538 research outputs found
Mathematical statistics functionally object model for monitoring and control
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
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 βwith each otherβ. 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 βTemAlβ and βProTemAl-Englβ developed in Voronezh State University. βTemAlβ processes Russian texts and βProTemAl-Englβ 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
ΠΠΠ’ΠΠΠΠ¦ΠΠΠΠ ΠΠ‘ΠΠΠΠ’Π ΠΠ‘ΠΠ₯ΠΠΠΠΠ¦ΠΠΠΠΠΠ Π‘Π’ΠΠΠ£ ΠΠΠΠΠ ΠΠΠ Π§ΠΠ‘ ΠΠΠΠΠ’ΠΠΠ‘Π’Π. ΠΠΠΠΠΠ Π₯ΠΠ ΠΠΠ’ΠΠ ΠΠ‘Π’ΠΠ ΠΠΠ‘Π’ΠΠ¦ΠΠΠΠΠ ΠΠΠΠΠΠΠΠ’Π Π£ ΠΠΠΠ’ΠΠΠ‘Π’Π Π’Π ΠΠΠΠΠΠΠ‘Π’Π
The aim of the study β 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 ΠΠ and ΠΠΠ 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 β 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 SΠ level) was statistically proved (ΡΒ <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 (ΡΒ <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.Β Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ β Π½Π° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΏΡΠΈΡ
ΠΎΡΠΌΠΎΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
, ΠΏΡΠΎΠ²Π΅ΡΡΠΈ Π°Π½Π°Π»ΠΈΠ· Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π³Π΅ΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π΄ΠΎΠΌΠΈΠ½Π°Π½ΡΡ Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΡΡΠ΅Π²ΠΎΠΆΠ½ΠΎΡΡΠΈ.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΎ 336 Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
Π²ΠΎ II ΠΈ III ΡΡΠΈΠΌΠ΅ΡΡΡΠ°Ρ
((26,29Β±0,55) Π½Π΅Π΄Π΅Π»ΠΈ). ΠΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎ-ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΈ ΠΈΡ
ΠΏΡΠΈΡ
ΠΎΡΠΌΠΎΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π»ΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ° ΠΏΡΠΈΡ
ΠΎΠ΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ², ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ: Π»ΠΈΡΠ½ΠΎΡΡΠ½ΡΠΉ ΠΎΠΏΡΠΎΡΠ½ΠΈΠΊ ΠΠ΅Ρ
ΡΠ΅ΡΠ΅Π²ΡΠΊΠΎΠΌ ΠΈΠ½ΡΡΠΈΡΡΡΠ°, ΡΠΊΠ°Π»Π° Π‘ΠΏΠΈΠ»Π±Π΅ΡΠ³Π΅ΡΠ° β Π₯Π°Π½ΠΈΠ½Π°, ΡΠ΅ΡΡ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π. Π.Β ΠΠΎΠ±ΡΡΠΊΠΎΠ²Π°. ΠΠ°ΡΠΈΠ°ΡΠΈΠΎΠ½Π½ΠΎ-ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»Π°ΡΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ Π°Π½Π°Π»ΠΈΠ·Π° Β«STATISTICA 6.0Β».Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΈΡ
ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΎΡΠ΅Π½ΠΊΠΈ Π»ΠΈΡΠ½ΠΎΡΡΠ½ΠΎΠΉ ΡΡΠ΅Π²ΠΎΠΆΠ½ΠΎΡΡΠΈ (ΠΠ’) ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΈ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡ, ΡΡΠΎ Ρ 330 (98,21 %) Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
Π΅Π΅ ΡΡΠΎΠ²Π΅Π½Ρ Π²ΡΡ
ΠΎΠ΄ΠΈΡ Π·Π° ΠΏΡΠ΅Π΄Π΅Π»Ρ Π½ΠΈΠ·ΠΊΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ. Π’Π°ΠΊΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½Ρ ΠΈ Π΄Π»Ρ ΡΠΈΡΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΡΡΠ΅Π²ΠΎΠΆΠ½ΠΎΡΡΠΈ (Π‘Π’), Π³Π΄Π΅ ΡΡΠ΅Π΄Π½ΠΈΠ΅ ΠΈ Π²ΡΡΠΎΠΊΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Ρ Ρ 256 Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
, ΡΡΠΎ ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΎ 76,19 %. ΠΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ Π²Π°ΡΠΈΠ°Π½Ρ ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ° Π³Π΅ΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π΄ΠΎΠΌΠΈΠ½Π°Π½ΡΡ (ΠΠΠΠ) ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ Π»ΠΈΡΡ Ρ 41 (12,20 %) Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ, Π° ΡΠΉΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΠΉ β Ρ 3 (0,89 %). Π£ 292 Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
, ΡΡΠΎ ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΎ 86,91 %, Π½Π΅ ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ ΠΏΡΠ΅ΠΎΠ±Π»Π°Π΄Π°Π½ΠΈΠ΅ Π±Π°Π»Π»ΠΎΠ² Π² ΠΏΠΎΠ»ΡΠ·Ρ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° ΠΠΠΠ (ΠΎΡΠΊΠ»ΠΎΠ½ΡΡΡΠΈΠΉΡΡ ΠΎΡ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ). ΠΠ½Π°Π»ΠΈΠ· Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π³Π΅ΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π΄ΠΎΠΌΠΈΠ½Π°Π½ΡΡ Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΡΡΠ΅Π²ΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΏΠΎΠΊΠ°Π·Π°Π», ΡΡΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π±Π°Π»Π»ΠΎΠ², Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΠΉ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ ΡΠΈΠΏ ΠΈ ΠΎΡΠ΅Π½Π΅Π½ Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΡΠΎΠ²Π½Ρ Π‘Π’, Π±ΡΠ» ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎ (Ρ<0,05) Π½ΠΈΠΆΠ΅ Ρ Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
