dataesr/scientific-paragraphs-categorization huggingface dataset:
Electrophoregrams were analyzed using Gensearch ® , or CodonCode Alligner ®. Exome Sequencing. The family members analyzed by exome sequencing where chosen based on DNA availability for at least 2 affected members of the family and presence of informed consent allowing for genetic studies with prioritization of phenotypic extremes when possible. These selected samples underwent exome sequencing at the Broad Institute after that the institutional review board and all participating sites approved the study protocols and all individuals who were selected for sequencing provided informed consent as previously described  19. Analysis of Exome Sequencing Data. In order to find the causal mutation out of thousands of different variations obtained by the exome sequencing, we used a comprehensive framework for prioritizing variants, which is commonly used in the analysis of exome sequencing studies  43.
Starting with the total number of variants shared by individuals from the family, we excluded variants conflicting with the ADH inheritance pattern and common variants that have a frequency of ≥ 1% in the general population, by examining different databases such as dbSNP, 1000Genome, Exac and gnomAD browsers. Then, we excluded silent and non-genic variants which do not alter protein sequence since most Mendelian syndromes are caused by coding or splice site mutations that alter the protein sequence. The remaining single nucleotide variants and short insertions or deletions were considered candidates. The variants were subsequently analyzed separately by exploring those whose genes have physical protein-protein interaction with the 4 known genes of ADH, or share the same biological pathways.
When an interesting variation is found, familial segregation was studied to demonstrate its possible co-segregation with the phenotype in the family. In silico Analyses. The frequency of the variations found by sequencing was estimated using different databases: Exome Variant Server (evs. gs. washington. edu/EVS/), dbSNP (ncbi. nlm. nih. gov/SNP/), and gnomAD browser (gnomad. broadinstitute. org/). The causal effect of each new molecular event was estimated with in silico prediction of protein function tools: Polyphen (genetics. bwh. harvard. edu/pph), SIFT (sift. jcvi. org), MutationTaster (mutationtaster. org) using Alamut Visual version 2. 7. 1. cDNAs, cell culture and transfections. The cDNAs encoding human LDLR  30  and human PCSK9 and its mutants  44  were cloned in pIRES2-EGFP (Clontech Labs), a bicistronic plasmid allowing the independent expression of a fluorescent EGFP from an internal ribosome entry site (IRES) and C-terminally V5-tagged wild-type (WT) human PCSK9 or its p.
Acknowledgements We would like to thank  Pr Sekar Kathiresan  ( Broad Institute of MIT and Harvard University , Cambridge, MA,  USA ; Cardiology Division,  Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA ) for his help in the exome sequencing experiments and analysis. S. K. was supported by  NIH   R01 HL107816. This work was supported by a grant from  Leducq Foundation  [FLQ #  13 CVD 03 ] through the  Transatlantic Networks of Excellence in Cardiovascular Research program  ('The function and regulation of PCSK9: a novel modulator of LDLR activity'); and  Institut National de la Santé et de la Recherche Médicale (INSERM) ; and  Conseil de la Recherche de l'Université Saint-Joseph , Beirut, Lebanon; and  Lebanese National Council for Scientific Research  ( CNRS-L ); and  Canadian Institutes of Health Research (CIHR) Foundation  grant (NGS; #  148363 ); and  Canada Research Chair  (NGS; #  950-231335 ); This work was also supported by the national project  CHOPIN (CHolesterol Personalized Innovation ) granted by the  Agence Nationale de la Recherche  ( ANR-16-RHUS-0007 ) and coordinated by the  Centre Hospitalo-Univesitaire (CHU) de Nantes.
Data analysis Experimental data were analyzed using R version 3. 4. 1 (The R Foundation; differential gene expression), FlowJo V10 (LLC, Ashland, OR; FACS analysis), Prism 7. 0a (GraphPad Software Inc. , San Diego, CA; CD marker expression levels and chemotaxis) or Image J 1. 50b (NIH; chemotaxis). All qPCR data were normalized against the housekeeper genes GAPDH and ACTB2 as described by the equation below to produce ΔΔC T values. The relative expression values were calculated compared to the relevant undifferentiated cell type.
