A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis

An increasingly popular and promising way for complex disease diagnosis is to employ artificial neural networks (ANN). Single nucleotide polymorphisms (SNP) data from individuals is used as the inputs of ANN to find out specific SNP patterns related to certain disease. Due to the large number of SNPs, it is crucial to select optimal SNP subset and their combinations so that the inputs of ANN can be reduced. With this observation in mind, a hybrid approach - a combination of genetic algorithms (GA) and ANN (called GANN) is used to automatically determine optimal SNP set and optimize the structure of ANN. The proposed GANN algorithm is evaluated by using both a synthetic dataset and a real SNP dataset of a complex disease.<br /

An embedded two-layer feature selection approach for microarray data analysis

Feature selection is an important technique in dealing with application problems with large number of variables and limited training samples, such as image processing, combinatorial chemistry, and microarray analysis. Commonly employed feature selection strategies can be divided into filter and wrapper. In this study, we propose an embedded two-layer feature selection approach to combining the advantages of filter and wrapper algorithms while avoiding their drawbacks. The hybrid algorithm, called GAEF (Genetic Algorithm with embedded filter), divides the feature selection process into two stages. In the first stage, Genetic Algorithm (GA) is employed to pre-select features while in the second stage a filter selector is used to further identify a small feature subset for accurate sample classification. Three benchmark microarray datasets are used to evaluate the proposed algorithm. The experimental results suggest that this embedded two-layer feature selection strategy is able to improve the stability of the selection results as well as the sample classification accuracy.<br /

An ensemble of classifiers with genetic algorithmBased Feature Selection

Different data classification algorithms have been developed and applied in various areas to analyze and extract valuable information and patterns from large datasets with noise and missing values. However, none of them could consistently perform well over all datasets. To this end, ensemble methods have been suggested as the promising measures. This paper proposes a novel hybrid algorithm, which is the combination of a multi-objective Genetic Algorithm (GA) and an ensemble classifier. While the ensemble classifier, which consists of a decision tree classifier, an Artificial Neural Network (ANN) classifier, and a Support Vector Machine (SVM) classifier, is used as the classification committee, the multi-objective Genetic Algorithm is employed as the feature selector to facilitate the ensemble classifier to improve the overall sample classification accuracy while also identifying the most important features in the dataset of interest. The proposed GA-Ensemble method is tested on three benchmark datasets, and compared with each individual classifier as well as the methods based on mutual information theory, bagging and boosting. The results suggest that this GA-Ensemble method outperform other algorithms in comparison, and be a useful method for classification and feature selection problems.<br /

CARBON DIOXIDE SEQUESTRATION: WHEN AND HOWMUCH?

We analyze carbon dioxide (CO sequestration as a strategy to manage future climate change in an optimal economic growth framework. We approach the problem in two ways: first, by using a simple analytical model, and second, by using a numerical optimization model which allows us to explore the problem in a more realistic setting. CO sequestration is not a perfect substitute for avoiding CO2 production because CO2 leaks back to the atmosphere and hence imposes future costs. The “efficiency factor” of CO2 sequestration can be expressed as the ratio of the avoided emissions to the economically equivalent amount of sequestered CO2 emissions. A simple analytical model in terms of a net-present value criterion suggests that short-term sequestration methods such as afforestation can be somewhat ( 60 %) efficient, while long term sequestration (such as deep aquifer or deep ocean sequestration) can be very ( 90%) efficient. A numerical study indicates that CO2 sequestration methods at a cost within the range of present estimates reduce the economically optimal CO2 concentrations and climate related damages. The potential savings associated with CO2 sequestration is equivalent in our utilitarian model to a one-time investment of several percent of present gross world product.

Analysis on the Significance of Effects from Operational Conditions on the Performances of Ultrasonic Atomization Dehumidifier with Liquid Desiccant

AbstractIn this work, simulations were carried out based on a L18×L8 cross-product orthogonal array to investigate the significance of the effects from inlet operational conditions on the performances of the ultrasonic atomization liquid desiccant dehumidification system (UADS), where dehumidification effectiveness was adopted as the performance indicator. Taguchi method was employed to analyze the results. It was found that though all of the inlet operational parameters revealed direct effects on the performances of UADS, the significance of their effects was quite different among which, the desiccant flow rate was the most sensible factor in improving DE while air humidity ratio exhibited the least significance. The results presented in this work may help in achieving the optimal running of the liquid desiccant dehumidification system

An agent-based hybrid system for microarray data analysis

This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.<br /

Buddleoside inhibits TLR4-related pathway in a mouse model of acute liver failure, promotes autophagy, and inhibits inflammation

Purpose: To study the inhibitory influence of buddleoside on TLR4-associated pathway, autophagy and inflammation in a mouse model of acute liver failure (ALF).Methods: Sixty male C57BL/6 mice were assigned to 5 groups: control, model, and three dose-groups of buddleoside, with 12 mice per group. Levels of interleukin (IL)-1, IL-6, TLR4 pathway-associated proteins, and autophagy-related proteins in each group were determined; cell adhesion in each group was also analyzed.Results: Levels of TLR4, MAPK and NF-кB-related pathways in model mice were significantly upregulated, relative to control mice, but they were more down-regulated in the 3 anthocyanin groups than in model group (p &lt; 0.05). There were significantly higher levels of TNF-α, IL- and IL-6 in model mice than in the control group, but they were down-regulated in high-, medium- and low-dose mice, relative to model mice. The population of adherent cells was significantly higher in ALF mice than in controls, butthere were markedly lower numbers of these cells in the 3 anthocyanin-treated mice than in model mice (p &lt; 0.05).Conclusion: Buddleoside mitigates ALF in mice by down-regulating inflammatory factors, reducing serum levels of ALT and AST, and up-regulating autophagy-related protein expressions by activating TLR4/MAPK/NF-кB signaling pathway. Thus, buddleoside may be useful in the treatment of acute liver failure, but this has to be curtained through clinical trials