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Background: Polypharmacy would increase the risk of adverse drug events and the burden of renal drug excretion among older people. Nevertheless, the association between the number of medication and the risk of chronic kidney disease (CKD) remains controversial. Therefore, this study aims to investigate the association between the number of medication and the incidence of CKD in older people. Methods: This study investigates the association between the number of medications and CKD in 2672 elderly people (≥ 65 years older) of the community health service center in southern China between 2019 and 2022. Logistic regression analysis was used to evaluate the relationship between polypharmacy and CKD. Results: At baseline, the average age of the study subjects was 71.86 ± 4.60, 61.2% were females, and 53 (2.0%) suffer from polypharmacy. During an average follow-up of 3 years, new-onset CKD developed in 413 (15.5%) participants. Logistic regression analysis revealed that taking a higher number of medications was associated with increase of CKD. Compared with people who didn't take medication, a higher risk of CKD was observed in the older people who taken more than five medications (OR 3.731, 95% CI 1.988, 7.003), followed by those who take four (OR 1.621, 95% CI 1.041, 2.525), three (OR 1.696, 95% CI 1.178, 2.441), two drugs (OR 1.585, 95% CI 1.167, 2.153), or one drug (OR 1.503, 95% CI 1.097, 2.053). Furthermore, age, systolic blood pressure (SBP), white blood cell (WBC), blood urea nitrogen (BUN) and triglyceride (TG) were also independent risk factors CKD (P < 0.05). Conclusion: The number of medications was associated with CKD in older people. As the number of medications taken increased, the risk of CKD was increased. [ABSTRACT FROM AUTHOR]

Northern China faces water scarcity, restricting water usage in place across Inner Mongolia's western region. The integrated irrigation and fertilization model for sugar beet is undergoing rapid development and application in production. However, there is a concerning trend in production where the frequency of irrigation and fertilization is being increased blindly, resulting in the wastage of valuable water and fertilizer resources. Limiting water and fertilizer usage is an effective approach to improve sugar beet production efficiency. Sugar beets are a significant sugar crop in China. A split-plot design was employed to examine the impact of reducing water and fertilizer use on sucrose metabolism in sugar beet root. Our study was performed at the Ulanqab Institute of Agricultural and Forestry Sciences in Inner Mongolia from 2022 to 2023. Three levels of fertilization and irrigation were utilized. We investigated the interactions between irrigation and fertilization on sucrose accumulation in sugar beet root. We examined key enzyme activities involved in sucrose metabolism alongside their gene expression levels. The findings suggested that reducing irrigation by 15%, fertilization by 10%, or both irrigation by 15% and fertilization by 10%, increased sucrose concentrations of sugar beets compared to the control group administered conventional water and fertilizer. Over the two-year period, the average sucrose concentration increased by 0.45, 0.57, and 0.65 degrees, respectively, under each treatment. Subsequent research verified that appropriately reducing water and fertilizer can regulate the expression of enzyme genes, thus influencing enzyme activity. Moreover, due to the higher efficiency of enzyme synthesis compared to decomposition, it contributed to an increase in net enzyme activity. These findings suggest that an appropriate reduction of water and fertilizer can improve sucrose synthesis rates and increase the sucrose concentration in sugar beets, providing a theoretical basis for environmentally friendly generation and enhanced efficiency in sugar beet growth. [ABSTRACT FROM AUTHOR]

Expansive soils are distributed across a wide area in China, and land transport and surface construction will inevitably involve these soils. To mitigate the deficiencies of single-method expansive soil modification, it is highly important to adopt the use of polyvinyl alcohol (PVA) to improve expansive soils and enhance the strength and toughness of modified soils. In addition, solidification technology can be utilized for the resource utilization of expansive soils. In this study, triaxial testing is employed to evaluate the mechanical properties of solidified soil. When the confining pressure is the same and with increasing PVA content, soil particles and PVA combine to form a cemented substance, which fills the internal pores of the soil samples, enhances the cohesion between soil particles, and improves the bearing capacity of the soil. The stress–strain curve for the modified soil first increases and then decreases. The shear strength peaks at a PVA content of 3%. Based on the improved soil with a 3% PVA content, GDS dynamic triaxial tests were carried out to investigate the effects of different confining pressures and frequencies on the dynamic stress–strain curves, dynamic modulus of elasticity, and variation rule for the damping ratio of the improved soil. The results show that the dynamic stress–strain curve for the improved soil increases with increasing confining pressure and frequency and that the dynamic stress–strain backbone curves exhibit significant nonlinearities at different frequencies and circumferential pressures. The dynamic elastic modulus increases with increasing confining pressure and frequency and decreases gradually with increasing dynamic strain. The initial dynamic modulus of elasticity increases with increasing envelope pressure and frequency but is less affected by frequency, and the damping ratio decreases with increasing confining pressure and frequency. Soil treatment can improve the pore distribution, inhibit the extension of soil cracks, and enhance soil compactness. [ABSTRACT FROM AUTHOR]