Data for the chemotaxis transwell migration assays were calculated as the chemotactic index (sample wells / basal wells from the same cell line i. e. each cell line's response was normalised to its own baseline) and represented as the mean ± SEM (n =  [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]. Statistical and principal component analysis. Changes in the gene expression values analysed by one-way ANOVA with Tukey post-hoc multiple comparison test compared to basal, with Ã P<0. 05, ÃÃ P<0. 01, ÃÃÃ P<0. 001 and ÃÃÃÃ P<0. 0001 deemed significant. Principal component analysis (PCA) was subsequently applied to the calculated the relative expression values, using singular value decomposition as implemented in the package scikit-learn  [22]. This analysis is a dimensionality reduction method that uses transformations to project a highdimensional set of data into a lower dimensional set of variables, called principal components (PCs)  [23].
The extracted PC values contain important information from the data, revealing its internal structure in a way that best explains its variance  [24]. PCs are ranked according to the percentage of total variance in the data they explain. PC1 explains the maximal total variance found within the data. The subsequent PC values represent the remaining variation, without being correlated with the preceding components. The script used for the analysis and plotting can be found at https://github. com/thomas-coudrat/pca_analysis. Compounds and reagents. The chemoattractants were purchased from Sigma-Aldrich (Castle Hill, NSW), Sapphire Biosciences (Redfern) and In Vitro Technologies Pty Ltd (Noble Park). Primer probe sets were purchased from Geneworks (Melbourne). Cell culture and molecular biology reagents were supplied by Thermo Fisher Scientific (Scoresby).
Interestingly, a significant downregulation of the interferon regulatory factor 8 (IRF8) was observed in all MCLCs. This gene is involved in monocyte lineage and typically is downregulated upon monocyte differentiation into a macrophage. Collectively these demonstrate that upon PMA treatment MCLCs differentiate into a macrophage-like cell type. To analyse these data sets in a non-biased approach a principal component analysis (PCA) was performed. PCA is a dimensionality reduction method that reveals values in the dataset that contribute variability between the combined data sets including both undifferentiated and differentiated cells, and individual genes. Cell lines and / or genes that display similar behaviour cluster together. This analysis revealed that human PBMCs cluster separately from all immortalised cells for the relative expression values (blue symbols and arrows; Fig 3A), indicating that the human primary cells are genotypically distinct from all of the monocyte-like cell lines.
However, as expected, a second clustering was evident between the non-PMAtreated and PMA-treated cells (burgundy, green and orange symbols and arrows), again correlating with those data obtained from the hierarchical clustering analysis. The composite of both PC1 (67%) and PC2 (12%) accounted for 79% of the total variability, indicating limited variation within the Ct dataset. When PCA was performed to examine clustering of gene families across all differentiated cells (irrespective of cell type), no clustering was seen at the level of the calculated relative expression values (Fig  3B ). Although in the latter PC analysis there does appear to be two "clusters; PC1-1. 0-0. 0 and 0. 5-1. 0", they bear no relationship to the functional role(s) of the genes. These representations broadly represent what is seen in Fig 1 ; namely that the human PBMCs (whether differentiated or polarised) cluster separately from the monocyte-like cell lines, again irrespective of their treatment.
Interestingly, they intervened at two different positions in NS1 gene according to the mosquito population: I2772T in Ae. albopictus Manaus and S3303N in Ae. albopictus PNMNI. Both substitutions reached fixation at the passage 10. Our data suggest that YFV has the potential for adaption to Ae. albopictus thereby posing a threat to most cities in South America where this mosquito is present. In early January 2018, a mass immunization campaign was implemented to prevent the arrival and re-urbanization of Yellow fever (YF) in the populated cities of Rio de Janeiro and São Paulo states in Southeastern Brazil, which were declared free of YF for more than 70 years until 2017 1 (http://portalms. saude. gov. br/noticias/ agencia-saude/42655-febre-amarela-ministerio-da-saude-atualiza-casos-no-pais).
To pool equimolarly the barcoded samples, a quantification step by the 2100 Bioanalyzer instrument (Agilent Technologies, California, USA) was performed. An emulsion PCR of the pools and loading on a 520 chip were realised using the automated Ion Chef instrument (ThermoFisher Scientific). Sequencing was performed using the S5 Ion torrent technology (ThermoFisher Scientific) following manufacturer's instructions. Consensus sequence was obtained after mapping the reads on reference (inoculum strain) using CLC genomics workbench software (Qiagen, Hilden, Germany). A de novo contig was also produced to ensure that the consensus sequence was not affected by the reference sequence.
