dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources and definitions Every month, Santé publique France (SpF) and the National Reference Center (NRC) of whooping cough and other Bordetella infections collect data on pertussis cases in the general population from the two main outpatient laboratories (Cerba and Eurofins-Biomnis) (Appendix, Text S1). We analysed laboratory results from nasopharyngeal swabs that were collected from symptomatic outpatients suspected of whooping cough between January 1, 2012 and December 31, 2019 in France, and tested for Bordetella spp (B. bronchiseptica or B. pertussis or B. holmesii) using PCR targeting insertion sequences IS481. A pertussis confirmed case was defined as a patient with a positive PCR result. We restricted our analysis to the 2-20-year-old population. The lower limit of 2 years old was chosen to only include children who were expected to have completed their initial immunization series (primary vaccination and first booster, Appendix, Text S2).
Statistical analyses of serological data The levels of antibody titres by age were compared between 2-5-year-old children born before February 1st, 2013 (supposed to be vaccinated with the 2/3/4+16 schedule) and 2-5-year-old children born after February 2013 (supposed to be vaccinated with the 2/4+11 schedule). Results are presented as geometric mean concentrations with 95% CI. We used independent samples t tests for comparisons between groups for log10-transformed antibody concentrations. Results with p-values <0. 05 were considered significant.
Sensitivity analyses The estimates found in sensitivity analyses differed slightly from the estimates found in the baseline analysis (best model), but confirmed our main findings. All model variants favored a higher risk of pertussis with the new vaccine schedule than with the former vaccine schedule, explained by a faster decay of the waning function (Table  S3  and Figure  S5 ). Among the differences observed, the decay rate of the former schedule was slightly higher than in the baseline analysis when only Biomnis laboratory data were analysed, when age was not included in the model, or when a different age-group definition was used. Among the six sensitivity analyses that we performed, the decay rate of the new schedule was slightly lower in five of them, and similar to the baseline analysis when negative samples were added in the model.
The waning function   is a proxy of waning immunity and is meant to represent the mean effect of waning immunity on the number of pertussis cases, in the whole cohort of children of a given age a in year y. First, we built a dataset of children born between February 1991 and December 2016 (i. e. the oldest individuals were aged 20 years in January 2012 and the youngest ones were aged 2 years in December 2018). We followed these cohorts of children at monthly time step from 2 to 20 years old, until December 2018. For each individual i of age a and each month of the study period, we computed the expected time elapsed since the last recommended vaccination (difference between age a and age of the last recommended vaccination,   ). We then defined a waning function   () of an individual i as a negative exponential function, decaying with the expected time  , since last vaccination: 𝑤 𝑖 (𝑎) = 𝑀𝑒 -𝜆𝑡 𝑖,𝑎 = 𝑀𝑒 -𝜆(𝑎-𝑎 𝑣𝑎𝑐𝑐 ) where  is the decay rate (in years -1 ) and  corresponds to the maximum of the function when  = 0 ( lies in the interval [0-1]).
Acknowledgements We thank  Martin Chalumeau  ( CRESS, INSERM, Paris, France ), for his contributions to the serological study. We thank  Laure Fonteneau ,  Yann Savitch  and  Julien Durand  ( Santé publique France ) for contributing to data analysis, and  Nicole Guiso  ( Institut Pasteur ) and  Matthieu Domenech de Cellès  ( Max Planck Institute for Infection Biology ) for useful discussions.
Fundings This project was funded by the  INCEPTION interdisciplinary project at Institut Pasteur , " Understanding whooping cough resurgence in Europe by combining genomic, epidemiological and sociological approaches " ( French Government Investissements d'Avenir  grant  ANR-16-CONV-0005 ). This work also received financial support from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases ' (grant n_ANR-10-LABX-62-IBEID). Institutional support was provided by  Institut Pasteur and Santé publique France.
