61 resultados para translation keys

em CentAUR: Central Archive University of Reading - UK


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Nucleolin is a multi-functional protein that is located to the nucleolus. In tissue Culture cells, the stability of nucleolin is related to the proliferation status of the cell. During development, rat cardiomyocytes proliferate actively with increases in the mass of the heart being due to both hyperplasia and hypertrophy. The timing of this shift in the phenotype of the myocyte from one capable of undergoing hyperplasia to one that can grow only by hypertrophy occurs within 4 days of post-natal development. Thus, cardiomyocytes are an ideal model system in which to study the regulation of nucleolin during growth in vivo. Using Western blot and quantitative RT-PCR (TaqMan) we found that the amount of nucleolin is regulated both at the level of transcription and translation during the development of the cardiomyocyte. However, in cells which had exited the cell cycle and were subsequently given a hypertrophic stimulus, nucleolin was regulated post-transcriptionally. (c) 2005 Elsevier Inc. All rights reserved.

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Unlike other positive-stranded RNA viruses that use either a 5'-cap structure or an internal ribosome entry site to direct translation of their messenger RNA, calicivirus translation is dependent on the presence of a protein covalently linked to the 50 end of the viral genome (VPg). We have shown a direct interaction of the calicivirus VPg with the cap-binding protein eIF4E. This interaction is required for calicivirus mRNA translation, as sequestration of eIF4E by 4E-BP1 inhibits translation. Functional analysis has shown that VPg does not interfere with the interaction between eIF4E and the cap structure or 4E-BP1, suggesting that VPg binds to eIF4E at a different site from both cap and 4E-BP1. This work lends support to the idea that calicivirus VPg acts as a novel 'cap substitute' during initiation of translation on virus mRNA.

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Purpose – The purpose of this paper is to propose a process model for knowledge transfer in using theories relating knowledge communication and knowledge translation. Design/methodology/approach – Most of what is put forward in this paper is based on a research project titled “Procurement for innovation and knowledge transfer (ProFIK)”. The project is funded by a UK government research council – The Engineering and Physical Sciences Research Council (EPSRC). The discussions are mainly grounded on a thorough review of literature accomplished as part of the research project. Findings – The process model developed in this paper has built upon the theory of knowledge transfer and the theory of communication. Knowledge transfer, per se, is not a mere transfer of knowledge. It involves different stages of knowledge transformation. Depending on the context of knowledge transfer, it can also be influenced by many factors; some positive and some negative. The developed model of knowledge transfer attempts to encapsulate all these issues in order to create a holistic framework. Originality/value of paper – An attempt has been made in the paper to combine some of the significant theories or findings relating to knowledge transfer together, making the paper an original and valuable one.

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In this paper, practical generation of identification keys for biological taxa using a multilayer perceptron neural network is described. Unlike conventional expert systems, this method does not require an expert for key generation, but is merely based on recordings of observed character states. Like a human taxonomist, its judgement is based on experience, and it is therefore capable of generalized identification of taxa. An initial study involving identification of three species of Iris with greater than 90% confidence is presented here. In addition, the horticulturally significant genus Lithops (Aizoaceae/Mesembryanthemaceae), popular with enthusiasts of succulent plants, is used as a more practical example, because of the difficulty of generation of a conventional key to species, and the existence of a relatively recent monograph. It is demonstrated that such an Artificial Neural Network Key (ANNKEY) can identify more than half (52.9%) of the species in this genus, after training with representative data, even though data for one character is completely missing.