Construction and enhancement of government capacity for international communication and promotion of Chinese Wushu.

BACKGROUND: In the process of international communication in Chinese Wushu (ICCW), the government controls the orientation, scale, pace. However, the ICCW currently lacks a standardised government capacity structural system, and a detailed study of framework construction may be required to ensure the smooth development of the ICCW. OBJECTIVES: This study aims to clarify these elements and construct a framework for a governmental capacity system for ICCW. METHODS: For this purpose, an expert interview outline was designed, and in-depth interviews were conducted with 61 experts. Using grounded theory in the qualitative research method, NVivo 12 software was used to conduct a three-level coding analysis of the interview text for data processing and analysis. RESULTS: We extracted 58 opening codes and 11 tree nodes and categorised them into three core categories: supply side government capacity, environment-side government capacity, and demand-side government capacity, accounting for 62.36 %, 24.76 %, and 12.86 % of the total, respectively, which jointly constructed the framework structure system of the governmental capacity system for the ICCW. CONCLUSIONS: This study found that these three-dimensional government capacities have synergistic effects and that multiple measures work together. The government should ensure the supply side's direct promotion effect; the environmental side's indirect influencing effect; and the demand side's internal driving effect to promote ICCW. Meanwhile, a closed-loop systematic study of communication processes should be conducted in combination with communication organisations and individuals.

Extracts of Thesium chinense inhibit SARS-CoV-2 and inflammation in vitro.

CONTEXT: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still spreading rapidly. Relevant research based on the antiviral effects of Thesium chinense Turcz (Santalaceae) was not found. OBJECTIVE: To investigate the antiviral and anti-inflammatory effects of extracts of T. chinense. MATERIALS AND METHODS: To investigate the anti-entry and replication effect of the ethanol extract of T. chinense (drug concentration 80, 160, 320, 640, 960 µg/mL) against the SARS-CoV-2. Remdesivir (20.74 µM) was used as positive control, and Vero cells were used as host cells to detect the expression level of nucleocapsid protein (NP) in the virus by real-time quantitative polymerase chain reaction (RT-PCR) and Western blotting. RAW264.7 cells were used as an anti-inflammatory experimental model under lipopolysaccharide (LPS) induction, and the expression levels of tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) were detected by enzyme-linked immunosorbent assay (ELISA). RESULTS: The ethanol extract of T. chinense significantly inhibited the replication (half maximal effective concentration, EC50: 259.3 µg/mL) and entry (EC50: 359.1 µg/mL) of SARS-CoV-2 into Vero cells, and significantly reduced the levels of IL-6 and TNF-α produced by LPS-stimulated RAW264.7 cells. Petroleum ether (EC50: 163.6 µg/mL), ethyl acetate (EC50: 22.92 µg/mL) and n-butanol (EC50: 56.8 µg/mL) extracts showed weak inhibition of SARS-CoV-2 replication in Vero cells, and reduced the levels of IL-6 and TNF-α produced by LPS-stimulated RAW264.7 cells. CONCLUSION: T. chinense can be a potential candidate to fight SARS-CoV-2, and is becoming a traditional Chinese medicine candidate for treating COVID-19.

Antibacterial Activity of Thesium chinense Turcz Extract Against Bacteria Associated with Upper Respiratory Tract Infections.

Purpose: The drug resistance of Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes and Haemophilus influenzae has become more and more serious, and it is urgent to seek new antibacterial drugs. In this study, Thesium chinense Turcz. extracts were tested for its potential antibacterial activities. Methods: T. chinense powder was extracted with 5 solvents of different polarity (ethyl alcohol, petroleum ether, ethyl acetate, n-butyl alcohol and double distilled water), and their antibacterial activities were tested. The Broth dilution method was used to evaluate the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of highly active plant extracts with a concentration of 1g/mL. The inhibitory activity of this extract on biofilm formation was investigated. Afterwards, we investigated its effect on the transcriptome of S. aureus. Results: The ethanol extract coded as BRY, only inhibited S. aureus, whereas the ethyl acetate extract coded as BY2 showed inhibitory effect on all the tested bacteria. The MIC of BRY on S. aureus was 128 mg/mL, and the MBC was 512 mg/mL. The MIC of BY2 against S. aureus, S. pneumoniae, S. pyogenes and H. influenzae were 8 mg/mL, 4 mg/mL, 4 mg/mL, and 4 mg/mL, respectively. The MBC of BY2 for these four bacteria ranged from 4 to 256 mg/mL. Mechanism studies have shown that BRY and BY2 have an impact on anti-formation of biofilms at MIC concentrations. Transcriptome sequencing results showed that 531 genes were up-regulated and 340 genes showed down-regulated expression in S. aureus after BY2 treatment. Conclusion: BY2 has a broader antibacterial spectrum than BRY. Meanwhile, the inhibitory effect of BY2 on S. aureus is better than BRY. The mechanism of BY2 against S. aureus may relate to its inhibition of ribosome synthesis, restriction of key enzymes of citric acid cycle, decrease of pathogenicity and influence on biofilm formation. The results confirmed that BY2 was the main antibacterial part of T. chinense, which can be used as a source of antibacterial agents.

