Article ; Online: Automated recognition and analysis of body bending behavior in C. elegans.
2023 Volume 24, Issue 1, Page(s) 175
Abstract: Background: Locomotion behaviors of Caenorhabditis elegans play an important role in drug activity screening, anti-aging research, and toxicological assessment. Previous studies have provided important insights into drug activity screening, anti-aging, ... ...
Abstract | Background: Locomotion behaviors of Caenorhabditis elegans play an important role in drug activity screening, anti-aging research, and toxicological assessment. Previous studies have provided important insights into drug activity screening, anti-aging, and toxicological research by manually counting the number of body bends. However, manual counting is often low-throughput and takes a lot of time and manpower. And it is easy to cause artificial bias and error in counting results. Results: In this paper, an algorithm is proposed for automatic counting and analysis of the body bending behavior of nematodes. First of all, the numerical coordinate regression method with convolutional neural network is used to obtain the head and tail coordinates. Next, curvature-based feature point extraction algorithm is used to calculate the feature points of the nematode centerline. Then the maximum distance between the peak point and the straight line between the pharynx and the tail is calculated. The number of body bends is counted according to the change in the maximum distance per frame. Conclusion: Experiments are performed to prove the effectiveness of the proposed algorithm. The accuracy of head coordinate prediction is 0.993, and the accuracy of tail coordinate prediction is 0.990. The Pearson correlation coefficient between the results of the automatic count and manual count of the number of body bends is 0.998 and the mean absolute error is 1.931. Different strains of nematodes are selected to analyze differences in body bending behavior, demonstrating a relationship between nematode vitality and lifespan. The code is freely available at https://github.com/hthana/Body-Bend-Count . |
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MeSH term(s) | Animals ; Caenorhabditis elegans ; Nematoda ; Caenorhabditis elegans Proteins ; Algorithms ; Aging |
Chemical Substances | Caenorhabditis elegans Proteins |
Language | English |
Publishing date | 2023-04-28 |
Publishing country | England |
Document type | Journal Article |
ZDB-ID | 2041484-5 |
ISSN | 1471-2105 ; 1471-2105 |
ISSN (online) | 1471-2105 |
ISSN | 1471-2105 |
DOI | 10.1186/s12859-023-05307-y |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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