dataesr/scientific-paragraphs-categorization huggingface dataset:
One could envisage exporting such models on different scales, including kinetic pathways of individual pathways  [38] , and genome-scale metabolic reconstructions  [39]. Another potential area of interest would be a greater focus on metabolomics data analysis  [40] , with the integration of mass spectra of chemical compounds run under standard conditions. With the addition of such data, one could envisage a system exploiting existing chemical metadata, standardised mass spectra and metabolic network proximity to provide a comprehensive metabolite identification service. The flexibility, extensibility and ease-of-use of graph database systems will provide enormous advantages in fast-moving areas such as biotechnological research over the coming years. bio-chem4j is an early example of their potential and can serve as a first step towards a family of related and interacting resources across a wide range of biological application domains.
Methods Populating biochem4j is built upon the freely available graph database, neo4j Community Edition, v3 (http://neo4j. com/), providing both a web and web service interface to the data. biochem4j is populated through a number of custom Python modules. The basic workflow for each of the modules is to download and parse flat (text) files from source databases, generating nodes and relationships that are used to initialize a neo4j instance. The source databases are ChEBI, MNXref, Rhea, KEGG, UniProt and the NCBI Taxonomy. As the modules download the latest version of the flat files (or utilise APIs that manage versioning  [41] ), the content of biochem4j can be regularly updated as source databases expand in scope. The key feature of the biochem4j is its integration of data. Such identifier mapping is a common problem in bioinformatics and has particular ramifications for the construction and merging of systems biology models  [42, 43].
Therefore, an important aspect of the population process is to ensure that a given entity only appears once in the database. An example of this is with chemicals, which are present in both ChEBI and MNXref. In order to ensure uniqueness, properties of each node are mapped to a common namespace, which is defined by identifiers. org  [44]. When a particular chemical entity is mapped to a KEGG entry in both ChEBI and MNXref, each source uses a different identifier to name the KEGG database (KEGG COM-POUND accession and kegg respectively). These are each mapped to the common identifiers. org namespace value, kegg. compound, allowing nodes representing the same entity but originating from different source databases to be merged. This allows a particular chemical species to maintain relationships in both the ChEBI ontology and also within the MNXref reaction network.
Another consideration of the biochem4j is in the specification of reaction definitions. In order to maximise the accuracy of the data and also to support future applications including constraint-based modelling  [45] , an attempt is made to balance all reaction data before submission. This makes use of an existing algorithm, ported from the SuBliMinaL Toolbox  [46]  which uses linear programming to balance reactions where possible, respecifying incorrect stoichiometries and adding commonly missing reactants such as protons and water. All code is available through open source and the MIT License at https://github. com/ synbiochem/biochem4j, with dependencies on core Python modules available from https:// github. com/synbiochem/synbiochem-py.
Acknowledgments NS, PC, MD, AJJ, MV, ARW,  J-LF , PM, DBK,  NSS  and RB acknowledge the funding from the  Biotechnology and Biological Sciences Research Council  (BBSRC; http://www. bbsrc. ac. uk) under grant  BB/M017702/1 , " Centre for synthetic biology of fine and speciality chemicals (SYNBIOCHEM) ". NS, DBK and PM thank the  BBSRC  for funding under grant  BB/K019783/ 1 , "Continued development of ChEBI towards better usability for the systems biology and metabolic modelling community". NS, RBN, PD, SA, DBK and PM thank the  BBSRC  for funding under grant  BB/M006891/1 , " Enriching Metabolic PATHwaY models with evidence from the literature (EMPATHY) ". NS thanks  Andrew Currin ,  Christopher Robinson  and  Sandra Taylor  for useful discussions during the development of this work. This is a contribution from the  Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM).
NS, PC, MD, AJJ, MV, ARW,  J-LF , PM, DBK,  NSS  and RB acknowledge the funding from the  Biotechnology and Biological Sciences Research Council  (BBSRC; http://www. bbsrc. ac. uk) under grant  BB/M017702/1 , " Centre for synthetic biology of fine and speciality chemicals (SYNBIOCHEM) ". NS, DBK and PM thank the  BBSRC  for funding under grant  BB/K019783/1 , "Continued development of ChEBI towards better usability for the systems biology and metabolic modelling community". NS, RBN, PD, SA, DBK and PM thank the  BBSRC  for funding under grant  BB/ M006891/1 , " Enriching Metabolic PATHwaY models with evidence from the literature (EMPATHY) ".
