Physical neural networks (PNNs) are a class of neural-like networks that make use of analogue physical systems to perform computations. Although at present confined to small-scale laboratory demonstrations, PNNs could one day transform how artificial intelligence (AI) calculations are performed. Could we train AI models many orders of magnitude larger than present ones? Could we perform model inference locally and privately on edge devices? Research over the past few years has shown that the answer to these questions is probably "yes, with enough research". Because PNNs can make use of analogue physical computations more directly, flexibly and opportunistically than traditional computing hardware, they could change what is possible and practical for AI systems. To do this, however, will require notable progress, rethinking both how AI models work and how they are trained-primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs, backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs and, so far, no method has been shown to scale to large models with the same performance as the backpropagation algorithm widely used in deep learning today. However, this challenge has been rapidly changing and a diverse ecosystem of training techniques provides clues for how PNNs may one day be used to create both more efficient and larger-scale realizations of present-scale AI models.

Land has a vital role in sustaining human communities, nurturing diverse ecosystems and regulating the climate of our planet. As such, current rates of land degradation pose a major environmental and socioeconomic threat, driving climate change, biodiversity loss and social crises. Preventing and reversing land degradation are key objectives of the United Nations Convention to Combat Desertification and are also fundamental for the other two Rio Conventions: the United Nations Framework Convention on Climate Change and the Convention on Biological Diversity. Here we argue that the targets of these conventions can only be met by 'bending the curve' of land degradation and that transforming food systems is fundamental for doing so. We showcase multiple actions for tackling land degradation that also yield climate and biodiversity benefits while fostering sustainable food systems that contribute to avoiding the risk of a global food crisis. We also propose ambitious 2050 targets for the three Rio Conventions related to land and food systems. Finally, we urge collective action to acknowledge the pivotal role of land in achieving the goals of the Rio Conventions and to embed food systems within intergovernmental agreements, enabling decisive progress on the complex and interconnected global crises that we face.

From fertilization onwards, the cells of the human body acquire variations in their DNA sequence, known as somatic mutations. These postzygotic mutations arise from intrinsic errors in DNA replication and repair, as well as from exposure to mutagens. Somatic mutations have been implicated in some diseases, but a fundamental understanding of the frequency, type and patterns of mutations across healthy human tissues has been limited. This is primarily due to the small proportion of cells harbouring specific somatic variants within an individual, making them more challenging to detect than inherited variants. Here we describe the Somatic Mosaicism across Human Tissues Network, which aims to create a reference catalogue of somatic mutations and their clonal patterns across 19 different tissue sites from 150 non-diseased donors and develop new technologies and computational tools to detect somatic mutations and assess their phenotypic consequences, including clonal expansions. This strategy enables a comprehensive examination of the mutational landscape across the human body, and provides a comparison baseline for somatic mutation in diseases. This will lead to a deep understanding of somatic mutations and clonal expansions across the lifespan, as well as their roles in health, in ageing and, by comparison, in diseases.

Since the discovery of resting-state functional connectivity in the human brain, this neuroimaging approach has revolutionized the study of neural architecture. Once considered noise, the functional significance of spontaneous low-frequency fluctuations across large-scale brain networks has now been investigated in more than 25,000 publications. In this Review, we provide a historical overview and thoughts regarding potential future directions for resting-state functional MRI (rsfMRI) research, highlighting the most informative analytic approaches that have been developed to reveal the brain's intrinsic spatiotemporal organization. We review the collaborative efforts that have led to the widespread use of rsfMRI in neuroscience, with an emphasis on methodological innovations that have been made possible by contributions from electrical and biomedical engineering, physics, mathematics and computer science. We focus on key theoretical and methodological advances that will be necessary for further progress in the field, highlighting the need for further integration with new developments in whole-brain computational modelling, more sophisticated approaches to brain-behaviour mapping, greater mechanistic insights from concurrent measurement of neurophysiology, and greater appreciation of the problem of generalization failure in machine learning applications. We propose that rsfMRI has the potential for even greater clinical relevance when it is fully integrated with population neuroscience and global health initiatives in the service of precision psychiatry.

