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Related Concept Videos

Translation01:31

Translation

156.6K
Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of...
156.6K
Translation01:31

Translation

17.8K
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...
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Initiation of Translation02:33

Initiation of Translation

39.0K
Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...
39.0K
Initiation of Translation02:33

Initiation of Translation

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8.1K
Termination of Translation01:44

Termination of Translation

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The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
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Termination of Translation01:44

Termination of Translation

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Lost in translation.

Parashkev Nachev1, Geraint Rees1,2,3,4, Richard Frackowiak1,5

  • 1Institute of Neurology, University College London, London, WC1N 3BG, UK.

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|January 16, 2019
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Summary
This summary is machine-generated.

Cognitive neuroscience translation requires higher-dimensional brain models for individual-level application. Advanced computing and large datasets can enable these complex models for better patient outcomes.

Keywords:
Translationcausalitycognitive neurosciencehigh-dimensional inferencemachine learning.neuroimaging

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Current cognitive neuroscience models often lack the necessary detail for individual patient application.
  • Progress in understanding the brain has not yet translated into effective clinical interventions.
  • Explanatory models must reach the individual level to enable real-world applications.

Purpose of the Study:

  • To argue for the necessity of high-dimensional modeling in cognitive neuroscience.
  • To identify the informational properties of the brain that necessitate greater model dimensionality.
  • To propose a path toward successful translation of neuroscience findings into clinical practice.

Main Methods:

  • Analysis of the informational properties of neural systems.
  • Examination of the widely distributed causality in adaptive neural mechanisms.
  • Discussion of recent advances in high-performance computing and large-scale data collection.

Main Results:

  • Adequate individualization of models requires significantly higher dimensionality than currently standard.
  • The adaptive and distributed nature of neural systems inherently demands complex, high-dimensional models.
  • High-performance computing and large datasets provide the means to create these necessary models.

Conclusions:

  • Successful translation in cognitive neuroscience hinges on adopting high-dimensional modeling approaches.
  • Bridging the gap between neuroscience research and patient impact requires a shift in modeling strategy.
  • The field should embrace advanced computational methods to achieve clinically relevant outcomes.