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An adjusted ELISpot-based immunoassay for evaluation of SARS-CoV-2-specific T-cell responses

Biosafety and Health 2022年第3期4卷 179-185页

作者： Hao Lin Jie Zhang Shaobo Dong Yaning Liu Peipei Liu George F.Gao William J.Liu Guizhen Wu National Health Commission Key Laboratory of Biosafety Research Units of Adaptive Evolution and Control of Emerging VirusesChinese Academy of Medical SciencesNational Institute for Viral Disease Control and PreventionChinese Center for Disease Control and Prevention(China CDC)Beijing 102206China Macheng Center for Disease Control and Prevention Huanggang 438300China Center for Biosafety Mega-Science Chinese Academy of SciencesWuhan 430071China

Like antibody evaluation,using an effective antigen‐specific T‐cell immunity assessment method in coronavirus disease 2019(COVID‐19)patients,survivors and vaccinees is crucial for understanding the immune persistence,prognosis assessment,and vaccine development for COVID‐*** study evaluated an empirically adjusted enzyme‐linked immunospot assay for detecting severe acute respiratory syndrome coronavirus 2(SARS‐CoV‐2)‐specific T‐cell immunity in 175 peripheral blood samples from COVID‐19 convalescents and healthy *** of viral nucleic acid were used as the gold standard of infection *** SARS‐CoV‐2M peptide pool had higher sensitivity of 85%and specificity of 71%for the single peptide *** combined peptide pools,the parallel evaluation(at least one of the peptide pools is positive)of total peptide pools(S1&S2&M&N)had higher sensitivity(up to 93%),and the serial evaluation(all peptide pools are positive)of total peptide pools had higher specificity(up to 100%).The result of the serial evaluation was better than that of the parallel evaluation as a *** detection efficiency of M and N peptide pool serial evaluation appeared the highest,with a sensitivity of 80%and specificity of 93%.This T‐cell immunity detection assay introduced in this report can achieve high operability and ***,it can be an effective SARS‐CoV‐2‐specific cellular immune function evaluation method.

关键词： SARS‐CoV‐2 COVID‐19 T‐cell response ELISpot assay Sensitivity Specificity

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Proactive Resilient Day-Ahead Unit Commitment with Cloud Computing Data centers

Proactive Resilient Day-Ahead Unit Commitment with Cloud Com...

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作者： Liu, Shengwei Zhao, Tianyang Liu, Xuan Li, Yuanzheng Wang, Peng Energy and Electricity Research Center Jinan University Zhuhai China School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

To enhance the resilience of power systems toward the temporal and spatial impacts caused by extreme weather events, e.g., hurricanes, the flexibility of cloud data centers (CDCs) is treated as a kind of efficient demand response. Since the workloads of CDCs have the shifting capacity between different locations and time slots, a day-ahead unit commitment problem including data centers is proposed to explore the integrated spatial and temporal flexibility of the workloads to its full extent through task migration. Considering the uncertainty of probability density functions, the line failure rates, workload arrival rates, power loads are integrated into an ambiguity set. The scheduling process of generators and CDCs is modeled as a two-stage distributionally robust optimization problem, which is reformulated as a large-scale deterministic mixed-integer linear programming problem and solved by the multicuts Benders decomposition method. The performance of the proposed scheduling strategy is tested in both the IEEE 24-bus RTS system and the three-area RTS-96 system. The results reveal that the method could mitigate the adverse impacts of hurricanes by enhancing the resilience of power systems and decreasing the dropping workloads of CDCs. © 1972-2012 IEEE.

