The NAC family of transcription factors includes no apical meristem (NAM), Arabidopsis thaliana transcription activator 1/2 (ATAF1/2), and cup-shaped cotyledon (CUC2) proteins, which are unique to plants, contributing significantly to their adaptation to environmental challenges. In the present study, we observed that the PvNAC52 protein is predominantly expressed in the cell membrane, cytoplasm, and nucleus. Overexpression of PvNAC52 in Arabidopsis strengthened plant resilience to salt, alkali, osmotic, and ABA stresses. PvNAC52 significantly (p < 0.05) reduced the degree of oxidative damage to cell membranes, proline content, and plant water loss by increasing the expression of MSD1, FSD1, CSD1, POD, PRX69, CAT, and P5CS2. Moreover, the expression of genes associated with abiotic stress responses, such as SOS1, P5S1, RD29A, NCED3, ABIs, LEAs, and DREBs, was enhanced by PvNAC52 overexpression. A yeast one-hybrid assay showed that PvNAC52 specifically binds to the cis-acting elements ABRE (abscisic acid-responsive elements, ACGTG) within the promoter. This further suggests that PvNAC52 is responsible for the transcriptional modulation of abiotic stress response genes by identifying the core sequence, ACGTG. These findings provide a theoretical foundation for the further analysis of the targeted cis-acting elements and genes downstream of PvNAC52 in the common bean.

This study aimed to develop an industrially viable method for rapid detoxification of raw kidney beans (Phaseolus vulgaris L.) while enhancing nutritional properties. Through optimized 5 min water soaking combined with intermittent microwaving (500 W, 5 min), we achieved significant reductions in key antinutrients, namely phytic acid (43–49%), tannins (74–90%), and saponins (59–68%)—all below safety thresholds—concurrently elevating antioxidant capacity (e.g., Ferric Reducing Antioxidant Power: +66–115%) across four colored varieties. Metabolomic analysis of 412 identified metabolites revealed substantial accumulation of key antioxidants including glutathione and quercetin derivatives. Pathway analysis demonstrated dual mechanisms: (1) Detoxification via activated phenylpropanoid biosynthesis degrading tannin precursors and glutathione metabolism reducing phytate; (2) Nutrient enrichment through upregulated phenolic biosynthesis and color-specific flavonoid/betalain pathways. This integrated approach achieves comparable detoxification to 30 min boiling in just 5 min, establishing water-assisted microwave processing as an efficient strategy for industrial-scale production of safer, nutrient-enhanced legumes.

The Northeast region in China is a major maize-producing area; however, low-temperature stress (TS) limits maize (Zea mays L.) seed germination, affecting population establishment and yield. In order to systematically explore the regulation mechanism of maize radicle which is highly sensitive to low-temperature environment response to TS, seeds of ZD958 and DMY1 were used to investigate germination responses under 15 °C (control) and 5 °C (TS) conditions. Phenotypic, physiological, transcriptomic, and metabolomic analyses were conducted on the radicles after 48 h of TS treatment. TS caused reactive oxygen species (ROS) imbalance and oxidative damage in radicle cells, inhibiting growth and triggering antioxidant defenses. Integrated transcriptomic and metabolomic analyses revealed that flavonoid metabolism may play a pivotal role in radicle responses to TS. Compared with the control treatment, ZD958 and DMY1 under TS treatment significantly increased (p < 0.01) the total flavonoid content, total antioxidant capacity, 4-coumarate-CoA ligase activity, and dihydroflavonol 4-reductase activity by 15.99% and 16.01%, 18.41% and 18.54%, 63.54% and 31.16%, and 5.09% and 7.68%, respectively. Despite genotypic differences, both followed a shared regulatory logic of “low-temperature signal-driven—antioxidant redirection—functional synergy.” This enabled ROS scavenging, redox balance, and antioxidant barrier formation, ensuring basal metabolism and radicle development.

Maize, the world’s most widely cultivated food crop, is critical in global food security. Low temperatures significantly hinder maize seedling growth, development, and yield formation. Efficient and accurate assessment of maize seedling quality under cold stress is essential for selecting cold-tolerant varieties and guiding field management strategies. However, existing evaluation methods lack a multimodal approach, resulting in inefficiencies and inaccuracies. This study combines phenotypic extraction technologies with a convolutional neural network–long short-term memory (CNN–LSTM) deep learning model to develop an advanced grading system for maize seedling quality. Initially, 27 quality indices were measured from 3623 samples. The RAGA-PPC model identified seven critical indices: plant height (x₁), stem diameter (x₂), width of the third spreading leaf (x₁₁), total leaf area (x₁₂), root volume (x₁₇), shoot fresh weight (x₂₂), and root fresh weight (x₂₃). The CNN–LSTM model, leveraging CNNs for feature extraction and LSTM for temporal dependencies, achieved a grading accuracy of 97.57%, surpassing traditional CNN and LSTM models by 1.28% and 1.44%, respectively. This system identifies phenotypic markers for assessing maize seedling quality, aids in selecting cold-tolerant varieties, and offers data-driven support for optimising maize production. It provides a robust framework for evaluating seedling quality under low-temperature stress.