dataesr/scientific-paragraphs-categorization huggingface dataset:
Tissue and cell-type enrichment analysis Tissue and cell-type enrichment analyses were performed using the GWAS summary statistics of the European ancestries meta-analysis and FUMA 32 software v1. 3. 6a. FUMA performs a genic aggregation analysis of GWAS association signals to calculate gene-wise association signals using MAGMA v1. 6 and subsequent tests whether tissues and cell-types are enriched for expression of these genes. For tissue enrichment analysis we used the GTEx v8 reference set. For cell-type enrichment analyses  33  we used human-derived single-cell RNA sequencing data on major brain cell-types (GSE67835 without fetal related traits: body mass index (anthropometric)  89  , years of schooling (educational attainment)  90  , alcoholic drinks per week, age of smoking initiation and cigarettes per day from Liu et al.
MR analysis was conducted in R using themr()` function in theTwoSampleMR` package  101. Subsequently, Radial MR analysis was conducted to determine if Wald ratio outliers needed to be removed from the IVW or MR-Egger MR estimates 37. In addition, we conducted a Q-test to identify outlier SNPs (P < 0. 05). These outliers were then removed from the original MR analyses (across all 5 MR methods). The Radial MR analysis was conducted using the RadialMR R package (https://github. com/WSpiller/RadialMR). In order to determine that the MR effects were orientated in the correct direction (from exposure to ALS) we conducted both reverse MR  102  and Steiger filtering  103  on our top MR findings. Finally, we explored whether the MR effects of our total and LDL cholesterol and systolic blood pressure exposures may be confounded by the effect we observed for years of schooling by conducting multivariate MR analysis  104.
For CBD, allele coding were missing and effect alleles were inferred by matching allele frequencies to those observed in the Haplotype Reference Consortium. Datasets and data preparation GWAS summary statistics for clinically-diagnosed Alzheimer's disease (AD) 26 , Parkinson's disease (PD) 27 , frontotemporal dementia (FTD) 28 , corticobasal degeneration (CBD) 29 , and progressive supranuclear palsy ( SNPs with minor allele frequency > 0. 4 were excluded. Since downstream methods rely on LD-scores or population-specific LD patterns, the European ancestry summary statistics from the present study were used for ALS. For sample size parameters, effective sample size was calculated as described previously.
Hidden neural states within DMN can predict upcoming perceptual transitions The previous evoked-response analysis showed that the biggest difference between BR and RPL conditions happened right before (∼1 second) perceptual transitions. However, the evoked response of the GFP is a crude measure as it is a grand average across all trials and all channels. To uncover the dynamic patterns embedded in the EEG signals on a trial-by-trial basis, we adopted the embedded-lag HMM method (https://github. com/OHBA-analysis/HMM-MAR) which takes advantage of the excellent temporal resolution and rich spectral profiles of the EEG data. If the intrinsic fluctuation of the DMN regional activity influences the perceptual transitions during BR, we should expect to find a correspondence between the temporal dynamics of DMN regions and the upcoming perceptual transitions.
Acknowledgments The authors declare that they have no competing interests or funding in support for this research. All data needed to evaluate the conclusions in the paper are publicly available in the repository: (https://datadryad. org/stash/dataset/doi:10. 5061/dryad. bf1b1). Acknowledgements are given to the research team led by  Bin He  from the  Department of Biomedical Engineering and Department of Psychology in the University of Minnesota  for data collection and deposition; and  Abhrajeet V. Roy  for the correspondence regarding to data clarification.
, 2019 ). This strategy is known to inflate the type I error rate and does not model the relationship between dependent variables  Fox (2015). The use of multivariate statistics allows for assessing how a combination of dependent variables reflects an effect of interest, and thus provides more information than the univariate GLM approach  ( McFarquhar et al. , 2016 , Stoyanov et al. , 2019 , Zufferey et al. , 2017 , Kherif et al. , 2002  Aug 1 ). The recently proposed implementation of multivariate GLM for neuroimaging data  ( McFarquhar et al. , 2016 ) , facilitates the modelling of either multi-contrast datasets or repeated measures datasets, but do not handle the case of datasets that have both characteristics. Spatial modelling challenges have rarely been considered given the sparsity of qMRI studies with multiple acquisition time points  ( Natu et al.
