Machine Learning for Characterization of Insect Vector Feeding.
Machine Learning for Characterization of Insect Vector Feeding.
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DOI：
10.1371/journal.pcbi.1005158
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发表时间：
2016-11
影响因子：
4.3
通讯作者：
Lapointe SL
中科院分区：
文献类型：
--
作者：
Willett DS;George J;Willett NS;Stelinski LL;Lapointe SL
以植物和动物体液为食的昆虫主要通过传播植物和动物的病原体，对全世界的人类、牲畜和农业造成毁灭性的破坏。通过监测昆虫-食物源喂食电路上的电压变化，可以记录吸吮昆虫成功传播病原体所需的喂食过程。这种监测的结果传统上是由人工检查的，这是一个缓慢而繁重的过程。我们教了一个计算机程序，自动分类先前描述的昆虫取食模式参与传播的病原体引起柑橘绿化病。我们还展示了这种分析如何有助于发现以前未被认识到的喂养状态，并可用于表征植物的抗性机制。这一进展大大减少了分析昆虫摄食所需的时间和精力，并应有助于开发，筛选和测试新的干预策略，以破坏影响农业，畜牧业和人类健康的病原体传播。昆虫媒介通过在宿主组织和体液中的探测，获得并传播引起传染病的病原体。通过电路连接昆虫和它们的食物来源，计算机使用机器学习算法，可以学习识别涉及病原体传播的昆虫进食模式。此外，这些机器学习算法可以向我们展示昆虫进食的新模式，并揭示导致病原体传播中断的机制。虽然我们使用这些技术来帮助挽救柑橘产业，使其免受昆虫传播的细菌病原体的严重影响，但这种对昆虫媒介饲养的智能监测将在破坏导致农业，牲畜和人类健康疾病的病原体传播方面取得进展。
Insects that feed by ingesting plant and animal fluids cause devastating damage to humans, livestock, and agriculture worldwide, primarily by transmitting pathogens of plants and animals. The feeding processes required for successful pathogen transmission by sucking insects can be recorded by monitoring voltage changes across an insect-food source feeding circuit. The output from such monitoring has traditionally been examined manually, a slow and onerous process. We taught a computer program to automatically classify previously described insect feeding patterns involved in transmission of the pathogen causing citrus greening disease. We also show how such analysis contributes to discovery of previously unrecognized feeding states and can be used to characterize plant resistance mechanisms. This advance greatly reduces the time and effort required to analyze insect feeding, and should facilitate developing, screening, and testing of novel intervention strategies to disrupt pathogen transmission affecting agriculture, livestock and human health. Insect vectors acquire and transmit pathogens causing infectious diseases through probing on host tissues and ingesting host fluids. By connecting insects and their food source via an electrical circuit, computers, using machine learning algorithms, can learn to recognize insect feeding patterns involved in pathogen transmission. In addition, these machine learning algorithms can show us novel patterns of insect feeding and uncover mechanisms that lead to disruption of pathogen transmission. While we use these techniques to help save the citrus industry from a major decline due to an insect-transmitted bacterial pathogen, such intelligent monitoring of insect vector feeding will engender advances in disrupting transmission of pathogens causing disease in agriculture, livestock, and human health.
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影响因子：
3.7
作者：
Ammar, El-Desouky;Richardson, Matthew L.;Shatters, Robert G., Jr.
通讯作者：
Shatters, Robert G., Jr.
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB
影响因子：
3.7
作者：
Dutt M;Barthe G;Irey M;Grosser J
通讯作者：
Grosser J
影响因子：
1.9
作者：
Bonani, J. P.;Fereres, A.;Lopes, J. R. S.
通讯作者：
Lopes, J. R. S.
影响因子：
2.2
作者：
Civolani, Stefano;Leis, Marilena;Tjallingii, W. Freddy
通讯作者：
Tjallingii, W. Freddy