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This paper proposes a new independent component analysis (ICA) method which is able to unmix overcomplete mixtures of sparce or structured signals like speech, music or images. Furthermore, the method is designed to be robust against outliers, which is a favorable feature for ICA algorithms since most of them are extremely sensitive to outliers. Our approach is based on a simple outlier index. However, instead of robustifying an existing algorithm by some outlier rejection technique we show how this index can be used directly to solve the ICA problem for super-Gaussian sources. The resulting inlier-based ICA (IBICA) is outlier-robust by construction and can be used for standard ICA as well as for overcomplete ICA (i.e. more source signals than observed signals). (c) 2005 Wiley Periodicals, Inc
Existing approaches in the area of knowledge-intensive processes focus on integrated knowledge and process management systems, the support of processes with KM systems, or the analysis of knowledge-intensive activities. For capturing knowledge-intensive business processes well known and established methods do not meet the requirements of a comprehensive and integrated approach of process-oriented knowledge management. These approaches are not able to visualise the decisions, actions and measures which are causing the sequence of the processes in an adequate manner. Parallel to conventional processes knowledge-intensive processes exist. These processes are based on conversions of knowledge within these processes. To fill these gaps in modelling knowledge-intensive business processes the Knowledge Modelling and Description Language (KMDL) got developed. The KMDL is able to represent the development, use, offer and demand of knowledge along business processes. Further it is possible to show the existing knowledge conversions which take place additionally to the normal business processes. The KMDL can be used to formalise knowledgeintensive processes with a focus on certain knowledge-specific characteristics and to identify process improvements in these processes. The KMDL modelling tool K-Modeler is introduced for a computer-aided modelling and analysing. The technical framework and the most important functionalities to support the analysis of the captured processes are introduced in the following contribution.
Diacylglycerol kinase (DGK) regulates the level of the second messenger diacylglycerol and produces phosphatidic acid (PA), another signaling molecule. The Arabidopsis thaliana genome encodes seven putative diacylglycerol kinase isozymes (named AtDGK1 to -7), structurally falling into three major clusters. So far, enzymatic activity has not been reported for any plant Cluster II DGK. Here, we demonstrate that a representative of this cluster, AtDGK7, is biochemically active when expressed as a recombinant protein in Escherichia coli. AtDGK7, encoded by gene locus At4g30340, contains 374 amino acids with an apparent molecular mass of 41.2 kDa. AtDGK7 harbors an N-terminal catalytic domain, but in contrast to various characterized DGKs (including AtDGK2), it lacks a cysteine-rich domain at its N terminus, and, importantly, its C-terminal DGK accessory domain is incomplete. Recombinant AtDGK7 expressed in E. coli exhibits Michaelis-Menten type kinetics with 1,2-dioleoyl-sn-glycerol as substrate. AtDGK7 activity was affected by pH, detergents, and the DGK inhibitor R59022. We demonstrate that both AtDGK2 and AtDGK7 phosphorylate diacylglycerol molecular species that are typically found in plants, indicating that both enzymes convert physiologically relevant substrates. AtDGK7 is expressed throughout the Arabidopsis plant, but expression is strongest in flowers and young seedlings. Expression of AtDGK2 is transiently induced by wounding. R59022 at similar to 80 mu M inhibits root elongation and lateral root formation and reduces plant growth, indicating that DGKs play an important role in plant development
Phosphatidylinositol signaling pathway and the relevant metabolites are known to be critical to the modulation of different aspects of plant growth, development, and stress responses. Inositol polyphosphate 5-phosphatase is a key enzyme involved in phosphatidylinositol metabolism and is encoded by an At5PTase gene family in Arabidopsis thaliana. A previous study shows that At5PTase11 mediates cotyledon vascular development probably through the regulation of intracellular calcium levels. In this study, we provide evidence that At5PTase13 modulates the development of cotyledon veins through its regulation of auxin homeostasis. A T-DNA insertional knockout mutant, At5pt13-1, showed a defect in development of the cotyledon vein, which was rescued completely by exogenous auxin and in part by brassinolide, a steroid hormone. Furthermore, the mutant had reduced auxin content and altered auxin accumulation in seedlings revealed by the DR5:beta-glucuronidase fusion construct in seedlings. In addition, microarray analysis shows that the transcription of key genes responsible for auxin biosynthesis and transport was altered in At5pt13-1. The At5pt13-1 mutant was also less sensitive to auxin inhibition of root elongation. These results suggest that At5PTase13 regulates the homeostasis of auxin, a key hormone controlling vascular development in plants
Inositol polyphosphates, such as inositol trisphosphate, are pivotal intracellular signaling molecules in eukaryotic cells. In higher plants the mechanism for the regulation of the type and the level of these signaling molecules is poorly understood. In this study we investigate the physiological function of an Arabidopsis (Arabidopsis thaliana) gene encoding inositol polyphosphate kinase (AtIPK2alpha), which phosphorylates inositol 1,4,5-trisphosphate successively at the D-6 and D-3 positions, and inositol 1,3,4,5-tetrakisphosphate at D-6, resulting in the generation of inositol 1,3,4,5,6-pentakisphosphate. Semiquantitative reverse transcription-PCR and promoter-beta-glucuronidase reporter gene analyses showed that AtIPK2alpha is expressed in various tissues, including roots and root hairs, stem, leaf, pollen grains, pollen tubes, the flower stigma, and siliques. Transgenic Arabidopsis plants expressing the AtIPK2alpha antisense gene under its own promoter were generated. Analysis of several independent transformants exhibiting strong reduction in AtIPK2alpha transcript levels showed that both pollen germination and pollen tube growth were enhanced in the antisense lines compared to wild-type plants, especially in the presence of nonoptimal low Ca2+ concentrations in the culture medium. Furthermore, root growth and root hair development were also stimulated in the antisense lines, in the presence of elevated external Ca2+ concentration or upon the addition of EGTA. In addition, seed germination and early seedling growth was stimulated in the antisense lines. These observations suggest a general and important role of AtIPK2alpha, and hence inositol polyphosphate metabolism, in the regulation of plant growth most likely through the regulation of calcium signaling, consistent with the well-known function of inositol trisphosphate in the mobilization of intracellular calcium stores
We present a technique that identifies truly interacting subsystems of a complex system from multichannel data if the recordings are an unknown linear and instantaneous mixture of the true sources. The method is valid for arbitrary noise structure. For this, a blind source separation technique is proposed that diagonalizes antisymmetrized cross- correlation or cross-spectral matrices. The resulting decomposition finds truly interacting subsystems blindly and suppresses any spurious interaction stemming from the mixture. The usefulness of this interacting source analysis is demonstrated in simulations and for real electroencephalography data
We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show that these easy-to-compute orderings allow us to detect outliers (i.e. very untypical points) with a performance comparable to or better than other often much more sophisticated methods. On the other hand, we show how to use these orderings to detect prototypes (very typical points) which facilitate exploratory data analysis algorithms such as noisy nonlinear dimensionality reduction and clustering. Comprehensive experiments demonstrate the validity of our approach.