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  • C68 - Computable General Equilibrium Models, Q11 - Aggregate Supply and Demand Analysis  (1)
  • Nucleic acid structure, RNA characterisation and manipulation, Computational Methods  (1)
  • Oxford University Press  (2)
  • 1
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    Technology adoption under US biofuel policies: do producers, consumers or taxpayers benefit? (2011)
    Oxford University Press
    Details
    Publication Date: 2011-12-27
    Description: The beneficiaries of technology adoption in agriculture and biofuel markets in the United States are heavily influenced by biofuel policies and market context. Biofuel mandates, one of the key pillars of domestic biofuel policies, may significantly alter the elasticity of demand for biofuels as well as the derived demand for maize used to produce a significant share of ethanol in the United States. Using a stochastic agriculture and biofuel model, it is determined that market context relative to biofuel policy is critically important in understanding the winners and losers from technology adoption. The results for both feedstock and biofuel producers as well as the US tax payers are used to discuss implications for the analysis of EU biofuel policies.
    Keywords: C68 - Computable General Equilibrium Models, Q11 - Aggregate Supply and Demand Analysis ; Prices, Q48 - Government Policy
    Print ISSN: 0165-1587
    Electronic ISSN: 1464-3618
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition , Economics
    Published by Oxford University Press
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    A comprehensive comparison of general RNA-RNA interaction prediction methods (2016)
    Oxford University Press
    Details
    Publication Date: 2016-04-21
    Description: RNA–RNA interactions are fast emerging as a major functional component in many newly discovered non-coding RNAs. Basepairing is believed to be a major contributor to the stability of these intermolecular interactions, much like intramolecular basepairs formed in RNA secondary structure. As such, using algorithms similar to those for predicting RNA secondary structure, computational methods have been recently developed for the prediction of RNA–RNA interactions. We provide the first comprehensive comparison comprising 14 methods that predict general intermolecular basepairs. To evaluate these, we compile an extensive data set of 54 experimentally confirmed fungal snoRNA–rRNA interactions and 102 bacterial sRNA–mRNA interactions. We test the performance accuracy of all methods, evaluating the effects of tool settings, sequence length, and multiple sequence alignment usage and quality. Our results show that—unlike for RNA secondary structure prediction—the overall best performing tools are non-comparative energy-based tools utilizing accessibility information that predict short interactions on this data set. Furthermore, we find that maintaining high accuracy across biologically different data sets and increasing input lengths remains a huge challenge, causing implications for de novo transcriptome-wide searches. Finally, we make our interaction data set publicly available for future development and benchmarking efforts.
    Keywords: Nucleic acid structure, RNA characterisation and manipulation, Computational Methods
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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