An AI-driven computational toolkit, Gcoupler, integrates ligand design, statistical modeling, and graph neural networks to predict endogenous metabolites that allosterically modulate the GPCR–Gα ...

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ABSTRACT: Magnetic Resonance Imaging (MRI) is commonly applied to clinical diagnostics owing to its high soft-tissue contrast and lack of invasiveness. However, its sensitivity to noise, attributable ...

Abstract: Physical-layer secret key generation (PSKG) is a well-known and effective method for boosting wireless security in the Internet of Things (IoT). This technique creates cryptographic keys ...

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...

Introduction: Predicting the relationship between diseases and microbes can significantly enhance disease diagnosis and treatment, while providing crucial scientific support for public health, ...

This data loss can obscure critical details, affect measurements, or even compromise downstream AI model performance. Therefore, it’s important to remove noise from the image to preserve its details ...

A group of scientists led by researchers from the University of New South Wales (UNSW) in Australia has developed a novel deep-learning method for denoising outdoor electroluminescence (EL) images of ...

X-ray computed tomography (CT) is widely used in clinical practice for screening and diagnosing patients, as it enables the acquisition of high-resolution images of internal tissues and organs in a ...

In PhotoniX, researchers report a self-supervised deep learning method that denoises dynamic fluorescence images in vivo without requiring clean training data. The figure shows in vivo venule images ...