dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Results were presented as median and interquartile ranges for quantitative variables and as relative frequencies for categorical variables. Baseline data were compared between groups using Fisher's exact test for categorical variables and the Student't test for continuous variables using SAS Enterprise Guide 7. 1 software (SAS Institute, Inc. Cary, NC, USA). The comparison of anti-and pro-inflammatory markers expression between group, at each time, were performed using two-way ANOVA with Bonferroni post-tests. For the flow cytometry data, two-way ANOVA was used with Tukey's multiple comparisons test. For the area under curve (AUC) representation of the flow cytometry data a Mann-Whitney test was used. All tests were two-sided, with a 5 % significance level. Statistical analyses were performed using Prism 9.
3 ). Expression of CD163, known to be reduced in IL-4 polarized macrophages at the gene expression level  [27] , was significantly different at D5, between D and ND subjects. Moreover, VEGF-A expression results were significantly different only at D0, between ND and D patients. Expression of IL-10, an antiinflammatory cytokine, was also measured. However, its expression was not significantly induced by IL-4, independently of the patient group or time point after MI (Fig. 3 ). Furthermore, under basal unpolarized conditions, the expression of these markers was not different between patients' groups, nor between time points (data not shown), suggesting that their expression was not preconditioned by the diabetic state.
Acknowledgement This work was supported by a local grant from the  Department of Clinical Research and Innovation of the University Hospital  of Nice 2016 ( 16-AOI-08 ), by the  "Agence Nationale de la Recherche " ( ANR-16-CE14-0001 ), by the " Fédération française de  cardiologie" to GC and by the  European Foundation for the Study of Diabetes (EFSD , to JGN).
B Followup of blood vRNA load in three macaques intravenously infected with SHIV 162P3 up to the day of euthanasia. SD indicates splenocyte donor. C Titration of splenocytes (40,000-160,000 cells) in coculture with 120,000 PBMCs. Viral production was detected by RT-qPCR 7 days postcoculture. D Titration of splenocytes (100,000-200,000 cells) in coculture with 10 4 TZM-bl cells. Infection was recorded as Relative Light Unit (RLU) at 48 h post co-culture after subtraction of the RLU of the control wells (TZM-bl only). Each bar represents the mean and standard error of the mean (SEM) of two independent experiments performed in triplicate. Each color represents a different animal. Source data are provided as a Source Data file. Ab-treated CM07 macaque (Table  5 ). Conversely, the virus was below the lower limit of detection (LOD = 46 copies/10 6 cells) in the female reproductive tract (cervix, uterus, vagina, and fallopian tubes) from both treated and control animals.
Detection of adaptive immune responses in SHIV-infected animals To assess the development of adaptive immunity in treated and control animals, we measured Env-binding IgG titers in serum during follow-up and T-cell responses to Env and Gag at the time of animal euthanasia. The five protected animals treated with the 10-1074 HEC gel, as well as the CM01 control animal, remained seronegative throughout the duration of the study, whereas all animals with detectable viremia developed anti-Env specific antibodies between 4 and 8 weeks after the first challenge (Fig. 5A ). T-cell responses were weak but detectable only in viremic animals, including the five treated with the placebo gel and the 10-1074 gel-treated animal CM07 (Fig. and C ). These data suggest that SHIV 162P3 -cell-associated infection fosters B and T cell responses in this model.
Two-different antibody panels were used: panel 1, targeting lymphocyte populations and T cell activation markers, and panel 2, targeting monocytes, dendritic cells, and neutrophils. A list of antibodies used for staining the splenocytes is shown in Table  S7. Data were acquired on an LSRFortessa (BD Biosciences) using Diva software (BD Biosciences) and analyzed with Flowjo v10 (BD Biosciences). Viral DNA extraction from splenocytes, quantification, and normalization DNA was extracted from frozen cells using the NucleoSpin Tissue XS kit (Macherey-Nagel), according to the manufacturer's instructions. SIV DNA was quantified in duplicate by real-time PCR Taqman assay using the Platinum® Quantitative PCR SuperMix-UDG kit (Thermo Fisher Scientific) with SIV-gag primers and probe, as described elsewhere  65.
Experiments were performed using a HydroSpeed microplate washer and Sunrise microplate absorbance reader (Tecan Männedorf), with optical density measurements made at 405 nm (OD405nm). The neutralization activity of the 10-1074 HEC gel was measured using TZM-bl reporter cells in duplicate as previously described in  67. IC50 values were calculated using Prism software by fitting duplicate values using the five-parameter sigmoidal dose-response model.
