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Harris County, Texas, remains at continuous risk to mosquito-borne diseases due to its geographic landscape and abundance of medically important mosquito vectors. Targeted mitigation of these mosquitoes requires accurate identification of these mosquitoes taxa. Currently, there is a paucity of genetic information to inform molecular identification and phylogenetic relationships beyond well-studied mosquito species. Here we utilized a genome skimming approach using shallow shot gun sequencing to generate data and assemble the mitochondrial genomes of 37 mosquito species collected in Harris County, Texas. This report includes the complete mitochondrial genome for 25 newly sequenced species spanning 10 genera; the genomes were consistent with reference genomes in the GenBank database having 37 genes (13 protein-coding, 2 rRNA and 22 tRNA), and average AT content of 78.74%. Bayesian and maximum likelihood tree topologies using just the easily aligned 13 concatenated protein coding genes confirmed phylogenetic placement of species for Aedes, Anopheles and Culex genera clustering in single clades as expected. Furthermore, this approach provided more robust phylogenetic placement/identity of study taxa when compared to the use of the traditional cytochrome oxidase I partial gene barcode sequence for molecular identification. This study demonstrates the utility of genome skimming as a cost-effective alternative approach to generate reference sequences for the validation of mosquito identification and taxonomic rectification, knowledge necessary for guiding targeted vector interventions.
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Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection
Abstract With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.
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- Award ID(s):
- 2039534
- PAR ID:
- 10385838
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- 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 Manuscript
- Journal Article:
- https://doi.org/10.1038/s41598-021-92891-9
