dataesr/scientific-paragraphs-categorization huggingface dataset:
The LWL2020 data objectivates this fact: when comparing patients groups diagnosed within 2 years of first symptoms to those diagnosed after 5 years, the burden on daily life was increased by 31% in the latter group. This trend is deemed robust as it was found across almost all European countries (table  2 ). Despite current obstacles, these results highlight the importance of improving current diagnosis delay for SLE as a way to improve the burden of the disease on daily life. The LWL2020 data also underlined the association between disease activity and the daily burden of the disease. While a majority of respondents (71. 8%) felt that their "lupus had been under control over the last 3 months", those who reported the contrary had a 40% higher burden on daily life. Again, this trend was found across almost all European countries Among the main limitations of the study is its design as an online survey.
Acknowledgements:  LUPUS EUROPE  is very thankful to the volunteers that helped build and translate the survey, and then disseminate it throughout Europe, as well as all the patients that have given their time and data to help us better understand the disease. Funding: The study has been funded by  LUPUS EUROPE. LUPUS EUROPE  is itself largely funded from unrestricted grants from industry, where no company exceeded 17% of total funds raised. Disclosures: All Authors report no disclosures related to the study.
TREC T-cell receptor Excision Circles (TRECs) were quantified on patient's PBMCs as described in  (45, 46). In brief, approximately 3x10 6 PBMCs were thawed, washed in DPBS and pelleted. Cells were then lyzed in 10mM Tris-HCl, 0. 05% Tween 20, 0. 05% NP40 and Proteinase K (100 µg/mL) for 30 minutes at 56°C and then 15 minutes at 98°C. Real-time PCR quantification of the different TRECs was performed using LightCycler technology (Roche Diagnostics). Through PCR amplification, any TREC was coamplified with the CD3g gene (used as a housekeeper). Specific primers for the sjTRECs (byproducts of the TCRd locus excision), DJbTRECs (byproducts of TCRBD1/TCRBJ1. 1 to TCRBJ1. 6 or TCRBD2/TCRBJ2. 1 to TCRBJ2. 7 rearrangements), and CD3g gene were defined on human sequences (GenBank accession numbers NG_001333, NC_00007.
For estimating the diversity of the TCR repertoire, the Shannon Entropy calculation (49) was used. Clonal expansions, retractions and tracking across samples was evaluated using VDJtools  (50). VDJviz  (51)  was used to assess the relative TCR usage frequency of each Vb family in CDR3 junction. Additional analysis of TRBV was performed with VDJtools and the tcR package  (52). The VDJdb database  (53, 54)  was screened for known public TCR sequences specific for pathogen antigens, restricted to matched V gene, J gene and CDR3 amino acid sequence where the recorded HLA allele matched the donor MHC haplotype. GLIPH2 analysis (  55 ) was employed to predict clusters within the datasets defined by shared 'local similarity' (patterns/motifs) of the TCR repertoire between samples. GLIPH2 analysis identifies motifs of amino acids within the CDR3 region that are statistically over-represented in one sample compared to another.
Analysis was performed using the local OSX binary obtained from http://50. 255. 35. 37:8080/ with default parameters for comparing the AHSCT donor repertoires at each timepoint to the GLIPH2 human reference v2 CD4 or CD8. Results were filtered for Fisher's score <0. 001 (p-value for contingency table comparing motif frequency in sample and reference) and Vb score <0. 05 (whether clusters included biased TRBV usage). To compare TRBV at a single timepoint to baseline, the baseline clonotypes for the 13 AHSCT subjects for CD4+RO+ (or CD8 +RO+) were pooled and the necessary reference files were generated with clonotypes, TRBV frequency and CDR3 length. The AHSCT baseline CD4 (or CD8) was then used as the reference dataset to analyse the 6-and 12-month timepoints adjusting minimum CDR3 length to 7 and kmers (AA sequences of k length) from 3 to 7 and results were filtered for Fisher's score <0.
