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Your search keyword '"Zeng, Yanyu"' showing total 24 results
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24 results on '"Zeng, Yanyu"'

10. A spatiotemporal steroidogenic regulatory network in human fetal adrenal glands and gonads

Author

Wang, Yifu, primary, Guo, Bingqian, additional, Guo, Yajie, additional, Qi, Nana, additional, Lv, Yufang, additional, Ye, Yu, additional, Huang, Yan, additional, Long, Xinyang, additional, Chen, Hongfei, additional, Su, Cheng, additional, Zhang, Liying, additional, Zhang, Qingyun, additional, Li, Minxi, additional, Liao, Jinling, additional, Yan, Yunkun, additional, Mao, Xingning, additional, Zeng, Yanyu, additional, Jiang, Jinghang, additional, Chen, Zhongyuan, additional, Guo, Yi, additional, Gao, Shuai, additional, Cheng, Jiwen, additional, Jiang, Yonghua, additional, and Mo, Zengnan, additional

Published
2022
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11. Corrigendum: Single-cell transcriptomics reveals the complexity of the tumor microenvironment of treatment-naive osteosarcoma

Author

Liu, Yun, primary, Feng, Wenyu, additional, Dai, Yan, additional, Bao, Mengying, additional, Yuan, Zhenchao, additional, He, Mingwei, additional, Qin, Zhaojie, additional, Liao, Shijie, additional, He, Juliang, additional, Huang, Qian, additional, Yu, Zhenyuan, additional, Zeng, Yanyu, additional, Guo, Binqian, additional, Huang, Rong, additional, Yang, Rirong, additional, Jiang, Yonghua, additional, Liao, Jinling, additional, Xiao, Zengming, additional, Zhan, Xinli, additional, Lin, Chengsen, additional, Xu, Jiake, additional, Ye, Yu, additional, Ma, Jie, additional, Wei, Qingjun, additional, and Mo, Zengnan, additional

Published
2022
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15. A spatiotemporal steroidogenic regulatory network in human fetal adrenal glands and gonads

Author

Wang, Yifu, primary, Guo, Bingqian, additional, Guo, Yajie, additional, Qi, Nana, additional, Lv, Yufang, additional, Ye, Yu, additional, Huang, Yan, additional, Long, Xinyang, additional, Chen, Hongfei, additional, Su, Cheng, additional, Zhang, Liying, additional, Zhang, Qingyun, additional, Li, Minxi, additional, Liao, Jinling, additional, Yan, Yunkun, additional, Mao, Xingning, additional, zeng, yanyu, additional, Jiang, Jinghang, additional, Chen, Zhongyuan, additional, Guo, Yi, additional, Gao, Shuai, additional, cheng, jiwen, additional, Jiang, Yonghua, additional, and Mo, Zengnan, additional

Published
2021
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16. Single-Cell Transcriptomics Reveals the Complexity of the Tumor Microenvironment of Treatment-Naive Osteosarcoma

Author

Liu, Yun, primary, Feng, Wenyu, additional, Dai, Yan, additional, Bao, Mengying, additional, Yuan, Zhenchao, additional, He, Mingwei, additional, Qin, Zhaojie, additional, Liao, Shijie, additional, He, Juliang, additional, Huang, Qian, additional, Yu, Zhenyuan, additional, Zeng, Yanyu, additional, Guo, Binqian, additional, Huang, Rong, additional, Yang, Rirong, additional, Jiang, Yonghua, additional, Liao, Jinling, additional, Xiao, Zengming, additional, Zhan, Xinli, additional, Lin, Chengsen, additional, Xu, Jiake, additional, Ye, Yu, additional, Ma, Jie, additional, Wei, Qingjun, additional, and Mo, Zengnan, additional

Published
2021
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18. Single‐cell transcriptomes reveal characteristic features of cell types within the human adrenal microenvironment

Author

Huang, Lin, primary, Liao, Jinling, additional, Chen, Yang, additional, Zou, Chunlin, additional, Zhang, Haiying, additional, Yang, Xiaobo, additional, Zhang, Qinyun, additional, Li, Tianyu, additional, Mo, Linjian, additional, Zeng, Yanyu, additional, Bao, Mengying, additional, Zhang, Fangxing, additional, Ye, Yu, additional, Yang, Zhanbin, additional, Cheng, Jiwen, additional, and Mo, Zengnan, additional

Published
2021
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20. Single-cell transcriptomics reveals cell type diversity of human prostate

Author

Chen, Yang, Zhang, Peng, Liao, Jinling, Cheng, Jiwen, Zhang, Qin, Li, Tianyu, Zhang, Haiying, Jiang, Yonghua, Zhang, Fangxing, Zeng, Yanyu, Mo, Linjian, Yan, Haibiao, Liu, Deyun, Zhang, Qinyun, Zou, Chunlin, Wei, Gong-Hong, and Mo, Zengnan

