Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning

Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

Comparison of sustainable community rating tools in Australia

The community is the basic unit of urban development, and appropriate assessment tools are needed for communities to evaluate and facilitate decision making concerning sustainable community development and reduce the detrimental effects of urban community actions on the environment. Existing research into sustainable community rating tools focuses primarily on those that are internationally recognized to describe their advantages and future challenges. However, the differences between rating tools due to different regional conditions, situations and characteristics have yet to be addressed. In doing this, this paper examines three sustainable community rating tools in Australia, namely Green Star-Communities PILOT, EnviroDevelopment and VicUrban Sustainability Charter (Master Planned Community Assessment Tool). In order to identify their similarities, differences and advantages these are compared in terms of sustainability coverage, prerequisites, adaptation to locality, scoring and weighting, participation, presentation of results, and application process. These results provide the stakeholders of sustainable community development projects with a better understanding of the available rating tools in Australia and assist with evaluation and decision making.

Technology-mediated tools for shaping experiences in urban commuter trains

This thesis presents the results of a study into ways that technology can be appropriated and designed to support urban rail commuters in their daily journeys. The study evaluated a mobile application prototype deployed along the Brisbane passenger rail network. This prototype was designed to support social interaction between passengers sharing the same trains. This thesis provides a step forward in showing the relevance of increasingly creating solutions that contribute to a more enjoyable and attractive public transport service.

A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.

Potential uses of Bayesian networks as tools for synthesis of systematic reviews of complex interventions

Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially useful for synthesising evidence or belief concerning a complex intervention, assessing the sensitivity of outcomes to different situations or contextual frameworks and framing decision problems that involve alternative types of intervention. Bayesian networks are useful extensions to logic maps when initiating a review or to facilitate synthesis and bridge the gap between evidence acquisition and decision-making. Formal elicitation techniques allow development of BNs on the basis of expert opinion. Such applications are useful alternatives to ‘empty’ reviews, which identify knowledge gaps but fail to support decision-making. Where review evidence exists, it can inform the development of a BN. We illustrate the construction of a BN using a motivating example that demonstrates how BNs can ensure coherence, transparently structure the problem addressed by a complex intervention and assess sensitivity to context, all of which are critical components of robust reviews of complex interventions. We suggest that BNs should be utilised to routinely synthesise reviews of complex interventions or empty reviews where decisions must be made despite poor evidence.

Collaborative learning from distributed places and spaces: Exploring the use of virtual collaborative tools for local and global student teamwork projects

Educating responsive graduates. Graduate competencies include reliability, communication skills and ability to work in teams. Students using Collaborative technologies adapt to a new working environment, working in teams and using collaborative technologies for learning. Collaborative Technologies were used not simply for delivery of learning but innovatively to supplement and enrich research-based learning, providing a space for active engagement and interaction with resources and team. This promotes the development of responsive ‘intellectual producers’, able to effectively communicate, collaborate and negotiate in complex work environments. Exploiting technologies. Students use ‘new’ technologies to work collaboratively, allowing them to experience the reality of distributed workplaces incorporating both flexibility and ‘real’ time responsiveness. Students are responsible and accountable for individual and group work contributions in a highly transparent and readily accessible workspace. This experience provides a model of an effective learning tool. Navigating uncertainty and complexity. Collaborative technologies allows students to develop critical thinking and reflective skills as they develop a group product. In this forum students build resilience by taking ownership and managing group work, and navigating the uncertainties and complexities of group dynamics as they constructively and professionally engage in team dialogue and learn to focus on the goal of the team task.

Development of molecular tools for the improvement of transgene expression in sugar cane

Biotechnology has the potential to improve sugar cane, one of the world's major crops for food and fuel. This research describes the detailed characterisation of introns and their potential for enhancing transgene expression in sugar cane via intron-mediated enhancement (IME). IME is a phenomenon whereby an intron enhances gene expression from a promoter. Current knowledge on the mechanism of IME or its potential for enhancing gene expression in sugar cane is limited. A better understanding of the factors responsible for IME will help develop new molecular tools that facilitate high levels of constitutive and tissue-specific gene expression in this crop.

Pedagogical tools designed to promote engagement and active learning in the biomedical sciences and allied health disciplines

Perceiving students, science students especially, as mere consumers of facts and information belies the importance of a need to engage them with the principles underlying those facts and is counter-intuitive to the facilitation of knowledge and understanding. Traditional didactic lecture approaches need a re-think if student classroom engagement and active learning are to be valued over fact memorisation and fact recall. In our undergraduate biomedical science programs across Years 1, 2 and 3 in the Faculty of Health at QUT, we have developed an authentic learning model with an embedded suite of pedagogical strategies that foster classroom engagement and allow for active learning in the sub-discipline area of medical bacteriology. The suite of pedagogical tools we have developed have been designed to enable their translation, with appropriate fine-tuning, to most biomedical and allied health discipline teaching and learning contexts. Indeed, aspects of the pedagogy have been successfully translated to the nursing microbiology study stream at QUT. The aims underpinning the pedagogy are for our students to: (1) Connect scientific theory with scientific practice in a more direct and authentic way, (2) Construct factual knowledge and facilitate a deeper understanding, and (3) Develop and refine their higher order flexible thinking and problem solving skills, both semi-independently and independently. The mindset and role of the teaching staff is critical to this approach since for the strategy to be successful tertiary teachers need to abandon traditional instructional modalities based on one-way information delivery. Face-to-face classroom interactions between students and lecturer enable realisation of pedagogical aims (1), (2) and (3). The strategy we have adopted encourages teachers to view themselves more as expert guides in what is very much a student-focused process of scientific exploration and learning. Specific pedagogical strategies embedded in the authentic learning model we have developed include: (i) interactive lecture-tutorial hybrids or lectorials featuring teacher role-plays as well as class-level question-and-answer sessions, (ii) inclusion of “dry” laboratory activities during lectorials to prepare students for the wet laboratory to follow, (iii) real-world problem-solving exercises conducted during both lectorials and wet laboratory sessions, and (iv) designing class activities and formative assessments that probe a student’s higher order flexible thinking skills. Flexible thinking in this context encompasses analytical, critical, deductive, scientific and professional thinking modes. The strategic approach outlined above is designed to provide multiple opportunities for students to apply principles flexibly according to a given situation or context, to adapt methods of inquiry strategically, to go beyond mechanical application of formulaic approaches, and to as much as possible self-appraise their own thinking and problem solving. The pedagogical tools have been developed within both workplace (real world) and theoretical frameworks. The philosophical core of the pedagogy is a coherent pathway of teaching and learning which we, and many of our students, believe is more conducive to student engagement and active learning in the classroom. Qualitative and quantitative data derived from online and hardcopy evaluations, solicited and unsolicited student and graduate feedback, anecdotal evidence as well as peer review indicate that: (i) our students are engaging with the pedagogy, (ii) a constructivist, authentic-learning approach promotes active learning, and (iii) students are better prepared for workplace transition.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

