Dear All,
We have two postdoc positions open in LBNL's Center for Advanced Mathematics
in Energy Research Applications (CAMERA; https://www.camera.lbl.gov/) to work
on a diverse set of problems in managing, cleaning and interpreting biophysical
data ranging from microscopy to crystallography.
Hi All,
How is the 'correct' resolution estimation related to the estimated error
on some observed hydrogen bond length of interest, or an error on the
estimated occupancy of a ligand or conformation or anything else that has
structural significance?
In crystallography, it isn't really (only in
Hi Jacob,
On Thu, Mar 12, 2020 at 9:13 AM Keller, Jacob
wrote:
> I would think the most information-reflecting representation for
> systematic absences (or maybe for all reflections) would be not I/sig but
> the reflection's (|log|) ratio to the expected intensity in that shell
> (median
I am not quite sure how it is done in Refmac, but in phenix it works as
follows
- difference maps are gradient maps for a least squares target and they do
not contain any amplitude contamination from twinning.
- Fo type maps are computed either via classic detwinning (solving the
linear
Hi Jessica,
If you see spots and they fit a lattice, you should use them. Use the b=10
solution in P1 that you have with 3 models (or two if one is iffy) and use
that as a start model for MR in b=20 and start searching for the rest.
Alternatively, use the b=10 model with stuff that looks decent
On Mon, Jul 6, 2020 at 10:33 PM Petr Kolenko
wrote:
> Dear Eleanor,
> The unit cell parameters are 117.385 155.506 155.611 90.00 90.00
> 90.00, as you expected. The twin law was recognized using phenix.xtriage.
> If I refine the structure using phenix.refine with no twin law and the
>
Hi Jacob,
What you describe sounds a lot like a phase transition. For instance, in
random graph methods, one often observes a sharp increase in the largest
connected component at a defined sparsity level.
This phenomenon has application in data analysis, machine learning, etc etc.
Predicting
Hi James,
This is what you need.
https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution
The distribution of a maximum of 1k random variates looks like this, and
the (fitted by eye) analytical distribution associated with it seems to
have a decent fit - as expected.
[image:
