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- 검색키워드
- 저자 : Kunpeng Zhou (검색결과 3 건)
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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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Hongtao Shen,Weicheng Hu,Qing-shan Yang,Fucheng Yang,Kunpeng Guo,Tong Zhou,Guowei Qian,Qinggen Xu,Ziting Yuan 한국풍공학회 2022 Wind and Structures, An International Journal (WAS Vol.35 No.6
In wind-resistant designs, wind velocity is assumed to be a Gaussian process; however, local complex topography may result in strong non-Gaussian wind features. This study investigates the non-Gaussian wind features over complex terrain under atmospheric turbulent boundary layers by the large eddy simulation (LES) model, and the turbulent inlet of LES is generated by the consistent discretizing random flow generation (CDRFG) method. The performance of LES is validated by two different complex terrains in Changsha and Mianyang, China, and the results are compared with wind tunnel tests and onsite measurements, respectively. Furthermore, the non-Gaussian parameters, such as skewness, kurtosis, probability curves, and gust factors, are analyzed in-depth. The results show that the LES method is in good agreement with both mean and turbulent wind fields from wind tunnel tests and onsite measurements. Wind fields in complex terrain mostly exhibit a left-skewed Gaussian process, and it changes from a softening Gaussian process to a hardening Gaussian process as the height increases. A reduction in the gust factors of about 2.0%-15.0% can be found by taking into account the non-Gaussian features, except for a 4.4% increase near the ground in steep terrain. This study can provide a reference for the assessment of extreme wind loads on structures in complex terrain.
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A New Kmeans Clustering Algorithm for Point Cloud
Kun Zhang,Weihong Bi,Xiaoming Zhang,Xinghu Fu,Li Zhu,Kunpeng Zhou 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.9
With development of 3D scanner, it becomes more convenient to access point data. However, for processing the large-scale point cloud, it raised a new challenge for computer graphics. This paper places an emphasis on the point data own characteristics, and then the point data have been divided into certain point sets by clustering algorithm, that is will be divided into different clusters. In order to suit for the point data organization or space division, the clustering algorithm would be improved. This paper provided a new Kmeans algorithm with density constraints. Before processing the point cloud by Kmeans algorithm with density constraints, the density of the point cloud have been defined in this paper, the density of the point cloud can be used for quantification of the convergence. Finally, the Kmeans algorithm with density constraints is verified by the experiment results. Our experiment showed that the improved Kmeans can reduce the processing time, especially, As the increase of the value of K, that is number of cluster, the calculating time of the clustering algorithm can be decreased greatly. In addition, with the increases of the the scale of data size, the stability of the improved Kmeans algorithm has been verified.
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