Identifying the molecular phenotype of renal progenitor cells

Although many of the molecular interactions in kidney development are now well understood, the molecules involved in the specification of the metanephric mesenchyme from surrounding intermediate mesoderm and, hence, the formation of the renal progenitor population are poorly characterized. In this study, cDNA microarrays were used to identify genes enriched in the murine embryonic day 10.5 (E10.5) uninduced metanephric mesenchyme, the renal progenitor population, in comparison with more rostral derivatives of the intermediate mesoderm. Microarray data were analyzed using R statistical software to determine accurately genes differentially expressed between these populations. Microarray outliers were biologically verified, and the spatial expression pattern of these genes at E10.5 and subsequent stages of early kidney development was determined by RNA in situ hybridization. This approach identified 21 genes preferentially expressed by the E10.5 metanephric mesenchyme, including Ewing sarcoma homolog, 14-3-3 theta, retinoic acid receptor-alpha, stearoyl-CoA desaturase 2, CD24, and cadherin-11, that may be important in formation of renal progenitor cells. Cell surface proteins such as CD24 and cadherin-11 that were strongly and specifically expressed in the uninduced metanephric mesenchyme and mark the renal progenitor population may prove useful in the purification of renal progenitor cells by FACS. These findings may assist in the isolation and characterization of potential renal stem cells for use in cellular therapies for kidney disease.

Attaching unit costs to Australia's National Survey of Mental Health and Wellbeing

Background: In mental health, policy-makers and planners are increasingly being asked to set priorities. This means that health economists, health services researchers and clinical investigators are being called upon to work together to define and measure costs. Typically, these researchers take available service utilisation data and convert them to costs, using a range of assumptions. There are inefficiencies, as individual groups of researchers frequently repeat essentially similar exercises in achieving this end. There are clearly areas where shared or common investment in the development of statistical software syntax, analytical frameworks and other resources could maximise the use of data. Aims of the Study: This paper reports on an Australian project in which we calculated unit costs for mental health admissions and community encounters. In reporting on these calculations, our purpose is to make the data and the resources associated with them publicly available to researchers interested in conducting economic analyses, and allow them to copy, distribute and modify them, providing that all copies and modifications are available under the same terms and conditions (i.e., in accordance with the 'Copyleft' principle), Within this context, the objectives of the paper are to: (i) introduce the 'Copyleft' principle; (ii) provide an overview of the methodology we employed to derive the unit costs; (iii) present the unit costs themselves; and (iv) examine the total and mean costs for a range of single and comorbid conditions, as an example of the kind of question that the unit cost data can be used to address. Method: We took relevant data from the Australian National Survey of Mental Health and Wellbeing (NSMHWB), and developed a set of unit costs for inpatient and community encounters. We then examined total and mean costs for a range of single and comorbid conditions. Results: We present the unit costs for mental health admissions and mental health community contacts. Our example, which explored the association between comorbidity and total and mean costs, suggested that comorbidly occurring conditions cost more than conditions which occur on their own. Discussion: Our unit costs, and the materials associated with them, have been published in a freely available form governed by a provision termed 'Copyleft'. They provide a valuable resource for researchers wanting to explore economic questions in mental health. Implications for Health Policies: Our unit costs provide an important resource to inform economic debate in mental health in Australia, particularly in the area of priority-setting. In the past, such debate has largely, been based on opinion. Our unit costs provide the underpinning to strengthen the evidence-base of this debate. Implications for Further Research: We would encourage other Australian researchers to make use of our unit costs in order to foster comparability across studies. We would also encourage Australian and international researchers to adopt the 'Copyleft' principle in equivalent circumstances. Furthermore, we suggest that the provision of 'Copyleft'-contingent funding to support the development of enabling resources for researchers should be considered in the planning of future large-scale collaborative survey work, both in Australia and overseas.

Methods for Categorical Longitudinal Survey Data: Understanding Employment Status of Australian Women

Many variables that are of interest in social science research are nominal variables with two or more categories, such as employment status, occupation, political preference, or self-reported health status. With longitudinal survey data it is possible to analyse the transitions of individuals between different employment states or occupations (for example). In the statistical literature, models for analysing categorical dependent variables with repeated observations belong to the family of models known as generalized linear mixed models (GLMMs). The specific GLMM for a dependent variable with three or more categories is the multinomial logit random effects model. For these models, the marginal distribution of the response does not have a closed form solution and hence numerical integration must be used to obtain maximum likelihood estimates for the model parameters. Techniques for implementing the numerical integration are available but are computationally intensive requiring a large amount of computer processing time that increases with the number of clusters (or individuals) in the data and are not always readily accessible to the practitioner in standard software. For the purposes of analysing categorical response data from a longitudinal social survey, there is clearly a need to evaluate the existing procedures for estimating multinomial logit random effects model in terms of accuracy, efficiency and computing time. The computational time will have significant implications as to the preferred approach by researchers. In this paper we evaluate statistical software procedures that utilise adaptive Gaussian quadrature and MCMC methods, with specific application to modeling employment status of women using a GLMM, over three waves of the HILDA survey.

Genetic algorithms in statistical tolerancing

An important aspect in manufacturing design is the distribution of geometrical tolerances so that an assembly functions with given probability, while minimising the manufacturing cost. This requires a complex search over a multidimensional domain, much of which leads to infeasible solutions and which can have many local minima. As well, Monte-Carlo methods are often required to determine the probability that the assembly functions as designed. This paper describes a genetic algorithm for carrying out this search and successfully applies it to two specific mechanical designs, enabling comparisons of a new statistical tolerancing design method with existing methods. (C) 2003 Elsevier Ltd. All rights reserved.

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in border to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a general-purpose tool to reduce the computational requirements of large-scale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study.