Clusters Of Spots Research Articles

Spatiotemporal patterns and factors contributing to neonatal mortality in Ethiopia: Data from EDHS 2000 to 2019.

Although Ethiopia has substantial improvements in various health indicators such as maternal and child mortality, the burden of neonatal mortality remains high. Between 2016 and 2019, neonatal mortality increased from 29 deaths per 1,000 live births to 33 deaths per 1,000 live births. This study aimed to explore the spatial patterns and factors contributing to neonatal mortality in Ethiopia. Data from the Ethiopian Demographic and Health Surveys (EDHS) for the years 2000, 2005, 2011, 2016, and 2019 were analyzed. The EDHS sampling design uses a two-stage cluster sampling technique, considering census enumeration areas as primary sampling units and households as secondary sampling units. We used the Spatial Scan analysis in SaTScan and Getis-Ord Gi* statistic in Geographic Information System (GIS), to analyse the spatiotemporal patterns of neonatal mortality. Maternal, newborn and health service-related factors contributing to neonatal mortality were also analyzed using a multilevel logistic regression model. Adjusted Odds Rios (AOR) with corresponding 95% CI were presented as a measure of association and a P-value of 0.05 was used to declare statistical significance. During the initial three consecutive surveys, there was a consistent pattern of hot spot clusters in the Amhara and Benshangul Gumuz regions, along with certain parts of the Oromia region. However, in later surveys, these clusters shifted to the eastern parts of the country, notably including the Somali region. Early initiation of breast feeding was associated with reduced chances of neonatal death (Adjusted Odds Ratio [AOR]) = 0.27; 95% Confidence Interval [CI]: 0.23, 0.32). Neonates born at home (AOR = 1.46; 95% CI: 1.16, 1.82) and male babies had a higher likelihood of mortality during the neonatal period compared to their counterparts (AOR = 1.36; 95% CI: 1.24, 1.51). The odds of neonatal mortality increased with the number of children a mother had ever given birth to (AOR = 1.36; 95% CI: 1.24, 1.51). In contrast, longer birth intervals were associated with a reduced risk of neonatal mortality (AOR = 0.76; 95% CI: 0.68, 0.83). The central southern, central-western, north-western, and northern parts of Ethiopia had most of the neonatal death clusters in the first three rounds of DHS while eastern Ethiopia had the highest neonatal mortality clusters in the latest two surveys. Our results underscore the importance for policymakers and health administrators to reassess intervention approaches and reallocate resources to regions identified as hot spots for neonatal mortality. Enhancing the initiation of breastfeeding within the first hour of birth would improve newborn survival rates. Special attention and care need to be given to babies born of smaller sizes.

Spatiotemporal evolution of ecosystem health and its driving factors in the southwestern karst regions of China

As one of the most typical karst landscapes globally, the karst regions in southwestern China are characterized by prominent rocky desertification and fragile ecological conditions. Consequently, exploring the spatiotemporal evolution and driving influences on ecosystem health (EH) in this region is of great significance for the improvement of ecosystems and green development. This study focuses on assessing EH in these regions from 2000 to 2020 using the “vitality-organization-recovery-service” (VORS) framework. Spatiotemporal changes in EH are analyzed through hotspot analysis, and the functional relationship between driving factors and EH is quantified using XGBoost and SHAP models. Key findings include: (1) Over the past two decades, the proportion of cities experiencing enhanced EH has generally improved in 73% of cities compared to 27% experiencing deterioration. (2) Spatial analysis reveals EH clustering in three regions. One cold spot cluster in the central north and two hot spot clusters in the southwest and southeast. (3) Urbanization level exhibits an inverse logarithmic relationship with EH. Precipitation affects EH in a cubic polynomial pattern—initial decrease, subsequent increase, and final decrease. Temperature impacts EH through a quartic polynomial function with fluctuating increases and decreases. PM2.5 shows a monotonically decreasing relationship with EH, while the normalized difference vegetation index demonstrates a monotonically increasing association. This research contributes to understanding EH dynamics in southwestern China’s karst landscapes, crucial for advancing ecosystem management and sustainable development efforts.

