Major depressive disorder (MDD) is a prevalent and debilitating disorder whose causes and consequences remain insufficiently understood. Genetic variants can be used to study causal relationships with other traits. Here we reviewed 201 MDD-associated traits and performed genetic correlation analyses for 115 traits, two-sample Mendelian randomization for 89 of them, and one-sample Mendelian randomization for an additional 43 outcomes, applying sensitivity tests and power analyses. We show that MDD liability increases risk for poorer circadian, cognitive, diet, medical disease, endocrine, functional, inflammatory, metabolic, mortality, physical activity, reproduction, risk behavior, social, socioeconomic and suicide outcomes. Most associations were bidirectional, although with weaker evidence for diet, disease and endocrine traits causing MDD risk. These findings provide a systematic overview of traits putatively causally linked to MDD-confirming known links and identifying new ones-and underscore MDD as a cross-cutting risk factor across medical, functional and psychosocial domains.

Quantum simulations of many-body systems are among the most promising applications of quantum computers1. In particular, models based on strongly correlated fermions are central to our understanding of quantum chemistry and materials problems2, and can lead to exotic, topological phases of matter3,4. However, owing to the non-local nature of fermions, such models are challenging to simulate with qubit devices5. Here we realize a digital quantum simulation architecture for two-dimensional fermionic systems based on reconfigurable atom arrays6. We utilize a fermion-to-qubit mapping based on Kitaev's model on a honeycomb lattice3, in which fermionic statistics are encoded using long-range entangled states7. We prepare these states efficiently using measurement8 and feedforward9, realize subsequent fermionic evolution through Floquet engineering10,11 with tunable entangling gates12 interspersed with atom rearrangement, and improve results with built-in error detection. Leveraging this fermion description of the Kitaev spin model, we efficiently prepare topological states across its complex phase diagram13 and verify the non-Abelian spin-liquid phase3 by evaluating an odd Chern number14,15. We further explore this two-dimensional fermion system by realizing tunable dynamics and directly probing fermion exchange statistics. Finally, we simulate strong interactions and study the dynamics of the Fermi-Hubbard model on a square lattice. These results pave the way for digital quantum simulations of complex fermionic systems for materials science, chemistry16 and high-energy physics17.

Out-of-equilibrium phases in many-body systems constitute a new paradigm in quantum matter-they exhibit dynamical properties that may otherwise be forbidden by equilibrium thermodynamics. Among these non-equilibrium phases are periodically driven (Floquet) systems1-5, which are generically difficult to simulate classically because of their high entanglement. Here we realize a Floquet topologically ordered state theoretically proposed in ref. 6, on an array of superconducting qubits. We image the characteristic dynamics of its chiral edge modes and characterize its emergent anyonic excitations. Devising an interferometric algorithm allows us to introduce and measure a bulk topological invariant to probe the dynamical transmutation of anyons for system sizes up to 58 qubits. Our work demonstrates that quantum processors can provide key insights into the thus-far largely unexplored landscape of highly entangled non-equilibrium phases of matter.

Globular clusters (GCs) are among the oldest and densest stellar systems in the Universe, yet how they form remains a mystery1. Here we present a suite of cosmological simulations in which both dark-matter-free GCs and dark-matter-rich dwarf galaxies naturally emerge in the Standard Cosmology. We show that these objects inhabit distinct locations in the size-luminosity plane and that they have similar ages, age spread, metallicity and metallicity spread to globulars and dwarfs in the nearby Universe. About half of our simulated globulars form by means of regular star formation near the centres of their host dwarf, with the rest forming further out, triggered by mergers. The latter are more tidally isolated and more likely to survive to the present day. Finally, our simulations predict the existence of a new class of object that we call 'globular-cluster-like dwarfs' (GCDs). These form from a single, self-quenching, star-formation event in low-mass dark-matter halos at high redshift and have observational properties intermediate between globulars and dwarfs. We identify several dwarfs in our Galaxy, such as Reticulum II (refs. 2-4), that could be in this new class. If so, they promise unprecedented constraints on dark-matter models and new sites to search for metal-free stars.

