dataesr/scientific-paragraphs-categorization huggingface dataset:
Ding group RNA structure predictions by the Ding group try to incorporate various structural data (both secondary and tertiary structures) available in the literature (e. g. , bioinformatics, hydroxyl radical foot printing, and in-line probing) or provided by the organizer (e. g. , SHAPE and proximity-mapping from the Das group). Briefly, for each given sequence, the database search with Rfam  (Burge et al. 2013; Daub et al. 2015)  is first performed to determine the RNA molecule's identity, function, homology, and multiple sequence alignment (MSA). In the case of Puzzle 4, where structures of homologous RNA sequences are experimentally known (e. g. , PDB ID: 2GIS), a homology modeling approach with multiscale discrete molecular dynamics (DMD) modeling  (Ding et al. 2008a ) is adopted. With the coarse-grained RNA model, sequence variations such as mutations, insertions, and deletions are straightforwardly carried out.
2009 ). The base pairs obtained from MSA are used if the RNA sequence is annotated in the Rfam database. Otherwise, base pairs derived from secondary structure predictions (with SHAPE if available) are used. Usually, 10 independent simulations are performed with different initial conditions. Next, the tertiary structural information including chemical probing by hydroxyl radical footprinting and in-line probing and proximity mapping are incorporated. The solvent accessibility information can be obtained from the chemical probing experiments  (Ding et al. 2012). Since the chemical probing data from the literature are often nonquantitative, the nucleotides are simply categorized as exposed, buried, and intermediate based on visual inspection of published plots. For the proximity-mapping data (e.
2008a ). Additional constraints can account for putative secondary structure, tertiary contacts, or ligand binding pockets. Replica exchange simulations are used to enhance sampling of the RNA conformational space. Next, the lowest (10% of) energy structures are selected from the replica exchange simulations and clustered hierarchically to identify the dominant state among the lowest energy ensemble. Finally, the centroids of the most populated clusters are selected for all-atom reconstruction. All-atom reconstruction is adapted from protein modeling  (Shirvanyants et al. 2012). The DMD-direct modeling process is fully automated  (Krokhotin et al. 2015). For Puzzle 4, the Dokholyan group built the initial tertiary structure of the target RNA based on the structure of a homologous sequence (PDB ID: 3IQP).
This structure was converted to a coarse-grained model, run through DMD simulations, and converted back to an all-atom model for refinement. In all other puzzles, the Dokholyan group performed simulations starting from extended RNA molecules providing a secondary structure as restraints. In Puzzles 7, 8, and 13, the secondary structure was predicted using Mfold  (Zuker 2003). For Puzzle 12, the Dokholyan group first searched for the sequence and obtained a single hit from T. tengcongensis, 5 ′ of the OppA9 gene, annotated as an ABC-type protein with nickel transporting ability. SHAPE-MaP data were then obtained with and without the (incorrectly) presumed ligand Ni 2+. Despite using the incorrect ligand, significant changes in SHAPE reactivity were observed as a function of Ni 2+. SHAPE-MaP-directed modeling  (Siegfried et al.
ACKNOWLEDGMENTS The Bujnicki group thanks its members who assisted in the work on individual problems, in particular Astha,  Bharat Madan ,  Radosław Pluta , and  Irina Tuszyn  ´ska. The Adamiak group thanks  Leszek Blaszczyk  for the literature review supporting prediction of Puzzles 12 and 13. Z. M. and E. W. are supported by  French Government  grant  ANR-10-BINF-02-02  " BACNET. "A. R. was supported by new faculty start-up funds from  Zhejiang University  and the  Thousand Young Talents Plan of China. Y. X. and J. W. are supported by the  National High Technology Research and Development Program of China  under grant number  2012AA020402  and the  National Natural Science Foundation of China  under grant number  31570722. S. C. is supported by  National Insitutes of Health  grant  R01-GM063732.
