dataesr/scientific-paragraphs-categorization huggingface dataset:
They were immediately frozen in liquid nitrogen and stored at -80 C until RNA extraction. As indicated in  Bourguet et al. (2010)  and  Delosière et al. (2020) , experimental procedures and animal holding facilities respected French animal protection legislation, including licensing of experimenters. The procedures were controlled and approved by the French Veterinary Services (agreement B63 345 17). The animal experimental design was described in and registered in the research unit quality management database.
(2012a). Data were preprocessed by Feature Extraction 10. 1 software (Agilent Technologies, Santa Clara, CA, USA) for all samples and probes. The probes not meeting the quality criteria (saturation and uniformity of spots, intensity above background noise, etc. ) were filtered out. Each array was normalized by dividing the raw intensity values of its probe by the median intensity of the control probes of the array. Each probe intensity was then normalized by dividing its raw value by the median of the corresponding probes from all arrays. After removing the probes with missing values, a log 2 transformation was applied to the data. The transcriptomic data were submitted to Gene Expression Omnibus (GEO) under the accession number GSE119912. Differential analyses were conducted via linear modelling with the diet supplementation Ã period Ã stress interaction factor to explain the probe levels.
An empirical Bayes method was used to moderate the standard errors of the estimated log-fold changes using the R/Limma package (http://bioinf. wehi. edu. au/limma/) as described in  Smyth, Yang & Speed (2003)  with a Benjamini and Hochberg multiple testing correction  (Benjamini & Hochberg, 1995). The genes for which at least 80% of the probes were consistently different at the adjusted p value 10% (i. e. , estimated rate of true positives in the probe list of 90%) were retained and considered differentially expressed genes (DEGs). All probe ratios were found to be consistent for each DEG, meaning that for one gene, all probe ratios were lower than 1 or greater than 1.
Gene Ontology enrichment Functional enrichment according to Gene Ontology Biological Process (GO BP) was assessed by submitting lists of accession numbers (for DEGs) or gene names (for common regulators and targets of the DEGs) to ProteINSIDEv2  (Kaspric et al. , 2015 , https://umrhbioinfo. clermont. inrae. fr/ProteINSIDE_2/). This workflow enables the analysis of lists of protein or gene identifiers from ruminant species and gathers biological information provided by functional annotations, putative protein secretion and protein interactions. It queries the g:Profiler database based on the most complete information available for Bos taurus. The list of array probes was used as a background list for enrichment analysis of the DEG lists. The GO enrichment test was declared significant at a Benjamini-Hochberg FDR < 0.
Identification of cis-transcriptional modules Promoter sequences were extracted using the program Gene2Promoter (version 3. 4. 1; Genomatix Software Suite, Munich, Germany, www. genomatix. de) using the default settings, 500 bp upstream and 100 bp downstream of the transcription start site. We selected bovine promoters with at least one relevant transcript and preferentially a high quality level (experimentally verified 5′ transcript or with 5′ end confirmed by PromoterInspector prediction) and for whose number of conserved orthologous promoters was at least 50% of loci. This was performed for DEGs and for all of the genes in the microarray. As the coregulation of mammalian genes usually depends on a combination of TFs rather than individual TFs alone, cis-acting regulatory elements are often organized into frameworks of motifs called cis-transcriptional modules.
The selected promoters were submitted to the 'ModelInspector' task of GEMS Launcher (version 4. 1; Genomatix Software, Munich, Germany, www. genomatix. de) to search for cis-transcriptional modules. For this purpose, the promoter sequences of the genes were scanned for matches to the Promoter Module 5. 4 Library (Vertebrate Module section). Fisher's exact test was then used to identify overrepresented cis-transcriptional modules in the DEG set compared to the total gene set of the microarray.
Construction of interaction networks Network analysis was performed with Pathway Studio software version 12. 0. 1. 9 using Elsevier's Resnet Mammal DataBase (Ariadne Genomics, Rockville, MD, USA). Gene interaction networks were built with the DEGs targeted by the most represented cistranscriptional modules for each muscle (targeting at least 5 and 4 DEGs for LT and ST, respectively) and with the DEGs targeted by the nine overrepresented cis transcriptional modules common to both muscles, generating two muscle-specific networks and one common network of stress response. For each set of genes, to reconstruct the network, Pathway Studio searches known relations between the genes and adds regulators and the expression targets common to them. Filters were applied to identify only key expression regulators and targets of each network.
