Schisandra chinensis lignans regulate and cooperate with endogenous cannabinoid systems to ameliorate intestinal barrier injury associated with depression.

BACKGROUND: Schisandra chinensis lignan (SCL), a major active component of the traditional functional Chinese medicine Schisandra chinensis, has been reported to have antidepressant effects. Its mechanisms include alleviating intestinal barrier injury (IBI) by resolving intestinal microflora, anti-inflammation, and neuroprotection. SCL also regulates endogenous cannabinoid system, and it is closely related to the onset and development of depression. PURPOSE: We investigated a new treatment strategy for depression, i.e., alleviating IBI by regulating the endogenous cannabinoid system for antidepressant effects, as well as conducted in-depth research to explore the specific mechanism. METHODS: Behavioral analysis was conducted to detect the occurrence of depressive-like behavior in C57BL/6 mice. We used hematoxylin-eosin staining, periodic acid-Schiff staining, and immunofluorescence to evaluate IBI. Network pharmacology and Western blotting (WB) were used to predict and confirm that the amelioration effect of SCL was associated with anti-inflammation and anti-apoptosis. Combined with the levels of anandamide (AEA) and 2-arachidonoylglycerol (2-AG), we conducted the Pearson analysis between the AEA, 2-AG levels and the major targets identified and validated by network pharmacology and WB. Subsequently, URB-597, a fatty acid amide hydrolase (FAAH) antagonist with an AEA hydrolase-inhibiting effect, was administered to the mice, and behavioral analysis and apoptotic proteins were verified. Plasma endocannabinoid levels after URB-597 supplementation were measured via 6470 Triple Quadrupole LC/MS. Finally, the cannabinoid receptor type 2 (CB2R) antagonist AM630 was administered to mice, and immunofluorescence and WB were performed to assess the proteins of IBI and anti-inflammation. RESULTS: The study demonstrated that SCL alleviated depressive-like behaviours and ameliorated IBI. Network pharmacology and WB confirmed that the improvement of IBI was related to the anti-inflammatory and anti-apoptotic pathways. Pearson results showed that AEA levels were positively correlated with inflammation and apoptosis, with a greater contribution to apoptosis. In-depth studies validated that the URB-597 administration reversed the positive effects of SCL on depressive-like behavior and anti-apoptosis. Similarly, URB-597 counteracted AEA levels reduced by SCL and decreased 2-AG levels. Furthermore, AM630 supplementation antagonized SCL's effect of improving IBI by reactivating the MAPK/NF-κB inflammation pathway. CONCLUSION: Overall, SCL, in collaboration with the endogenous cannabinoid system regulated by SCL, alleviates depression associated IBI. The specific mechanism involes SCL decreasing AEA levels to inhibit colon tissue cell apoptosis by up-regulating FAAH. Simultaneously, it directly triggers CB2R to reduce inflammation responses, further alleviating IBI.

What Factors Dominate the Change of PM2.5 in the World from 2000 to 2019? A Study from Multi-Source Data.

As the threat to human life and health from fine particulate matter (PM2.5) increases globally, the life and health problems caused by environmental pollution are also of increasing concern. Understanding past trends in PM2.5 and exploring the drivers of PM2.5 are important tools for addressing the life-threatening health problems caused by PM2.5. In this study, we calculated the change in annual average global PM2.5 concentrations from 2000 to 2020 using the Theil-Sen median trend analysis method and reveal spatial and temporal trends in PM2.5 concentrations over twenty-one years. The qualitative and quantitative effects of different drivers on PM2.5 concentrations in 2020 were explored from natural and socioeconomic perspectives using a multi-scale geographically weighted regression model. The results show that there is significant spatial heterogeneity in trends in PM2.5 concentration, with significant decreases in PM2.5 concentrations mainly in developed regions, such as the United States, Canada, Japan and the European Union countries, and conversely, significant increases in PM2.5 in developing regions, such as Africa, the Middle East and India. In addition, in regions with more advanced science and technology and urban management, PM2.5 concentrations are more evenly influenced by various factors, with a more negative influence. In contrast, regions at the rapid development stage usually continue their economic development at the cost of the environment, and under a high intensity of human activity. Increased temperature is known as the most important factor for the increase in PM2.5 concentration, while an increase in NDVI can play an important role in the reduction in PM2.5 concentration. This suggests that countries can achieve good air quality goals by setting a reasonable development path.

