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Pereira AC, Saraiva A, Oliva-Teles L, Guimarães L, Carvalho AP. Ecotoxicological Effects of Potassium Dichromate on the Tadpole Shrimp Triops longicaudatus. Animals (Basel) 2024; 14:358. [PMID: 38338000 PMCID: PMC10854805 DOI: 10.3390/ani14030358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/15/2024] [Accepted: 01/17/2024] [Indexed: 02/12/2024] Open
Abstract
The tadpole shrimp Triops longicaudatus is a freshwater crustacean with fast embryonic and larval development, short life cycle, and high fecundity. They are very active swimmers of a reasonable size, easy to spot and record. Such characteristics make it a promising candidate as an experimental model in ecotoxicology to evaluate the effects of aquatic pollutants, particularly using its locomotor behavior as an endpoint. To evaluate the sensitivity of T. longicaudatus and develop endpoints of interest, we conducted exposure experiments with lethal and sub-lethal concentrations of potassium dichromate, a compound known for its ecotoxicological importance and as a hexavalent chromium source. The endpoints evaluated were mortality, growth, sexual maturation, reproductive output, cholinesterase activity and locomotor/swimming behavior. The 96 h median lethal concentration was found to be 65 µg/L. Furthermore, exposure to potassium dichromate at higher concentrations had a significant negative impact on the growth rate of T. longicaudatus in terms of both body mass and length. The time for maturation was also delayed at higher concentrations. In addition, locomotor behavior allowed for the discrimination of all tested chromium concentrations and the control group and from each other, proving to be the most sensitive endpoint. Overall, the data support the potential of T. longicaudatus as a model for ecotoxicity testing, using apical endpoints with impact at the population level; in particular, results suggest that behavior assessments in this species might be useful for detecting hazardous compounds in environmental monitoring of freshwater ecosystems.
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Affiliation(s)
- André Carido Pereira
- CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4450-208 Matosinhos, Portugal; (A.C.P.); (A.S.); (L.O.-T.)
- Biology Department, FCUP—Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
- ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal
| | - Aurélia Saraiva
- CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4450-208 Matosinhos, Portugal; (A.C.P.); (A.S.); (L.O.-T.)
- Biology Department, FCUP—Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Luís Oliva-Teles
- CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4450-208 Matosinhos, Portugal; (A.C.P.); (A.S.); (L.O.-T.)
- Biology Department, FCUP—Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Laura Guimarães
- CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4450-208 Matosinhos, Portugal; (A.C.P.); (A.S.); (L.O.-T.)
- Biology Department, FCUP—Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - António Paulo Carvalho
- CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4450-208 Matosinhos, Portugal; (A.C.P.); (A.S.); (L.O.-T.)
- Biology Department, FCUP—Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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Oliva-Teles L, Pinto R, Vilarinho R, Carvalho AP, Moreira JA, Guimarães L. Environmental diagnosis with Raman Spectroscopy applied to diatoms. Biosens Bioelectron 2022; 198:113800. [PMID: 34838373 DOI: 10.1016/j.bios.2021.113800] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 10/04/2021] [Accepted: 11/12/2021] [Indexed: 12/30/2022]
Abstract
Freshwater quality has been changing due to the ever greater use of water resources and the contamination load resulting from human activities. Management of these systems, thus, requires constant diagnose of water quality with fast and efficient methodologies. The conventional methods adopted are, however, time-consuming, often very expensive, and require specialised expertise. Raman spectroscopy (RS) is a simple, fast and label-free technique that can be applied to environmental diagnosis using diatoms. Here, we developed a diagnostic method based on Raman spectroscopy applied to freshwater diatoms. For this, Raman spectra were recorded from diatoms of three lakes of a natural city park. The data acquired was analysed by chemometrics methods to describe the data (Partial Least Squares Regression), infer relationships in the dataset (Cluster Analysis) and produce classification models (Artificial Neural Network). The classification models developed diagnosed the lakes with excellent accuracy (89%) without requiring taxonomic information about the diatom species recorded. This study provides a proof-of-concept for the application of diatom Raman spectroscopy to diagnosing water quality, laying an important foundation for future environmental studies aiming at assessing freshwater systems, to be replicated at larger scales and to varied geographic settings.
