dataesr/scientific-paragraphs-categorization huggingface dataset:
Related work: Identifying causal explaining factors on sex/gender and COVID-19 relationship from epidemiological and clinical studies We review findings based on big data on gender and COVID-19 from two angles. First, we analyse a body of papers placing gender and sex as a risk factor towards COVID-19, focusing on explaining the reasons behind disparity. Second, we categorize the explaining variables into mediators and confounders and discuss possible fairness implications of the former results that could lead to discrimination decisions, with the aim of guiding the causal design of the underlying model. The amount of literature providing evidence on links between sex/gender and COVID-19 vulnerability is significant  (Besserve, Buchholz, & Schölkopf, 2021). Table  1  shows articles finding men to be more vulnerable to COVID-19 in comparison to women.
Gender-related lifestyle habits and COVID-19 vulnerability To help understand the association between gender and COVID-19, we conducted a more focused data analysis based on publicly available data to investigate the possible impact of sex and gender on the COVID-19 epidemic. 5 Even if ecological analysis 6  is considered the lowest form of epidemiological evidence, and potentially involves confounding variables, we are aware that it may not be a more accurate assessment than the individual level studies being surveyed in this article. Nonetheless, in this section we use this kind of analysis in order to elucidate plausible risk factors and unaccounted variables potentially explaining the disproportionate results. We constructed a database that aggregates confirmed cases statistics, COVID-19 deaths, ICU admissions and smoking data per gender for 61 countries spanning 5 continents.
The data sources are briefly described below. • The Global Health 50/50 7  project housed at University College of London, which is created by a live tracker that aggregates data on COVID-19 cases and mortality from published government reports. At the time of our analysis on April 05, 2021, sex-disaggregated data for 183 countries including confirmed cases, confirmed deaths, etc. was represented in the live tracker. • We also used a public dataset maintained by Our World in Data, 8  which also contains additional information such as smoking, population, and daily COVID-19 cases. (Wenham et al. , 2020)  Men are more likely to remain hospitalized an die and less likely to be discharged from the hospital than women. ✓(NSD: M) H ✓ ✓(S) Racial and gender based differences in COVID-19  (Kopel et al.
Data generation and model To illustrate different causal paths between gender and COVID-19 severity we construct a causal model based on the discussed literature. We note that causal models can also be learned from data directly with causal discovery methods such as  (Goudet et al. , 2018; Shimizu, Hoyer, Hyvärinen, Kerminen, & Jordan, 2006; Spirtes & Glymour, 1991). However, expert knowledge and previous research in the domain is important in informing what variables have to be included in the data. Furthermore, a recent study  (Binkyt ė-Sadauskien ė, Makhlouf, Pinzón, Zhioua, & Palamidessi, 2022)  shows that different causal discovery algorithms may not always agree on the resulting causal structure, therefore a combination of prior causal knowledge (for example, from experimental research) and statistical methods can help to achieve more robust results.
Some of the variables could be related to race or social status, and a more complex model is required to account for them. However, it goes beyond the scope of this article and could be foreseen for future work. The relationship between variables encoded in a DAG provides the means of recognizing conditional independence and identifying the set of parameters needed for any given computation  (Geiger & Pearl, 1990). The model allows to identify the set of covariates for performing mediation analysis to evaluate the effect of Sex on COVID-19 Severity in the synthetic dataset (faithful to the model). The reason behind using synthetic data is twofold. First, the scenarios we are seeking to illustrate are related to the impact on individuals and require individual level data which is not freely available.
Here we assume biological variables to have the largest overall effect on COVID-19 severity, lifestyle choices being the second, and gender role confounders as having the most moderate effect. The real proportions in the effect on COVID-19 severity can only be derived from a dataset including the relevant explaining variables for association between sex/gender and COVID-19 severity. We hope this article will encourage a causal analysis by proposing the model based on relevant research drawing attention to the relevance of causal knowledge for fair and explainable predictions. The data is generated as follows. For simplicity all variables are set to be binary Bernoulli variables  with domain  ∈ {0, 1} and parameters 0 ≤  ≤ 1 and  = 1 -. The initial probability of the Gender Role variables being Traditional or Equal is set to be the same, namely 0.
