Insects Neural Model: Potential Alternate to Mammals for Electrophysiological Studies

Gaburro, Julie; Nahavandi, Saeid; Bhatti, Asim

doi:10.1007/978-981-10-3957-7_6

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…Dengue virus adds to this burden with approximately 390 million infections annually [3]. These diseases not only impact human health but also affect mosquito behaviours, including locomotion and reproduction [4], with recent studies highlighting the effects of virus infection on mosquito nervous systems [5][6][7]. Despite ongoing persistent mosquito control efforts that have been active for over a century, mosquito-borne diseases still persist worldwide [8].…”

Section: Introductionmentioning

confidence: 99%

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes

Javed,

López-Denman,

Paradkar

et al. 2024

Preprint

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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 96.25–99.13% 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.

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Section: Introductionmentioning

confidence: 99%

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes

Javed,

López-Denman,

Paradkar

et al. 2024

Preprint

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“…These pathogens also affect the behaviours of mosquitoes [ 2 ], including locomotion [ 3 – 5 ], oviposition preferences [ 6 ], fertility [ 7 ] and feeding [ 8 , 9 ]. Moreover, recent research has shown that vector-borne viruses can also infect and significantly impact the vector nervous system [ 10 – 12 ]. Monitoring mosquitoes’ flight trajectories can help in understanding and defining their locomotion behaviour, which can ultimately assist in determining their fitness, improving existing epidemiological models [ 13 ] and developing effective mosquito traps [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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

2023

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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.

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“…Infections with pathogens can modulate different behavioural and fitness traits of mosquitoes such as locomotion, blood-feeding, fecundity, and fertility [ 7 ]. Mosquito-borne viruses can also significantly impact the mosquito’s nervous system, leading to behavioural changes that influence their transmission of mosquito-borne diseases [ 8 – 10 ]. In an epidemiological context, it is necessary to assess mosquito fitness, as this may aid in understanding how different mosquito populations adapt to environmental conditions [ 11 , 12 ] and the interactions between vectors and their pathogens [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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

Javed,

López-Denman,

Paradkar

et al. 2023

Parasites Vectors

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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. Graphical Abstract

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Insects Neural Model: Potential Alternate to Mammals for Electrophysiological Studies

Cited by 7 publications

References 49 publications

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes

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

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

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