Comprehensive spatial distribution of patients with first-episode psychosis (FEP) and its relation to socio-economic factors.

BACKGROUND: The functional-cognitive impact of first-episode psychosis (FEP) is extremely relevant and implies dysfunction from early life stages like adolescence and youth. Like other illnesses, FEP incidence is also influenced by environmental factors. It is necessary to attend to this age group with early interventions and to act on the environmental factors that the literature correlates with increased FEP incidence: socio-economic aspects, social adversity, bullying at school or cannabis use. In this context, identifying the areas of cities where FEP patients concentrate is important to perform early interventions. The spatial analysis of patient distribution in a whole city is one way to identify the most vulnerable areas and to propose psycho-social interventions for the possible prevention and/or early detection of FEP by improving urban mental health. METHODS: An epidemiological study of point patterns to determine the areas of a city with a higher incidence of patients with FEP. To do so, the addresses of FEP cases were georeferenced from 1 January 2016 to 31 October 2022, and 109 FEP patients were analysed. Data from a random sample of 383 controls, comprising their addresses, age, and sex, were randomly obtained from the official city council database. By GIS, the areas with higher FEP incidence were analysed to see if they coincided with the zones where inhabitants with lower incomes lived. RESULTS: The risk ratio of the FEP patients was compatible with the constant risk ratio in Albacete (p = 0.22). When performing the process separately with cases and controls only in men and women, the results were not significant for both distributions (p value: 0.12 and 0.57, respectively). Nonetheless, areas within the city had a significantly higher risk. These groups of cases coincided with those who had lower income and more inequality for women, but this pattern was not clear for men. CONCLUSIONS: Classifying city areas per income can help to determine the zones at higher risk of FEP, which would allow early healthcare and preventive measures for these zones.

Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis.

Diagnosis and prognosis of cancer are informed by the architecture inherent in cancer patient tissue sections. This architecture is typically identified by pathologists, yet advances in computational image analysis facilitate quantitative assessment of this structure. In this article, we develop a spatial point process approach to describe patterns in cell distribution within tissue samples taken from colorectal cancer (CRC) patients. In particular, our approach is centered on the Palm intensity function. This leads to taking an approximate-likelihood technique in fitting point processes models. We consider two Neyman-Scott point processes and a void process, fitting these point process models to the CRC patient data. We find that the parameter estimates of these models may be used to quantify the spatial arrangement of cells. Importantly, we observe characteristic differences in the spatial arrangement of cells between patients who died from CRC and those alive at follow up.

RPE Cell and Sheet Properties in Normal and Diseased Eyes.

Previous studies of human retinal pigment epithelium (RPE) morphology found spatial differences in density: a high density of cells in the macula, decreasing peripherally. Because the RPE sheet is not perfectly regular, we anticipate that there will be differences between conditions and when and where damage is most likely to begin. The purpose of this study is to establish relationships among RPE morphometrics in age, cell location, and disease of normal human and AMD eyes that highlight irregularities reflecting damage. Cadaveric eyes from 11 normal and 3 age-related macular degeneration (AMD) human donors ranging from 29 to 82 years of age were used. Borders of RPE cells were identified with phalloidin. RPE segmentation and analysis were conducted with CellProfiler. Exploration of spatial point patterns was conducted using the "spatstat" package of R. In the normal human eye, with increasing age, cell size increased, and cells lost their regular hexagonal shape. Cell density was higher in the macula versus periphery. AMD resulted in greater variability in size and shape of the RPE cell. Spatial point analysis revealed an ordered distribution of cells in normal and high spatial disorder in AMD eyes. Morphometrics of the RPE cell readily discriminate among young vs. old and normal vs. diseased in the human eye. The normal RPE sheet is organized in a regular array of cells, but AMD exhibited strong spatial irregularity. These findings reflect on the robust recovery of the RPE sheet after wounding and the circumstances under which it cannot recover.

Investigating Ecological Interactions in the Tumor Microenvironment using Joint Species Distribution Models for Point Patterns.

The tumor microenvironment (TME) is a complex and dynamic ecosystem that involves interactions between different cell types, such as cancer cells, immune cells, and stromal cells. These interactions can promote or inhibit tumor growth and affect response to therapy. Multitype Gibbs point process (MGPP) models are statistical models used to study the spatial distribution and interaction of different types of objects, such as the distribution of cell types in a tissue sample. Such models are potentially useful for investigating the spatial relationships between different cell types in the tumor microenvironment, but so far studies of the TME using cell-resolution imaging have been largely limited to spatial descriptive statistics. However, MGPP models have many advantages over descriptive statistics, such as uncertainty quantification, incorporation of multiple covariates and the ability to make predictions. In this paper, we describe and apply a previously developed MGPP method, the saturated pairwise interaction Gibbs point process model, to a publicly available multiplexed imaging dataset obtained from colorectal cancer patients. Importantly, we show how these methods can be used as joint species distribution models (JSDMs) to precisely frame and answer many relevant questions related to the ecology of the tumor microenvironment.

Spatial analysis of COVID-19 hospitalised cases in an entire city: The risk of studying only lattice data.

