Development of the pain-related beliefs and attitudes about sleep (PBAS) scale for the assessment and treatment of insomnia comorbid with chronic pain

Study Objectives Dysfunctional beliefs and attitudes about sleep is a cognitive-behavioral factor central to the development and perpetuation of insomnia. Previous works to unravel the complex interrelationship between pain and insomnia have not explored the role of inflexible beliefs about the sleep-pain interaction, possibly due to a lack of a valid instrument for doing so. The current study evaluated the psychometric and functional properties of a 10-item Pain-Related Beliefs and Attitudes about Sleep (PBAS) scale. Methods The PBAS scale was administered to four clinical samples of chronic pain patients with comorbid insomnia: to examine the scale’s psychometric properties (n=137), test-retest reliability (n=26), sensitivity to treatment (n=20), and generalizability (n=62). All participants completed the PBAS together with validated measures of pain interference, insomnia severity, and cognitive-behavioral processes hypothesized to underpin insomnia. Results The PBAS scale was found to be reliable, with adequate internal consistency and temporal stability. Factor analysis suggested a 2-factor solution representing beliefs about “pain as the primary cause of insomnia” and the “inevitable consequences of insomnia on pain and coping”. The PBAS total score was positively correlated with scores from the Insomnia Severity Index (ISI) scale, Dysfunctional Beliefs and Attitudes about Sleep (DBAS) scale, and the Anxiety and Preoccupation about Sleep Questionnaire (APSQ). It was a significant predictor of insomnia severity and pain interference. A significant reduction in PBAS was also observed in patients after receiving a hybrid cognitive-behavioral intervention for both pain and insomnia. Conclusions Pain-related sleep beliefs appear to be an integral part of chronic pain patients’ insomnia experience. The PBAS is a valid and reliable instrument for evaluating the role of these beliefs in chronic pain patients

Mapeamento dinâmico e colaborativo de alagamentos na cidade de São Paulo

A tendência de utilização de dados voluntários e colaborativos em contextos de desastres naturais é crescente. Esse fato aliado aos cenários de alagamentos que ocorrem na cidade de São Paulo traz a possibilidade de exploração sobre o modo voluntário e colaborativo de geração e transmissão da informação geográfica de forma dinâmica. E estas são proporcionadas por tecnologias acessíveis à população, como o GPS (Global Positioning System) embarcado em celulares e a internet. O presente artigo tem como objetivo a proposta de um esquema conceitual para um sistema dinâmico e colaborativo de mapeamento dos pontos alagados, cuja fonte dos dados advém das pessoas equipadas com aparelhos celulares que permitem a sua localização. Os resultados apresentados correspondem aos esquemas conceituais do sistema, bem como ao protótipo "Pontos de Alagamento" - mapa disponibilizado via web com os pontos de alagamento da cidade, fornecidos no momento da ocorrência do evento por pessoas comuns. O protótipo foi desenvolvido por meio da plataforma livre e de código aberto - Crowdmap/Ushahidi. O sistema foi avaliado através de um questionário respondido por usuários, os quais opinaram sobre a viabilidade do mesmo, bem como os ajustes que devem ser realizados para o uso efetivo da população

Communities in university mathematics

This paper concerns communities of learners and teachers that are formed, develop and interact in university mathematics environments through the theoretical lens of Communities of Practice. From this perspective, learning is described as a process of participation and reification in a community in which individuals belong and form their identity through engagement, imagination and alignment. In addition, when inquiry is considered as a fundamental mode of participation, through critical alignment, the community becomes a Community of Inquiry. We discuss these theoretical underpinnings with examples of their application in research in university mathematics education and, in more detail, in two Research Cases which focus on mathematics students' and teachers' perspectives on proof and on engineering students' conceptual understanding of mathematics. The paper concludes with a critical reflection on the theorising of the role of communities in university level teaching and learning and a consideration of ways forward for future research

Self-stabilised fractality of sea-coasts through damped erosion

Erosion of rocky coasts spontaneously creates irregular seashores. But the geometrical irregularity, in turn, damps the sea-waves, decreasing the average wave amplitude. There may then exist a mutual self-stabilisation of the waves amplitude together with the irregular morphology of the coast. A simple model of such stabilisation is studied. It leads, through a complex dynamics of the earth-sea interface, to the appearance of a stationary fractal seacoast with dimension close to 4/3. Fractal geometry plays here the role of a morphological attractor directly related to percolation geometry.Comment: 4 pages, 5 figure

Superhumps: Confronting Theory with Observation

We review the theory and observations related to the ``superhump'' precession of eccentric accretion discs in close binary sytems. We agree with earlier work, although for different reasons, that the discrepancy between observation and dynamical theory implies that the effect of pressure in the disc cannot be neglected. We extend earlier work that investigates this effect to include the correct expression for the radius at which resonant orbits occur. Using analytic expressions for the accretion disc structure, we derive a relationship between the period excess and mass-ratio with the pressure effects included. This is compared to the observed data, recently derived results for detailed integration of the disc equations and the equivalent empirically derived relations and used to predict values for the mass ratio based on measured values of the period excess for 88 systems.Comment: 11 pages, 7 figures, 4 tables, accepted for publication in MNRA

Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks

Ultrasound imaging is a commonly used technology for visualising patient anatomy in real-time during diagnostic and therapeutic procedures. High operator dependency and low reproducibility make ultrasound imaging and interpretation challenging with a steep learning curve. Automatic image classification using deep learning has the potential to overcome some of these challenges by supporting ultrasound training in novices, as well as aiding ultrasound image interpretation in patient with complex pathology for more experienced practitioners. However, the use of deep learning methods requires a large amount of data in order to provide accurate results. Labelling large ultrasound datasets is a challenging task because labels are retrospectively assigned to 2D images without the 3D spatial context available in vivo or that would be inferred while visually tracking structures between frames during the procedure. In this work, we propose a multi-modal convolutional neural network (CNN) architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure. We use a CNN composed of two branches, one for voice data and another for image data, which are joined to predict image labels from the spoken names of anatomical landmarks. The network was trained using recorded verbal comments from expert operators. Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels. We conclude that the addition of spoken commentaries can increase the performance of ultrasound image classification, and eliminate the burden of manually labelling large EUS datasets necessary for deep learning applications