dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions Based on these retrospective data that should only be considered as hypothesis generation, and taking into account the study's above-mentioned limitations, the French Collaborative Thoracic Intergroup (IFCT) launched a prospective, single-arm, first-line Phase 2 study, SAVIMMUNE (IFCT 1802, Eudract N • : 2018-004742-42, NCT04108026). This study's primary aim is to assess the anti-PD-L1 monoclonal antibody durvalumab in patients with high PD-L1 tumor expression (≥25% tumor cells) and poor general condition. Two sequential strata are scheduled, with only one including PS 2 patients. If no safety signals are noted, a second strata devoted to PS 3 patients will be investigated, with safety parameters as the primary study endpoint.
Patients and study design This monocentric study (Paoli-Calmettes Institute, Marseille, France) included forty-eight patients with newly diagnosed non-acute promyelocytic leukemia AML from the HEMATOBIO cohort (NCT02320656). Patients were aged 19 to 80 years (mean ±SD = 54. 0±14. 0). All patients received cytarabine and anthracycline-based induction chemotherapy as previously described. 43  Eight patients (16. 7%) were treated with allogeneic stem cell transplantation as a consolidation therapy. Patients' characteristics are summarized in Table  1.
Statistical analysis Statistical analyses were carried out using Graph Pad Prism V5. 01 (San Diego, CA, USA) and SPSS V9. 0 (Chicago, IL, USA). The χ 2 or Fisher's exact test was used to assess association between variables. For multiple comparisons of paired values, a Friedman test was performed followed by a Dunn's posttest. Groups of patients with a high or low frequency of CD56 -CD16 + NK cells were defined based on optimized cut-points using maximally selected log-rank statistics (maxstat package, R software V 3. 6. 2)  44  and on imposing that no group could represent fewer than 25% of patients. For survival analyses, overall survival (OS) was defined as the time from diagnosis until death from any cause, and event-free survival (EFS) as the time between induction and relapse, death from any cause, or induction failure, whatever occurred first.
Algorithm-based high-dimensional analysis NK cells were manually defined as CD13 -CD33 -CD34 -CD45 + CD3 -CD19 -CD56 + or CD13 -CD33 -CD34 -CD45 + CD3 -CD19 -CD56 -CD16 + and exported using FlowJo V10. 6. 2. The gating strategy is displayed in Supplemental Figure  1. Data were arcsinh-transformed with a cofactor of 5. NK cell populations were automatically defined using hierarchical stochastic neighbor embedding (h-SNE) analysis (Cytosplore V2. 2. 1) with default settings (30 perplexity and 1,000 iterations). 45  For the h-SNE analyses, consensus files were generated for each group of patients with a fixed number of NK cells in order to obtain a representative and balanced view of all patient groups. NK cell trajectory inferences were performed using the Wishbone algorithm. 32  The library was updated for Python V3.
Pseudo-time analysis The important question of the origin of these CD56 -NK cells remained. Interestingly, in patients with AML, accumulation of CD56 -CD16 + NK cells was associated with a decreased frequency of conventional (CD3 -CD56 + ) NK cells, thus suggesting that these NK cells are derived from the pool of conventional NK cells rather than expanded from the pool of immature precursor NK cells (Supplemental Figure  5 ). To probe this hypothesis, we performed a trajectory inference of NK cell maturation using the Wishbone algorithm. Wishbone is a recent algorithm used for analysis of developmental pathways in high-dimensional single-cell datasets. This algorithm positions single cells along bifurcating developmental trajectories on a k-nearest neighbor graph and pinpoints bifurcation points.
ACKNOWLEDGMENTS The authors thank the  CRCM cytometry core facility  as well as the  IPC/CRCM/UMR 1068 Tumor Bank , which operates under authorization #  AC-2007-33  granted by the  French Ministry of Research (Ministère de la Recherche et de l'Enseignement Supérieur). Louise Ball  from Angloscribe, an independent scientific language editing service, provided drafts and editorial assistance to the authors during preparation of this manuscript. This work has been financially supported by the  INCa  (grant  2012-064/2019-038  for D. O. , and A. T. ), the  Fondation de France  (grant  00076207  for A. S. C), the  SIRIC Marseille  (grant  INCa-DGOS-INSERM 6038 ), the  Cancéropôle PACA  (grants  K_CyTOF 2014  and  AML_CyTOF 2016  for J. A. N. ), the  GS IBiSA  and the  Agence Nationale de la Recherche  ( PHENOMIN  project for M.
