FlightTrackAI: a robust convolutional neural network-based tool for tracking the flight behaviour of Aedes aegypti mosquitoes.

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.

An overview of technologies available to monitor behaviours of mosquitoes.

Mosquito-borne diseases such as malaria, dengue, Zika, and chikungunya cause significant morbidity and mortality globally, resulting in over 600,000 deaths from malaria and around 36,000 deaths from dengue each year, with millions of people infected annually, leading to substantial economic losses. The existing mosquito control measures, such as long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS), helped to reduce the infections. However, mosquito-borne diseases are still among the deadliest diseases, forcing us to improve the existing control methods and look for alternative methods simultaneously. Advanced monitoring techniques, including remote sensing, and geographic information systems (GIS) have significantly enhanced the efficiency and effectiveness of mosquito control measures. Mosquitoes' behavioural traits, such as locomotion, blood-feeding, and fertility are the key determinants of disease transmission and epidemiology. Technological advancements, such as high-resolution cameras, infrared imaging, and artificial intelligence (AI) driven object detection models, including groundbreaking convolutional neural networks, have provided efficient and precise options to monitor various mosquito behaviours, including locomotion, oviposition, fertility, and host-seeking. However, they are not commonly employed in mosquito-based research. This review highlights the novel and significant advancements in behaviour-monitoring tools, mostly from the last decade, due to cutting-edge video monitoring technology and artificial intelligence. These advancements can offer enhanced accuracy, efficiency, and the ability to quickly process large volumes of data, enabling detailed behavioural analysis over extended periods and large sample sizes, unlike traditional manual methods prone to human error and labour-intensive. The use of behaviour-assaying techniques can support or replace existing monitoring techniques and directly contribute to improving control measures by providing more accurate and real-time data on mosquito activity patterns and responses to interventions. This enhanced understanding can help establish the role of behavioural changes in improving epidemiological models, making them more precise and dynamic. As a result, mosquito management strategies can become more adaptive and responsive, leading to more effective and targeted interventions. Ultimately, this will reduce disease transmission and significantly improve public health outcomes.

Understanding parents' perceptions of children's exposure to unhealthy food marketing in digital and retail environments.

Unhealthy food and non-alcoholic beverage marketing (UFM) adversely impacts children's selection and intake of foods and beverages, undermining parents' efforts to promote healthy eating. Parents' support for restrictions on children's exposure to food marketing can catalyse government action, yet research describing parent concerns is limited for media other than television. We examined parents' perceptions of UFM and their views on potential policies to address UFM in supermarkets and on digital devices - two settings where children are highly exposed to UFM and where little recent research exists. We conducted in-depth interviews with sixteen parents of children aged 7-12 from Victoria, Australia, analysing the data thematically. Parents perceived UFM as ubiquitous and viewed exposure as having an immediate but temporary impact on children's food desires and pestering behaviours. Parents were concerned about UFM in supermarkets as they viewed it as leading their children to pester them to buy marketed products, undermining their efforts to instil healthy eating behaviours. Parents generally accepted UFM as an aspect of contemporary parenting. Concern for digital UFM was lower compared to supermarkets as it was not directly linked to pestering and parents had limited awareness of what their children saw online. Nevertheless, parents felt strongly that companies should not be allowed to target their children with UFM online and supported government intervention to protect their children. While parents supported government policy actions for healthier supermarket environments, their views towards restricting UFM in supermarkets varied as some parents felt it was their responsibility to mitigate supermarket marketing. These findings could be used to advocate for policy action in this area.

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.

Comparative study of encoded and alignment-based methods for virus taxonomy classification.

The emergence of viruses and their variants has made virus taxonomy more important than ever before in controlling the spread of diseases. The creation of efficient treatments and cures that target particular virus properties can be aided by understanding virus taxonomy. Alignment-based methods are commonly used for this task, but are computationally expensive and time-consuming, especially when dealing with large datasets or when detecting new virus variants is time sensitive. An alternative approach, the encoded method, has been developed that does not require prior sequence alignment and provides faster results. However, each encoded method has its own claimed accuracy. Therefore, careful evaluation and comparison of the performance of different encoded methods are essential to identify the most accurate and reliable approach for virus taxonomy classification. This study aims to address this issue by providing a comprehensive and comparative analysis of the potential of encoded methods for virus classification and phylogenetics. We compared the vectors generated for each encoded method using distance metrics to determine their similarity to alignment-based methods. The results and their validation show that K-merNV followed by CgrDft encoded methods, perform similarly to state-of-the-art multi-sequence alignment methods. This is the first study to incorporate and compare encoded methods that will facilitate future research in making more informed decisions regarding selection of a suitable method for virus taxonomy.

