Adolescents' perceptions of social status: Development and evaluation of a new indicator

Objective. Eliminating health disparities, including those that are a result of socioeconomic status (SES), is one of the overarching goals of Healthy People 2010. This article reports on the development of a new, adolescent-specific measure of subjective social status (SSS) and on initial exploratory analyses of the relationship of SSS to adolescents' physical and psychological health. Methods. A cross-sectional study of 10 843 adolescents and a subsample of 166 paired adolescent/mother dyads who participated in the Growing Up Today Study was conducted. The newly developed MacArthur Scale of Subjective Social Status (10-point scale) was used to measure SSS. Paternal education was the measure of SES. Indicators of psychological and physical health included depressive symptoms and obesity, respectively. Linear regression analyses determined the association of SSS to depressive symptoms, and logistic regression determined the association of SSS to overweight and obesity, controlling for sociodemographic factors and SES. Results. Mean society ladder ranking, a subjective measure of SES, was 7.2 ± 1.3. Mean community ladder ranking, a measure of perceived placement in the school community, was 7.6 ± 1.7. Reliability of the instrument was excellent: the intraclass correlation coefficient was 0.73 for the society ladder and 0.79 for the community ladder. Adolescents had higher society ladder rankings than their mothers (µteen = 7.2 ± 1.3 vs µmom = 6.8 ± 1.2; P = .002). Older adolescents' perceptions of familial placement in society were more closely correlated with maternal subjective perceptions of placement than those of younger adolescents (Spearman's rhoteens

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.

Development and evaluation of a species diagnostic polymerase chain reaction-restriction fragment-length polymorphism procedure for cryptic members of the Culex sitiens (Diptera : Culicidae) subgroup in Australia and the southwest Pacific

Members of the Culex sitiens subgroup are important vectors of arboviruses, including Japanese encephalitis virus, Murray Valley encephalitis virus and Ross River virus. Of the eight described species, Cx. annulirostris Skuse, Cx. sitiens Wiedemann, and Cx. palpalis Taylor appear to be the most abundant and widespread throughout northern Australia and Papua New Guinea (PNG). Recent investigations using allozymes have shown this subgroup to contain cryptic species that possess overlapping adult morphology. We report the development of a polymerase chain reaction-restriction fragment-length polymorphism (PCR-RFLP) procedure that reliably separates these three species. This procedure utilizes the sequence variation in the ribosomal DNA ITS1 and demonstrates species-specific PCR-RFLP profiles from both colony and field collected material. Assessment of the consistency of this procedure was undertaken on mosquitoes sampled from a wide geographic area including Australia, PNG, and the Solomon Islands. Overlapping adult morphology was observed for Cx. annulirostris and Cx. palpalis in both northern Queensland and PNG and for all three species at one site in northwest Queensland.

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.

Model-based analysis of anaerobic acetate uptake by a mixed culture of polyphosphate-accumulating and glycogen-accumulating organisms

An increasing number of studies shows that the glycogen-accumulating organisms (GAOs) can survive and may indeed proliferate under the alternating anaerobic/aerobic conditions found in EBPR systems, thus forming a strong competitor of the polyphosphate-accumulating organisms (PAOs). Understanding their behaviors in a mixed PAO and GAO culture under various operational conditions is essential for developing operating strategies that disadvantage the growth of this group of unwanted organisms. A model-based data analysis method is developed in this paper for the study of the anaerobic PAO and GAO activities in a mixed PAO and GAO culture. The method primarily makes use of the hydrogen ion production rate and the carbon dioxide transfer rate resulting from the acetate uptake processes by PAOs and GAOs, measured with a recently developed titration and off-gas analysis (TOGA) sensor. The method is demonstrated using the data from a laboratory-scale sequencing batch reactor (SBR) operated under alternating anaerobic and aerobic conditions. The data analysis using the proposed method strongly indicates a coexistence of PAOs and GAOs in the system, which was independently confirmed by fluorescent in situ hybridization (FISH) measurement. The model-based analysis also allowed the identification of the respective acetate uptake rates by PAOs and GAOs, along with a number of kinetic and stoichiometric parameters involved in the PAO and GAO models. The excellent fit between the model predictions and the experimental data not involved in parameter identification shows that the parameter values found are reliable and accurate. It also demonstrates that the current anaerobic PAO and GAO models are able to accurately characterize the PAO/GAO mixed culture obtained in this study. This is of major importance as no pure culture of either PAOs or GAOs has been reported to date, and hence the current PAO and GAO models were developed for the interpretation of experimental results of mixed cultures. The proposed method is readily applicable for detailed investigations of the competition between PAOs and GAOs in enriched cultures. However, the fermentation of organic substrates carried out by ordinary heterotrophs needs to be accounted for when the method is applied to the study of PAO and GAO competition in full-scale sludges. (C) 2003 Wiley Periodicals, Inc.

