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.     

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.     

Influence of castration-induced testosterone and estradiol deficiency on obesity and glucose metabolism in male Göttingen minipigs

Low testosterone and estradiol concentrations are predictive for the development of the metabolic syndrome in men and women, respectively. The aim of this study was to investigate the influence of sex hormone deficiency on food intake, body weight, body composition and glucose metabolism in male Göttingen minipigs.Five adult male Göttingen minipigs were studied before castration (pre-cast), 10-18 days (post-cast 1) and 10-11 weeks (post-cast 2) after castration. Parameters of interest were food intake, body weight, body fat percentage and sex hormone concentrations. Furthermore glucose tolerance, glucagon suppression, insulin resistance, beta cell function and disposition index were evaluated by oral and intravenous glucose tolerance tests.Castration led to almost complete disappearance of circulating testosterone and estradiol and secondarily to increased food intake, body weight and body fat percentage. Ten-eighteen days sex hormone deficiency (post-cast 1) did not significantly change any of the investigated metabolic parameters compared to pre-cast levels. Ten weeks after castration (post-cast 2) significant insulin resistance, glucose intolerance and hyperglucagonemia was found, and the beta cell function and the disposition index both were decreased.In conclusion, castration-induced sex hormone deficiency in male Göttingen minipigs results in hyperphagia, obesity and disturbed glucose metabolism, which are some of the features typical for the human metabolic syndrome.     

Bivariate line-fitting methods for allometry

Fitting a line to a bivariate dataset can be a deceptively complex problem, and there has been much debate on this issue in the literature. In this review, we describe for the practitioner the essential features of line-fitting methods for estimating the relationship between two variables: what methods are commonly used, which method should be used when, and how to make inferences from these lines to answer common research questions. A particularly important point for line-fitting in allometry is that usually, two sources of error are present (which we call measurement and equation error), and these have quite different implications for choice of line-fitting method. As a consequence, the approach in this review and the methods presented have subtle but important differences from previous reviews in the biology literature. Linear regression, major axis and standardised major axis are alternative methods that can be appropriate when there is no measurement error. When there is measurement error, this often needs to be estimated and used to adjust the variance terms in formulae for line-fitting. We also review line-fitting methods for phylogenetic analyses. Methods of inference are described for the line-fitting techniques discussed in this paper. The types of inference considered here are testing if the slope or elevation equals a given value, constructing confidence intervals for the slope or elevation, comparing several slopes or elevations, and testing for shift along the axis amongst several groups. In some cases several methods have been proposed in the literature. These are discussed and compared. In other cases there is little or no previous guidance available in the literature. Simulations were conducted to check whether the methods of inference proposed have the intended coverage probability or Type I error. We identified the methods of inference that perform well and recommend the techniques that should be adopted in future work.     

Testing different 3D techniques using geometric morphometrics: Implications for cranial fluctuating asymmetry in humans

This study aimed to test the performance of 3D digitizer, CT scanner, and surface scanner in detecting cranial fluctuating asymmetry. Sets of 32 landmarks (6 in the midline and 13 bilateral) were acquired from 14 archeological crania using a 3D digitizer, and from 3D models generated from a CT scanner and surface scanner using Viewbox 4. Levels of shape variation were analyzed in MorphoJ using Procrustes analysis of variance and Principal component analysis. Intra-observer error accounted for 1.7%, 1.8%, and 4.5% of total shape variation for 3D digitizer, CT scanner, and surface scanner respectively. Fluctuating asymmetry accounted for 15%–16% of total shape variation. Variation between techniques accounted for 18% of total shape variation. We found a higher level of missing landmarks in our surface scan data than for both 3D digitizer and CT scanner data, and both 3D model-based techniques sometimes obscured taphonomic damage. All three 3D techniques are appropriate for measuring cranial fluctuating asymmetry. We advise against combining data collected with different techniques.     

Evolutionary and morphometric implications of morphological variation among flowers within an inflorescence: a case-study using European orchids

BACKGROUND AND AIMS: This study explores the previously largely ignored morphological variation that occurs among flowers within a single inflorescence. METHODS: Variation in four metric parameters (labellum length and width, spur length and width) that together strongly influence pollination frequency is documented within the simple racemose inflorescences of eight individuals that represent a primary hybrid and six species of European orchids. KEY RESULTS: Regression of each parameter against the location of each flower on the inflorescence, and calculation of correlation coefficients for each pair of parameters within each inflorescence, demonstrate significant decoupling of labellum and spur development, despite the fact that they are different portions of the same floral organ. Spur length and diameter are constant across inflorescences of Dactylorhiza other than the vestigial-spurred D. viridis, whereas in other genera spur length declines in parallel with labellum dimensions. These differences are likely to reflect selection pressures or developmental constraints. Strong negative deviations from the regression line for one or more parameters are evident in occasional flowers, occurring most frequently in the lowermost and uppermost one or two flowers, and so reflecting transitions in meristematic behaviour. Thus, population-level morphometric studies are best conducted on flowers taken from approximately the mid-point of the inflorescence. Moreover, in the two relatively large inflorescences where lower flowers were removed for measurement before the upper flowers had opened, labellum size increased significantly in the flowers immediately above the excisions, suggesting that excision liberated resources that were diverted into the opening buds. Repeat measurement of all flowers from one selected inflorescence demonstrated typical measurement errors of only +/- 30-80 micro m, irrespective of the size of the structure studied. If flowers are not mounted and measured immediately following excision, modest negative deviations of 30-50 micro m result from post-mounting shrinkage; this occurs less rapidly in the spur than in the thinner labellum, which should therefore be measured first. Variation in all four parameters among all the flowers of a single inflorescence is between 42 % and 107 % of that observed between a similar number of flowers sampled from a consistent location on different (but conspecific and coexisting) inflorescences. CONCLUSIONS: This result demonstrates the strong influence of epigenesis on flower morphology and further emphasizes the importance of (a) sampling from a consistent location within the inflorescences under comparison, (b) interpreting morphometric ordinations hierarchically, building from individuals to infraspecific taxa and species via populations, and (c) considering in any microevolutionary study the potentially profound effects of the cline in flower size within each inflorescence.     

参数仪器变量估计近似分布方差的一种算法

本文对更一般的结构模型给出了参数的一种常用的仪器变量估计近似分布方差的一种算法．并且给出了未知真值x服从指数分布的例子．此算法对生物科学中统计规律的探讨有一定的应用价值．     

Measures of precision for dissimilarity‐based multivariate analysis of ecological communities

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity‐based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity‐based standard error (MultSE) as a useful quantity for assessing sample‐size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided.