876
T FA</w> 6875
Q CT</w> 6875
methyl mercury</w> 6875
epri stone</w> 6875
v il 6874
Drin king</w> 6874
ste ering</w> 6873
8 1.8</w> 6872
P MR</w> 6872
epez il</w> 6872
L AM 6871
car s</w> 6871
gr asses</w> 6871
requis ites</w> 6871
if epristone</w> 6870
hepat os 6870
qual ification</w> 6870
en ers</w> 6869
tic ide</w> 6869
pol ishing</w> 6869
sk ipping</w> 6869
rs 7 6869
herpes viruses</w> 6869
hydraz ide</w> 6869
D 3 6868
9 2.3</w> 6867
thorac o 6867
P on 6866
c eph 6866
responsi vity</w> 6866
D K 6865
Ph ag 6865
Cam bo 6865
per idol</w> 6863
mon otonic</w> 6863
SB 20 6863
anno unc 6863
colon isation</w> 6862
cereb ell 6862
V OC</w> 6861
cu tis</w> 6861
transp iration</w> 6860
Acceler ated</w> 6859
15 ,000</w> 6858
Mat ter</w> 6858
Heter o 6858
Microbi ota</w> 6858
di chloro</w> 6857
TI M</w> 6857
hom onas</w> 6856
bul losa</w> 6856
kind led</w> 6856
amin ated</w> 6855
- 70</w> 6854
Resi li 6854
methoxy phenyl</w> 6854
o chol 6853
E STs</w> 6853
Col onic</w> 6853
sacro iliac</w> 6853
adenosyl methionine</w> 6853
a virulent</w> 6852
6 0.0</w> 6852
os ol</w> 6852
dri p</w> 6852
PEG ylated</w> 6852
aggrec an</w> 6852
F DP</w> 6851
scler otic</w> 6851
- 18</w> 6850
ti ans</w> 6850
TR US</w> 6850
accul turation</w> 6850
s as</w> 6849
in eal</w> 6849
explo its</w> 6849
cosme tics</w> 6849
offici als</w> 6849
e el</w> 6848
Post partum</w> 6848
credi ble</w> 6848
F OS</w> 6847
Phen otype</w> 6847
d up</w> 6846
N BD</w> 6846
ophthalm ologists</w> 6846
proce edings</w> 6846
P ediatrics</w> 6844
u ts</w> 6844
B yp 6844
Ap parently</w> 6844
CV P</w> 6844
thy retin</w> 6844
Propo fol</w> 6844
NT D</w> 6843
MS G</w> 6843
co el 6842
epilep sies</w> 6842
wave front</w> 6842
pres choolers</w> 6840
sensiti zes</w> 6840
milest ones</w> 6840
w ished</w> 6839
Neutroph ils</w> 6839
o rel 6838
electro catalytic</w> 6838
A AF</w> 6837
hypothalam o</w> 6837
PA X 6836
thi our 6836
recommend ing</w> 6836
Electro my 6836
5 90</w> 6835
am ental</w> 6835
T ar 6834
ro ot 6834
Cardi o 6834
IV D</w> 6834
jo in</w> 6834
gir dle</w> 6832
Cd Se</w> 6832
noro virus</w> 6832
H DM</w> 6831
- B</w> 6831
be et</w> 6831
Cre utz 6831
5 H</w> 6830
thi oate</w> 6830
b. i.d.</w> 6830
om ia</w> 6829
ol ocalization</w> 6829
de bulking</w> 6829
ru tin</w> 6829
3 4.3</w> 6828
valvul oplasty</w> 6828
4 16</w> 6827
viscer a</w> 6827
R α</w> 6826
We gener</w> 6826
thermo regulatory</w> 6826
4 37</w> 6825
E rec 6825
O FC</w> 6825
mis cellaneous</w> 6825
millil iter</w> 6825
electrocardi ograms</w> 6824
breast fed</w> 6823
floc k</w> 6823
camp us</w> 6823
min es</w> 6822
dra g</w> 6821
fel dt</w> 6821
oun saturated</w> 6820
Mo S</w> 6820
chromoph ores</w> 6820
s 2</w> 6819
K IF 6819
PW M</w> 6819
dithiocarb amate</w> 6819
H TP</w> 6818
I RES</w> 6818
p ium</w> 6818
M CT 6818
Z ambia</w> 6818
ec dysone</w> 6818
tic asone</w> 6818
P es 6817
Amer ic 6817
ligh tly</w> 6816
Smo oth</w> 6816
0 9 6815
4 6.2</w> 6815
Co 2</w> 6815
Medic ines</w> 6815
computer ised</w> 6815
buc kling</w> 6815
Consider ation</w> 6815
n r 6814
phenyl acetic</w> 6814
3 71</w> 6813
4 1.2</w> 6813
ter pene</w> 6813
to il 6813
Man if 6813
HT T</w> 6813
Ple ist 6813
1 13 6812
ph ere</w> 6812
ocardi tis</w> 6812
DU RES</w> 6812
Avail ability</w> 6812
IGF 1</w> 6811
pup al</w> 6811
d c</w> 6810
ru p</w> 6810
excit otoxic</w> 6810
C esarean</w> 6809
4 95</w> 6809
B P2</w> 6809
am mary</w> 6809
sensi bility</w> 6809
Te ach 6809
Sof tware</w> 6809
B HT</w> 6808
Ne wer</w> 6808
biom es</w> 6808
-- and</w> 6807
For mal 6807
sati vum</w> 6807
arabin oside</w> 6807
R ING</w> 6806
el u 6806
S co 6805
l unch</w> 6805
nucle osomal</w> 6805
SL I</w> 6805
tacti cs</w> 6805
mist aken</w> 6805
Opportun ities</w> 6805
credi bility</w> 6804
PROCE DURES</w> 6804
b A</w> 6803
sec tomized</w> 6803
3 2.2</w> 6802
N ER</w> 6802
ligam entous</w> 6802
7 3.3</w> 6801
In tran 6801
mal practice</w> 6801
hindr ance</w> 6801
pot ence</w> 6800
mox ifloxacin</w> 6800
discol oration</w> 6800
ep o 6799
col oration</w> 6799
ation . 6799
ret ard</w> 6799
imeth amine</w> 6799
bur den 6798
sper matic</w> 6798
pharyng itis</w> 6798
p si 6797
end onasal</w> 6797
rumin ant</w> 6797
den tinal</w> 6796
AP R</w> 6796
trou bles 6796
Brow nian</w> 6796
4 14</w> 6795
or onary</w> 6795
perfor ating</w> 6795
peri aqueductal</w> 6794
consul t</w> 6794
intracor tical</w> 6794
rec idi 6793
M X</w> 6792
end uring</w> 6792
exc ipients</w> 6792
methyl amine</w> 6792
C II</w> 6791
RN S</w> 6791
Ho use</w> 6791
Hind III</w> 6791
cont or 6790
psycho therapeutic</w> 6789
3 2.8</w> 6788
b low 6788
ab duc 6788
par ainflu 6788
orth olog</w> 6788
Br onchial</w> 6788
cauter y</w> 6788
bil ical</w> 6787
PD F</w> 6787
derang ements</w> 6787
enke phal 6786
choc olate</w> 6786
SM s</w> 6785
Quanti fying</w> 6785
re warming</w> 6784
tun ic 6784
ank yrin</w> 6784
Lac tococcus</w> 6784
Br achy 6783
Phase olus</w> 6783
G or 6782
R ico</w> 6782
ag ia</w> 6782
ob long 6782
sur amin</w> 6782
bio diesel</w> 6782
adap table</w> 6782
rehabil itative</w> 6782
og a</w> 6781
cr ine</w> 6781
Stere otactic</w> 6781
NZ B</w> 6781
enrol ment</w> 6781
In d 6780
Fi xed</w> 6780
4 17</w> 6779
spe ak</w> 6779
emet rexed</w> 6779
W it 6778
be am 6778
2. 28</w> 6778
Immunost aining</w> 6778
mon ate</w> 6776
anat om 6776
14 00</w> 6775
promp ting</w> 6775
moll usc 6775
ituit arism</w> 6775
b ol</w> 6774
oc erc 6774
di p</w> 6773
pro posing</w> 6773
mat er</w> 6773
F CR</w> 6772
ni acin</w> 6772
p ated</w> 6771
ol iv 6771
cyt olysis</w> 6771
Multi level</w> 6771
parasit emia</w> 6771
arbitr arily</w> 6771
D PH</w> 6770
leuko encephalopathy</w> 6770
C ephal 6769
ax otomy</w> 6769
pap ules</w> 6769
4 33</w> 6768
not och 6768
ethn ically</w> 6768
Fo rensic</w> 6768
Cri tically</w> 6768
B IO 6767
digit alis</w> 6767
R 2 6766
C GA</w> 6765
ped agog 6765
kappa B 6765
Lip opolysaccharide</w> 6765
si gh 6764
ynam ically</w> 6764
Am es</w> 6764
crani osynostosis</w> 6764
fru stration</w> 6763
G CF</w> 6761
tec tal</w> 6761
repres sive</w> 6761
hibern ation</w> 6761
re structuring</w> 6760
dec h 6760
2, 9 6760
NE FA</w> 6760
Mill er</w> 6760
pre menstrual</w> 6759
PC K</w> 6759
aler tness</w> 6759
u l</w> 6758
T empor 6758
sol d</w> 6758
Carbo hydrate</w> 6758
G DS</w> 6757
cistern ae</w> 6757
T Ps</w> 6756
poly vinyl 6756
predomin ately</w> 6756
epilep t 6756
summar ies</w> 6756
anthrac yclines</w> 6756
pleas ure</w> 6756
res ynchronization</w> 6755
phot ometric</w> 6755
Afr icans</w> 6755
syn decan</w> 6754
Dem ographics</w> 6753
TX A2</w> 6753
L MP</w> 6752
U sers</w> 6752
CV s</w> 6752
I AV</w> 6751
ter ahertz</w> 6751
actin in</w> 6751
LM W</w> 6751
insulin oma</w> 6751
pain ting</w> 6751
anticonvuls ants</w> 6751
vitr ification</w> 6751
put ting</w> 6749
Fl t</w> 6749
Tan dem</w> 6749
Cl in</w> 6748
carbo diimide</w> 6748
alex ithymia</w> 6748
S anta</w> 6746
of ascial</w> 6746
path om 6745
1 E</w> 6744
Ex pec 6744
liqu or</w> 6744
cartrid ge</w> 6744
olith ic</w> 6743
adv ise</w> 6742
don ated</w> 6742
S es 6741
k ur 6741
p BR 6741
ab l</w> 6741
bo died</w> 6741
plat e 6741
F MDV</w> 6740
We ighted</w> 6740
All ogeneic</w> 6739
rox icam</w> 6739
mon omethyl</w> 6738
wr ite</w> 6738
di gn 6737
un dec 6737
sub normal</w> 6737
nor floxacin</w> 6737
equip ot 6737
PROB LEM</w> 6737
ER B 6736
don ations</w> 6736
V 79</w> 6735
ro mo 6735
form is</w> 6735
AF LP</w> 6735
gang ren 6735
pay ers</w> 6735
3 L</w> 6734
F BP</w> 6734
non cardiac</w> 6734
anu fac 6734
ISRCT N 6734
W ill 6733
FI B</w> 6733
PI H</w> 6732
n af 6731
S NA</w> 6731
N TA</w> 6731
CA B</w> 6731
HL 60</w> 6730
-deoxy uridine</w> 6730
supervis ors</w> 6730
distinc tions</w> 6729
K az 6728
cir cles</w> 6728
preserv ative</w> 6728
Ne edle</w> 6727
HE L</w> 6727
P eyer</w> 6726
o ak</w> 6726
c yp 6726
I EC</w> 6725
inter phalangeal</w> 6725
intr usion</w> 6725
spin y</w> 6725
M AG</w> 6724
iev es</w> 6724
tryp an</w> 6724
myco plasma</w> 6724
Pac litaxel</w> 6724
sh otgun</w> 6723
Extr a</w> 6723
l isting</w> 6722
res titu 6722
overestim ate</w> 6722
gib berel 6722
dys genesis</w> 6721
alkal osis</w> 6721
Con sequences</w> 6720
quanti fiable</w> 6720
leng thy</w> 6720
o estrous</w> 6719
eu glycemic</w> 6719
evo kes</w> 6719
uncou pled</w> 6719
Daph nia</w> 6719
state wide</w> 6719
Vari ant</w> 6718
tetra hedral</w> 6718
compar ably</w> 6717
Pl an 6716
PN H</w> 6716
shi vering</w> 6716
Fc gamma 6716
stut tering</w> 6716
bio control</w> 6715
Dis sociation</w> 6715
con clusively</w> 6714
sp A</w> 6714
dis continu 6714
uk es</w> 6714
Multi disciplinary</w> 6714
n 1</w> 6713
enig matic</w> 6713
ec tod 6712
bour ne</w> 6712
Visc eral</w> 6712
ABI LITY</w> 6711
P osition</w> 6710
pent yl 6710
c occi</w> 6709
ann an</w> 6709
G DH</w> 6708
C 60</w> 6708
M PD</w> 6708
electro genic</w> 6708
acet onide</w> 6708
12 6 6708
T TC</w> 6707
fi res</w> 6706
glyc ogen 6706
tro chle 6705
mi Rs</w> 6705
ont ogenetic</w> 6705
CB 2</w> 6705
IT Y</w> 6705
B 10. 6704
in avir</w> 6704
For ces</w> 6704
nap us</w> 6704
FI D</w> 6703
Spectro scopic</w> 6703
Jak ob</w> 6703
in homogeneity</w> 6702
anti parallel</w> 6702
Com plex 6702
metallo protease</w> 6702
OH dG</w> 6702
T 7 6701
re pressors</w> 6701
ch e</w> 6701
am ple</w> 6701
is ep 6701
dec ays</w> 6701
2. 31</w> 6701
bre ech</w> 6701
L PD</w> 6700
- derived</w> 6700
aberr antly</w> 6700
pa x 6699
ill um</w> 6699
auto antigen</w> 6699
r i</w> 6698
l omerular</w> 6698
atic us</w> 6698
sha king</w> 6698
ib e</w> 6697
trapez ius</w> 6696
Ch iral</w> 6695
bre aths</w> 6695
pentag astrin</w> 6695
os umab</w> 6694
fil op 6694
tox igenic</w> 6694
DI AG 6694
succin ic</w> 6694
- negative</w> 6693
K 8</w> 6693
ren dipine</w> 6693
K E</w> 6692
dis place</w> 6692
nitro genase</w> 6692
weak ening</w> 6692
anthrac is</w> 6692
om agnetic</w> 6691
separ ates</w> 6691
Sil icon</w> 6691
oc er 6690
STAT 5</w> 6690
r . 6689
Expl oration</w> 6689
ADAM TS 6689
E b 6688
ov igilance</w> 6688
dig est 6688
summar ise</w> 6688
Sig ma</w> 6688
Mec kel</w> 6688
F MLP</w> 6686
HC H</w> 6686
parasi tized</w> 6686
second ly</w> 6686
b HLH</w> 6685
end olymphatic</w> 6685
W E 6684
4 3.8</w> 6684
cocul tured</w> 6684
encour ages</w> 6683
H erb 6682
C ut 6682
F LC</w> 6681
quin ol</w> 6681
TM B</w> 6681
depic t</w> 6681
F DR</w> 6680
Loc ation</w> 6680
Cle avage</w> 6680
with stand</w> 6679
chloro phenyl</w> 6679
multiv essel</w> 6679
con s</w> 6678
AI L 6678
reat t 6678
nitr ification</w> 6678
PU VA</w> 6678
le f 6677
interrup tions</w> 6677
ex os 6676
oper itoneal</w> 6676
philosoph ical</w> 6676
cef uroxime</w> 6674
TEN S</w> 6674
8 8. 6673
3 4.2</w> 6673
capac itors</w> 6673
CC 2</w> 6673
aff licted</w> 6672
question ing</w> 6672
chondro genesis</w> 6671
tad poles</w> 6671
leptomening eal</w> 6671
e um</w> 6670
ev ade</w> 6670
filari ae</w> 6670
3 86</w> 6669
op ol 6669
rele as 6669
ab ir 6668
inc in 6668
Rec i 6668
Fin ite</w> 6668
glo ve</w> 6668
hyper intense</w> 6667
GT N</w> 6667
nor theast</w> 6666
SE s</w> 6666
scap ularis</w> 6666
tann ins</w> 6666
Somat ostatin</w> 6666
g m 6665
ent e</w> 6665
Inv asion</w> 6665
D i</w> 6664
pro visions</w> 6664
FGF R1</w> 6664
ant e</w> 6663
Ad vis 6663
Cardi opulmonary</w> 6663
lu ting</w> 6662
S inus</w> 6661
2. 21</w> 6661
de acetylation</w> 6660
oc ou 6660
9 4. 6658
pyro sequencing</w> 6658
palin dromic</w> 6658
t ell</w> 6657
par anoid</w> 6657
isi tion</w> 6657
les bian</w> 6657
A AP</w> 6656
ca erul 6656
ov en</w> 6656
Cop enh 6656
estu arine</w> 6656
- 3-</w> 6655
myel omonocytic</w> 6655
Histor ical</w> 6655
s ons</w> 6654
K el 6654
Anti tumor</w> 6654
Coch lear</w> 6654
quinox aline</w> 6654
iz obium</w> 6653
organ izer</w> 6653
osom ally</w> 6651
overl ay</w> 6651
appendic eal</w> 6651
pyl orus</w> 6650
gall ic</w> 6650
B ub 6648
At g 6648
otechn ology</w> 6648
Thor ac 6648
and amide</w> 6647
tub ers</w> 6646
lignoc ellulosic</w> 6646
on ated</w> 6645
lipo ic</w> 6645
schem a</w> 6645
as alazine</w> 6644
dist ensibility</w> 6644
oste ochond 6644
DN MT 6644
cryp togenic</w> 6644
nc RNAs</w> 6644
v y</w> 6643
D PA</w> 6643
T AF 6643
cre ep</w> 6643
resi stivity</w> 6643
deterior ating</w> 6643
epti dases</w> 6642
ogu anine</w> 6642
urin alysis</w> 6642
oc a</w> 6641
tran shepatic</w> 6641
hyper polarized</w> 6640
log ist</w> 6640
su mat 6639
canc er 6639
Pleist ocene</w> 6638
M FC</w> 6637
B lu 6637
in activates</w> 6637
Scand in 6637
S ON</w> 6636
d u</w> 6636
emit ters</w> 6636
C 57</w> 6635
inter dig 6635
ish ments</w> 6634
supra optic</w> 6634
phys ostigmine</w> 6634
form yl 6633
cere brum</w> 6633
otrop ical</w> 6633
SV D</w> 6633
86 44</w> 6632
8 0.0</w> 6631
D acron</w> 6631
re v 6631
pre ganglionic</w> 6631
ELI SAs</w> 6631
e we</w> 6630
histomorph ometric</w> 6630
ot ation</w> 6629
dis continuing</w> 6629
Z er 6628
ific us</w> 6628
micro leakage</w> 6628
Sa O2</w> 6628
d rank</w> 6627
inhab iting</w> 6627
nob yl</w> 6627
M AA</w> 6626
plic ate</w> 6626
fo stering</w> 6626
Aff ected</w> 6626
quic ker</w> 6626
provoc ative</w> 6626
hydro philicity</w> 6625
fun goides</w> 6625
Coun tries</w> 6625
cep acia</w> 6625
end um</w> 6624
hus band</w> 6624
8 4.6</w> 6623
hyper variable</w> 6623
DI T</w> 6623
rs 18 6623
s ae</w> 6622
M y</w> 6622
antim yco 6622
polyke tide</w> 6622
deform able</w> 6621
S cho 6620
3 99</w> 6620
He ight</w> 6620
scler oti 6620
p Rb</w> 6619
neuro modulation</w> 6619
HC N</w> 6619
reticul o 6619
inf r 6618
methion yl</w> 6618
phyt ase</w> 6617
im in 6616
intrad ural</w> 6616
ati veness</w> 6615
un ifying</w> 6615
pe ts</w> 6615
4 75</w> 6614
M ON 6614
an j 6614
Run x2</w> 6612
B at 6611
Chr ys 6611
immen se</w> 6611
distr actors</w> 6610
lith otomy</w> 6610
scap e</w> 6610
Z IP</w> 6608
def ensin</w> 6608
ba it</w> 6608
S PA 6607
4 99</w> 6607
T p</w> 6607
it ers</w> 6607
sec ured</w> 6607
Un ified</w> 6607
al dose</w> 6606
varic osities</w> 6606
au str 6605
di m</w> 6604
St atic</w> 6604
stabil izers</w> 6604
Ech inococcus</w> 6604
mirro red</w> 6604
suprac lavicular</w> 6604
8 6.7</w> 6603
ef s</w> 6603
z onal</w> 6602
Min eral</w> 6602