Ρ Π²ΡΡΠΎΠΊΠΈΠΌ Π΅Π΅ ΡΡΠΎΠ²Π½Π΅ΠΌ ((4,10Β±0,60) Π±Π°Π»Π»Π° ) ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΌ Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
, ΠΊΠ°ΠΊ ΡΠΎ ΡΡΠ΅Π΄Π½ΠΈΠΌ ((4,68Β±0,22) Π±Π°Π»Π»Π°), ΡΠ°ΠΊ ΠΈ Π½ΠΈΠ·ΠΊΠΈΠΌ Π΅Π΅ ΡΡΠΎΠ²Π½Π΅ΠΌ ((4,94Β±0,36) Π±Π°Π»Π»Π°).ΠΡΠ²ΠΎΠ΄Ρ. ΠΡΠ΅Π½ΠΊΠ° ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ° Π³Π΅ΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π΄ΠΎΠΌΠΈΠ½Π°Π½ΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»Π° ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡ, ΡΡΠΎ Π΅Π³ΠΎ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ Π²Π°ΡΠΈΠ°Π½Ρ ΠΈΠΌΠ΅Π» ΠΌΠ΅ΡΡΠΎ Π»ΠΈΡΡ Ρ 12,20 % Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
, Π° Ρ Π±ΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²Π° Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
(86,91 %) Π½Π΅ ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ ΠΏΡΠ΅ΠΎΠ±Π»Π°Π΄Π°Π½ΠΈΠ΅ Π±Π°Π»Π»ΠΎΠ² Π² ΠΏΠΎΠ»ΡΠ·Ρ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠΏΠ°. ΠΠ½Π°Π»ΠΈΠ· Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π³Π΅ΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π΄ΠΎΠΌΠΈΠ½Π°Π½ΡΡ Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΡΡΠ΅Π²ΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΏΠΎΠΊΠ°Π·Π°Π», ΡΡΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π±Π°Π»Π»ΠΎΠ², Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΠΉ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ ΡΠΈΠΏ ΠΈ ΠΎΡΠ΅Π½Π΅Π½ Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΡΠΎΠ²Π½Ρ Π‘Π’, Π±ΡΠ» ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎ (Ρ<0,05) Π½ΠΈΠΆΠ΅ Ρ Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
Ρ Π²ΡΡΠΎΠΊΠΈΠΌ ΡΡΠΎΠ²Π½Π΅ΠΌ Π‘Π’ ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΌ Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
, ΠΊΠ°ΠΊ ΡΠΎ ΡΡΠ΅Π΄Π½ΠΈΠΌ, ΡΠ°ΠΊ ΠΈ Ρ Π½ΠΈΠ·ΠΊΠΈΠΌ Π΅Π΅ ΡΡΠΎΠ²Π½Π΅ΠΌ.ΠΠ΅ΡΠ° Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ: Π½Π° ΠΏΡΠ΄ΡΡΠ°Π²Ρ ΠΎΡΡΠ½ΠΊΠΈ ΠΏΡΠΈΡ
ΠΎΠ΅ΠΌΠΎΡΡΠΉΠ½ΠΎΠ³ΠΎ ΡΡΠ°Π½Ρ Π²Π°Π³ΡΡΠ½ΠΈΡ
, ΠΏΡΠΎΠ²Π΅ΡΡΠΈ Π°Π½Π°Π»ΡΠ· Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π³Π΅ΡΡΠ°ΡΡΠΉΠ½ΠΎΡ Π΄ΠΎΠΌΡΠ½Π°Π½ΡΠΈ Ρ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΡ ΡΡΠΈΠ²ΠΎΠΆΠ½ΠΎΡΡΡ.ΠΠΎΠ½ΡΠΈΠ½Π³Π΅Π½Ρ ΠΎΠ±ΡΡΠ΅ΠΆΠ΅Π½ΠΈΡ
Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ. ΠΠ±ΡΡΠ΅ΠΆΠ΅Π½ΠΎ 336 Π²Π°Π³ΡΡΠ½ΠΈΡ
Ρ ΠΠ ΡΠ° ΠΠΠ ΡΡΠΈΠΌΠ΅ΡΡΡΠ°Ρ
(26,29Β Β±Β 0,55 ΡΠΈΠΆΠ½Ρ). ΠΠ½Π΄ΠΈΠ²ΡΠ΄ΡΠ°Π»ΡΠ½ΠΎ-ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΡΡΠ½Ρ ΠΎΡΠΎΠ±Π»ΠΈΠ²ΠΎΡΡΡ Π²Π°Π³ΡΡΠ½ΠΈΡ
ΡΠ° ΡΡ
ΠΏΡΠΈΡ
ΠΎΠ΅ΠΌΠΎΡΡΠΉΠ½ΠΈΠΉ ΡΡΠ°Π½ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΡΠ²Π°Π»ΠΈ Π· Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½ΡΠΌ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΡ ΠΏΡΠΈΡ
ΠΎΠ΄ΡΠ°Π³Π½ΠΎΡΡΠΈΡΠ½ΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΡΠ²: ΠΎΡΠΎΠ±ΠΈΡΡΡΡΠ½ΠΈΠΉ ΠΎΠΏΠΈΡΡΠ²Π°Π»ΡΠ½ΠΈΠΊ ΠΠ΅Ρ
ΡΠ΅ΡΡΠ²ΡΡΠΊΠΎΠ³ΠΎ ΡΠ½ΡΡΠΈΡΡΡΡ, ΡΠΊΠ°Π»Π° Π‘ΠΏΡΠ»Π±Π΅ΡΠ³Π΅ΡΠ° β Π₯Π°Π½ΡΠ½Π°, ΡΠ΅ΡΡ Π²ΡΠ΄Π½ΠΎΡΠΈΠ½ Π²Π°Π³ΡΡΠ½ΠΎΡ Π.Π.Β ΠΠΎΠ±ΡΡΠΊΠΎΠ²Π°. ΠΠ°ΡΡΠ°ΡΡΠΉΠ½ΠΎ-ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½Π° ΠΎΠ±ΡΠΎΠ±ΠΊΠ° ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ² Π·Π΄ΡΠΉΡΠ½ΡΠ²Π°Π»Π°ΡΡ Π· Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½ΡΠΌ ΠΏΡΠΎΠ³ΡΠ°ΠΌ Π°Π½Π°Π»ΡΠ·Ρ βSTATISTICA6.0β.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΠΈ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Ρ ΡΠ° ΡΡ
ΠΎΠ±Π³ΠΎΠ²ΠΎΡΠ΅Π½Π½Ρ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΠΈ ΠΎΡΡΠ½ΠΊΠΈ ΠΎΡΠΎΠ±ΠΈΡΡΡΡΠ½ΠΎΡ ΡΡΠΈΠ²ΠΎΠΆΠ½ΠΎΡΡΡ (ΠΠ’) Π΄ΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΈ Π²ΡΡΠ°Π½ΠΎΠ²ΠΈΡΠΈ, ΡΠΎ Ρ 330 (98,21Β %) Π²Π°Π³ΡΡΠ½ΠΈΡ
ΡΡ ΡΡΠ²Π΅Π½Ρ Π²ΠΈΡ
ΠΎΠ΄ΠΈΡΡ Π·Π° ΠΌΠ΅ΠΆΡ Π½ΠΈΠ·ΡΠΊΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΡΠ². Π’Π°ΠΊΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½Ρ Ρ Π΄Π»Ρ ΡΠΈΡΡΠ°ΡΠΈΠ²Π½ΠΎΡ ΡΡΠΈΠ²ΠΎΠΆΠ½ΠΎΡΡΡ (Π‘Π’), Π΄Π΅ ΡΠ΅ΡΠ΅Π΄Π½Ρ ΡΠ° Π²ΠΈΡΠΎΠΊΡ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠΈ Π²ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Ρ Ρ 256 Π²Π°Π³ΡΡΠ½ΠΈΡ
, ΡΠΎ ΡΠΊΠ»Π°Π»ΠΎ 76,19Β %. ΠΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΈΠΉ Π²Π°ΡΡΠ°Π½Ρ ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ Π³Π΅ΡΡΠ°ΡΡΠΉΠ½ΠΎΡ Π΄ΠΎΠΌΡΠ½Π°Π½ΡΠΈ (ΠΠΠΠ) Π²ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠΉ Π»ΠΈΡΠ΅ Ρ 41 (12,20Β %) Π²Π°Π³ΡΡΠ½ΠΎΡ, Π° Π΅ΠΉΡΠΎΡΡΠΉΠ½ΠΈΠΉ β Ρ 3 (0,89Β %). Π£ 292 Π²Π°Π³ΡΡΠ½ΠΈΡ
, ΡΠΎ ΡΠΊΠ»Π°Π»ΠΎ 86,91Β %, Π½Π΅ Π²ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ ΠΏΠ΅ΡΠ΅Π²Π°ΠΆΠ°Π½Π½Ρ Π±Π°Π»ΡΠ² Π½Π° ΠΊΠΎΡΠΈΡΡΡ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠΏΡ ΠΠΠΠ (ΡΠΈΠΏ, ΡΠΊΠΈΠΉ Π²ΡΠ΄Ρ
