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- 저자 : ( Qingyun Dai ) (검색결과 4 건)
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Molecular Cloning and mRNA Expression of the Porcine Insulin-responsive Glucose Transporter (GLUT4)
Zuo, Jianjun,Dai, Fawen,Feng, Dingyuan,Cao, Qingyun,Ye, Hui,Dong, Zemin,Xia, Weiguang Asian Australasian Association of Animal Productio 2010 Animal Bioscience Vol.23 No.5
Insulin-responsive glucose transporter 4 (GLUT4) is a member of the glucose transporter family and mainly presents in skeletal muscle and adipose tissue. To clarify the molecular structure of porcine GLUT4, RACE was used to clone its cDNA. Several cDNA clones corresponding to different regions of GLUT4 were obtained by amplifying reverse-transcriptase products of total RNA extracted from Landrace porcine skeletal muscles. Nucleotide sequence analysis of the cDNA clones revealed that porcine GLUT4 cDNA was composed of 2,491 base pairs with a coding region of 509 amino acids. The deduced amino acid sequence was over 90% identical to human, rabbit and cattle GLUT4. The tissue distribution of GLUT4 was also examined by Real-time RT-PCR. The mRNA expression abundance of GLUT4 was heart>liver, skeletal muscle and brain>lung, kidney and intestine. The developmental expression of GLUT4 and insulin receptor (IR) was also examined by Real-time RT-PCR using total RNA extracted from longissimus dorsi (LM), semimembranosus (SM), and semitendinosus (SD) muscle of Landrace at the age of 1, 7, 30, 60 and 90 d. It was shown that there was significant difference in the mRNA expression level of GLUT4 in skeletal muscles of Landrace at different ages (p<0.05). The mRNA expression level of IR also showed significant difference at different ages (p<0.05). The developmental change in the mRNA expression abundance of GLUT4 was similar to that in IR, and both showed a higher level at birth and 30 d than at other ages. However, there was no significant tissue difference in the mRNA expression of GLUT4 or IR (p>0.05). These results showed that the nucleotide sequence of the cDNA clones was highly identical with human, rabbit and cattle GLUT4 and the developmental change of GLUT4 mRNA in skeletal muscles was similar to that of IR, suggesting that porcine GLUT4 might be an insulin-responsive glucose transporter. Moreover, the tissue distribution of GLUT4 mRNA showed that GLUT4 might be an important nutritional transporter in porcine skeletal muscles.
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A Max-Flow-Based Similarity Measure for Spectral Clustering
Jiangzhong Cao,Pei Chen,Yun Zheng,Qingyun Dai 한국전자통신연구원 2013 ETRI Journal Vol.35 No.2
In most spectral clustering approaches, the Gaussian kernel-based similarity measure is used to construct the affinity matrix. However, such a similarity measure does not work well on a dataset with a nonlinear and elongated structure. In this paper, we present a new similarity measure to deal with the nonlinearity issue. The maximum flow between data points is computed as the new similarity, which can satisfy the requirement for similarity in the clustering method. Additionally, the new similarity carries the global and local relations between data. We apply it to spectral clustering and compare the proposed similarity measure with other state-of-the-art methods on both synthetic and real-world data. The experiment results show the superiority of the new similarity: 1) The max-flow-based similarity measure can significantly improve the performance of spectral clustering; 2) It is robust and not sensitive to the parameters.
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Spectral Clustering with Sparse Graph Construction Based on Markov Random Walk
( Jiangzhong Cao ),( Pei Chen ),( Bingo Wing-kuen Ling ),( Zhijing Yang ),( Qingyun Dai ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.7
Spectral clustering has become one of the most popular clustering approaches in recent years. Similarity graph constructed on the data is one of the key factors that influence the performance of spectral clustering. However, the similarity graphs constructed by existing methods usually contain some unreliable edges. To construct reliable similarity graph for spectral clustering, an efficient method based on Markov random walk (MRW) is proposed in this paper. In the proposed method, theMRW model is defined on the raw k-NN graph and the neighbors of each sample are determined by the probability of the MRW. Since the high order transition probabilities carry complex relationships among data, the neighbors in the graph determined by our proposed method are more reliable than those of the existing methods. Experiments are performed on the synthetic and real-world datasets for performance evaluation and comparison. The results show that the graph obtained by our proposed method reflects the structure of the data better than those of the state-of-the-art methods and can effectively improve the performance of spectral clustering.
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