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Middle East and North African Health Informatics Association (MENAHIA): Technological initiatives for 'One Health'

Yearbook of Medical Informatics 2023年第1期32卷 320-329页

作者： Abd-Alrazaq, Alaa Al-Shorbaji, Najeeb Lakhdari, Kheira Elbokl, Ahmed Farouk, Hani Wahba, Hoda Elgasser, Naema Mohell, Rania Emara, Tamer Ayatollahi, Haleh Kalhori, Sharareh R. Niakan Rabiei, Reza Tara, Mahmood Akhu-Zaheya, Laila Al-Qutob, Raeda Alrazak, Sadam Alabed Al-Jafar, Eiman Alhuwail, Dari Ghazal, Hassan Dehbi, Zineb El Otmani Al Idrissi, Najib Fihri, Ouafaa Fassi Belyamani, Lahcen Shahzad, Tariq Ahmed, Zakiuddin Ahmad, Arfan Shah, Zubair Hamra, Eman Abu Taweel, Adel Nashwan, Abdulqadir J. Househ, Mowafa Hamdi, Mounir Al-Thani, Dena Alam, Tanvir Alshammari, Abdulwahhab Alnafrani, Sana Alali, Haitham AI Center for Precision Health Weill Cornell Medicine-Qatarc Doha Qatar EHealth Development Association Amman Jordan Abou Bekr Belkaid University of Tlemcen Chetouane Algeria Ain Shams University Virtual Hospital Ain Shams University Cairo Egypt Ain Shams University Cairo Egypt World Health Organization Cairo Egypt Alexandria Regional Blood Transfusion Center Alexandria University Alexandria Egypt Health Management and Economics Research Center Health Management Research Institute Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Health Management and Information Sciences Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran Peter L. Reichertz Institute for Medical Informatics (PLRI) Technical University of Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciencesc Tehran Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciencesc Mashhad Iran School of Nursing Jordan University of Science and Technology Jordan Jordan Centre for Disease Control and Prevention Amman Jordan Faculty of Applied Science and Engineering University of Toronto Toronto Canada Kuwait Health Informatics Association Kuwait Information Science Department College of Life Sciences Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Mohammed VI University of Health Sciences Casablanca Morocco National Center for Scientific and Technical Research (CNRST) Rabat Morocco Hassan II Agronomic and Veterinary Institute Rabat Morocco COMSATS University Islamabad Sahiwal Campus Islamabad Pakistan Digital Care Karachi Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Al Quds Unive

MENAHIA (Middle East and North African Health Informatics Association) is the International Medical Informatics Association chapter dedicated to the Middle East and North Africa region. This region is rapidly growing in terms of the use of health informatics or what has been recently coined digital health. Human health is highly affected by the health of the environment, animal health, food, nutrition, climate change, and many other factors that are beyond the biological or genetic structure of human beings. The impact of animal health and the health of the environment on people's health is an old phenomenon but recent reemerging and appearance of diseases have clearly demonstrated the link between these. The Novel Coronavirus disease (COVID-19) that almost all of us have been suffering from is an example of this. A number of countries in the region have already shown the depth and the work that they do to integrate the concept of 'One Health' in the public health surveillance system as they have described the work that has been done to capture data from databases other than those dealing with human beings. The examples that were provided to monitor the health of animals, agriculture, environmental health, climate change, and man-made and natural disasters are just examples of what countries have been registering in their databases and informing the health authorities of these changes and emerging trends. © The Author(s) 2023.

关键词： 'One Health' 'One Health' informatics Algeria Artificial Intelligence Climate Change Digital Health Egypt Health Informatics Health Information Technologies International Medical Informatics Association (IMIA) Iran Jordan Kuwait MENAHIA Middle East and North Africa Morocco Pakistan Palestine Qatar Saudi Arabia United Arab Emirates Zoonitic Diseases

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Coil2Coil: Self-supervised MR image denoising using phased-array coil images

arXiv

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arXiv 2022年

作者： Park, Juhyung Park, Dongwon Shin, Hyeong-Geol Choi, Eun-Jung An, Hongjun Kim, Minjun Shin, Dongmyung Chun, Se Young Lee, Jongho Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Electrical Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of Department of Radiology Johns Hopkins University School of Medicine BaltimoreMD United States F.M. Kirby Research Center for Functional Brain Imaging Kennedy Krieger Institute BaltimoreMD United States AI Engineering Division RadiSen Co. Ltd. Seoul Korea Republic of

