Much of the human genome’s non-protein-coding fraction acts directly through RNA, yet the structural and functional roles encoded in these sequences remain poorly understood. Applying deep learning is hindered by scarce RNA structural data and it remains unclear what biological constraints such models can recover directly from the abundant RNA sequences alone. Here, to address these challenges, we developed NucleicBERT, a self-supervised masked-language model that learns contextual representations from single sequences without evolutionary information. Explainable artificial intelligence analyses show that the model organizes RNA sequences in latent space and encodes structural properties indicating that biologically meaningful constraints are learned from sequence correlations alone. When fine-tuned for downstream structural and functional tasks, NucleicBERT requires only single sequences while matching or exceeding current RNA prediction models. This alignment-free framework addresses the scarcity of annotated 3D RNA data while providing a rapid, computational complement to experimental techniques. By bridging abundant unlabelled sequence data with scarce structural annotations, NucleicBERT advances RNA structure prediction and informs how large language models encode biological information. RNA structure and function are hard to infer because annotations are scarce, despite abundant sequence data. Upadhyay et al. trained a self-supervised model on large-scale RNA data that derives biologically meaningful patterns from sequence correlations.
While classical neural operators have transformed the solving of partial differential equations, developing efficient quantum counterparts remains challenging due to processing overheads and theoretical gaps. Specifically, existing quantum machine learning paradigms often demand prohibitive qubit scaling or deep circuits, rendering them impractical for near-term hardware. Here, to bridge this gap, we propose QuanONet, a quantum neural operator tailored for the noisy intermediate-scale quantum era. Theoretically, we extend the universal approximation theorem to the quantum domain for continuous nonlinear operators. Departing from conventional views relying on exponential Hilbert spaces, we prove that the architecture’s density matrix implicitly constructs a quadratic feature frame. We establish that, for operators, this implicit frame yields an $${\mathcal{O}}({p}^{2})$$ expressivity bound, circumventing the $${\mathcal{O}}(p)$$ linear capacity limits of matched classical models. To overcome the depth cost of spectrum alignment, we introduce a trainable-frequency (TF-QuanONet) strategy that adaptively spaces base frequencies to capture relatively high frequencies without parameter inflation. Extensive benchmark experiments demonstrate that TF-QuanONet notably outperforms quantum baselines and achieves competitive accuracy against classical frameworks under strictly matched-parameter conditions. Furthermore, in high-dimensional scaling regimes (p → 256), the architecture exhibits superior optimization robustness, consistently converging to the intrinsic error floor where classical baselines suffer from high variance. The physical deployment on IBM quantum processors validates its functional resilience on near-term hardware as a qualitative proof of concept. Wang et al. introduce a hardware-efficient quantum neural operator that overcomes classical linear capacity limits. Using an implicit quadratic frame, it offers accelerated expressivity for solving differential equations in the noisy intermediate-scale quantum era.
Representational alignment can reveal similarities between human brain activity and language models. Work now demonstrates that it can also guide learning, improving the reliability of artificial reasoning.
Clinical prediction models often underperform in deployment populations that differ from historical training cohorts because of covariate shifts, but outcome-based updating is infeasible when deployment outcomes are unavailable. This challenge is further amplified when the target training cohort has limited samples or sparse events. Here we propose CoxRTL, a transfer learning framework with deployment-covariate recalibration for survival prediction under covariate shift. CoxRTL borrows information from external cohorts to improve estimation, uses transferability screening and debiasing to mitigate external effect heterogeneity, and embeds density-ratio weighting into transfer estimation, yielding a distribution-adaptive weighted partial likelihood that aligns historical training cohorts with the deployment-covariate distribution. We evaluated CoxRTL in simulations and two cohorts. Simulations showed improved prediction under training–deployment covariate shift. Using early National Health and Nutrition Examination Survey data, we developed prognostic models for 20 chronic diseases in older adults and evaluated their performance in later survey cohorts. CoxRTL achieved higher discrimination than models trained only in older adults (median C-index improvement, 0.056; range, 0.016–0.126) or in the full population (0.042; 0.013–0.107). It also improved calibration, clinical utility and risk stratification, while identifying variables with greater predictive contribution across diseases. Independent replication in the Shanghai Suburban Adult Cohort and Biobank confirmed robustness under geographic and temporal shifts. These findings support CoxRTL as a feasible strategy for developing prediction models for prespecified deployment populations when target training data are limited. Pan et al. present CoxRTL, a recalibrated transfer learning strategy that leverages external cohorts to improve survival prediction under covariate shift when target training data are limited and deployment outcomes are unavailable.
Artists are often recognizable through collections of distinctive patterns (‘fingerprints’) in their work. Identifying such traits has important applications in authorship attribution, education, cultural heritage research and historical analysis. Here we focus on music, a domain with a rich tradition of theoretical and mathematical analysis. We train a variety of supervised learning models to identify 20 iconic jazz musicians from a curated dataset of 84 h of recordings. In particular, we introduce a multi-input architecture that represents four musical domains separately: melody, harmony, rhythm and dynamics. This design allows us to accurately identify individual performers (our best model obtains 94% accuracy across 20 classes) and to examine which musical elements most strongly distinguish between individual artists. We release open-source implementations of our models and an accompanying web application for exploring our results. Cheston et al. develop a machine learning pipeline that identifies 20 iconic jazz pianists from audio recordings with up to 94% accuracy, revealing how melody, harmony, rhythm and dynamics shape each performer’s individual musical fingerprint.
