Search results for "GOLD"

showing 10 items of 1320 documents

CCDC 2031247: Experimental Crystal Structure Determination

2021

Related Article: Araceli de Aquino, Francisco J. Caparrós, Khai-Nghi Truong, Kari Rissanen, Montserrat Ferrer, Yongsik Jung, Hyeonho Choi, João Carlos Lima, Laura Rodríguez|2021|Dalton Trans.|50|3806|doi:10.1039/D1DT00087J

bis(mu-bis(diphenylphosphino)methyl)-bis(phenanthren-9-yl)-tetra-gold unknown solvateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 817953: Experimental Crystal Structure Determination

2012

Related Article: J.Konu, T.Chivers, H.M.Tuononen|2011|Chem.-Eur.J.|17|11844|doi:10.1002/chem.201100891

bis(mu~2~-bis(diphenylphosphorothioyl)methanideselenolato)-gold(i)-gold(iii) dichloromethane solvateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 817954: Experimental Crystal Structure Determination

2012

Related Article: J.Konu, T.Chivers, H.M.Tuononen|2011|Chem.-Eur.J.|17|11844|doi:10.1002/chem.201100891

bis(mu~2~-bis(diphenylphosphorothioyl)methanidethiolato)-gold(i)-gold(iii) dichloromethane solvateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 962935: Experimental Crystal Structure Determination

2014

Related Article: Ilya S. Krytchankou, Dmitry V. Krupenya, Antti J. Karttunen, Sergey P. Tunik, Tapani A. Pakkanen, Pi-Tai Chou, Igor O. Koshevoy|2014|Dalton Trans.|43|3383|doi:10.1039/C3DT52658E

bis(mu~3~-((Phenylphosphinediyl)bis(methylene))bis(diphenylphosphine))-hexakis(mu~2~-eta^2^-2-(1-hydroxycyclohexyl)ethynyl)-di-copper-hexa-gold bis(hexafluorophosphate) acetone dichloromethane unknown solvateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 937899: Experimental Crystal Structure Determination

2013

Related Article: Thuy Minh Dau, Julia R. Shakirova, Antonio Doménech, Janne Jänis, Matti Haukka, Elena V. Grachova, Tapani A. Pakkanen, Sergey P. Tunik, Igor O. Koshevoy|2013|Eur.J.Inorg.Chem.||4976|doi:10.1002/ejic.201300615

bis(mu~3~-bis((Diphenylphosphino)methyl)phenylphosphine)-bis(4-(ferrocenyl)phenylethynyl)-tetra-gold bis(hexafluorophosphate) unknown solvateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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Gold-Catalyzed Suzuki Coupling of ortho -Substituted Hindered Aryl Substrates

2017

International audience; A method that allows hindered ortho-substituted aryl iodides to be efficiently coupled to phenylboronic acid using a gold-catalyzed C-C bond formation is presented. The use of a molecularly-defined dinuclear gold chloride catalytic precursor that is stabilized by a new tetradentate (N,N')-diamino-(P,P')-diphosphino ferrocene hybrid ligand in a Suzuki-type reaction is described for the first time. Electron-rich isopropyl groups on phosphorus were found essen-tial for a superior activity, while the performances of a set of analogous gold dinuclear complexes that were fully characterized by multinuclear NMR spectroscopy and XRD analysis, were investigated. Therefore, ar…

bromides010402 general chemistry01 natural sciencesBiochemistry[ CHIM ] Chemical SciencesCatalysischemistry.chemical_compoundSuzuki reactionPolymer chemistrygold(iii)Organic chemistry[CHIM]Chemical SciencesPhenylboronic acidacidscomplexes010405 organic chemistryLigandArylOrganic ChemistryGeneral ChemistryNuclear magnetic resonance spectroscopy0104 chemical sciencesreactivityhalideschemistryFerrocenenanoparticlesIsopropyl
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Simultaneous Color Contrast in Goldfish— a Quantitative Study

1997

AbstractA set of 9–15 colored test fields was presented to goldfish. In Experiment 1, test field hues ranged from green through yellow to red; in Experiment 2, the hues varied from blue through gray to yellow. In the training conditions, the test fields were presented with a gray or black surround. The fish learned to choose one intermediate test field hue by rewarding them with food. In the test conditions, the color of the surround was changed from gray to green, or red (Experiment 1), and from black to blue, or yellow (Experiment 2). The choice behavior of the goldfish changed substantially: one of the test fields other than the training test field was preferred. Direction and strength o…

business.industryColor visionColor visionCarassius auratusRadiusColor spaceSensory SystemsContrast SensitivityOphthalmologyOpticsColoredSpectrophotometrySpace PerceptionSimultaneous color contrastGoldfishField sizeCarassius auratusAnimalsLearningColor contrastbusinessAlgorithmsColor PerceptionMathematicsHueVision Research
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Automatic multi-seed detection for MR breast image segmentation

2017

In this paper an automatic multi-seed detection method for magnetic resonance (MR) breast image segmentation is presented. The proposed method consists of three steps: (1) pre-processing step to locate three regions of interest (axillary and sternal regions); (2) processing step to detect maximum concavity points for each region of interest; (3) breast image segmentation step. Traditional manual segmentation methods require radiological expertise and they usually are very tiring and time-consuming. The approach is fast because the multi-seed detection is based on geometric properties of the ROI. When the maximum concavity points of the breast regions have been detected, region growing and m…

business.industryComputer scienceComputer Science (all)Pattern recognitionImage segmentationGold standard (test)Breast MR030218 nuclear medicine & medical imagingTheoretical Computer Science03 medical and health sciencesSeed detection0302 clinical medicineRegion of interestRegion growing030220 oncology & carcinogenesisManual segmentationSegmentationSensitivity (control systems)Artificial intelligenceAutomatic segmentationMr imagesbusinessMaximum concavity point
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Transcarotid approach for TAVI: an optimal alternative to the transfemoral gold standard

2017

business.industryMasters of Cardiothoracic SurgeryGold standard (test)030204 cardiovascular system & hematologycomputer.software_genre03 medical and health sciences0302 clinical medicineMaterials ChemistryMedicineSurgery030212 general & internal medicineData miningCardiology and Cardiovascular Medicinebusinesscomputer
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Hidden Markov Random Field model and BFGS algorithm for Brain Image Segmentation

2016

Brain MR images segmentation has attracted a particular focus in medical imaging. The automatic image analysis and interpretation became a necessity. Segmentation is one of the key operations to provide a crucial decision support to physicians. Its goal is to simplify the representation of an image into items meaningful and easier to analyze. Hidden Markov Random Fields (HMRF) provide an elegant way to model the segmentation problem. This model leads to the minimization problem of a function. BFGS (Broyden-Fletcher-Goldfarb-Shanno algorithm) is one of the most powerful methods to solve unconstrained optimization problem. This paper presents how we combine HMRF and BFGS to achieve a good seg…

business.industrySegmentation-based object categorizationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationPattern recognitionImage segmentationMachine learningcomputer.software_genreSørensen–Dice coefficientBroyden–Fletcher–Goldfarb–Shanno algorithmSegmentationArtificial intelligenceHidden Markov random fieldbusinessHidden Markov modelcomputerMathematicsProceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence
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