The study identifies two maize genes, Discordia3a and Discordia3b, that encode the microtubule‑severing protein KATANIN. Loss‑of‑function allele combinations reduce microtubule severing, impair cell elongation, delay mitotic entry, and disrupt preprophase band and nuclear positioning, leading to dwarfed, misshapen plants.

The study evaluates the use of single-cell RNA sequencing (scRNA-seq) data to predict plant metabolic pathway genes (MPGs) in Arabidopsis thaliana, comparing five multi-label machine‑learning algorithms against traditional bulk RNA‑seq approaches. scRNA‑seq generated co‑expression networks that, while different, yielded significantly higher MPG classification accuracy, especially when data were split by genetic background or tissue type, and deep learning outperformed classical methods. The authors conclude that scRNA‑seq offers superior predictive power and should be incorporated into future MPG discovery pipelines.

The study identified lineage-specific long non‑coding RNAs (lncRNAs) from the aphid‑specific Ya gene family in Rhopalosiphum maidis and R. padi, demonstrating that these Ya lncRNAs are secreted into maize, remain stable, and move systemically. RNA interference of Ya genes reduced aphid fecundity, while ectopic expression of Ya lncRNAs in maize enhanced aphid colonization, indicating that Ya lncRNAs act as cross‑kingdom effectors that influence aphid virulence.

The study used a computer‑vision phenotyping pipeline (EarVision.v2) based on Faster R-CNN to map Ds‑GFP mutant kernels on maize ears and a statistical framework (EarScape) to assess spatial patterns of allele transmission from the apex to the base. They found that alleles causing pollen‑specific transmission defects often show significant spatial biases, whereas Mendelian alleles do not, indicating that reduced pollen fitness can shape the spatial distribution of progeny genotypes in Zea mays.

The study investigates how the pleiotropic maize genes GRASSY TILLERS1 (GT1) and RAMOSA3 (RA3) are differentially regulated to suppress axillary meristems and floral organs, using a newly developed high-throughput quantitative phenotyping method for grass flowers. Distinct environmental mechanisms were found to control each suppression process, and upstream regulatory pathways of GT1 and RA3 have diverged, illustrating how ancient developmental genes can be redeployed to increase genetic pleiotropy during evolution.

The study integrated weekly morphophysiological measurements with high-density genotyping-by-sequencing data and a machine‑learning pipeline to dissect flowering time variation in diverse Cannabis sativa landraces. By applying mutual information, recursive feature elimination, random forest, and support vector machine classifiers to over 234,000 combined genetic, phenotypic, and environmental features, the authors identified 53 key markers that classify early, medium, and late flowering types with 96.6% accuracy. Notable loci, including CsFT3 and CsCFL1, were highlighted as promising targets for breeding and smart‑crop strategies.

The study reveals that the maize catalytic trehalose-6-phosphate phosphatase RA3 interacts with the non‑catalytic TPS ZmTPS1, and together with the catalytic TPS ZmTPS14 they form a protein complex that enhances enzymatic activity. Genetic analyses show that mutations in ZmTPS1 and its paralog ZmTPS12 exacerbate ra3 branching phenotypes, while loss of the catalytic TPSs ZmTPS11 and ZmTPS14 causes embryonic lethality, indicating essential and regulatory roles for both catalytic and non‑catalytic TPS/TPP proteins in plant development.

Using CRISPR‑Cas9‑generated Zmcry mutants, the study shows that maize cryptochromes redundantly mediate blue‑light signaling, suppress mesocotyl elongation, and enhance UV‑B stress tolerance by upregulating genes for phenylpropanoid, flavonoid, and fatty‑acid pathways. Blue light also promotes epidermal wax accumulation, and ZmCRY1 directly interacts with GLOSSY2 in a light‑dependent manner to drive C32 aldehyde synthesis, linking cryptochrome activity to wax biosynthesis and UV‑B resistance.

The study used extensive gravimetric load‑cell and ambient sensor data collected over seven years from hundreds of greenhouse-grown crops to train machine‑learning models for predicting daily whole‑plant transpiration. Random Forest and XGBoost achieved the highest accuracy (R² up to 0.89), with ambient temperature identified as the dominant driver. These results highlight the promise of ML‑based tools for precise agricultural water management.

Metagenomic pool sequencing of infected maize leaves was used to monitor the population dynamics of the fungal pathogen Exserohilum turcicum, revealing a recent shift from local clonal lineages to tropical Kenyan lineages in a Swiss agricultural region. The novel leaf‑pooling approach enabled cost‑effective, large‑scale sampling, while phyllobiome analyses showed consistent microbial communities across maize varieties.