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[Objective]In the agricultural production, accurately classifying dried daylily grades is a critical task with significant economic implications. However, current target detection models face challenges such as inadequate accuracy and excessive parameters when applied to dried daylily grading, limiting their practical application and widespread use in real-world settings. To address these issues, an innovative lightweight YOLOv10-AD network model was proposed. The model aims to enhance detection accuracy by optimizing the network structure and loss functions while reducing parameters and computational costs, making it more suitable for deployment in resource-constrained agricultural production environments.[Methods]The dried daylilies selected from the Qingyang region of Gansu province as the research subject. A large number of images of dried daylilies, categorized into three grades superior, medium, and inferior, were collected using mobile phones under varying lighting conditions and backgrounds. The images were carefully annotated and augmented to build a comprehensive dataset for dried daylily grade classification. YOLOv10 was chosen as the base network, and a newly designed backbone network called AKVanillaNet was introduced. AKVanillaNet combines AKConv (adaptive kernel convolution) with VanillaNet's deep learning and shallow inference mechanisms. The second convolutional layer in VanillaNet was replaced with AKConv, and AKConv was merged with standard convolution layers at the end of the training phase to optimize the model for capturing the unique shape characteristics of dried daylilies. This innovative design not only improved detection accuracy but also significantly reduced the number of parameters and computational costs. Additionally, the DysnakeConv module was integrated into the C2f structure, replacing the Bottleneck layer with a Bottleneck-DS layer to form the new C2f-DysnakeConv module. This module enhanced the model's sensitivity to the shapes and boundaries of targets, allowing the neural network to better capture the shape information of irregular objects like dried daylilies, further improving the model's feature extraction capability. The Powerful-IOU (PIOU) loss function was also employed, which introduced a target-size-adaptive penalty factor and a gradient adjustment function. This design guided the anchor box regression along a more direct path, helping the model better fit the data and improve overall performance.[Results and Discussions]The testing results on the dried daylily grade classification dataset demonstrated that the YOLOv10-AD model achieved a mean average precision (mAP) of 85.7%. The model's parameters, computational volume, and size were 2.45 M, 6.2 GFLOPs, and 5.0 M, respectively, with a frame rate of 156 FPS. Compared to the benchmark model, YOLOv10-AD improved mAP by 5.7% and FPS by 25.8%, while reducing the number of parameters, computational volume, and model size by 9.3%, 24.4%, and 9.1%, respectively. These results indicated that YOLOv10-AD not only improved detection accuracy but also reduced the model's complexity, making it easier to deploy in real-world production environments. Furthermore, YOLOv10-AD outperformed larger models in the same series, such as YOLOv10s and YOLOv10m. Specifically, the weight, parameters, and computational volume of YOLOv10-AD were only 31.6%, 30.5%, and 25.3% of those in YOLOv10s, and 15.7%, 14.8%, and 9.8% of YOLOv10m. Despite using fewer resources, YOLOv10-AD achieved a mAP increase of 2.4% over YOLOv10s and 1.9% over YOLOv10m. These findings confirm that YOLOv10-AD maintains high detection accuracy while requiring significantly fewer resources, making it more suitable for agricultural production environments where computational capacity may be limited. The study also examined the performance of YOLOv10-AD under different lighting conditions. The results showed that YOLOv10-AD achieved an average accuracy of 92.3% in brighter environments and 78.6% in darker environments. In comparison, the YOLOv10n model achieved 88.9% and 71.0% in the same conditions, representing improvements of 3.4% and 7.6%, respectively. These findings demonstrate that YOLOv10-AD has a distinct advantage in maintaining high accuracy and confidence in grading dried daylilies across varying lighting conditions.[Conclusions]The YOLOv10-AD network model proposed significantly reduces the number of parameters and computational costs without compromising detection accuracy. This model presents a valuable technical reference for intelligent classification of dried daylily grades in agricultural production environments, particularly where resources are constrained.

