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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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贝叶斯后部密度估计显示了三维多个发射器定位的退化.

Raymond van Dijk1, Dylan Kalisvaart1, Jelmer Cnossen1

  • 1Delft Center for Systems and Control, Delft University of Technology, Delft, 2628 CD, The Netherlands.

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概括

一个新的贝叶斯算法通过准确配合重叠的发射器来改进3D单分子定位显微镜. 这种方法减少了不确定性,并提高了对密集样本的模型估计,推进了超高分辨率成像技术.

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科学领域:

  • * 生物物理 生物物理
  • * 光学显微镜技术
  • * 计算生物学 * 计算生物学

背景情况:

  • *单分子定位显微镜 (SMLM) 依赖于稀疏的发射器激活来克服衍射极限.
  • *在密度或厚度样本中重叠的发射器图像导致偏差的参数估计和标准SMLM中不正确的发射器计数.
  • * 现有的多个发射器安装方法与点传播函数 (PSF) 退化作斗争,导致模型和参数不确定性.

研究的目的:

  • * 开发一个强大的3D贝叶斯算法,用于在SMLM中安装多个重叠的发射器.
  • * 准确估计发射器参数 (3D位置,光子强度) 和相关的不确定性.
  • * 评估不同3D成像技术对多发射器配件的性能.

主要方法:

  • * 开发了一个3D贝叶斯式多发射器配合算法,利用可逆跳转马尔科夫链蒙特卡洛 (RJMCMC).
  • *该算法重建了模型 (发射器数量) 和参数的后方概率分布.
  • * 通过分析发射器分离能力,通过使用形和双平面PSF成像来评估算法性能.

主要成果:

  • *阿斯蒂格玛图像显示出发射器位置的多模式后部分布在PSF焦点标准偏差的2倍之内,表明位置模两可.
  • *双飞机成像成功地将发射器分离到0.75倍的PSF的焦点标准偏差,没有多模式.
  • *该算法有效地识别了PSF退化,并量化了成像技术的性能.

结论:

  • *开发的3D贝叶斯算法准确地估计了SMLM中重叠的发射器的参数和不确定性.
  • * 后部分布中的多态性是PSF退化和局部模糊性的指标.
  • *双平面成像在3D多发射器配件方面表现出优异的性能,相比于在某些方案中进行的形成像,在某些方案中表现出优异的性能.