A model for research supervision of international students in engineering and information technology disciplines

A large proportion (over 12 per cent) of international and non-English speaking background (NESB) postgraduate research students enrol in engineering and information technology (IT) programs in Australian universities. They find themselves in an advanced research culture, and are technically and scientifically challenged early in their programs. This is in addition to cultural, social and religious isolation and linguistic barriers they have to contend with. The project team surveyed this cohort at QUT and UWA, on the hypothesis that they face challenges that are more discipline-specific. The results of the survey indicate that existing supervisory frameworks which are limited to linguistic contexts are not fully assisting these students and supervisors to achieve high quality research. The goal of this project is to extend these supervisory frameworks to a holistic model that will address the unique needs and supervisory issues these students face in engineering and IT disciplines. The model will be useable by all other Australian universities.

The supervision tango : perceptions of supervisors of international graduate students in Engineering and IT

This paper, which was part of a larger study, reports on a survey that explored the perceptions of 69 graduate supervisors regarding issues in supervision from three higher education institutions in Australia. Factors that contribute to student success in higher education research degrees are many and diverse, including a complex dance of student factors, supervisor factors, and their supervisory context factors, and those informed by cultural and language differences. Therefore, a complex system approach using Bayesian network modelling was used to explore how student and/or supervisor factors influence the success of culturally and linguistically diverse (CALD) graduate students in Engineering and IT. Findings suggest that key factors include the experience of supervisors in terms of experience with the Australian higher education system, personal cross-cultural experience.

A biomimetic extracellular matrix for cartilage tissue engineering centered on photocurable gelatin, hyaluronic acid and chondroitin sulfate

The development of hydrogels tailored for cartilage tissue engineering has been a research and clinical goal for over a decade. Directing cells towards a chondrogenic phenotype and promoting new matrix formation are significant challenges that must be overcome for the successful application of hydrogels in cartilage tissue therapies. Gelatin-methacrylamide (Gel-MA) hydrogels have shown promise for the repair of some tissues, but they have not been extensively investigated for cartilage tissue engineering. We encapsulated human chondrocytes in gel-MA based hydrogels, and show that with the incorporation of small quantities of photo-crosslinkable hyaluronic acid methacrylate (HA-MA), and to a lesser extent chondroitin sulfate methacrylate (CS-MA), chondrogenesis and mechanical properties can be enhanced. The addition of HA-MA to Gel-MA constructs resulted in more rounded cell morphologies, enhanced chondrogenesis as assessed by gene expression and immunofluorescence, and increased quantity and distribution of the newly synthesised ECM throughout the construct. Consequently, while the compressive moduli of control Gel-MA constructs increased by 26 kPa after 8 weeks culture, constructs with HA-MA and CS-MA increased by 96 kPa. The enhanced chondrogenic differentiation, distribution of ECM, and improved mechanical properties make these materials potential candidates for cartilage tissue engineering applications.

Reimagining school libraries : children's perspectives and new opportunities

The Federal Government’s recent Building the Education Revolution program resulted in, among other features, the creation of over 600 new school libraries in Queensland alone. This paper reports on a component of a research project carried out with students in six primary schools and one secondary school that benefitted from the program, investigating the influences of these new physical environments on learning and teaching. In particular, this paper discusses one missing voice from the design process - that of the students who would be key users of the newly-created spaces in those schools. While opportunities for real involvement in design were minimal for most potential users of the new spaces, students’ imagined possibilities for school libraries, as submitted to the research project, suggest that students could have contributed different perspectives to enhance learning engagement through imaginative design elements. The findings of the project have relevance for teachers and teacher librarians in reconsidering the ways in which the new learning spaces are used as well as informing school designers in planning engaging school facilities. The findings may be extrapolated to the design and planning of general classrooms and other learning environments.

