dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics Statistical analysis was performed on GraphPad Prism software (version 7) by unpaired student's t-test for two group comparisons or one-way ANOVA correcting for multiple comparisons, with similar variance between groups being compared. Error bars represent s. e. m. ± SD, and P<0. 05 was considered statistically significant. For these experiments, each group is made up of at least three samples to achieve 80% power. No randomization nor blinding of investigators was performed.
S2H, I ). As a result, the IC50 for Dox was significantly reduced across multiple experiments (Fig. 2C, S2J ). In the Burkitt lymphoma cell line Daudi (p53 mutant, Myc Thr58 wild type), CHIR99021 also reduced the IC50 for Dox, albeit to a lesser degree (Fig. S2K ). Collectively these data demonstrate that GSK-3β inhibition enhances the response of Burkitt lymphoma cells to chemotherapy. Given that GSK-3β has targets other than Myc, we asked to what extent this pro-apoptotic effect was due to Myc stabilization. P493-6 p53shRNA cells were treated with vehicle or low dose tetracycline to reduce Myc levels, followed by DMSO or CHIR99021 and Dox. In vehicle-treated cells, CHIR99021 lead to increased activation of apoptosis following Dox (Fig. S3A , 'Vehicle' columns). Tetracycline-treated cells expressed around ~50% less Myc, and apoptosis following Dox alone was reduced ~50%.
Anti-GSK-3β adjuvant therapy engages and relies on extrinsic apoptosis To determine the underlying mechanism downstream of Myc, we profiled CHIR99021mediated transcriptional changes by performing RNA-Seq analysis on Ramos cells treated for 0, 3, or 6 hours with CHIR99021. We first looked for overlap between CHIR99021induced transcriptome changes and previously reported Myc signatures in two datasets pertaining to p493-6 cells-from microarray profiling ("Psathas Affy"  11  ) and array-based nuclear run-on (ANRO) assay ("Dang NRO"  28  ). We compared activated genes between the three datasets and found that there were highly statistically significant overlaps between the CHIR99021 and Myc datasets, attesting to the fact that Myc is a key downstream effector of GSK-3β (Fig. S5A ). To examine how apoptosis-related genes were being affected, we further analyzed our RNAseq dataset at the single gene level.
Consistent with engagement of extrinsic apoptosis, we found that CHIR99021 lowered the IC50 for the DR4/DR5 ligand TRAIL by 1 log (Fig. 7B ). Additionally, we utilized the human DR4 agonist antibody mapatumumab (a. k. a. HGS-ETR1)  30. Similar to the results with TRAIL, CHIR99021 sensitized Ramos cells to mapatumumab as seen by a half-log reduction in IC50 (Fig. 7C ). To determine if DR4 involvement is clinically relevant, we analyzed 10 Burkitt lymphoma clinical samples for IHC expression of DR4 and compared them to 5 normal tonsils. We found that while in the tonsillar germinal center the staining was very weakly cytoplasmic, the lymphoma samples displayed robust membrane and cytoplasmic staining (Fig. S6D ). Collectively these data demonstrate that GSK-3β inhibition potentiates the apoptotic activity of death receptors such as DR4 and reveals the critical dependence of anti-GSK-3β adjuvant therapy on extrinsic apoptosis.
ACKNOWLEDGEMENTS The authors thank the members of their laboratories and  CHOP Research Institute Normal and Malignant Hematopoiesis Research Affinity Group  for stimulating discussions, and  Daniel Martinez  and the  CHOP Pathology Core  for assistance in IHC stain analysis. iBet-151 was a kind gift from  Dr. Olena Barbash  (GlaxoSmithKline) and was first tested in our lab by  Daniel Soto de Jesus (University of Pennsylvania ). This work was supported by the  V Foundation for Cancer Research , the  NIH  grants  R01CA196299 & R21 CA183445  (ATT),  R01 HL110806 (PK) ,  T32 GM007229 & F31 CA217004  (CTH), and the  Institut National du Cancer  grant  PL-BIO 2016-179  (JW). The MAPPYACTS protocol is supported by the  Institut National du Cancer  grant  PHRC-K14-175  and the  Fondation ARC  grant  MAPY201501241.
