957 resultados para DATABASES


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Biomimetics involves transfer from one or more biological examples to a technical system. This study addresses four questions. What are the essential steps in a biomimetic process? What is transferred? How can the transferred knowledge be structured in a way useful for biologists and engineers? Which guidelines can be given to support transfer in biomimetic design processes? In order to identify the essential steps involved in carrying out biomimetics, several procedures found in the literature were summarized, and four essential steps that are common across these procedures were identified. For identification of mechanisms for transfer, 20 biomimetic examples were collected and modeled according to a model. of causality called the SAPPhIRE model. These examples were then analyzed for identifying the underlying similarity between each biological and corresponding analogue technical system. Based on the SAPPhIRE model, four levels of abstraction at which transfer takes place were identified. Taking into account similarity, the biomimetic examples were assigned to the appropriate levels of abstraction of transfer. Based on the essential steps and the levels of transfer, guidelines for supporting transfer in biomimetic design were proposed and evaluated using design experiments. The 20 biological and analogue technical systems that were analyzed were similar in the physical effects used and at the most abstract levels of description of their functionality, but they were the least similar at the lowest levels of abstraction: the parts involved. Transfer most often was carried out at the physical effect level of abstraction. Compared to a generic set of guidelines based on the literature, the proposed guidelines improved design performance by about 60%. Further, the SAPPhIRE model turned out to be a useful representation for modeling complex biological systems and their functionality. Databases of biological systems, which are structured using the SAPPhIRE model, have the potential to aid biomimetic concept generation.

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Purpose - The purpose of this paper is to discuss published research in rotorcraft which has taken place in India during the last ten years The helicopter research is divided into the following parts health monitoring smart rotor design optimization control helicopter rotor dynamics active control of structural response (ACSR) and helicopter design and development Aspects of health monitoring and smart rotor are discussed in detail Further work needed and areas for international collaboration are pointed out Design/methodology/approach - The archival journal papers on helicopter engineering published from India are obtained from databases and are studied and discussed The contribution of the basic research to the state of the art in helicopter engineering science is brought out Findings - It is found that strong research capabilities have developed in rotor system health and usage monitoring rotor blade design optimization ACSR composite rotor blades and smart rotor development Furthermore rotorcraft modeling and analysis aspects are highly developed with considerable manpower available and being generated in these areas Practical implications - Two helicopter projects leading to the advanced light helicopter and light combat helicopter have been completed by Hindustan Aeronautics Ltd These helicopter programs have benefited from the basic research and also provide platforms for further basic research and deeper industry academic collaborations The development of well trained helicopter engineers is also attractive for international helicopter design and manufacturing companies The basic research done needs to be further developed for practical and commercial applications Originality/value - This is the first comprehensive research on rotorcraft research in India an important emerging market manufacturing and sourcing destination for the industry

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A complete cDNA encoding a novel hybrid Pro-rich protein (HyPRP) was identified by differentially screening 3x10(4) recombinant plaques of a Cuscuta reflexa cytokinin-induced haustorial cDNA library constructed in lambda gt10. The nucleotide (nt) sequence consists of: (i) a 424-bp 5'-non coding region having five start codons (ATGs) and three upstream open reading frames (uORFs); (ii) an ORF of 987 bp with coding potential for a 329-amino-acid (aa) protein of M(r), 35203 with a hydrophobic N-terminal region including a stretch of nine consecutive Phe followed by a Pro-rich sequence and a Cys-rich hydrophobic C terminus; and (iii) a 178-bp 3'-UTR (untranslated region). Comparison of the predicted aa sequence with the NBRF and SWISSPROT databases and with a recent report of an embryo-specific protein of maize [Jose-Estanyol et al., Plant Cell 4 (1992) 413-423] showed it to be similar to the class of HyPRPs encoded by genes preferentially expressed in young tomato fruits, maize embryos and in vitro-cultured carrot embryos. Northern analysis revealed an approx. 1.8-kb mRNA of this gene expressed in the subapical region of the C. reflexa vine which exhibited maximum sensitivity to cytokinin in haustorial induction.

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Molecular understanding of disease processes can be accelerated if all interactions between the host and pathogen are known. The unavailability of experimental methods for large-scale detection of interactions across host and pathogen organisms hinders this process. Here we apply a simple method to predict protein-protein interactions across a host and pathogen organisms. We use homology detection approaches against the protein-protein interaction databases. DIP and iPfam in order to predict interacting proteins in a host-pathogen pair. In the present work, we first applied this approach to the test cases involving the pairs phage T4 - Escherichia coli and phage lambda - E. coli and show that previously known interactions could be recognized using our approach. We further apply this approach to predict interactions between human and three pathogens E. coli, Salmonella enterica typhimurium and Yersinia pestis. We identified several novel interactions involving proteins of host or pathogen that could be thought of as highly relevant to the disease process. Serendipitously, many interactions involve hypothetical proteins of yet unknown function. Hypothetical proteins are predicted from computational analysis of genome sequences with no laboratory analysis on their functions yet available. The predicted interactions involving such proteins could provide hints to their functions. (C) 2011 Elsevier B.V. All rights reserved.

