Spatiotemporal Interactions Research Articles

Temporal Adaptive RGBT Tracking with Modality Prompt

RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving. Existing RGBT trackers fully explore the spatial information between the template and the search region and locate the target based on the appearance matching results. However, these RGBT trackers have very limited exploitation of temporal information, either ignoring temporal information or exploiting it through online sampling and training. The former struggles to cope with the object state changes, while the latter neglects the correlation between spatial and temporal information. To alleviate these limitations, we propose a novel Temporal Adaptive RGBT Tracking framework, named as TATrack. TATrack has a spatio-temporal two-stream structure and captures temporal information by an online updated template, where the two-stream structure refers to the multi-modal feature extraction and cross-modal interaction for the initial template and the online update template respectively. TATrack contributes to comprehensively exploit spatio-temporal information and multi-modal information for target localization. In addition, we design a spatio-temporal interaction (STI) mechanism that bridges two branches and enables cross-modal interaction to span longer time scales. Extensive experiments on three popular RGBT tracking benchmarks show that our method achieves state-of-the-art performance, while running at real-time speed.

Scalable Spatio-temporal Top-k Interaction Queries on Dynamic Communities

Social media platforms generate massive amounts of data that reveal valuable insights about users and communities at large. Existing techniques have not fully exploited such data to help practitioners perform a deep analysis of large online communities. Lack of scalability hinders analyzing communities of large sizes and requires tremendous system resources and unacceptable runtime. This article proposes a new analytical query that identifies the top- k posts that a given user community has interacted with during a specific time interval and within a spatial range. We propose a novel indexing framework that captures the interactions of users and communities to provide a low query latency. Moreover, we propose exact and approximate algorithms to process the query efficiently and utilize the index content to prune the search space. The extensive experimental evaluation on real data has shown the superiority of our techniques and their scalability to support large online communities.

Abstract 298: Contribution of cancer associated fibroblasts to treatment response and resistance in high-risk multifocal prostate cancer

Abstract Patients with multifocal, locally advanced prostate cancer are at the highest risk of recurrence and death. Neoadjuvant therapies have limited response rates in this patient cohort and there remains a critical need to develop curative treatments. Prostate cancer (PC) shows marked heterogeneity, which is not limited to tumor cells but extends across immune and stromal compartments and has been linked to metastatic disease and therapeutic resistance. Cancer associated fibroblasts (CAFs) have been proposed to play a critical role in PC progression and invasion, but it is still unclear how CAFs interact with tumor cells. We evaluated CAFs from prostatectomy specimens as drivers of resistance to neoadjuvant androgen receptor signaling inhibitor (ARSi) and chemotherapy and assessed treatment response in co-culture systems in the context of CAF presence. We have utilized a novel radio-pathology tool that integrates PSMA PET/MRI scans to identify regions of interest that associate with heterogenous treatment response. MRI scans were utilized to print patient-specific 3D molds to micro dissect live tissue for subsequent cell sorting and molecular analyses. We have established a multi-parameter flow cytometry panel to characterize primary prostate fibroblasts from tumor foci and adjacent normal prostate tissue. In addition, co-culture assays were performed including immortalized CAF (hPrCSC-44) and MSC-derived fibroblast. To examine the tumor-promoting role of CAFs, fibroblasts were co-cultured with androgen-sensitive 3D tumor PC spheroids (LNCaP, C42B and LAPC4) in the integrated microfluidic STACKs platform that allows for the assessment of spatio-temporal interaction in a variety of culture microenvironments to model complex interactions. Cytotoxicity in 3D PC spheroids was assessed after Apalutamide, Darolutamide, or Docetaxel treatment by confocal microscopy. Integrated DNA, RNA, and radiology analysis of tumor biopsies have identified gene signatures associated with early biochemical recurrence. Most biopsies had low tumor purity, evidence of the cytotoxic impact of chemohormonal therapy while high tumor purity predicted high tumor expression. Poor clinical outcome was significantly associated with elevated stromal scores while epithelial and stromal enrichment were inversely correlated (rho= -0.80, p< 5 × 10−16). The presence of CAFs significantly decreased Docetaxel (C42B only vs C42B+CAF, 86.07% vs. 44.57%, respectively, p<0.001; LAPC4 only vs LAPC4+CAF, 70.98% vs 26.64%, respectively, p<0.001) and Darolutamide-induced (C42B only vs C42B+CAF, 52.59% vs 37.87%, respectively, p=0.0407). In conclusion, recurrent PC is linked to increased stromal content and the presence of CAFs supported tumor survival in response to ARSi and Docetaxel treatment. The molecular drivers of this phenomenon are currently being investigated. Citation Format: Nikolett Lupsa, Erika Heninger, Adeline Ding, Shannon R. Reese, Xavier T. Hazelberg, Aaron M. LeBeau, Brian P. Johnson, Peter P. Geiger, David J. Beebe, David A. Quigley, Joshua M. Lang. Contribution of cancer associated fibroblasts to treatment response and resistance in high-risk multifocal prostate cancer [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 298.

