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Identification of Predictive Biomarkers for the Progression to Severe COVID-19 Caused by Omicron Variants Using Metabolomics and Machine Learning Models

SSRN

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SSRN 2024年

作者： Zhang, Shuaijie Wang, Chunhua Zhang, Lu Ye, Fan Mao, Xiaogang Xia, Ying Lu, Junjie Yu, Tingyu Zhao, Jianzhong Jia, Yanlong Tang, Mengjun Yin, Lijuan Yang, Yang Xing, Hui Tan, Wenjie Shen, Liang Cao, Fengsheng Department of Central Laboratory Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science Hubei Province Xiangyang441021 China Department of Clinical Laboratory Xiangyang No. 1 People's Hospital Hubei University of Medicine Hubei Province Xiangyang441000 China Department of Critical Care Medicine Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science Hubei Province Xiangyang441021 China State Key Laboratory of Food Nutrition and Safety Key Laboratory of Industrial Microbiology Ministry of Education Tianjin Key Laboratory of Industry Microbiology National and Local United Engineering Lab of Metabolic Control Fermentation Technology China International Science and Technology Cooperation Base of Food Nutrition/Safety and Medicinal Chemistry College of Biotechnology Tianjin University of Science & Technology Tianjin300457 China Shenzhen Key Laboratory of Pathogen and Immunity National Clinical Research Center for infectious disease State Key Discipline of Infectious Disease Shenzhen Third People's Hospital Second Hospital Affiliated to Southern University of Science and Technology Shenzhen China NHC Key Laboratory of Biosafety National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing102206 China Xiangyang Central Hospital China

Background: The ongoing pandemic of COVID-19 has entered a new phase. Despite the reduced pathogenicity of the currently prevalent Omicron variant of SARS-CoV-2, there is still a risk of severe illness and death. Currently, there are no specific biomarkers available to accurately predict the progression and outcomes of Omicron induced COVID-19. Previous studies have mainly focused on untargeted or lipid metabolism analysis of individuals infected with the original strain of SARS-CoV-2 or Omicron with mild to moderate symptoms. Methods: We conducted a comprehensive targeted serum metabolomics analysis to analyze the metabolic profiles of COVID-19 patients infected with Omicron. We also correlated differentially expressed metabolites with laboratory test parameters. Finally, we developed a machine learning model that 'predict ' the progression and prognosis of severe COVID-19. Findings: We identified 450 DEMs between the non-severe and severe groups of COVID-19 patients. These differentially regulated metabolites are closely related to laboratory indicators such as inflammatory response, immune function. The results of the KEGG enrichment analysis on DEMs imply the association between the severity of COVID-19 in patients and host carbon homeostasis and resistance to oxidative stress, such as butyric acid metabolism, fructose and mannose metabolism. Subsequently, this study was able to accurately predict the progression of COVID-19 infection caused by the Omicron variant using three metabolites, Moreover, we first reported using five specific metabolites for predicting the outcomes of severe COVID-19 patients caused by the Omicron variant. Interpretation: 450 metabolites differ significantly between severe and non-severe Omicron-induced COVID-19 groups. We accurately predict the progression of COVID-19 infection caused by the Omicron variant using three metabolites. Funding: This research was supported by National Natural Science Foundation of China (82002192, 32302218

关键词： COVID-19

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CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose

arXiv

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arXiv 2021年

作者： Liu, Li Zhan, Xianghao Yang, Xikai Guan, Xiaoqing Wu, Rumeng Wang, Zhan Luo, Zhiyuan Wang, You Li, Guang The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University China The Computer Learning Research Center Royal Holloway University of London Egham Hill Surrey EghamTW20 0EX United Kingdom

In machine learning applications, the reliability of predictions is significant for assisted decision and risk control. As an effective framework to quantify the prediction reliability, conformal prediction (CP) was developed with the CPKNN (CP with kNN). However, the conventional CPKNN suffers from high variance and bias and long computational time as the feature dimensionality increases. To address these limitations, a new CP framework-conformal prediction with shrunken centroids (CPSC) is proposed. It regularizes the class centroids to attenuate the irrelevant features and shrink the sample space for predictions and reliability quantification. To compare CPKNN and CPSC, we employed them in the classification of 12 categories of alternative herbal medicine with electronic nose as a case and assessed them in two tasks: 1) offline prediction: the training set was fixed and the accuracy on the testing set was evaluated;2) online prediction with data augmentation: they filtered unlabeled data to augment the training data based on the prediction reliability and the final accuracy of testing set was compared. The result shows that CPSC significantly outperformed CPKNN in both two tasks: 1) CPSC reached a significantly higher accuracy with lower computation cost, and with the same credibility output, CPSC generally achieves a higher accuracy;2) the data augmentation process with CPSC robustly manifested a statistically significant improvement in prediction accuracy with different reliability thresholds, and the augmented data were more balanced in classes. This novel CPSC provides higher prediction accuracy and better reliability quantification, which can be a reliable assistance in decision support. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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Online progressive instance-balanced sampling for weakly supervised object detection

