dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors acknowledge enlightening discussions with  Clément Roussel ,  Andrey Aristov ,  Raphael Tomasi ,  Gabriel Amselem  and all members of the  Baroud team. Raphael Voituriez  is gratefully acknowledged for his comments on the probabilistic descriptions. P. B. is funded by  Institut Pasteur ,  Inserm  and an advanced grant from the  European Research Council (ENLIGHTEN). G. R. is funded by the  Direction Générale de l'Armement. The authors acknowledge the support of the Biomaterials and Microfluidics platform and the  FabLab at Institut Pasteur.
This work was supported by institutional funding from the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de la Santé et de la Recherche Médicale (INSERM) , by the  Excellence Initiative of Aix-Marseille Université (AMU)  (A*MIDEX), a  French "Investissements d'Avenir" programme , and  INSERM Transfert (CoPoC) ,  Agence Nationale de la Recherche  ( ANR-20-CE15-0016 ). J. M. P. is a recipient of a post-doctoral fellowship from the  Fondation de Coopération Scientifique "Méditerranée Infection ". Data Availability Statement: Not applicable. thank all members of the  JPG lab  for their support.
S6 ), as previously observed with BIO5464  20. Daily administration of the mixture to WT mice modestly induced the transcript level of Il22 in a NOD2/ RIP2-dependent manner, although there was no change in expression of Defa4, which is a NOD2-induced antimicrobial peptides (Fig. S6A, B ). The capacity of the mixture to promote accumulation of IL-22-producing ILC3 isolated from MLN was then confirmed by multiparameter flow cytometry analysis. As what observed with BIO5768 alone (Fig. 1D ), the mixture significantly elevated IL-22 production by NCR -ILC3 in WT mice but not in either Nod2-or Rip2-deficient mice (Fig. S6C ). In agreement with our in vitro data that are depicted in Fig. 1 , we failed to detect any change in the proportion of IL-17-producing CD4 T cells at steady state (Fig. S6D ).
Acknowledgements This work was supported by the  ANR   BiopaneX  project ( ANR-12-RPIB-0014 ), the  Institut Pasteur de Lille , the  "Institut National de la Santé et de la Recherche Médicale » (Inserm) , the  « Centre National de la Recherche Scientifique » (CNRS)  The  Region Centre Val de Loire  ( 2003-00085470 ), the  Conseil Général du Loiret , the  European Regional Development Fund (FEDER  No. 2016-00110366  and  EX005756 ) and  Charles University research program Cooperation   IMMU 207032. The authors thank  Anne Delanoye-Crespin ,  Emilie Floquet ,  Florence Savigny  and  Véronique Peucelle  for their excellent technical help in mice experimentation, histological and/or qRT-PCR analysis. We are also grateful to the staff of the  Centre de Dévelopement des Technologies Avancées (CDTA, Orleans, France) , the animal facility of the  Institut Pasteur de Lille  and the  BioImaging Center of Lille  and to  Nathalie Roudier  for critical review of the manuscript.
and Institut Francais de Bioinformatique (IFB) (ANR-11-INBS-0013) for the use of their resources and technical support. We thank Harry Sokol and Chantal Bridonneau for providing us the A2-165 Faecalibacterium prausnitzii bacterium. We acknowledge the contribution of the staff of Cytocell (Flow cytometry and FACS core facility, BioCore, CNRS 3556 -Inserm US016 -Nantes UniversitéUMS 3556, Nantes) for their expert technical assistance with flow cytometry material and methods.
Acknowledgments We thank the members of  INCIT INSERM unit 1302 (Immunology and New Concepts in ImmunoTherapy ), with a special emphasis for members of  Fredeŕic Altare's laboratory  for technical help and discussions. We also thank  Dr Nathalie Labarrière  for her kind gift of the K562 and U937 cell lines. We thank  Julie Tabiasco  for providing us with the CD39 inhibitor POM-1. We are most grateful to the  Genomics Core facility GenoA , member of Biogenouest and  France Genomique  and to the  Bioinformatics Core facility BIRD , member of Biogenouest.
Microbiology analyses The same laboratory procedures were conducted for samples obtained from different species. Rectal swabs and fecal samples were cultured within 3 days of sampling. Swabs were screened for ESBL-producing Enterobacteriaceae by direct incubation at 37 °C for 24h in ChromID ESBL agar (bioMérieux, Marcy l'Etoile, France). Samples exhibiting bacterial growth were subcultured on the same medium to confirm growth after 24 h of incubation, and up to 2 colonies with different morphotypes were selected per sample. Isolates were then stored at -80 °C and sent to the Laboratory of Microbiology of the Hospital Arnaud de Villeneuve in Montpellier (France) for bacterial species confirmation by direct sample preparation on a matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry using the MALDI Biotyper database DB 8326 MSP (Bruker Daltonics, Bremen, Germany).
