Accurate age-grading of field-aged mosquitoes reared under ambient conditions using surface-enhanced Raman spectroscopy and artificial neural networks
Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enh...
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| Published in | Journal of medical entomology Vol. 60; no. 5; pp. 917 - 923 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
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Entomological Society of America
01.09.2023
Oxford University Press |
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| Abstract | Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation (F2) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0–22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation. Graphical Abstract |
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| AbstractList | Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation (F2) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0–22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation. Graphical Abstract Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation (F2) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0-22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation. Abstract Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation (F2) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0–22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation. Graphical Abstract Graphical Abstract Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation ([F.sub.2]) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0-22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation. Key words: mosquitoes, age grading, SERS, artificial neural networks, Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation ([F.sub.2]) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0-22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation. Abstract Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation (F2) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0–22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation. |
| Audience | Academic |
| Author | Avila, Frank W. Harrington, Laura C. Zhu, Wei Gao, Zili Clark, John M. Barrientos, Luisa M. Alfonso-Parra, Catalina He, Lili |
| Author_xml | – sequence: 1 givenname: Zili orcidid: 0009-0003-9971-0052 surname: Gao fullname: Gao, Zili organization: Raman, IR and XRF Core Facility, University of Massachusetts, Amherst, MA 01003, USA – sequence: 2 givenname: Laura C. orcidid: 0000-0002-2143-2051 surname: Harrington fullname: Harrington, Laura C. organization: Department of Entomology, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA – sequence: 3 givenname: Wei surname: Zhu fullname: Zhu, Wei organization: Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003, USA – sequence: 4 givenname: Luisa M. surname: Barrientos fullname: Barrientos, Luisa M. organization: Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia – sequence: 5 givenname: Catalina orcidid: 0000-0002-5689-9356 surname: Alfonso-Parra fullname: Alfonso-Parra, Catalina organization: Instituto Colombiano de Medicina Tropical, Universidad CES, Sabaneta, Colombia – sequence: 6 givenname: Frank W. orcidid: 0000-0002-7278-0646 surname: Avila fullname: Avila, Frank W. organization: Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia – sequence: 7 givenname: John M. surname: Clark fullname: Clark, John M. organization: Department of Veterinary and Animal Sciences, University of Massachusetts, Amherst, MA 01003, USA – sequence: 8 givenname: Lili orcidid: 0000-0002-5046-9857 surname: He fullname: He, Lili organization: Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA |
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| Cites_doi | 10.1007/s00216-016-9540-x 10.1371/journal.pone.0209451 10.1016/j.actatropica.2015.02.010 10.2147/IJN.S71811 10.4269/ajtmh.14-0790 10.1186/s13071-017-2501-1 10.1039/C7EN00653E 10.1371/journal.pntd.0009218 10.3390/insects13040360 10.12688/wellcomeopenres.15201.1 10.1038/s41467-022-28980-8 10.1371/journal.pntd.0005040 10.1016/j.trac.2017.02.006 10.1016/j.pt.2019.10.011 10.1039/C7RA12238A 10.4269/ajtmh.1987.36.143 10.3390/insects13010052 10.1038/s41598-018-27998-7 10.1186/s13071-020-04031-3 10.1038/s41598-018-22712-z 10.1021/acs.analchem.6b04827 10.3389/fmicb.2020.584846 10.1186/1756-3305-6-298 10.1021/acsami.8b01320 10.1039/C6AN00306K 10.1603/0022-2585-41.2.231 10.1002/ansa.202100052 10.1146/annurev.ento.43.1.519 10.1016/j.cofs.2019.10.001 10.1093/acprof:oso/9780198515494.001.0001 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2023. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. 2023 The Author(s) 2023. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. COPYRIGHT 2023 Oxford University Press |
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| Snippet | Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so,... Abstract Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools... |
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| SubjectTerms | Accuracy Age age grading Aquatic insects Artificial neural networks Critical point Culicidae Dengue fever Disease transmission Entomology Epidemiology Females Machine learning Malaria Model accuracy MODELING/GIS, RISK ASSESSMENT, ECONOMIC IMPACT Mosquitoes Neural networks Pathogens Public health Raman spectroscopy Risk assessment SERS Spectroscopy Temperature effects Vectors (Biology) |
| Title | Accurate age-grading of field-aged mosquitoes reared under ambient conditions using surface-enhanced Raman spectroscopy and artificial neural networks |
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