Facility size as a determinant of spatiotemporal dynamics in logistic cluster patterns

Striatal spiny-projection neurons (SPNs) integrate glutamatergic inputs from the motor cortex and thalamus with neuromodulatory signals to regulate motor output. In vivo Ca2+ imaging has demonstrated that ensembles of direct and indirect pathway SPNs (dSPNs, iSPNs) are coactive during spontaneous movement. Co-activity is statistically greater among nearby neurons, correlates with behavioral state, and undergoes plasticity in an SPN-type-specific manner under pathological conditions. This spatially clustered co-activity could reflect shared excitatory inputs. However, whether and how synaptic mechanisms generate this distinctive spatiotemporal activity is unknown. Here, we show that the Group I metabotropic glutamate receptor 5 (mGluR5), which regulates synaptic strength at corticostriatal synapses, is a key mediator of spatially clustered SPN co-activity. Pharmacological modulation of mGluR5 signaling bidirectionally altered movement and spatially clustered dynamics, but not the absolute level of activity of dSPNs. Targeted deletion of mGluR5 in dSPNs recapitulated the effects on spatiotemporal neural dynamics and movement demonstrating a striatum-specific effect of mGluR5. Targeted deletion of mGluR5 also produced changes in the synaptic properties of dSPNs. These results show that properties of excitatory synapses influence motor function by shaping the characteristic spatially clustered patterns of co-activity that typify dSPN activation in vivo.

Addressing the need to harmonize environment conservation and sustainable economic development within the Yellow River Basin (YRB) requires a profound comprehension of the spatiotemporal dynamics of urban ecosystem resilience. This study developed an index system utilizing the resistance-adaptability-recovery framework to measure these dynamics. By applying the advanced multi-attribute boundary area comparison method and a spatial autocorrelation model, we investigated the spatiotemporal variations and spatial correlation patterns of urban ecological resilience across the YRB. The results of this study indicated that: (1) from 2011 to 2020, the value of urban ecological resilience index (UERI) in the YRB consistently ranged between 0.43 and 0.83, and the resilience degree of the urban ecosystem in the YRB progressively improved, with notably higher resilience in the southeast compared to the northwest; (2) the resilience degree of the urban ecosystem in the YRB was non-equilibrium in space. Spatial analysis indicates significant disparities in resilience levels across different areas within the YRB, marked by considerable fluctuations in the global Moran’s I index and significant changes in local autocorrelation clustering patterns; and (3) key factors such as wastewater discharge volume, sewage treatment rate, and the rate of non-hazardous treatment of domestic waste were identified as critical determinants of the overall ecological resilience. This research not only deepens our understanding of the factors driving urban ecological resilience but also aids in the formulation of strategic regional policy for sustainable development across the YRB.

Spatio-temporal dynamics within cells can now be recorded on film at appropriate resolutions thanks to advances made in fluorescence microscopy technologies. Even the single-particle tracking technique is now being applied to observations of biological molecules. Conversely, little is known about how reaction diffusion at the molecular level affects properties at the cellular level. Therefore, we propose an algorithm designed for the three-dimensional simulation of the reaction-diffusion dynamics of molecules, based on a particle model. Chemical reactions proceed through the interactions of particles in space. The activation energy determines the rate of these chemical reactions at each interaction. This energy-based model allows incorporating of the cellular membrane, membranes of other organelles, and cytoskeletons. The simulation algorithm was tested for a reversible enzyme reaction model and its validity verified. A snapshot image taken from simulated molecular interactions on the cellular membrane was shown to yield a clustering pattern associated with raft

The hilly and gully region of the Loess Plateau represents one of China’s most ecologically vulnerable landscapes, characterized by severe soil erosion, intensive land use, and pronounced disturbances to the structure and functionality of ecosystem services. Taking Zichang City as a case study, this research integrates grid-scale analysis with the InVEST-PLUS model and bivariate spatial autocorrelation techniques to examine the spatiotemporal dynamics and inter-relations of four critical ecosystem services—carbon storage, water yield, biodiversity, and soil retention—under varying land use intensity scenarios from 1990 to 2035. The findings indicate that (1) between 1990 and 2020, land use intensity in Zichang City steadily declined, exhibiting a spatial distribution pattern typified by central-area clustering and gradual peripheral transitions. (2) Across three development scenarios, the spatial distribution of the four ecosystem services aligned with the patterns observed in 2020, with central areas showing pronounced fluctuations, whereas peripheral regions experienced relatively minor changes. Specifically, from 1990 to 2020, the proportion of low-carbon storage areas increased by 2.89%, and high water yield areas expanded by 9.45%, while the shares of low habitat quality and low soil retention areas decreased by 5.59% and 6.25%, respectively. (3) A significant spatial autocorrelation was observed between land use intensity and the four ecosystem services, with widespread cold and hot spots reflecting dynamic spatial clustering patterns. These results offer valuable insights for optimizing land use strategies, improving ecosystem service performance, and advancing ecological conservation and sustainable development initiatives.

