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An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning.

ABSTRACT: Several Diptera species are known to transmit pathogens of medical and veterinary interest. However, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. A computer vision-based system that uses Wing interferential patterns (WIPs) to identify these insects could solve this problem. This study introduces a dataset for training and evaluating a recognition system for dipteran insects of medical and veterinary importance using WIPs. The dataset includes pictures of Culicidae, Calliphoridae, Muscidae, Tabanidae, Ceratopogonidae, and Psychodidae. The dataset is complemented by previously published datasets of Glossinidae and some Culicidae members. The new dataset contains 2,399 pictures of 18 genera, with each genus documented by a variable number of species and annotated as a class. The dataset covers species variation, with some genera having up to 300 samples.

SUBMITTER: Cannet A

PROVIDER: S-EPMC10761744 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning.

Cannet Arnaud A Simon-Chane Camille C Histace Aymeric A Akhoundi Mohammad M Romain Olivier O Souchaud Marc M Jacob Pierre P Sereno Darian D Bousses Philippe P Sereno Denis D

Scientific data 20240102 1

Several Diptera species are known to transmit pathogens of medical and veterinary interest. However, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. A computer vision-based system that uses Wing interferential patterns (WIPs) to identify these insects could solve this problem. This study introduces a dataset for training and evaluating a recognition system for dipteran insects of medical and veterinary importance using WIPs. The dataset ...[more]

PMID: 38168517

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Project description:BackgroundRibonucleosides and RNA are an underappreciated nutrient group essential during Drosophila larval development and growth. Detection of these nutrients requires at least one of the 6 closely related taste receptors encoded by the Gr28 genes, one of the most conserved insect taste receptor subfamilies.ObjectivesWe investigated whether blow fly larvae and mosquito larvae, which shared the last ancestor with Drosophila about 65 and 260 million years ago, respectively, can taste RNA and ribose. We also tested whether the Gr28 homologous genes of the mosquitoes Aedes aegypti and Anopheles gambiae can sense these nutrients when expressed in transgenic Drosophila larvae.MethodsTaste preference in blow flies was examined by adapting a 2-choice preference assay that has been well-established for Drosophila larvae. For the mosquito Aedes aegypti, we developed a new 2-choice preference assay that accommodates the aquatic environment of these insect larvae. Finally, we identified Gr28 homologs in these species and expressed them in Drosophila melanogaster to determine their potential function as RNA receptors.ResultsLarvae of the blow fly Cochliomyia macellaria and Lucilia cuprina are strongly attracted to RNA (0.5 mg/mL) in the 2-choice feeding assays (P < 0.05). Similarly, the mosquito Aedes aegypti larvae showed a strong preference for RNA (2.5 mg/mL) in an aquatic 2-choice feeding assay. Moreover, when Gr28 homologs of Aedes or Anopheles mosquitoes are expressed in appetitive taste neurons of Drosophila melanogaster larvae lacking their Gr28 genes, preference for RNA (0.5 mg/mL) and ribose (0.1 M) is rescued (P < 0.05).ConclusionsThe appetitive taste for RNA and ribonucleosides in insects emerged about 260 million years ago, the time mosquitoes and fruit flies diverged from their last common ancestor. Like sugar receptors, receptors for RNA have been highly conserved during insect evolution, suggesting that RNA is a critical nutrient for fast-growing insect larvae.

Dataset Information

An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning.

Publications

An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning.

Similar Datasets

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