Earth and Environmental Sciences

Abstract Alloparental care and division of labour are hallmarks of insect societies 1 . Social insect workers typically care for brood within the nest when they are young and transition to foraging outside the nest as they age 2–5 . This provides a powerful paradigm to study the neural basis of parenting and age-related behavioural change. Although previous work has interrogated aspects of these dynamics 6–14 , the underlying neural and molecular mechanisms remain poorly understood. Here, using an unbiased pharmacological screen of neuropeptides, we show that two ancestral regulators of feeding, neuropeptide F (NPF) and allatostatin A (AstA), modulate brood-care behaviour in the clonal raider ant. Through functional manipulations, we show that NPF increases brood-care behaviour, whereas AstA has the opposite effect. Furthermore, we find that the levels of NPF and AstA in the brain change naturally as ants age, suggesting that these changes underlie the age-related changes in brood-care behaviour. Finally, we show that, as in solitary species 15,16 , NPF and AstA remain sensitive to nutritional state, and nutritional state affects brood-care behaviour accordingly. Our results reveal that evolution has co-opted molecular mechanisms that regulated feeding ancestrally to enable cooperative brood care and age-associated division of labour.

Abstract Methanogens are central to global carbon cycling and among the largest biological sources of methane, a potent greenhouse gas 1 . At the heart of their energy metabolism lies the Hdr–Vhu–Fwd super-assembly, which couples H 2 oxidation with CO 2 reduction through flavin-based electron bifurcation. Here we present the cryogenic electron microscopy structure of the Hdr–Vhu–Fwd super-assembly from Methanococcus maripaludis , revealing an 8 MDa complex comprising 252 polypeptide chains and over 600 redox cofactors. Cryo-electron tomography further support that this super-assembly forms an intact structure within the cytoplasm of intact cells. This architecture comprises two hexameric HdrABC–Vhu rings linked by a tetrameric FwdF core, forming a continuous, circular electron chain. In this unique arrangement, 12 polyferredoxin subunits (VhuB) connect the Vhu–Hdr and Fwd complexes, thereby coupling electron bifurcation with CO 2 reduction and directly linking the last and the first step of methanogenesis. Moreover, we identify a modular variant of the complex in which the [NiFe]-hydrogenase Vhu is substituted by tungsten-containing formate dehydrogenase (FdhAB), indicating flexible integration of electron-input modules facilitating metabolic adaptation under diverse environmental conditions 2 . Analysis of the taxonomic distribution reveals that this architecture is specific to class I methanogens and is distinct from the smaller Hdr–Fmd complex of class II 3 . Together, our study reveals that the the Hdr–Vhu–Fwd super-assembly has a modular and adaptable bioenergetic assembly, suggesting a lineage-specific architecture to adapt to diverse anaerobic niches.

The circumstances into which individuals are born can place fundamental constraints on their future economic opportunities1–5, leading to a mismatch between talent, education and occupation. One major determinant of this inequality of opportunity is the absence of intergenerational educational mobility2. Here we extend existing knowledge on intergenerational mobility by presenting The European Atlas of Spatially Disaggregated Intergenerational Mobility (EUROPE-IGM-ATLAS), a panel database comprising indicators of intergenerational mobility for European subnational regions. In doing so, we make two contributions. First, we extend existing knowledge on the development of intergenerational mobility in European regions. The EUROPE-IGM-ATLAS reveals several spatiotemporal trends that characterize the changing geography of opportunity in Europe. For example, we show that observed increases in intergenerational mobility primarily stem from improvements in educational achievements among individuals from families at the lower end of the educational distribution, with fewer changes in rank across the educational spectrum. However, these increases are not uniformly distributed. Regions with a high degree of educational inequality also exhibit lower levels of intergenerational mobility, implying the co-existence of inequality both within and between generations. Second, we use this database to provide evidence on the relationship between intergenerational mobility and innovation. We provide large-scale time-series evidence that European regions with higher intergenerational mobility achieve higher innovation outcomes, one important driver of economic growth. Subsets of results further indicate that this relationship is nonlinear and that distinct mechanisms operate in major innovation hubs. The EUROPE-IGM-ATLAS reveals spatiotemporal trends that characterize the changing geography of opportunity in Europe and its relationship with regional innovation.