Optimized laser production of thermonuclear neutrons from plasma of submicron-sized clusters

The concept of maximizing the D-D fusion neutron yield from the laser-heated large volume of cluster medium by matching the focal spot size and cluster plasma structural scales to the laser pulse intensity was confirmed. For this purpose, the three-dimensional particle-in-cell GEANT4 simulations have been performed by zoning of the large interaction domain. While considering a small domain of the entire interaction volume, which is partitioned into successive zones along laser propagation direction, a special algorithm was proposed allowing to reconstruct the integral spectrum of deuterons and D-D neutron yield. We demonstrate that it makes possible to specify high-performance laser–cluster neutron source following this concept. For example, for the submicron heavy water droplets heated by femtosecond laser pulse of the intensity 3×1019 W/cm2 a D-D neutron yield may reach 107 neutrons per 1 J of deposited laser energy if the intensity contrast ratio prevents premature cluster destruction. Such yield is considerably higher than achieved to date for microstructured targets.

Unravelling the paradox of spatial homogeneity: An analysis of land use planning and monofunctional development in Wroclaw, Poland

Processes of urban development progressing across Europe, with built-up areas expanding at a faster rate than the population, especially in the Central and Eastern Europe region. The importance of ensuring a high quality of urban life and effective environmental planning is widely acknowledged for the sustainable development of functional urban areas. While the literature extensively covers the implementation stage of monofunctional development in Polish suburban zones, the origins of spatial homogeneity in planning have received limited attention in research. This paper aims to assess local zoning plans and examine the diversity of urban planning in the subregion of Wroclaw, Poland. Employing exploratory experts interviews and landscape metrics such as Patch Richness Density and Simpson Diversity Index, along with spatial statistics as the Hot Spot tool and Cluster and Outlier analysis, the findings reveal that monofunctional residential development in Polish functional urban areas may result from a complex interplay of various economic, institutional, educational, professional responsibility, and political, social, as well as historical factors on the urban planning stage. These results contribute to a deeper understanding of the extent and spatial distribution of planning diversity, opening paths for further discussions on the professional requirements of urban planners.

Spatial and temporal distribution characteristics and geographic contexts of civilized villages in China.

Rural civilization is the soul of rural revitalization strategies. As a booster of rural civilization, the civilized village is an indispensable force to promote the modernization of rural areas, in the meanwhile, the study of its spatial distribution has important theoretical significance and practical value in deepening the theory of rural geography, promoting the development of rural civilization, and helping rural revitalization. Considering civilized villages as the study topic, the spatial and temporal distribution characteristics and geographic context were discussed using spatial analysis methods. The number of civilized villages in the country has shown a clear upward trend since 2005. The spatial distribution pattern of civilized villages in China shows unbalanced patterns with a higher concentration in the east and south. Civilized villages in China present significant patterns of clustering with an increasing degree of spatial clustering, showing a multi-core spatial distribution pattern. Civilized villages in China demonstrate clear spatiotemporal clustering characteristics. A spatiotemporal hot spot cluster formed in the northwestern region from 2005 to 2011, and a spatiotemporal cold spot cluster formed in the southeastern region from 2005 to 2008. The spatial distribution of civilized villages in China has obvious geographical differentiation laws, and factors such as topography, climate, economy, transportation, and policy significantly affect their spatial distribution.

Spatiotemporal Analysis of HIV/AIDS Incidence in China From 2009 to 2019 and Its Association With Socioeconomic Factors: Geospatial Study.

The Joint United Nations Program on HIV/AIDS (UNAIDS) has set the "95-95-95" targets to ensure that 95% of all people living with HIV will know their HIV status, 95% of all people living with HIV will receive sustained antiretroviral therapy (ART), and 95% of all people receiving ART will achieve viral suppression (<1000 copies/mL). However, few countries have currently achieved these targets, posing challenges to the realization of the UNAIDS goal to eliminate the global HIV/AIDS epidemic by 2030. The Chinese government has implemented corresponding policies for HIV/AIDS prevention and control; however, it still faces the challenge of a large number of HIV/AIDS cases. Existing research predominantly focuses on the study of a particular region or population in China, and there is relatively limited research on the macro-level analysis of the spatiotemporal distribution of HIV/AIDS across China and its association with socioeconomic factors. This study seeks to identify the impact of these factors on the spatiotemporal distribution of HIV/AIDS incidence in China, aiming to provide scientific recommendations for future policy development. This study employed ArcGIS 10.2 (Esri) for spatial analysis, encompassing measures such as the imbalance index, geographical concentration index, spatial autocorrelation analysis (Moran I), and hot spot analysis (Getis-Ord Gi*). These methods were used to unveil the spatiotemporal distribution characteristics of HIV/AIDS incidence in 31 provinces of China from 2009 to 2019. Geographical Detector was used for ecological detection, risk area detection, factor detection, and interaction detection. The analysis focused on 9 selected socioeconomic indicators to further investigate the influence of socioeconomic factors on HIV/AIDS incidence in China. The spatiotemporal distribution analysis of HIV/AIDS incidence in China from 2009 to 2019 revealed distinct patterns. The spatial distribution type of HIV/AIDS incidence in China was random in 2009-2010. However, from 2011 to 2019, the distribution pattern evolved toward a clustered arrangement, with the degree of clustering increasing each year. Notably, from 2012 onwards, there was a significant and rapid growth in the aggregation of cold and hot spot clusters of HIV/AIDS incidence in China, stabilizing only by the year 2016. An analysis of the impact of socioeconomic factors on HIV/AIDS incidence in China highlighted the "urbanization rate" and "urban basic medical insurance fund expenditure" as the primary factors influencing the spatial distribution of HIV/AIDS incidence. Additionally, among social factors, indicators related to medical resources exerted a crucial influence on HIV/AIDS incidence. From 2009 to 2019, HIV/AIDS incidence in China was influenced by various socioeconomic factors. In the future, it is imperative to optimize the combination of different socioeconomic indicators based on regional incidence patterns. This optimization will facilitate the formulation of corresponding policies to address the challenges posed by the HIV/AIDS epidemic.

Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples

We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole-slide image (WSI), optimized for patient-derived xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.

Individual and Geospatial Determinants of Health Associated With School-Based Human Papillomavirus Immunization in Alberta: Population-Based Cohort Study.

Human papillomavirus (HPV) infection causes nearly all cervical cancer cases and is a cause of anogenital and oropharyngeal cancers. The incidence of HPV-associated cancers is inequitable, with an increased burden on marginalized groups in high-income countries. Understanding how immunization status varies by material and social deprivation, health system, and geospatial factors is valuable for prioritizing and planning HPV immunization interventions. The objective of this study was to describe school-based HPV immunization rates by individual and geospatial determinants of health in Alberta, Canada. Health administrative data for male and female individuals born in 2004 in Alberta were used to determine HPV immunization status based on age and the number of doses administered in schools during the 2014/2015-2018/2019 school years. Immunization status and its relationship with material and social deprivation and health system factors were assessed by a logistic regression model. Geospatial clustering was assessed using Getis-Ord Gi* hot spot analysis. Mean scores of material and social deprivation and health system factors were compared between hot and cold spots without full HPV immunization using independent samples t tests. A multidisciplinary team comprising researchers and knowledge users formed a co-design team to design the study protocol and review the study results. The cohort consisted of 45,207 youths. In the adjusted model, the odds of those who did not see their general practitioner (GP) within 3 years before turning 10 years old and not being fully immunized were 1.965 times higher (95% CI 1.855-2.080) than those who did see their GP. The odds of health system users with health conditions and health system nonusers not being fully immunized were 1.092 (95% CI 1.006-1.185) and 1.831 (95% CI 1.678-1.998) times higher, respectively, than health system users without health conditions. The odds of those who lived in areas with the most material and social deprivation not being fully immunized were 1.287 (95% CI 1.200-1.381) and 1.099 (95% CI 1.029-1.174) times higher, respectively, than those who lived in areas with the least deprivation. The odds of those who lived in rural areas not being fully immunized were 1.428 times higher (95% CI 1.359-1.501) than those who lived in urban areas. Significant hot spot clusters of individuals without full HPV immunization exist in rural locations on the northern and eastern regions of Alberta. Hot spots had significantly worse mean material deprivation scores (P=.008) and fewer GP visits (P=.001) than cold spots. Findings suggest that material and social deprivation, health system access, and rural residency impact HPV immunization. Such factors should be considered by public health professionals in other jurisdictions and will be used by the Alberta co-design team when tailoring programs to increase HPV vaccine uptake in priority populations and regions.

Abstract 5066: Integrating spatial and single cell transcriptomics to identify and characterise biologically driven subgroups in invasive lobular carcinoma

