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We report the enhanced bactericidal activity of ofloxacin in drug-containing Eudragit E100® dispersions ( Eu Cl- OFX) against Pseudomonas aeruginosa and the effect of the cationic polymer on bacterial membrane. Organisms treated with Eu Cl- OFX showed changes in cell morphology, altered outer membrane ( OM) and cytoplasm with low electrodensity areas. Zeta potential of bacterial surface was shifted to positive. Sensitization to lytic agents was also observed. A profound effect on bacterial size, granularity and membrane depolarization was found by flow cytometry. Cultures exposed to drug-free polymer also showed some damaged bacterial membranes, but there was no significant cell death. Inhibition of P. aeruginosa by Eu Cl- OFX may involve surface effect and, to some extent, permeation effect. The cationic polymer act to mitigate the electronegativity of cell surface in the process of disorganizing the OM, rendering it more permeable to antibiotic. In addition, cytoplasmic membrane depolarization turns bacterial cell more vulnerable. The effects on membranes combined with the mechanism of action of quinolone explain the improved bactericidal action exhibited by Eu Cl- OFX. The behavior described for Eudragit E100® against P. aeruginosa may be a useful tool to broaden the spectrum of antibiotics whose clinical use is limited by the impermeability of the bacterial OM. [ABSTRACT FROM AUTHOR]

This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copymark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but contextaware. Taken together, thiswork suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity. [ABSTRACT FROM AUTHOR]