Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons

We are developing computational tools supporting the detailed analysis of the dependence of neural electrophysiological response on dendritic morphology. We approach this problem by combining simulations of faithful models of neurons (experimental real life morphological data with known models of channel kinetics) with algorithmic extraction of morphological and physiological parameters and statistical analysis. In this paper, we present the novel method for an automatic recognition of spike trains in voltage traces, which eliminates the need for human intervention. This enables classification of waveforms with consistent criteria across all the analyzed traces and so it amounts to reduction of the noise in the data. This method allows for an automatic extraction of relevant physiological parameters necessary for further statistical analysis. In order to illustrate the usefulness of this procedure to analyze voltage traces, we characterized the influence of the somatic current injection level on several electrophysiological parameters in a set of modeled neurons. This application suggests that such an algorithmic processing of physiological data extracts parameters in a suitable form for further investigation of structure-activity relationship in single neurons.

Carbonyl compound dependent hydrolysis of mono-condensed Schiff bases: a trinuclear Schiff base complex and a mononuclear mixed-ligand ternary complex of copper(II)

Two Schiff bases, HL1 and HL2 have been prepared by the condensation of N-methyl-1,3-propanediamine (mpn) with salicylaldehyde and 1-benzoylacetone (Hbn) respectively. HL1 on reaction with Cu(ClO4)(2)center dot 6H(2)O in methanol produced a trinuclear Cu-II complex, [(CuL1)(3)(mu(3)-OH)](ClO4)(2)center dot H2O center dot 0.5CH(2)Cl(2) (1) but HL2 underwent hydrolysis under similar reaction conditions to result in a ternary Cu-II complex, [Cu(bn)(mpn)ClO4]. Both complexes have been characterised by single-crystal X-ray analyses, IR and UV-Vis spectroscopy and electrochemical studies. The partial cubane core [Cu3O4] of 1 consists of a central mu(3)-OH and three peripheral phenoxo bridges from the Schiff base. All three copper atoms of the trinuclear unit are five-coordinate with a distorted square-pyramidal geometry. The ternary complex 2 is mononuclear with the square-pyramidal Cu-II coordinated by a chelating bidentate diamine (mpn) and a benzoylacetonate (bn) moiety in the equatorial plane and one of the oxygen atoms of perchlorate in an axial position. The results show that the Schiff base (HL2) derived from 1-benzoylacetone is more prone to hydrolysis than that from salicylaldehyde (HL1). Magnetic measurements of 1 have been performed in the 1.8-300 K temperature range. The experimental data clearly indicate antiferromagnetism in the complex. The best-fit parameters for complex 1 are g = 2.18(1) and J = -15.4(2) cm(-1).

A hybrid data assimilation scheme for model parameter estimation: application to morphodynamic modelling

We present a novel algorithm for joint state-parameter estimation using sequential three dimensional variational data assimilation (3D Var) and demonstrate its application in the context of morphodynamic modelling using an idealised two parameter 1D sediment transport model. The new scheme combines a static representation of the state background error covariances with a flow dependent approximation of the state-parameter cross-covariances. For the case presented here, this involves calculating a local finite difference approximation of the gradient of the model with respect to the parameters. The new method is easy to implement and computationally inexpensive to run. Experimental results are positive with the scheme able to recover the model parameters to a high level of accuracy. We expect that there is potential for successful application of this new methodology to larger, more realistic models with more complex parameterisations.

