Ion hopping in crystalline and glassy spodumene LiAlSi2O6: Li7 spin-lattice relaxation and Li7 echo NMR spectroscopy

Nuclear magnetic resonance spectroscopy was used to study polycrystalline β-spodumene (β−LiAlSi2O6) as well as glassy specimens with the same chemical composition. 7Li spin-lattice relaxation measurements were carried out in a broad temperature range and for several Larmor frequencies. In addition to a pronounced rate maximum at high temperatures, stemming from the long-range Li motion in these aluminosilicates, we found a weak maximum in the crystalline modification near 120K. The latter result confirms the existence of a local double-well structure in which the Li ions reside. The ionic motion was also monitored by solid- and stimulated-echo spectra as well as by the decay of the Jeener-Broekaert echo. Under conditions which are discussed in detail, the latter is a direct measure of the hopping correlation function. For the glass this function was found to decay faster and more stretched than that of the crystal at a given temperature. Furthermore, the relevant barriers against the high-temperature long-range Li motion are larger in the crystal as compared to the glass. © 2005 The American Physical Society

Do health policy advisors know what the public wants? An empirical comparison of how health policy advisors assess public preferences regarding smoke-free air, and what the public actually prefers

Background: Health policy-making, a complex, multi-factorial process, requires balancing conflicting values. A salient issue is public support for policies; however, one reason for limited impact of public opinion may be misperceptions of policy makers regarding public opinion. For example, empirical research is scarce on perceptions of policy makers regarding public opinion on smoke-free public spaces. Methods: Public desire for smoke-free air was compared with health policy advisor (HPA) perception of these desires. Two representative studies were conducted: one with the public (N = 505), and the other with a representative sample of members of Israel’s health-targeting initiative, Healthy Israel 2020 (N = 34), in December 2010. Corresponding questions regarding desire for smoke-free areas were asked. Possible smoke-free areas included: 100% smoke-free bars and pubs; entrances to health facilities; railway platforms; cars with children; college campuses; outdoor areas (e.g., pools and beaches); and common areas of multi-dweller apartment buildings. A 1–7 Likert scale was used for each measure, and responses were averaged into a single primary outcome, DESIRE. Our primary endpoint was the comparison between public preferences and HPA assessment of those preferences. In a secondary analysis, we compared personal preferences of the public with personal preferences of the HPAs for smoke-free air. Results: HPAs underestimated public desire for smoke-free air (Public: Mean: 5.06, 95% CI:[4.94, 5.17]; HPA: Mean: 4.06, 95% CI:[3.61, 4.52]: p < .0001). Differences at the p = .05 level were found between HPA assessment and public preference for the following areas: 100% smoke-free bars and pubs; entrances to healthcare facilities; train platforms; cars carrying children; and common areas of multi-dweller apartment buildings. In our secondary comparison, HPAs more strongly preferred smoke-free areas than did the public (p < .0001). Conclusions: Health policy advisors underestimate public desire for smoke-free air. Better grasp of public opinion by policy makers may lead to stronger legislation. Monitoring policy-maker assessment of public opinion may shed light on incongruities between policy making and public opinion. Further, awareness of policy-maker misperceptions may encourage policy-makers to demand more accurate information before making policy

The Future of Legal Scholarship and Scholarly Communication: Publication in the Age of Cyberspace

In Part I of this paper, I will review the essentials of Hibbitts\u27s discussion, and his argument that electronic self-publication of legal scholarship soon willand shouldreplace the edited, printed law review as we know it today. In Part II, I apply sociological analysis to explore some special features of the audience for and functions of legal scholarship. I will build upon this discussion in Part III, which explains why legal scholarship is a poor candidate for electronic self-publication, and why self-publication is a poor use of the Internet\u27s potential for scholarly communication. In the concluding Part IV, I outline some counter-proposals for improving legal scholarship and scholarly communication in light of new dissemination technologies

TCT-749 The Impact of Race and Gender on Procedural Outcomes After Alcohol Septal Ablation for Symptomatic Hypertrophic Obstructive Cardiomyopathy

