Elucidating The Mechanisms And Consequences Of Clinical HIV-1 Resistance To The CCR5 Antagonist Maraviroc
Funder
National Health and Medical Research Council
Funding Amount
$622,732.00
Summary
CCR5 antagonists are a new class of anti-HIV drug, and maraviroc (MVC) is the only CCR5 antagonists that is licensed for use as a HIV treatment. Like all HIV treatments, drug resistance to MVC can develop in patients. This study will determine the mechanism of how HIV becomes resistant to MVC, which will permit the development of improved, second generation CCR5 antagonists, and will reveal ways to determine which patients are more likely to develop MVC resistance.
Mechanisms Underlying APOBEC3G Restriction Of HIV-1
Funder
National Health and Medical Research Council
Funding Amount
$540,075.00
Summary
In the fight against worldwide HIV-AIDS, understanding natural cell defenses to the HIV virus may identify new virus targets and strategies to block HIV in humans. Here, we will use state-of-the-art, high resolution, fluorescent microscopy to understand how the recently identified cell protein, APOBEC3G, blocks the HIV life cycle in human cells. We anticipate that APOBEC3G will stop HIV from invading the nucleus of human cells to defend against HIV, a strategy we can apply to new therapies.
Understanding HIV Resistance To Entry Inhibitors To Advance The Development Of Novel Antivirals
Funder
National Health and Medical Research Council
Funding Amount
$877,585.00
Summary
We cannot afford to be complacent in the search for improved anti HIV drugs for 2 principal reasons; First, worldwide a staggering 66% of infected individuals who need treatment are still unable to access therapy; and Second, the main reason why most treated patients are now living longer and more healthy lives is because we have never stopped developing newer therapies to provide options for patients. In this study we will develop and test newer drugs that block HIV infection of cells.
Intrinsic Host Antiviral Activity Against Pathogenic Filoviruses
Funder
National Health and Medical Research Council
Funding Amount
$488,754.00
Summary
Bats are a major reservoir for deadly human viruses including Ebola and Marburg virus. In contrast to humans, bats can be infected with these viruses without showing clinical signs of disease. The reason why bats can co-exist with these viruses is unknown. This study will determine if a bat antiviral molecule contributes to limiting virus release compared to the human version that could reveal strategies to prevent and control these deadly viruses in humans.
Norovirus Infection At The Stress Granule-PKR-p-elF2α Axis
Funder
National Health and Medical Research Council
Funding Amount
$505,967.00
Summary
This project application will aim to investigate and understand how viruses that cause vomiting and diarrhoea are able to infect, proliferate and spread within the human body. It aims to address how viruses are able to avoid and replicate in the presence of an effective immune response. We have evidence showing that Noroviruses are able to exploit certain antiviral proteins to paradoxically aid in virus replication and survival.
Next generation data mining techniques for analysing large evolving networks. In order to understand complex systems such as the Internet or gene interactions, we need to analyse how the networks in these systems function and evolve. This project will provide new methods for extracting knowledge from large network databases so that scientists can learn about the operation of these complex systems.
Multi source inference from heterogeneous dynamic networks. Sophisticated big data applications in engineering, the social sciences and biology are now generating flows of data across multiple sources possessing a variety of structures. An emerging challenge is how to develop data mining methods that can cope with this complexity and diversity to make inferences and provide practical insights. This project will develop methods in tensor data mining that provide a new foundation for extracting us ....Multi source inference from heterogeneous dynamic networks. Sophisticated big data applications in engineering, the social sciences and biology are now generating flows of data across multiple sources possessing a variety of structures. An emerging challenge is how to develop data mining methods that can cope with this complexity and diversity to make inferences and provide practical insights. This project will develop methods in tensor data mining that provide a new foundation for extracting useful knowledge from multi source heterogeneous data sets. This will help accelerate discoveries in the next generation of data driven science.Read moreRead less
Real-time and self-adaptive stream data analyser for intensive care management. The clinical benefit of this project will be in improved success rates and reduced mortality and risk in surgery and intensive care units. The Information and communication technology (ICT) benefit of this project is associated with the novel online algorithms and models aligned with the stream data research, and will be enhanced by our stream compression techniques. The stream data analyser developed in this projec ....Real-time and self-adaptive stream data analyser for intensive care management. The clinical benefit of this project will be in improved success rates and reduced mortality and risk in surgery and intensive care units. The Information and communication technology (ICT) benefit of this project is associated with the novel online algorithms and models aligned with the stream data research, and will be enhanced by our stream compression techniques. The stream data analyser developed in this project will be suitable for more than medical surveillance data; it will also improve the processing of other kinds of massive stream data (for example data from remote sensors, communication networks and other dynamic environments). The project involves a scientifically rich collaboration that will enhance the skills of PhD students and staff and drive the field forward.Read moreRead less
Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.