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- 저자 : Yuyao Li (검색결과 5 건)
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Zhu, Jia-Ying,Li, Yuyao,Cao, Dong-Mei,Yang, Hongjuan,Oh, Eunkyoo,Bi, Yang,Zhu, Shengwei,Wang, Zhi-Yong Elsevier 2017 Molecular cell Vol.66 No.5
<P><B>Summary</B></P> <P>The glycogen synthase kinase-3 (GSK3) family kinases are central cellular regulators highly conserved in all eukaryotes. In <I>Arabidopsis</I>, the GSK3-like kinase BIN2 phosphorylates a range of proteins to control broad developmental processes, and BIN2 is degraded through unknown mechanism upon receptor kinase-mediated brassinosteroid (BR) signaling. Here we identify KIB1 as an F-box E3 ubiquitin ligase that promotes the degradation of BIN2 while blocking its substrate access. Loss-of-function mutations of KIB1 and its homologs abolished BR-induced BIN2 degradation and caused severe BR-insensitive phenotypes. KIB1 directly interacted with BIN2 in a BR-dependent manner and promoted BIN2 ubiquitination in vitro. Expression of an F-box-truncated KIB1 caused BIN2 accumulation but dephosphorylation of its substrate BZR1 and activation of BR responses because KIB1 blocked BIN2 binding to BZR1. Our study demonstrates that KIB1 plays an essential role in BR signaling by inhibiting BIN2 through dual mechanisms of blocking substrate access and promoting degradation.</P> <P><B>Highlights</B></P> <P> <UL> <LI> KIB1 is an essential positive regulator in brassinosteroid signaling </LI> <LI> KIB1 mediates BR-induced ubiquitination and degradation of GSK3 kinase BIN2 </LI> <LI> KIB1 binding to BIN2 prevents BIN2-substrate interaction and promotes BIN2 degradation </LI> </UL> </P> <P><B>Graphical Abstract</B></P> <P>[DISPLAY OMISSION]</P>
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Yuyao Wang,Xiaozeng Xu,Xiaoqin Li,Lei Cheng,Hongbin Zhang 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.10
In this paper, the event-triggered filtering problem for continuous-time switched linear systems with overlapped mismatching intervals is investigated. The mismatching interval is the time period when the filter and the subsystem are asynchronous. It is assumed that only the sampled information of system state and switching signal is available to the H∞ filter at each sampling instant. Due to the overlaps between the mismatching intervals, multipleswitches are allowed during the adjacent sampling instants. Under this assumption, asynchronous time could be much longer than the modal dwell time (MDT). By constructing a time-dependent quadratic piecewise Lyapunov function, a new globally uniformly exponentially stability (GUES) result under MDT criteria is obtained, and theanalysis of the weighted H∞ filter is given. Moreover, the design method for the parameters of the filter and the eventtrigger are formulated and solved in the form of linear matrix inequalities (LMI). Finally, a numerical example at the end of this paper is proposed to illustrate the effectiveness and improvement of the proposed approach.
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Consistent Spatial-Temporal Longitudinal Atlas Construction for Developing Infant Brains
Zhang, Yuyao,Shi, Feng,Wu, Guorong,Wang, Li,Yap, Pew-Thian,Shen, Dinggang IEEE 2016 IEEE transactions on medical imaging Vol.35 No.12
<P>Brain atlases are an essential component in understanding the dynamic cerebral development, especially for the early postnatal period. However, longitudinal atlases are rare for infants, and the existing ones are generally limited by their fuzzy appearance. Moreover, since longitudinal atlas construction is typically performed independently over time, the constructed atlases often fail to preserve temporal consistency. This problem is further aggravated for infant images since they typically have low spatial resolution and insufficient tissue contrast. In this paper, we propose a novel framework for consistent spatial-temporal construction of longitudinal atlases for developing infant brain MR images. Specifically, for preserving structural details, the atlas construction is performed in spatial-temporal wavelet domain simultaneously. This is achieved by a patch-based combination of results from each frequency subband. Compared with the existing infant longitudinal atlases, our experimental results indicate that our approach is able to produce longitudinal atlases with richer structural details and also better longitudinal consistency, thus leading to higher performance when used for spatial normalization of a group of infant brain images.</P>
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Social stratification and housing wealth inequality in transitional urban China
Chengdong Yi,Yuyao Li,Yourong Wang,Haiyuan Wan 서울시립대학교 도시과학연구원 2021 도시과학국제저널 Vol.25 No.1
As the Chinese economy continues to transform, housing wealth has gradually become the single largest asset in most urban households. At the same time, housing wealth inequality has worsened, and housing stratification has become a major form of social stratification in the country. However, there is no empirical evidence of temporal changes in housing wealth inequality and their implications on social stratification. Pooling three waves of survey data from the 1995, 2008, and 2013 iterations of the Chinese Household Income Project, we present a dynamic image of housing wealth inequality in urban China. The results suggest that housing wealth inequality decreased initially and increased later. To further illustrate how housing wealth is stratified by social groups, we conducted regression analyses and structural tests, which revealed three main results: the education level of the household head has an increasingly positive impact on housing wealth inequality; a household head with superior occupational status generally has an advantage in terms of possession of housing wealth with no evidence that this advantage diminishes during the transitional period; and income has a significant, positive impact on housing wealth, and this effect exhibited an increasing trend over time. These results imply that the temporal changes in housing wealth inequality exacerbated the social stratification in transitional urban China.
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TCP-PPCC: Online-Learning Proximal Policy for Congestion Control
Shiwei Wang,Jing Li,Yuyao Guan,Pengpeng Ding 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
Effective network congestion control strategies are the key to secure the normal operation of complex and changeable networks. The fundamental assumptions of many existing TCP congestion control variants dominated by hand-crafted heuristic algorithms are no longer valid. We propose an algorithm called TCP-Proximal Policy Congestion Control (TCP-PPCC), which is based on deep reinforcement learning algorithm Proximal Policy Optimization (PPO). TCP-PPCC updates the policy offline from the features of the preceding network state and feedback from the current network environment and adjusts the congestion window online with the updated policy. The senders with TCP-PPCC can learn about the changes in network bandwidth more accurately and adjust the congestion window in time. We demonstrate the performance of TCP-PPCC by comparing it with the traditional congestion control algorithm NewReno in four network scenarios with the ns-3 simulator. The results show that in scenario 2, TCPPPCC takes 58.75% improvement in average delay and 27.80% improvement in throughput compared with NewReno.
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