Extreme space weather events on Earth occur during intervals of strong solar wind driving1. The solar wind drives plasma convection and currents in the near-Earth space environment2. For low values of the driver, the Earth's response is linear, estimated by parameters such as the polar cap index based on ground magnetometer activity3. Curiously, for extreme solar wind driving, the Earth's response appears not to increase beyond a saturation limit4. Theorists have advanced a host of explanations for this saturation effect, but there is no consensus5. Here we demonstrate that this saturation is a manifestation of the regression to the mean effect6 arising from random uncertainty in the timing and magnitude of solar wind measurements. Our results reveal that data analysis underpinning the saturation theories is nonlinearly biased, thereby challenging the validity of the theories. Correcting for the uncertainties reveals that the Earth's response to solar wind driving is linear throughout, and that the impact of extreme geomagnetic storms can be twice as large as previously thought. We show that regression to the mean is a fundamental property of the relationship between measurement and the truth, where the truth corresponding to the measurement is closer to the mean. This effect is particularly pronounced for uncertain measurements of extreme values and is likely to manifest across various fields, from extreme climate studies to chronic medical pain.

Molecular glues stabilize weak interactions to impart new functionalities to complexes1-3. Although molecular glues have been described in plant signalling and as human therapeutics4,5, it is unclear whether this modality provides endogenous regulation in human cells. Here we show that purine nucleotides are molecular glues that tether the rate-limiting enzyme in purine biosynthesis-phosphoribosyl pyrophosphate amidotransferase (PPAT)-to its inhibitor NUDT5. This mechanism allows cells to sense the levels of purines and to establish essential feedback control of their synthesis. We refer to such molecules as metabolite glues. Thiopurine chemotherapeutics6, which have been in clinical use since the 1950s, glue the same complex but adopt distinct orientations for enhanced function. Unlike most known glues, the PPAT-NUDT5 metabolite-glue pocket can adjust its conformation to notable compound alterations, enabling increased glue potency and improved on-target activity. We therefore identify endogenous metabolite glues as a mode of nutrient sensing that can be exploited for therapeutic benefit.

The transition of an atomic nucleus from an excited state to a state of lower energy is typically accompanied by the emission of photons. The likelihood of photon emission is a function of the photon energy and of properties of the initial and final nuclear states and is known as the photon (or γ-ray) strength function (γSF)1,2. Of the features present in the γSF, an enhancement in the low-energy region has been observed in some nuclei, but the electromagnetic nature of this enhancement remains an open question3,4. This low-energy enhancement in the γSF significantly impacts our understanding of how elements are created in stars and the structure of the nucleus5,6. Here we present the results from an experiment on the γSF of 70Zn that show that the enhancement in the γSF of this nucleus is of magnetic nature. This observation helps address a long-open question about γSFs in atomic nuclei and directly impacts our predictive capabilities in nuclear science.

Plastic waste accumulation poses a global threat to both the environment and public health1-3. Although catalytic upcycling to value-added chemicals holds promise, its industrial adoption is hindered by additive-induced catalyst deactivation, feedstock heterogeneity, process inflexibility and limited economic viability4. Here we report a catalyst-free upcycling strategy that makes use of in situ generation of hydroxyl radicals at microdroplet interfaces5-8 to enable oxidative cleavage of diverse waste plastics-from polyolefins to rubbers-into carboxylic acids under mild conditions. By eliminating catalyst-dependent pathways, this approach circumvents key challenges of catalyst design and poisoning, while substantially lowering technical barriers and costs9,10. Our method achieves complete conversion of polyethylene (PE) with selectivity to short-chain diacids approaching 69% under relatively mild conditions and demonstrated broad applicability to mixed commercial plastics, with scalability demonstrated up to the 300-g scale. Radical intermediate analysis reveals the crucial role of H2O in mediating a unique oxidative degradation mechanism: sequential hydroxyl radical addition to alkyl radicals, distinct from classical liquid-phase aerobic oxidation of alkane11. This interfacial radical-mediated strategy enables sustainable polymer upcycling with minimal infrastructure. More broadly, this work provides a scalable blueprint for the first, to our knowledge, industrial implementation of microdroplet chemistry, with transformative implications for oxidation processes in organic acid synthesis and beyond.

