Using Stem Cells And Bioengineered Scaffolds To Promote Regeneration Following Necrotic Brain Injury
Funder
National Health and Medical Research Council
Funding Amount
$710,857.00
Summary
A number of injuries, including stroke, result in tissue loss. Consequently promoting repair will require restoration of tissue structure, replacement cells and a supportive environment to promote integration of these new cells. This study will engineer and develop novel scaffolds that can replace tissue whilst additionally providing physical and chemical support for newly implanted stem cells. This work will be conducted in an animal model of stroke.
Standardising Protocols For The Differentiation And Integration Of Human Pluripotent Stem Cell-derived Neural Transplants In Parkinson's Disease
Funder
National Health and Medical Research Council
Funding Amount
$987,664.00
Summary
Clinical trials have shown that transplanting dopamine neurons (specific nerve cells) into the brain of Parkinson’s disease patients can improve symptoms. Trials use fetal tissue for implantation, which is unsustainable and highly variable. This proposal will examine stem cells as an alternative. We will establish a reliable protocol to instruct human stem cells to become dopamine neurons, develop methods to select these cells and, examine the integration of these transplanted cells in the brain
Understanding Universal Immunity To Influenza Viruses
Funder
National Health and Medical Research Council
Funding Amount
$687,975.00
Summary
A/Prof Kedzierska’s work combines cutting-edge basic research with unique clinical studies to define how to generate protective immunity against the pandemic and newly emerged influenza viruses. This research will identify key factors that drive the severe and fatal influenza disease in high-risk groups, including the young, elderly, pregnant women and Indigenous Austraians. Findings on the optimal human immunity to influenza viruses will be applicable to other infectious diseases and cancers.
Centre For Research Excellence In Stroke Rehabilitation And Brain Recovery
Funder
National Health and Medical Research Council
Funding Amount
$2,595,746.00
Summary
The Centre of Research Excellence in Stroke Rehabilitation and Brain Recovery will transform the stroke research and practice landscape in Australia, and accelerate the development of new interventions strongly supported by neuroscience. This unique collaboration will improve patient selection and rehabilitation research methods, create a training culture for the next generation of rehabilitation researchers and effectively implement proven cost effective interventions for Australians.
A computational theory of strategic deception. This artificial project aims to develop a theory of strategic deception and test it through an Artificial Intelligence model. The project will combine computational Theory-of-Mind concepts with recent scientific findings to allow us to better understand whether and how intelligent technologies of the future might deceive humans. The findings will provide new insights into how Artificial Intelligence technologies of the future will impact applied are ....A computational theory of strategic deception. This artificial project aims to develop a theory of strategic deception and test it through an Artificial Intelligence model. The project will combine computational Theory-of-Mind concepts with recent scientific findings to allow us to better understand whether and how intelligent technologies of the future might deceive humans. The findings will provide new insights into how Artificial Intelligence technologies of the future will impact applied areas of computing, where simulating advanced forms of social behaviour and cognition, including deception, will become increasingly significant.Read moreRead less
Active Visual Navigation in an Unexplored Environment. This project will develop a new method for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout, and navigating as an active observer in which the predictions informs actions. The outcome will be robotic agents capable of effective and efficient navigation and operation in previous ....Active Visual Navigation in an Unexplored Environment. This project will develop a new method for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout, and navigating as an active observer in which the predictions informs actions. The outcome will be robotic agents capable of effective and efficient navigation and operation in previously unseen environments, and the ability to control such agents with more human-like instructions. Such capabilities are desirable, and in some cases essential, for autonomous robots in a variety of important application areas including automated warehousing and high-level control of autonomous vehicles. Read moreRead less
Consumer-centric Adaptive Quality-assured Cloud Services Brokerage. This project aims to create a consumer-centric brokerage solution for managing cloud service provision. This aims to ensure consumers can receive cloud services that best satisfy their quality requirements in the most cost-effective manner in the presence of constant changes in performance and cost of cloud services available from different cloud providers. The intended outcome is a novel cloud brokerage framework and the associ ....Consumer-centric Adaptive Quality-assured Cloud Services Brokerage. This project aims to create a consumer-centric brokerage solution for managing cloud service provision. This aims to ensure consumers can receive cloud services that best satisfy their quality requirements in the most cost-effective manner in the presence of constant changes in performance and cost of cloud services available from different cloud providers. The intended outcome is a novel cloud brokerage framework and the associated mechanisms for proactive identification, prevention and mediation of cloud service-level agreement violation in the best interest of the cloud users. The findings of the project will support enterprises across Australian economy in shifting from traditional IT systems to the ubiquitous cloud computing that has already been recognised as a core enabling IT technology in addressing the societal challenge of lifting productivity and economic growth in a rapidly changing world economy.Read moreRead less
Decision making for lifetime affordable and tenable city housing. This project will study home buying decisions and outcomes and use this to provide new insights into housing affordability and liveability. The project will develop an innovative software tool for Australia's home buyers to explore affordability and liveability during home buying, and agent-based modelling of scenarios for urban development futures.
Enabling small businesses to more cost-effectively use big data on cloud computing platforms. This project will invent a new generic cost model for managing big data in cloud computing. This model will enable agent-based, innovative data management technologies to reduce the cost of storage, computation and bandwidth consumption in the cloud. Outcomes will enable small businesses to use big data in cloud computing more cost effectively.
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