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Artificial Intelligence (CAI), IEEE Conference on

作者： Xue Li Ben Cao Hongzhen Ding Na Kang Tao Song College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Centre for Computational Biology Duke-NUS Medical School Singapore School of Computer Science and Technology Dalian University of Technology Dalian China CFAR A*STAR Singapore The Ninth Department of Health Care Administration The Second Medical Center Chinese PLA General Hospital Beijing China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Molecular activities in vivo, such as protein-peptide binding residues, contribute to understanding disease mechanisms and discovering targeted drugs, but are often limited by the cost of wet experiments. Therefore, this paper proposes a Pre-trained module-based Fourier Network inspired by Fourier Neural Operators for predicting protein-peptide binding residues (PepPFN). First, the model captures highlatent protein representation and implicit information through Bert and ESM, respectively. Subsequently, the contrastive learning module is used to optimize the representation of binding residues to mitigate the effects of data diversity. Furthermore, the order and frequency information of the residual is obtained by strengthening the model with Fourier network. Comprehensive analysis of multiple tasks and protein visualization results demonstrates that PepPFN outperforms state-of-the-art methods on multiple performance metrics. Datasets and the source code is available at https://***/xueleecs/***.

关键词： Proteins Visualization Biological system modeling Source coding Contrastive learning Feature extraction Data models

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Predicting Phosphoglycerylation with Transformer Features and Deep Learning

Predicting Phosphoglycerylation with Transformer Features an...

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computer science and Data Engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Abel Chandra Alok Sharma Iman Dehzangi Tatsuhiko Tsunoda Abdul Sattar Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Department of Computer Science Rutgers University Camden NJ USA Center for Computational and Integrative Biology Rutgers University Camden USA Department of Biological Sciences Laboratory for Medical Science Mathematics School of Science The University of Tokyo Tokyo Japan Department of Computational Biology and Medical Sciences Laboratory for Medical Science Mathematics Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan

Understanding protein sequences can advance treatments for various diseases. However experimentally obtaining this information is laborious, time-consuming, and expensive. Traditional machine learning techniques, like support vector machine, random forest and logistic regression, offer potential to fast-track this process but are sometimes limited by data complexity. Deep learning algorithms, in contrast, tend to yield higher performance. In this study, we employed a convolutional neural network to predict protein phosphoglycerylation. Features were extracted from pre-trained transformer models and compared with conventional features, such as evolutionary information and physicochemical/biochemical properties. Our results indicate significant performance improvements across all feature types, with the combination of transformerbased features and the convolutional neural network being especially effective. This methodology holds potential for other protein property prediction tasks. Our software and datasets used in this study are publicly available at https://***/abelavit/***.

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"Unlocking Electrocatalytic Dynamics with Anti-Mxene Borides Monolayers for Nitrate Reduction"

SSRN

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SSRN 2024年

作者： Ho, Thi H. Nguyen, Quynh Anh T. Le, Bich-Tram Truong Kim, Seong-Gon Bui, Viet Q. Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Advanced Institute of Science and Technology The University of Danang 41 Le Duan Danang Viet Nam Department of Science and International Cooperation The University of Danang Danang551000 Viet Nam Department of Physics & Astronomy Center for Computational Sciences Mississippi State University MS39762 United States

In this comprehensive study, we explore the electrocatalytic behavior of novel two-dimensional (2D) monolayers composed of anti-MXene borides (TMB) towards the nitrate reduction reaction (NO3RR) using density functional theory (DFT). The analysis reveals that these TMB monolayers emerge as promising candidates for electrochemical NO3RR, characterized by their exceptional stability, advantageous selectivity, and effective activation properties. Notably, our results highlight the preferential progression of the NO3-to-N2 pathway over the NO3-to-NH3 pathway in these TMB systems, presenting a significant insight into the mechanism of nitrate reduction. Among the monolayers, MnB distinguishes itself with a limiting potential of 0.79 eV, underscoring its substantial potential for application in the electroreduction of nitrate. Additionally, this study sheds light on the broader applicability of these innovative two-dimensional anti-MXene TMB monolayers, suggesting their versatility as electrocatalysts in a variety of potential applications, thereby opening new avenues for future research and development in electrocatalysis. © 2024, The Authors. All rights reserved.

