Flight traits of dengue-infected Aedes aegypti mosquitoes

Understanding the flight behaviour of dengue-infected mosquitoes can play a vital role in various contexts, including modelling disease risks and developing effective interventions against dengue. Studies on the locomotor activity of dengue-infected mosquitoes have often faced challenges in terms of methodology. Some studies used small tubes, which impacted the natural movement of the mosquitoes, while others that used cages did not capture the three-dimensional flights, despite mosquitoes naturally flying in three dimensions. In this study, we utilised Mask RCNN (Region-based Convolutional Neural Network) along with cubic spline interpolation to comprehensively track the three-dimensional flight behaviour of dengue-infected Aedes aegypti mosquitoes. This analysis considered a number of parameters as characteristics of mosquito flight, including flight duration, number of flights, Euclidean distance, flight speed, and the volume (space) covered during flights. The accuracy achieved for mosquito detection and tracking was 98.34% for flying mosquitoes and 100% for resting mosquitoes. Notably, the interpolated data accounted for only 0.31%, underscoring the reliability of the results. Flight traits results revealed that exposure to the dengue virus significantly increases the flight duration (p-value 0.0135 × 10-3) and volume (space) covered during flights (p-value 0.029) whilst decreasing the total number of flights compared to uninfected mosquitoes. The study did not observe any evident impact on the Euclidean distance (p-value 0.064) and speed (p-value 0.064) of Aedes aegypti. These results highlight the intricate relationship between dengue infection and the flight behaviour of Aedes aegypti, providing valuable insights into the virus transmission dynamics. This study focused on dengue-infected Aedes aegypti mosquitoes; future research can explore the impact of other arboviruses on mosquito flight behaviour.

Engineered Antiviral Sensor Targets Infected Mosquitoes

Escalating vector disease burdens pose significant global health risks, as such innovative tools for targeting mosquitoes are critical. CRISPR-Cas technologies have played a crucial role in developing powerful tools for genome manipulation in various eukaryotic organisms. Although considerable efforts have focused on utilizing class II type II CRISPR-Cas9 systems for DNA targeting, these modalities are unable to target RNA molecules, limiting their utility against RNA viruses. Recently, the Cas13 family has emerged as an efficient tool for RNA targeting; however, the application of this technique in mosquitoes, particularly Aedes aegypti, has yet to be fully realized. In this study, we engineered an antiviral strategy termed REAPER (vRNA Expression Activates Poisonous Effector Ribonuclease) that leverages the programmable RNA-targeting capabilities of CRISPR-Cas13 and its potent collateral activity. REAPER remains concealed within the mosquito until an infectious blood meal is uptaken. Upon target viral RNA infection, REAPER activates, triggering programmed destruction of its target arbovirus such as chikungunya. Consequently, Cas13-mediated RNA targeting significantly reduces viral replication and viral prevalence of infection, and its promiscuous collateral activity can even kill infected mosquitoes within a few days. This innovative REAPER technology adds to an arsenal of effective molecular genetic tools to combat mosquito virus transmission.

EggCountAI: a convolutional neural network-based software for counting of Aedes aegypti mosquito eggs

BACKGROUND

Mosquito-borne diseases exert a huge impact on both animal and human populations, posing substantial health risks. The behavioural and fitness traits of mosquitoes, such as locomotion and fecundity, are crucial factors that influence the spread of diseases. In existing egg-counting tools, each image requires separate processing with adjustments to various parameters such as intensity threshold and egg area size. Furthermore, accuracy decreases significantly when dealing with clustered or overlapping eggs. To overcome these issues, we have developed EggCountAI, a Mask Region-based Convolutional Neural Network (RCNN)-based free automatic egg-counting tool for Aedes aegypti mosquitoes.

METHODS

The study design involves developing EggCountAI for counting mosquito eggs and comparing its performance with two commonly employed tools-ICount and MECVision-using 10 microscopic and 10 macroscopic images of eggs laid by females on a paper strip. The results were validated through manual egg counting on the strips using ImageJ software. Two different models were trained on macroscopic and microscopic images to enhance egg detection accuracy, achieving mean average precision, mean average recall, and F1-scores of 0.92, 0.90, and 0.91 for the microscopic model, and 0.91, 0.90, and 0.90 for the macroscopic model, respectively. EggCountAI automatically counts eggs in a folder containing egg strip images, offering adaptable filtration for handling impurities of varying sizes.

RESULTS

The results obtained from EggCountAI highlight its remarkable performance, achieving overall accuracy of 98.88% for micro images and 96.06% for macro images. EggCountAI significantly outperformed ICount and MECVision, with ICount achieving 81.71% accuracy for micro images and 82.22% for macro images, while MECVision achieved 68.01% accuracy for micro images and 51.71% for macro images. EggCountAI also excelled in other statistical parameters, with mean absolute error of 1.90 eggs for micro, 74.30 eggs for macro, and a strong correlation and R-squared value (0.99) for both micro and macro. The superior performance of EggCountAI was most evident when handling overlapping or clustered eggs.

CONCLUSION

Accurate detection and counting of mosquito eggs enables the identification of preferred egg-laying sites and facilitates optimal placement of oviposition traps, enhancing targeted vector control efforts and disease transmission prevention. In future research, the tool holds the potential to extend its application to monitor mosquito feeding preferences.

Engineered Antiviral Sensor Targets Infected Mosquitoes

Escalating vector disease burdens pose significant global health risks, so innovative tools for targeting mosquitoes are critical. We engineered an antiviral strategy termed REAPER (vRNA Expression Activates Poisonous Effector Ribonuclease) that leverages the programmable RNA-targeting capabilities of CRISPR Cas13 and its potent collateral activity. Akin to a stealthy Trojan Horse hiding in stealth awaiting the presence of its enemy, REAPER remains concealed within the mosquito until an infectious blood meal is up taken. Upon target viral RNA infection, REAPER activates, triggering programmed destruction of its target arbovirus such as chikungunya. Consequently, Cas13 mediated RNA targeting significantly reduces viral replication and its promiscuous collateral activity can even kill infected mosquitoes. This innovative REAPER technology adds to an arsenal of effective molecular genetic tools to combat mosquito virus transmission.