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Culicoides spp., a common biting midge genus, are haematophagous insects that can transmit pathogens to humans and other animals. Some species transmit arboviruses, including bluetongue virus, epizootic haemorrhagic disease virus, African horse sickness virus and Schmallenberg virus to vertebrates, which can be detrimental to livestock and wild animals. Culicoides spp. can also have a diversity of insect-specific viruses (ISVs) that can only be transmitted between insects and others related to known arboviruses. For Culicoides brevitarsis and other Culicoides spp. in Australia, the virome is largely unexplored. We used high-throughput sequencing to characterise the virome of C. brevitarsis collected from Casino, New South Wales, Australia. For virus detection, the total RNA was extracted from pools of C. brevitarsis followed by rRNA depletion and Illumina short-read-based RNA sequencing. The reads were quality-checked, filtered and assembled into contigs, compared with the non-redundant protein and conserved domain databases for viral detection and genome organisation, respectively. The phylogenetic analysis was used to further characterise the viruses. We detected new virus diversity including ten viruses belonging to eight different families with complete or near-complete coding regions. Seven of these were novel virus species belonging to the families: Chuviridae , Orthomyxoviridae , Peribunyaviridae , Qinviridae , Rhabdoviridae and Solemoviridae . In addition, the novel Peribunyaviridae virus should also be considered part of a new genus. Whilst most of the detected viruses grouped into families with viruses that can infect insects, animals or both, the novel species of Solemoviridae was closely related to an economically important plant pathogen, the sugarcane yellow leaf virus. Our quantitative PCR-based screening confirmed the absence of any Wolbachia endosymbiont within the collected samples. Furthermore, we detected fragments of three more virus families known to infect fungi and plants. The detection of potential arboviruses and ISVs in Culicoides spp. is important in understanding virus epidemiology.

Accurate counting of mosquito larval populations is essential for maintaining optimal conditions and population control within rearing facilities, assessing disease transmission risks, and implementing effective vector control measures. While existing methods for counting mosquito larvae have faced challenges such as the impact on larval mortality rate, multiple parameters adjustment and limitations in availability and affordability, recent advancements in artificial intelligence, particularly in AI-driven visual analysis, hold promise for addressing these issues. Here, we introduce LarvaeCountAI, an open-source convolutional neural network (CNN)-based tool designed to automatically count Culex annulirostris mosquito larvae from videos captured in laboratory environments. LarvaeCountAI does not require videos to be recorded using an advanced setup; it can count larvae with high accuracy from videos captured using a simple setup mainly consisting of a camera and commonly used plastic trays. Using the videos enables LarvaeCountAI to capitalise on the continuous movement of larvae, enhancing the likelihood of accurately counting a greater number of larvae. LarvaeCountAI adopts a non-invasive approach, where larvae are simply placed in trays and imaged, minimising any potential impact on larval mortality. This approach addresses the limitations associated with previous methods involving mechanical machines, which often increase the risk of larval mortality as larvae pass through multiple sections for counting purposes. The performance of LarvaeCountAI was tested using 10 video samples. Validation results demonstrated the excellent performance of LarvaeCountAI, with an accuracy ranging from 96.25 % to 99.13 % across 10 test videos and an average accuracy of 97.88 %. LarvaeCountAI represents a remarkable advancement in mosquito surveillance technology, offering a robust and efficient solution for monitoring larval populations. LarvaeCountAI can contribute to developing effective strategies for reducing disease transmission and safeguarding public health by providing timely and accurate data on mosquito larvae abundance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Japanese encephalitis virus (JEV) is transmitted by Culex species of mosquitoes. In 2022, JEV belonging to a previously unrecognized lineage of genotype IV (GIV) caused a major outbreak of JE in South-eastern Australia, resulting in human cases and affecting piggeries. Cx. annulirostris has previously been implicated as the major vector of JEV in northern Australia where the virus has circulated since its first detection in 1995. Here, we showed that experimental infection of a laboratory colony of Australian Cx. annulirostris with the isolate JEV NSW/22 resulted in a 100% mosquito infection rate, with 87% of mosquito saliva samples testing positive by RT-qPCR at 14 days post-infection. Immunohistochemistry confirmed the presence of a replicating virus in the mosquito midgut and dissemination throughout the body, including the salivary glands. Our results also showed evidence of transovarial transmission of this virus; however, transstadial transmission from the eggs to the adult stage was not found. Comparison with an Indonesian isolate of GIV JEV and previous Australian isolates belonging to genotypes I and II showed that infection with JEV NSW/22 resulted in higher viral titres in the early stage of infection and higher proportions of mosquitoes with JEV-positive saliva, indicating a greater transmission potential compared to other isolates. This study provides compelling experimental evidence that Australian Cx. annulirostris is a highly efficient vector for the 2022 Australian JEV GIV outbreak strain.

