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Diet

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Using artificial neural networks to predict pH, ammonia, and volatile fatty acid concentrations in the rumen.

Journal of dairy science
The objectives of this study were (1) to predict ruminal pH and ruminal ammonia and volatile fatty acid (VFA) concentrations by developing artificial neural networks (ANN) using dietary nutrient compositions, dry matter intake, and body weight as inp...

Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.

Artificial intelligence in medicine
BACKGROUND: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabet...

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods.

Scientific reports
Recent data indicate that up-to 30-40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have bee...

An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content.

Nutrients
BACKGROUND: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research out...

Duplicate portion sampling gives the most accurate iron intake estimate: Implications for iron status of female students.

International journal for vitamin and nutrition research. Internationale Zeitschrift fur Vitamin- und Ernahrungsforschung. Journal international de vitaminologie et de nutrition
Iron deficiency (ID) is one of the most common nutritional problems in the developing world, affecting primarily women of childbearing age. Poor dietary iron intake is a major reason of ID. Accurate measurement of iron intake is of crucial importance...

Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map.

Nutrients
An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume ...

Exploring the interactions between serum free fatty acids and fecal microbiota in obesity through a machine learning algorithm.

Food research international (Ottawa, Ont.)
Serum free fatty acids (FFA) are generally elevated in obesity. The gut microbiota is involved in the host energy metabolism through the regulation of body fat storage, and a link between diet, FFA and the intestinal microbiota seems to exist. Our ai...

Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features.

Sensors (Basel, Switzerland)
The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout () were fed either a fish-meal based diet (...

Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

Scientific reports
Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress...