Spatio-temporal Context Research Articles

From “life as lived” to “life as text”: gendered geographies of diaspora and exile in Clément Baloup’s Vietnamese memories

ABSTRACT Baloup’s approach to using personal narratives in graphic novels transcends the specific realm of refugee comics or human rights campaigns, yet it faces challenges when attempting to bridge the gap between the intimate narratives of refugees and a broader commercial audience. Blurring the lines between autobiography, historiography, reportage, testimony, and docufiction, Vietnamese Memories retraces the global dimensions of the Vietnamese diaspora while engaging with the representation of transgenerational trauma in ways that reflect the diversity of the Viet Kieu experience. Transitioning from family history in the first volume to inferred advocacy in the third, Baloup’s exploration and graphic representation of the Viet Kieu in various spatiotemporal contexts add layers of historical, memorial, and narrative complexity to the series with the publication of each new volume. The resulting multilayered narrative of the refugee experience elicits reader empathy, yet also raises ethical considerations regarding the representation of trauma, particularly for women. Central to this analysis is the exploration of Baloup’s framing of women’s narratives and its impact on reader engagement.

Vision-based displacement measurement method of large-scale bridges using tilt shift camera and fast spatio-temporal context learning

Tracking vibration displacement across multiple points on large-scale bridges in the field poses significant challenges due to perspective distortion by camera tilting, interference from adverse environmental factors and low-resolution images by long distance measurement. To address these issues, this study proposed a novel vision-based displacement measurement framework that combined the Fast Spatio-Temporal Context Learning algorithm (FSTC) with tilt shift cameras to enhance visual tracking accuracy. A procedure was introduced to select suitable tilt shift cameras to eliminate the adverse effects of potential lens distortion. The FSTC algorithm, which integrated the Spatio-Temporal Context Learning algorithm (STC) and FastFlownet, was employed to improve the displacement tracking accuracy of the target object under fog occlusion and during long-distance measurements. Experimental results from slider and shaking table tests demonstrated that the FSTC algorithm outperformed the existing vision-based methods such as STC, Digital Image Correlation (DIC), and Kanade-Lucas-Tomasi (KLT) algorithms in tracking the displacement of the target object, effectively controlling the displacement error (e.g., RMSE and peak error) to below 3 mm. The FSTC algorithm was less sensitive to fog occlusion and exhibited enhanced efficiency in object tracking. Furthermore, the FSTC algorithm, in combination with image resolution reconstruction, enabled subpixel accuracy in tracking the displacement of the target object. A preliminary field application was conducted to track the vibration displacement of the Sanchaji Bridge under wind load, validating the feasibility and applicability of the proposed framework.

Individual mobility prediction by considering current traveling features and historical activity chain

ABSTRACT Individual mobility prediction forecasts traveling activities of an individual traveler, and has wide applications in location-based services, public health, and transportation planning. Whereas, it remains challenging due to the complexity and uncertainty of human mobility. Existing methods mainly consider spatiotemporal contexts in current traveling, but overlook those in historical trips, as well as relationships between traversed road intersections. These issues hinder the model from effectively capturing complex mobility patterns. To fill this gap, we propose a novel method that incorporates current traveling features and historical activity chain to predict the coordinates of traveling destination. Specifically, (1) we construct current traveling features by extracting real-time moving states, and represent spatiotemporal correlations between traversed road intersections using word embedding; (2) we learn travel intentions as a probability vector for each historical trip, and combine it with spatiotemporal features to construct historical activity chain; (3) we construct an individual mobility prediction model using Long Short-Term Memory (LSTM) network and spatiotemporal scoring mechanism, to capture short-term and long-term dependencies in current trip and historical activity chain, respectively. Experiments on 21,890 trajectories over the whole Year 2019 of 20 representatives selected from 1916 private car travelers in Shenzhen City, reveal the effectiveness of our model. It outperforms four baselines, Random Forest (RF), Distant Neighboring Dependencies (DND), Location Semantics and Location Importance (LSI)-LSTM, as well as Intersection Transfer Preference and Current Movement Mode (ITP-CMM), by approximately 10%-15% improvement in accuracy. In addition, we further explore the impact of historical activity chain length, and destination visiting frequency on prediction, as well as the relationship between predictability and eight mobility pattern features. This study benefits potential applications such as personalized location-based service recommendations and targeted advertising, and also provides implications for understanding human mobility.

