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International Journal of Approximate Reasoning 2022年 151卷 271-271页

作者： Qian Guo Yuhua Qian Xinyan Liang Institute of Big Data Science and Industry Shanxi University Taiyuan 030006 Shanxi China Engineering Research Center for Machine Vision and Data Mining of Shanxi Province Taiyuan 030006 Shanxi China School of Computer and Information Technology Shanxi University Taiyuan 030006 Shanxi China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan 030006 Shanxi China

A Double-Helix Metal-Chain Metal-Organic Framework as a High-Output Triboelectric Nanogenerator Material for Self-Powered Anticorrosion

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Angewandte Chemie 2022年第40期134卷

作者： Dr. Zhichao Shao Junshuai Chen Kexin Gao Qiong Xie Xiaojing Xue Prof. Shuangyan Zhou Prof. Chao Huang Prof. Liwei Mi Prof. Hongwei Hou Center for Advanced Materials Research Zhongyuan University of Technology Zhengzhou 450007 P. R. China College of Chemistry Zhengzhou University Zhengzhou Henan 450001 P. R. China Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing 400065 China

The development of novel metal organic framework (MOF) friction power generation materials with high stability is important. This paper reports the first example of a double-helix metal chain organic framework with a network structure (ZUT-8). ZUT-8 shows high chemical stability, functional adjustability, and excellent output performance of friction power generation, which is superior to traditional coordination polymer materials. The cathodic protection system with ZUT-8 can prevent metal corrosion significantly. The output performance can be improved effectively by enhancing the conjugate effect of the linker. The theoretical calculation results showed that an increase in the degree of conjugation could significantly reduce the band gap, thereby affecting the friction power output signal. This study opens the door to constructing MOF materials with a double-helix metal chain and will promote their potential applications in self-powered electrochemical cathodic protection.

关键词： Ligand Exchange Metal Chain Organic Framework Self-Powered TENG

Medical Image Segmentation Using Deep Learning: A Survey

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arXiv 2020年

作者： Wang, Risheng Lei, Tao Cui, Ruixia Zhang, Bingtao Meng, Hongying Nandi, Asoke K. The School of Electronic Information and Artificial Intelligence The Shaanxi Joint Laboratory of Artificial Intelligence Shaanxi University of Science and Technology Xi'An710021 China The 'Laboratory of Hepatobiliary Surgery First Affiliated Hospital' 'National Engineering Laboratory of Big Data Algorithm and Analysis Technology Research' Xi'an Jiaotong University Xi'An710049 China The School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China The Department of Electronic and Electrical Engineering Brunel University London UxbridgeUB8 3PH United Kingdom

Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. In this paper, we present a comprehensive thematic survey on medical image segmentation using deep learning techniques. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi-level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyze literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia

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Science Advances 2024年第24期10卷 eadk6063页

作者： Jiang, Yuchao Palaniyappan, Lena Luo, Cheng Chang, Xiao Zhang, Jie Tang, Yingying Zhang, Tianhong Li, Chunbo Zhou, Enpeng Yu, Xin Li, Wei An, Dongmei Zhou, Dong Huang, Chu-Chung Tsai, Shih-Jen Lin, Ching-Po Cheng, Jingliang Wang, Jijun Yao, Dezhong Cheng, Wei Feng, Jianfeng Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Ministry of Education Shanghai China Douglas Mental Health University Institute Department of Psychiatry McGill University MontréalQC Canada Robarts Research Institute University of Western Ontario LondonON Canada Lawson Health Research Institute LondonON Canada Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of life Science and Technology University of Electronic Science and Technology of China Chengdu China High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province Center for Information in Medicine University of Electronic Science and Technology of China Chengdu China Chinese Academy of Medical Sciences Chengdu China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai200030 China Beijing China Department of Neurology West China Hospital Sichuan University Chengdu610041 China School of Psychology and Cognitive Science East China Normal University Shanghai China Shanghai Changning Mental Health Center Shanghai China Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan Institute of Neuroscience National Yang Ming Chiao Tung University Taipei Taiwan Department of MRI First Affiliated Hospital of Zhengzhou University Zhengzhou China Department of Neurology Huashan Hospital Fudan University Shanghai China Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence Zhejiang Normal University Jinhua China MOE Frontiers Center for Brain Science Fudan University Shanghai China Zhangjiang Fudan International Innovation Center Shanghai China School of Data Science Fudan University Shanghai China Department of Computer Science University of Warwick CoventryCV 4 7AL United Kingdom

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca’s area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual’s response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia. © 2024 the Authors, some rights reserved

关键词： Mapping

Syntax-enhanced Pre-trained model

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arXiv 2020年

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. Copyright © 2020, The Authors. All rights reserved.

