Liraglutide reduces cardiovascular events and mortality in type 2 diabetes mellitus independently of baseline low-density lipoprotein cholesterol levels and statin use results from the LEADER trial

The causal relationship between low-density lipoprotein cholesterol (LDL-C) and atherosclerotic cardiovascular events has been well established. In people with type 2 diabetes mellitus, LDL-C lowering with statins is strongly endorsed by clinical practice guidelines, with suggested LDL-C target levels including <100 mg/dL for high-risk patients, <70 mg/dL for very high-risk patients, and ≤55 mg/dL in patients with cardiovascular disease at extreme risk.2 Because LDL-C is a dominant pathophysiological mechanism of atherogenesis, questions pertain to whether newer antiatherosclerotic and cardiovascular protective therapies exhibit efficacy, even in the setting of low LDL-C. In this post hoc analysis of the LEADER trial, we evaluated the efficacy of liraglutide on cardiovascular outcomes and mortality across the spectrum of baseline LDL-C and statin use

Multilocal programming and applications

Preprint versionMultilocal programming aims to identify all local minimizers of unconstrained or constrained nonlinear optimization problems. The multilocal programming theory relies on global optimization strategies combined with simple ideas that are inspired in deflection or stretching techniques to avoid convergence to the already detected local minimizers. The most used methods to solve this type of problems are based on stochastic procedures and a population of solutions. In general, population-based methods are computationally expensive but rather reliable in identifying all local solutions. In this chapter, a review on recent techniques for multilocal programming is presented. Some real-world multilocal programming problems based on chemical engineering process design applications are described.Fundação para a Ciência e a Tecnologia (FCT

Modeling of time-resolved laser-induced incandescence transients for particle sizing in high-pressure spray combustion environments: a comparative study

Contains fulltext : 35660.pdf (publisher's version ) (Closed access)In this study experimental single-pulse, time-resolved laser-induced incandescence (TIRE-LII) signal intensity profiles acquired during transient Diesel combustion events at high pressure were processed. Experiments were performed between 0.6 and 7 MPa using a high-temperature high-pressure constant volume cell and a heavy-duty Diesel engine, respectively. Three currently available LII sub-model functions were investigated in their performance for extracting ensemble mean soot particle diameters using a least-squares fitting routine, and a "quick-fit" interpolation approach, respectively. In the calculations a particle size distribution as well as the temporal and spatial intensity profile of the heating laser was taken into account. For the poorly characterized sample environments of this work, some deficiencies in these state-of-the-art data evaluation procedures were revealed. Depending on the implemented model function, significant differences in the extracted particle size parameters are apparent. We also observe that the obtained "best-fit" size parameters in the fitting procedure are biased by the choice of their respective "first-guess" initial values. This behavior may be caused by the smooth temporal profile of the LII cooling curve, giving rise to shallow local minima on the multi-parameter least squares residuals, surface sampled during the regression analysis procedure. Knowledge of the gas phase temperature of the probed medium is considered important for obtaining unbiased size parameter information from TIRE-LII measurements

Impacts of Vegetation on the Decoupling between Air and Soil Temperatures across the Arctic

A rise in global air temperatures is expected to increase permafrost thaw and alter ecosystem carbon and water cycles in Arctic regions. The coupling between the soil temperature in the active layer (soil between the ground surface and permafrost) and air temperature is a key component in understanding permafrost stability and ecosystem change. Vegetation can affect soil temperature through a variety of mechanisms such as canopy shading, impacts on soil thermal conductivity via soil organic inputs or soil water uptake, albedo, and winter snow trapping. However, the relative importance of the vegetative effects on soil temperature is uncertain across large spatial scales and across different vegetative communities and ecosystem types. We compiled data on a Pan-Arctic scale pairing air and soil temperature with vegetation and ecosystem data to examine the impacts of vegetation on the decoupling of air and soil temperatures. We analyzed the summer thawing degree days, winter freezing degree days, and n factors (degree days soil/degree days air) from sites across the Arctic. Our results indicate that the decoupling between summer air and soil temperatures is more variable in boreal ecosystems than tundra ecosystems, and boreal ecosystems have lower winter n-factors than tundra ecosystems. Summer n-factors were more variable than winter n-factors, and had high variability within study sites. Vegetative and ecosystem characteristics can be key drivers of spatial and temporal variability in active layer soil temperature, particularly during the summer. Quantifying the impacts of vegetation on active layer temperature is critical to understanding how changes in vegetation under climate change can further affect permafrost stability and soil temperature