Using the World Wide Web to revolutionise technology transfer and training in the water and wastewater industries

Industry professionals of the near future will be supported by an IT infrastructure that enables them to complete a task by drawing on resources and people with expertise anywhere in the world, and access to knowledge through specific training programs that address the task requirements. The increasing uptake of new technologies enables information to reach a diverse population and to provide flexible learning environments 24 hours a day, 7 days a week. This paper examines one of the key areas where the World Wide Web will impact on the water and wastewater industries, namely technology transfer and training. The authors will present their experiences of developing online training courses for wastewater industry professionals over the last two years. The perspective is that of two people working at the coalface.

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.

Cross-reactivity of Sydney funnel-web spider antivenom: neutralization of the in vitro toxicity of other Australian funnel-web (Atrax and Hadronyche) spider venoms

Australian funnel-web spiders are recognized as one of the most venomous spiders to humans world-wide. Funnel-web spider antivenom (FWS AV) reverses clinical effects of envenomation from the bite of Atrax robustus and a small number of related Hadronyche species. This study assessed the in vitro efficacy of FWS AV in neutralization of the effects of funnel-web spider venoms, collected from various locations along the eastern seaboard of Australia, in an isolated chick biventer cervicis nerve-muscle preparation. Venoms were separated by SDS-PAGE electrophoresis to compare protein composition and transblotted for Western blotting and incubation with FWS AV. SDS-PAGE of venoms revealed similar low and high molecular weight protein bands. Western blotting with FWS AV showed similar antivenom binding with protein bands in all the venoms tested. Male funnel-web spider venoms (7/7) and female venoms (5110) produced muscle contracture and fasciculation when applied to the nerve-muscle preparation. Venom effects were reversed by subsequent application of FWS AV or prevented by pretreatment of the preparation with antivenom. FWS AV appears to reverse the in vitro toxicity of a number of funnel-web spider venoms from the eastern seaboard of Australia. FWS AV should be effective in the treatment of envenomation from most, if not all, species of Australian funnel-web spiders. (C) 2001 Elsevier Science Ltd. All rights reserved.

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.