dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion The research-policy collaborative partnership between ieDeA and wHO allows for a better understanding of current policy priorities and data and research limitations, leading to more well-timed and policy-relevant research. Regular communication provides a pathway to facilitate and expedite exchange of crucial knowledge and scientific evidence prior to peer-reviewed publication. Such partnerships that promote dialogue between stakeholders should be encouraged, to facilitate and improve uptake and integration of research evidence, to provide timely and reliable insights into progress and challenges in the global response to Hiv, and to support the development of health policy. ieDeA-wHO Research-Policy Collaboration 15 M Garcia Calleja, Daniel Low-Beer, Marina Penazzato and Marco vitoria).
Funding sources The international epidemiology Databases to evaluate AiDS (ieDeA) collaboration is supported by the National institute of Allergy And infectious Diseases (NiAiD), the eunice Kennedy Shriver National institute of Child Health and Human Development (NiCHD), the National Cancer institute (NCi), the National institute of Mental Health (NiMH) and the National institute on Drug Abuse (NiDA) under core grants: U01Ai069907 (Asia-Pacific), U01Ai069923 (CCASAnet), U01Ai096299 (Central Africa), U01Ai069911 (east Africa), U01Ai069918 (NA-ACCORD), U01Ai069924 (Southern Africa), U01Ai069919 (west Africa). Me was supported by special project funding (Grant No. 174281) from the Swiss National Science Foundation.
Acknowledgements we thank all patients, care providers and data managers in the seven ieDeA regions for contributing data for this project. we would also like to thank all the investigators participating in Data Harmonization wG ( Beverly Musick , Chair), the ieDeA Pediatrics wG (Rachel vreeman, Chair), the Strategic Data wG ( Constantin Yiannoutsos , Chair) and the ieDeA executive Committee ( Annette Sohn , Chair), as well as the wHO ( Meg Doherty , Jesus.
Study design A cross-sectional study was conducted among 227 farrowto-finish pig farms located in Belgium (n = 47), France (n = 60), Germany (n = 60) and Sweden (n = 60) between December 2012 and January 2014. Recruitment of farm is described by Postma et al. [8]. Briefly, a convenient sample of farms willing to participate and having more than 70 sows and 500 finishers produced annually was enrolled in the study. Each farm was visited once and data collected using a harmonised procedure prepared in collaboration within the research consortium. Collected data related to antimicrobial usage, herd characteristics and technical performance, biosecurity practices, occurrence of selected clinical signs of disease, vaccination scheme and farmers' attitudes and habits towards antimicrobial usage.
Antimicrobial usage data Detailed description of antimicrobial usage data collection is provided by Sjölund et al. [16]. Briefly, antimicrobial usage data of participating farms were collected for 1 year preceding the farm visit in Belgium, Germany and Sweden and for the last batch in France, using invoices from veterinarians and feed companies, farmers' treatment records or farmers' directed interview. Collected data included the product commercial name, total volume or mass, concentration of active substance and target animal age category. In each herd, the average number of animals of each age category present in a batch was estimated using data from the herd management information system. Antimicrobial usage was expressed in terms of 'treatment incidence' (TI) that represents the number of animals per 1000 receiving a daily dose of an antimicrobial during their production period  [17].
Herd characteristics and technical performances Herd characteristics were collected using farmer's interviews and included information on the farm management, i. e. number of present sows, number of employees per 100 sows, farrowing rhythm, weaning age and post-weaning use of zinc oxide, as well as information on the farmer, i. e. age, years of experience with pig farming and highest educational level. Technical performance data included both production (i. e. mortality rates, daily weight gain and feed conversion ratio) and reproductive (i. e. number of litters and weaned pigs per sow and per year) performance and were retrieved from the farm management system or via farmer's interviews.
Data on biosecurity practices Farm biosecurity status was described using the risk-based Biocheck. UGent™ scoring system  [20]. A farm visit together with farmer's interview was used to complete a questionnaire that provided detailed description of farms' practices to prevent pathogens from entering into the farm (i. e. external biosecurity) and to spread within the farm once they entered (i. e. internal biosecurity). Scores between 0 and 100 were then computed for six sub-categories each of external and internal biosecurity practices  [8, 21].
Farmers' attitudes and habits towards antimicrobial usage The collection of data related to farmers' attitudes and habits towards antimicrobial usage was already described by Visschers et al. [22]. Briefly, a seven-page questionnaire was sent to each farmer and collected during the farm visit. Questionnaire items were assessed on six-point Likert scales and were grouped into eight constructs exploring the farmer's worries about infectious diseases in the pigs, worries about AMR and worries about financial/legal issues, the farmer's perceived benefits of, need for and risks of using antimicrobials in pig production and the farmer's perceptions of the role of the veterinarian and feed expert regarding the reduction of antimicrobial usage  [13, 22]. After checking the internal reliabilities of the items per construct (i.
