Highly discriminatory single-nucleotide polymorphism interrogation of Escherichia coli by use of allele-specific real-time PCR and eBURST analysis

In total, 782 Escherichia coli strains originating from various host sources have been analyzed in this study by using a highly discriminatory single-nucleotide polymorphism (SNP) approach. A set of eight SNPs, with a discrimination value (Simpson's index of diversity [D]) of 0.96, was determined using the Minimum SNPs software, based on sequences of housekeeping genes from the E. coli multilocus sequence typing (MLST) database. Allele-specific real-time PCR was used to screen 114 E. coli isolates from various fecal sources in Southeast Queensland (SEQ). The combined analysis of both the MLST database and SEQ E. coli isolates using eight high-D SNPs resolved the isolates into 74 SNP profiles. The data obtained suggest that SNP typing is a promising approach for the discrimination of host-specific groups and allows for the identification of human-specific E. coli in environmental samples. However, a more diverse E. coli collection is required to determine animal- and environment-specific E. coli SNP profiles due to the abundance of human E. coli strains (56%) in the MLST database.

User-driven saliency maps for evaluating Region-of-Interest detection

Detection of Region of Interest (ROI) in a video leads to more efficient utilization of bandwidth. This is because any ROIs in a given frame can be encoded in higher quality than the rest of that frame, with little or no degradation of quality from the perception of the viewers. Consequently, it is not necessary to uniformly encode the whole video in high quality. One approach to determine ROIs is to use saliency detectors to locate salient regions. This paper proposes a methodology for obtaining ground truth saliency maps to measure the effectiveness of ROI detection by considering the role of user experience during the labelling process of such maps. User perceptions can be captured and incorporated into the definition of salience in a particular video, taking advantage of human visual recall within a given context. Experiments with two state-of-the-art saliency detectors validate the effectiveness of this approach to validating visual saliency in video. This paper will provide the relevant datasets associated with the experiments.

Investigation into the introduction of clonal strains of Pseudomonas aeruginosa to the cystic fibrosis population by consumption of raw salad vegetables

Most salad vegetables are eaten fresh by consumers. However, raw vegetables may pose a risk of transmitting opportunistic bacteria to immunocompromised people, including cystic fibrosis (CF) patients. In particular, CF patients are vulnerable to chronic Pseudomonas aeruginosa lung infections and this organism is the primary cause of morbidity and mortality in this group. Clonal variants of P. aeruginosa have been identified as emerging threats to people afflicted with CF; however it has not yet been proven from where these clones originate or how they are transmitted. Due to the organisms‟ aquatic environmental niche, it was hypothesised that vegetables may be a source of these clones. To test this hypothesis, lettuce, tomatoes, mushrooms and bean sprout packages (n = 150) were analysed from a green grocer, supermarket and farmers‟ market within the Brisbane region, availability permitting. The internal and external surfaces of the vegetables were separately analysed for the presence of clonal strains of P. aeruginosa using washings and homogenisation techniques, respectively. This separation was in an attempt to establish which surface was contaminated, so that recommendations could be made to decrease or eliminate P. aeruginosa from these foods prior to consumption. Soil and water samples (n = 17) from local farms were also analysed for the presence of P. aeruginosa. Presumptive identification of isolates recovered from these environmental samples was made based on growth on Cetrimide agar at 42°C, presence of the cytochrome-oxidase enzyme and inability to ferment lactose. P. aeruginosa duplex real-time polymerase chain reaction assay (PAduplex) was performed on all bacterial isolates presumptively identified as P. aeruginosa. Enterobacterial repetitive intergenic consensus strain typing PCR (ERIC-PCR) was subsequently performed on confirmed bacterial isolates. Although 72 P. aeruginosa were isolated, none of these proved to be clonal strains. The significance of these findings is that vegetables may pose a risk of transmitting sporadic strains of P. aeruginosa to people afflicted with CF and possibly, other immunocompromised people.

