dataesr/scientific-paragraphs-categorization huggingface dataset:
Hypothetical estimations of AMU in the Mekong Delta of Vietnam AMU data by species obtained from the survey were extrapolated for human and animal populations in the Mekong Delta (2019) using four different metrics: (i) no. doses-kg (either DDD kg or ADD kg ) related to the total amount of kg-body mass-time for each species per year; (ii) weight of AMU related to standing body mass of human and animal populations per year; (iii) weight of AMU related to standing body mass of human and biomass of animal populations per year (i. e. the methodology used in the OIE report);  6  (iv) weight of AMU related to standing body mass of human and PCU of animal populations per year (i. e. the ESVAC method). 24 he human standing body mass was obtained by multiplying the number of individuals (from census data) by their bodyweight (from their age and gender).
Participants and household farm characteristics A total of 101 household farms with 316 human residents present in the farm household at the time of the visit were investigated. Each farm had a median of 3 (IQR 2-4) residents, with a median age of 38 (IQR 13-55) years. Over half (51. 9%) were males. All interviewees were above 18 years old, and 82. 2% were male. A total of 186 (58. 8%) residents reported direct contact with animals in their farms (Table  S1 , available as Supplementary data at JAC-AMR Online). Chickens were predominant (71. 3% farms), followed by ducks (54. 5%), pigs (19. 8%) and Muscovy ducks (11. 9%). Other species raised were fish (10. 9%), cattle (5. 9%), frogs (4. 0%), goats (2. 0%), geese and eels (1. 0% each). Farms raising only one food animal species (42. 6%) were predominant, followed by farms raising two (40.
1 B ). This variant was present in the gnomAD v3. 1 public database but was extremely rare (MAF = 1. 3 × 10 -5 ) and found only in the heterozygous state (Fig. S1 D ). Given the crucial role of human IFNAR2 in the type I IFN pathway  (Duncan et al. , 2015)  and the previously described IFNAR1-deficient patient with YEL-AVD  (Hernandez et al. , 2019) , we hypothesized that the homozygous essential splice site variant of IFNAR2 might underlie the pathogenesis of the adverse reaction to YFV-17D in P1.
Whole-exome sequencing Exome capture was performed with the SureSelect Human All Exon 50 Mb kit (Agilent Technologies). Paired-end sequencing was performed on a HiSeq 2000 (Illumina) generating 100-base reads. We aligned the sequences with the GRCh37 reference build of the human genome with the Burrows-Wheeler transform  (Li and Durbin, 2009). Downstream processing and variant calling were performed with the Genome Analysis Toolkit, SAMtools, and Picard  (Li et al. , 2009; McKenna et al. , 2010). Substitution and InDel calls were made with the GATK Unified Genotyper. All variants were annotated with an annotation software system developed in house  (Adzhubei et al. , 2010; Kircher et al. , 2014; Ng and Henikoff, 2001). The patients reported here did not consent to the deposition of their genomic data.
Flow cytometry The cell surface expression of IFNAR2 was assessed with a PEconjugated mouse anti-IFNAR2 (21385-3PBL; Assay Science) Ab. Cells were stained and then washed twice with PBS and analyzed by flow cytometry. Data were acquired on a Gallios flow cytometer. Generation of IFNAR2-deficient HEK293T cells IFNAR2-deficient HEK293T cells were generated with the CRISPR/Cas9 system. Guide RNAs were designed with the Benchling design tool and inserted into lentiCRISPR v2, which was a gift from Feng Zhang (Broad Institute, Cambridge, MA; plasmid 52961; Addgene). The three guide RNAs were designed to bind and cut at different places in the IFNAR2 gene, one in exon 3 (forward: 59-CACCGCATATGAAATACCAAACACG-39; reverse: 59-AAACCGTGTTTGGTATTTCATATGC-39), one in exon 4 (forward: 59-CACCGCATTGCTGTATACAATCATG-39; reverse: 59-AAACCATGATTGTATACAGCAATGC-39), and one in exon 5 (forward: 59-CACCGTGAGTGGAGAAGCACACACG-39; reverse: 59-AAACCGTGTGTGCTTCTCCACTCAC-39).