Abstract

Extensive studies have been performed to describe the phenotypic changes occurring during malignant transformation of the prostate. However, the cell types and associated changes that contribute to the development of prostate diseases and cancer remain elusive, largely due to the heterogeneous composition of prostatic tissues. Here, we conduct a comprehensive evaluation of four human prostate tissues by single-cell RNA sequencing (scRNA-seq) to analyze their cellular compositions. We identify 18 clusters of cell types, each with distinct gene expression profiles and unique features; of these, one cluster of epithelial cells (Ep) is found to be associated with immune function. In addition, we characterize a special cluster of fibroblasts and aberrant signaling changes associated with prostate cancer (PCa). Moreover, we provide insights into the epithelial changes that occur during the cellular senescence and aging. These results expand our understanding of the unique functional associations between the diverse prostatic cell types and the contributions of specific cell clusters to the malignant transformation of prostate tissues and PCa development.

Published
2022
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22. The Endogenous Alterations of the Gut Microbiota and Feces Metabolites Alleviate Oxidative Damage in the Brain of LanCL1 Knockout Mice

Author

Zhang, Fangxing, primary, Qi, Nana, additional, Zeng, Yanyu, additional, Bao, Mengying, additional, Chen, Yang, additional, Liao, Jinling, additional, Wei, Luyun, additional, Cao, Dehao, additional, Huang, Shengzhu, additional, Luo, Qianqian, additional, Jiang, Yonghua, additional, and Mo, Zengnan, additional

Published
2020
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23. Enhancing Stock Trading Strategies: Integrating Discrete Wavelet Transformation with Deep Q-Network.

Author

Wang, Qi, Zhang, Liang, Zeng, Yanyu, Wu, Shize, Yu, Chuanwei, and Sun, Song

Abstract

The prediction of price trends in the stock market has always been a hot research topic in the financial field. However, due to the high instability and volatility of stock prices, it is very difficult to accurately predict stock trends. How to remove the noise of stock data, extract effective features, and pursue maximum value returns has always been a challenge. This paper proposes a hybrid model (DWT-DQN) that combines discrete wavelet transform with deep reinforcement learning to improve the accuracy and return rate of stock predictions. First, the model captures price fluctuation information on different scales by performing discrete wavelet transformation on the difference between long- and short-term moving averages of stock prices, and well extracts the changing characteristics of stock price data in the time domain and frequency domain. Then the feature data are input into the built DQN network for model training. The network can select the optimal trading action based on market status and historical experience and returns. At the same time, during the data sampling process, an attention mechanism is introduced to allow the model to further learn in the direction of maximizing returns. Through testing and verification on SSEC, HSI, NDX and SPX data sets, experiments show that the hybrid model proposed in this paper has excellent performance in terms of accuracy and return rate. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Distinct regulatory machineries underlying divergent chromatin landscapes distinguish innate lymphoid cells from T helper cells.

Author

Zhang Y, Hu L, Ren G, Zeng Y, Zhao X, and Zhong C

Subjects
Chromatin genetics, Chromatin metabolism, T-Lymphocytes, Helper-Inducer, Gene Expression Regulation, Lymphocytes, Immunity, Innate
Abstract

Innate lymphoid cells (ILCs), as the innate counterpart of CD4 + T helper (Th) cells, play crucial roles in maintaining tissue homeostasis. While the ILC subsets and their corresponding Th subsets demonstrate significant similarities in core programming related to effector function and regulatory mechanisms, their principal distinctions, given their innate and adaptive lymphocyte nature, remain largely unknown. In this study, we have employed an integrative analysis of 294 bulk RNA-sequencing results across all ILC and Th subsets, using scRNA-seq algorithms. Consequently, we identify two genesets that predominantly differentiate ILCs from Th cells, as well as three genesets that distinguish various immune responses. Furthermore, through chromatin accessibility analysis, we find that the ILC geneset tends to rely on specific transcriptional regulation at promoter regions compared with the Th geneset. Additionally, we observe that ILCs and Th cells are under differential transcriptional regulation. For example, ILCs are under stronger regulation by multiple transcription factors, including RORα, GATA3, and NF-κB. Otherwise, Th cells are under stronger regulation by AP-1. Thus, our findings suggest that, despite the acknowledged similarities in effector functions between ILC subsets and corresponding Th subsets, the underlying regulatory machineries still exhibit substantial distinctions. These insights provide a comprehensive understanding of the unique roles played by each cell type during immune responses., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Hu, Ren, Zeng, Zhao and Zhong.)

Published
2023
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