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Changde Sixian Opera is a cherished intangible cultural heritage traditional folk opera known for its vibrant melodies. The objective of this study is to propose guidelines for the literacy transmission of Changde Sixian Opera within schools and educational institutes. The study was conducted in Changde City, Hunan Province, and enlisted two key informants, including experienced Changde Sixian performers and educators. Employing a mixed-methods approach involving semi-structured interviews, observations, and document analysis, the study unearthed a multifaceted integration of Changde Sixian into primary and secondary education, higher education, community activities, television programming, and professional performance groups. The research findings underscore the importance of educational institutions in preserving and transmitting Changde Sixian Opera while emphasizing the need for ongoing strategies to address declining interest among younger generations, ensuring the continued vibrancy of this cultural treasure.

To intelligently detect the maturity of ‘Huping’ jujube in the natural environment, this study proposed a lightweight ‘Huping’ jujube maturity detection method based on the YOLO-FHLD. Firstly, the C2fF module was introduced to the YOLOv8n model to create a lightweight model and enhance the extraction ability of ‘Huping’ jujube features. Secondly, a new feature fusion module HS-FPAN was used to improve the expression and detection accuracy of ‘Huping’ jujube with different levels of maturity. Then, Focal Loss was employed as a loss function to address the class imbalance problem. Finally, knowledge distillation strategy further enhanced the detection accuracy of the model. The experimental results showed that the F1 score and mean average precision (mAP) of YOLO-FHLD increased by 1.24 % (reaching 79.48 %) and 2.96 % (reaching 85.40 %) compared with YOLOv8n, respectively. Furthermore, the model size was reduced to a mere 3.51MB, which accounted for only 58.99 % of the original model's size. In terms of Classification Error (Cls), Localization Error (Loc), and Background Error (Bkg), YOLO-FHLD decreased by 0.9, 0.68, and 0.38, respectively. For the ‘Huping’ jujubes during harvestable maturity period (cs) and non-harvestable maturity period (ws), the average precision (AP) of ‘Huping’ jujube detection reached 89.20 % (increased by 2.85 %) and 81.60 % (increased by 3.07 %), respectively. Compared to Faster R-CNN, SSD, YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5n, YOLOv7-Tiny, and YOLOv8s models, the mAP of YOLO-FHLD increased by 24.84 %, 4.72 %, 7.35 %, 1.3 %, 2.38 %, 8.2 %, and 0.79 %, respectively. The model size was only 3.24 %, 7.67 %, 21.09 %, 16.56 %, 94.61 %, 29.97 % and 16.36 % of the above seven models, respectively. Consequently, this model has advantages in both detection accuracy and model size. This study can provide a methodological support for target detection of ‘Huping’ jujube fruits in natural environment.

Abstract Objective This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). Materials and methods The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. Results The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. Discussion The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system’s high reliability and suitability has been affirmed through empirical validation. Conclusion The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.

Abstract Background Ensiling technology holds promise for preserving and providing high-quality forage. However, the preservation of rice straw poses challenges due to its high lignocellulosic content and low water-soluble carbohydrate levels. Developing highly effective lactic acid bacteria (LAB) for rice straw silage remains a priority. Results This study evaluated the impact of three LAB strains, Lactobacillus brevis R33 (Lac33), L. buchneri R17 (Lac17), and Leuconostoc pseudomesenteroides (Leu), on the fermentation quality of rice straw silage. Rice straw silage inoculated with Lac33 alone or in combination with other strains exhibited significantly lower neutral detergent fiber (NDF) (66.5% vs. 72.3%) and acid detergent fiber (ADF) (42.1% vs. 47%) contents, along with higher lactic acid (19.4 g/kg vs. not detected) and propionic acid (2.09 g/kg vs. 1.54 g/kg) contents compared to control silage. Bacterial community analysis revealed Lactobacillus dominance (> 80%) and suppression of unwanted Enterobacter and Clostridium. Metabolomic analysis highlighted increased carbohydrates and essential amino acids, indicating improved nutrient values in Lac33-inoculated rice straw silage and a potential explanation for Lac33 dominance. Conclusions This research identified a highly efficient LAB candidate for rice straw silage, advancing our comprehension of fermentation from integrated microbiology and metabolomic perspectives.

