Search Results (146)

Celery (Apium graveolens L.) is a widely cultivated leafy vegetable of significant agronomic and nutritional importance. Owing to its high nutritional value, global demand for celery has steadily increased. However, under natural cultivation conditions, uncontrolled light exposure often prolongs the seedling stage and impairs celery growth quality. Improving the nutritional quality of celery through artificial regulation of the light environment has therefore become an important research focus. This work aimed to elucidate the impact of varying light–dark cycles on the growth characteristics and nutritional attributes of celery. Six light–dark cycle treatments (4 h/2 h, 8 h/4 h, 16 h/8 h, 24 h/12 h, 32 h/16 h, and 40 h/20 h) were applied, using ‘Oster Ziyu Xiangqin’ as the plant material under a constant light intensity of 400 μmol·m⁻²·s⁻¹. The results revealed that the 24 h/12 h light–dark treatment significantly enhanced plant height, total fresh weight, and root vigor and showed superior performance in photosynthetic and chlorophyll fluorescence parameters. The 32 h/16 h treatment significantly enhanced the accumulation of soluble sugars, proteins, total phenolic compounds, and flavonoids, as well as the activities of antioxidant enzymes, while reducing nitrate-nitrogen levels. In conclusion, the 24 h/12 h light–dark cycle was most conducive to the growth and photosynthetic performance of celery, whereas the 32 h/16 h treatment optimally enhanced its nutritional quality and antioxidant capacity. Full article

Residual Networks (ResNet) address the vanishing gradient problem through skip connections and have become a fundamental architecture for computer vision tasks. However, standard convolutional layers exhibit limited capacity in modeling complex nonlinear relationships. We present EKAResNet, a residual backbone enhanced with a spline-based Kolmogorov–Arnold Network (KAN) head. Specifically, we introduce a KAN-based Feature Classification Module (KAN-FCM) that replaces a portion of the traditional fully connected classifier. This module employs piecewise polynomial (spline) approximation to achieve adaptive nonlinear mapping while maintaining a controlled parameter budget. We evaluate EKAResNet on CIFAR-10 and CIFAR-100, achieving top accuracies of 95.84% and 80.06%, respectively. Importantly, the model maintains a parameter count comparable to strong ResNet and WideResNet baselines. Ablation studies on spline configurations further confirm the contribution of the KAN head. These results demonstrate the effectiveness of integrating KAN structures into ResNet for modeling high-dimensional, complex features. Our work highlights a promising direction for designing deep learning architectures that balance accuracy and computational efficiency. Full article

The leaves of Apocynum venetum L. (A. venetum L.) are a functional food that plays an important role in antioxidation due to its high content of flavonoids and phenolic acids. Therefore, the extraction process of leaves of A. venetum L. is closely related to their activity. In this study, ultra-high-performance liquid chromatography (UHPLC) coupled with diode array detector (DAD), electrospray ionization (ESI), and quadrupole time-of-flight mass spectrometry (QTOF/MS) techniques has been established for qualitative and quantitative analysis of three phenolic acids and six flavonoids in the leaves of A. venetum L. Ultrasonic-assisted extraction conditions for the maximum recovery of phenolic and flavonoid compounds with a high antioxidation effect were optimized by response surface methodology (RSM). The optimum extraction conditions were as follows: ethanol concentration 64%, extraction time 20 min, and liquid-to-solid ratio 16:1 mL/g. The yields of three phenolic acids and six flavonoids under the optimal process were found to be 8.932 ± 0.091 and 20.530 ± 0.198 mg/g, respectively, which matched with those predicted (8.751 and 20.411 mg/g) within a 95% confidence level. Antioxidant activities based on ABTS and DPPH assays showed that the optimal extracts had strong activities compared with those of conventional reflux extraction methods. Moreover, the contribution of total and individual phenolic acids and flavonoids to antioxidant activity was also estimated by Pearson correlation analysis. Full article

