A method for determining the apoplastic water volume of conifer needles

A method for direct estimation of percentage apoplastic water volume (% APO) in conifer needles is described. The method presented here, and designated the pressure-needle (P-N) method, measures the relative water content of the needles to develop a curve similar to the pressure-volume (P-V) curve. P-V and P-N curves were developed for Picea pungens Engelm. cv. Hoopsi, Pinus sylvestris L., Abies gradis (Dougl.) L., and Pseudotsuga menziesii (Mirb) Franco. The % APO estimated by the two procedures varied as much as 2-fold, while other parameters were similar. The P-V method generated consistently higher and more variable % APO than the P-N method, due to the inclusion of the apoplastic water of the stem in the P-V method. For conifers, the P-N method offers a more accurate and precise method for determining % APO.     

The importance of juveniles in modelling growth: butterflyfish at Lizard Island

Synopsis I established and fitted von Bertalanffy growth functions to size-at-age data for four species of chaetodontids at Lizard Island. Special emphasis on juveniles provided detailed information of the early growth period. All four species demonstrated rapid initial growth achieving an average of 92% of maximum theoretical size in the first 2years. I used various constraints of the theoretical age at length zero (t₀) in an analysis of both complete data sets and data sets using only adult fish. An unconstrained value of t₀ resulted in the best-fit (maximum r²) curve when juveniles were included. When excluding juveniles, it was necessary to constrain t₀ to an approximate settling size to most closely represent the growth of the species.     

Addressing the intrinsic disorder bottleneck in structural proteomics

The Center for Eukaryotic Structural Genomics (CESG), as part of the Protein Structure Initiative (PSI), has established a high-throughput structure determination pipeline focused on eukaryotic proteins. NMR spectroscopy is an integral part of this pipeline, both as a method for structure determinations and as a means for screening proteins for stable structure. Because computational approaches have estimated that many eukaryotic proteins are highly disordered, about 1 year into the project, CESG began to use an algorithm (the Predictor of Naturally Disordered Regions, PONDR to avoid proteins that were likely to be disordered. We report a retrospective analysis of the effect of this filtering on the yield of viable structure determination candidates. In addition, we have used our current database of results on 70 protein targets from Arabidopsis thaliana and 1 from Caenorhabditis elegans, which were labeled uniformly with nitrogen-15 and screened for disorder by NMR spectroscopy, to compare the original algorithm with 13 other approaches for predicting disorder from sequence. Our study indicates that the efficiency of structural proteomics of eukaryotes can be improved significantly by removing targets predicted to be disordered by an algorithm chosen to provide optimal performance.     

Nonparametric estimation of ROC curves in the absence of a gold standard

In the evaluation of diagnostic accuracy of tests, a gold standard on the disease status is required. However, in many complex diseases, it is impossible or unethical to obtain such a gold standard. If an imperfect standard is used, the estimated accuracy of the tests would be biased. This type of bias is called imperfect gold standard bias. In this article we develop a nonparametric maximum likelihood method for estimating ROC curves and their areas of ordinal-scale tests in the absence of a gold standard. Our simulation study shows that the proposed estimators for the ROC curve areas have good finite-sample properties in terms of bias and mean squared error. Further simulation studies show that our nonparametric approach is comparable to the binormal parametric method, and is easier to implement. Finally, we illustrate the application of the proposed method in a real clinical study on assessing the accuracy of seven specific pathologists in detecting carcinoma in situ of the uterine cervix.     