MRI data acquisition and map calculation We acquired neuroimaging data on a 3T whole-body MRI system using 20-channel radiofrequency (RF) head and body coil for transmission (Magnetom Prisma, Siemens Medical Systems, Germany). The qMRI protocol consisted of three spoiled multi-echo 3D fast low-angle shot (FLASH) acquisitions with T1-, proton density-and magnetisation transfer weighting at 1 mm isotropic resolution  ( Weiskopf et al. , 2013 )  and  additional B1 acquisition to map spatial inhomogeneities of the RF transmit field  ( Lutti et al. , 2010 , Lutti et al. , 2012 ). We calculated quantitative maps of proton density (PD), longitudinal relaxation rate (R1), transversal relaxation rate R2 * and magnetisation transfer (MT) saturation using in-house routines in the framework of SPM12 (Wellcome Centre for Human Neuroimaging, London, UK; https://www.
Statistical analysis The change of depression score over time was tested using a linear mixed-model with the fixed effect factor time (4 levels: t0, t1, t2, t3) and a random intercept for each subject. For model estimation, we used the nlme package in R 3. 6. 0 followed by post hoc tests in the emmeans package  ( Pinheiro et al. , 2013 , Lenth, 2019 ). Posthoc t-tests were corrected for multiple comparisons using Tukey's method  Tukey (1949).
Multivariate general linear model implementation We implemented the multivariate General Linear Model in Matlab as an SPM12 toolbox (see code availability). Matrix of observations  , at each voxel, Y consisted in 180 observations (9 individuals × 4 time points × 5 maps) organised in 5 columns {  1 ,  2 ,  3 ,  4 ,  5 } where  1 is the vector of grey matter volume values,  2 -of PD * ,  3 -of MT saturation,  4 -of R1 and  5 -of R2 *. Each column of  included 36 observations (9 individuals × 4 time points) ordered by subject ( ) and time point (  ) : Design matrix : The design matrix included the four time points (t0, t1, t2, t3) as indicator variables. We also included the individuals' interaction terms of MADRS score x time as covariates and finally, to adjust for repeated measurements -we added a random intercept for each subject coded as a dummy variable ( Fig.
Acknowledgments and Funding We would like to thank all participants in the study. BD is supported by the  Swiss National Science Foundation (NCCR Synapsy , project grants Nr. 32003B_135679 ,  32003B_159780 ,  324730_192755  and  CRSK-3_190185 ) and the  Leenaards Foundation. The research leading to these results has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 720270  ( HBP SGA1 ). This work was carried out on the MRI platform of the  Département des Neurosciences Cliniques -Centre Hospitalier Universitaire Vaudois (CHUV) , which is generously supported by the  ROGER DE SPOELBERCH and Partridge Foundations. AL is supported by the  ROGER DE SPOELBERCH foundation  and the  Swiss National Science Foundation  ( 320030_184784 ).
ACKNOWLEDGEMENT LIST FOR ADNI PUBLICATIONS The Data and Publications Committee, in keeping with the publication policies adopted by the  ADNI Steering Committee , here provide lists for standardized acknowledgement. The list consists of three parts: I. ADNI Infrastructure Investigators and Site Investigators, II. DOD ADNI Infrastructure Investigators and Site Investigators and III. ADNI Depression Infrastructure Investigators and Site Investigators. Infrastructure Investigators represent the names responsible for leadership and infrastructure. Site Investigators represent the names of individuals at each recruiting site. All papers, including methodological papers, should have an acknowledgement list that consists of Infrastructure Investigators plus the FULL list.
Results Probiotic intervention at a low dose increases susceptibility to social stress. A timeline for the study is shown in Fig. 1a. Following both acute and repeated defeat training, hamsters were tested for social avoidance and social interaction with a novel, caged opponent. Repeated Measures, Two-way ANOVAs with Tukey's post hoc analyses were run to assess the effect of probiotic intervention (high and low dose or placebo) and number of defeats (acute or repeated) on social behavior. Behavioral data from 3 hamsters in the low dose probiotic group were not scored due to a technical error in saving the videos and thus the number of animals in this group was reduced. Analysis of social avoidance behavior revealed no interaction effect (F(2, 44) = 0. 5, p = 0. 6, η 2 = 0. 02) or main effect of defeat number (F(1, 44) = 2.
