dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, for every middle exon in each cell type, exon inclusion ratio (or ψ) was calculated (see methods), ranging from 0 to 1 (0 for exons not included, and 1 for exons included in all the transcripts). The genome-wide histone modification and DNAseI profiles for the cell types corresponding to the transcriptome data were obtained from the Roadmap Epigenomics consortium. For the Whole Genome Bisulfite Sequencing (WGBS) data, we computed three new tracks (figure  S1  †  ): CpG nucleotide density (consistent across cell and tissue types), CpG methylation ratio (average ratio of methylation at CpG sites in the window), CpG methylation density (number of methylated CpG sites in each window), for each sample, using the WGBS CpG coverage track as a control. For each pair of transcription feature and epigenetic modification, the associations were explored at two levels.
g. Stronger peak at the downstream hill than the upstream hill in H3K4me3 in the small intestine, figure 1A). H3K79me2 was particularly strongly asymmetric (figure  S2  † ), with a strong peak at around ± 500bp downstream of the TSS across 2 of the 3 cell lines for which the mark was studied in Roadmap Epigenomics. CpG density around the TSS was positively correlated with gene expression in all cell and tissues in the dataset. CpG methylation ratio and CpG methylation density near gene TSS were negatively correlated with gene expression levels. While CpG methylation ratio showed a flat profile near the TSS of non-expressed genes, CpG methylation density transitioned from a gap at highly expressed genes to a peak at non expressed genes at the TSS (figure  1A ). We further explored these trends for individual gene types.
Exon transcription and epigenetic modifications at middle exons Several studies have found a correlation between DNA methylation  26, 27, 36  or histone modifications  23, 30, 31  and exon and splicing events, in either single or a few cell and tissue contexts. We explored whether these observations hold true in the Roadmap Epigenomics dataset. We focused on middle exons and excluded first and last exons of protein coding genes. A total of 16,811 middle exons were expressed in at least one cell or tissue type in the Roadmap dataset. The expression level of an exon was defined as the sum of the TPM of the transcripts including that exon. Similar to TSS and TTS analysis, we performed epigenetic modifications enrichment analysis at middle exons. H3K36me3 showed enrichment at middle exons, correlated with the exon expression level within a cell or tissue type (figure  3A ).
Exon inclusion ratio and epigenetic modifications at middle exons Exon expression level in a cell type consists of both constitutive and alternative splicing events. To study association between epigenetic modifications and alternate splicing events, we calculated exon inclusion ratio for each exon. The exon inclusion ration was calculated for all transcripts of a given gene (see methods), in each cell or tissue type and sorted using the inclusion ratio (also known as Psi). Accordingly, we obtained Psi values for 17,517 exons, including 706 exons that were never included in the Roadmap datasets, but were part of genes that were expressed in this dataset. We checked whether epigenetic modifications were correlated with exon inclusion ratio and noted that only a few modifications; namely H3K27ac, H3K4me3, and H3K36me3 were associated with exon inclusion ratio within a cell type (figure  3B ).
A linear model for gene expression So far, we analysed the associations between epigenetic modifications and transcription control in a pair-wise manner. In order to better model the combinatorial effect of modifications, we selected 6 epigenetic modifications (DNA methylation, H3K4me1, H3K4me3, H3K9me3, H3K27me3 and H3K36me3) for which epigenetic and transcriptome data was available for 27 cell types in the Roadmap dataset, and regressed a linear model at four transcriptional features: (i) epigenetic modifications around TSS and gene expression level (figure  4A ), (ii) epigenetic modifications around TTS and gene expression level (figure  4B ), (iii) epigenetic modifications around the start of middle exons and exon expression level (figure  4C ), and (iv) epigenetic modifications around the start of middle exons and exon inclusion ratio (figure  4D ).