001. SONIA  (39, 56, 57)  was used to infer thymic selection pressures on features of amino acid CDR3 sequences. Following validation in hundreds of thousands of human ab TCRs, SONIA takes as input TCR CDR3 AA sequences along with V and J genes. Its output is selection factors for each AA, (relative) position, CDR3 length combinations and V/J gene choice. These selection factors are then used to apply a statistical probability of thymic generation and of thymic selection to each TCR. For each TCR sequence the algorithm computes P GEN ; the probability of being generated in the process of V-D-J recombination and P POST , the probability of entering the peripheral circulation following thymic selection. For determining TCR repertoire overlap across samples, the tcR package supported in R was used.
Similar trends were observed in the public TCR profiles of patients, and the increase in the proportion of public CD8+ memory clones in certain individuals (Figure  3A ) could not be readily explained by TCRs with reported affinity for common viruses (Supplementary Figure  6 ). No definable patterns were seen in clones annotated for self-antigen from the VDJdb. No centralised repository for TCR sequences identified in MS patients exists, however our data was screened against a publicly available dataset from a similar study  (25). 13. 7% of public clones in our data set could be identified in the accessed data. Next the TCR repertoire was analysed using the GLIPH2 algorithm  (55) , with the aim of identifying amino acid motifs which may signify a TCR 'signature' of MS. Pre-AHSCT TCRs (including clones shared with baseline samples from the natalizumab-treated cohort) were compared with a reference healthy population  (58, 59) , and then compared with posttransplant timepoints.
Indeed, an important tool generated in BTN3A research are activating mAbs including the anti-BTN3A agonist 20. 1, that mimics the pAg-induced Vg9Vd2 T cell activation  (43, 138, 139). After successfully showing proof-of-concept of preclinical efficacy  (140) , another BTN3A agonist mAb, ICT01, is currently under evaluation in the EVICTION phase I/II clinical trial (NCT04243499) sponsored by ImCheck Therapeutics in patients with solid tumors and hematological malignancies  (141, 142). The activating anti-BTN3A mAb could represent important therapeutic tools in infections to overcome the imbalances in immune responses observed in some patients. In this context, we are currently testing the ability of the agonist anti-BTN3A 20. 1 to modulate viral/bacterial replication in vitro in co-cultures of infected cells with Vg9Vd2 T cells  (143).
Zebrafish maintenance and mutant generation Zebrafish were raised, bred, and staged according to standard protocols  (Kimmel, et al. ,1995). The following strains were used: AB, Tg(mpeg1. 1:eGFP) (Ellett, et al. ,2011). For CRISPR9 mediated zbtb14 knockout zebrafish generation, guide RNA (gRNA) targeting exon1 of zbtb14 was designed using an online tool ZiFiT Targeter software (http://zifit. partners. org/ZiFiT), which was synthesized by cloning the annealed oligonucleotides into the gRNA transcription vector. Cas9 mRNA and gRNA were coinjected into one-cell stage zebrafish embryos. The injected F0 founder embryos were raised to adulthood and then outcrossed with wild type zebrafish. F1 embryos carrying potential indel mutations were raised to adulthood. Then PCR amplification and sequencing were performed on genomic DNA isolated from tail clips of F1 zebrafish to identify mutants.
Statistical analysis Data were analyzed by SPSS software (version 20) using two tailed Student t test for comparisons between two groups and one-way analysis of variance (ANOVA) among multiple groups. Differences were considered significant at P<0. 05. Data are expressed as mean ± standard error of the mean (SEM). doi:10. 1073/pnas. 0603324103 Zon LI. 1995. Developmental biology of hematopoiesis. BLOOD 86:2876-2891. Table  1. Primers for plasmid generation, luciferase assays, quantitative PCR, and CHIP-qPCR.
