diff --git a/manuscript/RSF.tex b/manuscript/RSF.tex
index 898804085d3dc741d3b65306ee92c51a809a3e43..0b5e4405a7a0981903993fdb2f3d84caf66ac15a 100644
--- a/manuscript/RSF.tex
+++ b/manuscript/RSF.tex
@@ -89,6 +89,7 @@
 tl;dr RKHS make subsequent optimization learning easier to implement and more likely to generalize better [primer]. 
 
 
+
 \subsection{Optical Coherence Tomography of the Retina}
 [Ronald?] -- ~ 4 paragraphs summarize impact of retinal OCT (Glaucoma, occulomics, etc.),  background on VFMD, background on metadata, imaging physics. 
 Key recent deep learning papers [Schuman et al.]. Other learning approaches [NNMF et al.].
@@ -101,16 +102,20 @@ Optical coherence tomography (OCT) is a non-invasive diagnostic imaging tool whi
 
 Key observation -- inherent anisotropy of imaging implements Frangi-like plate filter along the high resolution axis.
 
+
 \subsection{Kolmogorov complexity and the normalized information distance (NID)}
 
 FLIF \cite{FLIF} \dots
+\subsection{Semi-supervised spectral learning}
 
+\subsection{multi-scale image structure enhancement and positive semi-definite functions}
+Frangi's 3-D vesselness filter was a big advance in our ability to extract biological structure from low-SNR images.  
+\subsection{Kolmogorov structure function vs. }
+$h_{Kolmogorov}$ [Vereschegin and Vitanyi] \emph{vs.} $h_{Frangi}$
+\subsection{Quantization}
 
-\subsection{Semi-supervised spectral learning}
-\subsection{Kolmogorov structure function vs. multi-scale vessel ehancing image filters}
-$H_{Kolmogorov}$ [Vereschegin and Vitanyi] \emph{vs.} $H_{Frangi}$
 
-\section{The retinal structure function}
+\input{algorithmicStructure.tex}
 
 \section{Validating the NCD vs. prediction against functional changes of the visual field }
 
@@ -132,14 +137,18 @@ RKHS combinations: macula + ONH simultaneously, multi-resolution by channel vs.
 
 \section{Acknowledgements}
 
+\section{Hardware, software and data availability}
+A sample dataset of labeled OCT imaging data is being made available concurrent with this manuscript. The images and metadata can be explored using the LEVERSC [ref] web visualization toolkit: https://todo, or direct download at ??? 
 
-\section{Software and data availability}
 All of the software tools used are available free and open source, see \url{https://git-bioimage.coe.drexel.edu/opensource/rsf}.
 The image data together with segmentation and tracking results can be viewed interactively at \url{https://leverjs.net/ssfCluster}. The LEVERSC 4-D WEBGL viewer \cite[]{Cohen2022} renders 3-D kymographs and images, and the web API also supports downloading metadata and results directly. 
+
+The image processing pipeline described here requires dedicated hardware. Structure filters are GPU intensive, FLIF compression  is  CPU intensive. The reference hardware platform is 128 AMD X-???, 3 NVIDIA A6000 GPU, 2TB RAM, 1PB HDD. 
+
 \label{Sect.Software}
 
 \printbibliography
-
+BOOM!
 \newpage
 
   \begin{figure*}[!ht]