Quantification and analysis of ecdysis in the hornworm, Manduca sexta, using machine vision–based tracking

Alan Shimoide; Ian Kimball; Alba A. Gutierrez; Hendra Lim; Ilmi Yoon; John T. Birmingham; Rahul Singh; Megumi Fuse

doi:10.1007/s10158-012-0142-9

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Quantification and analysis of ecdysis in the hornworm, Manduca sexta, using machine vision–based tracking

Journal article

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Quantification and analysis of ecdysis in the hornworm, Manduca sexta, using machine vision–based tracking

Alan Shimoide, Ian Kimball, Alba A. Gutierrez, Hendra Lim, Ilmi Yoon, John T. Birmingham, Rahul Singh and Megumi Fuse

Invertebrate neuroscience, Vol.13(1), pp.45-55

06/01/2013

DOI: 10.1007/s10158-012-0142-9

PMCID: PMC3969263

PMID: 23007685

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https://www.ncbi.nlm.nih.gov/pmc/articles/3969263View

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Abstract

We have developed a machine vision–based method for automatically tracking deformations in the body wall to monitor ecdysis behaviors in the hornworm, Manduca sexta. The method utilizes naturally occurring features on the animal’s body (spiracles) and is highly accurate (>95 % success in tracking). Moreover, it is robust to unanticipated changes in the animal’s position and in lighting, and in the event tracking of specific features is lost, tracking can be reestablished within a few cycles without input from the user. We have paired our tracking technique with electromyography and have also compared our in vivo results to fictive motor patterns recorded from isolated nerve cords. We found no major difference in the cycle periods of contractions during naturally occurring ecdysis compared to ecdysis initiated prematurely through injection of the peptide ecdysis-triggering hormone, and we confirmed that the ecdysis period in vivo is statistically similar to that of the fictive motor pattern.

Biomedical and Life Sciences

Biomedicine

Cell Biology

Invertebrates

Molecular Medicine

Neurobiology

Neurosciences

Original Paper

Details

Title: Subtitle: Quantification and analysis of ecdysis in the hornworm, Manduca sexta, using machine vision–based tracking
Creators: Alan Shimoide - San Francisco State University
Ian Kimball - San Francisco State University
Alba A. Gutierrez - San Francisco State University
Hendra Lim - San Francisco State University
Ilmi Yoon - San Francisco State University
John T. Birmingham - Santa Clara University
Rahul Singh - San Francisco State University
Megumi Fuse - San Francisco State University
Resource Type: Journal article
Publication Details: Invertebrate neuroscience, Vol.13(1), pp.45-55
DOI: 10.1007/s10158-012-0142-9
PMID: 23007685
PMCID: PMC3969263
NLM abbreviation: Invert Neurosci
ISSN: 1354-2516
eISSN: 1439-1104
Publisher: Springer-Verlag
Language: English
Date published: 06/01/2013
Academic Unit: Computer Science
Record Identifier: 9984446267402771

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