Abstract Background: Invasive lobular carcinoma (ILC) is the second most common histological breast cancer subtype; however, little is known about its tumor microenvironment (TME). Here, we aimed to identify and to characterise ILC subgroups based on TME heterogeneity by combining spatial transcriptomics (ST) and single cell RNA-sequencing (scRNA-seq). Methods: We performed ST (Visium 10x Genomics) on frozen tumor samples from 41 primary hormone receptor positive (HR+), HER2-negative (HER2-) ILCs. Information coming from the morphological annotation of ST slides and the gene expression-based clustering of ST spots was integrated and used as input for a patient level classification (using intNMF algorithm). Subgroups of patients were annotated using morphology, gene set enrichment analysis and ST deconvolution based on scRNA-seq data (CARD software). SCAN-B bulk RNA-seq dataset was used as validation set (HR+, HER2- ILC samples, n = 853). Results: Four subgroups of patients were identified: proliferative (P, n = 12, enriched in tumor cells and proliferation-related pathways), normal-stroma enriched (NSE, n = 10, enriched in fibroblasts, stroma-related pathways and showing an higher level of colocalization between invasive and in situ carcinoma), metabolic (M, n = 9, enriched in metabolic related-pathways) and metabolic-immune enriched (MIE, n = 10, enriched in adipocytes, metabolic and immune-related pathways). Deconvolution of ST spots with single cell data revealed matching results with morphology (in terms of cell types). We observed an enrichment of myofibroblasts in NSE, an enrichment of cancer epithelial in P and, interestingly, an enrichment of endothelial cells in M subgroup (fdr &amp;lt; 0.25). MIE was characterized by perivascular like-endothelial cells and myeloid cells (monocytes and macrophages, fdr &amp;lt; 0.25). Of note, at the level of the ST slide, myeloid cells in MIE subgroup were more present in the adipose tissue compartment (fdr &amp;lt; 0.25) compared to the rest of the slide. NSE subgroup showed a trend for association with better prognosis. Gene signature-based assignment identified the same four subgroups in SCAN-B: GSEA analysis and comparisons among the four subgroups showed matching results between our dataset and SCAN-B. Of note, in SCAN-B, we observed differences in prognosis among the subgroups, with NSE showing better, M and MIE intermediate and P worse prognosis for overall survival and relapse free interval (p &amp;lt; 0.01). Conclusions: Four subgroups of ILC based on TME heterogeneity and showing differences in prognosis were identified and validated in external cohort. Of note, two of these groups were related to metabolism, highlighting the value of such process in ILC biology. Importantly, myeloid cells were the predominant immune cell types in ILC, and they showed a specific compartmentalization inside the TME. Further validation is needed. Citation Format: Matteo Serra, Andreas Papagiannis, Mattia Rediti, Frederic Lifrange, Nicola Occelli, Laetitia Collet, Delphine Vincent, Ghizlane Rouas, Danai Fimereli, Ligia Craciun, Denis Larsimont, David Venet, Miikka Vikkula, Francois P. Duhoux, Françoise Rothé, Christos Sotiriou. Integrating spatial and single cell transcriptomics to identify and characterise biologically driven subgroups in invasive lobular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5066.

Abstract 913: Automated reasoning artificial intelligence (AI) to model the cell-cell biochemical and cellular interactions in the tumor microenvironment (TME) of oral cancer (OC)

Abstract Our goal was to study the spatial complexity of HPV negative OC by integrating scRNAseq and spatial transcriptomics from 11 samples including 5 from never smokers never drinkers (NSND). We used Turing Biosystems proprietary graph database engine and automated reasoning AI to analyze and model the cell-cell biochemical and cellular interactions in the TME. The tumors H&amp;E images, Visium 10X, and single cell 10X data were pre-processed using standard methods. QC-based genes and cells filtering, gene expression normalization, dimensionality reduction, clustering of Visium spots, and identification of spatially variable genes were performed. We generated multiple networks from tumor data and prior biological knowledge to integrate different layers of information: single-cell and spatial transcriptomics data, cell-cell interactions networks, cell neighboring, spatial correlation networks. We first used a novel network-based technique to identify the spatial distribution of any cell subtypes (e.g. CAF, T cells), by integrating differentially expressed genes from scRNAseq, prior knowledge and prior data (e.g. TCGA). We used a multilayer network approach to integrate the different networks of data and an automated reasoning AI analysis which allows to interpret the results based on a reasoning on biological and clinical knowledge in opposition to machine learning methods based on statistical patterns. We identified CAFs/T cells/IFN-g to be strongly correlated within the tumor islets interacting with the stroma in NSND while they were more correlated within the stromal regions in smokers. We also identified 2 or 3 subtypes of tumor islets areas based on their functional states (e.g., immune pathways activated or metabolic differences), allowing us to unravel a more detailed map of the intratumoral heterogeneity when compared to histopathology annotations. In order to get some insights into the functional impact of the spatial distribution of cells, the tumors were then represented as multilayer networks to identify all possible cell-cell interactions in the TME. This was followed by a spatial simulation of these interactions using logic (e.g., inhibition, activation) and correlative (e.g., gene expression, imaging features correlations) interactions in the networks to identify spatial interactions. From that we analysed all the possible known protein-protein and metabolite-protein interactions between the cell types analysed and mapped the tumors with the gene expressions corresponding to the pairs of interacting proteins (or enzymes linked to metabolites). We found an interaction between CAF and Tregs via CXCL12 (CAFs) and CXCR4 (Tregs). In conclusion, automated reasoning AI integrating scRNAseq and spatial transcriptomics allows an in-depth analysis of cells spatial distribution and its functional impact in OC. Citation Format: Karène Mahtouk, Maxime Vincent, Yannick Le Meitour, Béatrice Vanbervliet-Defrance, Sonia Canjura, Cyril Degletagne, Laurie Tonon, Lucas Michon, Jebrane Bouaoud, Aude Excoffier, Philippe Zrounba, Rémy Boutonnet, Adam Amara, Pierre Saintigny. Automated reasoning artificial intelligence (AI) to model the cell-cell biochemical and cellular interactions in the tumor microenvironment (TME) of oral cancer (OC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 913.