Urban informatics, ubiquitous computing and social media for healthy cities

The increasing ubiquity of digital technology, internet services and location-aware applications in our everyday lives allows for a seamless transitioning between the visible and the invisible infrastructure of cities: road systems, building complexes, information and communication technology, and people networks create a buzzing environment that is alive and exciting. Driven by curiosity, initiative and interdisciplinary exchange, the Urban Informatics Research Lab at Queensland University of Technology (QUT), Brisbane, Australia, is an emerging cluster of people interested in research and development at the intersection of people, place and technology with a focus on cities, locative media and mobile technology. This paper introduces urban informatics as a transdisciplinary practice across people, place and technology that can aid local governments, urban designers and planners in creating responsive and inclusive urban spaces and nurturing healthy cities. Three challenges are being discussed. First, people, and the challenge of creativity explores the opportunities and challenges of urban informatics that can lead to the design and development of new tools, methods and applications fostering participation, the democratisation of knowledge, and new creative practices. Second, technology, and the challenge of innovation examines how urban informatics can be applied to support user-led innovation with a view to promote entrepreneurial ideas and creative industries. Third, place, and the challenge of engagement discusses the potential to establish places within cities that are dedicated to place-based applications of urban informatics with a view to deliver community and civic engagement strategies.

How do our 'quicks' generate pace? A cross sectional analysis of the Cricket Australia pace pathway

Technique and physical contributions to ball delivery speed in fast bowling have been popular research topics in sports science. However, a common limiting factor of this work is the level of expertise of participants and lack of within bowler investigations (Salter et al., 2007). The relationship between technique, anthropometry and ball speed has not been comprehensively investigated among elite fast bowlers. The purpose of this study was to examine the relationship between technique, anthropometric variables and ball speed using both within- and betweenbowler analyses in a cross section of the Cricket Australia high performance pace pathway.

What the experts think : fast bowling expertise acquisition and talent development

For applied sport scientists charged with developing talented performers an essential requirement is to identify components contributing to the development and maintenance of expertise. Previous qualitative analysis has revealed several psychological (e.g., mental focus, goal-setting and selfevaluation), socio-cultural (e.g. community and family support, cultural influence), physical (e.g., strength, height) and environmental (e.g., access to facilities and climate) constraints on successful Olympian development (Abbott et al., 2005). Open-ended interviews with expert athletes and/or expert coaches have been used to reveal competencies of elite performers to derive factors associated with success (Durand-Bush et al., 2002). However, the influence of these factors is likely to be sport-specific due to different task constraints and the changing nature of the performer-environment relationship through practice, coaching and competing (Vaeyens et al., 2008). So far, only one study on expertise acquisition in cricket has been undertaken. Weissensteiner, et al. (2009) found that development of expertise in cricket batting in Australia may be facilitated by early unstructured play (i.e. ‘backyard cricket’), a wide range of sport experience during development, and early exposure to playing with seniors.

Quantifying variability within technique and performance in elite fast bowlers : is technical variability dysfunctional or functional?

In fast bowling, cricketers are expected to produce a range of delivery lines and lengths while maximising ball speed. From a coaching perspective, technique consistency has been typically associated with superior performance in these areas. However, although bowlers are required to bowl consistently, at the elite level they must also be able to vary line, length and speed to adapt to opposition batters’ strengths and weaknesses. The relationship between technique and performance variability (and consistency) has not been investigated in previous fast bowling research. Consequently, the aim of this study was to quantify both technique (bowling action and coordination) and performance variability in elite fast bowlers from Australian Junior and National Pace Squads. Technique variability was analysed to investigate whether it could be classified as functional or dysfunctional in relation to speed and accuracy.

Multi-modal summarization of key events and top players in sports tournament videos

To detect and annotate the key events of live sports videos, we need to tackle the semantic gaps of audio-visual information. Previous work has successfully extracted semantic from the time-stamped web match reports, which are synchronized with the video contents. However, web and social media articles with no time-stamps have not been fully leveraged, despite they are increasingly used to complement the coverage of major sporting tournaments. This paper aims to address this limitation using a novel multimodal summarization framework that is based on sentiment analysis and players' popularity. It uses audiovisual contents, web articles, blogs, and commentators' speech to automatically annotate and visualize the key events and key players in a sports tournament coverage. The experimental results demonstrate that the automatically generated video summaries are aligned with the events identified from the official website match reports.

