Influencing factors and spatiotemporal heterogeneity of livestock greenhouse gas emission: Evidence from the Yellow River Basin of China.

Livestock is one of major sources of greenhouse gas (GHG) emissions in China. Clarifying spatiotemporal characteristics of GHG emissions from livestock and exploring influencing factors can provide reference for grasping regional changes of GHG emission and formulate strategies of carbon reduction for livestock industry. However, existing literatures considered both spatial and temporal impacts and dynamic evolution trend of these factors seldomly. This paper used the life cycle assessment (LCA) method to estimate GHG emissions of livestock in 114 cities of the YRB from 2000 to 2021. On this basis, spatiotemporal heterogeneity of influencing factors was analyzed by using geographically and temporally weighted regression (GTWR) model. Finally, future evolution trend of GHG emissions from livestock was predicted by combining traditional and spatial Markov chain. Four main results were listed as follows. Firstly, GHG emission in the life cycle of livestock industry increased from 57.202 million tons (Mt) carbon dioxide equivalent (CO2e) in 2000 to 77.568 Mt CO2e in 2021. Secondly, structure of livestock industry, labor flow and mechanization were vital factors that led to increase of GHG emissions from livestock. Positive effects of labor flow and mechanization were increasing year by year, while negative effect of urbanization and positive effect of economic development were decreasing year by year. Markov chain analysis shown that probability of keeping high level of GHG emissions of livestock in the YRB unchanged were 96% (T = 1) and 90% (T = 5), and there also existed a Matthew effect. In addition, probability of level transfer of GHG emission in urban livestock was spatially dependent. Government should formulate strategies for livestock development and optimize low-carbon transformation of energy structure for livestock and poultry husbandry based on local conditions and key driving factors in the future. Meanwhile, boundaries of administrative divisions should be broken to promote reduction of GHG emissions in livestock comprehensively.

Ecological disturbance effects of surface vegetation during coal mining in arid regions of Western China.

Coal mining in arid western regions is damaging the fragile ecology, causing problems such as surface damage, vegetation destruction, and soil erosion. These issues are obstacles to the development of green coal, as mining activities can disrupt the distribution of surface vegetation, leading to its spread outside the mining area and affecting surrounding areas. Based on Landsat data, the binary pixel model was used to calculate the vegetation coverage (FVC) in mining area from 2005 to 2021. Through vegetation coverage classification and regression trend analysis, the temporal and spatial changes and evolution trends of vegetation disturbance caused by coal mining and climate were analyzed. Correlation analysis revealed the range of ecological disturbance caused by coal mining at the coal mine scale and mining area scale. The results show that the vegetation coverage of the mining area showed a decreasing trend from 2005 to 2021. Winter and spring precipitation was the primary factor affecting vegetation growth in the area, while coal mining had indirect and secondary effects on vegetation. Human activities played a significant role in improving vegetation, and between 2015 and 2018, the area of vegetation improvement increased by 133.41% compared to that of 2009-2014. Compared to the reference area, the impact range of vegetation disturbance in the mining area is 2.5-5 km, while the impact range of vegetation disturbance in the coal mine is less than 500 m. Therefore, this study provides a theoretical basis for studying the impact of mining activities on vegetation and boundary identification.

Improving non-invasive hemoglobin measurement accuracy using nonparametric models.

Uncontrolled hemorrhage is a leading cause of preventable death among patients with trauma. Early recognition of hemorrhage can aid in the decision to administer blood transfusion and improve patient outcomes. To provide real-time measurement and continuous monitoring of hemoglobin concentration, the non-invasive and continuous hemoglobin (SpHb) measurement device has drawn extensive attention in clinical practice. However, the accuracy of such a device varies in different scenarios, so the use is not yet widely accepted. This article focuses on using statistical nonparametric models to improve the accuracy of SpHb measurement device by considering measurement bias among instantaneous measurements and individual evolution trends. In the proposed method, the robust locally estimated scatterplot smoothing (LOESS) method and the Kernel regression model are considered to address those issues. Overall performance of the proposed method was evaluated by cross-validation, which showed a substantial improvement in accuracy with an 11.3% reduction of standard deviation, 23.7% reduction of mean absolute error, and 28% reduction of mean absolute percentage error compared to the original measurements. The effects of patient demographics and initial medical condition were analyzed and deemed to not have a significant effect on accuracy. Because of its high accuracy, the proposed method is highly promising to be considered to support transfusion decision-making and continuous monitoring of hemoglobin concentration. The method also has promise for similar advancement of other diagnostic devices in healthcare.