Slope climbing of urban expansion worldwide: Spatiotemporal characteristics, driving factors and implications for food security.

The tendency of global urban expansion to be slope climbing has partly become possible with scarce cropland resources in plains. However, the scientific understanding of the quantity, intensity, pattern, and effect of the slope climbing of urban expansion (SCE) is minimal globally. In this study, we have attempted to quantify and evaluate global SCE from Suomi National Polar-orbiting Partnership (SNPP)-Visible Infrared Imaging Radiometer Suite (VIIRS)-like data and other auxiliary data. Results revealed that global SCE areas unevenly increased from 22,760 km2 to 90,720 km2 from 2000 to 2020, with an annual growth rate of 21.72%, in which low-environment cost type areas increased from 21,550 km2 to 84,010 km2 while high-environment cost type (HEC) areas increased from 1210 km2 to 6710 km2. One remarkable phenomenon is that China's SCE areas in 2020 were more than 11 times those in 2000. In addition, global SCE intensity increased by about 3.4-fold from 2000 to 2020 and the rapid growth of HEC intensity is concentrated in Asia and North America. SCE is mostly affected by urban population growth and terrain. Economic development also promotes its development to a certain extent. We also noted that global SCE potentially made a considerable contribution to saved cropland, saving about 46,747 km2 with a theoretical increased grain yield of 25,020 × 103 t. Our study provides timely and transparent monitoring of global SCE and offers new insights into sustainable urban development.

Schisandra chinensis Lignans Exert Antidepressant Effects by Promoting BV2 Microglia Polarization toward the M2 Phenotype through the Activation of the Cannabinoid Receptor Type-2-Signal Transducer and Activator of Transcription 6 Pathway.

Based on the current results, they showed that Schisandra chinensis lignans (SCL) ameliorated depressive-like behaviors in chronic unpredictable mild stress (CUMS) mice, alleviated neuroinflammation, and improved neuronal injury. This study aimed to explore whether SCL exerted antidepressant effects through inhibiting neuroinflammation, in turn improving neuronal injury. In vitro studies revealed that SCL blocked lipopolysaccharide-increased BV2 microglial M1 but promoted the M2 phenotype. The BV2-N2a interaction model suggested that increasing the M2 phenotype of BV2 played neuroprotective effects. The current studies demonstrated that SCL up-regulated the expression of CUMS- and LPS-decreased cannabinoid receptor type-2 (CB2R) mRNA. In vitro studies showed that the transfection of BV2 with siCrn2 blocked the SCL-increased M2 phenotype via the inactivating signal transducer and activator of transcription 6 (STAT6) pathway, further decreasing the viability of N2a cells. Finally, the possible pharmacodynamic compounds, γ-schisandrin and schisantherin A, were indicated by AutoDuck analysis. Overall, our study showed that SCL promoted microglia polarization toward the M2 phenotype, in turn exerting neuroprotective effects by activating CB2R-STAT6 signaling further to play antidepressant roles.

A long short-term memory-fully connected (LSTM-FC) neural network for predicting the incidence of bronchopneumonia in children.

Bronchopneumonia is the most common infectious disease in children, and it seriously endangers children's health. In this paper, a deep neural network combining long short-term memory (LSTM) layers and fully connected layers was proposed to predict the prevalence of bronchopneumonia in children in Chengdu based on environmental factors and previous prevalence rates. The mean square error (MSE), mean absolute error (MAE), and Pearson correlation coefficient (R) were used to detect the performance of the deep learning model. The values of MSE, MAE, and R in the test dataset are 0.0051, 0.053, and 0.846, respectively. The results show that the proposed model can accurately predict the prevalence of bronchopneumonia in children. We also compared the proposed model with three other models, namely, a fully connected (FC) layer neural network, a random forest model, and a support vector machine. The results show that the proposed model achieves better performance than the three other models by capturing time series and mitigating the lag effect.