Treatment for major depressive disorder (MDD) remains challenging as only 30-50% of patients respond to first-line antidepressant medications in primary care. Here we developed algorithms using predictors of response to sertraline and bupropion from a multisite study, and tested such markers in an independent, prospective clinical trial involving unmedicated individuals with MDD (NCT05537584). Leave-one-out cross-validation models achieved good performance in the training sample (area under the curve of 0.66-0.86). In the preregistered clinical trial, no significant differences in treatment outcomes emerged for those assigned a drug consistent versus inconsistent with their biomarkers. However, significant differences emerged in symptom reduction trajectories for those with positive markers for both medications (response rate: 71.4%) or either drug (65.4%) compared with those with two negative markers (42.9%). This is the first study using biobehavioral markers to prospectively guide assignment to two widely used antidepressants, yielding a 66.8% boost in response rate and providing foundations for larger personalized treatment studies of MDD.

Hydrogels are widely used in biomedical interfaces, in which effective gas exchange (for example, O2, CO2) within a water-rich environment is essential. However, hydrogels show intrinsically limited air exchange efficiency, owing to the low solubility (C) and diffusivity (D) of non-polar gases in the polar water medium1. This limitation poses a substantial bottleneck in long-term applications, such as wearable health monitors2-7 and tissue engineering8-12. Existing methods13-16 to enhance air permeability suffer from poor robustness and/or an inherent trade-off between permeability and water content (for example, <50 vol%). Here we introduce a viscoelastic phase separation17 (VPS)-enabled strategy to create a non-collapsible, air-rich network in high-water-content hydrogels, achieving a record-high oxygen permeability of 185 barrer with 70 vol% water-a tenfold increase compared with pristine hydrogels. VPS, a ubiquitous phenomenon in soft matter, is used to drive hydrophobic, dry gas particles within a hydrophilic, wet medium into a thin, stable three-dimensional network. This approach allows the facile and scalable fabrication of air-permeable hydrogels across diverse chemistries and form factors. Physiological tests over a 10-day continuous wear condition confirmed their effectiveness in preventing fluid accumulation and maintaining skin health. This strategy paves the way for hydrogels in long-term biomedical applications in which efficient and sustained air exchange becomes critical.

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.

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.

Over geological time, the growth of the ocean floor involves magmatic and tectonic extension1 at mid-ocean ridges (MORs). Because seismogeodetic monitoring of these submarine plate boundaries remains challenging2-7, little is known about how these systems operate on yearly timescales. Here we report the first, to our knowledge, in situ observation of a rifting event at a MOR segment that combines hydroacoustic, direct-path ranging and bottom-pressure measurements, with repeated seafloor mapping. This event started on 26 April 2024 at the axis of the Southeast Indian Ridge (SEIR) near 37° S, two months after instruments had been deployed across the ridge axis and nearby Amsterdam transform fault (TF). The event began as a rapidly migrating swarm of extensional seismicity along the axial valley. It caused 4 m of subsidence of the valley floor and more than a metre of horizontal extension across the valley. We interpret this as the deflation of a sill-like reservoir feeding propagating dykes along the ridge axis. The dykes eventually led to the outpouring of about 160 million m3 of lava at the seafloor in about 16 days, while inducing both seismic and aseismic slip on valley-bounding normal faults and finally triggering seismic activity on the abutting TFs. Large-scale aseismic slip induced by magmatic processes could therefore be the primary mechanism by which MOR normal faults accrue their displacement, which would account for their well-documented seismic deficit8,9.

Amazonia harbours more than 10% of the terrestrial biodiversity of the Earth1 and more than 400 Indigenous groups2. 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.

Recent theories propose that altered social understanding in autism stems from mismatches in social representations and inferential processes between non-autistic and autistic groups. Using a cross-sectional design, we examined whether these groups apply different types of social knowledge and learning strategies when inferring preferences of individuals from their own versus another diagnostic group. When comparing food and activity self-preferences in large samples, autistic adolescents' preferences showed greater group-level variability compared to non-autistic adults and adolescents. A sample of non-autistic adults (18-30y) and autistic adolescents (12-17y) learned about non-autistic and autistic adolescents. A computational modeling framework assessed how prior knowledge was integrated during learning. Aggregated group preference structures comprised knowledge for learning Both groups relied on a similar learning strategy - that is, integrating feedback via reinforcement learning scaled by fine-grained, in-group social knowledge-- but showed reduced accuracy when learning about autistic individuals. Since, autistic adolescents' preferences are more variable, aggregate knowledge about this group was less predictive. Within the autistic group, greater autistic traits, including rigidity, were associated with lower learning rates and accuracy, highlighting how our social learning models capture autistic trait-related variability.