Spatial Cluster Change of Schistosoma japonicum Transmission Foci in Indonesia During the Schistosomiasis Elimination Program.

The Government of Indonesia committed to eliminating schistosomiasis by 2025. Collaboratively snail control became one of the crucial strategies to ensure that the prevalence ofSchistosoma japonicuminOncomelania hupensis lindoensisreaches zero by the end of the program. This research investigated the spatial cluster change ofS. japonicumtransmission foci in Indonesia between 2017 and 2021. We mapped the snail foci, collected the snails, and calculated the snail density. We also conducted laboratory tests to detect the existence of cercariae in the snails. Identified infected snails were used to calculate the infection rate (IR) or snails' prevalence of schistosome cercariae among freshwater snails. We then analysed the spatial cluster using the Getis-Ord Gi* statistic to identify the hot and cold spots. The 5-year schistosomiasis elimination program successfully declined 18.84% of the snail foci and reduced 40.37% of the infected snail foci. Local spatial autocorrelation of snail density and infection rate identified that in 2017 and 2021, the number of cold spots decreased by 53.91% and 0%, while hot spots increased by 2.63% and 56.1%. The presence of more hotspots suggests a rise in the number of foci with high snail density and infection rates. The implementation of snail control was not optimal, and the parasite transmission through domestic animals still existed, causing the spatial cluster of hot spots to change during this period. Most hotspots have been observed near settlements, primarily in cocoa plantations, developed and deserted rice fields, grassland, and bush wetlands. During the schistosomiasis elimination program, the number of hot spots increased while cold spots decreased, and there were notable changes in the geographical distribution of hot spots, indicating a shift in the clustering pattern of schistosomiasis cases. The findings become essential for policymakers, particularly in selecting priority areas for intervention. In the Discussion section, we demonstrated the selection process based on the existence of hot and cold spots. Furthermore, we proposed that enhancing cross-sector integration is crucial, particularly in connection with the management of S. japonicum transmission through domestic animals.

Assessing the impact of booster vaccination on diphtheria transmission: Mathematical modeling and risk zone mapping

The COVID-19 pandemic caused significant disruptions in the healthcare system, affecting vaccinations and the management of diphtheria cases. As a consequence of these disruptions, numerous countries have experienced a resurgence or an increase in diphtheria cases. West Java province in Indonesia is identified as one of the high-risk areas for diphtheria, experiencing an upward trend in cases from 2021 to 2023. To analyze the situation, we developed an SIR model, which integrated DPT and booster vaccinations to determine the basic reproduction number, an essential parameter for infectious diseases. Through spatial analysis of geo-referenced data, we identified hotspots and explained diffusion in diphtheria case clusters. The calculation of R0 resulted in an R0 = 1.17, indicating the potential for a diphtheria outbreak in West Java. To control the increasing cases, one possible approach is to raise the booster vaccination coverage from the current 64.84% to 75.15%, as suggested by simulation results. Furthermore, the spatial analysis revealed that hot spot clusters were present in the western, central, and southern regions, posing a high risk not only in densely populated areas but also in rural regions. The diffusion pattern of diphtheria clusters displayed an expansion-contagious pattern. Understanding the rising trend of diphtheria cases and their geographic distribution can offer crucial insights for government and health authorities to manage the number of diphtheria cases and make informed decisions regarding the best prevention and intervention strategies.

