dataesr/scientific-paragraphs-categorization huggingface dataset:
These data indicate that FASTX-Toolkit is stringent to select high-quality reads. Altogether, PRINSEQ showed the best balance between quality and amount of reads, suggesting that this program uses a strategy that fits better for CLIP-seq data preprocessing than the other two programs. However, the balance of stringency and sensitivity can be tuned by changing the parameters of the programs to meet the needs of the researcher, such as the minimal quality of reads or their minimal length to be selected. Finally, the three methods achieved similar results regarding the collapse of duplicates, to remove redundant fractions of the data. A high redundancy of the data may be a consequence of low library complexity, which often occurs when samples are prepared from small amount of starting material or by performing too many polymerase chain reaction cycles for library construction.
In addition, an important issue concerns the consideration of multiple mapped reads (reads that map in many loci). Although allowing for multiple read mapping would increase the number of usable reads and the sensitivity of peak detection, this may also cause an increase of false-positive peaks, as it was also suggested for ChIP-seq analysis  [38]. To provide guidelines about the program to use, we ran two popular programs that achieved the best performances on RNAseq data  [39] , namely, Novoalign and STAR, on the in-house Ago2 CLIP-seq data set from P19 stem cells after preprocessing with the three aforementioned programs. For this analysis, we set the recommended parameters for Novoalign and used similar ones for STAR (Supplemental Information). As shown in Table  1 , regardless the preprocessing program used, Novoalign mapped uniquely between 20 and 30% more reads than STAR.
However, the full version of Novoalign is not freely downloadable, and the computational analysis can take much longer with the uncomplete free version compared with STAR, which is freely downloadable. An alternative strategy is to map the reads with one program and afterward run the unmapped reads with a second program. Because this strategy was not benchmarked yet, it is unclear whether it is really advantageous. Finally, the mappability of multiple mapped reads can be dealt by tuning the minimal length of reads or using paired-end sequencing  [38].
PIPE-CLIP and Pyicoclip group the reads based on positional overlap, while Piranha, MiCLIP  [47]  and HITS-CLIP data analyses (http://qbrc. swmed. edu/softwares. html) bin on genomic portions by fixed size. On the other hand, CIMS focuses on the identification of read clusters containing mutated cross-linked nucleotides  [25]. To discriminate enriched read clusters over the background, the peak-calling programs use different statistical models. For instance, PIPE-CLIP and Piranha use the zero-truncated negative binomial likelihoods, including also additional covariates to refine the peak detection, such as identification of cross-linked-dependent mutations or transcript abundance. On the other hand, Pyicoclip performs a background estimation implementing the modified false discovery rate procedure to determine which clusters of reads are significantly enriched in a list of genomic regions, by randomly placing the same number of reads within the region and iterating the process many times.
MiCLIP  [47]  and HITS-CLIP data analyses use hidden Markow models (HMMs) to model the spatial dependency of the reads that map in the cluster. Finally, CIMS assesses statistical significance using a permutationbased model. Specific statistical models are used for PAR-CLIP Importantly, different methods can give different results; thus, to help researchers in the choice of the right program for their peak-calling analysis, we recently performed a comprehensive, quantitative and qualitative comparative evaluation of four different publicly available programs for HITS-CLIP peakcalling step, including CIMS, PIPE-CLIP, Piranha and Pyicoclip, on four published Ago2 HITS-CLIP data sets  [9, 48, 49]  and one in-house Ago2 HITS-CLIP data set generated from P19 stem cells  [24]. By tuning the programs in default parameters, we found that Pyicoclip outperformed the other programs in terms of sensitivity, positional accuracy, agreement with TargetScan miRNA-binding site prediction program, specificity and for consistency in finding the same results on different data sets from the same tissue  [24].
