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Pengfei Liao,Jinqiu Huang,Puguo Tong,Wang Nie,Xin Yan,Yingmei Feng,Hui Peng,Xiaojue Peng,Shao-Bo Li 한국유전학회 2017 Genes & Genomics Vol.39 No.5
Genes encoding inositolphosphorylceramide synthase (IPCS), which play essential roles in sphingolipid biosynthesis, have been known to be widely distributed in plants. However, reports on identification, expression, and function analysis of plant IPCS family genes are very limited. In this study, a total of three OsIPCS genes were identified in the rice database, and their full-length coding regions were amplified by RT-PCR. These genes had 12–13 exons, and they encoded proteins of 313–326 amino acids with molecular mass of 35.8–37.6 kDa and isoelectric point of 10.99–12.61. Importantly, two typical and conserved motifs (D3 and D4) in plants were found in all the three putative OsIPCS proteins, indicating high similarity of OsIPCSs to the other plant IPCS proteins, which were confirmed by phylogenetic analysis. The transcripts of OsIPCS genes appeared in different rice organs including seedling roots, stems and leaves, and young panicles, but each gene showed a unique organ-specific expression pattern. Meanwhile, we found that all the three OsIPCS genes showed responses to stresses like drought, cold and salt, but their expression patterns were also to some extent different under stress treatments. Especially, under cold stress, OsIPCS1 and OsIPCS2 were significantly up-regulated in roots and stems but down-regulated in leaves, while OsIPCS3 was significantly up-regulated in all the measured organs. Actually, putative abiotic-stress regulatory elements were identified in the promoters of these genes. Our data suggest that OsIPCS genes should play important roles during rice growth and adversity adaptation.
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A novel HB-SC-MCCNN model for intelligent fault diagnosis of rolling bearing
Hui Liao,Pengfei Xie,Yan Zhao,Jinfang Gu,Lei Shi,Sier Deng,Hengdi Wang 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.12
The incompleteness and lack of bearing fault data have become important problems in bearing fault diagnosis. This paper presents an intelligent fault diagnosis method for rolling bearings based on a similarity clustering multi-channel convolution neural network with the hierarchical branch (HB-SC-MCCNN). First, the relevant features are extracted by MCCNN, and combined with the similarity clustering principle, the accurate binary classification is realized in the case of insufficient labeled data. Second, the similarity clustering module and additional loss are added to the SC-MCCNN network to form a hierarchical-branch network, which simplifies the problem of fault multi-classification into binary classification with multiple steps, and to reduces the dependence on the amount of label data in multi-classification. Finally, based on the self-learning characteristics of HB-SC-MCCNN, the unlabeled data and the missing fault types in the training set are re-labeled to realize the re-training of the network. On the benchmark dataset, the comparison experiment results with several salient deep learning models show that the method proposed in this paper successfully realizes the hierarchical diagnosis of bearing faults and presents more substantial competitiveness in the case of insufficient labeled data and missing fault types.
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