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The pulp and paper industry is an important contributor to global greenhouse gas emissions1,2. Country-specific strategies are essential for the industry to achieve net-zero emissions by 2050, given its vast heterogeneities across countries3,4. Here we develop a comprehensive bottom-up assessment of net greenhouse gas emissions of the domestic paper-related sectors for 30 major countries from 1961 to 2019—about 3.2% of global anthropogenic greenhouse gas emissions from the same period5—and explore mitigation strategies through 2,160 scenarios covering key factors. Our results show substantial differences across countries in terms of historical emissions evolution trends and structure. All countries can achieve net-zero emissions for their pulp and paper industry by 2050, with a single measure for most developed countries and several measures for most developing countries. Except for energy-efficiency improvement and energy-system decarbonization, tropical developing countries with abundant forest resources should give priority to sustainable forest management, whereas other developing countries should pay more attention to enhancing methane capture rate and reducing recycling. These insights are crucial for developing net-zero strategies tailored to each country and achieving net-zero emissions by 2050 for the pulp and paper industry.A bottom-up assessment of the net greenhouse gas emissions of the pulp and paper industries of 30 countries from 1961 to 2019 leads to country-specific strategies to achieve net zero by 2050. [ABSTRACT FROM AUTHOR]

The number of articles being retracted rose sharply this year. Integrity experts say that this is only the tip of the iceberg. [ABSTRACT FROM AUTHOR]

For a century, scientists pondered whether bird flight evolved by animals gliding down from trees or by creatures running and flapping from the ground up. A landmark 1974 paper reset the debate to focus on the evolution of the flight stroke instead. Fiftieth anniversary of an advance in understanding how flight evolved. [ABSTRACT FROM AUTHOR]

The paper-folding mechanism has been widely adopted in building of reconfigurable macroscale systems because of its unique capabilities and advantages in programming variable shapes and stiffness into a structure1–5. However, it has barely been exploited in the construction of molecular-level systems owing to the lack of a suitable design principle, even though various dynamic structures based on DNA self-assembly6–9 have been developed10–23. Here we propose a method to harness the paper-folding mechanism to create reconfigurable DNA origami structures. The main idea is to build a reference, planar wireframe structure24 whose edges follow a crease pattern in paper folding so that it can be folded into various target shapes. We realized several paper-like folding and unfolding patterns using DNA strand displacement25 with high yield. Orthogonal folding, repeatable folding and unfolding, folding-based microRNA detection and fluorescence signal control were demonstrated. Stimuli-responsive folding and unfolding triggered by pH or light-source change were also possible. Moreover, by employing hierarchical assembly26 we could expand the design space and complexity of the paper-folding mechanism in a highly programmable manner. Because of its high programmability and scalability, we expect that the proposed paper-folding-based reconfiguration method will advance the development of complex molecular systems.A method is presented to harness the paper-folding mechanism of reconfigurable macroscale systems to create reconfigurable DNA origami structures, in anticipation that it will advance the development of complex molecular systems. [ABSTRACT FROM AUTHOR]

Browser plug-in alerts users when studies — or their references — have been posted on a site known for raising integrity concerns. [ABSTRACT FROM AUTHOR]

Scientists compiling a review scan more than 1,000 papers and find troubling images in some 10%.Scientists compiling a review scan more than 1,000 papers and find troubling images in nearly 20%. [ABSTRACT FROM AUTHOR]

Theories of scientific and technological change view discovery and invention as endogenous processes1,2, wherein previous accumulated knowledge enables future progress by allowing researchers to, in Newton’s words, ‘stand on the shoulders of giants’3–7. Recent decades have witnessed exponential growth in the volume of new scientific and technological knowledge, thereby creating conditions that should be ripe for major advances8,9. Yet contrary to this view, studies suggest that progress is slowing in several major fields10,11. Here, we analyse these claims at scale across six decades, using data on 45 million papers and 3.9 million patents from six large-scale datasets, together with a new quantitative metric—the CD index12—that characterizes how papers and patents change networks of citations in science and technology. We find that papers and patents are increasingly less likely to break with the past in ways that push science and technology in new directions. This pattern holds universally across fields and is robust across multiple different citation- and text-based metrics1,13–17. Subsequently, we link this decline in disruptiveness to a narrowing in the use of previous knowledge, allowing us to reconcile the patterns we observe with the ‘shoulders of giants’ view. We find that the observed declines are unlikely to be driven by changes in the quality of published science, citation practices or field-specific factors. Overall, our results suggest that slowing rates of disruption may reflect a fundamental shift in the nature of science and technology.A decline in disruptive science and technology over time is reported, representing a substantive shift in science and technology, which is attributed in part to the reliance on a narrower set of existing knowledge. [ABSTRACT FROM AUTHOR]

