Colour Doppler ultrasound of the ocular circulation in patients with systemic lupus erythematosus identifies altered microcirculatory haemodynamics

We assessed whether quantitative analysis of Doppler flow velocity waveforms is able to identify subclinical microvascular abnormalities in SLE and whether eigenvector analysis can detect changes not detectable using the resistive index (RI). Fifty-four SLE patients with no conventional cardiovascular risk factors, major organ involvement or retinopathy were compared to 32 controls. Flow velocity waveforms were obtained from the ophthalmic artery (OA), central retinal artery (CRA) and common carotid artery (CA). The waveforms were analysed using eigenvector decomposition and compared between groups at each arterial site. The RI was also determined. The RI was comparable between groups. In the OA and CRA, there were significant differences in the lower frequency sinusoidal components (P <0.05 for each component). No differences were apparent in the CA between groups. Eigenvector analysis of Doppler flow waveforms, recorded in proximity of the terminal vascular bed, identified altered ocular microvascular haemodynamics in SLE. Altered waveform structure could not be identified by changes in RI, the traditional measure of downstream vascular resistance. This analytical approach to waveform analysis is more sensitive in detecting preclinical microvascular abnormalities in SLE. It may hold potential as a useful tool for assessing disease activity, response to treatment, and predicting future vascular complications.

SVM Training Phase Reduction Using Dataset Feature Filtering for Malware Detection

N-gram analysis is an approach that investigates the structure of a program using bytes, characters, or text strings. A key issue with N-gram analysis is feature selection amidst the explosion of features that occurs when N is increased. The experiments within this paper represent programs as operational code (opcode) density histograms gained through dynamic analysis. A support vector machine is used to create a reference model, which is used to evaluate two methods of feature reduction, which are 'area of intersect' and 'subspace analysis using eigenvectors.' The findings show that the relationships between features are complex and simple statistics filtering approaches do not provide a viable approach. However, eigenvector subspace analysis produces a suitable filter.

Identifying appropriate sampling and modelling approaches for analysing distributional patterns of Antarctic terrestrial arthropods along the Victoria Land latitudinal gradient

Biotic communities in Antarctic terrestrial ecosystems are relatively simple and often lack higher trophic levels (e. g. predators); thus, it is often assumed that species' distributions are mainly affected by abiotic factors such as climatic conditions, which change with increasing latitude, altitude and/or distance from the coast. However, it is becoming increasingly apparent that factors other than geographical gradients affect the distribution of organisms with low dispersal capability such as the terrestrial arthropods. In Victoria Land (East Antarctica) the distribution of springtail (Collembola) and mite (Acari) species vary at scales that range from a few square centimetres to regional and continental. Different species show different scales of variation that relate to factors such as local geological and glaciological history, and biotic interactions, but only weakly with latitudinal/altitudinal gradients. Here, we review the relevant literature and outline more appropriate sampling designs as well as suitable modelling techniques (e. g. linear mixed models and eigenvector mapping), that will more adequately address and identify the range of factors responsible for the distribution of terrestrial arthropods in Antarctica.

Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

Motivation: To date, Gene Set Analysis (GSA) approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations, or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes.

Results: In GSNCA, weight factors are assigned to genes in proportion to the genes' cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA, indeed, captures changes in the structure of genes' cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses.

Plant community assembly at small scales: spatial versus environmental factors in a central European grassland

Dispersal limitation and environmental conditions are crucial drivers of plant species distribution and establishment. As these factors operate at different spatial scales, we asked: Do the environmental factors known to determine community assembly at broad scales operate at fine scales (few meters)? How much do these factors account for community variation at fine scales? In which way do biotic and abiotic interactions drive changes in species composition? We surveyed the plant community within a dry grassland along a very steep gradient of soil characteristics like pH and nutrients. We used a spatially explicit sampling design, based on three replicated macroplots of 15x15, 12x12 and 12x12 meters in extent. Soil samples were taken to quantify several soil properties (carbon, nitrogen, plant available phosphorus, pH, water content and dehydrogenase activity as a proxy for overall microbial activity). We performed variance partitioning to assess the effect of these variables on plant composition and statistically controlled for spatial autocorrelation via eigenvector mapping. We also applied null model analysis to test for non-random patterns in species co-occurrence using randomization schemes that account for patterns expected under species interactions. At a fine spatial scale, environmental factors explained 18% of variation when controlling for spatial autocorrelation in the distribution of plant species, whereas purely spatial processes accounted for 14% variation. Null model analysis showed that species spatially segregated in a non-random way and these spatial patterns could be due to a combination of environmental filtering and biotic interactions. Our grassland study suggests that environmental factors found to be directly relevant in broad scale studies are present also at small scales, but are supplemented by spatial processes and more direct interactions like competition.