Content-based clustering of hallucinations across sensory modalities in a large online survey.

Hallucinations can have rather heterogeneous aetiology and presentation. This inspired the concept of different subtypes based on symptom profiles, especially in the field of auditory hallucinations. As many people experience hallucinations in more than one sensory modality, it seems important to investigate potential hallucination subtypes across different sensory modalities. We assessed the content of hallucinations as part of a large survey among the general Dutch population (n = 10,448) using the Questionnaire for Psychotic Experiences. Based on their descriptions, thematic categories were created in a data-driven cluster analysis. 2594 participants who experienced hallucinations over the past week that contained at least 2 different thematic categories were selected. Clustering of their hallucination content was performed with the HDBSCAN method. We identified 4 clusters, i.e., subtypes, which can be typified as 1. hallucinations of foul odors, 2. complex visual scenes, 3. a vast variety of rather common hallucinations possibly related to heightened alertness, and 4. possibly bereavement hallucinations. The bereavement subtype showed an increase in emotional loneliness and the presence of delusions. Our findings suggest that the content of hallucinations can be informative, especially when investigated across sensory modalities. Such subtypes may help to better understand their underlying mechanisms.

Reexploring the conception of heat-health risk: From the perspectives of dimensionality and spatiality.

Extreme heat events are more frequent and intense as a result of global climate change, thus posing tremendous threats to public health. However, extant literature exploring the multidimensional features of heat-health risks from a spatial perspective is limited. This study revisits extreme heat-health risk and decomposes this concept by integrating multi-sourced datasets, identifying compositional features, examining spatial patterns, and comparing classified characteristics based on local conditions. Using Maryland as the focal point, we found that the components of heat-health risk are different from traditional risk dimensions (i.e., vulnerability, hazards, and exposure). Through a local-level clustering analysis, heat-health risks were compared with areas having similar features, and among those with different features. The findings suggest a new perspective for understanding the socio-environmental and socio-spatial features of heat-health risks. They also offer an apt example of applying cross-disciplinary methods and tools for investigating an ever-changing phenomenon. Moreover, the spatial classification mechanism provides insights about the underlying causes of heat-health risk disparities and offers reference points for decision-makers regarding identification of vulnerable areas, resource allocation, and causal inferences when planning for and managing extreme heat disasters.

Enhanced Performance of the Optimized Dye CF583R in Direct Stochastic Optical Reconstruction Microscopy of Active Zones in Drosophila Melanogaster.

Super-resolution single-molecule localization microscopy (SMLM) of presynaptic active zones (AZs) and postsynaptic densities contributed to the observation of protein nanoclusters that are involved in defining functional characteristics and in plasticity of synaptic connections. Among SMLM techniques, direct stochastic optical reconstruction microscopy (dSTORM) depends on organic fluorophores that exert high brightness and reliable photoswitching. While multicolor imaging is highly desirable, the requirements necessary for high-quality dSTORM make it challenging to identify combinations of equally performing, spectrally separated dyes. Red-excited carbocyanine dyes, e.g., Alexa Fluor 647 (AF647) or Cy5, are currently regarded as "gold standard" fluorophores for dSTORM imaging. However, a recent study introduced a set of chemically modified rhodamine dyes, including CF583R, that promise to display similar performance in dSTORM. In this study, we defined CF583R's performance compared to AF647 and CF568 based on a nanoscopic analysis of Bruchpilot (Brp), a nanotopologically well-characterized scaffold protein at Drosophila melanogaster AZs. We demonstrate equal suitability of AF647, CF568 and CF583R for basal AZ morphometry, while in Brp subcluster analysis CF583R outperforms CF568 and is on par with AF647. Thus, the AF647/CF583R combination will be useful in future dSTORM-based analyses of AZs and other subcellularly located marker molecules and their role in physiological and pathophysiological contexts.

A novel super-resolution microscopy platform for cutaneous alpha-synuclein detection in Parkinson's disease.

Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson's disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a well-established validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylated-aSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001-0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status.

[Nitrogen Flow Characteristics of Agricultural Production and Consumption System in the Yangtze River Delta Region and Its Driving Factors].

