Climate change threatens global food systems1, but the extent to which adaptation will reduce losses remains unknown and controversial2. Even within the well-studied context of US agriculture, some analyses argue that adaptation will be widespread and climate damages small3,4, whereas others conclude that adaptation will be limited and losses severe5,6. Scenario-based analyses indicate that adaptation should have notable consequences on global agricultural productivity7-9, but there has been no systematic study of how extensively real-world producers actually adapt at the global scale. Here we empirically estimate the impact of global producer adaptations using longitudinal data on six staple crops spanning 12,658 regions, capturing two-thirds of global crop calories. We estimate that global production declines 5.5 × 1014 kcal annually per 1 °C global mean surface temperature (GMST) rise (120 kcal per person per day or 4.4% of recommended consumption per 1 °C; P < 0.001). We project that adaptation and income growth alleviate 23% of global losses in 2050 and 34% at the end of the century (6% and 12%, respectively; moderate-emissions scenario), but substantial residual losses remain for all staples except rice. In contrast to analyses of other outcomes that project the greatest damages to the global poor10,11, we find that global impacts are dominated by losses to modern-day breadbaskets with favourable climates and limited present adaptation, although losses in low-income regions losses are also substantial. These results indicate a scale of innovation, cropland expansion or further adaptation that might be necessary to ensure food security in a changing climate.

Modern colour image sensors face challenges in further improving sensitivity and image quality because of inherent limitations in light utilization efficiency1. A major factor contributing to these limitations is the use of passive optical filters, which absorb and dissipate a substantial amount of light, thereby reducing the efficiency of light capture2. On the contrary, active optical filtering in Foveon-type vertically stacked architectures still struggles to deliver optimal performance owing to their lack of colour selectivity, making them inefficient for precise colour imaging3. Here we introduce an innovative architecture for colour sensor arrays that uses multilayer monolithically stacked lead halide perovskite thin-film photodetectors. Perovskite bandgap tunability4 is utilized to selectively absorb the visible light spectrum's red, green and blue regions, eliminating the need for colour filters. External quantum efficiencies of 50%, 47% and 53% are demonstrated for the red, green and blue channels, respectively, as well as a colour accuracy of 3.8% in ΔELab outperforming the state-of-the-art colour-filter array and Foveon-type photosensors. The image sensor design improves light utilization in colour sensors and paves the way for the next generation of highly sensitive, artefact-free images with enhanced colour fidelity.

At least one-third of stroke survivors are affected by depression or anxiety, but no large-scale studies of real-world clinical practice have assessed whether psychological therapies are beneficial for these patients. Here we show that psychological treatment is effective for stroke survivors on average, using national healthcare records from National Health Service Talking Therapies services in England, including 7,597 patients with a hospital diagnosis of stroke before attendance. Following psychological treatment, stroke survivors experienced moderate reductions in depression and large reductions in anxiety symptoms. Patients who started attending the services a year or more after a stroke were less likely to reliably recover from symptoms of depression or anxiety than those seen within six months of a stroke, irrespective of differences in baseline characteristics including age, gender, local area deprivation and symptom severity. Compared with a matched sample of patients without a stroke, stroke survivors were less likely to reliably recover and more likely to reliably deteriorate after psychological treatment, although adjusting for level of physical comorbidity attenuated these relationships. It is crucial that clinicians working with stroke survivors screen for symptoms of depression and anxiety and consider referring patients to primary care psychological therapies as early as possible.

