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While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

A large body of literature has examined specificity and transfer of perceptual learning, suggesting a complex picture. Here, we distinguish between transfer over variations in a task-relevant feature (e.g., transfer of a learned orientation task to a different reference orientation) and transfer over a task-irrelevant feature (e.g., transfer of a learned orientation task to a different retinal location or different spatial frequency), and we focus on the mechanism for the latter. Experimentally, we assessed whether learning a judgment of one feature (such as orientation) using one value of an irrelevant feature (e.g., spatial frequency) transfers to another value of the irrelevant feature. Experiment 1 examined whether learning in eight-alternative orientation identification with one or multiple spatial frequencies transfers to stimuli at five different spatial frequencies. Experiment 2 paralleled Experiment 1, examining whether learning in eight-alternative spatial-frequency identification at one or multiple orientations transfers to stimuli with five different orientations. Training the orientation task with a single spatial frequency transferred widely to all other spatial frequencies, with a tendency to specificity when training with the highest spatial frequency. Training the spatial frequency task fully transferred across all orientations. Computationally, we extended the identification integrated reweighting theory (I-IRT) to account for the transfer data (Dosher, Liu, & Lu, 2023; Liu, Dosher, & Lu, 2023). Just as location-invariant representations in the original IRT explain transfer over retinal locations, incorporating feature-invariant representations effectively accounted for the observed transfer. Taken together, we suggest that feature-invariant representations can account for transfer of learning over a task-irrelevant feature.

Despite numerous studies in the field of dementia and Alzheimer's disease (AD), a comprehensive understanding of this devastating disease remains elusive. Bulk transcriptomics have provided insights into the underlying genetic factors at a high level. Subsequent technological advancements have focused on single-cell omics, encompassing techniques such as single-cell RNA sequencing and epigenomics, enabling the capture of RNA transcripts and chromatin states at a single cell or nucleus resolution. Furthermore, the emergence of spatial omics has allowed the study of gene responses in the vicinity of amyloid beta plaques or across various brain regions. With the vast amount of data generated, utilizing gene regulatory networks to comprehensively study this disease has become essential. This review delves into some techniques employed in the field of AD, explores the discoveries made using these techniques, and provides insights into the future of the field.

Traumatic brain injury (TBI) is a leading cause of death and disability, primarily caused by falls and motor vehicle collisions (MVC). However, understanding the spatial distribution of TBI risk and their correlation with area-based deprivation remains limited. This study addresses this gap by employing a Bayesian spatial modeling approach to analyze the spatial patterns of fall and MVC-related TBI and their associations with area-based deprivation. Data on TBI cases from 2003 to 2019 in the Halifax Regional Municipality were sourced from the Nova Scotia Trauma Registry. Patients' residential postal codes were geocoded to dissemination areas (DAs), and area-based deprivation status from the national census data was linked to DAs. Bayesian spatial models were utilized to analyze TBI incidence rates resulting from falls and MVCs across DAs. The analysis revealed a substantial spatial disparity in TBI risk, illustrated through maps showing both the relative risk and posterior exceedance probability. Furthermore, area-based deprivation emerged as a significant factor associated with TBI risk. Specifically, areas within the most economically deprived quintile increased the risk of fall related TBI nearly threefold (RR = 2.7, 95%CI 1.98–3.66) compared to those in the least deprived quintile. The most residentially unstable quintile had a 35% greater risk of fall-related TBI than the least deprived quintile (RR = 1.35, 95%CI = 1.01–1.80). The risk of MVC-related TBI increased by 71% among areas in the second most deprived quintile (RR = 1.71; 95% CI 1.18–2.49), highlighting situational vulnerability as a significant risk factor. Deprivation was mapped within a social determinants of health framework to understand the downstream effects of residential instability, economic dependency, and situational vulnerability. This study reveals the unique spatial risk patterns of TBI resulting from falls and MVCs and demonstrates the impact of areal-level deprivation on injury risk. By identifying high-risk areas and understanding the pathways through which deprivation acts on the physical environment and community behavior, public health interventions can be developed to mitigate injury risk. [ABSTRACT FROM AUTHOR]

Vaccination is a crucial public health intervention protecting individuals and communities from vaccine-preventable diseases. However, unvaccinated children in low- and middle-income countries pose a significant challenge. Ethiopia, a Global Alliance for Vaccines and Immunisation (GAVI)-supported country, ranks fifth in zero-dose immunisation burden, indicating concerning vaccine coverage gaps. Despite the severity of this issue, there is a dearth of research investigating the disparities, prevalence and contributing factors associated with zero-dose children in Ethiopia. This study aimed to assess the prevalence, spatial distribution and determinants of zero-dose children in Ethiopia. A community-based cross-sectional study was conducted using data from the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS). Data were collected from 21 March 2019 to 28 June 2019. The study included a total of 1334 children aged 12–35 months (weighted sample). For spatial and multilevel analyses, ArcGIS 10.8 and Stata 17 software were used, respectively. The measure of association was determined by computing the adjusted odds ratio (AOR) at a 95 % confidence interval (95 % CI), and a p-value <0.05 was considered statistically significant. The prevalence of zero-dose children in Ethiopia was 46.5 % (95 % CI: 43.8, 49.2). Southeast Amhara, Afar, Somali, Oromia and SNNPR (Southern Nations, Nationalities and Peoples' Region) regions had high zero-dose proportions. Maternal age 15–19 years (AOR = 1.63; 95 % CI: 1.05, 2.64), lack of antenatal care (AOR = 1.77; 95 % CI: 1.34, 2.35), rural residence (AOR = 1.94; 95 % CI: 1.17, 3.19) and region were significantly associated to zero-dose status in Ethiopia. The prevalence of zero-dose children in Ethiopia was high and the distribution exhibited significant variation across the country's clusters. Individual and community factors were key contributors. It is essential that areas with a high prevalence of zero-dose children have access to recommended childhood vaccines. This proactive approach can help protect children from morbidity and mortality caused by vaccine-preventable diseases. [ABSTRACT FROM AUTHOR]

The long-term monitoring of freshwater ecosystems has been globally recognized for its critical role in preserving natural biodiversity and human well-being over the past century, yet monitoring efforts remain heterogeneously distributed. The Anatolia region in Turkey is known to host three out of the world's 34 biodiversity hotspots, boasting high biodiversity, but is increasingly threatened by anthropogenic effects and invasive freshwater fish species. Although the introduction of non-native fish species and pollution has detrimental effects on different river catchments in this area, long-term biomonitoring studies in Anatolia remain scarce and inconsistent. Collecting information of the Gediz River basin, a major Anatolian river catchment as a model that has been significantly impacted by human activities, published between 1971 and 2023, a total of 21 fish species from 12 families were identified. The spatial and temporal occurrences of these species fluctuated significantly due to heterogeneous sampling efforts that did not follow a clear pattern, indicating a complex ecological and conservation scenario characterized by both spatial and temporal variability in species prevalence, endemism and abundance. The presented results emphasize the need for comprehensive long-term biodiversity monitoring for anthropogenically impacted catchments like the Gediz River basin. Only with a coherent basis of high-resolution monitoring data can the multiple stressors threatening Anatolia's freshwater biodiversity be disentangled and, subsequently, be mitigated. Using the Gediz River basin as an example, this study emphasizes the urgent need for comprehensive, long-term biodiversity monitoring in understudied regions worldwide to protect regional biodiversity and ensure ecosystem health. [ABSTRACT FROM AUTHOR]

