Proteomics: A new dimension to decode small cell lung cancer.

Small cell lung cancer (SCLC) is a recalcitrant malignancy. Conquering it will require deep insight into its biology. In this issue of Cell, Liu and colleagues describe proteomic and phosphoproteomic landscapes of resected SCLC tumors and illustrate the potential of this knowledge to identify new SCLC vulnerabilities.

Enhancing translation of science into non-English languages.

Translating scientific findings from English to other native languages is essential to make sure that they can be integrated into timely and informed dialogue with policymakers and a diverse range of audiences who are affected by the science. Here, we present innovative approaches how to enhance access to scientific knowledge in non-English languages.

Prediction of efficiencies for diverse prime editing systems in multiple cell types.

Applications of prime editing are often limited due to insufficient efficiencies, and it can require substantial time and resources to determine the most efficient pegRNAs and prime editors (PEs) to generate a desired edit under various experimental conditions. Here, we evaluated prime editing efficiencies for a total of 338,996 pairs of pegRNAs including 3,979 epegRNAs and target sequences in an error-free manner. These datasets enabled a systematic determination of factors affecting prime editing efficiencies. Then, we developed computational models, named DeepPrime and DeepPrime-FT, that can predict prime editing efficiencies for eight prime editing systems in seven cell types for all possible types of editing of up to 3 base pairs. We also extensively profiled the prime editing efficiencies at mismatched targets and developed a computational model predicting editing efficiencies at such targets. These computational models, together with our improved knowledge about prime editing efficiency determinants, will greatly facilitate prime editing applications.

The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation.

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains, provide a mechanistic understanding of the hippocampal role in generalization, and offer unifying principles underlying many entorhinal and hippocampal cell types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells display diverse properties resembling apparently bespoke spatial responses, such as grid, band, border, and object-vector cells. TEM hippocampal cells include place and landmark cells that remap between environments. Crucially, TEM also aligns with empirically recorded representations in complex non-spatial tasks. TEM also generates predictions that hippocampal remapping is not random as previously believed; rather, structural knowledge is preserved across environments. We confirm this structural transfer over remapping in simultaneously recorded place and grid cells.

A Well-Hung Horse: Sired by Knowledge and Imagination.

For more than a century, historians of science have been spinning a philosophical roulette wheel, pondering which is more important in the creative process: imagination or knowledge. The most original scientists (and artists) in our day discover newness by blending existing knowledge with imaginative thinking.

Bumblebees socially learn behaviour too complex to innovate alone.

Culture refers to behaviours that are socially learned and persist within a population over time. Increasing evidence suggests that animal culture can, like human culture, be cumulative: characterized by sequential innovations that build on previous ones1. However, human cumulative culture involves behaviours so complex that they lie beyond the capacity of any individual to independently discover during their lifetime1-3. To our knowledge, no study has so far demonstrated this phenomenon in an invertebrate. Here we show that bumblebees can learn from trained demonstrator bees to open a novel two-step puzzle box to obtain food rewards, even though they fail to do so independently. Experimenters were unable to train demonstrator bees to perform the unrewarded first step without providing a temporary reward linked to this action, which was removed during later stages of training. However, a third of naive observer bees learned to open the two-step box from these demonstrators, without ever being rewarded after the first step. This suggests that social learning might permit the acquisition of behaviours too complex to 're-innovate' through individual learning. Furthermore, naive bees failed to open the box despite extended exposure for up to 24 days. This finding challenges a common opinion in the field: that the capacity to socially learn behaviours that cannot be innovated through individual trial and error is unique to humans.

Functional and evolutionary significance of unknown genes from uncultivated taxa.