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.

Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network.

Mosquito-borne diseases cause a huge burden on public health worldwide. The viruses that cause these diseases impact the behavioural traits of mosquitoes, including locomotion and feeding. Understanding these traits can help in improving existing epidemiological models and developing effective mosquito traps. However, it is difficult to understand the flight behaviour of mosquitoes due to their small sizes, complicated poses, and seemingly random moving patterns. Currently, no open-source tool is available that can detect and track resting or flying mosquitoes. Our work presented in this paper provides a detection and trajectory estimation method using the Mask RCNN algorithm and spline interpolation, which can efficiently detect mosquitoes and track their trajectories with higher accuracy. The method does not require special equipment and works excellently even with low-resolution videos. Considering the mosquito size, the proposed method's detection performance is validated using a tracker error and a custom metric that considers the mean distance between positions (estimated and ground truth), pooled standard deviation, and average accuracy. The results showed that the proposed method could successfully detect and track the flying (≈ 96% accuracy) as well as resting (100% accuracy) mosquitoes. The performance can be impacted in the case of occlusions and background clutters. Overall, this research serves as an efficient open-source tool to facilitate further examination of mosquito behavioural traits.

Physiological Synchrony Predict Task Performance and Negative Emotional State during a Three-Member Collaborative Task.

Evaluation of team performance in naturalistic contexts has gained popularity during the last two decades. Among other human factors, physiological synchrony has been adopted to investigate team performance and emotional state when engaged in collaborative team tasks. A variety of methods have been reported to quantify physiological synchrony with a varying degree of correlation with the collaborative team task performance and emotional state, reflected in the inconclusive nature of findings. Little is known about the effect of the choice of synchrony calculation methods and the level of analysis on these findings. In this research work, we investigate the relationship between outcomes of different methods to quantify physiological synchrony, emotional state, and team performance of three-member teams performing a collaborative team task. The proposed research work employs dyadic-level linear (cross-correlation) and team-level non-linear (multidimensional recurrence quantification analysis) synchrony calculation measures to quantify task performance and the emotional state of the team. Our investigation indicates that the physiological synchrony estimated using multidimensional recurrence quantification analysis revealed a significant negative relationship between the subjectively reported frustration levels and overall task performance. However, no relationship was found between cross-correlation-based physiological synchrony and task performance. The proposed research highlights that the method of choice for physiological synchrony calculation has direct impact on the derived relationship of team task performance and emotional states.

Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.

COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally. Therefore, developing an efficient self-testing service for SARS-CoV-2 at home is vital. In this study, a two-stage vision-based framework, namely Fruit-CoV, is introduced for detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, audio signals are converted into Log-Mel spectrograms, and the EfficientNet-V2 network is used to extract their visual features in the first stage. In the second stage, 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN are employed to aggregate feature representations of the Log-Mel spectrograms and the waveform. Finally, the combined features are used to train a binary classifier. In this study, a dataset provided by the AICovidVN 115M Challenge is employed for evaluation. It includes 7,371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results indicate that the proposed model achieves an Area Under the Receiver Operating Characteristic Curve (AUC) score of 92.8% and ranks first on the final leaderboard of the AICovidVN 115M Challenge. Our code is publicly available.

Evaluating Operator Training Performance Using Recurrence Quantification Analysis of Autocorrelation Transformed Eye Gaze Data.