Key exclusion criteria were uveal melanoma and active intracranial disease. Additional inclusion and exclusion criteria are summarized in the TICIMEL protocol p22. All patients provided written, informed consent before study entry. The trial protocol is available online and was reviewed and approved by the French committee for the protection of persons and the French drug agency (Agence Nationale de S ecurit e du M edicament; date of approval August 4, 2017 EUDRACT 2016-005139-34). All study procedures were carried out in accordance to the International Council for Harmonization tripartite guideline on good clinical practice (Helsinki declaration) and approved by the French committee for the protection of persons (Comit e de protection des personnes Nord Ouest III). An independent data monitoring committee (IDMC) monitored and evaluated data from the study.
Ancillary analyses Flow cytometry on fresh blood: A total of 200 mL of fresh blood (collected on EDTA at week 0 and week 6) were incubated with antibodies directed against markers of T-cell maturation as well as markers allowing for the identification of Th-related subpopulations for 30 minutes (see Supplementary Table  S12 ). Red blood cells were then lysed using the BD Pharm Lyse 10Â buffer (BD Biosciences). Remaining immune cells were stained for 30 minutes in a PBS solution containing a fixable viability dye (Zombie NIR, BioLegend) and fixed for 10 minutes in a 2% formal saline solution. Samples were acquired using an LSR-Fortessa X-20 cytometer (BD Biosciences). Data have been analyzed using the FlowJo 10 software. For one patient from the infliximab cohort, staining for the T-cell maturation panel had to be made on peripheral blood mononuclear cells (PBMCs) for the W6 time point.
The certolizumab cohort displayed more adverse events and better response rates than the infliximab cohort. Our study lays the ground for further trials aiming at assessing the clinical efficacy of the combination. permeabilization buffer" (eBioscience, Thermo Fisher Scientific) for 30 minutes at 4 C. Cells were then washed twice in 1Â permeabilization buffer (eBioscience, Thermo Fisher Scientific) and incubated for 30 minutes at 4 C with antibodies directed against human Ki67. For the analysis of monocytes, following antibody staining, cells were washed and fixed for 10 minutes in 2% formal saline solution before acquisition. Samples were acquired using an LSR-Fortessa X-20 cytometer (BD Biosciences). Data have been analysed using the FlowJo 10 software. Plasma cytokine assessment: Plasmas were prepared within 1 hour following collection of patients' blood and frozen at À80 C.
Cells were then washed twice in 1Â permeabilization buffer (eBioscience, Thermo Fisher Scientific) and incubated for 30 minutes at 4 C with antibodies directed against human IFNg and TNF diluted in 1Â permeabilization buffer (see Supplementary Table  S12 ). Samples were acquired using an LSR-Fortessa X-20 cytometer (BD Biosciences). Data have been analyzed using the FlowJo 10 software.
Role of the funding source Claudius Regaud Institute (ICR) and Cancer Research Center of Toulouse (CRCT) designed the TICIMEL clinical trial. ICR and CRCT wrote the TICIMEL protocol, which has been reviewed and approved by BMS. BMS has funded the clinical trial and part of the ancillary study. Canc eropôle Grand Sud-Ouest (GSO), IUCT-O, and Association pour la Recherche sur le Cancer (ARC) have funded part of the ancillary study. Fondation Toulouse Cancer Sant e (FTCS) has funded the salary support of M. Virazels and S. Brayer. Fondation pour la Recherche M edicale (FRM) has funded the salary support of J. Milh es. Collection, analysis, and interpretation of data have been jointly assessed by ICR and CRCT. Decision to submit the paper for publication has been jointly taken by ICR, CRCT, and the co-senior authors.