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Affiliation(s)
- Luís Oliva-Teles
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros Do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal; Department of Biology, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal.
| | - Raquel Pinto
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros Do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal; Department of Biology, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal
| | - Rui Vilarinho
- IFIMUP, Department of Physics and Astronomy, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal
| | - António Paulo Carvalho
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros Do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal; Department of Biology, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal
| | - J Agostinho Moreira
- IFIMUP, Department of Physics and Astronomy, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal
| | - Laura Guimarães
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros Do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal; Department of Biology, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal
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Wang B, Zhu J, Wang A, Wang J, Wu Y, Yao W. Early detection of cyanide, organophosphate and rodenticide pollution based on locomotor activity of zebrafish larvae. PeerJ 2022; 9:e12703. [PMID: 35036170 PMCID: PMC8710045 DOI: 10.7717/peerj.12703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/07/2021] [Indexed: 11/20/2022] Open
Abstract
Cyanide, organophosphate and rodenticides are highly toxic substances widely used in agriculture and industry. These toxicants are neuro- and organotoxic to mammals at low concentrations, thus early detection of these chemicals in the aqueous environment is of utmost importance. Here, we employed the behavioral toxicity test with wildtype zebrafish larvae to determine sublethal concentrations of the above mentioned common environmental pollutants. After optimizing the test with cyanide, nine rodenticides and an organophosphate were successfully tested. The compounds dose-dependently initially (0-60-min exposure) stimulated locomotor activity of larvae but induced toxicity and reduced swimming during 60-120-min exposure. IC50 values calculated based on swimming distance after 2-h exposure, were between 0.1 and 10 mg/L for both first-generation and second-generation anticoagulant rodenticides. Three behavioral characteristics, including total distance travelled, sinuosity and burst count, were quantitatively analyzed and compared by hierarchical clustering of the effects measured by each three parameters. The toxicity results for all three behavioral endpoints were consistent, suggesting that the directly measured parameter of cumulative swimming distance could be used as a promising biomarker for the aquatic contamination. The optimized method herein showed the potential for utilization as part of a monitoring system and an ideal tool for the risk assessment of drinking water in the military and public safety.
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Affiliation(s)
- Binjie Wang
- The Department of Criminal Science and Technology, Zhejiang Police College, Key Laboratory of Drug Prevention and Control Technology of Zhejiang Province, Hangzhou, Zhejiang province, People's Republic of China
| | - Junhao Zhu
- The Department of Criminal Science and Technology, Zhejiang Police College, Key Laboratory of Drug Prevention and Control Technology of Zhejiang Province, Hangzhou, Zhejiang province, People's Republic of China
| | - Anli Wang
- The Department of Criminal Science and Technology, Zhejiang Police College, Key Laboratory of Drug Prevention and Control Technology of Zhejiang Province, Hangzhou, Zhejiang province, People's Republic of China.,College of Biosystems Engineering and Food Science, Zhejiang University, National Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Hangzhou, Zhejiang Province, People's Republic of China
| | - Jiye Wang
- The Department of Criminal Science and Technology, Zhejiang Police College, Key Laboratory of Drug Prevention and Control Technology of Zhejiang Province, Hangzhou, Zhejiang province, People's Republic of China
| | - Yuanzhao Wu
- The Department of Criminal Science and Technology, Zhejiang Police College, Key Laboratory of Drug Prevention and Control Technology of Zhejiang Province, Hangzhou, Zhejiang province, People's Republic of China
| | - Weixuan Yao
- The Department of Criminal Science and Technology, Zhejiang Police College, Key Laboratory of Drug Prevention and Control Technology of Zhejiang Province, Hangzhou, Zhejiang province, People's Republic of China
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Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:4105784. [PMID: 34691170 PMCID: PMC8531822 DOI: 10.1155/2021/4105784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/26/2021] [Accepted: 09/29/2021] [Indexed: 12/02/2022]
Abstract
As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural network algorithms. The results show that the multilayer self-organizing neural network can make the accuracy, recall, and F1 score of feature recognition reach 68.69%, 80.21%, and 70.19%, respectively, when there are many samples. Under the influence of strong noise, it keeps high efficiency of feature analysis. This shows that the multilayer self-organizing neural network has superior performance and can provide strong support for Chinese language feature analysis.
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