The study introduces a hybrid modeling framework that integrates a logistic ordinary differential equation with a Long Short-Term Memory neural network to form a Physics-Informed Neural Network (PINN) for predicting wheat plant height. Using only time and temperature as inputs, the PINN outperformed other longitudinal growth models, achieving the lowest average RMSE and reduced variability across multiple random initializations. The results suggest that embedding biological growth constraints within data‑driven models can substantially improve prediction accuracy for plant traits.

The study used Arabidopsis thaliana mutants with low (vtc2, vtc4) and high (vtc2/OE-VTC2) ascorbate levels to examine how ascorbate concentration affects gene expression and cellular homeostasis. Transcriptomic analysis revealed that altered ascorbate levels modulate defense and stress pathways, and that TAA1/TAR2‑mediated auxin biosynthesis is required for coping with elevated ascorbate in a light‑dependent manner.

The authors adapted OpenPlant kit CRISPR/Cas9 tools to enable multiplex gRNA expression from a single transcript using tRNA sequences in the liverwort Marchantia polymorpha, markedly enhancing editing efficiency and scalability. They coupled this vector system with a simplified, optimized thallus transformation protocol, providing a rapid and versatile platform for generating CRISPR/Cas9 mutants and advancing functional genomics in this model species.