The Effects of Urban Forms on the PM2.5 Concentration in China: A Hierarchical Multiscale Analysis.

For a better environment and sustainable development of China, it is indispensable to unravel how urban forms (UF) affect the fine particulate matter (PM2.5) concentration. However, research in this area have not been updated consider multiscale and spatial heterogeneities, thus providing insufficient or incomplete results and analyses. In this study, UF at different scales were extracted and calculated from remote sensing land-use/cover data, and panel data models were then applied to analyze the connections between UF and PM2.5 concentration at the city and provincial scales. Our comparison and evaluation results showed that the PM2.5 concentration could be affected by the UF designations, with the largest patch index (LPI) and landscape shape index (LSI) the most influential at the provincial and city scales, respectively. The number of patches (NP) has a strong negative influence (-0.033) on the PM2.5 concentration at the provincial scale, but it was not statistically significant at the city scale. No significant impact of urban compactness on the PM2.5 concentration was found at the city scale. In terms of the eastern and central provinces, LPI imposed a weighty positive influence on PM2.5 concentration, but it did not exert a significant effect in the western provinces. In the western cities, if the urban layout were either irregular or scattered, exposure to high PM2.5 pollution levels would increase. This study reveals distinct ties of the different UF and PM2.5 concentration at the various scales and helps to determine the reasonable UF in different locations, aimed at reducing the PM2.5 concentration.

A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China.

Exploring the coupling relationship between urban land and carbon emissions (CE) is one of the important premises for coordinating the urban development and the ecological environment. Due to the influence of the scale effect, a systematic evaluation of the CE at different scales will help to develop more reasonable strategies for low-carbon urban planning. However, corresponding studies are still lacking. Hence, two administrative scales (e.g., region and county) in Chongqing were selected as experimental objects to compare and analyze the CE at different scales using the spatiotemporal coupling and coupling coordination models. The results show that urban land and carbon emissions presented a significant growth trend in Chongqing at different scales from 2000 to 2015. The strength of the spatiotemporal coupling relationship between urban land and total carbon emissions gradually increased with increasing scale. At the regional scale, the high coupling coordination between urban land and total carbon emissions was mainly concentrated in the urban functional development region. Additionally, the high coupling coordination between urban land and carbon emission intensity (OI) was still located in the counties within the metropolitan region of Chongqing, but the low OI was mainly distributed in the counties in the northeastern and southeastern regions of Chongqing at the county level. This study illustrates the multiscale trend of CE and suggests differentiated urban land and carbon emission reduction policies for controlling urban land sprawl and reducing carbon emissions.

Effects of urban forms on CO2 emissions in China from a multi-perspective analysis.

Effectively evaluating the effects of urban forms on CO2 emissions has become a hot topic in socioeconomic sustainable development; however, few studies have been able to explore the urban form-CO2 emission relationships from a multi-perspective view. Here, we attempted to analyze the relationships between urban forms and CO2 emissions in 264 Chinese cities, with explicit consideration of the government policies, urban area size, population size, and economic structure. First, urban forms were calculated using the urban land derived from multiple-source remote sensing data. Second, we collected and processed CO2 emissions and three control variables. Finally, a correlation analysis was implemented to explore whether and to what extent the spatial patterns of urban forms were associated with CO2 emissions. The results show that urban form irregularity had a more significant impact on CO2 emissions in low-carbon pilot cities than in non-pilot cities. The impact of the complexity of urban forms on CO2 emissions was relatively significant in the small- and large-sized cities than in the medium-sized cities. Moreover, urban form complexity had a significant correlation with CO2 emissions in all of the cities, the level of which basically increased with the population size. This study provides scientific bases for use in policy-making to prepare effective policies for developing a low-carbon economy with consideration of the associations between urban forms and CO2 emissions in different scenarios.

Does China's Urban Development Satisfy Zipf's Law? A Multiscale Perspective from the NPP-VIIRS Nighttime Light Data.

