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.

The study examined how white lupin (Lupinus albus) cotyledons mobilize nitrogen and minerals during early seedling growth under nitrogen‑deficient conditions, revealing that 60 % of stored proteins degrade within eight days and are redirected to support development. Proteomic analyses showed dynamic shifts in nutrient transport, amino acid metabolism, and stress responses, and premature cotyledon removal markedly impaired growth, highlighting the cotyledon's essential role in nutrient supply and transient photosynthetic activity.