dataesr/scientific-paragraphs-categorization huggingface dataset:
ChIP was performed on native material (without crosslinking) using antibodies targeting histone modifications H3K4me2 (Merck ref. 07-030), H3K9me3 (Active Motif, Carlsbad, CA, USA; ref. 39161), or H3K27me3 (Active Motif, Carlsbad, CA, USA; ref. 39155). Three different ChIPs (i. e. three biological replicates) were performed for each of the histone modifications. Libraries were prepared from all biological replicates, individually, according to the Illumina TruSeq protocol "Ultra Low Input DNA library". Libraries were sequenced with an Illumina HiSeq 2000 genome analyzer at the Max Planck Genome centre Cologne, Germany (https://mpgc. mpipz. mpg. de/home/). Sequencing data are available under the GEO accession number GSE150127. 6.
Each WP has a list of tasks to fulfil within the initial 5-year timeframe (2019-2023) (see Table  2 ). The specific objectives of each WP and their respective tasks are detailed below in the next chapters. The project also builds upon the experience and outcomes of previous and current Europe-sponsored projects including: -I-MOVE (Influenza Monitoring Vaccine Effectiveness)  [17]  and I-MOVE+ (Integrated Monitoring of Vaccines)  [18] : the data collection of WP1 in retrospective databases in specific regions on influenza disease will be rechecked for validity with I-MOVE data. -ADVANCE (Accelerated Development of VAccine beNefit-risk Collaboration in Europe)  [19] : this program allows access to many different databases in Europe that can be consulted to test the research protocol developed in WP1.
WP1: burden of infectious diseases in ageing adults The aim of WP1 is to evaluate the total burden of infectious diseases in the 50 + age group in different European countries. The selection of that age group has been motivated by the need for understanding the progression of the infection problem with age. WP1 will make use of established datasets addressing the challenges of measuring the burden of vaccine-preventable [VP] infections, such as influenza, pneumococcal disease, zoster, pertussis, tetanus, for example, and potentially vaccine-preventable [PVP] infections, such as respiratory syncytial virus (RSV), norovirus, extra-intestinal pathogenic Escherichia Coli (ExPEC), and some others. WP1 tasks consist of reviewing methods and data sources that are available in the EU and to define risk factors on selected infectious diseases in ageing adults (pneumonia caused by Streptococcus pneumoniae, RSV, norovirus, ExPEC, Staphylococcus aureus) through the identification of gaps.
This will help to develop a protocol for collecting pro-and retrospective data on the burden of disease (BoD), risk factors, and co-morbidities on VP and PVP infectious diseases in specific areas. The protocols will be tested in two pilot regions of Valencia (Spain) and Denmark. The next task will be to transfer the protocols to other regions with local adjustments (Hungary, Italy, the Netherlands, Norway, and Serbia). This should allow us to build and consolidate a database on the BoD in ageing adults in Europe. With the database, it will be possible to identify potential risk factors and the development of an epidemiologic immuno-senescence score among ageing adults to help identify individuals who manifest regular infections. The data findings of WP1 will feed the research activities of WP3 and WP4, as seen in Fig.
VITAL should enable the development of models for reporting accurate estimations of the benefits to society with respect to lowered costs (spending and taxation), health, medical access, healthcare equity improvement, and better societal functioning of ageing adults. These models will serve to develop an optimal public health policy and to monitor its effects over time as the VITAL-database records the impact of new strategies. In addition, new knowledge on immune signatures could lead to novel strategies for developing improved vaccines. Through the information obtained from specific subgroups and models inferring the consequences of vaccination of specific target groups, VITAL will provide a balanced view on cost and health impact. Clear and favourable cost-effectiveness results for vaccination will enhance the impact of such interventions, increasing their affordability, and sustainability.
Plant Material RNA sequencing data were obtained from the progeny and the parents of a cross between a male and a female individual of the dioecious plant C. grandis, both grown in the experimental fields of IISER Pune, India. Seeds from the cross were collected as soon as the fruit matured. The 24 seedlings raised from these seeds took four to seven months to begin flowering, which allowed sexing of the individuals. Flower buds at early developmental stages 3-4 (defined in  [56] ) were sampled from plants being grown in the experimental fields. RNAs were isolated from 5 males (sons) and 5 females (daughters) from the F1 generation as well as from their parents.