PEREpigenomics, a web resource to explore associations between epigenetic modifications and transcription features Using the Roadmap dataset, we unravelled a range of associations between epigenetic modifications and transcription control. We have included a total of 9,024 stacked profiles of epigenetic modifications near TSS, TTS and middle exons, sorted according to the gene expression level, exon expression level or exon inclusion ratio, in about 30 different cell and tissue types. We have also provided stacked profiles for TSS and TTS for each gene type (protein-coding, RNA, pseudogene, other), as well as for short (≤ 1kb), long (> 3kb) and intermediate length genes. Users furthermore can generate regressions between an epigenetic modification at TSS or TTS and gene expression level across cell and tissue types for a gene of interest, and the same feature is available for middle exons and exon expression level or exon inclusion ratio as well.
Analysis across cell and tissue types For each gene and middle exon, we correlated the level of epigenetic modification with expression level or exon inclusion ratio across the different cell and tissue types in the Roadmap dataset. When regressing promoter DNA methylation with gene expression levels, 2,506 genes had a linear regression coefficient R 2 ≥ 0. 75 (5. 2% of all genes), including 1,173 protein coding genes (6. 3% of protein coding genes). The average slope was negative, indicating that for an individual gene, higher level of promoter DNA methylation is associated with lower gene expression level. Conversely, 2,485 genes had a linear regression coefficient R 2 ≥ 0. 75 (5. 2% of all genes) when regressing H3K4me3 levels at the promoter and gene expression level, including 1,367 protein coding genes (7.
3% of protein coding genes). The average slope in this case was positive, i. e. higher level of promoter H3K4me3 was associated with higher gene expression level. Nonetheless, most genes or exons did not have a regression coefficient ≥ 0. 25. This might be because many genes and exons might not have large enough epigenetic or expression variability in this dataset. Furthermore, ChIP-seq and DNAse-seq peak height can also be biased by the epigenetic modifications (or accessibility) the dominant fraction of alleles and cells, as well as biased by changes in ChIP efficiency due to hard-to-control experimental variations.
Data retrieving Gencode human annotation version 29 (main annotation file), were downloaded from theGencode as gff3 files. Reads from RNA-seq data were retrieved from the European Nucleotide Archive using the the Roadmap sample table as a reference. Roadmap Epigenomics whole genome bisulphite sequencing (WGBS) data sets were downloaded bigwig files of fractional methylation and read coverage from the Roadmap Epigenomics the Roadmap data portal. Histone modifications and DNAse1 data were downloaded as consolidated, not subsampled, tagAlign files from the the Roadmap data portal. Altogether, we retrieved 27 RNA-seq and corresponding WGBS datasets, 13 DNAse1 profiles (with matching controls), and 242 histone ChIP-seq datasets in 27 human cell lines or tissues (with 27 matching controls).
Epigenetic modifications data processing For WGBS, three different tracks were generated from the FractionalMethylation. bigwig files using bedtools  46  and rtracklayer  47  : number of CpG sites per window, mean DNA methylation ratio per window, density of mCpG sites per window, using windows of 250 base pair width, sliding by 100 base pairs. The WGBS coverage file was processed similarly to produce a fourth track serving as a control. No post-processing was done for DNAse1 and Histone tagAlign files.
Acknowledgements GD was funded for this work by the  People Program (Marie Curie Actions FP7/2007-2013)  under  REA  grant agreement No  PCOFUND-GA-2012-600181. AJ is supported by the  Bergen Research Foundation  Grant no. BFS2017TMT01. The authors would like to thanks  Olaf Sarnow ,  Kjell Petersen  and  Stanislav Oltu  for their help with the web server configuration, and  Anna Mantsoki  and  Barry Horne  for her help at the beginning of the project.
Overview We develop a foundational graph-theoretic toolkit that facilitates analysis of canonical and large-scale games, providing insights into their related topological structure in terms of their high-level strategic interactions. The prerequisite game theory background and technical details are provided in the Methods section, with full discussion of related works and additional details in Supplementary Note 1. Our results are summarized as follows. We use our toolkit to characterize a number of games, first analyzing motivating examples and canonical games with well-defined structures, then extending to largerscale empirical games datasets. For these larger games, we rely on empirical game-theoretic analysis  166, 174  , where we characterize an underlying game using a sample set of policies.
Acknowledgements The authors gratefully thank  Marc Lanctot  and  Thore Graepel  for insightful feedback on the paper. FCS acknowledges the support of  FCT-Portugal  through grants  PTDC/MAT/STA/3358/2014  and  UIDB/50021/2020. The authors thank the three anonymous reviewers and editor for their constructive and insightful feedback, which helped to significantly improve the clarity of the proposed method, experimental results, and discussions thereof.
