Plasma delipidation process induces rapid regression of atherosclerosis and mobilisation of adipose tissue

Cholesterol is a major component of atherosclerotic plaques. Cholesterol accumulation within the arterial intima and atherosclerotic plaques is determined by the difference of cellular cholesterol synthesis and/or influx from apo B-containing lipoproteins and cholesterol efflux. In humans, apo A-I Milano infusion has led to rapid regression of atherosclerosis in coronary arteries. We hypothesised that a multifunctional plasma delipidation process (PDP) would lead to rapid regression of experimental atherosclerosis and probably impact on adipose tissue lipids. In hyperlipidemic animals, the plasma concentrations of cholesterol, triglyceride and phospholipid were, respectively, 6-, 157-, and 18-fold higher than control animals, which consequently resulted in atherosclerosis. PDP consisted of delipidation of plasma with a mixture of butanol-diisopropyl ether (DIPE). PDP removed considerably more lipid from the hyperlipidemic animals than in normolipidemic animals. PDP treatment of hyperlipidemic animals markedly reduced intensity of lipid staining materials in the arterial wall and led to dramatic reduction of lipid in the adipose tissue. Five PDP treatments increased apolipoprotein A1 concentrations in all animals. Biochemical and hematological parameters were unaffected during PDP treatment. These results show that five PDP treatments led to marked reduction in avian atherosclerosis and removal of lipid from adipose tissue. PDP is a highly effective method for rapid regression of atherosclerosis.

Application and evaluation of classification trees for screening unwanted plants

Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical Study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which tender the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

Predicting residue-wise contact orders in proteins by support vector regression

Background: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Unplanned land clearing of Colombian rainforests: Spreading like disease?

Deforestation often occurs as temporal waves and in localized fronts termed 'deforestation hotspots' driven by economic pulses and population pressure. Of particular concern for conservation planning are 'biodiversity hotspots' where high concentrations of endemic species undergo rapid loss and fragmentation of habitat. We investigate the deforestation process in Caqueta, a biodiversity hotspot and major colonization front of the Colombian Amazon using multi-temporal satellite imagery of the periods 1989-1996-1999-2002. The probabilities of deforestation and regeneration were modeled against soil fertility, accessibility and neighborhood terms, using logistic regression analysis. Deforestation and regeneration patterns and rates were highly variable across the colonization front. The regional average annual deforestation rate was 2.6%, but varied locally between -1.8% (regeneration) and 5.3%, with maximum rates in landscapes with 40-60% forest cover and highest edge densities, showing an analogous pattern to the spread of disease. Soil fertility and forest and secondary vegetation neighbors showed positive and significant relationships with the probability of deforestation. For forest regeneration, soil fertility had a significant negative effect while the other parameters were marginally significant. The logistic regression models across all periods showed a high level of discrimination power for both deforestation and forest regeneration, with ROC values > 0.80. We document the effect of policies and institutional changes on the land clearing process, such as the failed peace process between government and guerillas in 1999-2002, which redirected the spread of deforestation and increased forest regeneration. The implications for conservation in biologically rich areas, such as Caqueta are discussed. (c) 2005 Elsevier B.V All rights reserved.

Optimal crossover designs for logistic regression models in pharmacodynamics

Pharmacodynamics (PD) is the study of the biochemical and physiological effects of drugs. The construction of optimal designs for dose-ranging trials with multiple periods is considered in this paper, where the outcome of the trial (the effect of the drug) is considered to be a binary response: the success or failure of a drug to bring about a particular change in the subject after a given amount of time. The carryover effect of each dose from one period to the next is assumed to be proportional to the direct effect. It is shown for a logistic regression model that the efficiency of optimal parallel (single-period) or crossover (two-period) design is substantially greater than a balanced design. The optimal designs are also shown to be robust to misspecification of the value of the parameters. Finally, the parallel and crossover designs are combined to provide the experimenter with greater flexibility.

Effect of sample geometry on regression rate of the melting interface for carbon steel burned in oxygen

Promoted-ignition testing on carbon steel rods of varying cross-sectional area and shape was performed in high pressure oxygen to assess the effect of sample geometry on the regression rate of the melting interface. Cylindrical and rectangular geometries and three different cross sections were tested and the regression rates of the cylinders were compared to the regression rates of the rectangular samples at test pressures around 6.9 MPa. Tests were recorded and video analysis used to determine the regression rate of the melting interface by a new method based on a drop cycle which was found to provide a good basis for statistical analysis and provide excellent agreement to the standard averaging methods used. Both geometries tested showed the typical trend of decreasing regression rate of the melting interface with increasing cross-sectional area; however, it was shown that the effect of geometry is more significant as the sample's cross sections become larger. Discussion is provided regarding the use of 3.2-mm square rods rather than 3.2-mm cylindrical rods within the standard ASTM test and any effect this may have on the observed regression rate of the melting interface.

Use of regression equation of peripheral pulse timing characteristics to predict hypertension in children

Studies have shown that an increase in arterial stiffening can indicate the presence of cardiovascular diseases like hypertension. Current gold standard in clinical practice is by measuring the blood pressure of patients using a mercury sphygmomanometer. However, the nature of this technique is not suitable for prolonged monitoring. It has been established that pulse wave velocity is a direct measure of arterial stiffening. However, its usefulness is hampered by the absence of techniques to estimate it non-invasively. Pulse transit time (PTT) is a simple and non-intrusive method derived from pulse wave velocity. It has shown its capability in childhood respiratory sleep studies. Recently, regression equations that can predict PTT values for healthy Caucasian children were formulated. However, its usefulness to identify hypertensive children based on mean PTT values has not been investigated. This was a continual study where 3 more Caucasian male children with known clinical hypertension were recruited. Results indicated that the PTT predictive equations are able to identify hypertensive children from their normal counterparts in a significant manner (p < 0.05). Hence, PTT can be a useful diagnostic tool in identifying hypertension in children and shows potential to be a non-invasive continual monitor for arterial stiffening.

Precision-recall operating characteristic (P-ROC) curves in imprecise environments

Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this paper, we consider the case of imprecise environments, where little may be known about these factors and they may well vary significantly when the system is applied. Specifically, the use of precision-recall analysis is investigated and compared to the more well known performance measures such as error-rate and the receiver operating characteristic (ROC). We argue that while ROC analysis is invariant to variations in class priors, this invariance in fact hides an important factor of the evaluation in imprecise environments. Therefore, we develop a generalised precision-recall analysis methodology in which variation due to prior class probabilities is incorporated into a multi-way analysis of variance (ANOVA). The increased sensitivity and reliability of this approach is demonstrated in a remote sensing application.