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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.

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]

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]

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]

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 < 0.05) and 35–49 years (aOR = 0.59, 95%CI = 0.46–0.77, p < 0.05) compared to younger women in the age group 15–24 years. An increasing level of education reduces the risk of odds of IV. Other predictors of IV were delivery at the health facility (aOR = 0.64, 95% CI = 053–0.76, p < 0.05), and media exposure (aOR = 0.63, 95%CI = 0.54–0.79, p < 0.05). Mothers’ characteristics explained most of the variability in the IV, relatively to smaller overall contributions from the community and state-level factors (p < 0.05). The level of IV against childhood diseases was high in Nigeria. However, disparities exist across the regions and other socioeconomic segments of the population. More efforts are required to improve vaccination sensitization programs and campaigns in Nigeria. [ABSTRACT FROM AUTHOR]

The conversion of agricultural land into built-up land is one of the factors causing the decline in rice production in Majalengka Regency and West Java Province. The agricultural sector still plays a vital role in local economic development, as evidenced by the GRDP of the agriculture, forestry, and fisheries sector being the second highest after the processing industry. Regional Regulation No. 11 of 2011 regarding the Spatial Planning of Majalengka Regency 2011-2031 stipulates a minimum of 39,190 ha of sustainable agricultural land. However, this amount has decreased to 30,966 ha with the issuance of Majalengka Regent Decree No. 520/KEP.1279- DKP3/2021 on the Designation of Sustainable Agricultural Land. To mitigate the negative impact of the reduction in land area, policy formulations supporting the preservation of agricultural land and implementing sustainable farming practices by local governments and related stakeholders are necessary. These efforts aim to maintain the area of food agricultural land within the framework of achieving food self-sufficiency in the Majalengka Regency. The study shows that to meet food (rice) needs until 2045, according to the direction of the RPJPN Development, a protected rice field area of 48,235 ha is required, with a surplus of 4,037 tons of rice. [ABSTRACT FROM AUTHOR]

Everyone learns differently, but individual performance is often ignored in favour of a group-level analysis. Using data from four different experiments, we show that generalised linear mixed models (GLMMs) and extensions can be used to examine individual learning patterns. Producing ellipsoids and cluster analyses based on predicted random effects, individual learning patterns can be identified, clustered and used for comparisons across various experimental conditions or groups. This analysis can handle a range of datasets including discrete, continuous, censored and non-censored, as well as different experimental conditions, sample sizes and trial numbers. Using this approach, we show that learning a face-named paired associative task produced individuals that can learn quickly, with the performance of some remaining high, but with a drop-off in others, whereas other individuals show poor performance throughout the learning period. We see this more clearly in a virtual navigation spatial learning task (NavWell). Two prominent clusters of learning emerged, one showing individuals who produced a rapid learning and another showing a slow and gradual learning pattern. Using data from another spatial learning task (Sea Hero Quest), we show that individuals' performance generally reflects their age category, but not always. Overall, using this analytical approach may help practitioners in education and medicine to identify those individuals who might need extra help and attention. In addition, identifying learning patterns may enable further investigation of the underlying neural, biological, environmental and other factors associated with these individuals. [ABSTRACT FROM AUTHOR]

This paper reports a comparison of different spatial and statistical models to predict the concentrations of the particulate matter (PM10), measured by the environmental stations of the Community of Madrid (Spain). Three methods were compared: Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK). The most accurate spatial interpolation method was the EBK. An interpolation map obtained by applying the EBK geostatistical method is presented to identify the areas with the highest pollution of PM10.] [ABSTRACT FROM AUTHOR]

This study aimed to determine the effect of spatial and climatic factors on the productivity of chestnut honey and chestnut propolis produced in chestnut forests and to reveal the potential contribution of their production to the economy. The study area included the provinces of Artvin, Trabzon, Ordu and Samsun in the Eastern and Central Black Sea Region, and covered the beekeepers producing chestnut honey and chestnut propolis in chestnut forests. The primary data of the study were obtained from the yield measurements of chestnut honey and chestnut propolis obtained from the hives, whereas the secondary data were obtained from the surveys conducted with beekeepers. Analysis of variance, independent sample t-test and correlation analysis were used to analyze the statistical difference between the chestnut honey and chestnut propolis yields of the beehives in different provinces. It was determined that there was a negative relationship between the yield of chestnut honey and chestnut propolis, relative humidity and wind speed, and a positive relationship between yield and altitude, temperature and precipitation. The potential contribution of chestnut honey and propolis production was US $ 51.10 million to the Eastern and Central Black Sea Region economy. [ABSTRACT FROM AUTHOR]