Evaluation of texture and geometry for dimensional facial expression recognition

Facial expression recognition (FER) algorithms mainly focus on classification into a small discrete set of emotions or representation of emotions using facial action units (AUs). Dimensional representation of emotions as continuous values in an arousal-valence space is relatively less investigated. It is not fully known whether fusion of geometric and texture features will result in better dimensional representation of spontaneous emotions. Moreover, the performance of many previously proposed approaches to dimensional representation has not been evaluated thoroughly on publicly available databases. To address these limitations, this paper presents an evaluation framework for dimensional representation of spontaneous facial expressions using texture and geometric features. SIFT, Gabor and LBP features are extracted around facial fiducial points and fused with FAP distance features. The CFS algorithm is adopted for discriminative texture feature selection. Experimental results evaluated on the publicly accessible NVIE database demonstrate that fusion of texture and geometry does not lead to a much better performance than using texture alone, but does result in a significant performance improvement over geometry alone. LBP features perform the best when fused with geometric features. Distributions of arousal and valence for different emotions obtained via the feature extraction process are compared with those obtained from subjective ground truth values assigned by viewers. Predicted valence is found to have a more similar distribution to ground truth than arousal in terms of covariance or Bhattacharya distance, but it shows a greater distance between the means.

SwarmPS: Rapid, semi-automated single particle selection software

Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that ∼104–105 particle projections are required to attain a 3 Å resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present SwarmPS, a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (∼102), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. SwarmPS is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites.

Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparse coefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.

Scene invariant crowd counting

This paper describes a scene invariant crowd counting algorithm that uses local features to monitor crowd size. Unlike previous algorithms that require each camera to be trained separately, the proposed method uses camera calibration to scale between viewpoints, allowing a system to be trained and tested on different scenes. A pre-trained system could therefore be used as a turn-key solution for crowd counting across a wide range of environments. The use of local features allows the proposed algorithm to calculate local occupancy statistics, and Gaussian process regression is used to scale to conditions which are unseen in the training data, also providing confidence intervals for the crowd size estimate. A new crowd counting database is introduced to the computer vision community to enable a wider evaluation over multiple scenes, and the proposed algorithm is tested on seven datasets to demonstrate scene invariance and high accuracy. To the authors' knowledge this is the first system of its kind due to its ability to scale between different scenes and viewpoints.

Identifying the beliefs which predict environmentally friendly behaviour in the Brisbane area : a foundation for informed interventions

A move to more sustainable living can provide immediate and long term health and environmental benefits. The Green Living Study consisted of a mail survey of 1186 South East Queensland residents and an online survey of a further 451 individuals, primarily from South East Queensland, and explored the predictors of environmentally friendly behaviour. This paper explores the underlying beliefs that were found to predict specific environmentally friendly behaviours, such as walking for transport, switching off lights when not in use, switching off unused appliances at the wall and shopping with reusable bags. Beliefs explored included social norms, advantages and disadvantages of performing the behaviours, and issues of control over ones behaviour. The findings showed that people’s environmentally friendly behaviours may be influenced by convenience, saving money and saving face; i.e. is it easy to do, will I be better off, and will I be seen as ‘different’? Understanding the beliefs which directly predict behaviour can help inform public policy and educational initiatives. A number of models for transferring this knowledge into policy and practice will be discussed.

Activity modelling in crowded environments : a soft decision approach

Probabilistic topic models have recently been used for activity analysis in video processing, due to their strong capacity to model both local activities and interactions in crowded scenes. In those applications, a video sequence is divided into a collection of uniform non-overlaping video clips, and the high dimensional continuous inputs are quantized into a bag of discrete visual words. The hard division of video clips, and hard assignment of visual words leads to problems when an activity is split over multiple clips, or the most appropriate visual word for quantization is unclear. In this paper, we propose a novel algorithm, which makes use of a soft histogram technique to compensate for the loss of information in the quantization process; and a soft cut technique in the temporal domain to overcome problems caused by separating an activity into two video clips. In the detection process, we also apply a soft decision strategy to detect unusual events.We show that the proposed soft decision approach outperforms its hard decision counterpart in both local and global activity modelling.

Assessing population limits : a carrying capacity approach to planning

While purporting to enhance Australia’s sustainability, the federal government’s Population Strategy rejects the assessment of the limiting factors to future population growth, thus avoiding urgent threshold issues such as resource depletion and environmental destruction. A more forward-thinking and whole-system perspective would assess and incorporate critical biophysical limits into governance processes with suitable prioritisation. It would encourage communities to examine their individual and collective responsibilities in the context of these limits in order to most equitably optimise outcomes; and it would employ both a resource-based examination of minimum population requirements, and an impact-based assessment of maximum thresholds. This carrying capacity approach to planning could help guide society towards a more sustainable future.