arXiv

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arXiv 2022年

作者： Chen, Minghao Tian, Yunong Li, Zhishuo Li, En Liang, Zize The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China Yuquan Road Beijing100049 China Engineering Laboratory of Industrial Vision and Intelligent Equipment Technology Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China

Based on multiple instance detection networks (MIDN), plenty of works have contributed tremendous efforts to weakly supervised object detection (WSOD). However, most methods neglect the fact that the overwhelming negative instances exist in each image during the training phase, which would mislead the training and make the network fall into local minima. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this paper. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module combining random sampling and IoU-balanced sampling progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances for making the network focus on positive instances. Extensive experimental results on the PASCAL VOC 2007 and 2012 datasets demonstrate the proposed method can significantly improve the baseline, which is also comparable to many existing state-of-the-art results. In addition, compared to the baseline, the proposed method requires no extra network parameters and the supplementary training overheads are small, which could be easily integrated into other methods based on the instance classifier refinement paradigm. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Emerging contaminants:A One Health perspective

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The Innovation 2024年第4期5卷 140-170,139页

作者： Fang Wang Leilei Xiang Kelvin Sze-Yin Leung Martin Elsner Ying Zhang Yuming Guo Bo Pan Hongwen Sun Taicheng An Guangguo Ying Bryan WBrooks Deyi Hou Damian EHelbling Jianqiang Sun Hao Qiu Timothy MVogel Wei Zhang Yanzheng Gao Myrna JSimpson Yi Luo Scott XChang Guanyong Su Bryan MWong Tzung-May Fu Dong Zhu Karl JJobst Chengjun Ge Frederic Coulon Jean Damascene Harindintwali Xiankui Zeng Haijun Wang Yuhao Fu Zhong Wei Rainer Lohmann Changer Chen Yang Song Concepcion Sanchez-Cid Yu Wang Ali El-Naggar Yiming Yao Yanran Huang Japhet Cheuk-Fung Law Chenggang Gu Huizhong Shen Yanpeng Gao Chao Qin Hao Li Tong Zhang Natàlia Corcoll Min Liu Daniel SAlessi Hui Li Kristian KBrandt Yolanda Pico Cheng Gu Jianhua Guo Jianqiang Su Philippe Corvini Mao Ye Teresa Rocha-Santos Huan He Yi Yang Meiping Tong Weina Zhang Fidèle Suanon Ferdi Brahushi Zhenyu Wang Syed AHashsham Marko Virta Qingbin Yuan Gaofei Jiang Louis A.Tremblay Qingwei Bu Jichun Wu Willie Peijnenburg Edward Topp Xinde Cao Xin Jiang Minghui Zheng Taolin Zhang Yongming Luo Lizhong Zhu Xiangdong Li DamiàBarceló Jianmin Chen Baoshan Xing Wulf Amelung Zongwei Cai Ravi Naidu Qirong Shen Janusz Pawliszyn Yong-guan Zhu Andreas Schaeffer Matthias C.Rillig Fengchang Wu Gang Yu James M.Tiedje State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China Department of Chemistry Hong Kong Baptist UniversityHong KongChina HKBU Institute of Research and Continuing Education Shenzhen Virtual University ParkShenzhenChina Technical University of Munich TUM School of Natural SciencesInstitute of Hydrochemistry85748 GarchingGermany School of Resources&Environment Northeast Agricultural UniversityHarbin 150030China Climate Air Quality Research UnitSchool of Public Health and Preventive MedicineMonash UniversityMelbourneVICAustralia Faculty of Environmental Science&Engineering Kunming University of Science&TechnologyKunming 650500China Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria College of Environmental Science and EngineeringNankai UniversityTianjin 300350China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory of Environmental Theoretical Chemistry South China Normal UniversityGuangzhouGuangdong 510006China Department of Environmental Science Baylor UniversityWacoTXUSA Center for Reservoir and Aquatic Systems Research(CRASR) Baylor UniversityWacoTXUSA School of Environment Tsinghua UniversityBeijing 100084China School of Civil and Environmental Engineering Cornell UniversityIthacaNY 14853USA Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province Zhejiang University of TechnologyHangzhou 310014China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Laboratoire d’Ecologie Microbienne Universite Claude Bernard Lyon 1UMR CNRS 5557UMR INRAE 1418VetAgro Sup69622 VilleurbanneFrance Department of Pla

