Hardware and software system for biotesting natural water bodies
Abstract
A hardware and software system for bioassays is proposed, which counts the number of mobile biological objects (ciliates) within a microscope's field of view. Video recording can be performed using simple tools, including a mobile phone camera. The program for counting the number of moving objects within a microscope's field of view is written in Python and utilizes OpenCV library functions. The user interface allows for adjustments to the number of frames processed, the brightness threshold, and the number of pixels for image blur. The program can be easily adapted for use with the simplest equipment, in frame-by-frame or continuous shooting modes. A comparison with other known bioassay tools is provided.
References
Skurlatov, Yu. I., Duka, G. G., & Ernestova, L. S. (1983). Toxication processes and self-purification mechanisms of natural water under anthropogenic influences. Izvestiya AN MSSR Series “Biological and chemical sciences”, (5), 3–20.
Leonova, I. B., & Metlenkin, D. A. (2016). Bioassays in assessing the quality of bottled drinking water. Modern science: current problems and solutions. (5), 43–48.
Dell, H. A. (1954). Stages in the development of an arrested scan type microscopic particle counter. British Journal of Applied Physics, 5(S3), S156–S161. https://doi.org/10.1088/0508-3443/5/S3/349.
Lojk, J., Čibej, U., Karlaš, D., Šajn, L., & Pavlin, M. (2015). Comparison of two automatic cell‐counting solutions for fluorescent microscopic images. Journal of microscopy, 260(1), 107-116. https://doi.org/10.1111/jmi.12272.
Peer, P., & Corzo, L. G. (2007). Local Pixel Value Collection Algorithm for Spot Segmentation in Two‐Dimensional Gel Electrophoresis Research. International Journal of Genomics, 2007(1), 089596. https://doi.org/10.1155/2007/89596
Kurnia, K. A., Sampurna, B. P., Audira, G., Juniardi, S., Vasquez, R. D., Roldan, M. J. M., ... & Hsiao, C. D. (2022). Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension. International Journal of Molecular Sciences, 23(11), 6009. https://doi.org/10.3390/ijms23116009.
Chesnokova, S. M., & Chugay, N. V. (2008). Biological methods for assessing the quality of environmental objects: a tutorial: in 2 parts. Part 2: Biotesting methods.
Cheremnykh, E.G., Voronina, L.P. Automation of soil biotesting based on image processing. Moscow Univ. Soil Sci. Bull. 62, 159–162 (2007). https://doi.org/10.3103/S0147687407030088.
PND FT 14.1:2:3:4.2-98 Methodology for determining water toxicity based on mortality and changes in fertility of daphnia.
Kuzmich V.N., Sokolova S.A., Kraynyukova A.N. Guidelines for Determining the Toxicity of Water, Bottom Sediments, Pollutants, and Drilling Fluids by Biotesting. Moscow: REFIA, NIA-Priroda, 2002.
Zurochka, A. V., Khaidukov, S. V., Kudryavtsev, I. V., & Chereshnev, V. A. (2018). Flow cytometry in biomedical research. Ekaterinburg: RIO UrO RAS.
Bergquist, P. L., Hardiman, E. M., Ferrari, B. C., & Winsley, T. (2009). Applications of flow cytometry in environmental microbiology and biotechnology. Extremophiles, 13(3), 389–401. https://doi.org/10.1007/s00792-009-0236-4.
Valeur, B., & Brochon, J. C. (Eds.). (2012). New trends in fluorescence spectroscopy: applications to chemical and life sciences (Vol. 1). Springer Science & Business Media.
Korobchevskaya, K., Lagerholm, B. C., Colin-York, H., & Fritzsche, M. (2017, July). Exploring the potential of airyscan microscopy for live cell imaging. In Photonics 4(3), 41. MDPI. https://doi.org/10.3390/photonics4030041.
Hogg, W. R., & Coulter, W. H. (1971). U.S. Patent No. 3,557,352. Washington, DC: U.S. Patent and Trademark Office.
Zhang, W., Hu, Y., Choi, G., Liang, S., Liu, M., & Guan, W. (2019). Microfluidic multiple cross-correlated Coulter counter for improved particle size analysis. Sensors and actuators B: Chemical, 296, 126615. https://doi.org/10.1016/j.snb.2019.05.092.
Grishagin, I. V. (2015). Automatic cell counting with ImageJ. Analytical biochemistry, 473, 63–65. https://doi.org/10.1016/j.ab.2014.12.007.
Collins, T. J. (2007). ImageJ for microscopy. Biotechniques, 43(sup1), S25–S30. https://doi.org/10.2144/000112517.
Farhan, A., Kurnia, K. A., Saputra, F., Chen, K. H. C., Huang, J. C., Roldan, M. J. M., ... & Hsiao, C. D. (2021). An opencv-based approach for automated cardiac rhythm measurement in zebrafish from video datasets. Biomolecules, 11(10), 1476. https://doi.org/10.3390/biom11101476.
Gallagher, S. R. (2014). Digital image processing and analysis with ImageJ. Current Protocols Essential Laboratory Techniques, 9(1), A-3C. https://doi.org/10.1002/9780470089941.eta03cs03.
Kolmogorov A. N. (1941). Soviet Mathematics. 31, 99–101.
Razumovsky N.K. (1940). The nature of distribution of metal content in ore deposits. Soviet Mathematics. 28(9), 815–817.
Razumovsky N.K. (1948). Logarithmic-normal distribution law of matter and its properties. Notes of the Leningrad Order of Lenin Mining Institute. XX, 105–107.
Ganias, K., Rakka, M., Vavalidis, T., & Nunes, C. (2010). Measuring batch fecundity using automated particle counting. Fisheries Research, 106(3), 570–574. https://doi.org/10.1016/j.fishres.2010.09.016.
Cai, Z., Chattopadhyay, N., Liu, W. J., Chan, C., Pignol, J.-P., & Reilly, R. M. (2011). Optimized digital counting colonies of clonogenic assays using ImageJ software and customized macros: Comparison with manual counting. International Journal of Radiation Biology, 87(11), 1135–1146. https://doi.org/10.3109/09553002.2011.622033.
Mussadiq, Z., Laszlo, B., Helyes, L., & Gyuricza, C. (2015). Evaluation and comparison of open source program solutions for automatic seed counting on digital images. Computers and Electronics in Agriculture, 117, 194–199. https://doi.org/10.1016/j.compag.2015.08.010.
Nichele, L., Persichetti, V., Lucidi, M., & Cincotti, G. (2020). Quantitative evaluation of ImageJ thresholding algorithms for microbial cell counting. Osa Continuum, 3(6), 1417–1427. https://doi.org/10.1364/OSAC.393971.
Danev, A., Yaneva, T., Bosakova-Ardenska, A., Gabrova, R., & Angelov, A. (2021). Software development for objective automatic counting of lactic acid bacterial colonies grown in MRS agar.
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