Determination of the Strength of Adhesion between Lipid Vesicles

A commonly used method to determine the strength of adhesion between adhering lipid vesicles is measuring their effective contact angle from experimental images. The aim of this paper is to estimate the interobserver variations in vesicles effective contact angle measurements and to propose a new method for estimating the strength of membrane vesicle adhesion. Theoretical model shows for the old and for the new measure a monotonic dependence on the strength of adhesion. Results obtained by both measuring techniques show statistically significant correlation and high interobserver reliability for both methods. Therefore the conventional method of measuring the effective contact angle gives qualitatively relevant results as the measure of the lipid vesicle adhesion. However, the new measuring technique provides a lower variation of the measured values than the conventional measures using the effective contact angle. Moreover, obtaining the adhesion angle can be automatized more easily than obtaining the effective contact angle

Numerical Study of Membrane Configurations

We studied biological membranes of spherical topology within the framework of the spontaneous curvature model. Both Monte Carlo simulations and the numerical minimization of the curvature energy were used to obtain the shapes of the vesicles. The shapes of the vesicles and their energy were calculated for different values of the reduced volume. The vesicles which exhibit in-plane ordering were also studied. Minimal models have been developed in order to study the orientational ordering in colloids coated with a thin sheet of nematic liquid crystal (nematic shells). The topological defects are always present on the surfaces with the topology of a sphere. The location of the topological defects depends strongly on the curvature of the surface. We studied the nematic ordering and the formation of topological defects on vesicles obtained by the minimization of the spontaneous curvature energy

Ključni izzivi pri modeliranju epidemije - dosedanje izkušnje pri modeliranju epidemije COVID-19

Mathematical modelling can be useful for predicting how infectious diseases progress, enabling us to show the likely outcome of an epidemic and help inform public health interventions. Different modelling techniques have been used to predict and simulate the spread of COVID-19, but they have not always been useful for epidemiologists and decision-makers. To improve the reliability of the modelling results, it is very important to critically evaluate the data used and to check whether or not due regard has been paid to the different ways in which the disease spreads through the population. As building an epidemiological model that is reliable enough and suits the current epidemiological situation within a country or region, certain criteria must be met in the modelling process. It might be necessary to use a combination of two or more different types of models in order to cover all aspects of epidemic modelling. If we want epidemiological models to be a useful tool in combating the epidemic, we need to engage experts from epidemiology, data science and statistics.Matematično modeliranje je lahko koristno za napovedovanje razvoja nalezljivih bolezni, saj s prikazom možnih izidov epidemije pomaga oblikovati javnozdravstvene ukrepe. Za napovedovanje in simulacijo širjenja v času epidemije COVID-19 so bile uporabljene različne tehnike modeliranja, vendar vse niso bile vedno koristne za epidemiologe in odločevalce. Da bi bili rezultati modeliranja zanesljivejši, je zelo pomembno kritično ovrednotiti uporabljene podatke ter preveriti, ali so bili upoštevani različni načini širjenja bolezni v populaciji ali ne. Izdelava dobrega epidemiološkega modela, ki je dovolj zanesljiv in ustreza trenutnim epidemiološkim razmeram v državi ali regiji, je zahtevna, zato je treba pri modeliranju slediti določenim kriterijem. Smiselno bi bilo tudi kombinirati dve različni vrsti modelov. Modeliranje bi bilo tako zanesljivejše, saj bi upoštevalo različne predpostavke. Če želimo, da bodo epidemiološki modeli koristno orodje v boju proti epidemiji, morajo pri modeliranju sodelovati strokovnjaki z različnih področij, predvsem epidemiologije, podatkovne znanosti in statistik

Key Challenges in Modelling an Epidemic – What Have we Learned from the COVID-19 Epidemic so far

Mathematical modelling can be useful for predicting how infectious diseases progress, enabling us to show the likely outcome of an epidemic and help inform public health interventions. Different modelling techniques have been used to predict and simulate the spread of COVID-19, but they have not always been useful for epidemiologists and decision-makers. To improve the reliability of the modelling results, it is very important to critically evaluate the data used and to check whether or not due regard has been paid to the different ways in which the disease spreads through the population. As building an epidemiological model that is reliable enough and suits the current epidemiological situation within a country or region, certain criteria must be met in the modelling process. It might be necessary to use a combination of two or more different types of models in order to cover all aspects of epidemic modelling. If we want epidemiological models to be a useful tool in combating the epidemic, we need to engage experts from epidemiology, data science and statistics