The evolution trend of availability of China's community-based care services and its impact on the cognitive function of elderly people: 2008-2018.

BACKGROUND: To address the challenge of the aging population, community-based care services (CBCS) have been developed rapidly in China as a new way of satisfying the needs of elderly people. Few studies have described the evolution trend of availability of CBCS in rural and urban areas and evaluated their effectiveness. This study aims to show the availability of China's CBCS and further analyze the effect of the CBCS on the cognitive function of elderly people. METHODS: Longitudinal analysis was performed using data from the 2008 to 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 23937 observations from 8421 elderly people were included in the study. The Chinese version of the Mini-Mental State Examination (MMSE) was used to assess cognitive function. We aggregated similar CBCS to generate three binary variable categories (daily life support, emotional comfort and entertainment services, medical support and health services) indicating the availability of CBCS (1 = yes, 0 = no). Multilevel growth models were employed to estimate the association between CBCS and cognitive function while adjusting for many demographic and socioeconomic characteristics. RESULTS: The availability of CBCS increased a lot from 2008 to 2018 in China. Although the availability of CBCS in urban areas was higher than that in rural areas in 2008, by 2018 the gap narrowed significantly. Emotional comfort and entertainment services (B = 0.331, 95% CI = 0.090 to 0.572) and medical support and health services (B = 1.041, 95% CI = 0.854 to 1.228) were significantly and positively associated with cognitive function after adjusting for the covariates. CONCLUSION: There was a significant increase in the availability of CBCS from 2008 to 2018 in China. This study sheds light on the positive correlation between CBCS and cognitive function among Chinese elderly individuals. The results suggest that policymakers should pay more attention to the development of CBCS and the equity of the supply of CBCS in urban and rural areas.

Research on the Evolution Trend of Group Psychological Security Risks Under Public Health Emergencies: Mining and Analysis Based on Social Media Data.

Background: In the digital age, people's attitudes and psychological security towards public health emergencies will be shared. Similar or identical psychological security states are prone to clustering and differentiation, while differentiated group psychological security is more prone to polarization, leading to group psychological security risks and then posing a threat to social stability and national security. However, existing studies mostly use qualitative analysis methods to study group emotional risks. There are still limitations in the study of dynamics of group psychological security risks through mining real data of social media. Purpose: The study aims to use intelligent analysis methods to understand how group psychological security risks dynamically change. Methods: The study draws on text sentiment analysis, Markov chains and time series analysis to construct a framework for the evolution of group psychological security risks. Based on this framework, text data was crawled on Sina Weibo platform, mainly consisting of posts during public health emergencies (March 1st to June 30th, 2022) in Shanghai, and a psychological security lexicon in the field of public health emergencies was constructed. This laid the foundation for identifying the tendencies, intensity, and transitions of individual text psychological security, and then exploring the evolution trend of group psychological security risks. Results: Compared with the generation and reduction periods, group psychological security risks are more likely to occur during the outbreak and recovery periods, and the intensity level is also higher. The overall intensity of group psychological security risks shows an evolution trend of first increasing, then decreasing, and then increasing again. Conclusion: The paper provides an opportunity to explore the dynamics of psychological security in the digital space. Meanwhile, we call on the government and relevant management departments to pay more attention to the group psychological security risks formed during the outbreak and recovery periods of public health emergencies, and take corresponding measures in a timely manner to guide the public to transform the extreme psychological security state into the normal psychological security state, in order to prevent and resolve group psychological security risks, promote social stability and national security.

Global distribution, cross-species transmission, and receptor binding of canine parvovirus-2: Risks and implications for humans.

