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Glycemic index diet for dummies
Eating fewer carbohydrates may be trendy-- but since your body needs them to function, eliminating too many carbs from your diet is neither healthy nor practical. Learn the glycemic index system to help you shed unwanted pounds and improve your overall health.-- Source other than Library of Congress.
Book
Genetic studies of body mass index yield new insights for obesity biology
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (
P
< 5 × 10
−8
), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
A genome-wide association study and Metabochip meta-analysis of body mass index (BMI) detects 97 BMI-associated loci, of which 56 were novel, and many loci have effects on other metabolic phenotypes; pathway analyses implicate the central nervous system in obesity susceptibility and new pathways such as those related to synaptic function, energy metabolism, lipid biology and adipogenesis.
Genetic correlates of obesity
In the second of two Articles in this issue from the GIANT Consortium, Elizabeth Speliotes and collegues conducted a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), commonly used to define obesity and assess adiposity, to find 97 BMI-associated loci, of which 56 were novel. Many of these loci have significant effects on other metabolic phenotypes. The 97 loci account for about 2.7% of BMI variation, and genome-wide estimates suggest common variation accounts for more than 20% of BMI variation. Pathway analyses implicate the central nervous system in obesity susceptibility including synaptic function, glutamate signaling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
Journal Article
Sensitivity of Ground-Based Remote Sensing Estimates of Wheat Chlorophyll Content to Variation in Soil Reflectance
Spectral indices (SI) derived from crop reflectance data are sensitive to chlorophyll a and b content (Chl). However, the SI-Chl relationship might be confounded by variation in leaf area index (LAI) and soil background reflectance, especially in semiarid environments where water determines crop growth. This study evaluated the sensitivity of SI to variation in soil reflectance and how this may affect overall SI performance for ground-based sensing of Chl in dryland wheat (Triticum aestivum L.). Selected SI were computed from spectra simulated by the PROSPECT-SAIL radiative-transfer model for 5 LAI values, 7 Chl values, and 121 dry soil surface reflectance spectra. These spectra represented soils across major wheat growing areas in the United States. Soil properties and reflectance varied widely among the soils indicated by the high SI variation for LAI values < 1.5. Overall, soil background variation contributed less to the observed SI variability (<6%) than LAI (<97%). Combined indices [i.e., Normalized Difference Red Edge Index (NDRE)/Normalized Difference Vegetation Index (NDVI) and Modified Chlorophyll Absorption Ratio Index (MCARI)/Second Modified Triangular Vegetation Index (MTVI)] were least affected by soil background variation than single indices (i.e., NDVI). Results showed that ground sensing of Chl may be improved by means of combined indices that are resistant to soil background and LAI. Empirical measurements verified that the modeling results were a reliable representation of the influence of Chl, LAI, and soil on canopy reflectance. Further research is needed to evaluate the effect of soil moisture, surface roughness, residue, growth stage, and shadow on SI.
Journal Article
Exchange-traded funds for dummies
Shows you in plain English how to weigh your options and confidently pick the ETFs that are right for you to build a lean, mean portfolio and optimize your profits.
Book