Zhang and colleagues engineered the poly(A) tail as a programmable regulatory element, showing that embedding cell-type-specific microRNA target sites directly within it confers robust, position-dependent silencing in off-target tissues while preserving activity in target cells. This strategy offers a new modular tool to enhance mRNA therapeutic safety and precision.

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10485-9

Bright squeezed vacuum light boosts nonlinear atomic tunnelling ionization more than 20-fold compared with coherent light, enabling quantum control of strong-field processes without increasing classical intensity.

arXiv:2506.22039v2 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) have achieved remarkable success through large-scale pretraining. However, their design primarily targets real-valued series, limiting their ability to handle general forecasting tasks involving diverse and often heterogeneous covariates -- such as categorical variables and multimodal data (e.g., images, text) -- which are typically task-specific and difficult to leverage during pretraining. To address this gap, we propose Unified Covariate Adaptation (UniCA), a framework to bridge TSFMs with general covariate-aware forecasting. UniCA first performs covariate homogenization to transform heterogeneous covariates into high-level homogeneous series representations and then fuses them via a unified attention-based fusion mechanism. UniCA is compatible and universal for adaptation with both homogeneous and heterogeneous covariates, incorporating extra covariate information while preserving the generalization ability of TSFMs.Extensive experiments on multiple unimodal and multimodal covariate-aware forecasting benchmarks demonstrate the superiority of UniCA, highlighting the promise of covariate-aware TSFM adaptation in real-world forecasting scenarios.Code: https://github.com/hanlu-nju/UniCA.