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Ashley Eisner,Pavithra Ramachandran,Conralyn Cabalbag,Dina Metti,Pouneh Shamloufard,Mark Kern,Mee Young Hong,Shirin Hooshmand 한국식품영양과학회 2020 Journal of medicinal food Vol.23 No.3
Consumption of fruits reduces the risk of chronic diseases such as cardiovascular disease; however, very few studies have investigated the effect of fruit consumption in overweight and obese children. We examined whether consuming dried apple as a snack is a practical solution for weight loss and improves body composition and metabolic markers. Thirty-eight overweight or obese children aged 10 to 16 years were randomly assigned to one of two groups consuming twice daily 120 kcal serving per day of either dried apple or a control snack (muffin) for 8 weeks. Body weight, height, waist circumference, and body composition were determined during an initial visit and after 8 weeks of intervention. Blood samples were collected to measure serum concentrations of blood lipids, glucose, insulin, proinsulin, total adiponectin, and C-reactive protein, as well as total antioxidant capacity and activity of glutathione peroxidase. Body weight increased in the muffin group (P = .01). BodPod and dual-energy X-ray absorptiometry showed that fat-free mass increased (P < .05) only in the muffin group. High-density lipoprotein cholesterol concentration increased (P = .04) after the 8-week treatment within the apple group. Overall, minor differences were detected in growing children who consumed snacks of either dried apples or muffins with similar macronutrient profiles for 8 weeks. Future research should evaluate the effects of consuming fresh apples that include the peel.
A MACHINE-LEARNING METHOD TO INFER FUNDAMENTAL STELLAR PARAMETERS FROM PHOTOMETRIC LIGHT CURVES
Miller, A. A.,Bloom, J. S.,Richards, J. W.,Lee, Y. S.,Starr, D. L.,Butler, N. R.,Tokarz, S.,Smith, N.,Eisner, J. A. IOP Publishing 2015 The Astrophysical journal Vol.798 No.2
<P>A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are >10(9) photometrically cataloged sources, yet modern spectroscopic surveys are limited to similar to fewx10(6) targets. As we approach the Large Synoptic Survey Telescope era, new algorithmic solutions are required to cope with the data deluge. Here we report the development of a machine-learning framework capable of inferring fundamental stellar parameters (T-eff, log g, and [Fe/H]) using photometric-brightness variations and color alone. A training set is constructed from a systematic spectroscopic survey of variables with Hectospec/ Multi-Mirror Telescope. In sum, the training set includes similar to 9000 spectra, for which stellar parameters are measured using the SEGUE Stellar Parameters Pipeline (SSPP). We employed the random forest algorithm to perform a non-parametric regression that predicts Teff, log g, and [Fe/H] from photometric time-domain observations. Our final optimized model produces a cross-validated rms error (RMSE) of 165 K, 0.39 dex, and 0.33 dex for T-eff, log g, and [Fe/H], respectively. Examining the subset of sources for which the SSPP measurements are most reliable, the RMSE reduces to 125 K, 0.37 dex, and 0.27 dex, respectively, comparable to what is achievable via low-resolution spectroscopy. For variable stars this represents a approximate to 12%-20% improvement in RMSE relative to models trained with single-epoch photometric colors. As an application of our method, we estimate stellar parameters for similar to 54,000 known variables. We argue that this method may convert photometric time-domain surveys into pseudo-spectrographic engines, enabling the construction of extremely detailed maps of the Milky Way, its structure, and history.</P>