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Groundwater is the most ubiquitous source of liquid freshwater globally, yet its role in supporting diverse ecosystems is rarely acknowledged1,2. However, the location and extent of groundwater-dependent ecosystems (GDEs) are unknown in many geographies, and protection measures are lacking1,3. Here, we map GDEs at high-resolution (roughly 30 m) and find them present on more than one-third of global drylands analysed, including important global biodiversity hotspots4. GDEs are more extensive and contiguous in landscapes dominated by pastoralism with lower rates of groundwater depletion, suggesting that many GDEs are likely to have already been lost due to water and land use practices. Nevertheless, 53% of GDEs exist within regions showing declining groundwater trends, which highlights the urgent need to protect GDEs from the threat of groundwater depletion. However, we found that only 21% of GDEs exist on protected lands or in jurisdictions with sustainable groundwater management policies, invoking a call to action to protect these vital ecosystems. Furthermore, we examine the linkage of GDEs with cultural and socio-economic factors in the Greater Sahel region, where GDEs play an essential role in supporting biodiversity and rural livelihoods, to explore other means for protection of GDEs in politically unstable regions. Our GDE map provides critical information for prioritizing and developing policies and protection mechanisms across various local, regional or international scales to safeguard these important ecosystems and the societies dependent on them.Mapping of groundwater-dependent ecosystems, which support biodiversity and rural livelihoods, shows they occur on more than one-third of global drylands analysed, but lack protections to safeguard these critical ecosystems and the societies dependent upon them from groundwater depletion. [ABSTRACT FROM AUTHOR]

Working from home has become standard for employees with a university degree. The most common scheme, which has been adopted by around 100 million employees in Europe and North America, is a hybrid schedule, in which individuals spend a mix of days at home and at work each week1,2. However, the effects of hybrid working on employees and firms have been debated, and some executives argue that it damages productivity, innovation and career development3–5. Here we ran a six-month randomized control trial investigating the effects of hybrid working from home on 1,612 employees in a Chinese technology company in 2021–2022. We found that hybrid working improved job satisfaction and reduced quit rates by one-third. The reduction in quit rates was significant for non-managers, female employees and those with long commutes. Null equivalence tests showed that hybrid working did not affect performance grades over the next two years of reviews. We found no evidence for a difference in promotions over the next two years overall, or for any major employee subgroup. Finally, null equivalence tests showed that hybrid working had no effect on the lines of code written by computer-engineer employees. We also found that the 395 managers in the experiment revised their surveyed views about the effect of hybrid working on productivity, from a perceived negative effect (−2.6% on average) before the experiment to a perceived positive one (+1.0%) after the experiment. These results indicate that a hybrid schedule with two days a week working from home does not damage performance.A trial investigating the effects of a hybrid working schedule in a Chinese technology firm in 2021–2022 shows that working from home two days a week improved job satisfaction, reduced quitting and did not affect performance. [ABSTRACT FROM AUTHOR]

From atomic crystals to animal flocks, the emergence of order in nature is captured by the concept of spontaneous symmetry breaking1–4. However, this cornerstone of physics is challenged when broken symmetry phases are frustrated by geometrical constraints. Such frustration dictates the behaviour of systems as diverse as spin ices5–8, confined colloidal suspensions9 and crumpled paper sheets10. These systems typically exhibit strongly degenerated and heterogeneous ground states and hence escape the Ginzburg–Landau paradigm of phase ordering. Here, combining experiments, simulations and theory we uncover an unanticipated form of topological order in globally frustrated matter: non-orientable order. We demonstrate this concept by designing globally frustrated metamaterials that spontaneously break a discrete Z 2 symmetry. We observe that their equilibria are necessarily heteregeneous and extensively degenerated. We explain our observations by generalizing the theory of elasticity to non-orientable order-parameter bundles. We show that non-orientable equilibria are extensively degenerated due to the arbitrary location of topologically protected nodes and lines where the order parameter must vanish. We further show that non-orientable order applies more broadly to objects that are non-orientable themselves, such as buckled Möbius strips and Klein bottles. Finally, by applying time-dependent local perturbations to metamaterials with non-orientable order, we engineer topologically protected mechanical memories11–19, achieve non-commutative responses and show that they carry an imprint of the braiding of the loads’ trajectories. Beyond mechanics, we envision non-orientability as a robust design principle for metamaterials that can effectively store information across scales, in fields as diverse as colloidal science8, photonics20, magnetism7 and atomic physics21.We describe globally frustrated matter as deformations of non-orientable surfaces, with the concept of non-orientable order we demonstrate topologically protected mechanical memories, suggesting a design principle for effectively storing information in frustrated metamaterials. [ABSTRACT FROM AUTHOR]

