Coordination analysis of flood-sediment transportation, eco-environment, and socio-economy coupling in the governance of the Yellow River Basin system

The watershed system has a complex game relationship between the benign operation and coordinated development of various elements of flood-sediment transportation, eco-environment, and socio-economy (FES). With the increasing breadth, depth, and intensity of human activities in watersheds, it is urgent to coordinate the FES. The relationship of water-sediment in the Yellow River Basin (YRB) is complex, with a prominent contradiction in water supply and a fragile ecosystem. This research tries to build a comprehensive evaluation model for FES and explore the complex interaction between FES in the YRB from 2000 to 2020. The results demonstrated that (1) the comprehensive flood-sediment transportation index (CFTI) and comprehensive eco-environment index (CEI) presented fluctuating growth. In contrast, the comprehensive socio-economy index (CSI) revealed a linear growth trend. The CFTI of Sanmenxia, CEI of Toudaokuan, and CSI of Ningxia had the highest growth rates, with 36.03%, 6.48%, and 107.5%, respectively. (2) FES's positive and negative effects were alternating, with heterogeneity in both time and space. (3) The coupling coordination degree (CCD) in the YRB indicated an increasing trend, ranging from 0.53 to 0.87, from reluctantly coordinated development to good coordinated development. The lagging subsystem was CFTI (2000-2001 and 2008-2020) and CSI (2002-2007), and the CEI was not lagging. (4) Exploratory Spatial Data Analysis (ESDA) demonstrated significant differences in the CCD of the YRB, and areas with similar CCD within the basin tend to be centrally distributed in space. At the same time, there was negative spatial autocorrelation in coordination. The results provide a scientific theoretical and methodological framework for strategic research on the YRB system's governance, protection, and management.

Exploring associations between built environment and crash risk of children in school commuting

Understanding how built environment are associated with crash risk (CR) in school commuting is essential to improving travel safety through land use and transportation policies. Scholars often assume that this relationship is consistent across space, but this may lead to inconsistent estimates. To address this issue, using data in Shenzhen, China, the data covers traffic accident data of children taken from police incident reports and supplemented with local land use, transportation network and specific school information. The measurement model of crash scale was conducted to represent crash severity, and the CR was further quantified. The study applies three models, spatial dubin model (SDM), geographically weighted regression (GWR), and mixed GWR (MGWR), to explore spatio-temporal heterogeneity relationships between built environment attributes and CR of children in school commuting. The findings reveal that the crash scale can better represent crash severity of school commuting than a single indicator. Policy interventions should be targeted at specific spatial scales, school types, and time windows to effectively improve travel safety. However, there are some common findings. It is recommended to use a scale of 200 m to explain the relationship between the variables. The MGWR model outperforms the other two models. To reduce CR, it is important to consider lower road network density, a reasonable layout of educational facilities, fewer bus routes, and more on-street parking spaces. Our findings can help to enrich the understanding of associations between land use and CR of children, as well as offer local planning and operating guidance for creating child-friendly environment.

Study on spatio-temporal variation and hydrological connectivity of tidal creek evolution in Yancheng coastal wetlands

The ecological changes have attracted extensive international attention at Jiangsu Yancheng coastal wetland on the west coast of the Pacific Ocean in recent decades. Tidal creek is an important channel for material exchange between sea and land and plays an important role in the connectivity of water in coastal wetlands. The tidal flats from Sheyang Port to Liangduo Estuary in Jiangsu Province were selected and divided into five sub-study areas using each port as a split point. Based on the remote sensing image data from 1987 to 2020, this study used seven parameters (grade, number, length, density, curvature, bifurcation ratio, and drainage efficiency) to analyze the spatio-temporal divergence patterns and hydrological connectivity of tidal creek network. The results showed that (1) the area of tidal flat was reduced from 1024.87 to 352.05 km², the total length of the tidal creek was directly reduced from 1061.27 to 640.74 km, the average density increased from 1.00 to 1.82 km/km², and the total number increased by 33% in 1987-2020, indicating the tidal creeks tended to be "short, fragmented, and parallelized." (2) The development degree of tidal creek networks showed a trend of gradually increasing from north to south, in which the γ index in areas II and V were closer to 1/3 of tree shape after 2002, indicating that the development of tidal creeks in these two areas was better. (3) The spatial heterogeneity of hydrological connectivity levels of tidal creeks in the study area was obvious, which gradually increased from north to south. In 2020, Area I was the lowest, and IC_min was 0.14, and Area V was the highest, and IC_max was 0.90. (4) Reclamation was the main factor leading to the shrinkage and degradation of tidal creeks, but it also increased tidal creek density and hydrological connectivity per unit area; the expansion of Spartina alterniflora had a certain influence on the development of tidal creeks. The results of this study are expected to provide data support for understanding and predicting the evolution of the morphological characteristics of tidal creeks under the influence of human and natural activities and provide scientific reference for the protection and restoration of hydrological connectivity in coastal wetlands.

[Geographical and temporal weighted regression model and its application in epidemiology: a review]

Geographical and temporal weighted regression (GTWR) model is a local regression linear model, which indirectly reflects the spatio-temporal non-stationary characteristics of the study data by calculating the trends for changes in parameters with space and time. Recently, GTWR model has become one of the hot topics in the study on spatio-temporal heterogeneity of diseases. This review summarizes the basic principles and study methods of the GTWR model, and describes the applications of this model in epidemiology.

