dataesr/scientific-paragraphs-categorization huggingface dataset:
The microbial diversity, characterized by the Shannon index, was also higher during the reactivation (Fig. 1B ). We lack microbiota data between the two HSV-2 reactivation events but we can detect elevated vaginal pH levels (above 4. 5), which could indicate variations in CSTs (Fig. 1H ). The last two detections of HSV-2 DNA and the onset of HPV66 infection (at DoF 168 and 182) coincided with a shift to CST III with a dominance of Lactobacillus iners (Fig. 1A, C,  and D ). The end of the reactivations and establishment of HPV66 infection, starting from DoF 207, was associated with a shift to CST I and a decrease in microbial diversity. Note that the participant reported repetitive topical and gynecological mycosis treatment between the DoF 207 and 275 (Supplementary Table  2 ). To characterize the immune response, we measured circulating blood antibodies (IgM and IgG) with a multiplex serology assay targeting HSV-2, some HPV genotypes, and Varicella Zoster Virus (Human alphaherpesvirus 3, VZV) as a control.
Acknowledgements The authors thank the participants of the PAPCLEAR study and the clinical staff and nurses for their help. We also thank  Baptiste Elie  ( Laboratoire MIVEGEC  ( UMR CNRS 5290 , IRD 224, UM), Montpellier, France) and  Noemi Bender (Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer Program ,  German Cancer Research Center (DKFZ) ,  Im Neuenheimer Feld, Heidelberg, Germany)  for their contributions.
Data collection Data retrospectively gathered from the medical records were age, sex, time from onset of fever to pediatric ED visit, CRP and PCT performed between H12-H36 from the onset of the fever, blood, urine, CSF and any other fluid cultures, duration of antibiotic therapy and final diagnoses. In addition, the neonates were categorized as either "not sick appearance" or "sick appearance" based on the medical note reported by the ED physician. "Sick appearance" was assigned if the neonate was reported to show signs of illness such as lethargy, irritability, cyanosis, dehydration or respiratory distress.
The study has some limitations. First, we only had 16 cases of IBI, due to the relative scarcity of these infections. Second, its retrospective design and based on standard of care may affect the quality of data collection. Some patients were excluded of the PCT performance calculations because not all biological tests were performed. Follow-up information was not recorded after hospital discharge and we cannot completely rule out the possibility of initially misdiagnosed SBI. However, this possibility is unlikely; the parents would have returned to our hospital emergency room and/or the parents would have contacted the service again if the outcome had not been favorable. Third, the time of PCT determination was calculated in relation to the time of onset of fever measured and reported at home by the parents.
Data Collection Patients were assessed according to a standardized procedure including a questionnaire, a physical examination by the patient's usual rheumatologist, and biological tests. All collected data were computerized and anonymized for analysis. The questionnaire collected data on (i) PsA characteristics: CASPAR criteria, ACPA (anti-citrullinated peptides antibodies) positivity, medication, and (ii) CVRFs: familial first-degree myocardial infarction or sudden death before the age of 55 in men and 65 in women, diabetes with its treatment and duration, smoking with cessation date and consumption, hypertension (HT) and its treatment, history of CVE with its treatment, and dyslipidaemia and its treatment. Physical examination included blood pressure measurement after 5 min of rest, twice, with a 5-min interval, sitting or lying down, with an automatic device.
The cohort was composed of patients recruited between 1967 and 1991 and is therefore outdated. Finally, it does not consider chronic inflammatory diseases as an independent cardiovascular risk factor. With the QRISK2, our study revealed an additional risk of mortality and cardiovascular events in 10 years in our PsA population. We also applied the "rheumatoid arthritis" item in the QRISK2 calculator for our PsA population (QRISK2-PsA). This increased the proportion of patients with a cardiovascular risk ≥ 10%, eligible for statin treatment according to the NICE recommendations, from 44. 9 to 53. 2%. This proportion was higher than the real-life prescription in our center (22. 9%). Algorithms to estimate CVR usually underestimate CVR in PsA. Published data on the adaptation of CVR equations are limited and need to be optimized for use in PsA  (32) (33) (34).
