dataesr/scientific-paragraphs-categorization huggingface dataset:
UK Biobank cohort The UK Biobank is an open-access longitudinal populationwide cohort study that includes 500k participants from all over the United Kingdom  ( Sudlow et al. , 2015 ). Data collection comprises detailed genotyping and a wide variety of endophenotypes ranging from health/activity questionnaires, extended demographics to neuroimaging and clinical health records. All participants provided informed consent and the study was approved by the North West Multi-Center Research Ethics Committee (MREC). This study used the February 2020 release (UK BioBank application number #64984). This release consisted of 36,940 participants, age range between 40 and 70 years (mean age = 54 ± 7. 45 years), with genotyping and resting-state functional MRI. To avoid any possible confounding effects related to ancestry, we restricted our analysis to individuals with British ancestry using the sample quality control information provided by UK Biobank  ( Bycroft et al.
, 2018 ). A final cohort of 32,186 participants (16,952 females, mean age = 55 ± 7. 51 years) were included in the study. We made use of the first ten principal components (Data field 22009) of the genotyping data's principal component analysis capturing population genetic diversity to account for population stratification. An independent replication dataset of 4,754 non-British individuals was also drawn from the UK Biobank. The age range of these participants was 40 to 70 (mean age = 53 ± 7. 55 years), 2,601 were female.
Resting-state functional MRI data The MRI data available from the UK Biobank are described in the UK Biobank Brain Imaging Documentation (v. 1. 7, January 2020) as well as in  Miller et al (2016 )  and  Alfaro-Almagro et al. (2018 ). Briefly, rsfMRI data were acquired using the following parameters: 3T Siemens Skyra scanner, TR = 0. 735 s, TE = 39 ms, duration = 6 min (490 time points), resolution: 2. 4 × 2. 4 × 2. 4 mm, Field-of-view = 88 × 88 × 64 matrix. During the resting-state scan, participants were instructed to keep their eyes fixated on a crosshair, to relax, and to think of nothing particular  ( Miller et al. , 2016 ). The preprocessing of the UK Biobank data includes motion correction, grand-mean intensity normalization, highpass temporal filtering including EPI (echo-planar imaging) unwarping with alignment to the T1 template and gradient non-linearity distortion correction (GDC) unwarping, brain masking, and registration to MNI (Montreal Neurological Institute-Hospital) space.
Diffusion-weighted MRI data The Diffusion-weighted MRI (DW-MRI) images were acquired using the following parameters; isotropic voxel size (resolution): 2 × 2 × 2 mm, five non diffusion-weighted image b = 0 s/mm2, diffusion-weighting of b = 1000, and 2000s/mm2 with 50 directions each, acquisition time: 7 min. Tensor fits utilize the b = 1000s/mm2 data and the NODDI (Neurite Orientation Dispersion and Density Imaging)  ( Zhang et al. , 2012 )  model is fit using AMICO (Accelerated Microstructure Imaging via Convex Optimization)  ( Daducci et al. , 2015 )  tool, creating outputs including nine diffusion indices maps. These ones were subject to a TBSS-style (Tract-based spatial statistics) analysis using FSL (FMRIB Software Library) tool resulting in a white matter skeleton.
Neuroimaging endophenotypes 2. 4. 1. FC endophenotypes The preprocessed resting-state BOLD signal was masked using the 25 ROIs and averaged at each time volume. A connectome matrix was computed using Nilearn  ( Abraham et al. , 2014 )  for each participant using a shrunk  ( Ledoit and Wolf, 2004  ) estimate of partial correlation  ( Marrelec et al. , 2006 ). This resulted in 300 ( = 25 × 24/2) edges connecting language ROIs for each individual. Each edge -also denoted functional connectivity (FC)-is further considered as a candidate endophenotype. See the Fig. 1 B.
Multivariate genome-wide association studies (mvGWAS) We performed a mvGWAS between the filtered imputed genotypes and the 142 significantly heritable FC endophenotypes, using the Multivariate Omnibus Statistical Test (MOSTest)  ( van der Meer et al. , 2020 ). All endophenotypes were pre-residualised controlling for covariates including sex, genotype array type, age, recruitment site, and ten genetic principal components provided by UK Biobank. In addition, MOSTest performs a rank-based inverse-normal transformation of the residualised endophenotypes to ensure that the inputs are normally distributed. The distributions across the participants of all endophenotypes were visually inspected before and after covariate adjustment. MOSTest generated summary statistics that capture the significance of the association across all heritable 142 language FC endophenotypes.