6×10 -9 , as genome-wide significant. In total, nine GWA scans were performed. Our multivariate analysis replicated five out of nine previously established  23  IgG Nglycosylation loci (ST6GAL1, B4GALT1, FUT8, SMARCB1-DERL3 and SYNGR1-TAB1-MGAT3; Fig. 1 , Supplementary Table  3 ) and detected five new loci (IGH, ELL2, HLA-B-C, AZI1, FUT6-FUT3, named by genes according to functional candidacy; Table  1 , Supplementary Table  6 , Supplementary Data 1). Among the five novel loci, four were not detectable using the conventional univariate analysis. The IGH locus could be identified using univariate GWAS on GP9, however, it was not mapped in the previous univariate GWAS with even larger discovery sample  23  (N = 2247). Genetic effects on phenotype scores are strongly replicated. To replicate and interpret the genetic effect of each newly identified locus (Table  1 , Supplementary Table  6 ), one could perform a multivariate test, resulting in a P-value, which, when significant, might be interpreted as replication.
Connection to immune-related tissues and disease. We used DEPICT software  30  to perform gene prioritization, gene set and tissue enrichment analyses. Eighteen analyses (GWAS of 9 trait -3 -2 -1 0 ORCADES -IGH 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 ORCADES -AZI1 1 2 3 -3 -2 -1 0 TWINSUK -IGH TWINSUK -AZI1 CROATIA -IGH CROATIA -AZI1 ORCADES -IGH 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 ORCADES -AZI1 1 2 3 Fig. 2  Comparison of the estimated genotype-phenotype partial correlations between discovery and replication cohorts. The IGH and AZI1 loci for the 17 traits in the IgG galactosylation group are displayed. The partial correlations were standardized to z-scores so that the regression slope and confidence intervals (red curves) represent the amount of correlation in the effect sizes between each named cohort.
The gray bars represent standard errors sets, using either genome-wide significant SNPs or SNPs with MANOVA P < 1×10 -5 ) were run. Results are reported in Supplementary Data 2. The gene sets demonstrating most significant enrichment included regulation of protein kinase activity and Endoplasmic Reticulum-nucleus signaling pathway. The top enriched tissues/cell types included B-lymphocytes, plasma cells, antibody-producing cells, plasma (see Fig. 3  and Supplemetary Fig. 11 -12 for trait group-specific results). To link to established knowledge and to provide insights regarding potential underlying functions of the IgG glycosylation loci, we searched association databases using PhenoScanner (http://www. phenoscanner. medschl. cam. ac. uk/) for our reported top variants (8 and 6 for the new and known loci, respectively) and obtained 2011 association records.
05 or imputation R-square < 0. 30 were excluded. GRAMMAR+ transformation  27  was implemented in the GenABEL-package  42  , part of the GenABEL-suite  43. The genomic relationship matrix used in the analyses was generated by the ibs() function (with weight = "freq" option), which uses SNP array data to estimate the realized pairwise kinship coefficient. The polygenic() function was used to obtain the GRAMMAR+ transformed phenotypes (grresidualY) from linear mixed models. All univariate GWAS inflation factors (lambda values) were close to 1, and the multivariate GWAS inflation factor was 1. 005, showing that this method efficiently accounts for family structure. We implement the above multivariate analysis in the Multivariate() function of the MultiABEL package. Implemented in this function is the MANOVA of multiple phenotypes against each single variant genotype dosage, and for the method option, in our analysis, Pillai's trace  44  was used as the test statistic for the multivariate association.
Acknowledgements We thank  Chris S. Haley ,  Yudi Pawitan  and  Jeanine Houwing-Duistermaat  for useful discussion of this manuscript. This study was supported by by  European Commission   FP7 grants MIMOmics  (contract # 305280 ),  HTP-GlycoMet  (contract # 324400 ), Pai-nOmics (contract # 602736 ) and  Integra-Life  (contract # 315997 ), and  H2020  projects GlySign (contract # 722095 ), SYSCID (contract # 733100 ) and IMforFuture (contract # 721815 ) as well as funding for the  Croatian National Centre of Research Excellence in Personalized Healthcare. X. S. was in receipt of a  Swedish Research Council  grant (No. 537-2014-371 ). The work of Y. S. A. was supported by the  Federal Agency of Scientific Organisations via the Institute of Cytology and Genetics  (project #  0324-2016-0008 ).
