dataesr/scientific-paragraphs-categorization huggingface dataset:
Secondly, we double-checked that the wristbands were recording data corresponding to our time-budget activity data. An example of the raw data from three individuals is also shown in the supplemental information (Figure  S1 ). Lastly, we regressed total step counts as the dependent variable and the previous days' time allocation data as the predictive variable, using a multivariate permutation regression (Permutation tests, also called randomization or re-randomization tests). 89 A few activities, such as teaching and studying (AT8, AT10, AT18, AT19), had a low participation rate; hence six activities were not included in this permutation test (see Table  S2 ). Because step counts are related to height and weight, we made sure to account for that in subsequent analysis. Covariates, like weight and height, did have an impact on the step counts, whereas the results of the generalized linear regression model also revealed that the time reported by participants undertaking subsistence work and gathering (AT3, AT4, AT5, AT7, codes presented in Table  S2 ) was significantly positively correlated with the total counts of steps.
QUANTIFICATION AND STATISTICAL ANALYSIS Generalized multilevel comparative analysis Data analyses were completed by R software, in which the "lmPerm" package was used to build the prediction model and permutation test,  76  a non-linear logged multi-level mixed model was built in "nlme,"  77  the model screening process was completed in "MuMIn,"  78  and "ggplot2," 79 "sf," 80 "stargazer" 81 packages were used for figures and tables. In regard to time-budget data and activity data, all the categories of activities and step counts show an overdispersion when we conducted generalized multilevel Poisson models. Additionally, a series of Poisson models did not perform well, even after we applied Poisson distribution and log link for daily step count data, as well as binomial distribution and logit link for proportional time-budget data.
ACKNOWLEDGMENTS We thank the local people for their participation in this research and their great help and assistance in fieldwork. We thank  Jiajia Wu ,  Yuping Yang ,  Bai Pengpeng ,  Almira Anwar ,  Yu Tang ,  Xin Zhu ,  Gang Jin ,  Hanzhi Zhang , and  Yishan Xie  for help with data collection. We thank  Bin Liu ,  Guo-Song Gao ,  Shi-Liang Li ,  Feng-Yi Xu , and other programmers from  Huami Company  for their immense help in programming and technical support. We are also grateful to two anonymous reviewers,  Andy Gardner ,  Alberto Micheletti , and  Mark Dyble  for their great comments. We also thank the  Human Evolutionary Ecology Group at UCL. This work was funded by the  European Research Council  (grant no. 834597 , Evobias: '' Sex-specific demography and the evolution of genderbiased harmful cultural practices ''),  University College London , and  Lanzhou University.
Implementation The server side of ViCloD website was implemented in python (version 3), Bash script and PHP (version 7. 4. 3, http://php. net/) that was used for interactive data processing. We also used the MySql database (mysql. com/) to control the user's pending jobs. The client-side web interface was implemented using HTML5, CSS, and JavaScript (javascript. com/) along with several libraries, including D3. js (d3js. org/), FileSaver. js (github. com/eligrey/FileSaver. js/), saveSVGAsPNG (github. com/exupero/saveSvgAsPng), PDFKit (pdfkit. org/), and JSZip (stuk. github. io/jszip/). Users submit their input files and can provide an email address to receive their results. We first check that the input file contains all the required information. Then, data are processed by the ViCloD Data processing tools, and all analyses are directly available or by clicking on the link sent by email.
However, in females with MS, cells expressing AR protein appeared to be increased in active lesions and perilesional areas of chronic active lesions, where there is potential to remyelinate, and fewer in the center of chronic active lesions and normal appearing white matter (Supplementary Fig. 1c-f ). In males, there was little AR expression in active or chronic active lesions (Supplementary Fig. 1j, k ). In females, some lesional AR + cells were CD68 + corresponding to activated microglia/macrophages (Supplementary Fig. 1g ). Also in support of these data, AR mRNA expression in microglia from MS and control donors from a publicly available singlenuclei RNA sequencing database  26  appeared higher in MS female samples compared to males (Supplementary Fig. 1l ). Altogether these data confirmed that the sexual discrepancy observed in mice may be relevant in human and microglia/macrophages may be a target for AR signaling during remyelination in females.
