• Title/Summary/Keyword: harmful cyanobacteria blooms

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Characterization of Filamentous Cyanobacteria Encapsulated in Alginate Microcapsules (알긴산염 마이크로캡슐 내부에 동결보존된 사상체 남세균의 특성 연구)

  • Park, Mirye;Kim, Z-Hun;Nam, Seung Won;Lee, Sang Deuk;Yun, Suk Min;Kwon, Dae Ryul;Lee, Chang Soo
    • Microbiology and Biotechnology Letters
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    • v.48 no.2
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    • pp.205-214
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    • 2020
  • Cyanobacteria are microorganisms which have important roles in the nitrogen cycle due to their ability to fix nitrogen in water and soil ecosystems. They also produce valuable materials that may be used in various industries. However, some species of cyanobacteria may limit the use of water resources by causing harmful algal blooms in water ecosystems. Many culture collection depositories provide cyanobacterial strains for research, but their systematic preservation is not well-developed in Korea. In this study, we developed a method for the cryopreservation of the cyanobacteria Trichormus variabilis (syn. Anabaena variabilis), using alginate microcapsules. Two approaches were used for the experiments and their outputs were compared. One of the methods involved the cryopreservation of cells using only a cryoprotectant and the other used the cryoprotectant within microcapsules. After cryopreservation for 35 days, cells preserved with both methods were successfully regenerated from the initial 1.0 × 105 cells/ml to a final concentration of 6.7 × 106 cells/ml and 1.1 × 107 cells/ml. Irregular T. variabilis shapes were found after 14 days of regeneration. T. variabilis internal structures were observed by transmission electron microscopy (TEM), revealing that lipid droplets were reduced after cryopreservation. The expression of the mreB gene, known to be related to cell morphology, was downregulated (54.7%) after cryopreservation. Cryopreservation using cryoprotectant alone or with microcapsules is expected to be applicable to other filamentous cyanobacteria in the future.

Machine- and Deep Learning Modelling Trends for Predicting Harmful Cyanobacterial Cells and Associated Metabolites Concentration in Inland Freshwaters: Comparison of Algorithms, Input Variables, and Learning Data Number (담수 유해남조 세포수·대사물질 농도 예측을 위한 머신러닝과 딥러닝 모델링 연구동향: 알고리즘, 입력변수 및 학습 데이터 수 비교)

  • Yongeun Park;Jin Hwi Kim;Hankyu Lee;Seohyun Byeon;Soon-Jin Hwang;Jae-Ki Shin
    • Korean Journal of Ecology and Environment
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    • v.56 no.3
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    • pp.268-279
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    • 2023
  • Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.