Are organ donation communication decisions reasoned or reactive? A test of the utility of an augmented Theory of Planned Behaviour with the Prototype/Willingness Model

Objectives: To explore whether people's organ donation consent decisions occur via a reasoned and/or social reaction pathway. --------- Design: We examined prospectively students' and community members' decisions to register consent on a donor register and discuss organ donation wishes with family. --------- Method: Participants completed items assessing theory of planned behaviour (TPB; attitude, subjective norm, perceived behavioural control (PBC)), prototype/willingness model (PWM; donor prototype favourability/similarity, past behaviour), and proposed additional influences (moral norm, self-identity, recipient prototypes) for registering (N=339) and discussing (N=315) intentions/willingness. Participants self-reported their registering (N=177) and discussing (N=166) behaviour 1 month later. The utility of the (1) TPB, (2) PWM, (3) augmented TPB with PWM, and (4) augmented TPB with PWM and extensions was tested using structural equation modelling for registering and discussing intentions/willingness, and logistic regression for behaviour. --------- Results: While the TPB proved a more parsimonious model, fit indices suggested that the other proposed models offered viable options, explaining greater variance in communication intentions/willingness. The TPB, augmented TPB with PWM, and extended augmented TPB with PWM best explained registering and discussing decisions. The proposed and revised PWM also proved an adequate fit for discussing decisions. Respondents with stronger intentions (and PBC for registering) had a higher likelihood of registering and discussing. --------- Conclusions: People's decisions to communicate donation wishes may be better explained via a reasoned pathway (especially for registering); however, discussing involves more reactive elements. The role of moral norm, self-identity, and prototypes as influences predicting communication decisions were highlighted also.

The Validity of the Brief Michigan Alcohol Screening Test (bMAST) as a Problem Drinking Severity Measure

Objective: The Brief Michigan Alcoholism Screening Test (bMAST) is a 10-item test derived from the 25-item Michigan Alcoholism Screening Test (MAST). It is widely used in the assessment of alcohol dependence. In the absence of previous validation studies, the principal aim of this study was to assess the validity and reliability of the bMAST as a measure of the severity of problem drinking. Method: There were 6,594 patients (4,854 men, 1,740 women) who had been referred for alcohol-use disorders to a hospital alcohol and drug service who voluntarily participated in this study. Results: An exploratory factor analysis defined a two-factor solution, consisting of Perception of Current Drinking and Drinking Consequences factors. Structural equation modeling confirmed that the fit of a nine-item, two-factor model was superior to the original one-factor model. Concurrent validity was assessed through simultaneous administration of the Alcohol Use Disorders Identification Test (AUDIT) and associations with alcohol consumption and clinically assessed features of alcohol dependence. The two-factor bMAST model showed moderate correlations with the AUDIT. The two-factor bMAST and AUDIT were similarly associated with quantity of alcohol consumption and clinically assessed dependence severity features. No differences were observed between the existing weighted scoring system and the proposed simple scoring system. Conclusions: In this study, both the existing bMAST total score and the two-factor model identified were as effective as the AUDIT in assessing problem drinking severity. There are additional advantages of employing the two-factor bMAST in the assessment and treatment planning of patients seeking treatment for alcohol-use disorders. (J. Stud. Alcohol Drugs 68: 771-779,2007)

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

The development of a serological-based diagnostic test for Dasheen mosaic potyvirus (DsMV)

Dasheen mosaic potyvirus (DsMV) is an important virus affecting taro. The virus has been found wherever taro is grown and infects both the edible and ornamental aroids, causing yield losses of up to 60%. The presence of DsMV, and other viruses,prevents the international movement of taro germplasm between countries. This has a significant negative impact on taro production in many countries due to the inability to access improved taro lines produced in breeding programs. To overcome this problem, sensitive and reliable virus diagnostic tests need to be developed to enable the indexing of taro germplasm. The aim of this study was to generate an antiserum against a recombinant DsMV coat protein (CP) and to develop a serological-based diagnostic test that would detect Pacific Island isolates of the virus. The CP-coding region of 16 DsMV isolates from Papua New Guinea, Samoa, Solomon Islands, French Polynesia, New Caledonia and Vietnam were amplified,cloned and sequenced. The size of the CP-coding region ranged from 939 to 1038 nucleotides and encoded putative proteins ranged from 313 to 346 amino acids, with the molecular mass ranging from 34 to 38 kDa. Analysis ofthe amino acid sequences revealed the presence of several amino acid motifs typically found in potyviruses,including DAG, WCIE/DN, RQ and AFDF. When the amino acid sequences were compared with each other and the DsMV sequences on the database, the maximum variability was21.9%. When the core region ofthe CP was analysed, the maximum variability dropped to 6% indicating most variability was present in the N terminus. Within seven PNG isolates ofDsMV, the maximum variability was 16.9% and 3.9% over the entire CP-coding region and core region, respectively. The sequence ofPNG isolate P1 was most similar to all other sequences. Phylogenetic analysis indicated that almost all isolates grouped according to their provenance. Further, the seven PNG isolates were grouped according to the region within PNG from which they were obtained. Due to the extensive variability over the entire CP-coding region, the core region ofthe CP ofPNG isolate Pl was cloned into a protein expression vector and expressed as a recombinant protein. The protein was purified by chromatography and SDS-PAGE and used as an antigen to generate antiserum in a rabbit. In western blots, the antiserum reacted with bands of approximately 45-47 kDa in extracts from purified DsMV and from known DsMV -infected plants from PNG; no bands were observed using healthy plant extracts. The antiserum was subsequently incorporated into an indirect ELISA. This procedure was found to be very sensitive and detected DsMV in sap diluted at least 1:1,000. Using both western blot and ELISA formats,the antiserum was able to detect a wide range ofDsMV isolates including those from Australia, New Zealand, Fiji, French Polynesia, New Caledonia, Papua New Guinea, Samoa, Solomon Islands and Vanuatu. These plants were verified to be infected with DsMV by RT-PCR. In specificity tests, the antiserum was also found to react with sap from plants infected with SCMV, PRSV-P, PRSV-W, but not with PVY or CMV -infected plants.

