stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics.

Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST. stDiff employs a conditional diffusion model, capturing gene expression abundance relationships in scRNA-seq data through two Markov processes: one introducing noise to transcriptomics data and the other denoising to recover them. The missing portion of ST is predicted by incorporating the original ST data into the denoising process. In our comprehensive performance evaluation across 16 datasets, utilizing multiple clustering and similarity metrics, stDiff stands out for its exceptional ability to preserve topological structures among cells, positioning itself as a robust solution for cell population identification. Moreover, stDiff's enhancement outcomes closely mirror the actual ST data within the batch space. Across diverse spatial expression patterns, our model accurately reconstructs them, delineating distinct spatial boundaries. This highlights stDiff's capability to unify the observed and predicted segments of ST data for subsequent analysis. We anticipate that stDiff, with its innovative approach, will contribute to advancing ST imputation methodologies.

Clusters of heterogeneity of tuberculosis-HIV coinfection in Brazil: a geospatial study.

OBJECTIVE: To analyze the geospatialization of tuberculosis-HIV coinfection in Brazil, from 2010 to 2021, and the correlation with socioeconomic, housing, and health indicators. METHODS: An ecological study of Brazilian municipalities and states, with data from HIV and tuberculosis information systems, previously reported by the Ministry of Health. The crude and smoothed coefficients were calculated by the local empirical Bayesian method of incidence of coinfection per 100,000 inhabitants in the population aged between 18 and 59 years. Univariate (identification of clusters) and bivariate (correlation with 20 indicators) Moran's indices were used. RESULTS: A total of 122,223 cases of coinfection were registered in Brazil from 2010 to 2021, with a mean coefficient of 8.30/100,000. The South (11.44/100,000) and North (9.93/100,000) regions concentrated the highest burden of infections. The coefficients dropped in Brazil, in all regions, in the years of covid-19 (2020 and 2021). The highest coefficients were observed in the municipalities of the states of Rio Grande do Sul, Mato Grosso do Sul, and Amazonas, with high-high clusters in the capitals, border regions, coast of the country. The municipalities belonging to the states of Minas Gerais, Bahia, Paraná, and Piauí showed low-low clusters. There was a direct correlation with human development indices and aids rates, as well as an indirect correlation with the proportion of poor or of those vulnerable to poverty and the Gini index. CONCLUSIONS: The spatial analysis of tuberculosis-HIV coinfection showed heterogeneity in the Brazilian territory and constant behavior throughout the period, revealing clusters with high-burden municipalities, especially in large urban centers and in states with a high occurrence of HIV and/or tuberculosis. These findings, in addition to alerting to the effects of the covid-19 pandemic, can incorporate strategic planning for the control of coinfection, aiming to eliminate these infections as public health problems by 2030.

Combining user-centered design and behavioral theory to enhance health technologies: A personas-based approach for a primary-care based multifactorial falls risk assessment tool.

INTRODUCTION: Multifactorial falls risk assessment tools (FRATs) can be an effective falls prevention method for older adults, but are often underutilized by health care professionals (HCPs). This study aims to enhance the use and implementation of multifactorial FRATs by combining behavioral theory with the user-centered design (UCD) method of personas construction. Specifically, the study aimed to (1) construct personas that are based on external (i.e., needs, preferences) and intrinsic user characteristics (i.e., behavioral determinants); and (2) use these insights to inform requirements for optimizing an existing Dutch multifactorial FRAT (i.e., the 'Valanalyse'). METHODS: Survey data from HCPs (n = 31) was used to construct personas of the 'Valanalyse.' To examine differences between clusters on 68 clustering variables, a multivariate cluster analysis technique with non-parametric analyses and computational methods was used. The aggregated external and intrinsic user characteristics of personas were used to inform key design and implementation requirements for the 'Valanalyse,' respectively, whereby intrinsic user characteristics were matched with appropriate behavior change techniques to guide implementation. RESULTS: Significant differences between clusters were observed in 20 clustering variables (e.g., behavioral beliefs, situations for use). These variables were used to construct six personas representing users of each cluster. Together, the six personas helped operationalize four key design requirements (e.g., guide treatment-related decision making) and 14 implementation strategies (e.g., planning coping responses) for optimizing the 'Valanalyse' in Dutch geriatric, primary care settings. CONCLUSION: The findings suggest that theory- and evidence-based personas that encompass both external and intrinsic user characteristics are a useful method for understanding how the use and implementation of multifactorial FRATs can be optimized with and for HCPs, providing important implications for developers and eHealth interventions with regards to encouraging technology adoption.

