Spatial Data Integration Research Articles

EPCO-01. FROM HIERARCHY TO GEOGRAPHY: MULTI-CLONES DRIVE THE CELLULAR DIVERSITY AND EVOLUTION OF HUMAN MEDULLOBLASTOMA

Abstract We’ve curated a comprehensive multi-omics database of medulloblastoma (MB) serving as a resource to understand spatiotemporal organization and guide therapeutic strategies. Herein, our analysis encompasses single-nucleus transcriptome (n = 51), chromatin accessibility (n = 46), and spatial transcriptomic profiles (n = 31) from human MB samples and patient-derived xenograft lines spanning four subgroups, augmented by bulk RNA (n = 322), whole-genome short-sequencing (n = 279), and bisulfite sequencing (n = 300) from matched samples. We delineate the malignant cellular hierarchy, identifying MB cancer stem-like cells (MB-CSCs), cycling cells, and more differentiated populations. Gene signatures of different cell subpopulations strongly correlate to clinical outcomes, while spatial character of patient-derived materials emphasizes the geographically heterogeneous nature of MB. Examination of somatic copy number variants (CNVs) and transcriptional signatures at single-nucleus resolution reveals geographically defined subclones harboring distinct CNVs, suggesting a competitive agglomeration of co-existing multi-clones for MB. Alongside the trajectorial transition, we observe distinct chromosomal alterations in apical MB-CSCs compared to more differentiated cells, indicative of extensive subclone-primed spatiotemporal evolution. Further analysis of reagent-gene interactions of subclonal lineages contributes to predicting the druggable candidates for treatment optimization. Integration of spatial architecture data highlights the recapitulation of subclone-determined niches through colocation with identified malignant lineages and their own inflammatory or fibrotic adaptions. Taken together, this comprehensive database, comprising five or six kinds of datatypes on the same MB cases, facilitates combinatorial analyses across different genomic modalities, offering genetic and geographic insights into therapeutic advancements.

Efficient Small Object Detection You Only Look Once: A Small Object Detection Algorithm for Aerial Images

Aerial images have distinct characteristics, such as varying target scales, complex backgrounds, severe occlusion, small targets, and dense distribution. As a result, object detection in aerial images faces challenges like difficulty in extracting small target information and poor integration of spatial and semantic data. Moreover, existing object detection algorithms have a large number of parameters, posing a challenge for deployment on drones with limited hardware resources. We propose an efficient small-object YOLO detection model (ESOD-YOLO) based on YOLOv8n for Unmanned Aerial Vehicle (UAV) object detection. Firstly, we propose that the Reparameterized Multi-scale Inverted Blocks (RepNIBMS) module is implemented to replace the C2f module of the Yolov8n backbone extraction network to enhance the information extraction capability of small objects. Secondly, a cross-level multi-scale feature fusion structure, wave feature pyramid network (WFPN), is designed to enhance the model’s capacity to integrate spatial and semantic information. Meanwhile, a small-object detection head is incorporated to augment the model’s ability to identify small objects. Finally, a tri-focal loss function is proposed to address the issue of imbalanced samples in aerial images in a straightforward and effective manner. In the VisDrone2019 test set, when the input size is uniformly 640 × 640 pixels, the parameters of ESOD-YOLO are 4.46 M, and the average mean accuracy of detection reaches 29.3%, which is 3.6% higher than the baseline method YOLOv8n. Compared with other detection methods, it also achieves higher detection accuracy with lower parameters.

Joint inference for telemetry and spatial survey data.

Data integration, the joint statistical analysis of data from different observation platforms, is pivotal for data-hungry disciplines such as spatial ecology. Pooled data types obtained from the same underlying process, analyzed jointly, can improve both precision and accuracy in models of species distributions and species-habitat associations. However, the integration of telemetry and spatial survey data has proved elusive because of the fundamentally different analytical approaches required by these two data types. Here, "spatial survey" denotes a survey that records spatial locations and has no temporal structure, for example, line or point transects but not capture-recapture or telemetry. Step selection functions (SSFs-the canonical framework for telemetry) and habitat selection functions (HSFs-the default approach to spatial surveys) differ in not only their specifications but also their results. By imposing the constraint that microscopic mechanisms (animal movement) must correctly scale up to macroscopic emergence (population distributions), a relationship can be written between SSFs and HSFs, leading to a joint likelihood using both datasets. We implement this approach using maximum likelihood, explore its estimation precision by systematic simulation, and seek to derive broad guidelines for effort allocation in the field. We find that complementarities in spatial coverage and resolution between telemetry and survey data often lead to marked inferential improvements in joint analyses over those using either data type alone. The optimal allocation of effort between the two methods (or the choice between them, if only one can be selected) depends on the overall effort expended and the pattern of environmental heterogeneity. Examining inferential performance over a broad range of scenarios for the relative cost between the two methods, we find that integrated analysis usually offers higher precision. Our methodological work shows how to integrate the analysis of telemetry and spatial survey data under a novel joint likelihood function, using traditional computational methods. Our simulation experiments suggest that even when the relative costs of the two methods favor the deployment of one field approach over another, their joint use is broadly preferable. Therefore, joint analysis of the two key methods used in spatial ecology is not only possible but also computationally efficient and statistically more powerful.

