Resin defect impacts on the value of graded recovery and evaluation of technologies for internal defect detection in slash pine logs.

The key outcome will be to identify a technology that is practical to use to scan logs identified by the modelling as suspect or marginal for sawing and to confirm their unsuitability for value adding sawing by internal scanning.

Detection of Polymyxa graminis in a barley crop in Australia.

Polymyxa graminis was detected in the roots of barley plants from a field near Wondai, Queensland, in 2009. P. graminis was identified by characteristic sporosori in roots stained with trypan blue. The presence of P. graminis f. sp. tepida (which is hosted by wheat and oats as well as barley) in the roots was confirmed by specific PCR tests based on nuclear ribosomal DNA. P. graminis is the vector of several damaging soil-borne virus diseases of cereals in the genera Furovirus, Bymovirus and Pecluvirus. No virus particles were detected in sap extracts from leaves of stunted barley plants with leaf chlorosis and increased tillering. Further work is required to determine the distribution of P. graminis in Australian grain crops and the potential for establishment and spread of the exotic soil-borne viruses that it vectors.

Establishing forest pest detection systems in South Pacific and Australia

The aim of the project is to reduce the risk of serious damage by exotic pests to the valuable timber resources of Fiji, Vanuatu and Australia by establishing efficient detection systems for target pests in high hazard sites. In particular, the project aims to minimise losses in the valuable plantations of Fiji and the emerging plantation industry of Vanuatu. This is part of a 'neighbourhood watch' approach to incursion management that will benefit all regional countries, including Australia.

Early detection of type 2 diabetes mellitus in Chinese and Indian adult population

Objectives of this study were to determine secular trends of diabetes prevalence in China and develop simple risk assessment algorithms for screening individuals with high-risk for diabetes or with undiagnosed diabetes in Chinese and Indian adults. Two consecutive population based surveys in Chinese and a prospective study in Mauritian Indians were involved in this study. The Chinese surveys were conducted in randomly selected populations aged 20-74 years in 2001-2002 (n=14 592) and 35-74 years in 2006 (n=4416). A two-step screening strategy using fasting capillary plasma glucose (FCG) as first-line screening test followed by standard 2-hour 75g oral glucose tolerance tests (OGTTs) was applied to 12 436 individuals in 2001, while OGTTs were administrated to all participants together with FCG in 2006 and to 2156 subjects in 2002. In Mauritius, two consecutive population based surveys were conducted in Mauritian Indians aged 20-65 years in 1987 and 1992; 3094 Indians (1141 men), who were not diagnosed as diabetes at baseline, were reexamined with OGTTs in 1992 and/or 1998. Diabetes and pre-diabetes was defined following 2006 World Health Organization/ International Diabetes Federation Criteria. Age-standardized, as well as age- and sex-specific, prevalence of diabetes and pre-diabetes in adult Chinese was significantly increased from 12.2% and 15.4% in 2001 to 16.0% and 21.2% in 2006, respectively. A simple Chinese diabetes risk score was developed based on the data of Chinese survey 2001-2002 and validated in the population of survey 2006. The risk scores based on β coefficients derived from the final Logistic regression model ranged from 3 – 32. When the score was applied to the population of survey 2006, the area under operating characteristic curve (AUC) of the score for screening undiagnosed diabetes was 0.67 (95% CI, 0.65-0.70), which was lower than the AUC of FCG (0.76 [0.74-0.79]), but similar to that of HbA1c (0.68 [0.65-0.71]). At a cut-off point of 14, the sensitivity and specificity of the risk score in screening undiagnosed diabetes was 0.84 (0.81-0.88) and 0.40 (0.38-0.41). In Mauritian Indian, body mass index (BMI), waist girth, family history of diabetes (FH), and glucose was confirmed to be independent risk predictors for developing diabetes. Predicted probabilities for developing diabetes derived from a simple Cox regression model fitted with sex, FH, BMI and waist girth ranged from 0.05 to 0.64 in men and 0.03 to 0.49 in women. To predict the onset of diabetes, the AUC of the predicted probabilities was 0.62 (95% CI, 0.56-0.68) in men and 0.64(0.59-0.69) in women. At a cut-off point of 0.12, the sensitivity and specificity was 0.72(0.71-0.74) and 0.47(0.45-0.49) in men; and 0.77(0.75-0.78) and 0.50(0.48-0.52) in women, respectively. In conclusion, there was a rapid increase in prevalence of diabetes in Chinese adults from 2001 to 2006. The simple risk assessment algorithms based on age, obesity and family history of diabetes showed a moderate discrimination of diabetes from non-diabetes, which may be used as first line screening tool for diabetes and pre-diabetes, and for health promotion purpose in Chinese and Indians.

