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Cryo-EM reveals mechanisms of natural RNA multivalency

Science (New York, N.Y.) 2025年第6746期388卷 545-550页

作者： Wang, Liu Xie, Jiahao Gong, Tao Wu, Hao Tu, Yifan Peng, Xin Shang, Sitong Jia, Xinyu Ma, Haiyun Zou, Jian Xu, Sheng Zheng, Xin Zhang, Dong Liu, Yang Zhang, Chong Luo, Yongbo Huang, Zirui Shao, Bin Ying, Binwu Cheng, Yu Guo, Yingqiang Lai, Ying Huang, Dingming Liu, Jianquan Wei, Yuquan Sun, Siqi Zhou, Xuedong Su, Zhaoming The State Key Laboratory of Biotherapy National Clinical Research Center for Geriatrics West China Hospital The State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases National Center for Stomatology West China Hospital of Stomatology Sichuan University Chengdu China Department of Cariology and Endodontics West China Hospital of Stomatology Sichuan University Chengdu China Mingle Scope (Chengdu) Chengdu China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China Key Laboratory for Bio-resources and Eco-environment of Ministry of Education College of Life Sciences Sichuan University Chengdu China Department of Laboratory Medicine West China Hospital Sichuan University Chengdu China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin New Territories Hong Kong China Cardiovascular Surgery Research Laboratory Department of Cardiovascular Surgery West China Hospital Sichuan University Chengdu China

Homo-oligomerization of biological macromolecules leads to functional assemblies that are critical to understanding various cellular processes. However, RNA quaternary structures have rarely been reported. Comparative genomics analysis has identified RNA families containing hundreds of sequences that adopt conserved secondary structures and likely fold into complex three-dimensional structures. In this study, we used cryo-electron microscopy (cryo-EM) to determine structures from four RNA families, including ARRPOF and OLE forming dimers and ROOL and GOLLD forming hexameric, octameric, and dodecameric nanostructures, at 2.6- to 4.6-angstrom resolutions. These homo-oligomeric assemblies reveal a plethora of structural motifs that contribute to RNA multivalency, including kissing-loop, palindromic base-pairing, A-stacking, metal ion coordination, pseudoknot, and minor-groove interactions. These results provide the molecular basis of intermolecular interactions driving RNA multivalency with potential functional relevance.

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Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年第11期12卷 17791-17806页

作者： Hongjian Zeng Yonghua Xiong Jinhua She Anjun Yu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Operation Management Department Jiangxi Ganyue Expressway Company Ltd. Nanchang Jiangxi China

Sparse mobile crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers’ mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms.

关键词： Sensors Costs Time factors Data collection Crowdsensing Evolutionary computation Data integrity Optimization Inference algorithms Mobile computing

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Memory-enhanced hierarchical and temporal semantic learning for multi-label patent classification

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Expert systems with Applications 2025年 285卷

作者： Xu, Xuejie Xu, Yi Miao, Ziqi Huang, Jian Sun, Leilei School of Software Beihang University Beijing100191 China Data Science and Intelligent Computing Laboratory Hangzhou International Innovation Institute Beihang University Hangzhou311115 China State Key Laboratory of Complex & Critical Software Environment Beihang University Beijing100191 China

Patent classification is widely acknowledged as a crucial part of patent management. Accurate automated patent classification can better assist domain experts in categorizing patents, especially with the continuous refinement of the International Patent Classification (IPC) and the surge in patent numbers. Current patent classification methods often rely on static patent texts and structural information, neglecting the dynamic and multi-faceted nature of patent information. To address these limitations, we propose a novel framework that captures comprehensive patent representations from multi-faceted information. Specifically, we first leverage a pre-trained model combined with a multi-scale convolutional neural network to learn contextual representation from patent text, Subsequently, we employ a tri-channel fusion mechanism to model patent publication trends from historical patent sequences across multi-assignee, integrating historical text, evolving hierarchical IPC label semantics, and temporal information. Furthermore, we design a memory-enhanced semantic learning module to capture and integrate correlations between hierarchical structure and semantic relationships in the IPC taxonomy. Finally,our method fuses the aforementioned multi-faceted representations to make multi-label prediction. Experiments on three real-world datasets show that our approach outperforms existing methods, providing insights into evolving technology trends through captured semantic changes in IPC codes. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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A semi-supervised multi-task assisted method for ultrasound medical image segmentation

