Information technologies for vaccine research

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

A model for the integration of legal research into Australian undergraduate law curricula

The marginalisation of the teaching and learning of legal research in the Australian law school curriculum is, in the author's experience, a condition common to many law schools. This is reflected in the reluctance of some law teachers to include legal research skills in the substantive law teaching schedule — often the result of unwillingness on the part of law school administrators to provide the resources necessary to ensure that such integration does not place a disproportionately heavy burden of assessment on those who are tempted. However, this may only be one of many reasons for the marginalisation of legal research in the law school experience. Rather than analyse the reasons for this marginalisation, this article deals with what needs to be done to rectify the situation, and to ensure that the teaching of legal research can be integrated into the law school curriculum in a meaningful way. This requires the use of teaching and learning theory which focuses on student-centred learning. This article outlines a model of legal research. It incorporates five transparent stages which are: analysis, contextualisation, bibliographic skills, interpretation and assessment and application.

An object-oriented designed finite-difference time-domain simulator for electromagnetic analysis and design in MRI - application to high field analyses

This paper presents a finite-difference time-domain (FDTD) simulator for electromagnetic analysis and design applications in MRI. It is intended to be a complete FDTD model of an MRI system including all RF and low-frequency field generating units and electrical models of the patient. The pro-ram has been constructed in an object-oriented framework. The design procedure is detailed and the numerical solver has been verified against analytical solutions for simple cases and also applied to various field calculation problems. In particular, the simulator is demonstrated for inverse RF coil design, optimized source profile generation, and parallel imaging in high-frequency situations. The examples show new developments enabled by the simulator and demonstrate that the proposed FDTD framework can be used to analyze large-scale computational electromagnetic problems in modern MRI engineering. (C) 2004 Elsevier Inc. All rights reserved.

Adjustment in mothers of children with Asperger syndrome: An application of the double ABCX model of family adjustment

The present study examined the applicability of the double ABCX model of family adjustment in explaining maternal adjustment to caring for a child diagnosed with Asperger syndrome. Forty-seven mothers completed questionnaires at a university clinic while their children were participating in an anxiety intervention. The children were aged between 10 and 12 years. Results of correlations showed that each of the model components was related to one or more domains of maternal adjustment in the direction predicted, with the exception of problem-focused coping. Hierarchical regression analyses demonstrated that, after controlling for the effects of relevant demographics, stressor severity, pile-up of demands and coping were related to adjustment. Findings indicate the utility of the double ABCX model in guiding research into parental adjustment when caring for a child with Asperger syndrome. Limitations of the study and clinical implications are discussed.

Cognitive deficits associated with cancer: A model of subjective and objective outcomes

Cancer and its treatment can affect many different aspects of quality of life. As a construct measured subjectively, quality of life shows an inconsistent relationship with objective outcome measures. That is, sometimes subjective and objective outcomes correspond with each other and sometimes they show little or no relationship. In this article, we propose a model for the relationship between subjective and objective outcomes using the example of cognitive function in people with cancer. The model and the research findings on which it is based help demonstrate that, in some circumstances, subjective measures of cognitive function correlate more strongly with psychosocial variables such as appraisal, coping, and emotions than with objective cognitive function. The model may provide a useful framework for research and clinical practice in quality of life for people with cancer.

Efficiency effects of agricultural economics research in the United States

Allocations of research funds across programs are often made for efficiency reasons. Social science research is shown to have small, lagged but significant effects on U.S. agricultural efficiency when public agricultural R&D and extension are simultaneously taken into account. Farm management and marketing research variables are used to explain variations in estimates of allocative and technical efficiency using a Bayesian approach that incorporates stylized facts concerning lagged research impacts in a way that is less restrictive than popular polynomial distributed lags. Results are reported in terms of means and standard deviations of estimated probability distributions of parameters and long-run total multipliers. Extension is estimated to have a greater impact on both allocative and technical efficiency than either R&D or social science research.

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.

Effect of different preparation routes on the structure and properties of rigid polyurethane-layered silicate nanocomposites

To identify the effect of reactive preparation on the structure and properties of rigid polyurethane (PU)layered silicate nanocomposite, a range of nanocomposites were prepared by combining the various precursors in different sequences. The morphology of the samples was characterized by XRD and TEM. Tensile properties and dynamic mechanical thermal properties were measured. The reactions between the layered silicates and PU precursors were monitored via FTIR to gain an understanding of the participation of nanofiller in the polymerization reaction, and the impact of this on system stoichiometry. The XRD and TEM results provided evidence that morphology can differ significantly if different synthesis methods are used. However, the mechanical properties are dominated by the stoichiometry imbalance induced by the addition of the layered silicates. (c) 2006 Wiley Periodicals, Inc.

Collaborative decision-making processes for maintaining biodiversity: Two Australian case studies

There have been many models developed by scientists to assist decision-makers in making socio-economic and environmental decisions. It is now recognised that there is a shift in the dominant paradigm to making decisions with stakeholders, rather than making decisions for stakeholders. Our paper investigates two case studies where group model building has been undertaken for maintaining biodiversity in Australia. The first case study focuses on preservation and management of green spaces and biodiversity in metropolitan Melbourne under the umbrella of the Melbourne 2030 planning strategy. A geographical information system is used to collate a number of spatial datasets encompassing a range of cultural and natural assets data layers including: existing open spaces, waterways, threatened fauna and flora, ecological vegetation covers, registered cultural heritage sites, and existing land parcel zoning. Group model building is incorporated into the study through eliciting weightings and ratings of importance for each datasets from urban planners to formulate different urban green system scenarios. The second case study focuses on modelling ecoregions from spatial datasets for the state of Queensland. The modelling combines collaborative expert knowledge and a vast amount of environmental data to build biogeographical classifications of regions. An information elicitation process is used to capture expert knowledge of ecoregions as geographical descriptions, and to transform this into prior probability distributions that characterise regions in terms of environmental variables. This prior information is combined with measured data on the environmental variables within a Bayesian modelling technique to produce the final classified regions. We describe how linked views between descriptive information, mapping and statistical plots are used to decide upon representative regions that satisfy a number of criteria for biodiversity and conservation. This paper discusses the advantages and problems encountered when undertaking group model building. Future research will extend the group model building approach to include interested individuals and community groups.