Neighborhood Interactions Research Articles

Balancing Environmental Sustainability and Economic Viability in Luxembourgish Farms: An Agent-Based Model with Multi-Objective Optimization

To curb the impacts arising from the agricultural sector, the actions undertaken by policymakers, and ultimately by the farmers, are of paramount importance. However, finding the best strategy to reduce impacts, and especially assessing the effects of the interactions and mutual influence among farmers, is very difficult. To this aim, this paper shows an application of an agent-based model (ABM) coupled with life cycle assessment (LCA), which also includes multi-objective optimization of farming activities (including both crop cultivation and livestock breeding) from an economic and environmental perspective. The environmental impacts are assessed using the impact assessment scores calculated with the Environmental Footprint 3.0 life cycle impact assessment method and the study is conducted “from cradle to farm gate”. The model is applied to all the farms in Luxembourg, whose network is built utilizing neighborhood interactions, through which a parameter known as farmer’s green consciousness is updated at each time step. The optimization module is instantiated at the end of each time step, and decision variables (the number of livestock units and land allocation) are assigned based on profitability and specified environmental impact categories. If only profit optimization is considered (i.e., when farmers’ green consciousness is de-activated), the results show a 9% reduction in the aggregated environmental impacts (obtained as the Environmental Footprint single score) and a 5.5% increase in overall profitability. At the farm level, simulations display a clear trade-off between environmental sustainability and financial stability, with a 25% reduction in overall emissions possible if farming activities are carried out using the single score impact in the objective function, though this results in an 8% reduction in profitability over 10 years.

Neighborhood structure, more than soil nutrients, influences net tree interactions among different functional types in a temperate forest

Tree-tree interactions are fundamental in shaping forest community dynamics, driven by factors such as nutrient availability and species composition. While recent research underscores the role of functional traits in influencing tree sensitivity to neighborhood interactions, a clear consensus is lacking on how these traits interact with biotic and abiotic factors. Based on a multi-level modeling approach, this study examines how specific leaf area (SLA), potential maximum tree height (Hmax), and wood density (WD) mediate the combined effects of soil nutrients and neighborhood biotic factors on net tree interactions among 4,210 individual stems for 13 species in a 30-ha temperate forest in northeastern China. The findings indicate that while functional traits independently account for a small portion of the variation in net tree interaction intensity (NIntC), they significantly mediate the effects of neighborhood crowding, neighborhood structure, and soil nutrients on NIntC. Specifically, only the response of growth NIntC of species with low-Hmax to available phosphorus (AP) was consistent with the stress gradient hypothesis. Conspecific neighborhood crowding consistently increases competitive NIntC, whereas heterospecific neighborhood crowding significantly facilitates survival NIntC. Moreover, the positive impact of heterospecific crowding on survival NIntC is significantly greater for high-Hmax species compared to low-Hmax species. Neighborhoods with higher tree species diversity, greater size heterogeneity, and more uniform distribution increase the facilitative NIntC of high-SLA or high-WD species but intensify the competitive NIntC of low-SLA or low-WD species. Our results also indicate that the relative importance of neighborhood structure exceeds that of soil nutrients in explaining variation in NIntC. When environmental conditions are difficult to change, adjusting the forest stand structure may be a more efficient way to regulate the intensity of interactions between trees and improve forest productivity.

Neighbor-Enhanced Representation Learning for Link Prediction in Dynamic Heterogeneous Attributed Networks

Dynamic link prediction aims to predict future connections among unconnected nodes in a network. It can be applied for friend recommendations, link completion, and other tasks. Network representation learning algorithms have demonstrated considerable effectiveness in various prediction tasks. However, most network representation learning algorithms are based on homogeneous networks and static networks for link prediction that do not consider rich semantic and dynamic information. Additionally, existing dynamic network representation learning methods neglect the neighborhood interaction structure of the node. In this work, we design a neighbor-enhanced dynamic heterogeneous attributed network embedding method (NeiDyHNE) for link prediction. In light of the impressive achievements of the heuristic methods, we learn the information of common neighbors and neighbors’ interaction in heterogeneous networks to preserve the neighbors proximity and common neighbors proximity. NeiDyHNE encodes the attributes and neighborhood structure of nodes as well as the evolutionary features of the dynamic network. More specifically, NeiDyHNE consists of the hierarchical structure attention module and the convolutional temporal attention module. The hierarchical structure attention module captures the rich features and semantic structure of nodes. The convolutional temporal attention module captures the evolutionary features of the network over time in dynamic heterogeneous networks. We evaluate our method and various baseline methods on the dynamic link prediction task. Experimental results demonstrate that our method is superior to baseline methods in terms of accuracy.

