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Twenty-seven villages were selected in southwest Burkina Faso to implement new vector control strategies in addition to long lasting insecticidal nets (LLINs) through a Randomized Controlled Trial (RCT). We conducted entomological surveys in the villages during the dry cold season (January 2017), dry hot season (March 2017) and rainy season (June 2017) to describe malaria vectors bionomics, insecticide resistance and transmission prior to this trial. We carried out hourly catches (from 17:00 to 09:00) inside and outside 4 houses in each village using the Human Landing Catch technique. Mosquitoes were identified using morphological taxonomic keys. Specimens belonging to the Anopheles gambiae complex and Anopheles funestus group were identified using molecular techniques as well as detection of Plasmodium falciparum infection and insecticide resistance target-site mutations. Eight Anopheles species were detected in the area. Anopheles funestus s.s was the main vector during the dry cold season. It was replaced by Anopheles coluzzii during the dry hot season whereas An. coluzzii and An. gambiae s.s. were the dominant species during the rainy season. Species composition of the Anopheles population varied significantly among seasons. All insecticide resistance mechanisms (kdr-w, kdr-e and ace-1 target site mutations) investigated were found in each members of the An. gambiae complex but at different frequencies. We observed early and late biting phenotypes in the main malaria vector species. Entomological inoculation rates were 2.61, 2.67 and 11.25 infected bites per human per month during dry cold season, dry hot season and rainy season, respectively. The entomological indicators of malaria transmission were high despite the universal coverage with LLINs. We detected early and late biting phenotypes in the main malaria vector species as well as physiological insecticide resistance mechanisms. These data will be used to evaluate the impact of complementary tools to LLINs in an upcoming RCT.

The mosquito family (Diptera: Culicidae) constitutes the most medically important group of arthropods because certain species are vectors of human pathogens. In some parts of the world, the diversity is so high that the accurate delimitation and/or identification of species is challenging. A DNA-based identification system for all animals has been proposed, the so-called DNA barcoding approach. In this study, our objectives were (i) to establish DNA barcode libraries for the mosquitoes of French Guiana based on the COI and the 16S markers, (ii) to compare distance-based and tree-based methods of species delimitation to traditional taxonomy, and (iii) to evaluate the accuracy of each marker in identifying specimens. A total of 266 specimens belonging to 75 morphologically identified species or morphospecies were analyzed allowing us to delimit 86 DNA clusters with only 21 of them already present in the BOLD database. We thus provide a substantial contribution to the global mosquito barcoding initiative. Our results confirm that DNA barcodes can be successfully used to delimit and identify mosquito species with only a few cases where the marker could not distinguish closely related species. Our results also validate the presence of new species identified based on morphology, plus potential cases of cryptic species. We found that both COI and 16S markers performed very well, with successful identifications at the species level of up to 98% for COI and 97% for 16S when compared to traditional taxonomy. This shows great potential for the use of metabarcoding for vector monitoring and eco-epidemiological studies.

An updated checklist of 235 mosquito species from Madagascar is presented. The number of species has increased considerably compared to previous checklists, particularly the last published in 2003 (178 species). This annotated checklist provides concise information on endemism, taxonomic position, developmental stages, larval habitats, distribution, behavior, and vector-borne diseases potentially transmitted. The 235 species belong to 14 genera: Aedeomyia (3 species), Aedes (35 species), Anopheles (26 species), Coquillettidia (3 species), Culex (at least 50 species), Eretmapodites (4 species), Ficalbia (2 species), Hodgesia (at least one species), Lutzia (one species), Mansonia (2 species), Mimomyia (22 species), Orthopodomyia (8 species), Toxorhynchites (6 species), and Uranotaenia (73 species). Due to non-deciphered species complexes, several species remain undescribed. The main remarkable characteristic of Malagasy mosquito fauna is the high biodiversity with 138 endemic species (59%). Presence and abundance of species, and their association, in a given location could be a bio-indicator of environmental particularities such as urban, rural, forested, deforested, and mountainous habitats. Finally, taking into account that Malagasy culicidian fauna includes 64 species (27%) with a known medical or veterinary interest in the world, knowledge of their biology and host preference summarized in this paper improves understanding of their involvement in pathogen transmission in Madagascar. Une liste mise à jour des 235 espèces de moustiques de Madagascar est présentée. Le nombre d’espèces a considérablement augmenté par rapport à celui donné dans les listes précédentes, en particulier la dernière, publiée en 2003 (178 espèces). Cette liste fournit des informations concises sur l’endémisme, la position taxonomique, les stades de développement, les gîtes larvaires, la distribution, le comportement des moustiques, et aussi sur les maladies potentiellement transmises par les moustiques vecteurs. Ces 235 espèces sont réparties dans 14 genres : Aedeomyia (3 espèces), Aedes (35 espèces), Anopheles (26 espèces), Coquillettidia (3 espèces), Culex (au moins 50 espèces), Eretmapodites (4 espèces), Ficalbia (2 espèces), Hodgesia (au moins une espèce), Lutzia (une espèce), Mansonia (2 espèces), Mimomyia (22 espèces), Orthopodomyia (8 espèces), Toxorhynchites (6 espèces), Uranotaenia (73 espèces). En raison de complexes d’espèces non résolus, plusieurs espèces sont toujours non décrites. La principale caractéristique remarquable de la faune malgache de moustiques est la grande biodiversité avec 138 espèces endémiques (59 %). La présence et l’abondance des espèces, et leur association dans un endroit donné, pourrait être un bio-indicateur des particularités environnementales telles que les habitats urbains, ruraux, forestiers, déboisés et montagneux. Enfin, compte tenu que la faune malgache de moustiques comprend 64 espèces (27 %) ayant un intérêt médical ou vétérinaire connu dans le monde, la connaissance de leur biologie et de leur préférence d’hôtes résumée dans le présent document permet de comprendre leur implication dans la transmission d’agents pathogènes à Madagascar.

