At present, various contradictions in most coal-energy cities have become increasingly prominent, which has become a disharmonious factor restricting the optimization and upgrading of coal-energy ...cities. Since the turn of the century, taking effective improvement and protection measures and promoting the economic transformation of cities reliant on coal as an energy source has been the primary job of departments at all levels in order to secure the sustainable growth of cities. In this paper, the economic transformation and sustainable growth of coal-energy cities are included in the enhanced TOPSIS economic transformation evaluation model based on the proposed entropy weight. This study examines the industrialization of cities that rely on coal energy resources, assesses the industrial efficacy of coal energy using the DEA technique, and proposes a plan for the industrialization of cities that rely on coal energy resources. This paper summarizes the industrial transformation process of coal-energy cities and designs an evaluation method for the industrial economic transformation of coal-energy cities. This paper determines a set of evaluation index systems suitable for the economic transformation of coal-based energy cities; constructs an evaluation model for the economic transformation of improved TOPSIS coal-based energy cities based on entropy weight; and, finally, calculates and analyzes the industrial economic statistics of a city over the years. It is found that, at the economic structure level, the transformation score of driving forces increases from 0.606 to 0.871; at the level of social economic structures, the transition score of the pressure system increases from 0.476 to 0.779, and the transition score of the state system increases from 0.401 to 0.699; at the level of urban construction structures, the transformation score of the pressure system increases from 0.467 to 0.568; and at the level of comprehensive transformation structures, the transformation score affecting the system increases from 0.611 to 0.716. This shows that, in the process of transformation, the driving force of industrial and economic development in coal-energy-based cities is sufficient, while the pressure of social and economic transformation is great. In the process of transformation, we should strengthen infrastructure construction and protect the urban environment.
Genome-wide association studies in autoimmune and inflammatory diseases (AID) have uncovered hundreds of loci mediating risk. These associations are preferentially located in non-coding DNA regions ...and in particular in tissue-specific DNase I hypersensitivity sites (DHSs). While these analyses clearly demonstrate the overall enrichment of disease risk alleles on gene regulatory regions, they are not designed to identify individual regulatory regions mediating risk or the genes under their control, and thus uncover the specific molecular events driving disease risk. To do so we have departed from standard practice by identifying regulatory regions which replicate across samples and connect them to the genes they control through robust re-analysis of public data. We find significant evidence of regulatory potential in 78/301 (26%) risk loci across nine autoimmune and inflammatory diseases, and we find that individual genes are targeted by these effects in 53/78 (68%) of these. Thus, we are able to generate testable mechanistic hypotheses of the molecular changes that drive disease risk.
Quercetin has demonstrated antioxidant, anti-inflammatory, hypoglycemic, and hypolipidemic activities, suggesting therapeutic potential against type 2 diabetes mellitus (T2DM) and Alzheimer's disease ...(AD). In this study, potential molecular targets of quercetin were first identified using the Swiss Target Prediction platform and pathogenic targets of T2DM and AD were identified using online Mendelian inheritance in man (OMIM), DisGeNET, TTD, DrugBank, and GeneCards databases. The 95 targets shared among quercetin, T2DM, and AD were used to establish a protein-protein interaction (PPI) network, top 25 core genes, and protein functional modules using MCODE. Metascape was then used for gene ontology and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. A protein functional module with best score was obtained from the PPI network using CytoHubba, and 6 high-probability quercetin targets (AKT1, JUN, MAPK, TNF, VEGFA, and EGFR) were confirmed by docking simulations. Molecular dynamics simulation was carried out according to the molecular docking results. KEGG pathway enrichment analysis suggested that the major shared mechanisms for T2DM and AD include "AGE-RAGE signaling pathway in diabetic complications," "pathways in cancer," and "MAPK signaling pathway" (the key pathway). We speculate that quercetin may have therapeutic applications in T2DM and AD by targeting MAPK signaling, providing a theoretical foundation for future clinical research.