Background: Generalized Joint Hyper-mobility (GJH) can aid in the diagnosis of Ehlers-Danlos Syndrome (EDS), a complex genetic connective tissue disorder with clinical features that can mimic other disease processes. Our study focuses on developing a unique image-based goniometry system, the HybridPoseNet, which utilizes a hybrid deep learning model. Objective: The proposed model is designed to provide the most accurate joint angle measurements in EDS appraisals. Using a hybrid of CNNs and HyperLSTMs in the pose estimation module of HybridPoseNet offers superior generalization and time consistency properties, setting it apart from existing complex libraries. Methodology: HybridPoseNet integrates the spatial pattern recognition prowess of MobileNet-V2 with the sequential data processing capability of HyperLSTM units. The system captures the dynamic nature of joint motion by creating a model that learns from individual frames and the sequence of movements. The CNN module of HybridPoseNet was trained on a large and diverse data set before the fine-tuning of video data involving 50 individuals visiting the EDS clinic, focusing on joints that can hyperextend. HyperLSTMs have been incorporated in video frames to avoid any time breakage in joint angle estimation in consecutive frames. The model performance was evaluated using Spearman's coefficient correlation versus manual goniometry measurements, as well as by the human labeling of joint position, the second validation step. Outcome: Preliminary findings demonstrate HybridPoseNet achieving a remarkable correlation with manual Goniometric measurements: thumb (rho = 0.847), elbows (rho = 0.822), knees (rho = 0.839), and fifth fingers (rho = 0.896), indicating that the newest model is considerably better. The model manifested a consistent performance in all joint assessments, hence not requiring selecting a variety of pose-measuring libraries for every joint. The presentation of HybridPoseNet contributes to achieving a combined and normalized approach to reviewing the mobility of joints, which has an overall enhancement of approximately 20% in accuracy compared to the regular pose estimation libraries. This innovation is very valuable to the field of medical diagnostics of connective tissue diseases and a vast improvement to its understanding. [ABSTRACT FROM AUTHOR]

The stability of energy generation is crucial for the functioning of every country. Currently, the EU policy is moving towards becoming independent of fossil energy sources, which can be replaced with sources that are not exhaustible, for example, energy from the sun. Public awareness of renewable energy is increasing. People are willing to invest in natural solutions. However, planning large photovoltaic farm projects is difficult due to complex location requirements. The study aimed to analyse the interferences/barriers to be considered when searching for a suitable location to install a photovoltaic farm. The analysis was conducted for the territory of Poland. The study used a literature and local legislation query and the Delphi method. The Delphi method identified the most important interferences from the investor's perspective. Eleven interferences have been identified, classified into legal, spatial, technical, social, and financial groups. Several are locally determined and only exist in selected locations (e.g., technical determinants of the power grid condition, etc.). In contrast, others are unitary (e.g., concerns about the impact of PV on human health, etc.). The decision-makers are aware of the existing interferences/barriers, and the proposed administrative, legal, and technical solutions marginally mitigate barriers. System solutions are recommended, allowing an easier way to find a suitable location for a PV system. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Overstory trees serve multiple functions in grassy savannas. Past research has shown that understory species can vary along gradients of canopy cover and basal area in savannas. This variation is frequently associated with light availability but could also be related to other mechanisms, such as heterogeneity in soil and litter depth and fire intensity. Several savanna studies have found differences in understory plant functional groups within the local environment near trees versus away from them in canopy openings. Although small‐scale variation is known to be high in southeastern U.S. pine savannas, patterns in understory species diversity have not been examined at the scale of individual overstory pine trees in this system. We conducted an observational study of the relationship between understory plant communities and proximity to individual pine trees in xeric and mesic pine savannas in frequently burned sites (1–3 year intervals). We recorded the plant community composition in plots adjacent to tree boles (basal) or outside crown driplines (open). Within each environment, raw species richness was significantly greater in open locations, where light transmittance was greater. In contrast, rarified species richness did not differ. Multivariate analyses showed that community composition differed significantly between basal and open plots. One native, woody species in each environment, Serenoa repens (W. Bartram) Small in mesic and Diospyros virginiana L. in xeric, was more abundant in basal plots. In mesic environments, eight species had greater occurrence in open plots. In xeric environments, four understory forbs were more abundant in open plots. Our results support previous research indicating that individual pine trees are associated with significant variation in understory vegetation in pine savannas. [ABSTRACT FROM AUTHOR]

