Mosquito-borne diseases continue to ravage humankind with >700 million infections and nearly one million deaths every year. Yet only a small percentage of the >3500 mosquito species transmit diseases, necessitating both extensive surveillance and precise identification. Unfortunately, such efforts are costly, time-consuming, and require entomological expertise. As envisioned by the Global Mosquito Alert Consortium, citizen science can provide a scalable solution. However, disparate data standards across existing platforms have thus far precluded truly global integration. Here, utilizing Open Geospatial Consortium standards, we harmonized four data streams from three established mobile apps—Mosquito Alert, iNaturalist, and GLOBE Observer’s Mosquito Habitat Mapper and Land Cover—to facilitate interoperability and utility for researchers, mosquito control personnel, and policymakers. We also launched coordinated media campaigns that generated unprecedented numbers and types of observations, including successfully capturing the first images of targeted invasive and vector species. Additionally, we leveraged pooled image data to develop a toolset of artificial intelligence algorithms for future deployment in taxonomic and anatomical identification. Ultimately, by harnessing the combined powers of citizen science and artificial intelligence, we establish a next-generation surveillance framework to serve as a united front to combat the ongoing threat of mosquito-borne diseases worldwide.

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IntroductionSilver (Ag) nanoparticles (NPs) are well documented for their broad-spectrum bactericidal effects. This study aimed to test the effect of bioactive Ag-hydrosol NPs on drug-resistant E. faecium 1449 strain and explore the use of artificial intelligence (AI) for automated detection of the bacteria.MethodsThe formation of E. faecium 1449 biofilms in the absence and presence of Ag-hydrosol NPs at different concentrations ranging from 12.4 mg/L to 123 mg/L was evaluated using a 3-dimentional culture system. The biofilm reduction was evaluated using the confocal microscopy in addition to the Transmission Electronic Microscopy (TEM) visualization and spectrofluorimetric quantification using a Biotek Synergy Neo2 microplate reader. The cytotoxicity of the NPs was evaluated in human nasal epithelial cells using the MTT assay. The AI technique based on Fast Regional Convolutional Neural Network architecture was used for the automated detection of the bacteria.ResultsTreatment with Ag-hydrosol NPs at concentrations ranging from 12.4 mg/L to 123 mg/L resulted in 78.09% to 95.20% of biofilm reduction. No statistically significant difference in biofilm reduction was found among different batches of Ag-hydrosol NPs. Quantitative concentration-response relationship analysis indicated that Ag-hydrosol NPs exhibited a relative high anti-biofilm activity and low cytotoxicity with an average EC50 and TC50 values of 0.0333 and 6.55 mg/L, respectively, yielding an average therapeutic index value of 197. The AI-assisted TEM image analysis allowed automated detection of E. faecium 1449 with 97% ~ 99% accuracy.DiscussionConclusively, the bioactive Ag-hydrosol NP is a promising nanotherapeutic agent against drug-resistant pathogens. The AI-assisted TEM image analysis was developed with the potential to assess its treatment effect.

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, and Culex quinquefasciatus – and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid and gravid). From these mosquitoes and stages, a total of 1,959 images were captured on a plain background via multiple smartphones. We then trained and validated an EfficientNet-B0-based model. With unseen data, the overall classification accuracy achieved by the model was 93.59%. Furthermore, we also assessed the explainability of our AI model, by implementing Grad-CAMs - a technique that highlights pixels in an image that were prioritized for classification. We observe that the highest significance was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. To the best of our knowledge, this work is the first to use computer vision techniques to identify the stages of the gonotrophic cycle of mosquitoes, and we discuss some potential practical applications of our techniques.