IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i8p2627-d344363.html
   My bibliography  Save this article

Analyzing the Response Behavior of Lumbriculus variegatus (Oligochaeta: Lumbriculidae) to Different Concentrations of Copper Sulfate Based on Line Body Shape Detection and a Recurrent Self-Organizing Map

Author

Listed:
  • Chang Woo Ji

    (Fisheries Science Institute, Chonnam National University, Yeosu 59626, Korea)

  • Young-Seuk Park

    (Department of Biology and Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Seoul 02447, Korea)

  • Yongde Cui

    (Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China)

  • Hongzhu Wang

    (Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China)

  • Ihn-Sil Kwak

    (Fisheries Science Institute, Chonnam National University, Yeosu 59626, Korea)

  • Tae-Soo Chon

    (Ecology and Future Research Association (EnFRA), Dusil-ro 45 beon-gil 21, Geumjeong-gu, Busan 46228, Korea)

Abstract

Point detection (e.g., the centroid of the body) of species has been conducted in numerous studies. However, line detection (i.e., the line body shape) of elongated species has rarely been investigated under stressful conditions. We analyzed the line movements of an Oligochaeta Lumbriculus variegatus in response to treatments with a toxic chemical, copper sulfate, at low concentrations (0.01 mg/L and 0.1 mg/L). The automatic line-tracking system was devised to identify the movement of body segments (body length) and the movements of segments (i.e., the speed and angles between segments) were recorded before and after treatment. Total body length was shortened from 31.22 (±5.18) mm to 20.91 (±4.65) mm after the 0.1 mg/L treatment. The Shannon entropy index decreased from 0.44 (±0.1) to 0.28 (±0.08) after treatment. On the other hand, the body and movement segments did not significantly change after the 0.01 mg/L treatment. Sequential movements of test organisms were further analyzed with a recurrent self-organizing map (RSOM) to determine the pattern of time-series line movements. The RSOM made it feasible to classify sequential behaviors of indicator organisms and identify various continuous body movements under stressful conditions.

Suggested Citation

  • Chang Woo Ji & Young-Seuk Park & Yongde Cui & Hongzhu Wang & Ihn-Sil Kwak & Tae-Soo Chon, 2020. "Analyzing the Response Behavior of Lumbriculus variegatus (Oligochaeta: Lumbriculidae) to Different Concentrations of Copper Sulfate Based on Line Body Shape Detection and a Recurrent Self-Organizing ," IJERPH, MDPI, vol. 17(8), pages 1-15, April.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:8:p:2627-:d:344363
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/8/2627/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/8/2627/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Greg J Stephens & Bethany Johnson-Kerner & William Bialek & William S Ryu, 2008. "Dimensionality and Dynamics in the Behavior of C. elegans," PLOS Computational Biology, Public Library of Science, vol. 4(4), pages 1-10, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Soon-Jin Hwang, 2020. "Eutrophication and the Ecological Health Risk," IJERPH, MDPI, vol. 17(17), pages 1-6, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Elke Braun & Bart Geurten & Martin Egelhaaf, 2010. "Identifying Prototypical Components in Behaviour Using Clustering Algorithms," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-15, February.
    2. Stanislav Nagy & Marc Goessling & Yali Amit & David Biron, 2015. "A Generative Statistical Algorithm for Automatic Detection of Complex Postures," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-23, October.
    3. Christophe Restif & Carolina Ibáñez-Ventoso & Mehul M Vora & Suzhen Guo & Dimitris Metaxas & Monica Driscoll, 2014. "CeleST: Computer Vision Software for Quantitative Analysis of C. elegans Swim Behavior Reveals Novel Features of Locomotion," PLOS Computational Biology, Public Library of Science, vol. 10(7), pages 1-12, July.
    4. Markus Reischl & Mazin Jouda & Neil MacKinnon & Erwin Fuhrer & Natalia Bakhtina & Andreas Bartschat & Ralf Mikut & Jan G Korvink, 2019. "Motion prediction enables simulated MR-imaging of freely moving model organisms," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-16, December.
    5. Sepideh Bazazi & Frederic Bartumeus & Joseph J Hale & Iain D Couzin, 2012. "Intermittent Motion in Desert Locusts: Behavioural Complexity in Simple Environments," PLOS Computational Biology, Public Library of Science, vol. 8(5), pages 1-10, May.
    6. Steffen Werner & Jochen C Rink & Ingmar H Riedel-Kruse & Benjamin M Friedrich, 2014. "Shape Mode Analysis Exposes Movement Patterns in Biology: Flagella and Flatworms as Case Studies," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-21, November.
    7. Chongbin Zheng & Evelyn Tang, 2024. "A topological mechanism for robust and efficient global oscillations in biological networks," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    8. Jeffrey P Nguyen & Ashley N Linder & George S Plummer & Joshua W Shaevitz & Andrew M Leifer, 2017. "Automatically tracking neurons in a moving and deforming brain," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-19, May.
    9. Laetitia Hebert & Tosif Ahamed & Antonio C Costa & Liam O’Shaughnessy & Greg J Stephens, 2021. "WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans," PLOS Computational Biology, Public Library of Science, vol. 17(4), pages 1-20, April.
    10. Li-Chun Lin & Han-Sheng Chuang, 2017. "Analyzing the locomotory gaitprint of Caenorhabditis elegans on the basis of empirical mode decomposition," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-14, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:8:p:2627-:d:344363. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.