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 employed ultra large‑scale 2D clinostats to grow tomato (Solanum lycopersicum) plants beyond the seedling stage under simulated microgravity and upright control conditions across five sequential trials. Simulated microgravity consistently affected plant growth, but the magnitude and direction of the response varied among trials, with temperature identified as a significant co‑variant; moderate heat stress surprisingly enhanced growth under simulated microgravity. These results highlight the utility of large‑scale clinostats for dissecting interactions between environmental factors and simulated microgravity in plant development.

The study investigated metabolic responses of kale (Brassica oleracea) grown under simulated microgravity using a 2-D clinostat versus normal gravity conditions. LC‑MS data were analyzed with multivariate tools such as PCA and volcano plots to identify gravity‑related metabolic adaptations and potential molecular markers for spaceflight crop health.

The study examined how soil phosphorus and nitrogen availability influence wheat root-associated arbuscular mycorrhizal fungal (AMF) communities and the expression of mycorrhizal nutrient transporters. Field sampling across two years combined with controlled pot experiments showed that P and N jointly affect AMF colonisation, community composition (with Funneliformis dominance under high P), and regulation of phosphate, ammonium, and nitrate transporters. Integrating metabarcoding and RT‑qPCR provides a framework to assess AMF contributions to crop nutrition.

The study investigated unexpected leaf spot symptoms in Psa3‑resistant kiwifruit (Actinidia) germplasm, finding that Psa3 was detectable by qPCR and metabarcoding despite poor culturing. Metabarcoding revealed distinct bacterial community shifts in lesions versus healthy tissue, and whole‑genome sequencing identified diverse Pseudomonas spp. that, while not individually more pathogenic, could enhance Psa3 growth, suggesting pathogenic consortia on resistant hosts.