Gene ordering in partitive clustering using microarray expressions

A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering and ordering the genes using gene expression data into homogeneous groups was shown to be useful in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on gene ordering in hierarchical clustering framework for gene expression analysis, there is no work addressing and evaluating the importance of gene ordering in partitive clustering framework, to the best knowledge of the authors. Outside the framework of hierarchical clustering, different gene ordering algorithms are applied on the whole data set, and the domain of partitive clustering is still unexplored with gene ordering approaches. A new hybrid method is proposed for ordering genes in each of the clusters obtained from partitive clustering solution, using microarry gene expressions. Two existing algorithms for optimally ordering cities in travelling salesman problem (TSP), namely, FRAG_GALK and Concorde, are hybridized individually with self organizing MAP to show the importance of gene ordering in partitive clustering framework. We validated our hybrid approach using yeast and fibroblast data and showed that our approach improves the result quality of partitive clustering solution, by identifying subclusters within big clusters, grouping functionally correlated genes within clusters, minimization of summation of gene expression distances, and the maximization of biological gene ordering using MIPS categorization. Moreover, the new hybrid approach, finds comparable or sometimes superior biological gene order in less computation time than those obtained by optimal leaf ordering in hierarchical clustering solution

Simulated annealing based pattern classification

A method is described for finding decision boundaries, approximated by piecewise linear segments, for classifying patterns in RN, N ≥2, using Simulated Annealing (SA). It involves generation and placement of a set of hyperplanes (represented by strings) in the feature space that yields minimum misclassification. Theoretical analysis shows that as the size of the training data set approaches infinity, the boundary provided by the SA based classifier will approach the Bayes boundary. The effectiveness of the classification methodology, along with the generalization ability of the decision boundary, is demonstrated for both artificial data and real life data sets having non-linear/overlapping class boundaries. Results are compared extensively with those of the Bayes classifier, k-NN rule and multilayer perceptron, and Genetic Algorithms, another popular evolutionary technique. Empirical verification of the theoretical claim is also provided

MONOSODIUM GLUTAMATE POTENTIATES THE CONTRACTION OF THE VISCERAL SMOOTH MUSCLE OF DUODENUM BY AUGMENTING THE ACTIVITY OF INTRINSIC CHOLINERGIC EFFERENTS, INDUCING OXIDATIVE STRESS AND PROLIFERATING SMOOTH MUSCLE CELLS

Objective: The objective of the present study was to examine the effects of monosodium glutamate (MSG) on the contraction of visceral smooth muscle (VSM) of the duodenum in a rat model to understand the MSG-induced impairment of the function of the small intestine. Methods: Male albino rats of Charles Foster strain were exposed with MSG at three different dosages (632, 1264, and 2528 mg/kg BW/day) for 30-day duration. The records of the contraction of the duodenum were achieved with isotonic transducer (IT-2245) coupled with RMS-Polyrite D by our standard laboratory protocol. Results: We have observed potentiation of contraction of duodenum ex vivo dose-dependently in MSG exposed groups of rats compared to control. Furthermore, the enzymatic activity of acetylcholinesterase (AChE) in VSM tissue homogenate and expression of AChE protein in fixed duodenal muscle cell layers have been decreased in a dosage response manner comparing to control rats. We have found a significant decrease in the activities of some antioxidant enzymes such as Cu-Zn superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase, glutathione-s-transferase, and increase in the level of malondialdehyde in MSG exposed VSM tissue homogenate of the duodenum. We have also observed thickening of muscularis externa layer and increase in the number of muscle cells in circular and longitudinal muscle layers of the duodenal wall in transverse duodenal wall sections stained with eosin-hematoxylin. Conclusion: MSG potentiates the contraction of VSM of duodenum by augmenting the activity of intrinsic cholinergic efferents predominantly, and inducing oxidative stress and proliferating smooth muscle cells

Enhancing Comfort of Occupants in Energy Buildings

International audienceAs buildings contribute significantly towards global energy consumption, it is essential that the occupants receive the best comfort without utilising further energy. This work treats building, environment and the occupants as a system, which presents the context and the occupants also provide their comfort criteria to a black box for yielding the a schedule of actions (opening/closing of doors/windows) for optimal comfort. The physical state of an office, situated in France, is recorded over a span of 100 days. This data is utilised by a physical model of the building to simulate the indoor ambience based on random sets of user actions from which an optimal schedule is obtained, representing equally best trade-off among minimal thermal and CO2 based air quality dissatisfaction. Results indicate that adopting the proposed schedule of user actions can efficiently enhance the occupants comfort

Differential Explanations for Energy Management in Buildings

International audienceIn the field of building energy efficiency, researchers generally focus on building performance and how to enhance it. The objective of this work is to empower the building occupants by putting them in the loop of efficient energy use, supporting them to achieve their objectives by pointing out how far their actions are from an optimal set of actions. Different levels of explanation are investigated. Indicators measuring the distance to optimality are, firstly, proposed. An algorithm that generates deeper explanations is then presented to determine how changing some actions impacts comfort. The paper emphasizes the importance of explanations with a real case study. It identifies the type and level of explanations needed for different occupants. The concept of replay is presented. An occupant can replay his past actions and learn from them