Key predictors of food security and nutrition in Africa: a spatio-temporal model-based study

There is voluminous literature on Food Security in Africa. This study explicitly considers the spatio-temporal factors in addition to the usual FAO-based metrics in modeling and understanding the dynamics of food security and nutrition across the African continent. To better understand the complex trajectory and burden of food insecurity and nutrition in Africa, it is crucial to consider space-time factors when modeling and interpreting food security. The spatio-temporal anova model was found to be superior(employing statistical criteria) to the other three models from the spatio-temporal interaction domain models. The results of the study suggest that dietary supply adequacy, food stability, and consumption status are positively associated with severe food security, while average food supply and environmental factors have negative effects on Food Security and Nutrition. The findings also indicate that severe food insecurity and malnutrition are spatially and temporally correlated across the African continent. Spatio-temporal modeling and spatial mapping are essential components of a comprehensive practice to reduce the burden of severe food insecurity. likewise, any planning and intervention to improve the average food supply and environment to promote sustainable development should be regional instead of one size fit all.

Targeting initial tumour-osteoclast spatiotemporal interaction to prevent bone metastasis.

Bone is the most common site of metastasis, and although low proliferation and immunoediting at the early stage make existing treatment modalities less effective, the microenvironment-inducing behaviour could be a target for early intervention. Here we report on a spatiotemporal coupling interaction between tumour cells and osteoclasts, and named the tumour-associated osteoclast 'tumasteoclast'-a subtype of osteoclasts in bone metastases induced by tumour-migrasome-mediated cytoplasmic transfer. We subsequently propose an in situ decoupling-killing strategy in which tetracycline-modified nanoliposomes encapsulating sodium bicarbonate and sodium hydrogen phosphate are designed to specifically release high concentrations of hydrogen phosphate ions triggered by tumasteoclasts, which depletes calcium ions and forms calcium-phosphorus crystals. This can inhibit the formation of migrasomes for decoupling and disrupt cell membrane for killing, thereby achieving early prevention of bone metastasis. This study provides a research model for exploring tumour cell behaviour in detail and a proof-of-concept for behaviour-targeting strategy.

Anthropogenic activities facilitate temporal overlaps and spatial partitions among sympatric canids in a human-modified landscape of Bulgaria

Mammalian carnivores (order Carnivora) occupy higher trophic levels in many terrestrial ecosystems, shaping community structures via direct predation and trophic cascades. The interference competition among sympatric carnivores (i.e., aggression and killing) has a major role in the intraguild interactions in which the large-dominant predators typically constrain behaviors and resource use patterns by exploitative (i.e., resource) competitions as well as the survival of smaller subordinate species by interference (i.e., direct) competitions. The free-roaming dog, Canis lupus familiaris, has been introduced worldwide and the species affects regional biodiversity, including native carnivore species. Using camera trapping, we investigated the temporal and spatial overlaps between the free-roaming dog and two wild canids, the golden jackal (Canis aureus) and the red fox (Vulpes vulpes), in different landscapes of central Bulgaria. We predict their interference interactions, namely that the dog, as the largest competitor, suppresses the golden jackal with intermediate body size, and, in turn, the golden jackal suppresses the smaller red fox. In mountainous forest landscapes where the free-roaming dog was absent or scarce, the golden jackal and red fox partitioned their diel activities, while there were moderate levels of spatial overlap. In agricultural lowlands where dogs were abundant and anthropogenic disturbances were relatively high, all three canids were primarily nocturnal with high temporal overlap, probably owing to human activities (e.g., hunting) in the daytime. The golden jackal was spatially separated from the dog, whereas the red fox spatially overlapped with large dogs. Our results indicated that, in human-modified landscapes with scattered forests and shrubs, spatial partitioning between the free-roaming dog and the golden jackal at fine spatial scales facilitates their sympatry by decreasing the probability of direct encounters. Furthermore, our findings also indicate that spatiotemporal interactions between the golden jackal and the red fox can change in association with various human disturbances, e.g., landscape modifications, human activities, and the introduction of dogs.

