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 performs a bibliometric analysis of 1,702 Scopus-indexed tomato omics publications over two decades, revealing a rapid surge in output after 2017 and highlighting dominant fields such as biochemistry, genetics, and molecular biology. Citation and co‑authorship network analyses identify key contributions in microRNA research and genome sequencing, major research hubs, and collaborative clusters, while keyword mapping underscores stress response, fruit quality, and immunity as priority topics.