Benzeneacetic acid, α-hydroxy-, phenylmethyl ester, (αR)-/Benzyl (2R)-hydroxy(phenyl)acetate/benzyl L-mandelate/benzyl (2R)-hydroxy(phenyl)ethanoate/benzeneacetic acid, a-hydroxy-, phenylmethyl ester, (aR)-/benzyl mandelate/D-(-)-MANDELIC ACID BENZYL ESTER/(-)-Mandelic acid benzyl ester/Benzyl D-(-)-Mandelate
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provides coniferyl ferulate(CAS#:97415-09-3) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate
Series of seventeen new multihalogenated 1-hydroxynaphthalene-2-carboxanilides was prepared and characterized. All the compounds were tested for their activity related to the inhibition of photosynthetic electron transport (PET) in spinach (Spinacia oleracea L.) chloroplasts. 1-Hydroxy-N-phenylnaphthalene-2-carboxamides substituted in the anilide part by 3,5-dichloro-, 4-bromo-3-chloro-, 2,5-dibromo- and 3,4,5-trichloro atoms were the most potent PET inhibitors (IC50 = 5.2, 6.7, 7.6 and 8.0 µM, respectively). The inhibitory activity of these compounds depends on the position and the type of halogen substituents, i.e., on lipophilicity and electronic properties of individual substituents of the anilide part of the molecule. Interactions of the studied compounds with chlorophyll a and aromatic amino acids present in pigment-protein complexes mainly in PS II were documented by fluorescence spectroscopy. The section between P680 and plastoquinone QB in the PET chain occurring on the acceptor side of PS II can be suggested as the site of action of the compounds. The structure-activity relationships are discussed.
hydroxynaphthalene-carboxamides, photosynthetic electron transport (PET) inhibition, spinach chloroplasts, structure-activity relationships
Halogenated 1-Hydroxynaphthalene-2-Carboxanilides Affecting Photosynthetic Electron Transport in Photosystem II †
Tomas Gonec,1,* Jiri Kos,1,2 Matus Pesko,3 Jana Dohanosova,4 Michal Oravec,5 Tibor Liptaj,4 Katarina Kralova,6 and Josef Jampilek2,*
The structures of two facially coordinated Group VII metal complexes, fac-[ReCl(C10H8N2O2)(CO)3]·C4H8O (I·THF) and fac-[MnBr(C10H8N2O2)(CO)3]·C4H8O (II·THF), are reported. In both complexes, the metal ion is coordinated by three carbonyl ligands, a halide ligand, and a 6,6′-dihydroxy-2,2′-bipyridine ligand in a distorted octahedral geometry. Both complexes co-crystallize with a non-coordinating tetrahydrofuran (THF) solvent molecule and exhibit intermolecular but not intramolecular hydrogen bonding. In both crystal structures, chains of complexes are formed due to intermolecular hydrogen bonding between a hydroxy group from the 6,6′-dihydroxy-2,2′-bipyridine ligand and the halide ligand from a neighboring complex. The THF molecule is hydrogen bonded to the remaining hydroxy group.
crystal structure, 6,6′-dihydroxy-2,2′-bipyridine ligand, rhenium complex, manganese complex, hydrogen bonding, selective catalysts for CO2 reduction
Crystal structures of fac-tricarbonylchlorido(6,6′-dihydroxy-2,2′-bipyridine)rhenium(I) tetrahydrofuran monosolvate and fac-bromidotricarbonyl(6,6′-dihydroxy-2,2′-bipyridine)manganese(I) tetrahydrofuran monosolvate
Sheri Lense,a,* Nicholas A. Piro,b Scott W. Kassel,b Andrew Wildish,a and Brent Jefferya
2016 Aug 1
Learning accurate models from ‘omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares – Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals’ outlierness based on the Cook’s distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level.
We applied this strategy for the classification of Triple-Negative Breast Cancer (TNBC) RNA-Seq and clinical data from the Cancer Genome Atlas (TCGA). The detected 24 outliers were identified as putative mislabeled samples, corresponding to individuals with discrepant clinical labels for the HER2 receptor, but also individuals with abnormal expression values of ER, PR and HER2, contradictory with the corresponding clinical labels, which may invalidate the initial TNBC label. Moreover, the model consensus approach leads to the selection of a set of genes that may be linked to the disease. These results are robust to a resampling approach, either by selecting a subset of patients or a subset of genes, with a significant overlap of the outlier patients identified.
The proposed ensemble outlier detection approach constitutes a robust procedure to identify abnormal cases and consensus covariates, which may improve biomarker selection for precision medicine applications. The method can also be easily extended to other regression models and datasets.
Electronic supplementary material
The online version of this article (10.1186/s12859-018-2149-7) contains supplementary material, which is available to authorized users.
Ensemble modeling, High-dimensionality, Outlier detection, Rank Product test, Triple-negative breast cancer
Ensemble outlier detection and gene selection in triple-negative breast cancer data
Marta B. Lopes,1 Andre Verissimo,1 Eunice Carrasquinha,1 Sandra Casimiro,2 Niko Beerenwinkel,3,4 and Susana Vingacorresponding author1,5