To be added to the network, target genes must be linked to a minimum of three bibliographic references and have at least six known relations in the Pathway Studio Database. For regulators, three bibliographic references and two known relations are also needed, except for the regulators of the nine common modules for which a cut-off of five relations was chosen. Finally, Venn diagrams were used to identify the major regulator genes and major target genes among the DEGs targeted by cis-transcriptional modules specific to LT and ST and in both muscles. Subnetworks between DEGs and their major regulator genes and between DEGs and their major target genes were extracted.
Validation of differential expression levels A RT-qPCR assay was performed on the LT samples of 10 animals/group for 4 genes (ATF3, CEBPD, SMAD7 and FOS) with the StepOne Plus TM Real-Time PCR System using the Power SYBR1 Green master mix (both Applied Biosystems, Foster City, CA, USA). The GeNorm algorithm  (Vandesompele et al. , 2002)  was used to determine the optimal number of reference genes required to effectively normalize the qPCR data. Four housekeeping genes were selected: UXT, MRPL39, CLN3 and TOP2B. The primer sequences (Table  S1 ) with an annealing temperature of 60 C were designed using Primer3 software. qPCR was performed using a StepOnePlus thermocycler (Applied Biosystems, Foster City, CA, USA). The PCR efficiency of each primer pair was tested with a 10-fold dilution series of purified cDNA.
Identification of genes corresponding to DEGs in quantitative trait loci (QTLs) A query of genetic information from the lists of the DEGs and the common regulators and targets of the DEGs was performed with the QTL module included in ProteINSIDEv2. Briefly, each DEG was searched on NCBI to retrieve the location of the corresponding gene on the genome. Then, this location was compared with the QTL positions in the QTL database "AnimalQTLdb". The location on the genome must be included entirely in a QTL to consider that the DEG is mapped in the QTL.
Transcriptomic profiles We recorded changes in gene expression profiles in the LT and the ST. Individual data are available in the GEO repository under accession number GSE119912. No effect was detected for the EP diet, the stress Ã EP diet or the stress Ã diet in either muscle, but an effect of stress was detected (P < 0. 1). In the stressed cows compared to cows handled with limited stress, microarray analysis revealed changes in the abundance of 67 transcripts in the LT (including 43 up-and 24 downregulated transcripts; P < 0. 1, Table  S2 ) corresponding to 62 DEGs with unique gene names (Fig. 1 ). In the ST, changes in 36 transcripts were detected (including 33 up-and 3 downregulated transcripts; P < 0. 1, Table  S2 ) corresponding to 32 DEGs with unique gene names (Fig. 1 ). Among the differentially expressed transcripts, 27 were common to both muscles, corresponding to 24 unique gene names (Fig.
1 ). They included eight known transcription factors (TFs): SMAD7, ETS2, MYOG, ATF3, HES6, CEBPD, HEYL, and FOS (Table  S2 ). In addition, muscle-specific genes were differentially expressed according to the stress status (38 in the LT and 8 in the ST; Fig. 1 ). These genes included four TFs (MYOD1, MYF6, CEBPB, and HES1) and one transcription cofactor (MED23) in the LT and a transcriptional activator (ZNF750) in the ST. The differential abundance of four TF transcripts (ATF3, CEBPD, SMAD7 and FOS) was checked by qPCR experiments in the LT and confirmed the observed changes, as illustrated in Table  1. Lists of DEGs according to stress status were submitted to biological information mining through Gene Ontology (GO) term enrichment compared to the background list of the microarray (Data S1).
For each considered GO BP, the list of genes included both common and muscle-specific DEGs. There were no significant GO cellular component terms at P < 0. 08 in both the LT and ST muscles (data not shown). In the LT, 18 GO molecular factor (MF) terms were enriched (P < 0. 08). In the ST, 19 GO MF terms were enriched (P < 0. 08). Sixteen of these GO terms were enriched in both the LT and ST. They mainly refer to DNA-binding and transcription regulator activity (Supplemental Dataset).
