Spatial Statistical Approach Research Articles

Thalamic-limbic circuit dysfunction and white matter topological alteration in Parkinson's disease are correlated with gait disturbance.

Limbic structures have recently garnered increased attention in Parkinson's disease (PD) research. This study aims to explore changes at the whole-brain level in the structural network, specifically the white matter fibres connecting the thalamus and limbic system, and their correlation with the clinical characteristics of patients with PD. Between December 2020 and November 2021, we prospectively enrolled 42 patients with PD and healthy controls at the movement disorder centre. All participants underwent diffusion tensor imaging (DTI), 3D T1-weighted imaging (3D-T1WI), and routine brain magnetic resonance imaging on a 3.0 T MR scanner. We employed the tract-based spatial statistical (TBSS) analytic approach, examined structural network properties, and conducted probabilistic fibre tractography to identify alterations in white matter pathways and the topological organisation associated with PD. In patients with PD, significant changes were observed in the fibrous tracts of the prefrontal lobe, corpus callosum, and thalamus. Notably, the fibrous tracts in the prefrontal lobe and corpus callosum showed a moderate negative correlation with the Freezing of Gait Questionnaire (FOG-Q) scores (r = -0.423, p = 0.011). The hippocampus and orbitofrontal gyrus exhibited more fibre bundle parameter changes than other limbic structures. The mean streamline length between the thalamus and the orbitofrontal gyrus demonstrated a moderate negative correlation with Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III (r = -0.435, p = 0.006). Topological parameters, including characteristic path length (L p), global efficiency (E g), normalised shortest path length (λ) and nodal local efficiency (N le), correlated moderately with the MDS-UPDRS, HAMA, MoCA, PDQ-39, and FOG-Q, respectively. DTI is a valuable tool for detecting changes in water molecule dispersion and the topological structure of the brain in patients with PD. The thalamus may play a significant role in the gait abnormalities observed in PD.

Geographic Patterns of Youth Suicide in San Diego County

ObjectiveOur objectives were to evaluate for any cluster patterns of youth suicide deaths and characterize the level of child opportunity in the communities where suicide deaths occurred. MethodsDecedents <18 years were identified from San Diego County Medical Examiner death reports from 2000 to 2020. We mapped decedents’ residential Zone Improvement Plan (ZIP) codes and calculated suicide rates per 10,000 youths. ZIP codes identified in overlapping spatial statistical approaches — the spatial scan statistic and Local Moran with Empirical Bayes (EB) rates — were considered a cluster for the final analysis. We obtained Child Opportunity Index (COI) scores for each ZIP code to determine if there were differences in: 1) ZIP codes with suicide deaths compared to ZIPs with no deaths 2) differences in distribution of suicide death rates across quintiles of COI. ResultsScan statistic identified 25 ZIP codes within a cluster (RR 2.6, P = 0.00066). Local Moran with EB rates identified two ZIP codes as a high-high cluster (P < 0.05). The location identified as a cluster in both approaches was in Alpine. The median COI for ZIP codes with suicide deaths was higher at 63.5 (IQR 38–83) compared to 47 (IQR 22.5–75.5) for ZIP codes without suicide deaths. There was a significant difference in suicide rates between Very Low and Moderate levels of Overall opportunity (P = .013). ConclusionWe identified a cluster of youth suicides in one of the most populous counties in the country. These findings serve to inform policies and prevention programs that aim to mitigate youth suicide mortality.

Intoxication in the city: Investigating spatial patterns and determinants of drugs and alcohol-related illegal activities in India's geostrategic corridor

Illicit drug and alcohol-related activities pose significant challenges to metropolitan centres globally, impacting public safety and socio-economic stability. This research analyzes the spatial and temporal patterns of drugs and alcohol-related illegal activities (DARIA) in Siliguri, known as the "Gateway of North-East India." The primary objectives are to explore DARIA patterns, identify clusters and concentrated areas, and investigate contributing factors. Using GIS-based spatial analysis and geographical models, we studied incidents recorded over three years from 2021 to 2023. Advanced spatial statistical approaches were applied to identify specific clusters of DARIA occurrences. The pure temporal analysis highlighted periods of increased illegal activity. Various geographical models, including ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR), were used to examine the spatial association. The MGWR model, with an explanatory power of R2 = 0.816, provided a detailed understanding of spatial relationships and variations in DARIA. Our findings indicate specific zones with frequent DARIA occurrences, mainly near major transportation hubs, commercial points, and areas with significant mobility. Key exploratory factors such as illiteracy rate, population density, proximity to playgrounds and slums were highly associated with DARIA incidents. These findings contribute to developing safer, more resilient, and more sustainable cities worldwide.

