Building Faculty Competence and Self-Efficacy for Using Z Space Virtual Reality (VR) Software in the Classroom

The environments of teaching and learning are changing as educational needs and technology advancements evolve. One of the newest technologies gaining momentum in healthcare education is virtual reality/augmented reality (VR/AR) that provides increased active learning opportunities by providing interaction. It has become imperative that faculty stay at the forefront of technology to educate the future care provider in navigating patient care. However, to capitalize on the advantages of using VR/AR technology software to enhance active learning, faculty need to know they exist, how they can use either and have confidence that they can use technology to enhance learning of today\u27s highly diverse and technology-savvy students. A literature review demonstrated a lack of confidence and knowledge by faculty on how to implement VR/AR technology software into the nursing curriculum. Further research reported that nursing faculty lacked time to research new technologies, lacked knowledge of technology use, not aware of best practices for the use of technologies, and lacked support by administration and peers. This project implemented an online educational module for nursing faculty focused on Z Space software learning. Measured levels of competence and self-efficacy were performed prior to and after the educational intervention. Data analyzed, demonstrated that both competence and self-efficacy improved on Z Space software as self-reported by faculty. Although this was a small sample size, it is recommended that nursing faculty participates in ongoing education on the use of VR technology software

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion

Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regionsof- interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability

Introducing coherent time control to cavity magnon-polariton modes

By connecting light to magnetism, cavity magnon-polaritons (CMPs) can link quantum computation to spintronics. Consequently, CMP-based information processing devices have emerged over the last years, but have almost exclusively been investigated with single-tone spectroscopy. However, universal computing applications will require a dynamic and on-demand control of the CMP within nanoseconds. Here, we perform fast manipulations of the different CMP modes with independent but coherent pulses to the cavity and magnon system. We change the state of the CMP from the energy exchanging beat mode to its normal modes and further demonstrate two fundamental examples of coherent manipulation. We first evidence dynamic control over the appearance of magnon-Rabi oscillations, i.e., energy exchange, and second, energy extraction by applying an anti-phase drive to the magnon. Our results show a promising approach to control building blocks valuable for a quantum internet and pave the way for future magnon-based quantum computing research

Shift-Symmetric Configurations in Two-Dimensional Cellular Automata: Irreversibility, Insolvability, and Enumeration

The search for symmetry as an unusual yet profoundly appealing phenomenon, and the origin of regular, repeating configuration patterns have long been a central focus of complexity science and physics. To better grasp and understand symmetry of configurations in decentralized toroidal architectures, we employ group-theoretic methods, which allow us to identify and enumerate these inputs, and argue about irreversible system behaviors with undesired effects on many computational problems. The concept of so-called configuration shift-symmetry is applied to two-dimensional cellular automata as an ideal model of computation. Regardless of the transition function, the results show the universal insolvability of crucial distributed tasks, such as leader election, pattern recognition, hashing, and encryption. By using compact enumeration formulas and bounding the number of shift-symmetric configurations for a given lattice size, we efficiently calculate the probability of a configuration being shift-symmetric for a uniform or density-uniform distribution. Further, we devise an algorithm detecting the presence of shift-symmetry in a configuration. Given the resource constraints, the enumeration and probability formulas can directly help to lower the minimal expected error and provide recommendations for system's size and initialization. Besides cellular automata, the shift-symmetry analysis can be used to study the non-linear behavior in various synchronous rule-based systems that include inference engines, Boolean networks, neural networks, and systolic arrays.Comment: 22 pages, 9 figures, 2 appendice

Emotion regulation in disordered eating : Psychometric properties of the Difficulties in Emotion Regulation Scale among Spanish adults and its interrelations with personality and clinical severity

Objective: The aims of the study were to (1) validate the Difficulties in Emotion Regulation Scale (DERS) in a sample of Spanish adults with and without eating disorders, and (2) explore the role of emotion regulation difficulties in eating disorders (ED), including its mediating role in the relation between key personality traits and ED severity. Methods: One hundred and thirty four patients (121 female, mean age = 29 years) with anorexia nervosa (n = 30), bulimia nervosa (n = 54), binge eating (n = 20), or Other Specified Feeding or Eating Disorders (n = 30) and 74 healthy control participants (51 female, mean age = 21 years) reported on general psychopathology, ED severity, personality traits and difficulties in emotion regulation. Exploratory and confirmatory factor analyses were conducted to examine the psychometrics of the DERS in this Spanish sample (Aim 1). Additionally, to examine the role of emotion regulation difficulties in ED (Aim 2), differences in emotion regulation difficulties across eating disorder subgroups were examined and structural equation modeling was used to explore the interrelations among emotion regulation, personality traits, and eating disorder severity. Results: Results support the validity and reliability of the DERS within this Spanish adult sample and suggest that this measure has a similar factor structure in this sample as in the original sample. Moreover, emotion regulation difficulties were found to differ as a function of eating disorder subtype and to mediate the relation between two specific personality traits (i.e., high harm avoidance and low self-directedness) and ED severity. Conclusions: Personality traits of high harm avoidance and low self-directedness may increase vulnerability to ED pathology indirectly, through emotion regulation difficulties

The long-term effectiveness of the New Zealand Green Prescription primary health care intervention on Christchurch residents

The aim of this research was to evaluate the long term effectiveness of the ‘Green Prescription’ programme, (GRx) in encouraging an increase in physical activity levels in previously inactive individuals, between Janurary 1st 2012 – May 1st 2014. Participants were a non-randomised subset of a larger GRx population. Prescribed Christchurch residents were seperated into two groups, the intervention group (discharged-independently active from the programme) and the control group (discharged-not registered-declined programme and discharged-registered-declined programme). These groups were then randomly selected using Microsoft Excel. A retrospective survey was administered and conducted via telephone. Completed surveys were attained from 147 of 498 participants, a total response rate of 29.9% between the two groups. Forty-one percent of participants in the intervention group reported increases in physical activity levels since being prescribed the GRx programme, 23.1% meet the national physical activity guidelines, and 73.6% were classified as non-sedentary. A higher proportion of the control group (46.4%) were classified as sedentary and only 16.1% met the national physical activity guidelines. Participants who had completed a GRx averaged 146.9 ± 173.5 (mean ± SD) physical activity minutes per week in comparision to the control group 83.1 ± 100.3. A decrease in meeting physical activity guidelines was observed the longer participants were off the Green Prescription Programme. Participants in the intervention group also reported higher levels of energy, increased mobility, a decrease in medication, body weight and aches and pains, had fewer breathing difficulties, felt stronger and more mentally relaxed compared to those in the control group

An automatic gait analysis pipeline for wearable sensors: a pilot study in Parkinson’s disease

The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events—bout segmentation, initial contact (IC), and final contact (FC)—from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson’s disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56–64.66 and 40.19–72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06–48.42, 40.19–72.70 and 36.06–60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials