Identifizierung, Klonierung und biochemische Charakterisierung der pflanzlichen Sulfitoxidase aus Arabidopsis thaliana

Der Molybdäncofaktor (Moco) ist essentieller Bestandteil aller eukaryotischer Molybdoenzyme, die eine Vielzahl diverser Redoxreaktionen katalysieren und so an zahlreichen unterschiedlichsten Stoffwechselwegen involviert sind. Seine universelle Struktur in Pro- und Eukaryoten, die auf dem namensgebenden Molybdopterin als Pterin-Derivat basiert, läßt einen ähnlichen Syntheseweg in allen Organismen vermuten. In Pflanzen konnten die Molybdoenzyme: Nitratreduktase, Aldehydoxidase und Xanthindehydrogenase/oxidase beschrieben werden. Im Vordergrund dieser Arbeit standen die Identifikation, Klonierung und biochemische Charakterisierung einer zur tierischen Sulfitoxidase (SOX) homologen Proteinsequenz. Die Ableitung der Proteinsequenz zeigte, dass es sich bei diesem neuen pflanzlichen Molybdoenzym, um das erste eukaryotische Molybdoenzym handelte, welches außer dem Moco kein weiteres aktives Redoxzentrum besaß. Den molekularen Analysen der kodierenden cDNA und des genomischen Bereiches folgte nach Klonierung und Überexpression im prokaryotischen Expressionssystem die biochemische Charakterisierung der rAt-SOX.. Die spektroskopischen Untersuchungen durch die UV/vis- und EPR-Spektroskopie bestätigten das bereits durch die molekularen Analysen nachgewiesene Fehlen einer Häm-Domäne im Vergleich zu den gut charakterisierten tierischen Sulfitoxidasen. Zusätzlich konnte eine andere Lokalisation der At-SOX im Gegensatz zu den tierischen Sulfitoxidasen in Peroxisomen gezeigt werden. Hierfür wurden polyklonale Antikörper gegen die rekombinante At-SOX generiert und für immunologische Analysen eingesetzt. Es zeigte sich eine große Verbreitung im gesamten Pflanzenreich, die von Monokotylen zu Dikotylen und von krautigen zu holzigen Vertretern reichte. Während die enzymkinetischen Messungen ein im gleichen Bereich liegenden Substratumsatz zeigten, konnte durch die EPR-Spektroskopie eine deutlich divergente Natur des aktiven Zentrums nachgewiesen werden.In mammals and birds, sulfite oxidase (SO) is a homodimeric Mo-enzyme consisting of a N-terminal heme-domain and a C-terminal Mo-domain (EC 1.8.3.1). In plants, the existence of SO has not yet been demonstrated, while sulfite reductase as part of sulfur assimilation is well characterized. Here we report the cloning of a plant sulfite oxidase gene from Arabidopsis thaliana and the biochemical characterization of the encoded protein (At-SO). At-SO is a Mo-enzyme with molybdopterin as organic component of the molybdenum cofactor. In contrast to homologous animal enzymes, At-SO lacks the heme domain which is evident both from the amino acid sequence and from its enzymological and spectral properties. Thus, among eukaryotes, At-SO is the only Mo-enzyme yet described possessing no redox-active centers other than the molybdenum. UV/visible and EPR spectra as well as apparent KM values are presented and compared to the hepatic enzyme. Subcellular analysis of crude cell extracts showed that SO was mostly found in the peroxisomal fraction. In molybdenum cofactor-mutants, the activity of SO was strongly reduced. Using antibodies directed against At-SO, we show that a cross-reacting protein of similar size occurs in a wide range of plant species, including both herbacious and woody plants

Development and Short-Range Testing of a 100 kW Side-Illuminated Millimeter-Wave Thermal Rocket

