dataesr/scientific-paragraphs-categorization huggingface dataset:
KEY PUBLIC HEALTH MESSAGE programmes collected data annually (n = 14), infraannually (n = 6) or multi-annually (e. g. every 3 years, n = 3); frequency of data collection was missing for one programme. Dissemination of the results occurred on an annual (n = 43), pluri-annual (n = 3) or infra-annual (n = 2) basis. Of 48 programmes, 16 also disseminated their results via open-access dashboards. Most programmes had national coverage (n = 43) and among them, 31 included at least one overseas territory (with some overseas territories, such as Réunion or Guadeloupe, covered by several national programmes, others not at all). Among the 43 nationwide programmes, all displayed their results at a national level, but 10 programmes with a higher granularity also displayed their results at a sub-national level.
By direct exchange with the coordinators of each programme, we are confident that our data are accurate and validated. We believe this mapping will be of interest to policymakers, as well as surveillance stakeholders, not only in France but also elsewhere since our methodology can easily be transferred to other countries and surveillance contexts. Nonetheless, this work also had some limitations. While we collected detailed data on ABR/ABU indicators together with other information generated by each programme, getting access to the actual programme databases, e. g. to look at data formats, or thesaurus, was beyond the study scope. Hence, we were unable to evaluate the inter-operability of existing data. Additionally, our mapping only provided a snapshot of the surveillance system in 2021 and did not capture changes over time.
Acknowledgements The authors would like to thank all actors and programmes coordinators who participated to the interviews and contributed to data collection and interpretation. We also acknowledge all members of the steering committee of the  Surv1Health  project for their useful feedback on project activities, especially  Laura Barbier ,  Gabriel Birgand ,  Richard Bonnet ,  Jocelyne Caillon ,  Céline Couderc-Obert ,  Christophe Dagot ,  Evelyne Jouvin-Marche ,  Simon Le Hello ,  Jérôme Robert ,  Yohann Lacotte ,  Loic Simon  and  Sonia Thibaut. Many thanks to  Claire Chauvin  and  Jean-Yves Madec  for their careful review of the manuscript.
PATIENTS AND METHODS Study design. NOVESA (ClinicalTrials. gov identifier: NCT03798366) was a 24-week, multicenter, phase IIa, randomized, double-blind, parallel-group, placebo-controlled study. Adults meeting the 2013 American College of Rheumatology (ACR)/EULAR criteria for SSc with dcSSc involvement were randomized to oral ziritaxestat or matching placebo for 24 weeks (Supplementary Figure  1 , available on the Arthritis & Rheumatology website at https://onlinelibrary. wiley. com/doi/10. 1002/art. 42477). This was in addition to any background immunosuppressant standard-of-care therapy. Patients were subsequently permitted to enter a 104-week OLE study (ClinicalTrials. gov identifier: NCT03976648). Patients entered the OLE at the rollover visit, coinciding with the week 24 visit of the parent study.
For bulk RNA-sequencing analysis, blood samples were collected in PAXgene blood RNA tubes (PreAnalytiX), and skin biopsies were stored in RNAlater (Ambion). RNA isolation, library preparation, and sequencing were performed by Genewiz Germany GmbH (Leipzig, Germany). Illumina sequencing libraries were prepared using poly(A) capture (messenger RNA and long noncoding RNA) after globin messenger RNA depletion. Sequencing libraries were multiplexed and sequenced using an Illumina NovaSeq 6000 system. Gene set variation analysis was applied to the data, followed by a differential expression analysis on the enrichment scores using Limma (Differential Pathway Activation) software version 3. 44. 3. Differentially regulated pathways were identified using the Molecular Signatures Database Hallmark 50 collection  (26).
Statistical analysis. The probability of observing a treatment effect of >4 points was determined a priori to be 63% based on 20 and 10 patients in the ziritaxestat and placebo groups, respectively, a common SD of 5 in MRSS change from baseline  (27) , a minimal clinically important difference of 4. 7  (28, 29) , and a 10% dropout rate. Both the full analysis set and safety analysis set included all randomized patients receiving ≥1 dose of ziritaxestat or placebo. The OLE full analysis set included all patients receiving ≥1 dose of ziritaxestat in the OLE study. The perprotocol analysis set included all patients in the full analysis set who did not have a protocol deviation affecting the efficacy results. This set was determined prior to database lock and unblinding. The PD analysis set was a subset of the safety analysis set, including all patients who had at least 1 postbaseline PD value and excluding patients with protocol deviations that could have an effect on PD analysis.
