Dataset Information

Evaporative cooling provides a major metabolic energy sink.

ABSTRACT:

Objective

Elimination of food calories as heat could help redress the excess accumulation of metabolic energy exhibited as obesity. Prior studies have focused on the induction of thermogenesis in beige and brown adipose tissues as the application of this principle, particularly because the ?-adrenergic environment associated with thermogenic activation has been shown to have positive health implications. The counterpoint to this strategy is the regulation of heat loss; we propose that mammals with inefficient heat conservation will require more thermogenesis to maintain body temperature.

Methods

Surface temperature thermography and rates of trans-epidermal water loss were integrated to profile the total heat transfer of genetically-engineered and genetically variable mice.

Results

These data were incorporated with energy expenditure data to generate a biophysical profile to test the significance of increased rates of evaporative cooling.

Conclusions

We show that mouse skins vary considerably in their heat retention properties, whether because of naturally occurring variation (SKH-1 mice), or genetic modification of the heat-retaining lipid lamellae (SCD1, DGAT1 or Agouti A^y obese mice). In particular, we turn attention to widely different rates of evaporative cooling as the result of trans-epidermal water loss; higher rates of heat loss by evaporative cooling leads to increased demand for thermogenesis. We speculate that this physiology could be harnessed to create an energy sink to assist with strategies aimed at treating metabolic diseases.

SUBMITTER: Kasza I

PROVIDER: S-EPMC6717770 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

Evaporative cooling provides a major metabolic energy sink.

Kasza Ildiko I Adler Doug D Nelson David W DW Eric Yen C-L CL Dumas Sabrina S Ntambi James M JM MacDougald Ormond A OA Hernando Diego D Porter Warren P WP Best Fred A FA Alexander C M CM

Molecular metabolism 20190701

<h4>Objective</h4>Elimination of food calories as heat could help redress the excess accumulation of metabolic energy exhibited as obesity. Prior studies have focused on the induction of thermogenesis in beige and brown adipose tissues as the application of this principle, particularly because the β-adrenergic environment associated with thermogenic activation has been shown to have positive health implications. The counterpoint to this strategy is the regulation of heat loss; we propose that ma ...[more]

PMID: 31302039

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Project description:MotivationClassification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting.MethodsWe introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection.ResultsOn simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder.Availability and implementationCode available at http://insilico.utulsa.edu/software/privateEC .Contactbrett-mckinney@utulsa.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

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Dataset Information

Evaporative cooling provides a major metabolic energy sink.

Objective

Methods

Results

Conclusions

Publications

Evaporative cooling provides a major metabolic energy sink.

Similar Datasets

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