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Experimental studying possibilities of the electron paramagnetic resonance (EPR) method application for the dose control using alanine deteectors in the process of technological irradiations with brake radiation at the accelerator ILU-10 INP RK has been carried out. The EPR signal stability and sensitivity of original alanine detectors have been studied, serviceability in standard modes was shown in the range of small doses of brake irradiation (0,005–1 kGy); for dose estimation the calibration on gamma-irrdiation was used. The obtained data confirmed the promise of alanine EPR dosimetry at the ILU-10 accelerator INP RK.

Netosis and abzyme activity are the significant components of the innate immunity. Spontaneous and induced netosis, various types of proteolytic and oxidoreductase abzyme activity in patients with COPD and BA were determined in this study. NETs were determined by both light and fluorescence microscopy using a previously developed staining method. As a result of the work, an increase of the induced netosis was found in patients with COPD, relative to patients with BA. A significant increase in the IgA peroxidase activity was found in patients with BA relative to patients with COPD. Data on hyaluronidase, catalase, elastase, dipeptidyl dipeptidase-4-similar activity were obtained. A correlation between the peroxidase and catalase activity of IgA in patients with BA was found.

New approaches to the analysis of Morlet wavelet spectra of electroencephalograms, electromyograms, and accelerometer signals are proposed. The proposed approaches are based on the analysis of time−frequency distributions of local extrema and ridges of wavelet spectrograms. The paper describes the results of application of the proposed techniques for the diagnosis of the early stage of Parkinson’s disease and essential tremor, monitoring of postoperative patients with epilepsy, and assessment of inter-channel phase coupling of EEG during cognitive tests of patients after traumatic brain injury.

An investigation of surface electromyogram (EMG) of antagonist muscles in patients with Parkinson's disease (PD) and essential tremor (ET) was carried out. A comparison was made between two methods for calculating instantaneous phases of envelopes of EMG signals. The first method is based on calculating the ridges of wavelet spectrograms of envelopes of EMG signals. The second method is based on using the Hilbert transform. Statistically significant difference between the mean values of the phase difference of the EMG signal envelopes was found in the antagonist muscles in PD and ET patients.

Hardly decomposable aboveground and underground plant residues are a substrate for the Fusarium graminearum, F. moniliforme and other dangerous infections of subsequent crops in the crop rotation. For the period 2014-2020, an infection of grain with F. moniliforme increased in the Kursk region by 2.3 times; in Volgograd region by 3.7 times; in Rostov region by 8.7 times; and in the Stavropol Territory by 5.4 times. After treatment of plant residues of corn with biological preparation-sanator, a rhizoctonia disappeared from the winter wheat agrophytocenosis, and an occurrence of the F. moniliforme decreased by 2.5 times; a frequency of occurrence of pathogenic fungi F. moniliforme and F. avenaceum on the aerial parts of a plant was 20%, and on the plant residues 10% or less. A microbial colonization of the underground residues of sunflower and corn plants by F. moniliforme after harvesting was up to 30% for three years. Intra-soil pulse continuous-discrete robotic system for application of a biological plant protection product has been developed to increase an impact of biological plant protection product on phytopathogen into the plant residue and topsoil. The method provides priority plant protection from harmful biological objects, improving soil health, increasing plant productivity and product quality.

A publicly available terahertz video dataset and a software platform for experimenting with the terahertz intelligent video surveillance are developed. The video dataset includes short videos of people with objects hidden under the clothing. The dataset is multimodal, that is, it contains synchronized videos of various kinds: terahertz, thermal, visible, near-infrared, and 3D. A special software platform is developed for the acquisition and preprocessing of the video data. The software platform includes a translator of the Actor Prolog language to Java and an open-source library of built-in classes for data acquisition and processing. In particular, the software enables one to project terahertz/thermal video data onto three-dimensional point clouds using 3D lookup tables. An experiment with the terahertz video data analysis based on various CNN architectures is described.

