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Background With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms don’t properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data. Results In this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes. Conclusions Our methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes.

This Handbook provides an overview of the field of visualization by presenting the basic concepts, providing a snapshot of current visualization software systems, and examining research topics that are advancing the field. This text is intended for a broad audience, including not only the visualization expert seeking advanced methods to solve a particular problem, but also the novice looking for general background information on visualization topics. The largest collection of state-of-the-art visualization research yet gathered in a single volume, this book includes articles by a who’s who of international scientific visualization researchers covering every aspect of the discipline, including:Virtual environments for visualizationBasic visualization algorithmsLarge-scale data visualizationScalar data isosurface methodsVisualization software and frameworksScalar data volume renderingPerceptual issues in visualizationVarious application topics, including information visualization.This covers a wide range of topics, in 47 chapters, representing the state-of-the-art of scientific visualization.

Understanding atmospheric phenomena involves analysis of large-scale spatiotemporal multivariate data. The complexity and heterogeneity of such data pose a significant challenge in discovering and understanding the association between multiple climate variables. To tackle this challenge, we present an interactive heuristic visualization system that supports climate scientists and the public in their exploration and analysis of atmospheric phenomena of interest. Three techniques are introduced: (1) web-based spatiotemporal climate data visualization; (2) multiview and multivariate scientific data analysis; and (3) data mining-enabled visual analytics. The Arctic System Reanalysis (ASR) data are used to demonstrate and validate the effectiveness and usefulness of our method through a case study of “The Great Arctic Cyclone of 2012”. The results show that different variables have strong associations near the polar cyclone area. This work also provides techniques for identifying multivariate correlation and for better understanding the driving factors of climate phenomena.

For finite element analyses within highly elastic and plastic structural domains, hexahedral meshes have historically offered some benefits over tetrahedral finite element meshes in terms of reduced error, smaller element counts, and improved reliability. However, hexahedral finite element mesh generation continues to be difficult to perform and automate, with hexahedral mesh generation taking several orders of magnitude longer than current tetrahedral mesh generators to complete. Thus, developing a better understanding of the underlying constraints that make hexahedral meshing difficult could result in dramatic reductions in the amount of time necessary to prepare a hexahedral finite element model for analysis. In this paper, we present a survey of constraints associated with hexahedral meshes (i.e., the conditions that must be satisfied to produce a hexahedral mesh). In presenting our formulation of these constraints, we will utilize the dual of a hexahedral mesh. We also discuss how incorporation of these constraints into existing hexahedral mesh generation algorithms could be utilized to extend the class of geometries to which these algorithms apply. We also describe a list of open problems in hexahedral mesh generation and give some context for future efforts in addressing these problems.

We assessed the extent to which failures in sustained attention were associated with chronic mild traumatic brain injury (mTBI) deficits in cognitive control among college-age young adults with and without a history of sport-related concussion. Participants completed the ImPACT computer-based assessment and a modified flanker task. Results indicated that a history of mTBI, relative to healthy controls, was associated with inferior overall flanker task performance with a greater number of omission errors and more frequent sequentially occurring omission errors. Accordingly, these findings suggest that failures in the ability to maintain attentional vigilance may, in part, underlie mTBI-related cognition deficits.

Biomedical simulations are often dependent on numerical approximation methods, including finite element, finite difference, and finite volume methods, to model the varied phenomena of interest. An important requirement of the numerical approximation methods above is the need to create a discrete decomposition of the model geometry into a ‘mesh’. Historically, the generation of these meshes has been a critical bottleneck in efforts to efficiently generate biomedical simulations which can be utilized in understanding, planning, and diagnosing biomedical conditions. In this paper we discuss a methodology for generating hexahedral meshes for biomedical models using an algorithm implemented in the SCIRun Problem Solving Environment. The method is flexible and can be utilized to build up conformal hexahedral meshes ranging from models defined by single isosurfaces to more complex geometries with multi-surface boundaries.

Biomedical computing applications often require a computational pipeline that integrates data from experimental measurements or from image acquisition into a modeling and visualization environment. The latter process often involves segmentation, mesh generation, and numerical simulations. An important requirement of the numerical approximation and visualization methods is the need to create a discrete decomposition of the model geometry into a ‘mesh’. The meshes produced are used both as input for computational simulation and as the geometric basis for many of the resulting visualizations. Historically, the generation of these meshes has been a significant bottleneck in efforts to efficiently create complex, three-dimensional biomedical models. In this paper, we will outline a pipeline for more efficiently generating meshes suitable for biomedical simulations. Because of the wide array of geometries and phenomena encountered in biomedical computing, this pipeline, SCIRun, will incorporate a flexible suite of tools that will offer some generality to mesh generation of biomedical models. We will discuss several tools that have been successfully used in past problems and how these tools have been incorporated into SCIRun. We will demonstrate mesh generation for example problems along with methods for verifying the quality of the meshes generated. Finally, we will discuss ongoing and future efforts to bring all of these tools into a common environment to dramatically reduce the difficulty of mesh generation for biomedical simulations.