Shifting the Focus. Incorporating knowledge about Aboriginal engineering into main stream content

BACKGROUND OR CONTEXT The concept of 'Aboriginal engineering' has had little exposure in conventional engineering education programs, despite more than 40,000 years of active human engagement with the diverse Australian environment. The work reported in this paper began with the premise that Indigenous Student Support Through Indigenous Perspectives Embedded in Engineering Curricula (Goldfinch, et al 2013) would provide a clear and replicable means of encouraging Aboriginal teenagers to consider a career in engineering. Although that remains a key outcome of this OLT project, the direction taken by the research had led to additional insights and perspectives that have wide implications for engineering education more generally. There has only been passing reference to the achievements of Aboriginal engineering in current texts, and the very absence of such references was a prompt to explore further as our work developed. PURPOSE OR GOAL Project goals focused on curriculum-based change, including development of a model for inclusive teaching spaces, and study units employing key features of the model. As work progressed we found we needed to understand more about the principles and practices informing the development of pre-contact Aboriginal engineering strategies for sustaining life and society within the landscape of this often harsh continent. We also found ourselves being asked 'what engineering did Aboriginal cultures have?' Finding that there are no easy-to- access answers, we began researching the question, while continuing to engage with specific curriculum trials. APPROACH Stakeholders in the project had been identified as engineering educators, potential Aboriginal students and Aboriginal communities local to Universities involved in the project. We realised, early on, that at least one more group was involved - all the non-Aboriginal students in engineering classes. This realisation, coupled with recognition of the need to understand Aboriginal engineering as a set of viable, long term practices, altered the focus of our efforts. Rather than focusing primarily on finding ways to attract Aboriginal engineering students, the shift has been towards evolving ways of including knowledge about Aboriginal practices and principles in relevant engineering content. DISCUSSION This paper introduces the model resulting from the work of this project, explores its potential influence on engineering curriculum development and reports on implementation strategies. The model is a static representation of a dynamic and cyclic approach to engaging with Aboriginal engineering through contact with local communities in regard to building knowledge about the social beliefs underlying Aboriginal engineering principles and practices. Ways to engage engineering educators, students and the wider community are evolving through the continuing work of the project team and will be reported in more detail in the paper. RECOMMENDATIONS/IMPLICATIONS/CONCLUSION While engineering may be considered by some to be agnostic in regard to culture and social issues, the work of this project is drawing attention to the importance of including such issues into curriculum materials at a number of levels of complexity. The paper will introduce and explore the central concepts of the research completed to date, as well as suggesting ways in which engineering educators can extend their knowledge and understanding of Aboriginal engineering principles in the context of their own specialisations.

Genome-wide mapping of cystitis due to Streptococcus agalactiae and Escherichia coli in mice identifies a unique bladder transcriptome that signifies pathogen-specific antimicrobial defense against urinary tract infection

The most common causes of urinary tract infections (UTIs) are Gram-negative pathogens such as Escherichia coli; however, Gram-positive organisms including Streptococcus agalactiae, or group B streptococcus (GBS), also cause UTI. In GBS infection, UTI progresses to cystitis once the bacteria colonize bladder, but the host responses triggered in the bladder immediately following infection are largely unknown. Here, we used genome-wide expression profiling to map the bladder transcriptome of GBS UTI in mice infected transurethrally with uropathogenic GBS that was cultured from a 35 year-old women with cystitis. RNA from bladders was applied to Affymetrix Gene-1.0ST microarrays; qRT-PCR was used to analyze selected gene responses identified in array datasets. A surprisingly small significant gene list of 172 genes was identified at 24h; this compared to 2507 genes identified in a side-by-side comparison with uropathogenic E. coli (UPEC). No genes exhibited significantly altered expression at 2h in GBS-infected mice according to arrays despite high bladder bacterial loads at this early time point. The absence of a marked early host response to GBS juxtaposed with broad-based bladder responses activated by UPEC at 2h. Bioinformatics analyses including integrative systems-level network mapping revealed multiple activated biological pathways in the GBS cystitis transcriptome that regulate leukocyte activation, inflammation, apoptosis, and cytokine-chemokine biosynthesis. These findings define a novel, minimalistic type of bladder host response triggered by GBS UTI, which comprises collective antimicrobial pathways that differ dramatically from those activated by UPEC. Overall, this study emphasizes the unique nature of bladder immune activation mechanisms triggered by distinct uropathogens.

