dataesr/scientific-paragraphs-categorization huggingface dataset:
Informed, written consent was obtained from each participant prior to the imaging study. The FreeSurfer software package (http://surfer. nmr. mgh. harvard. edu) was used to perform gray and white matter segmentation to define a cortical surface mesh with accurate surface normals. The FreeSurfer package extracts both gray/white and gray/cerebrospinal fluid (CSF) boundaries, but these surfaces can have different surface orientations. In particular, the gray/ white boundary has sharp gyri (the curvature changes rapidly) and smooth sulci (slowly changing surface curvature), while the gray/CSF boundary is the inverse, with smooth gyri and sharp sulci. We created a new surface that had a similar curvature for both gyri and sulci, avoiding these curvature discontinuities. The new surface generated by interpolating a position that was midway between the gray/white surface and the gray/CSF surface using the FreeSurfer function mris_expand.
Real data collection Visual Evoked Potentials were recorded from 9 adult observers with normal visual acuity and stereopsis. The participants viewed a display consisting of dynamic random-dot kinematograms that alternated at 1 Hz between coherent and incoherent motion states. The coherent state consisted of rotary coherent motion for 500 msec, alternated with incoherent motion for 500 msec. The direction of coherent motion alternated between being clock-wise vs counterclockwise so as to reduce the effects of motion adaptation. A full cycle of the stimulus thus lasted two seconds and 5 cycles of stimulation were presented as 10 sec trials (n = 10), with 1 sec of additional presentation at the beginning to allow for start-up transients associated with the onset of the dots from a blank screen.
Inversion over a groups of subjects As noted in the introduction, variable selection can be made not only within an ROI of a given subject, but also across subjects and this is expected to result in improved estimates. To demonstrate this collaborative effect, we selected 5 subjects at random from our database and made the same comparison as in the previous section on a single instance of simulated data. These results are shown in  Fig 3 (right column). While the results are qualitatively similar, Performance of the group-lasso (in red) and minimum-norm (in blue) on one instance of simulated data for one (left column) and five subjects (right column). Vertical lines correspond to the solutions chosen by optimizing the GCV error curve for each method, with the asterisks indicating the results from the minimum norm.
Acknowledgments Anthony Norcia ,  Justin Ales  and  Benoit Cottereau  were partially supported by grants  EY018875  and  EY015790  from the  National Eye Institute, National Institutes of Health. Trevor Hastie  and  Michael Lim  were partially supported by grant  DMS-1007719  from the  National Science Foundation , and grant  RO1-EB001988-15  from the  National Institutes of Health. The authors would like to thank  Peter J. Kohler  for his assistance in preparing the human VEP data for source modeling.
Populations used for a genome-wide association study (GWAS) and to analyse the prediction efficiency of diagnostic markers A population of 710 individuals from different origins was used to assess the efficiency of prediction based on SSR genotypes and alleles. This population was made up as follows: 151 individuals belonging to several commercial varieties (full-sib progenies) selected within a cocoa farm in Cameroon; 125 individuals from a breeding population from IRAD; 270 individuals from a breeding population from CEPLAC; 97 individuals from a collection of Nacional-type accessions from INIAP, resulting from germplasm conservation or new breeding steps; nine other accessions from Upper or Lower Amazon regions from CIRAD; and 58 individuals from a germplasm collection of Trinitario-and hybrid-types from the CRC (Trinidad and Tobago), previously evaluated for SC  (Bartley, 1970)  and stored in the ICGD database (http://www.
Genotyping F 2 progeny Our objective was to identify skewed regions with a total absence of recombinant plants, linked to the SI reaction, in the CH1 and CH4 regions of the F 2 progeny. Several approaches were used to define new markers in these two regions. SSR markers were defined using the whole sequence of the Criollo genome V1 (http://cocoa-genome-hub. southgreen. fr/ gbrowse)  (Argout et al. , 2011) , and the tool 'Search for SSR' in the ESTTIK database (http://esttik. cirad. fr/cgi-bin/SSR_server. cgi) (Supplementary Table  S1  at JXB online). PCR amplifications and genotyping of the different populations were performed according to Loor  Solorzano et al. (2012). Two different strategies were used to identify deletions/insertions. First, deletions/insertions were identified in the CH4 region between the Criollo genome and the Amelonado genome (http:// www.
