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Guanidine is a chemically stable nitrogen compound that is excreted in human urine and is widely used in manufacturing of plastics, as a flame retardant and as a component of propellants, and is well known as a protein denaturant in biochemistry1–3. Guanidine occurs widely in nature and is used by several microorganisms as a nitrogen source, but microorganisms growing on guanidine as the only substrate have not yet been identified. Here we show that the complete ammonia oxidizer (comammox) Nitrospira inopinataand probably most other comammox microorganisms can grow on guanidine as the sole source of energy, reductant and nitrogen. Proteomics, enzyme kinetics and the crystal structure of a N. inopinataguanidinase homologue demonstrated that it is a bona fide guanidinase. Incubation experiments with comammox-containing agricultural soil and wastewater treatment plant microbiomes suggested that guanidine serves as substrate for nitrification in the environment. The identification of guanidine as a growth substrate for comammox shows an unexpected niche of these globally important nitrifiers and offers opportunities for their isolation.

Chaperonins are large barrel-shaped complexes that mediate ATP-dependent protein folding1–3. The bacterial chaperonin GroEL forms juxtaposed rings that bind unfolded protein and the lid-shaped cofactor GroES at their apertures. In vitro analyses of the chaperonin reaction have shown that substrate protein folds, unimpaired by aggregation, while transiently encapsulated in the GroEL central cavity by GroES4–6. To determine the functional stoichiometry of GroEL, GroES and client protein in situ, here we visualized chaperonin complexes in their natural cellular environment using cryo-electron tomography. We find that, under various growth conditions, around 55–70% of GroEL binds GroES asymmetrically on one ring, with the remainder populating symmetrical complexes. Bound substrate protein is detected on the free ring of the asymmetrical complex, defining the substrate acceptor state. In situ analysis of GroEL–GroES chambers, validated by high-resolution structures obtained in vitro, showed the presence of encapsulated substrate protein in a folded state before release into the cytosol. Based on a comprehensive quantification and conformational analysis of chaperonin complexes, we propose a GroEL–GroES reaction cycle that consists of linked asymmetrical and symmetrical subreactions mediating protein folding. Our findings illuminate the native conformational and functional chaperonin cycle directly within cells.

Horses revolutionized human history with fast mobility1. However, the timeline between their domestication and their widespread integration as a means of transport remains contentious2–4. Here we assemble a collection of 475 ancient horse genomes to assess the period when these animals were first reshaped by human agency in Eurasia. We find that reproductive control of the modern domestic lineage emerged around 2200 bce, through close-kin mating and shortened generation times. Reproductive control emerged following a severe domestication bottleneck starting no earlier than approximately 2700 bce, and coincided with a sudden expansion across Eurasia that ultimately resulted in the replacement of nearly every local horse lineage. This expansion marked the rise of widespread horse-based mobility in human history, which refutes the commonly held narrative of large horse herds accompanying the massive migration of steppe peoples across Europe around 3000 bceand earlier3,5. Finally, we detect significantly shortened generation times at Botai around 3500 bce, a settlement from central Asia associated with corrals and a subsistence economy centred on horses6,7. This supports local horse husbandry before the rise of modern domestic bloodlines.

Despite its disordered liquid-like structure, glass exhibits solid-like mechanical properties1. The formation of glassy material occurs by vitrification, preventing crystallization and promoting an amorphous structure2. Glass is fundamental in diverse fields of materials science, owing to its unique optical, chemical and mechanical properties as well as durability, versatility and environmental sustainability3. However, engineering a glassy material without compromising its properties is challenging4–6. Here we report the discovery of a supramolecular amorphous glass formed by the spontaneous self-organization of the short aromatic tripeptide YYY initiated by non-covalent cross-linking with structural water7,8. This system uniquely combines often contradictory sets of properties; it is highly rigid yet can undergo complete self-healing at room temperature. Moreover, the supramolecular glass is an extremely strong adhesive yet it is transparent in a wide spectral range from visible to mid-infrared. This exceptional set of characteristics is observed in a simple bioorganic peptide glass composed of natural amino acids, presenting a multi-functional material that could be highly advantageous for various applications in science and engineering.