The Role of 3D Virtual Anatomy and Scanning Environmental Electron Microscopy in Understanding Morphology and Pathology of Ancient Bodies.

Mummy studies allow to reconstruct the characteristic of a population in a specific spatiotemporal context, in terms of living conditions, pathologies and death. Radiology represents an efficient diagnostic technique able to establish the preservation state of mummified organs and to estimate the patient's pathological conditions. However, the radiological approach shows some limitations. Although bone structures are easy to differentiate, soft tissue components are much more challenging, especially when they overlap. For this reason, computed tomography, a well-established approach that achieves optimal image contrast and three-dimensional reconstruction, has been introduced. This original article focuses attention on the role of virtual dissection as a promising technology for exploring human mummy anatomy and considers the potential of environmental scanning electron microscopy and X-ray spectroscopy as complementary approaches useful to understand the state of preservation of mummified remains. Ancient mummy corps have been analyzed through Anatomage Table 10 and environmental scanning electron microscope equipped with X-ray spectrometer; Results: Anatomage Table 10 through various volumetric renderings allows us to describe spine alteration due to osteoarthritis, dental state, and other clinical-pathological characteristics of different mummies. Environmental scanning electron microscope, with the advantage of observing mummified samples without prior specimen preparation, details on the state of tissue fragments. Skin, tendon and muscle show a preserved morphology and keratinocytes, collagen fibers and tendon structures are easily recognizable. Furthermore, X-ray spectrometer reveals in our tissue remains, the presence of compounds related to soil contamination. This investigation identifies a plethora of organic and inorganic substances where the mummies were found, providing crucial information about the mummification environment. These morphological and analytical techniques make it possible to study mummified bodies and describe their anatomical details in real size, in a non-invasive and innovative way, demonstrating that these interdisciplinary approaches could have great potential for improving knowledge in the study of ancient corpses.

Analysis of spatiotemporal distribution, variability, and trends of rainfall in Wollo area, Northeastern Ethiopia.

Ethiopia's agriculture is mostly dependent on rain, though the rainfall distribution and amount are varied in spatiotemporal context. The study was conducted to analyze the distribution, trends, and variability of monthly, seasonal, and annual rainfall data over the Wollo area from 1981 to 2022. To accomplish this, the study utilized the Climate Hazards Group Infrared Precipitation with Stations version two (CHIRPS-v2) data. Standard Rainfall Anomaly Index (SRA) and Coefficient of Variation (CV) were employed to examine rainfall variability and develop drought indices over southern Ethiopia. The Modified Mann Kendall (MMK) test, Sen's slope estimator and the innovative trend analysis (ITA) were employed to detect temporal changes in rainfall trends over the study period. The study found that the area experienced considerable rainfall variability and change, resulting in extended drought and flood events within the study period. Results from SRA and CV revealed interannual and seasonal rainfall variability, with the proportions of years below and above the long-term mean being estimated at 56% and 44%, respectively. The MMK test showed that the annual rainfall during the Kiremt (summer-main rainy season) had an increasing trend. On the other hand, rainfall for the Belg (short rain season for the study area) season and the Bega (winter) season showed a significantly decreasing trend (p < 0.05). Results from the innovative trend analysis (ITA) also revealed that the annual and seasonal rainfall trends exhibited different trends in varied magnitude for different stations. On a spatial basis, the eastern and northeastern regions of the study area showed trends of increasing rainfall during the Kiremt (JJA). Decision-makers and development planners need to design strategies to mitigate the risks posed by changes in rainfall variability and distribution and enhance community adaptation and mitigation capacities in Wollo, Ethiopia.

