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Search Results: 1 - 10 of 45 matches for " Olympia Kremmyda "
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Vestibular Loss and Balance Training Cause Similar Changes in Human Cerebral White Matter Fractional Anisotropy
Nadine Hummel, Katharina Hüfner, Thomas Stephan, Jennifer Linn, Olympia Kremmyda, Thomas Brandt, Virginia L. Flanagin
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0095666
Abstract: Patients with bilateral vestibular loss suffer from severe balance deficits during normal everyday movements. Ballet dancers, figure skaters, or slackliners, in contrast, are extraordinarily well trained in maintaining balance for the extreme balance situations that they are exposed to. Both training and disease can lead to changes in the diffusion properties of white matter that are related to skill level or disease progression respectively. In this study, we used diffusion tensor imaging (DTI) to compare white matter diffusivity between these two study groups and their age- and sex-matched controls. We found that vestibular patients and balance-trained subjects show a reduction of fractional anisotropy in similar white matter tracts, due to a relative increase in radial diffusivity (perpendicular to the main diffusion direction). Reduced fractional anisotropy was not only found in sensory and motor areas, but in a widespread network including long-range connections, limbic and association pathways. The reduced fractional anisotropy did not correlate with any cognitive, disease-related or skill-related factors. The similarity in FA between the two study groups, together with the absence of a relationship between skill or disease factors and white matter changes, suggests a common mechanism for these white matter differences. We propose that both study groups must exert increased effort to meet their respective usual balance requirements. Since balance training has been shown to effectively reduce the symptoms of vestibular failure, the changes in white matter shown here may represent a neuronal mechanism for rehabilitation.
False-Positive Head-Impulse Test in Cerebellar Ataxia
Olympia Kremmyda,Hanni Kirchner,Stefan Glasauer,Thomas Brandt,Klaus Jahn,Michael Strupp
Frontiers in Neurology , 2012, DOI: 10.3389/fneur.2012.00162
Abstract: The objective of this study was to compare the findings of the bedside head-impulse test (HIT), passive head rotation gain, and caloric irrigation in patients with cerebellar ataxia (CA). In 16 patients with CA and bilaterally pathological bedside HIT, vestibuloocular reflex (VOR) gains were measured during HIT and passive head rotation by scleral search coil technique. Eight of the patients had pathologically reduced caloric responsiveness, while the other eight had normal caloric responses. Those with normal calorics showed a slightly reduced HIT gain (mean ± SD: 0.73 ± 0.15). In those with pathological calorics, gains 80 and 100 ms after the HIT as well as the passive rotation VOR gains were significantly lower. The corrective saccade after head turn occurred earlier in patients with pathological calorics (111 ± 62 ms after onset of the HIT) than in those with normal calorics (191 ± 17 ms, p = 0.0064). We identified two groups of patients with CA: those with an isolated moderate HIT deficit only, probably due to floccular dysfunction, and those with combined HIT, passive rotation, and caloric deficit, probably due to a peripheral vestibular deficit. From a clinical point of view, these results show that the bedside HIT alone can be false-positive for establishing a diagnosis of a bilateral peripheral vestibular deficit in patients with CA.
Genetic Algorithms for a Parameter Estimation of a Fermentation Process Model: A Comparison
Olympia Roeva
Bioautomation , 2005,
Abstract: In this paper the problem of a parameter estimation using genetic algorithms is examined. A case study considering the estimation of 6 parameters of a nonlinear dynamic model of E. coli fermentation is presented as a test problem. The parameter estimation problem is stated as a nonlinear programming problem subject to nonlinear differential-algebraic constraints. This problem is known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based) local optimization methods fail to arrive satisfied solutions. To overcome their limitations, the use of different genetic algorithms as stochastic global optimization methods is explored. These algorithms are proved to be very suitable for the optimization of highly non-linear problems with many variables. Genetic algorithms can guarantee global optimality and robustness. These facts make them advantageous in use for parameter identification of fermentation models. A comparison between simple, modified and multi-population genetic algorithms is presented. The best result is obtained using the modified genetic algorithm. The considered algorithms converged very closely to the cost value but the modified algorithm is in times faster than other two.
