Cellular solids ubiquitously exist in natural systems and are crucial for living organisms1,2. Their unique smooth branch and node morphologies are often seen as adaptations for enhanced mechanical performance3,4. Exploring alternative evolutionary functions can enrich the understanding of cellular solids, but it is frequently neglected. Here we show that the biomineralized cellular solids in echinoderm stereom (for example, sea urchin spine) have unexpected mechanoelectrical perception with response potential and response time, both of which are one to three orders of magnitude greater than those of echinoderm vision5. This exceptional perception originates from the gradient cellular solids (with varying void- or solid-phase diameters) along the [001] spine axis, generating a differential charge density across the stereom surface during liquid flow. Inspired by this natural wisdom, we create artificial spine-like structures using three-dimensional printing technology that exhibit three-fold higher voltage output and eight-fold greater amplitude differential than gradient-free samples, as well as a nature-inspired metamaterial mechanoreceptor capable of time-resolved self-monitoring information underwater. Our findings advance the understanding of load-sensitive biomimetic cellular solids (such as wood, sponge and trabecular bone), with the potential to develop functional gradient cellular materials towards underwater spatiotemporal sensing and water resource utilization.

Main-group catalysts that mimic transition metal reactivity can expand substrate tolerance and enable transformations not possible at present with metal catalysis1. The discovery that PIII and PV phosphorus intermediates can undergo transition-metal-like two-electron chemistry raises the question of whether radical PIV intermediates can mimic other elementary steps in organometallic chemistry2,3. Here we describe a phosphine-photoredox catalyst system that promotes intermolecular Markovnikov hydroamination of unactivated terminal alkenes with numerous classes of N-H azoles, a reaction that is not possible with late transition metal catalysis. Experimental and computational mechanistic studies support a new elementary step for main-group catalysis, in which a phosphine radical cation activates the alkene to nucleophilic amination by the azole, a step otherwise associated with transition metals. Given the broad value of nucleophilic alkene functionalization in transition metal catalysis, this PIV mechanism could offer new opportunities for main-group element catalysis and chemical synthesis.

Anthropogenic nitrogen (N) pollution is a cause of eutrophication globally1. However, recent datasets indicate that some ecosystems may be experiencing widespread oligotrophication-declining N availability-which is suggested to be a response to elevated atmospheric carbon dioxide (CO2)2. Plant N isotope (δ15N) chronologies have served as primary evidence for oligotrophication, but there is wide disagreement whether rising CO2 or temporal changes in N deposition explain these patterns3-6. Here we construct δ15N tree-ring chronologies using archived samples from Sweden's 23.5-million-hectare forest area from 1961 to 2018. The study area spans a 1,500-km latitudinal distance where N deposition varies fourfold, but where rising CO2 is spatially uniform. Our data show declining δ15N chronologies throughout Sweden, including forests in the far north where atmospheric N deposition rates are very low. Linear mixed-effects models showed that rising CO2 is the strongest predictor of δ15N values, whereas N deposition variables, temperature and forest basal area had lower explanatory power. Our findings suggest that elevated atmospheric CO2 is causing oligotrophication in boreal forests, which has implications for predicting their future role as sinks in the global carbon cycle7-9.

Long-term preservation of digital information is vital for safeguarding the knowledge of humanity for future generations. Existing archival storage solutions, such as magnetic tapes and hard disk drives, suffer from limited media lifespans that render them unsuitable for long-term data retention1-3. Optical storage approaches, particularly laser writing in robust media such as glass, have emerged as promising alternatives with the potential for increased longevity. Previous work4-16 has predominantly optimized individual aspects such as data density but has not demonstrated an end-to-end system, including writing, storing and retrieving information. Here we report an optical archival storage technology based on femtosecond laser direct writing in glass that addresses the practical demands of archival storage, which we call Silica. We achieve a data density of 1.59 Gbit mm-3 in 301 layers for a capacity of 4.8 TB in a 120 mm square, 2 mm thick piece of glass. The demonstrated write regimes enable a write throughput of 25.6 Mbit s-1 per beam, limited by the laser repetition rate, with an energy efficiency of 10.1 nJ per bit. Moreover, we extend the storage ability to borosilicate glass, offering a lower-cost medium and reduced writing and reading complexity. Accelerated ageing tests on written voxels in borosilicate suggest data lifetimes exceeding 10,000 years.

