Chaos Analysis of the Brain Topology in Grey Matter Images for the Recognition of Psychosis

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis in the identification of people with psychosis. Structural MRI were acquired from 73 CHR, 77 FEP and 44 healthy controls (HC). Chaos analysis of the grey matter distribution was performed: first, the distances of each voxel from the center of mass in the grey matter image was calculated. Next, the distances multiplied by the voxel intensity was represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts how the grey matter topology changes. The classification of a subject’s clinical status was finally predicted by a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and b) matching the lambda series with the Morlet wavelet, which resulted in 100% accuracy in distinguishing between FEP and CHR. The proposed framework using spatial-series extraction enhances the classification decision for FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

Syntaxonomic and ecological peculiarities of extra-zonal pine forests with participation of Acer negundo L. from the forest-steppe and steppe zones of Altai Krai (South-Eastern Siberia)

The syntaxonomic analysis of pine forests with Acer negundo occurring on fluvio-glacial sandy deposits of Altai Krai (South-East Siberia) was made based on 93 releves. It was established that Acer negundo takes a different phytocenotic part in 2 associations, 2 variants, and 6 no-ranked communities of 4 classes and 4 orders according to the Braun-Blanquet approach. The method of detrended correspondence analysis (DCA coordination) implemented in the DECORANA software package was used to confirm the ecological and floristic integrity of the identified vegetation units. New data on the spatial syntaxa distributions depend on the complex humidity gradient, soil fertility, and anthropogenic factors. Acer negundo is most abundant and common in the communities of the Brachypodio-Betuletea pendulae class, which are characterized by habitats with moderate moistening and greater soil fertility. In the spatial series considered, according to the soil fertility and humidity gradients, we observe an increase in Acer negundo in the Vicia sylvatica – Pinus sylvestris community and an increase in the activity of mesophytes and mesohygrophytes that are more demanding to soil fertility.

A Simple Test of Spatial Autocorrelation for Centered Variables

We present a simple test of spatial autocorrelation based on the skedastic structure of the spatial series. Its distribution function is known for all sample sizes. Moreover, it is very simple to obtain, specially in a case of small samples where the new GQsp test has great power, higher than other alternatives existing in the literature.

Memory Effect in the Spatial Series Based on Diamond and Graphite Crystals

To study the relation between the structure of a compound and its properties is one of the fundamental trends in chemistry and materials science. A classic example is the well-known influence of the structures of diamond and graphite on their physicochemical properties, in particular, hardness. However, some other properties of these allotropic modifications of carbon, e.g., fractal properties, are poorly understood. In this work, the spatial series (interatomic distance histograms) calculated using the crystal structures of diamond and graphite are investigated. Hurst exponents H are estimated using detrended fluctuation analysis and power spectral density. The values of H are found to be 0.27–0.32 and 0.37–0.42 for diamond and graphite, respectively. The calculated data suggest that the spatial series have long memory with a negative correlation between the terms of the series; that is, they are antipersistent.

Water Quality Data Outlier Detection Method Based on Spatial Series Features

Outlier detection is one of the major branch in data mining which has been applied in different fields. Researchers have focused on the outlier detection in time series, but rarely spatial series. In this paper, we propose a new outlier detection method based on k-nearest neighbour (KNN) and Mahalanobis distance, which is first applied to the water field. Experimental results verify that the algorithm has good accuracy and effectiveness in outlier detection for water quality spatial series dataset.