Spatial Association Research Articles

Distribution and habitat use patterns of the endangered Central American clouded oncilla (Leopardus pardinoides oncilla) in Costa Rica.

Montane cloud forests are highly threatened ecosystems that are vulnerable to climate change. These complex habitats harbor many species that suffer the negative consequences of this global phenomenon, such as shifts in their distribution and habitat use. The Central American clouded oncilla (Leopardus pardinoides oncilla) is the smallest and most endangered wild cat in Mesoamerica and is primarily reported in cloud forests throughout its distribution. The species is poorly understood, with no studies conducted in Central America assessing its habitat preferences. To bridge this knowledge gap, we sampled two mountain ranges in Costa Rica with camera traps and conducted an occupancy analysis to understand the anthropogenic and environmental features that influence oncilla habitat use within them. Additionally, we conducted spatial predictions of habitat use across its northern and southern range in Costa Rica to identify priority conservation areas for the species. We found that Central American clouded oncilla habitat use is driven primarily by environmental factors. Our results showed that oncillas select habitats with denser tree cover at high elevations, closer to permanent water sources, which may provide them with high prey density and a favorable habitat structure for their survival. Spatial predictions identified two main regions as conservation priority areas where threat mitigation efforts and monitoring should be implemented: the Caribbean slope of the Talamanca mountains, and the Arenal-Monteverde forest complex. The occupancy modeling approach turned out to be very useful to assess the spatial associations of the species with the environment and mapping the conservation priority areas. Future research and mitigation actions should focus on potential threats that could negatively impact Central American clouded oncilla populations and habitat use, including the role of mesopredators and feral species.

Green Development of Chinese Agriculture from the Perspective of Bidirectional Correlation

China’s agricultural green development exhibits spatial imbalances. To promote the coordinated green development of agriculture across various regions, this paper explores the evolution of spatial association networks and driving factors of agricultural green development from a bidirectional correlation perspective, using social network analysis and spatial econometric methods. This research indicates that China’s agricultural green efficiency has steadily improved, with a complex multi-threaded network structure. Although the spatial interaction and spillover effects of the overall network structure have increased, they remain relatively weak. The individual network structure shows significant regional imbalances. The spatial association network of agricultural green efficiency forms four major blocks, with sparse connections within the blocks but close connections between blocks, demonstrating strong spillover effects. Regarding the driving factors, the proportion of the primary industry, labor level, and R&D investment have significant spatial impacts, while the spatial impacts of human capital level, degree of openness, economic development level, and new quality productivity level are not significant. Therefore, we believe that it is necessary to establish the concept of coordinated green development in agriculture, fully leverage regional associations and spillover effects, and formulate differentiated policies to improve agricultural green efficiency.

Spatiotemporal relationship between agriculture, livestock, deforestation, and visceral leishmaniasis in Brazilian legal Amazon

Visceral leishmaniasis (VL) is an urgent public health concern in Brazil. We evaluated the spatiotemporal distribution of VL to better understand the effects of economic activities related to agriculture, livestock, and deforestation on its incidence in the Brazilian Legal Amazon (BLA). The data on newly confirmed cases of VL in Brazilian municipalities from 2007 to 2020 were extracted from the Brazilian Notifiable Diseases Information System (SINAN) and analyzed. The data on agricultural production (planted area in hectares) and livestock (total number of cattle) were obtained from the Brazilian Institute of Geography and Statistics (IBGE), whereas deforestation data (in hectares) were obtained from the Amazon Deforestation Estimation Project (PRODES). SatScan and the local indicators of spatial association (LISA) were used to identify the spatial and temporal patterns of VL and its relationships with economic and environmental variables. The cumulative incidence rate was found to be 4.5 cases per 100,000 inhabitants. Based on the LISA results, areas with a high incidence of VL and deforestation were identified in the states of Roraima, Pará, and Maranhão. Strengthening deforestation monitoring programs and environmental enforcement actions can help implement public policies to control illegal deforestation and mitigate the socio-environmental vulnerability in the BLA. Therefore, areas identified in this study should be prioritized for controlling VL.