16S barcoding analysis Samples used for microbiota analyses are indicated in Table  2  of the Additional file 1. For each time point, 3 biological replicates were used. For each sample, 16S rDNA amplicon libraries were generated targeting the variable V3V4 loops for bacterial communities  [41]. Paired-end sequencing with a 250-bp length was performed at the McGill University (Génome Québec Innovation Centre, Montréal, Canada) for F1 generation and in Perpignan University (platform "bio-environnement, " Perpignan, France) for the F2 generation on a MiSeq instrument (Illumina). The bioinformatic pipeline for barcoding data treatment is represented in Fig. 2  of the Additional file 1. Community analysis was performed on R software (R Core Team, 2013) using the phyloseq package  [42]. Rarefaction curves of species richness were produced using the rarefy-even-depth and ggrare functions  [42].
One-way ANOVA or nonparametric Kruskal-Wallis test (when the normality of residuals was rejected (Shapiro test)) was used to compare alpha diversity indices between conditions. When the ANOVA or Kruskal-Wallis tests were significant, we performed pairwise comparisons between group levels with the pairwise t-test or the Dunn test (post hoc analyses) using Bonferroni corrections for multiple testing. The significance threshold was set at 0. 05 for all analyses. Principal coordinate analyses (PCoA) were computed to represent dissimilarities between samples using the Bray-Curtis distance matrix (beta diversity). Multivariate analysis of variance was tested using 999 permutations (adonis2 and betadisper from vegan package  [43] ). To compare the proportions for each genus between ME and control seawater or between ME-exposed and control oysters at day 2, we used the table of sum of sequences.
Transcriptome analysis (RNA-seq) Samples used for RNA-Seq analysis are indicated in Table  2  of the Additional file 1. For each time point, 3 biological replicates were sequenced. RNA-Seq library construction and sequencing were performed at McGill University (Génome Québec Innovation Centre, Montréal, Canada) (http:// www. genom equeb ec. com). NEB mRNA-stranded libraries were constructed and sequenced on a Hiseq 4000 (Illumina), in paired-end reads of 2 × 100 bp. The bioinformatic pipeline for RNAseq data treatment is represented in Fig. 3  of the Additional file 1, and quality of the metrics is indicated in Additional file 2. Functional annotation and enrichment analysis were performed with RBGOA using an adaptive clustering and a rank-based statistical test (Mann-Whitney U-test) combined to the adaptive clustering  [46].
"-log(qval)" (obtained from the Deseq2 analysis) was used as input for the RBGOA analysis to represent repressed or induced genes in ME-exposed compared to control oysters. The R and Perl scripts used can be downloaded at https:// github. com/ z0on/ GO_ MWU  [47]. Significantly, enriched biological processes were expressed as a ratio between the number of genes differentially expressed divided by the total number of genes assigned to that biological process and was represented in heatmaps with MeV  [48]. Because not all known C. gigas antimicrobial peptides (AMPs) were present in the C. gigas reference genome (assembly version V. 9), read counts for all of the time points were specifically obtained by alignment against a protein database which contains the AMP sequences using DIAMOND 0. 7.
Genetic analysis gDNA shotgun library construction and Hi-seq sequencing (Illumina, paired-end reads of 150 bp) were done at McGill University (Génome Québec Innovation Centre). Bioinformatic pipelines used for genetic analysis are described in Fig. 4  of the Additional file 1. A pool of 30 oysters was used to generate the genetic data. Quality metrics are indicated in Additional file 2. Principal component analyses (PCA) were generated with R software (R Core Team, 2013) from the allele frequency matrix using R packages "dplyr"  [50] , "tidyr"  [51] , "ggplot2"  [52] , "RcolorBrewer"  [53] , and "mixOmics"  [54, 55]. Evidence for adaptive selection at each SNP was tested using the FLK statistic  [56] , using a modification of the hapFLK software  [57]  allowing to input allele frequencies instead of individual genotypes  [58].
DNA methylation analysis Bisulfite conversion, BS-seq paired-end library construction, and sequencing were performed at McGill University (Génome Québec Innovation Centre). Sequencing was performed on a HiseqX using 150 nucleotide pairedend reads. Quality metrics are indicated in Additional file 2. The bioinformatics pipeline for BS-seq analysis is represented in Fig. 5  of the Additional file 1 and was performed on the local Galaxy platform  [60]  (http:// bioin fo. univ-perp. fr). Differential methylation analyses were performed with DMRseq package  [61]. Since this software is generally applied to vertebrate DNA methylation, the parameters were optimized using the DMRsim package in order to optimize the detection of true positives in our dataset. The DMRsim package was used to simulate differential methylation analysis on 180 DMRs artificially generated out of a dataset containing 700,000 methylated CpG using a cutoff value of 0.
