Spatial Context Research Articles

Artistic Production in a Necropolis in Motion

The present article studies aspects of the artistic production at New Kingdom Saqqara, a necropolis of the ancient Egyptian royal residence city Memphis. Following a brief review of the functions of ancient Egyptian tombs, this article will first set out to scrutinize the tomb-making section of society (e.g., size, membership). Second, the corpus of tombs will be reviewed to uncover the diverse nature of the tomb owners and to investigate access to resources required for tomb making. Third, the article will proceed to place the tombs in spatial and temporal context and reflect on the artistic production in a necropolis in motion.

A robust approach to Gaussian process implementation

Abstract. Gaussian process (GP) regression is a flexible modeling technique used to predict outputs and to capture uncertainty in the predictions. However, the GP regression process becomes computationally intensive when the training spatial dataset has a large number of observations. To address this challenge, we introduce a scalable GP algorithm, termed MuyGPs, which incorporates nearest-neighbor and leave-one-out cross-validation during training. This approach enables the evaluation of large spatial datasets with state-of-the-art accuracy and speed in certain spatial problems. Despite these advantages, conventional quadratic loss functions used in the MuyGPs optimization, such as root mean squared error (RMSE), are highly influenced by outliers. We explore the behavior of MuyGPs in cases involving outlying observations and, subsequently, develop a robust approach to handle and mitigate their impact. Specifically, we introduce a novel leave-one-out loss function based on the pseudo-Huber function (LOOPH) that effectively accounts for outliers in large spatial datasets within the MuyGPs framework. Our simulation study shows that the LOOPH loss method maintains accuracy despite outlying observations, establishing MuyGPs as a powerful tool for mitigating unusual observation impacts in the large data regime. In the analysis of US ozone data, MuyGPs provides accurate predictions and uncertainty quantification, demonstrating its utility in managing data anomalies. Through these efforts, we advance the understanding of GP regression in spatial contexts.

Spatial pattern and differential expression analysis with spatial transcriptomic data.

The emergence of spatial transcriptomic technologies has opened new avenues for investigating gene activities while preserving the spatial context of tissues. Utilizing data generated by such technologies, the identification of spatially variable (SV) genes is an essential step in exploring tissue landscapes and biological processes. Particularly in typical experimental designs, such as case-control or longitudinal studies, identifying SV genes between groups is crucial for discovering significant biomarkers or developing targeted therapies for diseases. However, current methods available for analyzing spatial transcriptomic data are still in their infancy, and none of the existing methods are capable of identifying SV genes between groups. To overcome this challenge, we developed SPADE for spatial pattern and differential expression analysis to identify SV genes in spatial transcriptomic data. SPADE is based on a machine learning model of Gaussian process regression with a gene-specific Gaussian kernel, enabling the detection of SV genes both within and between groups. Through benchmarking against existing methods in extensive simulations and real data analyses, we demonstrated the preferred performance of SPADE in detecting SV genes within and between groups. The SPADE source code and documentation are publicly available at https://github.com/thecailab/SPADE.

Asymmetric Deformable Spatio-temporal Framework for Infrared Object Tracking

The Infrared Object Tracking (IOT) task aims to locate objects in infrared sequences. Since color and texture information is unavailable in infrared modality, most existing infrared trackers merely rely on capturing spatial contexts from the image to enhance feature representation, where other complementary information is rarely deployed. To fill this gap, we in this article propose a novel Asymmetric Deformable Spatio-Temporal Framework (ADSF) to fully exploit collaborative shape and temporal clues in terms of the objects. Firstly, an asymmetric deformable cross-attention module is designed to extract shape information, which attends to the deformable correlations between distinct frames in an asymmetric manner. Secondly, a spatio-temporal tracking framework is coined to learn the temporal variance trend of the object during the training process and store the template information closest to the tracking frame when testing. Comprehensive experiments demonstrate that ADSF outperforms state-of-the-art methods on three public datasets. Extensive ablation experiments further confirm the effectiveness of each component in ADSF. Furthermore, we conduct generalization validation to demonstrate that the proposed method also achieves performance gains in RGB-based tracking scenarios.