To assess the impact of human activities on regional nitrogen （N） flow, based on the statistical data of 27 cities in the Yangtze River Delta Region （YRD）, N flow characteristics of the agricultural production and consumption system （APC） in the YRD from 2011 to 2020 were analyzed using substance flow analysis, and driving factors for N flow were analyzed using scenario analysis. The results showed that from 2011 to 2020, the mean N input intensity of the APC in the YRD was 194.6 kg·ï¼ˆhm2·a）-1, which was more than five times the national average value； thus, the YRD was a hotspot of N input intensity in China. Chemical N fertilizer was the largest component of N input, and the YRD changed from a net export area of grain and animal products to a net import area due to the rapid growth of food consumption demand. The N output of the system was mainly N loss to the environment, accounting for 53.2% on average. The N use efficiency （NUE） of cropland and the N recycling ratio of the APC ranged from 38.7-42.2% and 15.8-21.5%, respectively, which were both at a low level. In addition, the total amount of N input and output of the APC both showed a parabolic decline trend, decreasing by 11.3% and 10.0%, respectively. Spatially, the overall N input intensity showed a pattern of "high in the north and low in the south," and the spatial heterogeneity of N input intensity among cities was significant. Cities with high input intensity were mainly located in the north and east of Jiangsu, Shanghai, and northeast of Zhejiang. A significant positive spatial autocorrelation of the distribution of mean N input intensity was observed. The uncertainty of N flows was estimated using the error propagation equation. The uncertainty interval of N input and output ranged from 4.5% to 34.6%, which was roughly equivalent to the results of related studies, indicating that the model results were reliable. Based on the scenario analysis method, the decrease of the livestock scale led to a decrease of -0.27%-7.53% in the N input, making it the main reason for the decrease of total N input in the APC. Improving the NUE of cropland and re-establishing the linkage between cropland and livestock will help reduce N loss to the environment.

Spatial Clustering of Rabies by Animal Species in New Jersey, United States, from 1989 to 2023.

Identifying spatial clusters of rabies in animals aids policymakers in allocating resources for rabies prevention and control. This study aimed to investigate spatial patterns and hotspots of rabies in different animal species at the county level in New Jersey. Data on animal rabies cases from January 1989 to December 2023 were obtained from the New Jersey Department of Health and aggregated by county. Global Moran's index (I) statistics were computed for each species to detect global spatial clustering (GeoDa version 1.22). Local Moran's indicators of spatial association (LISA) were computed to identify local clusters of rabies. The results from the LISA analysis were mapped using ArcGIS Pro to pinpoint cluster locations. A total of 9637 rabies cases were analyzed among raccoons (n = 6308), skunks (n = 1225), bats (n = 1072), cats (n = 597), foxes (n = 225), and groundhogs (n = 210). A global Moran's test indicated significant global spatial clustering in raccoons (I = 0.32, p = 0.012), foxes (I = 0.29, p = 0.011), and groundhogs (I = 0.37, p = 0.005). The LISA results revealed significant spatial clustering of rabies in raccoons and foxes in southeastern New Jersey and in groundhogs in northern New Jersey. These findings could guide the development of targeted oral rabies vaccination programs in high-risk New Jersey counties, reducing rabies exposure among domestic animals and humans.

AFSC: A self-supervised augmentation-free spatial clustering method based on contrastive learning for identifying spatial domains.

Recent research in spatial transcriptomics allows researchers to analyze gene expression without losing spatial information. Spatial information can assist in cell communication, identification of new cell subtypes, which provides important research methods for multiple fields such as microenvironment interactions and pathological processes of diseases. Identifying spatial domains is an important step in spatial transcriptomics analysis, and improving spatial clustering methods can benefit for identifying spatial domains. In addition to eliminating noise in original gene expression, how to use spatial information to assist clustering has also become a new problem. A variety of calculating methods have been applied to spatial clustering, including contrastive learning methods. However, existing spatial clustering methods based on contrastive learning use data augmentation to generate positive and negative pairs, which will inevitably destroy the biological meaning of the data. We propose a new self-supervised spatial clustering method based on contrastive learning, Augmentation-Free Spatial Clustering (AFSC), which integrates spatial information and gene expression to learn latent representations. We construct a contrastive learning module without negative pairs or data augmentation by designing Teacher and Student Encoder. We also design an unsupervised clustering module to make clustering and contrastive learning be trained together. Experiments on multiple spatial transcriptomics datasets at different resolutions demonstrate that our method performs well in self-supervised spatial clustering tasks. Furthermore, the learned representations can be used for various downstream tasks including visualization and trajectory inference.