Research on Jiangxi Cultural Wisdom Tourism Industry Strategy Based on OHDSI General Data Model and Digital Art Empowerment

With the rapid development and wide application of a new round of information technologies, such as the global Internet of Things, the new generation of mobile Internet, cloud computing, etc., informatization has opened a new stage of smart tourism (ST), and ST has become the main trend of tourism development in China at present and for a long time to come. Tourism industry has become one of the important pillar industries in Jiangxi, and it plays an increasingly prominent role in stimulating and supporting economic and social development. Wisdom tourism is an important means to improve tourism service, tourism management and tourism marketing, and an important starting point to promote the upgrading of tourism industry in Jiangxi Province, and to achieve carry-over and leapfrog development and overtaking at corners. This paper is constantly improving and updating on the basis of data warehouse integration mechanism. Under the data warehouse mechanism, through the effective integration of spatial data, virtual reality data and dynamic data, a comprehensive data warehouse oriented to tourism promotion, scenic spot management and department management is formed. It aims to promote the rapid implementation of ST, bring tourists a comfortable tourism experience, save costs and improve efficiency for tourism service providers.

GS–CDNet: a remote sensing image cloud detection method with geographic spatial data integration

ABSTRACT In optical remote sensing images, clouds exhibit irregular scales and boundaries that vary with elevation across diverse geographical locations. To accurately capture the diverse visual patterns of clouds, we propose a cloud image segmentation approach named GS-CDNet (Geographic Spatial Data-Cloud Detection Network), which is based on the integration of geospatial data with multifaceted self-attention feature extraction, multi-scale feature aggregation, and boundary clarification techniques.Firstly, we utilize geographical coordinates from optical remote sensing images to extract a raster DEM (Digital Elevation Model) from SRTM3. This process creates a dataset consisting of elevation images, longitude, and latitude maps as geospatial data, enhancing the model’s capability in spatial positioning for cloud detection. Secondly, the proposed method consists of three interconnected modules within the cloud detection network: the Interleaved Self-Attention module(ISAM) utilizes a variety of self-attention mechanisms in an interleaved manner to extract multi-scale feature information.The Bidirectional Multi-Scale Feature Fusion Module(BIMFM) is responsible for integrating features, enabling a more comprehensive contextual understanding. The Boundary Extraction Module(BEM) utilizes a residual structure to generate a boundary cloud mask, effectively addressing the common issue of boundary blurring in multi-scale cloud masks. Finally, we compared and evaluated GS-CDNet with other cloud detection methods and conducted an ablation study on the key components of the method. The validation of generalization performance demonstrates the exceptional performance of the proposed model in cloud mask generation. Geospatial data and the different modules of the method play a significant role in the model.

Implementing geographic information systems in academic paper search: A framework and case study: GISAPS

The Geographic Information Systems Academic Publication Search (GISAPS) project pioneers the integration of spatial data into academic research through a novel online platform. Utilizing Web Map Service (WMS) and advanced search functionalities, GISAPS enables efficient access and analysis of georeferenced academic publications. This paper outlines the development and capabilities of GISAPS, including experimental WMS layer integration for enhanced data visualization. Key contributions include facilitating interdisciplinary research, improving spatial data accessibility, and promoting open science through its open-source framework. GISAPS's implications for academia and the research community are significant, offering new avenues for data-driven research and education. Future enhancements aim to incorporate real-time data feeds, advanced spatial analysis tools, and mobile accessibility, ensuring GISAPS remains at the forefront of GIS technology in academic settings.

Graspot: a graph attention network for spatial transcriptomics data integration with optimal transport.