Digitalitzat per Artypla

GENERATION AND SEGMENTATION OF 3D MODELS OF BONE FROM CT IMAGES BASED ON 3D POINT CLOUDS

The creation of 3D models of bone from CT images has become popular for surgical planning, the design of implants, and educational purposes. Software is available to convert CT images into 3D models of bone, however, these can be expensive and technically taxing. The goal of this project was to create an open-source and easy-to-use methodology to create 3D models of bone and allow the user to interact with the model to extract desired regions. The method was first created in MATLAB and ported to Python. The CT images were imported into Python and the images were then binarized using a desired threshold determined by the user and based on Hounsfield Units (HU). A Canny edge detector was applied to the binarized images, this extracted the inner and outer surfaces of the bone. Edge points were assigned x, y, and z coordinates based on their pixel location, and the location of the slice in the stack of CT images to create a 3D point cloud. The application of a Delaunay tetrahedralization created a mesh object, the surface was extracted and saved as an STL file. An add-on in Blender was created to allow the user to select the CT images to import, set a threshold, create a 3D mesh model, draw an ROI on the model, and extract that region based on the desired thickness and create a new 3D object. The method was fully open-sourced so was inexpensive and was able to create models of a skull and allow the segmentation of portions of that mesh to create new objects. Future work needs to be conducted to improve the quality of the mesh, implement sampling to reduce the time to create the mesh, and add features that would benefit the end-user.ThesisMaster of Applied Science (MASc)The creation of 3D models of bone from CT images has become popular for education, surgical planning, and the design of implants. Software is available to convert CT images into 3D models but can be expensive and technically taxing. The purpose of this project was to develop a process to allow surgeons to create and interact with models from imaging data. This project applied a threshold to binarize a set of CT images, extracted the edges using a Canny Edge detector, and used the edge pixels to create a 3D point cloud. The 3D point cloud was then converted to a mesh object. A user interface was implemented that allowed the selection of portions of the model and a new 3D model to be created from the selection. The process can be improved by improving the quality of the mesh output and adding features to the user interface

Magnetoencephalography for the investigation and diagnosis of Mild Traumatic Brain Injury

Mild Traumatic Brain Injury (mTBI), (or concussion), is the most common type of brain injury. Despite this, it often goes undiagnosed and can cause long term disability—most likely caused by the disruption of axonal connections in the brain. Objective methods for diagnosis and prognosis are needed but clinically available neuroimaging modalities rarely show structural abnormalities, even when patients suffer persisting functional deficits. In the past three decades, new powerful techniques to image brain structure and function have shown promise in detecting mTBI related changes. Magnetoencephalography (MEG), which measures electrical brain activity by detecting magnetic fields outside the head generated by neural currents, is particularly sensitive and has therefore gained interest from researchers. Numerous studies are proposing abnormal low-frequency neural oscillations and functional connectivity—the statistical interdependency of signals from separate brain regions—as potential biomarkers for mTBI. However, typically small sample sizes, the lack of replication between groups, the heterogeneity of the cohorts studied, and the lack of longitudinal studies impedes the adoption of MEG as a clinical tool in mTBI management. In particular, little is known about the acute phase of mTBI. In this thesis, some of these gaps will be addressed by analysing MEG data from individuals with mTBI, using novel as well as conventional methods. The potential future of MEG in mTBI research will also be addressed by testing the capabilities of a wearable MEG system based on optically pumped magnetometers (OPMs). The thesis contains three main experimental studies. In study 1, we investigated the signal dynamics underlying MEG abnormalities, found in a cohort of subjects scanned within three months of an mTBI, using a Hidden Markov Model (HMM), as growing evidence suggests that neural dynamics are (in part) driven by transient bursting events. Applying the HMM to resting-state data, we show that previously reported findings of diminished intrinsic beta amplitude and connectivity in individuals with mTBI (compared to healthy controls) can be explained by a reduction in the beta-band content of pan-spectral bursts and a loss in the temporal coincidence of bursts respectively. Using machine learning, we find the functional connections driving group differences and achieve classification accuracies of 98%. In a motor task, mTBI resulted in reduced burst amplitude, altered modulation of burst probability during movement and decreased connectivity in the motor network. In study 2, we further test our HMM-based method in a cohort of subjects with mTBI and non-head trauma—scanned within two weeks of injury—to ensure specificity of any observed effects to mTBI and replicate our previous finding of reduced connectivity and high classification accuracy, although not the reduction in burst amplitude. Burst statistics were stable over both studies—despite data being acquired at different sites, using different scanners. In the same cohort, we applied a more conventional analysis of delta-band power. Although excess low-frequency power appears to be a promising candidate marker for persistently symptomatic mTBI, insufficient data exist to confirm this pattern in acute mTBI. We found abnormally high delta power to be a sensitive measure for discriminating mTBI subjects from healthy controls, however, similarly elevated delta amplitude was found in the cohort with non-head trauma, suggesting that excess delta may not be specific to mTBI, at least in the acute stage of injury. Our work highlights the need for longitudinal assessment of mTBI. In addition, there appears to be a need to investigate naturalistic paradigms which can be tailored to induce activity in symptom-relevant brain networks and consequently are likely to be more sensitive biomarkers than the resting state scans used to date. Wearable OPM-MEG makes naturalistic scanning possible and may offer a cheaper and more accessible alternative to cryogenic MEG, however, before deploying OPMs clinically, or in pitch-side assessment for athletes, for example, the reliability of OPM-derived measures needs to be verified. In the third and final study, we performed a repeatability study using a novel motor task, estimating a series of common MEG measures and quantifying the reliability of both activity and connectivity derived from OPM-MEG data. These initial findings—presently limited to a small sample of healthy controls—demonstrate the utility of OPM-MEG and pave the way for this technology to be deployed on patients with mTBI