Recognition and enforcement in cross-border insolvency law: A proposal for judicial gap-filling

This Article analyzes the recognition and enforcement of cross-border insolvency judgments from the United States, United Kingdom, and Australia to determine whether the UNCITRAL Model Law’s goal of modified universalism is currently being practiced, and subjects the Model Law to analysis through the lens of international relations theories to elaborate a way forward. We posit that courts could use the express language of the Model Law text to confer recognition and enforcement of foreign insolvency judgments. The adoption of our proposal will reduce costs, maximize recovery for creditors, and ensure predictability for all parties.

A flexible hierarchical approach for facial age estimation based on multiple features

Age estimation from facial images is increasingly receiving attention to solve age-based access control, age-adaptive targeted marketing, amongst other applications. Since even humans can be induced in error due to the complex biological processes involved, finding a robust method remains a research challenge today. In this paper, we propose a new framework for the integration of Active Appearance Models (AAM), Local Binary Patterns (LBP), Gabor wavelets (GW) and Local Phase Quantization (LPQ) in order to obtain a highly discriminative feature representation which is able to model shape, appearance, wrinkles and skin spots. In addition, this paper proposes a novel flexible hierarchical age estimation approach consisting of a multi-class Support Vector Machine (SVM) to classify a subject into an age group followed by a Support Vector Regression (SVR) to estimate a specific age. The errors that may happen in the classification step, caused by the hard boundaries between age classes, are compensated in the specific age estimation by a flexible overlapping of the age ranges. The performance of the proposed approach was evaluated on FG-NET Aging and MORPH Album 2 datasets and a mean absolute error (MAE) of 4.50 and 5.86 years was achieved respectively. The robustness of the proposed approach was also evaluated on a merge of both datasets and a MAE of 5.20 years was achieved. Furthermore, we have also compared the age estimation made by humans with the proposed approach and it has shown that the machine outperforms humans. The proposed approach is competitive with current state-of-the-art and it provides an additional robustness to blur, lighting and expression variance brought about by the local phase features.

Development and preliminary validation of the Drinking Expectancy Sexual Vulnerabilities Questionnaire (DESV-Q): A purpose-specific instrument for rape-perception research

Background Alcohol expectancies likely play a role in people’s perceptions of alcohol-involved sexual violence. However, no appropriate measure exists to examine this link comprehensively. Objective The aim of this research was to develop an alcohol expectancy measure which captures young adults’ beliefs about alcohol’s role in sexual aggression and victimization. Method Two cross-sectional samples of young Australian adults (18–25 years) were recruited for scale development (Phase 1) and scale validation (Phase 2). In Phase 1, participants (N = 201; 38.3% males) completed an online survey with an initial pool of alcohol expectancy items stated in terms of three targets (self, men, women) to identify the scale’s factor structure and most effective items. A revised alcohol expectancy scale was then administered online to 322 young adults (39.6% males) in Phase 2. To assess the predictive, convergent, and discriminant validity of the scale, participants also completed established measures of personality, social desirability, alcohol use, general and context-specific alcohol expectancies, and impulsiveness. Results Principal axis factoring (Phase 1) and confirmatory factor analysis (Phase 2) resulted in a target-equivalent five-factor structure for the final 66-item Drinking Expectancy Sexual Vulnerabilities Questionnaire (DESV-Q). The factors were labeled: - (1) Sexual Coercion - (2) Sexual Vulnerability - (3) Confidence - (4) Self-Centeredness - (5) Negative Cognitive and Behavioral Changes The measure demonstrated effective items, high internal consistency, and satisfactory predictive, convergent, and discriminant validity. Conclusions The DESV-Q is a purpose-specific instrument that could be used in future research to elucidate people’s attributions for alcohol-involved sexual aggression and victimization.