Acknowledgements We thank  Songul Tolan  and  Golnoosh Farnadi  for their helpful feedback. We also acknowledge  Carlos Castillo  for early feedback at the conceptualization phase of the paper. Finally, we thank  COMETE team of Inria Saclay-Ile-de-France ,  Catuscia Palamidessi ,  Karima Makhlouf  and  Jasmine Nettiksimmons  for useful feedback discussion and support. The work of R ūta Binkyt ė and  Sami Zhioua  is funded by the  ERC  grant Hypatia (https://project. inria. fr/hypatia/)under the European Union's  Horizon 2020 research and innovation programme. Grant agreement №  835294. N. Díaz-Rodríguez  is supported by the  Spanish Government Juan de la Cierva Incorporación  contract ( IJC2019-039152-I ), the  Google   Research Scholar Grant and Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowship  with agreement ID: 101059332.
Study sample We used pooled data from three large prospective cohort studies of BRCA1 and BRCA2 pathogenic variant carriers: the International BRCA1/2 Carrier Cohort Study (IBCCS, consisting of 19 national multi-and singlecenter prospective cohort studies)  [22] , the Kathleen Cuningham Foundation Consortium for research into Familial Breast cancer (kConFab)  [24, 26] , and the Breast Cancer Family Registry (BCFR)  [23, 25]  (Additional file 1: Table  S1 ). The study sample comprised women who were 18 to 80 years of age at recruitment and tested positive for a pathogenic germline variant in BRCA1 or BRCA2. Women with pathogenic variants in both genes were excluded.
Data collection Study participants completed a baseline questionnaire and one or more follow-up questionnaires. The questionnaires asked about risk factors for breast cancer, including height, young-adult weight (age 18 or 20 years), weight at questionnaire completion, reproductive and medical history, surgical interventions, and menstrual history (age at menarche, age at last menstruation, whether the woman had had any periods in the past year, the number of years/months since the last menstruation, and reason(s) for periods stopping)  [27]. BMI was calculated as weight (kg) divided by height (m) squared. Weight change was calculated as the difference (in kg) between baseline weight and young-adult weight. For women who indicated no periods in the past year, age at menopause was determined by adding one year to "age at last menstruation.
All reported P values are two-sided. P values < 0. 05 were considered statistically significant. Statistical analyses were conducted using R 4. 1. 1 for Windows (R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https:// www. R-proje ct. org) and IBM SPSS Statistics for Windows Version 26 (IBM Corp, Armonk, NY).
Variant carriers may be highly motivated to attempt to modify their breast cancer risk through a healthy lifestyle  [50, 51]. Healthy behavior change might include better nutrition and weight loss, although a persistent change in lifestyle is hard to achieve, especially after menopause  [51]. Our retrospective results of low BMI and higher premenopausal breast cancer risk imply that underweight and unhealthy weight loss should be discouraged in premenopausal women. But further research on a larger dataset is necessary. The first intervention study on nutrition and physical activity for BRCA1/2-pathogenic variant carriers (the LIBRE trial in Germany) is currently recruiting  [52]. It aims to show that physical fitness and maintaining a healthy body weight are key to good quality of life and-in the long run-prevent breast cancer in both affected and unaffected carriers.
Acknowledgements The  Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON)  consists of the following Collaborating Centers:  Netherlands Cancer Institute  (coordinating center), Amsterdam, NL:  M. K. Schmidt ,  F. B. L. Hogervorst ,  F. E. van Leeuwen ,  M. A. Adank ,  D. J. Stommel-Jenner ,  R. de Groot ,  L. Hordijk ;  Erasmus Medical Center ,  Rotterdam , NL:  J. M. Collée ,  I. Geurts-Giele ,  M. J. Hooning ,  I. A. Boere ;  Leiden University Medical Center , NL:  C. J. van Asperen ,  P. Devilee ,.
Funding Open Access funding enabled and organized by Projekt DEAL. The HEBON study is supported by the  Dutch Cancer Society Grants   NKI1998-1854 ,  NKI2004-3088 ,  NKI2007-3756 ,  NKI 12535 , the  Netherlands Organisation of Scientific Research Grant   NWO 91109024 , the  Pink Ribbon Grants   110005  and  2014-187. WO76 , the  BBMRI  Grant  NWO 184. 021. 007/CP46 , and the Transcan Grant  JTC 2012 Cancer 12-054. The EMBRACE study is supported by  Cancer Research-UK  Grants:  PRCPJT-Nov21\100004 ,  C1287/A23382  and  C1287/A26886. MT is supported by the  NIHR Cambridge Biomedical Research Centre  ( BRC-1215-20014  and  NIHR203312 ). DGE was supported by the  NIHR Manchester Biomedical Research Centre  ( IS-BRC-1215-20007 ). The national French cohort, GENEPSO, had been supported by a grant from the  Fondation de France , the  Ligue Nationale Contre le Cancer , by a grant from  INCa  as part of the  European program ERA-NET on Translational Cancer Research  ( TRANSCAN-JTC2012 , no.
