Spatial Complexity Research Articles

From microbiome composition to functional engineering, one step at a time.

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.

Local fractal dimension of collagen detects increased spatial complexity in fibrosis

Increase of collagen content and reorganization characterizes fibrosis but quantifying the latter remains challenging. Spatially complex structures are often analyzed via the fractal dimension; however, established methods for calculating this quantity either provide a single dimension for an entire object or a spatially distributed dimension that only considers binary images. These neglect valuable information related to collagen density in images of fibrotic tissue. We sought to develop a fractal analysis that can be applied to 3-dimensional (3D) images of fibrotic tissue. A fractal dimension map for each image was calculated by determining a single fractal dimension for a small area surrounding each image pixel, using fiber thickness as the third dimension. We found that this local fractal dimension increased with age and with progression of fibrosis regardless of collagen content. Our new method of distributed 3D fractal analysis can thus distinguish between changes in collagen content and organization induced by fibrosis.

Impacts of a century of land‐use change on the eutrophication of large, shallow, prairie Lake Manitoba in relation to adjacent Lake Winnipeg (Manitoba, Canada)

Abstract Evaluation of large lake response to centennial changes in land use and climate can be complicated by high spatial and hydrological complexity within their catchments, particularly in regions of low relief. Furthermore, large lakes can exhibit abrupt changes in structure and function that obscure causes of eutrophication. We provide the first quantification of historical trends in lake production, cyanobacterial abundance, sediment geochemistry and diatom composition since c. 1800 in Lake Manitoba, the 29th largest lake in the world, and compared them to Lake Winnipeg, a morphologically similar, adjacent basin with a 10‐fold larger catchment and an abrupt increase in production around 1990. Before 1900, Lake Manitoba was mesotrophic, with low sedimentary concentrations of carbon, phosphorus, nitrogen, cyanobacteria and algal pigments, as well as assemblages of low‐light‐adapted benthic diatoms. Analysis of pigment time‐series with hierarchical generalised additive models revealed that Lake Manitoba eutrophied during 1900–1930 as a consequence of the development of intensive agriculture within its local catchment, but thereafter exhibited stable cyanobacterial densities with limited expansion of N2‐fixing cyanobacteria despite persistent eutrophication. Lake Manitoba did not undergo an abrupt change as seen in Lake Winnipeg. These findings suggest that catchment size had little influence on water quality degradation and that nutrient influx from proximal agricultural sources was sufficient to initially degrade these large prairie lakes. The abrupt change in Lake Winnipeg around 1990 required additional intensification of local land use that did not occur in the Lake Manitoba catchment.

Cross‐sectoral metrics as accountability tools for twin transitioning energy systems

AbstractAs energy systems become ever more closely intertwined in order to enable electrification and real‐time coordination across sectors, tracking the nature of change to ensure accountability during complex implementation processes presents novel challenges and requires renewed thinking on data infrastructures. For instance, sectors like electricity generation, electricity distribution and electrified urban transport have begun to interact more closely and with more spatial complexity than ever before. Correspondingly, this conceptual article articulates the evolving relationship between cross‐sectoral metrics (CSM) and twin transitions (i.e., digitalisation and decarbonisation) of energy systems in the Anthropocene. It argues for development of explicitly cross‐sectoral metrical analysis as an accountability tool for shifts to equitable, low‐carbon energy systems. It draws on three pertinent fields of study—calculative logics, institutionalisation, and degrees of digitalisation—to provide the basis for a theory of transformative metrics for application to evolving energy systems. Scholarship on calculative logics offers insights on the nature of metrics, work on institutionalisation helps understand the dynamics of integrating novel metrics into evolving sociotechnical systems, and consideration of degrees of digitalisation ensures mindfulness of differences across contexts. Resulting insights can serve as diagnostic tools to inform timely monitoring and implementation of twin transitions for energy systems. Work across three distinct lines of scholarship is specified to enable conceptual development, and an empirical case study is sketched to show how to operationalise and apply an analytical framework. This delineation serves as a step towards a theory of transformative metrics, for integrative study of CSM for accountable twin transitions.