The motivational benefits of specific versus general optimism

This study addresses the question whether a specific, work-related form of optimistic thinking has motivational consequences in terms of work engagement above and beyond general optimism over time. A specific form of optimistic thinking is focus on opportunities. Focus on opportunities is a future-oriented belief that describes how many plans, goals, and possibilities people expect to have in their future at work. Based on a cross-lagged panel design with a two-year time lag and data from a sample of 124 German business owners, results of structural equation modeling showed that focus on opportunities positively predicted changes in work engagement over time, even when controlling for general optimism. This finding supports propositions of social cognition and self-regulation theories that emphasize the importance of a specific form of optimism that has motivating potential by referring to future work goals and opportunities

Decision trees for detection of activity intensity in youth with cerebral palsy

PURPOSE To develop and test decision tree (DT) models to classify physical activity (PA) intensity from accelerometer output and Gross Motor Function Classification System (GMFCS) classification level in ambulatory youth with cerebral palsy (CP); and 2) compare the classification accuracy of the new DT models to that achieved by previously published cut-points for youth with CP. METHODS Youth with CP (GMFCS Levels I - III) (N=51) completed seven activity trials with increasing PA intensity while wearing a portable metabolic system and ActiGraph GT3X accelerometers. DT models were used to identify vertical axis (VA) and vector magnitude (VM) count thresholds corresponding to sedentary (SED) (<1.5 METs), light PA (LPA) (>/=1.5 and <3 METs) and moderate-to-vigorous PA (MVPA) (>/=3 METs). Models were trained and cross-validated using the 'rpart' and 'caret' packages within R. RESULTS For the VA (VA_DT) and VM decision trees (VM_DT), a single threshold differentiated LPA from SED, while the threshold for differentiating MVPA from LPA decreased as the level of impairment increased. The average cross-validation accuracy for the VC_DT was 81.1%, 76.7%, and 82.9% for GMFCS levels I, II, and III, respectively. The corresponding cross-validation accuracy for the VM_DT was 80.5%, 75.6%, and 84.2%, respectively. Within each GMFCS level, the decision tree models achieved better PA intensity recognition than previously published cut-points. The accuracy differential was greatest among GMFCS level III participants, in whom the previously published cut-points misclassified 40% of the MVPA activity trials. CONCLUSION GMFCS-specific cut-points provide more accurate assessments of MVPA levels in youth with CP across the full spectrum of ambulatory ability.

Detecting rare events using Kullback-Leibler divergence: A weakly supervised approach

Video surveillance infrastructure has been widely installed in public places for security purposes. However, live video feeds are typically monitored by human staff, making the detection of important events as they occur difficult. As such, an expert system that can automatically detect events of interest in surveillance footage is highly desirable. Although a number of approaches have been proposed, they have significant limitations: supervised approaches, which can detect a specific event, ideally require a large number of samples with the event spatially and temporally localised; while unsupervised approaches, which do not require this demanding annotation, can only detect whether an event is abnormal and not specific event types. To overcome these problems, we formulate a weakly-supervised approach using Kullback-Leibler (KL) divergence to detect rare events. The proposed approach leverages the sparse nature of the target events to its advantage, and we show that this data imbalance guarantees the existence of a decision boundary to separate samples that contain the target event from those that do not. This trait, combined with the coarse annotation used by weakly supervised learning (that only indicates approximately when an event occurs), greatly reduces the annotation burden while retaining the ability to detect specific events. Furthermore, the proposed classifier requires only a decision threshold, simplifying its use compared to other weakly supervised approaches. We show that the proposed approach outperforms state-of-the-art methods on a popular real-world traffic surveillance dataset, while preserving real time performance.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

Antigen-specific CD4 cells assist CD8 T-effector cells in eliminating keratinocytes