Fine-mapping: identification of genomic risk loci and functional annotation We performed functional annotation analysis using the FUMA (functional mapping and annotation) online platform v1. 3. 6a  ( Watanabe et al. , 2017 )  with default parameters. The genomic positions are reported according to the GRCh37 reference. SNPs were annotated for functional consequences on gene functions using ANNOVAR  ( Wang et al. , 2010 ) , Combined Annotation Dependent Depletion (CADD) scores  ( Kircher et al. , 2014 ) , and 15-core chromatin state prediction by ChromHMM  ( Ernst and Kellis, 2012 ). In addition, they were annotated for their effects on gene expression using expression Quantitative Trait Loci (eQTLs) of various tissue types. The eQTL module queried data from different tissue-datasets using GTEx v8  ( Consortium et al.
Multivariate genome-wide association analysis We performed a mvGWAS using the MOSTest ( van der Meer et al. , 2020 ) method, with the 142 FCs with significant SNP-based heritabilities. This analysis tested each SNP separately for its simultaneous association with the 142 FCs and yielded 4566 significant SNPs at a genomic threshold (see Table  SI3 ), distributed on chromosomes 2,  3, 5, 6, 10, 11, 14, 15, 17, 18 and 22. FUMA ( Watanabe et al. , 2017  ) software was used to analyze mvGWAS results and identify lead SNPs at each associated locus. Considering the genome-wide significance threshold p = 5e -8, there were 20 distinct genomic loci distributed on the 11 chromosomes, associated with different aspects of language FC ( Figs. 2 A, SI1, SI2, 2B, SI3 and Table  1  ) and represented by 20 lead SNPs.
Validation of lead SNPs associated with rsfMRI FCs The three lead SNPs were replicated at the nominal significance level ( p < 5e -2) on multivariate test in the independent non-British replication dataset: rs1440802 ( p = 9. 58e -3), rs35124509 ( p = 3. 25e -3), rs11187838 ( p = 2. 92e -2). Table  SI4  summarises these results. Moreover, these lead SNPs showed association at p < 0. 05 on univariate testing of all but three specific central traits identified in the discovery mvGWAS. Here, we present three of these loci that were replicated in an independent data set (refer to Section 2. 3 ). MOSTest results highlighted the three following genomic risk regions: (i) 15q14 locus (chr15, start = 39,598,529, length = 260 kb) with its strongest association related to the imputed SNP rs1440802 ( p = 1e -31); (ii) 3p11.
Identification of central endophenotypes associated with genomic risk regions For each lead SNP, we defined the 'central' endophenotypes that contributed the most in the multivariate association by using the individual univariate summary statistics performed by MOSTest and by considering the genome-wide significance threshold ( p < 5e -8) (Table  SI5 ). On 15q14, the lead SNP rs1440802 had two central FCs: the minor allele was associated with the partial correlation between (i) the precentral gyrus and the dorsal pars opercularis (Prec ↔F3opd). Both connected regions are in the left frontal lobe, and are labelled with a phonological linguistic component (Prec) and multi-labelled with semantic and sentence language processing (F3opd). (ii) The (PrecR ↔RolS) corresponds to the partial correlation between the precentral gyrus and the Rolandic sulcus.
All eQTL associations are presented in more detail in Table  SI7. Based on the human gene expression data from the Brainspan database, we found that THBS1 gene has relatively high mRNA (messenger RiboNucleic Acid) expression during early midprenatal to late prenatal stages, from 16 to 37 post-conceptional weeks; see Fig. 3 E. Indirect predictions might be added from the following annotation. RASGRP1 , identified by chromatin interaction mapping and which also appears to be under control of temporal expression during neurodevelopment, is reported as over-expressed in the perisylvian language areas  ( Johnson et al. , 2009 )  and as up-regulated in the dorsal striatum  ( Cirnaru et al. , 2020 ). Fig. 3  summarises these results, found by mvGWAS, associated to (Prec ↔F3opd) and (PrecR ↔RolS) FC endophenotypes.
