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Your search keyword '"Yang, Baofeng"' showing total 691 results
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691 results on '"Yang, Baofeng"'

4. Research on RMVSC Bidirectional Converter Based on Half-Bridge MMC

Author

Chen, Handong, Yang, Baofeng, He, Rixing, Luo, Zhenpeng, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Wen, Fushuan, editor, and Aris, Ishak Bin, editor

Published
2024
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11. The positive feedback loop of the NAT10/Mybbp1a/p53 axis promotes cardiomyocyte ferroptosis to exacerbate cardiac I/R injury

Author

Qu, Zhezhe, Pang, Xiaochen, Mei, Zhongting, Li, Ying, Zhang, Yaozhi, Huang, Chuanhao, Liu, Kuiwu, Yu, Shuting, Wang, Changhao, Sun, Zhiyong, Liu, Yingqi, Li, Xin, Jia, Yingqiong, Dong, Yuechao, Lu, Meixi, Ju, Tiantian, Wu, Fan, Huang, Min, Li, Na, Dou, Shunkang, Jiang, Jianhao, Dong, Xianhui, Zhang, Yi, Li, Wanhong, Yang, Baofeng, and Du, Weijie

Published
2024
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19. MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping

Author

Feng, Ruimin, Zhao, Jiayi, Wang, He, Yang, Baofeng, Feng, Jie, Shi, Yuting, Zhang, Ming, Liu, Chunlei, Zhang, Yuyao, Zhuang, Jie, and Wei, Hongjiang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (ki13,ki23,ki33) and the acquired single-orientation phase. The convolution neural networks are embedded into the physical model to learn a regularization term containing prior information. ki33 and phase induced by ki13 and ki23 terms were used as the labels for network training. Quantitative evaluation metrics (RSME, SSIM, and HFEN) were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications.

Published
2021

20. Research on NPC Three-Level Grid-Connected Inverter Based on Virtual Synchronous Generator Control

Author

Shi, Xin, Yang, Baofeng, Han, Junfei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Zeng, Pingliang, editor, Zhang, Xiao-Ping, editor, Terzija, Vladimir, editor, Ding, Yi, editor, and Luo, Yunxia, editor

Published
2023
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46. Hydroxychloroquine induces long QT syndrome by blocking hERG channel

Author

Zhao Xin, Sun Lihua, Chen Chao, Xin Jieru, Zhang Yan, Bai Yunlong, Pan Zhenwei, Zhang Yong, Li Baoxin, Lv Yanjie, and Yang Baofeng

Subjects
covid-19, hydroxychloroquine, lqt, herg, hsp90, Special situations and conditions, RC952-1245
Abstract

In March 2022, more than 600 million cases of Corona Virus Disease 2019 (COVID-19) and about 6 million deaths have been reported worldwide. Unfortunately, while effective antiviral therapy has not yet been available, chloroquine (CQ)/hydroxychloroquine (HCQ) has been considered an option for the treatment of COVID-19. While many studies have demonstrated the potential of HCQ to decrease viral load and rescue patients’ lives, controversial results have also been reported. One concern associated with HCQ in its clinical application to COVID-19 patients is the potential of causing long QT interval (LQT), an electrophysiological substrate for the induction of lethal ventricular tachyarrhythmias. Yet, the mechanisms for this cardiotoxicity of HCQ remained incompletely understood.

Published
2023
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