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 genotyped 1,013 hard red spring wheat lines using SNP arrays and targeted KASP markers to track changes in genetic diversity and the distribution of dwarfing Rht alleles over a century of North American breeding. It found shifts from Rht‑D1b to Rht‑B1b dominance, identified low‑frequency dwarf alleles at Rht24 and Rht25 that have increased recently, and revealed gene interactions that can fine‑tune plant height, along with evidence of recent selection for photoperiod sensitivity.

The study generated a phenotypic dataset for 550 Lactuca accessions, including 20 wild relatives, and applied an iterative two‑step GWAS using a jointly processed SNP set for cultivated lettuce (L. sativa) and its wild progenitor (L. serriola) to dissect trait loci. Known and novel QTLs for anthocyanin accumulation, leaf morphology, and pathogen resistance were identified, with several L. serriola‑specific QTLs revealing unique genetic architectures, underscoring the breeding value of wild lettuce species.

The study examined natural variation in leaf shape and linked functional-physiological traits of Chenopodium hircinum grown in a common garden, finding that leaf morphology correlates with the climate of population origin while functional traits associate directly with leaf shape. Landmark-based morphometric analysis identified a shape axis (deeply lobed versus rounded) linked to leaf mass per area and stomatal conductance, indicating morphology mediates a resource-use continuum and highlighting the importance of phenotypic plasticity for ecological adaptation.

A biparental Vicia faba mapping population was screened under glasshouse conditions for resistance to a mixture of Fusarium avenaceum and Fusarium oxysporum, revealing several families with moderate to high resistance. Using the Vfaba_v2 Axiom SNP array, a high-density linkage map of 6,755 SNPs was constructed, enabling the identification of a major QTL on linkage group 4 associated with partial resistance to foot and root rot.