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Transmissible spongiform encephalopathies (TSEs) are a group of fatal neurological conditions affecting a number of mammals, including sheep and goats (scrapie), cows (BSE), and humans (Creutzfeldt-Jakob disease). The diseases are widely believed to be caused by the misfolding of the normal prion protein to a pathological isoform, which is thought to act as an infectious agent. Outbreaks of the disease are commonly attributed to contaminated feed and genetic susceptibility. However, the implication of copper and manganese in the pathology of the disease, and its apparent geographical clustering, have prompted suggestions of a link with trace elements in the environment. Nevertheless, studies of soils at regional scales have failed to provide evidence of an environmental risk factor. This study uses geostatistical techniques to investigate the correlations between the distribution of TSE prevalence and soil geochemical variables across the UK according to different spatial scales. A similar spatial pattern in scrapie and BSE occurrence is identified, which may be linked with increasing pH and total organic carbon, and decreasing iodine concentration. However, the pattern also resembles that of the density of dairy farming. Nevertheless, despite the low spatial resolution of the TSE data available for this study, the fact that significant correlations are detected indicates there is a possibility of a link between soil geochemistry, scrapie, and BSE. It is suggested that further investigations of the prevalence of TSE and environmental exposure to trace metals should take into account the factors affecting their bioavailability.

Data-based methods of flow forecasting are becoming increasingly popular due to their rapid development times, minimum information requirements, and ease of real-time implementation, with transfer function and artificial neural network methods the most commonly applied methods in practice. There is much antagonism between advocates of these two approaches that is fuelled by comparison studies where a state-of-the-art example of one method is unfairly compared with an out-of-date variant of the other technique. This paper presents state-of-the-art variants of these competing methods, non-linear transfer functions and modified recurrent cascade-correlation artificial neural networks, and objectively compares their forecasting performance using a case study based on the UK River Trent. Two methods of real-time error-based updating applicable to both the transfer function and artificial neural network methods are also presented. Comparison results reveal that both methods perform equally well in this case, and that the use of an updating technique can improve forecasting performance considerably, particularly if the forecast model is poor.

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