Acral melanoma, which is not ultraviolet-associated, is the type of melanoma reported most commonly in several non-European-descent populations1-3, including in Mexican people4. Latin American samples are substantially under-represented in global cancer genomics studies5, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures6-8. To address this, we characterized the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients-a population notable because of its genetic admixture9. Compared with other studies of melanoma, we found fewer mutations in classical driver genes such as BRAF, NRAS or NF1. Although most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations. The tumours with activating BRAF mutations had a transcriptional profile more similar to cutaneous non-volar melanocytes, indicating that acral melanomas in these patients may arise from a distinct cell of origin compared with other tumours arising in these locations. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

Triple-negative breast cancer (TNBC) is frequently associated with metastatic relapse, even at an early stage1. Here we assessed an individualized neoantigen mRNA vaccine in 14 patients with TNBC following surgery and after neoadjuvant or adjuvant therapy. In peripheral blood of nearly all patients, high-magnitude, vaccine-induced, mostly de novo T cell responses to multiple neoantigens were detected that remained functional for several years. Characterization of individual patients revealed that a large proportion of these T cells developed into two subsets: a late-differentiated phenotype with markers indicative of 'ready-to-act' cytotoxic effector T cells, and T cells with a stem cell-like memory phenotype. Eleven patients remained relapse-free for up to six years post-vaccination. Recurrence occurred in three patients: the individual with the weakest vaccine-induced T cell response relapsed, but achieved complete remission on subsequent anti-PD-1 therapy; another patient had a tumour with low major histocompatibility complex (MHC) class I expression with MHC class I-deficient cells growing out under vaccination; and the third patient was BRCA-positive and had a recurrence from a genetically distinct primary tumour. These findings demonstrate the feasibility of individualized RNA vaccines in TNBC, document persistence of vaccine-induced, functional neoantigen-specific T cells and provide insights into possible immune escape mechanisms that will guide future approaches.

Intrinsically disordered proteins and regions (collectively IDRs) are found across all kingdoms of life and have critical roles in virtually every eukaryotic cellular process1. IDRs exist in a broad ensemble of structurally distinct conformations. This structural plasticity facilitates diverse molecular recognition and function2-4. Here we combine advances in physics-based force fields with the power of multi-modal generative deep learning to develop STARLING, a framework for rapid generation of accurate IDR ensembles and ensemble-aware representations from sequence. STARLING supports environmental conditioning across ionic strengths and demonstrates proof of concept for the interpolative ability of generative models beyond their training domain. Moreover, we enable ensemble refinement under experimental constraints using a Bayesian maximum-entropy reweighting scheme. Beyond ensemble characterization, STARLING sequence representations can be used in multiple ways. We showcase two examples: first, STARLING lets us perform ensemble-based search for 'biophysical look-alikes'. Second, we demonstrate how these latent representations can be used to accelerate ensemble-first sequence design from weeks or hours per candidate to seconds, enabling library-scale designs. Together, STARLING dramatically lowers the barrier to the computational interrogation of IDR function through the lens of emergent biophysical properties, complementing bioinformatic protein sequence analysis. We evaluate the accuracy of STARLING against extant experimental data and offer a series of vignettes illustrating how STARLING can enable rapid hypothesis generation for IDR function and aid the interpretation of experimental data.

Telecommunication systems are evolving towards ultrawide bandwidth and low latency, supporting wired and wireless links and their non-blocking interconnection1. However, a long-standing bandwidth mismatch between fibre communication and its wireless counterpart arises from fundamental disparities in signal architectures and hardware constraints2,3, which prevent high-speed and compatible transmission across the two domains. This challenge further complicates unified system design and hinders the realization of high-throughput-density, congestion-free fibre-wireless links under wideband-access scenarios4. Here we present an ultra-wideband (UWB) integrated photonics scheme that facilitates fibre-wireless communication over a shared-bandwidth infrastructure. Built on electro-optic (EO) and optic-electro (OE) conversions featuring 3-dB operational bandwidths exceeding 250 GHz and cross-architecture adaptability, our system demonstrates unprecedented data transmission capabilities in both wired and wireless links. Using the same set of devices and powered by the proposed complex bidirectional gated recurrent unit (complex-biGRU) algorithm, ultrahigh single-lane data rates of 512 Gbps for short-reach fibre and, for the first time to the authors' knowledge, 400-Gbps high-speed wireless transmission have been achieved. Furthermore, high-density access is enabled by an all-optically assisted ultra-broadband wireless scheme. Real-time multichannel 8K video transmission is successfully demonstrated across 86 channels, seamlessly using a spectral range from 138 to 223 GHz. These findings in unified telecommunication development show the potential for the development of high-speed, densified and low-latency communication networks.