01. The best parameters (blocksize = TRUE, minnumregion = 3, deltamax = 0. 25, bpspan = 1000, mininspan = 10, maxgapssmooth = 2500, smooth = TRUE) allowed for detection of 50% of true positives with 0% of false positives for a p-val < 0. 05 in our dataset and were used for the differential methylation analysis. The R scripts used here can be downloaded at https:// github. com/ IHPE/ DMRseq_ wrapp er. Statistically significant differentially methylated regions (DMRs) were checked by visual inspection using the Integrative Genomics Viewer (https:// softw are. broad insti tute. org/ softw are/ igv/). DMRs genomic positions were intersected with the annotation of C. gigas genome version 9  [62]  to identify DMRs that occurred within genes (differentially methylated genes (DMGs)) and within promoters (differentially methylated promoters (DMPs)).
The +2 kb region upstream of the transcription start site was defined as the promoter position. DMGs were used for functional annotation and enrichment analysis with RBGOA. A binary analysis was applied: a 1 score and a 0 score were attributed to each statistically significant or not significant DMG respectively, whatever the sense of the change in methylation level. The R and Perl scripts used here can be downloaded at https:// github. com/ z0on/ GO_ MWU  [47]. The following parameters were used for the adaptive clustering: largest = 0. 2; smallest = 5; clusterCutHeight = 0. 25. Statistically significantly enriched biological processes were classified manually into larger biological functions. Biological processes were graphically represented using Multiple Experiment Viewer (MeV). The color intensity represents the ratio: number of genes differentially methylated divided by the total number of genes assigned to that biological process.
Mean value and standard deviation were calculated for these 5000 intersections and were compared to the value that was obtained from the real dataset. Standard deviation for the real dataset was assumed to be the same percentage as the one obtained on the shuffled data. Based on this mean value and standard deviation, a t-test testing the null hypothesis was performed, and the null hypothesis was rejected if the absolute value of the statistical test was greater than 3. 090, a critical value expected for a sample size above 100 (https:// www. itl. nist. gov/ div898/ handb ook/ eda/ secti on3/ eda36 72. htm).
No evidence for genetic selection as the mechanism of increased immune capacity We investigated if a genetic selection could have occurred through ME exposure and would have selected more resistant oysters based on specific allele associations. To this end, we evaluated genome-wide SNP allele frequencies in juvenile oyster samples using whole genome sequencing (WGS). Principal component analysis (PCA) of these data showed little genetic divergence between the ME-exposed vs. control oysters for the F1 and F2 generations (Fig. 6a ). Next, we conducted a genome scan comparing allele frequencies in ME-exposed vs. control oysters using the FLK test to interrogate any signals of positive selection. The FLK statistic considers genomewide allele frequency data in a set of populations and aims at detecting positions where genetic differentiation between these populations is higher than expected under neutral evolution.
6  of the Additional file 3). To gain deeper insights into the impact of the ME exposure on methylation patterns of oysters, we used DMR-Seq software  [61]  to identify differentially methylated regions (DMRs) between ME-exposed and control oysters for each generation. The differential methylation analysis led to the detection of 4325 and 5531 DMRs for larvae (day 10) of the F1 and F2 generation, respectively, and 4985 and 5207 for juveniles (day 120) of the F1 and F2 generation, respectively (Table  4  of the Additional file 3, Additional file 9). Hyper-methylated DMRs in ME-exposed compared to control oysters were more frequent than hypomethylated DMRs at day 10 of the F1 generation (57. 4% hyper-methylated vs. 42. 6% hypomethylated DMRs). However, hypo-methylated DMRs in ME-exposed oysters were more frequent at day 120 of F1 generation (40.
To test whether this number of meiotically heritable DMRs was higher than would be expected by chance, we randomly generated 5000 files containing artificial DMRs of identical size and number as the DMRs detected in the real dataset of the F1 generation and intersected these with the real dataset of the F2 DMRs. Mean values of the number of intersections between F2 DMRs and these randomized regions were always significantly lower than the number of intersections between F1 and F2 DMRs from the real dataset (Table  1  and Additional file 11), indicating that the similarity of DMRs in F1 and F2 did not occur by chance. Taken together, our epigenetic analysis shows that microbial exposure during larval stages impacts the DNA methylation pattern in both the directly exposed oysters as well as their offspring.