Keratinocytes expressing tumor or viral antigens can be eliminated by antigen-primed CD8 cytotoxic T cells. CD4 T-helper cells help induction of CD8 cytotoxic T cells from naive precursors and generation of CD8 T-cell memory. In this study, we show, unexpectedly, that CD4 cells are also required to assist primed CD8 effector T cells in rejection of skin expressing human growth hormone, a neo-self-antigen, in keratinocytes. The requirement for CD4 cells can be substituted by CD40 costimulation. Rejection of skin expressing ovalbumin (OVA), a non-self-antigen, by primed CD8 cytotoxic T cells can in contrast occur without help from antigen-specific CD4 T cells. However, rejection of OVA expressing keratinocytes is helped by antigen-specific CD4 T cells if only low numbers of primed or naive OVA-specific CD8 T cells are available. Effective immunotherapy directed at antigens expressed in squamous cancer may therefore be facilitated by induction of tumor antigen-specific CD4 helper T cells, as well as cytotoxic CD8 T cells.

LRP5 regulates human body fat distribution by modulating adipose progenitor biology in a dose- and depot-specific fashion

Summary Common variants in WNT pathway genes have been associated with bone mass and fat distribution, the latter predicting diabetes and cardiovascular disease risk. Rare mutations in the WNT co-receptors LRP5 and LRP6 are similarly associated with bone and cardiometabolic disorders. We investigated the role of LRP5 in human adipose tissue. Subjects with gain-of-function LRP5 mutations and high bone mass had enhanced lower-body fat accumulation. Reciprocally, a low bone mineral density-associated common LRP5 allele correlated with increased abdominal adiposity. Ex vivo LRP5 expression was higher in abdominal versus gluteal adipocyte progenitors. Equivalent knockdown of LRP5 in both progenitor types dose-dependently impaired β-catenin signaling and led to distinct biological outcomes: diminished gluteal and enhanced abdominal adipogenesis. These data highlight how depot differences in WNT/β-catenin pathway activity modulate human fat distribution via effects on adipocyte progenitor biology. They also identify LRP5 as a potential pharmacologic target for the treatment of cardiometabolic disorders. © 2015 The Authors.

Elliptical summary randomisation for sensor-based human activity recognition

Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

Associations of serum perfluoroalkyl acid levels with T-helper cell-specific cytokines in children: By gender and asthma status

Perfluoroalkyl acids (PFAAs) are a group of common chemicals that ubiquitously exist in wildlife and humans. Experimental data suggest that they may alter T-lymphocyte functioning in situ by preferentially enhancing the development of T-helper 2 (TH2)- and inhibiting TH1-lymphocyte development and might increase allergic inflammation, but few human studies have been conducted. To evaluate the association between serum PFAAs concentrations and T-lymphocyte-related immunological markers of asthma in children, and further to assess whether gender modified this association, 231 asthmatic children and 225 non-asthmatic control children from Northern Taiwan were recruited into the Genetic and Biomarker study for Childhood Asthma. Serum concentrations of ten PFAAs and levels of TH1 [interferon (IFN)-γ, interleukin (IL)-2] and TH2 (IL-4 and IL-5) cytokines were measured. The results showed that asthmatics had significantly higher serum PFAAs concentrations compared with the healthy controls. When stratified by gender, a greater number of significant associations between PFAAs and asthma outcomeswere found in males than in females. Among males, adjusted odds ratios for asthma among those with the highest versus lowest quartile of PFAAs exposure ranged from 2.59 (95% CI: 1.14, 5.87) for the perfluorobutanesulfonate (PFBS) to 4.38 (95% CI: 2.02, 9.50) for perfluorooctanesulfonate (PFOS); and serum PFAAs were associated positively with TH2 cytokines and inversely with TH1 cytokines among male asthmatics. Among females, no significant associations between PFAAs and TH2 cytokines could be detected. In conclusion, increased serum PFAAs levels may promote TH cell dysregulation and alter the availability of key TH1 and TH2 cytokines, ultimately contributing to the development of asthma that may differentially impact males to a greater degree than females. These results have potential relevance in asthma prevention.