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4,612 results for “Labels”
Data from: Novel reverse radioisotope labelling experiment reveals carbon assimilation of marine calcifiers under ocean acidification conditions
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Data from: Non-invasive measurement of metabolic rates in wild, free-living birds using doubly labelled water
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Label-free three-photon imaging of intact human cerebral organoids: Tracking early events in brain development and deficits in Rett Syndrome
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Data from: Disentangling effects of air and soil temperature on C allocation in cold environments: a 14C pulse labelling study with two plant species
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Pacific black ducks tri-axial accelerometer data with behaviour labels
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Examined specimen data from collection labels in Megachile (Austrochile) taxonomic revision
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Learning protein fitness models from evolutionary and assay-labeled data
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Figure 3 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 3 A lateral image of ReVILE with the side panel removed to show the camera (a), turntable (b), stepper motor (c), Arduino Uno and motor driver controllers (d), front light panels (e), back light panel (f), light shield (g) and position of vial (arrow).
Figure 1 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 1 MALICE vial setup showing the acrylic mirrors (a) LEGO mirror tilt arms (b), formex base (c) and LEGO friction joint (d)
Figure 5 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 5 MALICE: Image output of MALICE including original output image (a) and the final processed image (b).
Figure 7 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 7 ReVILE: Two Rollout photography outputs of ReVILE. Left to right: a frame from the video (rotated 90° clockwise) showing the vial itself; the uncropped rollout image, covering more than one full rotation; the cropped rollout image, showing only one 360° rotation; the cropped rollout image, "shifted" across (by transferring a manually-defined block of pixel columns from the left side of the image to the right) to show the complete label
Ground truth labels for "BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation In Patients with Cerebrovascular Disease"
<p>Manual, voxel-wise segmentation ground truth labels for 20 healthy volunteers (4 from each age group) from the publicly available MIDAS data collection website under:<br> https://www.insight-journal.org/midas/community/view/21‌</p> <p> </p>
Mangrove-Non-Mangrove Manual Labels of Drone Imagery in Baja California Sur
<p>This database consists of shapefiles of mangrove-nonmangrove labels of 14 sites of mangroves from Puerto San Carlos and La Paz, Baja California Sur, from 2018 and 2019.</p> <p>Shapefiles of MNM_Labels_BCS</p> <ul> <li>OID: Corresponds to the unique identifier of each object</li> <li>Site: Corresponds to the name of the site imaged</li> <li>Class: Corresponds to mangrove or non-mangrove habitat (string)</li> <li>Label: Corresponds to mangrove or non-mangrove habitat (numerical)</li> </ul> <p>These sites were imaged using a Phantom 4 Pro at 120 meters altitude, resulting in a 3 cm per pixel resolution. Details of this flight capture can be found at <a href="https://escholarship.org/uc/item/2zv0z6zm">https://escholarship.org/uc/item/2zv0z6zm</a>. Mangrove and nonmangrove labels were labeled using QGIS. The imagery collection and labeling process was led by Astrid Hsu, Eric Lo, Beni Guerrero, and John Dorian.</p>
From leaf to label: A robust automated workflow for stomata detection: Light microscope images of stomata.
<p>All light microscope images used for training and testing of the deep learning model developed in the study: Meeus S., Van den Bulcke J., wyffels F. (2020) From leaf to label: A robust automated workflow for stomata detection. Ecol. Evol. <a href="https://doi.org/10.1002/ece3.6571">https://doi.org/10.1002/ece3.6571</a>.</p>
Neuronal cell labelled with anti PSD95 and homer antibodies - sample image for software testing of Easy SODA
<p><strong>Hippocampal neurons</strong> in culture seeded on glass coverslip. This 16 bits confocal picture of hippocampal neuron in culture has been taken at 63x (1024x1024 pixels - pixel size 92,45 nm) in sequential mode with two channels : one dedicated to the PSD95 protein, and the other one to Homer protein. Those protein are expressed at glutamatergic post-synaptic sites and can be found close within Post Synaptic Density. This is a good sample to test colocalization and association analysis. The homer channel can be used both to infere neuronal shape thanks to hk-means segmentation (<a href="http://icy.bioimageanalysis.org/protocol/easy-cell-shape-with-hk-means/">http://icy.bioimageanalysis.org/protocol/easy-cell-shape-with-hk-means/</a>), or to detect clusters thanks to wavelet segmentation (<a href="http://icy.bioimageanalysis.org/protocol/rainbow-spot-detector-scale-finder/">http://icy.bioimageanalysis.org/protocol/rainbow-spot-detector-scale-finder/</a>). This sample image can be used with Easy SODA protocol (Standard Object Distance Analysis) on Icy software (<a href="http://icy.bioimageanalysis.org/protocol/easy-soda-2-colors-1-image/">http://icy.bioimageanalysis.org/protocol/easy-soda-2-colors-1-image/) </a>to detect association between PSD95 and homer clusters.</p>
Neuronal cell labelled with anti MAP2 antibody - sample image for software testing
<p><strong>Hippocampal neurons</strong> in culture seeded on glass coverslip. Dendritic extensions are labeled with antibody against <strong>MAP2, a protein associated with microtubules and nucleus is labeled with DAPI</strong>. The resulting signal give acces to the neuronal cell body and dendrite. Here the axon is not labeled. This picture has been taken on a Leica DMRE microscope with a 40x objective. This is a sample demo to be used to test our automatic segmentation of neuronal cells thanks to the <strong>Easy cell shape protocol</strong>, available freely on Icy software. You can download the software (PC, Mac or linux) and have access to the full documentation of this solution on <a href="http://icy.bioimageanalysis.org/protocol/easy-cell-shape-with-hk-means/">http://icy.bioimageanalysis.org/protocol/easy-cell-shape-with-hk-means/ (</a>Publication ID: ICY-77638). It segments in a user friendly mode the cell shape using the hk-mean plugin that applies a N-class thresholding based on a K-Means classification of the image histogram.</p>
Data from: Proximity labeling reveals novel interactomes in live Drosophila tissue
Gametogenesis is dependent on intercellular communication facilitated by stable intercellular bridges connecting developing germ cells. During Drosophila oogenesis, intercellular bridges (referred to as ring canals) have a dynamic actin cytoskeleton that drives their expansion to a diameter of 10μm. While multiple proteins have been identified as components of ring canals (RCs), we lack a basic understanding of how RC proteins interact together to form and regulate the RC cytoskeleton. We optimized a procedure for proximity-dependent biotinylation in live tissue using the APEX enzyme to interrogate the RC interactome. APEX was fused to four different RC components (RC-APEX baits) and 55 unique high-confidence preys were identified. The RC-APEX baits produced almost entirely distinct interactomes that included both known RC proteins as well as uncharacterized proteins. The proximity ligation assay was used to validate close-proximity interactions between the RC-APEX baits and their respective preys. Further, an RNAi screen revealed functional roles for several high-confidence prey genes in RC biology. These findings highlight the utility of enzyme-catalyzed proximity labeling for protein interactome analysis in live tissue and expand our understanding of RC biology.