Densely stocked neighborhood reduces the risk of tree mortality in drylands: Evidence from Populus euphratica forests along the Tarim River corridor

To mitigate forest loss under current environmental challenges, it is increasingly urgent to model how tree mortality is mediated by both biotic and abiotic agents. However, the role of neighborhood interactions in mediating tree survival in dryland forests has been poorly understood, as it is often presupposed to be irrelevant in low-density stands coupled with overriding drought stress. Here, we conducted a failure time analysis of Populus euphratica forests (known as Tugay forests) in the Tarim River corridor (NW China) to examine the presupposition. We collected a spatial-explicit population inventory dataset with >7000 trees on ca. 14-ha land area with varying vapor pressure deficit (VPD), groundwater table depth (GWD), and stand density. Hegyi's competition index (CI) and stand density index (SDI, the number of large trees with an equivalent diameter of 25.4 cm) were employed to characterize neighborhood structures of each individual tree. Aridity stresses (higher VPD and GWD) tended to elevate tree mortality risk, and showed weaker effects than neighborhood structures did. Tree mortality risk increased drastically with higher CI, suggesting a strong role of competition for critical water resource. However, tree mortality risk reduced with higher SDI, likely due to ameliorated stresses of excessive light and/or temperature under less open canopies. This study highlights the importance of localized inter-tree interactions in regulating tree populations under prevalent drought stress and reiterates classic cautions against inferences about competition/facilitation in absence of spatial-explicit contexts. We recommend the priority of preserving large trees aggregated as closed stands in dryland forest conservation.

A multiclass logistic regression, Markov chain, and cellular automata integration for modelling urban land use change

Cellular Automata (CA) models employ simple transition rules and neighbourhood interactions to determine cells' evolution. Integrating CA with other algorithms is often essential in order to obtain necessary statistical inferences. The integration of Binary Logistic Regression (BLR) and Markov chain (MC) with CA forms common CA integrations. For convenience, urban land use change is often modelled as a binary process (e,g. ‘undeveloped land’ and ‘developed land’), whereas in the real world urban land use change phenomenon could go beyond two processes (e. g. ‘undeveloped land’, ‘sparsely developed land’, ‘semi-highly developed land’, ‘highly developed land’, ‘very highly developed land’, etc.). The BLR-MC-CA hybrid model yields a binary outcome. In order to extend the BLR-MC-CA hybrid model to multivariate problems, we introduce a novel hybrid model, by simply replacing the BLR with the new Multiclass Logistic Regression (MLR) to obtain the new MLR-MC-CA hybrid model. Satellite remote sensing imageries acquired in 1984, 2000, and 2020 are used as input data. Four scenarios of urban land use change are modelled: ‘nonurban region’, ‘highest urban region’, ‘high urban region’, and ‘medium urban region’. These four scenarios are modelled with twelve urban land use drivers: distance to water body, distance to residential areas, distance to industrial and commercial areas, distance to major roads, distance to railway, distance to Lagos Island, distance to international seaport, distance to University of Lagos, income potential, population potential, distance to international airport, and distance to Lagos State University. Urban transitions 1984–2000 and 2000–2020 are used to simulate the urban states in 2000 and 2020 respectively; as well as to obtain the urban state in 2060. The validation of the experiment is largely based on McNemar's test and Receiver Operating Characteristics (ROC) curves. The results of the experiment indicate significant fit between the reference and the predicted data.

Ants may buffer the Janzen-Connell effect in a tropical forest in Southwest China.

