Alfalfa (Medicago sativa L.) seed production in arid regions is significantly limited by water scarcity. However, achieving optimal irrigation management remains challenging due to intricate interactions between water supply, environmental conditions, and yield components. This study aimed to assess the impact of diverse irrigation treatments on alfalfa seed yield and develop an unmanned aerial vehicle (UAV)-based monitoring system that integrates machine learning and SHapley Additive exPlanations (SHAP) to support future precision irrigation strategies. A three-year field experiment (2023-2025) was conducted in the arid region of Northwest China, applying nine irrigation treatments ranging from 1200 to 3000 m & sup3; /ha. Multispectral images were captured using a DJI Mavic 3 Multispectral UAV at four crucial growth stages (branching, budding, flowering, and pod-setting) across five flight altitudes (12-100 m). A wide range of predictors, including spectral vegetation indices, RGB-derived color, and texture features, along with meteorological variables, were employed to predict seed yield through four machine learning algorithms: Random Forest, Support Vector Regression (SVR), Ridge Regression, and XGBoost. The results showed that treatment T3 (3000 m & sup3;/ha) achieved the highest seed yield (1309 kg/ha), while T7 (1200 m & sup3;/ha) displayed the highest irrigation water use efficiency (0.8 kg/m & sup3;), indicating a significant trade-off between yield maximization and water use efficiency. Structural equation modeling revealed complex yield formation processes, with florets per inflorescence having the largest negative direct impact on seed yield (beta = -0.49, P < 0.001). The machine learning models delivered consistent predictive performance, achieving R & sup2; values of 0.77-0.79 when utilizing integrated features. The optimal configuration was identified at a 20 m flight altitude and the combination of flowering and pod-setting stages (P3 + P4). SHAP analysis revealed that growing season temperature was the most influential predictor (scaled |SHAP| = 1.00), presenting strong negative correlations with yield (Tmin: r = -0.77; Tmax: r = -0.69). A complementary single-feature permutation analysis confirmed this dominance: permuting growing-season maximum temperature alone reduced cross-validated R & sup2; from 0.76 to 0.28, exceeding any UAV-derived feature by nearly an order of magnitude. Because pod-setting features fall outside the actionable window for irrigation decisions, SHAP analysis was further restricted to the budding (P2) and flowering (P3) stages, when irrigation interventions remain effective, to identify critical physiological thresholds during the budding (ExG < 0.251, NMDI < 0.341) and flowering stages (ExG < 0.262, LAB_L < 36.705, HSV_S > 0.377), demonstrating consistent temporal trends across years. By coupling causal inference (SEM), predictive interpretation (SHAP), and convergent permutation evidence, this framework distinguishes predictive from actionable features in alfalfa seed-production irrigation management. In summary, this study provides a robust scientific basis for transitioning from fixed irrigation schedules to adaptive, precision irrigation strategies guided by real-time monitoring of alfalfa physiological status through UAV remote sensing.

Drought stress severely restricts seed germination and seedling establishment in oat (Avena sativa). In this study, we synthesized a novel nanocomposite priming agent, 6-benzylaminopurine-loaded mesoporous silica nanoparticles (6-BA@MSNs), and investigated its effects on drought tolerance and the underlying regulatory mechanisms. Multiple analytical techniques, including transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FT-IR), were used to confirm the successful and stable loading of 6-BA onto MSNs. The synthesized composite exhibited a uniform spherical morphology (ca.55 nm) and good colloidal stability. Germination assays showed that 6-BA@MSNs restored oat germination percentage to levels comparable to those of the non-stressed control and significantly improved root length, seedling length, and fresh weight compared with free 6-BA, MSNs, or their physical mixture treatments. Physiological analyses demonstrated that 6-BA@MSNs alleviated oxidative damage by restoring the activities of drought-inhibited antioxidant enzymes (CAT, POD, and SOD), scavenging excess reactive oxygen species (H2O2 and O2 & sdot;-), reducing MDA accumulation, and improving chlorophyll accumulation and osmotic adjustment capacity. Transcriptomic analysis further revealed that 6-BA@MSNs coordinately regulated the expression of drought-responsive genes by upregulating POD70 to enhance reactive oxygen species (ROS) scavenging, downregulating POD2 and POD4 to alleviate excessive lignification and structural constraints on seedling growth, and upregulating the photosystem II-related genes PsbQ and PsbC to maintain photosynthetic efficiency, thereby supporting seedling development. These findings demonstrate that 6-BA@MSNs effectively integrates the hormonal regulatory effects of 6-BA with the priming advantages and structural protective functions of MSNs, thereby synergistically improving oat seed vigor and drought tolerance. The sustained-release properties of the composite reduced the required dosage of 6-BA and improved its utilization efficiency, highlighting its considerable potential as a sustainable nano-priming strategy for crop production.

