MDsrv: visual sharing and analysis of molecular dynamics simulations.

Molecular dynamics simulation is a proven technique for computing and visualizing the time-resolved motion of macromolecules at atomic resolution. The MDsrv is a tool that streams MD trajectories and displays them interactively in web browsers without requiring advanced skills, facilitating interactive exploration and collaborative visual analysis. We have now enhanced the MDsrv to further simplify the upload and sharing of MD trajectories and improve their online viewing and analysis. With the new instance, the MDsrv simplifies the creation of sessions, which allows the exchange of MD trajectories with preset representations and perspectives. An important innovation is that the MDsrv can now access and visualize trajectories from remote datasets, which greatly expands its applicability and use, as the data no longer needs to be accessible on a local server. In addition, initial analyses such as sequence or structure alignments, distance measurements, or RMSD calculations have been implemented, which optionally support visual analysis. Finally, based on Mol*, MDsrv now provides faster and more efficient visualization of even large trajectories compared to its predecessor tool NGL.

Visualizing simulated electrical fields from electroencephalography and transcranial electric brain stimulation: a comparative evaluation.

Electrical activity of neuronal populations is a crucial aspect of brain activity. This activity is not measured directly but recorded as electrical potential changes using head surface electrodes (electroencephalogram - EEG). Head surface electrodes can also be deployed to inject electrical currents in order to modulate brain activity (transcranial electric stimulation techniques) for therapeutic and neuroscientific purposes. In electroencephalography and noninvasive electric brain stimulation, electrical fields mediate between electrical signal sources and regions of interest (ROI). These fields can be very complicated in structure, and are influenced in a complex way by the conductivity profile of the human head. Visualization techniques play a central role to grasp the nature of those fields because such techniques allow for an effective conveyance of complex data and enable quick qualitative and quantitative assessments. The examination of volume conduction effects of particular head model parameterizations (e.g., skull thickness and layering), of brain anomalies (e.g., holes in the skull, tumors), location and extent of active brain areas (e.g., high concentrations of current densities) and around current injecting electrodes can be investigated using visualization. Here, we evaluate a number of widely used visualization techniques, based on either the potential distribution or on the current-flow. In particular, we focus on the extractability of quantitative and qualitative information from the obtained images, their effective integration of anatomical context information, and their interaction. We present illustrative examples from clinically and neuroscientifically relevant cases and discuss the pros and cons of the various visualization techniques.

Lexcube: Interactive Visualization of Large Earth System Data Cubes.

Many subsystems of Earth are constantly monitored in space and time and undergo continuous anthropogenic interventions. However, research into this transformation remains largely inaccessible to the public due to the complexity of the Big Data generated by models and Earth observation. To overcome this barrier, we present the Leipzig Explorer of Earth Data Cubes (lexcube.org), an interactive Earth data visualization that allows users to explore terabyte-scale datasets with minimal latency through space, time, variables, and model variants. With over 2800 users and 163,000 API requests since its public release in May 2022, lexcube.org is a novel interactive data cube visualization that embraces the concept of "data cubes," enabling an equal treatment of space and time. We expect this development to be particularly relevant for the emerging exascale Digital Twins of Earth, as interactive visualizations in real-time could remove access barriers and help democratize Earth system sciences.

"Feels Like an Indie Game"-Evaluation of a Virtual Field Trip Prototype on Radioactive Waste Management Research for University Education.

This article describes the design and evaluation of a virtual field trip on the topic of radioactive waste management research for university education. We created an interactive virtual tour through the Mont Terri underground research laboratory by enhancing the virtual experiment information system, designed for domain experts, with background information, illustrations, tasks, tests, and an improved user interface. To put the tour's content into context, a conventional introductory presentation on the final disposal of radioactive waste was added. A user study with 22 participants proved a good perceived usability of the virtual tour and the virtual field trip's ability to transfer knowledge. These results suggest a benefit of employing virtual field trips in geoscientific university courses. In addition, it is conceivable to use the virtual field trip as a tool for science communication in the context of participatory processes during nuclear waste disposal site selection processes.

Visualizing Higher-Order 3D Tensors by Multipole Lines.