Movement matters: uncovering life-course similarities and differences in residential environment perspectives

Neighbourhoods play a crucial role in residents’ everyday lives. People become attached to neighbourhood attributes throughout their lives and various activities performed within residential environments. Everyday time-space mobility routines actively impact how residents connect with their neighbourhoods. This paper aims to analyse residents’ links with their residential surroundings arising from their everyday spatial behaviours. It examines the topic in two dynamically transforming neighbourhood types in the Prague Metropolitan Area and from the perspectives of two population groups: older children from the suburbs and older adults from the gentrifying inner city. The study adopted qualitative data analysis using semi-structured interviews. Both groups fulfil neighbourhood attachments through various obligatory and optional movement types as well as accompanying social ties that constitute an important part of such moves. The neighbourhood links achieved through movement are shaped by complex mechanisms that occur at various spatial and temporal scales adding to the variety of functional and affective meanings of everyday mobility practices. They emerge along life-course shifts of individuals, changes in the neighbourhoods and activities happening within these spatial contexts. Residents then use numerous adaptive strategies to adjust their movements (and in turn their links with the neighbourhoods) to the combined effects of those conditions.

Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.

Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medical image segmentation methods have achieved state-of-the-art performance in various segmentation tasks. However, 3D medical images segmentation requires substantial global context and rich spatial semantic information, demanding much more GPU memory and computational resources. To address these challenges in 3D medical image segmentation, we propose a novel network- the MVEL-Net (Multi-view Ensemble Learning Network) for TMJ CBCT image segmentation. By resampling images along three dimensions, we generate multiple weak learners with different spatial semantic information. A subsequent strong learning network effectively integrates the outputs from these weak learners to achieve more accurate segmentation results. We evaluated our network model using a clinical dataset comprising 88 subjects with TMJ CBCT images. The average Dice similarity coefficient (DSC) was 0.9817 ± 0.0049, the average surface distance was 0.0540 ± 0.0179mm, and the 95% Hausdorff distance was 0.1743 ± 0.0550mm. Our proposed MVEL-Net demonstrates excellent segmentation performance on TMJ from CBCT images, while using fewer GPU memory resources compared to other 3D networks. The effectiveness of this method in capturing spatial context could be leveraged for tasks like organ segmentation from volumetric scans. This may facilitate wider adoption of AI-based solutions for automated analysis of 3D medical images.

Honour, Harmony and the King of Outspokenness. The Use of Stage Plays and Festive Culture in Fifteenth-Century Flemish Coastal Communities

ABSTRACT The history of fifteenth-century theatre in the Low Countries has long been studied primarily as the history of culture, literature and theatre. It has now attracted the attention of social historians and anthropologists and is being studied as an urban history of corporations, fraternities and chambers of rhetoric. That creative and art-loving activities in the Low Countries also frequently occurred in the fifteenth-century coastal communities, which largely comprised fishermen and sailors,, usually escapes the attention of historians. I argue these communities integrated drama into a survival strategy motivated by the spatial, economic and sociocultural context in which they were living.

