Solvent extraction of Am(III) and Eu(III) from nitrate solution using synergistic mixtures of n-tridentate heterocycles and chlorinated cobalt dicarbollide

The separation by solvent extraction of Am-241(III) from Eu-152(III), in 1 M NaNO3 weakly acidic (pH 4) aqueous solutions, into dilute (ca. 10(-2) M) solutions of triazinylbipyridine derivatives (diethylhemi-BTP or di(benzyloxyphenyl) hemi-BTP) and chlorinated cobalt dicarbollide (COSAN) in 1-octanol or nitrobenzene has been studied. The N-tridentate heterocyclic ligands, which are selective for Am(III) over Eu(III), secured efficient separation of the two metal ions, while COSAN, strongly hydrophobic and fully dissociated in polar diluents, enhanced the extraction of the metal ions by ion-pair formation. Molecular interactions between the two co-extractants, observed at higher concentrations, led to the precipitation of their 1: 1 molecular adduct. In spite of that, efficient separations of Am and Eu ions were attained, with high separation factors, SFAm/Eu of 40 and even 60, provided the concentration of hemi-BTP was significantly greater than that of COSAN. Excess COSAN concentrations caused an antagonistic effect, decreasing both the distribution ratio of the metal ions and their separation factor.

Self-assembly of peptide nanotubes in an organic solvent

The self-assembly of a modified fragment of the amyloid beta peptide, based on sequence A beta(16-20), KLVFF, extended to give AAKLVFF is studied in methanol. Self-assembly into peptide nanotubes is observed, as confirmed by electron microscopy and small-angle X-ray scattering. The secondary structure of the peptide is probed by FTIR and circular dichroism, and UV/visible spectroscopy provides evidence for the important role of aromatic interactions between phenylalanine residues in driving beta-sheet self-assembly. The beta-sheets wrap helically to form the nanotubes, the nanotube wall comprising four wrapped beta-sheets. At higher concentration, the peptide nanotubes form a nematic phase that exhibits spontaneous flow alignment as observed by small-angle neutron scattering.

Solvent-Assisted Formation of Vesicles by a Self-Assembling Ni-3-Schiff Base Complex

A linear trinuclear Ni-Schiff base complex [Ni-3(salpen)(2)(PhCH2COO)(2)(EtOH)] has been synthesized by combining Ni(ClO4)(2)center dot 6H(2)O, phenyl acetic acid (C6H5CH2COOH), and the Schiff base ligand, N,N'-bis(salicylidene)-1,3-pentanediamine (H(2)salpen). This complex is self-assembled through hydrogen bonding and C-H-g interaction in the solid state to generate a sheet-like architecture, while in organic solvent (CH2Cl2), it forms vesicles with a mean diameter of 290 nm and fused vesicles, depending upon the concentration of the solution. These vesicles act as an excellent carrier of dye molecules in CH2Cl2. The morphology of the complex has been determined by scanning electron microscopy and transmission electron microscopy experiments, and the encapsulation of dye has been examined by confocal microscopic image and electronic absorption spectra.

Comparison of heat-treated rapeseed expeller and solvent-extracted soya-bean meal as protein supplements for dairy cows given grass silage-based diets

Sixteen early to mid lactation Finnish Ayrshire dairy cows were used in a cyclic change-over experiment with four 21-day experimental periods and a 4 5 2 factorial arrangement of treatments to evaluate the effects of heat-treated rapeseed expeller and solvent-extracted soya-bean meal protein supplements on animal performance. Dietary treatments consisted of grass silage offered ad libitum supplemented with a fixed amount of a cereal based concentrate (10 kg/day on a fresh weight basis) containing 120, 150, 180 or 210 g crude protein (CP) per kg dry matter (DM). Concentrate CP content was manipulated by replacement of basal ingredients (g/kg) with either rapeseed expeller (R; 120, 240 and 360) or soya-bean meal (S; 80, 160 and 240). Increases in concentrate CP stimulated linear increases (P < 0.05) in silage intake (mean 22.5 and 23.8 g DM per g/kg increase in dietary CP content, for R and S, respectively) and milk production. Concentrate inclusion of rapeseed expeller elicited higher (P < 0.01) milk yield and milk protein output responses (mean 108 and 3.71 g/day per g/kg DM increase in dietary CP content) than soya-bean meal (corresponding values 62 and 2.57). Improvements in the apparent utilization of dietary nitrogen for milk protein synthesis (mean 0.282 and 0.274, for R and S, respectively) were associated with higher (P < 0.05) plasma concentrations of histidine, branched-chain, essential and total amino acids (35, 482, 902 and 2240 and 26, 410, 800 and 2119 mu mol/l, respectively) and lower (P < 0.01) concentrations of urea (corresponding values 4.11 and 4.52 mmol/l). Heat-treated rapeseed expeller proved to be a more effective protein supplement than solvent-extracted soya-bean meal for cows offered grass silage-based diets.