Building a fault-tolerant quantum computer will require vast numbers of physical qubits. For qubit technologies based on solid-state electronic devices1–3, integrating millions of qubits in a single processor will require device fabrication to reach a scale comparable to that of the modern complementary metal–oxide–semiconductor (CMOS) industry. Equally important, the scale of cryogenic device testing must keep pace to enable efficient device screening and to improve statistical metrics such as qubit yield and voltage variation. Spin qubits1,4,5based on electrons in Si have shown impressive control fidelities6–9but have historically been challenged by yield and process variation10–12. Here we present a testing process using a cryogenic 300-mm wafer prober13to collect high-volume data on the performance of hundreds of industry-manufactured spin qubit devices at 1.6 K. This testing method provides fast feedback to enable optimization of the CMOS-compatible fabrication process, leading to high yield and low process variation. Using this system, we automate measurements of the operating point of spin qubits and investigate the transitions of single electrons across full wafers. We analyse the random variation in single-electron operating voltages and find that the optimized fabrication process leads to low levels of disorder at the 300-mm scale. Together, these results demonstrate the advances that can be achieved through the application of CMOS-industry techniques to the fabrication and measurement of spin qubit devices.

Antibody and chimeric antigen receptor (CAR) T cell-mediated targeted therapies have improved survival in patients with solid and haematologic malignancies1–9. Adults with T cell leukaemias and lymphomas, collectively called T cell cancers, have short survival10,11and lack such targeted therapies. Thus, T cell cancers particularly warrant the development of CAR T cells and antibodies to improve patient outcomes. Preclinical studies showed that targeting T cell receptor β-chain constant region 1 (TRBC1) can kill cancerous T cells while preserving sufficient healthy T cells to maintain immunity12, making TRBC1 an attractive target to treat T cell cancers. However, the first-in-human clinical trial of anti-TRBC1 CAR T cells reported a low response rate and unexplained loss of anti-TRBC1 CAR T cells13,14. Here we demonstrate that CAR T cells are lost due to killing by the patient’s normal T cells, reducing their efficacy. To circumvent this issue, we developed an antibody–drug conjugate that could kill TRBC1+cancer cells in vitro and cure human T cell cancers in mouse models. The anti-TRBC1 antibody–drug conjugate may provide an optimal format for TRBC1 targeting and produce superior responses in patients with T cell cancers.

Neuromyelitis optica is a paradigmatic autoimmune disease of the central nervous system, in which the water-channel protein AQP4 is the target antigen1. The immunopathology in neuromyelitis optica is largely driven by autoantibodies to AQP42. However, the T cell response that is required for the generation of these anti-AQP4 antibodies is not well understood. Here we show that B cells endogenously express AQP4 in response to activation with anti-CD40 and IL-21 and are able to present their endogenous AQP4 to T cells with an AQP4-specific T cell receptor (TCR). A population of thymic B cells emulates a CD40-stimulated B cell transcriptome, including AQP4 (in mice and humans), and efficiently purges the thymic TCR repertoire of AQP4-reactive clones. Genetic ablation of Aqp4in B cells rescues AQP4-specific TCRs despite sufficient expression of AQP4 in medullary thymic epithelial cells, and B-cell-conditional AQP4-deficient mice are fully competent to raise AQP4-specific antibodies in productive germinal-centre responses. Thus, the negative selection of AQP4-specific thymocytes is dependent on the expression and presentation of AQP4 by thymic B cells. As AQP4 is expressed in B cells in a CD40-dependent (but not AIRE-dependent) manner, we propose that thymic B cells might tolerize against a group of germinal-centre-associated antigens, including disease-relevant autoantigens such as AQP4.

Tropical cyclones have far-reaching impacts on livelihoods and population health that often persist years after the event1–4. Characterizing the demographic and socioeconomic profile and the vulnerabilities of exposed populations is essential to assess health and other risks associated with future tropical cyclone events5. Estimates of exposure to tropical cyclones are often regional rather than global6and do not consider population vulnerabilities7. Here we combine spatially resolved annual demographic estimates with tropical cyclone wind fields estimates to construct a global profile of the populations exposed to tropical cyclones between 2002 and 2019. We find that approximately 560 million people are exposed yearly and that the number of people exposed has increased across all cyclone intensities over the study period. The age distribution of those exposed has shifted away from children (less than 5 years old) and towards older people (more than 60 years old) in recent years compared with the early 2000s. Populations exposed to tropical cyclones are more socioeconomically deprived than those unexposed within the same country, and this relationship is more pronounced for people exposed to higher-intensity storms. By characterizing the patterns and vulnerabilities of exposed populations, our results can help identify mitigation strategies and assess the global burden and future risks of tropical cyclones.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1–9. Deep learning approaches have aided in exploring chemical spaces1,10–15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureuswere enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus(MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