The work of S. S. was supported by  Russian Ministry of Science and Education  under 5-100 Excellence Programme. ORCADES was supported by the  Chief Scientist Office of the Scottish Government  ( CZB/4/276 ,  CZB/4/710 ), the  Royal Society , the  MRC Human Genetics Unit, Arthritis Research UK  and the  European Union  framework program 6  EUROSPAN  project (contract no. LSHG-CT-2006-018947 ). DNA extractions were performed at the  Edinburgh Clinical Research Facility. The  CROATIA-Vis  and  CROATIA-Korcula  acknowledge the  University of Split and Zagreb Medical Schools , the  Institute for Anthropological Research in Zagreb  and the  Croatian Institute for Public Health. Both studies were funded by grants from the  Medical Research Council (UK) , MSES grants ( 216-1080315-0302 ) and the  Croatian Science Foundation  ( 8875 ).
Code availability. The free and open source R package MultiABEL is available at: https://cran. r-project. org/package=MultiABEL. Its developer version is available at the GenABEL project repository: https://r-forge. r-project. org/R/?group_id=505. Tutorial of the multivariate GWA analysis procedure using MultiABEL is available at: https://github. com/xiashen/MultiABEL/. Data availability. The results of the nine genome-wide multivariate scans in the discovery analysis are available in the DataShare repository (http://dx. doi. org/ 10. 7488/ds/2069) of the University of Edinburgh. The remaining data are contained within the paper and Supplementary Files or available from the corresponding authors upon request.
Stiffness potentiates TCR/CD3-induced transcriptional response In a founding study  (Engler et al. , 2006) , substrate stiffness was shown to induce different programs of differentiation of mesenchymal stem cells. We thus asked whether different stiffness values could induce different programs of T cell differentiation or whether stiffness acts instead as a rheostat on TCR/CD3-induced activation. We first used the microarray data to perform pair-wise comparisons (presence vs. absence of aCD3 for each stiffness value) with the gene set enrichment analysis (GSEA) method. We used the publicly available Gene Ontology -Biological Processes (GO-BP) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway gene sets to identify the relatively enriched collections of genes and then extracted the enriched gene sets for each condition (Figure  3A , Supplementary file 2).
Results showed that most of the enriched gene sets were common for the three different substrates and these included cell-cycle-related and immune-response-related processes. The stiff 100 kPa gel caused the enrichment of a higher number of gene sets in response to aCD3 stimulation, due to specific enrichment of gene sets such as mitochondrial biogenesis, oxidative phosphorylation and glycolysis (Supplementary file 2). Pathway analysis on the differentially up-regulated genes, using the GO and the KEGG databases, showed that the 6. 4 and 100 kPa PA-gels induced mostly common pathways, which were mitotic cell cycle, transcription and translation related (Figure  3B , Figure  3 -figure supplement 1, Supplementary files 3, 4). For the 0. 5 kPa PA-gel, the two most prominent pathways induced were T cell activation and differentiation (Figure  3B ), showing that the TCR was responsive from the softest end of the stiffness range.
Video microscopy T cell migration on gels was monitored either with a Nikon TiE video microscope (Nikon, Tokyo, Japan) equipped with a cooled CCD camera (HQ2, Photometrics, Tucson, AZ) or a Nikon TE2000 microscope with a Cascade 1K camera (Photometrics). Experiments were performed in culture medium and transmission movies were acquired using a 10x or 20x objective, with one frame every 5 s. Analysis was performed using Fiji  (Schindelin et al. , 2012)  and R software. First, T cells were tracked for 5 min using the TrackMate plugin of Fiji. Then, track representation and analysis were obtained with R. For each T cell's track, we computed the mean instantaneous velocity and the maximum distance traveled on the substrate, which is the maximum distance separating two points within a single track.
mRNA microarrays and data analysis Following 24 hr cultures on PA-gels of varying stiffness, polysomal RNA was isolated from 6 Â 10 5 T cells from each condition using the NucleoSpin RNA kit (Macherey-Nagel, Dueren, Germany, cat. no. 740955. 50). For each condition, 100 ng of RNA was transcribed into cDNA. Labeled DNA was then hybridized on the Affymetrix human Gene ST2. 1, an oligonucleotide microarray, and processed on an Affymetrix GeneTitan device (Affymetrix, Santa Clara, CA). This analysis was performed for four different human donors. Microarray data were then processed into the open source software R (version 3. 1. 0, www. r-project. org) using packages from Bioconductor (www. bioconductor. org). CEL files with raw data were used and the quality control analysis was performed using ArrayQualityMetrics package  (Kauffmann et al.