The survival of animals is impacted by landscapes of spatially varying mortality factors including habitat type, predation risk or harvest risk, among others. Characterization of these spatial mortality processes is important for managing animal populations and their habitats, yet this information has proved challenging to capture. Advances in telemetry now make it possible to monitor tagged individuals' locations through time for a wide range of taxa, providing opportunity to assess movement and mortality simultaneously through spatial time‐to‐event data. Existing time‐to‐event modelling frameworks have largely ignored spatially varying mortality processes or have treated an animal's location as fixed at a regional level.Here we present a general spatial survival model for analysing time‐to‐event data arising from animal telemetry. Our model has a flexible Cox regression form and can estimate the effects of conventional non‐spatial risk factors (size, seasonality, etc.), spatial covariates (e.g. habitat type) and spatial variation in risk not explained by covariates on mortality. We show how to fit our model using Bayesian methods and demonstrate applications of our model with several simulated examples and two animal telemetry case studies.Our model produced consistent and unbiased parameter estimates throughout simulations with a variety of spatial and non‐spatial hazards. In the first case study, data from 147 tagged caribou in British Columbia, Canada, revealed a spatially heterogeneous mortality landscape with caribou survival varying by elevation, likely in response to space use by predators. Our second case study involved a dataset of 390 acoustically tagged Atlantic cod in a southern Norwegian fjord where a marine protected area (MPA) was established during the study. We found that the MPA led to a shift from mostly fishing mortality to mostly natural mortality within the fjord and that these risks had markedly different spatial hazard patterns.Our spatial time‐to‐event model can make use of data from a variety of widely used telemetry technologies to characterize landscapes of mortality risks for different taxa. Our work provides new opportunities to inform the spatial ecology and management of fish and wildlife populations. [ABSTRACT FROM AUTHOR]

ABSTRACT Wolf spiders can learn simple spatial navigation tasks. Previous studies have shown that the wolf spider Tigrosa helluo can use environmental edge features (reference frame landmarks) to learn the location of a dry target in flooded T‐mazes; however, the relative importance of different types or numbers of landmark cues to spatial learning remains unknown. We used a modified open arena water maze and recorded the ability of adult female T. helluo to find a target reward (a dark and dry cup) among cups that were identical to the target but flooded. We measured variation in spatial learning by measuring time to target with no landmark (control), with a beacon (a landmark that is part of the target), with an associative cue (a landmark associated with a specific navigational action), and with both a beacon and an associative cue (N = 92 subjects, n = 23 per landmark cue treatment). For each treatment, we tested females for five trials each on four consecutive days, with the last trial on the fourth day having an altered target location, totaling 19 training trials and one reversal trial (1840 trials). We found that spiders took significantly less time to find the target over subsequent trials within a day and learned more quickly when landmark cues were present, but we found no difference in the type or number of landmark features in the meantime to target entrance. After learning a target location, moving the landmark significantly increased the mean time to target entrance in the combined beacon and associative cue treatment relative to other treatments. Our results indicate that wolf spiders use visual beacons and associative cue landmarks alone or in combination and that performance improves across trials when landmarks are present and deteriorates more when multiple landmarks are moved. [ABSTRACT FROM AUTHOR]

Tumor-infiltrating B cells play a significant role in tumor development, progression, and prognosis, yet a comprehensive classification system is lacking. To address this gap, we present a pan-cancer single-cell RNA sequencing (scRNA-seq) atlas of tumor-infiltrating B and plasma cells across a large sample cohort. We identify key B cell subset signatures, revealing distinct subpopulations and highlighting the heterogeneity and functional diversity of these cells in the tumor microenvironment. We explore associations between B cell subsets and checkpoint inhibitor therapy responses, finding subset-specific effects on overall response. Additionally, we examine B and T cell crosstalk, identifying unique ligand-receptor pairs for specific B cell subsets, spatially validated. This comprehensive dataset serves as a valuable resource, providing a detailed atlas that enhances the understanding of B cell complexity in tumors and opens new avenues for research and therapeutic strategies. [Display omitted] • A scRNA-seq atlas identifies ten clusters of intratumoral B and plasma cells • Specific B cell subsets are associated with checkpoint inhibitor therapy responses • Atlas-derived B cell gene signatures validated in spatial analyses • Publicly available Shiny tool enables users to interactively explore the atlas Fitzsimons et al. present a comprehensive pan-cancer single-cell RNA-seq atlas of intratumoral B cells, identifying ten distinct B and plasma cell clusters. They uncover associations with checkpoint inhibitor therapy response, explore B-T cell crosstalk, validate findings with spatial data, and offer an interactive tool for further research. [ABSTRACT FROM AUTHOR]

Background: Peste des petits ruminant (PPR) is a contagious disease caused by the peste des petits ruminants virus (PPRV). The disease poses a significant economic threat to small ruminant production in Ethiopia, particularly to the striving pastoral production system. A cross-sectional study was conducted to estimate the seroprevalence and associated risk factors of PPR in the small ruminants of the Borena Zone. A total of 384 serum samples were collected randomly from sheep and goats and examined for the presence of PPRV antibodies using competition enzyme-linked immune sorbent assay (c-ELISA). Additionally, a retrospective analysis of five years of outbreak data was performed to provide insight into the spatial and temporal distribution of the disease. Results: The seroprevalence of PPRV antibodies in nonvaccinated, vaccinated, and unknown vaccination status of small ruminants in this study was found to be 32.1%, 68.8%, and 45.5%, respectively. Multivariable logistic analysis revealed a statistically significant association between PPRV seropositivity and several factors, including age, animal origin, flock size, and veterinary services status. A retrospective analysis revealed 53 PPR outbreaks in the Borena Zone from 2018 to 2022, exacerbated by low vaccination coverage relative to the at-risk animal population. Conclusion: The study revealed significant gaps in current vaccination efforts, with herd immunity levels falling below the FAO-WOAH recommended threshold of 80%. Despite Ethiopia's ambitious goal to eradicate PPR by 2027, the frequent outbreaks and insufficient herd immunity highlight the inadequacy of the existing strategies. To effectively move toward eradication, Ethiopia must align its approach with the global PPR eradication framework, which emphasizes a comprehensive strategy that includes diagnostics, surveillance, prevention, and the establishment of a robust veterinary regulatory system, rather than relying solely on vaccination. Overcoming logistical challenges, improving vaccination coverage, and optimizing the timing of vaccination campaigns, especially in hard-to-reach areas, will be crucial for reducing outbreaks and making progress toward eradication. [ABSTRACT FROM AUTHOR]

The article "Spatial clustering of dengue cases during the 2024 epidemic in Brazil" published in the Journal of Travel Medicine explores the spatial distribution of dengue cases in Brazil during the severe 2024 epidemic. The study reveals significant spatial heterogeneity in dengue incidence rates, with clusters primarily in the South and Southeast regions. Lower coverage rates of primary healthcare and family health teams were associated with higher dengue incidence, highlighting the importance of robust healthcare systems in controlling vector-borne diseases. The findings emphasize the need for targeted public health interventions, including enhanced vector control, infrastructure improvements, community health education, and strategic vaccine distribution, to reduce the burden of dengue in Brazil. [Extracted from the article]

We examine the relationship between economic participation rates and rates of assaults, vandalisms, and drug offenses in neighborhoods in Forsyth County, N.C. We use a unique dataset containing assault, vandalism, and drug offense rates at the neighborhood level from 2013 to 2020. We estimate a two-way fixed effects spatial Durbin regression model relating crime rates to economic participation rates for the three types of crime. The results show that neighborhoods, proxied with census tracts, with higher economic participation rates among its residents have significantly lower crime rates. The relationship exists even after controlling for unobserved heterogeneity and spatial autocorrelation. Workforce programs that recruit individuals from neighborhoods with low economic participation rates may have the additional benefit of reducing crime. [ABSTRACT FROM AUTHOR]

