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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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ANIMC: A soft approach for auto-weighted noisy and incomplete multi-view clustering

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multi-view clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multi-view clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering approach (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model. Firstly, by designing adaptive semi-regularized nonnegative matrix factorization (adaptive semi-RNMF), the soft auto-weighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Secondly, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results demonstrate its superior advantages over other state-of-the-art methods. Impact Statement-As an effective method to process data from multiple sources, multi-view clustering has attracted more and more attention. However, most previous works ignore missing instances and noises in original multi-view data, which limits the applications of these works. By a soft approach, our proposed ANIMC can effectively reduce the negative influence of missing instances and noises. Moreover, ANIMC outperforms the stateof- the-art works by about 20% in representative cases. With satisfactory performance on multiple rea

关键词： Regression analysis

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Society for Cardiovascular Magnetic Resonance recommendations toward environmentally sustainable cardiovascular magnetic resonance

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Journal of Cardiovascular Magnetic Resonance 2025年第1期27卷 101840页

作者： Hanneman, Kate Picano, Eugenio Campbell-Washburn, Adrienne E Zhang, Qiang Browne, Lorna Kozor, Rebecca Battey, Thomas Omary, Reed Saldiva, Paulo Ng, Ming Rockall, Andrea Law, Meng Kim, Helen Lee, Yoo Jin Mills, Rebecca Ntusi, Ntobeko Bucciarelli-Ducci, Chiara Markl, Michael Department of Medical Imaging University of Toronto Toronto ON Canada University Clinical Center of Serbia Cardiology Division University of Belgrade Serbia Cardiovascular Branch Division of Intramural Research National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States RDM Division of Cardiovascular Medicine & NDPH Big Data Institute University of Oxford Oxford United Kingdom Dept of Radiology Division of Pediatric Radiology Children's Hospital Colorado University of Colorado School of Medicine United States University of Sydney and Royal North Shore Hospital Sydney Australia Department of Radiology and Medical Imaging University of Virginia Health System Charlottesville VA United States Departments of Radiology & Biomedical Engineering Vanderbilt University Nashville TN United States Department of Pathology University of Sao Paulo School of Medicine Sao Paulo Brazil Department of Diagnostic Radiology School of Clinical Medicine Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Dept of Surgery and Cancer Faculty of Medicine Imperial College London United Kingdom Departments of Neuroscience Electrical and Computer Systems Engineering Monash University Australia Department of Radiology University of Washington WA United States Department of Radiology and Biomedical Engineering UCSF San Francisco CA United States University of Oxford Centre for Clinical Magnetic Resonance Research Oxford United Kingdom Groote Schuur Hospital Department of Medicine University of Cape Town Cape Town South Africa Royal Brompton and Harefield Hospitals Guys’ & St Thomas NHS Trust London United Kingdom Department of Radiology Feinberg School of Medicine Northwestern University Chicago IL United States Greenwell Project Nashville TN United States Department of Radiology Alfred Health Melbourne Australia School of Biomedical Engineering and Imaging Sciences Faculty of Life Sciences and Medicine King's College U

Delivery of health care, including medical imaging, generates substantial global greenhouse gas emissions. The cardiovascular magnetic resonance (CMR) community has an opportunity to decrease our carbon footprint, mitigate the effects of the climate crisis, and develop resiliency to current and future impacts of climate change. The goal of this document is to review and recommend actions and strategies to allow for CMR operation with improved sustainability, including efficient CMR protocols and CMR imaging workflow strategies for reducing greenhouse gas emissions, energy, and waste, and to decrease reliance on finite resources, including helium and waterbody contamination by gadolinium-based contrast agents. The article also highlights the potential of artificial intelligence and new hardware concepts, such as low-helium and low-field CMR, in achieving these aims. Specific actions include powering down magnetic resonance imaging scanners overnight and when not in use, reducing low-value CMR, and implementing efficient, non-contrast, and abbreviated CMR protocols when feasible. data on estimated energy and greenhouse gas savings are provided where it is available, and areas of future research are highlighted. © 2025 The Authors

关键词： Climate change Energy consumption Greenhouse gas emission Planetary health Sustainability

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TCIM: Triangle counting acceleration with processing-in-MRAM architecture

arXiv

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arXiv 2020年

作者： Wang, Xueyan Yang, Jianlei Zhao, Yinglin Qi, Yingjie Liu, Meichen Cheng, Xingzhou Jia, Xiaotao Chen, Xiaoming Qu, Gang Zhao, Weisheng Fert Beijing Research Institute School of Microelectronics Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering University of Maryland College ParkMD United States

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and FPGA. However, these approaches suffer from the bandwidth bottleneck because TC calculation involves a large amount of data transfers. In this paper, we propose to overcome this challenge by designing a TC accelerator utilizing the emerging processing-in-MRAM (PIM) architecture. The true innovation behind our approach is a novel method to perform TC with bitwise logic operations (such as AND), instead of the traditional approaches such as matrix computations. This enables the efficient in-memory implementations of TC computation, which we demonstrate in this paper with computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) arrays. Furthermore, we develop customized graph slicing and mapping techniques to speed up the computation and reduce the energy consumption. We use a device-to-architecture co-simulation framework to validate our proposed TC accelerator. The results show that our data mapping strategy could reduce 99.99% of the computation and 72% of the memory WRITE operations. Compared with the existing GPU or FPGA accelerators, our in-memory accelerator achieves speedups of 9× and 23.4×, respectively, and a 20.6× energy efficiency improvement over the FPGA accelerator. Copyright © 2020, The Authors. All rights reserved.

