538 research outputs found

    Mathematical statistics functionally object model for monitoring and control

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    The paper chain is seen as a complex system that is subject to management. The complexity of the process of monitoring and control is caused mainly complicated objects. To describe the operation of the facility built its functional and static mathematical model that completely describes the state of the object. The functional and statistical models to determine the probabilistic characteristics of information and communication network as object management. The model allows direct determination of the probability of the phase-out of the facility

    Linking markemes in Russian and British literary texts of the first half of the nineteenth century

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    The paper aims at visualizing and analyzing the markeme links in literary texts of Russian and British writers of the first half of nineteenth century. According to the target goal, the tasks of determining the preferred markeme links between the authors, substantial study of maximum force markeme links and comparing the linking markeme vocabulary in Russian and English literary texts have been solved. The topicality of this study is conditioned by the need to devise the ways of cognitive-graphical representations of analytic data and their semantic interpretation and by the scarcity of comparative quantitative studies of the language of Russian and English literary texts, which could present information for comparative and typological analysis of language and literary processes.The study of markeme links is one of the ways of formalized content analysis of the text. The scientists identified regularities to which the texts in natural languages are subject. This enables the use of the mathematical apparatus in linguistic studies. The use of both digital and traditional linguistic studies methods allows analyzing text corpora with mathematical and statistical methods and composing national text corpora, corpora of translations, interactive maps, creating social networks of writers, poets, philosophers, modeling script texts into picture line, analyzing text sentiment, running network analysis and so on. This article suggests analyzing markeme links of maximum force in pairs of authors when comparing β€œwith each other”. The method of markeme analysis proposed by A.A. Kretov as one of the means to formalize the semantical analysis of the text is used to solve the set tasks. It provides a means of presenting a complete picture of literary works language markeme composition of any chronological interval or historical period. It also gives the possibility to analyze texts practically of any wordage. Besides the method of markeme analysis allows analyzing markeme composition of literary (especially - fiction) works of individual authors or groups of authors, markeme specifications and the influence of social and cultural processes on markeme dynamics, studying the evolution of markeme vocabulary through several chronological intervals and establishing literary and genetic links between authors who belong to the same or different chronological intervals an individual DH prospect developed by A.A. Kretov, his colleagues and scholars. Its potential is not limited with solving the given problems. As a qualitative and quantitative analysis, markemological studies employ markeme analysis as a method. This method allows formalizing semantic analysis of texts. Markeme analysis is a method of computer-based identification of keywords, or markemes, followed by visualization of obtained data in the form of bar graphs, charts, graphs, clusters that undergo semantic interpretation. A.A. Kretov developed sharply defined notions on how to identify markemes using a special formula to calculate