Spatial transcriptomics (ST) technologies enable the measurement of mRNA expression while simultaneously capturing spot locations. By integrating ST data, the 3D structure of a tissue can be reconstructed, yielding a comprehensive understanding of the tissue's intricacies. Nevertheless, a computational challenge persists: how to remove batch effects while preserving genuine biological structure variations across ST data. To address this, we introduce Graspot, a graph attention network designed for spatial transcriptomics data integration with unbalanced optimal transport. Graspot adeptly harnesses both gene expression and spatial information to align common structures across multiple ST datasets. It embeds multiple ST datasets into a unified latent space, facilitating the partial alignment of spots from different slices. Demonstrating superior performance compared to existing methods on four real ST datasets, Graspot excels in ST data integration, including tasks that require partial alignment. In particular, Graspot efficiently integrates multiple ST slices and guides coordinate alignment. In addition, Graspot accurately aligns the spatio-temporal transcriptomics data to reconstruct human heart developmental processes. Graspot software is available at https://github.com/zhan009/Graspot.

Spatial computational modelling illuminates the role of the tumour microenvironment for treating glioblastoma with immunotherapies

Glioblastoma is the most common and deadliest brain tumour in adults, with a median survival of 15 months under the current standard of care. Immunotherapies like immune checkpoint inhibitors and oncolytic viruses have been extensively studied to improve this endpoint. However, most thus far have failed. To improve the efficacy of immunotherapies to treat glioblastoma, new single-cell imaging modalities like imaging mass cytometry can be leveraged and integrated with computational models. This enables a better understanding of the tumour microenvironment and its role in treatment success or failure in this hard-to-treat tumour. Here, we implemented an agent-based model that allows for spatial predictions of combination chemotherapy, oncolytic virus, and immune checkpoint inhibitors against glioblastoma. We initialised our model with patient imaging mass cytometry data to predict patient-specific responses and found that oncolytic viruses drive combination treatment responses determined by intratumoral cell density. We found that tumours with higher tumour cell density responded better to treatment. When fixing the number of cancer cells, treatment efficacy was shown to be a function of CD4 + T cell and, to a lesser extent, of macrophage counts. Critically, our simulations show that care must be put into the integration of spatial data and agent-based models to effectively capture intratumoral dynamics. Together, this study emphasizes the use of predictive spatial modelling to better understand cancer immunotherapy treatment dynamics, while highlighting key factors to consider during model design and implementation.

PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.

A Geospatial Framework of Food Demand Mapping

Spatial mapping of food demand is essential for understanding and addressing disparities in food accessibility, which significantly impact public health and nutrition. This research presents an innovative geospatial framework designed to map food demand, integrating individual dietary behaviors with advanced spatial analysis techniques. This study analyzes the spatial distribution of eating habits across Lithuania using a geospatial approach. The methodology involves dividing Lithuania into 60,000 points and interpolating survey data with Shepard’s operator, which relies on a weighted average of values at data points. This flexible approach allows for adjusting the number of points based on spatial resolution and sample size, enhancing the reliability and applicability of the generated maps. The procedure includes generating a structured grid system, incorporating measurements into the grid, and applying Shepard’s operator for interpolation, resulting in precise representations of food demand. This framework provides a comprehensive understanding of dietary behaviors, informing targeted policy interventions to improve food accessibility and nutrition. Traditional food spatial mapping approaches are often limited to specific polygons and lack the flexibility to achieve high granular detail. By applying advanced interpolation techniques and ensuring respondent location data without breaching privacy concerns, this study creates high-resolution maps that accurately represent regional differences in eating habits. The methodology’s flexibility allows for adjustments in spatial resolution and sample size, enhancing the maps’ validity and applicability. This novel approach facilitates the creation of detailed food demand maps at any granular level, providing valuable insights for policymakers and stakeholders. These insights enable the development of targeted strategies to improve food accessibility and nutrition. Additionally, the obtained information can be used for computer simulations to further analyze and predict food demand scenarios. By leveraging spatial data integration, this study contributes to a deeper understanding of the complex dynamics of food demand, identifying critical areas such as food deserts and swamps, and paving the way for more effective public health interventions and policies aimed at achieving equitable food distribution and better nutritional outcomes.

Visualization Of Spatial Distribution Of Village Development Index A Review Of Location Data In West Java Province

The spatial distribution of the Village Development Index (IDM) is the result of village development analysis for the purposes of mapping and assessing variations in the level of village development results in a region. The aim of the research is to analyze the spatial distribution of IDM, which is a comprehensive indicator that covers social, economic and environmental aspects in assessing village development progress. The methodology used in this research includes spatial analysis using a spatial data integration system with secondary data (statistical data). The combination of the internet of things (IoT) with WebGIS makes it possible to integrate data from various sources, in the form of spatial and non-spatial data into one easily accessible platform. This allows for more in-depth and accurate data analysis; and it is possible to disseminate information quickly and widely because it can be accessed by various stakeholders. That spatial distribution of the IDM can be used as an important tool in planning for a more inclusive and sustainable regional development. With WebGIS, data collected from IoT devices can be processed and analyzed to producing interactive maps and reports that will help in a better decision-making process, the research results can be accessed at https://jabar-e.netlify.app.

