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.

Will it ever be possible to sue anyone for damaging the climate? Twenty years after this question was first posed, we argue that the scientific case for climate liability is closed. Here we detail the scientific and legal implications of an 'end-to-end' attribution that links fossil fuel producers to specific damages from warming. Using scope 1 and 3 emissions data from major fossil fuel companies, peer-reviewed attribution methods and advances in empirical climate economics, we illustrate the trillions in economic losses attributable to the extreme heat caused by emissions from individual companies. Emissions linked to Chevron, the highest-emitting investor-owned company in our data, for example, very likely caused between US $791 billion and $3.6 trillion in heat-related losses over the period 1991-2020, disproportionately harming the tropical regions least culpable for warming. More broadly, we outline a transparent, reproducible and flexible framework that formalizes how end-to-end attribution could inform litigation by assessing whose emissions are responsible and for which harms. Drawing quantitative linkages between individual emitters and particularized harms is now feasible, making science no longer an obstacle to the justiciability of climate liability claims.

The semiconductor industry is experiencing an accelerated transformation to overcome the scaling limits of the transistor and to adapt to new requirements in terms of data storage and computation, especially driven by artificial intelligence applications and the Internet of Things. In this process, new materials, devices, integration strategies and system architectures are being developed and optimized. Among them, memristive devices and circuits-memristors are two-terminal memory devices that can also mimic some basic bioelectronic functions-offer a potential approach to create more compact, energy-efficient or better-performing systems. The memristor industry is growing quickly, raising abundant capital investment, creating new jobs and placing advanced products in the market. Here we analyse the status and prospects of the memristor industry, focusing on memristor-based products that are already commercially available, prototypes with a high technological readiness level that might affect the market in the near future, and discuss obstacles and pathways to their implementation.

Intersectionality describes interdependent systems of inequality related to sex, gender, race, age, class and other socio-political dimensions. By focusing on the compounded effects of social categories, intersectional analysis can enhance the accuracy and experimental efficiency of science. Here we extend intersectional approaches that were predominantly developed in the humanities, social sciences and public health to the fields of natural science and technology, where this type of analysis is less established. Informed by diverse global and disciplinary examples-from enhancing facial recognition for diverse user bases to mitigating the disproportionate impact of climate change on marginalized populations-we extract methods to demonstrate how quantitative intersectional analysis functions throughout the research process, from strategic considerations for establishing research priorities to formulating research questions, collecting and analysing data and interpreting results. Our goal is to offer a set of guidelines for researchers, peer-reviewed journals and funding agencies that facilitate systematic integration of intersectional analysis into relevant domains of science and technology. Precision in research best guides effective social and environmental policy aimed at achieving global equity and sustainability.

As Earth warms, regional climate signals are accumulating. Some signals, for example, land warming more than the ocean and the Arctic warming the most, were expected and successfully predicted. Underlying this success was the application of physical laws under the assumption that large and small spatial scales are well separated. This established what we call the standard approach, climate science's dominant paradigm. With additional warming, however, discrepancies between real-world signals and expectations based on this standard approach are piling up, especially at regional scales. At the same time, disruptive computational approaches are advancing new paradigms. Philosophers of science characterize situations where accumulating discrepancies (anomalies) and disruptions lead to a loss of confidence in the dominant paradigm as a 'crisis'. Here we articulate what we consider to be the dominant paradigm, or standard approach, and the discrepancies and disruptions that have emerged in recent years. The policy implications of a purported crisis are discussed, as well as paths forward, crisis or no crisis. These paths include using signals to test assumptions and processes driving a warming Earth for the first time, developing testable hypotheses, and revitalizing conceptual thinking by filling gaps across climate-system components and spatial scales.

Accurately interpreting medical images and generating insightful narrative reports is indispensable for patient care but places heavy burdens on clinical experts. Advances in artificial intelligence (AI), especially in an area that we refer to as multimodal generative medical image interpretation (GenMI), create opportunities to automate parts of this complex process. In this Perspective, we synthesize progress and challenges in developing AI systems for generation of medical reports from images. We focus extensively on radiology as a domain with enormous reporting needs and research efforts. In addition to analysing the strengths and applications of new models for medical report generation, we advocate for a novel paradigm to deploy GenMI in a manner that empowers clinicians and their patients. Initial research suggests that GenMI could one day match human expert performance in generating reports across disciplines, such as radiology, pathology and dermatology. However, formidable obstacles remain in validating model accuracy, ensuring transparency and eliciting nuanced impressions. If carefully implemented, GenMI could meaningfully assist clinicians in improving quality of care, enhancing medical education, reducing workloads, expanding specialty access and providing real-time expertise. Overall, we highlight opportunities alongside key challenges for developing multimodal generative AI that complements human experts for reliable medical report writing.

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.

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

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.