Cis-transcriptional modules A promoter analysis was performed with Gene2promoter of the Genomatix Software Suite to identify common TF binding sites in the promoter regions of genes-called cis transcriptional modules-that may account for coregulation among differential transcripts. For 52 of the DEGs in the LT, 168 promoters were retrieved from the Genomatix Promoter Table  1  Validation of some differentially expressed genes following pre-slaughter stress in the Longissimus thoracis muscle. The abundance of some DEGs detected by microarray analysis was quantified by qRT PCR in the Longissimus thoracis muscle of stress cows vs. cows handled with limited stress (2n = 20). Variation of reference genes used for normalization was computed with the GeNorm software package. Student t-test was used to test the significance of the difference between the two conditions.
Database, of which 111 were selected for further analysis (Table  S3 ). ModelInspector enabled the retrieval of 288 different cis-transcriptional modules (at 1,378 locations). For 28 of the DEGs in the ST, 84 promoters were retrieved. Of these, 57 promoters were further analysed with ModelInspector, and 201 cis-transcriptional modules were found (on 675 match positions). The same analysis was performed for all the genes represented on the microarray. As illustrated in Table  S3 , 24 cis-transcriptional modules were identified as overrepresented DEGs compared to the genes represented on the microarray (P < 0. 1) in the LT and 25 in the ST. Nine of the modules were overrepresented in both muscles. The cis-transcriptional modules and the DEGs targeted by these modules in each muscle as identified by ModelInspector are listed in Table  2.
Interaction networks and identification of regulators and main targets of DEGs Finally, with Pathway Studio 2, we constructed interaction networks between the DEGs targeted by the overrepresented cis-transcriptional modules for each muscle and between the DEGs targeted by the 9 overrepresented cis-transcriptional modules common to both muscles. We thus generated muscle-specific networks and one core network of stress responses. Then, using the Pathway Studio 2 database, we searched for the main regulators and the main targets of the 3 networks (Data S2, sheets 1-6). We next identified the similarities among the lists obtained from these datasets to identify the key common regulators and targets (Data S2, sheets 7-8). Ten main regulators of stress-responsive genes were identified: AKT1, EGF, HIF1A, IFNG, IL1B, INS, MAPK1, MAPK14, TGFB1, and TNF.
ACKNOWLEDGEMENTS The authors thank  Geneviève Gentes  and the  HELIXIO Company (formerly Imaxio, Clermont-Limagne Biopôle)  for performing the transcriptomic analyses and  Dominique Bauchart ,  Denis Durand ,  Claudia Terlouw ,  Brigitte Picard  and  Joelle Henry-Berger  for scientific discussions.
16 μm. Both the lower and upper limits of the Coulter Counter were set according to the size range of C. gelidus (e. g. ,  Chamnansinp et al. 2013). The instrument was routinely calibrated with 5 μm latex microspheres. Data blanks were generated with 0. 2 μm filtered ASW. Samples were diluted (0. 5 : 10) with 0. 2 μm filtered ASW to meet instrument operational requirements. Three consecutive counts were achieved for each culture sampling point. Before and after each run, both the aperture tube and electrode were rinsed with distilled water and the aperture was flushed with ASW. Samples were bubbled before each count to break C. gelidus chains. Growth rate was calculated as: μ ¼ ln N 2 =N 1 ð Þ = t 2 À t 1 ð Þ , ð1Þ where N 2 is the cell concentration at time t 2 and N 1 is the cell concentration at time t 1.
Acknowledgments The authors are most grateful to  Jonathan Gagnon  and  Gabrièle Deslongchamps  for their assistance with cellular content analysis. We thank  Flavienne Bruyant  for her valuable help during the experiments. This work was supported financially and logistically by the  Canada Excellence Research Chair (CERC) program , the strategic research cluster  Quebec-Océan  ( Fonds de Recherche du Québec -Nature et Technologie ) and by grants to J. -E. T. from the  ArcticNet Network Center of Excellence  and the  National Sciences and Engineering Research Council of Canada (NSERC). It contributes to the scientific programs of  Institut Nordique du Québec (INQ) ,  Québec-Océan  and the  Takuvik Joint International Laboratory  ( UMI 3376 ) of  Université Laval (Canada)  and the  Centre National de Recherche Scientifique (France).
