Parasite virulence: the role of activation and suppression of P2X7 receptors. Toxoplasmosis and leishmaniasis pose a threat to the health and well-being of the Australian human and animal populations. Around 6-8 million Australians are infected with Toxoplasma, a parasite that can cause severe problems in immunosuppressed individuals and birth defects and miscarriage in pregnant women with a primary infection. Toxoplasmosis is also the main cause of abortion and stillbirth in Australian sheep. L ....Parasite virulence: the role of activation and suppression of P2X7 receptors. Toxoplasmosis and leishmaniasis pose a threat to the health and well-being of the Australian human and animal populations. Around 6-8 million Australians are infected with Toxoplasma, a parasite that can cause severe problems in immunosuppressed individuals and birth defects and miscarriage in pregnant women with a primary infection. Toxoplasmosis is also the main cause of abortion and stillbirth in Australian sheep. Leishmaniasis, recently found in Australia, is a risk for overseas travellers, livestock and wildlife. This research will provide an understanding of what makes these parasites successful, paving the way for development of novel drugs to combat these chronic diseases.Read moreRead less
Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored ....Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored to individual characteristics. The success of this project could significantly advance the fundamental research in image analysis. Expected outcomes include new knowledge and algorithms for image analysis, which could benefit fields like biology and archaeology, where labeled images are hard to attain and scarce.Read moreRead less