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Related Concept Videos

Feedback Loops01:01

Feedback Loops

In most cases, excessive hormone production is prevented by negative feedback—a loop that starts with a stimulus inducing the release of a particular substance, like a hormone, to maintain a certain level before triggering a signal that results in a decrease in further release of the hormone.
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Feedback Inhibition

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Synthesis of new DNA molecules is carried out by the enzyme DNA polymerase, which adds nucleotides on the daughter strand complementary to the template DNA strand. DNA polymerase has a higher affinity to add the correct base and ensures fidelity during DNA replication. Furthermore,  it exhibits proofreading activity during replication, using an exonuclease domain that cuts off incorrect nucleotides from the nascent DNA strand.
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Internal Receptors

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Related Experiment Video

Updated: May 29, 2026

An In Vitro Approach to Photodynamic Therapy
04:53

An In Vitro Approach to Photodynamic Therapy

Published on: August 17, 2018

Author's Reply.

R M Haralick1

  • 1Machine Vision International, Ann Arbor, MI 48104.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

The Laplacian zero-crossing operator is less effective than the second directional derivative for edge detection. Our findings show it misses and misplaces edges, especially in noisy images.

Related Experiment Videos

Last Updated: May 29, 2026

An In Vitro Approach to Photodynamic Therapy
04:53

An In Vitro Approach to Photodynamic Therapy

Published on: August 17, 2018

Area of Science:

  • Computer Vision
  • Image Processing
  • Edge Detection Algorithms

Background:

  • Edge detection is crucial for image analysis.
  • Evaluating the effectiveness of different edge detection operators is an ongoing research area.
  • Previous studies by Grimson and Hildreth suggested comparable performance for certain edge operators.

Purpose of the Study:

  • To compare the effectiveness of the Laplacian zero-crossing operator against the second directional derivative edge operator.
  • To demonstrate the limitations of the Laplacian operator in utilizing neighborhood information.
  • To re-evaluate edge operator performance on noisy datasets.

Main Methods:

  • Applied the Laplacian of Gaussian (LoG) edge operator with a Gaussian smoother (std dev 5.0) and a large neighborhood size (50x50).
  • Tested the LoG operator on an aerial image of a mobile home park and a noisy checkerboard image.
  • Compared the results against the second directional derivative edge operator.

Main Results:

  • The LoG operator with the specified parameters missed and misplaced edges on the aerial image.
  • Performance of the LoG operator on a noisy checkerboard image was inferior to the second directional derivative operator.
  • Evidence suggests the LoG operator does not effectively use neighborhood information compared to the alternative.

Conclusions:

  • The Laplacian zero-crossing operator is less effective than the second directional derivative for edge detection, particularly with specific parameter choices.
  • The study highlights the importance of parameter selection and neighborhood information in edge operator performance.
  • Further research is needed on comprehensive edge operator evaluation methodologies.