Enhancing runoff simulation precision in the critical zone through spatiotemporal interpolation of areal rainfall with matrix decomposition

AbstractModelling hydrological process in the critical zone not only contributes to a better understanding of interactions across different Earth surface spheres but also holds significant practical implications for water resource management and disaster prevention. Rainfall‐runoff simulation in critical zones is particularly challenging due to the amalgamation of temporal and spatial complexity, rainfall variability, and data limitations. As a pivotal input variable of hydrological models, accurate estimation of areal rainfall is critical to successful runoff simulation. However, most estimation methods ignore temporal information, thereby increasing uncertainty in rainfall estimation and constraining the precision of rainfall‐runoff simulation. In this study, the matrix decomposition‐based estimation method (F‐SVD), which considers the spatial and temporal correlation of the rainfall process is employed to estimate areal rainfall. The superiority of the method in producing two‐dimensional rainfall information is evaluated through its application in runoff simulation with the Xin'anjiang model. The simulation results of selected flood events in the Jianxi basin in southeast China, spanning from 2009 to 2019, are compared with those of two widely used rainfall estimation methods, namely Arithmetical Mean (AM) and Thiessen Polygons (TP). The results show that (1) F‐SVD not only produces the highest Pearson correlation coefficient between rainfall and runoff series but also reduces the number of flood events with abnormal rainfall‐runoff relationships; (2) the Xin'anjiang model based on F‐SVD achieves the highest Nash‐Sutcliffe efficiency and lowest Relative Error, and performs best in simulating peak flow and its occurrence time as compared to AM and TP. This study contributes to a finer characterization of watershed rainfall distribution, enhancing the accuracy and sharpness of runoff simulation. It provides reliable data support for critical zone research and offers a scientific foundation for rationally allocating and managing water resources.

Diversity of spatial activities and parents' spatial talk complexity predict preschoolers' gains in spatial skills.

Children's spatial activities and parental spatial talk were measured to examine their associations with variability in preschoolers' spatial skills (N = 113, Mage = 4 years, 4 months; 51% female; 80% White, 11% Black, and 9% other). Parents who reported more diversity in daily spatial activities and used longer spatial talk utterances during a spatial activity had children with greater gains in spatial skills from ages 4 to 5 (β = .17 and β = .40, respectively). Importantly, this study is the first to move beyond frequency counts of spatial input and investigate the links among the diversity of children's daily spatial activities, as well as the complexity of parents' spatial language across different contexts, and preschoolers' gains in spatial skills, an important predictor of later STEM success.

Quantifying Spatial Complexity of Settlement Plans Through Fractal Analysis

In this paper, I investigate the possibilities and limitations of fractal analysis methods applied to archaeological and synthetic settlement plans, with the goal of providing quantitative measures of spatial randomness or noise, as well as potential tools for automated culture-historical attribution of settlement plans and socio-economic intra-site differentiation. The archaeological sample is made from Linear Pottery settlements in south-west Slovakia and Trypillia settlements in the Southern Bug—Dnieper interfluve in central Ukraine, all based upon high-quality geomagnetic site plans. Synthetic plans are constructed as geometrically ideal versions of the archaeological ones, with varying degrees of added spatial noise. A significant correlation between fractal dimension and noise level is revealed for synthetic settlement plans, independently of size, density, house-size distribution and basic layout. However, several methodological challenges persist, and further systematic exploration on larger samples is needed before these results may be generalised. All analysis is performed in the R language and the script is made freely available in order to facilitate further development.

Spatial transcriptomics in glioblastoma: is knowing the right zip code the key to the next therapeutic breakthrough?

Spatial transcriptomics, the technology of visualizing cellular gene expression landscape in a cells native tissue location, has emerged as a powerful tool that allows us to address scientific questions that were elusive just a few years ago. This technological advance is a decisive jump in the technological evolution that is revolutionizing studies of tissue structure and function in health and disease through the introduction of an entirely new dimension of data, spatial context. Perhaps the organ within the body that relies most on spatial organization is the brain. The central nervous system's complex microenvironmental and spatial architecture is tightly regulated during development, is maintained in health, and is detrimental when disturbed by pathologies. This inherent spatial complexity of the central nervous system makes it an exciting organ to study using spatial transcriptomics for pathologies primarily affecting the brain, of which Glioblastoma is one of the worst. Glioblastoma is a hyper-aggressive, incurable, neoplasm and has been hypothesized to not only integrate into the spatial architecture of the surrounding brain, but also possess an architecture of its own that might be actively remodeling the surrounding brain. In this review we will examine the current landscape of spatial transcriptomics in glioblastoma, outline novel findings emerging from the rising use of spatial transcriptomics, and discuss future directions and ultimate clinical/translational avenues.