Prepositions are among the most difficult parts of speech for ESL learners owing to their polysemous nature and how they behave in different languages. The present study aims to determine how English prepositions are represented in English Language textbooks designed by the National Council of Educational Research and Training (NCERT) for Classes I and II in India. For a comparison of the frequency of prepositions in English, we have selected the British National Corpus. The study revealed that though the choice of prepositions in the textbooks is as par with their occurrences in the standard English corpus, their senses are not always primary, i.e., spatial. We found instances of temporal and metaphorical senses as well. One significant finding of this study is the relatively very low frequency of temporal use of prepositions as compared to the proto-typical spatial sense in these textbooks. The metaphorical uses of some common prepositions, which are more abstract and complex, are higher in frequency, especially in the exercises for giving instructions. [ABSTRACT FROM AUTHOR]

Water quality of wetland ecosystems is a critical concern due to its implications on human health and aquatic life. This study was conducted to assess the spatial and temporal variation of water quality in Kotagala wetland to understand its impact on water safety for domestic uses and ecosystem health. Water samples were collected (n = 70) from six inlets and the wetland outlet from November 2021 to August 2022, and analyzed for pH, electrical conductivity (EC), salinity, total dissolved solids (TDS), total suspended solids (TSS), nitrate, and phosphate. The pH, EC, and nitrate showed significant correlations. Spatial clustering divided the monitoring sites into two clusters, as areas of higher and lower pollution levels. Discriminant analysis highlighted the significance of EC, pH, and nitrate concentrations in differentiating these clusters. Principal According to principal component analysis (PCA) 88.8% of the total variance of spatial variation in water quality was explained by the first two components. Temporal clustering following seasonal variations revealed the influence of rainfall pattern on water quality. EC, TDS, TSS, and nitrate concentrations emerged as the most important factors in this temporal categorization. In PCA, 76.8% of the total variance of temporal variation in water quality was explained by the first two components. Findings highlight the urgent need for sustainable strategies and policies to mitigate the impacts of human activities on wetland water quality, since water quality variations in the wetland was significantly impacted by direct human activities, and variations in rainfall trends in the area. [ABSTRACT FROM AUTHOR]

Agricultural nutrients nitrogen and phosphorus can subsequently be transported to waterways and are often managed through the adoption of best management practices (BMPs). However, we have a poor understanding of how the use of BMPs varies spatially and how BMP adoption might be related to nutrient yields in surface waters. To address this, we performed a survey of agricultural landowners across New York State and compared this with estimates of annual incremental nitrogen and phosphorus yields of agricultural origin from the Spatially Referenced Regressions On Watershed attributes (SPARROW) model. Using these socio-behavioral data and SPARROW predictions, we perform colocation analysis to identify areas where watersheds with high nutrient yield from agriculture are collocated with non-use of agricultural BMPs. This colocation analysis offers a novel methodology for identifying areas where monitoring of waterways and promotion of best management practices could be targeted to achieve the greatest benefits. [ABSTRACT FROM AUTHOR]

Structured Abstract: Background: Transcranial Magnetic Stimulation (TMS) is a widely used tool to explore the causal role of focal brain regions in cognitive processing. TMS effects over attentional processes are consistent and replicable, while at the same time subjected to individual variability. This individual variability needs to be understood to better comprehend TMS effects, and most importantly, its clinical applications. Objective: This study aimed to explore the role of white matter variability in TMS neuromodulatory effects on behavior in healthy participants (N = 50). Methods: Participants completed an attentional task in which orienting and alerting cues preceded near-threshold targets. Continuous Theta Burst Stimulation (cTBS) was applied over the left frontal eye field (FEF) or an active vertex condition. White matter was explored with diffusion-weighted imaging tractography and Tract-Based Spatial Statistics (TBSS). Results: Behaviorally, TMS over the left FEF slowed down reaction times (especially in the alerting task), impaired accuracy in the objective task, and reduced the proportion of seen targets (as compared to the vertex condition). Attentional effects increased, overall, when TMS was applied to the left FEF as compared to the vertex condition. Correlations between white matter and TMS effects showed i) reduced TMS effects associated with the microstructural properties of long-range white matter pathways such as the superior longitudinal fasciculus (SLF), and interhemispheric fibers of the corpus callosum (CC), and ii) increased TMS effects in participants with high integrity of the CC connecting the stimulated region with the opposite hemisphere. Additionally, variability in attentional effects was also related to white matter, showing iii) increased alerting effects in participants with low integrity of association, commissural, and projection fibers, and iv) increased orienting effects in participants with high integrity of the right SLF III. Conclusion: All these observations highlight the importance of taking into account individual variability in white matter for the understanding of cognitive processing and brain neuromodulation effects.

The primary aim of this investigation is to examine the historical (1989–2020) and future trends and magnitude of Reference Evapotranspiration (ET0) in terms of spatiotemporal measures, considering its significance as a hydro-meteorological parameter influenced by changing climate. The FAO-56 Penman-Monteith method is employed to analyze ET0, while the Modified Mann Kendall test is utilized to assess trends and Sen’s Slope Estimator is used for magnitude analysis. The future prediction is conducted using Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) models. Results show an increasing ET0 trend annually in the southeastern and northeastern zones, while decreased values are observed in other regions, particularly in the northwest (0.83 mm). Sen’s slope estimator reveals distinct fluctuations in ET0, with notable disparities in the Southeastern, Northeastern, Southwestern, and South-Central zones. Notably, Chattogram and Sitakunda experience an ET0 magnitude of 0.02 mm/Year, while other zones show a magnitude of 0.01 mm/Year. SVM outperformed other models, predicting rising ET0 in various seasons. These findings offer insights for optimizing irrigation systems and sustainable water management under changing climatic conditions.

Abstract The survival of animals is impacted by landscapes of spatially varying mortality factors including habitat type, predation risk or harvest risk, among others. Characterization of these spatial mortality processes is important for managing animal populations and their habitats, yet this information has proved challenging to capture. Advances in telemetry now make it possible to monitor tagged individuals' locations through time for a wide range of taxa, providing opportunity to assess movement and mortality simultaneously through spatial time‐to‐event data. Existing time‐to‐event modelling frameworks have largely ignored spatially varying mortality processes or have treated an animal's location as fixed at a regional level. Here we present a general spatial survival model for analysing time‐to‐event data arising from animal telemetry. Our model has a flexible Cox regression form and can estimate the effects of conventional non‐spatial risk factors (size, seasonality, etc.), spatial covariates (e.g. habitat type) and spatial variation in risk not explained by covariates on mortality. We show how to fit our model using Bayesian methods and demonstrate applications of our model with several simulated examples and two animal telemetry case studies. Our model produced consistent and unbiased parameter estimates throughout simulations with a variety of spatial and non‐spatial hazards. In the first case study, data from 147 tagged caribou in British Columbia, Canada, revealed a spatially heterogeneous mortality landscape with caribou survival varying by elevation, likely in response to space use by predators. Our second case study involved a dataset of 390 acoustically tagged Atlantic cod in a southern Norwegian fjord where a marine protected area (MPA) was established during the study. We found that the MPA led to a shift from mostly fishing mortality to mostly natural mortality within the fjord and that these risks had markedly different spatial hazard patterns. Our spatial time‐to‐event model can make use of data from a variety of widely used telemetry technologies to characterize landscapes of mortality risks for different taxa. Our work provides new opportunities to inform the spatial ecology and management of fish and wildlife populations.