OBJECTIVE: This research aimed to investigate the relationship between gaze behaviour dynamics and operator performance. BACKGROUND: Individuals differ in their approach when learning a new task often resulting in performance disparity. During training some individuals learn the structure and dynamics of the task and develop a systematic approach, whereas others may achieve the same result albeit with increased perceived workload, or indeed some may fail to achieve superior performance levels. Previous research has shown that comparing gaze of experts with novices can provide unique insights into cognitive functioning of superior performers. METHODS: Twenty-five individuals participated in a computer-based simulation task. The concept of coefficient of variation (CoV) of task scores was used to compute the participants' consistency of performance. Based on CoV, the cohort was split into two performance categories. The temporal patterns in participants gaze data were transformed using autocorrelation, and recurrence quantification analysis (RQA) was employed to analyse and quantify the patterns. RESULTS: A Mann-Whitney U analysis demonstrated significantly (p < .01) higher determinism, entropy and laminarity in the superior group compared to the moderate group. Pearson's correlation revealed a significant (p < .01) negative correlation between the consistency of task performance (CoV) and the RQA measures. CONCLUSION: The results demonstrated that eye gaze dynamics can be used as an objective measure of performance. Participants classified as superior performers consistently demonstrated a systematic gaze activity which were in line with the task structure. APPLICATION: The methods presented here are applicable to observe and evaluate operators' strategic distribution of gaze. Specifically, for tactical monitoring and decision making in task environments where spatial locations of elements-of-interest vary continuously.

Machine learning imaging applications in the differentiation of true tumour progression from treatment-related effects in brain tumours: A systematic review and meta-analysis.

INTRODUCTION: Chemotherapy and radiotherapy can produce treatment-related effects, which may mimic tumour progression. Advances in Artificial Intelligence (AI) offer the potential to provide a more consistent approach of diagnosis with improved accuracy. The aim of this study was to determine the efficacy of machine learning models to differentiate treatment-related effects (TRE), consisting of pseudoprogression (PsP) and radiation necrosis (RN), and true tumour progression (TTP). METHODS: The systematic review was conducted in accordance with PRISMA-DTA guidelines. Searches were performed on PubMed, Scopus, Embase, Medline (Ovid) and ProQuest databases. Quality was assessed according to the PROBAST and CLAIM criteria. There were 25 original full-text journal articles eligible for inclusion. RESULTS: For gliomas: PsP versus TTP (16 studies, highest AUC = 0.98), RN versus TTP (4 studies, highest AUC = 0.9988) and TRE versus TTP (3 studies, highest AUC = 0.94). For metastasis: RN vs. TTP (2 studies, highest AUC = 0.81). A meta-analysis was performed on 9 studies in the gliomas PsP versus TTP group using STATA. The meta-analysis reported a high sensitivity of 95.2% (95%CI: 86.6-98.4%) and specificity of 82.4% (95%CI: 67.0-91.6%). CONCLUSION: TRE can be distinguished from TTP with good performance using machine learning-based imaging models. There remain issues with the quality of articles and the integration of models into clinical practice. Future studies should focus on the external validation of models and utilize standardized criteria such as CLAIM to allow for consistency in reporting.

Use of artificial intelligence to enable dark nudges by transnational food and beverage companies: analysis of company documents.

OBJECTIVE: To describe the use of artificial intelligence (AI)-enabled dark nudges by leading global food and beverage companies to influence consumer behaviour. DESIGN: The five most recent annual reports (ranging from 2014-2018 or 2015-2019, depending on the company) and websites from 12 of the leading companies in the global food and beverage industry were reviewed to identify uses of AI and emerging technologies to influence consumer behaviour. Uses of AI and emerging technologies were categorised according to the Typology of Interventions in Proximal Physical Micro-Environments (TIPPME) framework, a tool for categorising and describing nudge-type behaviour change interventions (which has also previously been used to describe dark nudge-type approaches used by the alcohol industry). SETTING: Not applicable. PARTICIPANTS: 12 leading companies in the global food and beverage industry. RESULTS: Text was extracted from 56 documents from 11 companies. AI-enabled dark nudges used by food and beverage companies included those that altered products and objects' availability (e.g., social listening to inform product development), position (e.g., decision technology and facial recognition to manipulate the position of products on menu boards), functionality (e.g., decision technology to prompt further purchases based on current selections) and presentation (e.g., augmented or virtual reality to deliver engaging and immersive marketing). CONCLUSIONS: Public health practitioners and policymakers must understand and engage with these technologies and tactics if they are to counter industry promotion of products harmful to health, particularly as investment by the industry in AI and other emerging technologies suggests their use will continue to grow.

