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Nutrient based classification of Phyllospora comosa biomasses using machine learning algorithms: Towards sustainable valorisation.
Food Res Int
February 2025
Nutrition and Seafood Laboratory (NuSea.Lab), School of Life and Environmental Sciences, Deakin University, Queenscliff, VIC, Australia. Electronic address:
Sustainable seaweed value chains necessitate accurate biomass biochemical characterisation that leads to product development, geographical authentications and quality and sustainability assurances. Underutilised yet abundantly available seaweed species require a thorough investigation of biochemical characteristics prior to their valorisation. Abundantly available Australian seaweed species lack such comprehensive investigations within the global seaweed industrial value chains.
View Article and Find Full Text PDFOptimal network sizes for most robust Turing patterns.
Sci Rep
January 2025
Department of Life Sciences, Imperial College, London, SW7 2AZ, UK.
Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties.
View Article and Find Full Text PDFReal-world effectiveness of mepolizumab in Japanese asthma patients with diverse backgrounds: Improvements in rhinosinusitis imaging (J-Real-Mepo).
Allergol Int
January 2025
Division of Respiratory Medicine, Department of Internal Medicine, Nihon University School of Medicine, Tokyo, Japan.
Background: Although randomized controlled trials (RCT) have demonstrated the efficacy of mepolizumab for asthma, they have excluded certain patient subgroups. To bridge the gap between RCT and real-world practice, the effectiveness of mepolizumab in a diverse population, including those potentially excluded from RCT, was assessed. Its effects on imaging findings and symptoms of chronic rhinosinusitis (CRS) with asthma were also assessed.
View Article and Find Full Text PDFBridging the Healthcare Gap in Rural Populations: Challenges, Innovations, and Solutions.
Am J Med
January 2025
Southeast Iowa Regional Medical Center, West Burlington, IA, 52655. Electronic address:
Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data.
Nutrition
December 2024
Central University of Jharkhand, Ranchi, Jharkland, India. Electronic address:
Objectives: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the complex interplay of socioeconomic, demographic, and environmental factors. This study bridges this gap by employing machine learning methods to predict stunting at the household level, using data from the National Family Health Survey combined with satellite-driven datasets.
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