Boron, an essential micronutrient, plays a crucial role in plant reproductive processes. Boron deficiency is widespread in many regions and significantly reduces alfalfa (Medicago sativa) seed yield, yet the molecular mechanisms underlying its impact on reproductive organ development remain elusive. This study found that boron deficiency inhibited alfalfa pollen tube elongation and was accompanied by elevated reactive oxygen species (ROS) levels in the pollen tube. The application of exogenous hydrogen peroxide (H2O2) and ascorbic acid (AsA) indicated that maintaining ROS homeostasis was crucial for boron-mediated pollen tube elongation. Based on phylogenetic tree analysis and expression pattern analysis, MsWRKY49 was identified as a central regulator responding to low boron. Transgenic analysis showed that mswrky49 mutants alleviated the pollen tube phenotype and reduced ROS accumulation under low boron conditions, while overexpression of MsWRKY49 in alfalfa pollen tubes led to boron sensitivity declining and ROS accumulation. Furthermore, RNA-Seq analysis revealed that several antioxidant-related genes were significantly downregulated in the MsWRKY49-OE lines. Yeast one-hybrid and luciferase reporter assays demonstrated that MsWRKY49 suppressed the expression of MsAPX1, MsGSTU19, MsNADH and MsCu/Zn-SOD, thereby potentially leading to ROS overaccumulation and inhibiting pollen tube elongation. Our results reveal that MsWRKY49 inhibits pollen tube elongation under low-boron conditions by promoting ROS accumulation, providing a new perspective for understanding the molecular mechanism by which boron deficiency causes the decline of seed yield in alfalfa.

Spikelets play a crucial role in photosynthesis during seed formation. This study used two oat (Avena sativa) varieties with significantly different lemma colors, "Challenger" from Canada and "Qinghai444" from China, as experimental materials. Phenotypic, physiological, proteomic, and transcriptional analyses were conducted on oat glumes, lemmas, and paleas after nitrogen application during the grain-filling stage. Results indicated that glumes outperformed lemmas in photosynthetic efficiency. After nitrogen application, "Challenger" glumes exhibited increased stomatal area but decreased chlorophyll a content, maximum photochemical efficiency of photosystem II (Fv/fm), and the quantum yield of photosystem II in steady state (ΦPSII). Concurrently, chloroplast membrane structure was repaired, and the expression of CAO, PsbR, and genes encoding chlorophyll protein complexes (LHCs) was upregulated, enhancing net photosynthetic rate (Pn) and photosynthetic capacity. Conversely, "Qinghai444" glumes showed decreased stomatal area but increased chlorophyll a content, Fv/fm, ΦPSII, and non-photochemical quenching (NPQ). The chloroplast structure of glumes was improved, whereas that of the lemmas was damaged. The CP47 subunit of photosystem II (PSII) accumulated on the thylakoid lamella, and the expression of petA, PsbB, and PsbR genes was upregulated, with no change in Pn or photosynthetic capacity. This study revealed that photosynthetic responses to nitrogen varied among oat varieties and spikelet tissues, with "Challenger" showing more pronounced enhancements. The findings of this study elucidate the patterns of photosynthetic responses to nitrogen in oat spikelets, guiding nitrogen fertilizer use and supporting the breeding of high-yielding oat varieties.

