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Abstract BackgroundCulexmosquitoes are the primary vectors of West Nile virus (WNV) across the USA. Understanding when these vectors are active indicates times when WNV transmission can occur. This study determined the proportion of femaleCulexmosquitoes that were in diapause during the fall and winter and when they terminated diapause and began blood feeding in the spring. MethodsMosquitoes were collected from parks using various traps and/or aspirated from culverts in Franklin County, Ohio, from October to mid-May from 2019 to 2022.Culexmosquitoes were morphologically identified to species, and the ovaries of females were dissected to determine their diapause and parity statuses. ResultsBy early October 2021, roughly 95% ofCulex pipienscollected in culverts were in diapause and 98% ofCx. erraticuswere in diapause. Furthermore, gravid and blood-fedCulex salinarius,Cx. pipiens, andCx. restuanswere collected in late November in 2019 and 2021 in standard mosquito traps. In the winter of 2021, the proportions of non-diapausingCulexdecreased within culverts. The last non-diapausingCx. erraticuswas collected in late December 2021 while the final non-diapausingCx. pipienswas collected in mid-January 2022, both in culverts. Roughly 50% ofCx. pipiensterminated diapause by mid-March 2022, further supported by our collections of gravid females in late March in all 3 years of mosquito collection. In fact, male mosquitoes ofCx. pipiens,Cx. restuans, andCx. territanswere collected by the 1st week of May in 2022, indicating that multiple species ofCulexproduced a second generation that reached adulthood by this time. ConclusionsWe collected blood-fed and gravidCulexfemales into late November in 2 of the 3 years of our collections, indicating that it might be possible for WNV transmission to occur in late fall in temperate climates like Ohio. The persistence of non-diapausingCx. pipiensandCx. erraticusthroughout December has important implications for the winter survival of WNV vectors and our overall understanding of diapause. Finally, determining whenCulexterminate diapause in the spring may allow us to optimize mosquito management programs and reduce the spread of WNV before it is transmitted to humans. Graphical Abstract
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Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques
Abstract The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species—Aedes aegypti,Anopheles stephensi, andCulex quinquefasciatus—and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid, and gravid). From these mosquitoes and stages, a total of 1959 images were captured on a plain background via multiple smartphones. Subsequently, we trained four distinct AI model architectures (ResNet50,MobileNetV2,EfficientNet-B0, andConvNeXtTiny), validated them using unseen data, and compared their overall classification accuracies. Additionally, we analyzed t-SNE plots to visualize the formation of decision boundaries in a lower-dimensional space. Notably,ResNet50andEfficientNet-B0demonstrated outstanding performance with an overall accuracy of 97.44% and 93.59%, respectively.EfficientNet-B0demonstrated the best overall performance considering computational efficiency, model size, training speed, and t-SNE decision boundaries. We also assessed the explainability of thisEfficientNet-B0model, by implementing Grad-CAMs—a technique that highlights pixels in an image that were prioritized for classification. We observed that the highest weight was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. Our work has significant practical impact. First, image datasets for gonotrophic stages of mosquitoes are not yet available. Second, our algorithms can be integrated with existing citizen science platforms that enable the public to record and upload biological observations. With such integration, our algorithms will enable the public to contribute to mosquito surveillance and gonotrophic stage identification. Finally, we are aware of work today that uses computer vision techniques for automated mosquito species identification, and our algorithms in this paper can augment these efforts by enabling the automated detection of gonotrophic stages of mosquitoes as well.
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- Award ID(s):
- 2014547
- PAR ID:
- 10545412
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-023-47266-7