remot ely</w> 6602
Techn ologies</w> 6602
o dical</w> 6601
en uresis</w> 6601
pro social</w> 6601
D SP</w> 6600
X Y 6600
an x</w> 6600
mit o 6600
Ac tin</w> 6600
Cann abis</w> 6600
V AR</w> 6599
B SO</w> 6599
ut ory</w> 6599
intermit tently</w> 6599
g ers</w> 6598
R K</w> 6598
micro bic 6597
CT LA 6597
Anti viral</w> 6597
N U</w> 6596
fru iting</w> 6596
nephro logy</w> 6593
U 2</w> 6592
lin er</w> 6592
int olerant</w> 6592
LI C</w> 6592
com man 6591
60 6</w> 6591
sc h</w> 6590
PPAR α</w> 6590
me res</w> 6589
Bio film</w> 6589
cd c 6589
W W</w> 6588
trans venous</w> 6588
plan ting</w> 6588
yog ur 6588
S entinel</w> 6587
k h 6586
M cl</w> 6586
PC 2</w> 6586
A gain</w> 6585
8 4. 6585
muc ocutaneous</w> 6585
som n 6585
P ACS</w> 6584
F TC</w> 6584
al ar</w> 6584
20 30</w> 6584
log s</w> 6584
desc ended</w> 6584
en vis 6583
ol uminescent</w> 6583
du ties</w> 6583
En zymes</w> 6583
Bioph ys</w> 6583
di aphyseal</w> 6582
ab senteeism</w> 6582
SO X2</w> 6582
P un 6581
os tium</w> 6581
org estrel</w> 6581
arsen ate</w> 6581
Chlo rella</w> 6581
E Ps</w> 6580
re pulsive</w> 6580
Nanop article</w> 6580
b ounds</w> 6578
CB s</w> 6577
Anat omy</w> 6577
Erythro cyte</w> 6576
Ex tension</w> 6575
CF D</w> 6575
a .</w> 6574
ut aneously</w> 6574
contin ual</w> 6573
japon icus</w> 6573
steep er</w> 6573
alis ations</w> 6573
Creutz feldt</w> 6573
z ia</w> 6572
flex neri</w> 6572
vec tive</w> 6572
pyrrol idine</w> 6572
p estis</w> 6571
cotyled ons</w> 6571
borreli osis</w> 6571
I sle 6570
hor ns</w> 6568
dis lod 6567
ure ters</w> 6567
B AP 6566
mon ounsaturated</w> 6566
Af gh 6566
dendrim er</w> 6566
Membran es</w> 6566
legisl ative</w> 6566
S NF</w> 6565
for nix</w> 6565
Prom oter</w> 6565
profession alism</w> 6564
Bar thel</w> 6564
deline ating</w> 6564
Enrich ment</w> 6564
transduc e</w> 6563
zyg omatic</w> 6563
Rever sal</w> 6562
car nos 6561
Spl enic</w> 6561
occupati onally</w> 6561
fru it 6560
Bre ast 6560
Ori entation</w> 6560
M olecule</w> 6559
man dated</w> 6559
SN HL</w> 6559
C li 6558
EC V</w> 6558
schiz o 6558
25 th</w> 6558
Targ ets</w> 6557
C 10</w> 6556
O CTA</w> 6556
glycosi dic</w> 6556
DIAG NO 6556
ed in</w> 6555
P ick</w> 6554
psych opharmac 6554
oto acoustic</w> 6554
7 8. 6553
8 5. 6553
sub sided</w> 6553
bo ars</w> 6553
IC ER</w> 6553
hypoc ot 6553
trifluoro methyl</w> 6553
D Z</w> 6552
Th omas</w> 6552
he ading</w> 6552
3 5.6</w> 6551
yr idine</w> 6551
diver sified</w> 6551
P au 6550
ur in</w> 6550
ble eds</w> 6550
ra xia</w> 6549
r icular</w> 6548
on ization</w> 6548
hydrox yt 6548
So y 6548
Apl ysia</w> 6548
CH 1</w> 6547
Inhib iting</w> 6547
f l</w> 6546
t ling</w> 6546
acros omal</w> 6546
D APT</w> 6545
B PV</w> 6545
CR M</w> 6545
brac ket</w> 6545
lymphaden itis</w> 6545
ir regularities</w> 6544
T omat 6543
EV D</w> 6543
poli omyelitis</w> 6543
d les</w> 6542
proc alc 6542
differenti ates</w> 6542
employ er</w> 6542
fabr icating</w> 6542
de mineralized</w> 6541
DM H</w> 6541
IT D</w> 6541
P BM</w> 6540
2, 5 6540
Sh an 6540
LR RK2</w> 6540
Ret ur 6540
physio therapists</w> 6540
T CF 6539
pre transplant</w> 6538
pu pae</w> 6538
Bi variate</w> 6538
general ist</w> 6538
a R</w> 6537
4 13</w> 6537
hypo albumin 6537
inter conversion</w> 6536
nu trac 6536
HM B</w> 6536
Am ni 6536
resuscit ated</w> 6536
cyto kinin</w> 6535
Fe 3O4</w> 6535
2. 34</w> 6534
stre aming</w> 6534
DL PFC</w> 6534
glum ine</w> 6534
N m</w> 6533
arth ralgia</w> 6533
6, 6</w> 6533
H Z</w> 6532
t 3</w> 6532
s ICAM</w> 6531
Endome tri 6531
am icin</w> 6530
Innov ative</w> 6529
k ings</w> 6528
Graph ical</w> 6528
f ers</w> 6527
I ST</w> 6527
aut otrophic</w> 6527
myel ography</w> 6526
CP G</w> 6526
Arti cle</w> 6526
H im 6525
G HR</w> 6525
sol ani</w> 6525
conveni ently</w> 6525
M s 6524
N Rs</w> 6524
re vascularisation</w> 6524
2. 20</w> 6524
Organis ation</w> 6524
diop ters</w> 6524
PR IM 6523
tric arboxylic</w> 6523
ML ST</w> 6523
Mar ker</w> 6522
replic on</w> 6521
or able</w> 6520
trans aminases</w> 6520
aer odynamic</w> 6520
ocaly x</w> 6520
g allo 6519
inoc erebellar</w> 6519
s ap</w> 6518
9 0.9</w> 6518
sh ame</w> 6518
maglob ulinemia</w> 6518
F H 6517
di substituted</w> 6517
19 50s</w> 6517
gli adin</w> 6517
knowledge able</w> 6517
con strain</w> 6516
Lab eling</w> 6516
patern ity</w> 6516
obstruc tions</w> 6515
spectrophot ometer</w> 6515
F ab 6514
CN R</w> 6514
PV D</w> 6514
straw berry</w> 6514
S usp 6513
- 6. 6513
al utamide</w> 6513
4 8.5</w> 6512
con specific</w> 6512
2. 29</w> 6512
depar ture</w> 6512
in visible</w> 6511
tow ns</w> 6511
ord inarily</w> 6511
crystallo id</w> 6511
encycl idine</w> 6511
D CR</w> 6510
PH N</w> 6510
amni onitis</w> 6510
pill ars</w> 6510
Z 1</w> 6509
thalam ocortical</w> 6509
hypog onad 6509
en i 6508
guan ylyl</w> 6508
P ter 6507
R oss</w> 6507
Y am 6507
pre eclamptic</w> 6507
LI M</w> 6507
magne tically</w> 6506
aqu ifer</w> 6506
gon orrhea</w> 6506
Direc ted</w> 6506
u y 6505
ash i</w> 6505
Foll icular</w> 6505
is etron</w> 6504
mis c 6504
de amination</w> 6502
hom o</w> 6502
SO L</w> 6502
fire arm</w> 6502
ME Cs</w> 6501
wat ching</w> 6501
Il lin 6501
ble b</w> 6500
legitim ate</w> 6500
af ine</w> 6499
multi gene</w> 6499
P2 Y 6499
All erg 6499
d ment</w> 6498
ith ec 6498
2 3, 6497
s intering</w> 6497
consol idated</w> 6496
dec lar 6495
micro U</w> 6495
war dly</w> 6495
sack iev 6495
tri azine</w> 6494
ub stituted</w> 6494
her petic</w> 6494
gest ures</w> 6494
hex achloro 6494
Com pression</w> 6493
aren sis</w> 6493
dys arth 6492
prost anoid</w> 6492
2. 32</w> 6491
muc ociliary</w> 6491
interfer ometer</w> 6491
rein forces</w> 6491
Copenh agen</w> 6491
B us 6490
nin eteen</w> 6489
sp ace 6488
interpre table</w> 6488
Del ay</w> 6488
8 9. 6487
H TA</w> 6487
di l</w> 6487
op os 6487
tri acylglycerols</w> 6487
Re ad 6487
ine es</w> 6487
ef in</w> 6487
hyperprolactin emia</w> 6487
my otomy</w> 6486
multin omial</w> 6486
u ating</w> 6485
my algia</w> 6485
re assor 6484
De ter 6484
HI S</w> 6484
xyl itol</w> 6484
echinoc occosis</w> 6483
benz aldehyde</w> 6482
GAB AB</w> 6482
exer tional</w> 6482
CCR 2</w> 6482
H emis 6481
Challeng e</w> 6481
B LA</w> 6480
organ oids</w> 6480
hn RNP</w> 6480
rec tification</w> 6479
AP TT</w> 6479
ses sile</w> 6479
cos m 6479
Pot ent</w> 6479
O . 6478
cogni tions</w> 6478
Bart onella</w> 6478
dysmen orrhea</w> 6478
lin ess</w> 6477
pul pal</w> 6477
B PS</w> 6476
inter relationship</w> 6476
degrad ative</w> 6476
CV I</w> 6476
DHE AS</w> 6476
pl ase</w> 6475
ne ostigmine</w> 6475
usp irone</w> 6475
Zn S</w> 6474
NF kappaB</w> 6472
.00 8</w> 6472
myo fibroblast</w> 6472
r yl</w> 6471
min ers</w> 6471
pede strian</w> 6470
cockro ach</w> 6470
silk worm</w> 6470
P 50</w> 6469
3 3.8</w> 6469
2. 36</w> 6469
IFN gamma</w> 6469
check points</w> 6468
cyclin s</w> 6468
G 3 6467
3 0.1</w> 6467
Bi as</w> 6467
reproduc ing</w> 6467
ferro electric</w> 6467
at ement</w> 6466
arch ing</w> 6466
proxim ate</w> 6466
Ge ographic</w> 6466
lo x</w> 6465
mid life</w> 6465
LO C</w> 6465
refin ing</w> 6465
Fig ure</w> 6465
keton uria</w> 6465
M PT</w> 6464
cen sored</w> 6464
EB M</w> 6464
BA X</w> 6464
Integr ative</w> 6464
C y</w> 6463
sub chronic</w> 6463
Co ast</w> 6463
ostom ia</w> 6463
D V 6462
M other</w> 6462
N DI</w> 6462
in appropriately</w> 6462
calcane us</w> 6462
lever age</w> 6462
Stan ford</w> 6462
4 27</w> 6461
b ad 6461
di lat 6461
CD 163</w> 6461
biore actors</w> 6461
L ich 6460
K LH</w> 6460
K ip1</w> 6460
aut ografts</w> 6460
AM 1</w> 6460
histi ocytoma</w> 6460
Nucle ic</w> 6460
clean sing</w> 6460
Illin ois</w> 6460
O Me</w> 6459
Del hi</w> 6459
Retro grade</w> 6459
as u</w> 6458
200 ,000</w> 6458
n A</w> 6457
0.0 40</w> 6457
tax in</w> 6457
Bar cel 6457
UV R</w> 6457
dis organized</w> 6456
anti virals</w> 6455
centr ality</w> 6455
biop terin</w> 6455
fene stration</w> 6455
µ mol</w> 6454
ou re 6454
iz ona</w> 6454
slow s</w> 6454
tetrahydro cannabinol</w> 6454
i sia</w> 6453
W N</w> 6453
direc tor</w> 6453
ophthal mos</w> 6453
ation ale</w> 6452
3 d</w> 6451
J ane 6451
flu ent</w> 6451
omyel ia</w> 6451
synthet ases</w> 6451
ER AS</w> 6450
aden os 6450
CD11 c</w> 6450
RE F</w> 6449
sol idi 6449
punc h</w> 6449
hem i</w> 6448
pul ling</w> 6448
Nic kel</w> 6448
R ace</w> 6447
B an 6447
con vic 6446
constric ted</w> 6446
procalc itonin</w> 6446
pro pulsion</w> 6445
aden ed</w> 6445
Gluc agon</w> 6445
A ni 6444
4 36</w> 6444
un myelinated</w> 6444
AR V</w> 6444
MO G</w> 6444
A 11</w> 6443
L Ps</w> 6443
bis ulfite</w> 6443
catarrh alis</w> 6443
Refer ral</w> 6443
D URE</w> 6442
Pen insula</w> 6441
3 5.4</w> 6440
S100 B</w> 6440
cement less</w> 6440
splic e 6439
3 8.2</w> 6438
h unting</w> 6438
psych ophysiological</w> 6438
Candi date</w> 6438
eIF 4E</w> 6438
h atch</w> 6437
af e 6437
sen d</w> 6437
investig ative</w> 6437
domin ates</w> 6437
circ RNA</w> 6437
Chrom at 6437
ME s</w> 6436
Man dibular</w> 6436
ogr am 6436
8 6. 6435
l iraglutide</w> 6435
F G 6435
et e 6435
ton ometry</w> 6435
hin ders</w> 6435
GST P1</w> 6435
privileg ed</w> 6435
gluc o 6434
AP O</w> 6434
Sy n</w> 6434
Bur n</w> 6434
HS Ps</w> 6434
ych nine</w> 6434
accred ited</w> 6434
O ther 6433
ost osis</w> 6433
Resi dent</w> 6433
PROCE DURE</w> 6433
aerosol ized</w> 6433
ultras ensitive</w> 6432
DI P</w> 6432
T DM</w> 6431
ME K1</w> 6431
aminolev ulinic</w> 6431
met aphyseal</w> 6430
Cir cum 6430
multis ite</w> 6430
AV S</w> 6429
PW ID</w> 6429
intran asally</w> 6429
agrel or</w> 6429
EB NA</w> 6428
Ol factory</w> 6428
tetram ers</w> 6428
phosphoc reatine</w> 6428
-- an</w> 6427
panc ytopenia</w> 6427
fl ush</w> 6426
Immuno deficiency</w> 6426
muscul us</w> 6426
s aff 6425
FGF 2</w> 6425
Clos ed</w> 6425
Osc ill 6425
D J</w> 6424
Hyper tensive</w> 6424
P reservation</w> 6423
ediatr icians</w> 6423
on orgestrel</w> 6422
ol ism</w> 6422
con tention</w> 6422
Gib bs</w> 6422
equ als</w> 6421
down loaded</w> 6421
r igh 6420
wave guides</w> 6420
tap ering</w> 6419
C ranial</w> 6418
D Y</w> 6418
ox adi 6418
Hem odialysis</w> 6417
g a</w> 6416
th onous</w> 6416
ot i</w> 6416
Fr actional</w> 6416
4 94</w> 6415
tra de 6415
Gl ass</w> 6415
deoxyn ucleotide</w> 6415
c f</w> 6414
bios ensing</w> 6414
group ings</w> 6414
til ting</w> 6413
aver nous</w> 6413
o atrial</w> 6412
M PR</w> 6412
u ge</w> 6412
minim a</w> 6412
gu ardi 6411
Ad diction</w> 6411
ion otropic</w> 6411
down regulate</w> 6411
un ks</w> 6410
mit oses</w> 6410
phil ia</w> 6410
UPD RS</w> 6410
w ide 6409
ag ran 6409
pip iens</w> 6409
BLA ST</w> 6409
3 6.3</w> 6408
3 0.9</w> 6407
phor ia</w> 6407
n ity</w> 6406
k Vp</w> 6406
AR D</w> 6406
PAN SS</w> 6406
oper s</w> 6404
D HP</w> 6403
op a</w> 6403
proxim ally</w> 6403
In come</w> 6402
photo induced</w> 6402
p ear 6401
cas ei</w> 6401
ginsen oside</w> 6401
Acqu isition</w> 6401
enter itidis</w> 6400
hemodi lution</w> 6400
2 h</w> 6399
6 6.6</w> 6399
protec tant</w> 6399
gp 130</w> 6399
TA E</w> 6398
amin ophen 6397
bir ch</w> 6397
hedr a</w> 6397
conson ant</w> 6397
e ful</w> 6396
es eed</w> 6396
M us</w> 6395
B Cs</w> 6395
trop ics</w> 6395
E DA</w> 6394
vul v 6394
DM SA</w> 6394
therm ogra 6394
4 2.3</w> 6393
cinere a</w> 6393
tis ing</w> 6392
Sy novial</w> 6392
sub lines</w> 6391
antagon izes</w> 6391
dystroph ies</w> 6391
O lym 6390
ari piprazole</w> 6390
phyt on</w> 6390
trochan ter</w> 6390
in mates</w> 6389
az i</w> 6389
Var ic 6389
anteced ents</w> 6388
Bam HI</w> 6388
de differentiation</w> 6387
im potence</w> 6387
NH E</w> 6387
Cor rig 6386
4 98</w> 6385
vari eg 6385
CN TF</w> 6385
DMAR Ds</w> 6385
ox an</w> 6384
3 8.3</w> 6383
hem ocytes</w> 6383
Hist ochemical</w> 6383
cis -</w> 6383
Mel bourne</w> 6383
aph osph 6382
r t 6381
comm issure</w> 6381
perme ant</w> 6381
Cros s 6380
Lan gen 6380
W ater 6379
row n</w> 6379
D n 6378
ylo sis</w> 6378
dead ly</w> 6378
C GM</w> 6377
3 4.7</w> 6377
3 6.1</w> 6377
N VP</w> 6377
pol ished</w> 6377
fl ec 6377
cef oxitin</w> 6377
hydroly zing</w> 6377
Sem en</w> 6377
4 41</w> 6376
prud ent</w> 6376
9 9.9</w> 6374
in su 6374
con ate</w> 6374
tend inopathy</w> 6374
Pap illary</w> 6374
Ex clud 6373
al ogs</w> 6372
oc tapeptide</w> 6372
all yl 6372
osens or</w> 6372
Men i 6372
vir ally</w> 6371
domin ating</w> 6371
17 F</w> 6371
preg abalin</w> 6371
Erro r</w> 6371
3 4.1</w> 6370
immun ogen</w> 6370
pyr imethamine</w> 6370
dy ad</w> 6370
pass enger</w> 6370
duc t 6369
ir idium</w> 6369
gradu ated</w> 6369
symbi ont</w> 6369
Mong olian</w> 6369
cem entum</w> 6369
A mi 6368
ic ities</w> 6368
0. 1 6368
ax ially</w> 6368
competi tors</w> 6368
did actic</w> 6368
dy adic</w> 6367
hat ched</w> 6367
end om 6366
Micro wave</w> 6366
Ep it 6366
sympath o 6366
ni er</w> 6365
6 7. 6364
en ate</w> 6364
orth o 6364
Eg r</w> 6364
cab bage</w> 6364
idi xic</w> 6363
sumat riptan</w> 6363
f sky</w> 6362
S AL</w> 6362
m at</w> 6362
tri plex</w> 6362
Sup er</w> 6362
call osal</w> 6361
Eng agement</w> 6361
LDL T</w> 6361
E din 6360
st an 6360
Def ense</w> 6360
adipo kines</w> 6360
ethn ographic</w> 6359
201 Tl</w> 6359
mean while</w> 6358
tor so</w> 6357
ob ular</w> 6356
galac topyran 6356
3 8.6</w> 6355
O G 6355
Rab bits</w> 6355
N R1</w> 6354
omat oid</w> 6354
aver tebral</w> 6352
orth od 6352
Barcel ona</w> 6352
ang es</w> 6351
repor ters</w> 6351
RE ST</w> 6351
uro th 6351
her it 6351
Soci o</w> 6351
energ etically</w> 6351
Ach ieving</w> 6351
G K 6350
SU MO 6350
plic ated</w> 6350
diap ause</w> 6350
j ac 6349
corp uscular</w> 6348
sub divisions</w> 6347
deterior ate</w> 6347
Work shop</w> 6347
non users</w> 6346
lig ases</w> 6346
rever tants</w> 6346
AJ CC</w> 6346
3 alpha</w> 6345
D KA</w> 6345
pe eling</w> 6345
-0. 6</w> 6345
mast ery</w> 6345
berg hei</w> 6344
oc alin</w> 6343
reduc tases</w> 6343
d as 6342
B TV</w> 6342
te ens</w> 6342
par alytic</w> 6342
bas ins</w> 6342
deb ates</w> 6342
myelo ablative</w> 6342
re membered</w> 6341
Dys regulation</w> 6341
E motion</w> 6339
ll a</w> 6339
sulfonyl urea</w> 6339
T ree</w> 6338
with standing</w> 6338
ton sil 6338
Dist ance</w> 6338
promastig otes</w> 6338
a ul 6337
3 8.1</w> 6337
EB US</w> 6337
pass aged</w> 6337
