Woodwardgalloway7425

Family based multi-locus tests integrate information from individual loci by weighted averaging of the marginal statistics, and have been proven to be more efficient and robust than the single-locus tests in genetic association studies. The power depends on how much information the weights can extract from data. The currently published weighted sum methods are only applicable to either common or rare variants and may suffer from substantial power loss especially for rare variants. In this paper, we propose a novel data-driven weight to improve the power under both common and rare variant circumstances. We use the l1 regularization in Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the weight serving as a simultaneously adaptive marker selection process. Simulations for a dichotomous phenotype demonstrated that our LASSO-based approach outperformed the existing multi-locus methods in the sense of providing the highest statistical power while well controlled type I error rate under different scenarios. We also applied our methods to a real dataset for rheumatoid arthritis (GAW15 Problem 2). Two groups of alleles, in which individual SNPs had only modest and non-significant effects, were detected (P less then 0.00001) using our proposed methods, whereas traditional multi-locus methods failed to identify them. In conclusion, the novel LASSO-based approach represents a superior weight-choosing strategy for multi-locus tests.1-(2-ethylsulfonylethyl)-2-methyl-5-nitro-imidazole (1EMI) C8H13N3O4S also known as Tinidazole, selected for its antiprotozoal property is extensively used for spectroscopic elucidations and computational aspects using density functional methods. Along with spectral conclusions, further investigations on fundamental reactive properties such as electrical, optical, nonlinear combined with DFT simulations were performed. Molecular docking procedure supports the results of chosen appropriate antiprotozoal agent based on ligand-protein interactions. Experimental and simulated (B3LYP/6-311++G (d,p)) IR and Raman spectra showed concurrence. NLO analysis through first order hyperpolarizability parameter helps in finding the potential of 1EMI as a good NLO candidate. Charge delocalization and the stability of the compound were discussed using natural bond orbital (NBO) analysis. Furthermore, Electron localization function (ELF), local orbital locator (LOL), and Frontier molecular orbitals (FMO) were studied. Besides,ere presented.Our ability to understand and intervene on eating in the absence of hunger (EAH) as it occurs in peoples' natural environments is hindered by biased methods that lack ecological validity. One promising indicator of EAH that does not rely on self-report and is easily assessed in free-living individuals is glucose. Here, we hypothesize that elevated pre-prandial blood glucose concentrations (PPBG), which reflect a source of readily-available, short-term energy, are a biological indicator of EAH. This was a 7-day observational study of N = 41, 18-24 year old men and women with BMI less then 25 kg/m2 (60%) or BMI ≥ 25 kg/m2 (40%). We collected data using ecological momentary assessment from people in their natural environments. We defined EAH by self-report (perceived EAH) and by PPBG thresholds using two methods (standardized, PPBG less then 85 mg/dl; personalized, PPBG less then individual fasting levels). Multilevel modeling was used to analyze the data. N = 963 eating events were reported. There were significantly (p less then .05) fewer perceived EAH events (25%) as compared to standardized (62%) and personalized PPBG-defined EAH events (51%). Consistent with published literature, perceived EAH was more likely to occur at a higher PPBG (p less then .01), particularly among participants with a BMI ≥ 25 kg/m2 (pint less then .01). Additionally, discordance between perceived EAH and PPBG-defined EAH, indicating a perception of hunger at an eating event when PPBS was elevated, was less likely among participants with a BMI less then 25 kg/m2 vs. those with a BMI ≥ 25 kg/m2 (pint less then .01) as well as at snacks vs. meals (pint less then .01). These findings provide preliminary support for using PPBG as a biological indicator of EAH in free-living individuals.Approaching to the biological neural network, small-world neural networks have been demonstrated to improve the generalization performance of artificial neural networks. However, the architecture of small-world neural networks is typically large and predefined. This may cause the problems of overfitting and time consuming, and cannot obtain an optimal network structure automatically for a given problem. see more To solve the above problems, this paper proposes a pruning feedforward small-world neural network (PFSWNN), and applies it to nonlinear system modeling. Firstly, a feedforward small-world neural network (FSWNN) is constructed according to the rewiring rule of Watts-Strogatz. Secondly, the importance of each hidden neuron is evaluated based on its Katz centrality. If the Katz centrality of a hidden neuron is below the predefined threshold, this neuron is considered to be an unimportant node and then merged with its most correlated neuron in the same hidden layer. The connection weights are trained using the gradient-based algorithm, and the convergence of the proposed PFSWNN is theoretically analyzed in this paper. Finally, the PFSWNN model is tested on some problems for nonlinear system modeling, including the approximation for a rapidly changing function, CATS missing time-series prediction, four benchmark problems of UCI public datasets and a practical problem for wastewater treatment process. Experimental results demonstrate that PFSWNN exhibits superior generalization performance by small-world property as well as the pruning algorithm, and the training time of PFSWNN is shortened owning to a compact structure.A surge in the availability of data from multiple sources and modalities is correlated with advances in how to obtain, compress, store, transfer, and process large amounts of complex high-dimensional data. The clustering challenge increases with the growth of data dimensionality which decreases the discriminate power of the distance metrics. Subspace clustering aims to group data drawn from a union of subspaces. In such a way, there is a large number of state-of-the-art approaches and we divide them into families regarding the method used in the clustering. We introduce a soft subspace clustering algorithm, a Self-organizing Map (SOM) with a time-varying structure, to cluster data without any prior knowledge of the number of categories or of the neural network topology, both determined during the training process. The model also assigns proper relevancies (weights) to different dimensions, capturing from the learning process the influence of each dimension on uncovering clusters. We employ a number of real-world datasets to validate the model.