Computer simulation studies of the fidelity of DNA polymerases

Computer simulations can provide in principle quantitative correlation between the structures of DNA polymerases and the replication fidelity. This paper describes our progress in this direction. Using several theoretical approaches, including the free energy perturbation (FEP), linear response approximation (LRA), and the empirical valence bond (EVB) methods, we examined the stability of several mismatched base pairs in DNA duplex in aqueous solution, the contribution of binding energy to the fidelity of DNA polymerases beta and T7, and the mechanism and energetics of the polymerization reaction catalyzed by T7 DNA polymerase.     

A comparison of methods for predicting vegetation type

Predictive modeling of vegetation patterns has wide application in vegetation science. In this paper I discuss three methods of predictive modeling using data from the alpine treeline ecotone as a case study. The study area is a portion of Glacier National Park, Montana. Parametric general linear models (GLM), artificial neural networks (ANN) and classification tree (CT) methods of predicting vegetation type are compared to determine the relative strength of each predictive approach and how they may be used in concert to increase understanding of important vegetation – environment relations. For each predictive method, vegetation type within the alpine treeline ecotone is predicted using a suite of environmental indicator variables including elevation, moisture potential, solar radiation potential, snow potential index, and disturbance history. Results from each of the predictive methods are compared against the real vegetation types to determine the relative accuracy of the methods.When the entire data field is examined (i.e., not evaluated by smaller spatial aggregates of data) the ANN procedure produces the most accurate predictions (=0.571); the CT predictions are the least accurate (=0.351). The predicted patterns of vegetation on the landscape are considerably different using the three methods. The GLM and CT methods produce large contiguous swaths of vegetation types throughout the study area, whereas the ANN method produces patterns with much more heterogeneity and smaller patches.When predictions are compared to reality at catchment scale, it becomes evident that the accuracy of each method varies depending upon the specific situation. The ANN procedure remains the most accurate method in the majority of the catchments, but both the GLM and PCT produce the most accurate classifications in at least one basin each.The variability in predictive ability of the three methods tested here indicates that there may not be a single best predictive method. Rather it may be important to use a suite of predictive models to help understand the environment – vegetation relationships. The ability to use multiple predictive methods to determine which spatial subunits of a landscape are outliers is important when identifying locations useful for climate change monitoring studies.     

Transmission dynamics of lymphatic filariasis: density-dependence in the uptake of Wuchereria bancrofti microfilariae by vector mosquitoes

Gaining a better understanding of parasite infection dynamics in the vector mosquito (Diptera: Culicidae) population is central to improving knowledge regarding the transmission, persistence and hence control of lymphatic filariasis. Here, we use data on mosquito feeding experiments collated from the published literature to examine the available evidence regarding the functional form of the first component of this parasite-vector relationship for Wuchereria bancrofti (Filarioidea: Onchocercidae) causing Bancroftian filariasis, i.e. the rate of microfilariae (mf) uptake from the blood of infected humans by the feeding mosquito vector. Using a simple logarithmic regression model for describing the observed relationships between the mean numbers of mf ingested per mosquito and parasite load in humans in each study, and a linear mixed-effects meta-analytical framework for synthesizing the observed regressions across studies, we show here for the first time clear evidence for the existence of density-dependence in this process for all the three major filariasis transmitting mosquito vectors. An important finding of this study is that this regulation of mf uptake also varies significantly between the vector genera, being weakest in Culex, comparatively stronger in Aedes and most severe and occurring at significantly lower human mf loads in Anopheles mosquitoes. The analysis of the corresponding mf uptake prevalence data has further highlighted how density-dependence in mf uptake may influence the observed distributions of mf in vector populations. These results show that whereas strong regulation of mf uptake, especially when it leads to saturation in uptake at low human parasite intensities, can lead to static distributions of mf per mosquito with host parasite intensity, a weaker regulation of mf ingestion can give rise to changes in both mean mf loads and in the frequency distribution of parasites/mosquito with increasing human parasite intensity. These findings highlight the importance of considering local vector infection dynamics when attempting to predict the impacts of community-based filariasis control. They also emphasize the value of developing and applying robust meta-analytic methods for estimating functional relationships regarding parasitic infection from population ecological data.     

