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Vomiting, in turn, is suggested to reinforce binge eating and, in particular, the size of the binge This is for two reasons: rst, people learn that it is easier to vomit if they have eaten a large amount; second, people discount calories consumed in a binge if they believe that they are getting rid of them by purging Wilson (1989) has proposed a similar account and this account includes fear and social factors A broader model which includes other psychopathological mood states (eg depression), distorted body image and cognitive biases has been proposed by Williamson and colleagues (1985, 1990, 1999) A model in which binge eating is suggested to function as an escape from awareness has been proposed (Heatherton & Baumeister, 1991; Waller et al, 1995).

computes the distances between all pairs of gene expression pro les from two clusters and the average of these distances becomes the distance between the clusters. Single linkage de nes the distance between two clusters as the distance between the two closest representatives of these clusters. Complete linkage de nes the distance between two clusters as the distance between the two farthest representatives. The difference between these three de nitions is illustrated in Figure 4.15. 4.3.5.4 Cluster Validation Regardless of the type of clustering, all obtained clusters need to be evaluated for biological validity before proceeding to further analysis. Visual validation is aimed at determining whether there are outliers in clusters or whether the gene expression pro les within each cluster are correlated to each other. If a problem is detected by validation, clusters are often re ned by adjusting the number of clusters (parameter k), the distance measuring method, or even by repeating the clustering with a different clustering method. Microarray data sets are highly dimensional. It is often dif cult to provide a clear view of gene expression pro le types within each cluster. By reducing the dimension of the microarray data set to two or three dimensions, analysis can be simpli ed and a visual overview of the data can be generated, which may provide useful information on gene expression pro le clustering. Such a dimensionality reduction is typically achieved with principal component analysis (PCA). This technique nds the orthogonal components (also called principal components) of the input vectors and retains two or three orthogonal components with the highest variance. A visual examination of the projected clusters can help determine an appropriate number of distinct clusters for clustering as illustrated in Figure 4.16.

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The next example is again taken directly from life. It is discussed towards the end of this chapter and presented now as an exercise for self or group learning.

are added to one wavelength channel at source, dropped at an intermediate node, and then added to another wavelength channel at the intermediate node, and the routing is repeated until data are dropped at the destination In general, to realize a communication application speci ed by a set of traf c demands, algorithms are required to solve the following problems The logical topology design problem: Given a WDM network G, de ne a set R = {(u, v)} of connection requests over a set V of nodes of G such that for every pair of nodes s and t in V there is a path from s to t in R when R is viewed as a graph with a connection request as an edge For each request (u, v) R, data from u to v are transmitted by all-optical routing in G.

This model has been developed further by Waller and colleagues who propose a multilevel model of processing where abandonment issues play an early role in the activation of food schemata and hence inducing bulimic behavior as a means of reducing awareness of such cognitions (Meyer & Waller, 2000, p 333) Yet another model suggests that there are two pathways to bulimia nervosa, the rst concerning dietary restraint and the second relating to affect regulation (Stice, Nemeroff & Shaw, 1996; Stice, Shaw & Nemeroff, 1998; Stice et al, 1996)..

The ring topology is popular for optical networks due to its simple structure and symmetric property. The RWA and WA problems on ring networks have been extensively studied. In Section 18.3, the number of colors for supporting the full connectivity on Cn is given. We now introduce well-known algorithms for the WA and RWA problems for arbitrary connection requests on ring networks. We rst discuss the WA problem.

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