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RISS 인기검색어
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- 저자 : Xuemin Zhong (검색결과 3 건)
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Xuemin Zhong,Hongqi Liu,Xinyong Mao,Bin Li 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.10
This paper proposes a new method to identify the squareness errors of machine tools and gives an optimal method to improve the volumetric compensation accuracy based on the identified squareness errors. The volumetric error model based on screw theory is proposed and the effects of the squareness errors on the volumetric errors have been analyzed to clarify the possibility of improving volumetric error compensation by proper and accurate squareness errors. Then the identification method of the squareness errors is deduced in theory considering a 3-axis horizontal machine tool. With the identified squareness errors, an optimal method to improve volumetric error compensation in machine tools has been proposed. Experiments have verified that the 3 identified squareness errors are in accordance with the measured squareness errors within the accuracy range. Moreover, compared with the traditional measurement of squareness errors not considering the PDGEs, the proposed method is more effective and shows error compensation improvement in the volumetric error compensation process.
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A Generalized Markov Chain Model for IEEE 802.11 Distributed Coordination Function
( Ping Zhong ),( Jianghong Shi ),( Yuxiang Zhuang ),( Huihuang Chen ),( Xuemin Hong ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.2
To improve the accuracy and enhance the applicability of existing models, this paper proposes a generalized Markov chain model for IEEE 802.11 Distributed Coordination Function (DCF) under the widely adopted assumption of ideal transmission channel. The IEEE 802.11 DCF is modeled by a two dimensional Markov chain, which takes into account unsaturated traffic, backoff freezing, retry limits, the difference between maximum retransmission count and maximum backoff exponent, and limited buffer size based on the M/G/1/K queuing model. We show that existing models can be treated as special cases of the proposed generalized model. Furthermore, simulation results validate the accuracy of the proposed model.
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Han Peipei,Wu Shu,Li Limei,Li Danping,Zhao Jun,Zhang Haishan,Wang Yifang,Zhong Xuemin,Zhang Zhe,Li Ping,Matskova Liudmila,Zhou Xiaoying 한국유전학회 2022 Genes & Genomics Vol.44 No.4
Background: Acetyl-CoA acyltransferase 1 (ACAT1) is a key enzyme catalyzing the production of mitochondrial ketone bodies. We have shown that ACAT1 is down-regulated in kidney renal clear cell carcinoma (KIRC) previously. Objective: To investigate the reasons for downregulation of ACAT1 in KIRC and explore the underlying mechanisms involved in metastatic inhibition regulated by ACAT1. Methods: The Gene Expression Omnibus (GEO) database was queried for meta-analysis of ACAT1 mRNA expression in KIRC. The UALCAN website was used to compare the methylation levels of the ACAT1 promoter region in KIRC and normal tissues. RT-qPCR was used to quantitate ACAT1 transcription levels. The GCBI and Tarbase V.8 databases were used to predict miRNAs that may target the mRNA of ACAT1. The correlation between mRNA expression of ACAT1, MMP7 (matrix metallopeptidase 7), CDH1 (E-cadherin), EpCAM (epithelial cell adhesion molecule), and VIM (vimentin) was analyzed. Extracellular MMP7 protein was quantitated using an ELISA assay. Results: The methylation level of the ACAT1 promoter region in KIRC was significantly higher than that in the normal kidney tissues. The ACAT1 mRNA expression in the KIRC cell lines was restored after treatment with 5-aza-dC (p < 0.05). MiR-21-5p is a conserved microRNA targeting ACAT1. It is expressed at a significantly higher level in KIRC than in normal tissues (p < 0.001). MiR-21-5p miRNA expression negatively correlates with ACAT1 mRNA expression. The expression of miR-21-5p is higher at the T3-T4 stages and in the histologic grades G3-G4. Patients with high miR-21-5p expression tended to have lower overall survival, suggesting that miR-21-5p could serve as a potentially valuable diagnostic biomarker for KIRC (AUC = 0.957; p < 0.001). A mimetic of miR-21-5p inhibited the expression of ACAT1 mRNA and protein. In addition, ACAT1 mRNA expression positively correlates with CDH1 and EpCAM but is negatively correlated with VIM. Overexpression of ACAT1 suppresses the secretion of MMP7 in KIRC cells. Conclusion: Expression of ACAT1 in KIRC is controlled at two levels, firstly by the hypermethylation of the ACAT1 promoter region and secondly by overexpression of miR-21-5p. Downregulation of ACAT1 expression correlates with epithelial-mesenchymal transition (EMT).
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