0000000000101548

AUTHOR

K. Kramer

showing 3 related works from this author

Histaprodifens: synthesis, pharmacological in vitro evaluation, and molecular modeling of a new class of highly active and selective histamine H(1)-r…

2000

A new class of histamine analogues characterized by a 3, 3-diphenylpropyl substituent at the 2-position of the imidazole nucleus has been prepared outgoing from 4,4-diphenylbutyronitrile (4b) via cyclization of the corresponding methyl imidate 5b with 2-oxo-4-phthalimido-1-butyl acetate or 2-oxo-1,4-butandiol in liquid ammonia, followed by standard reactions. The title compounds displayed partial agonism on contractile H(1) receptors of the guinea-pig ileum and endothelium-denuded aorta, respectively, except 10 (histaprodifen; 2-[2-(3, 3-diphenylpropyl)-1H-imidazol-4-yl]ethanamine) which was a full agonist in the ileum assay. While 10 was equipotent with histamine (1), methylhistaprodifen (…

AgonistMaleModels MolecularRhodopsinRanidaeStereochemistrymedicine.drug_classGuinea PigsSubstituentIleumHistamine H1 receptorIn Vitro TechniquesChemical synthesis/dk/atira/pure/sustainabledevelopmentgoals/clean_water_and_sanitationHistamine Agonistschemistry.chemical_compoundStructure-Activity RelationshipIleumDrug DiscoverymedicineImidazoleAnimalsHumansVasoconstrictor AgentsReceptors Histamine H1Rats WistarAortaChemistryMethylhistaminesMuscle SmoothIn vitroProtein Structure TertiaryRatsReceptors Neurotransmittermedicine.anatomical_structureMolecular MedicineEndothelium VascularSDG 6 - Clean Water and SanitationHistamineMuscle ContractionJournal of medicinal chemistry
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HIGH GRADE GLIOMAS AND DIPG

2014

OncologyCancer Researchmedicine.medical_specialtybusiness.industry03 medical and health sciencesAbstracts0302 clinical medicineText miningOncology030220 oncology & carcinogenesisInternal medicinemedicineNeurology (clinical)business030217 neurology & neurosurgery
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A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data

2018

This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then a…

0106 biological sciencesFOS: Computer and information sciences010504 meteorology & atmospheric sciencesSpecific leaf areaClimateBos- en LandschapsecologieSoil ScienceFOS: Physical sciencesApplied Physics (physics.app-ph)010603 evolutionary biology01 natural sciencesStatistics - ApplicationsGoodness of fitAbundance (ecology)Machine learningForest and Landscape EcologyApplications (stat.AP)Computers in Earth SciencesPlant ecologyVegetatie0105 earth and related environmental sciencesRemote sensingMathematics2. Zero hungerPlant traitsVegetationData stream miningClimate; Landsat; Machine learning; MODIS; Plant ecology; Plant traits; Random forests; Remote sensing; Soil Science; Geology; Computers in Earth SciencesGlobal MapRegression analysisGeologyPhysics - Applied Physics15. Life on landRandom forestsRemote sensingPE&RCRandom forestMODISTraitVegetatie Bos- en LandschapsecologieVegetation Forest and Landscape EcologyLandsat
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