ΠΈΠ»Π΄ΡΡΡΡΡΡ Π²ΡΠ΄ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ). ΠΠ½Π°Π»ΡΠ· Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π³Π΅ΡΡΠ°ΡΡΠΉΠ½ΠΎΡ Π΄ΠΎΠΌΡΠ½Π°Π½ΡΠΈ Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΡ ΡΡΠΈΠ²ΠΎΠΆΠ½ΠΎΡΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π², ΡΠΎ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊ Π±Π°Π»ΡΠ², ΡΠΊΠΈΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΈΠΉ ΡΠΈΠΏ ΡΠ° ΠΎΡΡΠ½Π΅Π½ΠΈΠΉ Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½ΡΠΌ ΡΡΠ²Π½Ρ Π‘Π’, Π±ΡΠ² ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½ΠΎ Π΄ΠΎΡΡΠΎΠ²ΡΡΠ½ΠΎ (ΡΒ <Β 0,05) Π½ΠΈΠΆΡΠΈΠΌ Ρ Π²Π°Π³ΡΡΠ½ΠΈΡ
Π· Π²ΠΈΡΠΎΠΊΠΈΠΌ ΡΡΠ²Π½Π΅ΠΌ Π‘Π’ (4,10Β Β±Β 0,60 Π±Π°Π»ΠΈ) Ρ ΠΏΠΎΡΡΠ²Π½ΡΠ½Π½Ρ Π· Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠΎΠΌ Π²Π°Π³ΡΡΠ½ΠΈΡ
, ΡΠΊ Π· ΡΠ΅ΡΠ΅Π΄Π½ΡΠΌ (4,68Β Β±Β 0,22 Π±Π°Π»ΠΈ), ΡΠ°ΠΊ Ρ Π½ΠΈΠ·ΡΠΊΠΈΠΌ ΡΡ ΡΡΠ²Π½Π΅ΠΌ (4,94Β Β±Β 0,36 Π±Π°Π»ΠΈ).ΠΠΈΡΠ½ΠΎΠ²ΠΊΠΈ. ΠΠ° ΠΏΡΠ΄ΡΡΠ°Π²Ρ ΠΎΡΡΠ½ΠΊΠΈ ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ Π³Π΅ΡΡΠ°ΡΡΠΉΠ½ΠΎΡ Π΄ΠΎΠΌΡΠ½Π°Π½ΡΠΈ Π²ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΠΎ ΠΉΠΎΠ³ΠΎ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΈΠΉ Π²Π°ΡΡΠ°Π½Ρ ΠΌΠ°Π² ΠΌΡΡΡΠ΅ Π»ΠΈΡΠ΅ Ρ 12,20Β % Π²Π°Π³ΡΡΠ½ΠΈΡ
. Π£ Π±ΡΠ»ΡΡΠΎΡΡΡ Π²Π°Π³ΡΡΠ½ΠΈΡ
(86,91Β %) Π½Π΅ Π²ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ ΠΏΠ΅ΡΠ΅Π²Π°ΠΆΠ°Π½Π½Ρ Π±Π°Π»ΡΠ² Π½Π° ΠΊΠΎΡΠΈΡΡΡ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠΏΡ. ΠΠ½Π°Π»ΡΠ· Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π³Π΅ΡΡΠ°ΡΡΠΉΠ½ΠΎΡ Π΄ΠΎΠΌΡΠ½Π°Π½ΡΠΈ Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΡ ΡΡΠΈΠ²ΠΎΠΆΠ½ΠΎΡΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π², ΡΠΎ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊ Π±Π°Π»ΡΠ², ΡΠΊΠΈΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΈΠΉ ΡΠΈΠΏ ΡΠ° ΠΎΡΡΠ½Π΅Π½ΠΈΠΉ Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½ΡΠΌ ΡΡΠ²Π½Ρ Π‘Π’, Π±ΡΠ² ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½ΠΎ Π΄ΠΎΡΡΠΎΠ²ΡΡΠ½ΠΎ (ΡΒ <Β 0,05) Π½ΠΈΠΆΡΠΈΠΌ Ρ Π²Π°Π³ΡΡΠ½ΠΈΡ
Π· Π²ΠΈΡΠΎΠΊΠΈΠΌ ΡΡΠ²Π½Π΅ΠΌ Π‘Π’ Ρ ΠΏΠΎΡΡΠ²Π½ΡΠ½Π½Ρ Π· Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠΎΠΌ Π²Π°Π³ΡΡΠ½ΠΈΡ
, ΡΠΊ Π· ΡΠ΅ΡΠ΅Π΄Π½ΡΠΌ, ΡΠ°ΠΊ Ρ Π½ΠΈΠ·ΡΠΊΠΈΠΌ ΡΡ ΡΡΠ²Π½Π΅ΠΌ.