From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell–cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein–protein cell–cell communication interactions are routinely predicted from transcriptomic data. However, inferring metabolite-mediated intercellular signaling remains challenging, partially due to the limited size of intercellular prior knowledge resources focused on metabolites. Here, we leverage knowledge-graph infrastructure to integrate generalistic metabolite-protein with curated metabolite-receptor resources to create MetalinksDB. MetalinksDB is an order of magnitude larger than existing metabolite-receptor resources and can be tailored to specific biological contexts, such as diseases, pathways, or tissue/cellular locations. We demonstrate MetalinksDB's utility in identifying deregulated processes in renal cancer using multi-omics bulk data. Furthermore, we infer metabolite-driven intercellular signaling in acute kidney injury using spatial transcriptomics data. MetalinksDB is a comprehensive and customizable database of intercellular metabolite-protein interactions, accessible via a web interface (https://metalinks.omnipathdb.org/) and programmatically as a knowledge graph (https://github.com/biocypher/metalinks). We anticipate that by enabling diverse analyses tailored to specific biological contexts, MetalinksDB will facilitate the discovery of disease-relevant metabolite-mediated intercellular signaling processes. [ABSTRACT FROM AUTHOR]

Phalaris aquatica is pasture species introduced into Australia during early European settlement. Consumption of the plant can cause the neurological condition chronic phalaris toxicity (CPT) in sheep and cattle. In recent years, there has been an increase in reports of CPT in macropods, which has raised concerns regarding its impacts on their welfare. Currently, little is known about the distribution or seasonal patterns of this disease in wildlife, information pivotal in assessing its potential risks. Between 2021 and 2022, we conducted a survey targeting government bodies, veterinary businesses and wildlife organisations to investigate the locations and time of occurrence of CPT in macropods in the state of Victoria, Australia. We received 13 survey responses, 12 verbal reports, a full record of investigated cases from a university veterinary school and cases from a wildlife rescue organisation. Over the period of 11 years, Victoria had 918 cases of CPT recorded in macropods from 36 local government areas, with cases concentrated centrally just north of the state capital of Melbourne and July (midwinter) being the month with the highest case count (n = 220). There was a significant positive correlation between case count and both the abundance of kangaroos (Macropus giganteus and Macropus fuliginosus) (P < 0.01) and the abundance of P. aquatica (P = 0.009), and a significant negative correlation between annual case count and average rainfall of March (P = 0.016) and April (P = 0.02). Understanding these relationships will assist land and wildlife managers in predicting the risk and magnitude of disease outbreaks of CPT each in Victoria. [ABSTRACT FROM AUTHOR]

Competition within and between species affects community structure and is well documented in raptors. We investigated the spatial distribution of two sympatric raptors in Cyprus, the Bonelli's Eagle (BE) and the Long-legged Buzzard (LLB). Both species are facultative tree/cliff nesters, with overlapping nesting and foraging areas. In 2021, the BE population consisted of 40 pairs with a density of 1,3 pairs/100 km2, and the LLB population of 113 pairs with 2,9 pairs/100 km2. About 80% of BE nesting sites and 43% of LLB nesting sites were situated within the Natura 2000 (N2K) protected areas network while 97% of BE nesting sites and 91% of LLB's were situated within 2.5 km from N2K areas. The spatial distribution was compared between and within species based on Nearest Neighbour Distance (NND) analysis. Average intraspecific NND for BE was 6.158 ± 2.207 m (SD), almost double compared to LLB which was 3.151 ± 1.730 m (SD). The distribution of BE nesting territories showed a regular spacing, indicating a stronger effect of intraspecific competitive interactions, whereas LLB nesting territories did not differ significantly from random. Average interspecific NND for BE was 5.339 ± 3.719 m (SD) and for LLB 5.673 ± 3.931 m (SD). Only the LLB had a regular pattern, suggesting that LLB pairs take into account neighbouring BE active territories when establishing their own, while the opposite is not the case. Therefore, the LLB, which is the smaller of the two raptors, avoids BE nesting sites by placing its nests further than to conspecifics. NNDs in 2021 were lower compared to previous studies in Cyprus, in 2005 for LLB and 2009 for BE, indicating an increase in both species' densities. [ABSTRACT FROM AUTHOR]