lib er 6337
gen cies</w> 6336
bro adened</w> 6336
adap ter</w> 6336
hepat omegaly</w> 6336
idi osis</w> 6335
alk yne</w> 6335
ethyl en 6335
LE P</w> 6335
Byp ass</w> 6335
y on</w> 6334
oin d 6334
Sil ica</w> 6334
preclud es</w> 6334
sweet ened</w> 6334
p ud 6333
so aked</w> 6333
3 5.2</w> 6332
modul i</w> 6332
doub tful</w> 6332
P er</w> 6331
cl age</w> 6331
co conut</w> 6331
trans arterial</w> 6331
IN F</w> 6331
Si C</w> 6331
27 , 6331
D ukes</w> 6330
ib us</w> 6330
P Q 6329
V P2</w> 6329
- 17</w> 6328
mic utes</w> 6328
AC EI</w> 6328
ax illa</w> 6328
oct anol</w> 6328
Ay urve 6328
ic om 6327
ys ine</w> 6327
N ON 6326
ru brum</w> 6326
dex ter 6326
7 6. 6325
DQ A1</w> 6325
intestin alis</w> 6325
Other wise</w> 6325
n ilo 6323
met as 6323
hus bands</w> 6323
on i</w> 6322
methyl transferases</w> 6322
devi ated</w> 6322
R if 6321
F AI</w> 6321
ass e</w> 6321
depl ete</w> 6321
Incorpor ating</w> 6321
H PMC</w> 6320
en nium</w> 6320
Acet ylcholine</w> 6320
G OS</w> 6319
micro meters</w> 6319
rhe ic</w> 6319
TL S</w> 6319
Fri end</w> 6319
oligo deoxynucleotides</w> 6319
bombar dment</w> 6319
B CAA</w> 6318
Con trac 6318
Pol yp 6318
scre enings</w> 6316
adel ta</w> 6316
soci alization</w> 6315
Wat son</w> 6315
Hawai i</w> 6315
crib ing</w> 6314
CS R</w> 6314
N t 6313
infra renal</w> 6313
domes tication</w> 6313
cyano acrylate</w> 6313
inadver tent</w> 6313
reven ue</w> 6313
Chil ean</w> 6312
H il 6311
mechanis tically</w> 6311
tel es 6311
PH I</w> 6311
LC 50</w> 6311
on amide</w> 6310
Un treated</w> 6310
phag ocyte</w> 6310
AZ A</w> 6310
unil amellar</w> 6310
Ex plo 6309
biom icro 6309
Imp ul 6309
prognos tication</w> 6308
t Hcy</w> 6307
b id</w> 6307
nucle ob 6307
U MP</w> 6306
pos us</w> 6306
RE R</w> 6306
flic ker</w> 6306
per su 6305
dis counting</w> 6304
path ogn 6303
radi ations</w> 6302
eth oxy</w> 6302
polyp ectomy</w> 6302
pred atory</w> 6302
interview er</w> 6302
P CDD</w> 6301
ul arity</w> 6301
de marc 6301
tw enti 6301
spr uce</w> 6301
P ort</w> 6300
V AC</w> 6300
F ort</w> 6300
sal am 6300
hepati ca</w> 6300
don ating</w> 6300
gli oblastomas</w> 6300
D DR</w> 6299
contrac ts</w> 6298
manuscrip ts</w> 6298
W ES</w> 6297
BM P2</w> 6297
Micro vascular</w> 6297
pros th 6297
pseudot umor</w> 6297
or ia</w> 6296
intra arterial</w> 6296
stilb ene</w> 6296
Injec tions</w> 6295
tic ola</w> 6294
compl ementing</w> 6294
rop ia</w> 6294
DI A</w> 6294
sub specialty</w> 6293
ed itorial</w> 6293
2. 56</w> 6293
Phy toph 6293
Inc ident</w> 6293
AT F</w> 6292
opo res</w> 6292
Sol itary</w> 6292
ulcer ations</w> 6292
L SG</w> 6291
bro od</w> 6291
Plac ement</w> 6290
Erb B</w> 6290
Xanth omonas</w> 6290
ven o</w> 6289
FN AB</w> 6289
7 R</w> 6288
- D</w> 6288
cont empl 6288
Con ditional</w> 6288
profil ed</w> 6288
Com pan 6287
dic tate</w> 6286
but amide</w> 6286
Müll erian</w> 6286
em et</w> 6285
sub capsular</w> 6285
basoph il</w> 6285
transcriptom ics</w> 6285
n exin</w> 6284
B un 6284
0 -</w> 6283
SU S</w> 6283
DE R</w> 6283
ogra fted</w> 6283
Perman ent</w> 6283
Hem ip 6282
ped unc 6282
R el</w> 6281
vec uronium</w> 6281
Electr ic</w> 6281
isobut yl</w> 6280
tri ol</w> 6279
cos mid</w> 6279
clean ed</w> 6279
CD K2</w> 6278
Myo D</w> 6278
8 000</w> 6277
me glumine</w> 6277
poly omavirus</w> 6277
s g 6276
VI A</w> 6276
NF AT 6276
V KA</w> 6275
Altern aria</w> 6275
Lu te 6275
Jane iro</w> 6275
m are</w> 6274
HE MA</w> 6274
BA Y</w> 6274
M SD</w> 6273
Tam oxifen</w> 6273
A SCT</w> 6272
P RT</w> 6272
C over 6272
Th ic 6272
desc end 6272
Stock holm</w> 6272
c aged</w> 6271
tripl ets</w> 6271
pine al 6271
endomy ocardial</w> 6271
7 S</w> 6270
form ulating</w> 6270
PL C 6270
w ishes</w> 6269
trac tography</w> 6269
so oner</w> 6269
squ id</w> 6269
3 3.6</w> 6268
ps or 6267
Scor ing</w> 6267
mo ist</w> 6266
har dening</w> 6266
eicos anoid</w> 6266
retr o</w> 6266
vign ettes</w> 6266
rec umb 6265
cloac ae</w> 6265
E LF</w> 6264
en berg</w> 6264
is ocyanate</w> 6264
evalu ative</w> 6264
Cal ibration</w> 6264
radic ally</w> 6264
Apop totic</w> 6264
co aches</w> 6263
fac i 6263
bo o</w> 6263
ge ological</w> 6263
glob ule</w> 6263
glob ulins</w> 6263
MT CT</w> 6263
parasit oid</w> 6263
T ail 6262
actin ic</w> 6262
Relap se</w> 6262
bio equivalence</w> 6261
osom y</w> 6261
R n</w> 6260
V CR</w> 6260
blo ody</w> 6260
bio electrical</w> 6260
gradu ation</w> 6260
discer n</w> 6260
o a</w> 6259
PT U</w> 6259
uti lities</w> 6259
8 15</w> 6258
Cor respon 6258
mobil isation</w> 6258
2. 30</w> 6257
construc tive</w> 6257
aggra vate</w> 6257
Gene tically</w> 6257
sialy l</w> 6257
Edin burgh</w> 6257
P SE</w> 6256
S mal 6255
W MH</w> 6255
Aff ective</w> 6255
fis cal</w> 6255
thoraco abdominal</w> 6255
fun nel</w> 6254
6 9. 6253
renz epine</w> 6253
7 80</w> 6252
5 7.5</w> 6252
stri ke</w> 6252
H u</w> 6251
4 49</w> 6251
oc eptive</w> 6251
L 929</w> 6250
3 8.8</w> 6250
4 44</w> 6250
Dep osition</w> 6250
hand held</w> 6250
reflec tivity</w> 6250
V ein</w> 6249
ib ustion</w> 6249
equ ality</w> 6249
arc tica</w> 6249
E ud 6248
3 8.7</w> 6247
ad ness</w> 6247
ru vate</w> 6247
byp asses</w> 6247
Barri er</w> 6247
5 58</w> 6246
hydro lysed</w> 6246
guide wire</w> 6246
direc tives</w> 6245
atom istic</w> 6245
M oz 6244
osup pressive</w> 6244
un fortunately</w> 6243
poly protein</w> 6243
Phytoph thora</w> 6243
ac ade 6242
certific ate</w> 6242
P 22</w> 6241
o plasm</w> 6241
4 3.3</w> 6241
at ose</w> 6241
phenomen ology</w> 6241
CV R</w> 6241
Lati nos</w> 6241
3 5.8</w> 6240
F HR</w> 6240
anthrop ometry</w> 6240
L W</w> 6239
te icoplanin</w> 6239
3. 25</w> 6239
iontoph oresis</w> 6239
I BM 6238
ann ulation</w> 6238
bed time</w> 6238
cryptorch idism</w> 6238
P atch</w> 6237
un responsiveness</w> 6237
id yl</w> 6237
PF OA</w> 6237
crani opharyngi 6237
Con cept</w> 6236
PC NL</w> 6236
arthro graphy</w> 6235
hypop ituitarism</w> 6235
RA I</w> 6234
k iss</w> 6233
am eric</w> 6233
Re fractory</w> 6233
caffe ic</w> 6233
medro xyprogesterone</w> 6233
D 7</w> 6232
ac lop 6231
b p 6230
ent als</w> 6230
dynam in</w> 6230
Radi ographs</w> 6230
4 52</w> 6229
Am B</w> 6229
es tic</w> 6228
din o 6228
aliqu ots</w> 6228
fill ers</w> 6227
E ts</w> 6226
organ otypic</w> 6226
Fir micutes</w> 6226
enanti oselectivity</w> 6226
Moun tain</w> 6226
epigene tics</w> 6226
G W</w> 6225
C at</w> 6225
im poses</w> 6225
me sis</w> 6225
Pap ill 6225
ct DNA</w> 6225
B6 C3 6225
tran examic</w> 6224
ech ogenic</w> 6224
nucle ases</w> 6224
10. 10 6223
depic ting</w> 6223
H 9</w> 6222
4 53</w> 6222
pre adipocytes</w> 6222
bronch oscopic</w> 6222
thromb omodulin</w> 6221
analy ser</w> 6220
ST AR</w> 6220
osi der 6220
D AP 6219
fer ulic</w> 6219
haem ostasis</w> 6219
Sp O2</w> 6219
sphing omyel 6219
cinnam ic</w> 6219
G S 6218
S ac 6218
3 4.9</w> 6218
Ad 5</w> 6218
G 5</w> 6217
3 1.9</w> 6217
neur on 6217
H3 T3</w> 6216
od one</w> 6215
ta e</w> 6215
poly phenolic</w> 6215
na ev 6215
PON V</w> 6215
stilb estrol</w> 6215
E TB</w> 6214
ro idal</w> 6214
anti -</w> 6214
st ren 6213
H2 B</w> 6213
hypol ip 6213
aclop rid</w> 6213
P 1 6212
symp atric</w> 6212
perit umoral</w> 6212
Cen sus</w> 6212
encomp assed</w> 6211
19 63</w> 6209
multi sensory</w> 6208
contag ious</w> 6208
i. t.</w> 6207
70 5</w> 6207
dech lor 6207
Parathyro id</w> 6206
B FU</w> 6205
lob ules</w> 6205
Rec ipients</w> 6205
tel encephalon</w> 6205
4 45</w> 6204
ac ry 6204
he donia</w> 6204
clim ates</w> 6204
SV T</w> 6203
ec ologists</w> 6202
inf lo 6202
MD T</w> 6202
H3K 27 6202
c rom 6201
R TA</w> 6201
Ac cred 6201
IVI g</w> 6201
Ot ta 6201
Tomat o</w> 6201
t ell 6200
at i</w> 6200
i ro 6199
p 73</w> 6199
oste ocytes</w> 6199
60 8</w> 6199
di ps 6198
applic ator</w> 6198
arsen ess</w> 6198
phosphoryl ating</w> 6198
M Z 6197
CA E</w> 6197
intenti onally</w> 6197
Tak ay 6197
S tern 6196
F ist 6196
ou in</w> 6196
AI C</w> 6196
Prote ase</w> 6196
w ife</w> 6195
magne tite</w> 6195
C ow 6194
col leges</w> 6194
capac itation</w> 6194
MD C</w> 6194
crow ded</w> 6194
House hold</w> 6194
C 30</w> 6193
di atom</w> 6193
CO C</w> 6193
Sou thwest</w> 6193
pep sinogen</w> 6192
postin jury</w> 6192
Clinic opathological</w> 6192
m util 6191
5 R</w> 6191
per itone 6191
feno fibrate</w> 6190
V . 6189
T SP 6189
cer cari 6189
suff erers</w> 6189
un married</w> 6188
IT O</w> 6188
Gv HD</w> 6188
3 6.2</w> 6187
in gence</w> 6187
yl methyl</w> 6187
DB H</w> 6187
trimethyl ammonium</w> 6187
fall en</w> 6187
t ment</w> 6186
lact ulose</w> 6186
P PO</w> 6185
non etheless</w> 6185
60 7</w> 6185
k go</w> 6184
lor azepam</w> 6184
top iramate</w> 6184
TE N</w> 6184
Transcrip ts</w> 6184
Coron avirus</w> 6184
dis odium</w> 6183
Sy k</w> 6183
With draw 6183
ar m 6182
ev an 6182
Decre ases</w> 6182
convuls ant</w> 6182
n ad 6181
H II 6181
4 6.5</w> 6180
E TT</w> 6180
Dis rup 6180
Fr action 6180
8 7. 6179
sec ular</w> 6179
vag ue</w> 6179
Kar no 6179
B inary</w> 6178
st achian</w> 6178
ograph ers</w> 6178
wee ds</w> 6178
Trich inella</w> 6178
K or 6177
tra ver 6177
fung icides</w> 6177
proce eding</w> 6177
v 2</w> 6176
ozo ite</w> 6175
o G</w> 6174
p recor 6174
sp p.</w> 6174
quanti fies</w> 6174
Pak ist 6174
schizo affective</w> 6173
T NT</w> 6172
inter layer</w> 6172
pat ents</w> 6172
ro su 6171
ere sis</w> 6171
Det ails</w> 6171
mangro ve</w> 6171
al fentanil</w> 6170
ac arb 6170
Edi tion</w> 6170
maxim ized</w> 6169
CB C</w> 6169
phospho fructokinase</w> 6169
cul tur 6168
sho es</w> 6168
pain t</w> 6168
pu romycin</w> 6167
gastroenter ology</w> 6167
Td T</w> 6167
terpen es</w> 6167
anec dotal</w> 6166
pur posive</w> 6165
pow dered</w> 6165
hos tile</w> 6165
Bomb yx</w> 6165
I kappaB</w> 6164
O TUs</w> 6164
R PMI</w> 6163
echocardi ograms</w> 6163
ifi ers</w> 6163
4 2.8</w> 6162
ol u 6162
ep izo 6162
conjug ative</w> 6162
at em 6161
fluor imetric</w> 6161
lich en 6161
C erebellar</w> 6160
4 26</w> 6160
2. 37</w> 6160
phen othi 6160
bas ophilic</w> 6160
melan ocortin</w> 6160
Fr action</w> 6160
NI H3T3</w> 6160
3 5.1</w> 6159
In ner</w> 6159
Lin k</w> 6159
Foc using</w> 6159
4 7.6</w> 6158
harb our</w> 6158
in competent</w> 6157
ph orin</w> 6157
atten tive</w> 6157
MM P2</w> 6157
zol ed 6157
K 12</w> 6156
pseud ocyst</w> 6156
ont ological</w> 6155
Ra w</w> 6155
short ages</w> 6155
odor sal</w> 6155
Prol actin</w> 6155
ne st 6154
fer ret</w> 6154
oly tically</w> 6154
Multi modal</w> 6154
occup ies</w> 6154
septic aemia</w> 6154
re juven 6153
ex quis 6153
S HRs</w> 6152
vis tic</w> 6151
nitro phenol</w> 6151
M CR</w> 6150
sporozo ites</w> 6150
E uk 6149
O swe 6149
por tin</w> 6149
Ch olec 6149
TA Z</w> 6149
dac ry 6148
ot ella</w> 6147
hyper pigmentation</w> 6147
immunob lots</w> 6147
2. 54</w> 6146
transp orts</w> 6146
electro kinetic</w> 6145
gam ete</w> 6145
Or der</w> 6145
Wor d</w> 6145
Otta wa</w> 6145
bi ose</w> 6144
Dor sal</w> 6144
accent uated</w> 6144
GP IIb</w> 6143
7 5. 6142
pre biotic</w> 6142
let o 6142
C ognition</w> 6141
6 4.3</w> 6141
R AB 6141
oph yt 6141
Smar t</w> 6141
R ich</w> 6140
erg on 6140
Tr ace</w> 6140
o sim 6139
di et 6139
og los 6139
gras tim</w> 6139
F DC</w> 6138
al tru 6138
de mineralization</w> 6138
am ation</w> 6138
em ail</w> 6138
AR I 6138
draw back</w> 6138
V ital</w> 6136
ra rer</w> 6136
SGL T2</w> 6136
peristal sis</w> 6136
S arcoma</w> 6135
rh GH</w> 6135
FGF 21</w> 6135
mag ic</w> 6134
HC B</w> 6134
Met allo 6134
defibrill ators</w> 6134
Conserv ation</w> 6134
zyg otes</w> 6133
6 9.2</w> 6132
q 24</w> 6132
leng thened</w> 6132
DE C</w> 6132
Heterogene ous</w> 6132
obstetr icians</w> 6131
amoeb ae</w> 6131
v owels</w> 6130
lo fen 6130
3, 2</w> 6130
illu stration</w> 6130
ka empferol</w> 6129
dap sone</w> 6129
arcis sis 6128
t ardive</w> 6127
app raise</w> 6127
floc cul 6127
Retin opathy</w> 6126
rosu vastatin</w> 6126
i pr 6125
gl omus</w> 6125
pig let</w> 6125
IR B</w> 6125
hypophy sectomized</w> 6125
n esting</w> 6124
L CAT</w> 6124
CH L</w> 6124
AS R</w> 6124
othrom bo 6124
restitu tion</w> 6124
al ensis</w> 6123
Ab und 6123
res ort</w> 6122
sh re 6122
LG N</w> 6122
pyrethro id</w> 6122
ag asc 6121
2. 65</w> 6121
fur an 6121
gar den</w> 6121
sporozo ite</w> 6121
com yc 6120
mat s</w> 6120
2. 43</w> 6119
foc ally</w> 6119
innerv ate</w> 6119
drow ning</w> 6119
U W</w> 6118
fluo rosis</w> 6118
meta plastic</w> 6117
leuko plakia</w> 6117
B ang 6116
Diagnos tics</w> 6116
hir udin</w> 6116
v ores</w> 6115
Gen omics</w> 6115
fluoro genic</w> 6115
Morph ologic</w> 6115
P ath</w> 6114
or mycosis</w> 6114
Bi ob 6114
phal anx</w> 6114
butter fly</w> 6114
A HP</w> 6113
4 2.1</w> 6113
T PR</w> 6113
or ations</w> 6113
or he 6113
pl anted</w> 6113
met ap 6113
kerat ectomy</w> 6113
Influ ences</w> 6113
3 9.1</w> 6112
end osome</w> 6112
fem urs</w> 6112
hel pl 6112
thre e-</w> 6112
C ancers</w> 6111
3 7.2</w> 6111
em ens</w> 6111
compo unded</w> 6111
olip ase</w> 6111
f A</w> 6108
L id 6107
G DF 6107
resc ues</w> 6107
icul aris</w> 6107
top otecan</w> 6107
Oc clusion</w> 6107
squ at</w> 6106
telem etry</w> 6106
C ru 6105
os erine</w> 6105
MR M</w> 6105
hetero trimeric</w> 6105
3, 6</w> 6104
sou thwest</w> 6104
leth arg 6104
HI PEC</w> 6104
tetra ethylammonium</w> 6104
ag nes 6103
DAS 28</w> 6103
Karno fsky</w> 6103
re efs</w> 6102
os econd</w> 6102
parame ter 6102
ran s</w> 6101
amper ometric</w> 6100
flud arabine</w> 6099
ph asing</w> 6098
chemo prophylaxis</w> 6098
Gro EL</w> 6098
cercari ae</w> 6098
60 ,000</w> 6097
TM V</w> 6097
Oswe stry</w> 6097
s anc 6096
introg ression</w> 6096
Athl etes</w> 6096
vi als</w> 6095
Neur o</w> 6095
Ple ural</w> 6095
Suppor ting</w> 6095
penc il</w> 6095
7 74</w> 6094
S ound</w> 6094
th ens</w> 6094
methyl enedi 6094
s otalol</w> 6093
o ogenesis</w> 6093
T AT 6093
ur ines</w> 6093
commun icated</w> 6093
Fibro blasts</w> 6093
4 39</w> 6092
spir alis</w> 6092
estu ary</w> 6092
Dissem inated</w> 6092
Bi os 6090
MV P</w> 6090
f issures</w> 6089
o plast</w> 6089
5 6.5</w> 6089
Di els</w> 6089
3 9.3</w> 6088
di acetate</w> 6088
troph ozoites</w> 6088
PGF 2alpha</w> 6088
F av 6087
en venom 6087
di lator</w> 6087
ver atr 6087
off line</w> 6087
attrib utions</w> 6087
|
BioGPT/data/bpecodes/0
