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Background Social media has become an essential tool to implement risk communication, giving health information could gain more exposure by retweeting during the COVID-19 pandemic. Methods Content analysis was conducted to scrutinize the official (national and provincial) public health agencies’ Weibo posts ( n  = 4396) to identify features of information sources and message features (structure, style content). The Zero-Inflated Negative Binomial (ZINB) model was adopted to analyze the association between these features and the frequency of the retweeted messages. Results Results indicated that features of source and health information, such as structure, style, and content, were correlated to retweeting. The results of IRR further suggested that compared to provincial accounts, messages from national health authorities’ accounts gained more retweeting. Regarding the information features, messages with hashtags#, picture, video have been retweeted more often than messages without any of these features respectively, while messages with hyperlinks received fewer retweets than messages without hyperlinks. In terms of the information structure, messages with the sentiment (!) have been retweeted more frequently than messages without sentiment. Concerning content, messages containing severity, reassurance, efficacy, and action frame have been retweeted with higher frequency, while messages with uncertainty frames have been retweeted less often. Conclusions Health organizations and medical professionals should pay close attention to the features of health information sources, structures, style, and content to satisfy the public’s information needs and preferences to promote the public's health engagement. Designing suitable information systems and promoting health communication strategies during different pandemic stages may improve public awareness of the COVID-19, alleviate negative emotions, and promote preventive measures to curb the spread of the virus.

This paper analyzes an agency model of crisis bargaining where two states have private information about war payoffs. In the model, two leaders bargain on behalf of their own states. Importantly, owing to political bias and audience costs, a leader’s war payoff and peace payoff differ from those of her state at large. I establish general results about leaders’ bargaining strategies and the possibility of peaceful resolution. By examining incentive compatibility constraints, I show that in any equilibrium that has zero probability of costly war, a leader’s payoff net of audience costs cannot vary with their private information. After that, I identify the size of resource necessary to appease both states. If this necessary condition holds, which is affected by political bias, there exist properly specified audience costs that guarantee peaceful bargaining outcomes.

In heterogeneous image fusion problems, different imaging mechanisms have always existed between time-of-flight and visible light heterogeneous images which are collected by binocular acquisition systems in orchard environments. Determining how to enhance the fusion quality is key to the solution. A shortcoming of the pulse coupled neural network model is that parameters are limited by manual experience settings and cannot be terminated adaptively. The limitations are obvious during the ignition process, and include ignoring the impact of image changes and fluctuations on the results, pixel artifacts, area blurring, and the occurrence of unclear edges. Aiming at these problems, an image fusion method in a pulse coupled neural network transform domain guided by a saliency mechanism is proposed. A non-subsampled shearlet transform is used to decompose the accurately registered image; the time-of-flight low-frequency component, after multiple lighting segmentation using a pulse coupled neural network, is simplified to a first-order Markov situation. The significance function is defined as first-order Markov mutual information to measure the termination condition. A new momentum-driven multi-objective artificial bee colony algorithm is used to optimize the parameters of the link channel feedback term, link strength, and dynamic threshold attenuation factor. The low-frequency components of time-of-flight and color images, after multiple lighting segmentation using a pulse coupled neural network, are fused using the weighted average rule. The high-frequency components are fused using improved bilateral filters. The results show that the proposed algorithm has the best fusion effect on the time-of-flight confidence image and the corresponding visible light image collected in the natural scene, according to nine objective image evaluation indicators. It is suitable for the heterogeneous image fusion of complex orchard environments in natural landscapes.

In the context of global fighting against the unexpected COVID-19 pandemic, how to promote the public implementation of preventive behavior is the top priority of pandemic prevention and control. This study aimed at probing how the media would affect the public’s preventive behavior and excessive preventive intention accordingly. Data were collected from 653 respondents in the Chinese mainland through online questionnaires and further analyzed by using partial least squares structural equation modeling (PLS-SEM). Taking risk perception, negative emotions, and subjective norms as mediators, this study explored the impact of mass media exposure and social networking services involvement on preventive behavior and excessive preventive intention. Based on differences in the severity of the pandemic, the samples were divided into the Wuhan group and other regions group for multi-group comparison. The results showed that mass media exposure had a significant positive impact on subjective norms; moreover, mass media exposure could significantly enhance preventive behavior through subjective norms, and social networking services involvement had a significant positive impact on negative emotions; meanwhile, social networking services involvement promoted excessive preventive intention through negative emotions.

