dataesr/scientific-paragraphs-categorization huggingface dataset:
Fluorescence recovery after photobleaching (FRAP) confluent HFF cells for 24-30 h with or without auxin (500 μM). FRAP experiment were performed at the Bordeaux Imaging Center on a spinning disk microscope Leica DMI8 (Leica Microsystems, Wetzlar, Germany) equipped with a confocal Scanner Unit CSU-W1 T2 (Yokogawa Electric Corporation, Tokyo, Japan) using an HCX PL Apo CS2 63X oil NA 1. 4. The 37°C atmosphere was created with an incubator box and an air heating system (PeCon GmbH, Germany). This system was controlled by MetaMorph software (Molecular Devices, Sunnyvale, USA). Images were acquired using a sCMOS Prime 95B camera (Photometrics, Tucson, USA). The LASER diode used was at 488 nm (400 mW). Photo-manipulation experiments were done with an iLas² scanner system (Gataca Systems, Massy, France) using the same Laser bench as for imaging.
At least one parasite per GFP-expressing vacuole was photobleached with 10 pulses at 100% of the 488 nm laser power. Recovery from photobleaching was monitored by 20 consecutive acquisitions at 1 image per second followed by 1 image every 10 or 20 s for 180 s. Images were analyzed using home-made ImageJ script developed by Fabrice Cordelières from the Bordeaux Imaging Center (https://github. com/fabricecordelieres/IJ-Macro_FRAP-MM).
This suggests that BCC1 might be a cargo for MyoJ interacting in the tail region of this myosin heavy chain. Surprisingly, no calmodulin (CaM)-like protein able to act as a myosin light chain for MyoJ was identified in the mass spectrometry analysis, except CEN2. This protein shares 30% similarity with MLC1, the myosin light chain of the glideosome complexes  (Herm-Götz et al. , 2002; Freńal et al. , 2014)  and disappears from the basal complex upon deletion of MyoJ while MyoJ remains at the posterior cup in absence of CEN2  (Freńal et al. , 2017; Lentini et al. , 2019). These data suggest that CEN2 could act as a myosin light chain to regulate the function of MyoJ by binding to calcium  (Bombardi et al. , 2020). The kinase CDPK6 and the S/T phosphatase BCC5 co-localize with MyoJ at the posterior cup, but their depletion did not cause any detectable effect on the constriction of the basal complex or the localization of MyoJ.
Acknowledgments We are grateful to the  Bordeaux Imaging Center (BIC)  for the use of instrumentation and  Magali Mondin  and  Sebastien Marais  for their technical assistance. The BIC is a service unit of the  CNRS-INSERM , and  Bordeaux University  is a member of the national infrastructure  France BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). We also thank  Drs Jean-Francois Dubremetz ,  Maryse Lebrun ,  Sebastian Lourido ,  Markus Meissner ,  David L. Sibley , and  Dominique Soldati-Favre  for providing reagents and/or strains. We are grateful to  Dr Sunil Kumar Dogga  for his help with the OrthoFinder analysis and to  Drs Gaëlle Lentini  and  Meĺanie Bonhivers  for critically reading the manuscript.
Funding This study was supported by a Preciput 2019 from the  department Sciences Biologiques et Médicales of the University of Bordeaux  to KF, a CNRS dotation, and  LabEX ParaFrap  ( ANR-11-LABX-0024 ) to KF and DR, and by a Ph. D. fellowship from the  Ministry of Higher Education, Research and Innovation  to CR.
Study design, setting and participants Two cohorts of healthy volunteers were enrolled in this study. The first cohort (observational study -cohort 1) consisted of 150 healthy adults (aged between 19 and 95 years old) with serology data for four common herpesviruses. Individuals <65y were recruited among blood donors (Etablissement francais du sang) in Paris (France) while those aged >-5y were recruited at the geriatric department of the Piti e-Salpêtri ere Hospital (Paris, France) between 2015 and 2016. Individuals with malignancies, acute diseases, or severe chronic diseases, such as atherosclerosis, congestive heart failure, poorly controlled diabetes mellitus, renal or hepatic disease, various inflammatory conditions, or chronic obstructive pulmonary disease, as well as individuals on immunosuppressive therapy, were excluded from the study.
