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3,650 results for “antibody”
Fig. 1 in Age affects antibody levels and anthelmintic treatment efficacy in a wild rodent
Fig. 1. Dynamics of A) H. polygyrus burden, B) E. hungaryensis burden, C) total faecal IgA and D) H. polygyrus-specific IgG1 over the experimental period as predicted by minimal GAMM models. Red lines represent model estimates for animals treated with anthelmintic, black lines represent model estimates for control animals. Shaded areas represent estimated standard errors.
Fig. 2 in Age affects antibody levels and anthelmintic treatment efficacy in a wild rodent
Fig. 2. Relationship between mouse age (scaled ELW), treatment and post-treatment H. polygyrus burden as predicted by GLM model. The red line represents model estimates for treated animals, black lines represent model estimates for control animals. Shaded areas represent estimated standard errors. Points represent raw data.
Vaccination B cell dataset antibodies (DDW lab)
<p>The present vaccination dataset, obtained by the group of Deborah Dunn-Walters in 2012 ("vaccination") contains B cell repertoire data of six young (aged 19-45) and six elderly (aged 70-89) healthy volunteers. Three samples per donor have been taken: The first prior to vaccination (with Influvac and Pneumovax II) called "Day 0", the next seven days later ("Day 7") and the last one 28 days after vaccination ("Day 28"). This allows for a time-resolved monitoring of the immune response for the two different age groups. In total, the data set (as downloaded) contains 45784 observations.</p>
Fig. 1 in Presence of IgG antibodies is not a reliable marker of Toxoplasma gondii infection in feral mice
Fig. 1. Frequency distribution of body weight in Toxoplasma gondii B1 PCRnegative (in green) and positive mice (in red). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6 in Plasmodium relictum MSP-1 capture antigen-based ELISA for detection of avian malaria antibodies in African penguins (Spheniscus demersus)
Fig. 6. Analysis of the sensitivity and specificity of the P. relictum MSP-1 capture antigen-based ELISA. Serial dilutions of normal chicken serum and three sera samples from P. relictum-infected penguins (8776, 8783 and 8784) were used for coating the assay wells. Each dilution was performed in triplicate, and the data shown represent means of three independent experiments with standard error bars and levels of statistical significance (****: P <0.0001).
Fig. 4 in Plasmodium relictum MSP-1 capture antigen-based ELISA for detection of avian malaria antibodies in African penguins (Spheniscus demersus)
Fig. 4. Validation of ELISA. The ELISA developed using P. relictum MSP-1 protein was tested with known P. relictum positive penguin serum collected from penguin # 8790 at week 26. Black column represents positive penguin serum. Hatched and white columns represent negative controls containing normal chicken serum and PBS, respectively. Grey column represents the positive reaction containing streptavidin alkaline-phosphatase and its substrate pnitrophenyl phosphate. Each reaction was performed in triplicate, and the data shown represent means of three independent experiments with standard error bars and levels of statistical significance (****: P <0.0001).
Fig. 2 in Plasmodium relictum MSP-1 capture antigen-based ELISA for detection of avian malaria antibodies in African penguins (Spheniscus demersus)
Fig. 2. Verification of the biotinylation of recombinant P. relictum MSP-1. Purified recombinant P. relictum MSP-1 protein was labeled with biotin. Biotinylation of MSP-1 was confirmed using an enzyme (streptavidin alkaline phosphatase)-linking assay and absorbance read at 405 nm indicated the presence of biotinylated MSP-1. Black column represents biotinylated MSP-1 protein. Hatched column and white column represent negative controls containing non-biotinylated MSP-1 and PBS, respectively. Grey column represents the positive control containing streptavidin alkaline-phosphatase (SAP) and its substrate p-nitrophenyl phosphate (PNPP). Each reaction was performed in triplicate, and the data shown represent means of three independent experiments with standard error bars and levels of statistical significance (****: P <0.0001).
Fig. 3 in Plasmodium relictum MSP-1 capture antigen-based ELISA for detection of avian malaria antibodies in African penguins (Spheniscus demersus)
Fig. 3. Biotin-labeled MSP-1 protein titration curve. Serially diluted (300 ng/ μL to 0.003 ng/μL) biotinylated MSP-1 was used for coating the surfaces of the reaction wells overnight at 4 ◦C. The amount of biotinylated MSP-1 immobilized on the surface of the well was proportional to the intensity of the colored product generated which in turn was proportional to the absorbance value measured at 405 nm wavelength. Reactions were performed in triplicate, and the data shown represent means of three independent experiments with standard error bars.
