Cardiovascular disease and PPARdelta: Targeting the risk factors

Metabolism, in part, is regulated by the peroxisome proliferator-activated receptors (PPARs). The PPARs act as nutritional lipid sensors and three mammalian PPAR subtypes designated PPARalpha (NR1C1), PPARgamma (NR1C3) and PPARdelta (NR1C2) have been identified. This subgroup of nuclear hormone receptors binds DNA and controls gene expression at the nexus of pathways that regulate lipid and glucose homeostasis, energy storage and expenditure in an organ-specific manner. Recent evidence has demonstrated activation of PPARdelta in the major mass peripheral tissue (ie, adipose and skeletal muscle). It enhances glucose tolerance, insulin-stimulated glucose disposal, lipid catabolism, energy expenditure, cholesterol efflux and oxygen consumption. These effects positively influence the blood-lipid profile. Furthermore, PPARdelta activation produces a predominant type I/slow twitch/oxidative muscle fiber phenotype that leads to increased endurance, insulin sensitivity and resistance to obesity. PPARdelta has rapidly emerged as a potential target in the battle against dyslipidemia, insulin insensitivity, type II diabetes and obesity, with therapeutic efficacy in the treatment of cardiovascular disease risk factors. GW-501516 is currently undergoing phase II safety and efficacy trials in human volunteers for the treatment of dyslipidemia. The outcome of these clinical trials are eagerly awaited against a background of conflicting reports about cancer risks in genetically predisposed animal models. This review focuses on the potential pharmacological utility of selective PPARdelta agonists in the context of risk factors associated with metabolic and cardiovascular disease.

Analysis of stirred mill performance using DEM simulation: Part 1 - Media motion, energy consumption and collisional environment

Stirred mills are becoming increasingly used for fine and ultra-fine grinding. This technology is still poorly understood when used in the mineral processing context. This makes process optimisation of such devices problematic. 3D DEM simulations of the flow of grinding media in pilot scale tower mills and pin mills are carried out in order to investigate the relative performance of these stirred mills. Media flow patterns and energy absorption rates and distributions are analysed here. In the second part of this paper, coherent flow structures, equipment wear and mixing and transport efficiency are analysed. (C) 2006 Published by Elsevier Ltd.

One-dimensional interacting anyon gas: Low-energy properties and haldane exclusion statistics

The low-energy properties of the one-dimensional anyon gas with a delta-function interaction are discussed in the context of its Bethe ansatz solution. It is found that the anyonic statistical parameter and the dynamical coupling constant induce Haldane exclusion statistics interpolating between bosons and fermions. Moreover, the anyonic parameter may trigger statistics beyond Fermi statistics for which the exclusion parameter alpha is greater than one. The Tonks-Girardeau and the weak coupling limits are discussed in detail. The results support the universal role of alpha in the dispersion relations.

The inhibitory effects of free nitrous acid on the energy generation and growth processes of an enriched Nitrobacter culture

The inhibitory effects of nitrite (NO2-)/free nitrous acid (HNO2-FNA) on the metabolism of Nitrobacter were investigated using a method allowing the decoupling of the growth and energy generation processes. A lab-scale sequencing batch reactor was operated for the enrichment of a Nitrobacter culture. Fluorescent in situ hybridization (FISH) analysis showed that 73% of the bacterial population was Nitrobacter. Batch tests were carried out to assess the oxygen and nitrite consumption rates of the enriched culture at low and high nitrite levels, in the presence or absence of inorganic carbon. It was observed that in the absence of CO2, the Nitrobacter culture was able to oxidize nitrite at a rate that is 76% of that in the presence of CO2, with an oxygen consumption rate that is 85% of that measured in the presence of CO2. This enabled the impacts of nitrite/FNA on the catabolic and anabolic processes of Nitrobacter to be assessed separately. FNA rather than nitrite was likely the actual inhibitor to the Nitrobacter metabolism. It was revealed that FNA of up to 0.05 mg HNO2-N center dot L-1 (3.4 mu M), which was the highest FNA concentration used in this study, did not have any inhibitory effect on the catabolic processes of Nitrobacter. However, FNA initiated its inhibition to the anabolic processes of Nitrobacter at approximately 0.011 mg HNO2-N center dot L-1 (0.8 mu M), and completely stopped biomass synthesis at a concentration of approximately 0.023 mg HNO2-N center dot L-1 (1.6 mu M). The inhibitory effect could be described by an empirical inhibitory model proposed in this paper, but the underlying mechanisms remain to be revealed.

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.

Drying processess in the United Kingdom:assessment of industrial energy utilisation and efficiency of drying systems and the modelling of drying characters using neural networks (BL)

Drying is an important unit operation in process industry. Results have suggested that the energy used for drying has increased from 12% in 1978 to 18% of the total energy used in 1990. A literature survey of previous studies regarding overall drying energy consumption has demonstrated that there is little continuity of methods and energy trends could not be established. In the ceramics, timber and paper industrial sectors specific energy consumption and energy trends have been investigated by auditing drying equipment. Ceramic products examined have included tableware, tiles, sanitaryware, electrical ceramics, plasterboard, refractories, bricks and abrasives. Data from industry has shown that drying energy has not varied significantly in the ceramics sector over the last decade, representing about 31% of the total energy consumed. Information from the timber industry has established that radical changes have occurred over the last 20 years, both in terms of equipment and energy utilisation. The energy efficiency of hardwood drying has improved by 15% since the 1970s, although no significant savings have been realised for softwood. A survey estimating the energy efficiency and operating characteristics of 192 paper dryer sections has been conducted. Drying energy was found to increase to nearly 60% of the total energy used in the early 1980s, but has fallen over the last decade, representing 23% of the total in 1993. These results have demonstrated that effective energy saving measures, such as improved pressing and heat recovery, have been successfully implemented since the 1970s. Artificial neural networks have successfully been applied to model process characteristics of microwave and convective drying of paper coated gypsum cove. Parameters modelled have included product moisture loss, core gypsum temperature and quality factors relating to paper burning and bubbling defects. Evaluation of thermal and dielectric properties have highlighted gypsum's heat sensitive characteristics in convective and electromagnetic regimes. Modelling experimental data has shown that the networks were capable of simulating drying process characteristics to a high degree of accuracy. Product weight and temperature were predicted to within 0.5% and 5C of the target data respectively. Furthermore, it was demonstrated that the underlying properties of the data could be predicted through a high level of input noise.