Social behavior comparisons were analyzed by Repeated Measures, Two-way ANOVA with Tukey's post hoc analysis on GraphPad Prism 8. 2. 0 (GraphPad Software, La Jolla, CA) and effect sizes were calculated. Differences in post hoc analyses were denoted as significant at *p < 0. 05. While published reports indicate that the incidence of adverse events associated with these treatments are extremely unlikely  72  , hamsters were monitored daily throughout treatment and testing by the experimenters and the Department of Animal Resources staff, as well as veterinary technicians or veterinarians to assess coat quality and to make sure that food and water intake remained constant. Additionally, experimenters carefully observed all hamsters during each agonistic encounter for coprophagia and for any injury.
Diluted pooled samples were loaded on an Illumina MiSeq and sequenced using a 500-cycle (paired-end sequencing configuration of 2 × 250 bp) MiSeq Reagent Kit v3. 16S rRNA gene sequencing analysis. The sequences were demultiplexed and quality filtered using the Quantitative Insights Into Microbial Ecology 2 (QIIME 2, version 3. 5. 5) software package  75. Forward and reverse Illumina reads were joined using the fastq-join method. We used the QIIME 2 default parameters for quality filtering  75. Sequences were clustered using amplicon sequencing variant tables with Deblur 76. Clusters were then classified taxonomically using the Greengenes reference database, Version 13. 5  77. Clusters that did not match any Greengenes Operational taxonomic units (OTUs) were kept. A single representative sequence for each OTU was aligned and a phylogenetic tree was built using FastTree  78.
The phylogenetic tree described above was used to assess beta and alpha diversity  79, 80. Unweighted UniFrac 81 was used to compute distances and to measure beta diversity between groups using rarefied OTU table count. Principal coordinates analysis (PCoA) plots were used to further assess and visualize beta diversity. Groups were compared for distinct clustering using PERMANOVA method using vegan R-package through QIIME 2. The phylogeny-based metric, phylogenetic diversity whole tree (PD whole tree) measurement and the non phylogeny-based metric, Observed OTUs, were determined with QIIME 2. Kruskal-Wallis with Dunn's multiple comparison test was used to determine differences among groups on GraphPad Prism 8. 2. 0 (GraphPad Software, La Jolla, CA). Lastly, LEfSE (Linear Discriminate Analysis Effect Size) was used to compare abundance of specific taxa between groups  82.
The assay was conducted per the manufacturer's protocol using the recommended sample dilution (4×) and standard curve concentrations. All samples and standards were assayed in duplicates. This multiplex technology uses "xMAP"-based microspheres to detect protein concentrations of target analytes. The concentration of each analyte was determined by Bio-Plex Manager, and the analyte concentrations were compared across groups by ANOVA with Tukey's post hoc comparisons or Kruskal-Wallis with Dunn's multiple comparisons test on GraphPad Prism 8. 2. 0 (GraphPad Software, La Jolla, CA). A rat assay was chosen because no equivalent multiplex assay exists for hamster. Notably, most of the sequences for the analytes in the multi-plex assay share a 77-90% homology with hamster sequences, suggesting a high likelihood of crossreactivity with hamster proteins.
Acknowledgements The authors acknowledge the helpful contribution of  Thomas Tompkins ,  Chad MacPherson , and  Amanda Piano  at  Rosell Institute for Probiotics and Microbiome. This work was supported by  Lallemand Health Solutions , the  National Institutes of Health   R01MH062044  awarded to K. L. H. , and a  GSU Brains and Behavior Fellowship  awarded to K. A. P. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health , the  Brains and Behavior Program , the  Center for Behavioral Neuroscience, Lallemand Health Solutions , or  Georgia State University.
Importantly, we compared our RNA-seq data of dc-hiOLs and primary oligodendrocytes (pOls) with a previously published single-nuclei RNA-seq dataset from white matter areas of human brain  (Ja ¨kel et al. , 2019). When comparing the expression levels of representative glial, neuronal, and fibroblast genes, dc-hiOLs clustered together with primary pOls as well as oligodendrocytes and oligodendrocyte progenitor cells isolated from human brain samples and were clearly separated from fibroblasts (Figure  2B ). We next utilized an assay for transposase-accessible chromatin using sequencing (ATAC-seq) to interrogate changes in chromatin accessibility induced by SON. During transdifferentiation the cells undergo marked chromatin rearrangement as denoted by the analysis of differentially accessible ATAC-seq peaks between fibroblasts and the derived dc-hiOLs (Figures  2C  and S3F ).