Flow Spatiotemporal Moran's I: Measuring the Spatiotemporal Autocorrelation of Flow Data

Flows can reflect the spatiotemporal interactions or movements of geographical objects between different locations. Measuring the spatiotemporal autocorrelation of flows can help determine the overall spatiotemporal trends and local patterns. However, quantitative indicators of flows used to measure spatiotemporal autocorrelation both globally and locally are still rare. Therefore, we propose the global and local flow spatiotemporal Moran's I (FSTI). The global FSTI is used to assess the overall spatiotemporal autocorrelation degree of flows, and the local FSTI is applied to identify local spatiotemporal clusters and outliers. In the FSTI, to reflect flow spatiotemporal adjacency relationships, we establish flow spatiotemporal weights by multiplying the spatial and temporal weights of flows considering spatiotemporal orthogonality. The flow spatial weights include contiguity‐based (considering first/higher‐order and common border) and Euclidean distance‐based weights. The temporal weights consider ordinary and lagged cases. As flow attributes may follow a long‐tail distribution, we conduct Monte Carlo simulations to evaluate the statistical significance of the results. We assess the FSTI using synthetic datasets and Chinese population mobility datasets, and compare some results with those of recent flow‐related methods. Additionally, we perform a sensitivity analysis to select a suitable temporal threshold. The results show that the FSTI can be used to effectively detect spatiotemporal variations in the autocorrelation degree and type.

Accounting for spatial coupling to assess the interactions between human well-being and environmental performance

Understanding the spatio-temporal interactions between ecosystem services (ESs) and the progress of Sustainable Development Goals (SDGs) is crucial in arid regions due to the more intense conflicts between human well-being and environmental performance. However, the interactions over distance and their spatial connectivity network among regions are often neglected, influencing the advancements in policy making especially in arid basin. Here a comprehensive framework that integrated ESs and SDGs was proposed to assess the spatial coupling between human well-being and environmental performance within and across regions in the Heihe River Basin (HRB), the second largest inland river basin in China. Then the Random Forests method was employed to identify the key driving factors of the interactions. The results are as follows: (1) The coupling that accounts for both local and distant effects was higher than that only considers local effects. The interactions of all regions that considered both were more balanced, suggesting that spatial interactions among regions promoted the supplies of ES and the progress of SDGs. (2) The midstream of HRB formed a stronger spatial connectivity network. (3) Land use intensity, population density, and water scarcity showed a strong influence on the interactions. This study provides targeted suggestions to guide sustainable development for better human well-being while maintaining ecosystem health for better environmental performance.

Spatiotemporal distribution, interactions and toxic effect of microorganisms and ARGs/MGEs from the bioreaction tank in hospital sewage treatment facility

