VisME: Visual microsaccades explorer

This work presents a visual analytics approach to explore microsaccade distributions in high-frequency eye tracking data. Research studies often apply filter algorithms and parameter values for microsaccade detection. Even when the same algorithms are employed, different parameter values might be adopted across different studies. In this paper, we present a visual analytics system (VisME) to promote reproducibility in the data analysis of microsaccades. It allows users to interactively vary the parametric values for microsaccade filters and evaluate the resulting influence on microsaccade behavior across individuals and on a group level. In particular, we exploit brushing-and-linking techniques that allow the microsaccadic properties of space, time, and movement direction to be extracted, visualized, and compared across multiple views. We demonstrate in a case study the use of our visual analytics system on data sets collected from natural scene viewing and show in a qualitative usability study the usefulness of this approach for eye tracking researchers. We believe that interactive tools such as VisME will promote greater transparency in eye movement research by providing researchers with the ability to easily understand complex eye tracking data sets; such tools can also serve as teaching systems. VisME is provided as open source software

Overlap-free Drawing of Generalized Pythagoras Trees for Hierarchy Visualization

Generalized Pythagoras trees were developed for visualizing hierarchical data, producing organic, fractal-like representations. However, the drawback of the original layout algorithm is visual overlap of tree branches. To avoid such overlap, we introduce an adapted drawing algorithm using ellipses instead of circles to recursively place tree nodes representing the subhierarchies. Our technique is demonstrated by resolving overlap in diverse real-world and generated datasets, while comparing the results to the original approach

Visualizing dynamic software developer numbers in Work Spaces of Interest (WOIs)

Softwaresysteme werden häufig über mehrere Jahre von vielen Entwicklern bearbeitet. In dieser Zeit können sich sowohl die Beteiligung der Entwickler als auch die Arbeitsbereiche, in denen entwickelt wird, stark verändern. In dieser Arbeit wird das Konzept der AOI Rivers für die Visualisierung von Softwareentwicklungsprozessen angepasst, indem es zu WOI Rivers erweitert wird. Mit WOI Rivers ist es möglich, die dynamischen Verhaltensweisen von Entwicklergruppen zu beobachten. Es kann sichtbar gemacht werden, wie sich die Anzahl von Entwicklern oder die Häufigkeit ihrer Beteiligung an Dateiveränderungen in verschiedenen Arbeitsbereichen über die Zeit verändert. Zusätzlich kann über Transitionen gezeigt werden, wie zwischen verschiedenen Arbeitsbereichen gewechselt wird und wann bzw. wo neue Entwickler hinzukommen oder das Projekt wieder verlassen. Da sich Entwickler zur gleichen Zeit an vielen verschiedenen Dateien bzw. verschiedenen Arbeitsbereichen in unterschiedlichen Stärken beteiligen können, ist die Höhe, die jedem Entwickler in einem Intervall zugewiesen wird, variabel und muss auf mehrere Transitionen aufgeteilt werden. Hierfür werden verschiedene Möglichkeiten untersucht und es wird eine Methode entwickelt, Transitionen zwischen gleichen Arbeitsbereichen nicht unnötig aufzuteilen, um die Anzahl an Überkreuzungen, und dadurch Visual Clutter, zu reduzieren. Die Visualisierungstechnik wurde als interaktives Visualisierungswerkzeug implementiert. In diesem können Arbeitsbereiche, sogenannte Workspaces of Interest (WOIs), in einer Hierarchiedarstellung, Entwicklergruppen aus einer Liste aller Entwickler und der darzustellende Zeitbereich für die WOI River-Visualisierung festgelegt werden. Anhand dreier Open-Source-Softwareprojekte werden Fallstudien durchgeführt, um Einsichten in die Entwicklungsprozesse dieser Projekte zu erhalten

Roles of the Chr.9p21.3 ANRIL Locus in Regulating Inflammation and Implications for Anti-Inflammatory Drug Target Identification

Periodontitis (PD) is a common gingival infectious disease caused by an over-aggressive inflammatory reaction to dysbiosis of the oral microbiome. The disease induces a profound systemic inflammatory host response, that triggers endothelial dysfunction and pro-thrombosis and thus may aggravate atherosclerotic vascular disease and its clinical complications. Recently, a risk haplotype at the ANRIL/CDKN2B-AS1 locus on chromosome 9p21.3, that is not only associated with coronary artery disease / myocardial infarction (CAD/MI) but also with PD, could be identified by genome-wide association studies. The locus encodes ANRIL - a long non-coding RNA (lncRNA) which, like other lncRNAs, regulates genome methylation via interacting with specific DNA sequences and proteins, such as DNA methyltranferases and polycomb proteins, thereby affecting expression of multiple genes by cis and trans mechanisms. Here, we describe ANRIL regulated genes and metabolic pathways and discuss implications of the findings for target identification of drugs with potentially anti-inflammatory activity in general

Exploring visual quality of multidimensional time series projections

Dimensionality reduction is often used to project time series data from multidimensional to two-dimensional space to generate visual representations of the temporal evolution. In this context, we address the problem of multidimensional time series visualization by presenting a new method to show and handle projection errors introduced by dimensionality reduction techniques on multidimensional temporal data. For visualization, subsequent time instances are rendered as dots that are connected by lines or curves to indicate the temporal dependencies. However, inevitable projection artifacts may lead to poor visualization quality and misinterpretation of the temporal information. Wrongly projected data points, inaccurate variations in the distances between projected time instances, and intersections of connecting lines could lead to wrong assumptions about the original data. We adapt local and global quality metrics to measure the visual quality along the projected time series, and we introduce a model to assess the projection error at intersecting lines. These serve as a basis for our new uncertainty visualization techniques that use different visual encodings and interactions to indicate, communicate, and work with the visualization uncertainty from projection errors and artifacts along the timeline of data points, their connections, and intersections. Our approach is agnostic to the projection method and works for linear and non-linear dimensionality reduction methods alike

The iReAct study : A biopsychosocial analysis of the individual response to physical activity

Background:Physical activity is a substantial promoter for health and well-being. Yet, while an increasing number of studies shows that the responsiveness to physical activity is highly individual, most studies focus this issue from only one perspective and neglect other contributing aspects. In reference to a biopsychosocial framework, the goal of our study is to examine how physically inactive individuals respond to two distinct standardized endurance trainings on various levels. Based on an assessment of activity- and health-related biographical experiences across the life course, our mixed-method study analyzes the responsiveness to physical activity in the form of a transdisciplinary approach, considering physiological, epigenetic, motivational, affective, and body image-related aspects.Methods:Participants are randomly assigned to two different training programs (High Intensity Interval Training vs. Moderate Intensity Continuous Training) for six weeks. After this first training period, participants switch training modes according to a two-period sequential-training-intervention (STI) design and train for another six weeks. In order to analyse baseline characteristics as well as acute and adaptive biopsychosocial responses, three extensive mixed-methods diagnostic blocks take place at the beginning (t0) of the study and after the first (t1) and the second (t2) training period resulting in a net follow-up time of 15 weeks. The study is divided into five modules in order to cover a wide array of perspectives.Discussion:The study's transdisciplinary mixed-method design allows to interlace a multitude of subjective and objective data and therefore to draw an integrated picture of the biopsychosocial efficacy of two distinct physical activity programs. The results of our study can be expected to contribute to the development and design of individualised training programs for the promotion of physical activity.Trial registration The study was retrospectively registered in the German Clinical Trials Register on 12 June 2019 (DRKS00017446).publishe