Termination of pregnancy may be offered as an option to the pregnant woman and in some studies termination was chosen in up to 88% of the prenatally diagnosed cases (6, 10). Studies show increased mortality  (5, 11, 12)  and morbidity  (2, 5, 8, 13)  among adults with Klinefelter syndrome and indicates, that some of the physical and cognitive challenges can occur from the early childhood  (14). Studies describing morbidity and mortality in childhood for children diagnosed with Klinefelter syndrome are limited, often based on small populations and typically focusing on the speci c causes and not the burden of disease compared to the background population  (4, (15) (16) (17) (18) (19) (20). This study is part of the EUROlinkCAT project, a European population-based record-linkage cohort study, using the EUROCAT infrastructure to establish a network of standardized datasets containing information about survival and morbidity for children up to 10 years of age with major congenital anomalies  (21).
Linking of data Data on survival for children diagnosed with Klinefelter syndrome were obtained by linkage to death certi cates and vital statistics in the regions and countries  (23). Data on hospitalisations and surgical procedures for all three groups of children were obtained by electronic linkage to the hospital databases used in the regions and countries. Linkage success for linkage to hospital databases was 97% for children with congenital anomalies and 95% for reference children. Further details of the linkage method used in the EUROlinkCAT study have been published earlier  (22, 24)  Data on survival Data on survival for children with Klinefelter syndrome were available for thirteen registries in ten countries. The mortality rate for all liveborn children across Europe were obtained from the statistical o ce of the European Union, EUROSTAT and converted to a survival rate for comparison of the survival for children diagnosed with Klinefelter syndrome.
Data on hospitalisation Data on hospitalisation for children with Klinefelter syndrome were available for nine registries (Table  1 ). For six of the registries the hospital databases covered hospital stays in the whole country. For Wales, hospital stays in England were included. For the Valencian Region in Spain, Emilia Romagna and Tuscany in Italy, the hospital databases covered the same region as the EUROCAT registry. Data on hospitalisation of reference children and children with any congenital anomaly were obtained from the EUROlinkCAT study on length of stay for European children  (24).
Statistical analysis A common data model was developed for all the hospital databases and used to standardise the variables obtained by linkage from the local databases  (22). This enabled all registries to run centrally written syntax scripts in Stata version 13 for linkage quality checks and analyses. The aggregate tables and analytic results produced were sent to a Central Results Repository at Ulster University. No individual case data were shared. All analyses were performed separately for three age groups: <1 year (0-364 days), 1-4 years (365-1825 days) and 5-9 years (1826-3651 days). As there were insu cient data from 5-9 years of age, only the age groups < 1 year and 1-4 years were included in meta-analyses. The number of days spent in hospital between the day of admission and the day of discharge was calculated and if the date of admission and discharge occurred on the same day, the length of stay was determined to be 0.
5 days. Quantile estimation methods were used to obtain pooled estimates of the median number of days spent in hospital per year for those with at least one hospital admission in a random effects meta-analyses using the "metamedian" package in R, version 4. 0. 3. In each registry, the percentages of children hospitalised and with surgeries within each age group were calculated using Kaplan Meier (KM) survival analyses to allow for the censoring on December 31st 2015, death or emigration from the study. Pooled estimates of the percentage hospitalised and with surgery were calculated in a random effects meta-analyses using the ln(-ln) transformation in STATA, version 15. As very few children with Klinefelter syndrome had extended hospital stays, the percentage with extended stays, was calculated by summing the totals ignoring registry or censoring and the Wilson con dence interval for proportions was used.
Author's contributions JM, ML and EG designed the EUROlinkCAT study and obtained funding. ARA wrote the protocol for this study. ARA, SKU and JT analysed the data. AN, AP, CC, DT, DW, HdeW, KK, MGa, MGi, NL and EG contributed with data from their EUROCAT registries. ML and SKU performed the standardization of the variables. JM supervised the analysis scripts. ARA wrote the rst draft of the manuscript. All authors reviewed and revised the manuscript. Figures.
Patient identification: Patients were referred to us at Boston Children's Hospital from offsite centers through the very early onset IBD consortium (www. veoibd. org). Patients and parents included in this study provided informed consent and assent where applicable. Consent was obtained as per our research protocols as was publication of de-identified information in this work, through our Institutional Review Board (protocol# P00000529). De-identified information was stored on RedCap through the Boston Children's Hospital (BCH) Pediatric Gastrointestinal Data Registry and BioRepository.
