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Bioloc3D
No full textBioloc3D: an automatized and user-friendly toolset to quantify fluorescent profiles and their colocalization(s) in 3D.
Description
The goal of Bioloc3D is to quantify fluorescent labeling across entire stacks of confocal images and to automatize the statistical analysis of the data. It is an all-in-one bundle for the three-dimensional quantification of individualized fluorescent profiles.
Bioloc3D is a free alternative to commercial solutions with segmentation and analysis programs. It is a user-friendly tool that has been developed to offer the user a straight-to-the-point tool to avoid parasitic information or accessibility issues.
The bundle is made of two components. The first one, Bioloc3D-Imaging, is an ImageJ toolset that is used to individualize fluorescent elements from 2 different channels and identify possible colocalization(s) in 3D. To do so, the core of Bioloc3D has been structured around the “3D Objects Counter” plugin, used to enumerate the number of elements in 3D and provide valuable characteristics about it (e.g. volume, integrated density, etc). The second component, Bioloc3D-Plotting is a Python script that will automatically analyze Excel files obtained through the ImageJ tool to plot data and run the appropriate statistical test.
Installation
To install Bioloc3D in ImageJ, just drag/drop the .ijm file in the "toolset" folder of the application (restart ImageJ if needed).
Note: The macro will be completely open-source after publication.
Software Requirements
Bioloc3D needs plugins to run correctly on ImageJ :
- MorphoLibJ
- Read and Write Excel
- 3D Simple Segmentation
- 3D Objects Counter
Keep ImageJ updated, the last version of the software is advised.
Changelog B3D-Imaging
v2.1.1
Suppression "Remove outliers (Noise)" option because not adapted to all kinds of fluorescent staining.
NB : If tested before, adding this to the "Indiv_C" function (line 480) might be a way to save time for segmentation
v2.1.0
Add the possibility to test inputs for channels 3 and/or 4 in "Test settings Tool"
v2.0.1
Fix potential crash issues due to counting of 0 or 1 colocalization
v2.0.0
Implemented a loop to run several analysis in a row
Simplification of the GUI
Add simple segmentation option (for faster analysis)
Add colocalization per channel (counting of appositions on previously segmented elements) : C1xCo / C2xCo
Add soma counting (optionnal)
Add 2 additional channels to verify colocalization (e.g. using a synaptic marker) and soma estimates (e.g. with DAPI)
v1.0.1
Correction of minor bug concerning headless mode
To report any issues, please click hereAt this time, only the ImageJ toolset (B3D-Imaging) is available
Bioloc3D
No full textBioloc3D: an automatized and user-friendly toolset to quantify fluorescent profiles and their colocalization(s) in 3D.
Description
The goal of Bioloc3D is to quantify fluorescent labeling across entire stacks of confocal images and to automatize the statistical analysis of the data. It is an all-in-one bundle for the three-dimensional quantification of individualized fluorescent profiles.
Bioloc3D is a free alternative to commercial solutions with segmentation and analysis programs. It is a user-friendly tool that has been developed to offer the user a straight-to-the-point tool to avoid parasitic information or accessibility issues.
The bundle is made of two components. The first one, Bioloc3D-Imaging, is an ImageJ toolset that is used to individualize fluorescent elements from 2 different channels and identify possible colocalization(s) in 3D. To do so, the core of Bioloc3D has been structured around the “3D Objects Counter” plugin, used to enumerate the number of elements in 3D and provide valuable characteristics about it (e.g. volume, integrated density, etc). The second component, Bioloc3D-Plotting is a Python script that will automatically analyze Excel files obtained through the ImageJ tool to plot data and run the appropriate statistical test.
Installation
To install Bioloc3D in ImageJ, just drag/drop the .ijm file in the "toolset" folder of the application (restart ImageJ if needed).
Note: The macro will be completely open-source after publication.
Software Requirements
Bioloc3D needs plugins to run correctly on ImageJ :
- MorphoLibJ
- Read and Write Excel
- 3D Simple Segmentation
- 3D Objects Counter
Keep ImageJ updated, the last version of the software is advised.
To report any issues, please click hereAt this time, only the ImageJ toolset (B3D-Imaging) is available
Bioloc3D
No full textBioloc3D: an automatized and user-friendly toolset to quantify fluorescent profiles and their colocalization(s) in 3D.
Description
The goal of Bioloc3D is to quantify fluorescent labeling across entire stacks of confocal images and to automatize the statistical analysis of the data. It is an all-in-one bundle for the three-dimensional quantification of individualized fluorescent profiles.
Bioloc3D is a free alternative to commercial solutions with segmentation and analysis programs. It is a user-friendly tool that has been developed to offer the user a straight-to-the-point tool to avoid parasitic information or accessibility issues.
The bundle is made of two components. The first one, Bioloc3D-Imaging, is an ImageJ toolset that is used to individualize fluorescent elements from 2 different channels and identify possible colocalization(s) in 3D. To do so, the core of Bioloc3D has been structured around the “3D Objects Counter” plugin, used to enumerate the number of elements in 3D and provide valuable characteristics about it (e.g. volume, integrated density, etc). The second component, Bioloc3D-Plotting is a Python script that will automatically analyze Excel files obtained through the ImageJ tool to plot data and run the appropriate statistical test.
Installation
To install Bioloc3D in ImageJ, just drag/drop the .ijm file in the "toolset" folder of the application (restart ImageJ if needed).
Note: The macro will be completely open-source after publication.