Study design The online survey was initially designed to evaluate whether and how the COVID-19 pandemic influenced menstrual health. Retrospective and self-reported data on menstrual cycles, behaviour, life circumstances and health before and during the pandemic as well as SARS-CoV-2 infection and vaccination status were collected using an online survey hosted on the Qualtrics platform (www. qualtrics. com). All survey responses were anonymized using randomly generated IDs.
Exposures A total of 33 variables were extracted for this analysis. In addition to socio-demographic variables (age, income, education, gender, ethnic group, marital status), and standard proxies for health (BMI, smoking status, physical activity, regular use of vitamins/supplements, regular use of medicine), the dataset included vaccine-related, COVID and pandemic-related, and reproductive variables (See SI4 for the operationalization of variables). First, data on the type of vaccine received, of which only two had been approved for use in the UK at the time (Pfizer BioNTech/Oxford-AstraZeneca/Not sure), and the timing of the first vaccination (month/year) were included. Second, COVID status was operationalized in two ways: (i) based on whether people thought they had had COVID, as widespread testing had not been available in the UK in the early months of the pandemic which fell within the survey period, leading to three categories: No COVID, acute COVID (symptoms lasting less than 28 days) and Long Covid (symptoms lasting more than 28 days) as well as (ii) based on a combination of testing and self-diagnosis, leading to three categories: No COVID (no tests or negative tests), COVID tested + (positive test) and "Self-diagnosed positive" (referring to individuals who had a suspected or clinically diagnosed COVID infection but had not obtained positive PCR, antigen or antibody tests).
As an alternative way of selecting covariates for the multivariable models, and to improve model prediction accuracy, we also performed LASSO regression using the "glmnet" package in R  (22). As the range and scale of variables can influence the penalization for having too many variables in elastic net models, all ordinal variables were coded numerically and reclassed as continuous, and all continuous variables were centered and standardized. Nominal categorical variables were broken out into individual binary dummy variables for all response levels except for the reference level.
Missing data The analysis of complete cases only can introduce bias and lead to a substantial reduction of statistical power  (23) , especially if it is plausible that the data are missing at random or not completely at random. An evaluation of the missing data suggested that multiple imputation was advisable (SI6). The average proportion of missing values across all variables in the dataset was 3. 8%, which was mostly accounted for by the variable BMI (38% of missing data, SI6). To handle missing data, we used a multiple imputation approach using the R package 'missRanger'  (24) , which combines random forest imputation with predictive mean matching  (24). Prior to all analyses, we imputed 5 datasets, with a maximum of 10 iterations specified for each imputation. Each imputation was also weighted by the degree of missing data for each participant, such that the contribution of data from participants with higher proportions of missingness was weighted down in the imputation.
We set the maximum number of trees for the random forest to 200 but left all other random forest hyperparameters at their default. The average out-of-bag (OOB) error rate for multiple imputation across all imputed datasets was 0. 08 in women (range: 0 to 0. 77) and 0. 08 in men (range: 0 to 0. 69). Parameter estimates for all five datasets were pooled to provide more accurate estimates. A sensitivity analysis was also performed on the complete cases without missing data imputation (n=1,548 (SI7)).
The type of contraceptive used and the history of COVID infection, while correlated, did not offer good predictive value for whether an individual will report changes to their menstrual cycle. Each exposure alone contributed an increase of only 1 to 3% of explained variance. The AUCs for the multivariate models were low across the imputed datasets (0. 57 to 0. 61) and the complete case dataset (0. 63): the variables considered are not sufficient for predicting accurately whether an individual will report menstrual changes after vaccination. To improve the prediction accuracy of our models, we also performed a LASSO regression considering all 33 variables, but no improvement in AUC was obtained (SI12), suggesting that key variables are missing from our dataset and/or that the subjective outcome is not defined specifically enough for accurate prediction, especially if experiences of menstrual changes are diverse.
Discussion Statement of principal findings: Using data collected in the UK prior to widespread media attention to menstrual disturbances following COVID-19 vaccination, this study found that among pre-menopausal vaccinated individuals who menstruated in the 12 months preceding the survey, 20% reported any changes to their menstrual cycles up to 4 months after receiving their first injection. In this sample, there was an association between a history of COVID infection and an increased relative risk of reporting changes of menstrual cycles following vaccination against COVID-19, independently of how COVID status was determined, i. e. , using COVID type (Acute vs. Long Covid) or certainty of diagnosis (tested vs. self-diagnosed positive). This study also found that using contraceptives containing oestradiol (e.