Multi-Object Tracking in Agricultural Applications using a Vision Transformer for Spatial Association

This paper introduces a Multi-Object Tracking (MOT) framework for agricultural applications that estimates global positions in pixel coordinates using the local feature matching transformer — LoFTR. We design an efficient tracker that augments the capabilities of a state-of-the-art tracking algorithm by incorporating a novel association strategy based on spatial information of targets leaving and returning the camera field of view. We evaluate our framework using the publicly available LettuceMOT benchmark dataset and an adapted version of the AppleMOTS benchmark dataset that we denominate AppleMOT. Our experimental results demonstrate that our method outperforms cutting-edge algorithms for robotic plant tracking in the LettuceMOT dataset. The evaluation metrics show average improvements of up to 25% compared to the best publicly available results, demonstrating the benefits of our spatial association approach. For the AppleMOT dataset, we obtained bounding-box-based MOT evaluation metrics comparable to the segmentation-based (MOTS) counterparts presented in the original AppleMOTS paper. These findings highlight the effectiveness and potential of our approach in addressing the unique challenges posed by agricultural environments.

Contracted functional connectivity profiles in autism

ObjectiveAutism spectrum disorder (ASD) is a neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long- relative to short-range functional connections. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes.MethodsWe studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean ± SD age = 20.8 ± 8.1 years) and 108 neurotypical controls (NT, 19.2 ± 7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types.ResultsCompared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores.LimitationsDespite rigorous harmonization across the three different acquisition sites, heterogeneity in autism poses a potential limitation to the generalizability of our results. Additionally, we focussed male participants, warranting future studies in more balanced cohorts.ConclusionsOur study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.

Metallogenic model of the Lykling ophiolite-hosted lode Au deposit, Scandinavian Caledonides: Insight from fluid inclusions, mineral chemistry and stable isotope geochemistry

The Lykling lode Au deposit represents a unique example of gold mineralization in the Upper Allochthone of the Scandinavian Caledonides. The mineralization is hosted by the Early Ordovician Lykling Ophiolite Complex and the intruding trondhjemite unit and spatially associated with two generations of mafic dykes that crosscut both the ophiolitic complex and trondhjemite. Field observations indicate that the mafic dykes did not play an active role in the emplacement of Au at Lykling, however their contacts with the immediate host rocks (i.e., gabbro and trondhjemite) may have focused ore-forming fluids.Three main stages in the evolution of the hydrothermal system in the Lykling area are documented by two generations of auriferous quartz veins: 1) older quartz-carbonate veins hosted by moderately angled brittle-ductile shear zones and 2) younger quartz-sulfide veins that fill steeply dipping brittle faults. Stage 1 resulted in deposition of barren quartz and gold-free pyrite from moderately saline NaCl-CaCl2-H2O ± CO2 fluids at temperatures between ∼310–330 °C and pressures in the range from 2.7 to 3.5 kbars. Under the given physicochemical conditions Au was mobile in the form of its chloride complexes. Stage 2 is associated with multiple episodes of ductile deformation punctuated by concomitant brecciation reflecting brittle-ductile transition processes. Fluid inclusion data reveals a decrease in temperature and pressure that may result in the formation of stable Au-bisulfide complexes and making the hydrothermal mobility of Au sensitive to changes in pressure. Consequently, the fluctuation in pressure controlled by the development of brittle-ductile structures likely resulted in the precipitation of native gold in quartz veins. Stage 3 represents a pure brittle event which is accompanied by a significant decrease in the fCO2/fS2 ratio. Mixing of higher-temperature and moderate-salinity fluids with cooler fluids has been recognized as the principal trigger for deposition of sulfides and a paragenetically late phase of native gold in the Lykling hydrothermal system.The Lykling ophiolite-hosted lode Au deposit shows numerous similarities with orogenic gold deposits elsewhere, including structural controls during mineralizing events, deposition from moderately to low salinity CO2-bearing aqueous solutions, deposition from a focused fluid flow along trans-crustal fault zones with a mixed brittle-ductile character and the spatial association with regionally metamorphosed terranes. In contrast to the great majority of the known orogenic gold deposits, the Lykling ophiolite-hosted lode Au deposit records a magmatic origin of volatiles. Therefore, taking into consideration field relationships and the regional setting of the Lykling deposit, we argue that its formation is concomitant with emplacement of the Sunnhordland Batholith during post-collisional thinning of the crust and associated localized uplift within the Lykling Ophiolite Complex.

