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arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

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White spot syndrome virus (WSSV) modulates lipid metabolism in white shrimp

Communications Biology

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Communications Biology 2023年第1期6卷 546页

作者： Ng, Yen Siong Cheng, Cheng-Shun Ando, Masahiro Tseng, Yi-Ting He, Shu-Ting Li, Chun-Yuan Cheng, Shu-Wen Chen, Yi-Min Kumar, Ramya Liu, Chun-Hung Takeyama, Haruko Hirono, Ikuo Wang, Han-Ching Department of Biotechnology and Bioindustry Sciences College of Bioscience and Biotechnology National Cheng Kung University Tainan Taiwan Research Organization for Nano and Life Innovations Waseda University Tokyo Japan International Center for the Scientific Development of Shrimp Aquaculture National Cheng Kung University Tainan Taiwan Department of Aquaculture National Pingtung University of Science and Technology Pingtung Taiwan Department of Life Science and Medical Bioscience Waseda University Tokyo Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology Tokyo Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and Engineering Tokyo Japan Department of Marine Biosciences Tokyo University of Marine Science and Technology Tokyo Japan

In addition to the Warburg effect, which increases the availability of energy and biosynthetic building blocks in WSSV-infected shrimp, WSSV also induces both lipolysis at the viral genome replication stage (12 hpi) to provide material and energy for the virus replication, and lipogenesis at the viral late stage (24 hpi) to complete virus morphogenesis by supplying particular species of long-chain fatty acids (LCFAs). Here, we further show that WSSV causes a reduction in lipid droplets (LDs) in hemocytes at the viral genome replication stage, and an increase in LDs in the nuclei of WSSV-infected hemocytes at the viral late stage. In the hepatopancreas, lipolysis is triggered by WSSV infection, and this leads to fatty acids being released into the hemolymph. β-oxidation inhibition experiment reveals that the fatty acids generated by WSSV-induced lipolysis can be diverted into β-oxidation for energy production. At the viral late stage, WSSV infection leads to lipogenesis in both the stomach and hepatopancreas, suggesting that fatty acids are in high demand at this stage for virion morphogenesis. Our results demonstrate that WSSV modulates lipid metabolism specifically at different stages to facilitate its replication. © 2023, The Author(s).

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A Modified Clustering Using Representatives to Enhance and Optimize Tracking and Monitoring of Maritime Traffic in Real-time Using Automatic Identification System data

A Modified Clustering Using Representatives to Enhance and O...

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International Conference on computational science and computational Intelligence (CSCI)

作者： Cheronika Manyfield-Donald Tor A. Kwembe Jing-Ru C. Cheng Computational Data-Enabled Science and Engineering Jackson State University Jackson MS USA Department of Mathematics and Statistical Sciences Jackson State University Jackson MS USA Information Technology Lab. Engineer Research and Development Center U.S. Army Corps of Engineers Vicksburg MS USA

ISBN: (纸本)9781665458429

In this paper, we introduce a modification of the Clustering Using Representatives (CURE) algorithm to enhance and optimize the tracking and monitoring of maritime traffic in real-time using the Automatic Identification System (AIS) data. In doing so, we present a 2-D points data collection system for the utilization of a modified unsupervised machine learning clustering method of the CURE algorithm integrated with a data streaming algorithm to develop a more efficient method that will directly assist in adverting accidents and support in vessels avoiding dangerous environments. Results are presented that show tracking in inland as well as open sea waterways.

关键词： Machine learning algorithms Tracking Scientific computing Clustering methods Clustering algorithms Machine learning data collection

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Multilinear state space system identification with matrix product operators

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IFAC-PapersOnLine 2018年第15期51卷 640-645页

作者： Batselier, Kim Ko, Ching-Yun Phan, Anh-Huy Cichocki, Andrzej Wong, Ngai Electrical and Electronic Engineering Department The University of Hong Kong Hong Kong Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia Systems Research Institute Polish Academy of Science Warsaw Poland

