dataesr/scientific-paragraphs-categorization huggingface dataset:
Positive Selection The program CODEML from the PAML package was used to test if positive selection was present in the eight gene segments. The results showed that the M2a model (indicating the presence of positive selection) fitted the HA sequences significantly better, whereas this was not the case for the remaining genes (Table  3 ). Estimates of dN/dS ratios for individual codons in the HA gene, under the M2a model, strongly indicated the presence of positive selection at position 536 (numbering from first methionine) in the HA2 part of the gene, which encodes the stalk region. Further analysis of the HA amino acid alignment showed that a mutation from tryptophan to arginine at position 536 was present in four pigs at F4, F6 and F8 (F4_W1_13, F6_W1_P16, F8_W1_16, F8_W1_17). Interestingly, this specific position is located in a B-cell epitope identified in the A(H1N1)pdm09 virus and in a T-cell epitope identified among human seasonal H1N1 (Table  4 ).
Data analyses were performed using FlowJo X (FlowJo LLC ©). Trucount flow cytometry experiments were performed on 30 μl of total blood directly stained with antibodies washed into 2 ml of PBS then complemented with 25 μl of countBright absolute counting beads (C36950 Invitrogen) and resuspended into 2 ml of PBS before acquisition on a Attune NxT Flow Cytometer (ThermoFisher Scientific). Combined intra versus extracellular staining for Ly6G. Samples were collected and surface stained with classical methods of flow cytometry labelling. After 3 washes, cells were fixed and permeabilized using the FoxP3 staining kit from eBioscience (#00-5523-00). Then, cells were incubated with anti-Ly6G or anti-Gr1 antibodies or adequate isotype control labelled with distinct fluorochromes from the surface staining in the permeabilization buffer for 15 min at 4 °C.
RNA (1 μg) was used for reverse transcription using the High-Capacity cDNA Reverse Transcription Kit (4368814, Ther-moFisher Scientific). Real-time PCR was done using Taqman universal PCR master mix (4324018, ThermoFisher Scientific) and Taqman probes for the following genes: Ly6g: Mm04934123_m1; gene expression level normalization gene Rpl30: Mm01611464_g1 (ThermoFisher Scientific). Data were presented as ΔC T values. Survey of the scientific literature. We surveyed the scientific literature over a one-year period (April 2017 to March 2018) and found forty-five publications using the key words "neutrophils depletion" on PubMed, in which the authors reported efficient neutrophil depletion. One-third of these publications used anti-Gr1 and two-thirds anti-Ly6G. From each study, we noted the method used to validate neutrophil depletion, or if no validation was shown.
Acknowledgements We thank the  EPFL SV Flow Cytometry Core Facility  for access to instruments and technical help. This work was supported by the  Swiss National Science Foundation  ( 310030_179324 ), the  ISREC Foundation , the  Chercher et Trouver Foundation , the  Nuovo Soldati Foundation  (to G. B. ), the  MGH ECOR (Executive Committee on Research)   Tosteson Postdoctoral Fellowship  (to C. P. ),  NIH-R01-AI084880  (to M. J. P. ), a  postdoctoral fellowship  from the  Fondation pour la Recherche Médicale  ( SPF20160936218 ) (to J. M. ), the  French National Cancer Institute INCa   2014-093 ,  INCa   2017-158 , the Ligue contre le cancer, and the  LABEX DevWeCan.
Methods and findings The European consortium ABIRISK (Anti-Biopharmaceutical Immunization: prediction and analysis of clinical relevance to minimize the RISK) conducted a clinical and genomic multicohort prospective study of 560 patients with multiple sclerosis (MS, n = 147), rheumatoid arthritis (RA, n = 229), Crohn's disease (n = 148), or ulcerative colitis (n = 36) treated with 8 different biopharmaceuticals (etanercept, n = 84; infliximab, n = 101; adalimumab, n = 153; interferon [IFN]-beta-1a intramuscularly [IM], n = 38; IFN-beta-1a subcutaneously [SC], n = 68; IFN-beta-1b SC, n = 41; rituximab, n = 31; tocilizumab, n = 44) and followed during the first 12 months of therapy for time to ADA development. From the bioclinical data collected, we explored the relationships between patient-related factors and the occurrence of ADAs.
