Anaerobic organisms have molecular systems to detoxify reactive oxygen species when transiently exposed to oxygen. One of these systems is superoxide reductase, which reduces O2 (.-) to H2 O2 without production of molecular oxygen. In order to complete the reduction of superoxide anion, superoxide reductase requires an electron, delivered by its redox partners, which in Desulfovibrio gigas are rubredoxin and/or desulforedoxin. In this work, we characterized the interaction of Desulfovibrio gigas superoxide reductase with both electron donors by using steady-state kinetics, 2D NMR titrations, and backbone relaxation measurements. The rubredoxin surface involved in the electron transfer complex with superoxide reductase comprises the solvent-exposed hydrophobic residues in the vicinity of its metal center (Cys9, Gly10, Cys42, Gly43, and Ala44), and a Kd of 3 muM at 59 mM ionic strength was estimated by NMR. The ionic strength dependence of superoxide-mediated rubredoxin oxidation by superoxide reductase has a maximum kapp of (37 +/- 12) min(-1) at 157 mM. Relative to the electron donor desulforedoxin, its complex with superoxide reductase was not detected by chemical shift perturbation, though this protein is able to transfer electrons to superoxide reductase with a maximum kapp of (31 +/- 7) min(-1) at an ionic strength of 57 mM. Competition experiments using steady-state kinetics and NMR spectroscopy (backbone relaxation measurements and use of a paramagnetic relaxation enhancement probe) with Fe-desulforedoxin in the presence of (15) N-Zn-rubredoxin showed that these two electron donors compete for the same site on the enzyme surface, as shown in the model structure of the complex generated by using restrained molecular docking calculations. These combined strategies indicate that the two small electron donors bind in different manners, with the desulforedoxin complex being a short lived electron transfer complex or more dynamic, with many equivalent kinetically competent orientations.

The development of web applications that manipulate geo-referenced information is often supported by Application Programming Interfaces (APIs), allowing a fast development cycle for high quality applications. APIs can be used by programmers with different expertise levels and choosing an adequate API may
have a dramatic impact on the productivity achieved by those programmers. Our goal is to compare maps APIs with respect to their usability. We compare three different APIs: the Google Maps JavaScript API, the ArcGIS API for JavaScript, and the OpenLayers JavaScript Mapping Library. Our comparison is supported by
a set of software metrics and is performed in two orthogonal ways: the comparison of three implementations of the same system prototype, each using one of the APIs under scrutiny; the comparison of the APIs specifications. The main results of the study are related to the size of the APIs, with the Google API being significantly smaller than the others.

In this work, we obtained a charge state distribution inside an Ar plasma produced by an electron–cyclotron-resonance ion source. The processes that lead to the observed lines in x-ray spectra are identified and included in the simulated x-ray spectrum. The geometrical constraints of the flat double crystal spectrometer, used to measure the x-ray spectrum, are investigated as they are crucial for correctly obtaining the ion densities from the observed transition amplitudes. Multiple electron impact ionization is included, and a realistic electron velocity distribution is taken into account. The charge state distribution of the Ar ions is compared to measured extracted ionic currents.

The sand pile model, in conjunction with Bean model, is often applied to describe single grain bulk superconductors. However, in several applications such as electric motors, multiseeded bulks are needed, due to the need to increase sample dimensions. In this paper, an extension of the sand pile model is presented in order to manage this type of materials. Multiseeded HTS bulk superconductors, produced, e.g., by the top-seeded melt growth process, are characterized by intra- and intergrain currents, and these are reflected in the model. However, identifying these currents from flux density measurements is not straightforward, when considering more than one grain. In fact, the number of currents increases with the number of grains, and these have to be identified from the measured field surface. A method to identify these currents based on genetic algorithms is validated with artificial data and then used in real measurements.

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