For this purpose, in the pre-surgical stage, robust and efficient methods are required to identify atypical from typical representations. Given the frequent location of regions generating seizures in the vicinity of language networks, one important function to be considered is language. The risk of language impairment after surgery is determined pre-surgically by mapping language networks. In clinical settings, cognitive mapping is classically performed with fMRI. The fMRI analyses allowing the identification of atypical patterns of language networks in patients are not sufficiently robust and require additional statistic approaches. In this study, we report the use of a statistical nonlinear machine learning classification, the Extreme Gradient Boosting (XGBoost) algorithm, to identify atypical patterns and classify 55 participants as healthy subjects or patients with epilepsy.
XGBoost analyses were based on neurophysiological features in five language regions (three frontal and two temporal) in both hemispheres and activated with fMRI for a phonological (PHONO) and a semantic (SEM) language task. These features were combined into 135 cognitively plausible subsets and further submitted to selection and binary classification. Classification performance was scored with the Area Under the receiver operating characteristic curve (AUC). Our results showed that the subset SEM_LH BA_47-21 (left fronto-temporal activation induced by the SEM task) provided the best discrimination between the two groups (AUC of 91 ± 5%). The results are discussed in the framework of the current debates of language reorganization in focal epilepsy.
In this current study, we applied a ML classifier, the Extreme Gradient Boosting algorithm (XGBoost)  [26]  in order to discriminate the fMRI from epileptic patients and healthy subjects. This particular method was chosen due to its significant advantages: (a) dealing with missing values, (b) requiring data scaling, (c) implying a computationally efficient variant of gradient boosting algorithm  [27] , (d) providing satisfactory results in ML competitions  [28]  and was successfully used in other studies and domains (see  [29, 30] ). Using XGBoost, we expected to identify robust patterns of language representation which are able to distinguish patients and healthy people. To our knowledge, there are no studies using XGBoost to objectively classify two populations based on their neurophysiological features.
MR acquisition The experiment was performed in a whole-body 3T MR scanner (Bruker MedSpec S300) with 40 mT/m gradient strength at MR facility. For functional runs, the manufacturer-provided gradient-echo/T2*-weighted EPI method was used. Thirty-nine adjacent axial slices parallel to the bi-commissural plane were acquired in an interleaved mode. Slice thickness was 3. 5 mm. During each run, the cerebral volume was measured 150 times. The in-plane voxel size was 3 9 3 mm (216 9 216 mm field of view acquired with a 72 9 72 pixel data matrix, reconstructed with zero filling to 128 9 128 pixels). The main sequence parameters were: TR = 2. 5 s, TE = 40 ms, flip angle = 77°. To correct images for geometric distortions induced by local B0 inhomogeneity, a B0 field map was obtained from two gradient-echo datasets acquired with a standard 3D FLASH sequence (DTE = 9.
1 ms). The field map was used during data processing. A T1-weighted high- For each group, we mentioned the number of participants (N), the mean age and standard deviation (SD), the gender (F, female; M, male) and the handedness (right-handed, R; left-handed, L) resolution three-dimensional anatomical volume was also acquired, by using a 3D-modified driven equilibrium Fourier transform (MDEFT) sequence (field of view: 256 9 224 9 176 mm; resolution: 1. 333 9 1. 750 9 1. 375 mm; acquisition matrix: 192 9 128 9 128 pixels; reconstruction matrix: 256 9 128 9 128 pixels).
Spatial preprocessing of fMRI data Data analysis was performed by using the general linear model, GLM  [35]  for event-related designs with SPM12 (Wellcome Department of Imaging Neuroscience, London, UK, www. fil. ion. ucl. ac. uk/spm) implemented in MATLAB (MathWorks Inc. , Natick, MA, USA). Images were spatially preprocessed. First, the functional volumes were time-corrected with the 19th slice as reference (the acquired brain volume was composed of 39 slices) to correct artifacts caused by the delay of time acquisition between slices. Subsequently, all volumes were realigned to correct for head motion, by using a rigid body transformation. T1-weighted anatomical volume was co-registered to mean images created by the realignment procedure and was normalized within the MNI space. Anatomical normalization parameters were used for the normalization of functional volumes.
