The quasi-free-standing nature of graphene on H-saturated SiC(0001)

We report on an investigation of quasi-free-standing graphene on 6H-SiC(0001) which was prepared by intercalation of hydrogen under the buffer layer. Using infrared absorption spectroscopy we prove that the SiC(0001) surface is saturated with hydrogen. Raman spectra demonstrate the conversion of the buffer layer into graphene which exhibits a slight tensile strain and short range defects. The layers are hole doped (p = 5.0-6.5 x 10^12 cm^(-2)) with a carrier mobility of 3,100 cm^2/Vs at room temperature. Compared to graphene on the buffer layer a strongly reduced temperature dependence of the mobility is observed for graphene on H-terminated SiC(0001)which justifies the term "quasi-free-standing".Comment: 3 pages, 3 figures, accepted for publication in Applied Physics Letter

Portfolio selection problems in practice: a comparison between linear and quadratic optimization models

Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints. We propose a completely new approach for solving the LAM model, based on reformulation as a Standard Quadratic Program and on some recent theoretical results. With this approach we obtain optimal solutions both for some well-known financial data sets used by several other authors, and for some unsolved large size portfolio problems. We also test our method on five new data sets involving real-world capital market indices from major stock markets. Our computational experience shows that, rather unexpectedly, it is easier to solve the quadratic LAM model with our algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of the best commercial codes for mixed integer linear programming (MILP) problems. Finally, on the new data sets we have also compared, using out-of-sample analysis, the performance of the portfolios obtained by the Limited Asset models with the performance provided by the unconstrained models and with that of the official capital market indices

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure

Toxic Epidermal Necrolysis after Pemetrexed and Cisplatin for Non-Small Cell Lung Cancer in a Patient with Sharp Syndrome

Background: Pemetrexed is an antifolate drug approved for maintenance and second-line therapy, and, in combination with cisplatin, for first-line treatment of advanced nonsquamous non-small cell lung cancer. The side-effect profile includes fatigue, hematological and gastrointestinal toxicity, an increase in hepatic enzymes, sensory neuropathy, and pulmonary and cutaneous toxicity in various degrees. Case Report: We present the case of a 58-year-old woman with history of Sharp's syndrome and adenocarcinoma of the lung, who developed toxic epidermal necrolysis after the first cycle of pemetrexed, including erythema, bullae, extensive skin denudation, subsequent systemic inflammation and severe deterioration in general condition. The generalized skin lesions occurred primarily in the previous radiation field and responded to immunosuppressive treatment with prednisone. Conclusion: Although skin toxicity is a well-known side effect of pemetrexed, severe skin reactions after pemetrexed administration are rare. Caution should be applied in cases in which pemetrexed is given subsequent to radiation therapy, especially in patients with pre-existing skin diseases

Appropriate Inhibition of Orexigenic Hypothalamic Arcuate Nucleus Neurons Independently of Leptin Receptor/STAT3 Signaling

Leptin directly suppresses the activity of orexigenic neurons in the hypothalamic arcuate nucleus (ARC). We examined c-Fos-like immunoreactivity (CFLIR) as a marker of ARC neuronal activity in db/db mice devoid of the signaling form of the leptin receptor (LRb) and s/s mice that express LRbS1138 [which is defective for STAT3 (signal transducer and activator of transcription) signaling]. Both db/db and s/s animals are hyperphagic and obese. This analysis revealed that CFLIR in agouti related peptide-expressing orexigenic ARC neurons is basally elevated in db/db but not s/s mice. Consistent with these observations, electrophysiologic evaluation of a small number of neurons in s/s animals suggested that leptin appropriately suppresses the frequency of IPSCs on ARC proopiomelanocortin (POMC) neurons that are mediated by the release of GABA from orexigenic ARC neurons. CFLIR in POMC neurons of s/s mice was also increased compared with db/db animals. Thus, these data suggest that, although LRb→STAT3 signaling is crucial for the regulation of feeding, it is not required for the acute or chronic regulation of orexigenic ARC neurons, and the activation of STAT3-mediated transcription by leptin is not required for the appropriate development of leptin responsiveness in these neurons

Direct observation of free-exciton thermalization in quantum-well structures

We report on a direct observation of free-exciton thermalization in quantum-well structures. A narrow energy distribution of free 1s excitons is created in ZnSe-based quantum wells by emission of one LO phonon after optical excitation of the continuum states with picosecond laser pulses. The subsequent relaxation dynamics within the 1s-exciton dispersion is directly monitored by time-resolved studies of the phonon-assisted photoluminescence. It is demonstrated that the free-exciton distribution remains nonthermal for some 100 ps. The observed dynamics is in reasonable agreement with numerical results of a rate-equation model which accounts for the relevant exciton-phonon coupling mechanisms