Extreme event attribution assesses how climate change affected climate extremes, but typically focuses on single events1-4. Furthermore, these attributions rarely quantify the extent to which anthropogenic actors have contributed to these events5,6. Here we show that climate change made 213 historical heatwaves reported over 2000-2023 more likely and more intense, to which each of the 180 carbon majors (fossil fuel and cement producers) substantially contributed. This work relies on the expansion of a well-established event-based framework1. Owing to global warming since 1850-1900, the median of the heatwaves during 2000-2009 became about 20 times more likely, and about 200 times more likely during 2010-2019. Overall, one-quarter of these events were virtually impossible without climate change. The emissions of the carbon majors contribute to half the increase in heatwave intensity since 1850-1900. Depending on the carbon major, their individual contribution is high enough to enable the occurrence of 16-53 heatwaves that would have been virtually impossible in a preindustrial climate. We, therefore, establish that the influence of climate change on heatwaves has increased, and that all carbon majors, even the smaller ones, contributed substantially to the occurrence of heatwaves. Our results contribute to filling the evidentiary gap to establish accountability of historical climate extremes7,8.

The Perseverance rover has explored and sampled igneous and sedimentary rocks within Jezero Crater to characterize early Martian geological processes and habitability and search for potential biosignatures1-7. Upon entering Neretva Vallis, on Jezero Crater's western edge8, Perseverance investigated distinctive mudstone and conglomerate outcrops of the Bright Angel formation. Here we report a detailed geological, petrographic and geochemical survey of these rocks and show that organic-carbon-bearing mudstones in the Bright Angel formation contain submillimetre-scale nodules and millimetre-scale reaction fronts enriched in ferrous iron phosphate and sulfide minerals, likely vivianite and greigite, respectively. This organic carbon appears to have participated in post-depositional redox reactions that produced the observed iron-phosphate and iron-sulfide minerals. Geological context and petrography indicate that these reactions occurred at low temperatures. Within this context, we review the various pathways by which redox reactions that involve organic matter can produce the observed suite of iron-, sulfur- and phosphorus-bearing minerals in laboratory and natural environments on Earth. Ultimately, we conclude that analysis of the core sample collected from this unit using high-sensitivity instrumentation on Earth will enable the measurements required to determine the origin of the minerals, organics and textures it contains.

Psychotic-like experiences (PLEs) may arise from genetic and environmental risk leading to worsening cognitive and morphometry metrics over time, which in turn lead to worsening PLEs. Analyses used three waves of unique longitudinal Adolescent Brain Cognitive Development Study data (ages 9-13) to test whether changes in cognition and global morphometry metrics attenuate associations between genetic and environmental risk with persistent distressing PLEs. Multigroup univariate latent growth models examined three waves of cognitive metrics and global morphometry separately for three PLE groups: persistent distressing PLEs (n=356), transient distressing PLEs (n=408), and low-level PLEs (n=7901). Persistent distressing PLEs showed greater decreases (i.e., more negative slopes) of cognition and morphometry metrics over time compared to those in low-level PLE groups. Analyses also provided novel evidence for extant theories that worsening cognition and global morphometry metrics may partially account for associations between environmental risk with persistent distressing PLEs.

Geologically storing carbon is a key strategy for abating emissions from fossil fuels and durably removing carbon dioxide (CO2) from the atmosphere1,2. However, the storage potential is not unlimited3,4. Here we establish a prudent planetary limit of around 1,460 (1,290-2,710) Gt of CO2 storage through a risk-based, spatially explicit analysis of carbon storage in sedimentary basins. We show that only stringent near-term gross emissions reductions can lower the risk of breaching this limit before the year 2200. Fully using geologic storage for carbon removal caps the possible global temperature reduction to 0.7 °C (0.35-1.2 °C, including storage estimate and climate response uncertainty). The countries most robust to our risk assessment are current large-scale extractors of fossil resources. Treating carbon storage as a limited intergenerational resource has deep implications for national mitigation strategies and policy and requires making explicit decisions on priorities for storage use.