After washing in PBS supplemented with 0. 2% Tween20 (Sigma-Aldrich), the slide was incubated with secondary horseradish peroxidaseconjugated antibody for 30 min at room temperature (Supplementary Table  1 ). Antibody binding was revealed with diaminobenzidine (DAB, VECTOR Labs, Burlingame, CA, USA) as chromogenic substrate. The slide was then counterstained with hematoxylin (DAKO, Agilent technologies), dehydrated with increasing concentrations of ethanol and xylene, respectively, and mounted with Entellan Neó ® mounting solution (Millipore, Merck, Darmstadt, Germany). Stained sections were analyzed using VECTRA 3 imaging system (Akoya Biosciences, Marlborough, MA, USA). Images were shared, treated and edited using Omero. iviewer, Omero. figure from an OMERO image database online platform  (21).
They were then stored at +4°C. Each antibody-metal conjugate was validated by mass cytometry, using UltraComp eBeads (Life Technologies, Carlsbad, CA, USA) coated with antibodies recognizing antibody Fc-fraction. After the incubation of 0. 5 mL of each metal-antibody with 50 mL of beads and several washes, 5000 events were acquired in a Helios mass cytometer (Fluidigm) and the metal signal of each antibody was analyzed using FlowJo software (BD bioscience, San Jose, CA, USA).
ACKNOWLEDGMENTS The authors acknowledge the IPMC microscopy facility part of the  Microscopie Imagerie Cote d'Azur (GIS IBiSA labeled platform) , the iBV experimental histopathology facility (Nice, France) and the  Cytometry core facility Hyperion (Brest, France)  for their technical assistance. The authors thank  Julie Rousset  from  Centre de Resources Biologiques-CAL  for providing tumor specimens,  Drs Sonanda Bailleux ,  Ana Giordano  and  Florent Milliet  from  Hopitaux pediatriques de Nice CHU-Lenval  for providing tonsil specimens and  Heĺène Barelli  for providing access to VECTRA 3 imaging system. We thank  Dendrities/ Eurobio Scientific  for advice and reagants.
FUNDING This research was supported by  Centre National de la Recherche Scientifique ;  Institut national de la santéet de la recherche medicale; UniversitéCote d'Azur; Canceŕopole PACA; Reǵion Provence-Alpes-Côte d'Azur; Fondation d'entreprise SILAB Jean PAUFIQUE; Fondation d'Entreprise Bristol-Myers Squibb pour la Recherche en Immuno-Oncologie ;  Fondation ARC pour la recherche sur le Cancer; Ligue Nationale contre le Cancer ;  Fondation de l'Avenir; French Government (National Research Agency, ANR)  through the  "Investments for the Future" programs LABEX SIGNALIFE   ANR-11-LABX-0028  and  IDEX  UCAJedi  ANR-15-IDEX-01.
Monocyte purification, transcriptomic profiling, and statistical analysis Extended methodological details and details on statistical analysis are given in a supplementary file (Supplementary data 1). In brief, CD14 + monocytes were isolated from heparinized whole blood by positive magnetic selection using CD14 microbeads. In cohort 1, RNA isolation and gene chip hybridization were performed as previously described  [21]. In cohort 2, monocyte transcriptomes were generated by RNA-sequencing using the Illumina HiSeq4000 sequencer. For GeneChip microarray analysis, significant differences in expression were identified using high performance chip data analysis (HPCDA) (for details see Supplementary data 1). A HPCDA score of > 100 was considered significant. For RNA-sequencing, analysis of differential gene expression was carried out with edgeR using a linear model including the disease status, the gender and the sequencing experiment.
Scores reaching a p-value < 0. 05 were considered significant. Hierarchical clustering of transcriptomes was performed with Genes@Work software  [22]. Principal component analysis (PCA) was performed using Qlucore (Lund, Sweden). For functional allocation, gene set enrichment analysis (GSEA) was performed using the Molecular Signature Database (MSigDB) v7. 2  [23, 24]. Differentially expressed probe-sets and genes were analyzed for co-expression in 70 reference transcriptomes (all Affymetrix HG-U133 Plus 2. 0 transcriptomes) generated by our own or retrieved from Gene expression omnibus (GEO) data repository as previously described  [25]. In brief, data sets were harmonized by quantile normalization before application in co-expression analysis. Pearson correlation coefficients were calculated between the differentially expressed probe-sets of individual comparisons on the basis of the 70 reference transcriptomes.