Plant seedlings are sensitive to cultivation environment factors and highly susceptible to pathogenic infections under adverse conditions such as inappropriate light environment. In this study, five kinds of LED lighting sources with different red (R) and blue (B) light combinations were set up: R10B0, R7B3, R5B5, R2B8 and R0B10 (with R:B ratios of 10:0, 7:3, 5:5, 2:8 and 0:10, respectively) to explore their effects on tomato seedlings’ growth, AsA-GSH cycle, endogenous hormones, and resistance to Phytophthora infestans, providing a basis for factory seedling light-quality selection. The results showed that with the increase in the proportion of blue light in the composite light, the growth indicators, photosynthetic characteristic parameters and enzyme activities of tomato seedlings generally increased. The contents of AsA, reduced glutathione, and oxidized glutathione all reached the maximum under high-proportion blue-light treatments (R2B8 and R0B10). The high-blue-light groups (R2B8 and R0B10) had the highest AsA and glutathione contents. The red–blue combinations reduced inhibitory ABA and increased growth-promoting hormones (e.g., melatonin), while monochromatic light increased ABA to inhibit growth. After inoculation with P. infestans, the apoplastic glucose content was the highest under the red–blue-combined treatments (R5B5 and R2B8), while the total glucose content in leaves was the highest under the combined light R2B8 treatment. In conclusion, high-proportion blue-light treatment can greatly promote the photosynthetic process of tomato, enhance the AsA-GSH cycle, and achieve the best effect in improving the resistance of tomatoes to P. infestans. Given these, the optimal light environment setting was R:B = 2:8. Full article

The exceptional surface (ES) in non-Hermitian physics has attracted much attention due to its strong robustness and enhanced frequency splitting in the sensing field. However, the detection limit of the ES-based sensing structure is still limited by the mode linewidth in the optical microcavity. In this paper, we demonstrate that Sagnac–Fizeau shift in a microcavity based on an ES separates the dark mode from the bright mode, further enhancing the frequency splitting in the transmission spectrum. Moreover, a strategy for manipulating spectral line shape is realized by the phase in the reflection loop. Compared with the traditional ES-based sensing structure, the proposed nanoparticle sensing mechanism significantly reduces the detection limit for weak perturbations. This work will contribute to the development of high-precision nanoparticle sensors. Full article

A total of 216 Hy-Line brown laying hens (28 week old) were utilized for this experiment. The laying hens were randomly divided into three treatments. Each treatment had six replications, and there were 12 hens in each replication. The ambient temperature was 32 ± 1 °C from 9:00 am to 5:00 pm, and 26 ± 1 °C otherwise. Treatments included a basal diet (CON), basal diet +0.075% betaine (TRT1), and basal diet +0.15% betaine (TRT2). The results indicated that incorporating betaine into the diets of laying hens led to a significant improvement in egg production between weeks 6 and 8 (p < 0.05). HU height showed a linear improvement in week 8 alongside betaine supplementation. At week 8 of the experiment, there were significant increase in the digestibility of DM, nitrogen (p < 0.05). Moreover, there was a linear decrease in gas emission of NH_3, H₂S (p < 0.05), and Methyl mercaptans (p = 0.05) in week 8 in hens whose diet was supplemented with betaine. Betaine also linearly reduced blood cortisol (p < 0.01). In summary, increasing betaine supplementation in the diet of laying hens during the summer season enhanced egg production, egg quality, gas emission, and blood profile. Full article

