The cause of Mars's loss of surface habitability is unclear, with isotopic data suggesting a 'missing sink' of carbonate1. Past climates with surface and shallow-subsurface liquid water are recorded by Mars's sedimentary rocks, including strata in the approximately 4-km-thick record at Gale Crater2. Those waters were intermittent, spatially patchy and discontinuous, and continued remarkably late in Mars's history3-attributes that can be understood if, as on Earth, sedimentary-rock formation sequestered carbon dioxide as abundant carbonate (recently confirmed in situ at Gale4). Here we show that a negative feedback among solar luminosity, liquid water and carbonate formation can explain the existence of intermittent Martian oases. In our model, increasing solar luminosity promoted the stability of liquid water, which in turn formed carbonate, reduced the partial pressure of atmospheric carbon dioxide and limited liquid water5. Chaotic orbital forcing modulated wet-dry cycles. The negative feedback restricted liquid water to oases and Mars self-regulated as a desert planet. We model snowmelt as the water source, but the feedback can also work with groundwater as the water source. Model output suggests that Gale faithfully records the expected primary episodes of liquid water stability in the surface and near-surface environment. Eventually, atmospheric thickness approaches water's triple point, curtailing the sustained stability of liquid water and thus habitability in the surface environment. We assume that the carbonate content found at Gale is representative, and as a result we present a testable idea rather than definitive evidence.

Classical mechanics characterizes the kinetic energy of a particle, the energy it holds due to its motion, as consistently positive. By contrast, quantum mechanics describes the motion of particles using wave functions, in which regions of negative local kinetic energy can emerge1. This phenomenon occurs when the amplitude of the wave function experiences notable decay, typically associated with quantum tunnelling. Here, we investigate the quantum mechanical motion of particles in a system of two coupled waveguides, in which the population transfer between the waveguides acts as a clock, allowing particle speeds along the waveguide axis to be determined. By applying this scheme to exponentially decaying quantum states at a reflective potential step, we determine an energy-speed relationship for particles with negative local kinetic energy. We find that the smaller the energy of the particles-in other words, the more negative the local kinetic energy-the higher the measured speed inside the potential step. Our findings contribute to the ongoing tunnelling time debate2-6 and can be viewed as a test of Bohmian trajectories in quantum mechanics7-9. Regarding the latter, we find that the measured energy-speed relationship does not align with the particle dynamics postulated by the guiding equation in Bohmian mechanics.

An increasing number of scholars, policymakers and grassroots communities argue that artificial intelligence (AI) research-and computer-vision research in particular-has become the primary source for developing and powering mass surveillance1-7. Yet, the pathways from computer vision to surveillance continue to be contentious. Here we present an empirical account of the nature and extent of the surveillance AI pipeline, showing extensive evidence of the close relationship between the field of computer vision and surveillance. Through an analysis of computer-vision research papers and citing patents, we found that most of these documents enable the targeting of human bodies and body parts. Comparing the 1990s to the 2010s, we observed a fivefold increase in the number of these computer-vision papers linked to downstream surveillance-enabling patents. Additionally, our findings challenge the notion that only a few rogue entities enable surveillance. Rather, we found that the normalization of targeting humans permeates the field. This normalization is especially striking given patterns of obfuscation. We reveal obfuscating language that allows documents to avoid direct mention of targeting humans, for example, by normalizing the referring to of humans as 'objects' to be studied without special consideration. Our results indicate the extensive ties between computer-vision research and surveillance.

Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics1-4. Modern fabrication techniques have greatly expanded the feasible hardware5-8, but using these systems requires control software to translate the desired motions into actuator commands. Conventional robots can easily be modelled as rigid links connected by joints, but it remains an open challenge to model and control biologically inspired robots that are often soft or made of several materials, lack sensing capabilities and may change their material properties with use9-12. Here, we introduce a method that uses deep neural networks to map a video stream of a robot to its visuomotor Jacobian field (the sensitivity of all 3D points to the robot's actuators). Our method enables the control of robots from only a single camera, makes no assumptions about the robots' materials, actuation or sensing, and is trained without expert intervention by observing the execution of random commands. We demonstrate our method on a diverse set of robot manipulators that vary in actuation, materials, fabrication and cost. Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot. Because it enables robot control using a generic camera as the only sensor, we anticipate that our work will broaden the design space of robotic systems and serve as a starting point for lowering the barrier to robotic automation.

Planets are thought to form from dust and gas in protoplanetary disks, with debris disks being the remnants of planet formation. Aged a few million up to a few billion years, debris disks have lost their primordial gas, and their dust is produced by steady-state collisions between larger, rocky bodies1,2. Tens of debris disks, with sizes of tens, sometimes hundreds, of astronomical units have been resolved with high-spatial-resolution, high-contrast imagers at optical and near-infrared or (sub)millimetre interferometers3,4. They commonly show cavities, ring-like structures and gaps, which are often regarded as indirect signatures of the presence of planets that gravitationally interact with unseen planetesimals2,5. However, no planet responsible for these features has been detected yet, probably because of the limited sensitivity (typically 2-10 MJ) of high-contrast imaging instruments (see, for example, refs. 6-9) before the James Webb Space Telescope. Here we have used the unprecedented sensitivity of the James Webb Space Telescope's Mid-Infrared Instrument10,11 in the thermal infrared to search for such planets in the disk of the approximately 6.4-Myr-old star TWA 7. With its pole-on orientation, this three-ring debris disk is indeed ideally suited for such a detection. We unambiguously detected a source 1.5 arcsec from the star, which is best interpreted as a cold, sub-Jupiter-mass planet. Its estimated mass (about 0.3 MJ) and position (about 52 AU, de-projected) can thoroughly account for the main disk structures.