Mutualistic symbioses between ants and plants are widespread in nature. Ants can deter unwanted pests and provide protection for plants in return for food or housing rewards. Using a long-term demographic dataset in a tropical seasonal rain forest in Southwest China, we found that associations with ants positively influenced seedling survival and adult growth, and also, species with extrafloral nectaries experienced weaker conspecific negative density dependence compared with species without extrafloral nectaries. Furthermore, we found strong evidence suggesting that species in our forest experienced conspecific density dependence, which we interpreted as heavy pest pressure that may drive the development of anti-pest symbioses such as the plant-ant relationship. Our findings suggest that ants and conspecific neighbors play important but inverse roles on plant survival and growth and that ants can buffer tree neighborhood interactions in this tropical forest.

Effects of psychosocial characteristics on cognitive function in middle-aged and older adults: Focusing on change by year using the Korean Longitudinal Study of Aging panel data (2014-2018).

Old age is associated with a higher risk of dementia. Psychosocial characteristics frequently affect cognitive function; however, the exact mechanism underlying the effect of psychosocial factors on cognitive function is unclear. Therefore, this study aimed to investigate the effects of psychosocial characteristics on cognitive function. The participants comprised 4809 middle-aged and older (years 50+) adults. The analysis used data from the Korean Longitudinal Study of Aging from 2014 to 2018. The effects of neighborhood interaction, depression, life satisfaction, and economic activity on cognitive function were examined, and a linear mixed model analysis was performed to assess the change in cognitive function by year. A statistically significant association was found between neighborhood interaction and time. Additionally, cognitive function decreased in the presence of depression and with time. In men, significant interactions were found between depression and time and between economic activity and time. In women, significant interactions were found between life satisfaction and time. The findings indicate that since active neighborhood interaction positively affects cognitive function, it is necessary to develop various community-wide social activity programs for middle-aged and older adults. As depression is a risk factor for cognitive impairment, it is crucial to prevent cognitive decline through continuous management of depression. Given the positive effects of economic activity on cognitive function in men, it is essential to expand infrastructure to sustain economic activity by developing educational programs and creating job opportunities for middle-aged and older men.

Nourish: Food Donation App Using Android and ML

Abstract: This research paper introduces an App designed to modernize food donation practices by digitizing the process and offering support to volunteers, from organizations such, as NGOs orphanages and other charitable institutions. Encouraged by the recurring difficulties of food waste together with worldwide food scarcity the task looks for to develop an easy-to-use system promoting smooth and also clear contributions from people, services as well as dining establishments. To minimize the wastage of food this effort attempts to link the space in between excess food and hunger among the poorer. The Mobile application display the advancement of a user-friendly application user interface streamlining the uploading, with organized functions as well as a focus on transparency. Especially, the application integrates identity confirmation system enabling contributors to post and also confirm their identity by verifying their Aadhar card therefore guaranteeing authenticity of donors and recipients. Furthermore, the app serves as a hub, for resources providing people with access, to guidelines, best practices and other information aimed at raising awareness to combat food waste. By fostering neighborhood interaction and also forging collaborations with neighborhood organizations this study adds to a much more lasting future by making excess food easily offered to those encountering food instability therefore dealing with the double obstacles of food waste as well as needs while harnessing modern technology for favorable social effect

Occupant Needs for Neighborhood Outdoor Spaces Based on Maslow's Hierarchy of Needs: Evidence from Wuxi, China