Currently, whether the urban development in China satisfies Zipf's law across different scales is still unclear. Thus, this study attempted to explore whether China's urban development satisfies Zipf's law across different scales from the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data. First, the NPP-VIIRS data were corrected. Then, based on the Zipf law model, the corrected NPP-VIIRS data were used to evaluate China's urban development at multiple scales. The results showed that the corrected NPP-VIIRS data could effectively reflect the state of urban development in China. Additionally, the Zipf index (q) values, which could express the degree of urban development, decreased from 2012 to 2018 overall in all provinces, prefectures, and counties. Since the value of q was relatively close to 1 with an R2 value > 0.70, the development of the provinces and prefectures was close to the ideal Zipf's law state. In all counties, q > 1 with an R2 value > 0.70, which showed that the primate county had a relatively stronger monopoly capacity. When the value of q < 1 with a continuous declination in the top 2000 counties, the top 250 prefectures, and the top 20 provinces in equilibrium, there was little difference in the scale of development at the multiscale level with an R2 > 0.90. The results enriched our understanding of urban development in terms of Zipf's law and had valuable implications for relevant decision-makers and stakeholders.

Exploring the dynamic, forecast and decoupling effect of land natural capital utilization in the hinterland of the Three Gorges Reservoir area, China.

Natural capital utilization in ecologically sensitive areas is an important subject for ecologically sustainable quantification. Research on the natural capital flow and stock is helpful to analyze the utilization of natural capital and promote its sustainable development. Thus, we attempted to combine the three-dimensional ecological footprint model with the decoupling model and GM(1,1) model to analyze the current and future state of land natural capital utilization, and then analyze the status of decoupling between economic development and the sustainable use of the land natural capital. The results showed that: (1) Overall, the land footprint and land capacity in the hinterland of the Three Gorges Reservoir area increased. The occupation of the land natural capital flow increased, and the footprint depth first increased and then decreased. (2) The utilization ratio of capital stock to the flows of cultivated land increased, and the space for sustainable utilization of natural capital was larger. The gray prediction showed that the ratio of capital stock to the flows in all the districts and counties of the study area will increase from 2022. (3) A decoupling relationship existed between the utilization ratio of the stock flow and Gross Domestic Product as a whole, indicating that the pressure of economic development on the sustainable utilization of natural capital has always existed. This study has a certain reference value to construct an ecological civilization in the hinterland of the Three Gorges Reservoir and formulate relevant policies on the increase of rural land natural capital.

Are Karst Rocky Desertification Areas Affected by Increasing Human Activity in Southern China? An Empirical Analysis from Nighttime Light Data.

Due to remarkable socioeconomic development, an increasing number of karst rocky desertification areas have been severely affected by human activities in southern China. Effectively analyzing human activities in karst rocky desertification areas is a critical prerequisite for managing and restoring areas with tremendous negative impacts from desertification. At present, a timely and accurate way of quantifying the spatiotemporal variations of human activities in karst rocky desertification areas is still lacking. In this communication, we attempted to quantify human activities from the corrected Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) nighttime light composite data from 2012 to 2018 based on statistical analysis. The results show that a significant increase of night lights could be clearly identified during the study period. The total nighttime lights (TL) related to severe karst rocky desertification (S) were particularly concentrated in Guizhou and Yunnan. The nighttime light intensity (LI) related to the S areas in Chongqing was the strongest due to its rapid socioeconomic development. The annual growth rate of nighttime lights (GL) has been slow or even negative in Guangdong because of its various karst rocky desertification restoration programs. This communication could provide an effective approach for quantifying human activities and provide useful information about where prompt attention is required for policy-making on the restoration of the karst rocky desertification areas.

What Are the Driving Forces of Urban CO2 Emissions in China? A Refined Scale Analysis between National and Urban Agglomeration Levels.

