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.

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 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.

Radiative forcing from well-mixed greenhouse gases (WMGHGs) is a main driver of Earth's energy imbalance and global surface climate change1,2. It remains difficult to constrain, largely because its longwave (LW) instantaneous radiative forcing (IRF) component depends on atmospheric state and is subject to radiative parameterization error3-7. The IRF measures the immediate change in radiative fluxes at the tropopause8-10 caused by perturbations in WMGHG concentrations. Here we show that increasing WMGHG concentrations have enhanced LW IRF by 3.69 ± 0.07 W m-2 (95% confidence interval) since 1850. We first use global line-by-line radiative transfer simulations to provide a global benchmark of LW IRF for the main WMGHGs under realistic, all-sky conditions. We then identify a robust linear relationship between LW IRF and outgoing longwave radiation (OLR), enabling state-dependent LW IRF to be directly inferred from regressions against satellite-observed OLR. Furthermore, LW IRF explains 91% of the inter-model spread in effective radiative forcing (ERF, which includes rapid atmospheric adjustments beyond the IRF) for CO2 (ref. 11) across Earth system models. Benchmarking model-simulated IRF using the regression technique reveals that most discrepancies originate from radiation parameterizations and correcting LW IRF biases would reduce uncertainty in CO2 ERF by 50%. Our results establish a simple and robust framework for quantifying state-dependent radiative forcing of WMGHGs, providing an observation-informed pathway for future climate assessments.

Engineered T cells, reprogrammed to express chimeric antigen receptors (CAR) or T cell receptors (TCR), have transformed cancer treatment and are being explored as therapeutics for autoimmune and infectious diseases. Enhancing T cell function through genome editing, either by disrupting endogenous genes or precisely inserting DNA payloads, has shown considerable promise1. However, the ex vivo manufacturing process is lengthy and costly, limiting accessibility of these therapies. In vivo generation of CAR T cells could overcome these barriers, but current methods rely either on transient expression with limited durability, or on random integration of DNA payloads that lack specificity. Here we demonstrate that stable and cell-specific transgene expression can be achieved through in vivo site-specific integration of large DNA payloads. We developed a two-vector system to deliver CRISPR-Cas9 ribonucleoproteins and a DNA donor template, using enveloped delivery vehicles and adeno-associated viruses, respectively. We optimized both vectors for T cell-specific delivery and gene-targeting efficiency. By integrating a CAR transgene into a T cell-specific locus, we generate therapeutic levels of CAR T cells in vivo in humanized mouse models of B cell aplasia, and haematological and solid malignancies. These findings offer a pathway to more efficient, precise and widely accessible T cell therapies.

Insulating oxides are among the most abundant solid materials in the universe1-3. Of the many ways in which they influence natural phenomena, perhaps the most consequential is their capacity to transfer electrical charge during contact4-10-which occurs even between samples of the same oxide-yet the symmetry-breaking parameter that causes this remains unidentified11,12. Here we show that adventitious carbonaceous molecules adsorbed from the environment are the symmetry-breaking factor in same-material oxide contact electrification (CE). We use acoustic levitation to measure charge exchange between a sphere and a plate composed of identical amorphous silicon dioxide (SiO2). Although charging polarity is random for co-prepared samples, we control it with baking or plasma treatment. Observing the charge-exchange relaxation afterwards, we see dynamics over a timescale of hours and connect this directly to the presence of adventitious carbon with time-of-flight mass spectrometry, low-energy ion scattering and infrared spectroscopy. Going further, we confirm that adventitious carbon can even determine charge exchange among different oxides. Our results identify the symmetry-breaking parameter that causes insulating oxides to exchange charge in settings ranging from desert sands4 to volcanic plumes5,6, while simultaneously highlighting an overlooked factor in CE more broadly.

