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 used untargeted metabolomics and QTL mapping in a tomato recombinant inbred line population to characterize root exudate composition and identify genetic loci controlling specific metabolites. It reveals domestication-driven changes in exudate profiles and links metabolic QTLs with previously reported microbial QTLs, suggesting a genetic basis for shaping the root microbiome.

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.

The study benchmarked several sampling approaches for simultaneous profiling of root exudates and rhizosphere microbiota in soil-grown barley, revealing consistent exudate chemistry across methods but variation in root morphology and nitrogen exudation. High‑throughput amplicon sequencing and quantitative PCR showed protocol‑specific impacts on microbial composition, yet most rhizosphere-enriched microbes were captured by all approaches. The authors conclude that different protocols provide comparable integrated data, though methodological differences must be aligned with experimental objectives.

The study combined ecometabolomics of root exudates with fungal community profiling to assess how abiotic (soil moisture, temperature legacy) and biotic (microbial inoculum, plant density) treatments shape metabolite diversity and fungal assemblages in Guarea guidonia seedlings. While soil microbial legacy and moisture drove metabolite diversity, antimicrobial treatments altered metabolite composition, and fungal community structure was linked to metabolite profiles, revealing metabolite‑fungal associations as early indicators of plant response to disturbance.

The study investigated how plant roots promote water infiltration through dry soil layers using dye tracing in model soil microcosms. Results indicate that dissolved root exudates, possibly by altering surface tension, are the primary drivers of infiltration, with root architecture also contributing. These insights suggest that root traits influencing exudation and structure could improve drought resistance in crops.

The study examined how altering ethylene biosynthesis (ACO1) or perception (etr1.1) in a hybrid poplar (P. tremula × P. tremuloides T89) influences the assembly of root and shoot fungal and bacterial communities, using amplicon sequencing and confocal microscopy. Ethylene modulation had limited impact on the sterile plant metabolome but triggered distinct primary and secondary metabolic changes in microbe‑colonized plants, correlating with reduced fungal colonisation of shoots and increased root fungal colonisation, while arbuscular mycorrhizal fungi and bacterial communities were largely unchanged.