119 research outputs found
Lexical interaction of the modern Greek language with Germanic and Romance languages (with reference to the vocabulary of English, French and modern Greek languages)
The article deals with the issues of lexical interaction of the modern Greek language with Germanic and Romance languages (represented by English and French). There were singled out two areas in which this lexical interaction is rather significant: the vocabulary related to food and drinks and the vocabulary used to designate objects and concepts of clothing, shoes, haberdashery and perfumer
Application of information and analytical system of analysis of the financial condition and business activity of the enterprise
The work is devoted to the questions of the information-analytical system of analysis of the financial condition and business activity of the enterprise.Π Π°Π±ΠΎΡΠ° ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎ-Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΈ Π΄Π΅Π»ΠΎΠ²ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡ
THE ROLE OF GROWTH AND ENDOTHELIAL FACTORS IN HEMOSTATIC DISORDER IN CHILDREN WITH THE SYNDROM DELAYED FETAL
Violation of the natural defense mechanisms of endothelial dysfunction and an imbalance can occur many neurohumoral factors. The imbalance of angiogenic and antiangiogenic growth factors, endothelial, platelet factor closely related to disorders of hemostasis, promotes the development of generalized endothelial dysfunction and is the basis of placental insufficiency, and is central to fetal growth and neonatal characteristics of the childβs development. The mechanisms regulating these processes in normal and pathological conditions play an important role of thromboxane and prostacyclin, which are derivatives of polyunsaturated fatty acids. Which indicates the feasibility of studying the role of vascular endothelial growth factor and hemostasis disorders in children with fetal growth retardation syndrome
ΠΡΠΈΠ½ΡΠΈΠΏΡ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΠ°Π·Π²ΠΈΡΠΈΡ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΠ·Π° ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ ΠΎΡ Π°Π³ΡΠΎΠΌΠ΅ΡΠ΅ΠΎΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ ΡΡΠ»ΠΎΠ²ΠΈΠΉ
The purpose of the research: to develop verbal, analogous, mathematical models of projected growth of potato globoderosis depending on agrometeorological conditions. Materials and methods. Analysis of national and foreign literature regarding epiphytotiology, modeling was performed, as well as available literature data and personal supervision about influence of meteorological conditions on the potato globoderosis development were generalized. A database received in Kondrovo of Kaluga Region was used in order to develop verbal, analogous, mathematical model. One experimental field was chosen out of 40 experimental fields and plots of land where Sineglazka vulnerable variety was grown during 1979-1993. On the field with average soil quality the population density of nematodes varied from 14,900 to 27,300 (average 20,600) ovicells and larvas at the 100 cubic cm of soil. Globoderosis development was evaluated according to the scale for at-ground visual appraisal of defeat potato plants by globoderosis in points annually in July. Phenological, phytosanitary and phytohelminthological metering and supervision at the experimental field were conducted during vegetational season. Correlation and regression analysis of collected material was conducted using software application Microsoft Excel. Results and discussion. Π‘orrelation coefficient for meteorological factors closely