For canine parvovirus -2 (CPV-2), a zoonotic virus capable of cross-species transmission in animals, the amino acid changes of capsid protein VP2 are key factors when binding to other species' transferrin receptors (TfR). CPV-2 variants can spread from felines and canines, for example, to Carnivora, Artiodactyla, and Pholidota species, and CPV-2c variants are essential to spread from Carnivora to Artiodactyla and Pholidota species in particular. In our study, a CPV-2a variant maintained a relatively stable trend, and the proportion of CPV-2c gradually rose from 1980 to 2021. The VP2 amino acid sequence analysis showed that five amino acid mutations at 426E/D, 305H/D, and 297S may be necessary for the virus to bind to different host receptors. Meanwhile, receptor-binding loop regions and amino acid sites 87 L, 93 N, 232I, and 305Y were associated with CPV-2 cross-species transmission. The homology of TfRs in different hosts infected with CPV-2 ranged from 77.2 % to 99.0 %, and from pig to feline, canine, and humans was 80.7 %, 80.4 %, and 77.2 %, respectively. The amino acid residues of TfRs involved in the viral binding in those hosts are highly conserved, which suggests that CPV-2 may be capable of pig-to-human transmission. Our analysis of the origin, evolutionary trend, cross-species transmission dynamics, and genetic characteristics of CPV-2 when binding to host receptors provides a theoretical basis for further research on CPV-2's mechanism of cross-species transmission and for establishing an early warning and monitoring mechanism for the possible threat of CPV-2 to animal-human public security.

Spatiotemporal distribution and dynamics evolution of artificial intelligence development in China.

The quantified measurement and comprehensive analysis of artificial intelligence development (AIDEV) are vital for countries to form AI industrial ecology and promote the long-term development of regional AI technology. Based on the innovation ecosystems (IE) theory, this paper constructs an evaluation system to measure and analyze the spatiotemporal distribution and dynamic evolution of the AIDEV in China from 2011 to 2020. The results show that the AIDEV of China presents an overall upward trend and an obvious unbalance in the spatial distribution which is "eastern > central > western". Meanwhile, the provinces of low-level AIDEV are catching up with the high-level provinces, which leads to the regional difference of AIDEV narrowing. Moreover, the concentration and polarization phenomenon of AIDEV in China has been weakening and the AIDEV will continue to increase in the next three years. Further, there is a significantly positive spatial autocorrelation of AIDEV. Finally, high AIDEV provinces will increase the probability of surrounding provinces' AIDEV to develop. This paper expands the research stream in the field of AI research, extends the application scenarios of IE theory, and puts forward some relevant policy recommendations.

Inverted Classroom Teaching of Physiology in Basic Medical Education: Bibliometric Visual Analysis.

Background: Over the last decade, there has been growing interest in inverted classroom teaching (ICT) and its various forms within the education sector. Physiology is a core course that bridges basic and clinical medicine, and ICT in physiology has been sporadically practiced to different extents globally. However, students' and teachers' responses and feedback to ICT in physiology are diverse, and the effectiveness of a modified ICT model integrated into regular teaching practice in physiology courses is difficult to assess objectively and quantitatively. Objective: This study aimed to explore the current status and development direction of ICT in physiology in basic medical education using bibliometric visual analysis of the related literature. Methods: A bibliometric analysis of the ICT-related literature in physiology published between 2000 and 2023 was performed using CiteSpace, a bibliometric visualization tool, based on the Web of Science database. Moreover, an in-depth review was performed to summarize the application of ICT in physiology courses worldwide, along with identification of research hot spots and development trends. Results: A total of 42 studies were included for this bibliometric analysis, with the year 2013 marking the commencement of the field. University staff and doctors working at affiliated hospitals represent the core authors of this field, with several research teams forming cooperative relationships and developing research networks. The development of ICT in physiology could be divided into several stages: the introduction stage (2013-2014), extensive practice stage (2015-2019), and modification and growth stage (2020-2023). Gopalan C is the author with the highest citation count of 5 cited publications and has published 14 relevant papers since 2016, with a significant surge from 2019 to 2022. Author collaboration is generally limited in this field, and most academic work has been conducted in independent teams, with minimal cross-team communication. Authors from the United States published the highest number of papers related to ICT in physiology (18 in total, accounting for over 43% of the total papers), and their intermediary centrality was 0.24, indicating strong connections both within the country and internationally. Chinese authors ranked second, publishing 8 papers in the field, although their intermediary centrality was only 0.02, suggesting limited international influence and lower overall research quality. The topics of ICT in physiology research have been multifaceted, covering active learning, autonomous learning, student performance, teaching effect, blended teaching, and others. Conclusions: This bibliometric analysis and literature review provides a comprehensive overview of the history, development process, and future direction of the field of ICT in physiology. These findings can help to strengthen academic exchange and cooperation internationally, while promoting the diversification and effectiveness of ICT in physiology through building academic communities to jointly train emerging medical talents.