Many of the Earth's microbes remain uncultured and understudied, limiting our understanding of the functional and evolutionary aspects of their genetic material, which remain largely overlooked in most metagenomic studies1. Here we analysed 149,842 environmental genomes from multiple habitats2-6 and compiled a curated catalogue of 404,085 functionally and evolutionarily significant novel (FESNov) gene families exclusive to uncultivated prokaryotic taxa. All FESNov families span multiple species, exhibit strong signals of purifying selection and qualify as new orthologous groups, thus nearly tripling the number of bacterial and archaeal gene families described to date. The FESNov catalogue is enriched in clade-specific traits, including 1,034 novel families that can distinguish entire uncultivated phyla, classes and orders, probably representing synapomorphies that facilitated their evolutionary divergence. Using genomic context analysis and structural alignments we predicted functional associations for 32.4% of FESNov families, including 4,349 high-confidence associations with important biological processes. These predictions provide a valuable hypothesis-driven framework that we used for experimental validatation of a new gene family involved in cell motility and a novel set of antimicrobial peptides. We also demonstrate that the relative abundance profiles of novel families can discriminate between environments and clinical conditions, leading to the discovery of potentially new biomarkers associated with colorectal cancer. We expect this work to enhance future metagenomics studies and expand our knowledge of the genetic repertory of uncultivated organisms.

Artificial intelligence and illusions of understanding in scientific research.

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.

Large language models encode clinical knowledge.

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language Model1 (PaLM, a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM2 on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA3, MedMCQA4, PubMedQA5 and Measuring Massive Multitask Language Understanding (MMLU) clinical topics6), including 67.6% accuracy on MedQA (US Medical Licensing Exam-style questions), surpassing the prior state of the art by more than 17%. However, human evaluation reveals key gaps. To resolve this, we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLMs for clinical applications.

Remote collaboration fuses fewer breakthrough ideas.

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.

Geometry of abstract learned knowledge in the hippocampus.

Hippocampal neurons encode physical variables1-7 such as space1 or auditory frequency6 in cognitive maps8. In addition, functional magnetic resonance imaging studies in humans have shown that the hippocampus can also encode more abstract, learned variables9-11. However, their integration into existing neural representations of physical variables12,13 is unknown. Here, using two-photon calcium imaging, we show that individual neurons in the dorsal hippocampus jointly encode accumulated evidence with spatial position in mice performing a decision-making task in virtual reality14-16. Nonlinear dimensionality reduction13 showed that population activity was well-described by approximately four to six latent variables, which suggests that neural activity is constrained to a low-dimensional manifold. Within this low-dimensional space, both physical and abstract variables were jointly mapped in an orderly manner, creating a geometric representation that we show is similar across mice. The existence of conjoined cognitive maps suggests that the hippocampus performs a general computation-the creation of task-specific low-dimensional manifolds that contain a geometric representation of learned knowledge.

A framework for quantifying individual and collective common sense.

The notion of common sense is invoked so frequently in contexts as diverse as everyday conversation, political debates, and evaluations of artificial intelligence that its meaning might be surmised to be unproblematic. Surprisingly, however, neither the intrinsic properties of common sense knowledge (what makes a claim commonsensical) nor the degree to which it is shared by people (its "commonness") have been characterized empirically. In this paper, we introduce an analytical framework for quantifying both these elements of common sense. First, we define the commonsensicality of individual claims and people in terms of the latter's propensity to agree on the former and their awareness of one another's agreement. Second, we formalize the commonness of common sense as a clique detection problem on a bipartite belief graph of people and claims, defining [Formula: see text] common sense as the fraction [Formula: see text] of claims shared by a fraction [Formula: see text] of people. Evaluating our framework on a dataset of [Formula: see text] raters evaluating [Formula: see text] diverse claims, we find that commonsensicality aligns most closely with plainly worded, fact-like statements about everyday physical reality. Psychometric attributes such as social perceptiveness influence individual common sense, but surprisingly demographic factors such as age or gender do not. Finally, we find that collective common sense is rare: At most, a small fraction [Formula: see text] of people agree on more than a small fraction [Formula: see text] of claims. Together, these results undercut universalistic beliefs about common sense and raise questions about its variability that are relevant both to human and artificial intelligence.

Epistemic language in news headlines shapes readers' perceptions of objectivity.

