Molecular characterization of Thelastomatoidea (Nematoda : Oxyurida) from cockroaches in Australia

A molecular approach was used to genetically characterize 5 species (Aoruroides queenslandensis. Blattophila sphaerolaima, Cordonicola gibsoni, Desmicola ornato and Leidynemella fusiformis) belonging to the superfamily. Thelastomatoidea fi (Nematoda: Oxyurida), a group of pinworms that parasitizes terrestrial arthropods. The D3 domain of the large subunit Of nuclear ribosomal RNA (LSU) was sequenced for individual specimens, and the analysis of the sequence data allowed the genetic relationships of the 5 species to be studied dagger. The sequence variation in the D3 domain within individual species (0-1-8%) was significantly less than the differences among species (4(.)3-12(.)4%). Phylogenetic analyses, Using maximum parsimony, maximum likelihood, and neighbour-joining, tree-building methods, established relationships among the 5 species of Thelastomatoidea and Oxyuris equi (a species of the order Oxyurida). The molecular approach employed provides the prospect for developing DNA tools for the specific identification of the Thelastomatoidea, irrespective of developmental stage and sex, as a basis for systematic, ecological and/or population genetic investigations of members within this superfamily.

Building better wildlife-habitat models

Wildlife-habitat models are an important tool in wildlife management toda?, and by far the majority of these predict aspects of species distribution (abundance or presence) as a proxy measure of habitat quality. Unfortunately, few are tested on independent data, and of those that are, few show useful predictive st;ill. We demonstrate that six critical assumptions underlie distribution based wildlife-habitat models, all of which must be valid for the model to predict habitat quality. We outline these assumptions in a mete-model, and discuss methods for their validation. Even where all sis assumptions show a high level of validity, there is still a strong likelihood that the model will not predict habitat quality. However, the meta-model does suggest habitat quality can be predicted more accurately if distributional data are ignored, and variables more indicative of habitat quality are modelled instead.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

Building family/general practice research capacity

To promote the range of interventions for building family/general practice (family medicine) research capacity, we describe successful international examples. Such examples of interventions that build research capacity focus on diseases and illness research, as well as process research; monitor the output of research in family/general practice (family medicine); increase the number of family medicine research journals; encourage and enable research skills acquisition (including making it part of professional training); strengthen the academic base; and promote research networks and collaborations. The responsibility for these interventions lies with the government, colleges and academies, and universities. There are exciting and varied methods of building research capacity in family medicine.

West elevation, Neville Bonner Building, Brisbane

Riverside Expressway building, Expressway on right.

West elevation, Neville Bonner Building, Brisbane

William St building, facing Riverside Expressway. Landscaped courtyard space in foreground.

West elevation, Neville Bonner Building, Brisbane

William St building, facing Riverside Expressway.

Sunshading fins, Neville Bonner Building, Brisbane

Steel shading structure to East elevation of Riverside Expressway building. William St building and main entry area in background.

Windows, Neville Bonner Building, Brisbane

North elevation, Riverside Expressway building.

Facade articulation, Neville Bonner Building, Brisbane

Detail of precast concrete sunshading panels to freeway (West) elevation.

South elevation, Neville Bonner Building, Brisbane

William St building, as seen from across Riverside Expressway off-ramp.

Main entry, William St, Neville Bonner Building, Brisbane

William St building-Riverside Expressway building junction.

Facade articulation, Neville Bonner Building, Brisbane

Detail of precast concrete sunshading panels to freeway (West) elevation.

North elevation, Neville Bonner Building, Brisbane

As seen from Queens Wharf Road.