2014-008 ) and is being supported by  Institut National du Cancer-DGOS , Grant  PRT-K22-076  and by the  "Programmes labellisés (PGA) 2022 " of the  Fondation ARC  ( N°ARCPGA2022010004414_4863 ). The  Breast Cancer Family Registry (BCFR)  is supported by Grant  U01 CA164920  from the  USA National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the  National Cancer Institute  or any of the collaborating centers in the BCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the  USA Government  or the BCFR. A. O. is partially funded by FIS  PI19/00640  supported by  FEDER  funds and the  Spanish Network on Rare Diseases (CIBERER). The  Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab)  and the kConFab Follow-Up Study have been supported by grants from  Cancer Australia  ( 809195  and  1100868 ), the  Australian National Breast Cancer Foundation  (IF 17 kConFab), the  National Health and Medical Research Council  ( 454508 ,  288704 , and  145684 ), the  Queensland Cancer Fund , the  Cancer Councils of New South Wales, Victoria, Tasmania , and  South Australia , and the  Cancer Foundation of Western Australia.
The disease progresses gradually and begins with muscle atrophy of the shoulder and the pelvic girdles. The symptoms appear around late childhood or early adulthood and are heterogenous, ranging from temporary fatigability to the loss of ambulation up to severe cardiac and respiratory issues for some patients  [10, 14, 15]. To date, despite the relative efficiency of ribitol-5-phosphate therapy illustrated by the orally bioavailable BBP-418 small drug currently under investigation in a phase 2 clinical trial (NCT05775848), no curative treatment is available for patients suffering from this pathology. FKRP gene therapy represents one of the most promising therapeutic strategies to treat LGMDR9  [16]. Delivered by a recombinant adeno-associated virus (rAAV) vector, the sequence coding for the human full-length FKRP protein can be systemically distributed to the FKRP-deficient patient's muscles  [17] [18] [19] [20].
Pre-clinical studies  [20]  have paved the way for a phase 1/2 clinical trial sponsored by Atamyo Therapeutics/Généthon (NCT05224505), aiming to evaluate the safety and efficacy of a FKRP gene therapy product (GNT0006/ATA-100) in LGMDR9 patients. To demonstrate the product activity, quality, and consistency of a gene therapy produced throughout product and clinical development, regulatory authorities such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) recommend several quality controls  [21]. The cell-based potency assay aims to measure the biological activity of the gene therapy product using quantitative biological assays, reflecting the mechanism of action (MoA) of the therapeutic product. Despite limitations in determining an adequate and relevant in vitro experimental model  [22] , making the development of a cell-based potency test challenging, there is an urgent and unmet need for a relevant and robust in vitro model to assess the restoration of biological activity after drug treatment in LGMDR9 disease.
Statistics Results were analyzed using Prism (GraphPad Software Inc. , La Jolla, CA, USA). The comparison of cell groups for the fusion index was performed using one-way analysis of variance (ANOVA) with the post-hoc Tukey test. The determination of statistical differences between two groups was performed using Student's t-test with a two-tailed distribution, assuming equal or unequal sample variance depending on the equality of the variance. Data are presented as the mean ± SEM, with n indicating the number of independent biological replicates used in each group. Differences were considered significant at (*) p < 0. 05; (**) p < 0. 01; (***) p < 0. 001; and (****) p < 0. 0001.
Whereas rAAV-FKRP treatment in the LGMDR9 knock-in mouse model  [20]  leads to significantly increased α-DG glycosylation, resulting in histological and functional improvements, FKRP gene therapy produces only a slight and non-reproducible increase in glycosylation in LGMDR9 patients' cells (data not shown), highlighting the limitations of the current in vitro model as well as the relevance of a newly generated KO-FKRP cell line without any interference of the basal level of α-DG glycosylation to develop a strong potency assay. The new in vitro model may also pave the way for further pre-clinical studies to gain insight into FKRP functions and associated dysregulated pathways. We subsequently developed a valuable potency in vitro assay based on the OCW technology to assess a FKRP gene therapy product.