Locus in 3p11. 1 associated to semantic-language related endophenotypes Fourteen independent SNPs were identified in locus 3p11. 1 ( Fig. 4 C ). The rs35124509 SNP is a non-synonymous variant within exon 16 of EPHA3 protein-coding gene. The subregion around rs35124509 and rs113141104 has its chromatin state annotated as (weak) activelytranscribed states (Tx, TxWk) in the brain tissues, specifically in the Brain Germinal Matrix, the Ganglion Eminence derived primary cultured neurospheres, and in the Fetal Brain Female. Concerning the subregion around rs6551410, it has its chromatin state annotated as Weak transcription (TxWk) in the Fetal Brain Female, enhancer (enh) in the Brain Germinal Matrix and Repressed PolyComb (ReprPC) in both the Ganglion Eminence and Cortex derived primary cultured neurospheres.
Discussion In this study, we extracted individual language FC endophenotypes from the rsfMRI data of 32,186 participants from the UK Biobank cohort and conducted a mvGWAS. We found 4566 significantly associated SNPs distributed over 11 chromosomes. Three multivariate associations with lead SNPs were replicated in the non-British cohort, highlighting the robustness of these signals across different ancestries. Two functional connections, contributing in the perceptual motor interaction, associated with 15q14 locus located in the RP11-624L4. 1 antisense gene with modulatory effects on the expression of the THBS1 gene. Multiple FCs in the fronto-temporal semantic language network were found to be associated with SNPs regulating EPHA3 gene expression in 3p11. 1 locus. Each lead SNP was found to be associated with the neuroanatomical white matter tracts that support each of these FCs.
Locus regulating THBS1 associated with the perceptual motor interactions process A locus in 15q14 was associated with the precentral-opercularis FC (Prec ↔F3opd) and the precentral-Rolandic FC endophenotypes (PrecR ↔RolS). The L -R Prec regions in the ventral precentral gyrus Expression quantitative trait loci (eQTL) associations (data source: eQTLGen  ( V ˜osa et al. , 2018 ) , PsychENCODE  ( Wang et al. , 2018 ) , DICE  ( Schmiedel et al. , 2018 ) , BIOS QTL browser  ( Zhernakova et al. , 2017 ) , GTEx/v8  ( Consortium et al. , 2017 ) , eQTLcatalogue). (D) Overlap of the genomic region risk region identified from FUMA for MOSTest results and the nine FCs mentioned above. (E) Gene expression from BrainSpan for the interesting genes prioritised by FUMA. are both associated with phonology language component and considered relevant for pharynx and tongue fine-movement coordination in the human and nonhuman primates  ( Vigneau et al.
, 2009 ). EPH receptors have been linked to neurode velopmental disorders, including schizophrenia  ( Zhang et al. , 2010 )  and autism spectrum disorder  ( Casey et al. , 2012 ). Moreover, in  Rudov et al. (2013 ) , EPHA3 is found in silico , as putative gene implicated in dyspraxia, dyslexia and specific language impairment (SLI). Finally, we observed that EPHA3 is expressed in the human brain, in a consistent manner across developmental stages from early prenatal to late-mid prenatal (8-24 pcw, BrainSpan; see Figs. 4 E  and SI4 ). Taken together, these results indicate that EPHA3 in the 3p11. 1 locus, could be prioritised in the study of key genes playing a role in the fronto-temporal semantic network, and with the anatomical connectivity support of this network.
Compared to structural endophenotypes, the FCs have low amplitude which hinders the study in terms of statistical power. This observation constitutes a third limitation that is somehow surpassed when working on large scale cohorts and using multivariate approaches. Another potential limitation is the UK Biobank dataset in which this study is based. It should be noted that the UKB constitutes a relatively old sample. Future studies in other developmental stages (i. e. children, adolescent, youngadult) will inform us whether the observed associations are stable across development, or whether they reflect some age-related specificity.
Acknowledgments This research was conducted using the UK Biobank resource under application # 64984. This project was supported by the  Marie Sklodowska-Curie program  awarded to  Stephanie J. Forkel  (Grant agreement No. 101028551 ). Amaia Carrion-Castillo was supported by a  Juan de la Cierva fellowship  from the  Spanish Ministry of Science and Innovation , and a  Gipuzkoa Fellows fellowship  from the  Basque Government.
