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3,388 results on '"Wang, ZhiPing"'

4. Lipid-anchored proteasomes control membrane protein homeostasis.

Author

Zhang, Ruizhu, Pan, Shuxian, Zheng, Suya, Liao, Qingqing, Jiang, Zhaodi, Wang, Dixian, Li, Xuemei, Hu, Ao, Li, Xinran, Zhu, Yezhang, Shen, Xiaoqi, Lei, Jing, Zhong, Siming, Zhang, Xiaomei, Huang, Lingyun, Wang, Xiaorong, Huang, Lan, Shen, Li, Song, Bao-Liang, Zhao, Jing-Wei, Wang, Zhiping, Yang, Bing, and Guo, Xing

Subjects
Humans, Animals, Mice, Proteasome Endopeptidase Complex, Membrane Proteins, Proteostasis, Endoplasmic Reticulum-Associated Degradation, Mice, Nude, Lipids
Abstract

Protein degradation in eukaryotic cells is mainly carried out by the 26S proteasome, a macromolecular complex not only present in the cytosol and nucleus but also associated with various membranes. How proteasomes are anchored to the membrane and the biological meaning thereof have been largely unknown in higher organisms. Here, we show that N-myristoylation of the Rpt2 subunit is a general mechanism for proteasome-membrane interaction. Loss of this modification in the Rpt2-G2A mutant cells leads to profound changes in the membrane-associated proteome, perturbs the endomembrane system, and undermines critical cellular processes such as cell adhesion, endoplasmic reticulum-associated degradation and membrane protein trafficking. Rpt2G2A/G2A homozygous mutation is embryonic lethal in mice and is sufficient to abolish tumor growth in a nude mice xenograft model. These findings have defined an evolutionarily conserved mechanism for maintaining membrane protein homeostasis and underscored the significance of compartmentalized protein degradation by myristoyl-anchored proteasomes in health and disease.

Published
2023

5. Research on Hyperspectral Image Target Detection Algorithm Based on Depth Learning

Author

Wang, Zhiping, Tang, Cundong, Chen, Li, Wang, Yi, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Hung, Jason C., editor, Yen, Neil, editor, and Chang, Jia-Wei, editor

Published
2024
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13. Transperitoneal vs retroperitoneal laparoscopic radical nephrectomy: a double-arm, parallel-group randomized clinical trial

Author

Liu, Junyao, Zhang, Bin, Qi, Peng, Ren, Xiaowei, Zheng, Duo, He, Yang, Zheng, Xu, Yue, Zhongjin, Li, Ye, Yang, Ningqiang, Wang, Zhiping, Bao, Junsheng, Tian, Junqiang, Yang, Li, Zhai, Zhenxing, Zuo, Lingjun, Hou, Zizhen, Wang, Jiaji, Wang, Wei, Chang, Hong, Ma, Junhai, Zhang, Yunxin, Dong, Zhichun, Dong, Zhilong, Zhong, Ganping, Cheng, Hui, Lei, Pengyuan, Li, Zhongming, Wu, GongJin, and Shang, Panfeng

Published
2024
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41. USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence

Author

Yao, Jing, Dou, Zhicheng, Xie, Ruobing, Lu, Yanxiong, Wang, Zhiping, and Wen, Ji-Rong

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, H.3.3
Abstract

Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing both search and recommendation services, showing us the demand and opportunity to simultaneously handle both tasks. However, most platforms consider these two tasks independently -- they tend to train separate search model and recommendation model, without exploiting the relatedness and dependency between them. In this paper, we argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We investigate the interactions between these two tasks in the specific information content service domain. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks based on the unified sequence. More specifically, we design the Unified Information Search and Recommendation model (USER), which mines user interests from the integrated sequence and accomplish the two tasks in a unified way., Comment: 10 pages, 5 figures, CIKM 2021

Published
2021
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