关键词： Hurricanes

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Resilience Enhancement for Transmission Lines Under Decision-Dependent Uncertainty 42

Resilience Enhancement for Transmission Lines Under Decision...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Xinghua Yang, Xiang Sun, Yuzhen Zhao, Tianyang Wang, Peng Xi'an University of Technology Department of Power Grid Information and Control Engineering Xi'an710054 China State Grid Linshu County Power Supply Company of Shandong Province Linshu276700 China Energy and Electric Research Center Jinan University Zhuhai519070 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

ISBN: (纸本)9789887581543

Reinforcement of transmission lines before natural disasters such as typhoons is an effective measure to ensure that the power system can operate safely in face of extreme weather. To enhance the grid resilience, the uncertain problem is formulated as a two-stage robust optimization (TRO) problem for the resilient unit commitment. The exogenous uncertain polyhedral model does not address the coupling problem of typhoon hazard, reinforcement decision and transmission lines state, for which an decision-dependent uncertainty (DDU) set is constructed in this paper. Because the two-stage robust optimization problem based on decision-dependent uncertainty sets cannot be solved by the traditional column and constraint generation (C&CG) algorithm, a novel iterative algorithm based on the C&CG is invoked in this paper, in which advanced optimality cuts and feasibility cuts are designed to combine the uncertainty decision coupling. Lastly, the IEEE-5 bus test system is simulated under the influence of a typhoon to verify the effectiveness of the proposed method. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electric lines

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Robust Divergence Learning for Missing-Modality Segmentation

Robust Divergence Learning for Missing-Modality Segmentation

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Chinese Automation Congress (CAC)

作者： Runze Cheng Zhongao Sun Ye Zhang Chun Li MSU-BIT-SMBU Joint Research Center of Applied Mathematics Shenzhen MSU-BIT University Shenzhen China Institute of Control Theory and Control Engineering School of Automation Beijing Institute of Technology Beijing China School of Mathematics and Statistics Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Multimodal Magnetic Resonance Imaging (MRI) provides essential complementary information for analyzing brain tumor subregions. While methods using four common MRI modalities for automatic segmentation have shown success, they often face challenges with missing modalities due to image quality issues, inconsistent protocols, allergic reactions, or cost factors. Thus, developing a segmentation paradigm that handles missing modalities is clinically valuable. A novel single-modality parallel processing network framework based on Hölder divergence and mutual information is introduced. Each modality is independently input into a shared network backbone for parallel processing, preserving unique information. Additionally, a dynamic sharing framework is introduced that adjusts network parameters based on modality availability. A Hölder divergence and mutual information-based loss functions are used for evaluating discrepancies between predictions and labels. Extensive testing on the BraTS 2018 and BraTS 2020 datasets demonstrates that our method outperforms existing techniques in handling missing modalities and validates each component's effectiveness.

关键词： Image segmentation Three-dimensional displays Protocols Magnetic resonance imaging Brain tumors Parallel processing Brain modeling Mutual information Knowledge transfer Testing

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Optimizing Plant Process by Adding Soft Sensors

Optimizing Plant Process by Adding Soft Sensors

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Nayef Salman Al-Dossary Mohammed Aldahlan Abdul-Wahid A. Saif Control and Instrumentation Engineering Department Interdisciplinary Research Center for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

Process optimization cannot be done without continuous process measure and increasing plant margin cannot be done without continuous process optimization. Normally, products are kept within the specifications by using online monitoring or some time using laboratory off-line sampling. The second method practice has a huge time delay between process monitoring and control which add more challenges for process optimization. This work aims to come up with cost-effective solution to measure and predict the physical properties of distillate column in Ethylene Oxide (EO) plant, continuously, without having any additional on-line stream analyzers. Here, we apply soft sensors model to provide process optimization solution. The model output can be integrated with a Distributed control System (DCS) or Model Predictive control (MPC) system to minimize product giveaway and energy consumption. In this work, we will collect actual plant dataset and conduct the required training to come with the right model to be used for future predication to monitor plant process without any additional hardware and we will show the process of applying soft sensor. One of Artificial Intelligent solution is applying a neural network. Various structures of neural networks with different numbers of neurons in each hidden layer were created and assessed for their performance on the estimation of product composition in the distillate stream. Other solution of Artificial Intelligent is using regression model (Ordinary Least Squares OLS, Weighted Least Squares - WLS and Partial Least Squares PLS) based on soft sensor for online measurement which were developed in this work. The performance for each developed method was tested on the estimation of Ethylene Oxide composition in the distillate stream. The simulation results showed that the neural network has most accurate prediction. So, it will be the best choice to use for EO composition prediction, however, due to company recourses and restriction we hav

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A Passive Navigation Algorithm Based on Derivative Dynamic Time Regularization Geomagnetic Matching 2

A Passive Navigation Algorithm Based on Derivative Dynamic T...