Cytological observation of the proportion of fertilized ovules Several SC trees from the F 2 progeny, homozygous for the Amelonado allele for the CH4 locus and heterozygous for the CH1 locus, were self-pollinated or pollinated with a SC pollen (CCN 51) taken as a control. Young fruits were harvested 7 d after self-pollination and to determine the proportion of fertilized ovules, the fruits were boiled for 20 min in water and then dissected. Using a binocular microscope, the relatively large, white fertilized ovules could be distinguished from the poorly developed, yellow, aborted ovules. Genome-wide association studies GWAS was conducted using Tassel 5. 2. 31 software  (Glaubitz et al. , 2014)  on 570 individuals assessed for SC/SI and coded as 0 (SI) and 1 (SC). In addition, for a subsample of 388 individuals the percentage of fruit setting 14 d after self-pollination was determined as a quantitative variable to assess SC/SI status.
The 570 individuals were genotyped by sequencing using 16480 SNPs stored in the TropGENE database (http://tropgenedb. cirad. fr/tropgene/) and with a minor allele frequency >0,05. The mixed linear model method was applied to identify marker/trait association using the population structure and kinship matrix as covariates  (Bradbury et al. , 2007). The structure of the population was determined with a subset of 150 SNPs distributed over all chromosomes, and using a Bayesian clustering method implemented in the STRUCTURE software  (Pritchard et al. , 2000) , with a burning period of 100 000 iterations, 500 000 Markov Chain Monte Carlo repetitions, and 10 independent runs. The variances of LnP(D) (Estimated Ln Prob of Data) were checked for each run, and runs for which the variance was not homogeneous with variances of the other runs with the same K value were eliminated.
A model with admixture and correlated allele frequencies was chosen, and with a K number of sub-populations between 5 and 12. The value of K was estimated as in  Evanno et al. (2005). The contributions of individuals to the K groups were then used as covariates in the analysis. The kinship matrix was constructed from the 16480 SNPs. Significant associations between markers and SC/SI status were identified in two steps: first, the P values were computed, and second, to eliminate false-positive associations, the P-value threshold was determined using the QVALUE software to control the false discovery rate (FDR)  (Storey, 2002). The corresponding P-value threshold was given by the QVALUE with a cut-off for rejection of the null hypothesis (q value) of 0. 05 to ensure a 5% FDR.
The combined use of markers mSI_303 and mSI_460 Fig. 1. Genome-wide association study (GWAS) of self-incompatibility traits. GWAS-derived Manhattan plots showing significant P-values associated with self-incompatibility traits. Left: a qualitative variable was used with a score of 0 for self-incompatible trees (less than 10% of fruit setting after selfpollination) and 1 for self-compatible trees (more than 30% of fruit setting after self-pollination). Right: a quantitative variable: the percentage of fruit setting, assessed 14 d after self-pollination. A total of 16 480 genome-wide SNP markers identified by GBS were used for this analysis to generate the data, which was analysed using the MLM procedure of the Tassel software. Significant associations for CH1 to CH10, and for unmapped SNP (11) are shown by dots for a threshold of P-values <3e -5 [or -log 10 (P-value)>5.
Acknowledgements This project was supported by  Agropolis Fondation  under the reference ID  1403 -46  through the  'Investissements d'avenir' programme (Labex Agro :  ANR-10-LABX -001 -1 ). We also thank  Mondelēz International  for their financial contribution to this project. We thank  Marc Lartaud  and  Geneviève Conéjéro  for their helpful advice regarding immunolocalization experiments and imaging. We thank  Neba Ngwa Akongnwi , for his help in managing field pollinations in Cameroun and  Semi Melliti , and  Gerald Oliver  for the management of cocoa plants in the greenhouse in Montpellier.
SUPPLEMENTAL MATERIAL: R computation details RGMM were implemented in R by adding a Ridge regularization term νI to the covariance matrix Σ c , where the parameter ν has been tuned using a 5-fold cross-validation. HDDA method  [2]  has been implemented thanks to the R package HDclassif  [1]. Implemented using the R package e1071  [7] , SVM require the setting of the kernel scale parameter and the soft margin cost, tuned by a 5-fold cross-validation. Random Forest method has two parameters to be tuned: the number of trees in the forest and the Bagging number of variables. We decided to set the number of trees to 500, and the Bagging parameter was tuned by a 5-fold cross-validation  [5]. The Random Forest method was implemented thanks to the R package randomForest  [6]. As there is no standard R package to implement FMLM, we made our own functional adaptation of the multinomial logit model in decomposing the data on a cubic spline basis (degree 3)  [10] , then applying the standard multivariate multinomial logit model on the obtained coefficients (we used the R package splines  [8]  to compute the cubic spline basis and the R package nnet  [11]  to build the multinomial logit model).
