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Corrigendum to “Rational Protocols and Attacks in Blockchain System”

Security and Communication Networks 2021年第1期2021卷

作者： Tao Li Yuling Chen Yanli Wang Yilei Wang Minghao Zhao Haojia Zhu Youliang Tian Xiaomei Yu Yixian Yang State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou 550025 *** College of Computer Science and Technology Guizhou University Guiyang Guizhou 550025 *** School of Information Science and Engineering Qufu Normal University Rizhao 276825 *** School of Software Tsinghua University Beijing 100084 *** School of Information Science and Engineering Shandong Normal University Jinan 250014 ***

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Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge Computing Environment

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ACM Transactions on Sensor Networks 1000年

作者： Yongzhe Jia Bowen Liu Xuyun Zhang Fei Dai Arif Khan Lianyong Qi Wanchun Dou State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland College of Computer Science and Technology China University of Petroleum East China - Qingdao Campus Qingdao China

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML frameworks require resource-constrained devices to update the entire Deep Neural Network (DNN) models and are vulnerable to reconstruction attacks. Concurrently, the serial DCML frameworks suffer from training efficiency problems due to their serial training nature. In this paper, we propose a Model Pruning-enabled Federated Split Learning framework (MP-FSL) to reduce resource consumption with a secure and efficient training scheme. Specifically, MP-FSL compresses DNN models by adaptive channel pruning and splits each compressed model into two parts that are assigned to the client and the server. Meanwhile, MP-FSL adopts a novel aggregation algorithm to aggregate the pruned heterogeneous models. We implement MP-FSL with a real FL platform to evaluate its performance. The experimental results show that MP-FSL outperforms the state-of-the-art frameworks in model accuracy by up to 1.35%, while concurrently reducing storage and computational resource consumption by up to 32.2% and 26.73%, respectively. These results demonstrate that MP-FSL is a comprehensive solution to the challenges faced by DCML, with superior performance in both reduced resource consumption and enhanced model performance.

关键词： Federated learning split learning model pruning edge computing

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The mechanism of bone healing after traumatic brain injury

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Yuan Xiong Wenbin Zhong Bobin Mi School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore Singapore Department of Orthopedics Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China Contribution: Data curation Resources Software Supervision Writing - original draft Contribution: Data curation Software Supervision Writing - original draft Contribution: Conceptualization Investigation Supervision Validation Visualization Writing - review & editing

A growing body of evidence suggests that patients who experience traumatic brain injuries (TBIs) exhibit significantly shorter healing periods compared to those with isolated fractures. However, the precise underlying mechanism behind this phenomenon remains unclear. Recent studies have shed light on the potential role of hormonal signals and neural circuits originating in the hypothalamus, which play vital roles in regulating the skeletal system. Despite these advances, there is a lack of comprehensive research summarizing the crucial role of bone healing in TBIs and the underlying mechanisms. This review aimed to explore the underlying mechanisms responsible for the accelerated bone healing observed in TBI patients, with a specific focus on the intricate crosstalk between TBI and bone remodeling. Additionally, we comprehensively discuss and summarize the beneficial effects of TBI on the skeletal system and examine the TBI-induced signaling pathways that result in accelerated fracture healing and bone remodeling. By dissecting these pathways, we aim to identify potential targets for intervention and bone repair promotion.

关键词： bone crosstalk mechanisms TBI

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A mobile cloud-based ehealth scheme

arXiv

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arXiv 2020年

作者： Liu, Yihe Abbasi, Aaqif Afzaal Aghaei, Atefeh Abbasi, Almas Mosavi, Amir Shamshirband, Shahab Al-qaness, Mohammed A.A. Data Recovery Key Laboratory of Sichuan Province College of Computer Science and AI Neijiang Normal University Neijiang641100 China Department of Software Engineering Foundation University Islamabad44000 Pakistan Department of Communication Faculty of Social Sciences University of Tehran Tehran*** Iran Department of Computer Science International Islamic University Islamabad46000 Pakistan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Kalman Kando Faculty of Electrical Engineering Obuda University Budapest1034 Hungary School of the Built Environment Oxford Brookes University OxfordOX3 0BP United Kingdom Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam School of Computer Science Wuhan University Wuhan430072 China

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial *** Codes 68T05 Copyright © 2020, The Authors. All rights reserved.

