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arXiv 2024年

作者： Zhang, Ruoyu Wang, Lihui Tang, Kun Xu, Jingwen Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Cortical surface registration plays a crucial role in coordinating individual cortical functions and anatomical features, serving as a fundamental step in cortical surface analysis. Its aim is to align the anatomical or functional regions of different individuals, which is of great importance for neuroimaging studies across different populations. Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical registration methods is that for each pair of images to be registered, it is necessary to search for the optimal transformation in the deformation space according to a specific optimization algorithm until the similarity measure function converges, which cannot meet the requirements of real-time and high-precision in medical image registration. With the spectacular success of deep learning in the field of computer vision, researching cortical surface image registration techniques based on deep learning models has become a new direction. But so far, there are still only a few studies on cortical surface image registration based on deep learning. Moreover, although deep learning methods theoretically have stronger representation capabilities, surpassing the most advanced classical methods in registration accuracy and distortion control remains a challenge. Therefore, to address this challenge, this paper constructs a deep learning model to study the technology of cortical surface image registration. The specific work is as follows: (1) An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed, and a convolution method based on spherical harmonic transformation is introduced to register cortical surface data. This solves the problem of scale-inflexibility of spherical feature transformation and optimizes the multi-scale registration process. The results show that the proposed network outperforms the other deep learning-based registration m

关键词： Spheres

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation

arXiv

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arXiv 2024年

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical & Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we verify for the first time that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. Copyright © 2024, The Authors. All rights reserved.

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3D ANATOMICAL STRUCTURE-GUIDED DEEP LEARNING FOR ACCURATE DIFFUSION MICROSTRUCTURE IMAGING

arXiv

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arXiv 2025年

作者： Ma, Xinrui Cheng, Jian Fan, Wenxin Wu, Ruoyou Ye, Yongquan Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Pengcheng Laboratory Guangdong Shenzhen China United Imaging Healthcare HoustonTX United States

Diffusion magnetic resonance imaging (dMRI) is a crucial noninvasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation;however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients. © 2025, CC BY-NC-ND.

关键词： Tissue

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Microbiota enterotoxigenic Bacteroides fragilis-secreted BFT-1 promotes breast cancer cell stemness and chemoresistance through its functional receptor NOD1

引用

Protein & Cell 2024年第6期15卷 419-440页

作者： Wei Ma Lu Zhang Weilong Chen Zhaoxia Chang Juchuanli Tu Yuanyuan Qin Yuwen Yao Mengxue Dong Jiajun Ding Siqin Li Fengkai Li Qiaodan Deng Yifei Yang Tingting Feng Fanrong Zhang Xiying Shao Xueyan He Lixing Zhang Guohong Hu Quentin Liu Yi-Zhou Jiang Shu Zhu Zhi Xiao Dan Su Tong Liu Suling Liu Fudan University Shanghai Cancer Center & Institutes of Biomedical Sciences State Key Laboratory of Genetic EngineeringCancer InstitutesDepartment of OncologyKey Laboratory of Breast Cancer in ShanghaiThe Shanghai Key Laboratory of Medical EpigeneticsShanghai Key Laboratory of Radiation OncologyThe International Co-laboratory of Medical Epigenetics and MetabolismMinistry of Science and TechnologyShanghai Medical CollegeFudan UniversityShanghai 200032China Intelligent Pathology Institute and Department of Pathology The First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230071China Department of Thyroid Breast and Vascular SurgeryXijing HospitalFourth Military Medical UniversityXi’an 710032China Institute of Immunology CAS Key Laboratory of Innate Immunity and Chronic DiseaseSchool of Basic Medical SciencesDivision of Life Science and MedicineUniversity of Science and Technology of ChinaHefei 230027China Department of Pathology The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhou 310022China Department of Breast Surgery The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhou 310022China Department of Breast Medical Oncology The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhou 310022China Shanghai Institute of Nutrition and Health University of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South ChinaGuangzhou 510060China Department of Breast Surgery Xiangya HospitalChangsha 410008China Department of Breast Surgery Tumor Hospital of Harbin Medic

Tumor-resident microbiota in breast cancer promotes cancer initiation and malignant ***,targeting microbiota to improve the effects of breast cancer therapy has not been investigated in ***,we evaluated the microbiota composition of breast tumors and found that enterotoxigenic Bacteroides fragilis(ETBF)was highly enriched in the tumors of patients who did not respond to taxane-based neoadjuvant ***,albeit at low biomass,secreted the toxic protein BFT-1 to promote breast cancer cell stemness and *** studies showed that BFT-1 directly bound to NOD1 and stabilized NOD1 ***1 was highly expressed on ALDH+breast cancer stem cells(BCSCs)and cooperated with GAK to phosphorylate NUMB and promote its lysosomal degradation,thereby activating the NOTCH1-HEY1 signaling pathway to increase ***1 inhibition and ETBF clearance increase the chemosensitivity of breast cancer by impairing BCSCs.