Bioinformatic analysis. Quality control of the data. The quality control of the sequencing data has been performed using FastQC (v 0. 11. 5) (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and MultiQC (v 0. 7)  35. Reads were trimmed using Trimmomatic (v 0. 36)  36  with the parameters "Leading:2 Trailing:3 Slidingwindow:4:15 Minlen:36". In order to filter any reads which could come from the mosquito host, sequences were mapped using BWA mem (v 0. 7. 7)  37  against Ae. albopictus derived C6/36 cells (assembly GCA001876365. 2). The SAM file produced was converted, sorted and indexed using Samtools (v 1. 3)  38  and the unmapped reads were extracted using SamToFastq from Picard Tools (v 2. 8. 1) (http://broadinstitute. github. io/picard/). Variant calling. These unmapped reads were then mapped using BWA mem (v 0.
7. 7)  37  and default parameters against the reference genome YFV-74018  32. The duplicated reads were marked with MarkDuplicates from Picard Tools (v 2. 8. 1) (http://broadinstitute. github. io/picard/). Finally, the variant calling was done with freebayes (v 1. 1. 0)  39 using the option -pooled-continuous. A filter has been applied with vcffilter from vcflib (v 1. 0. 0)  39 to select only SNP variants with a quality score above 20. The results analysis has finally been done under R (v 3. 3. 1) (https:// www. R-project. org) with the packages VariantAnnotation (v 1. 20. 3)  40 , pheatmap (v 1. 0. 8) (https://CRAN. R-project. org/package=pheatmap) and ggplot2 (v2. 2. 1) (https://www. springer. com/us/book/9780387981413).
Acknowledgements We thank  Laurence Mousson  and  Pei-Shi Yen  for technical help. We warmly thank  Peter Sahlins  for correcting the manuscript. This study was funded by the  Institut Pasteur , the  French Government 's  Investissement d' Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant n°ANR-10-LABX-62-IBEID), the  PTR  (grant  n°528 ), the  CAPES-COFECUB  (grant  799-14 ), and the  FAPERJ-Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro  (grant  E-26/102. 351/2013-CNE ).
Amplicon quality was verified by gel electrophoresis and they were sent to the GenoToul plateform (Toulouse, France) for sequencing. All the samples were collected and processed in the same way. Analysis of the intestinal microbiota by 16s ribosomal RNA sequencing. The composition of the intestinal microbiota was analyzed by high-throughput sequencing with Illumina MiSeq technology, targeting the 16S ribosomal DNA V3-V4 region. Data was analyzed using QIIME v1. 9. 0. Paired-end reads were assembled with PANDAseq v 2. 7, to generate the sequence of a 450-base pair amplicon  37. Reads were demultiplexed and processed with the quantitative insights into microbial ecology (QIIME v1. 9. 0) pipeline, using its default parameters  38. Chimeric sequences were identified de novo, on the basis of references, and were removed with usearch61  39.
The non-chimeric sequences were then clustered into operational taxonomic units (OTUs) displaying at least 97. 0% sequence similarity, by a closed reference-based picking approach in UCLUST software applied to the Greengenes 13. 8 database of bacterial 16S rDNA sequences  40. The mean number of quality-controlled reads was 26008 ± 8106. 69 (mean ± SD) per sample (minimum count: 8370. 0, maximum count: 79884. 0). After rarefaction at 7,000 reads per sample, bacterial alpha diversity was estimated on the basis of the Shannon's index. OTUs with a prevalence <5% were removed from the analysis. We assessed beta diversity with weighted and unweighted UniFrac distances. The weighted Unifrac metric is weighted by the difference in the abundance of OTUs from each community, whereas unweighted UniFrac considers only the absence/presence of OTUs, providing different information.
The sizes of the circles in the cladogram plot are proportional to bacterial abundance. MaAsLin (multivariate analysis by linear models) was used to identify associations between clinical metadata and microbial community abundance or function  42. Sex, age, BMI, alcohol intake, smoking status, diabetes and proton pump intake were included as covariates in the MaAsLin. Relative abundances were subjected to arcsine root transformation before regression analyses using MaAsLin. Both LEfSe and MaAsLin were accessed online (http://huttenhower. sph. harvard. edu/galaxy/). For the MaAsLin compariason, a corrected p value < 0. 2 (p corr) was considerd statisicaly significant. The remaining comparisons were performed with R software v2. 14. 1. Unpaired t-tests or Mann-Whitney U-tests were used to compare continuous data between groups, according to data distribution.