A novel GFP-based strategy to quantitate cellular spatial associations in HSV-1 viral pathogenesis.

Periodic reactivation of herpes simplex virus type 1 (HSV-1) triggers immune responses that result in corneal scarring (CS), known as herpes stromal keratitis (HSK). Despite considerable research, fully understanding HSK and eliminating it remains challenging due to a lack of comprehensive analysis of HSV-1-infected immune cells in both corneas and trigeminal ganglia (TG). We engineered a recombinant HSV-1 expressing green fluorescent protein (GFP) in the virulent McKrae virus strain that does not require corneal scarification for efficient virus replication (GFP-McKrae). Next-generation sequencing (NGS) analysis, along with in vitro and in vivo assays, showed that GFP-McKrae virus was similar to WT-McKrae virus. Furthermore, corneal cells infected with GFP-McKrae were quantitatively analyzed using image mass cytometry (IMC). The single-cell reconstruction data generated cellular maps of corneas based on the expression of 25 immune cell markers in GFP-McKrae-infected mice. Corneas from mock control mice showed the presence of T cells and macrophages, whereas corneas from GFP-McKrae-infected mice on days 3 and 5 post-infection (PI) exhibited increased immune cells. Notably, on day 3 PI, increased GFP expression was observed in closely situated clusters of DCs, macrophages, and epithelial cells. By day 5 PI, macrophages and T cells became prominent. Finally, immunostaining methods detected HSV-1 or GFP and gD proteins in latently infected TG. This study presents a valuable strategy for identifying cellular spatial associations in viral pathogenesis and holds promise for future therapeutic applications.IMPORTANCEThe goal of this study was to establish quantitative approaches to analyze immune cell markers in HSV-1-infected intact corneas and trigeminal ganglia from primary and latently infected mice. This allowed us to define spatial and temporal interactions between specific immune cells and their potential roles in virus replication and latency. To accomplish this important goal, we took advantage of the utility of GFP-McKrae virus as a valuable research tool while also highlighting its potential to uncover previously unrecognized cell types that play pivotal roles in HSV-1 replication and latency. Such insights will pave the way for developing targeted therapeutic approaches to tackle HSV-1 infections more effectively.

Water and socioeconomic inequalities: spatial analysis of water consumption in Brazil

ABSTRACT To achieve equitable water access, it is necessary to understand and quantify the heterogeneity of water consumption. Thus, this study assessed the spatial dependence of water consumption for Brazil and an urban center. Two spatial autocorrelation tests were applied: the Global Moran’s Index (I) and the Local Indicator of Spatial Association (LISA). It was found that, both at the national and municipal scales, water consumption is not uniformly distributed in space, indicating the formation of clusters of regions with High-High, High-Low, Low-High, and Low-Low aggregation patterns. In this way, water and socioeconomic inequalities in Brazil are highlighted, emphasizing the gap between the Center-South and the North-Northeast regions. Additionally, water and socioeconomic disparities within the city of Fortaleza are also observed, resulting from its urbanization process. Finally, the spatial autocorrelation between water consumption and per capita income is strongly evident at both the national and municipal levels.

Six-Degree-of-Freedom Pose Estimation Method for Multi-Source Feature Points Based on Fully Convolutional Neural Network

An object’s six-degree-of-freedom (6DoF) pose information has great importance in various fields. Existing methods of pose estimation usually detect two-dimensional (2D)-three-dimensional (3D) feature point pairs, and directly estimates the pose information through Perspective-n-Point (PnP) algorithms. However, this approach ignores the spatial association between pixels, making it difficult to obtain high-precision results. In order to apply pose estimation based on deep learning methods to real-world scenarios, we hope to design a method that is robust enough in more complex scenarios. Therefore, we introduce a method for 3D object pose estimation from color images based on farthest point sampling (FPS) and object 3D bounding box. This method detects the 2D projection of 3D feature points through a convolutional neural network, matches it with the 3D model of the object, and then uses the PnP algorithm to restore the feature point pair to the object pose. Due to the global nature of the bounding box, this approach can be considered effective even in partially occluded or complex environments. In addition, we propose a heatmap suppression method based on weighted coordinates to further improve the prediction accuracy of feature points and the accuracy of the PnP algorithm in solving the pose position. Compared with other algorithms, this method has higher accuracy and better robustness. Our method yielded 93.8% of the ADD(-s) metrics on the Linemod dataset and 47.7% of the ADD(-s) metrics on the Occlusion Linemod dataset. These results show that our method is more effective than existing methods in pose estimation of large objects.