Microvessel isolation protocol for RNA visualization and profiling

Disruptions in pericyte and endothelial cell communication can compromise the integrity of the blood-brain barrier (BBB), leading to neurovascular dysfunction and the development of neurological disorders. However, the evaluation of microvessel RNAs has been limited to tissue homogenates, with spatial visualization only available for protein targets. The aim of the present study is the development of an innovative microvessel isolation technique that is RNA-friendly for the purpose of coupling with in situ hybridization RNAscope analysis. RNA-friendly microvessel isolation combined with RNAscope analysis enables the visualization of cell-specific RNA within the spatial and histological context of the BBB. Using this approach, we have gained valuable insights into the structural and functional differences associated with the microvessels of 5xFAD mice, a mouse model of Alzheimer’s disease (AD). RNAscope analysis revealed a decrease in pericytes from microvessels isolated from 5xFAD mice in comparison to wild-type mice. Additionally, the microvessels of 5xFAD mice exhibited an increase in TYRO protein tyrosine kinase binding protein (TYROBP) mRNA expression. These findings significantly advance our understanding of neurovascular interactions and hold great promise for guiding the development of targeted therapeutic interventions. This innovative approach enables visualization of cell RNA while preserving the spatial and histological context of the BBB, shedding light on the mechanisms underlying neurovascular unit communication.

Measuring self-control in a wild songbird using a spatial discounting task

Self-control allows animals to resist instant gratification and invest more time and/or energy in better outcomes. However, widespread temporal self-control tasks lack ecological validity for many species, and few studies have explored whether self-control can be measured in the wild. We used a spatial discounting task resembling natural foraging decisions to measure self-control in wild toutouwai (Petroica longipes), a songbird endemic to New Zealand. Birds chose between a near, low-quality food item and a high-quality food item further away. Toutouwai showed striking individual variation in their self-control abilities. Validation tests suggested that our task reliably measured self-control in a spatial foraging context. However, individual-level performance was confounded by food preferences and the satiation and/or learning effects associated with increasing trial number, limiting the applicability of this task as a measure of individual variation in self-control. Nonetheless, we found no correlation between an individual’s self-control and their inhibitory control measured using a detour task, suggesting that self-control is a distinct ability from the suppression of impulsive motor actions in toutouwai. This study demonstrates for the first time that a bird is capable of self-control in a spatial context and provides suggestions for how future researchers may robustly quantify individual differences in self-control in the wild.

Combining graph theory and spatially-explicit, individual-based models to improve invasive species control strategies at a regional scale

ContextInvasive species cause widespread species extinction and economic loss. There is an increasing need to identify ways to efficiently target control efforts from local to regional scales.ObjectivesOur goal was to test whether prioritizing managed habitat using different treatments based on spatial measures of connectivity, including graph-theoretic measures, can improve management of invasive species and whether the level of control effort affects treatment performance. We also explored how uncertainty in biological variables, such as dispersal ability, affects measures performance.MethodsWe used a spatially-explicit, individual-based model (sIBM) based on the American bullfrog (Lithobates catesbeianus), a globally pervasive invasive species. Simulations were informed by geographic data from part of the American bullfrog’s non-native range in southeastern Arizona, USA where they are known to pose a threat to native species.ResultsWe found that total bullfrog populations and occupancy declined in response to all treatments regardless of effort level or patch prioritization methods. The most effective spatial prioritization was effort-dependent and varied depending on spatial context, but frequently a buffer strategy was most effective. Treatments were also sensitive to dispersal ability. Performance of treatments prioritizing habitat patches using betweenness centrality improved with increasing dispersal ability, while performance of eigenvalue centrality improved as dispersal ability decreased.ConclusionsWith the careful application of connectivity measures to prioritize control efforts, similar reductions in invasive species population size and occupancy could be achieved with less than half the effort of sub-optimal connectivity measures at higher effort rates. More work is needed to determine if trait-based generalities may define appropriate connectivity measures for specific suites of dispersal abilities, demographic traits, and population dynamics.

Adding Highly Variable Genes to Spatially Variable Genes Can Improve Cell Type Clustering Performance in Spatial Transcriptomics Data.

Spatial transcriptomics has allowed researchers to analyze transcriptome data in its tissue sample's spatial context. Various methods have been developed for detecting spatially variable genes (SV genes), whose gene expression over the tissue space shows strong spatial autocorrelation. Such genes are often used to define clusters in cells or spots downstream. However, highly variable (HV) genes, whose quantitative gene expressions show significant variation from cell to cell, are conventionally used in clustering analyses. In this report, we investigate whether adding highly variable genes to spatially variable genes can improve the cell type clustering performance in spatial transcriptomics data. We tested the clustering performance of HV genes, SV genes, and the union of both gene sets (concatenation) on over 50 real spatial transcriptomics datasets across multiple platforms, using a variety of spatial and non-spatial metrics. Our results show that combining HV genes and SV genes can improve overall cell-type clustering performance.