Sensitivity of the lane change test as a measure of in-vehicle system demand

The Lane Change Test (LCT) is one of the growing number of methods developed to quantify driving performance degradation brought about by the use of in-vehicle devices. Beyond its validity and reliability, for such a test to be of practical use, it must also be sensitive to the varied demands of individual tasks. The current study evaluated the ability of several recent LCT lateral control and event detection parameters to discriminate between visual-manual and cognitive surrogate In-Vehicle Information System tasks with different levels of demand. Twenty-seven participants (mean age 24.4 years) completed a PC version of the LCT while performing visual search and math problem solving tasks. A number of the lateral control metrics were found to be sensitive to task differences, but the event detection metrics were less able to discriminate between tasks. The mean deviation and lane excursion measures were able to distinguish between the visual and cognitive tasks, but were less sensitive to the different levels of task demand. The other LCT metrics examined were less sensitive to task differences. A major factor influencing the sensitivity of at least some of the LCT metrics could be the type of lane change instructions given to participants. The provision of clear and explicit lane change instructions and further refinement of its metrics will be essential for increasing the utility of the LCT as an evaluation tool.

An empirical test of causal relationships of factors affecting ICT adoption for building project management : an Indian SME case study

Purpose - Building project management (BPM) requires effective coordination and collaboration between multiple project team organisations which can be achieved by real time information flow between all participants. In the present scenario, this can be achieved by the use of information communication technologies (ICT). The purpose of this paper is to present part of a research project conducted to study the causal relationships between factors affecting ICT adoption for BPM by small and medium enterprises. Design/methodology/approach - This paper discusses structural equation modelling (SEM) analysis conducted to test the causal relationships between quantitative factors. Data for quantitative analysis were gathered through a questionnaire survey conducted in the Indian construction industry. Findings - SEM analysis results help in demonstrating that an increased and matured use of ICT for general administration within the organisation would lead to: an improved ICT infrastructure within the organisation; development of electronic databases; and a staff that is confident of using information technology (IT) tools. In such a scenario, staff would use advanced software and IT technologies for project management (PM) processes and that would lead to an increased adoption of ICT for PM processes. But, for general administration also, ICT adoption would be enhanced if the organisation is interacting more with geographically separated agencies and senior management perceives that significant benefits would accrue by adoption of ICT. All the factors are inter-related and their effect cannot be maximized in isolation. Originality/value - The results provide direction to building project managements for strategically adopting the effective use of ICT within their organisations and for BPM general.

Assessment of pile driving refusal using the standard test (SPT)

The design of driven pile foundations involves an iterative process requiring an initial estimate of the refusal level to determine the depth of boreholes for subsequent analyses. Current procedures for determining borehole depths incorporate parameters typically unknown at the investigation stage. Thus, a quantifiable procedure more applicable at this preliminary stage would provide greater confidence in estimating the founding level of driven piles. This paper examines the effectiveness of the Standard Penetration Test (SPT) in directly estimating driven pile refusal levels. A number of significant correlations were obtained between SPT information and pile penetration records demonstrating the potential application of the SPT. Results indicated pile penetration was generally best described as a function of both the pile toe and cumulative shaft SPT values. The influence of the toe SPT increased when piles penetrated rock. A refusal criteria was established from the results to guide both the estimation of borehole depths and likely pile lengths during the design stage.