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2021 2nd International Conference on Modeling, Big Data Analytics and Simulation, MBDAS 2021

作者： Wang, Yiteng Bao, Jinhong Li, Zehua Science and Technology on Communication Information Security Control Laboratory China No.36 Research Institute of China Electronics Technology Group Corporation China College of Telecommunication Engineering Hangzhou Dianzi University Hangzhou310018 China

As satellite positioning has shortcomings such as susceptibility to multipath interference, we look forward to using a new algorithm to solve these problems. The method of derivative dynamic time regularization is used as the matching method of geomagnetic navigation. Due to the factors of the inertial device itself, a certain cumulative error will occur in the positioning solution of the inertial navigation (INS). Therefore, an outdoor error model needs to be established, and the positioning error is corrected by filtering technology. To solve this problem, the Unscented Kalman Filter is used to fuse the positioning results of the two to obtain a more accurate positioning, with an average accuracy within 1.5 meters. © Published under licence by IOP Publishing Ltd.

关键词： Kalman filters

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Nationwide survey of ticks on domesticated animals in Taiwan: Revealing the hidden threat to animal and public health

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Medical and Veterinary Entomology 2024年第1期38卷 99-107页

作者： Liao, Jhih-Rong Liao, Yi-Hao Liao, Kuei-Min Wu, Huai-Hui Tu, Wu-Chun Lin, Ying-Hsi Systematic Zoology Laboratory Department of Biological Sciences Tokyo Metropolitan University Tokyo Japan Department of Medical Laboratory Science and Biotechnology Yuanpei University of Medical Technology Hsinchu Taiwan National Mosquito-Borne Diseases Control Research Center National Health Research Institutes Tainan Taiwan Department of Biotechnology Tajen University Yanpu Township Taiwan Department of Entomology National Chung Hsing University Taichung Taiwan School of Life Sciences and Technology Bandung Institute of Technology Bandung Indonesia

Ticks are not only bloodsucking ectoparasites but also important vectors of tick-borne diseases (TBDs), posing significant threats to public and animal health. Domesticated animals serve as critical hosts for numerous ticks, highlighting the importance of understanding tick infestations in Taiwan. To address this knowledge gap, we conducted a nationwide survey to identify ticks on domesticated animals and associated environments in 2018 and 2019. A total of 6,205 ticks were collected from 1,337 host animals, revealing the presence of seven tick species, with Rhipicephalus microplus, and Rhipicephalus sanguineus being the dominant species. High infestation rates and widespread distribution of ticks were observed on domesticated animals, especially on dogs and cattle (yellow cattle and angus cattle), and the neighbouring grassland of yellow cattle. While this study has certain limitations, it provides valuable insights into the distribution and prevalence of ticks on domesticated animals in Taiwan and their implications for controlling TBDs. Further research is needed to comprehensively understand the complex interactions among ticks, hosts and pathogens. © 2023 Royal Entomological Society.