The intelligent detection of young peaches is the main technology of fruit-thinning robots, which is crucial for enhancing peach fruit quality and reducing labor costs. This study presents the lightweight YOLO-PEM model based on YOLOv8s to achieve high-precision and automatic detection of young “Okubo” peaches. Firstly, the C2f_P module was devised by partial convolution (PConv), replacing all C2f modules in YOLOv8s to achieve the model’s lightweight. Secondly, embedding the efficient multi-scale attention (EMA) module in the lightweight C2f_P_1 module of the backbone network enhanced the feature extraction capability and accuracy for young peaches. Finally, the MPDIoU loss function was utilized to replace the original CIoU loss function, which improved the detection accuracy of the bounding box while speeding up the convergence of the model. The experimental results demonstrate that the YOLO-PEM model achieved an average precision (AP) of 90.86%, F1 score of 86.70%, and model size of 16.1 MB, which was a 1.85% improvement in the AP, 0.85% improvement in the F1 score, and 5.3 MB reduction in the model size compared with YOLOv8s. The AP was 6.26%, 6.01%, 2.05%, 2.12%, and 1.87% higher compared with the other lightweight detection models YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv6s, and YOLOv7-tiny, respectively. Furthermore, the FPS of YOLO-PEM was 196.2 f·s-1, which can fulfill the demand for the real-time detection of young peaches. YOLO-PEM effectively detects young peaches in complex orchard environments and can offer a basis for the theoretical design of the vision system of the “Okubo” peach fruit-thinning robot and scientific management of orchards.

Soil ingestion by livestock is common in grazing ecosystems, but few studies have been conducted to assess its effect on the animal organism. The topic is worthy of attention because these potential effects are likely to be enriched in the food chain and interfere with animal and human health. In this study, we present an indoor feeding trial conducted based on a completely randomized design to comprehensively evaluate the effects of simulated soil ingestion during grazing on nutrient digestibility, rumen fermentation, and microflora, and mineral deposition in the organs and tissues of sheep. Eighteen Mutton Merino crossbred sheep (42.7 ± 2.34 kg) were randomly allotted to three treatments and fed diets containing 0% (Control), 5% (SOIL5), and 10% (SOIL10) for 62 d, including a 7-d metabolism trial. It was found that soil intake altered the rumen fermentation in sheep, as evidenced by a decrease in total volatile fatty acids (VFA) and acetate concentrations in rumen fluid of 50.6% and 51.3%, respectively (p

Abstract Background Health information avoidance is common in real life, but because it is not always conducive to health promotion and maintenance, people often actively switch to health information acquisition. Understanding this process of active change can facilitate intervention in unreasonable avoidance behaviors. However, studies so far have mostly focused on why and how avoidance takes place, little is known about the process of active change from avoidance to acquisition. We thus use a grounded theory approach (GT) to explore how the active change takes place, and to generate a grounded theoretical framework capable of illustrating stages and influencing factors involved in the active change process. Methods Straussian grounded theory (Corbin & Strauss, 2015) was used to analyze data collected through semi-structured interviews with 30 adults (14 in good health, 11 with disease, 5 in other health status) who had experienced health information behavior change from avoidance to acquisition. These interviews focused on how the change occurred and what effected the change. Results The core category of Health Information Avoidance Change and 12 categories were identified and integrated to form a theoretical framework termed the Health Information Avoidance Change Model (HIACM). This model describes the process using five non-linear stage variables (initiation, preparation, action, maintenance, and abandonment) and seven moderating factor variables (cognitive change, social stimulus, beliefs and attitudes, intrapsychic literacy, social resources, information source, time and material resources). Conclusions HIACM can be used to explain the process of active change from health information avoidance to health information acquisition. HIAC is a non-linear and holistic process, and it is necessary to dynamically analyze the impact of relevant factors and take targeted intervention measures in stages. HIAC is usually not only an individual behavior, but also a socialized behavior requiring the collaboration of individuals, families, health information providers, healthcare providers, and governments.