Acknowledgments: The authors are members of  Genopole , the first French biocluster dedicated to genetics, biotechnologies, and biotherapies. We acknowledge the  Genethon "Vector Core Facility"  for the production of the rAAV. We are grateful to the  "Imaging and Cytometry Core Facility" of Genethon  for the technical support, and to the  Ile-de-France Region , to  Conseil Départemental de l'Essonne (ASTRE) ,  INSERM , and  GIP Genopole ,  Evry  for the purchase of the equipment. We thank the members of the  Analytical Development Department and Progressive Muscular Dystrophies team  for the fruitful discussions and experimental assistance.
ScRNA-seq confirms DC-type identification in ZeST mice We harnessed ZeST mice to perform single-cell RNA sequencing (scRNA-seq) for the five DC types, on index sorting from the spleen of animals either infected or not infected with MCMV, using the gating strategy shown in Fig. 3b  and the FB5P-seq (FACS-based 5′-end scRNA-seq) protocol  27, 28. We first analyzed 343 splenocytes isolated from noninfected (NI) mice. They were clustered and annotated for cell types (Supplementary Table ) by Seurat, based on gene expression profiles (Extended Data Fig. 6a ), and by Rphenograph, based on surface marker expression (Extended Data Fig. 6b, c ). DC-type assignment to individual cells was rather consistent between these two strategies, but suggested heterogeneity of Seurat clusters II and III (Extended Data Fig.
6d ). Therefore, to unambiguously and reliably assign a DC type to individual cells in an unbiased manner, we used a combinatorial strategy: to be selected, a cell had to belong to the expected intersection between the Rphenograph and Seurat clusters (colored cells in Extended Data Fig. 6d ) and to be enriched for the corresponding DC-type-specific transcriptomic signatures  4, 29  as tested with single-cell connectivity Map (sgCMap; Extended Data Fig. 6e ). A final cell type was assigned to 205 cells: 103 pDCs, 26 pDC-like cells, 19 CD11c high tDCs, 23 cDC2s and 34 cDC1s (Extended Data Fig. 6e ,f and Supplementary Table ). Our next objective was to assign a DC type to the 951 cells from the whole dataset, from both NI and MCMV-infected mice (Extended Data Fig. 7a  and Supplementary Table ).
We aimed at achieving a robust DC-type assignment, irrespective of cell states and infection conditions, by combining transcriptomic and phenotypic analyses. Based on the intersection of the clustering with Seurat (Extended Data Fig. 7b ) versus Rphenograph (Extended Data Fig. 7c, d ), a final DC type was assigned to 851 cells (colored cells in Extended Data Fig. 7e ): 310 pDCs, 170 pDC-like cells, 146 CD11c high tDCs, 103 cDC2s and 122 cDC1s, with 100 cells left unannotated (Fig. 4a ). We confirmed DC-type assignment by a complementary method: we generated cell type-specific transcriptomic signatures from the dataset focused on cells from NI mice (Supplementary Table ) and used them for sgCMap analysis of the whole dataset (Extended Data Fig. 7f ). All the cells sorted as pDCs, and most of the tdT -pDCs, were computationally assigned to pDCs (Extended Data Fig.
4c ). The pDC-like cells expressed very high levels of Ly6c2 (Fig. 4c, d ). Hence, the pDC-like cells characterized in the present report were confirmed to align with both the originally described pDC-like cells  4  and the CD11c low Ly6C + tDCs  6. All cDC1s expressed the XCR1 protein (Fig. 4b ). However, a fraction was low/negative for Xcr1 and other genes specific of steady-state cDC1s (Gpr141b, Tlr3, Cadm1 and Naaa; Fig. 4c ), consistent with DC-type activation decreasing the expression of many of the genes used to identify them at steady state  [30] [31] [32]  , preventing use of individual genes for reliable DC-type identification in scRNA-seq datasets  33. As expected, only pDCs expressed rearranged immunoglobulin (Ig) genes, Ccr9, Klk1 and Cox6a2 (Fig. 4c ) as well as the tdT fluorescent reporter above autofluorescence levels (Fig.
4e ). The tdT -pDCs were largely overrepresented in our scRNA-seq dataset because we enriched them for characterization. As we did not use the tdT signal in our analysis, these results confirm the specificity of pDC-Tom mice for pDC identification; they also confirmed proper identification of pDC-like cells and CD11c high tDCs in ZeST mice, and allowed refining of their characterization through side-by-side pangenomic transcriptomic profiling.