Uncovering wastewater treatment plants as possible sources of legionellosis clusters through spatial statistics approach and environmental analysis.

Wastewater treatment plants (WWTPs) are suspected reservoirs of Legionella pneumophila (Lp). The required aeration and mixing steps lead to the emission and dispersion of bioaerosols potentially harboring Lp. The aim of the project is to evaluate municipal WWTPs as a possible source of legionellosis through the statistical analysis of case clusters. A space-time scanning statistical method was implemented in SaTScan software to identify and analyze WWTPs located within and close to spatiotemporal clusters of legionellosis detected in Quebec between 2016 and 2020. In parallel, WWTPs were ranked according to their pollutant load, flow rate and treatment type. These parameters were used to evaluate the WWTP susceptibility to generate and disperse bioaerosols. Results show that 37 of the 874 WWTPs are located inside a legionellosis cluster study zone, including six of the 40 WWTPs ranked most susceptible. In addition, two susceptible WWTPs located within an extended area of 2.5km from the study zone (2.5-km buffer) were included, for a total of 39 WWTPs. The selected 39 WWTPs were further studied to document proximity of population, dominant wind direction, and surrounding water quality. Samples collected from the influent and the effluent of six selected WWTPs revealed the presence of Legionella spp. in 92.3% of the samples. Lp and Lp serogroupg 1 (Lp sg1) were detected below the limit of quantification in 69% and 46% of the samples, respectively. The presence of Legionella in wastewater and the novel statistical approach presented here provides information to the public health authorities regarding the investigation of WWTPs as a possible source of Legionella exposure, sporadic cases, and clusters of legionellosis.

Association between the functional connectivity of ventral tegmental area-prefrontal network and pure apathy in Parkinson's disease: a cross-sectional study.

Apathy, characterized by diminished goal-directed behaviors, frequently occurs in patients with Parkinson's disease (PD). The dopamine-releasing neurons of the ventral tegmental area (VTA) have been closely related to this behavioral disruption and project widely to the corticolimbic areas, yet their functional and structural connectivity in regard to other brain regions remain unknown in patients with PD and pure apathy (PD-PA). This study thus aimed to characterize the alterations of functional connectivity (FC) of the VTA and white matter structural connectivity in PD-PA. In this study, 29 patients with PD-PA, 37 with PD but not pure apathy (PD-NPA), and 28 matched healthy controls (HCs) underwent T1-weighted, resting state functional magnetic resonance imaging, and diffusion tensor imaging scans. Patients of this cross-sectional retrospective study were consecutively recruited from The First Affiliated Hospital of Nanjing Medical University between April 2017 and October 2021. Meanwhile, HCs were consecutively recruited from the local community and the Health Examination Center of our hospital. An analysis of covariance and a general linear model were respectively conducted to investigate the functional and structural connectivity among three groups. The tract-based spatial statistics (TBSS) approach was used to investigate the white matter structural connectivity. Patients with PD-PA showed reduced FC of the VTA with the left medial superior frontal gyrus (SFGmed) when compared to the patients with PD-NPA [t=-3.67; voxel-level P<0.001; cluster-level family-wise error-corrected P (PFWE)<0.05]. Relative to the HCs, patients with PD-PA demonstrated reduced FC of the VTA with the left SFGmed (t=-4.98; voxel-level P<0.001; cluster-level PFWE<0.05), right orbital superior frontal gyrus (SFGorb) (t=-5.08; voxel-level P<0.001; cluster-level PFWE<0.05), and right middle frontal gyrus (MFG) (t=-5.08; voxel-level P<0.001; cluster-level PFWE<0.05). Moreover, the reductions in VTA FC with the left SFGmed were associated with severe apathy symptoms in patients with PD-PA (r=-0.600; P=0.003). However, a TBSS approach did not reveal any significant differences in fiber tracts between the three groups. This study identified reduced FC within the mesocortical network (VTA-SFGmed) of patients with PD-PA. These findings may provide valuable information for administering neuromodulation therapies in the alleviation of apathy symptoms in those with PD.