Background and aims: Effective hepatic blood flow (EHBF) decreases with liver disease progression, and identifying liver pathology is critical for patients with liver disease. This study was designed to elucidate the correlation between EHBF and liver pathology and explore the potential of EHBF for predicting the degree of liver pathology. Methods: In this study, 207 patients with hepatitis B virus (HBV) who underwent liver biopsy and indocyanine green (ICG) clearance tests were enrolled. EHBF was measured using the ICG clearance test, and liver tissue was histologically analyzed to determine the pathological stage according to the Scheuer scoring system. Demographic data, biochemical indexes, and FibroScan data were collected for statistical analysis. Results: EHBF levels decreased as the liver histological stages of inflammation and fibrosis increased (P

Abstract Evidence has demonstrated that microRNA-342-5p (miR-342-5p) is implicated in atherosclerosis (AS), but little is known regarding its intrinsic regulatory mechanisms. Here, we aimed to explore the effect of miR-342-5p targeting Wnt3a on formation of vulnerable plaques and angiogenesis of AS. ApoE−/− mice were fed with high-fat feed for 16 w to replicate the AS vulnerable plaque model. miR-342-5p and Wnt3a expression in aortic tissues of AS were detected. The target relationship between miR-342-5p and Wnt3a was verified. Moreover, ApoE−/− mice were injected with miR-342-5p antagomir and overexpression-Wnt3a vector to test their functions in serum lipid levels, inflammatory and oxidative stress-related cytokines, aortic plaque stability and angiogenesis in plaque of AS mice. miR-342-5p expression was enhanced and Wnt3a expression was degraded in aortic tissues of AS mice and miR-342-5p directly targeted Wnt3a. Up-regulating Wnt3a or down-regulating miR-342-5p reduced blood lipid content, inflammatory and oxidative stress levels, the vulnerability of aortic tissue plaque and inhibited angiogenesis in aortic plaque of AS mice. Functional studies show that depleting miR-342-5p can stabilize aortic tissue plaque and reduce angiogenesis in plaque in AS mice via restoring Wnt3a.

To detect quickly and accurately “Yuluxiang” pear fruits in non-structural environments, a lightweight YOLO-GEW detection model is proposed to address issues such as similar fruit color to leaves, fruit bagging, and complex environments. This model improves upon YOLOv8s by using GhostNet as its backbone for extracting features of the “Yuluxiang” pears. Additionally, an EMA attention mechanism was added before fusing each feature in the neck section to make the model focus more on the target information of “Yuluxiang” pear fruits, thereby improving target recognition ability and localization accuracy. Furthermore, the CIoU Loss was replaced with the WIoUv3 Loss as the loss function, which enhances the capability of bounding box fitting and improves model performance without increasing its size. Experimental results demonstrated that the enhanced YOLO-GEW achieves an F1 score of 84.47% and an AP of 88.83%, while only occupying 65.50% of the size of YOLOv8s. Compared to lightweight algorithms such as YOLOv8s, YOLOv7-Tiny, YOLOv6s, YOLOv5s, YOLOv4-Tiny, and YOLOv3-Tiny; there are improvements in AP by 2.32%, 1.51%, 2.95%, 2.06%, 2.92%, and 5.38% respectively. This improved model can efficiently detect “Yuluxiang” pears in non-structural environments in real-time and provides a theoretical basis for recognition systems used by picking robots.

Rapid and precise detection of cucumbers is a key element in enhancing the capability of intelligent harvesting robots. Problems such as near-color background interference, branch and leaf occlusion of fruits, and target scale diversity in greenhouse environments posed higher requirements for cucumber target detection algorithms. Therefore, a lightweight YOLOv5s-Super model was proposed based on the YOLOv5s model. First, in this study, the bidirectional feature pyramid network (BiFPN) and C3CA module were added to the YOLOv5s-Super model with the goal of capturing cucumber shoulder features of long-distance dependence and dynamically fusing multi-scale features in the near-color background. Second, the Ghost module was added to the YOLOv5s-Super model to speed up the inference time and floating-point computation speed of the model. Finally, this study visualized different feature fusion methods for the BiFPN module; independently designed a C3SimAM module for comparison between parametric and non-parametric attention mechanisms. The results showed that the YOLOv5s-Super model achieves mAP of 87.5%, which was 4.2% higher than the YOLOv7-tiny and 1.9% higher than the YOLOv8s model. The improved model could more accurately and robustly complete the detection of multi-scale features in complex near-color backgrounds while the model met the requirement of being lightweight. These results could provide technical support for the implementation of intelligent cucumber picking.

