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The reaction of copper(I) iodide with 6-n-propylthiouracil (ptu) in the presence or absence of the triphenylphosphine (tpp) or tri(p-tolyl)phosphine (tptp) in a 1:1:2 molar ratio forms the mixed ligand Cu(I) complex with formula [CuI(ptu)(2)](toluene) (1), [CuI(tpp)(2)(ptu)] (2), and [CuI(tptp)(2)(ptu)] (3). The complexes have been characterized by FT-IR, NMR, UV-vis, spectroscopic techniques, and single crystal X-ray crystallography. Two sulfur atoms from two ptu ligands and one iodide form a trigonal geometry around the metal center in 1. Intramolecular interactions through hydrogen bonds lead to a bend ribbon polymeric supramolecular architecture with zigzag conformation. Two phosphorus atoms from two arylphosphines, one sulfur atom, and one iodide anion form a tetrahedron around the copper ion in case of 2 and 3. Intramolecular hydrogen bonding interactions lead to dimerization. Complexes 1-3 and the already known ones with formulas, [(tpSb)(2)Cu(mu(2)-I)(2)Cu(tpSb)(2)] (4) (tbSb = triphenylstibine), [(tpp)Cu(mu(2)-I)(2)Cu(tpp)(2)] (5), [(tpp)Cu(mu(2)-Cl)(2)Cu(tpp)(2)] (6), [CuCl(tpp)(3)center dot(CH3CN)] (7), and [AuCl(tpp)] (8), were used to study their catalytic activity on the intermolecular cycloaddition of iodonium ylides toward benzo[b]furans formation. The results show that both the metal and the ligand type affect the catalytic affinity of the complexes. The highest yield of benzo[b]furan was derived when complexes 2, 3, and 4 were used as catalysts. The mechanism of the Cu(I)-catalyzed and uncatalyzed intramolecular cycloaddition of iodonium ylide has been also thoroughly explored by means of ab initio electronic structure calculation methods, and the results are compared with the experimental ones. Inorg Chem

Direct reaction of copper(I) chloride with triphenylphosphine (tpp) in molar ratio 2: 3 and 1: 3, results in the formation of the [(tpp)Cu(mu(2)-Cl)(2)Cu(tpp)(2)] (1) and {[CuCl(tpp)(3)]center dot(CH(3)CN)} (2) complexes. The complexes have been characterized by melting point, FT-IR, UV-Vis spectroscopic data and X-ray crystallography. Complex 1 is di-nuclear. Two mu(2)-Cl atoms bridge two copper(I) ions with tetrahedral and trigonal geometry respectively. The short copper-copper bond distance of 2.9039(6) angstrom in case of 1 indicates d(10)-d(10) interaction between metal centers. Thus, our studies were extended here in the determination of the quasi-aromaticity, which results in strong Cu-Cu interactions, using the computational method of nucleus-independent chemical shifts (NICS). The NICS calculated at the inner region of the Cu(2)Cl(2)P(3) core in complex 1 is shielded up to -6.05 ppm. Complex 2 is mono-nuclear where three phosphorus and one chloride atoms form a tetrahedron around the copper(I) ion. Photolysis of both complexes 1 and 2, results in the formation of triphenylphosphine oxide. The complexes 1 and 2, were tested for their in vitro cytotoxic activity against leiomyosarcoma cells (LMS) and human breast adenocarcinoma cells (MCF-7). The type of LMS cell death caused by the complexes was also evaluated by use of a flow cytometry assay. The results show that at concentration of 5 mu M of complexes 1 and 2, 34.1% (1) and 19.6 (2)% of LMS cells undergo programmed cell death (apoptosis), while at 10 mu M, 80.4% (1) and 65.2% (2) of LMS cells undergo apoptosis. The light sensitivity of the complex is discussed in relation with the biological activity. (C) 2009 Elsevier B. V. All rights reserved. Inorganica Chimica Acta

Normals and patients from three disorders have been selected for investigation: (1) motor neurone disease (MND) (2) Becker muscular distrophy (AMD) and (3) spinal muscular atrophy. The data from 36 macroelectromyograms were used for analysis. The results suggest that unsupervised learning neural networks generally produce better results than those produced by the supervised learning neural networks. No conclusion about the optimum size of the output grid can be reached from the results since the examined models for the 10 × 10 and 8 × 8 cases produced similar results. It is expected, however, that an optimum grid size should exist. This size will depend on the size and the variability of the training set. More epochs can improve the performance of a model up to a certain level, beyond which the number of epochs will have no positive effect. 305 312 Sponsors: IEEE Engineering in Medicine & Biology Soc IEEE Computer Soc IEEE Baltimore Section Conference code: 15496 Cited By :4