US velocimetry in participants with aortoiliac occlusive disease

The accurate quantification of blood flow in aortoiliac arteries is challenging but clinically relevant because local flow patterns can influence atherosclerotic disease. To investigate the feasibility and clinical application of two-dimensional blood flow quantification using high-frame-rate contrast-enhanced US (HFR-CEUS) and particle image velocimetry (PIV), or US velocimetry, in participants with aortoiliac stenosis. In this prospective study, participants with a recently diagnosed aortoiliac stenosis underwent HFR-CEUS measurements of the pre- and poststenotic vessel segments. Two-dimensional quantification of blood flow was achieved by performing PIV analysis, which was based on pairwise cross-correlation of the HFR-CEUS images. Visual inspection of the entire data set was performed by five observers to evaluate the ability of the technique to enable adequate visualization of blood flow. The contrast-to-background ratio and average vector correlation were calculated. In two participants who showed flow disturbances, the flow complexity and vorticity were calculated. Results: 35 participants were included. Visual scoring showed that flow quantification was achieved in 41 of 42 locations. In 25 locations, one or multiple issues occurred that limited optimal flow quantification, including loss of correlation during systole, shadow regions, a short vessel segment in the image plane, and loss of contrast during diastole. In the remaining 16 locations, optimal quantification was achieved. The contrast-to-background ratio was higher during systole than during diastole, whereas the vector correlation was lower. Flow complexity and vorticity were high in regions with disturbed flow. Blood flow quantification with US velocimetry is feasible in patients with an aortoiliac stenosis, but several challenges must be overcome before implementation into clinical practice

Figure-ground segregation in a recurrent network architecture

Proposes a model of how the visual brain segregate textured scenes into figures and background. During texture segregation, locations where the properties of texture elements change abruptly are assigned to boundaries, whereas image regions that are relatively homogeneous are grouped together boundary detection and grouping of image regions require different connection schemes, which are accommodated in single network architecture by implementing them in different layers. As a result, all units carry signals related to boundary detection as well as grouping of image regions, in accordance with cortical physiology. Boundaries yield an early enhancement of network responses, but at a later point, an entire figural region is grouped together, because units that respond to it are labeled with enhanced activity. The model predicts which image regions are preferentially perceived as figure or as background and reproduces the spatio-temporal profile of neuronal activity in the visual cortex during texture segregation in intact animals, as well as in animals with cortical lesions

Integration in urban climate adaptation: Lessons from Rotterdam on integration between scientific disciplines and integration between scientific and stakeholder knowledge

Based on the experience acquired in the Bergpolder Zuid district in the city of Rotterdam, The Netherlands, this paper presents lessons learned so far on science-policy interactions supporting the adaptation to climate change in an urban district. Two types of integration issues were considered: (1) Integration within science including integration of disciplines, methods, models and data, and (2) integration between science and the local stakeholders' society, involving a synthesis of scientific and practical knowledge, linking sectors, governance arrangements and organisations. At first sight, the issues around integration within science and beyond the science of climate change adaptation in cities resemble those generally observed in science-policy integration. However, the relative newness of urban adaptation to climate change poses specific challenges for both the scientists and the stakeholders involved in the process. The Rotterdam example discusses the use of multiple means of integration for enhancing integration between scientific disciplines and integration between scientific and stakeholder knowledge