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Avian influenza viruses suppress innate immunity by inducingtrans-transcriptional readthrough via SSU72

Cellular & Molecular Immunology 2022年第6期19卷 702-714页

作者： Yan Zhao Fengming Huang Zhen Zou Yuhai Bi Yang Yang Cong Zhang Qiang Liu Daozhen Shang Yiwu Yan Xiangwu Ju Song Mei Peng Xie Xiao Li Mingyao Tian Shuguang Tan Huijun Lu Zongsheng Han Kangtai Liu Yuqing Zhang Junbo Liang Zhu Liang Qingchao Zhang Jiahui Chang William JLiu Cong Feng Tanshi Li Michael Q.Zhang Xiaoyue Wang George FGao Yingxia Liu Ningyi Jin Chengyu Jiang State Key Laboratory of Medical Molecular Biology Institute of Basic Medical SciencesChinese Academy of Medical SciencesDepartment of BiochemistryPeking Union Medical CollegeBeijing 100005China Shenzhen Key Laboratory of Pathogen and Immunity State Key Discipline of Infectious DiseaseSecond Hospital Affiliated with Southern University of Science and TechnologyShenzhen Third People’s HospitalShenzhenGuangdong 518112China CAS Key Laboratory of Pathogenic Microbiology and Immunology Institute of MicrobiologyCenter for Influenza Research and Early-warning(CASCIRE)CAS-TWAS Center of Excellence for Emerging Infectious Diseases(CEEID)Chinese Academy of SciencesBeijing 100101China School of Life Science University of Science and Technology of ChinaHefeiAnhui 230027China Department of Automation Tsinghua UniversityBeijing 100084China Genetic Engineering Laboratory Institute of Military VeterinaryAcademy of Military Medical SciencesChangchunJilin 130062China State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy West China HospitalSichuan UniversityChengduSichuan 610000China State Key Laboratory of Medical Molecular Biology Department of Biochemistry and Center for BioinformaticsInstitute of Basic Medical SciencesChinese Academy of Medical SciencesSchool of Basic MedicinePeking Union Medical CollegeBeijing 100005China National Institute for Viral Disease Control and Prevention Chinese Centers for Disease Control and PreventionBeijing 102206China Department of Emergency General Hospital of the PLAHaidian DistrictBeijing 100853China Department of Biological Sciences and Center for Systems Biology The University of Texas at DallasRichardsonTXUSA

Innate immunity plays critical antiviral roles. The highly virulent avian influenza viruses (AIVs) H5N1, H7N9, and H5N6 can betterescape host innate immune responses than the less virulent seasonal H1N1 virus. Here, we report a mechanism by whichtranscriptional readthrough (TRT)-mediated suppression of innate immunity occurs post AIV infection. By using cell lines, mouselungs, and patient PBMCs, we showed that genes on the complementary strand (“trans” genes) influenced by TRT were involved inthe disruption of host antiviral responses during AIV infection. The trans-TRT enhanced viral lethality, and TRT abolishmentincreased cell viability and STAT1/2 expression. The viral NS1 protein directly bound to SSU72, and degradation of SSU72 inducedTRT. SSU72 overexpression reduced TRT and alleviated mouse lung injury. Our results suggest that AIVs infection induce TRT byreducing SSU72 expression, thereby impairing host immune responses, a molecular mechanism acting through the NS1-SSU72-trans-TRT-STAT1/2 axis. Thus, restoration of SSU72 expression might be a potential strategy for preventing AIV pandemics.

关键词： AIV infection TRT SSU72 NS1 Immune escape

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Tactile-Based Object Pose Estimation Employing Extended Kalman Filter

Tactile-Based Object Pose Estimation Employing Extended Kalm...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Qiguang Lin Chaojie Yan Qiang Li Yonggen Ling Yu Zheng Wangwei Lee Zhaoliang Wan Bidan Huang Xiaofeng Liu Jiangsu Key laboratory of Special Robotic Technology College of IoT Engineering Hohai University Changzhou Jiangsu P.R. China State Key Laboratory of Industrial Control and Technology Zhejiang University Institute of Cyber-System and Control Hangzhou P.R. China Neuroinformatics Group Center for Cognitive Interaction Technology (CITEC) Bielefeld University Bielefeld Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P.R.China

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks.

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Data-Based Online Optimal control for Multi-player Nonlinear Non-zero-Sum Games Using Recursive Least Squares

Data-Based Online Optimal Control for Multi-player Nonlinear...