With large wildfires becoming more frequent1,2, we must rapidly learn how megafires impact biodiversity to prioritize mitigation and improve policy. A key challenge is to discover how interactions among fire-regime components, drought and land tenure shape wildfire impacts. The globally unprecedented3,42019–2020 Australian megafires burnt more than 10 million hectares5, prompting major investment in biodiversity monitoring. Collated data include responses of more than 2,000 taxa, providing an unparalleled opportunity to quantify how megafires affect biodiversity. We reveal that the largest effects on plants and animals were in areas with frequent or recent past fires and within extensively burnt areas. Areas burnt at high severity, outside protected areas or under extreme drought also had larger effects. The effects included declines and increases after fire, with the largest responses in rainforests and by mammals. Our results implicate species interactions, dispersal and extent of in situ survival as mechanisms underlying fire responses. Building wildfire resilience into these ecosystems depends on reducing fire recurrence, including with rapid wildfire suppression in areas frequently burnt. Defending wet ecosystems, expanding protected areas and considering localized drought could also contribute. While these countermeasures can help mitigate the impacts of more frequent megafires, reversing anthropogenic climate change remains the urgent broad-scale solution.

Mutations accumulate in the genome of every cell of the body throughout life, causing cancer and other diseases1,2. Most mutations begin as nucleotide mismatches or damage in one of the two strands of the DNA before becoming double-strand mutations if unrepaired or misrepaired3,4. However, current DNA-sequencing technologies cannot accurately resolve these initial single-strand events. Here we develop a single-molecule, long-read sequencing method (Hairpin Duplex Enhanced Fidelity sequencing (HiDEF-seq)) that achieves single-molecule fidelity for base substitutions when present in either one or both DNA strands. HiDEF-seq also detects cytosine deamination—a common type of DNA damage—with single-molecule fidelity. We profiled 134 samples from diverse tissues, including from individuals with cancer predisposition syndromes, and derive from them single-strand mismatch and damage signatures. We find correspondences between these single-strand signatures and known double-strand mutational signatures, which resolves the identity of the initiating lesions. Tumours deficient in both mismatch repair and replicative polymerase proofreading show distinct single-strand mismatch patterns compared to samples that are deficient in only polymerase proofreading. We also define a single-strand damage signature for APOBEC3A. In the mitochondrial genome, our findings support a mutagenic mechanism occurring primarily during replication. As double-strand DNA mutations are only the end point of the mutation process, our approach to detect the initiating single-strand events at single-molecule resolution will enable studies of how mutations arise in a variety of contexts, especially in cancer and ageing.

Hybridizing superconductivity with the quantum Hall (QH) effect has notable potential for designing circuits capable of inducing and manipulating non-Abelian states for topological quantum computation1–3. However, despite recent experimental progress towards this hybridization4–15, concrete evidence for a chiral QH Josephson junction16—the elemental building block for coherent superconducting QH circuits—is still lacking. Its expected signature is an unusual chiral supercurrent flowing in QH edge channels, which oscillates with a specific 2ϕ0magnetic flux periodicity16–19(ϕ0= h/2eis the superconducting flux quantum, where his the Planck constant and eis the electron charge). Here we show that ultra-narrow Josephson junctions defined in encapsulated graphene nanoribbons exhibit a chiral supercurrent, visible up to 8 T and carried by the spin-degenerate edge channel of the QH plateau of resistance h/2e2≈ 12.9 kΩ. We observe reproducible 2ϕ0-periodic oscillations of the supercurrent, which emerge at a constant filling factor when the area of the loop formed by the QH edge channel is constant, within a magnetic-length correction that we resolve in the data. Furthermore, by varying the junction geometry, we show that reducing the superconductor/normal interface length is crucial in obtaining a measurable supercurrent on QH plateaus, in agreement with theories predicting dephasing along the superconducting interface19–22. Our findings are important for the exploration of correlated and fractional QH-based superconducting devices that host non-Abelian Majorana and parafermion zero modes23–32.

