Effects of sampling method on foliar δ13C of Leymus chinensis at different scales

Stable carbon isotope composition (δ¹³C) usually shows a negative relationship with precipitation at a large scale. We hypothesized that sampling method affects foliar δ¹³C and its response pattern to precipitation. We selected 11 sites along a precipitation gradient in Inner Mongolia and collected leaves of Leymus chinensis with five or six replications repeatedly in each site from 2009 to 2011. Additionally, we collected leaves of L. chinensis separately from two types of grassland (grazed and fenced) in 2011. Foliar δ¹³C values of all samples were measured. We compared the patterns that foliar δ¹³C to precipitation among different years or different sample sizes, the differences of foliar δ¹³C between grazed and fenced grassland. Whether actual annual precipitation (AAP) or mean annual precipitation (MAP), it was strongly correlated with foliar δ¹³C every year. Significant difference was found between the slopes of foliar δ¹³C to AAP and MAP every year, among the slopes of foliar δ¹³C to AAP from 2009 to 2011. The more samples used at each site the lower and convergent P‐values of the linear regression test between foliar δ¹³C and precipitation. Furthermore, there was significant lower foliar δ¹³C value in presence of grazed type than fenced type grassland. These findings provide evidence that there is significant effect of sampling method to foliar δ¹³C and its response pattern to precipitation of L. chinensis. Our results have valuable implications in methodology for future field sampling studies.     

PEDIGREE ERROR DUE TO EXTRA‐PAIR REPRODUCTION SUBSTANTIALLY BIASES ESTIMATES OF INBREEDING DEPRESSION

Understanding the evolutionary dynamics of inbreeding and inbreeding depression requires unbiased estimation of inbreeding depression across diverse mating systems. However, studies estimating inbreeding depression often measure inbreeding with error, for example, based on pedigree data derived from observed parental behavior that ignore paternity error stemming from multiple mating. Such paternity error causes error in estimated coefficients of inbreeding (f) and reproductive success and could bias estimates of inbreeding depression. We used complete “apparent” pedigree data compiled from observed parental behavior and analogous “actual” pedigree data comprising genetic parentage to quantify effects of paternity error stemming from extra‐pair reproduction on estimates of f, reproductive success, and inbreeding depression in free‐living song sparrows (Melospiza melodia). Paternity error caused widespread error in estimates of f and male reproductive success, causing inbreeding depression in male and female annual and lifetime reproductive success and juvenile male survival to be substantially underestimated. Conversely, inbreeding depression in adult male survival tended to be overestimated when paternity error was ignored. Pedigree error stemming from extra‐pair reproduction therefore caused substantial and divergent bias in estimates of inbreeding depression that could bias tests of evolutionary theories regarding inbreeding and inbreeding depression and their links to variation in mating system.     

Post-natal heart adaptation in a knock-in mouse model of calsequestrin 2-linked recessive catecholaminergic polymorphic ventricular tachycardia

Cardiac calsequestrin (CASQ2) contributes to intracellular Ca²⁺ homeostasis by virtue of its low-affinity/high-capacity Ca²⁺ binding properties, maintains sarcoplasmic reticulum (SR) architecture and regulates excitation–contraction coupling, especially or exclusively upon β-adrenergic stimulation. Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited arrhythmogenic disease associated with cardiac arrest in children or young adults. Recessive CPVT variants are due to mutations in the CASQ2 gene. Molecular and ultra-structural properties were studied in hearts of CASQ2^R33Q/R33Q and of CASQ2^−/− mice from post-natal day 2 to week 8. The drastic reduction of CASQ2-R33Q is an early developmental event and is accompanied by down-regulation of triadin and junctin, and morphological changes of jSR and of SR-transverse-tubule junctions. Although endoplasmic reticulum stress is activated, no signs of either apoptosis or autophagy are detected. The other model of recessive CPVT, the CASQ2^−/− mouse, does not display the same adaptive pattern. Expression of CASQ2-R33Q influences molecular and ultra-structural heart development; post-natal, adaptive changes appear capable of ensuring until adulthood a new pathophysiological equilibrium.     

