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SYNAPSE: An international roadmap to large brain imaging

Physics Reports 2023年第1期999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： Brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

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Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

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Journal of Healthcare Engineering 2022年 2022卷 8302674页

作者： Santosh, K.C. Pradeep, Nijalingappa Goel, Vikas Ranjan, Raju Pandey, Ekta Shukla, Piyush Kumar Nuagah, Stephen Jeswinde CSandE Department Bapuji Institute of Engineering and Technology Karnataka Davangere India Uttar Pradesh Ghaziabad201206 India School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Applied Science Department Bundelkhand Institute of Engineering and Technology Uttar Pradesh Jhansi India Computer Science and Engineering Department University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Technological University of Madhya Pradesh Bhopal462033 India Department of Electrical Engineering Tamale Technical University Tamale Ghana

The use of digital medical images is increasing with advanced computational power that has immensely contributed to developing more sophisticated machine learning techniques. Determination of age and gender of individuals was manually performed by forensic experts by their professional skills, which may take a few days to generate results. A fully automated system was developed that identifies the gender of humans and age based on digital images of teeth. Since teeth are a strong and unique part of the human body that exhibits least subject to risk in natural structure and remains unchanged for a longer duration, the process of identification of gender- and age-related information from human beings is systematically carried out by analyzing OPG (orthopantomogram) images. A total of 1142 digital X-ray images of teeth were obtained from dental colleges from the population of the middle-east part of Karnataka state in India. 80% of the digital images were considered for training purposes, and the remaining 20% of teeth images were for the testing cases. The proposed gender and age determination system finds its application widely in the forensic field to predict results quickly and accurately. The prediction system was carried out using Multiclass SVM (MSVM) classifier algorithm for age estimation and LIBSVM classifier for gender prediction, and 96% of accuracy was achieved from the system. © 2022 K. C. Santosh et al.

关键词： Learning algorithms

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E-government and E-commerce issues in Malaysia 2

E-government and E-commerce issues in Malaysia

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Kanaan, Anas Ghassan Pathmanathan, P. Ravindran Alawi, Nahg Abdulmajid Hatamleh, Amer Jusoh, Julaily Aida Aledinat, Lowai Saleh Aseh, Khairi Bamansoor, Samer UniSZA Faculty of Informatics and Computing Malaysia Petra University Faculty of Managment and Financial Sciences Business and E-Commerce Depaetment Amman Jordan International Institute of Applied Science Swiss School of Management Netherlands Geomatika University College Malaysia University of Aden Yemen Irbid National University Faculty of Financial and Business Science Head of Management Information System Department Irbid Jordan UniSZA Faculty of Informatics and Computing Malaysia Faculty of Computer and Information Technology MEDIU Malaysia University of Aden Aden Yemen

ISBN: (纸本)9781665432221

In Malaysia, an e-commerce company is still considered a new middleman. As well as the development of information and communication technology strongly affects business companies at the present time in terms of management, business strategies, production, sales and marketing, and other aspects that underpin business profitability in general. E-commerce, known as e-commerce, refers to a general term for a commercial activity using modern technologies, or a commercial transaction that includes the exchange of data via electronic frameworks, especially the Internet, as well as many modern systems used in personal computers, smart phones, etc. It is not just limited to doing business between companies but also related to online services between governments and individuals. Various investigations related to e-commerce have identified obstacles related to information technology (IT) organization, internet use, financial situation, government and administrative activities as important determinants of doing business online. Information and Communication Technology (ICT) has revolutionized businesses around the world. In this paper, e-commerce practices in Malaysia are analyzed by their characteristics, drawbacks, openings and risks. This paper provides a general review of the current issues considered by e-commerce organizations today. It also suggests the challenges and problems facing e-commerce companies at the present time and how to solve them using e-commerce concepts. © 2021 IEEE.

关键词： Smartphones

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HiPoNet: A Topology-Preserving Multi-View Neural Network For High Dimensional Point Cloud and Single-Cell Data

arXiv

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arXiv 2025年

作者： Viswanath, Siddharth Madhu, Hiren Bhaskar, Dhananjay Kovalic, Jake Johnson, Dave Ying, Rex Tape, Christopher Adelstein, Ian Perlmutter, Michael Krishnaswamy, Smita Department of Computer Science Yale University New Haven United States Department of Genetics Yale University New Haven United States Department of Applied Physics Yale University New Haven United States Program in Computing Boise State University Idaho United States Cell Communication Lab Department of Oncology University College London Cancer Institute London United Kingdom Department of Mathematics Yale University New Haven United States Department of Mathematics Boise State University Idaho United States Computational Biology and Bioinformatics Program Yale University New Haven United States Wu-Tsai Institute Yale University New Haven United States Program for Applied Math Yale University New Haven United States

In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionality exceeding the capabilities of existing methods for point clouds which are tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e. one on every patient), necessitating models that can process large, high-dimensional point clouds at scale. Most current approaches build a single nearest-neighbor graph, discarding important geometric information. In contrast, HiPoNet forms higher-order simplicial complexes through learnable feature reweighting, generating multiple data views that disentangle distinct biological processes. It then employs simplicial wavelet transforms to extract multiscale features—capturing both local and global topology. We empirically show that these components preserve topological information in the learned representations, and that HiPoNet significantly outperforms state-of-the-art point-cloud and graph-based models on single cell. We also show an application of HiPoNet on spatial transcriptomics datasets using spatial co-ordinates as one of the views. Overall, HiPoNet offers a robust and scalable solution for high-dimensional data analysis. © 2025, CC BY.