A very rare hydrogen-bridged hexanuclear CuII complex containing a triangular Cu3O core capped by an unusual triply coordinated perchlorate anion

An unusual hexanuclear Cu-II complex, [{[Cu(NHDEPO)](3)(mu(3)-O)(O3ClO)}(2)(mu-H)]center dot 7ClO(4)center dot 4H(2)O (1) was prepared starting from Cu(ClO4)(2)center dot 6H(2)O and the oxime-based Schiff base ligand NHDEPO (= 3-[3-(diethylamino)propylimino]butan-2-one oxime). Structural characterization of the complex reveals that it consists of two triangular Cu3O units, the copper ions being at the corners of an equilateral triangle, separated by an O center dot center dot center dot O distance of 2,447(5) angstrom, held together solely by a proton. In each triangle, the copper atoms are in square-pyramid environments. The equatorial plane consists of the bridging oxygen of the central OH-(O2-) group together with three atoms (N, N, O) of the Schiff base. All Unusual triply coordinated perchlorate ion (mu(3)-kappa O:kappa O':kappa O '') interacts in axial position with the three copper ions, Variable-temperature (2-300 K) magnetic susceptibility measurements show that complex 1 is antiferromagnetically Coupled (J = -148 cm(1-)). The EPR data at low temperature clearly indicates the presence of spin frustration phenomenon in the complex.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

Phylogenetic systematics of Sternbergia (Amaryllidaceae) based on plastid and ITS sequence data

The phylogenetics of Sternbergia (Amaryllidaceae) were studied using DNA sequences of the plastid ndhF and matK genes and nuclear internal transcribed spacer (ITS) ribosomal region for 38, 37 and 32 ingroup and outgroup accessions, respectively. All members of Sternbergia were represented by at least one accession, except S. minoica and S. schubertii, with additional taxa from Narcissus and Pancratium serving as principal outgroups. Sternbergia was resolved and supported as sister to Narcissus and composed of two primary subclades: S. colchiciflora sister to S. vernalis, S. candida and S. clusiana, with this clade in turn sister to S. lutea and its allies in both Bayesian and bootstrap analyses. A clear relationship between the two vernal flowering members of the genus was recovered, supporting the hypothesis of a single origin of vernal flowering in Sternbergia. However, in the S. lutea complex, the DNA markers examined did not offer sufficient resolving power to separate taxa, providing some support for the idea that S. sicula and S. greuteriana are conspecific with S. lutea

Synthesis, X-ray crystal structure and catalytic activity of the manganese(II) complex [Mn(bdoa)(H2O)3](bdoaH2 =benzene-1,2-dioxyacetic acid)

Benzene-1,2-dioxyacetic acid (bdoaH2) reacts with Mn(CH3CO2)2·4H2O in an ethanol-water mixture to give the manganese(II) complex [Mn(bdoa)(H2O)3]. The X-ray crystal structure of the complex shows the metal to be pseudo seven-coordinate. The quadridentate bdoa2− dicar☐ylate ligand forms an essentially planar girdle around the metal, being strongly bondedtransoid by a car☐ylate oxygen atom from each of the two car☐ylate moieties (mean MnO 2.199A˚) and also weakly chelated by the two internal ether oxygen atoms (mean MnO 2.413A˚). The coordination sphere about the manganese is completed by three water molecules (mean MnO 2.146A˚) lying in a meridional plane orthogonal to that of the bdoa2− ligand. Magnetic, conductivity and voltammetry data for the complex are given, and its use as a catalyst for the disproportionisation of H2O2 is described.

Synthesis, X-ray crystal structure and reactivity of the polymeric copper(II) dicarboxylic acid complex {Cu2(η1η1μ2-oda)2(py)4(H2O)2}n(odaH2 = octanedioic acid, PY = pyridine)

An aqueous solution of the α-ω-dicarboxylic acid octanedioic acid (odaH2) reacts with [Cu2(μ-O2CCH3)4(H2O)2] in the presence of an excess of pyridine (py) to give the crystalline copper(II) complex {Cu2(η1η1μ2-oda)2(py)4(H2O)2}n (1). structure of 1, as determined by X-ray crystallography, consists of polymeric chains in which bridging oda2− anions link two crystallographically identical copper atoms. The copper atoms are also ligated by two transoidal pyridine nitrogens and an oxygen atom from an apical water molecule, giving the metals an overall N2O3 square-pyramidal geometry. If the blue solid 1 is gently heated, or if it is left to stand in its mother liquor for prolonged periods, it loses one molecule of pyridine and half a molecule of water and the green complex {Cu (oda)(py)(H2O)0.5}n (2) is formed. Spectroscopic and magnetic data for both complexes are given, together with the electrochemical and thermogravimetric measurements for 1.