Paternal care is rare among mammals and the neural mechanisms governing its emergence are poorly understood1. We leveraged the natural paternal behaviour of African striped mice (Rhabdomys pumilio)2,3, and integrated brain-wide cFos mapping, single-nucleus RNA sequencing, virally mediated gene perturbation and environmental manipulation to dissect the neural basis of natural variation in male parenting. Here we find that socio-environmental conditions drive individual variation in male alloparenting such that postweaning social isolation increases paternal care whereas social living in higher density groups increases infanticide. This natural variation in care corresponds to neural activity in the medial preoptic area and changes in correlated activity across brain regions. Within the medial preoptic area, expression of agouti signalling protein (Agouti) in neurons is increased by group housing and is negatively associated with care, and overexpression of Agouti reduces care and enhances infanticide in previously tolerant mice. Naturalistic manipulations further reveal that Agouti integrates long-term housing conditions rather than food availability or hunger. Our findings reveal that variation in male paternal care reflects context-dependent regulation of conserved hypothalamic and melanocortin signalling mechanisms rather than the presence or absence of paternal capacity.

The brain displays the richest repertoire of post-transcriptional mechanisms regulating mRNA translation1-11. Among these, alternative splicing has been shown to drive cell-type specificity and, when disrupted, is strongly linked to neurological disorders12-17. However, genome-wide measurements of mRNA translation with isoform sensitivity at single-cell resolution have not been achieved. To address this, we deployed Surveying Ribosomal Targets by APOBEC-Mediated Profiling (Ribo-STAMP) coupled with short-read and long-read single-cell RNA sequencing in the brain18. We generated the first isoform-sensitive single-cell translatomes of the mouse hippocampus at postnatal day 25, discovering cell-type-specific translation of 3,857 alternative transcripts across 1,641 genes and identifying isoforms of the same genes undergoing differential translation within and across 8 different cell types. We defined high and low translational states in CA1 and CA3 neurons, with synaptic and metabolic genes enriched in high states. We found that CA3 exhibited higher basal translation compared with CA1, as confirmed by metabolic labelling of newly synthesized proteins and immunohistochemistry of translational machinery components. This accessible platform will expand our understanding of how cell-type-specific and isoform-specific translation drives brain physiology and disease.

Cytopathology, often abbreviated as cytology, has a central role in the early detection of cancer, such as cervical, lung and bladder cancers, owing to its speed, simplicity and minimally invasive nature1-9. However, its effectiveness is limited by variability in diagnostic accuracy stemming from subjective visual interpretation10-21. Although many artificial intelligence (AI)-powered systems have been proposed to improve consistency22-26, none have achieved fully autonomous, clinical-grade performance. Existing approaches serve as assistive tools and still rely on human oversight for interpretation and decision-making22-26. Here we present a clinical-grade autonomous cytopathology pipeline that combines high-resolution, real-time optical whole-slide tomography with edge computing to deliver end-to-end automation. The system achieves practical performance in imaging speed, quality and data volume, with localized data compression enabling streamlined storage and accelerated AI-driven analysis. In addition to supporting cell-level classification, the platform enables flow cytometry-like, population-wide morphological profiling for comprehensive interpretation of cellular distributions and patterns. A vision transformer achieved area under the receiver operating characteristic (ROC) curve (AUC) values exceeding 0.99 at the single-cell level for detecting low-grade squamous intraepithelial lesions (LSILs), high-grade squamous intraepithelial lesions (HSILs) and adenocarcinoma. In a multicentre evaluation of 1,124 cervical liquid-based cytology samples across four centres, the AI model achieved slide-level AUC values of 0.86-0.91 for LSIL+ and 0.89-0.97 for HSIL+, with LSIL counts correlating strongly with human papillomavirus positivity and HSIL counts scaling with diagnostic severity. The system enables autonomous triage cytology, offering a foundation for routine, scalable and objective diagnostics.