Diels-Alder Photoclick Patterning of Extracellular Matrix for Spatially Controlled Cell Behaviors.

Strategies that mimic the spatial complexity of natural tissues can provide cellular scaffolds to probe fundamental questions in cell biology and offer new materials for regenerative medicine. Here, the authors demonstrate a light-guided patterning platform that uses natural engineered extracellular matrix (ECM) proteins as a substrate to program cellular behaviors. A photocaged diene which undergoes Diels-Alder-based click chemistry upon uncaging with 365nm light is utilized. By interfacing with commercially available maleimide dienophiles, patterning of common ECM proteins (collagen, fibronectin Matrigel, laminin) with readily purchased functional small molecules and growth factors is achieved. Finally, the use of this platform to spatially control ERK activity and migration in mammalian cells is highlighted, demonstrating programmable cell behavior through patterned chemical modification of natural ECM.

A Transformer-Based Channel Estimation Method for OTFS Systems.

Orthogonal time frequency space (OTFS) is a novel modulation scheme that enables reliable communication in high-mobility environments. In this paper, we propose a Transformer-based channel estimation method for OTFS systems. Initially, the threshold method is utilized to obtain preliminary channel estimation results. To further enhance the channel estimation, we leverage the inherent temporal correlation between channels, and a new method of channel response prediction is performed. To enhance the accuracy of the preliminary results, we utilize a specialized Transformer neural network designed for processing time series data for refinement. The simulation results demonstrate that our proposed scheme outperforms the threshold method and other deep learning (DL) methods in terms of normalized mean squared error and bit error rate. Additionally, the temporal complexity and spatial complexity of different DL models are compared. The results indicate that our proposed algorithm achieves superior accuracy while maintaining an acceptable computational complexity.

A dual spatio-temporal network for short-term wind power forecasting

The intricate spatial and temporal complexities inherent in wind speed data necessitate the utilization of spatio-temporal networks for accurate wind power generation forecasting. But these complexities are made worse by the constant presence of high-frequency disturbances and clear outliers in the spatial and temporal correlations of wind speed, which makes forecasting wind power very difficult. Additionally, the intricate topological architectures that underpin current models often result in superfluous computational operations and susceptibility to overfitting. In response to these formidable challenges, we introduce an innovative solution in the form of the dual spatio-temporal (DST) network. This paradigm, characterized by meticulous design, excels in acquiring precise and resilient spatio-temporal representations essential for wind power forecasting. This is possible because of the careful development of two types of spatial correlation layers: synchronous layers and asynchronous layers. Each type is fine-tuned to recognize different spatial patterns. Recognizing that spatial and temporal correlations change over time, our method explores spatio-temporal representations at many different scales. This makes the system more adaptable and robust. Notably, for effective noise mitigation over extended forecasting horizons, we judiciously integrate wavelet decomposition. Empirical validation, rigorously conducted across twelve wind power stations in Anhui Province, China, firmly attests to the efficacy of our proposed DST network. In summary, our innovation presents a promising avenue for advancing wind power forecasting, characterized by adept noise reduction and improved precision while concurrently optimizing computational efficiency in modeling intricate spatial–temporal dependencies.

Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey.

Electron microscopy (EM) enables high-resolution imaging of tissues and cells based on 2D and 3D imaging techniques. Due to the laborious and time-consuming nature of manual segmentation of large-scale EM datasets, automated segmentation approaches are crucial. This review focuses on the progress of deep learning-based segmentation techniques in large-scale cellular EM throughout the last six years, during which significant progress has been made in both semantic and instance segmentation. A detailed account is given for the key datasets that contributed to the proliferation of deep learning in 2D and 3D EM segmentation. The review covers supervised, unsupervised, and self-supervised learning methods and examines how these algorithms were adapted to the task of segmenting cellular and sub-cellular structures in EM images. The special challenges posed by such images, like heterogeneity and spatial complexity, and the network architectures that overcame some of them are described. Moreover, an overview of the evaluation measures used to benchmark EM datasets in various segmentation tasks is provided. Finally, an outlook of current trends and future prospects of EM segmentation is given, especially with large-scale models and unlabeled images to learn generic features across EM datasets.