关键词： Signal analysis

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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A Text-Inception-Based Natural Language Processing Model for Sentiment Analysis of Drug Experiences

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Muhammad Swaileh A. Alzaidi Alya Alshammari Manar Almanea Haneen A. Al-khawaja Hanan Al Sultan Shoayee Alotaibi Wafa Almukadi Department of English Language College of Language Sciences King Saud University Riyadh Saudi Arabia Department of Applied Linguistics College of Languages Princess Nora bint Abdul Rahman University Riyadh Saudi Arabia Department of English College of Languages and Translation Imam Muhammad Ibn Saud Islamic University Riyadh Saudi Arabia Applied Science Private University Amman Jordan Department of English College of Arts King Faisal University Al-Ahsa Saudi Arabia Department of Artificial Intelligence and Data Science? College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Software Engineering College of Engineering and computer Science Jeddah University Jeddah Saudi Arabia

The study of sentiment in Natural Language Processing (NLP) is among the most successful research areas because of the availability of millions of user opinions online since the turn of the century. The economic, political, and medical fields are just some of the many that have benefited from studies of sentiment research. While numerous studies have examined more mainstream topics like consumer electronics, movies, and restaurants, relatively few have examined health and medical concerns. Considerable insight into where to direct efforts to improve public health might be gained by a study of how people feel about healthcare as a whole and of individual drug experiences in particular. When it comes to medicine, automatic analysis of online user evaluations paves the way for sifting through massive amounts of user feedback to find information regarding medications' efficacy and side effects that might be used to enhance pharmacovigilance programs. Simple rules-based methods have given way to more complex machine learning approaches like deep learning, which is developing as a technology for many natural language processing jobs. The opensource datasets have been analyzed with models that use word embeddings and term frequency-inverse document frequency (TF-IDF). A feature-enhanced text-inception model for sentiment classification was presented to work in tandem with this approach. The model first employed a cutting-edge text-inception module to glean useful shallow features from the text. K-MaxPooling was subsequently employed to reduce the dimensionality of its shallow and deep includes as well as enhance the generalization of characteristics, and a deep feature extraction module was formed using the bidirectional gated recurrent unit (Bi-GRU) and the capsule neural network to comprehend the text's semantic data. By combining traditional methods with cutting-edge artificial intelligence techniques, this hybrid approach can revolutionize public health initiatives, de

关键词： Natural Language Processing (NLP) User Opinions Healthcare Medical Sentiment Public Health Deep learning

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Cover Image, Volume 84, Issue 7

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Proteins: Structure, Function, and Bioinformatics 2016年第7期84卷

作者： Wei Wang Juan Liu Lin Sun Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Xinxiang 453007 China Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining Henan Province China Institute of Computer Software School of Computer Wuhan University Wuhan 430072 China

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Novel Truncated-rank Graph-structured and Tree-guided Sparse Linear Mixed Models for Variable Selection on Genome-wide Association Studies

Novel Truncated-rank Graph-structured and Tree-guided Sparse...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Liu Xueling Liu Jing Diao Mengyao Zheng Jihe Li Yongyi Xie Kang Lai Xiao Geng Yijun Song Linshan Jiang School of Computing National University of Singapore Group Technology Office National University Health System Department of Preventive Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology & National Clinical Research Center for Oral Diseases Harvard T.H. Chan School of Public Health Harvard University School of Software and Microelectronics Peking University Shanghai Skin Disease Hospital Institute of Dermatology School of Medicine Tongji University School of Mechanical & Aerospace Engineering Nanyang Technological University Orthopaedic Department Peking University Third Hospital Engineering Research Center of Bone and Joint Precision Medicine Ministry of Education Dongfang College Zhejiang University of Finance & Economics Institute of Data Science National University of Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nal̈ively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential.

