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Specific macronutrient clusters associated with lower mortality risk: Evidence from NHANES 1999–2018: Specific Macronutrient Clusters Associated with Lower Mortality Risk

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Journal of Advanced Research 2025年

作者： Yu, Jiaying Chen, Yang Li, Defang Zhang, Lan Zhang, Yuting Zhang, Jiaqi Zhu, Jiayu Li, Zican Fu, Hongxin Guan, Dongwei Zhang, Runan Liu, Liyan Wang, Cheng Sun, Changhao Feng, Rennan Department of Nutrition and Food Hygiene School of Public Health Harbin Medical University Heilongjiang Harbin 150081 China Key Laboratory of Precision Nutrition and Health of Ministry of Education School of Public Health Harbin Medical University Heilongjiang Harbin 150081 China Department of Environmental Hygiene School of Public Health Harbin Medical University Heilongjiang Harbin 150081 China Key Laboratory of Science and Engineering for the Multi-modal Prevention and Control of Major Chronic Diseases Ministry of Industry and Information Technology China

Introduction: Accumulating evidence suggest that imbalanced macronutrient composition would increase the risk of chronic diseases. However, previous studies that predominantly focused on individual macronutrients often failed to thoroughly elucidate this complex association. Objectives: This study aimed to comprehensively analyze the relationship between macronutrient clusters and all-cause mortality. Methods: The study included 26,615 adults aged 20–75 years from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. A three-dimensional cube method was employed to categorize clusters of macronutrients intake. The association between dietary macronutrient clusters and all-cause mortality was investigated using Cox proportional hazards modeling and restricted cubic spline (RCS) analysis. Results: Over a weighted median follow-up duration of 7.58 years, 3,998 deaths were recorded. After adjusting for potential confounders, compared with the reference Cluster _Pm:Fm:Cmh, 4 specific Clusters were associated with reduced all-cause mortality: Cluster _Pm:Fm:Cm (HR: 0.79, 95 % CI: 0.67–0.92), Cluster _Pm:Fmh:Cml (HR: 0.76, 95 % CI: 0.61–0.95), Cluster _Pm:Fmh:Cm (HR: 0.86, 95 % CI: 0.75–0.97), and Cluster _Pl:Fm:Cmh (HR: 0.73, 95 % CI: 0.60–0.89). Three-node RCS analysis revealed non-linear relationships between carbohydrate within Cluster _Pm:Fm:Cm and protein within Cluster _Pl:Fm:Cmh and overall mortality. Subgroup and sensitivity analyses corroborated the robustness of these associations across different age, gender, and energy intake levels. Conclusions: This study employed a three-dimensional cube approach to categorize the human macronutrients intake into 24 clusters. Cluster _Pm:Fm:Cm, Clusters _Pm:Fmh:Cml, Cluster _Pm:Fmh:Cm, and Cluster _Pl:Fm:Cmh exhibited a lower mortality risk. Different clusters of macronutrients could be a precondit

关键词： All-cause mortality Dietary protein, fat and carbohydrate intake Macronutrient Three-dimensional cube method

Cadherin dynamics and cortical tension in remodeling cell-cell adhesion during EMT

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Biophysical Journal 2025年第12期124卷 2041-2055页

作者： Zhu, Hongyuan Liu, Xiaoxi Zhang, Jiayu Zhao, Guoqing Wang, Jin Zhang, Huan Liu, Yan Guo, Hui Yang, Jin Wang, Zheng Lu, Tian Jian Xu, Feng Lin, Min The Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and Technology Xi'an Jiaotong University Xi'an China Bioinspired Engineering and Biomechanics Center (BEBC) Xi'an Jiaotong University Xi'an China State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration National Clinical Research Center for Oral Diseases Out-patient Department School of Stomatology The Fourth Military Medical University Xi'an Shaanxi China Department of Medical Oncology The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an China Phase I Clinical Trial Research Center The First Affiliated Hospital of Xi'an Jiaotong University Shaanxi China Department of Cancer Center The First Affiliated Hospital of Xi'an Jiaotong University Shaanxi China Department of Hepatobiliary Surgery First Affiliated Hospital of Xi'an Jiaotong University Xi'an China State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing China

