In patients with psychotic disorders, sleep spindles are reduced, supporting the hypothesis that the thalamus and glutamate receptors play a crucial etio-pathophysiological role, whose underlying mechanisms remain unknown. We hypothesized that a reduced function of NMDA receptors is involved in the spindle deficit observed in schizophrenia.
An electrophysiological multisite cell-to-network exploration was used to investigate, in pentobarbital-sedated rats, the effects of a single psychotomimetic dose of the NMDA glutamate receptor antagonist ketamine in the sensorimotor and associative/cognitive thalamocortical (TC) systems.
Under the control condition, spontaneously-occurring spindles (intra-frequency: 10–16 waves/s) and delta-frequency (1–4 Hz) oscillations were recorded in the frontoparietal cortical EEG, in thalamic extracellular recordings, in dual juxtacellularly recorded GABAergic thalamic reticular nucleus (TRN) and glutamatergic TC neurons, and in intracellularly recorded TC neurons. The TRN cells rhythmically exhibited robust high-frequency bursts of action potentials (7 to 15 APs at 200–700 Hz). A single administration of low-dose ketamine fleetingly reduced TC spindles and delta oscillations, amplified ongoing gamma-(30–80 Hz) and higher-frequency oscillations, and switched the firing pattern of both TC and TRN neurons from a burst mode to a single AP mode. Furthermore, ketamine strengthened the gamma-frequency band TRN-TC connectivity. The antipsychotic clozapine consistently prevented the ketamine effects on spindles, delta- and gamma−/higher-frequency TC oscillations.
The present findings support the hypothesis that NMDA receptor hypofunction is involved in the reduction in sleep spindles and delta oscillations. The ketamine-induced swift conversion of ongoing TC-TRN activities may have involved at least both the ascending reticular activating system and the corticothalamic pathway.
The article examines the international and Russian experience in teaching and learning digital humanities. Existing formats such as online courses, university courses, minors and programs, summer schools are identified and analyzed, examples are given. The structures that lead educational activities are described. In addition, the article proposes a methodology for constructing a Russian-language course and teaching digital humanities, describes the concept of the "Learning Digital Humanities" platform and the first experience of its use.
The most common tools to understand perception of food products are hall tests, surveys and observations. However, these approaches require large samples to get reliable results and they are rather costly and time-consuming. Furthermore, they are also highly expert-dependent and rely on the assumption that study participants can express their preferences consciously and explicitly. In our paper, we suggest an electroencephalography- based (EEG) approach to evaluate perceived product similarity in a cross-modal taste-visual task. We tested two potential neurometrics measured from Fz electrode: the amplitude of the N400-like evoked response potentials (ERP) and the power of induced gamma oscillations during 400-600 ms period after visual stimulus presentation. Both metrics showed a strong correlation with the perceived similarity scores at both individual and group levels; however, N400-like amplitude had greater inter-subject variability making it less suitable for practical applications. The results based on the power of induced gamma oscillations (N=18) could be compared to traditional hall-tests (N=200) and may potentially reveal subtle differences in food perception that can not be captured in the hall-tests.
In this paper, we address several aspects of applying classical machine learning algorithms to a regression problem. We compare the predictive power to validate our approach on a data about revenue of a large Russian restaurant chain. We pay special attention to solve two problems: data heterogeneity and a high number of correlated features. We describe methods for considering heterogeneity — observations weighting and estimating models on subsamples. We define a weighting function via Mahalanobis distance in the space of features and show its predictive properties on following methods: ordinary least squares regression, elastic net, support vector regression, and random forest.