Scalable integrated two-dimensional Fourier-transform spectrometry

Integrated spectrometers offer the advantages of small sizes and high portability, enabling new applications in industrial development and scientific research. Integrated Fourier-transform spectrometers (FTS) have the potential to realize a high signal-to-noise ratio but typically have a trade-off between the resolution and bandwidth. Here, we propose and demonstrate the concept of the two-dimensional FTS (2D-FTS) to circumvent the trade-off and improve scalability. The core idea is to utilize 2D Fourier transform instead of 1D Fourier transform to rebuild spectra. By combining a tunable FTS and a spatial heterodyne spectrometer, the interferogram becomes a 2D pattern with variations of heating power and arm lengths. All wavelengths are mapped to a cluster of spots in the 2D Fourier map beyond the free-spectral-range limit. At the Rayleigh criterion, the demonstrated resolution is 250 pm over a 200-nm bandwidth. The resolution can be enhanced to 125 pm using the computational method.

Application of Machine Learning in Grain-Related Clustering of Laue Spots in a Polycrystalline Energy Dispersive Laue Pattern

Application of Machine Learning in Grain-Related Clustering of Laue Spots in a Polycrystalline Energy Dispersive Laue Pattern

Hotspot areas of risky sexual behaviour and associated factors in Ethiopia: Further spatial and mixed effect analysis of Ethiopian demographic health survey.

Sexual behaviour needs to take a central position in the heart of public health policy makers and researchers. This is important in view of its association with Sexually Transmitted Infections (STIs), including HIV. Though the prevalence of HIV/AIDS is declining in Ethiopia, the country is still one of the hardest hit in the continent of Africa. Hence, this study was aimed at identifying hot spot areas and associated factors of risky sexual behavior (RSB). This would be vital for more targeted interventions which can produce a sexually healthy community in Ethiopia. In this study, a cross-sectional survey study design was employed. A further analysis of the 2016 Ethiopia Demographic and Health Survey data was done on a total weighted sample of 10,518 women and men age 15-49 years. ArcGIS version 10.7 and Kuldorff's SaTScan version 9.6 software were used for spatial analysis. Global Moran's I statistic was employed to test the spatial autocorrelation, and Getis-Ord Gi* as well as Bernoulli-based purely spatial scan statistics were used to detect significant spatial clusters of RSB. Mixed effect multivariable logistic regression model was fitted to identify predictors and variables with a p-value ≤0.05 were considered as statistically significant. The study subjects who had RSB were found to account about 10.2% (95% CI: 9.64%, 10.81%) of the population, and spatial clustering of RSB was observed (Moran's I = 0.82, p-value = 0.001). Significant hot spot areas of RSB were observed in Gambela, Addis Ababa and Dire Dawa. The primary and secondary SaTScan clusters were detected in Addis Ababa (RR = 3.26, LLR = 111.59, P<0.01), and almost the entire Gambela (RR = 2.95, LLR = 56.45, P<0.01) respectively. Age, literacy level, smoking status, ever heard of HIV/AIDS, residence and region were found to be significant predictors of RSB. In this study, spatial clustering of risky sexual behaviour was observed in Ethiopia, and hot spot clusters were detected in Addis Ababa, Dire Dawa and Gambela regions. Therefore, interventions which can mitigate RSB should be designed and implemented in the identified hot spot areas of Ethiopia. Interventions targeting the identified factors could be helpful in controlling the problem.

Trend, spatial distribution, and factors associated with HIV testing uptake among pregnant women in Ethiopia, based on 2005-2016 Ethiopia demographic and health survey: A multivariate decomposition analysis and geographically weighted regression.