Shade watch: Mapping citywide shade dynamics through ray tracing and LiDAR data in Hong Kong's complex 3-D built environment

The importance of urban shade in city planning has been underscored by its various associations with thermal comfort, living environment quality, human health and well-being. However, existing methods for urban shade mapping are often limited to street-level scopes or singular sampling observational times, leaving citywide shade mapping, its spatiotemporal patterns, and controlling factors largely unexplored. To fill these knowledge gaps, this study proposed a novel protocol that enables seamless mapping of spatiotemporal urban shade patterns over the entire city by fusing look-up-table (LUT)-based ray-tracing approach with Google Earth Engine cloud computing technology, as well as high-resolution digital surface model (DSM) derived from LiDAR data. This protocol was applied to Hong Kong, a city characterized by a complex 3-D built environment, and then delved into the spatiotemporal patterns of urban shade and the associated controlling factors. Validation results indicate that the proposed method can accurately quantify urban shade compared to ground-based photo references and high-resolution satellite imagery. The analysis uncovers that: (1) Shade exhibits spatial heterogeneity across different 3-D built environments while maintaining a consistent "U-shape" diurnal shade fraction during the day; (2) Solar geometry, regardless of solar zenith and azimuth angle, is positively associated with aggregated metrics of shade landscape and negatively associated with fragmented metrics. Moreover, solar zenith angles exert stronger controls over the citywide shade landscape patterns; and (3) Persistent shade significantly reduces the sunlight hours (i.e., the accumulated sunshine hours per day) across administrative districts, local climate zones (LCZs), and seasons. Due to shade effects originating from 3-D urban structures, Hong Kong typically experiences an average of 4-8 sunlight hours per day within a one-year cycle, which is substantially lower than the 11-14 hours of natural daylength. This study offers a practical protocol for large-scale urban shade mapping, enhancing our understanding of urban shade, its spatiotemporal dynamics, and benefits in a broader spatiotemporal context. The associated datasets and findings from this study can inform urban planners and policymakers in developing effective strategies to create healthier and sustainable urban environments in Hong Kong.

Mapping indefinite causal order processes to composable quantum protocols in a spacetime

Abstract Formalisms for higher order quantum processes provide a theoretical formalisation of quantum processes where the order of agents' operations need not be definite and acyclic, but may be subject to quantum superpositions. This has led to the concept of indefinite causal structures (ICS) which have garnered much interest. However, the interface between these information-theoretic approaches and spatiotemporal notions of causality is less understood, and questions relating to the physical realisability of ICS in a spatiotemporal context persist despite progress in their information-theoretic characterisation. Further, previous work suggests that composition of processes is not so straightforward in ICS frameworks, which raises the question of how this connects with the observed composability of physical experiments in spacetime. To address these points, we compare the formalism of quantum circuits with quantum control of causal order (QC-QC), which models an interesting class of ICS processes, with that of causal boxes, which models composable quantum information protocols in spacetime. We incorporate the set-up assumptions of the QC-QC framework into the spatiotemporal perspective and show that every QC-QC can be mapped to a causal box that satisfies these set up assumptions and acts on a Fock space while reproducing the QC-QC's behaviour in a relevant subspace defined by the assumptions. Using a recently introduced concept of fine-graining, we show that the causal box corresponds to a fine-graining of the QC-QC, which unravels the original ICS of the QC-QC into a set of quantum operations with a well-defined and acyclic causal order, compatible with the spacetime structure. Our results also clarify how the composability of physical experiments is recovered, while highlighting the essential role of relativistic causality and the Fock space structure.