Sexually dimorphic molecular mechanisms induced by DHT in EAE animals The sexual dimorphism mentioned above led us to investigate the molecular mechanisms putatively involved. We performed a transcriptomic comparison, by bulk RNA sequencing (RNA-Seq) of the spinal cords derived from EAE female and male mice treated or not (control) with DHT (Fig. 10a ). After dataset normalization (Supplementary Fig. 11 ), we found that both DHT-treated females and males were clearly separate from their respective controls by principal component and clustering analyses indicating a clear effect of DTH treatment (Fig. 10b, c  and Supplementary Fig. 12a-d ). A large number of differentially expressed genes (DEGs) was found in DHT-treated compared to control, both in females (3285 up-and 4185 down-regulated) and males (2061 up-and 1720 down-regulated) by using stringent statistical criteria (FDR < 0.
05) indicating strong gene expression changes upon DHT treatment in both sexes Supplementary Fig. 11b ; Supplementary Dataset 1). To assess the impact of DTH-treatment in oligodendrogenesis and myelination, we used OligoScore (https:// oligoscore. icm-institute. org/), a resource using a knowledge-driven scoring procedure for gene sets involved in oligodendrogenesis and (re)myelination, as described in Methods. Given that EAE model is characterized by the co-existence of demyelinating and remyelinating plaques (as in MS), genes either promoting or inhibiting the oligodendroglial processes could be detected among the DEGs (Supplementary Fig. 13a-j ; Supplementary Datasets 2-5). This analysis indicated that myelination was the main process impacted by DHT treatment, both in females and males (Fig.
10f-i , Supplementary Dataset 6 and Supplementary Fig. 12e ). These molecular features are thus fully consistent with the remyelinating effects and the functional improvement of female and male animals induced by DHT and also corroborate the differential capacity of DHT to regulate the inflammatory process in female and male demyelinated tissues. Given the sex-dependent discrepancies identified above regarding the local inflammatory cells, we further analyzed the RNA-Seq data focusing on these cells. We used gene sets characteristic of the main subpopulations of microglia constituting microglia diversity during aging or neurodegeneration, including homeostatic, activated, disease associated (DAM), and white matter associated (WAM) microglia  [28] [29] [30] [31] [32] [33] [34] [35]. Two subsets of genes were down-regulated in both DHT-treated females and males compared to their respective controls either without significant difference between female and male controls (including homeostatic genes Axl, Cd68, Csf1r, Cx3cr1 and Tmem119; DAM genes Axl, Trem2, Ctsl, Fth1, and Lyz2; WAM genes Anxa5, C1qb, Cd63, Cd74, Cst7, Ctsz, Fam20c, Fth1, Ftl1, H2-D1 and Lyz2) or with significant difference between female and male controls (including homeostatic genes ApoE, C1qc, Mertk, Rxra and Trem2; DAM genes ApoE, Ctsb,Ctsd, Lpl, Timp2 and Trem2; WAM genes ApoE, Atp6v0c and Ctsb; Supplementary Datasets 7 and 9) in agreement with the reduction of the inflammatory foci and demyelinated areas previously observed in spinal cord slices.
However, another gene subset (34 genes out of 103) was exclusively down-regulated in DHT-treated females without significant differences between female and male controls (including microglia genes Cd33, Fcgr2b, Tlr4, Tnf; microglia DAM genes B2m, Ccl6,Cd9,Clec7a, Csf1, Csf2, Itgax; microglia WAM genes Anxa2, C1qb, Capg, Cd52, Crip1, Ctss, Cybb, H2-K1, Ifitm3, Lgals1, Lgals3, Spp1, Tspo, Vim; microglia activated genes Il1b, Rpl14, Rpl21, Rpl35, Rpsa, Tmsb4x; microglia remyelination genes Ank, Fn1, Psat1; pink highlighted in Supplementary Dataset 9; tab DEGs Microglia). Several out of these genes are notable such as the genes encoding TLR4 whose activation leads to the production of pro-inflammatory cytokines  36  or the proinflammatory cytokine TNF-α, but also Csf2 and Il1b, which encode the well-known pro-inflammatory GM-CSF and IL-1β, respectively.
Quantitative RT-PCR amplification confirmed that Tnf and Csf2 were significantly down-regulated in DHT-treated females (Supplementary Fig. 14 ). To exclude bias putatively related to changes in microglia cell numbers between conditions, we first took advantage of tools available in the field by using a scRNA-Seq dataset from mouse EAE model 37 (GSE113973). Resulting dotplots revealed that 21 genes out of 31 displayed a high average and percent expression mostly in EAE microglia (Supplementary Fig. 15 ). By combining all microglial gene sets (105 genes), we performed gene set enrichment analysis (GSEA) with these genes ordered by their changes in expression either in female or male comparisons (DHT-treated versus non-treated). In line with previous results, this GSEA analysis showed large enrichment of many gene sets in DHT-treated females but almost none in males, with many of the suppressed gene sets (genes being downregulated) related to immune and inflammatory processes, including 'lymphocyte mediated immunity', 'response to stress', 'defense response', 'immune system process', 'immune response', and 'cytokine production' (Supplementary Fig.