Enhanced Reservoir Description via Areal Data Integration and Reservoir Fluid Geodynamics: A Case Study From Deepwater Gulf of Mexico

Accurate reservoir characterization is vital for effective decisions made throughout the life cycle of an oilfield reservoir, including management and development. Of all the components of reservoir description, hydraulic connectivity carries the highest amount of uncertainty, where inaccurate connectivity evaluation often results in production underperformance. Shortcomings are faced when applying conventional approaches of connectivity assessment. Seismic surveys are not always sufficient to evaluate lateral connectivity as detected faults can be transmissive or partially transmissive, while some faults are below the detection limits of seismic amplitude measurements. Vertical connectivity represents another uncertainty, where pressure measurements and well logs are often either unable to detect the baffles along oil columns or cannot assess whether detected baffles are relevant seals or flow diverters. Although conventional downhole fluid analysis (DFA) workflows have proven effective in delineating reservoir connectivity, enough DFA data are not always available, and with added complexity, uncertainties arise. Additionally, while equilibrated asphaltene gradients, measured through DFA probes, imply connectivity, ongoing reservoir fluid geodynamics (RFG) processes, such as current hydrocarbon charging, can preclude equilibration in a connected reservoir. Thus, a comprehensive assessment approach, that utilizes all available data streams, is needed to overcome the significant spatial complexity associated with moderately and heavily faulted reservoirs. In this paper, we employed our recently introduced interpretation workflow to evaluate the connectivity of a heavily faulted reservoir in the deepwater Gulf of Mexico. The field was divided into five investigation areas penetrated by 12 wells. Areal downhole fluid analysis (ADFA) was applied to assess local connectivity leading to reservoir-scale connectivity. Through integrating fluid/dynamic and rock/static data, each data type provided insights that were pieced together to enhance consistency and reduce uncertainty. Analyzed data included pressure-volume-temperature (PVT) reports, pressure surveys, well logs, and geochemistry. The study resulted in a verifiable connectivity description where faults, previously regarded as sealing, were classified into sealing or partially transmissive faults; unresolved faults were detected. Fault-block migration was detected, and fault throw was estimated; asphaltenes behavior was used to deduce original field structures prior to faulting. We also examined RFG processes to investigate oil biodegradation, where an asphaltene clustering trend was observed, causing high oil viscosities toward the bottom of one sandstone. A correlation was then derived and successfully implemented to estimate oil viscosity.

Early Detection of Coronary Microvascular Dysfunction Using Machine Learning Algorithm Based on Vectorcardiography and Cardiodynamicsgram Features

PurposeAs a main etiology of myocardial ischemia, coronary microvascular dysfunction (CMD) can occur in patients with or without obstructive coronary artery disease. Currently, there is a lack of a non-invasive approach for early detection of CMD. AimWe aim to develop a multilayer perceptron (MLP) algorithm to achieve non-invasive early detection of CMD based on vectorcardiography (VCG) and cardiodynamicsgram (CDG) features. MethodsElectrocardiograms of 82 CMD patients and 107 healthy controls were collected and synthesized into VCGs. The VCGs' ST-T segments were extracted and fed into a deterministic learning algorithm to develop CDGs. Temporal heterogeneity index, spatial heterogeneity index, sample entropy, approximate entropy, and complexity index were extracted from VCGs' ST-T segments and CDGs, entitled as STT- and CDG-based features, respectively. The most effective feature subsets were determined from CDG-based, STT-based, and the combined features (i.e., all features) via the sequential backward selection algorithm as inputs for CDG-, STT-, and CDG-STT-based MLP models optimized with an improved sparrow search algorithm, respectively. Finally, the classification capacity of the corresponding models was evaluated via five-fold cross-validations and tested on a testing dataset to verify the optimal one. ResultsThe CDG-STT-based MLP model had significantly higher evaluated metrics than CDG- and STT-based ones on the validation dataset, with the accuracy, sensitivity, specificity, F1 score, and AUC of 0.904, 0.925, 0.870, 0.870, and 0.897 on the testing dataset respectively. ConclusionsThe MLP model based on VCG and CDG features showed high efficiency in identifying CMD. The CDG-STT-based MLP model may afford a potential computer-aided tool for non-invasive detection of CMD.

