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Hume G.E.,Queensland Institute of Medical Research | Fowler E.V.,Queensland Institute of Medical Research | Griffths L.R.,Griffth University | Doecke J.D.,CSIRO | And 2 more authors.
Journal of Gastrointestinal and Liver Diseases | Year: 2012

Background & Aims: Peroxisome proliferator-activated receptor (PPAR) γ is a transcription factor, highly expressed in colonic epithelial cells, adipose tissue and macrophages, with an important role in the regulation of inflammatory pathways. The common PPARγ variants C161T and Pro12Ala have recently been associated with Ulcerative Colitis (UC) and an extensive UC phenotype respectively, in a Chinese population. PPARγ Pro12Ala variant homozygotes appear to be protected from the development of Crohn's disease (CD) in European Caucasians. Methods: A case-control study was performed for both variants (CD n=575, UC n=306, Controls n=360) using a polymerase chain reaction (PCR)-restriction fragment length polymorphism analysis in an Australian IBD cohort. A transmission disequilibrium test was also performed using CD trios for the PPARγ C161T variant. Genotype-phenotype analyses were also undertaken. Results: There was no significant difference in genotype distribution data or allele frequency between CD and UC patients and controls. There was no difference in allele transmission for the C161T variant. No significant relationship between the variants and disease location was observed. Conclusions: We were unable to replicate in a Caucasian cohort the recent association between PPARγ C161T and UC or between PPARγ Pro12Ala and an extensive UC phenotype in a Chinese population. There are significant ethnic differences in genetic susceptibility to IBD and its phenotypic expression. Source


Li H.,Jiangxi Normal University | Li H.,Griffth University | Shen F.,University of Electronic Science and Technology of China | Shen C.,University of Adelaide | And 2 more authors.
Pattern Recognition | Year: 2016

In the past decade, linear representation based face recognition has become a very popular research subject in computer vision. This method assumes that faces belonging to one individual reside in a low-dimensional linear subspace. In real-world applications, however, face images usually are of degraded quality due to expression variations, disguises, and partial occlusions. These problems undermine the validity of the subspace assumption and thus the recognition performance deteriorates significantly. In this work, we propose a simple yet effective framework to address the problem. Observing that the linear subspace assumption is more reliable on certain face patches rather than on the holistic face, Probabilistic Patch Representations (PPRs) are randomly generated, according to the Bayesian theory. We then train an ensemble model over the patch-representations by minimizing the empirical risk w.r.t. the "leave-one-out margins", which we term Linear Representation Ensemble (LRE). In the test stage, to handle the non-facial or novel face patterns, we design a simple inference method to dynamically tune the ensemble weights according to the proposed Generic Face Confidence (GFC). Furthermore, to accommodate immense PPR sets, a boosting-like algorithm is also derived. In addition, we theoretically prove two desirable property of the proposed learning methods. We extensively evaluate the proposed methods on four public face dataset, i.e., Yale-B, AR, FRGC and LFW, and the results demonstrate the superiority of both our two methods over many other state-of-the art algorithms, in terms of both recognition accuracy and computational efficiency. © 2015 Elsevier Ltd. Source


Suprun E.V.,Griffth University | Suprun E.V.,Belgorod State Technological University | Stewart R.A.,Griffth University
Construction Innovation | Year: 2015

Purpose: The aim of this study is to explore the current situation in the Russian construction industry and the obstacles, drivers and strategies that affect innovation implementation most significantly. The Russian construction industry is highly conservative and is often criticised for its lack of innovation. Construction firms invest relatively little in innovation adoption, development of new ideas and formal research and development. Design/methodology/approach: This study utilised an extensive literature review followed by a questionnaire survey incorporating some post hoc interviews with 52 experts from the Russian architecture, engineering and construction industry to identify the most significant drivers, enablers, barriers and strategies related to innovation diffusion in construction. Findings: Findings indicated that economic and financial difficulties, as well as inappropriate legislation, are the most significant barriers to innovation. Financial incentives, legislative improvements and the promotion of alternative construction procurement methods were viewed as the most critical strategies to improve the current lacklustre rate of innovation diffusion. Originality/value: While there is anecdotal evidence that the Russian construction industry is lagging in terms of technological advancement, its closed nature means that there is still little reported evidence on what are the main barriers to innovation diffusion in this country. Hence, there is a lack of focus on innovation diffusion rates in different construction sectors, such as building and civil infrastructure and limited consideration on how effectively the research and development sector contributes to innovation. © Emerald Group Publishing Limited. Source


Estivill-Castro V.,Griffth University | Fernandez E.,University Pompeu Fabra | Hexel R.,Griffth University
Australasian Conference on Robotics and Automation, ACRA | Year: 2013

We investigate the current state of the art in terms of formally verifiable protocols to form a formation of robots under unreliable communi- cation. We show that in practical terms, it is possible to obtain more efficient protocols with more appealing properties. Source


Pupunwiwat P.,Griffth University | Stantic B.,Griffth University
Wireless Networks | Year: 2013

Radio Frequency Identification (RFID) uses wireless radio frequency technology to automatically identify tagged objects. Despite the extensive development of the RFID technology in many areas, tags collisions still remain a major problem. This issue is known as the collision problem and can be solved by using anti-collision techniques. Current probabilistic anti-collision approaches suffer from tag starvation due to the inaccurate Backlog estimation and have a low performance in some cases. In this research, we propose a Probabilistic Cluster-Based Technique (PCT) to maximise the performance efficiency during the tag identification process. The PCT approach creates new tag grouping strategies using particular equations, according to the optimal efficiency obtained for a specific number of tags. Through extensive experimentation, we have demonstrated that the proposed concept performs better than the other current state-of-the-art approaches. © 2012 Springer Science+Business Media, LLC. Source

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