Steel truss bridges are constructed by connecting many different types of bars (components) to form a load-bearing structural system. Several disastrous collapses of this type of bridge have occurred as a result of initial component failure(s) propagating to the rest of the structure1-3. Despite the prevalence and importance of these structures, it is still unclear why initial component failures propagate disproportionately in some bridges but barely affect functionality in others4-7. Here we uncover and characterize the fundamental secondary resistance mechanisms that allow steel truss bridges to withstand the initial failure of any main component. These mechanisms differ substantially from the primary resistance mechanisms considered during the design of (undamaged) bridges. After testing a scaled-down specimen of a real bridge and using validated numerical models to simulate the failure of all main bridge components, we show how secondary resistance mechanisms interact to redistribute the loads supported by failed components to other parts of the structure. By studying the evolution of these mechanisms under increasing loads up to global failure, we are able to describe the conditions that enable their effective development. These findings can be used to enhance present bridge design and maintenance strategies, ultimately leading to safer transport networks.

We introduce Generative, Adaptive, Context-Aware 3D Printing (GRACE), a new approach combining 3D imaging, computer vision and parametric modelling to create tailored, context-aware geometries using volumetric additive manufacturing. GRACE rapidly and automatically generates complex structures capable of conforming directly around features ranging from cellular to macroscopic scales with minimal user intervention. Here we demonstrate its versatility in applications ranging from synthetic objects to biofabrication, including adaptive vascular-like geometries around cell-laden bioinks, resulting in improved functionality. GRACE also enables precise alignment of sequential prints, as well as the detection and overprinting of opaque surfaces through shadow correction. Compatible with various printing modalities1-4, GRACE transcends traditional additive manufacturing limitations in automating overprinting and adapting the printed designs to the content of the printable material. This opens new possibilities in tissue engineering and regenerative medicine.

For rocky planets, the presence of a solid inner core has notable implications on the composition and thermal evolution of the core and on the magnetic history of the planet1-3. On Mars, geophysical observations have confirmed that the core is at least partially liquid4-7, but it is unknown whether any part of the core is solid. Here we present an analysis of seismic data acquired by the InSight mission, demonstrating that Mars has a solid inner core. We identify two seismic phases, the deep core-transiting phase, PKKP, and the inner core boundary reflecting phase, PKiKP, indicative of the inner core. Our inversions constrain the radius of the Martian inner core to about 613 ± 67 km, with a compressional velocity jump of around 30% across the inner core boundary, supported by additional inner-core-related seismic phases. These properties imply a concentration of distinct light elements in the inner core, segregated from the outer core through core crystallization. This finding provides an anchor point for understanding the thermal and chemical state of Mars. Moreover, the relationship between inner core formation and the Martian magnetic field evolution could provide insights into dynamo generation across planetary bodies.

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with previous expectations to initiate movements and make decisions1. It is difficult to meet this challenge if different laboratories apply different analyses to different recordings in different regions during different behaviours. Here we report a comprehensive set of recordings from 621,733 neurons recorded with 699 Neuropixels probes across 139 mice in 12 laboratories. The data were obtained from mice performing a decision-making task with sensory, motor and cognitive components. The probes covered 279 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map and assess how neural activity encodes key task variables. Representations of visual stimuli transiently appeared in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of midbrain and hindbrain regions that also encoded choices. Neural responses correlated with impending motor action almost everywhere in the brain. Responses to reward delivery and consumption were also widespread. This publicly available dataset represents a resource for understanding how computations distributed across and within brain areas drive behaviour.

The neural representations of prior information about the state of the world are poorly understood1. Here, to investigate them, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with a prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions that, notably, span all levels of processing, from early sensory areas (the lateral geniculate nucleus and primary visual cortex) to motor regions (secondary and primary motor cortex and gigantocellular reticular nucleus) and high-level cortical regions (the dorsal anterior cingulate area and ventrolateral orbitofrontal cortex). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision-making areas. This study offers a brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large-scale recordings on a single standardized task.