This study investigates the correlation between the needs of residents in Wuxi, China, and the provision of neighborhood outdoor spaces, through the lens of Maslow's Hierarchy of Needs. The objective is to discern and comprehend the distinct needs residents have for outdoor spaces across various tiers of Maslow's hierarchy, encompassing physiological, safety, love/belonging, esteem, and self-actualization. This study utilizes quantitative research methods, integrating data obtained from a survey of 372 residents to acquire insights into their perspectives on outdoor spaces. The findings reveal that, at the physiological level, residents place a premium on access to clean and verdant spaces for relaxation and exercise. From a safety perspective, the availability of well-lit, secure, and meticulously maintained outdoor settings is deemed essential. In terms of love and belonging, environments that encourage neighborhood interaction and foster social bonds, such as playgrounds and community gardens, are highly valued. Regarding esteem, spaces that are aesthetically appealing and bolster local identity and pride hold significant importance. Finally, concerning self-actualization, areas that provide avenues for personal development and engagement with the environment, including educational workshops and neighborhood vegetable gardens, are in demand. The study underscores the necessity of adopting a holistic approach to urban planning that accounts for the multifaceted needs of residents at different levels of Maslow’s hierarchy. The insights garnered from Wuxi, China, suggest that satisfying these varied needs is pivotal to enhancing the overall well-being and contentment of residents with their community outdoor spaces. This research offers critical perspectives for urban planners, landscape architects, and policymakers dedicated to fostering more habitable, sustainable, and needs-focused outdoor environments in urban settings.

Digital “habitats”: neighborhood chat as a mechanism of social status construction (the case of housing estates in Irkutsk)

The article analyzes the role and place of neighborhood interaction in messengers in terms of social status construction. The digitalization of everyday life and the expanded construction of multi-apartment housing objects in Russian cities raise many questions for researchers about the peculiarities of social processes and the formation of social structure in these conditions. The aim of the article is to show, based on two years of observation and collected empirical material (more than 60,000 user messages), how the discourse of neighborly discussions of modern apartment complexes in Irkutsk articulates the narrative of social well-being. The main method of research is discourse analysis; the social status of residents seems to us to be an “empty sign” for the filling of which the discourse of neighborhood discussions “struggles.” MAXQDA 2020 software was used to preprocess the text array, preprocessing was done using thematic coding. A number of codes were identified that were somehow related to the problematic under study. The coded set of texts was summarized into three main tropes around which discussions of the topic of interest revolve — financial solvency of community members, material markers of wealth, and the image of residents of neighboring localities. The main theoretical resources used in the research were the ideas of P. Bourdieu and the “social construction of reality” of P. Berger and T. Luckmann. Neighborhood chat seems to us to be a mechanism that allows us to instrumentalize the attributes of social status in textual practices, thus crystallizing the community from within. The main factors determining the self-description and self-image of communities as carriers of high social status were identified as well-being and location in the urban space predetermined at the stage of realization by developers of new real estate.

Pedestrian trajectory prediction method based on automatic driving

Pedestrian trajectory prediction plays a crucial role in autonomous driving, as its accuracy directly affects the autonomous driving system’s comprehension of the environment and subsequent decision-making processes. Current trajectory prediction methods tend to oversimplify pedestrians to mere point coordinates, utilizing positional information to infer interactions among individuals while overlooking the temporal correlations between them, thereby excessively simplifying pedestrian characteristics. To address the aforementioned issues, this paper proposes a trajectory prediction model for autonomous driving applications, that takes into account both pedestrian motion characteristics and scene interaction. The model optimizes the LSTM unit structure twice, serving to learn correlations among long trajectories of pedestrians and to integrate multiple forms of information into the neighborhood interaction module. Furthermore, our model introduces dual attention mechanisms for individuals and scenes, focusing on the key motion points of individual pedestrians and their interactive behavior with others in busy scenarios. The efficacy of the model was validated on the MOT16 pedestrian dataset and the Daimler pedestrian path prediction dataset, outperforming mainstream methods with 8% and 10% reductions in Average Displacement Error and Final Displacement Error respectively.

Biophilic Experience in High-Rise Residential Areas in China: Factor Structure and Validity of a Scale

The increasing popularity of high-rise, high-density residential areas in urban environments has brought about problems such as isolation from nature and increasingly depauperate ecological conditions, and consequently, adverse effects on residents’ health and environmental sustainability. Therefore, creating a high-quality biophilic living space environment is key to solving this conflict, considering the health benefits that nature brings to people. However, there are currently no appropriate assessment tools to measure the biophilic living environment of high-rise residential areas. In response, we have developed an environment assessment scale for measuring the Biophilic Experience in High-Rise Residential Areas (BornA) based on resident experiences. Semi-structured interviews were conducted to identify the measurement items relevant to their biophilic living experiences and behaviors. Exploratory factor analysis and confirmatory factor analysis were performed to identify the scale’s structure and examine its reliability and validity. The study resulted in the development of an 18-item BornA, comprising five dimensions: natural landscape, natural interaction, cultural identity, neighborhood interaction, and personal space. The BornA contributes to the assessment of the biophilic living space environment in high-rise residential areas from the perspective of resident behavior and perception, identifying health intervention environments for sample populations and understanding how the residential environment affects residents’ health.