Preparation of the DNA library was carried out utilizing the Nextera XT library prep kit (Illumina) according to the manufacturer's protocol. The libraries were sequenced using a 2 × 250 bp paired-end reads method with a minimum coverage of 30× on a HiSeq platform (Illumina). Reads were adapter trimmed using Trimmomatic 0. 30  (Bolger et al. , 2014). Draft genome de novo assemblies were carried out by SPAdesv3. 7  (Bankevich et al. , 2012)  and contigs were annotated using Prokka 1. 11  (Seemann, 2014). Genome quality assessment was performed by QUAST  (Gurevich et al. , 2013). Library preparation, genome sequencing, reads trimming, assembly and quality control were provided by MicrobesNG. The Kraken software was used to confirm bacterial species identity  (Wood and Salzberg, 2014). Contigs are available in Genbank (Bioproject ID PRJNA666069).
From assembled contigs, we determined the presence of antibiotic resistance genes, E. coli sequence types (STs), plasmid replicon types and virulence genes using the bioinformatic tools of the Center for Genomic Epidemiology (CGE) (http://www. genomicepidemiology. org/) including ResFinder 2. 1 (for acquired resistance genes and chromosomal point mutations), MLST 2. 0. 4 using the PubMLST database (https://pubmlst. org/), PlasmidFinder 2. 0, and VirulenceFinder 1. 7. A 95% identity threshold and 80% minimum length coverage was used to predict genes and chromosomal point mutations, while 60% coverage was used to predict plasmids. Two isolates with new STs detected by the CGE tool were further submitted and assigned a ST in Enterobase (https://enterobase. warwick. ac. uk/). The MobileElementFinder 1.
0. 3 tool from CGE was used to assess whether resistance genes were detected in contigs also containing plasmids. Additionally, genetic similarities of E. coli isolates were estimated from core genome MLST analysis using hierarchical clustering of cgMLST v1. 1. 2 in Enterobase, comprising 2513 loci. The resulting phylogenetic tree was edited in FigTree version v1. 4. 4 (http://tree. bio. ed. ac. uk/software/ figtree/).
Antibiotic resistance genes and plasmids in ESBL-E. coli from bats and livestock A total of 33 isolates including 18 ESBL-E. coli isolates (out of 20 positive isolates) from bats and 15 (out of 20) isolates from livestock were successfully analyzed by WGS. The seven excluded isolates had insufficient sequencing data quality or were suspected to represent mixed cultures following genome quality control (genome length and n50 value). Details of WGS, including reads, coverage and contigs information for each isolate are given in Supplementary Table  S1. All isolates from bats belonged to different animals (one isolate/animal) whereas only two isolates (ID19 and ID20) belonged to the same sampled cow in Mala and had different antibiotic resistance patterns and genetic characteristics. E. coli species identity was confirmed by Kraken for all isolates.
ESBL-E. coli sequence types circulating in bats and livestock MLST and cgMLST analyses showed a high diversity of STs in both bats and livestock with 23 different STs according to the PubMLST database reported among 33 isolates, including two newly assigned STs (Fig. 4 ). Vampire bat isolates harbored 10 known STs and 1 new ST (ST 11919 in Enterobase), while livestock harbored 14 STs, and 1 new ST (ST 11921 in Enterobase) (Fig. 4 ). The cgMSLT phylogenomic reconstruction showed no universal isolate clustering by host species, year or sampling area (Fig. 4 ). However, E. coli isolates showing 100% identity on the cgMLST (out of 2513 loci) were collected in the same bat colony at different years (i. e. ,  ST 167 collected in Huacho in 2015 and 2017). Three of the four E. coli ST 167 carried the same bla genes ( blaCTX-M-15 , bla OXA-1 , bla TEM-156 ), mcr-1 gene, virulence genes and had five plasmid replicon types in common (IncFIA, IncFIB(AP001918), IncFII, IncI2, IncX1) (Table  S2 ).