Developing a universal representation space for cells that encompasses the tremendous molecular diversity of cell types across species would be transformative for cell biology. Recent work using single-cell transcriptomic approaches to create molecular definitions of cell types in the form of cell atlases has provided the necessary data for such an endeavour1–3. Here we present the universal cell embedding (UCE) foundation model. UCE was trained on a large corpus of cell data using self-supervision, creating a unified biological latent space that can represent cells across diverse tissues and species. This latent space captures important biological variation despite the presence of experimental noise. UCE’s universality means that new cells can be embedded with no data labelling, model training or fine-tuning. We used UCE to create the Integrated Mega-scale Atlas, embedding 36 million cells, with more than 1,000 uniquely named cell types, from hundreds of experiments, dozens of tissues and eight species. We gain insights into the organization of cell types and tissues within the space. UCE’s embedding space exhibits emergent behaviour, identifying biology that it was never trained for, such as identifying developmental lineages and embedding data from species that were not included in the training set. Overall, by enabling a universal representation for every cell state and type, UCE is a valuable tool for analysis, annotation and hypothesis generation over single-cell data. The universal cell embedding foundation model learns to capture the organization and variation of cells by training on 36 million cells from hundreds of experiments, dozens of tissues and eight species.

Abstract Neurons acquire polarity by specifying one neurite as the axon, whereas the others become dendrites. But how this fundamental asymmetry is established remains unclear 1 . Neuronal polarization has been thought to rely primarily on growth cones that sense external cues 2 . Here we show that growth cones alone do not direct this process and that the soma acts as a central organizer of neuronal polarization. Using live imaging and genetic loss-of-function approaches in vivo, combined with optogenetic control and local cytoskeletal perturbations in cultured neurons, we uncover a soma-initiated oscillatory program that primes axon selection. Periodic actin branching that depends on the actin-related protein 2/3 (ARP2/3) complex at the soma remodels a global actomyosin network, thereby generating an actin wave that retracts neurites before propagating into a single neurite tip. Exposure to this wave relaxes local actomyosin contractility, which drives a transient microtubule-based protrusion and biases this neurite towards axon fate. As the cell exits this oscillatory stage, this neurite can overcome global inhibition and extend independently of ARP2/3, whereas actomyosin activity suppresses axon formation in the remaining neurites so that they subsequently become dendrites. This soma-driven mechanism ensures the emergence of a single axon independent of environmental cues and underpins the unidirectional information flow in neuronal circuits.

. So far, however, no study has assessed how climate change and the loss of Indigenous languages may simultaneously impact its biological and cultural heritage. Here, to bridge this gap, we first assembled a database of 90,536 reports from 700 references to understand the societal benefits that native plants provide across all countries of the Amazon basin. We found that humans utilize 5,796 native plant species, which amounts to one-third of the known Amazon vascular seed plant flora. Next, analysing 8,429 species distribution models across three future climate scenarios (SSP1-2.6, SSP3-7.0 and SSP5-8.5), we show that climate change will produce a greater reduction in the ranges of utilized than of non-utilized species by 2060-2080. Locally, Indigenous cultures may lose an average of 28-34% of their utilized plant species and 18-23% of their associated services from climate change. Regionally, the loss of threatened Indigenous languages may result in a 26% reduction in the Amazonian knowledge pool. Overall, our results point to the strong climate and language vulnerability of Amazonian biocultural heritage. At the same time, these results-together with our publicly available dataset-may serve to guide biocultural restoration and reverse the growing global change effects on ecosystems and cultural traditions.