Studies to tackle air quality in Delhi and solve the mystery of an emerging pathogen are among those helping to raise the profile of Indian science. [ABSTRACT FROM AUTHOR]

Campaigners who want scrutiny of biometrics research on Uyghurs, Tibetans and other groups are frustrated by slow progress. [ABSTRACT FROM AUTHOR]

Theories of innovation emphasize the role of social networks and teams as facilitators of breakthrough discoveries1–4. Around the world, scientists and inventors are more plentiful and interconnected today than ever before4. However, although there are more people making discoveries, and more ideas that can be reconfigured in new ways, research suggests that new ideas are getting harder to find5,6—contradicting recombinant growth theory7,8. Here we shed light on this apparent puzzle. Analysing 20 million research articles and 4 million patent applications from across the globe over the past half-century, we begin by documenting the rise of remote collaboration across cities, underlining the growing interconnectedness of scientists and inventors globally. We further show that across all fields, periods and team sizes, researchers in these remote teams are consistently less likely to make breakthrough discoveries relative to their on-site counterparts. Creating a dataset that allows us to explore the division of labour in knowledge production within teams and across space, we find that among distributed team members, collaboration centres on late-stage, technical tasks involving more codified knowledge. Yet they are less likely to join forces in conceptual tasks—such as conceiving new ideas and designing research—when knowledge is tacit9. We conclude that despite striking improvements in digital technology in recent years, remote teams are less likely to integrate the knowledge of their members to produce new, disruptive ideas.Analysis of research articles and patent applications shows that members of teams that collaborate remotely are less likely to make breakthrough discoveries than members of on-site teams. [ABSTRACT FROM AUTHOR]

An unpublished analysis suggests that there are hundreds of thousands of bogus ‘paper-mill’ articles lurking in the literature. [ABSTRACT FROM AUTHOR]

This journal will double down on efforts to diversify the pool of corresponding authors and referees. [ABSTRACT FROM AUTHOR]

In some areas of social science, around half of studies can’t be replicated. A new test-fast, fail-fast initiative aims to show what research is hot — and what’s not. [ABSTRACT FROM AUTHOR]

The confidential 124-page report from the University of Rochester, disclosed in a lawsuit, details the extent of Ranga Dias’s scientific misconduct. [ABSTRACT FROM AUTHOR]

Some publishers say they are battling industrialized cheating. A Nature analysis examines the 'paper mill' problem — and how editors are trying to cope. [ABSTRACT FROM AUTHOR]

A popular theory about how trees cooperate has enchanted the public and raised the profile of forest conservation. But some ecologists think its scientific basis has been oversold. [ABSTRACT FROM AUTHOR]

Immune cells rely on transient physical interactions with other immune and non-immune populations to regulate their function1. To study these ‘kiss-and-run’ interactions directly in vivo, we previously developed LIPSTIC (labelling immune partnerships by SorTagging intercellular contacts)2, an approach that uses enzymatic transfer of a labelled substrate between the molecular partners CD40L and CD40 to label interacting cells. Reliance on this pathway limited the use of LIPSTIC to measuring interactions between CD4+ T helper cells and antigen-presenting cells, however. Here we report the development of a universal version of LIPSTIC (uLIPSTIC), which can record physical interactions both among immune cells and between immune and non-immune populations irrespective of the receptors and ligands involved. We show that uLIPSTIC can be used, among other things, to monitor the priming of CD8+ T cells by dendritic cells, reveal the steady-state cellular partners of regulatory T cells and identify germinal centre-resident T follicular helper cells on the basis of their ability to interact cognately with germinal centre B cells. By coupling uLIPSTIC with single-cell transcriptomics, we build a catalogue of the immune populations that physically interact with intestinal epithelial cells at the steady state and profile the evolution of the interactome of lymphocytic choriomeningitis virus-specific CD8+ T cells in multiple organs following systemic infection. Thus, uLIPSTIC provides a broadly useful technology for measuring and understanding cell–cell interactions across multiple biological systems.A paper reports the development of a universal tool for studying cellular interactions in biological systems, and demonstrates its coupling with single-cell transcriptomics methods to provide insights into the biology of the interactions. [ABSTRACT FROM AUTHOR]

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.Evaluation of evidence generated to test 19 proposed policy recommendations and guidance for the future. [ABSTRACT FROM AUTHOR]