A plasma global metabolic profiling approach applied to an exercise study monitoring the effects of glucose, galactose and fructose drinks during post-exercise recovery

A global metabolic profiling methodology based on gas chromatography coupled to time-of-flight mass spectrometry (GC-TOFMS) for human plasma was applied to a human exercise study focused on the effects of beverages containing glucose, galactose, or fructose taken after exercise and throughout a recovery period of 6 h and 45 min. One group of 10 well trained male cyclists performed 3 experimental sessions on separate days (randomized, single center). After performing a standardized depletion protocol on a bicycle, subjects consumed one of three different beverages: maltodextrin (MD)+glucose (2:1 ratio), MD+galactose (2:1), and MD+fructose (2:1), consumed at an average of ～1.25 g of carbohydrate (CHO) ingested per minute. Blood was taken straight after exercise and every 45 min within the recovery phase. With the resulting blood plasma, insulin, free fatty acid (FFA) profile, glucose, and GC-TOFMS global metabolic profiling measurements were performed. The resulting profiling data was able to match the results obtained from the other clinical measurements with the addition of being able to follow many different metabolites throughout the recovery period. The data quality was assessed, with all the labelled internal standards yielding values of <15% CV for all samples (n=335), apart from the labelled sucrose which gave a value of 15.19%. Differences between recovery treatments including the appearance of galactonic acid from the galactose based beverage were also highlighted.     

Regression Calibration in Semiparametric Accelerated Failure Time Models

Summary In large cohort studies, it often happens that some covariates are expensive to measure and hence only measured on a validation set. On the other hand, relatively cheap but error‐prone measurements of the covariates are available for all subjects. Regression calibration (RC) estimation method ( Prentice, 1982 , Biometrika 69 , 331–342) is a popular method for analyzing such data and has been applied to the Cox model by Wang et al. (1997, Biometrics 53 , 131–145) under normal measurement error and rare disease assumptions. In this article, we consider the RC estimation method for the semiparametric accelerated failure time model with covariates subject to measurement error. Asymptotic properties of the proposed method are investigated under a two‐phase sampling scheme for validation data that are selected via stratified random sampling, resulting in neither independent nor identically distributed observations. We show that the estimates converge to some well‐defined parameters. In particular, unbiased estimation is feasible under additive normal measurement error models for normal covariates and under Berkson error models. The proposed method performs well in finite‐sample simulation studies. We also apply the proposed method to a depression mortality study.     

Estimating Treatment Effects of Longitudinal Designs using Regression Models on Propensity Scores

Summary We derive regression estimators that can compare longitudinal treatments using only the longitudinal propensity scores as regressors. These estimators, which assume knowledge of the variables used in the treatment assignment, are important for reducing the large dimension of covariates for two reasons. First, if the regression models on the longitudinal propensity scores are correct, then our estimators share advantages of correctly specified model‐based estimators, a benefit not shared by estimators based on weights alone. Second, if the models are incorrect, the misspecification can be more easily limited through model checking than with models based on the full covariates. Thus, our estimators can also be better when used in place of the regression on the full covariates. We use our methods to compare longitudinal treatments for type II diabetes mellitus.     

Correction for Covariate Measurement Error in Nonparametric Longitudinal Regression

Summary We introduce a correction for covariate measurement error in nonparametric regression applied to longitudinal binary data arising from a study on human sleep. The data have been surveyed to investigate the association of some hormonal levels and the probability of being asleep. The hormonal effect is modeled flexibly while we account for the error‐prone measurement of its concentration in the blood and the longitudinal character of the data. We present a fully Bayesian treatment utilizing Markov chain Monte Carlo inference techniques, and also introduce block updating to improve sampling and computational performance in the binary case. Our model is partly inspired by the relevance vector machine with radial basis functions, where usually very few basis functions are automatically selected for fitting the data. In the proposed approach, we implement such data‐driven complexity regulation by adopting the idea of Bayesian model averaging. Besides the general theory and the detailed sampling scheme, we also provide a simulation study for the Gaussian and the binary cases by comparing our method to the naive analysis ignoring measurement error. The results demonstrate a clear gain when using the proposed correction method, particularly for the Gaussian case with medium and large measurement error variances, even if the covariate model is misspecified.