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Hearing characteristics under the conditions of spacial indetermination of overthreshold tonic signal were studied on dolphins Tursiops truncatus by the method of motordigestive conditional reflexes. High efficiency of spacial analysis on the frequencies 5, 20, 80, 100 kHz was shown. The criterium of relative loss of information in the channel was used for evaluating integral index of efficiency of dolphin spacial hearing.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
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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.
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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.
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.
Emerging spatial multiomics technologies provide an increasingly large amount of information content at multiple scales. However, it remains challenging to efficiently represent and harmonize diverse spatial datasets. Here we present Giotto Suite, a suite of modular packages that provides scalable and extensible end-to-end solutions for multiscale and multiomic data analysis, integration and visualization. At its core, Giotto Suite is centered around an innovative data framework, allowing the representation and integration of spatial omics data in a technology-agnostic manner. Giotto Suite integrates molecular, morphology, spatial and annotated feature information to create a responsive and flexible workflow, as demonstrated by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science in R, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive and multiscale ecosystem for spatial multiomic data analysis.
The analysis of spatial structure, ie, the encoding of relative positions between pattern elements, was studied in central and eccentric vision. In a two-alternative forced-choice task the observer had to discriminate between two patterns consisting of short line segments. At each trial the two patterns were flashed for 140 msec and the observer indicated whether the patterns were identical or mirror symmetric. Psychometric functions were measured by changing pattern size at each eccentricity in order to find the threshold size allowing 75% of correct responses. The scaling factor, required for discriminating between mirror symmetric and identical patterns independent of eccentricity, was found to be similar to the size-scaling proposed by Levi et al (Vision Res 25:963, 1985) for vernier acuity tasks.
We test various assumptions necessary for the interpretation of spatial autocorrelation analysis of gene frequency surfaces, using simulations of Wright's isolation-by-distance model with migration or selection superimposed. Increasing neighborhood size enhances spatial autocorrelation, which is reduced again for the largest neighborhood sizes. Spatial correlograms are independent of the mean gene frequency of the surface. Migration affects surfaces and correlograms when immigrant gene frequency differentials are substantial. Multiple directions of migration are reflected in the correlograms. Selection gradients yield clinal correlograms; other selection patterns are less clearly reflected in their correlograms. Sequential migration from different directions and at different gene frequencies can be disaggregated into component migration vectors by means of principal components analysis. This encourages analysis by such methods of gene frequency surfaces in nature. The empirical results of these findings lend support to the inference structure developed earlier for spatial autocorrelation analysis.
There is some evidence from grating experiments that the transient presentation of a stimulus pattern interferes with the encoding of positional relationships between pattern elements (i.e. the analysis of spatial structure) more in eccentric vision than in central vision. The present study investigated the effect of exposure duration on the analysis of spatial structure in eccentric vision using a task in which the observer discriminated between two mirror symmetric patterns consisting of short line segments. In each trial, the two patterns were flashed for 140 or 500 ms, and the observer had to decide whether the patterns were identical or mirror symmetric. Both constant-size and size-scaled patterns were used in eccentric vision. The longer exposure duration slightly increased the proportion of correct responses in eccentric vision but performance remained distinctly inferior to that in central vision.
The gene frequencies at eight loci in some European and Asian human populations have been subjected to spatial autocorrelation analysis, using Geary's c coefficient. Contrary to what is expected for markers affected only by gene flow and genetic drift, the spatial correlograms show distinct modes of gene frequency variation: there are significant clinal patterns (at the GLO and ESD loci), significant non-clinal patterns (AK, ADA, 6-PGD and GPT) and marginally significant patterns (PGP and SOD). Any hypothesis on the evolution of these polymorphisms should account for the observed heterogeneity of their geographical distributions.
Three statistical approaches, used to detect spatial clusters of disease associated with a point source exposure, are applied to childhood cancer data for the city of San Francisco (1973-88). The distributions of incident cases of leukemia (51 cases), brain cancer (35 cases), and lymphatic cancer (37 cases) among individuals less than 21 years of age are described using three measures of clustering: distance on a geopolitical map, distance on a density equalized transformed map, and relative risk. The point source of exposure investigated is a large microwave tower located southwest of the center of the city (Sutro Tower). The three analytic approaches indicate that the patterns of the major childhood cancers are essentially random with respect to the point source. These results and a statistical model for spatial clustering are used to explore distance and risk measures in the analysis of spatial data. Both types of measures of spatial clustering are shown to perform similarly when a specific area of exposure can be defined.