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 evaluated a transgenic soybean line (VPZ-34A) expressing Arabidopsis VDE, PsbS, and ZEP for combined improvements in light‑use efficiency and carbon assimilation under ambient and elevated CO2 in a FACE experiment. While VPZ‑34A showed enhanced maximum quantum efficiency of PSII under fluctuating light, it did not increase carbon assimilation efficiency or yield, and transcriptome analysis revealed limited gene expression changes. The results suggest that VPZ‑mediated photosynthetic gains are insufficient to boost productivity under elevated CO2.

The study examined how polyploidy, hybridization, and incomplete lineage sorting affect phylogenetic reconstructions in the genus Packera, evaluating several published paralog‑processing pipelines. Results showed that the choice of orthology and paralog handling methods markedly altered tree topology, time‑calibrated phylogenies, biogeographic histories, and detection of ancient reticulation, underscoring the need for careful methodological selection alongside comprehensive taxon sampling.