The second cohort (cohort 2) consisted of 137 individuals aged more than 70 years old, who received a full TBE vaccination course of three injections at week 0, 4 and 24 with a licensed inactivated whole virus vaccine (FSME Immun Ò CC, Baxter) as part of a clinical trial (registered through ClinicalTrials. gov: NCT00461695) conducted at the Epidemiology, Biostatistics and Prevention Institute (EBPI) at the University of Z€ urich Switzerland between 2007 and 2010 (see Table  S1  in supplemental materials). Here, we report the core data of this study including immunogenicity of TBE vaccination in healthy elderly stratified by CMV serostatus. 32À34 Included individuals were healthy according to a health questionnaire and a physical evaluation at study entry; 57% reported no comorbidities and no long-term medical treatment, 43% reported one co-morbidity requiring drug treatment (mostly hypertension).
Phenotypic analysis PBMCs were stained for surface markers using combinations of the following directly conjugated monoclonal antibodies: anti-CCR7ÀPE-Cy7 (clone 3D12; BD Biosciences), anti-CD3ÀBV605 (clone SK7; BD Biosciences), anti-CD8ÀAPC-Cy7 (clone SK1; BD Biosciences), anti-CD4 BUV395 (clone SK3; BD Biosciences) anti-CD27ÀAF700 (clone O323; BioLegend), anti-CD45RA-ÀECD (clone 2H4LDH11LDB9; Beckman Coulter) and anti-CD95ÀFITC (clone DX2; BD Biosciences). Nonviable cells were eliminated from the analysis using LIVE/DEAD Fixable Aqua (Thermo Fisher Scientific). Samples were acquired using an LSR Fortessa (BD Biosciences). Data were analyzed using FACSDiva software version 7 (BD Biosciences) and/or FlowJo software version 10 (FlowJo LLC).
Samples were then fixed and permeabilized, before staining with anti-IFNg-APC (clone B27; BD Biosciences). Samples were acquired using an LSR Fortessa (BD Biosciences) and data were analyzed using FACSDiva software version 7 (BD Biosciences) and/or FlowJo software version 10 (FlowJo LLC). Percentages of antigen-specific T cells were calculated after background subtraction of the unstimulated control.
Statistical analysis Scattered plot representations were used to describe changes in immunological parameters according to the age (Figures  1 to 4 , cohort 1) or CMV serostatus (Figures 5 and 6, cohort 2) of subjects. Data from the first cohort were divided in subgroups based on the number of herpesvirus infections. Univariate statistical analyses were performed using GraphPad Prism, R  41  and Rstudio  42  softwares with the packages Tidyverse  43  and Scales. 44 Groups were compared using the nonparametric Mann-Whitney test, as indicated in Figure legends. There is no allowance for multiplicity. Bonferroni correction was applied when performing multiple group comparisons. Spearman's rank test was used to determine correlations. P values below 0. 05 were considered statistically significant.
Different influence of age and persistent infections on systemic inflammation and the T-cell compartment We studied a cohort of healthy adults (n = 150, named "cohort 1") aged from 19 to 95 years old, grouped according to the number of persistent viruses they were infected with on the basis of the serology data for four common herpesviruses: CMV, EBV, HSV-1 and HSV-2 (see Table  1  for a distribution of donors according to their serology). Persistent infections with these viruses may induce sustained inflammatory responses in their host. To verify this hypothesis, we measured by digital ELISA circulating cytokines (IFN-a, IL-6, TNF and IL-10) commonly associated with inflammatory manifestations. We observed an age-dependent increase in all four inflammatory markers, also associated with increasing numbers of herpesvirus infections (Figure  1  and Table  S2 ).
De novo cellular immune responses to TBE vaccination were monitored at week 26 post-first immunization (2 weeks after the last boost), and compared to baseline values, using IFNg ELISpot with pools of overlapping peptides against all structural proteins of TBEv. Total TBEv-specific T-cell responses were statistically significantly lower in CMV + compared to CMV À elderly individuals (Figure  5a  and Table  S4 ). The frequency of TBEv-specific T cells induced upon vaccination correlated with the counts in naive CD4 + T cells prior to vaccination (Figure  5b ), but not naive CD8 + T cells (data not shown). This indicates a reliance of the capacity to mount de novo vaccine T-cell responses on the pool of naive CD4 + T cells, and suggests that CMV infection may therefore impact this capacity due to its effect on the naive CD4 + T-cell pool.