Data from: Using quantum dots as pollen labels to track the fates of individual pollen grains
1. Despite a long history of significant advances in understanding natural selection and evolution, the field of plant reproductive biology has largely studied plant mating without directly tracking pollen movement due to a lack of suitable pollen-tracking methods. 2. Here, we develop and test a novel pollen-tracking technique using quantum dots as pollen-grain labels. Quantum dots are semiconductor nanocrystals that are so small, they behave like atoms. When exposed to UV light, they emit extremely bright light in both visible and infrared wavelengths We tested the suitability of non-toxic CuInSexS2-x/ZnS (core/shell) quantum dots with oleic acid ligands as pollen-grain labels.Using a micropipette, we dispensed quantum dots dissolved in hexane in minute volumes (0.15–0.5 µl) directly onto dehisced anthers of four different plant species from four different families [Wachendorfia paniculata (Haemodoraceae), Sparaxis villosa (Iridaceae), Arctotheca calendula (Asteraceae), Oxalis purpurea (Oxalidaceae)]. 3. After application, the hexane solvent evaporated immediately, leaving behind quantum dots that remained attached to pollen grains of the four different plant species even after agitation in a polar solvent. This suggests a lipophilic interaction between oleic-acid ligands on quantum dots, and pollenkitt surrounding pollen grains. We also showed that most pollen grains within anthers of the same four plant species were labelled with quantum dots after applying a volume of quantum-dot solution sufficient to cover an individual anther. To test whether quantum-dot pollen-labels influenced pollen transport, we conducted pollen transfer trials (one donor, ten sequential recipients) on S. villosa using captively-reared honey bees to ensure bees were free of external pollen prior to experiments. We found no difference in pollen transport to recipients from donor flowers with labelled or unlabelled pollen grains. 4. We demonstrate that quantum dots can be used as pollen labels allowing subsequent tracking of pollen fates. This method is relatively inexpensive (<$500 for equipment and ca. $0.02 per labelled anther thereafter) and can be simply and directly applied to anthers of most flowers in the lab and field. The ability to track pollen grain movement in situ, may help to address an historically neglected aspect of plant reproductive ecology and evolution.
Data from: Label-free sensitive detection of influenza virus using PZT discs with a synthetic sialylglycopolymer receptor layer
We describe rapid, label-free detection of Influenza A viruses using the first radial mode of oscillations of lead zirconate titanate (PZT) piezoelectric disks with a 2-mm radius and 100-µm thickness fabricated from a piezoelectric membrane. The disks are modified with a synthetic sialylglycopolymer receptor layer,and the coated disks are inserted in a flowing virus suspension. Label-free detection of the virus is achieved by monitoring the disk radial mode resonance frequency shift. Piezo transducers with sialylglycopolymer sensor layers exhibited a long lifetime, a high sensitivity, and the possibility of regeneration. We demonstrate positive, label-free detection of Influenza A viruses at concentrations below 10^5 virus particles per millilitre. We show that label-free, selective, sensitive detection of Influenza viruses by home appliances is possible in principle.
Enhanced and unified anatomical labeling for a common mouse brain atlas
<p>Anatomical atlases in standard coordinates are necessary for the interpretation and integration of research findings in a common spatial context. However, the two most-used mouse brain atlases, the Franklin and Paxinos (FP) and the common coordinate framework (CCF) from the Allen Institute for Brain Science, have accumulated inconsistencies in anatomical delineations and nomenclature, creating confusion among neuroscientists. To overcome these issues, here we adopt the FP labels into the CCF to merge two labels in the single atlas framework. We use cell type specific transgenic mice and an MRI atlas to adjust and further segment our labels. Moreover, detailed segmentations are added to the dorsal striatum using cortico-striatal connectivity data. Lastly, we digitize our anatomical labels based on the Allen ontology, create a web-interface for visualization, and provide tools for comprehensive comparisons between the CCF and FP labels. Our open-source labels signify a key step towards a unified mouse brain atlas.</p>
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.