QOL-23. POTENTIAL MYELIN REPAIR AFTER IRRADIATED PEDIATRIC BRAIN TUMOR: A MAGNETIZATION TRANSFER IMAGING ANALYSIS

Abstract BACKGROUND Exercise training and metformin treatment may improve cognition after cranial irradiation in pediatric brain tumors (PBT), but the neuronal mechanisms underlying the effect of these interventions remains unknown. Magnetization transfer imaging (MTI) is an imaging technique that may be sensitive to white matter microstructure – including myelin. METHODS In this work, we analyzed changes in magnetization transfer ratio (MTR) over a 12-week period in irradiated PBT survivors who participated in pilot clinical trials entailing 12 weeks of exercise training (exercise group, n=17), or metformin treatment (n=12). Pre- and post-intervention assessment included MRI scanning and neurocognitive tests. A no intervention control group, (n=7) was also included and seen before and after a 12 week period of time. We used a longitudinal Tract-Based Spatial Statistics (TBSS) approach to analyze differences in MTR across the 12 weeks in the three groups, using as covariates: age at baseline, sex, handedness, age at diagnosis and delay from diagnosis. Then we performed correlations between mean MTR changes and behavioral outcomes. RESULTS Clusters of significant increase in MTR were observed in the right temporal lobe in the exercise group after intervention and in the right minor forceps in the metformin group after treatment. These changes respectively correlated with higher performance in free recall score in exercise group and with higher performances in inhibitory control and attention in metformin group. There was no difference in MTR changes between groups nor within the control group. DISCUSSION This exploratory work suggests either a 12-week period of exercise training or metformin may promote myelination in PBT survivors in brain regions. These changes appear to be associated with improved memory and executive function, two core domains of neurocognitive deficit in this population.

Disease Progression and Multiparametric Imaging Characteristics of Spinocerebellar Ataxia Type 3 With Spastic Paraplegia.

Spinocerebellar ataxia type 3 (SCA3) is a hereditary ataxia that occurs worldwide. Clinical patterns were observed, including the one characterized by marked spastic paraplegia. This study investigated the clinical features, disease progression, and multiparametric imaging aspects of patients with SCA3. We retrospectively analyzed 249 patients with SCA3 recruited from the Organization for Southeast China for cerebellar ataxia research between October 2014 and December 2020. Of the 249 patients, 145 were selected and assigned to 2 groups based on neurologic examination: SCA3 patients with spastic paraplegia (SCA3-SP) and SCA3 patients with nonspastic paraplegia (SCA3-NSP). Participants underwent 3.0-T brain MRI examinations, and voxel-wise and volume-of-interest-based approaches were used for the resulting images. A tract-based spatial statistical approach was used to investigate the white matter (WM) alterations using diffusion tensor imaging, neurite orientation dispersion, and density imaging metrics. Multiple linear regression analyses were performed to compare the clinical and imaging parameters between the 2 groups. The longitudinal data were evaluated using a linear mixed-effects model. Forty-three patients with SCA3-SP (mean age, 37.58years ± 11.72 [SD]; 18 women) and 102 patients with SCA3-NSP (mean age, 47.42years ± 12.50 [SD]; 39 women) were analyzed. Patients with SCA3-SP were younger and had a lower onset age but a larger cytosine-adenine-guanine repeat number, as well as higher clinical severity scores (all corrected p < 0.05). The estimated progression rates of the Scale for the Assessment and Rating of Ataxia (SARA) and International Cooperative Ataxia Rating Scale scores were higher in the SCA3-SP subgroup than in the SCA3-NSP subgroup (SARA, 2.136 vs 1.218 points; ICARS, 5.576 vs 3.480 points; both p < 0.001). In addition, patients with SCA3-SP showed gray matter volume loss in the precentral gyrus with a decreased neurite density index in the WM of the corticospinal tract and cerebellar peduncles compared with patients with SCA3-NSP. SCA3-SP differs from SCA3-NSP in clinical features, multiparametric brain imaging findings, and longitudinal follow-up progression.

