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138 results for “Toolbox”
SIMToolbox: A MATLAB toolbox for structured illumination fluorescence microscopy
<p>SIMToolbox is an open-source, modular set of functions for MATLAB equipped with a user-friendly graphical interface and designed for processing two-dimensional and three-dimen- sional data acquired by structured illumination microscopy (SIM). Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured il- lumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination.</p>
Data from: Image Calibration and Analysis Toolbox – a free software suite for measuring reflectance, colour, and pattern objectively and to animal vision
1. Quantitative measurements of colour, pattern, and morphology are vital to a growing range of disciplines. Digital cameras are readily available and already widely used for making these measurements, having numerous advantages over other techniques, such as spectrometry. However, off-the-shelf consumer cameras are designed to produce images for human viewing, meaning that their uncalibrated photographs cannot be used for making reliable, quantitative measurements. Many studies still fail to appreciate this, and of those scientists who are aware of such issues, many are hindered by a lack usable tools for making objective measurements from photographs. 2. We have developed an image processing toolbox that generates images that are linear with respect to radiance from the RAW files of numerous camera brands, and can combine image channels from multispectral cameras, including additional ultraviolet photographs. Images are then normalised using one or more grey standards to control for lighting conditions. This enables objective measures of reflectance and colour using a wide range of consumer cameras. Furthermore, if the camera's spectral sensitivities are known, the software can convert images to correspond to the visual system (cone-catch values) of a wide range of animals, enabling human and non-human visual systems to be modelled. The toolbox also provides image analysis tools that can extract luminance (lightness), colour, and pattern information. Furthermore, all processing is performed on 32-bit floating point images rather than commonly used 8-bit images. This increases precision and reduces the likelihood of data loss through rounding error or saturation of pixels, while also facilitating the measurement of objects with shiny or fluorescent properties. 3. All cameras tested using this software were found to demonstrate a linear response within each image and across a range of exposure times. Cone-catch mapping functions were highly robust, converting images to several animal visual systems and yielding data that agreed closely with spectrometer-based estimates. 4. Our imaging toolbox is freely available as an addition to the open source ImageJ software. We believe that it will considerably enhance the appropriate use of digital cameras across multiple areas of biology, in particular researchers aiming to quantify animal and plant visual signals.
Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia
<p>This upload is associated with the software, <a href="https://github.com/pr4deepr/GutAnalysisToolbox">Gut Analysis Toolbox</a> (GAT).</p> <p>If you use it please cite:</p> <p><strong><em>Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: <a href="https://doi.org/10.1242/jcs.261950" target="_blank" rel="noopener">https://doi.org/10.1242/jcs.261950</a></em></strong></p> <p>The upload contains<strong> StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and FPN+ResNet101 model for enteric ganglia in 2D in gut wholemount tissue.</strong> GAT is implemented in Fiji, but the models can be used in any software that supports StarDist and the use of 2D UNet models. The files here also consist of <strong>Python notebooks (Google Colab)</strong>, training and test data as well as reports on model performance.</p> <p>Note: The enteric ganglia model is has been updated to v3 which uses pytorch and is a different architecture (FPN+ResNet101).</p> <p>The model files are located in the respective folders as zip files. The folders have also been zipped:</p> <ul> <li>Neuron (Hu; <a href="https://github.com/stardist/stardist">StarDist</a> model): <ul> <li>Main folder: 2D_enteric_neuron_model_QA.zip</li> <li>StarDist Model File:2D_enteric_neuron_v4_1.zip </li> <li>DeepImageJ compatible model: 2D_enteric_neuron.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> <li>Neuronal subtype (<a href="https://github.com/stardist/stardist">StarDist</a> model): <ul> <li>Main folder: 2D_enteric_neuron_subtype_model_QA.zip</li> <li>Model File: 2D_enteric_neuron_subtype_v4.zip</li> <li>DeepImageJ compatible model: 2D_enteric_neuron_subtype.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> <li>Enteric ganglia (2D FPN_ResNet101; Use in FIJI with <a href="https://deepimagej.github.io/deepimagej/">deepImageJ</a>) <ul> <li>Main folder: 2D_enteric_ganglia_v3_training.zip</li> <li>Model File: 2D_Ganglia_RGB_v3.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> </ul> <p>For the all models, files included are:</p> <ol> <li>Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet.</li> <li>Training and Test datasets used for training.</li> <li>Google Colab notebooks used for training and quality assurance (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCost DL4Mic notebooks</a>).</li> <li>Python notebook and code for training ganglia model with QA.</li> <li>Quality assurance reports generated from above notebooks.</li> <li>StarDist model exported for use in QuPath.</li> </ol> <p>The model files can be used within can be used within the software, <a href="https://github.com/stardist/stardist">StarDist</a>. They are intended to be used within FIJI or QuPath, but can be used in any software that supports the implementation of StarDist in 2D.</p> <p><strong>Data:</strong></p> <p>All the images were collected from 4 different research labs and a public database (<a href="https://sparc.science/data?type=dataset">SPARC database</a>) to account for variations in image acquisition, sample preparation and immunolabelling.</p> <p>For enteric neurons the pan-neuronal marker, Hu has been used and the 2D wholemounts images from mouse, rat and human tissue.</p> <p>For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used..