Synthesis and X-ray crystal structure of the copper(I) dicarboxylic acid complex [Cu(η1-bdoaH)(PPh3)3] (bdoaH2 = benzene-1,2- dioxyacetic acid)

The copper(II) complex [Cu(bdoa)(H2O)2] (bdoaH2 = benzene-1,2-dioxyacetic acid) reacts with triphenylphosphine (1:4 mol ratio) to give the colourless copper(I) complex [Cu(η1-bdoaH)(PPh3)3] (1) in good yield. The X-ray crystal structure of the complex shows the copper atom at the centre of a distorted tetrahedron, and is ligated by the phosphorus atoms of the three triphenylphosphines and one carboxylate oxygen atom of the bdoaH− ligand. Significant intermolecular hydrogen-bonding exists between the pendant carboxylate OH function of one molecule and the uncoordinated “ketonic” oxygen of a neighbouring molecule. Complex 1 is non-conducting in chloroform but ionizes readily in acetonitrile. The cyclic voltammogram of an acetonitrile solution of 1 shows a single irreversible anodic peak for the oxidation of the PPh3 ligands and the copper(I) centre, and a single irreversible cathodic peak for the reduction of the bdoaH− ion. IR and mass spectral data for 1 are given.

Nonlinear data assimilation in geosciences: an extremely efficient particle filter

Almost all research fields in geosciences use numerical models and observations and combine these using data-assimilation techniques. With ever-increasing resolution and complexity, the numerical models tend to be highly nonlinear and also observations become more complicated and their relation to the models more nonlinear. Standard data-assimilation techniques like (ensemble) Kalman filters and variational methods like 4D-Var rely on linearizations and are likely to fail in one way or another. Nonlinear data-assimilation techniques are available, but are only efficient for small-dimensional problems, hampered by the so-called ‘curse of dimensionality’. Here we present a fully nonlinear particle filter that can be applied to higher dimensional problems by exploiting the freedom of the proposal density inherent in particle filtering. The method is illustrated for the three-dimensional Lorenz model using three particles and the much more complex 40-dimensional Lorenz model using 20 particles. By also applying the method to the 1000-dimensional Lorenz model, again using only 20 particles, we demonstrate the strong scale-invariance of the method, leading to the optimistic conjecture that the method is applicable to realistic geophysical problems. Copyright c 2010 Royal Meteorological Society

Simple solution for a complex problem: Proanthocyanidins, galloyl glucoses and ellagitannins fit on a single calibration curve in high performance-gel permeation chromatography

This study was undertaken to explore gel permeation chromatography (GPC) for estimating molecular weights of proanthocyanidin fractions isolated from sainfoin (Onobrychis viciifolia). The results were compared with data obtained by thiolytic degradation of the same fractions. Polystyrene, polyethylene glycol and polymethyl methacrylate standards were not suitable for estimating the molecular weights of underivatized proanthocyanidins. Therefore, a novel HPLC-GPC method was developed based on two serially connected PolarGel-L columns using DMF that contained 5% water, 1% acetic acid and 0.15 M LiBr at 0.7 ml/min and 50 degrees C. This yielded a single calibration curve for galloyl glucoses (trigalloyl glucose, pentagalloyl glucose), ellagitannins (pedunculagin, vescalagin, punicalagin, oenothein B, gemin A), proanthocyanidins (procyanidin B2, cinnamtannin B1), and several other polyphenols (catechin, epicatechin gallate, epicallocatechin gallate, amentoflavone). These GPC predicted molecular weights represented a considerable advance over previously reported HPLC-GPC methods for underivatized proanthocyanidins. (C) 2011 Elsevier B.V. All rights reserved.

