Prioritized Data Compression using Wavelets

The volume of data and the velocity with which it is being generated by com- putational experiments on high performance computing (HPC) systems is quickly outpacing our ability to effectively store this information in its full fidelity. There- fore, it is critically important to identify and study compression methodologies that retain as much information as possible, particularly in the most salient regions of the simulation space. In this paper, we cast this in terms of a general decision-theoretic problem and discuss a wavelet-based compression strategy for its solution. We pro- vide a heuristic argument as justification and illustrate our methodology on several examples. Finally, we will discuss how our proposed methodology may be utilized in an HPC environment on large-scale computational experiments

Hypothalamus And Pituitary Gland Development In The Common Snapping Turtle, Chelydra Serpentina, And Disruption With Atrazine Exposure

Neural control of the endocrine system originates in the hypothalamus in all vertebrates. The hypothalamus signals directly to the pituitary gland to maintain homeostasis by releasing hormones directly into the bloodstream. Hormones travel to peripheral endocrine organs, and regulate widespread activities including growth, metabolism, stress response, circadian rhythms, water balance, and reproduction. Many of these functions have sex-specific aspects, and therefore sexual dimorphism in morphology and function of the hypothalamus is present. Sex-specific genetic background and morphology is established during pre- and peri-natal development, during which sexually dimorphic cellular differentiation occurs and hormonal profiles are set. Disruption of development can therefore have widespread sex-specific effects. A thorough understanding of underlying molecular regulation of sex-specific development is therefore important for understanding disease and dysfunction. Here, we present four studies investigating the sex-specific development of the hypothalamus and pituitary gland in the common snapping turtle, a species with temperature-dependent sex determination. First, we tracked morphological development of the pituitary gland from its origin early in embryogenesis through hatching. Development is a highly conserved process between vertebrate species. Next, we analyzed sex differences in gene expression within the hypothalamus and pituitary gland in embryonic and hatchling turtles. We specifically analyze expression of genes involved in reproductive neuroendocrine function, and also use RNA-seq to identify all differentially expressed genes between sexes and development to further understanding of the underlying mechanisms of development and function. We identified overrepresented functional categories of the differentially expressed gene sets between sexes, and identify protein modification as a potential mechanism underlying sexual dimorphism. Last, we analyzed changes in neuroendocrine gene expression following embryonic exposure to the widely used herbicide atrazine, an endocrine disrupting chemical. Our results indicate an acute, embryonic exposure to atrazine can have persistent changes in expression of genes involved in stress response and reproduction in both the hypothalamus and pituitary gland. This dissertation adds to the body of literature on comparative neuroendocrine development, provides background information for numerous future experiments, and identifies the snapping turtle as another species with potential impacts from atrazine exposure

VAPOR: Visual, Statistical, and Structural Analysis of Astrophysical Flows

In this paper we discuss recent developments in the capabilities of VAPOR: a desktop application that leverages today’s powerful CPUs and GPUs to enable visualization and analysis of terascale data sets using only a commodity PC or laptop. We review VAPOR’s current capabilities, highlighting support for Adaptive Mesh Refinement (AMR) grids, and present new developments in interactive feature-based visualization and statistical analysis

Global e-learning tendencies and possibilities of using the internet for vocational and educational training

The Virtual Training Center (VTC) integrates the ideas of media-based learning environments into the idea of the learning platform which has increasingly influenced theoretical discussions at international congresses (e.g. Learntec 2000, Karlsruhe, see www.learntec.de). The VTC simulates a training center in the web and serves as a platform of internet or intranet based courses and self-learning media. Seminars and workshops can be held, media and documents can be worked on and administered and know-how can be exchanged. The basic idea of the VTC is to create a web-based infrastructure which shelters all possibilities of web-based teaching, learning and training

Segmentation and Visualization of Multivariate Features Using Feature-Local Distributions