An investigation of the 0.5–4 Hz little-studied frequency range of acceleration signals (ACC) was performed in patients with Parkinson’s disease (PD) and essential tremor (ET). In this frequency range, new neurophysiological regularities were revealed. A new method was used to analyze the wave train electrical activity of the muscles based on the analysis of Morlet wavelet spectrograms and ROC curves. The method idea is to find local extrema (named “wave trains”) in the Morlet wavelet spectrogram and to calculate various parameters describing these wave trains: the number of wave trains per second, the duration of the wave trains in periods, the leading frequency of the wave trains, the width of the wave train frequency band. The AUC functional dependence on the values of the bounds of the ranges of these parameters is investigated. This method is aimed at studying changes in the time-frequency parameters (the shape) of signals including changes that are not related to the power spectral density of the signal.

The problems associated with the development of a method for early and differential diagnosis of Parkinson's disease (PD) and essential tremor (ET) using the analysis of wave train muscle activity are investigated. In the study of patients with PD, it is very difficult t o fi nd un tr eated pa ti ents wh o ha ve not previously taken antiparkinsonian drugs. To solve the problem of small samples, it was proposed to increase artificially t he sample of subjects using the bootstrap method. Three artificially enlarged samples of subjects were created: a sample of patients with PD, a sample of patients with ET, and a sample of control subjects. When constructing models, two problems arise: the problem of inadequacy and the problem of insufficient g e neralizing ability of the model. Both of these issues must be considered when building models. The paper proposes models that, according to the authors, are promising for the early and differential diagnosis of PD and ET. Using these models, a good accuracy of PD recognition at the early stage was obtained (about 100%); the accuracy of differential diagnostics (distinguishing between PD and ET) was more than 90%.

Background. A major meta-analysis has confirmed the ability of statins to exert both diabetogenic and hyperglycaemic effects. To date, practical recommendations for predicting glucose dynamics during lipid-lowering therapy have not been developed. Aims. Identify the combination of factors that can predict changes in basal glycaemia during 6-month lipid-lowering therapy in patients at high risk of cardiovascular disease. Methods. This study reports on 50 patients with diabetes or impaired glucose tolerance, and 18 patients with coronary artery disease without disorders of carbohydrate metabolism. Of note, 29 of the 50 diabetic or glucose intolerant patients had documented ischaemic heart disease (stable angina). Patients were randomised into three groups: Gr.1 (n=33, atorvastatin therapy), Gr.2 (n=17, atorvastatin in combination with ezetimibe) and Gr.3 (n=18, rosuvastatin therapy). After treatment for 24 weeks, we assessed lipid profile dynamics, metabolism of glucose/insulin and the HOMA-IR index. Multivariate analysis was then performed to identify factors that predicted increases in basal glycaemia. Results. All of the included patients completed 24 weeks of treatment (N=68). Lipid-lowering effect was significant in all three groups, and overall, target LDL cholesterol level was achieved in 50% of patients (n=34). In Gr.2, basal glucose level increased from 5.5(5.36.6) to 6.3(5.67.8) mmol/l (p=0.0014), which was accompanied by an increase in HOMA-IR (p=0.024). No significant change in basal glycaemia was observed in Grs.1 and 3. Moreover, an increase in the basal glycaemia was observed in 48.5% of patients in Gr.1, 70.6% in Gr.2 and 44.4% in Gr.3. Multivariate discriminant analysis across all patient groups revealed a canonical linear discriminant function that included the following factors: baseline basal glucose levels, total cholesterol levels, triglycerides and ratio of LDL/HDL cholesterol. Sensitivity and specificity of the model accounted for 75%; 51 out of the 68 cases were correctly classified when predicting the dynamics of basal glucose during lipid-lowering therapy. Conclusions. Our data demonstrate the ability to predict the dynamics of the basal glycaemia during lipid-lowering therapy. This may allow for a new way to identify patients at high risk of statin-related increases in glycaemia.

