A comparison of methods to identify alcohol involvement in youth injury-related emergency department presentation data

Aims: To compare different methods for identifying alcohol involvement in injury-related emergency department presentation in Queensland youth, and to explore the alcohol terminology used in triage text. Methods: Emergency Department Information System data were provided for patients aged 12-24 years with an injury-related diagnosis code for a 5 year period 2006-2010 presenting to a Queensland emergency department (N=348895). Three approaches were used to estimate alcohol involvement: 1) analysis of coded data, 2) mining of triage text, and 3) estimation using an adaptation of alcohol attributable fractions (AAF). Cases were identified as ‘alcohol-involved’ by code and text, as well as AAF weighted. Results: Around 6.4% of these injury presentations overall had some documentation of alcohol involvement, with higher proportions of alcohol involvement documented for 18-24 year olds, females, indigenous youth, where presentations occurred on a Saturday or Sunday, and where presentations occurred between midnight and 5am. The most common alcohol terms identified for all subgroups were generic alcohol terms (eg. ETOH or alcohol) with almost half of the cases where alcohol involvement was documented having a generic alcohol term recorded in the triage text. Conclusions: Emergency department data is a useful source of information for identification of high risk sub-groups to target intervention opportunities, though it is not a reliable source of data for incidence or trend estimation in its current unstandardised form. Improving the accuracy and consistency of identification, documenting and coding of alcohol-involvement at the point of data capture in the emergency department is the most desirable long term approach to produce a more solid evidence base to support policy and practice in this field.

Recent advances in the design of artificial corneas

Purpose of review: Artificial corneas are being developed to meet a shortage of donor corneas as well as to address cases where allografting is contraindicated. A range of artificial corneas has been developed. Here we review several newer designs and especially those inspired by naturally occurring biomaterials found with the human body and elsewhere. Recent findings: Recent trends in the development of artificial corneas indicate a move towards the use of materials derived from native sources including decellularized corneal tissue and tissue substitutes synthesized by corneal cells in vitro when grown either on their own, or in conjunction with novel protein-based scaffolds. Biologically inspired materials are also being considered for implantation on their own with the view to promoting endogenous corneal tissue. Summary: More recent attempts at making artificial corneas have taken a more nature-based or nature-inspired approach. Several will in the near future be likely to be available clinically.

Comparing methods for modelling spreading cell fronts

Spreading cell fronts play an essential role in many physiological processes. Classically, models of this process are based on the Fisher-Kolmogorov equation; however, such continuum representations are not always suitable as they do not explicitly represent behaviour at the level of individual cells. Additionally, many models examine only the large time asymptotic behaviour, where a travelling wave front with a constant speed has been established. Many experiments, such as a scratch assay, never display this asymptotic behaviour, and in these cases the transient behaviour must be taken into account. We examine the transient and asymptotic behaviour of moving cell fronts using techniques that go beyond the continuum approximation via a volume-excluding birth-migration process on a regular one-dimensional lattice. We approximate the averaged discrete results using three methods: (i) mean-field, (ii) pair-wise, and (iii) one-hole approximations. We discuss the performace of these methods, in comparison to the averaged discrete results, for a range of parameter space, examining both the transient and asymptotic behaviours. The one-hole approximation, based on techniques from statistical physics, is not capable of predicting transient behaviour but provides excellent agreement with the asymptotic behaviour of the averaged discrete results, provided that cells are proliferating fast enough relative to their rate of migration. The mean-field and pair-wise approximations give indistinguishable asymptotic results, which agree with the averaged discrete results when cells are migrating much more rapidly than they are proliferating. The pair-wise approximation performs better in the transient region than does the mean-field, despite having the same asymptotic behaviour. Our results show that each approximation only works in specific situations, thus we must be careful to use a suitable approximation for a given system, otherwise inaccurate predictions could be made.

Selective combination of visual and thermal imaging for resilient localization in adverse conditions : day and night, smoke and fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions.