关键词： NOD1 breast chemotherapy

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UNIQUE SOLVABILITY AND ERROR ANALYSIS OF THE LAGRANGE MULTIPLIER APPROACH FOR GRADIENT FLOWS

arXiv

引用

arXiv 2024年

作者： Cheng, Qing Shen, Jie Wang, Cheng Department of Mathematics Tongji University Shanghai200092 China Key Laboratory of Intelligent Computing and Applications Tongji University Ministry of Education China Eastern Institute of Technology Zhejiang Ningbo315200 China Department of Mathematics University of Massachusetts North DartmouthMA02747 United States

The unique solvability and error analysis of the original Lagrange multiplier approach proposed in [8] for gradient flows is studied in this paper. We identify a necessary and sufficient condition that must be satisfied for the nonlinear algebraic equation arising from the original Lagrange multiplier approach to admit a unique solution in the neighborhood of its exact solution, and propose a modified Lagrange multiplier approach so that the computation can continue even if the aforementioned condition is not satisfied. Using Cahn-Hilliard equation as an example, we prove rigorously the unique solvability and establish optimal error estimates of a second-order Lagrange multiplier scheme assuming this condition and that the time step is sufficient small. We also present numerical results to demonstrate that the modified Lagrange multiplier approach is much more robust and can use much larger time step than the original Lagrange multiplier *** Codes 65M70, 65K15, 65N22 © 2024, CC BY-SA.

关键词： Error analysis

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Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning

arXiv

引用

arXiv 2024年

作者： Tang, Hao Lu, Junhao Huang, Guoheng Li, Ming Chen, Xuhang Zhong, Guo Tan, Zhengguang Li, Zinuo School of Computer Science and Technology Guangdong University of Technology Guangdong China School of Computer Science and Engineering Huizhou University Huizhou China Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China School of Physics Maths and Computing University of Western Australia WA Australia

In Few-Shot Learning (FSL), traditional metric-based approaches often rely on global metrics to compute similarity. However, in natural scenes, the spatial arrangement of key instances is often inconsistent across images. This spatial misalignment can result in mismatched semantic pixels, leading to inaccurate similarity measurements. To address this issue, we propose a novel method called the Layer-Wise Features Metric of Semantic-Pixel Matching (LWFM-SPM) to make finer comparisons. Our method enhances model performance through two key modules: (1) the Layer-Wise Embedding (LWE) Module, which refines the cross-correlation of image pairs to generate well-focused feature maps for each layer;(2)the Semantic-Pixel Matching (SPM) Module, which aligns critical pixels based on semantic embeddings using an assignment algorithm. We conducted extensive experiments to evaluate our method on four widely used few-shot classification benchmarks: miniImageNet, tieredImageNet, CUB-200-2011, and CIFAR-FS. The results indicate that LWFM-SPM achieves competitive performance across these benchmarks. Our code will be publicly available on https://***/Halo2Tang/Code-for-LWFM-SPM. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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A Proposed Radio Environment Monitoring System of Ali Observatory 35

A Proposed Radio Environment Monitoring System of Ali Observ...

引用

35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023

作者： Zhang, Haiyan Wang, Yu Huang, Shijie Hu, Hao Zhao, Chuanjiang Wu, Guifang National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing100101 China Hebei Key Laboratory of Radio Astronomy Technology Hebei Shijiazhuang050081 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Zhongtai Street Yuhang District Zhejiang Hangzhou311100 China Beijing Boer Communication Technology Co. Ltd Zhongguancun Software Park No. 8 Dongbei Wangxi Road Haidian District Beijing100089 China China Electric Power Research Institute Co. Ltd 15 Qinghe Xiaoying East Road Haidian District Beijing100192 China

ISBN: (纸本)9789463968096

With its extremely dry air and high altitude above 5000 meters, the Ail Observatory located in the Tibet plateau has been a promising site for several astronomical instruments working in the optical or millimeter wave bands. In addition to protect the dark sky around the Ali Observatory, the measures to protect local radio environment have also been discussed. Based on the requirements of radio astronomical instruments, a radio environment monitoring system has been proposed. This paper describes the preliminary design of the system, including the technical specification and the system composition. In the near future, this system will be put into use. © 2023 International Union of Radio Science.

关键词： Observatories

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Hydrogel Immobilized Papain-Cuso4@Sio2 Biomimetic Nanozyme for Real-Time Colorimetric Detection

SSRN

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SSRN 2025年

作者： Hu, Qiannan Zhang, Yaoqi Yang, Xuxu Dong, Yingying Xiang, Yizhou Wong, Tuck-Whye Luo, Zisheng Ban, Zhaojun Xuan, Jinghong Li, Li College of Biosystems Engineering and Food Science Fuli Institute of Food Science Key Laboratory of Agro-Products Postharvest Handling Ministry of Agriculture and Rural Affairs Zhejiang University Hangzhou310023 China National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang Key Laboratory for Agro-Food Processing Zhejiang Engineering Laboratory of Food Technology and Equipment Zhejiang University Hangzhou310023 China Center for X-Mechanics Department of Engineering Mechanics Zhejiang University Hangzhou310027 China Sustainable and Smart Materials Laboratory Department of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia Johor Johor Bahru81310 Malaysia School of Biological and Chemical Engineering Zhejiang University of Science and Technology Hangzhou310023 China Agricultural and Rural Development Service Center of Liaoning Province Shenyang110032 China

Nanozyme-based biosensors hold great promise for hazardous substance detection but face challenges in scalability, stability, and real-time application. This study presents a biomimetic papain-CuSO4 nanozyme synthesized via a simple, one-step hydrothermal process. Inspired by natural copper-containing enzymes, the nanozyme exhibits excellent peroxidase-like activity, driven by a catalytic mechanism involving electron transfer from cysteine residues to Cu2+ ions, forming Cu+ and Cu-S catalytic centers. These centers mediate Fenton-like reactions, generating hydroxyl radicals (⋅OH) that oxidize 3,3',5,5'-tetramethylbenzidine (TMB), producing a colorimetric signal. To further enhance stability and catalytic performance, SiO2 was incorporated to obtain papain-CuSO4@SiO2 nanozyme, which prevents aggregation, promotes Cu+ regeneration, and exposes more active sites. To demonstrate its practical utility, the nanozyme was immobilized in a hydrogel matrix, leveraging the confinement effect to amplify activity and stability. Integrated into a multi-enzyme cascade, the system demonstrated rapid, sensitive solanine detection, utilizing acetylcholinesterase (ACHE) inhibition as the sensing mechanism. A smartphone-based platform enabled detection within 40 minutes, with a limit of detection (LOD) of 0.0335 mg L−1. The system exhibited high specificity and stability in complex matrices, validated through tests on spiked potato and lake water samples. This study introduces a portable, cost-effective nanozyme-hydrogel system for real-time toxic compound detection. Its practical applicability and mechanistic insights provide a foundation for advancing nanozyme-based biosensors in food safety and environmental monitoring. © 2025, The Authors. All rights reserved.

关键词： Papain

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Privacy-Preserving Federated Learning Framework with Resisting Source Inference Attacks in Smart Healthcare

SSRN

引用

SSRN 2024年

作者： Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without sharing their local sensitive dataset with others. Despite its benefits, FL is susceptible to source inference attacks and model poisoning. To avoid these issues, we propose a federated learning framework for smart healthcare that preserves privacy and employs certificateless ring signatures to counteract potential attacks. During the collaborative training process of the FL model, certificateless ring signatures are used to obscure the origin of parameter updates, ensuring the anonymity of the parameter sources. This approach has the advantage of being computationally efficient due to no complicated pairing operations. Furthermore, compared with the previous certificateless ring signature schemes, our proposed scheme is more effective in reducing the success rate of source inference attacks, and is efficient in terms of computation and communication overhead. © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Intracardiac Electrogram Signals key Feature Points Recognition Using Dense Convolutional Network

Intracardiac Electrogram Signals Key Feature Points Recognit...

引用

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. The feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. Therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. The dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. The final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. The final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. The research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data Medical services Myocardium Feature extraction Pattern recognition Testing

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