Systematic assessments of species extinction risk at regular intervals are necessary for informing conservation action1,2. Ongoing developments in taxonomy, threatening processes and research further underscore the need for reassessment3,4. Here we report the findings of the second Global Amphibian Assessment, evaluating 8,011 species for the International Union for Conservation of Nature Red List of Threatened Species. We find that amphibians are the most threatened vertebrate class (40.7% of species are globally threatened). The updated Red List Index shows that the status of amphibians is deteriorating globally, particularly for salamanders and in the Neotropics. Disease and habitat loss drove 91% of status deteriorations between 1980 and 2004. Ongoing and projected climate change effects are now of increasing concern, driving 39% of status deteriorations since 2004, followed by habitat loss (37%). Although signs of species recoveries incentivize immediate conservation action, scaled-up investment is urgently needed to reverse the current trends.

Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals1. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample.

The nearby radio galaxy M87 is a prime target for studying black hole accretion and jet formation1,2. Event Horizon Telescope observations of M87 in 2017, at a wavelength of 1.3 mm, revealed a ring-like structure, which was interpreted as gravitationally lensed emission around a central black hole3. Here we report images of M87 obtained in 2018, at a wavelength of 3.5 mm, showing that the compact radio core is spatially resolved. High-resolution imaging shows a ring-like structure of 8.4−1.1+0.5Schwarzschild radii in diameter, approximately 50% larger than that seen at 1.3 mm. The outer edge at 3.5 mm is also larger than that at 1.3 mm. This larger and thicker ring indicates a substantial contribution from the accretion flow with absorption effects, in addition to the gravitationally lensed ring-like emission. The images show that the edge-brightened jet connects to the accretion flow of the black hole. Close to the black hole, the emission profile of the jet-launching region is wider than the expected profile of a black-hole-driven jet, suggesting the possible presence of a wind associated with the accretion flow.

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.

The thick filament is a key component of sarcomeres, the basic units of striated muscle1. Alterations in thick filament proteins are associated with familial hypertrophic cardiomyopathy and other heart and muscle diseases2. Despite the central importance of the thick filament, its molecular organization remains unclear. Here we present the molecular architecture of native cardiac sarcomeres in the relaxed state, determined by cryo-electron tomography. Our reconstruction of the thick filament reveals the three-dimensional organization of myosin, titin and myosin-binding protein C (MyBP-C). The arrangement of myosin molecules is dependent on their position along the filament, suggesting specialized capacities in terms of strain susceptibility and force generation. Three pairs of titin-α and titin-β chains run axially along the filament, intertwining with myosin tails and probably orchestrating the length-dependent activation of the sarcomere. Notably, whereas the three titin-α chains run along the entire length of the thick filament, titin-β chains do not. The structure also demonstrates that MyBP-C bridges thin and thick filaments, with its carboxy-terminal region binding to the myosin tails and directly stabilizing the OFF state of the myosin heads in an unforeseen manner. These results provide a foundation for future research investigating muscle disorders involving sarcomeric components.

Fanconi anaemia (FA), a model syndrome of genome instability, is caused by a deficiency in DNA interstrand crosslink repair resulting in chromosome breakage1–3. The FA repair pathway protects against endogenous and exogenous carcinogenic aldehydes4–7. Individuals with FA are hundreds to thousands fold more likely to develop head and neck (HNSCC), oesophageal and anogenital squamous cell carcinomas8(SCCs). Molecular studies of SCCs from individuals with FA (FA SCCs) are limited, and it is unclear how FA SCCs relate to sporadic HNSCCs primarily driven by tobacco and alcohol exposure or infection with human papillomavirus9(HPV). Here, by sequencing genomes and exomes of FA SCCs, we demonstrate that the primary genomic signature of FA repair deficiency is the presence of high numbers of structural variants. Structural variants are enriched for small deletions, unbalanced translocations and fold-back inversions, and are often connected, thereby forming complex rearrangements. They arise in the context of TP53loss, but not in the context of HPV infection, and lead to somatic copy-number alterations of HNSCC driver genes. We further show that FA pathway deficiency may lead to epithelial-to-mesenchymal transition and enhanced keratinocyte-intrinsic inflammatory signalling, which would contribute to the aggressive nature of FA SCCs. We propose that the genomic instability in sporadic HPV-negative HNSCC may arise as a result of the FA repair pathway being overwhelmed by DNA interstrand crosslink damage caused by alcohol and tobacco-derived aldehydes, making FA SCC a powerful model to study tumorigenesis resulting from DNA-crosslinking damage.