Evolutionary Trends, Regional Differences and Influencing Factors of the Green Efficiency of Agricultural Water Use in China Based on WF-GTWR Model.

Improving the green efficiency of agricultural water use is a key way to promote the sustainable utilization of agricultural water resources and sustainable development of economy and society. This work calculated and analyzed the evolution trend, regional differences and driving factors of the green efficiency of agricultural water use in China from the perspective of the water footprint. The results show that the green efficiency of agricultural water use in China shows a fluctuation trend of first declining and then rising from 1997 to 2020, after which the average efficiency dropped from 0.538 in 1997 to 0.406 in 2009, and then rose rapidly to 0.989 in 2020, with an average annual growth rate of about 3.6%. From a regional perspective, the green efficiency of agricultural water use in the eastern region was the highest (0.594), above the national average (0.538), followed by the western region (0.522), with the central region in last (0.491), with significant regional differences. The spatial differences in the green efficiency of available agricultural water in China shows a fluctuating downward trend. The Gini coefficient fluctuated from 0.271 in 1997 to 0.182 in 2020, with an average annual growth rate of about -1.4%. The main source of this regional difference was super-variable density, followed by the difference between the eastern and the central regions. The influence of urbanization level, water-saving level and agricultural trade on the green efficiency of agricultural water use was always positive and the influence of industrialization level was always negative; among them, the urbanization level, water-saving level and industrialization level had a greater impact on Northeast China, and agricultural trade had a greater impact on Southeast China. Therefore, this work puts forward relevant policy recommendations.

Urban land use carbon emission intensity in China under the "double carbon" targets: spatiotemporal patterns and evolution trend.

In-depth research on the spatiotemporal patterns and evolution trend of urban land use carbon emission intensity (ULUCEI) can reveal the internal relationship between urban land use and carbon emissions, which is crucial for achieving carbon emission reduction and "double carbon" targets. This paper proposed a conceptual framework of ULUCEI; the methods of kernel density estimation (KDE), exploratory spatial data analysis (ESDA), and spatial Markov chains were adopted for exploring the spatiotemporal patterns and evolution trend of China's ULUCEI from 2000 to 2017. The following conclusions are drawn through research. (1) There was an increasing trend in ULUCEI in China from 0.102 in 2000 to 0.283 in 2017. From the regional perspective, the ULUCEI in the eastern region is markedly higher than that in the central and western regions. Moreover, the results of nuclear density estimation indicate that China's ULUCEI shows an obvious upward and polarized trend. (2) China's ULUCEI shows a positive spatial autocorrelation. The types of spatial agglomeration include "high-high" agglomeration, "high-low" polarization, "low-high" collapse, and "low-low" homogeneity, and there are obvious disparities in the distribution rules of cities with different spatial agglomeration forms. (3) China's ULUCEI presents strong stability and "club convergence" trend. Moreover, spatial factors significantly affect the dynamic transition of China's ULUCEI, and its effect on the shifting upwards gradually enhances with increasing lag type. This paper therefore suggests that policymakers should formulate differentiated urban land low-carbon use models and carbon emission reduction policies to reduce ULUCEI.

Convergence of energy carbon emission efficiency: evidence from manufacturing sub-sectors in China.

