Forecasting with serially correlated regression models

In this article we investigate the asymptotic and finite-sample properties of predictors of regression models with autocorrelated errors. We prove new theorems associated with the predictive efficiency of generalized least squares (GLS) and incorrectly structured GLS predictors. We also establish the form associated with their predictive mean squared errors as well as the magnitude of these errors relative to each other and to those generated from the ordinary least squares (OLS) predictor. A large simulation study is used to evaluate the finite-sample performance of forecasts generated from models using different corrections for the serial correlation.

Evaluation of individual protein errors in silver-stained two-dimensional gels

The relationship between spot volume and variation for all protein spots observed on large format 2D gels when utilising silver stain technology and a model system based on mammalian NSO cell extracts is reported. By running multiple gels we have shown that the reproducibility of data generated in this way is dependent on individual protein spot volumes, which in turn are directly correlated with the coefficient of variation. The coefficients of variation across all observed protein spots were highest for low abundant proteins which are the primary contributors to process error, and lowest for more abundant proteins. Using the relationship between spot volume and coefficient of variation we show it is necessary to calculate variation for individual protein spot volumes. The inherent limitations of silver staining therefore mean that errors in individual protein spot volumes must be considered when assessing significant changes in protein spot volume and not global error. (C) 2003 Elsevier Science (USA). All rights reserved.

Hydrodynamic evaluation of kangaroo aortic valve matrices for tissue valve engineering

We evaluated the hydrodynamic performance of kangaroo aortic valve matrices (KMs) (19, 21, and 23 mm), as potential scaffolds in tissue valve engineering using a pulsatile left heart model at low and high cardiac outputs (COs) and heart rates (HRs) of 60 and 90 beats/min. Data were measured in two samples of each type, pooled in two CO levels (2.1 +/- 0.7 and 4.2 +/- 0.6 L/min; mean +/- standard errors on the mean), and analyzed using analysis of variance with CO level, HR, and valve type as fixed factors and compared to similar porcine matrices (PMs). Transvalvular pressure gradient (Delta P) was a function of HR (P < 0.001) and CO (P < 0.001) but not of valve type (P = 0.39). Delta P was consistently lower in KMs but not significantly different from PMs. The effective orifice area and performance index of kangaroo matrices was statistically larger for all sizes at both COs and HRs.