The increase in world temperature or global warming is a form of imbalance in the average temperature on Earth. The increase in air temperature will increase the risk of disasters, which will occur more frequently in the future. Rising global temperatures are expected to cause changes that can have fatal consequences. To anticipate the dangers are predicted by predicting the future air temperature increase. One of the methods that can be used is spatial extreme value theory, which uses the Gaussian copula model approach and Student's t copula, where the choice of these two methods was based on the flexibility they offer in capturing tail dependencies due to their capacity to describe the dependence structure between many variables simultaneously. . This makes it possible to get a return level or predicted value of air temperature by considering the elements of location in it. This research discusses both approaches and uses the maximum likelihood estimation (MLE) and pseudo maximum likelihood estimation (PMLE) methods to estimate the parameters. In addition, since spatial elements need to be considered, the trend surface model is also used. Akaike information criterion (AIC) is used to determine the best model for predicting air temperature based on extreme n air temperature data in East Java Province from nine air temperature observation stations. The results show that the highest air temperature value is around the Banyuwangi temperature observation station located in Banyuwangi Regency in the next two-year return period. The AIC results show that the best model produced is the Gaussian copula approach with a smaller AIC value than the student's t-copula approach, which is 8.0174. This value used to compare the relative quality of different statistical models with a lower AIC value generally indicates a better-fitting model.This value with a lower AIC value generally indicates a better-fitting model.

Abstract Introduction Cambodia is a low-income country with limited access to family planning (FP) in terms of modern contraceptive utilization. Despite several FP programs designed to make contraceptives accessible, adoption of contraceptives has been difficult in Cambodia, which has high rates of fertility and maternal mortality. This gap in essential care can put women and adolescent girls at greater risk of adverse outcomes such as stillbirth, spontaneous abortion, unwanted pregnancy, or even maternal death. One of the goals set by the government to decrease both maternal and child mortality and morbidity was increasing the modern contraceptives utilization. So, the main objective of this study was to evaluate spatial variations in modern contraceptives utilization and its contributing factors among women. Methods Data from the Cambodia 2021–22 Demographic and Health Survey datasets were used for secondary data analysis. A total of 19,496 women of reproductive age participated in the study. A spatial and multilevel mixed effects analysis was done on the factors affecting modern contraceptives utilization among Cambodian women. Finally, the percentage, odd ratio, together with their 95% confidence intervals and the results of the spatial analysis were provided. Result The prevalence of modern contraceptive use was 31.2% in Cambodia. Living in an urban area [AOR = 1.224; 95% CI = (1.126.1.330); P = 0.0001]; being married [AOR = 34.131; 95% CI= (12.673, 91.921); P = 0.0001]; and having a history of terminated pregnancy [AOR = 1.137; 95% CI= (1.055, 1.225); P = 0.0001] were found to be positively associated with modern contraceptive utilization. In contrast to this, being between the age range of 46–49 [AOR = 0.421; 95% CI = (0.364, 0.487); P = 0.0001]; being a female-headed household [AOR = 0.784; 95% CI = (0.723, 0.850); P = 0.0001]; and current breast feeding [AOR = 0.84; 95% CI = (0.75, 0.93); P = 0.010] were found to be negatively associated with modern contraceptive utilization. Additionally, the spatial analysis of modern contraceptive utilization showed that a higher proportion was utilized in the southern and southwest regions of Cambodia. Conclusion In this study, living in urban area, being married and having history of terminated pregnancy were found positively associated with modern contraceptive utilization. In contrast to this, being old age, being female headed household and being currently breast-feeding women were found negatively associated with modern contraceptive utilization. In addition to this, there were geographic (spatial) variations in modern contraceptive utilization among Cambodian’s women across the country.

Abstract Background Peste des petits ruminant (PPR) is a contagious disease caused by the peste des petits ruminants virus (PPRV). The disease poses a significant economic threat to small ruminant production in Ethiopia, particularly to the striving pastoral production system. A cross-sectional study was conducted to estimate the seroprevalence and associated risk factors of PPR in the small ruminants of the Borena Zone. A total of 384 serum samples were collected randomly from sheep and goats and examined for the presence of PPRV antibodies using competition enzyme-linked immune sorbent assay (c-ELISA). Additionally, a retrospective analysis of five years of outbreak data was performed to provide insight into the spatial and temporal distribution of the disease. Results The seroprevalence of PPRV antibodies in nonvaccinated, vaccinated, and unknown vaccination status of small ruminants in this study was found to be 32.1%, 68.8%, and 45.5%, respectively. Multivariable logistic analysis revealed a statistically significant association between PPRV seropositivity and several factors, including age, animal origin, flock size, and veterinary services status. A retrospective analysis revealed 53 PPR outbreaks in the Borena Zone from 2018 to 2022, exacerbated by low vaccination coverage relative to the at-risk animal population. Conclusion The study revealed significant gaps in current vaccination efforts, with herd immunity levels falling below the FAO-WOAH recommended threshold of 80%. Despite Ethiopia’s ambitious goal to eradicate PPR by 2027, the frequent outbreaks and insufficient herd immunity highlight the inadequacy of the existing strategies. To effectively move toward eradication, Ethiopia must align its approach with the global PPR eradication framework, which emphasizes a comprehensive strategy that includes diagnostics, surveillance, prevention, and the establishment of a robust veterinary regulatory system, rather than relying solely on vaccination. Overcoming logistical challenges, improving vaccination coverage, and optimizing the timing of vaccination campaigns, especially in hard-to-reach areas, will be crucial for reducing outbreaks and making progress toward eradication.

A coastal rice field is generally characterized by salt-affected soil and low soil quality for rice cultivation. Identifying soil chemical properties in these areas is necessary to determine soil management options for rice production. Therefore, soil samples were collected from 33 sampling points in the Wedung Sub-district of Demak Regency in the late dry season of 2021 to evaluate the variation among soil chemical characteristics in a coastline rice field. Soil samples were obtained beneath the topsoil (0-20 cm soil depth) and observed for electrical conductivity, exchangeable potassium, sodium, calcium, magnesium, and cation exchange capacity. Soils in the research field were categorized as slightly salty (0.75-2 dS m-1) to lightly salty (2.0-4.0 dS m-1) with very high sodium (>2 cmol(+) kg-1). Exchangeable potassium was dominated by moderate (0.3-0.7 cmol(+) kg-1) and low categories (0.2-0.3 cmol(+) kg-1). Based on soil calcium-to-magnesium ratios, around 6% of all samples were classified as calcium-deficient. The range of soil cation exchange capacity was 22-30 cmol(+) kg-1 and classified as high soil cation exchange capacity. Rice productivity in the salt-affected soil was around 4 t ha-1. Strategies for soil and controlling plants, such as soil amelioration and salt-tolerant rice cultivars, should be pursued to support plant growth and enhance rice productivity in the salt-affected soil, particularly in the coastal area.