With the development of modern agricultural technology, plant factories have become an important way to achieve efficient and sustainable crop production. Accurate understanding of the light received by plants is the key to improving the light energy utilization efficiency of lamps and ensuring the benefits of plant factories. Ray tracing technology, as one of the key technologies in plant factories, is of great significance to analyze the growing light environment of vegetables. Spinach has high nutritional value and is loved by the public and is one of the main crops grown in plant factories. In this paper, LightTools, TracePro, and Ansys Lumerical FDTD Solution, which are currently mature light environment tracking software in the field of lighting, are selected as the research objects to investigate their performance in simulating the light environment of spinach leaf surfaces under different planting arrangements and different lamp source distances. The results show as follows: Under the rectangular planting arrangement, the leaves received more light, and the plants grew faster. Different planting arrangements of plants had little effect on the simulation effect of the same kind of software, but the simulation effect of the three kinds of software under the same planting arrangement was significantly different, and the difference between the simulated value and the measured value of TracePro was the least. Further, TracePro was used to trace and simulate the leaf surface light conditions of spinach under a rectangular planting arrangement at different lighting distances, and the simulation results showed that there was no significant difference between the software simulation value and the measured value, and the simulation accuracy was the highest when the distance from the light source was 30 cm. Therefore, TracePro software can accurately simulate the light intensity of spinach leaves during the growth process and is most suitable for monitoring the change of light environment of spinach growth in plant factories. Full article

The AP2/ERF (APETALA2/ethylene-responsive element binding factor) family represents one of the largest transcription factor families in plants, playing pivotal roles in abiotic stress responses and hormone signaling pathways. Through genome-wide analysis, we identified 163 AnAP2/ERF genes in Adiantum nelumboides. Transcriptome data revealed that 12 AnAP2/ERF genes were significantly upregulated under either drought or flooding stress, with 8 genes responding to both conditions. qRT-PCR validation confirmed that all 12 selected AnAP2/ERF genes exhibited differential expression under both stress types. Notably, these genes also showed significant induction by abscisic acid (ABA), auxin (IAA), and gibberellin (GA), suggesting their potential involvement in stress responses through hormone crosstalk. This study establishes a foundation for investigating AnAP2/ERF gene functions and their molecular mechanisms in abiotic stress adaptation in A. nelumboides. Full article

Microorganisms play a crucial role in food processes, safety, and quality through their dynamic interactions with other organisms. In recent years, biosensors have become essential tools for monitoring these processes in the dairy, meat, and fresh produce industries. This review highlights how microbial diversity, starter cultures, and interactions, such as competition and quorum sensing, shape food ecosystems. Diverse biosensor platforms, including electrochemical, optical, piezoelectric, thermal, field-effect transistor-based, and lateral flow assays, offer distinct advantages tailored to specific food matrices and microbial targets, enabling rapid and sensitive detection. Biosensors have been developed for detecting pathogens in real-time monitoring of fermentation and tracking spoilage. Control strategies, including bacteriocins, probiotics, and biofilm management, support food safety, while decontamination methods provide an additional layer of protection. The integration of new techniques, such as nanotechnology, CRISPR, and artificial intelligence, into Internet of Things systems is enhancing precision, particularly in addressing regional food safety challenges. However, their adoption is still hindered by complex food matrices, high costs, and the growing challenge of antimicrobial resistance. Looking ahead, intelligent systems and wearable sensors may help overcome these barriers. Although gaps in standardization and accessibility remain, biosensors are well-positioned to revolutionize food microbiology, linking ecological insights to practical solutions and paving the way for safer, high-quality food worldwide. Full article

Farming is an important development direction of agriculture in the future, which is affected by various environmental factors, among which light plays an important role, and it is essential for the growth of organisms in nature. LED technology can regulate the growth and development of vegetables by adjusting the spectral composition of light. In order to explore light quality formulation with the aim of improving the quality and yield of celery, we set up six experimental treatments: W (white light), R (red light), B (blue light), 3R1B (red light/blue light = 3:1), 4R1B (red light/blue light = 4:1), and 5R1B (red light/blue light = 5:1). The results indicated that the 3R1B and 4R1B illumination treatments were conducive to promoting the growth of celery, enhancing plant height and root length. Specifically, the 3R1B treatment optimized the nutritional quality of celery by increasing the levels of soluble protein, soluble sugar, and total flavonoids while reducing nitrate and cellulose contents and elevating the anthocyanin content in petioles. Additionally, both treatments enhanced the contents of Ca and Mg in celery leaves and petioles. Furthermore, the 3R1B treatment promoted the accumulation of photosynthetic pigments, upregulated the activities of ANS and FNS enzymes, and induced the upregulation of gene expression levels of FNS and ANS, thereby enhancing the nutritional value of celery. Full article

