Local Neighborhood Research Articles

Semantic-spatial feature-fused cortical surface parcellation: a scale-unified spatial learning network with boundary contrastive loss.

The cortical surface parcellation provides prior guidance for studying mental disorders and human cognition. Graph neural networks (GNNs) have gained popularity in this task to preserve its spatial structure. However, previous GNNs struggled to effectively exploit the information contained in the complex spatial structure of the cortical surface and generally encountered an uneven node distribution issue. Meanwhile, labeling boundary nodes was also identified as a widespread problem in this task. Accordingly, this paper develops a scale-unified spatial learning network with a boundary contrastive loss (SSLNet) for cortical surface parcellation. Its core is the scale-unified spatial learning module. It devises neighbor feature extraction and aggregation strategies by fully integrating spatial coordinates and semantic structure to learn effective spatial features of local neighborhoods. More importantly, spatial scale unification is incorporated into this module to mitigate the negative effect on spatial learning caused by node distribution differences among local areas. Additionally, a universal boundary contrastive loss is constructed, enhancing the feature discriminability of boundary nodes by constraining them to be close to the same class nodes and apart from different class nodes in the feature space. It considerably improves boundary performance without increasing parameters or changing the network structure. Experiments regarding public Mindboggle demonstrate that the dice score and accuracy of SSLNet achieve and , respectively, surpassing existing methods.

An integrated optimization strategy for mission planning and control resource allocation for long-endurance unmanned aerial vehicle

PurposeWith burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.Design/methodology/approachWe consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.FindingsNumerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.Originality/valueDrawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

Local geometry of NAE-SAT solutions in the condensation regime

AbstractThe local behavior of typical solutions of random constraint satisfaction problems (csp) describes many important phenomena including clustering thresholds, decay of correlations, and the behavior of message passing algorithms. When the constraint density is low, studying the planted model is a powerful technique for determining this local behavior which in many examples has a simple Markovian structure. The work of Coja-Oghlan, Kapetanopoulos, Müller (Comb Prob Comput 29:346-422, 2020) showed that for a wide class of models, this description applies up to the so-called condensation threshold. Understanding the local behavior after the condensation threshold is more complex due to long-range correlations. In this work, we revisit the random regular nae-sat model in the condensation regime and determine the local weak limit which describes a random solution around a typical variable. This limit exhibits a complicated non-Markovian structure arising from the space of solutions being dominated by a small number of large clusters. This is the first description of the local weak limit in the condensation regime for any sparse random csps in the one-step replica symmetry breaking (1rsb) class. Our result is non-asymptotic and characterizes the tight fluctuation $$O(n^{-1/2})$$ O ( n - 1 / 2 ) around the limit. Our proof is based on coupling the local neighborhoods of an infinite spin system, which encodes the structure of the clusters, to a broadcast model on trees whose channel is given by the 1rsb belief-propagation fixed point. We believe that our proof technique has broad applicability to random csps in the 1rsb class.

A quantified local-to-global principle for Morse quasigeodesics

Kapovich, Leeb and Porti (2014) gave several new characterizations of Anosov representations \Gamma \to G , including one where geodesics in the word hyperbolic group \Gamma map to “Morse quasigeodesics” in the associated symmetric space G/K . In analogy with the negative curvature setting, they prove a local-to-global principle for Morse quasigeodesics and describe an algorithm which can verify the Anosov property of a given representation in finite time. However, some parts of their proof involve non-constructive compactness and limiting arguments, so their theorem does not explicitly quantify the size of the local neighborhoods one needs to examine to guarantee global Morse behavior. In this paper, we supplement their work with estimates in the symmetric space to obtain the first explicit criteria for their local-to-global principle. This makes their algorithm for verifying the Anosov property effective. As an application, we demonstrate how to compute explicit perturbation neighborhoods of Anosov representations with two examples.

