Participants' perceptions of their involvement in a cardiovascular disease risk factor reduction program

A cardiovascular disease risk factor reduction program was implemented in the Niagara region. To gain an understanding of this program from the participants ' perspective, 10 participants of the program were interviewed to document their perceptions of what they learned in the program, their perceptions of their behaviour change and their perceptions of factors that facilitated or impeded any behaviour change. The learning style inventory and PET test were also given to the participants to further understand their perceptions. Findings unique to this study highlighted aspects of the andragogical model, self-directed learning theory, learning style preference and psychological type that were prominent in the participants' comments and perspectives. Implications for practice, theory development and further research are suggested.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.