Artificial intelligence (AI) and combinatorial optimization drive applications across science and industry, but their increasing energy demands challenge the sustainability of digital computing. Most unconventional computing systems1-7 target either AI or optimization workloads and rely on frequent, energy-intensive digital conversions, limiting efficiency. These systems also face application-hardware mismatches, whether handling memory-bottlenecked neural models, mapping real-world optimization problems or contending with inherent analog noise. Here we introduce an analog optical computer (AOC) that combines analog electronics and three-dimensional optics to accelerate AI inference and combinatorial optimization in a single platform. This dual-domain capability is enabled by a rapid fixed-point search, which avoids digital conversions and enhances noise robustness. With this fixed-point abstraction, the AOC implements emerging compute-bound neural models with recursive reasoning potential and realizes an advanced gradient-descent approach for expressive optimization. We demonstrate the benefits of co-designing the hardware and abstraction, echoing the co-evolution of digital accelerators and deep learning models, through four case studies: image classification, nonlinear regression, medical image reconstruction and financial transaction settlement. Built with scalable, consumer-grade technologies, the AOC paves a promising path for faster and sustainable computing. Its native support for iterative, compute-intensive models offers a scalable analog platform for fostering future innovation in AI and optimization.

Sustainable development is an imperative worldwide1-3 but metrics and data on poverty and quality of life have remained too coarse and abstract to characterize challenges adequately and guide practical progress4,5. Nowhere is this challenge greater than in Africa4-6, where we still know little about the spatial details of development3,7-9. Here we leverage a comprehensive, high-precision dataset of building footprints to identify infrastructure deficits and infer informal settlements down to the street block level10-12 everywhere in sub-Saharan Africa. We identify a general pattern of informality with cities showing, on average, greater access to infrastructure and services than rural and peri-urban areas. We show that such patterns of informality are characterized by consistent statistical distributions reflecting uneven local development2,13,14. We also show that these physical measures of informality are systematically associated with many indicators of human deprivation, which form a single principal component co-varying predictably with specific changes in street access to buildings. These results demonstrate that the localization of sustainable development is possible down to the street level at a continental scale and provide a general distributed strategy for accelerating progress in infrastructure and service expansion that taps local innovations in systematic, equitable and context-appropriate ways7,11,12,15.

The forthcoming sixth-generation and beyond wireless networks are poised to operate across an expansive frequency range-from microwave, millimetre wave to terahertz bands-to support ubiquitous connectivity in diverse application scenarios1-3. This necessitates a one-size-fits-all hardware solution that can be adaptively reconfigured within this wide spectrum to support full-band coverage and dynamic spectrum management4. However, existing electrical or photonic-assisted solutions face a lot of challenges in meeting this demand because of the limited bandwidths of the devices and the intrinsically rigid nature of system architectures5. Here we demonstrate adaptive wireless communications over an unprecedented frequency range spanning over 100 GHz, driven by a thin-film lithium niobate (TFLN) photonic wireless system. Leveraging the Pockels effect and scalability of the TFLN platform, we achieve monolithic integration of essential functional elements, including baseband modulation, broadband wireless-photonic conversion and reconfigurable carrier and local signal generation. Powered by broadband tunable optoelectronic oscillators, our signal sources operate across a record-wide frequency range from 0.5 GHz to 115 GHz with high-frequency stability and consistent coherence. Based on the broadband and reconfigurable integrated photonic solution, we realize full-link wireless communication across nine consecutive bands, achieving record lane speeds of up to 100 Gbps. The real-time reconfigurability further enables adaptive frequency allocation, a crucial ability to ensure enhanced reliability in complex spectrum environments. Our proposed system represents a marked step towards future full-spectrum and omni-scenario wireless networks.

Large urban gullies cause damage in many tropical cities across the Global South1,2. They can result from inappropriate urban planning and insufficient infrastructure to safely store and evacuate rainfall in environments that are already highly sensitive to soil erosion1,3,4. Although they can cause large destruction and societal impacts such as population displacement1,2,5, the magnitude of this geo-hydrological hazard remains poorly documented and understood6,7. Here we provide an assessment of the extent and impact of urban gullies at the scale of the Democratic Republic of the Congo (DRC). Through mapping, we identify 2,922 urban gullies across 26 cities. By combining their formation and growth rates with population density data8, we estimate that around 118,600 people (uncertainty range: ± 44,400 people) have been displaced by urban gullies over the period 2004-2023. We find that average displacement rates increased from about 4,650 persons yr-1 (pre-2020) to about 12,200 persons yr-1 (post-2020). Between 2010 and 2023, the number of people living in the potential expansion zone of urban gullies doubled from 1.6 (±0.6) to 3.2 (±1.3) million, with more likely to be exposed due to urban sprawl9,10 and climate change11. We suggest that there is a need for tools and strategies to prevent and mitigate this hazard.