Acknowledgments We are indebted to  Matthew Albert  ( Insitro, San Francisco, CA ) for constructive scientific discussion. We are very grateful to the researchers and technical staff of the  Division of Infectious Diseases and Hospital Epidemiology of the University Hospital of Zurich (Switzerland) , the  Institute of Social and Preventive Medicine of the University of Zurich (Switzerland) , the  Center for Virology of the Medical University of Vienna (Austria) , and the  Laboratoire d'Immunologie et d'Histocompatibilit e of the Hopital Saint-Louis (Paris, France)  for their scientific advice and expert technical assistance. This work was supported by the  ANR  (Project  ANR-14-CE14-0030-01 ) and by  Universit a ItaloFrancese/Univerist e FrancoItalienne  ( Galileo  Project  G10-718 ;  PHC Galilee  Project  39582TJ ), by the  Swiss National Science Foundation  (grant  PP0033-110737  to UK), by the  Heuberg Foundation (Zurich, Switzerland) , by the  AETAS Foundation (Geneva, Switzerland)  and by a  Senior IdEx Chair  of the  University of Bordeaux (France).
EC, VB, CA, MA, DD and AT were supported by the  French Government 's  Investissement d'Avenir Program, Laboratoire d'Excellence  " Milieu Int erieur " Grant  ANR-10-LABX-69-01. EC and AT are supported by the  Agence Nationale de la Recherche  (Project RANKLthym  ANR-19-CE18-0021-02 ).
MATERIALS AND METHODS Study design and patients The analyses reported here include data from two randomised, double-blind, phase III trials in patients with active PsA: SPIRIT-P1 (NCT01695239)  15 16  and SPIRIT-P2 (NCT02349295). 17 18 Details of these clinical trials have been published previously. Briefly, SPIR-IT-P1 enrolled biological-naïve patients, and SPIRIT-P2 enrolled patients with an inadequate response or intolerance to one or two tumour necrosis factor inhibitors (TNFi) (TNFi-inadequate responders, TNFi-IR). For both trials, patients were at least 18 years of age, had a PsA diagnosis of at least 6 months, met classification criteria for PsA (ClASsification for Psoriatic ARthritis, (CASPAR) criteria), had ≥3 tender joints and ≥3 swollen joints, and either currently had or had a history of plaque psoriasis.
Statistical analyses Composite measures were calculated at various time points, per protocol for each measure (online supplemental table 2), through week 52 (mCPDAI through week 24). Treatment comparisons (IXE Q4W and IXE Q2W vs placebo) were made using a logistic regression model for categorical data with missing values imputed by non-responder imputation. A mixed model for repeated measures analysis was used for treatment comparisons for continuous data. Data from patients who were inadequate responders at week 16 were censored after week 16 and up to week 24. Patients on active biological treatment (ie, patients treated with IXE and ADA in SPIRIT-P1 and IXE in SPIR-IT-P2) were pooled for post-hoc comparisons between the composite measures at weeks 24 and 52. Comparisons between the composite measures were made for the complete integrated data set to explore the impact of baseline functional impairment and structural damage on composite outcomes and in subgroups defined by baseline Health Assessment Questionnaire-Disability Index (HAQ-DI) (HAQ-DI <1; HAQ-DI >1 and <1.
Acknowledgements The authors thank  Gina Moore ,  Lydia Morris  and  Kathy Oneacre  ( Syneos Health, Morrisville, North Carolina, USA ), for medical writing support and assistance with preparation and submission of this paper. Contributors LCC contributed to interpretation of data for the work and critical revision of the manuscript for important intellectual content and is the guarantor for this paper. JSS contributed to interpretation of data for the work and critical revision of the manuscript for important intellectual content. PJM contributed to acquisition and interpretation of data for the work and critical revision of the manuscript for important intellectual content. EH contributed to interpretation of data for the work and critical revision of the manuscript for important intellectual content.
CLEC-1 reduces cDC1 cross-presentation of dead cell-associated antigens to CD8 + T cells We previously described that CLEC-1 is expressed by different types of myeloid cells such as monocytes, macrophages, neutrophils, and DCs  (8, 9). We noticed in mouse and human public datasets (ImmGen MNP Open-source) that Clec1a is expressed under steadystate conditions by several subtypes of DCs and that among DCs, the highest expression is by the subset of conventional cDC1 (XCR1 + and CD141/BDCA-3 + in mouse and human, respectively), which are especially efficient at processing material from dead cells (fig. S5A )  (20, 21). We confirmed higher cell-surface expression of CLEC-1 at the protein level on cDC1s compared to cDC2s from the blood of healthy individuals (Fig. 5A ). We therefore evaluated whether CLEC-1 in DCs might play a role in the cross-presentation of dead cell-associated antigens to CD8 + T cells.