2 in the experiment and where we denoted C i1 and C i2 the first and second coordinates of the point C i. Finally, we have set N = 50 000, ε = 0. 002 and τ = 0. 001 and we have run 2000 timesteps. The computation takes less than six hours on a single core of a regular laptop. Note that it does not seem straighforward to adapt the techniques used in the proofs of convergence presented here to this setting, where the force depends on the density of the particle. Our purpose with this test case is merely to show that the numerical scheme behaves reasonably well in more complex situations. Software. The software developed for generating the results presented in this article is publicly available at https://github. com/mrgt/EulerLagrangianOT.
T. O. G was supported by  ANR  grant  ISOTACE  ( ANR-12-MONU-0013 ) and by the  Fonds de la Recherche Scientifique -FNRS  under Grant  MIS F. 4539. 16. Q. M. is supported by  ANR  grant  MAGA  ( ANR-16-CE40-0014 ). COMMUNICATED BY EITAN TADMOR.
Exon Ontology tree and exon annotation Large-scale RNA sequencing technologies allow characterization of the expression level of cellular transcripts as well as their exon content. Computational analyses based on Gene Ontology (GO), which relies on gene functional annotations (or GO terms), allow prediction of the biological processes (enriched GO terms) that are likely to be impacted by changes in gene expression level (Fig. 1A ). We developed Exon Ontology to identify protein domains and features that are impacted by alternative splicing variations (Fig. 1A ). For this purpose, we defined Exon Ontology (EXONT) terms from existing databases including Sequence Ontology, Protein Modification Ontology, InterPro, and Gene Ontology  (Montecchi-Palazzi et al. 2008; Mungall et al. 2011; Gene Ontology Consortium 2015; Mitchell et al.
2015). A total of 5312 Exon Ontology terms was used to generate the Exon Ontology tree (Fig. 1C ; Supplemental Table  S2 ). Meanwhile, protein annotations retrieved from reference tools and databases were mapped to the genomic exons defined in the FasterDB genome annotation database that we previously developed (Fig. 1B ;  Mallinjoud et al. 2014). In so doing, FasterDB genomic exons were associated with one or several EXONT terms and a web interface was developed in order to easily retrieve the EXONT terms associated with genomic exons (Fig. 1B ). A large proportion of the 190,617 coding exons defined in FasterDB was associated with "Structure"-, "PTM" (post-translational modification)-, "Binding"-, "Localization"-, and/or "Catalytic"-associated terms (Fig. 1D ). It is important to emphasize that Exon Ontology is based on exon-level annotations and relies neither on full-length transcript annotations nor on transcript/gene-associated GO terms.
Looking for the enrichment of Gene Ontology (GO) terms associated with these genes allows prediction of the biological processes and cellular activities that are likely to be impacted by gene expression level modifications. Exon Ontology aims at identifying protein features associated with changes of exon content owing to alternative splicing regulation, which may contribute to cellular phenotypes. Both GO and Exon Ontology predictions can next be addressed by dedicated experimental approaches. (B) The Exon Ontology workflow is based on ontological terms (EXONT terms) that were derived from existing ontologies and databases (e. g. , GO, Sequence Ontology, PSI-MOD, and InterPro). Protein features were derived from reference tools and databases and were mapped to annotated genomic exons in the 'Faster DB' database.
Genomic exons can thus be associated with one or several EXONT terms. A computational suite (Exon Ontology) then calculates a dedicated EXONT term score and looks for potential EXONT term enrichment by statistical analysis. (C) Protein features and domains have been assigned Exon Ontology (EXONT) terms based on existing ontologies and databases as described in panel B. The EXONT terms were organized in an Exon Ontology tree based on height classes of protein features (e. g. , catalytic, binding). Each class was divided in categories and contains a more or less large number of associated terms. For example, the "Localization" class was divided into "Nuclear Localization Signal" (NLS), "Nuclear Export Signal" (NES), "Mitochondrial Targeting Signal" (MTS), "Peroxisomal Targeting Signal" (PTS), "Endoplasmic Reticulum Signal Sequence" (ERSS), "Endosomal Localization Signal" (EL), and "Signal Peptide" (SP) categories based on the "Sequence Ontology" resource.
