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 generated a dataset of 420 sgRNAs targeting promoters, exons, and introns of 137 tomato genes in protoplasts, linking editing efficiency to chromatin accessibility, genomic context, and sequence features. Open chromatin sites showed higher editing rates, while transcriptional activity had little effect, and a subset of guides produced near‑complete editing with microhomology‑mediated deletions. Human‑trained prediction models performed poorly, highlighting the need for plant‑specific guide design tools.

The study examined how elevated atmospheric CO₂ (550 ppm) affects immunity in the C₄ cereal maize (Zea mays L.) by exposing plants grown under ambient and elevated CO₂ to a range of pathogens. Elevated CO₂ increased susceptibility to sugarcane mosaic virus, decreased susceptibility to several bacterial and fungal pathogens, and left susceptibility to others unchanged, with reduced bacterial disease linked to heightened basal immune responses. These findings provide a baseline for future investigations into CO₂‑responsive defense mechanisms in C₄ crops.

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 provides a comprehensive genome-wide catalog and single‑cell expression atlas of the carbonic anhydrase (CA) gene family in maize, identifying 18 CA genes across α, β, and γ subfamilies and detailing their structural and regulatory features. Phylogenetic, synteny, promoter motif, bulk tissue RNA‑seq, and single‑cell RNA‑seq analyses reveal distinct tissue and cell‑type specific expression patterns, highlighting β‑CAs as key players in C4 photosynthesis and γ‑CAs in ion/pH buffering, and propose cell‑type‑specific CA genes as targets for improving stress resilience.

The study integrates genome, transcriptome, and chromatin accessibility data from 380 soybean accessions to dissect the genetic and regulatory basis of symbiotic nitrogen fixation (SNF). Using GWAS, TWAS, eQTL mapping, and ATAC-seq, the authors identify key loci, co‑expression modules, and regulatory elements, and validate the circadian clock gene GmLHY1b as a negative regulator of nodulation via CRISPR and CUT&Tag. These resources illuminate SNF networks and provide a foundation for soybean improvement.

The study integrated genetic architecture derived from maize GWAS into phenotypic simulations of hybrid populations, using ≥200 top GWAS hits and adjusting marker effect sizes, which increased the correlation between simulated and empirical trait data across environments (r = 0.397–0.915). These informed simulations produced realistic trait distributions and genomic prediction results that closely matched empirical observations, demonstrating improved utility for digital breeding programs.

The study compared physiological, ion‑balance, and metabolic responses of two maize inbred lines—salt‑sensitive C68 and salt‑tolerant NC326—under salinity stress. Untargeted metabolomics identified 56 metabolites and, together with genetic analysis, linked 10 candidate genes to key protective metabolites, revealing constitutive and inducible mechanisms of salt tolerance.

The study models maize flowering time plasticity using a physiological reaction norm derived from multi-environment trial data, revealing genotype-specific differences in temperature-driven development and photoperiod perception. It introduces an envirotyping metric that shows genotypes can experience markedly different photoperiods even within the same environment, and demonstrates distinct adaptive strategies between tropical and temperate germplasm.

The study generated a temporal physiological and metabolomic map of leaf senescence in diverse maize inbred lines differing in stay‑green phenotype, identifying 84 metabolites associated with senescence and distinct metabolic signatures between stay‑green and non‑stay‑green lines. Integration of metabolite data with genomic information uncovered 56 candidate genes, and reverse‑genetic validation in maize and Arabidopsis demonstrated conserved roles for phenylpropanoids such as naringenin chalcone and eriodictyol in regulating senescence.