A neural network approach bridging Lagrangian and Eulerian perspectives transforms sparse observations into physically accurate, high-resolution maps of complex and chaotic fluid motions.
Knowledge-constrained network inference enables the reconstruction of regulatory programmes from high-dimensional biological data, yet the conditions under which shared and context-specific structures can be reliably identified remain poorly understood. Pablo Rodriguez-Mier et al. recently introduced CORNETO, a framework for multi-sample constrained inference. Here we reproduce, evaluate and extend CORNETO in terms of robustness, identifiability and bias–variance trade-offs. Using dynamic haematopoiesis and stable cortical datasets, we systematically assess four network reconstruction paradigms while preserving the original method-specific evaluation procedures and benchmark definitions. We show that multi-sample inference generally improves robustness and reproducibility, although gains in predictive performance depend on biological context and the completeness of the prior network. To enhance stability, we introduce bootstrap aggregation and selective re-expansion strategies that improve consensus robustness and recovery while preserving context-specific signals without modifying the underlying optimization scheme or sacrificing generality. Across datasets and methods, our results reveal distinct inference regimes governed by sample availability, prior knowledge and regularization. Together, these findings position network reconstruction as a regime-dependent learning problem and provide practical principles for robust multi-sample biological network inference. This work studies the reusability of CORNETO, a previously developed framework for learning biological networks from multi-omics data. Robust network inference is found to depend on data availability, prior knowledge, regularization and biological context.
Medical image quality is essential for reliable clinical diagnosis and downstream artificial intelligence analysis, yet routine images are often degraded by noise, motion artefacts, low contrast, low-dose acquisition or modality-specific artefacts. Here we present AMIFound, an all-in-one foundation model for medical image enhancement and restoration across computed tomography, magnetic resonance imaging, positron emission tomography, endoscopy, ultrasound, fundus imaging and X-ray. AMIFound is trained and evaluated on 3.76 million paired high-quality and degraded images spanning 7 representative degradation patterns. A shared encoder captures modality and degradation cues, while a dynamically routed expert restoration pool adapts the enhancement pathway for each input. Across 7 modalities, AMIFound outperforms 38 task-specific and generalist baselines on full-reference image quality metrics, clinician assessment and robustness tests under combined degradations as well as on naturally degraded clinical images. Enhanced images further improve 44 disease detection tasks, tumour stage prediction and survival risk stratification in non-small-cell lung cancer, including improved individual-level prognostic ranking. These results support AMIFound as a domain-specific foundation model for improving low-quality clinical images and strengthening downstream medical artificial intelligence workflows while preserving clinically relevant information. Cao et al. present AMIFound, a foundation model that unifies medical image restoration across seven imaging modalities, delivering robust enhancement across diverse degradations and preserving clinically relevant information for diagnosis.
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.
