Study of accumulation behaviour of tungsten based composite using electron probe micro analyser for the application in bone tissue engineering

In this research, a proto-type study we have conducted, where we have synthesized tungsten based composite materials which are tungsten along with combined oxides of other elements like calcium, scandium, barium, and aluminium in the form of powder with bones powder of mice devised by high energy ball mill and later on fabricating high dense pellets by sintering by spark plasma. The particle sizes of the composite materials are found to be 1–2 µm, as evidenced by the electron microscope, suggesting synthesized materials are of micron size. The quantitative and qualitative analysis of sintered pellets are well confirmed by electron probe micro analyzer (EPMA) and energy dispersive X-ray spectrometer (EDS) which illustrate the greater percentage of tungsten presents in the profound scan areas with other elements of the composite. The absence of pores across the 3D geometry suggesting dense sample, which is quite revealed by the X-ray tomography inspection. The prepared sintered pellets from the tungsten based composites are found to be ≈ 99.5% density with the observation of tungsten to be accumulated uniformly across the scan regions along with focussed hot spots as implied by EPMA. This study paves the way, to examine how the tungsten accumulation and the distribution with the other elements for future understanding in bone tissue engineering application and the in vivo specification of tungsten.     

Measurement of field scale N2O emission fluxes from a wheat crop using micrometeorological techniques

Measurements of N₂O emission fluxes from a 3 ha field of winter wheat were measured using eddy covariance and relaxed eddy accumulation continuously over 10 days during April 1994. The measurements averaged fluxes over approximately 10⁵ m² of the field, which was fertilised with NH₄NO₃ at a rate of 43 kg N ha^-1 at the beginning of the measurements. The emission fluxes became detectable after the first heavy rainfall, which occured 4 days after fertiliser application. Emissions of N₂O increased rapidly during the day following the rain to a maximum of 280 ng N m^-2s^-1 and declined over the following week. During the period of significant emission fluxes, a clear diurnal cycle in N₂O emission was observed, with the daytime maximum coinciding with the soil temperature maximum at 12 cm depth. The temperature dependence of the N₂O emission was equivalent to an activation energy for N₂O production of 108 kJ mol^-1. The N₂O fluxes measured using relaxed eddy accumulation, averaged over 30 to 270 min, were in agreement with those of the eddy covariance system within 60%. The total emission of N₂O over the period of continuous measurement (10 days) was equivalent to about 10 kg N₂O-N, or 0.77% of the N fertiliser applied.     

Diallel analysis for sex-linked and maternal effects

Genetic models including sex-linked and maternal effects as well as autosomal gene effects are described. Monte Carlo simulations were conducted to compare efficiencies of estimation by minimum norm quadratic unbiased estimation (MINQUE) and restricted maximum likelihood (REML) methods. MINQUE(1), which has 1 for all prior values, has a similar efficiency to MINQUE(), which requires prior estimates of parameter values. MINQUE(1) has the advantage over REML of unbiased estimation and convenient computation. An adjusted unbiased prediction (AUP) method is developed for predicting random genetic effects. AUP is desirable for its easy computation and unbiasedness of both mean and variance of predictors. The jackknife procedure is appropriate for estimating the sampling variances of estimated variances (or covariances) and of predicted genetic effects. A t-test based on jackknife variances is applicable for detecting significance of variation. Worked examples from mice and silkworm data are given in order to demonstrate variance and covariance estimation and genetic effect prediction.     

The ecosystem wilting point defines drought response and recovery of a Quercus-Carya forest

Soil and atmospheric droughts increasingly threaten plant survival and productivity around the world. Yet, conceptual gaps constrain our ability to predict ecosystem-scale drought impacts under climate change. Here, we introduce the ecosystem wilting point (Ψ_EWP), a property that integrates the drought response of an ecosystem's plant community across the soil–plant–atmosphere continuum. Specifically, Ψ_EWP defines a threshold below which the capacity of the root system to extract soil water and the ability of the leaves to maintain stomatal function are strongly diminished. We combined ecosystem flux and leaf water potential measurements to derive the Ψ_EWP of a Quercus-Carya forest from an “ecosystem pressure–volume (PV) curve,” which is analogous to the tissue-level technique. When community predawn leaf water potential (Ψ_pd) was above Ψ_EWP (=−2.0 MPa), the forest was highly responsive to environmental dynamics. When Ψ_pd fell below Ψ_EWP, the forest became insensitive to environmental variation and was a net source of carbon dioxide for nearly 2 months. Thus, Ψ_EWP is a threshold defining marked shifts in ecosystem functional state. Though there was rainfall-induced recovery of ecosystem gas exchange following soaking rains, a legacy of structural and physiological damage inhibited canopy photosynthetic capacity. Although over 16 growing seasons, only 10% of Ψ_pd observations fell below Ψ_EWP, the forest is commonly only 2–4 weeks of intense drought away from reaching Ψ_EWP, and thus highly reliant on frequent rainfall to replenish the soil water supply. We propose, based on a bottom-up analysis of root density profiles and soil moisture characteristic curves, that soil water acquisition capacity is the major determinant of Ψ_EWP, and species in an ecosystem require compatible leaf-level traits such as turgor loss point so that leaf wilting is coordinated with the inability to extract further water from the soil.     

