Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.Comment: 8 pages, 5 figure

Educational data comics:What can comics do for education in visualization?

This paper discusses the potential of comics for explaining concepts with and around data visualization. With the increasing spread of visualizations and the democratization of access to visualization tools, we see a growing need for easily approachable resources for learning visualization techniques, applications, design processes, etc. Comics are a promising medium for such explanation as they concisely combine graphical and textual content in a sequential manner and they provide fast visual access to specific parts of the explanations. Based on a first literature review and our extensive experience with the subject, we survey works at the respective intersections of comics, visualization and education: data comics, educational comics, and visualization education. We report on five potentials of comics to create and share educational material, to engage wide and potentially diverse audiences, and to support educational activities. For each potential we list, we describe open questions for future research. Our discussion aims to inform both the application of comics by educators and their extension and study by researchers

Expression Analysis of Fibronectin Type III Domain-Containing (FNDC) Genes in Inflammatory Bowel Disease and Colorectal Cancer

Background. Fibronectin type III domain-containing (FNDC) proteins fulfill manifold functions in tissue development and regulation of cellular metabolism. FNDC4 was described as anti-inflammatory factor, upregulated in inflammatory bowel disease (IBD). FNDC signaling includes direct cell-cell interaction as well as release of bioactive peptides, like shown for FNDC4 or FNDC5. The G-protein-coupled receptor 116 (GPR116) was found as a putative FNDC4 receptor. We here aim to comprehensively analyze the mRNA expression of FNDC1, FNDC3A, FNDC3B, FNDC4, FNDC5, and GPR116 in nonaffected and affected mucosal samples of patients with IBD or colorectal cancer (CRC). Methods. Mucosa samples were obtained from 30 patients undergoing diagnostic colonoscopy or from surgical resection of IBD or CRC. Gene expression was determined by quantitative real-time PCR. In addition, FNDC expression data from publicly available Gene Expression Omnibus (GEO) data sets (GDS4296, GDS4515, and GDS5232) were analyzed. Results. Basal mucosal expression revealed higher expression of FNDC3A and FNDC5 in the ileum compared to colonic segments. FNDC1 and FNDC4 were significantly upregulated in IBD. None of the investigated FNDCs was differentially expressed in CRC, just FNDC3A trended to be upregulated. The GEO data set analysis revealed significantly downregulated FNDC4 and upregulated GPR116 in microsatellite unstable (MSI) CRCs. The expression of FNDCs and GPR116 was independent of age and sex. Conclusions. FNDC1 and FNDC4 may play a relevant role in the pathobiology of IBD, but none of the investigated FNDCs is regulated in CRC. GPR116 may be upregulated in advanced or MSI CRC. Further studies should validate the altered FNDC expression results on protein levels and examine the corresponding functional consequences

Perspectives of Visualization Onboarding and Guidance in VA

A typical problem in Visual Analytics is that users are highly trained experts in their application domains, but have mostly no experience in using VA systems. Thus, users often have difficulties interpreting and working with visual representations. To overcome these problems, user assistance can be incorporated into VA systems to guide experts through the analysis while closing their knowledge gaps. Different types of user assistance can be applied to extend the power of VA, enhance the user's experience, and broaden the audience for VA. Although different approaches to visualization onboarding and guidance in VA already exist, there is a lack of research on how to design and integrate them in effective and efficient ways. Therefore, we aim at putting together the pieces of the mosaic to form a coherent whole. Based on the Knowledge-Assisted Visual Analytics model, we contribute a conceptual model of user assistance for VA by integrating the process of visualization onboarding and guidance as the two main approaches in this direction. As a result, we clarify and discuss the commonalities and differences between visualization onboarding and guidance, and discuss how they benefit from the integration of knowledge extraction and exploration. Finally, we discuss our descriptive model by applying it to VA tools integrating visualization onboarding and guidance, and showing how they should be utilized in different phases of the analysis in order to be effective and accepted by the user.Comment: Elsevier Visual Informatics (revised version under review