The high fat content in oat seeds makes them susceptible to aging during storage, leading to reduced seed vigor, delayed germination, and even seed death. Much evidence suggests that lipid remodeling is closely associated with successful seed germination. However, the dynamic behavior and response mechanisms of lipids during the germination of aged oat seeds remain unclear. In this study, ‘Monida’ (aging-tolerant) and ‘Haywire’ (aging-sensitive), were used to investigate the lipid profiles in the embryo and endosperm and the dynamic transcriptomic differences in the embryo during the germination. The results demonstrate that phospholipid alterations during the germination of aged seeds are more significant compared to unaged seeds, indicating that aging affects lipid remodeling during germination, particularly in the ‘Haywire’. Further analysis revealed that the most critical lipid response events occurred at the end of germination stage II (32 h) in embryo, primarily regulated through the PLC-DGK pathway to modulate phospholipid and glycerolipid molecules. Specifically, transcripts of PLC, DGK, and DGAT were upregulated, promoting the generation of diacylglycerol (DG) from various phospholipids, which further increased the monogalactosyldiacylglycerol/digalactosyldiacylglycerol (MGDG/DGDG) ratio, thereby influencing membrane repair. Additionally, at 6 h of germination in aged seeds, PC(3:0/0:0) levels significantly decreased. Compared to ‘Monida,’ the aging-sensitive ‘Haywire’ seeds exhibited substantial production of PE(19:0/0:0) and PC(15:0/0:0) at 32 h of germination, which may be key factors contributing to the seed's sensitivity to aging and the significant reduction in germination percentage after aging. Therefore, PC(3:0/0:0), PE(19:0/0:0), and PC(15:0/0:0) could serve as important lipid metabolic markers in future studies on the mechanisms of oat seed vigor. The findings of this study provide insights into the specificity of lipid remodeling and its response mechanisms during the germination of aged oat seeds, providing a theoretical foundation for the safe preservation of oat germplasm and the development of aging-tolerant varieties.

Smooth bromegrass (Bromus inermis) was adopted as experiment materials for identifying the seed maturity using a combination of multispectral imaging and machine learning. The trials were conducted to investigate the effects of three nitrogen application levels (0, 100 and 200 kg N ha− 1, defined as CK, N1 and N2 respectively) and two spikelet grain positions: superior grain (SG) at the basal position and inferior grain (IG) at the upper position, on smooth bromegrass seeds. The germination characteristics of the seeds revealed that the variations in nitrogen application and grain positions significantly influenced seeds vigor. The seed vigor of increased gradually with their maturity, reaching a high level at 30 and 36 days after anthesis. A stacking ensemble learning approach was employed to identify the seed maturity based on multispectral imaging and autofluorescence imaging. The results demonstrated that the Ensemble model outperformed Support Vector Machine, Bayesian, XGBoost and Random Forest across all evaluated metrics in different scenarios. The model accuracy in CK, N1 and N2 were 89

Moisture significantly impacts seed sales, storage, and processing. Traditional moisture testing methods are often slow, labor-intensive, and inadequate for the rapid detection demands of modern agriculture, particularly for non-destructive testing of individual seeds. This study applied multispectral imaging to obtain morphological and spectral data from alfalfa seeds at six moisture levels (4 %, 8 %, 12 %, 16 %, 25 %, and 41 %). By integrating algorithms such as Support Vector Machines (SVM), Random Forests (RF), Linear Discriminant Analysis (LDA), Back Propagation Neural Network (BPNN), and normalized typical discriminant analysis (nCDA) algorithms, classification models were developed to distinguish between safe and unsafe moisture levels. The Results indicated that spectral data alone significantly improved model accuracy and prediction. nCDA visualizations effectively illustrated spatial moisture distribution, highlighting stark color differences between seeds in the safe moisture range (4 %, 8 %, 12 %) and those in the unsafe range (16 %, 25 %, 41 %). BPNN exhibited high model precision, achieving a recognition accuracy rate of 90.1 % for safe and unsafe moisture content. Key wavelengths identified by the Permutation method included 970, 880, 570, and 490 nm. Pearson correlation analysis showed a significant positive correlation between germination indicators and spectral data, which strengthened with longer seed storage. These findings confirm the potential of multispectral imaging for assessing the safe moisture content of alfalfa seeds, supporting the development of detection systems for evaluating moisture content in individual seeds. This advancement enables the rapid removal of high-moisture seeds, preventing deterioration during storage.