关键词： Network topology

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Sguq: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis Using Multi-Omics Data

SSRN

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SSRN 2024年

作者： Tao, Liang Xie, Yixin Deng, Jeffrey D. Shen, Hui Deng, Hong-Wen Zhou, Weihua Zhao, Chen Department of Computer Science Kennesaw State University MariettaGA30060 United States Department of Information Technology Kennesaw State University MariettaGA30060 United States Geisel School of Medicine Dartmouth College HanoverNH03755 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Applied Computing Michigan Technological University HoughtonMI49931 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

Background and Objective: Alzheimer’s disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia, significantly impacting cost, mortality, and burden worldwide. The advent of high-throughput omics technologies, such as genomics, transcriptomics, proteomics, and epigenomics, has revolutionized the molecular understanding of AD. Conventional AI approaches typically require the completion of all omics data at the outset to achieve optimal AD diagnosis, which are inefficient and may be unnecessary. We aim to propose a cost-effective method for AD ***: To reduce the clinical cost and improve the accuracy of AD diagnosis using multi-omics data, we propose a novel staged graph convolutional network with uncertainty quantification (SGUQ) model. SGUQ begins with mRNA and progressively incorporates miRNA and DNA methylation data only when necessary, reducing overall costs and exposure to harmful ***: Experimental results indicate that 0.94% of the samples can be reliably predicted using only single-modal omics data (mRNA), while an additional 33.96% of the samples can achieve reliable predictions when combining two omics data types (mRNA + miRNA). In addition, the proposed staged SGUQ achieved an accuracy of 0.878 on ROSMAP dataset, which outperformed existing methods ***: The proposed SGUQ can not only be applied to AD diagnosis using multi-omics data, but also has the potential for clinical decision making using multi-viewed data. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Survey on Explainable Artificial Intelligence for Cybersecurity

arXiv

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision

Artificial Intelligence for Climate Smart Forestry: A Forwar...

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Feng Luo Ling Liu G. Geoff Wang Vijay Kumar Mark S. Ashton Jacob Abernethy Fatemeh Afghah Matthew H. E. M Browning David Coyle Philip Dames Tom O'Halloran James Hays Patrick Heisl Chenfanfu Jiang Puskar Khanal Venkat Narayan Krovi Sara Kuebbing Nianyi Li JingJing Liang Ninghao Liu Steve McNulty Christopher M. Oswalt Neil Pederson Demetri Terzopoulos Christopher W. Woodall Yongkai Wu Jian Yang Yin Yang Liang Zhao School of Computing Clemson University Clemson SC USA College of Computing Georgia Institute of Technology Atlanta GA USA Department of Forestry and Environmental Conservation Clemson University Clemson SC USA School of Engineering and Applied Science University of Pennsylvania Philadelphia PA USA The Forest School Yale University New Haven CT USA Department of Electrical and Computer Engineering Clemson University Clemson SC USA Department of Parks Recreation and Tourism Management Clemson University Clemson SC USA Department of Mechanical Engineering Temple University Philadelphia PA USA Department of Mathematics University of California Los Angeles Los Angeles CA USA Department of Automotive Engineering Clemson University Clemson SC USA Department of Forestry and Natural Resources Purdue University West Lafayette IN USA School of Computing University of Georgia Athens GA USA Northern Research Station USDA Forestry Service Durham NH USA South Research Station USDA Forestry Service Knoxville TN USA The Harvard Forest Harvard University Petersham MA USA Computer Science Department University of California Los Angeles Los Angeles CA USA Department of Forestry and Natural Resources University of Kentucky Lexington KY USA School of Computing University of Utah Salt Lake City UT USA Department of Computer Science Emory University Atlanta GA USA

Forests and forest ecosystems are vital to our social, economic, and environmental well-being. However, climate change and climate-driven disturbances (CDDs) are undermining the health and resilience of forests worldwide and pose significant uncertainty to sustainable forest management. Climate-smart forestry (CSF) remains a grand challenge in practice due to our limited knowledge of how forests respond to climate change and our abilities to collect related information to empower decision making. Rapid advances in artificial intelligence (AI) can offer a timely opportunity to address the challenges in CSF. We argue that the AI-enabled, next-generation CSF can be achievable through synergistically coordinated and transdisciplinary efforts that develop and advance foundational and use-inspired AI technologies that can lead to building next-generation forest decision support systems.