Motif discovery and other features Following the peak calling, the analysis mainly focuses on the characterization of the RBP-RNA interactions, especially looking for possible binding sequence signature(s), using a candidate screening or a de novo motifs identification. For the candidate screening approach, programs like FIMO  [54]  can be used to screen peak sequences for the identification of known RNAbinding motifs, such as those from the database of Ray et al. [55]. If the user is looking for unknown RNA-binding motifs, a de novo motif identification could be performed. For this task, two main parameters should be calibrated before launching the analysis. The first parameter is the nucleotide length of the motif. Mitchell and Parker  [56]  recently showed that different RNA-binding domains bind in average a precise number of nucleotides.
Downstream analysis The last step of CLIP-seq analysis involves functional characterization of the target RNAs identified to provide clues about the molecular function of the RBP(s) or the miRNA(s) of interest. Many programs/databases address this task, including GoTermFinder  [75]  and topGO  [76]  to perform GO Term enrichment; GeneMania  [77]  to predict the function of a set of genes; STRING  [78]  and Cytoscape  [79]  to predict and visualize the protein interaction networks; GSEA  [80]  to determine whether a set of genes show similar expression differences between two biological conditions; and RAIN that integrates noncoding RNAs and protein-protein interaction networks  [81]. Furthermore, the results from CLIP-seq analysis can be coupled with data sets from other genome-wide technologies, including RNA expression profile or alternative splicing.
Ago2 CLIP-seq data ameliorate miRNA target prediction To date, the main miRNA target prediction programs take into account the following miRNA::RNA interaction features: (i) occurrence of perfect complementarity match between miRNA seed sequence and target mRNAs, (ii) sequence conservation of the target sequence across species, (iii) the free energy of the miRNA::RNA heteroduplex and (iv) the target site accessibility  [88] [89] [90] [91]. Even considering all these features, the rate of false positives and negatives is still high  [70] , indicating that more is needed to better predict miRNA target sequences. The presence of >15 Ago2 CLIP-seq analyses performed in several cells or tissues and deposited in the Starbase database  [92, 93]  provides an important resource for a genome-wide investigation of the miRNA targeting features.
STarMir implements a logistic prediction models based on thermodynamic parameters of the miRNA::RNA heteroduplexes and the secondary structure features of the target mRNAs from CLIP-seq analyses  [103]. MiRTar2GO is a rule-based machine learning approach to predict cell type-specific miRNA target mRNAs, which are ranked using validated binding sites from luciferase assay or Ago CLASH data sets  [105]. Lu and Leslie  [106]  developed the program chimiRic that uses a discriminative machine learning approach on Ago2 CLIP-seq and CLASH data to train a novel miRNA target prediction model. On the other hand, some of the first-generation miRNA prediction programs have been refined and updated thanks to Ago2 CLIP-seq analyses. For example, DIANA-micro-T-CDS  [102]  is an extension of the first-generation algorithm DIANA-micro-T  [107]  that uses a machine learning approach to identify the most relevant features of miRNA targeting from CLIP-seq data sets.
We believe that this review benefits scientists in RNA biology, gene expression fields and beyond. CLIP-seq analysis provides a huge amount of data that often are not fully exploited by the researchers because of the lack of time or tools dedicated to perform/integrate different analyses. A further effort should be done to make these data available in a user-friendly format and resource databases to collect them. Some databases, such as StarBase  [92] , CLIPdb  [127]  and CLIPZ  [27] , collect raw data of CLIP-seq studies and provide peak sequences and/or coordinates. However, they are not always updated and use standard pipelines that may not be suitable for every RBPs. Therefore, the development of integrated platform of CLIP-seq data analysis and databases could be a direction to be taken in the near future.
In particular, these platforms should combine multiple software, such as pipelines covering multiple steps of data analysis or multiple databases including other high-throughput techniques as RIP-seq, ChIP-seq, RNAseq and quantitative proteomics data, making the analysis faster and more comprehensive. On the other hand, improvement of the experimental protocol or conditions, such as quantification of the benefits of using replicates and/or control experiments, is needed to improve the reproducibility of the data and increase the efficiency of the data mining. Overall, we have shown that CLIP-seq experiments associated to sophisticated bioinformatics analysis have become nowadays an essential instrument to gain insight into the direct regulatory network(s) of RBP-RNA interactions to address central questions of RNA biology and gene expression control in normal and pathological events of physiology or development.