EEG microstates are associated with music training experience.

Background: Music training facilitates the development of individual cognitive functions and influences brain plasticity. A comprehensive understanding of the pathways and processes through which music affects the human brain, as well as the neurobiological mechanisms underlying human brain perception of music, is necessary to fully harness the plasticity that music offers for brain development. Aims: To investigate the resting-state electroencephalogram (EEG) activity of individuals with and without music training experience, and explore the microstate patterns of EEG signals. Method: In this study, an analysis of electroencephalogram (EEG) microstates from 57 participants yielded temporal parameters(mean duration, time coverage, occurrence, and transition probability)of four classic microstate categories (Categories A, B, C, and D) for two groups: those with music training experience and those without. Statistical analysis was conducted on these parameters between groups. Results: The results indicate that compared to individuals without music training experience, participants with music training experience exhibit significantly longer mean durations of microstate A, which is associated with speech processing. Additionally, they show a greater time coverage of microstate B, which is associated with visual processing. Transition probabilities from microstate A to microstate B were greater in participants with music training experience compared to those without. Conversely, transition probabilities from microstate A to microstate C and from microstate C to microstate D were greater in participants without music training experience. Conclusion: Our study found differences in characteristic parameters of certain microstates between individuals with and without music training experience. This suggests distinct brain activity patterns during tasks related to speech, vision, and attention regulation among individuals with varying levels of music training experience. These findings support an association between music training experience and specific neural activities. Furthermore, they endorse the hypothesis of music training experience influencing brain activity during resting states. Additionally, they imply a facilitative role of music training in tasks related to speech, vision, and attention regulation, providing initial evidence for further empirical investigation into the cognitive processes influenced by music training.

Quantification of collective signalling in time-lapse microscopy images.

Live-cell imaging of fluorescent biosensors has demonstrated that space-time correlations in signalling of cell collectives play an important organisational role in morphogenesis, wound healing, regeneration, and maintaining epithelial homeostasis. Here, we demonstrate how to quantify one such phenomenon, namely apoptosis-induced ERK activity waves in the MCF10A epithelium. We present a protocol that starts from raw time-lapse fluorescence microscopy images and, through a sequence of image manipulations, ends with ARCOS, our computational method to detect and quantify collective signalling. We also describe the same workflow in the interactive napari image viewer to quantify collective phenomena for users without prior programming experience. Our approach can be applied to space-time correlations in cells, cell collectives, or communities of multicellular organisms, in 2D and 3D geometries.

Analysis of the spatiotemporal changes in global tropospheric ozone concentrations from 1980 to 2020.

Tropospheric ozone affects human health, ecosystems, and climate change. Previous studies on Tropospheric Column Ozone (TCO) have primarily concentrated on specific regions or global geographic divisions. This has led to insufficient exploration of the spatiotemporal characteristics and influencing factors of TCO in global and rational subregions. In this study, TCO is calculated using the Modern Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data and corrected using satellite data. Cluster analysis is conducted to explore the temporal characteristics of TCO variations in different regions. The results show that the global TCO is basically distributed latitudinally, with higher TCO in the northern hemisphere, which is related to atmospheric circulation, radiation, stratospheric transport, and the distribution of ozone precursors. Between 1980 and 2020, the global average annual TCO showed an increasing trend at 0.09 DU yr-1 due to rising anthropogenic emissions of ozone precursors (NOx at 589547.86 t yr-1 and NMVOC at 1070818.24 t yr-1), increasing tropopause height (-0.10 hPa yr-1), and the enhanced ozone flux at the tropopause (0.22 ppbv m s-2 yr-1). Cluster analysis reveals different trends in TCO changes across regions. The ocean south of 60°S and parts of West Antarctica (Region 2), the region from 30°N to 60°N and the western oceanic region of 30°S (Region 3), and the region from the equator to 60°S and the region north of 60°N (Region 5) exhibit increasing trends (with rates of 0.08 DU yr-1, 0.07 DU yr-1, and 0.11 DU yr-1, respectively), linked to the enhanced ozone flux at the tropopause, the rising tropopause height and increasing ozone p precursors. Conversely, the decreasing TCO trends in the equatorial Pacific (Region 1) and East Antarctica (Region 4) (with rates of -0.01 DU yr-1 and -0.02 DU yr-1) may be related to increased cloudiness and weakened photochemical reactions.

