Tunable “Nano-Shearing” : A physical mechanism to displace nonspecific cell adhesion during rare cell detection

We report a tunable alternating current electrohydrodynamic (ac-EHD) force which drives lateran fluid motion within a few nanometers of an electrode surface. Because the magnitude of this fluid shear force can be tuned externally (e.g., via the application of an ac electric field), it provides a new capability to physically displace weakly (nonspecifically) bound cellular analytes. To demonstrate the utility of the tunable nanoshearing phenomenon, we present data on purpose-built microfluidic devices that employ ac-EHD force to remove nonspecific adsorption of molecular and cellular species. Here, we show that an ac-EHD device containing asymmetric planar and microtip electrode pairs resulted in a 4-fold reduction in nonspecific adsorption of blood cells and also captured breast cancer cells in blood, with high efficiency (approximately 87%) and specificity. We therefore feel that this new capability of externally tuning and manipulating fluid flow could have wide applications as an innovative approach to enhance the specific capture of rare cells such as cancer cells in blood.

Improving the detection of donations within the RHD negative blood donor panel which weakly express the RHD antigen

This project improved the detection and classification of very weakly expressed RhD variants in the Australian blood donor panel and contributed to the knowledge of anti-D reactivity patterns of RHD alleles that are undescribed. As such, the management of donations possessing these RHD alleles can be improved upon and the overall safety of transfusion medicine pertaining to the Rh blood group system will be increased. Future projects at ARCBS will be able to utilise the procedures developed in this project, thereby decreasing throughput time. The specificity of current testing will be improved and the need for outsourced RHD testing diminished.

Plasma-enabled graded nanotube biosensing arrays on a Si nanodevice platform : catalyst-free integration and in situ detection of nucleation events

Low-temperature plasmas in direct contact with arbitrary, written linear features on a Si wafer enable catalyst-free integration of carbon nanotubes into a Si-based nanodevice platform and in situ resolution of individual nucleation events. The graded nanotube arrays show reliable, reproducible, and competitive performance in electron field emission and biosensing nanodevices.

Asymptotic minimax robust and misspecified Lorden quickest change detection for dependent stochastic processes

The quick detection of an abrupt unknown change in the conditional distribution of a dependent stochastic process has numerous applications. In this paper, we pose a minimax robust quickest change detection problem for cases where there is uncertainty about the post-change conditional distribution. Our minimax robust formulation is based on the popular Lorden criteria of optimal quickest change detection. Under a condition on the set of possible post-change distributions, we show that the widely known cumulative sum (CUSUM) rule is asymptotically minimax robust under our Lorden minimax robust formulation as a false alarm constraint becomes more strict. We also establish general asymptotic bounds on the detection delay of misspecified CUSUM rules (i.e. CUSUM rules that are designed with post- change distributions that differ from those of the observed sequence). We exploit these bounds to compare the delay performance of asymptotically minimax robust, asymptotically optimal, and other misspecified CUSUM rules. In simulation examples, we illustrate that asymptotically minimax robust CUSUM rules can provide better detection delay performance at greatly reduced computation effort compared to competing generalised likelihood ratio procedures.

Participatory Data Analytics: Collaborative interfaces for data composition and visualisation

This research proposes the development of interfaces to support collaborative, community-driven inquiry into data, which we refer to as Participatory Data Analytics. Since the investigation is led by local communities, it is not possible to anticipate which data will be relevant and what questions are going to be asked. Therefore, users have to be able to construct and tailor visualisations to their own needs. The poster presents early work towards defining a suitable compositional model, which will allow users to mix, match, and manipulate data sets to obtain visual representations with little-to-no programming knowledge. Following a user-centred design process, we are subsequently planning to identify appropriate interaction techniques and metaphors for generating such visual specifications on wall-sized, multi-touch displays.

Detection of sinkhole attack in wireless sensor networks

Generally wireless sensor networks rely of many-to-one communication approach for data gathering. This approach is extremely susceptible to sinkhole attack, where an intruder attracts surrounding nodes with unfaithful routing information, and subsequently presents selective forwarding or change the data that carry through it. A sinkhole attack causes an important threat to sensor networks and it should be considered that the sensor nodes are mostly spread out in open areas and of weak computation and battery power. In order to detect the intruder in a sinkhole attack this paper suggests an algorithm which firstly finds a group of suspected nodes by analyzing the consistency of data. Then, the intruder is recognized efficiently in the group by checking the network flow information. The proposed algorithm's performance has been evaluated by using numerical analysis and simulations. Therefore, accuracy and efficiency of algorithm would be verified.

Using collaborative and activity-based learning for engaging IT students

Active and collaborative learning are becoming essential strategies to attract, engage and retain students. These methods have been adopted within the Science and Engineering Faculty of Queensland University of Technology for use in its Science, Information Technology and Engineering degrees. This paper describes the adoption and application of these techniques in a specific first year unit in a new Bachelor of Information Technology degree which has majors in Computer Science and Information Systems. The paper reports on the design, development and implementation of this foundation subject and discusses how it uses active and collaborative learning to teach design thinking through a series of design challenges, and how it uses critiquing and reflection to ensure that students become more aware of design and team processes.

Telomerase activity : detection and clinical implications in human cancer

Telomerase is an extremely important enzyme required for the immortalisation of tumour cells. Because the gene is activated in the vast majority of tumour tissues and remains unused in most somatic cells, it represents a marker with huge diagnostic, prognostic and treatment implications in cancer. This article summarises the basic structure and functions of telomerase and considers its clinical implications in colorectal and other cancers.

