Genetic and morphologic diversity of European fan palm (Chamaerops humilis L.) populations from different environments from Sicily

Chamaerops humilis is decreasing in abundance in Mediterranean Europe, which has induced the European community to call for its protection in Special Areas of Conservation. However, information about its genetic and morphological variability, which is crucial to the development of any conservation strategies, is insufficient. The present study aimed to investigate the genetic and morphological variability of C. humilis in Sicily, which was selected as a model because of the high number of dense populations. The relationships between morphological traits and climatic variables were studied to highlight patterns of adaptation to the environment, along with the genetic similarity among the populations. Ten natural populations were sampled, analyzed using 28 specifically designed SSR primers, and evaluated based on 29 morphological traits. The populations were clustered similarly based on genetic and morphological traits. Heterozygosity was high and inbreeding coefficients were low. These results, along with higher intra‐ than inter‐population differentiation, suggest that C. humilis populations in Sicily differentiated from a common ancestor and that inter‐population variation arose from secondary evolution processes induced by ecological adaptation. The correlations between climatic variables and morphological traits suggest that the morphological adaptation to arid environments depends more on summer temperatures than on evapotranspiration or rainfall and that autumn and winter temperatures are determinants of the species establishment at new sites. Considering the response of C. humilis to seasonal temperatures, the present results indicate this species as a candidate for tracking climatic changes in Europe. Further studies are needed to highlight the adaptation of C. humilis to cold environments. Palaeo‐climatological and ‐ecological studies could help clarify its strategies for the colonization of new sites. © 2014 The Linnean Society of London, Botanical Journal of the Linnean Society, 2014, 176 , 66–81.     

Why have so few proteomic biomarkers “survived” validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC‐MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC‐MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.     

Bayesian Methods for Regression Using Surrogate Variables

If a dependent variable in a regression analysis is exceptionally expensive or hard to obtain the overall sample size used to fit the model may be limited. To avoid this one may use a cheaper or more easily collected “surrogate” variable to supplement the expensive variable. The regression analysis will be enhanced to the degree the surrogate is associated with the costly dependent variable. We develop a Bayesian approach incorporating surrogate variables in regression based on a two‐stage experiment. Illustrative examples are given, along with comparisons to an existing frequentist method. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Hadamard randomization: a valid restriction of random permuted blocks

Random permuted blocks can result in treatment imbalance if entry to the trial stops in mid‐block. This paper presents a restriction of this method of randomization. The restriction avoids severe treatment imbalance but gives unbiased estimators of the treatment difference and its variance.     

Robustness of Generalized Estimating Equation (GEE) Tests of Significance against Misspecification of the Error Structure Model

Generalized linear model analyses of repeated measurements typically rely on simplifying mathematical models of the error covariance structure for testing the significance of differences in patterns of change across time. The robustness of the tests of significance depends, not only on the degree of agreement between the specified mathematical model and the actual population data structure, but also on the precision and robustness of the computational criteria for fitting the specified covariance structure to the data. Generalized estimating equation (GEE) solutions utilizing the robust empirical sandwich estimator for modeling of the error structure were compared with general linear mixed model (GLMM) solutions that utilized the commonly employed restricted maximum likelihood (REML) procedure. Under the conditions considered, the GEE and GLMM procedures were identical in assuming that the data are normally distributed and that the variance‐covariance structure of the data is the one specified by the user. The question addressed in this article concerns relative sensitivity of tests of significance for treatment effects to varying degrees of misspecification of the error covariance structure model when fitted by the alternative procedures. Simulated data that were subjected to monte carlo evaluation of actual Type I error and power of tests of the equal slopes hypothesis conformed to assumptions of ordinary linear model ANOVA for repeated measures except for autoregressive covariance structures and missing data due to dropouts. The actual within‐groups correlation structures of the simulated repeated measurements ranged from AR(1) to compound symmetry in graded steps, whereas the GEE and GLMM formulations restricted the respective error structure models to be either AR(1), compound symmetry (CS), or unstructured (UN). The GEE‐based tests utilizing empirical sandwich estimator criteria were documented to be relatively insensitive to misspecification of the covariance structure models, whereas GLMM tests which relied on restricted maximum likelihood (REML) were highly sensitive to relatively modest misspecification of the error correlation structure even though normality, variance homogeneity, and linearity were not an issue in the simulated data.Goodness‐of‐fit statistics were of little utility in identifying cases in which relatively minor misspecification of the GLMM error structure model resulted in inadequate alpha protection for tests of the equal slopes hypothesis. Both GEE and GLMM formulations that relied on unstructured (UN) error model specification produced nonconservative results regardless of the actual correlation structure of the repeated measurements. A random coefficients model produced robust tests with competitive power across all conditions examined. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Set-up uncertainty during postmastectomy radiotherapy with Segmented Photon Beams Technique

Aim

To verify the reproducibility of patients irradiated after mastectomy on the immobilization system designed and manufactured for our hospital and to compare the Internal Protocol (IP) with the modified-No Action Level Protocol.

