On strongly stable normal operators in Krein spaces

For bounded normal operators in Krein spaces we give a necessary and sufficient condition for strong stability. The same result for unitary operators was obtained by M.G.Krein [1] (see also [2]). For selfadjoint operators we refer to the papers of P.Jonas, H.Langer [3] and H.Langer [4].     

Targeting cell surface receptors with ligand-conjugated nanocrystals

To explore the potential for use of ligand-conjugated nanocrystals to target cell surface receptors, ion channels, and transporters, we explored the ability of serotonin-labeled CdSe nanocrystals (SNACs) to interact with antidepressant-sensitive, human and Drosophila serotonin transporters (hSERT, dSERT) expressed in HeLa and HEK-293 cells. Unlike unconjugated nanocrystals, SNACs were found to dose-dependently inhibit transport of radiolabeled serotonin by hSERT and dSERT, with an estimated half-maximal activity (EC(50)) of 33 (dSERT) and 99 microM (hSERT). When serotonin was conjugated to the nanocrystal through a linker arm (LSNACs), the EC(50) for hSERT was determined to be 115 microM. Electrophysiology measurements indicated that LSNACs did not elicit currents from the serotonin-3 (5HT(3)) receptor but did produce currents when exposed to the transporter, which are similar to those elicited by antagonists. Moreover, fluorescent LSNACs were found to label SERT-transfected cells but did not label either nontransfected cells or transfected cells coincubated with the high-affinity SERT antagonist paroxetine. These findings support further consideration of ligand-conjugated nanocrystals as versatile probes of membrane proteins in living cells.     

The use of acoustical test fixtures for the measurement of hearing protector attenuation. Part I: Review of previous work and the design of an improved test fixture

This paper gives a comprehensive progress report on the development of objective methods for measuring the attenuation of hearing protection devices (HPD's), and focuses on the use of acoustic test fixtures (ATF's), i.e., artificial heads. While there are many publications on ATF's for the evaluation of circumaural HPD's (earmuffs), only one serious attempt to construct an ATF for the evaluation of intra-aural HPD's (earplugs) could be found. Consequently, no ATF for testing earplugs has been standardized so far, while two standardized ATF's currently exist for testing earmuffs [see ANSI S3.19-1974 (1975) and ISO/DIS 6290 (1983)]. Both ATF's are suited, however, only for production testing and are not designed for HPD-type testing. It is believed that both ATF's do not provide sufficiently high accuracy for HPD-type testing. A new ATF with appropriate circumaural and intra-aural flesh simulations was constructed, including a suitable ear simulator and a cast of an average pinna. Objectives for design and construction of the new ATF are discussed. The effect of using artificial flesh on the insertion loss of earmuffs (max. 5 dB at 125 and/or 250 Hz) and the effect of using a pinna (max. 12 dB lower insertion loss at 2 kHz) were evaluated.     

Ueber den Multiplicator bei der Bestimmung des Zuckers im Harn nach Roberts

Ohne Zusammenfassung     

Refined elliptic tropical enumerative invariants

We suggest a new refined (i.e., depending on a parameter) tropical enumerative invariant of toric surfaces. This is the first known enumerative invariant that counts tropical curves of positive genus with marked vertices. Our invariant extends the refined rational broccoli invariant invented by L. Göttsche and the first author, though there is a serious difference between the invariants: our elliptic invariant counts weights assigned partly to individual tropical curves and partly to collections of tropical curves, and our invariant is not always multiplicative over the vertices of the counted tropical curves as was the case for other known tropical enumerative invariants of toric surfaces. As a consequence we define elliptic broccoli curves and elliptic broccoli invariants as well as elliptic tropical descendant invariants for any toric surface.     

Broccoli curves and the tropical invariance of Welschinger numbers

In this paper we introduce broccoli curves, certain plane tropical curves of genus zero related to real algebraic curves. The numbers of these broccoli curves through given points are independent of the chosen points — for arbitrary choices of the directions of the ends of the curves, possibly with higher weights, and also if some of the ends are fixed. In the toric Del Pezzo case we show that these broccoli invariants are equal to the Welschinger invariants (with real and complex conjugate point conditions), thus providing a proof of the independence of Welschinger invariants of the point conditions within tropical geometry. The general case gives rise to a tropical Caporaso–Harris formula for broccoli curves which suffices to compute all Welschinger invariants of the plane.     

A probabilistic approach to classifying metabolic stability

Metabolic stability is an important property of drug molecules that should-optimally-be taken into account early on in the drug design process. Along with numerous medium- or high-throughput assays being implemented in early drug discovery, a prediction tool for this property could be of high value. However, metabolic stability is inherently difficult to predict, and no commercial tools are available for this purpose. In this work, we present a machine learning approach to predicting metabolic stability that is tailored to compounds from the drug development process at Bayer Schering Pharma. For four different in vitro assays, we develop Bayesian classification models to predict the probability of a compound being metabolically stable. The chosen approach implicitly takes the "domain of applicability" into account. The developed models were validated on recent project data at Bayer Schering Pharma, showing that the predictions are highly accurate and the domain of applicability is estimated correctly. Furthermore, we evaluate the modeling method on a set of publicly available data.     

Accurate solubility prediction with error bars for electrolytes: a machine learning approach

Accurate in silico models for predicting aqueous solubility are needed in drug design and discovery and many other areas of chemical research. We present a statistical modeling of aqueous solubility based on measured data, using a Gaussian Process nonlinear regression model (GPsol). We compare our results with those of 14 scientific studies and 6 commercial tools. This shows that the developed model achieves much higher accuracy than available commercial tools for the prediction of solubility of electrolytes. On top of the high accuracy, the proposed machine learning model also provides error bars for each individual prediction.