Differential roles for endothelial ICAM-1, ICAM-2, and VCAM-1 in shear-resistant T cell arrest, polarization, and directed crawling on blood-brain barrier endothelium

Endothelial ICAM-1 and ICAM-2 were shown to be essential for T cell diapedesis across the blood-brain barrier (BBB) in vitro under static conditions. Crawling of T cells prior to diapedesis was only recently revealed to occur preferentially against the direction of blood flow on the endothelial surface of inflamed brain microvessels in vivo. Using live cell-imaging techniques, we prove that Th1 memory/effector T cells predominantly crawl against the direction of flow on the surface of BBB endothelium in vitro. Analysis of T cell interaction with wild-type, ICAM-1-deficient, ICAM-2-deficient, or ICAM-1 and ICAM-2 double-deficient primary mouse brain microvascular endothelial cells under physiological flow conditions allowed us to dissect the individual contributions of endothelial ICAM-1, ICAM-2, and VCAM-1 to shear-resistant T cell arrest, polarization, and crawling. Although T cell arrest was mediated by endothelial ICAM-1 and VCAM-1, T cell polarization and crawling were mediated by endothelial ICAM-1 and ICAM-2 but not by endothelial VCAM-1. Therefore, our data delineate a sequential involvement of endothelial ICAM-1 and VCAM-1 in mediating shear-resistant T cell arrest, followed by endothelial ICAM-1 and ICAM-2 in mediating T cell crawling to sites permissive for diapedesis across BBB endothelium.

Catheter-directed ultrasound-accelerated thrombolysis for the treatment of acute pulmonary embolism

Systemic thrombolysis rapidly improves right ventricular (RV) dysfunction in patients with acute pulmonary embolism (PE) but is associated with major bleeding complications in up to 20%. The efficacy of low-dose, catheter-directed ultrasound-accelerated thrombolysis (USAT) on the reversal of RV dysfunction is unknown.

A Flow Trace Generator using Graph-based Traffic Classification Techniques

We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Evidence for a cognitive control network for goal-directed attention in simple sustained attention

The deterioration of performance over time is characteristic for sustained attention tasks. This so-called "performance decrement" is measured by the increase of reaction time (RT) over time. Some behavioural and neurobiological mechanisms of this phenomenon are not yet fully understood. Behaviourally, we examined the increase of RT over time and the inter-individual differences of this performance decrement. On the neurophysiological level, we investigated the task-relevant brain areas where neural activity was modulated by RT and searched for brain areas involved in good performance (i.e. participants with no or moderate performance decrement) as compared to poor performance (i.e. participants with a steep performance decrement). For this purpose, 20 healthy, young subjects performed a carefully designed task for simple sustained attention, namely a low-demanding version of the Rapid Visual Information Processing task. We employed a rapid event-related functional magnetic resonance imaging (fMRI) design. The behavioural results showed a significant increase of RT over time in the whole group, and also revealed that some participants were not as prone to the performance decrement as others. The latter was statistically significant comparing good versus poor performers. Moreover, high BOLD-responses were linked to longer RTs in a task-relevant bilateral fronto-cingulate-insular-parietal network. Among these regions, good performance was associated with significantly higher RT-BOLD correlations in the pre-supplementary motor area (pre-SMA). We concluded that the task-relevant bilateral fronto-cingulate-insular-parietal network was a cognitive control network responsible for goal-directed attention. The pre-SMA in particular might be associated with the performance decrement insofar that good performers could sustain activity in this brain region in order to monitor performance declines and adjust behavioural output.