dataesr/scientific-paragraphs-categorization huggingface dataset:
py); • decide whether the FTS is live with FTS4VMC or whether the MTSυ is live with VMC; • specify a v-ACTL formula in FTS4VMC or in VMC; • decide whether the v-ACTL formula is a v-ACTLive formula with VMC; The front-end FTS4VMC accepts FTSs in the. dot format, a wellknown graphical notation supported by the graphviz open source graph visualization software (cf. https://www. graphviz. org). Once the FTS has been uploaded and verified to be in the correct format, the user (modeler) is offered a variety of (analysis) tasks: Full ambiguities analysis analyzes the FTS for all three types of ambiguities: once done, it outputs an updated version of the FTS with dead transitions highlighted in blue, false optional transitions highlighted in green, and hidden deadlock states highlighted in red; moreover, it enables the ambiguity removal tasks mentioned below; Liveness analysis analyzes the FTS for liveness, i.
CONCLUSION We presented FTS4VMC, developed specifically as a front-end for the research tool VMC, with a user-friendly It has code to The results, mentioned in this paper, that form the basis of the verification methodologies depicted in Figs. 1  and 2  indicate specific cases in which verification of live FTSs reduces to verification (with a linear complexity) of corresponding MTSs and LTSs that are obtained straightforwardly by ignoring the feature expressions (and distinguishing necessary and optional transitions in case of MTSs). However, if either (i) the property to be verified is not a v-ACTLive or LTL formula, or (ii) the result of the verification is false, then the formula needs to be verified with a classical family-based modelchecking tool or by means of product-based model checking.
We integrated original DNA methylation data from our laboratory (the Gene SMART cohort) with available open-access data from multiple repositories and published studies. Firstly, we aimed to identify robust age-related CpGs in skeletal muscle in an EWAS meta-analysis of age, combining n ¼ 908 samples from 10 datasets. Second, we performed enrichment analyses to unravel the potential functional consequences of these robust age-related DNA methylation changes. Thirdly, we integrated age-related methylome changes with transcriptome and proteome changes in skeletal muscle using two external, large-scale studies. Finally, we updated our skeletal muscle epigenetic clock  9  with an additional 371 samples, reaching a total of 1053 human skeletal muscle methylomes from 16 datasets. Importantly, we have made the results of our analysis available as an open-access, user-friendly, interactive web-based tool, MetaMeth (https://sarah-voisin.
Epigenome-wide association study meta-analysis of age in skeletal muscle We combined four datasets of genome-wide DNA methylation in skeletal muscle [the Gene Skeletal Muscle Adaptive Response to Training (SMART),  13  the Limb Immobilisation and Transcriptional/Epigenetic Responses (LITER) study,  9  the Biological Atlas of Severe Obesity (ABOS) study,  14  and the Epigenetica & Kracht (EPIK) study 10 ], with five datasets from the open-access Gene Expression Omnibus platform (GSE49908, 8 GSE50498, 7 GSE114763, 15 GSE38291,  16  and GSE135063  17  ), and the Finland-United States Investigation of NIDDM Genetics (FUSION) study  18 (phs000867. v1. p1 ). These summed up to a total of n ¼ 908 skeletal muscle samples collected from men and women across the lifespan (age range 18-89 years old, Supporting Information, Figure  S1  and Table  S1 ).
Samples were 98% Caucasian and 71% male (Table  S1 ). We excluded cohorts from our recently published paper  9  with a narrow age range (age standard deviation <5 years) as age-related differences in DNA methylation cannot be detected if age is invariant; we also excluded datasets with a limited number of samples (n < 20) for robustness. Samples from the Gene SMART cohort (n ¼ 234) include two batches, and our recently published paper  9  only includes the first batch of 75 samples available on the Gene Expression Omnibus platform (GSE151407). The additional 159 samples from the second batch include both men and women, before and after exercise intervention. Different preprocessing pipelines may result in DNA methylation differences between studies. To overcome this issue, we downloaded and preprocessed the data using the same pipeline for 9/10 datasets whose raw data were available (Table  S1 ).
