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2,121 results on '"Liu Yuxi"'

101. BCGGAN: Ballistocardiogram artifact removal in simultaneous EEG-fMRI using generative adversarial network

Author

Lin, Guang, Zhang, Jianhai, Liu, Yuxi, Gao, Tianyang, Kong, Wanzeng, Lei, Xu, and Qiu, Tao

Subjects
Computer Science - Machine Learning
Abstract

Due to its advantages of high temporal and spatial resolution, the technology of simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) acquisition and analysis has attracted much attention, and has been widely used in various research fields of brain science. However, during the fMRI of the brain, ballistocardiogram (BCG) artifacts can seriously contaminate the EEG. As an unpaired problem, BCG artifact removal now remains a considerable challenge. Aiming to provide a solution, this paper proposed a novel modular generative adversarial network (GAN) and corresponding training strategy to improve the network performance by optimizing the parameters of each module. In this manner, we hope to improve the local representation ability of the network model, thereby improving its overall performance and obtaining a reliable generator for BCG artifact removal. Moreover, the proposed method does not rely on additional reference signal or complex hardware equipment. Experimental results show that, compared with multiple methods, the technique presented in this paper can remove the BCG artifact more effectively while retaining essential EEG information.

Published
2020
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102. The short-term seasonal analyses between atmospheric environment and COVID-19 in epidemic areas of Cities in Australia, South Korea, and Italy

Author

Liu, Yuxi, Lin, Xin, and Qin, Shaowen

Subjects
Quantitative Biology - Populations and Evolution, Physics - Physics and Society
Abstract

The impact of the outbreak of COVID-19 on health has been widely concerned. Disease risk assessment, prediction, and early warning have become a significant research field. Previous research suggests that there is a relationship between air quality and the disease. This paper investigated the impact of the atmospheric environment on the basic reproduction number (R$_0$) in Australia, South Korea, and Italy by using atmospheric environment data, confirmed case data, and the distributed lag non-linear model (DLNM) model based on Quasi-Poisson regression. The results show that the air temperature and humidity have lag and persistence on short-term R$_0$, and seasonal factors have an apparent decorating effect on R$_0$. PM$_{10}$ is the primary pollutant that affects the excess morbidity rate. Moreover, O$_3$, PM$_{2.5}$, and SO$_2$ as perturbation factors have an apparent cumulative effect. These results present beneficial knowledge for correlation between environment and COVID-19, which guiding prospective analyses of disease data.

Published
2020

103. A knowledge transfer model for COVID-19 predicting and non-pharmaceutical intervention simulation

Author

Wang, Jingyuan, Lin, Xin, Liu, Yuxi, Qilegeri, Feng, Kai, and Lin, Hui

Subjects
Quantitative Biology - Populations and Evolution, Mathematics - Dynamical Systems, Mathematics - Optimization and Control, Physics - Physics and Society
Abstract

Since December 2019, A novel coronavirus (2019-nCoV) has been breaking out in China, which can cause respiratory diseases and severe pneumonia. Mathematical and empirical models relying on the epidemic situation scale for forecasting disease outbreaks have received increasing attention. Given its successful application in the evaluation of infectious diseases scale, we propose a Susceptible-Undiagnosed-Infected-Removed (SUIR) model to offer the effective prediction, prevention, and control of infectious diseases. Our model is a modified susceptible-infected-recovered (SIR) model that injects undiagnosed state and offers pre-training effective reproduction number. Our SUIR model is more precise than the traditional SIR model. Moreover, we combine domain knowledge of the epidemic to estimate effective reproduction number, which addresses the initial susceptible population of the infectious disease model approach to the ground truth. These findings have implications for the forecasting of epidemic trends in COVID-19 as these could help the growth of estimating epidemic situation., Comment: 8 pages, 8 figures

Published
2020

111. Study on the Relationship Between External Characteristic Parameters and Aging Mechanism of Lithium-Ion Batteries Based on an Electrochemical Model

Author

Kang, Jianqiang, Zhou, Longlong, Liu, Yuxi, Gui, Yupeng, Wang, Jing V., Zhu, Guorong, 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, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, Dong, Xuzhu, editor, and Ma, Weiming, editor

Published
2023
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113. Development of Bipolar Current-Type Tester for Measuring Cerebral Cortical Epilepsy Threshold Based on STM32

Author

Zhang, Jingchun, Lei, Zhipeng, Hao, Weicheng, Liu, Yuxi, Ma, Jiuhong, Sun, Meizhen, Zhang, Jianhua, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, 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, Xie, Kaigui, editor, Hu, Jianlin, editor, Yang, Qingxin, editor, and Li, Jian, editor

Published
2023
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114. Auto(mated)nomous Assembly

Author

Liu, Yuxi, Belousov, Boris, Funk, Niklas, Chalvatzaki, Georgia, Peters, Jan, Tessmann, Oliver, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Gomes Correia, António, editor, Azenha, Miguel, editor, Cruz, Paulo J. S., editor, Novais, Paulo, editor, and Pereira, Paulo, editor

Published
2023
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115. The Design and Implementation of Mobile Operation Platform Based on Domain Driven Design

Author

Shi, Wenjuan, Wang, Shulong, Ma, Jing, Zhang, Junquan, Liu, Yuxi, Jiang, Zaiyu, Hu, Yang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, 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, Sun, Jiande, editor, Wang, Yue, editor, Huo, Mengyao, editor, and Xu, Lexi, editor

Published
2023
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119. Transcription analysis on response of porcine alveolar macrophages to Haemophilus parasuis

Author

Wang Yang, Liu Chong, Fang Ying, Liu Xiaoli, Li Wentao, Liu Shuqing, Liu Yingyu, Liu Yuxi, Charreyre Catherine, Audonnet Jean-Christophe, Chen Pin, and He Qigai

