MOANA: Multi-objective ant nesting algorithm for optimization problems.

This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporates adaptive mechanisms, such as deposition weight parameters, to balance exploration and exploitation, while a polynomial mutation strategy ensures diverse and high-quality solutions. The algorithm is evaluated on standard benchmark datasets, including ZDT functions and the IEEE Congress on Evolutionary Computation (CEC) 2019 multi-modal benchmarks.

Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.

Neutrophil NADPH oxidase promotes bacterial eradication and regulates NF-κB-Mediated inflammation via NRF2 signaling during urinary tract infections.

The precise role of neutrophil-derived reactive oxygen species (ROS) in combating bacterial uropathogens during urinary tract infections (UTI) remains largely unexplored. In this study, we elucidate the antimicrobial significance of NADPH oxidase 2 (NOX2)-derived ROS, as opposed to mitochondrial ROS, in facilitating neutrophil-mediated eradication of uropathogenic Escherichia coli (UPEC), the primary causative agent of UTI. Furthermore, NOX2-derived ROS regulate NF-κB-mediated inflammatory responses in neutrophils against UPEC by inducing the release of nuclear factor erythroid 2-related factor 2 (Nrf2) from its inhibitor, Kelch-like ECH-associated protein 1 (Keap1).