Investigation of the Clinical Value of Four Visualization Modalities for Congenital Heart Disease.

Diagnosing congenital heart disease (CHD) remains challenging because of its complex morphology. Representing the intricate structures of CHD on conventional two-dimensional flat screens is difficult owing to wide variations in the pathologies. Technological advancements, such as three-dimensional-printed heart models (3DPHMs) and virtual reality (VR), could potentially address the limitations of viewing complex structures using conventional methods. This study aimed to investigate the usefulness and clinical value of four visualization modalities across three different cases of CHD, including ventricular septal defect, double-outlet right ventricle, and tetralogy of Fallot. Seventeen cardiac specialists were invited to participate in this study, which was aimed at assessing the usefulness and clinical value of four visualization modalities, namely, digital imaging and communications in medicine (DICOM) images, 3DPHM, VR, and 3D portable document format (PDF). Out of these modalities, 76.4% of the specialists ranked VR as the best for understanding the spatial associations between cardiac structures and for presurgical planning. Meanwhile, 94.1% ranked 3DPHM as the best modality for communicating with patients and their families. Of the various visualization modalities, VR was the best tool for assessing anatomical locations and vessels, comprehending the spatial relationships between cardiac structures, and presurgical planning. The 3DPHM models were the best tool for medical education as well as communication. In summary, both 3DPHM and VR have their own advantages and outperform the other two modalities, i.e., DICOM images and 3D PDF, in terms of visualizing and managing CHD.

A study on the spatial correlation network structure and its influencing factors of coupling coordination between FDI flow network and carbon transfer network in the belt and road initiative countries

Clarifying the spatial correlation characteristics and influencing factors of coupling coordination between Foreign Direct Investment (FDI) flow networks and carbon transfer networks in countries along the Belt and Road Initiative is of utmost importance for the formulation of regional carbon governance strategies and the establishment of a high-quality Green Silk Road. This study used a comprehensive approach combining social network analysis and coupling coordination model to measure the coupling coordination degree of FDI flow networks and carbon transfer networks of 67 Belt and Road countries from 2010 to 2016. In addition, a modified gravity model is used to characterize the spatial correlation network structure of coupling coordination between the two networks, and the QAP regression analysis method is applied to investigate the factors influencing the spatial association network. The results are as follows: 1) The spatial correlation network of coupling coordination between the two networks has good accessibility and relatively high overall network stability. 2) Countries such as Qatar and the United Arab Emirates occupy central positions in the network, while Bahrain and Jordan are positioned on the periphery of the network. 3) The spatial correlation network can be divided into three sectors: net outflow, net inflow, and bidirectional overflow sectors. 4) Spatial adjacency, bilateral investment treaties, economic development, and institutional quality have significant positive effects on the spatial association network, while the industrial structure and the level of infrastructure development have a significant negative impact. This study proposes an indicator system for the coupling coordination between FDI flow networks and carbon transfer networks. The aim is to investigate the coupling coordination relationship between FDI flow networks and carbon emission transfer networks in countries along the Belt and Road Initiative, providing important guidance for the formulation of regional cooperative carbon emission reduction strategies in other regions.

