Postantifungal Effect of Micafungin against the Species Complexes of Candida albicans and Candida parapsilosis

Micafungin is an effective antifungal agent useful for the therapy of invasive candidiasis. Candida albicans is the most common cause of invasive candidiasis; however, infections due to non-C. albicans species, such as Candida parapsilosis, are rising. Killing and postantifungal effects (PAFE) are important factors in both dose interval choice and infection outcome. The aim of this study was to determinate the micafungin PAFE against 7 C. albicans strains, 5 Candida dubliniensis, 2 Candida Africana, 3 C. parapsilosis, 2 Candida metapsilosis and 2 Candida orthopsilosis. For PAFE studies, cells were exposed to micafungin for 1 h at concentrations ranging from 0.12 to 8 mu g/ml. Time-kill experiments (TK) were conducted at the same concentrations. Samples were removed at each time point (0-48 h) and viable counts determined. Micafungin (2 mu g/ml) was fungicidal (>= 3 log(10) reduction) in TK against 5 out of 14 (36%) strains of C. albicans complex. In PAFE experiments, fungicidal endpoint was achieved against 2 out of 14 strains (14%). In TK against C. parapsilosis, 8 mu g/ml of micafungin turned out to be fungicidal against 4 out 7 (57%) strains. Conversely, fungicidal endpoint was not achieved in PAFE studies. PAFE results for C. albicans complex (41.83 +/- 2.18 h) differed from C. parapsilosis complex (8.07 +/- 4.2 h) at the highest tested concentration of micafungin. In conclusion, micafungin showed significant differences in PAFE against C. albicans and C. parapsilosis complexes, being PAFE for the C. albicans complex longer than for the C. parapsilosis complex.

Multi-agent modelling for the simulation of a simple smart microgrid

The smart grid is a highly complex system that is being formed from the traditional power grid, adding new and sophisticated communication and control devices. This will enable integrating new elements for distributed power generation and also achieving an increasingly automated operation so for actions of the utilities as for customers. In order to model such systems a bottom-up method is followed, using only a few basic elements which are structured into two layers: a physical layer for the electrical power transmission, and one logical layer for element communication. A simple case study is presented to analyse the possibilities of simulation. It shows a microgrid model with dynamic load management and an integrated approach that can process both electrical and communication flows.

Complete agent based simulation of mini-grids

EuroPES 2009

An agent-based multi-scale wind generation model

(EuroPES 2009)

Agent based modeling of energy networks

Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed.

Optimization of the pyrolysis process for the production of a biomass derived reducing agent and hydrogen-rich gases

221 p.