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PubMed1977

[Spatial analysis of the electrophysiological changes in ventricular loading].

Space analysis methods of the changes in heart electromotive force are described (EMF). The size of the space vector in millivolts, azimuth and elevation are obtained by formula substitution in the mathematical analysis. The same values, in graphis analysis, are obtained by the "Circle for space vectors determination", proposed by the authors. Axial and space analysis of the changes in the electrogenesis of right ventricle was performed in 27 patients with pulmonary stenosis, confirmed by catheterization, on the base of the corrective orthogonal electrocardiogram according to Frank. The axial indices, indicating the size of the forces directed to the right (Sxmv) and forward (Qzmv) are compared as well as the size, elevation and azimuth of the maximal space vector, directed to the right (Vmax.sp-x) and forward (Vmax.sp-z). The index from the axial analysis (sensitivity 62%, r = 0.65) shows a priority for the forces, directed to the right. The priority is on the side of the indices from space analysis (sensitivity 33%, r = 0.78) in case of forces directed forward. There is no statistically significant difference in the diagnostic value of the two methods.

Adult↗
PubMed1995

An estimate of the prevalence of drug misuse in Liverpool and a spatial analysis of known addiction.

BACKGROUND: The objective of this study was to determine the prevalence and distribution of opiate and cocaine misuse in the City of Liverpool in 1991. The databases included residents of the city using opiates or cocaine, who were known to the Drug Dependency Units or the Infectious Diseases Unit, or who were arrested for possession of drugs in 1991. METHODS: A three-sample log-linear capture-recapture method was applied to databases containing details of drug users with City of Liverpool postcodes, to determine the prevalence of drug misuse in 1991. Linear regression analysis was performed to correlate the prevalence of known drug misuse with indices of material deprivation in each electoral ward. RESULTS: Data on 1427 individuals were analysed, producing an estimate of the drug-using population of 2344 [95 per cent confidence interval (CI) = 1972-2716] and a period prevalence of 5.2 per 1000 [95 per cent CI = (4.5-6.0) per 1000]. In the 15-29 year age group, the prevalence of drug abuse was 16.9 per 1000 [95 per cent CI = (13.9-19.9) per 1000]. There was a negative interdependence between the police and Drug Dependency Unit databases with attenders at the Unit being 7.2 (95 per cent CI = 4.6-11.4) times less likely to be arrested for possession than non-attenders. There was a strong correlation between the distribution of known drug use and material deprivation, as measured by the Townsend index (r = 0.75; p < 0.001). CONCLUSIONS: The capture-recapture method allows the prevalence of drug misuse to be estimated and provides more meaningful information than is available from the notification system. The study suggests that people in contact with drug services are less likely to commit crimes of possession of class A drugs than those not in contact with drug services. There is a strong association between drug abuse and deprivation, and therefore the purchasing of services for drug misusers should be focused on areas of deprivation.

Adolescent↗
PubMed1993

[Temporal and spatial analysis of hospitalization for selected diseases in Poland associated with certain environmental factors. I. Current trends in hospitalization for certain diseases].

In the present study of hospitalization rates during the period 1979-1990 for some of the diseases considered to be related to environmental contamination were analyzed. Selected diagnosis included malignant melanoma and other malignant neoplasm of skin, malignant neoplasm of bladder, myeloid and monocytic leukaemia, aplastic anaemia, chronic obstructive pulmonary disease and asthma, diseases of skin and subcutaneous tissue, spontaneous abortion and congenital anomalies. The performed analysis has shown that in 1979-1990 discharge rates had significant growing trends in the both urban and rural population in the case of malignant neoplasm of bladder, leukaemia, spontaneous abortion and congenital anomalies in infants. There were no significant trends in hospitalization of malignant melanoma and other malignant neoplasm of skin, aplastic anaemia and asthma.

Abortion, Spontaneous↗
PubMed2005

[Analysis of spatial data in public health: methods, problems, and perspectives].