The hippocampus (HPC) and retrosplenial cortex (RSC) are key components of the brain's memory and navigation systems. Lesions of either region produce profound deficits in spatial cognition and HPC neurons exhibit well‐known spatial firing patterns (place fields). Recent studies have also identified an array of navigation‐related firing patterns in the RSC. However, there has been little work comparing the response properties and information coding mechanisms of these two brain regions. In the present study, we examined the firing patterns of HPC and RSC neurons in two tasks which are commonly used to study spatial cognition in rodents, open field foraging with an environmental context manipulation and continuous T‐maze alternation. We found striking similarities in the kinds of spatial and contextual information encoded by these two brain regions. Neurons in both regions carried information about the rat's current spatial location, trajectories and goal locations, and both regions reliably differentiated the contexts. However, we also found several key differences. For example, information about head direction was a prominent component of RSC representations but was only weakly encoded in the HPC. The two regions also used different coding schemes, even when they encoded the same kind of information. As expected, the HPC employed a sparse coding scheme characterized by compact, high contrast place fields, and information about spatial location was the dominant component of HPC representations. RSC firing patterns were more consistent with a distributed coding scheme. Instead of compact place fields, RSC neurons exhibited broad, but reliable, spatial and directional tuning, and they typically carried information about multiple navigational variables. The observed similarities highlight the closely related functions of the HPC and RSC, whereas the differences in information types and coding schemes suggest that these two regions likely make somewhat different contributions to spatial cognition. [ABSTRACT FROM AUTHOR]

Despite a remarkable reduction in global poverty and famines, substantial childhood malnutrition continues to persist. In 2017, over 50 million and 150 million young children suffered from acute malnutrition (wasting) and chronic malnutrition (stunting), respectively. Yet, the measurable impact of determinants is obscure. We evaluate proposed socio-environmental related determinants of stunting and wasting across Kenya and Nigeria and quantify their effectiveness. We combine health and demographic data from Kenya and Nigeria Demographic Health Surveys (2003, 2008–2009, 2013, 2014) with spatially explicit precipitation, temperature, and vegetation data. Geospatial and disaggregated data help to understand better who is at risk and where to target mitigation efforts. We evaluate the responsiveness of malnutrition indicators using a four-level random intercept hierarchical generalized logit model. We find that spatial and hierarchical relationships explain 28% to 36% of malnutrition outcome variation. Temporal variation in precipitation, temperature, and vegetation corresponds with more than a 50% change in malnutrition rates. Wasting is most impacted by mother’s education, family wealth, clinical delivery, and vaccinations. Stunting is most impacted by family wealth, mother’s education, clinical delivery, vaccinations, and children asymptomatic of fever, cough, or diarrhea. Remotely monitored climatic variables are powerful determinants, however, their effects are inconsistent across different indicators and locations. [ABSTRACT FROM AUTHOR]

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.

Tens of thousands of plant species globally are harvested to varying extents as non-timber forest products (NTFPs). Local populations of many NTFPs face significant pressures from harvesters, often leading to calls for harvesting guidelines to promote sustainable offtakes. However, most studies to determine sustainable harvest levels implicitly assume that the entire population is available to harvesters. Yet, certain areas or portions of particular NTFP populations outside of protected areas may not be open to harvesting because they are in some sort of cultural or spatial refuge. Using a mixed-methods approach we assessed the prevalence and efficacy of refugia for 70 NTFP plant species at seven communal land sites in south-eastern South Africa. Based on reports from local harvesters, approximately 10 % of the 70 species had part of their population in a refuge. For most of these, populations within a refuge showed lower levels of harvesting and were in a better condition than adjacent populations not in a refuge. These results demonstrate that studies assessing the sustainability of harvesting of NTFP populations should account for whether refugia are present.

