Investigations on diurnal and seasonal variations of Schumann resonance intensities in the auroral region

Measurements of the magnetic component of the Schumann resonance in the frequency range 6-14 Hz were performed at high latitude location (TNB Antarctica; geographic coordinates: 74.7°S, 164.1°E; geomagnetic coordinates: 80.0°S, 307.7°E; LT=UT+13; MLT=UT8; altitude=28 m a.s.l.), during the two years 1996-1997. TNB is a particularly important observation site located in a region characterised by a high electromagnetic activity in the ELF and VLF bands. Moreover its remote location in Antarctica provides the important advantage that electromagnetic background noise is not corrupted by anthropogenic noise and that the continental lightning activity is very low. The combination of low additional anthropogenic electromagnetic radiation and low atmospheric noise in this area allows detailed investigations into wave generation and amplification in the polar ionosphere and magnetosphere not possible anywhere else in the world. This paper reports the study of the magnetic power of the 8 Hz Schumann resonance mode. For both the years considered diurnal and long-term seasonal variations were observed

Investigations on diurnal and seasonal variations of Schumann resonance intensities in the auroral region

Measurements of the magnetic component of the Schumann resonance in the frequency range 6-14 Hz were performed at high latitude location (TNB Antarctica; geographic coordinates: 74.7°S, 164.1°E; geomagnetic coordinates: 80.0°S, 307.7°E; LT=UT+13; MLT=UT–8; altitude=28 m a.s.l.), during the two years 1996-1997. TNB is a particularly important observation site located in a region characterised by a high electromagnetic activity in the ELF and VLF bands. Moreover its remote location in Antarctica provides the important advantage that electromagnetic background noise is not corrupted by anthropogenic noise and that the continental lightning activity is very low. The combination of low additional anthropogenic electromagnetic radiation and low atmospheric noise in this area allows detailed investigations into wave generation and amplification in the polar ionosphere and magnetosphere not possible anywhere else in the world. This paper reports the study of the magnetic power of the 8 Hz Schumann resonance mode. For both the years considered diurnal and long-term seasonal variations were observed

Fourier Velocity Encoded MRI: Acceleration and Velocity Map Estimation

Fourier velocity encoding (FVE) is an alternative to phase contrast imaging (PC). FVE provides considerably higher SNR than PC, due to its higher dimensionality and larger voxel sizes. Furthermore, FVE is robust to partial voluming, as it resolves the velocity distribution within each voxel. FVE data are usually acquired with low spatial resolution, due to scan-time restrictions associated with its higher dimensionality. FVE is capable of providing the velocity distribution associated with a large voxel, but does not directly provides a velocity map. Knowing the velocity distribution on a voxel is important for accurate diagnosis of stenosis in vessels on the scale of spatial resolution. Velocity maps, however, are useful for visualizing the actual blood flow through a vessel and can be used in different studies and diagnosis. In this context, this chapter deals with two aspects of the FVE MRI technique: acceleration and estimation of velocity map. First, are introduced six different acceleration techniques that can be applied to FVE acquisition. Methods such as variable-density sampling and compressive sampling. Then, is proposed a novel method to estimate velocity maps with high spatial resolution from low-resolution FVE data. Finally, it can be concluded that FVE datasets can be acquired in time scale comparable to PC, it contains more velocity information, since it resolves a velocity distribution within a voxel, and also provides an accurate estimation of the velocity map

Computational fluid dynamics simulations of blood flow regularized by 3D phase contrast MRI

Abstract Background Phase contrast magnetic resonance imaging (PC-MRI) is used clinically for quantitative assessment of cardiovascular flow and function, as it is capable of providing directly-measured 3D velocity maps. Alternatively, vascular flow can be estimated from model-based computation fluid dynamics (CFD) calculations. CFD provides arbitrarily high resolution, but its accuracy hinges on model assumptions, while velocity fields measured with PC-MRI generally do not satisfy the equations of fluid dynamics, provide limited resolution, and suffer from partial volume effects. The purpose of this study is to develop a proof-of-concept numerical procedure for constructing a simulated flow field that is influenced by both direct PC-MRI measurements and a fluid physics model, thereby taking advantage of both the accuracy of PC-MRI and the high spatial resolution of CFD. The use of the proposed approach in regularizing 3D flow fields is evaluated. Methods The proposed algorithm incorporates both a Newtonian fluid physics model and a linear PC-MRI signal model. The model equations are solved numerically using a modified CFD algorithm. The numerical solution corresponds to the optimal solution of a generalized Tikhonov regularization, which provides a flow field that satisfies the flow physics equations, while being close enough to the measured PC-MRI velocity profile. The feasibility of the proposed approach is demonstrated on data from the carotid bifurcation of one healthy volunteer, and also from a pulsatile carotid flow phantom. Results The proposed solver produces flow fields that are in better agreement with direct PC-MRI measurements than CFD alone, and converges faster, while closely satisfying the fluid dynamics equations. For the implementation that provided the best results, the signal-to-error ratio (with respect to the PC-MRI measurements) in the phantom experiment was 6.56 dB higher than that of conventional CFD; in the in vivo experiment, it was 2.15 dB higher. Conclusions The proposed approach allows partial or complete measurements to be incorporated into a modified CFD solver, for improving the accuracy of the resulting flow fields estimates. This can be used for reducing scan time, increasing the spatial resolution, and/or denoising the PC-MRI measurements.http://deepblue.lib.umich.edu/bitstream/2027.42/116061/1/12938_2015_Article_104.pd

Frailty assessment in an unselected population admitted to an intensive cardiac care unit

Abstract Background Although interest in frailty has expanded among cardiology experts over the past 2 decades, its integration, as part of cardiovascular disease management, is still lacking, above all in the acute cardiac care setting. The Clinical Frailty Scale (CFS) is a brief guided tool to assess frailty in hospital settings without specialist equipment. Purpose Our objective was to test the performance of the CFS in an older, unselected population, admitted to an Intensive Cardiac Care Unit (ICCU) during the year 2019. Methods The study sample included 431 patients ≥65 years old, admitted to an ICCU of a tertiary cardiac center in Italy. The CFS ranged from "very fit: 1" to "terminally ill: 9", but it was considered present at a score ≥5. Our primary endpoint was defined by a combination of severe complications requiring critical care and in-hospital death. The data were collected from the hospital discharge summary and the electronic chart records. Results 158 patients (36.7%) were frail. These individuals had greater comorbidity and higher in-hospital mortality (Table 1). After a multivariable logistic regression analysis, 4 predictors were identified: signs of congestive heart failure (OR: 8.51, 95% Confidence Interval-CI: 4.63–14.6; p<0,001), systolic blood pressure (OR per 1 mmHg increasing: 0.98, 95% CI: 0.97–0.99; p<0,001), smoking habit (OR: 0.49, 95% CI: 0.22–1.11; p=0.09) and the CFS ≥5 (OR: 1.86, 95% CI: 1.08–3.23: p=0,026). Conclusions The CFS is a simple guided frailty tool that may enhance outcome prediction in the acute cardiac care setting. These findings merit evaluation in larger cohorts of unselected patients. Funding Acknowledgement Type of funding sources: None