Also, the reduction in collagen degradation markers may reflect reduced collagen turnover associated with ziritaxestat treatment. Biomarker data from blood samples demonstrated changes in the expression of genes related to immune activation and inflammation in ziritaxestat-treated patients compared with those who received placebo. MS4A4A, an M2 macrophage marker  gene, has previously been incorporated into weighted modeling of a longitudinal, PD skin biomarker for SSc (2GSSc skin biomarker), which exhibited a high correlation with MRSS  (37). In NOVESA, analysis of blood samples detected reductions in MS4A4A expression in ziritaxestat-treated patients that were linked to significant improvements in MRSS. A similar reduction in expression of the 2GSSc skin biomarker has been reported in skin biopsies from patients with SSc receiving fresolimumab  (38)  and tocilizumab  (39).
Unexpected changes in the expression of genes linked to the development of fibrosis have been reported previously, including transforming growth factor β downstream signaling where there is suppression of canonical Smad transcripts (45) associated with attenuation of fibroblast activation. In discontinued phase III studies of ziritaxestat in IPF, ISA-BELA 1 and 2 (ClinicalTrials. gov identifiers: NCT03711162 and NCT03733444), no improvements in primary or secondary efficacy end points were observed versus placebo (manuscript in preparation). Results from NOVESA are not in agreement with those of ISABELA. A possible explanation for the ziritaxestat efficacy observed in NOVESA may be the prominent role of autotaxin in the pathogenesis of early SSc. It is postulated that ziritaxestat could target immune activation, inflammation, and downstream fibrosis.
This phase IIa trial in patients with dcSSc demonstrated that ziritaxestat is significantly more effective than placebo in improving MRSS after 24 weeks of treatment. Longer-term data indicated that switching from placebo to ziritaxestat results in a similar improvement in MRSS to that experienced by patients receiving ziritaxestat in the parent study. Blood biomarker analysis suggested that ziritaxestat lowers the circulatory level of fibrosis markers and also has potentially beneficial effects in reducing the expression of genes associated with inflammation, oxidative phosphorylation, and mitochondrial function. Ziritaxestat was generally well tolerated, and no new safety signals emerged in this small population. Results from NOVESA support a possible role for the autotaxin/LPA pathway in modulating immune activation and inflammation in patients with dcSSc, which may promote improvement of MRSS, and should be confirmed in a larger, adequately powered clinical trial.
ACKNOWLEDGMENTS We would like to thank the investigators involved in recruiting patients for this study:  Vanessa Smith  (Belgium; 7 patients),  Ellen De Langhe  (Belgium; 5 patients),  Patricia Carreira  (Spain; 2 patients),  Alfredo Guillen-del-Castillo  (Spain; 1 patient),  Chris Denton  (UK; 4 patients),  Marina Anderson  (UK; 2 patients),  Marco Matucci-Cerinic  (Italy; 1 patient),  Lorenzo Dagna  (Italy; 1 patient),  Dinesh Khanna  (US; 4 patients),  Daniel Furst  (US; 1 patient),  Maureen Mayes  (US; 1 patient),  Joseph Forstot  (US; 1 patient),  Suzanne Kafaja  (US; 2 patients), and  Stanley Cohen  (US; 1 patient). The authors would also like to thank  Emmanuel Karouzakis  ( Galapagos NV ) for his contribution to the interpretation of biomarker data and  Cianna Leatherwood  ( Gilead Sciences, Inc.
Background. Mast cells are potential contributors to chronic changes in kidney transplants (KTx). Here, the role of mast cells (MCs) in KTx is investigated in patients with minimal inflammatory lesions. Methods. Fourty-seven KTx biopsies (2009-2018) with borderline pathological evidence for T cell-mediated rejection according to the Banff'17 Update were retrospectively included and corresponding clinical data was collected. Immunohistochemistry for tryptase was performed on formalin-fixed paraffin-embedded sections. Cortical MCs were counted and corrected for area (MC/mm²). Interstitial fibrosis was assessed by Sirius Red staining and quantified using digital image analysis (QuPath). Results. Increased MC number was correlated to donor age (spearman's r = 0. 35, P = 0. 022), deceased donor kidneys (mean difference = 0.