We introduce an iterative feature-based transfer function de- sign that extracts and systematically incorporates multivariate feature- local statistics into a texture-based volume rendering process. We argue that an interactive multivariate feature-local approach is advantageous when investigating ill-defined features, because it provides a physically meaningful, quantitatively rich environment within which to examine the sensitivity of the structure properties to the identification parameters. We demonstrate the efficacy of this approach by applying it to vortical structures in Taylor-Green turbulence. Our approach identified the exis- tence of two distinct structure populations in these data, which cannot be isolated or distinguished via traditional transfer functions based on global distributions

Structures in magnetohydrodynamic turbulence: detection and scaling

We present a systematic analysis of statistical properties of turbulent current and vorticity structures at a given time using cluster analysis. The data stems from numerical simulations of decaying three-dimensional (3D) magnetohydrodynamic turbulence in the absence of an imposed uniform magnetic field; the magnetic Prandtl number is taken equal to unity, and we use a periodic box with grids of up to 1536^3 points, and with Taylor Reynolds numbers up to 1100. The initial conditions are either an X-point configuration embedded in 3D, the so-called Orszag-Tang vortex, or an Arn'old-Beltrami-Childress configuration with a fully helical velocity and magnetic field. In each case two snapshots are analyzed, separated by one turn-over time, starting just after the peak of dissipation. We show that the algorithm is able to select a large number of structures (in excess of 8,000) for each snapshot and that the statistical properties of these clusters are remarkably similar for the two snapshots as well as for the two flows under study in terms of scaling laws for the cluster characteristics, with the structures in the vorticity and in the current behaving in the same way. We also study the effect of Reynolds number on cluster statistics, and we finally analyze the properties of these clusters in terms of their velocity-magnetic field correlation. Self-organized criticality features have been identified in the dissipative range of scales. A different scaling arises in the inertial range, which cannot be identified for the moment with a known self-organized criticality class consistent with MHD. We suggest that this range can be governed by turbulence dynamics as opposed to criticality, and propose an interpretation of intermittency in terms of propagation of local instabilities.Comment: 17 pages, 9 figures, 5 table

Visualization-driven Structural and Statistical Analysis of Turbulent Flows

Knowledge extraction from data volumes of ever increasing size requires ever more flexible tools to facilitate interactive query. In- teractivity enables real-time hypothesis testing and scientific discovery, but can generally not be achieved without some level of data reduction. The approach described in this paper combines multi-resolution access, region-of-interest extraction, and structure identification in order to pro- vide interactive spatial and statistical analysis of a terascale data volume. Unique aspects of our approach include the incorporation of both local and global statistics of the flow structures, and iterative refinement fa- cilities, which combine geometry, topology, and statistics to allow the user to effectively tailor the analysis and visualization to the science. Working together, these facilities allow a user to focus the spatial scale and domain of the analysis and perform an appropriately tailored mul- tivariate visualization of the corresponding data. All of these ideas and algorithms are instantiated in a deployed visualization and analysis tool called VAPOR, which is in routine use by scientists internationally. In data from a 10243 simulation of a forced turbulent flow, VAPOR allowed us to perform a visual data exploration of the flow properties at interac- tive speeds, leading to the discovery of novel scientific properties of the flow, in the form of two distinct vortical structure populations. These structures would have been very difficult (if not impossible) to find with statistical overviews or other existing visualization-driven analysis ap- proaches. This kind of intelligent, focused analysis/refinement approach will become even more important as computational science moves to- wards petascale applications

Alternatives to Contour Visualizations for Power Systems Data

Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system -- Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.Comment: IEEE Vis 202

Self‐reported food and drug allergy in Maputo, Mozambique

Public Health. 2005 Jul;119(7):587-9. Self-reported food and drug allergy in Maputo, Mozambique. Lunet N, Falcão H, Sousa M, Bay N, Barros H. SourceDepartment of Hygiene and Epidemiology, University of Porto Medical School, Portugal. [email protected] PMID: 15925673 [PubMed - indexed for MEDLINE