Isolation of a heat-stable maize endosperm ADP-glucose pyrophosphorylase variant

Heat stress reduces maize yield and several lines of evidence suggest that the heat lability of maize endosperm ADP-glucose pyrophosphorylase (AGPase) contributes to this yield loss. AGPase catalyzes a rate-limiting step in starch synthesis. Herein, we present a novel maize endosperm AGPase small subunit variant, termed BT2-TI that harbors a single amino acid change of residue 462 from threonine to isoleucine. The mutant was isolated by random mutagenesis and heterologous expression in a bacterial system. BT2-TI exhibits enhanced heat stability compared to wildtype maize endosperm AGPase.The TI mutation was placed into another heat-stable small subunit variant, MP. MP is composed of sequences from the maize endosperm and the potato tuber small subunit. The MP-TI small subunit variant exhibited greater heat stability than did MP. Characterization of heat stability as well as kinetic and allosteric properties suggests that MP-TI may lead to increased starch yield when expressed in monocot endosperms.     

Sharp intense line in the bioluminescence emission of the firefly

Numerous investigations have been carried out on the spectral distribution of the light of different species of fireflies. Here we record the emission spectrum of the Indian species of the firefly Luciola praeusta Kiesenwetter 1874 (Coleoptera : Lampyridae : Luciolinae) on a color film. Green and red color-sectors, with an intense yellow one in between, appear in this spectrum. Intensity profile of this spectrum reveals a hitherto undetected strong narrow yellow line, which lies within the full-width-at-half maximum (FWHM) of the intensity profile. The spectrum recorded in a high-resolution spectrometer confirms the presence of this sharp intense line. This finding lends support to an earlier drawn analogy between the in vivo emission of the firefly and laser light.     

Bayesian Quantile Regression for Longitudinal Studies with Nonignorable Missing Data

Summary .  We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introducing a   ℓ₂   penalty in the usual QR check function to shrink the subject-specific intercepts and slopes toward the common population values. The informative missing data are assumed to be related to the longitudinal outcome process through the shared latent random effects. We assess the performance of the proposed method using simulation studies, and illustrate it with data from a pediatric AIDS clinical trial.     

Modelling biomass of mountainous grasslands by including a species composition map

High mountain grasslands offer multiple goods and services to society but are severely threatened by improper land use practices such as abandonment or rapid intensification. In order to reduce abandonment and strengthen the common extensive agricultural practice a sustainable land use management of high mountain grasslands is needed. A spatially detailed yield assessment helps to identify possible meadows or, on the contrary, areas with a low carrying capacity in a region, making it easier to manage these sites. Such assessments are rarely available for remote and inaccessible areas. Remotely sensed vegetation indices are able to provide valuable information on grassland properties. These indices tend, however, to saturate for high biomass. This affects their applicability to assessments of high-yield grasslands.The main aim of this study was to model a spatially explicit grassland yield map and to test whether saturation issues can be tackled by consideration of plant species composition in the modelling process. The high mountain grassland of the subalpine belt (1800 – 2500 m a.s.l.) in the Kazbegi region, Greater Caucasus, Georgia, was chosen as test site for its strong species composition and yield gradients.We first modelled the species composition of the grassland described as metrically scaled gradients in the form of ordination axes by random forest regression. We then derived vegetation indices from Rapid Eye imagery, and topographic variables from a digital elevation model, which we used together with the multispectral bands as predictive variables. For comparison, we performed two yield models, one excluding the species composition maps and one including the species composition map as predictors. Moreover, we performed a third individual model, with species composition as predictors and a split dataset, to produce the final yield map.Three main grassland types were found in the vegetation analysis: Hordeum violaceum-meadows, Gentianella caucasea-grassland and Astragalus captiosus-grassland. The three random forest regression models for the ordination axes explained 64%, 33% and 46% of the variance in species composition. Independent validation of modelled ordination scores against a validation data set resulted in an R² of 0.64, 0.32 and 0.46 for the first, second and third axes, respectively. The model based on species composition resulted in a R² = 0.55, whereas the benchmark model showed weaker relationships between yield and the multispectral reflectance, vegetation indices, and topographical parameters (R² = 0.42). The final random forest yield model used to derive the yield map resulted in 62% variance explained and an R² = 0.64 between predicted and observed biomass. The results further indicate that high yields are generally difficult to predict with both models.The benefit of including a species composition map as a predictor variable for grassland yield lies in the preservation of ecologically meaningful features, especially the occurrence of high yielding vegetation type of Hordeum violaceum meadows is depicted accurately in the map. Even though we used a gradient based design, sharp boundaries or immediate changes in productivity were visible, especially in small structures such as arable fields or roads (Fig. 6b), making it a valuable tool for sustainable land use management. The saturation effect however, was mitigated by using species composition as predictor variables but is still present at high yields.     

