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Multi-band image fusion in biological systems aims to integrate image data from various spectral bands to obtain more comprehensive, accurate, and effective image information. However, developing efficient and low-power artificial vision multi-band image fusion systems inspired by biological vision systems remains a challenge. Here, an artificial visual neuron based on the integration of InO/PY-IT phototransistor and NbO Mott memristor is proposed, which can simultaneously sense optical signals in the UV and near-infrared bands and achieve pulse encoding of different frequencies through light stimulation of different intensities. In addition, the pulse signals encoded by artificial neurons are processed through a Pulse Coupled Neural Network for image fusion, which successfully integrates image information under different lighting scenes and demonstrates the bionic functionality of the artificial vision fusion system. Such artificial visual neurons provide a solid foundation for constructing integrated, functional, and low-power artificial visual systems and serve as building blocks for hardware-based multi-band perception.
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http://dx.doi.org/10.1002/smll.202505337 | DOI Listing |
Interv Neuroradiol
September 2025
University Clinic for Neuroradiology, University Hospital Magdeburg, Magdeburg, Germany.
BackgroundAt present, nonvirtual neurovascular training can be performed using either an angiographic suite under fluoroscopic guidance (entailing radiation exposure) or direct optical visualization with a camera-based system. The angiographic approach offers high-fidelity visualization and catheter control but is constrained by the limited availability of such specialized facilities, whereas the camera-based approach can be implemented virtually anywhere yet lacks comparable realism in key procedural aspects. The objective of this work is to develop and evaluate a novel camera-based angiography training system (CBATS) that generates artificial angiograms and roadmaps, thereby combining the advantages of both imaging techniques while eliminating radiation exposure.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, 41 Dinh Tien Hoang, District 1, Ho Chi Minh City 700000, Vietnam.
Molecular property prediction has become essential in accelerating advancements in drug discovery and materials science. Graph Neural Networks have recently demonstrated remarkable success in molecular representation learning; however, their broader adoption is impeded by two significant challenges: (1) data scarcity and constrained model generalization due to the expensive and time-consuming task of acquiring labeled data and (2) inadequate initial node and edge features that fail to incorporate comprehensive chemical domain knowledge, notably orbital information. To address these limitations, we introduce a Knowledge-Guided Graph (KGG) framework employing self-supervised learning to pretrain models using orbital-level features in order to mitigate reliance on extensive labeled data sets.
View Article and Find Full Text PDFChemistry
September 2025
Institute of Visual Computing, Graz University of Technology, Inffeldgasse 16c, Graz, 8010, Austria.
Polyoxometalates (POMs) are nanoscale, structurally versatile metal-oxo clusters with emerging applications in sustainability, energy, nanoelectronics, and life science technologies. Owing to their structural complexity, some all-inorganic POMs are often perceived as serendipitous outcomes from self-assembly processes, which poses challenges for scalable rational design. From this perspective, we therefore examine how the development of POM informatics and, more generally, data-driven POM exploration can pave the way for the molecular engineering of new POM-based materials targeting customized applications.
View Article and Find Full Text PDFAnat Sci Educ
September 2025
Human Anatomy, Vita-Salute San Raffaele University, Milan, Italy.
As emerging technologies reshape both the body and how we represent it, anatomical education stands at a threshold. Virtual dissection tools, AI-generated images, and immersive platforms are redefining how students learn anatomy, while real-world bodies are becoming hybridized through implants, neural interfaces, and bioengineered components. This Viewpoint explores what it means to teach human anatomy when the body is no longer entirely natural, and the image is no longer entirely real.
View Article and Find Full Text PDFAm J Ophthalmol
September 2025
Department of Ophthalmology, Université de Montréal, Montréal, Québec, Canada.
Purpose: To evaluate the incidence, risk factors, management strategies, and visual outcomes of retinal detachment (RD) following Boston Keratoprosthesis Type 1 (KPro) implantation.
Design: Single-center, retrospective observational case series.
Methods: Medical records of 157 eyes from 122 adult patients who underwent Boston Type 1 KPro implantation at a tertiary care center between 2008 and 2022 were reviewed.