The study examined nitrogen use strategies in the model alga Chlamydomonas reinhardtii by comparing growth on ammonium, nitrate, and urea, finding similar molar nitrogen utilization efficiency under saturating conditions. Rapid nitrogen uptake and storage were demonstrated through pulse experiments, and source‑specific transcriptome analysis revealed distinct regulation of assimilation pathways and transporters, supporting a model of flexible nitrogen acquisition and storage.

The study assessed how well common deep learning models (ResNet, EfficientNet, Inception, MobileNet) generalize across different tomato pest and disease image datasets. While models performed well on the dataset they were trained on, they suffered substantial accuracy drops when applied to other datasets, indicating that architectural changes alone cannot overcome dataset variability. The results highlight the necessity for more diverse, representative training data to improve real-world deployment of PPD diagnostic tools.

The study demonstrates that hyperspectral imaging can non‑destructively differentiate active nitrogen‑fixing root nodules from non‑fixing nodules and root tissue based on distinct spectral signatures. By integrating deep‑learning models, the authors created an automated nodule counting pipeline that works across multiple legume species and growth conditions, eliminating labor‑intensive manual counting and reliably detecting nodules within dense root systems.

The study investigates how maternal environmental conditions, specifically temperature and light intensity, influence seed longevity in eight Arabidopsis thaliana natural accessions. Seeds developed under higher temperature (27 °C) and high light showed increased longevity, with transcriptome analysis of the Bor-4 accession revealing dynamic changes in stored mRNAs, including upregulation of antioxidant defenses and raffinose family oligosaccharides. These findings highlight the genotype‑dependent modulation of seed traits by the maternal environment.

The study generated deep proteome and phosphoproteome datasets from guard cell‑enriched tissue to examine how phosphorylation regulates stomatal movements. Comparative analysis revealed increased phosphorylation of endomembrane trafficking and vacuolar proteins in closed stomata, supporting a role for phospho‑regulated trafficking in stomatal dynamics.

The study examined seed maturation and germination in the endangered legume Paubrasilia echinata using proteomic and polyamine analyses at 4, 6, and 8 weeks post-anthesis, identifying over 2,000 proteins and linking specific polyamines to developmental stages. Mature seeds (6 weeks) showed elevated proteasome components, translation machinery, LEA proteins, and heat shock proteins, while polyamine dynamics revealed putrescine dominance in early development and spermidine/spermine association with desiccation tolerance and germination. These findings uncover dynamic molecular shifts underlying seed development and provide insights for conservation and propagation.

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 provides a comprehensive proteomic analysis of seed mitochondria from white lupin, revealing fully assembled OXPHOS complexes ready for immediate energy production upon imbibition. Quantitative mass‑spectrometry identified 1,162 mitochondrial proteins, highlighting tissue‑specific transporter and dehydrogenase profiles and dynamic remodeling during early germination, while many uncharacterized proteins suggest novel legume‑specific functions.

The study introduces the Botanical Spectrum Analyzer (BSA), a GUI that incorporates a modified U‑Net deep neural network for accurate segmentation of plant images from RGB and hyperspectral (VNIR and SWIR) data. BSA was tested on wheat, barley, and Arabidopsis datasets, achieving >99% accuracy and F1‑scores above 98%, and markedly outperformed commercial tools on root segmentation tasks.

The study examined how prior light‑acclimation influences the fitness and rapid photoprotective reprogramming of Chlamydomonas during transitions between low and high diurnal light intensities. While high‑light‑acclimated cells struggled to grow and complete the cell cycle after shifting to low light, low‑light‑acclimated cells quickly remodeled thylakoid ultrastructure, enhanced photoprotective quenching, and altered photosystem protein levels, recovering chloroplast function within a single day. Transcriptomic and proteomic profiling revealed swift induction of stress‑response genes, indicating high flexibility in diurnal light acclimation.