New approach to improved detection and tracking performance in track-while-scan radars. Part 2: Detection, track nitiation and association

he paper presents, in three parts, a new approach to improve the detection and tracking performance of a track-while-scan (TWS) radar. Part 1 presents a review of current status. In this part, Part 2, it is shown how the detection can be improved by utilising information from tracker. A new multitarget tracking algorithm, capable of tracking manoeuvring targets in clutter, is then presented. The algorithm is specifically tailored so that the solution to the combinatorial problem presented in a companion paper can be applied. The implementation aspects are discussed and a multiprocessor architecture identified to realise the full potential of the algorithm. Part 3 presents analytical derivations for quantitative assessment of the performance of the TWS radar system. It also shows how the performance can be optimised.

Detection and use of QTL for complex traits in multiple environments.

QTL mapping methods for complex traits are challenged by new developments in marker technology, phenotyping platforms, and breeding methods. In meeting these challenges, QTL mapping approaches will need to also acknowledge the central roles of QTL by environment interactions (QEI) and QTL by trait interactions in the expression of complex traits like yield. This paper presents an overview of mixed model QTL methodology that is suitable for many types of populations and that allows predictive modeling of QEI, both for environmental and developmental gradients. Attention is also given to multi-trait QTL models which are essential to interpret the genetic basis of trait correlations. Biophysical (crop growth) model simulations are proposed as a complement to statistical QTL mapping for the interpretation of the nature of QEI and to investigate better methods for the dissection of complex traits into component traits and their genetic controls.

An innovative microplate assay to facilitate the detection of antimicrobial activity in plant extracts.

A microplate assay was modified for the detection of antimicrobial activity in plant extracts. The aim was to develop an in vitro assay that could rapidly screen plant extracts to provide quantitative data on inhibition of microbial growth. A spectrophotometric assay using a microplate with serial dilutions of the plant extract and the bacteria was developed. Two bacteria, Staphylococcus aureus and Escherichia coli, were used for this study. Essential oils, oregano (Origanum vulgare) and lemon myrtle (Backhousia citriodora), and three active components carvacrol, thymol and citral were evaluated. The reproducibility of the assay was high, with correlation coefficients (r aureus and E. coli between 0.9321 and 0.9816. Similarly, r and 0.9814. This assay could also be used to measure antimicrobial activity in plant extracts which vary in pH and color.

Specific detection of the Old World screwworm fly, Chrysomya bezziana, in bulk fly trap catches using real-time PCR.

The Old World screwworm fly (OWS), Chrysomya bezziana Villeneuve (Diptera: Calliphoridae), is a myiasis-causing blowfly of major concern for both animals and humans. Surveillance traps are used in several countries for early detection of incursions and to monitor control strategies. Examination of surveillance trap catches is time-consuming and is complicated by the presence of morphologically similar flies that are difficult to differentiate from Ch. bezziana, especially when the condition of specimens is poor. A molecular-based method to confirm or refute the presence of Ch. bezziana in trap catches would greatly simplify monitoring programmes. A species-specific real-time polymerase chain reaction (PCR) assay was designed to target the ribosomal DNA internal transcribed spacer 1 (rDNA ITS1) of Ch. bezziana. The assay uses both species-specific primers and an OWS-specific Taqman MGB probe. Specificity was confirmed against morphologically similar and related Chrysomya and Cochliomyia species. An optimal extraction protocol was developed to process trap catches of up to 1000 flies and the assay is sensitive enough to detect one Ch. bezziana in a sample of 1000 non-target species. Blind testing of 29 trap catches from Australia and Malaysia detected Ch. bezziana with 100% accuracy. The probability of detecting OWS in a trap catch of 50 000 flies when the OWS population prevalence is low (one in 1000 flies) is 63.6% for one extraction. For three extractions (3000 flies), the probability of detection increases to 95.5%. The real-time PCR assay, used in conjunction with morphology, will greatly increase screening capabilities in surveillance areas where OWS prevalence is low.

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.

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.