Both psychiatric vulnerability and cortical structure are shaped by the cumulative effect of common genetic variants across the genome. However, the shared genetic underpinnings between psychiatric disorders and brain structural phenotypes, such as thickness and surface area of the cerebral cortex, remain elusive. In this study, we employed pleiotropy-informed conjunctional false discovery rate analysis to investigate shared loci across genome-wide association scans of regional cortical thickness, surface area, and 8 psychiatric disorders in individuals of European ancestry. Aggregating regional measures, we identified 55 independent genetic loci shared between psychiatric disorders and surface area, as well as 29 independent genetic loci shared with cortical thickness. Risk alleles exhibited bidirectional effects on both cortical thickness and surface area, such that some risk alleles for each disorder were associated with increased regional brain size while other risk alleles were associated with decreased regional brain size. Due to bidirectional effects, in many cases we observed extensive pleiotropy between an imaging phenotype and a psychiatric disorder even in the absence of a significant genetic correlation between them. The impact of genetic risk for psychiatric disorders on regional brain structure did exhibit a consistent pattern across highly comorbid psychiatric disorders, with 80% of the independent genetic loci shared across multiple disorders displaying consistent directions of effect. Cortical patterning of genetic overlap revealed a hierarchical genetic architecture, with the association cortex and sensorimotor cortex representing two extremes of shared genetic influence on psychiatric disorders and brain structural variation. Integrating multi-scale functional annotations and transcriptomic profiles, we observed that shared genetic loci were enriched in active genomic regions, converged on neurobiological and metabolic pathways, and showed differential expression in postmortem brain tissue from individuals with psychiatric disorders. Cumulatively, these findings provide a significant advance in our understanding of the overlapping polygenic architecture between psychopathology and cortical brain structure.

Most stars, including our Sun, will one day evolve into red giants and, subsequently, white dwarfs. Several planet candidates have recently been identified orbiting white dwarfs1-4, demonstrating that planets can survive the stellar post-main-sequence stage intact. Little is known about the atmospheric composition of post-main-sequence planets, with the most evolved transiting planets with atmospheric detections so far orbiting subgiants5,6. Here we report an atmospheric detection for the white dwarf planet WD 1856 b, achieved through transmission spectroscopy with the James Webb Space Telescope (JWST) Near-Infrared Spectrograph (NIRSpec) PRISM. Our 0.5-5.0-μm spectrum reveals the presence of hydrocarbons (odds ratio of 167:1-5,377:1, with CH4 preferred at 17:1-30:1), aerosols (2 × 105:1-2 × 106:1) and thermal emission from the planetary nightside (2 × 1063:1-2 × 1073:1). Our spectral analysis constrains the mass of WD 1856 b to 4.3-10.9 MJ, finds a carbon-enriched atmosphere (with a CH4 abundance of approximately 7%) and an effective temperature exceeding the expected planetary equilibrium temperature (390-412 K versus 160 K). On the basis of cooling models, these results indicate that WD 1856 b underwent a migration-related reheating event 3.0-5.5 Gyr into the white dwarf phase, consistent with post-main-sequence tidal evolution to the present-day 0.02-AU circular orbit. Our results provide a window into the ultimate fate of giant planets orbiting stars with masses similar to our Sun.

Constraining the complex dynamics of the inner Earth unites research efforts across several scientific disciplines, including geochemistry, geophysics and geodynamics. Seismological and geodynamic studies offer insights into the present state of the mantle structure, whereas geochemical approaches characterize its chemical and isotopic heterogeneities1, shedding light on the complexity of its evolution. One key challenge is determining the age and origin of its chemical heterogeneities. Here we present new high-precision Nd isotopic measurements in present-day volcanism that identify heterogeneities dating back to the Earth's earliest history. We report significantly positive 142Nd anomalies in lavas from the submarine Fani Maoré volcano in the Comoros archipelago. These anomalies require the preservation, in the mantle, of material depleted in light rare-earth elements (REE) and formed within the first 100 million years (Myr) of Earth's history. We suggest that this material is mainly composed of bridgmanite that crystallized from an early Earth magma ocean. This Hadean bridgmanite may be more widespread in the present-day mantle than previously expected, raising new questions about its survival over billions of years of plate tectonics and vigorous mantle convection.

