Author: bugman
Date: Wed May 9 00:52:00 2007
New Revision: 3291
URL: http://svn.gna.org/viewcvs/relax?rev=3291&view=rev
Log:
Renamed "Kay's paradigm" to "the diffusion seeded paradigm".
Kay's paradigm was accidentally mislabelled. This paradigm should have been
stated to be the
diffusion seeded paradigm using the T1/T2 ratio to initially estimate the
diffusion tensor!
Modified:
1.2/docs/latex/model-free.tex
Modified: 1.2/docs/latex/model-free.tex
URL:
http://svn.gna.org/viewcvs/relax/1.2/docs/latex/model-free.tex?rev=3291&r1=3290&r2=3291&view=diff
==============================================================================
--- 1.2/docs/latex/model-free.tex (original)
+++ 1.2/docs/latex/model-free.tex Wed May 9 00:52:00 2007
@@ -485,18 +485,18 @@
-% Kay's paradigm -- the initial diffusion tensor estimate.
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\section{Kay's paradigm -- the initial diffusion tensor estimate}
-
-Ever since \citet{Kay89}, the question of how to obtain the model-free
description of the system has followed the route in which the diffusion
tensor is initially estimated. Using this rough estimate, the model-free
models are optimised for each spin system $i$, the best model selected, and
then the global model $\Space$ of the diffusion model $\Diffset$ with each
model-free model $\Mfset_i$ is optimised. This procedure is then repeated
using the diffusion tensor parameters of $\Space$ as the initial input.
Finally the global model is selected. The full protocol is illustrated in
Figure~\ref{fig: init diff estimate}.
-
-
-% Kay's paradigm figure.
+% The diffusion seeded paradigm.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\section{The diffusion seeded paradigm}
+
+Ever since the original Lipari and Szabo papers \citep{LipariSzabo82a,
LipariSzabo82b}, the question of how to obtain the model-free description of
the system has followed the route in which the diffusion tensor is initially
estimated. Using this rough estimate, the model-free models are optimised
for each spin system $i$, the best model selected, and then the global model
$\Space$ of the diffusion model $\Diffset$ with each model-free model
$\Mfset_i$ is optimised. This procedure is then repeated using the diffusion
tensor parameters of $\Space$ as the initial input. Finally the global model
is selected. The full protocol is illustrated in Figure~\ref{fig: init diff
estimate}.
+
+
+% The diffusion seeded paradigm figure.
\begin{figure}
\centerline{\includegraphics[width=0.8\textwidth, bb=0 0 437
523]{images/model_free/init_diff_est.eps.gz}}
-\caption[Model-free analysis using Kay's paradigm -- the initial diffusion
tensor estimate]{A schematic of model-free analysis using Kay's paradigm --
the initial diffusion tensor estimate -- together with AIC model selection
and model elimination. The initial estimates of the parameters of $\Diffset$
are held constant while model-free models $m0$ to $m9$ (\ref{model:
m0}--\ref{model: m9}) of the set $\Mfset_i$ for each spin system $i$ are
optimised, model elimination applied to remove failed models, and AIC model
selection used to determine the best model. The global model $\Space$, the
union of $\Diffset$ and all $\Mfset_i$, is then optimised. These steps are
repeated until convergence of the global model. The entire iterative process
is repeated for each of the Brownian diffusion models. Finally AIC model
selection is used to determine the best description of the dynamics of the
molecule by selecting between the global models $\Space$ including the
sphere, oblate spheroid, prolate spheroid, and ellipsoid. Once the solution
has been found, Monte Carlo simulations can be utilised for error analysis.}
\label{fig: init diff estimate}
+\caption[Model-free analysis using the diffusion seeded paradigm -- the
initial diffusion tensor estimate]{A schematic of model-free analysis using
the diffusion seeded paradigm -- the initial diffusion tensor estimate --
together with AIC model selection and model elimination. The initial
estimates of the parameters of $\Diffset$ are held constant while model-free
models $m0$ to $m9$ (\ref{model: m0}--\ref{model: m9}) of the set $\Mfset_i$
for each spin system $i$ are optimised, model elimination applied to remove
failed models, and AIC model selection used to determine the best model. The
global model $\Space$, the union of $\Diffset$ and all $\Mfset_i$, is then
optimised. These steps are repeated until convergence of the global model.
The entire iterative process is repeated for each of the Brownian diffusion
models. Finally AIC model selection is used to determine the best
description of the dynamics of the molecule by selecting between the global
models $\Space$ including the sphere, oblate spheroid, prolate spheroid, and
ellipsoid. Once the solution has been found, Monte Carlo simulations can be
utilised for error analysis.} \label{fig: init diff estimate}
\end{figure}
r3291 - /1.2/docs/latex/model-free.tex