The study tracked molecular changes in plastoglobules and thylakoids of Zea mays B73 during heat stress and recovery, revealing increased plastoglobule size, number, and adjacent lipid droplets over time. Proteomic and lipidomic analyses uncovered up‑regulation of specific plastoglobule proteins and alterations in triacylglycerol, plastoquinone derivatives, and phytol esters, suggesting roles in membrane remodeling and oxidative defense. These insights highlight plastoglobule‑associated pathways as potential targets for enhancing heat resilience in maize.

The study used an interpretable convolutional neural network to predict ABA responsiveness from proximal promoter sequences in Arabidopsis thaliana, revealing both known ABF-binding motifs and novel regulatory elements. Model performance was boosted by advanced data augmentation, and predicted regulatory regions were experimentally validated using reporter lines, confirming the inferred cis‑regulatory code.

The study identifies the serine/threonine protein kinase CIPK14/SNRK3.15 as a regulator of sulfate‑deficiency responses in Arabidopsis thaliana seedlings, with mutants showing diminished early adaptive and later salvage responses under sulfur starvation. While snrk3.15 mutants exhibit no obvious phenotype under sufficient sulfur, the work provides a novel proteomic dataset comparing wild‑type and mutant seedlings under sulfur limitation.

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 presents GenoRetriever, an interpretable deep learning framework trained on STRIPE-seq data from soybean and other crops, that predicts transcription start site locations and usage by identifying 27 core promoter motifs. Validation using in silico motif insertions, saturation mutagenesis, and CRISPR‑Cas9 promoter editing demonstrates high predictive accuracy and reveals domestication‑driven motif usage shifts and lineage‑specific effects. The tool is provided via a web server for promoter analysis and design, offering a new resource for plant functional genomics and crop improvement.

The study examined how white lupin (Lupinus albus) cotyledons mobilize nitrogen and minerals during early seedling growth under nitrogen‑deficient conditions, revealing that 60 % of stored proteins degrade within eight days and are redirected to support development. Proteomic analyses showed dynamic shifts in nutrient transport, amino acid metabolism, and stress responses, and premature cotyledon removal markedly impaired growth, highlighting the cotyledon's essential role in nutrient supply and transient photosynthetic activity.

The study characterizes the protein composition of extracellular vesicles (EVs) secreted by the oomycete Phytophthora infestans, revealing enrichment of transmembrane proteins and RxLR effectors, while EV-independent secretions are dominated by cell wall–modifying enzymes. Two MARVEL‑domain proteins, PiMDP1 and PiMDP2, are identified as EV-associated markers that co‑localize with RxLR effectors, with PiMDP2 specifically accumulating at the haustorial interface during early infection, suggesting a role in effector delivery.

The study provides an in‑depth proteomic characterization of brewer's spent grain (BSG) and tracks proteome dynamics during malting and mashing, revealing that 29% of identified proteins change in abundance and that B3‑Hordein dominates the BSG protein pool. BSG contains a high proportion of intracellular proteins and over 45% of its proteins are potential allergens or antinutritional factors, underscoring the need for targeted downstream processing to create safe, functional food ingredients.

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 introduces an in-soil fiber Bragg grating (FBG) sensing system that continuously records three-dimensional strain from growing pseudo-roots, enabling non‑destructive monitoring of root architecture. Using two ResNet models, the system predicts root width and depth with over 90% accuracy, and performance improves to 96‑98% after retraining on data from actual corn (Zea mays) roots over a 30‑day period. This prototype demonstrates potential for scalable, real‑time root phenotyping and broader soil environment sensing.