Scientific papers made free to access during the pandemic are rumoured to be disappearing behind paywalls. They aren’t — yet. [ABSTRACT FROM AUTHOR]

Poorly performing antibodies have plagued biomedical sciences for decades. Several fresh initiatives hope to change this. [ABSTRACT FROM AUTHOR]

Nobel prizewinner Alain Aspect's 'crackpot' studies of quantum entanglement have explored the tricks reality plays on us — and built on ideas from a physics great. [ABSTRACT FROM AUTHOR]

Can cycles of dieting increase your risk of heart attack? In mice, an alternating high-fat and low-fat diet promotes plaque build-up in arteries by modulating the body’s innate immune responses.Cycling between a high-fat and low-fat diet exacerbates atherosclerosis. [ABSTRACT FROM AUTHOR]

Tiny cellular tubes between neurons and brain cells called microglia serve as conduits for the export of toxic protein aggregates from neurons and the import of healthy organelles, keeping neurodegeneration at bay.Neurons and microglia trade materials. [ABSTRACT FROM AUTHOR]

An ultra-precise laser synchronized to one of the world’s most precise clocks has been used to excite rapid nuclear oscillations — promising a timekeeper that could help to tackle fundamental questions about the Universe.Frequency comb custom built for the thorium-229 nuclear transition. [ABSTRACT FROM AUTHOR]

Papers in engineering are under-represented, even neglected, in the journal. We want to change that. [ABSTRACT FROM AUTHOR]

Don’t rely solely on academic papers to raise your professional profile. General readers are interested in your opinions, too. [ABSTRACT FROM AUTHOR]

Although Jonathan Pruitt, the researcher at the centre of a retractions scandal, has resigned, former lab members and collaborators continue dealing with the fallout. [ABSTRACT FROM AUTHOR]

The decline might be another sign of politics affecting cross-country scientific collaborations. [ABSTRACT FROM AUTHOR]

How certain subgroups of a childhood brain tumour called a medulloblastoma arise has been unclear. Evidence now implicates a cell type found only in developing human brains as the originator of these tumours. Brain cells that give rise to certain types of cancer identified. [ABSTRACT FROM AUTHOR]

Phase segregation is ubiquitously observed in immiscible mixtures, such as oil and water, in which the mixing entropy is overcome by the segregation enthalpy1–3. In monodispersed colloidal systems, however, the colloidal–colloidal interactions are usually non-specific and short-ranged, which leads to negligible segregation enthalpy4. The recently developed photoactive colloidal particles show long-range phoretic interactions, which can be readily tuned with incident light, suggesting an ideal model for studying phase behaviour and structure evolution kinetics5,6. In this work, we design a simple spectral selective active colloidal system, in which TiO2 colloidal species were coded with spectral distinctive dyes to form a photochromic colloidal swarm. In this system, the particle–particle interactions can be programmed by combining incident light with various wavelengths and intensities to enable controllable colloidal gelation and segregation. Furthermore, by mixing the cyan, magenta and yellow colloids, a dynamic photochromic colloidal swarm is formulated. On illumination of coloured light, the colloidal swarm adapts the appearance of incident light due to layered phase segregation, presenting a facile approach towards coloured electronic paper and self-powered optical camouflage.A simple spectral selective active colloidal system is designed in which TiO2 colloidal species are coded with dyes to form a photochromic swarm that adapts the appearance of incident light due to layered phase segregation. [ABSTRACT FROM AUTHOR]

Franklin was no victim in how the DNA double helix was solved. An overlooked letter and an unpublished news article, both written in 1953, reveal that she was an equal player. [ABSTRACT FROM AUTHOR]