Details on the preprocessing steps can be found elsewhere. 9 We have also filtered out additional probes that have been identified as cross-hybridizing by Pidsley et al. 19  We did not preprocess all datasets together because age distributions varied widely between datasets (Figure  S1 ). As age was confounded with dataset, normalizing datasets together may overcorrect/under-correct DNA methylation profiles and artificially introduced noise. Therefore, we analysed each dataset separately and only then perform a meta-analysis, which preserves each dataset's specificity while combining results across datasets. We conducted independent EWAS of age in skeletal muscle in each dataset, using linear models and moderated Bayesian statistics as implemented in limma  20  ; to isolate the contribution of age to DNA methylation variability, we regressed DNA methylation level against age and adjusted, when the dataset included these covariates, for sex, body mass index (BMI), diabetes status, batch, and time point (baseline/post-intervention or training); we also added, when the dataset included repeated measures on the same individuals or related individuals, a random intercept using the duplicateCorrelation function to account for repeated measures from the same individuals or to account for twinship.
We adjusted each EWAS for bias and inflation using the empirical null distribution as implemented in bacon (Figure  S2 ). 21 Inflation and bias in EWAS are caused by unmeasured technical and biological confounding, such as population substructure, batch effects, and cellular heterogeneity. 22 The inflation factor is higher when the expected number of true associations is high (as it is for age); it is also greater for studies with higher statistical power. 21 he figures we found (Figure  S2 ) were consistent with the inflation factors and biases reported in an EWAS of age in blood. 21 esults from the independent EWAS were combined using an inverse variance weighted meta-analysis with METAL. 12 e used METAL because it does not require all DNA methylation datasets to include every CpG site on the HumanMethylation arrays.
Different sets of CpGs may be filtered out during preprocessing of each individual dataset, which means the overlap between the datasets is imperfect and a given CpG may only be present in five out of 10 datasets or eight out of 10 datasets. For robustness, we only included CpGs present in at least six of the 10 cohorts (649 250 CpGs). We used a fixed effects (as opposed to random effects) meta-analysis, assuming one true effect size of age on DNA methylation, which is shared by all the included studies. Nevertheless, Cochran's Q-test for heterogeneity was performed to test whether effect sizes were homogeneous between studies [a heterogeneity index (I 2 ) >50% reflects heterogeneity between studies]. The CpGs associated with age at a stringent meta-analysis false discovery rate (FDR) <0. 005 were considered differentially methylated positions (DMPs).
Update of the muscle epigenetic clock (MEAT 2. 0) Since the development of the original muscle clock that used 682 samples from 12 datasets to predict age from DNA methylation data,  9  we gathered additional 371 samples from five datasets (+159 from Gene SMART, +65 from ABOS, +42 from LITER, +57 from GSE135063, and +48 from EPIK). We therefore updated the clock with these new samples, using the same algorithm and methodology. 9 Briefly, we first preprocessed each dataset separately (i. e. probe/sample filtering, adjustment of type I and type II probes, and correction for batch effects); then, we reduced each dataset to all the CpGs that were common between them (18 747 CpGs). To obtain DNA methylation profiles that were comparable between datasets, we calibrated each dataset to GSE50498 using an adapted version of the BMIQ algorithm.
Widespread age-related DNA methylation changes at genes involved in skeletal muscle structure, development, and function We first conducted an EWAS meta-analysis of age in skeletal muscle using 10 datasets (total n ¼ 908 samples from 601 individuals, Table  1 ) and uncovered a small, widespread effect of ageing on the skeletal muscle epigenome. Six per cent of all tested CpGs were associated with age in skeletal muscle (40 479 DMPs corresponding to 6710 DMRs, both at FDR < 0. 005, Figure  1A  and Tables  S2  and S3 ). We found slightly more hypomethylated than hypermethylated DMPs (57% hypo-DMPs and 43% hyper-DMPs, Table  S2 ). The magnitude of age-related DNA methylation changes was small and similar for both hypo-DMPs and hyper-DMPs: hypo-DMPs lost an average of ~0. 8% in methylation per decade of life, and hyper-DMPs gained an average of ~0.