Different divergence processes of isoglosses of folk nomenclature between wild trees and rice landraces imply the need for different conservation planning based on the type of plant resources.

BACKGROUND: The intensification of production and socio-economic changes have accelerated the loss of local traditional knowledge and plant resources. Understanding the distribution and determinants of such biocultural diversity is essential in planning efficient surveys and conservation efforts. Because the concept of biocultural diversity in socio-ecological adaptive systems comprises biological, cultural, and linguistic diversity, linguistic information should serve as a surrogate for the distribution of local biological and cultural diversity. In this study, we spatio-linguistically evaluated the names of local trees and rice landraces recorded in Ehime Prefecture, southwestern Japan. METHODS: Hierarchical clustering was performed separately for the names of local trees and rice landraces. By considering innate flora differences and species having multiple local names, a novel distance index was adopted for local tree names. For the names of rice landraces, Jaccard distance was adopted. V-measure and factor detector analysis were used to evaluate the spatial association between the isogloss maps of the folk nomenclature derived from the clustering and multiple thematic maps. RESULTS: Local tree names showed stronger spatial association with geographical factors than rice landrace names. One folk nomenclature group of trees overlapped well with the slash-and-burn cultivation area, suggesting a link between the naming of trees and the traditional production system. In contrast, rice landraces exhibited stronger associations with folklore practices. Moreover, influences of road networks and pilgrimages on rice landraces indicated the importance of human mobility and traditional rituals on rice seed transfer. High homogeneity and low completeness in the V-measure analysis indicated that the names of local trees and rice landraces were mostly homogenous within current municipalities and were shared with a couple of adjacent municipalities. The isogloss maps help to illustrate how the biological and cultural diversity of wild trees and rice landraces are distributed. They also help to identify units for inter-municipal collaboration for effective conservation of traditional knowledge related to those plant resources and traditional rice varieties themselves. CONCLUSIONS: Our spatio-linguistic evaluation indicated that complex geographical and sociological processes influence the formation of plant folk nomenclature groups and implies a promising approach using quantitative lexico-statistical analysis to help to identify areas for biocultural diversity conservation.

Barriers and facilitators for oral health screening among tobacco users: a mixed-methods study.

OBJECTIVES: Tobacco consumption adversely affects general and oral health and is considered one of the significant public health burdens globally. The present study aims to assess the barriers and facilitators for attending oral and dental health screening among tobacco users who seek cessation advice. METHODOLOGY: The present mixed-methods study used group concept mapping (GCM) to identify the facilitators/barriers to attending oral health screening among young adults attending face-to-face and virtual Tobacco Cessation Clinic at King Saud University (Riyadh, Saudi Arabia) between September 2022 and April 2023. Study investigators included healthcare social workers, dental interns, and oral and maxillofacial medicinists. Information about demographics, general health, oral/dental health and tobacco use were collected using self-completed questionnaires. The barriers and facilitators were assessed following GCM by brainstorming, sorting, rating, and interpretation activities. Descriptive, multidimensional scaling and hierarchical cluster analysis were used to describe the study participants and produce concept maps of the generated statements. RESULTS: The study included 148 participants who generated 67 statements summarised into 28 statements as facilitators or barriers. Based on a 5-point importance scale, the participants indicated the importance of facilitators under health-related cluster [e.g. when I feel pain] as the highest, followed by personal [e.g. to maintain my mouth hygiene], social [e.g. the quality of treatment] and financial clusters [e.g. the reasonable cost]. Concerning barriers, financial factors [e.g. high cost] acted as the highest-rated barrier, followed by personal [e.g. lack of dental appointments] and health-related [e.g. worry that dental problems will worsen]. The social factors were the least considerable barrier [e.g. lack of time]. Clustering these facilitators/barriers on the concept map indicated their conceptual similarity by an average stress value of 0.23. CONCLUSION: Pain was the most important facilitator to attending oral health screening by young adults seeking tobacco cessation advice. Notable barriers included the high cost of dental treatment and the lack of scheduled appointments. Thus, oral health care providers need to consider scheduling periodic and timely dental check-ups to prevent and reduce the burden of tobacco-associated and pain-causing oral diseases.