Editors threaten to resign over ‘no-reject’ model that others see as the future of research journals. [ABSTRACT FROM AUTHOR]

A programmable RNA that bridges a genetic donor and a target could herald a safer and more flexible approach to large-scale chromosome changes. [ABSTRACT FROM AUTHOR]

RNA-guided recombinase enzymes have been discovered that herald a new chapter for genome editing — enabling the insertion, inversion or deletion of long DNA sequences at user-specified genome positions.Recombinase enzymes that recognize DNA sequences using a ‘bridge’ RNA. [ABSTRACT FROM AUTHOR]

There are gaps in our understanding of how and why digital misinformation propagates. To help design effective interventions to minimize the spread of falsehoods, researchers need data and transparency from online platforms. [ABSTRACT FROM AUTHOR]

Researchers are aiming to create fully accessible versions of the latest iteration of DeepMind’s blockbuster protein-structure model. [ABSTRACT FROM AUTHOR]

Theoretical physicist saw his eponymous particle discovered after 48 years. [ABSTRACT FROM AUTHOR]

The digitization of health records and growing availability of tumour DNA sequencing provide an opportunity to study the determinants of cancer outcomes with unprecedented richness. Patient data are often stored in unstructured text and siloed datasets. Here we combine natural language processing annotations 1,2 with structured medication, patient-reported demographic, tumour registry and tumour genomic data from 24,950 patients at Memorial Sloan Kettering Cancer Center to generate a clinicogenomic, harmonized oncologic real-world dataset (MSK-CHORD). MSK-CHORD includes data for non-small-cell lung (n = 7,809), breast (n = 5,368), colorectal (n = 5,543), prostate (n = 3,211) and pancreatic (n = 3,109) cancers and enables discovery of clinicogenomic relationships not apparent in smaller datasets. Leveraging MSK-CHORD to train machine learning models to predict overall survival, we find that models including features derived from natural language processing, such as sites of disease, outperform those based on genomic data or stage alone as tested by cross-validation and an external, multi-institution dataset. By annotating 705,241 radiology reports, MSK-CHORD also uncovers predictors of metastasis to specific organ sites, including a relationship between SETD2 mutation and lower metastatic potential in immunotherapy-treated lung adenocarcinoma corroborated in independent datasets. We demonstrate the feasibility of automated annotation from unstructured notes and its utility in predicting patient outcomes. The resulting data are provided as a public resource for real-world oncologic research., (© 2024. The Author(s).)

Crystalline solids typically show robust long-range structural ordering, vital for their remarkable electronic properties and use in functional electronics, albeit with limited customization space. By contrast, synthetic molecular systems provide highly tunable structural topologies and versatile functionalities but are often too delicate for scalable electronic integration. Combining these two systems could harness the strengths of both, yet realizing this integration is challenging owing to distinct chemical bonding structures and processing conditions. Two-dimensional atomic crystals comprise crystalline atomic layers separated by non-bonding van der Waals gaps, allowing diverse atomic or molecular intercalants to be inserted without disrupting existing covalent bonds. This enables the creation of a diverse set of layered hybrid superlattices (LHSLs) composed of alternating crystalline atomic layers of variable electronic properties and self-assembled atomic or molecular interlayers featuring customizable chemical compositions and structural motifs. Here we outline strategies to prepare LHSLs and discuss emergent properties. With the versatile molecular design strategies and modular assembly processes, LHSLs offer vast flexibility for weaving distinct chemical constituents and quantum properties into monolithic artificial solids with a designable three-dimensional potential landscape. This opens unprecedented opportunities to tailor charge correlations, quantum properties and topological phases, thereby defining a rich material platform for advancing quantum information science., (© 2024. Springer Nature Limited.)