Antibiotic resistance genes (ARGs) can be emitted from wastewater to ambient air and impose unignorable inhalable hazards, which could be exacerbated in antibiotic-concentrated hospital sewage. However, whether the ARG-carrying pathogens are more likely to infect cells remains largely unknown. Here, this study investigated and analyzed the spatiotemporal distribution, interaction, and toxicity of airborne microorganisms and their hosting ARGs in a hospital sewage treatment facility. The average concentration of ARGs/MGEs in sewage of bioreaction tank (BRT-W) was 2.27 × 104 gene copies/L. In the air of bioreaction tank (BRT-A), the average concentration of ARGs/MGEs was 15.86 gene copies/m3. In the four seasons, the ARGs concentration of sewage gradually decreased over time; The concentration of ARGs in the air first decreased and then increased. In spring, the concentration of ARGs/MGEs (qacedelta1-01) in BRT-W was highest (1.05 × 105 gene copies/L); The concentration of ARGs/MGEs (strB) in BRT-A in winter was higher than other seasons (26.18 gene copies/m3). Different from the past, this study also paid attention to the pathogenic potential of ARGs/MGEs in the air. The results of cell experiments showed that the cytotoxicity of drug-resistant Escherichia coli could reach Grade V. This suggested that the longer the drug-resistant E. coli were exposed to cells, the greater the cytotoxicity. Moreover, the cytotoxicity of bacteria increased with the increase in exposure time. In spring, the toxic effect of ARGs/MGEs in sewage of BRT-W was highest. Traceability analysis proved that BRT-W was an essential source of microorganisms and ARGs/MGEs in BRT-A. Furthermore, the combined risk of people exposed to the air of BRT in spring was higher than that in other seasons.

TRPV4-dependent Ca2+ influx determines cholesterol dynamics at the plasma membrane

The activities of the transient receptor potential vanilloid 4 (TRPV4), a Ca2+-permeable nonselective cation channel, are controlled by its surrounding membrane lipids (e.g., cholesterol, phosphoinositides). The transmembrane region of TRPV4 contains a cholesterol recognition amino acid consensus (CRAC) motif and its inverted (CARC) motif located in the plasmalemmal cytosolic leaflet. TRPV4 localizes in caveolae, a bulb-shaped cholesterol-rich domain at the plasma membrane. Here, we visualized the spatiotemporal interactions between TRPV4 and cholesterol at the plasma membrane in living cells by dual-color single-molecule imaging using total internal reflection fluorescence microscopy. To this aim, we labeled cholesterol at the cytosolic leaflets of the plasma membrane using a cholesterol biosensor, D4H. Our single-molecule tracking analysis showed that the TRPV4 molecules colocalize with D4H-accessible cholesterol molecules mainly in the low fluidity membrane domains in which both molecules are highly clustered. Colocalization of TRPV4 and D4H-accessible cholesterol was observed both inside and outside of caveolae. Agonist-evoked TRPV4 activation remarkably decreased colocalization probability and association rate between TRPV4 and D4H-accessible cholesterol molecules. Interestingly, upon TRPV4 activation, the particle density of D4H-accessible cholesterol molecules was decreased and the D4H-accessible cholesterol molecules in the fast-diffusing state were increased at the plasma membrane. The introduction of skeletal dysplasia-associated R616Q mutation into the CRAC/CARC motif of TRPV4, which reduced the interaction with cholesterol clusters, could not alter the D4H-accessible cholesterol dynamics. Mechanistically, TRPV4-mediated Ca2+ influx and the C-terminal calmodulin-binding site of TRPV4 are essential for modulating the plasmalemmal D4H-accessible cholesterol dynamics. We propose that TRPV4 remodels its surrounding plasmalemmal environment by manipulating cholesterol dynamics through Ca2+ influx.

Spatio-temporal interaction and constraint effects between ecosystem services and human activity intensity in Shaanxi Province,China

Identifying spatial heterogeneity in ecosystem services and understanding the spatial and temporal correlations between ecosystem service exploration and human activity intensity are vital for achieving regional harmony between human activities and the environment, ultimately fostering sustainable development. Previous studies primarily concentrated on the impact of human activities on ecosystem services while overlooking the intricate spatiotemporal dynamics and nonlinear relationships. In this study, which leveraged multi-source data, we found increasing trends in Human Footprint Index (HFI), most pronounced in the central region of Shaanxi Province. From 2000 to 2020, Shaanxi Province consistently exhibited an ecosystem service distribution pattern characterized by a 'high in the central-south and low in the northwest' trend. Habitat Quality (HQ) consistently maintained high values, signifying favorable habitat conditions, while Food Production (FP) remained relatively stable. Carbon Sequestration (CS) and Soil Conservation (SC) displayed diverse temporal patterns, whereas Water Yield (WY) showed noticeable fluctuations.This analysis revealed a negative correlation between HFI indicators and Total Ecosystem Services (TES). Constraint lines delineated hump-shaped relationships between TES and human activity intensity across various geomorphic areas. Notably, the differences between regions were significant, and threshold dynamics were identified. HFI negatively correlated with TES, moderate HFI growth improved services, yet exceeding ecological thresholds caused decline. Overall,this study informs necessary ecosystem management in Shaanxi, highlighting the need for balanced policies and predictive models that ensure sustainable development without crossing critical ecological thresholds. Future work should concentrate on developing predictive models that incorporate environmental, technological, and socio-economic variables, to better anticipate and mitigate the ecological impacts of human activities.

