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2,428 results on '"Li, Ziwei"'

75. The phosphorylation-deubiquitination positive feedback loop of the CHK2-USP7 axis stabilizes p53 under oxidative stress

Author

Liu, Jingwei, Cao, Liangzi, Wang, Yubang, Zou, Yu, Guo, Qiqiang, Chen, Shu, Jiang, Bo, Wu, Xuan, Zheng, Lixia, Zhang, Siyi, Lu, Songming, Zhou, Keshen, Jiang, Pengcheng, Xiao, Yutong, Yang, Ruohan, Dong, Shiyuan, Li, Ziwei, Chen, Di, Zhang, Ying, Zhang, Naijin, Sun, Guozhe, Xing, Chengzhong, Song, Xiaoyu, Wang, Zhenning, and Cao, Liu

Published
2024
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84. NDF is a transcription factor that stimulates elongation by RNA polymerase II

Author

Fei, Jia, Xu, Jun, Li, Ziwei, Xu, Kevin, Wang, Dong, Kassavetis, George A, and Kadonaga, James T

Subjects
Biotechnology, Genetics, Underpinning research, 1.1 Normal biological development and functioning, Generic health relevance, Humans, Nucleosomes, RNA Polymerase II, Saccharomyces cerevisiae, Transcription Factors, Transcription, Genetic, Transcriptional Elongation Factors, transcription, RNA polymerase II, elongation, NDF, TFIIS, gene expression, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Developmental Biology
Abstract

RNA polymerase II (Pol II) elongation is a critical step in gene expression. Here we found that NDF, which was identified as a bilaterian nucleosome-destabilizing factor, is also a Pol II transcription factor that stimulates elongation with plain DNA templates in the absence of nucleosomes. NDF binds directly to Pol II and enhances elongation by a different mechanism than that used by transcription factor TFIIS. Moreover, yeast Pdp3, which is related to NDF, binds to Pol II and stimulates elongation. Thus, NDF is a Pol II binding transcription elongation factor that is localized over gene bodies and is conserved from yeast to humans.

Published
2022

87. Tropical precipitation clusters as islands on a rough water-vapor topography

Author

Li, Ziwei, O'Gorman, Paul A., and Rothman, Daniel H.

Subjects
Physics - Atmospheric and Oceanic Physics, Physics - Geophysics
Abstract

Tropical precipitation clusters exhibit power-law frequency distributions in area and volume (integrated precipitation), implying a lack of characteristic scale in tropical convective organization. However, it remains unknown what gives rise to the power laws and how the power-law exponents for area and volume are related to one another. Here, we explore the perspective that precipitation clusters are islands above a convective threshold on a rough column-water-vapor (CWV) topography. This perspective is supported by the agreement between the precipitation clusters and CWV islands in their frequency distributions as well as fractal dimensions. Power laws exist for CWV islands at different thresholds through the CWV topography, suggesting that the existence of power-laws is not specifically related to local precipitation dynamics, but is rather a general feature of CWV islands. Furthermore, the frequency distributions and fractal dimensions of the clusters can be reproduced when the CWV field is modeled to be self-affine with a roughness exponent of 0.3. Self-affine scaling theory relates the statistics of precipitation clusters to the roughness exponent; it also relates the power-law slopes for area and volume without involving the roughness exponent. Thus, the perspective of precipitation clusters as CWV islands provides a useful framework to consider many statistical properties of the precipitation clusters, particularly given that CWV is well-observed over a wide range of length scales in the tropics. However, the statistics of CWV islands at the convective threshold imply a smaller roughness than is inferred from the power spectrum of the bulk CWV field, and further work is needed to understand the scaling of the CWV field., Comment: 22 pages, 9 figures

Published
2021
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88. Learn Like The Pro: Norms from Theory to Size Neural Computation

Author

Trautner, Margaret, Li, Ziwei, and Ravela, Sai

Subjects
Computer Science - Machine Learning, Mathematics - Dynamical Systems, Nonlinear Sciences - Chaotic Dynamics, 68T07, I.2.6, G.1.7
Abstract

The optimal design of neural networks is a critical problem in many applications. Here, we investigate how dynamical systems with polynomial nonlinearities can inform the design of neural systems that seek to emulate them. We propose a Learnability metric and its associated features to quantify the near-equilibrium behavior of learning dynamics. Equating the Learnability of neural systems with equivalent parameter estimation metric of the reference system establishes bounds on network structure. In this way, norms from theory provide a good first guess for neural structure, which may then further adapt with data. The proposed approach neither requires training nor training data. It reveals exact sizing for a class of neural networks with multiplicative nodes that mimic continuous- or discrete-time polynomial dynamics. It also provides relatively tight lower size bounds for classical feed-forward networks that is consistent with simulated assessments., Comment: 7 pages

Published
2021

89. Intradermally delivered mRNA-encapsulating extracellular vesicles for collagen-replacement therapy

Author

You, Yi, Tian, Yu, Yang, Zhaogang, Shi, Junfeng, Kwak, Kwang Joo, Tong, Yuhao, Estania, Andreanne Poppy, Cao, Jianhong, Hsu, Wei-Hsiang, Liu, Yutong, Chiang, Chi-Ling, Schrank, Benjamin R., Huntoon, Kristin, Lee, DaeYong, Li, Ziwei, Zhao, Yarong, Zhang, Huan, Gallup, Thomas D., Ha, JongHoon, Dong, Shiyan, Li, Xuefeng, Wang, Yifan, Lu, Wen-Jing, Bahrani, Eman, Lee, Ly James, Teng, Lesheng, Jiang, Wen, Lan, Feng, Kim, Betty Y. S., and Lee, Andrew S.

Published
2023
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