A water quality parameter retrieval scheme based on the UAV push-broom hyperspectral images was designed and validated for assessing the ecological health of Zhang Wei Xin River in Dezhou distinct, China. First, a UAV carrying a push-broom hyperspectral imager that is lightweight and has a small size was used to acquire high spatial and hyperspectral resolution images. Then, the mosaicked reflectance data of the whole river were produced by a seamless image mosaicking method with high geometrical accuracy and spectral fidelity. Next, the in-field measurements of different parameters and the corresponding spectral reflectance from the mosaicked images at the sampling points were used to build the water quality parameter retrieval models for total phosphorus (TP), chlorophyll a (Chla), and total suspended solids (TSS). To validate the model, the retrieval results of the testing sampling points were compared with the measured parameters. The coefficients of determination R2 of TP, Chla, and TSS were 0.886, 0.918, and 0.968, respectively. The retrieved TP, Chla, and TSS maps showed that the water pollution of Zhang Wei Xin River is serious, the total phosphorus exceeds the standard, and the water body is in a state of eutrophication. The UAV-based hyperspectral remote sensing technique provides a cost-effective method for inland water monitoring at a local scale with high accuracy. [ABSTRACT FROM AUTHOR]

The South-to-North Water Diversion Project (SNWDP) is one of the largest cross-basin and cross-region water transfer projects in the world. The Danjiangkou reservoir area, a haven of diverse species, serves as a core water source for the Central Line of the SNWDP. Yet, less research has been conducted on changes in land use and ecosystem services (ESs) in the Danjiangkou reservoir area in the context of the implementation of the SNWDP and other national projects. In this study, we aim to reveal evolutions of land uses and ESs in the Danjiangkou reservoir area and the response of ESs to natural and socio-economic factors. This is essential to enhance the regional sustainable management of the Danjiangkou reservoir area. Based on classified land use maps and the InVEST model, we first analyzed the land use changes and evaluated three typical types of ESs (i.e., water yield (WY), carbon storage (CS) and habitat quality (HQ)) in the Danjiangkou reservoir area during 2000 to 2018. Then, we detected the spatial clustering characteristics and tradeoffs and synergistic relationships of multiple ESs through hot spot analysis and correlation analysis. Finally, we adopt the geographical detector model (GDM) to identify key driving factors of ESs changes. The results show that: (1) During 2000–2018, the area of arable land and woodland decreased by 1.65% and 0.8%, respectively, while the proportion of construction land and water area increased by 1.31% and 1.39%, respectively. (2) The greatest decrease was in WY, decreasing by 59%, while the change in HQ was relatively stable, but showed spatial heterogeneity. (3) The northern, southern and western districts of the reservoir area showed mainly synergies among multiple ESs, while other regions showed mainly trade-offs. (4) Road network density, proportion of construction land and normalized difference vegetation index are the leading factors for ESs variations. These findings can provide reference for formulating more reasonable ecological protection strategies, so as to realize the sustainable management of SNWDP and its headwaters region. [ABSTRACT FROM AUTHOR]