To orchestrate ribosomal peptide synthesis, transfer RNAs (tRNAs) must be aminoacylated, with activated amino acids, at their 2',3'-diol moiety1,2, and so the selective aminoacylation of RNA in water is a key challenge that must be resolved to explain the origin of protein biosynthesis. So far, there have been no chemical methods to effectively and selectively aminoacylate RNA-2',3'-diols with the breadth of proteinogenic amino acids in water3-5. Here we demonstrate that (biological) aminoacyl-thiols (1) react selectively with RNA diols over amine nucleophiles, promoting aminoacylation over adventitious (non-coded) peptide bond formation. Broad side-chain scope is demonstrated, including Ala, Arg, Asp, Glu, Gln, Gly, His, Leu, Lys, Met, Phe, Pro, Ser and Val, and Arg aminoacylation is enhanced by unprecedented side-chain nucleophilic catalysis. Duplex formation directs chemoselective 2',3'-aminoacylation of RNA. We demonstrate that prebiotic nitriles, N-carboxyanhydrides and amino acid anhydrides, as well as biological aminoacyl-adenylates, all react with thiols (including coenzymes A and M) to selectively yield aminoacyl-thiols (1) in water. Finally, we demonstrate that the switch from thioester to thioacid activation inverts diol/amine selectivity, promoting peptide synthesis in excellent yield. Two-step, one-pot, chemically controlled formation of peptidyl-RNA is observed in water at neutral pH. Our results indicate an important role for thiol cofactors in RNA aminoacylation before the evolution of proteinaceous synthetase enzymes.

Generative models cover various application areas, including image and video synthesis, natural language processing and molecular design, among many others1-11. As digital generative models become larger, scalable inference in a fast and energy-efficient manner becomes a challenge12-14. Here we present optical generative models inspired by diffusion models4, where a shallow and fast digital encoder first maps random noise into phase patterns that serve as optical generative seeds for a desired data distribution; a jointly trained free-space-based reconfigurable decoder all-optically processes these generative seeds to create images never seen before following the target data distribution. Except for the illumination power and the random seed generation through a shallow encoder, these optical generative models do not consume computing power during the synthesis of the images. We report the optical generation of monochrome and multicolour images of handwritten digits, fashion products, butterflies, human faces and artworks, following the data distributions of MNIST15, Fashion-MNIST16, Butterflies-10017, Celeb-A datasets18, and Van Gogh's paintings and drawings19, respectively, achieving an overall performance comparable to digital neural-network-based generative models. To experimentally demonstrate optical generative models, we used visible light to generate images of handwritten digits and fashion products. In addition, we generated Van Gogh-style artworks using both monochrome and multiwavelength illumination. These optical generative models might pave the way for energy-efficient and scalable inference tasks, further exploiting the potentials of optics and photonics for artificial-intelligence-generated content.

Brain cortical morphology, indexed by its surface area and thickness, is known to be highly heritable. Previous research has suggested a relationship of cortical morphology with several neuropsychiatric phenotypes. However, the multitude of potential confounders makes it difficult to establish causal relationships. Here, we employ Generalized Summary-data-based Mendelian Randomization and a series of sensitivity analyses to investigate causal links between 70 cortical morphology measures and 199 neuropsychiatric, behavioral, and metabolic phenotypes. We show that total brain cortical surface area (TSA) has significant positive causal effects on 18 phenotypes. The strongest effects include TSA positively influencing cognitive performance, while reverse analyses reveal small effects of cognitive performance on TSA. Global mean cortical thickness (MTH) exhibits significant causal effects on five phenotypes, including schizophrenia. MTH reduces schizophrenia risk and bidirectional causality is found between MTH and smoking initiation. Finally, in regional analyses we detect positive influences of the transverse temporal surface area on cognitive performance and negative influences of transverse temporal thickness on schizophrenia risk. Overall, our results highlight bidirectional associations between TSA, MTH, and neuropsychiatric traits. These insights offer potential avenues for intervention studies aimed at improving brain health.