Abstract Background The coronavirus disease (COVID-19), a pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown its destructiveness with more than one million confirmed cases and dozens of thousands of death, which is highly contagious and still spreading globally. World-wide studies have been conducted aiming to understand the COVID-19 mechanism, transmission, clinical features, etc. A cross-language terminology of COVID-19 is essential for improving knowledge sharing and scientific discovery dissemination. Methods We developed a bilingual terminology of COVID-19 named COVID Term with mapping Chinese and English terms. The terminology was constructed as follows: (1) Classification schema design; (2) Concept representation model building; (3) Term source selection and term extraction; (4) Hierarchical structure construction; (5) Quality control (6) Web service. We built open access for the terminology, providing search, browse, and download services. Results The proposed COVID Term include 10 categories: disease, anatomic site, clinical manifestation, demographic and socioeconomic characteristics, living organism, qualifiers, psychological assistance, medical equipment, instruments and materials, epidemic prevention and control, diagnosis and treatment technique respectively. In total, COVID Terms covered 464 concepts with 724 Chinese terms and 887 English terms. All terms are openly available online (COVID Term URL: http://covidterm.imicams.ac.cn ). Conclusions COVID Term is a bilingual terminology focused on COVID-19, the epidemic pneumonia with a high risk of infection around the world. It will provide updated bilingual terms of the disease to help health providers and medical professionals retrieve and exchange information and knowledge in multiple languages. COVID Term was released in machine-readable formats (e.g., XML and JSON), which would contribute to the information retrieval, machine translation and advanced intelligent techniques application.

Abstract Background The results of a previous study verified that umbilical cord mesenchymal stem cells (UCMSCs) have good therapeutic effects for the treatment of HBV-related acute-on-chronic liver failure (ACLF) and liver cirrhosis (LC). Nevertheless, it is still unknown whether the effects of UCMSCs are affected by recipient age. Methods Patients treated with UCMSCs who met the criteria of HBV-related ACLF and liver cirrhosis were identified in this retrospective observational study. Patients were divided into subgroups according to the World Health Organization (WHO) age criteria (

(1) Estimation of grazing livestock intake is the basis for studying animal–plant relationships and the nutritional status of grazing livestock and has important implications for grassland composition and productivity. (2) We used the saturated alkanes method to determine the feed intake and vegetation nutrient digestibility of livestock at different grazing intensities and in different months. (3) We found that C31 had the highest concentration in both pasture and fecal output, and the average recovery of C31 was 77.99%. The different grazing intensities significantly affected livestock intake. As the grazing intensity increased, there was a decreasing trend of livestock intake and the highest livestock feed intake was 6.11 kg DM/day in light grazing. With the increase in grazing season months, the highest livestock intake was 6.67 kg DM/day in the cold period in September. The month also had a significant effect on the digestibility of livestock for all nutrient variables when compared to the grazing intensity. Livestock weight and medium palatability species are more important for livestock intake. (4) Our study provides a more accurate measurement of grazing livestock intake, which can be used as a reference for the scientific management of grazing livestock and the rational use of grazing pastures.