Bibliometric study of approaches to studying collective intelligence for sustainable territorial innovation

Collective intelligence and collaborative innovation are two closely related but distinct concepts. By combining the two, actors in a supportive entrepreneurial ecosystem can generate knowledge, ideas and innovative solutions based on active participation and synergy to solve complex problems. The aim of this article is to provide an in-depth and detailed overview of collective intelligence and collaborative innovation in previous literature and how they are mobilized and combined to address the challenges of sustainable development. Moreover, this study aims to explore the methods and approaches adopted in research carried out in this field. A scoping review following a systematic literature approach was carried out using the PRISMA-ScR method. 78 scientific articles were selected between 2013 and 2023 from two online databases. To achieve our objectives, our research questions were subdivided into 4 tasks and missions. By following our research process and strategy, and after conducting a review of the different phases of our research, the results enabled us to inform and inform about the evolution, origin and development of our subject in a spatiotemporal context. In addition, the analysis revealed a diversity of research approaches and methods totalling 17. We should also mention that artificial intelligence and technology were clearly the themes most frequently dominated in the 10 most relevant articles. The current state of the art shows that no systematic literature review on collective intelligence and collaborative innovation in territorial development has proposed an exhaustive list of the specific methods and approaches addressed in the course of this subject.

Spatiotemporal context feedback bidirectional attention network for breast cancer segmentation based on DCE-MRI

Spatiotemporal context feedback bidirectional attention network for breast cancer segmentation based on DCE-MRI

Modeling Disease Dynamics From Spatially Explicit Capture‐Recapture Data

ABSTRACTOne of the main aims of wildlife disease ecology is to identify how disease dynamics vary in space and time and as a function of population density. However, monitoring spatiotemporal and density‐dependent disease dynamics in the wild is challenging because the observation process is error‐prone, which means that individuals, their disease status, and their spatial locations are unobservable, or only imperfectly observed. In this paper, we develop a novel spatially‐explicit capture‐recapture (SCR) model motivated by an SCR data set on European badgers (Meles meles), naturally infected with bovine tuberculosis (Mycobacterium bovis, TB). Our model accounts for the observation process of individuals as a function of their latent activity centers, and for their imperfectly observed disease status and its effect on demographic rates and behavior. This framework has the advantage of simultaneously modeling population demographics and disease dynamics within a spatial context. It can therefore generate estimates of critical parameters such as population size; local and global density by disease status and hence spatially‐explicit disease prevalence; disease transmission probabilities as functions of local or global population density; and demographic rates as functions of disease status. Our findings suggest that infected badgers have lower survival probability but larger home range areas than uninfected badgers, and that the data do not provide strong evidence that density has a non‐zero effect on disease transmission. We also present a simulation study, considering different scenarios of disease transmission within the population, and our findings highlight the importance of accounting for spatial variation in disease transmission and individual disease status when these affect demographic rates. Collectively these results show our new model enables a better understanding of how wildlife disease dynamics are linked to population demographics within a spatiotemporal context.

„Kõik on muidugi selle poolt.” Koosoleku motiiv sotsialistliku korra retrospektiivsel kujutamisel

”Everybody’s in favour, of course.” Meetings as a motif in retrospective depictions of the socialist order Keywords: comparative literature, prose, motif, meetings, totalitarianism, Soviet regime This article examines the portrayal of meetings in four novels: Enn Vetemaa’s “The Musician” (Pillimees), Herta Müller’s “The Land of Green Plums” (Herztier), Elena Chizhova’s “The Time of Women” (Vremia zhenshchin), and Eugen Ruge’s “In Times of Fading Light” (In Zeiten des abnehmenden Lichts). These works all explore the experience of living under a totalitarian regime, where the system had either fundamentally changed or collapsed by the time of the novels’ publication. Meetings play a significant role in conveying this experience. In The Musician, the meeting unfolds as a trap, destroying the accused while leaving others trapped in endless contemplation of the events. “The Land of Green Plums” employs meetings to articulate the trauma of life under totalitarianism. Chizhova’s novel uses meetings as manifestations of the suffocating effects of the totalitarian regime and the way desperate people reinforce the system. In Ruge’s work, the meeting motif serves primarily to reveal the spatiotemporal context and the prevailing worldview of the era. In these narratives, meetings often appear as recollections that disrupt the contemporary plotline or paralyze the character’s perception. Instead of advancing dialogue, the texts focus on the formality of the meetings, emphasizing their ritualistic nature and the characters’ lack of agency. During and after the meetings, protagonists feel alienated from the events and even from themselves. In most cases, meetings leave behind traumas that time fails to heal. Across these novels, the meeting emerges as a powerful symbol of an oppressive, at times even life-threatening, system. This motif serves to reflect on the enduring impact of a bygone regime – or, in some cases, its remnants that continue to persist.