, 2009). The raw data were preprocessed using the RMA method available in the oligo package  (Carvalho and Irizarry, 2010). Probes with no annotation were removed from analysis. Finally, the resulting matrix comprised data for 53617 probes. The PCA of T cell profiles was performed using the Ade4 package  (Dray and Dufour, 2007)  of the R software on all probes, except the not annotated ones. The barycenters were computed from the set of observations in each condition and projected into the PCA plot. Confidence ellipses (95% of confidence) around the barycenters of conditions were then drawn. Identification of differentially expressed genes was performed with the limma package  (Smyth, 2005)  by computing moderated t-tests. We considered a gene as differentially expressed if its adjusted p-value using the Benjamini-Hochberg method was lower than 0.
Gene set enrichment analysis Data from the mRNA microarrays were used to perform pair-wise comparisons (presence vs. absence of aCD3 for each stiffness value) with the gene set enrichment analysis (GSEA) method (www. broad. mit. edu/gsea). Statistical analysis was performed evaluating nominal p-values and false discovery rates (FDR) based on 1000 permutations. We used the Gene Ontology -Biological Processes (GO-BP) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway gene sets as ranked data sets. Results were considered significant when the p-value was lower than 0. 05 and the FDR was lower than 0. 25, according to developer's instructions  (Subramanian et al. , 2007). The GSEA output is mainly characterized by two parameters: the normalized enrichment score (NES) and the false discovery rate (FDR).
Pathway analysis Pathway analysis of differentially expressed genes and gene clusters was performed with the publicly available tool EnrichR (http://amp. pharm. mssm. edu/Enrichr) that provides access to various gene-set libraries, including the GO-BP and the KEGG databases, and computes enrichment of specific pathways  (Chen et al. , 2013; Kuleshov et al. , 2016). We considered pathways as enriched if their adjusted p-value was lower than 0. 05 and ranked them with it.
Metamorph software (Molecular Devices, Sunnyvale, CA) was used for all acquisitions. The cell area was measured from the Z-plane closest to the substrate using ImageJ software. In order to measure the Young's modulus of HeLa cells cultured on PDMS gels of varying stiffness, we used a custom-made glass cantilever with a spherical tip. HeLa cells in confluent layers were indented with the cantilever, which was calibrated in stiffness, allowing us to measure the indentation force from its deflection. Using the Hertz contact model for a spherical probe  (Sneddon, 1965) , the force-indentation relationship could be written as: F ¼ 4 3 ffiffiffi R p E Ã d 3=2 where F is the indentation force, R the radius of the probe, d the indentation depth, and E Ã the reduced Young's modulus: 1 E Ã ¼ 1 À n 2 cell E cell þ 1 À n 2 probe E probe where v is the Poisson's ratio.
Acknowledgements The authors thank  Virginie Bazin  ( IBPS, UniversiteP. et M. Curie, Paris ) for her help obtaining SEM images, and  Laurence Bataille ,  Luigia Pace  and  Andres Zucchetti  for critically reading the manuscript. This work was supported by funds from  ANR  ( ANR-12-BSV5-0007-01 ,  ImmunoMeca ;  ANR-13-BSV2-0018  ' NeuroImmunoSynapse '),  DC-Biol Labex  ( ANR-10-IDEX-0001-02 PSL*  and  ANR-11-LABX-0043 ), ' Who Am I?' Labex  ( ANR-11-IDEX-0005-02  and  ANR-11-LABX-0071 ) and  Fondation pour la Recherche Medicale (FRM ,  FRM DEQ20140329513 ).
Flowers were hand pollinated and each plant was pruned and allowed to set a single fruit. In CAM, lines producing at least two fruits were included in statistical analyses. • 31 ′ 41 ′′ N- 2 • 23 ′ 34. 8 ′′ W), Fruits were harvested at 55 days after pollination (DAP) in all locations and data for weight, length and width were collected. Soluble solids content (SSC) ( • Brix) was measured by first cutting a 2 cm radial section from around the center of the melon fruit perpendicular to the longitudinal axis, and then taking four 1 cm diameter core samples equidistant from around the melon ring. The core samples were homogenized and the juice extracted and analyzed with a digital hand-held refractometer (Atago Co. Ltd. , Tokyo, Japan). A second set of four core samples was taken and each core was then cut into three equal sized pieces.
