5 resultados para database indexing

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP)


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This article documents the addition of 512 microsatellite marker loci and nine pairs of Single Nucleotide Polymorphism (SNP) sequencing primers to the Molecular Ecology Resources Database. Loci were developed for the following species: Alcippe morrisonia morrisonia, Bashania fangiana, Bashania fargesii, Chaetodon vagabundus, Colletes floralis, Coluber constrictor flaviventris, Coptotermes gestroi, Crotophaga major, Cyprinella lutrensis, Danaus plexippus, Fagus grandifolia, Falco tinnunculus, Fletcherimyia fletcheri, Hydrilla verticillata, Laterallus jamaicensis coturniculus, Leavenworthia alabamica, Marmosops incanus, Miichthys miiuy, Nasua nasua, Noturus exilis, Odontesthes bonariensis, Quadrula fragosa, Pinctada maxima, Pseudaletia separata, Pseudoperonospora cubensis, Podocarpus elatus, Portunus trituberculatus, Rhagoletis cerasi, Rhinella schneideri, Sarracenia alata, Skeletonema marinoi, Sminthurus viridis, Syngnathus abaster, Uroteuthis (Photololigo) chinensis, Verticillium dahliae, Wasmannia auropunctata, and Zygochlamys patagonica. These loci were cross-tested on the following species: Chaetodon baronessa, Falco columbarius, Falco eleonorae, Falco naumanni, Falco peregrinus, Falco subbuteo, Didelphis aurita, Gracilinanus microtarsus, Marmosops paulensis, Monodelphis Americana, Odontesthes hatcheri, Podocarpus grayi, Podocarpus lawrencei, Podocarpus smithii, Portunus pelagicus, Syngnathus acus, Syngnathus typhle,Uroteuthis (Photololigo) edulis, Uroteuthis (Photololigo) duvauceli and Verticillium albo-atrum. This article also documents the addition of nine sequencing primer pairs and sixteen allele specific primers or probes for Oncorhynchus mykiss and Oncorhynchus tshawytscha; these primers and assays were cross-tested in both species.

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Searching in a dataset for elements that are similar to a given query element is a core problem in applications that manage complex data, and has been aided by metric access methods (MAMs). A growing number of applications require indices that must be built faster and repeatedly, also providing faster response for similarity queries. The increase in the main memory capacity and its lowering costs also motivate using memory-based MAMs. In this paper. we propose the Onion-tree, a new and robust dynamic memory-based MAM that slices the metric space into disjoint subspaces to provide quick indexing of complex data. It introduces three major characteristics: (i) a partitioning method that controls the number of disjoint subspaces generated at each node; (ii) a replacement technique that can change the leaf node pivots in insertion operations; and (iii) range and k-NN extended query algorithms to support the new partitioning method, including a new visit order of the subspaces in k-NN queries. Performance tests with both real-world and synthetic datasets showed that the Onion-tree is very compact. Comparisons of the Onion-tree with the MM-tree and a memory-based version of the Slim-tree showed that the Onion-tree was always faster to build the index. The experiments also showed that the Onion-tree significantly improved range and k-NN query processing performance and was the most efficient MAM, followed by the MM-tree, which in turn outperformed the Slim-tree in almost all the tests. (C) 2010 Elsevier B.V. All rights reserved.

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Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

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The study of pharmacokinetic properties (PK) is of great importance in drug discovery and development. In the present work, PK/DB (a new freely available database for PK) was designed with the aim of creating robust databases for pharmacokinetic studies and in silico absorption, distribution, metabolism and excretion (ADME) prediction. Comprehensive, web-based and easy to access, PK/DB manages 1203 compounds which represent 2973 pharmacokinetic measurements, including five models for in silico ADME prediction (human intestinal absorption, human oral bioavailability, plasma protein binding, bloodbrain barrier and water solubility).

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Usually, a Petri net is applied as an RFID model tool. This paper, otherwise, presents another approach to the Petri net concerning RFID systems. This approach, called elementary Petri net inside an RFID distributed database, or PNRD, is the first step to improve RFID and control systems integration, based on a formal data structure to identify and update the product state in real-time process execution, allowing automatic discovery of unexpected events during tag data capture. There are two main features in this approach: to use RFID tags as the object process expected database and last product state identification; and to apply Petri net analysis to automatically update the last product state registry during reader data capture. RFID reader data capture can be viewed, in Petri nets, as a direct analysis of locality for a specific transition that holds in a specific workflow. Following this direction, RFID readers storage Petri net control vector list related to each tag id is expected to be perceived. This paper presents PNRD cornerstones and a PNRD implementation example in software called DEMIS Distributed Environment in Manufacturing Information Systems.