Spatial regression, particularly the Spatial Error Model (SERM), was utilized in prior studies to analyze Human Development Index (HDI) modeling. However, the studies were unable to determine which dimension among the three defined by the UN and BPS had the significant impact on HDI, as they constructed models based on the indicators used for the interpretation of the dimensions. Therefore, a comprehensive analysis combining spatial regression and Structural Equation Modeling (SEM), known as spatial SEM, was deemed necessary. This is the reason the current study aimed to develop SERM-SEM modeling holistically. The model parameters were estimated using the Generalized Method of Moments (GMM). To assess spatial dependency, the Lagrange Multiplier (LM) method was employed, with a distinct model error distribution compared to the error distribution of the traditional spatial model. The result of the LM test development showed that, under the null hypothesis, the LM test statistics followed a distribution. The results of the SERM-SEM model development were applied to HDI modeling using data in 2022 with three latent variables, namely a Long and Healthy Life (LHL), Knowledge (Know_L), and a Decent Standard of Living (DLS) (based on UN standards). The assessment of the outer model in SEM was based on the loading factor values that exceed 0.5 and their significance. This evaluation aimed to identify indicators that effectively explained or measured latent variables, so it got the revised model in SEM. These indicators are LHL2 and LHL 4 to form LHL. DLS1 and DLS3 are indicators to make up DLS, and for Know_L, they are K2 and K3. The revised SEM model was analyzed using spatial. The results of the spatial dependency test showed that the HDI model significantly led to the SERM-SEM model. Knowledge and a decent standard of living variables significantly influence HDI.

Abstract Background Mental illness poses a substantial global public health challenge, including in Thailand, where exploration of access to mental health services is limited. The spatial and temporal dimensions of mental illness in the country are not extensively studied, despite the recognized association between poor mental health and socioeconomic inequalities. Gaining insights into these dimensions is crucial for effective public health interventions and resource allocation. Methods This retrospective study analyzed mental health service utilization data in Thailand from 2015 to 2023. Temporal trends in annual numbers of individuals visiting mental health services by diagnosis were examined, while spatial pattern analysis employed Moran’s I statistics to assess autocorrelation, identify small-area clustering, and hotspots. The implications of our findings for mental health resource allocation and policy were discussed. Results Between 2015 and 2023, mental health facilities documented a total of 13,793,884 visits. The study found anxiety, schizophrenia, and depression emerged as the top three illnesses for mental health visits, with an increase in patient attendance following the onset of the COVID-19 outbreak. Spatial analysis identified areas of significance for various disorders across different regions of Thailand. Positive correlations between certain disorder pairs were found in specific regions, suggesting shared risk factors or comorbidities. Conclusions This study highlights spatial and temporal variations in individuals visiting services for different mental disorders in Thailand, shedding light on service gaps and socioeconomic issues. Addressing these disparities requires increased attention to mental health, the development of appropriate interventions, and overcoming barriers to accessibility. The findings provide a baseline for policymakers and stakeholders to allocate resources and implement culturally responsive interventions to improve mental health outcomes.

Background: Uninhabited shophouses become breeding places for mosquitoes which cause high levels of dengue hemorrhagic fever (DHF), whereas the existence of squatters causes a reduction in green open space, one of which becomes a breeding place for Aedes sp. Methods: This study aimed to develop a DHF risk model in shophouses and squatters in Batam city. This research applied an ecological study design using a spatial approach with the geographically weighted regression (GWR) method. The variables studied were the incidence of dengue fever, vector density, and rainfall. Results: The results of the analysis show that for the vector density in the shophouses, the odds ratio (OR) was 4.71 while for the vector density in squatters, the OR was 6.76. For the rainfall in the shophouses, the OR was 0.83 while for the rainfall in the Squatters, the OR was 1.68. Model analysis shows that the higher shophouses and squatters can cause an increase in dengue cases. Conclusion: Risk analysis shows that factors that can increase DHF vulnerability include high vector density with a risk of 4.71 times (shophouses) and 6.76 times (squatters), and high rainfall in squatters with a risk of 1680 times. The distribution of dengue cases shows that Sambau dan Batu Selicin are subdistricts that have a high distribution of vulnerabilities in both shophouses and squatters. The mathematical model of dengue risk shows that every construction of one shophouse or one squatter can cause an increase in dengue cases by one case.