Optimization of E. coli Cultivation Model Parameters Using Firefly Algorithm
Olympia Roeva
International Journal Bioautomation , 2012,
Abstract: In this paper, a novel meta-heuristics algorithm, namely the Firefly Algorithm (FA), is adapted and applied for a model parameter identification of an E. coli fed-batch cultivation process. A system of ordinary nonlinear differential equations is used to model the biomass growth and substrate utilization. Parameter optimization is performed using real experimental data set from an E. coli MC4110 fed-batch cultivation process. The FA adjustments are done based on several pre-tests according to the optimization problem considered here. The simulation results indicate that the applied algorithm is effective and efficient. As a result, a model with high degree of accuracy is obtained applying the FA.
Doing Postgraduate Research
Olympia Palikara
Educate~ , 2007,
Abstract:
Writing for Academic Success: A Postgraduate Guide
Olympia Palikara
Educate~ , 2009,
Abstract:
Modelling of Escherichia coli Cultivations: Acetate Inhibition in a Fed-batch Culture
Olympia Roeva,Stoyan Tzonkov
Bioautomation , 2006,
Abstract: A set of three competing, unstructured models has been proposed to model biomass growth, glucose utilization, acetate formation, dissolved oxygen consumption and carbon dioxide accumulation of a fed-batch cultivation process of Escherichia coli. The inhibiting effect of acetate on growth of E. coli cultures is included in the considered models. The model identification is carried out using experimental data from the cultivation process. Genetic algorithms are used for parameter estimation. The model discrimination is based on the four criteria, namely sum of square errors, Fisher criterion, Akaike information criterion and minimum description length criterion. The most suitable model is identified that reflects the state variables curves adequately by considering acetate inhibited growth according to the Jerusalimsky approach.
Optimal Feed Rate Control of Escherichia coli Fed-batch Fermentation
Olympia Roeva,Stoyan Tzonkov
Bioautomation , 2005,
Abstract: In this paper an optimal control algorithm for E. coli fed-batch fermentation has been developed. A simple material balance model is used to describe the E. coli fermentation process. The optimal feed rate control of a primary metabolite process is studied and a biomass production is used as an example. An optimization of a fed-batch fermentation process is usually done using the calculus of variations to determine an optimal feed rate profile. In the optimal control literature the problem is formulated as a free final time problem where the control objective is to maximise biomass at the end of the process. The obtained optimal feed rate profile consists of sequences of maximum and minimum feed rates. The obtained results are used for optimization of E. coli fed-batch fermentation and the presented simulations show a good efficiency of the developed optimal feed rate profile.
Generalized Net Model of Brevibacterium flavul 22LD Fermentation Process
Olympia Roeva,Tania Pencheva
Bioautomation , 2005,
Abstract: In order to render the specific peculiarities of the fermentation processes, as well as to avoid the complexity of mathematical description with systems of differential equations, the elaboration of some new methods and approaches for their modelling and control is predetermined. As a new, alternative approach for modelling of fermentation processes, an application of generalized nets is presented in this paper. The theory of generalized nets is applied to the fermentation process of Brevibacterium flavul 22LD for L-lysine production. A generalized net model of considered process is developed. For comparison and completeness, model with differential equations is also provided. The generalized nets model developed for the fed-batch cultivation of Brevibacterium flavul 22LD allows changing the concentration of the feeding solution and the aeration rate. In this way some inhibition effects are prevented and a possibility for optimal carrying out of the considered fermentation process is provided.
A Genetic Algorithm for Feeding Trajectory Optimisation of Fed-batch Fermentation Processes
Olympia Roeva,Stoyan Tzonkov
Bioautomation , 2009,
Abstract: In this work a genetic algorithm is proposed with the purpose of the feeding trajectory optimization during a fed-batch fermentation of E. coli. The feed rate profiles are evaluated based on a number of objective functions. Optimization results obtained for different feeding trajectories demonstrate that the genetic algorithm works well and shows good computational performance. Developed optimal feed profiles meet the defined criteria. The ration of the substrate concentration and the difference between actual cell concentration and theoretical maximum cell concentration is defined as the most appropriate objective function. In this case the final cell concentration of 43 g·l-1 and final product concentration of 125 g·l-1 are achieved and there is not significant excess of substrate.
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