Acknowledgments The authors are grateful to  Y Chen  and  J Jin  (both from  Shanghai Jiao Tong University School of Medicine, Shanghai, China ) for technical support. We thank  Dr. X Jiao  (from the  Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ 08854, USA ) for his critical manuscript reading. This work was supported by research funding from the  National Natural Science Foundation of China  ( NO. 32171097 ). (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
, 2005; Warren et al. , 2002)  have shown equivocal results in patients with sepsis  (Fang et al. , 2019; Suffredini and Munford, 2011). Despite this, post-hoc analyses have shown specific benefits (or harms) in groups of patients when stratified by immunological phenotype or transcriptomic profile  (Antcliffe et al. , 2019; Kernan et al. , 2019; Shakoory et al. , 2016) , suggesting a role for personalized immunomodulation in sepsis. Recently, therapies targeting the restoration of monocyte (m)HLA-DR level with interferon (IFN)-g as gain interest to reverse sepsis-induced immunodepression  (Nguyen et al. , 2021; Payen et al. , 2019)  (ClinicalTrials. gov Identifier: NCT01649921, NCT01374711). Decreased expression of HLA-DR -an MHC class II molecule required for antigen presentation to T cells -on the cell surface of circulating monocytes is widely recognized as a marker of sepsis-induced immunodepression and is strongly associated with both sepsis-induced mortality and increased risk of secondary infections (Conway  Morris et al.
, 2022). Therefore, this study provides an opportunity to discuss an example of the use of  of Trieste, Trieste, Italy) for help with paper revisions. The study was supported by a BQR ''Bonus Qualite Ŕecherche'' grant from the University Paris Sud to P. T. and medications and routinely measured laboratory markers. Peripheral blood was sampled into EDTA anticoagulants tubes (Vacutainer, Becton Dickinson, USA) at days 1, 2, 7, 14, 21 and 28 following ICU admission for mHLA-DR assays, and upon recruitment to the study for genotyping. Of 221 patients with septic shock recruited to the study, daily data from day of ICU admission to R7 days and genomic DNA was available from 203 patients (Figure  S1 ). The median age of the study cohort was 61 years (range: 18-94), with a majority of men (65%) (Table  1 ).
Cells were then incubated with fluorescently labeled antibodies were added for 15minat 4 C. The antibodies mix contained: Fluorescein isothiocyanate (FITC)-conjugated anti-CD20 (mouse monoclonal IgG1k, clone LT20, Miltenyi Biotec, Bergisch Gladbach, Germany), phycoerythrin (PE)-conjugated anti-CD14 (mouse monoclonal IgG2a,k, clone M5E2, BD Biosciences, Franklin Lakes NJ) and PE-cyanine 7 (PE-Cy7)-conjugated anti-HLADR (mouse monoclonal IgG2a,k, clone G46-6, BD Biosciences, Franklin Lakes, NJ). After two PBS washes, cells were suspended in 1% paraformaldehyde. Flow cytometry was performed on a BD FACSCanto (BD Biosciences, Franklin Lakes, NJ) and results analyzed by the software FlowJo (TreeStar Inc, Ashland, OR). PBMCs were cultured in 24-well plates at 2 3 10 6 cells/ml in RPMI with 10% FBS at 37 C, 5% CO 2.
Total RNA isolation and quantitative PCR analysis Total RNA was isolated from monocytes and B cells using the NucleoSpinâ RNA XS (Macherey-Nagel, Du ¨ren, Deutschland), following manufacturer's instruction. cDNA was generated from 200 ng of total RNA using iScriptä cDNA Synthesis Kit (Bio-Rad, Hercules, CA). Primers for each CIITA isoform and for the large subunit of ribosomal protein (RPLPO), were designed using the software Beacon Designer 7. 0 (PREMIER Biosoft, Palo Alto, CA) and are reported in together with working concentrations and amplification efficiencies (Table  S1 ). RPLPO was used as a reference gene and the relative quantity of the transcript was calculated by the 2-DDCt method  (Livak and Schmittgen, 2001). PCR reactions were performed in a CFX96 real time PCR system (Bio-Rad Hercules, CA) using the SsoFast EvaGreen Supermix (Bio-Rad, Hercules, CA).
