Bug-beats: using wing beat patterns for automated detection of mosquitoes

Bug-beats：使用翅膀拍打模式自动检测蚊子

• 批准号: 2333391
• 金额: --
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2333391
• 关键词: Bug; beats; using; wing; beat

Mosquito-borne diseases are a major challenge for human health, affecting 700 million people every year and resulting in over one million deaths. In animals, mosquitoes also transmit a number of diseases that have major effects on animal health and welfare, and cause significant economic losses. Reliable information on population and fine-scale spatial distribution of key vector mosquito species is of major importance for surveillance and implementation of appropriate control methods and development of eco-epidemiologic models. Current mosquito monitoring methods rely heavily on traps and are hindered by laborious procedures where each insect has to be counted and identified by hand, or by PCR which is expensive. Recent advances in communications technologies offer a unique opportunity for exploitation in vector-borne disease surveillance. Combined with our understanding of mosquito wing beat patterns and frequencies, an automated method of detecting mosquitoes through a microphone could be a reality. Mosquito wing beat patterns have been shown to differ significantly between species and, therefore, could be used to identify mosquito species and sex (which is important for surveillance). They could also be used further to determine whether mosquitoes are infected with a pathogen or resistant to insecticides. For example, mosquito host-seeking behaviour and fitness is known to be significantly affected by their resistance and infection statuses, and, therefore, their wing beat patterns are likely to be altered, but whether wing beat or flight patterns can be used to characterise infection or resistance status has never been investigated. Fundamental mosquito behavioural and computational studies are needed before sound can be used as a reliable predictor for identification of mosquitoes in practice. Here, we propose a fusion of several disciplines to create a unique and compelling studentship which will investigate the wing beat patterns of mosquitoes of medical and veterinary importance to determine whether species, sex, resistance status and malaria infection status can be detected. The objectives are to:1. Investigate mosquito flight patterns and wing kinematic patterns of three different mosquito species (Anopheles gambiae, Culex quinquefasciatus and Aedes aegypti), in olfactometer behavioural and electrophysiology studies, and use image and acoustic processing to determine whether these patterns can be used as predictors to speciate mosquitoes (at RVC and LSHTM) (Year 1)2. Investigate wing kinematics of a) insecticide susceptible and resistant strains of Anopheles gambiae, and b) malaria-infected or uninfected females to determine whether there are distinct patterns that can be used as a predictor of resistance and infection status (at LSHTM and RVC; Year 2)3. Develop a detection system, incorporating miniature microphone technology, and an algorithm that can be used to detect specific wing beat patterns (at RVC; Year 2) 4. Design and build a prototype trap using a lure (during the Rentokil placement), containing a prototype detection device and algorithm that can be trialled with mosquitoes in small-scale behavioural olfactometer assays, followed by larger scale free flight rooms at LSHTM (Year 3 and 4)5. Test the new surveillance trap and detection device prototype in a small scale pilot field trial in Kenya (at an operational Rentokil site), and compare with conventional trapping and identification methods (Year 4).This interdisciplinary project is novel, challenging and we believe it is feasible within the timeframe of the studentship. It will provide a unique opportunity for a student to train with specialists in medical and veterinary disciplines across entomology, analytical chemistry, insect behaviour, computational biology, aerospace engineering, biomechanics. The results are likely to underpin improvements in both human and animal health. Thus, the project aligns the mission of the academi

蚊媒疾病是人类健康面临的重大挑战，每年影响7亿人，造成100多万人死亡。在动物中，蚊子也传播许多疾病，对动物健康和福利产生重大影响，并造成重大经济损失。关于主要病媒蚊种的种群和小尺度空间分布的可靠信息对于监测和实施适当的控制方法以及开发生态流行病学模型至关重要。目前的蚊子监测方法严重依赖于诱捕器，并且受到费力的程序的阻碍，其中每种昆虫必须手工计数和识别，或者通过昂贵的PCR进行。通信技术的最新进展为利用媒介传播疾病监测提供了独特的机会。结合我们对蚊子翅膀拍打模式和频率的理解，通过麦克风检测蚊子的自动方法可能成为现实。蚊子翅膀的拍打模式在不同物种之间有很大的差异，因此可以用来识别蚊子的物种和性别（这对监测很重要）。它们还可以进一步用于确定蚊子是否感染了病原体或对杀虫剂有抗药性。例如，已知蚊子寻找宿主的行为和适应性受到它们的抵抗力和感染状态的显著影响，因此，它们的翅膀拍打模式可能会改变，但翅膀拍打或飞行模式是否可以用于抵抗感染或抵抗状态从未被研究过。在将声音作为可靠的预测因子用于实际识别蚊子之前，需要进行基本的蚊子行为和计算研究。在这里，我们提出了几个学科的融合，以创建一个独特的和令人信服的助学金，将调查的医学和兽医重要性的蚊子的翅膀拍模式，以确定是否可以检测到物种，性别，耐药性状态和疟疾感染状态。目标是：1.在嗅觉仪行为和电生理学研究中调查三种不同蚊子物种（冈比亚按蚊、致倦库蚊和埃及伊蚊）的蚊子飞行模式和翅膀运动模式，并使用图像和声学处理来确定这些模式是否可用作蚊子物种识别的预测因子（在RVC和LSHTM）（第1年）2。研究a）冈比亚按蚊的杀虫剂敏感和抗性品系，和B）疟疾感染或未感染雌性的翅运动学，以确定是否存在可用作抗性和感染状态的预测因子的不同模式（在LSHTM和RVC;第2年）3.开发一个检测系统，结合微型麦克风技术，和一个算法，可用于检测特定的翅膀拍模式（在RVC; 2年）4。使用诱饵设计和建造原型诱捕器（在能多洁放置期间），包含原型检测设备和算法，可以在小规模的行为嗅觉仪试验中对蚊子进行试验，然后在LSHTM进行更大规模的自由飞行室（第3年和第4年）5。在肯尼亚的一个小规模试点现场试验中测试新的监测陷阱和检测设备原型（在能多洁的一个运营现场），并与传统的诱捕和识别方法进行比较（4年级）。这个跨学科的项目是新颖的，具有挑战性的，我们相信它在学生奖学金的时间范围内是可行的。它将为学生提供一个独特的机会，与昆虫学，分析化学，昆虫行为，计算生物学，航空航天工程，生物力学等医学和兽医学科的专家进行培训。这些结果可能会支持人类和动物健康的改善。因此，该项目与学院的使命保持一致，