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Related Concept Videos

Protein Denaturation01:28

Protein Denaturation

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The function of proteins depends on their native three-dimensional structure, which is dictated by the amino acid sequence of the specific protein. Folding of the polypeptide chain takes place under specific conditions that energetically favor the folded conformation. In contrast, protein denaturation occurs spontaneously under unfavorable conditions that disrupt the integrity of the folded conformation. Thus, the chemical and physical environment of a protein, such as significant changes in pH...
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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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¹H NMR of Labile Protons: Deuterium (²H) Substitution00:48

¹H NMR of Labile Protons: Deuterium (²H) Substitution

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This lesson illustrates the role of deuterium substitution in simplifying the NMR spectrum of compounds comprising labile protons. One method employed is the use of deuterium. Amongst the three isotopes of hydrogen, deuterium (2H) has a nucleus composed of one proton and one neutron. When the D2O solvent is added to a pure dry ethanol solution, its labile proton is substituted with deuterium.
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¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Updated: Jun 4, 2025

Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy
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Investigating self-supervised image denoising with denaturation.

Hiroki Waida1, Kimihiro Yamazaki2, Atsushi Tokuhisa3

  • 1Department of Mathematical and Computing Science, Institute of Science Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|December 19, 2024
PubMed
Summary
This summary is machine-generated.

Self-supervised learning with denatured data effectively denoises images. Theoretical analysis shows performance depends on task difficulty, aligning with experimental results for improved algorithms.

Keywords:
Self-supervised image denoisingTheory on denoising

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Area of Science:

  • Machine Learning
  • Computer Vision
  • Signal Processing

Background:

  • Self-supervised learning is vital for image denoising with noisy, denatured data.
  • Theoretical understanding of self-supervised denoising using denatured data is limited.

Purpose of the Study:

  • To theoretically analyze a self-supervised denoising algorithm using denatured data.
  • To investigate the algorithm's performance through numerical experiments.

Main Methods:

  • Theoretical analysis of optimization problem and risk guarantees.
  • Empirical investigation using an extended self-supervised denoising algorithm.
  • Evaluation based on denaturation levels and task hardness.

Main Results:

  • The algorithm converges to desired solutions for population risk.
  • Guarantee for empirical risk is contingent on denoising task difficulty.
  • Experimental results confirm algorithm effectiveness and alignment with theory.

Conclusions:

  • Self-supervised image denoising with denatured data is viable.
  • Theoretical insights guide understanding of performance guarantees.
  • Findings offer directions for future algorithm enhancements.