Xem 1-20 trên 98 kết quả Biology cluster
  • The Brazilian Symposium on Bioinformatics (BSB) 2008 was held at Santo Andr´e (S˜ao Paulo), Brazil, August 28–30, 2008. BSB 2008 was the third symposium in the BSB series, although BSB was preceded by the Brazilian Workshop on Bioinformatics (WOB). This previous event had three consecutive editions in 2002 (Gramado, Rio Grande do Sul), 2003 (Maca´e, Rio de Janeiro), and 2004 (Bras´ılia, Distrito Federal). The change from workshop to symposium reflects the increasing quality and interest behind this meeting.

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  • Running today’s non-critical business applications in virtualized environments has shown to be efficient and cost saving. More sophisticated or highly available applications on the other hand were most likely incompatible with commonly used software based virtualization solutions. The availability of Oracle VM overcomes this obstacle.

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  • X-ray crystallography of the nonheme manganese catalase fromLactobacillus plantarum(LPC) [Barynin, V.V., Whit-taker, M.M., Antonyuk, S.V., Lamzin, V.S., Harrison, P.M., Artymiuk, P.J. & Whittaker, J.W. (2001)Structure9, 725–738] has revealed the structure of the dimanganese redox cluster together with its protein environment. The oxidized [Mn(III)Mn(III)] cluster is bridged by two solvent molecules (oxo and hydroxo, respectively) together with a l1,3bridging glutamate carboxylate and is embedded in a web of hydrogen bonds involving an outer sphere tyrosine residue (Tyr42). ...

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  • MicroRNAs (miRNAs) have been proven to play important roles at the post-transcriptional level in animals and plants. To investigate clustering patterns and specific sequence characteristics in the flanking regions of plant miRNA genes, we performed genome-wide analyses ofArabidopsis thaliana, Populus trichocarpa, Oryza sativaand Sorghum bicolor.

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  • The Protein Data Bank contains the description of approximately 27 000 protein–ligand binding sites. Most of the ligands at these sites are biologi-cally active small molecules, affecting the biological function of the protein. The classification of their binding sites may lead to relevant results in drug discovery and design.

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  • Metallothionein-3, also called neuronal growth-inhibitory factor, is one of the four members of the mammalian metallothionein family, which in turn belongs to the metallothionein, a class of ubiquitously occurring low-molecular-weight cysteine- and metal-rich proteins containing metal– thiolate clusters.

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  • In an effort to explore the role of glycine clusters on the cold adaptation of enzymes, we designed point mutations aiming to alter the distribution of glycine residues close to the active site of the psychrophilic alkaline phosphatase from the Antarctic strain TAB5. The mutagenesis targets were residues Gly261 and Gly262. The replacement of Gly262 by Ala resulted in an inactive enzyme. Substitution of Gly261 by Ala resulted to an enzyme with lower stability and increased energy of activation.

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  • Stowers Institute for Medical Research, 1000 E 50th Street, Kansas City, MO 64110, USA. †Department of Microbiology, Molecular Genetics, and Immunology, University of Kansas Medical Center, Kansas City, KS 66160, USA. Correspondence: Galina V Glazko. E-mail: gvg@stowers-institute.org reviews Published: 27 April 2004 Genome Biology 2004, 5:R32 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2004/5/5/R32

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  • Managing and Mining Graph Data part 1 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 3 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 62 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 2 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 5 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 6 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 11 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 61 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 4 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 7 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 8 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 9 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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