The study demonstrates that plasmodesmata‑located protein 5 (PDLP5) interacts with plasma membrane intrinsic proteins (PIPs) to inhibit H2O2 transport across the plasma membrane in Arabidopsis. Overexpression of PDLP5 reduces H2O2 uptake and diminishes H2O2‑induced root growth inhibition, whereas pdlp5 mutants show enhanced sensitivity, with PIP2;5 identified as a key target of this regulation.

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 authors compiled and standardized published data on Rubisco dark inhibition for 157 flowering plant species, categorizing them into four inhibition levels and analyzing phylogenetic trends. Their meta‑analysis reveals a complex, uneven distribution of inhibition across taxa, suggesting underlying chloroplast microenvironment drivers and providing a new resource for future photosynthesis improvement efforts.