Physics, medicine, earth sciences, mechanical engineering, geo-engineering, bio-engineering and many more application areas use tensorial data. For example, tensors are used in formulating the balance equations of charge, mass, momentum, or energy as well as the constitutive relations that complement them. Some of these tensors (i.e., stiffness tensor, strain gradient, photo-elastic tensor) are of order higher than two. Currently, there are nearly no visualization techniques for such data beyond glyphs. An important reason for this is the limit of currently used tensor decomposition techniques. In this article, we propose to use the deviatoric decomposition to draw lines describing tensors of arbitrary order in three dimensions. The deviatoric decomposition splits a three-dimensional tensor of any order with any type of index symmetry into totally symmetric, traceless tensors. These tensors, called deviators, can be described by a unique set of directions (called multipoles by J. C. Maxwell) and scalars. These multipoles allow the definition of multipole lines which can be computed in a similar fashion to tensor lines and allow a line-based visualization of three-dimensional tensors of any order. We give examples for the visualization of symmetric, second-order tensor fields as well as fourth-order tensor fields. To allow an interpretation of the multipole lines, we analyze the connection between the multipoles and the eigenvectors/eigenvalues in the second-order case. For the fourth-order stiffness tensor, we prove relations between multipoles and important physical quantities such as shear moduli as well as the eigenvectors of the second-order right Cauchy-Green tensor.

Electromechanical Coupling in Electroactive Polymers - a Visual Analysis of a Third-Order Tensor Field.

Electroactive polymers are frequently used in engineering applications due to their ability to change their shape and properties under the influence of an electric field. This process also works vice versa, such that mechanical deformation of the material induces an electric field in the EAP device. This specific behavior makes such materials highly attractive for the construction of actuators and sensors in various application areas. The electromechanical behaviour of electroactive polymers can be described by a third-order coupling tensor, which represents the sensitivity of mechanical stresses concerning the electric field, i.e., it establishes a relation between a second-order and a first-order tensor field. Due to this coupling tensor's complexity and the lack of meaningful visualization methods for third-order tensors in general, an interpretation of the tensor is rather difficult. Thus, the central engineering research question that this contribution deals with is a deeper understanding of electromechanical coupling by analyzing the third-order coupling tensor with the help of specific visualization methods. Starting with a deviatoric decomposition of the tensor, the multipoles of each deviator are visualized, which allows a first insight into this highly complex third-order tensor. In the present contribution, four examples, including electromechanical coupling, are simulated within a finite element framework and subsequently analyzed using the tensor visualization method.

Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network.

Purpose: The interpretation of image data plays a critical role during acute brain stroke diagnosis, and promptly defining the requirement of a surgical intervention will drastically impact the patient's outcome. However, determining stroke lesions purely from images can be a daunting task. Many studies proposed automatic segmentation methods for brain stroke lesions from medical images in different modalities, though heretofore results do not satisfy the requirements to be clinically reliable. We investigate the segmentation of brain stroke lesions using a geometric deep learning model that takes advantage of the intrinsic interconnected diffusion features in a set of multi-modal inputs consisting of computer tomography (CT) perfusion parameters. Approach: We propose a geometric deep learning model for the segmentation of ischemic stroke brain lesions that employs spline convolutions and unpooling/pooling operators on graphs to excerpt graph-structured features in a fully convolutional network architecture. In addition, we seek to understand the underlying principles governing the different components of our model. Accordingly, we structure the experiments in two parts: an evaluation of different architecture hyperparameters and a comparison with state-of-the-art methods. Results: The ablation study shows that deeper layers obtain a higher Dice coefficient score (DCS) of up to 0.3654. Comparing different pooling and unpooling methods shows that the best performing unpooling method is the proportional approach, yet it often smooths the segmentation border. Unpooling achieves segmentation results more adapted to the lesion boundary corroborated with systematic lower values of Hausdorff distance. The model performs at the level of state-of-the-art models without optimized training methods, such as augmentation or patches, with a DCS of 0.4553±0.0031. Conclusions: We proposed and evaluated an end-to-end trainable fully convolutional graph network architecture using spline convolutional layers for the ischemic stroke lesion prediction. We propose a model that employs graph-based operations to predict acute stroke brain lesions from CT perfusion parameters. Our results prove the feasibility of using geometric deep learning to solve segmentation problems, and our model shows a better performance than other models evaluated. The proposed model achieves improved metric values for the DCS metric, ranging from 8.61% to 69.05%, compared with other models trained under the same conditions. Next, we compare different pooling and unpooling operations in relation to their segmentation results, and we show that the model can produce segmentation outputs that adapt to irregular segmentation boundaries when using simple heuristic unpooling operations.

