BIGDATA: Collaborative Research: IA: Large-Scale Multi-Parameter Analysis of Honeybee Behavior in their Natural Habitat

BIGDATA：协作研究：IA：蜜蜂自然栖息地行为的大规模多参数分析

• 批准号: 1633184
• 负责人: Jose Agosto Rivera
• 金额: $ 34.32万
• 依托单位: University of Puerto Rico-Rio Piedras
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633184&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; IA; Large

Honey bees exhibit highly complex behavior and are vital for our agriculture. Due to the rich social organization of bees, the overall performance and health of a bee colony depends both on a successful division of labor among the bees and on adequate reaction to the environment, which involves complex behavioral patterns and biological mechanisms. Much remains to be discovered on these matters as research is currently limited by our ability to effectively collect and analyze individual's behavior at large scale, out of the laboratory. The technology developed in this project will enable biologists to study the individual behavior of thousands of bees over extended periods of time. It builds on innovative algorithms and software to analyze big data collected from colonies in the field. Study of behavioral patterns at such scale will provide unique information to advance knowledge on biological processes such as circadian rhythms that influence bee behavior in addition to playing an important role in animals and humans. The models developed will help better understand factors involved in colony collapse disorder, thus guiding future research on threats to such an important pollinator. This work will be performed through the tight collaboration of a multi-disciplinary team of researchers to combine the latest advances in computer science and data science with expertise in biology. It will provide the opportunity to train students from underrepresented minority on research at the intersection of these fields and to reach more than 600 undergraduate students, high school students, and the general public about how the Big Data approach can contribute to current scientific and ecological challenges.The project will develop a platform for the high-throughput analysis of individual insect behaviors and gain new insights into the role of individual variations of behavior on bee colony performance. Joint video and sensor data acquisition will monitor marked individuals at multiple colonies over large continuous periods, generating the first datasets of bee activities of this kind on such a scale. Algorithms and software will be developed to take advantage of a High Performance Computing facility to perform the analysis of these massive datasets. Semi-supervised machine learning will leverage the large amount of data available to facilitate the creation of new detectors for parameters such as pollen carrying bees or fanning behavior, currently annotated manually. Predictive models and functional data analysis methods will be developed to find patterns in individual behavior based on multiple parameters and over large temporal scales. These advances are expected to help uncover mechanisms of individual variations previously unobservable. They will enable the first large scale biological study on the circadian rhythms of the bee based on the variations in behavior of individuals in multiple activities instead of reasoning on single activities or averages. Progress, datasets and software will be shared with the community on the project website (sites.google.com/a/upr.edu/bigdbee).

蜜蜂表现出高度复杂的行为，对我们的农业至关重要。由于蜜蜂丰富的社会组织，蜂群的整体表现和健康既取决于蜜蜂之间的成功分工，也取决于蜜蜂对环境的充分反应，这涉及复杂的行为模式和生物机制。在这些问题上还有很多有待发现的地方，因为目前的研究受到我们在实验室之外大规模有效收集和分析个人行为的能力的限制。在这个项目中开发的技术将使生物学家能够在很长一段时间内研究成千上万只蜜蜂的个体行为。它建立在创新算法和软件的基础上，分析从野外菌落收集的大数据。对这种规模的行为模式的研究将提供独特的信息，以推进生物过程的知识，如影响蜜蜂行为的昼夜节律，除了在动物和人类中发挥重要作用之外。开发的模型将有助于更好地了解蜂群崩溃失调的相关因素，从而指导未来对这种重要传粉媒介的威胁的研究。这项工作将通过一个多学科研究团队的紧密合作来完成，将计算机科学和数据科学的最新进展与生物学的专业知识结合起来。它将提供机会，培训来自未被充分代表的少数民族的学生在这些领域的交叉点进行研究，并向600多名本科生、高中生和公众介绍大数据方法如何为当前的科学和生态挑战做出贡献。该项目将为个体昆虫行为的高通量分析开发一个平台，并获得个体行为变化对蜂群性能的作用的新见解。联合视频和传感器数据采集将在大的连续时期内监测多个蜂群中的标记个体，从而产生第一个如此大规模的蜜蜂活动数据集。算法和软件将被开发，以利用高性能计算设备来执行这些海量数据集的分析。半监督机器学习将利用大量可用数据来促进创建新的参数检测器，例如花粉携带蜜蜂或扇风行为，目前手工注释。将开发预测模型和功能数据分析方法，以发现基于多个参数和大时间尺度的个人行为模式。这些进展有望帮助揭示以前无法观察到的个体变异的机制。它们将使基于个体在多种活动中的行为变化的蜜蜂昼夜节律的第一次大规模生物学研究成为可能，而不是基于单一活动或平均值的推理。项目进展、数据集和软件将在项目网站（sites.google.com/a/upr.edu/bigdbee）上与社区分享。

项目成果

Clustering Honeybees by Its Daily Activity [Clustering Honeybees by Its Daily Activity]

按蜜蜂的每日活动进行聚类 [按每日活动对蜜蜂进行聚类]

• DOI: 10.5220/0007387505980604
• 发表时间: 2019
• 期刊: Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods
• 作者: Acuna, Edgar;Palomino, Velcy;Agosto, José;Mégret, Rémi;Giray, Tugrul;Prado, Alberto;Alaux, Cédric;Conte, Yves
• 通讯作者: Conte, Yves

Soybean aphid biotype 1 genome: Insights into the invasive biology and adaptive evolution of a major agricultural pest

• DOI: 10.1016/j.ibmb.2020.103334
• 发表时间: 2020-05-01
• 期刊: INSECT BIOCHEMISTRY AND MOLECULAR BIOLOGY
• 影响因子: 3.8
• 作者: Giordano, Rosanna;Donthu, Ravi Kiran;Zhan, Shuai
• 通讯作者: Zhan, Shuai

Parallel mechanisms of visual memory formation across distinct regions of the honey bee brain

蜜蜂大脑不同区域视觉记忆形成的并行机制

• DOI: 10.1242/jeb.242292
• 发表时间: 2021
• 期刊: Journal of Experimental Biology
• 影响因子: 2.8
• 作者: Avalos, Arián;Traniello, Ian M.;Pérez Claudio, Eddie;Giray, Tugrul
• 通讯作者: Giray, Tugrul

LabelBee: a web platform for large-scale semi-automated analysis of honeybee behavior from video

LabelBee：用于从视频中大规模半自动分析蜜蜂行为的网络平台

• DOI: 10.1145/3359115.3359120
• 发表时间: 2019
• 期刊: Proceedings of Artificial Intelligence for Data Discovery and Reuse (AIDR’19
• 作者: Mégret, Rémi;Rodriguez, Ivan F.;Ford, Isada Claudio;Acuña, Edgar;Agosto-Rivera, Jose L.;Giray, Tugrul
• 通讯作者: Giray, Tugrul

Genomic regions influencing aggressive behavior in honey bees are defined by colony allele frequencies

• DOI: 10.1073/pnas.1922927117
• 发表时间: 2020-07-21
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Avalos, Arian;Fang, Miaoquan;Hudson, Matthew E.
• 通讯作者: Hudson, Matthew E.