Receptor multivariate

Moreover, multivariate optimization, the simultaneous optimization of several properties, will increasingly come into focus. A drug should have high selectivity in binding to different receptors and minimal toxicity, good solubility and penetration, and so on. A hair color should have a brilliant shine, be absorbed well, not be washed out, not damage the hair, not be toxic, and be stable under sunlight, etc. 

Van der Graaf PH, Nilsson J, Van Schaick EA, Danhof M. Multivariate quantitative structure-pharmacokinetic relationships (QSPKR) analysis of adenosine A1 receptor agonists in rat. J Pharm Sci 1999 88 306-12. 

G. Calomme and P.J. Lewi, Multivariate analysis of structure-activity data. Spectral map of opioid narcotics in receptor binding. Actual. Chim. Therap., S.l 1 (1984) 121-126. 

Multivariate data analysis and experimental design, 25 (1988) 291 Muscarinic Receptors, 43 (2005) 105... 

Receptor Modeling for Air Quality Management, edited by P.K. Hopke Design and Optimization in Organic Synthesis, by R. Carlson Multivariate Pattern Recognition in Chemometrics, illustrated by case studies, edited by R.G. Brereton... 

Objectives Optimize biological activity of drugs Find new active lead compounds Characteristics Response in isolated systems Effects are specific and well defined Specific mechanism of action Receptor is known in most cases Techniques Hansch Approach Multivariate Analysis Computerized molecular modeling Estimate rates of fate processes Analyze Processes Whole organism response Net effects (mortality growth, etc.) Specific nonspecific mechanisms Receptor unknown in most cases Hansch Approach Multivariate Analysis Molecular modeling not applied... 

Millan Ml, Maiofiss L, Cussac D, Audinot V, Boutin JA, Newman-Tancredi A. (2002) Differential actions of antiparkinson agents at multiple classes of monoaminergic receptor. I. A multivariate analysis of the binding profiles of 14 drags at 21 native and cloned human receptor subtypes. J Pharmacol Exp Ther 303 791-804. 

Among the multivariate statistical techniques that have been used as source-receptor models, factor analysis is the most widely employed. The basic objective of factor analysis is to allow the variation within a set of data to determine the number of independent causalities, i.e. sources of particles. It also permits the combination of the measured variables into new axes for the system that can be related to specific particle sources. The principles of factor analysis are reviewed and the principal components method is illustrated by the reanalysis of aerosol composition results from Charleston, West Virginia. An alternative approach to factor analysis. Target Transformation Factor Analysis, is introduced and its application to a subset of particle composition data from the Regional Air Pollution Study (RAPS) of St. Louis, Missouri is presented. 

There are two general types of aerosol source apportionment methods dispersion models and receptor models. Receptor models are divided into microscopic methods and chemical methods. Chemical mass balance, principal component factor analysis, target transformation factor analysis, etc. are all based on the same mathematical model and simply represent different approaches to solution of the fundamental receptor model equation. All require conservation of mass, as well as source composition information for qualitative analysis and a mass balance for a quantitative analysis. Each interpretive approach to the receptor model yields unique information useful in establishing the credibility of a study s final results. Source apportionment sutdies using the receptor model should include interpretation of the chemical data set by both multivariate methods. 

A source apportionment study using the receptor model should include interpretation of the chemical data set by both multivariate and chemical mass balance methods The most critical steps in a receptor model study are the initial review of potential source characteristics and the development of an appropriate study plan. 

Three generic types of receptor model have been identified, chemical mass balance, multivariate, and microscopical identification. Each one has certain requirements for input data to provide a specified output. An approach which combines receptor and source models, source/ receptor model hybridization, has also been proposed, but it needs further study. 

In an attempt to provide this focus, forty-seven active receptor model users from government, university, consulting and industry met for 2 1/2 days in February 1980 it. They addressed the models and the information required to use them in six separate task forces 1) Chemical Element Balance Receptor Models, 2) Multivariate Receptor Models, 3) Microscopic Identification Receptor Models, 4) Field Study Design and Data Management, 5) Source Characterization, and 6) Analytical Methods. The objectives of these interrelated task forces were to ... 

Receptor models presently in use can be classified into one of four categories chemical mass balance, multivariate, microscopic, and source/receptor hybrids. Each classification will be treated individually, though it will become apparent that they are closely related. 

While the chemical mass balance receptor model is easily derivable from the source model and the elements of its solution system are fairly easy to present, this is not the case for multivariate receptor models. Watson (9) has carried through the calculations of the source-receptor model relationship for the correlation and principal components models in forty-three equation-laden pages. 

The input data required of the multivariate receptor models at their present state-of-the-art are the ambient concentrations, The output with this input, however, is only qualitative. With... 

The microscopic receptor model can include many more aerosol properties than have been used in the chemical mass balance and multivariate models. The data inputs required for this model are the ambient properties measurements and the source properties measurements. To estimate the confidence Interval of the calculated source contributions the uncertainties of the source and receptor measurements are also required. Microscopists generally agree that a list of likely source contributors, their location with respect to the receptor, and windflow during sampling are helpful in confirming their source assignments. 

Source Characterization. All receptor models, even the source/receptor hybrids, require input data about the particulate matter sources. The multivariate models, which can conceivably be used to better estimate source compositions, require an initial knowledge of the chemical species associations in sources. 

While there has been a great deal of work on emissions from motor vehicles, with emphasis on why the VOC and CO emissions have been historically underestimated, a similar problem appears to exist with respect to stationary source emissions, at least in some areas. For example, Flenry et al. (1997) measured organic gases in an industrial area in Houston, Texas, and compared them to reported emissions inventories. Application of a multivariate receptor model revealed large inconsistencies between the measurements and expected concentrations. 

A widely used approach for estimation of source contributions at receptor sites is receptor modelling [31, 32], In receptor models, source contributions are estimated based on the measurements of various chemical constituents in a sufficiently large number of ambient PM samples, often filter samples that are collected during 24 h. Depending on the available knowledge about the main sources, CMB or multivariate statistical models can be applied CMB requires a priori knowledge of the chemical profile of all relevant sources, i.e. the percentage of the chemical... 

Thorpe et al. [116] proposed the roadside incremental concentration of coarse particles above the urban background as a first estimate of the sum of source strength road dust resuspension and the coarse fraction of wear emissions. Other studies succeeded in separating different traffic emissions by means of multivariate receptor models applied to PM size distribution data ([84, 117]. 

In many chemical studies, the measured properties of the system can be regarded as the linear sum of the fundamental effects or factors in that system. The most common example is multivariate calibration. In environmental studies, this approach, frequently called receptor modeling, was first applied in air quality studies. The aim of PCA with multiple linear regression analysis (PCA-MLRA), as of all bilinear models, is to solve the factor analysis problem stated below ... 

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