Torreya grandis (T. grandis, Taxaceae) is a well-known nut tree species. Its fruit aril and leaves possess a unique aroma, making it an ideal natural raw material for extracting essential oils (EOs). This study aims to comprehensively compare the composition, biological activities, and pharmacological mechanism of EOs extracted from the arils (AEO) and leaves (LEO) of T. grandis. The results revealed that the chemical composition of the two EOs was highly consistent, with α-pinene and D-limonene as the main components. Both EOs significantly reduced cellular melanin production and inhibited tyrosinase activity in α-MSH-stimulated B16 cells (p < 0.05). AEO and LEO suppressed inflammatory responses in LPS-stimulated RAW 264.7 macrophages, significantly inhibiting cellular NO production and proinflammatory cytokines such as TNF-α and IL-6 (p < 0.05). A network pharmacology analysis reveals that AEO and LEO share similar molecular mechanisms and pharmacological pathways for treating skin pigmentation and inflammation. Regulating inflammatory cytokines may be a critical pathway for AEO and LEO in treating skin pigmentation. These findings suggest that AEO and LEO have potential for cosmetic applications. The leaves of T. grandis could be a valuable source of supplementary materials for producing T. grandis aril EO. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the level of inactivated A71 (EV-A71) vaccination in Pudong New Area of Shanghai and its effects on the epidemiology and pathogen spectrum of hand–foot–mouth disease (HFMD) in this area, as well as to provide a basis for improving the prevention and control strategy of HFMD in this area. Data were collected from the "Comprehensive Management Cloud Platform for Immunization Program" system from December 2016 to December 2022. The data on HFMD cases from January 2012 to December 2022 were extracted from the "China Information System for Disease Control and Prevention". A total of 484,056 doses were administered. The vaccination rate of the first dose was 14.03%, and the full vaccination rate was 13.33%. There were significant differences between the first dose and the full vaccination rate in different years (χ2 = 46,538.831, p < 0.001, χ2 = 50,013.946, p < 0.001). A total of 91625 cases of HFMD were reported, including 58 severe cases, and no deaths. Before and after the administration of the inactivated EV-A71 vaccine, there were statistical differences in the distribution of HFMD cases in terms of gender, household registration, occupation, and age (p < 0.001). The etiological surveillance results showed that the rate of enterovirus positivity was 84.15%, with 9.85% being EV-A71, 23.74% CV-A16, and 50.56% non-EV-A71 and non-CV-A16. The coverage rate of the inactivated EV-A71 vaccine in Pudong New Area was not high, and the incidence of HFMD showed a downward trend after the postmarketing of the vaccine. The majority of HFMD infections were non-EV-A71 and non-CV-A16, with CV-A6 accounting for the highest proportion. It is recommended to accelerate the development of combined vaccines to provide more antibody protection. [ABSTRACT FROM AUTHOR]

Shading and drought are considered crucial abiotic stress factors that limit the normal growth of plants. Under natural conditions, the quality of Bletilla striata pseudobulbs (BP), a Chinese traditional medicinal crop, is often affected by the dual stresses of shading and drought. However, the relationship and mechanism of the interaction between the two stress factors in B. striata remain unclear. In this study, we examined the changes in photosynthetic properties and active ingredients of B. striata under shading (L), drought (W), and shading-drought dual stresses (LW). We aimed to explore the metabolite mechanism that led to these changes using GC-MS-based non-targeted metabolomics techniques. The results indicated a significant reduction in the polysaccharide content of BP under W and LW treatments compared to the control (CK). The total phenol content was significantly reduced under L treatment, while the total flavonoid content did not change significantly under the three stresses. The significant increase in militarine content under all three stresses implies that B. striata may modulate its biosynthesis in response to different environmental stresses. Transpiration rate and stomatal conductance were reduced, amino acid expression was up-regulated, and carbohydrate expression was down-regulated in B. striata under L treatment. The net photosynthesis rate, stomatal conductance, and transpiration rate exhibited significant reductions, and the tuber metabolic disorder marker Homocysteine increased and organic acid content as well under W treatment. The net photosynthetic rate, transpiration rate, stomatal conductance, and water use efficiency of B. striata were further reduced under LW compared with single stress, which is in agreement with the "trade-off theory". Pseudobulb metabolite changes, in combination with the results of the two single stresses, showed an up-regulation of amino acids and disaccharide compounds and a down-regulation of monosaccharide compounds. A support vector machine model (SVM) was used to screen 10 marker metabolites and accurately predict the changes in active ingredient content through an artificial neural network model (ANN). The results suggest that an appropriate stress environment can enhance the content of the target active ingredients based on cultivation goals. [ABSTRACT FROM AUTHOR]