P031 SPATIAL ASSOCIATION BETWEEN HYPERTENSION AND AIR POLLUTION IN ULAANBAATAR, MONGOLIA

Background and Objective: This study aims to assess the spatial association between hypertension prevalence and PM2.5 levels in Ulaanbaatar. Methods: To estimate the hypertension prevalence, we conducted a complex survey with a two-stage cluster sampling method in 2018. For PM2.5 level estimation, we used data collected during cold weather months (October to March) from 2015 to 2019. These estimates were used to predict PM2.5 levels across the entire city, using the Inverse Distance Weighted (IDW) interpolation method. The same approach was applied to predict the hypertension prevalence. We fitted a multiple linear regression model to estimate the association between PM2.5 and hypertension prevalence with adjustment for income, a proxy of socioeconomic factors to examine effect modification. In exploratory analysis, we estimated the association between PM2.5 level and income level. We used RStudio 1.3.1093 and ArcMap 10.8.2 for data analysis. Results: A total of 4515 participants were included in the sample. The hypertension prevalence was 46.5% with a threshold of 130/80 mm Hg. Adjusting for other covariates, a 10-unit increase in PM2.5 level is associated with a 6.66 (95% CIs;[10.23,10.23]) point increase in hypertension prevalence (p<0.001). With each 100,000 (MNT) increase in income level, the impact of 10-unit increases in PM2.5 level on hypertension decreases by 2.98 (95% CIs;[-5.48,-0.47]) points (p=0.02). In the partial F-test, an increase in income level was associated with a lower hypertension prevalence (p=0.002). In the exploratory analysis, a 100,000 (MNT) increase in income level was associated with a 0.23 (95% CIs;[-0.41,-0.05]) point decrease in PM2.5 level (p=0.011). Figure 1. Spatial association between hypertension prevalence. A).Hypertension prevalence (%); B).Income level (MNT) PM2.5 level; C).PM 2.5 (μg/m3); D).Linear regression model. Note: Inverse Distance Weighted method is used for predictions. Green points indicate the PM2.5-measuring site locations. Conclusions: The people who live in lower-income areas experience a stronger influence of air pollution on hypertension prevalence compared to others in Ulaanbaatar.

Examining relations between sea-level anomalies in the Niño 3.4 region and terrestrial hydroclimatic conditions in China

As a key driver of global climate variability, El Niño–Southern Oscillation (ENSO) significantly impacts the regional hydrological cycle. Similar to the recognized ENSO monitoring index (i.e., sea surface temperature anomaly), a potential advancement in the usage of sea level anomaly (SLA) in the equatorial Pacific is expected to enhance our knowledge of ENSO and its influence on regional terrestrial hydroclimatic conditions (THCs). In this study, we employed wavelet coherence and multi-channel singular spectral analysis to investigate the ability of satellite altimetry-based SLA, and its two major components (i.e., steric SLA (SSL) and non-steric SLA (NSSL)) averaged over Niño 3.4 region, to identify ENSO-related signals. Subsequently, we explored their spatial association with THCs at interannual scales in China using a cross-correlation method. We found that the time series of sea surface temperature anomaly index (SSTaI), SLA index (SLAI), and SSL index (SSLI) displayed high consistency. SSTaI was found to present higher correlations to SLAI (0.91) and SSLI (0.88), compared to NSSL index (NSSLI; 0.40). Furthermore, the high (low) wavelet coherence and in-phase (orthogonal) relationship between SSTaI and SLAI/SSLI (NSSLI) was also revealed. This indicated that SLAI and SSLI may be good complementary indices to SSTaI in observing ENSO-related signals. Similar to the temporal association of SSTaI and SLAI/SSLI, their spatial correlation pattern with THCs also displayed consistency. The time lag characterizing the relationship between SSTaI/SLAI/SSLI and THCs was also consistent, despite some regional differences. In summary, our results indicated that the combined analyses of the time lag determined between SSTaI/SLAI/SSLI and THCs can eventually complement the time lag analysis based solely on SSTaI. This, in turn, provides a more comprehensive understanding of ENSO’s influence on THCs in China.

Midkine is upregulated in the hippocampus following bothspatial and olfactory reward association learning and enhances memory.

Hippocampal neuronal plasticity is a fundamental process underpinning learning and memory formation and requiring elaborate molecular mechanisms that result in the dynamic remodelling of synaptic connectivity. The neurotrophic properties of midkine (Mdk) have been implicated in the development and repair of the nervous system, while Mdk knockout resulted in deficits in the formation of certain types of memory. The role of Mdk in the process of memory-associated neuronal plasticity, however, remains poorly understood. We investigated the learning-induced regulation of Mdk in spatial navigation and association learning using the water maze and the odour reward association learning paradigms, characterising a temporal profile of Mdk protein expression post-learning. Both learning events revealed similar patterns of upregulation of expression of the protein in the rat hippocampal dentate gyrus, which were rapid and transient. Moreover, administration of recombinant Mdk during the endogenous Mdk upregulation following learning enhanced memory in the water maze task revealing a pro-cognitive action of Mdk. We further show that, within the adult hippocampus, Mdk mRNA is predominantly expressed in granular and pyramidal neurons and that hippocampal neuronal Mdk expression is regulated by the canonical plasticity-associated neurotransmitter glutamate. Finally, we confirm that the positive action of Mdk on neurite outgrowth previously noted in cortical and cerebellar neurons extends to hippocampal neurons. Together, our findings suggest a role for Mdk in glutamate-mediated hippocampal neuronal plasticity important for long-term memory consolidation.