These mismatches create a BspHI restriction site in the mutant PCR product which cuts the 193 bp amplicon into two fragments of https://doi. org/ T 20 bp and 173 bp. Digested and undigested PCR products were separated a 8% acrylamide gel. In order to analyze the conservation of mutated amino acids, we used the Web interface for EMBOSS Stretcher (https://www. ebi. ac. uk/Tools/psa/emboss_stretcher/) to calculate an optimal global alignment of human and S. cerevisiae MT-ATP6 and MT-ATP8 sequences. In order to predict putative impact of these substitutions on ATP synthase structure and function, we looked at their positioning on the crystallographic structure of S. cerevisiae mitochondrial ATP synthase  [11, 12]  using the interface Swiss-Pdb Viewer (https://spdbv. vital-it. ch/, PDB code: 6B8H).
Case report The patient is a boy, born of non-consanguinous parents with no family history. He has a healthy younger sister. He presented hypotonia and microcephaly (-2DS) since age 4 months. He started associating words at age 2 years, but with dysarthria needing therapy. Walking was acquired at age 29 months and became stable only by age 6 years. Property was also acquired at 6 years. He gradually developed ataxia. Today, at age 13 years he presents ataxia increasing with effort, dysarthria, gait disorders, slowness, learning difficulties, exercise intolerance and fatigue needing to rest. Ophthalmological examination shows bilateral retinal hypoplasia. Cardiac, pulmonary and abdominal examinations are normal. Brain MRI showed bilaterally symmetric lesions in the basal ganglia, that correspond to a typical aspect of Leigh syndrome (data not shown).
V. EXPERIMENTAL RESULTS The aim of this section is twofold. In the first part we compare the testability of ACFs for lattices obtained with  [4]  and  [10]  synthesis methods. In the second part we evaluate the effect of the lattice restructuring methods proposed in Section IV on the testability of lattices obtained with  [4]. The experiments have been run on a machine with two AMD Opteron 4274HE for a total of 16 CPUs at 2. 5 GHz and 128 GByte of main memory, running Linux CentOS 7. The benchmark functions (expressed in PLA form) are taken from LGSynth93  [13]. Each output of a benchmark is implemented as a separate Boolean function for a total of 520 functions. Since the simulation time depends on the number of variables, we consider lattices with a number of variables lower than 6. Notice that this limitation is due to the onerous procedure for the fault simulation, and it is not due to our proposed algorithms for improving testability.
The software used for simulations is written in C++. We used ESPRESSO to implement the method described in  [4] , and a collection of Python scripts for computing minimumarea lattices by transformation to a series of SAT problems, to simulate the results reported in  [10]. A. Testability of lattices synthesized with methods  [4]  and  [10]  In Table  I  we compare the number of testable cells for each ACF between lattices synthesized as described in  [4]  and  [10]. The first column in the table reports the name and the number of the considered output of each function; the second column reports the number of input variables. The following columns report, for each synthesis method, the dimensions (r, s and area) and the percentage of testable cells (T R , T L , T T , T B ) w. r. t. the lattice area.
GPE GPE Figure  1 : An example from the  ACE-2004 dataset (Doddington et al. , 2004)  in which two GPEs (Geographical Entities) are nested in a Facility Entity. Due to the semantic structures within natural language, nested entities can be ubiquitous: e. g. 47% of the entities in the test split of  ACE-2004 (Doddington et al. , 2004)  dataset overlap with other entities, and 42% of the sentences contain nested entities. Various approaches  (Alex et al. , 2007; Lu and Roth, 2015; Katiyar and Cardie, 2018; Muis and Lu, 2017; Wang and Lu, 2018)  have been proposed in the past decade to extract nested named entities. However, these models are designed explicitly for recognizing nested named entities. They usually do not perform well on nonoverlapping named entity recognition compared to sequence labeling models.
To tackle the aforementioned drawbacks, we propose a novel neural framework, named MGNER, for Multi-Grained Named Entity Recognition. It is suitable for tackling both Nested NER and Non-overlapping NER. The idea of MGNER is natural and intuitive, which is to first detect entity positions in various granularities via a Detector and then classify these entities into different pre-defined categories via a Classifier. MGNER has five types of modules: Word Processor, Sentence Processor, Entity Processor, Detection Network, and Classification Network, where each module can adopt a wide range of neural network designs. In summary, the contributions of this work are: • We propose a novel neural framework named MGNER for Multi-Grained Named Entity Recognition, aiming to detect both nested and non-overlapping named entities effectively in a single model.
