Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables

In this paper very simple nonparametric classification rule for mixtures of discrete and continuous random variables is described. It is based on the method of nearest neighbor proposed by Cover and Hart (1967). The bounds on the limit of the nearest neighbor rule risks are given. Both lower and upper bound depend on the Bayes risk and the loss function. Finally the method is compared with other existing methods on some practical data set.     

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.     

Environmental factors influencing the vertical migration of planktonic rotifers in a hypereutrophic tarn

The diel vertical migration of planktonic rotifers in a small, hypereutrophic tarn was investigated on four occasions in 1983. When the tarn was isothermal the rotifers were distributed throughout the water column. After stratification, the rotifers were confined to the top 1–2 m of oxygenated water. On all four dates the rotifers were aggregated at specific depths in the water column. On some occasions, the pattern of aggregation changed as the animals performed distinct diurnal migrations. Keratella cochlearis, K. quadrata and Polyarthra vulgaris usually followed the reverse migrations of the phytoplankton. In contrast, the movements of Anuraeopsis fissa were less pronounced and were associated with variations in the depth of the oxycline.     

Cranial variation in European populations: a spatial autocorrelation study at three time periods

This study reports on spatial variation of 10 cranial variables in European populations at 3 time periods. Means for these variables, based on 137, 108, and 183 samples from the Early Medieval, Late Medieval, and Recent periods, were subjected to one-dimensional and directional spatial autocorrelation analyses. Significant spatial structure was found for most variables. It becomes more pronounced as time progresses. The spatial patterns are not strongly clinal. Correlograms based on distances computed from all variables are monotonic only to 900, 1,650, and 1,350 km for the three periods. Regional patterns are seen for most variables and become more structured and significant with time. There is little similarity among the correlograms of the variables at any one period and virtually none among periods. Inferences about spatial structure of these populations, based on spatial autocorrelation analysis, suggest a pattern dominated by migration, followed by expansion and admixture rather than selection or chance fluctuations. The patterns of morphometric change seem to reflect the patterns of linguistic change in these areas.