Determination of markers of systemic inflammation is one of the important directions in the study of pathogenesis and improvement of diagnosis of chronic obstructive pulmonary disease (COPD), asthma-COPD overlap (ACO), and bronchial asthma (BA). The aim of our work was a comparative study of the features of changes in serum levels of IL-17, IL-18, and TNF-αin patients with COPD, ACO, and BA with various severity of the disease, as well as evaluation of the relationship between the level of these cytokines and lung ventilation function. A total of 147 patients with COPD (n=58), ACO (n=57), and BA (n=32) during a stable period have been examined in this study. The control group included 21 healthy nonsmokers with similar sex-age indicators. Serum levels of IL-17, IL-18, and TNF-αwere determined by ELISA. The concentrations of these cytokines in the circulation in the studied patients with COPD, ACO, and BA were higher than those in healthy nonsmokers (p≤0.001). IL-17 and IL-18 levels in the blood serum were comparable in all examined patients. The mean TNF-αconcentrations in the circulation in COPD and ACO were significantly higher than those in BA (p<0.001). In patients with COPD, the levels of IL-17 and TNF-αincreased progressively against the background of a decrease in numerous spirometric indicators, which allows us to consider these cytokines as systemic biomarkers of disease severity. In BA, the inverse correlations between the level of IL-17 and FEV1/FVC (%) and FEV1 have been found. In patients with ACO, the increase in IL-18 levels was associated with a decrease in FEV1 and TNF-αwith FEV1/FVC (%). These findings indicate that IL-17, IL-18, and TNF-αcan participate in the mechanisms of systemic inflammation and the genesis of disorders of airway obstruction in COPD, AСO, and BA. An increase in the levels of IL-17 and TNF-αmay be associated with impaired bronchial patency in COPD and BA. The established associations of the IL-18 concentration in the blood serum and FEV1 only in patients with ACO allow using the level of IL-18 as a potential marker of the degree of impaired airway obstruction in this disease.

The frequency with which we use different words changes all the time, and every so often, a new lexical item is invented or another one ceases to be used. Beyond a small sample of lexical items whose properties are well studied, little is known about the dynamics of lexical evolution. How do the lexical inventories of languages, viewed as entire systems, evolve? Is the rate of evolution of the lexicon contingent upon historical factors or is it driven by regularities, perhaps to do with universals of cognition and social interaction? We address these questions using the Google Books N-Gram Corpus as a source of data and relative entropy as a measure of changes in the frequency distributions of words. It turns out that there are both universals and historical contingencies at work. Across several languages, we observe similar rates of change, but only at timescales of at least around five decades. At shorter timescales, the rate of change is highly variable and differs between languages. Major societal transformations as well as catastrophic events such as wars lead to increased change in frequency distributions, whereas stability in society has a dampening effect on lexical evolution.

In this paper we explore to what extent text parameters, such as average number of words per sentence, syllables per word, nouns per sentence, frequency of content words, etc. can successfully rank Russian academic texts for different age and grade levels. We provide a brief overview of previous research on readability of Russian texts and describe the corpus of school textbooks on Social Studies (from 5-th to 11-th grade) compiled by the authors. We share our experience of using a variety of quantitative text complexity metrics and evaluate the measures of existing Russian text complexity formulas. Based on the tests of a set of extended text features, we propose one innovative metric for better prediction of Russian text complexity, i.e. the number of adjectives. As the results obtained compare favorably with the previously published results on the established complexity metrics for Russian texts, the study encourages the development of valid, reliable and transparent complexity tools for Russian texts.

The paper provides a survey of semantic methods for solution of fundamental tasks in mathematical knowledge management. Ontological models and formalisms are discussed. We propose an ontology of...

The dramatic expansion of modern linguistic research and enhanced accuracy of linguistic analysis have become a reality due to the ability of artificial neural networks not only to learn and adapt, but also carry out automate linguistic analysis, select, modify and compare texts of various types and genres. The purpose of this article and the journal issue as a whole is to present modern areas of research in computational linguistics and linguistic complexology, as well as to define a solid rationale for the new interdisciplinary field, i.e. discourse complexology. The review of trends in computational linguistics focuses on the following aspects of research: applied problems and methods, computational linguistic resources, contribution of theoretical linguistics to computational linguistics, and the use of deep learning neural networks. The special issue also addresses the problem of objective and relative text complexity and its assessment. We focus on the two main approaches to linguistic complexity assessment: “parametric approach” and machine learning. The findings of the studies published in this special issue indicate a major contribution of computational linguistics to discourse complexology, including new algorithms developed to solve discourse complexology problems. The issue outlines the research areas of linguistic complexology and provides a framework to guide its further development including a design of a complexity matrix for texts of various types and genres, refining the list of complexity predictors, validating new complexity criteria, and expanding databases for natural language.