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Unblur is used to align the frames of movies recorded on an electron microscope to reduce image blurrig due to beam-induced motion. It reads stacks of movies that are stored in MRC/CCP4 format. Unblur generates frame sums that can be used in subsequent image processing steps and optionally applies an exposure-dependent filter to maximize the signal at all resolutions in the frame averages. Movie frame sums can also be calculated using Summovie, which uses the alignment resuls from a prior run of Unblur.
OpenCV is an open-source library that includes several hundreds of computer vision algorithms.
In December 2021 OSC updated its firewall to enhance security. As a result, SSH sessions are being closed more quickly than they used to be. It is very easy to modify your SSH options in the client you use to connect to OSC to keep your connection open.
In ~/.ssh/config (use the command
touch ~/.ssh/config to create it if there is no exisitng one), you can set 3 options:
MotionCor2 uses multi-GPU acceleration to correct anisotropic cryo-electron microscopy images at the single pixel level across the whole frame, making it suitable for single particle and tomographic images. Iterative, patch-based motion detection is combined with spatial and temporal constraints and dose weighting.
Availability and Restrictions
The following versions are available on OSC clusters:
MyOSC now offers the ability for PIs to view the billing statements of current un-billed usage for their projects.
Login to my.osc.edu and navigate to Project -> Billing Statements.
There are two sections:
The current usage section provides information usage charges up to the current day.
Keep protected data in proper locations
SUG Conference - October 7, 2021
VisIt is an Open Source, interactive, scalable, visualization, animation and analysis tool for visualizing data defined on two- and three-dimensional structured and unstructured meshes.
HMMER is used for searching sequence databases for sequence homologs, and for making sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMS). HMMER is designed to detect remote homologs as sensitively as possible, relying on the strength of its underlying probability models.