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2021 China Automation Congress, CAC 2021

作者： Yang, Gaofu Song, Ruizhuo Wei, Qinglai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

In this paper, optimal control of nonlinear systems for non-zero-sum games is solved with a data-based and recursive least square. The new adjustment law is similar to experience replay algorithm which refer history data and the covariance matrix is introduced for storing historical data, which can avoid the waste of space and time caused by storing a large amount of historical data and collecting data. In tuning process, we use a discount factor which shows how trust to the collected data to guide adjustment law to update the weight. Meanwhile, discount factor can avoid the covariance matrix tending to zero due to accumulation of data. After that, the convergence of the weight is analyzed. Finally, using a simulation to verify the effectiveness of the tuning law. © 2021 IEEE

关键词： Covariance matrix

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Research on Cardiac Medical Image Segmentation Based on an Improved U-Net Model

Research on Cardiac Medical Image Segmentation Based on an I...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Yuqing Wu Jun Wang Rui Wang Jian Wang Kecheng Zhou Xiaoxin Zhang School of Computer Science and Technology Shenyang University of Chemical Technology Key Laboratory of Industrial Intelligence Technology on Chemical Process Liaoning Province Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Emergency Zhongyi Northeast International Hospital Co. Ltd. Shenyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This paper presents a cardiac MRI image segmentation model based on an improved U-Net architecture. Accurate segmentation of cardiac MRI images is critical for the diagnosis and treatment of cardiovascular diseases, yet existing U-Net models exhibit limitations in handling complex cardiac structures and multi-scale features. To address these challenges, this paper proposes two key enhancements. First, we propose a Multi-Dimensional Context Attention module, designed to improve the integration of global and local information within the skip connections, thereby enhancing segmentation accuracy for intricate cardiac structures. Furthermore, we propose a Reverse Feature Modulation module, which generates reverse masks and dynamically adjusts feature weights across different classes using adaptive weighting, effectively mitigating class imbalance issues in multi-class segmentation tasks and improving focus on difficult-to-segment regions. Experimental results demonstrate that the proposed model outperforms the standard U-Net on the ACDC dataset, achieving significant improvements in evaluation metrics such as the Dice coefficient. These enhancements underscore the model's efficacy and robustness in complex cardiac image segmentation tasks, offering new technical support for automated cardiovascular disease diagnosis.

关键词： Image segmentation Accuracy Magnetic resonance imaging Computational modeling Modulation Context awareness Robustness Medical diagnosis Cardiovascular diseases Context modeling

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Hot Spot Detection of Photovoltaic Module Infrared Near-field Image based on Convolutional Neural Network 4

Hot Spot Detection of Photovoltaic Module Infrared Near-fiel...

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2022 4th International Conference on Energy systems and Electrical Power, ICESEP 2022

作者： Fan, Fu Na, ZhiXiong Zhang, ChangZhi Li, HaoRan Tong, ChaoLiu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China State Grid e-commerce Co. Ltd Beijing100053 China State Grid Tianjin Electric Power Company Tianjin300010 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

With the installation and use of large-scale photovoltaic systems around the world, the detection of photovoltaic system operation and maintenance has become increasingly important. This research uses a convolutional neural network training model to detect and classify the infrared near-field images of photovoltaic modules from small-scale photovoltaic plants in the laboratory. This model classifies the images into two categories: with and without hot spots, with a classification accuracy of 96.58%. The experimental results show that the convolutional neural network training model has a good classification result. © 2022 institute of Physics Publishing. All rights reserved.

关键词： Convolutional neural networks

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Vehicle Trajectory Prediction Based on Graph Attention Network 6th

Vehicle Trajectory Prediction Based on Graph Attention Netwo...

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6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

作者： Chaochen, Zhuolei Zhang, Qichao Li, Ding Li, Haoran Pang, Zhonghua North China University of Technology Beijing100144 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Hunan University Changsha410082 China