Software Requirements
Bioloc3D needs plugins to run correctly on ImageJ :
- MorphoLibJ
- Read and Write Excel
- 3D Simple Segmentation
- 3D Objects Counter
To avoid any issues, the last version of ImageJ is advised.
Changelog B3D-Imaging
v1.0.1
Correction of minor bug concerning headless mode
To report any issues, please click hereAt this time, only the ImageJ toolset (B3D-Imaging) is available
Bioloc3D
No full text<p><strong>Bioloc3D: an automatized and user-friendly toolset to quantify fluorescent profiles and their colocalization(s) in 3D.</strong></p>
<p><strong>Description</strong></p>
<p>The goal of Bioloc3D is to quantify fluorescent labeling across entire stacks of confocal images and to automatize the statistical analysis of the data. It is an all-in-one bundle for the three-dimensional quantification of individualized fluorescent profiles.</p>
<p>Bioloc3D is a free alternative to commercial solutions with segmentation and analysis programs. It is a user-friendly tool that has been developed to offer the user a straight-to-the-point tool to avoid parasitic information or accessibility issues.</p>
<p>The bundle is made of two components. The first one, Bioloc3D-Imaging, is an ImageJ toolset that is used to individualize fluorescent elements from 2 different channels and identify possible colocalization(s) in 3D. To do so, the core of Bioloc3D has been structured around the “3D Objects Counter” plugin, used to enumerate the number of elements in 3D and provide valuable characteristics about it (e.g. volume, integrated density, etc). The second component, Bioloc3D-Plotting is a Python script that will automatically analyze Excel files obtained through the ImageJ tool to plot data and run the appropriate statistical test.</p>
<p><strong>Installation</strong></p>
<p>To install Bioloc3D in ImageJ, just drag/drop the .ijm file in the "toolset" folder of the application (restart ImageJ if needed).</p>
<p>Note: The macro will be completely open-source after publication.</p>
<p><strong>Software Requirements</strong></p>
<p>Bioloc3D needs plugins to run correctly on ImageJ : <br>
- MorphoLibJ<br>
- Read and Write Excel<br>
- 3D Simple Segmentation<br>
- 3D Objects Counter </p>
<p>Keep ImageJ updated, the last version of the software is advised.</p>
<p><strong>Changelog B3D-Imaging</strong></p>
<p>v2.0.0</p>
<ul>
<li>Implemented a loop to run several analysis in a row</li>
<li>Simplification of the GUI</li>
<li>Add simple segmentation option (for faster analysis)</li>
<li>Add colocalization per channel (counting of appositions on previously segmented elements) : C1xCo / C2xCo</li>
<li>Add soma counting (optionnal)</li>
<li>Add 2 additional channels to verify colocalization (e.g. using a synaptic marker) and soma estimates (e.g. with DAPI)</li>
</ul>
<p>v1.0.1</p>
<ul>
<li>Correction of minor bug concerning headless mode</li>
</ul>
<p><a href="mailto:[email protected]?subject=B3D%20issues&body=Context%20%3A%20%0A%0AScreenshot%20of%20the%20log%20window%20%3A%0A%0AVersion%20of%20ImageJ%20%3A%20%0A%0AVersion%20of%20B3D%20%3A%0A%0A">To report any issues, please click here</a></p>
<p> </p>At this time, only the ImageJ toolset (B3D-Imaging) is available
Bioloc3D
No full textBioloc3D: an automatized and user-friendly toolset to quantify fluorescent profiles and their colocalization(s) in 3D.
Description
The goal of Bioloc3D is to quantify fluorescent labeling across entire stacks of confocal images and to automatize the statistical analysis of the data. It is an all-in-one bundle for the three-dimensional quantification of individualized fluorescent profiles.
Bioloc3D is a free alternative to commercial solutions with segmentation and analysis programs. It is a user-friendly tool that has been developed to offer the user a straight-to-the-point tool to avoid parasitic information or accessibility issues.
The bundle is made of two components. The first one, Bioloc3D-Imaging, is an ImageJ toolset that is used to individualize fluorescent elements from 2 different channels and identify possible colocalization(s) in 3D. To do so, the core of Bioloc3D has been structured around the “3D Objects Counter” plugin, used to enumerate the number of elements in 3D and provide valuable characteristics about it (e.g. volume, integrated density, etc). The second component, Bioloc3D-Plotting is a Python script that will automatically analyze Excel files obtained through the ImageJ tool to plot data and run the appropriate statistical test.
Installation
To install Bioloc3D in ImageJ, just drag/drop the .ijm file in the "toolset" folder of the application (restart ImageJ if needed).
Note: The macro will be completely open-source after publication.
Software Requirements
Bioloc3D needs plugins to run correctly on ImageJ :
- MorphoLibJ
- Read and Write Excel
- 3D Simple Segmentation
- 3D Objects Counter
Keep ImageJ updated, the last version of the software is advised.
Changelog B3D-Imaging
v2.0.1
Fix potential crash issues due to counting of 0 or 1 colocalization
v2.0.0
Implemented a loop to run several analysis in a row
Simplification of the GUI
Add simple segmentation option (for faster analysis)
Add colocalization per channel (counting of appositions on previously segmented elements) : C1xCo / C2xCo
Add soma counting (optionnal)
Add 2 additional channels to verify colocalization (e.g. using a synaptic marker) and soma estimates (e.g. with DAPI)
v1.0.1
Correction of minor bug concerning headless mode
To report any issues, please click hereAt this time, only the ImageJ toolset (B3D-Imaging) is available
The Austrian Environment en Miniature: „Official“ Perceptions of the Austrian Landscape Reflected through Postal Stamps since 1945
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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