Abstract 73: Spatial profiling of pediatric rhabdomyosarcoma to elucidate their immunosuppressive tumor microenvironment

Abstract Background: Pediatric rhabdomyosarcoma (RMS) is a mesenchymal soft tissue malignancy. Despite intensive chemotherapy regimens, there is currently a gap in improving treatment outcomes for RMS. Chemotherapy can modulate the tumor microenvironment (TME) by altering the immune cell composition or their functions. However, the impact of chemotherapy in pediatric RMS TME is unclear. RMS tumors are also considered as “cold” tumors due to low immune infiltration, which could be a determinant of why immunotherapy is unlikely to work. Additional investigations are required to determine the reasons of why RMS is “cold” and whether a combined immunotherapy and chemotherapy approach should be administered for this hard-to-cure cancer. Methods We conducted for the first time spatial GeoMx whole transcriptome atlas and 51-plex Akoya CODEX profiling for a tissue microarray (TMA) from a cohort of pediatric RMS patients (n = 12) with pre-chemotherapy (n = 5) and post-chemotherapy (n = 5) samples. Regions of interest (ROIs) were selected based on tumor areas with immune cell infiltration, with the lymphocytes, tumor cells, and nuclei visualized using fluorescent morphological markers CD45, Desmin, and DAPI. ROIs were segmented into tumor stroma compartments and immune cell compartments. Computational analyses were conducted on the transcriptomics data using R package standR including quality control, normalization, differential expression analysis, gene set enrichment analysis, and cell type deconvolution. For CODEX data, we preformed cell segmentation, followed by further quality control, cell phenotyping, cellular neighborhood and cellular interaction analysis. Results: From differential expression analysis of the immune compartment, we have identified type I interferon-related genes (DTX3L, ADAP2, RNF135) and reactive oxygen species (ROS)-induced gene (NCF1) are upregulated, and an antioxidant enzyme gene (PRDX2) is downregulated in post-chemotherapy samples compared to pre-chemotherapy samples. Gene set enrichment analysis of the immune compartment revealed an enriched antigen presentation pathway in post-chemotherapy samples. In the tumor stroma compartment, we identified increased TGF-β binding pathway in post-chemotherapy samples. Cellular deconvolution revealed a higher proportion of endothelial cells in post-chemotherapy samples, which is validated via CODEX imaging analysis. Cellular neighborhoods and cell-cell interaction analysis for CODEX data also reveal how chemotherapy affect functional units and cellular interactions in RMS TME with spatial context preserved. Conclusion: By applying spatial transcriptomic and multiplexed imaging to RMS samples, we have revealed key genes, pathways and cell types involved in chemotherapy-induced alterations in the TME. This will provide a new perspective for developing novel chemoimmunotherapy for RMS patients. Citation Format: Cui Tu, Chin Wee Tan, Ning Liu, Arutha Kulasinghe, Fernando Souza-Fonseca-Guimaraes. Spatial profiling of pediatric rhabdomyosarcoma to elucidate their immunosuppressive tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 73.