Regulation of the AKT signaling pathway by epithelial cellenriched splicing factors As already mentioned, the Exon Ontology database contains experimentally validated PTMs, including phosphorylation sites retrieved from several databases (see Methods). The Exon Ontology-based analysis revealed that the 81 selected exons are ). The enrichment Z-score and statistical significance are calculated by comparing the calculated EXONT score as described above to the scores obtained from a large number of sets of control exons having approximately the same size as the test-list. This analysis is available on the Exon Ontology website for the 91 EXONT terms that are most frequently associated with exons (listed in Supplemental Table  S2 ). enriched for "Phosphorylated Residue," "O-phospho-L-serine," and "O-phospho-L-threonine" terms, but not for the "O4'-phosterm when compared to CEs or ASEs (Fig.
S6 , panel A; Supplemental Table  S4 ;  Belov and Mohammadi 2012). The co-occurrence of phospho-residues, IUPRs, and/or protein binding motifs in the protein segments coded by the 81 Mes-Epi exons suggested that alternative splicing of these exons may affect protein-protein interaction networks. We therefore looked within the IntAct database (http://www. ebi. ac. uk/intact) for the partners of the 81 proteins harboring differentially spliced Mes-Epi exons. Interestingly, these partners are involved in biological processes relying on "nonmembranebounded organelles," "vesicles," "autophagy vacuole," and "exocytosis" (Supplemental Fig. S6 , panels B and C). In addition, several genes bearing exons regulated by mesenchymal cell-enriched splicing factors interact with autophagic factors (Fig. 6C-E ).
H3 (histone H3) is used as a loading control. right panel). Remarkably, it also mimicked the splicing effects by MBNL1/2 silencing (Fig. 6I ). These results support a model where mesenchymal cell-enriched splicing factors control alternative splicing of autophagic regulators that, in turn, regulate MBNL1 and MBNL2 protein expression level. Because a computational approach allowing prediction of the protein features affected by alternative splicing will be useful to the research community, we created a freely available web interface (http://fasterdb. ens-lyon. fr/ExonOntology/) that, after uploading the genomic coordinates of selected exons, gives access to the information stored in the Exon Ontology database, allows acquisition of potentially enriched protein features, and retrieval of relevant protein-protein networks (Supplemental Fig.
Annotations Annotations were derived from reference tools and databases, including InterProScan (version 5. 3-46. 0) (Jones et al. 2014) , TMHMM (version 2. 0c)  (Krogh et al. 2001)   (Matlock et al. 2015) , and D2P2 (Dec. 2014)  (Oates et al. 2013 ). We only used the "experimental" data sets of UniProt, dbOGAP, and dbPTM3. Localization motifs were identified using a custom Perl (version 5. 10. 1) script based on regular expressions. These annotations were mapped at the exon level using our splicing database FasterDB and stored in a MySQL database (version 14. 14 distribution 5. 1. 73). EXONT score, Z-score, and FDR For a given exon and a given feature, the EXONT score is computed by dividing the feature size (in nucleotides) by the exon size (in kilo-nucleotides). Only the coding part of the exon is considered.
RNA analysis RNA extraction, RT-PCR, and RT-qPCR were described previously  (Dardenne et al. 2012; Samaan et al. 2014). qPCR data were normalized with the RNA18S5 gene as a control. Statistical analyses on means were performed using Student's t-tests (unilateral, paired, P < 0. 05). Primer sequences for PCR and qPCR are provided in Supplemental Table  S1.
Membranes were then incubated with primary antibodies (overnight, 4°C) and washed before being incubated with secondary HRP-conjugated antibodies for 1 h. Primary and secondary antibodies are listed in Supplemental Table  S1. Image acquisitions were performed using the ChemiDoc Touch Imaging System (Bio-Rad), and quantification was performed using Image Lab software (v. 5. 2. 1, Biorad) and normalized with histone H3 or total nonphosphoprotein. Statistical analyses on means were made using Student's t-tests (unilateral, paired, P < 0. 05).
Figure1. (A) Genome-wide transcriptomic analyses allow identification of genes whose expression levels are modified when comparing two experimental conditions. Looking for the enrichment of Gene Ontology (GO) terms associated with these genes allows prediction of the biological processes and cellular activities that are likely to be impacted by gene expression level modifications. Exon Ontology aims at identifying protein features associated with changes of exon content owing to alternative splicing regulation, which may contribute to cellular phenotypes. Both GO and Exon Ontology predictions can next be addressed by dedicated experimental approaches. (B) The Exon Ontology workflow is based on ontological terms (EXONT terms) that were derived from existing ontologies and databases (e. g. , GO, Sequence Ontology, PSI-MOD, and InterPro).
