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Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Suppose a positive test charge moves away from a positive static charge, then the Coulomb force does positive work, and its electric potential energy decreases. The potential energy per unit charge is defined as the electric potential. The electric potential is independent of the test charge.
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Operational amplifiers (op-amps) are versatile devices that extend beyond amplification. In this context, two specific op-amp configurations are explored: the summing and difference amplifiers.
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Frequency difference mapping applied to the corpus callosum at 7T.

Benjamin C Tendler1,2, Richard Bowtell1

  • 1Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, United Kingdom.

Magnetic Resonance in Medicine
|December 25, 2018
PubMed
Summary

A new Frequency Difference Mapping (FDM) algorithm reveals white matter microstructure details using gradient echo signals. This method simplifies phase processing without needing phase unwrapping or complex imaging techniques.

Keywords:
corpus callosumfrequency difference mappingmicrostructurephase processingthree-pool modelwhite matter

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Medical Physics

Background:

  • Gradient Echo (GE) signals are crucial in Magnetic Resonance Imaging (MRI).
  • Phase processing of GE signals can provide insights into tissue properties.
  • Nonlinear temporal evolution of phase contains valuable information.

Purpose of the Study:

  • Introduce a novel Frequency Difference Mapping (FDM) processing algorithm.
  • Demonstrate FDM's ability to reveal white matter microstructure.
  • Develop a simplified phase processing technique for MRI.

Main Methods:

  • Applied a novel FDM algorithm to multi-echo GE scans at 7T.
  • Processed phase data from ten healthy subjects.
  • Examined temporal evolution of signal magnitude and frequency difference in corpus callosum regions.

Main Results:

  • Observed consistent frequency difference contrast in the corpus callosum (CC) and superior cerebellar peduncle.
  • Identified distinct variations in frequency difference curves across CC regions.
  • Noted larger frequency differences and faster signal decay in the genu and splenium of the CC.

Conclusions:

  • The novel FDM algorithm provides images sensitive to tissue microstructure.
  • FDM effectively highlights microstructural differences within the corpus callosum.
  • This simplified FDM approach avoids phase unwrapping and complex image processing.