Background: This study adds to the current literature by using a novel device-based method to explore where preschool children are physically active outside of home and childcare settings. This study combined accelerometry with geospatial data to explore the influence of the environment on preschool children's physical activity by objectively identifying the locations where preschool children engage in moderate to vigorous physical activity (MVPA) within and outside of their neighborhood. Methods: Accelerometry, Global Positioning System, and Geographic Information Systems data from 168 preschool children aged 2–5 years were processed in ArcGIS Pro to identify locations (per 25 × 25-m fishnet cell) with high MVPA counts. Locations with high MVPA counts were defined as those with the top 20% of MVPA counts per fishnet cell. The land use for high MVPA count location was determined for 3 domains: <500 m from home, 500 to 1600 m from home, and >1600 m from home. Results: Locations with high MVPA counts <500 m from home were playgrounds (66.6%), schools (16.7%), and parks (16.7%). Locations with high MVPA counts 500 to 1600 m from home included playgrounds (33.3%), nonhome residential (29.6%), childcare centers (11.1%), and parks (3.7%). Locations with high MVPA counts >1600 m from home included nonhome residential (54.7%), sports and recreation centers (11.1%), playgrounds (6.8%), and parks (5.3%). Conclusions: Our findings highlight that local parks and playgrounds provide physical activity opportunities for preschool children, yet beyond the local neighborhood, the homes of others are important locations for preschool children to accumulate MVPA. These findings can be used to inform the design of current and future neighborhood places to better accommodate preschool children's MVPA. [ABSTRACT FROM AUTHOR]

Abstract Dengue, a zoonotic viral disease transmitted by Aedes mosquitoes, poses a significant public health concern throughout the Lao People’s Democratic Republic (Lao PDR). This study aimed to describe spatial–temporal patterns and quantify the effects of environmental and climate variables on dengue transmission at the district level. The dengue data from 2015 to 2020 across 148 districts of Lao PDR were obtained from the Lao PDR National Center for Laboratory and Epidemiology (NCLE). The association between monthly dengue occurrences and environmental and climate variations was investigated using a multivariable Zero-inflated Poisson regression model developed in a Bayesian framework. The study analyzed a total of 72,471 dengue cases with an incidence rate of 174 per 100,000 population. Each year, incidence peaked from June to September and a large spike was observed in 2019. The Bayesian spatio-temporal model revealed a 9.1% decrease (95% credible interval [CrI] 8.9%, 9.2%) in dengue incidence for a 0.1 unit increase in monthly normalized difference vegetation index at a 1-month lag and a 5.7% decrease (95% CrI 5.3%, 6.2%) for a 1 cm increase in monthly precipitation at a 6-month lag. Conversely, dengue incidence increased by 43% (95% CrI 41%, 45%) for a 1 °C increase in monthly mean temperature at a 3-month lag. After accounting for covariates, the most significant high-risk spatial clusters were detected in the southern regions of Lao PDR. Probability analysis highlighted elevated trends in 45 districts, emphasizing the importance of targeted control strategies in high-risk areas. This research underscores the impact of climate and environmental factors on dengue transmission, emphasizing the need for proactive public health interventions tailored to specific contexts in Lao PDR.

Abstract Osteosarcomas are intricate cellular ecosystems, where heterotypic interactions significantly influence disease progression and therapeutic outcomes. Despite their importance, a detailed understanding of their cellular composition and organizational structure remains elusive. In this study, we provide a comprehensive single-cell and spatially resolved transcriptomics analysis of human osteosarcomas. We construct a cellular meta-map to dissect spatial transcriptomic data, unveiling a detailed atlas of osteosarcoma compositional subgroups. We meticulously characterize the unique gene signatures and functional states of each subgroup and investigate the impact of chemotherapy on these cellular subpopulations. Additionally, our spatial transcriptomics analysis identifies a distinct spatial niche, located at the forefront of tumor necrotic zones, potentially associated with chemotherapy resistance. We also delve into the crosstalk between different cellular subgroups. This study furnishes a comprehensive transcriptional atlas of osteosarcoma's cellular architecture, enriching our comprehension of its complexity and laying the groundwork for more targeted therapeutic approaches.

Abstract High childhood disease prevalence and under-five mortality rates have been consistently reported in Nigeria. Vaccination is a cost-effective preventive strategy against childhood diseases. Therefore, this study aimed to identify the determinants of Incomplete Vaccination (IV) among children aged 12–23 months in Nigeria. This cross-sectional design study utilized the 2018 Nigeria Demographic and Health Survey (NDHS) dataset. A two-stage cluster sampling technique was used to select women of reproductive age who have children (n = 5475) aged 12–23 months. The outcome variable was IV of children against childhood diseases. Data were analyzed using Integrated Nested Laplace Approximation and Bayesian binary regression models (α0.05). Visualization of incomplete vaccination was produced using the ArcGIS software. Children’s mean age was 15.1 ± 3.2 months and the median number of vaccines received was four. Northern regions contributed largely to the IV. The likelihood of IV was lower among women aged 25–34 years (aOR = 0.67, 95% CI = 0.54–0.82, p

Introduction: Mycobacterium tuberculosis bacteria can cause tuberculosis disease. In 2022, East Java Province reported 8,398 cases of child tuberculosis with a discovery coverage of 105.6%. This study aims to describe the distribution of child tuberculosis incidence in East Java Province by mapping and analyzing the correlation between livable houses, BCG immunization coverage, and population density with the incidence of child tuberculosis in 38 districts/cities in East Java Province from 2020 to 2022. Methods: This was a descriptive approach with a population correlation study design using the Spearman correlation statistical test. Secondary records from the East Java Health Profile 2020, 2021, and 2022 were used in this study. Thirty-eight districts/cities in the province of East Java comprised the study's population. Results and Discussion: This study showed that there has been a positive correlation between BCG immunization coverage in 2020 (r=0.043); 2021 (r=0.140); 2022 (r=0.214) and population density 2020 (r=0.082); 2021 (r=0.102); and 2022 (r=0.174) with the incidence of child tuberculosis. Meanwhile, the correlation between livable houses and the incidence of child tuberculosis is negative in 2020 (r=-0.990) and 2022 (r=-0.047) but positive in 2021 (r=0.078). Conclusion: The increase in BCG immunization coverage and population density aligns with the increase in child tuberculosis cases. Meanwhile, an increase in the percentage of access to livable houses is followed by a decrease in the incidence of child tuberculosis.

As the value ranges presented in the literature with tables and graphs that feature soil index properties related to the soil swelling potential are intertwined, their interpretation may pose certain challenges. In this study, the spatial distribution maps of soil swelling potential were created using soil index data obtained from this research, and those values of ranges from the swelling potential of the study area were assessed by combining all maps, resulting in a single comprehensive map and new limit ranges in the high plasticity cohesive soils. Soils in the study area were evaluated according to the newly determined limit value ranges. The findings show that the high plasticity cohesive soils in the region should have swelling potential in all parameters of the new limit value ranges to show swelling potential. The limit values for high plasticity cohesive soils to demonstrate a high swelling potential are: % natural water content 90, liquid limit >65, plasticity index >21, swelling pressure >240 kPa, and % swelling percentage >6.

Abstract Introduction The United Nations established the Sustainable Development Goals (SDGs) in 2015 to enhance global development. In this study, we examine an SDG indicator: the percentage of women aged 15–49 whose family planning needs are met by modern contraception (mDFPS). We evaluate both the factors influencing its coverage and its progress since 2015. Methods We used nationally representative surveys data (Demographic and Health Surveys (DHS) and Performance Monitoring for Action (PMA)) from Ethiopia, Kenya, and Nigeria. We assessed predictors of mDFPS. We also computed mDFPS coverage across countries and subnational areas, assessing coverage changes from the SDGs onset to the most recent period, using a Bayesian model-based geostatistical approach. We assessed whether the subnational areas exceeded the minimum recommended WHO mDFPS coverage of 75%. Results Varied individual and community-level determinants emerged, highlighting the countries’ uniqueness. Factors such as being part of a female-headed household, and low household wealth, lowered the odds of mDFPS, while rural-residence had low odds only in Ethiopia and Nigeria. The results indicate mDFPS stagnation in most administrative areas across the three countries. Geographic disparities persisted over time, favouring affluent regions. The predicted posterior proportion of mDFPS and exceedance probability (EP) for WHO target for Ethiopia was 39.85% (95% CI: [4.51, 83.01], EP = 0.08) in 2016 and 46.28% (95% CI: [7.15, 85.99], EP = 0.13) in 2019. In Kenya, the adjusted predicted proportion for 2014 was 30.19% (95% CI: [2.59, 80.24], EP = 0.06) and 44.16% (95%CI: [9.35, 80.24], EP = 0.13) in 2022. In Nigeria, the predicted posterior proportion of mDFPS was 17.91% (95% CI: [1.24, 61.29], EP = 0.00) in 2013, and it was 23.08% (95% CI: [1.80, 56.24], EP = 0.00) in 2018. None of the sub-national areas in Ethiopia and Nigeria exceeded the WHO target. While 9 out of 47 counties in Kenya in 2022 exceeded the WHO mDFPS target. Conclusion The study unveils demographic, geographic, and socioeconomic mDFPS disparities, signalling progress and stagnation across administrative areas. The findings offer policymakers and governments insights into targeting interventions for enhanced mDFPS coverage. Context-specific strategies can address local needs, aiding SDG attainment.