Four individuals were reported to take melatonin for sleeping problems with good effect. "Multivitamin cocktails" containing for example coenzyme Q 10 , riboflavin, and biotin, either prescribed by their physician or as over-the-counter drugs in different combinations and with different doses, were taken by many individuals. Reliable data were available for only 5 individuals, for whom none of the caretakers or physicians reported any discernible clinical difference. Treatment of drooling in MEGDHEL syndrome has been evaluated separately. 25 In addition to the described successful surgical correction, P28 and P65 were given atropine 0. 5% eye drops orally (1 drop every 6 hours), which reduced drooling to a normal level without any side effects.
With regard to the homogeneity of the clinical phenotype, we cannot exclude that our data may be biased, as the vast majority of patients were only diagnosed after occurrence of neurological symptoms. However, the individuals in our cohort diagnosed in the first months of life as well as the ones with the milder phenotypes also developed the characteristic combination of signs and symptoms. Furthermore, from our experience with exome sequencing for rare pediatric disorders (the Munich databases [12,000 exomes] encompass, eg, >1,000 exomes of suspected mitochondrial cases, >1,000 intellectual disability cases, >500 epilepsy cases, and >100 acute liver failure cases), we did not identify individuals with different or partial phenotypes. We classify MEGDHEL syndrome primarily as a disorder of the biosynthesis of complex lipids with secondary mitochondrial dysfunction, although MEGDHEL syndrome displays typical findings and the progressive course of a mitochondrial disorder, including lactic acidosis and 3-MGA-uria.
Acknowledgment M. S. was supported by the  Else Kr€ oner-Fresenius Stiftung " This study was supported by the  German Bundesministerium f€ ur Bildung und Forschung (BMBF)  and  Horizon2020  through the  E-Rare  project  GENOMIT  ( 01GM1603  and  01GM1207  for HP and  FWF I 2741-B26  for J. A. M. J. A. M. , S. B. W. , W. S. were supported by the Vereinigung zur F€ orderung P€ adiatrischer Forschung und Fortbildung Salzburg. R. W. T. was supported by the  Wellcome Centre for Mitochondrial Research  ( 203105/Z/16/Z ), the  Medical Research Council (MRC) Centre for Translational Research in Neuromuscular Disease, Mitochondrial Disease Patient Cohort (UK)  ( G0800674 ), the  Lily Foundation  and the  UK NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults and Children.
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors  (Kiros et al. , 2015)  on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks.
Two questions need to be solved in order to build such an encoder, namely: what is the preferable neural network architecture; and how and on what task should such a network be trained. Following existing work on learning word embeddings, most current approaches consider learning sentence encoders in an unsupervised manner like SkipThought  (Kiros et al. , 2015)  or FastSent  (Hill et al. , 2016). Here, we investigate whether supervised learning can be leveraged instead, taking inspiration from previous results in computer vision, where many models are pretrained on the ImageNet  (Deng et al. , 2009)  before being transferred. We compare sentence embeddings trained on various supervised tasks, and show that sentence embeddings generated from models trained on a natural language inference (NLI) task reach the best results in terms of transfer accuracy.
We hypothesize that the suitability of NLI as a training task is caused by the fact that it is a high-level understanding task that involves reasoning about the semantic relationships within sentences. Unlike in computer vision, where convolutional neural networks are predominant, there are multiple ways to encode a sentence using neural networks. Hence, we investigate the impact of the sentence encoding architecture on representational transferability, and compare convolutional, recurrent and even simpler word composition schemes. Our experiments show that an encoder based on a bi-directional LSTM architecture with max pooling, trained on the Stanford Natural Language Inference (SNLI) dataset  (Bowman et al. , 2015) , yields state-of-the-art sentence embeddings compared to all existing alternative unsupervised approaches like SkipThought or FastSent, while be-ing much faster to train.