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Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data

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computers in Biology and Medicine 2024年 179卷 108813-108813页

作者： Zhao, Chen Su, Kuan-Jui Wu, Chong Cao, Xuewei Sha, Qiuying Li, Wu Luo, Zhe Qing, Tian Qiu, Chuan Zhao, Lan Juan Liu, Anqi Jiang, Lindong Zhang, Xiao Shen, Hui Zhou, Weihua Deng, Hong-Wen Department of Computer Science Kennesaw State University 680 Arntson Dr MariettaGA30060 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Biostatistics University of Texas MD Anderson Pickens Academic Tower 1400 Pressler St. HoustonTX77030 United States Department of Mathematical Sciences Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems and Health Research Institute Michigan Technological University HoughtonMI49931 United States

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research. © 2024

关键词： Metabolites

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An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

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Healthcare Analytics 2022年 2卷 100096-100096页

作者： Ukwandu, Ogechukwu Hindy, Hanan Ukwandu, Elochukwu Department of Computer Communication and Information Systems School of Engineering and Built Environment Glasgow Caledonian University United Kingdom Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff CF5 2YB United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

Timely and rapid diagnoses are core to informing on optimum interventions that curb the spread of COVID-19. The use of medical images such as chest X-rays and CTs has been advocated to supplement the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test, which in turn has stimulated the application of deep learning techniques in the development of automated systems for the detection of infections. Decision support systems relax the challenges inherent to the physical examination of images, which is both time consuming and requires interpretation by highly trained clinicians. A review of relevant reported studies to date shows that most deep learning algorithms utilised approaches are not amenable to implementation on resource-constrained devices. Given the rate of infections is increasing, rapid, trusted diagnoses are a central tool in the management of the spread, mandating a need for a low-cost and mobile point-of-care detection systems, especially for middle- and low-income nations. The paper presents the development and evaluation of the performance of lightweight deep learning technique for the detection of COVID-19 using the MobileNetV2 model. Results demonstrate that the performance of the lightweight deep learning model is competitive with respect to heavyweight models but delivers a significant increase in the efficiency of deployment, notably in the lowering of the cost and memory requirements of computing resources. © 2022 The Author(s)

关键词： COVID-19 detection Diagnostic analytics Lightweight deep learning techniques Machine learning Point-of-care Resource-constrained devices

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Determinants of customer purchase intention using Zalora mobile commerce application 2

Determinants of customer purchase intention using Zalora mob...

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： El-Ebiary, Yousef A. Baker Pathmanathan, P. Ravindran Tarshany, Yasser Mohamed Abdelrahman Jusoh, Julaily Aida Aseh, Khairi Al Moaiad, Yazeed Al-Kofahi, Mahmoud Pande, Bishwajeet Bamansoor, Samer UniSZA Faculty of Informatics and Computing Malaysia International Institute of Applied Science Swiss School of Management Netherlands MEDIU Faculty of Islamic Sciences Department of Fiqh and Usul Fiqh Malaysia MEDIU Faculty of Computer and Information Technology Malaysia Irbid National University Faculty of Administrative and Financial Sciences Irbid Jordan Gyancity Research Consultancy Pvt Ltd Motihari India Faculty of Computer and Information Technology MEDIU Malaysia

ISBN: (纸本)9781665432221

In the era of modern technologies and with the development of marketing, nowadays the customer can save time buying things without having to visit the store. They just need to shop online, find what they are buying and pay it with just a few simple clicks. With this convenience in mind, the indulgence of people buying online has risen quite well. The customer's decision to purchase a product through Internet commerce under the concept of electronic commerce - which has been developed into e-commerce or mobile commerce - usually takes a serious action with perceived confidence in the services and their suitability. Therefore, customer behaviour is an important factor to facilitate online electronic service. Hence, it is important to understand customer perceptions on these services and their intention of using it. Zalora is one of the fastest online search platforms providing kind of world-famous brands in Malaysia. The objective of this study is to examine factors that influence Malaysian consumers' purchase intention through Zalora mobile application. Based on the theory of planned behaviour along with other factors, the study proposes a theoretical model for the empirical examination of the customer intention for purchasing via Zalora mobile application. Data were collected from sixteen respondents to test the relationship between variables. Data analysis was carried out using PLS-SEM. The empirical results indicated that perceived friendliness, perceived convenience, and perceived influence have a significant impact on purchase intention. The result may be preserved by providing an easy shopping experience to the user when using commerce sites and building trust to maintain the intent to buy. In addition, this analysis is helpful for Zalora marketers to suggest the most effective promotion methods in reference to further marketing of their products. © 2021 IEEE.

关键词： Mobile commerce

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