关键词： Syntactics

Accelerated evolution of an Lhx2 enhancer shapes mammalian social hierarchies

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细胞研究（英文版） 2020年第5期30卷 408-420页

作者： Yuting Wang Guangyi Dai Zhili Gu Guopeng Liu Ke Tang Yi-Hsuan Pan Yujie Chen Xin Lin Nan Wu Haoshan Chen Su Feng Shou Qiu Hongduo Sun Qian Li Chuan Xu Yanan Mao Yong Edward Zhang Philipp Khaitovich Yan-Ling Wang Qunxiu Liu Jing-Dong Jackie Han Zhen Shao Gang Wei Chun Xu Naihe Jing Haipeng Li CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of SciencesYueyang Road 320 200031 Shanghai China University of Chinese Academy of Sciences Beijing China Precise Genome Engineering Center School of Life SciencesGuangzhou University Guangzhou 510405 Guangdong China Key Laboratory of Brain Functional Genomics of Ministry of Education School of Life Science East China Normal University 200062 Shanghai China Bio-Med Big Data Center CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of Sciences Yueyang Road 320 200031 Shanghai China Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Yueyang Road 320 200031 Shanghai China State Key Laboratory of Cell Biology Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Yueyang Road 320 200031 Shanghai China Key Laboratory of Zoological Systematics and Evolution & State Key Laboratory of Integrated Management of Pest Insects and Rodents Institute of Zoology Chinese Academy of Sciences 100101 Beijing China Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences 650223 Kunming China State Key Laboratory of Stem cell and Reproductive Biology Institute of Zoology Chinese Academy of Sciences 100101 Beijing China Shanghai Zoological Park 200335 Shanghai China

Social hierarchies emerged during evolution,and social rank influences behavior and health of ***,the evolutionary mechanisms of social hierarchy are still unknown in *** we developed a new method and performed a genome-wide screening for identifying regions with accelerated evolution in the ancestral lineage of placental mammals,where mammalian social hierarchies might have initially *** functional analyses were conducted for the most accelerated region designated as placental-accelerated sequence 1 (PAS1,P=3.15 × 10-18).Multiple pieces of evidence show that PAS1 is an enhancer of the transcription factor gene Lhx2 involved in brain ***1s isolated from various amniotes showed different cis-regulatory activity in vitro,and affected the expression of Lhx2 differently in the nervous system of mouse ***1 knock-out mice lack social ***1 knock-in mouse models demonstrate that PAS1s determine the social dominance and subordinate of adult mice,and that social ranks could even be turned over by mutated *** homozygous mutant mice had normal huddled sleeping behavior,motor coordination and ***,PAS1-Lhx2 modulates social hierarchies and is essential for establishing social stratification in amniotes,and positive Darwinian selection on PAS1 plays pivotal roles in the occurrence of mammalian social hierarchies.

关键词： Bioinformatics Comparative genomics

Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives

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arXiv 2025年

作者： Liu, Xiaokun Rastegari, Sayedmohammadreza Huang, Yijun Cheong, Sxe Chang Liu, Weikang Zhao, Wenjie Tian, Qihao Wang, Hongming Zhou, Shuo Guo, Yingjie Tabakhi, Sina Liu, Xianyuan Zhu, Zheqing Sang, Wei Lu, Haiping Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Evolutionary Science Intelligence of Shanxi Province Shanxi Taiyuan China Faculty of Computer Engineering University of Isfahan Isfahan Iran The First Clinical Medical School Shanxi Medical University Taiyuan China School of Medicine & Population Health University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom Department of Biochemistry and Molecular Biology School of Basic Medical Sciences Shanxi Medical University Taiyuan China Institute of Medical Technology Shanxi Medical University Taiyuan China

In cancer therapeutics, protein-metal binding mechanisms critically govern drug pharmacokinetics and targeting efficacy, thereby fundamentally shaping the rational design of anticancer metallodrugs. While conventional laboratory methods used to study such mechanisms are often costly, low throughput, and limited in capturing dynamic biological processes, machine learning (ML) has emerged as a promising alternative. Despite increasing efforts to develop protein–metal binding datasets and ML algorithms, the application of ML in tumor protein-metal binding remains limited. Key challenges include a shortage of high-quality, tumor-specific datasets, insufficient consideration of multiple data modalities, and the complexity of interpreting results due to the "black box" nature of complex ML models. This paper summarizes recent progress and ongoing challenges in using ML to predict tumor protein-metal binding, focusing on data, modeling, and interpretability. We present multimodal protein-metal binding datasets and outline strategies for acquiring, curating, and preprocessing them for training ML models. Moreover, we explore the complementary value provided by different data modalities and examine methods for their integration. We also review approaches for improving model interpretability to support more trustworthy decisions in cancer research. Finally, we offer our perspective on research opportunities and propose strategies to address the scarcity of tumor protein data and the limited number of predictive models for tumor protein–metal binding. We also highlight two promising directions for effective metal-based drug design: integrating protein–protein interaction data to provide structural insights into metal-binding events and predicting structural changes in tumor proteins after metal binding. Copyright © 2025, The Authors. All rights reserved.