Lanthanides have shown magnetic memory at both the atomic1,2 and molecular3,4 level. The magnetic remanence temperatures of lanthanide single-molecule magnets can surpass d-transition metal examples5,6, and since 2017, energy barriers to magnetic reversal (Ueff) from 1,237(28) cm-1 to 1,631(25) cm-1 and open magnetic hysteresis loops between 40 K and 80 K have typically been achieved with axial dysprosium(III) bis(cyclopentadienyl) complexes7-17. It has been predicted that linear dysprosium(III) compounds could deliver greater mJ (the projection of the total angular momentum, J, on a quantization axis labelled z) state splitting and therefore higher Ueff and hysteresis temperatures18-21, but as lanthanide bonding is predominantly ionic22,23, so far dysprosium bis(amide) complexes have shown highly bent geometries that promote fast magnetic reversal24,25. Here we report a dysprosium bis(amide)-alkene complex, [Dy{N(SiiPr3)[Si(iPr)2C(CH3)=CHCH3]}{N(SiiPr3)(SiiPr2Et)}][Al{OC(CF3)3}4] (1-Dy), that shows Ueff = 1,843(11) cm-1 and slow closing of soft magnetic hysteresis loops up to 100 K. Calculations show that the Ueff value for 1-Dy arises from the charge-dense amide ligands, with a pendant alkene taking a structural role to enforce a large N-Dy-N angle while imposing only a weak equatorial interaction. This leads to molecular spin dynamics up to 100 times slower than the current best single-molecule magnets above 90 K.

Emergence of universal collective behaviour from interactions within a sufficiently large group of elementary constituents is a fundamental scientific concept1. In physics, correlations in fluctuating microscopic observables can provide key information about collective states of matter, such as deconfined quark-gluon plasma in heavy-ion collisions2 or expanding quantum degenerate gases3,4. Mesoscopic colliders, through shot-noise measurements, have provided smoking-gun evidence on the nature of exotic electronic excitations such as fractional charges5,6, levitons7 and anyon statistics8. Yet, bridging the gap between two-particle collisions and the emergence of collectivity9 as the number of interacting particles increases10 remains a challenging task at the microscopic level. Here we demonstrate all-body correlations in the partitioning of electron droplets containing up to N = 5 electrons, driven by a moving potential well through a Y-junction in a semiconductor device. Analysing the partitioning data using high-order multivariate cumulants and finite-size scaling towards the thermodynamic limit reveals distinctive fingerprints of a strongly correlated Coulomb liquid. These fingerprints agree well with a universal limit at which the partitioning of a droplet is predicted by a single collective variable. Our electron-droplet scattering experiments illustrate how coordinated behaviour emerges through interactions of only a few elementary constituents. Studying similar signatures in other physical platforms such as cold-atom simulators4,11 or collections of anyonic excitations8,12 may help identify emergence of exotic phases and, more broadly, advance understanding of matter engineering.

The pursuit of strong yet ductile alloys has been ongoing for centuries. However, for all alloys developed thus far, including recent high-entropy alloys, those possessing good tensile ductility rarely approach 2-GPa yield strength at room temperature. The few that do are mostly ultra-strong steels1-3; however, their stress-strain curves exhibit plateaus and serrations because their tensile flow suffers from plastic instability (such as Lüders strains)1-4, and the elongation is pseudo-uniform at best. Here we report that a group of carefully engineered multi-principal-element alloys, with a composition of Fe35Ni29Co21Al12Ta3 designed by means of domain knowledge-informed machine learning, can be processed to reach an unprecedented range of simultaneously high strength and ductility. An example of this synergy delivers 1.8-GPa yield strength combined with 25% truly uniform elongation. We achieved strengthening by pushing microstructural heterogeneities to the extreme through unusually large volume fractions of not only coherent L12 nanoprecipitates but also incoherent B2 microparticles. The latter, being multicomponent with a reduced chemical ordering energy, is a deformable phase that accumulates dislocations inside to help sustain a high strain hardening rate that prolongs uniform elongation.