Patients We performed a retrospective search in the database of the Department of Pathology, Erasmus Medical Center, Rotterdam, The Netherlands. All biopsies with the diagnosis bTCMR according to the Banff'17 Update between 2009 and 2018 were included. The exclusion criteria were BK viremia, biopsies with antibody-mediated rejection, and biopsies with a TCMR in the 3 mo before the indication biopsy. Patient characteristics and demographics were collected with a follow-up time of 2 y. The status of the graft function after 2 y was noted (such as function, no function, death of the patient, or unknown). No function was defined as return to dialysis. DGF was defined as the need for renal replacement therapy within the first week after KTx. This research has been performed following the Declaration of Helsinki.
Digital Quantification After staining, the slides were scanned with a digital scanner (Hamamatsu NanoZoomer, Japan) under 40x enlargement for further analysis, resulting in a resolution of 0. 2277 µm/pixel. The scanned slides were analyzed using QuPath: Quantitative Pathology and Bioimage analysis software, version 0. 2. 3. 35  Cortical areas were annotated by hand, with the following structures being excluded from the annotation: the outer renal capsule; all medium-and large-sized arteries, veins, arterioles, and venules bigger than the adjacent tubule; tissue artifacts that in any way could distort the analysis. A pixel thresholder was used to calculate the total area stained by Sirius Red. MCs were counted using the QuPath manual counting function.
Statistical Analysis Statistical analysis was performed using SPSS software version 25 (IBM Corp. , 2017). Mann-Whitney U tests were performed to compare clinical and biological data with graft function. Independent samples (Student's) t-tests and Welch's tests were performed to compare biopsy data with clinical outcome parameters. G-tests (log-likelihood ratio, a derivative of χ²) were performed between categorical variables (donortype, [no] DGF, and graft function/loss at t1). A 2-sided significance level (α) of 0. 05 was used in all analyses.
Bioinformatics analysis. Paired-end Illumina sequences data were first demultiplexed by samples, then filtered according to their Sequence Counts, their Mean Quality score and their Adaptater content obtained with fastQC. After the pre-process, reads were analyzed using Quantitative Insights Into Microbial Ecology 2 (QIIME2), mixomics, DEseq2 and philoseq. Reads were denoised (i. e. dereplicated), chimeras identified and quality filtered using DEBLUR in the QIIME2 software. Then, ouputs were merged and clustered into OTUs (Operational Taxonomic Units) using vsearch in QIIME2. Sequences with ≥ 97% identities were assigned to the same OTUs. The SILVA database was used to classifly each OTU into the sample. In order to get alpha and beta diversities, OTUs were first rarified. Alpha diversity was calculated using the Shannon entropy, and beta diversity was calculated employing the Jaccard index of similarity.
The level of significance was determined at P < 0. 05. Microbial profile data were displayed using the plotbar in ggplot2 and phyloseq packages in the R software. To visualize the impact in the microbia of each group of animals, phyla, class and families are displayed in plot bars with phyloseq. The large decrease of the abundance is observed in the plot bar with absolute abundances. Whereas in the plot bar with related abundances, the differences in proportions of certain families are highlighted. The search for components to describe and discriminate samples according to their diversity and abundance is carried out with the Partial Least Squares Discriminant Analysis (PLSda) in the mixomics software. In addition, a differential OTU abundance analysis based on the negative binomial distribution was performed, using DEseq2.
Acknowledgements We thank the  @BRIDGe INRAE platform ,  UMR 1313  GABI, 78350, Jouy-en-Josas, France, for technical support and advice, in particular M. N. Rossignol  and  D. Jardet. We thank the  CRB Ressources génétiques animales CARARE, UR ASSET ,  Domaine de Duclos , Prise d' eau, 97170  Petit-Bourg , for technical support. This study was supported by the projects  CRB PSA  ( Centre de Ressources Biologiques Productions et Santé Animales ) and.