Advanced Engineering of Lipid Metabolism in Nicotiana benthamiana Using a Draft Genome and the V2 Viral Silencing-Suppressor Protein

The transient leaf assay in Nicotiana benthamiana is widely used in plant sciences, with one application being the rapid assembly of complex multigene pathways that produce new fatty acid profiles. This rapid and facile assay would be further improved if it were possible to simultaneously overexpress transgenes while accurately silencing endogenes. Here, we report a draft genome resource for N. benthamiana spanning over 75% of the 3.1 Gb haploid genome. This resource revealed a two-member NbFAD2 family, NbFAD2.1 and NbFAD2.2, and quantitative RT-PCR (qRT-PCR) confirmed their expression in leaves. FAD2 activities were silenced using hairpin RNAi as monitored by qRT-PCR and biochemical assays. Silencing of endogenous FAD2 activities was combined with overexpression of transgenes via the use of the alternative viral silencing-suppressor protein, V2, from Tomato yellow leaf curl virus. We show that V2 permits maximal overexpression of transgenes but, crucially, also allows hairpin RNAi to operate unimpeded. To illustrate the efficacy of the V2-based leaf assay system, endogenous lipids were shunted from the desaturation of 18:1 to elongation reactions beginning with 18:1 as substrate. These V2-based leaf assays produced ~50% more elongated fatty acid products than p19-based assays. Analyses of small RNA populations generated from hairpin RNAi against NbFAD2 confirm that the siRNA population is dominated by 21 and 22 nt species derived from the hairpin. Collectively, these new tools expand the range of uses and possibilities for metabolic engineering in transient leaf assays. © 2012 Naim et al.

De Novo Transcriptome Sequence Assembly and Analysis of RNA Silencing Genes of Nicotiana benthamiana

Background: Nicotiana benthamiana has been widely used for transient gene expression assays and as a model plant in the study of plant-microbe interactions, lipid engineering and RNA silencing pathways. Assembling the sequence of its transcriptome provides information that, in conjunction with the genome sequence, will facilitate gaining insight into the plant's capacity for high-level transient transgene expression, generation of mobile gene silencing signals, and hyper-susceptibility to viral infection. Methodology/Results: RNA-seq libraries from 9 different tissues were deep sequenced and assembled, de novo, into a representation of the transcriptome. The assembly, of16GB of sequence, yielded 237,340 contigs, clustering into 119,014 transcripts (unigenes). Between 80 and 85% of reads from all tissues could be mapped back to the full transcriptome. Approximately 63% of the unigenes exhibited a match to the Solgenomics tomato predicted proteins database. Approximately 94% of the Solgenomics N. benthamiana unigene set (16,024 sequences) matched our unigene set (119,014 sequences). Using homology searches we identified 31 homologues that are involved in RNAi-associated pathways in Arabidopsis thaliana, and show that they possess the domains characteristic of these proteins. Of these genes, the RNA dependent RNA polymerase gene, Rdr1, is transcribed but has a 72 nt insertion in exon1 that would cause premature termination of translation. Dicer-like 3 (DCL3) appears to lack both the DEAD helicase motif and second dsRNA binding motif, and DCL2 and AGO4b have unexpectedly high levels of transcription. Conclusions: The assembled and annotated representation of the transcriptome and list of RNAi-associated sequences are accessible at www.benthgenome.com alongside a draft genome assembly. These genomic resources will be very useful for further study of the developmental, metabolic and defense pathways of N. benthamiana and in understanding the mechanisms behind the features which have made it such a well-used model plant. © 2013 Nakasugi et al.

Description of plant tRNA-derived RNA fragments (tRFs) associated with argonaute and identification of their putative targets

tRNA-derived RNA fragments (tRFs) are 19mer small RNAs that associate with Argonaute (AGO) proteins in humans. However, in plants, it is unknown if tRFs bind with AGO proteins. Here, using public deep sequencing libraries of immunoprecipitated Argonaute proteins (AGO-IP) and bioinformatics approaches, we identified the Arabidopsis thaliana AGO-IP tRFs. Moreover, using three degradome deep sequencing libraries, we identified four putative tRF targets. The expression pattern of tRFs, based on deep sequencing data, was also analyzed under abiotic and biotic stresses. The results obtained here represent a useful starting point for future studies on tRFs in plants. © 2013 Loss-Morais et al.; licensee BioMed Central Ltd.