Patients aged 18 years or more who had been prescribed the identified BP for the first time by a physician independently of the study were followed for 12 months from the start of the therapy, during which 7 to 12 visits were performed (specific to each disease study protocol), clinical data were recorded into an electronic Case Report Form (eCRF), and DNA samples and serum samples were collected for genetic analyses and ADA testing, respectively. ABI-MS-P01 study. Patients with MS or Clinically Isolated Syndrome (CIS) diagnosis according to McDonald criteria and who were prescribed IFN-beta-1a intramuscularly (IM), IFN-beta-1a subcutaneously (SC), or IFN-beta-1b SC (original product or biosimilar) were recruited from January 30, 2014 to January 22, 2016. Patients who were not naive to IFN-beta were excluded.
All the data from the different sites have been merged with the ADA test results and gathered in a unique database (TranSMART) hosted by the eTRIKS consortium, following standardized data-loading procedures according to CDISC terminology. Data recorded at the baseline visit included demographic characteristics, vital signs and disease-specific clinical scores, smoking history, familial history of disease, medical and surgical history, vaccines in the year before the study, previous medications, and concomitant medications. Data recorded at each study visit included new vaccines, adverse events, and concomitant medications during the study. At the end of the study, familial history of disease, medical and surgical history, and adverse events were encoded to standardized terms with the Med-DRA dictionary, and previous and concomitant medications and vaccines were encoded with the WHODrug dictionary.
2 BeadChip. DNA samples were genotyped according to Infinium HTS Automated Protocol (Illumina, San Diego, CA, USA) starting from 200 ng of double-stranded DNA. A first quality control check of the genotyping data and genotype calling were performed by Genome Studio software 2011. 1 with Genotyping module v1. 9 (Illumina). Genotypes are called by comparing the generated data with those in the supplied cluster file. Samples with a call rate (percentage of SNPs genotyped by samples) lower than 95% in the Illumina Cluster were excluded. From this data set, a second quality control check was performed, first on the level of the individual and then on the level of the genetic marker, using the R package "gaston"  [10]. Individuals were excluded for ambiguous sex (genotypic sex different from phenotypic sex from the eCRF), abnormal heterozygosity (deviated more than 3 standard deviations [SDs] from the mean heterozygosity of the sample), genotyping completeness less than 99%, and non-European ethnicity admixture detected as outliers from a principal component analyses of a linkage-disequilibrium-pruned data set (with a deviation of at least 6 SDs from the mean of at least one of the first 10 principal components).
The quality control left 457 genotyped individuals for the analyses. The initial number of genotyped SNPs was 713,599. Markers were excluded from the analyses if they had a Minor Allele Frequency (MAF) below 5%, if they had a call rate below 99%, or if they deviated significantly from the estimated allele frequencies at Hardy-Weinberg equilibrium (based on a threshold of p < 1 × 10 -5 ). In order to prevent departure from the asymptotic null distribution of the log-rank test related to treatment group imbalance, we performed the genome-wide association analysis only on the variants with an MAF higher than 20%. The final number of SNPs included in the analysis was 351,824 SNPs. Concerning HLA allele imputation, a statistical imputation of classical HLA alleles was performed using SNP data with the HIBAG method, with attribute bagging trained from individuals of European ancestries and based on Infinium OmniExpress 24-chip  [11].
Acknowledgments We would like to thank the ABIRISK clinicians who contributed to the recruitment of patients in the ABIRISK prospective study:  Andrea Matucci ,  Università di Firenze; Philippe Seksik, Hopital Saint Antoine, Paris ;  Antony Buisson ,  CHU de Clermont Ferrand ;  Chanteloup Elis ,  Hopital Saint-Joseph ;  Stephane Nancey ,  CHU de Lyon Sud ;  Edouard Louis ,  CHU Sart Tilman, Liège ;  Xavier Hebuterne ,  CHU de Nice ;  Marion Simon ,  Institut Montsouris ;  Maxime Dougados ,  AP-HP Hopital Cochin, Paris ;  Bruno Fautrel ,  Hopital Pitie-Salpêtrière, Paris; Philippe Dieude, Hopital Bichat, Paris; Francis Berenbaum, Hopital Saint Antoine, Paris; Aleth Pedriger, Hopital Sud, Rennes ;  Hubert Marotte ,  CHU de Saint-Etienne, Hopital Nord, Saint-Etienne ;  Alain Cantagrel  and  Arnaud Constantin ,  CHU de Toulouse, Toulouse ;  Olivier Vittecoq  and  Thierry Lequere ,  CHU de Rouen, Hopital Bois Guillaume, Rouen ;  Alain Saraux ,  CHU de la Cavale Blanche, Brest ;  Rene-Marc Flipo , Hopital  Roger Salengro , Lille;  Jean Sibilia ,  CHU de Strasbourg, Hopital de Hautepierre, Strasbourg ;  Jacques Morel ,  CHU Lapeyronie, Montpellier ;  Prof.