000 most variable CpGs clearly separated neonatal samples from dc-hiOLs derived from adult and old-aged donors (Figure  S5A ). We applied Horvath's epigenetic clock  (Horvath, 2013)  as age predictor to our  (legend continued on next page) methylation dataset and confirmed the distinction of fibroblasts and the derived dc-hiOLs in separate age groups (Figure  S5B ). Overall, our results show that dc-hiOLs preserve the epigenetic age of donor cells, at least with respect to DNA methylation profile, and represent a promising model to study age-associated myelin deficits.
Data and Code availability The accession number for the RNA-seq data reported in this paper is ArrayExpress: E-MTAB-7838. The RNA-seq data and will be made publicly available upon publication. The R script for the comparison of our RNA-seq data with the published dataset by Ja ¨kel et al. (  2019 ) is available on github (https://github. com/ctho1/ compareRNAseq). post-infection. Scale Bar: 75μm. F) Fibroblasts were infected with SON-IRES-RFP lentivirus enabling detection of transgene expression. Flow cytometry analysis for O4 and RFP in neonatal fibroblasts infected with SON-RFP lentivirus at day 16 identifies RFP-and O4+ cells in addition to RFP-O4-; RFP+ O4-and RFP+ O4+. G) Quantitative RT-PCR analysis showing the upregulation of endogenous Sox10 and Olig2 transcripts in dc-hiOLs at reprogramming day 18 in both virus-silenced (RFP-O4+) and non-silenced (RFP+O4+) cells.
Raw single-nucleus RNAseq read count data from Jäkel et al. 2019  (Jakel et al. , 2019)  was retrieved from GEO (accession number: GSE118257) and loaded into the R environment (version 3. 6. 0). Each single nucles was annotated according to the subclusters defined by Jäkel et al. and comprised 3530 oligodendrocytes (oligodendrocytes 1-6), 273 oligodendrocyte progenitor cells (OPCs), 81 immune oligodendroglia (ImOLG), 153 committed oligodendrocyte precursors (COPs), 1768 neurons (neurons 1-5), 454 astrocytes, 134 pericytes and 78 microglial cells. After read counts of each subcluster were averaged, the resulting data matrix was combined with read counts derived from our own RNA sequencing experiment. Quantile normalization was performed across the whole dataset. The resulting matrix was imported into DESeq2 (v1.
26) using the DESeqDataSetFromMatrix() function. For visualisation, the expression dataset was transformed using variance stabilizing transformations (VST). For clustering analysis, 35 marker genes for OPCs, oligodendrocytes, neurons, astroglia, microglia and fibroblasts were selected. Samples were clustered using the Euclidean distance as the distance measure and complete linkage as the clustering method. The R script for the analysis is available on github (https://github. com/ctho1/compareRNAseq).
2. 6  (Feng et al. , 2012)  with parameters '-g hs --nomodel --qvalue 0. 05 -broad'. Peaks were annotated using ChIPSeeker v1. 2. Fragments mapping to peaks were summarized using featureCounts v1. 6. 5  (Liao et al. , 2014)  and differential accessibility was assessed using DESeq2 v1. 24  (Love et al. , 2014). Motif enrichment: Short nucleosome free regions within differentially accessible peaks were called using NucleoATAC  (Schep et al. , 2015). HOMER v4. 7 was used to scan for motifs found in the JASPAR database  (Khan et al. , 2018). Resulting motifs were plotted, clustered and aggregated using universalmotif v1. 2. 1 and R v3. 6. 1.
DNA methylation array analysis To compare the methylation profile of fibroblasts and dc-hiOLs three independent samples per age-group were analyzed. Following total DNA extraction using the QIAamp DNA Mini Kit (Quiagen) all samples were analyzed using the MethylationEPIC BeadChip array (Illumina Inc. , San Diego, CA). On-chip quality metrics of all samples were carefully controlled. Raw signal intensities from IDAT files were loaded into the R environment (version 3. 6. 0) using the minfi package (version 1. 30). Quantile normalization was applied with the preprocessquantile function of the minfi package. The following filtering criteria were applied: removal of probes targeting the X and Y chromosomes, removal of probes containing a single nucleotide polymorphism (dbSNP132 Common) within five base pairs of and including the targeted CpG site, and probes not mapping uniquely to the human reference genome (hg19) allowing for one mismatch.