Acknowledgments We thank the patients who participated in this study; the clinical research staff, including all subinvestigators, nurses, clinical research associates, data managers, and secretaries. We thank  Bristol-Myers Squibb (BMS)  for funding the clinical trial and part of the ancillary study. We thank  Fondation ARC pour la recherche sur le cancer, Canc eropôle Grand Sud-Ouest (GSO) , and  Institut Universitaire du cancer Toulouse-Oncopole (IUCT-O)  for funding part of the ancillary study. We thank  Fondation Toulouse Cancer Sant e (FTCS)  and  Fondation pour la Recherche M edicale (FRM)  for covering the salary supports of  M. Virazels ,  S. Brayer  and  J. Milh  es. We thank the  IDMC  for critical analyses of the results and recommendations. We thank the D el egation a la recherche clinique et a l'innovation (DRCI) du  CHU de Toulouse  and the technology cluster of the CRCT.
Statistical analysis The number of subjects needed was estimated at 42 patients in each group for an risk 0. 05 under the unilateral assumption that MRI abnormalities should be more frequent in the NR-axSpA group. The interobserver reproducibility (Cohen's Kappa coefficient/ICC) of lesions observed on MRI was calculated for both readers (0. 81 -1. 00: excellent; 0. 61 -0. 80: good; 0. 41 -0. 60: moderate)  (15). Demographic, clinical, biological and MRI data were described in terms of percentages and numbers (qualitative) or means and standard deviations (quantitative). Independent quantitative variables were compared between the NR-axSpA group and the "other diagnoses" group using a Student's T-test, qualitative variables were compared using a Fischer's exact test, and qualitative variables were ordered using a Mann-Whitney test.
Data collection For each patient, epidemiological, demographic, clinical, biological, radiological, treatment and outcome data were collected from medical records. Comorbidities were collected systematically and are summarized in Table 1  (Du et al. , 2020). The Charlson Comorbidity Index, with and without age, was also assessed. Data on clinical signs at hospital admission were collected systematically and are summarized in Table  2. The following biological data were collected at hospital admission: neutrophils, lymphocytes, fibrinogen, C-reactive protein, albumin, pre-albumin, creatinine, glomerular filtration rate, urea and alanine aminotransferase. Radiological data [computer tomography (CT) scan] were classified based on severity, as follows: non-evocative, 0%; minimal, 1-15%; moderate, 15-25%; extensive, 25-50%; severe, 50-75%; and critical, >75%.
For each patient, three clinical scores were calculated: the National Early Warning Score 2 (NEWS2), the Quick Sepsis-related Organ Failure Assessment (qSOFA) score  (Singer et al. , 2016; Goulden et al. , 2018) , and the CRB65 (confusion, respiratory rate, blood pressure, 65 years old) score  (Brabrand and Henriksen, 2018). The three scores were defined at different times following hospital admission (when possible): Day (D) 0 (D0), D7, D10, D14, D21 and D28. For antibiotic therapy, data on firstand second-line (if any) treatments were collected systematically for all patients. Antibiotics were classified as: amoxicillin, amoxicillin + clavulanate, third-generation cephalosporin, third-generation cephalosporin + macrolide, piperacillin + tazobactam, and other. Patients who were not prescribed any antibiotics throughout their hospital stay were classified as receiving no antibiotic therapy.
When biological data were missing, data imputation using the multiple imputation method was performed to estimate the propensity score. Next, Cox regression was undertaken using complete data weighted by 1propensity score (a patient with a score of 0. 1 is weighted 0. 9 and a patient with a score of 0. 9 is weighted 0. 1). The model included the same covariates as used in the primary analysis. Statistical analyses were performed using SAS software. P < 0. 05 was considered to indicate statistical significance.
Acknowledgements The authors wish to thank all the doctors, residents and medical students of the  Infectious Diseases Department, Dijon University Hospital  for their help and work during the pandemic. The authors also wish to thank the  Virology Laboratory  for performing SARS-CoV-2 RT-PCR assays, the patients, and  Dr Claire Breniaux  ( Doctoral Researcher in British Society and Politics and Teaching Fellow at the University of Burgundy ) for linguistic support.
