Artificial light at night (ALAN) marks the global impact of humanity1,2. Yet, our understanding of its true ebb and flow has been limited, often based on temporally aggregated satellite data that obscure finer dynamics. Here, using daily night-time satellite imagery3 and a continuous change detection approach4,5, we created global maps of high-frequency ALAN dynamics (2014-2022). Our findings challenge the prevailing perspective that changes in light radiance are largely gradual and unidirectional. Instead, the nightlights of Earth are surprisingly dynamic, characterized by frequent and coexisting brightening and dimming. On average, each location experiencing change underwent 6.6 distinct shifts over the 9 years. Driven by this volatility, the cumulative area of total ALAN change comprised 2.05 million km2 of abrupt changes and 19.04 million km2 of gradual changes. Brightening contributed a radiance increase equivalent to 34% of the 2014 global baseline, whereas dimming offset this by 18%. Notably, both brightening and dimming have markedly intensified over the past decade. This evidence of increasing volatility in human night-time activity provides an important dynamic dimension for understanding urban evolution, energy transitions, policy impacts and ecological consequences of rapidly changing illuminated nights.

The outcomes of modern particle physics experiments, such as proton-proton collisions at the Large Hadron Collider at CERN (European Organization for Nuclear Research), depend crucially on the precise description of the scattering processes in terms of the fundamental forces. Among all the known forces that contribute, the limited understanding of the strong nuclear force is a key source of inaccuracy. At the fundamental level, the strong force is described by quantum chromodynamics, the theory of quarks and gluons. Their coupling, αs, becomes weaker at high energies (asymptotic freedom), enabling power series expansions in αs, but the confinement of quarks in hadronic bound states usually requires additional model assumptions. Consequently, determinations of αs from experiment mostly remain with large systematic theory errors1,2. Here we report the model-free determination of αs with unprecedented precision from low-energy experimental input combined with large-scale numerical simulations of the first-principles formulation of quantum chromodynamics on a space-time lattice. The uncertainty, about half that of all other results combined3, originates predominantly from the statistical Monte Carlo evaluation and has a clear probabilistic interpretation. The result for αs describes both low-energy hadronic physics with the help of lattice quantum chromodynamics and high-energy scattering using the perturbative expansion. By removing a source of theoretical uncertainty, our estimate of αs could enable markedly improved analyses of many high-energy experiments4. This will contribute to the likelihood that small effects of yet unknown physics are uncovered, as well as enable stringent precision tests of the Standard Model.

In the standard model of particle physics, the masses of the W and Z bosons, the carriers of the weak interaction, are uniquely related. A precise determination of their masses is important because quantum loops of heavy, undiscovered particles could modify this relationship. Although the Z mass is known to the remarkable precision of 22 parts per million (2.0 MeV), the W mass is known much less precisely. A global fit to measured electroweak observables predicts the W mass with 6 MeV uncertainty1-3. Reaching a comparable experimental precision would be a sensitive and fundamental test of the standard model, made even more urgent by a recent challenge to the global fit prediction by a measurement from the CDF Collaboration at the Fermilab Tevatron collider4. Here we report the measurement of the W mass by the CMS Collaboration at the CERN Large Hadron Collider, based on a large data sample of W → μν events collected in 2016 at the proton-proton collision energy of 13 TeV. The measurement exploits a high-granularity maximum likelihood fit to the kinematic properties of muons produced in W decays. By combining an accurate determination of experimental effects with marked in situ constraints of theoretical inputs, we reach a precise measurement of the W mass, of 80,360.2 ± 9.9 MeV, in agreement with the standard model prediction.

Artificial intelligence chatbots have achieved unprecedented adoption, with millions now using these systems for emotional support and companionship in contexts of widespread social isolation and capacity-constrained mental health services. While some users report psychological benefits, concerning edge cases are emerging, including reports of suicide, violence, and delusional thinking linked to emotional relationships with chatbots. To understand these risks we need to consider the interaction between human cognitive-emotional biases and chatbot behavioural tendencies, the latter including companionship-reinforcing behaviours such as sycophancy, role-play and anthropomimesis. Individuals with preexisting mental health conditions may face increased risks of chatbot-induced changes in beliefs and behaviour, particularly where these conditions manifest in altered belief-updating, reality-testing, and social isolation. To address this emerging public health concern, we need coordinated action across clinical practice, AI development, and regulatory frameworks.