Development of a liquid chromatographic time-of-flight mass spectrometric method for the determination of unlabelled and deuterium-labelled alpha-tocopherol in blood components

A method is described for the analysis of deuterated and undeuterated alpha-tocopherol in blood components using liquid chromatography coupled to an orthogonal acceleration time-of-flight (TOF) mass spectrometer. Optimal ionisation conditions for undeuterated (d0) and tri- and hexadeuterated (d3 or d6) alpha-tocopherol standards were found with negative ion mode electrospray ionisation. Each species produced an isotopically resolved single ion of exact mass. Calibration curves of pure standards were linear in the range tested (0-1.5 muM, 0-15 pmol injected). For quantification of d0 and d6 in blood components following a standard solvent extraction, a stable-isotope-labelled internal standard (d3-alpha-tocopherol) was employed. To counter matrix ion suppression effects, standard response curves were generated following identical solvent extraction procedures to those of the samples. Within-day and between-day precision were determined for quantification of d0- and d6-labelled alpha-tocopherol in each blood component and both averaged 3-10%. Accuracy was assessed by comparison with a standard high-performance liquid chromatography (HPLC) method, achieving good correlation (r(2) = 0.94), and by spiking with known concentrations of alpha-tocopherol (98% accuracy). Limits of detection and quantification were determined to be 5 and 50 fmol injected, respectively. The assay was used to measure the appearance and disappearance of deuterium-labelled alpha-tocopherol in human blood components following deuterium-labelled (d6) RRR-alpha-tocopheryl acetate ingestion. The new LC/TOFMS method was found to be sensitive, required small sample volumes, was reproducible and robust, and was capable of high throughput when large numbers of samples were generated. Copyright (C) 2003 John Wiley Sons, Ltd.

Solvent influences on metastable polymorph lifetimes: real-time interconversions using energy dispersive X-ray diffractometry

Solvent influences on the crystallization of polymorph and hydrate forms of the nootropic drug piracetam (2-oxo-pyrrolidineacetamide) were investigated from water, methanol, 2-propanol, isobutanol, and nitromethane. Crystal growth profiles of piracetam polymorphs were constructed using time-resolved diffraction snapshots collected for each solvent system. Measurements were performed by in situ energy dispersive X-ray diffraction recorded in Station 16.4 at the synchrotron radiation source (SRS) at Daresbury Laboratory, CCLRC UK. Crystallizations from methanol, 2-propanol, isobutanol, and nitromethane progressed in a similar fashion with the initial formation of form I which then converted relatively quickly to form II with form III being generated upon further cooling. However, considerable differences were observed for the polymorphs lifetime and both the rate and temperature of conversion using the different solvents. The thermodynamically unstable form I was kinetically favored in isobutanol and nitromethane where traces of this polymorph were observed below 10 degrees C. In contrast, the transformation of form II and subsequent growth of form III were inhibited in 2-propanol and nitromethane solutions. Aqueous solutions produced hydrate forms of piracetam which are different from the reported monohydrate; this crystallization evolved through successive generation of transient structures which transformed upon exchange of intramolecular water between the liquid and crystalline phases. (c) 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 96:1069-1078, 2007.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Fully complex-valued radial basis function networks for orthogonal least squares regression

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Kernel density construction using orthogonal forward regression

An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

Orthogonal forward selection for constructing the radial basis function network with tunable nodes

An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process.

An orthogonal forward regression technique for sparse kernel density estimation

Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights of the selected sparse model are finally updated using the multiplicative nonnegative quadratic programming algorithm, which ensures the nonnegative and unity constraints for the kernel weights and has the desired ability to reduce the model size further. Except for the kernel width, the proposed method has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Several examples demonstrate the ability of this simple regression-based approach to effectively construct a SKID estimate with comparable accuracy to that of the full-sample optimised PW density estimate. (c) 2007 Elsevier B.V. All rights reserved.