• MGNER is highly modularized. Each module in MGNER can adopt a wide range of neural network designs. Moreover, MGNER can be easily extended to many other related information extraction tasks, such as chunking (Ramshaw and Marcus, 1999) and slot filling  (Mesnil et al. , 2015). • Experimental results show that MGNER is able to achieve new state-of-the-art results on both Nested Named Entity Recognition tasks and Non-overlapping Named Entity Recognition tasks.
Related Work Existing approaches for recognizing nonoverlapping named entities usually treat the NER task as a sequence labeling problem. Various sequence labeling models achieve decent performance on NER, including probabilistic graph models such as Conditional Random Fields (CRF)  (Ratinov and Roth, 2009) , and deep neural networks like recurrent neural networks or convolutional neural networks (CNN). Hammerton (2003)   (Rei et al. , 2016). External resources have been used to further improve the NER performance. Peters et al. (2017)  add pre-trained context embeddings from bidirectional language models to NER. Peters et al. (2018)  learn a linear combination of internal hidden states stacked in a deep bidirectional language model, ELMo, to utilize both higher-level states which capture context-dependent aspects and lower-level states which model aspects of syntax.
The Proposed Framework An overview of the proposed MGNER framework for multi-grained entity recognition, is illustrated in Figure  2. Specifically, MGNER consists of two sub-networks: the Detector and the Classifier. The Detector detects all the possible entity positions while the Classifier aims at classifying detected entities into pre-defined entity categories. The Detector has three modules: 1) Word Processor which extracts word-level semantic features, 2) Sentence Processor that learns context information for each utterance and 3) Detection Network that decides whether a word segment is an entity or not. The Classifier consists of 1) Word Processor which has the same structure as the one in the Detector, 2) Entity Processor that obtains entity features and 3) Classification Network that classifies entity into pre-defined categories.
In addition, a self-attention mechanism is adopted in the En-tity Processor to help the model capture and utilize entity-related contextual information. Each module in MGNER can be replaced with a wide range of different neural network designs. For example, BERT  (Devlin et al. , 2018)  can be used as the Word Processor and a capsule model  (Sabour et al. , 2017; Xia et al. , 2018)  can be integrated into the Classification Network. It is worth mentioning that in order to improve the learning speed as well as the performance of MGNER, the Detector and the Classifier are trained with a series of shared input features, including the pre-trained word embeddings and the pre-trained language model features. Sentencelevel semantic features trained in the Detector are also transferred into the Classifier to introduce and utilize the contextual information.
The Detector The Detector is aimed at detecting possible entity positions within each utterance. It takes an utterance as the input and outputs a set of entity candidates. Essentially, we use a semi-supervised neural network inspired by  (Peters et al. , 2017)  to model this process. The architecture of the Detector is illustrated in the left part of Figure 2. Three major modules are contained in the Detector: Word Processor, Sentence Processor and Detection Network. More specifically, pre-trained word embeddings, POS tag information and character-level word information are used for generating semantically meaningful word representations. Word representations obtained from the Word Processor and the language model embeddings-ELMo  (Peters et al. , 2018) , are concatenated together to produce context-aware sentence representations.
Sentence Processor To learn the contextual information from each sentence, another bidirectional LSTM, named word LSTM, is applied to sequentially encode the utterance. For each token, the forward hidden states → h k = LSTM f w (x k , ← h k-1 ), ← h k = LSTM bw (x k , ← h k+1 ), h k = [ → h k ; ← h k ]. (1) Besides, we also utilize the language model embeddings pre-trained in an unsupervised way as the ELMo model in  (Peters et al. , 2018). The pretrained ELMo embeddings and the hidden states in the word LSTM h k are concatenated. Hence, the concatenated hidden states h k for each token can be reformulated as: h k = [ → h k ; ← h k ; ELMo k ], (2) where ELMo k is the ELMo embeddings for token t k. Speficially, a three-layer bi-LSTM neural network is trained as the language model. Since the lower-level LSTM hidden states have the ability to model syntax properties and higher-level LSTM hidden states can capture contextual information, ELMo computes the language model embeddings as a weighted combination of all the bidirectional LSTM hidden states: ELMo k = γ L l=0 u j h LM k,l , (3) where γ is a task-specified scale parameter which indicates the importance of the entire ELMo vector to the NER task.
