RadioGenes2: Modelling complex biomolecular interactions in radiated tumours: Towards understanding the genesis of therapeutic radioresistance. About 45% of bladder cancer patients require radiotherapy or surgery. Radiotherapy has a failure rate of ~50%. Surgery (bladder removal) diminishes quality of life considerably. Modelling complex gene interactions in radiated cancer cells will provide crucial knowledge on the molecular genesis of radiotherapy-resistance of tumours. Our findings will prov ....RadioGenes2: Modelling complex biomolecular interactions in radiated tumours: Towards understanding the genesis of therapeutic radioresistance. About 45% of bladder cancer patients require radiotherapy or surgery. Radiotherapy has a failure rate of ~50%. Surgery (bladder removal) diminishes quality of life considerably. Modelling complex gene interactions in radiated cancer cells will provide crucial knowledge on the molecular genesis of radiotherapy-resistance of tumours. Our findings will provide: (i) an accurate mathematical/computational model for diagnosing radiosensitivity; (ii) further insights to be applied in the pharmaceutical sector such as the discovery of novel molecular targets that have the potential to increase radiotherapy success ratios; (iii) a holistic modelling technique applicable to a larger diversity of tumours.Read moreRead less
Characterisation and improvement of radiation beams used for radiotherapy of small lesions. This project aims to characterise the radiation dose from a medical linear accelerator after the beam has been shaped by a mini-multileaf collimator. The characterisation will be achieved through a combination of computer simulations and experimental investigation of the beam using the technique of three-dimensional gel dosimetry. When the dosage characteristics are known, techniques will be developed to ....Characterisation and improvement of radiation beams used for radiotherapy of small lesions. This project aims to characterise the radiation dose from a medical linear accelerator after the beam has been shaped by a mini-multileaf collimator. The characterisation will be achieved through a combination of computer simulations and experimental investigation of the beam using the technique of three-dimensional gel dosimetry. When the dosage characteristics are known, techniques will be developed to improve radiotherapy treatments in patients with small lesions with sizes of up to a few centimetres. This will lead to an improved outcome for some cancer patients.Read moreRead less
A Phase III Trial Comparing Adjuvant Versus Salvage Radiotherapy For High Risk Patients Post Radical Prostatectomy
Funder
National Health and Medical Research Council
Funding Amount
$819,138.00
Summary
About half of all patients Treated with an operation to remove their prostate cancer have a high chance of the cancer coming back. Giving immediate radiotherapy to all patients will improve cure rates but does not benefit all men and can cause significant side effects. This study explores whether it is safe to wait and only give radiotherapy when there is a rising PSA after surgery indicating active cancer. A total of 470 men from Australasia will enter this study comparing the two approaches.
Correction of the Effects of Gradient Field Nonlinearity in Magnetic Resonance Imaging - A Complete 3-Dimensional Approach. The outcomes of this research will have direct benefits to various areas of diagnostic and interventional medicine especially in neurological diseases such as Alzheimer's disease, stroke, multiple sclerosis or brain tumors. The techniques developed in this project will in general enable MRI to provide a higher quality service to the community.
Robotics for zero-tillage agriculture. This project will develop small agricultural robots to increase broad-acre crop production and reduce environmental impact. These robots will have advanced navigation capability, will cooperate to cover large areas and resupply themselves, while causing less soil damage and applying herbicide more intelligently.
Lifelong robotic navigation using visual perception. Service robots are becoming a major part of our working and personal environments, in much the same way as personal computers already have. This project will develop new methods of practical and useful robot navigation that will enable Australia's industries and services to remain internationally competitive.
Discovery Early Career Researcher Award - Grant ID: DE120100995
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Visual navigation for sunny summer days and stormy winter nights. This project will develop innovative techniques for camera-based navigation that recognise locations under a wide range of environmental conditions caused by day-night cycles, weather and seasonal change. These techniques will enable the widespread use of cheap and lightweight cameras in robot and personal navigation systems.
Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently ....Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL210100156
Funder
Australian Research Council
Funding Amount
$2,716,041.00
Summary
Re-Evolving Nature’s Best Positioning Systems for People and Their Machines. The aim is to develop next-generation positioning capabilities that reduce Australia’s increasingly risky strategic reliance on vulnerable GPS satellites owned by other countries, and that enable transformation of Australia’s most important sectors through enhanced automation and robotics. Our approach re-evolves, re-engineers, and re-combines the best performing and best understood components of nature’s best positioni ....Re-Evolving Nature’s Best Positioning Systems for People and Their Machines. The aim is to develop next-generation positioning capabilities that reduce Australia’s increasingly risky strategic reliance on vulnerable GPS satellites owned by other countries, and that enable transformation of Australia’s most important sectors through enhanced automation and robotics. Our approach re-evolves, re-engineers, and re-combines the best performing and best understood components of nature’s best positioning systems with new technological advances in sensing and computation. The expected outcomes are high-performance positioning systems that improve the competitiveness of Australia’s leading industries and provide the positioning reliability required by the defence sector to keep Australia secure.Read moreRead less
Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility ....Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility services delivery and lower customer utility bills. Project benefits include enabling utilities to better manage and plan resources in the information age, while empowering customers with real-time water end-use data and behaviour changing consumption recommendations.Read moreRead less