A methodological framework to characterize the wildlife-livestock interface: The case of wild boar in mainland Spain.

BACKGROUND: The representation of wildlife-livestock interface (WLI) at an accurate spatial resolution poses several challenges. Furthermore, there is a lack of published material providing detailed descriptions of geospatial techniques for the purpose of producing visual results that are interpretable and contrastable for epidemiological analysis. OBJECTIVES: Our aim is to develop a standardized, applicable, and scalable methodological framework for describing and characterizing the WLI across a large spatial extent. Subsequently, we aim to employ this framework to depict specific WLI based on different epidemiological scenarios determined by the abundance of wild boar (Sus scrofa) and domestic ungulates as an illustrative case, specifically focusing on mainland Spain. METHODS: To establish a methodological framework, we merged data from both wild and domestic sources into a hexagonal grid. We utilized data on wild boar hunting and the locations of pig, cattle, sheep, and goat farms in mainland Spain. New variables were derived from this combined dataset to illustrate the overlapping abundance between wild boar and domestic species. Finally, a cluster analysis of the generated variables was carried out, with the aim of distinguishing and characterizing various scenarios of the wild boar-domestic ungulate interface in mainland Spain. RESULTS: The hexagonal grid proved appropriate to represent and evaluate the WLI at fine spatial resolution over such broad extent. Despite the inability to ascribe a dominant livestock type and production system to a specific region, we were able to identify fifteen main areas of interest in terms of overlap. As for extensive livestock, normally at the highest risk of interaction with wild boar, the primary regions in Spain were those with dehesa agroecosystem and the Atlantic areas. Certain scenarios were particularly relevant in terms of risk for interaction and subsequent transmission of disease, namely, the case of extensive pig production in south western Spain (dehesa agroecosystem), which is especially concerned about the potential introduction of African Swine fever (ASF) in the Country. DISCUSSION AND CONCLUSIONS: We provide a basis for visualizing and understanding of different WLI scenarios, which is extensible to other regions and interfaces, and automatable where precise source of data from wildlife and livestock are available. This spatial statistics framework enables the utilization of high-resolution data, ensuring consistency on uniform grids. This aligns with the needs of high-resolution disease dissemination models based on wildlife behaviour. Such aspects are crucial for developing risk assessment and improving strategies for the prevention, control, and eradication of shared priority emerging diseases at national and international levels, such as ASF.

An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data.

Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many non-spatial and spatial clustering approaches have been used to partition the entire region into mutually exclusive spatial domains based on the SRT high-dimensional molecular profile, most require an ad hoc selection of less interpretable dimensional-reduction techniques. To overcome this challenge, we propose a zero-inflated negative binomial mixture model to cluster spots or cells based on their molecular profiles. To increase interpretability, we employ a feature selection mechanism to provide a low-dimensional summary of the SRT molecular profile in terms of discriminating genes that shed light on the clustering result. We further incorporate the SRT geospatial profile via a Markov random field prior. We demonstrate how this joint modeling strategy improves clustering accuracy, compared with alternative state-of-the-art approaches, through simulation studies and 3 real data applications.

Assessing green production efficiency and spatial characteristics of China's real estate industry based on the undesirable super-SBM model.

