Aortic coarctation (CoA) accounting for 3-11% of congenital heart disease can be successfully treated. Long-term results, however, have revealed decreased life expectancy associated with abnormal hemodynamics. Accordingly, an assessment of hemodynamics is the key factor in treatment decisions and successful long-term results. In this study, 3D angiography whole heart (3DWH) and 4D phase-contrast magnetic resonance imaging (MRI) data were acquired. Geometries of the thoracic aorta with CoAs were reconstructed using ZIB-Amira software. X-ray angiograms were used to evaluate the post-treatment geometry. Computational fluid dynamics models in three patients were created to simulate pre- and post-treatment situations using the FLUENT program. The aim of the study was to investigate the impact of the inlet velocity profile (plug vs. MRI-based) with a focus on the peak systole pressure gradient and wall shear stress (WSS). Results show that helical flow at the aorta inlet can significantly affect the assessment of pressure drop and WSS. Simplified plug inlet velocity profiles significantly (p < 0.05) overestimate the pressure drop in pre- and post-treatment geometries and significantly (p < 0.05) underestimate surface-averaged WSS. We conclude that the use of the physiologically correct but time-expensive 4D MRI-based in vivo velocity profile in CFD studies may be an important step towards a patient-specific analysis of CoA hemodynamics.

This study compared pressure fields by 4-dimensional (4D), velocity-encoded cine (VEC) cardiac magnetic resonance imaging (CMR) with pressures measured by the clinical gold standard catheterization. Thirteen patients (n = 7 male, n = 6 female) with coarctation were studied. The 4D-VEC-CMR pressure fields were computed by solving the Pressure-Poisson equation. The agreement between catheterization and CMR-based methods was determined at 5 different measurement sites along the aorta. For all sites, the correlation coefficients between measures varied between 0.86 and 0.97 (p < 0.001). The Bland-Altman test showed good agreement between peak systolic pressure gradients across the coarctation. The nonsignificant (p > 0.2) bias was +2.3 mm Hg (± 6.4 mm Hg, 2 SDs) for calibration with dynamic pressures and +1.5 mm Hg (± 4.6 mm Hg, 2 SDs) for calibration with static pressure. In a clinical setting of coarctation, pressure fields can be accurately computed from 4D-VEC-CMR-derived flows. In patients with coarctation, this noninvasive technique might evolve to an alternative to invasive catheterization.

In randomized controlled trials, maintenance treatment for relapse prevention has been proven to be efficacious in patients responding in acute treatment, its efficacy in long-term outcome in "real-world patients" has yet to be proven. Three-year long-term data from a large naturalistic multisite follow-up were presented. Severe relapse was defined as suicide, severe suicide attempt, or rehospitalization. Next to relapse rates, possible risk factors including antidepressant medication were identified using univariate generalized log-rank tests and multivariate Cox proportional hazards model for time to severe relapse. Overall data of 458 patients were available for analysis. Of all patients, 155 (33.6%) experienced at least one severe relapse during the 3-year follow-up. The following variables were associated with a shorter time to a severe relapse in univariate and multivariate analyses: multiple hospitalizations, presence of avoidant personality disorder, continuing antipsychotic medication, and no further antidepressant treatment. In comparison with other studies, the observed rate of severe relapse during 3-year period is rather low. This is one of the first reports demonstrating a beneficial effect of long-term antidepressant medication on severe relapse rates in naturalistic patients. Concomitant antipsychotic medication may be a proxy marker for treatment resistant and psychotic depression.

Many datasets have multiple perspectives – for example space, time and description – and often analysts are required to study these multiple perspectives concurrently. This concurrent analysis becomes difficult when data are grouped and split into small multiples for comparison. A design challenge is thus to provide representations that enable multiple perspectives, split into small multiples, to be viewed simultaneously in ways that neither clutter nor overload. We present a design framework that allows us to do this. We claim that multi‐perspective comparison across small multiples may be possible by superimposing perspectives on one another rather than juxtaposing those perspectives side‐by‐side. This approach defies conventional wisdom and likely results in visual and informational clutter. For this reason we propose designs at three levels of abstraction for each perspective. By flexibly varying the abstraction level, certain perspectives can be brought into, or out of, focus. We evaluate our framework through laboratory‐style user tests. We find that superimposing, rather than juxtaposing, perspective views has little effect on performance of a low‐level comparison task. We reflect on the user study and its design to further identify analysis situations for which our framework may be desirable. Although the user study findings were insufficiently discriminating, we believe our framework opens up a new design space for multi‐perspective visual analysis.

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Current virtualization technologies enable hosting of a large number of Virtual Machines (VMs) on a common physical host. The hypervisor interconnects these VMs via Virtual Networks (VNs). These VNs underlie the same security requirements as physical networks. Network elements such as stateful firewalls contribute in enforcing this security. With the advent of stateful firewalls on the hypervisor level, a new challenge arises when it comes to VM migration. Not only the VM itself, but also the associated Security Context (SC) has to migrate. Current open-source hypervisors do not address this issue. In this paper we present the architecture and implementation of our framework for migrating SC along with VMs.

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