Abstract Background Umbilical cord mesenchymal stem cells (UCMSCs) have been demonstrated to have good therapeutic effects in the treatment of HBV-related liver diseases. However, the therapeutic effect of UCMSCs on HBV-related liver failure and liver cirrhosis and the variations in the efficacy of UCMSCs after different treatment courses remain poorly understood. Therefore, this study was designed to answer these two questions. Methods This was an observational study that retrospectively considered a 3-year period during which 513 patients who received stem cell infusion and met the criteria of hepatic failure and liver cirrhosis were identified from the databases of the Third Affiliated Hospital of Sun Yat-sen University. The eligible patients were categorized into the liver failure group and liver cirrhosis group. The two groups were divided into different subgroups according to the duration of stem cell therapy. In the liver failure group, group A received more than 4 weeks and group B received less than 4 weeks of stem cell therapy. In the liver cirrhosis group, patients who received more than 4 weeks of stem cell therapy belonged to group C, and the patients in group D received less than 4 weeks of stem cell therapy. The patients were followed up for 24 weeks. The demographics, clinical characteristics, biochemical factors, and model for end-stage liver disease (MELD) scores were recorded and compared among different groups. Results A total of 64 patients met the criteria for liver failure, and 59 patients met the criteria for liver cirrhosis. After UCMSC treatment, the levels of alanine aminotransferase (ALT), glutamic-oxaloacetic transaminase (AST), and total bilirubin (TBIL) at all postbaseline time points were significantly lower than those at baseline in the liver failure group and liver cirrhosis group; the prothrombin activity (PTA) and MELD scores gradually improved in only the liver failure group. Four weeks after UCMSC treatment, patients who received prolonged treatment with UCMSCs had a larger decrease in TBIL levels than patients who terminated treatment with UCMSCs. After more than 4 weeks of UCMSC treatment, there were no statistically significant differences in the changes in ALT, AST, TBIL, and PTA values and MELD scores between patients with liver failure who received prolonged treatment with UCMSCs and patients with liver cirrhosis who received prolonged treatment with UCMSCs at any time point. However, the median decrease and cumulative decrease in the TBIL level of patients with liver failure with a standard 4-week treatment course were larger than those of patients with liver cirrhosis with a standard 4-week treatment course. Conclusion Peripheral infusion of UCMSCs showed good therapeutic effects for HBV-related liver failure and liver cirrhosis. Prolonging the treatment course can increase the curative effect of UCMSCs for end-stage liver disease, especially for patients with cirrhosis.

Abstract Background Kinesin superfamily proteins (KIFs) can transport membranous organelles and protein complexes in an ATP-dependent manner. Kinesin family member 15 (KIF15) is overexpressed in various cancers. However, the function of KIF15 in gastric cancer (GC) is still unclear. Methods GC patients’ data from The Cancer Genome Atlas (TCGA) were analyzed by bioinformatics methods. The expression of KIF15 was examined in GC and paracarcinoma tissues from 41 patients to verify the analysis results. The relationship between KIF15 expression and clinical characteristics were also observed by bioinformatics methods. Kaplan–Meier survival analysis of 122 GC patients in our hospital was performed to explore the relationship between KIF15 expression levels and GC patients’ prognosis. KIF15 was downregulated in GC cell lines AGS and SGC-7901 by transfecting a lentivirus-mediated shRNA plasmid targeting KIF15. In vitro, GC cell proliferation and apoptosis were detected by MTT assay, colony formation assay, and Annexin V-APC staining. In vivo, xenograft experiments were used to verify the in vitro results. Furthermore, Human Apoptosis Antibody Array kit was used to screen possible targets of KIF15 in GC cell lines. Results The bioinformatics results showed that KIF15 expression levels were higher in GC tissues than in normal tissues. IHC showed same results. High expression of KIF15 was statistical correlated with high age and early histologic stage. Kaplan–Meier curves indicated that high KIF15 expression predict poor prognosis in patients with GC. MTT assay and colony formation assay showed that KIF15 promote GC cell proliferation. Annexin V-APC staining found that KIF15 can inhibit GC cell apoptosis. Xenograft experiments reveal that downregulating KIF15 can inhibit GC tumor growth and promote GC apoptosis. Through detection of 43 anti-apoptotic proteins by the Human Apoptosis Antibody Array kit, it was confirmed that knocking down KIF15 can reduce seven anti-apoptotic proteins expression. Conclusions Taken together, our study revealed a critical role for KIF15 to inhibit GC cell apoptosis and promote GC cell proliferation. KIF15 may decrease anti-apoptotic proteins expression by regulating apoptosis pathways. High expression of KIF15 predicts a poor prognosis in patients with GC. KIF15 might be a novel prognostic biomarker and a therapeutic target for GC.