Acknowledgements The authors thank the  NED team (Olivier Zemb ,  Béatrice Gabinaud ) and the  GeT-PlaGe platform  for their help with the sequencing data. This work was supported by  INSERM ,  Univ Paris-Sud ,  SNFGE  ( Société Nationale Francophone de Gastro-entérologie) ,  AFEF  ( French Association for the Study of Liver ),  IREB/FRA (Institut de Recherches sur les Boissons/Fondation pour la recherche en alcoologie) ,  IRIS  ( Institut de Recherches Internationales Servier ),  ERAB (The European Foundation for Alcohol Research) ,  FRM  ( Fondation pour la Recherche Médicale ) and Biocodex. INSERM U996 is a member of the  Laboratory of Excellence LERMIT  supported by a grant from the  Agence Nationale de la Recherche  ( ANR-10-LABX-33 ). DC was supported by research fellowship from  INSERM.
*OTU identified using BLASTN program (vBLAST + 2. 6. 0) from NCBI Blast against the NCBI 16 s Microbial database. Only hits with an overall sequence identity of 97% or more were considered. **Covariates: age, sex, alcohol intake, smoking status, BMI, diabetes and proton pump inhibitors use. ACP: alcoholic chronic pancreatitis, AC: alcoholic controls, LEfSe: LDA Effect Size, MaAsLin: Multivariate Association with Linear Models. In bold taxa with a p corr < 0. 2 in MaAsLin.
94 0. 00139 0. 00824 0. 05 0. 18 2. 89 0. 04 in the intestinal microbota between chronic alcoholic pancreatitis (CAP) and severe alcoholic hepatitis (sAH) at the genus level. * Operational Taxonomic Unit (OTU) identified using BLASTN program (vBLAST + 2. 6. 0) from NCBI Blast against the NCBI 16 s Microbial database. Only hits with an overall sequence identity of 97% or more were considered. ** Covariates: age, sex, alcohol intake, smoking status, BMI, diabetes and proton pump inhibitors use. ACP: alcoholic chronic pancreatitis, sAH: severe alcoholic hepatitis, LEfSe: LDA Effect Size, MaAsLin: Multivariate Association with Linear Models. In bold taxa with a p corr < 0. 2 in MaAsLin.
Apparatus and Procedure In Paris, the movies were presented on a 17 ′′ TFT monitor (1280 × 1024 pixel resolution) with an integrated Tobii T60 eyetracking system which was run by a Dell computer. The presentation of the stimuli and the storing of the data were performed with the Tobii Studio software. In Hong Kong, a Tobii TX300 was used, which was run by a Dell computer and with videos presented on a Tobii TX300 screen unit with a 1920 × 1080 pixel resolution. Each participant was tested individually in a quiet, dimly lit laboratory room and watched 24 testing trials in total. As French-and Mandarin-speaking participants had no knowledge of Cantonese they received a warm-up trial in Cantonese, in which the two pseudowords used were phonetically different in every single segment (/ka/ -/su/) and in which subtitles were presented, in order to familiarize them with the task.
Data Analysis The eye-tracking data used for the analysis consisted of the binocular gaze position (X and Y coordinates) at each timestamp, that is, every 16. 6 ms for French-speaking adults and every 3. 3 ms for Mandarin-and Cantonese-speaking adults. Trials in which no data was available for the postnaming phase were discarded from the analyses (27/1728 trials). The data was analyzed in R (version 3. 4. 3, R Core Team, 2017, http://www. r-project. org) using the eyetrackingR package  (Dink and Ferguson, 2015 , http://www. eyetrackingr. com) for the latency and the growth curve analysis as well as for the cluster-based permutation analysis.
e. , within the next 200 ms) as these saccades were probably programmed before the name of the target was processed (Cantonese: 21%; Mandarin: 22%; French: 10%) or did not shift at all (Cantonese: 1%; Mandarin: 6%; French: 7%) as well as outliers, that is values greater or smaller than 2. 5 standard deviations from the mean (Cantonese: 1%; Mandarin: 2%; French: 2%). Mean latencies and standard deviations are shown in Table  3  for each language and condition. We used a linear mixed model using the function lmer of the R package lm4, with random effects for participants and items  (Bates et al. , 2015) , and the package languageR  (Baayen, 2015)  to obtain p-values. The model included fixed effects of condition (compared in sliding contrasts: Consonants-Vowels; Vowels-Tones), language group (also compared in sliding contrasts: French vs.
