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Survival parametric modeling for patients with heart failure based on Kernel learning.
BMC Med Res Methodol
January 2025
Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis. Kernels are used to handle even more complicated and enormous quantities of medical data by injecting non-linearity into linear models.
View Article and Find Full Text PDFPhysics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing.
Sensors (Basel)
December 2024
Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan 333326, Taiwan.
The proliferation of sophisticated counterfeiting poses critical challenges to global security and commerce, with annual losses exceeding $2.2 trillion. This paper presents a novel physics-constrained deep learning framework for high-precision security ink colorimetry, integrating three key innovations: a physics-informed neural architecture achieving unprecedented color prediction accuracy (CIEDE2000 (ΔE00): 0.
View Article and Find Full Text PDFFFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things.
Sensors (Basel)
December 2024
Stony Brook Institute at Anhui University, Hefei 230000, China.
The Internet of Things (IoT) contains many devices that can compute and communicate, creating large networks. Industrial Internet of Things (IIoT) represents a developed application of IoT, connecting with embedded technologies in production in industrial operational settings to offer sophisticated automation and real-time decisions. Still, IIoT compels significant cybersecurity threats beyond jamming and spoofing, which could ruin the critical infrastructure.
View Article and Find Full Text PDFOptimized Spheroid Model of Pancreatic Cancer Demonstrates Influence of Macrophage-T Cell Interaction for Intratumoral T Cell Motility.
Cancers (Basel)
December 2024
Department of Gastroenterology, Endocrinology, Infectious Diseases and Metabolism, University Hospital Marburg, 35043 Marburg, Germany.
Background: Most spheroid models use size measurements as a primary readout parameter; some models extend analysis to T cell infiltration or perform caspase activation assays. However, to our knowledge, T cell motility analysis is not regularly included as an endpoint in imaging studies on cancer spheroids.
Methods: Here, we intend to demonstrate that motility analysis of macrophages and T cells is a valuable functional endpoint for studies on molecular interventions in the tumor microenvironment.
Bioinformatics and Deep Learning Approach to Discover Food-Derived Active Ingredients for Alzheimer's Disease Therapy.
Foods
January 2025
Department of Bioconvergence, Hoseo University, Asan 31499, Republic of Korea.
Alzheimer's disease (AD) prevention is a critical challenge for aging societies, necessitating the exploration of food ingredients and whole foods as potential therapeutic agents. This study aimed to identify natural compounds (NCs) with therapeutic potential in AD using an innovative bioinformatics-integrated deep neural analysis approach, combining computational predictions with molecular docking and in vitro experiments for comprehensive evaluation. We employed the bioinformatics-integrated deep neural analysis of NCs for Disease Discovery (BioDeepNat) application in the data collected from chemical databases.
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