Weather Interaction-Aware Spatio-Temporal Attention Networks for Urban Traffic Flow Prediction

As the cornerstone of intelligent transportation systems, accurate traffic prediction can reduce the pressure of urban traffic, reduce the cost of residents’ travel time, and provide a reference basis for urban construction planning. Existing traffic prediction methods focus on spatio-temporal dependence modeling, ignoring the influence of weather factors on spatio-temporal characteristics, and the prediction task has complexity and an uneven distribution in different spatio-temporal scenarios and weather changes. In view of this, we propose a weather interaction-aware spatio-temporal attention network (WST-ANet), in which we integrate feature models and dynamic graph modules in the encoder and decoder, and use a spatio-temporal weather interaction perception module for prediction. Firstly, the contextual semantics of the traffic flows are fused using a feature embedding module to improve the adaptability to weather drivers; then, an encoder–decoder is constructed by combining the Dynamic Graph Module and the WSTA Block, to extract spatio-temporal aggregated correlations in the roadway network; finally, the feature information of the encoder was weighted and aggregated using the cross-focusing mechanism, and attention was paid to the hidden state of the encoding. Traffic flow was predicted using the PeMS04 and PeMS08 datasets and compared with multiple typical baseline models. It was learned through extensive experiments that the accuracy evaluation result is the smallest in WST-ANet, which demonstrated the superiority of the proposed model. This can more accurately predict future changes in traffic in different weather conditions, providing decision makers with a basis for optimizing scenarios.

Modelling spatiotemporal patterns of visceral leishmaniasis incidence in two endemic states in India using environment, bioclimatic and demographic data, 2013-2022.

As of 2021, the National Kala-azar Elimination Programme (NKAEP) in India has achieved visceral leishmaniasis (VL) elimination (<1 case / 10,000 population/year per block) in 625 of the 633 endemic blocks (subdistricts) in four states. The programme needs to sustain this achievement and target interventions in the remaining blocks to achieve the WHO 2030 target of VL elimination as a public health problem. An effective tool to analyse programme data and predict/ forecast the spatial and temporal trends of VL incidence, elimination threshold, and risk of resurgence will be of use to the programme management at this juncture. We employed spatiotemporal models incorporating environment, climatic and demographic factors as covariates to describe monthly VL cases for 8-years (2013-2020) in 491 and 27 endemic and non-endemic blocks of Bihar and Jharkhand states. We fitted 37 models of spatial, temporal, and spatiotemporal interaction random effects with covariates to monthly VL cases for 6-years (2013-2018, training data) using Bayesian inference via Integrated Nested Laplace Approximation (INLA) approach. The best-fitting model was selected based on deviance information criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) and was validated with monthly cases for 2019-2020 (test data). The model could describe observed spatial and temporal patterns of VL incidence in the two states having widely differing incidence trajectories, with >93% and 99% coverage probability (proportion of observations falling inside 95% Bayesian credible interval for the predicted number of VL cases per month) during the training and testing periods. PIT (probability integral transform) histograms confirmed consistency between prediction and observation for the test period. Forecasting for 2021-2023 showed that the annual VL incidence is likely to exceed elimination threshold in 16-18 blocks in 4 districts of Jharkhand and 33-38 blocks in 10 districts of Bihar. The risk of VL in non-endemic neighbouring blocks of both Bihar and Jharkhand are less than 0.5 during the training and test periods, and for 2021-2023, the probability that the risk greater than 1 is negligible (P<0.1). Fitted model showed that VL occurrence was positively associated with mean temperature, minimum temperature, enhanced vegetation index, precipitation, and isothermality, and negatively with maximum temperature, land surface temperature, soil moisture and population density. The spatiotemporal model incorporating environmental, bioclimatic, and demographic factors demonstrated that the KAMIS database of the national programmme can be used for block level predictions of long-term spatial and temporal trends in VL incidence and risk of outbreak / resurgence in endemic and non-endemic settings. The database integrated with the modelling framework and a dashboard facility can facilitate such analysis and predictions. This could aid the programme to monitor progress of VL elimination at least one-year ahead, assess risk of resurgence or outbreak in post-elimination settings, and implement timely and targeted interventions or preventive measures so that the NKAEP meet the target of achieving elimination by 2030.

