dataesr/scientific-paragraphs-categorization huggingface dataset:
HSPC subtype assignment In order to assign known FACS cell identity in our HSPC pool, we used CaSTLe (Classification of single cells by transfer learning), a supervised classification method consisting in labeling cells in a scRNA-seq experiment, using knowledge learnt from other experiments on similar subtypes  [64]. We chose as source dataset a published scRNA-seq dataset obtained from FACS isolated HSPCs  [11]. Cells from this data set (approximately 2000/per type) were divided into 4 subsets: the LTHSC (Lin-Sca1 + Kit + Flt3 -Cd150 + Cd48 -), the STHSC (Lin-Sca1 + Kit + Flt3 -Cd150 - Cd48 -), the MPP2s (Lin-Sca1 + Kit + Flt3 -Cd150 + Cd48 + ), and the MPP3 (Lin-Sca1 + Kit + Flt3 -Cd150 - Cd48 + ). HscScores were computed as previously described  [35].
Dataset integration, data scaling, and cell cycle regression To minimize batch effect between datasets, we integrated our 4 sample datasets (Young_A, Young_B, Old_ A, Old_B) following the procedure of Seurat 3  [25]. Integration was done also for young and aged conditions separately. Briefly, the highly variable genes for each dataset were selected with the FindVariableFeatures function (selection. method ="vst") and ranked according to the number of datasets in which they were independently identified as highly variable. The top highly variable 2000 genes were thus integrated by iteratively merging pairs of datasets according to a given distance. Integration anchors, representing two cells that are predicted to originate from a common biological state in both datasets using a Canonical Correlation Analysis (CCA), were done using the FindIntegrationAnchors function (dims=1:15).
Then, the expression of the target dataset was corrected using the difference in expression between the two expression vectors for each pair of anchor cells. This step was realized using IntegrateData function (dims=1:15). This process resulted in an expression matrix that contains the batch-effect-corrected expression for the 2000 selected genes of the cells from the 4 samples. Standardized (i. e. , centered and reduced) expression values with cell to cell variations due to cell cycle effect regressed were obtained with the ScaleData function of Seurat using the G2/M, S and G1/G0 scores previously computed for each cell by cyclone for the var. to. regress argument (cf Cell cycle phase classification).
Pseudotime ordering Unsupervised ordering of the HSPCs was done with the Seurat 3 integrated results as input to build a tree like differentiation trajectory using the DDRTree algorithm of the R package Monocle v2  [31]. Integrated data from (i) all samples (young and aged) excluding the primed pL2 cluster cells, (ii) young cells only, or (iii) aged cells only were processed with Monocle. For the three pseudotime ordering analyses (all cells, young only, and aged only), the 2000 gene expression matrix, scaled and regressed for cell cycle effect (see Data scaling and cell cycle regression) issued from the Seurat 3 integrated samples was loaded into Monocle using the newCellDa-taSet function (lowerDetectionLimit = 0. 1, expression-Family = uninormal()). The 2000 genes were set as ordering genes and trajectory building was made by calling the reduceDimension Monocle function (max_components = 2, reduction_method = 'DDRTree', norm_ method = "none", pseudo_expr =0).
25 (logfc. threshold = 0. 25) were tested. A p-adjusted value (Bonferroni correction) threshold of 0. 05 was applied to filter out nonsignificant markers. Aging markers for the global population were obtained with the FindConservedMarkers Seurat function (min. pct = 0. 1, logfc. threshold = 0) using the sequencing platform as grouping variable to minimize batch effect (Young_A, Old_A were processed on HalioDx platform and Young_B, Old_B on TGML platform). The Wilcoxon rank-sum test was performed on the lognormalized data between all young versus all aged cells (Additional file 6: Table  S5 ) from each batch separately and the two p values for each gene were combined using the Tipett's method. Genes presenting an opposite variation between the 2 batches were filtered out. Aging markers for each cluster (Additional file 7: Table  S6 ) and for each Monocle state (Additional file 11: Table  S10B ) were obtained with the same method by looking at the difference cluster per cluster and state per state (min.