Ecological factors associated with fox feces density in an Echinococcus multilocularis endemic zone in Japan

IntroductionHuman alveolar echinococcosis caused by Echinococcus multilocularis is an important zoonotic disease in the northern hemisphere. The life cycle of E. multilocularis is maintained primarily in wild animals and requires an intermediate host (mainly small mammals). Human can become an intermediate host through accidental ingestion of E. multilocularis eggs. Hokkaido Prefecture is the only area of Japan in which human alveolar echinococcosis is endemic. The purposes of this study were to elucidate the land use ecological factors associated with the density of red fox feces along paved roads and the relationship between the distributions of red fox (Vulpes vulpes) populations and fox feces, which determine the level of hazard from eggs.MethodsA series of surveys was conducted in the central part of the Nemuro Peninsula of Hokkaido, excluding urban areas, over a total of 4 years in May–June in 2014 and 2016–2018 when red foxes remain with their cubs around the dens. Transects of 500 m were set up on paved roads, and feces within the transects were counted. Univariable and multivariable analyses were performed to examine ecological factors including the principal components (PCs) of five land use–type occupancy proportions within 500 m and 1 km, respectively, as explanatory fixed-effect variables. The number of feces in each transect was examined as the response variable using integrated nested Laplace approximation with negative binomial errors with a spatio-temporal autocorrelations structure to separate the effects of similarities of neighboring locations and annual variation. The multivariable models with the lowest widely applicable information criterion values were selected.ResultsThe feces density was explained by the PC of the 500- m buffer (−0.27, 2.5th and 97.5th percentiles: −0.44, −0.10) characterized by mixed forests (−0.82) and scarcity of residential areas (0.29) and the proximity to the nearest livestock farm house (−0.35, 2.5th and 97.5th percentiles: −0.53, −0.17). This suggested that foxes defecate in the areas where prey is abundant, avoiding humans.DiscussionPolicy discussions regarding bait distribution design targeting these conditions should be initiated.

Wheeler-DeWitt canonical quantum gravity in hydrogenoid atoms according to de Broglie-Bohm and the geodesic hypothesis. Einstein’s Quantum Field Equation

We explore the geodesic hypothesis of orbital trajectories of the electrons in hydrogenoid atoms, in the frame of de Broglie-Bohm quantum theory. It is intended that the space-time can be curved, at very short distances, by the effect of the joint action of the energy content of the atomic system and the contribution of the electric and quantum potentials. The geodesic hypothesis would explain the non-lose of energy in the electron orbital trajectories. So we explore a conception where particles and waves interact in a closed system: the waves guide the particles and the particles generate the spacetime perturbation that acts as a wave, beyond the pilot-wave theory. We establish the equivalence, in a local neighborhood, between the electron trajectory of an hydrogenoid atom in the Minkowskian space where the de Broglie-Bohm can be cast with its movement in a Lorentzian manifold, according to the concept of metric tangent. Through the geodesic condition and the invariance of the elemental length, we establish a relationship between some components of the metrics. But as the particles in microphysics do not follow the Einstein’s field equation, we consider the 3+1 decomposition according to ADM and the quantization in the Wheeler De Witt theory and with a so-called quantum Einstein field equation, with a decomposition of spacetime into three-dimensional sheets of a spatial character. Then we derive from the moment-energy tensor a further equation between the components of the metrics. It opens the avenue to characterize the metric by an exact solution of the Einstein’s field equations.

Local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on local fuzzy self information

The advent of the era of big data is accompanied by the generation of large-scale data of various types. Extracting the potential value and rules from such data has always been a challenge. Due to various external and internal factors, it is commonplace for large-scale data to exhibit the phenomenon of missing limited labels. In addressing a large-scale mixed information system with limited label missing (LSMDISLML), local neighborhood rough set model (LNRS-model) is typically employed. However, the identical neighborhood radius is often used by such model when confronted with numerical attributes, which could potentially attenuate the classification capability of the data. Local fuzzy rough set model (LFRS-model) can overcomes this point. This paper studies local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on LFRS-model via local fuzzy self information and overlap degree function. First, leveraging the statistical distribution of data as a foundation, fuzzy relations on the entire sample set are established, which has the advantage of being able to use different fuzzy similarity radii to calculate similarity, thereby adapting to different data distributions. Subsequently, the samples with missing labels are discarded as they constitute a small proportion of the entire sample set and have little impact on overall performance of dataset. The limited computing resources and storage space are focused on the sample set with complete labels (denoted as target set). Thereafter, based on the target set, local fuzzy λ-upper and lower approximations are defined, and LFRS-model is constructed. This model not only reduces processing time and sources of error in large-scale data but also improves data quality and enhances the reliability of the experimental results. Then, local fuzzy λ-self information is introduced and used to design a local fuzzy rough attribute reduction algorithm in a LSMDISLML. Furthermore, a overlap degree function is introduced to evaluate and reorder the attributes based on their importance, prioritizing the elimination of redundant attributes with high overlap and low importance from the preordered attribute set. This strategy effectively improves the efficiency of obtaining the optimal subset. Finally, a series of experiments are carried out. The experiment results demonstrate that the designed algorithm exhibits excellent performance in classification tasks and outlier detection tasks, surpassing existing four algorithms.