Quantum error correction (QEC) is the primary strategy for protecting a quantum computer from the environment1,2. The prerequisite of QEC is that errors must remain sufficiently rare, which requires perpetually adapting the control parameters of the computer to the drifting environmental conditions. The current solution to this problem is to terminate the entire quantum computation for recalibration, but it is incompatible with the long runtimes of future quantum algorithms3,4. Here we address this challenge by unifying calibration with computation. We grant the QEC process5–11 a dual role: its error-detection events are not only used to correct the logical quantum state but are also repurposed as a learning signal, teaching a reinforcement learning agent12–16 to continuously steer the control parameters and stabilize the quantum system during computation. We experimentally demonstrate this framework on a Willow superconducting processor, improving the logical stability of the surface code 3.5-fold against injected drift. By synthesizing our full suite of technological advances, we achieve record performance of the surface and colour codes, with average logical error per cycle of 7.72(9) × 10−4 and 8.19(14) × 10−3, respectively. Numerical simulations of large codes with tens of thousands of control parameters confirm the scalability of our RL framework, revealing an optimization speed that is independent of system size. This work thus enables a new paradigm: a quantum computer that learns from its errors and never stops computing. By integrating reinforcement learning with quantum error correction, a quantum computer continuously self-calibrates during computation, achieving record logical error rates and enhanced resilience to drift.

Accurate forecasting of severe convective events is vital for meteorologists, as it directly supports their efforts to understand atmospheric risk patterns and enable effective early-warning systems. This paper presents an Integrating Heuristic Knowledge with Attention-based LSTM Networks for Thunderstorm Prediction (HoST) for probabilistic thunderstorm prediction. The proposed framework integrates attention-enhanced recurrent modelling with physically informed heuristic constraints, allowing the model to capture complex nonlinear atmospheric dynamics while maintaining meteorological consistency. The model is evaluated on a real-world observational dataset, where it demonstrates strong predictive capability in capturing spatiotemporal convective patterns. Furthermore, the framework is assessed in a quasi-operational forecasting setting, exhibiting low-latency inference, computational efficiency, and stable predictive performance across multiple forecast lead times (5-60 minutes). To further validate the robustness of the approach, controlled experiments are conducted using synthetically generated atmospheric scenarios that emulate key thermodynamic and kinematic relationships. Results show improved classification stability, enhanced probabilistic calibration, and superior performance compared to Random Forest, SVM, Improved Decision Support, Deep Neural Network, SALAMA, BLSTM-GRU, MetNet, FourCastNet, GraphCast, HRRR, and AROME models. Overall, the findings highlight the effectiveness of integrating heuristic knowledge with data-driven learning, demonstrating the potential of HoST as a physically consistent and operationally viable framework for short-term thunderstorm forecasting.

El Niño is the leading mode of interannual climate variability, driving seasonal predictability worldwide. It has been found that tropical variability, as that in the Atlantic, can impact on ENSO in certain decades, affecting its predictability. Nevertheless, a comprehensive analysis of the role of the Tropical Atlantic in changing ENSO predictability along time has not been assessed so far. This work analyses the Atlantic-Pacific connection and its impact on ENSO prediction using the 20th century reforecast of the ECMWF operational seasonal forecast model, SEAS5-20C, for which changes in ENSO predictive skill over the century have been previously found. Using this reforecast, multidecadal variability of tropical basin interactions appear together with changes in tropical Atlantic and Pacific predictability. It is found how the connection between tropical basins is related to the improvement in ENSO prediction, even in the second year after the initialization (+12 month lead-time), confirming that changes in the background conditions modulate these changes in predictability.

Abstract Whether Archean arc-like volcanism reflects subduction remains debated. We present high-resolution geochemical data from a well-preserved 3.13-3.10 Ga arc-like volcanic succession in Australia’s Pilbara Craton, a rare Archean analog of modern arc volcanism retaining fluid-mobile element concentrations consistent with primary magmatic values. The sequence records three primitive lava series typical of modern arcs: tholeiitic, calc-alkaline, and the oldest stratigraphically extensive genuine boninites. Geochemical modelling shows this melt diversity requires at least two mantle sources with distinct depletion histories. The mantle H 2 O required for fluid-assisted melting to produce these lavas substantially exceeds primitive mantle, approaching the H 2 O-saturated solidus of modern mantle wedges. We infer hydrous melting was triggered by dripduction, the short-lived inclined foundering of hydrated lithosphere without laterally continuous plate boundaries, in an off-plateau setting. Dripduction locally recycled surface water and generated arc-like magmas without self-sustained plate tectonics, possibly promoting mantle-ocean-atmosphere volatile exchange during the Archean.