RNA Sequencing The flower buds were sent to IPS2 Paris, France using RNA later ICE kits by Thermo Fisher. Total RNA was extracted from 12 flower bud samples using Agilent's spin column purification method, mRNA was isolated with Oligo-dT Beads from NEB, and RNA-seq libraries were constructed with the Directional Kit from NEB. Sequencing was performed at IPS2 Paris, France, with Illumina NextSeq500 following a paired-end protocol of library preparation (fragment lengths 100-150 bp, 75 bp sequenced from each end). RNA samples were checked for quality, individually tagged, and sequenced (see Supplementary Table  S1  for library sizes). The sequence data are available in the ENA database under the study accession number PRJEB39318.
De Novo Transcriptome Assembly A reference transcriptome was built for C. grandis using Trinity  [57]  on the combined libraries of the 12 individuals (the parents and their ten offspring). For each individual, 100% identical reads, assumed to be PCR duplicates, were filtered out using the ConDeTri v2. 3 trimming software  [58]. Reads were then filtered out for sequencing adapters and low quality using ea-utils FASTQ processing utilities v1. 04. 636  [59]. Cleaned reads from all male and female individuals were combined and assembled with Trinity version 2. 4. 0 with default settings  [57]  leading to 276,225 contigs. Poly-A tails were removed from contigs using PRINSEQ v0. 20. 4  [60]  with parameters -trim_tail_left 5 -trim_tail_right 5. rRNA-like sequences were removed using riboPicker version 0.
4. 3  [61]  with parameters-i 90 -c 50 -l 50 and the following databases: SILVA Large subunit reference database, SILVA Small subunit reference database, the GreenGenes database, and the Rfam database. To ensure that X and Y gametologs are assembled in consensus contigs (required for the SEX-DETector analysis,  [50] ), Trinity components were merged using Cap3  [62] , with parameter -p 90 and custom perl scripts. Coding sequences were predicted using Trinity TransDecoder version 3. 0. 1  [57]  and including Pfam domain searches as ORF retention criteria. This assembly included 128,904 ORFs. To avoid mapping X and Y reads on separate contigs of the same gene, we chose to work on the longest ORF predicted per Trinity isoforms, which resulted in a final set of 82,699 contigs (see Table  1 ). BUSCO v3.
0. 2 (Benchmarking Universal Single-Copy Orthologs) was used to assess the completeness of our transcriptome according to conserved gene content from the Plant Dataset  [63]. Results are shown for full assembly with all ORFs and for the longest ORF per Trinity isoforms, hereafter referred to as our reference transcriptome (128,904 and 82,699 contigs respectively) in Supplementary Table  S2.
Inferring Sex-Linked Contigs The raw Illumina reads were mapped on the reference transcriptome using BWA mapping  [65]  version 0. 7. 15 with the following commands: bwa aln -n 5 and bwa sampe. Mapping statistics are shown in Supplementary Table  S3. Mapped reads were kept with Samtools version 1. 3. 1 and individual genotypes were predicted with reads2snps version 2. 0. 64 with paralog detection  [66, 67] , option -aeb which allows alleles to have different expression levels, and -par 0 to avoid removal of paralogous positions by the paraclean program, which tends to filter out X/Y SNPs. SEX-DETector version 1. 0 (3rd September 2017) was used to infer contig segregation types using a stochastic expectation maximization (SEM) algorithm  [50]. The detected SNPs were filtered using Perl scripts to retrieve the autosomal or sex-linked SNPs, when their posterior probability to be either autosomal or sex-linked was higher than 0.
Correcting Mapping Bias To avoid biases towards the reference allele in expression level estimates, a second mapping was done with GSNAP  [68] , a SNP-tolerant mapping software (see Supplementary Table  S3 ). A SNP file of X/Y SNPs identified in the first run of SEX-DETector was produced with home-made perl scripts as described in  [17]. Raw Illumina reads were mapped with GSNAP version 2017-11-15 and parameters -m 10 and -N 1. Only uniquely mapped and concordant paired reads were kept for expression analysis. SEX-DETector was run a second time on this new mapping, and the new inferences were used afterwards for all analyses. No significant difference in the number of sex-linked inferences was observed: 1196 X/Y and 168 X-hemizygous contigs were found as shown in Table  2.
