Short-term invasibility patterns in burnt and unburnt experimental Mediterranean grassland communities of varying diversities

This paper reports the findings of a short-term natural invasibility field study in constructed Mediterranean herbaceous communities of varying diversities, under a fire treatment. Three components of invasibility, i.e. species richness, density and biomass of invaders, have been monitored in burnt and unburnt experimental plots with resident diversity ranging from monocultures to 18-species mixtures. In general, species richness, density and biomass of invaders decreased significantly with the increase of resident species richness. Furthermore, the density and biomass of invading species were significantly influenced by the species composition of resident communities. Although aboveground biomass, leaf area index, canopy height and percent bare ground of the resident communities explained a significant part of the variation in the success of invading species, these covariates did not fully explain the effects of resident species richness. Fire mainly influenced invasibility via soil nutrient levels. The effect of fire on observed invasibility patterns seems to be less important than the effects of resident species richness. Our results demonstrate the importance of species richness and composition in controlling the initial stages of plant invasions in Mediterranean grasslands but that there was a lack of interaction with the effects of fire disturbance.     

Effects of feral cats on the evolution of anti-predator behaviours in island reptiles: insights from an ancient introduction

Exotic predators have driven the extinction of many island species. We examined impacts of feral cats on the abundance and anti-predator behaviours of Aegean wall lizards in the Cyclades (Greece), where cats were introduced thousands of years ago. We compared populations with high and low cat density on Naxos Island and populations on surrounding islets with no cats. Cats reduced wall lizard populations by half. Lizards facing greater risk from cats stayed closer to refuges, were more likely to shed their tails in a standardized assay, and fled at greater distances when approached by either a person in the field or a mounted cat decoy in the laboratory. All populations showed phenotypic plasticity in flight initiation distance, suggesting that this feature is ancient and could have helped wall lizards survive the initial introduction of cats to the region. Lizards from islets sought shelter less frequently and often initially approached the cat decoy. These differences reflect changes since islet isolation and could render islet lizards strongly susceptible to cat predation.     

Gene expression profiling and its practice in drug development

The availability of sequenced genomes of human and many experimental animals necessitated the development of new technologies and powerful computational tools that are capable of exploiting these genomic data and ask intriguing questions about complex nature of biological processes. This gave impetus for developing whole genome approaches that can produce functional information of genes in the form of expression profiles and unscramble the relationships between variation in gene expression and the resulting physiological outcome. These profiles represent genetic fingerprints or catalogue of genes that characterize the cell or tissue being studied and provide a basis from which to begin an investigation of the underlying biology. Among the most powerful and versatile tools are high-density DNA microarrays to analyze the expression patterns of large numbers of genes across different tissues or within the same tissue under a variety of experimental conditions or even between species. The wide spread use of microarray technologies is generating large sets of data that is stimulating the development of better analytical tools so that functions can be predicted for novel genes. In this review, the authors discuss how these profiles are being used at various stages of the drug discovery process and help in the identification of new drug targets, predict the function of novel genes, and understand individual variability in response to drugs.     

Predicting final extent of ischemic infarction using artificial neural network analysis of multi-parametric MRI in patients with stroke

In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted - DWI, T₁-weighted – T1WI, T₂-weighted-T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated (r = 0.80, p<0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).