QUANTIFICATION AND STATISTICAL ANALYSIS Allele and genotype frequencies were calculated by direct counting. Hardy-Weinberg equilibrium for the two SNPs was assessed by the software Arlequin v3. 11. Analysis of association between genotype and binomial clinical outcomes using different genetic models was performed by using the software plink (http://pngu. mgh. harvard. edu/$purcell/plink/). Results are presented as range and median for continuous variables and as proportion of total for categorical variables. Differences in HLA-DR, SOFA and SAPS II scores for the different genotypes were analyzed by using the Kruskal-Wallis test run in Stata software. The Wilcoxon rank-sum test was applied to analyze continuous variables between different genotypes. Mann-Whitney Test was applied for analysis of luciferase experiments by using the software GraphPad Prism 9.
We report here a novel HLA-DPB1*11:01 allele, now named DPB1*11:01:06 that carries one nucleotide substitution in exon 2 when compared to the DPB1*11:01:01:01 allele, identified in a volunteer bone marrow donor. The HLA typing was performed using Next Generation Sequencing (AllType NGS, One Lambda, Canoga Park, CA) on the Ion S5 system platform (ThermoFisher Scientific, Waltham, MA),  1   1 ), not resulting in a coding change. This nucleotide change was confirmed using other NGS reagents provided by GenDX NGSgo-MX6-1 (Utrecht, Nederlands) run on the Illumina MiSeq system (San Diego, CA) and analyzed with the NGSEngine software (GenDX, version 2. 26). We were confident in the phasing as the sample displayed a mean read length of 304 base pairs over all the loci, the mismatched G base was attributed 139 times to the new HLA-DPB1*11:01:06 allele and can be only attributed to this allele because it was possible to discriminate from the associated HLA-DPB1*104:01:01:01 allele by virtue of 4 variant positions each distant by less than 100 base pairs.
HLA typing by Luminex reverse sequencespecific oligonucleotide (SSO) was performed (One Lambda Labtype, Canoga Park, CA). 3 With this assay (lot 010, catalog RSSO2P_010_02), the most likely HLA-typing of the patient was DPB1*11:01, 104:01 without any bead modification. Indeed the IPD-IMGT/HLA Database 3. 50. 0 release describe no other HLA-DPB1 alleles displaying a ACG sequence in codon 21, explaining why the manufacturer did not include probes targeting this codon. The nucleotide sequence of the new allele has been submitted to the GenBank database (Accession No. OP393479) and to the IPD-IMGT/HLA Database (Submission No. HWS10062888). The name DPB1*11:01:06 has been officially assigned by the WHO Nomenclature Committee for Factors of the HLA System in September 2022. This follows the agreed policy that, subject to the conditions stated in the most recent Nomenclature Report, 4 names will be assigned to new sequences as they are identified.
from exons 2 to 5. The reads were analyzed using the TypeStream Visual Software version 2. 1 (One Lambda). This donor was found to have a new DPB1*11:01 allele and was consequently typed A*02:01, 30:02; B*18:01, 44:03; C*05:01, 16:01; DRB1*03:01, 07:01; DRB3*02:02; DRB4*01:01; DQA1*02:01, 05:01; DQB1*02:01, 02:02; DPA1*01:03, 02:01; DPB1*11:01:06, 104:01:01. Using the IPD-IMGT/HLA Database, 2 nucleotide sequence alignment with HLA-DPB1 alleles shows that this new allele has one nucleotide change from DPB1*11:01:01:01 in codon 21 in exon 2, where A ! G, (ACA ! ACG, Figure.
To put this T reg increase into context, two published NPC RNA-Seq expression data sets were combined with data on thousands of samples from 32 solid tumor types from the Tumor Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases. We further subset EBV + GC (GC_EBV) from the TCGA GC samples based on prior annotation  [30]. We grouped these samples by tumor type and examined FOXP3, CCL17, and CCL22 expression levels. NPCs and EBV + GC had the first and second highest median expression of FOXP3, a T reg marker, as well as elevated CCL17 and CCL22 compared to the other tumors (Fig  3 ). Examination of control genes and sample clustering confirmed that the NPC and EBV + GC samples were broadly similar to other tumors (S3 and S4 Figs).