author's weight or Index of Textual Markedness. The computational formula expresses functionality between a frequency weight and a length weight of a word. As the length weight of a word is constant because it depends on the length of the word in letters or sounds, it is the value of frequency weight that determines the value of InTeM. When a word distribution in the text exceeds a standard frequency distribution threshold for this word, the value of its InTeM becomes positive thereby expressing the level of significance for the word in the particular text. The texts are processed with word thematic analysis manipulation programs β€œTemAl” and β€œProTemAl-Engl” developed in Voronezh State University. β€œTemAl” processes Russian texts and β€œProTemAl-Engl” does the same with English texts. These programs calculate the value of InTeM for each word as well. To guarantee comparability of markeme weights of different authors the procedure of normalizing InTeM values is carried out. This is due to the fact that too often there is great difference both in number of works and their length in words written by different authors and their availability in digital form. InTeM normalizing eliminates their incorrect correlation. The analysis of linking markemes that establish markeme links between two or more authors allows determining the degree of markeme similarity between the authors of chronological interval. Mutual markemes are selected from each author markeme list. Index of Markeme Similarity (IMaS) is the measurable parameter that gives possibility to determine the degree of generality of markeme lexicon of two authors. The computation of IMaS in each pair of authors belonging to the chronological interval is based on the value of total normalized indices of textual markedness of their mutual markemes. The mutual markemes of those two writers that have the largest value of IMaS are their linking markemes. The value of IMaS determines the power of markeme link. When the value of IMaS is the largest for only one writer in the pair, a directional or oriented link of maximum power is formed. In case the value of IMaS is the largest for both writers in the pair, mutually oriented link is formed between them. The present study results in the analysis of linking markemes in the texts of Russian and British writers of the first half of the nineteenth century in reference to the distinguished centre of attraction. The method of visualizing the links between the authors who belong to the same chronological interval allows to distinguish the centre of attraction and intermediate centres of zero, first and second degree, to compute the power of centripetal links, to stratify and analyze linking markemes, to study markeme specificity of the centre of attraction and to distinguish markemes that provide an indirect link between the centre of attraction and intermediate centres. The use of the algorithm of visualizing markeme relations between the authors provides a means of revealing existing centrifugal and centripetal markeme links between the writers, distinguishing the centre of attraction, identifying its major figures and the authors who have direct or indirect markeme links of maximum force with each of them. Obtained data make it possible to compute the power of the center of attraction and the semantic study of maximum force markeme links leads to the specification of both the intermediate centres that are represented by key figures of the centre of attraction and the centre of attraction itself

    ΠœΠžΠ’Π˜Π’ΠΠ¦Π†Π™ΠΠ† ΠΠ‘ΠŸΠ•ΠšΠ’Π˜ ПБИΠ₯ΠžΠ•ΠœΠžΠ¦Π†Π™ΠΠžΠ“Πž БВАНУ Π–Π†ΠΠžΠš ΠŸΠ†Π” ЧАБ Π’ΠΠ“Π†Π’ΠΠžΠ‘Π’Π†. АНАЛІЗ Π₯ΠΠ ΠΠšΠ’Π•Π Π˜Π‘Π’Π˜Πš Π“Π•Π‘Π’ΠΠ¦Π†Π™ΠΠžΠ‡ Π”ΠžΠœΠ†ΠΠΠΠ’Π˜ Π£ ΠšΠžΠΠ’Π•ΠšΠ‘Π’Π† Π’Π Π˜Π’ΠžΠ–ΠΠžΠ‘Π’Π†