Stochastic Description of Aircraft Simulation Models and Numerical Approaches

Der Artikel befasst sich mit der Unsicherheitsquantifizierung eines Flugzeugsimulationsmodells. Mathematisch gesehen handelt es sich bei dem Flugzeugmodell um ein System von Differentialgleichungen zweiter Ordnung. Dabei hängt das System von Eingangsparametern ab, die der Masse, der Aerodynamik und der Struktur des Flugzeugs zugeordnet sind. Die aerodynamischen Eingangsparameter sind als Zufallsvariablen und -prozesse modelliert, deren Wahrscheinlichkeitsverteilungen nach dem Maximum-Entropie-Prinzip und verfügbaren experimentellen Daten ausgewählt werden. Für das Flugdynamikmodell wird die Unsicherheitsentwicklung der Flugzustandsverläufe mittels sogenannter nicht-intrusiver numerischer Verfahren abgeschätzt. Die Verfahren sind beispielsweise direkte Integrationsverfahren, stochastische Kollokationsverfahren und Pseudo-Galerkin Verfahren. Diese numerischen Verfahren basieren auf Stichproben von Flugzustandsverläufen, welche durch das Lösen der zugehörigen deterministischen gewöhnlichen Differentialgleichungen erhalten werden.This paper is concerned with the uncertainty quantification of an aircraft simulation model. Mathematically speaking the aircraft model represents a system of second order differential equations dependent on a set of input parameters related to the mass, aerodynamics and the structure of the aircraft. The input aerodynamic parameters are modelled as random variables and processes whose probability distributions are chosen according to the maximum entropy principle and available experimental data. For a flight dynamics model the evolution of uncertainties in the aircraft state trajectory is estimated with the help of so-called non-intrusive numerical approaches, examples of which are the direct integration method, the stochastic collocation approach and the pseudo-Galerkin method. These numerical methods rely on a set of samples of aircraft state trajectories simply obtained by solving the corresponding systems of deterministic ordinary differential equations

Whole Exome Sequencing of Patients with Steroid-Resistant Nephrotic Syndrome

BACKGROUND AND OBJECTIVES: Steroid-resistant nephrotic syndrome overwhelmingly progresses to ESRD. More than 30 monogenic genes have been identified to cause steroid-resistant nephrotic syndrome. We previously detected causative mutations using targeted panel sequencing in 30% of patients with steroid-resistant nephrotic syndrome. Panel sequencing has a number of limitations when compared with whole exome sequencing. We employed whole exome sequencing to detect monogenic causes of steroid-resistant nephrotic syndrome in an international cohort of 300 families. DESIGN, SETTING, PARTIIPANTS AND MEASUREMENTS: Three hundred thirty-five individuals with steroid-resistant nephrotic syndrome from 300 families were recruited from April of 1998 to June of 2016. Age of onset was restricted to <25 years of age. Exome data were evaluated for 33 known monogenic steroid-resistant nephrotic syndrome genes. RESULTS: In 74 of 300 families (25%), we identified a causative mutation in one of 20 genes known to cause steroid-resistant nephrotic syndrome. In 11 families (3.7%), we detected a mutation in a gene that causes a phenocopy of steroid-resistant nephrotic syndrome. This is consistent with our previously published identification of mutations using a panel approach. We detected a causative mutation in a known steroid-resistant nephrotic syndrome gene in 38% of consanguineous families and in 13% of nonconsanguineous families, and 48% of children with congenital nephrotic syndrome. A total of 68 different mutations were detected in 20 of 33 steroid-resistant nephrotic syndrome genes. Fifteen of these mutations were novel. NPHS1, PLCE1, NPHS2, and SMARCAL1 were the most common genes in which we detected a mutation. In another 28% of families, we detected mutations in one or more candidate genes for steroid-resistant nephrotic syndrome. CONCLUSIONS: Whole exome sequencing is a sensitive approach toward diagnosis of monogenic causes of steroid-resistant nephrotic syndrome. A molecular genetic diagnosis of steroid-resistant nephrotic syndrome may have important consequences for the management of treatment and kidney transplantation in steroid-resistant nephrotic syndrome

Quantitative analysis of spectroscopic Low Energy Electron Microscopy data: High-dynamic range imaging, drift correction and cluster analysis

For many complex materials systems, low-energy electron microscopy (LEEM) offers detailed insights into morphology and crystallography by naturally combining real-space and reciprocal-space information. Its unique strength, however, is that all measurements can easily be performed energy-dependently. Consequently, one should treat LEEM measurements as multi-dimensional, spectroscopic datasets rather than as images to fully harvest this potential. Here we describe a measurement and data analysis approach to obtain such quantitative spectroscopic LEEM datasets with high lateral resolution. The employed detector correction and adjustment techniques enable measurement of true reflectivity values over four orders of magnitudes of intensity. Moreover, we show a drift correction algorithm, tailored for LEEM datasets with inverting contrast, that yields sub-pixel accuracy without special computational demands. Finally, we apply dimension reduction techniques to summarize the key spectroscopic features of datasets with hundreds of images into two single images that can easily be presented and interpreted intuitively. We use cluster analysis to automatically identify different materials within the field of view and to calculate average spectra per material. We demonstrate these methods by analyzing bright-field and dark-field datasets of few-layer graphene grown on silicon carbide and provide a high-performance Python implementation