Predicting people’s intention to donate their body to medical science and research

Predictors of people’s intention to register with a body bequest program for donating their deceased body to medical science and research were examined using standard theory of planned behavior (TPB) predictors (attitude, subjective norm, perceived behavioral control) and adding moral norm, altruism, and knowledge. Australian students (N = 221) at a university with a recently established body bequest program completed measures of the TPB’s underlying beliefs (behavioral, normative, and control beliefs) and standard and extended TPB predictors, with a sub-sample reporting their registration-related behavior 2 months later. The standard TPB accounted for 43.6%, and the extended predictors an additional 15.1% of variance in intention. The significant predictors were attitude, subjective norm, and moral norm, partially supporting an extended TPB in understanding people’s body donation intentions. Further, important underlying beliefs can inform strategies to target prospective donors.

From News Blogs to News on Twitter: Gatewatching and Collaborative News Curation

This chapter, explores the role of the second tier of independent news blogs as it developed in the years following the Seattle WTO protests in 1999, and outlines the practice of gatewatching as a key element of news bloggers’ activities. We critique perceptions of the news blogosphere as an echo chamber or filter bubble whose discussions about current events are detached from journalistic coverage, and demonstrate instead the close interconnections between independent news bloggers and professional journalists in the wider media ecology. Finally, we sketch the gradual transition and broadening of gatewatching practices in the news blogosphere towards the collaborative curation of news sharing in contemporary social media spaces, and outline the further research questions which emerge from such transformations of the flows of news and discussion.

Complete-linkage clustering for voice activity detection in audio and visual speech

We propose a novel technique for conducting robust voice activity detection (VAD) in high-noise recordings. We use Gaussian mixture modeling (GMM) to train two generic models; speech and non-speech. We then score smaller segments of a given (unseen) recording against each of these GMMs to obtain two respective likelihood scores for each segment. These scores are used to compute a dissimilarity measure between pairs of segments and to carry out complete-linkage clustering of the segments into speech and non-speech clusters. We compare the accuracy of our method against state-of-the-art and standardised VAD techniques to demonstrate an absolute improvement of 15% in half-total error rate (HTER) over the best performing baseline system and across the QUT-NOISE-TIMIT database. We then apply our approach to the Audio-Visual Database of American English (AVDBAE) to demonstrate the performance of our algorithm in using visual, audio-visual or a proposed fusion of these features.

Bangarra Dance Theatre: Copyright law and Indigenous culture

This article considers the artistic and legal practices of Bangarra Dance Theatre in a case study of copyright law management in relation to Indigenous culture. It is grounded in the particular local experience, knowledge and understanding of copyright law displayed by the performing arts company. The first part considers the special relationship between Bangarra Dance Theatre and the Munyarrun Clan. It examines the contractual arrangements developed to recognise communal ownership. The next section examines the role of the artistic director and choreographer. It looks at the founder, Carole Johnson, and her successor, Stephen Page. The third part of the article focuses on the role of the composer, David Page. It examines his ambition to set up a Indigenous recording company, Nikinali. Part 4 focuses upon the role of the artistic designers. It looks at the contributions of artistic designers such as Fiona Foley. Part 5 deals with broadcasts of performances on television, film, and multi-media. Part 6 considers the collaborations of Bangarra Dance Theatre with the Australian Ballet, and the Sydney Organising Committee for the Olympic Games. The conclusion considers how Bangarra Dance Theatre has played a part ina general campaign to increase protection of Indigenous copyright law.

Capnography monitoring during procedural sedation and analgesia: A systematic review protocol

Background An important potential clinical benefit of using capnography monitoring during procedural sedation and analgesia (PSA) is that this technology could improve patient safety by reducing serious sedation-related adverse events, such as death or permanent neurological disability, which are caused by inadequate oxygenation. The hypothesis is that earlier identification of respiratory depression using capnography leads to a change in clinical management that prevents hypoxaemia. As inadequate oxygenation/ventilation is the most common reason for injury associated with PSA, reducing episodes of hypoxaemia would indicate that using capnography would be safer than relying on standard monitoring alone. Methods/design The primary objective of this review is to determine whether using capnography during PSA in the hospital setting improves patient safety by reducing the risk of hypoxaemia (defined as an arterial partial pressure of oxygen below 60 mmHg or percentage of haemoglobin that is saturated with oxygen [SpO2] less than 90 %). A secondary objective of this review is to determine whether changes in the clinical management of sedated patients are the mediating factor for any observed impact of capnography monitoring on the rate of hypoxaemia. The potential adverse effect of capnography monitoring that will be examined in this review is the rate of inadequate sedation. Electronic databases will be searched for parallel, crossover and cluster randomised controlled trials comparing the use of capnography with standard monitoring alone during PSA that is administered in the hospital setting. Studies that included patients who received general or regional anaesthesia will be excluded from the review. Non-randomised studies will be excluded. Screening, study selection and data extraction will be performed by two reviewers. The Cochrane risk of bias tool will be used to assign a judgment about the degree of risk. Meta-analyses will be performed if suitable. Discussion This review will synthesise the evidence on an important potential clinical benefit of capnography monitoring during PSA within hospital settings. Systematic review registration: PROSPERO CRD42015023740