In this article, we introduce a matrix product operator, also called tensor train matrix, representation of discrete-time multilinear state space models and develop a corresponding system identification method. Using matrix product operators allows us to store the exponential number of model coefficients with a linear storage complexity. The derived system identification algorithm estimates the matrix product operator of the multilinear state space model directly from the measured data. This results in lower computational complexity compared to traditional nonlinear optimization methods. The effectiveness of our proposed model and method is demonstrated by a numerical experiment, where the identification of a degree-16 multilinear state space system in MATLAB on a standard desktop computer takes about 8 minutes with a relative validation error of 0.003%. © 2018

关键词： Matrix algebra

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Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation

arXiv

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arXiv 2023年

作者： Acosta, Fernando Torales Mikuni, Vinicius Nachman, Benjamin Arratia, Miguel Karki, Bishnu Milton, Ryan Karande, Piyush Angerami, Aaron Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Nuclear and Chemical Science Division Lawrence Livermore National Laboratory LivermoreCA94550 United States

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared. © 2023, CC BY.

关键词： Calorimeters

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Towards a widespread usage of computational fluid dynamics simulations for automated virtual nasal surgery planning

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Future Generation Computer Systems 2026年 174卷

作者： Mario Rüttgers Moritz Waldmann Fabian Hübenthal Klaus Vogt Makoto Tsubokura Sangseung Lee Andreas Lintermann Data-Driven Fluid Engineering (DDFE) Laboratory Inha University Incheon South Korea Institute of Aerodynamics and Chair of Fluid Mechanics (AIA) RWTH Aachen University Germany Faculty of Medicine Center of Experimental Surgery University of Latvia 1586 Riga Latvia Department of Computational Science Graduate School of System Informatics Kobe University Japan Complex Phenomena Unified Simulation Research Team RIKEN Center for Computational Science Kobe Japan Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich GmbH Germany

Efficient computational approaches are crucial for advancing computational fluid dynamics (CFD)-based automated planning in nasal surgeries, such as septoplasties and turbinectomies. This study introduces a hybrid lattice-Boltzmann and level-set method to address the trade-off between computational cost and automation. By interpolating geometry variations in discrete steps between pre-surgical and target states, the approach achieves computational efficiency with only 21 surface variations per intervention. Previous methods rely on more costly coupling strategies, such as reinforcement learning or thermal modeling, which may still be appropriate for complex planning scenarios involving multiple intervention sites or thermal flow analysis. In contrast, the presented method reduces complexity while retaining key predictive capabilities, making it particularly suitable for widespread, time-sensitive clinical use focused on a single surgical intervention. Fluid mechanical metrics, including pressure loss and volume flow rate balance, are evaluated alongside tissue removal volume to recommend optimized surgical plans. Case studies on three patients demonstrate tissue savings of 12–25% without compromising key flow parameters. Additionally, a non-linear regression model trained on as few as 11 CFD simulations predicts pressure loss and flow rates with errors below 4%, and reduces computational costs by 50%. The proposed framework represents a significant step toward making CFD-based virtual nasal surgery planning more accessible and practical.

关键词： Septoplasty Turbinectomy Non-linear regression

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Intratumoral heterogeneity of second-harmonic generation scattering from tumor collagen and its effects on metastatic risk prediction 21

Intratumoral heterogeneity of second-harmonic generation sca...

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Multiphoton Microscopy in the Biomedical sciences XXI 2021

作者： Desa, Danielle E. Wu, Wencheng Hill, Robert L. Martens, J.W.M. Strawderman, Robert L. Turner, Bradley M. Brown, Edward B. Dept. of Biomedical Engineering University of Rochester 201 Goergen Hall RochesterNY14627 United States Goergen Institute for Data Science University of Rochester 1209 Wegmans Hall RochesterNY14627 United States Harmonigenic Corp. RochesterNY United States Dept. of Medical Oncology Erasmus University Medical Center PO Box 5201 Rotterdam3008 AE Netherlands Dept. of Biostatistics and Computational Biology University of Rochester Medical Center 265 Crittenden Blvd. RochesterNY14642 United States Dept. of Pathology and Laboratory Medicine University of Rochester Medical Center 601 Elmwood Ave. RochesterNY14642 United States