Scientists worry that ill-informed use of artificial intelligence is driving a deluge of unreliable or useless research. [ABSTRACT FROM AUTHOR]

Text- and image-generating tools present a new hurdle for efforts to tackle the growing number of fake papers making their way into the academic literature. [ABSTRACT FROM AUTHOR]

In a huge collaborative effort, millions of cells in the mouse brain have been mapped in detail. Two scientists examine the resulting wealth of insights into gene regulation in brain cells, neuronal connections and how our own brains evolved.Transcriptomic and epigenomic data from millions of mouse brain cells. [ABSTRACT FROM AUTHOR]

By designing an autonomous system that fed prompts to the chatbot, researchers produced a paper that was fluent and insightful. Yet they still have concerns. [ABSTRACT FROM AUTHOR]

Ranga Dias will have a second paper revoked. A journal’s investigation found apparent data fabrication. [ABSTRACT FROM AUTHOR]

An algorithm that takes just seconds to scan a paper for duplicated images racks up more suspicious images than a person. [ABSTRACT FROM AUTHOR]

A world of AI-assisted writing and reviewing might transform the nature of the scientific paper. [ABSTRACT FROM AUTHOR]

Some researchers publish a new paper every five days, on average. Data trackers suspect not all their manuscripts were produced through honest labour. [ABSTRACT FROM AUTHOR]

An analysis of DOIs suggests that digital preservation is not keeping up with burgeoning scholarly knowledge. [ABSTRACT FROM AUTHOR]

By joining a collecting society, researchers can ensure they are paid when copyrighted book content and papers are reproduced. [ABSTRACT FROM AUTHOR]

A Nature survey finds that scientists are concerned, as well as excited, by the increasing use of artificial-intelligence tools in research. [ABSTRACT FROM AUTHOR]

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks1. Accurate and timely warnings are critical for mitigating flood risks2, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.Artificial intelligence-based forecasting improves the reliability of predicting extreme flood events in ungauged watersheds, with predictions at five days lead time that are as good as current systems are for same-day predictions. [ABSTRACT FROM AUTHOR]

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.The proliferation of artificial intelligence tools in scientific research risks creating illusions of understanding, where scientists believe they understand more about the world than they actually do. [ABSTRACT FROM AUTHOR]

Publishers are deploying AI-based tools to detect suspicious images, but generative AI threatens their efforts. [ABSTRACT FROM AUTHOR]

Astronomer Fred Hoyle supposedly coined the catchy term to ridicule the theory of the Universe’s origins — 75 years on, it’s time to set the record straight. [ABSTRACT FROM AUTHOR]

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10–11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.Genome-wide association studies of individuals from an isolated population (data from the Finnish biobank study FinnGen) and consequent meta-analyses facilitate the identification of previously unknown coding variant associations for both rare and common diseases. [ABSTRACT FROM AUTHOR]

Studies involving hundreds, even thousands, of scientists are on the rise, but how do such large groups coordinate their work? [ABSTRACT FROM AUTHOR]

Neuroscientist Sergiy Sylantyev tells Nature that science and war cannot be separated. [ABSTRACT FROM AUTHOR]

Universities must declare all their retractions and launch investigations into misconduct cases; a Nature analysis reveals that since 2021 there have been more than 17,000 retractions with Chinese co-authors. [ABSTRACT FROM AUTHOR]

Chemistry can transform disposable cups into industrially useful structures called cellulose nanocrystals.Most paper cups are not recycled, but a new technique could turn them into biodegradable packaging films.: Chemistry can transform disposable cups into industrially useful structures called cellulose nanocrystals. [ABSTRACT FROM AUTHOR]

A study of review reports identifies dozens of adjectives that could indicate text written with the help of chatbots. [ABSTRACT FROM AUTHOR]

Large language models are transforming scientific writing and publishing. But the productivity boost that these tools bring could have a downside. [ABSTRACT FROM AUTHOR]

Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1–4. Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype–protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort5. We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene–protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. STAB1 and STAB2, encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in NLRC4, identifying potential biomarkers for a fatty liver disease-associated variant in HSD17B13 and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between TET2-CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery.A set of three papers in Nature reports a new proteomics resource from the UK Biobank and initial analysis of common and rare genetic variant associations with plasma protein levels. [ABSTRACT FROM AUTHOR]

Large language models and other artificial-intelligence systems could be excellent at synthesizing scientific evidence for policymakers — but only with appropriate safeguards and humans in the loop. [ABSTRACT FROM AUTHOR]

Its rise to the top has been long forecast, but what next for Chinese science in the post-pandemic era? [ABSTRACT FROM AUTHOR]