Statistical analyses of fMRI data Statistical analyses were subsequently performed on the preprocessed data. For each participant, each task (PHONO and SEM) was declared as a specific fMRI run. Thus, for each run PHONO or SEM, we included two regressors, PHONO (task) and Control-PHONO, and SEM (task) and Control-SEM, respectively. Each of them was convolved with a canonical hemodynamic response function (HRF). Movement parameters derived from the realignment corrections (three translations and three rotations) were included into the design matrix as additional factors of no interest. The GLM was then used to generate the parameter estimates of activity for each voxel, each condition and each participant. Statistical parametric maps were generated from the linear contrasts between the HRF parameter estimates for the four experimental conditions (i.
The space of all feature subsets is generated by the strategy defined above. An exhaustive search with these features involves around one million combinations 2 20  ð Þ. After evaluation, some filter methods (low variance, Fisher score, CFS, Laplacian score, spectral score) and the forward (SFS) and backward selection (SBS) wrapper methods were considered unsuccessful (i. e. , with no feature subset stability and with no significance of the performance metrics). Filter methods were tested with scikit-feature Python libraries implementation  [39]. SFS and SBS wrapper methods were performed with Mlxtend Python libraries  [40]. Thus, we decided to perform another wrapper method, a reduced exhaustive search among a selection of 135 feature subsets. We did not perform an exhaustive search with the 2 20 combinations of features, which would have been too long to compute and would have been prone to overfitting.
Classification method The Extreme Gradient Boosting (XGBoost) algorithm  [43]  was used for classification. It was implemented using the scikit-learn  [40]  Python libraries for all ML processes. Parameters for the algorithm were fixed (cf. Table  3 ) and not optimized by a grid search for the whole ML process (see  [44] ). Four parameters out of fifteen were set to particular values. The learning rate was typically set to 0. 01 (default 0. 3) as small values lead to much better generalization  [45]. The number of boosted trees usually between some hundreds and thousands was set to 1200 (estimators). To prevent overfitting, the subsample was set to 0. 7 (default 1). This added randomness and made the training robust to noise. The maximum depth of a tree was set to 3 (default 6) to reduce the model complexity.
These subsets were evaluated for three thematic sets according to task (semantic only, SEM only; phonological only, PHONO only; semantic and phonological combined, SEM ? PHONO) and hemisphere (left hemisphere, right hemisphere and bilateral-both hemispheres) on the validation set that was held out from the feature selection step. All splits were performed in a stratified way to get the same ratio of patients with epilepsy. In order to assure stability of feature selection and get correct generalization of the classification performance, we repeated this process twelve times by randomly shuffling the dataset before splitting into training and validation sets. We finally obtained twelve performance metrics (cf. Table  3 ) for twelve feature subsets, based on a distribution of samples.
Discussion This is a proof of concept study illustrating the ability of a specific ML approach, the XGBoost algorithm, to classify subjects in two distinct classes or categories, healthy/typical versus patients with epilepsy/atypical, according to their language representation, as determined with fMRI. This statistical method does not require manipulation of data and uses neurophysiological features reflecting the amount of activated language regions, for two main processes: semantic and phonological. Our results showed that a specific subset best distinguished the two categories of participants, namely the subset SEM_LH BA_47-21, showing that the left fronto-temporal activation induced by the SEM task was the most relevant to classify patients. This result can be discussed in the framework of current debates on language representation and reorganization in focal epilepsy  [3].
An important contribution to this reorganization is added by the interaction between semantic language and memory processes, given that a part of the anatomical subjacent regions are common to both language and memory. This also explains why these two cognitive functions are increasingly examined together rather than separately  [62]. These observations are reflected and confirmed by the neuropsychological testing, showing that these patients frequently have semantic (both language and memory) deficits. In conclusion, this biomarker of intrahemispheric reorganization of fronto-temporal semantic networks revealed by the XGBoost algorithm for distinguishing patients from controls is in agreement with our knowledge on semantic processing. This result is in agreement with data from patients with epilepsy.