The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. [115303], resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. BH received support from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy  within the framework of the  Munich Cluster for Systems Neurology  ( EXC 2145 SyNergy -ID 390857198 ). The biobank of the  Department of Neurology as part of the Joint Biobank Munich in the framework of the German Biobank Node  supported the study. OA received grants from the  German Research Foundation (DFG)  and the  German Ministry of Education and Research (BMBF).
TD's institution received grants from the  Swiss National Research Foundation, University of Basel , and  Swiss MS Society. EKH and PN were supported by the  Czech Ministry of Education  research project  PROGRES Q27/LF1  and  MSMT-35199/2012-32. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Biogen ,  Novartis ,  Merck Serono , and  Sanofi. JA has served on scientific advisory boards for  Roche, BMS, Abbvie, Nordic, Boerhinger , and  Sanofi-Genzyme ; he has received speaking honoraria from  Pfizer ,  MSD ,  BMS , and  Novartis ; and he has received research grants from  BMS  and  Pfizer. received speaker honoraria and/or.
3b , Supplementary Data 5). Notably, cluster I included 374 highly expressed genes in P1's, which were slightly downregulated upon differentiation into P2 cells and further repressed within P3's. Cluster II exhibited an expression profile of 33 genes which were highly upregulated in P2's compared to P1 and P3 subsets. Upon P2's converting into P3 cells, 299 genes associated with alveolar macrophage homeostasis  27  became more prominent in cluster III compared to cluster I and II (Fig. 3b ). Pathway enrichment analysis was performed to understand the biological processes associated with each gene cluster. Genes upregulated during the P2-P3 cellular switching were associated with efferocytosis, cell cycle, biosynthetic processes, secretion of ECM components and growth factors, pathways all involved in repair.
Tissue autofluorescence was minimized using 0. 1% Sudan Black (Sigma-Aldrich, Paris, France). Sections were incubated with primary antibodies, (Supplementary Table  1 ). IL-33 staining was amplified using Tyramide Signal Amplification (TSA ™ ) from PerkinElmer, according to the manufacturer's protocol. For co-localization experiments, secondary donkey antibodies conjugated with Alexa Fluor 488, Alexa 568 or Alexa 647 (Invitrogen/Molecular probes, Saint Aubin, France) were used. Sections were counterstained with nuclear Dapi. Fluorescent images were obtained using a confocal Leica TCS SP8 microscope and LAS AF (Leica Application Suite Advanced Fluorescence, Paris, France) microscope software. Images were captured with 25× and 40× oil immersion objectives. CCSP quantification. Bronchioles (diameter = 150-350 μm) were examined for CCSP expression in Naïve, NA-treated, depleted and transferred mice.
Mean intensity of fluorescence per bronchiole was used as quantitative dataset and normalized to the mean fluorescence intensity of CCSP expression in naive mice to calculate the total CCSP expression per bronchiole. Then, the mean intensity of fluorescence was quantified within each club cell. Club cells displaying less or higher than 50% of the mean fluorescence intensity of the reference, (CCSP expression in club cells of naïve mice), were qualified as CCSP low or CCSP high , respectively. Finally, Ki-67 + cells were counted within CCSP low cells. Flow cytometry analysis of BAL monocytes and macrophages. BAL and lung cells from naïve, NA-injected mice, administered with PBS-or CL-containing liposomes, were fixed and permeabilized using cytofix/cytoperm buffer (R&D systems, Lille, France).
Flow cytometry (Canto II and LSRII/Fortessa, Becton Dickinson, Paris, France) was performed on fixed 0. 5 × 10 6 BAL cells, after incubation with 50 μL of Fc block anti-mouse CD16/CD32 (Becton Dickinson Pharmingen) on ice for 15 min, followed by staining with anti-mouse antibodies, (Supplementary Table  2 ), according to manufacturer's protocols, as well as their isotype controls. Biotinylated antibody-antigen complexes were detected using streptavidin conjugated with Brilliant violet 421 (Biolegend, Yvelines, France). Cell cycle was examined using Propidium Iodide (Biolegend). FACS data was acquired using BD LSR Fortessa ™ Flow Cytometer and were analyzed by Flow Jo 9. 5 software. After gating out debris and neutrophils (F4/80 low CD11b hgh CD64 low ), monocytes and macrophages were analyzed on CD64 gate.
