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The significance of interactive pedagogy in English education cannot be overstated, as it fosters student engagement, collaboration, and active learning, thereby enhancing both academic performance and comprehensive capabilities. This article presents a structured three-step strategy designed to integrate curriculum thinking into the teaching of English at higher vocational levels through a blended online and offline approach. Central to this methodology is the development of a robust “online + offline” teaching quality evaluation index system, innovatively constructed using a combination of rough set theory and a backpropagation (BP) neural network algorithm. Furthermore, the effectiveness of this pedagogical approach is quantitatively analyzed using a Markov chain model, which evaluates the progression of students’ English proficiency. The analysis reveals that sustained application of the proposed teaching methodology is likely to elevate students’ English performance to an estimated score of 84.273, marking a substantial improvement of 5.443 points over traditional methods. Empirical validation of the model’s effectiveness was achieved through student evaluations across three critical areas: Civic Integration Effectiveness, Pre-Class Preview, and Follow-Up Testing. The results of the post-test assessments yielded mean scores of 3.565, 3.564, and 3.695, respectively, indicating a significant enhancement in learning outcomes, as evidenced by p-values of less than 0.05. This statistical analysis confirms the significant impact of the innovative pedagogical strategy on student learning outcomes.

This paper firstly explores the power mechanism of agricultural insurance to help rural revitalization from three aspects of agriculture, farmers and rural three farmers, respectively, and constructs the rural revitalization development index system by combining the entropy power method. Then, the three-stage DEA model and the three-stage Malmquist index model are used to measure the efficiency of agricultural insurance boosting rural revitalization by combining the selected agricultural insurance boosting indicators. Finally, taking province A as an example, after descriptive statistical analysis, the method of this paper is combined with empirical analysis, and the practical path of agricultural insurance to help farmers is proposed from the three aspects of the insurance system, publicity as well and supply. The results show that after the optimization of agricultural insurance, the value of technical efficiency is distributed in [0.600, 0.738], the value of pure technical efficiency is distributed in [0.716, 0.826], and the value of scale efficiency is distributed in [0.738, 0.833] from 2010 to 2019. The mean value of the agricultural technical efficiency index in each city is 1.091, the mean value of the progress index is 1.033, and the mean value of total factor productivity is 1.029, which is an upward trending technology, and all of them have a growth rate of more than 5%. Based on this study, agricultural insurance can help improve the efficiency of rural revitalization.

Background: Thyroid cancer is a common thyroid malignancy. The majority of thyroid lesion needs intraoperative frozen pathology diagnosis, which provides important information for precision operation. As digital whole slide images (WSIs) develop, deep learning methods for histopathological classification of the thyroid gland (paraffin sections) have achieved outstanding results. Our current study is to clarify whether deep learning assists pathology diagnosis for intraoperative frozen thyroid lesions or not. Methods: We propose an artificial intelligence-assisted diagnostic system for frozen thyroid lesions that applies prior knowledge in tandem with a dichotomous judgment of whether the lesion is cancerous or not and a quadratic judgment of the type of cancerous lesion to categorize the frozen thyroid lesions into five categories: papillary thyroid carcinoma, medullary thyroid carcinoma, anaplastic thyroid carcinoma, follicular thyroid tumor, and non-cancerous lesion. We obtained 4409 frozen digital pathology sections (WSI) of thyroid from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) to train and test the model, and the performance was validated by a six-fold cross validation, 101 papillary microcarcinoma sections of thyroid were used to validate the system's sensitivity, and 1388 WSIs of thyroid were used for the evaluation of the external dataset. The deep learning models were compared in terms of several metrics such as accuracy, F1 score, recall, precision and AUC (Area Under Curve). Results: We developed the first deep learning-based frozen thyroid diagnostic classifier for histopathological WSI classification of papillary carcinoma, medullary carcinoma, follicular tumor, anaplastic carcinoma, and non-carcinoma lesion. On test slides, the system had an accuracy of 0.9459, a precision of 0.9475, and an AUC of 0.9955. In the papillary carcinoma test slides, the system was able to accurately predict even lesions as small as 2 mm in diameter. Tested with the acceleration component, the cut processing can be performed in 346.12 s and the visual inference prediction results can be obtained in 98.61 s, thus meeting the time requirements for intraoperative diagnosis. Our study employs a deep learning approach for high-precision classification of intraoperative frozen thyroid lesion distribution in the clinical setting, which has potential clinical implications for assisting pathologists and precision surgery of thyroid lesions. [ABSTRACT FROM AUTHOR]