As China strives to balance rapid urbanization with environmental conservation, increasing attention is being paid to the pursuit of green production efficiency (GPE) in the real estate industry. The undesirable super-SBM model was used to calculate the GPE of China's real estate industry from 2001 to 2020. Additionally, GPE spatial distribution characteristics in China's real estate industry were analyzed using the standard deviation ellipse (SDE), Moran's index, Theil index, random kernel density estimation (RKDA), and spatial Markov chain (SMC) methods. The GPE exhibited a U-shaped trend, with 2008 as the inflection point, first decreasing and then increasing. It reached a maximum value of 0.747 in 2020. The Theil index increased from 0.043 to 0.121 nationwide, indicating the overall characteristics of low-level slow growth, and imbalance. Discrepancies in input-output scales, the southward shift of economic centers, and population movements contribute significantly to the disparities between the east and west, north and south, and regions divided by the Hu Huanyong Line (Hu Line). The GPE exhibited club convergence characteristics; however, polarization phenomena exist in local areas. Spatial spillover effects were also observed in GPE. Finally, we provide recommendations for promoting green development in the real estate industry, including green building technology, fiscal subsidy investment, and population migration management.

iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis.

Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.

Identifying obesogenic environment through spatial clustering of body mass index among adults.

BACKGROUND: The escalating trend of obesity in Malaysia is surmounting, and the lack of evidence on the environmental influence on obesity is untenable. Obesogenic environmental factors often emerge as a result of shared environmental, demographic, or cultural effects among neighbouring regions that impact lifestyle. Employing spatial clustering can effectively elucidate the geographical distribution of obesity and pinpoint regions with potential obesogenic environments, thereby informing public health interventions and further exploration on the local environments. This study aimed to determine the spatial clustering of body mass index (BMI) among adults in Malaysia. METHOD: This study utilized information of respondents aged 18 to 59 years old from the National Health and Morbidity Survey (NHMS) 2014 and 2015 at Peninsular Malaysia and East Malaysia. Fast food restaurant proximity, district population density, and district median household income were determined from other sources. The analysis was conducted for total respondents and stratified by sex. Multilevel regression was used to produce the BMI estimates on a set of variables, adjusted for data clustering at enumeration blocks. Global Moran's I and Local Indicator of Spatial Association statistics were applied to assess the general clustering and location of spatial clusters of BMI, respectively using point locations of respondents and spatial weights of 8 km Euclidean radius or 5 nearest neighbours. RESULTS: Spatial clustering of BMI independent of individual sociodemographic was significant (p < 0.001) in Peninsular and East Malaysia with Global Moran's index of 0.12 and 0.15, respectively. High-BMI clusters (hotspots) were in suburban districts, whilst the urban districts were low-BMI clusters (cold spots). Spatial clustering was greater among males with hotspots located closer to urban areas, whereas hotspots for females were in less urbanized areas. CONCLUSION: Obesogenic environment was identified in suburban districts, where spatial clusters differ between males and females in certain districts. Future studies and interventions on creating a healthier environment should be geographically targeted and consider gender differences.

A Pipeline for Dynamic Analysis of Mitochondrial Content in Developing T Cells: Bridging the Gap Between High-Throughput Flow Cytometry and Single-Cell Microscopy Analysis.

Analyzing the dynamics of mitochondrial content in developing T cells is crucial for understanding the metabolic state during T cell development. However, monitoring mitochondrial content in real-time needs a balance of cell viability and image resolution. In this chapter, we present experimental protocols for measuring mitochondrial content in developing T cells using three modalities: bulk analysis via flow cytometry, volumetric imaging in laser scanning confocal microscopy, and dynamic live-cell monitoring in spinning disc confocal microscopy. Next, we provide an image segmentation and centroid tracking-based analysis pipeline for automated quantification of a large number of microscopy images. These protocols together offer comprehensive approaches to investigate mitochondrial dynamics in developing T cells, enabling a deeper understanding of their metabolic processes.

Complete spatially resolved gene expression is not necessary for identifying spatial domains.

Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, termed BINARY, relying exclusively on binarized SRT data to accurately delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.

Prevalence and spatial distribution characteristics of human echinococcosis: A county-level modeling study in southern Xinjiang, China.