Gene set enrichment analysis To characterize the identified clusters with Seurat, we performed gene set enrichment analysis on cluster markers with g:Profiler v0. 6. 7  [66]  with default arguments except for background set to all genes expressed in the whole dataset (i. e. , genes that passed filtering during quality control). We tested enrichments in GO terms (GO:BP, GO: MF, GO:CC) as well as in terms from KEGG, REAC, TF, MI, CORUM, HP, HPA, and OMIM databases (Fig. 1c  and Additional file 4: Table  S3 ). Cluster markers were also tested for enrichment in previously published gene set signatures related to HSPCs (Additional file 13: Table  S12 ). Signatures tested were Bcell_Chambers, Diff_Chambers, Gran_Chambers, HSC_Chambers, Lymph_Chambers, Mono_Chambers, Mye_Chambers, NK_Chambers, Nai-veT_Chambers, and Ner_Chambers lineage priming of HSC signatures C1, C2, C3, Mk, Er, Ba, Neu, Mo, Mo2, preDC, preB and preT  [11] , and HSCs and aging signatures Mm_HSC_Runx1_Wu, Mm_HSC_Tcf7_Wu  [67] , Mm_LT_HSC_Venezia, Mm_Proliferation_Venezia, Mm_ Quiescence_Venezia  [68] , Polarity_factors_Ting, Novel_ HSC_regul_polar_Ting  [57] , HSC aging Svendsen  [30]  and MGA-MEP  [34].
Cluster marker enrichment for the different signatures in comparison to all dataset genes was tested using a hypergeometric test (phyper R function). To perform enrichment analysis of aging markers with a consistent gene number, we gathered the overexpressed (resp. underexpressed) markers from at least one cluster and used gprofiler as describe above (Additional file 8: Table  S7A  & B). Expression scores of the signatures or of selected aging features from the enrichment analysis were calculated for each individual cell using the AddModule-Score Seurat function (on log-normalized data) with default parameters, using as input the genes of the signatures or the aging markers annotated for the selected features. The Svendsen signature score was computed in the same way taking the aging markers common to our study and those of Svendsen's re-analysis  [30].
Regulon analysis pySCENIC (1. 10. 0) was used with its command line implementation  [38]. The raw expression matrix for the cells of all samples was filtered, by keeping genes with a total expression greater than 2*0. 01*(number of cell). 10, 698 genes passed the filtering. pyscenic grn command was used with grnboos2 method and default options and a fixed seed to derive co-expression modules between transcription factors and potential targets. We used as input all the markers of the Seurat clusters for which a transcription factor binding motif was available in the motifs-v9-nr. mgi-m0. 001-o0. 0 database provided by Scenic, plus several TFs involved in early hematopoiesis, Spi1, Tal1, Zfpm1, Cbfa2t3, Erg, Fli1, Gata1, Gata2, Hhex, Runx1, Smad6  [69] , Gfi1b  [70] , and Zbtb16  [71]. The obtained modules were refined by pruning targets that did not have an enrichment for a corresponding motif of the TF with pyscenic ctx command withmaskdropouts option using the motif database motifs-v9-nr.
mgi-m0. 001-o0. 0 and the cis-target database mm9-tss-centered-10 kb-7species. mc9nr. Only positive regulons (i. e. , those with a positive correlation between the TF and its targets) were kept for downstream analysis (Additional file 10: Table  S9A ). AUCell scores (regulon activities) in each cell were computed with pycenic aucell command (default options). To be noted that number of target genes was highly variable from a regulon to another (Additional file 1: Fig. S9 ). For young and aged HSPCs, two heatmaps of regulon activity scores, along pseudotime, were made in order to analyze transcriptional activity at the two bifurcation points for both ages. See Additional file 10; Supplementary methods for detailed regulons heatmaps construction.
Statistics Statistics were computed with R software v3. 5. 1. The statistical tests for gene expression and signature or regulon activity scores were performed with and are detailed above. In each cluster and in non-primed/ primed clusters gathered, the enrichment of age was tested using a hypergeometric test (phyper R function Fig. 2b ). Chi 2 tests (chisq. test R function) were performed to test independence between cell cycle phase and age, in all cells (Fig. 6a ) and in each HSPC subtype separately (Fig. 6b ), and in the cells at the departure of the trajectory (Pseudotime < 2, Additional file 11: Fig. S10 ) and to test independence between Monocle state and age in all Monocle states (Fig. 4i ) and in the states 2, 3, and 5 only (Fig. 4j ). Fisher's exact test (fisher. test R function) was performed to test independence between Monocle state and age in each Seurat cluster (Additional file 9: Supplemental Fig.