Construction of China's food security evaluation index system and spatiotemporal evolution.

Food security is a vital material foundation for a nation's development and has been a topic of significant concern on the international stage in recent years. With a population exceeding 1.4 billion, China is not only a major producer but also a substantial consumer of food. Ensuring food security in China is not only a top priority for its socio-economic development but also a driving force in maintaining the stability of the global food supply chain and reducing the number of hungry people worldwide. However, a lack of comprehensive research into the Chinese food security system remains. This study addresses this gap by constructing a comprehensive evaluation framework encompassing four dimensions: food supply, accessibility, production stability, and sustainability. Utilizing the Moran's Index and generating LISA (Local Indicators of Spatial Association) maps, we analyze the spatial correlations of food security. The Dagum Gini coefficient and kernel density estimation are applied to assess heterogeneity and spatial disparities. Furthermore, this research employs the Exponential Smoothing (ETS) model to forecast food security trends. The findings reveal that the overall composite food security score exhibited fluctuations, initially increasing and reaching its peak of 0.407 in 2003, followed by a subsequent sharp decline after 2019. Spatially, food security exhibits correlations, with the Huang-Huai-Hai Plain and Northeast regions consistently showing high-high clustering. In contrast, the Western and Southern regions exhibit low-low clustering at specific periods. The Dagum Gini coefficient indicates that overall food security disparities are relatively small. However, these disparities have gradually expanded in recent years, with inter-group differences becoming predominant after 2005. As indicated by the kernel density estimation, the dynamic distribution of food security initially widens and then narrows, suggesting a shift from dispersed to concentrated data distribution. This phenomenon is accompanied by polarization and convergence trends, particularly evident after 2015. According to the ETS model, the study forecasts a substantial risk of declining food security in China over the next decade, largely influenced by the ongoing pandemic. In conclusion, this research provides a comprehensive assessment of the changing status of food security in China. It offers early warnings through predictive analysis, addressing the existing research gaps in the field of food security.

Measurement of China's public health level: compilation and research of an index.

BACKGROUND: With the development of the economy, public health has become increasingly important. Therefore, it is important to establish a comprehensive and scientific the public health level index (PHL) system to measure public health level as a research priority. The current research has limitations in exploring the PHL system; therefore, the field still lacks a comprehensive indicator system to measure the level of public health. Therefore, this paper aims to develop a multi-level public health index system and utilizes China as a case study to evaluate its public health status. The objective is to offer insights and recommendations for the improvement of public health initiatives in China and other regions. METHODS: Utilizing data from 2011 to 2020, a comprehensive PHL was developed to encompass three vital indices: the Public Health Service Index (PHS), the Public Health Resource Index (PHR), and the Population Health Level Index (PHL). Subsequently, the PHL, PHS, PHR, and PH were meticulously calculated using a comprehensive evaluation method. Amid the current disparity between public health and economic progress, both the spatial Durbin model and the spatial lag model were finally employed to examine the influence of economic level (EL) on PHL, thus affirming the consistent reliability and accuracy of PHS. RESULTS: Our findings revealed the following: (i) the PHL, PHS, and PHR indices show increasing trends in China; (ii) both EL and PHL exhibit high-high clustering and low-low clustering states; (iii) the PHL in the area has a positive spatial spillover effect on the surrounding area; (iv) EL will result in the siphoning effect of PHL; and (v) EL can enhance PHL through urbanization, PH, and PHS. CONCLUSIONS: The PHL system constructed in this paper demonstrates multiple levels, pluralism, spatio-temporal comparability, and robustness. It can reflect not only the input and output of public health initiatives but also the interconnectedness and autonomy within the public health system. Therefore, it can be widely utilized in other areas of public health research.