With China's economy entering the stage of high-quality development, manufacturing energy carbon emission efficiency has become the focus of academic attention. It is of great significance to study the convergence of manufacturing energy carbon emission efficiency for realizing high-quality development of manufacturing in China. Based on the panel data of China's manufacturing sub-sectors, this paper measures and analyzes the evolution trend of manufacturing energy carbon emission and its efficiency. On this basis, this paper uses the coefficient of variation and convergence model to test the convergence of manufacturing energy carbon emission efficiency. The results show that China's manufacturing energy carbon emissions and its efficiency demonstrate an increasing trend. Coal was the main source of manufacturing energy carbon emissions. The manufacturing energy carbon emission efficiency does not have σ convergence, but has [Formula: see text] convergence, and its convergence has industry heterogeneity. The manufacturing energy carbon emission efficiency exits scale effect and technology effect, but not the effect of opening to the outside world and institutional effect, and its effect exists industry heterogeneity. By reducing carbon emissions, adopting differentiated policies, adjusting the industry scale, and enhancing the industry technology intensity, China's manufacturing can improve the energy carbon emission efficiency and promote high-quality economic development.

Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China.

The coordinated promotion of urban digitalization and green development is an inevitable requirement for sustainable development in the digital age. Based on the coupling mechanism of urban digitalization and green development, in this study, we took 282 cities at the prefecture level and above in China from 2011 to 2019 as the research object, and we constructed the evaluation index system and calculated the coupling coordination degree (CD&GDD) of the two through the coupling coordination degree model. We further used the Dagum Gini coefficient, kernel density estimation, Markov chain and Moran's I to assess the spatial effects of the regional differences, dynamic evolution trends and degree of coupling coordination. The results show the following: (1) The level of urban digitalization and green development show a fluctuating upward trend, and the interaction between the two is obvious. (2) Although the CD&GDD of most cities is continuously improving, it is still at a low level. There are large differences in the levels between the regions. (3) The inter-regional differences are the main source of the large overall differences in the CD&GDD in China, and these are mainly composed of the hypervariable density and net differences between the regions. (4) The phenomenon of "club convergence" exists in the CD&GDD. (5) The coupling coordination relationship between cities has a substantial spatial effect, and the spatial effect has obvious regional heterogeneity. The results and conclusions provide a reference for developing countries to promote green and low-carbon urban development.

Spatial distribution, regional differences, and dynamic evolution of the medical and health services supply in China.

The imbalance of medical and health services supply (MHSS) is a significant public health concern as regional economic development disparities widen in China. Based on the provincial panel data of medical and health services, this paper constructed an evaluation index system and used the two-stage nested entropy method to measure the MHSS level of 31 provinces in China from 2005 to 2020. Then we used the standard deviation ellipse, Dagum Gini coefficient, ß convergence model, kernel density estimation and Markov chain to investigate the spatial distribution, regional differences, and dynamic evolution of MHSS. According to the results of these analysis, the conclusions are drawn as follows: (1) In general, the MHSS level in China showed a significant up-ward trend from 2005 to 2020. However, the MHSS level among different provinces showed a non-equilibrium characteristic. (2) Regional comparison shows that the eastern region had the highest level, and the central region had the lowest level. The eastern and central regions presented polarization, while the western region showed unremarkable gradient effect. (3) During the period, the overall regional differences, intra-regional differences, and inter-regional differences of MHSS level all showed convergence. (4) The economic development, urbanization rate, fiscal self-sufficiency rate, and foreign direct investment had significant impacts on the convergence. (5) The provinces with high levels had the positive spillover effect. The findings of this paper provide theoretical supports for optimizing the allocation of health resources and improving the equity of MHSS.

Spatiotemporal Differences, Dynamic Evolution and Trend of the Coupled Coordination Relationship between Urbanization and Food Security in China.