Acknowledgements We warmly thank the staff of the  Ifremer stations of Argenton and Bouin (Ifremer LPI-PFOM  and  SG2M  laboratories) for technical support in the animal experimentation and for the production of the two generations of oysters. We also thank  Camille Clerissi  and  Nelia Luviano  for fruitful discussions. The authors are grateful to  Jean-Francois Allienne  from the  Bioenvironment platform  for technical support in library preparation and sequencing. The present study was supported by the  ANR  project  DECIPHER  ( ANR-14-CE19-0023 ), by the chercheur d'avenir project  TRANSGIGAS (Region Languedoc-Roussillon ), by the  EU  funded project  VIVALDI  ( H2020 program , n°  678589 ), and by  Ifremer ,  CNRS ,  Université de Montpellier and Université de Perpignan Via Domitia , with the support of  LabEx CeMEB , an  ANR  " Investissements d'avenir" program  ( ANR-10-LABX-04-01 ) through the  Environmental Epigenomics platform.
Funding ANR project  DECIPHER  ( ANR-14-CE19-0023 ); the chercheur d'avenir project  TRANSGIGAS  ( Region Languedoc-Roussillon ); the  University of Perpignan Via Domitia ,  ED305 ; the  EU -funded project  VIVALDI  ( H2020 program , n° 678589);  LabEx "CeMEB"  ( ANR-10-LABX-04-01 ) through the  Environmental Epigenomics Platform; and LabEx "TULIP"  ( ANR-10-LABX-41 ).
Availability of data and materials The datasets generated, analyzed during the current study, and supporting the conclusions of this article (RNA-seq, WGBS, WGS, 16S barcoding data) are available at the following link: https:// datav iew. ncbi. nlm. nih. gov/ object/ PRJNA 609264? revie wer= hg0tt fia8i g7ae6 tbam1 ssapv8.
RNA-seq data processing and analysis Read quality control was performed using FastQC (version 0. 11. 7) (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Due to the good quality of the reads, no filtering or adapter removal was performed. The reads were then aligned to the human genome assembly GRCh38. p10 using HISAT2 (version 2. 0. 5) (Kim, Langmead & Salzberg, 2015). Read counting was performed in strand-specific mode by the htseq-count script from Python library HTSeq (version 0. 12. 4)  (Anders, Pyl & Huber, 2015)  using GENCODE v26 gene annotation (ALL). Genes with no counts for all samples, as well as highly expressed ribosomal genes, were filtered out, resulting in the expression set of 32,120 genes across 12 samples. Principal component analysis (PCA) was performed on the rlog-transformed count data, and the first two principal components were extracted with the corresponding percentage of explained variance.
Differential expression analysis was performed with the R package DESeq2 (version 1. 30. 1) (Love, Huber & Anders, 2014). We declared the gene to be differentially expressed if padj (p value adjusted by the Benjamini-Hochberg procedure) was smaller than 0. 05 and the fold change was larger than 1. 5 in any direction (Table  S1 ). The statistical power for protein-coding genes (with median 80 aligned reads) was calculated with R package RNASeqPower (version 1. 30. 0) and was 0. 91. Each group of samples consisted of three biological replicates. Overrepresentation analysis (ORA) and gene set enrichment analysis (GSEA)  (Subramanian et al. , 2005)  were performed with the R package clusterProfiler (version 3. 18. 1) (Yu et al. , 2012). GSEA was performed on a preranked list of genes ordered by the stat column of DESeq2 results.
The KEGG (release 99)  (Kanehisa & Goto, 2000)  and GO biological process  (Ashburner et al. , 2000; The Gene Ontology Consortium, 2017)  databases were used as the sources of gene sets for ORA and GSEA. GO annotation was obtained from the R package org. Hs. eg. db (version 3. 12. 0) (Marc Carlson, 2020 ). An adjusted p value cutoff of 0. 05 was used to select statistically significant categories. REVIGO  (Supek et al. , 2011)  with a cutoff parameter of 0. 4 was used to remove redundant GO terms. To assess the possible additional activation of EBV genes due to Tat protein expression in RPMI Tat cells, the same pipeline for differential expression analyses was used. The EBV gene annotation file in GTF format was obtained from GenBank file NC_007605. 1 using a custom Python script. Raw reads were aligned to combined human and viral genomes.