Epithelial-to-mesenchymal transition (EMT), a key process in cancer metastasis and fibrosis, disrupts cellular adhesion by replacing epithelial E-cadherin with mesenchymal N-cadherin. While, how the shift from E-cadherin to N-cadherin impacts molecular-scale adhesion mechanics and cluster dynamics—and how these changes weaken adhesion under varying mechanical and environmental conditions—remains poorly understood, limiting our ability to target EMT-driven pathological adhesion dynamics. Here, we developed a unified lattice-clutch model to investigate cadherin clustering, cortical tension, and adhesion strength during EMT. Using atomic force microscopy experiments, we measured the mechanical properties of single cadherin trans-bonds and cadherin-mediated cell-cell and cell-matrix adhesions across varying conditions. Our results demonstrate that N-cadherin trans-bonds are mechanically weaker than E-cadherin trans-bonds, leading to reduced adhesion strength during EMT. Computational modeling and experimental validation further revealed that EMT impairs cadherin clustering and cortical tension regulation, which collectively weaken both cell-cell and cell-matrix adhesions, particularly on stiff substrates. These findings highlight how EMT disrupts adhesion strength at multiple scales—from individual cadherin bonds to collective cluster dynamics. Our study elucidates how EMT-driven changes in cadherin type weaken adhesion strength and mechanotransduction, providing insights into cellular adhesion mechanics and potential therapeutic strategies for targeting EMT-associated diseases such as cancer metastasis and tissue remodeling. © 2025 Biophysical Society

关键词： Cadherin Clustering Cortical Tension Epithelial-to-mesenchymal transition Mechanical Model Mechanochemical Coupling

Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning

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arXiv 2023年

作者： Wu, Qiong Li, Jiahan Dai, Pingyang Ye, Qixiang Cao, Liujuan Wu, Yongjian Ji, Rongrong Institute of Artificial Intelligence The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Peng Cheng Laboratory Shenzhen518066 China The School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Media Analytics and Computing Lab Department of Computer Science School of Informatics Xiamen University Xiamen361005 China The Youtu Laboratory Tencent Shanghai200233 China The Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

An Application of Pixel Interval Down-sampling (PID) for Dense Tiny Microorganism Counting on Environmental Microorganism Images

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arXiv 2022年

作者： Zhang, Jiawei Zhao, Xin Jiang, Tao Rahaman, Md Mamunur Yao, Yudong Lin, Yu-Hao Zhang, Jinghua Pan, Ao Grzegorzek, Marcin Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China School of Resources and Civil Engineering Northeastern University Shenyang China Stevens Institute of Technology United States Department of Environmental Engineering National Chung Hsing University 145 Xingda Rd. Taichung402 Taiwan School of Control Engineering Chengdu University of Information Technology China Institute of Medical Informatics University of Luebeck Germany School of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder-decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. This addresses the limitation of contour conglutination of dense objects while counting. The evaluation was conducted using classical segmentation metrics (the Dice, Jaccard and Hausdorff distance) as well as counting metrics. The experimental results show that the proposed PID-Net had the best performance and potential for dense tiny object counting tasks, which achieved 96.97% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches, such as Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting. © 2022, CC0.

关键词： Pixels

Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders 1

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2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020

作者： Chen, Chao Zhu, Xuequan Belkacem, Abdelkader Nasreddine Lu, Lin Hao, Long You, Jia Shin, Duk Tan, Wenjun Huang, Zhaoyang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Department of Computer and Network Engineering College of Information Technology UAE University Al Ain15551 United Arab Emirates Zhonghuan Information College Tianjin University of Technology Tianjin300380 China Department of Electronics and Mechatronics Tokyo Polytechnic University Atsugi243-0297 Japan School of Computer Science and Engineering Northeastern University Shenyang110189 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing100088 China