Advances in Understanding Vector Behavioural Traits after Infection.

Vector behavioural traits, such as fitness, host-seeking, and host-feeding, are key determinants of vectorial capacity, pathogen transmission, and epidemiology of the vector-borne disease. Several studies have shown that infection with pathogens can alter these behavioural traits of the arthropod vector. Here, we review relevant publications to assess how pathogens modulate the behaviour of mosquitoes and ticks, major vectors for human diseases. The research has shown that infection with pathogens alter the mosquito's flight activity, mating, fecundity, host-seeking, blood-feeding, and adaptations to insecticide bed nets, and similarly modify the tick's locomotion, questing heights, vertical and horizontal walks, tendency to overcome obstacles, and host-seeking ability. Although some of these behavioural changes may theoretically increase transmission potential of the pathogens, their effect on the disease epidemiology remains to be verified. This study will not only help in understanding virus-vector interactions but will also benefit in establishing role of these behavioural changes in improved epidemiological models and in devising new vector management strategies.

Genomic mutations and changes in protein secondary structure and solvent accessibility of SARS-CoV-2 (COVID-19 virus).

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.

Efficient neural spike sorting using data subdivision and unification.

Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activities of the brain at very high resolution, data rate and fidelity. However, the evolution in spike sorting algorithms to deal with the aforementioned technological advancement and capability to quantify higher density data sets is somewhat limited. Both supervised and unsupervised clustering algorithms do perform well when the data to quantify is small, however, their efficiency degrades with the increase in the data size in terms of processing time and quality of spike clusters being formed. This makes neural spike sorting an inefficient process to deal with large and dense electrophysiological data recorded from brain. The presented work aims to address this challenge by providing a novel data pre-processing framework, which can enhance the efficiency of the conventional spike sorting algorithms significantly. The proposed framework is validated by applying on ten widely used algorithms and six large feature sets. Feature sets are calculated by employing PCA and Haar wavelet features on three widely adopted large electrophysiological datasets for consistency during the clustering process. A MATLAB software of the proposed mechanism is also developed and provided to assist the researchers, active in this domain.

Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting.

Deciphering useful information from electrophysiological data recorded from the brain, in-vivo or in-vitro, is dependent on the capability to analyse spike patterns efficiently and accurately. The spike analysis mechanisms are heavily reliant on the clustering algorithms that enable separation of spike trends based on their spatio-temporal behaviors. Literature review report several clustering algorithms over decades focused on different applications. Although spike analysis algorithms employ only a small subset of clustering algorithms, however, not much work has been reported on the compliance and suitability of such clustering algorithms for spike analysis. In our study, we have attempted to comment on the suitability of available clustering algorithms and performance capacity when exposed to spike analysis. In this regard, the study reports a compatibility evaluation on algorithms previously employed in spike sorting as well as the algorithms yet to be investigated for application in sorting neural spikes. The performance of the algorithms is compared in terms of their accuracy, confusion matrix and accepted validation indices. Three data sets comprising of easy, difficult, and real spike similarity with known ground-truth are chosen for assessment, ensuring a uniform testbed. The procedure also employs two feature-sets, principal component analysis and wavelets. The report also presents a statistical score scheme to evaluate the performance individually and overall. The open nature of the data sets, the clustering algorithms and the evaluation criteria make the proposed evaluation framework widely accessible to the research community. We believe that the study presents a reference guide for emerging neuroscientists to select the most suitable algorithms for their spike analysis requirements.

Dengue virus infection changes Aedes aegypti oviposition olfactory preferences.

Aedes aegypti mosquitoes, main vectors for numerous flaviviruses, have olfactory preferences and are capable of olfactory learning especially when seeking their required environmental conditions to lay their eggs. In this study, we showed that semiochemical conditions during Aedes aegypti larval rearing affected future female choice for oviposition: water-reared mosquitoes preferred to lay eggs in water or p-cresol containers, while skatole reared mosquitoes preferred skatole sites. Using two independent behavioural assays, we showed that this skatole preference was lost in mosquitoes infected with dengue virus. Viral RNA was extracted from infected female mosquito heads, and an increase of virus load was detected from 3 to 10 days post infection, indicating replication in the insect head and possibly in the central nervous system. Expression of selected genes, potentially implied in olfactory learning processes, were also altered during dengue infection. Based on these results, we hypothesise that dengue virus infection alters gene expression in the mosquito's head and is associated with a loss of olfactory preferences, possibly modifying oviposition site choice of female mosquitoes.