关键词： Electrocatalysis

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Efficiently Verifiable Quantum Advantage on Near-Term Analog Quantum Simulators

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PRX Quantum 2025年第1期6卷 010341-010341页

作者： Zhenning Liu Dhruv Devulapalli Dominik Hangleiter Yi-Kai Liu Alicia J. Kollár Alexey V. Gorshkov Andrew M. Childs Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA Joint Quantum Institute NIST/University of Maryland College Park Maryland 20742 USA Department of Computer Science University of Maryland College Park Maryland 20742 USA Simons Institute for the Theory of Computing University of California at Berkeley California 94720 USA Applied and Computational Mathematics Division National Institute of Standards and Technology (NIST) Gaithersburg Maryland 20899 USA Department of Physics University of Maryland College Park Maryland 20742 USA Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA

Existing schemes for demonstrating quantum computational advantage are subject to various practical restrictions, including the hardness of verification and challenges in experimental implementation. Meanwhile, analog quantum simulators have been realized in many experiments to study novel physics. In this work, we propose a quantum advantage protocol based on single-step Feynman-Kitaev verification of an analog quantum simulation, in which the verifier need only run an O(λ2)-time classical computation, and the prover need only prepare O(1) samples of a history state and perform O(λ2) single-qubit measurements, for a security parameter λ. We also propose a near-term feasible strategy for honest provers and discuss potential experimental realizations.

关键词： Quantum computers

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis

arXiv

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arXiv 2022年

作者： Golovanevsky, Michal Eickhoff, Carsten Singh, Ritambhara Department of Computer Science Brown University United States Center for Biomedical Informatics Brown University United States Center for Computational Molecular Biology Brown University United States

Alzheimer’s Disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis. We present a Multimodal Alzheimer’s Disease Diagnosis framework (MADDi) to accurately detect the presence of AD and mild cognitive impairment (MCI) from imaging, genetic, and clinical data. MADDi is novel in that we use cross-modal attention, which captures interactions between modalities - a method not previously explored in this domain. We perform multi-class classification, a challenging task considering the strong similarities between MCI and AD. We compare with previous state-of-the-art models, evaluate the importance of attention, and examine the contribution of each modality to the model’s performance. MADDi classifies MCI, AD, and controls with 96.88% accuracy on a held-out test set. When examining the contribution of different attention schemes, we found that the combination of cross-modal attention with self-attention performed the best, and no attention layers in the model performed the worst, with a 7.9% difference in F1-Scores. Our experiments underlined the importance of structured clinical data to help machine learning models contextualize and interpret the remaining modalities. Extensive ablation studies showed that any multimodal mixture of input features without access to structured clinical information suffered marked performance losses. This study demonstrates the merit of combining multiple input modalities via cross-modal attention to deliver highly accurate AD diagnostic decision support. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling

arXiv

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arXiv 2022年

作者： Friedman, Roy Hassan, Sultan Department of Computer Science The Hebrew University of Jerusalem Jerusalem Israel Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States

Extracting the maximum amount of cosmological and astrophysical information from upcoming large-scale surveys remains a challenge. This includes evaluating the exact likelihood, parameter inference and generating new diverse synthetic examples of the incoming high-dimensional data sets. In this work, we propose the use of normalizing flows as a generative model of the neutral hydrogen (HI) maps from the CAMELS project. Normalizing flows have been very successful at parameter inference and generating new, realistic examples. Our model utilizes the spatial structure of the HI maps in order to faithfully follow the statistics of the data, allowing for high-fidelity sample generation and efficient parameter inference. © 2022, CC BY.

关键词： Clustering algorithms

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Enhancement of Sensitivity of Radio Frequency Based Biomolecule Detection in the Presence of Gold Nanoparticles: A Feasibility Study

Enhancement of Sensitivity of Radio Frequency Based Biomolec...