16 ; Supplementary Dataset 10). Therefore, these results agree with those obtained by immunofluorescence analyses supporting a strong anti-inflammatory activity of DHT in EAE female mice, but not males. To try to exclude bias putatively related to changes in cell numbers between conditions, we used Cibersortx, a machine learning method to determine cell type abundance and expression from bulk tissues  38  , together with a single cell RNA-Seq dataset from mouse EAE model 37 (GSE113973). This deconvolution of our bulk-RNA-Seq datasets suggested that while microglial clusters did not change in proportions upon DHT-treatment in males, DHT-treated females presented some changes in microglial clusters and EAE immune-OL/OPC clusters  37  (Supplementary Dataset 11), likely due to the abovementioned dysregulation of microglial/inflammatory genes.
Indeed, 21 genes out of 31 downregulated genes only in DHT-treated females are expressed in the EAE microglial cells from this scRNA-Seq dataset (Supplementary Fig. 15 ). Altogether these results are in agreement with our flow cytometry data, which indicated that DHT did not modify the proportion of microglia/macrophages neither in female nor in male spinal cords compared to their controls (Fig. 8f ) and they support the existence of true molecular differences in the effects of DHT in female compared to male mice. Similarly, we used sets of genes previously implicated in the characterization of different astrocyte subsets  39, 40. Among the 165 DEGs related to astrocytes, 62 were deregulated in both DHT-treated females and males. Most importantly, 83 were deregulated by DHT exclusively in females whereas only 20 were deregulated exclusively in males compared to their own controls.
Sequential amplifications were performed at 40 °C and the mRNA signal was developed by horseradish peroxidase incubation followed by incubation with Opal 570 (1:750; FP1488001KT, Akoya Biosciences). For Iba1 immunofluorescence, sections were then incubated with anti-rabbit Alexa Fluor 647 secondary antibody (1:750; A-21244, Thermo Fischer Scientific), counterstained with DAPI and mounted with ProLong Glass Antifade mounting medium (Invitrogen). The entire sections were imaged using the Opera Phenix Plus system (PerkinElmer) under a 20x water-immersion objective. Image analysis was performed in QuPath software  71. Regions of interest were selected to contain different lesion types in MS samples, or at random in control samples. Iba1 + cells that contained AR + puncta were manually counted in at least 3 different regions of interest per lesion type and expressed as percentage of the total number of Iba1 + cells.
Image acquisition and analysis Images were taken using the microscope analyzing system Axiovision 4. 2 (Carl Zeiss, Inc. ) and the confocal Zeiss LSM 510-Meta Confocor 2. Analyses were performed with ImageJ software. A total of 3-5 sections per mouse were analyzed. For the brains derived from the LPC-injected animals, the immunofluorescent-positive cells or areas were determined in one every other 5 sections throughout the whole demyelinated lesion per mouse and averaged for each animal. The lesion surface was determined by measuring the area of the nuclear densification (correlated with myelin loss visualized by MBP staining) one every other 5 slices through the whole demyelinated lesion. For the human post-mortem tissue analysis, the entire sections were imaged using a ZEISS Axio Scan. Z1 slide scanner.
Electron microscopy Ultrathin sections of lumbar spinal cords were examined using transmission electron microscope (1011 JEOL) equipped with a Gatan digital camera. The g ratio (the ratio between the axon diameter and fiber diameter corresponding to myelin sheath + axon diameter) was estimated by measuring the minimum and maximum axon diameter and fiber diameter for each axon using ImageJ software. A total of 100 to 300 randomly chosen myelinated axons were evaluated for each animal.
