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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Translational regulation in prokaryotes ensures efficient protein synthesis by controlling ribosome access to mRNA. This regulation is mediated by secondary RNA structures, including translational riboswitches, RNA thermometers, and small RNAs (sRNAs), which respond to intracellular and environmental signals to modulate gene expression.Translational RiboswitchesRiboswitches in the leader region of mRNAs can regulate translation by altering the accessibility of the Shine-Dalgarno (SD) sequence,...
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Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research.

Denis Newman-Griffis1, Jill Fain Lehman2, Carolyn Rosé3

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Translational Natural Language Processing (NLP) bridges basic and applied research, ensuring innovations are applied and key questions are studied. This new paradigm facilitates the exchange between fundamental principles and real-world application needs.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Natural Language Processing (NLP) research traditionally separates basic science from applied science.
  • The exchange between basic and applied NLP is often assumed, leading to underutilized innovations and unaddressed research questions.

Purpose of the Study:

  • To introduce a new research paradigm: Translational NLP.
  • To structure and facilitate the bidirectional exchange between basic and applied NLP research.
  • To focus on how application needs can drive innovation in basic NLP science and technology.

Main Methods:

  • Describing a new paradigm of Translational NLP.
  • Presenting a conceptual framework for translational research in NLP.
  • Outlining stakeholders and key questions within translational NLP.

Main Results:

  • Many significant NLP advances arise from the intersection of basic principles and application needs.
  • A conceptual framework is provided to guide Translational NLP development.
  • General translational principles are identified to enhance research exchange.

Conclusions:

  • Translational NLP offers a structured approach to connect fundamental research with practical applications.
  • This paradigm shift can accelerate innovation and ensure the relevance of NLP research.
  • The proposed framework serves as a roadmap for establishing Translational NLP as a distinct research area.