Brain clocks track the deviations between predicted brain age and chronological age (brain age gaps, BAGs). These BAGs can be used to measure accelerated aging, monitoring deviations from the healthy brain trajectories associated with brain diseases and different cumulative burdens. However, the underlying biophysical mechanisms associated with BAGs in aging and dementia remain unclear. Here, we combine source space connectivity (EEG) with generative brain modeling in healthy controls (HCs) from the global south and north, alongside Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) patients (N=1,399). BAGs in aging were influenced by geography (south>north), income (low>high), sex (female>male), and education (low>high), with larger BAGs in patients, especially females with AD. Biophysical modeling revealed BAGs related to hyperexcitability and structural disintegration in aging, while hypoexcitability and severe disintegration were linked to dementia. Our work sheds light on the biophysical mechanisms of accelerated aging and dementia in diverse populations.

Cortical high gamma-band activity (HGA) is used in many scientific investigations1-18, yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly-that it predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. To test these hypotheses, here we trained monkeys (Macaca mulatta) to decouple local spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them suggested that HGA is probably not generated simply by summed local spiking. Instead, HGA correlated with co-firing of neuronal populations that were widely distributed across millimetres of cortex. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises mainly from summed postsynaptic potentials triggered by the synchronous co-firing of widely distributed neurons.

Electronic coherences resulting from molecular photoionization underlie the process of attosecond charge migration, widely investigated as a possible path towards controlled charge-directed reactivity1-4. However, photoionization often creates entangled ions and photoelectrons. This entanglement compromises the ability to explore coherent ultrafast electron dynamics within ions or of their accompanying photoelectrons5-8. Here we present experiments and calculations in which hydrogen molecules are ionized by the combination of a phase-locked pair of isolated attosecond laser pulses and a few-cycle near-infrared (NIR) laser pulse. The electronic coherence in the dissociating H2+ ion is influenced by ion-photoelectron entanglement. We demonstrate experimental control over the degree of entanglement by varying the delay between the two attosecond pulses and the delay between these pulses and the few-cycle NIR pulse. Our work demonstrates the importance of proper consideration of the role of quantum entanglement for the optimal observation of electronic coherences in attosecond experiments.

Ensuring safe and effective use of artificial intelligence (AI) requires understanding and anticipating its performance on new tasks, from advanced scientific challenges to transformed workplace activities1-3. So far, benchmarking has guided progress in AI but has offered limited explanatory and predictive power for general-purpose AI systems4-8, attributed to limited transferability across specific tasks9-11. Here we introduce general scales for AI evaluation that elicit demand profiles explaining what capabilities common AI benchmarks truly measure, extract ability profiles quantifying the general strengths and limits of AI systems and robustly predict AI performance for new task instances. Our fully automated methodology builds on 18 rubrics, capturing a broad range of cognitive and intellectual demands, which place different task instances on the same general scales, illustrated on 15 large language models (LLMs) and 63 tasks. Both the demand and the ability profiles on these scales bring new insights such as construct validity through benchmark sensitivity and specificity and explain conflicting claims about whether AI has reasoning capabilities. Ultimately, high predictive power at the instance level becomes possible using the general scales, providing superior estimates over strong black-box baseline predictors, especially in out-of-distribution settings (new tasks and benchmarks). The scales, rubrics, battery, techniques and results presented here constitute a solid foundation for a science of AI evaluation, underpinning the reliable deployment of AI in the years ahead.

Contrail cirrus clouds are a main contributor to the climate forcing from aviation1. Yet, the number of contrail ice crystals forming behind aircraft with modern lean-burn engines is unknown. Theory spans a four orders of magnitude range in ice crystal numbers2,3-rendering related climate effects unpredictable. Here we show that lean-burn combustion reduces soot particle number emissions by three orders of magnitude compared with conventional rich-quench-lean engines4,5-but does not significantly decrease volatile particles or contrail ice crystal numbers-both can exceed 1015 particles per kg of burned fuel. Our findings arise from in-flight observations behind an A321neo aircraft with lean-burn engines, thus providing real-world confirmation of some laboratory work6 and narrowing the range of theoretical expectations. Our results indicate that the tested lean-burn engine configurations alone are unlikely to reduce the warming effect of contrails, suggesting that modifications of fuel composition and lubrication oil venting architecture may be required. We show that contrail ice particle numbers in the low-soot regime can be reduced by using low-sulfur fuels and that organic fuel constituents and lubrication oil vapours can increase contrail ice particle numbers. Future research should explore how reductions in volatile particles, apart from soot, affect contrail ice formation.