Application to the present study The mbPLS regression was applied to the present study to achieve two objectives: (i) describe, in each country, the relative contribution of selected explanatory blocks of variables to the explanation of antimicrobial usage (block Y) and (ii) identify within each block, the variables mainly contributing to the explanation of antimicrobial usage (block Y). Collected data were organised into seven meaningful blocks of variables, namely: a block of response variables (block Y) describing the herd level of antimicrobial usage, and six blocks of explanatory variables, i. e. the potential risk factors, that related with the herd characteristics (block X 1 ), biosecurity scores (block X 2 ), occurrence of clinical signs (block X 3 ), vaccination scheme (block X 4 ), farmer's attitudes and habits (block X 5 ) and herd technical performance (block X 6 ).
Data pre-processing The mbPLS regression did not allow for missing data in the dataset; 20 farms were therefore excluded from further analysis because they had missing data for an entire explanatory block. Thus, 207 farms, including 38 Belgian, 56 French, 54 German and 59 Swedish farms were included in the models. Remaining farms had sparse missing data for few variables (i. e. <3% of the farms); missing values were imputed using regularised iterative principal component and multiple correspondence analyses algorithms for quantitative and qualitative variables, respectively  [30, 31]. Besides, variables of the block Y were normalised using logarithm transformation, after adding one to the original variable to adjust for zero values in the data. The herd farrowing rhythm was converted into a dummy variable, using a 3-week system as the reference class.
Acknowledgements. The authors thank the farmers and veterinarians who contributed to the study. They also thank the  French Pork and Pig Institute  for providing access to their technical database. The authors also thank  Murielle Guiard  and  Jean-Yves Audiart  who assisted with data collection and data entry in France. This study was conducted as part of the  MINAPIG  project, funded by the  ERA-NET  programme EMIDA ( EMIDA19 ) and by the participating national funding agencies. Declaration of Interest. None.
Flow cytometry analysis 7] [38]  Antihuman monoclonal antibodies (mAbs) included the following: CD3 (UCHT1), CD4 (RPA-T4), CD8 (RPA-T8), CD19 (HIB19), CD31 (WM59), CD45RA (HI100), CD45RO (UCHL1), CD27 (L128), CD25 (2A3), immunoglobulin D (IgD) (IA6-2), CD38 (HIT2), CD24 (ML5), and CTLA-4 (BNI3) from BD Pharmingen (San Diego, CA), and GITR (eBioAITR) and FoxP3 (PCH101) from eBioscience (San Diego, CA). Cells were acquired in FACSCalibur (BD Biosciences) cytometer and analyzed with Flow Jo (TreeStar) software. All analyses were performed on fresh blood.
The library was prepared using Nextera XT index kit (Illumina) and Taq platinum high fidelity according to manufacturer's instructions before purification using AMPure XP beads. Denatured library was paired-end (2 3 250) sequenced on a MiSeq (Illumina). Unique molecular identifiers were treated using pRESTO,  45  and CDR3 clonotypes were assembled using MiTCR software. 46 Postanalysis of TCR repertoire diversity, segment usage, spectratyping, clonotype tracking, and repertoire overlap were performed by VDJTools. 47 Raw sequencing data were deposited in the NCBI SRA database (SRP106516).
Statistical analysis Patient characteristics are described as mean 6 standard error (SE). Nonparametric 2-tailed Wilcoxon's signed-rank test was performed to compare pre-and posttransplant values. Continuous variables in 2 different groups were compared by nonparametric 2-tailed Mann-Whitney U test, and results were expressed as median 6 interquartile range (IQR). Correlations were assessed using nonparametric Spearman test. When indicated, logarithmic transformation was performed on skewed data prior to correlation. SPSS Statistics 20 (IBM, Armonk, NY), GraphPad 7 (La Jolla, CA) or R Project Package in VDJTools environment were used for figures and analyses. 47 Significance was set at 0. 05.