S8B ). Wilcoxon rank-sum test was used to test for median difference between pseudotime value distributions of young and aged cells (Fig. 4h ). In each cluster, a linear regression was computed between the average log fold change (in the cluster) and the global (in all cells) average log fold change of the aging markers recovered in the cluster (lm R function Additional file 6: Supplemental Fig. S5 ). Smooth curves of module score expression in pseudotime through the different fates for young and aged cells were drawn for quiescence and proliferation signature (Fig. 6c ) using the geom_smooth function ggplot2 R package  [72]  with the gam function of mgcv R package  [73]. grant from Aix Marseille University; MP's postdoctoral fellowship was supported by the Fondation de (#2017-00076284).
Acknowledgements We thank  Dr. Lionel Spinelli  for critically reading the manuscript and the code. We are grateful to the core flow cytometry and the animal facilities of the  CRCM  for providing supportive help and to the  HaliodX  and the TGML sequencing facilities for the single cell capture and sequencing. Computing resources for this study was provided both by the computing facilities DISC (Datacenter IT and Scientific Computing) of the  Centre de Recherche en Cancérologie de Marseille  and by the facilities of the  Institut de Mathématiques de Marseille  thanks to  Dr. Olivier Chabrol.
Funding This work was partly supported by the  Excellence Initiative of Aix-Marseille University  -A * MIDEX, a  French "Investissements d'Avenir" program  to ED and  ER, Institut Thématique Multi-Organisme -cancer, by l 'Institut National du Cancer  (grant number  20141PLBIO06  to ED) and the  ARC  foundation (PJA# 20161204989  to ED). LH was the recipient of an interdisciplinary PhD.
Patient Collective The cohort for this study includes 319 formalin-fixed paraffin-embedded primary BC tissues that were collected from patients who underwent surgery from the year 2000 to 2002 at the Department of Gynecology and Obstetrics of the Ludwig Maximillian University in Munich, Germany. The total collective (TC) included 319 patients (Table  1 ). After an observation period of up to 10 years, DFS and OS were statistically analyzed, and these follow-up data were retrieved from the Munich Cancer Registry. The Union for International Cancer Control (UICC) TNM classification was completed to assess the size of the primary tumor (pT)  [53, 54] , lymph node involvement (pN), and distant metastasis (pM). An experienced pathologist of the LMU Department of Pathology determined the tumor grade and histological status.
Ethical Approval The tissue samples used in this study were left over material after all diagnostics had been completed and were retrieved from the archive of Gynecology and Obstetrics, Ludwig Maximilian University, Munich, Germany. All patients gave their consent to participate in the study. All patient data and clinical information from the Munich Cancer Registry were fully anonymized and encoded for statistical analysis. The study was performed according to the standards set in the Declaration of Helsinki, 1975. The current study was approved by the Ethics Committee of Ludwig Maximilian University, Munich, Germany (approval number 048-08). The authors were blinded from the clinical information during the experimental analysis.
Statistical Analysis Statistical analysis was performed with the IBM Statistical Package for the Social Sciences (IBM SPSS Statistic v26. 0 Inc. , Chicago, IL, USA). The gathered results were inserted into the SPSS database in the implied manner, building the TC. The Chi-squared test was used to assess the distribution of clinical-pathological variables. Correlations between findings of immunohistochemical staining were determined with Spearman's analysis. The nonparametric Kruskal-Wallis test was used to test for differences in cytoplasmic and nuclear RXRα expression regarding the set prognostic markers. OS (in years) and disease-free survival (DFS) (in years) was compared using Kaplan-Meier graphics and differences in patient survival times were tested for significance using the Chi-squared statistics of the log rank test.
There are some factors limiting our study. First, it was a retrospective analysis based on a single dataset. Second, the sample size was comparatively low and may thus be possibly insufficient to elucidate all the heterogenous entities in breast cancer. Furthermore, specific information on possible toxic environmental aspects or a history of endocrine therapy and other patient characteristics could enrich the investigation of how additional factors interact with RXRα. Hormone treatment with estrogen might influence breast cancer growth, since a cooperative antiproliferative interaction between RXR and ER has been described  [47, 48]. In addition, the guidelines for surgical, radiation oncology and chemotherapy treatment options changed substantially within the observation time of the study.