related to globoderosis development over 15 years were calculated with the help of correlation analysis. Predictors (average daily temperature, quantity and amount of rainfall) for short period forecast of globoderosis development while planting of potatoes with average level of fertility were determined. As the result of regression analysis mathematic models of projected growth globoderosis depending on agrometeorological factors were received. Confidence of a mathematical model, i.e. differences of expectancy record from retrospective average 3.6%. Correspondence of mathematic model was checked by historical data using correlation coefficient between the result of projected growth globoderosis and measurement data. It is 0.83. Accuracy of forecast varied from -36.8 to 36.5%.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ: ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°ΡΡ Π²Π΅ΡΠ±Π°Π»ΡΠ½ΡΡ, Π°Π½Π°Π»ΠΎΠ³ΠΎΠ²ΡΡ, ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΠ°Π·Π²ΠΈΡΠΈΡ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΠ·Π° ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ Π°Π³ΡΠΎΠΌΠ΅ΡΠ΅ΠΎΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ»ΠΎΠ²ΠΈΠΉ. ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· ΠΎΡΠ΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΈ Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΠΎΠΉ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ ΠΏΠΎ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ ΡΠΏΠΈΡΠΈΡΠΎΡΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Ρ ΠΈΠΌΠ΅ΡΡΠΈΠ΅ΡΡ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΈ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΠ΅ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ ΠΏΠΎ Π²Π»ΠΈΡΠ½ΠΈΡ ΠΌΠ΅ΡΠ΅ΠΎΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ»ΠΎΠ²ΠΈΠΉ Π½Π° ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΠ·Π° ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ. ΠΠ»Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ Π²Π΅ΡΠ±Π°Π»ΡΠ½ΠΎΠΉ, Π°Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠΉ, ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π±ΡΠ»Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° Π±Π°Π·Π° Π΄Π°Π½Π½ΡΡ
, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π² Π³. ΠΠΎΠ½Π΄ΡΠΎΠ²ΠΎ ΠΠ°Π»ΡΠΆΡΠΊΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ. ΠΠ· 40 ΠΎΠΏΡΡΠ½ΡΡ
ΡΡΠ°ΡΡΠΊΠΎΠ² ΠΈ Π΄Π΅Π»ΡΠ½ΠΎΠΊ Π±ΡΠ» Π²ΡΠ±ΡΠ°Π½ ΠΎΠ΄ΠΈΠ½, Π½Π° ΠΊΠΎΡΠΎΡΠΎΠΌ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 1979-1993 Π³Π³. Π²ΡΡΠ°ΡΠΈΠ²Π°Π»ΠΈ Π²ΠΎΡΠΏΡΠΈΠΈΠΌΡΠΈΠ²ΡΠΉ ΡΠΎΡΡ Π‘ΠΈΠ½Π΅Π³Π»Π°Π·ΠΊΠ°. ΠΠ° ΡΡΠ°ΡΡΠΊΠ΅ ΡΠΎ ΡΡΠ΅Π΄Π½ΠΈΠΌ ΠΏΠ»ΠΎΠ΄ΠΎΡΠΎΠ΄ΠΈΠ΅ΠΌ ΠΏΠΎΡΠ²Ρ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ Π½Π΅ΠΌΠ°ΡΠΎΠ΄Ρ ΠΊΠΎΠ»Π΅Π±Π°Π»Π°ΡΡ ΠΎΡ 14 900 Π΄ΠΎ 27 300 (Π² ΡΡΠ΅Π΄Π½Π΅ΠΌ 20 600) ΡΠΈΡ ΠΈ Π»ΠΈΡΠΈΠ½ΠΎΠΊ Π½Π° 100 ΠΊΡΠ±. ΡΠΌ ΠΏΠΎΡΠ²Ρ. Π Π°Π·Π²ΠΈΡΠΈΠ΅ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΠ·Π° ΠΎΡΠ΅Π½ΠΈΠ²Π°Π»ΠΈ Π΅ΠΆΠ΅Π³ΠΎΠ΄Π½ΠΎ Π² ΠΈΡΠ»Π΅ ΠΏΠΎ ΡΠΊΠ°Π»Π΅ Π΄Π»Ρ Π½Π°Π·Π΅ΠΌΠ½ΠΎΠΉ Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΠ·ΠΎΠΌ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ Π² Π±Π°Π»Π»Π°Ρ
. Π ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΏΠ΅ΡΠΈΠΎΠ΄Π° ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ ΡΠ΅Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅, ΡΠΈΡΠΎΡΠ°Π½ΠΈΡΠ°ΡΠ½ΡΠ΅ ΠΈ ΡΠΈΡΠΎΠ³Π΅Π»ΡΠΌΠΈΠ½ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΡΠ΅ΡΡ ΠΈ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ Π½Π° ΠΎΠΏΡΡΠ½ΠΎΠΌ ΡΡΠ°ΡΡΠΊΠ΅. ΠΠΎΡΡΠ΅Π»ΡΡΠΈΠΎΠ½Π½ΡΠΉ ΠΈ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΠ²Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΎΠ±ΡΠ°Π½Π½ΠΎΠ³ΠΎ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π° Π±ΡΠ» ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ Microsoft Excel. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅. Π‘ ΠΏΠΎΠΌΠΎΡΡΡ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π±ΡΠ»ΠΈ ΡΠ°ΡΡΡΠΈΡΠ°Π½Ρ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΡ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ Π΄Π»Ρ ΠΌΠ΅ΡΠ΅ΠΎΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΡΠ΅ΡΠ½ΠΎ ΡΠ²ΡΠ·Π°Π½Π½ΡΡ