Functional evaluation of anti-cytokine auto-Abs The blocking activity of anti-IFN-α and anti-IFN-ω auto-Abs was determined by assessing STAT1 phosphorylation in healthy control cells following stimulation with the appropriate cytokines in the presence of 10% serum/plasma from a healthy control or a patient. Surface-stained healthy control PBMCs (350,000/reaction) were cultured in serum-free Roswell Park Memorial Institute medium supplemented with 10% healthy control or patient serum/plasma and were either left unstimulated or stimulated with IFN-α or IFN-ω (10 ng/ml) for 15 min at 37°C. Each sample was tested once. Cells were fixed, permeabilized, and stained for intranuclear phospho-STAT1 (Y701). Cells were acquired on a BD LSRFortessa cytometer with gating on CD14 + monocytes and analyzed with FlowJo software.
Acknowledgments We thank the patients and their families for placing their trust in us. We warmly thank  Y. Nemirovskaya ,  D. Papandrea ,  M. Woollet ,  D. Liu ,  S. Boucherit ,  C. Rivalain , and  C. Patissier  for administrative assistance. We warmly thank  S. Meyer  and  A. -L. Neehus  for their German translation. We thank  S. Pellegrini  and  Z. Li  for providing the IFNAR2 plasmids. We thank the Imagine sequencing platform for all their help and understanding. We thank  Stephen J. Seligman  for initiating the recruitment of all the patients and for establishing a collaboration with the late  Reinaldo de Menezes Martins , resulting in the inclusion of the Brazilian patients. The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute , The  Rockefeller University , the  St.
Giles Foundation , the  National Institutes of Health  ( R01AI088364 ), the  National Center for Advancing Translational Sciences , the  National Institutes of Health Clinical and Translational Science Award program  ( UL1 TR001866 ), a  Fast Grant  from  Emergent Ventures , the  Mercatus Center at George Mason University , the  Yale Center for Mendelian Genomics , the  Genome Sequencing Program Coordinating Center  funded by the  National Human Genome Research Institute  ( UM1HG006504 andU24HG008956 ), the  French National Research Agency  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for Medical Research  ( EQU201903007798 ), the  French Foundation for Medical Research and French National Research Agency   GENCOVID  project,  ANRS-COV05 , the  Square Foundation, Grandir-Fonds de Solidarité pour l'Enfance , the  SCOR Corporate Foundation for Science, Institut National de la Santé et de la Recherche Médicale , and the  University of Paris.
FUNDING The work was supported by State funding from the  Agence Nationale de la Recherche  under " Investissments d'avenir" program  ( ANR-10-IAHU-01 ), by  ANR - AID  ( ANR-19-CE17-0012-01 ;  ANR-19-CE17-0012-04 ) and by  INSERM. SK is a  Centre National de la Recherche Scientifique  staff researcher.
.  Study cohorts The frequency of the particular single nucleotide null variant of SIGLEC in the general population is described in the genome aggregation database (https://gnomad. broadinstitute. org/variant/20-3687141-C-A). The association of the SIGLEC null variant Glu88Ter with TB was investigated in two different cohorts. First, the Swiss HIV Cohort Study (SHCS; www. shcs. ch), which comprises 3732 participants that are part of a large cohort study prospectively enrolling HIV-1 infected individuals since 1988 in Switzerland  (Schoeni-Affolter et al. , 2010). Demographics, route of transmission, AIDS defining illnesses, co-morbidities, and behavioural, clinical and laboratory data are systematically and prospectively collected. Plasma and cell samples are stored longitudinally every 6-12 months.
.  SIGLEC-1 genetic analyses Genotyping for the HIV-1 Swiss cohort SHCS has been previously described  (Martinez-Picado et al. , 2016). For the Russian cohort, genomic DNA was isolated from frozen whole peripheral blood using the Gentra Puregene Blood Isolation kit (Qiagen) according to the manufacturer's protocol. Samples from individuals with TB diagnosis and control subjects were genotyped for the SIGLEC null variant Glu88Ter (SNP rs150358287C > A) in the SIGLEC gene by TaqMan SNP Genotyping Assay (Assay ID: C_167368973_10; Applied Biosystems). Reactions were performed in a StepOnePlus Real-Time PCR System (Applied Biosystems) and results were analyzed using StepOne Software v2. 3 (Applied Biosystems).
.  Murine histopathological analysis Right upper lung lobe samples were fixed in formaldehyde (Biopsafe), embedded in paraffin and cut in 5-μm sections that were stained with haematoxylin-eosin for microscopic observation (Nikon Instruments Inc. ). Four distinct sections of each tissue block were used to determine the damaged area as percentage of the total lung area using the NISElements D version 3. 0x software package (Nikon Instruments Inc. ).
