Species Identification of the Major Japanese Encephalitis Vectors within the Culex vishnui Subgroup (Diptera: Culicidae) in Thailand Using Geometric Morphometrics and DNA Barcoding

Wing geometric morphometrics and DNA barcoding to distinguish three closely related species of Armigeres mosquitoes (Diptera: Culicidae) in Thailand

Armigeres subalbatus, a mosquito species widely found in Thailand and other Asian countries, serves as a vector for filarial parasites, affecting both humans and animals. However, the surveillance of this vector is complicated because of its morphological similarity to two other species, Armigeres dohami and Armigeres kesseli. To differentiate these morphologically similar species, our study employed both wing geometric morphometrics (GM) and DNA barcoding, offering a comprehensive approach to accurately identify these closely related Armigeres species in Thailand. Our GM analyses based on shape demonstrated significant accuracy in differentiating Armigeres species. Specifically, the outline-based GM method focusing on the 3rd posterior cell exhibited an accuracy rate of 82.61%, closely followed by the landmark-based GM method with 81.54%. Both these GM techniques effectively distinguished Ar. subalbatus from Ar. dohami and Ar. kesseli. Regarding DNA barcoding, our investigation of pairwise intra- and interspecific divergences revealed a "barcoding gap". Furthermore, the results of species confirmation using both species delimitation methods including the automatic barcode gap discovery method (ABGD) and the Multi-rate Poisson tree process (mPTP) were consistent with those of morphological identification, sequence comparisons with the GenBank and Barcode of Life Data System (BOLD) databases, and the neighbor-joining tree construction. These consistent results emphasize the efficacy of DNA barcoding in the precise identification of Armigeres species.

Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system

Mosquito-borne diseases such as dengue fever and malaria are the top 10 leading causes of death in low-income countries. Control measure for the mosquito population plays an essential role in the fight against the disease. Currently, several intervention strategies; chemical-, biological-, mechanical- and environmental methods remain under development and need further improvement in their effectiveness. Although, a conventional entomological surveillance, required a microscope and taxonomic key for identification by professionals, is a key strategy to evaluate the population growth of these mosquitoes, these techniques are tedious, time-consuming, labor-intensive, and reliant on skillful and well-trained personnel. Here, we proposed an automatic screening, namely the deep metric learning approach and its inference under the image-retrieval process with Euclidean distance-based similarity. We aimed to develop the optimized model to find suitable miners and suggested the robustness of the proposed model by evaluating it with unseen data under a 20-returned image system. During the model development, well-trained ResNet34 are outstanding and no performance difference when comparing five data miners that showed up to 98% in its precision even after testing the model with both image sources: stereomicroscope and mobile phone cameras. The robustness of the proposed-trained model was tested with secondary unseen data which showed different environmental factors such as lighting, image scales, background colors and zoom levels. Nevertheless, our proposed neural network still has great performance with greater than 95% for sensitivity and precision, respectively. Also, the area under the ROC curve given the learning system seems to be practical and empirical with its value greater than 0.960. The results of the study may be used by public health authorities to locate mosquito vectors nearby. If used in the field, our research tool in particular is believed to accurately represent a real-world scenario.

Molecular and morphometric differentiation of secondary filariasis vector Coquillettidia mosquitoes (Diptera: Culicidae) in Thailand

Coquillettidia mosquitoes are important nuisance-biting pests and a vector of brugian filariasis in Thailand. However, comprehensive information about these mosquitoes remains unavailable such as molecular and morphometric differences among species. The lack of vector knowledge on Coquillettidia species could affect future disease control. This study aims to investigate differences in molecular variations based on mitochondrial cytochrome oxidase subunit I (COI) gene and wing geometric traits of three Coquillettidia species, namely Cq. crassipes, Cq. nigrosignata, and Cq. ochracea in Thailand. The results of molecular analyses revealed the differences among three Coquillettidia species. The genetic difference measure based on the Kimura two-parameter model among three Coquillettidia species showed low intraspecific distances (0%-3.05%) and large interspecific distances (10.10%-12.41%). The values of intra- and inter-genetic differences of three Coquillettidia species did not overlap which showed the existence of a barcoding gap indicating the efficiency of the identification based on the COI gene. As with molecular analysis, the landmark-based geometric morphometrics approach based on wing shape analysis indicated three distinct species groups which were supported by the high total performance score of cross-validated classification (97.16%). These results provide the first evidence of taxonomic signal based on molecular and wing geometric differences to support species identification and biological variations of Coquillettidia mosquitoes in Thailand for understanding these rare vector mosquitoes in depth and leading to effective further mosquito control.