In the face of increasing global unsustainable risks such as poverty, hunger, and pollution. Building sustainable agriculture (SA) in the digital age is a fundamental task for human survival. Based on the coupled coordination perspective, this paper constructs an SA system that takes into account more stakeholders by considering poverty alleviation and income increase (PI), food security (FS), and green agriculture (GA) as subsystems. The impact of digital technology on SA is systematically analyzed through data from 276 prefecture-level and above cities in China from 2005 to 2020. The study shows that digital technology has a significant upgrading effect on SA and its subsystems. And digital technology is more likely to promote SA in the developed eastern region and peripheral cities. Moreover, agricultural productivity and labor productivity play a mediating mechanism in the process of digital technology for SA. Digital financial inclusion fuels the high input process of digital technology for incentivizing SA, PI, and GA, but it cannot affect the highly technical process of digital farming. Further research found that the incentives of digital technology for SA and GA are characterized by the nonlinear characteristic of increasing marginal effects. Due to the second digital divide, there is a U-shaped incentive process of digital technology for PI to fall and then rise. Finally, the spillover nature of digital technology leads to spatial spillovers in its contribution to SA development.

In order to improve the effectiveness of intrusion detection, an intrusion detection method of the Internet of Things (IoT) is proposed by suppressed fuzzy clustering (SFC) algorithm and principal component analysis (PCA) algorithm. In this method, the data are classified into high-risk data and low-risk data at first, which are detected by high frequency and low frequency, respectively. At the same time, the self-adjustment of the detection frequency is carried out according to the suppressed fuzzy clustering algorithm and the principal component analysis algorithm. Finally, the key factors influencing the algorithm are analyzed deeply by simulation experiment. The results shows that, compared to traditional method, this method has better adaptability.

Quercetin, a polyphenolic bioflavonoid, possesses multiple pharmacological actions including anti-inflammatory and antitumor properties. However, the precise action mechanisms of quercetin remain unclear. Here, we reported the regulatory actions of quercetin on cyclooxygenase-2 (COX-2), an important mediator in inflammation and tumor promotion, and revealed the underlying mechanisms. Quercetin significantly suppressed COX-2 mRNA and protein expression and prostaglandin (PG) E(2) production, as well as COX-2 promoter activation in breast cancer cells. Quercetin also significantly inhibited COX-2-mediated angiogenesis in human endothelial cells in a dose-dependent manner. The in vitro streptavidin-agarose pulldown assay and in vivo chromatin immunoprecipitation assay showed that quercetin considerably inhibited the binding of the transactivators CREB2, C-Jun, C/EBPβ and NF-κB and blocked the recruitment of the coactivator p300 to COX-2 promoter. Moreover, quercetin effectively inhibited p300 histone acetyltransferase (HAT) activity, thereby attenuating the p300-mediated acetylation of NF-κB. Treatment of cells with p300 HAT inhibitor roscovitine was as effective as quercetin at inhibiting p300 HAT activity. Addition of quercetin to roscovitine-treated cells did not change the roscovitine-induced inhibition of p300 HAT activity. Conversely, gene delivery of constitutively active p300 significantly reversed the quercetin-mediated inhibition of endogenous HAT activity. These results indicate that quercetin suppresses COX-2 expression by inhibiting the p300 signaling and blocking the binding of multiple transactivators to COX-2 promoter. Our findings therefore reveal a novel mechanism of action of quercetin and suggest a potential use for quercetin in the treatment of COX-2-mediated diseases such as breast cancers.