Social deficits are potential risk factors for premature mortality. Most research has focused on social deficits measured at single points in time. It remains unclear if the chronicity of loneliness affects its impact on adverse health outcomes. This study assessed the effects of chronic loneliness and isolation in predicting incident functional impairment and all-cause and cause-specific mortality. This longitudinal study used panel data from the English Longitudinal Study of Ageing, including 14 years of follow-up (waves 2-9, in 2004-2018). Social deficits over three waves (4 years) were measured using the UCLA loneliness scale and social isolation index, categorized as not present, fluctuating or chronic. We estimated the all-cause mortality risk with Cox proportional hazard modeling, and the Fine-Gray competing risk modeling was used to test the risk of functional impairment onset and cause-specific mortality. We analyzed 5,131 participants (mean age 67.6 years (s.d. 9.8)) in the mortality cohort (follow-up 9.8 years (IQR: 6.67-10.08)) and 4,279 participants (mean age 67.0 years (s.d. 9.6)) who were functional disability-free at baseline (follow-up 9.8 years (IQR: 7.17-10.17)). Compared with not being lonely/isolated, there was a higher risk of incident functional impairment among those with fluctuating loneliness (sub-hazard ratio (sHR) 1.30, 95% CI: 1.03-1.63) and chronic loneliness (sHR 1.58, 1.12-2.23), as well as chronic social isolation (sHR 1.41, 1.02-1.94). In survival analyses, compared with people who were not lonely/isolated, people experiencing fluctuating loneliness and social isolation had higher risks of all-cause mortality (loneliness HR 1.29, 1.13-1.48; isolation HR 1.15, 1.01-1.31). People with chronic isolation also had higher risks of all-cause mortality (HR 1.27, 1.05-1.55) and cancer-related mortality (sHR 1.69, 1.23-2.31). Over a 14 year follow-up, we found that chronic loneliness and isolation phenotypes were associated with aggravated risks of incident functional impairment and mortality. There was a potential dose-response relationship between chronicity of loneliness phenotypes and functional impairment onset and mortality. Preventing the onset of and transition to chronic loneliness and isolation in older age is a crucial target to support both the healthspan and the lifespan.

The Global Flourishing Study is a longitudinal panel study of over 200,000 participants in 22 geographically and culturally diverse countries, spanning all six populated continents, with nationally representative sampling and intended annual survey data collection for 5 years to assess numerous aspects of flourishing and its possible determinants. The study is intended to expand our knowledge of the distribution and determinants of flourishing around the world. Relations between a composite flourishing index and numerous demographic characteristics are reported. Participants were also surveyed about their childhood experiences, which were analyzed to determine their associations with subsequent adult flourishing. Analyses are presented both across and within countries, and discussion is given as to how the demographic and childhood relationships vary by country and which patterns appear to be universal versus culturally specific. Brief comment is also given on the results of a whole series of papers in the Global Flourishing Study Special Collection, employing similar analyses, but with more-specific aspects of well-being. The Global Flourishing Study expands our knowledge of the distribution and determinants of well-being and provides foundational knowledge for the promotion of societal flourishing.

Trace elements and isotopes (TEIs) are important to marine life and are essential tools for studying ocean processes1. Two different frameworks have arisen regarding marine TEI cycling: reversible scavenging favours water-column control on TEI distributions2-5, and seafloor boundary exchange emphasizes sedimentary imprints on water-column biogeochemistry6,7. These two views lead to disparate interpretations of TEI behaviours8-10. Here we use rare earth elements and neodymium isotopes as exemplar tracers of particle scavenging11 and boundary exchange6,7,12. We integrate these data with models of particle cycling and sediment diagenesis to propose a general framework for marine TEI cycling. We show that, for elements with greater affinity for manganese oxide than biogenic particles, scavenging is a net sink throughout the water column, contrary to a common assumption for reversible scavenging3,13. In this case, a benthic flux supports increasing elemental concentrations with water depth. This sedimentary source consists of two components: one recycled from elements scavenged by water-column particles, and another newly introduced to the water column through marine silicate weathering inside sediment8,14,15. Abyssal oxic diagenesis drives this benthic source, and exerts a strong influence on water-column biogeochemistry through seafloor geometry and bottom-intensified turbulent mixing16,17. Our findings affirm the role of authigenic minerals, often overshadowed by biogenic particles, in water-column cycling18, and suggest that the abyssal seafloor, often regarded as inactive, is a focus of biogeochemical transformation19,20.

Compounds consisting only of the element nitrogen (polynitrogens or nitrogen allotropes) are considered promising clean energy-storage materials owing to their immense energy content that is much higher than hydrogen, ammonia or hydrazine, which are in common use, and because they release only harmless nitrogen on decomposition1. However, their extreme instability poses a substantial synthetic challenge and no neutral molecular nitrogen allotrope beyond N2 has been isolated2,3. Here we present the room-temperature preparation of molecular N6 (hexanitrogen) through the gas-phase reaction of chlorine or bromine with silver azide, followed by trapping in argon matrices at 10 K. We also prepared neat N6 as a film at liquid nitrogen temperature (77 K), further indicating its stability. Infrared and ultraviolet-visible (UV-Vis) spectroscopy, 15N-isotope labelling and ab initio computations firmly support our findings. The preparation of a metastable molecular nitrogen allotrope beyond N2 contributes to our fundamental scientific knowledge and possibly opens new opportunities for future energy-storage concepts.