Code availability This study used openly available software and codes, specifically GCTA (https://cnsgenomics. com/software/gcta/#GREML), PLINK (http://zzz. bwh. harvard. edu/plink/), MOSTest (https:// github. com/precimed/mostest), and FUMA (https://fuma. ctglab. nl/). The anatomical connectivity atlas used is available at (http://www. bcblab. com/BCB/Atlas_of_Human_Brain_Connections. html).
GaTewAY is a post-processing tool for molecular dynamics (MD) simulation databased on graph theoretical techniques. The main idea is to construct 2D graphs from the 3D structures. Then, GaTewAY identifies the conformations over time based on the dynamics of bonds and the isomorphism test. The use of graph theory methods/tools does not only provide a direct and fast methodology to identify conformers, but also allows following in time the interconversion reactions between the different conformers as shown by graphs of transitions. In this paper, we describe the methodology used to develop GaTeWAY. Then, we describe the code with its different modules. We present the inputs of GaTeWAY, the threshold values, the usage of the code and the main outputs. Our software has been tested and validated on DFT-MD and QM-MM trajectories of gas phase molecules and clusters.
We will, in particular, illustrate results for peptides to have an idea about the outputs. The algorithm's transferability is further demonstrated on a more complex air/liquid water interface. We designed our algorithm so that it can be extended and consider any types of bonds/interactions and parameters; hence, GaTeWAY can be applied to any molecular system. Ongoing work aims to analyze reaction networks in clusters for catalysis. The results are promising. Another manuscript has just been submitted, which illustrates the performance of GateWAY for these systems.
Introduction This paper describes the GaTewAY software, a graph theoretical-based tool for conformational dynamics analysis. Given one MD trajectory, GaTewAY provides the different conformers explored in this trajectory, their residence time and how the molecular system goes from one conformer to another. GaTewAY reads the atoms' cartesian coordinates from the input file and converts them to graph objects corresponding to conformations. It provides the set of conformations that are not isomorphic  1  and the graph of transitions representing all transitions observed between these conformations. GaTewAY can be applied to DFT-MD/QM-MM trajectories 2-6 of the molecular system in the gas phase as presented in the examples, but also to any simulations, for different phases, and to more complex systems as interfaces.
The software is implemented so that it can be extended and consider any other types of bonds and parameters to analyze the conformational dynamics. Some uses of the GaTewAY results are launching more efficient simulations: by using relevant starting points, computing accurate spectra, etc. The use of graph theory methods/tools not only provides a direct and fast methodology to identify conformers but also allows following in time the interconversion reactions between the different conformers as shown by graphs of transitions. Graph theory tools will be ever more precious when applied to bigger molecular systems, for instance, to conformational dynamics and folding of proteins. Within graph theory, a molecular system can be defined as a graph such that vertices represent atoms of the molecular system and edges represent the bonds formed between these atoms (covalent bonds, hydrogen bonds, intermolecular electrostatic interactions, etc.
Single analysis module This module is used to analyze one trajectory. The main steps of the analysis are as follows: GaTewAY reads the first snapshot of the trajectory and provides the first conformation by computing the different bond sets as described in section 2. 2. Then, at each following snapshot of the trajectory, GaTewAY reads the new snapshot and provides the associated conformation. To that end, it applies either a matrices' adjacency comparison or isomorphic tests (see refs  12, 21  ) with the last identified conformation or with the set of conformations already identified in order to decide if the current snapshot defines a new conformation or not. As defined in section 2. 2, the program uses reference snapshots and orbits to reduce (optimize) the application of the isomorphic tests.
In graph theory, this result is obtained by analyzing the connected components(i. e. a subgraph in which every vertex is connected to every other node in the sub-graph and is not connected to any other node not being part of the sub-graph) of each conformer. For an illustration of 200 snapshots extracted from a total of 20 ps ab initio MD trajectory (composed of an average of 44 water molecules, 132 atoms), the same 2D-H-bond network as in ref. 22  has been obtained by the graph theory algorithms of GaTewAY  21. The interface analysis module uses the same algorithms as the single and multiple analysis modules to identify the conformational dynamics. It performs more treatments to get details about the organization of the molecular system. One treatment consists of identifying the molecular system's connectivity, which provides the distribution of the connected components, the H-bond patterns, networks formed, etc.