Natural disasters trigger complex chains of events within human societies 1 . Immediate deaths and damage are directly observed after a disaster and are widely studied, but delayed downstream outcomes, indirectly caused by the disaster, are difficult to trace back to the initial event 1,2 . Tropical cyclones (TCs)-that is, hurricanes and tropical storms-are widespread globally and have lasting economic impacts 3-5 , but their full health impact remains unknown. Here we conduct a large-scale evaluation of long-term effects of TCs on human mortality in the contiguous United States (CONUS) for all TCs between 1930 and 2015. We observe a robust increase in excess mortality that persists for 15 years after each geophysical event. We estimate that the average TC generates 7,000-11,000 excess deaths, exceeding the average of 24 immediate deaths reported in government statistics 6,7 . Tracking the effects of 501 historical storms, we compute that the TC climate of CONUS imposes an undocumented mortality burden that explains a substantial fraction of the higher mortality rates along the Atlantic coast and is equal to roughly 3.2-5.1% of all deaths. These findings suggest that the TC climate, previously thought to be unimportant for broader public health outcomes, is a meaningful underlying driver for the distribution of mortality risk in CONUS, especially among infants (less than 1 year of age), people 1-44 years of age, and the Black population. Understanding why TCs induce this excess mortality is likely to yield substantial health benefits., (© 2024. The Author(s).)

Large language models (LLMs) have enabled the generation of high-quality synthetic text, often indistinguishable from human-written content, at a scale that can markedly affect the nature of the information ecosystem 1-3 . Watermarking can help identify synthetic text and limit accidental or deliberate misuse 4 , but has not been adopted in production systems owing to stringent quality, detectability and computational efficiency requirements. Here we describe SynthID-Text, a production-ready text watermarking scheme that preserves text quality and enables high detection accuracy, with minimal latency overhead. SynthID-Text does not affect LLM training and modifies only the sampling procedure; watermark detection is computationally efficient, without using the underlying LLM. To enable watermarking at scale, we develop an algorithm integrating watermarking with speculative sampling, an efficiency technique frequently used in production systems 5 . Evaluations across multiple LLMs empirically show that SynthID-Text provides improved detectability over comparable methods, and standard benchmarks and human side-by-side ratings indicate no change in LLM capabilities. To demonstrate the feasibility of watermarking in large-scale-production systems, we conducted a live experiment that assessed feedback from nearly 20 million Gemini 6 responses, again confirming the preservation of text quality. We hope that the availability of SynthID-Text 7 will facilitate further development of watermarking and responsible use of LLM systems., (© 2024. The Author(s).)

Biobanks aim to improve our understanding of health and disease by collecting and analysing diverse biological and phenotypic information in large samples. So far, biobanks have largely pursued a population-based sampling strategy, where the individual is the unit of sampling, and familial relatedness occurs sporadically and by chance. This strategy has been remarkably efficient and successful, leading to thousands of scientific discoveries across multiple research domains, and plans for the next wave of biobanks are underway. In this Perspective, we discuss the strengths and limitations of a complementary sampling strategy for future biobanks based on oversampling of close genetic relatives. Such family-based samples facilitate research that clarifies causal relationships between putative risk factors and outcomes, particularly in estimates of genetic effects, because they enable analyses that reduce or eliminate confounding due to familial and demographic factors. Family-based biobank samples would also shed new light on fundamental questions across multiple fields that are often difficult to explore in population-based samples. Despite the potential for higher costs and greater analytical complexity, the many advantages of family-based samples should often outweigh their potential challenges., (© 2024. Springer Nature Limited.)