Spatially resolved metabolomics: From metabolite mapping to function visualising.

Mass spectrometry imaging (MSI)-based spatially resolved metabolomics addresses the limitations inherent in traditional liquid chromatography-tandem mass spectrometry (LC-MS)-based metabolomics, particularly the loss of spatial context within heterogeneous tissues. MSI not only enhances our understanding of disease aetiology but also aids in the identification of biomarkers and the assessment of drug toxicity and therapeutic efficacy by converting invisible metabolites and biological networks into visually rendered image data. In this comprehensive review, we illuminate the key advancements in MSI-driven spatially resolved metabolomics over the past few years. We first outline recent innovations in preprocessing methodologies and MSI instrumentation that improve the sensitivity and comprehensiveness of metabolite detection. We then delve into the progress made in functional visualization techniques, which enhance the precision of metabolite identification and annotation. Ultimately, we discuss the significant potential applications of spatially resolved metabolomics technology in translational medicine and drug development, offering new perspectives for future research and clinical translation. HIGHLIGHTS: MSI-driven spatial metabolomics preserves metabolite spatial information, enhancing disease analysis and biomarker discovery. Advances in MSI technology improve detection sensitivity and accuracy, expanding bioanalytical applications. Enhanced visualization techniques refine metabolite identification and spatial distribution analysis. Integration of MSI with AI promises to advance precision medicine and accelerate drug development.

A universal hippocampal memory code across animals and environments.

How learning is affected by context is a fundamental question of neuroscience, as the ability to generalize learning to different contexts is necessary for navigating the world. An example of swift contextual generalization is observed in conditioning tasks, where performance is quickly generalized from one context to another. A key question in identifying the neural substrate underlying this ability is how the hippocampus (HPC) represents task-related stimuli across different environments, given that HPC cells exhibit place-specific activity that changes across contexts (remapping). In this study, we used calcium imaging to monitor hippocampal neuron activity as animals performed a conditioning task across multiple spatial contexts. We investigated whether hippocampal cells, which encode both spatial locations (place cells) and task-related information, could maintain their task representation even when their spatial encoding remapped in a new spatial context. To assess the consistency of task representations, we used advanced dimensionality reduction techniques combined with machine learning to develop manifold representations of population level HPC activity. The results showed that task-related neural representations remained stable even as place cell representations of spatial context changed, thus demonstrating similar embedding geometries of neural representations of the task across different spatial contexts. Notably, these patterns were not only consistent within the same animal across different contexts but also significantly similar across different animals, suggesting a standardized neural encoding or 'neural syntax' in the hippocampus. These findings bridge a critical gap between memory and navigation research, revealing how the hippocampus maintains cognitive consistency across different spatial environments. These findings also suggest that hippocampal function is governed by a neural framework shared between animals, an observation that may have broad implications for understanding memory, learning, and related cognitive processes. Looking ahead, this work opens new avenues for exploring the fundamental principles underlying hippocampal encoding strategies.

Next-Generation Cancer Phenomics: A Transformative Approach to Unraveling Lung Cancer Complexity and Advancing Precision Medicine.

