The central nervous system (CNS) requires a tightly controlled environment free of toxins and pathogens to provide the proper chemical composition for neural function. This environment is maintained by the ‘blood brain barrier’ (BBB), which is composed of blood vessels whose endothelial cells display specialized tight junctions and extremely low rates of transcellular vesicular transport (transcytosis)1–3. In concert with pericytes and astrocytes, this unique brain endothelial physiological barrier seals the CNS and controls substance influx and efflux4–6. While BBB breakdown has recently been associated with initiation and perpetuation of various neurological disorders, an intact BBB is a major obstacle for drug delivery to the CNS7–10. A limited understanding of the molecular mechanisms that control BBB formation has hindered our ability to manipulate the BBB in disease and therapy. Here, we identify mechanisms governing the establishment of a functional BBB. First, using a novel embryonic tracer injection method, we demonstrate spatiotemporal developmental profiles of BBB functionality and find that the mouse BBB becomes functional at embryonic day 15.5 (E15.5). We then screen for BBB-specific genes expressed during BBB formation, and find that major facilitator super family domain containing 2a (Mfsd2a) is selectively expressed in BBB-containing blood vessels in the CNS. Genetic ablation of Mfsd2a results in a leaky BBB from embryonic periods through adulthood, while maintaining the normal patterning of vascular networks. Electron microscopy examination reveals a dramatic increase in CNS endothelial cell vesicular transcytosis in Mfsd2a−/− mice, without obvious tight junction defects. Finally we show that MFSD2A endothelial expression is regulated by pericytes to facilitate BBB integrity. These findings identify MFSD2A as a key regulator of BBB function that may act by suppressing transcytosis in CNS endothelial cells. Further our findings may aid in efforts to develop therapeutic approaches for CNS drug delivery.

No abstract

Correlation filter (CF) based trackers have aroused increasing attentions in visual tracking field due to the superior performance on several datasets while maintaining high running speed. For each frame, an ideal filter is trained in order to discriminate the target from its surrounding background. Considering that the target always undergoes external and internal attributes during tracking procedure, the trained filter should take consideration of not only the external distractions but also the target appearance variation synchronously. To this end, we present a State-aware Anti-drift CF tracker (SAT) in this paper, which joint model the discrimination and reliability information in filter learning. Specifically, global context patches are incorporated into filter training stage to better distinguish the target from backgrounds. Meanwhile, a color-based reliable mask is learned to encourage the filter to focus on more reliable regions suitable for tracking. We show that the proposed optimization problem could be solved using Alternative Direction Method of Multipliers and fully carried out in frequency domain to speed up. Extensive experiments are conducted on OTB-100 datasets to compare the SAT tracker (both hand-crafted feature and CNN feature) with other relevant state-of-the-art methods. Both quantitative and qualitative evaluations further demonstrate the effectiveness and robustness of the proposed work.

This paper presents a model of cascading failures in cyber-physical power systems (CPPSs) based on an improved percolation theory, and then proposes failure mitigation strategies. In this model, the dynamic development of cascading failures is divided into several iteration stages. The power flow in the power grid, along with the data transmission and delay in the cyber layer, is considered in the improved percolation theory. The interaction mechanism between two layers is interpreted as the observability and controllability analysis and data update analysis influencing the node state transformation and security command execution. The resilience indices of the failures reflect the influence of cascading failures on both topological integrity and operational state. The efficacy of the proposed mitigation strategies is validated, including strategies to convert some cyber layer nodes into autonomous nodes and embed unified power flow controller (UPFC) into the physical layer. The results obtained from simulations of cascading failures in a CPPS with increasing initial failure sizes are compared for various scenarios. Dynamic cascading failures can be separated into rapid and slow processes. The interdependencies and gap between the observable and controllable parts of the physical layer with the actual physical network are two fundamental reasons for first-order transition failures. Due to the complexity of the coupled topological and operational relations between the two layers, mitigation strategies should be simultaneously applied in both layers.

No abstract