On determining the noon polar cap boundary from SuperDARN HF radar backscatter characteristics

International audiencePrevious work has shown that ionospheric HF radar backscatter in the noon sector can be used to locate the footprint of the magnetospheric cusp particle precipitation. This has enabled the radar data to be used as a proxy for the location of the polar cap boundary, and hence measure the flow of plasma across it to derive the reconnection electric field in the ionosphere. This work used only single radar data sets with a field of view limited to ~2 h of local time. In this case study using four of the SuperDARN radars, we examine the boundary determined over 6 h of magnetic local time around the noon sector and its relationship to the convection pattern. The variation with longitude of the latitude of the radar scatter with cusp characteristics shows a bay-like feature. It is shown that this feature is shaped by the variation with longitude of the poleward flow component of the ionospheric plasma and may be understood in terms of cusp ion time-of-flight effects. Using this interpretation, we derive the time-of-flight of the cusp ions and find that it is consistent with approximately 1 keV ions injected from a subsolar reconnection site. A method for deriving a more accurate estimate of the location of the open-closed field line boundary from HF radar data is described.Key words: Ionosphere (ionosphere?magnetosphere interactions; plasma convection) · Magnetospheric physics (magnetopause · cusp · and boundary layers

Fostering the exchange of real-life data across different countries to answer primary care research questions: a protocol for an UNLOCK study from the IPCRG

[Excerpt] This protocol describes a study that will explore the lessons of UNLOCK (Uncovering and Noting Long-term COPD and asthma to enhance Knowledge) over the past 5 years of sharing real-life primary care data from different countries to answer research questions on the diagnosis and management of chronic respiratory diseases. UNLOCK is an international collaboration between primary care researchers and practitioners to coordinate and share data sets of relevant diagnostic and follow-up variables for chronic obstructive pulmonary disease (COPD) and asthma management in primary care. It was set up by members of the International Primary Care Respiratory Group (IPCRG) in response to the identified research need for research in primary care, which recruits patients representative of primary care populations, evaluates interventions realistically delivered within primary care and draws conclusions that will be meaningful to professionals working within primary care.1,2 [...]The IPCRG provided funding for this research project as an UNLOCK Group study for which the funding was obtained through an unrestricted grant by Novartis AG, Basel, Switzerland. Novartis has no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

PACE and EISCAT radar observations of short-lived flow bursts on the nightside

Concurrent observations from two widely spaced radar experiments of quasi periodic flow bursts in the nightside are presented. The flow bursts closely resemble single radar observations reported by Williams et al. By using the Polar Anglo-American Conjugate Experiment (PACE) HF radar array at Halley Bay in conjunction with the EISCAT Common Program (CP) 2-D experiment, the flow bursts are shown to be a global phenomenon and important information as to their development and propagation can be determined

Clinical implications of the Royal College of Physicians three questions in routine asthma care: A real-life validation study

BACKGROUND: Annual recording of the Royal College of Physicians three questions (RCP3Q) morbidity score is rewarded within the UK 'pay-for-performance' Quality and Outcomes Framework. AIMS: To investigate the performance of the RCP3Qs for assessing control in real-life practice compared with the validated Asthma Control Questionnaire (ACQ) administered by self-completed questionnaire. METHODS: We compared the RCP3Q score extracted from a patient's computerised medical record with the ACQ self-completed after the consultation. The anonymous data were paired by practice, age, sex, and dates of completion. We calculated the sensitivity and specificity of the RCP3Q scale compared with the threshold for good/poor asthma control (ACQ greater than 1). RESULTS: Of 291 ACQ questionnaires returned from 12 participating practices, 129 could be paired with complete RCP3Q data. Twenty-five of 27 patients who scored zero on the RCP3Q were well controlled (ACQ less than 1). An RCP3Q score greater than 1 predicted inadequate control (ACQ greater than 1) with a sensitivity of 0.96 and specificity of 0.34. Comparable values for RCP3Q greater than 2 were sensitivity 0.50 and specificity 0.94. The intraclass correlation coefficient of 0.13 indicated substantial variability between practices. Exacerbations and use of reliever inhalers were moderately correlated with ACQ (Spearman's rho 0.3 and 0.35) and may reflect different aspects of control. CONCLUSIONS: In routine practice, an RCP3Q score of zero indicates good asthma control and a score of 2 or 3 indicates poor control. An RCP3Q score of 1 has good sensitivity but poor specificity for suboptimal control and should provoke further enquiry and consideration of other aspects of control such as exacerbations and use of reliever inhalers

Prostate Motion Modelling Using Biomechanically-Trained Deep Neural Networks on Unstructured Nodes

In this paper, we propose to train deep neural networks with biomechanical simulations, to predict the prostate motion encountered during ultrasound-guided interventions. In this application, unstructured points are sampled from segmented pre-operative MR images to represent the anatomical regions of interest. The point sets are then assigned with point-specific material properties and displacement loads, forming the un-ordered input feature vectors. An adapted PointNet can be trained to predict the nodal displacements, using finite element (FE) simulations as ground-truth data. Furthermore, a versatile bootstrap aggregating mechanism is validated to accommodate the variable number of feature vectors due to different patient geometries, comprised of a training-time bootstrap sampling and a model averaging inference. This results in a fast and accurate approximation to the FE solutions without requiring subject-specific solid meshing. Based on 160,000 nonlinear FE simulations on clinical imaging data from 320 patients, we demonstrate that the trained networks generalise to unstructured point sets sampled directly from holdout patient segmentation, yielding a near real-time inference and an expected error of 0.017 mm in predicted nodal displacement