Optimizing the relative humidity to improve the stability of a proton exchange membrane by segmented fuel cell technology

The segmented fuel cell technology was applied to investigate the effects of the humidification conditions on the internal locally resolved performance and the stability of the fuel cell system. It was found at certain operating conditions, the time-dependent oscillation of current at potentio-static state appeared. The appearance of positive spikes of current indicated a temporary improved performance, while the negative current spikes indicated a temporary decreased performance. The periodic build-up and removal of liquid water in the cell caused unstable cell performance. Through the analyses of the evolution of the locally resolved current density distributions, the reasons for the positive or the negative spikes of current peaks with respect to a stationary value were found, which might be due to the drying-out of the membrane or the flooding of the membrane. The contour of the current density mapping differed to each other at the period of current peaks up or down, which might be due to different effect of the drying-out or flooding on the membrane. Through optimizing the relative humidity of anode (RHa) or cathode (RHc) of the fuel cell, the oscillation of the current disappeared and the performance of the cell became stable. RHc affects the performance of fuel cell much more obviously than RHa. The stability of the fuel cell system is also dependent on the imposed voltage. With the cell voltage decreased, the amplitude and the frequency of positive spikes of current increased.     

DIC image reconstruction on large cell scans

The width of the emission spectrum of a common fluorophore allows only for a limited number of spectral distinct fluorescent markers in the visible spectrum, which is also the regime where CCD-cameras are used in microscopy. For imaging of cells or tissues, it is required to obtain an image from which the morphology of the whole cell can be extracted. This is usually achieved by differential interference contrast (DIC) microscopy. These images have a pseudo-3D appearance, easily interpreted by the human brain. In the age of high throughput and high content screening, manual image processing is not an option. Conventional algorithms for image processing often use threshold-based criteria to identify objects of interest. These algorithms fail for DIC images as they have a range from dim to bright with an intermediate intensity equal to the background, so as to produce no clear object boundary. In this article we compare different reconstruction methods for up to 100 MB-large DIC images and implement a new iterative reconstruction method based on the Hilbert Transform that enables identification of cell boundaries with standard threshold algorithms.     

Uric Acid: The Sperm-Release Pheromone of the Marine Polychaete Platynereis dumerilii

In the marine polychaete, Platynereis dumerilii, reproductive behavior in the two sexes is synchronized by the consecutive discharge of male and female sex-specific pheromones. After the female releases the eggs into the free water column, immediate fertilization is achieved by several males circling around the eggs emitting sperm clouds. We report the isolation and identification of the sperm-release pheromone present in the coelomic fluid of sexually mature females. Each step in isolation was guided by bioassay. Isolation methods included extraction and solvent partitioning and separation methods included ultrafiltration and high-performance liquid chromatography. Uric acid was identified as the sperm-release pheromone that is discharged by the female with release of the eggs. The threshold concentration for sperm release by males was determined as 0.6 M.     

On the evaluation of unsupervised outlier detection: measures,datasets, and an empirical study

The evaluation of unsupervised outlier detection algorithms is a constant challenge in data mining research. Little is known regarding the strengths and weaknesses of different standard outlier detection models, and the impact of parameter choices for these algorithms. The scarcity of appropriate benchmark datasets with ground truth annotation is a significant impediment to the evaluation of outlier methods. Even when labeled datasets are available, their suitability for the outlier detection task is typically unknown. Furthermore, the biases of commonly-used evaluation measures are not fully understood. It is thus difficult to ascertain the extent to which newly-proposed outlier detection methods improve over established methods. In this paper, we perform an extensive experimental study on the performance of a representative set of standard k nearest neighborhood-based methods for unsupervised outlier detection, across a wide variety of datasets prepared for this purpose. Based on the overall performance of the outlier detection methods, we provide a characterization of the datasets themselves, and discuss their suitability as outlier detection benchmark sets. We also examine the most commonly-used measures for comparing the performance of different methods, and suggest adaptations that are more suitable for the evaluation of outlier detection results.     

Decision support in early development phases—A case study from machine engineering

In the case study presented in this paper we consider early development phases of a mechanical product. We want to evaluate different concepts and decide which one(s) to pursue. A problem in early phases is that usually no test runs are available. In our case study, based on a standard, there are ways to compute the lifetime distributions of the components of the different concepts. Some parameters needed for these computations are not known precisely. Unfortunately, the lifetime distributions of the components are highly sensitive to these parameters. Our approach is to equip these parameters with distributions. These distributions would be called prior distributions in Bayesian terminology, but no update is possible since no test runs are available. Our approach implies that the distribution of the system lifetime for each concept is random, i.e. we get random elements in the space of lifetime distributions. Using Monte-Carlo simulations, we demonstrate several ways to compare the random lifetime distributions of the concepts. Some of these comparisons use stochastic orderings. We also introduce a new stochastic ordering which is particularly suitable for reliability purposes. Our case study, consisting of three scenarios, allows us to demonstrate some conclusions that can be reached.     

Structure‐Based Mechanism of Oleate Hydratase from Elizabethkingia meningoseptica

Hydratases provide access to secondary and tertiary alcohols by regio‐ and/or stereospecifically adding water to carbon‐carbon double bonds. Thereby, hydroxy groups are introduced without the need for costly cofactor recycling, and that makes this approach highly interesting on an industrial scale. Here we present the first crystal structure of a recombinant oleate hydratase originating from Elizabethkingia meningoseptica in the presence of flavin adenine dinucleotide (FAD). A structure‐based mutagenesis study targeting active site residues identified E122 and Y241 as crucial for the activation of a water molecule and for protonation of the double bond, respectively. Moreover, we also observed that two‐electron reduction of FAD results in a sevenfold increase in the substrate hydration rate. We propose the first reaction mechanism for this enzyme class that explains the requirement for the flavin cofactor and the involvement of conserved amino acid residues in this regio‐ and stereoselective hydration.     

Parametric G n blending of curves and surfaces

We introduce a simple blending method for parametric curves and surfaces that produces families of parametrically defined, G ⁿ –continuous blending curves and surfaces. The method depends essentially on the parameterizations of the curves/surfaces to be blended. Hence, the flexibility of the method relies on the existence of suitable parameter transformations of the given curves/surfaces. The feasibility of the blending method is shown by several examples. The shape of the blend curve/surface can be changed in a predictable way with the aid of two design parameters (thumb weight and balance).     

Dietary Apigenin Exerts Immune-Regulatory Activity in Vivo by Reducing NF-κB Activity,Halting Leukocyte Infiltration and Restoring Normal Metabolic Function

The increasing prevalence of inflammatory diseases and the adverse effects associated with the long-term use of current anti-inflammatory therapies prompt the identification of alternative approaches to reestablish immune balance. Apigenin, an abundant dietary flavonoid, is emerging as a potential regulator of inflammation. Here, we show that apigenin has immune-regulatory activity in vivo. Apigenin conferred survival to mice treated with a lethal dose of Lipopolysaccharide (LPS) restoring normal cardiac function and heart mitochondrial Complex I activity. Despite the adverse effects associated with high levels of splenocyte apoptosis in septic models, apigenin had no effect on reducing cell death. However, we found that apigenin decreased LPS-induced apoptosis in lungs, infiltration of inflammatory cells and chemotactic factors’ accumulation, re-establishing normal lung architecture. Using NF-κB luciferase transgenic mice, we found that apigenin effectively modulated NF-κB activity in the lungs, suggesting the ability of dietary compounds to exert immune-regulatory activity in an organ-specific manner. Collectively, these findings provide novel insights into the underlying immune-regulatory mechanisms of dietary nutraceuticals in vivo.