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Model receptor

Receptor models are powerful tools for source apportionment of particulates because a vast amount of particulate species characterization data have been collected at many sampling sites worldwide, and because many aerosol species are primary pollutants. Most of the information available is for elemental concentrations, eg, lead, nickel, and alurninum, although more recent measurements have provided data on concentrations of ionic species and carbonaceous compounds. At a sampling (or receptor) site, the aerosol mass concentration of each species i is... [Pg.379]

Receptor Models. Receptor models, by their formulation, are effective in determining the contributions of various sources to particulate matter concentrations. In classic studies, sources contributing to airborne particle loadings have been identified in Washington, D.C. (78), St. Louis (9,24), Los Angeles (7,12), Portiand, Oregon (78), and Boston (79—81), as well as other areas including the desert (82). [Pg.385]

P. K. Hopke, Receptor Modeling in Environmental Chemistry, oEn Wiley Sons, Inc., New York, 1985. [Pg.387]

Watson, J. G. (ed.), "Receptor Models in Air Resources Management." APCA Transaction Series, No. 14. Air and Waste Management Association, Pittsburgh, 1989. [Pg.343]

N-Heterocycles as receptor models based on molecular recognition at membrane surfaces 97YGK436. [Pg.237]

In this histamine H2-receptor model, both histamine and its antagonists bind to [18]aneN6 3 H + forming 1 1 complexes at neutral pH, and the histamine antagonists are more strongly bound to the receptor model (see Fig. 9). Although this picture... [Pg.139]

The form of that function is shown in Figure 3.2. There are two specific parameters that can be immediately observed from this function. The first is that the maximal asymptote of the function is given solely by the magnitude of A/B. The second is that the location parameter of the function (where it lies along the input axis) is given by C/B. It can be seen that when [Input] equals C/B the output necessarily will be 0.5. Therefore, whatever the function the midpoint of the curve will lie on a point at Input = C/B. These ideas are useful since they describe two essential behaviors of any dmg-receptor model namely, the maximal response (A/B) and the potency (concentration of input required for effect C/B). Many of the complex equations... [Pg.43]

From the time of A. J. Clark until the late 1970s, receptor models have been refined to describe drug affinity and efficacy. These ideas are collectively referred to as classical receptor theory. [Pg.52]

This model also can accommodate dose-response curve having Hill coefficients different from unity. This can occur if the stimulus-response coupling mechanism has inherent cooperativity. A general procedure can be used to change any receptor model into a variable slope operational function. This is done by passing the receptor stimulus through a forcing function. [Pg.55]

There are statistical methods to determine the verisimilitude of experimental data to models. One major procedure to do this is nonlinear curve fitting to dose-response curves predicted by receptor models. [Pg.254]

Atmospheric emissions of sulphur dioxide are either measured or estimated at their source and are thus calculated on a provincial or state basis for both Canada and the United States (Figure 2). While much research and debate continues, computer-based simulation models can use this emission information to provide reasonable estimates of how sulphur dioxide and sulphate (the final oxidized form of sulphur dioxide) are transported, transformed, and deposited via atmospheric air masses to selected regions. Such "source-receptor" models are of varying complexity but all are evaluated on their ability to reproduce the measured pattern of sulphate deposition over a network of acid rain monitoring stations across United States and Canada. In a joint effort of the U.S. Environmental Protection Agency and the Canadian Atmospheric Environment Service, eleven linear-chemistry atmospheric models of sulphur deposition were evaluated using data from 1980. It was found that on an annual basis, all but three models were able to simulate the observed deposition patterns within the uncertainty limits of the observations (22). [Pg.45]

Legislation enacted by both Canada and the United States (see the US-Canada Air Quality Accord, 1991) will, when implemented, reduce the North American emissions of sulphur dioxide by about 50% based upon the 1980 baseline. These projected emission fields have been appplied in the atmospheric source-receptor models that were described above, to provide a projected deposition field for acidic sulphate that would be expected (14). The predicted sulphate deposition fields have then subsequently been appUed in aquatic effects models that provide estimates of regional surface water acidification distributions (50). The regional acidification profiles have then been used in a model of fish species richness (51) that results in an estimate of the expected presence of fish species as compared to that expected in an unacidified case. [Pg.58]

Figure 6. Interconverting receptor model. In the presence of L, membrane R and G become associated to form the ternary complex LRG, which is sequentially interconverted into LR and LRX prior to internalization. Figure 6. Interconverting receptor model. In the presence of L, membrane R and G become associated to form the ternary complex LRG, which is sequentially interconverted into LR and LRX prior to internalization.
W. Van Borm, Source Apportionment of Atmospheric Particles by Electron Probe X-ray Microanalysis and Receptor Models. Doctoral Thesis, University of Antwerp, 1989. [Pg.158]