Bioactive Calcium Phosphate Coatings on Metallic Implants
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
Recommended from our members
What can mathematical, computational and robotic models tell us about the origins of syntax?
Mammalian end binding proteins control persistent microtubule growth
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
ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΎΡΡΠ°Π²Π° ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΠΉ ΠΌΠΈΠΊΡΠΎΡΠ»ΠΎΡΡ ΠΌΠΎΠ»ΠΎΠΊΠ° ΠΏΠΎΡΠ»Π΅ ΠΏΠ°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ
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 10β20Β 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 ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΡΒ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠΎΡΡΠ°Π²Π° ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΠΉ ΠΌΠΈΠΊΡΠΎΡΠ»ΠΎΡΡΒ ΠΏΠ°ΡΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ»ΠΎΠΊΠ° Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ Π±Π°ΠΊΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΠ΅ΠΉΠ·Π°ΠΆΠ° ΠΈ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΠΎΠ±ΡΠ΅ΠΌΠ΅Π½Π΅Π½Π½ΠΎΡΡΠΈ ΡΡΡΠΎΠ³ΠΎΒ ΠΌΠΎΠ»ΠΎΠΊΠ°. ΠΠ·ΡΡΠ΅Π½Π° ΡΠ΅ΡΠΌΠΎΡΡΠ°Π±ΠΈΠ»ΡΠ½ΠΎΡΡΡ ΡΠ΅ΡΡ-ΠΊΡΠ»ΡΡΡΡ ΠΌΠΈΠΊΡΠΎΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠΎΠ², Π·Π½Π°ΡΠΈΠΌΠΎ Π²Π»ΠΈΡΡΡΠΈΡ
Π½Π° ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ ΠΈ Ρ
ΡΠ°Π½ΠΈΠΌΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΡΠ΅ΡΠΌΠ΅Π½ΡΠΈΡΡΠ΅ΠΌΡΡ
Β ΠΌΠΎΠ»ΠΎΡΠ½ΡΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ². ΠΠ»ΡΒ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΡΠ°Π²Π° ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΠΉ ΠΌΠΈΠΊΡΠΎΡΠ»ΠΎΡΡ ΠΌΠΎΠ»ΠΎΠΊΠ° ΠΏΠΎΡΠ»Π΅Β ΠΏΠ°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠ΅ΡΠΈΠ»ΡΠ½ΠΎΠ΅ ΠΌΠΎΠ»ΠΎΠΊΠΎ Π·Π°ΡΠ°ΠΆΠ°Π»ΠΈ ΡΠ΅ΡΡ-ΠΊΡΠ»ΡΡΡΡΠ°ΠΌΠΈ ΠΌΠΈΠΊΡΠΎΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠΎΠ²Β Π² Π΄ΠΎΠ·Π°Ρ