Power calculation for detecting interaction effect in cross-sectional stepped-wedge cluster randomized trials: an important tool for disparity research.

BACKGROUND: The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes. METHODS: We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods. RESULTS: Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD. CONCLUSIONS: We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE.

Spatial and temporal assessment of snake encounters in urban Delhi, India.

Delhi, the capital city of India is, highly urbanized and surrounded by remnant forest, farms, ridges, and other green areas experience regular snake encounters in and around residential, institutional, and industrial areas. A total of 41 months of sampling from January 2019 to May 2022 was conducted wherein we, studied the snake assemblage in Delhi to determine the species composition, encounter frequency, seasonal activity patterns, and probable encounter sites in an urban setup. We documented 372 individuals belonging to 15 species from seven families out of 23 species found in Delhi. Snakes were found inside forests, public parks, homes, drain networks, streets, office buildings, and even in school-college buildings. The most recorded species being Ptyas mucosa (37.37%, n = 139), Naja naja (19.62%, n = 73), and Lycodon aulicus (13.44%, n = 50). The highest numbers of incidents were reported in the month of July (22.04%, n = 82) and August (19.89%, n = 74) during the peak monsoon season, for identifying high encounter sites, we used a geostatistical modeling tool, Ordinary kriging to identify places having more snake occurrences. We further used a statistical spatial method called average nearest neighbor distance to detect the pattern distribution of snake species. Spatial interpolation done through Ordinary kriging highlighted two areas having concentrated snake encounters. The results of the average nearest neighbor distance analysis showed three species having clustered and two species having dispersed distribution. The incidence of snake encounters was found to be highly seasonal and appeared to be associated mainly with monthly rainfall, temperature, and humidity. The findings of this study on snakes' distribution patterns provide valuable insights into the conservation of these species. Understanding their habitat preferences and spatial distribution is crucial for the implementation of effective conservation strategies.

A new proposal for phenotypic classification and outcome assessment of dermatomyositis based on clinical manifestations and serological testing.

BACKGROUND: Dermatomyositis (DM) is an infrequent disease subgroup of idiopathic inflammatory myopathies characterized by distinct skin lesions. However, high heterogeneity makes clinical diagnosis and treatment of DM very challenging. OBJECTIVES: Unsupervised classification in DM patients and analysis of key factors related to clinical outcomes. METHODS: This retrospective study was conducted between 2017 and 2022 at the Department of Rheumatology, Xiangya Hospital, Central South University. 162 DM patients were enrolled for unsupervised hierarchical cluster analysis. In addition, we divided the clinical outcomes of DM patients into four subgroups: withdrawal, stabilization, aggravation, and death, and compared the clinical profiles amongst the subgroups. RESULTS: Out of 162 DM patients, three clusters were defined. Cluster 1 (n = 40) was mainly grouped by patients with prominent muscular involvement and mild Interstitial Lung Disease (ILD). Cluster 2 (n = 72) grouped patients with skin rash, anti-Melanoma Differentiation Associated protein 5 positive (anti-MDA5+), and Rapid Progressive Interstitial Lung Disease (RP-ILD). Cluster 3 (n = 50) grouped patients with the mildest symptoms. The proportion of death increased across the three clusters (cluster 3 < cluster 1 < cluster 2). STUDY LIMITATIONS: The number of cases was limited for the subsequent construction and validation of predictive models. We did not review all skin symptoms or pathological changes in detail. CONCLUSIONS: We reclassified DM into three clusters with different risks for poor outcome based on diverse clinical profiles. Clinical serological testing and cluster analysis are necessary to help clinicians evaluate patients during follow-up and conduct phenotype-based personalized care in DM.

How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method.

Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fundamental for the execution of effective public health interventions and policies. Methods to couple big public health data with modern statistical techniques offer greater granularity for describing and understanding data quality, disease distributions, and potential predictive connections between population-level indicators with areal-based health outcomes. This study applied clustering techniques to explore patterns of diabetes burden correlated with local socio-economic inequalities in Malaysia, with a goal of better understanding the factors influencing the collation of these clusters. Through multi-modal secondary data sources, district-wise diabetes crude rates from 271,553 individuals with diabetes sampled from 914 primary care clinics throughout Malaysia were computed. Unsupervised machine learning methods using hierarchical clustering to a set of 144 administrative districts was applied. Differences in characteristics of the areas were evaluated using multivariate non-parametric test statistics. Five statistically significant clusters were identified, each reflecting different levels of diabetes burden at the local level, each with contrasting patterns observed under the influence of population-level characteristics. The hierarchical clustering analysis that grouped local diabetes areas with varying socio-economic, demographic, and geographic characteristics offer opportunities to local public health to implement targeted interventions in an attempt to control the local diabetes burden.

CDC Guidance for Community Assessment and Investigation of Suspected Suicide Clusters, United States, 2024.

This report is the second of three reports in the MMWR supplement updating CDC's guidance for investigating and responding to suicide clusters. The first report, Background and Rationale - CDC Guidance for Assessing, Investigating, and Responding to Suicide Clusters, United States, 2024, describes an overview of suicide clusters, methods used to develop the supplement guidance, and intended use of the supplement reports. The final report, CDC Guidance for Community Response to Suicide Clusters, United States, 2024, describes how local public health and community leaders can develop a response plan for suicide clusters. This report provides updated guidance for the approach to assessing and investigating suspected suicide clusters. Specifically, this approach will guide lead agencies in determining whether a confirmed suicide cluster exists, what concerns are in the community, and what the specific characteristics are of the suspected or confirmed suicide cluster. The guidance in this report is intended to support and assist lead agencies and their community prepare for, assess, and investigate suicide clusters. The steps provided in this report can be adapted to the local context, culture, capacity, circumstances, and needs for each suspected suicide cluster.

Spatiotemporal trends in the burden of aortic aneurysms caused by high sodium intake from 1990 to 2019: A global, regional, and national analysis.

BACKGROUND AND AIMS: This study, drawing on Global Burden of Disease (GBD) data, examines spatiotemporal trends in mortality and disability-adjusted life years (DALYs) linked to aortic aneurysm (AA) from high sodium intake. The aim is a comprehensive analysis globally, regionally, and nationally spanning 1990 to 2019. METHODS AND RESULTS: Quantifying AA deaths and DALYs due to high sodium intake, incorporating age-standardized mortality rate (ASMR) and age-standardized DALYs rate (ASDR), revealed a global surge. Deaths rose by 86.09 %, DALYs by 74.02 % from 1990 to 2019. EAPC for ASMR and ASDR displayed negative trends (-0.72 and -0.77). High/middle-high Socio-demographic Index (SDI) regions bore higher burdens than lower SDI regions. Males consistently had higher burdens across SDI regions, with both genders showing a slight downward trend. Age-wise, AA deaths and DALYs rose with age, followed by decline. A positive correlation existed between SDI and global burden, inversely related to EAPC for ASMR and ASDR. CONCLUSION: AA burden from high sodium intake is pronounced in high SDI regions, necessitating targeted interventions. The global data highlights a significant increase in AA deaths and DALYs due to high sodium intake, urging prompt and effective control measures.

Physicochemical assessment of industrial effluents of Kala Sanghian drain, Punjab, India.

