发表机构
Universidade Federal do Rio Grande do Sul（南里奥格兰德联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过实验测量草履虫和卤虫在胁迫环境下的运动轨迹，构建粗粒度随机模型，揭示环境胁迫改变运动统计特征，表明轨迹指标可作为生理应激指示器。
AI 中文摘要
微生物的运动为研究活性物质及其对环境扰动的响应提供了一个自然框架。我们在受控条件下实验研究了草履虫（Paramecium caudatum）和卤虫（Artemia salina）的运动轨迹。利用视频显微技术，我们随时间重建了个体轨迹。在基线条件之外，我们还分析了胁迫环境，其中草履虫暴露于有毒试剂，而卤虫被置于蒸馏水中。为了表征运动，我们计算了基于轨迹的指标，包括均方位移、速度分布、持续时间、曲折度和回转半径。这些观测量量化了不同物种和条件下运动模式的变化。为了解释动力学，我们开发了直接从实验数据参数化的粗粒度随机模型。草履虫在跑动-翻滚（run-and-tumble）框架内描述，包括胁迫下的逆转事件。相比之下，卤虫需要一种带有持续取向噪声的类跑动-翻滚描述，以解释跑动过程中持续的方向波动。这些模型重现了用于比较的主要轨迹级观测量，即曲折度和回转半径。这种结合方法提供了关于环境胁迫如何改变运动统计特征的见解。我们的结果突出了运动统计特征的系统性差异，表明轨迹指标可以作为生理应激的指示器。这项工作为活性物质实验提供了一个可访问的框架，其中个体级统计可以直接测量和建模。
英文摘要
The motility of microorganisms provides a natural framework for studying active matter and its response to environmental perturbations. We experimentally investigate the trajectories of \textit{Paramecium caudatum} and \textit{Artemia salina} under controlled conditions. Using video microscopy, we reconstruct individual trajectories over time. Beyond baseline conditions, we analyze stressed environments where \textit{P. caudatum} are exposed to a toxic agent and \textit{A. salina} are placed in distilled water. To characterize the motion, we compute trajectory-based metrics, including mean square displacement, velocity distributions, persistence times, tortuosity, and radius of gyration. These observables quantify changes in motility patterns across species and conditions. To interpret the dynamics, we develop coarse-grained stochastic models parameterized directly from experimental data. \textit{P. caudatum} is described within a run-and-tumble framework, including reversal events under stress. In contrast, \textit{A. salina} requires a run-and-tumble-like description with persistent orientational noise, accounting for continuous directional fluctuations during runs. The models reproduce the main trajectory-level observables used for comparison, namely tortuosity and radius of gyration. This combined approach provides insight into how environmental stress modifies locomotion statistics. Our results highlight systematic differences in the statistical signatures of motion, suggesting that trajectory metrics may serve as indicators of physiological stress. This work provides an accessible framework for active matter experiments where individual-level statistics can be directly measured and modeled.
CommentsAccepted for publication in Soft Matter
Journal refSoft Matter (2026)