Reliable future climate projections and water deficiency assessments require precipitation observations that are both spatially comprehensive and temporally complete, yet many global regions still suffer from observation sparsity1,2. Here we evaluate the distribution of 221,483 internationally exchanged precipitation gauges worldwide, with records across 1900-2022, and further explore where new gauges are most needed under different scenarios. We find that at present only 13.4% of the global land surface meets the World Meteorological Organization requirements for annual precipitation monitoring, indicating widespread scarcity that has serious socioeconomic implications. Europe has the highest continental gauge density (2.4 gauges per 1,000 km2), with Germany leading among countries over 50,000 km2 (22.4 gauges per 1,000 km2). Globally, 25% of land surface already requires urgent expansion of gauge networks because of climate variability, including northern South America, northern North America, Central Africa and southern Asia. Considering projected precipitation changes and socioeconomic conditions under a high-emission scenario further identifies high-need regions in India, Greenland, Bolivia and China because of climate sensitivity and socioeconomic vulnerabilities, increasing this share to 32.1% of global land. Our findings highlight important gaps in global precipitation monitoring that require strategic investments in new gauges and underscore the need for open data access.

The conditions under which magma accumulates and is stored are fundamental to unravelling the processes of crust formation, planetary differentiation, geothermal heat recharge and volcanic eruptions. Storage pressure, temperature and volatile saturation are typically inferred from erupted volcanic products. However, changes during kilometres of magma ascent induce disequilibrium crystallization and vesiculation, and inverting back to storage conditions comes with unresolvable uncertainties. Here we explore opportunities arising from magma drilling at Krafla volcano, Iceland, to reconstruct real, in situ magmatic conditions. The findings show that, over the approximately 5 min in which the magma is quenched, vapour bubbles consisting of H2O and CO2 exsolve, grow and resorb, but the changes can be accounted for by multiparametric inversion (for chemistry, vesicularity and vitrification), and that the magma was stored under volatile-saturated lithostatic conditions, unlike previous assertions of lower vapour pressures based on classic methods1. These new disequilibrium simulations reconcile the glass chemistry with conceptual models of magma storage and provide us with the unique pairing of precisely measured depth and volatile pressure on a single magma body and thus a robust method to improve our understanding of magma storage conditions and evolution.

The automation of science is a long-standing ambition in artificial intelligence (AI) research1,2. Although the community has made substantial progress in automating individual components of the scientific process, a system that autonomously navigates the entire research life cycle-from conception to publication-has remained out of reach. Here we present a pipeline for automating the entire scientific process end to end. We present The AI Scientist, which creates research ideas, writes code, runs experiments, plots and analyses data, writes the entire scientific manuscript, and performs its own peer review. Its ideas, execution and presentation are of sufficient quality that the manuscript generated by this AI system passed the first round of peer review for a workshop of a top-tier machine learning conference. The workshop had an acceptance rate of 70%. Our system leverages modern foundation models3-5 within a complex agentic system. We evaluate The AI Scientist in two settings: a focused mode using human-provided code templates as an initial scaffold for conducting research on a specific topic and a template-free, open-ended mode that leverages agentic search for wider scientific exploration6,7. Both settings produce diverse ideas and automatically test, report on and evaluate them. This achievement demonstrates the growing capacity of AI for making scientific contributions and signifies a potential paradigm shift in how research is conducted. As with any impactful new technology, there could be important risks, including taxing overwhelmed review systems and adding noise to the scientific literature. However, if developed responsibly, such autonomous systems could greatly accelerate scientific discovery.

Effectively communicating worst-case projections of global future climate-hereinafter referred to as worst-case climate outcomes-is essential for risk assessment and developing robust adaptation strategies to global warming1-7. Yet, current approaches for identifying spatially consistent climate outcomes are limited, with worst-case global climates typically communicated via the average of climate model projections at high global warming levels, such as 3 °C or 4 °C above the preindustrial era8,9. Here we show that extreme global climate outcomes may occur even under moderate 2 °C warming for several sectors. For droughts in global key breadbasket regions, precipitation extremes over highly populated areas and fire weather extremes across forests, global climatic impact-drivers at 2 °C of global warming may turn out to be much more extreme than model-averaged projections at 3 °C or 4 °C warming. We derive these results by identifying sector-specific, spatially consistent potential high- and low-impact global climate outcomes through spatially averaging projected sector-relevant climatic impact-drivers across key global regions. Our approach can easily be adapted to a wide range of sectors to support the improvement of sector-specific climate risk assessment and to inform climate policy. As global warming approaches 1.5 °C (ref. 10), these findings underscore the urgency of rapid mitigation to limit warming well below 2 °C, as even a 2 °C world may entail severe impacts.

