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您的检索式：作者名="Zicong Peng"
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| 1 | JASMONATE-ZIM DOMAIN proteins engage Polycomb chromatin modifiers to modulate Jasmonate signaling in Arabidopsis显示文摘Jasmonate(JA)regulates various aspects of plant growth and development and stress responses,with prominent roles in male reproductive development and defenses against herbivores and necrotrophic pathogens.JASMONATE-ZIM DOMAIN(JAZ)proteins are key regulators in the JA signaling pathway and function to repress the expression of JA-responsive genes.Here,we show that JAZ proteins directly interact with several chromatin-associated Polycomb proteins to mediate repressive chromatin modifications at JA-responsive genes and,thus,their transcriptional repression in Arabidopsis.Genetic analyses revealed that the developmental defects,including anther and pollen abnormalities,resulting from loss or block of JA signaling were partially rescued by loss of Polycomb protein-mediated chromatin silencing(Polycomb repression).We further found that JAZ-mediated transcriptional repression during anther and pollen development requires Polycomb proteins at four key Regulatory loci.Analysis of genome-wide occupancy of a Polycomb factor and transcriptome reprogramming in response to JA revealed that Polycomb repression is involved in the repression of various JA-responsive genes.Taken together,our study reveals an important chromatin-based mechanism for JAZ-mediated transcriptional repression and JA signaling in plants. | Zicong Li Xiao Luo Yang Ou Huijun Jiao Li Peng Xing Fu Alberto P.Macho Renyi Liu Yuehui He | 2021 | Molecular Plant2021，14，5： | 1 |
| 2 | The role of machine learning in carbon neutrality:Catalyst property prediction,design,and synthesis for carbon dioxide reduction显示文摘Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation of natural resources that have accompanied the development of human society.The carbon dioxide reduction reaction(CO_(2)RR)is a promising strategy to capture and convert carbon dioxide(CO_(2))into value-added chemical products.However,the traditional trial-and-error method makes it expensive and time-consuming to understand the deeper mechanism behind the reaction,discover novel catalysts with superior performance and lower cost,and determine optimal support structures and electrolytes for the CO_(2)RR.Emerging machine learning(ML)techniques provide an opportunity to integrate material science and artificial intelligence,which would enable chemists to extract the implicit knowledge behind data,be guided by the insights thereby gained,and be freed from performing repetitive experiments.In this perspective article,we focus on recent ad-vancements in ML-participated CO_(2)RR applications.After a brief introduction to ML techniques and the CO_(2)RR,we first focus on ML-accelerated property prediction for potential CO_(2)RR catalysts.Then we explore ML-aided prediction of catalytic activity and selectivity.This is followed by a discussion about ML-guided catalyst and electrode design.Next,the potential application of ML-assisted experimental synthesis for the CO_(2)RR is discussed. | Zhuo Wang Zhehao Sun Hang Yin Honghe Wei Zicong Peng Yoong Xin Pang Guohua Jia Haitao Zhao Cheng Heng Pang Zongyou Yin | 2023 | eScience2023，3，4： | 0 |
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