The rate of river migration affects the stability of Arctic infrastructure and communities 1,2 and regulates the fluxes of carbon 3,4 , nutrients 5 and sediment 6,7 to the oceans. However, predicting how the pace of river migration will change in a warming Arctic 8 has so far been stymied by conflicting observations about whether permafrost 9 primarily acts to slow 10,11 or accelerate 12,13 river migration. Here we develop new computational methods that enable the detection of riverbank erosion at length scales 5-10 times smaller than the pixel size in satellite imagery, an innovation that unlocks the ability to quantify erosion at the sub-monthly timescales when rivers undergo their largest variations in water temperature and flow. We use these high-frequency observations to constrain the extent to which erosion is limited by the thermal condition of melting the pore ice that cements bank sediment 14 , a requirement that will disappear when permafrost thaws, versus the mechanical condition of having sufficient flow to transport the sediment comprising the riverbanks, a condition experienced by all rivers 15 . Analysis of high-resolution data from the Koyukuk River, Alaska, shows that the presence of permafrost reduces erosion rates by 47%. Using our observations, we calibrate and validate a numerical model that can be applied to diverse Arctic rivers. The model predicts that full permafrost thaw may lead to a 30-100% increase in the migration rates of Arctic rivers., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Semiconductors have already had a very profound effect on society, accelerating scientific research and driving greater connectivity. Future semiconductor hardware will open up new possibilities in quantum computing, artificial intelligence and edge computing, for applications such as cybersecurity and personalized healthcare. By nature of its ethos, open hardware provides opportunities for even greater collaboration and innovations across education, academic research and industry. Here we present Flex-RV, a 32-bit microprocessor based on an open RISC-V (ref.  1 ) instruction set fabricated with indium gallium zinc oxide thin-film transistors 2 on a flexible polyimide substrate, enabling an ultralow-cost bendable microprocessor. Flex-RV also integrates a programmable machine learning (ML) hardware accelerator inside the microprocessor and demonstrates new instructions to extend the RISC-V instruction set to run ML workloads. It is implemented, fabricated and demonstrated to operate at 60 kHz consuming less than 6 mW power. Its functionality when assembled onto a flexible printed circuit board is validated while executing programs under flat and tight bending conditions, achieving no worse than 4.3% performance variation on average. Flex-RV pioneers an era of sub-dollar open standard non-silicon 32-bit microprocessors and will democratize access to computing and unlock emerging applications in wearables, healthcare devices and smart packaging., (© 2024. The Author(s).)

The prevailing methods to make large language models more powerful and amenable have been based on continuous scaling up (that is, increasing their size, data volume and computational resources 1 ) and bespoke shaping up (including post-filtering 2,3 , fine tuning or use of human feedback 4,5 ). However, larger and more instructable large language models may have become less reliable. By studying the relationship between difficulty concordance, task avoidance and prompting stability of several language model families, here we show that easy instances for human participants are also easy for the models, but scaled-up, shaped-up models do not secure areas of low difficulty in which either the model does not err or human supervision can spot the errors. We also find that early models often avoid user questions but scaled-up, shaped-up models tend to give an apparently sensible yet wrong answer much more often, including errors on difficult questions that human supervisors frequently overlook. Moreover, we observe that stability to different natural phrasings of the same question is improved by scaling-up and shaping-up interventions, but pockets of variability persist across difficulty levels. These findings highlight the need for a fundamental shift in the design and development of general-purpose artificial intelligence, particularly in high-stakes areas for which a predictable distribution of errors is paramount., (© 2024. The Author(s).)

Twenty genetic therapies have been approved by the US Food and Drug Administration to date, a number that now includes the first CRISPR genome-editing therapy for sickle cell disease-CASGEVY (exagamglogene autotemcel, Vertex Pharmaceuticals). This extraordinary milestone is widely celebrated owing to the promise for future genome-editing treatments of previously intractable genetic disorders and cancers. At the same time, such genetic therapies are the most expensive drugs on the market, with list prices exceeding US$4 million per patient. Although all approved cell and gene therapies trace their origins to academic or government research institutions, reliance on for-profit pharmaceutical companies for subsequent development and commercialization results in prices that prioritize recouping investments, paying for candidate product failures and meeting investor and shareholder expectations. To increase affordability and access, sustainable discovery-to-market alternatives are needed that address system-wide deficiencies. Here we present recommendations of a multidisciplinary task force assembled to chart such a path. We describe a pricing structure that, once implemented, could reduce per-patient cost tenfold and propose a business model that distributes responsibilities while leveraging diverse funding sources. We also outline how academic licensing provisions, manufacturing innovation and supportive regulations can reduce cost and enable broader patient treatment., (© 2024. Springer Nature Limited.)

