Arbovirus Activation And Modulation Of NLRP3 Inflammasome
Funder
National Health and Medical Research Council
Funding Amount
$779,720.00
Summary
This project aims to establish how mosquito borne viruses such as Ross River and dengue viruses interacts with the human host to cause disease, including how the virus evades the host’s immune response to persist and cause disease for prolonged periods. Knowing how differences in the virus and the host’s immune system interplay to cause asymptomatic to severely disabling disease will assist in devising new treatments and prevention programs to lessen the impact of these diseases in Australia.
New Insights Into Viral Inflammatory Disease Mechanisms And Approaches To Therapy
Funder
National Health and Medical Research Council
Funding Amount
$631,010.00
Summary
This fellowship aims to establish how viruses cause disease, including how they evade the immune response to persist and cause disease for prolonged periods. My vision is that knowing how the virus and the immune system interact to determine disease severity will assist in devising new treatments and prevention programs to lessen the impact of viral diseases in Australia and worldwide.
Mosquito-borne alphaviruses such as Ross River and chikungunya viruses cause widespread epidemics and exert extreme pressure on the public health systems of affected regions. Alphaviruses spreads to joints and triggers a severe disease in those affected. There are no effective treatments or vaccines. The project will investigate virus-host interaction at the bite site. The outcome will be new knowledge to treat infection at the mosquito bite site to prevent joint disease.
Novel Insights Into The Pathobiology Of Alphavirus Infections
Funder
National Health and Medical Research Council
Funding Amount
$827,660.00
Summary
Infections with mosquito-borne viruses are increasing at an alarming rate worldwide. Ross River virus is endemic in parts of Australia, PNG and Pacific islands, while chikungunya virus is distributed globally and causes recurrent pandemics that involve millions of people. These viruses cause severe musculoskeletal disease for several months after infection. This project aims to establish how these viruses interact with the human host to cause disease and may provide a basis for new treatments.
Glycotherapeutics; A New Class Of Treatment For Alphavirus-induced Musculoskeletal Disease
Funder
National Health and Medical Research Council
Funding Amount
$449,868.00
Summary
The hallmark of alphavirus disease is crippling pain and joint arthritis, which often has an extended duration. Currently there is no licenced specific treatment for alphavirus disease and the increasing spread of infection highlights an urgent need for therapeutic intervention strategies. This grant looks at the potential of pentosan polysulfate as a promising drug-repurposing candidate for the treatment of alphavirus-induced arthritis.
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less
Fine-grained Human Action Recognition with Deep Graph Neural Networks. This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and e ....Fine-grained Human Action Recognition with Deep Graph Neural Networks. This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and enabling techniques for more objective human action analysis in many domains such as sports and health. This should provide significant benefits to any application domain involving big and complex spatial-temporal data for finer analytics and better knowledge discovery.Read moreRead less
Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Personalised Online Learning Analytics by Exploring Multilayer Graph Data . Learning analytics is becoming a significant factor in reducing the drop-out rate of students in online learning. The aim of this project is to develop a reliable, robust, real-time analysis system that automatically reveals multilayered relationships, evaluates students' learning performance, and generates a personal study plan through discovery. This project includes the design of novel algorithms for multilayer graph ....Personalised Online Learning Analytics by Exploring Multilayer Graph Data . Learning analytics is becoming a significant factor in reducing the drop-out rate of students in online learning. The aim of this project is to develop a reliable, robust, real-time analysis system that automatically reveals multilayered relationships, evaluates students' learning performance, and generates a personal study plan through discovery. This project includes the design of novel algorithms for multilayer graph processing, pattern recognition in learning activities, learning performance assessment, and personalised study plan recommendations. The success of this project will significantly enhance the success of online education both in Australia and worldwide and; hence, will save time, money and resources for end users.Read moreRead less
Domain-driven information, quality assurance and interoperability for road transport systems. This project addresses the ever increasing need for improved information quality in the area of road transport and urban planning. Public bodies, like State and Federal authorities, require highly accurate sets of personal records as they are central authorities for the identification of individuals. This project will develop techniques for the enhancement of these records.