Transcending disciplinary, cultural and national boundaries: Emergent technologies, new education landscape and the cloud workshop project

As technology continues to become more accessible, miniaturised and diffused into the environment, the potential of wearable technology to impact our lives in significant ways becomes increasingly viable. Wearables afford unique interaction, communication and functional capabilities between users, their environment as well as access to information and digital data. Wearables also demand an inter-disciplinary approach and, depending on the purpose, can be fashioned to transcend cultural, national and spatial boundaries. This paper presents the Cloud Workshop project based on the theme of ‘Wearables and Wellbeing; Enriching connections between citizens in the Asia-Pacific region’, initiated through a cooperative partnership between Queensland University of Technology (QUT), Hong Kong Baptist University (HKBU) and Griffith University (GU). The project was unique due to its inter-disciplinary, inter-cultural and inter-national scope that occurred simultaneously between Australia and Hong Kong.

Scoping study for a national survey of the prevalence of child abuse and neglect

This is a report produced as a result of a study commissioned by the Australian Government Royal Commission into Institutional Responses to Child Sexual Abuse.

Absolute quantification of human tear lactoferrin using multiple reaction monitoring technique with stable-isotopic labeling

The mass spectrometry technique of multiple reaction monitoring (MRM) was used to quantify and compare the expression level of lactoferrin in tear films among control, prostate cancer (CaP), and benign prostate hyperplasia (BPH) groups. Tear samples from 14 men with CaP, 15 men with BPH, and 14 controls were analyzed in the study. Collected tears (2 μl) of each sample were digested with trypsin overnight at 37 °C without any pretreatment, and tear lactoferrin was quantified using a lactoferrin-specific peptide, VPSHAVVAR, both using natural/light and isotopic-labeled/heavy peptides with MRM. The average tear lactoferrin concentration was 1.01 ± 0.07 μg/μl in control samples, 0.96 ± 0.07 μg/μl in the BPH group, and 0.98 ± 0.07 μg/μl in the CaP group. Our study is the first to quantify tear proteins using a total of 43 individual (non-pooled) tear samples and showed that direct digestion of tear samples is suitable for MRM studies. The calculated average lactoferrin concentration in the control group matched that in the published range of human tear lactoferrin concentration measured by enzyme-linked immunosorbent assay (ELISA). Moreover, the lactoferrin was stably expressed across all of the samples, with no significant differences being observed among the control, BPH, and CaP groups.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.

Novel application of a discrete choice experiment to identify preferences for a national healthcare-associated infection surveillance programme: A cross-sectional study

Objective: To identify key stakeholder preferences and priorities when considering a national healthcare-associated infection (HAI) surveillance programme through the use of a discrete choice experiment (DCE). Setting: Australia does not have a national HAI surveillance programme. An online web-based DCE was developed and made available to participants in Australia. Participants: A sample of 184 purposively selected healthcare workers based on their senior leadership role in infection prevention in Australia. Primary and secondary outcomes: A DCE requiring respondents to select 1 HAI surveillance programme over another based on 5 different characteristics (or attributes) in repeated hypothetical scenarios. Data were analysed using a mixed logit model to evaluate preferences and identify the relative importance of each attribute. Results: A total of 122 participants completed the survey (response rate 66%) over a 5-week period. Excluding 22 who mismatched a duplicate choice scenario, analysis was conducted on 100 responses. The key findings included: 72% of stakeholders exhibited a preference for a surveillance programme with continuous mandatory core components (mean coefficient 0.640 (p<0.01)), 65% for a standard surveillance protocol where patient-level data are collected on infected and non-infected patients (mean coefficient 0.641 (p<0.01)), and 92% for hospital-level data that are publicly reported on a website and not associated with financial penalties (mean coefficient 1.663 (p<0.01)). Conclusions: The use of the DCE has provided a unique insight to key stakeholder priorities when considering a national HAI surveillance programme. The application of a DCE offers a meaningful method to explore and quantify preferences in this setting.