Estimating daily minimum grass temperature to quantify frost damage to winter wheat during stem elongation in the central area of Huang-Huai plain in China.

Frost damage to winter wheat during stem elongation frequently occurred in the Huang-Huai plain of China, leading to considerable yield losses. Minimum Stevenson screen temperature (STmin) and minimum grass temperature (GTmin) have long been used to quantify frost damage. Although GTmin has higher accuracy than STmin, it is limited in application due to the lack of data. Therefore, this study aimed to select appropriate environmental variables to estimate GTmin, as well as to quantify the frost damage. Shangqiu, a frost-prone winter wheat area in the central Huang-Hui plain, was selected as the study area. From the descriptive statistics of ST, air relative humidity (RH), wind speed (WS), cloud fraction (CF), and volumetric soil water content (VWC) during temperature decreasing and increasing, seven variables significantly correlated with GTmin were selected, including STmin, maximum reduction of ST (RST), maximum increase of ST (IST), minimum RH during temperature increasing (RHmin), WS at STmin occurrence (WS), minimum VWC during temperature decreasing (VWCmin), and nightly CF. Multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and K-nearest neighbor (KNN) were adopted for estimating GTmin based on the various combinations of the variables. Results showed the more variables, the higher the accuracy for the MLR and SVR. However, this pattern was not always true for the KNN and RF. The KNN based on STmin, RST, IST, RHmin, and WS achieved the highest accuracy, with R2 of 0.9992, RMSE of 0.14 ℃, and MAE of 0.076 ℃. The overall classification accuracy for frost damage identified by the estimated GTmin reached 97.1% during stem elongation of winter wheat from 2017 to 2021. The integrated frost stress (IFS) index calculated by the estimated and measured GTmin maintained high linear fitting accuracy. The KNN with fewer variables demonstrated good applicability at the regional scale.

Chemical Constituents of Thesium chinense Turcz and Their In Vitro Antioxidant, Anti-Inflammatory and Cytotoxic Activities.

Three novel compounds (1-3) along with twenty-six known compounds, two known steroids (4-5) and twenty-four known phenylpropanoids (6-29) were isolated from the whole plant of Thesium chinense Turcz. The structures of the three new compounds were elucidated on the basis of ESI-MS, HR-ESIMS, 1D and 2D NMR, IR, UV spectroscopic data. The absolute stereochemistry of compound 1 was determined by the Gauge-Including Atomic Orbitals (GIAO) method. The in vitro antioxidant, anti-inflammatory and cytotoxic activities of the isolated compounds were evaluated by DPPH radical-scavenging assay, LPS-activated RAW 264.7 cells model and CCK-8 kit, respectively. Compound 11 showed high antioxidant activity with an SC50 value of 16.2 ± 1.6 µM. Compound 21 showed considerable anti-inflammatory activity with an IC50 value of 28.6 ± 3.0 µM. Compounds 4 and 5 displayed potent cytotoxic activity against human NCI-H292, SiHa, A549, and MKN45 cell lines, with the compound 4 having IC50 values of 17.4 ± 2.4, 22.2 ± 1.1, 9.7 ± 0.9, 9.5 ±0.7 µM, and the compound 5 having all IC50 values less than 0.1 µM in vitro.

Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020.

A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the "Liandaowan" region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002-2014), exhibited changes and reductions in response to the change (2015-2017), and exhibited optimization and recovery after this change (2018-2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy.

Longitudinal changes in mental health among medical students in China during the COVID-19 epidemic: depression, anxiety and stress at 1-year follow -up.

