Mingzhen Lu, Sili Wang, Avni Malhotra, Shersingh Joseph Tumber-Dávila, Samantha Weintraub-Leff, M. Luke McCormack, Xingchen Tony Wang & Robert B. Jackson
Nature Communications volume 16, Article number: 5281 (2025)

An improved understanding of root vertical distribution is crucial for assessing plant-soil-atmosphere interactions and their influence on the land carbon sink. Here, we analyze a continental-scale dataset of fine roots reaching 2 meters depth, spanning from Alaskan tundra to Puerto Rican forests. Contrary to the expectation that fine root abundance decays exponentially with depth, we found root bimodality at ~20% of 44 sites, with secondary biomass peaks often below 1 m. Root bimodality was more likely in areas with low total fine root biomass and was more frequent in shrublands than grasslands. Notably, secondary peaks coincided with high soil nitrogen content at depth. Our analyses suggest that deep soil nutrients tend to be underexploited, while root bimodality offers plants a mechanism to tap into deep soil resources. Our findings add to the growing recognition that deep soil dynamics are systematically overlooked, and calls for more research attention to this deep frontier in the face of global environmental change.

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Reed Bender, Karina Kofman, Blaise Agüera y Arcas, Michael Levin

The question of “what is life?” has challenged scientists and philosophers for centuries, producing an array of definitions that reflect both the mystery of its emergence and the diversity of disciplinary perspectives brought to bear on the question. Despite significant progress in our understanding of biological systems, psychology, computation, and information theory, no single definition for life has yet achieved universal acceptance. This challenge becomes increasingly urgent as advances in synthetic biology, artificial intelligence, and astrobiology challenge our traditional conceptions of what it means to be alive. We undertook a methodological approach that leverages large language models (LLMs) to analyze a set of definitions of life provided by a curated set of cross-disciplinary experts. We used a novel pairwise correlation analysis to map the definitions into distinct feature vectors, followed by agglomerative clustering, intra-cluster semantic analysis, and t-SNE projection to reveal underlying conceptual archetypes. This methodology revealed a continuous landscape of the themes relating to the definition of life, suggesting that what has historically been approached as a binary taxonomic problem should be instead conceived as differentiated perspectives within a unified conceptual latent space. We offer a new methodological bridge between reductionist and holistic approaches to fundamental questions in science and philosophy, demonstrating how computational semantic analysis can reveal conceptual patterns across disciplinary boundaries, and opening similar pathways for addressing other contested definitional territories across the sciences.

Read the full article at: arxiv.org

Sai Krishna Katla, Chenyu Lin, and Juan Pérez-Mercader

PNAS 122 (22) e2412514122

Self-reproduction is one of the most fundamental features of natural life. This study introduces a biochemistry-free method for creating self-reproducing polymeric vesicles. In this process, nonamphiphilic molecules are mixed and illuminated with green light, initiating polymerization into amphiphiles that self-assemble into vesicles. These vesicles evolve through feedback between polymerization, degradation, and chemiosmotic gradients, resulting in self-reproduction. As vesicles grow, they polymerize their contents, leading to their partial release and their reproduction into new vesicles, exhibiting a loose form of heritable variation. This process mimics key aspects of living systems, offering a path for developing a broad class of abiotic, life-like systems.

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Ryan Hill, Yian Yin, Carolyn Stein, Xizhao Wang, Dashun Wang & Benjamin F. Jones
Nature (2025)

Scientists and inventors set the direction of their work amid evolving questions, opportunities and challenges, yet the understanding of pivots between research areas and their outcomes remains limited1,2,3,4,5. Theories of creative search highlight the potential benefits of exploration but also emphasize difficulties in moving beyond one’s expertise6,7,8,9,10,11,12,13,14. Here we introduce a measurement framework to quantify how far researchers move from their existing work, and apply it to millions of papers and patents. We find a pervasive ‘pivot penalty’, in which the impact of new research steeply declines the further a researcher moves from their previous work. The pivot penalty applies nearly universally across science and patenting, and has been growing in magnitude over the past five decades. Larger pivots further exhibit weak engagement with established mixtures of prior knowledge, lower publication success rates and less market impact. Unexpected shocks to the research landscape, which may push researchers away from existing areas or pull them into new ones, further demonstrate substantial pivot penalties, including in the context of the COVID-19 pandemic. The pivot penalty generalizes across fields, career stage, productivity, collaboration and funding contexts, highlighting both the breadth and depth of the adaptive challenge. Overall, the findings point to large and increasing challenges in effectively adapting to new opportunities and threats, with implications for individual researchers, research organizations, science policy and the capacity of science and society as a whole to confront emergent demands.

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Miguel A. González-Casado, Andreia Sofia Teixeira & Angel Sánchez 
Communications Physics volume 8, Article number: 227 (2025)

How do networks of social relationships evolve over time? This study addresses the lack of longitudinal analyses of social networks grounded in mathematical modelling. We analyse a dataset tracking the social interactions of 900 individuals over four years. Despite shifts in individual relationships, the macroscopic structure of the network remains stable, fluctuating within predictable bounds. We link this stability to the concept of equilibrium in statistical physics. Specifically, we show that the probabilities governing link dynamics are stationary over time, and that key network features align with equilibrium predictions. Moreover, the dynamics also satisfy the detailed balance condition. This equilibrium persists despite ongoing turnover, as individuals join, leave, and shift connections. This suggests that equilibrium arises not from specific individuals but from the balancing act of human needs, cognitive limits, and social pressures. Practically, this equilibrium simplifies data collection, supports methods relying on single network snapshots (like Exponential Random Graph Models), and aids in designing interventions for social challenges. Theoretically, it offers insights into collective human behaviour, revealing how emergent properties of complex social systems can be captured by simple mathematical models.

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