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary *** global efforts to mitigate legacy pollutants,the continuous introduction of new substances remains a major threat to both people and the *** response,global initiatives are focusing on risk assessment and regulation of emerging contaminants,as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental Science-Policy Panel on Chemicals,Waste,and Pollution *** review identifies the sources and impacts of emerging contaminants on planetary health,emphasizing the importance of adopting a One Health *** for monitoring and addressing these pollutants are discussed,underscoring the need for robust and socially equitable environmental policies at both regional and international *** actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

关键词： pollution planet contaminants

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Kinetic modelling and steady-state optimization of cooling crystallization by continuous oscillatory baffled crystallizer

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Chemical Engineering Journal 2025年 519卷

作者： Mingyan Zhao Tao Liu Bo Song Ji Fan Xiongwei Ni Zoltan K. Nagy Rolf Findeisen Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian 116024 China Institute of Advanced Measurement & Control Technology Dalian University of Technology Dalian 116024 China School of Engineering and Physical Science Heriot-Watt University Edinburgh UK Davidson School of Chemical Engineering Purdue University West Lafayette IN 47907 United States Control and Cyber-physical Systems Laboratory Technical University of Darmstadt Darmstadt 64283 Germany

To address an open issue of sufficiently describing crystallization kinetics in a continuous oscillatory baffled crystallizer (COBC), a comprehensive kinetic modelling method is proposed in this paper, along with a steady-state optimization approach (SOA) for operating the COBC. Taking into account the axial dispersion of crystal quantity (ADCQ), velocity dispersion of crystal population (VDCP), and growth rate dispersion (GRD), a non-ideal plug flow micro-distribution model (NPF-MDM) is firstly established, which could be used to predict the crystal size distribution (CSD) and mean crystal size (MCS) in each zone of COBC. The model parameters are estimated by heterogeneous tracer experiments and continuous cooling crystallization (CCC) experiments in a real COBC named DN15. Based on the established NPF-MDM, an SOA is provided for operating the COBC. The tube length distribution across different temperature zones of COBC is optimized to determine the maximum attainable region of product MCS. By introducing an objective function related to the target crystal size and the CSD width of product crystals, a sensitivity analysis (SA) is presented to identify the critical operating conditions (COCs), including the seed recipe and net flow rate. Subsequently, the SA-based SOA is carried out. A growth optimizer algorithm is offered to solve the related nonconvex optimization problems. Experiments on the CCC of L-glutamic acid (LGA) via DN15 are performed to validate the proposed modelling and SOA.

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Capacity Configuration of Battery Energy Storage System for Photovoltaic Generation System Considering the High Chargerate 10

Capacity Configuration of Battery Energy Storage System for ...

引用

2020 10th International Conference on Power, Energy and Electrical Engineering, CPEEE 2020

作者： Li, Jiaming Qiao, Ying Liu, Guojing Lu, Zongxiang State Key Lab of Control and Simulation of Power Systems and Generation Equipments Dept. of Electrical Engineering Tsinghua University Beijing100084 China Economic Institute of Technology State Grid Jiangsu Electric Power Company Nanjing210005 China

Battery energy storage system (BESS) is one of the important solutions to improve the accommodation of large-scale grid connected photovoltaic (PV) generation and increase its operation economy. However, the strong intra-day volatility and severe curtailment of PV power sets a high demand of BESS charge-rate that is a key factor in operation models but ignored in current planning researches. This paper proposes a BESS capacity configuration model for PV generation systems which takes BESS's ability to (dis)charge exceeds its rated power into account. The best charge-rate and power & energy capacity of BESS are optimized by particle swarm optimization (PSO) algorithm. Through an analysis of the annual output statistics of PV power station in the northwest of China, the results show that when considering the high charge-rate of BESS, the optimal BESS capacity configuration rises and comprehensive income of the PV-BESS system will increase. © 2020 The Authors, published by EDP Sciences.