A Taxonomy of Uncertainty Events in Visual Analytics.

Visual analytics (VA) has become a standard tool to process and analyze data visually to generate novel insights. Unfortunately, each component can introduce uncertainty in the visual analytics process. These uncertainty events can originate from many effects and need to be differentiated. In this work, we propose a taxonomy of potential uncertainty events in the visual analytics cycle. Here, we structure the taxonomy along the components included in the visual analytics cycle. Based on this taxonomy, we provide a list of dependencies between these events. At last, we show how to use our taxonomy by providing a real-world example.

BarMap: RNA folding on dynamic energy landscapes.

Dynamical changes of RNA secondary structures play an important role in the function of many regulatory RNAs. Such kinetic effects, especially in time-variable and externally triggered systems, are usually investigated by means of extensive and expensive simulations of large sets of individual folding trajectories. Here we describe the theoretical foundations of a generic approach that not only allows the direct computation of approximate population densities but also reduces the efforts required to analyze the folding energy landscapes to a one-time preprocessing step. The basic idea is to consider the kinetics on individual landscapes and to model external triggers and environmental changes as small but discrete changes in the landscapes. A "barmap" links macrostates of temporally adjacent landscapes and defines the transfer of population densities from one "snapshot" to the next. Implemented in the BarMap software, this approach makes it feasible to study folding processes at the level of basins, saddle points, and barriers for many nonstationary scenarios, including temperature changes, cotranscriptional folding, refolding in consequence to degradation, and mechanically constrained kinetics, as in the case of the translocation of a polymer through a pore.

An Interactive Decision Support System for Land Reuse Tasks.

Experts face the task of deciding where and how land reuse-transforming previously used areas into landscape and utility areas-can be performed. This decision is based on which area should be used, which restrictions exist, and which conditions have to be fulfilled for reusing this area. Information about the restrictions and the conditions is available as mostly textual, nonspatial data associated to areas overlapping the target areas. Due to the large amount of possible combinations of restrictions and conditions overlapping (partially) the target area, this decision process becomes quite tedious and cumbersome. Moreover, it proves to be useful to identify similar regions that have reached different stages of development within the dataset which in turn allows determining common tasks for these regions. We support the experts in accomplishing these tasks by providing aggregated representations as well as multiple coordinated views together with category filters and selection mechanisms implemented in an interactive decision support system. Textual information is linked to these visualizations enabling the experts to justify their decisions. Evaluating our approach using a standard SUS questionnaire suggests that especially the experts were very satisfied with the interactive decision support system.

Visual Cue Based Corrective Feedback for Motor Skill Training in Mixed Reality: A Survey.

When learning a motor skill it is helpful to get corrective feedback from an instructor. This will support the learner to execute the movement correctly. With modern technology, it is possible to provide this feedback via mixed reality. In most cases, this involves visual cues to help the user understand the corrective feedback. We analyzed recent research approaches utilizing visual cues for feedback in mixed reality. The scope of this paper is visual feedback for motor skill learning, which involves physical therapy, exercise, rehabilitation etc. While some of the surveyed literature discusses therapeutic effects of the training, this paper focuses on visualization techniques. We categorized the literature from a visualization standpoint, including visual cues, technology and characteristics of the feedback. This provided insights into how visual feedback in mixed reality is applied in the literature and how different aspects of the feedback are related. The insights obtained can help to better adjust future feedback systems to the target group and their needs. This paper also provides a deeper understanding of the characteristics of the visual cues in general and promotes future, more detailed research on this topic.

Stress Visualization for Interface Optimization of a Hybrid Component Using Surface Tensor Spines.