This paper considers a dynamic ventilation system of the underground mining. The research is relevant from the point of view of safety and energy saving of mining operations, since the process of ventilation of underground mining companies consumes from 30 to 50% of all company electricity. Existing methods of ventilation control often do not ensure rational energy consumption, as they do not take into account the dynamics of air distribution and changes in environmental parameters. The proposed method includes basic algorithms for calculating the interrelationship of physical parameters of general natural draught between the trunks. The method includes: calculation of the draught’s power; calculation of productivity and the choice of the required mode of operation for the main fan unit (MFU) considering the inertia of the ventilation system; dynamic calculation of the control signal on the fan unit taking into account the impact of the general natural draught. The method is focused on the implementation of the TICK stack used to create IoT applications as part of the Cyber-Physical System (CPS) for ventilation based on the InfluxData platform. The proposed CPS architecture consists of four subsystems: physical object subsystem, IoT network and computing infrastructure - ICT infrastructure, digital twin, user interface. CPS architecture provides processing of data from energy meters, controllers and air environment parameters, implemented in on-line and off-line calculation units
Understanding neurological mechanisms of motor recovery after stroke is important for selecting appropriate therapeutic and rehabilitation strategies. One of the most widely-used but yet rather controversial MRI predictors is a co-called lesion load on the cortico-spinal tract (CST). This metric corresponds to the overlap between the volumes of the lesion and the cortico-spinal tract which is responsible for conducting neuronal signals that lead to motion generation. In this study we evaluated the potential of the lesion load to explain the motor outcome in a cohort of patients with chronic ischemic stroke. Lesions were automatically identified on structural T1-weighted images using LINDA package. Once lesions are identified, lesion loads on CST were calculated automatically using PALS software package (Ito et al., 2018). Finally, the obtained results were used to classify patients according to their motor outcome using decision tree classifier J48 implemented in WEKA software. However, the classification accuracy was much lower compared to the classification results based on another widely accepted MRI parameter: asymmetry of the fractional anisotropy in the internal capsule of the CST.
The degree of mental attention in childhood and adolescence determines in the future the effectiveness of working memory (ability to store and manipulate information). Attention has been previously found to be related to the prefrontal and parietal areas of the human cortex. But the relationship between attention and white matter properties are still largely unknown. The goal of this study was to identify the relationships between attention and fractional anisotropy (FA) of diffusion MRI in bilateral superior longitudinal fasciculus (in three subdivisions SLF 1- 3), arcuate fasciculus (AF), and corpus callosum (CC) in children and adolescents. Subjects: 14 children (9-11 years) and 13 teenagers (12-15 years). During the experiments participants had to establish a match between the colors on the screen and the colors on the previous slide. The task had six difficulty levels and both performance accuracy (m-score) and reaction time (RT) were measured. There was a positive correlation for m-score and a negative correlation for RT with FA in СС (levels 1-3) in the children's group (p<0.05). On the contrary, when FA increases in the right SLF 3 (level 6), there is a decrease in m-score, and when FA increases in the left SLF 3 and AF, there is an increase in RT at 2,3,4 and 6 levels. In contrast, a decrease in RT with an increase FA of bilateral SLF 3 (level 6) and left AF (level 4) was observed for adolescents, which reflects the redistribution of the roles between fiber tracts with age. FA values of the left (level 2) and right (level 1) SLF 2 negatively correlated with mscore (p <0.05) in the same group. For females (n=13) (regardless the age), there was only a negative correlation for m-score (2,3,5 levels) and the only positive correlation for RT (level 2) with FA of the right SLF 1, left and right SLF 2, in the left SLF 3 and СС (p<0.05). For males (n=13), on the contrary, there were positive correlations between m-score and FA of the СС (1,3,4 levels) and the left SLF 1 (5 level), and inverse correlations between RT and FA for the same fibers of the white matter (1 level) (p<0,05). Interestingly, an increase in FA with age was found in males in all the components of the white matter (p<0.01), except for the СС, and in females, on thecontrary – only in the СС. Further research is needed, taking into account gender, to fully understand the influence of white matter on the development of mental attention.
The dynamics of the background EEG of women during the reproductive period (from 16 to 45 years)has not been studied. However, it is the background indicators that reflect the system properties of the cerebral cortex and determine the dynamic changes in indicators during activity . The purpose of the study was to identify patterns of dynamics of the amplitude, power and frequency of the Pho‑ new EEG of female individuals during the reproductive period.
Patent foramen ovale (PFO) is an important cause of embolic cryptogenic stroke (ECS) in young patients. The main mechanism in this case is paradoxical embolism (PE), the basis for which is a right-to-left (R-L) shunt. Objective: to comparatively characterize patients who have undergone ECS, with and without an R-L shunt, as evidenced by transcranial Doppler with the bubble test (TCD-BT).
