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(-)-Mandelic acid benzyl ester

$77

  • Brand : BIOFRON

  • Catalogue Number : BN-O1040

  • Specification : 98%(HPLC)

  • CAS number : 97415-09-3

  • Formula : C15H14O3

  • Molecular Weight : 242.27

  • Volume : 5mg

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Catalogue Number

BN-O1040

Analysis Method

Specification

98%(HPLC)

Storage

2-8°C

Molecular Weight

242.27

Appearance

Botanical Source

Structure Type

Category

SMILES

C1=CC=C(C=C1)COC(=O)C(C2=CC=CC=C2)O

Synonyms

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

IUPAC Name

Density

1.204

Solubility

Flash Point

163 ºC

Boiling Point

387 ºC

Melting Point

104-107ºC

InChl

InChI=1S/C31H46O18S2/c1-14(2)9-21(33)47-24-23(49-51(42,43)44)22(48-50(39,40)41)18(13-32)46-26(24)45-17-11-29(4)19-6-5-16-10-30(19,25(34)15(16)3)8-7-20(29)31(12-17,27(35)36)28(37)38/h14,16-20,22-26,32,34H,3,5-13H2,1-2,4H3,(H,35,36)(H,37,38)(H,39,40,41)(H,42,43,44)

InChl Key

WGK Germany

RID/ADR

HS Code Reference

Personal Projective Equipment

Correct Usage

For Reference Standard and R&D, Not for Human Use Directly.

Meta Tag

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

No Technical Documents Available For This Product.

PMID

29023407

Abstract

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.

KEYWORDS

hydroxynaphthalene-carboxamides, photosynthetic electron transport (PET) inhibition, spinach chloroplasts, structure-activity relationships

Title

Halogenated 1-Hydroxynaphthalene-2-Carboxanilides Affecting Photosynthetic Electron Transport in Photosystem II †

Author

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,*

Publish date

2017 Oct

PMID

27536414

Abstract

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.

KEYWORDS

crystal structure, 6,6′-dihy­droxy-2,2′-bi­pyridine ligand, rhenium complex, manganese complex, hydrogen bonding, selective catalysts for CO2 reduction

Title

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

Author

Sheri Lense,a,* Nicholas A. Piro,b Scott W. Kassel,b Andrew Wildish,a and Brent Jefferya

Publish date

2016 Aug 1

PMID

29728051

Abstract

Background
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.

Results
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.

Conclusions
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.

KEYWORDS

Ensemble modeling, High-dimensionality, Outlier detection, Rank Product test, Triple-negative breast cancer

Title

Ensemble outlier detection and gene selection in triple-negative breast cancer data

Author

Marta B. Lopes,1 Andre Verissimo,1 Eunice Carrasquinha,1 Sandra Casimiro,2 Niko Beerenwinkel,3,4 and Susana Vingacorresponding author1,5

Publish date

2018;


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