8a ). Hence, pDC-like cells and CD11c high tDCs underwent divergent activation in vivo during infection. Indeed,   7a ). DC-type assignment is the same as in Fig. 4a. Seurat clusters are indicated on the UMAP (Extended Data Fig. 7b ). Activation states were assigned based on mining of the marker genes of Seurat clusters (see Supplementary Table ). b, Violin plots showing the expression of selected phenotypic markers across DC types and activation states. c, Heatmap showing mRNA expression levels of selected genes (rows) across all 951 individual cells (columns), with hierarchical clustering of genes using Euclidean distance, and ordering of individual cells (column) according to their assignment into cell types and activation states using the same color code (top) as in a. The color scale for gene expression levels is the same as in Fig.
Flow cytometry analysis Extracellular staining was performed in PBS 1× supplemented with 2 mM EDTA (Sigma-Aldrich) and 1% FCS. All extracellular staining was performed for at least 30 min at 4 °C. Dead cell staining (LIVE/DEAD Fixable Aqua Dead Cell Stain or Blue Dead Cell Stain, Life Technologies) was performed in PBS 1× according to the manufacturer's recommendations. Samples were acquired with a FACS Fortessa X20 (BD Biosciences) or sorted with FACS ARIA III (BD Biosciences) using BD Diva v. 9. 0. All data were analyzed with FlowJo v. 10. 8. 1 software. For the unsupervised analysis, t-SNE plots were generated with the dedicated plugin in FlowJo software. Dimensional reduction was performed on singlets/nonautofluorescent/live dead -/lineage -/CD19 -/CD11c + and/or SiglecH + cells. Dimension reduction was calculated with the following markers: B220, BST2, CD11b, CD11c, CCR9, CX3CR1, Ly6D, SiglecH, tdT, XCR1 and Zbtb46-GFP.
We used the HyperFinder plugin of FlowJo for the generation of unsupervised gating strategy. The calculation was performed on singlets/nonautofluorescent/live dead -cells. We defined the pDC population for the calculation as being lineage -/ CD19 -/CD11b -/XCR1 -/CD11c low /BST2 high /tdT +. The software calculated the best and fastest gating strategy with the following markers: B220, BST2, CD11b, CD11c, CD19, CCR9, CX3CR1, lineage (CD3/Ly6G/NK1. 1), Ly6D, SiglecH and XCR1.
5% BSA) and resuspended in PBS. In all flow cytometric plots, doublets, aggregates, dead cells and autofluorescence were excluded. Acquisition was performed on an Aurora five lasers (Cytek Biosciences) using SpectroFlo v. 3. 0. 1 software (Cytek Biosciences); the quality control (QC) indicates a similarity and complexity index of 11. 7. Data were analyzed using OMIQ (app. OMIQ. ai). First, the data were run through flowCut  51  to check for aberrant signal patterns or events. Second, the data were cleaned by manual gating to remove doublets, debris and dead cells. Third, the autofluorescence of the tissue was subtracted. UMAP  52  was run to reduce the dimensions to a two-dimensional space and thus group phenotypically similar events into 'islands' to illustrate differences both between and inside each population.
ScRNA-seq data generation For the generation of the scRNA-seq data, we followed the FB5P method previously published  28. Briefly, single cells were FACS sorted into ice-cold 96-well PCR plates (Thermo Fisher Scientific) containing 2 μl of lysis mix per well. Immediately after cell sorting, each plate was covered with an adhesive film (Thermo Fisher Scientific), briefly spun down in a benchtop plate centrifuge and frozen on dry ice. The reverse transcription (RT) reaction was performed with SuperScript II (Thermo Fisher Scientific) in the presence of RNaseOUT (Thermo Fisher Scientific), dithiothreitol (Thermo Fisher Scientific), betaine (Sigma-Aldrich), MgCl 2 (Sigma-Aldrich) and well-specific template-switching oligonucleotide. For complementary DNA amplification, KAPA HiFi HotStart ReadyMix (Roche Diagnostics) was used with adapted primers.
Bioinformatics analyses of scRNA-seq data A mark-up file of how the scRNA-seq analysis was performed is provided (Supplementary Fig. 2 ). FB5P-seq data were aligned and mapped to a reference genome using STAR (v. 2. 5. 3a) and HTSeqCount (v. 0. 9. 1) and processed to generate a single-cell, unique molecular identifier (UMI) counts matrix as described  28. The counts matrix was loaded to R (v. 4. 0. 3) and Seurat (v. 3. 2. 0)  54  was used for downstream analyses as described  27. Gene expression is shown as log(normalized values) and protein expression as inverse hyperbolic arcsine (asinh) of fluorescence intensity-scaled values (asinh(fluorescence)/100). For dimensionality reduction, we performed UMAP, using the RunUMAP function. The differentially expressed genes (DEGs) were determined using the FindMarkers function.