For fluidigm, qPCR was performed using the fluidigm Biomark Dynamic array loaded with the probes found in Supplementary Table  3  (Fluidigm Corp, South San Francisco, USA). Single-cell RNAseq of mouse bronchial epithelial cells. Mouse bronchioles were collected, and single cell suspensions were prepared as described in lung tissue preparation above and enriched for Epcam positive cells. For single-cell RNAsequencing, we followed the manufacturer's protocol (Chromium ™ Single Cell 3′ Reagent Kit, v2 Chemistry) to obtain single cell 3′ libraries for Illumina sequencing. Libraries were assessed for quality (TapeStation 4200, Agilent), and then sequenced on NextSeq 500 or HiSeq 4000 instruments (Illumina). Initial data processing was performed using the Cell Ranger version 2. 0 pipeline (10x Genomics).
Post-processing, including filtering by number of genes expressed per cell, was performed using the Seurat package V2. 2 and R 3. 4 and visualization was generated using UCSC Cell Browser. Clustering was realized with t-Distributed Stochastic Neighbor Embedding (t-SNE). Identification of cell clusters in mouse bronchial epithelial cells was guided by marker genes. Scgb1a1, Scgb3a1, Foxj1, and Il33 gene expression was performed for each cluster. t-SNE plots were generated using Seurat. RNA-sequencing and bioinformatics analysis. RNA sequencing was performed using Illumina HiSeq 2000 platform and sequence data (fastq) was quality checked using FastQC and further aligned to mouse genome (mm10) using Hisat2 (version 2. 0. 2). Gene count table was generated using HTSeq and further normalized to count per million (CPM) using edgeR.
Differential expression gene analysis was performed using edgeR and genes with false-discovery rate < 0. 05 and fold change (FC) > 2 were considered as significant if not specified separately. Heatmap visualization was built using pheatmap in R after removing low expressing genes, (CPM < 150 if not specified separately). CPM in log scale was used to generate heatmaps for growth factor, extracellular modeling and immune response and Zscore was used to generate heatmaps with DEG. Gene signatures were clustered using hierarchical clustering based on Euclidean distance. Tree was cut based on dendrogram using R cutree function. Pathway enrichment analysis for gene cluster was performed by metabase R package (v4. 2. 0, Clarivate Analytics) and process networks constructed by Thomson Reuters. Detailed pathway was classified based on network ontology, (Supplemental Tables  4  and 5 ).
Gene Set Enrichment Analysis (GSEA) was performed in GenePattern using curated gene sets from Reactome database (c2. cp. reactome), and detailed pathway was further classified according to Reactome ontology (reactome. org/). Quantification of cytokines and growth factors. The levels of cytokines were measured by multiplex magnetic bead immunoassay system, (Milliplex MAP kit, Merck Millipore, Molsheim, France), in BAL supernatants that had been previously concentrated 5× using Amicon Ultra-centrifugal filters, (Merck Millipore). Mouse Chemokine production in BAL cell lysates. Naïve and NA-treated mice were euthanized and BAL fluid was obtained by injecting and recovering 2 × 1 mL of sterile PBS through a tracheal catheter. After centrifugation, (400g, 5 min, 4 °C), BAL cell pellets were resuspended at 5 × 10 5 cells per mL in RPMI medium and seeded in 24-well plate (500 μl per well) overnight to allow macrophage adhesion to the well bottom.
IL-13 from cultured ILCs was determined by ELISA (R&D). IL-33 and sST2 quantification. The right upper lung lobes of naïve and NAinjected mice were homogenized in 1 mL lysis buffer, and the supernatant was collected for IL-33 quantification, (R&D Systems), in standardized sandwich ELISA, according to the manufacturer's protocol. BAL levels of sST2 were measured by ELISA (R&D Systems). Statistical analysis. Statistical analyses were performed using GraphPad Prism v8. 0. 1. ANOVA with Bonferroni post-test was used to determine statistical significance between the groups in experiments involving more than two groups or time-points. The data were considered significant when p values were ≤0. 05. For all graphs and experiments, * P < 0. 05, ** P < 0. 01, *** P < 0. 001, **** P < 0. 0001 comparing the treated group or timepoint the mark is directly above to the naïve or unstimulated for that same genotype of mouse or cells; * P < 0.
