Pretranslational regulation of the expression of the lipoprotein lipase (EC 3.1, l.34) gene by dietary fatty acids in the rat

Although there have been a number of studies of effects of diet and hormones on lipoprotein lipase (EC 3.1.1.34; LPL) activity and levels of LPL mRNA (Raynolds et al. 1990), there have been no studies which have investigated effects of different dietary fatty acids on LPL gene expression. In the present study male Wistar Albino rats were pair-fed diets containing 50 g fat/kg of different fatty acid composition for 2 weeks. The diets fed were (1) a mixed oil (450 g saturated fatty acids, 420 g monounsaturated fatty acids, 130 g polyunsaturated fatty acids/kg; n 8), (2) maize oil (n 8), or (3) fish oil (n 8). Animals were killed, RNA was extracted from liver and perirenal and epididymal fat pads, and analysed by ‘Northern methodology’. Samples were hybridized to a human cDNA probe for LPL (Gotoda et al. 1989). Two transcripts were identified in epididymai and perirenal adipose tissue which were approximately 3·7 and 1·7 kb in size. The results suggested that (1) fish oil-fed animals had significantly greater production of LPL mRNA in epididymai adipose tissue compared with maize oil-fed animals (P < 0·05), (2) maize oil-fed animals had significantly greater production of LPL mRNA in perirenal fat compared with the other dietary groups (P < 0·05), (3) expression in the liver was not significant. Rats fed on a fish oil diet had significantly reduced plasma triacylglycerol concentrations compared with the mixed-oil group (P < 0·05), but there were no significant differences in plasma cholesterol. The differences in LPL could not be explained directly by the changes in plasma immunoreactive-insulin and glucose-dependent insulinotrophic polypeptide levels in the three groups.

Computationally efficient rule-based classification for continuous streaming data

Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules.

Effect of high temperature and water stress on groundnuts under field conditions

Groundnuts cultivated in the semiarid tropics are often exposed to water stress (mid-season and end season) and high temperature (> 34 °C) during the critical stages of flowering and pod development. This study evaluated the effects of both water stress and high temperature under field conditions at ICRISAT, India. Treatments included two irrigations (full irrigation, 100 % of crop evapotranspiration; and water stress, 40 % of crop evapotranspiration), four temperature treatments from a combination of two sowing dates and heat tunnels with mean temperatures from sowing to maturity of 26.3° (T1), 27.3° (T2), 29.0° (T3) and 29.7 °C (T4) and two genotypes TMV2 and ICGS 11. The heat tunnels were capable of raising the day temperature by > 10 °C compared to ambient. During the 20-day high-temperature treatment at flowering, mean temperatures were 33.8° (T1), 41.6° (T2), 38.7° (T3) and 43.5°C (T4). The effects of water stress and high temperature were additive and temporary for both vegetative and pod yield, and disappeared as soon as high-temperature stress was removed. Water use efficiency was significantly affected by the main effects of temperature and cultivar and not by water stress treatments. Genotypic differences for tolerance to high temperature can be attributed to differences in flowering pattern, flower number, peg-set and harvest index. It can be inferred from this study that genotypes that are tolerant to water stress are also tolerant to high temperature under field conditions. In addition, genotypes with an ability to establish greater biomass and with a significantly greater partitioning of biomass to pod yield would be suitable for sustaining higher yields in semiarid tropics with high temperature and water stress.