Growth Curve Analysis Third, we conducted a Growth Curve Analysis (GCA) which includes time as a predictor to estimate if differences between conditions emerged over time within each language group. As dependent measure we took the transformed proportion data during the postnaming phase using the empirical logit (elog, aggregated in 100 ms time bins) and analyzed it with a weighted mixed-effects linear regression model within the eyetrackingR package (modeled after  Mirman et al. , 2008). For each language group separately, we entered condition (again compared in sliding contrasts: Consonants-Vowels; Vowels-Tones), orthogonal polynomials (linear, quadratic and cubic time component), and the interaction between each time term and condition as fixed effects. Participants and items were entered as random effects into the model.
MatrixDB (http://matrixdb. univ-lyon1. fr/) is an interaction database focused on biomolecular interactions established by extracellular matrix (ECM) proteins and glycosaminoglycans (GAGs). It is an active member of the International Molecular Exchange (IMEx) consortium (https://www. imexconsortium. org/). It has adopted the HUPO Proteomics Standards Initiative standards for annotating and exchanging interaction data, either at the MIMIx (The Minimum Information about a Molecular Interaction eXperiment) or IMEx level. The following items related to GAGs have been added in the updated version of MatrixDB: (i) cross-references of GAG sequences to the GlyTouCan database, (ii) representation of GAG sequences in different formats (IUPAC and GlycoCT) and as SNFG (Symbol Nomenclature For Glycans) images and (iii) the GAG Builder online tool to build 3D models of GAG sequences from GlycoCT codes.
The database schema has been improved to represent n-ary experiments. Gene expression data, imported from Expression Atlas (https://www. ebi. ac. uk/gxa/home), quantitative ECM proteomic datasets (http://matrisomeproject. mit. edu/ecm-atlas), and a new visualization tool of the 3D structures of biomolecules, based on the PDB Component Library and LiteMol, have also been added. A new advanced query interface now allows users to mine MatrixDB data using combinations of criteria, in order to build specific interaction networks related to diseases, biological processes, molecular functions or publications.
Interactions may be identified by high-throughput assays, but many are reported in publications that focus on specific proteins. In order to investigate them at the scale of a biological process, a tissue or an organ, these interactions must be captured individually in the literature and stored in databases. We have built a database, MatrixDB (http://matrixdb. univ-lyon1. fr/), focused on biomolecular interactions established by ECM proteins, matricryptins and GAGs  (7) (8) (9). MatrixDB is an active member of the International Molecular Exchange (IMEx) consortium (https://www. imexconsortium. org/)  (10)  and has adopted the HUPO Proteomics Standards Initiative standards for manual curation of the literature and the exchange of interaction data, either at the MIMIx (The Minimum Information about a Molecular Interaction eXperiment  (11) ) or IMEx level.
Curation is per-formed via the curation interface of the IntAct database (https://www. ebi. ac. uk/intact/ (12)). We have updated MatrixDB with a focus on GAGs by adding cross-references of GAG entries to the Gly-TouCan database  (13) , representation of GAG sequences in different formats (IUPAC and GlycoCT (  14 )) and as SNFG (Symbol Nomenclature For Glycans) images  (15) , and GAG Builder (http://glycan-builder. cermav. cnrs. fr/gag/ (16)) to build 3D models of GAG sequences from Gly-coCT codes. Gene expression data from Expression Atlas (https://www. ebi. ac. uk/gxa/home/ (17)), and quantitative ECM proteomic datasets (http://matrisomeproject. mit. edu/ecm-atlas/ (2)) have been imported into MatrixDB. A new visualization tool of the 3D structures of biomolecules, based on the PDB Component Library (http://www.
ebi. ac. uk/pdbe/pdb-component-library/index. html) and LiteMol  (18)  has been added on the Biomolecule Report pages. The database schema has been deeply modified to speed up queries, ease data import and export and represent n-ary experiments. Last, advanced queries have been designed to create lists of biomolecules of interest based on combined criteria in order to build their interaction networks with MatrixDB iNavigator  (9).