DiffSeer: Difference-based Dynamic Weighted Graph Visualization

Existing dynamic weighted graph visualization approaches rely on users' mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based node reordering strategy is developed to group nodes with similar evolution patterns and highlight interesting graph structure details in each timeslice. We conducted two case studies on real-world graph datasets and in-depth interviews with 12 target users to evaluate DiffSeer. The results demonstrate its effectiveness in visualizing dynamic weighted graphs

Visualizing practical knowledge: The Haughton-Mars Project

To improve how we envision knowledge, we must improve our ability to see knowledge in everyday life. That is, visualization is concerned not only with displaying facts and theories, but also with finding ways to express and relate tacit understanding. Such knowledge, although often referred to as "common," is not necessarily shared and may be distributed socially in choreographies for working together—in the manner that a chef and a maitre d’hôtel, who obviously possess very different skills, coordinate their work. Furthermore, non-verbal concepts cannot in principle be inventoried. Reifying practical knowledge is not a process of converting the implicit into the explicit, but pointing to what we know, showing its manifestations in our everyday life. To this end, I illustrate the study and reification of practical knowledge by examining the activities of a scientific expedition in the Canadian Arctic—a group of scientists preparing for a mission to Mar

φxANES: In vivo imaging of metal-protein coordination environments

© 2016, Nature Publishing Group. All rights reserved. We have developed an X-ray absorption near edge structure spectroscopy method using fluorescence detection for visualizing in vivo coordination environments of metals in biological specimens. This approach, which we term fluorescence imaging XANES (φXANES), allows us to spatially depict metal-protein associations in a native, hydrated state whilst avoiding intrinsic chemical damage from radiation. This method was validated using iron-challenged Caenorhabditis elegans to observe marked alterations in redox environment