Aggregates of medin amyloid (a fragment of the protein MFG-E8, also known as lactadherin) are found in the vasculature of almost all humans over 50 years of age1,2, making it the most common amyloid currently known. We recently reported that medin also aggregates in blood vessels of ageing wild-type mice, causing cerebrovascular dysfunction3. Here we demonstrate in amyloid-β precursor protein (APP) transgenic mice and in patients with Alzheimer’s disease that medin co-localizes with vascular amyloid-β deposits, and that in mice, medin deficiency reduces vascular amyloid-β deposition by half. Moreover, in both the mouse and human brain, MFG-E8 is highly enriched in the vasculature and both MFG-E8 and medin levels increase with the severity of vascular amyloid-β burden. Additionally, analysing data from 566 individuals in the ROSMAP cohort, we find that patients with Alzheimer’s disease have higher MFGE8expression levels, which are attributable to vascular cells and are associated with increased measures of cognitive decline, independent of plaque and tau pathology. Mechanistically, we demonstrate that medin interacts directly with amyloid-β to promote its aggregation, as medin forms heterologous fibrils with amyloid-β, affects amyloid-β fibril structure, and cross-seeds amyloid-β aggregation both in vitro and in vivo. Thus, medin could be a therapeutic target for prevention of vascular damage and cognitive decline resulting from amyloid-β deposition in the blood vessels of the brain.

A spinal cord injury interrupts pathways from the brain and brainstem that project to the lumbar spinal cord, leading to paralysis. Here we show that spatiotemporal epidural electrical stimulation (EES) of the lumbar spinal cord1–3applied during neurorehabilitation4,5(EESREHAB) restored walking in nine individuals with chronic spinal cord injury. This recovery involved a reduction in neuronal activity in the lumbar spinal cord of humans during walking. We hypothesized that this unexpected reduction reflects activity-dependent selection of specific neuronal subpopulations that become essential for a patient to walk after spinal cord injury. To identify these putative neurons, we modelled the technological and therapeutic features underlying EESREHABin mice. We applied single-nucleus RNA sequencing6–9and spatial transcriptomics10,11to the spinal cords of these mice to chart a spatially resolved molecular atlas of recovery from paralysis. We then employed cell type12,13and spatial prioritization to identify the neurons involved in the recovery of walking. A single population of excitatory interneurons nested within intermediate laminae emerged. Although these neurons are not required for walking before spinal cord injury, we demonstrate that they are essential for the recovery of walking with EES following spinal cord injury. Augmenting the activity of these neurons phenocopied the recovery of walking enabled by EESREHAB, whereas ablating them prevented the recovery of walking that occurs spontaneously after moderate spinal cord injury. We thus identified a recovery-organizing neuronal subpopulation that is necessary and sufficient to regain walking after paralysis. Moreover, our methodology establishes a framework for using molecular cartography to identify the neurons that produce complex behaviours.

Photoelectrochemical (PEC) artificial leaves hold the potential to lower the costs of sustainable solar fuel production by integrating light harvesting and catalysis within one compact device. However, current deposition techniques limit their scalability1, whereas fragile and heavy bulk materials can affect their transport and deployment. Here we demonstrate the fabrication of lightweight artificial leaves by employing thin, flexible substrates and carbonaceous protection layers. Lead halide perovskite photocathodes deposited onto indium tin oxide-coated polyethylene terephthalate achieved an activity of 4,266 µmol H2g−1h−1using a platinum catalyst, whereas photocathodes with a molecular Co catalyst for CO2reduction attained a high CO:H2selectivity of 7.2 under lower (0.1 sun) irradiation. The corresponding lightweight perovskite-BiVO4PEC devices showed unassisted solar-to-fuel efficiencies of 0.58% (H2) and 0.053% (CO), respectively. Their potential for scalability is demonstrated by 100 cm2stand-alone artificial leaves, which sustained a comparable performance and stability (of approximately 24 h) to their 1.7 cm2counterparts. Bubbles formed under operation further enabled 30–100 mg cm−2devices to float, while lightweight reactors facilitated gas collection during outdoor testing on a river. This leaf-like PEC device bridges the gulf in weight between traditional solar fuel approaches, showcasing activities per gram comparable to those of photocatalytic suspensions and plant leaves. The presented lightweight, floating systems may enable open-water applications, thus avoiding competition with land use.