Abstract
Summary
Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an ...open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work.
Availability and implementation
RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home
Supplementary information
Supplementary data are available at Bioinformatics online.
Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new ...method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.
Patients with gastric cancer (GC) are more likely to be infected with 2019 coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the prognosis is ...worse. It is urgent to find effective treatment methods.
This study aimed to explore the potential targets and mechanism of ursolic acid (UA) on GC and COVID-19 by network pharmacology and bioinformatics analysis.
The online public database and weighted co-expression gene network analysis (WGCNA) were used to screen the clinical related targets of GC. COVID-19-related targets were retrieved from online public databases. Then, a clinicopathological analysis was performed on GC and COVID-19 intersection genes. Following that, the related targets of UA and the intersection targets of UA and GC/COVID-19 were screened. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome Analysis (KEGG) pathway enrichment analyses were performed on the intersection targets. Core targets were screened using a constructed protein-protein interaction network. Finally, molecular docking and molecular dynamics simulation (MDS) of UA and core targets were performed to verify the accuracy of the prediction results.
A total of 347 GC/COVID-19-related genes were obtained. The clinical features of GC/COVID-19 patients were revealed using clinicopathological analysis. Three potential biomarkers (TRIM25, CD59, MAPK14) associated with the clinical prognosis of GC/COVID-19 were identified. A total of 32 intersection targets of UA and GC/COVID-19 were obtained. The intersection targets were primarily enriched in FoxO, PI3K/Akt, and ErbB signaling pathways. HSP90AA1, CTNNB1, MTOR, SIRT1, MAPK1, MAPK14, PARP1, MAP2K1, HSPA8, EZH2, PTPN11, and CDK2 were identified as core targets. Molecular docking revealed that UA strongly binds to its core targets. The MDS results revealed that UA stabilizes the protein-ligand complexes of PARP1, MAPK14, and ACE2.
This study found that in patients with gastric cancer and COVID-19, UA may bind to ACE2, regulate core targets such as PARP1 and MAPK14, and the PI3K/Akt signaling pathway, and participate in antiinflammatory, anti-oxidation, anti-virus, and immune regulation to exert therapeutic effects.
We conducted a cross-trait meta-analysis of genome-wide association study on schizophrenia (SCZ) (n = 65,967), bipolar disorder (BD) (n = 41,653), autism spectrum disorder (ASD) (n = 46,350), ...attention deficit hyperactivity disorder (ADHD) (n = 55,374), and depression (DEP) (n = 688,809). After the meta-analysis, the number of genomic loci increased from 14 to 19 in ADHD, from 3 to 10 in ASD, from 45 to 57 in DEP, from 8 to 54 in BD, and from 64 to 87 in SCZ. We observed significant enrichment of overlapping genes among different disorders and identified a panel of cross-disorder genes. A total of seven genes were found being commonly associated with four out of five psychiatric conditions, namely GABBR1, GLT8D1, HIST1H1B, HIST1H2BN, HIST1H4L, KCNB1, and DCC. The SORCS3 gene was highlighted due to the fact that it was involved in all the five conditions of study. Analysis of correlations unveiled the existence of two clusters of related psychiatric conditions, SCZ and BD that were separate from the other three traits, and formed another group. Our results may provide a new insight for genetic basis of the five psychiatric disorders.
