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  • 1995-1999  (2)
  • 1925-1929
  • 1997  (2)
  • High Order networks  (1)
  • Key words: Bamboo mosaic virus — Satellite RNA — Satellite-encoded protein — Sequence variation  (1)
Material
Years
  • 1995-1999  (2)
  • 1925-1929
Year
  • 1997  (2)
  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of molecular evolution 44 (1997), S. 207 -213 
    ISSN: 1432-1432
    Keywords: Key words: Bamboo mosaic virus — Satellite RNA — Satellite-encoded protein — Sequence variation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract. Satellite RNA of bamboo mosaic potexvirus (satBaMV) is a linear RNA molecule which encodes a 20-kDa nonstructural protein. Sequences of seven different satBaMV isolates from bamboo hosts in three genera showed 0.7% to 7.5% base variation which spanned the whole RNA molecule. However, the putative 20-kDa open reading frame was all preserved in these isolates. The phylogenetic relationship based on the nucleotide sequence did not show particular grouping of satBaMV from the host in one genus; neither was the grouping of satBaMV evident by location of sampling. Putative secondary structures of the 3′ untranslated regions showed a basic pattern with conserved hexanucleotides (ACCUAA) and polyadenylation signal (AAUAAA) located in the loop regions. Although the satBaMV-encoded 20-kDa protein is a nonstructural protein, its predicted secondary structure contains eight-stranded β-sheets which may form ``jelly-roll'' structure similar to that found in capsid protein encoded by satellite virus of panicum mosaic virus.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1573-7497
    Keywords: Neural Networks ; Boltzmann Machines ; High Order networks ; Classification Problems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This work reports the results obtained with the application of High Order Boltzmann Machines without hidden units to construct classifiers for some problems that represent different learning paradigms. The Boltzmann Machine weight updating algorithm remains the same even when some of the units can take values in a discrete set or in a continuous interval. The absence of hidden units and the restriction to classification problems allows for the estimation of the connection statistics, without the computational cost involved in the application of simulated annealing. In this setting, the learning process can be sped up several orders of magnitude with no appreciable loss of quality of the results obtained.
    Type of Medium: Electronic Resource
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