关键词： Adaptation models Input variables Biological system modeling High performance computing Noise Genomics Medical services data models Robustness Bioinformatics

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Calling Cards: A Customizable Platform to Longitudinally Record Protein-DNA Interactions Over Time in Cells and Tissues (vol 3, e883, 2023)

CURRENT PROTOCOLS

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CURRENT PROTOCOLS 2023年第10期3卷 e883页

作者： Yen, Allen Mateusiak, Chase Sarafinovska, Simona Gachechiladze, Mariam A. Guo, Juanru Chen, Xuhua Moudgil, Arnav Cammack, Alexander J. Hoisington-Lopez, Jessica Crosby, MariaLynn Brent, Michael R. Mitra, Robi D. Dougherty, Joseph D. Department of Genetics Washington University School of Medicine Saint Louis Missouri Department of Psychiatry Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Software Supervision Validation Visualization Writing - original draft Writing - review & editing Edison Family Center for Genome Sciences and Systems Biology Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Data curation Formal analysis Methodology Software Visualization Writing - original draft Contribution: Formal analysis Funding acquisition Investigation Visualization Writing - original draft Writing - review & editing Contribution: Formal analysis Funding acquisition Investigation Visualization Writing - review & editing Contribution: Data curation Methodology Software Writing - review & editing Contribution: Methodology Writing - review & editing Current address: Department of Ophthalmology Stanford University School of Medicine Stanford California Contribution: Conceptualization Methodology Software Validation Department of Neurology Washington University School of Medicine Saint Louis Missouri Current address: Department of Neurodegenerative Diseases University College London London United Kingdom Contribution: Conceptualization Methodology Validation Contribution: Investigation Writing - review & editing Department of Computer Science and Engineering Washington University Saint Louis Missouri Contribution: Funding acquisition Contribution: Conceptualization Funding acquisition Project administration Resources Software Supervision Writing - review & editing Intellectual and Developmental Disabilities Research Center Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Funding acquisition Project administration Resources Supervision Writing - origin

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next-generation sequencing. Compared with other genomic assays, readouts of which provide a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon “Calling Cards” into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile-specific transcription factor (TF) binding with custom TF-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Cards reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. © 2023 Wiley Periodicals LLC. This article was corrected on 16 October 2023. See the end of the full text for details. Basic Protocol 1 : Preparation and delivery of Calling Cards reagents Support Protocol 1 : Next-generation sequencing quantification of barcode

关键词： calling cards epigenetics enhancer usage gene regulation transcription factor binding

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The PLATO mission

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Experimental Astronomy 2025年第3期59卷 1-111页