Supported metal catalysts that integrate atomically dispersed species with controlled structures lie at the forefront of catalytic materials design, offering exceptional control over reactivity and high metal utilization, approaching the precision of molecular systems1-3. However, accurately resolving the local metal coordination environments remains challenging, hindering the advancement of structure-activity relationships needed to optimize their design for diverse applications1,2. Although electron microscopy reveals atomic dispersion, conventional spectroscopic methods used in heterogeneous catalysis only provide average structural information. Here we demonstrate that 195Pt solid-state nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for characterizing atomically dispersed Pt sites on various supports, so called single-atom catalysts (SACs). Monte Carlo simulations allow the conversion of NMR spectra into SAC signatures that describe coordination environments with molecular precision, enabling quantitative assessment of Pt-site distribution and homogeneity. This methodology can track the influence of synthetic parameters, uncovering the impact of specific steps and support types, and can also monitor changes upon reaction. It offers critical insights for the reproducible development of SACs with targeted structures. Beyond SACs, this approach lays the foundation for studying more complex architectures, such as dual-atom or single-cluster catalysts, containing various NMR-active metals.

North Atlantic Ocean circulation and temperature patterns profoundly influence global and regional climate across all timescales, from synoptic1 to seasonal2-4, decadal5, multidecadal6,7 and beyond8,9. During 2023, an extreme and near-basin-scale marine heatwave developed during Northern Hemisphere summer, peaking in July. The warming spread across virtually all regions of the North Atlantic, including the subpolar ocean, where a cooling trend over the past 50-100 years has been linked to a slowdown in the meridional overturning circulation10,11. Yet the mechanisms that led to this exceptional surface ocean warming remain unclear. Here we use observationally constrained atmospheric reanalyses alongside ocean observations and model simulations to show that air-sea heat fluxes acting on an extremely shallow surface mixed layer, rather than anomalous ocean heat transport, were responsible for this extreme ocean warming event. The dominant driver is shown to be anomalously weak winds leading to strongly shoaling (shallowing) mixed layers, resulting in a rapid temperature increase in a shallow surface layer of the North Atlantic. Furthermore, solar radiation anomalies made regional-scale warming contributions in locations that approximately correspond to some of the region's main shipping lanes, suggesting that reduced sulfate emissions could also have played a localized role. With a trend towards shallower mixed layers observed over recent decades, and projections that this will continue into the future, the severity of North Atlantic marine heatwaves is set to worsen.

Building a useful photonic quantum computer requires robust techniques to synthesize optical states that can encode qubits. Gottesman-Kitaev-Preskill (GKP) states1 offer one of the most attractive classes of such qubit encodings, as they enable the implementation of universal gate sets with straightforward, deterministic and room temperature-compatible Gaussian operations2. Existing pioneering demonstrations generating optical GKP states3 and other complex non-Gaussian states4-11 have relied on free-space optical components, hindering the scaling eventually required for a utility-scale system. Here we use an ultra-low-loss integrated photonic chip fabricated on a customized multilayer silicon nitride 300-mm wafer platform, coupled over fibre with high-efficiency photon number resolving detectors, to generate GKP qubit states. These states show critical mode-level features necessary for fault tolerance, including at least four resolvable peaks in both p and q quadratures, and a clear lattice structure of negative Wigner function regions, in this case a 3 × 3 grid. We also show that our GKP states show sufficient structure to indicate that the devices used to make them could, after further reduction in optical losses, yield states for the fault-tolerant regime. This experiment validates a key pillar of bosonic architectures for photonic quantum computing2,12, paving the way for arrays of GKP sources that will supply future fault-tolerant machines.

Lattice gauge theories (LGTs)1-4 can be used to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials5-7. Studying dynamical properties of emergent phases can be challenging, as it requires solving many-body problems that are generally beyond perturbative limits8-10. Here we investigate the dynamics of local excitations in a Z2 LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit that prepares low-energy states that have a large overlap with the ground state; then we create charge excitations with local gates and simulate their quantum dynamics by means of a discretized time evolution. As the electric field coupling constant is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the electric field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT, from which we uncover two distinct regimes inside the confining phase: for weak confinement, the string fluctuates strongly in the transverse direction, whereas for strong confinement, transverse fluctuations are effectively frozen11,12. We also demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a new set of techniques for investigating emergent excitations and string dynamics.

