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

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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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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Dynamic Simulation Analysis of the Flexible Swap Device

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Journal of Donghua University(English Edition) 2015年第2期32卷 300-304页

作者：崔艳波赵腊生张强张建新 Key Laboratory of Advanced Design and Intelligent Computing Dalian University

With the development of space science and technology,the on-orbit servicing technologies of spacecraft get more and more *** to the design criterion of existing spacecraft in orbit module replacement technology,the flexible swap device is designed and the dynamics simulation of institutions by the automatic dynamic analysis of mechanical systems(ADAMS)simulation software is *** the analysis process,this paper studies the effect of collision force of flexible mechanism and provides a basis for the optimization of flexible plug agencies.

关键词： collision swap spacecraft institutions trajectory freedom kinematic criterion constraints replacement

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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IEEE International Symposium on Biomedical Imaging

作者： Fan Yang S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment.

关键词： Training Measurement Adaptation models Three-dimensional displays Smoothing methods Head Kernel Biomedical imaging

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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Multi-process digital twin closed-loop machining through shape-feature state update and error propagation knowledge graph

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advanced Engineering Informatics 2025年 65卷

作者： Lin, Zhiwen Zhang, Yueze Chen, Chuanhai Guo, Jinyan Qi, Baobao Yan, Jun Liu, Zhifeng Key Laboratory of CNC Equipment Reliability Ministry of Education Jilin University Jilin Province Changchun130025 China Jilin Provincial Key Laboratory of Advanced Manufacturing and Intelligent Technology for High-end CNC Equipment Jilin University Jilin Province Changchun130025 China School of Mechanical and Aerospace Engineering Jilin University Jilin Province Changchun130025 China Beijing Key Laboratory of Advanced Manufacturing Technology Beijing University of Technology Beijing100124 China Machinery Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Technology Beijing University of Technology Beijing100124 China

Machining digital twin systems represent a novel platform for achieving full autonomy in machine tools. However, in multi-process machining, error propagation between different processes presents significant challenges to current digital twin systems: real-time model updates, error evolution analysis and optimization. To address these issues, this study proposes a method for rapid state updates of shape features and the construction of an error propagation knowledge graph, enabling dynamic control in a multi-process machining digital twin system. First, a state update method based on multi-level voxel computation is introduced, utilizing the transformation relationship between cutter-workpiece engagement (CWE) and voxels to ensure rapid updates of multi-process digital twin models. Second, digital threads for multi-process machining are developed to identify machining states and track errors. To analyze error evolution and implement effective control measures, an error propagation knowledge graph based on Taylor expansion model is constructed, enabling closed-loop control. Finally, the proposed system was validated for its advancement in error control and closed-loop optimization through the machining of typical multi-process components in the transportation field. The results of comparative experiments and ablation experiments indicate that the error updating efficiency based on multi-level voxel computation threads improved by an average of 59.26% compared to other benchmarks. The error propagation knowledge graph based on the Taylor model achieved a 42.75% improvement in reasoning accuracy compared to the ablated Taylor model. The developed digital twin closed-loop optimization system successfully ensured that the errors in 1280 processes of the case study remained within the tolerance range. © 2025 Elsevier Ltd

关键词： Machining centers

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Ladle Furnace Temperature Prediction Model Based on Large-scale Data With Random Forest

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 770-774页

作者： Xiaojun Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of EducationDalian UniversityDalian 116622 China

In ladle furnace, the prediction of the liquid steel temperature is always a hot topic for the researchers. The most of the existing temperature prediction models use small sample set. Today, the precision of them can not satisfy practical production. Fortunately, the large sample set is accumulated from the practical production process. However, a large sample set makes it difficult to build a liquid steel temperature model. To deal with the issue, the random forest method is preferred in this paper, which is a powerful regression method with low complexity and can be designed very quickly. It is with the parallel ensemble structure,uses sample subsets,and employs a simple learning algorithm of sub-models. Then, the random forest method is applied to establish a temperature model by using the data sampled from the production process. The experiments show that the random forest temperature model is more precise than other temperature models.