Background

Application of forward IMRT techniques requires a good reproducibility of patient positioning. To minimize the set-up error, an effective immobilization system is important.

Materials and methods

The study was performed for two groups of 65 each. In the first group, portal images for anterior field were taken in 1–3 fractions and, subsequently, three times a week. In this group, the mNAL protocol was used. In the second group, the IP was used. The portal images from the anterior field and from the gantry 0 were taken during the 1–3 and 10 fractions. In both groups, image registration was performed off-line. For each group the systematic and random errors and PTV margin were calculated.

Results

In the first group the value of the population systematic errors and random errors were 1.6 ± 1.6 mm for the left–right, and 1.5 ± 1.7 mm for the cranial–caudal directions, respectively, 1.7 ± 1.3 mm, and 1.9 ± 1.3 mm for the second group. The PTV margins for the left–right and cranial–caudal directions were 5.1 and 4.9 mm for the first group and 5.4 and 6.4 mm for the second group.

Conclusions

For patients immobilized with our support device treated according to the mNAL protocol or IP, a good set-up reproducibility was obtained. Implementation of IP limits the number of required images.     

Modeling the biotic and abiotic factors that describe the number of active off‐host Amblyomma americanum larvae

Amblyomma americanum (L.) is a three‐host tick that spends most of its life off‐host and is an important vector of pathogens in the eastern United States. Our objectives were to develop a predictive statistical model that describes the number of active, off‐host larvae from 2007 to 2011 and to determine the environmental variables associated with this pattern. Data used in this study came from monitoring conducted in northeast Missouri in which off‐host ticks were collected from a permanent plot in a forest and an old field habitat every other week from approximately February to December. Variables examined were day length, degree days, total precipitation prior to sampling, wind speed, saturation deficit, number of adults prior to sampling, and collection site. Of the four regression models tested, the negative binomial model was selected. Fitted candidate models were compared relative to one another using values of eight model selection criteria and model averaging was used to develop a predictive model. The residual plots indicated that the weighted average model performs well in describing the number of larvae. Of the variables considered, the number of larvae was most strongly associated with increasing degree days, the number of active adults prior to sampling, and the forested site.     

Sex ratios and mental health: Evidence from China

While sex ratios (i.e., relative numbers of men and women) have been linked to various economic and social outcomes, how sex ratios affect mental health is underexplored. Using nationally representative data from the China Family Panel studies (CFPS) and Population Census, we evaluate the impact of sex ratios on mental health among Chinese men and explore potential mechanisms. Employing the instrumental variables (IV) approach where the One-Child Policy’s mandated fertility limits and implementation are used as exogenous variations in local sex ratios, we find that higher local sex ratios increase depressive symptoms and probability of depression among Chinese men. The impact is stronger for men with lower levels of education and living in rural areas. Analyses of potential mechanisms show that higher sex ratios increase the likelihood of marriage delay and unemployment for men, and prolong working hours for the employed men. The findings are of direct relevance to the health and population policy in China.     

A generic rate law for surface-active enzymes

Many biochemical reactions are confined to interfaces, such as membranes or cell walls. Despite their importance, no canonical rate laws describing the kinetics of surface-active enzymes exist. Combining the approach chosen by Michaelis and Menten 100 years ago with concepts from surface chemical physics, we here present an approach to derive generic rate laws of enzymatic processes at surfaces. We illustrate this by a simple reversible conversion on a surface to stress key differences to the classical case in solution. The available area function, a concept from surface physics which enters the rate law, covers different models of adsorption and presents a unifying perspective on saturation effects and competition between enzymes. A remarkable implication is the direct dependence of the rate of a given enzyme on all other enzymatic species able to bind at the surface. The generic approach highlights general principles of the kinetics of surface-active enzymes and allows to build consistent mathematical models of more complex pathways involving reactions at interfaces.