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1. Global mosquito observations dashboard (GMOD): creating a user-friendly web interface fueled by citizen science to monitor invasive and vector mosquitoes

   https://doi.org/10.1186/s12942-023-00350-7  

   Uelmen, Jr., Johnny A. ; Clark, Andrew ; Palmer, John ; Kohler, Jared ; Van Dyke, Landon C. ; Low, Russanne ; Mapes, Connor D. ; Carney, Ryan M. ( October 2023 , International Journal of Health Geographics)

   Mosquitoes and the diseases they transmit pose a significant public health threat worldwide, causing more fatalities than any other animal. To effectively combat this issue, there is a need for increased public awareness and mosquito control. However, traditional surveillance programs are time-consuming, expensive, and lack scalability. Fortunately, the widespread availability of mobile devices with high-resolution cameras presents a unique opportunity for mosquito surveillance. In response to this, the Global Mosquito Observations Dashboard (GMOD) was developed as a free, public platform to improve the detection and monitoring of invasive and vector mosquitoes through citizen science participation worldwide.

   GMOD is an interactive web interface that collects and displays mosquito observation and habitat data supplied by four datastreams with data generated by citizen scientists worldwide. By providing information on the locations and times of observations, the platform enables the visualization of mosquito population trends and ranges. It also serves as an educational resource, encouraging collaboration and data sharing. The data acquired and displayed on GMOD is freely available in multiple formats and can be accessed from any device with an internet connection.

   Since its launch less than a year ago, GMOD has already proven its value. It has successfully integrated and processed large volumes of real-time data (~ 300,000 observations), offering valuable and actionable insights into mosquito species prevalence, abundance, and potential distributions, as well as engaging citizens in community-based surveillance programs.

   GMOD is a cloud-based platform that provides open access to mosquito vector data obtained from citizen science programs. Its user-friendly interface and data filters make it valuable for researchers, mosquito control personnel, and other stakeholders. With its expanding data resources and the potential for machine learning integration, GMOD is poised to support public health initiatives aimed at reducing the spread of mosquito-borne diseases in a cost-effective manner, particularly in regions where traditional surveillance methods are limited. GMOD is continually evolving, with ongoing development of powerful artificial intelligence algorithms to identify mosquito species and other features from submitted data. The future of citizen science holds great promise, and GMOD stands as an exciting initiative in this field.

    

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2. Human biting mosquitoes and implications for West Nile virus transmission

   https://doi.org/10.1186/s13071-022-05603-1  

   Uelmen, Jr., Johnny A. ; Lamcyzk, Bennett ; Irwin, Patrick ; Bartlett, Dan ; Stone, Chris ; Mackay, Andrew ; Arsenault-Benoit, Arielle ; Ryan, Sadie J. ; Mutebi, John-Paul ; Hamer, Gabriel L. ; et al ( January 2023 , Parasites & Vectors)

   West Nile virus (WNV), primarily vectored by mosquitoes of the genusCulex, is the most important mosquito-borne pathogen in North America, having infected thousands of humans and countless wildlife since its arrival in the USA in 1999. In locations with dedicated mosquito control programs, surveillance methods often rely on frequent testing of mosquitoes collected in a network of gravid traps (GTs) and CO₂-baited light traps (LTs). Traps specifically targeting oviposition-seeking (e.g. GTs) and host-seeking (e.g. LTs) mosquitoes are vulnerable to trap bias, and captured specimens are often damaged, making morphological identification difficult.

   This study leverages an alternative mosquito collection method, the human landing catch (HLC), as a means to compare sampling of potential WNV vectors to traditional trapping methods. Human collectors exposed one limb for 15 min at crepuscular periods (5:00–8:30 am and 6:00–9:30 pm daily, the time whenCulexspecies are most actively host-seeking) at each of 55 study sites in suburban Chicago, Illinois, for two summers (2018 and 2019).

   A total of 223 human-seeking mosquitoes were caught by HLC, of which 46 (20.6%) were mosquitoes of genusCulex. Of these 46 collectedCulexspecimens, 34 (73.9%) wereCx. salinarius, a potential WNV vector species not thought to be highly abundant in upper Midwest USA. Per trapping effort, GTs and LTs collected > 7.5-fold the number of individualCulexspecimens than HLC efforts.

   The less commonly used HLC method provides important insight into the complement of human-biting mosquitoes in a region with consistent WNV epidemics. This study underscores the value of the HLC collection method as a complementary tool for surveillance to aid in WNV vector species characterization. However, given the added risk to the collector, novel mitigation methods or alternative approaches must be explored to incorporate HLC collections safely and strategically into control programs.