No technical replicates were performed, primarily because of very limited amount of clinical material, but also as our previous experience show very low technical variability between MS runs using HiRIEF LC-MS. The validity of the cohort was, however, confirmed by the low variability between samples and the fact that AD and control samples separated well in a principal component analysis (PCA) (Fig. 2A ). The hypothesis tested for each protein was that they were differentially expressed in AD samples compared with control samples. As we cannot assume normal distribution in the data using Shapiro-Wilk test, a non-parametric statistical method, DEqMS (Differential Expression analysis of quantitative Mass Spectrometry data, Zhou et al. , manuscript under consideration for publication elsewhere, R package in Bioconductor) was used.
DEqMS works on top of limma package and considers the detected number of peptide spectra matches (PSMs) per peptide while calculating t-statistics. Benjamini-Hochberg method was used for correction for multiple hypothesis testing. An overview of the experimental workflow is shown in Fig. 1. The details of the experimental design and statistical rationale are explained in each subsection. Case Selection-Frozen hippocampal tissues were obtained from five sporadic AD and five control cases from the Netherlands Brain Bank (Exploratory cohort). AD cases were clinically diagnosed according to previously published research criteria  (29) , and definitive diagnosis was confirmed pathologically by using immunohistochemical staining against amyloid plaques and neurofibrillary tangles. We selected AD cases with relatively early stages of AD pathology (AD Braak stage 4 and amyloid deposits stage C).
0)  (37). PSMs found at 1% false discovery rate (FDR) were used to infer gene identities. Protein quantification was based on TMT10plex reporter ions and calculated using TMT PSM ratios to the entire sample set (all 10 TMT-channels) and normalized to the sample median. The median PSM TMT reporter ratio from peptides that are unique to a gene symbol was then used for quantification. Only one unique peptide was required to identify a protein, and a protein FDR cut-off of 1% was applied to the list of gene-centric proteins using the picked-FDR method  (38). Peptide sequences, m/z, modifications, peptide identification scores, accession numbers, number of peptides, percentage coverage and quantification measurements for each peptide and protein has been deposited to the ProteomeXchange Consortium using the PRIDE repository (dataset identifier: PXD014557).
Proteins that were not identified in all 10 cases were excluded in the analysis. Statistical Analysis-Alterations in average protein expression between AD and control cases were calculated using DEqMS package in R (Zhou et al. , manuscript under consideration for publication elsewhere), because the data did not show normal distribution using Shapiro-Wilk test. DEqMS works on top of limma package and considers the detected number of PSMs per peptide while calculating t-statistics. DEqMS is available at Bioconductor  (39) , and the package is also deposited at GitHub (https://github. com/yafeng/DEqMS). Benjamini-Hochberg method was used for multiple hypothesis testing and a cut-off level of 10% FDR was applied. Multivariate data analyses were performed to find the biggest variation in our dataset using an unsupervised PCA in SIMCA v15.
This type of analysis is based on the molecular signature database (v6. 1) that contains gene sets derived from gene ontology (GO) annotations  (41). We ran a running-sum statistic method, in which all of the identified genes were ranked from the most increased to the most decreased expression regardless of their p-and FDR-values (GO database from 16 -02-2019). Ontologies with p Ͻ 0. 01 and FDR Ͻ5% were considered significantly enriched which is considerably more stringent than the GSEA guideline  (40). Additionally, we performed a second type of enrichment analysis using the GOrilla tool (GO database from 27-04-2019)  (42). Two unranked lists of proteins, a target list consisting of differentially expressed proteins (FDR Ͻ10%) and a background list, which is all the identified proteins, were uploaded.