Bacillary dysentery remains a common intestinal infectious disease in China. With economic development and improvements in sanitation, the incidence rate of bacillary dysentery has changed substantially across counties in Shaanxi Province. This study aims to elucidate its epidemiological characteristics and spatio-temporal dynamics to inform prevention and control strategies. Surveillance data of confirmed bacillary dysentery cases in Shaanxi Province from 2005 to 2024 were obtained from the China Information System for Disease Control and Prevention. We analyzed epidemiological features across temporal, regional, and population distributions. Long-term incidence trends were assessed using Joinpoint regression, while spatial autocorrelation and spatio-temporal scan statistics were employed to identify clustering patterns at the county level. A total of 156,656 cases of bacillary dysentery were reported in Shaanxi Province from 2005 to 2024. The reported incidence decreased from 65.30 per 100,000 in 2005 to 3.33 per 100,000 in 2024. A unimodal seasonal pattern was observed, with 76.09% of cases occurring between May and October. Children under five years of age were the most susceptible group, particularly infants under one. The incidence was higher in males than in females. The majority of cases were farmers (27.08%), home-care children (22.35%), and students (17.71%). Joinpoint regression revealed a significant overall decreasing trend (Average Annual Percent Change, AAPC = -15.0%; 95% CI: -18.4%, -11.4%; p < 0.001). Geographically, high-incidence counties were primarily located in the northern and central-western regions before 2014. After 2015, the number of high-incidence counties markedly decreased. By 2024, the incidence in 84.07% (95/113) of counties had fallen below 5 per 100,000. Global spatial autocorrelation analysis indicated significant clustered distributions annually. Local spatial autocorrelation identified 174 "High-High" clusters. Spatio-temporal scan statistics showed that primary clusters were located in the northern and central-western regions before 2010, shifting to the central region thereafter. Both the incidence and the spatial clustering of bacillary dysentery have decreased significantly in Shaanxi Province. However, targeted interventions during the summer and autumn seasons, focusing on children under five and populations in the central region, remain crucial for future control efforts.

Understanding the spatiotemporal dynamics of vegetation in response to climatic variability is critical for ecosystem monitoring and sustainable land management, particularly in ecologically sensitive regions. Northern Iran, specifically Guilan Province, with its humid subtropical climate and complex topography, is highly vulnerable to hydroclimatic fluctuations. However, comprehensive assessments integrating multi-source satellite data remain limited. This study aims to investigate spatiotemporal vegetation-climate interactions in Guilan Province from 2020 to 2024 using an integrated analytical framework developed within Google Earth Engine (GEE). The objectives are to quantify interannual changes in vegetation greenness (NDVI and EVI), assess relationships with land surface temperature (LST) and precipitation, and develop a novel Vegetation-Climate Sensitivity Index (VCSI) to evaluate ecological vulnerability. Methodology employed multi-source satellite datasets: Landsat 8-derived NDVI and EVI, MODIS MOD11A2 LST, and CHIRPS precipitation data. All datasets were harmonized to 1km resolution and analyzed within GEE. The VCSI was constructed by integrating standardized vegetation indices with normalized precipitation and LST. Spatial autocorrelation analyses (Moran's I, Getis-Ord Gi*) identified clustering patterns of vegetation-climate sensitivity. Results revealed marked interannual variability, with 2022 exhibiting the lowest NDVI/EVI and highest LST, indicating severe climatic stress, followed by partial ecosystem recovery in 2024. Strong positive correlations were found between vegetation indices and precipitation (r = 0.88-0.92), while negative correlations with LST (r = -0.62) confirmed thermal stress, particularly during summer (mean summer VCSI = 56.96). Lagged response analysis showed vegetation responded to precipitation with one- to two-month delays, highlighting soil moisture retention effects. The VCSI effectively captured spatial heterogeneity: high values (resilient zones) concentrated in forested highlands of the west and north, while low values (vulnerable zones) characterized agricultural lowlands and urbanized areas. Spatial autocorrelation confirmed strong clustering of ecosystem sensitivity (Global Moran's I > 0.93, p < 0.001), with persistent hotspots in highland forests and coldspots in lowland anthropogenic landscapes, intensified during 2022 climatic stress. In conclusion, vegetation responses to climate variability in Guilan Province are spatially structured, temporally lagged, and highly sensitive to hydroclimatic drivers. The integrated GEE-based framework combining VCSI with spatial statistics provides a robust approach for mapping ecological vulnerability. Findings offer actionable insights for adaptive land-use planning and climate adaptation interventions in northern Iran and similar humid subtropical regions.

The rational construction of development zones is one of many effective ways to solve the problem of uncoordinated and imbalanced regional economic development. We analyse the spatiotemporal dynamics and drivers of 2046 development zones in China from 1984 to 2018 using ESTDA, standard deviation ellipse, and Geodetector – GWR models. Results reveal a north-westward shift in development centres, distinct clustering patterns, and increasing roles of natural and social factors. By distinguishing the spatial heterogeneity between national and provincial zones, we identify differentiated planning needs. This study provides empirical evidence and methodological advances for optimizing spatial layout in support of inclusive, sustainable development, aligning with the United Nations’ Sustainable Development Goals (SDGs) 8, 9, 10, and 11.