Abstract 5488: AI-enhanced spatial proteomics reveals B-cells & FoxP3+ cells interactions in HPV(+) oropharyngeal squamous cell carcinoma Progression

Abstract Background: Analyzing interactions and the microanatomical distribution of the tumor immune microenvironment (TIME) is essential for a comprehensive understanding of tumor progression. Imaging Mass Cytometry (IMC) is a high-dimensional tissue imaging system that enables the deeper and multiparametric in situ exploration of tumor microenvironments at a single-cell level. This study describes a pipeline for analyzing 39-plex IMC images of human HPV(+) oropharyngeal squamous cell carcinoma (OPSCC) tumor sections, elucidating immune cells' nature, functions, and interactions with tumor cells. The IMC data analysis provides valuable information for clinical studies that could be used for the identification of prognostic biomarkers and mechanisms of resistance to current immunotherapies. Methods: A retrospective study was conducted on (N=20) patients (10 progressors and 10 non-progressors) with HPV(+) OPSCC. The dataset comprises paired hematoxylin and eosin (H&E) images with IMC data from multiple Regions of Interest (ROIs) (N=82), enabling the in-situ profiling of 39 proteins in primary tumor tissue. Cell segmentation was followed by employing original images and their segmentation masks for cell phenotype identification using a probabilistic neural network method to spatially profile cancer cells, macrophages, NK-M cells, B-cells, and distinct T-cell populations. We identified protein expression at single-cell resolution and investigated the impact of multicellular spatial organization on the disease progression. The spatial neighborhood interaction score of tumor cells with various other cells was computed for all the ROIs across the two groups. Results: The markers showed varying expression levels of intensity distribution, likely due to differences in tissue sample composition. Specifically, CD3, CD4, CD8, CD19, CD20, and CD68 showed significantly higher intensity in non-progressors. Interestingly, FoxP3 also had a significantly higher intensity level in non-progressors. Cell phenotyping emphasized that non-progressors generally have increased levels of NK-M cell markers. Spatial analysis revealed a significant increase in interaction between CD3 cells and tumor cells in non-progressors (p-value=0.07, Mann-Whitney U test). Furthermore, a significant rise in the interaction between B-cells and FoxP3+ cells was observed in non-progressors (p-value<0.05, Mann-Whitney U test) suggesting a coordinated suppression of tumor growth. Conclusion: We developed a deep learning-based pipeline to analyze spatial relationships in the TIME of IMC data, demonstrating its clinical relevance in predicting disease progression. Our findings emphasize the importance of spatial relationships in the TME for response and suggest that proximity between either CD3+ T-cells to cancer cells or B-cells to FoxP3+ cells are candidate biomarkers. Citation Format: Sumanth Reddy Nakkireddy, Routman David, Kathleen Bartemes, Daniel Ma, Tae Hyun Hwang, Kathryn Van Abel, Chadi Nimeh Abdel-Halim. AI-enhanced spatial proteomics reveals B-cells & FoxP3+ cells interactions in HPV(+) oropharyngeal squamous cell carcinoma Progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5488.

Revealing per-grain and neighbourhood stress interactions of a deforming ferritic steel via three-dimensional X-ray diffraction

The structural performance of polycrystalline alloys is strongly controlled by the characteristics of individual grains and their interactions, motivating this study to understand the dynamic micromechanical response within the microstructure. Here, a high ductility single-phase ferritic steel during uniaxial deformation is explored using three-dimensional X-ray diffraction. Grains well aligned for dislocation slip are shown to possess a wide intergranular stress range, controlled by per-grain dependent hardening activity. Contrariwise, grains orientated poorly for slip have a narrow stress range. A grain neighbourhood effect is observed of statistical significance: the Schmid factor of serial adjoining grains influences the stress state of a grain of interest, whereas parallel neighbours are less influential. This phenomenon is strongest at low plastic strains, with the effect diminishing as grains rotate during plasticity to eliminate any orientation dependent load shedding. The ability of the ferrite to eliminate such neighbourhood interactions is considered key to the high ductility possessed by these materials.

