Identification And Characterisation Of Novel Genes For Congenital Cataract
Funder
National Health and Medical Research Council
Funding Amount
$432,750.00
Summary
Cataracts are the leading cause of blindness worldwide. The term describes a clouding of the lens which may lead to visual impairment. Congenital cataracts (present at birth) are less common than age-related cataract but the lifelong impact on vision can be severe, with a third of patients remaining legally blind. Late complications such as aphakic glaucoma may be blinding. We have shown that congenital cataracts are often inherited and have performed a population-based study in South-Eastern Au ....Cataracts are the leading cause of blindness worldwide. The term describes a clouding of the lens which may lead to visual impairment. Congenital cataracts (present at birth) are less common than age-related cataract but the lifelong impact on vision can be severe, with a third of patients remaining legally blind. Late complications such as aphakic glaucoma may be blinding. We have shown that congenital cataracts are often inherited and have performed a population-based study in South-Eastern Australia over the past 5 years to determine the causative genes. A large number of families have been involved in the study and solid progress has been made in identifying mutations in cataract genes and understanding what effect these may have on the patient's prognosis. We have recently identified a new gene in a large Australian family with a syndrome of cataract, mental retardation and teeth problems. This syndrome, known as Nance-Horan syndrome was originally described in Australia 30 years ago and we have worked with the original family to find the exact gene responsible. We already know that this gene causes the same syndrome in other families and in this project we will examine whether it can cause cataract without the other features or mental retardation without cataract. We will perform a series of experiments to learn what this gene does and how it causes the disease. We have also selected 3 other very interesting families with congenital cataracts for further study as we either know already or strongly suspect that they will enable us to identify further new genes for cataract, and in one case mental retardation. Our work in other diseases indicates that understanding the genes in severe young onset cases can give valuable clues to the causes of age-related forms and may in the future enable new ways to prevent and treat the commonest cause of worldwide blindness.Read moreRead less
Differential Evolution Framework for Intelligent Charging Scheduling. Smart charging scheduling is a vital challenge as dynamic environment with traffic networks and various unexpected issues. This project aims to develop a differential evolution framework for intelligent charging scheduling. The framework consists of a comprehensive charging scheduling model with various road networks and factors. The project outcomes include a distributed evolutionary computation framework, differential evolut ....Differential Evolution Framework for Intelligent Charging Scheduling. Smart charging scheduling is a vital challenge as dynamic environment with traffic networks and various unexpected issues. This project aims to develop a differential evolution framework for intelligent charging scheduling. The framework consists of a comprehensive charging scheduling model with various road networks and factors. The project outcomes include a distributed evolutionary computation framework, differential evolution algorithms, and cooperative co-evolutionary strategies. The outcome results will be demonstrated by practical evaluations over public datasets and comparisons to related works. The project is beneficial to the nation in both theory of artificial intelligence techniques and applications of real transport systems.Read moreRead less
A Data-Centric Mobile Edge Platform for Resilient Logistics & Supply Chain. This project aims to develop a secure mobile edge computing platform for resilient logistic and supply chain management. It consists of easy-used functions that help businesses realise low latency, high reliability, low cost, and high security in their logistics and supply chain system. To cope with the vast generated application data, we invent new data replication, placement, and deduplication techniques to optimise th ....A Data-Centric Mobile Edge Platform for Resilient Logistics & Supply Chain. This project aims to develop a secure mobile edge computing platform for resilient logistic and supply chain management. It consists of easy-used functions that help businesses realise low latency, high reliability, low cost, and high security in their logistics and supply chain system. To cope with the vast generated application data, we invent new data replication, placement, and deduplication techniques to optimise the mobile edge computing platform from the computation, storage, and network aspects. The invented mobile edge computing platform will enable more intelligent business applications for various industries, e.g., IT, manufacturing, and media, to appear, thus benefiting both the economy of Australia.Read moreRead less
Efficient and effective methods for classifying massive time series data. This project aims to transform the theory and practice of time series classification. The current state of the art cannot handle the massive numbers of time series that describe many critical problems facing humanity, such as disease transmission and climate change. This project seeks to develop methods that can analyse dynamic processes at global scale, delivering the most accurate classifiers feasible within a given comp ....Efficient and effective methods for classifying massive time series data. This project aims to transform the theory and practice of time series classification. The current state of the art cannot handle the massive numbers of time series that describe many critical problems facing humanity, such as disease transmission and climate change. This project seeks to develop methods that can analyse dynamic processes at global scale, delivering the most accurate classifiers feasible within a given computational budget. Expected outcomes of this project include efficient, effective and broadly applicable time series classification technologies. This should provide significant benefits to myriad sectors, transforming data science for time series problems and supporting innovation in industry, commerce and government.Read moreRead less