A spatial statistical approach to estimate bus stop demand using GIS-processed data

This study integrates the fields of geography, urban transit planning, and statistical learning to develop a sophisticated methodology for predicting bus demand at the stop level. It uses a Generalized Additive Model that captures non-linear relationships and incorporates spatial dependence, improving traditional methods. It showcases a high predictive capacity with a pseudo R-squared of 0.79 during its validation, ensuring substantial explanatory power for new observations. A large number of variables, including land-use characteristics, socioeconomic factors, and transit supply, are analysed. These widely available predictors facilitate the transferability of the methodology to other urban areas. Transit supply predictor considers the number of annual trips per stop and area as well as the location of stops along the lines that serve them. GIS processing of the data allows the calculation of variables within the areas of influence of each stop, obtained by following the walkable street network. For the case study, the presence of universities, hospitals, and lodgings areas, as well as inhabitants and ratio of bus trips show a positive impact on bus demand. This geo-analysis process employs accurate disaggregated data, such as information on uses in each building, as well as methods for assigning socioeconomic information from local areas to residential buildings. This study highlights the complex relationship between the location of transit network stops, both along the bus line and in terms of geographical proximity, their transit supply, and its surrounding factors. The results indicate that there is spatial dependence for stops less than 1.15 km apart. The developed methodology provides reliable information to transit network planners for decision making. Specifically, this proposed methodology can contribute to designing new routes, optimizing stop locations, and estimating the impact of changes in the transit network or urban planning on bus demand. All these improvement measures promote sustainable urban mobility, consequently fostering environmental and social benefits.

Whole-farm yield map datasets – Data validation for exploring spatiotemporal yield and economic stability

CONTEXTStatistical methods used for delineation of field management zones and yield stability are frequently only applied to relatively small areas, with few studies performing rotational, whole-farm economic spatiotemporal appraisals. To enable accurate economic analysis, yield map datasets must contain minimal errors while cleaning procedures are often used to remove errors, it is rare that cleaned data is validated before its application. OBJECTIVEThe objective of this study was to process, validate and combine spatial statistical approaches for a rotational yield map dataset from a whole-farm across 7 crops in a winter wheat based rotation. Developing a framework for using validated yield map datasets to support precision agriculture techniques that are applicable for farm-level decision making. METHODSThe rotational completeness of a 10 year combine yield map dataset for a 435 ha farm in Eastern England was assessed. The dataset was cleaned statistically, and its accuracy assessed by comparison with recorded yields from trailer weigh cells. The cleaned, validated, and corrected yield map dataset was used to identify management zones across the whole farm using fuzzy clustering. The temporal stability of management zones and economic performance across the rotation was also assessed. RESULTS AND DISSCUSIONData cleaning methods removed 16% of data points, improving the degree of spatial correlation within the individual yield maps. Independent validation demonstrated varied accuracy of yield maps from combine harvester data and errors in wheat ranged from 0.53 to 1.53 t/ha RMSE. These errors have implications for researchers using combine yield data to develop and validate precision agriculture technologies. This data set required correction before yield data can be applied with confidence for on-farm decision making. Compared to the zones with the highest margin in each field, 34% of zones had an average annual margin loss of >£100 ha. The temporal stability of the resulting management zones also varied. Areas with the lowest economic performance and greatest yield stability across years will potentially see the greatest economic and environmental benefits from precision agriculture techniques. SIGNIFICANCEThe accuracy of combine yield map data should not be assumed. The application of these datasets, including for the identification of management zones or in developing precision agriculture techniques should attempt to address this through data cleaning and validation procedures. Only then should it be used for on farm decision making, such as identifying areas with the most economic benefit by applying precision agriculture tools such as variable rate nutrient applications.

Latitudinal diversity of planktonic copepods in the Eastern Pacific: overcoming sampling biases and predicting patterns

Gradients of latitudinal diversity are one of the biogeographic features calling the most attention in ecology and macroecology. However, in pelagic communities of the marine environment, geographic trends and patterns are poorly known. We evaluated the latitudinal variation in species richness of marine planktonic copepods in the Eastern Pacific using spatial statistical models and approaches that mitigate and account for biases in occurrence data. A Boosted Regression Tree (BRT) and regression-Kriging based models allowed us to estimate and predict alpha diversity in poorly sampled regions, whereas beta diversity patterns were assessed using generalized dissimilarity analysis (GDM). Species richness showed a bimodal pattern, with a maximum of 291 species in the Northern Hemisphere and Tropical Eastern Pacific Ocean. Particulate Organic Carbon, salinity (max), spatial autocovariate, range of salinity and temperature, and Mixed Layer Depth, explained 85.2% of the latitudinal variability of copepods. Beta diversity was structured into four macrozones associated with the main water masses of the North and South Pacific.Our analytical approaches can overcome the limitations of data gaps, predicting greater diversity in subtropical and coastal areas, while providing insights into key drivers modulating spatial diversity patterns.

