Unique X-linked familial FSGS with co-segregating heart block disorder is associated with a mutation in the NXF5 gene

Focal segmental glomerulosclerosis (FSGS) is the consequence of a disease process that attacks the kidney's filtering system, causing serious scarring. More than half of FSGS patients develop chronic kidney failure within 10 years, ultimately requiring dialysis or renal transplantation. There are currently several genes known to cause the hereditary forms of FSGS (ACTN4, TRPC6, CD2AP, INF2, MYO1E and NPHS2). This study involves a large, unique, multigenerational Australian pedigree in which FSGS co-segregates with progressive heart block with apparent X-linked recessive inheritance. Through a classical combined approach of linkage and haplotype analysis, we identified a 21.19 cM interval implicated on the X chromosome. We then used a whole exome sequencing approach to identify two mutated genes, NXF5 and ALG13, which are located within this linkage interval. The two mutations NXF5-R113W and ALG13-T141L segregated perfectly with the disease phenotype in the pedigree and were not found in a large healthy control cohort. Analysis using bioinformatics tools predicted the R113W mutation in the NXF5 gene to be deleterious and cellular studies support a role in the stability and localization of the protein suggesting a causative role of this mutation in these co-morbid disorders. Further studies are now required to determine the functional consequence of these novel mutations to development of FSGS and heart block in this pedigree and to determine whether these mutations have implications for more common forms of these diseases in the general population.

A combined method for mitigating sparsity problem in tag recommendation

Tag recommendation is a specific recommendation task for recommending metadata (tag) for a web resource (item) during user annotation process. In this context, sparsity problem refers to situation where tags need to be produced for items with few annotations or for user who tags few items. Most of the state of the art approaches in tag recommendation are rarely evaluated or perform poorly under this situation. This paper presents a combined method for mitigating sparsity problem in tag recommendation by mainly expanding and ranking candidate tags based on similar items’ tags and existing tag ontology. We evaluated the approach on two public social bookmarking datasets. The experiment results show better accuracy for recommendation in sparsity situation over several state of the art methods.

Towards the retrieval of accurate OD matrices from Bluetooth data : lessons learned from 2 years of data

The Bluetooth technology is being increasingly used to track vehicles throughout their trips, within urban networks and across freeway stretches. One important opportunity offered by this type of data is the measurement of Origin-Destination patterns, emerging from the aggregation and clustering of individual trips. In order to obtain accurate estimations, however, a number of issues need to be addressed, through data filtering and correction techniques. These issues mainly stem from the use of the Bluetooth technology amongst drivers, and the physical properties of the Bluetooth sensors themselves. First, not all cars are equipped with discoverable Bluetooth devices and the Bluetooth-enabled vehicles may belong to some small socio-economic groups of users. Second, the Bluetooth datasets include data from various transport modes; such as pedestrian, bicycles, cars, taxi driver, buses and trains. Third, the Bluetooth sensors may fail to detect all of the nearby Bluetooth-enabled vehicles. As a consequence, the exact journey for some vehicles may become a latent pattern that will need to be extracted from the data. Finally, sensors that are in close proximity to each other may have overlapping detection areas, thus making the task of retrieving the correct travelled path even more challenging. The aim of this paper is twofold. We first give a comprehensive overview of the aforementioned issues. Further, we propose a methodology that can be followed, in order to cleanse, correct and aggregate Bluetooth data. We postulate that the methods introduced by this paper are the first crucial steps that need to be followed in order to compute accurate Origin-Destination matrices in urban road networks.