Associative graph convolution network for point cloud analysis

Since point cloud is the raw output of most 3D sensors, its effective analysis is in huge demand in the field of autonomous driving and robotic manipulation. However, directly processing point clouds is challenging because point clouds are a kind of disordered and unstructured geometric data. Recently, numerous graph convolution neural networks are proposed for introducing graph structure to point clouds yet far from perfect. Specially, DGCNN tries to learn local geometric of points in semantic space and recomputes the graph using nearest neighbors in the feature space in each layer. However, it discards all the information of the previous graph after each graph update, which neglects the relations between each dynamic update. To this end, we propose an associative graph convolution neural network (AGCN) which mainly consists of associative graph convolution (AGConv) and two kinds of residual connections. AGConv additionally considers the information from the previous graph when computing the edge function on current local neighborhoods in each layer, and it can precisely and continuously capture the local geometric features on point clouds. Residual connections further explore the semantic relations between layers for effective learning on point clouds. Extensive experiments on several benchmark datasets show that our network achieves competitive classification and segmentation results.

Information Dependency in Mitigating Disruption Cascades

Problem definition: Shocks that trigger supply chain disruptions inflict initial losses by damaging firms’ assets. The disruption can then cascade when an affected firm fails to deliver to its buyer, thereby interrupting the buyer’s operations, and continue thus across multiple levels (tiers) in the supply chain. To protect against such disruption cascades, firms can make ex ante investments in risk mitigation. These investments depend heavily on the operational characteristics of network participants and their interconnections. Gathering operational information can be challenging. Our aim is to shed light on the forces that govern information requirements for risk mitigation. Methodology/results: We introduce a game-theoretic model to characterize the equilibrium mitigation by firms in a decentralized arborescent network facing severe disruptions. We find that when the trigger shocks are nonconcurrent events, the equilibrium mitigation by a firm displays a limited vertical dependence on the operational attributes of suppliers that are farther away in tier (network) distance. Specifically, we show that information about a firm’s extended local neighborhood—up to its tier 2 suppliers—suffices to characterize its equilibrium mitigation. Allowing for concurrent shocks to simultaneously strike multiple firms increases the information requirement at partner firms that typically lie within two tiers downstream from the firms experiencing concurrent shocks. Managerial implications: Full supply chain visibility is costly. The literature offers little guidance on how to prioritize efforts to enhance visibility into the attributes of supply chain partners. Rather than a blanket call for greater visibility, our results proffer nuanced managerial prescriptions for the extent to which risk mitigation requires such visibility. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0408 .

Disruption of a massive molecular cloud by a supernova in the Galactic Centre

The Milky Way’s Central Molecular Zone (CMZ) differs dramatically from our local solar neighbourhood, both in the extreme interstellar medium conditions it exhibits (e.g. high gas, stellar, and feedback density) and in the strong dynamics at play (e.g. due to shear and gas influx along the bar). Consequently, it is likely that there are large-scale physical structures within the CMZ that cannot form elsewhere in the Milky Way. In this paper, we present new results from the Atacama Large Millimeter/submillimeter Array (ALMA) large programme ACES (ALMA CMZ Exploration Survey) and conduct a multi-wavelength and kinematic analysis to determine the origin of the M0.8–0.2 ring, a molecular cloud with a distinct ring-like morphology. We estimate the projected inner and outer radii of the M0.8–0.2 ring to be 79″ and 154″, respectively (3.1 pc and 6.1 pc at an assumed Galactic Centre distance of 8.2 kpc) and calculate a mean gas density >104 cm−3, a mass of ~106 M⊙, and an expansion speed of ~20 km s−1, resulting in a high estimated kinetic energy (>1051 erg) and momentum (>107 M⊙ km s−1). We discuss several possible causes for the existence and expansion of the structure, including stellar feedback and large-scale dynamics. We propose that the most likely cause of the M0.8–0.2 ring is a single high-energy hypernova explosion. To viably explain the observed morphology and kinematics, such an explosion would need to have taken place inside a dense, very massive molecular cloud, the remnants of which we now see as the M0.8–0.2 ring. In this case, the structure provides an extreme example of how supernovae can affect molecular clouds.