Studies in which spatial distribution and geographic information systems (GIS) play a central role are becoming more common in the public health literature. However, methods and software to implement such approaches still pose serious limitations, due to unfriendliness and lack of integration. Additionally, most researchers and public health professionals are not familiar with either the techniques or the software. The aim of this work, besides presenting a systematic review of spatial analysis in health, is to discuss some representative applications of methods that deal with the analysis of spatial patterns of health events, analyzing advantages, disadvantages, and applicability of the proposed models in ecological and health services utilization studies.

Epidemiologic Methods↗
PubMed1992

Measurement processes and spatial principal components analysis.

Spatial principal components analysis (SPCA) applied to the ongoing EEG yields factor loadings which, when mapped, consistently reveal symmetrical patterns resembling the spherical harmonics. In this paper, we consider the mechanisms responsible for these characteristic patterns. In doing so, we demonstrate that volume conduction is one of a family of processes capable of generating such patterns with SPCA. It is shown that any series of measurements on a sphere in which the covariance is only a function of measurement site angular separation (shift invariant processes) will yield the spherical harmonics as the eigenvectors or factor loadings of the covariance matrix. Simulations further indicate that this effect is robust and not determined by the geometry of the measurement sites. In situations where shift invariant signals coexist with those generated at specific sites (anatomically specific processes), such as evoked potentials and some artifacts, it is shown that the anatomically specific signals do not influence the eigenvectors of the covariance matrix in a uniform or random fashion. The factors most influenced are those whose symmetry is similar to that of the site specific signal.

Brain↗
PubMed2005

Analysis of spatial patterns at a geographical scale over north-western Europe from point-referenced aphid count data.

The spatial analysis by distance indices (SADIE) technique was developed to evaluate the spatial pattern of point-referenced count data as well as the spatial association between two sets of data sharing the same point locations. This paper presents an analysis of spatial patterns in aphid count data and the association of these data with climate across north-west Europe. The paper tests the applicability of the technique to large geographical areas. Aggregation and cluster indices were calculated for the total annual abundance of the peach-potato aphid Myzus persicae (Sulzer) and for the annual mean rainfall and temperature at aphid monitoring sites. Association indices demonstrated the stability in time of aphid spatial structures and the correlation between aphid density and climate patterns. Groups of relatively large numbers of aphids, termed patches, and groups of relatively small numbers of aphids, termed gaps, were located and their mean size estimated. The aphid patterns were quite stable in time and the spatial patterns of temperature and rainfall were weakly associated with M. persicae annual abundance. Similarities were observed between the results of SADIE and those from the more widely used technique of spatial autocorrelation (SAC). However, the SADIE association index has the advantage of quantifying the possible associations between aphid data and the factors that determine population distribution. Thus, high temperature and low rainfall were identified as environmental factors that were positively associated with aphid abundance across north-west Europe.

Animals↗
PubMed2004

Discussion on the choice of separated components in fMRI data analysis by spatial independent component analysis.

By measuring the changes of magnetic resonance signals during a stimulation, the functional magnetic resonance imaging (fMRI) is able to localize the neural activation in the brain. In this report, we discuss the fMRI application of the spatial independent component analysis (spatial ICA), which maximizes statistical independence over spatial images. Included simulations show the possibility of the spatial ICA on discriminating asynchronous activations or different response patterns in an fMRI data set. An in vivo visual stimulation fMRI test was conducted, and the result shows a proper sum of the separated components as the final image is better than a single component, using fMRI data analysis by spatial ICA. Our result means that spatial ICA is a useful tool for the detection of different response activations and suggests that a proper sum of the separated independent components should be used for the imaging result of fMRI data processing.

Algorithms↗
PubMed1999

[Analysis for spatial distribution of Oncomelania snail in mainland China by geographic information system(GIS) database].