XML Views of Relational Databases: Semantics and Update Problems. XML is the standard for representing, publishing and exchanging data over the Internet and relational database is the dominant technology for data management. Updating XML views over relational data is fundamental to bring these two technologies together to serve Internet-based applications. Australia has been a leading country in both developing and applying internet technologies. The theoretic outcomes of this project will contr ....XML Views of Relational Databases: Semantics and Update Problems. XML is the standard for representing, publishing and exchanging data over the Internet and relational database is the dominant technology for data management. Updating XML views over relational data is fundamental to bring these two technologies together to serve Internet-based applications. Australia has been a leading country in both developing and applying internet technologies. The theoretic outcomes of this project will contribute to the advance in database and web research communities and establish us as an internationally leading group in this research area. The technological outcomes will help organisations in Australia effectively and efficiently conduct e-Business on the Internet. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100165
Funder
Australian Research Council
Funding Amount
$443,847.00
Summary
Evolving privacy and utility in data storage and publishing. This project aims to develop a distributed evolutionary computation-based framework to optimize data privacy and utility in distributed database systems. It intends to synchronously solve the conflicting challenges of privacy preservation and utility maintenance in multi-objective, dynamic, and multitasking scenarios. Expected outcomes include a new computation framework as a service and freely available distributed computation models, ....Evolving privacy and utility in data storage and publishing. This project aims to develop a distributed evolutionary computation-based framework to optimize data privacy and utility in distributed database systems. It intends to synchronously solve the conflicting challenges of privacy preservation and utility maintenance in multi-objective, dynamic, and multitasking scenarios. Expected outcomes include a new computation framework as a service and freely available distributed computation models, evolutionary algorithms, and knowledge-transfer strategies. Anticipated benefits include theoretical contributions to artificial intelligence, cyber security, distributed computation, and a service to eliminate data owners’ privacy concerns while guaranteeing the value of data in further utilization.Read moreRead less
Fast effective clustering technologies for highly dynamic massive networks. Clustering is a fundamental data mining and analysis task. In an interconnected evolving world, friendships and information flows are modelled as large dynamic networks. Structural clustering and correlation clustering are important and well-studied approaches for static networks; for evolving networks, where links appear and disappear over time, we lack efficient techniques. Anticipated outcomes are new practical cluste ....Fast effective clustering technologies for highly dynamic massive networks. Clustering is a fundamental data mining and analysis task. In an interconnected evolving world, friendships and information flows are modelled as large dynamic networks. Structural clustering and correlation clustering are important and well-studied approaches for static networks; for evolving networks, where links appear and disappear over time, we lack efficient techniques. Anticipated outcomes are new practical clustering algorithms for dynamic networks – with performance guarantees of efficiency and clustering quality – and prototype software, guiding us to pick a good clustering. Expected benefits include better understanding of spread in evolving social networks, accelerating the software testing cycle, and improved topic detection.Read moreRead less
Cost-effective and Reliable Edge Caching for Software Vendors. This project aims to deliver a suite of models and techniques for cost-effective and reliable data caching in the multi-access edge computing (MEC) environment facilitated by 5G mobile network. MEC offers great promises for rapidly advancing mobile and IoT applications in various domains in Australia, e.g., smart cities, remote medical services, advanced manufacturing, etc. Combining graph analytics, optimisation techniques and game ....Cost-effective and Reliable Edge Caching for Software Vendors. This project aims to deliver a suite of models and techniques for cost-effective and reliable data caching in the multi-access edge computing (MEC) environment facilitated by 5G mobile network. MEC offers great promises for rapidly advancing mobile and IoT applications in various domains in Australia, e.g., smart cities, remote medical services, advanced manufacturing, etc. Combining graph analytics, optimisation techniques and game theory, this project tackles the new challenges in the placement, update and adaptation of edge data faced by software vendors embracing 5G. The outcomes can ease software vendors' cost and security concerns during the transition from 4G to 5G, and significantly promote the wave of 5G innovation in Australia.Read moreRead less
Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research ....Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research will include new data privacy models, new privacy-preserving data mining algorithms, and a prototype of cloud data mining software. These will help businesses cut costs for data mining and privacy protection, and provide significant benefits toward helping Australia achieve its national cyber security strategy and potentially provide economic impact from commercialisation of new software technology for the industry partner.Read moreRead less
Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment. This project aims to alleviate cyberattacks which are increasingly being crafted to attack software vulnerabilities and weaknesses by utilising advanced knowledge graphs and deep learning techniques. This project expects to construct an innovative software vulnerability knowledge graph and develop advanced graph-based algorithms and models. Expected outcomes of this project include the enhanced capacity to defend agains ....Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment. This project aims to alleviate cyberattacks which are increasingly being crafted to attack software vulnerabilities and weaknesses by utilising advanced knowledge graphs and deep learning techniques. This project expects to construct an innovative software vulnerability knowledge graph and develop advanced graph-based algorithms and models. Expected outcomes of this project include the enhanced capacity to defend against cyberattacks for both organisations and individuals in Australia and beyond, theory development in graph theory, refined graph neural network models and improved graph transfer learning algorithms.Read moreRead less