The severity of malaria varies substantially between individuals, but the mechanisms that underlie these differences remain unclear. Because erythrocytes have a key role in malaria biology, genetic variants associated with the development of these cells could inform the mechanisms that determine disease severity. Here we investigate the mechanistic basis of the association of the variant rs112233623-T with erythrocyte properties, and examine its role in modulating malaria severity. This variant is associated with increased levels of haemoglobin A2, increased erythrocyte size and reduced erythrocyte number1,2. It is found in an erythroid enhancer of CCND3, which encodes cyclin D3-a cell-division activator that enhances the pentose phosphate pathway and thereby helps to counteract reactive oxygen species (ROS)3. We show that rs112233623-T disrupts a binding site for the transcription factor SMAD3, weakens enhancer activity and, in erythrocyte precursors (erythroblasts), is associated with reduced CCND3 expression and inhibition of the G1-S cell-cycle transition, concomitant with a reduction in the number of erythrocytes and an increase in their size. Using population genetic methods, we observe signatures of positive selection for rs112233623-T in the genetic history of Sardinia, a region in which malaria was once prevalent. Furthermore, we show that parasite growth is impaired in cultured Plasmodium falciparum-infected erythrocytes from rs112233623-T carriers, and that this impairment correlates with ROS levels. This mirrors our observations in erythrocytes from individuals who are deficient in the pentose-phosphate-pathway enzyme G6PD-a trait associated with protection against malaria in some settings-and highlights a common ROS-based mechanism of malaria resistance. Our results suggest that a reduction in CCND3 in erythroblasts constitutes a mechanism of resistance to malaria, and could enable therapeutic interventions.

Neurodevelopmental disorders that arise from de novo mutations in chromatin-remodelling genes lack targeted treatments. Snijders Blok-Campeau syndrome (SNIBCPS)1, which is caused by pathogenic variants in CHD3, manifests with intellectual disability, autistic-like behaviours and motor deficits2. Whether somatic gene correction can reverse such phenotypes in vivo remains unknown. Here we show that modelling the recurrent CHD3 variant p.R1025W in a humanized mouse model (Chd3hR1025W/+) recapitulates key features of SNIBCPS, including reduced CHD3 protein levels and abnormalities in social communication, cognition and motor coordination. We engineered a TadA-embedded adenine base editor (TeABE) and delivered it brain-wide using a dual adeno-associated virus (AAV) system and achieved efficient on-target A•T-to-G•C correction across multiple cortical and hippocampal regions with minimal bystander activity. This intervention restored CHD3 levels and ameliorated behavioural abnormalities in vivo. Furthermore, intrathecal dual AAV delivery in nonhuman primates resulted in widespread neuronal transduction and efficient TeABE reconstitution, a result that supports its translational feasibility. These findings establish in vivo base editing as a viable therapeutic approach for CHD3-related neurodevelopmental disease. More broadly, they demonstrate that precise single-base correction in the postnatal brain can restore protein dosage and function, thereby offering a framework for the treatment of monogenic neurodevelopmental disorders.

Centromeres ensure accurate chromosome segregation, yet their DNA evolves rapidly across eukaryotes leaving the origins of new centromere architectures unclear1-4. The brewer's yeast Saccharomyces cerevisiae exemplifies this long-standing puzzle. Its centromeres shifted ancestrally from large, repeat-rich, epigenetically specified forms to the compact, genetically defined 'point' centromeres1,5. How this transition occurred has remained unresolved6. Here we identify evolutionarily related 'proto-point' centromeres that provide a resolution to the evolutionary origins of point centromeres. Proto-point centromeres contain a single centromeric nucleosome positioned over an AT-rich core, accompanied by relaxed organization and sequence variability of flanking cis-elements. In two species, these proto-point centromeres lie within retrotransposon-derived repeat clusters, linking ancestral repeat-rich centromeres to genetically encoded ones. Comparative and phylogenetic analyses indicate that proto-point and point centromeres evolved in an ancestor with retrotransposon-rich centromeres. These results identify long-terminal-repeat retrotransposons, specifically Ty5 sequences, as the genetic substrate for point-centromere evolution and provide a mechanistic route by which an epigenetic centromere can become genetically specified. More broadly, they show how selfish elements can be co-opted to perform essential chromosomal functions.

