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3,650 results for “antibody”
Data from: Maternally-derived anti-helminth antibodies predict offspring survival in a wild mammal
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Source data for: Human monoclonal antibodies against Staphylococcus aureus surface antigens recognize in vitro biofilm and in vivo implant infections
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Plasmodium infection is associated with cross-reactive antibodies to carbohydrate epitopes on the SARS-CoV-2 Spike protein
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A novel TNFR2 agonist antibody expands highly potent regulatory T cells
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Antibody features towards VAR2CSA and CSA binding infected erythrocytes in a cohort of pregnant women from PNG
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Schistosoma mansoni IgG antibody response and Kato-Katz infection among pre-school aged children in Mbita, Western Kenya 2012-2014
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Multiplex IgG antibody response and malaria parasitemia among children ages 1-59 months in the MORDOR Niger trial, 2015-2018
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Random peptide sequences binding amyloid monoclonal antibodies
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Link to dataset related to article "Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry"
<p>The interrogation of single cells is revolutionizing biology, especially our understanding of the immune system. Flow cytometry is still one of the most versatile and high-throughput approaches for single-cell analysis, and its capability has been recently extended to detect up to 28 colors, thus approaching the utility of cytometry by time of flight (CyTOF). However, flow cytometry suffers from autofluorescence and spreading error (SE) generated by errors in the measurement of photons mainly at red and far-red wavelengths, which limit barcoding and the detection of dim markers. Consequently, development of 28-color fluorescent antibody panels for flow cytometry is laborious and time consuming. Here, we describe the steps that are required to successfully achieve 28-color measurement capability. To do this, we provide a reference map of the fluorescence spreading errors in the 28-color space to simplify panel design and predict the success of fluorescent antibody combinations. Finally, we provide detailed instructions for the computational analysis of such complex data by existing, popular algorithms (PhenoGraph and FlowSOM). We exemplify our approach by designing a high-dimensional panel to characterize the immune system, but we anticipate that our approach can be used to design any high-dimensional flow cytometry panel of choice. The full protocol takes a few days to complete, depending on the time spent on panel design and data analysis.</p> <p> </p> <p> </p> <p>link related to dataset: https://flowrepository.org/id/FR-FCM-ZYV3</p>
EMERGENCE OF DRIFT VARIANTS THAT MAY AFFECT COVID-19 VACCINE DEVELOPMENT AND ANTIBODY TREATMENT
<p>Supplemental File 1</p>
Dataset: On the Aggregation and Nucleation Mechanism of the Monoclonal Antibody Anti-CD20 Near Liquid- Liquid Phase Separation (LLPS)
<p>Dataset related to the paper: Pantuso et al., On the Aggregation and Nucleation Mechanism of the Monoclonal Antibody Anti-CD20 Near Liquid-Liquid Phase Separation (LLPS), Scientific Reports (2020) 10:8902.</p>
Figure 4 from: Padilla-Sanchez V (2020) In silico analysis of SARS-CoV-2 spike glycoprotein and insights into antibody binding. Research Ideas and Outcomes 6: e55281. https://doi.org/10.3897/rio.6.e55281
Figure 4 Docking energies and interface score charts. A shows the Rosetta Dock results, binding energies from PDBEPISA server in B shows good results for ΔG. Developability of this antibody shows all green flags in C.
Figure 3 from: Padilla-Sanchez V (2020) In silico analysis of SARS-CoV-2 spike glycoprotein and insights into antibody binding. Research Ideas and Outcomes 6: e55281. https://doi.org/10.3897/rio.6.e55281
Figure 3 Docking interface between the modified 80R antibody and the RBD of the SARS-CoV-2 spike protein. The model shows the structural interface with the 80R antibody above and the RBD below. The seven substitutions in 80R are shown in magenta and RBD residues are shown in cyan. Notice how the substitutions in 80R allow new aromatic-aromatic interactions that improve binding to the RBD and are not present in wild type 80R. E484 is shown pointing towards the beta strand of 80R and a glycine substitution was therefore introduced to avoid clashes.
