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Size-Exclusion Chromatography01:08

Size-Exclusion Chromatography

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In size-exclusion chromatography (SEC), also known as molecular-exclusion or gel-permeation chromatography, molecules are separated based on their sizes. This technique is important for separating large molecules such as polymers and biomolecules. The two classes of micron-sized stationary phases encountered in SEC are silica particles and cross-linked polymer resin beads. Both materials are porous, but their pore sizes vary significantly.
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Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
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Centrifugation is a separation technique based on differences in density or size. It is commonly used to separate solids from aqueous interferents. During centrifugation, the sample is placed in centrifugation tubes and spun at high angular velocity, which allows centrifugal force to act differentially on the different densities or masses of the components. After spinning, the supernatant liquid is decanted. Depending on the specific application, either the pellet or the supernatant is retained...
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通过可分离性指数和光谱聚类来提高尖峰分类效率.

Leila Ranjbar1, Hossein Parsaei2, Mohammad Mehdi Movahedi3

  • 1Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.

Medical engineering & physics
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概括

频谱聚类有效地使用原始数据对神经尖端进行排序. 一个新的可分离性指数预测了尖端分类难度,提高了大数据集的效率和准确性.

关键词:
功能提取 功能提取神经系统, 动作潜力, 尖分类主要组件分析,可分离性指数频谱聚类是指光谱的聚类.

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科学领域:

  • 计算神经科学是一种神经科学.
  • 信号处理 信号处理

背景情况:

  • 尖峰分类对于分析神经活动至关重要.
  • 由于信号的变化,精确的尖峰分类是具有挑战性的.

研究的目的:

  • 为了评估用于尖峰分类的光谱聚类.
  • 引入一个可分离性指数来评估尖端分类难度.
  • 将光谱聚类与现有方法进行比较.

主要方法:

  • 对各种特征集 (原样,衍生品,PCA) 应用的光谱聚类.
  • 开发和验证一个新的可分离性指数.
  • 对两个已建立的尖峰分类算法进行比较分析.

主要成果:

  • 原始样本通过光谱聚类实现了73.84%的准确性.
  • 可分离性指数有效预测了分类难度 (相关性=0.71).
  • 拟议的方法比以前的技术高出23%的准确性.

结论:

  • 光谱聚类是一种可行的尖端分类方法,特别是在原始数据中.
  • 分离性指数为计算成本和方法比较提供了一个预测工具.
  • 这种方法提高了神经数据分析的效率和准确性.