Subjects
Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background Haemophilus parasuis (H. parasuis) is the etiological agent of Glässer's disease in pigs. Currently, the molecular basis of this infection is largely unknown. The innate immune response is the first line of defense against the infectious disease. Systematical analysis on host innate immune response to the infection is important for understanding the pathogenesis of the infectious microorganisms. Results A total of 428 differentially expressed (DE) genes were identified in the porcine alveolar macrophages (PAMs) 6 days after H. parasuis infection. These genes were principally related to inflammatory response, immune response, microtubule polymerization, regulation of transcript and signal transduction. Through the pathway analysis, the significant pathways mainly concerned with cell adhesion molecules, cytokine-cytokine receptor interaction, complement and coagulation cascades, toll-like receptor signaling pathway, MAPK signaling pathway, suggesting that the host took different strategies to activate immune and inflammatory response upon H. parasuis infection. The global interactions network and two subnetworks of the proteins encoded by DE genes were analyzed by using STRING. Further immunostimulation analysis indicated that mRNA levels of S100 calcium-binding protein A4 (S100A4) and S100 calcium-binding protein A6 (S100A6) in porcine PK-15 cells increased within 48 h and were sustained after administration of lipopolysaccharide (LPS) and Poly (I:C) respectively. The s100a4 and s100a6 genes were found to be up-regulated significantly in lungs, spleen and lymph nodes in H. parasuis infected pigs. We firstly cloned and sequenced the porcine coronin1a gene. Phylogenetic analysis showed that poCORONIN 1A belonged to the group containing the Bos taurus sequence. Structural analysis indicated that the poCORONIN 1A contained putative domains of Trp-Asp (WD) repeats signature, Trp-Asp (WD) repeats profile and Trp-Asp (WD) repeats circular profile at the N-terminus. Conclusions Our present study is the first one focusing on the response of porcine alveolar macrophages to H. parasuis. Our data demonstrate a series of genes are activated upon H. parasuis infection. The observed gene expression profile could help screening the potential host agents for reducing the prevalence of H. parasuis and further understanding the molecular pathogenesis associated with H. parasuis infection in pigs.

Published
2012
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135. Ask2Me VarHarmonizer: A Python-Based Tool to Harmonize Variants from Cancer Genetic Testing Reports and Map them to the ClinVar Database

Author

Liu, Yuxi, Yin, Kanhua, Lamichhane, Basanta, Sandbach, John F., Patel, Gayle, Compagnoni, Gia, Kanak, Richard H., Rosen, Barry, Ondrula, David P., Smith, Linda, Brown, Eric, Gold, Linsey, Whitworth, Pat, App, Colleen, Euhus, David, Semine, Alan, Lyons, S. Dwight, Lazarte, Melford Allan C., Parmigiani, Giovanni, Braun, Danielle, and Hughes, Kevin S.

Subjects
Quantitative Biology - Quantitative Methods, Quantitative Biology - Genomics
Abstract

PURPOSE: The popularity of germline genetic panel testing has led to a vast accumulation of variant-level data. Variant names are not always consistent across laboratories and not easily mappable to public variant databases such as ClinVar. A tool that can automate the process of variants harmonization and mapping is needed to help clinicians ensure their variant interpretations are accurate. METHODS: We present a Python-based tool, Ask2Me VarHarmonizer, that incorporates data cleaning, name harmonization, and a four-attempt mapping to ClinVar procedure. We applied this tool to map variants from a pilot dataset collected from 11 clinical practices. Mapping results were evaluated with and without the transcript information. RESULTS: Using Ask2Me VarHarmonizer, 4728 out of 6027 variant entries (78%) were successfully mapped to ClinVar, corresponding to 3699 mappable unique variants. With the addition of 1099 unique unmappable variants, a total of 4798 unique variants were eventually identified. 427 (9%) of these had multiple names, of which 343 (7%) had multiple names within-practice. 99% mapping consistency was observed with and without transcript information. CONCLUSION: Ask2Me VarHarmonizer aggregates and structures variant data, harmonizes names, and maps variants to ClinVar. Performing harmonization removes the ambiguity and redundancy of variants from different sources.

Published
2019

140. Clinical implications of conflicting variant interpretations in the cancer genetics clinic

Author

Zukin, Elyssa, Culver, Julie O., Liu, Yuxi, Yang, Yunqi, Ricker, Charité N., Hodan, Rachel, Sturgeon, Duveen, Kingham, Kerry, Chun, Nicolette M., Rowe-Teeter, Courtney, Singh, Kathryn, Zell, Jason A., Ladabaum, Uri, McDonnell, Kevin J., Ford, James M., Parmigiani, Giovanni, Braun, Danielle, Kurian, Allison W., Gruber, Stephen B., and Idos, Gregory E.

Published
2023
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149. Multi-layer Echo State Network with Nonlinear Vector Autoregression Reservoir for Time Series Prediction

Author

Wang, Heshan, Liu, Yuxi, Wang, Dongshu, Luo, Yong, Xin, Jianbin, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhang, Haijun, editor, Chen, Yuehui, editor, Chu, Xianghua, editor, Zhang, Zhao, editor, Hao, Tianyong, editor, Wu, Zhou, editor, and Yang, Yimin, editor

Published
2022
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150. A Novel Deep Learning System for Breast Lesion Risk Stratification in Ultrasound Images

Author

Liu, Ting, An, Xing, Liu, Yanbo, Liu, Yuxi, Lin, Bin, Jiang, Runzhou, Xu, Wenlong, Cong, Longfei, Zhu, Lei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Linwei, editor, Dou, Qi, editor, Fletcher, P. Thomas, editor, Speidel, Stefanie, editor, and Li, Shuo, editor

Published
2022
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