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.

Six new Viola species and two reinstated species from China were identified using field surveys, detailed morphological comparison, and phylogenetic analysis of ITS and GPI gene sequences, placing them in section Plagiostigma subsect. Diffusae. The GPI data offered higher resolution, indicating complex relationships possibly due to ancient hybridization or incomplete lineage sorting, thereby clarifying species boundaries and evolutionary patterns in Chinese Viola.

The study reports the creation and validation of a high‑density Axiom SNP array for Solanum tuberosum, based on 10X Genomics sequencing of 108 diverse clones and integration of existing Illumina markers. The array demonstrated high reproducibility and, after filtering, provided 206,616 informative markers for population structure analysis, GWAS of polyphenol oxidase activity, and genomic prediction with accuracies up to 0.86.