    

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3. A phenomic modeling approach for using chlorophyll-a fluorescence-based measurements on coral photosymbionts

   https://doi.org/10.3389/fmars.2023.1092202  

   Hoadley, Kenneth D. ; Lockridge, Grant ; McQuagge, Audrey ; Pahl, K. Blue ; Lowry, Sean ; Wong, Sophie ; Craig, Zachary ; Petrik, Chelsea ; Klepac, Courtney ; Muller, Erinn M. ( March 2023 , Frontiers in Marine Science)

   We test a newly developed instrument prototype which utilizes time-resolved chlorophyll- a fluorescence techniques and fluctuating light to characterize Symbiodiniaceae functional traits across seven different coral species under cultivation as part of ongoing restoration efforts in the Florida Keys. While traditional chlorophyll- a fluorescence techniques only provide a handful of algal biometrics, the system and protocol we have developed generates > 1000 dynamic measurements in a short (~11 min) time frame. Resulting ‘high-content’ algal biometric data revealed distinct phenotypes, which broadly corresponded to genus-level Symbiodiniaceae designations determined using quantitative PCR. Next, algal biometric data from Acropora cervicornis (10 genotypes) and A. palmata (5 genotypes) coral fragments was correlated with bleaching response metrics collected after a two month-long exposure to high temperature. A network analysis identified 1973 correlations (Spearman R > 0.5) between algal biometrics and various bleaching response metrics. These identified biomarkers of thermal stress were then utilized to train a predictive model, and when tested against the same A. cervicornis and A. palmata coral fragments, yielded high correlation (R = 0.92) with measured thermal response (reductions in absorbance by chlorophyll-a). When applied to all seven coral species, the model ranked fragments dominated by Cladocopium or Breviolum symbionts as more bleaching susceptible than corals harboring thermally tolerant symbionts ( Durusdinium ). While direct testing of bleaching predictions on novel genotypes is still needed, our device and modeling pipeline may help broaden the scalability of existing approaches for determining thermal tolerance in reef corals. Our instrument prototype and analytical pipeline aligns with recent coral restoration assessments that call for the development of novel tools for improving scalability of coral restoration programs. 

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4. Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection

   https://doi.org/10.1038/s41598-021-92891-9  

   Goodwin, Adam ; Padmanabhan, Sanket ; Hira, Sanchit ; Glancey, Margaret ; Slinowsky, Monet ; Immidisetti, Rakhil ; Scavo, Laura ; Brey, Jewell ; Sai Sudhakar, Bala Murali ; Ford, Tristan ; et al ( December 2021 , Scientific Reports)

   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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5. Using body size as an indicator for age structure in field populations of Aedes aegypti (Diptera: Culicidae)

   https://doi.org/10.1186/s13071-022-05605-z  

   Gutiérrez, Eileen H. Jeffrey ; Riehle, M. A. ; Walker, K. R. ; Ernst, K. C. ; Davidowitz, G. ( December 2022 , Parasites & Vectors)

   TheAedesaegyptimosquito is a vector of several viruses including dengue, chikungunya, zika, and yellow fever. Vector surveillance and control are the primary methods used for the control and prevention of disease transmission; however, public health institutions largely rely on measures of population abundance as a trigger for initiating control activities. Previous research found evidence that at the northern edge ofAe.aegypti’s geographic range, survival, rather than abundance, is likely to be the factor limiting disease transmission. In this study, we sought to test the utility of using body size as an entomological index to surveil changes in the age structure of field-collected femaleAedesaegypti.

   We collected femaleAe.aegyptimosquitoes using BG sentinel traps in three cities at the northern edge of their geographic range. Collections took place during their active season over the course of 3 years. Female wing size was measured as an estimate of body size, and reproductive status was characterized by examining ovary tracheation. Chronological age was determined by measuring transcript abundance of an age-dependent gene. These data were then tested with female abundance at each site and weather data from the estimated larval development period and adulthood (1 week prior to capture). Two sources of weather data were tested to determine which was more appropriate for evaluating impacts on mosquito physiology. All variables were then used to parameterize structural equation models to predict age.

   In comparing city-specific NOAA weather data and site-specific data from HOBO remote temperature and humidity loggers, we found that HOBO data were more tightly associated with body size. This information is useful for justifying the cost of more precise weather monitoring when studying intra-population heterogeneity of eco-physiological factors. We found that body size itself was not significantly associated with age. Of all the variables measured, we found that best fitting model for age included temperature during development, body size, female abundance, and relative humidity in the 1 week prior to capture . The strength of models improved drastically when testing one city at a time, with Hermosillo (the only study city with seasonal dengue transmission) having the best fitting model for age. Despite our finding that there was a bias in the body size of mosquitoes collected alive from the BG sentinel traps that favored large females, there was still sufficient variation in the size of females collected alive to show that inclusion of this entomological indicator improved the predictive capacity of our models.

   Inclusion of body size data increased the strength of weather-based models for age. Importantly, we found that variation in age was greater within cities than between cities, suggesting that modeling of age must be made on a city-by-city basis. These results contribute to efforts to use weather forecasts to predict changes in the probability of disease transmission by mosquito vectors.

    

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