Perceptual cue-guided adaptive image downscaling for enhanced semantic segmentation on large document images

AbstractImage downscaling is an essential operation to reduce spatial complexity for various applications and is becoming increasingly important due to the growing number of solutions that rely on memory-intensive approaches, such as applying deep convolutional neural networks to semantic segmentation tasks on large images. Although conventional content-independent image downscaling can efficiently reduce complexity, it is vulnerable to losing perceptual details, which are important to preserve. Alternatively, existing content-aware downscaling severely distorts spatial structure and is not effectively applicable for segmentation tasks involving document images. In this paper, we propose a novel image downscaling approach that combines the strengths of both content-independent and content-aware strategies. The approach limits the sampling space per the content-independent strategy, adaptively relocating such sampled pixel points, and amplifying their intensities based on the local gradient and texture via the content-aware strategy. To demonstrate its effectiveness, we plug our adaptive downscaling method into a deep learning-based document image segmentation pipeline and evaluate the performance improvement. We perform the evaluation on three publicly available historical newspaper digital collections with differences in quality and quantity, comparing our method with one widely used downscaling method, Lanczos. We further demonstrate the robustness of the proposed method by using three different training scenarios: stand-alone, image-pyramid, and augmentation. The results show that training a deep convolutional neural network using images generated by the proposed method outperforms Lanczos, which relies on only content-independent strategies.

Enlarged housing space and increased spatial complexity enhance hippocampal neurogenesis but do not increase physical activity in mice.

Environmental enrichment (EE) improves various health outcomes, such as hippocampal neurogenesis, in rodents, which is thought to be caused, in part, by increased physical activity. However, the specific effect of each enrichment component, such as enlarged housing spaces and increased spatial complexity with a variety of objects, on physical activity remains unclear because of methodological limitations in measuring physical activity. We aimed to examine whether enlarged housing spaces and increased spatial complexity increase physical activity in mice using a body-implantable actimeter. Adult male C57BL/6J mice were assigned to either standard housing or EE groups. The housing environment in the EE mice was gradually enriched by enlarging the housing space and the placement of a variety of objects. Physical activity was measured using a body-implanted actimeter. Hippocampal neurogenesis was immunohistochemically examined. Enlarged housing spaces and the placement of a variety of objects did not increase physical activity in mice. In contrast, hippocampal neurogenesis was enhanced in the EE mice, suggesting that environmental interventions successfully provided enriched housing conditions for these mice. These results indicate that enlarged housing spaces and increased spatial complexity do not increase physical activity in mice. Furthermore, we found that EE enhanced hippocampal neurogenesis without increasing activity volume. Besides the current understanding that increasing the amount of physical activity is key to improving hippocampal function, our result suggests that the environment in which physical activity takes place is also a crucial contextual factor in determining the impact of physical activity on hippocampal function.

Scale-dependent complexity in administrative units and implications for data-driven decision-making models

Through analysis and discussion of basic systemic properties of a rural municipality, this paper explores how aggregating data in planning and land use modeling can potentially obscure intricate real-world behavior. Complexity theory is applied as a theoretical framework for explaining this hypothesis. Thus, the aim of this study is to address the author’s desire to understand systemic complexity when designing a data-driven decision-making model for rural planning. The novelty of this approach is two-fold: one, most studies on scalability issues in planning addresses spatial complexity, more so than systemic complexity within the complex system that the very act of planning strives to dictate. Two, although delimited to the scope of the study, the accessibility to and use of complete and valid socio-demographic data enables a rarely demonstrated accurate representation of an entire population. It is ultimately observed that on the disaggregated municipal level, systemic dispersion increases parallelly with population size, a correlation that is significantly influenced by gender ratio in any given parish – a characteristic that was not visible at the aggregated municipal level. In addition to advancing the understanding and placement of complexity science within spatial data science, these insights will make it easier to assess the generalizability of any given administrative unit by quantifying basic complexity attributes; in this case based on the correlation dispersion caused by the fragmentation of a municipality into its comprising parishes.