关键词： data transfer

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Genome assembly and transcriptome analysis provide insights into the antischistosome mechanism of Microtus fortis

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Journal of Genetics and Genomics 2020年第12期47卷 743-755页

作者： Hong Li Zhen Wang Shumei Chai Xiong Bai Guohui Ding Yuanyuan Li Junyi Li Qingyu Xiao Benpeng Miao Weili Lin Jie Feng Mingyue Huang Cheng Gao Bin Li Wei Hu Jiaojiao Lin Zhiqiang Fu Jianyun Xie Yixue Li Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Shanghai Veterinary Research Institute Chinese Academy of Agricultural SciencesKey Laboratory of Animal ParasitologyMinistry of AgricultureShanghai 200241China Shanghai Laboratory Animal Research Center Shanghai 201203China Institute for Digital Health International Human Phenome Institutes(Shanghai)Shanghai 200433China Shanghai Center for Bioinformation Technology Shanghai Academy of Science and TechnologyShanghai 201203China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)ShenzhenGuangdong 518055China Shanghai Institute of Immunology Department of Immunology and MicrobiologyShanghai JiaoTong University School of MedicineShanghai 200025China State Key Laboratory of Genetic Engineering Ministry of Education Key Laboratory of Contemporary AnthropologyCollaborative Innovation Center for Genetics and DevelopmentSchool of Life SciencesFudan UniversityShanghai 200438China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhouZhejiang 330106China

Microtus fortis is the only mammalian host that exhibits intrinsic resistance against Schistosoma japonicum ***,the underlying molecular mechanisms of this resistance are not yet ***,we perform the first de novo genome assembly of ***,comprehensive gene annotation analysis,and evolution ***,we compare the recovery rate of schistosomes,pathological changes,and liver transcriptomes between *** and mice at different time points after *** observe that the time and type of immune response in *** are different from those in *** activates immune and inflammatory responses on the 10th day post infection,such as leukocyte extravasation,antibody activation,Fc-gamma receptor-mediated phagocytosis,and the interferon signaling cascade,which play important roles in preventing the development of *** contrast,an intense immune response occurrs in mice at the late stages of infection and could not eliminate *** mice suffer severe pathological injury and continuous decreases in cell cycle,lipid metabolism,and other *** findings offer new insights into the intrinsic resistance mechanism of *** against schistosome *** genome sequence also provides the basis for future studies of other important traits in ***.

关键词： Genome assembly Microtus fortis Schistosome Immune Transcriptome

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Non-Orthogonal Superimposed Pilot for Goodput Enhancement in Digital Semantic Communication

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zou, Jian Xiao, Jian Shen, Han Xie, Wenwu Meng, Fanyang Yang, Liang Liang, Yongsheng Shenzhen University College of Applied Technology Shenzhen518060 China Central China Normal University College of Physical Science and Technology Department of Electronics and Information Engineering Wuhan430079 China Harbin Institute of Technology School of Electronics and Information Engineering Shenzhen518055 China Pengcheng Laboratory Research Center of Networks and Communications Shenzhen518060 China Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Jishou University School of Communication and Electronic Engineering Jishou416000 China Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China

A novel digital semantic communication (D-SemCom) framework is proposed to enhance the adaptability and goodput of multi-user-multiple-input multiple-output (MU-MIMO) orthogonal frequency division multiplexing (OFDM) systems by integrating the non-orthogonal superposition pilot (NOSIP) scheme. However, this introduces challenges, such as inter-user interference, which limit the performance of traditional signal processing methods. To address these challenges, we propose SANet, which realizes the nonlinear mapping of received signals to log-likelihood ratios. SANet leverages a common subspace and a multi-head self-attention mechanism to effectively suppress interference and optimize signal decoding performance. Additionally, SANet addresses the coupling between the semantic encoder-decoder and the channel environment, reducing the dependency on channel variations and enabling the system to adapt more flexibly to different channel and data scenarios. We also introduce an adaptive data augmentation strategy that dynamically adjusts the data representation based on channel conditions, further enhancing the reliability and performance of D-SemCom. Numerical results show that: 1) SANet improves the goodput of D-SemCom by 7.01% compared to traditional approaches;2) the goodput of D-SemCom is improved by 37.15% with the NOSIP scheme compared to the orthogonal pilot scheme;and 3) the effectiveness of the adaptive data augmentation strategy in enhancing D-SemCom performance is validated. © 2025 IEEE.