Current practice in nutrition assessment for the management of Parkinson’s disease in Australia and Canada

AIM: To document and compare current practice in nutrition assessment of Parkinson’s disease by dietitians in Australia and Canada in order to identify priority areas for review and development of practice guidelines and direct future research. METHODS: An online survey was distributed to DAA members and PEN subscribers through their email newsletters. The survey captured current practice in the phases of the Nutrition Care Plan. The results of the assessment phase are presented here. RESULTS: Eighty-four dietitians responded. Differences in practice existed in the choice of nutrition screening and assessment tools, including appropriate BMI ranges. Nutrition impact symptoms were commonly assessed, but information about Parkinson’s disease medication interactions were not consistently assessed. CONCLUSIONS: he variation in practice related to the use of screening and assessment methods may result in the identification of different goals for subsequent interventions. Even more practice variation was evident for those items more specific to Parkinson’s disease and may be due to the lack of evidence to guide practice. Further research is required to support decisions for nutrition assessment of Parkinson’s disease.

Residential care workers and residents : the New Zealand story

Aims To describe the nature and size of long-term residential care homes in New Zealand; funding of facilities; and the ethnic and gender composition of residents and residential care workers nationwide. Methods A postal, fax, and email survey of all long-term residential care homes in New Zealand. Results Completed surveys were received from an eligible 845 facilities (response rate: 55%). The majority of these (54%) facilities housed less than 30 residents. Of the 438 (94%) facilities completing the questions about residents’ ethnicity, 432 (99%) housed residents from New Zealand European (Pakeha) descent, 156 (33%) housed at least 1 Maori resident, 71 (15%) at least 1 Pacific (Islands) resident, and 61 (13%) housed at least 1 Asian resident. Facilities employed a range of ethnically diverse staff, with 66% reporting Maori staff. Less than half of all facilities employed Pacific staff (43%) and Asian staff (33%). Registered nursing staff were mainly between 46 and 60 years (47%), and healthcare assistant staff were mostly between 25 and 45 years old (52%). Wide regional variation in the ethnic make up of staff was reported. About half of all staff were reported to have moved within the previous 2 years. Conclusions The age and turnover of the residential care workforce suggests the industry continues to be under threat from staffing shortages. While few ethnic minority residents live in long-term care facilities, staff come from diverse backgrounds, especially in certain regions.

Vision-based estimation of airborne target pseudobearing rate using hidden Markov model filters

The problem of estimating pseudobearing rate information of an airborne target based on measurements from a vision sensor is considered. Novel image speed and heading angle estimators are presented that exploit image morphology, hidden Markov model (HMM) filtering, and relative entropy rate (RER) concepts to allow pseudobearing rate information to be determined before (or whilst) the target track is being estimated from vision information.

Self excitation in equity indices

A "self-exciting" market is one in which the probability of observing a crash increases in response to the occurrence of a crash. It essentially describes cases where the initial crash serves to weaken the system to some extent, making subsequent crashes more likely. This thesis investigates if equity markets possess this property. A self-exciting extension of the well-known jump-based Bates (1996) model is used as the workhorse model for this thesis, and a particle-filtering algorithm is used to facilitate estimation by means of maximum likelihood. The estimation method is developed so that option prices are easily included in the dataset, leading to higher quality estimates. Equilibrium arguments are used to price the risks associated with the time-varying crash probability, and in turn to motivate a risk-neutral system for use in option pricing. The option pricing function for the model is obtained via the application of widely-used Fourier techniques. An application to S&P500 index returns and a panel of S&P500 index option prices reveals evidence of self excitation.

Personalized ontology learning for enhancing text mining effectiveness

Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.

The role of fathers in child feeding : perceived responsibility and predictors of participation