We live in a global pandemic caused by the COVID-19 disease where severe social distancing measures are necessary. Some of these measures have been taken into account by the administrative boundaries within cities (neighborhoods, postal districts, etc.). However, considering only administrative boundaries in decision making can prove imprecise, and could have consequences when it comes to taking effective measures. To solve the described problems, we present an epidemiological study that proposes using spatial point patterns to delimit spatial units of analysis based on the highest local incidence of hospitalisations instead of administrative limits during the first COVID-19 wave. For this purpose, the 579 addresses of the cases hospitalised between March 3 and April 6, 2020, in Albacete (Spain), and the addresses of the random sample of 383 controls from the Inhabitants Register of the city of Albacete, were georeferenced. The risk ratio in those hospitalised for COVID-19 was compatible with the constant risk ratio in Albacete (p = 0.49), but areas with a significantly higher risk were found and coincided with those with greater economic inequality (Gini Index). Moreover, two districts had areas with a significantly high incidence that were masked by others with a significantly low incidence. In conclusion, taking measures conditioned exclusively by administrative limits in a pandemic can cause problems caused by managing entire districts with lax measures despite having interior areas with high significant incidences. In a pandemic context, georeferencing disease cases in real time and spatially comparing them to updated random population controls to automatically and accurately detect areas with significant incidences are suggested. This would facilitate decision making, which must be fast and accurate in these situations.

A spatial randomness test based on the box-counting dimension.

Statistical modelling of a spatial point pattern often begins by testing the hypothesis of spatial randomness. Classical tests are based on quadrat counts and distance-based methods. Alternatively, we propose a new statistical test of spatial randomness based on the fractal dimension, calculated through the box-counting method providing an inferential perspective contrary to the more often descriptive use of this method. We also develop a graphical test based on the log-log plot to calculate the box-counting dimension. We evaluate the performance of our methodology by conducting a simulation study and analysing a COVID-19 dataset. The results reinforce the good performance of the method that arises as an alternative to the more classical distances-based strategies.

Modeling road traffic safety based on point patterns of wildlife-vehicle collisions.

Wildlife-vehicle collisions represent one of the main coexistence problems that appear between human populations and the environment. In general terms, this affects road safety, wildlife management, and the building of road infrastructures. These accidents are a great danger to the life and safety of car drivers, cause property damage to vehicles, and affect wildlife populations. In this work, we develop a new approach based on algorithms used to obtain minimum paths between vertices in weighted networks to get the optimal (safest) route between two points (departure and destination points) in a road structure based on wildlife-vehicle collision point patterns together with other road variables such as traffic volume (traffic flow information), road speed limits, and vegetation density around roads. For this purpose, we have adapted the road structure into a mathematical linear network as described in the field of Graph Theory and added weights to each linear segment based on the intensity of accidents. Then, the resulting network structure allows us to consider some graph theory methodologies to manipulate and apply different calculations to analyze the network. This new approach has been illustrated with a real data set involving the locations of 491 wildlife-vehicle collisions in a square region (40 km × 40 km) around the city of Lleida, during the period 2010-2014, in the region of Catalonia, North-East of Spain. Our results show the usefulness of our new approach to model road traffic safety based on point patterns of wildlife-vehicle collisions. As such, optimal path selection on linear networks based on wildlife-vehicle collisions can be considered to find the safest path between two pairs of points, avoiding more dangerous routes and even routes containing hotspots of accidents.

Spatial Statistics-Based Image Analysis Methods for the Study of Vascular Morphogenesis.

Several studies are available addressing the mechanisms of vascular morphogenesis in order to unravel how cooperative cell behavior can follow from the underlying, genetically regulated behavior of endothelial cells and from cell-to-cell and cell-to-extracellular matrix interactions. From the morphological standpoint several aspects of the process are of interest. They include the way the pattern of vessels fills the available tissue space and how the network grows during the angiogenic process, namely how a main trunk divides into smaller branches, and how branching occurs at different distances from the root point of a vascular tree. A third morphological aspect of interest concerns the spatial relationship between vessels and tissue cells able to secrete factors modulating endothelial cells self-organization, thus influencing vascular rearrangement.In the present chapter image analysis methods allowing for a quantitative characterization of these morphological aspects will be detailed and discussed. They are almost based on concepts derived from the theoretical framework represented by spatial statistics.

Spatial patterns and interspecific relationships of two dominant cushion plants at three elevations on the Kunlun Mountain, China.

One of the most important ecological processes is the formation of interspecific relationships in relation to spatial patterns among alpine cushion plants in extreme environmental habitats. However, such relationships remain poorly understood. Here, we examined the spatial patterns of alpine cushion plants along an altitudinal gradient of environmental severity and the interspecific relationship between two cushion species (Thylacospermum caespitosum and Androsace tangulashanensis) on the eastern Kunlun Mountain of China. Our results showed that the two species were highly aggregated within a distance of 2.5-5 m at the mid (S2) altitude, whereas they were randomly distributed at the low (S1) and high (S3) altitudes. A positive spatial interaction between the two species was observed over shorter distances at the mid (S2) altitude, and the spatial patterns were related to the size of individuals of the two species. Moreover, the impact of A. tangulashanensis on T. caespitosum (RIIT. caespitosum) was negative in all the study plots, and a positive impact of T. caespitosum on A. tangulashanensis (RIIA. tangulashanensis) was only observed at the mid (S2) altitude. Together, these results demonstrated that the spatial patterns of these two cushions varied with environmental severity, since the outcome of the interactions were different, to some extent, at the three altitudes. Plant size is the main factor affecting the spatial correlation and interspecific relationship between two cushions. Therefore, its potential influence should be considered when discussing interspecific relationships among cushions and their community construction at small scales in alpine ecosystems.