Ρ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ΠΌ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΠ·Π° Π·Π° 15 Π»Π΅Ρ. ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΎΡΡ (ΡΡΠ΅Π΄Π½Π΅ΡΡΡΠΎΡΠ½Π°Ρ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ°, ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΈ ΡΠΈΡΠ»ΠΎ ΠΎΡΠ°Π΄ΠΊΠΎΠ²) Π΄Π»Ρ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΡΡ
ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΎΠ² ΡΠ°Π·Π²ΠΈΡΠΈΡ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΠ·Π° Π½Π° ΠΏΠΎΡΠ°Π΄ΠΊΠ°Ρ
ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ ΡΠΎ ΡΡΠ΅Π΄Π½ΠΈΠΌ ΡΡΠΎΠ²Π½Π΅ΠΌ ΠΏΠ»ΠΎΠ΄ΠΎΡΠΎΠ΄ΠΈΡ. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΠΎΠ»ΡΡΠ΅Π½Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΠ°Π·Π²ΠΈΡΠΈΡ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΠ·Π° Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ Π°Π³ΡΠΎΠΌΠ΅ΡΠ΅ΠΎΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ². ΠΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΡΡΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ, Ρ.Π΅. ΠΎΡΠ»ΠΈΡΠΈΠ΅ ΡΠ°ΡΡΠ΅ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΎΡ ΡΠ΅ΡΡΠΎΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
Π² ΡΡΠ΅Π΄Π½Π΅ΠΌ ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ 3,6%. ΠΠ΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎΡΡΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΡΠΎΠ²Π΅ΡΠ΅Π½Π° Π½Π° ΡΠ΅ΡΡΠΎΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠΌ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΠ°Π·Π²ΠΈΡΠΈΡ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΠ·Π° ΠΈ ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ Π΄Π°Π½Π½ΡΠΌΠΈ. ΠΠ½ ΡΠ°Π²Π΅Π½ 0,83. ΠΠΏΡΠ°Π²Π΄ΡΠ²Π°Π΅ΠΌΠΎΡΡΡ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° Π²Π°ΡΡΠΈΡΡΠ΅Ρ ΠΎΡ -36,8 Π΄ΠΎ 35,6%
Cognitive conceptualization and categorization of time definition in english, french and russian linguistics
The article is devoted to the description of linguocognitive categorization and conceptualization of time by the material of English, French and Russian languages. The process of categorization is aimed at combining similar or identical units into larger categories, and the process of conceptualization is aimed at highlighting the minimum meaningful units of human experience. The multilevel structure of the concept of TIME, due to the complementary nature of the analyzed category, is also considere
Characteristics of the Plasma Disturbance Excited at Altitudes of 450β500 km During the βSuraβ Facility Operation
Β© 2018, Springer Science+Business Media, LLC, part of Springer Nature. We discuss the results of measuring characteristics of the artificial plasma disturbances excited at altitudes of 450β500 km with the ionospheric F2 layer modified by high-power HF radio waves from the Sura facility. It is found that at these altitudes there are plasma temperature and density variations in the HF-perturbed magnetic flux tube. No ducts with increased plasma density that were previously observed at altitudes of about 660 km were detected. The results of the studies are compared with the data from the DEMETER satellite and the results of radiotomographic measurements. It is noted that the field-aligned currents induced in a perturbed ionosphere during the Sura operation were detected for the first time using SWARM satellites
