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Protocol of the Nutritional, Psychosocial, and Environmental Determinants of Neurodevelopment and Child Mental Health (COINCIDE) study.
Wellcome Open Res
November 2024
Indian Institute of Public Health-Bengaluru, Public Health Foundation of India, Bangalore, India.
Background: Over 250 million children are developing sub-optimally due to their exposure to early life adversities. While previous studies have examined the effects of nutritional status, psychosocial adversities, and environmental pollutants on children's outcomes, little is known about their interaction and cumulative effects.
Objectives: This study aims to investigate the independent, interaction, and cumulative effects of nutritional, psychosocial, and environmental factors on children's cognitive development and mental health in urban and rural India.
Classical and machine learning tools for identifying yellow-seeded by fusion of hyperspectral features.
Front Genet
January 2025
School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia.
Introduction: Due to its favorable traits-such as lower lignin content, higher oil concentration, and increased protein levels-the genetic improvement of yellow-seeded rapeseed has attracted more attention than other rapeseed color variations. Traditionally, yellow-seeded rapeseed has been identified visually, but the complex variability in the seed coat color of has made manual identification challenging and often inaccurate. Another method, using the RGB color system, is frequently employed but is sensitive to photographic conditions, including lighting and camera settings.
View Article and Find Full Text PDFCECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses.
J Multidiscip Healthc
January 2025
Department of Nuclear Medicine, The First People's Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650032, People's Republic of China.
Objective: This study aimed to explore the value of a radiomic nomogram based on contrast-enhanced computed tomography (CECT) for differentiating benign and malignant solid-containing renal masses.
Materials And Methods: A total of 122 patients with pathologically confirmed benign (n=47) or malignant (n=75) solid-containing renal masses were enrolled in this study. Radiomic features were extracted from the arterial, venous and delayed phases and further analysed by dimensionality reduction and selection.
Estimation of leaf area index by combining multi-source remote sensing data and machine learning optimization model.
Front Plant Sci
January 2025
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
The Leaf Area Index (LAI) is an essential parameter that affects the exchange of energy and materials between the vegetative canopy and the surrounding environment. Estimating LAI using machine learning models with remote sensing data has become a prevalent method for large-scale LAI estimation. However, existing machine learning models have exhibited various flaws, hindering the accurate estimation of LAI.
View Article and Find Full Text PDFAnalysis and prediction of atmospheric ozone concentrations using machine learning.
Front Big Data
January 2025
Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland.
Atmospheric ozone chemistry involves various substances and reactions, which makes it a complex system. We analyzed data recorded by Switzerland's National Air Pollution Monitoring Network (NABEL) to showcase the capabilities of machine learning (ML) for the prediction of ozone concentrations (daily averages) and to document a general approach that can be followed by anyone facing similar problems. We evaluated various artificial neural networks and compared them to linear as well as non-linear models deduced with ML.
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