CSSI-PRO: a method for secondary structure type editing, assignment and estimation in proteins using linear combination of backbone chemical shifts

Estimation of secondary structure in polypeptides is important for studying their structure, folding and dynamics. In NMR spectroscopy, such information is generally obtained after sequence specific resonance assignments are completed. We present here a new methodology for assignment of secondary structure type to spin systems in proteins directly from NMR spectra, without prior knowledge of resonance assignments. The methodology, named Combination of Shifts for Secondary Structure Identification in Proteins (CSSI-PRO), involves detection of specific linear combination of backbone H-1(alpha) and C-13' chemical shifts in a two-dimensional (2D) NMR experiment based on G-matrix Fourier transform (GFT) NMR spectroscopy. Such linear combinations of shifts facilitate editing of residues belonging to alpha-helical/beta-strand regions into distinct spectral regions nearly independent of the amino acid type, thereby allowing the estimation of overall secondary structure content of the protein. Comparison of the predicted secondary structure content with those estimated based on their respective 3D structures and/or the method of Chemical Shift Index for 237 proteins gives a correlation of more than 90% and an overall rmsd of 7.0%, which is comparable to other biophysical techniques used for structural characterization of proteins. Taken together, this methodology has a wide range of applications in NMR spectroscopy such as rapid protein structure determination, monitoring conformational changes in protein-folding/ligand-binding studies and automated resonance assignment.

Developing molecular diagnostics for the detection of strawberry viruses

Developing molecular diagnostics for the detection of strawberry viruses.

Detection and differentiation of bovine herpesvirus 1 and 5 using a multiplex real-time polymerase chain reaction.

A multiplex real-time PCR was developed for the detection and differentiation of two closely related bovine herpesviruses 1 (BoHV-1) and 5 (BoHV-5). The multiplex real-time PCR combines a duplex real-time PCR that targets the DNA polymerase gene of BoHV-1 and BoHV-5 and a real-time PCR targeting mitochondrial DNA, as a house-keeping gene, described previously by Cawthraw et al. (2009). The assay correctly identified 22 BoHV-1 and six BoHV-5 isolates from the Biosecurity Sciences Laboratory virus collection. BoHV-1 and BoHV-5 were also correctly identified when incorporated in spiked semen and brain tissue samples. The detection limits of the duplex assay were 10 copies of BoHV-1 and 45 copies of BoHV-5. The multiplex real-time PCR had reaction efficiencies of 1.04 for BoHV-1 and 1.08 for BoHV-5. Standard curves relating Ct value to template copy number had correlation coefficients of 0.989 for BoHV-1 and 0.978 for BoHV-5. The assay specificity was demonstrated by testing bacterial and viral DNA from pathogens commonly isolated from bovine respiratory and reproductive tracts. The validated multiplex real-time PCR was used to detect and differentiate BoHV-1 and BoHV-5 in bovine clinical samples with known histories.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Seizure resistance of cast aluminium alleys containing dispersed graphite particles of differing sizes

The seizure resistance of cast graphite-aluminium composite alloys containing graphite particles of various sizes was studied using a Hohman wear tester. If the graphite content is more than 2% these alloys can be selfmated without seizure under conditions of boundary lubrication. The size and shape of the graphite particles had no significant effect on seizure resistance. Owing to the extensive deformation and fragmentation of graphite, the low yield strength of the aluminium matrix and the low flow stress of the graphite particles, a continuous layer of graphite is formed on the mating surfaces even after a short running-in period. This layer persisted even after extensive wear deformation.

Real-time PCR as a surveillance tool for the detection of Trichinella infection in muscle samples from wildlife

Trichinella nematodes are the causative agent of trichinellosis, a meat-borne zoonosis acquired by consuming undercooked, infected meat. Although most human infections are sourced from the domestic environment, the majority of Trichinella parasites circulate in the natural environment in carnivorous and scavenging wildlife. Surveillance using reliable and accurate diagnostic tools to detect Trichinella parasites in wildlife hosts is necessary to evaluate the prevalence and risk of transmission from wildlife to humans. Real-time PCR assays have previously been developed for the detection of European Trichinella species in commercial pork and wild fox muscle samples. We have expanded on the use of real-time PCR in Trichinella detection by developing an improved extraction method and SYBR green assay that detects all known Trichinella species in muscle samples from a greater variety of wildlife. We simulated low-level Trichinella infections in wild pig, fox, saltwater crocodile, wild cat and a native Australian marsupial using Trichinella pseudospiralis or Trichinella papuae ethanol-fixed larvae. Trichinella-specific primers targeted a conserved region of the small subunit of the ribosomal RNA and were tested for specificity against host and other parasite genomic DNAs. The analytical sensitivity of the assay was at least 100 fg using pure genomic T. pseudospiralis DNA serially diluted in water. The diagnostic sensitivity of the assay was evaluated by spiking log of each host muscle with T. pseudospiralis or T. papuae larvae at representative infections of 1.0, 0.5 and 0.1 larvae per gram, and shown to detect larvae at the lowest infection rate. A field sample evaluation on naturally infected muscle samples of wild pigs and Tasmanian devils showed complete agreement with the EU reference artificial digestion method (k-value = 1.00). Positive amplification of mouse tissue experimentally infected with T. spiralis indicated the assay could also be used on encapsulated species in situ. This real-time PCR assay offers an alternative highly specific and sensitive diagnostic method for use in Trichinella wildlife surveillance and could be adapted to wildlife hosts of any region. (C) 2012 Elsevier B.V. All rights reserved.