Tree mycorrhizal type mediates the responses of foliar stoichiometry and tree growth to functionally dissimilar neighbours in a subtropical forest experiment

Abstract Plant nutrient stoichiometry is of critical importance to productivity and nutrient cycling in terrestrial ecosystems. The impacts of tree species diversity on productivity have been well studied at the stand level. However, it is unclear how neighbourhood interactions impact the foliar nutrient stoichiometry of trees at the neighbourhood scale and how plant mycorrhizal associations can mediate such effects. We randomly selected eight tree species from a large‐scale biodiversity experiment with mixtures up to 32 tree species in subtropical China to assess the effects of species richness, phylogenetic and trait dissimilarities and competition on the foliar nutrient stoichiometry of focal trees associated with either arbuscular mycorrhizal (AM) or ectomycorrhizal (EM) fungi. We further investigated whether neighbourhood diversity can alter focal tree growth by regulating C:N:P stoichiometry. Neighbourhood species richness had no significant impact on the foliar C:N, N:P or C:P for both AM and EM trees. Increased neighbourhood phylogenetic dissimilarity significantly decreased the foliar N:P and C:P of AM trees but did not affect those of EM tree species. Foliar C:N, N:P and C:P of AM trees decreased with increasing neighbour trait (specific leaf area, root diameter, wood density dissimilarity, total trait) dissimilarities, while those of EM trees increased or remained unchanged. The increase of the neighbourhood competition index resulted in an increase in the foliar C:N of AM tree species but not EM tree species. The structural equation model analysis revealed that the increase of neighbourhood phylogenetic dissimilarity and functional trait dissimilarity indirectly enhanced tree growth of AM trees by decreasing foliar C:N. Conversely, the increase of neighbourhood‐specific root length and wood density dissimilarity indirectly reduced the growth of EM trees by increasing foliar N:P. Synthesis. Our results indicate that neighbourhood trait dissimilarity regulated tree foliar stoichiometry and growth performance, but the effects depended on the mycorrhizal type of trees. Our findings highlight the importance of tree mycorrhizal associations for better understanding the relationship between plant diversity and ecosystem functions. Read the free Plain Language Summary for this article on the Journal blog.

Perceived neighborhood walkable environment, neighborhood interaction and mental health among residents of different age groups: a case study in Shanghai, China

Purpose This study aims to investigate the relationships between the perceived neighborhood walkable environment (PNWE), neighborhood interaction (NI) and residents’ mental health, with a focus on examining differences among residents of different age groups.Design/methodology/approach Using an electronic survey questionnaire, data on PNWE, NI and mental health were collected from 1,159 residents across 205 communities in Shanghai, China. Our study utilized a structural equation modeling (SEM), employing the maximum likelihood estimation method. The structural equation model was fitted using the MPLUS software.Findings The mental health of young and middle-aged adults is generally poor, and they are at higher risk of depression than children and older adults. The effects of PNWE and NI on the mental health of residents varied among different age groups. As residents get older, their mental health is more affected by the PNWE. In addition, the influence of the PNWE on children and older adults’ mental health is direct and not mediated by NI. For young and middle-aged adults, the influence of the PNWE on their mental health needs to be mediated by NI.Originality/value This study marks the first examination of the relationship between PNWE, NI and mental health among different age groups of residents in China. The findings of this research can assist policymakers in gaining a deeper understanding of the underlying mechanisms by which PNWE affects mental health. Furthermore, it can contribute to the development of more targeted walkable environment designs aimed at enhancing mental health among various age groups.

Neighbourhood characteristics and socioeconomic inequalities in child mental health: Cross-sectional and longitudinal findings from the Growing Up in Ireland study

This study examined the role of neighbourhood characteristics in explaining socioeconomic inequalities in child mental health (the total difficulties score from the Strengths and Difficulties Questionnaire) using data from Cohort ’08 of Growing Up in Ireland Waves 3 (age 5; baseline) and 5 (age 9; follow-up). Twenty neighbourhood items were grouped into neighbourhood safety, built environments, cohesion, interaction, and disorder. Data were analysed using regression, single and multiple mediation, and network psychometric analyses. We found that neighbourhood safety, cohesion, interaction, and disorder were associated with child mental health. These four domains separately (by up to 18 %) or in concert (by up to 23 %) partially explained socioeconomic inequalities in child mental health. Built environments may explain socioeconomic inequalities in mental health in urban children only. Findings from network analysis indicated that specific concerns over “people being drunk or taking drugs in public” and “this is a safe neighbourhood” had the strongest connections with child mental health. Improving neighbourhood characteristics may be important to reduce socioeconomic inequalities in child mental health in Ireland.