关键词： Battery storage

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EVA-planner: Environmental adaptive quadrotor planning

arXiv

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arXiv 2020年

作者： Quan, Lun Zhang, Zhiwei Zhong, Xingguang Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China National Engineering Research Center for Industrial Automation Ningbo Institute Ningbo315000 China

The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while ensuring flight smoothness. Although many works on quadrotor planning have been proposed, a research gap exists in incorporating self-adaptation into a planning framework to enable a drone to automatically fly slower in denser environments and increase its speed in a safer area. In this paper, we propose an environmental adaptive planner to adjust the flight aggressiveness effectively based on the obstacle distribution and quadrotor state. Firstly, we design an environmental adaptive safety aware method to assign the priority of the surrounding obstacles according to the environmental risk level and instantaneous motion tendency. Then, we apply it into a multi-layered model predictive contouring control (Multi-MPCC) framework to generate adaptive, safe, and dynamical feasible local trajectories. Extensive simulations and real-world experiments verify the efficiency and robustness of our planning framework. Benchmark comparison also shows superior performances of our method with another advanced environmental adaptive planning algorithm. Moreover, we release our planning framework as open-source ros-packages. Copyright © 2020, The Authors. All rights reserved.

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Process Monitoring with Sparse Bayesian Model for industrial Methanol Distillation

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IFAC-PapersOnLine 2020年第2期53卷 424-430页

作者： Lin Luo Lei Xie Hongye Su Jiusun Zeng School of Information and Control Engineering Liaoning Shihua University Fushun 113001 Liaoning PR China State Key Laboratory of Industrial Control Technology Institute of Cyber-systems and Control Zhejiang University Hangzhou 310027 Zhejiang PR China College of Metrology & Measurement Engineering China Jiliang University Hangzhou 310018 Zhejiang PR China

Following the intuition that not all latent variables in probabilistic principal component analysis method shifts simultaneously, this paper proposes a spike-and-slab regularization technique for nonlinear fault detection and isolation. Different from the existing probabilistic latent variable models, a spike-and-slab prior is introduced to downweight the irrelevant information of latent variables for the discriminative model. The resulting latent subspace supported by regularization parameters is not only sensitive to the informative variables, but it also eliminates the influence of the non-informative ones. The feasibility and efficiency of the proposed approach will be tested on an industrial methanol distillation dataset. Moreover, the performance will be compared with conventional probabilistic latent variables methods.

关键词： Process monitoring fault detection isolation Bayesian variable selection spike-and-slab prior industrial methanol distillation

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H∞$$ {H}_{\infty } $$optimal output tracking control for Markov jump systems: A reinforcement learning-based approach

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International Journal of Robust and Nonlinear control 2024年第8期34卷

作者： Ying Shen Cai-Kang Yao Bo Chen Wei-Wei Che Zheng-Guang Wu Department of Automation Zhejiang University of Technology Hangzhou People's Republic of China Zhejiang Provincial United Key Laboratory of Embedded Systems Zhejiang University of Technology Hangzhou People's Republic of China Institute of Complexity Science and the Shandong Key Laboratory of Industrial Control Technology Qingdao University Qingdao People's Republic of China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou People's Republic of China

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pH drives the spatial variation of antibiotic resistance gene profiles in riparian soils at a watershed scale

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Environmental Pollution 2023年第1期326卷 121486页

作者： Fu, Chenxi Qin, Yuan Xiang, Qian Qiao, Min Zhu, Yongguan State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China University of Chinese Academy of Sciences 19A Yuquan Road Beijing100049 China Key Lab of Urban Environment and Health Institute of Urban Environment Chinese Academy of Sciences Xiamen361021 China Key Laboratory of Urban Environment and Health Ningbo Urban Environment Observation and Research Station Institute of Urban Environment Chinese Academy of Sciences Xiamen361021 China Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control CAS Haixi Industrial Technology Innovation Center in Beilun Ningbo315830 China

Owing to convenient water access, riparian areas are often sites for intensive livestock breeding industries and agriculture, which can increase the spread of antibiotic resistance genes (ARGs). However, studies on ARG profiles in riparian soils are limited and there is little information regarding the factors influencing ARGs at a watershed scale. Here, we analyzed ARG profiles, bacterial communities, and soil properties in riparian soils under different land-use types. A total of 124 ARGs and 25 mobile genetic elements (MGEs) were detected in the riparian soils, which covered almost all major classes of antibiotics. Non-metric multidimensional scaling analysis showed that both the distance to the water reservoir and land-use types played important roles in shaping ARG profiles in riparian soils at a watershed scale. Downstream soils harbored three times the abundance of ARGs compared with upstream and midstream soils. Distance-decay analysis indicated that the similarity of ARG profiles and bacterial community composition decreased significantly with the increase of geographical distance (p © 2023

关键词： Soils

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