The WHO recently endorsed an ambitious plan, "Defeating Meningitis by 2030", that aims to control/eradicate invasive bacterial infection epidemics by 2030. Vaccination is one of the pillars of this road map, with the goal to reduce the number of cases and deaths due to Neisseria meningitidis, Streptococcus pneumoniae, Haemophilus influenzae and Streptococcus agalactiae. The risk of developing invasive bacterial infections (IBI) due to these bacterial species includes genetic and acquired factors that favor repeated and/or severe invasive infections. We searched the PubMed database to identify host risk factors that increase the susceptibility to these bacterial species. Here, we describe a number of inherited and acquired risk factors associated with increased susceptibility to invasive bacterial infections.
In a third review, Goruganthu et al. discuss the effects of Notch ligand receptor recruitment on anti-tumor immune responses, as well as major clinical and preclinical findings that highlight the potential therapeutic activity of targeting this pathway. Finally, mining GEO and TCGA databases, Li et al. identify an immune risk signature that could serve as a predictor of survival and immune activity in colon cancer. Thanks to this collection of articles, this issue provides insightful perspectives on immunosuppressive mechanisms at play in the TME, their impact on anti-tumor immunity, and the identification of novel targets for the immunotherapy of cancer.
Background Ivermectin is known to cause severe encephalopathies in subjects infected with loiasis, an endemic parasite in Sub-Saharan Africa (SSA). In addition, case reports have described ivermectin-related serious adverse drug reactions (sADRs) such as toxidermias, hepatic and renal disorders. The aim of this study was to identify suspected sADRs reported after ivermectin administration in VigiBase, the World Health Organization's global individual case safety reports database and analyze their frequency relative to the frequency of these events after other antinematodal drugs reported in SSA and other areas of the world (ROW).
These reactions reflect the inflammatory phenomena associated with the destruction of mf by the drug. Since the early 1990, ivermectin has been known to cause potentially fatal encephalopathies in individuals with very high microfilarial density of L. loa in the blood (loiasis is endemic only in Central Africa)  [5, 6] , also referred to as "Possible/Probable L. loa encephalopathy temporally related to Mectizan" (PLERM). PLERM can occur in subjects with L. loa microfilarial density >10,000 mf/mL if measured before treatment or >1,000 mf/mL if measured after treatment  [7]. Since then, few studies have been conducted to investigate the frequency of these Loa-related adverse drug reactions (ADR) and the mechanisms by which they occur  [8]. In 2017, an analysis of the WHO Global individual case safety report (ICSR) database (Vigi-Base) for serious neurological adverse events was conducted  [9].
This study raised questions about the mechanisms underlying the appearance of these neurological effects. To our knowledge, these studies are the only two that have used a pharmacovigilance database to evaluate the occurrence of post-ivermectin ADRs and they have focused exclusively on neurological events. Our systematic search of the literature for non-neurological adverse events found 10 cases where ivermectin was associated with cutaneous reactions  [11] [12] [13] [14] [15] , nephropathy  [16] , psychiatric disorders  [17, 18] , hepatic disorders  [19, 20]  and multiorgan dysfunction syndrome  [21]. Clinical trials and observational studies have reported common adverse events such as headache, pruritus, muscle pain, cough, dyspnea, nausea, vomiting, diarrhea, blurred vision, postural hypotension and confusion and more anecdotal effects such as serious skin reactions and edematous swelling  [22] [23] [24].
Data source Data were extracted from the WHO Global Individual Case Safety Report (ICSR) database, VigiBase  [25]  which includes more than 20 million cases of suspected ADRs reported by national pharmacovigilance centers in more than 130 countries participating in the WHO Program for International Drug Monitoring  [26]. An ICSR is an anonymized report for a single individual who experienced adverse event(s) that may be linked to the use of one or more drugs. ICSR contains sociodemographic information (age, sex, reporter qualification, country of origin, year of report), information about the drug administration (frequency, dosage, comedication) and information about the reported adverse event. The latter include the seriousness according to the criteria of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH)  [27] , adverse event verbatim description and associated terms from the Medical Dictionary for Regulatory Activities (Med-DRA) developed by the ICH.