The paper presents the results of the model experiment on spring barley (Hordeum vulgare L.) grown in polluted soil. The influence of separate and combined application of wood biochar and heavy metal-tolerant bacteria on morpho-physiological, anatomical and ultrastructural parameters of H. vulgare L. has been studied. The joint application of biochar and bacteria increased the shoot length by 2.1-fold, root length by 1.7-fold, leaf length by 2.3-fold and dry weight by threefold compared to polluted variant, bringing the plant parameters to the control level. The maximal quantum yield of photosystem II decreased by 8.3% in H. vulgare L. grown in contaminated soil, whereas this decrease was less in biochar (7%), bacteria (6%) and in combined application of bacteria and biochar (5%). As for the transpiration rate, the H. vulgare L. grown in polluted soil has shown a decrease in transpiration rate by 26%. At the same time, the simultaneous application of biochar and bacteria has led to a significant improvement in the transpiration rate (14%). The H. vulgare L. also showed anatomical (integrity of epidermal, vascular bundles, parenchymal and chlorenchymal cells) and ultrastructural (chloroplasts, thylakoid system, plastoglobules, starch grains, mitochondria, peroxisomes, ribosomes, endoplasmic reticulum, vacuoles) changes, revealed by light-optical and transmission electron microscopy of leaf sections. The effects were most prominent in H. vulgare L., grown in polluted soil but gradually improved with application of biochar, bacteria and their combination. The use of biochar in combination with metal-tolerant bacteria is an efficient tool for remediation of soils, contaminated with heavy metals. The positive changes caused by the treatment can be consistently traced at all levels of plant organization.

A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time–frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time–frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time–frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson’s disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson’s disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams.

the article presents the study of the content of organic ecotoxicants – polycyclic aromatic hydrocarbons in the soils of the impact zone of the energy industry enterprise of Novocherkasskaya power plant (NPP) – the largest producer of electricity in southern Russia. The study showed a high level of total PAH content in the soils of the studied territory, compared to the background territories. The degree of contamination of soils in the zone of influence of the energy industry enterprises with Benzo(a)pyrene is classified as highly contaminated soils. The diagnostic relationships of individual PAHs showed a significant effect of the NchGRES on the soils of sites located along the line of the prevailing wind from NPP. For these sites, the pyrogen-coal origin of PAHs was established.

The terahertz video surveillance opens up new unique opportunities in the field of security in public places, as it allows to detect and thus to prevent usage of hidden weapons and other dangerous items. Although the first generation of terahertz video surveillance systems has already been created and is available on the security systems market, it has not yet found wide application. The main reason for this is in that the existing methods for analyzing terahertz images are not capable of providing hidden and fully-automatic recognition of weapons and other dangerous objects and can only be used under the control of a specially trained operator. As a result, the terahertz video surveillance appears to be more expensive and less efficient in comparison with the standard approach based on the organizing security perimeters and manual inspection of the visitors. In the paper, the problem of the development of a method of automatic analysis of the terahertz video images is considered. As a basis for this method, it is proposed to use the semantic fusion of video images obtained using different physical principles, the idea of which is in that the semantic content of one video image is used to control the processing and analysis of another video image. For example, the information about 3D coordinates of the body, arms, and legs of a person can be used for analysis and proper interpretation of color areas observed on a terahertz video image. Special means of the object-oriented logic programming are developed for the implementation of the semantic fusion of the video data, including special built-in classes of the Actor Prolog logic language for acquisition, processing, and analysis of video data in the visible, infrared, and terahertz ranges as well as 3D video data.

Object-oriented logical means for a real-time analysis of video were developed. The software includes a translator of the Actor Prolog object-oriented logic language to Java, an open source Java library that contains built-in classes of Actor Prolog, and a programming environment of the Actor Prolog language. An example of using the software for intelligent video monitoring of abnormal behavior in people is considered. Object-oriented means of the Actor Prolog language make it possible to divide a logic program into several parallel processes corresponding to various stages of the video processing. The translation of logic programs to Java provides reliability, portability, and openness of the intelligent video surveillance programs.