Search for iflavirus sequences We searched for sequences matching iflaviruses in public tick transcriptome assemblies (available from TSA, GenBank, as of August 2020). For each transcriptome assembly, we searched for matches using tblastn  [12] , using the polyprotein sequence of ISIV (accession number BBD75427) as a query and RNA-Seq contigs (nucleotide sequences) as targets. Of note, several of these transcriptomes were produced by our research group for a phylogenetic study of hard ticks  [13]  or for expression profiling studies of I. ricinus (BioProject accession numbers PRJEB40724 and PRJNA662253). We used low-stringency matching criteria (e-value threshold 1-e3) to ensure that even relatively distantly related sequences could be found. Since new genome sequences identified in our study were retrieved from transcriptome data, they represent uncultivated virus genomes (UViGs).
Genomes were defined as representing novel species if their sequence identity to members of existing species was less than 90%, according to the species delineation criteria of the International Committee on Taxonomy of Viruses (ICTV) for iflaviruses (https:// talk. ictvo nline. org/ ictv-repor ts/ ictv_ online_ report/ posit ive-sense-rna-virus es/w/ iflav iridae). We also performed a BLASTp search on the nr database, selecting all of the matches described as being associated with ticks and adding them to our data set. This approach ensured that our study included all of the tick-associated iflavirus genome sequences published to date (as of September 2020).
The corrected sequence, based on the majority of reads, contained an intact ORF and no frameshift. We therefore corrected this contig, and all subsequent analysis of this sequence was based on the corrected sequence. Additionally, a 5' partial sequence was also found in a transcriptome assembly of I. holocyclus (2,039 amino acids, IhIV-2). This sequence was similar but not identical to that of the first iflavirus genome (IhIV) identified for that tick species. The new sequences were deposited in the GenBank database (accession numbers in Table  1 ) with metadata specifying the source of these viruses, the assembly methods used, and the quality of the sequence data (following the guidelines for the Minimum Information about an Uncultivated Virus Genome, or MIUVIG  [26] ). All other assemblies obtained from multiple genera of hard and soft ticks gave negative results.
Finally, other sequences were found after a BLASTp search against the GenBank protein database (nr), which allowed the inclusion of sequences from viruses associated with Amblyomma americanum (lone star tick dicistrovirus), Haemaphysalis flava (HfFV), Hyalomma asiaticum (Bole Hyalomma virus), and Ixodes holocyclus (IhIV), Ixodes uriae (Gerbovich virus) as well as viruses from pools of tick species (Table  1 ).
Phylogeny The iflavirus genome sequences included in our work are described in Table  1 , including nine newly identified sequences from the present study (Fig. 2 ). The best-fit model (BIC criteria) was LG+G4. An ML tree (consensus from 1,000 bootstrap trees) showed that the five variants found in I. ricinus and the ISIV sequence grouped closely Table  1  List of iflavirus sequences found in tick transcriptomes (with tblastn or blastp, using the ISIV polyprotein sequence as a query). Columns: Species, Tissues (HEM: haematocytes, MG: midgut, MT: Malpighian tubules, OV: ovaries, SG: salivary glands, SYN: synganglion, WB: whole bodies) followed by details on the stages or conditions between parentheses, when available, Location of the sampling (or source of the strains), Accessions: GenBank accessions, protein and nucleotide, and if available, related TSA or BioProject accession between parentheses, Publication (or authors of the sequences), Percent identity with ISIV -% id at the amino acid level of the first hsp (tblastn)-and query range of the match.
Relative abundance of an iflavirus associated with I. ricinus, in different field populations and life-stages. Abundance was assessed by read counts per million in different RNA-Seq libraries, comparing A) different field populations (each library was obtained from a pool of 50 nymphs), data from BioProject PRJNA662080 B) different life-stages and conditions, for a lab-reared strain, data from BioProject PRJNA595586. A dot indicates a negative library (i. e. less than the one read per million threshold), where the virus is presumed absent from the pool of individuals.