The discharge of industrial effluents has a significant impact on the Water Quality Index (WQI) of the water bodies and is a major source of contamination of groundwater. The present study investigated the physicochemical characteristics and scrutinized the pollution potential of the tannery, textile, and electroplating effluents uploading into the Kala Sanghian drain, located in Jalandhar, Punjab, India. In this study, 12 samples were collected from the four sites (leather complex drain (LD), leather complex outlet (LO), focal point drain (FD), and Bulandpur drain (BD)) of Kala Sanghian drain in the dry season. The result showed that the drain under consideration is very much contaminated and the water is not suitable for irrigation and agricultural purposes. Rather it has a bad impact on the health of local people, the physiology of aquatic organisms, and the soil quality of agricultural land nearby. The present study confirmed the water quality index was more than 100, indicating a highly contaminated drain and water is unfit for any use. The correlation analysis shows that there exists a positive correlation between TDS and temperature (r = 0.994), DO and pH (r = 0.808), BOD and temperature (r = 0.987), BOD and TDS (r = 0.978), EC and temperature (r = 0.963), EC and TDS (r = 0.954), and EC and BOD (r = 0.956). The principal component analysis (PCA) confirms that PC1 alone has more than 89% of the variance with high positive loading for TDS, temperature, EC, and BOD. The hierarchical cluster analysis (HCA) reflected two clusters where cluster 1 consists of pH, DO, temperature, and BOD of water while cluster 2 consists of TDS and EC of water. The PCA and HCA study of the data set confirms the high degree contribution of anthropogenic activities through the application of chemicals in agriculture, disposal of municipal waste, and industrial effluents in the deterioration of water quality. The results of the study will help to enhance the sustainable action plan for the management of industrial effluents in the studied area.

Analyzing Multiple Social Determinants of Health Using Different Clustering Methods.

Social determinants of health (SDoH) have become an increasingly important area to acknowledge and address in healthcare; however, dealing with these measures in outcomes research can be challenging due to the inherent collinearity of these factors. Here we discuss our experience utilizing three statistical methods-exploratory factor analysis (FA), hierarchical clustering, and latent class analysis (LCA)-to analyze data collected using an electronic medical record social risk screener called Protocol for Responding to and Assessing Patient Assets, Risks, and Experience (PRAPARE). The PRAPARE tool is a standardized instrument designed to collect patient-reported data on SDoH factors, such as income, education, housing, and access to care. A total of 2380 patients had complete PRAPARE and neighborhood-level data for analysis. We identified a total of three composite SDoH clusters using FA, along with four clusters identified through hierarchical clustering, and four latent classes of patients using LCA. Our results highlight how different approaches can be used to handle SDoH, as well as how to select a method based on the intended outcome of the researcher. Additionally, our study shows the usefulness of employing multiple statistical methods to analyze complex SDoH gathered using social risk screeners such as the PRAPARE tool.

Assessment of environmental risk areas based on airborne pollen patterns as a response to land use and land cover distribution.

Allergic respiratory diseases are considered to be among the most important public health concerns, and pollen is the main cause of allergic respiratory diseases worldwide. However, the biological component of air quality is largely underestimated, and there is an important gap in the legislation in this area. The aims of this study were to characterise the occurrence and incidence of pollen exposure in relation to potential pollen sources and to delineate the main areas of aerobiological risk in the Madrid Autonomous Region based on homogeneous patterns of pollen exposure. This study uses the historical aerobiological database of the Madrid Region Palynological Network (central Spain) from ten pollen stations from 1994 to 2022, and the land-use information from the Corine Land Cover. Multiple clustering approaches were followed to group the sampling stations and subsequently all the 1 × 1km pixels for the Madrid Autonomous Region. The clustering dendrogram for land-use distribution was compared to the dendrogram for historical airborne pollen data. The two dendrograms showed a good alignment with a very high correlation (0.95) and very low entanglement (0.15), which indicates a close correspondence between the distribution of the potential pollen sources and the airborne pollen dynamics. Based on this knowledge, the Madrid Autonomous Region was divided into six aerobiological risk areas following a clear anthropogenic gradient in terms of the potential pollen sources that determine pollen exposure in the Madrid Region. Spatial regionalisation is a common practice in environmental risk assessment to improve the application of management plans and optimise the air quality monitoring networks. The risk areas proposed by scientific criteria in the Madrid Autonomous Region can be adjusted to other operational criteria following a framework equivalent to other air quality networks.

A Clustering Study of Dietary Patterns and Physical Activity among Workers of the Uruguayan State Electrical Company.

