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相关概念视频

Applications Of NMR In Biology01:25

Applications Of NMR In Biology

3.6K
Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
3.6K
NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

589
When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
589
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

922
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
922

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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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为了优化基于神经网络的NMR代谢量化NMR代谢量化.

Hayden Johnson1, Aaryani Tipirneni-Sajja1,2

  • 1Department of Biomedical Engineering, The University of Memphis, Memphis, TN 38152, USA.

Metabolites
|April 25, 2025
PubMed
概括

变压器擅长从核磁共振 (NMR) 光谱中量化代谢物,为代谢学提供快速,自动化的解决方案. 这种先进的深度学习方法提高了准确性,特别是对于复杂的样本.

科学领域:

  • 计算化学是一种计算化学.
  • 生物化学 生物化学
  • 机器学习 机器学习

背景情况:

  • 从NMR光谱中精确,高吞吐量量化代谢物对代谢学至关重要.
  • 神经网络在定量NMR代谢学中未得到充分利用,尽管它们具有速度和吞吐量的潜力.
  • 传统的峰值配合软件对于复杂的光谱来说可能很慢,效率也很低.

研究的目的:

  • 通过直接从模拟的NMR光谱中使用神经网络调查数据集和模型开发以量化代谢物.
  • 为了比较多层感知子,卷积神经网络和转换器模型对NMR代谢学的性能.
  • 优化模型架构,训练参数和数据集,以准确量化代谢物.

主要方法:

  • 使用模拟的400MHz 1H-NMR光谱测试了含有8,44或86种代谢物的混合物.
  • 三个神经网络模型 (MLP,CNN,变压器) 被训练并优化.
  • 模型在100MHz和800MHz模拟的光谱上进行了验证.

主要成果:

  • 变压器模型在NMR代谢物量化方面表现出卓越的性能.
  • 变压器在增加代谢物数量,低度或大动态范围时最有效.
  • 通过广泛的光谱范围 (100-MHz至800-MHz) 实现了精确的量化.
关键词:
核磁共振光谱法 (NMR) 是一种光谱法.卷积神经网络是一种卷积神经网络.低场NMR是一种低场NMR.多层多层的感知器变压器的变压器是一个变压器.

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Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts
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结论:

  • 变压器显示出在NMR代谢学中准确,实时,完全自动化代谢物量化的巨大潜力.
  • 进一步开发实验数据可能会导致先进的自动化定量NMR代谢学软件.
  • 这种方法为复杂的光谱分析提供了传统方法的有希望的替代方案.