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Title: Purification, detection and biological effects of cyanobacterial toxins.
Authors: McElhiney, Jacqueline
Supervisors: Lawton, Linda A.
Edwards, Christine
Issue Date: May-1999
Publisher: The Robert Gordon University
Citation: LAWTON, L. A., MCELHINEY, J. and EDWARDS, C., 1999. Purification of closely eluting hydrophobic microcystins (peptide cyanotoxins) by normal-phase and reversed-phase flash chromatography. Journal of Chromatography A 848, pp. 515-522
MCELHINEY, J., LAWTON, L. A., EDWARDS, C. and GALLACHER, S., 1998. Development of a bioassay employing the desert locus (Schistocerca gregaria) for the detection of saxitoxin and related compounds in cyanobacteria and shellfish. Toxicon, 36 (2), pp. 417-420
Abstract: The aesthetic beauty of a landscape is a very subjective issue: every person has their own opinions and their own idea of what beauty is. However, all people have a common evolutionary history, and, according to the Biophilia hypothesis, a genetic predisposition to liking certain types of landscapes. It is possible that this common inheritance allows us to attempt to model scenic preference for natural landscapes. The ideal type of model for such predictions is the psychophysical preference model, integrating psychological responses to landscapes with objective measurements of quantitative and qualitative landscape variables. Such models commonly predict two thirds of the variance in the predications of the general public for natural landscapes. In order to create such a model three sets of data were required: landscape photographs (surrogates of the actual landscape), landscape preference data and landscape component variable measurements. The Internet was used to run a questionnaire survey; a novel, yet flexible, environmentally friendly and simple method of data gathering, resulting in one hundred and eighty responses. A geographic information system was used to digitise ninety landscape photographs and measure their landforms (based on elevation) in terms of areas and perimeters, their colours and proxies for their complexity and coherence. Landscape preference models were created by running multiple linear regressions using normalised preference data and the landscape component variables, including mathematical transformations of these variables. The eight models created predicted over sixty percent of variance in the responses and had moderate to high correlations with a second set of landscape preference data. A common base to the models were the variables of complexity, water and mountain landform, in particular the presence or absence of water and mountains was noted as being significant in determining landscape scenic preference. In order to fully establish the utility of these models, they were further tested against: changes in weather and season; the addition of cultural structures; different photographers; alternate film types; different focal lengths; and composition. Results showed that weather and season were not significant in determining landscape preference; cultural structures increased preferences for landscapes; and photographs taken by different people did not produce consistent results from the predictive models. It was also found that film type was not significant and that changes in focal length altered preferences for landscapes.
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