Development And Validation Of A Latent Tuberculosis Diagnostic
Funder
National Health and Medical Research Council
Funding Amount
$534,865.00
Summary
Globally, tuberculosis is a leading cause of death with 9.6 million new diagnoses in 2014. The diagnosis of latent TB infection is important, but is difficult to make because current assays are suboptimal. We have developed a very simple assay which detects responses to TB antigens by co-expression of two surface markers expressed by CD4+ T cells. We propose to develop this into a highly standardised kit for the diagnosis of TB with our commercial partner Cytognos.
Activation Of BMP4 Signalling To Inhibit Breast Cancer Metastasis
Funder
National Health and Medical Research Council
Funding Amount
$748,742.00
Summary
The spread of cancer cells to other organs is a common cause of breast cancer-related death in women. Current therapies for advanced breast cancer are often palliative since the secondary tumours become resistant to the chemotherapy. Here, we are using preclinical models of advanced breast cancer to develop a treatment that should be effective in patients with secondary tumours and should reduce the risk of dying of this disease.
Drug-induced Immune Thrombocytopenia: Understanding The Disease Mechanisms Is The Key To Better Treatment
Funder
National Health and Medical Research Council
Funding Amount
$509,550.00
Summary
Many very commonly used medications cause an allergic reaction in a small number of patients that receive them. The allergic reaction results in platelets being destroyed and puts the patients at risk of bleeding. The patient recovers slowly if the drug is stopped but there is no other treatment and no way to reverse the effect quickly if the patient starts to bleed. This project will try to understand the mechanism of the condition and test a potential treatment.
Identifying And Exploiting Novel Pharmacological Targets For Breast Cancer
Funder
National Health and Medical Research Council
Funding Amount
$431,000.00
Summary
Breast cancers are made up of different types of cancer cells, and not all cells contribute equally. A subset of cancer cells may be uniquely capable of driving tumor growth, rebuilding fatal tumors after therapy and establishing new tumors at distant sites. Identifying and exploiting the pathways that regulate the activity and survival of these cells will lead to better modes of treatment, and move towards a relapse-free future for breast cancer patients.
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.
Discovery Early Career Researcher Award - Grant ID: DE230100495
Funder
Australian Research Council
Funding Amount
$422,154.00
Summary
Structured Federated Learning for Personalised Intelligence on Devices. The project aims to develop a new structured federated machine-learning framework to enhance the customisation of artificial intelligence across mobile and smart devices. It seeks to enable users to receive customised services on their devices without sending their sensitive personal data to a cloud service provider. Anticipated benefits include greater privacy, data security and device performance, as well as better end-use ....Structured Federated Learning for Personalised Intelligence on Devices. The project aims to develop a new structured federated machine-learning framework to enhance the customisation of artificial intelligence across mobile and smart devices. It seeks to enable users to receive customised services on their devices without sending their sensitive personal data to a cloud service provider. Anticipated benefits include greater privacy, data security and device performance, as well as better end-user experience. Expected outcomes of this research include new knowledge, toolkits and algorithms for use in developing machine-learning based secure, efficient and fault-tolerant technologies for software applications, mobile services, cloud computing, autonomous vehicles and advanced manufacturing processes.Read moreRead less
Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowled ....Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowledge and an approach to fast-adapt bilevel optimization solutions when required computing resources change. The anticipated outcomes should significantly improve the reliability of ML with benefits for safety learning and computing resource optimisation in ML-based data analytics.Read moreRead less
Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language m ....Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language models, and human-guided continuous correction for safety improvements. The established theories and developed algorithms will advance frontier technologies in AI and contribute to a wide range of real applications of safe RL, such as robotics and autonomous driving, bringing enormous social and economic benefits. Read moreRead less
Targeting Fungal Phospholipid Metabolism For Antifungal Drug Discovery
Funder
National Health and Medical Research Council
Funding Amount
$828,557.00
Summary
Invasive fungal infections are a serious and escalating health problem. They cause severe disease with a high death rate and are very costly to the health system. New antifungal drugs with novel properties are needed now because there are problems with current drugs. This project aims to develop potent new antifungal drugs that are effective in many fungal diseases and are well-tolerated.
An intelligent condition-monitoring system for mineral screening machines. This project aims to develop an intelligent condition-monitoring system for screening machines which are widely used for classifying mineral particles in the mining industry. This project will develop new vibration-based methodologies and techniques for fault diagnostics and remaining useful life prediction of bearings and gears in situations with multiple complex sources and interferences. The monitoring system, as the e ....An intelligent condition-monitoring system for mineral screening machines. This project aims to develop an intelligent condition-monitoring system for screening machines which are widely used for classifying mineral particles in the mining industry. This project will develop new vibration-based methodologies and techniques for fault diagnostics and remaining useful life prediction of bearings and gears in situations with multiple complex sources and interferences. The monitoring system, as the expected outcomes of this project, will modernise the current maintenance practices towards condition-based predictive maintenance, reducing unplanned downtime, increasing productivity and reducing maintenance costs for the Australian mining industry. It will also add more value to the Australian manufactured products. Read moreRead less