Drought stress affects plant photosynthesis, leading to a reduction in the quality and yield of crop production. Non-foliar organs play a complementary role in photosynthesis during plant growth and development and are important sources of energy. However, there are limited studies on the performance of non-foliar organs under drought stress. The photosynthetic-responsive differences of oat spikelet organs (glumes, lemmas and paleas) and flag leaves to drought stress during the grain-filling stage were examined. Under drought stress, photosynthetic performance of glume is more stable. Intercellular CO2 concentration (Ci), chlorophyll b, maximum photochemical efficiency of photosystem II. (Fv/Fm), and electron transport rate (ETR) were significantly higher in the glume compared to the flag leaf. The transcriptome data revealed that stable expression of the RCCR gene under drought stress was the main reason for maintaining higher chlorophyll content in the glume. Additionally, no differential expression genes (DEGs) related to Photosystem Ⅰ (PSI) reaction centers were found, and drought stress primarily affects the Photosystem II (PSII) reaction center. In spikelets, the CP43 and CP47 subunits of PSII and the AtpB subunit of ATP synthase were increased on the thylakoid membrane, contributing to photosynthetic stabilisation of spikelets as a means of supplementing the limited photosynthesis of the leaves under drought stress. The results enhanced understanding of the photosynthetic performance of oat spikelet during the grain-filling stage, and also provided an important basis on improving the photosynthetic capacity of non-foliar organs for the selection and breeding new oat varieties with high yield and better drought resistance.

BACKGROUND:Medicago sativa, often referred to as the "king of forage", is prized for its high content of protein, minerals, carbohydrates, and digestible nutrients. However, various abiotic stresses can hinder its growth and development, ultimately resulting in reduced yield and quality, including water deficiency, high salinity, and low temperature. The ethylene-insensitive 3 (EIN3)/ethylene-insensitive 3-like (EIL) transcription factors are key regulators in the ethylene signaling pathway in plants, playing crucial roles in development and in the response to abiotic stresses. Research on the EIN3/EIL gene family has been reported for several species, but minimal information is available for M. sativa. RESULTS:In this study, we identified 10 MsEIN3/EIL genes from the M. sativa genome (cv. Zhongmu No.1), which were classified into three clades based on phylogenetic analysis. The conserved structural domains of the MsEIN3/EIL genes include motifs 1, 2, 3, 4, and 9. Gene duplication analyses suggest that segmental duplication (SD) has played a significant role in the expansion of the MsEIN3/EIL gene family throughout evolution. Analysis of the cis-acting elements in the promoters of MsEIN3/EIL genes indicates their potential to respond to various hormones and environmental stresses. We conducted a further analysis of the tissue-specific expression of the MsEIN3/EIL genes and assessed the gene expression profiles of MsEIN3/EIL under various stresses using transcriptome data, including cold, drought, salt and abscisic acid treatments. The results showed that MsEIL1, MsEIL4, and MsEIL5 may act as positive regulatory factors involved in M. sativa's response to abiotic stress, providing important genetic resources for molecular design breeding. CONCLUSION:This study investigated MsEIN3/EIL genes in M. sativa and identified three candidate transcription factors involved in the regulation of abiotic stresses. These findings will offer valuable insights into uncovering the molecular mechanisms underlying various stress responses in M. sativa.

Abiotic stresses have deleterious effects on seed germination and seedling establishment, leading to significant crop yield losses. Adverse environmental conditions can cause the accumulation of methylglyoxal (MG) within plant cells, which can negatively impact plant growth and development. The glyoxalase system, which consists of the glutathione (GSH)-dependent enzymes glyoxalase I (GLX1) and glyoxalase II (GLX2), as well as the GSH-independent glyoxalase III (GLX3 or DJ-1), plays a crucial role in detoxifying MG. However, genome-wide analysis of glyoxalase genes has not been performed for one of the agricultural important species, oat (Avena sativa). This study identified a total of 26 AsGLX1 genes, including 8 genes encoding Ni2+-dependent GLX1s and 2 genes encoding Zn2+-dependent GLX1s. Additionally, 14 AsGLX2 genes were identified, of which 3 genes encoded proteins with both lactamase B and hydroxyacylglutathione hydrolase C-terminal domains and potential catalytic activity, and 15 AsGLX3 genes encoding proteins containing double DJ-1 domains. The domain architecture of the three gene families strongly correlates with the clades observed in the phylogenetic trees. The AsGLX1, AsGLX2, and AsGLX3 genes were evenly distributed in the A, C, and D subgenomes, and gene duplication of AsGLX1 and AsGLX3 genes resulted from tandem duplications. Besides the core cis-elements, hormone responsive elements dominated the promoter regions of the glyoxalase genes, and stress responsive elements were also frequently observed. The subcellular localization of glyoxalases was predicted to be primarily in the cytoplasm, chloroplasts, and mitochondria, with a few presents in the nucleus, which is consistent with their tissue-specific expression. The highest expression levels were observed in leaves and seeds, indicating that these genes may play important roles in maintaining leaf function and ensuring seed vigor. Moreover, based on in silico predication and expression pattern analysis, AsGLX1-7A, AsGLX2-5D, AsDJ-1-5D, AsGLX1-3D2, and AsGLX1-2A were suggested as promising candidate genes for improving stress resistance or seed vigor in oat. Overall, the identification and analysis of the glyoxalase gene families in this study can provide new strategies for improving oat stress resistance and seed vigor.

