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 investigates how cortical microtubule orientations are coordinated across tissues, testing the hypothesis that they sense mechanical stress. By quantifying microtubule behavior on different cell faces and edges, the authors find that tissue-level biases arise from cell‑geometric factors and edge‑specific mechanisms rather than direct stress sensing, and they develop a combinatorial model incorporating face‑specific and edge‑specific microtubule dynamics to explain the observed coordination.

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 isolated the Plant Cysteine Oxidase/Ethylene Response Factor VII oxygen‑sensing circuit from Arabidopsis thaliana and reconstituted it in Saccharomyces cerevisiae, using a reporter to compare hypoxia‑induced transcriptional dynamics in yeast and plants. Both systems showed rapid ERFVII stabilization, but plants exhibited a larger response, which could be enhanced in yeast by adding a hypoxia‑inducible feedback loop. Computational modeling identified promoter competition and hypoxia‑inducible PCOs as key determinants of early hypoxia responses.

The study investigates the altered timing of the core circadian oscillator gene ELF3 in wheat compared to Arabidopsis, revealing that dawn-specific expression in wheat arises from repression by TOC1. An optimized computational model integrating experimental expression data and promoter architecture predicts that wheat’s circadian oscillator remains robust despite this shift, indicating flexibility in plant circadian network design.