In lightweight construction, engineers focus on designing and optimizing lightweight components without compromising their strength and durability. In this process, materials such as polymers are commonly considered for a hybrid construction, or even used as a complete replacement. In this work, we focus on a hybrid component design combining metal and carbon fiber reinforced polymer parts. Here, engineers seek to optimize the interface connection between a polymer and a metal part through the placement of load transmission elements in a mechanical millimetric mesoscale level. To assist engineers in the placement and design process, we extend tensor spines, a 3-D tensor-based visualization technique, to surfaces. This is accomplished by combining texture-based techniques with tensor data. Moreover, we apply a parametrization based on a remeshing process to provide visual guidance during the placement. Finally, we demonstrate and discuss real test cases to validate the benefit of our approach.

Extrapolation in the analysis of lung aeration by computed tomography: a validation study.

INTRODUCTION: Computed tomography (CT) is considered the gold standard for quantification of global or regional lung aeration and lung mass. Quantitative CT, however, involves the exposure to ionizing radiation and requires manual image processing. We recently evaluated an extrapolation method which calculates quantitative CT parameters characterizing the entire lung from only 10 reference CT-slices thereby reducing radiation exposure and analysis time. We hypothesized that this extrapolation method could be further validated using CT-data from pigs and sheep, which have a different thoracic anatomy. METHODS: We quantified volume and mass of the total lung and differently aerated lung compartments in 168 ovine and 55 porcine whole-lung CTs covering lung conditions from normal to gross deaeration. Extrapolated volume and mass parameters were compared to the respective values obtained by whole-lung analysis. We also tested the accuracy of extrapolation for all possible numbers of CT slices between 15 and 5. Bias and limits of agreement (LOA) were analyzed by the Bland-Altman method. RESULTS: For extrapolation from 10 reference slices, bias (LOA) for the total lung volume and mass of sheep were 18.4 (-57.2 to 94.0) ml and 4.2 (-21.8 to 30.2) grams, respectively. The corresponding bias (LOA) values for pigs were 5.1 (-55.2 to 65.3) ml and 1.6 (-32.9 to 36.2) grams, respectively. All bias values for differently aerated lung compartments were below 1% of the total lung volume or mass and the LOA never exceeded ± 2.5%. Bias values diverged from zero and the LOA became considerably wider when less than 10 reference slices were used. CONCLUSIONS: The extrapolation method appears robust against variations in thoracic anatomy, which further supports its accuracy and potential usefulness for clinical and experimental application of quantitative CT.

The Making of Continuous Colormaps.

Continuous colormaps are integral parts of many visualization techniques, such as heat-maps, surface plots, and flow visualization. Despite that the critiques of rainbow colormaps have been around and well-acknowledged for three decades, rainbow colormaps are still widely used today. One reason behind the resilience of rainbow colormaps is the lack of tools for users to create a continuous colormap that encodes semantics specific to the application concerned. In this paper, we present a web-based software system, CCC-Tool (short for Charting Continuous Colormaps) under the URL https://ccctool.com, for creating, editing, and analyzing such application-specific colormaps. We introduce the notion of "colormap specification (CMS)" that maintains the essential semantics required for defining a color mapping scheme. We provide users with a set of advanced utilities for constructing CMS's with various levels of complexity, examining their quality attributes using different plots, and exporting them to external application software. We present two case studies, demonstrating that the CCC-Tool can help domain scientists as well as visualization experts in designing semantically-rich colormaps.

Visualizing Multimodal Deep Learning for Lesion Prediction.

A U-Net is a type of convolutional neural network that has been shown to output impressive results in medical imaging segmentation tasks. Still, neural networks in general form a black box that is hard to interpret, especially by noncomputer scientists. This work provides a visual system that allows users to examine U-Nets that were trained to predict brain lesions caused by stroke using multimodal imaging. We provide several visualization views that allow users to load trained U-Nets, run them on different patient data, and examine the results while visually following the computation of the U-Net. With these visualizations, we can provide useful information for our medical collaborators showing how the training database can be improved and which features are best learned by the neural network.

Illustrative stream surfaces.