Voluntary muscle contraction is triggered by the neurotransmitter acetylcholine binding its receptors on the postsynaptic membrane of the neuromuscular junction, opening ion channels that allow cation influx and initiate depolarization1-3. Mutations in muscle acetylcholine receptors disrupt this process by either impairing (fast-channel) or prolonging (slow-channel) channel openings1,4. These defects cause congenital myasthenic syndromes (CMS), characterized by severe muscle weakness that is often present at birth and, in some cases, progresses to paralysis and death5,6. The structural mechanisms underlying these pathogenic defects and their pharmacological correction remain unknown. Here, using cryogenic electron microscopy, chemical biology and electrophysiology, we determined the structures and functional consequences of representative CMS mutant receptors with and without drugs. In fast-channel disease-associated mutants, we discovered a cryptic allosteric site targeted by positive modulators that restore gating in a mutation-specific manner. In receptor mutants associated with slow-channel disease, quinidine, fluoxetine and reboxetine act as pore blockers; notably, the antidepressant reboxetine selectively blocks desensitized receptors in a mutation-independent fashion, suggesting repurposing potential. Mechanistically, fast-channel mutations uncouple agonist binding from gating, whereas slow-channel mutations stabilize an abnormally widened, desensitized-like pore. These findings reveal unifying principles of CMS pathogenesis and provide a framework for precision therapies.

Huntington's disease (HD) is a devastating movement disorder without a cure at present1. Although the monogenic basis of HD is well defined2, the complex downstream effects that underlie behavioural symptoms are poorly understood. These effects include cortical dysfunction3,4, yet the roles of specific cortical neuronal subtypes in HD symptoms remain largely unexplored. Here we used longitudinal in vivo two-photon calcium imaging to examine the activity of three cortical inhibitory neuron (IN) subtypes and excitatory corticostriatal (CStr) projection neurons in the motor cortex of the transgenic R6/2 HD mouse model throughout disease progression. We found that motor deficits in R6/2 mice were accompanied by neuron subtype-specific abnormalities in movement-related activity. This included marked hypoactivity of vasoactive intestinal peptide (VIP)-INs and CStr neurons, which was also observed in the knock-in zQ175DN HD mouse model. Optogenetic activation of VIP-INs in R6/2 mice restored healthy levels of activity in VIP-INs and their downstream CStr neurons and ameliorated motor deficits in R6/2 mice; behavioural improvements persisted for days after stimulation. Our findings highlight cortical INs as a potential therapeutic target for HD.

Tin (Sn2+) halide perovskites are promising lead-free semiconductors for optoelectronic and electronic devices, owing to their tunable bandgaps and favourable charge transport1,2. However, their practical implementation is fundamentally limited by an intrinsic redox instability at undercoordinated Sn2+ sites, which drives uncontrolled self-p-doping and rapid oxidative degradation3,4. Here we introduce a volatile-assisted coordination strategy that reconstructs the perovskite surface through transient acetate coordination and volatilization, which transforms reactive SnI2-terminated surfaces into chemically equilibrated and defect-mitigated interfaces. This surface reconstruction suppresses undercoordinated Sn-related trap states and stabilizes the local stoichiometry, thus enabling p-type transistors with robust transport characteristics, a near-zero threshold voltage and high on/off ratios exceeding 108. More importantly, the reconstructed interface acts as a self-passivating and thermally resilient barrier, resulting in markedly enhanced environmental stability, with devices maintaining stable operation for over 1 month at 100 °C. These results establish volatile-assisted surface reconstruction as an effective method for defect equilibration in metastable semiconductors, and they provide a general strategy for enabling durable, device-grade functionality in Sn2+-based materials.

Cells must rapidly counteract heat stress-induced hyperfluidization of the plasma membrane to prevent membrane damage1,2, yet how cells achieve such early protection remains unknown. Here we show that in rice (Oryza sativa), the P4-ATPase OsALA5, together with its β-subunit OsALIS2, mediates a heat-responsive flipping of saturated phosphatidylcholines that rapidly stabilizes plasma membrane fluidity. Using leaflet-resolved lipidomics and complementary transport assays, we demonstrate that heat exposure induces a minute-timescale shift in OsALA5 transport activity that leads to selective enrichment of saturated phosphatidylcholines in the cytoplasmic plasma membrane leaflet. This OsALA5-mediated saturated phosphatidylcholine flipping prevents plasma membrane hyperfluidization upon heat stress, thus mitigating ion leakage and cell death. Our analyses of OsALA5 orthologues in Arabidopsis thaliana and yeast support functional conservation of a rapid heat-associated response within a subset of plasma membrane-localized, phosphatidylcholine-transporting P4-ATPases. We identified a rare haplotype of OsALA5 that confers both heat tolerance and yield stability in multi-year, multi-location field trials. Thus, beyond identifying this P4-ATPase-mediated flipping of saturated phosphatidylcholines in response to heat stress and providing genetic resources to advance breeding of heat-tolerant crops, our study reveals how cells counteract heat stress-driven plasma membrane hyperfluidization at an earlier stage than the previously known transcription-dependent lipid remodelling response.