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Neurocomputing 2025年 639卷

作者： Li, Honghe Yang, Jinzhu Qu, Mingjun Feng, Yong Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang110819 China

The accurate segmentation of the left ventricle in echocardiography is critical for assessing cardiac function, but challenges such as blurred boundaries, high morphological variability, and limited annotated data hinder the performance of traditional methods. Recent advances in semi-supervised learning show promise in leveraging unlabeled data, yet existing techniques often rely on network or data-level perturbation, which may not fully exploit spatial and positional information essential for precise segmentation. To address these challenges, we propose a novel multi-task assisted semi-supervised segmentation framework. Our method combines segmentation, landmark detection, and image reconstruction into a unified model with a shared encoder and dual decoders. A feature cross-fusion module based on a cross-attention mechanism integrates features across tasks to enhance spatial and positional awareness. We have also introduced a contrastive learning mechanism to refine the segmentation boundaries, especially in areas where the edges are blurred. In addition, we utilize multi-scale supervision to better adapt the model to targets of different scales. The framework employs an exponential moving average student–teacher model to effectively utilize unlabeled data for training. Experiments on CAMUS and EchoNet-Dynamic datasets demonstrate that the proposed method achieves state-of-the-art performance, delivering near fully-supervised results with only 10%–20% labeled data. This highlights its potential for high-quality segmentation in low-annotation scenarios, outperforming existing semi-supervised learning methods. © 2025

关键词： Contrastive Learning

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Foreign‐Object Detection in High‐Voltage Transmission Line Based on Improved YOLOv8m

arXiv

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arXiv 2025年

作者： Wang, Zhenyue Yuan, Guowu Zhou, Hao Ma, Yi Ma, Yutang School of Information Science and Engineering Yunnan University Kunming650504 China Electric Power Research Institute Yunnan Power Grid Co. Ltd. Kunming650214 China Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650504 China

The safe operation of high‐voltage transmission lines ensures the power grid’s security. Various foreign objects attached to the transmission lines, such as balloons, kites and nesting birds, can significantly affect the safe and stable operation of high‐voltage transmission lines. With the advancement of computer vision technology, periodic automatic inspection of foreign objects is efficient and necessary. Existing detection methods have low accuracy because foreign objects attached to the transmission lines are complex, including occlusions, diverse object types, significant scale variations, and complex backgrounds. In response to the practical needs of the Yunnan Branch of China Southern Power Grid Co., Ltd., this paper proposes an improved YOLOv8m‐based model for detecting foreign objects on transmission lines. Experiments are conducted on a dataset collected from Yunnan Power Grid. The proposed model enhances the original YOLOv8m by incorporating a Global Attention Module (GAM) into the backbone to focus on occluded foreign objects, replacing the SPPF module with the SPPCSPC module to augment the model’s multiscale feature extraction capability, and introducing the Focal‐EIoU loss function to address the issue of high‐ and low‐quality sample imbalances. These improvements accelerate model convergence and enhance detection accuracy. The experimental results demonstrate that our proposed model achieves a 2.7% increase in mAP_0.5, a 4% increase in mAP_0.5:0.95, and a 6% increase in recall. © 2025, CC0.

关键词： Power transmission lines

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Single-cell omics: experimental workflow, data analyses and applications

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Science China(Life Sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Unified output feedback based prescribed performance consensus tracking control of heterogeneous multi-agent systems

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Luo, Dahui Wang, Yujuan Lewis, Frank L. Song, Yongduan Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University The School of Automation Chongqing University Chongqing400044 China Star Institute for Intelligent Systems Chongqing400044 China Uta Research Institute University of Texas at Arlington Fort WorthTX76118 United States