When do we have the power to detect biological interactions in spatial point patterns?

Uncovering the roles of biotic interactions in assembling and maintaining species-rich communities remains a major challenge in ecology. In plant communities, interactions between individuals of different species are expected to generate positive or negative spatial interspecific associations over short distances. Recent studies using individual-based point pattern datasets have concluded that (a) detectable interspecific interactions are generally rare, but (b) are most common in communities with fewer species; and (c) the most abundant species tend to have the highest frequency of interactions. However, it is unclear how the detection of spatial interactions may change with the abundances of each species, or the scale and intensity of interactions. We ask if statistical power is sufficient to explain all three key results.We use a simple two-species model, assuming no habitat associations, and where the abundances, scale and intensity of interactions are controlled to simulate point pattern data. In combination with an approximation to the variance of the spatial summary statistics that we sample, we investigate the power of current spatial point pattern methods to correctly reject the null model of pairwise species independence.We show the power to detect interactions is positively related to both the abundances of the species tested, and the intensity and scale of interactions, but negatively related to imbalance in abundances. Differences in detection power in combination with the abundance distributions found in natural communities are sufficient to explain all the three key empirical results, even if all pairwise interactions are identical. Critically, many hundreds of individuals of both species may be required to detect even intense interactions, implying current abundance thresholds for including species in the analyses are too low. Sy n thesis. The widespread failure to reject the null model of spatial interspecific independence could be due to low power of the tests rather than any key biological process. Since we do not model habitat associations, our results represent a first step in quantifying sample sizes required to make strong statements about the role of biotic interactions in diverse plant communities. However, power should be factored into analyses and considered when designing empirical studies.

Detection of spatial aggregation of cases of cancer from data on patients and health centres contained in the Minimum Basic Data Set.

The feasibility of the Minimum Basic Data Set (MBDS) as a tool in cancer research was explored monitoring its incidence through the detection of spatial clusters. Case-control studies based on MBDS and marked point process were carried out with the focus on the residence of patients from the Prince of Asturias University Hospital in Alcalá de Henares (Madrid, Spain). Patients older than 39 years with diagnoses of stomach, colorectal, lung, breast, prostate, bladder and kidney cancer, melanoma and haematological tumours were selected. Geocoding of the residence address of the cases was done by locating them in the continuous population roll provided by the Madrid Statistical Institute and extracting the coordinates. The geocoded control group was a random sample of 10 controls per case matched by frequency of age and sex. To assess case clusters, differences in Ripley K functions between cases and controls were calculated. The spatial location of clusters was explored by investigating spatial intensity and its ratio between cases and controls. Results suggest the existence of an aggregation of cancers with a common risk factor such as tobacco smoking (lung, bladder and kidney cancers). These clusters were located in an urban area with high socioeconomic deprivation. The feasibility of designing and carrying out case-control studies from the MBDS is shown and we conclude that MBDS can be a useful epidemiological tool for cancer surveillance and identification of risk factors through case-control spatial point process studies.

Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis.

The biggest problem when analyzing the brain is that its synaptic connections are extremely complex. Generally, the billions of neurons making up the brain exchange information through two types of highly specialized structures: chemical synapses (the vast majority) and so-called gap junctions (a substrate of one class of electrical synapse). Here we are interested in exploring the three-dimensional spatial distribution of chemical synapses in the cerebral cortex. Recent research has showed that the three-dimensional spatial distribution of synapses in layer III of the neocortex can be modeled by a random sequential adsorption (RSA) point process, i.e., synapses are distributed in space almost randomly, with the only constraint that they cannot overlap. In this study we hypothesize that RSA processes can also explain the distribution of synapses in all cortical layers. We also investigate whether there are differences in both the synaptic density and spatial distribution of synapses between layers. Using combined focused ion beam milling and scanning electron microscopy (FIB/SEM), we obtained three-dimensional samples from the six layers of the rat somatosensory cortex and identified and reconstructed the synaptic junctions. A total volume of tissue of approximately 4500µm(3) and around 4000 synapses from three different animals were analyzed. Different samples, layers and/or animals were aggregated and compared using RSA replicated spatial point processes. The results showed no significant differences in the synaptic distribution across the different rats used in the study. We found that RSA processes described the spatial distribution of synapses in all samples of each layer. We also found that the synaptic distribution in layers II to VI conforms to a common underlying RSA process with different densities per layer. Interestingly, the results showed that synapses in layer I had a slightly different spatial distribution from the other layers.