5), CD8α (clone 53-6. 7), CD44 (clone IM7), CD45RB (clone C363. 16 A), CD11b (clone M1/70), CD11c (clone N4/8), Ly-6G (clone RB6-8C5), NK1. 1 (clone PK136), CD45 (clone 30-F11), CD206 (clone MR5D3) and the transcription factors Foxp3 (clone FJK-16s), T-bet (clone eBio4BIO) and RORγt (clone B2D) (ThermoFisher Scientific, BD Bioscience). The use of a mAb to the mouse Fcγ receptor (clone 93, eBioscience) avoided non-specific antibody binding. At least 20,000 events were analyzed for each sample. Cell debris, dead cells, and doublets were gated out using the FSC and SSC parameters. Data acquisition was performed at the Flow Cytometry Core Facility IPSIT (Clamart, France). Flow cytometry data were analyzed using FlowJo (Treestar) software.
Bulk RNA sequencing and analysis The spinal cords from 4 animals for each group were dissected and frozen in liquid nitrogen for further processing. Total RNA was isolated with the Trizol Reagent protocol (ThermoFisher) from spinal cords and RNeasy Mini Kit (Qiagen) according to instructions of the provider. The RNA-Seq libraries were prepared using the NEBNext Ultra II Directional RNA Library Prep Kit (NEB) and sequenced with the Novaseq 6000 platform (ILLUMINA, 32 × 10 6 100 bp pair-end reads per sample). Quality of raw data was evaluated with FastQC. Poor quality sequences were trimmed or removed with fastp tool, with default parameters, to retain only good quality paired reads. Illumina DRAGEN bio-IT Plateform (v3. 6. 3) was used for mapping on mm10 reference genome and quantification with gencode vM25 annotation gtf file.
Library orientation, library composition and coverage along transcripts were checked with Picard tools. Following analyses were conducted with R software. Female and male datasets were first integrated by using normalization by housekeeping genes  72  present in the two datasets, as a previously demonstrated strategy to reduce unwanted variation from RNA-Seq data  73  (RUVSeq). The integrated data were then normalized with edgeR (v3. 28. 0) bioconductor packages, prior to differential analysis with glm framework likelihood ratio test from edgeR package workflow. Multiple hypothesis adjusted p-values were calculated with the Benjamini-Hochberg procedure to control FDR. For the differential expression analyses, low expressed genes were filtered, sex was used as covariable (when relevant) and the cut-offs applied were FDR < 0.
05. Finally, gene ontology (GO) enrichment analysis of biological processes of the differentially expressed genes (DEGs) was conducted with clusterProfiler R package (v3. 14. 3). R script detailing these analyses has been deposited in https://github. com/ParrasLab/Androgensignaling-and-remyelination-Nat-Commun-paper (https://doi. org/10. 5281/zenodo. 7560637).
Gene set enrichment analysis (GSEA) We used gseGO function of Cluster profiler R package to find gene sets enriched in the gene list of 105 microglial genes (Supplementary Dataset 10) ranged by the differential expression (logarithmic fold change, logFC) in DTH-treated versus non-treated females and males, respectively. We found 219 gene set enriched in females but only 2 in males. Dotplot and gseaplot functions were used for visualization of enriched gene sets. All gene sets enriched are provided in Supplementary Dataset 10. R script has been deposited in https://github. com/ ParrasLab/Androgen-signaling-and-remyelination-Nat-Commun-paper (https://doi. org/10. 5281/zenodo. 7560637).
scRNA-Seq analysis EAEraw. RData object was obtained from Gonçalo Castelo-Branco's lab and processed in R (4. 0) using the following packages: Seurat (3. 0) for data processing and ggplot2 (v3. 3. 6) for graphical plots. Seurat objects were first generated using CreateSeuratObject function (min. cells = 5, min. features = 100). Normalized with sctransform function. Cell neighbors and clusters were found using FindNeighbors (dims = 1:30) and FindClusters (resolution = 0. 8) functions. RunPCA, and RunUMAP functions with default parameters. Clusters were annotated based on cell-subtype markers as detailed in the R script, which has been deposited in https://github. com/ParrasLab/Androgen-signaling-andremyelination-Nat-Commun-paper (https://doi. org/10. 5281/zenodo. 7560637).
Statistical analysis Statistical analysis of mouse histological staining was performed with GraphPad Prism 7. 0 software (La Jolla, CA). The significance of differences between means was evaluated by two-tailed, unpaired Student's t test for two independent group comparisons and ANOVA followed by Tukey's or Holm-Sidak's post tests for comparisons of more than two groups and/or several variables. In case of absence of distribution normality (analyzed via D'Agostino & Pearson normality test and Shapiro-Wilk normality test), non-parametric tests (Mann-Whitney two-tailed, Kruskal-Wallis with Dunn's post tests for comparison) were used. Appropriate corrections were done according to the determination of the variance of each sample. The values are the means ± SEM from the number of animals indicated in each plotted graph or as indicated in the corresponding legends.