Functional data analysis with covariate-dependent mean and covariance structures

Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external covariates, as most functional models assume a common covariance structure. We propose a new functional regression model with covariate-dependent mean and covariance structures. Particularly, by allowing variances of random scores to be covariate-dependent, we identify eigenfunctions for each individual from the set of eigenfunctions that govern the variation patterns across all individuals, resulting in high interpretability and prediction power. We further propose a new penalized quasi-likelihood procedure that combines regularization and B-spline smoothing for model selection and estimation and establish the convergence rate and asymptotic normality of the proposed estimators. The utility of the developed method is demonstrated via simulations, as well as an analysis of the Avon Longitudinal Study of Parents and Children concerning parental effects on the growth curves of their offspring, which yields biologically interesting results.     

THE MEASUREMENT OF SELECTION ON QUANTITATIVE TRAITS: BIASES DUE TO ENVIRONMENTAL COVARIANCES BETWEEN TRAITS AND FITNESS

The use of regression techniques for estimating the direction and magnitude of selection from measurements on phenotypes has become widespread in field studies. A potential problem with these techniques is that environmental correlations between fitness and the traits examined may produce biased estimates of selection gradients. This report demonstrates that the phenotypic covariance between fitness and a trait, used as an estimate of the selection differential in estimating selection gradients, has two components: a component induced by selection itself and a component due to the effect of environmental factors on fitness. The second component is shown to be responsible for biases in estimates of selection gradients. The use of regressions involving genotypic and breeding values instead of phenotypic values can yield estimates of selection gradients that are not biased by environmental covariances. Statistical methods for estimating the coefficients of such regressions, and for testing for biases in regressions involving phenotypic values, are described.     

Causes of covariation of phenotypic traits among populations

Morphological and life-history traits often vary among populations of a species. Traits generally do not vary independently, but show patterns of covariation that can arise from genetic and environmental influences on phenotype. Covariance of traits may arise at an among-population level when genetically influenced traits diverge among populations in a correlated manner. Genetic correlations caused by pleiotropy and/or gene linkage can cause traits to evolve together, but among-population covariance can also arise among traits that are not genetically correlated. For example, “selective covariance” can arise when natural selection directly causes correlated change in a suite of traits. Similarly, mutation, migration, and drift may also sometimes cause correlated genetic changes among populations. Because covariation of traits among populations can arise by several different processes, the evolution of suites of traits must be interpreted with great caution. We discuss the sources of among-population covariance and illustrate one approach to identifying the sources' using data on floral traits of Dalechampia scandens (Euphorbiaceae).     

MIXED MODEL APPROACHES FOR ESTIMATING GENETIC VARIANCES AND COVARIANCES

The limitations of methods for analysis of variance(ANOVA)in estimating genetic variances are discussed. Among the three methods(maximum likelihood ML, restricted maximum likelihood REML, and minimum norm quadratic unbiased estimation MINQUE)for mixed linear models, MINQUE method is presented with formulae for estimating variance components and covariances components and for predicting genetic effects. Several genetic models, which cannot be appropriately analyzed by ANOVA methods, are introduced in forms of mixed linear models. Genetic models with independent random effects can be analyzed by MINQUE(1)method whieh is a MINQUE method with all prior values setting 1. MINQUE(1)method can give unbiased estimation for variance components and covariance components, and linear unbiased prediction (LUP) for genetic effects. There are more complicate genetic models for plant seeds which involve correlated random effects. MINQUE(0/1)method, which is a MINQUE method with all prior covariances setting 0 and all prior variances setting 1, is suitable for estimating variance and covariance components in these models. Mixed model approaches have advantage over ANOVA methods for the capacity of analyzing unbalanced data and complicated models. Some problems about estimation and hypothesis test by MINQUE method are discussed.