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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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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.
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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.
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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.
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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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Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network

A Hagiwara1,2, Y Otsuka3,4, M Hori3

  • 1From the Departments of Radiology (A.H., Y.O., M.H., Y.T., S.F., C.A., K.K., R.I., S.K., T.M., L.C., A.W., M.Y.T., S.A.) a-hagiwara@juntendo.ac.jp.

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Deep learning significantly enhances synthetic FLAIR MRI quality, reducing artifacts and improving contrast for better Multiple Sclerosis lesion visualization.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Synthetic FLAIR images often exhibit lower quality compared to conventional FLAIR images.
  • Improving synthetic FLAIR image quality is crucial for accurate diagnostic interpretation.

Purpose of the Study:

  • To enhance synthetic FLAIR image quality using deep learning.
  • To achieve pixel-by-pixel translation via conditional generative adversarial network (cGAN) training.

Main Methods:

  • Forty patients with Multiple Sclerosis (MS) underwent 3T MRI to acquire synthetic and conventional FLAIR images.
  • A cGAN was trained to generate improved FLAIR images from synthetic MR imaging data, using conventional FLAIR as targets.
  • Quantitative metrics (SNR, NRMSE, Dice index) and visual assessments of lesion conspicuity and artifacts were performed.

Main Results:

  • Generated FLAIR images showed significantly improved peak signal-to-noise ratio (SNR) and normalized root mean square error (NRMSE) compared to synthetic FLAIR images (P < .001).
  • Lesion conspicuity and Dice index were comparable between generated and synthetic FLAIR images (P = 1 and P = .59).
  • Generated FLAIR images exhibited significantly fewer granular (P = .003) and swelling artifacts than synthetic FLAIR images.

Conclusions:

  • Deep learning effectively improves synthetic FLAIR image quality.
  • Generated FLAIR images offer contrast closer to conventional FLAIR with reduced artifacts, preserving lesion contrast.
  • This advancement holds promise for more reliable MS diagnosis and monitoring using synthetic imaging.