Pharmacokinetics studies in NHPs A PK study of the 10-1074 Ab-containing gel was conducted in two naïve female CMs (PK01 and PK02) pre-treated intramuscularly with 30 mg medroxyprogesterone acetate (Depo-Provera, Pfizer) 30 days before the gel application. Two mL of HEC gels containing 10-1074 Ab at 5 mg/g were applied into the vaginal vault with a French catheter connected to a ready-to-use syringe. Before procedures, the animals were anesthetized with a 2. 5 mg/kg intra-muscular injection of Zole-til®100 (Virbac, Carros, France). Animals were monitored for product leakage. Blood was collected before and at 24, 48, and 72 h after gel application. CVFs were collected before and at 1, 2, 4, 6, 24, 48, and 72 h after gel application, by placing pre-weighted Weck-Cel sponges (Beaver Visitec International) into the vaginal vault.
Next, the luciferase activity was measured using a TriStar LB941 luminometer (Berthold Technologies GmbH & Co. KG. , Bad Wildbad, Germany) and expressed as relative light units (RLU) and expressed as a percentage of that in positive control wells. IC 50 was calculated in GraphPad Prism 9. 2. 0 using non-linear regression (GraphPad Software, San Diego, CA, USA) and used to estimate the compound concentrations in CVL.
Data visualization and statistical analysis All data visualization and statistical analysis were performed using GraphPad Prism version 9. 2. 0 software (GraphPad Software, La Jolla, USA). Dose-response inhibition curves were plotted using sigmoid dose-response curves (variable slope). Wilcoxon rank-sum test was used to determine if there were any differences in weight (kg) and age (years) between the placebo controls and 10-1074 gel-treated animals at the beginning of the efficacy study. Survival analysis was used to compare time to infection in 10-1074 gel-treated animals relative to the placebo group. A Kaplan-Meier survival analysis was computed and graphed. The log-rank test was computed to determine a statistically significant difference in time to infection between the two groups of macaques.
The map1-2 gene is a member of a multigene family of Major Antigenic Proteins in the Anaplasmataceae (Pfam PF01617)  [42] , which are surface exposed and have been identified as potential vaccine targets  [43]. Some members of the map gene family have been experimentally characterized as porins  [44]  and are suspected to be involved in host cell adhesion  [45, 46]. Thus, its mutation appears to be a potential candidate for attenuation. However, a study of the map1-2 gene in several different strains of E. ruminantium failed to provide evidence of transcription of this gene in any of the strains they tested (Senegal virulent and attenuated, Gardel, Welgevonden and Sankat 430) in either ticks or bovine endothelial cells  [43]. A different study did however report the transcription of the map1-2 gene in Welgevonden  [47].
Acknowledgments The authors would like to thank  Ludovic Pruneau  for technical assistance with growth of the Senegal strains and Valerie Barbe at the  French National Sequencing Centre Genoscope  for sequencing. The genome assemblies for the ERSA and ERSB strains have been submitted to GenBank (Accessions  GCA_002019755. 1 & GCA_002189655. 1, respectively).
This study was partly conducted in the framework of the project  MALIN  " Surveillance , diagnosis, control and impact of infectious diseases of humans, animals and plants in tropical islands", grant #  2015-FED-186 , supported by the  European Union  through the  European Regional Development Fund (ERDF)  and was also supported by  European  project  EPIGENESIS FP7-REGPOT-2012-2013-1 , grant #  31598 , in the framework of the fund for  Research Potential of Convergence Regions (REGPOT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
By comparing our allo-HCT scRNA-Seq data with a public data set on blood B cells from healthy donors (HDs) and non-HCT patient groups (HIV, malaria), we found signature gene profiles unique to allo-HCT within these ABC and other memory subsets. Thus, we hypothesize that scRNA-Seq will provide insight into B cell transcriptional programs when tolerance is maintained or lost after allo-HCT and identify potential molecular targets for future study in alleviating cGVHD and possibly other B cell-associated autoimmune diseases.
Unsupervised clustering analysis of the 8-patient scRNA-Seq data set identified 10 clusters of B cells (Figure  1B ). All 10 B cell clusters were represented in both the 4 active cGVHD and 4 no cGVHD patients (Figure  1C ) and were numbered 1-10 based on largest to smallest total B cell number in both patient groups combined (Figure  1D ). To validate known and potentially novel B cell subsets in allo-HCT patients, we identified hallmark signature (marker) genes for all 10 clusters. We then plotted log 2 -normalized expression values for 16 major B cell development and function genes from all 8 patients (Figure  1, E-H ), each validated using findMarkers in the R package Seurat  (32). These signature genes were remarkably homogeneous within clusters among all 8 allo-HCT patients. Levels of the transitional B cell molecules VPREB3, CD24, and interferon regulatory factor 8 (IRF8) were highest in clusters 1 and 2 and lowest in clusters 9 and 10 (Figure  1E ).