Western blotting Whole-cell lysates in RIPA (with phosphatase and protease inhibitors) were boiled for 10 minutes in LDS buffer with reducing agent, separated by 4-12% SDS-polyacrylamide gel electrophoresis, and transferred to nitrocellulose membrane. Membranes were blocked with 5% milk in Tris-buffered saline-Tween 20 and incubated overnight at 4˚C with primary antibodies: anti-LMP1 (A301-957A, ThermoFisher, TFS), anti-LMP2A (MA1-81921, TFS), anti-EBNA1 (sc-81581, Santa Cruz Biotechnology), anti-HSP90 (4874S, New England Biolabs, NEB), or anti-Actin (4970S, NEB). Membranes were washed and incubated with HRP-conjugated secondary antibodies for 1hr at room temperature (RT), followed by rewashing and visualization with enhanced chemiluminescence reagent. Densitometry was done with Li-Cor Image Studio Lite software.
Human T reg migration study-Raji/Daudi When Raji tumors reached volumes of 200-400 mm 3 , mice were treated with 50 mg/kg CCR4-351 and 3 hours later injected with 8x10 6 human iT reg (>90% purity by Flow analysis of CD4, CD25 and CCR4). Tumors were harvested after 7 days into digestion buffer with DNase (89836, TFS) and lysed in gentleMACS C tubes (130-093-237, Miltenyi Biotec) using the Gentle MACS Octodissociator. Staining was performed with: TruStain FcX (anti-mCD16/32) antibody (101320, Biolegend), human TruStain FcX (422302, Biolegend), hCD4 APC Cy7 (317450, Biolegend), hCD45 PE Cy7 (368532, Biolegend), mCD45 BV510 (103138, Biolegend), hCD19 APC (392504, Biolegend), hCCR4 APC (59407, Biolegend), 7-AAD live/dead stain PERCP Cy5. 5 (420404, Biolegend). Data was collected on BD Fortessa and analyzed using FlowJo software.
Bulk RNA-Seq Solid tumor TCGA and TARGET RNA-Seq datasets were downloaded from the UCSC Xena data hub  [57]  on June 18 th , 2017. NPC datasets GSE102349  [58]  and GSE68799 were downloaded from NCBI GEO and processed with Kallisto  [59]. Counts across all data sets were quantile-normalized using preprocessCore  [60]. EBV status for GC was obtained from cBio-Portal  [61]. S1 Table shows tumor-type abbreviations.
Single cell RNA-Seq After tumors reached 200-300 mm 3 , they were collected and digested with collagenase buffer in a 37˚C bath, pipetting every 10 minutes to dissociate. Cells from 5 tumors were pooled per sample. Cell surface protein feature barcoding (CITE seq) antibody (BioLegend TotalSeq-A) incubation was performed per manufacturer protocols, and cells were processed for 10X Chromium 3' RNA seq (v3) chemistry reagents with slight modifications for CITE seq. FASTQ files were aligned to the mouse mm10 genome with CellRanger (v3. 1. 0). Data was analyzed in R  [62]  using Seurat (v3. 2. 2)  [63]  and tidyseurat  [64]  packages. scRNA data deposited as PRJNA736082 to SRA.
Acknowledgments This research was supported by  RAPT Therapeutics, formally known as FLX Bio. Urvi Kolhatkar  helped with densitometry. We thank  Abood Okal  for providing insight and expertise that greatly assisted the research. We are grateful to  Steve Wong ,  Martin Brovarney ,  Justy Guagua ,  Jerick Sanchez ,  David Chan  and  Angela Wadsworth  for providing their technical expertise during the course of this research. We are very grateful to  Heather Milestone  for chemokineinduction experiments that did not appear in the manuscript. Finally, we thank  Brian Wong ,  CEO of RAPT Therapeutics , for manuscript review and continual support for this work.