Reliable automatic camera-laser calibration

Camera-laser calibration is necessary for many robotics and computer vision applications. However, existing calibration toolboxes still require laborious effort from the operator in order to achieve reliable and accurate results. This paper proposes algorithms that augment two existing trustful calibration methods with an automatic extraction of the calibration object from the sensor data. The result is a complete procedure that allows for automatic camera-laser calibration. The first stage of the procedure is automatic camera calibration which is useful in its own right for many applications. The chessboard extraction algorithm it provides is shown to outperform openly available techniques. The second stage completes the procedure by providing automatic camera-laser calibration. The procedure has been verified by extensive experimental tests with the proposed algorithms providing a major reduction in time required from an operator in comparison to manual methods.

Combining multiple sensor modalities for a localisation robust to smoke

This paper proposes an approach to obtain a localisation that is robust to smoke by exploiting multiple sensing modalities: visual and infrared (IR) cameras. This localisation is based on a state-of-the-art visual SLAM algorithm. First, we show that a reasonably accurate localisation can be obtained in the presence of smoke by using only an IR camera, a sensor that is hardly affected by smoke, contrary to a visual camera (operating in the visible spectrum). Second, we demonstrate that improved results can be obtained by combining the information from the two sensor modalities (visual and IR cameras). Third, we show that by detecting the impact of smoke on the visual images using a data quality metric, we can anticipate and mitigate the degradation in performance of the localisation by discarding the most affected data. The experimental validation presents multiple trajectories estimated by the various methods considered, all thoroughly compared to an accurate dGPS/INS reference.

Towards reliable perception for unmanned ground vehicles in challenging conditions

This work aims to promote reliability and integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicle (UGV) autonomy. For this purpose, a comprehensive UGV system, comprising many different exteroceptive and proprioceptive sensors has been built. The first contribution of this work is a large, accurately calibrated and synchronised, multi-modal data-set, gathered in controlled environmental conditions, including the presence of dust, smoke and rain. The data have then been used to analyse the effects of such challenging conditions on perception and to identify common perceptual failures. The second contribution is a presentation of methods for mitigating these failures to promote perceptual integrity in adverse environmental conditions.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Supplementary information : weighted tree kernels for sequence analysis

Genomic sequences are fundamentally text documents, admitting various representations according to need and tokenization. Gene expression depends crucially on binding of enzymes to the DNA sequence at small, poorly conserved binding sites, limiting the utility of standard pattern search. However, one may exploit the regular syntactic structure of the enzyme's component proteins and the corresponding binding sites, framing the problem as one of detecting grammatically correct genomic phrases. In this paper we propose new kernels based on weighted tree structures, traversing the paths within them to capture the features which underpin the task. Experimentally, we and that these kernels provide performance comparable with state of the art approaches for this problem, while offering significant computational advantages over earlier methods. The methods proposed may be applied to a broad range of sequence or tree-structured data in molecular biology and other domains.

Development of an integrated GIS and land use planning course : impacts of hybrid instructional methods

This study reports an action research undertaken at Queensland University of Technology. It evaluates the effectiveness of the integration of GIS within the substantive domains of an existing land use planning course in 2011. Using student performance, learning experience survey, and questionnaire survey data, it also evaluates the impacts of incorporating hybrid instructional methods (e.g., in-class and online instructional videos) in 2012 and 2013. Results show that: students (re)iterated the importance of GIS in the course justifying the integration; the hybrid methods significantly increased student performance; and unlike replacement, the videos are more suitable as a complement to in-class activity.

Nutrition screening and assessment in Parkinson's disease : a comparison of methods

