Field evaluation of a suite of biomarkers in an Australian Tropical Reef Species, Stripey Seaperch (Lutjanus carponotatus) : Assessment of Produced Formation water from the Harriet A Platform

There is paucity of data regarding hydrocarbon exposure of tropical fish species inhabiting the waters near oil and gas platforms on the Northwest Shelf of Australia. A comprehensive field study assessed the exposure and potential effects associated with the produced water (PW) plume from the Harriet A production platform on the northwest shelf in a local reef species, Stripey seaperch (Lutjanus carponotatus). This field study was a continuation of an earlier pilot study which concluded that there were “warning signs” of potential biological effects on fish populations exposed to PW. A 10-day field caging study was conducted deploying 15 individual fish into 6 separate steel cages set 1-m subsurface at 3 stations in a concentration gradient moving away from the platform. A battery of biomarkers were evaluated including hepatosomatic index (HSI), total cytochrome P450, bile metabolites, CYP1A-, CYP2K- and CYP2M-like proteins, cholinesterase (ChE) activity, and histopathology of liver and gill tissues. Water column and PW effluent samples was also collected. Results confirmed that PAH metabolites in bile, CYP1A-, CYP2K-, and CYP2M-like proteins and liver histopathology provided evidence of significant exposure and effects after 10 days at the near-field site (~200 m off the Harriet A platform). Hepatosomatic index, total cytochrome P450, and ChE did not provide site-specific differences by day 10 of exposure to PW. CYP proteins were shown by principal component analysis (PCA) to be the best diagnostic tool for determining exposure and associated biological effects of PW on L. carponotatus. Using a suite of biomarkers has been widely advocated as a vital component in environmental risk assessments worldwide. This study demonstrates the usefulness of biomarkers for assessing the Harriet A PW discharge into Australian waters with broader applications for other PW discharges. This approach has merit as a valuable addition to environmental management strategies for protecting Australia’s tropical environment and its rich biodiversity.

Changes in intervertebral disc morphology persist 5 mo after 21-day bed rest

As part of the nutrition-countermeasures (NUC) study in Cologne, Germany in 2010, seven healthy male subjects underwent 21 days of head-down tilt bed rest and returned 153 days later to undergo a second bout of 21-day bed rest. As part of this model, we aimed to examine the recovery of the lumbar intervertebral discs and muscle cross-sectional area (CSA) after bed rest using magnetic resonance imaging and conduct a pilot study on the effects of bed rest in lumbar muscle activation, as measured by signal intensity changes in T(2)-weighted images after a standardized isometric spinal extension loading task. The changes in intervertebral disc volume, anterior and posterior disc height, and intervertebral length seen after bed rest did not return to prebed-rest values 153 days later. While recovery of muscle CSA occurred after bed rest, increases (P ≤ 0.016) in multifidus, psoas, and quadratus lumborum muscle CSA were seen 153 days after bed rest. A trend was seen for greater activation of the erector spinae and multifidus muscles in the standardized loading task after bed rest. Greater reductions of multifidus and psoas CSA muscle and greater increases in multifidus signal intensity with loading were associated with incidence of low back pain in the first 28 days after bed rest (P ≤ 0.044). The current study contributes to our understanding of the recovery of the lumbar spine after 21-day bed rest, and the main finding was that a decrease in spinal extensor muscle CSA recovers within 5 mo after bed rest but that changes in the intervertebral discs persist.

Stable feature selection with support vector machines

The support vector machine (SVM) is a popular method for classification, well known for finding the maximum-margin hyperplane. Combining SVM with l1-norm penalty further enables it to simultaneously perform feature selection and margin maximization within a single framework. However, l1-norm SVM shows instability in selecting features in presence of correlated features. We propose a new method to increase the stability of l1-norm SVM by encouraging similarities between feature weights based on feature correlations, which is captured via a feature covariance matrix. Our proposed method can capture both positive and negative correlations between features. We formulate the model as a convex optimization problem and propose a solution based on alternating minimization. Using both synthetic and real-world datasets, we show that our model achieves better stability and classification accuracy compared to several state-of-the-art regularized classification methods.

Understanding toxicities and complications of cancer treatment: a data mining approach

Cancer remains a major challenge in modern medicine. Increasing prevalence of cancer, particularly in developing countries, demands better understanding of the effectiveness and adverse consequences of different cancer treatment regimes in real patient population. Current understanding of cancer treatment toxicities is often derived from either “clean” patient cohorts or coarse population statistics. It is difficult to get up-to-date and local assessment of treatment toxicities for specific cancer centres. In this paper, we applied an Apriori-based method for discovering toxicity progression patterns in the form of temporal association rules. Our experiments show the effectiveness of the proposed method in discovering major toxicity patterns in comparison with the pairwise association analysis. Our method is applicable for most cancer centres with even rudimentary electronic medical records and has the potential to provide real-time surveillance and quality assurance in cancer care.