Background In malaria-endemic countries, large proportions of individuals infected with Plasmodium falciparum are asymptomatic and constitute a reservoir of parasites for infection of newly hatched mosquitoes. Methods Two studies were run in parallel in Burkina Faso to evaluate the impact of systematic identification and treatment of asymptomatic carriers of P. falciparum , detected by rapid diagnostic test, on disease transmission and susceptibility to clinical malaria episodes. A clinical study assessed the incidence of symptomatic malaria episodes with a parasite density >5,000/μL after three screening and treatment campaigns ~1 month apart before the rainy season; and an entomological study determined the effect of these campaigns on malaria transmission as measured by entomological inoculation rate. Results The intervention arm had lower prevalence of asymptomatic carriers of asexual parasites and lower prevalence of gametocyte carriers during campaigns 2 and 3 as compared to the control arm. During the entire follow-up period, out of 13,767 at-risk subjects, 2,516 subjects (intervention arm 1,332; control arm 1,184) had symptomatic malaria. Kaplan-Meier analysis of the incidence of first symptomatic malaria episode with a parasite density >5,000/μL showed that, in the total population, the two treatment arms were similar until Week 11–12 after campaign 3, corresponding with the beginning of the malaria transmission season, after which the probability of being free of symptomatic malaria was lower in the intervention arm (logrank p < 0.0001). Similar trends were observed in infants and children <5 years and in individuals ≥5 years of age. In infants and children <5 years old who experienced symptomatic malaria episodes, the geometric mean P. falciparum density was lower in the intervention arm than the control arm. This trend was not seen in those individuals aged ≥5 years. Over the year, monthly variation in mosquito density and entomological inoculation rate was comparable in both arms, with September peaks in both indices. Conclusion Community screening and targeted treatment of asymptomatic carriers of P. falciparum had no effect on the dynamics of malaria transmission, but seemed to be associated with an increase in the treated community’s susceptibility to symptomatic malaria episodes after the screening campaigns had finished. These results highlight the importance of further exploratory studies to better understand the dynamics of disease transmission in the context of malaria elimination.

The tribe Aedini (Family Culicidae) contains approximately one-quarter of the known species of mosquitoes, including vectors of deadly or debilitating disease agents. This tribe contains the genus Aedes, which is one of the three most familiar genera of mosquitoes. During the past decade, Aedini has been the focus of a series of extensive morphology-based phylogenetic studies published by Reinert, Harbach, and Kitching (RH&K). Those authors created 74 new, elevated or resurrected genera from what had been the single genus Aedes, almost tripling the number of genera in the entire family Culicidae. The proposed classification is based on subjective assessments of the \"number and nature of the characters that support the branches\" subtending particular monophyletic groups in the results of cladistic analyses of a large set of morphological characters of representative species. To gauge the stability of RH&K's generic groupings we reanalyzed their data with unweighted parsimony jackknife and maximum-parsimony analyses, with and without ordering 14 of the characters as in RH&K. We found that their phylogeny was largely weakly supported and their taxonomic rankings failed priority and other useful taxon-naming criteria. Consequently, we propose simplified aedine generic designations that 1) restore a classification system that is useful for the operational community; 2) enhance the ability of taxonomists to accurately place new species into genera; 3) maintain the progress toward a natural classification based on monophyletic groups of species; and 4) correct the current classification system that is subject to instability as new species are described and existing species more thoroughly defined. We do not challenge the phylogenetic hypotheses generated by the above-mentioned series of morphological studies. However, we reduce the ranks of the genera and subgenera of RH&K to subgenera or informal species groups, respectively, to preserve stability as new data become available.