Notably, clusters 1, 2, 4, 6, and 8 retained 1 or more hallmark genes of B cell immaturity, while clusters 7, 3, 9, 10, and 5 expressed markers of antigen activation, cytoskeletal activity, or the capacity to produce antibodies (Figure  2 , A and B). We further validated this ordering of the 10 B cell clusters by interrogating the KEGG  (37)  and GO (38) databases using signature genes of interest for major molecular pathways. Across pathways, clusters 9 and 10 showed striking increases in signature genes involved in B cell-T cell interactions, transcription, proliferation, survival/apoptosis, and metabolic processes (Figure  2 , A and C-F, and Supplemental Figure  2, A  and B ). Interestingly, clusters 7 and 10 shared multiple ribosomal signature genes (Figure  2E ), suggesting increased protein synthesis potential.
Since functional memory B cell recovery is delayed after allo-HCT (13), we assessed memory B cell signatures. Leveraging a similar publicly available scRNA-Seq data set generated from blood B cells of HDs (n = 3), patients with HIV (n = 3), and patients with malaria (n = 3), published by Holla et al. (35) , we first identified total CD27 + ITGAX -, CD27 -ITGAX + , and CD27 + ITGAX + B cells from all groups, which should collectively represent BCR-experienced memory subsets. We then assessed the frequency of B cells within these cluster 5 subsets expressing known genes associated with typical memory B cells or ABCs  (39) (40) (41) (42). For most memory signature genes, the proportion of positive B cells in the 3 subsets was similar among all 5 groups (Supplemental Figure  3 ). Remarkably, B cells expressing ADA, associated with a form of SCID with disrupted B cell tolerance  (43) ; the T cell-attracting chemokine CCL22  (44) ; and 3 G protein-coupled receptors (GPCRs) important for B cell homing to lymphoid niches, CCR7, S1PR1, and S1PR2  (45) (46) (47) (48) (49) , were increased in proportion across CD27/ITGAX subsets in allo-HCT patients, irrespective of cGVHD status (Figure  3 ).
By contrast, ITGB2, which pairs with various integrin α chains to mediate migration, was decreased in allo-HCT  (35). Allo-HCT patients also had increased proportions of B cells across these subsets expressing survival regulators with roles in memory B cell maintenance including BCL2  (50)  and TRADD (51) (Figure  4 ). By  contrast, allo-HCT patients had a paucity in B cells expressing FOS, important for apoptosis during the GC response  (52). Together, these data reveal that the memory B cell pool in allo-HCT possesses intrinsic molecular hallmarks of altered tolerance, migration, SLO homing, and survival. BCR-experienced cluster 5 harbors distinct CD27 + B cell and ABC populations, with the latter expanded in active cGVHD. Since antigen-experienced B cells likely comprise the pathogenic B cells that emerge in cGVHD, as occurs in other diseases such as lupus  (1, 53) , we further interrogated "memory" cluster 5 for the presence of CD27 + typical memory B cells and for the presence of ABCs, which express CD11c (encoded by the ITGAX gene).
Accordingly, regions of B cells in the scRNA-Seq data having increased TNFRSF13B/TACI transcripts were readily evident in active cGVHD (Figure  6F ). These results support studies implicating BAFF as a potential driver of B cell hyperresponsiveness and alloantibody production in cGVHD  (7, 8, 10, 11). To compare allo-HCT with the non-HCT setting, we also performed the PhenoGraph analysis on HD blood B cells. The HD analysis also predicted 15 clusters, but HDs had a greater number of B cell clusters marking with CD27 (Supplemental Figure  5 , clusters m, n, o) compared with allo-HCT patients (Supplemental Figure  4 , clusters l, o only). Furthermore, in allo-HCT patients, cluster o represented mostly CD27 bright B cells that lacked CD21, CD24, IgD, and CD11c expression, suggestive of isotype-switched plasmablasts (PBs, Supplemental Figure  4 ).