Recent studies have shown that certain nutrients, specific food groups, or general dietary patterns (DPs) can promote health and prevent noncommunicable chronic diseases (NCCDs). Both developed and developing countries experience a high prevalence of NCCDs due to poor lifestyle habits, DPs, and low physical activity levels. This study aims to examine the dietary, physical activity, sociodemographic, and lifestyle patterns of Uruguayan State Electrical Company workers (the IN-UTE study). A total of 2194 workers participated in the study, providing information about their sociodemographics, lifestyles, and dietary habits through different questionnaires. To identify DPs from 16 food groups, principal component analysis (PCA) was performed. A hierarchical cluster algorithm was used to combine food groups and sociodemographic/lifestyle variables. Four DPs were extracted from the data; the first DP was related to the intake of energy-dense foods, the second DP to the characteristics of the job, the third DP to a Mediterranean-style diet, and the fourth DP to age and body mass index. In addition, cluster analysis involving a larger number of lifestyle variables produced similar results to the PCA. Lifestyle and sociodemographic factors, including night work, working outside, and moderate and intense PA, were significantly correlated with the dietary clusters, suggesting that working conditions, socioeconomic status, and PA may play an important role in determining DPs to some extent. Accordingly, these findings should be used to design lifestyle interventions to reverse the appearance of unhealthy DPs in the UTE population.

Segmentation of OECD countries on the basis of selected global environmental indicators using k-means non-hierarchical clustering.

In order to allocate resources and describe progress, frequently nations are grouped together by many international authorities. A variety of pertinent indicators can provide a more useful basis for classification for each specific area of interest. Based on commonalities between various variables connected to the global environmental sector, we developed a novel typology of country clusters. Four indicators were chosen after a review of the literature. In order to optimize data availability across as many OECD nations as feasible, indicators were chosen based on their relevance for all the OECD countries. Countries were arranged into a natural cluster using the hierarchical clustering method. Four groups, covering 31 countries, were the result of two stages of grouping. These four clusters were found to be more compact and clearly divided which gives policymakers a clear-cut idea as to how these environmental indicators are deteriorating day by day and year by year and what needs to be done to be more environmentally sustainable and responsible.

Deep learning based decision tree ensembles for incomplete medical datasets.

BACKGROUND: In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose. OBJECTIVE: To introduce a novel approach, namely Deep Learning-based Decision Tree Ensembles (DLDTE), which borrows the bounding box and sliding window strategies used in deep learning techniques to divide an incomplete dataset into a number of subsets and learning from each subset by a decision tree, resulting in decision tree ensembles. METHOD: Two medical domain problem datasets contain several hundred feature dimensions with the missing rates of 10% to 50% are used for performance comparison. RESULTS: The proposed DLDTE provides the highest rate of classification accuracy when compared with the baseline decision tree method, as well as two missing value imputation methods (mean and k-nearest neighbor), and the case deletion method. CONCLUSION: The results demonstrate the effectiveness of DLDTE for handling incomplete medical datasets with different missing rates.

A Bayesian nonparametric approach for handling item and examinee heterogeneity in assessment data.

We propose a novel nonparametric Bayesian item response theory model that estimates clusters at the question level, while simultaneously allowing for heterogeneity at the examinee level under each question cluster, characterized by a mixture of binomial distributions. The main contribution of this work is threefold. First, we present our new model and demonstrate that it is identifiable under a set of conditions. Second, we show that our model can correctly identify question-level clusters asymptotically, and the parameters of interest that measure the proficiency of examinees in solving certain questions can be estimated at a n rate (up to a log term). Third, we present a tractable sampling algorithm to obtain valid posterior samples from our proposed model. Compared to the existing methods, our model manages to reveal the multi-dimensionality of the examinees' proficiency level in handling different types of questions parsimoniously by imposing a nested clustering structure. The proposed model is evaluated via a series of simulations as well as apply it to an English proficiency assessment data set. This data analysis example nicely illustrates how our model can be used by test makers to distinguish different types of students and aid in the design of future tests.