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 used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

The study genotyped 1,013 hard red spring wheat lines using SNP arrays and targeted KASP markers to track changes in genetic diversity and the distribution of dwarfing Rht alleles over a century of North American breeding. It found shifts from Rht‑D1b to Rht‑B1b dominance, identified low‑frequency dwarf alleles at Rht24 and Rht25 that have increased recently, and revealed gene interactions that can fine‑tune plant height, along with evidence of recent selection for photoperiod sensitivity.

The study maps the spatial and temporal patterns of H3K4me1, H3K9me2, and phosphorylated RNA Polymerase II in male and female floral meristems of the dioecious plant Silene latifolia, revealing links between epigenetic marks, transcriptional activity, and sex determination. By employing AI‑assisted nuclei segmentation and high‑content imaging, a single‑cell resolution atlas was generated that highlights sex‑specific chromatin reprogramming, such as H3K9me2 association with pollen mother cell formation.

The study uncovers that dioecy in the plant Eurycorymbus cavaleriei is governed by two Y-linked mutations—a truncated D-class MADS-box allele (YUN{Delta}) and a novel sRNA locus (SUNMAO) that silences the X-linked SUN gene—forming an interlocking SUN‑KUN‑YUN regulatory module that balances male‑female developmental trade‑offs. These findings suggest a stepwise evolution of the XY sex‑determination system via linked mutations, translocation, and recombination‑suppressing inversions.

The study investigates the genetic basis of sex determination in Cannabis sativa, identifying a X‑chromosome locus (Monoecy1) that governs the switch between dioecy and monoecy. Transcriptomic and genomic analyses reveal three tightly linked genes with sex‑specific expression, suggesting their combined action controls both flower type and individual sex phenotype.

The study reveals that the dioecious monocot Dioscorea tokoro employs a male heterogametic (XY) sex-determination system with sex-determining regions on chromosome 3, including X- and Y-specific pericentromeric regions. Two Y-specific candidate genes, BLH9 (a homeobox protein) and HSP90 (a molecular chaperone), are identified as likely mediators of female organ suppression and pollen development, respectively, providing insight into the evolution of dioecy in plants.

A biparental Vicia faba mapping population was screened under glasshouse conditions for resistance to a mixture of Fusarium avenaceum and Fusarium oxysporum, revealing several families with moderate to high resistance. Using the Vfaba_v2 Axiom SNP array, a high-density linkage map of 6,755 SNPs was constructed, enabling the identification of a major QTL on linkage group 4 associated with partial resistance to foot and root rot.