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1,359 results on '"Yang, Baofeng"'

1. Making Your Dreams A Reality: Decoding the Dreams into a Coherent Video Story from fMRI Signals

Author

Fu, Yanwei, Gao, Jianxiong, Yang, Baofeng, and Feng, Jianfeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper studies the brave new idea for Multimedia community, and proposes a novel framework to convert dreams into coherent video narratives using fMRI data. Essentially, dreams have intrigued humanity for centuries, offering glimpses into our subconscious minds. Recent advancements in brain imaging, particularly functional magnetic resonance imaging (fMRI), have provided new ways to explore the neural basis of dreaming. By combining subjective dream experiences with objective neurophysiological data, we aim to understand the visual aspects of dreams and create complete video narratives. Our process involves three main steps: reconstructing visual perception, decoding dream imagery, and integrating dream stories. Using innovative techniques in fMRI analysis and language modeling, we seek to push the boundaries of dream research and gain deeper insights into visual experiences during sleep. This technical report introduces a novel approach to visually decoding dreams using fMRI signals and weaving dream visuals into narratives using language models. We gather a dataset of dreams along with descriptions to assess the effectiveness of our framework., Comment: Work in progress

Published
2025

9. Binding of LncDACH1 to dystrophin impairs the membrane trafficking of Nav1.5 protein and increases ventricular arrhythmia susceptibility

Author

Genlong Xue, Jiming Yang, Yang Zhang, Ying Yang, Ruixin Zhang, Desheng Li, Tao Tian, Jialiang Li, Xiaofang Zhang, Changzhu Li, Xingda Li, Jiqin Yang, Kewei Shen, Yang Guo, Xuening Liu, Guohui Yang, Lina Xuan, Hongli Shan, Yanjie Lu, Yang Baofeng, and Zhenwei Pan

Subjects
LncDACH1, dystrophin, Nav1.5, ventricular arrhythmia, Medicine, Science, Biology (General), QH301-705.5
Abstract

Dystrophin is a critical interacting protein of Nav1.5 that determines its membrane anchoring in cardiomyocytes. Long noncoding RNAs (lncRNAs) are involved in the regulation of cardiac ion channels, while their influence on sodium channels remains unexplored. Our preliminary data showed that lncRNA-Dachshund homolog 1 (lncDach1) can bind to dystrophin, which drove us to investigate if lncDach1 can regulate sodium channels by interfering with dystrophin. Western blot and immunofluorescent staining showed that cardiomyocyte-specific transgenic overexpression of lncDach1 (lncDach1-TG) reduced the membrane distribution of dystrophin and Nav1.5 in cardiomyocytes. Meanwhile, peak INa was reduced in the hearts of lncDach1-TG mice than wild-type (WT) controls. The opposite data of western blot, immunofluorescent staining and patch clamp were collected from lncDach1 cardiomyocyte conditional knockout (lncDach1-cKO) mice. Moreover, increased ventricular arrhythmia susceptibility was observed in lncDach1-TG mice in vivo and ex vivo. The conservative fragment of lncDach1 inhibited membrane distribution of dystrophin and Nav1.5, and promoted the inducibility of ventricular arrhythmia. Strikingly, activation of Dystrophin transcription by dCas9-SAM system in lncDach1-TG mice rescued the impaired membrane distribution of dystrophin and Nav1.5, and prevented the occurrence of ventricular arrhythmia. Furthermore, lncDach1 was increased in transaortic constriction (TAC) induced failing hearts, which promoted the inducibility of ventricular arrhythmia. And the expression of lncDach1 is regulated by hydroxyacyl-CoA dehydrogenase subunit beta (hadhb), which binds to lncDach1 and decreases its stability. The human homologue of lncDACH1 inhibited the membrane distribution of Nav1.5 in human iPS-differentiated cardiomyocytes. The findings provide novel insights into the mechanism of Nav1.5 membrane targeting and the development of ventricular arrhythmias.

Published
2025
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10. 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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20. 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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21. 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

28. 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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