Significance of p < 0. 05 was used for all analyses. *p ≤ 0. 05; **p ≤ 0. 01; ***p ≤ 0. 001; ****p < 0. 0001. For human histological staining, data were analyzed using linear mixed-effects models on R Studio with the lmerTest package, adding diagnosis, sex and lesion type as main effects and accounting for multiple measurements from each sample by the random effects. To determine main effects ANOVAs were used (stats package), and posthoc comparisons between groups were made using pairwise comparisons in the emmeans package with Tukey method. To ensure data met model assumptions, normal distribution was assessed by Shapiro-Wilk test, and homogeneity of variance by Levene's test. For transcriptomic analyses, multiple testing correction aimed at controlling the false discovery rate (FDR) was performed using the Benjamini-Hochberg method.
Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. cytometry analysis used FlowJo v10. 8. 1 (Treestar) software available at https://www. flowjo. com. Statistical analysis was performed with GraphPad Prism 7. 0 software (La Jolla, CA) available at https://www. graphpad. com. RNA-seq analyses were conducted with R software available https://www. r-project. org and including edgeR (v3. 28. 0) bioconductor packages for normalization, edgeR package workflow for differential analysis, clusterProfiler R package (v3. 14. 3) for gene ontology enrichment analysis, gseGO function of Cluster profiler R package used for gene Set Enrichment Analysis. EAE raw. RData GSE113973 were processed in R (4. 0) using the packages: Seurat (3.
0) for data processing and ggplot2 (v3. 3. 6) for graphical plots. The deconvolution of our bulk RNA-Seq datasets used the CIBERSORTx tool on the docker (v20. 10. 12) module Cibersortx/fractions available at https://cibersortx. stanford. edu. R script detailing these analyses has been deposited at https://github. com/ParrasLab/Androgen-signalingand-remyelination-Nat-Commun-paper (https://doi. org/10. 5281/ zenodo. 7560637). Scoring of differentially expressed genes for their impact in oligodendrogenesis used the OligoScore resource that has been deposited at https://oligoscore. icm-institute. org/.
Acknowledgements This work was supported by the  French Multiple Sclerosis Foundation ARSEP  [ RAK17128LLA ;  RAK19176LLA ;  RAK21128LLA  to E. T. ]. A. Z. was funded by  Mattern Foundation. A. K. was funded by grants from the  French Government and ARSEP. A. W. , L. Z. and F. T. were funded by the  MS Society UK. We thank  UMS44  ( Le Kremlin-Bicêtre ),  A. Schmidt  and  Imaging platform  ( Hôpital Cochin, Paris ),  Y. Marie  and  Genotyping/ Sequencing core facility  ( ICM, Paris ),  D. Langui ,  A. Baskaran  and  ICM Quant platform (ICM, Paris ) for technical assistance, ICM plateforms iSeq and DAC, particularly  F-X. Lejeune  for help in transcriptome normalization using housekeeping genes and  C. Raoux  for help in bulk RNAseq deconvolution.
Data availability All metadata associated with RNA sequencing generated in the present manuscript are available at https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE225254. The publicly available independent singlenuclei RNA sequencing database from MS donors is available at https:// malhotralab. shinyapps. io/MS_broad/. Human MS data are available at https://ega-archive. org/studies/EGAS00001006345. scRNA-Seq dataset from mouse EAE model is available at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE113973. Mm10 reference genome (org. Mm. eg. db_3. 15. 0. ) is available at https://bioconductor. org (DOI: 0. 18129/B9. bioc. BSgenome. Mmusculus. UCSC. mm10). The readers can expect to receive any raw data from their request. Source data are provided with this paper.
Results ExTrack is a maximum-likelihood method to detect different diffusion states in single-molecule tracks We developed ExTrack, a maximum-likelihood estimation (MLE) method that contains two main modules: A fitting module fits a multi-state Markovian diffusion model to a data set of noisy single-molecule tracks. This module infers global model parameters including localization error, diffusion lengths, transition rates, and the initial fractions of molecules (at the first time point of all tracks). Part of these global parameters can also be provided by the user, and localization error can even be provided for each peak  (Thompson et al. , 2002; Quan et al. , 2010)  if desired. ExTrack is flexible in terms of the number of states and spatial dimensions. Additionally, it can explicitly consider molecules leaving the field of view, which otherwise introduces bias  (Hansen et al.