Estimating the Age of Sex Chromosomes The X and Y ORF sequences were produced by SEX-DETector using only X/Y segregating SNPs, and pairwise dS was estimated by the codeml program implemented in the PAML suite  [69]  version 4. 8 (see Supplementary Figure  S1 ). The age of the sex chromosomes can be obtained from the X/Y gene pairs with the highest synonymous divergence (the first to stop recombining,  [3, 4] ). To get age estimates in millions years, we used three Brassicaceae molecular clocks: 1. 5 × 10 -8 substitution/synonymous site/year, derived from an assumed divergence time of Barbarea and Cardamine of 6. 0 Myr [70], 7. 1 × 10 -9 substitutions/site/generation based on spontaneous mutations in Arabidopsis thaliana  [71] , and 4 × 10 -9 substitutions/synonymous site/year, derived from a phylogeny calibrated with six Brassicales fossils  [72].
Identifying Contigs with Sex-Biased Expression We used the R packages DESeq2  [75]  version 1. 18. 1, edgeR  [76, 77]  version 3. 20. 8, and Limma-Voom  [78]  version 3. 34. 8 in R version  3. 4. 3 (2017-11-30)  to perform biased gene expression analysis between males and females. DGE analysis was performed on the raw read counts (untransformed, not normalized for sequencing depth). Contigs with a CPM (count per million) lower than 0. 5 (corresponding to a count of 10) were filtered out. Regularized log-transformation of the DESeq2 package was used to reduce variance of low read counts. Normalization with edgeR was made with a tagwise dispersion and GLM normalization method (calcNormFactors, estimateTagwiseDisp, and glmLRT functions). With LimmaVoom, counts were fitted to a linear model and differential expression was computed by empirical Bayes (lmFit and eBayes functions).
To compare these correlation coefficients between control and arthritic rats, they were Fisher-transformed to normalize their distributions. In cases of normality, Welch's test was performed; otherwise, the non-parametric Mann-Whitney test was used. Benjamini-Hochberg's correction for multiple comparisons was performed for each plane dataset, and a false discovery rate of 0. 05 was adopted. Seed ROI regions were chosen based on a hypothesis-driven approach. Correlation matrix analysis. For each acquisition, a correlation matrix was constructed as previously described  6  , based on the rat Paxinos atlas  35  at Bregma -0. 6 mm and using the filtered and normalized signals of the seed-based analysis. Correlation matrices were constructed for all rats of the control group and all rats of the arthritic group.
Correlation matrices of FC alterations and behavior. To investigate a possible link between the development of FC alterations and chronic pain behavior, we correlated FC alterations (as characterized by the seed-based, correlation matrix and FC dynamics) with behavior. With the seed-based analysis, we constructed a vector containing the correlation coefficients of the significantly altered ROI pairs and the behavioral data for each rat. As described previously  6  , we correlated each element of the vector with all the other elements. The resulting correlation matrix can be decomposed in three parts: first, the top left is the correlation of the seed-based dataset with itself; second, the top right is the correlation of the data with behavior; third and last, the bottom left is the correlation of the behavioral dataset with itself.
ROC curves. Using the results of the seed-based analysis, we were able to plot sensitivity-specificity ROC curves using MedCalc Statistical Software version 16. 4. 3 (MedCalc Software bvba, Ostend, Belgium; https://www. medcalc. org; 2016). A ROC curve represents the true positive rate, or sensitivity, as a function of the false positive rate. In order to assess the corresponding diagnosis power, ROC curves were plotted using a logistic model output containing the correlation values of the seed-based analysis, with an increasing number of ROI pairs included in the calculation. The first ROC curve enabled the determination of the correlation threshold, using the first cohort of arthritic rats (N = 9) and a subset (N = 5) of the control animals ("Test cohort"). A second cohort of independent animals, the "Validation cohort" consisting of (N = 8) arthritic rats and another subset of (N = 4) control animals was used to backcross the previous findings.