ISBN: (数字)9789811612886

ISBN: (纸本)9789811612879

Nowadays, Sleep disorder is a common disease, and spindle spindles are important features of the second stage non-rapid eye movement (NREM) sleep. In this paper, we propose an improved automatic detection method of spindles based on wavelet transform. The spindles automatic detector is mainly composed of wavelet transform and clustering. We collected the electroencephalography (EEG) signals of six patients with sleep disorders all night for ten hours, and then preprocessed the data and other operations, and then used our improved method to detect the sleep EEG signals by spindles. By comparing with the previous automatic detection method not improved and another automatic detection method, the results show that the accuracy of sleep spindles detection can be effectively improved. The accuracy of the improved detector is 5.19% higher than before, and 9.7% higher than that of another method based on amplitude threshold. Finally, we made a simple comparison between people with sleep disorders and normal people. We found that there were significant differences in spindle density between people with sleep disorders and people without sleep disorders. The average spindle density in the normal population averaged 2.59 spindles per minute. People with sleep disorders had an average spindle density of 1.32 spindles per minute. In future research, our research direction is to improve the accuracy of spindles automatic detection by improving the spindles detector and study the difference of spindles between patients with sleep disorders and normal people in a large number of samples, so that the difference of spindles can be used as the basis for the diagnosis of sleep disorders. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Wavelet transforms

Real-time and ultrafast optical pulse quantization based on slicing the supercontinuum

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Chinese Optics Letters 2021年第8期19卷 69-74页

作者： Ya Guo Qiang Cai Zhiwei Jia Bingjie Xu Zhensen Gao Qianwu Zhang Ruonan Zhang Adonis Bogris K.Alan Shore Yuncai Wang Pu Li Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai UniversityShanghai 200444China School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710072China Science and Technology on Communication Security Laboratory Institute of Southwestern CommunicationChengdu 610041China Department of Informatics and Computer Engineering University of West AtticaAthens 12243Greece School of Electronic Engineering Bangor UniversityWales LL571UTUnited Kingdom

All-optical analog-to-digital conversion is a paramount issue in modern science. How to implement real-time and ultrafast quantization to optical pulses with different intensities in an all-optical domain is a central problem. Here, we report a realtime demonstration of an all-optical quantization scheme based on slicing the supercontinuum in a nonlinear fiber. In comparison with previous schemes through off-line analysis of the power of different optical spectral components in the supercontinuum, this, to the best of our knowledge, is the first demonstration of such functionality online in the time ***, the extinction ratio among the quantized outputs can exceed 10 d B, which further confirms the feasibility of the proposed quantization scheme. The current 3 bit resolution in the proof-of-principle experiment is limited by the current experimental condition, but it can be expected to be greatly enhanced through improving both the spectral width of the generated supercontinuum and the number of filtering channels used.

关键词： all-optical analog-to-digital conversion photonic quantization all-optical signal processing supercontinuum generation

EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization laboratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

Application of Graph-Curvature Features in Auxiliary Diagnosis for Histopathological Image Identification of Gastric Cancer

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SSRN

SSRN 2023年

作者： He, Ruilin Li, Chen Yang, Xinyi Yang, Jinzhu Jiang, Tao Grzegorzek, Marcin Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute for Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering

Background: Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors, but it has some drawbacks. This paper explores a computer-aided diagnostic method that can identify benign and malignant gastric cancer with histopathology images. Method: This article obtains the most suitable process through multiple experiments, compared multiple methods and features for classification. Firstly, the U-net is applied to segment the image. Next, the nucleus is extracted from the segmented image and the Minimum Spanning Tree (MST) diagram structure is drawn. The third step is to extract the graph-curvature features of histopathology image according to the MST image. Finally, by inputting graph-curvature features into the classifier, the recognition results for benign or malignant can be obtained. Result: During the experiment, we use various methods for comparison. In the image segmentation stage, U-net, watershed algorithm and Otsu threshold segmentation methods are used respectively. Combined with multiple indicators, we find that the U-net method is the most suitable for segmentation of histopathology images. In the feature extraction stage, in addition to extracting graph-edge feature and graph-curvature feature, several basic image features are also extracted, including Red, Green, Blue feature, Gray-Level Co-occurrence Matrix feature, Histogram of Oriented Gradient feature, and Local Binary Pattern feature. In the classifier design stage, we experimented with various methods, such as Support vector machine (SVM), Random forest, Artificial Neural Network, K Nearest Neighbors, VGG-16 and Inception-V3. Through the comparison and analysis, the classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into SVM classifier. Conclusion: This paper has created a unique feature, graph-curvature feature based on MST to represent and analyze histopathology images. This graph-based feature can be used

关键词： Local binary pattern