Lexical, sentiment and correlation analysis of sacred writings. A tale of cultural influxes and different ways to interpret reality

Natural Language Processing (NLP) has transformative potential for decoding sacred writings, bridging linguistic and temporal relationships between cultures. These texts, laden with cultural and religious significance. The study analyzes texts from 14 belief systems using lexical, sentiment and correlation assessment. The analysis revealed that sacred texts are complex due to archaic language, but tend to show similar themes, historical contexts, and emotional tones. The study highlights common terms found throughout the texts, but also revealing specific terms that are influenced by the cultural context of the belief system. It also explores the various depictions of fauna and flora, uncovering the impact of spatio-temporal contexts on the composition of sacred writings. Sentiment analysis reveals polarity variations between cultures and suggest conflicts of style during translation of the texts. Comparative analysis uncovers text clusters with high similarity and cultural influences between religions that coexisted. This work showcases NLP’s potential to enhance comprehension of sacred texts and promote cross-cultural understanding.

Climatic and edaphic niche shifts during plant radiation in the Mediterranean biodiversity hotspot.

Ecological speciation is frequently invoked as a driver of plant radiation, but the behaviour of environmental niches during radiation is contentious, with patterns ranging from niche conservatism to niche divergence. Here we investigated climatic and edaphic niche shifts during radiation in a western Mediterranean lineage of the genus Linaria (Plantaginaceae). Detailed distributional, phylogenomic and environmental data were integrated to analyse changes in climatic and edaphic niches in a spatiotemporal context, including calculation of niche overlap, niche equivalency and similarity tests, maximum entropy modelling, phylogenetic comparative methods and biogeographic analyses. Active divergence of climatic and edaphic niches within a limited subset of available conditions was detected among the eight study species, and particularly between sister species. Speciation and niche divergence is estimated to have happened in the southern Iberian Peninsula under Mediterranean conditions, followed by waxing and waning of distribution ranges resulting from the Quaternary climatic cycles. Results support the idea that the prevalence of niche conservatism or niche divergence patterns is a matter of phylogenetic scale. Habitat isolation pertaining to both climatic and soil conditions appears to have played a role in plant speciation in the western Mediterranean biodiversity hotspot, most likely in combination with pollinator isolation and some degree of geographic isolation. These findings are in agreement with an adaptive radiation scenario incorporating certain non-adaptive features.

Developmental Modeling of Childhood Cancers.

Growing evidence indicates that childhood cancer is a developmental disease and the oncogenic impact of mutations depends on spatiotemporal developmental contexts. This dependency leads to distinct molecular, genetic, and clinical characteristics across various cancer (sub)types. However, the underlying molecular mechanisms of tumorigenesis are not fully understood, and the development of precision medicine for childhood cancers is still an ongoing effort, partially due to their relative rarity. Therefore, it is crucial to develop and use "developmental models" that replicate both mutations and specific developmental contexts that determine their impact. In this review, we summarize recent advances in the growing field of developmental modeling of childhood cancers, which enhance our understanding of the pathogenic mechanisms and pave the way for the development of new therapeutic approaches.

