Dear all,
I have found many fundamental studies on image processing and
refinement indexes concerning the decision on cutting resolution for a
dataset, always meant to get better models, the final objective. Paired
refinement has been a procedure mostly indicated.
I have been searching studies alike concerning, in these days of
thousands of collected images and strong x ray beams, the cutting (or
truncation) of the (sequentially due to rotation method) recorded images
in a dataset due to radiation damage. Once again, I understand the idea
is to always produce better models.
On one hand, the more images one uses, the higher the multiplicity,
what (higher multiplicity) leads to better averaged intensity (provided
scaling makes a good job), on the other hand, the more images one uses,
lower intensity (due to the radiation damage) equivalent reflections
come into play for scaling, etc. How to balance this? I have seen a case
in which truncating images with some radiation damage led to worse
CC(1/2) and <I/sigI> (at the same high resolution shell, multiplicities
around 12.3 and then 5.7), but this might not be the general finding. In
a word, are there indicators of the point where to truncate more
precisely the images such that the dataset will lead to a better model?
I understand tracing a sharp borderline might not be trivial, but even a
blurred borderline might help, specially in the moment of image processing.
I find that in
https://ccp4i2.gitlab.io/rstdocs/tasks/aimless_pipe/scaling_and_merging.html#estimation-of-resolution
there is a suggestion to try refinement with both truncating and not
truncating.
Sure other factors come into play here, like diffraction anisotropy,
crystal internal symmetry, etc., but to start one might consider just
the radiation damage due to exposure to x rays. Yes, further on, it
would be nice the talk evolves to those cases when we see peaks and
valleys along the rotation due to crystal anisotropy, whose average
height goes on diminishing.
Comments and indications to papers and material to study are welcome.
Thanks.
Yours,
Jorge
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