Browse Articles

Oral Infigratinib Therapy in Children with Achondroplasia

How China can become a biotechnology superpower

‘One of the true final frontiers’ — Sally Ride biopic highlights the struggle of gay astronauts

Could a novelty indicator improve science?

Freeze frame: cracking molecular motion

Transparent peer review to be extended to all of Nature’s research papers

I can’t show my face because of pangolin poachers

Minuscule worms form living towers to hunt for food

A long-predicted cosmic collision might not happen after all

Hungry caterpillars can brew exotic molecules in their guts

A pangenome reference of wild and cultivated rice

Abyssal seafloor as a key driver of ocean trace-metal biogeochemical cycles

Abstract Trace elements and isotopes (TEIs) are important to marine life and are essential tools for studying ocean processes1. Two different frameworks have arisen regarding marine TEI cycling: reversible scavenging favours water-column control on TEI distributions2–5, and seafloor boundary exchange emphasizes sedimentary imprints on water-column biogeochemistry6,7. These two views lead to disparate interpretations of TEI behaviours8–10. Here we use rare earth elements and neodymium isotopes as exemplar tracers of particle scavenging11 and boundary exchange6,7,12. We integrate these data with models of particle cycling and sediment diagenesis to propose a general framework for marine TEI cycling. We show that, for elements with greater affinity for manganese oxide than biogenic particles, scavenging is a net sink throughout the water column, contrary to a common assumption for reversible scavenging3,13. In this case, a benthic flux supports increasing elemental concentrations with water depth. This sedimentary source consists of two components: one recycled from elements scavenged by water-column particles, and another newly introduced to the water column through marine silicate weathering inside sediment8,14,15. Abyssal oxic diagenesis drives this benthic source, and exerts a strong influence on water-column biogeochemistry through seafloor geometry and bottom-intensified turbulent mixing16,17. Our findings affirm the role of authigenic minerals, often overshadowed by biogenic particles, in water-column cycling18, and suggest that the abyssal seafloor, often regarded as inactive, is a focus of biogeochemical transformation19,20.

Trace elements in the ocean attributed to a surprising source

A cancer-causing mutation meets its match

PRDM16-dependent antigen-presenting cells induce tolerance to gut antigens

Abstract The gastrointestinal tract is continuously exposed to foreign antigens in food and commensal microorganisms with potential to induce adaptive immune responses. Peripherally induced T regulatory (pTreg) cells are essential for mitigating inflammatory responses to these agents1–4. Although RORγt+ antigen-presenting cells (APCs) have been shown to programme gut microbiota-specific pTreg cells5–7, their definition remains incomplete, and the APC responsible for food tolerance has remained unknown. Here we identify an APC subset that is required for differentiation of both food- and microbiota-specific pTreg cells and for establishment of oral tolerance. Development and function of these APCs require expression of the transcription factors PRDM16 and RORγt, as well as a unique Rorc(t) cis-regulatory element. Gene expression, chromatin accessibility, and surface marker analysis establish the pTreg-inducing APCs as myeloid in origin, distinct from type 3 innate lymphoid cells, and sharing epigenetic profiles with classical dendritic cells, and designate them PRDM16+RORγt+ tolerizing dendritic cells (tolDCs). Upon genetic perturbation of tolDCs, we observe a substantial increase in food antigen-specific T helper 2 cells in lieu of pTreg cells, leading to compromised tolerance in mouse models of asthma and food allergy. Single-cell analyses of freshly resected mesenteric lymph nodes from a human organ donor, as well as multiple specimens of human intestine and tonsil, reveal candidate tolDCs with co-expression of PRDM16 and RORC and an extensive transcriptome shared with tolDCs from mice, highlighting an evolutionarily conserved role across species. Our findings suggest that a better understanding of how tolDCs develop and how they regulate T cell responses to food and microbial antigens could offer new insights into developing therapeutic strategies for autoimmune and allergic diseases as well as organ transplant tolerance.