Objectives: To estimate the coverage and willingness of pneumococcal vaccination and further explore the influencing factors of vaccination willingness among people in mainland China.Methods: Literature searches were conducted independently by two researchers in English- and Chinese-language databases from database inception to 6 October 2021. A random-effects meta-analysis model was used to derive summary vaccination coverage and willingness. Predefined subgroup analysis and meta-regression were performed to explore the sources of heterogeneity.Results: A total of 97 studies were included in this meta-analysis. The summary vaccination coverage in 76 included studies was 21.7% (95% confidence interval [CI]: 17.2%-26.5%). Subgroup analysis shows that the summary coverage was 29.0% (95% CI, 20.4%-39.1%) among the permanent residents and 20.7% (95% CI, 12.4%-35.9%) among the floating residents. The eastern and central regions presented higher coverage than the western region. Notable differences were observed between the various study populations. A total of 27 studies provided an estimation of vaccination willingness, with a summary willingness of 51.2% (95% CI, 40.4%-61.9%). In subgroup analysis, the summary willingness was 57.9% (95% CI, 48.3%-67.2%) in urban areas and 52.3% (95% CI, 40.8%-63.8%) in rural areas. Parents with children and people with a history of pneumonia were more willing to be vaccinated than the elderly. Recommendations by family members and physicians, previous pneumococcal and influenza vaccination, perceived vaccination effectiveness and severity of disease and a history of pneumonia contributed to vaccination willingness.Conclusions: Compared to global estimates and other countries, pneumococcal vaccination coverage and willingness are at a lower level in mainland China. Recommendations for vaccination by family members and doctors, a history of vaccination and the perception of pneumonia and vaccination are associated with greater willingness to be vaccinated. [ABSTRACT FROM AUTHOR]

Ecosystem services (ESs) are irreplaceable natural resources, and their value is closely related to global change and to human well-being. Research on ecosystem services value (ESV) and its influencing factors can help rationalize ecological regulatory policies, and is especially relevant in such an ecologically significant region as the Yellow River Basin (YRB). In this study, the ecological contribution model was used to measure the contribution of intrinsic land use change to ESV, the bivariate spatial autocorrelation model was applied to investigate the relationship between land use degree and ESV, and the geographical detector model (GDM) and geographically weighted regression (GWR) were applied to reveal the impact of natural and socio-economic factors on ESV. Results showed that: (1) The total ESV increased slightly, but there were notable changes in spatial patterns of ESV in the YRB. (2) Land use changes can directly lead to ESV restoration or degradation, among which, conversion from grassland to forest land and conversion from unused land to grassland are vital for ESV restoration in the YRB, while degradation of grassland is the key factor for ESV deterioration. (3) According to GDM, NDVI is the most influential factor affecting ESV spatial heterogeneity, and the combined effect of multiple factors can exacerbate ESV spatial heterogeneity. (4) GWR reveals that NDVI is always positively correlated with ESV, GDP is mainly positively correlated with ESV, and population density is mainly negatively correlated with ESV, while positive and negative correlation areas for other factors are roughly equal. The findings can provide theoretical support and scientific guidance for ecological regulation in the YRB. [ABSTRACT FROM AUTHOR]

'Requirements for human haematopoietic stem/progenitor cells' is the first set of guidelines on human haematopoietic stem/progenitor cells in China, jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research. This standard specifies the technical requirements, inspection methods, inspection rules, instructions for usage, labelling requirements, packaging requirements, storage requirements and transportation requirements for human haematopoietic stem/progenitor cells, which is applicable to the quality control for human haematopoietic stem/progenitor cells. We hope that publication of these guidelines will promote institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of human haematopoietic stem/progenitor cells for applications. [ABSTRACT FROM AUTHOR]

The Fuyang River system (FRS) in north China, for a long time, is seriously polluted with organic compounds and heavy metals due to industrialization. However, the information on heavy metal pollution in this area is still limited, and health risks raised by trace elements are neglected up to now. To characterize the heavy metal pollutants and assess their potential ecological risks scientifically in FRS, surface sediments were collected from 66 sampling sites selected according to the hydrological and anthropogenic conditions along the river. A total of twelve metal pollutants (e.g., Cr, As, and Hg) in the sediments were detected among the distributaries. A combining application of geoaccumulation index (I geo) , ratio of secondary phase and primary phase (RSP) , and the ecological risk factor (Er i ) in this study gave systematic assessment results of single or combined pollution status raised by heavy metals in this area. The results show that Cr, Ni, Cu, As, Cd, Co, and Sn are mainly dispersed in the river reaches of Xingtai City and pose potential health risks in midstream, as per the geoaccumulation index and Pearson's correlation analyses. In particular, Cd accumulates strongly in sediments of Ming River and Aixinzhuang dam from Xingtai City. In upstream and downstream of FRS, the potential ecological risk is low, except in Yongnian County where high ecological risk was caused by Cd and Hg. These findings provide new insights into the pollution characteristics and assessment of the potential ecological risks induced by heavy metals along FRS, which suggest new directions should strategically tend to typical pollutants control by policy formulation and taking effective measures to prevent and manage heavy metal pollution in North China. [Display omitted] • Multiple heavy metals pollution in FRS in North China was surveyed and assessed. • Distribution and pollution status of heavy metals in the FRS were characterized. • The health risk raised by heavy metals along the FRS was systematically evaluated. • The correlations between pollution and human activities along the river was built. Heavy metal pollution in Fuyang river system in North China are systematically characterized and the ecological risks are assessed through I geo , RSP, and Er i . [ABSTRACT FROM AUTHOR]