Acknowledgements We thank  O. Rais ,  M. Voordouw , and  Franck Boué , who provided part of the material used for transcriptome sequencing of I. ricinus. We acknowledge the support of the  Genotoul bioinformatics platform Toulouse Midi-Pyrénées (Bioinfo Genotoul ) for bioinformatic analyses. Thanks to  Sara Moutailler  ( ANSES ) for helpful comments on the manuscript.
Funding This work was supported by the  Genoscope , the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA) and France Génomique  ( ANR-10-INBS-09-08 ), by the  Italian Ministry of Education, University and Research (MIUR) :  Dipartimenti di Eccellenza Program  ( 2018-2022 ) -Dept. of  Biology and Biotechnology "L. Spallanzani", University of Pavia  (to D. S. ), and by a grant from the  Institut Carnot France Futur Elevage  ( XENOBIOTICK  project).
Ixodes ricinus WB (wild nymphs) Nancy, France QPI13032. 1, MT050464 (PRJNA662080) Rispe et al (unpub. ) 98. 2% 1-2991 IricIV-5 Tissues and Location Accession numbers Publication Identity Match range(s) Virus name conditions Amblyomma WB (wild quest- NY and Con- ASU47553. 1, Tokarz et al. 65. 8% 1142-2838 Lone star tick americanum ing ticks) necticut, USA KX774633. 1 2018 dicistrovirus Haemaphysalis WB Japan BBK20270. 1, Kobayashi et al. 45. 6% 63-2937 HflFV flava LC483655. 1 2020 Hyalomma WB China APG77501. 1, Shi et al. 2016 48. 4% 379-2990 Bole Hyalomma asiaticum KX883729. 1 asiaticum Hyalomma dromedarii SG, wild ticks Tunisia BK012003 (GFGI01) Bensaoud et al. 2018 40. 0% 1-1880 and 2000-2989 HydromIV Ixodes frontalis WB Carquefou, France QPI13027. 1, MT008333 (PRJNA528282) Charrier et al.
2019 41. 1% 560-2989 IfronIV Ixodes holocy- SG,WB,MG QLD and NSW, AQZ42314. 1, O'Brien et al. 62. 2% 1-2989 IhIV clus Australia KY020412. 1 2018 (GIBQ01) Ixodes holocy-clus WB NSW, Australia QPI13026. 1, MT008332 (PRJNA528282) Charrier et al. 2019 65. 4% 913-2989 IhIV-2 Ixodes ricinus SYN (wild unfed ticks) Chizé, France QPI13029. 1, MT008330. 1 (PRJEB40724) Rispe et al (unpub. ) 97. 7% 1-2991 IricIV-1 Ixodes ricinus SG, OV, MT (feeding adult females) Neuchâtel (Switzerland), lab strain QPI13030. 1, MT008331 (PRJNA662253) Daveu et al (unpub. ) 98. 4% 1-2991 IricIV-2 Ixodes ricinus WB (various stages) Czech Republic BK012002 (GIDG01012278) Vechtova et al. (unpub. ) 98. 1% 1-2991 IricIV-3 Ixodes ricinus WB (wild nymphs) Switzerland QPI13031. 1, MT050463 (PRJNA662080) Rispe et al (unpub. ) 98.
3% 6-2991 IricIV-4 Ixodes scapu- Cell line ISE6 - BBD75427. 1, Nakao et al. 100. 0% 1-2991 ISIV laris LC094426. 1 2017 Ixodes uriae WB Antartic penin- QIS88066. 1, Wille et al. 2020 42. 0% 383-2991 Gerbovich virus sula MT025175. 1 Ixodes versper-tilionis WB Maine-et-Loire, France QPI13028, MT008334 (PRJNA528282) Charrier et al. 2019 68. 2% 1016-2989 IvespIV Pool of tick spe- WB China YP_009336552. 1, Shi et al. 2016 38. 6% 381-2991 Ht-V1 cies NC_032764. 1 Pool of tick spe- WB China YP_009336542. 1, Shi et al. 2016 44. 3% 391-2991 Ht-V2 cies NC_032758. 1 Pool of tick spe- WB China YP_009336533. 1, Shi et al. 2016 46. 1% 63-2989 Ht-V3 cies NC_032751. 1 [2]ether (see below for details on genetic distances). This group therefore represents variants of the same virus species. This was also the case of the two variants of I.