Volume estimation of biological objects by systematic sections

The absolute volume of biological objects is often estimated stereologically from an exhaustive set of systematic sections. The usual volume estimator is the sum of the section contents times the distance between sections. For systematic sectioning with a random start, it has been recently shown that is unbiased when m, the ratio between projected object length and section distance, is an integer number (Cruz-Orive 1985). As this quantity is no integer in the real world, we have explored the properties of in the general and realistic situation m . The unbiasedness of under appropriate sampling conditions is demonstrated for the arbitrary compact set in 3 dimensions by a rigorous proof. Exploration of further properties of for the general triaxial ellipsoid leads to a new class of non-elementary real functions with common formal structure which we denote as np-functions. The relative mean square error (CE ²) of in ellipsoids is an oscillating differentiable np-function, which reduces to the known result CE ²= 1/(5m ⁴) for integer m. As a biological example the absolute volumes of 10 left cardiac ventricles and their internal cavities were estimated from systematic sections. Monte Carlo simulation of replicated systematic sectioning is shown to be improved by using m instead of m . In agreement with the geometric model of ellipsoids with some added shape irregularities, mean empirical CE was proportional to m ^–1.36 and m^–1.73 in the cardiac ventricle and its cavity. The considerable variance reduction by systematic sectioning is shown to be a geometric realization of the principle of antithetic variates.     

Vascular response in a non-uterine site to implantation-stage embryos following interspecies transfers between the rat,mouse, and guinea-pig

Summary Pre-implantation-stage embryos from rats, mice, and guinea-pigs were transferred to a non-uterine site — the anterior chamber of the eye — of female recipients. All 9 combinations of transfers were performed: 3 allogeneic (intraspecies) transfers as controls, and 6 xenogeneic (interspecies) transfers. Implantation, as judged by extravasation from blood vessels of the iris or ciliary body occurred with success rates of 90.4% per transfer in the control rat group, 76.9% in the control mouse group, and 81.8% in the control guinea-pig group. Significantly reduced implantation rates occurred in the rat to guinea-pig (0%), mouse to rat (46.9%), mouse to guinea-pig (6.7%), and guinea-pig to rat (0%) groups compared to controls. Reductions, although not significant, also occurred in the other 2 groups: rat to mouse (77.8%), and guinea-pig to mouse (44.4%). These results together with some ultrastructural and lightmicroscopical observations suggest a degree of species specificity involved in the vascular response to the implanting embryo. We propose that the peri-implantation embryo produces a signal(s) which is to some extent species specific and which in the normal allogeneic situation is responsible for the early vascular effects seen at implantation in most eutherian mammals.     

The biennial life strategy in a random environment

In a previous paper (J. Math. Biol. 26, 199–215 (1988)) we calculated the mean and variance of the long-run geometric growth rate of a discrete-time population model with two age classes in a random environment. The formula which was used in that paper as the starting point for the computation of the variance represents only the contribution of the one-period variances. Here we supplement these results by a calculation of the exact variance. All qualitative conclusions reached before are unaffected.