Facial Expression Recognition-You Only Look Once-Neighborhood Coordinate Attention Mamba: Facial Expression Detection and Classification Based on Neighbor and Coordinates Attention Mechanism.

In studying the joint object detection and classification problem for facial expression recognition (FER) deploying the YOLOX framework, we introduce a novel feature extractor, called neighborhood coordinate attention Mamba (NCAMamba) to substitute for the original feature extractor in the Feature Pyramid Network (FPN). NCAMamba combines the background information reduction capabilities of Mamba, the local neighborhood relationship understanding of neighborhood attention, and the directional relationship understanding of coordinate attention. The resulting FER-YOLO-NCAMamba model, when applied to two unaligned FER benchmark datasets, RAF-DB and SFEW, obtains significantly improved mean average precision (mAP) scores when compared with those obtained by other state-of-the-art methods. Moreover, in ablation studies, it is found that the NCA module is relatively more important than the Visual State Space (VSS), a version of using Mamba for image processing, and in visualization studies using the grad-CAM method, it reveals that regions around the nose tip are critical to recognizing the expression; if it is too large, it may lead to erroneous prediction, while a small focused region would lead to correct recognition; this may explain why FER of unaligned faces is such a challenging problem.

Recurrence prediction of gastric cancer based on multi-resolution feature fusion and context information

Pathological images of gastric cancer serve as the gold standard for diagnosing this malignancy. However, the recurrence prediction task often encounters challenges such as insignificant morphological features of the lesions, insufficient fusion of multi-resolution features, and inability to leverage contextual information effectively. To address these issues, a three-stage recurrence prediction method based on pathological images of gastric cancer is proposed. In the first stage, the self-supervised learning framework SimCLR was adopted to train low-resolution patch images, aiming to diminish the interdependence among diverse tissue images and yield decoupled enhanced features. In the second stage, the obtained low-resolution enhanced features were fused with the corresponding high-resolution unenhanced features to achieve feature complementation across multiple resolutions. In the third stage, to address the position encoding difficulty caused by the large difference in the number of patch images, we performed position encoding based on multi-scale local neighborhoods and employed self-attention mechanism to obtain features with contextual information. The resulting contextual features were further combined with the local features extracted by the convolutional neural network. The evaluation results on clinically collected data showed that, compared with the best performance of traditional methods, the proposed network provided the best accuracy and area under curve (AUC), which were improved by 7.63% and 4.51%, respectively. These results have effectively validated the usefulness of this method in predicting gastric cancer recurrence.

Quantifying mobile robot localization safety for an EKF-based SLAM estimator: An integrity monitoring approach

The safety of SLAM-based estimation for mobile robots is a challenging research problem, particularly for life- or mission-critical exploratory applications. To address this problem, this work proposes a method to utilize integrity risk, a widely used performance metric in aviation, to quantify SLAM-based mobile robot’s localization safety. More importantly, the approach accounts for sensor measurement faults, unknown deterministic errors that cannot be modeled via Gaussian white noise. The method is tailored for an EKF-based SLAM estimator, a chi-squared failure detector, and a local nearest neighbors data association criterion. The results show that data association errors can cause significant positioning performance degradation that can only be predicted using the proposed integrity risk metric. Furthermore, the study demonstrates that as the map’s landmark density increases, mobile robot localization safety improves, except when the landmark map’s density is too high to make the features indistinguishable, which leads to positioning safety degradation.

Does growing up in a physical activity-friendly neighborhood increase the likelihood of remaining active during adolescence and early adulthood?

BackgroundThe SOPHYA-cohort-study investigated whether the objectively characterized and perceived residential neighborhood of Swiss youth predict accelerometer-measured physical activity and activity in specific domains (participation in a sports club and cycling) five years later.MethodsAt baseline in 2014, 1230 children and adolescents aged 6 to 16 years participated and wore accelerometers for 7 days. Of these children, 447 participated again in the follow-up study in 2019 and provided longitudinal accelerometer measurements. Sociodemographic factors and perceptions of the local neighbourhood were assessed by questionnaire. Specific objective environmental data (e.g. built environment or social environment) was modelled to the children’s address at baseline. Multivariate linear and logistic regression models were applied to identify short- and long-term characteristics that are associated with accelerometer-based physical activity, cycling and participation in organised sport.ResultsIf the neighborhood-score as perceived by the parents in 2014 was in the middle or lowest tertile, children were significantly less active cross-sectionally in 2014 (-41.1 (-78.0;-4.2) and -52.4 (-88.6;-16.2) counts per minute, cpm), and five years later (-52.4 (-88.6;-16.2) and 48.1 (-86.6;-9.7) cpm). In addition, they were also less likely to accumulate active minutes above the median at both measuring points compared to peers of the same age and sex. Using objective environmental data modeled around the children’s residential address, similar associations were found: In the tertile with the lowest proportion of green space children achieved less cpm in 2014, while a high main street density and a low socioeconomic environment, respectively, hindered physical activity tracking above the median longitudinally. Also for cycling and participation in a sport club, the associations with the perceived and objective environment were more pronounced in the longitudinal analyses.ConclusionThe results suggest that growing up in a physical activity friendly neighborhood increases the likelihood of remaining active during adolescence and early adulthood. Interventions should be implemented to ensure that children growing up in an unfavorable neighborhood do not fall behind at an early stage.

