Low-temperature stress (TS) limits maize (Zea mays L.) seed germination and agricultural production. Exposure to TS during germination inhibits radicle growth, triggering seedling emergence disorders. Here, we aimed to analyse the changes in gene expression in the radicles of maize seeds under TS by comparing Demeiya1 (DMY1) and Zhengdan958 (ZD958) (the main Northeast China cultivars) and exposing them to two temperatures: 15 °C (control) and 5 °C (TS). TS markedly decreased radicle growth as well as fresh and dry weights while increasing proline and malondialdehyde contents in both test varieties. Under TS treatment, the expression levels of 5301 and 4894 genes were significantly different in the radicles of DMY1 and ZD958, respectively, and 3005 differentially expressed genes coexisted in the radicles of both varieties. The phenylpropanoid biosynthesis pathway was implicated within the response to TS in maize radicles, and peroxidase may be an important indicator for assessing low-temperature tolerance during maize germination. Peroxidase-encoding genes could be important candidate genes for promoting low-temperature resistance in maize germinating radicles. We believe that this study enhances the knowledge of mechanisms of response and adaptation of the maize seed germination process to TS and provides a theoretical basis for efficiently assessing maize seed low-temperature tolerance and improving maize adversity germination performance.

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.