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    The aim of the study – to analyze gestational dominant within the framework of anxiety based on estimation of psychoemotional state of pregnant women.Materials and Methods. 336 pregnant women were examined in Π†Π† and Π†Π†Π† trimesters (26.29Β Β±Β 0.55 week). Individual and psychological features of pregnant women and their psychoemotional state were studied using the complex of psychodiagnostic methods: Personality Questionnaire of Bekhterev University, Spielberger-Hanin scale, Pregnant Woman Attitude Test of I.V. Dobryakov. Variation and statistical processing of results was performed with STATISTICA 6.0 analysis programs.Results and Discussion. Results of estimation of trait anxiety (TA) permit to determine that its level of 330 (98.21Β %) pregnant women exceeds the bounds of low indices. Also such results are typical for state anxiety (SA) where medium and high indices are determined in 256 pregnant women that made 76.19Β %. Optimal variant of psychological component of gestational dominant (PCGD) is determined in 41 (12.20Β %) pregnant women and euphoric one – in 3 (0.89Β %) pregnant women. Prevalence of points in favour of certain type of PCGD (type which deviates from the optimal variant) was not determined in 292 pregnant women that made 86.91Β %. Analysis of characteristics of gestational dominant within framework of anxiety has shown that indicator of points (which characterizes optimal type and is estimated with consideration of SА level) was statistically proved (Ρ€Β <Β 0.05) and lower in pregnant women with high SA level (4.10Β Β±Β 0.60 points) comparing to appropriate index of pregnant women with both medium (4.68Β Β±Β 0.22 points) and lower (4.94Β Β±Β 0.36 points) levels.Conclusions. Estimation of psychological component of gestational dominant permitted to determine the fact that its optimal variant occurred only in 12.20 % of pregnant women. Prevalence of points in favour of certain type was not determined in majority of pregnant women (86.91Β %). Analysis of characteristics of gestational dominant within framework of anxiety showed that index of points which characterizes optimal type and is estimated with consideration of SA level was statistically proved (Ρ€Β <Β 0.05) and lower in pregnant women with high SA level comparing to appropriate index of pregnant women with both medium and low level of SA. ЦСль исслСдования – Π½Π° основании ΠΎΡ†Π΅Π½ΠΊΠΈ ΠΏΡΠΈΡ…ΠΎΡΠΌΠΎΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ состояния Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ…, провСсти Π°Π½Π°Π»ΠΈΠ· характСристик гСстационной Π΄ΠΎΠΌΠΈΠ½Π°Π½Ρ‚Ρ‹ Π² контСкстС трСвоТности.ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Ρ‹ ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹. ОбслСдовано 336 Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ… Π²ΠΎ II ΠΈ III тримСстрах ((26,29Β±0,55) Π½Π΅Π΄Π΅Π»ΠΈ). Π˜Π½Π΄ΠΈΠ²ΠΈΠ΄ΡƒΠ°Π»ΡŒΠ½ΠΎ-психологичСскиС особСнности Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ… ΠΈ ΠΈΡ… ΠΏΡΠΈΡ…ΠΎΡΠΌΠΎΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½ΠΎΠ΅ состояниС исслСдовали с использованиСм комплСкса психодиагностичСских ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ², Ρ‚Π°ΠΊΠΈΡ… ΠΊΠ°ΠΊ: личностный опросник БСхтСрСвском института, шкала Π‘ΠΏΠΈΠ»Π±Π΅Ρ€Π³Π΅Ρ€Π° – Π₯Π°Π½ΠΈΠ½Π°, тСст ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠΉ Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½ΠΎΠΉ И. Π’. Добрякова. Π’Π°Ρ€ΠΈΠ°Ρ†ΠΈΠΎΠ½Π½ΠΎ-статистичСская ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² ΠΎΡΡƒΡ‰Π΅ΡΡ‚Π²Π»ΡΠ»Π°ΡΡŒ с использованиСм ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ Π°Π½Π°Π»ΠΈΠ·Π° Β«STATISTICA 6.0Β».Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ исслСдования ΠΈ ΠΈΡ… обсуТдСниС. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΎΡ†Π΅Π½ΠΊΠΈ личностной трСвоТности (Π›Π’) ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΈ ΡƒΡΡ‚Π°Π½ΠΎΠ²ΠΈΡ‚ΡŒ, Ρ‡Ρ‚ΠΎ Ρƒ 330 (98,21 %) Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ… Π΅Π΅ ΡƒΡ€ΠΎΠ²Π΅Π½ΡŒ Π²Ρ‹Ρ…ΠΎΠ΄ΠΈΡ‚ Π·Π° ΠΏΡ€Π΅Π΄Π΅Π»Ρ‹ Π½ΠΈΠ·ΠΊΠΈΡ… ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ. Π’Π°ΠΊΠΈΠ΅ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€Π½Ρ‹ ΠΈ для ситуативной трСвоТности (Π‘Π’), Π³Π΄Π΅ срСдниС ΠΈ высокиС ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ установлСны Ρƒ 256 Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ…, Ρ‡Ρ‚ΠΎ составило 76,19 %. ΠžΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΉ Π²Π°Ρ€ΠΈΠ°Π½Ρ‚ психологичСского ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚Π° гСстационной Π΄ΠΎΠΌΠΈΠ½Π°Π½Ρ‚Ρ‹ (ΠŸΠšΠ“Π”) установлСн лишь Ρƒ 41 (12,20 %) Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½ΠΎΠΉ, Π° эйфоричСский – Ρƒ 3 (0,89 %). Π£ 292 Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ…, Ρ‡Ρ‚ΠΎ составило 86,91 %, Π½Π΅ установлСно ΠΏΡ€Π΅ΠΎΠ±Π»Π°Π΄Π°Π½ΠΈΠ΅ Π±Π°Π»Π»ΠΎΠ² Π² ΠΏΠΎΠ»ΡŒΠ·Ρƒ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠ³ΠΎ Ρ‚ΠΈΠΏΠ° ΠŸΠšΠ“Π” (ΠΎΡ‚ΠΊΠ»ΠΎΠ½ΡΡŽΡ‰ΠΈΠΉΡΡ ΠΎΡ‚ ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ). Анализ характСристик гСстационной Π΄ΠΎΠΌΠΈΠ½Π°Π½Ρ‚Ρ‹ Π² контСкстС трСвоТности ΠΏΠΎΠΊΠ°Π·Π°Π», Ρ‡Ρ‚ΠΎ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ Π±Π°Π»Π»ΠΎΠ², Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡŽΡ‰ΠΈΠΉ ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΉ Ρ‚ΠΈΠΏ ΠΈ ΠΎΡ†Π΅Π½Π΅Π½ с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ уровня Π‘Π’, Π±Ρ‹Π» статистичСски достовСрно (Ρ€<0,05) Π½ΠΈΠΆΠ΅ Ρƒ Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ… с высоким Π΅Π΅ ΡƒΡ€ΠΎΠ²Π½Π΅ΠΌ ((4,10Β±0,60) Π±Π°Π»Π»Π° ) ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с ΡΠΎΠΎΡ‚Π²Π΅Ρ‚ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΌ Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ…, ΠΊΠ°ΠΊ со срСдним ((4,68Β±0,22) Π±Π°Π»Π»Π°), Ρ‚Π°ΠΊ ΠΈ Π½ΠΈΠ·ΠΊΠΈΠΌ Π΅Π΅ ΡƒΡ€ΠΎΠ²Π½Π΅ΠΌ ((4,94Β±0,36) Π±Π°Π»Π»Π°).Π’Ρ‹Π²ΠΎΠ΄Ρ‹. ΠžΡ†Π΅Π½ΠΊΠ° психологичСского ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚Π° гСстационной Π΄ΠΎΠΌΠΈΠ½Π°Π½Ρ‚Ρ‹ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»Π° ΡƒΡΡ‚Π°Π½ΠΎΠ²ΠΈΡ‚ΡŒ, Ρ‡Ρ‚ΠΎ Π΅Π³ΠΎ ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΉ Π²Π°Ρ€ΠΈΠ°Π½Ρ‚ ΠΈΠΌΠ΅Π» мСсто лишь Ρƒ 12,20 % Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ…, Π° Ρƒ Π±ΠΎΠ»ΡŒΡˆΠΈΠ½ΡΡ‚Π²Π° Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ… (86,91 %) Π½Π΅ установлСно ΠΏΡ€Π΅ΠΎΠ±Π»Π°Π΄Π°Π½ΠΈΠ΅ Π±Π°Π»Π»ΠΎΠ² Π² ΠΏΠΎΠ»ΡŒΠ·Ρƒ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠ³ΠΎ Ρ‚ΠΈΠΏΠ°. Анализ характСристик гСстационной Π΄ΠΎΠΌΠΈΠ½Π°Π½Ρ‚Ρ‹ Π² контСкстС трСвоТности ΠΏΠΎΠΊΠ°Π·Π°Π», Ρ‡Ρ‚ΠΎ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ Π±Π°Π»Π»ΠΎΠ², Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡŽΡ‰ΠΈΠΉ ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΉ Ρ‚ΠΈΠΏ ΠΈ ΠΎΡ†Π΅Π½Π΅Π½ с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ уровня Π‘Π’, Π±Ρ‹Π» статистичСски достовСрно (Ρ€<0,05) Π½ΠΈΠΆΠ΅ Ρƒ Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ… с высоким ΡƒΡ€ΠΎΠ²Π½Π΅ΠΌ Π‘Π’ ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с ΡΠΎΠΎΡ‚Π²Π΅Ρ‚ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΌ Π±Π΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ…, ΠΊΠ°ΠΊ со срСдним, Ρ‚Π°ΠΊ ΠΈ с Π½ΠΈΠ·ΠΊΠΈΠΌ Π΅Π΅ ΡƒΡ€ΠΎΠ²Π½Π΅ΠΌ.ΠœΠ΅Ρ‚Π° дослідТСння: Π½Π° підставі ΠΎΡ†Ρ–Π½ΠΊΠΈ психоСмоційного стану Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ…, провСсти Π°Π½Π°Π»Ρ–Π· характСристик гСстаційної Π΄ΠΎΠΌΡ–Π½Π°Π½Ρ‚ΠΈ Ρƒ контСксті тривоТності.ΠšΠΎΠ½Ρ‚ΠΈΠ½Π³Π΅Π½Ρ‚ обстСТСних Ρ– ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈ дослідТСння. ΠžΠ±ΡΡ‚Π΅ΠΆΠ΅Π½ΠΎ 336 Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ… Ρƒ Π†Π† Ρ‚Π° Π†Π†Π† тримСстрах (26,29Β Β±Β 0,55 Ρ‚ΠΈΠΆΠ½Ρ–). Π†Π½Π΄ΠΈΠ²Ρ–Π΄ΡƒΠ°Π»ΡŒΠ½ΠΎ-психологічні особливості Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ… Ρ‚Π° Ρ—Ρ… психоСмоційний стан дослідТували Π· використанням комплСксу психодіагностичних ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ–Π²: особистісний ΠΎΠΏΠΈΡ‚ΡƒΠ²Π°Π»ΡŒΠ½ΠΈΠΊ Π‘Π΅Ρ…Ρ‚Π΅Ρ€Ρ”Π²ΡΡŒΠΊΠΎΠ³ΠΎ інституту, шкала Π‘ΠΏΡ–Π»Π±Π΅Ρ€Π³Π΅Ρ€Π° – Π₯Π°Π½Ρ–Π½Π°, тСст відносин Π²Π°Π³Ρ–Ρ‚Π½ΠΎΡ— Π†.