Quantification of testosterone undecanoate in human hair by liquid chromatography–tandem mass spectrometry

Testosterone undecanoate (T‐C11) can be used by athletes in order to improve performance. After oral intake, T‐C11 is rapidly metabolized, hampering discrimination between exogenous and endogenous testosterone. A possible alternative is to detect the intact ester in hair. A method based on liquid chromatography–tandem mass spectrometry was developed for the determination of T‐C11 in hair. The sample procedure consisted of digestion of 200 mg of pulverized hair with tris(2‐carboxyethyl)phosphine hydrochloride and liquid–liquid extraction with n‐pentane. Several parameters such as the mobile phase, the ionization source and the washing step were optimized. The method was validated at different spiked levels obtaining satisfactory values for accuracy (between 92 and 102%) with relative standard deviations lower than 7% and a limit of detection of 0.2 ng/g. The applicability of the method was checked by the analysis of three samples from patients using T‐C11. A peak for the analyte was detected in all samples with concentrations between 0.4 and 8.4 ng/g. Copyright © 2009 John Wiley & Sons, Ltd.     

Multiple Observations for Secret-Key Binding with SRAM PUFs

We present a new Multiple-Observations (MO) helper data scheme for secret-key binding to an SRAM-PUF. This MO scheme binds a single key to multiple enrollment observations of the SRAM-PUF. Performance is improved in comparison to classic schemes which generate helper data based on a single enrollment observation. The performance increase can be explained by the fact that the reliabilities of the different SRAM cells are modeled (implicitly) in the helper data. We prove that the scheme achieves secret-key capacity for any number of enrollment observations, and, therefore, it is optimal. We evaluate performance of the scheme using Monte Carlo simulations, where an off-the-shelf LDPC code is used to implement the linear error-correcting code. Another scheme that models the reliabilities of the SRAM cells is the so-called Soft-Decision (SD) helper data scheme. The SD scheme considers the one-probabilities of the SRAM cells as an input, which in practice are not observable. We present a new strategy for the SD scheme that considers the binary SRAM-PUF observations as an input instead and show that the new strategy is optimal and achieves the same reconstruction performance as the MO scheme. Finally, we present a variation on the MO helper data scheme that updates the helper data sequentially after each successful reconstruction of the key. As a result, the error-correcting performance of the scheme is improved over time.     

Activity of the acyl-CoA synthetase ACSL6 isoforms: role of the fatty acid Gate-domains

Background  

Activation of fatty acids by acyl-CoA synthetase enzymes is required for de novo lipid synthesis, fatty acid catabolism, and remodeling of biological membranes. Human long-chain acyl-CoA synthetase member 6, ASCL6, is a form present in the plasma membrane of cells. Splicing events affecting the amino-terminus and alternative motifs near the ATP-binding site generate different isoforms of ACSL6.     

Step-shape angular spin distribution in layered systems by Fe Mössbauer spectroscopy: A general treatment

In the so-called ‘step-shape’ angular spin distribution model for layered systems, the non-collinear directions of the atomic magnetic moments are confined to the film plane and form a homogeneous fan spanning inside an (in-plane) angular interval Δφ centered at an angle φ₀. A general approach for deriving the two parameters φ₀ and Δφ via ⁵⁷Fe Mössbauer spectroscopy measurements is discussed. The analysis extends our previously reported treatment, which assumed that the angular aperture Δφ develops symmetrically versus a fixed direction φ₀ (e.g., the in-plane easy axis of magnetization) oriented either along or perpendicular to the in-plane projection of the Mössbauer γ-ray direction. The proposed approach is also applicable for those cases when not only the spin aperture Δφ is changing but also the aperture center φ₀ is rotating under the influence of different external parameters, such as applied field, temperature, stress, etc. The method is suitable for applications to nanoscale layered heterostructures with in-plane uniaxial or unidirectional magnetic anisotropy. The method is applied to experimental data obtained on a 2-nm thick defected Fe layer with in-plane magnetic texture.     

Atomic Vibrational Dynamics of Thin Films Studied by Nuclear Resonant Inelastic X-Ray Scattering: Amorphous Tb1−x Fe x Alloys

Hyperfine Interactions - Nuclear resonant inelastic X-ray scattering (NRIXS) of 14.4125 keV synchrotron radiation was used to measure directly the partial vibrational density of states (VDOS),...     

Robust optimization of uncertain multistage inventory systems with inexact data in decision rules

In production-inventory problems customer demand is often subject to uncertainty. Therefore, it is challenging to design production plans that satisfy both demand and a set of constraints on e.g. production capacity and required inventory levels. Adjustable robust optimization (ARO) is a technique to solve these dynamic (multistage) production-inventory problems. In ARO, the decision in each stage is a function of the data on the realizations of the uncertain demand gathered from the previous periods. These data, however, are often inaccurate; there is much evidence in the information management literature that data quality in inventory systems is often poor. Reliance on data “as is” may then lead to poor performance of “data-driven” methods such as ARO. In this paper, we remedy this weakness of ARO by introducing a model that treats past data itself as an uncertain model parameter. We show that computational tractability of the robust counterparts associated with this extension of ARO is still maintained. The benefits of the new model are demonstrated by a numerical test case of a well-studied production-inventory problem. Our approach is also applicable to other ARO models outside the realm of production-inventory planning.