How we reason about objectivity-whether an assertion has a ground truth-has implications for belief formation on wide-ranging topics. For example, if someone perceives climate change to be a matter of subjective opinion similar to the best movie genre, they may consider empirical claims about climate change as mere opinion and irrelevant to their beliefs. Here, we investigate whether the language employed by journalists might influence the perceived objectivity of news claims. Specifically, we ask whether factive verb framing (e.g., "Scientists know climate change is happening") increases perceived objectivity compared to nonfactive framing (e.g., "Scientists believe [...]"). Across eight studies (N = 2,785), participants read news headlines about unique, noncontroversial topics (studies 1a-b, 2a-b) or a familiar, controversial topic (climate change; studies 3a-b, 4a-b) and rated the truth and objectivity of the headlines' claims. Across all eight studies, when claims were presented as beliefs (e.g., "Tortoise breeders believe tortoises are becoming more popular pets"), people consistently judged those claims as more subjective than claims presented as knowledge (e.g., "Tortoise breeders know…"), as well as claims presented as unattributed generics (e.g., "Tortoises are becoming more popular pets"). Surprisingly, verb framing had relatively little, inconsistent influence over participants' judgments of the truth of claims. These results demonstrate how, apart from shaping whether we believe a claim is true or false, epistemic language in media can influence whether we believe a claim has an objective answer at all.

Human microbiome variation associated with race and ethnicity emerges as early as 3 months of age.

Human microbiome variation is linked to the incidence, prevalence, and mortality of many diseases and associates with race and ethnicity in the United States. However, the age at which microbiome variability emerges between these groups remains a central gap in knowledge. Here, we identify that gut microbiome variation associated with race and ethnicity arises after 3 months of age and persists through childhood. One-third of the bacterial taxa that vary across caregiver-identified racial categories in children are taxa reported to also vary between adults. Machine learning modeling of childhood microbiomes from 8 cohort studies (2,756 samples from 729 children) distinguishes racial and ethnic categories with 87% accuracy. Importantly, predictive genera are also among the top 30 most important taxa when childhood microbiomes are used to predict adult self-identified race and ethnicity. Our results highlight a critical developmental window at or shortly after 3 months of age when social and environmental factors drive race and ethnicity-associated microbiome variation and may contribute to adult health and health disparities.

A novel TOX3-WDR5-ABCG2 signaling axis regulates the progression of colorectal cancer by accelerating stem-like traits and chemoresistance.

The eradication of cancer stem cells (CSCs) with drug resistance confers the probability of local tumor control after chemotherapy or targeted therapy. As the main drug resistance marker, ABCG2 is also critical for colorectal cancer (CRC) evolution, in particular cancer stem-like traits expansion. Hitherto, the knowledge about the expression regulation of ABCG2, in particular its upstream transcriptional regulatory mechanisms, remains limited in cancer, including CRC. Here, ABCG2 was found to be markedly up-regulated in CRC CSCs (cCSCs) expansion and chemo-resistant CRC tissues and closely associated with CRC recurrence. Mechanistically, TOX3 was identified as a specific transcriptional factor to drive ABCG2 expression and subsequent cCSCs expansion and chemoresistance by binding to -261 to -141 segments of the ABCG2 promoter region. Moreover, we found that TOX3 recruited WDR5 to promote tri-methylation of H3K4 at the ABCG2 promoter in cCSCs, which further confers stem-like traits and chemoresistance to CRC by co-regulating the transcription of ABCG2. In line with this observation, TOX3, WDR5, and ABCG2 showed abnormal activation in chemo-resistant tumor tissues of in situ CRC mouse model and clinical investigation further demonstrated the comprehensive assessment of TOX3, WDR5, and ABCG2 could be a more efficient strategy for survival prediction of CRC patients with recurrence or metastasis. Thus, our study found that TOX3-WDR5/ABCG2 signaling axis plays a critical role in regulating CRC stem-like traits and chemoresistance, and a combination of chemotherapy with WDR5 inhibitors may induce synthetic lethality in ABCG2-deregulated tumors.

Principles of metabolome conservation in animals.

Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

Ants combine object affordance with latent learning to make efficient foraging decisions.