Study of the structure formation during compression for selecting multiaxial deformation conditions for an Mg-Ca Alloy
The structure homogeneity of the Mg-0.8% Ca alloy is shown to increase with temperature during uniaxial compression. Plastic deformation localizes in narrow and wide deformation bands at 250-350Β°C. A small number of regions with recrystallized grains is observed only after deformation at 400Β°C. A relatively homogeneous recrystallized structure with an average grain size of about 20 ΞΌm forms after uniaxial compression at t = 450Β°
ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π₯Π°ΡΡΠ΅Π»Π° Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ Π·ΠΎΠ»ΠΎΡΠΈΡΡΠΎΠΉ ΠΊΠ°ΡΡΠΎΡΠ΅Π»ΡΠ½ΠΎΠΉ Π½Π΅ΠΌΠ°ΡΠΎΠ΄Ρ ΠΏΠΎΡΠ»Π΅ Π²ΡΡΠ°ΡΠΈΠ²Π°Π½ΠΈΡ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠΎΡΡΠ° ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ
The purpose of the research is evaluation the possibility of application the Hassell model for prediction the dynamics of population density of golden nematode of potato in the ground after growing Globodera resistant variety of potato in the single-crop.Materials and methods. For the research database of population density of golden nematode of potato (amount of ootids and larvae) was used on the three plots where amenable varieties of potato were grown and one plot where Globodera resistant variety of potato Kardinal was grown in the Kaluga Region within 14 years (1979β1993). Non-linear least-squares method which is the version of least-squares method for non-linear systems was used as the method for evaluation the model parameters. Toolset of search for solution in the program Excel was used for analysis.Results and discussion. Hassell model for prediction the dynamics of population density of golden nematode of potato in the ground after growing Globodera resistant variety of potato in the single-crop demonstrated high confidence. (R2 = 0.94). Based on the Hassell model for development golden nematode of potato population in plot soil with growing nematode resistant variety modeling of introduction of Globodera resistant variety was conducted for different initial amount of golden nematode of potato and different levels of fertility of soil: low, average, high. Hassell model has confirmed that it is the most multifunctional in the category of discrete one-period models for prediction dynamics of population density of golden nematode of potato in the ground and permits to predict the amount of golden nematode of potato for agrobiocenosis with single-crop of potato of amenable and Globodera resistant varieties.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ: ΠΎΡΠ΅Π½ΠΊΠ° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π₯Π°ΡΡΠ΅Π»Π° Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ Π·ΠΎΠ»ΠΎΡΠΈΡΡΠΎΠΉ ΠΊΠ°ΡΡΠΎΡΠ΅Π»ΡΠ½ΠΎΠΉ Π½Π΅ΠΌΠ°ΡΠΎΠ΄Ρ (ΠΠΠ) Π² ΠΏΠΎΡΠ²Π΅ ΠΏΠΎΡΠ»Π΅ Π²ΡΡΠ°ΡΠΈΠ²Π°Π½ΠΈΡ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΡΡΡΠΎΠΉΡΠΈΠ²ΡΡ
ΡΠΎΡΡΠΎΠ² ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ Π² ΠΌΠΎΠ½ΠΎΠΊΡΠ»ΡΡΡΡΠ΅.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΠ»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ ΠΌΠ°ΡΡΠΈΠ² Π΄Π°Π½Π½ΡΡ
ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΉ (ΡΠΈΡΠ»ΠΎ ΡΠΈΡ ΠΈ Π»ΠΈΡΠΈΠ½ΠΎΠΊ) ΠΠΠ Π½Π° ΡΡΠ΅Ρ
ΡΡΠ°ΡΡΠΊΠ°Ρ