Detection of dynamic primary user with cooperative spectrum sensing

In this paper we demonstrate that existing cooperative spectrum sensing formulated for static primary users cannot accurately detect dynamic primary users regardless of the information fusion method. Performance error occurs as the sensing parameters calculated by the conventional detector result in sensing performance that violates the sensing requirements. Furthermore, the error is accumulated and compounded by the number of cooperating nodes. To address this limitation, we design and implement the duty cycle detection model for the context of cooperative spectrum sensing to accurately calculate the sensing parameters that satisfy the sensing requirements. We show that longer sensing duration is required to compensate for dynamic primary user traffic.

Modelling total duration of traffic incidents including incident detection and recovery time

Traffic incidents are key contributors to non-recurrent congestion, potentially generating significant delay. Factors that influence the duration of incidents are important to understand so that effective mitigation strategies can be implemented. To identify and quantify the effects of influential factors, a methodology for studying total incident duration based on historical data from an ‘integrated database’ is proposed. Incident duration models are developed using a selected freeway segment in the Southeast Queensland, Australia network. The models include incident detection and recovery time as components of incident duration. A hazard-based duration modelling approach is applied to model incident duration as a function of a variety of factors that influence traffic incident duration. Parametric accelerated failure time survival models are developed to capture heterogeneity as a function of explanatory variables, with both fixed and random parameters specifications. The analysis reveals that factors affecting incident duration include incident characteristics (severity, type, injury, medical requirements, etc.), infrastructure characteristics (roadway shoulder availability), time of day, and traffic characteristics. The results indicate that event type durations are uniquely different, thus requiring different responses to effectively clear them. Furthermore, the results highlight the presence of unobserved incident duration heterogeneity as captured by the random parameter models, suggesting that additional factors need to be considered in future modelling efforts.

A pilot study to assess the appropriateness of prescribing from a collaborative pharmacist prescribing study in a surgical pre admission clinic

Background Current evidence to support non-medical prescribing is predominantly qualitative, with little evaluation of appropriateness. This study aims to evaluate the appropriateness of prescribing, and significance of omissions, from a doctor pharmacist collaborative prescribing model in an elective surgery pre admission clinic (PAC). Method A modified version of the Medication Appropriate Index (MAI) was developed, piloted and subsequently used by an expert panel, comprised of a surgeon, anaesthetist, clinical pharmacologist, pharmacist, resident medical officer (RMO) and clinical nurse. The tool was used to rate the appropriateness of prescribing of medications, and the significance of omissions in a 5% sample (N=19) of the total cohort from a randomised, controlled two arm trial of doctor-pharmacist collaborative prescribing. Results When reviewer assessments were combined, 32 out of 294 (10.9%) medications assessed for appropriateness in the control arm were classed as inappropriate, compared to 13 of 266 (4.9%) in the intervention arm. Out of 89 regular medications in the control arm, 25 (28%) were omitted from the medication charts, compared to 1 out of 55 (2%) in the intervention arm (p<0.001, fishers exact) On average, 52% of omissions in the control arm were judged to have potential for patient harm or ward inconvenience. Conclusion For the appropriateness of prescribing, overall results were similar between arms, as judged by individual panel members. Medication charts in the control arm contained significantly more omissions than in the intervention arm, a number of which were rated by the panel members as having the potential for patient harm or ward inconvenience.

Making space to learn : leading collaborative classroom design

Design principles for learning spaces for 21st century learning emphasise flexibility and collaboration. Yet it is rare in Australian schools for school leaders to invite students and teachers to collaborate in the design process or to prepare them for the transition into innovative physical learning spaces that are often designed to challenge and change existing learning habits. This article presents a qualitative case study of how one primary school leader led a successful transition for a teacher and her students by inviting them to design their future physical learning space and reconstruct their pedagogic relationships. This article analyses her leadership practices drawing from literature in the learning space, student voice and leadership fields to consider the benefits and challenges experienced by the collaborators when making space to learn.

Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease

Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

Collaborative extension of biodiversity monitoring protocols in the bird watching community

Citizen science projects have demonstrated the advantages of people with limited relevant prior knowledge participating in research. However, there is a difference between engaging the general public in a scientific project and entering an established expert community to conduct research. This paper describes our ongoing acoustic biodiversity monitoring collaborations with the bird watching community. We report on findings gathered over six years from participation in bird walks, observing conservation efforts, and records of personal activities of experienced birders. We offer an empirical study into extending existing protocols through in-context collaborative design involving scientists and domain experts.

Topic dependent language modelling for spoken term detection

This paper investigates the effect of topic dependent language models (TDLM) on phonetic spoken term detection (STD) using dynamic match lattice spotting (DMLS). Phonetic STD consists of two steps: indexing and search. The accuracy of indexing audio segments into phone sequences using phone recognition methods directly affects the accuracy of the final STD system. If the topic of a document in known, recognizing the spoken words and indexing them to an intermediate representation is an easier task and consequently, detecting a search word in it will be more accurate and robust. In this paper, we propose the use of TDLMs in the indexing stage to improve the accuracy of STD in situations where the topic of the audio document is known in advance. It is shown that using TDLMs instead of the traditional general language model (GLM) improves STD performance according to figure of merit (FOM) criteria.