This paper proposes an output-feedback based prescribed performance consensus tracking control methodology for a class of heterogeneous multi-agent systems (HMASs) with inconsistent system structure, where the performance behavior is allowed to be different from that of each other. Both the heterogeneous system structures and the nonidentical performance requirements make the control problem much more challenging than that of MASs with identical structure and performance requirement. This is mainly due to the coupling effect of the system dynamics and performance restriction of each agent in the cooperative control action. The key to solve this problem is to introduce a dual-phase performance-guaranteed method, in which the consensus tracking error is decomposed into auxiliary tracking error and filter tracking error and then the whole performance control is decomposed into two phases. By confining the two errors respectively, the practical tracking error can be proved to be explicitly confined within an arbitrarily given performance envelope by merely adjusting the design parameters rather than modifying control structure. Moreover, the prescribed performance control (PPC) result is not only uniform with any initial conditions and design parameters, allowing it to be global, but also unifying both the global and semi-global result into one frame, distinguishing itself from most existing PPC works where either only global or only semi-global result is guaranteed. Finally, the effectiveness of the proposed control scheme is confirmed by the simulation conducted on a group of tunnel-diode circuits (TDC). © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Levees

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A Stealthy False Data Injection Attack Scheme Against Sensor Measurements Using Partial System Knowledge

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IEEE Transactions on Automatic Control 2025年

作者： Guo, Haibin Pang, Zhong-Hua Sun, Jian Han, Qing-Long Liu, Guo-Ping North China University of Technology Key Laboratory of Fieldbus Technology and Automation of Beijing Beijing100144 China Beijing University of Technology School of Information Science and Technology Beijing100124 China Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing100081 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia Southern University of Science and Technology School of Automation and Intelligent Manufacturing Shenzhen518055 China

Attack design is indispensable for analyzing potential risks of networked control systems (NCSs). However, the remote control center is usually well protected and its knowledge is difficult to be disclosed, which becomes a major obstacle in developing stealthy false data injection (FDI) attack scheme because only partial system knowledge (i.e., the system matrices of the physical plant) could be used. To meet this challenge, a novel stealthy FDI attack scheme against the sensor measurement is proposed by employing the normal and compromised self-governed filters held by malicious attackers, where the normal one is adopted to estimate the system state and the compromised one is used as the virtual attacked target. The corresponding attack strategy is obtained by maximizing the estimation error of the compromised self-governed filter. Then, the residual of the compromised system is derived to prove attack stealthiness. Next, it is derived and found that the attack impact on system estimation performance is the same as that based on full system knowledge. Furthermore, the divergence condition of NCSs under the attack is presented. Finally, all the theoretical analyses are verified by simulation results. © 1963-2012 IEEE.

关键词： Kalman filters

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CacheAlarm: Monitoring Sensitive Behaviors of Android Apps Using Cache Side Channel

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Tian, Jianwen Ma, Haoyu Gao, Debin Kuang, Xiaohui Academy of Military Sciences Institute of System Engineering China Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100045 China Singapore Management University School of Computing and Information Systems Singapore

Malware attack has been a serious threat to the security and privacy of both individual and corporation users of the Android platform. Business entities seek to protect themselves by means of monitoring privacy-related sensitive behaviors conducted on company-issued Android devices. However, due to Android's own access control and privacy protection policies, this is difficult to be done with third-party apps using only normal privileges. Existing works proposed using side-channel readings from leaky APIs and system virtual files to speculate runtime app behaviors, which could be unreliable due to future system updates (that ban exploited resources), hardware jittering, etc. In this paper, we argue that a more traditional side-channel attack strategy, namely the CPU-cache-based side channel, could be exploited in the benign scenario of app behavior surveillance. Specifically, we propose CacheAlarm, a sensitive app behavior monitor and foreground app identification system, which works by measuring cache side-channel readings of selected methods within the Android framework, and conducted in-lab and in-the-wild user studies to compare the effectiveness of our scheme against SideNet, a previous Android app behavior surveillance scheme using API-based side channels. Results of the studies suggested that CacheAlarm outperforms SideNet on the accuracy of detecting sensitive behaviors in addition to gaining the capability of detecting apps running at foreground of the user device. © 2025 IEEE. All rights reserved.

关键词： Android malware

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