Data Population study. Data originate from a cohort of N = 29 subjects who received up to three injections of Bnt162b2 (ClinicalTrials. gov:NCT04750720 and ClinicalTrials. gov: NCT05315583). In brief SARS-CoV-2 naive patients were recruited in Orleans, France between August 27, 2020 and May 24, 2022. Individuals were followed for up to 483 days after their first vaccine injections (see more details on the data in  [11, 20, 21] ). Two patients without longitudinal follow-up and 1 immunocompromised individual were not included in our analysis. In total, N = 26 individuals were analyzed (see Table  1 ). Briefly, all subjects received at least 2 doses, administered on average 27 days after the first injection. N = 22 subjects received a third injection, administered on average 269 days after the first injection.
During the followup N = 12 had a positive PCR, and only data prior to infection were analyzed, leaving an average follow-up of 11 visits and a median follow-up time of 362 days. Longitudinal markers of immune response. Two types of measurements were available at each visit: 1) anti-spike binding IgGs, measured in BAU/mL) neutralization titers of sera provided in ED 50 , which is the effective dilution required to neutralize 50% of an arbitrary viral load of reference (eg, the higher the ED 50 the larger the protection level). Neutralization capacity was assessed against historical strain (D614G), Alpha, Beta, Delta, and Omicron variants (strains BA. 1, BA. 2, and BA. 5). In brief IgGs markedly increased after each dose, but rapidly declined over time, with a rate that did not substantially differ after the second or the third dose (Fig  1A ).
We assume that other parameters in vector η except error measurements are also estimated in log-transformation and are common to all individuals in the population. Altogether, the vector of parameters to estimate is given by θ = (η, O, σ BAU , σ ν ). Estimation procedure. Parameters were estimated (and named ŷ in the following) with the SAEM algorithm implemented in MONOLIX software version 2022R1  [28]  allowing to handle left censored data  [29]. Likelihood was estimated using the importance sampling method and standard error were obtained by asymptotic approximation and inversion of the Fisher Information Matrix. Graphical and statistical analyses were performed using R version 3. 4. 3. Simulation of long-term humoral response. Next, we used the model to predict the long-term evolution of Ab and ED n 50 over time.
Mechanistic model for humoral response We first aimed to investigate whether there is a proportional relationship between the evolution of concentration of binding antibodies and its neutralization capacity. Fig  2 displays  the observed relationship from data between antibody concentration and ED n 50 for each VoC after each injection. First, we notice that these ratios are different for the variants. Then, we compared the evolution of these ratios with respect to the previous vaccination. In most cases, the ratios improved significantly, indicating an intrinsic gain in neutralization that cannot be explained by the variation in antibody concentration alone, justifying the need to quantify this phenomenon precisely. This is supported by the linear regressions of ED n 50 on Ab after each injection presented in Table  3  (made with the R package Censreg  [33]  to account for censored data).
Acknowledgments We thank  Isabelle Staropoli ,  Florence Guivel-Benhassine ,  Francoise Porrot  and all the members of the  Virus and Immunity Unit  for discussion and help, as well as  Fabienne Peira ,  Vanessa Legros ,  Barbara De Dieuleveult ,  Aurelie Theillay ,  Sandra Pallay  and  Daniela Pires Roteia  ( CHR Orle  ´ans) for their help with the cohorts. Part of the experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by  Inria ,  CNRS  (LABRI and  IMB) ,  Universitede Bordeaux ,  Bordeaux INP  and  Conseil Regional d'Aquitaine  (see https://www. plafrim. fr). We thank  Simulations Plus ,  Lixoft division  for the free academic use of the MonolixSuite.
Work in OS lab is funded by  Institut Pasteur ,  Urgence COVID-19 Fundraising Campaign of Institut Pasteur ,  Fondation pour la Recherche  Medicale (FRM),  ANRS , the  Vaccine Research Institute  ( ANR-10-LABX-77 ),  Labex IBEID  ( ANR-10-LABX-62-IBEID ),  ANR / FRM Flash Covid PROTEO-SARS -CoV-2 ,  ANR Coronamito ,  HERA european  funding,  Sanofi  and  IDISCOVR. This work has received funding from the  French Agency for Research on AIDS and Emerging Infectious Diseases  via the  EMERGEN  project ( ANRS0151 ). This work was supported by  INSERM  and the  Investissements d'Avenir program ,  Vaccine Research Institute (VRI) , managed by the  ANR  under reference  ANR-10-LABX-77-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data collection In-depth, face-to-face interviews were conducted in December 2021 (phase 1) and February 2022 (phase 2) by the first author and a team of researchers composed of one researcher from TUAF, one from the Institute of Anthropology of Hanoi, two free-lance researchers in sociology and a variable number of students from TUAF depending on the phase. The translation was done by a student and one sociology researcher for phase 1 and another sociology researcher for phase 2. Members of the team were trained on research objectives, data collection methods, and participatory epidemiology. Before the data collection, a pilot study in Phu Binh district was organized in November 2021 with one farmer and one drug seller. As there were few changes made to the interview guide and considering the importance of the information collected, this farmer's interview was included in the study.