Π’. Добрякова. Π’Π°Ρ€Ρ–Π°Ρ†Ρ–ΠΉΠ½ΠΎ-статистична ΠΎΠ±Ρ€ΠΎΠ±ΠΊΠ° Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ–Π² Π·Π΄Ρ–ΠΉΡΠ½ΡŽΠ²Π°Π»Π°ΡΡŒ Π· використанням ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌ Π°Π½Π°Π»Ρ–Π·Ρƒ β€œSTATISTICA6.0”.Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈ Π΄ΠΎΡΠ»Ρ–Π΄ΠΆΠ΅Π½ΡŒ Ρ‚Π° Ρ—Ρ… обговорСння. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈ ΠΎΡ†Ρ–Π½ΠΊΠΈ особистісної тривоТності (ОВ) Π΄ΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΈ встановити, Ρ‰ΠΎ Ρƒ 330 (98,21Β %) Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ… Ρ—Ρ— Ρ€Ρ–Π²Π΅Π½ΡŒ Π²ΠΈΡ…ΠΎΠ΄ΠΈΡ‚ΡŒ Π·Π° ΠΌΠ΅ΠΆΡ– Π½ΠΈΠ·ΡŒΠΊΠΈΡ… ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΡ–Π². Π’Π°ΠΊΡ– Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈ Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€Π½Ρ– Ρ– для ситуативної тривоТності (Π‘Π’), Π΄Π΅ сСрСдні Ρ‚Π° високі ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠΈ встановлСні Ρƒ 256 Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ…, Ρ‰ΠΎ склало 76,19Β %. ΠžΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½ΠΈΠΉ Π²Π°Ρ€Ρ–Π°Π½Ρ‚ психологічного ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚Ρƒ гСстаційної Π΄ΠΎΠΌΡ–Π½Π°Π½Ρ‚ΠΈ (ΠŸΠšΠ“Π”) встановлСний лишС Ρƒ 41 (12,20Β %) Π²Π°Π³Ρ–Ρ‚Π½ΠΎΡ—, Π° Π΅ΠΉΡ„ΠΎΡ€Ρ–ΠΉΠ½ΠΈΠΉ – Ρƒ 3 (0,89Β %). Π£ 292 Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ…, Ρ‰ΠΎ склало 86,91Β %, Π½Π΅ встановлСно пСрСваТання Π±Π°Π»Ρ–Π² Π½Π° ΠΊΠΎΡ€ΠΈΡΡ‚ΡŒ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠ³ΠΎ Ρ‚ΠΈΠΏΡƒ ΠŸΠšΠ“Π” (Ρ‚ΠΈΠΏ, який Π²Ρ–Π΄Ρ…ΠΈΠ»Π΄ΡΡ”Ρ‚ΡŒΡΡ Π²Ρ–Π΄ ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ). Аналіз характСристик гСстаційної Π΄ΠΎΠΌΡ–Π½Π°Π½Ρ‚ΠΈ Π² контСксті тривоТності ΠΏΠΎΠΊΠ°Π·Π°Π², Ρ‰ΠΎ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊ Π±Π°Π»Ρ–Π², який Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡ” ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½ΠΈΠΉ Ρ‚ΠΈΠΏ Ρ‚Π° ΠΎΡ†Ρ–Π½Π΅Π½ΠΈΠΉ Π· урахуванням рівня Π‘Π’, Π±ΡƒΠ² статистично достовірно (Ρ€Β <Β 0,05) Π½ΠΈΠΆΡ‡ΠΈΠΌ Ρƒ Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ… Π· високим Ρ€Ρ–Π²Π½Π΅ΠΌ Π‘Π’ (4,10Β Β±Β 0,60 Π±Π°Π»ΠΈ) Ρƒ порівнянні Π· Π²Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π½ΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠΎΠΌ Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ…, як Π· сСрСднім (4,68Β Β±Β 0,22 Π±Π°Π»ΠΈ), Ρ‚Π°ΠΊ Ρ– низьким Ρ—Ρ— Ρ€Ρ–Π²Π½Π΅ΠΌ (4,94Β Β±Β 0,36 Π±Π°Π»ΠΈ).Висновки. На підставі ΠΎΡ†Ρ–Π½ΠΊΠΈ психологічного ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚Ρƒ гСстаційної Π΄ΠΎΠΌΡ–Π½Π°Π½Ρ‚ΠΈ встановлСно, Ρ‰ΠΎ ΠΉΠΎΠ³ΠΎ ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½ΠΈΠΉ Π²Π°Ρ€Ρ–Π°Π½Ρ‚ ΠΌΠ°Π² місцС лишС Ρƒ 12,20Β % Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ…. Π£ Π±Ρ–Π»ΡŒΡˆΠΎΡΡ‚Ρ– Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ… (86,91Β %) Π½Π΅ встановлСно пСрСваТання Π±Π°Π»Ρ–Π² Π½Π° ΠΊΠΎΡ€ΠΈΡΡ‚ΡŒ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠ³ΠΎ Ρ‚ΠΈΠΏΡƒ. Аналіз характСристик гСстаційної Π΄ΠΎΠΌΡ–Π½Π°Π½Ρ‚ΠΈ Π² контСксті тривоТності ΠΏΠΎΠΊΠ°Π·Π°Π², Ρ‰ΠΎ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊ Π±Π°Π»Ρ–Π², який Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡ” ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½ΠΈΠΉ Ρ‚ΠΈΠΏ Ρ‚Π° ΠΎΡ†Ρ–Π½Π΅Π½ΠΈΠΉ Π· урахуванням рівня Π‘Π’, Π±ΡƒΠ² статистично достовірно (Ρ€Β <Β 0,05) Π½ΠΈΠΆΡ‡ΠΈΠΌ Ρƒ Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ… Π· високим Ρ€Ρ–Π²Π½Π΅ΠΌ Π‘Π’ Ρƒ порівнянні Π· Π²Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π½ΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠΎΠΌ Π²Π°Π³Ρ–Ρ‚Π½ΠΈΡ…, як Π· сСрСднім, Ρ‚Π°ΠΊ Ρ– низьким Ρ—Ρ— Ρ€Ρ–Π²Π½Π΅ΠΌ.