A method for the analysis of the time–frequency dynamics of the background brain activity is proposed and used to reveal three main features of the early-stage Parkinson disease (PD): hemispheric asymmetry of the time–frequency characteristics of electroencephalogram in the central lead of the motor zone of the cerebral cortex, generation of the rhythm of electroencephalogram in these leads in a frequency interval of 4–6 Hz and its correlation with electromyograms and mechanical tremor of contralateral limbs upon Parkinsonian tremor, and disorganization of the dominant rhythm corresponding to the general concept of the disorganization of various systems under PD.

The problem of the video monitoring the laboratory rats by the means of the object-oriented logic programming is considered. The main task of the video monitoring is the analysis of the behavior of the animals in cognitive testing. An essential feature of the video records is in that the experiments are conducted in the same cage where the animal lives, that is, the background of the cage is sawdust. The color of the animals is about the same as the color of the sawdust; thus the detection of the animals is not a simple task. An additional difficulty is in that the videos were recorded simultaneously with electroencephalograms (EEG) in the animals; thus the head of the rat is connected with EEG cable that moves and causes false detections of recognition algorithms. In the paper, development of low-level algorithms for video analysis as well as logical methods for the analysis of the animal behavior is discussed. The methods and algorithms are implemented in the Actor Prolog object-oriented logic language.

International audience; Experimental means developed in the Actor Prolog parallel object-oriented logic language for implementation of heterogeneous multichannel intelligent visual surveillance systems are considered. These means are examined by the instance of a logic program for permanent monitoring of people's body parts temperature in the area of visual surveillance. The logic program implements a fusion of heterogeneous data acquired by two devices: (1) 3D coordinates of human body are measured using a time-of-flight (ToF) camera; (2) 3D coordinates of human body skeleton are computed on the base of 3D coordinates of the body; (3) a thermal video is acquired using a thermal imaging camera. Special built-in classes are developed in the Actor Prolog language for the acquisition and analysis of 3D and 2D video data: the Kinect and KinectBuffer classes implement interaction between the logic program and the ToF camera of the Kinect-2 device; the BufferedScene class implements storing and transferring 3D images; the Canvas3D class implements 3D graphics based on the Java3D open source library. In the considered example, the thermal video is projected to the 3D surface of the human body; then the temperature of the human body is projected to the vertices and edges of the skeleton. A special logical agent (i.e., the logic program written in Actor Prolog) implements these operations in real-time and transfers the data to another logical agent. The latter agent implements a time average of the temperature of the human skeletons and displays colored 3D images of the skeletons; the average temperature of the vertices and edges of the skeletons is depicted by colors. A method of remote predicate calls is used for the interaction of the logical agents; this method was developed and implemented in Actor Prolog to support the agent logic programming paradigm. The logic programming means under consideration are developed for the purpose of the implementation of logical analysis of video scene semantics in the intelligent visual surveillance systems. © 2018 Radioelektronika, Nanosistemy, Informacionnye Tehnologii. All rights reserved.

The problem of video monitoring of laboratory rodents by the means of object-oriented logic programming is considered. The videos are produced in neurophysiological experiments on the study of convulsive electrical activity of the brain cortex. Videos of behaviour of laboratory rats were recorded simultaneously with electroencephalograms (EEG) in the animals. A comparison of EEG data with the behaviour of the animals is necessary because sharp motions of the animals can result in EEG signals that are very similar to the epileptic discharges. Thus, the first task of the video monitoring is recognition of the sharp motions of the animals and using this information for proper interpretation of the results of the experiments. The second task of the video monitoring is analysis of behaviour of animals in cognitive testing. Essential feature of the video records is in that the experiments are conducted in the same cage where the animal lives, that is, the background of the cage is sawdust. The colour of the animals is about the same as the colour of the sawdust; thus the detection of the animals is not a simple task. In the paper, development of low-level algorithms for video analysis as well as logical methods for the analysis of the animal behaviour is discussed. The methods and algorithms are implemented in the Actor Prolog object-oriented logic language.

