Genetic variation in BEACON influences quantitative variation in metabolic syndrome-related phenotypes

The BEACON gene (also known as UBL5) was identified as differentially expressed between lean and obese Psammomys obesus, a polygenic animal model of obesity, type 2 diabetes, and dyslipidemia. The human homologue of BEACON is located on chromosome 19p, a region likely to contain genes affecting metabolic syndrome–related quantitative traits as established by linkage studies. To assess whether the human BEACON gene may be involved in influencing these traits, we exhaustively analyzed the complete gene for genetic variation in 40 unrelated individuals and identified four variants (three novel). The two more common variants were tested for association with a number of quantitative metabolic syndrome–related traits in two large cohorts of unrelated individuals. Significant associations were found between these variants and fat mass (P = 0.026), percentage of fat (P = 0.001), and waist-to-hip ratio (P = 0.031). The same variants were also associated with total cholesterol (P = 0.024), LDL cholesterol (P = 0.019), triglycerides (P = 0.006), and postglucose load insulin levels (P = 0.018). Multivariate analysis of these correlated phenotypes also yielded a highly significant association (P = 0.0004), suggesting that BEACON may influence phenotypic variation in metabolic syndrome–related traits.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.