Efficiency at maximum power of low-dissipation Carnot engines

We study the efficiency at maximum power, η*, of engines performing finite-time Carnot cycles between a hot and a cold reservoir at temperatures Th and Tc, respectively. For engines reaching Carnot efficiency ηC=1-Tc/Th in the reversible limit (long cycle time, zero dissipation), we find in the limit of low dissipation that η* is bounded from above by ηC/(2-ηC) and from below by ηC/2. These bounds are reached when the ratio of the dissipation during the cold and hot isothermal phases tend, respectively, to zero or infinity. For symmetric dissipation (ratio one) the Curzon-Ahlborn efficiency ηCA=1-√Tc/Th] is recovered.     

Universality of synchrony: critical behavior in a discrete model of stochastic phase-coupled oscillators

We present the simplest discrete model to date that leads to synchronization of stochastic phase-coupled oscillators. In the mean field limit, the model exhibits a Hopf bifurcation and global oscillatory behavior as coupling crosses a critical value. When coupling between units is strictly local, the model undergoes a continuous phase transition which we characterize numerically using finite-size scaling analysis. In particular, the onset of global synchrony is marked by signatures of the XY universality class, including the appropriate classical exponents beta and nu, a lower critical dimension d(lc) = 2, and an upper critical dimension d(uc) = 4.     

A novel methodological approach for δ18O analysis of sugars using gas chromatography-pyrolysis-isotope ratio mass spectrometry

Although the instrumental coupling of gas chromatography-pyrolysis-isotope ratio mass spectrometry (GC-Py-IRMS) for compound-specific δ¹⁸O analysis has been commercially available for more than a decade, this method has been hardly applied so far. Here we present the first GC-Py-IRMS δ¹⁸O results for trimethylsilyl-derivatives of plant sap-relevant sugars and a polyalcohol (glucose, fructose, sucrose, raffinose and pinitol). Particularly, we focus on sucrose, which is assimilated in leaves and which is the most important transport sugar in plants and hence of utmost relevance in plant physiology and paleoclimate studies. Replication measurements of sucrose standards and concentration series indicate that the GC-Py-IRMS δ¹⁸O measurements are not stable over time and that they are amount (area) dependent. We, therefore, suggest running sample batch replication measurements in alternation with standard concentration series of reference material. This allows for carrying out (i) a drift correction, (ii) a calibration against reference material and (iii) an amount (area) correction. Tests with ¹⁸O-enriched water do not provide any evidence for oxygen isotope exchange reactions affecting sucrose and raffinose. We present the first application of GC-Py-IRMS δ¹⁸O analysis for sucrose from needle extract (soluble carbohydrate) samples. The obtained δ¹⁸O_{sucrose/ Vienna Standard Mean Ocean Water (VSMOW)} values are more positive and vary in a wider range (32.1–40.1 ‰) than the δ¹⁸O_{bulk/ VSMOW} values (24.6–27.2 ‰). Furthermore, they are shown to depend on the climate parameters maximum day temperature, relative air humidity and cloud cover. These findings suggest that δ¹⁸O_sucrose of the investigated needles very sensitively reflects the climatically controlled evaporative ¹⁸O enrichment of leaf water and thus highlights the great potential of GC-Py-IRMS δ¹⁸O_sucrose analysis for plant physiology and paleoclimate studies.     

The Endomorphism Type of Certain Bipartite Graphs and a Characterization of Projective Planes

Fan (in Southeast Asian Bull Math 25, 217–221, 2001) determines the endomorphism type of a finite projective plane. In this note we show that Fan’s result actually characterizes the class of projective planes among the finite bipartite graphs of diameter three. In fact, this will follow from a generalization of Fan’s theorem and its converse to all finite bipartite graphs with diameter d and girth g such that (1) d + 1<g≤2d, and (2) every pair of adjacent edges is contained in a circuit of length g.     

A Polyketide Synthase Component for Oxygen Insertion into Polyketide Backbones

Enzymatic core components from trans‐acyltransferase polyketide synthases (trans‐AT PKSs) catalyze exceptionally diverse biosynthetic transformations to generate structurally complex bioactive compounds. Here we focus on a group of oxygenases identified in various trans‐AT PKS pathways, including those for pederin, oocydins, and toblerols. Using the oocydin pathway homologue (OocK) from Serratia plymuthica 4Rx13 and N‐acetylcysteamine (SNAC) thioesters as test surrogates for acyl carrier protein (ACP)‐tethered intermediates, we show that the enzyme inserts oxygen into β‐ketoacyl moieties to yield malonyl ester SNAC products. Based on these data and the identification of a non‐hydrolyzed oocydin congener with retained ester moiety, we propose a unified biosynthetic pathway of oocydins, haterumalides, and biselides. By providing access to internal ester, carboxylate pseudostarter, and terminal hydroxyl functions, oxygen insertion into polyketide backbones greatly expands the biosynthetic scope of PKSs.     

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.