RAINFALL MODELING USING THE GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD

Rainfall is an important parameter in understanding the climate and environment in the Malang Regency area. This research aims to model the distribution of rainfall in this region using the Geographically Weighted Poisson Regression (GWPR) method. GWPR is a spatial statistical approach that allows us to understand changes in inhomogeneous rainfall patterns throughout the Malang Regency area. Rainfall data collected from weather stations over several years was used in this study. We use GWR to study the relationship between various environmental factors, such as topography, vegetation, and land use, and rainfall distribution in Malang Regency. The results of the GWR analysis provide a deeper understanding of the spatial differences in the influence of these factors on rainfall. By applying GWR, we can find out how certain factors contribute to different rainfall patterns in certain regions. Rainfall modeling using the Geographically Weighted Poisson Regression (GWPR) method combines the power of Poisson regression in analyzing calculated data with the advantages of GWR in modeling spatial variability. GWPR allows us to identify and map rainfall distribution patterns that vary in geographic space. The main advantage of GWPR is its ability to provide local adjustments and capture the spatial variability associated with rainfall distribution. The results of the modeling analysis show that the GWPR is better, marked by the smallest AIC value, namely 336.84, compared to the generalized poisson regression model, namely 337.76.

Spatial Assessment of Groundwater Salinity and Its Impact on Vegetation Cover Conditions in the Agricultural Lands of Al-Ahsa Oasis, Saudi Arabia

This study investigated the complex relationship among groundwater salinity, soil texture, and vegetation health in Al-Ahsa Oasis, Saudi Arabia. Utilizing vegetation condition index data from 5 years of satellite imagery, alongside ground measurements of groundwater salinity and soil analysis, the study unveiled significant spatial heterogeneity. The vegetation health and groundwater salinity in the study area ranged from 27 to 70% and from 1.7 to 9.6 dS m−1, respectively, and exhibited contrasting patterns, with a high vegetation condition index (healthy vegetation) in the north and east versus high salinity in the southwest. The applied cross-tabulation statistics revealed a strong negative correlation between the vegetation condition index and salinity, with “good” and “normal” vegetation health primarily coinciding with “slightly saline” groundwater. Soil texture further modulated the vegetation response, as “Silt loam” soils corresponded with “good” VCI, while “silty clay loam” only supported “dry” and “normal” conditions. The applied spatial autocorrelation analysis confirmed this, showing low vegetation clustering with “silt” texture and high salinity in the mid-west of the study area (p &lt; 0.05). Outlier districts hint at local factors beyond simple salinity–vegetation health relationships. Finally, a conditional plot, applied as a spatial statistical approach, revealed that low vegetation health spatially correlates with high groundwater salinity (&gt;5 dS m−1) on “silt loam” in the mid-west, while a higher vegetation condition index aligned with lower groundwater salinity in the east and north. These findings highlight the critical role of spatial variability and the need for advanced modeling and data integration to understand and predict the responses of vegetation to environmental challenges, and will be crucial for optimizing water management to ensure the long-term sustainability of Al-Ahsa Oasis.

From Space to Field: Combining Satellite, UAV and Agronomic Data in an Open-Source Methodology for the Validation of NDVI Maps in Precision Viticulture

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. Researchers have currently investigated different aspects of these sources, mainly comparing different vegetation indexes and exploring potential relationships with agronomic variables. However, due to the costs and limitations of such an approach, a standardized methodology for agronomic purposes is still missing. This study aims to fill such a methodology gap by overcoming the potential flaws or shortages of previous works. To achieve this, an image acquisition campaign covering 6 months and over 17 hectares was carried out, followed by an NDVI comparison between Sentinel-2 and UAV to eventually explore relationships with agronomic variables. Comparative analyses were performed by using both classical (Ordinary Least Squares regression and Pearson Correlation) and spatial (Moran’s Index) statistical approaches: here, 90% of cases show r and MI scores above 0.6 for plain images, with these scores expectedly lowering to 72% and 52% when considering segmented images. Moreover, NDVI thematic maps were classified into clusters and validated by the Chi-squared test. Finally, the relationship and distribution of agronomic variables within NDVI and clustered maps were consistently validated through the ANOVA test. The proposed open-source pipeline allows to strengthen existing UAV and satellite applications in Precision Agriculture by integrating more agronomic variables.