A major difficulty in integrating large scale wind power generation in an electrical power system is that wind generated power appears to be erratic, intermittent, and volatile. In this paper, we demonstrate the efficacies of a novel ultra short term 1-step ahead wind generated power prediction model, by combining two best of breed machine learning models in their respective areas of applications: a deep convolutional neural network (CNN) model, known to be effective in classification problems, and a bi-directional long short term memory (Bi-LSTM) model, known to be effective in 1-step ahead time series prediction problems, using a cross attention (XA) mechanism on three challenging practical datasets: the East-China dataset, the Yalova (Turkey) dataset, and the 16 MW dataset. There are two alternative cross attention models: (1) using the CNN features as the key–value pair, and the Bi-LSTM features as the query, this we called a XA model, and (2) using the Bi-LSTM features as the key–value pair, and the CNN features as the query, this we called a XA_I model; we found empirically for a properly chosen sliding window length, whichever CNN model or Bi-LSTM model performs well in the 1-step ahead prediction, then its features should be used as the query in the cross attention mechanism, thus, the XA achieves better results than XA_I on the East-China dataset, while XA_I achieves better results than XA on the Yalova dataset, and on the 16 MW dataset. Where comparable results using other state-of-the-art models are available on the Yalova dataset and the 16 MW dataset, we found that our XA_I results are superior. Moreover, it is found empirically, that the cross attention mechanism model produces stable k -step ahead predictions, for k ≤ W 2 , where W is the sliding window length. • A novel prediction model for wind generated power based on cross attention mechanism has been proposed. • The proposed method demonstrated on three practical datasets to have superior performance. • The cross attention mechanism based models give a stable multi-step ahead prediction. [ABSTRACT FROM AUTHOR]

PM2.5 is closely linked to both air quality and public health. Many studies have used models combined with remote sensing and auxiliary data to inverse a large range of PM2.5 concentrations. However, the data's spatial resolution is limited. and better results might have been obtained if higher resolution data had been used. Therefore, this paper establishes a geographical and temporal weighted regression model (GTWR) and estimates the PM2.5 concentration in Xinjiang from 2015 to 2020 using 1 km resolution MCD19A2 (MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006) data and 9 auxiliary variables. The findings indicate that the GTWR model performs better than the simple linear regression (SLR) and geographically weighted regression (GWR) models in terms of accuracy and feasibility in retrieving PM2.5 concentrations in Xinjiang. Simultaneously, by combining the GTWR model with MCD19A2 data, a spatial distribution map of PM2.5 with better spatial resolution can be obtained. Next, the regional distribution of annual PM2.5 concentrations in Xinjiang is consistent with the terrain from 2015 to 2020. The low value area is primarily found in the mountainous area with higher terrain, while the high value area is primarily in the basin with lower terrain. Overall, the southwest is high and the northeast is low. In terms of time change, the six-year PM2.5 shows a single peak distribution with 2016 as the inflection point. Lastly, from 2015 to 2020, the seasonal average PM2.5 concentration in Xinjiang has a significant difference, thereby showing winter (66.15μg/m3)>spring (52.28μg/m3)>autumn (40.51μg/m3)>summer (38.63μg/m3). The research shows that the combination of MCD19A2 data and GTWR model has good applicability in retrieving PM2.5 concentration., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Quan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The occurrence of major health events can have a significant impact on public mood and mental health. In this study, we selected Shanghai during the 2019 novel coronavirus pandemic as a case study and Weibo texts as the data source. The ERNIE pre-training model was used to classify the text data into five emotional categories: gratitude, confidence, sadness, anger, and no emotion. The changes in public sentiment and potential influencing factors were analyzed with the emotional sequence diagram method. We also examined the causal relationship between the epidemic and public sentiment, as well as positive and negative emotions. The study found: (1) public sentiment during the epidemic was primarily affected by public behavior, government behavior, and the severity of the epidemic. (2) From the perspective of time series changes, the changes in public emotions during the epidemic were divided into emotional fermentation, emotional climax, and emotional chaos periods. (3) There was a clear causal relationship between the epidemic and the changes in public emotions, and the impact on negative emotions was greater than that of positive emotions. Additionally, positive emotions had a certain inhibitory effect on negative emotions.