UAV Visual Object Tracking Based on Spatio-Temporal Context

To balance the real-time and robustness of UAV visual tracking on a single CPU, this paper proposes an object tracker based on spatio-temporal context (STCT). STCT integrates the correlation filter and Siamese network into a unified framework and introduces the target’s motion model, enabling the tracker to adapt to target scale variations and effectively address challenges posed by rapid target motion, etc. Furthermore, a spatio-temporal regularization term based on the dynamic attention mechanism is proposed, and it is introduced into the correlation filter to suppress the aberrance of the response map. The filter solution is provided through the alternating direction method of multipliers (ADMM). In addition, to ensure efficiency, this paper proposes the average maximum response value-related energy (AMRE) for adaptive tracking state evaluation, which considers the time context of the tracking process in STCT. Experimental results show that the proposed STCT tracker can achieve a favorable balance between tracking robustness and real-time performance for UAV object tracking while running at ∼38 frames/s on a low-cost CPU.

When multi-view meets multi-level: A novel spatio-temporal transformer for traffic prediction

Traffic prediction is a vital aspect of Intelligent Transportation Systems with widespread applications. The main challenge is accurately modeling the complex spatial and temporal relationships in traffic data. Spatial–temporal Graph Neural Networks (GNNs) have emerged as one of the most promising methods to solve this problem. However, several key issues have not been well addressed in existing studies. Firstly, traffic patterns have significant periodic trends, existing methods often overlook the importance of periodicity. Secondly, most methods model spatial dependencies in a static manner, which limits the ability to learn dynamic traffic patterns. Lastly, achieving satisfactory results for both long-term and short-term forecasting remains a challenge. To tackle the above problems, this paper proposes a Multi-level Multi-view Augmented Spatio-temporal Transformer (LVSTformer) for traffic prediction, which captures spatial dependencies from three different levels: local geographic, global semantic, and pivotal nodes, along with long- and short-term temporal dependencies. Specifically, we design three spatial augmented views to delve into the spatial information from above three levels. By combining three spatial augmented views with three parallel spatial self-attention mechanisms, the model can comprehensively captures spatial dependencies at different levels. We design a gated temporal self-attention mechanism to dynamically capture long- and short-term temporal dependencies. Furthermore, a spatio-temporal context broadcasting module is introduced between two spatio-temporal layers to ensure a well-distributed allocation of attention scores, alleviating overfitting and information loss, and enhancing the generalization ability and robustness of the model. A comprehensive set of experiments are conducted on six well-known traffic benchmarks, the experimental results demonstrate that LVSTformer achieves state-of-the-art performance compared to competing baselines, with the maximum improvement reaching up to 4.32%.

COVID-19 latent age-specific mortality in US states: a county-level spatio-temporal analysis with counterfactuals.

During the COVID-19 pandemic, which spanned much of 2020-2023 and beyond, daily case and death counts were recorded globally. In this study, we examined available mortality counts and associated case counts, with a focus on the estimation missing information related to age distributions. In this paper, we explored a model-based paradigm for generating age distributions of mortality counts in a spatio-temporal context. We pursued this aim by employing Bayesian spatio-temporal lagged dependence models for weekly mortality at the county level. We compared three US states at the county level: South Carolina (SC), Ohio, and New Jersey (NJ). Models were developed for mortality counts using Bayesian spatio-temporal constructs, incorporating both dependence on current and cumulative case counts and lagged dependence on previous deaths. Age dependence was predicted based on total deaths in proportion to population estimates. This latent age field was generated as counterfactuals and then compared to observed deaths within age groups. The optimal retrospective space-time models for weekly mortality counts were those with lagged dependence and a function of caseload. Added random effects were found to vary across states: Ohio favored a spatially correlated model, while SC and NJ favored a simpler formulation. The generation of age-specific latent fields was performed for SC only and compared to a 15-month, 13-county data set of observed >65 age population. It is possible to model spatio-temporal variations in mortality at the county level with lagged dependencies, spatial effects, and case dependencies. In addition, it is also possible to generate latent age-specific fields based on estimates of death risk (using population proportions or more sophisticated modeling approaches). More detailed data will be needed to make more calibrated comparisons for future epidemic monitoring. The proposed discrepancy tool could serve as a useful resource for public health planners in tailoring interventions during epidemic situations.