Beginning with Edward Jenner's discovery of the smallpox vaccine, the ever-expanding repertoire of vaccines against pathogens has saved many lives. During the COVID-19 pandemic, a revolutionary mRNA injectable vaccine emerged that effectively controlled the severity of disease caused by SARS-CoV-2. This vaccine induced potent antigen-specific neutralizing serum IgG antibodies, but was limited in its ability to prevent viral invasion at the respiratory surfaces. Nasal vaccines have attracted attention as a potential strategy to combat respiratory infections and prepare for future pandemics. Input from disciplines such as microbiology, biomaterials, bioengineering and chemistry have complemented the immunology to create innovative delivery systems. This approach to vaccine delivery has yielded nasal vaccines that induce secretory IgA as well as serum IgG antibodies, which are expected to prevent pathogen invasion, thereby diminishing transmission and disease severity. For a nasal vaccine to be successful, the complexity of the relevant anatomical, physiological and immunological properties, including the proximity of the central nervous system to the nasal cavity, must be considered. In this Review, we discuss past and current efforts as well as future directions for developing safe and effective nasal vaccines for the prevention of respiratory infections.

Osteoarthritis is the third most rapidly growing health condition associated with disability, after dementia and diabetes1. By 2050, the total number of patients with osteoarthritis is estimated to reach 1 billion worldwide2. As no disease-modifying treatments exist for osteoarthritis, a better understanding of disease aetiopathology is urgently needed. Here we perform a genome-wide association study meta-analyses across up to 489,975 cases and 1,472,094 controls, establishing 962 independent associations, 513 of which have not been previously reported. Using single-cell multiomics data, we identify signal enrichment in embryonic skeletal development pathways. We integrate orthogonal lines of evidence, including transcriptome, proteome and epigenome profiles of primary joint tissues, and implicate 700 effector genes. Within these, we find rare coding-variant burden associations with effect sizes that are consistently higher than common frequency variant associations. We highlight eight biological processes in which we find convergent involvement of multiple effector genes, including the circadian clock, glial-cell-related processes and pathways with an established role in osteoarthritis (TGFβ, FGF, WNT, BMP and retinoic acid signalling, and extracellular matrix organization). We find that 10% of the effector genes express a protein that is the target of approved drugs, offering repurposing opportunities, which can accelerate translation.

From experimental evolution in the laboratory to sustained measurements of natural selection in the wild, long-term studies have revolutionized our understanding of evolution. By directly investigating evolutionary dynamics in real time, these approaches have provided unparallelled insights into the complex interplay between evolutionary process and pattern. These approaches can reveal oscillations, stochastic fluctuations and systematic trends that unfold over extended periods, expose critical time lags between environmental shifts and population responses, and illuminate how subtle effects may accumulate into significant evolutionary patterns. Long-term studies can also reveal otherwise cryptic trends that unfold over extended periods, and offer the potential for serendipity: observing rare events that spur new evolutionary hypotheses and research directions. Despite the challenges of conducting long-term research, exacerbated by modern funding landscapes favouring short-term projects, the contributions of long-term studies to evolutionary biology are indispensable. This is particularly true in our rapidly changing, human-dominated world, where such studies offer a crucial window into how environmental changes and altered species interactions shape evolutionary trajectories. In this Review article, we showcase the groundbreaking discoveries of long-term evolutionary studies, underscoring their crucial role in advancing our understanding of the complex nature of evolution across multiple systems and timescales.

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI.

The nervous and immune systems have complementary roles in the adaptation of organisms to environmental changes. However, the mechanisms that mediate cross-talk between the nervous and immune systems, called neuroimmune interactions, are poorly understood. In this Review, we summarize advances in the understanding of neuroimmune communication, with a principal focus on the central nervous system (CNS): its response to immune signals and the immunological consequences of CNS activity. We highlight these themes primarily as they relate to neurological diseases, the control of immunity, and the regulation of complex behaviours. We also consider the importance and challenges linked to the study of the neuroimmune connectome, which is defined as the totality of neuroimmune interactions in the body, because this provides a conceptual framework to identify mechanisms of disease pathogenesis and therapeutic approaches. Finally, we discuss how the latest techniques can advance our understanding of the neuroimmune connectome, and highlight the outstanding questions in the field.

Over the past billion years, the fungal kingdom has diversified to more than two million species, with over 95% still undescribed. Beyond the well-known macroscopic mushrooms and microscopic yeast, fungi are heterotrophs that feed on almost any organic carbon, recycling nutrients through the decay of dead plants and animals and sequestering carbon into Earth's ecosystems. Human-directed applications of fungi extend from leavened bread, alcoholic beverages and biofuels to pharmaceuticals, including antibiotics and psychoactive compounds. Conversely, fungal infections pose risks to ecosystems ranging from crops to wildlife to humans; these risks are driven, in part, by human and animal movement, and might be accelerating with climate change. Genomic surveys are expanding our knowledge of the true biodiversity of the fungal kingdom, and genome-editing tools make it possible to imagine harnessing these organisms to fuel the bioeconomy. Here, we examine the fungal threats facing civilization and investigate opportunities to use fungi to combat these threats.