.  Murine cytokine profiling Frozen serum samples and snap-frozen lung homogenates from the right lower and middle lung lobes were assessed. Lung samples were thawed, weighted and homogenized on ice with lysis buffer containing 0. 05% sodium azide, 0. 5% Triton X-100, 1:500 Protease inhibitor cocktail (all from Sigma-Aldrich) in sterile PBS, using 1 ml per 100 mg of tissue. Homogenates were incubated for 1 h at 4 • C and centrifuged at 3000 × g for 10 min. Supernatants were collected and stored at -80 • C until use. Cytokines were measured by Luminex xMAP technology and analyzed with xPONENT 3. 1 software (Luminex Corporation). IFN-γ, TNF-α, IL-6, LIX and IL-17 were measured in serum samples, using the MCYTOMAG-70K kit. In lung homogenates, IFN-γ, TNF-α, IL-4, IL-6, LIX, IL-1β, IL-10, IL-12, IP-10, KC, MCP-1 and VEGF were analyzed with the MCYTOMAG-70K kit, and TGFβ with the TGFBMAG-64K kit (EMD Millipore Corporation) following the manufacturer's instructions.
Culture media was renewed every 3 days for 7 days. Monocyte-derived macrophages were then infected with 10 5 Mtb H37Rv-DsRed per well (MOI = 0. 2) for 4 h at 37 • C, washed with PBS to remove extracellular Mtb and replaced with fresh media. At day 0, 5 or 7 post-infection, cells were washed with PBS and lysed with 0. 01% Triton X100 (Sigma-Aldrich), serially diluted in PBS and plated onto 7H11-OADC agar medium (Difco) to assess the bacterial intracellular growth. CFU were determined at days 14 and 21 post-plating. Of note, a significant increase in bacterial growth over time was observed for all cellular types tested (P < 0. 0001; likelihood ratio test for time effect in a fitted linear mixed-effects model). At day 7, part of the cells was assessed for Siglec-1 expression with an APC-anti-Siglec-1 mAb (BioLegend) or the corresponding isotype control by flow cytometry using BD LSRFortessa flow cytometer (BD Biosciences, TRI Genotoul platform) and the associated BD FACSDiva software.
Data were analyzed with software version build 3. 1. 46, with detection threshold set to 5, and blur, Min track Length and Max Jump Distance set to auto. Samples were diluted in filtered PBS to remove particles in suspension and reach optimal concentration for instrument linearity: 20-120 particles/frame as advised by the manufacturer. Readings were taken on triplicates of 60 s at 30 frames/s, at a camera level set to 16 and with manual monitoring of temperature.
Following acquisition images were deconvoluted using Huygens Professional software and theoretical PSF parameters corresponding to the system's effective observation spot or point-spread-function. All subsequent image manipulation steps were performed using Fiji (ImageJ distribution) software with the exception of the isosurface generation, which was performed using Imaris. For flow cytometry analyses, mDCs were left untreated or pre-incubated for 15 min at RT with 10 μg/ml of anti-Siglec-1 mAbs (7-239 or 7D2; both from Abcam) or IgG1 isotype control (BD Biosciences) before pulse with CFSE labelled-extracellular vesicles. After extensive washing, mDCs cells were acquired with FACS Calibur (BD Biosciences), and the percentage of positive cells was determined using FlowJo v. 10. 6 software. Forward and side-scatter light gating were used to exclude dead cells and debris from all analyses.
Monocytes were also isolated with CD14 + beads from frozen PBMCs obtained from a homozygous HIV-1 null individual and common allele HIV-1 individual with confirmed genotype for the SIGLEC null variant. Monocytes were activated with 1000 U/ml of Interferon-2α (Sigma-Aldrich) for 24 h to induce Siglec-1 expression. 2 × 10 5 monocytes were pulsed with CFSE labelled-extracellular vesicles for 4 h at 37 • C to assess uptake as described for mDCs. Siglec-1 expression on these cells was assessed by flow cytometer, blocking cells with 1 mg/ml of hIgGs and staining them with α-Siglec-1-PE mAb 7-239 or matched isotype-PE control (BioLegend) at 4 • C for 30 min. Samples were analyzed with FACSCanto II (BD Biosciences) using FlowJo v. 10. 6 software.
.  Statistical analyses The association between the SIGLEC null allele and the TB phenotypes was assessed by an enrichment analysis using a Chisquared test, a hypergeometric test, a proportion test or a logistic regression, according to the design of the cohorts. Mean or median changes were analyzed using a paired t-test or a Mann-Whitney U-test, respectively. Mean changes from 100% of data normalized to percentages were assessed with a one-sample t-test. Longitudinal values of bacterial replication were analyzed using linear models of mixed effects. Test results were considered significant at P < 0. 05. All analyses and figures were generated with R or GraphPad Prism v. 8 software.