Fruits with various maturity levels coexist among the harvested jujubes, and have different tastes and uses. Manual grading has a low efficiency and a strong subjectivity. The number of “Hupingzao” jujubes between different maturity levels is unbalanced, which affects the performance of the classifier. To solve the above issue, the class balance loss (CB) was used to improve the MobileNet V2 network, and a transfer learning strategy was used to train the model. The model was optimized based on the selection of an optimizer and learning rate. The model achieved the best classification results using the AdamW optimizer and a learning rate of 0.0001. The application of transfer learning and class balance loss improved the model’s performance. The precision was 96.800~100.000%, the recall was 95.833~100.000%, and the F1 score was 0.963~1.000. To compare the CB-MobileNet V2 performance, the CB-AlexNet, CB-GoogLeNet, CB-ShuffleNet, CB-Inception V3, CB-ResNet 50, and CB-VGG 16 with transfer learning were used to build classification models. Achieving a validation accuracy of 99.058%, and a validation loss value of 0.055, the CB-MobileNet V2 model showed a better overall performance compared with other models. The maturity detection system of “Hupingzao” jujubes was developed to test the model. The testing accuracy of the CB-MobileNet V2 model was 99.294%. The research indicates that the CB-MobileNet V2 model improves the performance of maturity classification, and provides a theoretical basis for intelligent classification of the quality of “Hupingzao” jujubes.

With increasing consumer expectations for the quality and safety of agricultural products, intelligent quality detection and gradation have considerable significance in agricultural production. The surface defect is an important indicator of quality, but is classified mainly using inefficient manual identification for “Yuluxiang” pears. Because of the uncertainty and high difficulty of image acquisition in agriculture, the data imbalance between categories is a common problem. For the resolution of these problems, the class balance (CB) was used to re-weight the sigmoid cross-entropy loss (SGM-CE), softmax cross-entropy loss (SM-CE), focal loss (FL) functions in this study. CB-SGM-CE, CB-SM-CE, and CB-FL were used to construct a GoogLeNet network as a convolutional neural network (CNN) generalized feature extractor, and transfer learning was combined to build detection models, respectively. The results showed that CB-SGM-CE, CB-SM-CE, and CB-FL were better than SGM-CE, SM-CE, and FL, respectively. CB-FL achieved the best detection results (F1 score of 0.993–1.000) in 3 CB loss functions. Then, CB-FL was used to construct VGG 16, AlexNet, SqueezeNet, and MobileNet V2 networks based on transfer learning, respectively. Machine learning (ML) and CNN were used to build classification models in this study. Compared with ML models and the other 4 CNN models, the CB-FL-GoogLeNet model achieved the best detection results (accuracy of 99.78%). A system for surface defect detection was developed. The results showed that the testing accuracy of the CB-FL-GoogLeNet model was 95.28% based on this system. This study realizes the surface defect detection of the “Yuluxiang” pear with an unbalanced dataset, and provides a method for intelligent detection in agriculture.

To solve the failure problem of the visible/near infrared (VIS/NIR) spectroscopy model, soluble solids content (SSC) detection for fresh jujubes cultivated in different modes was carried out based on the method of variable optimization and model update. Iteratively retained informative variables (IRIV) and successive projections algorithm (SPA) algorithms were used to extract characteristic wavelengths, and least square support vector machine (LS-SVM) was used to establish detection models. Compared with IRIV, IRIV-SPA achieved better performance. Combined with the offset properties of the wavelength, repeated wavelengths were removed, and wavelength recombination was carried out to create a new combination of variables. Using these fused wavelengths, the model was recalibrated based on the Euclidean distance between samples. The LS-SVM detection model of SSC was established using the update method of wavelength fusion-Euclidean distance. Good prediction results were achieved using the proposed model. The determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the test set on SSC of fresh jujubes cultivated in the open field were 0.82, 1.49%, and 2.18, respectively. The R2, RMSE, and RPD of the test set on SSC of fresh jujubes cultivated in the rain shelter were 0.81, 1.44%, and 2.17, respectively. This study realized the SSC detection of fresh jujubes with different cultivation and provided a method for the establishment of a robust VIS/NIR detection model for fruit quality, effectively addressing the industry need for identifying jujubes grown in the open field.