Treatment with antihuman CLEC-1 antagonist mAbs enhances antitumor immunity in preclinical models To evaluate the clinical relevance of CLEC-1 in patients with cancer, we analyzed scRNA-seq datasets of myeloid cells from human lung TME  (23). We observed the highest expression of CLEC1A by a particular subset of monocytes sharing characteristics of immature DC, by DC1, and by subsets of macrophages and neutrophils displaying protumoral features (Fig. 6A ). This expression profile was similar in mice. In addition, pan-cancer analysis (breast, colorectal, lung, and ovarian) identified CLEC1A expression in various subsets of myeloid cells from human TME, such as in CD14 + (C1) and CD16 + Downloaded from https://www. science. org on November 21, 2022 S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 10 of 15 (C2) clusters of monocytes and in different subtypes of macrophages expressing antitumoral M1 (C3 and C4) or protumoral M2 (C5 to C7) markers (Fig.
6B )  (24). Among DCs, CLEC1A expression was retrieved mostly in the C1 cluster corresponding to cDC1 cells. Analysis of datasets from The Cancer Genome Atlas (TCGA) with GEPIA tool  (25)  indicated that a broad spectrum of human cancers such as pancreatic adenocarcinoma or glioblastoma overexpress TRIM21 compared to normal tissues (Fig. 6C ). These data suggest that tumors may exploit their overexpression of TRIM21 to suppress the function of myeloid cells via CLEC-1. Therefore, to evaluate the translational potential of CLEC-1 as an immunotherapeutic target, we generated several antihuman CLEC-1 mAbs able to block the interaction of human CLEC-1 with its putative ligand(s) in human necrotic cells (fig. S7, A  and C ). Knowing the cross-reactivity of the ligands, we tested the therapeutic potential of these antihuman CLEC-1 antagonist mAbs in the context of a fully functional immune system using a newly generated CLEC-1 humanized mouse model.
Cells were encapsulated in droplets using the Chromium Controller (10X Genomics, San Francisco, CA) (load of 20,000 cells) and marked with anti-mouse TotalSeq hashtag antibodies (BioLegend). scRNAseq libraries were prepared with the Chromium Single Cell 3′ Library and Gel Bead Kit v2 (PN-120267) and Chromium Next GEM Single Cell 3′ Library and Gel Bead Kit v3. 1 (PN-1000121) (10X Genomics). Library was prepared by sequencing in a NovaSeq 6000 (Illumina, SFR, Nantes). The Cell Ranger Suite (version 6. 0. 0; 10X Genomics) was used to perform sample demultiplexing, barcode processing, and single-cell gene unique molecular identifier counting. The gene cell barcode matrices were merged into one matrix (20,473 genes × 31,976 cells) and then loaded into the R package Seurat, version 4. 0. 1 (https://satijalab.
Acknowledgments: We thank  D. McIlroy  for language editing. We acknowledge the  IBISA MicroPICell facility (Biogenouest) , member of the national infrastructure  France-Bioimaging  supported by the  French national research agency  ( ANR-10-INBS-04 ), the  Genomics Core Facility GenoA , member of Biogenouest and  France Genomique  and the  Bioinformatics Core Facility BiRD , member of  Biogenouest  and  Institut Français de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ) for the use of their resources and their technical support. Funding: This work was supported by  LIGUE 44 ,  56 , and 29 (2018) and LIGUE 44 (2021) (to E. C. );  LabEX IGO programs  ( ANR-11-LABX-0016-01 ) from  National Research Agency (ANR) for French government  (to E. C. ); and  ANR  generique PRCE 2018 from  National Research Agency (ANR) for French government  (to E.
The pandemic situation at the time urged for a collaborative approach with a statistical method that enables continuous synthesis of the data from all ongoing trials. It can take months or years to collect and analyse the data of all trials and perform an individualparticipant-data (IPD) meta-analysis  (Tierney, Riley, Smith, Clarke, & Stewart, 2021). By simplifying the structure of the data collection and inviting all ongoing trials, the current study aimed to collect accumulating data and perform an Anytime Live and Leading INterim (ALL-IN) meta-analysis. The results presented here are based on the limited data that was accumulated on an ongoing basis. The analysis is rich in its representation of the history of the evidence and timing of events, yet it does not contain all the (subgroup/covariate-adjusted) analyses that would be possible by combining all full trial datasets.