Walk-centric deliberations

This article outlines methods of practice-oriented teaching at universities integrating democracy, sustainability, and learning in adult education.. The methods highlight the potential for democratic innovation in local communities in which diverse perspectives come together to address societal challenges. With a focus on sustainability and the connection among place, space, and pedagogy it introduces ‘Voice-Resonance Walks,’ a deliberative method piloted in Stuttgart, Germany, for social work education. It also highlights pilot deliberations at the district (Mini-Studentlab Deliberative Walks) and cityscape (Public Transport Walks) levels in Hamburg, Germany, involving interdisciplinary student groups in lifelong learning. These examples illustrate how higher education institutions are engaging with the public sphere, with a particular focus on vulnerable groups of citizens and on fostering civic learning. They aim to equip students with the knowledge and skills to become future experts, decision-makers, and active citizens, capable of understanding the challenges facing both their local neighborhoods and diverse urban spaces.

Enhancing Graph Neural Networks via Memorized Global Information

Graph neural networks (GNNs) have gained significant attention for their impressive results on different graph-based tasks. The essential mechanism of GNNs is the message-passing framework, whereby node representations are aggregated from local neighborhoods. Recently, Transformer-based GNNs have been introduced to learn the long-range dependencies, enhancing performance. However, their quadratic computational complexity, due to the attention computation, has constrained their applicability on large-scale graphs. To address this issue, we propose MGIGNN ( M emorized G lobal I nformation G raph N eural N etwork), an innovative approach that leverages memorized global information to enhance existing GNNs in both transductive and inductive scenarios. Specifically, MGIGNN captures long-range dependencies by identifying and incorporating global similar nodes, which are defined as nodes exhibiting similar features, structural patterns and label information within a graph. To alleviate the computational overhead associated with computing embeddings for all nodes, we introduce an external memory module to facilitate the retrieval of embeddings and optimize performance on large graphs. To enhance the memory-efficiency, MGIGNN selectively retrieves global similar nodes from a small set of candidate nodes. These candidate nodes are selected from the training nodes based on a sparse node selection distribution with a Dirichlet prior. This selecting approach not only reduces the memory size required but also ensures efficient utilization of computational resources. Through comprehensive experiments conducted on ten widely-used and real-world datasets, including seven homogeneous datasets and three heterogeneous datasets, we demonstrate that our MGIGNN can generally improve the performance of existing GNNs on node classification tasks under both inductive and transductive settings.

A Spatial Interpolation Method for Meteorological Data Based on a Hybrid Kriging and Machine Learning Approach

ABSTRACTConventional spatial interpolation methods for meteorological data are usually based on linear interpolation. However, with the improvements in the temporal and spatial resolution of observational data, local neighbouring stations are susceptible to the influence of underlying surface changes and high terrain gradients. Moreover, for interpolation at a single time point, the inability to extract continuous change information effectively from adjacent times limits the interpolation performance. In this paper, an improved hybrid deep learning‐kriging method is proposed that combines a graph neural networks (GNNs) prediction model with the kriging interpolation algorithm. The GNNs considers dynamic changes over time and combines spatial and temporal information to estimate (interpolate) meteorological data at target weather stations using reanalysis data as input. The experimental results show that the hybrid method exhibits good performance in interpolating station data in complex terrain areas and under uneven surface conditions. The interpolation effectiveness of this method is markedly improved compared to that of traditional kriging methods. Moreover, when applied to station‐to‐grid interpolation, the hybrid method still provides better interpolation results than those of kriging methods. Therefore, this research provides a new method and perspective for meteorological data interpolation.

