Regulation of 3D Cell Migration by Microtubule-Dependent Processes. The overarching aim of this research is to elucidate the molecular mechanisms that cells use to move in 3D environments: a basic biological function essential to development and homeostasis. During these processes, cells interact with their surroundings where they translate biophysical forces into biochemical signals to adapt their shape to move. This requires distinct signalling, controlled in space and time, to regulate the cr ....Regulation of 3D Cell Migration by Microtubule-Dependent Processes. The overarching aim of this research is to elucidate the molecular mechanisms that cells use to move in 3D environments: a basic biological function essential to development and homeostasis. During these processes, cells interact with their surroundings where they translate biophysical forces into biochemical signals to adapt their shape to move. This requires distinct signalling, controlled in space and time, to regulate the crosstalk between organelles and the cytoskeleton. To date, the role of microtubules remains elusive. Using interdisciplinary approaches combining advanced imaging technology with novel cell biology methods, the project aims to uncover fundamental knowledge about how cells interact with their environment.Read moreRead less
A networked robotic telescope array for coincident detection of transient phenomena in the optical, gravitational wave, neutrino and radio spectra. An international collaboration of scientists will employ a global network of rapid response robotic telescopes and detectors to study exotic transient phenomena in the early Universe. Potential spin-offs include the application of novel image analysis techniques for identifying and tracking dangerous space junk.
Testing theories of two-phase fluid flow in porous media through experiment, imaging and modelling. The process underlying oil extraction, groundwater flow and the sequestration of carbon dioxide is that of one fluid pushing another out of the microscopic spaces in porous rocks and soils. Using the latest three-dimensional X-ray microscopes and computing technology, the project will image and model these fluid flows, allowing theories to be tested for the first time.
Multi-modal virtual microscopy for quantitative diagnostic pathology. This project will contribute to the next generation of virtual microscopy systems that provide innovative features capable of significantly increasing the adoption of digital imaging technology throughout the field of diagnostic pathology. These tools will especially contribute to the screening and diagnosis of cervical, lung and bladder cancer.
Imaging the foundation of the nervous system. This Project aims to understand the formation of the neural tube; a fundamental tissue structure that generates the brain and the spinal cord. Using interdisciplinary approaches and exploiting recent advances in transgenic and imaging technologies, the Project expects to reveal the complex interplay of molecular, cellular and mechanical processes that direct neural tissue formation and cell fate specification. Outcomes from the Project include knowle ....Imaging the foundation of the nervous system. This Project aims to understand the formation of the neural tube; a fundamental tissue structure that generates the brain and the spinal cord. Using interdisciplinary approaches and exploiting recent advances in transgenic and imaging technologies, the Project expects to reveal the complex interplay of molecular, cellular and mechanical processes that direct neural tissue formation and cell fate specification. Outcomes from the Project include knowledge of previously intractable developmental processes, training of future scientists and development of international collaborations. This should provide enhanced imaging capacity, a higher quality scientific workforce and position Australia at the forefront of developmental biology.
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Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi ....Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.Read moreRead less
Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imagin ....Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imaging scenarios with high heterogeneity and limited training data. The frameworks will facilitate high-throughput processing for a wide range of microscopy image modalities and biological applications, and potentially become the next generation computational platform to support fundamental research in human biology.Read moreRead less