6% in methylation per decade of life (Figure  1B ). Each dataset had a unique study design that required adjustment for factors that are known to affect DNA methylation levels, such as sex,  40  BMI,  41  and T2D. 42 We adjusted each dataset for these factors, but we noted that age was associated with BMI or T2D in some datasets (Table  S1 ). For example, older individuals from the GSE50498 dataset had a higher BMI than younger individuals (4. 1 kg/m 2 heavier, P ¼ 0. 0011), so it is possible that the age-related signal captured in this dataset was partially confounded by BMI. We repeated the meta-analysis without GSE50498, but results were largely unchanged (Figure  S3a ). We also repeated the meta-analysis excluding T2D patients from the FUSION, ABOS, and GSE38291 datasets, but results remained unchanged (Figure  S3b ).
We also repeated the meta-analysis without the ABOS dataset whose muscle of origin differed from that of the other datasets (rectus abdominis vs. vastus lateralis muscle). However, results remained unchanged (Figure  S3c ). Finally, we repeated the meta-analysis omitting eight non-Caucasian individuals from the Gene SMART and LITER cohorts. However, results remained unchanged (Figure  S3d ). This confirms that our results are not confounded by BMI, T2D, the type of skeletal muscle, or the presence of a few ethnically diverse individuals. We then focused on the DMRs for all downstream analyses, as DMRs remove spatial redundancy (CpG sites within ~500 bp are typically highly correlated  24  ), and they may provide more robust and functionally important information than DMPs. 25, 26 As with DMPs, we found slightly more hypomethylated than hypermethylated DMRs (61% hypo-DMRs and 39% hyper-DMRs, Table  S3 ).
g. genomic region, annotated gene, position with respect to CpG islands, chromatin states in male and female skeletal muscle, and TF binding). To download summary statistics for DMPs or DMRs in a table format, users can go to the 'Summary Tables' tab and download the data as an excel or csv file, after optionally filtering data based on genomic location and statistics. Finally, we have also displayed the scatter plot of genes showing methylation, and mRNA and protein changes with age as an interactive graph: users simply need to hover their mouse on one point of the graph to be shown the name of the gene and the number of DMRs annotated to it. The code used to produce the website is available in open access on Sarah Voisin's GitHub account (https://github. com/sarah-voisin/MetaMeth).
More samples in the muscle epigenetic clock do not change age prediction accuracy The present EWAS meta-analysis of age utilized all of the datasets included in the original muscle epigenetic clock (MEAT) that we recently published, with the exception of datasets that were invariant in age and the datasets that were too small (n < 20) (see Methods). 9 The present study included an additional 371 samples from five datasets. Using the same algorithm and methodology, we updated the muscle clock with these new samples, reaching a total of n ¼ 1053 human skeletal muscle samples from 16 datasets. The updated version of the clock (MEAT 2. 0) uses DNA methylation at 156 CpGs to predict age, 73 of which were in common with MEAT (Figure  6A ). We found that MEAT 2. 0 only slightly outperforms MEAT, with an average Pearson correlation coefficient of 0.
Discussion To paint a comprehensive picture of age-related DNA methylation changes in human skeletal muscle, we conducted an EWAS meta-analysis of age in human muscle across the lifespan, combining 908 samples from 10 independent datasets. In this study, we were able to demonstrate a profound effect of age on the muscle methylome. Additionally, we have provided a detailed account of the genomic context of age-affected regions, reported putatively affected pathways, and integrated methylome changes with known transcriptome and proteome changes in muscle. To maximize the usefulness of this large-scale EWAS meta-analysis to the scientific community, we created a website named MetaMeth (https://sarah-voisin. shinyapps. io/MetaMeth/), which allows researchers to visualize results in an interactive and user-friendly manner.
Finally, we updated our muscle clock  9  with 371 newly acquired DNA methylation samples and found that the original version of the clock was already at optimal prediction accuracy. Previous studies on the overall pattern of age-related DNA methylation changes in muscle showed mixed results, three reporting more hypermethylation with age  7, 8, 11  and one finding slightly more hypomethylation with age. 10  We included three of these studies (GSE49908, GSE50498, and EPIK) in our meta-analysis and found balanced amounts of hypomethylation and hypermethylation. Differences in coverage between studies are unlikely to explain the discrepancy in results, because the three HumanMethylation arrays were represented in these studies (27k, 450k, and 850k). It is more likely that the overall direction of age-related DNA methylation change became more nuanced once these small-scale studies were combined with the other nine datasets.
