scholarly journals EMDA: A Python package for Electron Microscopy Data Analysis

2021 ◽  
pp. 107826
Author(s):  
Rangana Warshamanage ◽  
Keitaro Yamashita ◽  
Garib N. Murshudov
Keyword(s):  
Electron Microscopy ◽  
Data Analysis ◽  
Electron Microscopy Data ◽  
Microscopy Data ◽  
Python Package

scholarly journals Towards on-the-fly Cryo-Electron Microscopy Data Processing by High Performance Data Analysis

2018 ◽  
Vol 955 ◽  
pp. 012005 ◽  
Author(s):  
Evgeny Pichkur ◽  
Timur Baimukhametov ◽  
Anton Teslyuk ◽  
Anton Orekhov ◽  
Roman Kamyshinsky ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Data Analysis ◽  
Data Processing ◽  
High Performance ◽  
Performance Data ◽  
Cryo Electron Microscopy ◽  
Electron Microscopy Data ◽  
Microscopy Data

scholarly journals EMDA: A Python package for Electron Microscopy Data Analysis

2021 ◽  
Author(s):  
Rangana Warshamanage ◽  
Keitaro Yamashita ◽  
Garib N Murshudov
Keyword(s):  
Electron Microscopy ◽  
Coordinate Frame ◽  
Command Line ◽  
Model Errors ◽  
Local Correlation ◽  
Relative Movement ◽  
Structural Comparison ◽  
Electron Microscopy Data ◽  
Microscopy Data ◽  
Python Package

An open-source Python library EMDA for cryo-EM map and model manipulation is presented with a specific focus on validation. The architecture of the library and its programmable and command-line interfaces are presented. The use of several functionalities in the library is presented through several examples. The utility of local correlation as a metric for identifying model errors and unmodeled regions in maps, and how it is used as a metric of map-model validation is demonstrated. The mapping of local correlation to individual atoms, and its use to draw insights on local signal variations are discussed. EMDA`s likelihood-based map overlay is demonstrated by carrying out a superposition of two domains in two related structures. The overlay is carried out globally to bring both maps into the same coordinate frame and then locally to estimate the relative movement of domains. Finally, the map magnification refinement in EMDA is presented with an example to highlight the importance of correcting map magnification in structural comparison studies.

scholarly journals Label-free fluorescence predictions from large-scale correlative light and electron microscopy data

2021 ◽  
Vol 27 (S1) ◽  
pp. 94-95
Author(s):  
Ryan Lane ◽  
Luuk Balkenende ◽  
Simon van Staalduine ◽  
Anouk Wolters ◽  
Ben Giepmans ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Large Scale ◽  
Light And Electron Microscopy ◽  
Label Free ◽  
Electron Microscopy Data ◽  
Microscopy Data

scholarly journals Automatic instance segmentation of mitochondria in electron microscopy data

2021 ◽  
Author(s):  
Luke Nightingale ◽  
Joost de Folter ◽  
Helen Spiers ◽  
Amy Strange ◽  
Lucy M Collinson ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Public Repository ◽  
Post Processing ◽  
Human Cortex ◽  
Electron Microscopy Data ◽  
Processing Procedure ◽  
Microscopy Data ◽  
Instance Segmentation

We present a new method for rapid, automated, large-scale 3D mitochondria instance segmentation, developed in response to the ISBI 2021 MitoEM Challenge. In brief, we trained separate machine learning algorithms to predict (1) mitochondria areas and (2) mitochondria boundaries in image volumes acquired from both rat and human cortex with multi-beam scanning electron microscopy. The predictions from these algorithms were combined in a multi-step post-processing procedure, that resulted in high semantic and instance segmentation performance. All code is provided via a public repository.

scholarly journals Влияние температуры деформации в камере Бриджмена на особенности формирования дефектной структуры технически чистого титана

2018 ◽  
Vol 44 (20) ◽  
pp. 70
Author(s):  
Н.А. Шурыгина ◽  
А.М. Глезер ◽  
Д.Л. Дьяконов ◽  
А.А. Томчук ◽  
А.Г. Кадомцев ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Hydrostatic Pressure ◽  
High Hydrostatic Pressure ◽  
Defect Structures ◽  
Cryogenic Temperatures ◽  
Two Phase ◽  
Α Phase ◽  
Electron Microscopy Data ◽  
Phase Mixture ◽  
Microscopy Data

AbstractTransmission electron microscopy data showed evidence of the formation of structural regions corresponding to deformation (dislocated) fragments and dynamically recrystallized grains in α-phase titanium upon torsion at high hydrostatic pressure at room and cryogenic temperatures. It is shown that the previously proposed “two-phase mixture” model is applicable to description of these defect structures.

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