Variations in cone photoreceptor abundance and the visual ecology of birds

The relative abundance and topographical distribution of retinal cone photoreceptors was measured in 19 bird species to identify possible correlations between photoreceptor complement and visual ecology. In contrast to previous studies, all five types of cone photoreceptor were distinguished, using bright field and epifluorescent light microscopy, in four retinal quadrants. Land birds tended to show either posterior dorsal to anterior ventral or anterior dorsal to posterior ventral gradients in cone photoreceptor distribution, fundus coloration and oil droplet pigmentation across the retina. Marine birds tended to show dorsal to ventral gradients instead. Statistical analyses showed that the proportions of the different cone types varied significantly across the retinae of all species investigated. Cluster analysis was performed on the data to identify groups or clusters of species on the basis of their oil droplet complement. Using the absolute percentages of each oil droplet type in each quadrant for the analysis produced clusters that tended to reflect phylogenetic relatedness between species rather than similarities in their visual ecology. Repeating the analysis after subtracting the mean percentage of a given oil droplet type across the whole retina (the 'eye mean') from the percentage of that oil droplet type in each quadrant, i.e. to give a measure of the variation about the mean, resulted in clusters that reflected diet, feeding behaviour and habitat to a greater extent than phylogeny.

Patterns of community integration 2-5 years post-discharge from brain injury rehabilitation

Outcome after traumatic brain injury (TBI) is characterized by a high degree of variability which has often been difficult to capture in traditional outcome studies. The purpose of this study was to describe patterns of community integration 2-5 years after TBI. Participants were 208 patients admitted to a Brain Injury Rehabilitation Unit between 1991-1995 in Brisbane, Australia. The design comprised retrospective data collection and questionnaire follow-up by mail. Mean follow-up was 3.5 years. Demographic, injury severity and functional status variables were retrieved from hospital records. Community integration was assessed using the Community Integration Questionnaire (CIQ), and vocational status measured by a self administered questionnaire. Data was analysed using cluster analysis which divided the data into meaningful subsets. Based on the CIQ subscale scores of home, social and productive integration, a three cluster solution was selected, with groups labelled as working (n = 78), balanced (n = 46) and poorly integrated (n = 84). Although 38% of the sample returned to a high level of productive activity and 22% achieved a balanced lifestyle, overall community integration was poor for the remainder. This poorly integrated group had more severe injury characterized by longer periods of acute care and post-traumatic amnesia (PTA) and greater functional disability on discharge. These findings have implications for service delivery prior to and during the process of reintegration after brain injury.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

A classification of subantarctic Heard Island vegetation

The vascular and bryophyte floras of subantarctic Heard Island were classified using cluster analysis into six vegetation communities: Open Cushion Carpet, Mossy Feldmark, Wet Mixed Herbfield, Coastal Biotic Vegetation, Saltspray Vegetation, and Closed Cushion Carpet. Multidimensional scaling indicated that the vegetation communities were not well delineated but were continua. Discriminant analysis and a classification tree identified altitude, wind, peat depth, bryophyte cover and extent of bare ground, and particle size as discriminating variables. The combination of small area, glaciation, and harsh climate has resulted in reduced vegetation variety in comparison to those subantarctic islands north of the Antarctic Polar Front Zone. Some of the functional groups and vegetation communities found on warmer subantarctic islands are not present on Heard Island, notably ferns and sedges and fernbrakes and extensive mires, respectively.

Communities of the postindustrial city

The authors discern the community structure of the postindustrial city, with reference to Australia. They focus empirically on three major types of Australian urban center: urban regions. metropolitan areas that are not part of urban regions, and other major cities. These three account for almost three-quarters of the Australian population. The authors draw on a conceptualization formulated by Marcuse and van Kempen to guide the analysis, with a combination of cluster analysis and discriminant analysis being applied to aggregate (essentially census) data to identify the communities. Nine major Australian urban communities are identified-four are affluent. four are disadvantaged. and one is a working-class community. The communities found, however, differed greatly from those cited in the Marcuse and van Kempen schema.

"Owned by...". Country of origin's new cue

This study identifies and explores a new country of origin (COO) cue, “owned by….” The importance of three extrinsic cues “owned by …,” “made in …” and price was examined using conjoint analysis. Data were collected from a sample of 268 undergraduate students familiar with color televisions. Segments were formed using cluster analysis and analyzed using multiple discriminant analysis. “Owned by …” was found to be important and distinct from the “made in …” cue. Segments based on the two COO cues were identified using importance weights and individual utilities. When segments were formed using individual utilities the individual difference construct, economic nationalism, provided discriminatory power while consumer ethnocentrism did not, supporting the hypothesis that economic nationalism and consumer ethnocentrism differ. Practitioners can now use “owned by …” knowing that it forms an important and distinct marketing tool. Limitations and future research are discussed.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.