Climate anomalies and neighbourhood crowding interact in shaping tree growth in old‐growth and selectively logged tropical forests

Abstract Climate extremes and biotic interactions at the neighbourhood scale affect tropical forest dynamics with long‐term consequences for biodiversity, global carbon cycling and climate change mitigation. However, forest disturbance may change crowding intensity, and thus the relative contribution of climate extremes and neighbourhood interactions on tree growth, thereby influencing tropical forest resistance and resilience to climate change. Here, we aim to evaluate the separate and interactive effects of climate and neighbours on tree growth in old‐growth and disturbed tropical forests. We used 30 years of growth measurements for over 300 tropical tree species from 15 forest plots in French Guiana to investigate the separate and interactive effects of climate anomalies (in solar radiation, maximum temperature, vapour pressure deficit and climatic water deficit) and neighbourhood crowding on individual tree growth. Contrasting old‐growth and selectively logged forests, we also examined how disturbance history affects tree growth sensitivity to climate and neighbours. Finally, for the most abundant 100 species, we evaluated the role of 12 functional traits pertaining to water relations, light and carbon use in mediating tree growth sensitivity to climate anomalies, neighbourhood crowding and their interactions. Climate anomalies tied to heat and drought stress and neighbourhood crowding independently reduced tree growth, and showed positive interactive effects which attenuated their separate effects on tree growth. Their separate and interactive effects were stronger in disturbed than undisturbed forests. Fast‐growing species (i.e. higher intrinsic growth rates) were more abundant in disturbed forests and more sensitive to climate anomalies and neighbourhood crowding. Traits related to water relations, light and carbon use captured species sensitivities to different climate anomalies and neighbourhood crowding levels but were weak predictors of their interactions. Synthesis: Our results demonstrate that climate anomalies and neighbourhood crowding can interact to shape tropical tree growth, suggesting that considering the biotic context may improve predictions of tropical forest dynamics facing altered climate regimes. Furthermore, species traits can capture tree growth sensitivity to the separate effects of climate and neighbours, suggesting that better representing leading functional dimensions in tropical tree strategies offers a promising way towards a better understanding of the underlying ecological mechanisms that govern tropical forest dynamics.

Improved graph representation learning based on neighborhood aggregation and interaction fusion

The adaptive fusion module with an attention mechanism functions by employing a dual-channel graph convolutional network to aggregate neighborhood information. The resulting embeddings are then utilized to calculate interaction terms, thereby incorporating additional information. To enhance the relevance of fusion information, an adaptive fusion module with an attention mechanism is constructed. This module selectively combines the neighborhood aggregation and interaction terms, prioritizing the most pertinent information. Through this adaptive fusion process, the algorithm effectively captures both neighborhood features and other nonlinear information, leading to improved overall performance. Neighborhood Aggregation Interaction Graph Convolutional Network Adaptive Fusion (NAIGCNAF) is a graph representation learning algorithm designed to obtain low-dimensional node representations while preserving graph properties. It addresses the limitations of existing algorithms, which tend to focus solely on aggregating neighborhood features and overlook other nonlinear information. NAIGCNAF utilizes a dual-channel graph convolutional network for neighborhood aggregation and calculates interaction terms based on the resulting embeddings. Additionally, it incorporates an adaptive fusion module with an attention mechanism to enhance the relevance of fusion information. Extensive evaluations on three citation datasets demonstrate that NAIGCNAF outperforms other algorithms such as GCN, Neighborhood Aggregation, and AIR-GCN. NAIGCNAF achieves notable improvements in classification accuracy, ranging from 1.0 to 1.6 percentage points on the Cora dataset, 1.1 to 2.4 percentage points on the Citeseer dataset, and 0.3 to 0.9 percentage points on the Pubmed dataset. Moreover, in visualization tasks, NAIGCNAF exhibits clearer boundaries and stronger aggregation within clusters, enhancing its effectiveness. Additionally, the algorithm showcases faster convergence rates and smoother accuracy curves, further emphasizing its ability to improve benchmark algorithm performance.