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1,458 results on '"Zheng, Rui"'

9. Asymptomatic infection and disappearance of clinical symptoms of COVID-19 infectors in China 2022–2023: a cross-sectional study

Author

Zhang, Kaige, Zhong, Xiaoying, Fan, Xiaodan, Yu, Dongdong, Chen, Zhuo, Zhao, Chen, Zhang, Xiaoyu, Guan, Zhiyue, Wei, Xuxu, Wan, Siqi, Zhang, Xuecheng, Zhao, Mengzhu, Dai, Qianqian, Liu, Wenjing, Xu, Qianqian, Kong, Yifan, Han, Songjie, Lin, Hongyuan, Wang, Wenhui, Jiang, Huiru, Gu, Chunling, Zhang, Xiaowei, Jiang, Tong, Liu, Shuling, Cui, Herong, Yang, Xinyu, Jiang, Yin, Chen, Zhao, Sun, Yang, Tao, Liyuan, Zheng, Rui, Qiu, Ruijin, You, Liangzhen, and Shang, Hongcai

Published
2024
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18. TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency

Author

Zhang, Dan, Zheng, Rui, Gadeng, Luosang, and Yang, Pei

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper addresses the task of semantic segmentation in computer vision, aiming to achieve precise pixel-wise classification. We investigate the joint training of models for semantic edge detection and semantic segmentation, which has shown promise. However, implicit cross-task consistency learning in multi-task networks is limited. To address this, we propose a novel "decoupled cross-task consistency loss" that explicitly enhances cross-task consistency. Our semantic segmentation network, TriangleNet, achieves a substantial 2.88\% improvement over the Baseline in mean Intersection over Union (mIoU) on the Cityscapes test set. Notably, TriangleNet operates at 77.4\% mIoU/46.2 FPS on Cityscapes, showcasing real-time inference capabilities at full resolution. With multi-scale inference, performance is further enhanced to 77.8\%. Furthermore, TriangleNet consistently outperforms the Baseline on the FloodNet dataset, demonstrating its robust generalization capabilities. The proposed method underscores the significance of multi-task learning and explicit cross-task consistency enhancement for advancing semantic segmentation and highlights the potential of multitasking in real-time semantic segmentation., Comment: Accepted for publication in the journal "International Journal of Intelligent Systems"

Published
2022
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19. Diterpenoids target SARS-CoV-2 RdRp from the roots of Euphorbia fischeriana Steud

Author

Ting Ruan, Zheng-Rui Xiang, Yun-Wu Zhang, Shi-Rui Fan, Juan Ren, Qian Zhao, Xiao-Long Sun, Shi-Li Wu, Li-Li Xu, Miao Qiao, Chen-Xu Jing, Xiao-Jiang Hao, and Duo-Zhi Chen

Subjects
Euphorbia fischeriana Steud, diterpenoids, antiviral, SARS-CoV-2 RdRp, microscale thermophoresis, Plant culture, SB1-1110
Abstract

IntroductionCurrently, the development of new antiviral drugs against COVID-19 remains of significant importance. In traditional Chinese medicine, the herb Euphorbia fischeriana Steud is often used for antiviral treatment, yet its therapeutic effect against the COVID-19 has been scarcely studied. Therefore, this study focuses on the roots of E. fischeriana Steud, exploring its chemical composition, antiviral activity against COVID-19, and the underlying basis of its antiviral activity.MethodsIsolation and purification of phytochemicals from E. fischeriana Steud. The elucidation of their configurations was achieved through a comprehensive suite of 1D and 2D NMR spectroscopic analyses as well as X-ray diffraction. Performed cytopathic effect assays of SARS-CoV-2 using Vero E6 cells. Used molecular docking to screen for small molecule ligands with binding to SARS-CoV-2 RdRp. Microscale thermophoresis (MST) was used to determine the dissociation constant Kd.ResultsUltimately, nine new ent-atisane-type diterpenoid compounds were isolated from E. fischeriana Steud, named Eupfisenoids A-I (compounds 1-9). The compound of 1 was established as a C-19-degraded ent-atisane-type diterpenoid. During the evaluation of these compounds for their antiviral activity against COVID-19, compound 1 exhibited significant antiviral activity. Furthermore, with the aid of computer virtual screening and microscale thermophoresis (MST) technology, it was found that this compound could directly bind to the RNA-dependent RNA polymerase (RdRp, NSP12) of the COVID-19, a key enzyme in virus replication. This suggests that the compound inhibits virus replication by targeting RdRp.DiscussionThrough this research, not only has our understanding of the antiviral components and material basis of E. fischeriana Steud been enriched, but also the potential of atisane-type diterpenoid compounds as antiviral agents against COVID-19 has been discovered. The findings mentioned above will provide valuable insights for the development of drugs against COVID-19.

Published
2024
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