作者： Rauer, Heike Aerts, Conny Cabrera, Juan Deleuil, Magali Erikson, Anders Gizon, Laurent Goupil, Mariejo Heras, Ana Walloschek, Thomas Lorenzo-Alvarez, Jose Marliani, Filippo Martin-Garcia, César Mas-Hesse, J. Miguel O’Rourke, Laurence Osborn, Hugh Pagano, Isabella Piotto, Giampaolo Pollacco, Don Ragazzoni, Roberto Ramsay, Gavin Udry, Stéphane Appourchaux, Thierry Benz, Willy Brandeker, Alexis Güdel, Manuel Janot-Pacheco, Eduardo Kabath, Petr Kjeldsen, Hans Min, Michiel Santos, Nuno Smith, Alexis Suarez, Juan-Carlos Werner, Stephanie C. Aboudan, Alessio Abreu, Manuel Acuña, Lorena Adams, Moritz Adibekyan, Vardan Affer, Laura Agneray, François Agnor, Craig Aguirre Børsen-Koch, Victor Ahmed, Saad Aigrain, Suzanne Al-Bahlawan, Ashraf Alcacera Gil, Ma de los Angeles Alei, Eleonora Alencar, Silvia Alexander, Richard Alfonso-Garzón, Julia Alibert, Yann Allende Prieto, Carlos Almeida, Leonardo Alonso Sobrino, Roi Altavilla, Giuseppe Althaus, Christian Alvarez Trujillo, Luis Alonso Amarsi, Anish Ammler-von Eiff, Matthias Amôres, Eduardo Andrade, Laerte Antoniadis-Karnavas, Alexandros António, Carlos Aparicio del Moral, Beatriz Appolloni, Matteo Arena, Claudio Armstrong, David Aroca Aliaga, Jose Asplund, Martin Audenaert, Jeroen Auricchio, Natalia Avelino, Pedro Baeke, Ann Baillié, Kevin Balado, Ana Ballber Balagueró, Pau Balestra, Andrea Ball, Warrick Ballans, Herve Ballot, Jerome Barban, Caroline Barbary, Gaële Barbieri, Mauro Barceló Forteza, Sebastià Barker, Adrian Barklem, Paul Barnes, Sydney Barrado Navascues, David Barragan, Oscar Baruteau, Clément Basu, Sarbani Baudin, Frederic Baumeister, Philipp Bayliss, Daniel Bazot, Michael Beck, Paul G. Belkacem, Kevin Bellinger, Earl Benatti, Serena Benomar, Othman Bérard, Diane Bergemann, Maria Bergomi, Maria Bernardo, Pierre Biazzo, Katia Bignamini, Andrea Bigot, Lionel Billot, Nicolas Binet, Martin Biondi, David Biondi, Federico Birch, Aaron C. Bitsch, Bertram Bluhm Ceballos, Paz Victoria Bódi, Attila Bognár, Zsófia Boisse, Isabelle Bolmont, Emeline Bonanno, Alfio Bonavita, Institut für Planetenforschung Deutsches Zentrum für Luft- und Raumfahrt DLR Rutherfordstrasse 2 Berlin 12489 Germany Fachbereich Geowissenschaften Institut für Geologische Freie Universität Berlin Malteserstr. 74-100 Berlin 12249 Germany Institute of Astronomy KU Leuven Leuven Belgium CNRS CNES LAM Aix Marseille University Marseille France Max Planck Institute for Solar System Research Göttingen Germany LESIA Paris Observatory PSL University Sorbonne University Paris Cité University CNRS Meudon France ESTEC European Space Agency Noordwijk Netherlands ESAC European Space Agency Madrid Spain Centro de Astrobiología CSIC-INTA Campus ESAC Madrid Spain Center for Space and Habitability University of Bern Bern Switzerland INAF - Osservatorio Astrofisico di Catania Catania Italy Department of Physics and Astronomy University of Padua Padua Italy Department of Physics University of Warwick Coventry United Kingdom Astronomical Observatory of Padova INAF - Italian National Institute for Astrophysics Padua Italy Armagh Observatory and Planetarium College Hill Armagh United Kingdom Geneve Observatory University of Geneve Geneve Switzerland IAS University of Paris-Saclay Orsay France AlbaNove University Centre Stockholm University Stockholm Sweden Department of Astrophysics University of Vienna Vienna Austria Department of Astronomy University of Sao Paulo Sao Paulo Brazil Astronomical Institute of the Czech Academy of Sciences Ondřejov Czech Republic Department of Physics and Astronomy Aarhus University Aarhus Denmark Institute for Space Research SRON Leiden Netherlands Instituto de Astrofísica e Ciências do Espaço CAUP Universidade do Porto Porto Portugal Departamento de Física e Astronomia Faculdade da Ci ncias Universidade do Porto Porto Portugal Mullard Space Science Laboratory University College London Holmbury Saint Mary United Kingdom Dept. Theoretical Physics and the Cosmos University of Granada Granada Spain Centre for Plan

PLATO (PLAnetary Transits and Oscillations of stars) is ESA’s M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2REarth) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5%, 10%, 10% for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO‘s target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile towards the end of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases. © The Author(s) 2025.

关键词： Asteroseismology Exoplanets PLATO mission

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