Acknowledgments We thank  C. Tello  and  Elizabeth Rojas-Paniagua  for assistance in field sampling collection. DGS and JAB were funded by a  Sir Henry Dale Fellowship , jointly funded by the  Wellcome Trust and Royal Society  (Grant  102507/Z/13/Z ). DGS was funded by a  Wellcome Trust   Senior Research Fellowship  ( 217221/Z/19/Z ). CS, JAB, NF and DGS were also funded by a  CONCYTEC-UK Embassy  grant (No. 003-2016-FONDECYT ). AO-C was funded by the  National Fund for Scientific and Technological Development of Chile (FONDECYT-Iniciación , grant number  11190602 ) and by the  ANID Millennium Science Initiative/Millennium Initiative for Collaborative Research on Bacterial Resistance, MICROB-R ,  NCN17_081. JAB was funded by the  National Fund for Scientific and Technological Development of Chile (FONDECYT-Iniciación , grant number  11181017 ).
Annexin V and PI Flow Cytometry Cell apoptosis analysis was carried out using FITC-Annexin V and propidium iodide (apoptosis detection kit, BD Biosciences, Franklin Lake, WI, USA) according to the manufacturer's instructions. Briefly, cells were seeded in 10 cm Petri dishes and treated with cetuximab +/-cisplatin for 24 h or 48 h. Cells were harvested and counted, diluted in annexin buffer (BD Biosciences, Franklin Lake, WI, USA) at a concentration of 1 × 10 6 cells per 100 µL, and stained with 10 µL of propidium iodide and 5 µL of FITC-Annexin V. After 15 min of incubation, cells were analyzed in flow cytometer on a BD LSRFortessa TM (BD Biosciences, Franklin Lake, WI, USA) after satisfying QC using CST beads. Acquisition and data analyses have been performed using the BD FACSDiva TM Software.
Protein-related signals were acquired on a Pxi Imager (Syngene ® , Cambridge, United Kingdom). Protein expression (e. g. , cleaved Caspase-3, CRT, and HMGB1) was quantified by measuring the SDS-PAGE gel bands using the ImageJ software. In short, and according to the manufacturer's recommendations, a box was drawn in lanes around gel-band signals using the rectangle tool, making sure to include some of the empty gel between lanes and white space outside of the band. The same box was used for all gel-bands on the same blot. Signal acquisition of pixels was converted into peaks by the ImageJ software, and the area of each peak (which correlates with the gel-band signal intensity) was recovered. Each recovered value was normalized to their respective loading control in the same lane (cell "housekeeping" proteins (actin or GADPH) in the case of cleaved caspase-3 or intracellular HMGB1; cell-culture medium Bovine Serum Albumin (BSA) in the case of extracellular expression of released HMGB1).
Statistical Analysis MTT assays results were analyzed with a Mann-Whiney test. For all other data sets, the, the hypothesis of normality (d'Agostino and Pearson test; Shapiro-Wilk test) and homogeneity of variances (Levene test for equality of variances) of data sets were analyzed. If the sample did not meet at least one of these conditions, then a non-parametric test was used (Kruskal Wallis with Dunn post-test). Otherwise, parametric tests were used (Student t-test; Anova and Tuckey post-test). The tumor-free survival of mice challenged with a hEGFR-MOC2-C1 cell injection was evaluated with Kaplan-Meier survival analysis (see below) and log-rank post-test. Statistical tests were performed using GraphPad Prism 8. For all analyzes, statistical significance is represented in graphs using asterisks: * p < 0.
During this period, infection may remain undetected because it does not always strongly stimulate peripheral immune responses and results in negative skin tests ["unresponsive period;"  (25) ]. The bTB epidemiological latency in cattle is estimated to span from 8 days through to 7 years  (25, 76)  although the latent period may be shorter in calves  (25). As a result, infected animals without macroscopic visible lesions may still be infectious  (23) ; indeed, 50-80% of skin test reactors are non-visible lesion at slaughter  (23). Analysis of large field-based datasets, of animals removed from breakdown herds in Northern Ireland (reactors and exposed in-contact animals), on the relationship between ageclass and post-mortem metrics of infection have revealed that age-classes around 1-2 years of age had higher probability of disclosure and higher numbers of lesions detected than older cohorts  (17, 71).