Sexual reproduction relies on meiotic chromosome pairing to form bivalents, a process that is complicated in polyploids owing to the presence of multiple subgenomes1. Uneven ploidy mostly results in sterility due to unbalanced chromosome pairing and segregation during meiosis. However, pentaploid dogroses (Rosa sect. Caninae; 2n = 5x = 35) achieve stable sexual reproduction through a unique mechanism: 14 chromosomes form bivalents and are transmitted biparentally, while the remaining 21 chromosomes are maternally inherited as univalents2,3. Despite being studied for over a century, the role of centromeres in this process has remained unclear. Here we analyse haplotype-resolved chromosome-level genome assemblies for three pentaploid dogroses. Subgenome phasing revealed a bivalent-forming subgenome with two highly homozygous chromosome sets and three divergent subgenomes lacking homologous partners, therefore explaining their meiotic behaviour. Comparative analyses of chromosome synteny, phylogenetic relationships and centromere composition indicate that the subgenomes originated from two divergent clades of the genus Rosa. Pollen genome analysis shows that subgenomes from different evolutionary origins form bivalents, supporting multiple origins of dogroses and highlighting variation in subgenome contributions. We reveal that bivalent-forming centromeres are enriched with ATHILA retrotransposons, contrasting with larger tandem-repeat-based centromeres mainly found in univalents. This centromere structural bimodality possibly contributes to univalent drive during female meiosis. Our findings provide insights into the unique reproductive strategies of dogroses, advancing our understanding of genome evolution, centromere diversity and meiotic mechanisms in organisms with asymmetrical inheritance systems.

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

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

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

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

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

Ultracold fermionic atoms in optical lattices offer pristine realizations of Hubbard models1, which are fundamental to modern condensed-matter physics2,3. Despite notable advancements4-6, the accessible temperatures in these optical lattice material analogues are still too high to address many open problems7-10. Here we demonstrate a several-fold reduction in temperature6,11-13, bringing large-scale quantum simulations of the Hubbard model into an entirely new regime. This is accomplished by transforming a low-entropy product state into strongly correlated states of interest via dynamic control of the model parameters14,15, which is extremely challenging to simulate classically10. At half-filling, the long-range antiferromagnetic order is close to saturation, leading to a temperature of T/t=0.05-0.05+0.06 based on comparisons with numerically exact simulations. Doped away from half-filling, it is exceedingly challenging to realize systematically accurate and predictive numerical simulations9. Importantly, we are able to use quantum simulation to identify a new pathway for achieving similarly low temperatures with doping. This is confirmed by comparing short-range spin correlations to state-of-the-art, but approximate, constrained-path auxiliary-field quantum Monte Carlo simulations16-18. Compared with the cuprates2,19,20, the reported temperatures correspond to a reduction from far above to below room temperature, at which physics such as the pseudogap and stripe phases may be expected3,19,21-24. Our work opens the door to quantum simulations that solve open questions in material science, develop synergies with numerical methods and theoretical studies, and lead to discoveries of new physics8,10.

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

Despite the success of fructose as a low-cost food additive, epidemiological evidence suggests that high fructose consumption during pregnancy or adolescence is associated with disrupted neurodevelopment1-3. An essential step in appropriate mammalian neurodevelopment is the phagocytic elimination of newly formed neurons by microglia, the resident professional phagocyte of the central nervous system4. Whether high fructose consumption in early life affects microglial phagocytosis and whether this directly affects neurodevelopment remains unknown. Here we show that offspring born to female mice fed a high-fructose diet and neonates exposed to high fructose exhibit decreased phagocytic activity in vivo. Notably, deletion of the high-affinity fructose transporter GLUT5 (also known as SLC2A5) in neonatal microglia completely reversed microglia phagocytic dysfunction, suggesting that high fructose directly affects neonatal development by suppressing microglial phagocytosis. Mechanistically, we found that high-fructose treatment of mouse and human microglia suppresses phagocytosis capacity, which is rescued in GLUT5-deficient microglia. Additionally, we found that high fructose drives significant GLUT5-dependent fructose uptake and catabolism to fructose 6-phosphate, rewiring microglial metabolism towards a hypo-phagocytic state in part by enforcing mitochondrial localization of the enzyme hexokinase 2. Mice exposed to high fructose as neonates develop anxiety-like behaviour as adolescents-an effect that is rescued in GLUT5-deficient mice. Our findings provide a mechanistic explanation for the epidemiological observation that high-fructose exposure during early life is associated with increased prevalence of adolescent anxiety disorders.

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

Plasma membrane integrity is vital for nearly all aspects of cell functioning1. Mechanical forces can cause plasma membrane damage2, but it is not known whether there are large molecules that regulate plasma membrane integrity under mechanical strain. Here we constructed a 384-well cellular stretch system that delivers precise, reproducible strain to cultured cells. Using the system, we screened 10,843 siRNAs targeting 2,726 multi-pass transmembrane proteins for strain-induced membrane permeability changes. The screen identified NINJ1, a protein recently proposed to regulate pyroptosis and other lytic cell death3,4, as the top hit. We demonstrate that NINJ1 is a critical regulator for mechanical strain-induced plasma membrane rupture (PMR), without the need of stimulating any cell death programs. NINJ1 level on the plasma membrane is inversely correlated to the amount of force required to rupture the membrane. In the pyroptosis context, NINJ1 on its own is not sufficient to fully rupture the membrane, and additional mechanical force is required for full PMR. Our work establishes that NINJ1 functions as a bona fide determinant of membrane biomechanical properties. Our study also suggests that PMR across tissues of distinct mechanical microenvironments is subjected to fine tuning by differences in NINJ1 expression and external forces.

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