Background. Dysphagia is a common sequelae after stroke. Noninvasive brain stimulation (NIBS) is a tool that has been used in the rehabilitation process to modify cortical excitability and improve ...dysphagia. Objective. To systematically evaluate the effect of NIBS on dysphagia after stroke and compare the effects of two different NIBS. Methods. Randomized controlled trials about the effect of NIBS on dysphagia after stroke were retrieved from databases of PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Data, VIP, and CBM, from inception to June 2021. The quality of the trials was assessed, and the data were extracted according to the Cochrane Handbook for Systematic Reviews of Interventions. A statistical analysis was carried out using RevMan 5.3 and ADDIS 1.16.8. The effect size was evaluated by using the standardized mean difference (SMD) and a 95% confidence interval (CI). Results. Ultimately, 18 studies involving 738 patients were included. Meta-analysis showed that NIBS could improve the dysphagia outcome and severity scale (DOSS) score (standard mean difference SMD=1.44, 95% CI 0.80 to 2.08, P<0.05) and the water swallow test score (SMD=6.23, 95% CI 5.44 to 7.03, P<0.05). NIBS could reduce the standardized swallowing assessment (SSA) score (SMD=−1.04, 95% CI -1.50 to -0.58, P<0.05), the penetration-aspiration scale (PAS) score (SMD=−0.85, 95% CI -1.33 to -0.36, P<0.05), and the functional dysphagia scale score (SMD=−1.05, 95% CI -1.48 to -0.62, P<0.05). Network meta-analysis showed that the best probabilistic ranking of the effects of two different NIBS on the DOSS score is rTMS P=0.52>tDCS P=0.48, the best probabilistic ranking of the SSA score is rTMS P=0.72>tDCS P=0.28, and the best probabilistic ranking of the PAS score is rTMS P=0.68>tDCS P=0.32. Conclusion. Existing evidence showed that NIBS could improve swallowing dysfunction and reduce the occurrence of aspiration after stroke, and that rTMS is better than tDCS. Limited by the number of included studies, more large-sample, multicenter, double-blind, high-quality clinical randomized controlled trials are still needed in the future to further confirm the results of this research.
Alopecia areata (AA) is a prevalent autoimmune disease with 10 known susceptibility loci. Here we perform the first meta-analysis of research on AA by combining data from two genome-wide association ...studies (GWAS), and replication with supplemented ImmunoChip data for a total of 3,253 cases and 7,543 controls. The strongest region of association is the major histocompatibility complex, where we fine-map four independent effects, all implicating human leukocyte antigen-DR as a key aetiologic driver. Outside the major histocompatibility complex, we identify two novel loci that exceed the threshold of statistical significance, containing ACOXL/BCL2L11(BIM) (2q13); GARP (LRRC32) (11q13.5), as well as a third nominally significant region SH2B3(LNK)/ATXN2 (12q24.12). Candidate susceptibility gene expression analysis in these regions demonstrates expression in relevant immune cells and the hair follicle. We integrate our results with data from seven other autoimmune diseases and provide insight into the alignment of AA within these disorders. Our findings uncover new molecular pathways disrupted in AA, including autophagy/apoptosis, transforming growth factor beta/Tregs and JAK kinase signalling, and support the causal role of aberrant immune processes in AA.
Motivation: Yeast two-hybrid screens are an important method to map pairwise protein interactions. This method can generate spurious interactions (false discoveries), and true interactions can be ...missed (false negatives). Previously, we reported a capture–recapture estimator for bait-specific precision and recall. Here, we present an improved method that better accounts for heterogeneity in bait-specific error rates. Result: For yeast, worm and fly screens, we estimate the overall false discovery rates (FDRs) to be 9.9%, 13.2% and 17.0% and the false negative rates (FNRs) to be 51%, 42% and 28%. Bait-specific FDRs and the estimated protein degrees are then used to identify protein categories that yield more (or fewer) false positive interactions and more (or fewer) interaction partners. While membrane proteins have been suggested to have elevated FDRs, the current analysis suggests that intrinsic membrane proteins may actually have reduced FDRs. Hydrophobicity is positively correlated with decreased error rates and fewer interaction partners. These methods will be useful for future two-hybrid screens, which could use ultra-high-throughput sequencing for deeper sampling of interacting bait–prey pairs. Availability: All software (C source) and datasets are available as supplemental files and at http://www.baderzone.org under the Lesser GPL v. 3 license. Contact: joel.bader@jhu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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