Filamentous enzymes have been found in all domains of life, but the advantage of filamentation is often elusive1. Some anaerobic, autotrophic bacteria have an unusual filamentous enzyme for CO2fixation—hydrogen-dependent CO2reductase (HDCR)2,3—which directly converts H2and CO2into formic acid. HDCR reduces CO2with a higher activity than any other known biological or chemical catalyst4,5, and it has therefore gained considerable interest in two areas of global relevance: hydrogen storage and combating climate change by capturing atmospheric CO2. However, the mechanistic basis of the high catalytic turnover rate of HDCR has remained unknown. Here we use cryo-electron microscopy to reveal the structure of a short HDCR filament from the acetogenic bacterium Thermoanaerobacter kivui. The minimum repeating unit is a hexamer that consists of a formate dehydrogenase (FdhF) and two hydrogenases (HydA2) bound around a central core of hydrogenase Fe-S subunits, one HycB3 and two HycB4. These small bacterial polyferredoxin-like proteins oligomerize through their C-terminal helices to form the backbone of the filament. By combining structure-directed mutagenesis with enzymatic analysis, we show that filamentation and rapid electron transfer through the filament enhance the activity of HDCR. To investigate the structure of HDCR in situ, we imaged T. kivuicells with cryo-electron tomography and found that HDCR filaments bundle into large ring-shaped superstructures attached to the plasma membrane. This supramolecular organization may further enhance the stability and connectivity of HDCR to form a specialized metabolic subcompartment within the cell.

A long-standing question in nuclear physics is whether chargeless nuclear systems can exist. To our knowledge, only neutron stars represent near-pure neutron systems, where neutrons are squeezed together by the gravitational force to very high densities. The experimental search for isolated multi-neutron systems has been an ongoing quest for several decades1, with a particular focus on the four-neutron system called the tetraneutron, resulting in only a few indications of its existence so far2–4, leaving the tetraneutron an elusive nuclear system for six decades. Here we report on the observation of a resonance-like structure near threshold in the four-neutron system that is consistent with a quasi-bound tetraneutron state existing for a very short time. The measured energy and width of this state provide a key benchmark for our understanding of the nuclear force. The use of an experimental approach based on a knockout reaction at large momentum transfer with a radioactive high-energy 8He beam was key.

New particle formation in the upper free troposphere is a major global source of cloud condensation nuclei (CCN)1–4. However, the precursor vapours that drive the process are not well understood. With experiments performed under upper tropospheric conditions in the CERN CLOUD chamber, we show that nitric acid, sulfuric acid and ammonia form particles synergistically, at rates that are orders of magnitude faster than those from any two of the three components. The importance of this mechanism depends on the availability of ammonia, which was previously thought to be efficiently scavenged by cloud droplets during convection. However, surprisingly high concentrations of ammonia and ammonium nitrate have recently been observed in the upper troposphere over the Asian monsoon region5,6. Once particles have formed, co-condensation of ammonia and abundant nitric acid alone is sufficient to drive rapid growth to CCN sizes with only trace sulfate. Moreover, our measurements show that these CCN are also highly efficient ice nucleating particles—comparable to desert dust. Our model simulations confirm that ammonia is efficiently convected aloft during the Asian monsoon, driving rapid, multi-acid HNO3–H2SO4–NH3nucleation in the upper troposphere and producing ice nucleating particles that spread across the mid-latitude Northern Hemisphere.