Low clonotype overlap and high TCR diversity are related to clinical response after AHSCT Normalized unique clonotype counts were used to estimate the TCR diversity in 5 responder and 3 nonresponder transplanted patients (Table  2 ). 47 Minimal transplant-induced changes in the TCR repertoire and clonotype specificities were assessed by NGS. 28, 51 At 1-year posttransplantation, the TCR diversity had significantly increased from pretransplant levels, indicating the generation of a new immune system (Figure  2A ). These data were further confirmed by an increase of Chao1 diversity estimation 52 at 1-year post-AHSCT (supplemental Figure  5A ), with higher frequency of singletons (clonotypes with single occurrence in the sample) (supplemental Figure  5B ). Estimation of TCR diversity showed that, for responder patients, overall specificities increased following thymic rebound at 1 and 2 years (Figure  2B ).
Acknowledgments This work was achieved by obtaining a co-financing  Region Auvergne-Rhone-Alpes  and  FEDER  in 2015 ("Thematiques emergentes"). MD and EB were supported by grants from the  Region Auvergne-Rhone-Alpes. This work was supported by the  Ministère de la Recherche et de la Technologie, Inserm and UniversiteClermont Auvergne  [ UMR1107 ,  UMR1071]; INRA [USC-2018 ]; CNRS [UMR6023]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Electron microscopy preparations were all performed by the  "Centre d'Imagerie Cellulaire Sante" (CICS, Clermont-Ferrand, France).
DNA libraries were constructed using the Nextera ® DNA Sample Preparation kit (Illumina ® , CA, USA), following the manufacturer's instructions. For all RNA and DNA library preparations, an average of 2 μg and of 50 ng of purified nucleic acid were used for each sample library, respectively. Quality control and absolute quantification of libraries were carried out in the ECO ™ system (Eco Real-Time PCR System, Illumina ® ) using the KAPA Library Quantification kit (KAPA Biosystems, MA, USA), according to the manufacturers' specifications. Libraries were diluted to a final concentration of 12 pM and NGS by synthesis was carried out in an Illumina ® HiSeq 2500 platform (2 × 100 paired-end runs). Sequencing data files are available in the SRA database under.
Bioinformatics and statistical analyses Quality analysis of the generated sequencing reads was conducted with FastQC (Babraham Bioinformatics, Cambridge, UK) and reads with good quality (Phred score > 30 and ≥ 90 bp) were selected with Sickle-Master  [25]. Reads were submitted to a BLASTX search  [26]  onto the Gen-Bank nonredundant viral protein database, with a minimal e-value cutoff of 1e.
Viral diversity in enteric samples of western lowland gorillas To assess the diversity of the enteric virome of wild gorillas, RNA and DNA libraries of  23   BLASTX analysis showed that approximately 24 and 29% of normalized reads could be annotated for identifying virus families using a viral database for RNA and DNA libraries, respectively (Fig. 1 ). The Siphoviridae bacteriophage family was more frequent in SIVgor-infected individuals (32. 7 vs 23. 4% in uninfected individuals), while two other bacteriophage families were more frequent in uninfected individuals, Myoviridae (31. 7 vs 25. 8% in infected individuals) and Podoviridae (12. 9 vs 9. 0% in infected individuals). Altogether, these three viral families accounted for 67. 5 and 68% of the total annotated reads in SIVgor-infected and uninfected individuals, respectively (Table  2 ).
Discussion Studies on natural history of lentiviral infections in apes have been mostly limited to captive or semi-free ape communities due to the restrictions of working in the wild and to the endangered status of these species. However, noninvasive techniques developed during the last 30 years allowed for the study of prevalence, diversity and impact of SIV infection in several wild primate populations  [6]. These advances prompted us to assess novel dysbiosis markers in previously studied wild gorilla communities, using fecal samples collected in CP. In this study, 24 and 29% of normalized reads from RNA and DNA libraries, respectively, were annotated using a virus database. These rates were in agreement with similar reports on the complexity of virome annotation  [31]  and the finding of a large proportion of reads frequently found in the "No Hit" category in fecal samples  [28].
Acknowledgements We wish to thank lab personnel form the  Institut de Recherche pour le Développement-IRD  (Montpellier, France) and  Institut Bouisson Bertrand-IBB (France) , mainly  Dr. Eric Delaporte  ( Head of the Laboratory   UMI233/INSERM1175  and  Head of the IBB)  for providing infrastructure for the conduction of the study and  Amandine Esteban  (technician of the laboratory  UMI233/INSERM1175 ) for sample processing.
Funding This work was supported by grants from the  Rio de Janeiro State Science Foundation (FAPERJ)   E26/170. 026/2008  and the  Brazilian Research Council (CNPq)   573806/2008-0 , both funded by the  Brazilian National Institute of Science and Technology (INCT) for Cancer Control  to H. N. S and M. A. S. , the  Graduate Program in Genetics (PGGEN)  of  Universidade Federal do Rio de Janeiro (UFRJ)  Grant # 1629742  to M. D. , the  National Institute of Health  ( RO1 AI 50529 ) and the  Agence Nationale de Recherches sur le SIDA  ( ANRS 12125/12182/12325 ) to M. P. and A. A.
