Dual‐fluorescence imaging and automated trophallaxis detection for studying multi‐nutrient regulation in superorganisms

In the related manuscript, we present a dual-fluorescence imaging setup designed to track two food sources, each labeled with a different fluorophore, as they are disseminated throughout a freely behaving colony of individually tagged ants. Additionally, our image-based deep learning algorithm for automatic detection of ant trophallaxis events efficiently yields a detailed record of all food-transfer interactions. Using a series of calibration experiments, we demonstrate the reliability of our measurements. We then exemplify the capabilities of our new method by tracking food dissemination in a colony of Camponotus sanctus ants supplied with two nutritionally-distinct food sources. This dataset contains data and Matlab code related to: 1. Calibration and validation of the dual-fluorescence imaging technique 2. Training and employing a deep neural network for detecting trophallaxis 3. Sample data from a multinutrient feeding experiment在相关研究手稿中，我们搭建了一套双荧光成像装置，用于追踪两种分别标记有不同荧光团（fluorophore）的食物源在自由活动且个体均被标记的蚁群中的传播过程。此外，我们开发了基于图像的深度学习算法，可自动检测蚂蚁交哺行为（trophallaxis）事件，能够高效生成所有食物转移交互行为的详细记录。我们通过一系列校准实验验证了测量结果的可靠性，随后以饲喂两种营养成分存在差异的食物源的圣弓背蚁（Camponotus sanctus）蚁群为研究对象，追踪其食物传播过程，以此展示新方法的应用能力。本数据集包含与以下内容相关的数据及Matlab代码：1. 双荧光成像技术的校准与验证；2. 用于检测交哺行为的深度神经网络的训练与部署；3. 多营养饲喂实验的样本数据。