Introduction: Bunaken Island still lacks proper waste management infrastructure, resulting in the accumulation of garbage around residences and along the coastline. This prevalent issue significantly impacts public health and the overall quality of life on the island. The research seeks to evaluate the waste management situation on Bunaken Island and identify suitable locations for waste disposal. Methods: The study employed random sampling techniques to select households within the study area for primary data collection. The research utilized Boolean and spatial analysis methods to pinpoint appropriate waste disposal zones and propose sustainable waste management strategies for Bunaken Island. Results and Discussion: Findings revealed that Bunaken Island spans a land area of 794.12 hectares and is inhabited by a population of 3,843. The analysis identified two recommended locations for final disposal zones, totaling 3.40 and 0.45 hectares, respectively. Moreover, the estimated waste generation over the next decade is approximately 4,277,893 kilograms, necessitating 0.0241 hectares of land for waste accommodation through terracing methods. Conclusion: Addressing this issue requires the identification of two viable disposal zones in distinct locations. Additionally, implementing recycling practices to utilize both organic and inorganic waste from local and marine sources and promoting reuse before landfill disposal are crucial strategies for effective waste management on Bunaken Island.

Abstract Background Diarrhea, defined as three or more loose stool per day, is a major cause of child mortality. Exploring its spatial distribution, prevalence, and influencing factors is crucial for public health decision and targeted interventions. This study aimed to investigate these aspects using 2019 Rwanda demographic health survey data. Method A total 7,978 (weighted) under-five children were included in this study. Spatial clustering (hotspots areas) were mapped using ArcGIS and SaTscan software. A multilevel logistic regression model was fitted to assessed factors associated with diarrhea, reporting significance at p

Abstract Background Malaria is one of the most devastating tropical diseases, resulting in loss of lives each year, especially in children under the age of 5 years. Malaria burden, related deaths and stall in the progress against malaria transmission is evident, particularly in countries that have moderate or high malaria transmission. Hence, mitigating malaria spread requires information on the distribution of vectors and the drivers of insecticide resistance (IR). However, owing to the impracticality in establishing the critical need for real-world information at every location, modelling provides an informed best guess for such information. Therefore, this review examines the various methodologies used to model spatial, temporal and spatio-temporal patterns of IR within populations of malaria vectors, incorporating pest-biology parameters, adopted ecological principles, and the associated modelling challenges. Methods The review focused on the period ending March 2023 without imposing restrictions on the initial year of publication, and included articles sourced from PubMed, Web of Science, and Scopus. It was also limited to publications that deal with modelling of IR distribution across spatial and temporal dimensions and excluded articles solely focusing on insecticide susceptibility tests or articles not published in English. After rigorous selection, 33 articles met the review's elibility criteria and were subjected to full-text screening. Results Results show the popularity of Bayesian geostatistical approaches, and logistic and static models, with limited adoption of dynamic modelling approaches for spatial and temporal IR modelling. Furthermore, our review identifies the availability of surveillance data and scarcity of comprehensive information on the potential drivers of IR as major impediments to developing holistic models of IR evolution. Conclusions The review notes that incorporating pest-biology parameters, and ecological principles into IR models, in tandem with fundamental ecological concepts, potentially offers crucial insights into the evolution of IR. The results extend our knowledge of IR models that provide potentially accurate results, which can be translated into policy recommendations to combat the challenge of IR in malaria control. Graphical Abstract

Abstract Background Anemia is a global public health problem among women of reproductive age group, especially in developing countries, which affect health, social and economic development that result in low physical activity, increased maternal morbidity and mortality and adverse neonatal outcome especially those with severe anemia. However, there is limited reliable and updated data on the spatial variations of anemia and its associated factors among reproductive-age women in Nigeria. Methods Secondary data analysis was conducted using data from the recent Nigeria malaria indicators survey datasets. The study comprised a total of 14,476 reproductive-age women. Spatial and multilevel mixed effect analysis on determinants factors of anemia among reproductive age women in Nigeria evidenced by the recent Nigerian malaria indicators survey. Finally, the percentage and odd ratio, its 95% confidence intervals, and the result of spatial analysis were reported. Result This study includes a total weighted sample of 14,476 reproductive-age women from the Nigeria malaria indicators survey. The prevalence of anemia was 24.6% in Ethiopia. Being between the age range of 30–34 years [AOR: 0.217, 95% CI (0.171, 0.274)], Attending higher education [AOR: 0.848, 95%CI (0.740, 0.972)] and being male headed household [AOR: 0.540, 95% CI (0.471, 0.620)] were protective for anemia. On the other hand being poorest [AOR: 1.542 95%CI (1.299, 1.830)] and being listening radio less than once a week [AOR: 1.013, 95% CI (0.908, 1.131)] were risk for anemia. Conclusion In this study Individual level factors were associated with anemia and also there were spatial variations in anemia across the region among reproductive-age women. Empowering women to have better educational status, improving the wealth index, and promoting education about prevention and control strategies of anemia through media especially in developing regions were the key factors to reduce anemia among reproductive age women in Nigeria.

