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新型图像分析方法揭示了黏菌网络适应性微调方面的新见解。

Novel image-analytic approach reveals new insights in fine-tuning of slime mould network adaptation.

作者信息

Rosina Philipp, Grube Martin

机构信息

Institute of Biology, University of Graz, Graz 8010, Austria.

出版信息

R Soc Open Sci. 2024 Oct 30;11(10):240950. doi: 10.1098/rsos.240950. eCollection 2024 Oct.

Abstract

This study introduces a novel methodology to explore the network dynamics of , an organism celebrated for its remarkable adaptive capabilities. We used two innovative techniques to analyse its growth behaviour and network modifications under stress conditions, including starvation and differential epinephrine exposures. The first method provided a quantitative assessment of growth and exploration over time. The second method provided a detailed examination of vein diameter and contraction patterns, illuminating the physiological adjustments undergoes in response to environmental challenges. By integrating these approaches, we were able to estimate the total network volume of the organism, with a focus on the normalized estimated volume, unveiling insightful aspects of its structural adaptations. While starvation reduced the volume, indicating a significant structural compromise, low and high epinephrine concentrations maintained a volume-to-area ratio comparable with the control. Determining the fractal dimension of the networks over time revealed a fine-tuning of the network complexity in response to environmental conditions, with significant reductions under stress indicating a constrained network adaptation strategy. These methods, novel in their application to , provide a framework for future studies and a basis for exploring complex network behaviours with potential applications in bioengineering and adaptive network design.

摘要

本研究引入了一种新颖的方法来探索[生物名称]的网络动态,该生物以其卓越的适应能力而闻名。我们使用了两种创新技术来分析其在应激条件下的生长行为和网络变化,包括饥饿和不同肾上腺素暴露。第一种方法提供了随时间的生长和探索的定量评估。第二种方法详细检查了静脉直径和收缩模式,阐明了[生物名称]在应对环境挑战时所经历的生理调节。通过整合这些方法,我们能够估计该生物的总网络体积,重点是归一化估计体积,揭示其结构适应的深刻方面。饥饿减少了体积,表明结构上有重大损害,而低和高肾上腺素浓度保持了与对照相当的体积与面积比。随着时间的推移确定网络的分形维数揭示了网络复杂性响应环境条件的微调,应激下的显著降低表明网络适应策略受到限制。这些方法在应用于[生物名称]时是新颖的,为未来的研究提供了一个框架,并为探索具有生物工程和自适应网络设计潜在应用的复杂网络行为奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b45/11528663/2855fcd7f0c3/rsos.240950.f001.jpg

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