|
{
"file_path": "BioGPT/data/bpecodes",
"repo_id": "BioGPT",
"token_count": 332329
}
| 145 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import sys
import re
import json
data_dir=sys.argv[1]
prefix=sys.argv[2]
def build_source_seq(question, context, long_answer=None):
if long_answer:
src = "question: {} context: {} answer: {}".format(question.strip(), context.strip(), long_answer.strip())
else:
src = "question: {} context: {} ".format(question.strip(), context.strip())
return src
def build_target_seq(tgt):
tgt = 'the answer to the question given the context is ' + tgt + '.'
return tgt
def loader(fname, fn, required_long_answer=False):
ret = []
cnt = 0
with open(fname, 'r') as file:
data = json.load(file)
for pmid, content in data.items():
cnt += 1
question = content['QUESTION']
context = ' '.join(sen.strip() for sen in content['CONTEXTS'])
context = re.sub(r'\n', ' ', context)
# remove duplicate spaces
context = re.sub(r'\s+', ' ', context)
long_answer = content['LONG_ANSWER']
if required_long_answer:
source = build_source_seq(question, context, long_answer)
else:
source = build_source_seq(question, context)
if 'final_decision' in content:
label = content['final_decision']
target = fn(label)
else:
target = ''
if isinstance(target, list):
for i in range(len(target)):
data_pair = [source, target[i]]
ret.append(data_pair)
else:
data_pair = [source, target]
ret.append(data_pair)
print(f"{cnt} samples in {fname} has been processed")
return ret
def dumper(content_list, prefix):
fw_source = open(prefix + ".x", "w")
fw_target = open(prefix + ".y", "w")
for ele in content_list:
print(ele[0], file=fw_source)
print(ele[1], file=fw_target)
fw_source.close()
fw_target.close()
def worker(fname, prefix, fn):
ret = loader(fname, fn)
dumper(ret, prefix)
worker(os.path.join(f"{data_dir}", "train_set.json"), os.path.join(f"{data_dir}", f"{prefix}_train"), build_target_seq)
worker(os.path.join(f"{data_dir}", "dev_set.json"), os.path.join(f"{data_dir}", f"{prefix}_valid"), build_target_seq)
worker(os.path.join(f"{data_dir}", "test_set.json"), os.path.join(f"{data_dir}", f"{prefix}_test"), build_target_seq)
|
BioGPT/examples/QA-PubMedQA/rebuild_data.py/0
|
{
"file_path": "BioGPT/examples/QA-PubMedQA/rebuild_data.py",
"repo_id": "BioGPT",
"token_count": 1071
}
| 146 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from bitblas.utils.target_detector import auto_detect_nvidia_target
from bitblas import Matmul, MatmulConfig
import argparse
# Initialize the parser
parser = argparse.ArgumentParser(
description="Benchmark BitBLAS int4 on a specific target."
)
# Add arguments to the parser
parser.add_argument(
"--target",
type=str,
default=auto_detect_nvidia_target(),
help="Specify the target device for benchmarking."
)
parser.add_argument(
"--group_size",
type=int,
default=None,
help="Group size for grouped quantization."
)
parser.add_argument(
"--A_dtype",
type=str,
default="float16",
choices=["float16", "float32", "float64", "int32", "int8"], # Assuming these are the valid choices
help="Data type of activation A."
)
parser.add_argument(
"--W_dtype",
type=str,
default="int4",
choices=["float16", "float32", "float64", "int32", "int8", "int4", "int2", "int1", "nf4", "fp4_e2m1"], # Assuming these are the valid choices
help="Data type of weight W."
)
parser.add_argument(
"--accum_dtype",
type=str,
default="float16",
choices=["float16", "int32"], # Assuming these are the valid choices
help="Data type for accumulation."
)
parser.add_argument(
"--out_dtype",
type=str,
default="float16",
choices=["float16", "float32", "int32", "int8"], # Assuming these are the valid choices
help="Data type for output."
)
parser.add_argument(
"--layout",
type=str,
default="nt",
choices=["nt", "nn"], # Assuming these are the valid choices
help="Matrix layout, 'nt' for non-transpose A and transpose W."
)
parser.add_argument(
"--with_bias",
action="store_true",
help="Include bias in the benchmark."
)
parser.add_argument(
"--with_scaling",
action="store_true",
help="Include scaling factor in the quantization."
)
parser.add_argument(
"--with_zeros",
action="store_true",
help="Include zeros in the quantization."
)
parser.add_argument(
"--zeros_mode",
type=str,
default=None,
choices=["original", "rescale", "quantized"], # Replace with actual modes if applicable
help="Specify the mode for calculating zeros."
)
# Parse the arguments
args = parser.parse_args()
# Assign arguments to variables
target = args.target
group_size = args.group_size
A_dtype = args.A_dtype
W_dtype = args.W_dtype
accum_dtype = args.accum_dtype
out_dtype = args.out_dtype
layout = args.layout
with_bias = args.with_bias
group_size = args.group_size
with_scaling = args.with_scaling
with_zeros = args.with_zeros
zeros_mode = args.zeros_mode
test_shapes = [
# square test
(MatmulConfig, Matmul, (1, 16384, 16384, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
# BLOOM-176B
(MatmulConfig, Matmul, (1, 43008, 14336, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (1, 14336, 14336, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (1, 57344, 14336, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (1, 14336, 57344, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
# # OPT-65B
(MatmulConfig, Matmul, (1, 9216, 9216, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (1, 36864, 9216, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (1, 9216, 36864, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (1, 22016, 8192, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
# # LLAMA-70B/65B
(MatmulConfig, Matmul, (1, 8192, 22016, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (1, 8192, 8192, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (1, 28672, 8192, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (1, 8192, 28672, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
# square test
(MatmulConfig, Matmul, (16384, 16384, 16384, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
# BLOOM-176B
(MatmulConfig, Matmul, (8192, 43008, 14336, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (8192, 14336, 14336, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (8192, 57344, 14336, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (8192, 14336, 57344, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
# # OPT-65B
(MatmulConfig, Matmul, (8192, 9216, 9216, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (8192, 36864, 9216, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (8192, 9216, 36864, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (8192, 22016, 8192, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
# # LLAMA-70B/65B
(MatmulConfig, Matmul, (8192, 8192, 22016, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (8192, 8192, 8192, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (8192, 28672, 8192, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
(MatmulConfig, Matmul, (8192, 8192, 28672, A_dtype, W_dtype, out_dtype, accum_dtype, layout, with_bias, group_size, with_scaling, with_zeros, zeros_mode)),
]
benchmark_sets = []
benchmark_sets.extend(test_shapes)
# fmt:on
benchmark_results = {}
for config, operator, input_args in benchmark_sets:
config = config(*input_args)
matmul = operator(config, target=target, enable_tuning=True)
kernel_latency = matmul.profile_latency()
if matmul.input_transform is not None:
kernel_latency += matmul.ladder_permutate_a.profile_latency()
print("Time cost is: {:.3f} ms".format(kernel_latency))
profile_config = {
f"{operator.__name__}-{'-'.join([str(i) for i in input_args])}": {
"BitBLAS_top20_latency": kernel_latency,
}
}
benchmark_results.update(profile_config)
# Define headers for the table
headers = [
"PrimFunc",
"Input Arguments",
"BitBLAS Top20 Latency",
]
col_widths = [0, 0, 0]
for config, values in benchmark_results.items():
args = config.split("-")
func_name = args[0]
input_args = "-".join(args[1:])
col_widths[0] = max((max(len(str(headers[0])), len(func_name)) + 2), col_widths[0])
col_widths[1] = max((max(len(str(headers[1])), len(input_args)) + 2, col_widths[1]))
col_widths[2] = max(max(len(str(headers[2])), len(f"{values['BitBLAS_top20_latency']:.3f} ms")) + 2, col_widths[2])
break
for i, header in enumerate(headers):
headers[i] = header.ljust(col_widths[i])
print("".join(headers))
print("-" * sum(col_widths))
for config, values in benchmark_results.items():
args = config.split("-")
func_name = args[0]
input_args = "-".join(args[1:])
row = [
func_name,
input_args,
f"{values['BitBLAS_top20_latency']:.3f} ms",
]
print("".join([str(i).ljust(col_widths[j]) for j, i in enumerate(row)]) + "\n")
|
BitBLAS/benchmark/operators/benchmark_bitblas_matmul.py/0
|
{
"file_path": "BitBLAS/benchmark/operators/benchmark_bitblas_matmul.py",
"repo_id": "BitBLAS",
"token_count": 3838
}
| 147 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import argparse
import torch
from modeling_bitnet import BitnetForCausalLM
torch.set_grad_enabled(False)
parser = argparse.ArgumentParser()
parser.add_argument("--hf_path", default="1bitLLM/bitnet_b1_58-3B", type=str)
parser.add_argument("--batch_size", default=1, type=int)
parser.add_argument("--seq_len", default=1, type=int)
args = parser.parse_args()
seq_len = args.seq_len
batch_size = args.batch_size
def profile(model, input_data):
import time
import numpy as np
model = model.cuda()
model.eval()
def get_runtime(num_repeats=1):
tic = time.time()
for _ in range(num_repeats):
_ = model(input_data)
torch.cuda.synchronize()
return (time.time() - tic) * 1000 / num_repeats
with torch.no_grad():
st = time.time()
while time.time() - st < 1.0:
get_runtime() # warmup
warmup_runtime = get_runtime()
num_repeats = max(1, int(1000 / warmup_runtime))
times = get_runtime(num_repeats)
return np.mean(times)
def main():
model = BitnetForCausalLM.from_pretrained(
"1bitLLM/bitnet_b1_58-3B",
device_map="auto",
low_cpu_mem_usage=True,
use_flash_attention_2=True,
torch_dtype=torch.float16,
).half()
with torch.no_grad():
model._post_process_weights()
torch.cuda.empty_cache()
input_id = torch.ones(batch_size, seq_len).long().cuda()
for _ in range(10000):
_ = model(input_id)
if __name__ == "__main__":
main()
|
BitBLAS/integration/BitNet/benchmark_model_10k_loops.py/0
|
{
"file_path": "BitBLAS/integration/BitNet/benchmark_model_10k_loops.py",
"repo_id": "BitBLAS",
"token_count": 698
}
| 148 |
#!/bin/bash
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# if dist and build directories exist, remove them
if [ -d dist ]; then
rm -r dist
fi
if [ -d build ]; then
rm -r build
fi
python setup.py bdist_wheel
|
BitBLAS/maint/scripts/local_distribution.sh/0
|
{
"file_path": "BitBLAS/maint/scripts/local_distribution.sh",
"repo_id": "BitBLAS",
"token_count": 83
}
| 149 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""PrimFunc Wrapper and Block information Analaysis"""
import tvm
from tvm import tir
from tvm.tir import IterVar, PrimFunc
from typing import Any, Dict, List, Tuple, Optional
from tvm.tir.schedule.schedule import BlockRV
import numpy as np
import functools
from ..analysis import BlockInfo, get_reduction_blocks
from .. import analysis
from .. import normalize_prim_func
from .shape_inference import get_analyzer_by_tir
def pre_order_traverse(block_analyzer, blocks, func):
visited = set()
def _traverse(block):
if block in visited:
return
visited.add(block)
for dep_blocks in block_analyzer.get_consumer_blocks(block):
_traverse(dep_blocks)
func(block)
for block in blocks:
_traverse(block)
class BlockAnalyzer(object):
def __init__(self, sch) -> None:
self.sch: tir.Schedule = sch
self.block_infos: List[BlockInfo] = normalize_prim_func(self.sch)
def get_block_name(self, block: BlockRV) -> str:
return self.sch.get(block).name_hint
def get_block_info(self, block: BlockRV) -> BlockInfo:
for block_info in self.block_infos:
if self.get_block_name(block) == block_info.name:
return block_info
return None
def get_spatial_axis(self, block: BlockRV) -> List[IterVar]:
block_info = self.get_block_info(block)
axis = []
for iter in block_info.iters:
if iter.kind == "S":
axis.append(iter)
return axis
def get_reduce_axis(self, block: BlockRV) -> List[IterVar]:
block_info = self.get_block_info(block)
raxis = []
for iter in block_info.iters:
if iter.kind == "R":
raxis.append(iter)
return raxis
def get_input_buffers(self, block: BlockRV) -> List[tir.Buffer]:
buffers = []
for read in self.sch.get(block).reads:
buffers.append(read.buffer)
return buffers
def get_output_buffers(self, block: BlockRV) -> List[tir.Buffer]:
buffers = []
for write in self.sch.get(block).writes:
buffers.append(write.buffer)
return buffers
def get_buffers(self, block: BlockRV) -> List[tir.Buffer]:
return self.get_input_buffers(block) + self.get_output_buffers(block)
def get_producer_blocks(self, block: BlockRV) -> List[BlockRV]:
return self.sch.get_producers(block)
def get_consumer_blocks(self, block: BlockRV) -> List[BlockRV]:
return self.sch.get_consumers(block)
class Node(object):
def __init__(self, tags: Optional[Dict] = None) -> None:
if tags is None:
tags = {}
self._dtypes = []
self._tag: Dict = {}
for tag in tags:
self.add_tag(tag, tags[tag])
def set_tag(self, k: str, v: Any = True) -> None:
self.add_tag(k, v)
def add_tag(self, k: str, v: Any = True) -> None:
self._tag[k] = v
def get_tag(self, k: str) -> Any:
if k not in self._tag:
return None
return self._tag[k]
class PrimFuncNode(Node):
def __init__(self, prim_func: PrimFunc, tags: Optional[Dict] = None) -> None:
super().__init__(tags)
self.prim_func = self._specialize_func(prim_func)
self.sch: tir.Schedule = tir.Schedule(self.prim_func)
self.block_analyzer: BlockAnalyzer = BlockAnalyzer(self.sch)
self.schedule_stages: List[BlockRV] = []
self.blocks: List[BlockRV] = []
self.output_blocks: List[BlockRV] = None
self.reduction_block: BlockRV = None
self.raxis = []
self.input_buffers = []
self.output_buffers = []
self.buffers = []
self.args = []
self._analysis_funcinfo()
self.ana = get_analyzer_by_tir(self.block_analyzer, self.blocks)
def _specialize_func(self, func: PrimFunc):
