3 resultados para Myosin Type V

em eResearch Archive - Queensland Department of Agriculture


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An urgent need exists for indicators of soil health and patch functionality in extensive rangelands that can be measured efficiently and at low cost. Soil mites are candidate indicators, but their identification and handling is so specialised and time-consuming that their inclusion in routine monitoring is unlikely. The aim of this study was to measure the relationship between patch type and mite assemblages using a conventional approach. An additional aim was to determine if a molecular approach traditionally used for soil microbes could be adapted for soil mites to overcome some of the bottlenecks associated with soil fauna diversity assessment. Soil mite species abundance and diversity were measured using conventional ecological methods in soil from patches with perennial grass and litter cover (PGL), and compared to soil from bare patches with annual grasses and/or litter cover (BAL). Soil mite assemblages were also assessed using a molecular method called terminal-restriction fragment length polymorphism (T-RFLP) analysis. The conventional data showed a relationship between patch type and mite assemblage. The Prostigmata and Oribatida were well represented in the PGL sites, particularly the Aphelacaridae (Oribatida). For T-RFLP analysis, the mite community was represented by a series of DNA fragment lengths that reflected mite sequence diversity. The T-RFLP data showed a distinct difference in the mite assemblage between the patch types. Where possible, T-RFLP peaks were matched to mite families using a reference 18S rDNA database, and the Aphelacaridae prevalent in the conventional samples at PGL sites were identified, as were prostigmatids and oribatids. We identified limits to the T-RFLP approach and this included an inability to distinguish some species whose DNA sequences were similar. Despite these limitations, the data still showed a clear difference between sites, and the molecular taxonomic inferences also compared well with the conventional ecological data. The results from this study indicated that the T-RFLP approach was effective in measuring mite assemblages in this system. The power of this technique lies in the fact that species diversity and abundance data can be obtained quickly because of the time taken to process hundreds of samples, from soil DNA extraction to data output on the gene analyser, can be as little as 4 days.

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Multidrug-resistant Escherichia colt sequence type 131 (51131) has recently emerged as a globally distributed cause of extraintestinal infections in humans. Diverse factors have been investigated as explanations for ST131's rapid and successful dissemination, including transmission through animal contact and consumption of food, as suggested by the detection of ST131 in a number of nonhuman species. For example, ST131 has recently been identified as a cause of clinical infection in companion animals and poultry, and both host groups have been confirmed as faecal carriers of ST131. Moreover, a high degree of similarity has been shown among certain ST131 isolates from humans, companion animals, and poultry based on resistance characteristics and genomic background and human and companion animal ST131 isolates tend to exhibit similar virulence genotypes. However, most ST131 isolates from poultry appear to possess specific virulence genes that are typically absent from human and companion animal isolates, including genes associated with avian pathogenic E. coli. Since the number of reported animal and food-associated ST131 isolates is quite small, the role of nonhuman host species in the emergence, dissemination, and transmission of ST131 to humans remains unclear. Nevertheless, given the profound public health importance of the emergent ST131 clonal group, even the limited available evidence indicates a pressing need for further careful study of this significant question.

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On-going, high-profile public debate about climate change has focussed attention on how to monitor the soil organic carbon stock (C(s)) of rangelands (savannas). Unfortunately, optimal sampling of the rangelands for baseline C(s) - the critical first step towards efficient monitoring - has received relatively little attention to date. Moreover, in the rangelands of tropical Australia relatively little is known about how C(s) is influenced by the practice of cattle grazing. To address these issues we used linear mixed models to: (i) unravel how grazing pressure (over a 12-year period) and soil type have affected C(s) and the stable carbon isotope ratio of soil organic carbon (delta(13)C) (a measure of the relative contributions of C(3) and C(4) vegetation to C(s)); (ii) examine the spatial covariation of C(s) and delta(13)C; and, (iii) explore the amount of soil sampling required to adequately determine baseline C(s). Modelling was done in the context of the material coordinate system for the soil profile, therefore the depths reported, while conventional, are only nominal. Linear mixed models revealed that soil type and grazing pressure interacted to influence C(s) to a depth of 0.3 m in the profile. At a depth of 0.5 m there was no effect of grazing on C(s), but the soil type effect on C(s) was significant. Soil type influenced delta(13)C to a soil depth of 0.5 m but there was no effect of grazing at any depth examined. The linear mixed model also revealed the strong negative correlation of C(s) with delta(13)C, particularly to a depth of 0.1 m in the soil profile. This suggested that increased C(s) at the study site was associated with increased input of C from C(3) trees and shrubs relative to the C(4) perennial grasses; as the latter form the bulk of the cattle diet, we contend that C sequestration may be negatively correlated with forage production. Our baseline C(s) sampling recommendation for cattle-grazing properties of the tropical rangelands of Australia is to: (i) divide the property into units of apparently uniform soil type and grazing management; (ii) use stratified simple random sampling to spread at least 25 soil sampling locations about each unit, with at least two samples collected per stratum. This will be adequate to accurately estimate baseline mean C(s) to within 20% of the true mean, to a nominal depth of 0.3 m in the profile.