The current image registration models have problems such as low feature point matching accuracy, high memory consumption, and significant computational complexity in heterogeneous image registration, especially in complex environments. In this context, significant differences in lighting and leaf occlusion in orchards can result in inaccurate feature extraction during heterogeneous image registration. To address these issues, this study proposes an AD-ResSug model for heterogeneous image registration. First, a VGG16 network was included as the encoder in the feature point encoder system, and the positional encoding was embedded into the network. This enabled us to better understand the spatial relationships between feature points. The addition of residual structures to the feature point encoder aimed to solve the gradient diffusion problem and enhance the flexibility and scalability of the architecture. Then, we used the Sinkhorn AutoDiff algorithm to iteratively optimize and solve the optimal transmission problem, achieving optimal matching between feature points. Finally, we carried out network pruning and compression operations to minimize parameters and computation cost while maintaining the model’s performance. This new AD-ResSug model uses evaluation indicators such as peak signal-to-noise ratio and root mean square error as well as registration efficiency. The proposed method achieved robust and efficient registration performance, verified through experimental results and quantitative comparisons of processing color with ToF images captured using heterogeneous cameras in natural apple orchards.

To solve the issue that existing yield prediction methods do not fully capture the interaction between multiple factors, we propose a winter wheat yield prediction framework with triple cross-attention for multi-source data fusion. This framework consists of three modules: a multi-source data processing module, a multi-source feature fusion module, and a yield prediction module. The multi-source data processing module collects satellite, climate, and soil data based on the winter wheat planting range, and constructs a multi-source feature sequence set by combining statistical data. The multi-source feature fusion module first extracts deeper-level feature information based on the characteristics of different data, and then performs multi-source feature fusion through a triple cross-attention fusion mechanism. The encoder part in the production prediction module adds a graph attention mechanism, forming a dual branch with the original multi-head self-attention mechanism to ensure the capture of global dependencies while enhancing the preservation of local feature information. The decoder section generates the final predicted output. The results show that: (1) Using 2021 and 2022 as test sets, the mean absolute error of our method is 385.99 kg/hm2, and the root mean squared error is 501.94 kg/hm2, which is lower than other methods. (2) It can be concluded that the jointing-heading stage (March to April) is the most crucial period affecting winter wheat production. (3) It is evident that our model has the ability to predict the final winter wheat yield nearly a month in advance.

The purpose of this study was to employ bibliometric analysis to visualize hot spots and evolving trends in the studies on the relationship between vitamin D and obesity. From the Web of Science Core Collection database, articles on vitamin D and obesity from 2001 to 2021 were retrieved. For the bibliometric visualization analysis, CiteSpace was employed. Some of the figures were created using GraphPad software. 4454 pieces of articles and reviews were found, with an average citation of 30.68 times. There are many more published papers in the area of \"nutrition dietetics\" (1166, 26.179 %). The United States possesses the largest number of publications (1297, 29.12 %) and demonstrates definitive leadership in this field. The League of European Research Universities generates a higher percentage of publications (256, 5.748 %) than other institutions. Major studies are funded by the United States Department of Health and Human Services (531, 11.922 %) and the National Institutes of Health, USA (528, 11.855 %). The top five keywords with the highest co-occurrence frequency are “obesity” (1260), “vitamin d” (943), “insulin resistance” (651), “risk” (642), and “d deficiency” (636). The biggest keyword cluster was #0 \"adolescent\" among the 18 keyword clusters. The three latest keywords in the keyword burst were \"mineral density\"、\"d insufficiency\" and \"25 hydroxyvitamin d concentration\". This bibliometric analysis shows an overview of the current status of the research on the association between vitamin D and obesity. The prevalence of vitamin D deficiency and the relationship between vitamin D and metabolic syndrome in obese individuals remains hot topics. We speculate that the effect of obesity on vitamin D levels and bone mineral density, and the influence of vitamin D insufficiency on various body systems in obese populations will be future trends. •The USA and European countries had an advantage in the number of publications about vitamin D and obesity.•The prevalence of Vitamin D deficiency and its link to metabolic syndrome in obesity are still hot topics.•Obesity's impact on vitamin D and bone density, and vitamin D's effects on obese body systems, are future trends.