Archaeological evidence suggests that dogs diverged from wolves during the Palaeolithic, more than 15,000 years ago1-7. The earliest unequivocal genetic evidence, however, is associated with dog remains from Mesolithic archaeological contexts approximately 10,900 years ago8,9. Here we generate both nuclear and mitochondrial genomes from canid remains at Pınarbaşı in Türkiye (15,800 years ago)10 and Gough's Cave in the UK (14,300 years ago)11, as well as from dogs excavated from two Mesolithic sites in Serbia (Padina between 11,500-7,900 years ago and Vlasac 8,900 years ago)12,13. Our analyses indicate that a genetically homogeneous dog population was already widely distributed across Europe and Anatolia during the Late Upper Palaeolithic (by at least 14,300 years ago). This finding suggests that dogs were exchanged among genetically and culturally distinct western Eurasian Late Palaeolithic human populations, namely the Magdalenian, Epigravettian and Anatolian hunter-gatherers10,14-16. Last, we identify a major influx of eastern Eurasian dog ancestry during the Mesolithic, concomitant with the movement of eastern hunter-gatherer populations into Europe14, which led to the establishment of the primary ancestry characteristics that define European dog populations today.

Climate change is causing measurable harm globally1,2. Political and legal efforts seek to link these damages with specific emissions, including in discussions of loss and damage (L&D)3,4; however, no quantitative definition of L&D exists5,6, nor is there a framework to link past and future emissions from specific sources to monetized, location-specific damages. Here we develop such a framework, which is integrated with recent efforts to estimate the social cost of carbon7. Using empirical estimates of the non-linear relationship between temperature and aggregate economic output, we show that future damages from past emissions-one component of L&D-are at least an order of magnitude larger than historical damages from the same emissions. For instance, one tonne of CO2 emitted in 1990 caused US$180 in discounted global damages by 2020 ($40-530) and will cause an additional $1,840 through 2100 ($500-5,700). Thus, settling debts for past damages will not settle debts for past emissions. In other illustrative estimates, a single long-haul flight per year over the past decade leads to about $25k ($6,000-77,000) in future damages by 2100, and US emissions since 1990 caused $500 billion ($180-1,300 billion) of damage in India and $330 billion ($110-820 billion) in Brazil. Carbon removal offers an alternative to transfer payments for settling L&D, but is increasingly ineffective in limiting damages as the delay between emission and recapture increases.

The earliest morphologically identifiable dogs are from Europe and date to at least 14,000 years ago1-5, although early remains are also found in other regions. The origin of early dogs in Europe, and their relationships to other dogs, has remained elusive in the absence of genome-wide data. Similarly, although dogs were the only domestic animal to predate agriculture, little is known about how the arrival of Neolithic farmers from Southwest Asia affected the dogs living with European Mesolithic hunter-gatherers. Here we analysed 216 canid remains, including 181 from Palaeolithic and Mesolithic Europe. We developed a genome-wide capture approach that enriched endogenous DNA by 10-100-fold and could distinguish dog from wolf ancestry for 141 of 216 remains. The oldest dog data that we recovered are from a 14,200-year-old dog from the Kesslerloch site in Switzerland, and we find that it shares ancestry with later worldwide dogs-inconsistent with the hypothesis that European Upper Palaeolithic dogs derived wholly from a separate domestication process. The Kesslerloch dog already displays more affinity to Mesolithic, Neolithic and present-day European dogs than to Asian dogs, demonstrating that dog genetic diversification had started well before 14,200 years ago. We find a Neolithic influx of Southwest Asian ancestry into Europe, but this seems to have been of smaller magnitude than in humans, suggesting that Mesolithic dogs contributed substantially to Neolithic, and, ultimately, probably also modern, European dogs.

Carbon capture and storage (CCS) has the potential to help nations meet their Paris Agreement CO2 reduction commitments1,2. The ability to capture CO2 within mafic and ultramafic rocks through mineralization of carbon is an example of such a CCS technology3,4, but large-scale deployment has yet to be achieved5,6. Each geologic environment in the Earth's crust requires a distinct carbon storage solution. Whereas some regions of the subsurface contain saline aquifers and sedimentary traps suitable for traditional carbon storage through the injection of high-pressure, dense CO2 below impermeable caprocks, other regions may lack caprocks5-9. In these regions, carbon storage is possible through the mineralization of injected water-dissolved CO2 forming stable carbonate minerals through its reactions with reactive silicate rocks and minerals6,10,11. A notable challenge to applying this process at scale is that it can require 20-50 times or more water than the mass of CO2 stored12. Here we report on an industrial-scale pilot project designed to find a carbon disposal solution for western Saudi Arabia. This arid region has large point-source CO2 emitters, including petroleum refining and desalination facilities, but lacks saline aquifers and sedimentary traps13-17. We find that a CO2 injection approach based on the recirculation of subsurface fluids can eliminate the need for external water. Our results demonstrate the feasibility of carbon mineral storage in regions in which access to water resources may be limited.