Psychoactive pharmaceuticals are among the most frequently prescribed drugs, contributing to persistent measurable concentrations in aquatic systems. Typically, it is assumed that such contaminants have no human health implications because they exist in extremely low concentrations. We exposed juvenile fathead minnows (Pimephales promelas) to three pharmaceuticals, fluoxetine, venlafaxine and carbamazepine, individually and in a mixture, and measured their effect on the induction of gene expression in fish brains using microarray analysis. Gene expression changes were accompanied by behavioral changes and validated by qPCR analysis. Gene Set Enrichment Analysis was used to perform gene-class analysis of gene expression, testing for enrichment of gene sets known to be involved in human neuronal development, regulation and growth. We found significant enrichment of gene sets for each of the treatments, with the largest induction of expression by the mixture treatment. These results suggest that the psychoactive pharmaceuticals are able to alter expression of fish genes associated with development, regulation and differentiation of synapses, neurons and neurotransmitters. The results provide a new perspective for the consideration of potential consequence for human health due to environmental exposure to unmetabolized psychoactive pharmaceuticals.
Available resources limit the performance and lifetime of a wireless sensor network. In order to increase network lifetime in the face of depleting resources, networks that can be reconfigured to meet specified performance criteria are of interest. For a target localization scenario using binary sensor data, we present an approach in which network reconfiguration is guided using a performance criterion derived from the Cramer-Rao Lower Bound on the covariance of the errors in target location estimation. Inactive sensors are strategically placed within the sensor field to provide redundancy. When a sensor fails, sets of inactive sensors are identified as candidates for activation to replace it. The performance expected to be achieved by the activation of each candidate is determined, and decisions about which candidates to activate are made based on criteria of interest for the given application. Example scenarios are provided and some possible applications are discussed.
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