On criteria for evaluating models of absolute risk

Absolute risk is the probability that an individual who is free of a given disease at an initial age, a, will develop that disease in the subsequent interval (a, t]. Absolute risk is reduced by mortality from competing risks. Models of absolute risk that depend on covariates have been used to design intervention studies, to counsel patients regarding their risks of disease and to inform clinical decisions, such as whether or not to take tamoxifen to prevent breast cancer. Several general criteria have been used to evaluate models of absolute risk, including how well the model predicts the observed numbers of events in subsets of the population ("calibration"), and "discriminatory power," measured by the concordance statistic. In this paper we review some general criteria and develop specific loss function-based criteria for two applications, namely whether or not to screen a population to select subjects for further evaluation or treatment and whether or not to use a preventive intervention that has both beneficial and adverse effects. We find that high discriminatory power is much more crucial in the screening application than in the preventive intervention application. These examples indicate that the usefulness of a general criterion such as concordance depends on the application, and that using specific loss functions can lead to more appropriate assessments.     

Prediction of methylated CpGs in DNA sequences using a support vector machine

DNA methylation plays a key role in the regulation of gene expression. The most common type of DNA modification consists of the methylation of cytosine in the CpG dinucleotide. At the present time, there is no method available for the prediction of DNA methylation sites. Therefore, in this study we have developed a support vector machine (SVM)-based method for the prediction of cytosine methylation in CpG dinucleotides. Initially a SVM module was developed from human data for the prediction of human-specific methylation sites. This module achieved a MCC and AUC of 0.501 and 0.814, respectively, when evaluated using a 5-fold cross-validation. The performance of this SVM-based module was better than the classifiers built using alternative machine learning and statistical algorithms including artificial neural networks, Bayesian statistics, and decision trees. Additional SVM modules were also developed based on mammalian- and vertebrate-specific methylation patterns. The SVM module based on human methylation patterns was used for genome-wide analysis of methylation sites. This analysis demonstrated that the percentage of methylated CpGs is higher in UTRs as compared to exonic and intronic regions of human genes. This method is available on line for public use under the name of Methylator at http://bio.dfci.harvard.edu/Methylator/.     

Analysis of slow-binding enzyme inhibitors at elevated enzyme concentrations

The improvement in the characterization of slow-binding inhibitors achieved by performing experiments at elevated enzyme concentrations is presented. In particular, the characterization of slow-binding inhibitors conforming to a two-step mode of inhibition with a steady-state dissociation constant that is much lower than the initial dissociation constant with enzyme is discussed. For these systems, inhibition is rapid and low steady-state product concentrations are produced at saturating inhibitor concentrations. By working at elevated enzyme concentrations, improved signal-to-noise ratios are achieved and data may be collected at saturating inhibitor levels. Numerical simulations confirmed that improved parameter estimates are obtained and useful data to discern the mechanism of slow-binding inhibition are produced by working at elevated enzyme concentrations. The saturation kinetics that were unobservable in two previous studies of an enzyme inhibitor system were measured by performing experiments at an elevated enzyme concentration. These results indicate that consideration of the quality of the data acquired using a particular assay is an important factor when selecting the enzyme concentration at which to perform experiments used to characterize the class of enzyme inhibitors examined herein.     

The five-parameter logistic: a characterization and comparison with the four-parameter logistic

Improvements in assay technology have reduced the amount of random variation in measured responses to the point where even slight asymmetry of the assay data can be more significant than random variation. Use of the five-parameter logistic (5PL) function to fit dose-response data easily accommodates such asymmetry. The 5PL can dramatically improve the accuracy of asymmetric assays over the use of symmetric models such as the four-parameter logistic (4PL) function. Until recently, however, the process of fitting the 5PL function has been difficult, with the result that the 4PL function has continued to be used even for highly asymmetric data. Various ad hoc modifications of the 4PL method have been developed in an attempt to address asymmetric data. However, recent advances in numerical methods and assay analysis software have rendered easier the fitting of the 5PL routine. This paper demonstrates how use of the 5PL function can improve assay performance over the 4PL and its variants. Specifically, the improvement in the accuracy of concentration estimates that can be obtained using the 5PL over the 4PL as a function of the asymmetry present in the data is studied. The behavior of the 5PL curve and how it differs from the 4PL curve are discussed. Common experimental designs, which can lead to ill-conditioned regression problems, are also examined.