Denoising of magnetic resonance images is beneficial in improving the quality of low signal-to-noise ratio images. Recently, denoising using deep neural networks has demonstrated promising results. Most of these networks, however, utilize supervised learning, which requires large training images of noise-corrupted and clean image pairs. Obtaining training images, particularly clean images, is expensive and time-consuming. Hence, methods such as Noise2Noise (N2N) that require only pairs of noise-corrupted images have been developed to reduce the burden of obtaining training datasets. In this study, we propose a new self-supervised denoising method, Coil2Coil (C2C), that does not require the acquisition of clean images or paired noise-corrupted images for training. Instead, the method utilizes multichannel data from phased-array coils to generate training images. First, it divides and combines multichannel coil images into two images, one for input and the other for label. Then, they are processed to impose noise independence and sensitivity normalization such that they can be used for the training images of N2N. For inference, the method inputs a coil-combined image (e.g., DICOM image), enabling a wide application of the method. When evaluated using synthetic noise-added images, C2C shows the best performance against several self-supervised methods, reporting comparable outcomes to supervised methods. When testing the DICOM images, C2C successfully denoised real noise without showing structure-dependent residuals in the error maps. Because of the significant advantage of not requiring additional scans for clean or paired images, the method can be easily utilized for various clinical applications. © 2022, CC BY.

关键词： Magnetic resonance imaging

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DQ-SGD: Dynamic quantization in SGD for communication-efficient distributed learning

arXiv

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arXiv 2021年

作者： Yan, Guangfeng Huang, Shao-Lun Lan, Tian Song, Linqi Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China Data Science and Information Technology Research Center Tsinghua-Berkeley Shenzhen Institute Shenzhen China Department of Electrical and Computer Engineering George Washington University WashingtonDC United States

Gradient quantization is an emerging technique in reducing communication costs in distributed learning. Existing gradient quantization algorithms often rely on engineering heuristics or empirical observations, lacking a systematic approach to dynamically quantize gradients. This paper addresses this issue by proposing a novel dynamically quantized SGD (DQ-SGD) framework, enabling us to dynamically adjust the quantization scheme for each gradient descent step by exploring the trade-off between communication cost and convergence error. We derive an upper bound, tight in some cases, of the convergence error for a restricted family of quantization schemes and loss functions. We design our DQSGD algorithm via minimizing the communication cost under the convergence error constraints. Finally, through extensive experiments on large-scale natural language processing and computer vision tasks on AG-News, CIFAR-10, and CIFAR-100 datasets, we demonstrate that our quantization scheme achieves better tradeoffs between the communication cost and learning performance than other state-of-the-art gradient quantization methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

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A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

arXiv

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arXiv 2024年

作者： Orlichenko, Anton Qu, Gang Zhou, Ziyu Liu, Anqi Deng, Hong-Wen Ding, Zhengming Stephen, Julia M. Wilson, Tony W. Calhoun, Vince D. Wang, Yu-Ping Department of Biomedical Engineering Tulane University New OrleansLA70118 United States Department of Computer Science Tulane University New OrleansLA70118 United States Center for Biomedical Informatics and Genomics Tulane Integrated Institute of Data & Health Sciences Tulane University New OrleansLA70112 United States Institute for Human Neuroscience Boys Town National Research Hospital Boys TownNE United States Mind Research Network AlbuquerqueNM United States Georgia State University Georgia Institute of Technology Emory University AtlantaGA United States

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results:We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data con- ditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds. © 2024, CC BY.

关键词： Neurodegenerative diseases

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Modern deep learning in bioinformatics

引用

Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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Modular Extremely Large-Scale Array Communication: Near-Field Modelling and Performance Analysis

arXiv

引用

arXiv 2022年

作者： Li, Xinrui Lu, Haiquan Zeng, Yong Jin, Shi Zhang, Rui The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

This paper investigates wireless communications based on a new antenna array architecture, termed modular extremely large-scale array (XL-array), where an extremely large number of antenna elements are regularly arranged on a common platform in a modular manner. Each module consists of a flexible/moderate number of antenna elements, and different modules are separated with an inter-module spacing that is typically much larger than the inter-element spacing/signal wavelength for ease of deployment. By properly modelling the variations of signal phase, amplitude and projected aperture across different array modules/elements, we develop the new channel model and analyze the signal-to-noise ratio (SNR) performance of the modular XL-array based communications. Under the practical non-uniform spherical wave (NUSW) model, the closed-form expression of the maximum achievable SNR is derived in terms of key geometric parameters, including the total planar array size, module separation distances along each dimension, as well as the user's location in the three-dimensional (3D) space. Besides, the asymptotic SNR scaling laws are revealed as the number of modules along different dimensions goes to infinity. Moreover, we show that our developed near-field modelling and performance analysis include the existing ones for the collocated XL-array, the far-field uniform plane wave (UPW) model, as well as the one-dimensional (1D) modular extremely large-scale uniform linear array (XL-ULA) as special cases. Extensive simulation results are provided to validate our obtained results. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos 38

Retrospective for the Dynamic Sensorium Competition for pred...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 Competition Track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024 Neural information processing systems foundation. All rights reserved.

