Effects of age and physical performance capacity on distribution and composition of high-density lipoprotein subfractions in men

The influences of age and maximal aerobic capacity (VO2max) on serum lipoproteins with special regard to the concentration, composition and distribution of high density lipoprotein (HDL) subfractions were investigated in 51 healthy males of different characteristics: younger than 35 years, untrained (n = 14, mean age 28.2 years, SD 6.0; VO2max, 47.9 ml.kg-1.min-1, SD 5.8) and trained (n = 11, mean age 27.9 years, SD 4.3; VO2max, 61.1 ml.kg-1.min-1, SD 5.1), older than 50 years untrained (n = 14, mean age 58.9 years, SD 5.9, VO2max, 29.3 ml.kg-1.min-1, SD 5.3) and trained (n = 12, mean age 59.3 years, SD 7.2, VO2max, 45.7 ml.kg-1.min-1, SD 7.7). The fasting-state serum concentrations of total cholesterol, tri-acylglycerol and lipoprotein-cholesterol were measured. The HDL-subfractions were separated by density (rho) gradient ultracentrifugation. Concentrations of cholesterol, cholesterylester, tri-acylglycerol, phospholipids, apolipoprotein (apo) A-I and A-II were measured in the subfractions HDL2b: rho = 1.063-1.100 g.ml-1; HDL2al: rho = 1.00-1.110 g.ml-1; HDL2a2: rho = 1.110-1.150 g.ml-1; HDL3: rho = 1.150-1.210 g.ml-1. Elderly untrained subjects showed increased serum concentrations of total-, very low- and low density lipoprotein-cholesterol and elevated tri-acylglycerol levels. The HDL-cholesterol concentration was decreased, due to reduced concentrations of HDL2-subfractions. Significant changes in the composition of HDL2-subfractions were found in elderly untrained subjects. The HDL2-subfractions had more protein, a decreased apoA-I:A-II ratio and less phospholipids in comparison to HDL2-subfractions from younger untrained and trained, and elderly trained subjects.(ABSTRACT TRUNCATED AT 250 WORDS)     

Exploring two methods for statistical downscaling of Central European phenological time series

In this study we set out to investigate the possibility of linking phenological phases throughout the vegetation cycle, as a local-scale biological phenomenon, directly with large-scale atmospheric variables via two different empirical downscaling techniques. In recent years a number of methods have been developed to transfer atmospheric information at coarse General Circulation Model's grid resolutions to local scales and individual points. Here multiple linear regression (MLR) and canonical correlation analysis (CCA) have been selected as downscaling methods. Different validation experiments (e.g. temporal cross-validation, split-sample tests) are used to test the performance of both approaches and compare them for time series of 17 phenological phases and air temperatures from Central Europe as microscale variables. A number of atmospheric variables over the North Atlantic and Europe are utilized as macroscale predictors. The period considered is 1951–1998. Temporal cross-validation reveals that the CCA model generally performs better than MLR, which explains 20%–50% of the phenological variances, whereas the CCA model shows a range from 40% to over 60% throughout most of the vegetation cycle. To show the validity of employing phenological observations for downscaling purposes both methods (MLR and CCA) are also applied to gridded local air temperature time series over Central Europe. In this case there is no obvious superiority of the CCA model over the MLR model. Both models show explained variances from 40% to over 70% in the temporal cross-validation experiment. The results of this study indicate that time series of phenological occurrence dates are very compatible with the needs of empirical downscaling originally developed of local-scale atmospheric variables.     

Multi-linear regression analysis, preliminary biotic ligand modeling, and cross species comparison of the effects of water chemistry on chronic lead toxicity in invertebrates

The current study examined the chronic toxicity of lead (Pb) to three invertebrate species: the cladoceran Ceriodaphnia dubia, the snail Lymnaea stagnalis and the rotifer Philodina rapida. The test media consisted of natural waters from across North America, varying in pertinent water chemistry parameters including dissolved organic carbon (DOC), calcium, pH and total CO(2). Chronic toxicity was assessed using reproductive endpoints for C. dubia and P. rapida while growth was assessed for L. stagnalis, with chronic toxicity varying markedly according to water chemistry. A multi-linear regression (MLR) approach was used to identify the relative importance of individual water chemistry components in predicting chronic Pb toxicity for each species. DOC was an integral component of MLR models for C. dubia and L. stagnalis, but surprisingly had no predictive impact on chronic Pb toxicity for P. rapida. Furthermore, sodium and total CO(2) were also identified as important factors affecting C. dubia toxicity; no other factors were predictive for L. stagnalis. The Pb toxicity of P. rapida was predicted by calcium and pH. The predictive power of the C. dubia and L. stagnalis MLR models was generally similar to that of the current C. dubia BLM, with R(2) values of 0.55 and 0.82 for the respective MLR models, compared to 0.45 and 0.79 for the respective BLMs. In contrast the BLM poorly predicted P. rapida toxicity (R(2)=0.19), as compared to the MLR (R(2)=0.92). The cross species variability in the effects of water chemistry, especially with respect to rotifers, suggests that cross species modeling of invertebrate chronic Pb toxicity using a C. dubia model may not always be appropriate.     

3D-QSAR analysis of 2,4,5- and 2,3,4,5-substituted imidazoles as potent and nontoxic modulators of P-glycoprotein mediated MDR.

3D-Quantitative structure-activity relationships of 2,4,5- and 2,3,4,5-substituted imidazoles as a novel class of potent and nontoxic modulators of Pgp mediated MDR were investigated using CoMFA and COMSIA approaches. The best CoMFA model obtained from 46 imidazole analogues is a two-component model with the following statistics. R(2)(cv)=0.643, RMSE(cv)=0.360 for the cross-validation, and R(2)=0.767, RMSE=0.290 for the fitted. The best COMSIA model obtained is also a two-component model with the following statistics. R(2)(cv)=0.619, RMSE(cv)=0.372 for the cross-validation, and R(2)=0.765, RMSE=0.292 for the fitted.