Acknowledgments: We thank the  GERMETHEQUE Biobank (Rennes)  and the  Direction de la Recherche Clinique (DRC)  of  CHU de Rennes. C. R. and V. M. are thankful to the  Rennes1 University  for the fellowship of S. D. V. M. and M. J. L. F. are also thankful to the  University of Catania  for the fellowship of RC.
healthy monocytes, FDR<0. 05) using XGR software and the Reactome pathway database demonstrated a number of expected pathways involved in the innate immune response to pathogens, including type I IFN signaling, cytokine signaling, interactions between lymphoid and non-lymphoid cells, NLR sensing, etc (Supplementary Figure  8  and Supplementary Table  6 ). However, when we focused our analysis on pathways enriched in upregulated genes in activated COVID-19 monocytes compared to activated healthy monocytes, the most significantly enriched pathways were involved in hemostasis and coagulation, including integrin signaling, extracellular matrix organization, signaling by PDGF, interactions with activated platelets and general hemostasis (Figure  4c  and Supplementary Table  7 ). Integrin receptors are used by cells to interact with other cells and with the extracellular matrix, by binding numerous matrix proteins including collagen, actin and laminin being also involved in hemostasis and platelet aggregation  40.
The lack of cytokine expression, activation of costimulatory receptors, impaired antigen presentation potential and metabolic impairments displayed by moderate COVID-19 monocytes resembled the phenotype observed in LPS-induced tolerance  51. We have previously defined an endotoxin tolerance gene expression signature from publicly available microarray data on monocytes stimulated in vitro with LPS  52  that comprises 398 genes. Out of these, 318 genes were detected in our RNA-seq dataset. We tested for correlation of the endotoxin tolerance signature with ex vivo (Figure  5c ) and activated (Figure  5d ) COVID-19 monocytes, and while ex vivo COVID-19 monocytes did not display a clear correlation with the tolerance signature, activated COVID-19 monocytes displayed similar directionality of expression in those genes from the tolerance signature that were detected in the dataset.
RNA-seq analysis. RNA-sequencing was performed by the Oxford Genomics Centre. PolyA-enriched strand-specific libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kits (Illumina). All samples were pooled together and 150bp PE reads were sequenced on a Novaseq system, resulting in a median read count of 28M per sample. Raw data was processed using the Sanger Nextflow RNA-seq pipeline (https://github. com/wtsihgi/nextflow-pipelines). Briefly, reads were aligned to the reference genome (GRCh38. 99) using STAR v2. 7. 3 66 in the two-pass mode (ENCODE recommended parameters) and gene expression was quantified using featureCounts  67. Mapping statistics and quality control metrics from FastQC and RNA-SeQC 68 indicated high data quality for all samples with no outliers detected.
RNA-seq data analysis was performed in R v4. 1 in Rstudio Server. Features that did not have at least 10 reads in at least 6 samples (the size of the smallest biological subgroup) were filtered out using the genefilter package 69 , resulting in a processed data set on 16,328 features. Principal component analysis (PCA) with the prcomp function was used to explore the relationship between samples, after the filtered gene counts were transformed using a regularized log transformation from the DESeq2 70 package. Differential gene expression analysis was carried out using DESeq2, comparing unstimulated monocytes from COVID-19 patients (n=10) to unstimulated monocytes from healthy controls (HC) (n=6), and SARS-CoV-2-stimulated monocytes from COVID-19 patients (n=14) to stimulated monocytes from HC (n=12).
Genes with FDR<0. 05 and a fold change (FC)>1. 5 were deemed significantly differentially expressed. Pathway enrichment analysis was performed using Fisher's exact test in XGR  29  with annotations from Reactome, using all genes retained in the processed RNA-seq data as the background, and employing the xEnrichConciser options. An adjusted p-value (BH FDR) threshold of 0. 05 was used to identify significantly enriched pathways. Pheatmap package was used to draw heatmaps illustrating variation in gene expression across samples. For testing the enrichment of the sepsis signature in our datasets, publicly available microarray gene expression data on sepsis patients and healthy controls were accessed using GEOquery (GSE46955)  50. Gene expression between patients and controls was compared using limma  71  , for both the unstimulated and stimulated conditions.
Subsequently, the estimated fold changes were tested for correlation with those from the COVID-19 vs HC results. Where multiple probes were available for the same gene in the microarray dataset, the top ranked probe was selected for the comparison. For comparison to the endotoxin-induced tolerance signature, we have previously defined an endotoxin tolerance gene signature  72  from publicly available microarray data on in vitro LPSstimulated monocytes. Briefly, two datasets (GSE15219 52 and GSE22248 73 ) were accessed through GEO. Genes that were differentially expressed following a single LPS treatment (LPS response genes), and that were also differentially expressed between singly-and doubly-stimulated cells were identified. This resulted in an endotoxin tolerance gene signature comprising 398 genes, of which 318 were detected in the RNA-seq dataset.