Based on these assumptions, we suggest that the ML-XGBoost algorithm could be a useful tool to detect the intra-hemispheric atypical reorganization patterns, more difficult to assess in patients, but having a major role in the neuroplasticity of language in patients with epilepsy. In terms of suitable tasks to map language networks, our results show that compared to phonological task, the semantic task is more reliable for classifying patients, even if both tasks activate fronto-temporal regions. The advantage of using a semantic task is that this task induces a more spread-out activation within frontal and temporal language networks. This assumption is in agreement with the findings by Billingsley et al. [9] , showing that language reorganization in patients is mainly revealed by a semantic task, whereas a phonological task results in more specific prefrontal activation.
Conclusions The ML-XGBoost is a powerful statistical method of classification which detects nonlinear patterns in datasets with missing values. It shows significant potential for classifying patients with epilepsy based on the cerebral region, hemisphere and processing of their language representation. One subset, or a specific combination of features, the SEM_LH BA_47-21, was the most powerful, for identifying patients. The importance of this particular subset is plausible given the cognitive and clinical observations made with these patients. L. Torlay is a computer science engineer, specialist in predictive statistical analyses, machine learning, and their implementation into applications and technological tools. Specifically, he develops explanatory or predictive models of cognitive functions in healthy subject and patients presenting cerebral lesions.
Numerous mutations in each of the mitochondrial aminoacyl-tRNA synthetases (aaRSs) have been implicated in human diseases. The mutations are autosomal and recessive and lead mainly to neurological disorders, although with pleiotropic effects. The processes and interactions that drive the etiology of the disorders associated with mitochondrial aaRSs (mt-aaRSs) are far from understood. The complexity of the clinical, genetic, and structural data requires concerted, interdisciplinary efforts to understand the molecular biology of these disorders. Toward this goal, we designed MiSynPat, a comprehensive knowledge base together with an ergonomic Web server designed to organize and access all pertinent information (sequences, multiple sequence alignments, structures, disease descriptions, mutation characteristics, original literature) on the disease-linked human mt-aaRSs.
DESCRIPTION OF MISYNPAT COMPONENTS AND TOOLS The MiSynPat infrastructure is organized around three main components: (i) a knowledge base; (ii) an updating system for new 3D structure and literature information with access restricted to authorized experts; (iii) a Web interface to query the database. The knowledge base is organized around four main SQL tables and two junction tables (see Materials and Methods and Supp. Fig. S1 ), which provide the list of mutations and related information, information on protein sequences, homologs and related 3D structures as well as links to the original bibliography and associated diseases. Each mutation is characterized at the disease, genetic, evolutionary, and structural levels and an access is provided to the statistics calculated for individual or all synthetases.
Currently, the MiSynPat knowledge base contains: (ii) 3D coordinates for the three available crystallographic structures of human mt-aaRSs and for the models obtained by automated homology modeling of the 19 mt-aaRSs at a PHYRE2 confidence level >90% for all systems. (i) (iii) Two hundred and eight disease-related variations with the allelic composition of each patient and their correlation with the eight distinct major disorders. (iv) One hundred and twenty-five abstracts and PMIDs linked to the NCBI PubMed database for the publications retained by the double sieve updating system (see below). A set of helper tools (JSmol, Modeler, LSQMan, NAccess, PolyPhen, SIFT) is attached to the knowledge base to provide and manage the data. In addition, in-house statistical packages and updating systems have been developed.
Updating system The quality and lifetime of a knowledge-based Web server strongly depend on the balance between maximum automation for its update and minimum human dependency. To incorporate new bibliographic information into the database, the chosen strategy is based on a double sieve mechanism. Firstly, publications concerning human mt-aaRS diseases are automatically retrieved from PubMed through the NCBI Web service using a specialized filter. Secondly, the articles selected by the first sieve are presented through a Web form to an expert who validates or rejects them and enters the relevant information regarding mutations/diseases into the database. Missense, nonsense, insertion, deletion, and splicing defects present in the mt-aaRS exons or introns are all registered and those impacting the protein sequence are further visualized.
Single mt-aaRS window To Disease-related protein sequence variations are indicated within MiSynPat tabs following the HGVs international nomenclature standards (e. g. , p. Arg179His, which indicates that Arginine at position 179 is replaced by an Histidine) and using the one letter code (e. g. , R179H) in order to be easily recognizable within the multiple sequence alignments. It should be noted that help is provided in MiSynPat through tooltips when applicable, and a dedicated Help Page is accessible throughout the Website.