OBJECTIVE: To analyze spatial distribution of Oncomelania snail populations in mainland China by geographic information system (GIS) database. METHODS: Genetic variation experiment data and experimental data of susceptibility of 34 snail populations were collected from nine provinces in China, to set up a database. A world digital map was linked with the database in a software ArcView Release 3.0 a to be used to divide zones with ArcView spatial analyze function and GIS overlaying function. AVHRR NDVI satellite images were collected to form a new one for prevalent season, which were overlaid with distribution maps of heterozygous indices of snail populations, polymorphic locus percentage and infection rates of snail population with schistosome for classifying the images. RESULTS: Spatial analysis showed that distribution maps of genetic heterozygous indices of snail, percentage of polymophic loci and infection rate of snail can be divided into two large zones, i.e., east zone and west zone, both with varied pictures. Analysis for overlaying three-stratum distribution map with AVHRR NDVI satellite image showed four distinctly different spatial zones, including west Sichuan zone, west Yunnan zone, middle zone of river, lake and marshland, and southeast coastal zone. CONCLUSION: It is the first time using GIS to analyze data of Oncomelania population genetics and confirmed the population structure of Oncomelania snails presented discrete sub-population model, which supports the theory of sub-species of Oncomelania spp. Exited in mainland China.

Animals↗
PubMed1993

Isolation by distance, trend surface analysis, and spatial autocorrelation.

The isolation by distance model is both a population process and a surface model. In this model the surface is, on average, flat in every direction. By contrast, probably most observed genetic surfaces exhibit trends generated by complex long-distance populational processes. When one estimates the parameters of a Malécot-Morton equation for those surfaces, the isolation by distance model does not fit. In the simplest case, in first-order trends (two-dimensional clines) the genetic differentiation increases dramatically, faster per unit distance than it would by isolation by distance alone. When isolation by distance takes place but is hidden through the apparent and complicated relief of a surface, another surface model incorporating trend spatial analysis can bypass the difficulty of estimating the isolation by distance process if approached through the Malécot-Morton equation or through a measure of spatial autocorrelation.

ABO Blood-Group System↗
PubMed2006

Geostatistical analysis of spatial and temporal variations of groundwater level.

Groundwater and water resources management plays a key role in conserving the sustainable conditions in arid and semi-arid regions. Applying management tools which can reveal the critical and hot conditions seems necessary due to some limitations such as labor and funding. In this study, spatial and temporal analysis of monthly groundwater level fluctuations of 39 piezometric wells monitored during 12 years was carried out. Geostatistics which has been introduced as a management and decision tool by many researchers has been applied to reveal the spatial and temporal structure of groundwater level fluctuation. Results showed that a strong spatial and temporal structure existed for groundwater level fluctuations due to very low nugget effects. Spatial analysis showed a strong structure of groundwater level drop across the study area and temporal analysis showed that groundwater level fluctuations have temporal structure. On average, the range of variograms for spatial and temporal analysis was about 9.7 km and 7.2 months, respectively. Ordinary and universal kriging methods with cross-validation were applied to assess the accuracy of the chosen variograms in estimation of the groundwater level drop and groundwater level fluctuations for spatial and temporal scales, respectively. Results of ordinary and universal krigings revealed that groundwater level drop and groundwater level fluctuations were underestimated by 3% and 6% for spatial and temporal analysis, respectively, which are very low and acceptable errors and support the unbiasedness hypothesis of kriging. Although, our results demonstrated that spatial structure was a little bit stronger than temporal structure, however, estimation of groundwater level drop and groundwater level fluctuations could be performed with low uncertainty in both space and time scales. Moreover, the results showed that kriging is a beneficial and capable tool for detecting those critical regions where need more attentions for sustainable use of groundwater. Regions in which were detected as critical areas need to be much more managed for using the current water resources efficiently. Conducting water harvesting systems especially in critical and hot areas in order to recharge the groundwater, and altering the current cropping pattern to another one that need less water requirement and applying modern irrigation techniques are highly recommended; otherwise, it is most likely that in a few years no more crop would be cultivated.