As changes in the epigenetic landscape are one of the primary hallmarks of ageing, understanding its origin would narrow down our focus on putative genetic or/and epigenetic regions, with the ultimate goal of targeting them with lifestyle or pharmacological interventions to slow down the ageing process at the molecular level. Future studies should aim to find interventions easily accessible to a wide range of people, such as exercise training or dietary interventions, to slow down, or perhaps even reverse, age-related epigenetic changes in skeletal muscle. Recently, we established an epigenetic clock for human skeletal muscle, using 682 samples from 12 datasets. 9 Here we updated this clock (MEAT 2. 0) by using 1053 samples from 16 datasets, particularly adding more female and middle-aged individuals that were under-represented in MEAT.
This suggests that the original muscle clock was already sufficiently accurate for age prediction in human skeletal muscle using the Illumina HumanMethylation array technology. We have however updated the R package MEAT on Bioconductor with this new clock, providing users the possibility to choose between the original version (MEAT) and updated version (MEAT 2. 0) of the clock for their analyses. The age-related changes in the muscle methylome uncovered herein and the epigenetic age calculated from the MEAT clock reflect both intracellular changes in methylation levels and age-related changes in muscle cell-type composition. Older muscle tends to have a greater proportion of type IIx (hybrid) muscle fibres,  46  shows fat  61  and macrophage  62  infiltration, and displays lower numbers of satellite cells,  63  which can alter the methylome of bulk muscle tissue.
Acknowledgements We would like to thank  Dr Mark Ziemann  from  Deakin University  for his critical feedback on the manuscript. We also greatly acknowledge  Erika Guzman  at the  ATGC/IHBI/QUT  for performing the HMEPIC assays in the LITER study. The authors of this manuscript certify that they comply with the ethical guidelines for authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle. 64.
Data availability statement The GSE151407, GSE49908, GSE50498, GSE114763, GSE38291, and GSE135063 are available in the Gene Expression Omnibus repository (https://www. ncbi. nlm. nih. gov/ geo). The FUSION study is available on the dbGaP repository (https://www. ncbi. nlm. nih. gov/gap/). The ABOS and EPIK studies datasets are available from the corresponding author on reasonable request. The online webtool (MetaMeth) gen-erated during the current study is available at https://sarahvoisin. shinyapps. io/MetaMeth/?_ga=2. 176854501. 15133379 47. 1598765391-1534254090. 1593032027. The source code for MetaMeth is available at https://github. com/sarahvoisin/MetaMeth.
Delineation of the regulatory graph controlling Th1-Th17 cell differentiation We first conducted an extensive analysis of published data to identify the key signaling events and molecules driving Th17 differentiation upon exposure to antigen and various cytokine combinations, including IL-1β, IL-23, IL-6 and TGF-β. We also curated the literature for the IL-12 pathway in T cells in order to understand its interplay with known Th17 inducers. The construction of the regulatory graph relied on 91 original articles, two KEGG pathways (hsa04350, hsa04660, https://www. genome. jp/kegg/), the T-MOD database  [20] , a Th17 differentiation data-driven model  [21] , and two preexisting Th differentiation dynamical models  [22, 23]. We also used public ChIP-seq data to further validate several putative interactions.
2 ). IL-1β signaling has an inherent relationship with the TCR module, as it is able to activate phosphoinositide 3kinases (PI3K) and to induce myeloid differentiation primary response 88 (Myd88) expression, which results in AP-1 activation (IL-1β module, Fig. 2 ). The IL-12 module includes the input cytokine IL-12, which can trigger the activation of both STAT1 and STAT4, leading to the expression of the master transcriptional regulator T-bet and to the production of IFN-γ cytokine (IL-12 module, Fig. 2 ). It is usually considered that Th1 and Th17 differentiation programs are mutually exclusive, with mutual inhibitions between Tbet and RORγt. However, based on our literature search, we were led to consider additional cross-talks, in A model is first built based on information collected from publications, databases and previous models.
