Using ten Phaeodactylum tricornutum mutant strains with graded constitutive Lhcx1 expression, the study links NPQ induction under high light to physiological outcomes (oxidized QA, increased cyclic electron flow) and extensive transcriptomic reprogramming, affecting nearly half the genome. The approach demonstrates that higher NPQ mitigates PSII damage, boosts ATP production for repair, and drives distinct gene regulatory networks, providing a model framework for dissecting photosynthetic and gene expression integration.

The study establishes a tractable system using the large bloom-forming diatom Coscinodiscus granii and its natural oomycete parasite Lagenisma coscinodisci, enabling manual isolation of single host cells and stable co-cultures. High‑quality transcriptomes for both partners were assembled, revealing diverse oomycete effectors and a host transcriptional response involving proteases and exosome pathways, while also profiling the co‑occurring heterotrophic flagellate Pteridomonas sp. This tripartite platform provides a unique marine model for dissecting molecular mechanisms of oomycete‑diatom interactions.

The study evaluated a transgenic soybean line (VPZ-34A) expressing Arabidopsis VDE, PsbS, and ZEP for combined improvements in light‑use efficiency and carbon assimilation under ambient and elevated CO2 in a FACE experiment. While VPZ‑34A showed enhanced maximum quantum efficiency of PSII under fluctuating light, it did not increase carbon assimilation efficiency or yield, and transcriptome analysis revealed limited gene expression changes. The results suggest that VPZ‑mediated photosynthetic gains are insufficient to boost productivity under elevated CO2.

PlantCV v4 is an open-source Python framework that simplifies image-based plant phenotyping by providing extensive tutorials and streamlined installation, enabling users with limited coding skills to automate trait extraction. The release adds support for fluorescence, thermal, and hyperspectral imaging and introduces a new subpackage for morphological measurements such as leaf angle, which is validated against manual data collection methods.

The study combined high-throughput image-based phenotyping with genome-wide association studies to uncover the genetic architecture of tolerance to the spittlebug Aeneolamia varia in 339 interspecific Urochloa hybrids. Six robust QTL were identified for plant damage traits, explaining up to 21.5% of variance, and candidate genes linked to hormone signaling, oxidative stress, and cell‑wall modification were highlighted, providing markers for breeding.

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 investigates the role of the chromatin regulator MpSWI3, a core subunit of the SWI/SNF complex, in the liverwort Marchantia polymorpha. A promoter mutation disrupts male gametangiophore development and spermiogenesis, causing enhanced vegetative propagation, and transcriptomic analysis reveals that MpSWI3 regulates genes controlling reproductive initiation, sperm function, and asexual reproduction, highlighting its ancient epigenetic role in balancing vegetative and reproductive phases.

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 examined drought tolerance across nine provenances of the conifer Pinus nigra using high‑throughput phenotyping combined with metabolomic and transcriptomic analyses under controlled soil‑drying conditions. Drought tolerance, measured by the decline in Fv/Fm, varied among provenances but was not linked to a climatic gradient and was independent of growth, with tolerant provenances showing distinct flavonoid and diterpene profiles and provenance‑specific gene expression patterns. Integrating phenotypic and molecular data revealed metabolic signatures underlying drought adaptation in this non‑model conifer.

The study examined how the bioinoculant Trichoderma afroharzianum T22 influences Pisum sativum growth under iron-sufficient versus iron-deficient conditions, finding pronounced benefits—enhanced photosynthesis, Fe/N accumulation, and stress‑related gene expression—only during iron deficiency. RNA‑seq revealed distinct gene expression patterns tied to symbiosis, iron transport, and redox pathways, and microbiome profiling showed T22 reshapes the root bacterial community under deficiency, suggesting context‑dependent mutualism.