Phenotypic Data Analysis Statistical analysis of SSC, sugar, and organic acid content was performed using JMP (version 8. 0. 1 for Windows, SAS institute, Cary, N. C. ) and SAS (SAS institute, Cary, N. C. ) for both the SC NILs and TRI-derived lines. For the SC NILs, a one-way analysis of variance of data was conducted using SAS PROC ANOVA with the hovtest option of the MEANS statement to test for homogeneity of variance and Welch ANOVA employed when variances were unequal. Pearson correlation coefficients, and Spearman rank correlations, for measured traits within and between environments, respectively, were calculated using SAS PROC CORR. For the SC NILs and subNILs, estimations of heritability (h 2 ) were derived from the ANOVA, and two-way analyses of variance were conducted to examine genotype × environment interactions as described in  Eduardo et al.
, 2012). Using a new assembly and annotation of the genome  (Garcia-Mas et al. , unpublished) , the SUPER-W pipeline  (Sanseverino et al. , 2015)  was used for the variant calling procedure and vcftools  (Danecek et al. , 2011)  and in house script for a post-filtering process. This allowed selecting high quality variants with a genotype depth >5 and genotpe quality >30. In addition, since a high rate of homozygous variants are expected in melon, heterozygous variants were discarded. SnpEff v4  (Cingolani et al. , 2012)  was used for imputing the gene region in which the variants fall (promoter, intron, exon, UTRs) and the putative impact of the variants on protein functionality. Subsequently analyses on exonic SNPs and INDELs were performed in order to estimate the deleterious degree of the variants.
To do this, a combination of different prediction web tools was exploited, including PROVEAN (Protein Variation Effect Analyzer) (http://provean. jcvi. org/index. php), SIFT (http://sift. jcvi. org/), SNAP2 (https:// rostlab. org/services/snap2web/), and Hope (http://www. cmbi. ru. nl/hope/method/). iTASSER (Iterative Threading ASSEmbly Refinement) (http://zhanglab. ccmb. med. umich. edu/I-TASSER/) (Zhang, 2008)  was used to predict the conformational structures and protein-ligand binding sites based on sequence variants. The GSDS v2 software  (Hu et al. , 2015)  was used to design gene structure of the candidate genes.
Expression analysis was performed on a LightCycler R 480 Real-Time PCR System using SYBR R Green I Mix (Roche Applied Science, USA). The relative amounts of specific transcripts were determined using cyclophilin (CmCYP7) as a reference gene, as in previous experiments  (Mascarell-Creus et al. , 2009; Saladie et al. , 2015). Primers for amplification of target and reference genes were designed with Primer3 software (http://primer3. wi. mit. edu/) (Table  S3 ). To maximize efficiency of qPCR reactions, primers pairs were checked for the presence of secondary structures with NetPrimer (http://www. premierbiosoft. com/netprimer/) and redesigned if necessary. Calculation of intra-assay variation, primer efficiencies, specificity of the PCR amplification, and the presence of genomic contamination were as described previously.
Sequence Comparison and Expression Patterns of Candidate Genes for sscqsc5. 1 and sucqsc5. 1 Based on functional annotations in the melon genome database (http://www. melonomics. net/genome/) the QTL interval contained four predicted genes (Table  4 ): MELO3C014519 is a predicted gene of 3. 9 kbp with four exons that encodes a BEL1-like 1 homeodomain protein belonging to the TALE (for three amino acid loop extension) superclass of transcription factors  (Burglin, 1997) ; MELO3C014520 is most similar to a E3 ubiquitin-protein ligase (PUB22) with a gene size Positions and sizes in base pairs (bp) on chromosome (chr) 5 pseudomolecule (PM). of 1. 3 kb and a single exon; MELO3C014521 is a gene of size 2. 1 kbp also contained in a single exon and predicted to encode a chloroplastic fantastic four (FAF-like) protein with a domain of unknown function and a transit peptide targeting it to the chloroplast (http://www.
uniprot. org/uniprot/ Q0V865); Lastly, MELO3C014522 is a larger gene of 14. 6 kbp with 13 exons predicted as a serine/threonine-protein phosphatase (BSL2). The interval also contains two predicted transposons (CM3B0062380TE, CM3B0062557TE) adjacent to MELO3C014519. Expressed sequence tags (ESTs) were detected in public databases for MELO3C014519 and MELO3C014521 in both developing and mature melon fruit libraries, for MELO3C014522 in mature fruit (http://www. icugi. org/cgibin/ICuGI/tool/blast. cgi). Although ESTs were present in callus and root tissue libraries, for MELO3C014520, none were present in fruit libraries. Data from transcriptomic studies, including the parental lines used in developing the NIL and subNIL populations, showed expression of MELO3C014519, MELO3C014521, and MELO3C014522 in developing and mature fruits up to 55 DAP  (Saladie et al.