Multivariate character process models for the analysis of two or more correlated function-valued traits

Various methods, including random regression, structured antedependence models, and character process models, have been proposed for the genetic analysis of longitudinal data and other function-valued traits. For univariate problems, the character process models have been shown to perform well in comparison to alternative methods. The aim of this article is to present an extension of these models to the simultaneous analysis of two or more correlated function-valued traits. Analytical forms for stationary and nonstationary cross-covariance functions are studied. Comparisons with the other approaches are presented in a simulation study and in an example of a bivariate analysis of genetic covariance in age-specific fecundity and mortality in Drosophila. As in the univariate case, bivariate character process models with an exponential correlation were found to be quite close to first-order structured antedependence models. The simulation study showed that the choice of the most appropriate methodology is highly dependent on the covariance structure of the data. The bivariate character process approach proved to be able to deal with quite complex nonstationary and nonsymmetric cross-correlation structures and was found to be the most appropriate for the real data example of the fruit fly Drosophila melanogaster

Measurement of Electromagnetic Activity of Yeast Cells at 42 GHz

This paper discusses the possibility of using a device composed of a resonant cavity, preamplifiers, and a spectrum analyzer to detect electromagnetic emission of yeast cells at a frequency of about 42 GHz. Measurement in this frequency range is based on the Frohlich\'s postulate of coherent polar oscillations as a fundamental biophysical property of biological systems and on the experiments of Grundler and Keilmann who disclosed effects of exposure to the electromagnetic field at 42 GHz on the growth rate of yeast cells. This article includes a detailed description of the laboratory equipment and the methods used to evaluate the obtained results

A link function approach to model heterogeneity of residual variances over time in lactation curve analyses

Several studies with test-day models for the lactation curve show heterogeneity of residual variance over time. The most common approach is to divide the lactation length into subclasses, assuming homogeneity within these classes and heterogeneity between them. The main drawbacks of this approach are that it can lead to many parameters being estimated and that classes have to be arbitrarily defined, whereas the residual variance changes continuously over time. A methodology that overcomes these drawbacks is proposed here. A structural model on the residual variance is assumed in which the covariates are parametric functions of time. In this model, only a few parameters need to be estimated, and the residual variance is then a continuous function of time. The analysis of a sample data set illustrates this methodology

Contrasting models for lactation curve analysis

Several statistical models have been proposed for the genetic evaluation of production traits in dairy cattle based on test-day records. Three main approaches have been put forward in the literature: random regression, orthogonal polynomials, and, more recently, character process models. The aim of this paper is to show how these different approaches are related, to compare their performance for the genetic analysis of lactation curves, and to assess equivalence between sire and animal models for repeated measures analyses. It was found that, with an animal model, a character process model with 11 parameters performed better, regarding the likelihood criterion, than a quartic random regression model (with 31 parameters). However, although the likelihood was higher, the genetic variance was very different with the character process model from the unstructured model, which raises important issues concerning model selection criteria. There are advantages in combining methodologies. A quadratic random regression model for the environmental part, combined with a character process model for the residual, performed better than the quartic random regression model and had fewer parameters. A character process structure allowing for a correlation pattern modeled the residual better than a simple quadratic variance, and had only one extra parameter

Electrochemical Boron-Doped Diamond Film Microcells Micromachined with Femtosecond Laser: Application to the Determination of Water Framework Directive Metals

Planar electrochemical microcells were micromachined in a microcrystalline boron-doped diamond (BDD) thin layer using a femtosecond laser (Photo 1). The electrochemical performances of the new laser-machined BDD microcell were assessed by differential pulse anodic stripping voltammetry (DPASV) determinations, at nM level, of the four heavy metal ions of the European Water Framework Directive (WFD): Cd(II), Ni(II), Pb(II), Hg(II). The results are compared with those of previously published BDD electrodes [1]. The calculated detection limits are 0.4 nM, 6.8 nM and 5.5 nm 2.3 nM, and the linearities go up to 35nM, 97nM, 48nM and 5nM for respectively Cd(II), Ni(II) Pb(II) and Hg(II). The detection limits meet with the environmental quality standard of the WFD for three of the four metals. It was shown that the four heavy metals could be detected simultaneously, in the concentration ratio usually measured in sewage or runoff waters

Structure, electrochemical properties and functionalization of amorphous CN films deposited by femtosecond pulsed laser ablation

Amorphous carbon nitride (a-C:N) material has attracted much attention in research and development. Recently, it has become a more promising electrode material than conventional carbon based electrodes in electrochemical and biosensor applications. Nitrogen containing amorphous carbon (a-C:N) thin films have been synthesized by femtosecond pulsed laser deposition (fs-PLD) coupled with plasma assistance through Direct Current (DC) bias power supply. During the deposition process, various nitrogen pressures (0 to 10 Pa) and DC bias (0 to ¿ 350 V) were used in order to explore a wide range of nitrogen content into the films. The structure and chemical composition of the films have been studied by using Raman spectroscopy, electron energy-loss spectroscopy (EELS) and high-resolution transmission electron microscopy (HRTEM). Increasing the nitrogen pressure or adding a DC bias induced an increase of the N content, up to 21 at.%. Nitrogen content increase induces a higher sp2 character of the film. However DC bias has been found to increase the film structural disorder, which was detrimental to the electrochemical properties. Indeed the electrochemical measurements, investigated by cyclic voltammetry (CV), demonstrated that a-C:N film with moderate nitrogen content (10 at.%) exhibited the best behavior, in terms of reversibility and electron transfer kinetics. Electrochemical grafting from diazonium salts was successfully achieved on this film, with a surface coverage of covalently bonded molecules close to the dense packed monolayer of ferrocene molecules. Such a film may be a promising electrode material in electrochemical detection of electroactive pollutants on bare film, and of biopathogen molecules after surface grafting of the specific affinity receptor.This work is produced with the financial support of the Future Program Lyon Saint-Etienne (PALSE) from the University of Lyon (ANR-11-IDEX-0007), under the “Investissements d'Avenir” program managed by the National Agency Research (ANR)

Linkage analysis of longitudinal data and design consideration

BACKGROUND: Statistical methods have been proposed recently to analyze longitudinal data in genetic studies. So far, little attention has been paid to examine the relationship among key factors in genetic longitudinal studies including power, the number of families or sibships, and the number of repeated measures per individual subjects. RESULTS: We proposed a variance component model that extends classic variance component models for a single quantitative trait to mapping longitudinal traits. Our model includes covariate effects and allows genetic effects to vary over time. Using our proposed model, we examined the power, pedigree structures, and sample size through simulation experiments. CONCLUSION: Our simulation results provide useful insights into the study design for genetic, longitudinal studies. For example, collecting a small number of large sibships is much more powerful than collecting a large number of small sibships or increasing the number of repeated measures, when the total number of measurements is comparable