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 authors developed a high‑throughput, semi‑automated image analysis pipeline that couples a Blackbird CNC microscopy robot with YOLO11‑based computer‑vision models to identify life stages of the two‑spotted spider mite across multiple host plants. The three‑class detection model achieved >87% precision and recall, enabling reliable fecundity measurements for resistance breeding, though mortality assessment was less accurate at high mite densities or on unseen host backgrounds.

The study examines how ectopic accumulation of methionine in Arabidopsis thaliana leaves, driven by a deregulated AtCGS transgene under a seed‑specific promoter, reshapes metabolism, gene expression, and DNA methylation. High‑methionine lines exhibit increased amino acids and sugars, activation of stress‑hormone pathways, and reduced expression of DNA methyltransferases, while low‑methionine lines show heightened non‑CG methylation without major transcriptional changes. Integrated transcriptomic and methylomic analyses reveal a feedback loop linking sulfur‑carbon metabolism, stress adaptation, and epigenetic regulation.

Using genome‑wide association studies in Arabidopsis thaliana, the authors identified the chromatin‑associated protein CDCA7 as a trans‑regulator that specifically controls CG methylation (mCG) and TE silencing. CDCA7 and its paralog CDCA7β bind the remodeler DDM1, modulating its activity without broadly affecting non‑CG methylation or histone variant deposition, and natural variation in CDCA7 regulatory sequences correlates with local ecological adaptation.

The study demonstrates that the chromatin remodeler DDM1 is essential for biomass heterosis in Arabidopsis thaliana hybrids, as loss of DDM1 function leads to reduced rosette growth and extensive genotype‑specific transcriptomic and DNA methylation changes. Whole‑genome bisulfite sequencing revealed widespread hypomethylation in ddm1 mutants, while salicylic acid levels were found unrelated to heterosis, indicating that epigenetic divergence, rather than SA signaling, underpins hybrid vigor.

The study reveals that leaf wounding in Arabidopsis triggers localized cooling and activation of cold-responsive genes, with the subsequent loss of cooling marking wound healing. It identifies CBF transcription factors as mediators of this cooling signal and introduces a computer‑vision and deep‑learning workflow to quantitatively monitor healing dynamics.

The study shows that the SnRK1 catalytic subunit KIN10 directs tissue-specific growth‑defense programs in Arabidopsis thaliana by reshaping transcriptomes. kin10 knockout mutants exhibit altered root transcription, reduced root growth, and weakened defense against Pseudomonas syringae, whereas KIN10 overexpression activates shoot defense pathways, increasing ROS and salicylic acid signaling at the cost of growth.

The study combined long‑read whole‑genome assembly, multi‑omics profiling (DNA methylation, mRNA, ribosome‑associated transcripts, tRNA abundance, and protein levels) in two Arabidopsis thaliana accessions to evaluate how genomic information propagates through the Central Dogma. Codon usage in gene sequences emerged as the strongest predictor of both mRNA and protein abundance, while methylation, tRNA levels, and ribosome‑associated transcripts contributed little additional information under stable conditions.

The Global Wheat Dataset Consortium released a comprehensive semantic segmentation dataset (GWFSS) of wheat organs across developmental stages, comprising 1,096 fully annotated images and 52,078 unannotated images from 11 institutions. Models based on DeepLabV3Plus and Segformer were trained, with Segformer achieving ≈90% mIoU for leaves and spikes but lower precision (54%) for stems, while also enabling weed exclusion and discrimination of necrotic, senescent, and residue tissues.

The study performed a comprehensive computational analysis of the Arabidopsis thaliana proteome, classifying 48,359 proteins by melting temperature (Tm) and melting temperature index (TI) and linking thermal stability to amino acid composition, molecular mass, and codon usage. Machine‑learning and evolutionary analyses revealed that higher molecular mass and specific codon pairs correlate with higher Tm, and that gene duplication has driven the evolution of high‑Tm proteins, suggesting a genomic basis for stress resilience.