SimpliFly: A Methodology for Simplification and Thematic Enhancement of Trajectories.

Movement data sets collected using today's advanced tracking devices consist of complex trajectories in terms of length, shape, and number of recorded positions. Multiple additional attributes characterizing the movement and its environment are often also included making the level of complexity even higher. Simplification of trajectories can improve the visibility of relevant information by reducing less relevant details while maintaining important movement patterns. We propose a systematic stepwise methodology for simplifying and thematically enhancing trajectories in order to support their visual analysis. The methodology is applied iteratively and is composed of: (a) a simplification step applied to reduce the morphological complexity of the trajectories, (b) a thematic enhancement step which aims at accentuating patterns of movement, and (c) the representation and interactive exploration of the results in order to make interpretations of the findings and further refinement to the simplification and enhancement process. We illustrate our methodology through an analysis example of two different types of tracks, aircraft and pedestrian movement

Human mesenchymal stromal cells inhibit platelet activation and aggregation involving CD73-converted adenosine

Background: Mesenchymal stromal cells (MSCs) are promising cell therapy candidates. Clinical application is considered safe. However, minor side effects have included thromboembolism and instant blood-mediated inflammatory reactions suggesting an effect of MSC infusion on hemostasis. Previous studies focusing on plasmatic coagulation as a secondary hemostasis step detected both procoagulatory and anticoagulatory activities of MSCs. We now focus on primary hemostasis and analyzed whether MSCs can promote or inhibit platelet activation. Methods: Effects of MSCs and MSC supernatant on platelet activation and function were studied using flow cytometry and further platelet function analyses. MSCs from bone marrow (BM), lipoaspirate (LA) and cord blood (CB) were compared to human umbilical vein endothelial cells or HeLa tumor cells as inhibitory or activating cells, respectively. Results: BM-MSCs and LA-MSCs inhibited activation and aggregation of stimulated platelets independent of the agonist used. This inhibitory effect was confirmed in diagnostic point-of-care platelet function analyses in platelet-rich plasma and whole blood. Using inhibitors of the CD39–CD73–adenosine axis, we showed that adenosine produced by CD73 ectonucleotidase activity was largely responsible for the LA-MSC and BM-MSC platelet inhibitory action. With CB-MSCs, batch-dependent responses were obvious, with some batches exerting inhibition and others lacking this effect. Conclusions: Studies focusing on plasmatic coagulation suggested both procoagulatory and anticoagulatory activities of MSCs. We now show that MSCs can, dependent on their tissue origin, inhibit platelet activation involving adenosine converted from adenosine monophosphate by CD73 ectonucleotidase activity. These data may have strong implications for safety and risk/benefit assessment regarding MSCs from different tissue sources and may help to explain the tissue protective mode of action of MSCs. The adenosinergic pathway emerges as a key mechanism by which MSCs exert hemostatic and immunomodulatory functions

First-principles study of TMNan (TM= Cr, Mn, Fe, Co, Ni; n = 4-7) clusters

Geometry, electronic structure, and magnetic properties of TMNan (TM=Cr-Ni; n = 4-7) clusters are studied within a gradient corrected density functional theory (DFT) framework. Two complementary approaches, the first adapted to all-electron calculations on free clusters, and the second been on plane wave projector augmented wave (PAW) method within a supercell approach are used. Except for NiNan, the clusters in this series are found to retain the atomic moments of the TM atoms, and the magnetic moment presented an odd-even oscillation with respect to the number of Na atoms. The origin of these odd-even oscillations is explained from the nature of chemical bonding in these clusters. Differences and similarities between the chemical bonding and the magnetic properties of these clusters and the TMNan (TM = Sc, V and Ti; n = 4-6) clusters on one hand, and TM-doped Au and Ag clusters on the other hand, are discussed

Variable binned scatter plots

The scatter plot is a well-known method of visualizing pairs of two continuous variables. Scatter plots are intuitive and easy-to-use, but often have a high degree of overlap which may occlude a significant portion of the data. To analyze a dense non-uniform data set, a recursive drill-down is required for detailed analysis. In this article, we propose variable binned scatter plots to allow the visualization of large amounts of data without overlapping. The basic idea is to use a non-uniform (variable) binning of the x and y dimensions and to plot all data points that are located within each bin into the corresponding squares. In the visualization, each data point is then represented by a small cell (pixel). Users are able to interact with individual data points for record level information. To analyze an interesting area of the scatter plot, the variable binned scatter plots with a refined scale for the subarea can be generated recursively as needed. Furthermore, we map a third attribute to color to obtain a visual clustering. We have applied variable binned scatter plots to solve real-world problems in the areas of credit card fraud and data center energy consumption to visualize their data distributions and cause-effect relationships among multiple attributes. A comparison of our methods with two recent scatter plot variants is included

Visual Analytics of Large Multi-Dimensional Data Using Variable Binned Scatter Plots

The scatter plot is a well-known method of visualizing pairs of two-dimensional continuous variables. Multidimensional data can be depicted in a scatter plot matrix. They are intuitive and easy-to-use, but often have a high degree of overlap which may occlude a significant portion of data. In this paper, we propose variable binned scatter plots to allow the visualization of large amounts of data without overlapping. The basic idea is to use a non-uniform (variable) binning of the x and y dimensions and plots all the data points that fall within each bin into corresponding squares. Further, we map a third attribute to color for visualizing clusters. Analysts are able to interact with individual data points for record level information. We have applied these techniques to solve real-world problems on credit card fraud and data center energy consumption to visualize their data distribution and cause-effect among multiple attributes. A comparison of our methods with two recent well-known variants of scatter plots is included