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Repeats are two or more contiguous segments of amino acid residues that are believed to have arisen as a result of intragenic duplication, recombination and mutation events. These repeats can be utilized for protein structure prediction and can provide insights into the protein evolution and phylogenetic relationship. Therefore, to aid structural biologists and phylogeneticists in their research, a computing resource (a web server and a database), Repeats in Protein Sequences (RPS), has been created. Using RPS, users can obtain useful information regarding identical, similar and distant repeats (of varying lengths) in protein sequences. In addition, users can check the frequency of occurrence of the repeats in sequence databases such as the Genome Database, PIR and SWISS-PROT and among the protein sequences available in the Protein Data Bank archive. Furthermore, users can view the three-dimensional structure of the repeats using the Java visualization plug-in Jmol. The proposed computing resource can be accessed over the World Wide Web at http://bioserver1.physics.iisc.ernet.in/rps/.

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The Basic Local Alignment Search Tool (BLAST) is one of the most widely used sequence alignment programs with which similarity searches, for both protein and nucleic acid sequences, can be performed against large databases at high speed. A large number of tools exist for processing BLAST output, but none of them provide three-dimensional structure visualization. This shortcoming has been addressed in the proposed tool BLAST Server for Structural Biologists (BSSB), which maps a BLAST output onto the three-dimensional structure of the subject protein. The three-dimensional structure of the subject protein is represented using a three-color coding scheme (identical: red; similar: yellow; and mismatch: white) based on the pairwise alignment obtained. Thus, the user will be able to visualize a possible three-dimensional structure for the query protein sequence. This information can be used to gain a deeper insight into the sequence-structure correlation. Furthermore, the additional structure-level information enables the user to make coherent and logical decisions regarding the type of input model structure or fragment that can be used for molecular replacement calculations. This tool is freely available to all users at http://bioserver1.physics.iisc.ernet.in/bssb/.

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In this paper, power management algorithms for energy harvesting sensors (EHS) that operate purely based on energy harvested from the environment are proposed. To maintain energy neutrality, EHS nodes schedule their utilization of the harvested power so as to save/draw energy into/from an inefficient battery during peak/low energy harvesting periods, respectively. Under this constraint, one of the key system design goals is to transmit as much data as possible given the energy harvesting profile. For implementational simplicity, it is assumed that the EHS transmits at a constant data rate with power control, when the channel is sufficiently good. By converting the data rate maximization problem into a convex optimization problem, the optimal load scheduling (power management) algorithm that maximizes the average data rate subject to energy neutrality is derived. Also, the energy storage requirements on the battery for implementing the proposed algorithm are calculated. Further, robust schemes that account for the insufficiency of battery storage capacity, or errors in the prediction of the harvested power are proposed. The superior performance of the proposed algorithms over conventional scheduling schemes are demonstrated through computations using numerical data from solar energy harvesting databases.

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In data mining, an important goal is to generate an abstraction of the data. Such an abstraction helps in reducing the space and search time requirements of the overall decision making process. Further, it is important that the abstraction is generated from the data with a small number of disk scans. We propose a novel data structure, pattern count tree (PC-tree), that can be built by scanning the database only once. PC-tree is a minimal size complete representation of the data and it can be used to represent dynamic databases with the help of knowledge that is either static or changing. We show that further compactness can be achieved by constructing the PC-tree on segmented patterns. We exploit the flexibility offered by rough sets to realize a rough PC-tree and use it for efficient and effective rough classification. To be consistent with the sizes of the branches of the PC-tree, we use upper and lower approximations of feature sets in a manner different from the conventional rough set theory. We conducted experiments using the proposed classification scheme on a large-scale hand-written digit data set. We use the experimental results to establish the efficacy of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.

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Many of the research institutions and universities across the world are facilitating open-access (OA) to their intellectual outputs through their respective OA institutional repositories (IRs) or through the centralized subject-based repositories. The registry of open access repositories (ROAR) lists more than 2850 such repositories across the world. The awareness about the benefits of OA to scholarly literature and OA publishing is picking up in India, too. As per the ROAR statistics, to date, there are more than 90 OA repositories in the country. India is doing particularly well in publishing open-access journals (OAJ). As per the directory of open-access journals (DOAJ), to date, India with 390 OAJs, is ranked 5th in the world in terms of numbers of OAJs being published. Much of the research done in India is reported in the journals published from India. These journals have limited readership and many of them are not being indexed by Web of Science, Scopus or other leading international abstracting and indexing databases. Consequently, research done in the country gets hidden not only from the fellow countrymen, but also from the international community. This situation can be easily overcome if all the researchers facilitate OA to their publications. One of the easiest ways to facilitate OA to scientific literature is through the institutional repositories. If every research institution and university in India set up an open-access IR and ensure that copies of the final accepted versions of all the research publications are uploaded in the IRs, then the research done in India will get far better visibility. The federation of metadata from all the distributed, interoperable OA repositories in the country will serve as a window to the research done across the country. Federation of metadata from the distributed OAI-compliant repositories can be easily achieved by setting up harvesting software like the PKP Harvester. In this paper, we share our experience in setting up a prototype metadata harvesting service using the PKP harvesting software for the OAI-compliant repositories in India.