We performed this analysis for decreased and increased proteins separately. Ontologies with FDR Ͻ5% were considered statistically significant. Both in GSEA and GOrilla tools, Benjamini-Hochberg method was used to calculate FDR-values. Pathway Analyses-Ingenuity pathway analysis (IPA, Qiagen, Hilden, Germany) was used for systemic analysis of our dataset in order to interpret our findings in a biological context. AD/C protein ratio (log 2 ) of all quantified proteins, p-and FDR-values were uploaded into the IPA, and core analysis was performed. We allowed both direct and indirect relationships and a search on published data carried out in mouse, rat and human species, and applied 10% FDR cut-off to avoid having relations that could occur just by chance. Differentially expressed proteins were then analyzed in three categories: biological functions, enriched molecular networks, and upstream regulators.
Sections were scanned using Nikon Eclipse E800 microscope and Nikon DS-Ri2 camera (10ϫ objective, gain 1, exposure 2 ms) and NIS Elements software (Nikon Instrument, Inc. , Japan), and images were analyzed on 8-bit scale using ImageJ Fiji (NIH). Mean pixel intensities of the area of interest, covering the middle and outer ML of the DG, were measured in a blinded fashion and the background, defined as the signal intensity of a region outside the tissue, was subtracted  (43). Additionally, gross morphological observations of the staining intensity in the region of interest was checked by subjective assessment done by two independent researchers (C. E. and S. F. ) in a blinded fashion. Visual scoring was performed based on a scale from 0 to 3 (0, absent; 1, mild; 2, moderate; 3, strong).
Considering the fact this region is highly vulnerable to AD-related pathologic changes, we used LMD to dissect it from five AD and five neurologically healthy control cases and performed quantitative MS. All cases were labeled with a different isobaric tag on a peptide level, allowing simultaneous detection of ten samples, and thereby, eliminating variation between runs. Prior to LC-MS/MS, labeled peptides were fractionated into 72 fractions based on their isoelectric focusing point  (32). Our indepth proteomic analysis led to an identification of 68,755 peptides mapping to 7322 proteins (gene-centric) using the MSGFϩ Percolator software (1% FDR cut-off in peptide level). Protein false discovery rates were calculated using the picked-FDR method using gene symbols as protein groups and limited to 1% FDR  (38).
The complete list of proteins that were quantified in all cases can be found in supplemental Table  S3. Quantitative information about both peptide and protein level has also been deposited to the Proteome-Xchange Consortium using the PRIDE repository (dataset identifier: PXD014557). PCA shows a relatively good separation between AD and control cases, except for one of the AD cases, ID-4 (Fig. 2A ). The clinical and neuropathological details of this AD patient do not suggest any prominent evidence that could explain this discrepancy. The distribution of fold change versus p values for all identified proteins is shown in a volcano plot displaying a considerable change in the protein levels between AD and control (Fig. 2B ). After adjusting for multiple hypothesis comparison using Benjamini-Hochberg method, 724 proteins were found to be significantly altered (p Ͻ 0.
01 and FDR Ͻ10%), consisting of 382 decreased and 342 increased proteins. The expression levels of the most altered proteins (FDR Ͻ10%) are shown in the heat map (Fig. 2C ), and the top 20 proteins with the largest fold change are listed in Table  I. We observed an over-representation of presynaptic proteins among the decreased proteins including complexins, RAB3A, metabotropic glutamate receptor 4 (GRM4) and SYNGR1. Interestingly, however, postsynaptic proteins such as AMPA and NMDA receptors, drebrin, PSD95, HOMER1 and synaptopodin were not altered in AD (supplemental Table  S4 ). There was no difference in the levels of amyloid precursor protein (APP) and microtubule-associated protein tau (MAPT) between AD and control cases in our dataset. However, this is not surprising, because even if the region of interest contains amyloid plaques and neurofibrillary tangles, they were most probably not dissolved using the current methodology.
In addition, calcium ion regulated exocytosis and SV localization were also found to be negatively enriched in AD (supplemental Table  S5 ). On the other hand, among the top 10 positively enriched GO terms, RNA processing and ribonucleoprotein complex biogenesis were more prominent (Fig. 2E ). Additionally, using the GOrilla tool, we investigated the enrichment of biological processes when only the differentially expressed proteins (FDR Ͻ10%) (target list) were compared with all identified proteins in our dataset (background change and analyzed using GSEA regardless of their p-and FDR-values. The top 10 negatively (D) and positively (E) enriched biological processes (BPs) in AD were plotted. GO terms with p Ͻ 0. 01 and FDR Ͻ5% were considered statistically significant. F-G, The significantly decreased or increased proteins (FDR Ͻ10%, target lists) were compared with the entire dataset (background list) by GOrilla.