Objectives: Human echinococcosis remains an important public health problem. The aim of this study was to analyze the prevalence and spatial distribution characteristics of human echinococcosis cases in southern Xinjiang, China from 2005 to 2021. Methods: Human echinococcosis cases were collected from the National Infectious Disease Reporting System. Joinpoint regression analysis was performed to explore the trends. Spatial autocorrelation, hot spot analysis, as well as spatial-temporal clustering analysis were conducted to confirm the distribution and risk factors. Results: A total of 4580 cases were reported in southern Xinjiang during 2005-2021, with a mean annual incidence of 2.56/100,000. Echinococcosis incidence showed an increasing trend from 2005 to 2017 (APC = 17.939, 95%CI: 13.985 to 22.029) and a decreasing trend from 2017 to 2021 (APC = -18.769, 95%CI: 28.157 to -8.154). Echinococcosis cases had a positive spatial autocorrelation in 2005-2021 (Moran's I = 0.19, P < 0.05). The disease hotspots were located in the east and west in these areas, then returned to the east clusters, including Hejing, Heshuo, Wuqia, Atushi, Aheqi, and Yanqi Hui Autonomous County. Meanwhile, spatial-temporal analysis identified the first cluster comprised of five counties (cities): Yanqi Hui Autonomous County, Korla City, Bohu County, Hejing County, and Heshuo County. And secondary clusters 1-3 are predominantly in Wushi County, Aheqi County, Keping County, Atushi City, Wuqia County and Cele County. Conclusions: Our findings suggest that echinococcosis is still an important zoonotic parasitic disease in southern Xinjiang, yet it showed a certain degree of spatial clustering. It is crucial to implement comprehensive prevention and control measures to effectively combat the epidemic of echinococcosis.

Joint Bayesian estimation of cell dependence and gene associations in spatially resolved transcriptomic data.

Recent technologies such as spatial transcriptomics, enable the measurement of gene expressions at the single-cell level along with the spatial locations of these cells in the tissue. Spatial clustering of the cells provides valuable insights into the understanding of the functional organization of the tissue. However, most such clustering methods involve some dimension reduction that leads to a loss of the inherent dependency structure among genes at any spatial location in the tissue. This destroys valuable insights of gene co-expression patterns apart from possibly impacting spatial clustering performance. In spatial transcriptomics, the matrix-variate gene expression data, along with spatial coordinates of the single cells, provides information on both gene expression dependencies and cell spatial dependencies through its row and column covariances. In this work, we propose a joint Bayesian approach to simultaneously estimate these gene and spatial cell correlations. These estimates provide data summaries for downstream analyses. We illustrate our method with simulations and analysis of several real spatial transcriptomic datasets. Our work elucidates gene co-expression networks as well as clear spatial clustering patterns of the cells. Furthermore, our analysis reveals that downstream spatial-differential analysis may aid in the discovery of unknown cell types from known marker genes.

A Scoping Review of GIS Mapping of Type 1 Diabetes in Children: Identifying Current Gaps and Future Research Directions Using PRISMA-ScR.

BACKGROUND: Type 1 Diabetes poses a significant public health threat, especially in low-and-middle countries, where resources are limited. The use of geographical information systems in diabetes research has shown the potential to reveal several epidemiological risk factors. AIMS: This scoping review aimed to identify the scope and extent of the current literature and explore its limitations on the geographical mapping of children with type 1 diabetes. METHODS: A scoping review was conducted using five electronic databases and included studies published between the years 2000 and 2023. The search terms included: "Type 1 Diabetes Mellitus", "GIS mapping", "Juvenile Onset Diabetes Mellitus", "Spatial Epidemiology", "Spatial Clustering", "Spatial analysis", and "Geographic information system". Relevant full-text articles that met the inclusion criteria were selected for review. RESULTS: The search identified 17 studies that met the criteria for inclusion in the review. More than half the studies were conducted before 2015 (n=11; 61%). All studies were conducted in High-Income Countries. More than 10 articles studied environmental factors, 3 of them focused on the environment, 6 of them included sociodemographic factors, and 1 study incorporated nutrition (as a variable) in environmental factors. 2 studies focused on the accessibility of health services by pediatric patients. CONCLUSION: Studies on type 1 diabetes highlight the complex relationship between incidence and risk, suggesting comprehensive prevention and treatment. Geographical mapping has potential in low- and middle-income nations, but further research is needed to develop innovative strategies. The importance of geomappping in understanding the risk factors for Type 1 Diabetes is highlighted in this scoping review, which also suggests a possible direction for focused interventions, particularly in settings with low resources.