Monitoring the flight behaviour of mosquitoes is crucial for assessing their fitness levels and understanding their potential role in disease transmission. Existing methods for tracking mosquito flight behaviour are challenging to implement in laboratory environments, and they also struggle with identity tracking, particularly during occlusions. Here, we introduce FlightTrackAI, a robust convolutional neural network (CNN)-based tool for automatic mosquito flight tracking. FlightTrackAI employs CNN, a multi-object tracking algorithm, and interpolation to track flight behaviour. It automatically processes each video in the input folder without supervision and generates tracked videos with mosquito positions across the frames and trajectory graphs before and after interpolation. FlightTrackAI does not require a sophisticated setup to capture videos; it can perform excellently with videos recorded using standard laboratory cages. FlightTrackAI also offers filtering capabilities to eliminate short-lived objects such as reflections. Validation of FlightTrackAI demonstrated its excellent performance with an average accuracy of 99.9%. The percentage of correctly assigned identities after occlusions exceeded 91%. The data produced by FlightTrackAI can facilitate analysis of various flight-related behaviours, including flight distance and volume coverage during flights. This advancement can help to enhance our understanding of mosquito ecology and behaviour, thereby informing targeted strategies for vector control., Competing Interests: We declare we have no competing interests., (© 2024 The Authors.)

Mosquitoes of the Culex genus are responsible for a large burden of zoonotic virus transmission globally. Collectively, they play a significant role in the transmission of medically significant diseases such as Japanese encephalitis virus and West Nile virus. Climate change, global trade, habitat transformation and increased urbanisation are leading to the establishment of Culex mosquitoes in new geographical regions. These novel mosquito incursions are intensifying concerns about the emergence of Culex -transmitted diseases and outbreaks in previously unaffected areas. New mosquito control methods are currently being developed and deployed globally. Understanding the complex interaction between pathogens and mosquitoes is essential for developing new control strategies for Culex species mosquitoes. This article reviews the role of Culex mosquitos as vectors of zoonotic disease, discussing the transmission of viruses across different species, and the potential use of Wolbachia technologies to control disease spread. By leveraging the insights gained from recent successful field trials of Wolbachia against Aedes -borne diseases, we comprehensively discuss the feasibility of using this technique to control Culex mosquitoes and the potential for the development of next generational Wolbachia -based control methods.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.

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., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Escalating vector disease burdens pose significant global health risks, so innovative tools for targeting mosquitoes are critical. We engineered an antiviral strategy termed REAPER (v R NA E xpression A ctivates P oisonous E ffector R ibonuclease) 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., Competing Interests: Competing interests O.S.A is a founder of both Agragene, Inc. and Synvect, Inc. with equity interest. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. L.A is an adviser to Synvect, Inc and Biocentis Ltd., with financial interest in each. All other authors declare no competing interests.

The involvement of the nucleus during flavivirus infection has been observed in only a small number of cases and can be limited to primarily two viral proteins; the structural protein C and the RNA polymerase NS5. Previously we observed that by blocking nuclear transport, WNV strain Kunjin (WNV KUN ) replication is severely affected and through mutation of the identified NLS in WNV KUN NS5 protein. In this study, we interrogated the potential nuclear functions of WNV KUN NS5 has on the host transcriptome, by means of RNA sequencing (RNAseq). In a direct comparison between wild type and mutant NS5, it can also be determined that the nuclear translocation of NS5 results in a significant down-regulation of host genes involved in the innate immune response. When compared to published RNAseq data from WNV infection, many of these genes were overlapping indicting the role of NS5 induced transcription during infection., (Crown Copyright © 2021. Published by Elsevier Inc. All rights reserved.)