1 Animal experimental studies have demonstrated that inducible nitric oxide synthase (iNOS) expression correlates with neointima formation and is prevented by HMG-CoA reductase inhibitors (statins). In the present study we have investigated the underlying mechanism of action of these drugs in isolated segments of the rat aorta. 2 Western blot analysis and immunohistochemistry revealed that tumour necrosis factor a (TNFa) plus interferon-g (IFNg) synergistically induce iNOS gene expression in the endothelium but not in the smooth muscle of these segments while constitutive endothelial NO synthase (eNOS) abundance was markedly reduced. 3 Pre-treatment with 1 ± 10 mM atorvastatin, cerivastatin or pravastatin decreased TNFa plus IFNg stimulated iNOS expression in the endothelium irrespective of the presence of the HMG-CoA reductase product mevalonate (400 mM). 4 Electrophoretic mobility shift assay experiments con®rmed that the combination of TNFa plus IFNg causes activation of the transcription factors STAT-1 and NF-kB in native endothelial cells. Neutralization of these transcription factors by employing the corresponding decoy oligonucleotides con®rmed their involvement in TNFa plus IFNg stimulated iNOS expression. Translocation of both transcription factors was attenuated by atorvastatin, and this eect was insensitive to exogenous mevalonate. 5 The present ®ndings thus demonstrate a speci®c HMG-CoA reductase-independent inhibitory eect of statins, namely atorvastatin, on cytokine-stimulated transcription factor activation in native endothelial cells in situ and the subsequent expression of a gene product implicated in vascular in¯ammation. This eect may be therapeutically relevant and in addition provide an explanation for the reported rapid onset of action of these drugs in humans.
a b s t r a c tQuantification and forecasting of cost uncertainty for aerospace innovations is challenged by conditions of small data which arises out of having few measurement points, little prior experience, unknown history, low data quality, and conditions of deep uncertainty. Literature research suggests that no frameworks exist which specifically address cost estimation under such conditions. In order to provide contemporary cost estimating techniques with an innovative perspective for addressing such challenges a framework based on the principles of spatial geometry is described. The framework consists of a method for visualising cost uncertainty and a dependency model for quantifying and forecasting cost uncertainty. Cost uncertainty is declared to represent manifested and unintended future cost variance with a probability of 100% and an unknown quantity and innovative starting conditions considered to exist when no verified and accurate cost model is available. The shape of data is used as an organising principle and the attribute of geometrical symmetry of cost variance point clouds used for the quantification of cost uncertainty. The results of the investigation suggest that the uncertainty of a cost estimate at any future point in time may be determined by the geometric symmetry of the cost variance data in its point cloud form at the time of estimation. Recommendations for future research include using the framework to determine the "most likely values" of estimates in Monte Carlo simulations and generalising the dependency model introduced. Future work is also recommended to reduce the framework limitations noted.
a b s t r a c tThe lack of defensible methods for quantifying cost estimate uncertainty over the whole product life cycle of aerospace innovations such as propulsion systems or airframes poses a significant challenge to the creation of accurate and defensible cost estimates. Based on the axiomatic definition of uncertainty as the actual prediction error of the cost estimate, this paper provides a comprehensive overview of metrics used for the uncertainty quantification of cost estimates based on a literature review, an evaluation of publicly funded projects such as part of the CORDIS or Horizon 2020 programs, and an analysis of established approaches used by organizations such NASA, the U.S. Department of Defence, the ESA, and various commercial companies. The metrics are categorized based on their foundational character (foundations), their use in practice (state-of-practice), their availability for practice (state-of-art) and those suggested for future exploration (state-of-future). Insights gained were that a variety of uncertainty quantification metrics exist whose suitability depends on the volatility of available relevant information, as defined by technical and cost readiness level, and the number of whole product life cycle phases the estimate is intended to be valid for. Information volatility and number of whole product life cycle phases can hereby be considered as defining multi-dimensional probability fields admitting various uncertainty quantification metric families with identifiable thresholds for transitioning between them. The key research gaps identified were the lacking guidance grounded in theory for the selection of uncertainty quantification metrics and lacking practical alternatives to metrics based on the Central Limit Theorem. An innovative uncertainty quantification framework consisting of; a set-theory based typology, a data library, a classification system, and a corresponding input-output model are put forward to address this research gap as the basis for future work in this field.
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