Fig. 7. MSP-1 in Plasmodium relictum MSP-1 capture antigen-based ELISA for detection of avian malaria antibodies in African penguins (Spheniscus demersus)
Fig. 7. MSP-1 capture antigen-based ELISA analysis of 370 sera samples collected from 11 penguins during three consecutive seasons of Spring (March to May), Summer (June to August) and Fall (September to November). (A) ELISA absorbance readings for the 370 sera samples collected from Spring to Fall are arranged in ascending order. Time of collection is indicated by color. Light grey represents Spring, dark grey represents Summer, and black represents Fall. Black dashed line indicates the single cut-off point (0.488) determined by change-point analysis. (B) Left: percentage of positive (A405 ≥ 0.488) and negative (A405 <0.488) sera samples; Right: distribution of positive samples in Spring (March to May), Summer (June to August), and Fall (September to November). (C) Percentage of penguin sera samples that tested positive (A405> = 0.488) in each month from March to November.
Fig. 5. P. relictum MSP-1 in Plasmodium relictum MSP-1 capture antigen-based ELISA for detection of avian malaria antibodies in African penguins (Spheniscus demersus)
Fig. 5. P. relictum MSP-1 capture antigen-based ELISA analysis of test sera from penguins. Sera samples (370 total) from eleven penguins collected from Spring through Fall season were used as test samples in the assay to determine anti-P. relictum antibodies level. Light grey, dark grey, and black columns represent penguin sera samples collected in Spring, Summer, and Fall, respectively. Panel A–K represents ELISA results for individual penguins' sera collected at different time points from Spring to Fall. Panel L represents average ELISA absorbances for all 11 penguins at different sampling points. Each sample was assayed in triplicate, and the data shown represent means of three independent experiments with standard error bars.
Fig. 1 in Comparison of a commercial ELISA and indirect hemagglutination assay with the modified agglutination test for detection of Toxoplasma gondii antibodies in giant panda (Ailuropoda melanoleuca)
Fig. 1. Receiver operating characteristics (ROC) analysis of the ELISA. ROC analysis shows an area under the curve (AUC) of 0.861 (95% CI: 0.712–1.000) for ELISA (a), 0.894 (95% CI: 0.791–0.997) for ELISA (b), and 0.902 (95% CI: 0.799–1.000) for ELISA (c).
OMAP-21: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Palatine Tonsil with MICS on MACSima 1.5
<p>OMAP-21 was designed for MICS (MACSima imaging cyclic staining) imaging of FFPE human tonsil samples. Tissue fixation and antigen retrieval is described in (<a href="https://www.biorxiv.org/content/biorxiv/early/2023/11/07/2023.10.27.564191.full.pdf">Spatial protein and RNA analysis on the same tissue section using MICS technology</a>). The MACSima technology is described in detail in the following publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">MACSima imaging cyclic staining (MICS) technology reveals combinatorial target pairs for CAR T cell treatment of solid tumors</a>). Most, but not all, antibodies in this panel are recombinant antibodies with a mutated human IgG1 constant region. The described mutation removes the Fc receptor binding capacity of human IgG1, eliminating the need for additional blocking steps and reducing non-specific binding of human antibodies on human tissues. Highly multiplexed imaging is achieved through cycles of immunolabeling with FITC, PE, and APC conjugated antibodies and photobleaching to eliminate fluorescence signal between imaging cycles. The panel contains 47 antibodies and the nuclear marker DAPI for image alignment and nuclear segmentation. This OMAP provides a spatial context for all anatomical structures and most cell types present in the human palatine tonsil. OMAP-21 follows closely OMAP-1 described for human lymph nodes (<a href="https://cdn.humanatlas.io/hra-releases/v1.4/docs/omap/omap-1-human-lymph-node-ibex.html">omap-1-human-lymph-node-ibex</a>) and OMAP-10 (<a href="https://cdn.humanatlas.io/hra-releases/v1.4/docs/omap/omap-10-palatine-tonsil-macsima.html">omap-10-palatine-tonsil-macsima</a>).</p> <p>All reagents were obtained from Miltenyi Biotec and have been rigorously tested through an internal quality control system to have minimal variation between lots. For this reason, lot information is not included in this table. Analysis was performed by an accompanied software package MACSIQ View Analysis also described in the MACSima publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">https://doi.org/10.1038/s41598-022-05841-4</a>). The result of the analysis is included in the uploaded dataset. In brief, the software processes the raw images of the MACSima run, generates stitched images, and then allows downstream analysis including cell segmentation, cell gating, data normalization, dimension reduction plots (tSNE, UMAP), heat maps, distance analyses and cluster analyses, all of which are interactively linked together. The MACSima system is continuously evolving, this is the second OMAP for the MACSima system. A representative dataset created using OMAP-21 can be found here:<a href="https://doi.org/10.5281/zenodo.7875937"> </a><strong> 10.5281/zenodo.11281609 .</strong></p>