ISBN: (纸本)9781510641310

Metastases are the leading cause of breast cancer-related deaths. Tumor metastasis is affected by the microenvironment, including the extracellular matrix (ECM). Fibrillar collagen in the ECM produces an intrinsic optical signal called secondharmonic generation (SHG). The ratio of forward- to backward-scattered SHG photons (F/B) is sensitive to the collagen fiber internal structure and has been shown to be an independent prognostic indicator of metastasis-free survival time. We evaluated the effects of heterogeneity in the tumor ECM on F/B's prognostic ability. Using SHG imaging we identified two distinct regions within 95 untreated primary tumor excisions: the tumor bulk and surrounding collagenous tumorstroma interface. We found that F/B measured in the tumor-stroma interface, but not tumor bulk, is prognostic of metastasis-free survival time (MFS) using both an intensity threshold selected by a blinded observer and adaptive thresholding. We calculated a 21-gene recurrence score (surrogate OncotypeDX®, or S-ODX) for each patient and using a Random Survival Forest method we found that F/B from the tumor-stroma interface, but not tumor bulk, and S-ODX contribute to predicting MFS in this patient cohort. These results suggest that F/B from the tumor-stroma interface of primary tumor excisions may provide information independent of genomic methods to stratify patients by metastatic risk and identify need for post-operative treatment. © 2021 SPIE. All rights reserved.

关键词： Harmonic generation

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Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

arXiv

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arXiv 2022年

作者： Moridian, Parisa Ghassemi, Navid Jafari, Mahboobeh Salloum-Asfar, Salam Sadeghi, Delaram Khodatars, Marjane Shoeibi, Afshin Khosravi, Abbas Ling, Sai Ho Subasi, Abdulhamit Alizadehsani, Roohallah Gorriz, Juan M. Abdulla, Sara A. Acharya, U. Rajendra Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Deakin University GeelongVIC Australia UltimoNSW Australia Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Dept. of Computer Science College of Engineering Effat University Jeddah Saudi Arabia Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming;therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Exploiting dynamic nonlinearity in upconversion nanoparticles for super-resolution imaging

arXiv

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arXiv 2022年

作者： Chen, Chaohao Ding, Lei Liu, Baolei Du, Ziqin Liu, Yongtao Di, Xiangjun Shan, Xuchen Lin, Chenxiao Zhang, Min Xu, Xiaoxue Zhong, Xiaolan Wang, Jianfeng Chang, Lingqian Halkon, Ben J. Chen, Xin Cheng, Faliang Wang, Fan Guangdong Engineering and Technology Research Center for Advanced Nanomaterials School of Environment and Civil Engineering Dongguan University of Technology Dongguan523808 China Department of Chemical Engineering Shaanxi Key Laboratory of Energy Chemical Process Intensification Institute of Polymer Science in Chemical Engineering School of Chemical Engineering and Technology Xi'an Jiaotong University Xi'An710049 China School of Mathematical and Physical Sciences Faculty of Science University of Technology SydneyNSW2007 Australia School of Physics Beihang University Beijing100191 China Smart Computational Imaging Laboratory Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China Department for Electrochemical Energy Storage Helmholtz-Zentrum Berlin für Materialien und Energie Hahn-Meitner-Platz Berlin14109 Germany School of Biomedical Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing100191 China Centre for Audio Acoustics & Vibration Faculty of Engineering & IT University of Technology Sydney UltimoNSW2007 Australia School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

Single-beam super-resolution microscopy, also known as superlinear microscopy, exploits the nonlinear response of fluorescent probes in confocal microscopy. The technique requires no complex purpose-built system, light field modulation, or beam shaping. Here, we present a strategy to enhance this technique's spatial resolution by modulating excitation intensity during image acquisition. This modulation induces dynamic optical nonlinearity in upconversion nanoparticles (UCNPs), resulting in variations of higher spatial-frequency information in the obtained images. The high-order information can be extracted with a proposed weighted finite difference imaging algorithm from raw fluorescence images, to generate an image with a higher resolution than superlinear microscopy images. We apply this approach to resolve two adjacent nanoparticles within a diffraction-limited area, improving the resolution to 130 nm. This work suggests a new scope for developing dynamic nonlinear fluorescent probes in super-resolution nanoscopy. © 2022, CC BY.

关键词： Nanoparticles

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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