Scientific assessment of the coupled coordination degree between urbanization and food security (CDUFS) revealed regional differences and sources. Dynamic evolution and trends are important references for achieving a coordinated interaction between high-quality urbanization and ensuring food security. Specifically, the CDUFS was measured using prefectural panel data in China from 2000 to 2019 and the coupling coordination degree model, which revealed its spatial correlation and differentiation. On this basis, in order to examine the spatiotemporal differences and evolution of the CDUFS, the Dagum-Gini coefficient and Kernel density estimation were innovatively used to analyze its regional differences and evolution distribution. The spatial Markov chain was further employed to examine the evolution trend of the CDUFS. The study found that the CDUFS showed a downward trend in fluctuation within the low coordination interval. There was a positive spatial correlation, with a more stable distribution pattern of high-high and low-low clusters. The regional differences in the CDUFS were obvious and the overall difference has expanded. The main source of regional differences among different food functional areas was inter-regional differences, followed by intra-regional differences. The regional difference between food main producing areas and food main marketing areas was the highest. The CDUFS shows a single-peak distribution; the imbalance between regions was still prominent with a left trailing phenomenon and no convergence. The CDUFS has the stability of maintaining the original state, and the probability of leapfrogging evolution is low in the short term. Finally, the geospatial effect plays an important role in the dynamic evolution of the CDUFS.

Topic Evolution Analysis for Omics Data Integration in Cancers.

One of the vital challenges for cancer diseases is efficient biomarkers monitoring formation and development are limited. Omics data integration plays a crucial role in the mining of biomarkers in the human condition. As the link between omics study on biomarkers discovery and cancer diseases is deepened, defining the principal technologies applied in the field is a must not only for the current period but also for the future. We utilize topic modeling to extract topics (or themes) as a probabilistic distribution of latent topics from the dataset. To predict the future trend of related cases, we utilize the Prophet neural network to perform a prediction correction model for existing topics. A total of 2,318 pieces of literature (from 2006 to 2020) were retrieved from MEDLINE with the query on "omics" and "cancer." Our study found 20 topics covering current research types. The topic extraction results indicate that, with the rapid development of omics data integration research, multi-omics analysis (Topic 11) and genomics of colorectal cancer (Topic 10) have more studies reported last 15 years. From the topic prediction view, research findings in multi-omics data processing and novel biomarker discovery for cancer prediction (Topic 2, 3, 10, 11) will be heavily focused in the future. From the topic visuallization and evolution trends, metabolomics of breast cancer (Topic 9), pharmacogenomics (Topic 15), genome-guided therapy regimens (Topic 16), and microRNAs target genes (Topic 17) could have more rapidly developed in the study of cancer treatment effect and recurrence prediction.

The Evolutionary Trends of Health Behaviors in Chinese Elderly and the Influencing Factors of These Trends: 2005⁻2014.

As China is now facing the severe challenge of rapid population ageing, the health behaviors in Chinese elderly people are of great significance for realizing the goal of "Healthy Ageing" and the construction of a "Healthy China". Little is known about the evolutionary trends of health behaviors in the Chinese elderly and about the factors influencing these trends; thus, the purposes of this paper are: (1) To describe the classes and evolutionary trends of health behaviors in the Chinese elderly; and (2) to explore the factors that influence the changes in the health behaviors in the elderly in China. Latent class analysis (LCA) is applied in this study to analyze the classes of health behaviors in the Chinese elderly. Growth mixture modelling (GMM) is employed to describe the evolutionary trends of the health behaviors in elderly people in China. In addition, the Bivariate analysis model is adopted to identify the influencing factors of the evolution of health behaviors. The data were derived from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 2005 to 2014. The results reveal that the health behaviors in the Chinese elderly can be grouped into five classes: Negative, relatively negative, fair, relatively positive, and positive. Approximately 77.2% of the health behaviors in the Chinese elderly have the characteristics of "modified", with a positive tendency. Moreover, approximately 22.8% of the health behaviors in Chinese elderly people have the characteristics of "non-modified", with a negative tendency or remaining unchanged. The evolution of the health behaviors in the elderly in China is more affected by economic factors such as timely medical treatment during childhood, pension, occupations before the age of 60 and family income, as well as by self-rated health (SRH) and demographic characteristics such as household registration, age, and education level. Hence, various possible interventions should be made to improve the health behaviors in elderly people.