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- 저자 : Yanpeng Wu (검색결과 2 건)
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Yanpeng Wu,Ying Wu 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.2
This paper is concerned with the stochastic stabilization problem for a class of networked control system(NCS) with destabilizing transmission factors. By introducing the effective sampling instant to model randomtime delays and successive packet dropouts as two independent Markov chains, NCS is modeled as a discrete-timeMarkovian jump linear system with mixed integrated Markovian jumping parameters. In this way, a novel frameworkto analyze the stochastic stabilization problem of NCS is provided. The necessary and sufficient conditions forthe stochastic stabilization of the NCS are obtained by the Lyapunov method and the state-feedback controller gainthat depends on the delay modes is obtained in terms of the linear matrix inequalities (LMIs) formulation via theSchur complement theory. Finally, numerical examples are provided to illustrate the effectiveness of the proposedmethod.
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Yong Liao,Wen Cao,Kunpeng Zhang,Yang Zhou,Xin Xu,Xiaoling Zhao,Xu Yang,Jitao Wang,Shouwen Zhao,Shiyu Zhang,Longfei Yang,Dengxiang Liu,Yanpeng Tian,Weizhong Wu 한국유전학회 2021 Genes & Genomics Vol.43 No.6
Background lncRNAs–miRNAs–mRNAs networks play an important role in Gastric adenocarcinoma (GA). Identifcation of these networks provide new insight into the role of these RNAs in gastric cancer. Objectives Biological information databases were screened to characterize and examine the regulatory networks and to further investigate the potential prognostic relationship this regulation has in GA. Methods By mining The Cancer Genome Atlas (TCGA) database, we gathered information on GA-related lncRNAs, miRNAs, and mRNAs. We identifed diferentially expressed (DE) lncRNAs, miRNAs, and mRNAs using R software. The lncRNA–miRNA–mRNA interaction network was constructed and subsequent survival examination was performed. Representative genes were selected out using The Biological Networks Gene Ontology plug-in tool on Cytoscape. Additional analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms were used to screen representative genes for functional enrichment. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) were used to identify the expression of fve candidate diferential expressed RNAs. Results Information of samples from 375 cases of gastric cancer and 32 healthy cases (normal tissues) were downloaded from the TCGA database. A total of 1632 DE-mRNAs, 1008 DE-lncRNAs and 104 DE-miRNAs were identifed and screened. Among them, 65 DE-lncRNAs, 10 DE-miRNAs, and 10 DE-mRNAs form lncRNAs–miRNAs–mRNAs regulatory network. Additionally, 10 lncRNAs and 2 mRNAs were associated with the prognosis of GA. Multivariable COX analysis revealed that AC018781.1 and VCAN-AS1 were independent risk factors for GA. GO functional enrichment analysis found DE-mRNA was signifcantly enriched TERM (P<0.05). The KEGG signal regulatory network analysis found 11 signifcantly enrichment networks, the most prevailing was for the AGE-RAGE signaling pathway associated with Diabetic complications. Results of RT-qPCR was consistent with the in silico results. Conclusions The results of the present study represent a view of GA from a analysis of lncRNA, miRNA and mRNA. The network of lncRNA–miRNA–mRNA interactions revealed here may potentially further experimental studies and may help biomarker development for GA.
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