They further obtained better results using layer-norm regularization of their model in  (Ba et al. , 2016). Hill et al. (2016)  showed that the task on which sentence embeddings are trained significantly impacts their quality. In addition to unsupervised methods, they included supervised training in their comparisonnamely, on machine translation data (using the WMT'14 English/French and English/German pairs), dictionary definitions and image captioning data (see also  Kiela et al. (2017) ) from the COCO dataset  (Lin et al. , 2014). These models obtained significantly lower results compared to the unsupervised Skip-Thought approach. Recent work has explored training sentence encoders on the SNLI corpus and applying them on the SICK corpus  (Marelli et al. , 2014) , either using multi-task learning or pretraining  (Mou et al.
The Natural Language Inference task The SNLI dataset consists of 570k humangenerated English sentence pairs, manually labeled with one of three categories: entailment, contradiction and neutral. It captures natural language inference, also known in previous incarnations as Recognizing Textual Entailment (RTE), and constitutes one of the largest high-quality labeled resources explicitly constructed in order to require understanding sentence semantics. We hypothesize that the semantic nature of NLI makes it a good candidate for learning universal sentence embeddings in a supervised way. That is, we aim to demonstrate that sentence encoders trained on natural language inference are able to learn sentence representations that capture universally useful features. Models can be trained on SNLI in two different ways: (i) sentence encoding-based models that explicitly separate the encoding of the individual sentences and (ii) joint methods that allow to use encoding of both sentences (to use cross-features or attention from one sentence to the other).
LSTM and GRU Our first, and simplest, encoders apply recurrent neural networks using either LSTM  (Hochreiter and Schmidhuber, 1997)  or GRU  (Cho et al. , 2014)  modules, as in sequence to sequence encoders  (Sutskever et al. , 2014). For a sequence of T words (w 1 ,. , w T ), the network computes a set of T hidden representations h 1 ,. , h T , with h t = ----→ LSTM(w 1 ,. , w T ) (or using GRU units instead). A sentence is represented by the last hidden vector, h T. We also consider a model BiGRU-last that concatenates the last hidden state of a forward GRU, and the last hidden state of a backward GRU to have the same architecture as for SkipThought vectors.
Binary and multi-class classification We use a set of binary classification tasks (see Table  1 ) that covers various types of sentence classification, including sentiment analysis (MR, SST), question-type (TREC), product reviews (CR), subjectivity/objectivity (SUBJ) and opinion polarity (MPQA). We generate sentence vectors and train a logistic regression on top. A linear classifier requires fewer parameters than an MLP and is thus suitable for small datasets, where transfer learning is especially well-suited. We tune the L2 penalty of the logistic regression with grid-search on the validation set. Entailment and semantic relatedness We also evaluate on the SICK dataset for both entailment (SICK-E) and semantic relatedness (SICK-R). We use the same matching methods as in SNLI and learn a Logistic Regression on top of the joint representation.
For semantic relatedness evaluation, we follow the approach of  (Tai et al. , 2015)  and learn to predict the probability distribution of relatedness scores. We report Pearson correlation. STS14 -Semantic Textual Similarity While semantic relatedness is supervised in the case of SICK-R, we also evaluate our embeddings on the 6 unsupervised SemEval tasks of STS14  (Agirre et al. , 2014). This dataset includes subsets of news articles, forum discussions, image descriptions and headlines from news articles containing pairs of sentences (lower-cased), labeled with a similarity score between 0 and 5. These tasks evaluate how the cosine distance between two sentences correlate with a human-labeled similarity score through Pearson and Spearman corre-.