Smart Grid Test Bed Design and Implementation

Given there is currently a migration trend from traditional electrical supervisory control and data acquisition (SCADA) systems towards a smart grid based approach to critical infrastructure management. This project provides an evaluation of existing and proposed implementations for both traditional electrical SCADA and smart grid based architectures, and proposals a set of reference requirements which test bed implementations should implement. A high-level design for smart grid test beds is proposed and initial implementation performed, based on the proposed design, using open source and freely available software tools. The project examines the move towards smart grid based critical infrastructure management and illustrates the increased security requirements. The implemented test bed provides a basic framework for testing network requirements in a smart grid environment, as well as a platform for further research and development. Particularly to develop, implement and test network security related disturbances such as intrusion detection and network forensics. The project undertaken proposes and develops an architecture of the emulation of some smart grid functionality. The Common Open Research Emulator (CORE) platform was used to emulate the communication network of the smart grid. Specifically CORE was used to virtualise and emulate the TCP/IP networking stack. This is intended to be used for further evaluation and analysis, for example the analysis of application protocol messages, etc. As a proof of concept, software libraries were designed, developed and documented to enable and support the design and development of further smart grid emulated components, such as reclosers, switches, smart meters, etc. As part of the testing and evaluation a Modbus based smart meter emulator was developed to provide basic functionality of a smart meter. Further code was developed to send Modbus request messages to the emulated smart meter and receive Modbus responses from it. Although the functionality of the emulated components were limited, it does provide a starting point for further research and development. The design is extensible to enable the design and implementation of additional SCADA protocols. The project also defines an evaluation criteria for the evaluation of the implemented test bed, and experiments are designed to evaluate the test bed according to the defined criteria. The results of the experiments are collated and presented, and conclusions drawn from the results to facilitate discussion on the test bed implementation. The discussion undertaken also present possible future work.

Corneal confocal microscopy : a novel noninvasive test to diagnose and stratify the severity of human diabetic neuropathy

OBJECTIVE: The accurate quantification of human diabetic neuropathy is important to define at-risk patients, anticipate deterioration, and assess new therapies. ---------- RESEARCH DESIGN AND METHODS: A total of 101 diabetic patients and 17 age-matched control subjects underwent neurological evaluation, neurophysiology tests, quantitative sensory testing, and evaluation of corneal sensation and corneal nerve morphology using corneal confocal microscopy (CCM). ---------- RESULTS: Corneal sensation decreased significantly (P = 0.0001) with increasing neuropathic severity and correlated with the neuropathy disability score (NDS) (r = 0.441, P < 0.0001). Corneal nerve fiber density (NFD) (P < 0.0001), nerve fiber length (NFL), (P < 0.0001), and nerve branch density (NBD) (P < 0.0001) decreased significantly with increasing neuropathic severity and correlated with NDS (NFD r = −0.475, P < 0.0001; NBD r = −0.511, P < 0.0001; and NFL r = −0.581, P < 0.0001). NBD and NFL demonstrated a significant and progressive reduction with worsening heat pain thresholds (P = 0.01). Receiver operating characteristic curve analysis for the diagnosis of neuropathy (NDS >3) defined an NFD of <27.8/mm2 with a sensitivity of 0.82 (95% CI 0.68–0.92) and specificity of 0.52 (0.40–0.64) and for detecting patients at risk of foot ulceration (NDS >6) defined a NFD cutoff of <20.8/mm2 with a sensitivity of 0.71 (0.42–0.92) and specificity of 0.64 (0.54–0.74). ---------- CONCLUSIONS: CCM is a noninvasive clinical technique that may be used to detect early nerve damage and stratify diabetic patients with increasing neuropathic severity. Established diabetic neuropathy leads to pain and foot ulceration. Detecting neuropathy early may allow intervention with treatments to slow or reverse this condition (1). Recent studies suggested that small unmyelinated C-fibers are damaged early in diabetic neuropathy (2–4) but can only be detected using invasive procedures such as sural nerve biopsy (4,5) or skin-punch biopsy (6–8). Our studies have shown that corneal confocal microscopy (CCM) can identify early small nerve fiber damage and accurately quantify the severity of diabetic neuropathy (9–11). We have also shown that CCM relates to intraepidermal nerve fiber loss (12) and a reduction in corneal sensitivity (13) and detects early nerve fiber regeneration after pancreas transplantation (14). Recently we have also shown that CCM detects nerve fiber damage in patients with Fabry disease (15) and idiopathic small fiber neuropathy (16) when results of electrophysiology tests and quantitative sensory testing (QST) are normal. In this study we assessed corneal sensitivity and corneal nerve morphology using CCM in diabetic patients stratified for the severity of diabetic neuropathy using neurological evaluation, electrophysiology tests, and QST. This enabled us to compare CCM and corneal esthesiometry with established tests of diabetic neuropathy and define their sensitivity and specificity to detect diabetic patients with early neuropathy and those at risk of foot ulceration.