Enzymatic Synthesis and Antimicrobial Activity of Oligomer Analogues of Medicinal Biopolymers from Comfrey and Other Species of the Boraginaceae Family

This study reports the first enzymatic synthesis leading to several oligomer analogues of poly[3-(3,4-dihydroxyphenyl)glyceric acid]. This biopolymer, extracted from plants of the Boragi-naceae family has shown a wide spectrum of pharmacological properties, including antimicrobial activity. Enzymatic ring opening polymerization of 2-methoxycarbonyl-3-(3,4-dibenzyloxyphenyl)oxirane (MDBPO) using lipase from Candida rugosa leads to formation of poly[2-methoxycarbonyl-3-(3,4-dibenzyloxyphenyl)oxirane] (PMDBPO), with a degree of polymerization up to 5. Catalytic debenzylation of PMDBPO using H2 on Pd/C yields poly[2-methoxycarbonyl-3-(3,4-dihydroxyphenyl)oxirane] (PMDHPO) without loss in molecular mass. Antibacterial assessment of natural polyethers from different species of Boraginaceae family Symhytum asperum, S. caucasicum, S. grandiflorum, Anchusa italica, Cynoglossum officinale, and synthetic polymers, poly[2-methoxycarbonyl-3-(3,4-dimethoxyphenyl)oxirane (PMDMPO) and PMDHPO, reveals that only the synthetic analogue produced in this study (PMDHPO) exhibits a promising antimicrobial activity against pathogenic strains S.aureus ATCC 25923 and E.coli ATCC 25922 the minimum inhibitory concentration (MIC) being 100 µg/mL

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling

Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical model parameters that results in a simulation best matching an empirical observation. When applied to brain tumor modeling, one of the instances of image-based model personalization in medical image computing, the overarching drawback of the methods is the time complexity of finding such a set. In a clinical setting with limited time between imaging and diagnosis or even intervention, this time complexity may prove critical. As the history of quantitative science is the history of compression (Schmidhuber and Fridman, 2018), we align in this paper with the historical tendency and propose a method compressing complex traditional strategies for solving an inverse problem into a simple database query task. We evaluated different ways of performing the database query task assessing the trade-off between accuracy and execution time. On the exemplary task of brain tumor growth modeling, we prove that the proposed method achieves one order speed-up compared to existing approaches for solving the inverse problem. The resulting compute time offers critical means for relying on more complex and, hence, realistic models, for integrating image preprocessing and inverse modeling even deeper, or for implementing the current model into a clinical workflow. The code is available at https://github.com/IvanEz/for-loop-tumor

Comparison of the Holiday Climate Index (HCI) and the Tourism Climate Index (TCI) in Tbilisi

A comparative analysis of data on the monthly values of Tourism Climate Index (TCI) and Holiday Climate Index (HCI) in Tbilisi is presented. Period of observation – 1956-2015. Average monthly values of HCI for the entire observation period varied from 62.0 (“Good”, January) to 83.8 (“Excellent”, May). As in the case with the TCI, according to the HCI, the bioclimatic conditions in Tbilisi are favorable for resort and tourist purposes all year round. Comparison of the values and categories of the Tourism Climate Index and Holiday Climate Index shows that the intra-annual variation of both indices is similar and has a bimodal form. However, given that the TCI is calculated for the so-called “average tourist” (regardless of gender, age, physical condition), the values and categories of this index is lower than the HCI values and categorie

Dynamics of the thirty-year moving average values of the air temperature in Tbilisi and St.-Petersburg with 1851 to 2010 and their extrapolation to 2051-2080.

On the basis of 100-years (1907-2006) and 163-years (1850-2012) time-series of observations the analysis of the dynamics of the changeability of the average annual air temperature in Tbilisi and St.- Petersburg was carried out. Prognostic calculations showed that in 2051-2080 the average annual air temperature in Tbilisi is expected 14.0±0.4 ºС (ARIMA) and 14.8±1.4 ºС (EXPERTMODELER) against 13.7 ºС in 1981-2010, while in St.-Petersburg - 6.4±0.4 ºС (ARIMA) and 8.6±4.0 ºС (EXPERTMODELER) against 5.8 ºС in 1981-2010. The comparative analysis of the indicated results with the obtained earlier prognostic estimations of the air temperature in Tbilisi, St.-Petersburg, and also its mean global values is carried ou

Statistical Analysis of Angstrom Fire Index for Kutaisi, Georgia

The problem of fires, including forest fires, is actual for many countries of world. This problem is also important for Georgia, where forest fires are frequent. In recent years this problem is aggravated by the global and local climate warming which facilitates an increase in the fire hazard. In Georgia, the top 3 regions were responsible for 53% of all tree cover loss between 2001 and 2020. Samtskhe-Javakheti had the most tree cover loss at 3.24 kha, then Kakheti (1.24 kha) and Imereti (1.01 kha) [https://www.globalforestwatch.org/dashboards/country/GEO]. For evaluating the fire hazard in locality the set of indices is developed. One of the simple of these indices is the Swedish Angstrom Fire Index (AFI). Earlier, data on AFI for Tbilisi and Telavi were presented. In this work results of a statistical analysis of daily values of AFI for Kutaisi are presented. AFI = (R/20) + (27-T)/10, where R is the minimum relative humidity, T is the maximum air temperature. Data of the about daily values of T and R in the period 2011-2020 are used [http://www.pogodaiklimat.ru/archive.php?id=ru®ion=07]. The gradations of the values of AFI are as follows: I. AFI ≥ 4.1 – Low, II. AFI = 4.0 ÷ 3.0 - Moderate, III. AFI = 2.9 ÷ 2.5 - High, IY. AFI = 2.4 ÷ 2.0 – Very High, Y. AFI = <2.0 - Extreme. In particular, it was found that an Extreme fire hazard in Kutaisi is observed on average within 59 days a year (16.0 % of cases), Very High – 46 days a year (12.7 % of cases), High - 64 days a year (17.6 % of cases), Moderate – 100 days a year (27.5 % of cases), Low – 96 days a year (26.2 % of cases). The highest repeatability of AFI values for its various gradations is as follows: Extreme – 33.3 % (September), Very High – 22.6 % (August), High – 30.3 % (July), Moderate – 37.3 % (November), Low 48.7 % (January). The values of AFI in Kutaisi are compared with their values in Tbilisi and Telavi. In particular, it was found that a repeatability of Extreme fire hazard in Kutaisi is lower, than in Tbilisi (19.1 % of cases) and Telavi (18.5 % of cases). This result is in good agreement with the data on loss of forest cover from fires in Kakheti and Imereti, indicated above. Further, it is planned to expand work on this issue (using other more complex fire hazard indices, studying their trends in connection with climate change, determining these indices for other points in Georgia, etc.)

