Weak conformance of process models with respect to data objects

Process models specify behavioral aspects by describing ordering constraints between tasks which must be accomplished to achieve envisioned goals. Tasks usually exchange information by means of data objects, i.e., by writing information to and reading information from data objects. A data object can be characterized by its states and allowed state transitions. In this paper, we propose a notion which checks conformance of a process model with respect to data objects that its tasks access. This new notion can be used to tell whether in every execution of a process model each time a task needs to access a data object in a particular state, it is ensured that the data object is in the expected state or can reach the expected state and, hence, the process model can achieve its goals.

Managing your research data

Presentation by Tony Beatton, QUT Business School, at Managing your research data seminar, 2012

Managing data. Why do it?

Presentation by Siobhann McCafferty, Institute for Future Environments, at Managing your research data seminar, 2012

Bayesian P-splines with a multiplicative term in EMG trace data

A method is proposed to describe force or compound muscle action potential (CMAP) trace data collected in an electromyography study for motor unit number estimation (MUNE). Experimental data was collected using incre- mental stimulation at multiple durations. However, stimulus information, vital for alternate MUNE methods, is not comparable for multiple duration data and therefore previous methods of MUNE (Ridall et al., 2006, 2007) cannot be used with any reliability. Hypothesised ring combinations of motor units are mod- elled using a multiplicative factor and Bayesian P-spline formulation. The model describes the process for force and CMAP in a meaningful way.

Managing Research Data: Mechanical testing and modelling of bone-implant constructs

Presentation by Dr Caroline Grant, Science & Engineering Faculty, IHBI, at Managing your research data seminar, 2012

Managing Research Data at QUT

Presentation by Paula Callan, eResearch Access Coordinator, at Managing your research data seminar, 2012

Research Data Management Update on National Programs: New infrastructure and opportunities

Presentation by Dr Joe Young, ITS-HPC and Research Support, Managing your research data seminar, 2012

Lumbar epidural steroids are not cost effective in sciatica

Background Lumbar Epidural Steroids Injections (ESI’s) have previously been shown to provide some degree of pain relief in sciatica. Number Needed To Treat (NNT) to achieve 50% pain relief has been estimated at 7 from the results of randomised controlled trials. Pain relief is temporary. They remain one of the most commonly provided procedures in the UK. It is unknown whether this pain relief represents good value for money. Methods 228 patients were randomised into a multi-centre Double Blind Randomised Controlled Trial. Subjects received up to 3 ESI’s or intra-spinous saline depending on response and fall off with the first injection. All other treatments were permitted. All received a review of analgesia, education and physical therapy. Quality of life was assessed using the SF36 at 6 points and compared using independent sample t-tests. Follow up was up to 1 yr. Missing data was imputed using last observation carried forward (LOCF). QALY’s (Quality of Life Years) were derived from preference based heath values (summary health utility score). SF-6D health state classification was derived from SF-36 raw score data. Standard gambles (SG) were calculated using Model 10. SG scores were calculated on trial results. LOCF was not used for this. Instead average SG were derived for a subset of patients with observations for all visits up to week 12. Incremental QALY’s were derived as the difference in the area between the SG curve for the active group and placebo group. Results SF36 domains showed a significant improvement in pain at week 3 but this was not sustained (mean 54 Active vs 61 Placebo P<0.05). Other domains did not show any significant gains compared with placebo. For derivation of SG the number in the sample in each period differed. In week 12, average SG scores for active and placebo converged. In other words, the health gain for the active group as measured by SG was achieved by the placebo group by week 12. The incremental QALY gained for a patient under the trial protocol compared with the standard care package was 0.0059350. This is equivalent to an additional 2.2 days of full health. The cost per QALY gained to the provider from a patient management strategy administering one epidural as suggested by results was £25 745.68. This result was derived assuming that the gain in QALY data calculated for patients under the trial protocol would approximate that under a patient management strategy based on the trial results (one ESI). This is above the threshold suggested by some as a cost effective treatment. Conclusions The transient benefit in pain relief afforded by ESI’s does not appear to be cost-effective. Further work is needed to develop more cost-effective conservative treatments for sciatica.

The importance of accurate beam data measurement for SRT/SRS treatment planning

Introduction This study investigated the sensitivity of calculated stereotactic radiotherapy and radiosurgery doses to the accuracy of the beam data used by the treatment planning system. Methods Two sets of field output factors were acquired using fields smaller than approximately 1 cm2, for inclusion in beam data used by the iPlan treatment planning system (Brainlab, Feldkirchen, Germany). One set of output factors were measured using an Exradin A16 ion chamber (Standard Imaging, Middleton, USA). Although this chamber has a relatively small collecting volume (0.007 cm3), measurements made in small fields using this chamber are subject to the effects of volume averaging, electronic disequilibrium and chamber perturbations. The second, more accurate, set of measurements were obtained by applying perturbation correction factors, calculated using Monte Carlo simulations according to a method recommended by Cranmer-Sargison et al. [1] to measurements made using a 60017 unshielded electron diode (PTW, Freiburg, Germany). A series of 12 sample patient treatments were used to investigate the effects of beam data accuracy on resulting planned dose. These treatments, which involved 135 fields, were planned for delivery via static conformal arcs and 3DCRT techniques, to targets ranging from prostates (up to 8 cm across) to meningiomas (usually more than 2 cm across) to arterioveinous malformations, acoustic neuromas and brain metastases (often less than 2 cm across). Isocentre doses were calculated for all of these fields using iPlan, and the results of using the two different sets of beam data were evaluated. Results While the isocentre doses for many fields are identical (difference = 0.0 %), there is a general trend for the doses calculated using the data obtained from corrected diode measurements to exceed the doses calculated using the less-accurate Exradin ion chamber measurements (difference\0.0 %). There are several alarming outliers (circled in the Fig. 1) where doses differ by more than 3 %, in beams from sample treatments planned for volumes up to 2 cm across. Discussion and conclusions These results demonstrate that treatment planning dose calculations for SRT/SRS treatments can be substantially affected when beam data for fields smaller than approximately 1 cm2 are measured inaccurately, even when treatment volumes are up to 2 cm across.