Electrophysiological evidence of RML12 mosquito cell line towards neuronal differentiation by 20-hydroxyecdysdone.

Continuous cell lines from insect larval tissues are widely used in different research domains, such as virology, insect immunity, gene expression, and bio pharmacology. Previous study showed that introduction of 20-hydroxyecdysone to Spodoptera cell line induced a neuron-like morphology with neurite extensions. Despite some results suggesting potential presence of neuro-receptors, no study so far has shown that these neuron-induced cells were functional. Here, using microelectrode arrays, we showed that the mosquito cell line, RML12, differentiated with 20-hydroxyecdysone, displays spontaneous electrophysiological activity. Results showed that these cells can be stimulated by GABAergic antagonist as well as nicotinic agonist. These results provide new evidence of neuron-like functionality of 20-hydroxyecdysone induced differentiated mosquito cell line. Finally, we used this new model to test the effects of two insecticides, temephos and permethrin. Our analysis revealed significant changes in the spiking activity after the introduction of these insecticides with prolonged effect on the neuronal activity. We believe that this differentiated mosquito neuronal cell model can be used for high-throughput screening of new pesticides on insect nervous system instead of primary neurons or in vivo studies.

Neurotropism and behavioral changes associated with Zika infection in the vector Aedes aegypti.

Understanding Zika virus infection dynamics is essential, as its recent emergence revealed possible devastating neuropathologies in humans, thus causing a major threat to public health worldwide. Recent research allowed breakthrough in our understanding of the virus and host pathogenesis; however, little is known on its impact on its main vector, Aedes aegypti. Here we show how Zika virus targets Aedes aegypti's neurons and induces changes in its behavior. Results are compared to dengue virus, another flavivirus, which triggers a different pattern of behavioral changes. We used microelectrode array technology to record electrical spiking activity of mosquito primary neurons post infections and discovered that only Zika virus causes an increase in spiking activity of the neuronal network. Confocal microscopy also revealed an increase in synapse connections for Zika virus-infected neuronal networks. Interestingly, the results also showed that mosquito responds to infection by overexpressing glutamate regulatory genes while maintaining virus levels. This neuro-excitation, possibly via glutamate, could contribute to the observed behavioral changes in Zika virus-infected Aedes aegypti females. This study reveals the importance of virus-vector interaction in arbovirus neurotropism, in humans and vector. However, it appears that the consequences differ in the two hosts, with neuropathology in human host, while behavioral changes in the mosquito vector that may be advantageous to the virus.

Zika virus-induced hyper excitation precedes death of mouse primary neuron.

BACKGROUND: Zika virus infection in new born is linked to congenital syndromes, especially microcephaly. Studies have shown that these neuropathies are the result of significant death of neuronal progenitor cells in the central nervous system of the embryo, targeted by the virus. Although cell death via apoptosis is well acknowledged, little is known about possible pathogenic cellular mechanisms triggering cell death in neurons. METHODS: We used in vitro embryonic mouse primary neuron cultures to study possible upstream cellular mechanisms of cell death. Neuronal networks were grown on microelectrode array and electrical activity was recorded at different times post Zika virus infection. In addition to this method, we used confocal microscopy and Q-PCR techniques to observe morphological and molecular changes after infection. RESULTS: Zika virus infection of mouse primary neurons triggers an early spiking excitation of neuron cultures, followed by dramatic loss of this activity. Using NMDA receptor antagonist, we show that this excitotoxicity mechanism, likely via glutamate, could also contribute to the observed nervous system defects in human embryos and could open new perspective regarding the causes of adult neuropathies. CONCLUSIONS: This model of excitotoxicity, in the context of neurotropic virus infection, highlights the significance of neuronal activity recording with microelectrode array and possibility of more than one lethal mechanism after Zika virus infection in the nervous system.