Background The role of fathers in shaping their child’s eating behaviour and weight status through their involvement in child feeding has rarely been studied. This study aims to describe the fathers’ perceived responsibility for child feeding, and to identify predictors of how frequently fathers eat meals with their child. Methods Four hundred and thirty-six Australian fathers (M age=37 years, SD=6 years; 34% university educated) of a 2-5 year old child (M age=3.5 years, SD=0.9 years; 53% boys) were recruited via contact with mothers enrolled in existing research projects or a University staff and student email list. Data were collected from fathers via a self-report questionnaire. Descriptive and hierarchical linear regression analyses were conducted. Results The majority of fathers reported that the family often/mostly ate meals together (79%). Many fathers perceived that they were responsible at least half of the time for feeding their child in terms of organizing meals (42%); amount offered (50%) and deciding if their child eats the ‘right kind of foods’ (60%). Time spent in paid employment was inversely associated with how frequently fathers ate meals with their child (β=-0.23, p<0.001); however, both higher perceived responsibility for child feeding (β=-0.16, p<0.004) and a more involved and positive attitude toward their role as a father (β=0.20, p<0.001) were positively related to how often they ate meals with their child, adjusting for a range of paternal and child covariates, including time spent in paid employment. Conclusions Fathers from a broad range of educational backgrounds appear willing to participate in research studies on child feeding. Most fathers were engaged and involved in family meals and child feeding. This suggests that fathers, like mothers, should be viewed as potential agents for the implementation of positive feeding practices within the family.

A high-throughput panel for identifying clinically relevant mutation profiles in melanoma

Success with molecular-based targeted drugs in the treatment of cancer has ignited extensive research efforts within the field of personalized therapeutics. However, successful application of such therapies is dependent on the presence or absence of mutations within the patient's tumor that can confer clinical efficacy or drug resistance. Building on these findings, we developed a high-throughput mutation panel for the identification of frequently occurring and clinically relevant mutations in melanoma. An extensive literature search and interrogation of the Catalogue of Somatic Mutations in Cancer database identified more than 1,000 melanoma mutations. Applying a filtering strategy to focus on mutations amenable to the development of targeted drugs, we initially screened 120 known mutations in 271 samples using the Sequenom MassARRAY system. A total of 252 mutations were detected in 17 genes, the highest frequency occurred in BRAF (n = 154, 57%), NRAS (n = 55, 20%), CDK4 (n = 8, 3%), PTK2B (n = 7, 2.5%), and ERBB4 (n = 5, 2%). Based on this initial discovery screen, a total of 46 assays interrogating 39 mutations in 20 genes were designed to develop a melanoma-specific panel. These assays were distributed in multiplexes over 8 wells using strict assay design parameters optimized for sensitive mutation detection. The final melanoma-specific mutation panel is a cost effective, sensitive, high-throughput approach for identifying mutations of clinical relevance to molecular-based therapeutics for the treatment of melanoma. When used in a clinical research setting, the panel may rapidly and accurately identify potentially effective treatment strategies using novel or existing molecularly targeted drugs

Filter and control performance bounds in the presence of model uncertainties with aerospace applications

This thesis establishes performance properties for approximate filters and controllers that are designed on the basis of approximate dynamic system representations. These performance properties provide a theoretical justification for the widespread application of approximate filters and controllers in the common situation where system models are not known with complete certainty. This research also provides useful tools for approximate filter designs, which are applied to hybrid filtering of uncertain nonlinear systems. As a contribution towards applications, this thesis also investigates air traffic separation control in the presence of measurement uncertainties.

A social matching system : using implicit and explicit information for personalized recommendation in online dating service

Online dating websites enable a specific form of social networking and their efficiency can be increased by supporting proactive recommendations based on participants' preferences with the use of data mining. This research develops two-way recommendation methods for people-to-people recommendation for large online social networks such as online dating networks. This research discovers the characteristics of the online dating networks and utilises these characteristics in developing efficient people-to-people recommendation methods. Methods developed support improved recommendation accuracy, can handle data sparsity that often comes with large data sets and are scalable for handling online networks with a large number of users.

Policing Transnational Organized Crime and Corruption : Exploring the Role of Communication Interception Technology

Technology has advanced in such a manner that the world can now communicate in means previously never thought possible. These new technologies have not been overlooked by transnational organized crime groups and networks of corruption, and have been exploited for criminal success. This text explores the use of communication interception technology (CIT), such as phone taps or email interception, and its potential to cause serious disruption to these criminal enterprises. Exploring the placement of communication interception technology within differing policing frameworks, and how they integrate in a practical manner, the authors demonstrate that CIT is best placed within a proactive, intelligence-led policing framework. They also indicate that if law enforcement agencies in Western countries are serious about fighting transnational organized crime and combating corruption, there is a need to re-evaluate the constraints of interception technology, and the sceptical culture that surrounds intelligence in policing. Policing Transnational Organized Crime and Corruption will appeal to scholars of Law, Criminal Justice and Police Science as well as intelligence analysts and police and security intelligence professionals.

