Literature DB >> 18342950

Automated detection and analysis of foraging behavior in Caenorhabditis elegans.

Kuang-Man Huang1, Pamela Cosman, William R Schafer.   

Abstract

Foraging is a rapid, side-to-side movement of the nose generated by Caenorhabditis elegans as it explores its environment. In this paper, we present an automated method to detect and analyze foraging behavior of C. elegans in a video sequence. Several morphological image-processing methods are used to locate the precise nose position of the worm in each image. Then foraging events are detected by measuring the bending angle of the nose and investigating the overall bending curve using periodograms. We measure foraging-related parameters which have not previously been studied. The algorithm has applications in classifying and characterizing genetic mutations associated with this behavior.

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Year:  2008        PMID: 18342950     DOI: 10.1016/j.jneumeth.2008.01.027

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  13 in total

Review 1.  Strategies for automated analysis of C. elegans locomotion.

Authors:  Steven D Buckingham; David B Sattelle
Journal:  Invert Neurosci       Date:  2008-08-08

2.  Locomotion Behavior Is Affected by the GαS Pathway and the Two-Pore-Domain K+ Channel TWK-7 Interacting in GABAergic Motor Neurons in Caenorhabditis elegans.

Authors:  Dieter-Christian Gottschling; Frank Döring; Kai Lüersen
Journal:  Genetics       Date:  2017-03-24       Impact factor: 4.562

3.  Rapid and accurate developmental stage recognition of C. elegans from high-throughput image data.

Authors:  Amelia G White; Patricia G Cipriani; Huey-Ling Kao; Brandon Lees; Davi Geiger; Eduardo Sontag; Kristin C Gunsalus; Fabio Piano
Journal:  Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit       Date:  2010-08-05

4.  A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches.

Authors:  Pingli Ma; Chen Li; Md Mamunur Rahaman; Yudong Yao; Jiawei Zhang; Shuojia Zou; Xin Zhao; Marcin Grzegorzek
Journal:  Artif Intell Rev       Date:  2022-06-07       Impact factor: 9.588

5.  Multi-environment model estimation for motility analysis of Caenorhabditis elegans.

Authors:  Raphael Sznitman; Manaswi Gupta; Gregory D Hager; Paulo E Arratia; Josué Sznitman
Journal:  PLoS One       Date:  2010-07-22       Impact factor: 3.240

6.  Self-assemblage and quorum in the earthworm Eisenia fetida (Oligochaete, Lumbricidae).

Authors:  Lara Zirbes; Yves Brostaux; Mark Mescher; Maxime Jason; Eric Haubruge; Jean-Louis Deneubourg
Journal:  PLoS One       Date:  2012-03-01       Impact factor: 3.240

7.  A database of Caenorhabditis elegans behavioral phenotypes.

Authors:  Eviatar Yemini; Tadas Jucikas; Laura J Grundy; André E X Brown; William R Schafer
Journal:  Nat Methods       Date:  2013-07-14       Impact factor: 28.547

8.  Model-independent phenotyping of C. elegans locomotion using scale-invariant feature transform.

Authors:  Yelena Koren; Raphael Sznitman; Paulo E Arratia; Christopher Carls; Predrag Krajacic; André E X Brown; Josué Sznitman
Journal:  PLoS One       Date:  2015-03-27       Impact factor: 3.240

9.  AutoEPG: software for the analysis of electrical activity in the microcircuit underpinning feeding behaviour of Caenorhabditis elegans.

Authors:  James Dillon; Ioannis Andrianakis; Kate Bull; Steve Glautier; Vincent O'Connor; Lindy Holden-Dye; Christopher James
Journal:  PLoS One       Date:  2009-12-29       Impact factor: 3.240

10.  Light microscopy applications in systems biology: opportunities and challenges.

Authors:  Paul Michel Aloyse Antony; Christophe Trefois; Aleksandar Stojanovic; Aidos Sagatovich Baumuratov; Karol Kozak
Journal:  Cell Commun Signal       Date:  2013-04-11       Impact factor: 5.712

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