High-resolution carbon emission mapping and spatial-temporal analysis based on multi-source geographic data: A case study in Xi'an City, China

Cities, contributing over 70% of global emissions, are key areas for climate change mitigation. Heterogeneity within cities determines the need for spatialized urban emissions reduction policies. However, few studies have attempted to characterize the spatial distribution of carbon emissions at the urban scale. To address this issue, a novel mapping method was proposed, using Xi'an as an example to explore the spatial distribution of carbon emissions at the city scale. Firstly, multiple geospatial open-source data, such as point of interest (POI), road networks, and land use, were utilized to identify the locations of emission sources. High-resolution carbon emission distributions were then mapped by allocating emissions based on the Intergovernmental Panel on Climate Change (IPCC) methodology. The study employed Global Moran's I to analyze the changes in spatial heterogeneity at different resolutions. Additionally, the Local Indicators of Spatial Association index (LISA) and Standard Deviation Ellipses (SDE) were adopted to examine the spatiotemporal characteristics of carbon emissions in Xi'an. The results show that carbon emissions at Xi'an City rises from 45.112 million tons to 72.701 million tons between 2010 and 2021. The construction of multi-scale carbon emissions spatial distributions, with a resolution of up to 30 m, allowed for a more detailed characterization of carbon emissions, especially in urban fringe areas. In addition, the results indicate that urban carbon emissions exhibit the strongest spatial autocorrelation at a resolution of 350 m. The study can provide a reference for the development of regional carbon emission reduction policies and spatial planning. In addition, the proposed spatialized method of city carbon emissions depends on open-source data, which allows it to have the potential for application in other cities.

A multiscale and multiperspective quantifying framework for spatial patterns and influencing mechanisms of geographical indications

Geographical indications (GIs) embody distinctive regional elements characterized by widespread spatial distribution, serving as vital agricultural intellectual properties and significant tourism assets. This case study introduces a multiscale and multi-perspective quantification framework for Shandong Province, focusing on spatial agglomeration, association, and heterogeneity. This approach quantitatively explores GIs’ spatial patterns and influencing mechanisms, analyzing both at the monomer and regional scales, considering static and dynamic perspectives. Leveraging methods such as kernel density analysis, Moran’s I index, Geary’s C index, Internet hot word analysis, and Geodetector, the coastal municipalities, prosperous urban areas, and zones with high-quality arable soils were identified as spatial agglomeration areas for GIs. The study revealed a strong correlation between the spatial distribution pattern of socioeconomic factors and GIs’ spatial density from a static perspective. However, diminished spatial autocorrelation was observed for GIs’ public perception from a dynamic standpoint, indicating a reduced influence of Internet connectivity. The spatial heterogeneity in GIs is determined by multiple interconnected factors, displaying an enhancement effect between any two influencing factors. Among the 15 influencing factors analyzed, urbanization rate; gross value of agricultural, forestry, animal husbandry, and fishery production; and per capita gross domestic product significantly contribute to spatial heterogeneity effects at both urbanization rate, gross value of agriculture, forestry, animal husbandry, fishery production, and per capita gross domestic product significantly contributed to spatial heterogeneity effects at regional and monomer scales. The results suggest that GIs are likely to flourish with regional economic growth. The analytical framework and quantification methods support exploring and sustainably utilizing GIs, improving output quality, and modernizing GIs’ production, ultimately contributing to regional agriculture’s high-quality and sustainable development.

Spatial Clustering of Rabies by Animal Species in New Jersey, United States, from 1989 to 2023.