关键词： Ladle furnace random forest regression tree temperature prediction

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intelligent energy management in hybrid electric cycling work vehicles: A multi-task learning approach

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Energy 2025年 326卷

作者： Zhang, Wei Li, Jinchen Ma, Hongfeng Niu, Chengyong Wang, Jixin Yang, Zhiyu Li, Lijun Huang, Linsen Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment Chongqing Technology and Business University Chongqing 400067 China State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery Xuzhou221004 China Vehicle and Intelligent Driving Testing and Research Department China Merchants Testing Vehicle Technology Research Institute Co. Ltd. Chongqing 401329 China School of Mechanical and Aerospace Engineering Jilin University Changchun130012 China School of Transportation Science and Engineering Beihang University Beijing100191 China

Learning-based approaches have become mainstream solutions for achieving intelligent energy management in hybrid electric vehicles (HEVs). For hybrid electric cycling work vehicles (HECWVs), predictable driving routes facilitate model learning. Nevertheless, effectively tailoring and deploying learning-based energy management strategies specifically for HECWVs encounters significant hurdles. These primarily involve substantial data requirements to capture diverse operational tasks, achieving robust generalization across heterogeneous scenarios, and ensuring the interpretability of the resultant control policies. To address these issues in HECWVs, this study pioneers the introduction of a multi-task learning mechanism and presents a customized energy management approach. Specifically, a work condition clustering module using Self-Organizing Maps (SOM) enables efficient dimensionality reduction and intuitive visual clustering. Additionally, the prioritized sweeping method is introduced within the multi-task learning framework to enhance learning efficiency and establish an efficient cross-task sharing mechanism. Finally, this study introduces an asymmetric sharing mechanism to accommodate diverse working conditions among HECWVs, thereby enhancing strategic decision-making responsiveness. This enhancement is achieved by guiding targeted interactions among tasks through an asymmetric parameter-sharing structure aligned with the proportion of working conditions. Real data analysis demonstrates improved model interpretability and adaptability in sparse data scenarios. Additionally, it outperforms traditional model-based learning approaches in both optimality and strategic response speed in environments with heterogeneous driving characteristics. Notably, fuel economy is improved by 5.3 %, and the learning response speed is enhanced by 388 %. © 2025 Elsevier Ltd

关键词： Multi-task learning

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VFLMonitor: Defending One-Party Hijacking Attacks in Vertical Federated Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4828-4843页

作者： Xiangrui Xu Yiwen Zhao Yufei Han Yongsheng Zhu Zhen Han Guangquan Xu Bin Wang Shouling Ji Wei Wang School of Information Science and Technology Qingdao University of Science and Technology Qingdao China Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA INRIA Rennes Brittany France Institute of Computing Technologies China Academy of Railway Sciences Corporation Ltd. Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China Tianjin Key Laboratory of Advanced Networking (TANK) College of Intelligence and Computing Tianjin University Tianjin China Zhejiang Key Laboratory of Artificial Intelligence of Things (AIoT) Network and Data Security Hangzhou China College of Computer Science and Technology Zhejiang University Zhejiang China Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China

Vertical Federated Learning (VFL) is susceptible to various one-party hijacking attacks, such as Replay and Generation attacks, where a single malicious client can manipulate the model to produce attacker-specified results, thereby compromising its reliability in real-world deployments. In this paper, we first uncover the underlying mechanisms of these attacks and observe that successful attacks induce significant discrepancies in the embedding-label associations across different clients. We establish a theoretical framework demonstrating how these discrepancies can serve as reliable indicators for detecting hijacking attempts. Building upon this insight, we propose VFLMonitor, a robust defense mechanism that leverages these embedding-label discrepancies to detect and mitigate hijacking attacks. Specifically, VFLMonitor identifies suspicious queries by analyzing differences in label estimations from multiple clients and applies a majority voting rule to correct or filter out these malicious queries. Moreover, VFLMonitor introduces a novel regularization strategy during training to reduce intra-class variance in embeddings, thereby enhancing their discriminative power and improving defense effectiveness. Extensive experiments were conducted on 5 real-world datasets against 2 different attack types under 3 attack scenarios. The results demonstrate that VFLMonitor can effectively identify and exclude potential hijacked requests in all types of one-party hijacking attacks, while maintaining a meager false positive rate for legitimate queries.

关键词： Training Servers Electronic mail Artificial intelligence Reliability theory Predictive models Federated learning Data models Computational modeling Collaboration

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