# Specialize the function to make it more friendly for analysis.
# set attrs
for k, v in func.attrs.items():
self.set_tag(k, v)
if self.get_tag("is_speclized"):
return func
opt_shapes = self.get_tag("opt_shapes")
if opt_shapes:
for name, shape in opt_shapes.items():
var = analysis.find_var_from_func(func, name)
if var is not None:
func = func.specialize({var: shape.astype(var.dtype)})
return func
def _analysis_funcinfo(self):
root_block = analysis.get_root_block(self.sch)
blocks = self.sch.get_child_blocks(root_block)
self.blocks = blocks
self.output_blocks = self.sch.get_output_blocks(root_block)
reduction_blocks = get_reduction_blocks(self.sch, blocks)
if reduction_blocks is None:
self.reduction_block = None
self.schedule_stages.append(*self.output_blocks)
else:
# analysis on the last reduction block
self.reduction_block = reduction_blocks[-1]
# set raxis
reduce_block_info = self.block_analyzer.get_block_info(self.reduction_block)
for iter in reduce_block_info.iters:
if iter.kind == "R":
self.raxis.append(iter)
self.schedule_stages.append(self.reduction_block)
# collect output buffers
for output_block in self.output_blocks:
for write in self.sch.get(output_block).writes:
if write not in self.output_buffers:
self.output_buffers.append(write.buffer)
for param in self.prim_func.params:
if param not in self.prim_func.buffer_map:
# in case of dynamic symbolic may in params
continue
buffer = self.prim_func.buffer_map[param]
if buffer not in self.output_buffers:
self.input_buffers.append(buffer)
self.args = self.input_buffers + self.output_buffers
self.buffers = [buffer for buffer in self.prim_func.buffer_map.values()]
# set dtype
self.set_dtype(tvm.DataType(self.output_buffers[0].dtype))
def get_opt_shape(self, name) -> int:
opt_shapes = self.get_tag("opt_shapes")
if opt_shapes is None:
return None
return opt_shapes[name]
def extent_wrapper(self, value) -> int:
if isinstance(value, tvm.tir.Var):
return self.get_opt_shape(value.name)
elif isinstance(value, tvm.tir.IntImm):
return int(value)
else:
return value
@functools.lru_cache()
def get_space_dim(self) -> List[int]:
dim_size = []
if self.reduction_block:
block_info = self.block_analyzer.get_block_info(self.reduction_block)
for iter in block_info.iters:
if iter.kind == "S":
if isinstance(iter.dom.extent, tvm.tir.IntImm):
dim_size.append(int(iter.dom.extent))
else:
assert isinstance(iter.dom.extent, tvm.tir.Var)
dim_size.append(self.get_opt_shape(iter.dom.extent.name))
else:
# assume outer stage has the same shape
loops = self.sch.get_loops(self.schedule_stages[0])
for loop in loops:
dim_size.append(int(self.sch.get(loop).extent))
return [int(x) for x in dim_size]
def set_dtype(self, dtype: tvm.DataType, id=0) -> None:
assert isinstance(dtype, tvm.DataType), type(dtype)
if dtype == tvm.DataType("bool"):
dtype = tvm.DataType("int8")
if len(self._dtypes) <= id:
self._dtypes.extend([None for _ in range(id - len(self._dtypes) + 1)])
elif self._dtypes[id] is not None:
assert self._dtypes[id] == dtype, (self._dtypes, dtype)
self._dtypes[id] = dtype
def get_dtype(self, id=0) -> tvm.DataType:
return self._dtypes[id]
def get_buffer_dtype(self, buffer: tir.Buffer) -> tvm.DataType:
return tvm.DataType(buffer.dtype)
def propagate(self, tile, rstep: Optional[Dict] = None, targets=None):
if rstep is None:
rstep = {}
shape = {
self.block_analyzer.get_output_buffers(block)[0].name:
[tvm.arith.ConstIntBound(0, val - 1) for val in tile] for block in self.schedule_stages
}
return self.ana.infer(shape, rstep, targets)
def propagate_inputs(self, tile, rstep: Optional[Dict] = None) -> List[List[int]]:
if rstep is None:
rstep = {}
read_idx_offset = len(self.input_buffers)
targets = [t.name for t in self.args[:read_idx_offset]]
shapes, intermediate_bind = self.propagate(tile, rstep, targets)
results = []
for i, arg in enumerate(self.args[:read_idx_offset]):
if arg.name in intermediate_bind:
results.append(shapes[arg.name])
continue
# should not exceed original shape
trimmed_shape = [
self.extent_wrapper(i)
for i in list(map(min, zip(shapes[arg.name], self.input_buffers[i].shape)))
]
results.append(trimmed_shape)
return results
# Propagate inputs only on reduction block
def propagate_inputs_on_reduction(self, tile, rstep: Optional[Dict] = None) -> List[List[int]]:
if rstep is None:
rstep = {}
reduction_block = self.reduction_block
args = self.block_analyzer.get_input_buffers(reduction_block)
targets = [t.name for t in args]
shapes, intermediate_bind = self.propagate(tile, rstep, targets)
results = []
for i, arg in enumerate(args):
if arg.name in intermediate_bind:
results.append(shapes[arg.name])
continue
# should not exceed original shape
propagate_shape = shapes[arg.name]
buffer_shape = args[i].shape
if len(buffer_shape) > len(propagate_shape):
buffer_shape = buffer_shape[-len(propagate_shape):]
trimmed_shape = [
self.extent_wrapper(j) for j in list(map(min, zip(propagate_shape, buffer_shape)))
]
results.append(trimmed_shape)
return results
def propagate_outputs(self, tile, rstep: Optional[Dict] = None) -> List[List[int]]:
if rstep is None:
rstep = {}
read_idx_offset = len(self.input_buffers)
targets = [t.name for t in self.args[read_idx_offset:]]
shapes, _ = self.propagate(tile, rstep, targets)
results = []
for i, arg in enumerate(self.args[read_idx_offset:]):
# should not exceed original shape
trimmed_shape = list(map(min, zip(shapes[arg.name], self.input_buffers[i].shape)))
results.append(trimmed_shape)
return results
def propagate_reduction_inputs(self,
shape,
rstep: Optional[Dict] = None) -> Dict[str, List[int]]:
if rstep is None:
rstep = {}
if self.reduction_block is None:
return {}
targets = [b.name for b in self.block_analyzer.get_input_buffers(self.reduction_block)]
results, _ = self.propagate(shape, rstep, targets)
return results
def get_reduce_inputs_dtype(self):
if self.reduction_block is None:
return {}
return {
b.name: tvm.DataType(b.dtype)
for b in self.block_analyzer.get_input_buffers(self.reduction_block)
}
@functools.lru_cache()
def infer_tensorcore_axis(self) -> Tuple[int]:
# axis is fixed for one expression, so only inference and cached
assert self.get_tag("tensorcore_config")
C_ax_m, C_ax_n = self.get_tag("tensorcore_config")
wmma_m, wmma_n, wmma_k = [16, 16, 16] # just for testing, any number is ok
output_buffer_shape = (
self.block_analyzer.sch.get(self.reduction_block).writes[0].buffer.shape)
valid_region = []
for region in output_buffer_shape:
if region.value == 1:
continue
valid_region.append(region)
num_nvalid_regions = len(output_buffer_shape) - len(valid_region)
self.set_tag("num_nvalid_regions", num_nvalid_regions)
def get_cl_shapes(c_ax_m, c_ax_n, num_nvalid_regions):
spatial_dim = self.get_space_dim()
assert len(valid_region) == len(
spatial_dim), f" {valid_region} mismatch with {spatial_dim}"
cl_shapes = [1] * len(spatial_dim)
cl_shapes[c_ax_m - num_nvalid_regions] = wmma_m
cl_shapes[c_ax_n - num_nvalid_regions] = wmma_n
return cl_shapes
CL_shape = get_cl_shapes(C_ax_m, C_ax_n, num_nvalid_regions)
self.set_tag("tensorcore_config", [s - num_nvalid_regions for s in [C_ax_m, C_ax_n]])
shapes = self.propagate_reduction_inputs(CL_shape, {x.var.name: 1 for x in self.raxis})
A_deps, B_deps = shapes.values()
A_ax_m = A_deps.index(wmma_m)
B_ax_n = B_deps.index(wmma_n)
CL_shape = [1] * len(self.get_space_dim())
shapes = self.propagate_reduction_inputs(CL_shape, {x.var.name: wmma_k for x in self.raxis})
A_deps, B_deps = shapes.values()
A_ax_k = len(A_deps) - 1 - A_deps[::-1].index(wmma_k)
B_ax_k = len(B_deps) - 1 - B_deps[::-1].index(wmma_k)
tc_axis = (A_ax_m, A_ax_k, B_ax_k, B_ax_n, C_ax_m, C_ax_n)
return tc_axis
def footprint(self, shape, rstep, stride_map: Optional[Dict] = None) -> int:
if stride_map is None:
stride_map = {}
result = 0
shapes, _ = self.propagate(shape, rstep)
def is_broadcast_pattern(buffer, output_buffer):
return (buffer in self.args and
len(shapes[output_buffer.name]) > len(shapes[buffer.name]) and
np.prod(shapes[output_buffer.name]) > np.prod(shapes[buffer.name]))
def is_after_reduce_stage(block):
if not self.reduction_block:
return False
reduce_dependent_blocks = getattr(self, "reduce_dependent_blocks", None)
if reduce_dependent_blocks is None:
reduce_dependent_blocks = set()
pre_order_traverse(
self.block_analyzer,
[self.reduction_block],
lambda block: reduce_dependent_blocks.add(block),
)
self.reduce_dependent_blocks = reduce_dependent_blocks
return block not in reduce_dependent_blocks
# compute cached stages
cached_tensor = []
for block in self.blocks:
output_buffer = self.block_analyzer.get_output_buffers(block)[0]
for buffer in self.block_analyzer.get_input_buffers(block):
cache = buffer.name not in cached_tensor and (
is_broadcast_pattern(buffer, output_buffer) or
self.block_analyzer.get_block_info(block).is_reduction)
if not cache:
continue
cached_tensor.append(buffer.name)
if is_after_reduce_stage(block):
continue # cache after reduce op can often reuse buffer in reduce stage
if buffer.name in stride_map:
num_elem = stride_map[buffer.name].compute_elements_from_shape(
shapes[buffer.name])
else:
num_elem = np.prod(shapes[buffer.name])
buffer_len = num_elem * int((tvm.DataType(buffer.dtype).bits + 7) // 8)
buffer_len = (buffer_len + 31) // 32 * 32
result += buffer_len
return result, cached_tensor
def get_input_buffers(self) -> List[tir.Buffer]:
return self.block_analyzer.input_buffers
|
BitBLAS/python/bitblas/base/roller/node.py/0
|
{
"file_path": "BitBLAS/python/bitblas/base/roller/node.py",
"repo_id": "BitBLAS",
"token_count": 7738
}
| 150 |
# Copyright 2018 The apache/tvm Authors. All Rights Reserved.
# 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.
#
# /* Modifications Copyright (c) Microsoft. */
# The code below is mostly copied from apache/tvm base.py in dlight.
"""Base schedule rule for GPU operators."""
from tvm.target import Target
from ..base import ScheduleRule
class GPUScheduleRule(ScheduleRule): # pylint: disable=too-few-public-methods
"""The Schedule Rule specific to GPU targets, will return None if the target is not GPU."""
def is_target_available(self, target: Target) -> bool:
"""Check whether the target is available for gpu rule.
Parameters
----------
target : Target
The compilation target to check.
Returns
-------
available : bool
Whether the target is available for this rule.
"""
return super().is_target_available(target) and "gpu" in target.keys
|
BitBLAS/python/bitblas/gpu/base.py/0
|
{
"file_path": "BitBLAS/python/bitblas/gpu/base.py",
"repo_id": "BitBLAS",
"token_count": 498
}
| 151 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# pylint: disable=missing-docstring
"""Utility methods for generic GPU."""
from typing import List, Optional
from tvm import tir
from tvm.target import Target
def max_threads_per_block(target: Target) -> int:
"""Get the maximum number of threads per block for a given target.
Parameters
----------
target : Target
The target to get the maximum number of threads per block for.
Returns
-------
max_threads_per_block : int
The maximum number of threads per block for the given target.
"""
for name in ["max_threads_per_block", "max_num_threads"]:
result = target.attrs.get(name, None)
if result is not None:
return result
if target.kind.name == "cuda":
return 1024
return 256
def suggest_threads_per_block(
target: Target,
loops: List[tir.For],
max_threads_for_dynamic_loop: int = 32,
) -> List[int]:
if target.kind.name == "cuda":
threads = 1024
elif target.kind.name == "rocm":
threads = 256
elif target.kind.name == "metal":
threads = 256
else:
threads = 64
results: List[Optional[int]] = []
dynamic: List[int] = []
for i, loop in enumerate(loops):
loop_extent = loop.extent
if isinstance(loop_extent, tir.IntImm):
loop_extent = loop_extent.value
extent = 1
while extent <= loop_extent and extent <= threads:
extent *= 2
extent //= 2
assert extent >= 1
assert threads % extent == 0
threads //= extent
results.append(extent)
else:
results.append(None)
dynamic.append(i)
for i in dynamic:
extent = 1
while extent <= max_threads_for_dynamic_loop and extent <= threads:
extent *= 2
extent //= 2
assert extent >= 1
assert threads % extent == 0
threads //= extent
results[i] = extent
if dynamic:
results[dynamic[0]] *= threads
return results
def get_sm_version(target: Target) -> int:
if target.kind.name != "cuda":
return -1
arch = target.arch
sm_version = arch.replace("sm_", "")
return int(sm_version) if sm_version.isdigit() else -1
|
BitBLAS/python/bitblas/gpu/utils.py/0
|
{
"file_path": "BitBLAS/python/bitblas/gpu/utils.py",
"repo_id": "BitBLAS",
"token_count": 991
}
| 152 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from tvm.target import Target
from typing import Literal, Union
from .operator import Operator, TransformKind
from .impl.param_permutate_impl import select_implementation
from dataclasses import dataclass
@dataclass(frozen=True)
class ParamPermutateConfig:
M: int
N: int
datatype: Literal["float16"] = "float16"
transpose_matrix: bool = True
group_size: int = -1
propagate_kind: TransformKind = TransformKind.NonTransform
target_instruction: Literal["nvidia-mma"] = (
"nvidia-mma" # maybe extend to "cdna-mfma" in future.
)
def __post_init__(self):
if isinstance(self.propagate_kind, bool):
object.__setattr__(
self,
"propagate_kind",
(TransformKind.IntraWarpTransform
if self.propagate_kind else TransformKind.NonTransform),
)
elif isinstance(self.propagate_kind, int):
object.__setattr__(self, "propagate_kind", TransformKind(self.propagate_kind))
class ParamPermutate(Operator):
def __init__(
self,
config: ParamPermutateConfig,
name: str = "permutate",
target: Union[str, Target] = "llvm", # assume to do permutation on cpu.