Lung cancer remains one of the leading causes of cancer-related deaths globally, with its complexity driven by intricate and intertwined genetic, epigenetic, and environmental factors. Despite advances in genomics, transcriptomics, and proteomics, understanding the phenotypic diversity of lung cancer has lagged behind. Next-generation phenomics, which integrates high-throughput phenotypic data with multiomics approaches and digital technologies such as artificial intelligence (AI), offers a transformative strategy for unraveling the complexity of lung cancer. This approach leverages advanced imaging, single-cell technologies, and AI to capture dynamic phenotypic variations at cellular, tissue, and whole organism levels and in ways resolved in temporal and spatial contexts. By mapping the high-throughput and spatially and temporally resolved phenotypic profiles onto molecular alterations, next-generation phenomics provides deeper insights into the tumor microenvironment, cancer heterogeneity, and drug efficacy, safety, and resistance mechanisms. Furthermore, integrating phenotypic data with genomic and proteomic networks allows for the identification of novel biomarkers and therapeutic targets in ways informed by biological structure and function, fostering precision medicine in lung cancer treatment. This expert review examines and places into context the current advances in next-generation phenomics and its potential to redefine lung cancer diagnosis, prognosis, and therapy. It highlights the emerging role of AI and machine learning in analyzing complex phenotypic datasets, enabling personalized therapeutic interventions. Ultimately, next-generation phenomics holds the promise of bridging the gap between molecular alterations and clinical and population health outcomes, providing a holistic understanding of lung cancer biology that could revolutionize its management and improve patient survival rates.

Functional analysis of regA paralog rlsD in Volvox carteri.

Volvox carteri is an excellent system for investigating the origins of cell differentiation because it possesses just two cell types, reproductive gonidia and motile somatic cells, which evolved relatively recently. The somatic phenotype depends on the regA gene, which represses cell growth and reproduction, preventing cells expressing it from growing large enough to become gonidia. regA encodes a putative transcription factor and was generated in an undifferentiated ancestor of V. carteri through duplication of a progenitor gene whose ortholog in V. carteri is named rlsD. Here we analyze the function of rlsD through knockdown, overexpression, and RNA-seq experiments, to gain clues into the function of a member of an understudied putative transcription factor family and to obtain insight into the origins of cell differentiation in the volvocine algae. rlsD knockdown was lethal, while rlsD overexpression dramatically reduced gonidial growth. rlsD overexpression led to differential expression of approximately one-fourth of the genome, with repressed genes biased for those typically overexpressed in gonidia relative to somatic cells, and upregulated genes biased toward expression in soma, where regA expression is high. Notably, rlsD overexpression affects accumulation of transcripts for genes/Pfam domains involved in ribosome biogenesis, photosynthetic light harvesting, and sulfate generation, functions related to organismal growth, and responses to resource availability. We also found that in the wild type, rlsD expression is induced by light deprivation. These findings are consistent with the idea that cell differentiation in V. carteri evolved when a resource-responsive, growth-regulating gene was amplified, and a resulting gene duplicate was co-opted to repress growth in a constitutive, spatial context.

METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning

Imaging mass spectrometry is a powerful technology enabling spatial metabolomics, yet metabolites can be assigned only to a fraction of the data generated. METASPACE-ML is a machine learning-based approach addressing this challenge which incorporates new scores and computationally-efficient False Discovery Rate estimation. For training and evaluation, we use a comprehensive set of 1710 datasets from 159 researchers from 47 labs encompassing both animal and plant-based datasets representing multiple spatial metabolomics contexts derived from the METASPACE knowledge base. Here we show that, METASPACE-ML outperforms its rule-based predecessor, exhibiting higher precision, increased throughput, and enhanced capability in identifying low-intensity and biologically-relevant metabolites.

Spatial Resolution Enhancement Framework Using Convolutional Attention-Based Token Mixer

Spatial resolution enhancement in remote sensing data aims to augment the level of detail and accuracy in images captured by satellite sensors. We proposed a novel spatial resolution enhancement framework using the convolutional attention-based token mixer method. This approach leveraged spatial context and semantic information to improve the spatial resolution of images. This method used the multi-head convolutional attention block and sub-pixel convolution to extract spatial and spectral information and fused them using the same technique. The multi-head convolutional attention block can effectively utilize the local information of spatial and spectral dimensions. The method was tested on two kinds of data types, which were the visual-thermal dataset and the visual-hyperspectral dataset. Our method was also compared with the state-of-the-art methods, including traditional methods and deep learning methods. The experiment results showed that the method was effective and outperformed state-of-the-art methods in overall, spatial, and spectral accuracies.