Elevated [CO₂] enhances light interception and carboxylation efficiency in plants. The combined effects of [CO₂] and photosynthetic photon flux density (PPFD) on stomatal morphology, leaf anatomy, and photosynthetic capacity in tomato seedlings remain unclear. This study subjected tomato seedlings (Solanum lycopersicum Mill. cv. Jingpeng No.1) to two [CO₂] (ambient [a[CO₂], 400 µmol·mol⁻¹] and enriched [e[CO₂], 800 µmol·mol⁻¹]) and three PPFD levels (L; low[Ll: 200 µmol·m⁻²·s⁻¹], moderate[Lm: 300 µmol·m⁻²·s⁻¹], and high[Lh: 400 µmol·m⁻²·s⁻¹]) to assess their interactive impacts. Results showed that e[CO₂] and increased PPFD synergistically improved relative growth rate and net assimilation rate while reducing specific leaf area and leaf area ratio. Notably, e[CO₂] decreased stomatal aperture (−13.81%) and density (−27.76%), whereas elevated PPFD promoted stomatal morphological adjustments. Additionally, Leaf thickness increased by 72.98% under e[CO₂], with Lm and Lh enhancing this by 10.79% and 41.50% compared to Ll. Furthermore, photosynthetic performance under e[CO₂] was further evidenced by improved chlorophyll fluorescence parameters (excluding non-photochemical quenching). While both e[CO₂] and increased PPFD Photosynthetic performance under e[CO₂] was further evidenced by improved chlorophyll fluorescence parameters (excluding non-photochemical quenching). Moreover, e[CO₂]-Lh treatment maximized total dry mass and seedling health index. Correlation analysis indicated that synergistic optimization of stomatal traits and leaf structure under a combination of e[CO₂] and increased PPFD enhanced light harvesting and CO₂ diffusion, thereby promoting carbon assimilation. These findings highlight e[CO₂]-Lh as an optimal strategy for tomato seedling growth, providing empirical guidance for precision CO₂ fertilization and light management in controlled cultivation. Full article

Asparagus (Asparagus officinalis L.) is a highly nutritious vegetable rich in various bioactive compounds. Ensuring both yield improvement and quality preservation is a shared goal for producers and researchers. As novel green yield-enhancing technologies in facility agriculture, electrostatic fields and elevated CO₂ application hold significant potential. This study investigated the effects of the interaction between electrostatic fields and elevated CO₂ on the growth and physiological characteristics of asparagus. The results demonstrated that the combined treatment of electrostatic fields and elevated CO₂ significantly increased total yield, tender stem number, and single tender stem weight of asparagus, while also shortening the harvesting period and promoting rapid shoot growth. Additionally, the treatment markedly enhanced the total chlorophyll content in asparagus leaves, improving photosynthetic capacity. By boosting antioxidant enzyme activities (e.g., SOD, APX) and reducing malondialdehyde (MDA) levels, the treatment maintained the redox homeostasis of asparagus shoots, effectively mitigating oxidative damage. In terms of nutrient accumulation, the interaction between electrostatic fields and elevated CO₂ significantly promoted the synthesis and accumulation of key nutrients, including soluble sugars, reducing sugars, soluble proteins, total phenolics, total flavonoids, and ascorbic acid, thereby substantially improving the nutritional quality of asparagus. Comprehensive analysis using fuzzy membership functions revealed that the combined treatment of electrostatic fields and elevated CO₂ outperformed individual treatments in enhancing asparagus growth and physiological characteristics. This study provides important theoretical insights and technical support for the efficient and sustainable cultivation of asparagus in facility agriculture. Full article