):
super().__init__(name, config, target)
if target.kind.name != "llvm":
raise ValueError("Currently only support llvm target for Permutation")
self.target = target
self._build_runtime_module(target)
# select implementation based on the Operator config
def _select_implementation(self):
return select_implementation(
M=self.M,
N=self.N,
datatype=self.datatype,
transpose_matrix=self.transpose_matrix,
group_size=self.group_size,
propagate_kind=self.propagate_kind,
target_instruction=self.target_instruction,
)
@property
def M(self):
return self.config.M
@property
def N(self):
return self.config.N
@property
def datatype(self):
return self.config.datatype
@property
def propagate_kind(self):
return self.config.propagate_kind
@property
def transpose_matrix(self):
return self.config.transpose_matrix
@property
def group_size(self):
return self.config.group_size
@property
def target_instruction(self):
return self.config.target_instruction
__all__ = ["ParamPermutate", "ParamPermutateConfig"]
|
BitBLAS/python/bitblas/ops/param_permutate.py/0
|
{
"file_path": "BitBLAS/python/bitblas/ops/param_permutate.py",
"repo_id": "BitBLAS",
"token_count": 1099
}
| 153 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import pytest
import bitblas
from bitblas.utils import auto_detect_nvidia_target
from bitblas.ops.matmul_dequantize import (
MatmulWeightOnlyDequantize,
MatmulWeightOnlyDequantizeConfig,
)
import tvm
import logging
from bitblas import set_log_level
set_log_level(logging.DEBUG)
target = tvm.target.Target(auto_detect_nvidia_target())
def get_codegen_result(ops, target):
code = ops.get_source(target=target)
return code
# fmt: off
@pytest.mark.parametrize(
"M,N,K,in_dtype,out_dtype,accum_dtype,bit,storage_dtype,source_format,with_scaling,with_zeros,group_size,fast_decoding,with_bias,layout,propagate_a,propagate_b,zeros_mode",
[
(16, 768, 768, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1, True,
False, "nt", False, False, "original"),
(1, 768, 768, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1, True,
False, "nt", False, False, "original"),
(1, 768, 768, "float16", "float16", "float16", 4, "int8", "uint", True, True, -1, True,
True, "nt", False, True, "original"),
],
)
def test_matmul_dequantize_codegen_default(M, N, K, in_dtype, out_dtype, accum_dtype, bit,
storage_dtype, source_format, with_scaling, with_zeros,
group_size, fast_decoding, with_bias, layout,
propagate_a, propagate_b, zeros_mode):
matmul_config = MatmulWeightOnlyDequantizeConfig(
M=M,
N=N,
K=K,
in_dtype=in_dtype,
out_dtype=out_dtype,
accum_dtype=accum_dtype,
bit=bit,
storage_dtype=storage_dtype,
source_format=source_format,
with_scaling=with_scaling,
with_zeros=with_zeros,
group_size=group_size,
fast_decoding=fast_decoding,
with_bias=with_bias,
propagate_a=propagate_a,
propagate_b=propagate_b,
layout=layout,
zeros_mode=zeros_mode,
)
matmul = MatmulWeightOnlyDequantize(
config=matmul_config,
target=target,
)
assert get_codegen_result(matmul, target)
@pytest.mark.parametrize(
"M,N,K,in_dtype,out_dtype,accum_dtype,bit,storage_dtype,source_format,with_scaling,with_zeros,group_size,fast_decoding,with_bias,propagate_a,propagate_b,layout",
[
(1, 1024, 1024, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1, False,
False, False, False, "nt"),
],
)
def test_matmul_dequantize_retrieve_weight_shape(
M,
N,
K,
in_dtype,
out_dtype,
accum_dtype,
bit,
storage_dtype,
source_format,
with_scaling,
with_zeros,
group_size,
fast_decoding,
with_bias,
propagate_a,
propagate_b,
layout,
):
matmul_config = MatmulWeightOnlyDequantizeConfig(
M=M,
N=N,
K=K,
in_dtype=in_dtype,
out_dtype=out_dtype,
accum_dtype=accum_dtype,
bit=bit,
storage_dtype=storage_dtype,
source_format=source_format,
with_scaling=with_scaling,
with_zeros=with_zeros,
group_size=group_size,
fast_decoding=fast_decoding,
with_bias=with_bias,
propagate_a=propagate_a,
propagate_b=propagate_b,
layout=layout,
)
matmul = MatmulWeightOnlyDequantize(
config=matmul_config,
target=target,
)
assert matmul.retrieve_weight_shape()
@pytest.mark.parametrize(
"M,N,K,in_dtype,out_dtype,accum_dtype,bit,storage_dtype,source_format,with_scaling,with_zeros,group_size,fast_decoding,with_bias,propagate_a,propagate_b,layout",
[
(
1,
1024,
1024,
"float16",
"float16",
"float16",
4,
"int8",
"uint",
False,
False,
-1,
False,
False,
False,
False,
"nt",
),
(
1,
1024,
1024,
"float16",
"float16",
"float16",
4,
"int8",
"uint",
False,
False,
-1,
False,
False,
False,
True,
"nt",
),
],
)
def test_matmul_dequantize_codegen_finetune(
M,
N,
K,
in_dtype,
out_dtype,
accum_dtype,
bit,
storage_dtype,
source_format,
with_scaling,
with_zeros,
group_size,
fast_decoding,
with_bias,
propagate_a,
propagate_b,
layout,
):
matmul_config = MatmulWeightOnlyDequantizeConfig(
M=M,
N=N,
K=K,
in_dtype=in_dtype,
out_dtype=out_dtype,
accum_dtype=accum_dtype,
bit=bit,
storage_dtype=storage_dtype,
source_format=source_format,
with_scaling=with_scaling,
with_zeros=with_zeros,
group_size=group_size,
fast_decoding=fast_decoding,
with_bias=with_bias,
propagate_a=propagate_a,
propagate_b=propagate_b,
layout=layout,
)
matmul = MatmulWeightOnlyDequantize(
config=matmul_config,
target=target,
)
matmul.hardware_aware_finetune(topk=20)
assert get_codegen_result(matmul, target)
@pytest.mark.parametrize(
"M,N,K,in_dtype,out_dtype,accum_dtype,bit,storage_dtype,source_format,with_scaling,with_zeros,group_size,fast_decoding,with_bias,propagate_a,propagate_b,layout",
[
(
1,
1024,
1024,
"float16",
"float16",
"float16",
4,
"int8",
"uint",
False,
False,
-1,
False,
False,
False,
False,
"nt",
),
(
1,
1024,
1024,
"float16",
"float16",
"float16",
4,
"int8",
"nf",
False,
False,
-1,
False,
False,
False,
False,
"nt",
),
(
1024,
1024,
1024,
"float16",
"float16",
"float16",
4,
"int8",
"nf",
False,
False,
-1,
False,
False,
False,
False,
"nt",
),
(
1024,
1024,
1024,
"float16",
"float16",
"float16",
4,
"int8",
"nf",
False,
False,
-1,
False,
False,
False,
True,
"nt",
),
(
1024,
1024,
1024,
"float16",
"float16",
"float16",
4,
"int8",
"nf",
False,
False,
-1,
False,
False,
True,
True,
"nt",
),
(
1024,
1024,
1024,
"float16",
"float16",
"float16",
4,
"int8",
"nf",
True,
False,
-1,
False,
False,
True,
True,
"nt",
),
(
1024,
1024,
1024,
"float16",
"float16",
"float16",
4,
"int8",
"nf",
True,
False,
128,
False,
False,
True,
True,
"nt",
),
],
)
def test_matmul_dequantize_profile_latency(
M,
N,
K,
in_dtype,
out_dtype,
accum_dtype,
bit,
storage_dtype,
source_format,
with_scaling,
with_zeros,
group_size,
fast_decoding,
with_bias,
propagate_a,
propagate_b,
layout,
):
matmul_config = MatmulWeightOnlyDequantizeConfig(
M=M,
N=N,
K=K,
in_dtype=in_dtype,
out_dtype=out_dtype,
accum_dtype=accum_dtype,
bit=bit,
storage_dtype=storage_dtype,
source_format=source_format,
with_scaling=with_scaling,
with_zeros=with_zeros,
group_size=group_size,
fast_decoding=fast_decoding,
with_bias=with_bias,
propagate_a=propagate_a,
propagate_b=propagate_b,
layout=layout,
)
matmul = MatmulWeightOnlyDequantize(
config=matmul_config,
target=target,
)
matmul.hardware_aware_finetune(topk=20)
latency = matmul.profile_latency()
assert latency
@pytest.mark.parametrize(
"M,N,K,in_dtype,out_dtype,accum_dtype,bit,storage_dtype,source_format,with_scaling,with_zeros,group_size,fast_decoding,with_bias,propagate_a,propagate_b,layout,zeros_mode",
[
(1, 1024, 1024, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1, False,
False, False, False, "nt", "rescale"),
(1, 1024, 1024, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1, True,
False, False, False, "nt", "rescale"),
(1, 1024, 1024, "float16", "float16", "float16", 4, "int8", "int", False, False, -1, False,
False, False, False, "nt", "rescale"),
(1, 1024, 1024, "float16", "float16", "float16", 4, "int8", "int", False, False, -1, True,
False, False, False, "nt", "rescale"),
(1, 1024, 1024, "float16", "float16", "float16", 2, "int8", "int", False, False, -1, True,
False, False, False, "nt", "rescale"),
(1, 1024, 1024, "float16", "float16", "float16", 2, "int8", "int", True, False, -1, True,
False, False, False, "nt", "rescale"),
(1, 1024, 1024, "float16", "float16", "float16", 2, "int8", "int", True, False, 128, True,
False, False, False, "nt", "rescale"),
(1, 1024, 1024, "float16", "float16", "float16", 2, "int8", "uint", True, True, 128, False,
False, False, False, "nt", "rescale"),
(1, 1024, 4096, "float16", "float16", "float16", 2, "int8", "uint", True, True, 128, True,
False, False, False, "nt", "rescale"),
(1024, 1024, 1024, "float16", "float16", "float16", 2, "int8", "int", True, False, 128,
False, False, False, False, "nt", "rescale"),
(1024, 1024, 1024, "float16", "float16", "float16", 2, "int8", "int", True, False, 128,
False, False, False, True, "nt", "rescale"),
(1024, 1024, 1024, "float16", "float16", "float16", 2, "int8", "int", True, False, 128,
False, False, True, True, "nt", "rescale"),
(1024, 1024, 1024, "float16", "float16", "float16", 2, "int8", "int", True, False, 128,
False, False, True, True, "nt", "original"),
([1, 1024], 1024, 1024, "float16", "float16", "float16", 4, "int8", "uint", False, False,
-1, False, False, False, False, "nt", "original"),
],
)
def test_matmul_dequantize_torch_forward(M, N, K, in_dtype, out_dtype, accum_dtype, bit,
storage_dtype, source_format, with_scaling, with_zeros,
group_size, fast_decoding, with_bias, propagate_a,
propagate_b, layout, zeros_mode):
import torch
torch.random.manual_seed(0)
import numpy as np
from bitblas.quantization.utils import general_compress
matmul_config = MatmulWeightOnlyDequantizeConfig(
M=M,
N=N,
K=K,
in_dtype=in_dtype,
out_dtype=out_dtype,
accum_dtype=accum_dtype,
bit=bit,
storage_dtype=storage_dtype,
source_format=source_format,
with_scaling=with_scaling,
with_zeros=with_zeros,
group_size=group_size,
fast_decoding=fast_decoding,
with_bias=with_bias,
propagate_a=propagate_a,
propagate_b=propagate_b,
layout=layout,
zeros_mode=zeros_mode)
matmul = MatmulWeightOnlyDequantize(
config=matmul_config,
target=target,
)
if not isinstance(M, int):
M = 32
matmul.hardware_aware_finetune(topk=20)
input_shape = (M, K)
weight_shape = (N, K) if layout == "nt" else (K, N)
output_shape = (M, N)
inputs = []
inputs.append(torch.rand(input_shape, dtype=torch.float16).cuda() - 0.5)
maxq = 2 ** (bit - 1)
zeros = maxq
if source_format == "uint":
inputs.append(torch.randint(0, maxq, weight_shape, dtype=torch.int8).cuda())
elif source_format == "int":
inputs.append(torch.randint(-maxq, maxq, weight_shape, dtype=torch.int8).cuda())
else:
raise NotImplementedError
inputs.append(torch.rand(output_shape, dtype=torch.float16).cuda())
intweight = inputs[1]
intweight = intweight.cpu().numpy().astype(np.int8)
if source_format == "int":
intweight = intweight + maxq
if with_zeros:
inputs[1] = inputs[1] - zeros
bias = torch.rand((output_shape[-1],), dtype=torch.float16).cuda()
ref_result = torch.matmul(inputs[0],
(inputs[1].t() if layout == "nt" else inputs[1]).to(torch.float16))
if with_bias:
ref_result = ref_result + bias
qw_np = general_compress(intweight, source_bits=bit, storage_dtype=np.int8)
qw_torch = torch.from_numpy(qw_np).cuda()
permuted_inputs = []
if matmul.input_transform is not None:
permuted_inputs.append(matmul.input_transform(inputs[0].cpu()).cuda())
else:
permuted_inputs.append(inputs[0])
if matmul.weight_transform is not None:
permuted_inputs.append(matmul.weight_transform(qw_torch.cpu()).cuda())
else:
permuted_inputs.append(qw_torch)
if with_scaling:
if group_size == -1:
group_size = K
permuted_inputs.append(torch.ones([N, K // group_size], dtype=torch.float16).cuda())
if with_zeros:
permuted_inputs.append(torch.ones([N, K // group_size], dtype=torch.float16).cuda() * zeros)
if with_bias:
permuted_inputs.append(bias)
permuted_inputs.append(inputs[2])
matmul(*permuted_inputs)
if zeros_mode == "rescale":
torch.testing.assert_close(permuted_inputs[-1], ref_result, rtol=1e-0, atol=1e-0)
else:
torch.testing.assert_close(permuted_inputs[-1], ref_result, rtol=1e-0, atol=1e-1)
@pytest.mark.parametrize(
"M,N,K,in_dtype,out_dtype,accum_dtype,bit,storage_dtype,source_format,with_scaling,with_zeros,group_size,fast_decoding,with_bias,layout,zeros_mode",
[
(16, 768, 768, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1, True,
False, "nt", "original"),
(16, 768, 768, "float16", "float16", "float16", 4, "int8", "uint", False, True, -1, True,
True, "nt", "original"),
(16, 3072, 768, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1, True,
False, "nt", "original"),
(16, 768, 3072, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1, True,
False, "nt", "original"),
],
)
def test_matmul_dequantize_propgate_comparison(M, N, K, in_dtype, out_dtype, accum_dtype, bit,
storage_dtype, source_format, with_scaling,
with_zeros, group_size, fast_decoding, with_bias,
layout, zeros_mode):
import torch
torch.random.manual_seed(0)
original_matmul_config = MatmulWeightOnlyDequantizeConfig(
M=M,
N=N,
K=K,
in_dtype=in_dtype,
out_dtype=out_dtype,
accum_dtype=accum_dtype,
bit=bit,
storage_dtype=storage_dtype,
source_format=source_format,
with_scaling=with_scaling,
with_zeros=with_zeros,
group_size=group_size,
fast_decoding=False,
with_bias=with_bias,
propagate_a=False,
propagate_b=False,
layout=layout,
zeros_mode=zeros_mode)
original_matmul = MatmulWeightOnlyDequantize(
config=original_matmul_config,
target=target,
)
if not isinstance(M, int):
M = 32
if group_size == -1:
group_size = K
input_shape = (M, K)
weight_shape = (N, K // 2) if layout == "nt" else (K, N)
output_shape = (M, N)
scales_shape = (N, K // group_size)
zeros_shape = (N, K // group_size)
bias_shape = (N,)
inputs = []
input_tensor = torch.rand(input_shape, dtype=torch.float16).cuda()
weight_tensor = torch.randint(0, 2**(bit - 1) - 1, weight_shape, dtype=torch.int8).cuda()
scales_tensor = torch.rand(scales_shape, dtype=torch.float16).cuda()
zeros_tensor = torch.rand(zeros_shape, dtype=torch.float16).cuda()
bias_tensor = torch.rand(bias_shape, dtype=torch.float16).cuda()
output_tensor = torch.zeros(output_shape, dtype=torch.float16).cuda()
inputs.append(input_tensor)
inputs.append(weight_tensor)
if with_scaling:
inputs.append(scales_tensor)
if with_zeros:
inputs.append(zeros_tensor)
if with_bias:
inputs.append(bias_tensor)
inputs.append(output_tensor)
ref_result = original_matmul(*inputs)
propagated_matmul_config = MatmulWeightOnlyDequantizeConfig(
M=M,
N=N,
K=K,
in_dtype=in_dtype,
out_dtype=out_dtype,
accum_dtype=accum_dtype,
bit=bit,
storage_dtype=storage_dtype,
source_format=source_format,
with_scaling=with_scaling,
with_zeros=with_zeros,
group_size=group_size,
fast_decoding=fast_decoding,
with_bias=with_bias,
propagate_a=False,
propagate_b=True,
layout=layout,
zeros_mode=zeros_mode)
propagated_matmul = MatmulWeightOnlyDequantize(
config=propagated_matmul_config,
target=target,
)
propagated_matmul.hardware_aware_finetune(topk=20)
propagated_inputs = []
propagated_inputs.append(input_tensor)
if propagated_matmul.weight_transform is not None:
propagated_inputs.append(propagated_matmul.weight_transform(weight_tensor.cpu()).cuda())
else:
propagated_inputs.append(weight_tensor)
if with_scaling:
propagated_inputs.append(scales_tensor)
if with_zeros:
propagated_inputs.append(zeros_tensor)
if with_bias:
propagated_inputs.append(bias_tensor)
propagated_inputs.append(torch.zeros(output_shape, dtype=torch.float16).cuda())
propagated_result = propagated_matmul(*propagated_inputs)
torch.testing.assert_close(ref_result, propagated_result, rtol=1e-2, atol=1e-2)
@pytest.mark.parametrize(
"M,N,K,in_dtype,out_dtype,accum_dtype,bit,storage_dtype,source_format,with_scaling,with_zeros,group_size,fast_decoding,with_bias,propagate_a,propagate_b,layout,source_zeros_mode,target_zeros_mode",
[
(1, 1024, 1024, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1, False,
False, False, False, "nt", "rescale", "quantized"),
(1, 1024, 1024, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1, False,
False, False, False, "nt", "rescale", "original"),
(1024, 1024, 1024, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1,
False, False, False, False, "nt", "rescale", "quantized"),
(1024, 1024, 1024, "float16", "float16", "float16", 4, "int8", "uint", False, False, -1,
False, False, False, False, "nt", "rescale", "original"),
],
)
