434 research outputs found

    A Study of the Dynamics of Cardiac Ischemia using Experimental and Modeling Approaches

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    The dynamics of cardiac ischemia was investigated using experimental studies and computer simulations. An experimental model consisting of an isolated and perfused canine heart with full control over blood flow rate to a targeted coronary artery was used in the experimental study and a realistically shaped computer model of a canine heart, incorporating anisotropic conductivity and realistic fiber orientation, was used in the simulation study. The phenomena investigated were: (1) the influence of fiber rotation on the epicardial potentials during ischemia and (2) the effect of conductivity changes during a period of sustained ischemia. Comparison of preliminary experimental and computer simulation results suggest that as the ischemic region grows from the endocardium towards the epicardium, the epicardial potential patterns follow the rotating fiber orientation in the myocardium. Secondly, in the experimental studies it was observed that prolonged ischemia caused a subsequent reduction in the magnitude of epicardial potentials. Similar results were obtained from the computer model when the conductivity of the tissue in the ischemic region was reduce

    Energy spread of ultracold electron bunches extracted from a laser cooled gas

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    Ultrashort and ultracold electron bunches created by near-threshold femtosecond photoionization of a laser-cooled gas hold great promise for single-shot ultrafast diffraction experiments. In previous publications the transverse beam quality and the bunch length have been determined. Here the longitudinal energy spread of the generated bunches is measured for the first time, using a specially developed Wien filter. The Wien filter has been calibrated by determining the average deflection of the electron bunch as a function of magnetic field. The measured relative energy spread σUU=0.64±0.09%\frac{\sigma_{U}}{U} = 0.64 \pm 0.09\% agrees well with the theoretical model which states that it is governed by the width of the ionization laser and the acceleration length

    The Impact of Torso Signal Processing on Noninvasive Electrocardiographic Imaging Reconstructions

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    Goal: To evaluate state-of-the-art signal processing methods for epicardial potential-based noninvasive electrocardiographic imaging reconstructions of single-site pacing data. Methods: Experimental data were obtained from two torso-tank setups in which Langendorff-perfused hearts (n = 4) were suspended and potentials recorded simultaneously from torso and epicardial surfaces. 49 different signal processing methods were applied to torso potentials, grouped as i) high-frequency noise removal (HFR) methods ii) baseline drift removal (BDR) methods and iii) combined HFR+BDR. The inverse problem was solved and reconstructed electrograms and activation maps compared to those directly recorded. Results: HFR showed no difference compared to not filtering in terms of absolute differences in reconstructed electrogram amplitudes nor median correlation in QRS waveforms (p > 0.05). However, correlation and mean absolute error of activation times and pacing site localization were improved with all methods except a notch filter. HFR applied post-reconstruction produced no differences compared to pre-reconstruction. BDR and BDR+HFR significantly improved absolute and relative difference, and correlation in electrograms (p < 0.05). While BDR+HFR combined improved activation time and pacing site detection, BDR alone produced significantly lower correlation and higher localization errors (p < 0.05). Conclusion: BDR improves reconstructed electrogram morphologies and amplitudes due to a reduction in lambda value selected for the inverse problem. The simplest method (resetting the isoelectric point) is sufficient to see these improvements. HFR does not impact electrogram accuracy, but does impact post-processing to extract features such as activation times. Removal of line noise is insufficient to see these changes. HFR should be applied post-reconstruction to ensure over-filtering does not occur

    The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

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    The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

    Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators

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    Prior knowledge about the imaging physics provides a mechanistic forward operator that plays an important role in image reconstruction, although myriad sources of possible errors in the operator could negatively impact the reconstruction solutions. In this work, we propose to embed the traditional mechanistic forward operator inside a neural function, and focus on modeling and correcting its unknown errors in an interpretable manner. This is achieved by a conditional generative model that transforms a given mechanistic operator with unknown errors, arising from a latent space of self-organizing clusters of potential sources of error generation. Once learned, the generative model can be used in place of a fixed forward operator in any traditional optimization-based reconstruction process where, together with the inverse solution, the error in prior mechanistic forward operator can be minimized and the potential source of error uncovered. We apply the presented method to the reconstruction of heart electrical potential from body surface potential. In controlled simulation experiments and in-vivo real data experiments, we demonstrate that the presented method allowed reduction of errors in the physics-based forward operator and thereby delivered inverse reconstruction of heart-surface potential with increased accuracy.Comment: 11 pages, Conference: Medical Image Computing and Computer Assisted Interventio

    Cardiac structure and function in adolescent Sherpa; effect of habitual altitude and developmental stage

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    The purpose of this study was to examine ventricular structure and function in Sherpa adolescents to determine whether age-specific differences in oxygen saturation (S

    Risk Factors of Microbial Keratitis in Uganda: A Case Control Study.

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    Purpose: Microbial keratitis (MK), is a frequent cause of sight loss worldwide, particularly in low and middle-income countries. This study aimed to investigate the risk factors of MK in Uganda.Methods: Using a nested case control, we recruited healthy community controls for patients presenting with MK at the two main eye units in Southern Uganda between December 2016 and March 2018. Controls were individually matched for age, gender and village of the cases on a 1:1 ratio. We collected information on demographics, occupation, HIV and Diabetes Mellitus status. In STATA version 14.1, multivariable conditional logistic regression was used to generate odds ratios for risk factors of MK and a likelihood ratio test used to assess statistical significance of associations.Results: Two hundred and fifteen case-control pairs were enrolled. The HIV positive patients among the cases was 9% versus 1% among the controls, p = .0003. Diabetes 7% among the cases versus 1.4% among the controls, p = .012. Eye trauma was 29% versus 0% among the cases and controls. In the multivariable model adjusted for age, sex and village, HIV (OR 83.5, 95%CI 2.01-3456, p = .020), Diabetes (OR 9.38, 95% CI 1.48-59.3, p = .017) and a farming occupation (OR 2.60, 95%CI 1.21-5.57, p = .014) were associated with MK. Compared to a low socio-economic status, a middle status was less likely to be associated with MK (OR 0.29, 95%CI 0.09-0.89, p < .0001).Conclusion: MK was associated with HIV, Diabetes, being poor and farming as the main occupation. More studies are needed to explore how these factors predispose to MK

    Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification

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    Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter summarizing contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, this highlights the impact of material property variation on predicted artery stresses and presents a pipeline to explore and characterize uncertainty in computational biomechanics.Comment: To appear: Biomechanics and Modeling in Mechanobiolog
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