Construct design for efficient, effective and high-throughput gene silencing in plants

Post-transcriptional silencing of plant genes using anti-sense or co-suppression constructs usually results in only a modest proportion of silenced individuals. Recent work has demonstrated the potential for constructs encoding self-complementary 'hairpin' RNA (hpRNA) to efficiently silence genes. In this study we examine design rules for efficient gene silencing, in terms of both the proportion of independent transgenic plants showing silencing, and the degree of silencing. Using hpRNA constructs containing sense/anti-sense arms ranging from 98 to 853 nt gave efficient silencing in a wide range of plant species, and inclusion of an intron in these constructs had a consistently enhancing effect. Intron-containing constructs (ihpRNA) generally gave 90-100% of independent transgenic plants showing silencing. The degree of silencing with these constructs was much greater than that obtained using either co-suppression or anti-sense constructs. We have made a generic vector, pHANNIBAL, that allows a simple, single PCR product from a gene of interest to be easily converted into a highly effective ihpRNA silencing construct. We have also created a high-throughput vector, pHELLSGATE, that should facilitate the cloning of gene libraries or large numbers of defined genes, such as those in EST collections, using an in vitro recombinase system. This system may facilitate the large-scale determination and discovery of plant gene functions in the same way as RNAi is being used to examine gene function in Caenorhabditis elegans.

Expression patterns of vascular-specific promoters RolC and Sh in transgenic potatoes and their use in engineering PLRV-resistant plants

The expression patterns of GUS fusion constructs driven by the Agrobacterium rhizogenes RolC and the maize Sh (Shrunken: sucrose synthase-1) promoters were examined in transgenic potatoes (cv. Atlantic). RolC drove high-level gene expression in phloem tissue, bundle sheath cells and vascular parenchyma, but not in xylem or non-vascular tissues. Sh expression was exclusively confined to phloem tissue. Potato leafroll luteovirus (PLRV) replicates only in phloem tissues, and we show that when RolC is used to drive expression of the PLRV coat protein gene, virus-resistant lines can be obtained. In contrast, no significant resistance was observed when the Sh promoter was used.

Fraud and its PREY : conceptualising social engineering tactics and its impact on financial literacy outcomes

Financial literacy may not be as effective as previously thought in protecting against fraud victimisation. It does not inoculate investors from persuasion or social engineering tactics used by offenders to secure investment in fraudulent schemes. In fact, recent research indicates that overconfidence in investment knowledge may make individuals more susceptible to fraud. Using boiler room fraud as a case study, this article introduces the PREY (Profiled, Relational, Exploitable and Yielding) model to capture the psychological tactics used by fraud perpetrators to influence the thoughts and decision-making processes of individuals. The PREY model operationalizes the tenets of social engineering and demonstrates how such tactics could be re-engineered to increase the effectiveness of fraud prevention within the financial literacy context.

Large-scale analysis of formations in soccer

Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a "role-representation", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.

Predicting shot locations in tennis using spatiotemporal data

Over the past decade, vision-based tracking systems have been successfully deployed in professional sports such as tennis and cricket for enhanced broadcast visualizations as well as aiding umpiring decisions. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this high quality data for quantitative player performance and prediction has been lacking. In this paper, we present a method which predicts the location of a future shot based on the spatiotemporal parameters of the incoming shots (i.e. shot speed, location, angle and feet location) from such a vision system. Having the ability to accurately predict future short-term events has enormous implications in the area of automatic sports broadcasting in addition to coaching and commentary domains. Using Hawk-Eye data from the 2012 Australian Open Men's draw, we utilize a Dynamic Bayesian Network to model player behaviors and use an online model adaptation method to match the player's behavior to enhance shot predictability. To show the utility of our approach, we analyze the shot predictability of the top 3 players seeds in the tournament (Djokovic, Federer and Nadal) as they played the most amounts of games.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

Swimmer localization from a moving camera

At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.