Analyzing tiger interaction and home range shifts using a time-geographic approach

BackgroundInteraction through movement can be used as a marker to understand and model interspecific and intraspecific species dynamics, and the collective behavior of animals sharing the same space. This research leverages the time-geography framework, commonly used in human movement research, to explore the dynamic patterns of interaction between Indochinese tigers (Panthera tigris corbeti) in the western forest complex (WEFCOM) in Thailand.MethodsWe propose and assess ORTEGA, a time-geographic interaction analysis method, to trace spatio-temporal interactions patterns and home range shifts among tigers. Using unique GPS tracking data of tigers in WEFCOM collected over multiple years, concurrent and delayed interaction patterns of tigers are investigated. The outcomes are compared for intraspecific tiger interaction across different genders, relationships, and life stages. Additionally, the performance of ORTEGA is compared to a commonly used proximity-based approach.ResultsAmong the 67 tracked tigers, 42 show concurrent interactions at shared boundaries. Further investigation of five tigers with overlapping home ranges (two adult females, a male, and two young male tigers) suggests that the mother tiger and her two young mostly stay together before their dispersal but interact less post-dispersal. The male tiger increases encounters with the mother tiger while her young shift their home ranges. On another timeline, the neighbor female tiger mostly avoids the mother tiger. Through these home range dynamics and interaction patterns, we identify four types of interaction among these tigers: following, encounter, latency, and avoidance. Compared to the proximity-based approach, ORTEGA demonstrates better detects concurrent mother–young interactions during pre-dispersal, while the proximity-based approach misses many interactions among the dyads. With larger spatial buffers and temporal windows, the proximity-based approach detects more encounters but may overestimate the duration of interaction.ConclusionsThis research demonstrates the applicability and merits of ORTEGA as a time-geographic based approach to animal movement interaction analysis. We show time geography can develop valuable, data-driven insights about animal behavior and interactions. ORTEGA effectively traces frequent encounters and temporally delayed interactions between animals, without relying on specific spatial and temporal buffers. Future research should integrate contextual and behavioral information to better identify and characterize the nature of species interaction.

Land cover preferences and spatiotemporal associations of ungulates within a Scottish mammal community.

In the degraded and modified environment of the Scottish Highlands, novel ungulate communities have arisen following local extinctions, reintroductions, and the introduction of non-native species. An understanding of the dynamics and interactions within these unique mammal communities is important as many of these mammals represent keystone species with disproportionate impacts on the environment. Using a camera trap survey, we investigated land cover preferences and spatiotemporal interactions within a Scottish ungulate community: the sika deer (Cervus nippon), the roe deer (Capreolus capreolus), the red deer (Cervus elaphus), and the wild boar (Sus scrofa). We used generalised linear models to assess land cover preferences and the effect of human disturbance; spatiotemporal interactions were characterised using time interval modelling. We found that sika deer and roe deer preferred coniferous plantations and grasslands, with sika deer additionally preferring woodland. For red deer, we found a slight preference for wetland over woodland; however, the explained variance was low. Finally, wild boar preferred grassland and woodland and avoided coniferous plantations, heathland, and shrubland. Contrary to our expectations, we found no evidence that human disturbance negatively impacted ungulates' distributions, potentially because ungulates temporally avoid humans or because dense vegetation cover mitigates the impacts of humans on their distributions. Furthermore, we detected a spatiotemporal association between sika deer and roe deer. Although the underlying cause of this is unknown, we hypothesise that interactions such as grazing facilitation or an anti-predator response to culling could be driving this pattern. Our work provides a preliminary analysis of the dynamics occurring within a novel ungulate community and also highlights current knowledge gaps in our understanding of the underlying mechanisms dictating the observed spatiotemporal associations.