The integration of Indigenous perspectives and knowledge with biomedical approaches in neurosciences can significantly broaden the understanding of the human brain and mind. Drawing upon the writings of Elders in Canada, we refer to this integration as Two-Eyed Seeing or Etuaptmumk. We discuss how Two-Eyed Seeing and other dual perspectives can bring both breadth of knowledge and humility to the development of research and clinical practices for brain health. In this forward-looking discussion, we include both traditional academic and non-academic traditions and the work of Indigenous scholars on methodologies, life, health, culture, language and history. To describe challenges and consider solutions, we offer broad strategies for allyship, humility and universalism and situate them in four specific examples pertaining to disability, suicide, migration and the environment. We further advance the power of Two-Eyed Seeing in the context of new considerations for communication and public engagement. Two-Eyed Seeing, per se, is only one approach, but as neuroscience becomes ever more global, inclusive and ethically proactive, it must universally see the world of brain and mental health through the eyes of both reductionism and holism.

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.

Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficiently realizes artificial neural networks. Neuromorphic designers apply the principles of biointelligence discovered by neuroscientists to design efficient computational systems, often for applications with size, weight and power constraints. With this research field at a critical juncture, it is crucial to chart the course for the development of future large-scale neuromorphic systems. We describe approaches for creating scalable neuromorphic architectures and identify key features. We discuss potential applications that can benefit from scaling and the main challenges that need to be addressed. Furthermore, we examine a comprehensive ecosystem necessary to sustain growth and the new opportunities that lie ahead when scaling neuromorphic systems. Our work distils ideas from several computing sub-fields, providing guidance to researchers and practitioners of neuromorphic computing who aim to push the frontier forward.

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.

Our understanding of type 2 immunity has undergone a substantial transformation in recent years, revealing previously unknown functions. Beyond its canonical role in defence against parasitic helminth infections, type 2 immunity safeguards the host through additional mechanisms, including the suppression of excessive type 1 immune responses, regulation of tissue repair and maintenance of adipose tissue homeostasis. However, unlike type 1 immune responses, type 2 immunity is perceived as a potential promoter of tumorigenesis. Emerging evidence challenges this perspective, painting a more nuanced picture in which type 2 immunity might protect against or even actively suppress tumour growth and progression. In this Review, we explore discoveries that highlight the potential of type 2 immunity in reshaping the landscape of cancer immunotherapies.

Microscopy and crystallography are two essential experimental methodologies for advancing modern science. They complement one another, with microscopy typically relying on lenses to image the local structures of samples, and crystallography using diffraction to determine the global atomic structure of crystals. Over the past two decades, computational microscopy, encompassing coherent diffractive imaging (CDI) and ptychography, has advanced rapidly, unifying microscopy and crystallography to overcome their limitations. Here, I review the innovative developments in CDI and ptychography, which achieve exceptional imaging capabilities across nine orders of magnitude in length scales, from resolving atomic structures in materials at sub-ångstrom resolution to quantitative phase imaging of centimetre-sized tissues, using the same principle and similar computational algorithms. These methods have been applied to determine the 3D atomic structures of crystal defects and amorphous materials, visualize oxygen vacancies in high-temperature superconductors and capture ultrafast dynamics. They have also been used for nanoscale imaging of magnetic, quantum and energy materials, nanomaterials, integrated circuits and biological specimens. By harnessing fourth-generation synchrotron radiation, X-ray-free electron lasers, high-harmonic generation, electron microscopes, optical microscopes, cutting-edge detectors and deep learning, CDI and ptychography are poised to make even greater contributions to multidisciplinary sciences in the years to come.

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.

In the quest for environmental sustainability, the rising demand for electric vehicles and renewable energy technologies has substantially increased the need for efficient lithium extraction methods. Traditional lithium production, relying on geographically concentrated hard-rock ores and salar brines, is associated with considerable energy consumption, greenhouse gas emissions, groundwater depletion and land disturbance, thereby posing notable environmental and supply chain challenges. On the other hand, low-quality brines-such as those found in sedimentary waters, geothermal fluids, oilfield-produced waters, seawater and some salar brines and salt lakes-hold large potential owing to their extensive reserves and widespread geographical distribution. However, extracting lithium from these sources presents technical challenges owing to low lithium concentrations and high magnesium-to-lithium ratios. This Review explores the latest advances and continuing challenges in lithium extraction from these demanding yet promising sources, covering a variety of methods, including precipitation, solvent extraction, sorption, membrane-based separation and electrochemical-based separation. Furthermore, we share perspectives on the future development of lithium extraction technologies, framed within the basic principles of separation processes. The aim is to encourage the development of innovative extraction methods capable of making use of the substantial potential of low-quality brines.

As the field of neural organoids and assembloids expands, there is an emergent need for guidance and advice on designing, conducting and reporting experiments to increase the reproducibility and utility of these models. In this Perspective, we present a framework for the experimental process that encompasses ensuring the quality and integrity of human pluripotent stem cells, characterizing and manipulating neural cells in vitro, transplantation techniques and considerations for modelling human development, evolution and disease. As with all scientific endeavours, we advocate for rigorous experimental designs tailored to explicit scientific questions as well as transparent methodologies and data sharing to provide useful knowledge for current research practices and for developing regulatory standards.

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.