The researchers performed a large‑scale field trial with 105 wheat (Triticum aestivum) genotypes inoculated by Fusarium culmorum, combining quantitative deoxynivalenol (DON) profiling and untargeted metabolomics to uncover molecular signatures of infection. Sesquiterpene‑derived metabolites tracked toxin accumulation, whereas glycosylated diterpene conjugates were enriched in low‑DON samples, indicating a potential defensive metabolic pathway.

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 used comparative transcriptomics of dorsal and ventral petals across development, alongside expression profiling in floral symmetry mutants, to identify genes linked to dorsal (AmCYC-dependent) and ventral (AmDIV-dependent) identities in Antirrhinum majus. In situ hybridisation validated axis‑specific and boundary‑localized expression patterns, revealing that a conserved NGATHA‑LIKE1‑BRASSINAZOLE‑RESISTANT1‑miR164 module has been co‑opted to regulate AmDIV targets and shape the corolla. These findings delineate regulatory modules coordinating dorsoventral and proximal‑distal patterning in zygomorphic flowers.

The study combined high-throughput image-based phenotyping with genome-wide association studies to uncover the genetic architecture of tolerance to the spittlebug Aeneolamia varia in 339 interspecific Urochloa hybrids. Six robust QTL were identified for plant damage traits, explaining up to 21.5% of variance, and candidate genes linked to hormone signaling, oxidative stress, and cell‑wall modification were highlighted, providing markers for breeding.

The study investigates how the timing of the vegetative phase change (VPC) in Arabidopsis thaliana influences drought adaptation, revealing strong genotype-by-environment interactions that create stage-specific fitness tradeoffs. Genotypes from warmer, drier Iberian climates transition earlier, and genome-wide association mapping identifies loci linked to VPC timing and drought response, with several candidates validated using T‑DNA insertion lines.

The study sequenced genomes of ericoid mycorrhiza‑forming liverworts and experimentally reconstituted the symbiosis, revealing a nutrient‑regulated state that supports intracellular colonization. Comparative transcriptomics identified an ancestral gene module governing intracellular symbiosis, and functional validation in Marchantia paleacea through genetic manipulation, phylogenetics, and transactivation assays confirmed its essential role. The findings suggest plants have retained and independently recruited this ancestral module for diverse intracellular symbioses.

The study assessed how well common deep learning models (ResNet, EfficientNet, Inception, MobileNet) generalize across different tomato pest and disease image datasets. While models performed well on the dataset they were trained on, they suffered substantial accuracy drops when applied to other datasets, indicating that architectural changes alone cannot overcome dataset variability. The results highlight the necessity for more diverse, representative training data to improve real-world deployment of PPD diagnostic tools.

The study investigates the wheat Pm3 NLR allelic series, revealing that near-identical Pm3d and Pm3e alleles confer broad-spectrum resistance by recognizing multiple, structurally diverse powdery mildew effectors. Using chimeric NLR constructs, the authors pinpoint specificity-determining polymorphisms and demonstrate that engineered combinations of Pm3d and Pm3e further expand effector recognition, showcasing the potential for durable wheat protection through NLR engineering.

The study demonstrates that hyperspectral imaging can non‑destructively differentiate active nitrogen‑fixing root nodules from non‑fixing nodules and root tissue based on distinct spectral signatures. By integrating deep‑learning models, the authors created an automated nodule counting pipeline that works across multiple legume species and growth conditions, eliminating labor‑intensive manual counting and reliably detecting nodules within dense root systems.

The study introduces the Botanical Spectrum Analyzer (BSA), a GUI that incorporates a modified U‑Net deep neural network for accurate segmentation of plant images from RGB and hyperspectral (VNIR and SWIR) data. BSA was tested on wheat, barley, and Arabidopsis datasets, achieving >99% accuracy and F1‑scores above 98%, and markedly outperformed commercial tools on root segmentation tasks.