Identification of Key Determinants on Quality of Life Related Transportation: A Spatial Statistical Modeling Approach Based on Transport Accessibility and Subjective Well-Being

Identification of Key Determinants on Quality of Life Related Transportation: A Spatial Statistical Modeling Approach Based on Transport Accessibility and Subjective Well-Being

Diffusion tensor imaging reveals distributed white matter abnormalities in primary trigeminal neuralgia: Tract-based spatial statistics study

BackgroundPrimary trigeminal neuralgia (PTN) is a prevalent chronic pain disorder whose pathogenesis is not limited to the trigeminal system. Despite the significant advances in uncovering underlying mechanisms, there is a paucity of comprehensive and consistent data regarding the role of white matter throughout the entire brain in PTN. MethodsWe performed a prospective case-control study. Sixty patients with PTN and 28 age- and sex-matched healthy controls were evaluated using diffusion tensor imaging (DTI). A tract-based spatial statistical approach was performed to investigate white matter impairment in patients with PTN with several metrics, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). Additionally, ROI-based analysis was performed for each white matter tract to compare FA values between groups with correction for patient age and sex. Correlations between DTI data and nerve root compression severity, as well as pain severity, were also evaluated in patients with PTN. ResultsOur analysis demonstrated a widespread and symmetrical reduction in FA values among TN patients when compared to the control group (p < 0.05). Specifically, this FA decrease was predominantly observed in regions such as the corona radiata, internal capsule, optic radiation, and thalami, as well as structures within the posterior fossa, notably the cerebellar peduncles. No statistically significant differences were found between patients and the control group during the MD, AD and RD map analyses. ROI-based analysis did not reveal statistically significant changes in FA values in white matter tracts (p > 0.05 in all comparisons, FDR-corrected); however, there were trends towards FA value decreases in the internal capsule (p = 0.08, FDR-corrected) and inferior fronto-occipital fasciculus (p = 0.09, FDR-corrected). ConclusionsOur findings indicate the presence of microstructural abnormalities in white matter among individuals with primary trigeminal neuralgia, which may potentially play a role in the development and progression of the condition.

Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility.

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

Can digitalization and low-carbonization progress in harmony? Evidence from Chinese cities.

Achieving high-quality development of the city requires actively promoting coordinated digitalization and low-carbon development. Previous studies have focused on the unidirectional impact of urban digitalization on low-carbonization and there is a lack of research on their interactions. This study uses the generalized spatial three-stage least squares method and the spatial simultaneous equation to investigate the endogenous interactions between urban digitalization and low-carbonization. The properties of the spatiotemporal evolution are then examined using linked coordination degree models, kernel density, and spatial statistical approaches. Finally, using the spatial panel metering model, this study empirically investigates the motivations behind the synergistic advancement of digitalization and low-carbonization. The results show that: (1) There is an endogenous interaction between urban digitalization and low-carbonization and that this interaction pattern is closely linked to geographical proximity. (2) In general, both urban digitalization and low-carbonization have a positive spatial impact and a negative spatial interaction, and their coordination levels have a significant spatial impact. (3) Throughout the research period, the coordination degree of urban digitalization and low carbonization continued to increase, showing a positive spatial correlation and a balanced development trend. (4) Economic development, industrial structure, and human capital accumulation are vital internal drivers of the synergistic advancement of urban digitalization and low carbonization. Government capacities and technological innovations are key external factors that contribute to the synergistic advancement of urban digitalization and low-carbonization. Overall, the paper is essential not only to deepen understanding of the relationship between urban digitalization and low-carbonization but also to formulate policies for their coordinated development.

Soil texture mediates the surface cooling effect of urban and peri-urban green spaces during a drought period in the city area of Hamburg (Germany)