ΠΎΡΒ 101 ΠΠΠ/ΡΠΌ3 Π΄ΠΎ 107 ΠΠΠ/ΡΠΌ3.Β ΠΠΎΡΠ»Π΅Β Π·Π°ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΌΠΎΠ»ΠΎΠΊΠΎ ΠΏΠ°ΡΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π»ΠΈ ΠΏΡΠΈΒ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ°Ρ
(72 Β± 1)Β Β°C ΠΈΒ (80 Β± 1) Β°C Ρ Π²ΡΠ΄Π΅ΡΠΆΠΊΠΎΠΉ 10β20Β ΡΠ΅ΠΊΡΠ½Π΄.Β ΠΡΡΠ²Π»Π΅Π½ΠΈΠ΅ ΠΈΒ ΠΏΠΎΠ΄ΡΡΠ΅Ρ ΠΌΠΈΠΊΡΠΎΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠΎΠ²Β ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»ΠΈ ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΠΌΠΈ ΠΌΠΈΠΊΡΠΎΠ±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ. ΠΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΠΌΠΈΠΊΡΠΎΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΎΠΉ Π³ΠΎΡΠΏΠΎΠ΄ΡΡΠ²ΡΡΡΠΈΡ
ΠΊΠΎΠ»ΠΎΠ½ΠΈΠΉ ΠΈ ΠΌΠΎΡΡΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΊΠ»Π΅ΡΠΎΠΊ Π² ΠΌΠΈΠΊΡΠΎΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ°Ρ
. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Π° ΡΠ΅ΡΠΌΠΎΡΡΠ°Π±ΠΈΠ»ΡΠ½ΠΎΡΡΡ ΠΌΠΈΠΊΡΠΎΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠΎΠ², Π·Π½Π°ΡΠΈΠΌΡΡ
Π΄Π»ΡΒ ΠΌΠΎΠ»ΠΎΡΠ½ΡΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ², Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ ΡΡΡΠΎΠ²,Β ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ ΠΊΠΎΡΠΎΡΡΡ
ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΡΡΠΎΠ΅Β ΠΌΠΎΠ»ΠΎΠΊΠΎ. Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΡΠΎ ΠΈΠ· ΠΊΠΎΠΊΠΊΠΎΠ²ΡΡ
ΡΠΎΡΠΌΒ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΠΈΠ΅ ΡΠΈΡΠΊΠΈΒ ΡΠ²ΡΠ·Π°Π½Ρ Ρ ΡΠ½ΡΠ΅ΡΠΎΠΊΠΎΠΊΠΊΠ°ΠΌΠΈ. ΠΠΈΡΠ΅ΡΠ½Π°Ρ ΠΏΠ°Π»ΠΎΡΠΊΠ° ΠΏΡΠΈ Π΄ΠΎΠ·Π°Ρ
Β Π·Π°ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π²ΡΡΠ΅ 106 ΠΠΠ/ΡΠΌ3Β ΡΠ°ΡΡΠΈΡΠ½ΠΎ ΡΠΎΡ
ΡΠ°Π½ΡΠ΅Ρ ΠΆΠΈΠ·Π½Π΅ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΠΊΠ°ΠΊ ΠΏΡΠΈ Π½ΠΈΠ·ΠΊΠΎΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ½ΠΎΠΉ, ΡΠ°ΠΊ ΠΈ ΠΏΡΠΈ Π²ΡΡΠΎΠΊΠΎΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ½ΠΎΠΉ ΠΏΠ°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ. ΠΠ° ΡΠΏΠΎΡΠΎΠ²ΡΠ΅ ΠΏΠ°Π»ΠΎΡΠΊΠΈ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΡ ΠΏΠ°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈΒ Π½Π΅Β ΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ Π»Π΅ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π΄Π΅ΠΉΡΡΠ²ΠΈΡ, ΠΈΡ
