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491 results on '"Ji, ZhiYong"'

7. Single-cell transcriptome atlas of the human corpus cavernosum

Author

Zhao, LiangYu, Han, Sha, Su, HengChuan, Li, JianYing, Zhi, ErLei, Li, Peng, Yao, ChenCheng, Tian, RuHui, Chen, HuiXing, Chen, HuiRong, Luo, JiaQiang, Shi, ChenKun, Ji, ZhiYong, Hu, JianLin, Wu, Gang, Zhou, WeiDong, Tang, YuXin, Chen, YuZhuo, Lin, GuiTing, Lue, Tom F, Wu, DengLong, and Li, Zheng

Subjects
Contraception/Reproduction, Urologic Diseases, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Erectile Dysfunction, Humans, Male, Muscle, Smooth, Penile Erection, Penis, Transcriptome
Abstract

The corpus cavernosum is the most important structure for penile erection, and its dysfunction causes many physiological and psychological problems. However, its cellular heterogeneity and signalling networks at the molecular level are poorly understood because of limited access to samples. Here, we profile 64,993 human cavernosal single-cell transcriptomes from three males with normal erection and five organic erectile dysfunction patients. Cell communication analysis reveals that cavernosal fibroblasts are central to the paracrine signalling network and regulate microenvironmental homeostasis. Combining with immunohistochemical staining, we reveal the cellular heterogeneity and describe a detailed spatial distribution map for each fibroblast, smooth muscle and endothelial subcluster in the corpus cavernosum. Furthermore, comparative analysis and related functional experiments identify candidate regulatory signalling pathways in the pathological process. Our study provides an insight into the human corpus cavernosum microenvironment and a reference for potential erectile dysfunction therapies.

Published
2022

26. Primate‐Specific DAZ Regulates Translation of Cell Proliferation‐Related mRNAs and is Essential for Maintenance of Spermatogonia

Author

Ou, Ningjing, primary, Wang, Yuci, additional, Xu, Shuai, additional, Luo, Jiaqiang, additional, Zhang, Chenwang, additional, Zhang, Yangyi, additional, Shi, Xiaoyan, additional, Xiong, Minggang, additional, Zhao, Liangyu, additional, Ji, Zhiyong, additional, Zhang, Yuxiang, additional, Zhao, Jingpeng, additional, Bai, Haowei, additional, Tian, Ruhui, additional, Li, Peng, additional, Zhi, Erlei, additional, Huang, Yuhua, additional, Chen, Wei, additional, Wang, Ruiqi, additional, Jin, Yuxuan, additional, Wang, Dian, additional, Li, Zheng, additional, Chen, Hao, additional, and Yao, Chencheng, additional

Published
2024
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38. MULTI-OBJECTIVE TOPOLOGY OPTIMIZATION DESIGN OF HYDROGEN-FUELED TRUCK FRAME

Author

LI YanXing, WANG Tie, JI ZhiYong, LI GuoXing, and LI YuanJie

Subjects
Hydrogen-fueled truck, Frame, Variable density method, Multi-objective topology optimization, Mechanical engineering and machinery, TJ1-1570, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Taking a hydrogen-fueled truck frame as the research object, based on the topology optimization theory of variable density method, the goal of maximizing the static multi-working stiffness and improving the dynamic low-order natural frequency of the frame is realized. The attachments of the frame were retained, and the topological optimization space was established according to the original frame outline size. A multi-objective topology optimization was carried out by using the isotropic material penalty function to obtain the best frame structure layout, and then the new frame structure was designed. The results show that the middle bearing capacity of the new frame is better than that of the original frame which has been verified by modal testing, and the maximum stress decreases by 35.6%. The first-order natural frequency increases by 1.9 Hz, which can avoid the maximum excitation energy region of the road. Meanwhile, the frame weight is reduced by 7.8%, which realizes the specialized and lightweight design of a hydrogen-fueled truck frame.

Published
2022
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41. An Efficient Prediction Model on the Operation Quality of Medical Equipment Based on Improved Sparrow Search Algorithm-Temporal Convolutional Network-BiLSTM.

Author

Lin, Zicong and Ji, Zhiyong

Subjects
*MEDICAL equipment, *TIME series analysis, *ARTIFICIAL intelligence, *DIAGNOSTIC equipment, *PREDICTION models
Abstract

Combining medical IoT and artificial intelligence technology is an effective approach to achieve the intelligence of medical equipment. This integration can address issues such as low image quality caused by fluctuations in power quality and potential equipment damage, and this study proposes a predictive model, ISSA-TCN-BiLSTM, based on a bi-directional long short-term memory network (BiLSTM). Firstly, power quality data and other data from MRI and CT equipment within a 6-month period are collected using current fingerprint technology. The key factors affecting the active power of medical equipment are explored using the Pearson coefficient method. Subsequently, a Temporal Convolutional Network (TCN) is employed to conduct multi-layer convolution operations on the input temporal feature sequences, enabling the learning of global temporal feature information while minimizing the interference of redundant data. Additionally, bidirectional long short-term memory (BiLSTM) is integrated to model the intermediate active power features, facilitating accurate prediction of medical equipment power quality. Additionally, an improved Sparrow Search Algorithm (ISSA) is utilized for hyperparameter optimization of the TCN-BiLSTM model, enabling optimization of the active power of different medical equipment. Experimental results demonstrate that the ISSA-TCN-BiLSTM model outperforms other comparative models in terms of RMSE, MSE, and R2, with values of 0.1143, 0.1157, 0.0873, 0.0817, 0.95, and 0.96, respectively, for MRI and CT equipment. This model exhibits both prediction speed and accuracy in power prediction for medical equipment, providing valuable guidance for equipment maintenance and diagnostic efficiency enhancement. [ABSTRACT FROM AUTHOR]

Published
2024
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