What Determines Size Distributions of Heavy Drops in a Synthetic Turbulent Flow?

We present results from an individual particle based model for the collision, coagulation and fragmentation of heavy drops moving in a turbulent flow. Such a model framework can help to bridge the gap between the full hydrodynamic simulation of two phase flows, which can usually only study few particles and mean field based approaches for coagulation and fragmentation relying heavily on parameterization and are for example unable to fully capture particle inertia. We study the steady state that results from a balance between coagulation and fragmentation and the impact of particle properties and flow properties on this steady state. We compare two different fragmentation mechanisms, size-limiting fragmentation where particles fragment when exceeding a maximum size and shear fragmentation, where particles break up when local shear forces in the flow exceed the binding force of the particle. For size-limiting fragmentation the steady state is mainly influenced by the maximum stable particle size, while particle and flow properties only influence the approach to the steady state. For shear fragmentation both the approach to the steady state and the steady state itself depend on the particle and flow parameters. There we find scaling relationships between the steady state and the particle and flow parameters that are determined by the stability condition for fragmentation.Comment: 14 pages, 7 figure

Development and Evolution of Neural Networks in an Artificial Chemistry

We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and cell stimulation. We demonstrate the model's power with a hand-written genome that leads to the growth of a simple network which performs classical conditioning. To evolve more complex structures, we implemented a platform-independent, asynchronous, distributed Genetic Algorithm (GA) that allows users to participate in evolutionary experiments via the World Wide Web.Comment: 8 pages LaTeX, style file included, 8 embedded postscript figures. To be published in Proc. of 3rd German Workshop on Artificial Life (GWAL

Bioassay Analysis Using R

We describe an add-on package for the language and environment R which allows simultaneous fitting of several non-linear regression models. The focus is on analysis of dose response curves, but the functionality is applicable to arbitrary non-linear regression models. Features of the package is illustrated in examples.

Influence of pH and type of myrosinase complex on the products obtained in the myrosinase catalysed hydrolysis of glucosinolates – a MECC study

Environmental conditions, e.g. pH and the presence of Fe2+ are well known factors that influence the product profile of the myrosinase catalysed hydrolysis of glucosinolates. Depending on the plant genera, the species and tissue of origin myrosinase isoenzymes (thioglucohydrolase EC 3.2.1.147) have different characteristics in terms of MW, subunit composition and pI. However, the influence of these parameters on the outcome of glucosinolate hydrolysis has not been traditionally studied, which hinders the full exploitation of the catalytic potential of these enzymes. In the present experiments the effect of myrosinase type on the products obtained in the hydrolysis of glucosibarin was studied by MECC using two B. carinata myrosinase preparations differing on their affinity to the Con A material, Con A 1 (first eluting fractions) and Con A 2 (last eluting fractions). At pH 3 Con A 1 isoenzymes were more active than Con A 2 isoenzymes. At pH 5 and 6.5 Con A 1 isoenzymes produced oxazolidine-2-thione to a higher extent than Con A 2 isoenzymes. The production of nitriles by Con A 1 isoenzymes was not influenced by pH and at pH 5 and 6.5 the amount of nitrile produced by Con A 1 isoenzymes was lower than that produced by Con A 2 isoenzymes. Formation of nitriles requires the presence of two redox equivalents which leads to the release of the sulphur atom from the aglucone. Isothiocyanates and nitriles differ in their bioactivity towards different targets; therefore the possibility for directing the glucosinolate hydrolysis towards the desired compound in a particular situation is of great relevance