ISBN: (纸本)9789811692468

Trajectory prediction is one of the main challenges to autonomous vehicles. Except for the predicted vehicle historical trajectory information, viable solutions for this task must also consider the static geometric context, such as lane centerlines, and the dynamic interaction information among traffic participants, such as the influence of surrounding vehicles or pedestrians on the predicted vehicle. Recently, mainstream methods are mostly based on recurrent neural networks, which have achieved state-of-the-art (SOTA) performance on distance-based prediction metrics. In order to consider the influence of static geometric context and dynamic interaction information better for the vehicle trajectory prediction, we propose a two-stage prediction framework and a combined VAE-GAT model. Stage I estimates the future destination point of the predicted vehicle using a destination point estimation module based on VAE. In stage II, it produces multiple trajectories using a multiple trajectories prediction module based on GAT which consists of an LSTM-based encoder-decoder and a graph attention network. Finally, one specific trajectory is selected from multiple trajectories conditioned on the estimated destination point. The experimental results and performance comparison based on the Argoverse motion forecasting dataset are given to prove the effectiveness of the proposed method. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Robust Tracking for Electromagnetic-Actuated Microrobot Via Sliding Mode control

Robust Tracking for Electromagnetic-Actuated Microrobot Via ...

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Annual Conference of industrial Electronics Society

作者： Yihang Wu Ziyao Qu Xiaocong Chang Zhijian Hu Rongni Yang Renjie Ma State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Control Science and Engineering Shandong University Jinan China

Microrobots can work in small, enclosed spaces and complete complex tasks. It has potential applications in the field of biomedical and life sciences. This paper focuses on investigating robust dynamic tracking for electromagnetic-actuated microrobot via sliding mode control. First, we generalize the planar motion to the case of spatial trajectory tracking and then establish the three-dimensional tracking error model of microrobot. Then, we deploy a novel sliding control strategy and ensure the stability of sliding mode dynamics, once the robotic state reaches the sliding surface and sustains there in the sequel. Finally, the numerical simulation is conducted, compared to the traditional proportion-integration-differentiation control strategy, to verify the effectiveness and advantages of our proposed control scheme.

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A Multi-Model Fusion Approach for Transformer Fault Detection

A Multi-Model Fusion Approach for Transformer Fault Detectio...

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Information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ke Zhou Wei Zhang Ligang Li Shaoming Pan Chengwei Huang Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid CO. LTD. Nanning State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Ltd.

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Addressing the weak generalization capability and suboptimal prediction performance of single mechanistic and data-driven models, this paper proposes a transformer fault detection method based on the fusion of mechanism and data models. Firstly, vibration and current data are collected on-site to train the SVM vibration model and the Adaboost current model. Secondly, current simulation data is obtained using laboratory simulation software. This simulation data, combined with the current data collected under the same conditions on-site, is used as input to train the GBDT error compensation model. Finally, these three models are used as inputs to the GRNN, completing the overall model design. This fusion of mechanistic and data-driven models not only addresses the parameter mismatch issue of the mechanistic model under complex working conditions but also deeply mines time-series data. Experimental results show that The mean squared error of the fusion model is 0.002 lower than that of the data model, and the coefficient of determination is improved by 5.07% compared to the mechanistic model, enhancing the overall predictive accuracy of the model.

关键词： Vibrations Support vector machines Training Accuracy Biological system modeling Fault detection Predictive models Transformers Prediction algorithms Data models

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LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane

arXiv

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arXiv 2022年

作者： Wang, Ze Yang, Kailun Shi, Hao Li, Peng Gao, Fei Wang, Kaiwei State Key Laboratory of Modern Optical Instrumentation Zhejiang University China Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany State Key Laboratory of Industrial Control Technology Zhejiang University China Huzhou Institute of Zhejiang University Zhejiang University China

Visual-inertial-odometry has attracted extensive attention in the field of autonomous driving and robotics. The size of Field of View (FoV) plays an important role in Visual-Odometry (VO) and Visual-Inertial-Odometry (VIO), as a large FoV enables to perceive a wide range of surrounding scene elements and features. However, when the field of the camera reaches the negative half plane, one cannot simply use [u, v, 1]T to represent the image feature points anymore. To tackle this issue, we propose LF-VIO, a real-time VIO framework for cameras with extremely large FoV. We leverage a three-dimensional vector with unit length to represent feature points, and design a series of algorithms to overcome this challenge. To address the scarcity of panoramic visual odometry datasets with ground-truth location and pose, we present the PALVIO dataset, collected with a Panoramic Annular Lens (PAL) system with an entire FoV of 360◦×(40◦∼120◦) and an IMU sensor. With a comprehensive variety of experiments, the proposed LF-VIO is verified on both the established PALVIO benchmark and a public fisheye camera dataset with a FoV of 360◦×(0◦∼93.5◦). LF-VIO outperforms state-of-the-art visual-inertial-odometry methods. Our dataset and code are made publicly available at https://***/flysoaryun/LF-VIO. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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