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Corrigendum to "Brain network communication in remission: a comparative study of bipolar and unipolar depression" [J. Psychiatr. Res. 186 (2025) 1-8]

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Journal of psychiatric research 2025年

作者： Haiyan Liu Yi Xia Lingling Hua Hao Sun Rui Yan Zhijian Yao Jiaolong Qin Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University Nanjing China. Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University Nanjing China. Electronic address: zjyao@***. The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China.

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A multi-branch hybrid transformer network for corneal endothelial cell segmentation

arXiv

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arXiv 2021年

作者： Zhang, Yinglin Higashita, Risa Fu, Huazhu Xu, Yanwu Zhang, Yang Liu, Haofeng Zhang, Jian Liu, Jiang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Cixi Institute of Biomedical Engineering Chinese Academy of Sciences China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Tomey Corporation Nagoya451-0051 Japan Global Big Data Technologies Centre University of Technology SydneyNSW Australia Inception Institute of Artificial Intelligence United Arab Emirates Intelligent Healthcare Unit Baidu Beijing100085 China

Corneal endothelial cell segmentation plays a vital role in quantifying clinical indicators such as cell density, coefficient of variation, and hexagonality. However, the corneal endothelium’s uneven reflection and the subject’s tremor and movement cause blurred cell edges in the image, which is difficult to segment, and need more details and context information to release this problem. Due to the limited receptive field of local convolution and continuous downsampling, the existing deep learning segmentation methods cannot make full use of global context and miss many details. This paper proposes a Multi-Branch hybrid Transformer Network (MBT-Net) based on the transformer and body-edge branch. Firstly, We use the convolutional block to focus on local texture feature extraction and establish long-range dependencies over space, channel, and layer by the transformer and residual connection. Besides, We use the body-edge branch to promote local consistency and to provide edge position information. On the self-collected dataset TM-EM3000 and public Alisarine dataset, compared with other State-Of-The-Art (SOTA) methods, the proposed method achieves an improvement. Copyright © 2021, The Authors. All rights reserved.

关键词： Endothelial cells

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Visualtracking based on moving monocular camera

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International Journal of Performability engineering 2020年第8期16卷 1215-1224页

作者： Kan, Xiu He, Jia Xi, Zhenghao School of Mathematics Southeast University Nanjing210096 China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China State Key Laboratory of Intelligent Technology and Systems Department of Computer Science Tsinghua University Shanghai201620 China

This paper investigates the problem of moving target tracking in the case of a monocular mobile camera. Firstly, a scene space model was established under a moving camera based on the constraint conditions of ground plane. Thus, a three-dimensional (3D) motion model of target could be established. The scene space model and the target 3D motion model could both be compensated by the posture of the camera, which can be calculated according to the vanishing point in the image or the motion parameters of the camera acquired by sensors. Secondly, an interception function was established to extract the target motion range according to the target 3D motion model. Both the influence of background factors and the computational complexity of the algorithm were reduced. Then, a dual-kernels tracking method was used to track the target under a situation of non-obvious color characteristics and partial shielding. Finally, a public data set was used to conduct comparison experiments with three other algorithms to verify the effectiveness and robustness of the proposed algorithm. © 2020 Totem Publisher, Inc.

关键词： Motion compensation

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Thermal Simulation for a Mechanical Spindle With External Cooler Across Grease Coated Interface

Research Square

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Research Square 2021年

作者： Zhao, Liang Lei, Mohan Ren, Hongdi Wang, Jinshi Wang, Shuai Li, Ben Q. Yang, Jun Mei, Xuesong Xi'an Jiaotong University China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'An710049 China Shaanxi Key Laboratory of Intelligent Robots Xi'an Jiaotong University Xi'An710049 China School of Mechanical and Precision Instrument Engineering Xi'an University of Technology 710048 China State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong University Xi’an710049 China Department of Mechanical Engineering College of Engineering and Computer Science University of Michigan MI48128 United States