Fractal and structural analysis of the different sculptured Mn-based nanostructures

Surface morphology and fractal properties of 3D nanostructures of the circular helical, square helical, and chevron were investigated. The nanostructures were deposited on the glass substrate using the oblique angle deposition technique. The surface properties of the nanostructures were investigated by atomic force microscopy (AFM) and field emission scanning electron microscopy (FESEM). The autocorrelation function (ACF), height-height correlation function (H(r)), power density function (PSDF), and Minkowski function are used for fractal characterization. The results showed that the circular helical nanostructure has more surface roughness, roughness exponent, and grain size compared to the square helical and chevron nanostructures. The fractal dimension of the surface of all three nanostructures showed that the chevron nanostructure with Df=2.88 has higher spatial complexity. The Minkowski functionals showed similar behavior for all three nanostructures. However, more negative and positive Minkowski connectivity was obtained for the circular helical nanostructure compared to the other two nanostructures.

Mechanism of gas-phase passivation inhibition during FeS self-ignition based on pore structure and fractal characteristics

Reactive FeS that spontaneously combusts was prepared under field conditions and subjected to gas-phase passivation using various atmospheres and durations. The goal was to uncover how gas-phase passivation inhibits the spontaneous combustion of reactive FeS. The surface characteristics and elemental composition of FeS were analyzed at different gas-phase passivation levels using scanning electron microscopy (SEM) and X-ray energy-dispersive spectroscopy (EDS). The effect of gas-phase passivation on the evolution of the FeS pore structure was investigated using low-temperature liquid nitrogen adsorption, and the FHH fractal theory was employed to address changes in the fractal properties of the FeS surface. The results highlighted the intricate and densely dispersed pore structure of reactive FeS, along with its pronounced surface roughness. With increased levels of gas-phase passivation, the FeS surface became smoother and more regular, the number of oxidation-expanding pores increased, most sulfur elements were converted into sulfur dioxide gas, and iron trioxide passivation films formed on the FeS surface. Gas-phase passivation significantly reduced the specific surface area and cumulative pore volume of reactive FeS. Specifically, the cumulative pore volume decreased by approximately 20.9%, while the specific surface area decreased by 28.5% as gas-phase passivation deepened. The passivation process transformed more micropores into transition pores, with resulting iron-oxygen compounds adhering to the FeS surface. This reduced the number of reactive adsorption sites and substantially slowed down spontaneous combustion. As gas-phase passivation deepened, the volume proportion of FeS micropores decreased to 16.5%, and the micropore area decreased by around 6%. Gas-phase passivation had a significant impact on reducing the spatial fractal dimension and complexity of FeS, with a greater influence on spatial complexity than surface complexity. The passivation effect was initially enhanced by higher oxygen concentrations within the first 6-hours of passivation, but its effectiveness diminished thereafter.

Regional immunity complexes: A plurospherical and autoimmunological reimagining of regional security

This article proposes a categorical reframing of regional security theory that builds on and extends the recent theoretical turn towards immunology. Specifically, the article argues that the systems of regional security should be recast as systems of regional immunity in order to draw attention not only to the spatial complexities of post-national and increasingly deterritorialised regionalism, fuelled by the spatial modalities of late modernity, but also to the autoimmunitary processes that are at work in all protective and resilience-building systems, biological and political alike. The discussion departs from Peter Sloterdijk's immunological theory of spheres in order to delineate the broad outlines of what the article terms Regional Immunity Complexes (RIC's). Through this reading, structures of regional security take on the form of a plurosphere of atmospherically differentiated and immunised lifeworlds that rhizomatically unfold through the connected isolations of Sloterdijk's foams. Through the exploration of specific examples, the article shows how the dynamics of regional plurospheres affect the processes of regional security and resilience and how these then engender the very counter-processes of autoimmunological erasure.