LETTERS TO THE EDITOR We conducted an observational, retrospective study including patients >18 years who were treated with daratumumab for ITP and/or warm AIHA (primary or secondary) according to international criteria. 13, 14 The patients were identified through the French National Reference Center for Adult Immune Cytopenia network from November 2019 to November 2021. Any patient who opposed data collection was not included. All patients were informed and gave consent to 'off-label' use of daratumumab. The initial treatment regimen was extrapolated from the one commonly used in myeloma  4  (i. e. , at least 4 daratumumab infusions at a dose of 16 mg/kg per week combined with oral dexamethasone 20 mg before each infusion). For ITP, complete response was defined as a.
A B platelet count >100x10 9 /L and response as a platelet count between 30 and 100x10 9 /L with at least a 2-fold increase from baseline. 13 For warm AHAI, complete response was defined as a hemoglobin level ≥12 g/dL in the absence of recent transfusion and response as a hemoglobin level ≥10 g/dL with an increase of at least 2 g/dL from the pre-treatment level in the absence of recent transfusion (<1 month). 14 Patients who required any other treatment for autoimmune cytopenia including rescue therapy >6 weeks after the first daratumumab infusion were considered non-responders regardless of platelet or hemoglobin levels. The first daratumumab infusion was considered day 0 for subsequent time points. Clinical and biological data were collected with a standardized form. The study received institutional review board approval (00011558, UPEC University, AP-HP).
In conclusion, daratumumab may provide clinical benefit in a subset of patients with ITP or warm AIHA refractory to standard therapy. However, the efficacy seems relatively modest and hypogammaglobulinemia exposes patients to a risk of infection. Future prospective trials will evaluate the use of CD38-directed monoclonal antibodies for the management of patients with immune cytopenias, such as daratumumab in ITP (NCT04703621) or isatuximab in warm AIHA (NCT04661033).
Study population This study included genome-wide genotyped data from 9846 patients with SSc and 18 333 healthy individuals from the same source population. 13 The patients fulfilled the 2013 American College of Rheumatology/the European League Against Rheumatism classification criteria or the criteria proposed by LeRoy and Medsger for early SSc. 22 23 In addition, patients were stratified by the main clinical classifications (lcSSc or dcSSc) and main autoantibody status (ACA, ATA or ARA). Details of the cohorts, genotyping methods and quality control (QC) for genotyped data are described elsewhere. 13.
SNP and HLA imputation After genotyping QC, SNPs, classical HLA alleles and amino acid variants, were all imputed for each case-control dataset separately in the extended MHC region in chromosome 6. 24 The SNP2HLA  25  software was used for imputation using a reference panel consisting of 5225 European individuals in the Type 1 Diabetes Genetic Consortium,  26  containing data of 8961 variants across the MHC region, and two and four digit-resolution allelic identities of the HLA class I (HLA-A, HLA-B and HLA-C) and II genes (HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1 and HLA-DRB1) as well as their amino acid make-up. Imputed data were also filtered for 95% success call rate for alleles and amino acids, deviation from Hardy-Weinberg equilibrium (HWE) considering a p-value of <0. 001 for SNPs in controls and 95% total call rate for individuals.
Statistical analysis Association analyses were performed with PLINK  27  using logistic regressions in each of the 14 independent datasets, including sex and the five first principal components (PCs) as covariates. Briefly, PC analysis was performed using ~1 00 000 quality-filtered independent SNPs outside the MHC region using PLINK and GCTA64. Outliers were identified and removed as described elsewhere. 13 We tested SNPs, classical HLA alleles and all possible combinations of amino acid residues per position. Inverse variance fixed effects meta-analysis was conducted with PLINK to evaluate the consistency of effects across studies. The genome-wide significance was established at a p-value≤5×10 -8. Considering the main clinical SSc subtypes and serological classifications, stratification of cases was performed following the same procedure as for the global analysis and comparisons were made with the control group and intracases, namely, dcSSc with lcSSc, and ATA with ACA (patients without available data or positive for both autoantibodies were excluded from the analysis).
Only classical alleles whose results outperformed those from the global analysis and that were significantly associated in both comparisons were declared as private. To identify independent signals within the region, sequential conditional association analyses were performed with the software GCTA-COJO  19 28 29  using the summary statistics from the meta-analysis (global and stratified by clinical and serological subtypes) and separately for each variant type (SNPs, alleles,and amino acids). The Manhattan plot was obtained with an in-house R script. The Protein Data Bank entries 3pdo, 1a1m, 3lqz and 2bvp were used to produce the 3D models of the HLA molecules with the UCSF Chimaera software. 30.