Spindles in precision boring machine often work in low speed and heavy load without internal cooling, and the thermal error is nonnegligible. So an external cooling system was designed, and the effectiveness of the designed scheme needs to be preliminarily verified by simulation before building the cooling system. Thermal simulations of the spindle with an external cooler require calculating the thermal resistance of the thermal grease-coated interface between the cooler and spindle. Models describing the contact thermal resistance and total thermal resistance for metal contact filled with silicone grease based on solid-liquid interface force equivalence were described in this paper, and experiments were also conducted to verify the accuracy of these models. The contact thermal resistances between the cast iron/copper and silicone grease on flat or arc surfaces were calculated, and the bulk thermal resistance of the silicone grease layer was calculated. The total heat transferred between the cooler and the silicone grease-coated interface of the spindle were calculated. Heat transfer and heat generation in the spindle were calculated, and a finite element model was established to verify the effectiveness of the designed external cooling scheme. Finally, results from experiments for the spindle in different conditions show that the external cooling system decreases the time to reach thermal equilibrium by more than 60%. The RMSE of the simulated thermal elongation is less than 5.7044 μm when the rotating speed of 3000 rpm, and is less than 3.9714 μm when the rotating speed is 1500 rpm. © 2021, CC BY.

关键词： Cooling

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

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

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

关键词： Biotic

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DeepAmp: A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation

Research Square

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Research Square 2021年

作者： Azim, Sayed Mehedi Sharma, Alok Shatabda, Swakkhar Dehzangi, Abdollah Department of Computer Science and Engineering United International University Plot-2 United City Madani Avenue Badda Dhaka1212 Bangladesh Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama230-0045 Japan Department of Computer Science Rutgers University CamdenNJ08102 United States Center for Computational and Integrative Biology Rutgers University CamdenNJ08102 United States

AMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bond. AMPylators catalyze this process as covalent attachment of adenosine monophosphate to the amino acid side chain of a peptide. Recent studies have shown that this post-translational modification is directly responsible for regulation of neurodevelopment and neurodegeneration and also involved in many physiological processes. Despite the importance of this post-translational modification, there is no peptide sequence dataset available for conducting computational analysis. Therefore, so far, no computational approach has been proposed for predicting AMPylation. In this study, we introduce a new dataset of this distinct post-translational modification and develop a new machine learning tool using a deep convolutional neural network called DeepAmp to predict AMPylation sites in proteins. DeepAmp achieves 77.7%, 79.1%, 76.8%, and 0.55 in terms of Accuracy, Sensitivity, Specificity, and Matthews Correlation Coefficient (MCC) for AMPylation site prediction task, respectively. As the first machine learning model, DeepAmp demonstrate promising results which highlight its potential to solve this problem. Our presented dataset and DeepAmp as a standalone predictor are publicly available at https://***/MehediAzim/DeepAmp. © 2021, CC BY.

关键词： Peptides

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Iterated Problem Reformulation for Evolutionary Large-Scale Multiobjective Optimization

Iterated Problem Reformulation for Evolutionary Large-Scale ...

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Congress on Evolutionary Computation

作者： Cheng He Ran Cheng Ye Tian Xingyi Zhang Department of Computer Science and Engineering University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Southern University of Science and Technology Shenzhen China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Due to the curse of dimensionality, two main issues remain challenging for applying evolutionary algorithms (EAs) to large-scale multiobjective optimization. The first issue is how to improve the efficiency of EAs for reducing computation cost. The second one is how to improve the diversity maintenance of EAs to avoid local optima. Nevertheless, these two issues are somehow conflicting with each other, and thus it is crucial to strike a balance between them in practice. Thereby, we propose an iterated problem reformulation based EA for large-scale multiobjective optimization, where the problem reformulation based method and the decomposition based method are used iteratively to address the aforementioned issues. The proposed method is compared with several state-of-the-art EAs on a variety of large-scale multiobjective optimization problems. Experimental results demonstrate the effectiveness of our proposed iterated method in large-scale multiobjective optimization.

关键词： Sociology Convergence Pareto optimization Indexes computer science

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and systems 2024年 1049 LNNS卷 v-vi页

作者： Abraham, Ajith Siarry, Patrick Hanne, Thomas Hong, Tzung-Pei Pllana, Sabri Bajaj, Anu School of Artificial Intelligence Bennett University Uttar Pradesh Greater Noida India University of Applied Sciences and Arts Northwestern Olten Switzerland Computer Science and Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India Laboratory of Images Signals and Intelligent Systems University Paris-Est Créteil Vitry-sur-Seine France

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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On PID control for synchronization of complex dynamical network with delayed nodes

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science China(Technological sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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