Reaction of a phospholipid monolayer with gas-phase ozone at the air-water interface: measurement of surface excess and surface pressure in real time

The reaction between gas-phase ozone and monolayers of the unsaturated lipid 1-palmitoy1-2-oleoyl-sn-glycero-3-phosphocholine, POPC, on aqueous solutions has been studied in real time using neutron reflection and surface pressure measurements. The reaction between ozone and lung surfactant, which contains POPC, leads to decreased pulmonary function, but little is known shout the changes that occur to the interfacial material as a result of oxidation. The results reveal that the initial reaction of ozone with POPC leads to a rapid increase in surface pressure followed by a slow decrease to very low values. The neutron reflection measurements, performed on an isotopologue of POPC with a selectively deuterated palmitoyl strand, reveal that the reaction leads to loss of this strand from the air-water interface. suggesting either solubilization of the product lipid or degradation of the palmitoyl strand by a reactive species. Reactions of H-1-POPC on D2O reveal that the headgroup region of the lipids in aqueous solution is not dramatically perturbed by the reaction of POPC monolayers with ozone supporting degradation of the palmitoyl strand rather than solubilization. The results are consistent with the reaction of ozone with the oleoyl strand of POPC at the air water interface leading to the formation of OH radicals. the highly reactive OH radicals produced can then go on to react with the saturated palmitoyl strands leading to the formation or oxidized lipids with shorter alkyl tails.

Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.

Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Mononuclear single helices of lanthanum(III) and europium(III). Metal dependent diastereomeric excess

Using the 1: 2 condensate of benzildihydrazone and 2-acetylpyridine as a tetradentate N donor ligand L, LaL(NO3)(3) (1) and EuL(NO3)(3) (2), which are pale yellow in colour, are synthesized. While single crystals of 1 could not be obtained, 2 crystallises as a monodichloromethane solvate, 2 center dot CH2Cl2 in the space group Cc with a = 11.7099(5) angstrom, b = 16.4872(5) angstrom, c = 17.9224(6) angstrom and beta = 104.048(4)degrees. From the X-ray crystal structure, 2 is found to be a rare example of monohelical complex of Eu(III). Complex 1 is diamagnetic. The magnetic moment of 2 at room temperature is 3.32 BM. Comparing the FT-IR spectra of 1 and 2, it is concluded that 1 also is a mononuclear single helix. H-1 NMR reveals that both 1 and 2 are mixtures of two diastereomers. In the case of the La(III) complex (1), the diastereomeric excess is only 10% but in the Eu(III) complex 2 it is 80%. The occurrence of diastereomerism is explained by the chiralities of the helical motif and the type of pentakis chelates present in 1 and 2.

An introduction to radial basis functions for system identification: a comparison with other neural network methods

A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RBFs are firstly considered in detail themselves and are subsequently compared with a multi-layered perceptron (MLP), in terms of performance and usage.

Belowground biomass functions and expansion factors in high elevation Norway spruce

Biomass allocation to above- and belowground compartments in trees is thought to be affected by growth conditions. To assess the strength of such influences, we sampled six Norway spruce forest stands growing at higher altitudes. Within these stands, we randomly selected a total of 77 Norway spruce trees and measured volume and biomass of stem, above- and belowground stump and all roots over 0.5 cm diameter. A comparison of our observations with models parameterised for lower altitudes shows that models developed for specific conditions may be applicable to other locations. Using our observations, we developed biomass functions (BF) and biomass conversion and expansion factors (BCEF) linking belowground biomass to stem parameters. While both BF and BCEF are accurate in belowground biomass predictions, using BCEF appears more promising as such factors can be readily used with existing forest inventory data to obtain estimates of belowground biomass stock. As an example, we show how BF and BCEF developed for individual trees can be used to estimate belowground biomass at the stand level. In combination with existing aboveground models, our observations can be used to quantify total standing biomass of high altitude Norway spruce stands.

The Ferritin-like superfamily: evolution of the biological iron storeman from a rubrerythrin-like ancestor

Background: The Ferritins are part of the extensive ‘Ferritin-like superfamily’ which have diverse functions but are linked by the presence of a common four-helical bundle domain. The role performed by Ferritins as the cellular repository of excess iron is unique. In many ways Ferritins act as tiny organelles in their ability to secrete iron away from the delicate machinery of the cell, and then to release it again in a controlled fashion avoiding toxicity. The Ferritins are ancient proteins, being common in all three domains of life. This ubiquity reflects the key contribution that Ferritins provide in achieving iron homeostasis. Scope of the review: This review compares the features of the different Ferritins and considers how they, and other members of the Ferritin-like superfamily, have evolved. It also considers relevant features of the eleven other known families within the Ferritin-like superfamily, particularly the highly diverse rubrerythrins. Major conclusions: The Ferritins have travelled a considerable evolutionary journey, being derived from far more simplistic rubrerythrin-like molecules which play roles in defence against toxic oxygen species. The forces of evolution have moulded such molecules into three distinct types of iron storing (or detoxifying) protein: the classical and universal 24-meric ferritins; the haem-containing 24-meric bacterioferritins of prokaryotes; and the prokaryotic 12-meric Dps proteins. These three Ferritin types are similar, but also possess unique properties that distinguish them and enable then to achieve their specific physiological purposes. General significance: A wide range of biological functions have evolved from a relatively simple structural unit.

Wiener System identification using B-spline functions with De Boor recursion

A simple and effective algorithm is introduced for the system identification of Wiener system based on the observational input/output data. The B-spline neural network is used to approximate the nonlinear static function in the Wiener system. We incorporate the Gauss-Newton algorithm with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialization scheme. The efficacy of the proposed approach is demonstrated using an illustrative example.

Asymptotic expansions for Taylor coefficients of the composition of two functions

We give an asymptotic expansion for the Taylor coe±cients of L(P(z)) where L(z) is analytic in the open unit disc whose Taylor coe±cients vary `smoothly' and P(z) is a probability generating function. We show how this result applies to a variety of problems, amongst them obtaining the asymptotics of Bernoulli transforms and weighted renewal sequences.