After agriculture, wood harvest is the human activity that has most reduced the storage of carbon in vegetation and soils1,2. Although felled wood releases carbon to the atmosphere in various steps, the fact that growing trees absorb carbon has led to different carbon-accounting approaches for wood use, producing widely varying estimates of carbon costs. Many approaches give the impression of low, zero or even negative greenhouse gas emissions from wood harvests because, in different ways, they offset carbon losses from new harvests with carbon sequestration from growth of broad forest areas3,4. Attributing this sequestration to new harvests is inappropriate because this other forest growth would occur regardless of new harvests and typically results from agricultural abandonment, recovery from previous harvests and climate change itself. Nevertheless some papers count gross emissions annually, which assigns no value to the capacity of newly harvested forests to regrow and approach the carbon stocks of unharvested forests. Here we present results of a new model that uses time discounting to estimate the present and future carbon costs of global wood harvests under different scenarios. We find that forest harvests between 2010 and 2050 will probably have annualized carbon costs of 3.5–4.2 Gt CO2e yr−1, which approach common estimates of annual emissions from land-use change due to agricultural expansion. Our study suggests an underappreciated option to address climate change by reducing these costs.A new model uses time discounting to estimate the present and future carbon costs of global wood harvests under different scenarios, suggesting an underappreciated option to address climate change. [ABSTRACT FROM AUTHOR]

Investigations suggest that, in some fields, at least one-quarter of clinical trials might be problematic or even entirely made up, warn some researchers. They urge stronger scrutiny. [ABSTRACT FROM AUTHOR]

Concerns about image integrity have so far led to 17 retractions, corrections or expressions of concern for papers co-authored by geneticist Gregg Semenza. [ABSTRACT FROM AUTHOR]

Journals have begun retracting publications with suspicious links to sites trading in author positions. [ABSTRACT FROM AUTHOR]

Online integrity hub will contain tools to help publishers combat bogus research and image manipulation. [ABSTRACT FROM AUTHOR]

Groundwater resources are vital to ecosystems and livelihoods. Excessive groundwater withdrawals can cause groundwater levels to decline1–10, resulting in seawater intrusion11, land subsidence12,13, streamflow depletion14–16 and wells running dry17. However, the global pace and prevalence of local groundwater declines are poorly constrained, because in situ groundwater levels have not been synthesized at the global scale. Here we analyse in situ groundwater-level trends for 170,000 monitoring wells and 1,693 aquifer systems in countries that encompass approximately 75% of global groundwater withdrawals18. We show that rapid groundwater-level declines (>0.5 m year−1) are widespread in the twenty-first century, especially in dry regions with extensive croplands. Critically, we also show that groundwater-level declines have accelerated over the past four decades in 30% of the world’s regional aquifers. This widespread acceleration in groundwater-level deepening highlights an urgent need for more effective measures to address groundwater depletion. Our analysis also reveals specific cases in which depletion trends have reversed following policy changes, managed aquifer recharge and surface-water diversions, demonstrating the potential for depleted aquifer systems to recover.Analysis of about 170,000 monitoring wells and 1,693 aquifer systems worldwide shows that extensive and often accelerating groundwater declines are widespread in the twenty-first century, but that groundwater levels are recovering in some cases. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have demonstrated tremendous capabilities in solving complex tasks, from quantitative reasoning to understanding natural language. However, LLMs sometimes suffer from confabulations (or hallucinations), which can result in them making plausible but incorrect statements1,2. This hinders the use of current large models in scientific discovery. Here we introduce FunSearch (short for searching in the function space), an evolutionary procedure based on pairing a pretrained LLM with a systematic evaluator. We demonstrate the effectiveness of this approach to surpass the best-known results in important problems, pushing the boundary of existing LLM-based approaches3. Applying FunSearch to a central problem in extremal combinatorics—the cap set problem—we discover new constructions of large cap sets going beyond the best-known ones, both in finite dimensional and asymptotic cases. This shows that it is possible to make discoveries for established open problems using LLMs. We showcase the generality of FunSearch by applying it to an algorithmic problem, online bin packing, finding new heuristics that improve on widely used baselines. In contrast to most computer search approaches, FunSearch searches for programs that describe how to solve a problem, rather than what the solution is. Beyond being an effective and scalable strategy, discovered programs tend to be more interpretable than raw solutions, enabling feedback loops between domain experts and FunSearch, and the deployment of such programs in real-world applications.FunSearch makes discoveries in established open problems using large language models by searching for programs describing how to solve a problem, rather than what the solution is. [ABSTRACT FROM AUTHOR]