953 resultados para Discovery and exploration, Spanish


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"Reprint edition ... [includes] index prepared in 1912 by Morgan Poitiaux Robinson for the Virginia State Library."

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Includes bibliographical references and index.

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Imprint of v. 2: Richmond : Drinker and Morris, 1848.

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Mode of access: Internet.

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This paper investigates the hypotheses that the recently established Mexican stock index futures market effectively serves the price discovery function, and that the introduction of futures trading has provoked volatility in the underlying spot market. We test both hypotheses simultaneously with daily data from Mexico in the context of a modified EGARCH model that also incorporates possible cointegration between the futures and spot markets. The evidence supports both hypotheses, suggesting that the futures market in Mexico is a useful price discovery vehicle, although futures trading has also been a source of instability for the spot market. Several managerial implications are derived and discussed. (C) 2004 Elsevier B.V. All rights reserved.

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Cyclotides are mini-proteins of 28-37 amino acid residues that have the unusual feature of a head-to-tail cyclic backbone surrounding a cystine knot. This molecular architecture gives the cyclotides heightened resistance to thermal, chemical and enzymatic degradation and has prompted investigations into their use as scaffolds in peptide therapeutics. There are now more than 80 reported cyclotide sequences from plants in the families Rubiaceae, Violaceae and Cucurbitaceae, with a wide variety of biological activities observed. However, potentially limiting the development of cyclotide-based therapeutics is a lack of understanding of the mechanism by which these peptides are cyclized in vivo. Until now, no linear versions of cyclotides have been reported, limiting our understanding of the cyclization mechanism. This study reports the discovery of a naturally occurring linear cyclotide, violacin A, from the plant Viola odorata and discusses the implications for in vivo cyclization of peptides. The elucidation of the cDNA clone of violacin A revealed a point mutation that introduces a stop codon, which inhibits the translation of a key Asn residue that is thought to be required for cyclization. The three-dimensional solution structure of violacin A was determined and found to adopt the cystine knot fold of native cyclotides. Enzymatic stability assays on violacin A indicate that despite an increase in the flexibility of the structure relative to cyclic counterparts, the cystine knot preserves the overall stability of the molecule. (c) 2006 Elsevier Ltd. All rights reserved.

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Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.

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The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.