Co-expression analysis of the axSpA signature genes points towards distinct LPS/TNFα activation and reveals a neutrophil-like phenotype of monocytes To decipher transcriptional alterations of axSpA monocytes in the context of BM adaptation and LPS/cytokine activation, we applied mapping of monocyte profiles with previously published profiles from GEO data repositories. Up-and downregulated probe-sets are analyzed separately; for analysis of cohort 2 the matched GeneChip probe-sets were used. The reference profiles comprised (1) 34 transcriptomes of 11 different cell types of early and late myelopoiesis from bone marrow (BM; GSE42519)  [29] ; (2) 15 transcriptomes from blood cells comprising BDCA1 + (n = 3) and BDCA3 + DCs (n = 3), CD15 + PMN (n = 3) and blood monocyte subsets including classical CD14 + CD16 - monocytes (Mo-CD16 -; n = 3), and non-classical monocytes CD14 -CD16 + (Mo-CD16 + ; n = 3) [8] (GSE18565); (3) six transcriptomes of blood leukocytes from healthy donors before and after treatment with G-CSF (GSE7400)  [30] ; and (4) 12 transcriptomes of blood purified monocytes incubated without stimulus or stimulated with TNFα, LPS, IFNγ, or IFNα (GSE38351)  [21, 31].
A Hierarchical clustering of active and inactive axSpA patients according to 1367 significantly different probe-sets (HPCDA score > 100). B Gene set enrichment analysis (GSEA) using MSigDB as input database identified overlap of upregulated and downregulated genes with the indicated pathways. C Coexpression analysis in reference transcriptomes performed as in Fig. 2   To determine if disease activity in axSpA is associated with transcriptional changes in monocytes we compared transcriptomes from active (BASDAI ≥ 4) vs. inactive patients (BASDAI < 4) in cohort 1, in which none of the patients was treated with biologics. The "active axSpA signature" resulted in only reasonable clustering of active and inactive patients, which is not unexpected given that BASDAI is patient-reported without molecular parameters of inflammation.
Acknowledgments The authors would like to thank  Renu Dua ,  Estelle Piwowar Manning ,  Claire Vincent ,  Patrick Tordjman ,  Ana Olivo  and laboratory staff at the  Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States , the  Eurofins Biomnis Laboratory, Ivry sur Seine, France , and the  Infectious Disease Research, Abbott ,  Core Diagnostics ,  Abbott Park, IL, United States  for their assistance with sample management and testing.
Raw read processing Raw data in FASTQ format were processed for TRB sequence annotation using MiXCR software (v. 3. 0. 3)  (24) , which ensures PCR and sequencing error corrections and provides for each sample a list of unique amino acid TRBs and their corresponding counts. Annotated sequences were then analyzed using an in-house workflow to eliminate long amino acid CDR3 sequences (length higher than the mean + 8). The resulting datasets are summarized in Table  S2.
Statistical comparison Sample overlap was tested by computing the Jaccard distance (25) using the "vegan" R package and plotted into a heatmap using the "pheatmap" package  (26). Clustering analysis was performed using the 'Ward' method. Principal component analysis was performed using the "stats" R package. Renyi entropy (27) was calculated using "vegan" and represents the distribution of clonal expansions as a function of the parameter alpha. Probability density plots were plotted using the "ggplot2" R package  (28).
TCR Database Mouse CDR3βs associated with diabetes (n=52), EAE (n=50), SLE (n=49) and cancer (n=52) were collected from Friedman's published database  (32). These sequences were derived from CD4 + T cells and identified by different methods including reactivity assays, tetramer staining, selection of diabetes-specific TCRs following diabetes induction and by sequencing of tumor T cell infiltrates in the context of cancer studies.
Statistical analyses Statistical analyses were performed using the nonparametric Wilcoxon test with Holm multiple testing correction using "ggpubr" R package  (33). A p value of ≤0. 05 was considered statistically significant; * p ≤0. 05, * * p ≤0. 01, * * * p ≤0. 001, * * * * p ≤0. 0001 and n. s. denotes not significant (p > 0. 05). Mean percentage comparisons of diabetes, EAE, SLE and cancer CDR3s were done using the Wilcoxon paired test with diabetes as a reference group.