Advances in optical imaging technology using rapid and non-destructive methods have led to improvements in the efficiency of seed quality detection. Accurately timing the harvest is crucial for maximizing the yield of higher-quality Siberian wild rye seeds by minimizing excessive shattering during harvesting. This research applied integrated optical imaging techniques and machine learning algorithms to develop different models for classifying Siberian wild rye seeds based on different maturity stages and grain positions. The multi-source fusion of morphological, multispectral, and autofluorescence data provided more comprehensive information but also increases the performance requirements of the equipment. Therefore, we employed three filtering algorithms, namely minimal joint mutual information maximization (JMIM), information gain, and Gini impurity, and set up two control methods (feature union and no-filtering) to assess the impact of retaining only 20% of the features on the model performance. Both JMIM and information gain revealed autofluorescence and morphological features (CIELab A, CIELab B, hue and saturation), with these two filtering algorithms showing shorter run times. Furthermore, a strong correlation was observed between shoot length and morphological and autofluorescence spectral features. Machine learning models based on linear discriminant analysis (LDA), random forests (RF) and support vector machines (SVM) showed high performance (>0.78 accuracies) in classifying seeds at different maturity stages. Furthermore, it was found that there was considerable variation in the different grain positions at the maturity stage, and the K-means approach was used to improve the model performance by 5.8%-9.24%. In conclusion, our study demonstrated that feature filtering algorithms combined with machine learning algorithms offer high performance and low cost in identifying seed maturity stages and that the application of k-means techniques for inconsistent maturity improves classification accuracy. Therefore, this technique could be employed classification of seed maturity and superior physiological quality for Siberian wild rye seeds.

Seed vigour is an important indicator to evaluate the seed quality, and the testing of seed vigour is crucial during the growth and development of seeds. Multispectral imaging is an emerging non-destructive testing technology that has been gradually utilized in the field of seed quality testing in recent years. In this study, we used multispectral imaging to obtain image and spectral information of smooth bromegrass seeds with different maturity levels and different harvest years, and combined with five multivariate analysis methods of principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to distinguish and estimate. Results showed that LDA could predict the seed vigour of smooth bromegrass more accurately, and the accuracy of seed prediction could reach 94.2%-99.6% for different maturity levels and 90.0%-99.2% for different harvest years, which was the best differentiation model. On the prediction of seed germination, the accuracy of the LDA model in predicting normal seedlings of smooth bromegrass seeds in different harvest years (93.6%) was higher than different maturity levels (66.7%). The prediction accuracy of nCDA could reach 93.7% and 91.3% for dead seeds with different maturity levels and different harvest years, and 91.1% and 91.3% for high vigour seeds, respectively. The above results confirmed the feasibility of the multispectral imaging techniques combined with the multivariate analysis methods for rapid and non-destructive differentiation of seed vigour of smooth bromegrass, and it also showed good application prospects in

为探究胚根线粒体抗坏血酸-谷胱甘肽(AsA-GSH)循环响应低温胁迫的抗氧化作用机制,以紫花苜蓿种子为材料,研究了不同温度(10和20℃)处理下发芽特性、不同吸胀时间(6、12和24 h)胚根线粒体AsA-GSH循环酶活性、抗氧化物以及过氧化氢(H2O2)含量的变化规律.结果表明,吸胀12和24 h后,10℃处理的胚根线粒体H2O2含量高于20℃.吸胀24 h期间,10℃处理的胚根线粒体谷胱甘肽还原酶(GR)活性均低于20℃.吸胀12和24 h后,10℃处理的胚根线粒体抗坏血酸(AsA)含量均低于20℃.10℃条件下吸胀24 h后,与20℃处理相比,胚根线粒体谷胱甘肽(GSH)含量显著降低(P<0.05).苜蓿种子在10℃条件下吸胀萌发时,主要通过降低AsA-GSH循环中GR、单脱氢抗坏血酸还原酶(MDHAR)、过氧化物酶(POD)活性和AsA、GSH含量,使胚根线粒体内的H2O2积累,产生氧化损伤,继而影响种子萌发的正常进程.