Abstract Under-five children’s diarrhea is a significant public health threat and the World Health Organization (WHO) reported it as the second leading cause of children’s death worldwide. In this study area, little is known about the spatiotemporal distribution of under-5 diarrhea incidence. This study was therefore, conducted among all districts in the Central Gondar zone to assess the spatial, temporal, and spatiotemporal variation in diarrhea incidence among under-five children in the Central Gondar zone. The data for children under 5 years of age with diarrhea was obtained from Central Gondar Zone Health Department diarrhea reports from January 2019 to December 2022. All districts were included and geo-coded. The spatial data were created in ArcGIS 10.8.1. Global and local spatial autocorrelation were used to detect hot spots and cold spots. The Poisson model was generated by applying the Kulldorff method in SaTScan™9.6 to analyse the the purely temporal, spatial, and space-time clusters. The study revealed spatial variation of under-5 diarrhea where Gondar City, Gondar Zuria, East Dembia, and Lay Armacho districts were the high-rate spatial clusters during the study period. A year search window for temporal scan statistic identified 01 January 2020-30 December 2021 as risk periods across all districts. Spatiotemporal scan statistics detected high-rate clusters at Gondar City, Gondar Zuria, East Dembia, Lay Armacho, and Alefa between 2019 and 2022. In conclusion, there has been a spatial, temporal, and spatiotemporal variability of under-5 children’s diarrhea in the Central Gondar Zone. Interventional and preventive strategies should be developed and given priority to the areas that has been detected as a hotspot in this study to reduce the mortality and morbidity of under 5 children.

AbstractBuilding upon prior work, we propose an alternative way to look at the pattern of spatial crime concentration and temporal stability of it. We first identify a high-crime cluster using the sample block groups in New York City by employing a k-means clustering method. We then examine the temporal stability of the high-crime cluster over time. We also longitudinally assess how our high-crime cluster classification is associated with the actual amount of crime while accounting for the measures of social and physical environments. We observed that about 6–12% of total areas are identified to be in the high-crime cluster. We also found that block groups identified to be high-crime cluster in one year are more likely to be that way in the next year. We hope future research may consider using data-driven approaches to expand understanding of spatial and temporal crime patterns. [ABSTRACT FROM AUTHOR]

Avian influenza (AI) is a viral infection that profoundly affects global poultry production. This study aimed to identify the spatial and temporal factors associated with AI in Thailand, using a geographic information system (GIS)–based multi‐criteria decision analysis (MCDA) approach. We discovered that high‐risk areas for AI were primarily concentrated in the central and lower northern regions of the country, with fewer occurrences in the northeastern and southern regions. Model validation using historical outbreak data showed moderate agreement (AUC = 0.60, 95% CI = 0.58–0.61). This study provides valuable insights for planning national AI surveillance programs and aiding in disease prevention and control efforts. The efficiency and effectiveness of disease surveillance at the national level can be improved using this GIS‐based MCDA, in conjunction with temporal risk factor analysis. [ABSTRACT FROM AUTHOR]

Dengue, a zoonotic viral disease transmitted by Aedes mosquitoes, poses a significant public health concern throughout the Lao People's Democratic Republic (Lao PDR). This study aimed to describe spatial–temporal patterns and quantify the effects of environmental and climate variables on dengue transmission at the district level. The dengue data from 2015 to 2020 across 148 districts of Lao PDR were obtained from the Lao PDR National Center for Laboratory and Epidemiology (NCLE). The association between monthly dengue occurrences and environmental and climate variations was investigated using a multivariable Zero-inflated Poisson regression model developed in a Bayesian framework. The study analyzed a total of 72,471 dengue cases with an incidence rate of 174 per 100,000 population. Each year, incidence peaked from June to September and a large spike was observed in 2019. The Bayesian spatio-temporal model revealed a 9.1% decrease (95% credible interval [CrI] 8.9%, 9.2%) in dengue incidence for a 0.1 unit increase in monthly normalized difference vegetation index at a 1-month lag and a 5.7% decrease (95% CrI 5.3%, 6.2%) for a 1 cm increase in monthly precipitation at a 6-month lag. Conversely, dengue incidence increased by 43% (95% CrI 41%, 45%) for a 1 °C increase in monthly mean temperature at a 3-month lag. After accounting for covariates, the most significant high-risk spatial clusters were detected in the southern regions of Lao PDR. Probability analysis highlighted elevated trends in 45 districts, emphasizing the importance of targeted control strategies in high-risk areas. This research underscores the impact of climate and environmental factors on dengue transmission, emphasizing the need for proactive public health interventions tailored to specific contexts in Lao PDR. [ABSTRACT FROM AUTHOR]

A field study was conducted on the course of the Shatt al-Arab for the period from September 2021 to August 2022. to track the space-time changes in the quality of moving water in the course of the Shatt al-Arab under the influence of the movement of tidal cycles and their interference with fresh water coming from the Tigris River. through four study stations located in the areas of Al-Sada Al-Nour, the paper factory, Al-Ashar, and Sehan. and the study aimed to find out the extent of variation in the values of salinity. the concentration of dissolved solids and concentrations of some Cations and anions spatially and temporally and the extent to which they are affected by the movement of tides. Aquatic samples were collected from the study stations according to the four seasons of the study period and from three depths and three repeats from each depth. The results of the statistical analysis showed significant differences in the values of all studied vocabulary, namely EC, pH, Ca+2, Mg+2, TH, Na+1, HCO3-1, Cl-1, SO4-2 based on the stations and seasons studied, and the values of the studied vocabulary showed a spatial and temporal variation, as the salinity values increased as we headed south towards the mouth of the river and the ranges of values reached 1.92-8.45 dS-1 .The pH values tended to be light basal and ranged between 7.79 – 8.06, and the concentrations of cations and anions varied spatially and temporally as they were affected by the movement of the tides and the values of the concentrations ranged between 116.43 - 661.67, 35.16 -147.62, 167.67 - 1118.91, 221.43-2337.53 and 344.88 - 1073.68 mg L-1 for ions Ca+2, Mg+2, Na+1, Cl-1, SO4-2 respectively. [ABSTRACT FROM AUTHOR]

In this study, we ran a meta-analysis of neuroimaging studies to pinpoint the neural regions that are commonly activated across space, time, and numerosity, and we tested the existence of gradient transitions among these magnitude representations in the brain. Following PRISMA guidelines, we included in the meta-analysis 112 experiments (for space domain), 114 experiments (time domain), and 115 experiments (numerosity domain), and we used the activation likelihood estimation method. We found a system of brain regions that was commonly recruited in all the three magnitudes, which included bilateral insula, the supplementary motor area (SMA), the right inferior frontal gyrus, and bilateral intraparietal sulci. Gradiental transitions between different magnitudes were found along all these regions but insulae, with space and numbers leading to gradients mainly over parietal regions (and SMA) whereas time and numbers mainly over frontal regions. These findings provide evidence for the GradiATOM theory (Gradient Theory of Magnitude), suggesting that spatial proximity given by overlapping activations and gradients is a key aspect for efficient interactions and integrations among magnitudes. [ABSTRACT FROM AUTHOR]