This study aimed to evaluate the influence of COVID-19 on the mental health of Chinese medical students at 1-year of follow-up. From 2 February 2020 to 23 February 2021, we conducted three waves of research online (T1 = during outbreak, T2 = controlling period, T3 = 1 year after outbreak). The survey collected demographic data and several self reporting questionnaires to measure the depressive, anxiety and stress symptoms. A total of 4002 participants complete the whole research phases. The study major, grade level and gender were the main factors related to psychological distress caused by the COVID-19 crisis. Importantly, medical knowledge has a protective effect on medical students' psychological distress during the COVID-19 period.

Attention-optimized DeepLab V3 + for automatic estimation of cucumber disease severity.

BACKGROUND: Automatic and accurate estimation of disease severity is critical for disease management and yield loss prediction. Conventional disease severity estimation is performed using images with simple backgrounds, which is limited in practical applications. Thus, there is an urgent need to develop a method for estimating the disease severity of plants based on leaf images captured in field conditions, which is very challenging since the intensity of sunlight is constantly changing, and the image background is complicated. RESULTS: This study developed a simple and accurate image-based disease severity estimation method using an optimized neural network. A hybrid attention and transfer learning optimized semantic segmentation model was proposed to obtain the disease segmentation map. The severity was calculated by the ratio of lesion pixels to leaf pixels. The proposed method was validated using cucumber downy mildew, and powdery mildew leaves collected under natural conditions. The results showed that hybrid attention with the interaction of spatial attention and channel attention can extract fine lesion and leaf features, and transfer learning can further improve the segmentation accuracy of the model. The proposed method can accurately segment healthy leaves and lesions (MIoU = 81.23%, FWIoU = 91.89%). In addition, the severity of cucumber leaf disease was accurately estimated (R2 = 0.9578, RMSE = 1.1385). Moreover, the proposed model was compared with six different backbones and four semantic segmentation models. The results show that the proposed model outperforms the compared models under complex conditions, and can refine lesion segmentation and accurately estimate the disease severity. CONCLUSIONS: The proposed method was an efficient tool for disease severity estimation in field conditions. This study can facilitate the implementation of artificial intelligence for rapid disease severity estimation and control in agriculture.

Growth monitoring of greenhouse lettuce based on a convolutional neural network.

Growth-related traits, such as aboveground biomass and leaf area, are critical indicators to characterize the growth of greenhouse lettuce. Currently, nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. In this study, a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network (CNN). Taking lettuce images as the input, a CNN model was trained to learn the relationship between images and the corresponding growth-related traits, i.e., leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA). To compare the results of the CNN model, widely adopted methods were also used. The results showed that the values estimated by CNN had good agreement with the actual measurements, with R2 values of 0.8938, 0.8910, and 0.9156 and normalized root mean square error (NRMSE) values of 26.00, 22.07, and 19.94%, outperforming the compared methods for all three growth-related traits. The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno. Generalization tests were conducted by using images of Tiberius from another growing season. The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits, with R2 values of 0.9277, 0.9126, and 0.9251 and NRMSE values of 22.96, 37.29, and 27.60%. The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce.

Highly carbonylated cellulose nanofibrous membranes utilizing maleic anhydride grafting for efficient lysozyme adsorption.

Construction of adsorptive materials for simple, efficient, and high-throughput adsorption of proteins is critical to meet the great demands of highly purified proteins in biotechnological and biopharmaceutical industry; however, it has proven extremely challenging. Here, we report a cost-effective strategy to create carbonyl groups surface-functionalized nanofibrous membranes under mild conditions for positively charged protein adsorption. Our approach allows maleic anhydride to in situ graft on cellulose nanofibrous membranes (CMA) to construct adsorptive membranes with large surface area and tortuous porous structure. Thereby, the resultant CMA membranes exhibited high adsorption capacity of 160 mg g(-1), fast equilibrium within 12 h, and good reversibility to lysozyme. Moreover, the dynamic adsorption was performed under low pressure-drops (750 Pa), with a relatively high saturation adsorption amount of 118 mg g(-1), which matched well with the requirements for proteins purification. Considering the excellent adsorption performance of the as-prepared adsorptive membranes, this simple and intriguing approach may pave a way for the design and development of robust and cost-effective adsorption membranes to meet the great demands for fast and efficient adsorption of positively charged proteins.