The origin of the eukaryotic cell, with its compartmentalized nature and generally large size compared with bacterial and archaeal cells, represents a cornerstone event in the evolution of complex life on Earth. In a process referred to as eukaryogenesis, the eukaryotic cell is believed to have evolved between approximately 1.8 and 2.7 billion years ago from its archaeal ancestors, with a symbiosis with a bacterial (proto-mitochondrial) partner being a key event. In the tree of life, the branch separating the first from the last common ancestor of all eukaryotes is long and lacks evolutionary intermediates. As a result, the timing and driving forces of the emergence of complex eukaryotic features remain poorly understood. During the past decade, environmental and comparative genomic studies have revealed vital details about the identity and nature of the host cell and the proto-mitochondrial endosymbiont, enabling a critical reappraisal of hypotheses underlying the symbiotic origin of the eukaryotic cell. Here we outline our current understanding of the key players and events underlying the emergence of cellular complexity during the prokaryote-to-eukaryote transition and discuss potential avenues of future research that might provide new insights into the enigmatic origin of the eukaryotic cell., (© 2024. Springer Nature Limited.)

Radiocarbon ( 14 C) is essential for creating chronologies to study the timings and drivers of pivotal events in human history and the Earth system over the past 55,000 years. It is also a fundamental proxy for investigating solar processes, including the potential of the Sun for extreme activity. Until now, fluctuations in past atmospheric 14 C levels have limited the dating precision possible using radiocarbon. However, the discovery of solar super-storms known as extreme solar particle events (ESPEs) has driven a series of advances with the potential to transform the calendar-age precision of radiocarbon dating. Organic materials containing unique 14 C ESPE signatures can now be dated to annual precision. In parallel, the search for further storms using high-precision annual 14 C measurements has revealed fine-scaled variations that can be used to improve calendar-age precision, even in periods that lack ESPEs. Furthermore, the newly identified 14 C fluctuations provide unprecedented insight into solar variability and the carbon cycle. Here, we review the current state of knowledge and share our insights into these rapidly developing, diverse research fields. We identify links between radiocarbon, archaeology, solar physics and Earth science to stimulate transdisciplinary collaboration, and we propose how researchers can take advantage of these recent developments., (© 2024. Springer Nature Limited.)

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., (© 2024. Springer Nature Limited.)

The open data revolution won’t happen unless the research system values the sharing of data as much as authorship on papers. [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]

Scientists are closing in on a tool that can reliably identify AI-generated text without affecting the user’s experience. But the technology’s robustness remains a challenge. [ABSTRACT FROM AUTHOR]

Wide distribution of findings shows how analytical choices drive conclusions. [ABSTRACT FROM AUTHOR]

Layers of a thin semiconductor material overlap in a particular pattern, giving rise to particle currents carrying a fraction of the charge of an electron — with potential for encoding quantum information.Fractional quantum Hall effect in the absence of a magnetic field. [ABSTRACT FROM AUTHOR]

The inner workings of a family of proteins, known as adhesion G-protein-coupled receptors, have finally been visualized at high resolution — revealing the structural basis of their self-activation mechanism.The inner workings of a class of G-protein-coupled receptors. [ABSTRACT FROM AUTHOR]

Two differing approaches that are used to study common and rare genetic causes of schizophrenia reveal convergent clues about the biology underlying this complex disorder. Rare and common genetic variants associated with schizophrenia. [ABSTRACT FROM AUTHOR]

Researchers have tried a bunch of strategies to get more negative results into the literature. Nature asks whether they are working. [ABSTRACT FROM AUTHOR]

How plants sense their orientation in relation to gravity and steer their root and shoot growth accordingly is not fully understood. The discovery of key steps needed for this process fills in some of the gaps in our knowledge.LAZY proteins hitchhike when roots reorient relative to gravity. [ABSTRACT FROM AUTHOR]

Self-reliance and a wish to set the agenda might explain concerted push to produce domestic journals. [ABSTRACT FROM AUTHOR]

Researchers and policymakers often exist in different worlds and speak different languages. Here are three ways to bridge the divide. [ABSTRACT FROM AUTHOR]

Analysis of which researchers publish, get credit, move around, get funding, collaborate and receive citations shows how deeply ingrained the bias against women is. [ABSTRACT FROM AUTHOR]

Periodic waves of changing electron density are linked to the ability of some materials to conduct electricity without resistance. Four studies reveal that such waves could emerge in more materials than expected.Pair density waves observed in three superconductors. [ABSTRACT FROM AUTHOR]