Entity Processor The word representation is fed into a bidirectional word LSTM with hidden size D wl and the hidden states are concatenated with the ELMo language model embeddings as the entity features. A bidirectional LSTM with hidden size D el is applied to the entity feature to capture sequence information among the entity words. The last hidden states of the forward and backward Entity LSTM are concatenated as the entity representation e ∈ R 2D el. The same word in different contexts may have different semantic meanings. To this end, in our model, we take the contextual information into consideration when learning the semantic representations of entity candidates. We capture the contextual information from other words in the same utterance. Denote c as the context feature vector for these context words, and it can be extracted from the sentence representation f k in the Detector.
Datasets We mainly evaluate our framework on  ACE-2004 and ACE-2005 (Doddington et al. , 2004)  with the same splits used by previous works  (Luo et al. , 2015; Wang and Lu, 2018)  for the nested NER task. Specifically, seven different types of entities such as person, facility, weapon and vehicle, are contained in the ACE datasets. For the traditional NER task, we use the  CoNLL-2003 dataset (Tjong Kim Sang and De Meulder, 2003)  which contains four types of named entities: location, organization, person and miscellaneous. An overview of these three datasets is illustrated in Table  1. It can be observed that most entities are less or equal to 6 tokens, and thus we select the maximum entity length R = 6.
Implementation Details We performed random search  (Bergstra and Bengio, 2012)  for hyper-parameter optimization and selected the best setting based on performance on the development set. We employ the Adam optimizer (Kingma and Ba, 2014) with learning rate decay for all the experiments. The learning rate is set as 0. 001 at the beginning and exponential decayed by 0. 9 after each epoch. The batch size of utterances is set as 20. In order to balance the influence of positive proposals and negative proposals, we use down-sampling for negative ones and the total proposal number N b for each batch is 128. To alleviate over-fitting, we add dropout regularizations after the word representation layer and all the LSTM layers with a dropout rate of 0. 5. In addition, we employ the early stopping strategy when there is no performance improvement on the development dataset after three epochs.
The pretrained word embeddings are from GloVe  (Pennington et al. , 2014) , and the word embedding dimension D w is 300. Besides, the ELMo 5. 5B data 1 is utilized in the experiment for the language model embedding. Moreover, the size of character embedding c k is 100, and the hidden size of the Character LSTM D cl is also 100. The size of POS tag embedding p k is 300 for the ACE datassets and no POS tag information is used in the CoNLL-2003 dataset. The hidden dimensions of 1 https://allennlp. org/elmo the Word LSTM layer D wl , the Sentence LSTM layer D sl , the Entity LSTM layer D el and the Attention LSTM layer D ml are all set to 300. The hidden dimension of the classification layer D h is 50. The margin ∆ in the hinge-ranking loss for the entity category classification is set to 5. The ELMo scale parameter γ used in the Detector is 3.
Results Nested NER Task. The proposed MGNER is very suitable for detecting nested named entities since every possible entity will be examined and classified. In order to validate this advantage, we compare MGNER with numerous baseline models: 1)  Lu and Roth (2015)    To analyze how well our model performs on overlapping and non-overlapping entities, we split the test data into two portions: sentences with and without overlapping entities (follow the splits used by  Wang and Lu (2018) ). Four state-of-theart nested NER models are compared with our proposed framework MGNER on the ACE-2005 dataset. As illustrated in Table  3 , MGNER consistently performs better than the baselines on both portions, especially for the non-overlapping part. This observation indicates that our model can better recognize non-overlapping entities than previous nested NER models.