Stream surfaces are an intuitive approach to represent 3D vector fields. In many cases, however, they are challenging objects to visualize and to understand, due to a high degree of self-occlusion. Despite the need for adequate rendering methods, little work has been done so far in this important research area. In this paper, we present an illustrative rendering strategy for stream surfaces. In our approach, we apply various rendering techniques, which are inspired by the traditional flow illustrations drawn by Dallmann and Abraham \& Shaw in the early 1980s. Among these techniques are contour lines and halftoning to show the overall surface shape. Flow direction as well as singularities on the stream surface are depicted by illustrative surface streamlines. ;To go beyond reproducing static text book images, we provide several interaction features, such as movable cuts and slabs allowing an interactive exploration of the flow and insights into subjacent structures, e.g., the inner windings of vortex breakdown bubbles. These methods take only the parameterized stream surface as input, require no further preprocessing, and can be freely combined by the user. We explain the design, GPU-implementation, and combination of the different illustrative rendering and interaction methods and demonstrate the potential of our approach by applying it to stream surfaces from various flow simulations. ;

Visualization of graph products.

Graphs are a versatile structure and abstraction for binary relationships between objects. To gain insight into such relationships, their corresponding graph can be visualized. In the past, many classes of graphs have been defined, e.g. trees, planar graphs, directed acyclic graphs, and visualization algorithms were proposed for these classes. Although many graphs may only be classified as "general" graphs, they can contain substructures that belong to a certain class. Archambault proposed the TopoLayout framework: rather than draw any arbitrary graph using one method, split the graph into components that are homogeneous with respect to one graph class and then draw each component with an algorithm best suited for this class. Graph products constitute a class that arises frequently in graph theory, but for which no visualization algorithm has been proposed until now. In this paper, we present an algorithm for drawing graph products and the aesthetic criterion graph product's drawings are subject to. We show that the popular High-Dimensional Embedder approach applied to cartesian products already respects this aestetic criterion, but has disadvantages. We also present how our method is integrated as a new component into the TopoLayout framework. Our implementation is used for further research of graph products in a biological context.

Detection and Visualization of Splat and Antisplat Events in Turbulent Flows.

Splat and antisplat events are a widely found phenomenon in three-dimensional turbulent flow fields. Splats are observed when fluid locally impinges on an impermeable surface transferring energy from the normal component to the tangential velocity components, while antisplats relate to the inverted situation. These events affect a variety of flow properties, such as the transfer of kinetic energy between velocity components and the transfer of heat, so that their investigation can provide new insight into these issues. Here, we propose the first Lagrangian method for the detection of splats and antisplats as features of an unsteady flow field. Our method utilizes the concept of strain tensors on flow-embedded flat surfaces to extract disjoint regions in which splat and antisplat events of arbitrary scale occur. We validate the method with artificial flow fields of increasing complexity. Subsequently, the method is used to analyze application data stemming from a direct numerical simulation of the turbulent flow over a backward facing step. Our results show that splat and antisplat events can be identified efficiently and reliably even in such a complex situation, demonstrating that the new method constitutes a well-suited tool for the analysis of turbulent flows.

Analysis of the Near-Wall Flow in a Turbine Cascade by Splat Visualization.

Turbines are essential components of jet planes and power plants. Therefore, their efficiency and service life are of central engineering interest. In the case of jet planes or thermal power plants, the heating of the turbines due to the hot gas flow is critical. Besides effective cooling, it is a major goal of engineers to minimize heat transfer between gas flow and turbine by design. Since it is known that splat events have a substantial impact on the heat transfer between flow and immersed surfaces, we adapt a splat detection and visualization method to a turbine cascade simulation in this case study. Because splat events are small phenomena, we use a direct numerical simulation resolving the turbulence in the flow as the base of our analysis. The outcome shows promising insights into splat formation and its relation to vortex structures. This may lead to better turbine design in the future.

Visual exploration of climate variability changes using wavelet analysis.

Due to its nonlinear nature, the climate system shows quite high natural variability on different time scales, including multiyear oscillations such as the El Niño Southern Oscillation phenomenon. Beside a shift of the mean states and of extreme values of climate variables, climate change may also change the frequency or the spatial patterns of these natural climate variations. Wavelet analysis is a well established tool to investigate variability in the frequency domain. However, due to the size and complexity of the analysis results, only few time series are commonly analyzed concurrently. In this paper we will explore different techniques to visually assist the user in the analysis of variability and variability changes to allow for a holistic analysis of a global climate model data set consisting of several variables and extending over 250 years. Our new framework and data from the IPCC AR4 simulations with the coupled climate model ECHAM5/MPI-OM are used to explore the temporal evolution of El Niño due to climate change.