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Translation01:31

Translation

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Translation01:31

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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.
Translation Produces the Building Blocks of...
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Translation01:31

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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.
Translation Produces the Building Blocks of Life
Proteins are...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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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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Improving Translational Accuracy02:07

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Real-Time Lexicon-Free Scene Text Localization and Recognition.

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    This study introduces a real-time method for text localization and recognition using Extremal Regions (ERs). The approach achieves state-of-the-art performance on challenging datasets, improving text detection and recognition accuracy.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Accurate text localization and recognition are crucial for various applications.
    • Existing methods often struggle with real-time performance and robustness to image variations.

    Purpose of the Study:

    • To develop an end-to-end, real-time method for text localization and recognition.
    • To improve robustness against blur, low contrast, illumination, and color variations.

    Main Methods:

    • Character detection and segmentation framed as sequential selection from Extremal Regions (ERs).
    • A two-stage ER detection process utilizing constant-time and computationally expensive features.
    • Efficient clustering for text line grouping and an OCR classifier for character labeling.
    • Context-aware selection of the most probable character sequence.

    Main Results:

    • Achieved state-of-the-art results in text localization on the ICDAR 2013 dataset.
    • Significantly outperformed state-of-the-art methods on the challenging SVT dataset.
    • Demonstrated improved performance by incorporating prior knowledge about detected text.

    Conclusions:

    • The proposed method offers an efficient and robust solution for real-time text localization and recognition.
    • It serves as a strong baseline for robust reading competitions, comparing favorably to existing methods.