Protein features were derived from reference tools and databases and were mapped to annotated genomic exons in the 'Faster DB' database. Genomic exons can thus be associated with one or several EXONT terms. A computational suite (Exon Ontology) then calculates a dedicated EXONT term score and looks for potential EXONT term enrichment by statistical analysis. (C) Protein features and domains have been assigned Exon Ontology (EXONT) terms based on existing ontologies and databases as described in panel B. The EXONT terms were organized in an Exon Ontology tree based on height classes of protein features (e. g. , catalytic, binding). Each class was divided in categories and contains a more or less large number of associated terms. For example, the "Localization" class was divided into "Nuclear Localization Signal" (NLS), "Nuclear Export Signal" (NES), "Mitochondrial Targeting Signal" (MTS), "Peroxisomal Targeting Signal" (PTS), "Endoplasmic Reticulum Signal Sequence" (ERSS), "Endosomal Localization Signal" (EL), and "Signal Peptide" (SP) categories based on the "Sequence Ontology" resource.
Acknowledgments This work was funded by  Fondation ARC  ( Programme Labellisé Fondation   ARC 2014 ,  PGA120140200853 ),  INCa  ( 2014-154 ),  Inserm  " Plan Cancer 2009-2013 ,"  AFM-Téléthon , and LNCC. Doctoral fellowships are from  Région Rhône-Alpes  (C. B. -P. ) and post-doctoral fellowships from LNCC (M. A. M. G. ) and Fondation ARC (F. Z. C. , F. O. D. , and L. C. T. ). The authors thank  Claire Burny  for helpful discussions on statistical analyses,  Marion Dubarry  for the interaction networks, and  Hélène Polveche  for computational assistance.
Software availability The Exon Ontology version used and released in this manuscript is v1. 5. 0. The ontology (i. e. , the EXONT terms) and the human annotation (i. e. , the association between EXONT terms and human coding exons) are available on the Exon Ontology website (except for the dbPTM data set, which is still available from the dbPTM website). In addition, the scripts to run Exon Ontology analyses from the command line are available in our GitLab repository (https:// gitlab. com/ExonOntology/ExonOntology), and in Supplemental Data S1.
g. , GO, Sequence Ontology, PSI-MOD, and InterPro). Protein features were derived from reference tools and databases and were mapped to annotated genomic exons in the 'Faster DB' database. Genomic exons can thus be associated with one or several EXONT terms. A computational suite (Exon Ontology) then calculates a dedicated EXONT term score and looks for potential EXONT term enrichment by statistical analysis. (C) Protein features and domains have been assigned Exon Ontology (EXONT) terms based on existing ontologies and databases as described in panel B. The EXONT terms were organized in an Exon Ontology tree based on height classes of protein features (e. g. , catalytic, binding). Each class was divided in categories and contains a more or less large number of associated terms. For example, the "Localization" class was divided into "Nuclear Localization Signal" (NLS), "Nuclear Export Signal" (NES), "Mitochondrial Targeting Signal" (MTS), "Peroxisomal Targeting Signal" (PTS), "Endoplasmic Reticulum Signal Sequence" (ERSS), "Endosomal Localization Signal" (EL), and "Signal Peptide" (SP) categories based on the "Sequence Ontology" resource.
Approximately 600 ng of Cy3-labeled cRNA was hybridized onto the SurePrint G3 Porcine GE microarray (8 × 60 K) following the manufacturer's instructions. Slides were scanned immediately after washing on an Agilent G2505C Microarray Scanner using the Agilent Scan Control A. 8. 5. 1 software, and the fluorescence signals were extracted using the Agilent Feature Extraction software v10. 10. 1. 1 with the default parameters. The microarray data were analyzed using R (www. r-project. org, R v. 3. 1. 2) and the Bioconductor packages (www. bioconductor. org, v 3. 0) as described in GEO entry GSE97821, which also contains all experimental details. A total of 16 samples (4 replicates per treatment group) was processed. Network analysis and functional analysis of the DE genes were performed using the Ingenuity Pathway Analysis tool (IPA, http://www.