Spatiotemporal interactions and influencing factors for carbon emission efficiency of cities in the Yangtze River Economic Belt, China

Exploring the spatiotemporal evolution and influencing factors of carbon emission efficiency (CEE) is crucial for achieving the goal of urban carbon neutrality. However, most of the existing studies ignore the temporal dependence of the spatial pattern evolution of CEE and the scale variability of the factors influencing CEE. With the help of an exploratory spatiotemporal data analysis framework, this paper examined the spatiotemporal interactions of CEE across 110 cities in the Yangtze River Economic Belt (YREB). In addition, a multiscale geographically weighted regression model was employed to reveal the scale effects of influencing factors on CEE. The main conclusions are as follows: first, the CEE of cities in the YREB shows a fluctuating upward trend, but the overall level is still low, and a certain polarization phenomenon exists. Second, the spatial pattern of the CEE of cities in the YREB is generally relatively stable, with strong spatial integration and path dependence. Finally, the factors influencing CEE exhibit obvious scale variability and spatial heterogeneity. Our findings can provide a basis for localized and differentiated carbon emission reduction decision-making at the city level, as well as new insights for the formulation of sustainable urban spatial planning and low-carbon development strategies.

Vehicle Trajectory Prediction Considering Multi-feature Independent Encoding Based on Graph Neural Network

Background: Today, self-driving cars are already on the roads. However, driving safety remains a huge challenge. Trajectory prediction of traffic targets is one of the important tasks of an autonomous driving environment perception system, and its output trajectory can provide necessary information for decision control and path planning. Although there are many patents and articles related to trajectory prediction, the accuracy of trajectory prediction still needs to be improved. Objective: This paper aimed to propose a novel scheme that considers multi-feature independent encoding trajectory prediction (MFIE). Methods: MFIE is an independently coded trajectory prediction algorithm that consists of a spacetime interaction module and trajectory prediction module, and considers speed characteristics and road characteristics. In the spatiotemporal interaction module, an undirected and weightless static traffic graph is used to represent the interaction between vehicles, and multiple graph convolution blocks are used to perform data mining on the historical information of target vehicles, capture temporal features, and process spatial interaction features. In the trajectory prediction module, three long short-term memory (LSTM) encoders are used to encode the trajectory feature, motion feature, and road constraint feature independently. The three hidden features are spliced into a tensor, and the LSTM decoder is used to predict the future trajectory. Results: On datasets, such as Apollo and NGSIM, the proposed method has shown lower prediction error than traditional model-driven and data-driven methods, and predicted more target vehicles at the same time. It can provide a basis for vehicle path planning on highways and urban roads, and it is of great significance to the safety of autonomous driving. Conclusion: This paper has proposed a multi-feature independent encoders’ trajectory prediction data-driven algorithm, and the effectiveness of the algorithm is verified with a public dataset. The trajectory prediction algorithm considering multi-feature independent encoders provides some reference value for decision planning.

Microglial roles in Alzheimer's disease: An agent-based model to elucidate microglial spatiotemporal response to beta-amyloid.