5, 200 mM NaCl, 1 mM DTT and 5% glycerol. The complex was flash frozen and stored at -80 °C. NMR data acquisition, resonance assignment and structural calculations. For NMR experiments, the RNA samples were dissolved in a buffer containing 10 mM potassium phosphate, 70 mM NaCl, and 0. 1 mM EDTA, pH 5. 2. All NMR experiments were acquired by using Bruker 700 or 800 MHz instruments equipped with cryogenic probes. Spectra for observing non-exchangeable protons were collected at 298 K in 99. 96% D 2 O, whereas those for exchangeable protons were at 283 and 298 K in 10% D 2 O. For NOESY experiments, mixing times were set to 200 ms. Assignments of 7SK-SL1 apical were obtained first by transferring assignments of the 7SK-SL1 top and 7SK-SL1 bottom to the regions that are shared with them, and then by analyzing 1 H-1 H 2D NOESY spectra of fully protonated sample.
For TAR samples, NOESY datasets were recorded using unlabeled and various combinations of nucleotide-specific labeled samples  48. Samples for 7SK-SL1 apical :Tat-RBD and TAR:Tat-RBD complex for structure determination were prepared at a 1:0. 9 equivalents, respectively, to avoid any non-specific binding by the Tat RBD. Assignments for non-exchangeable 1 H and  13  C signals of 7SK-SL1 apical and TAR free and in complex with Tat-RBD were obtained by analyzing two-dimensional 1 H-1 H NOESY recorded with non-labeled samples, two-dimensional  13  C-HMQC, and three-dimensional  13  C-edited HMQC-NOESY spectra. Initial structural models were generated using manually assigned restraints in CYANA  48, 49. Upper-limit distance restraints of 2. 7, 3. 3, and 5. 0 A were employed for direct NOE cross-peaks of strong, medium and weak intensities, respectively.
Small angle X-ray scattering. SAXS data were obtained at SIBYLS beamline of Advanced Light Source at Lawrence Berkeley National Laboratory. Measurements were performed in buffer containing 10 mM sodium phosphate, 70 mM NaCl, 0. 1 mM EDTA, pH 5. 2. The background scattering was subtracted from the sample scattering to obtain the scattering intensity from the solute molecules. Data from four different concentrations (20, 30, 40, and 50 uM) were compared with scattering intensities at q = 0 Å -1 [I(0)], as determined by Guinier analysis, to detect possible interparticle interactions. Data were analyzed by using ScÅtter software, and the ab initio envelope structures were reconstructed by using DAMMIF/ DAMMIN software.
Data availability Coordinates, restraints, and structures for the final ensembles of 7SK-SL1 apical , 7SK-SL1 apical : Tat RBD and TAR: Tat RBD structures have been deposited in the Biological Magnetic Resonance Bank under 30512, 30511, and 30510 BMRB codes, and in the Protein Data Bank with PDB ID codes 6MCI, 6MCF, and 6MCE, respectively. All other data supporting the findings of this study are available from the authors upon request.
Evidence for platelet activation during human drug-induced anaphylaxis Having demonstrated that hFcγRIIA-expressing platelets contribute to IgG anaphylaxis severity in mouse models, we sought to evaluate the translation of these findings to the clinic. To this end, we investigated circulating platelet numbers and activation status in blood samples from 67 patients suffering from mild or severe anaphylaxis to perioperative neuromuscular blocking agent (NMBA) administration and their matched controls, in the case-control "NASA" study (https://clinicaltrials. gov/ct2/show/NCT01637220). Significantly fewer single platelets, identified as CD61 + /FSC lo cells by flow cytometry (Fig. S7 ), were detected 30 minutes after anaphylaxis induction (i. e. NMBA injection) in the blood of patients suffering from severe versus mild anaphylaxis (1.
Clinical study design Detailed description of the NASA clinical study design and cohort can be found in (38 and Jönsson, de Chaisemartin et al, submitted). In brief, the multi-center NASA ("Neutrophil Activation in Systemic Anaphylaxis") study involved 11 hospital anesthesia departments in the Ile-de-France region in France (https://clinicaltrials. gov/ct2/show/NCT01637220). The purpose of the NASA study was to evaluate the role of neutrophils during anaphylactic reactions to NMBA in humans through a case/control study design, with a 6-8 weeks' follow-up in cases, with clinical signs consistent with intra-anesthetic anaphylaxis to NMBA whatever the grade of the reaction. Patients were retrospectively classified in severity grade according to the Ring and Messmer (39) classification by two independent evaluations of clinician experts.
