Developing feasible in situ control of mange disease in wombats. Our goal is the development of feasible in situ control of sarcoptic mange in wombat populations. Globally important, the Sarcoptes scabiei mite infects >100 mammal species and is among the 50 most common human diseases, causing health, welfare and population impacts. This infection is treatable, and we will test a new treatment (fluralaner), develop new models to guide management, and conduct replicated field trials. This will ena ....Developing feasible in situ control of mange disease in wombats. Our goal is the development of feasible in situ control of sarcoptic mange in wombat populations. Globally important, the Sarcoptes scabiei mite infects >100 mammal species and is among the 50 most common human diseases, causing health, welfare and population impacts. This infection is treatable, and we will test a new treatment (fluralaner), develop new models to guide management, and conduct replicated field trials. This will enable science-based guidelines, advancing disease control, local eradication, and regulatory approval for wombats. Our research framework is adaptable to other mange-impacted species, and advance methods and theory for control of treatable disease in wildlife.Read moreRead less
The ins and outs of HIV biology. This project aims to delineate the fundamental mechanisms that regulate the production of HIV and the ability of HIV to cause AIDS in infected patients. It will utilise state-of-the-art technologies to unearth new clues that govern the biology of HIV, with the ultimate goal to develop novel vaccine and treatment strategies against HIV.
Artificial intelligence algorithms to predict risk of injury in racehorses. This project will address the urgent need for predicting and preventing catastrophic and career limiting limb injuries and cardiac arrhythmias in racehorses due to over (or under) training. Using data from GPS and movement sensors integrated into saddlecloths, artificial intelligence algorithms will convert cumulative data on speed, gait, and stride characteristics during training, along with injury data, into a risk mat ....Artificial intelligence algorithms to predict risk of injury in racehorses. This project will address the urgent need for predicting and preventing catastrophic and career limiting limb injuries and cardiac arrhythmias in racehorses due to over (or under) training. Using data from GPS and movement sensors integrated into saddlecloths, artificial intelligence algorithms will convert cumulative data on speed, gait, and stride characteristics during training, along with injury data, into a risk matrix. Recorded heart rate and ECG data will also be analysed using artificial intelligence to detect early evidence of the development of cardiac arrhythmias. The system will improve racehorse welfare, providing a simple interface to warn trainers when risk of injury becomes high, in order to prevent catastrophic breakdown.Read moreRead less
Developing mathematical models of infection and transmission to link biology, epidemiology and public health policy. Infectious diseases constitute a significant burden on the health of the population. Understanding how best to control them requires a multi-faceted approach, combining data from biology, medicine and population health with mathematical and computational models of disease transmission. This project will investigate the "flu" and other diseases.
ARC Centre of Excellence for Plant Success in Nature and Agriculture. The ARC CoE for Plant Success in Nature and Agriculture will discover the adaptive strategies underpinning productivity and resilience in diverse plants and deepen knowledge of the genetic and physiological networks driving key traits. Using novel quantitative and computational approaches, the Centre will link gene networks with traits across biological levels, giving breeders an unparalleled predictive capacity. The Centre wi ....ARC Centre of Excellence for Plant Success in Nature and Agriculture. The ARC CoE for Plant Success in Nature and Agriculture will discover the adaptive strategies underpinning productivity and resilience in diverse plants and deepen knowledge of the genetic and physiological networks driving key traits. Using novel quantitative and computational approaches, the Centre will link gene networks with traits across biological levels, giving breeders an unparalleled predictive capacity. The Centre will accelerate technologies to transfer successful networks into crops and build legal frameworks to secure this knowledge. With a uniquely multidisciplinary team, the Centre will deliver new strategies to address the problems of food security and climate change, establishing Australia as a global leader in these areas.Read moreRead less