CTCombine : rotating and combining CT data with an accurate detector model to simulate radiotherapy portal imaging at non-zero beam angles

Established Monte Carlo user codes BEAMnrc and DOSXYZnrc permit the accurate and straightforward simulation of radiotherapy experiments and treatments delivered from multiple beam angles. However, when an electronic portal imaging detector (EPID) is included in these simulations, treatment delivery from non-zero beam angles becomes problematic. This study introduces CTCombine, a purpose-built code for rotating selected CT data volumes, converting CT numbers to mass densities, combining the results with model EPIDs and writing output in a form which can easily be read and used by the dose calculation code DOSXYZnrc...

The big data lottery

Big data is certainly the buzz term in executive networking circles at the moment. Heralded by management consultancies and research organisations alike as the next big thing in business efficiency, it is shooting up the Gartner hype cycle to the giddy heights of the peak of inflated expectations before it tumbles down in to the trough of disillusionment

Accelerometer data reduction : a comparison of four reduction algorithms on select outcome variables

Purpose Accelerometers are recognized as a valid and objective tool to assess free-living physical activity. Despite the widespread use of accelerometers, there is no standardized way to process and summarize data from them, which limits our ability to compare results across studies. This paper a) reviews decision rules researchers have used in the past, b) compares the impact of using different decision rules on a common data set, and c) identifies issues to consider for accelerometer data reduction. Methods The methods sections of studies published in 2003 and 2004 were reviewed to determine what decision rules previous researchers have used to identify wearing period, minimal wear requirement for a valid day, spurious data, number of days used to calculate the outcome variables, and extract bouts of moderate to vigorous physical activity (MVPA). For this study, four data reduction algorithms that employ different decision rules were used to analyze the same data set. Results The review showed that among studies that reported their decision rules, much variability was observed. Overall, the analyses suggested that using different algorithms impacted several important outcome variables. The most stringent algorithm yielded significantly lower wearing time, the lowest activity counts per minute and counts per day, and fewer minutes of MVPA per day. An exploratory sensitivity analysis revealed that the most stringent inclusion criterion had an impact on sample size and wearing time, which in turn affected many outcome variables. Conclusions These findings suggest that the decision rules employed to process accelerometer data have a significant impact on important outcome variables. Until guidelines are developed, it will remain difficult to compare findings across studies

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Comparison of 3 accelerometer data reduction approaches, step counts, and 2 self-report measures for estimating physical activity in free-living adults

Background Accelerometers have become one of the most common methods of measuring physical activity (PA). Thus, validity of accelerometer data reduction approaches remains an important research area. Yet, few studies directly compare data reduction approaches and other PA measures in free-living samples. Objective To compare PA estimates provided by 3 accelerometer data reduction approaches, steps, and 2 self-reported estimates: Crouter's 2-regression model, Crouter's refined 2-regression model, the weighted cut-point method adopted in the National Health and Nutrition Examination Survey (NHANES; 2003-2004 and 2005-2006 cycles), steps, IPAQ, and 7-day PA recall. Methods A worksite sample (N = 87) completed online-surveys and wore ActiGraph GT1M accelerometers and pedometers (SW-200) during waking hours for 7 consecutive days. Daily time spent in sedentary, light, moderate, and vigorous intensity activity and percentage of participants meeting PA recommendations were calculated and compared. Results Crouter's 2-regression (161.8 +/- 52.3 minutes/day) and refined 2-regression (137.6 +/- 40.3 minutes/day) models provided significantly higher estimates of moderate and vigorous PA and proportions of those meeting PA recommendations (91% and 92%, respectively) as compared with the NHANES weighted cut-point method (39.5 +/- 20.2 minutes/day, 18%). Differences between other measures were also significant. Conclusions When comparing 3 accelerometer cut-point methods, steps, and self-report measures, estimates of PA participation vary substantially.

Design of a garment for data collection of toddler language and physical activity

Design process phases of development, evaluation and implementation were used to create a garment to simultaneously collect reliable data of speech production and intensity of movement of toddlers (18-36 months). A series of prototypes were developed and evaluated that housed accelerometer-based motion sensors and a digital transmitter with microphone. The approved test garment was a top constructed from loop-faced fabric with interior pockets to house devices. Extended side panels allowed for sizing. In total, 56 toddlers (28 male; 28 female; 16-36 months of age) participated in the study providing pilot and baseline data. The test garment was effective in collecting data as evaluated for accuracy and reliability using ANOVA for accelerometer data, transcription of video for type of movement, and number and length of utterances for speech production. The data collection garment has been implemented in various studies across disciplines.