Segmenting eukaryotic genomes with the generalized Gibbs sampler

Eukaryotic genomes display segmental patterns of variation in various properties, including GC content and degree of evolutionary conservation. DNA segmentation algorithms are aimed at identifying statistically significant boundaries between such segments. Such algorithms may provide a means of discovering new classes of functional elements in eukaryotic genomes. This paper presents a model and an algorithm for Bayesian DNA segmentation and considers the feasibility of using it to segment whole eukaryotic genomes. The algorithm is tested on a range of simulated and real DNA sequences, and the following conclusions are drawn. Firstly, the algorithm correctly identifies non-segmented sequence, and can thus be used to reject the null hypothesis of uniformity in the property of interest. Secondly, estimates of the number and locations of change-points produced by the algorithm are robust to variations in algorithm parameters and initial starting conditions and correspond to real features in the data. Thirdly, the algorithm is successfully used to segment human chromosome 1 according to GC content, thus demonstrating the feasibility of Bayesian segmentation of eukaryotic genomes. The software described in this paper is available from the author's website (www.uq.edu.au/similar to uqjkeith/) or upon request to the author.

Cognitive change process during group cognitive behaviour therapy for depression

Background: This study extended that of Kwon and Oei [Kwon, S.M., Oei, T.P.S., 2003. Cognitive change processes in a group cognitive behavior therapy of depression. J. Behav. Ther. Exp. Psychiatry, 3, 73-85], which outlined a number of testable models based on Beck's cognitive theory of depression. Specifically, the current study tested the following four competing models: the causal, consequential, fully and partially interactive cognitive models in patients with major depressive disorder. Methods: A total of 168 clinically depressed outpatients were recruited into a 12-week group cognitive behaviour therapy program. Data was collected at three time points: baseline, mid- and at termination of therapy using the ATQ DAS and BD1. The data were analysed with Amos 4.01 (Arbuckle, J.L., 1999. Amos 4.1. Smallwaters, Chicago.) structural equation modelling. Results: Results indicated that dysfunctional attitudes, negative automatic thoughts and symptoms of depression reduced significantly during treatment. Both the causal and consequential models equally provided an adequate fit to the data. The fully interactive model provided the best fit. However, after removing non-significant pathways, it was found that reduced depressive symptom contributed to reduced depressogenic automatic thoughts and dysfunctional attitudes, not the reverse. Conclusion: These findings did not fully support Beck's cognitive theory of depression that cognitions are primary in the reduction of depressed mood. (c) 2006 Elsevier B.V. All rights reserved.