A molecular understanding of complementary chromatic adaptation

Photosynthetic activity and the composition of the photosynthetic apparatus are strongly regulated by environmental conditions. Some visually dramatic changes in pigmentation of cyanobacterial cells that occur during changing nutrient and light conditions reflect marked alterations in components of the major light-harvesting complex in these organisms, the phycobilisome. As noted well over 100 years ago, the pigment composition of some cyanobacteria is very sensitive to ambient wavelengths of light; this sensitivity reflects molecular changes in polypeptide constituents of the phycobilisome. The levels of different pigmented polypeptides or phycobiliproteins that become associated with the phycobilisome are adjusted to optimize absorption of excitation energy present in the environment. This process, called complementary chromatic adaptation, is controlled by a bilin-binding photoreceptor related to phytochrome of vascular plants; however, many other regulatory elements also play a role in chromatic adaptation. My perspectives and biases on the history and significance of this process are presented in this essay. This revised version was published online in August 2006 with corrections to the Cover Date.     

Testing for the presence of previously identified QTL for milk production traits in new populations

Five chromosomal regions previously associated with milk production traits were tested in 16 families of Black and White cattle from the UK. The traits were also linearly transformed into genetically and phenotypically independent variables normalized by phenotypic variances ('canonical traits'). Significant associations with the untransformed and canonical traits were found for bovine chromosome 6. There was also evidence that chromosome 9 influenced these traits. The linear transformation clarified the effects of chromosomal regions; regions with effects on all three untransformed traits (milk, fat and protein yields) were generally condensed into an effect on a single canonical trait. Comparison of our results with those reported previously for American Holstein cattle suggested that the QTL may not be the same.     

On power and sample size calculations for likelihood ratio tests in generalized linear models

A direct extension of the approach described in Self, Mauritsen, and Ohara (1992, Biometrics 48, 31-39) for power and sample size calculations in generalized linear models is presented. The major feature of the proposed approach is that the modification accommodates both a finite and an infinite number of covariate configurations. Furthermore, for the approximation of the noncentrality of the noncentral chi-square distribution for the likelihood ratio statistic, a simplification is provided that not only reduces substantial computation but also maintains the accuracy. Simulation studies are conducted to assess the accuracy for various model configurations and covariate distributions.     

Structural time series models with feedback mechanisms

Structural time series models have applications in many different fields such as biology, economics, and meteorology. A structural times series model can be represented as a state-space model where the states of the system represent the unobserved components and the structural parameters have clear interpretations. This paper introduces a class of structural time series models that incorporate feedback from the latent components of the history. An iterative procedure is proposed for estimation. These models allow flexible and robust feedback mechanisms, have clear interpretations, and have a computationally efficient estimation procedure. They are applied to hormone data to characterize hormone secretion and to explore a potential feedback mechanism.     

Estimation of temperature and precipitation from morphological characters of dicotyledonous leaves

The utility of regression and correspondence models for deducing climate from leaf physiognomy was evaluated by the comparative application of different predictive models to the same three leaf assemblages. Mean annual temperature (MAT), mean annual precipitation (MAP), and growing season precipitation (GSP) were estimated from the morphological characteristics of samples of living leaves from two extant forests and an assemblage of fossil leaves. The extant forests are located near Gainesville, Florida, and in the Florida Keys; the fossils were collected from the Eocene Clarno Nut Beds, Oregon. Simple linear regression (SLR), multiple linear regression (MLR), and canonical correspondence analysis (CCA) were used to estimate temperature and precipitation. The SLR models used only the percentage of species having entire leaf margins as a predictor for MAT and leaf size as a predictor for MAP. The MLR models used from two to six leaf characters as predictors, and the CCA used 31 characters. In comparisons between actual and predicted values for the extant forests, errors in prediction of MAT were 0.6°-5.7°C, and errors in prediction of precipitation were 6-89 cm (=6-66%). At the Gainesville site, seven models underestimated MAT and only one overestimated it, whereas at the Keys site, all eight models overestimated MAT. Precipitation was overestimated by all four models at Gainesville, and by three of them at the Keys. The MAT estimates from the Clarno leaf assemblage ranged from 14.3° to 18.8°C, and the precipitation estimates from 227 to 363 cm for MAP and from 195 to 295 cm for GSP.