Comparative evolution of molecular markers: An analysis of genetic variation within the blue marlin (Makaira nigricans)

Blue marlin diversity was assessed at mtDNA, scnDNA, microsatellite DNA, and allozyme molecular markers. Hierarchical analysis of molecular variance (AMOVA) revealed that most genetic variation was maintained within populations, with a non-significant fraction attributable to variation among temporal replicates and between locations within oceans. In contrast, inter-ocean divergence was highly significant for a majority of loci within each marker class. Previous studies of mitochondrial DNA (mtDNA; n = 104) genetic variation within the blue marlin revealed two distinct clades of haplotypes, one of which was present only in the Atlantic (the \u27Atlantic clade\u27), at a frequency of 40% &(F\sb{lcub}st{rcub}& = 0.39). ScnDNA and allozyme markers exhibited lower levels of diversity and inter-ocean divergence than mtDNA (average &F\sb{lcub}st{rcub}& = 0.08). Enhanced genetic drift among populations, due to the four-fold lower effective population size of mtDNA, was emphasized as causing the greater mtDNA inter-ocean divergence. The low mutation rate of nuclear markers, and greater male dispersal may have contributed to the difference detected. Microsatellite loci were hypervariable, and displayed a wide range of divergence estimates (average &F\sb{lcub}st{rcub}& = 0.14). A nuclear \u27Atlantic clade\u27 of alleles was detected at one locus, indicating that the historical forces that generated the mitochondrial Atlantic clade (Pleistocene allopatry) also strongly influenced the nuclear genome. Although some microsatellite loci were much more sensitive than scnDNA markers, on average, these differences were not significant, due to the wide range of microsatellite patterns detected. The mean and variance of inter-ocean divergence &(F)& estimates were not significantly different among marker classes, suggesting a minor influence of selection. Correlations between diversity and divergence within and among marker classes were non-significant, indicating that difference in mutation rate can not explain the lower nuclear divergence. The patterns of diversity obtained within and among marker classes is consistent with expected values under migration-drift equilibrium

Period-Luminosity Relations Derived from the OGLE-III Fundamental Mode Cepheids

In this Paper, we have derived Cepheid period-luminosity (P-L) relations for the Large Magellanic Cloud (LMC) fundamental mode Cepheids, based on the data released from OGLE-III. We have applied an extinction map to correct for the extinction of these Cepheids. In addition to the VIW band P-L relations, we also include JHK and four Spitzer IRAC band P-L relations, derived by matching the OGLE-III Cepheids to the 2MASS and SAGE datasets, respectively. We also test the non-linearity of the Cepheid P-L relations based on extinction-corrected data. Our results (again) show that the LMC P-L relations are non-linear in VIJH bands and linear in KW and the four IRAC bands, respectively.Comment: 6 pages, 3 figures and 3 tables, ApJ accepte

Structural Inference in Transition Measurement Error Models for Longitudinal Data

We propose a new class of models, transition measurement error models, to study the effects of covariates and the past responses on the current response in longitudinal studies when one of the covariates is measured with error. We show that the response variable conditional on the error-prone covariate follows a complex transition mixed effects model. The naive model obtained by ignoring the measurement error correctly specifies the transition part of the model, but misspecifies the covariate effect structure and ignores the random effects. We next study the asymptotic bias in naive estimator obtained by ignoring the measurement error for both continuous and discrete outcomes. We show that the naive estimator of the regression coefficient of the error-prone covariate is attenuated, while the naive estimators of the regression coefficients of the past responses are generally inflated. We then develop a structural modeling approach for parameter estimation using the maximum likelihood estimation method. In view of the multidimensional integration required by full maximum likelihood estimation, an EM algorithm is developed to calculate maximum likelihood estimators, in which Monte Carlo simulations are used to evaluate the conditional expectations in the E-step. We evaluate the performance of the proposed method through a simulation study and apply it to a longitudinal social support study for elderly women with heart disease. An additional simulation study shows that the Bayesian information criterion (BIC) performs well in choosing the correct transition orders of the models.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66146/1/j.1541-0420.2005.00446.x.pd

Bivariate least squares linear regression: towards a unified analytic formalism

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts is reviewed using a new formalism in terms of deviation (matrix) traces. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. Linear models of regression lines are considered in the general case of correlated errors in X and in Y for heteroscedastic data. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases. In the limit of homoscedastic data, the results determined for functional models are compared with their counterparts related to extreme structural models. While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with a single exception. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors for both heteroscedastic and homoscedastic data. Conversely, samples related to different methods produce discrepant results, due to the presence of (still undetected) systematic errors, which implies no definitive statement can be made at present. A comparison is also made between different expressions of regression line slope and intercept variance estimators, where fractional discrepancies are found to be not exceeding a few percent, which grows up to about 20% in presence of large dispersion data.Comment: 56 pages, 2 tables, and 2 figures. New Astronomy, accepte

Confidence interval estimation for the changepoint of treatment stratification in the presence of a qualitative covariate-treatment interaction

The goal in stratified medicine is to administer the \textquotedblbest\textquotedbl treatment to a patient. Not all patients might benefit from the same treatment; the choice of best treatment can depend on certain patient characteristics. In this article, it is assumed that a time-to-event outcome is considered as a patient-relevant outcome and a qualitative interaction between a continuous covariate and treatment exists, ie,~that patients with different values of one specific covariate should be treated differently. We suggest and investigate different methods for confidence interval estimation for the covariate value, where the treatment recommendation should be changed based on data collected in a randomized clinical trial. An adaptation of Fieller's theorem, the delta method, and different bootstrap approaches (normal, percentile-based, wild bootstrap) are investigated and compared in a simulation study. Extensions to multivariable problems are presented and evaluated. We observed appropriate confidence interval coverage following Fieller's theorem irrespective of sample size but at the cost of very wide or even infinite confidence intervals. The delta method and the wild bootstrap approach provided the smallest intervals but inadequate coverage for small to moderate event numbers, also depending on the location of the true changepoint. For the percentile-based bootstrap, wide intervals were observed, and it was slightly conservative regarding coverage, whereas the normal bootstrap did not provide acceptable results for many scenarios. The described methods were also applied to data from a randomized clinical trial comparing two treatments for patients with symptomatic, severe carotid artery stenosis, considering patient's age as predictive marker

Uncertainty of Forest Biomass Estimates in North Temperate Forests Due to Allometry: Implications for Remote Sensing

Estimates of above ground biomass density in forests are crucial for refining global climate models and understanding climate change. Although data from field studies can be aggregated to estimate carbon stocks on global scales, the sparsity of such field data, temporal heterogeneity and methodological variations introduce large errors. Remote sensing measurements from spaceborne sensors are a realistic alternative for global carbon accounting; however, the uncertainty of such measurements is not well known and remains an active area of research. This article describes an effort to collect field data at the Harvard and Howland Forest sites, set in the temperate forests of the Northeastern United States in an attempt to establish ground truth forest biomass for calibration of remote sensing measurements. We present an assessment of the quality of ground truth biomass estimates derived from three different sets of diameter-based allometric equations over the Harvard and Howland Forests to establish the contribution of errors in ground truth data to the error in biomass estimates from remote sensing measurements