HIV testing during pregnancy is an integral component and first step of prevention for mother to child transmission, initiation of antiretroviral treatment and diagnosis of HIV/AIDS. However, Ethiopia and other sub-Saharan African countries face challenges in meeting the first target of the 95-95-95 global initiatives. This study examines trends, spatial distribution, and factors influencing HIV testing among pregnant women in Ethiopia from 2005 to 2016, using data from the Ethiopia Demographic and Health Surveys. The study was based on three consecutive demographic and health survey in Ethiopia. A total weighted sample of 13,020 women who gave birth within 2 year proceeding each survey year was included in each survey. Logit based decomposition analysis technique was employed to identify factors contributing to the change in HIV testing uptake among pregnant women overtime. ArcGIS version 10.7.1 and SaT Scan version 10.1software were used for the spatial analysis and geographically weighted regression. HIV testing uptake among pregnant women has significantly increased from 0.51% in 2005 to 32.4% in 2016 with 2.9% annual rate of increment in Ethiopia. About 75.9% of the overall increase in HIV testing uptake among pregnant from 2005-2016 was due to increases in women's composition with knowledge of Mother to child transmission of HIV (3.2%), HIV counseling (10.3%), 4 or more antenatal care visits (31.4%), health facility delivery (6.3%), not perceiving distance from the health facility as a big problem (1.1%), and urban residence (0.6%). Spatial variation of low proportion of HIV testing was non-random in all three surveys (Moran index, p-value<0.05). Hot spot clusters exhibited in all the three waves includes Tigray and SNNPRs in 2005 and consistent hotspot areas in Benishangul-Gumuz, Somali, SNNPR, and Gambella in 2011 and 2016 EDHS. Lack of knowledge of Mother to child transmission of HIV, lack of antenatal care visit, lack of media exposure, and health facility delivery were significant predictors for the spatial variation of low proportion of HIV testing uptake across regions in Ethiopia in 2016. Over all, there has been a substantial increase in HIV testing uptake among pregnant women overtime in Ethiopia, but it still far away from achieving the 2025 HIV testing targets. Knowledge of Mother to child transmission of HIV, HIV counseling, Number of Antenatal care visit, previous place of delivery, residence and distance to health facility were significant contributing factors for the change in HIV testing uptake. There was geographical disparity in HIV testing uptake across regions in all three EDHS. Lack of knowledge of Mother to child transmission of HIV, lack of ANC visit, lack media exposure, and health facility delivery were significant predictors. Geographic-based interventions, together with broader public health strategies, are essential for advancing HIV testing uptake.

Spatial Transcriptomics Unveils Novel Potential Mechanisms of Disease in a MI cγ1 Multiple Myeloma in vivo Model

Multiple Myeloma (MM) is a malignant plasma cell (PC) disorder characterized by a heterogeneous distribution of PCs in the bone marrow (BM) where the interactions between the PCs and the BM microenvironment guide the pathogenesis of the disease. The development of single cell technologies has contributed to understand the transcriptional heterogeneity of the disease both from the tumor and the microenvironment point of view, yet the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. To address the role of spatially resolved interactions in MM, we performed spatial transcriptomic analysis in BM from the MI cγ1mice strain (Larrayoz et al Nat Med. 2023) a recently described mouse model that recapitulates the characteristics of a MYC driven human MM disease using the Visium Spatial Gene Expression analysis (10x Genomics). Using formalin-fixed paraffin-embedded (FFPE) BM tissues from the femur of control mice, we first characterized the healthy BM. Considering that each spot contains an averaged transcriptomic profile of 3-10 cells, we generated a single-cell reference set that included the most prevalent cell types in the BM to identify the cell type contribution per spot. The relative proportions of the estimations are consistent with percentage estimations observed by previous works (Hongzhe et al, eLife. 2023). Next, using the “cell proportion estimations per spot” we identified four different clusters of spots. A deeper characterization of those clusters revealed a positive correlation between erythroblasts and B cells, and a negative correlation between both cell types and neutrophils. Additionally, we spatially defined clusters 1 and 2, those with larger proportion of erythroblasts, in the metaphysis trabecular regions of the bone marrow. We followed the same analysis framework to characterize the spatial configuration in the MI cγ1 mice BM with a developed MM. To this end, we incorporated the MM-derived single-cell PCs in the single-cell reference set. Importantly, the pathological PCs were identified and organized in large groups in the BM space. Cell proportion estimations in these samples revealed a distribution in 7 clusters, 4 of them concentrated in the periphery of the PC hot spots and 3 mirroring the PC gradient observed in previous analysis (Figure 1A). We next performed a targeted differential analysis to confirm previous observed markers identities of different MM processes as Cd44 (de Jong et al, Nat Immunol. 2021), a signature of dormant cells (Khoo et al, Blood. 2019), the 38 MM-associated surface-protein-encoding genes (Yao et al, Cancer Res. 2023), Mmp9 and different profiles of T cell exhaustion markers within the 3 clusters with higher PC concentration. These analyses allowed us to verify two different markers, Cd44 and Mmp9 localized in the periphery of the hot spots of PC which may explain an invasive mechanism of the cells localized in the outer areas of the MM combining an inflammatory environment, manifested by a Cd44 increment, with a degradative mechanism driven by Mmp9. Simultaneously, an untargeted differential analysis aimed to identify markers characterizing the clusters with high PC prevalence, revealed novel marker genes never described before in MM and not associated with PC proportion, as the Transmembrane Immune Signaling Adaptor ( Tyrobp) in the cluster 5 (peripheral cluster), which is predictive of a poor prognosis and high tumor immune infiltration in other described tumors (Lu et al BMC Cancer 2021) (Fig 1B). Finally, as a proof of principle of this technology potential in human samples, spatial transcriptomic was applied to FFPE samples from BM biopsies of 7 MM patients with different degrees of PC infiltration, providing a source for validation of the results obtained in the mouse model. In conclusion, our findings demonstrate that the application of spatial transcriptomics represents a useful tool for understanding the spatial architecture and niche specific interactions in human diseases, offering a systemic approach to dissect the role of the spatial interactions in the pathogenesis of MM.

Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics.

The recent development of spatially resolved transcriptomics (SRT)technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data. We introduce a statistical spatial transcriptomics cell assignment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments. https://github.com/ChengZ352/SPAN.

Exploring the spatial pattern of animal bites in Iran (2021–2022)

ObjectiveKnowledge of the spatial pattern of animal bites can be helpful for targeted resource allocation and to develop and deliver effective intervention programs. The aim of this study was to explore the spatial pattern of the animal bites in Iran during 2021–2022. MethodsAnimal bite cases from all provinces and counties in Iran were obtained from a nationally based registry. Global Moran's I was applied to check spatial autocorrelation. The spatially adjusted standardized incidence ratios (SIRs) were estimated using the Besag, York, and Mollie (BYM) model. Spatial clusters were identified by local indicators of spatial association (LISA) and purely spatial scan statistic. ResultsA total of 260,470 animal bites were registered during the study period. There was a positive spatial autocorrelation (global Moran's I=0.27, p-value=0.001). The majority of SIRs greater than 1.00 was found in counties in the northern belt of Iran (e.g., observed greater than expected animal bites). LISA found that approximately 7% of counties in the north and northeast, 18% in the west and south, and 3% in the central part of Iran were significant hot spots, cold spots, and spatial outliers (p-value≤0.05). Spatial scan statistic detected primary hot spot cluster in the counties in the Mazandaran and Alborz provinces (Relative Risk=2.56, p-value<0.001), while primary cold spot cluster involved counties in Kurdistan and Kermanshah province (0.37, <0.001). ConclusionAnimal bites were unevenly distributed in Iran. Further prevention and control programs as well as appropriately resource allocation are needed in order to reduce the observed animal bites spatial disparity.

Frequency, inequalities and spatial distribution of oral health services utilization in Peruvian children under twelve years of age: a population-based comparative analysis of the years 2017 and 2021

BackgroundOral diseases pose a significant public health challenge among Peruvian children. However, oral health services utilization among them is marked by inequalities, which may have been exacerbated by the COVID-19 pandemic. The objective was to compare the frequency, inequalities, determinants of inequality, and spatial distribution in oral health services utilization in Peruvian children under 12 years of age in 2017 and 2021.MethodsComparative secondary data analysis from the Demographic and Family Health Survey (ENDES) for the years 2017 (38,787 minors) and 2021 (36,729 minors). Age-adjusted prevalence ratios (PR) with 95% confidence intervals (CI) were calculated to assess the change in oral health services utilization in the last 12 months between the years 2017 and 2021, stratifying by demographic and socioeconomic covariates. Inequality was assessed by decomposing the Erreygers Concentration Index (ECI) and calculating the percentage contribution to inequality of the independent variables. Spatial analysis was performed using spatial autocorrelation methods, Hot Spot Analysis, Cluster and Outlier analysis, Inverse Distance Weighting, ordinary Kriging, and Kulldorff analysis.ResultsThe probability of Peruvian children under 12 years of age using oral health services in the last 12 months was reduced by 45% in 2021 compared to 2017. The ECI identified a significant reduction in oral health services utilization inequalities at the national level (Diff: -0.0963; p < 0.001). The main contributor to inequality was higher tutor education level (55.2% in 2017 and 82.7% in 2021). In the comparison of spatial distribution, there was a greater dispersion of the conglomerates in which the use of oral health services is concentrated in 2021.ConclusionsThe frequency of oral health services utilization in the Peruvian children under 12 years of age was halved between 2017 and 2021. This problem is transversal to the entire population at the demographic and socioeconomic level. The key factor contributing to inequalities in the utilization of oral health services was the higher educational attainment of caregivers or guardians. Despite the improvement observed in inequalities and spatial distribution of the concentration of oral health services utilization, it is necessary to keep monitoring these patterns to guide decision-making.