Naringin exerts antibacterial and anti-inflammatory effects on mice with Staphylococcus aureus-induced osteomyelitis.

Osteomyelitis is an invasive bone infection that can lead to severe pain and even disability, posing a challenge for orthopedic surgery. Naringin can reduce bone-related inflammatory conditions. This study aimed to elucidate the function and mechanism of naringin in a Staphylococcus aureus-induced mouse model of osteomyelitis. Femurs of S. aureus-infected mice were collected after naringin administration and subjected to microcomputed tomography to analyze cortical bone destruction and bone loss. Bacterial growth in femurs was also assessed. Proinflammatory cytokine levels in mouse femurs were measured using enzyme-linked immunosorbent assays. Pathological changes and bone resorption were analyzed using hematoxylin and eosin staining and tartrate-resistant acid phosphatase staining, respectively. Quantitative reverse transcription polymerase chain reaction and western blot analysis were used to quantify the messenger RNA and protein expression of osteogenic differentiation-associated genes in the femurs. The viability of human bone marrow-derived stem cells (hBMSCs) was determined using cell counting kit-8. Alizarin Red S staining and alkaline phosphatase staining were performed to assess the formation of mineralization nodules and bone formation in vitro. Notch signaling-related protein levels in femur tissues and hBMSCs were assessed using western blot analysis. Experimental results revealed that naringin alleviated S. aureus-induced cortical bone destruction and bone loss in mice by increasing the bone volume/total volume ratio. Naringin suppressed S. aureus-induced bacterial growth and inflammation in femurs. Moreover, it alleviated histopathological changes, inhibited bone resorption, and increased the expression of osteogenic markers in osteomyelitic mice. It increased the viability of hBMSCs and promoted their differentiation and bone mineralization in vitro. Furthermore, naringin activated Notch signaling by upregulating the protein levels of Notch1, Jagged1, and Hes1 in the femurs of model mice and S. aureus-stimulated hBMSCs. In conclusion, naringin reduces bacterial growth, inflammation, and bone resorption while upregulating the expression of osteogenic markers in S. aureus-infected mice and hBMSCs by activating Notch signaling.

How to promote the development of a green economy: Talent or technology?-Evidence from China's high-speed rail.

The green economy is essential in supporting sustainable economic development and relies on talents and technologies. From the perspective of traditional economic theory, this study explores the impact of high-speed rail and innovation on the green economy from the perspectives of talent and technology. Using the data of 281 prefecture-level cities in China from 2008 to 2018, this study constructs empirical models to discuss the driving factors of the green economy. Empirical results show that high-speed rail and innovation can promote the development of a green economy, and the opening of high-speed rail can strengthen the positive association between innovation and a green economy. The accessibility of high-speed rail improves the flow of talent between different cities and greatly stimulates the positive impact of innovation on green economic activities. In the further test, this study explores the impact of high-speed rail and innovation on the green economy from different dimensions, including government policy, economic strength, and administrative level. During China's 12th Five-Year Plan, high-speed rail and innovation had a positive impact on the green economy, but the impact of innovation can still be significant after this period. Moreover, the opening of high-speed rail may motivate the migration of talents from developed cities to developing ones, while developed cities can rely on technological advantages to support green economic activities. Furthermore, low-administrative level cities will rely on attracting more talents to promote a green economy due to technological disadvantages. Innovation can play a critical role in enhancing the green economy of cities with high administrative levels. Talents and technology are both important to green economic activities, and the construction of high-speed rail changes the impact of technology on the green economy through the flow of talent. Our findings can explain why the opening of high-speed rail can promote the development of a green economy and effectively help governments achieve the goal of sustainable development.

Linking What I Say and What I Do: Evidence From Perceived Competition Networks.

The enterprise network is of great significance in explaining the risk-taking of individual firm. However, some unobservable networks hidden in different firms have long been neglected. Using the text data of the annual reports of China's listed firms from 2007 to 2018, this paper adopts a textual analysis method to capture the managers' perceptions of pressure, and build a special kind of hidden inter-firm networks, that is, the perceived competition networks of managers. In addition, this paper discusses the impact of network characteristics on corporate risk-taking behavior. Empirically, there is a positive association between competition strength and corporate risk-taking, as well as the density of perceived competition network. Furthermore, this paper explores the risk-taking behaviors of peer firms in focal firm's perceived competition network, and finds that the improvement of peer firms' risk-taking significantly increases the risk bearing level of focal firm, that is, the positive spillover effect of risk-taking behavior among firms in perceived competition networks. Moreover, managers' personal traits significantly moderate the impact of network characteristics on corporate risk-taking, which is mainly reflected in younger and male managers. Our findings can enrich the literature on social interactions and corporate behaviors, and help firms to improve their understanding of perceptible peer firms.

A Comprehensive Survey on Graph Neural Networks.

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.

How High-Polluting Firms Suffer from Being Distracted form Intended Purpose: A Corporate Social Responsibility Perspective.

