1
|
Zhao T, Shen Z, Zhong P, Zou H, Han M. Detection and prediction of pathogenic microorganisms in aquaculture (Zhejiang Province, China). ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:8210-8222. [PMID: 38175512 DOI: 10.1007/s11356-023-31612-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024]
Abstract
The detection and prediction of pathogenic microorganisms play a crucial role in the sustainable development of the aquaculture industry. Currently, researchers mainly focus on the prediction of water quality parameters such as dissolved oxygen for early warning. To provide early warning directly from the pathogenic source, this study proposes an innovative approach for the detection and prediction of pathogenic microorganisms based on yellow croaker aquaculture. Specifically, a method based on quantitative polymerase chain reaction (qPCR) is designed to detect the Cryptocaryon irritans (Cri) pathogenic microorganisms. Furthermore, we design a predictive combination model for small samples and high noise data to achieve early warning. After performing wavelet analysis to denoise the data, two data augmentation strategies are used to expand the dataset and then combined with the BP neural network (BPNN) to build the fusion prediction model. To ensure the stability of the detection method, we conduct repeatability and sensitivity tests on the designed qPCR detection technique. To verify the validity of the model, we compare the combined BPNN to long short-term memory (LSTM). The experimental results show that the qPCR method provides accurate quantitative measurement of Cri pathogenic microorganisms, and the combined model achieves a good level. The prediction model demonstrates higher accuracy in predicting Cri pathogenic microorganisms compared to the LSTM method, with evaluation indicators including mean absolute error (MAE), recall rate, and accuracy rate. Especially, the accuracy of early warning is increased by 54.02%.
Collapse
Affiliation(s)
- Tong Zhao
- College of Information and Electrical Engineering, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, China
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China
| | - Zhencai Shen
- College of Science, China Agricultural University, Beijing, 100083, China
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, China
| | - Ping Zhong
- College of Information and Electrical Engineering, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, China
- College of Science, China Agricultural University, Beijing, 100083, China
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, China
| | - Hui Zou
- College of Science, China Agricultural University, Beijing, 100083, China.
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China.
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China.
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, China.
| | - Mingming Han
- Zhejiang Academy of Agricultural Sciences, Zhejiang, 310021, China
| |
Collapse
|
2
|
Chen H, Zhou T, Li S, Feng J, Li W, Li L, Zhou X, Wang M, Li F, Zhao X, Ren L. Living Magnetotactic Microrobots Based on Bacteria with a Surface-Displayed CRISPR/Cas12a System for Penaeus Viruses Detection. ACS APPLIED MATERIALS & INTERFACES 2023; 15:47930-47938. [PMID: 37811735 DOI: 10.1021/acsami.3c09690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Bacterial microrobots are an emerging living material in the field of diagnostics. However, it is an important challenge to make bacterial microrobots with both controlled motility and specific functions. Herein, magnetically driven diagnostic bacterial microrobots are prepared by standardized and modular synthetic biology methods. To ensure mobility, the Mms6 protein is displayed on the surface of bacteria and is exploited for magnetic biomineralization. This gives the bacterial microrobot the ability to cruise flexibly and rapidly with a magnetization intensity up to about 18.65 emu g-1. To achieve the diagnostic function, the Cas12a protein is displayed on the bacterial surface and is used for aquatic pathogen nucleic acid detection. This allows the bacterial microrobot to achieve sensitive, rapid, and accurate on-site nucleic acid detection, with detection limits of 8 copies μL-1 for decapod iridescent virus 1 (DIV1) and 7 copies μL-1 for white spot syndrome virus (WSSV). In particular, the diagnostic results based on the bacterial microrobots remained consistent with the gold standard test results when tested on shrimp tissue. This approach is a flexible and customizable strategy for building bacterial microrobots, providing a reliable and versatile solution for the design of bacterial microrobots.
Collapse
Affiliation(s)
- Haoxiang Chen
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Tao Zhou
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Shuo Li
- College of Life Science and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou 310018, P. R. China
| | - Junya Feng
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Wenlong Li
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Lihuang Li
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Xi Zhou
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Miao Wang
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Fang Li
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, P. R. China
| | - Xueqin Zhao
- College of Life Science and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou 310018, P. R. China
| | - Lei Ren
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
- State Key Lab of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, P. R. China
| |
Collapse
|