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International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Annesha Mazumder Prabhakar Bhimalapuram Syed Azeemuddin Tapan K. Sau Center for VLSI and Embedded System Technology International Institute of Information Technology Hyderabad India Center for Computational Natural Science and Bioinformatics International Institute of Information Technology Hyderabad India Department of Electrical and Computer Engineering University of Mississippi Oxford MS USA

In this work, the role of gold nanoparticles in enhancing the sensitivity of Radio Frequency based biomolecule detection is assessed. A facile, efficient and rapid microstrip patch antenna based sensor, resonating at 61.747 GHz is utilized for the analysis. Gold nanoparticles are placed in the vicinity of biomolecules, and the extent of improvement is studied under various conditions. In particular, different morphological variations and arrangements of gold particles are investigated. The results indicate that, in general, the presence of gold nanoparticles contributes towards sensitivity improvement. However, the amount of enhancement strongly depends upon the morphological features and arrangements of gold particles and varies within 5-65% for the scenarios studied in this case.

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The nature of epidemic criticality in temporal networks

arXiv

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arXiv 2023年

作者： Cai, Chao-Ran Nie, Yuan-Yuan Holme, Petter School of Physics Northwest University Xi’an710127 China Shaanxi Key Laboratory for Theoretical Physics Frontiers Xi’an710127 China Department of Computer Science Aalto University Espoo Finland Center for Computational Social Science Kobe University Kobe Japan

Analytical studies of network epidemiology almost exclusively focus on the extreme situations where the time scales of network dynamics are well separated (longer or shorter) from that of epidemic propagation. In realistic scenarios, however, these time scales could be similar, which has profound implications for epidemic modeling (e.g., one can no longer reduce the dimensionality of epidemic models). We build a theory for the critical behavior of susceptible-infected-susceptible (SIS) epidemics in the vicinity of the critical threshold on the activity-driven model of temporal networks. We find that the persistence of links in the network leads to increasing recovery rates reducing the threshold. Dynamic correlations (coming from being close to infected nodes increases the likelihood of infection) drive the threshold in the opposite direction. These two counteracting effects make epidemic criticality in temporal networks a remarkably complex phenomenon. Copyright © 2023, The Authors. All rights reserved.

关键词： Epidemiology

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Development and performance evaluation of a Bengali-adapted mHealth app for early detection of impaired visual acuity

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Heliyon 2024年第23期10卷 e40853页

作者： Haque, Monzurul Zaman, Marzia Islam, Ashraful Sarker, Farhana Ferdausi, Nahid Mamun, Khondaker A. AIMS Lab IRIIC United International University Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Dhaka Bangladesh Gopalganj Eye Hospital and Training Institute Gopalganj Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh CMED Health Limited Dhaka Bangladesh

This paper presents a comprehensive study on the development and performance evaluation of “Dristi,” a mobile health (mHealth) app designed to facilitate eye screening for impaired visual acuity (VA) in Bangladesh. Recognizing the critical importance of vision and the challenges posed by visual impairments, particularly in low-resource settings, this study explores an innovative solution to enhance eye care accessibility. The app, developed for Android devices, integrates features such as VA testing using adapted Bengali letters, color blindness tests, and eye health education, aiming to make eye care more user-friendly and accessible. Inspired by the Snellen chart, the app employs a simplified method for VA assessment, suitable for the constraints of mobile devices. The study involved a cross-sectional comparative design, conducted between June and September 2022, to clinically validate the app's feature of testing distant vision and near vision. Participants were recruited from one government eye hospital, one semi-private eye hospital, one high school, and a private clinic in Bangladesh, encompassing a diverse demographic. The validation study revealed promising results in terms of the app's accuracy, sensitivity, and specificity in both distant and near vision testing, although some variability was observed across different data sources. The study also identified a significant number of participants with diabetes and hypertension, common comorbidities affecting eye health. Overall, “Dristi” demonstrates potential as a valuable tool in the early detection and management of visual impairments in Bangladesh. Its development marks a significant step towards utilizing mHealth solutions to overcome healthcare barriers and improve eye care services in low-resource settings. Future directions include expanding the app's features, enhancing its integration with healthcare systems, and extending its reach to other similar contexts. © 2024 The Author(s)

关键词： Distant vision Eye mHealth Near vision Vision problem Visual acuity

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