def test_matmul_dequantize_diff_zero_types(M, N, K, in_dtype, out_dtype, accum_dtype, bit,
storage_dtype, source_format, with_scaling, with_zeros,
group_size, fast_decoding, with_bias, propagate_a,
propagate_b, layout, source_zeros_mode,
target_zeros_mode):
import torch
torch.random.manual_seed(0)
import numpy as np
from bitblas.quantization.utils import general_compress
source_quantized_matmul_config = MatmulWeightOnlyDequantizeConfig(
M=M,
N=N,
K=K,
in_dtype=in_dtype,
out_dtype=out_dtype,
accum_dtype=accum_dtype,
bit=bit,
storage_dtype=storage_dtype,
source_format=source_format,
with_scaling=with_scaling,
with_zeros=with_zeros,
group_size=group_size,
fast_decoding=fast_decoding,
with_bias=with_bias,
propagate_a=propagate_a,
propagate_b=propagate_b,
layout=layout,
zeros_mode=source_zeros_mode)
source_quantized_matmul = MatmulWeightOnlyDequantize(
config=source_quantized_matmul_config,
target=target,
)
if not isinstance(M, int):
M = 32
source_quantized_matmul.hardware_aware_finetune(topk=20)
input_shape = (M, K)
weight_shape = (N, K) if layout == "nt" else (K, N)
output_shape = (M, N)
inputs = []
inputs.append(torch.rand(input_shape, dtype=torch.float16).cuda() - 0.5)
maxq = 2**(bit - 1) - 1
zeros = maxq
if source_format == "uint":
inputs.append(torch.randint(0, maxq, weight_shape, dtype=torch.int8).cuda())
elif source_format == "int":
inputs.append(torch.randint(-maxq, maxq, weight_shape, dtype=torch.int8).cuda())
else:
raise NotImplementedError
inputs.append(torch.rand(output_shape, dtype=torch.float16).cuda())
intweight = inputs[1]
intweight = intweight.cpu().numpy().astype(np.int8)
if source_format == "int":
intweight = intweight + maxq
if with_zeros:
inputs[1] = inputs[1] - zeros
bias = torch.rand((output_shape[-1],), dtype=torch.float16).cuda()
qw_np = general_compress(intweight, source_bits=bit, storage_dtype=np.int8)
qw_torch = torch.from_numpy(qw_np).cuda()
permuted_inputs = []
if source_quantized_matmul.input_transform is not None:
permuted_inputs.append(source_quantized_matmul.input_transform(qw_torch.cpu()).cuda())
else:
permuted_inputs.append(inputs[0])
if source_quantized_matmul.weight_transform is not None:
permuted_inputs.append(source_quantized_matmul.weight_transform(qw_torch.cpu()).cuda())
else:
permuted_inputs.append(qw_torch)
if with_scaling:
if group_size == -1:
group_size = K
permuted_inputs.append(torch.rand([N, K // group_size], dtype=torch.float16).cuda())
if with_zeros:
if source_zeros_mode == "original":
permuted_inputs.append(
torch.ones([N, K // group_size], dtype=torch.float16).cuda() * zeros)
elif source_zeros_mode == "rescale":
original_zeros = torch.ones([N, K // group_size], dtype=torch.float16).cuda() * zeros
scaled_zeros = original_zeros * permuted_inputs[-1]
permuted_inputs.append(scaled_zeros)
elif source_zeros_mode == "quantized":
original_zeros = torch.ones([K // group_size, N], dtype=torch.int8).cuda() * zeros
qzeros = general_compress(
original_zeros.cpu().numpy(), source_bits=bit, storage_dtype=np.int8)
permuted_inputs.append(torch.from_numpy(qzeros).cuda())
else:
raise NotImplementedError
if with_bias:
permuted_inputs.append(bias)
permuted_inputs.append(inputs[2])
source_quantized_matmul(*permuted_inputs)
ref_result = permuted_inputs[-1]
target_quantized_matmul_config = MatmulWeightOnlyDequantizeConfig(
M=M,
N=N,
K=K,
in_dtype=in_dtype,
out_dtype=out_dtype,
accum_dtype=accum_dtype,
bit=bit,
storage_dtype=storage_dtype,
source_format=source_format,
with_scaling=with_scaling,
with_zeros=with_zeros,
group_size=group_size,
fast_decoding=fast_decoding,
with_bias=with_bias,
propagate_a=propagate_a,
propagate_b=propagate_b,
layout=layout,
zeros_mode=target_zeros_mode)
target_quantized_matmul = MatmulWeightOnlyDequantize(
config=target_quantized_matmul_config,
target=target,
)
if not isinstance(M, int):
M = 32
target_quantized_matmul.hardware_aware_finetune(topk=20)
input_shape = (M, K)
weight_shape = (N, K) if layout == "nt" else (K, N)
output_shape = (M, N)
target_inputs = []
target_inputs.append(permuted_inputs[0])
target_inputs.append(permuted_inputs[1])
if with_scaling:
target_inputs.append(permuted_inputs[2])
if with_zeros:
if target_zeros_mode == "original":
target_inputs.append(
torch.ones([N, K // group_size], dtype=torch.float16).cuda() * zeros)
elif target_zeros_mode == "rescale":
original_zeros = torch.ones([N, K // group_size], dtype=torch.float16).cuda() * zeros
scaled_zeros = original_zeros * target_inputs[-1]
target_inputs.append(scaled_zeros)
elif target_zeros_mode == "quantized":
original_zeros = torch.ones([K // group_size, N], dtype=torch.int8).cuda() * zeros
qzeros = general_compress(
original_zeros.cpu().numpy(), source_bits=bit, storage_dtype=np.int8)
target_inputs.append(torch.from_numpy(qzeros).cuda())
else:
raise NotImplementedError
if with_bias:
target_inputs.append(bias)
target_inputs.append(torch.zeros_like(inputs[2]))
target_quantized_matmul(*target_inputs)
torch.testing.assert_close(target_inputs[-1], ref_result, rtol=1e-2, atol=1e-2)
# fmt: on
if __name__ == "__main__":
bitblas.testing.main()
|
BitBLAS/testing/python/operators/test_matmul_dequantize_ops.py/0
|
{
"file_path": "BitBLAS/testing/python/operators/test_matmul_dequantize_ops.py",
"repo_id": "BitBLAS",
"token_count": 13779
}
| 154 |
from ..datasets import VQAv2Dataset
from .datamodule_base import BaseDataModule
from collections import defaultdict
class VQAv2DataModule(BaseDataModule):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@property
def dataset_cls(self):
return VQAv2Dataset
@property
def dataset_name(self):
return "vqa"
def setup(self, stage):
super().setup(stage)
train_answers = self.train_dataset.table["answers"].to_pandas().tolist()
val_answers = self.val_dataset.table["answers"].to_pandas().tolist()
train_labels = self.train_dataset.table["answer_labels"].to_pandas().tolist()
val_labels = self.val_dataset.table["answer_labels"].to_pandas().tolist()
all_answers = [c for c in train_answers + val_answers if c is not None]
all_answers = [l for lll in all_answers for ll in lll for l in ll]
all_labels = [c for c in train_labels + val_labels if c is not None]
all_labels = [l for lll in all_labels for ll in lll for l in ll]
self.answer2id = {k: v for k, v in zip(all_answers, all_labels)}
sorted_a2i = sorted(self.answer2id.items(), key=lambda x: x[1])
self.num_class = max(self.answer2id.values()) + 1
self.id2answer = defaultdict(lambda: "unknown")
for k, v in sorted_a2i:
self.id2answer[v] = k
|
BridgeTower/src/datamodules/vqav2_datamodule.py/0
|
{
"file_path": "BridgeTower/src/datamodules/vqav2_datamodule.py",
"repo_id": "BridgeTower",
"token_count": 634
}
| 155 |
from collections import OrderedDict
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = LayerNorm(d_model)
self.mlp = nn.Sequential(OrderedDict([
("c_fc", nn.Linear(d_model, d_model * 4)),
("gelu", QuickGELU()),
("c_proj", nn.Linear(d_model * 4, d_model))
]))
self.ln_2 = LayerNorm(d_model)
self.attn_mask = attn_mask
def attention(self, x: torch.Tensor, x_mask:torch.Tensor):
if x_mask is not None:
x_mask = x_mask.to(dtype=torch.bool, device=x.device)
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=x_mask)[0]
def forward(self, x: torch.Tensor, x_mask:torch.Tensor=None):
x = x + self.attention(self.ln_1(x), x_mask)
x = x + self.mlp(self.ln_2(x))
return x
class Transformer(nn.Module):
def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, model_type: str = "BT", vit_remove_last: bool = False):
super().__init__()
self.width = width
self.layers = layers
if vit_remove_last:
self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers - 1)])
else:
self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
self.model_type = model_type
def forward(self, x: torch.Tensor, x_mask: torch.Tensor=None):
xs = []
for block in self.resblocks:
x = block(x, x_mask)
if self.model_type == 'BT':
xs.append(x)
if self.model_type == 'BT':
return xs
else:
return x
class VisualTransformer(nn.Module):
def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int, resolution_after: int, model_type: str = "BT", vit_layernorm_shared: bool = True, vit_remove_last: bool = False):
super().__init__()
self.input_resolution = input_resolution
self.output_dim = output_dim
self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
scale = width ** -0.5
self.class_embedding = nn.Parameter(scale * torch.randn(width))
self.positional_embedding = nn.Parameter(scale * torch.randn((resolution_after // patch_size) ** 2 + 1, width))
self.ln_pre = LayerNorm(width)
self.transformer = Transformer(width, layers, heads, model_type=model_type, vit_remove_last=vit_remove_last)
self.ln_post = LayerNorm(width)
self.model_type = model_type
self.vit_layernorm_shared = vit_layernorm_shared
if not vit_layernorm_shared:
# self.cross_modal_ln_separate
self.ln_separate = nn.ModuleList([LayerNorm(width) for _ in range(layers)])
def forward(self, x: torch.Tensor, x_mask):
x = self.conv1(x) # shape = [*, width, grid, grid]
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
t = self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device)
x = torch.cat([t, x], dim=1) # shape = [*, grid ** 2 + 1, width]
x = x + self.positional_embedding.to(x.dtype)
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
if self.model_type == 'BT':
xs = self.transformer(x, x_mask)
xs = torch.stack(xs, dim=0) # shape = [layers, width, *, grid ** 2]
xs = xs.permute(0, 2, 1, 3) # shape = [layers, *, width, grid ** 2]
if self.vit_layernorm_shared:
xs = self.ln_post(xs)
else:
xs_ = []
for x, ln in zip(xs, self.ln_separate):
x = ln(x)
xs_.append(x)
xs = torch.stack(xs_, dim=0) # shape = [layers, *, width, grid ** 2]
return xs
else:
x = self.transformer(x, x_mask)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.ln_post(x)
return x
def forward_pre(self, x: torch.Tensor):
x = self.conv1(x) # shape = [*, width, grid, grid]
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
t = self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device)
x = torch.cat([t, x], dim=1) # shape = [*, grid ** 2 + 1, width]
x = x + self.positional_embedding.to(x.dtype)
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
return x
def forward_post(self, x: torch.Tensor):
x = x.permute(1, 0, 2)
x = self.ln_post(x)
return x
class CLIP(nn.Module):
def __init__(self,
embed_dim: int,
# vision
image_resolution: int,
vision_layers: Union[Tuple[int, int, int, int], int],
vision_width: int,
vision_patch_size: int,
# text
context_length: int,
vocab_size: int,
transformer_width: int,
transformer_heads: int,
transformer_layers: int,
resolution_after=224,
model_type="BT",
vit_layernorm_shared=True,
vit_remove_last=False
):
super().__init__()
self.context_length = context_length
vision_heads = vision_width // 64
self.visual = VisualTransformer(
input_resolution=image_resolution,
patch_size=vision_patch_size,
width=vision_width,
layers=vision_layers,
heads=vision_heads,
output_dim=embed_dim,
resolution_after=resolution_after,
model_type=model_type,
vit_layernorm_shared=vit_layernorm_shared,
vit_remove_last=vit_remove_last,
)
self.vocab_size = vocab_size
self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
self.ln_final = LayerNorm(transformer_width)
self.initialize_parameters()
def initialize_parameters(self):
nn.init.normal_(self.positional_embedding, std=0.01)
proj_std = (self.visual.transformer.width ** -0.5) * ((2 * self.visual.transformer.layers) ** -0.5)
attn_std = self.visual.transformer.width ** -0.5
fc_std = (2 * self.visual.transformer.width) ** -0.5
for block in self.visual.transformer.resblocks:
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
@property
def dtype(self):
return self.visual.conv1.weight.dtype
def forward(self, image, image_mask=None):
return self.visual(image.type(self.dtype), image_mask)
def available_models():
"""Returns the names of available CLIP models"""
return list(_MODELS.keys())
_MODELS = {
"CLIP-ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
"CLIP-ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
"CLIP-ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
"CLIP-ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
}
import os
import hashlib
import urllib
from tqdm import tqdm
import warnings
def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
os.makedirs(root, exist_ok=True)
filename = os.path.basename(url)
expected_sha256 = url.split("/")[-2]
download_target = os.path.join(root, filename)
if os.path.exists(download_target) and not os.path.isfile(download_target):
raise RuntimeError(f"{download_target} exists and is not a regular file")
if os.path.isfile(download_target):
if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
return download_target
else:
warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop:
while True:
buffer = source.read(8192)
if not buffer:
break
output.write(buffer)
loop.update(len(buffer))
if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
return download_target
def adapt_position_encoding(model, patch_size=32, after=384,
suffix='visual.positional_embedding'):
keys = [k for k in model if k.endswith(suffix)]
assert len(keys) == 1
key = keys[0]
origin_pos_embed = model[key]
origin_dim2 = False
if len(origin_pos_embed.shape) == 2:
origin_dim2 = True
origin_pos_embed = origin_pos_embed.unsqueeze(0)
grid_before = int(np.sqrt(origin_pos_embed.shape[1] - 1))
before = int(grid_before*patch_size)
assert (before % patch_size) == 0
grid_after = after // patch_size
assert (after % patch_size) == 0
embed_dim = origin_pos_embed.shape[-1]
pos_embed = origin_pos_embed[0, 1:, :].reshape((grid_before, grid_before, embed_dim))
new_size = (grid_after, grid_after)
pos_embed = torch.nn.functional.interpolate(pos_embed.permute((2, 0, 1)).unsqueeze(0), size=new_size, mode='bicubic')
pos_embed = pos_embed.squeeze(0).permute((1, 2, 0)).reshape((-1, embed_dim))
pos_embed = torch.cat((origin_pos_embed[0, 0:1, :], pos_embed), dim=0).unsqueeze(0)
assert pos_embed.shape == (1, grid_after * grid_after + 1, embed_dim)
if origin_dim2:
assert pos_embed.shape[0] == 1
pos_embed = pos_embed.squeeze(0)
model[key] = pos_embed
return model
def build_model(name, resolution_after=224, model_type="BT", vit_layernorm_shared=True, vit_remove_last=False):
if name in _MODELS:
model_path = _download(_MODELS[name])
elif os.path.isfile(name):
model_path = name
else:
raise RuntimeError(f"Model {name} not found; available models = {available_models()}"
)
try:
model = torch.jit.load(model_path, map_location="cpu")
state_dict = None
except RuntimeError:
if jit:
warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
jit = False
state_dict = torch.load(model_path, map_location="cpu")
state_dict = state_dict or model.state_dict()
vit = "visual.proj" in state_dict
vision_width = state_dict["visual.conv1.weight"].shape[0] # Feature Dimension
vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
image_resolution = vision_patch_size * grid_size
embed_dim = state_dict["text_projection"].shape[1]
context_length = state_dict["positional_embedding"].shape[0]
vocab_size = state_dict["token_embedding.weight"].shape[0]
transformer_width = state_dict["ln_final.weight"].shape[0]
transformer_heads = transformer_width // 64
transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
model = CLIP(
embed_dim,
image_resolution, vision_layers, vision_width, vision_patch_size,
context_length, vocab_size, transformer_width, transformer_heads, transformer_layers,
resolution_after, model_type, vit_layernorm_shared, vit_remove_last,
)
for key in ["input_resolution", "context_length", "vocab_size"]:
if key in state_dict:
del state_dict[key]
model_dict = model.state_dict()
pretrained_dict = state_dict
if resolution_after != image_resolution:
pretrained_dict = adapt_position_encoding(pretrained_dict, after=resolution_after, patch_size=vision_patch_size)
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model.load_state_dict(model_dict)
return model
|
BridgeTower/src/modules/clip_model.py/0
|
{
"file_path": "BridgeTower/src/modules/clip_model.py",
"repo_id": "BridgeTower",
"token_count": 6470
}
| 156 |
# Based on https://github.com/peteanderson80/bottom-up-attention/blob/master/data/genome/create_splits.py
''' Determine visual genome data splits to avoid contamination of COCO splits.'''