Golf ball sponges are small, sometimes inconspicuous, sponges. They can be found across a range of habitats varying from perturbed and pristine coral reefs to harbours and marine lakes and from the deep sea to shallow waters. They can be difficult to distinguish in the field and have presented some problems with taxonomists lumping and splitting species due to the difficulty in defining clear species boundaries. In the present study, we sampled golf ball sponges from Indo‐Pacific and Caribbean locations and used 16S gene amplicon sequencing to study their prokaryotic communities. We show that golf ball sponges harbour a wide variety of prokaryotic communities. Among the most prevalent operational taxonomic units (OTUs), several belonged to a range of taxa, including the bacterial AqS1 and EC94 groups, which have been associated with genes known to facilitate interactions between hosts and microbes. Certain host taxa were enriched with OTUs classified to the SAR202 clade of Chloroflexi. Our findings show that prokaryotic dissimilarity varied as a function of space (geographical distance) and host dissimilitude. The importance of space and host dissimilitude, however, varied depending on the data transformation with host dissimilitude a more important predictor of untransformed data whereas space was a more important predictor of log‐transformed data. Given that log‐transformation downscales the influence of abundant taxa, we interpret these results by the tendency of closely related host organisms to host similar sets of abundant symbiotic microorganisms; distantly sampled specimens, in contrast, tend to harbour less abundant prokaryotic microorganisms found in the surrounding environment (e.g., seawater or sediment). [ABSTRACT FROM AUTHOR]

This study proposed a spatially explicit non-linear function for estimating the benefits following the release and establishment of the koinobiont endoparasitoid, Fopius arisanus at a landscape scale in Kenya to control the oriental fruit fly, Bactrocera dorsalis. The proposed model relates the bioecology of the parasitoid to its impacts on fruit growers' economic benefits per fruit farm. In addition, we used financial information attributed to the cost of importation, conducting associated research, and the release of F. arisanus to estimate the return on investment from the parasitoid's release in Kenya. We observed distinct variations in potential annual benefits across the diverse mango cultivation regions throughout Kenya. In Kenya, the potential benefits range from negligible benefit to high benefit per acre (US$201- US$255) annually when classical biological control is applied. Furthermore, the cost-benefit ratio analysis showed that in Kenya, for every US$1 invested, the return benefit was US$33.92. In addition in Kenya, the investment yielded a significant net present value of US$13.7 million over a 16-year period and an attractive internal rate of return of 73% compared to the reference discount rate of 12%. The study suggests the need to facilitate further release of the parasitoid (F. arisanus) across major fruit production regions of countries affected by the invasive fruit fly (B. dorsalis). Highlights: This study presents a spatially explicit non-linear function for estimating the economic benefits at scale following parasitoid release and establishment to control an invasive fruit fly. Costs incurred in the exploration, importation, lab research, and field release were employed to estimate the return on investment in the bio-control of fruit flies using F. arisanus as an agent in Kenya. The findings will stimulate government investment in mass rearing of the parasitoid for up-scaling sustainable biological control techniques of invasive agricultural pests. [ABSTRACT FROM AUTHOR]

Solar energy reaching a horizontal surface can possess fluctuations that impact electricity generation at a solar plant. Despite this, energy access remains inadequate, particularly in rural areas, with an estimated 82% deficiency. This drives us to assess the regressive and spatial-temporal accessibility of solar energy in the southern and mid regions of Mozambique. This evaluation aims to determine the actual availability of energy for electrification purposes. Data on global horizontal irradiation from approximately 8 stations across all provinces in the specified regions, collected between 2012 and 2014 at intervals of 1 and 10 min, were analyzed using regression and correlation methods along with a specialized algorithm for classifying days based on clear sky index terms. The statistical analysis identified days with significant potential for energy accessibility, exceeding 50% of the average. The findings suggest a correlation coefficient of approximately 0.30 for energy and non-linear regression with clear sky index coefficients around 0.80. The method employed demonstrated accuracy when compared to theoretical simulations of the clear sky index in the region, indicating its potential applicability in other regions of interest. [ABSTRACT FROM AUTHOR]