Tumor Microenvironment and Immune Escape in the Time Course of Glioblastoma

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor with a malignant prognosis. GBM is characterized by high cellular heterogeneity and its progression relies on the interaction with the central nervous system, which ensures the immune-escape and tumor promotion. This interplay induces metabolic, (epi)-genetic and molecular rewiring in both domains. In the present study, we aim to characterize the time-related changes in the GBM landscape, using a syngeneic mouse model of primary GBM. GL261 glioma cells were injected in the right striatum of immuno-competent C57Bl/6 mice and animals were sacrificed after 7, 14, and 21 days (7D, 14D, 21D). The tumor development was assessed through 3D tomographic imaging and brains were processed for immunohistochemistry, immunofluorescence, and western blotting. A human transcriptomic database was inquired to support the translational value of the experimental data. Our results showed the dynamic of the tumor progression, being established as a bulk at 14D and surrounded by a dense scar of reactive astrocytes. The GBM growth was paralleled by the impairment in the microglial/macrophagic recruitment and antigen-presenting functions, while the invasive phase was characterized by changes in the extracellular matrix, as shown by the analysis of tenascin C and metalloproteinase-9. The present study emphasizes the role of the molecular changes in the microenvironment during the GBM progression, fostering the development of novel multi-targeted, time-dependent therapies in an experimental model similar to the human disease.

Analyzing the spatio-temporal variation of the CO2 emissions from district heating systems with "Coal-to-Gas" transition: Evidence from GTWR model and satellite data in China

Understanding the spatio-temporal heterogeneous effects of socioeconomic and meteorological factors on CO₂ emissions from combinations of different district heating systems with "Coal-to-Gas" transition can contribute to the development of future low-carbon energy systems that are efficient and effective. This work downscales city-level CO₂ emissions to a 3 × 3 km² gridded level in northern China during 2012 to 2018. By employing the Geographically and Temporally Weighted Regression (GTWR) model, nighttime light (NTL) data are adopted as a proxy of the level of urbanization, and the Temperature-Humidity-Wind (THW) Index is used as a proxy of meteorological factors in the downscaling model. The results show that, for more than 85% of the cities, urbanization significantly enhances the CO₂ emissions of district heating systems, while the THW Index shows negative impacts on CO₂ emissions. Significant spatial and temporal heterogeneity exists. The grids with the highest CO₂ emissions from coal-fired boilers (grids with annual variation >0.59 Gg CO₂/year) are mainly located in nonurban areas of the two megacities Beijing and Tianjin and also in the capital cities of each province. Urbanization has larger effects on the CO₂ emissions of natural gas-fired boilers than of coal-fired boilers and combined heat and power (CHP). The average growth rate of CO₂ emissions of gas-fired boilers in the urban areas of the study regions was approximately 4.7 times that of nonurban areas. The spatio-temporal heterogeneous impacts of urbanization on CO₂ emissions should therefore be considered in future discussions of clean heating policies and climate response strategies.

Quantifying geographic regions of excess stillbirth risk in the presence of spatial and spatio-temporal heterogeneity

Motivated by population-based geocoded data for Iowa stillbirths and live births delivered during 2005-2011, we sought to identify spatio-temporal variation of stillbirth risk. Our high-quality data consisting of point locations of these delivery events allows use of a Bayesian Poisson point process approach to evaluate the spatial pattern of events. With this large epidemiologic dataset, we implemented the integrated nested Laplace approximation (INLA) to fit the conditional formulation of the point process via a Bayesian hierarchical model and empirically showed that INLA, compared to Markov chain Monte Carlo (MCMC) sampling, is an attractive approach. Furthermore, we modeled the temporal variability in stillbirth to better understand how stillbirths are geographically linked over the seven-year study period and demonstrate the similarity between the conditional formulation of the spatio-temporal model and a log Gaussian Cox process governed by discrete space-time random fields. After controlling for important features of the data, the Bayesian temporal relative risk maps identified areas of increasing and decreasing stillbirth risk over the birth period, which may warrant further public health investigation in the regions identified.

Fine-scale spatial and temporal variation of clinical malaria incidence and associated factors in children in rural Malawi: a longitudinal study

Background

Spatio-temporal variations in malaria burden are currently complex and costly to measure, but are important for decision-making. We measured the spatio-temporal variation of clinical malaria incidence at a fine scale in a cohort of children under five in an endemic area in rural Chikhwawa, Malawi, determined associated factors, and monitored adult mosquito abundance.

Methods

We followed-up 285 children aged 6–48 months with recorded geolocations, who were sampled in a rolling malaria indicator survey, for one year (2015–2016). Guardians were requested to take the children to a nearby health facility whenever ill, where health facility personnel were trained to record malaria test results and temperature on the child’s sick-visit card; artemisinin-based combination therapy was provided if indicated. The cards were collected and replaced 2-monthly. Adult mosquitoes were collected from 2-monthly household surveys using a Suna trap. The head/thorax of adult Anopheles females were tested for presence of Plasmodium DNA. Binomial logistic regression and geospatial modelling were performed to determine predictors of and to spatially predict clinical malaria incidence, respectively.

Results

Two hundred eighty two children, with complete results, and 267.8 child-years follow-up time were included in the analysis. The incidence rate of clinical malaria was 1.2 cases per child-year at risk; 57.1% of the children had at least one clinical malaria case during follow-up. Geographical groups of households where children experienced repeated malaria infections overlapped with high mosquito densities and high entomological inoculation rate locations.

Conclusions

Repeated malaria infections within household groups account for the majority of cases and signify uneven distribution of malaria risk within a small geographical area.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2730-y) contains supplementary material, which is available to authorized users.