Gerencia De Proyectos

We report on a measurement of the tensor-analyzing power T20 in elastic electron-deuteron scattering in the range of four-momentum transfer from 1.8 to 3.2 fm-1. Electrons of 704 MeV were scattered from a polarized deuterium internal target. The tensor polarization of the deuterium nuclei was determined with an ion-extraction system, allowing an absolute measurement of T20. The data are described well by a non-relativistic calculation that includes the effects of meson-exchange currents.

The interaction of Escherichia coli agmatinase (EC 3.5.3.11) with the substrate guanidinium group was investigated by kinetic and site-directed mutagenesis studies. Putrescine and guanidinium ions (Gdn + ) were slope-linear, competitive inhibitors with respect to agmatine and their bindings to the enzyme were not mutually exclusive. By site-directed mutagenesis, the E274A variant exhibiting about 1-2% of wild-type activity was obtained. Mutation produced a moderate, but significant, increase in the K m value for agmatine (from 1.1 ± 0.2 mM to 6.3 ± 0.3 mM) and the K i value for competitive inhibition by Gdn + (from 15.0 ± 0.1 mM to 44.2 ± 2.1 mM), but the K i value for putrescine inhibition (2.8 ± 0.2 mM) was not altered. The tryptophan fluorescence properties (k max = 342 nm) and circular dichroism spectra were not significantly altered by the Glu274 fi Ala mutation. The dimeric structure of the enzyme was also maintained. We conclude that Glu274 is involved in binding and positioning of the guanidinium moiety of the substrate for efficient catalysis. A kinetic mechanism involving rapid equilibrium random release of products is proposed for E. coli agmatinase.

To examine the interaction of human arginase II (EC 3.5.3.1) with substrate and manganese ions, the His120Asn, His145Asn and Asn149Asp mutations were introduced separately. About 53% and 95% of wild-type arginase activity were expressed by fully manganese activated species of the His120Asn and His145Asn variants, respectively. The Km for arginine (1.4–1.6 mm) was not altered and the wild-type and mutant enzymes were essentially inactive on agmatine. In contrast, the Asn149Asp mutant expressed almost undetectable activity on arginine, but significant activity on agmatine. The agmatinase activity of Asn149Asp (Km = 2.5 ± 0.2 mm) was markedly resistant to inhibition by arginine. After dialysis against EDTA, the His120Asn variant was totally inactive in the absence of added Mn2+ and contained < 0.1 Mn2+·subunit−1, whereas wild-type and His145Asn enzymes were half active and contained 1.1 ± 0.1 Mn2+·subunit−1 and 1.3 ± 0.1 Mn2+·subunit−1, respectively. Manganese reactivation of metal-free to half active species followed hyperbolic kinetics with Kd of 1.8 ± 0.2 × 10−8 m for the wild-type and His145Asn enzymes and 16.2 ± 0.5 × 10−8 m for the His120Asn variant. Upon mutation, the chromatographic behavior, tryptophan fluorescence properties (λmax = 338–339 nm) and sensitivity to thermal inactivation were not altered. The Asn149→Asp mutation is proposed to generate a conformational change responsible for the altered substrate specificity of arginase II. We also conclude that, in contrast with arginase I, Mn2+A is the more tightly bound metal ion in arginase II.

Los algoritmos genéticos se han aplicado con éxito a una gran variedad de problemas de ingeniería. Este trabajo propone un algoritmo genético para resolver el problema del flujo óptimo de potencia teniendo en cuenta solamente la potencia activa del sistema eléctrico de potencia. A diferencia de las tradicionales técnicas de programación matemática, el algoritmo genético puede usar funciones objetivo sin tener conocimiento del problema que va a resolver, esta característica hace que el algoritmo sea sencillo de aplicar, además permite la inclusión de restricciones no lineales sin necesidad de obtener un equivalente lineal de ellas, lo cual acerca más la solución obtenida a la realidad del problema por resolver. Este método se probará utilizando el sistema de prueba IEEE 30 y 118 buses.

En el ámbito de la teoría de conjuntos difusos y de la teoría de la posibilidad se han desarrollado muchas aplicaciones para ingeniería sin prestar mucha atención a la semántica involucrada en su uso. Como consecuencia, existe una muy amigable y sólida estructura matemática para combinar conjuntos difusos, aunque frecuentemente se presenta sin asignarla a alguna estructura interpretativa. Al hacerlo así se corre el riesgo, por un lado, de confundir su uso, y por otro, de privar a los usuarios de esta técnica, de lineamientos sobre cómo y en qué situaciones es admisible su aplicación. Esta situación lleva irremediablemente al simple desarrollo de operaciones semánticamente sin sentido, dejando los resultados obtenidos sin el soporte de una estructura interpretativa. En efecto, en este documento se presentan explicaciones y ejemplos, que buscan dilucidar y resaltar tales diferencias subyacentes de tal modo que haya mejor uso de los conjuntos difusos en problemas de decisión.