Using the threshold defined above (using the "Test cohort"), the sensitivity and specificity were measured using seed-based data from animals of the "Validation cohort". Data and code availability statement. All data supporting the findings of this study associated with figures are available. These data can be downloaded in the supplementary materials of the publication on the repository website 'Zenodo' under the name 'Dataset Raw data of the article 'Ultrafast ultrasound imaging pattern analysis reveals distinctive dynamic brain states and potent sub-network alterations in arthritic animals", (DOI 10. 5072/zenodo. 520533).
Acknowledgements The authors wish to thank the  CNRS, INSERM  and  ESPCI  for their financial support. This work was supported by the the  European Research Council (ERC)  Advanced Grant FUSIMAGINE. Line Rahal was supported by a fellowship from the  'Fondation NRJ'-Institut de France  award granted to  Mickael Tanter. Finally , this work was supported by the  Inserm ART (Technology Research Accelerator) in "Biomedical Ultrasound ".
The number of active operating theatres was reduced from 20 to 5. Nevertheless, proper patient selection is mandatory to avoid any loss of chance among patients with life-threatening conditions. The retained surgical indications were surgical emergencies, oncologic surgery, and vital organ transplantation. One major challenge is to reorganize the hospital system to limit the risk of exposure to Sars-COV2 in surgical patients more prone to develop serious forms of Covid-19  [3]  while keeping enough acute care resources for managing both COVID+ and COVID-. Our institution combines 2 specificities: firstly, it consists of one of the leading infectious disease departments in France and Europe, and https://doi. org/10. 1016/j. ijsu. 2020. 07. 023 Received 8 May 2020; Received in revised form 6 July 2020; Accepted 7 July 2020 the principal investigator of the large-scale European DISCOVERY study (NCT04315948) belongs to the institution.
Surgical departments Data from patients undergoing surgery were collected during the first 4 weeks (23rd March to 19th April) after the opening of the dedicated units and diffusion of the institutional protocols for intrahospital screening. Each surgical procedure has been classified according to the POSSUM scale, which has been adapted to head and neck procedures  [17, 18]. Briefly, this classification makes it possible to classify the interventions from minor to major plus according to the degree of severity and surgical technicality. The total number of patients operated on was 112 patients for the three specialties described in this article (Table  1 ). Among the patients.
Cell culture and Virus preparation Vero E6 (ATCC, CLR-1586) cells were cultured at 37°C in 9% CO2 in Dulbecco's modified Eagle's medium (DMEM, Gibco™, ThemoFisher) supplemented with 5% fetal calf serum (FCS) and 0. 5% penicillinstreptomycin. Cells were passaged by trypsinization every 2 days. The SARS-CoV-2 strains 2019-nCoV/Italy-INMI1 (Genbank MT066156) was provided by the Lazzaro Spallanzani National Institute of Infectious Diseases (Rome, Italy) via the EVAg network (European Virus Archive goes global). SARS-CoV-2 stocks used in the experiments had undergone two passages on Vero E6 cells and were stored at -80°C. Virus titer was determined by standard plaque assay (1x10 7 pfu/ml).
Quantification of viral RNA by qRT-PCR SARS-CoV-2 RNA from cell culture supernatant samples and supernatant plus cells was isolated using the NucleoSpin RNA Virus, Mini kit for viral RNA from cell-free fluids (Macherey Nagel) according to the manufacturer's instructions. RNA was subjected to qRT-PCR analysis using the SuperScript III Platinum One-Step qRT-PCR Kit (ThermoFisher) and a CFX96 Touch Real-Time PCR Detection System Thermal Cycler (BioRad). Primers targeting IP2 and IP4 (RdRp) were as recommended  [14]  Standard curves were created using in vitro transcribed RNA derived from strain BetaCoV_Wuhan_WIV04_2019 (EPI_ISL_402124). The transcript contains the amplification regions of the RdRp and E gene as positive strand. Each microtube contains 1011 copies of target sequences diluted in presence of yeast tRNAs, and lyophilised.
Data analysis Principal component analysis was done as previously described  [18]. Co-expression cluster analysis was obtained using the Bioconductor R package coseq v1. 5. 2  [19]. The protein abundance matrix was used as an input in coseqR. Log CLR-transformation was applied to the matrix to normalize the abundance of proteins and the K-means algorithm was chosen to detect the co-expressed clusters across the different time points. The K-mean algorithm was repeated 20 times in order to determine the optimal number of clusters. The resulting number of clusters in each run was recorded, and the most parsimonious cluster partition was selected using the slope heuristics approach. Both the PCA and the coseq analysis were performed after removal of proteins with spectral counts lower than three (1402 host protein groups retained).