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

An analysis of long-term trends, seasonality and short-term dynamics in water quality data from Plynlimon, Wales

This paper examines two hydrochemical time-series derived from stream samples taken in the Upper Hafren catchment, Plynlimon, Wales. One time-series comprises data collected at 7-hour intervals over 22 months (Neal et al., submitted, this issue), while the other is based on weekly sampling over 20 years. A subset of determinands: aluminium, calcium, chloride, conductivity, dissolved organic carbon, iron, nitrate, pH, silicon and sulphate are examined within a framework of non-stationary time-series analysis to identify determinand trends, seasonality and short-term dynamics. The results demonstrate that both long-term and high-frequency monitoring provide valuable and unique insights into the hydrochemistry of a catchment. The long-term data allowed analysis of long-termtrends, demonstrating continued increases in DOC concentrations accompanied by declining SO4 concentrations within the stream, and provided new insights into the changing amplitude and phase of the seasonality of the determinands such as DOC and Al. Additionally, these data proved invaluable for placing the short-term variability demonstrated within the high-frequency data within context. The 7-hour data highlighted complex diurnal cycles for NO3, Ca and Fe with cycles displaying changes in phase and amplitude on a seasonal basis. The high-frequency data also demonstrated the need to consider the impact that the time of sample collection can have on the summary statistics of the data and also that sampling during the hours of darkness provides additional hydrochemical information for determinands which exhibit pronounced diurnal variability. Moving forward, this research demonstrates the need for both long-term and high-frequency monitoring to facilitate a full and accurate understanding of catchment hydrochemical dynamics.

Extracting quantitative structural parameters for disordered polymers from neutron scattering data

The organization of non-crystalline polymeric materials at a local level, namely on a spatial scale between a few and 100 a, is still unclear in many respects. The determination of the local structure in terms of the configuration and conformation of the polymer chain and of the packing characteristics of the chain in the bulk material represents a challenging problem. Data from wide-angle diffraction experiments are very difficult to interpret due to the very large amount of information that they carry, that is the large number of correlations present in the diffraction patterns.We describe new approaches that permit a detailed analysis of the complex neutron diffraction patterns characterizing polymer melts and glasses. The coupling of different computer modelling strategies with neutron scattering data over a wide Q range allows the extraction of detailed quantitative information on the structural arrangements of the materials of interest. Proceeding from modelling routes as diverse as force field calculations, single-chain modelling and reverse Monte Carlo, we show the successes and pitfalls of each approach in describing model systems, which illustrate the need to attack the data analysis problem simultaneously from several fronts.

A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems

We present an approach for dealing with coarse-resolution Earth observations (EO) in terrestrial ecosystem data assimilation schemes. The use of coarse-scale observations in ecological data assimilation schemes is complicated by spatial heterogeneity and nonlinear processes in natural ecosystems. If these complications are not appropriately dealt with, then the data assimilation will produce biased results. The “disaggregation” approach that we describe in this paper combines frequent coarse-resolution observations with temporally sparse fine-resolution measurements. We demonstrate the approach using a demonstration data set based on measurements of an Arctic ecosystem. In this example, normalized difference vegetation index observations are assimilated into a “zero-order” model of leaf area index and carbon uptake. The disaggregation approach conserves key ecosystem characteristics regardless of the observation resolution and estimates the carbon uptake to within 1% of the demonstration data set “truth.” Assimilating the same data in the normal manner, but without the disaggregation approach, results in carbon uptake being underestimated by 58% at an observation resolution of 250 m. The disaggregation method allows the combination of multiresolution EO and improves in spatial resolution if observations are located on a grid that shifts from one observation time to the next. Additionally, the approach is not tied to a particular data assimilation scheme, model, or EO product and can cope with complex observation distributions, as it makes no implicit assumptions of normality.