The first step in MGNER is to detect entity positions using the Detector, where the effectiveness of proposing correct entity candidates immediately affects the performance of the whole model. To this end, we provide the experiment results of detecting correct entities in the Detector module here. The precision, recall and F1 score are  85. 23 , 91. 84, 88. 41   NER Task. We also evaluate the proposed MGNER framework on the NER task which needs to reorganize non-overlapping entities. Two types of baseline models are compared here: sequence labelling models which are designed specifically for non-overlapping NER task and nested NER models which also provide the ability to detect non-overlapping mentions. The first type of models including 1)  Lample et al. (2016)  which adopt the LSTM-CRF structure; 2)  Ma and Hovy (2016)  which use a LSTM-CNNs-CRF architecture; 3)  Chiu and Nichols (2016)  which propose a CNN-LSTM-CRF model; 4)  Peters et al.
(2017)  which add semi-supervised language model embeddings; and 5)  Peters et al. (2018)  which utilize the state-of-the-art ELMo language model embeddings. The second types include four Nested models mentioned in the Nested NER section: 1)  Luo et al. (2015) ; 2) Muis and  Lu (2017) ; 3)  Xu et al. (2017) ; 4)  (Wang and Lu, 2018). Table  4  shows the F1 scores of different approaches on CoNLL-2003 devlopement set and test set for the English NER task. Mean and standard deviation across five runs are reported. It can be observed from Table  4  that the proposed MGNER model outperforms all the baselines. The models designed for non-overlapping entity detection usually performs better than Nested NER models for the NER task. Our proposed framework outperforms state-of-the-art results both on the NER and Nested NER task.
Xu et al. (2017)  is the best baseline model among the Nested models since it shares a similar idea of our proposed framework by individually examin-ing each entity proposal. From the ablation study, we can observe that by purely adding the context information, the F1 score on the CoNLL-2003 test set improves from 92. 23 to 92. 26, and by adding the attention mechanism, the F1 score improves to 92. 28. We also provide the performance of detecting non-overlapping entities in the Detector here. The precision, recall and F1 score are 95. 33, 95. 69 and 95. 51 on the CoNLL-2003 dataset.
We can observe from Table2that, our proposed framework MGNER outperforms all the baseline approaches. For both datasets, our model improves the state-of-the-art result by around 4% in terms of precision, recall, as well as the F1 score. To study the contribution of different modules in MGNER, we also report the performance of two ablation variations of the proposed MGNER at the bottom of Table2. MGNER w/o attention is a variation of MGNER which removes the selfattention mechanism and MGNER w/o context removes all the context information. To remove the self-attention mechanism, we feed the context feature C directly into a bi-directional LSTM to obtain context representation m, other than the attentive context vector C att. As for MGNER w/o context, we only use entity representation e to do classification other than the context-aware entity representation o.
Data from the Clinical Trial in Organ Transplantation 09 (CTOT-09), a study where kidney transplant recipients underwent tacrolimus withdrawal, show that urinary CXCL9 at a positivity threshold of ≥200 pg/ml can detect acute rejection 3 to 30 days before clinical presentation  [22]. Data from a case-series indicate that urinary CXCL9 can also be used to monitor response to therapy for acute rejection  [23]. Therefore, serial measurements of urinary CXCL9 could allow to identify early patients at risk for acute cellular rejection during conversion from CNI to belatacept and to treat them promptly. Based on this background, we developed a strategy of gradual (over a 2-month time period) conversion from CNI to belatacept to reduce the risk of acute rejection. In the present study, we tested the safety and feasibility of such approach in the real-life setting of a large transplant center and we tested the association between changes in urinary CXCL9 and acute rejection episodes during conversion.
Measurement of urinary CXCL9 Urinary samples for CXCL9 analyses were centrifuged at 2,000g for 30 min at 4°C within 4 h from collection. The supernatant was divided into aliquots and frozen at -80°C. Urinary CXCL9 was measured in batches at the end of the study. We strictly followed the protocol defined by a multicenter validation study for CXCL9 measurement  [21]  and reported the data as pg/ml without creatinine correction accordingly. We used the same previously defined threshold for positivity of 200 pg/mL  [21]. Frozen aliquots of urine supernatants were diluted (1:1) in 0. 05% Tween 20/0. 4% bovine serum albumin in phosphate-buffered saline (pH 7. 2-7. 4) and tested by ELISA for CXCL9 (R&D Systems, Minneapolis, MN), as previously reported  [21].