A severe acute hypersensitivity reaction (AHR) was defined as a grade 3 or 4 (i. e. a severe organ failure or a cardiac/respiratory arrest). As case patients were not in a medical and psychological state to provide study consent during the AHR, their consent was obtained as soon as they were judged able. If the patients did not recover the day after the AHR, consent was obtained from a next of kin. Non-inclusion criteria were the absence of consent. The study protocol was approved by a local ethics committee (committee for the protection of Individuals "Ile-de-France X") and prospectively registered at ClinicalTrials. gov (Identifier: NCT0163722). The biological collection was approved and labeled by the National Health Authority, and registered within the French Ministry of Research and ARH ("Agences Régionales de l'Hospitalisation").
Acknowledgments: We are thankful to our colleagues  F. Abdallah ,  B. Iannascoli  and  O. Richard-Le Goff  at  Institut Pasteur, Paris  for technical help. We are thankful to our colleagues for their generous gifts:  M. P. Reilly  and  S. McKenzie  ( Jefferson Medical College, Philadelphia, PA ) for FCGR2A-transgenic mice and  LFB Biomédicaments  ( Les Ulis, France ) for human albumin and fibrinogen. Funding: This work was supported by the  European Research Council (ERC) - Seventh Frame-work Program  ( ERC-2013-CoG 616050 ), by a  JCJC grant  from the  Agence National de la Recherche  ( ANR-16-CE15-0012-01 ), the  Institut Pasteur , the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Société Française d'Allergologie (SFA ;  Soutien de la Recherche en Allergologie ).
The presence of these markers was tested using PE-conjugated anti-Sca-1, anti-CD44, and anti-CD45 antibodies and FITC-conjugated anti-CD34 obtained from eBioscience, USA. Unstained cells and cells of the proper isotype were used as controls, and the data were analyzed using FlowJo software. The second passage of MSCs was used for experiments. BM-MSCS can differentiate into osteocytes or adipocytes when cultured under appropriate differentiation conditions (DMEM supplemented with 0. 5 μM ascorbyl phosphate, 1 μM dexamethasone, and 200 μM indomethacin) for 10 days. Cells that had been treated in this manner were stained with 0. 5% Oil Red for 10 min. To differentiate isolated cells into osteocytes, they were cultured in a specific differentiation medium (DMEM supplemented with 0. 5 μM ascorbyl phosphate, 10 mM β-glycerophosphate, and 1 μM dexamethasone) for 21 days and stained with 1% Alizarin Red for 10 min.
Total mRNA extracted from MSC-induced Tregs after 6 h, 12 h, 24 h, 48 h, 72 h, and 5 days of coculture, and mRNA expression of TRAF6, GRAIL, USP7, UBC13, and STUB1 assessed by quantitative RT-PCR. Allogeneic MLR performed; CD4 + CD25 -effector T cells isolated 5 days after MLR used as negative control, and TGF-β-induced Treg cells used as positive control. mRNA samples normalized to expression of endogenous housekeeping gene (GAPDH) and compared with negative control. Data presented as mean ± SEM; n = 4 independent experiments. Significant results: *p < 0. 05; **p < 0. 01; ***p < 0. 001. Correlation of mRNA expression of each gene with that of FOXP3 mRNA shown under expression graph. Spearman correlation coefficient r and significance levels shown at top of graph. Significance levels of correlation coefficients: P*** (0.
2f ). These data suggested that the absence of netrin-1 in the hematopoietic compartment could protect against the development of AAA. Since inflammation and ECM degradation are key hallmarks of AAA, we therefore assessed macrophage infiltration and elastin damage. The accrual of macrophages characterized by mRNA abundance of Adgre1 (Fig. 2g ) and immunostaining directed against CD68 (Fig. 2h ) was significantly reduced in Ntn1 -/-→ApoE -/-as opposed to WT→ApoE -/-aortas that were prone to AAA. Extensive elastin damage depicted by focal breaks and fiber thinning was consistently observed across the circumference of the aortic sections of the WT→ApoE -/-mice whereas those examined from Ntn1 -/-→ApoE -/-sections were in opposite mirror to the latter findings (Fig. 2i ). These exciting key observations predicted that the expression of netrin-1 could contribute to elastin damage and precipitate to vessel rupture.