Enzymes immobilized in Langmuir-Blodgett films: Why determining the surface properties in Langmuir monolayer is important?

ABSTRACT In this review we discuss about the immobilization of enzymes in Langmuir-Blodgett films in order to determine the catalytic properties of these biomacromolecules when adsorbed on solid supports. Usually, the conformation of enzymes depends on the environmental conditions imposed to them, including the chemical composition of the matrix, and the morphology and thickness of the film. In this review, we show an outline of manuscripts that report the immobilization of enzymes as LB films since the 1980’s, and also some examples of how the surface properties of the floating monolayer prepared previously to the transfer to the solid support are important to determine the efficiency of the resulting device

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

Evaluating the LIHLA lexical aligner on Spanish, Brazilian Portuguese and Basque parallel texts

El alineamiento de palabras y de unidades multipalabra desempeña un papel importante en muchas aplicaciones del procesamiento de lenguaje natural, tales como la traducción automática basada en ejemplos, la inducción de reglas de transferencia para la traducción automática, la lexicografía bilingüe, la desambiguación de la polisemia, etc. En esta comunicación describimos LIHLA, un alineador de palabras que utiliza léxicos probabilísticos bilingües generados por un paquete de herramientas libremente disponible (NATools) y heurísticas independientes del idioma para encontrar alineamientos entre palabras y unidades multipalabra en textos paralelos alineados por oraciones. El método ha alcanzado una precisión de un 92.44% y un 85.09% y una cobertura de un 91.13% y un 64.66% en textos paralelos escritos en portugués brasileño–español y español–euskera, respectivamente.Alignment of words and multiword units plays an important role in many natural language processing applications, such as example-based machine translation, transfer rule learning for machine translation, bilingual lexicography, word sense disambiguation, etc. In this paper we describe LIHLA, a lexical aligner which uses bilingual probabilistic lexicons generated by a freely available set of tools (NATools) and language-independent heuristics to find links between single words and multiword units in sentence-aligned parallel texts. The method has achieved a precision of 92.44% and 85.09% and a recall of 91.13% and 64.66% on Brazilian Portuguese–Spanish and Spanish–Basque parallel texts, respectively.FAPESP, CAPES, CNPq and the Spanish Ministry of Science & Technology (Project TIC2003-08681-C02-01)