Seaweed has the potential to become a significant fisheries commodity in the coastal areas of Bone Regency. This study aimed to analyze the socioeconomic aspects of seaweed cultivation in Bone Regency as part of a strategy for its sustainable management. The research was conducted in three villages known for their seaweed production: Palette Village, Mallari Village, and Waetuwo Village. Data were collected using purposive sampling techniques, including observation and interviews with 90 seaweed farmers. Socioeconomic data were analyzed using percentage and SWOT analysis, while spatial analysis was used to examine the distribution of respondents and cultivation locations. The results showed that seaweed management strategies in Bone Regency fall within quadrant 1 of the SO (Strengths-Opportunities) strategy. This strategy focuses on increasing community involvement in government programs that support the development of seaweed as a key commodity, leveraging local knowledge and experience. The recommendations include developing training and education programs to improve cultivation skills, enhance productivity, and raise the quality of seaweed products to meet growing demand. Additionally, optimizing local and national marketing networks, implementing digital marketing strategies, and expanding access to international markets are essential. Finally, encouraging younger farmers to utilize land more effectively, in line with RZWP3K (spatial planning regulations), could increase the area available for seaweed cultivation. [ABSTRACT FROM AUTHOR]

Flashiness refers to the rapidity and frequency of fluctuations in river flow. It can provide insights into flooding, by capturing dramatic increases in river flow, as well as contaminant transport, relating to concentrations of diffuse pollution. Despite a very well gauged river system, there is limited research in Great Britain targeting this component of river flow. This study addresses that gap in knowledge, with a detailed spatio-temporal analysis of river flashiness in Great Britain. Using 513 gauging stations, with historical records of at least 30 years, the average Richards–Baker flashiness index ( R B I ¯ ) was calculated for 1990–2020, showing an overall west- (0.6–0.8) to east-coast (0.1–0.2) gradient, being higher in the west (with the exception of some gauges in the south-east). Employing random forest models, the main predictor for flashiness was found to be soil composition, with some additional region-specific predictors. These include flood attenuation by reservoirs and catchment areas, affecting flashiness in the north and west of Great Britain. Additionally, using a subset of 208 gauging stations with data recorded from 1970 to 2020, a temporal analysis examined significant breakpoints and/or trends in yearly flashiness, using the Pettitt test and Mann–Kendall trend test, respectively. Increases in flashiness were found mainly in the north-east and south-west of Great Britain, with implications in flooding and river health. On a seasonal scale, and using a monthly R B I ¯ , the timing of flashy events was found to oscillate between autumn and spring over the 50 years, gravitating around winter. [ABSTRACT FROM AUTHOR]

Many ecosystems are defined and shaped by one or a few common, foundation species. Even though such species hold a key role in the restoration of these ecosystems, the demographic processes involved in their re‐establishment have rarely been studied. Foundation species' population dynamics, re‐establishment history, and the abiotic and biotic factors that affect individual establishment at restored sites can be studied by addressing population spatial patterns and age structure. Such an approach to studying population dynamics is particularly relevant for long‐lived species with low mortality, such as shrubs in alpine areas. We studied a population of the foundation species Empetrum nigrum ssp. hermaphroditum at an alpine spoil heap site and found evidence of population re‐establishment starting within a decade after construction. High Empetrum densities close to the spoil heap edges indicated that short distances to seed sources in the surroundings had a strong positive effect on establishment of individuals. Empetrum individuals were significantly clustered, which indicated intraspecific facilitation. As revealed by spatial analyses of recruits and older, established individuals, clustering developed gradually over time, which indicated a shift from no interaction to increased facilitation. We conclude that intraspecific facilitation promotes Empetrum reestablishment at the studied alpine spoil heap. Synthesis: We show that population spatial patterns and age structure can be successfully used to unveil the re‐establishment history of a foundation species in a restoration context. Efficient seed dispersal and intraspecific facilitation seem to be important factors behind Empetrum's successful re‐establishment at alpine spoil heaps. Identification of abiotic and biotic factors determining foundation species' establishment success at restored sites can support planning and improve success of restoration. [ABSTRACT FROM AUTHOR]

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime‐switching models that have abrupt regime‐switching governed by a Markov chain were developed to model volatility in financial time series data. Volatility modelling was also extended to spatially interconnected time series, resulting in spatial variants of ARCH models. This inspired us to propose a Markov switching framework of the spatio‐temporal log‐ARCH model. In this article, we discuss the Markov‐switching extension of the model, the estimation procedure and the smooth inferences of the regimes. The Monte Carlo simulation studies show that the maximum likelihood estimation method for our proposed model has good finite sample properties. The proposed model was applied to 28 stock indices' data that were presumably affected by the 2015–2016 Chinese stock market crash. The results showed that our model is a better fit compared to that of the one‐regime counterpart. Furthermore, the smoothed inference of the data indicated the approximate periods where structural breaks occurred. This model can capture structural breaks that simultaneously occur in nearby locations. [ABSTRACT FROM AUTHOR]

Small area estimation (SAE) has become an important tool in official statistics, used to construct estimates of population quantities for domains with small sample sizes. Typical area-level models function as a type of heteroscedastic regression, where the variance for each domain is assumed to be known and plugged in following a design-based estimate. Recent work has considered hierarchical models for the variance, where the design-based estimates are used as an additional data point to model the latent true variance in each domain. These hierarchical models may incorporate covariate information but can be difficult to sample from in high-dimensional settings. Utilizing recent distribution theory, we explore a class of Bayesian hierarchical models for SAE that smooth both the design-based estimate of the mean and the variance. In addition, we develop a class of unit-level models for heteroscedastic Gaussian response data. Importantly, we incorporate both covariate information as well as spatial dependence, while retaining a conjugate model structure that allows for efficient sampling. We illustrate our methodology through an empirical simulation study as well as an application using data from the American Community Survey. [ABSTRACT FROM AUTHOR]

Children with Attention Deficit Hyperactivity Disorder (ADHD) demonstrate a heterogeneous sensorimotor, emotional, and cognitive profile. Comorbid sensorimotor imbalance, anxiety, and spatial disorientation are particularly prevalent among their non-core symptoms. Studies in other populations presented these three comorbid dysfunctions in the context of vestibular hypofunction. To test whether there is a subgroup of children with ADHD who have vestibular hypofunction presenting with concomitant imbalance, anxiety, and spatial disorientation. Children with ADHD-only (n = 28), ADHD + Developmental Coordination Disorder (ADHD + DCD; n = 38), and Typical Development (TD; n = 19) were evaluated for vestibular function by the Dynamic Visual Acuity test (DVA-t), balance by the Bruininks-Oseretsky Test of motor proficiency (BOT-2), panic anxiety by the Screen for Child Anxiety Related Emotional Disorders questionnaire-Child version (SCARED-C), and spatial navigation by the Triangular Completion test (TC-t). Children with ADHD vs. TD presented with a high rate of vestibular hypofunction (65 vs. 0 %), imbalance (42 vs. 0 %), panic anxiety (27 vs. 11 %), and spatial disorientation (30 vs. 5 %). Children with ADHD + DCD contributed more frequent and severe vestibular hypofunction and imbalance than children with ADHD-only (74 vs. 54 %; 58 vs. 21 %, respectively). A concomitant presence of imbalance, anxiety, and spatial disorientation was observed in 33 % of children with ADHD, all sharing vestibular hypofunction. Vestibular hypofunction may be the common pathophysiology of imbalance, anxiety, and spatial disorientation in children. These comorbidities are preferentially present in children with ADHD + DCD rather than ADHD-only, thus likely related to DCD rather than to ADHD disorder. Children with this profile may benefit from a vestibular rehabilitation intervention. • A subgroup of children with ADHD, mainly with ADHD + DCD, demonstrated vestibular hypofunction. • The subgroup with vestibular hypofunction presented with concomitant imbalance, anxiety, and spatial disorientation. • Identification of this subgroup in the clinical milieu may redirect the treatment to the vestibular system. [ABSTRACT FROM AUTHOR]

