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您的检索式：作者名="Cheng Zuoren"
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| 1 | Comparative phosphoproteomic analysis of BR-defective mutant reveals a key role of GhSK13 in regulating cotton fiber development显示文摘Brassinosteroid(BR), a steroid phytohormone, whose signaling transduction pathways include a series of phosphorylation and dephosphorylation events, and GSK3 s are the main negative regulator kinases. BRs have been shown to play vital roles in cotton fiber elongation. However, the underlying mechanism is still elusive. In this study, fibers of a BR-defective mutant Pagoda 1(pag1), and its corresponding wild-type(ZM24) were selected for a comparative global phosphoproteome analysis at critical developmental time points: fast-growing stage(10 days after pollination(DPA)) and secondary cell wall synthesis stage(20DPA). Based on the substrate characteristics of GSK3, 900 potential substrates were identified. Their GO and KEGG annotation results suggest that BR functions in fiber development by regulating GhSKs(GSK3s of Gossypium hirsutum L.) involved microtubule cytoskeleton organization, and pathways of glucose, sucrose and lipid metabolism. Further experimental results revealed that among the GhSK members identified, GhSK13 not only plays a role in BR signaling pathway, but also functions in developing fiber by respectively interacting with an AP2-like ethylene-responsive factor GhAP2L, a nuclear transcription factor GhDNFYB19, and a homeodomain zipper member GhHDZ5. Overall, our phosphoproteomic research advances the understanding of fiber development controlled by BR signal pathways especially through GhSKs, and also offers numbers of target proteins for improving cotton fiber quality. | Lingling Wang Han Cheng Fangjie Xiong Shuya Ma Lei Zheng Yun Song Kexuan Deng Huanhuan Wu Fuguang Li Zuoren Yang | 2020 | Science China(Life Sciences)2020，63，12： | 1 |
| 2 | A new platform-based orthogonal SoC design methodology显示文摘 | Li Li Gao Minglun Cheng Zuoren | 2003 | ASIC2003，，： | 1 |
| 3 | Lateral interaction by Laplacian‐based graph smoothing for deep neural networks显示文摘Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions.Linear self‐organising map(SOM)introduces lateral interaction in a general form in which signals of any modality can be used.Some approaches directly incorporate SOM learning rules into neural networks,but incur complex operations and poor extendibility.The efficient way to implement lateral interaction in deep neural networks is not well established.The use of Laplacian Matrix‐based Smoothing(LS)regularisation is proposed for implementing lateral interaction in a concise form.The authors’derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS‐regulated k‐means,and they both show the topology‐preserving capability.The authors also verify that LS‐regularisation can be used in conjunction with the end‐to‐end training paradigm in deep auto‐encoders.Additionally,the benefits of LS‐regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated.Furthermore,the topologically ordered structure introduced by LS‐regularisation in feature extractor can improve the generalisation performance on classification tasks.Overall,LS‐regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models. | Jianhui Chen Zuoren Wang Cheng‐Lin Liu | 2023 | CAAI Transactions on Intelligence Technology2023，8，4： | 0 |
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