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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
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Nuclear Overhauser Enhancement (NOE)01:07

Nuclear Overhauser Enhancement (NOE)

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Irradiation of a spin-active nucleus causes an increase or decrease in the signal intensity of neighboring nuclei that are not necessarily chemically bonded or involved in J-coupling.  This phenomenon, called the Nuclear Overhauser Enhancement (NOE), results from through-space interactions between the nuclear spins. The NOE effect decreases with increasing internuclear distance and is generally not observed beyond 4 angstroms. In NOE, dipole-dipole interactions between neighboring...
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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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Nuclear Transmutation03:20

Nuclear Transmutation

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Nuclear transmutation is the conversion of one nuclide into another. It can occur by the radioactive decay of a nucleus, or the reaction of a nucleus with another particle. The first manmade nucleus was produced in Ernest Rutherford’s laboratory in 1919 by a transmutation reaction, the bombardment of one type of nuclei with other nuclei or with neutrons. Rutherford bombarded nitrogen-14 atoms with high-speed α particles from a natural radioactive isotope of radium and observed...
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Atomic Nuclei: Nuclear Spin State Population Distribution01:14

Atomic Nuclei: Nuclear Spin State Population Distribution

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Near absolute zero temperatures, in the presence of a magnetic field, the majority of nuclei prefer the lower energy spin-up state to the higher energy spin-down state. As temperatures increase, the energy from thermal collisions distributes the spins more equally between the two states. The Boltzmann distribution equation gives the ratio of the number of spins predicted in the spin −½ (N−) and spin +½ (N+) states.
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相关实验视频

Updated: Sep 11, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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电子核截面从转移学习的截面

Krzysztof M Graczyk1, Beata E Kowal1, Artur M Ankowski1

  • 1University of Wrocław, Institute of Theoretical Physics, plac Maxa Borna 9, 50-204 Wrocław, Poland.

Physical review letters
|August 18, 2025
PubMed
概括
此摘要是机器生成的。

转移学习使深度神经网络 (DNN) 能够适应新的物理问题. 经过电子-碳散射训练的DNN在微调后准确地预测其他核目标的截面.

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科学领域:

  • 核物理 核物理 核物理
  • 计算物理 计算物理
  • 机器学习 机器学习

背景情况:

  • 转移学习是一种机器学习技术,在这种技术中,在一个任务上训练的模型被重新用于第二个相关任务.
  • 深度神经网络 (DNN) 在各种科学领域都显得有前途.

研究的目的:

  • 在核物理中研究使用DNN转移学习的应用.
  • 评估在一个散射过程中训练的微调DNN的有效性,以预测相关过程的结果.

主要方法:

  • 在包括电子-碳散射数据的深度神经网络中进行训练.
  • 为新的预测任务微调训练好的DNN.
  • 对各种核目标的实验横截面数据验证预测.

主要成果:

  • 微调的DNN准确地预测了电子与核目标相互作用的截面.
  • 在从-3到铁的各种核中成功应用转移学习.

结论:

  • 转移学习是加速核物理预测的可行和有效技术.
  • DNN可以有效地适应模拟各种电子核散射过程.