Background: The number and proportion of HIV/AIDS patients among older people are continuously and rapidly increasing in China. We conducted a detailed molecular epidemiological analysis of HIV-1 epidemic strains in a developed city in eastern China and found that elderly people play a crucial role in the transmission of subtypes and high pretreatment drug resistance (PDR). Methods: A total of 1048 samples were obtained from 1129 (92.8%) newly confirmed HIV-1-positive and treatment-naive patients between 2019 and 2023. The 1316 bp target fragment of the pol gene was amplified by reverse transcription polymerase chain reaction (RT‒PCR) and nested PCR, and Maximum-likelihood (ML) phylogenetic trees and molecular transmission network were constructed to analyse the subtypes and transmission clusters. Molecular transmission network was visualized using Cytoscape with the distance threshold of 0.0075. PDR-associated mutations were determined according to the Stanford University HIV Drug Resistance Database. Results: A total of 933 pol sequences (89.0%, 933/1048) were successfully obtained, and twelve HIV-1 subtypes were detected. CRF07_BC was the predominant subtype, accounting for 48.1% (449/933) of sequences, followed by CRF01_AE (29.4%, 274/933). A total of 398 individuals (42.7%, 398/933) formed 89 clusters in the network. Multivariable logistic regression analysis revealed that age, nationality, subtype, and PDR were the most significant factors associated with clustering in the transmission network. The prevalence of PDR was 14.6% (136/933).PDR associated with non-nucleoside reverse transcriptase inhibitors (10.0%, 93/933) was much more common than that associated with nucleoside reverse transcriptase inhibitors (1.8%, 17/933) and protease inhibitors (3.2%, 30/933) (χ2 = 77.961, p < 0.001). The most frequent NNRTI mutations were K103N/S/KN/NS (52.2%, 71/136), which led to the highest proportion of high-level resistance to nevirapine and efavirenz (52.2%). Conclusions: Our study revealed the important influence of elderly people on CRF07_BC transmission and the high prevalence of PDR. The clustering of drug-resistant cases was significant, which suggested the potential for localized widespread transmission of drug-resistant strains. HIV screening and the determination of PDR are recommended for older patients to improve early detection and reduce treatment failure and second-generation transmission. [ABSTRACT FROM AUTHOR]

