Molecular characterisation of virulence graded field isolates of myxoma virus

<p>Abstract</p> <p>Background</p> <p><it>Myxoma virus </it>(MV) has been endemic in Europe since shortly after its deliberate release in France in 1952. While the emergence of more resistant hosts and more transmissible and attenuated virus is well documented, there have been relatively few studies focused on the sequence changes incurred by the virus as it has adapted to its new host. In order to identify regions of variability within the MV genome to be used for phylogenetic studies and to try to investigate causes of MV strain attenuation we have molecularly characterised nine strains of MV isolated in Spain between the years 1992 and 1995 from wide ranging geographic locations and which had been previously graded for virulence by experimental infection of rabbits.</p> <p>Results</p> <p>The findings reported here show the analysis of 16 genomic regions accounting for approximately 10% of the viral genomes. Of the 20 genes analysed 5 (M034L, M069L, M071L, M130R and M135R) were identical in all strains and 1 (M122R) contained only a single point mutation in an individual strain. Four genes (M002L/R, M009L, M036L and M017L) showed insertions or deletions that led to disruption of the ORFs.</p> <p>Conclusions</p> <p>The findings presented here provide valuable tools for strain differentiation and phylogenetic studies of MV isolates and some clues as to the reasons for virus attenuation in the field.</p

Design and implementation of the canadian kidney disease cohort study (CKDCS): A prospective observational study of incident hemodialysis patients

<p>Abstract</p> <p>Background</p> <p>Many nephrology observational studies use renal registries, which have well known limitations. The Canadian Kidney Disease Cohort Study (CKDCS) is a large prospective observational study of patients commencing hemodialysis in five Canadian centers. This study focuses on delineating potentially reversible determinants of adverse outcomes that occur in patients receiving dialysis for end-stage renal disease (ESRD).</p> <p>Methods/Design</p> <p>The CKDCS collects information on risk factors and outcomes, and stores specimens (blood, dialysate, hair and fingernails) at baseline and in long-term follow-up. Such specimens will permit measurements of biochemical markers, proteomic and genetic parameters (proteins and DNA) not measured in routine care. To avoid selection bias, all consenting incident hemodialysis patients at participating centers are enrolled, the large sample size (target of 1500 patients), large number of exposures, and high event rates will permit the exploration of multiple potential research questions.</p> <p>Preliminary Results</p> <p>Data on the baseline characteristics from the first 1074 subjects showed that the average age of patients was 62 (range; 50-73) years. The leading cause of ESRD was diabetic nephropathy (41.9%), and the majority of the patients were white (80.0%). Only 18.7% of the subjects received dialysis in a satellite unit, and over 80% lived within a 50 km radius of the nearest nephrologist's practice.</p> <p>Discussion</p> <p>The prospective design, detailed clinical information, and stored biological specimens provide a wealth of information with potential to greatly enhance our understanding of risk factors for adverse outcomes in dialysis patients. The scientific value of the stored patient tissue will grow as new genetic and biochemical markers are discovered in the future.</p

ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing

The four-dimensional magnetic point and space groups

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