The paper considers applied scientific aspects of algorithmic and software design of cyber-physical building system (cyber-physical system, CPS). The building's CPS is a basic element of the Smart City IT architecture and represents a set of life support system control devices, communication and computing facilities integrated into the building, which are necessary and sufficient for the implementation of user services. In a building's cyberphysical system, all equipment and subsystems are integrated into a single ecosystem to improve comfort and safety, as well as to reduce operating costs and save resources. The paper investigates the requirements for the building's CPS software. Presented are groups of design patterns, which, in practice, does not only significantly reduce the time to program and configure the CPS elements of the building, but also increases the interoperability of developed information applications. Consideration is given to the algorithms of CPS software under the conditions of the Internet of Things (IoT) application. Using the proposed templates, software developers will be able to quickly form new services, quickly integrate and maintain them. The patterns presented in the study are most effective in the implementation of services to control indoor and outdoor lighting, power loads and electrical appliances, as well as systems such as heating, air conditioning, ventilation, security alarm, access control, water leakage control, audio and video equipment. CPS software architecture design templates can be in demand for manufacturers of systems and services of management of private or apartment houses, developers of software systems of automation of commercial real estate objects and state organizations, developers and administrators of software of industrial constructions, objects of agroindustrial complex. The research is carried out within the framework of the priority science development direction of the Perm branch of the National Research University Higher School of Economics "Research of control methods in cyber-physical systems".
Processing of mathematical operations and solving numerical tasks implicate a distributed set of brain regions. These regions include the superior and inferior parietal lobules that underlie numerical processing such as size judgments, and additional prefrontal regions that are needed for formal mathematical operations such as addition, subtraction and multiplication [Arsalidou, Taylor, 2011]. Critically, little is known about the connectivity between these regions and the association between math performance and the anatomical structure of white matter tracts. The present study investigates connectivity and white matter tracks associated with networks related to math performance: arcuate fasciculus (AF) and superior longitudinal fasciculus (SLF). Participants performed a computerized task with mathematical operations (addition, subtraction, multiplication, and division) with three levels of difficulty; accuracy and reaction time were recorded. Diffusion tensor imagining (DTI) recordings provided indices on fractional anisotropy (FA) — a measure of the direction of white matter tracks in the brain. The relation between FA and math performance scores is reported.
This paper is an empirical study of the changing nature of the dependence of fundamental factors on the stock market index, which is the trend identified earlier in the Russian stock market. We empirically test the impact of daily values of fundamental factors on the MOEX Russia Index from 2003 to 2018. The analysis of the ARIMA-GARCH (1,1) model with a rolling window reveals that the change in the power and direction of the influence of the fundamental factors on the Russian stock market persists. The Quandt-Andrews breakpoint test and Bai-Perron test identify the number and likely location of structural breaks. We find multiple breaks probably associated with the dramatic falls of the stock market index. The results of the regression models over the different regimes, defined by the structural breaks, can vary markedly over time. This research is of value in macroeconomic forecasting and in the investment strategy development
In this paper, we analyze a new approach for demand prediction in retail. One of the signicant gaps in demand prediction by machine learning methods is the unaccounted sales data censorship. Econometric approaches to modeling censored demand are used to obtain consistent and unbiased estimates of parameters. These approaches can also be transferred to different classes of machine learning models to reduce the prediction error of sales volume. In this study we build two ensemble models to predict demand with and without demand censorship, aggregating predictions for machine learning methods such as Linear regression, Ridge regression, LASSO and Random forest. Having estimated the predictive properties of both models, we test the best predictive power of the models with accounting for the censored nature of demand.
The texts I have revised testify the line developed from the more “naïve” and ontologizing discourse of Ps.-Macarius to the discourse where the unity is spoken of as a phenomenon revealed for intellectual abilities, which suggests an indication of unity in a certain respect and preserves from interpreting this unity as a merge of unified natures. For the latter we can distinguish the strategy of cognition / recognition, which Maximus the Confessor and Symeon the New Theologian adhered to. According to this strategy, by the properties displayed by incandescent iron, we can discern the nature of fire revealed in iron, and, in the same way, we can conclude that human is god (of small letter) on the basis of that he manifests himself as God, that is, exposes the divine features.