Comprehensive assessments of species’ extinction risks have documented the extinction crisis1and underpinned strategies for reducing those risks2. Global assessments reveal that, among tetrapods, 40.7% of amphibians, 25.4% of mammals and 13.6% of birds are threatened with extinction3. Because global assessments have been lacking, reptiles have been omitted from conservation-prioritization analyses that encompass other tetrapods4–7. Reptiles are unusually diverse in arid regions, suggesting that they may have different conservation needs6. Here we provide a comprehensive extinction-risk assessment of reptiles and show that at least 1,829 out of 10,196 species (21.1%) are threatened—confirming a previous extrapolation8and representing 15.6 billion years of phylogenetic diversity. Reptiles are threatened by the same major factors that threaten other tetrapods—agriculture, logging, urban development and invasive species—although the threat posed by climate change remains uncertain. Reptiles inhabiting forests, where these threats are strongest, are more threatened than those in arid habitats, contrary to our prediction. Birds, mammals and amphibians are unexpectedly good surrogates for the conservation of reptiles, although threatened reptiles with the smallest ranges tend to be isolated from other threatened tetrapods. Although some reptiles—including most species of crocodiles and turtles—require urgent, targeted action to prevent extinctions, efforts to protect other tetrapods, such as habitat preservation and control of trade and invasive species, will probably also benefit many reptiles.

Transcription-coupled DNA repair removes bulky DNA lesions from the genome1,2and protects cells against ultraviolet (UV) irradiation3. Transcription-coupled DNA repair begins when RNA polymerase II (Pol II) stalls at a DNA lesion and recruits the Cockayne syndrome protein CSB, the E3 ubiquitin ligase, CRL4CSAand UV-stimulated scaffold protein A (UVSSA)3. Here we provide five high-resolution structures of Pol II transcription complexes containing human transcription-coupled DNA repair factors and the elongation factors PAF1 complex (PAF) and SPT6. Together with biochemical and published3,4data, the structures provide a model for transcription–repair coupling. Stalling of Pol II at a DNA lesion triggers replacement of the elongation factor DSIF by CSB, which binds to PAF and moves upstream DNA to SPT6. The resulting elongation complex, ECTCR, uses the CSA-stimulated translocase activity of CSB to pull on upstream DNA and push Pol II forward. If the lesion cannot be bypassed, CRL4CSAspans over the Pol II clamp and ubiquitylates the RPB1 residue K1268, enabling recruitment of TFIIH to UVSSA and DNA repair. Conformational changes in CRL4CSAlead to ubiquitylation of CSB and to release of transcription-coupled DNA repair factors before transcription may continue over repaired DNA.

The remaining carbon budget for limiting global warming to 1.5 degrees Celsius will probably be exhausted within this decade1,2. Carbon debt3generated thereafter will need to be compensated by net-negative emissions4. However, economic policy instruments to guarantee potentially very costly net carbon dioxide removal (CDR) have not yet been devised. Here we propose intertemporal instruments to provide the basis for widely applied carbon taxes and emission trading systems to finance a net-negative carbon economy5. We investigate an idealized market approach to incentivize the repayment of previously accrued carbon debt by establishing the responsibility of emitters for the net removal of carbon dioxide through ‘carbon removal obligations’ (CROs). Inherent risks, such as the risk of default by carbon debtors, are addressed by pricing atmospheric CO2storage through interest on carbon debt. In contrast to the prevailing literature on emission pathways, we find that interest payments for CROs induce substantially more-ambitious near-term decarbonization that is complemented by earlier and less-aggressive deployment of CDR. We conclude that CROs will need to become an integral part of the global climate policy mix if we are to ensure the viability of ambitious climate targets and an equitable distribution of mitigation efforts across generations.

It has been the historic responsibility of the social sciences to investigate human societies. Fulfilling this responsibility requires social theories, measurement models and social data. Most existing theories and measurement models in the social sciences were not developed with the deep societal reach of algorithms in mind. The emergence of ‘algorithmically infused societies’—societies whose very fabric is co-shaped by algorithmic and human behaviour—raises three key challenges: the insufficient quality of measurements, the complex consequences of (mis)measurements, and the limits of existing social theories. Here we argue that tackling these challenges requires new social theories that account for the impact of algorithmic systems on social realities. To develop such theories, we need new methodologies for integrating data and measurements into theory construction. Given the scale at which measurements can be applied, we believe measurement models should be trustworthy, auditable and just. To achieve this, the development of measurements should be transparent and participatory, and include mechanisms to ensure measurement quality and identify possible harms. We argue that computational social scientists should rethink what aspects of algorithmically infused societies should be measured, how they should be measured, and the consequences of doing so.