关键词：

Incomplete multi-view clustering via efficient anchor tensor recovery framework

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Neural networks : the official journal of the International Neural Network Society 2025年 190卷 107652页

作者： Jintian Ji Songhe Feng Jie Huang Taotao Wei Xiang Feng Peiwu Lv Bing Li School of Computer Science and Technology Beijing Jiaotong University Beijing 100044 China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing 100044 China. Electronic address: 22120385@***. Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing 100044 China. Electronic address: shfeng@***. Jiangsu Railway Group Co. Ltd. Nanjing 210012 China Jiangsu Province Engineering Research Center of Intelligent and Green Railway Nanjing 210012 China. Electronic address: huangj@***. Jiangsu Province Engineering Research Center of Intelligent and Green Railway Nanjing 210012 China. Electronic address: weitt@***. Jiangsu Province Engineering Research Center of Intelligent and Green Railway Nanjing 210012 China. Electronic address: fengx@***. Jiangsu Province Engineering Research Center of Intelligent and Green Railway Nanjing 210012 China. Electronic address: lvpw@***. The National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) Beijing 100190 China. Electronic address: bli@***.

Tensor-based Incomplete Multi-view Clustering (TIMC) methods have received widespread attention due to the powerful data recovery capability of capturing cross-view high-order correlation. Although such methods have achieved remarkable progress, they still suffer from the following problems: (1) The extremely high computational complexity makes it hard for tensor-based methods to handle large-scale multi-view data. (2) Geometric structure constraints in the sample space often lead to high computational complexity and redundancy of structural information. (3) The commonly used Tensor Nuclear Norm (TNN) over-penalizes the primary rank components, leading to a sub-optimal representation tensor. Being aware of these, we propose Incomplete Multi-View Clustering with Efficient Anchor Tensor Recovery Framework (EATER). Specifically, it learns a group of anchors to construct a low-rank anchor tensor to recover the missing data with the high-order correlation among views and the geometric structure in the learned representation tensor is enhanced by an Anchor Laplacian Regularization (ALR). Moreover, instead of employing TNN, we adopt a tighter Non-convex Tensor Rank (NTR) to capture the multi-view high-order correlation effectively. An efficient iterative optimization algorithm is designed to solve the EATER, which is time-economical and enjoys favorable convergence. Extensive experimental results on various datasets demonstrate the superiority of the proposed algorithm as compared to state-of-the-art methods.

关键词： Geometric structure constraint High-order correlation Incomplete multi-view clustering Non-convex tensor rank

Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

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Chemosphere 2024年 352卷 141329页

作者： Yaseen, Zaher Mundher Melini Wan Mohtar, Wan Hanna Homod, Raad Z. Alawi, Omer A. Abba, Sani I. Oudah, Atheer Y. Togun, Hussein Goliatt, Leonardo Ul Hassan Kazmi, Syed Shabi Tao, Hai Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi 43600 Malaysia Environmental Management Centre Institute of Climate Change Universiti Kebangsaan Malaysia Selangor UKM Bangi 43600 Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Department of Thermofluids School of Mechanical Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru 81310 Malaysia Department of Computer Sciences College of Education for Pure Science University of Thi-Qar Nasiriyah 64001 Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Department of Mechanical Engineering College of Engineering University of Baghdad Baghdad Iraq Computational and Applied Mechanics Department Federal University of Juiz de Fora 36036-900 Brazil Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou 515063 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Faculty of Data Science and Information Technology INTI International University 71800 Malaysia

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. © 2024 Elsevier Ltd

关键词： Artificial intelligence Heavy metals Sensitivity analysis Soil contamination

Micro-supervised disturbance learning: A perspective of representation probability distribution

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arXiv 2020年

作者： Chu, Jielei Liu, Jing Wang, Hongjun Meng, Hua Gong, Zhiguo Li, Tianrui The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China The School of Business Sichuan University Sichuan Chengdu610065 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of labels prompt us to explore more expressive representation learning methods which depends on the labels as few as possible. To address these issues, the small-perturbation ideology is firstly introduced on the representation learning model based on the representation probability distribution. The positive small-perturbation information (SPI) which only depend on two labels of each cluster is used to stimulate the representation probability distribution and then two variant models are proposed to fine-tune the expected representation distribution of RBM, namely, Micro-supervised Disturbance GRBM (Micro-DGRBM) and Micro-supervised Disturbance RBM (Micro-DRBM) models. The Kullback-Leibler (KL) divergence of SPI is minimized in the same cluster to promote the representation probability distributions to become more similar in Contrastive Divergence (CD) learning. In contrast, the KL divergence of SPI is maximized in the different clusters to enforce the representation probability distributions to become more dissimilar in CD learning. To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has not any external stimulation. Experimental results demonstrate that the proposed deep Micro-DL architecture shows better performance in comparison to the baseline method, the most related shallow models and deep frameworks for clustering. © 2020, CC0.

关键词： Probability distributions