</p> <p>25 images were used from the following entries in the <a href="https://sparc.science/data?type=dataset">SPARC database</a>:</p> <ul> <li><a href="https://doi.org/10.26275/9FFG-482D">Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. </a></li> <li><a href="https://doi.org/10.26275/PZEK-91WX">Graham, K. D., Huerta-Lopez, S., Sengupta, R., Shenoy, A., Schneider, S., Wright, C. M., Feldman, M., Furth, E., Lemke, A., Wilkins, B. J., Naji, A., Doolin, E., Howard, M., & Heuckeroth, R. (2020). Robust 3-Dimensional visualization of human colon enteric nervous system without tissue sectioning (Version 1) [Data set]. SPARC Consortium.</a></li> <li>Wang, L., Yuan, P.-Q., Gould, T. and Tache, Y. (2021). Antibodies Tested in theColon – Mouse (Version 1) [Data set]. SPARC Consortium. doi:10.26275/i7dl-58h</li> </ul> <p>Additional images for new ganglia model:</p> <ul> <li>Hamnett, R., Dershowitz, L. B., Sampathkumar, V., Wang, Z., Gomez-Frittelli, J., De Andrade, V., Kasthuri, N., Druckmann, S. and Kaltschmidt, J. A. (2022b). Regional cytoarchitecture of the adult and developing mouse enteric nervous system. Curr. Biol. 32, 4483-4492.e5.</li> </ul> <p>The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using: </p> <ul> <li> <p>Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3) </p> </li> <li> <p>Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88) </p> </li> <li> <p>Zeiss Axio Imager M2 (20X HC PL APO NA 0.3) </p> </li> <li> <p>Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45) </p> </li> </ul> <p>Human tissue images were acquired using: </p> <ul> <li> <p>IX71 Olympus microscope (10X HC PL APO NA 0.3) </p> </li> </ul> <p>For more information, visit the <a href="https://gut-analysis-toolbox.gitbook.io/docs" target="_blank" rel="noopener">Documentation</a> website.</p> <p><strong>NOTE:</strong> The images for enteric neurons and neuronal subtypes have been rescaled to 0.568 µm/pixel for mouse and rat. For human neurons, it has been rescaled to 0.9 µm/pixel . This is to ensure the neuronal cell bodies have similar pixel area across images. The area of cells in pixels can vary based on resolution of image, magnification of objective used, animal species (larger animals -> larger neurons) and potentially how the tissue is stretched during wholemount preparation </p> <p>Average neuron area for neuronal model: 701.2 ± 195.9 pixel<sup>2 </sup>(Mean ± SD, 6267 cells)</p> <p>Average neuron area for neuronal subtype model: 880.9 ± 316 pixel<sup>2 </sup>(Mean ± SD, 924 cells)</p> <p><strong>Software References:</strong></p> <p><strong><a href="https://github.com/stardist/stardist">Stardist</a></strong></p> <p>Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em> (pp. 265-273). Springer, Cham.</p> <p><strong><a href="https://deepimagej.github.io/deepimagej/">deepImageJ</a></strong></p> <p>Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia, A. and Sage, D., 2021. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. <em>Nature Methods</em>, <em>18</em>(10), pp.1192-1195.</p> <p><strong><a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCost DL4Mic</a></strong></p> <p>von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. <em>Nature communications</em>, <em>12</em>(1), pp.1-18.</p>
Supplementary material 1 from: Dang NA, Jackson BM, Tomscha SA, Lilburne L, Burkhard K, Tran DD, Phi LH, Benavidez R (2022) Guidelines and a supporting toolbox for parameterising key soil hydraulic properties in hydrological studies and broader integrated modelling. One Ecosystem 7: e76410. https://doi.org/10.3897/oneeco.7.e76410
Supplementary Material S1
Supplementary material 2 from: Dang NA, Jackson BM, Tomscha SA, Lilburne L, Burkhard K, Tran DD, Phi LH, Benavidez R (2022) Guidelines and a supporting toolbox for parameterising key soil hydraulic properties in hydrological studies and broader integrated modelling. One Ecosystem 7: e76410. https://doi.org/10.3897/oneeco.7.e76410
Supplementary Material S2
Learning and practice of complementary therapies in pulmonary transplant patients: the toolbox prospective open study
<p><span>Objective</span><span>:</span> <span>In addition to pharmacologic treatments, complementary therapies are a reliable option to improve pain and quality-of-life during and after lung transplantation.</span> <span>The objective of the present study was to highlight the possibility to implement a toolbox-kit of complementary techniques in lung transplantation which could improve patients' experience.</span></p> <p><span>Design</span><span>: Prospective open single-center study.</span></p> <p><span>Setting</span><span>: Foch University Hospital, Suresnes, France.</span></p> <p><span>Participants</span><span>: Adult patients undergoing double-lung transplantation.</span></p> <p><span>Results</span><span>:</span><span> </span><span>Among the 80 patients included from May 2017 to September 2020, 59 were evaluated at the 4</span><span>th</span><span> postoperative month. Over the </span><span>4359 sessions performed</span><span>, the most frequent technique </span><span>used</span><span> before surgery</span><span> was relaxation. After transplantation, the techniques most frequently used were relaxation and TENS</span><span>. </span><span>TENS was the best performing technique in terms of autonomy, usability, adaptation, and compliance. Self-appropriation of relaxation was the easiest while self-appropriation of holistic gymnastics was difficult but appreciated by patients.</span></p> <p><span>Conclusion</span><span>: The appropriation by patients of complementary therapies such as mind-body therapies, TENS and holistic gymnastics are feasible in lung transplantation. Even after a short training session, patients regularly practiced these therapies, mainly TENS and relaxation.</span></p>
Figure 2 from: Ito K, Anglin J, Liew S (2017) Semi-automated Robust Quantification of Lesions (SRQL) Toolbox. Research Ideas and Outcomes 3: e12259. https://doi.org/10.3897/rio.3.e12259
Figure 2 - We tested our toolbox on a mock lesion mask. A. The stroke subject's T1 anatomical scan; B. The mock lesion mask is the red sphere; the blue mask is the lesion segmentation. The white matter was intentionally covered within the mock lesion mask, but as shown here, white matter voxels are removed by the white matter correction.