Funding This work was supported by the  Spanish Ministry of Science and Innovation  (grant ref. SAF2015-66761-P  and  RTI20181013  ( 32-B-100 )),  Red de Investigación en Inflamación y Enfermedades Reumáticas  from  Instituto de Salud Carlos III  ( RD16/0012/0013 ) and grants from  National Institutes of Health  ( R01AR073284 ) and  DoD  ( W81XWH-16-1-0296 ). MAH was funded by the  Spanish Ministry of Science and Innovation  through the  Juan de la Cierva Incorporacion program  (ref. IJC2018-035131-I ). GO, AB and ALH were supported by the  NIHR Manchester Biomedical Research Centre and Versus Arthritis  (grant ref  21754 ).
designed experiments and analysed data. S. P. performed two-photon imaging, laser ablations, Matlab and IMARIS analysis, mouse genetics and toxin injections. C. M. -M. performed two-photon imaging, whole-mount staining, FACS preparatory work and analysis and mouse genetics. D. G. G. assisted with two-photon imaging, Matlab and IMARIS analysis, and experimental discussions throughout the project. E. A. L. assisted with whole mounts and mouse genetics. J. D. B. assisted with mouse genetics. C. M. P assisted with the HRAS model development. E. M. assisted with laser ablations and Matlab analysis.
3b ). This indicates that LCs and DETCs are regularly and non-randomly distributed, consistent with observations of human LCs  49, 50. To determine whether the distribution of LCs is dependent on the distribution of DETCs and vice versa, we compared actual imaging coordinates of one immune population to actual or artificially generated random distributions of the other. This analysis showed that LCs and DETCs do not maintain a specific distance from each other (Fig. 3c ). This corroborates the above findings that the depletion of LCs or DETCs does not influence the density or distribution of the remaining population (Extended Data Figs. 1a  and 7a-d ). To further understand the global distribution of immune cells, we measured the area that each LC and DETC occupies using a Voronoi diagram.
However, LC Rac1KO lack key molecular markers of activation, such as the upregulation of MHCII or CD86, when compared with wild type LCs or physiologically activated LCs from lymph nodes (Extended Data Fig. 9a-e ). In addition, LCs maintained expression of the adherent junction component E-cadherin (Extended Data Fig. 9f-h ), which is downregulated during LC activation  65, 66. These data suggest that LC Rac1KO in the epidermis are not activated and maintain cell-cell contact with epithelial cells. To assess LC Rac1KO immune cell patterning, we revisited the same region over time. Notably, LC Rac1KO lost their tiling pattern by week 1 after induction and this irregular distribution persisted for at least 3 weeks (Fig. 5c-e ). For instance, LC Rac1KO cells at 3 weeks after induction have a wider range of minimum distances and come into closer contact than wild-type LCs, consistent with a loss of self-avoidance (Fig.
Image analysis. Raw image stacks were imported into Fiji (v1. 52p, National Institutes of Health) or Imaris software (v. 9. 5. 1; Bitplane/Oxford Instruments) for further analysis. Imaris software was used to track cells and obtain xyz coordinates from individual tracked cells over time. All cell tracks were individually examined and only cells that could be tracked for the full duration of a time-lapse series or of revisit images were included in the final analysis. Prism software (Graphpad, v. 8. 0. 0) was used to graph the data. The tiled images were stitched by a grid/collection stitching plugin in Fiji. Migrating cell tracking for rose plot analysis and 3D reconstitution were performed in Imaris software. Voronois, displacement analysis and minimum distances were analysed using Matlab (Mathworks, USA, vR2019b).
Lymph nodes were incubated in 400 U ml -1 collagenase D (Roche Applied Science) for 30 min before filtration through a 70 μm filter. All samples were pretreated with rat serum (Sigma-Aldrich) and incubated with anti-mouse/human CD207 PE (Biolegend; 144204, 1:200) and either anti-mouse MHCII APC (Biolegend; 107614, 1:250), anti-mouse CD86 APC (Biolegend; 105012, 1:100) or anti-mouse CD324 biotin (E-cadherin) (Thermofisher; 13-3249-82, 1:100) at 4 °C. Biotin samples were incubated with secondary antibody Streptavidin-APC (Thermofisher; SA1005, 1:500) for 20 min. Samples were run on a Becton Dickinson LSRII outfitted with Diva software v. 8. 0. 1, and the data were analysed using Flowjo v. 10. 6. 2.