Live In-Vivo Neuroimaging Reveals the Transport of Lipophilic Cargo Through the Blood-Retina Barrier with Modified Amphiphilic Poly-N-Vinylpyrrolidone Nanoparticles.

The blood-retina barrier (BRB), analogous to the blood-brain barrier, is a major hurdle for the passage of drugs from the blood to the central nervous system. Here, we designed polymeric nanoparticles from amphiphilic poly-/V-vinylpyrrolidone (Amph-PVP NPs) as a new carrier-system and investigated their ability to pass the BRB using a live In-Vivo neuroimaging system for the retina in rats and ex-vivo wholemounted retinae preparation. Amph-PVP NPs were loaded with hydrophobic fluorescent markers as a surrogate for hydrophobic drugs. Linking these NPs with the hydrophobic fluorescence marker Carboxyfluorescein-succinimidyl-ester (CFSE) to the surface, induced the passage of the cargo into the retina tissue. In particular, we observed a substantial internalization of the CFSE-linked NPs into blood cells. We propose surface- modified Amph-PVP NPs as a potential new nano-carrier platform to target posterior eye and potentially brain diseases while camouflaged by blood cells.

A modular degree-of-interest specification for the visual analysis of large dynamic networks.

Large dynamic networks are targets of analysis in many fields. Tracking temporal changes at scale in these networks is challenging due in part to the fact that small changes can be missed or drowned-out by the rest of the network. For static networks, current approaches allow the identification of specific network elements within their context. However, in the case of dynamic networks, the user is left alone with finding salient local network elements and tracking them over time. In this work, we introduce a modular DoI specification to flexibly define what salient changes are and to assign them a measure of their importance in a time-varying setting. The specification takes into account neighborhood structure information, numerical attributes of nodes/edges, and their temporal evolution. A tailored visualization of the DoI specification complements our approach. Alongside a traditional node-link view of the dynamic network, it serves as an interface for the interactive definition of a DoI function. By using it to successively refine and investigate the captured details, it supports the analysis of dynamic networks from an initial view until pinpointing a user's analysis goal. We report on applying our approach to scientific coauthorship networks and give concrete results for the DBLP data set.

Supporting the visual analysis of dynamic networks by clustering associated temporal attributes.

The visual analysis of dynamic networks is a challenging task. In this paper, we introduce a new approach supporting the discovery of substructures sharing a similar trend over time by combining computation, visualization and interaction. With existing techniques, their discovery would be a tedious endeavor because of the number of nodes, edges as well as time points to be compared. First, on the basis of the supergraph, we therefore group nodes and edges according to their associated attributes that are changing over time. Second, the supergraph is visualized to provide an overview of the groups of nodes and edges with similar behavior over time in terms of their associated attributes. Third, we provide specific interactions to explore and refine the temporal clustering, allowing the user to further steer the analysis of the dynamic network. We demonstrate our approach by the visual analysis of a large wireless mesh network.

In situ exploration of large dynamic networks.

The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization--a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown--right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.