GaTewAY usages GaTewAY is a series of codes written primarily in C. The software has two usage modes: online (direct) usage and advanced usage. The first mode consists in using a web user-friendly interface to analyze trajectories without caring about the code. The second mode consists in compiling the code on the user machine and then running the code. To that end, the user has full access to the code and can hence modify the code lines and call GaTewAY from other programs. This section describes the two usage mods independently of the molecular systems. Applications on different molecular systems are presented in a related paper  21. As demonstrated in  21  , the graph theory algorithms developed here are directly transferable without modifications.
On-line usage (direct usage) Hereafter, we describe the first usage mode of GaTewAY, which consists in using a web interface. We aim to provide a simple, intuitive and responsive web interface that lets users get things done with less effort and time. To achieve this objective, we have developed the interface based on the pop-up and drop-down menus and windows. Such menus make the available functions more apparent and easier to scan. Users can choose any functionality with just a couple of clicks. Figure  8  shows a screenshot of the GaTewAY web interface home page. The home page contains an introduction to the GaTewAY program and a menu to access different functionalities. The web interface provides to the user the possibility to analyse his own trajectories. He has to click the "new analysis" button which enables a drop-down menu (see figure  9 ).
All parameters are described in section 3. 2. Once all fields are completed, the user clicks the "Get conformations" at the bottom of the page to run the GaTewAY program. Internal automatic checks are triggered to ensure that the user inter the correct values in the different inputs and fills out the required ones. Then, the GaTewAY program is run in the background. Depending on the trajectories' size and number, this could take a few minutes. After running, a new page appears. This page contains the conformational dynamics analysis results of the input file. The user can download the different results and/or launch a new analysis with the same trajectory(ies) but with other parameters and then compare the results, which would be useful to get the right threshold accuracy. A full description of the web interface is provided on the "How does it work?" section of the interface.
In addition, we have put some examples of trajectories already analyzed by GaTewAY to understand more about the analyses performed by the software. Finally, the web interface is available through any standard web browser (chrome, safari, etc. ) and uses a responsive design. The user can access this interface from various devices (laptop, mobile phone, tablet, etc. ). For the time being, the interface is reachable only through the laboratory wired network. Ongoing work aims to add a system of authentification to the interface, which allows us to distribute the interface publicly.
Advanced usage The user can run and test the code directly on his machine. He has full access to the code and can edit whatever he wants. The GaTewAY package is freeware and can be downloaded from the developed web interface or GitHub. The code will be publicly available upon the acceptance of this paper. The GaTewAY consists of the source files written in C, a header file, a Makefile for compilation, examples of input and output files stored in the Examples folder, and a readme file. First, the user has to check the installation of the required external software used by GaTewAY. These free software are : nauty 14 , gnuplot  24  , and graphviz  25. Nauty is used to perform the isomorphism 1, 14 between the conformations mixed graphs. Gnuplot is used to draw plots like the time evolution of hydrogen bonds.
Finally, graphviz is used to draw graphs as the 2D simplified graphs of conformations and the graph of transitions. If no error has occurred, this line command will generate executable files : • singanalysis. exe : this executable is used to analyse a single trajectory. • multanalysis. exe : this executable is used to analyse a set of trajectories. • interface. exe : this executable is used to analyse molecular systems in condensed phase (interfaces). Each module (executable) has a set of arguments. For example, the user can set the system type, the bonds to take into account, etc. The user's manual and the readMe file provide details about each module. Finally, running the GaTewAY program will generate all results files in a directory with the same name as the input file/directory described hereafter.
The covalent bonds are represented by black edges. Arcs represent the H-bonds from the heavy atom (tail of the arc) to the acceptor atom (head of the arc). The arc is red as long as no proton transfer occurs. It becomes blue when there is a proton transfer. The electrostatic interactions are represented by blue edges. GaTewAY also saves one snapshot for each conformation in a. xyz file to allow the user to visualize the 3D representation of the conformation. The program saves the first snapshot related to the conformation appearance. • Graph of transitions. Such an oriented graph (see figure  4 ) provides the statistical relationship between the conformations found along the trajectory and the time sequence of their appearance. Each state of the graph represents one conformation, while arcs represent transitions between them.