An approach to the 3D intelligent video surveillance based on the means of the object-oriented logic programming is proposed. In contrast to the conventional 2D video surveillance, the methods of 3D vision provide reliable recognition of parts of the human body that makes possible a new statement of the problem and efficient practical application of methods of people behaviour analysis in the video surveillance systems. The logic-based approach to the intelligent video surveillance allows easily definition of people complex behaviour in terms of simpler activities and postures. The goal of this work is to implement the advantages of the logic programming approach in the area of 3D intelligent video surveillance.

A new method for analyzing the time-frequency dynamics of brain's background electrical activity is described. It is used to detect at least three main features of Parkinson's disease (PD) in its early stages: (1) hemispheric asymmetry in the time-frequency characteristics (EEG) in the central recording areas of the motor cortex, (2) the emergence in these recording areas of EEG rhythms in the frequency range of 4---6 Hz and its relation to electromyograms (EMG) and the mechanical tremor of contralateral limbs in the case of tremor-dominant PD, and (3) the disruption of the dominant rhythm corresponding to views generally held on the disorganization of different systems in PD.

A new method has been developed for the analysis of the wave train electrical activity of muscles based on the wavelet analysis and ROC analysis that enables to study the time-frequency characteristics of electromyograms (EMG) and acceleration (ACC) signals in patients with Parkinson’s disease (PD). The idea of the method is to find local maxima (that correspond to the wave trains) in the wavelet spectrogram and to calculate various characteristics describing these maxima: the leading frequency, the duration of the wave trains in periods, the bandwidth of the wave trains, the number of wave trains per second. The degree of difference between a group of patients and a control group of volunteers in the space of these parameters is analyzed. ROC analysis is used for this purpose. The functional dependence of AUC (the area under the ROC curve) on the values of the boundaries of parameters’ ranges under consideration is investigated. The developed method involves investigation of a big number of ranges of selected characteristics; therefore a multiple comparisons problem appears during statistical hypothesis testing. It is necessary to find a compromise between the degree of detail of the studied characteristics and the magnitude of the Bonferroni correction. The paper describes the statistical hypothesis testing on the data of early Parkinson’s disease patients.

The goal of this study is development of a novel signal processing and analysis method for detailed investigation of the time-frequency dynamics of brain cortex electrical activity. The idea of our method of electroencephalograms (EEG) analyzing is in that we consider EEG signal as a composition of so-called wave trains. The wave train term is used to denote a signal localized in time, frequency, and space. We consider the wave train as a typical component of EEG, but not as a special kind of EEG signals. In contrast to papers devoted to detecting wave trains of one or two specific types, such as alpha spindles and sleep spindles, we analyze any kind of wave trains in a wide frequency band. Using this method, we have found three interesting frequency areas where differences were detected between a group of Parkinson’s disease (PD) patients and a control group of healthy volunteers. The goal of this work is to check whether the regularities in the mu and beta frequency bands are independent ones, that is, the beta wave trains observed in the analysis were not the second harmonics of the mu wave trains. We have developed a special algorithm that eliminates from the analysis all beta wave trains in EEG signal that were observed simultaneously with the mu wave trains. Analysis of a real experimental data set processed by this algorithm has confirmed that the beta frequency band regularity is separate from the mu frequency band regularity. Moreover, a new significant difference between the left hand tremor and right hand tremor Parkinson’s disease patients was discovered.

A method of analysis of EEG wave trains based on wavelets and nonparametric statistics is developed. The method is compared with standard methods based on Fourier spectra and complex Morlet wavelets by the example of Parkinson's disease experimental data. We demonstrate that these methods are complementary, that is, the standard methods and the wave train analysis method reveal sufficiently different effects in the EEG data.