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- 저자 : Ningnan Zhang (검색결과 2 건)
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Junfeng Yang,Wei Li,Zhuorui Zhang,Jie Shen,Ningnan Zhang,Min Yang,Maolin Yang,Yanhong Yu 한국유전학회 2018 Genes & Genomics Vol.40 No.5
The aim of this study is to make a comparative evaluation on association of PSCArs2294008 C/T polymorphism with the risk of bladder cancer in Bai, Dai, and Han people in China. A potential mechanism of the T allele risk was also investigated. T allele increased the occurring risk of bladder cancer in Han (OR 1.34; 95% CI 1.17–1.69), Dai, (OR 1.33; 95% CI 1.12–1.70), and Bai (OR 1.14; 95% CI 1.08–1.57) people. T genotype was also observed to associate with invasive bladder cancer in all the three populations (Bai, OR 1.15, 95% CI 1.07–1.87; Dai, OR 1.17, 95% CI 1.05–2.23; Han, OR 1.22, 95% CI 1.10–2.09). PSCA m-RNA levels in T genotype bladder cancer tissues were significantly lower than those in C genotype. An enhancement of PSCA m-RNA level by over-expressing C or T genotype in bladder cancer cells both decreased the cell proliferation and migration, but not affected cell cycle. The increased cell apoptasis due to the over-expression of the two variants was observed. Those change of cell proliferation, migration, and apoptasis was more remarkable in over-expressed C genotype cells than those in over-expressed T genotype. T genotype was genetically high risk to the occurrence of bladder cancer. The decreased PSCA m-RNA levels were involved in the progress of bladder cancer. T allele takes more responsibility for PSCA m-RNA down-regulation to promote cell proliferation and migration and hinder cell apoptasis, thus leading to a higher risk.
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Load Pattern Window Aware Power Supply Device Clustering
Wanxing Sheng,Ke-yan Liu,Yixi Yu,Rungong An,Ningnan Zhou,Xiao Zhang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.8
Data-driven decision in big data era is becoming ubiquitous in electronic grid. In particular, daily collected power consumption records enable workload aware device clustering, which is crucial for critical domain applications such as device functionality identification. In this paper, we propose a load pattern window aware method for clustering power supply devices. Our approach overcomes the drawbacks in existing works, such as fuzzy based clustering, K-means based clustering and neutral network based clustering. After investigating the large scale records from power supply devices, our approach partitions device records into disjoint time intervals with parameterized window size, which indicate the load pattern feature for a period of time given a specific device. Devices are then decomposed into a mixture of these features, and those devices with similar dominating features are grouped together. The experimental results demonstrate the effectiveness and efficiency of our solution based on the real data collected from power grid in China.
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