Determination of purines including 2,8-dihydroxyadenine in urine using capillary electrophoresis

For the purpose of rapid drug monitoring, methods have been developed for the determination of 2,8-dihydroxyadenine, allopurinol, oxypurinol, adenine, hypoxanthine, hippuric acid and xanthine in urine with and without sodium dodecyl sulfate as additive in sodium tetraborate running buffer. No sample preparation is necessary. 6-methylmercaptopurine and etofylline have been used as the internal standards. The limit of detection is 5 microM and the range of quantification stretches from 20 to 2000 microM. The capillary electrophoresis methods are simple, fast and robust.     

Synthetic α,β(1→4)-Glucan Oligosaccharides as Models for Heparan Sulfate

Abstract

α,β-(1→4)-Glucans were devised as models for heparan sulfate with the simplifying assumptions that carboxyl-reduction and sulfation of heparan sulfate does not decrease the SMC antiproliferative activity and that N-sulfates in glucosamines can be replaced by O-sulfates. The target oligo-saccharides were synthesized using maltosyl building blocks. Glycosylation of methyl 2,3,6,2′,3′,6′-hexa-O-benzyl-β-maltoside (1) with hepta-O-acetyl-α-maltosyl bromide (2) furnished tetrasaccharide 3 which was deprotected to α-D-Glc-(1→4)-β-D-Glc-(1→4)-α-D-Glc-(1→4)-β-D-Glc-(1→OCH₃) (5) or, alternatively, converted to the tetrasaccharide glycosyl acceptor (8) with one free hydroxyl function (4?′-OH). Further glycosylation with glucosyl or maltosyl bromide followed by deblocking gave the pentasaccharide [β-D-Glc-(1→4)-α-D-Glc-(1→4)]₂-β-D-Glc-(1→OCH₃) (11) and hexasaccharide [α-D-Glc-(1→4)-β-D-Glc-(1→4)₂-α-D-Glc-(1→4)-β-D-Glc-(1→OCH₃) (14). The protected tetrasaccharide 3 and hexasaccharide 12 were fully characterized by ¹H and ¹³C NMR spectroscopy. Assignments were possible using 1D TOCSY, T-ROESY, ¹H,¹H 2D COSY supplemented by ¹H-detected one-bond and multiple-bond ¹H,¹³C 2D COSY experiments.     

A High Performing Zn‐Ion Battery Cathode Enabled by In Situ Transformation of V2O5 Atomic Layers

Developing high capacity and stable cathodes is a key to successful commercialization of aqueous Zn‐ion batteries (ZIBs). Pure layered V₂O₅ has a high theoretical capacity (585 mAh g^?1), but it suffers severe capacity decay. Pre‐inserting cations into V₂O₅ can substantially stabilize the performance, but at an expense of lowered capacity. Here we show that an atomic layer deposition derived V₂O₅ can be an excellent ZIB cathode with high capacity and exceptional cycle stability at once. We report a rapid in situ on‐site transformation of V₂O₅ atomic layers into Zn₃V₂O₇(OH)₂?2 H₂O (ZVO) nanoflake clusters, also a known Zn‐ion and proton intercalatable material. High concentration of reactive sites, strong bonding to the conductive substrate, nanosized thickness and binder‐free composition facilitate ionic transport and promote the best utilization of the active material. We also provide new insights into the V₂O₅‐dissolution mechanisms for different Zn‐salt aqueous electrolytes and their implications to the cycle stability.     

Performance limitations of flat-histogram methods

We determine the optimal scaling of local-update flat-histogram methods with system size by using a perfect flat-histogram scheme based upon the exact density of states of 2D Ising models. The typical tunneling time needed to sample the entire bandwidth does not scale with the number of spins N as the minimal N2 of an unbiased random walk in energy space. While the scaling is power law for the ferromagnetic and fully frustrated Ising model, for the +/-J nearest-neighbor spin glass the distribution of tunneling times is governed by a fat-tailed Fréchet extremal value distribution that obeys exponential scaling. Furthermore, the shape parameters of these distributions indicate that statistical sample means become ill defined already for moderate system sizes within these complex energy landscapes.     

Predicting thermal displacements in modular tool systems

In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated first from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and again get promising results. The thermally induced errors can be estimated with 1-2 microm accuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems.     

Short-term forecasting of life-threatening cardiac arrhythmias based on symbolic dynamics and finite-time growth rates

Ventricular tachycardia or fibrillation (VT-VF) as fatal cardiac arrhythmias are the main factors triggering sudden cardiac death. The objective of this study is to find early signs of sustained VT-VF in patients with an implanted cardioverter-defibrillator (ICD). These devices are able to safeguard patients by returning their hearts to a normal rhythm via strong defibrillatory shocks; additionally, they store the 1000 beat-to-beat intervals immediately before the onset of a life-threatening arrhythmia. We study these 1000 beat-to-beat intervals of 17 chronic heart failure ICD patients before the onset of a life-threatening arrhythmia and at a control time, i.e., without a VT-VF event. To characterize these rather short data sets, we calculate heart rate variability parameters from the time and frequency domain, from symbolic dynamics as well as the finite-time growth rates. We find that neither the time nor the frequency domain parameters show significant differences between the VT-VF and the control time series. However, two parameters from symbolic dynamics as well as the finite-time growth rates discriminate significantly both groups. These findings could be of importance in algorithms for next generation ICD's to improve the diagnostics and therapy of VT-VF.