Methods during the second phase were only mentioned but not ranked. The interviews were completed by on-farm observation of feeding, housing, herding activities and drug packaging. With the drug sellers, we observed the different products sold in the shop and the interactions between the participants and other sellers or buyers. During phase 1 and phase 2, we stayed on two different farms that were interviewed during the data collection period to observe farm activities over several days (Fig  1 ). The study's objectives were explained to the participants by the interviewers and written consent was obtained before starting the interview. The anonymity of the participants was guaranteed throughout the study. This study was approved by the Ethics Review Board for biomedical research of Hanoi University of Public Health with the application number 021-391/ DD-YTCC.
Data analysis Interviews were recorded, fully transcribed in Vietnamese, translated into English by a professional and compiled with field notes. Data from farmers' transcripts were analyzed by thematic analysis using the six-step method explained by Castleberry and Nolen  [37]. Thematic analysis is an inductive approach, developed from grounded theory, that allows meaning to emerge from the data itself. The coding was performed on NVivo software (V. 12,2, QSR International). The grid was first constructed a priori, according to the interview guide, and validated by the team. All the transcripts were first read in full, coded by the first author and the grid was refined during the analysis. When no new themes emerged from the transcripts, the codebook was validated after team discussion. The codes were analyzed together in light of the research questions and the context of the study to generate themes using thematic hierarchies.
Acknowledgments We are thankful to all the participants that have contributed to this study. We would like to thanks the  SubDAHLP of Thai   Nguyen , the agriculture extension center of Phu Binh district, and the authorities of the municipalities for their great support. We are thankful to the students of TUAF that have participated in the data collection:  Hien ,  Linh , and Xuan. We also thank  Nguyen Thi Phuong Le  for the transcription and translation of the interviews and  Anita Saxena  for the professional English proof reading.
The  ROADMAP  project is funded by the  European Union 's  Horizon 2020 research and innovation programme  under the Grant Agreement Number  817626  (https://www. roadmap-h2020. eu/ ). The project started in June 2019 and will end in May 2023. Part of this study was funded by the GREASE network (https://www. grease-network. org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
All files are available from the database (DOI https://doi. org/10. 18167/ DVN1/GITSRP). We have carefully removed all sensitive information including names, ages, addresses and specific dates that could be used to identify the respondents to protect their anonymity. The transcripts have been uploaded in a data repository with the following DOI: https://doi. org/ 10. 18167/DVN1/GITSRP under the the following: irb@huph. edu. vn.
2) (Biolegend), IL-10 (JES5-16E3) (Thermo Fisher Scientific), and IL-17A (TC11-18H10) (BD Biosciences) (Table  S2 ). After intracellular labelling, cells were fixed and stored using FACS Lysing Solution 1X (BD Biosciences). All data were collected using a BD Biosciences FACSCanto II or Lyric and analyzed using FlowJo software.
16S sequencing data processing Bacterial diversity was determined for each sample by targeting a portion of the ribosomal genes. PCR was performed to prepare amplicons using V3-V4 oligonucleotides (PCR1F_460: 5' CTTT CCCTACACGACGCTCTTCCGATCTACGGRAGGCAGCAG 3', PCR1R_460: 5' GGAGTTCAGACGTGTGCTCTTCCGATCT TACCAGGGTATCTAATCCT 3'), with 30 amplification cycles at an annealing temperature of 65°C. Amplicon quality was verified by gel electrophoresis, and PCR products were sent to the @BRIDGe platform for sequencing on an Illumina MiSeq platform (Illumina, San Diego, CA, USA). The 16S sequences were demultiplexed and quality-filtered using the QIIME version 2. 1. 0 software package  (40). Sequences were analyzed and normalized using the pipeline FROGS (Find Rapidly OTU with Galaxy Solution)  (41, 42).