This study aims to analyze the determinants of processes of knowledge creation and knowledge spillovers that affect the volume of innovative goods and services. It investigates the role of spatial and non-spatial proximities in knowledge creation in Russian regions within a spatial econometric modeling framework. This study also estimates the effect of expenditure on technological innovations and human capital on the knowledge production function in the spatial autoregressive model, the spatial Durbin model, and the extended spatial error model. The results indicate that, whereas cognitive proximity enhanced knowledge spillovers in 2005 and had a positive impact on the volume of innovative goods and services, technological proximity had no effect on knowledge creation. Spatial proximity existed between Russian regions but was not based on either R&D or human capital. The results of the study can be used for developing policies oriented towards integration projects and joint regional programs of the Russian Federation. [ABSTRACT FROM AUTHOR]

Occupancy models are commonly used in statistical ecology to model binary detection/non-detection data. These hierarchical models make a distinction between detection/non-detection and presence/absence by treating true occupancy as a latent process. In this paper, we propose a multi-species, multi-season occupancy model to jointly model detection/non-detection data on multiple species. Existing literature has shown that models that account for various sources of dependence in the latent occupancy process improve estimation, especially in the single-survey setting. However, the detection process in the model has not received much attention, even though detectability of a species is expected to relate to the detectability of other species and to detectability in previous time periods. In this work, we propose a model to capture this phenomenon by incorporating a multivariate temporal random effect in the detection process. We perform a simulation study to show that the proposed model yields more accurate inference than models that only use covariates to quantify detection. We apply our model to detection/non-detection data on three species—Thomson's gazelle, zebra, and wildebeest—in Serengeti National Park of Tanzania, Africa. [ABSTRACT FROM AUTHOR]

High-quality geospatial data are required to examine how the places in which we reside, work and play determine health outcomes; however, seldom is the quality of nationwide geospatial data reported. We examined the quality of geospatial data of public drinking water distribution zones (WDZ) across all territorial authorities in Aotearoa New Zealand to investigate structural inequities in data quality. In our national dataset of WDZ, we identified several differences in the quality of geospatial information that are associated with the population, area-level deprivation, ethnicity and most of all urban/rural classification. Our research highlights structural data inequity, which may undermine efforts to reduce health inequity. [ABSTRACT FROM AUTHOR]

Remote sensing is essential in construction management by providing valuable information and insights throughout the project lifecycle. Due to the rapid advancement of remote sensing technologies, their use has been increasingly adopted in the architecture, engineering, and construction industries. This review paper aims to advance the understanding, knowledge base, and practical implementation of remote sensing technologies in the construction industry. It may help support the development of robust methodologies, address challenges, and pave the way for the effective integration of remote sensing into construction management processes. This paper presents the results of a comprehensive literature review, focusing on the challenges faced in using remote sensing technologies in construction management. One hundred and seventeen papers were collected from eight relevant journals, indexed in Web of Science, and then categorized by challenge type. The results of 44 exemplary studies were reported in the three types of remote sensing platforms (satellite, airborne, and ground-based remote sensing). The paper provides construction professionals with a deeper understanding of remote sensing technologies and their applications in construction management. The challenges of using remote sensing in construction were collected and classified into eleven challenges. According to the number of collected documents, the critical challenges were shadow, spatial, and temporal resolution issues. The findings emphasize the use of unmanned airborne systems (UASs) and satellite remote sensing, which have become increasingly common and valuable for tasks such as preconstruction planning, progress tracking, safety monitoring, and environmental management. This knowledge allows for informed decision-making regarding integrating remote sensing into construction projects, leading to more efficient and practical project planning, design, and execution. [ABSTRACT FROM AUTHOR]

The fragile environment of the Himalayan region is prone to natural hazards, which are intensified by climate change, leading to food and livelihood insecurity for inhabitants. Therefore, building resilience in the most dominant livelihood sector, i.e., the agricultural sector, has become a priority in development and planning. To assess the perils induced by climate change on the agriculture sector in the ecologically fragile region of Kashmir Valley, a study has been conducted to evaluate the risk using the Intergovernmental Panel on Climate Change (IPCC) framework. The risk index has been derived based on socioeconomic and ecological indicators for risk determinants, i.e., vulnerability, hazard, and exposure. Furthermore, the study also evaluated the future risk to the agriculture sector under changing climatic conditions using Shared Socioeconomic Pathways (SSPs) for SSP2-4.5 and SSP5-8.5 at mid- and late-century timescales. It was observed that districts such as Bandipora (0.59), Kulgam (0.56), Ganderbal (0.56), and Kupwara (0.54) are most vulnerable due to drivers like low per capita income, yield variability, and areas with >30% slope. Shopian and Srinagar were found to be the least vulnerable due to adaptive capacity factors like livelihood diversification, crop diversification, percentage of tree crops, and percentage of agriculture labor. In terms of the Risk index, the districts found to be at high risk are Baramulla (0.19), Pulwama (0.16), Kupwara (0.15), and Budgam (0.13). In addition, the findings suggested that the region would experience a higher risk of natural hazards by the mid- (MC) and end-century (EC) due to the projected increase in temperature with decreasing precipitation, which would have an impact on crop yields and the livelihoods of farmers in the region. [ABSTRACT FROM AUTHOR]

With the rise of forced displacement, attention has turned to the economic impact of refugees. However, few studies investigate long-term impacts. We use data for Tanzania for the period 1985–2015 to examine the effect of camps on urbanisation and local development, exploiting a unique satellite-derived dataset of high spatial resolution and temporal frequency. We show a modest but significant effect of refugee camps on built-up area up to a 100 km distance. We then match camp locations to regional gross domestic product, local consumption spending and employment patterns. Output in areas with camps grew at a faster rate during camp operation, but closure of camps was associated with change in economic activity. Activity induced by camps is largely in non-tradeable goods and services rather than inducing longer run structural transformation. [ABSTRACT FROM AUTHOR]

Osteosarcomas are intricate cellular ecosystems, where heterotypic interactions significantly influence disease progression and therapeutic outcomes. Despite their importance, a detailed understanding of their cellular composition and organizational structure remains elusive. In this study, we provide a comprehensive single-cell and spatially resolved transcriptomics analysis of human osteosarcomas. We construct a cellular meta-map to dissect spatial transcriptomic data, unveiling a detailed atlas of osteosarcoma compositional subgroups. We meticulously characterize the unique gene signatures and functional states of each subgroup and investigate the impact of chemotherapy on these cellular subpopulations. Additionally, our spatial transcriptomics analysis identifies a distinct spatial niche, located at the forefront of tumor necrotic zones, potentially associated with chemotherapy resistance. We also delve into the crosstalk between different cellular subgroups. This study furnishes a comprehensive transcriptional atlas of osteosarcoma's cellular architecture, enriching our comprehension of its complexity and laying the groundwork for more targeted therapeutic approaches. [ABSTRACT FROM AUTHOR]