Seed pelleting is an advanced technology that can improve seed sowing and germination under abiotic stresses such as drought, promoting sustainable agriculture. However, the development of conventional seed pelleting formulations is often characterized by prolonged timelines and significant labor costs. This study aimed to accelerate the development of water retention agents (WRAs) pelleting formulations for alfalfa seed under drought stress using multispectral imaging technology. The efficacy of these formulations was evaluated under drought stress conditions by measuring seedling physiological characteristics and collecting multispectral im-ages. Results showed that WRA-pelleting formulations with HS0.5, BS1, and BS2 outperformed other WRA-pelleting formulations in terms of catalase, seedling length, superoxide dismutase, proline, and shoot length by principal component analysis. Additionally, backpropagation neural network, support vector machine, and random forest models were employed to identify various WRA-pelleting formulations using multispectral data from seedlings and cotyledons. All three models had an accuracy above 0.9 based on multispectral data from cotyledons, with support vector machine achieving the highest accuracy of 100%. The SHapley Additive ex-Planations method was applied to explain the prediction mechanism of the support vector machine model and identified important features such as 970, 850, 880, 940, and 780 nm. Significant correlations were found be-tween spectra and physiological indicators such as antioxidant, chlorophyll, and drought stress. In conclusion, our new approach has great potential to reduce the time and cost of developing seed pelleting formulations for mitigating drought stress.

Germination of aged seeds may be associated with specific metabolic changes. The objective of this study was to examine physiological and metabolic alterations before and after germination of control and aged oat (Avena sativa) seeds. The activity of antioxidant enzymes and the level of storage compounds were measured in the embryo and endosperm at 0, 4, 16, and 32 h of imbibition for control seeds and 0, 4, 16, 32, and 60 h of imbibition for medium vigor seeds after artificially accelerated aging; metabolomic changes were determined in embryos at 16 and 32 h of seed imbibition. In aged oat seeds, superoxide dismutase activity and catalase activity increased in the late imbibition stage. The content of soluble sugars decreased significantly in the later stages of imbibition, while the content of proteins increased in 32 h of seed imbibition eventually producing mannitol and proline. The mobilization of fat in deteriorated seeds was mainly through the sphingolipid metabolic pathway generated by cell growth-promoting dihydrosphingosine-1-phosphate. Ascorbic acid, avenanthramide and proline levels increased significantly at 60 h of imbibition, playing an important role in the germination of aged oat seeds.

Seed aging, a common physiological phenomenon during forage seed storage, is a crucial factor contributing to a loss of vigor, resulting in delayed seed germination and seedling growth, as well as limiting the production of hay. Extensive bodies of research are dedicated to the study of seed aging, with a particular focus on the role of the production and accumulation of reactive oxygen species (ROS) and the ensuing oxidative damage during storage as a primary cause of decreases in seed vigor. To preserve optimal seed vigor, ROS levels must be regulated. The excessive accumulation of ROS can trigger programmed cell death (PCD), which causes the seed to lose vigor permanently. LESION SIMULATING DISEASE (LSD) is one of the proteins that regulate PCD, encodes a small C2C2 zinc finger protein, and plays a molecular function as a transcriptional regulator and scaffold protein. However, genome-wide analysis of LSD genes has not been performed for alfalfa (Medicago sativa), as one of the most important crop species, and, presently, the molecular regulation mechanism of seed aging is not clear enough. Numerous studies have also been unable to explain the essence of seed aging for LSD gene regulating PCD and affecting seed vigor. In this study, we obtained six MsLSD genes in total from the alfalfa (cultivar Zhongmu No. 1) genome. Phylogenetic analysis demonstrated that the MsLSD genes could be classified into three subgroups. In addition, six MsLSD genes were unevenly mapped on three chromosomes in alfalfa. Gene duplication analysis demonstrated that segmental duplication was the key driving force for the expansion of this gene family during evolution. Expression analysis of six MsLSD genes in various tissues and germinating seeds presented their different expressions. RT-qPCR analysis revealed that the expression of three MsLSD genes, including MsLSD2, MsLSD5, and MsLSD6, was significantly induced by seed aging treatment, suggesting that they might play an important role in maintaining seed vigor. Although this finding will provide valuable insights into unveiling the molecular mechanism involved in losing vigor and new strategies to improve alfalfa seed germinability, additional research must comprehensively elucidate the precise pathways through which the MsLSD genes regulate seed vigor.