The objective of the phase described here of the Millimeter-Wave Thermal Launch System (MTLS) Project was to launch a small thermal rocket into the air using millimeter waves. The preliminary results of the first MTLS flight vehicle launches are presented in this work. The design and construction of a small thermal rocket with a planar ceramic heat exchanger mounted along the axis of the rocket is described. The heat exchanger was illuminated from the side by a millimeter-wave beam and fed propellant from above via a small tank containing high pressure argon or nitrogen. Short-range tests where the rocket was launched, tracked, and heated with the beam are described. The rockets were approximately 1.5 meters in length and 65 millimeters in diameter, with a liftoff mass of 1.8 kilograms. The rocket airframes were coated in aluminum and had a parachute recovery system activated via a timer and Pyrodex. At the rocket heat exchanger, the beam distance was 40 meters with a peak power intensity of 77 watts per square centimeter. and a total power of 32 kilowatts in a 30 centimeter diameter circle. An altitude of approximately 10 meters was achieved. Recommendations for improvements are discussed

Fast algorithm for smoothing parameter selection in multidimensional generalized P-splines

A new computational algorithm for estimating the smoothing parameters of a multidimensional penalized spline generalized model with anisotropic penalty is presented. This new proposal is based on the mixed model representation of a multidimensional P-spline, in which the smoothing parameter for each covariate is expressed in terms of variance components. On the basis of penalized quasi-likelihood methods (PQL), closed-form expressions for the estimates of the variance components are obtained. This formulation leads to an efficient implementation that can considerably reduce the computational load. The proposed algorithm can be seen as a generalization of the algorithm by Schall (1991) - for variance components estimation - to deal with non-standard structures of the covariance matrix of the random effects. The practical performance of the proposed computational algorithm is evaluated by means of simulations, and comparisons with alternative methods are made on the basis of the mean square error criterion and the computing time. Finally, we illustrate our proposal with the analysis of two real datasets: a two dimensional example of historical records of monthly precipitation data in USA and a three dimensional one of mortality data from respiratory disease according to the age at death, the year of death and the month of deathThe authors would like to express their gratitude for the support received in the form of the Spanish Ministry of Economy and Competitiveness grants MTM2011-28285-C02-01 and MTM2011-28285-C02-02. Work of Mar a Xose Rodríguez - Alvarez was supported by grant CA09/0053 from the Instituto de Salud Carlos III. The research of Dae-Jin Lee was funded by an NIH grant for the Superfund Metal Mixtures, Biomarkers and Neurodevelopment project 1PA2ES016454-01A

Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm

A new computational algorithm for estimating the smoothing parameters of a multidimensional penalized spline generalized linear model with anisotropic penalty is presented. This new proposal is based on the mixed model representation of a multidimensional P-spline, in which the smoothing parameter for each covariate is expressed in terms of variance components. On the basis of penalized quasi-likelihood methods, closed-form expressions for the estimates of the variance components are obtained. This formulation leads to an efficient implementation that considerably reduces the computational burden. The proposed algorithm can be seen as a generalization of the algorithm by Schall (1991)-for variance components estimation-to deal with non-standard structures of the covariance matrix of the random effects. The practical performance of the proposed algorithm is evaluated by means of simulations, and comparisons with alternative methods are made on the basis of the mean square error criterion and the computing time. Finally, we illustrate our proposal with the analysis of two real datasets: a two dimensional example of historical records of monthly precipitation data in USA and a three dimensional one of mortality data from respiratory disease according to the age at death, the year of death and the month of death.The authors would like to express their gratitude for the support received in the form of the Spanish Ministry of Economy and Competitiveness grants MTM2011-28285-C02-01 and MTM2011-28285-C02-02. The research of Dae-Jin Lee was funded by an NIH grant for the Superfund Metal Mixtures, Biomarkers and Neurodevelopment project 1PA2ES016454-01A2

Direct imaging of the structural change generated by dielectric breakdown in MgO based magnetic tunnel junctions

MgO based magnetic tunnel junctions are prepared to investigate the dielectric breakdown of the tunnel barrier. The breakdown is directly visualized by transmission electron microscopy measurements. The broken tunnel junctions are prepared for the microscopy measurements by focussed ion beam out of the junctions characterized by transport investigations. Consequently, a direct comparison of transport behavior and structure of the intact and broken junctions is obtained. Compared to earlier findings in Alumina based junctions, the MgO barrier shows much more microscopic pinholes after breakdown. This can be explained within a simple model assuming a relationship between the current density at the breakdown and the rate of pinhole formation

Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry

Thyroid volumetry is crucial in the diagnosis, treatment, and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D and tracked 3D ultrasound with an automatic thyroid segmentation based on a deep neural network regarding inter- and intraobserver variability, time, and accuracy. Volume reference was MRI. 28 healthy volunteers (24—50 a) were scanned with 2D and 3D ultrasound (and by MRI) by three physicians (MD 1, 2, 3) with different experience levels (6, 4, and 1 a). In the 2D scans, the thyroid lobe volumes were calculated with the ellipsoid formula. A convolutional deep neural network (CNN) automatically segmented the 3D thyroid lobes. 26, 6, and 6 random lobe scans were used for training, validation, and testing, respectively. On MRI (T1 VIBE sequence) the thyroid was manually segmented by an experienced MD. MRI thyroid volumes ranged from 2.8 to 16.7ml (mean 7.4, SD 3.05). The CNN was trained to obtain an average Dice score of 0.94. The interobserver variability comparing two MDs showed mean differences for 2D and 3D respectively of 0.58 to 0.52ml (MD1 vs. 2), −1.33 to −0.17ml (MD1 vs. 3) and −1.89 to −0.70ml (MD2 vs. 3). Paired samples t-tests showed significant differences for 2D (p = .140, p = .002 and p = .002) and none for 3D (p = .176, p = .722 and p = .057). Intraobsever variability was similar for 2D and 3D ultrasound. Comparison of ultrasound volumes and MRI volumes showed a significant difference for the 2D volumetry of all MDs (p = .002, p = .009, p <.001), and no significant difference for 3D ultrasound (p = .292, p = .686, p = 0.091). Acquisition time was significantly shorter for 3D ultrasound. Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times

Orbit design for satellite swarm-based interferometric radiometers for super-resolution earth observation

Soil moisture and ocean salinity mapping by earth observation satellites has contributed significantly towards a better understanding of the earth’s climate and hydrosphere. Nevertheless, an increased spatial resolution of radiometric data could yield a more complete picture of global hydrological and climate processes. High-resolution radiometers, such as SMOS, have already approached prohibitive sizes for spacecraft due to the required large antenna apertures. Radiometer concepts based on satellites flying in close proximity have been proposed as a possible solution. Individual receivers placed on a large number of smaller satellites orbiting a central satellite would form a combined interferometric array. Recent technological progress in formation flying, satellite miniaturisation, inter-satellite links and data processing could make a future satellite swarm-based radiometer possible. The design of such a system requires a methodology which enables the determination of orbit parameters in a way that optimizes radiometer performance and ensures system feasibility. In the past, the optimization of interferometric array configurations has only aimed to optimize the image quality without taking into account system constraints, such as satellite collision risk, satellite fuel consumption and other feasibility considerations. This resulted in idealized array configurations that might put unrealistic constraints on the satellite system. A current research project of the DLR Microwaves and Radar Institute investigates methodologies for the orbit optimization of large satellite swarm-based interferometric radiometers regarding future earth observation radiometry missions. For this purpose a system simulator has been created for the study of radiometers based on a large number of spacecraft. First results have indicated that an approach based on statistical methods for the quantification of radiometer performance and the use of numeric optimization solvers can yield promising orbit configurations. This paper provides an overview of the optimization approach and first results in generating a feasible and performant satellite swarm configuration for interferometric radiometry purposes

A trivariate additive regression model with arbitrary link functions and varying correlation matrix

In many empirical situations, modelling simultaneously three or more outcomes as well as their dependence structure can be of considerable relevance. Copulae provide a powerful framework to build multivariate distributions and allow one to view the specification of the marginal responses’ equations and their dependence as separate but related issues. We propose a generalizationof the trivariate additive probit model where the link functions can in principle be derived from any parametric distribution and the parameters describing the residual association between the responses can be made dependent on several types of covariate effects (such as linear, nonlinear, random, and spatial effects). All the coefficients of the model are estimated simultaneously within a penalized likelihood framework that uses a trust region algorithm with integrated automatic multiple smoothing parameter selection. The effectiveness of the model is assessed in simulation as well as empirically by modelling jointly three adverse birth binary outcomes in North Carolina. The approach can be easily employed via the gjrm() function in the R package GJRM