Phylogenetic analysis to define feline immunodeficiency virus subtypes in 31 domestic cats in South Africa

Feline immunodeficiency virus (FIV), a lentivirus, is an important pathogen of domestic cats around the world and has many similarities to human immunodeficiency virus (HIV). A characteristic of these lentiviruses is their extensive genetic diversity which has been an obstacle in the development of successful vaccines. Of the FIV genes, the envelope gene is the most variable and sequence differences in a portion of this gene have been used to define 5 FIV subtypes (A, B, C, D and E). In this study, the proviral DNA sequence of the V3-V5 region of the envelope gene was determined in blood samples from 31 FIV positive cats from 4 different regions of South Africa. Phylogenetic analysis demonstrated the presence of both subtypes A and C, with subtype A predominating. These findings contribute to the understanding of the genetic diversity of FIV

Mutation analysis to verify feature matrices for isolating errors in simulation models

Software simulation models are computer programs that need to be verified and debugged like any other software. In previous work, a method for error isolation in simulation models has been proposed. The method relies on a set of feature matrices that can be used to determine which part of the model implementation is responsible for deviations in the output of the model. Currrently these feature matrices have to be generated by hand from the model implementation, which is a tedious and error-prone task. In this paper, a method based on mutation analysis, as well as prototype tool support for the verification of the manually generated feature matrices is presented. The application of the method and tool to a model for wastewater treatment shows that the feature matrices can be verified effectively using a minimal number of mutants.

A comparison of high-content screening versus manual analysis to assay the effects of mesenchymal stem cell-conditioned medium on neurite outgrowth in vitro

Bone marrow mesenchymal stem cells (MSCs) promote nerve growth and functional recovery in animal models of spinal cord injury (SCI) to varying levels. The authors have tested high-content screening to examine the effects of MSC-conditioned medium (MSC-CM) on neurite outgrowth from the human neuroblastoma cell line SH-SY5Y and from explants of chick dorsal root ganglia (DRG). These analyses were compared to previously published methods that involved hand-tracing individual neurites. Both methods demonstrated that MSC-CM promoted neurite outgrowth. Each showed the proportion of SH-SY5Y cells with neurites increased by ~200% in MSC-CM within 48 h, and the number of neurites/SH-SY5Y cells was significantly increased in MSC-CM compared with control medium. For high-content screening, the analysis was performed within minutes, testing multiple samples of MSC-CM and in each case measuring >15,000 SH-SY5Y cells. In contrast, the manual measurement of neurite outgrowth from >200 SH-SY5Y cells in a single sample of MSC-CM took at least 1 h. High-content analysis provided additional measures of increased neurite branching in MSC-CM compared with control medium. MSC-CM was also found to stimulate neurite outgrowth in DRG explants using either method. The application of the high-content analysis was less well optimized for measuring neurite outgrowth from DRG explants than from SH-SY5Y cells.

Using quantitative analysis to implement autonomic IT systems

The software underpinning today’s IT systems needs to adapt dynamically and predictably to rapid changes in system workload, environment and objectives. We describe a software framework that achieves such adaptiveness for IT systems whose components can be modelled as Markov chains. The framework comprises (i) an autonomic architecture that uses Markov-chain quantitative analysis to dynamically adjust the parameters of an IT system in line with its state, environment and objectives; and (ii) a method for developing instances of this architecture for real-world systems. Two case studies are presented that use the framework successfully for the dynamic power management of disk drives, and for the adaptive management of cluster availability within data centres, respectively.

A peak-clustering method for MEG group analysis to minimise artefacts due to smoothness

Magnetoencephalography (MEG), a non-invasive technique for characterizing brain electrical activity, is gaining popularity as a tool for assessing group-level differences between experimental conditions. One method for assessing task-condition effects involves beamforming, where a weighted sum of field measurements is used to tune activity on a voxel-by-voxel basis. However, this method has been shown to produce inhomogeneous smoothness differences as a function of signal-to-noise across a volumetric image, which can then produce false positives at the group level. Here we describe a novel method for group-level analysis with MEG beamformer images that utilizes the peak locations within each participant's volumetric image to assess group-level effects. We compared our peak-clustering algorithm with SnPM using simulated data. We found that our method was immune to artefactual group effects that can arise as a result of inhomogeneous smoothness differences across a volumetric image. We also used our peak-clustering algorithm on experimental data and found that regions were identified that corresponded with task-related regions identified in the literature. These findings suggest that our technique is a robust method for group-level analysis with MEG beamformer images.