Whole Exome and Whole Genome Sequencing: Whole Exome Sequencing (WES) was performed in collaboration with Merck Research Labs using an Agilent v5 Sureselect capture kit and Illumina 2500 sequencing technology as previously described  70. For each sample, paired end reads (2X100 bp) were obtained, processed and mapped to the human reference genome (hg19). We used the BWA mem algorithm (version 0. 7. 12)  71  for alignment of the sequence reads. The HaplotypeCaller algorithm of GATK version 3. 4 was applied for variant calling, as recommended in the best practice pipeline  72. KGG-seq v. 08 was used for annotation of identified variants  73  and in house scripts were applied for filtering based on family pedigree and local dataset of variants detected in previous sequencing projects. Whole genome sequencing was performed as described  74  using the HiSeq platform (Illumina) with the Burrows-Wheeler aligner, and Picard was used for basic alignment and sequence quality control.
The same capture kits and strategies were employed for the genome sequencing as described above for whole exome sequencing. Next Generation Sequencing data was analyzed by a biostatistician (MF) in conjunction with trained gastroenterologists (JO, SBS). Proband variants were filtered by rare, loss-of-function (LOF) or missense variants inherited according to standard Mendelian inheritance patterns. Rare variants were those with minor allele frequency (MAF) <1% for homozygous, hemizygous, and de novo mutations and <5% for compound heterozygous mutations according to ExAC 59 and the 1000 genomes project  58.
5μM -0. 125 μM -0. 0625 μM) and association to IFNLR1 was measured over 300s. Following the association step, the biosensor was transferred in a well containing assay buffer alone and dissociation was measured over 600s. Association curves were normalized based on the signal of the last 2 seconds of the loading step. Dissociation constants (KD) were calculated using the Octet Data Analysis HT Software.
Quantification and Statistical Analysis. To compare two groups unpaired t-test were performed. When data did not meet the normality assumption, significance was assessed by Mann-Whitney test. To establish the appropriate test, normal distribution and variance similarity were assessed with the D'Agostino-Pearson omnibus normality test. Dose response curves were fitted with a Richard's five parameters dose response curve. EC50 and area under the curve (AUC) were calculated with GraphPad PRISM software. All statistical analyses were two-sided and performed using GraphPad PRISM software software and are indicated in figure legends. Throughout the paper significant is defined as follows: ns, not significant (p > 0. 05); *p < 0. 05, **p < 0. 01, ***p < 0. 001, and ****p < 0. 0001. Figure Legends:.
Acknowledgements: SBS is supported by  NIH  grant  P30DK03485 ,  RC2DK122532 ,  R01DK115217 , the  Wolpow Family Chair in IBD Treatment and Research , the  Translational Investigator Service at Boston Children's Hospital , and the  Children's Rare Disease Cohort (CRDC) Study. IZ is supported by  NIH  grant  1R01AI121066 ,  1R01DK115217 ,  1R01AI165505  and contract no. 75N93019C00044 ,  Lloyd J. Old STAR  Program  CRI3888 , and holds an Investigators in the  Pathogenesis of Infectious Disease Award  from the  Burroughs Wellcome Fund. AB is supported by: the  excellence initiative of Aix Marseille Université-A*Midex, a French "investissements d'Avenir" program :  AMX-20-CE-01 ; the  FRM amorçage de jeunes equipes  grant  AJE202010012468  and the  ANR-JCJC  grant " INTERMICI "  ANR-21-CE15-0022.
Background: The emergence and diffusion of strains of pathogenic bacteria resistant to antibiotics constitutes a real public health challenge. Antibiotic resistance genes (ARGs) can be carried by both pathogenic and non-pathogenic bacteria, including commensal bacteria from the human microbiota, which require special monitoring in the fight against antimicrobial resistance. Methods: We analyzed the proteomes of 335 new bacterial species from human microbiota to estimate its whole range of ARGs using the BLAST program against ARGs reference databases. Results: We found 278 bacteria that harbor a total of 883 potential ARGs with the following distribution: 264 macrolides-lincosamides-streptogramin, 195 aminoglycosides, 156 tetracyclines, 58 β-lactamases, 58 fosfomycin, 51 glycopeptides, 36 nitroimidazoles, 33 phenicols and 32 rifamycin.