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[1] arXiv:2508.03584 (交叉列表自 eess.SP) [中文pdf, pdf, html, 其他]

标题： 解码和工程化植物生物群通信以实现智能农业 显示英文标题

标题： Decoding and Engineering the Phytobiome Communication for Smart Agriculture

Fatih Gulec, Hamdan Awan, Nigel Wallbridge, Andrew W. Eckford

评论： 正在修订中，供IEEE通信杂志使用

主题： 信号处理 (eess.SP) ; 人工智能 (cs.AI) ; 新兴技术 (cs.ET) ; 网络与互联网架构 (cs.NI) ; 分子网络 (q-bio.MN)

智能农业应用将物联网和机器学习/人工智能（ML/AI）等技术整合到农业中，有望解决日益增长的粮食需求、环境污染和水资源短缺等现代挑战。 随着植物生物组（phytobiome）概念的提出，该概念定义了包括植物、其环境和相关生物在内的区域，以及分子通信（MC）的最新出现，利用通信理论来推进农业科学和实践存在重要机遇。 在本文中，我们旨在使用通信工程的视角，以全面理解植物生物组通信，并弥合植物生物组通信与智能农业之间的差距。 首先，介绍了通过分子和电生理信号进行植物生物组通信的概述，并提出了一个将植物生物组建模为通信网络的多尺度框架。 然后，通过植物实验展示了如何利用该框架对电生理信号进行建模。 此外，还提出了通过工程化植物生物组通信实现的智能农业应用，例如智能灌溉和农药的定向输送。 这些应用将ML/AI方法与由MC支持的生物纳米事物互联网相结合，为更高效、可持续和环保的农业生产铺平道路。 最后，讨论了这些应用的实施挑战、开放的研究问题和工业前景。 显示英文摘要

Smart agriculture applications, integrating technologies like the Internet of Things and machine learning/artificial intelligence (ML/AI) into agriculture, hold promise to address modern challenges of rising food demand, environmental pollution, and water scarcity. Alongside the concept of the phytobiome, which defines the area including the plant, its environment, and associated organisms, and the recent emergence of molecular communication (MC), there exists an important opportunity to advance agricultural science and practice using communication theory. In this article, we motivate to use the communication engineering perspective for developing a holistic understanding of the phytobiome communication and bridge the gap between the phytobiome communication and smart agriculture. Firstly, an overview of phytobiome communication via molecular and electrophysiological signals is presented and a multi-scale framework modeling the phytobiome as a communication network is conceptualized. Then, how this framework is used to model electrophysiological signals is demonstrated with plant experiments. Furthermore, possible smart agriculture applications, such as smart irrigation and targeted delivery of agrochemicals, through engineering the phytobiome communication are proposed. These applications merge ML/AI methods with the Internet of Bio-Nano-Things enabled by MC and pave the way towards more efficient, sustainable, and eco-friendly agricultural production. Finally, the implementation challenges, open research issues, and industrial outlook for these applications are discussed.

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[2] arXiv:2403.17202 (替换) [中文pdf, pdf, html, 其他]

标题： 大型生化网络的通用温度响应 显示英文标题

标题： The generic temperature response of large biochemical networks

Julian B. Voits, Ulrich S. Schwarz (Heidelberg University)

评论： 24页，26图

主题： 生物物理 (physics.bio-ph) ; 统计力学 (cond-mat.stat-mech) ; 分子网络 (q-bio.MN)

生物系统对相对较小的温度变化非常敏感。最明显的例子是发烧，当体温仅升高几开尔文时，就会对我们的免疫系统及其对抗病原体的方式产生强烈影响。另一个非常重要的例子是气候变化，即使温度变化很小，也会导致生态系统发生剧烈变化。尽管普遍认为温度升高的主要效应是根据阿伦尼乌斯方程加速生化反应，但尚不清楚它如何影响具有复杂结构的大型生化网络。对于果蝇和青蛙这样的发育系统，已经表明系统对温度的响应以一种特有的方式偏离单个反应的线性阿伦尼乌斯图，但尚未给出严格的解释。在这里，我们使用生化主方程的平均首次通过时间的图论解释，来提供一个统计描述。我们发现，在系统规模较大且网络偏向于目标状态的情况下，阿伦尼乌斯图通常是二次的，这与大型网络的数值模拟以及果蝇和青蛙发育时间的实验数据高度一致。我们还讨论了在什么条件下这种普遍响应可能被破坏，例如对于只有一棵生成树的线性链。 显示英文摘要

Biological systems are remarkably susceptible to relatively small temperature changes. The most obvious example is fever, when a modest rise in body temperature of only few Kelvin has strong effects on our immune system and how it fights pathogens. Another very important example is climate change, when even smaller temperature changes lead to dramatic shifts in ecosystems. Although it is generally accepted that the main effect of an increase in temperature is the acceleration of biochemical reactions according to the Arrhenius equation, it is not clear how it affects large biochemical networks with complicated architectures. For developmental systems like fly and frog, it has been shown that the system response to temperature deviates in a characteristic manner from the linear Arrhenius plot of single reactions, but a rigorous explanation has not been given yet. Here we use a graph-theoretical interpretation of the mean first-passage times of a biochemical master equation to give a statistical description. We find that in the limit of large system size and if the network has a bias towards a target state, then the Arrhenius plot is generically quadratic, in excellent agreement with numerical simulations for large networks as well as with experimental data for developmental times in fly and frog. We also discuss under which conditions this generic response can be violated, for example for linear chains, which have only one spanning tree.