Spatio-temporal attention based collaborative local–global learning for traffic flow prediction

Traffic flow prediction is crucial for intelligent transportation systems (ITS), providing valuable insights for traffic control, route planning, and operation management. Existing work often separately models the spatial and temporal dependencies and primarily relies on predefined graphs to represent spatio-temporal dependencies, neglecting the traffic dynamics caused by unexpected events and the global relationships among road segments. Unlike previous models that primarily focus on local feature extraction, we propose a novel collaborative local–global learning model (LOGO) that employs spatio-temporal attention (STA) and graph convolutional networks (GCN). Specifically, LOGO simultaneously extracts hidden traffic features from both local and global perspectives. In local feature extraction, a novel STA is devised to directly attend to spatio-temporal coupling interdependencies instead of separately modeling temporal and spatial dependencies, and to capture in-depth spatio-temporal traffic context with an adaptive graph focusing on the dynamics in traffic flow. In global feature extraction, a global correlation matrix is constructed and GCNs are utilized to propagate messages on the obtained matrix to achieve interactions between both adjacent and similar road segments. Finally, the obtained local and global features are concatenated and fed into a gated aggregation to forecast future traffic flow. Extensive experiments on four real-world traffic datasets sourced from the Caltrans Performance Measurement System (PEMS03, PEMS04, PEMS07, and PEMS08) demonstrate the effectiveness of our proposed model. LOGO achieves the best performance over 18 state-of-the-art baselines and the best prediction performance with the highest improvement of 6.06% on the PEMS07 dataset. Additionally, two real-world case studies further substantiate the robustness and interpretability of LOGO.

ANGI-05. UNSUPERVISED DECONVOLUTION OF SPATIOTEMPORAL HETEROGENEITY REVEALS HIGH-RESOLUTION INVASION STATES IN GLIOBLASTOMA

Abstract Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. Towards this goal, we applied a novel computational workflow to a spatiotemporally profiled GBM xenograft cohort, leveraging the ability to distinguish human tumor from mouse TME to overcome previous limitations in analysis of diffuse invasion. Our analytic approach, based on unsupervised deconvolution, performs reference-free discovery of cell types and cell activities within the complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor cell programs set within the spatiotemporal context of 90 mouse brain and TME cell types, cell activities, and anatomic structures. Distinct tumor programs related to invasion were aligned with routes of perivascular, white matter, and parenchymal invasion. Furthermore, sub-modules of genes serving as program network hubs were highly prognostic in GBM patients. The compendium of programs presented here provides a basis for rational targeting of tumor and/or TME components. We anticipate that our approach will facilitate an ecosystem-level understanding of immediate and long-term consequences of such perturbations, including identification of compensatory programs that will inform improved combinatorial therapies.

The socioecology of sexual minority stigma: Advancing theory on stigma-based mechanisms underlying sexual orientation-based disparities in health

Research in public health and psychology has identified sexual minority stigma-related risk factors that contribute to sexual orientation-based health disparities across settings and societies worldwide. Existing scholarship, however, has predominantly focused on these factors as independent, stand-alone risk factors, neglecting their interconnected nature across different levels. This article theoretically explores how sexual minority stigma may function as a multilevel socioecological system, by building on prevailing theories and emphasizing the interplay between structural, interpersonal, and intrapersonal stigma-related factors. Drawing on the minority stress and socioecological theories, three central tenets are proposed, namely 1) chronosystem with immediate, accumulating, or lasting effects across spatiotemporal contexts, 2) nested multilevel system with cross-level effects, and 3) mechanistic pathways linking stigma exposure to health. By providing a nuanced and comprehensive understanding of how sexual minority stigma may operate as a multilevel socioecological system, this article reflects on the novel implications of this interpretation for future research and aims to guide future conceptualizations and studies, acknowledging the complexity of sexual minority stigma exposure across historical contexts, societies, and the individual life course in shaping physical and mental health of sexual minorities. Limitations of current research and recommendations for future research are being discussed.