Antimicrobial and antiviral activities of an actinomycete (Streptomyces sp.) isolated from a Brazilian tropical forest soil

A promising producer of bioactive compounds isolated from a Brazilian tropical soil was tested for its range of antimicrobial activities. Strain 606, classified as Streptomyces sp., could not be identified up to species level, suggesting a possible new taxon. The supernatant and 10 extracts and fractions, obtained by extraction and chromatographic techniques, presented antimicrobial activity using antibiograms. The methanolic fraction was highly active against pathogenic bacteria, phytopathogenic fungi and the human pathogenic yeast Candida albicans. It also possessed high antiviral activity inhibiting the propagation of an acyclovir-resistant herpes simplex virus type 1 strain on HEp-2 cells at non-cytotoxic concentration. The strong cytotoxic effect suggests an antitumour action. This revised version was published online in August 2006 with corrections to the Cover Date.     

Environment versus dispersal in the assembly of western Amazonian palm communities

Aim It is a central issue in ecology and biogeography to understand what governs community assembly and the maintenance of biodiversity in tropical rain forest ecosystems. A key question is the relative importance of environmental species sorting (niche assembly) and dispersal limitation (dispersal assembly), which we investigate using a large dataset from diverse palm communities. Location Lowland rain forest, western Amazon River Basin, Peru. Methods We inventoried palm communities, registering all palm individuals and recording environmental conditions in 149 transects of 5 m × 500 m. We used ordination, Mantel tests and indicator species analysis (ISA) to assess compositional patterns, species responses to geographical location and environmental factors. Mantel tests were used to assess the relative importance of geographical distance (as a proxy for dispersal limitation) and environmental differences as possible drivers of dissimilarity in palm species composition. We repeated the Mantel tests for subsets of species that differ in traits of likely importance for habitat specialization and dispersal (height and range size). Results We found a strong relationship between compositional dissimilarity and environmental distance and a weaker but also significant relationship between compositional dissimilarity and geographical distance. Consistent with expectations, relationships with environmental and geographical distance were stronger for understorey species than for canopy species. Geographical distance had a higher correlation with compositional dissimilarity for small‐ranged species compared with large‐ranged species, whereas the opposite was true for environmental distance. The main environmental correlates were inundation and soil nutrient levels. Main conclusions The assembly of palm communities in the western Amazon appears to be driven primarily by species sorting according to hydrology and soil, but with dispersal limitation also playing an important role. The importance of environmental characteristics and geographical distance varies depending on plant height and geographical range size in agreement with functional predictions, increasing our confidence in the inferred assembly mechanisms.     

An Efficient Regression Type Estimator in Survey Sampling

For the estimation of population mean in simple random sampling, an efficient regression-type estimator is proposed which is more efficient than the conventional regression estimator and hence than mean per unit estimator, ratio and product estimators and many other estimators proposed by various authors. Some numerical examples are included for illustration.     

Semiparametric Regression in Size-Biased Sampling

Summary .  Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.     

Confidence Intervals for the Risk Ratio in Cohort Studies under Inverse Sampling

Three simple interval estimates for the risk ratio in inverse sampling are considered. The first two interval estimates are derived on the basis of Fieller's Theorem and the delta method with the logarithmic transformation, respectively. The third interval estimate is derived on the basis of an F-test statistic proposed by BENNETT (1981) for testing equal probabilities of a disease between two comparison groups when the disease is rare. To evaluate the performance of these three methods, a Monte Carlo simulation is used to compare the actual coverage probability with the nominal confidence level for each method and to estimate the expected length of the corresponding confidence interval in a variety of situations. On the basis of the results found in the simulation, we have concluded that the method with the logarithmic transformation is either equivalent to or better than the other two methods for all situations considered here.