(a) Individual Participant Data (IPD); reducing data problems The data was shared in file formats with a row of data per participant. With full information on the date of randomisation, events and follow-up, the evolution of evidence over time could be retrospectively processed, based on each new data upload. Only information for the time-to-event analysis in the two primary outcomes (COVID-19 infections and hospitalisations) was collected, along with information on the stratifying factor, limiting the amount of work in ongoing data cleaning. The IPD approach encourages close collaboration between the trial data-uploaders and meta-analysis statisticians. This improves problem solving in data cleaning and optimises data quality. The IPD approach also limits the risk of falsified or fabricated data entering the meta-analysis because more than one person inspects all the data.
Identification of trials To identify trials for inclusion in the meta-analysis, ClinicalTrials. gov was searched for the terms "BCG AND (COVID OR corona OR SARS-CoV-2)". We also screened a database constructed for the Kaggle hackathon on BCG and COVID-19 clinical trials  (Kaggle, 2020). Finally, we used snowballing by regularly asking the trial investigators involved whether they were aware of trials that were not yet included (final elicitation October 3, 2022). Trials were eligible if they met the following criteria: 1) individual randomisation, 2) comparison of BCG to either placebo or no intervention, 3) population consists of adult healthcare workers, 4) COVID-19 and COVID-19 related hospitalisation are among the primary or secondary outcome measures.
From the original risk of bias tool, we removed the domain selective reporting as there is no reporting of results in a protocol, and we changed the domain incomplete outcome data to method of outcome assessment. In this domain we assessed whether the method of collecting outcomes was appropriate and whether we expected that any outcomes could be missed by the researchers. Risk of bias was scored independently by two reviewers: one reviewer with a methodological background (AD) and one of two reviewers with a clinical background (KH and JW). All risk of bias domains (agreements and disagreements) were subsequently discussed within the review team. During advisory board meetings, each trial's risk-of-bias assessment was discussed, and advisory board members were invited to ask clarifying questions and to share their opinion.
In cases where it is possible to test differences between trials this would of course be recommended before acting on the evidence. However, the possibility of detailed subgroup analyses was not anticipated given the expected small effect sizes and large amounts of data needed to distinguish differences between small effects. This rationale of decision making requires a specific argument of expected homogeneity in the effect sizes as a key criterium in the decision on trial inclusion, such that the meta-analysis could be the leading source of information. Trials were only included if we expected each of them to have an effect in the same direction (benefit, not harm) -if there would be benefit in one trial, we expect it in all trials. Moreover, trials were included if they could be expected to have an effect of a certain minimal size (if BCG has an effect at all).
Data extraction Each trial had a designated data-uploader that also attended several Advisory board meetings and would become a meta-analysis co-author. IPD was extracted from each trial and uploaded to a secured cloud server in repeated uploads described in the 'Working instruction for data-uploaders', Statistical Analysis Plan and a webinar that explained the statistical methodology (ALL-IN-META-BCG-CORONA Replication Package). Data-uploaders were encouraged to check their data as it appeared in the dashboard (see Figure  2 ) based on a data processing tutorial (ALL-IN-META-BCG-CORONA Replication Package) and upload new data if available. The following variables were included: intervention randomised to (control or BCG), calendar date of randomisation, the stratification factor 'hospital' (e.
The analysis assumes that the HR can vary from trial to trial, and is a two-stage analysis. A Cox proportional hazards model (maximum-likelihood) estimate was obtained for each trial, stratified by hospital (i. e. a single HR per trial that follows from evaluating all events with regard to the risk set in their hospital alone instead of the risk set in the full trial). These trial estimates were combined into a meta-analysis HR estimate using inverse-variance weighting. The event times are analysed in calendar time such that all participants within the same hospital at a given date are at the same risk, regardless of their own time since inclusion. More details about the exact scripts used are available in the Appendix section Detailed methods and supplementary material R code (ALL-IN-META-BCG-CORONA Replication Package).
Trial inclusion A total of 20 protocols were identified of trials of BCG for prevention of COVID-19 in healthcare workers. Of these, data from 6 trials were included in the current primary meta-analysis. A secondary analysis was defined that adds COVID-19 infections based on serology, and includes the AF trial as the 7nd trial. This process is described in Figure  3  and trial characteristics and summary data are described in Table  1  and Table 2. More trial details are provided in the Appendix section Detailed results. Three protocols for risk-of-bias assessment were published, for the NL trial  (Ten Doesschate, et al. , 2020) , the DK trial  (Madsen, et al. , 2020)  and the BR trial  (Junqueira-Kipnis, et al. , 2020). Following the rationale of a certain expected homogeneity in effect sizes (see Methods section Expected homogeneity in effect sizes), it was decided prospectively to exclude trials in the elderly or.