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相关概念视频

X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal crystal...
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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相关实验视频

Updated: Jun 19, 2026

Rapid Analysis and Exploration of Fluorescence Microscopy Images
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使用 Python 代码进行生物图像自动分析的协议.

Benjamin Sacks1, Jiya Mody1, Yiming Huang1

  • 1Department of Neurology, Robert Wood Johnson Medical School, Institute for Neurological Therapeutics at Rutgers, Rutgers Biomedical and Health Sciences, Piscataway, NJ 08854, USA.

STAR protocols
|February 26, 2026
PubMed
概括
此摘要是机器生成的。

本协议详细介绍了用于生物研究的Python图像分析,为特征量化提供了一个开源替代方案. 科学家可以学会处理,细分和分析图像,而编码经验最小.

关键词:
生物信息学是一种生物信息学.细胞生物学 细胞生物学计算机科学 计算机科学显微镜的使用方法

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科学领域:

  • 生物图像分析分析
  • 计算生物学是一种计算生物学.
  • 科学成像科学成像

背景情况:

  • 商用图像分析软件可能昂贵且限制性.
  • 在生物研究中,人们越来越需要易于使用,灵活的工具.
  • Python 为科学计算提供了一个强大的开源平台.

研究的目的:

  • 提供一个用户友好的协议,用于Python中的定量图像分析.
  • 赋予具有有限编码经验的科学家分析生物图像的权力.
  • 展示Python在生物图像分析方面的灵活性和能力.

主要方法:

  • 阅读和准备各种生物图像文件类型.
  • 图像细分技术用于特征隔离.
  • 从细分图像中提取定量测量.
  • 数据出口用于进一步分析.

主要成果:

  • 使用Python成功处理和分析图像的演示.
  • 在各种常见的生物图像类型中的特征的量化.
  • 为下游应用生成可导出数据.

结论:

  • 基于Python的图像分析是商业软件的可行和灵活的替代方案.
  • 该协议使科学家能够使用最小的编码进行定量生物图像分析.
  • 开源工具提高了科学研究中的可访问性和可重复性.