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Mining association rules from a large collection of databases is based on two main tasks. One is generation of large itemsets; and the other is finding associations between the discovered large itemsets. Existing formalism for association rules are based on a single transaction database which is not sufficient to describe the association rules based on multiple database environment. In this paper, we give a general characterization of association rules and also give a framework for knowledge-based mining of multiple databases for association rules.

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Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams with temporal interdependencies. Over the last decade many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as statistics, machine learning and databases, the literature is scattered among many different sources. In this article, we present an overview of techniques of temporal data mining.We mainly concentrate on algorithms for pattern discovery in sequential data streams.We also describe some recent results regarding statistical analysis of pattern discovery methods.

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The rapidly growing structure databases enhance the probability of finding identical sequences sharing structural similarity. Structure prediction methods are being used extensively to abridge the gap between known protein sequences and the solved structures which is essential to understand its specific biochemical and cellular functions. In this work, we plan to study the ambiguity between sequence-structure relationships and examine if sequentially identical peptide fragments adopt similar three-dimensional structures. Fragments of varying lengths (five to ten residues) were used to observe the behavior of sequence and its three-dimensional structures. The STAMP program was used to superpose the three-dimensional structures and the two parameters (Sequence Structure Similarity Score (Sc) and Root Mean Square Deviation value) were employed to classify them into three categories: similar, intermediate and dissimilar structures. Furthermore, the same approach was carried out on all the three-dimensional protein structures solved in the two organisms, Mycobacterium tuberculosis and Plasmodium falciparum to validate our results.

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Luteal insufficiency affects fertility and hence study of mechanisms that regulate corpus luteum (CL) function is of prime importance to overcome infertility problems. Exploration of human genome sequence has helped to study the frequency of single nucleotide polymorphisms (SNPs). Clinical benefits of screening SNPs in infertility are being recognized well in recent times. Examining SNPs in genes associated with maintenance and regression of CL may help to understand unexplained luteal insufficiency and related infertility. Publicly available microarray gene expression databases reveal the global gene expression patterns in primate CL during the different functional state. We intend to explore computationally the deleterious SNPs of human genes reported to be common targets of luteolysin and luteotropin in primate CL Different computational algorithms were used to dissect out the functional significance of SNPs in the luteinizing hormone sensitive genes. The results raise the possibility that screening for SNPs might be integrated to evaluate luteal insufficiency associated with human female infertility for future studies. (C) 2012 Elsevier B.V. All rights reserved,

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The Shola habitat on the high elevation sky islands of the Western Ghats in southern India is a unique habitat. Although this habitat hosts a disproportionately high level of endemism and is threatened by anthropogenic modifications, it has received little research attention. We compiled publications of research conducted in this habitat from scientific databases and the grey literature to examine trends in publication. For a quantitative summary, all publications were classified according to the taxa of research and the broad topic of research. We identified 279 publications from 1964 and found an almost threefold increase in the number of publications and diversity of research topics studied over the last decade. Studies on flora, birds and mammals have been numerous (62% of the studies examined), but certain taxa like fish (1%) have been ignored. Most studies (65%) are descriptive, focusing on diversity, distribution trends and management suggestions, while surprisingly few have concentrated on climate change, ecological restoration and invasive species, all major threats to this landscape. We have identified some key gaps in research and conservation focus that future studies could address. We also suggest that initiatives like edited volumes and special journal sections, along with the use of creative commons licensed data-sharing portals, can be used to usher unpublished work into the public domain.

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The rapid recent increase in microarray-based gene expression studies in the corpus luteum (CL) utilizing macaque models gathered increasing volume of data in publically accessible microarray expression databases. Examining gene pathways in different functional states of CL may help to understand the factors that control luteal function and hence human fertility. Co-regulation of genes in microarray experiments may imply common transcriptional regulation by sequence-specific DNA-binding transcriptional factors. We have computationally analyzed the transcription factor binding sites (TFBS) in a previously reported macaque luteal microarray gene set (n = 15) that are common targets of luteotropin (luteinizing hormone (LH) and human chorionic gonadotropin (hCG)) and luteolysin (prostaglandin (PG) F-2 alpha). This in silico approach can reveal transcriptional networks that control these important genes which are representative of the interplay between luteotropic and luteolytic factors in the control of luteal function. Our computational analyses revealed 6 matrix families whose binding sites are significantly over-represented in promoters of these genes. The roles of these factors are discussed, which might help to understand the transcriptional regulatory network in the control of luteal function. These factors might be promising experimental targets for investigation of human luteal insufficiency. (C) 2012 Elsevier B.V. All rights reserved.