Urban green spaces (UGS) and peri-urban green spaces (P-UGS) play a crucial role in reducing the land surface temperature within the urban environment, especially during heat waves. Although their cooling effect generally is due to shading and evaporation, the role of soil texture and soil water availability on surface cooling remains largely unexplored. This study investigated the impact of soil texture on the spatio-temporal patterns of LST in different UGSs and P-UGSs in Hamburg (Germany) during a hot summer drought period. The LST and the Normalized Differentiated Moisture and Vegetation Indices (NDMI, NDVI) were calculated based on two Landsat 8 OLI/TIRS images from July 2013. Non-spatial and spatial statistical approaches such as stepwise backward regression or Hotspot (Getis-Ord Gi*) analyses were applied explaining LST distributions in relation to soil texture within each UGS and P-UGS. All GSs were clearly characterized as surface cooling islands whereas, for each GS, an individual thermal footprint was observed. Within all GSs, the LST patterns showed a significant negative relationship to NDMI values, whereas the NDVI values and the elevation were of minor importance. Soil texture was found to influence the LST distribution significantly in most UGSs and P-UGSs, where sites on clay-rich soils showed the highest LST values compared to sites on sand- or silt-rich soils. For example, in parks, clayey soils showed a mean LST of 25.3 °C whereas sand-dominated sites had a mean LST of only 23.1 °C. This effect was consistent throughout all statistical approaches, for both dates and across most GSs. This unexpected result was explained by the very low unsaturated hydraulic conductivity in clayey soils which limits plant water uptake and transpiration rates responsible for the evaporative cooling effect. We concluded that soil texture has to be considered for understanding and managing the surface cooling capacity of UGSs and P-UGSs.

The impact of water legislation on groundwater sustainability in an arid region: Spatial statistical approach

Sustainable use of groundwater resources and preservation of groundwater quality are critical issues for society as these resources directly impact the health of the environmental system and the population that rely on them for consumption. Despite this, and considering the importance of legal frameworks for establishing and enforcing sustainable practices, there is a lack of research looking at the effectiveness of environmental legislation on groundwater resources which ultimately inhibits opportunities for improvement. Considering this, this study aims to investigate the impact of water legislation on groundwater usage in Jordan, a small country located in a typically arid region, using hotspot analysis (HSA) and time series trend analysis (TA). In addition, the study assesses legislation's effectiveness on groundwater quality through data collected on static water level, water extractions, total dissolved solids (TDS), chloride (Cl−), and nitrate (NO3−).The study results show that the groundwater levels declined (average of 1 m/yr) in most of the monitored wells in the observed time irrespective of the legislation implemented to monitor and reduce extraction. In fact, the HSA results showed a significant increase in the volume of water extracted after legislation was introduced. In addition, water quality also deteriorated particularly in areas of intensive agricultural activity where there are high levels of water usage and chemical run-off. In particular, TDS and Cl-levels increased during the assessed period by more than 40% over the Jordanian standards for drinking water.As such, the study concludes that existing legislation has been ineffective at preserving groundwater quantity and quality. It is therefore recommended that legislation be reformed to ensure that quality standards are upheld and extraction of groundwater resources is permitted at sustainable rates without exceeding the basin yield. This could be achieved by implementing an integrated institutional framework that better facilitates coordination between relevant ministries; introducing an advanced monitoring system; substituting existing crops for low-water use crops; introducing higher tariffs; and enforcing a stricter penalty system.

Altered microstructural pattern of the cortex and basal forebrain cholinergic system in wilson's disease: an automated fiber quantification tractography study.

Basal forebrain (BF) cholinergic projection neurons form a highly extensive input to the cortex. Failure of BF cholinergic circuits is responsible for the cognitive impairment associated with Wilson's disease (WD), but whether and how the microstructural changes in fiber projections between the BF and cerebral cortex influence prospective memory (PM) remain poorly understood. We collected diffusion tensor imaging (DTI) data from 21 neurological WD individuals and 26 healthy controls (HCs). The experiment reconstructed the probabilistic streamlined tractography of 18 white matter tracts using an automated fiber quantification (AFQ) toolkit. Tract properties (FA, MD, RD, and AD) were computed for 100 points along each tract for each participant, and the differences between the groups were examined. Subsequently, correlation analysis was performed to evaluate whether abnormal microstructural white matter integrity measures correlate with PM performance. Additional investigations used a tract-based spatial statistics (TBSS) approach to identify regions with altered white matter structure between groups and verify the reliability of the AFQ results. The highest nonoverlapping DTI-related differences were detected in the anterior thalamic radiation (ATR), corticospinal tract (CST), corpus callosum, association fibers, and limbic system fibers. Additionally, PM parameters of the patient group were highly correlated with white matter microstructure changes in the inferior longitudinal fasciculus. Our study highlights that the performance of projections between cholinergic input and output areas-the cerebral cortex and BF-may serve as neural biomarkers of PM and disease prognosis.