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8 results on '"Sun, Yuzhe"'

6. The trans-omics landscape of COVID-19

Author

Wu, Peng, Chen, Dongsheng, Ding, Wencheng, Wu, Ping, Hou, Hongyan, Bai, Yong, Zhou, Yuwen, Li, Kezhen, Xiang, Shunian, Liu, Panhong, Ju, Jia, Guo, Ensong, Liu, Jia, Yang, Bin, Fan, Junpeng, He, Liang, Sun, Ziyong, Feng, Ling, Wang, Jian, Wu, Tangchun, Wang, Hao, Cheng, Jin, Xing, Hui, Meng, Yifan, Li, Yongsheng, Zhang, Yuanliang, Luo, Hongbo, Xie, Gang, Lan, Xianmei, Tao, Ye, Li, Jiafeng, Yuan, Hao, Huang, Kang, Sun, Wan, Qian, Xiaobo, Li, Zhichao, Huang, Mingxi, Ding, Peiwen, Wang, Haoyu, Qiu, Jiaying, Wang, Feiyue, Wang, Shiyou, Zhu, Jiacheng, Ding, Xiangning, Chai, Chaochao, Liang, Langchao, Wang, Xiaoling, Luo, Lihua, Sun, Yuzhe, Yang, Ying, Zhuang, Zhenkun, Li, Tao, Tian, Lei, Zhang, Shaoqiao, Zhu, Linnan, Chang, Ashley, Chen, Lei, Wu, Yiquan, Ma, Xiaoyan, Chen, Fang, Ren, Yan, Xu, Xun, Liu, Siqi, Yang, Huanming, Wang, Lin, Sun, Chaoyang, Ma, Ding, Jin, Xin, Chen, Gang, Bai, Yong [0000-0001-5960-8000], Li, Kezhen [0000-0001-7943-5389], Liu, Panhong [0000-0003-4568-3119], Guo, Ensong [0000-0002-7388-4324], Yang, Bin [0000-0002-6218-2773], Fan, Junpeng [0000-0002-9413-408X], Sun, Wan [0000-0002-0347-2519], Wang, Shiyou [0000-0003-1901-3920], Yang, Ying [0000-0003-4843-1689], Ren, Yan [0000-0002-4007-8625], Xu, Xun [0000-0002-5338-5173], Liu, Siqi [0000-0001-9744-3681], Sun, Chaoyang [0000-0003-2469-1638], and Apollo - University of Cambridge Repository

Subjects
38/43, 631/553/1833, 631/250/255/2514, 38/23, 631/250/2502, article, 631/250/2499, 38/91
Abstract

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.

Published
2021

8. License Plate Character Recognition Research Based on Shape Context

Author

Lan Shanzhen, Sun Yuzhe, and Li Shaobin

Subjects
Chain code, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Sampling (statistics), Pattern recognition, Position (vector), Active shape model, Key (cryptography), Code (cryptography), Shape context, Enhanced Data Rates for GSM Evolution, Artificial intelligence, business
Abstract

Classic shape context algorithm uses the correspondence points between contour points. Those points represent the outline of a shape characteristic. Select a different position and number of sampling points to produce different effect on the similar shape feature description. Uniform random sampling algorithm can not solve the problem of selective retention for classic shape context with similar characters discrimination of contour points. Improved Freeman chain code algorithm describe the edge profile, and the code value controls the location of sampling points. Experimental results show the proposed method has better effect on retaining the similar characters outline key points. Using shape context features can be accurate license plate character recognition.

Published
2014

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