Drought is one of the most common and complex natural hazards affecting the environment, economies and populations globally1-4. However, there are significant uncertainties in global drought trends4-6, and a limited understanding of the extent to which a key driver, atmospheric evaporative demand (AED), impacts the recent evolution of the magnitude, frequency, duration and areal extent of droughts. Here, by developing an ensemble of high-resolution global drought datasets for 1901-2022, we find an increasing trend in drought severity worldwide. Our findings suggest that AED has increased drought severity by an average of 40% globally. Not only are typically dry regions becoming drier but also wet areas are experiencing drying trends. During the past 5 years (2018-2022), the areas in drought have expanded by 74% on average compared with 1981-2017, with AED contributing to 58% of this increase. The year 2022 was record-breaking, with 30% of the global land area affected by moderate and extreme droughts, 42% of which was attributed to increased AED. Our findings indicate that AED has an increasingly important role in driving severe droughts and that this tendency will likely continue under future warming scenarios.

Rivers and streams are an important pathway in the global carbon cycle, releasing carbon dioxide (CO2) and methane (CH4) from their water surfaces to the atmosphere1,2. Until now, CO2 and CH4 emitted from rivers were thought to be predominantly derived from recent (sub-decadal) biomass production and, thus, part of ecosystem respiration3-6. Here we combine new and published measurements to create a global database of the radiocarbon content of river dissolved inorganic carbon (DIC), CO2 and CH4. Isotopic mass balance of our database suggests that 59 ± 17% of global river CO2 emissions are derived from old carbon (millennial or older), the release of which is linked to river catchment lithology and biome. This previously unrecognized release of old, pre-industrial-aged carbon to the atmosphere from long-term soil, sediment and geologic carbon stores through lateral hydrological routing equates to 1.2 ± 0.3 Pg C year-1, similar in magnitude to terrestrial net ecosystem exchange. A consequence of this flux is a greater than expected net loss of carbon from aged organic matter stores on land. This requires a reassessment of the fate of anthropogenic carbon in terrestrial systems and in global carbon cycle budgets and models.

Anyons1,2 are low-dimensional quasiparticles that obey fractional statistics, hence interpolating between bosons and fermions. In two dimensions, they exist as elementary excitations of fractional quantum Hall states3-5 and are believed to enable topological quantum computing6,7. One-dimensional anyons have been theoretically proposed, but their experimental realization has proven to be difficult. Here we observed emergent anyonic correlations in a one-dimensional strongly interacting quantum gas, resulting from the phenomenon of spin-charge separation8-10. A mobile impurity provides the necessary spin degree of freedom to engineer anyonic correlations in the charge sector and simultaneously acts as a probe to reveal these correlations. Starting with bosons, we tune the statistical phase to transmute bosons through anyons to fermions and observe an asymmetric momentum distribution11-14, a hallmark of anyonic correlations. Going beyond equilibrium conditions, we observed dynamical fermionization of the anyons15. This study opens the door to the exploration of non-equilibrium anyonic phenomena in a highly controllable setting15-17.

Every generation, the human genome is shuffled during meiosis and a single fertilized egg gives rise to all of the cells of the body1. Meiotic errors leading to chromosomal abnormalities are known causes of pregnancy loss2,3, but genetic aetiologies of euploid pregnancy loss remain largely unexplained4. Here we characterize sequence diversity in early pregnancy loss through whole-genome sequencing of 1,007 fetal samples and 934 parental samples from 467 trios affected by pregnancy loss (fetus, mother and father). Sequenced parental genomes enabled us to determine both the parental and meiotic origins of chromosomal abnormalities, detected in half of our set. It further enabled us to assess de novo mutations on both homologous chromosomes from parents transmitting extra chromosomes, and date them, revealing that 6.6% of maternal mutations occurred before sister chromatid formation in fetal oocytes. We find a similar number of de novo mutations in the trios affected by pregnancy loss as in 9,651 adult trios, but three times the number of pathogenic small (<50 bp) sequence variant genotypes in the loss cases compared with adults. Overall, our findings indicate that around 1 in 136 pregnancies is lost due to a pathogenic small sequence variant genotype in the fetus. Our results highlight the vast sequence diversity that is lost in early pregnancy.

Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive1. Recent advances in artificial intelligence (AI) have shown promise in improving both predictive performance and efficiency2,3, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. Aurora outperforms operational forecasts in predicting air quality, ocean waves, tropical cyclone tracks and high-resolution weather, all at orders of magnitude lower computational cost. With the ability to be fine-tuned for diverse applications at modest expense, Aurora represents a notable step towards democratizing accurate and efficient Earth system predictions. These results highlight the transformative potential of AI in environmental forecasting and pave the way for broader accessibility to high-quality climate and weather information.