Imaging of Vascular Smooth Muscle Cells with Soft X-Ray Spectromicroscopy

Using X-ray microscopy and spectromicroscopy, vascular smooth muscle cells (VSMCs) were imaged, prepared without using additional embedding material or staining, but by applying simple, noncryo fixation techniques. The cells were imaged with a compact source transmission X-ray microscope and a scanning transmission X-ray microscope (STXM). With the STXM, spectromicroscopy was performed at the C K-edge and the Ca LIII,II-edges. VSMCs were chosen because of their high amount of actin stress fibers, so that the actin cytoskeleton should be visible. Other parts of the cell, such as the nucleus and organelles, were also identified from the micrographs. Both in the spectra and the images, the effects of the different preparation procedures were observable. Furthermore, Ca hotspots were detected and their density is determine

MSCar: Enhancing Message Sequence Charts with Interactivity for Analysing (Automotive) Communication Sequences

Message Sequence Charts (MSCs) are a standardized and widespread form to visually describe interactions in distributed systems. Our approach proposes the enrichment of large scaled MSCs with novel interaction and design techniques used in the field of information visualization. Additionally, we show a graphical solution to visualize parallel, multi-directed communication processes in MSCs. Instead of the common application to specify system behaviours our interactive MSCs are aimed at exploring and diagnosing dependencies in network communication in general and, regarding our special requirements, within in-car communication traces. We implemented a prototype called MSCar with Focus and Context techniques, Dynamic Path Highlighting, Details on Demand and Colour Coding to support the users' cognitive abilities. A qualitative user study on MSCar gave us preliminary feedback and disclosed potentials of our approach

Visual parameter optimisation for biomedical image processing

Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches

Visual interaction with dimensionality reduction: a structured literature analysis

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a “human in the loop” process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities

Multifunctional supramolecular polymer networks as next-generation consolidants for archaeological wood conservation.

The preservation of our cultural heritage is of great importance to future generations. Despite this, significant problems have arisen with the conservation of waterlogged wooden artifacts. Three major issues facing conservators are structural instability on drying, biological degradation, and chemical degradation on account of Fe(3+)-catalyzed production of sulfuric and oxalic acid in the waterlogged timbers. Currently, no conservation treatment exists that effectively addresses all three issues simultaneously. A new conservation treatment is reported here based on a supramolecular polymer network constructed from natural polymers with dynamic cross-linking formed by a combination of both host-guest complexation and a strong siderophore pendant from a polymer backbone. Consequently, the proposed consolidant has the ability to chelate and trap iron while enhancing structural stability. The incorporation of antibacterial moieties through a dynamic covalent linkage into the network provides the material with improved biological resistance. Exploiting an environmentally compatible natural material with completely reversible chemistries is a safer, greener alternative to current strategies and may extend the lifetime of many culturally relevant waterlogged artifacts around the world.This is the author's accepted manuscript. The final version is available from PNAS at http://www.pnas.org/content/111/50/17743.long

Design Space of Visual Feedforward And Corrective Feedback in XR-Based Motion Guidance Systems

Extended reality (XR) technologies are highly suited in assisting individuals in learning motor skills and movements -- referred to as motion guidance. In motion guidance, the "feedforward" provides instructional cues of the motions that are to be performed, whereas the "feedback" provides cues which help correct mistakes and minimize errors. Designing synergistic feedforward and feedback is vital to providing an effective learning experience, but this interplay between the two has not yet been adequately explored. Based on a survey of the literature, we propose design space for both motion feedforward and corrective feedback in XR, and describe the interaction effects between them. We identify common design approaches of XR-based motion guidance found in our literature corpus, and discuss them through the lens of our design dimensions. We then discuss additional contextual factors and considerations that influence this design, together with future research opportunities for motion guidance in XR.Comment: To appear in ACM CHI 202

GraphUnit: Evaluating Interactive Graph Visualizations Using Crowdsourcing

We present GraphUnit, a framework and online service that automates the process of designing, running and analyzing results of controlled user studies of graph visualizations by leveraging crowdsourcing and a set of evaluation modules based on a graph task taxonomy. User studies play an important role in visualization research but conducting them requires expertise and is time consuming. GraphUnit simplifies the evaluation process by allowing visualization designers to easily configure user studies for their web-based graph visualizations, deploy them online, use Mechanical Turk to attract participants, collect user responses and store them in a database, and analyze incoming results automatically using appropriate statistical tools and graphs. We demonstrate the effectiveness of GraphUnit by replicating two published evaluation studies on network visualization, and showing that these studies could be configured in less than an hour. Finally, we discuss how GraphUnit can facilitate quick evaluations of alternative graph designs and thus encourage the frequent use of user studies to evaluate design decisions in iterative development processes

Design Patterns for Situated Visualization in Augmented Reality

Situated visualization has become an increasingly popular research area in the visualization community, fueled by advancements in augmented reality (AR) technology and immersive analytics. Visualizing data in spatial proximity to their physical referents affords new design opportunities and considerations not present in traditional visualization, which researchers are now beginning to explore. However, the AR research community has an extensive history of designing graphics that are displayed in highly physical contexts. In this work, we leverage the richness of AR research and apply it to situated visualization. We derive design patterns which summarize common approaches of visualizing data in situ. The design patterns are based on a survey of 293 papers published in the AR and visualization communities, as well as our own expertise. We discuss design dimensions that help to describe both our patterns and previous work in the literature. This discussion is accompanied by several guidelines which explain how to apply the patterns given the constraints imposed by the real world. We conclude by discussing future research directions that will help establish a complete understanding of the design of situated visualization, including the role of interactivity, tasks, and workflows.Comment: To appear in IEEE VIS 202