Multiscalar Integration of Dense and Sparse Spatial Data: an Archaeological Case Study with Magnetometry and Geochemistry

Multiscalar Integration of Dense and Sparse Spatial Data: an Archaeological Case Study with Magnetometry and Geochemistry

Retraction Note: Integration of clinical and spatial data to explore lipid metabolism-related genes for predicting prognosis and immune microenvironment in gliomas.

Retraction Note: Integration of clinical and spatial data to explore lipid metabolism-related genes for predicting prognosis and immune microenvironment in gliomas.

Estimating water balance in a Brazilian semiarid watershed using different spatial data

This study advances water balance (WB) estimation in the Paraguaçu River watershed, Brazil, by leveraging spatial data to mitigate the challenges of sparse monitoring, particularly in the semiarid region. By combining precipitation (PPT) and actual evapotranspiration (ETR) data from diverse methodologies, including IMERG, TMPA, TerraClimate (TC), and IDW interpolation, alongside ETR sources like MOD16, GLEAM, and TerraClimate, we aim to capture the watershed's physical and climatic nuances. The analysis reveals that TerraClimate data (PPTTC and ETRTC) most accurately reflect spatial variations, effectively capturing orographic effects and the semiarid climate, thus emerging as the optimal combination for depicting WB (WBTC_TC). Key findings highlight the variable precision of PPT and ETR data sources in characterizing spatial distribution and magnitude across the watershed, with TerraClimate data showing superior sensitivity to regional disparities. This sensitivity is crucial for accurately modeling the hydrological dynamics in regions with contrasting climatic conditions, from humid orographic areas to the semiarid zones. Moreover, the study underscores the importance of data validation and careful integration of spatial data, particularly in under-monitored or climatically extreme regions, to enhance the reliability of hydrological analyses. The successful integration of spatial data underscores its value in remote or data-scarce regions, reinforcing the necessity for rigorous spatial and temporal validation. This approach not only improves water management strategies but also enriches the integration with climatic and hydrological models, offering a comprehensive toolkit for addressing the complexities of watershed management in the face of climate variability.

The non-linear dynamics of South Australian regional housing markets: A machine learning approach

Traditional linear models often struggle to capture regional housing markets' complex, non-linear dynamics. This study addresses this gap by developing and applying advanced machine learning algorithms to unlock unique insights into South Australian housing price behavior. Leveraging a comprehensive dataset of over 10,000 regional house sales from 2010 to 2021, we explore the non-linear relationships between housing prices and microeconomic factors (e.g., house size, land area, building quality) and socioeconomic characteristics (e.g., proximity to amenities and income levels). Our analysis employs a multi-step approach, including feature engineering, spatial data integration, correlation tests, multilevel modeling, and non-linear machine learning algorithms including Decision Tree, Random Forest, Gradient-Boosted Tree, and Artificial Neural Network. The key finding is that machine learning models outperform traditional econometric models in predicting regional housing prices, with higher accuracy and greater goodness of fit. Furthermore, we identify specific non-linear relationships, such as the increasing marginal impact of proximity to the sea on house prices as distance decreases. These findings offer valuable insights for policymakers, real estate professionals, and stakeholders, informing regional planning, infrastructure provision, and economic development strategies. This study sheds light on the complex dynamics of South Australian housing markets and lays the foundation for further research.

Exploring GIS Techniques in Sea Level Change Studies: A Comprehensive Review

Sea level change, a consequence of climate change, poses a global threat with escalating impacts on coastal regions. Since 1880, global mean sea level has risen by 8–9 inches (21–24 cm), reaching a record high in 2021. Projections by NOAA suggest an additional 10–12-inch increase by 2050. This paper explores research methodologies for studying sea level change, focusing on Geographic Information System (GIS) techniques. GIS has become a powerful tool in sea level change research, allowing the integration of spatial data, coastal process modeling, and impact assessment. This paper sets the link with sustainability and reviews key factors influencing sea level change, such as thermal expansion and ice-mass loss, and examines how GIS is applied. It also highlights the importance of using different scenarios, like Representative Concentration Pathways (RCP), for accurate predictions. The paper discusses data sources, index variables like the Coastal Vulnerability Index, and GIS solutions for modeling sea level rise impacts. By synthesizing findings from previous research, it contributes to a better understanding of GIS methodologies in sea level change studies. This knowledge aids policymakers and researchers in developing strategies to address sea level change challenges and enhance coastal resilience. Furthermore, global analysis highlights the pivotal roles of the United States and China in sea level change (SLC) and GIS research. In the Gulf Cooperation Council (GCC) region, rising temperatures have substantial impacts on local sea levels and extreme weather events, particularly affecting vulnerable coastal areas.