Figure 6 from: Padilla-Sanchez V (2020) In silico analysis of SARS-CoV-2 spike glycoprotein and insights into antibody binding. Research Ideas and Outcomes 6: e55281. https://doi.org/10.3897/rio.6.e55281
Figure 6 m396 mutations docking results. A shows Rosetta Dock funnels for the original partners SARS-CoV and m396, SARS-CoV-2 and m396 and the SARS-CoV-2 and mutated m396. Notice how the binding is improved to the level of the original partners. B shows the ΔG energies again notice the improvement of binding when the mutations are introduced. Finally, C shows the developability flags with only one warning that is not that far from green flag.
Figure 5 from: Padilla-Sanchez V (2020) In silico analysis of SARS-CoV-2 spike glycoprotein and insights into antibody binding. Research Ideas and Outcomes 6: e55281. https://doi.org/10.3897/rio.6.e55281
Figure 5 Docking interface between the modified m396 antibody and SARS-CoV-2 spike protein RBD. In magenta is m396 mutant and in cyan SARS-CoV-2 RBD. These five mutations introduce many electrostatic interactions between the partners therefore stabilizing very much the binding.
Figure 2 from: Padilla-Sanchez V (2020) In silico analysis of SARS-CoV-2 spike glycoprotein and insights into antibody binding. Research Ideas and Outcomes 6: e55281. https://doi.org/10.3897/rio.6.e55281
Figure 2 Structural analysis of SARS-CoV spike glycoprotein. In A the SARS-CoV spike protein (PDB ID: 6ACG) is shown bound to ACE2 (brown) and 80R antibody (cyan), superimposed on the same binding site. In B the spike protein is shown bound only to the 80R antibody (PDB ID: 2GHW), with the structural model of the RBD of the SARS-CoV-2 spike protein (magenta) containing the missing loops. This homology model served as the basis for the docking experiments. In C it is shown a spike colored by subunit and showing the glycans. There are only two possible glycans in RBD region at 331 and 343 and neither of these sites affect the 80R binding.
Figure 1 from: Padilla-Sanchez V (2020) In silico analysis of SARS-CoV-2 spike glycoprotein and insights into antibody binding. Research Ideas and Outcomes 6: e55281. https://doi.org/10.3897/rio.6.e55281
Figure 1 Structural model of SARS-CoV-2 infection. This structural model was built with UCSF Chimera using high-performance computers (Bridges Large and Frontera). The model shows 16 viruses, with the spike proteins shown in green (PDB ID: 6VSB) and an actual lipid bilayer membrane, with ACE2 dimers shown in magenta. All these structures are at atomic resolution. The length of the membrane is approximately 1 micrometer.
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: Differential changes in bone strength of two inbred mouse strains following administration of a sclerostin-neutralizing antibody during growth
Administration of sclerostin-neutralizing antibody (Scl-Ab) treatment has been shown to elicit an anabolic bone response in growing and adult mice. Prior work characterized the response of individual mouse strains but did not establish whether the impact of Scl-Ab on whole bone strength would vary across different inbred mouse strains. Herein, we tested the hypothesis that two inbred mouse strains (A/J and C57BL/6J (B6)) will show different whole bone strength outcomes following sclerostin-neutralizing antibody (Scl-Ab) treatment during growth (4.5 – 8.5 weeks of age). Treated B6 femurs showed a significantly greater stiffness (S) (68.8% vs. 46.0%) and maximum load (ML) (84.7% vs. 44.8%) compared to A/J. Although treated A/J and B6 femurs showed greater cortical area (Ct.Ar) similarly relative to their controls (37.7% in A/J and 41.1% in B6), the location of new bone deposition responsible for the greater mass differed between strains and may explain the greater whole bone strength observed in treated B6 mice. A/J femurs showed periosteal expansion and endocortical infilling, while B6 femurs showed periosteal expansion. Post-yield displacement (PYD) was smaller in treated A/J femurs (-61.2%, p < 0.001) resulting in greater brittleness compared to controls; an effect not present in B6 mice. Inter-strain differences in S, ML, and PYD led to divergent changes in work-to-fracture (Work). Work was 27.2% (p = 0.366) lower in treated A/J mice and 66.2% (p < 0.001) greater in treated B6 mice relative to controls. Our data confirmed the anabolic response to Scl-Ab shown by others, and provided evidence suggesting the mechanical benefits of Scl-Ab administration may be modulated by genetic background, with intrinsic growth patterns of these mice guiding the location of new bone deposition. Whether these differential outcomes will persist in adult and elderly mice remains to be determined.
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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.