scholarly journals Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science

2021 ◽  
Vol 6 ◽  
pp. 76
Author(s):  
Mick A. Phillips ◽  
David Miguel Susano Pinto ◽  
Nicholas Hall ◽  
Julio Mateos-Langerak ◽  
Richard M. Parton ◽  
...  
Keyword(s):  
Open Source ◽  
Adaptive Optics ◽  
High Performance ◽  
Medical Science ◽  
Machine Learning Algorithms ◽  
Spatial Light Modulators ◽  
Structured Illumination ◽  
Light Modulators ◽  
Wide Range ◽  
User Friendly

We have developed “Microscope-Cockpit” (Cockpit), a highly adaptable open source user-friendly Python-based Graphical User Interface (GUI) environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses “Python-Microscope” (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging.

scholarly journals Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science

2021 ◽  
Author(s):  
Mick A Phillips ◽  
David Miguel Susano Pinto ◽  
Nicholas Hall ◽  
Julio Mateos-Langerak ◽  
Richard M Parton ◽  
...  
Keyword(s):  
Open Source ◽  
Adaptive Optics ◽  
Medical Science ◽  
Machine Learning Algorithms ◽  
Spatial Light Modulators ◽  
List Type ◽  
Structured Illumination ◽  
Wide Range ◽  
Automated Microscopy ◽  
User Friendly

AbstractWe have developed “Microscope-Cockpit” (Cockpit), a highly adaptable open source user-friendly Python-based GUI environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near-instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses “Python-Microscope” (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging.HighlightsUser-friendly setup and use for simple to complex bespoke microscopes.Facilitates collaborations between biomedical scientists and microscope technologists.Touchscreen for near-instantaneous navigation of specimen landscape.Uses Python-Microscope, for abstracted open source hardware device control.Well-suited for user training of AI-algorithms for automated microscopy.

scholarly journals High-speed multiplane structured illumination microscopy of living cells using an image-splitting prism

Nanophotonics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 143-148
Author(s):  
Adrien Descloux ◽  
Marcel Müller ◽  
Vytautas Navikas ◽  
Andreas Markwirth ◽  
Robin van den Eynde ◽  
...  
Keyword(s):  
High Speed ◽  
Three Dimensional ◽  
Super Resolution ◽  
Optical Element ◽  
Spatial Light Modulators ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Image Stack ◽  
Light Modulators ◽  
3D Information

AbstractSuper-resolution structured illumination microscopy (SR-SIM) can be conducted at video-rate acquisition speeds when combined with high-speed spatial light modulators and sCMOS cameras, rendering it particularly suitable for live-cell imaging. If, however, three-dimensional (3D) information is desired, the sequential acquisition of vertical image stacks employed by current setups significantly slows down the acquisition process. In this work, we present a multiplane approach to SR-SIM that overcomes this slowdown via the simultaneous acquisition of multiple object planes, employing a recently introduced multiplane image splitting prism combined with high-speed SIM illumination. This strategy requires only the introduction of a single optical element and the addition of a second camera to acquire a laterally highly resolved 3D image stack. We demonstrate the performance of multiplane SIM by applying this instrument to imaging the dynamics of mitochondria in living COS-7 cells.

Practical issues for the use of liquid crystal spatial light modulators in adaptive optics

1998 ◽  
Vol 148 (4-6) ◽  
pp. 323-330 ◽  
Author(s):  
G.T Bold ◽  
T.H Barnes ◽  
J Gourlay ◽  
R.M Sharples ◽  
T.G Haskell
Keyword(s):  
Liquid Crystal ◽  
Adaptive Optics ◽  
Spatial Light Modulators ◽  
Light Modulators

scholarly journals BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis

2019 ◽  
Author(s):  
Manoj Kumar ◽  
Cameron Thomas Ellis ◽  
Qihong Lu ◽  
Hejia Zhang ◽  
Mihai Capota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Open Source ◽  
Programming Languages ◽  
High Performance ◽  
Large Scale ◽  
Markov Models ◽  
Matrix Analysis ◽  
Fmri Analysis ◽  
User Friendly

Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data, and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computationally efficient techniques with other Python packages (e.g., Nilearn, Scikit-learn) for file handling, visualization, and machine learning. To disseminate these powerful tools, we developed user-friendly tutorials (in Jupyter format; https://brainiak.org/tutorials/) for learning BrainIAK and advanced fMRI analysis in Python more generally. These materials cover techniques including: MVPA (pattern classification and representational similarity analysis); parallelized searchlight analysis; background connectivity; full correlation matrix analysis; inter-subject correlation; inter-subject functional connectivity; shared response modeling; event segmentation using hidden Markov models; and real-time fMRI. For long-running jobs or large memory needs we provide detailed guidance on high-performance computing clusters. These notebooks were successfully tested at multiple sites, including as problem sets for courses at Yale and Princeton universities and at various workshops and hackathons. These materials are freely shared, with the hope that they become part of a pool of open-source software and educational materials for large-scale, reproducible fMRI analysis and accelerated discovery.

scholarly journals corrfunc – a suite of blazing fast correlation functions on the CPU

2019 ◽  
Vol 491 (2) ◽  
pp. 3022-3041 ◽  
Author(s):  
Manodeep Sinha ◽  
Lehman H Garrison
Keyword(s):  
Correlation Function ◽  
Galaxy Formation ◽  
Correlation Functions ◽  
High Performance ◽  
Computing Time ◽  
Next Generation ◽  
Galaxy Surveys ◽  
Wide Range ◽  
Improved Performance ◽  
User Friendly

ABSTRACT The two-point correlation function (2PCF) is the most widely used tool for quantifying the spatial distribution of galaxies. Since the distribution of galaxies is determined by galaxy formation physics as well as the underlying cosmology, fitting an observed correlation function yields valuable insights into both. The calculation for a 2PCF involves computing pair-wise separations and consequently, the computing time-scales quadratically with the number of galaxies. The next-generation galaxy surveys are slated to observe many millions of galaxies, and computing the 2PCF for such surveys would be prohibitively time-consuming. Additionally, modern modelling techniques require the 2PCF to be calculated thousands of times on simulated galaxy catalogues of at least equal size to the data and would be completely unfeasible for the next-generation surveys. Thus, calculating the 2PCF forms a substantial bottleneck in improving our understanding of the fundamental physics of the Universe, and we need high-performance software to compute the correlation function. In this paper, we present corrfunc – a suite of highly optimized, openmp parallel clustering codes. The improved performance of corrfunc arises from both efficient algorithms as well as software design that suits the underlying hardware of modern CPUs. corrfunc can compute a wide range of 2D and 3D correlation functions in either simulation (Cartesian) space or on-sky coordinates. corrfunc runs efficiently in both single- and multithreaded modes and can compute a typical two-point projected correlation function [wp(rp)] for ∼1 million galaxies within a few seconds on a single thread. corrfunc is designed to be both user-friendly and fast and is publicly available at https://github.com/manodeep/Corrfunc.

scholarly journals MOSGA: Modular Open-Source Genome Annotator

2020 ◽  
Author(s):  
Roman Martin ◽  
Thomas Hackl ◽  
Georges Hattab ◽  
Matthias G Fischer ◽  
Dominik Heider
Keyword(s):  
Open Source ◽  
Source Code ◽  
Supplementary Information ◽  
Web Interface ◽  
Fully Integrated ◽  
Sequencing Technologies ◽  
A Genome ◽  
Wide Range ◽  
User Friendly ◽  
Eukaryotic Genomes

Abstract Motivation The generation of high-quality assemblies, even for large eukaryotic genomes, has become a routine task for many biologists thanks to recent advances in sequencing technologies. However, the annotation of these assemblies—a crucial step toward unlocking the biology of the organism of interest—has remained a complex challenge that often requires advanced bioinformatics expertise. Results Here, we present MOSGA (Modular Open-Source Genome Annotator), a genome annotation framework for eukaryotic genomes with a user-friendly web-interface that generates and integrates annotations from various tools. The aggregated results can be analyzed with a fully integrated genome browser and are provided in a format ready for submission to NCBI. MOSGA is built on a portable, customizable and easily extendible Snakemake backend, and thus, can be tailored to a wide range of users and projects. Availability and implementation We provide MOSGA as a web service at https://mosga.mathematik.uni-marburg.de and as a docker container at registry.gitlab.com/mosga/mosga: latest. Source code can be found at https://gitlab.com/mosga/mosga Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document