Scientific Computing and Automation (Europe) Conference, 12-15 June 1990, Maastricht, The Netherlands, edited by E.J. Karjalainen Volume 7 Receptor Modeling for Air Quality Management, edited by P.K. Hopke Volume 8 Design and Optimization in Organic Synthesis, by R. Carlson... [Pg.717]

The prevalent receptor model for the excitatory amino acid is a tetrameric complex. As mentioned in the text, there is evidence that the channel conductance depends on the number of subunits that bind a ligand. Estimate the EC50 value and Hill coefficient for a dose-response curve assuming that the occupation at each subunit has a Kd value of 1 pi I, an % of 1, and that activation induces a transition to an active state independent of the state of the other subunits ... [Pg.128]

Seel, C., Galan, A., de Mendoza, J. Molecular Recognition of Organic Acids and Anions -Receptor Models for Carboxylates, Amino Acids, and Nucleotides. 175, 101-132 (1995). [Pg.298]


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Adenosine receptors binding site models

Adenyl cyclase hormone receptor model

Aerosol receptor models, composition

Affinity receptor site models

Animal Models of Xenobiotic Nuclear Receptors and Their Utility in Drug Development

Animal models, of xenobiotic nuclear receptors

Anionic receptor sites model

Anticonvulsant receptor models

Approaches to Modeling Enzymes, Transporters, Channels, and Receptors

Benzo receptor model

Capsaicin receptor model

Cell receptor complexes modeling

Chemical receptor model

Chemokine receptors model

Chemometrics receptor modeling

Docking (Modelling Receptor

Double receptor model

Empirical Orthogonal Function Receptor Models

Epidermal growth factor receptors model

Functional models used to study dopamine receptor ligands

Genetically Evolved Receptor Models (GERM)

Genetically evolved receptor models

Histamine receptor antagonists molecular modeling

Hybrid receptor model

Hybrid receptor model approach

In silico molecular homology modeling of neurotransmitter receptors

Interconverting receptor model

Ionotropic glutamate receptors modeling

Ligand-Olfactory Receptor Modelling

Ligand-receptor dynamics, model

Ligand-receptor interactions molecular modeling

Membranes bound receptor modelling

Metabotropic glutamate receptors model

Mobile receptor model

Model based on desensitization of the cAMP receptor

Model receptor sites

Model receptor-based

Modeling Receptor Neuron Convergence

Modeling of Selective Pharmacophores at the arAdrenergic Receptors

Models early receptor potential

Models receptor/ligand binding kinetics

Molecular modeling known receptors

Molecular modeling unknown receptors

Molecular modelling receptor binding

Molecular modelling receptor/pharmacophore mapping

Multivariate receptor models

Nicotinic acetylcholine receptor molecular models

Olfactory Receptor Modelling

Opioid receptors computational models

Opioid receptors ligand binding model

Opioid receptors models

Partial agonism and the two-state model of receptor activation

Pharmacophore Identification and Pseudo-Receptor Modeling

Pharmacophore Models into Receptor Site

Pharmacophore pseudo-receptor models

Porphyrins and Metalloporphyrins as Receptor Models in Molecular Recognition

Proteochemometric Modeling of Chimeric MC Receptors Interacting with MSH Peptides

Pseudo-receptor models

Publication receptor-interaction, model

Receptor Modeling Methods

Receptor Surface Models (RSM)

Receptor binding models thyroid hormones

Receptor binding site models

Receptor characterization homology modelling

Receptor complexes ternary complex models

Receptor cooperativity model

Receptor extended ternary complex model

Receptor homology-built models

Receptor model anionic

Receptor model opiate anionic

Receptor modeling

Receptor modeling

Receptor modeling case study

Receptor modeling opiate

Receptor modeling theory

Receptor models Portoghese

Receptor models Snyder

Receptor models, variability

Receptor structure and modelling

Receptor surface model

Receptor ternary complex model

Receptor-binding kinetics model

Source chemical receptor model

Statistical Models 1 Receptor Modeling Methods

Statistical receptor models

Structural Insights for Homology Modeling of Chemokine Receptors

Sweet-taste receptor, model

Taste-receptor model

Toward a Model of the Receptor-G Protein Complex

Tracer hybrid receptor model

Two Receptor Model

Two-state receptor model

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