U and V are learned linear transformations that project the caption x and the image y to the same embedding space. We use a margin α = 0. 2 and 30 contrastive terms. We use the same splits as in  (Karpathy and Fei-Fei, 2015) , i. e. , we use 113k images from the COCO dataset (each containing 5 captions) for training, 5k images for validation and 5k images for test. For evaluation, we split the 5k images in 5 random sets of 1k images on which we compute Recall@K, with K ∈ {1, 5, 10} and median (Med r) over the 5 splits. For fair comparison, we also report SkipThought results in our setting, using 2048-dimensional pretrained ResNet-101  (He et al. , 2016)   Table  3 : Performance of sentence encoder architectures on SNLI and (aggregated) transfer tasks. Dimensions of embeddings were selected according to best aggregated scores (see Figure  5 ).
Architecture impact Model We observe in Table  3  that different models trained on the same NLI corpus lead to different transfer tasks results. The BiLSTM-4096 with the max-pooling operation performs best on both SNLI and transfer tasks. Looking at the micro and macro averages, we see that it performs significantly better than the other models LSTM, GRU, BiGRU-last, BiLSTM-Mean, inner-attention and the hierarchical-ConvNet. Table  3  also shows that better performance on the training task does not necessarily translate in better results on the transfer tasks like when comparing inner-attention and BiLSTM-Mean for instance. We hypothesize that some models are likely to over-specialize and adapt too well to the biases of a dataset without capturing general-purpose information of the input sentence.
MultiGenre NLI The MultiNLI corpus  (Williams et al. , 2017)  was recently released as a multi-genre version of SNLI. With 433K sentence pairs, MultiNLI improves upon SNLI in its coverage: it contains ten distinct genres of written and spoken English, covering most of the complexity of the language. We augment Table  4  with our model trained on both SNLI and MultiNLI (AllNLI). We observe a significant boost in performance overall compared to the model trained only on SLNI. Our model even reaches AdaSent performance on CR, suggesting that having a larger coverage for the training task helps learn even better general representations. On semantic textual similarity STS14, we are also competitive with PPDB based paragramphrase embeddings with a pearson score of 0. 70. Interestingly, on caption-related transfer tasks such as the COCO image caption retrieval task, training our sentence encoder on other genres from MultiNLI does not degrade the performance compared to the model trained only SNLI (which contains mostly captions), which confirms the generalization power of our embeddings.
Conclusion This paper studies the effects of training sentence embeddings with supervised data by testing on 12 different transfer tasks. We showed that models learned on NLI can perform better than models trained in unsupervised conditions or on other supervised tasks. By exploring various architectures, we showed that a BiLSTM network with max pooling makes the best current universal sentence encoding methods, outperforming existing approaches like SkipThought vectors. We believe that this work only scratches the surface of possible combinations of models and tasks for learning generic sentence embeddings. Larger datasets that rely on natural language understanding for sentences could bring sentence embedding quality to the next level.
Statistics For each antibody, the statistical significance of the comparisons were assessed using non-parametric matched pairs-Wilcoxon tests to account for any possible effect of age or gender on the observed traits (IMN and MS/CIS are age-dependent traits and gender is a well-established dimorphism in MS). Neu5gc sensitization may be associated with age as observed in the controls (r = 0. 14, p = 0. 048). Statistical analyses were performed using Graphpad, Stata 13 SE and R statistical software.
Simulated events The simulation of Z resonances is performed with the leading-order MADGRAPH v5. 2. 2. 2  [49]  Monte Carlo (MC) program using SM values for the left-and right-handed Z couplings to top quarks. The simulation is performed for a range of Z masses between 0. 5 and 4. 0 TeV, and for the three relative width hypotheses of 1%, 10%, and 30%. Higher-order QCD multijet processes for up to three extra partons are simulated at tree level. The Z boson is required to decay into a tt pair in all generated events. The parton showering and hadronization is modeled with PYTHIA 8. 205  [50, 51] , and the MLM algorithm  [52]  is used to match the parton shower to the matrix element calculation with a merging scale of 35 GeV. The simulation of KK excitations of a gluon is performed with the PYTHIA program.