Malbor Asllani, Alex Arenas

Phys. Rev. E 111, 044306

Chimera states, marked by the coexistence of order and disorder in systems of coupled oscillators, have captivated researchers with their existence and intricate patterns. Despite ongoing advances, a full understanding of the genesis of chimera states remains challenging. This work formalizes a systematic method by evoking pattern formation theory to explain the emergence of chimera states in complex networks, in a similar way to how Turing patterns are produced. Employing linear stability analysis and the spectral properties of complex networks, we show that the randomness of network topology, as reflected in the localization of the graph Laplacian eigenvectors, determines the emergence of chimera patterns, underscoring the critical role of network structure. In particular, this approach explains how amplitude and phase chimeras arise separately and explores whether phase chimeras can be chaotic or not. Our findings suggest that chimeras result from the interplay between local and global dynamics at different timescales. Validated through simulations and empirical network analyses, our method enriches the understanding of coupled oscillator dynamics.

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Jin Liu, Wenbin Yu, ChengJun Zhang, JiaRui Gu, Louyang Yu, Guancheng Zhong

Physica A: Statistical Mechanics and its Applications

Identifying influential spreaders in complex networks remains a significant research topic. Previous studies have primarily focused on estimating the source of spread. Our research focuses on identifying whether an infected node has sustained infection capabilities during the spreading process. We define a node with a continuous infection capability as an active node with high node activity. We propose an algorithm based on node centrality to calculate the node activity. Unlike the established paradigms, we posit that node centrality is negatively correlated with node activity. Nodes with lower centrality exhibited higher activity and infectiousness. In contrast, nodes with higher centrality may have recovered from the infection and resulted in lower activity and a diminished capacity to propagate the virus. Experiments on artificial and empirical networks demonstrate that the proposed method can effectively identify nodes with sustained infection capability. The proposed method enhances our understanding of the spreading dynamics and provides a valuable tool for managing and controlling the spread of information or diseases in complex networks.

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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.

Read the full article at: link.springer.com