Reactive oxygen species (ROS) are an important part of adaptation to biotic and abiotic stresses and regulate seed germination through positive or negative signaling. Seed adaptation to abiotic stress may be mediated by hydrogen peroxide (H₂O₂). The effects of the ROS scavenger N,N′-dimethylthiourea (DMTU) on maize seed germination through endogenous H₂O₂ regulation is unclear. In this study, we investigated the effects of different doses of DMTU on seed endogenous H₂O₂ and radicle development parameters using two maize varieties (ZD958 and DMY1). The inhibitory effect of DMTU on the germination rate and radicle growth was dose-dependent. The inhibitory effect of DMTU on radicle growth ceased after transferring maize seeds from DMTU to a water medium. Histochemical analyses showed that DMTU eliminated stable H₂O₂ accumulation in the radicle sheaths and radicles. The activity of antioxidant enzyme and the expression of antioxidant enzyme-related genes (ZmAPX2 and ZmCAT2) were reduced in maize seeds cultured with DMTU compared with normal culture conditions (0 mmol·dm⁻³ DMTU). We suggest the use of 200 mmol·dm⁻³ DMTU as an H₂O₂ scavenger to study the ROS equilibrium mechanisms during the germination of maize seeds, assisting in the future with the efficient development of plant growth regulators to enhance the seed germination performance of test maize varieties under abiotic stress.

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.

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.

Background: Low-temperature stress significantly restricts maize germination, seedling growth and development, and yield formation. However, traditional methods of evaluating maize seedling quality are inefficient. This study established a method of grading maize seedling quality based on phenotypic extraction and deep learning. Methods: A pot experiment was conducted using different low-temperature combinations and treatment durations at six different stages between the sowing and seedling phases. Changes in 27 seedling quality indices, including plant morphology and photosynthetic performance, were investigated 35 d after sowing and seedling quality grades were classified based on maize yield at maturity. The 27 quality indices were extracted, and a total of 3623 sample datasets were obtained and grouped into training and test sets in a 3:1 ratio. A convolutional neural network-based grading method was constructed using a deep learning model. Results: The model achieved an average precision of 98.575%, with a recall and F1-Score of 98.7% and 98.625%, respectively. Compared with the traditional partial least squares and back propagation neural network, the model improved recognition accuracy by 8.1% and 4.19%, respectively. Conclusions: This study provided an accurate grading of maize seedling quality as a reference basis for the standardized production management of maize in cold regions.

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.