Secondly, IPA can create molecular networks that can provide more information about the relationship between the identified proteins. A total of 25 molecular networks were created and a network called "cellto-cell signaling and interaction/nervous system development and function/cellular assembly and organization" was found to be affected the most in AD (Fig. 3C ). Interestingly, this network consists of pathways mediating neurotransmission (p ϭ 4. 92E-13), synaptic transmission (p ϭ 2. 24E-11), transport of SVs (p ϭ 3. 74E-10), and release of neurotransmitter (p ϭ 7. 46E-09). Lastly, the upstream regulator tool was used to identify which upstream molecules can potentially explain the observed changes in protein expression in our dataset. MAP kinase-interacting serine/threonine-protein kinase 1 (MKNK1), transcription factor 7-like 2 (TCF7L2), hemoglobin subunit alpha (HBA1/HBA2), and nuclear factor erythroid 2-related factor 2 (NFE2L2) were identified as upstream regulators and all predicted to be inhibited with significant zscores (supplemental Table  S10 ).
C, The molecular networks tool generated 25 different networks. The network "Cell-to-cell signaling and interaction/Nervous system development and function/Cellular assembly and organization", shown in (C), was ranked with the highest score and consisted of pathways mediating neurotransmission. An asterisk indicates presynaptic proteins in this network. D, The upstream regulator tool predicted the transcription factor Nuclear factor erythroid 2-related factor 2 (NFE2L2) as an upstream regulator of its target proteins that were identified in our dataset. particularly components of complex I, III, and IV, which were found to be slightly decreased, could still have an impact on mitochondrial function. Considering that synaptic transmission requires high amount of energy, altered mitochondrial bioenergetics could impair synaptic homeostasis, which has been previously observed in AD  (44).
The "synaptic vesicle pathway" (hsa04721) was modified from KEGG  (66) , and the proteins with altered expression in AD in our MS dataset were highlighted in different shades of green according to their ratio. The proteins with dashed line were added to the original pathway. proteins have been previously shown to be decreased in AD brain  (59, 60). These discrepancies could occur because of the local alterations in our region of interest. Activation of cell spreading and shape change of tumor cell lines as well as cell survival processes are perhaps surprising in a neurodegeneration context. However, many of these proteins, e. g. GFAP, CD44, ITGB1, EGFR, and GAP43, are also involved in other processes and it is possible that IPA predicted the abovementioned pathways because of a potential over-representation of published literature in the cancer field.
Acknowledgments-The postmortem human brain tissue for proteomics study was supplied by the  Netherlands Brain Bank  (Amsterdam, the Netherlands), and for immunohistochemical study was provided by the  Carroll A. Campbell Jr. Neuropathology Laboratory (CCNL) Brain Bank at the Medical University of South Carolina. We thank all the donors for the tissue used in this study. Mass spectrometry analysis was performed by the  Clinical Proteomics Mass Spectrometry facility ,  Karolinska Institutet/Karolinska University Hospital/ Science for Life Laboratory. We would like to thank to  Dr Christian Sibbersen  for his help on proteomics data analysis, as well as  Ms Anah Gilmore  and  Ms Laura Columbo  for brain dissections and processing at the CCNL brain bank.
Across the glacial-interglacial cycles of the late Pleistocene (∼700 k. y. ), temperature variability at low latitudes is often considered to have been negligible compared to changes in precipitation. However, a paucity of quantified temperature records makes this difficult to reliably assess. In this study, we used the Bayesian method CREST (Climate REconstruction SofTware) to produce a 790,000 yr quantified temperature reconstruction from a marine pollen record from southeast Africa. The results reveal a strong similarity between temperature variability in subtropical Africa and global ice volume and CO 2 concentrations, indicating that temperature in the region was not controlled by local insolation, but followed global trends at these time scales, with an amplitude of ∼4 °C between glacial minima and interglacial maxima.