    Bioactive Calcium Phosphate Coatings on Metallic Implants

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    Biocomposites based on bioinert metals or alloys and bioactive calcium phosphate coatings are a promising tendency of the new-generation implants development. In recent years, the approach of regenerative medicine based on the use of biodegradable biomaterials has been priority direction. Such materials are capable of initiating the bone tissue regeneration and replaced by the newly formed bone. The microarc oxidation (MAO) method allows obtaining the bioactive coatings with a porous structure, special functional properties, and modified by the essential elements. During the last decade, the investigations in the field of the nanostructured biocomposites based on bioinert Ti, Zr, Nb and their alloys with a calcium phosphate coatings deposited by the MAO method have been studied in the Institute of Strength Physics and Materials Science SB RAS, Tomsk. In this article the possibility to produce the bioactive coatings with high antibacterial and osseoconductive properties due to the introduction in the coatings of Zn, Cu, Ag, La, Si elements and wollastonite CaSiO[3] was shown. The high hydrophilic and bioresorbed coatings stimulate the processes of osseointegration of the implant into the bone tissue. A promising direction in the field of the medical material science is a development of the metallic implants with good biomechanical compatibility to the bone, such as Ti-Nb alloys with a low elastic modulus that can be classified as biomaterials of the second generation. Zr and its alloys are promising materials for the dentistry and orthopedic surgery due to their high strength and corrosion resistance. Biodegradable Mg alloys are biomaterials of third generation. Such materials can dissolve with a certain speed in human body and excreted from the body thereby excluding the need for reoperation. This article presents the analysis of the study results of bioactive MAO coatings on Ti, Ti-Nb, Zr-Nb and Mg alloys and their promising medical application

    Mammalian end binding proteins control persistent microtubule growth

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    Β© 2009 Komarova et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0. The definitive version was published in Journal of Cell Biology 184 (2009): 691-706, doi:10.1083/jcb.200807179.End binding proteins (EBs) are highly conserved core components of microtubule plus-end tracking protein networks. Here we investigated the roles of the three mammalian EBs in controlling microtubule dynamics and analyzed the domains involved. Protein depletion and rescue experiments showed that EB1 and EB3, but not EB2, promote persistent microtubule growth by suppressing catastrophes. Furthermore, we demonstrated in vitro and in cells that the EB plus-end tracking behavior depends on the calponin homology domain but does not require dimer formation. In contrast, dimerization is necessary for the EB anti-catastrophe activity in cells; this explains why the EB1 dimerization domain, which disrupts native EB dimers, exhibits a dominant-negative effect. When microtubule dynamics is reconstituted with purified tubulin, EBs promote rather than inhibit catastrophes, suggesting that in cells EBs prevent catastrophes by counteracting other microtubule regulators. This probably occurs through