Preclinical Development Of A Therapeutic Anticancer Antibody To C-Met
Funder
National Health and Medical Research Council
Funding Amount
$435,530.00
Summary
Many common cancers cannot be effectively treated. A range of these cancers (e.g. gastric and lung cancer) display the molecule c-Met on their cell surface. c-Met promotes tumour growth; therefore, blocking c-Met is a promising strategy for treating these cancers. However, no antibodies or drugs that target c-Met have been licensed. The therapeutics that are being developed to target c-Met all have considerable limitations. Thus, there is an opportunity to develop a 'best-in-class' therapeutic.
Discovery Early Career Researcher Award - Grant ID: DE230101058
Funder
Australian Research Council
Funding Amount
$437,254.00
Summary
Glass-box Deep Machine Perception for Trustworthy Artificial Intelligence. Explainability and Transparency are the key values for development and deployment of Artificial Intelligence (AI) in Australia’s AI Ethics Framework for industry and governments. This project aims to build new tools to make the central technology of AI - deep learning - transparent and explainable. Its expected outputs are novel theory-driven algorithms and unconventional foundational blocks for deep learning that will al ....Glass-box Deep Machine Perception for Trustworthy Artificial Intelligence. Explainability and Transparency are the key values for development and deployment of Artificial Intelligence (AI) in Australia’s AI Ethics Framework for industry and governments. This project aims to build new tools to make the central technology of AI - deep learning - transparent and explainable. Its expected outputs are novel theory-driven algorithms and unconventional foundational blocks for deep learning that will allow humans to clearly interpret the reasoning process of this technology, which is currently not possible. It is expected to significantly advance our knowledge in machine intelligence and perception. Due to their fundamental nature, the project outcomes are likely to benefit industry and scientific frontiers alike.Read moreRead less
Improving Stroke Outcomes: Attenuating Progression And Recurrence
Funder
National Health and Medical Research Council
Funding Amount
$9,331,996.00
Summary
Stroke is the second most common cause of death and major cause disability. There are few proven interventions, so we need to introduce new ones. We developed a bench to bedside program to introduce new stroke therapies and its early secondary prevention. Our general goal is to provide evidence for their effectiveness and safety. We will use animal stroke models, markers in the blood to help diagnose and predict stroke outcome and imaging to help select patients for several clinical trials.
Assistive technologies for Autism support. Growing numbers of children are diagnosed with Autism Spectrum Disorder, leading to a massive financial burden on educational, medical and social service systems. This project aims to construct technological solutions to ease this cost with software frameworks for both children and parents. These novel tools and techniques address key issues: personalisation of early intervention, extension of interventions in alternate contexts, and parental support th ....Assistive technologies for Autism support. Growing numbers of children are diagnosed with Autism Spectrum Disorder, leading to a massive financial burden on educational, medical and social service systems. This project aims to construct technological solutions to ease this cost with software frameworks for both children and parents. These novel tools and techniques address key issues: personalisation of early intervention, extension of interventions in alternate contexts, and parental support through analysis of social media. The outcomes aim to include algorithms and prototype applications for flexible early intervention and support for parents and carers, including evaluation of the developed tools in real-world settings.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100090
Funder
Australian Research Council
Funding Amount
$250,000.00
Summary
Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object ....Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object recognition in images, speech recognition and automatic translation, bringing the prospect of machine intelligence closer than ever. Modern machine learning techniques have had huge impact in the last decade in fields such as robotics, computer vision and data analytics. The facility would enable Australian researchers to develop, learn and apply deep networks to problems of national importance in robotic vision and big data analytics. Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH170100013
Funder
Australian Research Council
Funding Amount
$2,962,655.00
Summary
ARC Research Hub for Digital Enhanced Living. The ARC Research Hub for Digital Enhanced Living aims to address the growing challenges of aging people living in their own home or residential care. This will be through inventing new personalised medical technologies through an innovative approach, with a multi-disciplinary team leveraging diverse expertise. An enhanced capacity to create and deploy fit-for-purpose personalised health solutions will result in revenues from new and repurposed device ....ARC Research Hub for Digital Enhanced Living. The ARC Research Hub for Digital Enhanced Living aims to address the growing challenges of aging people living in their own home or residential care. This will be through inventing new personalised medical technologies through an innovative approach, with a multi-disciplinary team leveraging diverse expertise. An enhanced capacity to create and deploy fit-for-purpose personalised health solutions will result in revenues from new and repurposed devices, analytics and integration platforms. New jobs and improved care will see cost reductions, better use of resources and enhanced mental, physical and social well-being.Read moreRead less
Computational tools to analyse and exploit the social media revolution. We aim to create technologies to analyse social media communities, which are rapidly growing in reach, complexity, and content produced and shared. Powerful techniques to tap this resource will lead to commercial outcomes for marketing and search industries, alongside deeper insight into the cultural and social impact of this Internet revolution.
Most eye diseases have a genetic contribution, whether rare disorders affecting children such as retinoblastoma or congenital cataracts through to common disorders of older people such as myopia, age-related macular degeneration or glaucoma. We will continue our successful research to find genes that cause these diseases and use this to improve patient care and prevent blindness. We will work out how families can use this genetic information to participate in trials to develop new treatments.
Antimalarial Drugs In Pregnancy: Preclinical And Clinical Studies Of Conventional And Novel Agents
Funder
National Health and Medical Research Council
Funding Amount
$470,115.00
Summary
Women in malaria-endemic areas such as coastal PNG are at high risk of malaria in pregnancy. To prevent the substantially increased malaria-associated morbidity and mortality in mother and child, and because even asymptomatic infections can be deleterious, there has been a move to giving antimalarial drugs regularly during pregnancy regardless of the mother's clinical or parasitological status. In poor tropical countries, such treatment usually comprises safe and inexpensive agents such as chlor ....Women in malaria-endemic areas such as coastal PNG are at high risk of malaria in pregnancy. To prevent the substantially increased malaria-associated morbidity and mortality in mother and child, and because even asymptomatic infections can be deleterious, there has been a move to giving antimalarial drugs regularly during pregnancy regardless of the mother's clinical or parasitological status. In poor tropical countries, such treatment usually comprises safe and inexpensive agents such as chloroquine and Fansidar. There are two main issues with this approach. First, the efficacy of such conventional agents is waning and this increases the risk of break-through malaria. Second, there are few data on how the drugs are handled in pregnancy on which to base recommendations for treatment. We plan to collect information on the disposition and effectiveness of chloroquine and Fansidar in women with malaria in pregnancy in PNG that should allow a critical appraisal of the usefulness of current regimens in PNG and in other tropical countries where parasite resistance to these agents is emerging. Artemisinin combination therapy (ACT) in the form of a novel artemisinin drug and a longer-acting partner has been suggested as the most promising alternative therapy for malaria in pregnancy if conventional drugs fail. We plan to assess the safety of a leading ACT formulation, namely dihydroartemisinin and the chloroquine-like drug piperaquine (DHA-PQ), in animals before extending our studies to women with malaria in PNG. These latter studies will allow an evaluation of the safety and efficacy of DHA-PQ as novel therapy for malaria in pregnancy in PNG and other tropical countries.Read moreRead less
A theoretical framework for practical partial fingerprint identification. Fingerprints captured from a crime scene are often partial and poor quality which makes it difficult to identify the criminal suspects from large databases. This project will find mathematical models which can estimate the missing information located in the blank areas of a partial fingerprint and effectively identify it.