Background: Nonalcoholic fatty liver disease (NAFLD) is a prevalent chronic liver ailment that can lead to serious conditions such as cirrhosis and hepatocellular carcinoma. Hepatic Nogo-B regulates glucose and lipid metabolism, and its inhibition has been shown to be protective against metabolic syndrome. Increasing evidence suggests that imbalances in the gut microbiota (GM) and lipid metabolism disorders are significant contributors to NAFLD progression. Nevertheless, it is not yet known whether Nogo-B can affect NAFLD by influencing the gut microbiota and metabolites. Hence, the aim of the present study was to characterize this process and explore its possible underlying mechanisms. Methods: A NAFLD model was constructed by administering a high-fat diet (HFD) to Nogo-B−/− and WT mice from the same litter, and body weight was measured weekly in each group. The glucose tolerance test (GTT) and insulin tolerance test (ITT) were performed to assess blood glucose levels. At the end of the 12-week period, samples of serum, liver, and intestinal contents were collected and used for serum biochemical marker and inflammatory factor detection; pathology evaluation; and gut microbiome and metabolomics analysis. Spearman's correlation analysis was performed to determine possible correlations between differential gut microbiota and differential serum metabolites between groups. Results: Nogo-B deficiency attenuated the effects of the HFD, including weight gain, liver weight gain, impaired glucose tolerance, hepatic steatosis, elevated serum lipid biochemicals levels, and liver function. Nogo-B deficiency suppressed M1 polarization and promoted M2 polarization, thus inhibiting inflammatory responses. Furthermore, Nogo-B−/−-HFD-fed mice presented increased gut microbiota richness and diversity, decreased Firmicutes/Bacteroidota (F/B) ratios, and altered serum metabolites compared with those of WT-HFD-fed mice. During analysis, several differential gut microbiota, including Lachnoclostridium, Harryflintia, Odoribacter, UCG-009, and unclassified_f_Butyricoccaceae, were screened between groups. These microbiota were found to be positively correlated with upregulated purine metabolism and bile acid metabolites in Nogo-B deficiency, while they were negatively correlated with downregulated corticosterone and tricarboxylic acid cyclic metabolites in Nogo-B deficiency. Conclusion: Nogo-B deficiency delayed NAFLD progression, as demonstrated by reduced hepatocellular lipid accumulation, attenuated inflammation and liver injury, and ameliorated gut microbiota dysbiosis and metabolic disorders. Importantly, Odoribacter was strongly positively correlated with ALB and taurodeoxycholic acid, suggesting that it played a considerable role in the influence of Nogo-B on the progression of NAFLD, a specific feature of NAFLD in Nogo-B−/− mice. The regulation of bile acid metabolism by the gut microbiota may be a potential target for Nogo-B deficiency to ameliorate NAFLD. [ABSTRACT FROM AUTHOR]

Cucumber is one of the top ten vegetables globally and is widely cultivated worldwide. However, Fusarium wilt, caused by Fusarium oxysporum f. sp. Cucumerinum, is one of the most serious soil-borne diseases in cucumber cultivation, causing significant economic losses. Biological control has great potential in the prevention of cucumber wilt disease, but the mechanism involved still needs further research. In this study, biocontrol isolate Bacillus subtilis 1JN2, which was isolated in our previous work, was evaluated in field conditions against Fusarium wilt, and the rhizosphere fungal diversity was analyzed. The results indicated that the biocontrol efficacy of B. subtilis 1JN2 reached 58.5% compared with the blank control, and the population density of F. oxysporum in the rhizosphere decreased from 495 copies/g of soil before inoculation to 20 copies/g 14 days after treatment. High-throughput sequencing demonstrated that after an inoculation of 1JN2, the populations that decreased significantly include the genera of Olpidium and Pseudallescheria, from more than 20% to less than 8%. And the most increased population belonged to the family Chaetomiaceae, from 6.82% to 18.77%, 12.39%, 44.41%, and 19.41% at the four sample time points after treatment. In addition, soil-related enzyme activities, including catalase, soil dehydrogenase, alkaline phosphatase, and polyphenol oxidase, were analyzed before and after treatment with 1JN2. The results indicated that all the enzyme activities showed an upward trend following inoculation. These findings demonstrate the potential of using B. subtilis 1JN2 as a biocontrol agent for controlling Fusarium wilt in cucumber. [ABSTRACT FROM AUTHOR]

Distributed Inverse Synthetic Aperture Radar (ISAR) systems employ multiple spatially separated radars to observe a target, with the potential to enhance radar imaging azimuth resolution and gather more target information. Due to the existence of gaps in the observation angles of each radars, the coherence between pulses of radar echo is disrupted during fusion imaging, making it challenging for the Range-Doppler (RD) algorithm to achieve well-focused images. This paper proposes an imaging method based on sparse signal recovery (SSR), using the reweighted ${l_1}$ l 1 norm with convolution and total variation as regularization terms. This approach not only yields well-focused ISAR images but also better preserves the overall target information compared to other methods, while also exhibiting excellent noise resistance. In addition, the alternating direction method of multipliers (ADMM) algorithm is utilized for the solution to reduce computational complexity. Simulation and measured data verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]