Understanding population trajectories of psychological capacities can guide interventions to protect and enhance them across the life course. We conducted an umbrella review of systematic reviews examining the trajectories of a wide range of psychological capacity measures. Searches were performed in MEDLINE, EMBASE, PsycINFO, the Cochrane Database of Systematic Reviews and Google Scholar (11 December 2023 and 26 June 2025). Thirty-six reviews synthesizing 1,307 primary studies were included. Here we show that most reviews focused on depression, anxiety and trauma-related symptoms, with stable low-symptom trajectories being most common. Being a girl/woman and socioeconomic disadvantage were frequent risk factors, while social support emerged as protective. We found a comparative lack of reviews focused on less common mental-health conditions, positive outcomes and older adults. Future reviews should engage with a robust quality assessment of the analytical approach used and the (lack of) geographical and sociodemographic diversity in the primary studies included. Similarly, more evidence on the Global South and on minoritized and marginalized groups within populations is needed. The protocol is pre-registered in PROSPERO (CRD42023490490).

Epigenetic processes serve as both mediators of and responders to social and environmental challenges, influencing biological outcomes. Therefore, pinpointing epigenetic factors associated with social adversity and traumatic stress enables understanding of the mechanisms underlying vulnerability and resilience. We hypothesized that micro-RNA (miRNA) expression may be associated with post-traumatic stress disorder symptom severity in the context of social adversity. To test this hypothesis, we leveraged longitudinal data from the Detroit Neighborhood Health Study, a community-based, prospective cohort of predominantly African Americans. This includes blood-derived RNA samples from 389 participants in wave 2 and 243 participants in wave 4. Social adversity data were available for all included participants (n = 483). Results identified 86 miRNAs that are associated with social adversities (financial difficulties, perceived discrimination, cumulative trauma) and post-traumatic stress severity. These miRNAs are involved primarily in the immune response, brain and neural function, as well as cell cycle and differentiation, and 23 (25%) have previously been associated with conditions related to post-traumatic stress disorder, including traumatic brain injury and stress response. Our findings offer a fresh perspective on understanding the epigenetic role of miRNA in the interaction between social adversity and traumatic stress.

Adolescence is a sensitive period of brain development marked by rapid cortical thinning and increased risk for psychiatric disorders, yet the biological drivers of atypical trajectories remain unclear. Here, using longitudinal data from the Adolescent Brain Cognitive Development Study, we examined whether genetic predisposition to systemic inflammation, indexed by polygenic scores for C-reactive protein (PGS-CRP), influences brain development and psychopathology. Higher PGS-CRP was associated with accelerated cortical thinning, particularly in medial temporal and insular regions, and with increased externalizing symptoms. Early-life infections independently predicted greater depressive and externalizing symptoms but did not interact with genetic risk. Mediation analyses indicated that cortical thinning partially accounted for the association between PGS-CRP and externalizing psychopathology. Biological annotation further identified the regional similarity between cortical effects of PGS-CRP and several neurotransmitter systems. Together, these findings suggest that genetic susceptibility to inflammation may shape adolescent brain maturation and contribute to mental health vulnerability via neuroimmune pathways.

Detailed and accurate 3D mapping of dynamic environments is essential for machines to interface with their surroundings1-3 and for human-machine interaction4,5. Although considerable effort has been made to create the equivalent of the complementary metal-oxide-semiconductor (CMOS) image sensor for the 3D world, scalable, high-performance, reliable solutions have proven elusive6-11. Focal plane array (FPA) sensors using frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) have shown potential to meet all of these requirements and also provide direct measurement of radial velocity as a fourth dimension. Previous demonstrations12,13, although promising, have not achieved the simultaneous scale and performance required by commercial applications. Here we present a large-scale, coherent LiDAR FPA enabled by comprehensive chip-scale optoelectronic integration. A 4D imaging camera is built around the FPA and used to acquire point clouds. At the core is a 352 × 176-pixel 2D FMCW LiDAR FPA comprising more than 0.6 million photonic components, all integrated on-chip together with their associated electronics. This represents a five times increase in pixel count with respect to previous demonstrations12. The pixel architecture combines the outbound and inbound optical paths within the pixel in a monostatic configuration, together with coherent detectors and electronics. Frequency-modulated light is directed sequentially to groups of pixels by in-plane thermo-optic switches with integrated electronics for driving and calibration. An integrated serial digital interface controls both optical switching and readout synchronously. Point clouds of objects ranging from 4 to 65 m with per-pixel integration time compatible with frame rates from 3 to 15 frames per second (fps) are shown. This result demonstrates the capabilities of FMCW LiDAR FPA sensors as enablers of ubiquitous, low-cost, compact coherent 4D imaging cameras.