Ranking of microRNA target prediction scores by Pareto front analysis

Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure. which encourages further research towards a higher-dimensional analysis of Pareto fronts. (C) 2010 Elsevier Ltd. All rights reserved.

Biosensor based toxicity dissection of tannery and associated environmental samples

Kenyan tannery and associated environmental samples were selected for ecotoxicological assessment. A tool-kit of techniques was developed, including whole-cell biosensor and chemical assays. A luminescence based bacterial biosensor (Escherichia coli HB101 pUCD607) (via a multi-copy plasmid) was used for toxicity assessment. Samples were manipulated prior to biosensor interrogation to identify the nature of the toxic contaminants. Untreated samples (before any manipulations) showed a strong toxic effect at the discharge point in comparison to other sampling points. Sparging was used to identify toxicity associated with volatile organics. The toxicity of contaminants, removed by treatment with activated charcoal was identified for all the sampling points except for those upstream of effluent discharges. Filtration identified toxicity associated with suspended solids. Changes in availability of toxic contaminants due to pH adjustment of most samples from the tannery effluent treatment pits were also associated with the extreme pH values (4.0 and 8.0). The approach used has highlighted the complexicity of toxic pollutants in effluent from the tanning industry and the dissection of toxicity points to possible remediation strategies for effluents from the tanning industry.

Power Capping: What Works, What Does Not

Peak power consumption is the first order design constraint of data centers. Though peak power consumption is rarely, if ever, observed, the entire data center facility must prepare for it, leading to inefficient usage of its resources. The most prominent way for addressing this issue is to limit the power consumption of the data center IT facility far below its theoretical peak value. Many approaches have been proposed to achieve that, based on the same small set of enforcement mechanisms, but there has been no corresponding work on systematically examining the advantages and disadvantages of each such mechanism. In the absence of such a study,it is unclear what is the optimal mechanism for a given computing environment, which can lead to unnecessarily poor performance if an inappropriate scheme is used. This paper fills this gap by comparing for the first time five widely used power capping mechanisms under the same hardware/software setting. We also explore possible alternative power capping mechanisms beyond what has been previously proposed and evaluate them under the same setup. We systematically analyze the strengths and weaknesses of each mechanism, in terms of energy efficiency, overhead, and predictable behavior. We show how these mechanisms can be combined in order to implement an optimal power capping mechanism which reduces the slow down compared to the most widely used mechanism by up to 88%. Our results provide interesting insights regarding the different trade-offs of power capping techniques, which will be useful for designing and implementing highly efficient power capping in the future.