Tens of thousands of plant species globally are harvested to varying extents as non-timber forest products (NTFPs). Local populations of many NTFPs face significant pressures from harvesters, often leading to calls for harvesting guidelines to promote sustainable offtakes. However, most studies to determine sustainable harvest levels implicitly assume that the entire population is available to harvesters. Yet, certain areas or portions of particular NTFP populations outside of protected areas may not be open to harvesting because they are in some sort of cultural or spatial refuge. Using a mixed-methods approach we assessed the prevalence and efficacy of refugia for 70 NTFP plant species at seven communal land sites in south-eastern South Africa. Based on reports from local harvesters, approximately 10 % of the 70 species had part of their population in a refuge. For most of these, populations within a refuge showed lower levels of harvesting and were in a better condition than adjacent populations not in a refuge. These results demonstrate that studies assessing the sustainability of harvesting of NTFP populations should account for whether refugia are present. [ABSTRACT FROM AUTHOR]

Climate change is increasing the frequency and intensity of extreme precipitation events, raising the risk of urban flood disasters. This study uses a crowd‐sourced municipal call database to characterize the spatial distribution of flood risk in Detroit, MI. Call data including dates and addresses were obtained from the City of Detroit Department of Public Works for 2021. Calls were mapped and aggregated to census tract counts and merged with neighborhood‐level data. Associations of predictors with flood calls were tested using spatial regression models. Flooding calls were located throughout the city but were concentrated in specific areas. Multivariate models of census tract level call counts indicated that increased poverty and Black, immigrant, and older residents were positively associated with flood calls, while increased elevation was associated with protective effects. Longer distances from waste water interceptors were associated with higher risk for calls. Crowd‐sourced flood hotline call data can be used for effective spatial flood risk assessment. Though flooding occurs throughout the city of Detroit, infrastructural, neighborhood, and household factors influence flooding extent. Limitations included the self‐reported nature of calls. Future modeling efforts might include input from local stakeholders to improve spatial risk assessment. [ABSTRACT FROM AUTHOR]

Compositional Data Analysis (CoDa) has gained popularity in recent years. This type of data consists of values from disjoint categories that sum up to a constant. Both Dirichlet regression and logistic-normal regression have become popular as CoDa analysis methods. However, fitting this kind of multivariate models presents challenges, especially when structured random effects are included in the model, such as temporal or spatial effects. To overcome these challenges, we propose the logistic-normal Dirichlet Model (LNDM). We seamlessly incorporate this approach into the R-INLA package, facilitating model fitting and model prediction within the framework of Latent Gaussian Models. Moreover, we explore metrics like Deviance Information Criteria, Watanabe Akaike information criterion, and cross-validation measure conditional predictive ordinate for model selection in R-INLA for CoDa. Illustrating LNDM through two simulated examples and with an ecological case study on Arabidopsis thaliana in the Iberian Peninsula, we underscore its potential as an effective tool for managing CoDa and large CoDa databases. [ABSTRACT FROM AUTHOR]

Spatial regression, particularly the Spatial Error Model (SERM), was utilized in prior studies to analyze Human Development Index (HDI) modeling. However, the studies were unable to determine which dimension among the three defined by the UN and BPS had the significant impact on HDI, as they constructed models based on the indicators used for the interpretation of the dimensions. Therefore, a comprehensive analysis combining spatial regression and Structural Equation Modeling (SEM), known as spatial SEM, was deemed necessary. This is the reason the current study aimed to develop SERM-SEM modeling holistically. The model parameters were estimated using the Generalized Method of Moments (GMM). To assess spatial dependency, the Lagrange Multiplier (LM) method was employed, with a distinct model error distribution compared to the error distribution of the traditional spatial model. The result of the LM test development showed that, under the null hypothesis, the LM test statistics followed a distribution. The results of the SERM-SEM model development were applied to HDI modeling using data in 2022 with three latent variables, namely a Long and Healthy Life (LHL), Knowledge (Know_L), and a Decent Standard of Living (DLS) (based on UN standards). The assessment of the outer model in SEM was based on the loading factor values that exceed 0.5 and their significance. This evaluation aimed to identify indicators that effectively explained or measured latent variables, so it got the revised model in SEM. These indicators are LHL2 and LHL 4 to form LHL. DLS1 and DLS3 are indicators to make up DLS, and for Know_L, they are K2 and K3. The revised SEM model was analyzed using spatial. The results of the spatial dependency test showed that the HDI model significantly led to the SERM-SEM model. Knowledge and a decent standard of living variables significantly influence HDI. [ABSTRACT FROM AUTHOR]