Population Genetic Structure The pairwise genetic differentiation estimates (F ST ) between most of the sampling sites are statistically significant after Bonferroni correction, except for between sites 1 and 2, 8 and 9, 8 and 10, 9 and 10, 7 and 11, and 7 and 12 (Table  4 ). The PCA (Figure  1 ) showed a structuration among populations with the population from the west (1-4) separated from the population in the east (sites 7-12). Random sampling of two miracidia by patient does not change the latter result (Supplementary Figure  S3 ) This genetic structure was confirmed using Structure software that revealed the highest probability for two clusters (K = 2) (Figure  2 ).
haematobium are similar to S. mansoni  [38, 39]. Regardless of the method used (PCA or Bayesian analyses using Structure software), we showed a clear clustering into two groups of populations: one from the west (populations 1-4) and one from the east (populations 7-12). S. haematobium populations are usually not well structured compared to S. mansoni  [36]. These two parasite species possess similar transmission dynamics that could influence the parasite's genetic structure. For instance, for both species, the transmission is focused on water bodies, the intermediate and definitive hosts have similar mobility, and the number of intermediate host species is restricted. It is well established that S. haematobium has less genetic diversity than S. bovis or S. mansoni  [6, 36]. This low genetic diversity reduces the power of determination of structuring units.
Sequence Analysis The Cox1 and ITS2 sequences were assembled separately and edited with a 4. 5 sequencer version: (Gene Codes Corporation; Ann Arbor, MI, USA). The sequences were aligned using BioEdit Version 7. 0. 9 and ClustalW software. The aligned sequences were compared with the sequences in the GenBank Nucleotide Database for species designation: (https://www. ncbi. nlm. nih. gov/nucleotide/ accessed on 30 March 2022). The nuclear ITS2 region between S. haematobium and S. bovis differs at five polymorphic sites, hence the sequence chromatograms were checked at these SNPs to identify any possible heterozygosity (Supplementary Figure  S2 ). We constructed a Cox1 gene phylogenetic tree only using S. bovis sequences because the Cox1 S. haematobium gene is known to be poorly variable and a phylogenetic study revealed only two clusters in all the areas of repartition of the parasites  [42].
The phylogenetic tree was constructed using MEGA version 6. 0. 6 (Pennsylvania State University, Philadelphia, PA, USA) using an HKY + G nucleotide substitution model identified as the best model describing data. The support for tree nodes was calculated with 1000 bootstrap iterations. The phylogenetic analysis includes S. bovis sequences from various African countries retrieved from GenBank databases with a minimum length of 778 bp (see Supplementary Table  S1 ). The tree was rooted in the S. haematobium haplotypes of the present study. All sequences were uploaded onto the GenBank database (OL840258-OL840278).
This was followed by a final extending period of 30 s at 60 • C  [43]. The microsatellite PCR products were sent to Genoscreen, Lille, France for genotyping. Each microsatellite locus was visibly peak called with GS500Liz size standard (Applied Biosystem) and GeneMarker software. Eighty percent of our samples were successfully amplified by 14 loci and were used for result analysis while 4 markers (C131, Sh4, Sh8, and Sh15), which amplified less than 20% of the samples, were excluded.
Population Genetic Structure Linkage disequilibria and departures from Hardy-Weinberg expectations were tested using exact tests (1200 permutations) adjusted for multiple tests using Bonferroni's correction as implemented in the FSTAT software 2. 9. 3. 2  [44]. We analyzed the genetic variability of schistosomes from each study site by computing the expected heterozygosity (He), number of alleles (A), allelic richness (Ar), and the inbreeding coefficient (Fis) in each microsatellite's locus with FSTAT v. 2. 9. 3. 2  [44]. Heterozygosity (He) and allelic richness (Ar) between the populations were compared using the pairwise Friedman rank test followed by Nemenyi post hoc test. Genetic structure was first assessed by calculating pairwise F ST values between sites according to  [45]  using FSTAT version 2.