Background: The late embryogenesis abundant (LEA) gene family plays a critical role in abiotic stress tolerance during plant growth and development. Myricaria laxiflora, as a key pioneer species in the extreme hydrological fluctuation zone of the Yangtze River, has evolved unique adaptation mechanisms potentially linked to gene family evolution. However, the molecular mechanisms underlying how the LEA gene family responds to alternating flooding–drought cycles remain unclear. Methods and Results: In this study, we identified 31 LEA genes through whole-genome and transcriptome analyses using bioinformatics approaches, and classified them into nine subfamilies based on protein sequence similarity. These genes were distributed across 12 chromosomes. Our analysis revealed that LEA promoters contain cis-acting elements associated with anaerobic induction, abscisic acid (ABA) response, and combined low-temperature/light stress, suggesting their role in a multi-tiered environmental signal integration network. Spatio-temporal expression profiling further indicated that root-specific LEA genes maintain cellular integrity via membrane lipid binding, while leaf-predominant members cooperate with the antioxidant system to mitigate photoinhibition damage. Conclusions: This study elucidates the dynamic regulatory mechanisms of the LEA gene family during flooding-drought adaptation in M. laxiflora, providing molecular targets for ecological restoration in the Yangtze River Basin. Full article

Accurate permafrost mapping in mountainous regions is hindered by sparse in situ observations and heterogeneous terrain. This study develops a GPR-augmented machine learning framework to map mountain permafrost in the northeastern Qinghai–Tibet Plateau. A total of 1037 presence–absence samples were compiled from boreholes, soil pits, 128 GPR transects collected in 2009, and 22 additional empirical points above 4700 m, covering diverse topographic and thermal conditions. Thirteen classification algorithms were evaluated using 5-fold cross-validation repeated 40 times, with LightGBM, CatBoost, XGBoost, and RF achieving top performance (F1 > 0.98). Elevation-based spatial comparisons revealed that LightGBM and CatBoost produced more terrain-adaptive predictions at high altitudes and slope transitions. Aspect-controlled permafrost boundaries were captured, with modeled lower elevation limits varying by >200 m across slope directions. SHAP analysis showed that climate and soil variables contributed nearly 80% to model outputs, with LST, FDD, BD, and TDD being dominant. Several predictors exhibited threshold or nonlinear responses, reinforcing their physical relevance. Additional experiments confirmed that integration of GPR and high-elevation constraint samples significantly improved model generalization, especially in underrepresented terrain zones. This study demonstrates that a GPR-augmented machine learning framework can support cost-effective, physically informed mapping of frozen ground in complex alpine environments. Full article

This work utilized seabuckthorn seed meal protein (SSP) to develop hypoglycemic peptides via controlled protease catalyzed hydrolysis. Among the SSP hydrolysates (SSPHs) obtained by means of various proteases, the SSP hydrolyzed by dispase (SSPD) exhibited extraordinary inhibitory abilities against three key enzymes involved in glucose metabolism: α-glucosidase, α-amylase, and dipeptidyl peptidase-IV (DPP-IV). Following process optimization and purification, SSPD displayed remarkable inhibitions to α-glucosidase (IC₅₀: 3.45 ± 0.18 mg/mL) and DPP-IV (IC₅₀: 5.01 ± 0.21 mg/mL), respectively. Molecular docking analysis and in vitro verification revealed three peptides in the SSPD with α-glucosidase inhibition: FHF, FFI, and FGI (IC₅₀: 3.98 ± 0.16 mM, 8.21 ± 0.21 mM, 11.57 ± 0.20 mM), and three peptides with DPP-IV inhibition: IYF, IGF, and LFF (IC₅₀: 5.32 ± 0.15 mM, 7.17 ± 0.14 mM, 7.62 ± 0.19 mM). These findings demonstrate that SSP holds promise as a significant natural resource for the creation of multifunctional hypoglycemic peptides, which can be utilized in nutritional and functional food applications. Full article