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Critical Risk Indicators (CRIs) for the electric power grid: a survey and discussion of interconnected effects

引用

Environment Systems and Decisions 2021年第4期41卷 594-615页

作者： Che-Castaldo, Judy P. Cousin, Rémi Daryanto, Stefani Deng, Grace Feng, Mei-Ling E. Gupta, Rajesh K. Hong, Dezhi McGranaghan, Ryan M. Owolabi, Olukunle O. Qu, Tianyi Ren, Wei Schafer, Toryn L. J. Sharma, Ashutosh Shen, Chaopeng Sherman, Mila Getmansky Sunter, Deborah A. Tao, Bo Wang, Lan Matteson, David S. Conservation & Science Department Lincoln Park Zoo 2001 N. Clark St. Chicago Chicago IL United States International Research Institute for Climate and Society Earth Institute/Columbia University New York United States Department of Plant and Soil Sciences College of Agriculture Food and Environment University of Kentucky Lexington United States Department of Statistics and Data Science Cornell University New York United States Halicioglu Data Science Institute and Department of Computer Science & Engineering University of California San Diego CA United States Atmospheric and Space Technology Research Associates Louisville CO United States Department of Civil and Environmental Engineering Tufts University Medford MA United States Department of Finance Isenberg School of Management UMASS Amherst Amherst MA United States Civil and Environmental Engineering Pennsylvania State University State College PA United States Department of Hydrology Indian Institute of Technology Roorkee Roorkee India Department of Mechanical Engineering Tufts University Medford MA United States Tufts Institute of the Environment Tufts University Medford MA United States Center for International Environment and Resource Policy at The Fletcher School Tufts University Medford MA United States Department of Management Science Miami Herbert Business School University of Miami Coral Gables FL United States

The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as an infrastructure is shaped by human activity and public policy in terms of demand and supply requirements. Further, the grid is subject to changes and stresses due to diverse factors including solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic, posing novel risks, and presenting new challenges to manage the coupled human–natural system. This paper provides a survey of models and methods that seek to explore the significant interconnected impact of the electric power grid and interdependent domains. We also provide relevant critical risk indicators (CRIs) across diverse domains that may be used to assess risks to electric grid reliability, including climate, ecology, hydrology, finance, space weather, and agriculture. We discuss the convergence of indicators from individual domains to explore possible systemic risk, i.e., holistic risk arising from cross-domain interconnections. Further, we propose a compositional approach to risk assessment that incorporates diverse domain expertise and information, data science, and computer science to identify domain-specific CRIs and their union in systemic risk indicators. Our study provides an important first step towards data-driven analysis and predictive modeling of risks in interconnected human–natural systems. © 2021, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Critical risk indicator Electric power grid Multi-disciplinary Risk Systemic risk Uncertainty

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Towards Faster Fully Homomorphic Encryption Implementation with Integer and Floating-point Computing Power of GPUs

Towards Faster Fully Homomorphic Encryption Implementation w...

引用

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Guang Fan Fangyu Zheng Lipeng Wan Lili Gao Yuan Zhao Jiankuo Dong Yixuan Song Yuewu Wang Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cryptography University of Chinese Academy of Sciences Beijing China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing China Ant Group Hangzhou China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Cyber Security University of Science and Technology of China Hefei China

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without knowledge of the plaintext message and currently has been the focus of both academia and industry. However, the performance issue hinders its large-scale application, highlighting the urgent requirements of high-performance FHE *** noticing the tremendous potential of GPUs in the field of cryptographic acceleration, this paper comprehensively investigates how to convert the available computing resources residing in GPUs into FHE workhorses, and implement a full set of low-level and middle-level FHE primitives based on two arithmetic units (i.e., INT32 and FP64 units) with three types of data precision (i.e., INT32, INT64 and FP64). This paper gives a comprehensive evaluation and comparison based on each road-map. Our implementations of fundamental functions outperform the implementations on the same platform by 1.7× to 16.7×. Taking CKKS FHE schemes as a case study, our implementation of homomorphic multiplication achieves 3.2× speedup over the state-of-the-art GPU-based implementation, even considering the difference of platforms. The detailed evaluation and comparison of this paper would offer a vital reference for the follow-up work to choose appropriate underlying arithmetic units and important primitive optimizations in GPU-based FHE implementations.

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Training wake word detection with synthesized speech data on confusion words

arXiv

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arXiv 2020年

作者： Jia, Yan Cai, Zexin Ma, Murong Zhao, Zeqing Wang, Xuyang Wang, Junjie Li, Ming Data Science Research Center Duke Kunshan University Kunshan China Department of Electrical and Computer Engineering Duke University Durham United States AI Lab of Lenovo Research Beijing China

Confusing-words are commonly encountered in real-life keyword spotting applications, which causes severe degradation of performance due to complex spoken terms and various kinds of words that sound similar to the predefined keywords. To enhance the wake word detection system’s robustness on such scenarios, we investigate two data augmentation setups for training end-to-end KWS systems. One is involving the synthesized data from a multi-speaker speech synthesis system, and the other augmentation is performed by adding random noise to the acoustic feature. Experimental results show that augmentations help improve the system’s robustness. Moreover, by augmenting the training set with the synthetic data generated by the multi-speaker text-to-speech system, we achieve a significant improvement regarding confusing words scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Wakes

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