HIV Tat protein affects the gene expression profile of RPMI 8866 cells To determine the genes regulated by HIV-1 Tat, total RNA from RPMI 8866, RPMI EGFP , RPMI Tat and RPMI Cys cell lines was collected, and RNA-seq was performed. Three biological replicates were sequenced for each cell line. From 16 to 24 million 51 nt sequencing reads were generated for the RPMI Tat , RPMI Cys , RPMI EGFP and RPMI samples. On average, 67. 17% of reads in every sample were uniquely aligned to the reference genome GRCh38, and 78. 93% of them were nonambiguously mapped to the GENCODE gene annotation (Fig. S2 ). To assess variability between replicates and between cell lines, rlog-transformed filtered count data were visualized by principal component analysis (Fig. S3A ). Biological replicates proved to be highly alike by clustering tightly according to sample type.
RPMI 8866 cells express EBNA1, -2, -3A, -3B, -3C, and LMP-1, -2A, and -2B proteins and several noncoding RNAs. To explore the possibility that changes in gene expression were a result of Tat-induced changes in the expression of EBV genes, we performed differential gene expression analysis of the EBV transcriptome and found no evidence of Tat or TatC22G protein impact on the expression of viral genes in RPMI Tat or RPMI Cys cells (Fig. S4 ); thus we concluded that we observed a direct effect of Tat or TatC22G on the host cell. Next, we performed gene set enrichment analysis (GSEA) and overrepresentation analysis (ORA) to search for activated or suppressed functional gene groups and molecular pathways as defined by the GO Biological Process (GO BP) and KEGG databases. The analysis of modified biological processes (GO BP) was performed by GSEA with subsequent removal of redundant GO terms with REVIGO (Fig.
adjust = 8. 4 × 10 -3 )). We additionally analyzed changes in signaling pathways, for which overrepresentation analysis (ORA) using KEGG databases was performed (Fig. 3 ). Analysis of up-and downregulated DEGs revealed activation of innate immunity pathways, such as Toll-like receptor signaling pathways (hsa04620; p. adjust = 3. 1 × 10 -2 ) and NOD-like receptor signaling pathways (hsa04621; p. adjust = 3. 1 × 10 -2 ), as well as the Influenza A (hsa05164; p. adjust = 3. 1 × 10 -2 ) pathway, which was highly overlapping with the former two. Herpes simplex virus 1 infection pathway (hsa05168; p. adjust = 3. 1 × 10 -2 ) seems to be related to the antiviral response (Fig. 3A ). Several zinc finger proteins (ZNFs) were annotated as related to this pathway. Notably, several genes of the JAK-STAT signaling pathway (JAK1 and STAT1) were general for all detected pathways (Fig.
Both Tat and TatC22G proteins modulate the expression of similar and overlapping sets of genes (Fig. 4A ). Comparison of the transcriptome profiles of the RPMI Tat and RPMI Cys cell lines revealed 201 protein-coding DEGs (p. adj < 0. 05; fold change >1. 5 in any direction). Overrepresentation analysis against the KEGG database also demonstrated little gene set enrichment, reflecting relatively small differences between RPMI Cys and RPMI Tat cells (Fig. 4B ). Notably, the Cytokine-cytokine receptor interaction pathway (hsa04060) downregulated by HIV-1 Tat was further suppressed by mutant TatC22G protein in RPMI Cys cells compared to RPMI Tat cells. ex vivo experimental model, in which lymphocytes isolated from the blood of healthy donors were incubated in the presence of Tat protein (for example,  (Germini et al.
ACKNOWLEDGEMENTS We are grateful to  A. V. Lazarev  for technical support. The following reagent was obtained through the  NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH :  HIV-1 HXB2   GST-Tat Expression Vector  ( GST-Tat 1 (86R )) from  Dr. Andrew Rice. This work was carried out within the framework of the  International Research Network (IRN)   ONCO3D  and the  IDB RAS Government basic research program  ( 0088-2021-0007 ). The flow cytometry facility became available in the framework of the  Moscow State University Development Program.
While the study did not collect HRQoL according to validated scales, this large set of data nonetheless offered a unique opportunity to assess the impact of diagnosis delay and disease activity upon the daily burden of the disease. The LWL2020 survey highlighted that despite much progress in technology such as remote consultation, establishment and dissemination of new classification criteria, the median diagnosis delay is of 2 years and was greater than 5 years in approximately one third of patients. This is obviously a key concern for people living with lupus, because on one hand the pre-diagnosis period is known to be prone to high anxiety and difficulty to live with a series of unexplained symptoms, and on the other hand because of the concern that late means late start of treatments, potentially leading to higher disease burden on daily life.