Nephroblastoma-specific dysregulated gene SNHG15 with prognostic significance: scRNA-Seq with bulk RNA-Seq data and experimental validation

Wilms tumor (WT) is the most common malignancy of the genitourinary system in children. Currently, the Integration of single-cell RNA sequencing (scRNA-Seq) and Bulk RNA sequencing (RNA-Seq) analysis of heterogeneity between different cell types in pediatric WT tissues could more accurately find prognostic markers, but this is lacking. RNA-Seq and clinical data related to WT were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Small nucleolar RNA host gene 15 (SNHG15) was identified as a risk signature from the TARGET dataset by using weighted gene co-expression network analysis, differentially expressed analysis and univariate Cox analysis. After that, the functional mechanisms, immunological and molecular characterization of SNHG15 were investigated at the scRNA-seq, pan-cancer, and RNA-seq levels using Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), ESTIMATE, and CIBERSORT. Based on scRNA-seq data, we identified 20 clusters in WT and annotated 10 cell types. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing M2 macrophages as hubs for intercellular communication. In addition, in vitro cellular experiments showed that siRNAs interfering with SNHG15 significantly inhibited the proliferation and migration of G401 cells and promoted the apoptosis of G401 cells compared with the control group. The effect of siRNAs interfering with SNHG15 on EMT-related protein expression was verified by Western blotting assay. Thus, our findings will improve our current understanding of the pathogenesis of WT, and they are potentially valuable in providing novel prognosis markers for the treatment of WT.

Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system.

The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.

Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics

Dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) is a non-invasive imaging technique for hemodynamic measurements. Various perfusion parameters, such as cerebral blood volume (CBV) and cerebral blood flow (CBF), can be derived from DSC-MRP, hence this non-invasive imaging protocol is widely used clinically for the diagnosis and assessment of intracranial pathologies. Currently, most institutions use commercially available software to compute the perfusion parametric maps. However, these conventional methods often have limitations, such as being time-consuming and sensitive to user input, which can lead to inconsistent results; this highlights the need for a more robust and efficient approach like deep learning. Using the relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) perfusion maps generated by FDA-approved software, we trained a multistage deep learning model. The model, featuring a combination of a 1D convolutional neural network (CNN) and a 2D U-Net encoder-decoder network, processes each 4D MRP dataset by integrating temporal and spatial features of the brain for voxel-wise perfusion parameters prediction. An auxiliary model, with similar architecture, but trained with truncated datasets that had fewer time-points, was designed to explore the contribution of temporal features. Both qualitatively and quantitatively evaluated, deep learning-generated rCBV and rCBF maps showcased effective integration of temporal and spatial data, producing comprehensive predictions for the entire brain volume. Our deep learning model provides a robust and efficient approach for calculating perfusion parameters, demonstrating comparable performance to FDA-approved commercial software, and potentially mitigating the challenges inherent to traditional techniques.

Supporting Asset Management with GIS and Business Intelligence Technologies: The Case Study of the University of Turin

Despite the promising outcomes achieved over time in Asset Management, data accessibility, correlation, analysis, and visualization still represent challenges. The integration, readability, and interpretation of heterogeneous information by different stakeholders is a further concern, especially at the urban scale, where spatial data integration is required to correlate virtual information with the real world. The Geographic Information System (GIS) allows these connections, representing and digitizing extensive areas with significant benefits for asset analysis, management, and decision-making processes. Such benefits are central for managing large and widespread university campuses as they are comparable to small cities, covering a wide urban region and including resources highly integrated into the urban context. The paper presents how GIS integrated into Business Intelligence (BI) tools can support university Asset Management System (AMS) creation for the optimal use of resources, illustrating the University of Turin case study. The results discussion considers the relationship between the different elements of the assets and their synergy with the city. It focuses on four themes, dealing with the asset identification of buildings and resources, especially the educational ones, asset spatiotemporal evolution, and buildings’ distances for proximity analysis. The benefits achievable through the AMS, related challenges, and possible future developments are highlighted.