Design and Development of an In-Vehicle Human Machine Interface to Improve Fuel Efficiency & Safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behaviour change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, this research seeks to use human factors related theories and practices to inform the design and evaluation of an in-vehicle Human Machine Interface (HMI) providing real-time driver feedback with the aim of improving both fuel efficiency and safety.

Trait components provide tools to dissect the genetic susceptibility of migraine

The commonly used "end diagnosis" phenotype that is adopted in linkage and association studies of complex traits is likely to represent an oversimplified model of the genetic background of a disease. This is also likely to be the case for common types of migraine, for which no convincingly associated genetic variants have been reported. In headache disorders, most genetic studies have used end diagnoses of the International Headache Society (IHS) classification as phenotypes. Here, we introduce an alternative strategy; we use trait components--individual clinical symptoms of migraine--to determine affection status in genomewide linkage analyses of migraine-affected families. We identified linkage between several traits and markers on chromosome 4q24 (highest LOD score under locus heterogeneity [HLOD] 4.52), a locus we previously reported to be linked to the end diagnosis migraine with aura. The pulsation trait identified a novel locus on 17p13 (HLOD 4.65). Additionally, a trait combination phenotype (IHS full criteria) revealed a locus on 18q12 (HLOD 3.29), and the age at onset trait revealed a locus on 4q28 (HLOD 2.99). Furthermore, suggestive or nearly suggestive evidence of linkage to four additional loci was observed with the traits phonophobia (10q22) and aggravation by physical exercise (12q21, 15q14, and Xp21), and, interestingly, these loci have been linked to migraine in previous studies. Our findings suggest that the use of symptom components of migraine instead of the end diagnosis provides a useful tool in stratifying the sample for genetic studies.

Validating adult and paediatric nutrition screening and assessment tools in Vietnamese language in two acute hospital in Ho Chi Minh City, Vietnam

Despite being commonly prevalent in acute care hospitals worldwide, malnutrition often goes unidentified and untreated due to a lack in the implementation of a nutrition care pathway. The aim of this study was to validate nutrition screening and assessment tools in Vietnamese language. After converting into Vietnamese, Malnutrition Screening Tool (MST) and Subjective Global Assessment (SGA) were used to identify malnutrition in the adult setting; and the Paediatric Nutrition Screening Tool (PNST) and paediatric Subjective Global Nutritional Assessment (SGNA) were used in the paediatric setting in two acute care hospitals in Vietnam. This cross-sectional observational study sampled 123 adults (median age 78 years [39–96 years], 63% males) and 105 children (median age 20 months [2–100 months], 66% males). In adults, nutrition risk and malnutrition were identified in 29% and 45% of the cohort respectively. Nutrition risk and malnutrition were identified in 71% and 43% of the paediatric cohort respectively. The sensitivity and specificity of the screening tools were: 62% and 99% for the MST compared to the SGA; 89% and 42% for the PNST compared to the SGNA. This study provides a stepping stone to the potential use of evidence-based nutrition screening and assessment tools in Vietnamese language within the adult and paediatric Vietnamese acute care setting. Further work is required into integrating a complete nutrition care pathway within the acute care setting in Vietnamese hospitals.

Web 2.0 technology: Social learning tools in higher education in Saudi Arabia

Information and communication technology (ICT) has created opportunities for students' online interaction in higher education throughout the world. Limited research has been done in this area in Saudi Arabia. This study investigated university students' engagement and perceptions of online collaborative learning using Social Learning Tools (SLTs). In addition, it explored the quality of knowledge construction that occurred in this environment. A mixed methods case study approach was adopted, and the data was gathered from undergraduate students (n=43) who were enrolled in a 15-week course at a Saudi university. The results showed that while the students had positive perceptions towards SLTs and their engagement, data gathered from their work also showed little evidence of high levels of knowledge construction.

Tools for compositional data with a total

Compositional data analysis usually deals with relative information between parts where the total (abundances, mass, amount, etc.) is unknown or uninformative. This article addresses the question of what to do when the total is known and is of interest. Tools used in this case are reviewed and analysed, in particular the relationship between the positive orthant of D-dimensional real space, the product space of the real line times the D-part simplex, and their Euclidean space structures. The first alternative corresponds to data analysis taking logarithms on each component, and the second one to treat a log-transformed total jointly with a composition describing the distribution of component amounts. Real data about total abundances of phytoplankton in an Australian river motivated the present study and are used for illustration.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.