Study population The description, baseline data and main results of the AURORA study have been published previously  [24] [25] [26]. In short, AURORA is a double-blind, randomized, multicenter study involving 2773 men and women aged 50-80 years, who had been treated with maintenance hemodialysis or hemofiltration for at least 3 months. After providing written informed consent, eligible patients were randomly assigned in a 1:1 ratio to receive either rosuvastatin, 10 mg daily or matching placebo. There was no significant effect of rosuvastatin on the composite primary endpoint of major adverse cardiovascular event (MACE) (i. e. , nonfatal myocardial infarction, nonfatal stroke, or death from CV causes) [HR 0. 96 (95% CI 0. 84-1. 11), p = 0. 59). Rosuvastatin also exhibited no significant effects on the secondary endpoint of all-cause mortality [HR 0.
Statistical analysis Categorical variables are expressed as frequencies (%) and continuous variables are expressed as means ± standard deviation or median (25th and 75th percentiles), depending on the variable distribution. Associations of PICP and Gal-3 with CV death, allcause mortality or MACE were assessed using both continuous and categorized variables (tertiles) using Cox models. Models were adjusted on clinical and biological covariates based on previously published data in AURORA  [23]  (age, history of CV disease, diabetes mellitus, albumin, and high sensitivity C-reactive protein), and variables correlated to PICP and Gal-3 concentrations (sex, dialysis vintage, body mass index, systolic blood pressure). History of CV disease is defined by history of coronary heart disease (i. e. , prior myocardial infarction, prior coronary angioplasty or stent, and coronary artery bypass graft), history of vascular disease (i.
However, this does not necessarily mean that other therapeutic intervention directly targeting fibrosis could not be proposed, based on these biomarkers in the future. Of note, the anti-fibrotic drug class prototype of the mineralocorticoid receptors antagonists is currently tested in two multicenter randomized study CV prevention studies in hemodialysis achieve (aldosterone blockade for health improvement evaluation in end-stage renal disease trial; NCT03020303), and alchemist (aldosterone antagonist chronic hemodialysis interventional survival trial; NCT01848639)  [62]. Our work suggests that Gal-3 may be a key mechanistic pathway underlying fibrosis in dialysis patients, anti-Gal3 therapy for CV prevention, such as with modified citrus pectin (MCP) may be worth investigating in this setting.
Funding The AURORA trial was sponsored by  Astra-Zeneca. This research is part of  IMPROVE-PD  project which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement number  812699. PR, FZ, NG are supported by the  RHU Fight-HF , a public grant overseen by the  French National Research Agency (ANR)  as part of the second " Investissements d'Avenir" program  (reference:  ANR-15-RHUS-0004 ), by the  French PIA  project " Lorraine Université d'Excellence " (reference:  ANR-15-IDEX-04-LUE ),  Contrat de Plan Etat Lorraine   IT2MP  and  FEDER Lorraine. Reagents for galectin-3 were generously provided for free by  Abbott GmbH, Wiesbaden, Germany.
Background: Flow cytometry (FCM) aids the diagnosis and prognostic stratification of patients with suspected or confirmed myelodysplastic syndrome (MDS). Over the past few years, significant progress has been made in the FCM field concerning technical issues (including software and hardware) and pre-analytical procedures. Methods: Recommendations are made based on the data and expert discussions generated from 13 yearly meetings of the European LeukemiaNet international MDS Flow working group.