Β ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π² ΠΏΠ°ΡΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠΌ ΠΌΠΎΠ»ΠΎΠΊΠ΅ Π½Π΅ ΡΠ½ΠΈΠΆΠ°Π΅ΡΡΡ, Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΠΎ ΠΎΡ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠΉ Π΄ΠΎΠ·ΡΒ Π·Π°ΡΠ°ΠΆΠ΅Π½ΠΈΡ. ΠΠΈΠ·ΠΊΠΎΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ½Π°Ρ ΠΏΠ°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΡ Π°ΠΊΡΠΈΠ²ΠΈΠ·ΠΈΡΡΠ΅Ρ ΠΏΡΠΎΡΠ΅ΡΡ ΠΏΡΠΎΡΠ°ΡΡΠ°Π½ΠΈΡ ΡΠΏΠΎΡΒ ΠΊΠ»ΠΎΡΡΡΠΈΠ΄ΠΈΠΉ. Π‘ΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΠΊ ΡΠ΅Π°ΠΊΡΠΈΠ²Π°ΡΠΈΠΈ ΠΊΠ»Π΅ΡΠΎΠΊ ΠΏΠΎΡΠ»Π΅ ΡΠ΅ΡΠΌΠΎΡΠΎΠΊΠ° Π½Π°Π±Π»ΡΠ΄Π°Π»Π°ΡΡ Ρ ΠΊΠΈΡΠ΅ΡΠ½ΠΎΠΉ ΠΏΠ°Π»ΠΎΡΠΊΠΈ, ΡΡΠ°ΡΠΈΠ»ΠΎΠΊΠΎΠΊΠΊΠ°, ΠΏΡΠ΅Π²Π΄ΠΎΠΌΠΎΠ½Π°Π΄ ΠΈ ΠΏΠ»Π΅ΡΠ½Π΅Π²ΡΡ
Π³ΡΠΈΠ±ΠΎΠ². Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, ΠΎΡΡΠ°ΡΠΎΡΠ½Π°Ρ ΠΌΠΈΠΊΡΠΎΡΠ»ΠΎΡΠ° ΠΌΠΎΠ»ΠΎΠΊΠ°, ΠΏΠΎΠ΄Π²Π΅ΡΠ³Π½ΡΡΠΎΠ³ΠΎ Π½ΠΈΠ·ΠΊΠΎΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ½ΠΎΠΉ ΠΏΠ°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π°Β ΡΠ½ΡΠ΅ΡΠΎΠΊΠΎΠΊΠΊΠ°ΠΌΠΈ, ΡΠ΅ΡΠΌΠΎΡΠΈΠ»ΡΠ½ΡΠΌ ΡΡΡΠ΅ΠΏΡΠΎΠΊΠΎΠΊΠΊΠΎΠΌ, ΠΌΠΈΠΊΡΠΎΠΊΠΎΠΊΠΊΠ°ΠΌΠΈ, ΡΡΠ°ΡΠΈΠ»ΠΎΠΊΠΎΠΊΠΊΠ°ΠΌΠΈ, Π°ΡΠΏΠΎΡΠΎΠ³Π΅Π½Π½ΡΠΌΠΈ ΠΏΠ°Π»ΠΎΡΠΊΠ°ΠΌΠΈ ΠΈΒ ΡΠΏΠΎΡΠΎΠ²ΡΠΌΠΈ Π±Π°ΠΊΡΠ΅ΡΠΈΡΠΌΠΈ.Β ΠΡΡΠ΅ΠΏΠ΅ΡΠ΅ΡΠΈΡΠ»Π΅Π½Π½ΡΠ΅ ΠΌΠΈΠΊΡΠΎΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΡΒ ΡΠΎΡΡΠ°Π²Π»ΡΡΡ ΠΎΡΡΠ°ΡΠΎΡΠ½ΡΡ ΠΌΠΈΠΊΡΠΎΡΠ»ΠΎΡΡ ΠΏΠ°ΡΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ»ΠΎΠΊΠ° ΠΈ ΡΡΠ°ΡΡΠ²ΡΡΡ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ°Ρ
ΡΠΎΠ·ΡΠ΅Π²Π°Π½ΠΈΡ ΡΡΡΠΎΠ², ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡ ΠΈΡ
ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ ΠΈ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΡ, Π²Π»ΠΈΡΡΡ Π½Π° Ρ
ΡΠ°Π½ΠΈΠΌΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ Π³ΠΎΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΄ΡΠΊΡΠ°
- β¦