Enrique M. Muro, Fernando J. Ballesteros, Bartolo Luque, and Jordi Bascompte

PNAS 122 (13) e2422968122

The origin of eukaryotes represents one of the most significant events in evolution since it allowed the posterior emergence of multicellular organisms. Yet, it remains unclear how existing regulatory mechanisms of gene activity were transformed to allow this increase in complexity. Here, we address this question by analyzing the length distribution of proteins and their corresponding genes for 6,519 species across the tree of life. We find a scale-invariant relationship between gene mean length and variance maintained across the entire evolutionary history. Using a simple model, we show that this scale-invariant relationship naturally originates through a simple multiplicative process of gene growth. During the first phase of this process, corresponding to prokaryotes, protein length follows gene growth. At the onset of the eukaryotic cell, however, mean protein length stabilizes around 500 amino acids. While genes continued growing at the same rate as before, this growth primarily involved noncoding sequences that complemented proteins in regulating gene activity. Our analysis indicates that this shift at the origin of the eukaryotic cell was due to an algorithmic phase transition equivalent to that of certain search algorithms triggered by the constraints in finding increasingly larger proteins.

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Adriano B. L. Tort, Diego A. Laplagne, Andreas Draguhn & Joaquin Gonzalez 

Nature Reviews Neuroscience (2025)

Neuronal activities that synchronize with the breathing rhythm have been found in humans and a host of mammalian species, not only in brain areas closely related to respiratory control or olfactory coding but also in areas linked to emotional and higher cognitive functions. In parallel, evidence is mounting for modulations of perception and action by the breathing cycle. In this Review, we discuss the extent to which brain activity locks to breathing across areas, levels of organization and brain states, and the physiological origins of this global synchrony. We describe how waves of sensory activity evoked by nasal airflow spread through brain circuits, synchronizing neuronal populations to the breathing cycle and modulating faster oscillations, cell assembly formation and cross-area communication, thereby providing a mechanistic link from breathing to neural coding, emotion and cognition. We argue that, through evolution, the breathing rhythm has come to shape network functions across species.

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Selin E. Nugen

AI & SOCIETY

This paper examines the application of evolutionary analogy in AI (artificial intelligence) research, focussing on narratives that perpetuate individuated and autonomous imaginaries of AI systems through biological diction. AI research has long drawn inspiration from evolution to design and predict algorithmic change. Occasionally, these narratives extend inspiration to reimagine AI as a non-human species subject to the same evolutionary pressures as biological organisms. As AI technologies embed more pervasively in public life and require critical perspectives on their social impacts, these comparisons in AI discourse raise critical questions about the limits of and responsibility in employing such analogies and their potential impact on how broader audiences consume and perceive AI systems. This paper examines the diverse ways and intentions behind how evolution is invoked in AI research narratives by analysing the adaptation of individuating evolutionary language and concepts across three fields of AI-related research: evolutionary computing, Artificial Life, and existential risk. It scrutinises the challenge of accurate scientific communication when drawing inspiration from biological evolution and assigning organismal attributes to digital technologies whilst decontextualising wider evolutionary scholarly discourses. I argue that the intertwined history between evolutionary theory and technological change paired with the potential risks to wider perceptions of AI and biological evolution, requires (1) strategic consideration about the limits of evolutionary analogies in categorising AI in relation to biological organisms, balancing creative inspiration with scientific caution and (2) active, collaborative multidisciplinary engagement with addressing potential misinformation, recognising that biological narratives have sociopolitical implications that influence human interaction with machines.

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Karan Singh, V. K. Chandrasekar, Wei Zou, Jürgen Kurths & D. V. Senthilkumar 

Communications Physics volume 8, Article number: 170 (2025)

Cascading failures pose a significant threat to the stability and functionality of complex systems, making their mitigation a crucial area of research. While existing strategies aim to enhance network robustness, identifying an optimal set of critical nodes that mediates the cascade for protection remains a challenging task. Here, we present a robust and pragmatic framework that effectively mitigates the cascading failures by strategically identifying and securing critical nodes within the network. Our approach leverages a graph coloring technique to identify the critical nodes using the local network topology, and results in a minimal set of critical nodes to be protected yet maximally effective in mitigating the cascade thereby retaining a large fraction of the network intact. Our method outperforms existing mitigation strategies across diverse network configurations and failure scenarios. An extensive empirical validation using real-world networks highlights the practical utility of our framework, offering a promising tool for enhancing network robustness in complex systems.

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