Figure 2 from: Ito K, Anglin J, Kim H, Liew S (2017) Semi-automated Robust Quantification of Lesions (SRQL) Toolbox. Research Ideas and Outcomes 3: e13395. https://doi.org/10.3897/rio.3.e13395
Figure 2 - Testing of SRQL toolbox on a single lesion mask. A. The stroke subject's T1 anatomical scan; B. The original lesion mask in red; C. the overlayed purple mask is the lesion segmentation after white matter intensity correction. As shown here, voxels considered to be in the healthy range have been removed by the SRQL toolbox.
Figure 1 from: Garzon-Lopez C, Hattab T, Skowronek S, Aerts R, Ewald M, Feilhauer H, Honnay O, Decocq G, Van De Kerchove R, Somers B, Schmidtlein S, Rocchini D, Lenoir J (2018) The DIARS toolbox: a spatially explicit approach to monitor alien plant invasions through remote sensing. Research Ideas and Outcomes 4: e25301. https://doi.org/10.3897/rio.4.e25301
Figure 1 DIARS toolbox workflow. The green gears correspond to the sections of the toolbox and are accompanied by boxes stating its main goal. The gray gears describe the advantages of the DIARS toolbox.
Figure 5 from: Garzon-Lopez C, Hattab T, Skowronek S, Aerts R, Ewald M, Feilhauer H, Honnay O, Decocq G, Van De Kerchove R, Somers B, Schmidtlein S, Rocchini D, Lenoir J (2018) The DIARS toolbox: a spatially explicit approach to monitor alien plant invasions through remote sensing. Research Ideas and Outcomes 4: e25301. https://doi.org/10.3897/rio.4.e25301
Figure 5 Some examples of reconstructed images: A. Sylt island reconstructed image and plot locations (wavelengths: 170R, 65G, 17B). B. Compiègne Forest reconstructed image and plot locations (wavelengths: 207R, 65G, 10B).
Figure 3 from: Garzon-Lopez C, Hattab T, Skowronek S, Aerts R, Ewald M, Feilhauer H, Honnay O, Decocq G, Van De Kerchove R, Somers B, Schmidtlein S, Rocchini D, Lenoir J (2018) The DIARS toolbox: a spatially explicit approach to monitor alien plant invasions through remote sensing. Research Ideas and Outcomes 4: e25301. https://doi.org/10.3897/rio.4.e25301
Figure 3 Example of the workflow used for the mapping of alien plants. The same approach was used for all the tutorials.
The Appetite Toolbox for Preschools
ClinicalTrials.gov study NCT05486403. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Faith-Based Toolbox for African Americans With Dementia
ClinicalTrials.gov study NCT04325204. IPD Sharing: YES. Countries: 1. Publications: 0.
The Effect of Toolbox Training on Radiation Protection Practices of Health Professionals in Radioactive Workplaces
ClinicalTrials.gov study NCT06530121. IPD Sharing: YES. Countries: 0. Publications: 1.
Data from: An analysis toolbox to explore mesenchymal migration heterogeneity reveals adaptive switching between distinct modes
Open the record for dataset details and reuse information.
The bivalve shell biomineralization toolbox: bulging, not barren
Open the record for dataset details and reuse information.
Data from: Image Calibration and Analysis Toolbox – a free software suite for measuring reflectance, colour, and pattern objectively and to animal vision
Open the record for dataset details and reuse information.
Learning and practice of complementary therapies in pulmonary transplant patients: the toolbox prospective open study
Open the record for dataset details and reuse information.
LASSIM -a network inference toolbox for genome-wide mechanistic modeling [USF2, KLF6/COPEB, CBL and HIS1 siRNA]]
GEO Series GSE95508. Homo sapiens. 31 samples. Type: Expression profiling by array.
A versatile IRES toolbox to determine IRES-like activity exemplified by Hoxa mRNA expression and translation in mouse embryonic tissues
GEO Series GSE250214. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.