The extent to which landscape spatial patterns can impact the dynamics and distribution of biodiversity is a key geography and ecology issue. However, few previous studies have quantitatively analyzed the spatial relationship between the landscape pattern and habitat quality from a simulation perspective. In this study, the landscape pattern in 2031 was simulated using a patch-generating simulation (PLUS) model for the Yellow River Basin. Then, the landscape pattern index and habitat quality from 2005 to 2031 were evaluated using the Fragstats 4.2 and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Furthermore, we analyzed the spatial distribution characteristics and spatial spillover effects of habitat quality using spatial autocorrelation analysis. Finally, the spatial association between the landscape pattern index and habitat quality was quantitatively revealed based on a spatial lag model. The simulation results showed that: (1) from 2005 to 2031, the landscape of the Yellow River Basin would be dominated by grassland and unused land, and the areas of construction land and water body will increase significantly, while the area of grassland will decrease; (2) patch density (PD) and Shannon's diversity index (SHDI) show significant increases, while edge density (ED), landscape shape index (LSI), mean patch area (AREA&#95;MN), and contagion index (CONTAG) decrease; (3) from 2005 to 2031, habitat quality would decrease. The high-value areas of habitat quality are mainly distributed in the upper reaches of the Yellow River Basin, and the low-value areas are distributed in the lower reaches. Meanwhile, both habitat quality and its change rate present positive spatial autocorrelation; and (4) the spatial relationships of habitat quality with PD and COHESION are negative, while ED and LSI have positive impacts on habitat quality. Specifically, landscape fragmentation caused by high PD has a dominant negative influence on habitat quality. Therefore, this study can help decision makers manage future landscape patterns and develop ecological conservation policy in the Yellow River Basin.

Against the background of "the emotional turn" in geography, the study of emotional identification is attracting increasing attention among researchers. Edible landscape resources can satisfy the emotional needs of teachers and students by enabling them to experience pastoral landscapes that carry cultural and landscape values to campus environments. Based on a questionnaire survey of 419 students and teachers at Chenggong University Town in China, this study improved the structural equation modeling (SEM) method to construct a model to analyze the emotional identification mechanism of the campus edible landscape. The research found that emotional identification played an intermediary role between perception and behavioral intention, manifested as an association mechanism in which surface values influence perception, perception influences emotional identification, and emotional identification influences behavioral intention. The emotional identification model revealed the relationship between teachers and students' emotional identification and the value of campus edible landscape resources for the first time. It also uncovered the universality of the association mechanism in the research of emotional geography.

An increased land use intensity due to rapid urbanization and socio-economic development would alter the structure and function of regional ecosystems and cause prominent environmental problems. Revealing the impact of land use intensity on ecosystem services (ES) would provide guidance for more informed decision making to promote the sustainable development of human and natural systems. In this study, we selected the Hanjiang River Basin (HRB) in Hubei Province (China) as our study area, explored the correlation between land use intensity and ecosystem Services' Value (ESV), and investigated impacts of natural and socio-economic factors on ESV variations based on the Geographical Detector Model (GDM) and Geographically Weighted Regression (GWR). The results show that (1) from 2000 to 2020, land use intensity in HRB generally showed an upward trend, with a high spatial agglomeration in the southeast and low in the northwest; (2) the total ESV increased from 295.56 billion CNY in 2000 to 296.93 billion CNY in 2010, and then decreased to 295.63 CNY in 2020, exhibiting an inverted U-shaped trend, with regulation services contributing the most to ESV; (3) land use intensity and ESV had a strong negative spatial correlation, with LH (low land use intensity vs. high ESV) aggregations mainly distributed in the northwest, whereas HL (high land use intensity vs. low ESV) aggregations were located in the southeast; (4) natural factors, including annual mean temperature, the percentage of forest land, and slope were positively associated with ESV, while socio-economic factors, including GDP and population density, were negatively associated with ESV. To achieve the coordinated development of the socio-economy and the environment, ES should be incorporated into spatial planning and socio-economic development policies.