Supercomputing: What have we learned from the TOP500 Project?

Since 1993 we compile and publish twice a year a list of the most powerful supercomputers in the world. In this article we analyze some major trends in High-Performance Computing (HPC) based on the quantitative data gathered over the years in this TOP500 project (see www.top500.org for a complete access to all data). We start with an overview of the project, including the motivation and rationale behind it. We present the results of the TOP500 project as of November 2003 focusing on the changes over time with respect to the performance levels, the overall dynamics in this market, the manufacturers active in this market-segment, the architectures, and technologies used for these systems, and the major centers using such systems.     

Dietary Apigenin Exerts Immune-Regulatory Activity in Vivo by Reducing NF-κB Activity,Halting Leukocyte Infiltration and Restoring Normal Metabolic Function

The increasing prevalence of inflammatory diseases and the adverse effects associated with the long-term use of current anti-inflammatory therapies prompt the identification of alternative approaches to reestablish immune balance. Apigenin, an abundant dietary flavonoid, is emerging as a potential regulator of inflammation. Here, we show that apigenin has immune-regulatory activity in vivo. Apigenin conferred survival to mice treated with a lethal dose of Lipopolysaccharide (LPS) restoring normal cardiac function and heart mitochondrial Complex I activity. Despite the adverse effects associated with high levels of splenocyte apoptosis in septic models, apigenin had no effect on reducing cell death. However, we found that apigenin decreased LPS-induced apoptosis in lungs, infiltration of inflammatory cells and chemotactic factors’ accumulation, re-establishing normal lung architecture. Using NF-κB luciferase transgenic mice, we found that apigenin effectively modulated NF-κB activity in the lungs, suggesting the ability of dietary compounds to exert immune-regulatory activity in an organ-specific manner. Collectively, these findings provide novel insights into the underlying immune-regulatory mechanisms of dietary nutraceuticals in vivo.     

Identification of the Thermodynamically Stable Fe‐Containing Phase in Aged Cement Pastes

Current developments in cement chemistry increasingly rely on predictive thermodynamic modeling of the phase composition in cementitious composites with the aim of linking the performance of the material with the phase composition of the material. This approach requires identification of the cement phases that form in hydrating cementitious materials using standard techniques, such as X‐ray diffraction (XRD) and thermal analysis (DTA, TGA), but also state‐of‐the‐art synchrotron‐based techniques, in particular for those cases in which the signals of solid solutions overlap in XRD and TGA measurements. In this study, two ordinary Portland cements, with different chemical compositions and subject to different hydration times (~10, ~50 yr), were investigated aiming at identifying the most stable Fe‐containing cement phase in the cement pastes. The Fe‐containing cement phases and their solid solutions with the Al analogues in the complex cement matrix were analyzed with X‐ray absorption spectroscopy, indicating the formation of a mixed Fe–Al siliceous hydrogarnet as the major Fe‐containing phase. The presence of this phase after long hydration periods and upon selective dissolution of the pastes further indicates that, independent of the chemical compositions of cements, formation of the mixed Fe–Al siliceous hydrogarnet is thermodynamically favored in aged pastes, which is supported by published thermodynamic calculations.     

Structure‐Based Mechanism of Oleate Hydratase from Elizabethkingia meningoseptica

Hydratases provide access to secondary and tertiary alcohols by regio‐ and/or stereospecifically adding water to carbon‐carbon double bonds. Thereby, hydroxy groups are introduced without the need for costly cofactor recycling, and that makes this approach highly interesting on an industrial scale. Here we present the first crystal structure of a recombinant oleate hydratase originating from Elizabethkingia meningoseptica in the presence of flavin adenine dinucleotide (FAD). A structure‐based mutagenesis study targeting active site residues identified E122 and Y241 as crucial for the activation of a water molecule and for protonation of the double bond, respectively. Moreover, we also observed that two‐electron reduction of FAD results in a sevenfold increase in the substrate hydration rate. We propose the first reaction mechanism for this enzyme class that explains the requirement for the flavin cofactor and the involvement of conserved amino acid residues in this regio‐ and stereoselective hydration.     

A dynamic solution of the shear band propagation in submerged landslides

A failure mechanism of submarine landslides based on the phenomenon of shear band propagation has been investigated using the energy balance approach. Dynamic analysis includes inertia effects in the sliding layer and viscous resistance of the water, but ignores elastic and plastic wave propagation in the sliding layer. The resulting differential equation has been solved numerically. Analytical approximation of this numerical solution provides a convenient basis for the parametric and sensitivity studies. The solution allows assessing the velocity and acceleration of both the landslide and the shear band at the moment when the slide fails due to the limiting equilibrium (i.e., the initial post-failure velocity). The effects of the initial landslide velocity on the tsunami wave height are discussed and validated for a number of landslide examples.     

On the evaluation of unsupervised outlier detection: measures,datasets, and an empirical study

The evaluation of unsupervised outlier detection algorithms is a constant challenge in data mining research. Little is known regarding the strengths and weaknesses of different standard outlier detection models, and the impact of parameter choices for these algorithms. The scarcity of appropriate benchmark datasets with ground truth annotation is a significant impediment to the evaluation of outlier methods. Even when labeled datasets are available, their suitability for the outlier detection task is typically unknown. Furthermore, the biases of commonly-used evaluation measures are not fully understood. It is thus difficult to ascertain the extent to which newly-proposed outlier detection methods improve over established methods. In this paper, we perform an extensive experimental study on the performance of a representative set of standard k nearest neighborhood-based methods for unsupervised outlier detection, across a wide variety of datasets prepared for this purpose. Based on the overall performance of the outlier detection methods, we provide a characterization of the datasets themselves, and discuss their suitability as outlier detection benchmark sets. We also examine the most commonly-used measures for comparing the performance of different methods, and suggest adaptations that are more suitable for the evaluation of outlier detection results.