Managing milling train operation and performance

This paper discusses the main milling train management tasks necessary for maintaining good extraction performance through a season. The main activities discussed are making week by week decisions about shredder and mill setting adjustments, and selecting preseason mill settings. To maintain satisfactory milling train extraction performance, the main factors affecting extraction should be examined: cane preparation with pol in open cells or shredder torque, delivery nip compaction through the load or torque controller outputs such as roll lift, feed chute flap position or pressure feeder to mill speed ratio, and added water rate. To select mill settings for the coming season, delivery nip compaction and feed chute exit compaction can be inferred from the previous seasons.

SOTrain: A new program for training supervisors and operators in Australian sugar factories

The provision of effective training of supervisors and operators is essential if sugar factories are to operate profitably and in an environmentally sustainable and safe manner. The benefits of having supervisor and operator staff with a high level of operational skills are reduced stoppages, increased recovery, improved sugar quality, reduced damage to equipment, and reduced OH&S and environmental impacts. Training of new operators and supervisors in factories has traditionally relied on on-the-job training of the new or inexperienced staff by experienced supervisors and operators, supplemented by courses conducted by contractors such as Sugar Research Institute (SRI). However there is clearly a need for staff to be able to undertake training at any time, drawing on the content of online courses as required. An improved methodology for the training of factory supervisors and operators has been developed by QUT on behalf of a syndicate of mills. The new methodology provides ‘at factory’ learning via self-paced modules. Importantly, the training resources for each module are designed to support the training programs within sugar factories, thereby establishing a benchmark for training across the sugar industry. The modules include notes, training guides and session plans, guidelines for walkthrough tours of the stations, learning activities, resources such as videos, animations, job aids and competency assessments. The materials are available on the web for registered users in Australian Mills and many activities are best undertaken online. Apart from a few interactive online resources, the materials for each module can also be downloaded. The acronym SOTrain (Supervisor and Operator Training) has been applied to the new training program.

A tensor-based morphometry study of genetic influences on brain structure using a new fluid registration method

We incorporated a new Riemannian fluid registration algorithm into a general MRI analysis method called tensor-based morphometry to map the heritability of brain morphology in MR images from 23 monozygotic and 23 dizygotic twin pairs. All 92 3D scans were fluidly registered to a common template. Voxelwise Jacobian determinants were computed from the deformation fields to assess local volumetric differences across subjects. Heritability maps were computed from the intraclass correlations and their significance was assessed using voxelwise permutation tests. Lobar volume heritability was also studied using the ACE genetic model. The performance of this Riemannian algorithm was compared to a more standard fluid registration algorithm: 3D maps from both registration techniques displayed similar heritability patterns throughout the brain. Power improvements were quantified by comparing the cumulative distribution functions of the p-values generated from both competing methods. The Riemannian algorithm outperformed the standard fluid registration.

Statistically assisted fluid registration algorithm - SAFIRA

In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the regularizing (i.e., the non-conservative Lagrangian) terms, creating four versions of the algorithm. We evaluated the accuracy of each algorithm variant using the manually labeled LPBA40 dataset, which provides us with ground truth anatomical segmentations. We also compared the power of the different algorithms using tensor-based morphometry -a technique to analyze local volumetric differences in brain structure- applied to 46 3D brain scans from healthy monozygotic twins.

