Studies designed to examine effects of weight reduction by dieting on total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), high-density-lipoprotein cholesterol (HDL-C), very-low-density-lipoprotein cholesterol (VLDL-C), and triglycerides (TGs) have reported inconsistent results. The purpose of this study was to quantify effects of weight loss by dieting on lipids and lipoproteins through the review method of meta-analysis. Results from the 70 studies analyzed indicated that weight reduction was associated with significant decreases (P less than or equal to 0.001) and correlations (P less than or equal to 0.05) for TC (r = 0.32), LDL-C (r = 0.29), VLDL-C (r = 0.38), and TG (r = 0.32). For every kilogram decrease in body weight, a 0.009-mmol/L increase (P less than or equal to 0.01) in HDL-C occurred for subjects at a stabilized, reduced weight and a 0.007-mmol/L decrease (P less than or equal to 0.05) for subjects actively losing weight. Our results indicate that weight reduction through dieting can be a viable approach to help normalize plasma lipids and lipoproteins in overweight individuals.

Childhood obesity is currently one of the most prevailing and challenging public health issues among industrialized countries and of international priority. The global prevalence of obesity poses such a serious concern that the World Health Organization (WHO) has described it as a “global epidemic.” Recent literature suggests that the genesis of the problem occurs in the first years of life as feeding patterns, dietary habits, and parental feeding practices are established. Obesity prevention evidence points to specific dietary factors, such as the promotion of breastfeeding and appropriate introduction of nutritious complementary foods, but also calls for attention to parental feeding practices, awareness of appropriate responses to infant hunger and satiety cues, physical activity/inactivity behaviors, infant sleep duration, and family meals. Interventions that begin at birth, targeting multiple factors related to healthy growth, have not been adequately studied. Due to the overwhelming importance and global significance of excess weight within pediatric populations, this narrative review was undertaken to summarize factors associated with overweight and obesity among infants and toddlers, with focus on potentially modifiable risk factors beginning at birth, and to address the need for early intervention prevention.

The latest exhaustive survey of dietary patterns in infants from the Feeding Infants and Toddlers Study (FITS) in North America documents and quantifies current trends in infant feeding. These include higher than generally recommended energy, protein, and saturated fat intakes. The majority of infants are bottle fed at some point in their first year of life, and their weaning diet often includes low intakes of fruits and vegetables, with high starchy, rather than green or yellow, vegetables. Early introduction of solids, use of cow's milk prior to 1 year of age, and high juice intake in the first 2 years - all less desirable diet practices - are improving, but are still prevalent. More preschoolers are likely to get sweets or sweetened beverages than a serving of fruit or a vegetable on a given day. These food intake patterns mimic the adult American diet and are associated with an increased risk of obesity in childhood and later life. But more importantly, these patterns appear to be set as early as 18 months of age, and by 20 months of age, they mimic the adult diet. Despite increase in total energy intake, and greater variety of foods, the basic characteristics of macronutrient intake distribution and food group contribution of energy to the diet before 2 years of age remain remarkably stable and similar to the family table. Obesity prevention needs to include specific targets in terms of breastfeeding and adequate formula feeding, as well as appropriate introduction of weaning foods with goals of changing the inadequate patterns documented in the FITS. These interventions will also require addressing parent and caregiver behaviors, including attending to hunger satiety cues (responsive feeding), and shaping early food preferences. This needs to be done starting at birth, in the first months of life. Early intervention offers a unique and potentially efficacious opportunity to shape the future dietary patterns of the next generation.

NASA's Transiting Exoplanet Survey Satellite (TESS) presents us with an unprecedented volume of space-based photometric observations that must be analyzed in an efficient and unbiased manner. With at least ∼1,000,000 new light curves generated every month from full-frame images alone, automated planet candidate identification has become an attractive alternative to human vetting. Here we present a deep learning model capable of performing triage and vetting on TESS candidates. Our model is modified from an existing neural network designed to automatically classify Kepler candidates, and is the first neural network to be trained and tested on real TESS data. In triage mode, our model can distinguish transit-like signals (planet candidates and eclipsing binaries) from stellar variability and instrumental noise with an average precision (the weighted mean of precisions over all classification thresholds) of 97.0% and an accuracy of 97.4%. In vetting mode, the model is trained to identify only planet candidates with the help of newly added scientific domain knowledge, and achieves an average precision of 69.3% and an accuracy of 97.8%. We apply our model on new data from Sector 6, and present 288 new signals that received the highest scores in triage and vetting and were also identified as planet candidates by human vetters. We also provide a homogeneously classified set of TESS candidates suitable for future training.

For years, scientists have used data from NASA's Kepler Space Telescope to look for and discover thousands of transiting exoplanets. In its extended K2 mission, Kepler observed stars in various regions of sky all across the ecliptic plane, and therefore in different galactic environments. Astronomers want to learn how the population of exoplanets are different in these different environments. However, this requires an automatic and unbiased way to identify the exoplanets in these regions and rule out false positive signals that mimic transiting planet signals. We present a method for classifying these exoplanet signals using deep learning, a class of machine learning algorithms that have become popular in fields ranging from medical science to linguistics. We modified a neural network previously used to identify exoplanets in the Kepler field to be able to identify exoplanets in different K2 campaigns, which range in galactic environments. We train a convolutional neural network, called AstroNet-K2, to predict whether a given possible exoplanet signal is really caused by an exoplanet or a false positive. AstroNet-K2 is highly successful at classifying exoplanets and false positives, with accuracy of 98% on our test set. It is especially efficient at identifying and culling false positives, but for now, still needs human supervision to create a complete and reliable planet candidate sample. We use AstroNet-K2 to identify and validate two previously unknown exoplanets. Our method is a step towards automatically identifying new exoplanets in K2 data and learning how exoplanet populations depend on their galactic birthplace.