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.

arXiv:2502.14043v3 Announce Type: replace-cross Abstract: Most learning algorithms with formal regret guarantees essentially rely on trying all possible behaviors, which is problematic when some errors cannot be recovered from. Instead, we allow the learning agent to ask for help from a mentor and to transfer knowledge between similar states. We show that this combination enables the agent to learn both safely and effectively. Under standard online learning assumptions, we provide an algorithm whose regret and number of mentor queries are both sublinear in the time horizon for Markov decision processes with irreversible dynamics and infinite state spaces. Our proof involves a sequence of three reductions, making our result more general than a single algorithm. Conceptually, our result may be the first formal proof that it is possible for an agent to obtain high reward while becoming self-sufficient in an unknown, unbounded, and high-stakes environment without resets.

Nature Biotechnology, Published online: 02 September 2026; doi:10.1038/s41587-026-03294-y

DNA recombination in rice is optimized by engineering genomic attachment sites.