Rare diseases affect more than 300 million people worldwide1-3, yet timely and accurate diagnosis remains an urgent challenge1,3-5. Patients often endure a prolonged 'diagnostic odyssey' exceeding 5 years, marked by repeated referrals, misdiagnoses and unnecessary interventions, leading to delayed treatment and substantial emotional and economic burden4,5. Here we present DeepRare-a multi-agent system for rare disease differential diagnosis decision support6-8 powered by large language models, integrating more than 40 specialized tools and up-to-date knowledge sources. DeepRare processes heterogeneous clinical inputs, including free-text descriptions, structured human phenotype ontology terms and genetic testing results to generate ranked diagnostic hypotheses with transparent reasoning linked to verifiable medical evidence. Evaluated across nine datasets from literature, case reports and clinical centres across Asia, North America and Europe spanning 14 medical specialties, DeepRare demonstrates exceptional performance on 2,919 diseases. In human-phenotype-ontology-based tasks, it achieves an average Recall@1 of 57.18%, outperforming the next best method by 23.79%; in multi-modal tests, it reaches 69.1% compared with Exomiser's 55.9% on 168 cases. Expert review achieved 95.4% agreement on its reasoning chains, confirming their validity and traceability. Our work not only advances rare disease diagnosis but also demonstrates how the latest powerful large-language-model-driven agentic systems can reshape current clinical workflows.

The occurrence of treatment resistance in women with postpartum depression (PPD) and risk factors for treatment resistance remain less studied. This study aimed to determine the rate of treatment resistance and the associated risk factors among women with PPD in a nationwide setting. Here we conducted a nationwide register-based cohort study of 58,618 patients with a first-ever PPD during 2006-2021 in Sweden. Information on demographics, pregnancy characteristics, pre-existing physical and psychiatric conditions and treatment was retrieved from Swedish national registers. The outcome was treatment-resistant PPD (TRPPD) within 1 year following PPD diagnosis. Associations between potential risk factors and TRPPD were assessed using multivariable Poisson regression. Among the 58,618 patients with PPD, 3,522 (6.0%) met the criteria for TRPPD during 1 year after PPD diagnosis. Lower educational level, lower household income, being non-cohabiting, smoking in early pregnancy, delivery by cesarean section, pre-existing physical conditions and pre-existing psychiatric disorders were significantly associated with a higher risk of TRPPD. In addition, patients with two births (versus primiparity) or with a prior premenstrual disorder had a lower risk of TRPPD. Treatment resistance in patients with PPD is common and is notably associated with specific demographic and clinical profiles. These findings may provide grounds for practical risk assessment at PPD diagnosis and highlight the need for personalized management strategies.

Suicidality is increased among middle-aged and older autistic adults, but little is known about the underlying factors linking autism with suicidality in midlife and older age. Here we report a cross-sectional observational study of 9,979 adults (76% female) aged 50+ years who completed questionnaires measuring autistic traits, current mental health, social connections and suicidality (suicidal ideation and suicidal self-harm). We use path analysis to explore the relationship between autistic traits and suicidality and the mediating effects of current mental health, social connectedness and male/female sex. Our results find that depression, anxiety, post-traumatic stress disorder (PTSD), loneliness and social isolation all significantly mediate the relationship between autistic traits and suicidal ideation, with small effect sizes. For suicidal self-harm, male sex, depression, PTSD and social isolation were found to be mediators. We conclude that mental health difficulties and social isolation mediate higher rates of suicidality in 50+-year-olds with high autistic traits. Targeted and individually tailored interventions for people on the autism spectrum across the lifespan are important.

Identifying a catalyst class to optimize the enantioselectivity of a new reaction, either involving a different combination of known substrate types or an entirely unfamiliar class of compounds, is a formidable challenge. Statistical models trained on a reported set of reactions can help predict out-of-sample transformations1-5 but often face two challenges: (1) only sparse data that offer limited information on catalyst-substrate interactions are available; and (2) simple stereoelectronic parameters may fail to describe mechanistically complex transformations6,7. Here we report a descriptor generation strategy that accounts for changes in the enantiodetermining step with catalyst or substrate identity, allowing us to model reactions involving distinct ligand and substrate types. As validating case studies, we collected data on enantioselective nickel-catalysed C(sp3) couplings8 and trained statistical models with features extracted from the transition states and intermediates proposed to be involved in asymmetric induction. These models allow the optimization of poorly performing examples reported in a substrate scope and are applicable to unseen ligands and reaction partners. This approach offers the opportunity to streamline catalyst and reaction development, quantitatively transferring knowledge learned on sparse data to chemical spaces.