Database architecture The database (see Supp. Fig. S1 ) consists of four main tables called Synthetase, Disease, Mutation, and Bibliography. Two junction F I G U R E 3 Screen capture of the DARS2 Integrative Analysis tab. From top to bottom, the page provides: UniProt/NCBI and MSeqDR links, the interactive ModMutGimmick with a black line indicating the compound heterozygous status of the patient, who has the R179H mutation associated with the E425X mutation, the linear sequence of the human mt-AspRS, the JSmol 3D structure viewer window (either a crystallographic structure or a 3D model) with its control panel, the 3D Mutation toolbox to select and render the 3D model of a known disease-related mutation or a userdefined mutation. Upon selection of the R179H mutation in the dropdown menu, the conservation status, the relative solvent accessibility, and all known alleles with the bibliographic references are displayed.
g. , Dmito_Homo. sapi stands for the human DARS2). Each alignment has been treated by the MACSIMS program  (Thompson et al. , 2006)  in order to collect and propagate associated functional/structural features (PFAM-A domains, PhyloBlock, secondary structures). We defined three conservation levels: residues with 100% identity over a column, residues with >80% identity over a column and columns with >60% of similar residues using the six sim-.
Structural information To date, only three human mt-aaRS crystallographic structures have been solved [mt-TyrRS  (Bonnefond et al. , 2007) , mt-PheRS  (Klipcan et al. , 2012) , and mt-AspRS  (Neuenfeldt et al. , 2013) ]. Models of all other human mt-aaRS are built using the PHYRE2 Web server with the "intensive" option  (Kelley, Mezulis, Yates, Wass, & Sternberg, 2015). Using the PDB  (Berman et al. , 2000)   (Fiser & Sali, 2003). The protein residue Relative Solvent Accessibility (RSA) is computed with the NAccess program  (Hubbard & Thornton, 1993) , using a default probe radius of 1. 4 Å and a standard Ala-XXX-Ala tripeptide as the full accessibility reference for amino acid XXX. The PolyPhen  (Adzhubei et al. , 2010)  and SIFT  (Sim et al. , 2012)  predictions for a given mutation are computed on the fly within the Integrative Analysis tab.
Implementation All MiSynPat SQL tables are implemented in a SQLite3 database (v3. 8. 8. 3). The Web server is written in Tcl (v 8. 6. 4) hosted by an Apache server (v2. 4) running the Rivet (v2. 2) module under Ubuntu. The client side in written in HTML5/jQuery 1. 11 and uses the JSmol plugin (an open-source HTML5 viewer for chemical structures in 3D; http://wiki. jmol. org/index. php/JSmol).
It is therefore timely to extract and collect existing and emerging data related to mutations affecting human mt-aaRSs to allow the community of clinicians and fundamental researchers to analyze them comprehensively and systematically within a dedicated and continuously updated computing infrastructure. To achieve this, we developed MiSynPat (for Mitochondrial aminoacyl-tRNA Synthetases and Pathologies), a knowledge database and a Web server, for the characterization, analysis, and querying of the interrelations between genetics, mutations, sequences, 3D structures, multiple alignments, sequence/structure conservation, and biological functional domains. The architecture of MySinPat allows to search for data via four main interconnected information levels focused on the disease-causing mutations: protein sequence alignments, 3D structures, literature, and statistics.
concerning human mt-aaRS diseases are automatically retrieved from PubMed at the NCBI through the E-utilities Web service, using a filter built from a training set composed of 102 relevant articles published from 2007 and 2014. The first sieve of the filter consists of a set of relevant terms linked by Boolean operators to query the NCBI PubMed fields: Title/Abstract and Text Word. The coarseness of the filter has been adjusted so that no article of the training set is rejected. The selected articles are presented through a Web form to an expert who validates or rejects the proposed articles and enters the relevant information regarding all types of mutations (missense, nonsense, insertion, deletion, splicing defect) or diseases into the database. The bibliography search and validation process is performed daily and has been tested over a period of 18 months.