Identifying spatial clusters of rabies in animals aids policymakers in allocating resources for rabies prevention and control. This study aimed to investigate spatial patterns and hotspots of rabies in different animal species at the county level in New Jersey. Data on animal rabies cases from January 1989 to December 2023 were obtained from the New Jersey Department of Health and aggregated by county. Global Moran's index (I) statistics were computed for each species to detect global spatial clustering (GeoDa version 1.22). Local Moran's indicators of spatial association (LISA) were computed to identify local clusters of rabies. The results from the LISA analysis were mapped using ArcGIS Pro to pinpoint cluster locations. A total of 9637 rabies cases were analyzed among raccoons (n = 6308), skunks (n = 1225), bats (n = 1072), cats (n = 597), foxes (n = 225), and groundhogs (n = 210). A global Moran's test indicated significant global spatial clustering in raccoons (I = 0.32, p = 0.012), foxes (I = 0.29, p = 0.011), and groundhogs (I = 0.37, p = 0.005). The LISA results revealed significant spatial clustering of rabies in raccoons and foxes in southeastern New Jersey and in groundhogs in northern New Jersey. These findings could guide the development of targeted oral rabies vaccination programs in high-risk New Jersey counties, reducing rabies exposure among domestic animals and humans.

FINet: Handwriting trajectory reconstruction of Chinese characters based on the font imitate network

Recovering online handwriting trajectory from Chinese handwritten images is still a difficult problem because of abundant strokes, complex structures and different writing styles of Chinese characters. To address this problem, this paper proposes a handwriting trajectory reconstruction method for Chinese characters called the Font Imitate Network. The Font Imitate Network integrates two networks, the spatial encoder uses HRNet to encode the spatial association information of each position on the offline image; the temporal decoder consists of a multilayer perceptron and a recurrent neural network. The stroke feature network is designed to help predict the next handwriting point by focusing on the stroke path in the form of heatmap. Experiments are conducted on the CASIA-OLHWDB1.1 with DTW, LDTW, RMSE and character recognition accuracy as the evaluation metrics. The experimental results show our FINet outperforms the state-of-the-art methods and the recognition accuracy of offline characters is improved by the handwriting reconstruction.

Mapping Nutritional Inequality: A Primary Socio-Spatial Analysis of Food Deserts in Santiago de Chile

This study investigates the socio-spatial distribution of food deserts in Santiago de Chile, aiming to understand how urban planning and socioeconomic factors influence access to nutritious food. Employing geospatial analysis techniques with data from OpenStreetMap and the 2017 Census, the research identifies areas within Santiago where access to healthy food is limited. The novelty of this study lies in its application of spatial autocorrelation methods, specifically Local Indicators of Spatial Association (LISA), to reveal clusters of nutritional inequality. The findings indicate significant concentrations of food deserts in both lower socioeconomic peripheral areas and, surprisingly, in some high-income central areas. These results suggest that both poverty and urban infrastructure, including car dependency, play critical roles in shaping access to healthy food. By highlighting over two million residents affected by food deserts, the study underscores the urgent need for integrated urban planning and public health strategies. This research contributes to the understanding of urban nutritional inequality and provides a replicable methodological framework for other cities. The implications extend beyond Santiago, offering insights into how urban design can be leveraged to improve public health outcomes through better access to nutritious food.

Cerebral blood flow alterations and host genetic association in individuals with long COVID: A transcriptomic-neuroimaging study.

Neuroimaging studies have indicated that altered cerebral blood flow (CBF) was associated with the long-term symptoms of postacute sequelae of SARS-CoV-2 infection (PASC), also known as "long COVID". COVID-19 and long COVID were found to be strongly associated with host gene expression. Nevertheless, the relationships between altered CBF, clinical symptoms, and gene expression in the central nervous system (CNS) remain unclear in individuals with long COVID. This study aimed to explore the genetic mechanisms of CBF abnormalities in individuals with long COVID by transcriptomic-neuroimaging spatial association. Lower CBF in the left frontal-temporal gyrus was associated with higher fatigue and worse cognition in individuals with long COVID. This CBF pattern was spatially associated with the expression of 2,178 genes, which were enriched in the molecular functions and biological pathways of COVID-19. Our study suggested that lower CBF is associated with persistent clinical symptoms in long COVID individuals, possibly as a consequence of the complex interactions among multiple COVID-19-related genes, which contributes to our understanding of the impact of adverse CNS outcomes and the trajectory of development to long COVID.