Tourism climate index in the coastal and mountain locality of Adjara, Georgia

The determination of the Tourism Climate Index (TCI) to four coastal and mountain localities of the Adjarian Autonomous Republic (known tourist and health resort region of Georgia) is carried out (Batumi, capital of the Adjarian Autonomous Republic: 9 m a. s. l.; Kobuleti: 3 m a. s. l., distance from Batumi - 23 km along the coast of the Black sea; Khulo: 921 m a. s. l., distance from Batumi - 56 km; Goderdzi: 2025 m a. s. l., distance from Batumi - 73 km ). For the indicated localities the monthly average values of TCI in the period from 1961 through 2010 are calculated. The contrast of TCI values in dependence on area relief is revealed. The special features of the variability of TCI values during this period of time in connection with climate changeability are studied. The most favorable from the point of view of the bioclimatic characteristics of a locality for the development of different forms of tourism periods of the year are determined (Sun & Beach Tourism, Eco Tourism, Birdwatching, Sport Tourism, Rural Tourism, Cruise Tourism, Wine Tourism, Ski & Mountain Resorts, MICE Tourism, Gambling Tourism, etc

Comparison of the Mean Max Annual, Seasonal and Monthly Air Temperature Variability in Tbilisi and Shovi in 1956-2022

Some results of comparative analysis of the average maximum annual, seasonal and monthly air temperature variability in Tbilisi and Shovi during 1956-2022 against the background of global warming are presented.. The statistical characteristics of the mean max annual, seasonal and monthly air temperature in the period 1956-2022 (T), 1956-1985 (T₁) and 1993-2022 (T₂) for each point were studied. It is shown that compared to Tbilisi, climate warming in Shovi is much more significant. For example, the increase in the mean annual max air temperature in Tbilisi in 1993-2022 compared to 1956-1885 was 0.8 ˚C, while in Shovi it was 1.9 ˚C. The situation is similar for the warm and cold half of the year. The maximum increase in the mean max monthly air temperature at both points was observed in August. At the same time, in Tbilisi - 1.9 ˚C, and in Shovi - 3.7 ˚C. It is shown that the trend of the mean max annual and seasonal air temperature in 1956-2022 in Tbilisi is described by the second power polynomial, and in Shovi - by the third power polynomial. Using these equations, the average annual rate of increase in air temperature at both points was calculated. In particular, it was found that in 2011-2020 this speed in Shovi is three times higher than in Tbilisi

Variability of the Mean Max Annual Air Temperature in 39 Locations of Georgia in 1956-2015

The research results of the variability of the mean max annual air temperature at 39 locations in Georgia against the background of global climate change in 1956-2015 are presented. The statistical characteristics of the mean max annual air temperature in the period 1956-2015 (T), 1956-1985 (T₁) and 1986-2015 (T₂) for each point were studied. It has been established that in the second period compared to the first, there is a significant increase in the average max air temperature at the 29 stations from 0.3˚С (Stepantsminda) to 1.2˚С (Bakuriani); for Shovi this difference is 1.1˚С. It is shown that between the T₁, T₂ values and the terrain height there has been observed the high inverse linear correlation and regression relationship there. At the same time, the lines of the regression equations are parallel with the increasing in the second period compared to the first by 0.6 ˚С. It is shown, that a significant value of (T₂- T₁)/T changes from 2.0 % (Chokhatauri) to 11.0% (Bakuriani); for Shovi this indicator is 8.4 %. The significant value (T₂- T₁)/T increases with terrain height in accordance with a second power polynomial

Assessment of touristical-recreation potential of Georgia on background regional climate change

The determination of the climatic potential of tourism to Tbilisi (the capital of Georgia) into the correspondence with that frequently utilized in other countries of the “tourism climate index” is carried out

Formation and stabilisation of single current filaments in planar dielectric barrier discharge

In the experimental part we report on a typical bifurcation scenario of the current distribution in the discharge plane of a planar dielectric barrier discharge system. Increasing the amplitude $\hat U$ of the sinusoidal driving voltage after breakdown a large number of dynamic solitary filaments is observed and the subsequent decrease of $\hat U$ results in a pronounced hysteresis with decreasing number of filaments. In this way isolated single stationary filaments can be generated. In the theoretical part the latter are modeled by a reaction-drift-diffusion equation that is solved in three dimensional space numerically without any fitting procedure. As a result we obtain well defined stationary filaments of which size an shape essentially are independent of the initial conditions and having a width and an amplitude that agree with experiment rather well. On the basis of the numerical results we consider mechanisms of filament stabilisation. This includes the discussion of the well known surface charges as well as an additional focusing effect of volume charges