Treg specificity to diabetes-related antigens The existence of public databases containing TCRs with known peptide specificities allowed us to investigate the presence of antigen-specific sequences in our repertoires. Diabetes-related CDR3s identified from the database were overrepresented in IL-2treated NOD Tregs and most connected in LD=1 networks, as shown by their high number of neighbors. Linking the CDR3 similarity networks to annotated TCR datasets was previously described by Madi et al. , and showed that CDR3s with similar specificity tend to cluster together  (34). This indicates that diabetes-specific CDR3s could have core motifs engaged in pancreatic antigen recognition, and shows the potential of such a tool in identifying novel pathogenic TCRs. Indeed, repertoire analysis of islet-infiltrating T cells revealed an accumulation of TCRs that were reactive in vitro to uncharacterized autoantigens, distinct from the well-known glutamic acid decarboxylase and insulin antigens  (13).
Acknowledgments We thank  F. Jebbawi  and  L. Barientos  for their help in sample collection and cell sorting,  M. Mendez  and  N. Papaya  for their help in RNA extraction and library preparations, and  K. El Soufi  for his help in network analyses. We thank the staff of the animal care facility for their excellent handling of mice. We also thank the  UMR 8199 LIGAN-PM Genomics platform (Lille, France)  which belongs to the ' Federation de Recherche' 3508 Labex EGID (European Genomics Institute for Diabetes ;  ANR-10-LABX-46 ) and was supported by the  ANR   Equipex 2010 session , for performing the sequencing of our libraries.
Sample Size The sample size was calculated using an equation for the infinite population proportion  [36]  and a prevalence (p), taken from a previous study in Thailand  [19]. Bangkok and Nakhon Sawan provinces were defined as urban and suburban areas, respectively. Hence, the previous proportion (p = 4. 38%) was re-calculated from data on settlement and rain-fed areas derived from the previous study. For the sample size calculation, the maximum tolerated error (d) and alpha (α) were set at 5%. In total, the calculated sample size was 65 rodents based on the following equation: n = Z 2 1-α 2 × p × (1 -p) d 2.
Analysis of DNA Sequence The obtained DNA sequences were trimmed using the Chromatogram Explorer Lite version 5. 0. 2 software (http://www. dnabaser. com, accessed on 20 November 2021) under the default low-quality end trimming conditions (75% of good bases, 18 bases of window length, and 25 quality value (QV) of good base). The trimmed DNA chromatograms were edited using the SnapGene ® Viewer version 5. 3. 2 software (https://www. snapgene. com/ snapgene-viewer, accessed on 20 November 2021) and analyzed using BLASTn (https: //blast. ncbi. nlm. nih. gov/Blast. cgi, accessed on 20 November 2021). A phylogenetic tree was constructed using the neighbor-joining method based on a proper substitution model with 1000 bootstrapping replications in the Molecular Evolutionary Genetics Analysis (MEGA) version X software (https://www.
megasoftware. net, accessed on 20 November 2021). Bartonella positive sequences were analyzed for polymorphism based on the number of variable sites (VS), the proportion of G + C content (GC), the number of haplotypes (h), the average number of nucleotide differences (k), haplotype diversity (Hd), and nucleotide diversity (π) using the DNA Sequence Polymorphism (DnaSP) version 6. 12. 03 software (http://www. ub. edu/dnasp, accessed on 20 November 2021). Then, the sequences were analyzed based on a median-joining network using the Population Analysis with Reticulate Trees (PopART) version 1. 7 software (http://popart. otago. ac. nz/index. shtml, accessed on 20 November 2021) with the default setting (epsilon = 0). All Bartonella-matched sequences were submitted to GenBank with accession numbers: OK381826-OK381850.
Several genetic events including mutation, demography, and recombination were factors regulating haplotype diversity  [11]. Trimmed gltA sequences (approximately the 327 base pair) have been acclaimed for taxonomic classification in the genus Bartonella  [66]  and for distinguishing among subspecies and species  [2, 37, 65, 66]. However, an additional RNA-polymerase beta subunit (rpoB) gene has been noted to increase identification efficacy, especially for the classification of new species  [66]. Remarkably, gltA sequencing was suggested as a common method for Bartonella diversity study in wild animals  [6] , although homologous recombination was an important point of this gene  [67, 68]. Compared with sequences in the NCBI database, the gltA sequences of Bartonella spp. have been continuously updated, and this has facilitated more species to be distinguished  [47, 65].