关键词： domesticated animals nationwide investigation tick surveillance vector control zoonotic diseases

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Spatial-Temporal Hypergraph Neural Network based on Attention Mechanism for Multi-view Data Action Recognition 10

Spatial-Temporal Hypergraph Neural Network based on Attentio...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Wu, Zhixuan Ma, Nan Zhi, Tao Xu, Genbao Beijing Union University Beijing Key Laboratory of Information Service Engineering College of Robotics Beijing100101 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing University of Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing100124 China Yunji Technology Beijing100081 China

ISBN: (纸本)9798350304770

To address the issue of large network computing parameters for spatial-temporal features of actions in multi-view video sequences, this paper proposes a Spatial-Temporal Hypergraph Neural Network based on Attention Mechanism (STHGNN-AM). This method consists of a Temporal Attention Mechanism based on Trainable Threshold (TAM-TT) and a Multi-scale Spatial-Temporal Residual Module (MS-STRM), achieving multi-view data action recognition. Specifically, TAM-TT is constructed by using a learnable threshold to extract key frames of actions from different view video frames input to the module. MS-STRM is employed to further improve the model performance, and high-order semantic features of actions are learned in a hypergraph neural network. The MS-STRM extracts features using a multi-scale approach, modeling long-term and short-term semantic information to capture the temporal information changes between different frames. Comparative experiments on the NTU RGB+D and imitating traffic police gestures datasets evince the superior performance and heightened recognition accuracy exhibited by the proposed methodology, effectively enhancing the cognitive ability of machine-human body language interaction. © 2023 IEEE.

关键词： Semantics

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Achievements,challenges and global implications of China’s carbon neutral pledge

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Frontiers of Environmental science & engineering 2022年第8期16卷 285-288页

作者： Hong Yang Xianjin Huang Jianlin Hu Julian R.Thompson Roger J.Flower Department of Geography and Environmental Science University of ReadingReading RG66ABUK School of Geography and Ocean Science Nanjing UniversityNanjing 210023China Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control(AEMPC)School of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing 210044China Department of Geography University College LondonLondon WC1E 6BTUK

China has been committed to achieving carbon neutrality by ***’s pledge of carbon neutrality will play an essential role in galvanising global climate action,which has been largely deferred by the Covid-19 ***’s carbon neutrality could reduce global warming by approximately 0.2–0.3°C and save around 1.8 million people from premature death due to air *** with domestic benefits,China’s pledge of carbon neutrality is a“game-changer”for global climate action and can inspire other large carbon emitters to contribute actively to mitigate carbon emissions,particularly countries along the Belt and Road Initiative(BRI)*** order to achieve carbon neutrality by 2060,it is necessary to decarbonise all sectors in China,including energy,industry,transportation,construction,and ***,this transition will be very challenging,because major technological breakthroughs and large-scale investments are *** policies and implementation plans are essential,including sustainable demand,decarbonizing electricity,electrification,fuel switching,and negative *** particular,if China can peak carbon emissions earlier,it can lower the costs of the carbon neutral transition and make it easier to do so over a longer time ***’s pledge of carbon neutrality by 2060 and recent pledges at the 26th UN Climate Change Conference of the Parties(COP26)are significant contributions and critical steps for global climate ***,countries worldwide need to achieve carbon neutrality to keep the global temperature from growing beyond the level that will cause catastrophic damages globally.

关键词： Carbon neutrality Carbon peak Renewable energy Negative emission Carbon capture Utilisation and storage Nature-based solution

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Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

arXiv

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arXiv 2023年

作者： Desai, Tushar Oliva, Federico Ferrari, Riccardo M.G. Carnevale, Daniele Delft Center for Systems and Control Delft University of Technology Delft Netherlands Department of Civil Engineering and Computer Science Engineering University of Rome "Tor Vergata" Rome Italy

For reliable and safe battery operations, accurate and robust State of Charge (SOC) and model parameters estimation is vital. However, the nonlinear dependency of the model parameters on battery states makes the problem challenging. We propose a Moving-Horizon Estimation (MHE)-based robust approach for joint state and parameters estimation. Dut to all the time scales involved in the model dynamics, a multi-rate MHE is designed to improve the estimation performance. Moreover, a parallelized structure for the observer is exploited to reduce the computational burden, combining both multi-rate and a reduced-order MHEs. Results show that the battery SOC and parameters can be effectively estimated. The proposed MHE observers are verified on a Simulink-based battery equivalent circuit model. © 2023, CC BY.

关键词： Simulink

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