Diabetic Retinopathy in Native and Nonnative Canadians

High prevalence rates of type 2 diabetes are being observed in native Canadian communities. It is believed that native populations have a higher prevalence rate of vascular complications than nonnatives. The Southern Alberta Study of Diabetic Retinopathy (DR) examined the prevalence and incidence of DR and associated metabolic abnormalities in native and nonnative subjects. Prevalence rates of DR in type 2 diabetic native and nonnative subjects were identical, with a prevalence rate of 40%. Native subjects with retinopathy, however, tended to have more advanced changes of retinopathy compared to the nonnative subjects. Key factors such as A1c, blood pressure, duration of diabetes, and lipid values were not significantly different between the two cohorts. These data indicate that ethnicity does play a role in the development and severity of DR but potential risk factors that may affect the development of retinopathy are not significantly different between native and nonnative groups

Integration over song classification replicates: Song variant analysis in the hihi

Human expert analyses are commonly used in bioacoustic studies and can potentially limit the reproducibility of these results. In this paper, a machine learning method is presented to statistically classify avian vocalizations. Automated approaches were applied to isolate bird songs from long field recordings, assess song similarities, and classify songs into distinct variants. Because no positive controls were available to assess the true classification of variants, multiple replicates of automatic classification of song variants were analyzed to investigate clustering uncertainty. The automatic classifications were more similar to the expert classifications than expected by chance. Application of these methods demonstrated the presence of discrete song variants in an island population of the New Zealand hihi (Notiomystis cincta). The geographic patterns of song variation were then revealed by integrating over classification replicates. Because this automated approach considers variation in song variant classification, it reduces potential human bias and facilitates the reproducibility of the results

Liver Development, Regeneration, and Carcinogenesis

The identification of putative liver stem cells has brought closer the previously separate fields of liver development, regeneration, and carcinogenesis. Significant overlaps in the regulation of these processes are now being described. For example, studies in embryonic liver development have already provided the basis for directed differentiation of human embryonic stem cells and induced pluripotent stem cells into hepatocyte-like cells. As a result, the understanding of the cell biology of proliferation and differentiation in the liver has been improved. This knowledge can be used to improve the function of hepatocyte-like cells for drug testing, bioartificial livers, and transplantation. In parallel, the mechanisms regulating cancer cell biology are now clearer, providing fertile soil for novel therapeutic approaches. Recognition of the relationships between development, regeneration, and carcinogenesis, and the increasing evidence for the role of stem cells in all of these areas, has sparked fresh enthusiasm in understanding the underlying molecular mechanisms and has led to new targeted therapies for liver cirrhosis and primary liver cancers

Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes

<p>Abstract</p> <p>Background</p> <p>New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, <it>Caenorhabditis elegans, Drosophila melanogaster, Mus musculus </it>and <it>Saccharomyces cerevisiae</it>, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset.</p> <p>Results</p> <p>Cross-species analyses showed that the evolutionary conservation of genes and the presence of essential orthologues are each strong predictors of essentiality in eukaryotes. Absence of paralogues was also found to be a general predictor of increased relative essentiality. By combining several orthology and essentiality criteria one can select gene sets with up to a five-fold enrichment in essential genes compared with a random selection. We show how quantitative application of such criteria can be used to predict a ranked list of potential drug targets from <it>Ancylostoma caninum </it>and <it>Haemonchus contortus </it>- two blood-feeding strongylid nematodes, for which there are presently limited sequence data but no functional genomic tools.</p> <p>Conclusions</p> <p>The present study demonstrates the utility of using orthology information from multiple, diverse eukaryotes to predict essential genes. The data also emphasize the challenge of identifying essential genes among those in a parasite that are absent from its host.</p

Lithium dihydropyridine dehydrogenation catalysis : a group 1 approach to cyclisation of diamine-boranes

In reactions restricted previously to a ruthenium catalyst, a 1-lithium-2-alkyl-1,2-dihydropyridine complex is shown to be a competitive alternative dehydrogenation catalyst for the transformation of diamine boranes to cyclic 1,3,2-diazaborolidines, which can in turn be smoothly arylated in good yields. This study establishes the conditions and solvent dependence of the catalysis via NMR monitoring, with mechanistic insight provided by NMR (including DOSY) experiments and X-ray crystallographic studies of several model lithio intermediates

Leaf Growth and Senescence Rates in Brown-Back Wallaby Grass, \u3cem\u3eRytidosperma duttonianum\u3c/em\u3e

Knowledge of leaf turnover in grasses is necessary to model curing (the accumulation of dead material in the sward), which is not well represented in current pasture growth models, nor for many Australian native species. Leaf turnover begins with the appearance of successive leaves, which elongate until typically, a leaf ligule develops to indicate a mature, fully expanded length. Green leaf life span extends from appearance to the beginning of senescence, which ultimately leads to death (Fig. 1). Here, the individual rates of leaf growth and senescence for the Australian native brown-back wallaby grass, Rytidosperma duttonianum (Cashmore) Connor & Edgar, over the whole life cycle, are reported

Modelling the response of surface fuel to climate change across south-eastern Australia: consequences for future fire regimes

Geophysical Research Abstracts of EGU General Assembly 2014, held 27 April - 2 May, 2014 in Vienna, Austria

A Plant-Physiology Approach to a Fire-y Problem

As vegetation dies, it dries and becomes more flammable. Fire agencies require accurate and timely assessments of curing (the percentage of dead material in the sward) to model grass fire behaviour and calculate fire danger ratings (Cheney and Sullivan 2008). Visual observation is commonplace and the more objective use of the Levy Rod is recommended, although both have drawbacks (Anderson et al. 2011). There is great potential for pasture growth models to provide curing estimates to assist with the management of wild grass fires (Gill et al. 2010). This PhD project focused on plant physiological characters to populate models that could be used to predict curing assessments for fire management purposes