The affordance of an object refers to its functional properties. For example, a bowl has the affordance of holding water, but a sieve does not. Here, we report that ants learn the affordance of a novel object without this attribute being rewarded, and use the memory of this affordance to avoid predicted, but never experienced, crowding. Ants were trained to feeders, which could support either only one ant or many. Two feeders were encountered, each of identical design but differently scented. After training, on the outward journey, half the ants encounter nestmates, which had fed on food matching one of the training feeders. Encountering returning nestmates reduced preference for the feeder matching the scent of the encountered nestmates, but only for ants trained on a limited-access feeder; ants trained on an unlimited feeder were unaffected. In other words, only if ants know that the food access is limited, and receive information that this feeder is heavily visited, do they reduce their preference for this feeder. To achieve this, the ants had to combine memories of the feeders' affordance with the presence of nestmates. Then they had to use semantic knowledge that restricted food access combined with nestmate presence predicts a likelihood of crowding, or a rule such as "if the food is restricted and there are nestmates on the path, go to another food source." Regardless of the mechanism, these results demonstrate that ants latently learn the affordance of their surroundings, an unexpected cognitive ability for an invertebrate.

A critical analysis of plant science literature reveals ongoing inequities.

The field of plant science has grown dramatically in the past two decades, but global disparities and systemic inequalities persist. Here, we analyzed ~300,000 papers published over the past two decades to quantify disparities across nations, genders, and taxonomy in the plant science literature. Our analyses reveal striking geographical biases-affluent nations dominate the publishing landscape and vast areas of the globe have virtually no footprint in the literature. Authors in Northern America are cited nearly twice as many times as authors based in Sub-Saharan Africa and Latin America, despite publishing in journals with similar impact factors. Gender imbalances are similarly stark and show remarkably little improvement over time. Some of the most affluent nations have extremely male biased publication records, despite supposed improvements in gender equality. In addition, we find that most studies focus on economically important crop and model species, and a wealth of biodiversity is underrepresented in the literature. Taken together, our analyses reveal a problematic system of publication, with persistent imbalances that poorly capture the global wealth of scientific knowledge and biological diversity. We conclude by highlighting disparities that can be addressed immediately and offer suggestions for long-term solutions to improve equity in the plant sciences.

Navigating toward resilient and inclusive seed systems.

Food systems face new climatic and socioecological challenges and farmers need a diversity of new plant varieties to respond to these. While plant breeding is important, institutional innovations in seed systems are critical to ensure that new traits and varieties make their way into farmers' fields. This Perspective reviews the state of knowledge on seed system development, outlining insights emerging from the literature that can help navigate the way forward. We synthesize evidence on the contributions and limitations of the different actors, activities, and institutions pertaining to all seed systems smallholder farmers use, formal and informal. To do so, we structure our analysis on three functions-variety development and management, seed production, and seed dissemination-and two contextual factors-seed governance and food system drivers-that can be used to describe any seed system. Our review reveals the strengths and weaknesses of the activities of different actors along the entire chain of functions and demonstrates the multifaceted efforts to strengthen seed systems. We document that a new agenda for seed system development is taking root, based on the view that formal and farmers' seed systems are complementary. Because needs differ from crop to crop, farmer to farmer, and between agroecological and food system contexts, a variety of pathways are needed to ensure farmers' seed security. While the complexity of seed systems eludes a simple roadmap, we conclude by planting a "signpost" with principles to guide efforts to develop resilient and inclusive seed systems.

Diverse mathematical knowledge among indigenous Amazonians.

We investigate number and arithmetic learning among a Bolivian indigenous people, the Tsimane', for whom formal schooling is comparatively recent in history and variable in both extent and consistency. We first present a large-scale meta-analysis on child number development involving over 800 Tsimane' children. The results emphasize the impact of formal schooling: Children are only found to be full counters when they have attended school, suggesting the importance of cultural support for early mathematics. We then test especially remote Tsimane' communities and document the development of specialized arithmetical knowledge in the absence of direct formal education. Specifically, we describe individuals who succeed on arithmetic problems involving the number five-which has a distinct role in the local economy-even though they do not succeed on some lower numbers. Some of these participants can perform multiplication with fives at greater accuracy than addition by one. These results highlight the importance of cultural factors in early mathematics and suggest that psychological theories of number where quantities are derived from lower numbers via repeated addition (e.g., a successor function) are unlikely to explain the diversity of human mathematical ability.