R E S O U R C E A N D T E C H N I C A L A D V A N C E JCI Insight 2023;8(11):e169732 https://doi. org/10. 1172/jci. insight. 169732 consistent with recent PhenoGraph findings in pediatric patients with severe autoimmune syndromes  (63). Together these data further elucidate the allo-HCT memory B cell pool and begin to validate our scRNA-Seq data by showing TACI expression increases in ABCs or their precursors in cGVHD. Genes critical for B cell fate and function are differentially expressed in active cGVHD. We next asked whether intrinsic B cell programs were altered in patients with active cGVHD by examining DEGs between allo-HCT patient groups in the scRNA-Seq data set. Bar graphs in Figure  7  represent total DEGs by cluster, either increased (up, Figure  7A ) or decreased (down, Figure  7B ), in active cGVHD compared with no cGVHD.
These DEG data thus suggest altered follicle and GC  movement of B cells in human cGVHD, consistent with findings in cGVHD mouse studies  (3, 20, 75). Finally, genes associated with cell cycle were among the observed DEGs in active cGVHD. These included CDKN1B (P27KIP1) and CCND3 (up, Figure  7C  and Supplemental Table  4 ) and CDKN1A (P21CIP1) (down, Figure  7D  and Supplemental Table  4 ). This is consistent with our previous observations that active cGVHD B cells demonstrate enhanced proliferation to some important stimuli  (9). Numerous DEGs described above influence B cell migration and homing, which compelled analysis of cGVHD diseased tissue sites. We performed scRNA-Seq on a punch biopsy of lesional dermal skin from a patient with sclerodermatous cGVHD and compared it with a biopsy of dermal skin from an HD.
R E S O U R C E A N D T E C H N I C Trajectories for B cell subset diversification in patients with active cGVHD reflect potentially reversible maturation defects. Our past findings suggest a maturation block is linked to aberrant activation of circulating active cGVHD B cells  (9). Thus, we performed pseudotime trajectory analysis on our scRNA-Seq data set using Slingshot  (80). As shown in Figure  7E , Slingshot predicted 5 pseudotime trajectories (designated Traj A-E) for each allo-HCT patient group. Asterisks indicate the origin for the trajectories, empirically assigned to cluster 1 because of its transitional like, IGKC (Igκ + ) phenotype. Traj A, B, and C were remarkably similar between patient groups (Figure  7E , similar trajectories). Traj A consisted primarily of B cell clusters 1, 2, and 4 only, possibly indicating termination at cluster 4 that had generally low transcripts (Figure  1, E-H ), consistent with anergy.
Employing protein phosphoarrays, we tested purified B cell lysates from an independent cohort of allo-HCT patients. P27 KIP1 phosphorylated T198 (phospho-T198) was detected at a 6-to 10-fold greater level in active cGVHD B cells compared to no cGVHD B cells (Figure  9 , F and G and Supplemental Figure  7 ). Active site phosphorylation on AMPKα2 and RSKs, kinases known to phosphorylate P27 KIP1 T198  (85, 86) , were similar between allo-HCT patient B cells, suggesting increased kinase activity was not responsible for enhanced phospho-T198 in active cGVHD. Western blot analysis of total protein supported the concept of P27 KIP1 being protected in active cGVHD B cells, with significantly higher levels compared with no cGVHD B cells (Figure  9H ). These data enable formulation of a model for potential P27 KIP1 regulation in active cGVHD B cells (Figure  9I ), whereby broadly increased CKS2 expression, combined with increased CDKN1B/P27KIP1 transcription in some subsets (Figure  7 , C and E), may lead to P27 KIP1 accumulation.
BAFF influence may additionally propagate by increases in BAFF receptors such as TACI/TNFRSF13B, as published  (11)  and herein described for B cell subsets. It is also noteworthy that multiple other TNF superfamily and TNF receptor superfamily members were increased DEGs in cGVHD B cell clusters, including the metabolically active clusters 9 and 10. Therefore, ongoing studies of these molecules as potential drivers of ABC expansion in cGVHD is warranted. Plasticity or malleability has been demonstrated in newly formed B cells and during the generation of memory  (91, 92). Our scRNA-Seq data set and supporting work provide insight into these processes (Figure  10 , allo-HCT plasticity). Signature gene profiles defined 10 distinct B cell subsets (Figure  10 ) that continually developed and circulated under constant exposure to alloantigens.
Methods Experimental procedures. See Supplemental Methods for information on B cell isolation and culture, scRNA-Seq sample preparation, scRNA-Seq data analysis, dermal skin B cell isolation and scRNA-Seq analysis, qPCR, flow cytometry and PhenoGraph analyses, phosphoarray sample preparation and analysis, and Western blot analysis. Original, uncropped array and blot images are included in the online supplemental materials. Statistics. For scRNA-Seq data, clustering analysis was conducted using R statistical environment and extension package Seurat v3. 1. 4 (32). DEG analysis with respect to disease status (active cGVHD vs. no cGVHD) within each cluster was performed based on the read counts across all cells within each sample using the Bioconductor package DESeq2 (version 1. 26. 0). Global interaction between disease status and the B cell clusters also was analyzed.