Mosquitoes track odors, locate hosts, and find mates visually. The color of a food resource, such as a flower or warm-blooded host, can be dominated by long wavelengths of the visible light spectrum (green to red for humans) and is likely important for object recognition and localization. However, little is known about the hues that attract mosquitoes or how odor affects mosquito visual search behaviors. We use a real-time 3D tracking system and wind tunnel that allows careful control of the olfactory and visual environment to quantify the behavior of more than 1.3 million mosquito trajectories. We find that CO 2 induces a strong attraction to specific spectral bands, including those that humans perceive as cyan, orange, and red. Sensitivity to orange and red correlates with mosquitoes’ strong attraction to the color spectrum of human skin, which is dominated by these wavelengths. The attraction is eliminated by filtering the orange and red bands from the skin color spectrum and by introducing mutations targeting specific long-wavelength opsins or CO 2 detection. Collectively, our results show that odor is critical for mosquitoes’ wavelength preferences and that the mosquito visual system is a promising target for inhibiting their attraction to human hosts. Vision in mosquitoes plays a critical but understudied role in their attraction to hosts. Here, the authors show that encounter with an attractive odor gates the mosquito attraction to specific colors, especially the long wavelengths reflected from human skin. Filtering the long wavelengths reflected from the human skin or knocking-out the ability for the mosquito to detect the wavelengths, suppressed their attraction. This work transforms our understanding of mosquito vision from the conventional view that vision does little in mediating mosquito-host interactions, to the recognition that vision plays a critical role.

Mosquitoes collected from mid-December 2020 to early March 2021 from hibernacula in northeastern Germany, a region of West Nile virus (WNV) activity since 2018, were examined for WNV-RNA. Among the 6101 mosquitoes tested in 722 pools of up to 12 specimens, one pool of 10 Culex pipiens complex mosquitoes collected in early March 2021 in the cellar of a medieval castle in Rosslau, federal state of Saxony-Anhalt, tested positive. Subsequent mosquito DNA analysis produced Culex pipiens biotype pipiens. The pool homogenate remaining after nucleic acid extraction failed to grow the virus on Vero and C6/36 cells. Sequencing of the viral NS2B-NS3 coding region, however, demonstrated high homology with virus strains previously collected in Germany, e.g., from humans, birds, and mosquitoes, which have been designated the East German WNV clade. The finding confirms the expectation that WNV can overwinter in mosquitoes in Germany, facilitating an early start to the natural transmission season in the subsequent year. On the other hand, the calculated low infection prevalence of 0.016–0.20%, depending on whether one or twelve of the mosquitoes in the positive pool was/were infected, indicates a slow epidemic progress and mirrors the still-hypoendemic situation in Germany. In any case, local overwintering of the virus in mosquitoes suggests its long-term persistence and an enduring public health issue.

Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.

Mosquitoes are insects of medical importance due their role as vectors of different pathogens to humans. There is a lack of information about the evolutionary history and phylogenetic positioning of the majority of mosquito species. Here we characterized the mitogenomes of mosquito species through low-coverage whole genome sequencing and data mining. A total of 37 draft mitogenomes of different species were assembled from which 16 are newly-sequenced species. We datamined additional 49 mosquito mitogenomes, and together with our 37 mitogenomes, we reconstructed the evolutionary history of 86 species including representatives from 15 genera and 7 tribes. Our results showed that most of the species clustered in clades with other members of their own genus with exception of Aedes genus which was paraphyletic. We confirmed the monophyletic status of the Mansoniini tribe including both Coquillettidia and Mansonia genus. The Aedeomyiini and Uranotaeniini were consistently recovered as basal to other tribes in the subfamily Culicinae, although the exact relationships among these tribes differed between analyses. These results demonstrate that low-coverage sequencing is effective to recover mitogenomes, establish phylogenetic knowledge and hence generate basic fundamental information that will help in the understanding of the role of these species as pathogen vectors.

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.