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 performed comparative gene expression profiling across four rice accessions—from shoot apical meristem to primordia stage P5—to delineate developmental and photosynthetic transitions in leaf development. By integrating differential expression and gene regulatory network analyses, the authors identified stage-specific regulatory events and key transcription factors, such as RDD1, ARID2, and ERF3, especially in the wild rice Oryza australiensis, offering a comprehensive framework for optimizing leaf function.

The study examined how soil phosphorus and nitrogen availability influence wheat root-associated arbuscular mycorrhizal fungal (AMF) communities and the expression of mycorrhizal nutrient transporters. Field sampling across two years combined with controlled pot experiments showed that P and N jointly affect AMF colonisation, community composition (with Funneliformis dominance under high P), and regulation of phosphate, ammonium, and nitrate transporters. Integrating metabarcoding and RT‑qPCR provides a framework to assess AMF contributions to crop nutrition.

The study functionally characterizes three tomato CNR/FWL proteins (SlFWL2, SlFWL4, SlFWL5) and demonstrates that SlFWL5 localizes to plasmodesmata, where it regulates leaf size and morphology by promoting cell expansion likely through cell‑to‑cell communication. Gain‑ and loss‑of‑function transgenic tomato lines reveal that SlFWL5 is a key regulator of organ growth via modulation of plasmodesmatal signaling.

The study constructs a ~1‑million‑cell single‑nuclei transcriptome atlas of Arabidopsis leaves to reveal that drought stress accelerates transcriptional programs associated with maturation and aging, thereby limiting leaf growth in proportion to stress intensity. Targeted upregulation of FERRIC REDUCTION OXIDASE 6 in mesophyll cells partially rescues leaf growth under drought, demonstrating the functional relevance of these transcriptional changes.