Acknowledgements The authors wish to thank to  Nassima Ferhani ,  Sophie Kozelko , and  Sophie Moog  ( Inserm UMR1152 ) for their help with the NA experimentation, and the  Investissements d'Avenir program   ANR-11-IDEX-0005-02 ,  Sorbonne Paris Cité, Laboratoire d'Excellence INFLAMEX. Yoichiro Ohne  for critical reading of the paper.
Data availability Sequence data that support the findings of this study have been deposited in the Gene Expression Omnibus with accession numbers, GSE155356 and GSE155359, for cellsorted bulk RNAseq data for lung monocyte/macrophage subsets from NA-treated WT mice (Fig. 3 ), and NA-treated ST2 -/-(Fig. 6 ). scRNAseq data are deposited with accession numbers, GSE155261, for subtyping naïve mouse bronchial epithelial cells (Fig. 4 ). The data that support the findings from these studies are available from the corresponding authors upon request. Source data are provided as a Source Data file. Source data are provided with this paper.
INTRODUCTION Reduced representation sequencing (RRS) has become very popular in the last few years among scientists studying the genetic variation of non-model organisms  (Andrews, Good, Miller, Luikart, & Hohenlohe, 2016; Beichman, Huerta-Sanchez, & Lohmueller, 2018; Narum, Buerkle, Davey, Miller, & Hohenlohe, 2013) , with special emphasis on digestion-based techniques, here commonly referred to as RAD (restriction-site-associated DNA). Coupled with high-throughput sequencing technologies, RAD libraries produce sequencing data covering thousands to millions of short (<1000 bp) loci throughout the entire genome that can be replicated in large amounts of individuals, within and between related species. The various protocols (reviewed in  Andrews et al. , 2016)  allow for different levels of genome coverage, and are differently suited to studies involving population genetics, phylogenetics, linkage mapping, genomic scans and association mapping.
However, not only the number of loci may vary, but the complete locus repertoire will change across different ranges of fragment lengths. It has been shown that the set of loci covered can be affected by slight changes in library building protocols  (DaCosta & Sorenson, 2014)  or by simply including individual libraries in different pools during size selection  (Franchini, Monné Parera, Kautt, & Meyer, 2017). Large sensitivity to changes in the protocol affects reproducibility and may not only increase costs but also prevent entire datasets from being comparable. Although a few approaches have been suggested in attempts to homogenize the outcome of library building across many specimens (e. g. Franchini et al. , 2017) , the type of data generated and the lack of a reference genome prevent researchers from understanding what issues may have caused a drop in individual coverage and/or in the set of loci overlapping among samples.
, 2020)  and extensive repetitive regions  (Phillippy, 2017). Moreover, it is still largely unknown how genome assembly quality can affect RAD data analysis. Regardless of the approach chosen -whether de novo or reference-based -to analyse RAD data, the main goal is to ensure that variation among individuals is retrieved from comparisons among orthologous loci (or true homologues), in contrast to paralogous loci. A key challenge to identify true homologous alleles is the definition of identity thresholds between sequences, which should be established through an empirically-justifiable protocol  (McCartney-Melstad, Gidiş, & Shaffer, 2019). If thresholds are too high, they will over-split orthologous alleles, while too low thresholds will cluster paralogous alleles into the same putative locus.
Preliminary in silico tests and reference genome A draft genome of C. mydas (CheMyd_1. 0, GenBank accession number GCA_000344595. 1;  Wang et al. , 2013)  was used to perform preliminary estimations of fragment yield for different enzyme combinations and size ranges before designing the study, using a dedicated python script (RAD_digestion. py) that performs in silico digestions according to the ddRAD workflow  (Peterson et al. , 2012). We have chosen the enzyme pair EcoRI (a 6-bp cutter) and MseI (a 4-bp cutter) based on the number of expected genome fragments (20,000-30,000 loci) within a size range of 400-500 bp. For analyses performed after the sequencing data was obtained, we have utilized the version of CheMyd_1. 0 genome scaffolded by the DNAzoo project  (Dudchenko et al. , 2017) , identified here as CheMyd_1.
Unmapped read pairs were compared against CheMyd_1. 0_DNAzoo using blastn as implemented in NCBI's BLAST+ package (v. 2. 6. 0) with a maximum e-value of 10 -20 and subsequently against the freshwater turtle Chrysemys picta  (Shaffer et al. , 2013; Chrysemys_picta_bellii-3. 0. 3, NC_024218. 1). Finally, reads that did not produce matches were aligned against the GenBank nucleotide (nt) database using the same parameters. The output from blast was visualized using MEGAN v. 6. 8. 18  (Huson et al. , 2018).