Background & Aims Nutrition screening and assessment enable early identification of malnourished people and those at risk of malnutrition. Appropriate assessment tools assist with informing and monitoring nutrition interventions. Tool choice needs to be appropriate to the population and setting. Methods Community-dwelling people with Parkinson’s disease (>18 years) were recruited. Body mass index (BMI) was calculated from weight and height. Participants were classified as underweight according to World Health Organisation (WHO) (≤18.5kg/m2) and age specific (<65 years,≤18.5kg/m2; ≥65 years,≤23.5kg/m2) cut-offs. The Mini-Nutritional Assessment (MNA) screening (MNA-SF) and total assessment scores were calculated. The Patient-Generated Subjective Global Assessment (PG-SGA), including the Subjective Global Assessment (SGA), was performed. Sensitivity, specificity, positive predictive value, negative predictive value and weighted kappa statistic of each of the above compared to SGA were determined. Results Median age of the 125 participants was 70.0(35-92) years. Age-specific BMI (Sn 68.4%, Sp 84.0%) performed better than WHO (Sn 15.8%, Sp 99.1%) categories. MNA-SF performed better (Sn 94.7%, Sp 78.3%) than both BMI categorisations for screening purposes. MNA had higher specificity but lower sensitivity than PG-SGA (MNA Sn 84.2%, Sp 87.7%; PG-SGA Sn 100.0%, Sp 69.8%). Conclusions BMI lacks sensitivity to identify malnourished people with Parkinson’s disease and should be used with caution. The MNA-SF may be a better screening tool in people with Parkinson’s disease. The PG-SGA performed well and may assist with informing and monitoring nutrition interventions. Further research should be conducted to validate screening and assessment tools in Parkinson’s disease.  

Integrin-functionalized artificial membranes as test platforms for monitoring small integrin ligand binding by surface plasmon-enhanced fluorescence spectroscopy

The design and synthesis of molecularly or supramolecularly defined interfacial architectures have seen in recent years a remarkable growth of interest and scientific research activities for various reasons. On the one hand, it is generally believed that the construction of an interactive interface between the living world of cells, tissue, or whole organisms and the (inorganic or organic) materials world of technical devices such as implants or medical parts requires proper construction and structural (and functional) control of this organism–machine interface. It is still the very beginning of generating a better understanding of what is needed to make an organism tolerate implants, to guarantee bidirectional communication between microelectronic devices and living tissue, or to simply construct interactive biocompatibility of surfaces in general. This exhaustive book lucidly describes the design, synthesis, assembly and characterization, and bio-(medical) applications of interfacial layers on solid substrates with molecularly or supramolecularly controlled architectures. Experts in the field share their contributions that have been developed in recent years.

Different in vitro activation methods for latent transforming growth factors (TGF)–β : considerable exogenous factors to promote higher mesenchymal-origin cell proliferation in a bioprocessing platform

Regenerative medicine includes two efficient techniques, namely tissue-engineering and cell-based therapy in order to repair tissue damage efficiently. Most importantly, huge numbers of autologous cells are required to deal these practices. Nevertheless, primary cells, from autologous tissue, grow very slowly while culturing in vitro; moreover, they lose their natural characteristics over prolonged culturing period. Transforming growth factors-beta (TGF-β) is a ubiquitous protein found biologically in its latent form, which prevents it from eliciting a response until conversion to its active form. In active form, TGF-β acts as a proliferative agent in many cell lines of mesenchymal origin in vitro. This article reviews on some of the important activation methods-physiochemical, enzyme-mediated, non-specific protein interaction mediated, and drug-induced- of TGF-β, which may be established as exogenous factors to be used in culturing medium to obtain extensive proliferation of primary cells.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Sensitive methods for studying the environmental performance of protective coatings

The safe working lifetime of a structure in a corrosive or other harsh environment is frequently not limited by the material itself but rather by the integrity of the coating material. Advanced surface coatings are usually crosslinked organic polymers such as epoxies and polyurethanes which must not shrink, crack or degrade when exposed to environmental extremes. While standard test methods for environmental durability of coatings have been devised, the tests are structured more towards determining the end of life rather than in anticipation of degradation. We have been developing prognostic tools to anticipate coating failure by using a fundamental understanding of their degradation behaviour which, depending on the polymer structure, is mediated through hydrolytic or oxidation processes. Fourier transform infrared spectroscopy (FTIR) is a widely-used laboratory technique for the analysis of polymer degradation and with the development of portable FTIR spectrometers, new opportunities have arisen to measure polymer degradation non-destructively in the field. For IR reflectance sampling, both diffuse (scattered) and specular (direct) reflections can occur. The complexity in these spectra has provided interesting opportunities to study surface chemical and physical changes during paint curing, service abrasion and weathering, but has often required the use of advanced statistical analysis methods such as chemometrics to discern these changes. Results from our studies using this and related techniques and the technical challenges that have arisen will be presented.