A lagrangian formulation for statistical fluid registration

We defined a new statistical fluid registration method with Lagrangian mechanics. Although several authors have suggested that empirical statistics on brain variation should be incorporated into the registration problem, few algorithms have included this information and instead use regularizers that guarantee diffeomorphic mappings. Here we combine the advantages of a large-deformation fluid matching approach with empirical statistics on population variability in anatomy. We reformulated the Riemannian fluid algorithmdeveloped in [4], and used a Lagrangian framework to incorporate 0 th and 1st order statistics in the regularization process. 92 2D midline corpus callosum traces from a twin MRI database were fluidly registered using the non-statistical version of the algorithm (algorithm 0), giving initial vector fields and deformation tensors. Covariance matrices were computed for both distributions and incorporated either separately (algorithm 1 and algorithm 2) or together (algorithm 3) in the registration. We computed heritability maps and two vector and tensorbased distances to compare the power and the robustness of the algorithms.

A nonconservative Lagrangian framework for statistical fluid registration - SAFIRA

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented, where the deformation was regularized by penalizing deviations from a zero rate of strain. In, the terms regularizing the deformation included the covariance of the deformation matrices Σ and the vector fields (q). Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.

Brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (HARDI) study

We developed an analysis pipeline enabling population studies of HARDI data, and applied it to map genetic influences on fiber architecture in 90 twin subjects. We applied tensor-driven 3D fluid registration to HARDI, resampling the spherical fiber orientation distribution functions (ODFs) in appropriate Riemannian manifolds, after ODF regularization and sharpening. Fitting structural equation models (SEM) from quantitative genetics, we evaluated genetic influences on the Jensen-Shannon divergence (JSD), a novel measure of fiber spatial coherence, and on the generalized fiber anisotropy (GFA) a measure of fiber integrity. With random-effects regression, we mapped regions where diffusion profiles were highly correlated with subjects' intelligence quotient (IQ). Fiber complexity was predominantly under genetic control, and higher in more highly anisotropic regions; the proportion of genetic versus environmental control varied spatially. Our methods show promise for discovering genes affecting fiber connectivity in the brain.

Fluid registration of diffusion tensor images using information theory

We apply an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or $J$-divergence, to fluid registration of diffusion tensor images. The difference between diffusion tensors is quantified based on the sKL-divergence of their associated probability density functions (PDFs). Three-dimensional DTI data from 34 subjects were fluidly registered to an optimized target image. To allow large image deformations but preserve image topology, we regularized the flow with a large-deformation diffeomorphic mapping based on the kinematics of a Navier-Stokes fluid. A driving force was developed to minimize the $J$-divergence between the deforming source and target diffusion functions, while reorienting the flowing tensors to preserve fiber topography. In initial experiments, we showed that the sKL-divergence based on full diffusion PDFs is adaptable to higher-order diffusion models, such as high angular resolution diffusion imaging (HARDI). The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data.

Improving fluid registration through white matter segmentation in a twin study design

Robust and automatic non-rigid registration depends on many parameters that have not yet been systematically explored. Here we determined how tissue classification influences non-linear fluid registration of brain MRI. Twin data is ideal for studying this question, as volumetric correlations between corresponding brain regions that are under genetic control should be higher in monozygotic twins (MZ) who share 100% of their genes when compared to dizygotic twins (DZ) who share half their genes on average. When these substructure volumes are quantified using tensor-based morphometry, improved registration can be defined based on which method gives higher MZ twin correlations when compared to DZs, as registration errors tend to deplete these correlations. In a study of 92 subjects, higher effect sizes were found in cumulative distribution functions derived from statistical maps when performing tissue classification before fluid registration, versus fluidly registering the raw images. This gives empirical evidence in favor of pre-segmenting images for tensor-based morphometry.

Automated 3D mapping & shape analysis of the lateral ventricles via fluid registration of multiple surface-based atlases

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles, designed for monitoring degenerative disease effects in clinical neuroscience studies and drug trials. First we used a set of parameterized surfaces to represent the ventricles in a manually labeled set of 9 subjects' MRIs (atlases). We fluidly registered each of these atlases and mesh models to a set of MRIs from 12 Alzheimer's disease (AD) patients and 14 matched healthy elderly subjects, and we averaged the resulting meshes for each of these images. Validation experiments on expert segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease-related alterations monotonically improved as the number of atlases, N, was increased from 1 to 9. We then combined the segmentations with a radial mapping approach to localize ventricular shape differences in patients. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases, and we formulated a statistical stopping criterion to determine the optimal value of N. Anterior horn anomalies in Alzheimer's patients were only detected with the multi-atlas segmentation, which clearly outperformed the standard single-atlas approach.