Multigenerational cell tracking of DNA replication and heritable DNA damage

Abstract Cell heterogeneity is a universal feature of life. Although biological processes affected by cell-to-cell variation are manifold, from developmental plasticity to tumour heterogeneity and differential drug responses, the sources of cell heterogeneity remain largely unclear1,2. Mutational and epigenetic signatures from cancer (epi)genomics are powerful for deducing processes that shaped cancer genome evolution3–5. However, retrospective analyses face difficulties in resolving how cellular heterogeneity emerges and is propagated to subsequent cell generations. Here, we used multigenerational single-cell tracking based on endogenously labelled proteins and custom-designed computational tools to elucidate how oncogenic perturbations induce sister cell asymmetry and phenotypic heterogeneity. Dual CRISPR-based genome editing enabled simultaneous tracking of DNA replication patterns and heritable endogenous DNA lesions. Cell lineage trees of up to four generations were tracked in asynchronously growing cells, and time-resolved lineage analyses were combined with end-point measurements of cell cycle and DNA damage markers through iterative staining. Besides revealing replication and repair dynamics, damage inheritance and emergence of sister cell heterogeneity across multiple cell generations, through combination with single-cell transcriptomics, we delineate how common oncogenic events trigger multiple routes towards polyploidization with distinct outcomes for genome integrity. Our study provides a framework to dissect phenotypic plasticity at the single-cell level and sheds light onto cellular processes that may resemble early events during cancer development.

Vanadium-Mediated Carbon Isotope Exchange of Terminal Alkenes

Machine learning-based drought prediction using Palmer Drought Severity Index and TerraClimate data in Ethiopia

Accurate drought prediction is essential for proactive water management and agricultural planning, especially in regions like Ethiopia that are highly susceptible to climate variability. This study investigates the classification of the Palmer Drought Severity Index (PDSI) using machine learning models trained on TerraClimate data, incorporating variables such as precipitation, temperature, soil moisture, and vapor pressure deficit. We employed several classifiers Logistic Regression, Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Naive Bayes, AdaBoost, and XGBoost with Logistic Regression serving as a baseline statistical approach for comparison. To address data imbalance across drought classes, we applied a hybrid resampling method combining manual upsampling and SMOTE. Hyperparameter tuning was conducted using grid search and cross-validation. Random Forest outperformed all models, achieving an accuracy of 71.18%, F1-score of 0.71, and ROC AUC of 0.9000. Gradient Boosting and SVM also performed well with ROC AUC values of 0.8982 and 0.8681, respectively. SHAP analysis revealed that soil moisture, precipitation, and vapor pressure deficit were the most influential features in predicting drought severity. For benchmarking, an ARIMA (3,1,2) time-series model was applied but yielded poor performance (RMSE = 1.789, R² = −0.077), confirming the advantages of non-linear machine learning techniques for complex climate data. The results highlight the utility of ensemble learning in environmental modelling, offering valuable insights for drought early warning systems and climate resilience planning in Ethiopia. Future work should explore integrating localized predictors and real-time data to enhance prediction robustness.

Ultrasound-Triggered Activation of <i>p</i>-Azidobenzyloxycarbonyl-Based Prodrugs via Radical-Mediated Cascade Elimination

A comprehensive crop suitability assessment under modern irrigation system in arid croplands

Agricultural suitability analysis using traditional methods is still arguable due to the uncertainty and subjectivity resulting from manual evaluations. The current work provides a novel framework for integrating the analytical hierarchy process (AHP) with fuzzy logic under the geographic information system (GIS) platform to generate suitability maps for cultivating wheat, broad bean, and maize under center pivot irrigation systems. The research was executed in an arid region (30229 ha) in the western Nile Delta fringes, Egypt. Meteorological data, digital elevation model, and samples collected from seventy soil profiles and fourteen artesian wells were analyzed to characterize local climate conditions, landscape characteristics, and irrigation water quality. The main and sub-criteria were prone to AHP to specify the relative importance (weight) of each factor. Using GIS tools, raster layers were created, assigned scores (fuzzy membership functions) according to crop requirements, and complied in accordance with the weighted sum algorithm to produce final crop suitability maps. Results revealed that climate conditions were highly (S1) and moderately (S2) suitable for winter crops (wheat and broad bean) but marginally suitable (S3) for summer crop (maize). Soil salinity, sodicity, and depth were the most important determinants of landscape suitability. Accordingly, the land resources in the studied region were suitable (S1, S2, and S3) for the selected crops; nevertheless, 193 and 275 ha were currently not suitable (N1) for broad bean and maize, respectively. Potential salinity and specific ion (sodium and chloride) toxicity hazards were the main constraints for groundwater irrigation. The center pivot irrigation would meet wheat and maize requirements but adversely affect broad bean yield. Overall, groundwater quality contributed to 46% of site suitability for crop production followed by landscape factors that contributed to 42% and climate conditions that accounted for 13%. The final suitability maps affirmed high priority for wheat cultivation in the studied region since the S1 and S2 classes encompassed 90 and 10%, respectively. Moreover, maize ranked as the second suitable crop with 55, 42, and 35 of the total area fitting the S1, S2, and S3 classes, respectively. The third place was due to broad bean with S2 and S3 classes representing 53 and 47% of the total area, respectively. Our study can offer a replicable framework to integrate AHP with GIS-fuzzy logic for sustainable food crop production in drylands.