Joint FAO/WHO ad hoc Expert Committee, Energy and Protein Requirements, WHO Technical Report Series 522.

Abstract

Most adults have built-in controls for appetite: their food intake is governed by physiological needs, and the control is so good that body weight varies but little over long periods of time. The regularity of the growth

Sleep Patterns and Tryptophan Consumption among Students at Spanish Universities: The Unihcos Project

“…In this extensive university student study, we identified that more than 50% of participants experience sleep problems, with differences noted by gender, and dietary tryptophan consumption exceeded the current recommended levels [39,41,42]. This prevalence of sleep problems in university students is higher than that described in other countries [57,58].…”

“…However, while in this study causality cannot be attributed, an association with tryptophan intake level and sleep problems is present. Students in the lowest quartiles of intake have a higher risk of sleep problems than those with higher intakes even though they mainly comply with the higher end of the commonly accepted recommendations for tryptophan intake of 4.5 mg/kg of bodyweight [39][40][41][42].…”

“…Intake was then stratified into quartiles for analysis. To evaluate tryptophan intake adequacy, the estimated daily recommended intake was calculated using the recommendation of 4.5 mg/kg of bodyweight [39][40][41][42].…”

“…Tryptophan is an essential amino acid and a precursor of serotonin and melatonin, found mostly in animal products, such as beef, lamb, pork, poultry, and dairy, as well as in nuts and seeds, whole grains, and legumes, which are directly related to sleep quality and duration. The reference dietary intake for tryptophan is 4.5 mg per kilogram of body weight [39][40][41][42]. Therefore, a person weighing 70 kg should consume around 315 mg of tryptophan per day.…”

Interactions between fecal gut microbiome, enteric pathogens, and energy regulating hormones among acutely malnourished rural Gambian children

“…Used in the context aggregate networks and predictive modeling Protein energy malnutrition PEM Protein-energy malnutrition defined as a range of pathological conditions arising from inadequate calories and/or protein intake. [1] Modeling Clairvoyance Feature selection algorithm leveraged for phenotype-discriminative community detection [58] Hierarchical Ensemble of Classifiers HEC Graphical model where each internal node is a customized sub-model classifier with a unique feature set [58] Sub-model Machine-learning classification model used as internal node in a HEC model Leave subject out cross-validation LSOCV Cross-validation designed to simulate performance on a new subject This study Networks Background network BN Networks created from individuals who were WN for all This study Perturbed background network PBN Networks created when adding in a query individual to the background network This study Sample-specific network SSN Network with unique properties for each sample [80] Sample-specific perturbation network SSPN Network created from perturbation between BN and SSPN distributions This study Aggregate network AN Networks created from fitted sub-model coefficients This study Node The discrete objects within a network Edge Weighted connections between nodes Edge weight Association or perturbation strength of edge Perturbation Change in association strength of an edge between SSN and BN distributions Connectivity k ...…”