Mitigating loss of genetic diversity is a major global biodiversity challenge1-4. To meet recent international commitments to maintain genetic diversity within species5,6, we need to understand relationships between threats, conservation management and genetic diversity change. Here we conduct a global analysis of genetic diversity change via meta-analysis of all available temporal measures of genetic diversity from more than three decades of research. We show that within-population genetic diversity is being lost over timescales likely to have been impacted by human activities, and that some conservation actions may mitigate this loss. Our dataset includes 628 species (animals, plants, fungi and chromists) across all terrestrial and most marine realms on Earth. Threats impacted two-thirds of the populations that we analysed, and less than half of the populations analysed received conservation management. Genetic diversity loss occurs globally and is a realistic prediction for many species, especially birds and mammals, in the face of threats such as land use change, disease, abiotic natural phenomena and harvesting or harassment. Conservation strategies designed to improve environmental conditions, increase population growth rates and introduce new individuals (for example, restoring connectivity or performing translocations) may maintain or even increase genetic diversity. Our findings underscore the urgent need for active, genetically informed conservation interventions to halt genetic diversity loss.

Bipolar disorder is a leading contributor to the global burden of disease1. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown2. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings3, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder4, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.

Many human diseases are the result of early developmental defects. As most paediatric diseases and disorders are rare, children are critically underrepresented in research. Functional genomics studies primarily rely on adult tissues and lack critical cell states in specific developmental windows. In parallel, little is known about the conservation of developmental programmes across non-human primate (NHP) species, with implications for human evolution. Here we introduce the developmental Genotype-Tissue Expression (dGTEx) projects, which span humans and NHPs and aim to integrate gene expression, regulation and genetics data across development and species. The dGTEx cohort will consist of 74 tissue sites across 120 human donors from birth to adulthood, and developmentally matched NHP age groups, with additional prenatal and adult animals, with 126 rhesus macaques (Macaca mulatta) and 72 common marmosets (Callithrix jacchus). The data will comprise whole-genome sequencing, extensive bulk, single-cell and spatial gene expression profiles, and chromatin accessibility data across tissues and development. Through community engagement and donor diversity, the human dGTEx study seeks to address disparities in genomic research. Thus, dGTEx will provide a reference human and NHP dataset and tissue bank, enabling research into developmental changes in expression and gene regulation, childhood disorders and the effect of genetic variation on development.

The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body at unprecedented resolution and scale. Here we introduce a big-science endeavour called π-HuB (proteomic navigator of the human body). The aim of the π-HuB project is to (1) generate and harness multimodality proteomic datasets to enhance our understanding of human biology; (2) facilitate disease risk assessment and diagnosis; (3) uncover new drug targets; (4) optimize appropriate therapeutic strategies; and (5) enable intelligent healthcare, thereby ushering in a new era of proteomics-driven phronesis medicine. This ambitious mission will be implemented by an international collaborative force of multidisciplinary research teams worldwide across academic, industrial and government sectors.

Wearable sensors are a recent paradigm in healthcare, enabling continuous, decentralized, and non- or minimally invasive monitoring of health and disease. Continuous measurements yield information-rich time series of physiological data that are holistic and clinically meaningful. Although most wearable sensors were initially restricted to biophysical measurements, the next generation of wearable devices is now emerging that enable biochemical monitoring of both small and large molecules in a variety of body fluids, such as sweat, breath, saliva, tears and interstitial fluid. Rapidly evolving data analysis and decision-making technologies through artificial intelligence has accelerated the application of wearables around the world. Although recent pilot trials have demonstrated the clinical applicability of these wearable devices, their widespread adoption will require large-scale validation across various conditions, ethical consideration and sociocultural acceptance. Successful translation of wearable devices from laboratory prototypes into clinical tools will further require a comprehensive transitional environment involving all stakeholders. The wearable device platforms must gain acceptance among different user groups, add clinical value for various medical indications, be eligible for reimbursements and contribute to public health initiatives. In this Perspective, we review state-of-the-art wearable devices for body-fluid analysis and their translation into clinical applications, and provide insight into their clinical purpose.

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behaviour associations1,2. Several recent studies have shown that thousands of study participants are required for good replicability of BWAS1-3. Here we performed analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional MRI studies from the Lifespan Brain Chart Consortium4 (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized effect sizes and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability of the covariate and longitudinal studies have larger reported standardized effect size. Analysing age effects on global and regional brain measures from the UK Biobank and the Alzheimer's Disease Neuroimaging Initiative, we showed that modifying study design through sampling schemes improves standardized effect sizes and replicability. To ensure that our results are generalizable, we further evaluated the longitudinal sampling schemes on cognitive, psychopathology and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrated that commonly used longitudinal models, which assume equal between-subject and within-subject changes can, counterintuitively, reduce standardized effect sizes and replicability. Explicitly modelling the between-subject and within-subject effects avoids conflating them and enables optimizing the standardized effect sizes for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.

Herpes simplex virus 1 (HSV-1) encephalitis (HSE) is the most common sporadic viral encephalitis in humans. It is life-threatening and has a first peak of incidence in childhood, during primary infection. Children with HSE are not particularly prone to other infections, including HSV-1 infections of tissues other than the brain. About 8-10% of childhood cases are due to monogenic inborn errors of 19 genes, two-thirds of which are recessive, and most of which display incomplete clinical penetrance. Childhood HSE can therefore be sporadic but genetic, enabling new diagnostic and therapeutic approaches. In this Review, we examine essential cellular and molecular mechanisms of cell-intrinsic antiviral immunity in the brain that are disrupted in individuals with HSE. These mechanisms include both known (such as mutations in the TLR3 pathway) and previously unknown (such as the TMEFF1 restriction factor) antiviral pathways, which may be dependent (for example, IFNAR1) or independent (for example, through RIPK3) of type I interferons. They operate in cortical or brainstem neurons, and underlie forebrain and brainstem infections, respectively. Conversely, the most severe inborn errors of leukocytes, including a complete lack of myeloid and/or lymphoid blood cells, do not underlie HSE. Thus congenital defects in intrinsic immunity in brain-resident neurons that underlie HSE broaden natural host defences against HSV-1 from the leukocytes of the immune system to other cells in the organism.

Crop translational genomics applies breeding techniques based on genomic datasets to improve crops. Technological breakthroughs in the past ten years have made it possible to sequence the genomes of increasing numbers of crop varieties and have assisted in the genetic dissection of crop performance. However, translating research findings to breeding applications remains challenging. Here we review recent progress and future prospects for crop translational genomics in bringing results from the laboratory to the field. Genetic mapping, genomic selection and sequence-assisted characterization and deployment of plant genetic resources utilize rapid genotyping of large populations. These approaches have all had an impact on breeding for qualitative traits, where single genes with large phenotypic effects exert their influence. Characterization of the complex genetic architectures that underlie quantitative traits such as yield and flowering time, especially in newly domesticated crops, will require further basic research, including research into regulation and interactions of genes and the integration of genomic approaches and high-throughput phenotyping, before targeted interventions can be designed. Future priorities for translation include supporting genomics-assisted breeding in low-income countries and adaptation of crops to changing environments.

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

In this Review, we explore natural product antibiotics that do more than simply inhibit an active site of an essential enzyme. We review these compounds to provide inspiration for the design of much-needed new antibacterial agents, and examine the complex mechanisms that have evolved to effectively target bacteria, including covalent binders, inhibitors of resistance, compounds that utilize self-promoted entry, those that evade resistance, prodrugs, target corrupters, inhibitors of 'undruggable' targets, compounds that form supramolecular complexes, and selective membrane-acting agents. These are exemplified by β-lactams that bind covalently to inhibit transpeptidases and β-lactamases, siderophore chimeras that hijack import mechanisms to smuggle antibiotics into the cell, compounds that are activated by bacterial enzymes to produce reactive molecules, and antibiotics such as aminoglycosides that corrupt, rather than merely inhibit, their targets. Some of these mechanisms are highly sophisticated, such as the preformed β-strands of darobactins that target the undruggable β-barrel chaperone BamA, or teixobactin, which binds to a precursor of peptidoglycan and then forms a supramolecular structure that damages the membrane, impeding the emergence of resistance. Many of the compounds exhibit more than one notable feature, such as resistance evasion and target corruption. Understanding the surprising complexity of the best antimicrobial compounds provides a roadmap for developing novel compounds to address the antimicrobial resistance crisis by mining for new natural products and inspiring us to design similarly sophisticated antibiotics.