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Comparison of the biocidal activity of disinfectants against biofilm
Šlitrová, Barbora ; Votava,, Miroslav (referee) ; Omelková, Jiřina (advisor)
A common vector of nosocomial infections is Pseudomonas aeruginosa. This opportunistic pathogen is resistant against different classes of antibiotics as well as against biocides. Some P. aeruginosa strains are able to form biofilm. The biofilm formation on medical devices represent a common occurrence that can lead to serious illness and death. Bacteria in biofilms are more resistant than their planktonic counterparts. Therefore it is important to find disinfectants that will be effective also against biofilm. Bactericidal efficacy of four disinfectants has been evaluated on biofilm and on planktonic forms of P. aeruginosa.
Výběr volitelných parametrů částečného zapomínání
Votava, Adam ; Kárný, Miroslav (advisor) ; Šmíd, Martin (referee)
Presented work deals with the choice of optional parameters determining partial forgetting. The main objective is to design an algorithm for the development of the optional parameters in time in the optimal way, which would be better than usage of constant parameters. For this purpose, the Bayesian dynamic decision making, general principles of tracking the slowly varying parameters via forgetting and partial forgetting method are presented. To make computations feasible the exponential family of probability distribution functions is used. Applied algorithm is described mathematically using Bayesian learning. The stress is laid on the forgetting factor's choice, that is regarded as a Bayesian hypothesis testing. Moreover, the set of hypotheses on the forgetting factor varies in time. To hypotheses, forgetting is also applied. The presented methods are then applied to the normal regression model. However, the generality of the theoretical part allows the application to other models, e.g. Markov chain model, too. The algorithm is then programmed within the Python environment and tested on the real traffic data and on the simulated data as well.
Výběr volitelných parametrů částečného zapomínání
Votava, Adam ; Kárný, Miroslav (advisor) ; Šmíd, Martin (referee)
Presented work deals with the choice of optional parameters determining partial forgetting. The main objective is to design an algorithm for the development of the optional parameters in time in the optimal way, which would be better than usage of constant parameters. For this purpose, the Bayesian dynamic decision making, general principles of tracking the slowly varying parameters via forgetting and partial forgetting method are presented. To make computations feasible the exponential family of probability distribution functions is used. Applied algorithm is described mathematically using Bayesian learning. The stress is laid on the forgetting factor's choice, that is regarded as a Bayesian hypothesis testing. Moreover, the set of hypotheses on the forgetting factor varies in time. To hypotheses, forgetting is also applied. The presented methods are then applied to the normal regression model. However, the generality of the theoretical part allows the application to other models, e.g. Markov chain model, too. The algorithm is then programmed within the Python environment and tested on the real traffic data and on the simulated data as well.
Comparison of the biocidal activity of disinfectants against biofilm
Šlitrová, Barbora ; Votava,, Miroslav (referee) ; Omelková, Jiřina (advisor)
A common vector of nosocomial infections is Pseudomonas aeruginosa. This opportunistic pathogen is resistant against different classes of antibiotics as well as against biocides. Some P. aeruginosa strains are able to form biofilm. The biofilm formation on medical devices represent a common occurrence that can lead to serious illness and death. Bacteria in biofilms are more resistant than their planktonic counterparts. Therefore it is important to find disinfectants that will be effective also against biofilm. Bactericidal efficacy of four disinfectants has been evaluated on biofilm and on planktonic forms of P. aeruginosa.

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