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provides coniferyl ferulate(CAS#:62949-77-3) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate
The title compound, C20H15BrO3S, was prepared by the oxidation of 7-bromo-2-methyl-1-(4-tolylsulfanyl)naphtho[2,1-b]furan with 3-chloroperoxybenzoic acid. The 4-tolyl ring makes a dihedral angle of 70.96 (6)° with the plane of the naphthofuran fragment. The crystal structure is stabilized by aromatic π-π stacking interactions, with centroid-centroid distances of 3.672 (3) and 3.858 (3) a between the central benzene and furan rings, and between the brominated benzene and central benzene rings of the naphthofuran system of neighbouring molecules, respectively. In addition, the stacked molecules exhibit C—H⋯π and inter- and intramolecular C—H⋯O interactions.
Hong Dae Choi,a Pil Ja Seo,a Byeng Wha Son,b and Uk Leeb,*
2008 Jun 1;
One in five older adults in Taiwan have been diagnosed with diabetes. This study drew on disability data for 5,121 nationally representative middle-aged and older adults from the 1996-2003 Survey of Health and Living Status of the Elderly in Taiwan (SHLSET). By employing cohort sequential design and multilevel models, it combined cross-sectional and longitudinal evidence to characterize the age trajectory of physical disability from midlife to older adulthood and to discern the extent to which diabetes contributes to the variation in that trajectory. The main effects of diabetes and diabetes × age interaction in the fully controlled model provide evidence that diabetes independently and consistently changes physical functioning over and above natural aging processes in Taiwanese adults. In addition, while adding diabetes in the age trajectory of physical disability explained 3.2% and 1.6% of the variance in levels of and linear changes in physical disability trajectory, respectively, further adding follow-up status, sociodemographic factors and comorbidities altogether explained 40.5% and 29.1% of the variance in levels of and linear changes in that trajectory. These results imply that preventing the incidence of diabetes-related comorbidities may reduce the deterioration in both levels of and rates of change in physical disability.
Cohort-sequential design (accelerated longitudinal design), Multilevel modeling, Taiwan elders, Diabetes
Diabetes-Related Change in Physical Disability from Midlife to Older Adulthood: Evidence from 1996-2003 Survey of Health and Living Status of the Elderly in Taiwan
Ching-Ju Chiu, PhD,1 Linda A. Wray, PhD,1 and Mary Beth Ofstedal, PhD2
2012 Mar 1.
Bioassay data analysis continues to be an essential, routine, yet challenging task in modern drug discovery and chemical biology research. The challenge is to infer reliable knowledge from big and noisy data. Some aspects of this problem are general with solutions informed by existing and emerging data science best practices. Some aspects are domain specific, and rely on expertise in bioassay methodology and chemical biology. Testing compounds for biological activity requires complex and innovative methodology, producing results varying widely in accuracy, precision, and information content. Hit selection criteria involve optimizing such that the overall probability of success in a project is maximized, and resource-wasteful “false trails” are avoided. This “fail-early” approach is embraced both in pharmaceutical and academic drug discovery, since follow-up capacity is resource-limited. Thus, early identification of likely promiscuous compounds has practical value.
Here we describe an algorithm for identifying likely promiscuous compounds via associated scaffolds which combines general and domain-specific features to assist and accelerate drug discovery informatics, called Badapple: bioassay-data associative promiscuity pattern learning engine. Results are described from an analysis using data from MLP assays via the BioAssay Research Database (BARD) http://bard.nih.gov. Specific examples are analyzed in the context of medicinal chemistry, to illustrate associations with mechanisms of promiscuity. Badapple has been developed at UNM, released and deployed for public use two ways: (1) BARD plugin, integrated into the public BARD REST API and BARD web client; and (2) public web app hosted at UNM.
Badapple is a method for rapidly identifying likely promiscuous compounds via associated scaffolds. Badapple generates a score associated with a pragmatic, empirical definition of promiscuity, with the overall goal to identify “false trails” and streamline workflows. Unlike methods reliant on expert curation of chemical substructure patterns, Badapple is fully evidence-driven, automated, self-improving via integration of additional data, and focused on scaffolds. Badapple is robust with respect to noise and errors, and skeptical of scanty evidence.
Electronic supplementary material
The online version of this article (doi:10.1186/s13321-016-0137-3) contains supplementary material, which is available to authorized users.
Drug discovery informatics, High-throughput screening (HTS), Compound promiscuity, Molecular scaffolds, Statistical learning
Badapple: promiscuity patterns from noisy evidence
Jeremy J. Yang, Oleg Ursu, Christopher A. Lipinski, Larry A. Sklar, Tudor I. Oprea, and Cristian G. Bologacorresponding author