James G. Wilson

For many years, there has been increasing concern about the effects of the presence of hazardous substances in the environment. The chemical and biological effect (BE) monitoring of these pollutants has proven difficult due to low environmental concentrations, variable bioavailability, and the generalised nature of ecological responses to these substances. The over- or under-expression of key genes has proven to be useful in understanding the molecular mechanisms of the toxicity of contaminants. This study uses a quantitative PCR array to detect the changes in gene expression in flounder livers after exposure to both laboratory- and field-based contaminants. The model contaminants included 17β-estradiol (E2), 3-methylcholanthrene (3-MC), a commercial mixture of polychlorinated biphenyls (PCB, Arochlor), perfluoroctanoic acid (PFOA), and lindane. Multivariate analysis was used to investigate relationships between higher-organisational-level biomarkers, supporting parameters, and genes. A scoring system enabled the visualisation of biological effect responses and contaminants in field samples. Although gene expression was useful for inferring the pathways of toxicity in this organism, we recommend that this array be used in combination with existing and recommended higher-level biomarkers and should not be used as a replacement for traditional biomarkers currently used in monitoring.

Harvesting the commercially significant lobster, Nephrops norvegicus, is a multimillion dollar industry in Europe. Stock assessment is essential for maintaining this activity but it is conducted by manually inspecting hours of underwater surveillance videos. To improve this tedious process, we propose an automated procedure. This procedure uses mosaics for detecting the Nephrops, which improves visibility and reduces the tedious video inspection process to the browsing of a single image. In addition to this novel application approach, key contributions are made for handling the difficult lighting conditions in these kinds of videos. Mosaics are built using 1-10 minutes of footage and candidate Nephrops regions are selected using image segmentation based on local image contrast and colour features. A K-Nearest Neighbour classifier is then used to select the respective Nephrops from these candidate regions. Our final decision accuracy at 87.5% recall and precision shows a corresponding 31.5% and 79.4% improvement compared with previous work [1].