Intercultural Communicative Competence (ICC) is a crucial issue for the internationalization of higher education. In recent years, intercultural online learning has gradually become a new form of internationalization in higher education, which presents both challenges and opportunities for enhancing students' ICC. This study constructs an analytical framework for ICC within the context of online learning, drawing from existing research on ICC and classroom environment theory. Leveraging the "Global Integrated Classroom Learning Survey" by Tsinghua University's Global Integrated Classroom Teaching Research Group, 141 valid samples were collected and analyzed through descriptive statistics and regression analysis. Two main conclusions were drawn: (1) Students generally exhibit a relatively high level of ICC in online learning contexts, yet differences in ICC exist among different student groups; (2) Within online teaching, three contextual features—information literacy, instructional scaffolding, and technological support—significantly influence university students' ICC.

混合课程在高校教育教学和人才培养模式建设中受到广泛关注,但如何建设符合研究生教学特点的混合课程则是高校教学改革的重点和难点.针对研究生课堂教学中存在的问题,以硕士研究生课程——"牧草与草坪草种子科学与技术"的教学改革探索和实践为主要内容,通过线上线下混合课程的建设,拓展线上教学资源,利用不同教学组织形式,改善课堂教学的质量、课程学习的积极性和主动性,以期探索出适宜于研究生专业课程的教学模式,提升专业人才培养质量.

Computational intelligence refers to an algorithm inspired by the body structure of natural animals and plants and unique landscapes. It can perform simultaneous multithreaded work. In the processing process of the food industry, multiple factors must be considered at the same time to optimize various parameters simultaneously. Therefore, this research combines computational intelligence algorithms with the processing flow of the food industry, conducts research on the optimization of food processing systems and processes, and fully understands the needs of the market, the competitiveness of the company’s products, and the people facing marketing. The MA-CI model was used to optimize the entire process of food processing and production, and multiple variables were integrated. The following conclusions were drawn: (1) When the node size = 100 , calculate the optimal value of the intelligent model MA − CI = 0.3196 , the worst value = 0.3120 , and the average plus − minus variance = 0.3155 ± 4.6278 × 10 − 6 , in each index. Both are better than the Greedy algorithm model and the EA model. When the node size = 200 , the optimal value of the calculation intelligence model MA − CI = 0.3050 , the worst value = 0.2900 , and the average plus and minus variance = 0.2970 ± 8.9251 × 10 − 6 ; when the node size = 300 , the calculation intelligence model (MA-CI) optimal value = 0.2608 , the worst value = 0.2499 , and the mean plus − minus variance = 0.2551 ± 1.0028 × 10 − 5 ; when the node size = 500 , calculate the optimal value of intelligent model MA − CI = 0.2857 , the worst value = 0.2812 , and the mean plus and minus variance = 0.2834 ± 2.8230 × 10 − 6 . (2) In the electronic circuit network, calculate the intelligent model (MA-CI): Rc = 0.3174 , NMI = 0.7388 , Δ Ec = 0 , and k = 14.43 ; in the USAir network, the computational intelligence model (MA-CI) is as follows: Rc = 0.3041 , NMI = 0.8632 , Δ Ec = 0 , and k = 7.43 ; the computational intelligence model (the uniqueness and optimality of MA-CI) has resulted in the optimal solution for the optimization of the global machining process. (3) Using six online databases to predict and verify the MA-CI model for process optimization in the food industry, it was found that the MA-CI model for process optimization in the food industry was better than the standard value. (4) Perform performance testing on the optimized MA-CI model. It is found that under the MA-CI model, MUTAG = 98.2 ± 4.3 , ENZYMES = 96.2 ± 4.3 , PTC = 85.2 ± 4.3 , PROTEINS = 82.0 ± 3.2 , NCI 1 = 80.2 ± 2.0 , and D & D = 91.9 ± 0.5 , which are better than the other models. The optimized MA-CI model can control the profit problem in food processing and production, and it is found that the MA-CI model can make the enterprise obtain the maximum profit in the shortest period.