In order to check whether the observed phenotypes could be reached in the corresponding input condition, starting from Th0 conditions, we performed stochastic simulations using the software MaBoSS (see Material and Methods) to recapitulate the differentiation of Th1, Th17, Th1-Th17 hybrid cells. After tuning several logical rules, our model was able to qualitatively reproduce the polarizing events observed experimentally. Firstly, we could recover the polarization towards Th1 and Th17 cell types, characterized by the expression of IFN-γ and IL-17A/IL-17F, respectively, under the corresponding polarizing cytokine environments (Fig. 5a  and b ) (Supplementary Table  3 , top). We then verified that IL-17F + IFN-γ + IL-17A -and IL-17F + cell phenotypes could be induced in the IL-12 + IL-1β condition (Fig.
Regulatory graph construction We built a comprehensive logical model using the software GINsim, version 3. 0 alpha  [46] , relying on preexisting molecular maps and models. GINsim implements the multivalued logical modeling formalism initially introduced by René Thomas  [47]. This formalism relies on the delineation of a regulatory graph, where each component (e. g. protein or gene) is represented by a logical node (taking the values 0 or 1, or additional values when justified), and each regulatory interaction between a pair of components is represented by a signed arc (activation, inhibition, or more rarely a dual interaction, i. e. with a sign depending on the regulator level or on the presence of co-regulators). RNA-seq and ChIP-seq data in unstimulated naive CD4 + T cells were used to support several interactions of the regulatory graph.
Dynamical model analysis To obtain a dynamical model, we further assigned a logical rule to each node of the regulatory graph, which determines its activation level according to the levels of its regulators. These logical rules involve literals (component values) combined with the classical logical operators AND, OR, and NOT  [46]. We are particularly interested in the stable states of such models, as they typically represent cellular phenotypes. We computed the stable states of our model using an algorithm implemented in GINsim  [46]. In order to estimate the reachability of stable states from relevant initial conditions, we performed stochastic simulations with the software MaBoSS, version 2. 0  [48] , which translates Boolean networks into continuous time Markov processes. In this framework, each node activation and inactivation is associated with an up and a down rates, which specify the propensity of the corresponding transitions.
Availability of data and materials The ChIP-seq datasets used in this study can be downloaded from the database GEO (https://www. ncbi. nlm. nih. gov/geo/), with the identifiers listed in the supplementary Table  1. The logical model can be downloaded from the GINsim model repository at the url: http://ginsim. org/model/ThIL17diff. This file (with a. zginml extension), to be open with the Java software GINsim, version 3. 0, freely available at the url http://ginsim. org. The model is also publicly available the database BioModels in the SBML-qual format (https://www. ebi. ac. uk/ biomodels/MODEL2101150001).
First, we determined that scDRS cell type-disease associations are highly consistent between data sets collected using different technologies (TMS FACS vs. TMS droplet) and reasonably consistent between mouse and human data (TMS FACS vs. TS FACS) (Supp. Fig. 10 ). Second, we determined that cell type-disease associations are highly consistent between scDRS and 4 existing cell type-level association methods (LDSC-SEG 12 and 3 methods in Bryois et al. 8  ; Supp. Fig. 11 ). Third, since the scDRS results may be biased towards major cell types with many cells, we implemented a version of scDRS that adjusts for cell type proportions, and determined that it was highly consistent with the default version (median of 0. 97 across 74 diseases for the scDRS disease score correlation computed across all TMS FACS cells) and well-calibrated in null simulations (Supp.
Heterogeneous subpopulations of T cells associated with autoimmune disease We sought to further understand the heterogeneity across T cells in the TMS FACS data in their association with autoimmune diseases (Fig. 3 ). We jointly analyzed all T cells in the TMS FACS data (3,769 cells, spanning 15 tissues). Since the original study clustered cells from different tissues separately  19  , we reclustered these T cells, resulting in 11 clusters (Fig. 4A ; Methods); we verified that batch effects were not observed for tissue, age, or sex (Supp. Fig. 15 ). We considered 10 autoimmune diseases: IBD, Crohn's disease (CD), ulcerative colitis (UC), RA, MS, AIT, hypothyroidism (HT), eczema, asthma (ASM), and respiratory and ear-nose-throat diseases (RR-ENT) (Supp. Table  1 ); we also considered height as a negative control trait.