Thermal performance curves are an example of continuous reaction norm curves of common shape. Three modes of variation in these curves--vertical shift, horizontal shift, and generalist-specialist trade-offs--are of special interest to evolutionary biologists. Since two of these modes are nonlinear, traditional methods such as principal components analysis fail to decompose the variation into biological modes and to quantify the variation associated with each mode. Here we present the results of a new method, template mode of variation (TMV), that decomposes the variation into predetermined modes of variation for a particular set of thermal performance curves. We illustrate the method using data on thermal sensitivity of growth rate in Pieris rapae caterpillars. The TMV model explains 67% of the variation in thermal performance curves among families; generalist-specialist trade-offs account for 38% of the total between-family variation. The TMV method implemented here is applicable to both differences in mean and patterns of variation, and it can be used with either phenotypic or quantitative genetic data for thermal performance curves or other continuous reaction norms that have a template shape with a single maximum. The TMV approach may also apply to growth trajectories, age-specific life-history traits, and other function-valued traits.

IMPORTANCEDabigatran and rivaroxaban are non-vitamin K oral anticoagulants approved for stroke prevention in patients with nonvalvular atrial fibrillation (AF). There are no randomized head-to-head comparisons of these drugs for stroke, bleeding, or mortality outcomes. OBJECTIVE To compare risks of thromboembolic stroke, intracranial hemorrhage (ICH), major extracranial bleeding including major gastrointestinal bleeding, and mortality in patients with nonvalvular AF who initiated dabigatran or rivaroxaban treatment for stroke prevention. DESIGN, SETTING, AND PARTICIPANTSRetrospective new-user cohort study of 118 891 patients with nonvalvular AF who were 65 years or older, enrolled in fee-for-service Medicare, and who initiated treatment with dabigatran or rivaroxaban from November 4, 2011, through June 30, 2014. Differences in baseline characteristics were adjusted using stabilized inverse probability of treatment weights based on propensity scores. The data analysis was performed from May 7, 2015, through June 30, 2016.EXPOSURES Dabigatran, 150 mg, twice daily; rivaroxaban, 20 mg, once daily.MAIN OUTCOMES AND MEASURES Adjusted hazard ratios (HRs) for the primary outcomes of thromboembolic stroke, ICH, major extracranial bleeding including major gastrointestinal bleeding, and mortality, with dabigatran as reference. Adjusted incidence rate differences (AIRDs) were also estimated.RESULTS A total of 52 240 dabigatran-treated and 66 651 rivaroxaban-treated patients (47% female) contributed 15 524 and 20 199 person-years of on-treatment follow-up, respectively, during which 2537 primary outcome events occurred. Rivaroxaban use was associated with a statistically nonsignificant reduction in thromboembolic stroke (HR, 0.81; 95% CI, 0.65-1.01; P = .07; AIRD = 1.8 fewer cases/1000 person-years), statistically significant increases in ICH (HR, 1.65; 95% CI, 1.20-2.26; P = .002; AIRD = 2.3 excess cases/1000 person-years) and major extracranial bleeding (HR, 1.48; 95% CI, 1.32-1.67; P < .001; AIRD = 13.0 excess cases/1000 person-years), including major gastrointestinal bleeding (HR, 1.40; 95% CI, 1.23-1.59; P < .001; AIRD = 9.4 excess cases/1000 person-years), and with a statistically nonsignificant increase in mortality (HR, 1.15; 95% CI, 1.00-1.32; P = .051; AIRD = 3.1 excess cases/1000 person-years). In patients 75 years or older or with CHADS 2 score greater than 2, rivaroxaban use was associated with significantly increased mortality compared with dabigatran use. The excess rate of ICH with rivaroxaban use exceeded its reduced rate of thromboembolic stroke. CONCLUSIONS AND RELEVANCETreatment with rivaroxaban 20 mg once daily was associated with statistically significant increases in ICH and major extracranial bleeding, including major gastrointestinal bleeding, compared with dabigatran 150 mg twice daily.

Two major goals of laboratory evolution experiments are to integrate from genotype to phenotype to fitness, and to understand the genetic basis of adaptation in natural populations. Here we demonstrate that both goals are possible by re-examining the outcome of a previous laboratory evolution experiment in which the bacteriophage G4 was adapted to high temperatures. We quantified the evolutionary changes in the thermal reaction norms—the curves that describe the effect of temperature on the growth rate of the phages—and decomposed the changes into modes of biological interest. Our analysis indicated that changes in optimal temperature accounted for almost half of the evolutionary changes in thermal reaction norm shape, and made the largest contribution toward adaptation at high temperatures. Genome sequencing allowed us to associate reaction norm shape changes with particular nucleotide mutations, and several of the identified mutations were found to be polymorphic in natural populations. Growth rate measures of natural phage that differed at a site that contributed substantially to adaptation in the lab indicated that this mutation also underlies thermal reaction norm shape variation in nature. In combination, our results suggest that laboratory evolution experiments may successfully predict the genetic bases of evolutionary responses to temperature in nature. The implications of this work for viral evolution arise from the fact that shifts in the thermal optimum are characterized by tradeoffs in performance between high and low temperatures. Optimum shifts, if characteristic of viral adaptation to novel temperatures, would ensure the success of vaccine development strategies that adapt viruses to low temperatures in an attempt to reduce virulence at higher (body) temperatures.

While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages.Here we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can be applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data.Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.

Trade-offs can exist within and across environments, and constrain evolutionary trajectories. To examine the effects of competition and resource availability on trade-offs, we grew individuals of recombinant inbred lines of Impatiens capensis in a factorial combination of five densities with two light environments (full light and neutral shade) and used a Bayesian logistic growth analysis to estimate intrinsic growth rates. To estimate across-environment constraints, we developed a variance decomposition approach to principal components analysis, which accounted for sample size, model-fitting, and within-RIL variation prior to eigenanalysis. We detected negative across-environment genetic covariances in intrinsic growth rates, although only under fulllight. To evaluate the potential importance of these covariances, we surveyed natural populations of I. capensis to measure the frequency of different density environments across space and time. We combined our empirical estimates of across-environment genetic variance-covariance matrices and frequency of selective environments with hypothetical (yet realistic) selection gradients to project evolutionary responses in multiple density environments. Selection in common environments can lead to correlated responses to selection in rare environments that oppose and counteract direct selection in those rare environments. Our results highlight the importance of considering both the frequency of selective environments and the across-environment genetic covariances in traits simultaneously. 4 Authors contributed equally.