A framework and tool support for the systematic testing of model-based specifications

Formal specifications can precisely and unambiguously define the required behavior of a software system or component. However, formal specifications are complex artifacts that need to be verified to ensure that they are consistent, complete, and validated against the requirements. Specification testing or animation tools exist to assist with this by allowing the specifier to interpret or execute the specification. However, currently little is known about how to do this effectively. This article presents a framework and tool support for the systematic testing of formal, model-based specifications. Several important generic properties that should be satisfied by model-based specifications are first identified. Following the idea of mutation analysis, we then use variants or mutants of the specification to check that these properties are satisfied. The framework also allows the specifier to test application-specific properties. All properties are tested for a range of states that are defined by the tester in the form of a testgraph, which is a directed graph that partially models the states and transitions of the specification being tested. Tool support is provided for the generation of the mutants, for automatically traversing the testgraph and executing the test cases, and for reporting any errors. The framework is demonstrated on a small specification and its application to three larger specifications is discussed. Experience indicates that the framework can be used effectively to test small to medium-sized specifications and that it can reveal a significant number of problems in these specifications.

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

We consider the problem of assessing the number of clusters in a limited number of tissue samples containing gene expressions for possibly several thousands of genes. It is proposed to use a normal mixture model-based approach to the clustering of the tissue samples. One advantage of this approach is that the question on the number of clusters in the data can be formulated in terms of a test on the smallest number of components in the mixture model compatible with the data. This test can be carried out on the basis of the likelihood ratio test statistic, using resampling to assess its null distribution. The effectiveness of this approach is demonstrated on simulated data and on some microarray datasets, as considered previously in the bioinformatics literature. (C) 2004 Elsevier Inc. All rights reserved.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Development and evaluation of the Clinician Suicide Risk Assessment Checklist

This paper describes the development and evaluation of a new instrument – the Clinician Suicide Risk Assessment Checklist (CSRAC). The instrument assesses the clinician’s competency in three areas: clinical interviewing, assessment of specific suicide risk factors, and formulating a management plan. A draft checklist was constructed by integrating information from 1) literature review 2) expert clinician focus group and 3) consultation with experts. It was utilised in a simulated clinical scenario with clinician trainees and a trained actor in order to test for inter-rater agreement. Agreement was calculated and the checklist was re-drafted with the aim of maximising agreement. A second phase of simulated clinical scenarios was then conducted and inter-rater agreement was calculated for the revised checklist. In the first phase of the study, 18 of 35 items had inadequate inter-rater agreement (60%>), while in the second phase, using the revised version, only 3 of 39 items failed to achieve adequate inter-rater agreement. Further evidence of reliability and validity are required. Continued development of the CSRAC will be necessary before it can be utilised to assess the effectiveness of risk assessment training programs.

Right ventricle extraction by low level and model-based algorithm

In this paper we present an algorithm as the combination of a low level morphological operation and model based Global Circular Shortest Path scheme to explore the segmentation of the Right Ventricle. Traditional morphological operations were employed to obtain the region of interest, and adjust it to generate a mask. The image cropped by the mask is then partitioned into a few overlapping regions. Global Circular Shortest Path algorithm is then applied to extract the contour from each partition. The final step is to re-assemble the partitions to create the whole contour. The technique is deemed quite reliable and robust, as this is illustrated by a very good agreement between the extracted contour and the expert manual drawing output.