Methylation Sensitive Genes And The Transition To Allergic Disease: A Twin Study
Funder
National Health and Medical Research Council
Funding Amount
$493,843.00
Summary
Australia has amongst the highest reported prevalence allergic conditions (including asthma) in the world. Despite this, little is known about how these conditions arise. Mounting evidence implicates environmentally induced disruption of the genetic blueprint via a process known as epigenetics. We are combining the strengths of a unique collection of identical twins where one of a pair is sensitive to house dust mite, with cutting edge genomics, to characterise the pathways leading to allergy in ....Australia has amongst the highest reported prevalence allergic conditions (including asthma) in the world. Despite this, little is known about how these conditions arise. Mounting evidence implicates environmentally induced disruption of the genetic blueprint via a process known as epigenetics. We are combining the strengths of a unique collection of identical twins where one of a pair is sensitive to house dust mite, with cutting edge genomics, to characterise the pathways leading to allergy in children.Read moreRead less
Design of dynamic visual signals. Models of the design of visual signals depend heavily upon analyses of static ornaments. Nothing is known about dynamic visual signals. We will use an array of new techniques to tackle this problem for the first time. Motion analyses will define the task faced by the visual system. Sensory limitations will be measured to identify constraints on signal evolution. Digital video playback studies will assess recognition and explain aspects of signal design. Results ....Design of dynamic visual signals. Models of the design of visual signals depend heavily upon analyses of static ornaments. Nothing is known about dynamic visual signals. We will use an array of new techniques to tackle this problem for the first time. Motion analyses will define the task faced by the visual system. Sensory limitations will be measured to identify constraints on signal evolution. Digital video playback studies will assess recognition and explain aspects of signal design. Results will test the generality of principles that have been developed in studies of female mate choice and extend these ideas to address intra-sexual selection operating through opponent assessment.Read moreRead less
Uncovering The Basis Of Inflammatory And Immunodeficiency Diseases
Funder
National Health and Medical Research Council
Funding Amount
$15,718,075.00
Summary
A world-class team from 3 institutions, spanning disciplines of clinical and experimental immunology, therapeutics, signalling and genetics, will identify how immune and inflammatory responses are controlled in both health and disease. The major outcomes of this work will be the generation of new knowledge, concepts and approaches to diagnose, prevent and treat the major human health problems of autoimmune diseases, inflammation, allergy and immunodeficiency.
It is feasible to sequence patient genomes but we need to know more about how genetic variants cause complex disease. We have sequenced genomes from patients with immune deficiency and will test the idea that genetic variation causes consistent changes in particular white blood cells, thus providing a bridge between genomic information and clinical diagnosis. Outcomes will include more accurate diagnosis, better understanding of immunity, and a strategy for using whole genome information.
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less
Investigating B Cell Development, Maintenance And High-affinity Antibody Production By ENU Mutagenesis
Funder
National Health and Medical Research Council
Funding Amount
$408,388.00
Summary
B cells are essential for the protection against infections. This application aims to identify new genes that are crucial for the development or function of B cells and will investigate how mutations in newly discovered genes contribute to defects in the development and function of B cells and the pathogenesis of B cell leukaemia.
Dynamic Trafficking Of Amino Acid Transporters At Synapses And Their Role In Regulating Neurotransmission
Funder
National Health and Medical Research Council
Funding Amount
$421,219.00
Summary
Brain cells release chemical neurotransmitters to activate their neighbours. The most abundant neurotransmitter is glutamate, which mediates most of the communication in the brain. Following release, this neurotransmitter must be rapidly recycled to prevent levels being depleted and neurotransmission failing. The subject of this grant is to understand what molecules and pathways are used to recycle glutamate in the brain, and how its supply is controlled to sustain continual brain activation.
Democratisation of Deep Learning: Neural Architecture Search at Low Cost. The need to manually design Deep Learning-based Neural Networks (DNNs) limits their usage to AI experts and hinders the exploitation of their true potential more broadly, e.g., in farming, humanities. We aim to replace this tedious process through novel AI methods capable of generating DNNs that can perform significantly better and at a lower computational cost than manually designed DNNs. We further expand this idea to so ....Democratisation of Deep Learning: Neural Architecture Search at Low Cost. The need to manually design Deep Learning-based Neural Networks (DNNs) limits their usage to AI experts and hinders the exploitation of their true potential more broadly, e.g., in farming, humanities. We aim to replace this tedious process through novel AI methods capable of generating DNNs that can perform significantly better and at a lower computational cost than manually designed DNNs. We further expand this idea to solve complex real-world problems with both labelled and unlabelled data found in various applications including energy and climate change. The expected outcomes include the novel AI methods, highly trained AI researchers and a number of critical applications that will bring significant benefits to Australia and the world.Read moreRead less
Machine education for trusted multi-skilled evolutionary learners . Transforming data assets into organisational knowledge assets sits in the hands of a few, highly specialised, data scientists. The aim of this research is to design educational instruments to support non-experts to teach artificial intelligence (AI) systems in a similar way to educating human teachers to teach human learners. The significance of the project lies in affording the wider smart, but not necessarily AI expert, commun ....Machine education for trusted multi-skilled evolutionary learners . Transforming data assets into organisational knowledge assets sits in the hands of a few, highly specialised, data scientists. The aim of this research is to design educational instruments to support non-experts to teach artificial intelligence (AI) systems in a similar way to educating human teachers to teach human learners. The significance of the project lies in affording the wider smart, but not necessarily AI expert, community the ability to contribute to growing our knowledge-based society in a safe, transparent and trustworthy manner. Outcomes will include innovative instruments to teach machines, novel knowledge creation, trusted and transparent AI systems, and a new generation of human teachers specialised in educating AI systems.Read moreRead less
Extending fuzzy logic. Fuzzy logic is good for dealing with uncertain data somewhat like people do, and this technique has been used in train braking systems, computer animation etc, but can be slow for problems with large or complex data especially if the data are changing with time. The project will design efficient fuzzy logic algorithms capable of dealing with complex real world problems.