关键词： Frequency division multiplexing

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Adversarial directed graph embedding

arXiv

引用

arXiv 2020年

作者： Zhu, Shijie Li, Jianxin Peng, Hao Wang, Senzhang He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

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GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS

arXiv

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arXiv 2025年

作者： Li, Zhenlong Ning, Huan Gao, Song Janowicz, Krzysztof Li, Wenwen Arundel, Samantha T. Yang, Chaowei Bhaduri, Budhendra Wang, Shaowen Zhu, A. Xing Gahegan, Mark Shekhar, Shashi Ye, Xinyue McKenzie, Grant Cervone, Guido Hodgson, Michael E. Geoinformation and Big Data Research Lab Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of Wisconsin – Madison WI United States STKO Lab Department of Geography and Regional Research University of Vienna Vienna Austria Spatial Analysis Research Center School of Geographical Sciences and Urban Planning Arizona State University AZ United States Center of Excellence for Geospatial Information Science U.S. Geological Survey VA United States NSF Spatiotemporal Innovation Center Department of Geography & Geoinformation Science George Mason University VA United States TN United States CyberGIS Center for Advanced Digital and Spatial Studies Department of Geography and Geographic Information Science University of Illinois Urbana-Champaign IL United States School of Computer Science University of Auckland New Zealand Department of Computer Science & Engineering University of Minnesota MN United States Department of Landscape Architecture & Urban Planning Center for Geospatial Sciences Applications & Technology Texas A&M University TX United States Platial Analysis Lab Department of Geography McGill University Quebec Canada Institute for Computational and Data Sciences Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of South Carolina SC United States

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five autonomous levels, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modeling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future. © 2025, CC BY.

关键词： Image retrieval

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A Competitive Swarm Algorithm for Image Segmentation Guided by Opposite Fuzzy Entropy

A Competitive Swarm Algorithm for Image Segmentation Guided ...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Mohamed Abd Elaziz Ahmed A. Ewees Dalia Yousri Diego Oliva Songfeng Lu Erik Cuevas Hubei Engineering Research Center on Big Data Security School of cyber science and Engineering Huazhong university of Science and Technology Wuhan China Department of Computer Damietta University Egypt Department of Electrical Engineering Fayoum University Fayoum Egypt Depto. de Ciencias Computacionales Universidad de Guadalajara CUCEI Guadalajara Mexico Depto. de Ciencias Computacionales Universidad de Guadalajara CUCEI Guadalajara Mexico

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.

关键词： Image segmentation Entropy Optimization Linear programming Fuzzy sets Thresholding (Imaging) Histograms

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White Matter Abnormalities in Major Depression Biotypes Identified by Diffusion Tensor Imaging

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Neuroscience Bulletin 2019年第5期35卷 867-876页

作者： Sugai Liang Qiang Wang Xiangzhen Kong Wei Deng Xiao Yang Xiaojing Li Zhong Zhang Jian Zhang Chengcheng Zhang Xin-min Li Xiaohong Ma Junming Shao Andrew J. Greenshaw Tao Li Mental Health Centre West China Hospital Sichuan University Chengdu 610041 China Huaxi Brain Research Centre West China Hospital Sichuan University Chengdu 610041 China Language and Genetics Department Max Planck Institute for Psycholinguistics 6525 XD Nijmegen Netherlands Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China Department of Psychiatry University of Alberta Edmonton T6G 2B7 Canada

Identifying data-driven biotypes of major depressive disorder(MDD) has promise for the clarification of diagnostic heterogeneity. However, few studies have focused on white-matter abnormalities for MDD subtyping. This study included 116 patients with MDD and118 demographically-matched healthy controls assessed by diffusion tensor imaging and neurocognitive *** clustering was applied to the major fiber tracts, in conjunction with tract-based spatial statistics, to reveal white-matter alterations associated with *** and neurocognitive differences were compared between identified subgroups and healthy controls. With fractional anisotropy extracted from 20 fiber tracts, cluster analysis revealed 3 subgroups based on the patterns of abnormalities. Patients in each subgroup versus healthy controls showed a stepwise pattern of white-matter alterations as follows: subgroup 1(25.9% of patient sample),widespread white-matter disruption;subgroup 2(43.1% of patient sample), intermediate and more localized abnormalities in aspects of the corpus callosum and left cingulate;and subgroup 3(31.0% of patient sample),possible mild alterations, but no statistically significant tract disruption after controlling for family-wise error. The neurocognitive impairment in each subgroup accompanied the white-matter alterations: subgroup 1, deficits in sustained attention and delayed memory;subgroup 2, dysfunction in delayed memory;and subgroup 3, no significant deficits. Three subtypes of white-matter abnormality exist in individuals with major depression, those having widespread abnormalities suffering more neurocognitive impairments, which may provide evidence for parsing the heterogeneity of the disorder and help optimize typespecific treatment approaches.

关键词： Major depressive disorder Hierarchal clustering Diffusion tensor imaging Biotype Heterogeneity

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