import argparse
import os
import random
from random import shuffle
import shutil
import subprocess
import sys
import json
random.seed(0) # Make dataset splits repeatable
# The root directory which holds all information of the dataset.
dataDir = '~/BT/dataset/vg'
# First determine train, val, test splits (x, 5000, 5000)
train = set()
val = set()
test = set()
# Load coco test ids
coco_test_ids = set()
with open(os.path.join(dataDir, 'coco_splits/image_info_test2014.json')) as f:
coco_data = json.load(f)
for item in coco_data['images']:
coco_test_ids.add(item['id'])
print("There are %d coco test images" % len(coco_test_ids))
# Load karpathy coco splits
karpathy_train = set()
with open(os.path.join(dataDir, 'coco_splits/karpathy_train_images.txt')) as f:
for line in f.readlines():
image_id=int(line.split('.')[0].split('_')[-1])
karpathy_train.add(image_id)
karpathy_val = set()
with open(os.path.join(dataDir, 'coco_splits/karpathy_val_images.txt')) as f:
for line in f.readlines():
image_id=int(line.split('.')[0].split('_')[-1])
karpathy_val.add(image_id)
karpathy_test = set()
with open(os.path.join(dataDir, 'coco_splits/karpathy_test_images.txt')) as f:
for line in f.readlines():
image_id=int(line.split('.')[0].split('_')[-1])
karpathy_test.add(image_id)
print( "Karpathy splits are %d, %d, %d (train, val, test)" % (len(karpathy_train), len(karpathy_val), len(karpathy_test)))
# Load VG image metadata
coco_ids = set()
with open(os.path.join(dataDir, 'image_data.json')) as f:
metadata = json.load(f)
for item in metadata:
if item['coco_id']:
coco_ids.add(item['coco_id'])
print( "Found %d visual genome images claiming coco ids" % len(coco_ids))
print( "Overlap with COCO test is %d" % len(coco_test_ids & coco_ids))
print( "Overlap with Karpathy train is %d" % len(karpathy_train & coco_ids))
print( "Overlap with Karpathy val is %d" % len(karpathy_val & coco_ids))
print( "Overlap with Karpathy test is %d" % len(karpathy_test & coco_ids))
# Output
# There are 40775 coco test images
# Karpathy splits are 113287, 5000, 5000 (train, val, test)
# Found 51208 visual genome images claiming coco ids
# Overlap with COCO test is 0
# Overlap with Karpathy train is 46944
# Overlap with Karpathy val is 2126
# Overlap with Karpathy test is 2138
# Determine splits
remainder = []
for item in metadata:
if item['coco_id']:
if item['coco_id'] in karpathy_train:
train.add(item['image_id'])
elif item['coco_id'] in karpathy_val:
val.add(item['image_id'])
elif item['coco_id'] in karpathy_test:
test.add(item['image_id'])
else:
remainder.append(item['image_id'])
else:
remainder.append(item['image_id'])
with open(os.path.join(dataDir, 'vgqa/coco_ids.json'), 'w') as f:
json.dump({'train': list(train), 'val': list(val), 'test': list(test)}, f)
shuffle(remainder)
while len(test) < 5000:
test.add(remainder.pop())
while len(val) < 5000:
val.add(remainder.pop())
train |= set(remainder)
assert len(test) == 5000
assert len(val) == 5000
assert len(train) == len(metadata) - 10000
print(len(train)) # 98077
with open(os.path.join(dataDir, 'vgqa/ids.json'), 'w') as f:
json.dump({'train': list(train), 'val': list(val), 'test': list(test)}, f)
|
BridgeTower/src/utils/vgqa_split.py/0
|
{
"file_path": "BridgeTower/src/utils/vgqa_split.py",
"repo_id": "BridgeTower",
"token_count": 1484
}
| 157 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from data.base_dataset import BaseDataset, get_params, get_transform
from PIL import Image
import util.util as util
import os
class Pix2pixDataset(BaseDataset):
@staticmethod
def modify_commandline_options(parser, is_train):
parser.add_argument(
"--no_pairing_check",
action="store_true",
help="If specified, skip sanity check of correct label-image file pairing",
)
return parser
def initialize(self, opt):
self.opt = opt
label_paths, image_paths, instance_paths = self.get_paths(opt)
util.natural_sort(label_paths)
util.natural_sort(image_paths)
if not opt.no_instance:
util.natural_sort(instance_paths)
label_paths = label_paths[: opt.max_dataset_size]
image_paths = image_paths[: opt.max_dataset_size]
instance_paths = instance_paths[: opt.max_dataset_size]
if not opt.no_pairing_check:
for path1, path2 in zip(label_paths, image_paths):
assert self.paths_match(path1, path2), (
"The label-image pair (%s, %s) do not look like the right pair because the filenames are quite different. Are you sure about the pairing? Please see data/pix2pix_dataset.py to see what is going on, and use --no_pairing_check to bypass this."
% (path1, path2)
)
self.label_paths = label_paths
self.image_paths = image_paths
self.instance_paths = instance_paths
size = len(self.label_paths)
self.dataset_size = size
def get_paths(self, opt):
label_paths = []
image_paths = []
instance_paths = []
assert False, "A subclass of Pix2pixDataset must override self.get_paths(self, opt)"
return label_paths, image_paths, instance_paths
def paths_match(self, path1, path2):
filename1_without_ext = os.path.splitext(os.path.basename(path1))[0]
filename2_without_ext = os.path.splitext(os.path.basename(path2))[0]
return filename1_without_ext == filename2_without_ext
def __getitem__(self, index):
# Label Image
label_path = self.label_paths[index]
label = Image.open(label_path)
params = get_params(self.opt, label.size)
transform_label = get_transform(self.opt, params, method=Image.NEAREST, normalize=False)
label_tensor = transform_label(label) * 255.0
label_tensor[label_tensor == 255] = self.opt.label_nc # 'unknown' is opt.label_nc
# input image (real images)
image_path = self.image_paths[index]
assert self.paths_match(
label_path, image_path
), "The label_path %s and image_path %s don't match." % (label_path, image_path)
image = Image.open(image_path)
image = image.convert("RGB")
transform_image = get_transform(self.opt, params)
image_tensor = transform_image(image)
# if using instance maps
if self.opt.no_instance:
instance_tensor = 0
else:
instance_path = self.instance_paths[index]
instance = Image.open(instance_path)
if instance.mode == "L":
instance_tensor = transform_label(instance) * 255
instance_tensor = instance_tensor.long()
else:
instance_tensor = transform_label(instance)
input_dict = {
"label": label_tensor,
"instance": instance_tensor,
"image": image_tensor,
"path": image_path,
}
# Give subclasses a chance to modify the final output
self.postprocess(input_dict)
return input_dict
def postprocess(self, input_dict):
return input_dict
def __len__(self):
return self.dataset_size
|
Bringing-Old-Photos-Back-to-Life/Face_Enhancement/data/pix2pix_dataset.py/0
|
{
"file_path": "Bringing-Old-Photos-Back-to-Life/Face_Enhancement/data/pix2pix_dataset.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 1742
}
| 158 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import re
import importlib
import torch
from argparse import Namespace
import numpy as np
from PIL import Image
import os
import argparse
import dill as pickle
def save_obj(obj, name):
with open(name, "wb") as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_obj(name):
with open(name, "rb") as f:
return pickle.load(f)
def copyconf(default_opt, **kwargs):
conf = argparse.Namespace(**vars(default_opt))
for key in kwargs:
print(key, kwargs[key])
setattr(conf, key, kwargs[key])
return conf
# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8, normalize=True, tile=False):
if isinstance(image_tensor, list):
image_numpy = []
for i in range(len(image_tensor)):
image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
return image_numpy
if image_tensor.dim() == 4:
# transform each image in the batch
images_np = []
for b in range(image_tensor.size(0)):
one_image = image_tensor[b]
one_image_np = tensor2im(one_image)
images_np.append(one_image_np.reshape(1, *one_image_np.shape))
images_np = np.concatenate(images_np, axis=0)
return images_np
if image_tensor.dim() == 2:
image_tensor = image_tensor.unsqueeze(0)
image_numpy = image_tensor.detach().cpu().float().numpy()
if normalize:
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
else:
image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
image_numpy = np.clip(image_numpy, 0, 255)
if image_numpy.shape[2] == 1:
image_numpy = image_numpy[:, :, 0]
return image_numpy.astype(imtype)
# Converts a one-hot tensor into a colorful label map
def tensor2label(label_tensor, n_label, imtype=np.uint8, tile=False):
if label_tensor.dim() == 4:
# transform each image in the batch
images_np = []
for b in range(label_tensor.size(0)):
one_image = label_tensor[b]
one_image_np = tensor2label(one_image, n_label, imtype)
images_np.append(one_image_np.reshape(1, *one_image_np.shape))
images_np = np.concatenate(images_np, axis=0)
# if tile:
# images_tiled = tile_images(images_np)
# return images_tiled
# else:
# images_np = images_np[0]
# return images_np
return images_np
if label_tensor.dim() == 1:
return np.zeros((64, 64, 3), dtype=np.uint8)
if n_label == 0:
return tensor2im(label_tensor, imtype)
label_tensor = label_tensor.cpu().float()
if label_tensor.size()[0] > 1:
label_tensor = label_tensor.max(0, keepdim=True)[1]
label_tensor = Colorize(n_label)(label_tensor)
label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
result = label_numpy.astype(imtype)
return result
def save_image(image_numpy, image_path, create_dir=False):
if create_dir:
os.makedirs(os.path.dirname(image_path), exist_ok=True)
if len(image_numpy.shape) == 2:
image_numpy = np.expand_dims(image_numpy, axis=2)
if image_numpy.shape[2] == 1:
image_numpy = np.repeat(image_numpy, 3, 2)
image_pil = Image.fromarray(image_numpy)
# save to png
image_pil.save(image_path.replace(".jpg", ".png"))
def mkdirs(paths):
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def atoi(text):
return int(text) if text.isdigit() else text
def natural_keys(text):
"""
alist.sort(key=natural_keys) sorts in human order
http://nedbatchelder.com/blog/200712/human_sorting.html
(See Toothy's implementation in the comments)
"""
return [atoi(c) for c in re.split("(\d+)", text)]
def natural_sort(items):
items.sort(key=natural_keys)
def str2bool(v):
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
def find_class_in_module(target_cls_name, module):
target_cls_name = target_cls_name.replace("_", "").lower()
clslib = importlib.import_module(module)
cls = None
for name, clsobj in clslib.__dict__.items():
if name.lower() == target_cls_name:
cls = clsobj
if cls is None:
print(
"In %s, there should be a class whose name matches %s in lowercase without underscore(_)"
% (module, target_cls_name)
)
exit(0)
return cls
def save_network(net, label, epoch, opt):
save_filename = "%s_net_%s.pth" % (epoch, label)
save_path = os.path.join(opt.checkpoints_dir, opt.name, save_filename)
torch.save(net.cpu().state_dict(), save_path)
if len(opt.gpu_ids) and torch.cuda.is_available():
net.cuda()
def load_network(net, label, epoch, opt):
save_filename = "%s_net_%s.pth" % (epoch, label)
save_dir = os.path.join(opt.checkpoints_dir, opt.name)
save_path = os.path.join(save_dir, save_filename)
if os.path.exists(save_path):
weights = torch.load(save_path)
net.load_state_dict(weights)
return net
###############################################################################
# Code from
# https://github.com/ycszen/pytorch-seg/blob/master/transform.py
# Modified so it complies with the Citscape label map colors
###############################################################################
def uint82bin(n, count=8):
"""returns the binary of integer n, count refers to amount of bits"""
return "".join([str((n >> y) & 1) for y in range(count - 1, -1, -1)])
class Colorize(object):
def __init__(self, n=35):
self.cmap = labelcolormap(n)
self.cmap = torch.from_numpy(self.cmap[:n])
def __call__(self, gray_image):
size = gray_image.size()
color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)
for label in range(0, len(self.cmap)):
mask = (label == gray_image[0]).cpu()
color_image[0][mask] = self.cmap[label][0]
color_image[1][mask] = self.cmap[label][1]
color_image[2][mask] = self.cmap[label][2]
return color_image
|
Bringing-Old-Photos-Back-to-Life/Face_Enhancement/util/util.py/0
|
{
"file_path": "Bringing-Old-Photos-Back-to-Life/Face_Enhancement/util/util.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 2895
}
| 159 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import sys
import time
import shutil
import platform
import numpy as np
from datetime import datetime
import torch
import torchvision as tv
import torch.backends.cudnn as cudnn
# from torch.utils.tensorboard import SummaryWriter
import yaml
import matplotlib.pyplot as plt
from easydict import EasyDict as edict
import torchvision.utils as vutils
##### option parsing ######
def print_options(config_dict):
print("------------ Options -------------")
for k, v in sorted(config_dict.items()):
print("%s: %s" % (str(k), str(v)))
print("-------------- End ----------------")
def save_options(config_dict):
from time import gmtime, strftime
file_dir = os.path.join(config_dict["checkpoint_dir"], config_dict["name"])
mkdir_if_not(file_dir)
file_name = os.path.join(file_dir, "opt.txt")
with open(file_name, "wt") as opt_file:
opt_file.write(os.path.basename(sys.argv[0]) + " " + strftime("%Y-%m-%d %H:%M:%S", gmtime()) + "\n")
opt_file.write("------------ Options -------------\n")
for k, v in sorted(config_dict.items()):
opt_file.write("%s: %s\n" % (str(k), str(v)))
opt_file.write("-------------- End ----------------\n")
def config_parse(config_file, options, save=True):
with open(config_file, "r") as stream:
config_dict = yaml.safe_load(stream)
config = edict(config_dict)
for option_key, option_value in vars(options).items():
config_dict[option_key] = option_value
config[option_key] = option_value
if config.debug_mode:
config_dict["num_workers"] = 0
config.num_workers = 0
config.batch_size = 2
if isinstance(config.gpu_ids, str):
config.gpu_ids = [int(x) for x in config.gpu_ids.split(",")][0]
print_options(config_dict)
if save:
save_options(config_dict)
return config
###### utility ######
def to_np(x):
return x.cpu().numpy()
def prepare_device(use_gpu, gpu_ids):
if use_gpu:
cudnn.benchmark = True
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
if isinstance(gpu_ids, str):
gpu_ids = [int(x) for x in gpu_ids.split(",")]
torch.cuda.set_device(gpu_ids[0])
device = torch.device("cuda:" + str(gpu_ids[0]))
else:
torch.cuda.set_device(gpu_ids)
device = torch.device("cuda:" + str(gpu_ids))
print("running on GPU {}".format(gpu_ids))
else:
device = torch.device("cpu")
print("running on CPU")
return device
###### file system ######
def get_dir_size(start_path="."):
total_size = 0
for dirpath, dirnames, filenames in os.walk(start_path):
for f in filenames:
fp = os.path.join(dirpath, f)
total_size += os.path.getsize(fp)
return total_size
def mkdir_if_not(dir_path):
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##### System related ######
class Timer:
def __init__(self, msg):
self.msg = msg
self.start_time = None
def __enter__(self):
self.start_time = time.time()
def __exit__(self, exc_type, exc_value, exc_tb):
elapse = time.time() - self.start_time
print(self.msg % elapse)
###### interactive ######
def get_size(start_path="."):
total_size = 0
for dirpath, dirnames, filenames in os.walk(start_path):
for f in filenames:
fp = os.path.join(dirpath, f)
total_size += os.path.getsize(fp)
return total_size
def clean_tensorboard(directory):
tensorboard_list = os.listdir(directory)
SIZE_THRESH = 100000
for tensorboard in tensorboard_list:
tensorboard = os.path.join(directory, tensorboard)
if get_size(tensorboard) < SIZE_THRESH:
print("deleting the empty tensorboard: ", tensorboard)
#
if os.path.isdir(tensorboard):
shutil.rmtree(tensorboard)
else:
os.remove(tensorboard)
def prepare_tensorboard(config, experiment_name=datetime.now().strftime("%Y-%m-%d %H-%M-%S")):
tensorboard_directory = os.path.join(config.checkpoint_dir, config.name, "tensorboard_logs")
mkdir_if_not(tensorboard_directory)
clean_tensorboard(tensorboard_directory)
tb_writer = SummaryWriter(os.path.join(tensorboard_directory, experiment_name), flush_secs=10)
# try:
# shutil.copy('outputs/opt.txt', tensorboard_directory)
# except:
# print('cannot find file opt.txt')
return tb_writer
def tb_loss_logger(tb_writer, iter_index, loss_logger):
for tag, value in loss_logger.items():
tb_writer.add_scalar(tag, scalar_value=value.item(), global_step=iter_index)
def tb_image_logger(tb_writer, iter_index, images_info, config):
### Save and write the output into the tensorboard
tb_logger_path = os.path.join(config.output_dir, config.name, config.train_mode)
mkdir_if_not(tb_logger_path)
for tag, image in images_info.items():
if tag == "test_image_prediction" or tag == "image_prediction":
continue
image = tv.utils.make_grid(image.cpu())
image = torch.clamp(image, 0, 1)
tb_writer.add_image(tag, img_tensor=image, global_step=iter_index)
tv.transforms.functional.to_pil_image(image).save(
os.path.join(tb_logger_path, "{:06d}_{}.jpg".format(iter_index, tag))
)
def tb_image_logger_test(epoch, iter, images_info, config):
url = os.path.join(config.output_dir, config.name, config.train_mode, "val_" + str(epoch))
if not os.path.exists(url):
os.makedirs(url)
scratch_img = images_info["test_scratch_image"].data.cpu()
if config.norm_input:
scratch_img = (scratch_img + 1.0) / 2.0
scratch_img = torch.clamp(scratch_img, 0, 1)
gt_mask = images_info["test_mask_image"].data.cpu()
predict_mask = images_info["test_scratch_prediction"].data.cpu()
predict_hard_mask = (predict_mask.data.cpu() >= 0.5).float()
imgs = torch.cat((scratch_img, predict_hard_mask, gt_mask), 0)
img_grid = vutils.save_image(
imgs, os.path.join(url, str(iter) + ".jpg"), nrow=len(scratch_img), padding=0, normalize=True
)
def imshow(input_image, title=None, to_numpy=False):
inp = input_image
if to_numpy or type(input_image) is torch.Tensor:
inp = input_image.numpy()
fig = plt.figure()
if inp.ndim == 2:
fig = plt.imshow(inp, cmap="gray", clim=[0, 255])
else:
fig = plt.imshow(np.transpose(inp, [1, 2, 0]).astype(np.uint8))
plt.axis("off")
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.title(title)
###### vgg preprocessing ######
def vgg_preprocess(tensor):
# input is RGB tensor which ranges in [0,1]
# output is BGR tensor which ranges in [0,255]
tensor_bgr = torch.cat((tensor[:, 2:3, :, :], tensor[:, 1:2, :, :], tensor[:, 0:1, :, :]), dim=1)
# tensor_bgr = tensor[:, [2, 1, 0], ...]
tensor_bgr_ml = tensor_bgr - torch.Tensor([0.40760392, 0.45795686, 0.48501961]).type_as(tensor_bgr).view(
1, 3, 1, 1
)
tensor_rst = tensor_bgr_ml * 255
return tensor_rst
def torch_vgg_preprocess(tensor):
# pytorch version normalization
# note that both input and output are RGB tensors;
# input and output ranges in [0,1]
# normalize the tensor with mean and variance
tensor_mc = tensor - torch.Tensor([0.485, 0.456, 0.406]).type_as(tensor).view(1, 3, 1, 1)
tensor_mc_norm = tensor_mc / torch.Tensor([0.229, 0.224, 0.225]).type_as(tensor_mc).view(1, 3, 1, 1)
return tensor_mc_norm
def network_gradient(net, gradient_on=True):
if gradient_on:
for param in net.parameters():
param.requires_grad = True
else:
for param in net.parameters():
param.requires_grad = False
return net
|
Bringing-Old-Photos-Back-to-Life/Global/detection_util/util.py/0
|
{
"file_path": "Bringing-Old-Photos-Back-to-Life/Global/detection_util/util.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 3479
}
| 160 |
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