Chiral superconductors are unconventional superconducting states that break time reversal symmetry spontaneously and typically feature Cooper pairing at non-zero angular momentum. Such states may host Majorana fermions and provide an important platform for topological physics research and fault-tolerant quantum computing1-7. Despite intensive search and prolonged studies of several candidate systems8-26, chiral superconductivity has remained elusive so far. Here we report the discovery of robust unconventional superconductivity in rhombohedral tetra- and penta-layer graphene without moiré superlattice effects. We observed two superconducting states in the gate-induced flat conduction bands with Tc up to 300 mK and charge density ne down to 2.4*1011 cm-2 in five devices. Spontaneous time-reversal-symmetry-breaking due to electron's orbital motion is found, and several observations indicate the chiral nature of these superconducting states, including: 1. In the superconducting state, Rxx shows magnetic hysteresis in varying out-of-plane magnetic field B⊥-absent from all other superconductors; 2. the superconducting states are robust against in-plane magnetic field and are developed within a spin- and valley-polarized quarter-metal phase; 3. the normal states show anomalous Hall signals at zero magnetic field and magnetic hysteresis. We also observed a critical B⊥ of 1.4 Tesla, higher than any graphene superconductivity and indicates a strong-coupling superconductivity close to the BCS-BEC crossover27. Our observations establish a pure carbon material for the study of topological superconductivity, with the promise to explore Majorana modes and topological quantum computing.

Every generation, the human genome is shuffled during meiosis and a single fertilized egg gives rise to all of the cells of the body1. Meiotic errors leading to chromosomal abnormalities are known causes of pregnancy loss2,3, but genetic aetiologies of euploid pregnancy loss remain largely unexplained4. Here we characterize sequence diversity in early pregnancy loss through whole-genome sequencing of 1,007 fetal samples and 934 parental samples from 467 trios affected by pregnancy loss (fetus, mother and father). Sequenced parental genomes enabled us to determine both the parental and meiotic origins of chromosomal abnormalities, detected in half of our set. It further enabled us to assess de novo mutations on both homologous chromosomes from parents transmitting extra chromosomes, and date them, revealing that 6.6% of maternal mutations occurred before sister chromatid formation in fetal oocytes. We find a similar number of de novo mutations in the trios affected by pregnancy loss as in 9,651 adult trios, but three times the number of pathogenic small (<50 bp) sequence variant genotypes in the loss cases compared with adults. Overall, our findings indicate that around 1 in 136 pregnancies is lost due to a pathogenic small sequence variant genotype in the fetus. Our results highlight the vast sequence diversity that is lost in early pregnancy.

Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive1. Recent advances in artificial intelligence (AI) have shown promise in improving both predictive performance and efficiency2,3, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. Aurora outperforms operational forecasts in predicting air quality, ocean waves, tropical cyclone tracks and high-resolution weather, all at orders of magnitude lower computational cost. With the ability to be fine-tuned for diverse applications at modest expense, Aurora represents a notable step towards democratizing accurate and efficient Earth system predictions. These results highlight the transformative potential of AI in environmental forecasting and pave the way for broader accessibility to high-quality climate and weather information.

To grow at distant sites, metastatic cells must overcome major challenges posed by the unique cellular and metabolic composition of secondary organs1. Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease that metastasizes to the liver and lungs. Despite evidence of metabolic reprogramming away from the primary site, the key drivers that dictate the ability of PDAC cells to colonize the liver or lungs and survive there remain undefined. Here we identified PCSK9 as predictive of liver versus lung colonization by integrating metastatic tropism data of human PDAC cell lines2, in vivo metastasis modelling in mice and gene expression correlation analysis. PCSK9 negatively regulates low density lipoprotein (LDL)-cholesterol import and, accordingly, PCSK9-low PDAC cells preferentially colonize LDL-rich liver tissue. LDL-cholesterol taken up by liver-avid PCSK9-low cells supports activation of pro-growth mTORC1 activation at the lysosome, and through conversion into the signalling oxysterol, 24(S)-hydroxycholesterol, reprogrammes the microenvironment to release nutrients from neighbouring hepatocytes. Conversely, PCSK9-high, lung-avid PDAC cells rely on transcriptional upregulation of the distal cholesterol synthesis pathway to generate intermediates-7-dehydrocholesterol and 7-dehydrodesmosterol-with protective action against ferroptosis, a vulnerability in the oxygen-rich microenvironment of the lung. Increasing the amount of PCSK9 redirected liver-avid cells to the lung whereas ablating PCSK9 drove lung-avid cells to the liver, thereby establishing PCSK9 as necessary and sufficient for secondary organ site preference. Our studies reveal PCSK9-driven differential utilization of the distal cholesterol synthesis pathway as a key and potentially actionable driver of metastatic growth in PDAC.

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.

Glioblastoma is the most common and aggressive primary brain cancer and shows minimal response to therapies. The immunosuppressive tumour microenvironment in glioblastoma contributes to the limited therapeutic response. Astrocytes are abundant in the central nervous system and have important immunoregulatory roles. However, little is known about their role in the immune response to glioblastoma1. Here we used single-cell and bulk RNA sequencing of clinical glioblastoma samples and samples from preclinical models, multiplexed immunofluorescence, in vivo CRISPR-based cell-specific genetic perturbations and in vitro mouse and human experimental systems to address this gap in knowledge. We identified an astrocyte subset that limits tumour immunity by inducing T cell apoptosis through the death receptor ligand TRAIL. Moreover, we identified that IL-11 produced by tumour cells is a driver of STAT3-dependent TRAIL expression in astrocytes. Astrocyte signalling through STAT3 and TRAIL expression were associated with a shorter time to recurrence and overall decreased survival in patients with glioblastoma. Genetic inactivation of the IL-11 receptor or TRAIL in astrocytes extended survival in mouse models of glioblastoma and enhanced T cell and macrophage responses. Finally, treatment with an oncolytic HSV-1 virus engineered to express a TRAIL-blocking single-chain antibody in the tumour microenvironment extended survival and enhanced tumour-specific immunity in preclinical models of glioblastoma. In summary, we establish that IL-11-STAT3-driven astrocytes suppress glioblastoma-specific protective immunity by inducing TRAIL-dependent T cell apoptosis, and engineered therapeutic viruses can be used to target this mechanism of astrocyte-driven tumour immunoevasion.