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Emerging antimicrobial-resistant (AMR) Candida auris presents a formidable global health challenge, causing severe healthcare-associated infections (HAIs) with high mortality rates. Its ability to colonize surfaces and resist standard disinfectants undermines traditional hygiene practices, prompting an urgent need for new strategies. Ultraviolet C (UVC) light offers a promising approach with rapid and broad-spectrum germicidal efficacy. This review examines current literature on UVC LED technology in combating C. auris, highlighting its effectiveness, limitations, and applications in healthcare hygiene. UVC light has potent activity against C. auris, with up to 99.9999% inactivation depending on certain conditions such as microbial load, type of organism, surface, environmental, equipment, and UVC radiation factors. UVC LEDs can effectively combat C. auris, driving down healthcare costs and reducing attributable global mortality. Here, we explore implementation strategies for the targeted disinfection of high-risk areas and equipment, air handling units (AHUs), and water treatment systems. Challenges associated with UVC LED disinfection devices in healthcare settings, current performance limitations, and radiation safety are discussed. This will help in optimizing application protocols for effective disinfection and radiation safety. To further strengthen healthcare facility hygiene practices and curb the global spread of C. auris, recommendations for integrating UVC LED disinfection into infection control programs are shared. [ABSTRACT FROM AUTHOR]

The successful application of deep learning approaches in remote sensing image classification requires large hyperspectral image (HSI) datasets to learn discriminative spectral–spatial features simultaneously. To date, the HSI datasets available for image classification are relatively small to train deep learning methods. This study proposes a deep 3D/2D genome graph-based network (abbreviated as HybridGBN-SR) that is computationally efficient and not prone to overfitting even with extremely few training sample data. At the feature extraction level, the HybridGBN-SR utilizes the three-dimensional (3D) and two-dimensional (2D) Genoblocks trained using very few samples while improving HSI classification accuracy. The design of a Genoblock is based on a biological genome graph. From the experimental results, the study shows that our model achieves better classification accuracy than the compared state-of-the-art methods over the three publicly available HSI benchmarking datasets such as the Indian Pines (IP), the University of Pavia (UP), and the Salinas Scene (SA). For instance, using only 5% labeled data for training in IP, and 1% in UP and SA, the overall classification accuracy of the proposed HybridGBN-SR is 97.42%, 97.85%, and 99.34%, respectively, which is better than the compared state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Human noroviruses (HuNoVs) are a major cause of gastroenteritis and are associated with high morbidity because of their ability to survive in the environment and small inoculum size required for infection. Norovirus is transmitted through water, food, high touch-surfaces, and human-to-human contact. Ultraviolet Subtype C (UVC) light-emitting diodes (LEDs) can disrupt the norovirus transmission chain for water, food, and surfaces. Here, we illuminate considerations to be adhered to when picking norovirus surrogates for disinfection studies and shine light on effective use of UVC for norovirus infection control in water and air and validation for such systems and explore the blind spot of radiation safety considerations when using UVC disinfection strategies. This perspective also discusses the promise of UVC for norovirus mitigation to save and ease life. [ABSTRACT FROM AUTHOR]

Recently developed hybrid models that stack 3D with 2D CNN in their structure have enjoyed high popularity due to their appealing performance in hyperspectral image classification tasks. On the other hand, biological genome graphs have demonstrated their effectiveness in enhancing the scalability and accuracy of genomic analysis. We propose an innovative deep genome graph-based network (GGBN) for hyperspectral image classification to tap the potential of hybrid models and genome graphs. The GGBN model utilizes 3D-CNN at the bottom layers and 2D-CNNs at the top layers to process spectral–spatial features vital to enhancing the scalability and accuracy of hyperspectral image classification. To verify the effectiveness of the GGBN model, we conducted classification experiments on Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SA) datasets. Using only 5% of the labeled data for training over the SA, IP, and UP datasets, the classification accuracy of GGBN is 99.97%, 96.85%, and 99.74%, respectively, which is better than the compared state-of-the-art methods. [ABSTRACT FROM AUTHOR]