their action on microtubule ends, because catastrophe suppression does not require the EB domains needed for binding to known EB partners.This work was supported by the Netherlands Organization for Scientifi c Research grants to A.A., by Funda Γ§ Γ£ o para a Ci Γͺ ncia e a Tecnologia fellowship to S.M. Gouveia, by a FEBS fellowship to R.M. Buey, by the National Institutes of Health grant GM25062 to G.G. Borisy and by the Swiss National Science Foundation through grant 3100A0-109423 and by the National Center of Competence in Research Structural Biology program to M.O. Steinmetz

    ИсслСдованиС состава остаточной ΠΌΠΈΠΊΡ€ΠΎΡ„Π»ΠΎΡ€Ρ‹ ΠΌΠΎΠ»ΠΎΠΊΠ° послС пастСризации

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    The article presents the results of studies of the composition of the residual microflora of pasteurized milk, depending on the bacterial landscape and the initial contamination of raw milk. The thermal stability of testΒ  cultures of microorganisms that significantly affect theΒ  quality andΒ  storage capacity of fermented dairy products has beenΒ  studied. To studyΒ  theΒ  composition of theΒ  residual microflora of milk afterΒ  pasteurization, sterile milk was infected withΒ  testΒ  cultures of microorganisms at dosesΒ  fromΒ  101 CFU/cm3 to 107 CFU/cm3. After infection, theΒ  milk was pasteurized at temperatures of (72 Β± 1) Β°C andΒ  (80 Β± 1) Β°C withΒ  a holding timeΒ  of 10–20Β  seconds. The detection andΒ  enumeration of microorganisms was carried outΒ  by standardized microbiological methods. Microorganisms were identified by visual assessment of dominant colonies and cell morphology in micropreparations. The thermal stability of microorganisms important for dairy products, in particular cheeses, the source of whichΒ  is raw milk, has beenΒ  studied. It has beenΒ  established that of theΒ  coccalΒ  forms,Β  theΒ  greatest risks are associated withΒ  enterococci. Escherichia coli atΒ  infection dosesΒ  above 106 CFU/cm3 partially retains viability bothΒ  at low-temperature andΒ  at high-temperature pasteurization. Pasteurization temperatures do not haveΒ  a lethal effect on sporeΒ  bacilli, their number in pasteurized milk does not decrease, regardless of theΒ  initial dose of infection. Low-temperature pasteurization activates the process of clostridial sporeΒ  germination. The ability to reactivate cells afterΒ  thermal shock was observed in Escherichia coli, Staphylococcus aureus, Pseudomonas, andΒ  moldΒ  fungi.Β  Thus,Β  theΒ  residual microflora of milkΒ  subjected toΒ  low-temperature pasteurization is represented by enterococci, thermophilic streptococci, micrococci, staphylococci, asporogenous bacilliΒ  andΒ  spore bacteria. The above microorganisms constituteΒ  theΒ  residual microflora of pasteurized milk and are involved in theΒ  maturation of cheeses, determining their quality andΒ  safety,Β  [as well as] affecting theΒ  storage capacity of the finished product.Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ прСдставлСны  Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ исслСдований состава остаточной ΠΌΠΈΠΊΡ€ΠΎΡ„Π»ΠΎΡ€Ρ‹Β  пастСризованного ΠΌΠΎΠ»ΠΎΠΊΠ° Π² зависимости ΠΎΡ‚ Π±Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ ΠΏΠ΅ΠΉΠ·Π°ΠΆΠ° ΠΈ исходной обсСмСнСнности сырого  ΠΌΠΎΠ»ΠΎΠΊΠ°. Π˜Π·ΡƒΡ‡Π΅Π½Π° Ρ‚Π΅Ρ€ΠΌΠΎΡΡ‚Π°Π±ΠΈΠ»ΡŒΠ½ΠΎΡΡ‚ΡŒ тСст-ΠΊΡƒΠ»ΡŒΡ‚ΡƒΡ€ ΠΌΠΈΠΊΡ€ΠΎΠΎΡ€Π³Π°Π½ΠΈΠ·ΠΌΠΎΠ², Π·Π½Π°Ρ‡ΠΈΠΌΠΎ Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° качСство ΠΈ Ρ…Ρ€Π°Π½ΠΈΠΌΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡ‚ΡŒ Ρ„Π΅Ρ€ΠΌΠ΅Π½Ρ‚ΠΈΡ€ΡƒΠ΅ΠΌΡ‹Ρ…Β  ΠΌΠΎΠ»ΠΎΡ‡Π½Ρ‹Ρ… ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ². Для  исслСдования состава остаточной ΠΌΠΈΠΊΡ€ΠΎΡ„Π»ΠΎΡ€Ρ‹ ΠΌΠΎΠ»ΠΎΠΊΠ° послС  пастСризации ΡΡ‚Π΅Ρ€ΠΈΠ»ΡŒΠ½ΠΎΠ΅ ΠΌΠΎΠ»ΠΎΠΊΠΎ Π·Π°Ρ€Π°ΠΆΠ°Π»ΠΈ тСст-ΠΊΡƒΠ»ΡŒΡ‚ΡƒΡ€Π°ΠΌΠΈ ΠΌΠΈΠΊΡ€ΠΎΠΎΡ€Π³Π°Π½ΠΈΠ·ΠΌΠΎΠ²Β  Π² Π΄ΠΎΠ·Π°Ρ… ΠΎΡ‚Β  101 ΠšΠžΠ•/см3 Π΄ΠΎ 107 ΠšΠžΠ•/см3.Β  ПослС  зараТСния ΠΌΠΎΠ»ΠΎΠΊΠΎ пастСризовали ΠΏΡ€ΠΈΒ  Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π°Ρ… (72 Β± 1)Β  Β°C ΠΈΒ  (80 Β± 1) Β°C с Π²Ρ‹Π΄Π΅Ρ€ΠΆΠΊΠΎΠΉ 10–20Β  сСкунд.Β  ВыявлСниС ΠΈΒ  подсчСт ΠΌΠΈΠΊΡ€ΠΎΠΎΡ€Π³Π°Π½ΠΈΠ·ΠΌΠΎΠ²Β  осущСствляли стандартизованными микробиологичСскими ΠΌΠ΅Ρ‚ΠΎΠ΄Π°ΠΌΠΈ. Π˜Π΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΡŽ ΠΌΠΈΠΊΡ€ΠΎΠΎΡ€Π³Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π²ΠΈΠ·ΡƒΠ°Π»ΡŒΠ½ΠΎΠΉ ΠΎΡ†Π΅Π½ΠΊΠΎΠΉ Π³ΠΎΡΠΏΠΎΠ΄ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΡ… ΠΊΠΎΠ»ΠΎΠ½ΠΈΠΉ ΠΈ ΠΌΠΎΡ€Ρ„ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΊΠ»Π΅Ρ‚ΠΎΠΊ Π² ΠΌΠΈΠΊΡ€ΠΎΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚Π°Ρ…. ИсслСдована Ρ‚Π΅Ρ€ΠΌΠΎΡΡ‚Π°Π±ΠΈΠ»ΡŒΠ½ΠΎΡΡ‚ΡŒ ΠΌΠΈΠΊΡ€ΠΎΠΎΡ€Π³Π°Π½ΠΈΠ·ΠΌΠΎΠ², Π·Π½Π°Ρ‡ΠΈΠΌΡ‹Ρ… для  ΠΌΠΎΠ»ΠΎΡ‡Π½Ρ‹Ρ… ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ², Π² частности сыров,Β  источником ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Ρ… являСтся сыроС  ΠΌΠΎΠ»ΠΎΠΊΠΎ. УстановлСно, Ρ‡Ρ‚ΠΎ ΠΈΠ· ΠΊΠΎΠΊΠΊΠΎΠ²Ρ‹Ρ… Ρ„ΠΎΡ€ΠΌΒ  наибольшиС риски  связаны с энтСрококками. ΠšΠΈΡˆΠ΅Ρ‡Π½Π°Ρ ΠΏΠ°Π»ΠΎΡ‡ΠΊΠ° ΠΏΡ€ΠΈ Π΄ΠΎΠ·Π°Ρ…Β  зараТСния Π²Ρ‹ΡˆΠ΅ 106 ΠšΠžΠ•/см3Β  частично сохраняСт ΠΆΠΈΠ·Π½Π΅ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡ‚ΡŒ ΠΊΠ°ΠΊ ΠΏΡ€ΠΈ Π½ΠΈΠ·ΠΊΠΎΡ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π½ΠΎΠΉ, Ρ‚Π°ΠΊ ΠΈ ΠΏΡ€ΠΈ высокотСмпСратурной пастСризации. На споровыС ΠΏΠ°Π»ΠΎΡ‡ΠΊΠΈ Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Ρ‹ пастСризации  Π½Π΅Β  ΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‚ Π»Π΅Ρ‚Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ дСйствия, ΠΈΡ…Β  количСство Π² пастСризованном ΠΌΠΎΠ»ΠΎΠΊΠ΅ Π½Π΅ сниТаСтся, нСзависимо ΠΎΡ‚ исходной Π΄ΠΎΠ·Ρ‹Β  зараТСния. НизкотСмпСратурная пастСризация Π°ΠΊΡ‚ΠΈΠ²ΠΈΠ·ΠΈΡ€ΡƒΠ΅Ρ‚ процСсс прорастания спор  клостридий. Π‘ΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡ‚ΡŒ ΠΊ Ρ€Π΅Π°ΠΊΡ‚ΠΈΠ²Π°Ρ†ΠΈΠΈ ΠΊΠ»Π΅Ρ‚ΠΎΠΊ послС Ρ‚Π΅Ρ€ΠΌΠΎΡˆΠΎΠΊΠ° наблюдалась Ρƒ ΠΊΠΈΡˆΠ΅Ρ‡Π½ΠΎΠΉ ΠΏΠ°Π»ΠΎΡ‡ΠΊΠΈ, стафилококка, псСвдомонад ΠΈ плСснСвых Π³Ρ€ΠΈΠ±ΠΎΠ². Π’Π°ΠΊΠΈΠΌ ΠΎΠ±Ρ€Π°Π·ΠΎΠΌ, остаточная ΠΌΠΈΠΊΡ€ΠΎΡ„Π»ΠΎΡ€Π° ΠΌΠΎΠ»ΠΎΠΊΠ°, ΠΏΠΎΠ΄Π²Π΅Ρ€Π³Π½ΡƒΡ‚ΠΎΠ³ΠΎ Π½ΠΈΠ·ΠΊΠΎΡ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π½ΠΎΠΉ пастСризации, прСдставлСна  энтСрококками, Ρ‚Π΅Ρ€ΠΌΠΎΡ„ΠΈΠ»ΡŒΠ½Ρ‹ΠΌ стрСптококком, ΠΌΠΈΠΊΡ€ΠΎΠΊΠΎΠΊΠΊΠ°ΠΌΠΈ, стафилококками, аспорогСнными ΠΏΠ°Π»ΠΎΡ‡ΠΊΠ°ΠΌΠΈ ΠΈΒ  споровыми бактСриями.Β  Π’Ρ‹ΡˆΠ΅ΠΏΠ΅Ρ€Π΅Ρ‡ΠΈΡΠ»Π΅Π½Π½Ρ‹Π΅ ΠΌΠΈΠΊΡ€ΠΎΠΎΡ€Π³Π°Π½ΠΈΠ·ΠΌΡ‹Β  ΡΠΎΡΡ‚Π°Π²Π»ΡΡŽΡ‚ ΠΎΡΡ‚Π°Ρ‚ΠΎΡ‡Π½ΡƒΡŽ ΠΌΠΈΠΊΡ€ΠΎΡ„Π»ΠΎΡ€Ρƒ пастСризованного ΠΌΠΎΠ»ΠΎΠΊΠ° ΠΈ ΡƒΡ‡Π°ΡΡ‚Π²ΡƒΡŽΡ‚ Π² процСссах созрСвания сыров, опрСдСляя ΠΈΡ… качСство ΠΈ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡ‚ΡŒ, Π²Π»ΠΈΡΡŽΡ‚ Π½Π° Ρ…Ρ€Π°Π½ΠΈΠΌΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡ‚ΡŒ Π³ΠΎΡ‚ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Π°
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