High-polluting industries are regarded as the main sources of air pollutant emissions and the major factors that significantly destroy the ecological environment. Corporate innovation in high-polluting industries improves the energy consumption efficiency and reduces the emission of air pollutant, which mitigates the conflict between environment and economy. Using the sample of China's listed firms from 2010 to 2017, this study examines the impact of corporate social responsibility (CSR) and financialization on corporate innovation in high-polluting industries. The results show that there is a positive association between CSR and corporate innovation, while there is a negative association between financialization and corporate innovation. Furthermore, the financialization of high-polluting firms can alleviate the promotion role of CSR in the innovation process. The financialization of state-owned enterprises in high-polluting industries may not have a crowding-out effect on research and development (R&D), but it can limit the R&D promotion effect of CSR engagements. In contrast, the financialization of non-state-owned enterprises will hinder corporate innovation, but it will not affect the association between CSR and technology innovation. We also find that the financialization of high-polluting firms with low financial constraints can alleviate the promotion role of CSR engagements in innovation. Meanwhile, the CSR engagements of high-polluting firms with high financial constraints play a stronger role in corporate innovation. During the implementation of environmental policies, the negative association between financialization and corporate innovation has been strengthened. Our findings can encourage high-polluting firms to make more efforts in environmental protection and social stability.

Energy and Environment Performance of Resource-Based Cities in China: A Non-Parametric Approach for Estimating Hyperbolic Distance Function.

Scientific determination of energy and environmental efficiency and productivity is the key foundation of green development policy-making. The hyperbolic distance function (HDF) model can deal with both desirable output and undesirable output asymmetrically, and measure efficiency from the perspective of "increasing production and reducing pollution". In this paper, a nonparametric linear estimation method of an HDF model including uncontrollable index and undesirable output is proposed. Under the framework of global reference, the changes of energy environmental efficiency and productivity and their factorization of 107 resource-based cities in China from 2003 to 2018 are calculated and analyzed. With the classification of resource-based cities by resource dependence (RD) and region, we discuss the feature in green development quality of those cities. The results show that: (1) On the whole, the average annual growth rate of energy and environmental productivity of resource-based cities in China is 2.6%, which is mainly due to technological changes. The backward of relative technological efficiency hinders the further growth of productivity, while the scale diseconomy is the main reason for the backward of relative technological efficiency. (2) For the classification of RD, the energy and environmental efficiency of the high-dependent group are significantly lower than the other two, and the growth of productivity of the medium-dependent group is the highest. (3) In terms of classification by region, the energy and environmental efficiency of the eastern region is the highest, and that of the middle and western regions is not as good as that of the eastern and northeastern regions. The middle region shows the situation of "middle collapse" in both static efficiency and dynamic productivity change, and the main reason for its low productivity growth is the retreat of relatively pure technical efficiency. This conclusion provides practical reference for the classification and implementation of regional energy and environmental policies.

Assessing the applicability of flow measurement by using non-contact observation methods in open channels.

River discharge is one of the important hydraulic data to evaluate and manage the regional water resources. Estimating river discharge is generally based on field measurements. The measurement data are then applied to construct water level-discharge rating curves. However, it is sometimes difficult to obtain accurate discharge data due to the high uncertainty of flow. A commonly used technique is the propeller-type flowmeters (PTF), which average the results of 1-, 2-, or 3-point methods to obtain a vertical mean velocity. In this study, three types of flowmeters were employed to compare the accuracy of flow measured. The devices were calibrated using a tow tank testing: PTF, acoustic Doppler profiler (ADP), and radar surface velocimeter (RSV). To assess the applicability of the non-contact observation method, a series of 16 experiments in channels were conducted. Surface velocity measurement using the RSV was compared with the measurements obtained by PTF. The relationship between measured surface velocity of RSV and measured vertical mean velocity of PTF was established. The results show that the RSV can effectively estimate the river discharge in the open channel flow.

Labor Costs, Market Environment and Green Technological Innovation: Evidence from High-Pollution Firms.

Emerging economies face the challenge of increasing labor costs but also provide an opportunity to promote environmental governance and green development. Based on the perspectives of impetus and capability, the effects of rising labor costs and market environment on green technological innovation are investigated in this study. The empirical studies used the data of high-pollution firms in China from 2009 to 2018. Results demonstrate that rising labor costs deteriorates high-pollution firm performance, while highly competitive industries are affected more than other industries. Meanwhile, the influence of rising labor costs on green technological innovation has a threshold effect which illustrates an "inversely U-shaped" variation trend with the increase of degree of market monopoly. The labor costs will make biggest impact on the green technological innovation in the moderately concentrated market environment. Basing from these results, this study provides the following suggestions for emerging economies' green development: Take rising labor cost as an opportunity to advance technological progress to the green direction, establish a sound market competition environment, and develop green finance to reduce the financing constraints of green technological innovation.

The Effect of Corporate Social Responsibility and Public Attention on Innovation Performance: Evidence from High-polluting Industries.

High-polluting industries are important sources of pollutant emissions, and closely related to many environmental issues. High-polluting firms face the pressure to exploit technological innovation for improving their environmental operations. This paper explores the impact of corporate social responsibility and public attention on the innovation performance of high-polluting firms. Based on a sample of China's listed firms in high-polluting industries from 2011 to 2016, we use a panel data model to investigate the associations among corporate social responsibility, public attention and innovation performance. The results show that there is a positive association between corporate social responsibility and innovation performance. There is a positive association between public attention and innovation performance as well. The pressure of regional economies can hinder innovation performance. Furthermore, in the subsample of state-owned enterprises, the association between public attention and innovation performance is more pronounced. Meanwhile, the corporate social responsibility of non-state-owned enterprises plays a stronger role for innovation performance, but its effect will be limited by the pressure of regional economies. Our results can help high-polluting firms implement the innovation strategies for obtaining more environmental benefits and achieving sustainable development.