All (economic) politics is local: Voting responses to localized price shocks during the great recession

AbstractThe connection between individual and macroeconomic conditions and voting behavior is well‐established. We contribute to the less resolved “spatial gap” in the literature that centers on how the localized economic conditions of where voters live influence their likelihood to vote. We test how space mediates the tension between voter mobilization and withdrawal in the face of economic shocks. We consider a scenario, the Great Recession, where economic shocks were quite localized and sudden, and compile an extensive dataset of all registered voters in the four‐county Tampa metropolitan area between 2006 and 2015. Using sales prices and property characteristics from the tax assessor rolls, we estimate a neighborhood‐level shock to housing values induced by the Great Recession. Results show that when we do not account for local neighborhood variation, the Great Recession is associated with a significant decrease in voter turnout. However, when we account for localized economic shocks, we find that residents in neighborhoods with negative price shocks were more likely to vote after the Recession, especially in non‐local elections. In addition, the propensity to vote increases with the size of the negative price shock. There is some evidence that variation at the neighborhood level matters more than voter‐level heterogeneity. The positive voting response is most profound in predominantly Black neighborhoods, and, to a lesser extent, in predominantly Hispanic and the lowest income neighborhoods. Increases in the propensity to vote are robust to models controlling for baseline economic vulnerabilities, such as localized unemployment, the weakness of the local housing market and exposure to sectors hit hardest by the Recession. The results indicate that dramatic and sudden changes in localized economic conditions can drive voting behavior, and in ways that are distinct from macroeconomic drivers. In addition, the housing asset channel appears to be a powerful one, which can induce significant voting responses at the national level apart from other localized economic drivers, especially among homeowners.

6676 Exposure to Neighborhood Violence Rewires Glucocorticoid Receptor Binding and Drives Proliferation and Invasiveness in Lung Tumor Samples

Abstract Disclosure: H. Heath: None. J. Yoo: None. S. Akter: None. A. Jain: None. V. Sharma: None. H. McGee: None. A. Soliman: None. A. Mohamed: None. A.K. Matthews: None. R. Winn: None. Z. Madak Erdogan: None. S. Kim: None. Background: Despite the lower rates and frequency of smoking, Black men experience a higher incidence of lung cancer compared to white men. This health disparity is even more prevalent in Black men from Chicago, suggesting local neighborhood factors impacting tumorigenesis. Preliminary data indicate that, compared to white men, Black men are more likely to reside in neighborhoods with high levels of violence and have elevated levels of hair cortisol. Objective: To understand the link between lung tumorigenesis and exposure to neighborhood violence, our objective is to investigate the chromatin recruitment of the receptor for cortisol, called the glucocorticoid receptor (GR), and to correlate this recruitment with target gene expression levels. Methods: Utilizing CUT & RUN, we identified the gene binding sites of GR using 15 lung tumor and corresponding healthy tissue samples from lung cancer patients living in Chicago. GR recruitment to chromatin was correlated with the neighborhood homicide rate obtained using patients’ zip codes. Spatial transcriptome profiles for these 15 lung tumor samples were obtained from the Space Ranger pipeline and gene expression hot spots of GR target genes were visualized using Loupe Browser (10x Genomics). Results: GR recruitment to regulatory regions of chromatin increases with greater zip code level violence rates. In patients from high violence neighborhoods, tumor samples had higher magnitude of binding compared to normal neighboring tissue. In tumor samples from patients living in high violence compared to low violence neighborhoods, GR binding is seen in genes that are associated with increased tumor aggressiveness. Hot-spot analysis of these genes found strong co-expression of genes associated with proliferation and invasion in samples from high violence but not low violence neighborhoods. Conclusion: Exposure to neighborhood violence may impact tumor biology via increased GR recruitment to genes associated with proliferation and invasion. Presentation: 6/2/2024

Blossom everywhere: the territoriality of Hong Kong’s neighbourhood Lennon walls

ABSTRACT This empirical study investigates the neighborhood Lennon Walls of Hong Kong during the Anti-Extradition Bill Movement in 2019, which served as a platform for the public to express their opinions using sticky notes. Drawing on the discourse of territoriality, we argue that the Lennon Walls campaign de-territorialized public and private properties in local neighborhoods, thereby making them a social space for communication and broadening support and participation. The Lennon Wall sub-campaign strengthened the entire social movement. The findings enrich the debate of territoriality discourse in a social movement from protesters’ perspective and detail the understanding of the de-territoriality process.