Study design We performed disproportionality analyses using the case/non-case method which allows to identify disproportionate reporting, i. e. a higher than expected number of adverse reaction reports compared to other reactions recorded in the database by calculating Reporting Odds Ratios (ROR). ROR compares the odds of exposure to ivermectin between cases and non-cases  [30, 31]. Cases were defined as reports of each suspected sADR of interest identified by a MedDRA PT for severe headache, encephalopathies, confusional disorders, seizures, toxidermias (drug reaction with eosinophilia and systemic symptoms, Stevens-Johnson syndrome, toxic epidermal necrolysis and acute generalized exanthematous pustulosis), psychiatric disorders, suicidal behavior, severe acute respiratory syndrome (SARS), renal disorders, hepatic disorders, cardiac failure, rhythm disorders and Mazzotti reaction.
Discussion Our study used a case-non-case approach to assess the association between the use of ivermectin and the reporting of neurological as well as non-neurological suspected sADRs, recorded in the WHO drug adverse events database from 2003 to 2020 (see S3 Table for STROBE checklist of case-control studies). To our knowledge, it is the first to globally review the main serious ADRs reported with ivermectin. Some strong significant disproportionality signals were found, showing more frequent reporting of encephalopathies after ivermectin than after benzimidazoles, both in SSA countries and in the RoW. Disproportionality signals were also identified for serious toxidermias, serious confusional disorders and serious Mazzotti reactions with ivermectin when compared with benzimidazole drugs or all non-ivermectin antinematodal drugs.
In contrast, benzimidazoles have little short-term effect on the mf of any filarial species, and thus do not induce Mazzotti reactions, but impair the production of new mf by the adult female worms. The US Food and Drug Administration (FDA) approved product information for ivermectin mentions that "Rarely, patients with onchocerciasis who are also heavily infected with Loa loa may develop a serious or even fatal encephalopathy either spontaneously or following treatment with an effective microfilaricide. " [2]  In our study, we confirmed the findings of a previous analysis of the data in VigiBase  [10]  which identified encephalopathies reported with ivermectin also outside of SSA where L. loa is not endemic. In addition, we quantified this association by estimating aRORs for ivermectin-induced encephalopathy.
These findings could be of great interest for clinicians considering ivermectin treatment in patients at risk for these sADRs or assessing the causality of ivermectin in the development of a toxidermia. Our study has several strengths. First, we used the global ADRs database VigiBase intended to collect information on suspected ADRs from nearly all national pharmacovigilance centers in the world, allowing us to estimate ROR for rare events with sufficient statistical power and to stratify on geographical origin (SSA vs. RoW). Second, analyses were performed with adjustment for several potential confounders such as origin, gender, age and period of notification. Third, nearly all results of our principal analysis were confirmed in our sensitivity analyses considering all antinematodal agents.
Fourth, our results are consistent with already known risk associated with ivermectin (encephalopathy in SSA and Mazzotti reactions). Limitations of this study include the concern about under-reporting of suspected ADRs and differences in the under-reporting between different countries as well as the lack of information on the number of drug administrations, which is a major disadvantage inherent in studies using pharmacovigilance databases  [33, 34]. Although under-reporting may be less important since we focused on serious ADRs (which are more likely to be reported)  [35] , our analyses cannot measure the real risk of ADR but only the differences in reported events. Indeed, subjects in the control group (non-cases) are not healthy controls but patients with other various reported ADRs and pharmacovigilance data do not consider the total amount of patients exposed to the drug.
To do so, 340day-old cured or age-matched naïve mice were inoculated with LLC tumor cells in the absence of CD8 + T cells. Both naïve-and cured-age-matched mice developed tumors (Figs. 6g, h ). However, the tumor growth was significantly reduced in cured mice and three out of the six mice did not have tumors (Fig. 6g , right panel). Cured mice survival was also significantly increased in comparison to naïve mice (Fig. 6i ). Collectively, these results suggest that circulating CD8 + T cells participate in the antitumor immune response induced by HEI3090. P2RX7 is positively correlated with high infiltration of antitumor immune cells in NSCLC patients. Using the lung adenocarcinomas (LUAD) TCGA dataset, we analyzed the effect of P2RX7 expression levels on the recruitment of cytotoxic immune cells.