Acknowledgements The authors are indebted to  Dr Silvia Meschi  ( National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, via Portuense 292, 00149 Rome, Italia ) for making the Human 2019-nCoV strain 2019-nCoV/Italy-INMI1 ( 008N-03893 ) available. The authors thank all their colleagues from  Li2D (CEA)  for strong support, as well as all those who make the experimental work possible while facing the Covid19 pandemic and lockdown.
Funding This work was funded in part by the  French Alternative Energies and Atomic Energy Commission (CEA) , the  French joint ministerial program  of R&D against CBRNE threats, and the  ANR program  " Phylopeptidomics " ( ANR-17-CE18-0023-01 ). This publication was supported by the  European Virus Archive goes Global (EVAg)  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  N°653316.
sequences; 10,847,418 residues) containing the UniProt Chlorocebus sequences (downloaded March 2020) and the Italy-INMI1 SARS-CoV-2 protein sequences was queried after a first analysis to remove contaminant spectra against an in-house 'common contaminants' database (384 sequences; 187,250 residues) encompassing 361 contaminants classically observed in proteomics (cRAP + additional contaminants) and 23 Bos taurus sequences corresponding to the most abundant proteins from foetal calf serum present in the cell culture medium [16]. Peptide-to-MS/MS spectrum assignation was done with the following parameters: full trypsin specificity, maximum of two missed cleavages, mass tolerances on the parent ion of 5 ppm and 0. 02 Da on the MS/MS, static modification of carbamidomethylated cysteine (+57. 0215), and oxidized methionine (+15.
Methods To identify suitable studies, MEDLINE, Embase, and PsycINFO and preexisting study consortia were searched from inception to December 2017. Authors of prospective longitudinal human studies or trials of antihypertensives were contacted for data sharing and collaboration. Outcome measures were incident dementia or incident cognitive decline (classified using the reliable change index method). Data were separated into mid and late-life (>65 years) and each antihypertensive class was compared to no treatment and to treatment with other antihypertensives. Meta-analysis was used to synthesize data.
Data sources and searches To identify studies for inclusion in this systematic review and meta-analysis, the databases MEDLINE, MEDLINE In-Process, Embase, and PsycINFO were searched from inception to December 2017. The search terms used were (dementia or cognit* or mild cognitive impairment or Alzheimer disease or dementia vascular or dementia multi-infarct) and (antihypertensives or antihypertensive agents or diuretic or diuretics or thiazide or thiazide-like or calcium channel blocker or calcium channel blockers or calcium antagonist or angiotensin-converting enzyme inhibitor or angiotensinconverting enzyme inhibitors or ACE inhibitors or angiotensin receptor blocker or angiotensin receptor blockers or ARB or β-blocker or adrenergic β-antagonist). Details of the search strategy are given in appendix e-A (doi.
org/10. 5061/dryad. t9n4n3p). Reference lists and lists of studies contained within established study consortia relating to cognitive outcomes were screened for potentially relevant published articles and studies. Experts in the field were also consulted and searches were carried out for relevant trials using the following sources: The lead reviewer (R. P. ) carried out the literature searches. All identified abstracts, or titles where abstracts were unavailable, were double-read and a list of potentially relevant evidence compiled independently by each of the 2 reviewers (R. P. , J. P. ). The lists were compared with differences resolved by discussion. Once the list of possible publications was agreed upon, full texts of relevant documents were independently read and assessed for relevance.
To minimize the effect of publication bias, a list of potentially eligible studies was also compiled by examining those included in preexisting consortia, i. e. , collaborative groups of longitudinal studies with a focus on cognitive outcomes. Publications, protocols, and web information were searched for each of the studies in the consortia to identify whether they might have suitable data for inclusion. The lead or corresponding author from each publication/study was then contacted and asked to provide raw data or aggregate data summaries, using a standard template, for use in a study-level meta-analysis. AHM classes were defined as calcium channel blockers (CCB), angiotensin-converting enzyme inhibitors (ACE-I), diuretics, β-blockers (BB), and angiotensin receptor blockers (ARB).