*p<0. 05, ***p<0. 001 for healthy individual comparisons, #p<0. 05, ##p<0. 005 for mild COVID-19 patient comparisons, $$$p<0. 001 for moderate COVID-19 patient comparisons. ****p<0. 0001. Correlation plot of sepsis vs. healthy individual gene expression signature and ex vivo COVID-19 vs. healthy individual monocyte gene expression signature. Each point represents a gene detected in both the public sepsis dataset and our COVID-19 RNA-seq dataset. The log2FC between sepsis and healthy controls is plotted against the log2FC for ex vivo COVID-19 monocytes vs. healthy control monocytes, and the points are colored according to the significance and direction of effect in the COVID-19 contrast (grey, not significant; red, significantly upregulated, blue, significantly downregulated). b. Correlation plot of sepsis vs.
05), with the fold enrichment plotted on the x axis as log2 (FC) and the bars labelled with the adjusted p value. f. healthy (n=14, white dots), mild (n=15, light blue dots) and moderate (n=10, dark blue dots) COVID-19 monocytes stimulated with LPS for 60 minutes. Mixed model with Tukey's post-test for multiple comparisons for g and i. One-way ANOVA with Tukey's test for multiple comparisons in h. c. Correlation plot of endotoxin-induced tolerance gene signature and ex vivo COVID-19 vs. healthy monocyte signature. Each point represents a gene detected in both the endotoxin gene signature and our COVID-19 vs. healthy RNA-seq dataset. The log2FC between endotoxin tolerance and LPS-response is plotted against the log2FC for ex vivo COVID-19 vs. healthy monocytes, and the points colored according to the significance and direction of effect in the COVID-19 contrast.
Some of the most differentially expressed genes in the COVID-19 vs. healthy monocyte dataset are identified in the plot. d. Correlation plot of endotoxin-induced tolerance gene signature and SARS-CoV-2-stimulated COVID-19 vs. healthy monocyte signature. Each point represents a gene detected in both the endotoxin gene signature and our COVID-19 vs. healthy RNA-seq dataset. The log2FC between endotoxin tolerance and LPS-response is plotted against the log2FC for SARS-CoV-2-stimulated COVID-19 vs healthy monocytes, and the points colored according to the significance and direction of effect in the COVID-19 contrast. Some of the most differentially expressed genes in the COVID-19 vs. healthy monocyte dataset are identified in the plot. e. Barcode plot showing enrichment of the endotoxin tolerance gene set (ET) in the differential gene expression results for SARS-CoV-2-stimulated COVID-19 vs healthy monocytes.
Objectives To evaluate real-world persistence and effectiveness of the IL-12/23 inhibitor, ustekinumab or a tumour necrosis factor inhibitor (TNFi) for psoriatic arthritis over 3 years. Methods PsABio (NCT02627768), a prospective, observational study, followed patients with PsA prescribed first-line to third-line ustekinumab or a TNFi. Persistence and effectiveness (achievement of clinical Disease Activity for PSA (cDAPSA) low disease activity (LDA)/remission and minimal disease activity/very LDA (MDA/VLDA)) were assessed every 6 months. Safety data were collected over 3 years. Analyses to compare the modes of action were adjusted on baseline differences by propensity scores (PS).
Study design PsABio (NCT02627768) is a multinational, prospective, observational study of patients with PsA, designed to evaluate the persistence, effectiveness and safety of ustekinumab and TNFi as first-line to third-line in patients with PsA. The choice of bDMARD therapy was made by the treating rheumatologist, reflecting real-world clinical practice. The study duration per participant was up to 3 years, with follow-up twice yearly. This final, 3-year analysis reports comparative drug persistence data, extended effectiveness outcomes of achievement of cDAPSA LDA/remission and MDA/VLDA, as well as safety data. Due to the COVID-19 pandemic and consequent impediments to seeing patients routinely, the study was closed prematurely, resulting in 63 patients (7. 0%) not being able to reach the minimum of 1005 days for a 3-year assessment on initial treatment due to late enrolment.
Populations Analyses of persistence and effectiveness were based on the effectiveness set, comprising all patients with baseline data and any post-baseline effectiveness data up to the upper limit of the Month 36 visit window, which is up to 1200 days follow-up (including patients who switched/stopped treatment due to AEs, lack of efficacy or other reasons). Endpoint analyses used the last observation carried forward (LOCF) for patients whose last available assessment was earlier than 1005 days and for those whose last visit was cancelled due to study stop. A 36-month LOCF endpoint was created in addition to the observed case analysis. The safety set included all patients with baseline and any available follow-up data.