Background: Tsantan Sumtang originated from Four Tantras, which consisted of Choerospondias axillaris (Roxb.) B. L. Burtt and A. W. Hill, Santalum album L., and Myristica fragrans Houtt. The three herbs are in ratio 1:1:1. This medication is widely used for cardiovascular diseases.Aims: The purpose of this study was to explore the effect of Tsantan Sumtang on right ventricular (RV) function in hypoxia-induced pulmonary hypertension (HPH) rats and investigate the underlying mechanism.Methods: Sixty male Sprague-Dawley (SD) rats were divided into control, hypoxia, and hypoxia + Tsantan Sumtang (1.0, 1.25, and 1.5 g•kg−1•d−1) groups. Chronic hypoxia was induced by putting the rats inside a hypobaric chamber for four weeks and adjusting the inner pressure and oxygen content to match an altitude of 4500 m. Echocardiography was used to assess RV function and right ventricular-pulmonary arterial (RV-PA) coupling. The physiological parameters of the animals were also evaluated. Morphological characteristics of RV were assessed by hematoxylin and eosin (H&E) staining and TEM. Masson’s trichrome staining, immunohistochemical staining, western blotting, and TUNEL assay were used to assess fibrosis and apoptosis levels. The antioxidant and anti-apoptosis properties of Tsantan Sumtang were also evaluated. The effect of Tsantan Sumtang on ROCK signaling pathway was evaluated using real-time quantitative PCR and western blotting.Results: We established an HPH rat model as indicated by the significant increases in the physiological parameters of the rats. Tsantan Sumtang showed a significant cardiac-protective function and an improved effect on RV-PA coupling. Moreover, Tsantan Sumtang treatment inhibited fibrosis and alleviated apoptosis and oxidative stress in RV. In terms of mechanism, Tsantan Sumtang reduced the expression of ROCK (ROCK1, ROCK2) in RV, inhibited cardiac remodeling-related transcription factors (NFATc3, P-STAT3), and regulated apoptosis-related proteins.Conclusion: Tsantan Sumtang was able to restore RV function, improve RV-PA coupling, recover hemodynamic and hematological indexes, and protect RV against structural maladaptive remodeling in the HPH rats. These findings demonstrated that Tsantan Sumtang protects the function of RV in HPH rats. The antioxidant and anti-apoptosis properties of Tsantan Sumtang may be responsible for inhibiting the ROCK signaling pathway.

The efficient identification of the field flat jujube is the first condition to realize its automated picking. Consequently, a lightweight algorithm of target identification based on improved YOLOv5 (you only look once) is proposed to meet the requirements of high-accuracy and low-complexity. At first, the proposed method solves the imbalance of data distribution by improving the methods of data enhancement. Then, to improve the accuracy of the model, we adjust the structure and the number of the Concentrated-Comprehensive Convolution Block modules in the backbone network, and introduce the attention mechanisms of Efficient Channel Attention and Coordinate Attention. On this basis, this paper makes lightweight operations by using the Deep Separable Convolution to reduce the complexity of the model. Ultimately, the Complete Intersection over Union loss function and the non-maximum suppression of Distance Intersection over Union are used to optimize the loss function and the post-processing process, respectively. The experimental results show that the mean average precision of improved network reaches 97.4%, which increases by 1.7% compared with the original YOLOv5s network; and, the parameters, floating point of operations, and model size are compressed to 35.39%, 51.27%, and 37.5% of the original network, respectively. The comparison experiments are conducted around the proposed method and the common You Only Look Once target detection algorithms. The experimental results show that the mean average precision of the proposed method is 97.4%, which is higher than the 90.7%, 91.7%, and 88.4% of the YOLOv3, YOLOv4, and YOLOx-s algorithms, and the model size decreased to 2.3%, 2.2%, and 15.7%, respectively. The improved algorithm realizes a reduction of complexity and an increase in accuracy, it can be suitable for lightweight deployment to a mobile terminal at a later stage, and it provides a certain reference for the visual detection of picking robots.

Thin-walled tubes have gained wide applications in aerospace, automobile and other engineering fields due to their excellent energy absorption and lightweight properties. In this study, a novel method of entropy-weighted TOPSIS was adopted to study the energy absorption characteristics of a thin-walled circular tube under axial crushing. Three types of thin-walled circular tubes, namely, aluminum (Al) tubes, carbon-fiber-reinforced plastics (CFRP) tubes and CFRP-Al hybrid thin-walled tubes, were fabricated. Quasi-static axial crushing tests were then carried out for these specimens, and their failure modes and energy absorption performance were analyzed. The CFRP material parameters were obtained through tensile, compression and in-plane shear tests of CFRP laminates. The finite element models for the quasi-static axial crushing of these three types of circular tubes were established. The accuracy of the finite element models was verified by comparing the simulation results with the test results. On this basis, the effects of the geometric dimension and ply parameters of a CFRP-Al hybrid thin-walled circular tube on the axial crushing energy absorption characteristics were studied based on an orthogonal design and entropy-weighted TOPSIS method. The results showed that Al tube thickness, CFRP ply thickness and orientation have great effect on the energy absorption performance of a CFRP-Al hybrid thin-walled circular tube, whereas the tube diameter and length have little effect. The energy absorption capability of a CFRP-Al hybrid tube can be improved by increasing the thickness of the Al tube and the CFRP tube as well as the number of ±45° plies.