Multiple testing was accounted for within the framework of control of the false discovery rate using the qvalue approach (version 2. 14. 1) (https://github. com/StoreyLab/qvalue; commit ID 9b3f9a8). Statistical tests used in supporting experiments are described in the figure legends. For all comparisons in the scRNA-Seq data set, an adjusted P value of less than 0. 05 was considered statistically significant. For all other statistical comparisons, standard P values were used to determine significance, as follows: *, P < 0. 05; **, P < 0. 01; ***, P < 0. 001. Study approval. Deidentified whole blood samples or apheresis samples were obtained from allo-HCT patients under IRB protocols from Duke University, the National Cancer Institute (NIH), or the Dana-Farber Cancer Institute. Informed written consent was obtained from all patients, with the scope of the research and the minimal risks fully explained.
Acknowledgments The authors thank  Laney Weber , Certified Editor in the Life Sciences, for the critical review of this manuscript and formatting assistance. The authors also thank  Lauren S. Riley  and  Serach Patterson  for proofreading assistance. Funding was provided by  NIH National Heart, Lung, and Blood Institute  grant  R01HL129061  (to SS);  Leukemia & Lymphoma Society  grant  6497-16  (to SS);  NIH National Institute of Allergy and Infectious Diseases  grant  R38AI140297  (to SJB); and  European Union 's  Horizon 2020 Research and Innovation Program  under the  Marie Sklodowska-Curie grant  agreement  888743  (to JV, SS). Address correspondence to:  Stefanie Sarantopoulos ,  Duke Adult Bone Marrow , 2400  Pratt Street , Suite 9000, DUMC Box 3961, Durham, North Carolina 27710, USA.
Data availability. All blood B cell scRNA-Seq library data from the 8 allo-HCT patients (16 samples) are available through the Gene Expression Omnibus database (https://www. ncbi. nlm. nih. gov/geo/) under accession code GSE161343. The R scripts to reproduce the analyses of these scRNA-Seq data are available at this site: https://gitlab. oit. duke. edu/dcibioinformatics/pubs/sarantopoulos-10x-cgvhd/; commit ID 93fac70a. The Supporting Data Values file is included in the online supplemental materials.
The simplest model best describing the progress of the change of considered attribute is then selected using statistical scores such as (corrected) Akaike and Bayesian Information Criteria (AIC/BIC)  18. In a few cases where AIC/BIC criteria disagreed, several 'best models' were considered to perform multiple bootstraps on each model ranking them by AIC and BIC weighted scores to obtain realistic predictions. Using this multiple model bootstrap (MMB) approach, bootstrap was made on a single model, with a number of loops (N loops ) being proportional to its respective values of the weights wAIC and wBIC. The AKTS-Thermokinetics software  19  (version 5. 5) was used to perform AKM and associated stability predictions. Alternatively, SAS  20  (version 9. 4) was used for stability modeling of mAb B8.
For comparison, classical linear regressions of stability data obtained under recommended storage conditions 2 were performed using JMP  21   (version 16). Shelf-life Cards (AKTS) were used to monitor during the transport or storage of vaccines the variations of temperature, humidity, luminosity, and geolocation in real-time of vaccines experiencing temperature excursions. As described above, the previously created kinetic models, are combined with the Shelf-life Cards (SLC) (1) dα dt = v × A 1 × exp - Ea1 RT × (1 -α 1 ) n1 × α m1 1 × C p1 +(1 -v) × A 2 × exp - Ea2 RT × (1 -α 2 ) n2 × α m2 2 × C p2 through the AKTS data management software solution. The advanced kinetic analysis combined with the SLCs allows continuous monitoring of the level of vaccine degradation within the management software.
Results Various examples of accelerated stability studies were collected, covering a wide range of applications, including screening of formulations, estimation of shelf-life and impact of an excursion of temperature at the end of shelf-life (in-use conditions). Stability Predictions of Biotherapeutics. Various stability attributes were monitored during accelerated stability studies up to 40 °C, 45 °C or 55 °C for three or six months for various mAbs (Fig. 1a -g) and one   fusion protein (Fig. 1h ). In all cases kinetic models led to stability predictions in full agreement with real-time stability data at 5 °C up to 3 years. Aggregation, considered as a main degradation pathway of protein in solution, was monitored during accelerated stability studies up to 45 °C for three or six months for various biotherapeutics, including mAbs (Fig.