With the advancement of society and the economy, environmental problems have increasingly emerged, in particular, problems with urban CO2 emissions. Exploring the driving forces of urban CO2 emissions is necessary to gain a better understanding of the spatial patterns, processes, and mechanisms of environmental problems. Thus, the purpose of this study was to quantify the driving forces of urban CO2 emissions from 2000 to 2015 in China, including explicit consideration of a comparative analysis between national and urban agglomeration levels. Urban CO2 emissions with a 1-km spatial resolution were extracted for built-up areas based on the anthropogenic carbon dioxide (ODIAC) fossil fuel emission dataset. Six factors, namely precipitation, slope, temperature, population density, normalized difference vegetation index (NDVI), and gross domestic product (GDP), were selected to investigate the driving forces of urban CO2 emissions in China. Then, a probit model was applied to examine the effects of potential factors on urban CO2 emissions. The results revealed that the population, GDP, and NDVI were all positive driving forces, but that temperature and precipitation had negative effects on urban CO2 emissions at the national level. In the middle and south Liaoning urban agglomeration (MSL), the slope, population density, NDVI, and GDP were significant influencing factors. In the Pearl River Delta urban agglomeration (PRD), six factors had significant impacts on urban CO2 emissions, all of which were positive except for slope, which was a negative factor. Due to China's hierarchical administrative levels, the model results suggest that regardless of which level is adopted, the impacts of the driving factors on urban CO2 emissions are quite different at the national compared to the urban agglomeration level. The degrees of influence of most factors at the national level were lower than those of factors at the urban agglomeration level. Based on an analysis of the forces driving urban CO2 emissions, we propose that it is necessary that the environment play a guiding role while regions formulate policies which are suitable for emission reductions according to their distinct characteristics.

Mapping and evaluating cultivated land fallow in Southwest China using multisource data.

Accurately and effectively mapping and evaluating cultivated land fallow has already become an important issue that has received much attention in China. However, systematically analysing regional cultivated land fallow remains inadequate because current studies have mainly focused on quantifying cultivated land fallow using statistical data based on administrative units or a single aspect of cultivated land fallow using high or medium spatial resolution images at the local or regional scales. Against the existing shortcomings, this study first developed an integrated index of cultivated land fallow (ILF) for mapping and evaluating cultivated land fallow in Southwest China using multisource spatial data. The performance of the ILF was validated by comparing its results with Google Earth images and ecological carrying capacity of cultivated land (TEC). And the spatial distribution of cultivated land fallow in Southwest China was evaluated at the regional, provincial and metropolitan scales. The results revealed that the ILF provided a reliable evaluation of cultivated land fallow in Southwest China. Compared to the Google earth images, the pixel with the high ILF value was the cultivated land that was found to prioritize fallow. There was also a significant correlation between ILF and TEC at the prefectural level in Sichuan, with an R2 value >0.65. In Southwest China, the cultivated land related to highly appropriate fallow (HAF) accounted for 5.73% of the total cultivated land in 2010. The cultivated land related to inappropriate fallow (IF) accounted for 53.26% and 37.36% in Sichuan and Chongqing but only comprised 22.90% and 19.72% in Yunnan and Guizhou, respectively. Special attention needs to be paid to Guiyang and Kunming, where the HAF made up 25.38% and 17.48% of their total cultivated land, respectively. Human activities have been found to already become the most important impact factors for cultivated land fallow in Southwest China. This study is especially valuable for providing a scientific basis for policy-making on viable cultivated land fallow policy in Southwest China.

[Spatiotemporal Dynamics of CO2 Emissions in Chongqing: An Empirical Analysis at the County Level].

China's CO2 emissions present obvious temporal and spatial distribution characteristics. Therefore, the study of spatiotemporal dynamics of CO2 emissions could provide useful information for the government and policy-makers on viable CO2 emissions mitigation in China. Using Chongqing as a case study, we investigated the spatiotemporal dynamics of CO2 emissions at the county level (38 counties) from 1997 to 2012.The mathematical statistical method, spatial autocorrelation, and rank size rule were employed to evaluate the CO2 emissions change in detail. The results showed that all of the counties in Chongqing have experienced a rapid growth of CO2 emissions, but the two dimensional structure of CO2 emissions has not changed. The Global Moran's I clearly decreases with a small fluctuation, and these values gradually decrease from 0.56 in 1997 to 0.40 in 2012.In addition, the HH clusters are concentrated in some counties in the downtown areas. Based on the rank size rule analysis, the slope values q decrease from -1.35 in 1997 to -0.88 in 2012, indicating a clear scattered pattern of CO2 emissions in Chongqing at the county level. It has also been proven that the proportion of second industries and the urbanization rate are more important impact factors for CO2 emissions than the population.