A pepper quality detection and classification model based on transfer learning combined with convolutional neural network is proposed as a solution for low efficiency of manual pepper sorting at the current stage. The pepper dataset was amplified with data pre-processing methods including rotation, luminance switch, and contrast ratio switch. To improve training speed and precision, a network model was optimized with a fine-tuned VGG 16 model in this research, transfer learning was applied after parameter optimization, and comparative analysis was performed by combining ResNet50, MobileNet V2, and GoogLeNet models. It turned out that the VGG 16 model output anticipation precision was 98.14%, and the prediction loss rate was 0.0669 when the dropout was settled as 0.3, learning rate settled as 0.000001, batch normalization added, and ReLU as activation function. Comparing with other finetune models and network models, this model was of better anticipation performance, as well as faster and more stable convergence rate, which embodied the best performance. Considering the basis of transfer learning and integration with strong generalization and fitting capacity of the VGG 16 finetune model, it is feasible to apply this model to the external quality classification of pepper, thus offering technical reference for further realizing the automatic classification of pepper quality.

To perform accurate and synchronous detection of the soluble solid contents (SSC) in fresh jujubes at different stages of maturity, hyperspectral imaging was used to establish robust models. The combined data constituting four maturation stages were used to build the grid-search least squares support vector machine (GS-LS-SVM) model. The determination coefficient (Rp2), the root-mean-square error (RMSEP), and the residual predictive deviation (RPD) of the prediction set for samples of the overall stages were 0.98, 1.10%, and 7.85, respectively. Furthermore, a successive projections algorithm (SPA) was used to extract the characteristic wavelengths of the combined data. An artificial bee colony (ABC) algorithm (for the prediction set, Rp2 = 0.98, RMSEP = 1.19%, RPD = 7.25) was used to improve the SPA-LS-SVM model, which was better than the SPA-GS-LS-SVM model (for the prediction set, Rp2 = 0.98, RMSEP = 1.24%, RPD = 6.96). Lastly, visualization of the SSC distribution map was performed based on the SPA-ABC-LS-SVM model, which clearly showed that the SSC gradually increased during maturation. The results indicated that it was realistic to construct a detection model of the multimaturity stage. This research also demonstrated that the combination of hyperspectral imaging and the ABC had good application values in the testing of agricultural products.

Receptor for advanced glycation end products (AGEs; RAGE) binds to both AGEs and amyloid-beta peptides. RAGE is involved in chronic complications of type 2 diabetes and Alzheimer's disease. We aimed to investigate the roles of RAGE, AGEs and the Gly82Ser polymorphism of RAGE in mild cognitive impairment (MCI) among type 2 diabetes patients.Of the 167 hospitalized type 2 diabetes patients recruited, 82 satisfied the diagnostic criteria for MCI, and 85 matched control individuals were classified as non-MCI. Demographic data were collected, and the soluble RAGE (sRAGE) concentrations, serum AGE-peptide (AGE-P) levels, RAGE Gly82Ser genotype and neuropsychological test results were examined.The MCI group exhibited a decreased sRAGE level (0.87±0.35 vs. 1.05±0.52 ng/ml, p0.05).The RAGE pathway partially mediates AGE-induced MCI in diabetic patients. The serum AGE-P level may serve as a serum biomarker of MCI in these individuals, and sRAGE represents a predictor and even a potential intervention target of early cognitive decline in type 2 diabetes patients.Advanced Glycation End Products Induced Cognitive Impairment in Diabetes: BDNF Signal Meditated Hippocampal Neurogenesis ChiCTR-OCC-15006060.