(C) 2010 American Institute of Physics. [doi: 10.1063/1.3367974]“
“A major analytical challenge in computational biology is the detection and description of clusters of specified site types, such as polymorphic or substituted sites within DNA or protein sequences. Progress has been stymied by a lack of suitable methods to detect clusters and to estimate the extent of clustering in discrete linear sequences, particularly when there is no a priori specification of cluster size or cluster count. Here we derive and demonstrate a maximum likelihood method of hierarchical clustering. Our method

incorporates a tripartite divide-and-conquer strategy that models sequence heterogeneity, delineates clusters, and yields a profile of the level of clustering associated with each site. selleckchem The clustering model may be evaluated via model selection using the Akaike Information AC220 Criterion, the corrected Akaike Information

Criterion, and the Bayesian Information Criterion. Furthermore, model averaging using weighted model likelihoods may be applied to incorporate model uncertainty into the profile of heterogeneity across sites. We evaluated our method by examining its performance on a number of simulated datasets as well as on empirical polymorphism data from diverse natural alleles of the Drosophila alcohol dehydrogenase gene. Our method yielded greater power for the detection of clustered sites across a breadth of parameter ranges, and achieved better accuracy and precision of estimation of clusters, than did the existing empirical cumulative distribution function statistics.”
“ObjectiveThis

study PND-1186 concentration evaluates satisfaction with care (SC) in cancer patients treated at a Spanish day hospital to identify SC determinants and assess the relationship between SC and quality of life.

MethodsOne hundred seventy-six patients with different tumour sites and disease stages completed the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30), the Cancer Outpatient Satisfaction with Care questionnaire for chemotherapy (OUT-PATSAT35 CT), the Oberst patients’ perception of care quality and satisfaction scales, and an item on intention to recommend the hospital. Frequencies in the SC instruments, Spearman correlations between each scale of the OUT-PATSAT35 CT and overall satisfaction and between the subscales of OUT-PATSAT35 CT and of QLQ-C30 were calculated, and the determinants of patients’ SC were calculated through multivariate regression models.

ResultsSatisfaction with care was high: mean scores were >70 in all OUT-PATSAT35 CT areas except doctor availability and environment. These scores were in line with the other SC instruments. Correlation with overall satisfaction was high and statistically significant (p<0.01) for all subscales, especially for the nurses domain, which also had higher SC scores. Correlations between the EORTC QLQ-C30 and the OUT-PATSAT35 CT were low (0.