, Π³Π΄Π΅ Π²ΡΡΠ°ΡΠΈΠ²Π°Π»ΠΈ Π²ΠΎΡΠΏΡΠΈΠΈΠΌΡΠΈΠ²ΡΠ΅ ΡΠΎΡΡΠ° ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ, ΠΈ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΡΠ°ΡΡΠΊΠ°, Π³Π΄Π΅ Π²ΡΡΠ°ΡΠΈΠ²Π°Π»ΠΈ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΡΡΡΠΎΠΉΡΠΈΠ²ΡΠΉ ΡΠΎΡΡ ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ ΠΠ°ΡΠ΄ΠΈΠ½Π°Π», Π² ΠΠ°Π»ΡΠΆΡΠΊΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ Π·Π° 14-Π»Π΅ΡΠ½ΠΈΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄ (1979β1993 Π³Π³.). Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΎΡΠ΅Π½ΠΊΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈ Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π½Π°ΠΈΠΌΠ΅Π½ΡΡΠΈΡ
ΠΊΠ²Π°Π΄ΡΠ°ΡΠΎΠ², ΡΠ²Π»ΡΡΡΠΈΠΉΡΡ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠ΅ΠΉ ΠΌΠ΅ΡΠΎΠ΄Π° Π½Π°ΠΈΠΌΠ΅Π½ΡΡΠΈΡ
ΠΊΠ²Π°Π΄ΡΠ°ΡΠΎΠ² Π΄Π»Ρ Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ. ΠΠ»Ρ Π°Π½Π°Π»ΠΈΠ·Π° Π±ΡΠ» ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ ΠΏΠ°ΠΊΠ΅Ρ ΠΏΠΎΠΈΡΠΊΠ° ΡΠ΅ΡΠ΅Π½ΠΈΡ Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ΅ Excel.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅. ΠΠΎΠ΄Π΅Π»Ρ Π₯Π°ΡΡΠ΅Π»Π° Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΠΠΠ Π² ΠΏΠΎΡΠ²Π΅ ΠΏΠΎΡΠ»Π΅ Π²ΡΡΠ°ΡΠΈΠ²Π°Π½ΠΈΡ Π² ΠΌΠΎΠ½ΠΎΠΊΡΠ»ΡΡΡΡΠ΅ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΡΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠΎΡΡΠ° ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ ΠΏΠΎΠΊΠ°Π·Π°Π»Π° Π²ΡΡΠΎΠΊΡΡ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΡΡΡ. (R2 = 0,94). ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π₯Π°ΡΡΠ΅Π»Π° Π΄Π»Ρ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΠΠΠ Π² ΠΏΠΎΡΠ²Π΅ ΡΡΠ°ΡΡΠΊΠ° Ρ Π²ΡΡΠ°ΡΠΈΠ²Π°Π½ΠΈΠ΅ΠΌ Π½Π΅ΠΌΠ°ΡΠΎΠ΄ΠΎΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠΎΡΡΠ° ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π²Π²Π΅Π΄Π΅Π½ΠΈΡ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠΎΡΡΠ° Π΄Π»Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠΉ Π½Π°ΡΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ ΠΠΠ ΠΈ ΡΠ°Π·Π½ΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ ΠΏΠ»ΠΎΠ΄ΠΎΡΠΎΠ΄ΠΈΡ: Π½ΠΈΠ·ΠΊΠΎΠ³ΠΎ, ΡΡΠ΅Π΄Π½Π΅Π³ΠΎ ΠΈ Π²ΡΡΠΎΠΊΠΎΠ³ΠΎ. ΠΠΎΠ΄Π΅Π»Ρ Π₯Π°ΡΡΠ΅Π»Π° ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠ΄ΠΈΠ»Π°, ΡΡΠΎ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π² ΠΊΠ»Π°ΡΡΠ΅ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΡΡ
ΠΎΠ΄Π½ΠΎΠΏΠ΅ΡΠΈΠΎΠ΄Π½ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΠΠΠ Π² ΠΏΠΎΡΠ²Π΅ ΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΡ ΠΠΠ Π΄Π»Ρ Π°Π³ΡΠΎΠ±ΠΈΠΎΡΠ΅Π½ΠΎΠ·ΠΎΠ² Ρ ΠΌΠΎΠ½ΠΎΠΊΡΠ»ΡΡΡΡΠΎΠΉ ΠΊΠ°ΡΡΠΎΡΠ΅Π»Ρ Π²ΠΎΡΠΏΡΠΈΠΈΠΌΡΠΈΠ²ΡΡ
ΠΈ Π³Π»ΠΎΠ±ΠΎΠ΄Π΅ΡΠΎΡΡΡΠΎΠΉΡΠΈΠ²ΡΡ
ΡΠΎΡΡΠΎΠ²
A clinical case of congenital hyperinsulinism in an early child
A clinical case of congenital hyperinsulinism, diffuse form, pharmacoresistant course (heterozygous mutation of p. 1361 1363 dup CGG in the GCK gene) in an early child is presented as an example of an orphan severe disease with an extremely unfavorable course and a probability of deterioration of the long-term prognosis. The goal was to highlight the clinical manifestations, course options, and complexity of treatment of this pathology to a wide range of doctors of different specialties in the field of Pediatrics in terms of improving the quality and timeliness of diagnosis, reducing the number of complications with the formation of irreparable consequences. Attention is drawn to the most severe course of hypoglycemic conditions in the early neonatal period, the dependence of the formation of a pronounced neurological deficit on the degree and duration of hypoglycemia, which emphasizes the importance of their timely correction to preserve the quality of life of this contingent of children
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