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<DIV>Holger</DIV>
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<DIV>To my knowledge my review from a few years ago is the most recent comprehensive overview of weight average. It finishes with a mention of other averages & who has applied them. </DIV>
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<DIV>J.J. CORREIA (2000) ”The Analysis of Weight Average Sedimentation Data.” Methods in Enzymol., vol 321, 81-100.</DIV>
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<DIV>Soon after I published that review John Philo published a paper claiming typical data is too noisy for higher averages, and demonstrated so by simulations. As Walter mentioned these averages are also calculated in the DCDT portion of SEDANAL. Peter Schuck also suggests they are too noisy. Attached please find a test of those claims, derived from XLA data using g(s) analysis in SEDANAL. Clearly if done properly they are not too noisy! (I have compared this analysis with results from Philo's DCDTplus2 and the error bars are comparable for Sw, Sz and Sz+1 - John doesn't report Sn.) </DIV>
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<DIV>As to claims about no theoretical meaning, moments or averages of distributions are what they are. One could easily write a program to fit these moments and compare them to some model. We have done this but I generally only attempt such complexity when the system is very complex. I find it much more useful to compare weight average fitting with direct boundary fitting. For my self association systems the K's derived typically agree to within 20%. The direct boundary fitting usually reveals what causes the disagreement, typically aggregation at the centrifugal edge of the boundary.</DIV>
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<DIV>Applying these averages to different signals has its pitfalls, but Schachman 1st described absorbance average moments in his book, so the use of different signals is an old idea and easy to apply as a fitter. We again prefer to resort to direct boundary fitting, combining different wavelengths and extinction coefficients to different components and species. I strongly agree the information is in the shape of the sedimenting boundary. (This does not necessarily mean the shape of the derived distribution.) Comparisons to averages should in principle work with the usual caveats about reversibility, degree of aggregation, the presence of plateaus, etc. </DIV>
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<DIV>I hope you try this approach, apply your own direct statistical tests, and convince yourself of its potential utility. I have no doubts there may be a system dependency. My bias continues to be direct boundary fitting is the most informative, although we use weight average for model building, extrapolation to end points, and estimation of K's for initial guesses.</DIV>
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<DIV>Good luck!</DIV>
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<DIV>-------------------------------------------------------------------<BR> Dr. John J. "Jack" Correia<BR> Department of Biochemistry<BR> University of Mississippi Medical Center<BR> 2500 North State Street<BR> Jackson, MS 39216<BR> (601) 984-1522 <BR> fax (601) 984-1501 <BR> email address: <A href="mailto:jcorreia@biochem.umsmed.edu">jcorreia@biochem.umsmed.edu</A> <BR> homepage location: <A href="http://biochemistry.umc.edu/correia.html">http://biochemistry.umc.edu/correia.html</A><BR> dept homepage location: <A href="http://biochemistry.umc.edu/">http://biochemistry.umc.edu/</A><BR>-------------------------------------------------------------------<BR> <BR> <BR></DIV></BODY></HTML>