Geography↗
PubMed2003

Advantage of single-trial models for response to selection in wheat breeding multi-environment trials.

An investigation was conducted to evaluate the impact of experimental designs and spatial analyses (single-trial models) of the response to selection for grain yield in the northern grains region of Australia (Queensland and northern New South Wales). Two sets of multi-environment experiments were considered. One set, based on 33 trials conducted from 1994 to 1996, was used to represent the testing system of the wheat breeding program and is referred to as the multi-environment trial (MET). The second set, based on 47 trials conducted from 1986 to 1993, sampled a more diverse set of years and management regimes and was used to represent the target population of environments (TPE). There were 18 genotypes in common between the MET and TPE sets of trials. From indirect selection theory, the phenotypic correlation coefficient between the MET and TPE single-trial adjusted genotype means [ r(p(MT))] was used to determine the effect of the single-trial model on the expected indirect response to selection for grain yield in the TPE based on selection in the MET. Five single-trial models were considered: randomised complete block (RCB), incomplete block (IB), spatial analysis (SS), spatial analysis with a measurement error (SSM) and a combination of spatial analysis and experimental design information to identify the preferred (PF) model. Bootstrap-resampling methodology was used to construct multiple MET data sets, ranging in size from 2 to 20 environments per MET sample. The size and environmental composition of the MET and the single-trial model influenced the r(p(MT)). On average, the PF model resulted in a higher r(p(MT)) than the IB, SS and SSM models, which were in turn superior to the RCB model for MET sizes based on fewer than ten environments. For METs based on ten or more environments, the r(p(MT)) was similar for all single-trial models.

Agriculture↗
PubMed1996

Interactive spatial data analysis in medical geography.

Interactive spatial data analysis involves the use of software environments that permit the visualization, exploration and, perhaps, modelling of geographically-referenced data. Such systems are of obvious value in epidemiological research, both of an environmental and geographical nature. There is an increasing number of such software environments available on a variety of platforms and operating systems. This paper considers the use of the proprietary Geographical Information System, ARC/INFO, in a spatial analysis context, showing how the spatial analytic tools that may be added to it can be exploited by geographical epidemiologists; such tools include those for modelling possible raised incidence of disease around suspected sources of pollution. The paper also reviews the use of systems such as S-Plus and XLISP-STAT, statistical programming environments to which spatial analysis functions or libraries may be added. The use of INFO-MAP, a system designed to aid in the teaching of interactive spatial data analysis, is also highlighted. The various software environments are illustrated with reference to examples concerned with: clustering of childhood leukaemia in part of Lancashire, England; Burkitt's lymphoma in Uganda; larynx cancer in Lancashire; and childhood mortality in Auckland, New Zealand.

Adolescent↗
PubMed2026

stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images.

MOTIVATION: Spatially resolved transcriptomics (SRT) and spatially resolved proteomics (SRP) data enable the study of gene expression and protein abundances within their precise spatial and cellular contexts in tissues. Certain SRT and SRP technologies also capture corresponding morphology images, adding another layer of valuable information. However, few existing methods developed for SRT data effectively leverage these supplementary images to enhance clustering performance. RESULTS: Here, we introduce stDyer-image, an end-to-end deep learning framework designed for clustering for SRT and SRP datasets with images. Unlike existing methods that utilize images to complement gene expression data, stDyer-image directly links image features to cluster labels. This approach draws inspiration from pathologists, who can visually identify specific cell types or tumor regions from morphological images without relying on gene expression or protein abundances. Benchmarks against state-of-the-art tools demonstrate that stDyer-image achieves superior performance in clustering. Moreover, it is capable of handling large-scale datasets across diverse technologies, making it a versatile and powerful tool for spatial omics analysis. AVAILABILITY AND IMPLEMENTATION: The source code of stDyer-image and detailed tutorials are available at https://github.com/ericcombiolab/stDyer-image.

Proteomics↗