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 reveals that each individual plant possesses a statistically unique leaf appearance that can be discriminated using convolutional neural network (CNN) based deep learning, enabling a "plant face" recognition concept. Applications demonstrated include distinguishing leaves from the same species/cultivar, analyzing leaflet positional patterns on compound leaves, assessing bilateral symmetry, and detecting morphological differences linked to stem chirality, highlighting the encoding of genetic, environmental, and developmental information in leaf morphology.

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 evaluated whether integrating genomic, transcriptomic, and drone-derived phenomic data improves prediction of 129 maize traits across nine environments, using both linear (rrBLUP) and nonlinear (SVR) models. Multi-omics models consistently outperformed single-omics models, with transcriptomic data especially enhancing cross‑environment predictions and capturing genotype‑by‑environment interactions. The results highlight the added value of combining transcriptomics and phenomics with genotypes for more accurate and generalizable trait prediction in maize.

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 used Arabidopsis thaliana mutants with low (vtc2, vtc4) and high (vtc2/OE-VTC2) ascorbate levels to examine how ascorbate concentration affects gene expression and cellular homeostasis. Transcriptomic analysis revealed that altered ascorbate levels modulate defense and stress pathways, and that TAA1/TAR2‑mediated auxin biosynthesis is required for coping with elevated ascorbate in a light‑dependent manner.

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.