In total, 749. 312 CpG sites were kept for analysis. Unsupervised t-SNE analysis: Unsupervised t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis was performed on the 5000 most variable methylation probes (standard deviation) across the whole dataset using the Rtsne package (version 0. 15). The the following non-default parameter adjustments were used for t-SNE analysis: theta=0, max_iter=2000, perplexity=2. The results matrix was visualized with ggplot2 (version 3. 2. 1). Focused analysis of MBP, MAG and MOG methylation levels: CpG probe annotation was performed with the getAnnotation function from minfi. Genomic coordinates of the human reference genome (hg19) from MBP, MAG and MOG were retrieved from NCBI  (MBP chr18:74690789-74844774, MAG chr19:35782989-35804710, MOG chr6:29624758-29640149).
Mean beta values for each gene were then compared between the fibroblasts and dciOL using the Wilcoxon test and results were visualized using ggplot2. Differential methylation analyses: Differentially probes between aged and neonatal samples were calculated using the limma package (3. 40. 6). A total of 57. 106 CpG sites were differentially methylated. The top 1000 differentially methylated probes were then visualized using the pheatmap package (1. 0. 12). Differentially methylated regions between fibroblasts and dciOL samples were analyzed and visualized with DMRcate (2. 0. 7).
Data from Ja ¨kel et al. (2019) include different subclusters of neurons (Neuron1-Neuron5) and oligodendrocytes (Oligo1-Oligo6), and additional clusters of OPCs, committed OL precursors (COPs), immune oligodendroglia (imOLG), astrocytes (Astrocytes), pericytes (Pericytes), and immune cells (Macrophages and Microglia_Macrophages). Unsupervised hierarchical clustering shows that dc-hiOL1-3 cluster with the other oligodendroglial clusters, whereas fibroblasts (Fib1-Fib3) form a separate cluster. Unexpectedly, in the dataset published by Ja ¨kel et al. (2019), MBP was expressed in comparable levels in oligodendrocytes and astrocytes. This finding might be an RNA-seq artifact. (C) Global chromatin accessibility comparison using ATAC-seq. MA plot comparing accessibility at ATAC-seq peaks between dc-hiOLs and fibroblasts.
ACKNOWLEDGMENTS We thank  Dr. Thomas Zobel  from the  Imaging Network, Mu ¨nster  for helping with the 3D reconstruction analysis and  Dr. Laura Starost  for providing hiOLs. This work was supported by the  German Research Foundation  ( DFG CRC-TR-128B07  to T. K. ),  Interdisciplinary Center for Clinical Research  ( KuT3/007/20  to T. K. ),  Progressive MS Alliance (collaborative research network   PA-1604-08492  (BRAVEinMS) to G. M. , J. A. , A. B. -V. E. , and T. K. ),  AFM-TELETHON 20270  (to A. B. -V. E. ), the  National MS Society  ( RG-1801-30020  to T. K. ), and the  EC FP7  project  LEUKOTREAT  and  "les amis de Ianis" association  (to O. B. -T. ). Work in the Vaquerizas laboratory was funded by the  Max Planck Society , the  Deutsche Forschungsgemeinschaft (DFG)   Priority Program SPP 2202 'Spatial Genome Architecture in Development and Disease'  (project no.
Obesity and Diabetes Mellitus: Epidemiology and Physiopathology In the past few decades, obesity prevalence has considerably increased: in 1975, 5% of the world population was obese, compared to 13% in 2016, according to the WHO database  [1]. Obesity is defined as an excessive fat accumulation that leads to health complications  [1]. The development of obesity is exacerbated by a modern diet rich in sugar and lipids. This type of hypercaloric food promotes hyperglycemia and insulin secretion that induce fat accumulation in adipose tissue  [9]. Adipose tissue, in addition to its storage function, is an important endocrine organ that releases free fatty acids (FFA) which are partly responsible for insulin resistance  [10]. FFA induces the inactivation of kinase proteins involved in the insulin signaling pathway by phosphorylation and leads to diabetes mellitus  [11].
, 2020). This underlines the importance of social context on functional connectivity between social nodes. However, it remains unclear how communication across a wider network of regions changes during the perception of other's social interactions. Here we address two questions concerning neural responses to social interactions in the monkey brain: (1) how activations and network interactions are affected when viewing social interactions, and crucially, (2) how these dynamics change with respect to the nature of these interactions. We collected fMRI data from monkeys while they freely viewed videos of social interactions between nonhuman primates. These included situations where the context was clear (e. g. , aggressive, affiliative) and ambiguous (e. g. , 2 animals approaching each other but where the outcome was uncertain).