Alzheimer's disease (AD) is characterized by beta-amyloid (Aβ) plaques in the brain and widespread neuronal damage. Because of the high drug attrition rates in AD, there is increased interest in characterizing neuroimmune responses to Aβ plaques. In response to AD pathology, microglia are innate phagocytotic immune cells that transition into a neuroprotective state and form barriers around plaques. We seek to understand the role of microglia in modifying Aβ dynamics and barrier formation. To quantify the influence of individual microglia behaviors (activation, chemotaxis, phagocytosis, and proliferation) on plaque size and barrier coverage, we developed an agent-based model to characterize the spatiotemporal interactions between microglia and Aβ. Our model qualitatively reproduces mouse data trends where the fraction of microglia coverage decreases as plaques become larger. In our model, the time to microglial arrival at the plaque boundary is significantly negatively correlated (p < 0.0001) with plaque size, indicating the importance of the time to microglial activation for regulating plaque size. In addition, in silico behavioral knockout simulations show that phagocytosis knockouts have the strongest impact on plaque size, but modest impacts on microglial coverage and activation. In contrast, the chemotaxis knockouts had a strong impact on microglial coverage with a more modest impact on plaque volume and microglial activation. These simulations suggest that phagocytosis, chemotaxis, and replication of activated microglia have complex impacts on plaque volume and coverage, whereas microglial activation remains fairly robust to perturbations of these functions. Thus, our work provides insights into the potential and limitations of targeting microglial activation as a pharmacological strategy for the treatment of AD.

Incorporating spatio-temporal correlation and interaction into a generalized ordered logit model for examining injury severities of traffic accidents

Spatio-temporal correlation and interaction are significant features of traffic accident data. To account for the spatio-temporal correlation and interaction and the ordinal nature simultaneously, spatio-temporal generalized ordered logit models with different temporal treatments are advocated to examine accident injury severities. In the context of Bayesian inference, we estimate the advocated models and compare them with traditional generalized ordered logit model and spatial generalized ordered logit model using six-year (2014-2019) accident data from the Dongguang section of Guangzhou-Shenzhen Coastal Freeway in China. The results indicate that there are significant spatio-temporal effects and interaction in the accident severity data, and that the spatio-temporal generalized ordered logit models yield low values of deviation information criterion and more reasonable parameter estimates than the generalized ordered logit and spatial generalized ordered logit models do. The estimation results reveal that covariates related to vehicle type, accident type, season, time, horizontal curvature, and bridge have significant effects on accident injury severities. The findings above demonstrate the superiority of the proposed spatio-temporal generalized ordered logit models for predicting accident severities.

Spatio-Temporal Relationship between Land Use Carbon Emissions and Ecosystem Service Value in Guanzhong, China

In the context of escalating global concerns for “carbon neutrality and peak carbon” and the urgent need for ecological conservation, deciphering the spatiotemporal interactions between carbon emissions and the ecosystem service value (ESV) in relation to land use changes becomes critically significant. Identifying areas to bolster ecosystem services and curtail carbon emissions, especially within the Guanzhong urban agglomeration, is crucial for advancing sustainable and low-carbon regional development. The study focuses on the urban agglomeration of Guanzhong, using land use and socio-economic data from three periods between 2010 and 2020. Methods such as grid analysis and bivariate spatial autocorrelation models are employed to explore the temporal and spatial evolution characteristics and interaction patterns of carbon emissions and ESV in relation to land use. The findings reveal: (1) during 2010–2020, the Guanzhong urban agglomeration experienced varied transitions in land use types, marked by a significant net decrease in arable land and net increases in grasslands and urban construction areas. (2) The ESV in the Guanzhong urban agglomeration witnessed a consistent rise, exhibiting a spatial distribution pattern with higher values in the southwest and lower in the northeast. Among the categorized ecosystem service functions, services related to hydrological and climate regulation stood out. (3) The Guanzhong urban agglomeration observed an average annual growth rate of 5.03% in carbon emissions due to land use, with a spatial trend that was higher in the center and tapered towards the periphery. Predominant carbon sources included arable lands and urban construction areas, while forests accounted for 94% of carbon sequestration. (4) A pronounced negative correlation between the ESV and carbon emissions was discerned in Guanzhong. Regions with a stronger correlation were primarily centered in Guanzhong, notably around Xi’an and Baoji. The results emphasize the pivotal role of the primary sector’s qualitative development in harmonizing the ESV and carbon emission dynamics in the Guanzhong urban agglomeration. This research provides valuable insights for optimizing land resource management, aligned with the rural revitalization strategy, streamlining carbon dynamics, bolstering ESV, augmenting carbon sequestration efficiency, and guiding ecological spatial planning.