Multigrid Reduction‐In‐Time Convergence for Advection Problems: A Fourier Analysis Perspective

ABSTRACTA long‐standing issue in the parallel‐in‐time community is the poor convergence of standard iterative parallel‐in‐time methods for hyperbolic partial differential equations (PDEs), and for advection‐dominated PDEs more broadly. Here, a local Fourier analysis (LFA) convergence theory is derived for the two‐level variant of the iterative parallel‐in‐time method of multigrid reduction‐in‐time (MGRIT). This closed‐form theory allows for new insights into the poor convergence of MGRIT for advection‐dominated PDEs when using the standard approach of rediscretizing the fine‐grid problem on the coarse grid. Specifically, we show that this poor convergence arises, at least in part, from inadequate coarse‐grid correction of certain smooth Fourier modes known as characteristic components, which was previously identified as causing poor convergence of classical spatial multigrid on steady‐state advection‐dominated PDEs. We apply this convergence theory to show that, for certain semi‐Lagrangian discretizations of advection problems, MGRIT convergence using rediscretized coarse‐grid operators cannot be robust with respect to CFL number or coarsening factor. A consequence of this analysis is that techniques developed for improving convergence in the spatial multigrid context can be re‐purposed in the MGRIT context to develop more robust parallel‐in‐time solvers. This strategy has been used in recent work to great effect; here, we provide further theoretical evidence supporting the effectiveness of this approach.

A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes.

Recent advances in spatial transcriptomics technologies have provided multi-modality data integrating gene expression, spatial context, and histological images. Accurately identifying spatial domains and spatially variable genes is crucial for understanding tissue structures and biological functions. However, effectively combining multi-modality data to identify spatial domains and determining SVGs closely related to these spatial domains remains a challenge. In this study, we propose spatial transcriptomics Multi-modality and Multi-granularity Collaborative Learning (spaMMCL). For detecting spatial domains, spaMMCL mitigates the adverse effects of modality bias by masking portions of gene expression data, integrates gene and image features using a shared graph convolutional network, and employs graph self-supervised learning to deal with noise from feature fusion. Simultaneously, based on the identified spatial domains, spaMMCL integrates various strategies to detect potential SVGs at different granularities, enhancing their reliability and biological significance. Experimental results demonstrate that spaMMCL substantially improves the identification of spatial domains and SVGs. The code and data of spaMMCL are available on Github: Https://github.com/liangxiao-cs/spaMMCL. Supplementary data are available at Bioinformatics online.

Spatial context and informal caregivers’ Well-being: A case study of a Carer Café project in Hong Kong

This paper evaluates how a Hong Kong community Carer Café program reduce the informal (unpaid) caregivers’ navigation burden of social and health care service system. The cafés took place once biweekly at pantries and activity rooms of social service centers, community centers, and churches, which installed temporary decorations to create a café-like environment. his article will use the conceptual framework of spatial context, including spatial propinquity, spatial composition, and spatial configuration, to highlight four mechanisms in the process of informalization. They are (a) changing layout; (b) promoting spatial propinquity of caregivers; (c) creating a spatial composition facilitating social interaction; and (d) allowing the caregivers to use informal spaces flexibly. Qualitative data from 26 respondents, including social workers, project staff, volunteers, and users, sheds light on the details of four mechanisms. Results show that the Carer Cafés were transformed into a safe and caregiver friendly third place by the informalization process. Thus, caregivers from communities visit cafés frequently. These frequent visits facilitate the Cafés become a hub for informal caregivers access different resources and supports. This paper suggest that spatial context would be an important consideration to further explore in the future community caregiver support service practice and policy.