Publicly verifiable ciphertexts

In many applications, where encrypted traffic flows from an open (public) domain to a protected (private) domain, there exists a gateway that bridges the two domains and faithfully forwards the incoming traffic to the receiver. We observe that indistinguishability against (adaptive) chosen-ciphertext attacks (IND-CCA), which is a mandatory goal in face of active attacks in a public domain, can be essentially relaxed to indistinguishability against chosen-plaintext attacks (IND-CPA) for ciphertexts once they pass the gateway that acts as an IND-CCA/CPA filter by first checking the validity of an incoming IND-CCA ciphertext, then transforming it (if valid) into an IND-CPA ciphertext, and forwarding the latter to the recipient in the private domain. “Non-trivial filtering'' can result in reduced decryption costs on the receivers' side. We identify a class of encryption schemes with publicly verifiable ciphertexts that admit generic constructions of (non-trivial) IND-CCA/CPA filters. These schemes are characterized by existence of public algorithms that can distinguish between valid and invalid ciphertexts. To this end, we formally define (non-trivial) public verifiability of ciphertexts for general encryption schemes, key encapsulation mechanisms, and hybrid encryption schemes, encompassing public-key, identity-based, and tag-based encryption flavours. We further analyze the security impact of public verifiability and discuss generic transformations and concrete constructions that enjoy this property.

The multi-platform user engagement and data visualisation tool

The ability to identify and assess user engagement with transmedia productions is vital to the success of individual projects and the sustainability of this mode of media production as a whole. It is essential that industry players have access to tools and methodologies that offer the most complete and accurate picture of how audiences/users engage with their productions and which assets generate the most valuable returns of investment. Drawing upon research conducted with Hoodlum Entertainment, a Brisbane-based transmedia producer, this project involved an initial assessment of the way engagement tends to be understood, why standard web analytics tools are ill-suited to measuring it, how a customised tool could offer solutions, and why this question of measuring engagement is so vital to the future of transmedia as a sustainable industry. Working with data provided by Hoodlum Entertainment and Foxtel Marketing, the outcome of the study was a prototype for a custom data visualisation tool that allowed access, manipulation and presentation of user engagement data, both historic and predictive. The prototyped interfaces demonstrate how the visualization tool would collect and organise data specific to multiplatform projects by aggregating data across a number of platform reporting tools. Such a tool is designed to encompass not only platforms developed by the transmedia producer but also sites developed by fans. This visualisation tool accounted for multiplatform experience projects whose top level is comprised of people, platforms and content. People include characters, actors, audience, distributors and creators. Platforms include television, Facebook and other relevant social networks, literature, cinema and other media that might be included in the multiplatform experience. Content refers to discreet media texts employed within the platform, such as tweet, a You Tube video, a Facebook post, an email, a television episode, etc. Core content is produced by the creators’ multiplatform experiences to advance the narrative, while complimentary content generated by audience members offers further contributions to the experience. Equally important is the timing with which the components of the experience are introduced and how they interact with and impact upon each other. Being able to combine, filter and sort these elements in multiple ways we can better understand the value of certain components of a project. It also offers insights into the relationship between the timing of the release of components and user activity associated with them, which further highlights the efficacy (or, indeed, failure) of assets as catalysts for engagement. In collaboration with Hoodlum we have developed a number of design scenarios experimenting with the ways in which data can be visualised and manipulated to tell a more refined story about the value of user engagement with certain project components and activities. This experimentation will serve as the basis for future research.