Dilated cardiomyopathy (DCM) is a condition of impaired ventricular remodeling and systolic diastole that is often complicated by arrhythmias and heart failure with a poor prognosis. This study attempted to identify autophagy-related genes (ARGs) with diagnostic biomarkers of DCM using machine learning and bioinformatics approaches. Differential analysis of whole gene microarray data of DCM from the Gene Expression Omnibus (GEO) database was performed using the NetworkAnalyst 3.0 platform. Differentially expressed genes (DEGs) matching (|log2FoldChange ≥ 0.8, p value < 0.05|) were obtained in the GSE4172 dataset by merging ARGs from the autophagy gene libraries, HADb and HAMdb, to obtain autophagy-related differentially expressed genes (AR-DEGs) in DCM. The correlation analysis of AR-DEGs and their visualization were performed using R language. Gene Ontology (GO) enrichment analysis and combined multi-database pathway analysis were served by the Enrichr online enrichment analysis platform. We used machine learning to screen the diagnostic biomarkers of DCM. The transcription factors gene regulatory network was constructed by the JASPAR database of the NetworkAnalyst 3.0 platform. We also used the drug Signatures database (DSigDB) drug database of the Enrichr platform to screen the gene target drugs for DCM. Finally, we used the DisGeNET database to analyze the comorbidities associated with DCM. In the present study, we identified 23 AR-DEGs of DCM. Eight (PLEKHF1, HSPG2, HSF1, TRIM65, DICER1, VDAC1, BAD, TFEB) molecular markers of DCM were obtained by two machine learning algorithms. Transcription factors gene regulatory network was established. Finally, 10 gene-targeted drugs and complications for DCM were identified.
Gravity currents are important in many fields, including the estuarine sciences, meteorology and hydraulic engineering. The NHWAVE (non-hydrostatic wave) model was applied to simulate the detailed interface structure between a lock-release gravity current and the ambient fluid. The simulated structures, including the front height, front position and velocity of the current, are consistent with the results of laboratory experiments. However, the internal structure of the current is different from that revealed by previous research. The Kelvin–Helmholtz phenomenon in the interface and the interface vortices were successfully captured by the NHWAVE model. The difference in velocity between the front and rear vortices leads to entrainment, further causing changes in the shapes and amount of vortices. Flow field results obtained by the NHWAVE model reveal the existence of a significant circular flow, as well as some small eddies within it. The significant circular flow supports the forward movement of the current, whereas the small eddies reflect interface vortices. In contrast, hydrostatic simulation with the same model settings fails to capture the vortices. This research shows that the NHWAVE model performs better than a hydrostatic model when simulating the Kelvin–Helmholtz instability phenomenon and vortex entrainment in a lock-release gravity current.
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