Dataset for RNA-binding protein FUS antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the RNA-binding protein FUS protein. Version 2 contains the FUS FSC file describing the experimental set up used for the Flow Cytometry experiment. The original study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.5259945">https://doi.org/10.5281/zenodo.5259945</a>).</em></p>
Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains
<p>Antibodies are a popular and powerful class of therapeutic due to their ability to exhibit high affinity and specificity to target proteins. However, the majority of antibody therapeutics are not genetically human, with initial therapeutic designs typically obtained from animal models. Humanisation of these precursors is essential to reduce immunogenic risks when administered to humans.</p> <p>To aid humanisation we developed <a href="https://github.com/oxpig/Humatch" target="_blank" rel="noopener">Humatch</a>, a computational tool designed to offer experimental-like joint humanisation of heavy and light chains in seconds. Humatch consists of three lightweight Convolutional Neural Networks (CNNs) trained to identify human heavy V-genes, light V-genes, and well-paired antibody sequences with near-perfect accuracy. We show that these CNNs, alongside germline similarity, can be used for fast humanisation that aligns well with known experimental data. Throughout the humanisation process, a sequence is guided towards a specific target gene and away from others via multiclass CNN outputs and gene-specific germline data. This guidance ensures final humanised designs do not sit `between' genes, a trait that is not naturally observed. Humatch's optimisation towards specific genes and good VH/VL pairing increases the chances that final designs will be stable and express well and reduces the chances of immunogenic epitopes forming between the two chains.</p> <p>Here we share the data used to train and evaluate Humatch and the CNN weights and germline likeness lookup arrays that guide its humanisation.</p>
Interferon-induced activation of dendritic cells and monocytes by yellow fever vaccination correlates with early antibody responses
<p>Bulk RNA-seq analysis of sorted subpopulations isolated from PBMC of yellow fever vaccinees from before and 3, 7, 14 and 28 days after vaccination and single cell RNA-seq analysis of sorted DC and monocytes fractions isolated from PBMC of of yellow fever vaccinees from before and 3 and 7 days after vaccination.</p>
Data for "Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations"
Open the record for dataset details and reuse information.
Risk of bias assessments for the Cochrane review 'SARS-CoV-2-neutralising monoclonal antibodies for treatment of COVID-19'
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: SARS-CoV-2-neutralising monoclonal antibodies for treatment of COVID-19.</p>
Datasets for the manuscript "In silico proof of principle of machine learning-based antibody design at unconstrained scale"
<p>The zip file contains dataset files for the manuscript "In silico proof of principle of machine learning-based antibody design at unconstrained scale"</p>
Seroprevalence of IgG antibodies against SARS coronavirus 2 in Belgium – a serial prospective cross-sectional nationwide study of residual samples (March – October 2020)
<p>This dataset contains information on seven prospective cross-sectional nationwide residual sera collection rounds. The samples were analyzed for IgG antibodies against S1 proteins of SARS-CoV-2 with a semi-quantitative commercial ELISA (EuroImmun, Luebeck, Germany).</p> <p>We provide a CSV file containing the following variables:</p> <ul> <li><strong>code</strong>: unique sample code</li> <li><strong>age_cat</strong>: age categories by 10-year age bands (0-10, 10-20, ..., 80-90, 90-Inf), the lower limit is included, e.g. 0-10 = [0,10)</li> <li><strong>sex</strong>: sex (f = female, m = male)</li> <li><strong>province</strong>: province of residence (11 categories)</li> <li><strong>region</strong>: region of residence (3 categories: Brussels, Flanders, Walloon)</li> <li><strong>collection_round</strong>: collection round (values 1 to 7)</li> <li><strong>collection_start</strong>: start date of the collection round</li> <li><strong>collection_end</strong>: end date of the collection round</li> <li><strong>igg_orig</strong>: measured IgG OD value as character (note, a semi-quantitative ELISA was used, i.e. this should not be interpreted continiously)</li> <li><strong>igg_cat</strong>: categorized IgG OD values <ul> <li><em>LoD</em>: IgG OD < 0.15</li> <li><em>negative</em>: 0.15 ≤ IgG OD < 0.8</li> <li><em>borderline</em>: 0.8 ≤ IgG OD < 1.1</li> <li><em>positive</em>: 1.1 ≤ IgG OD</li> </ul> </li> </ul> <p>Please see publication mentioned underneath for more details (<a href="https://doi.org/10.1101/2020.06.08.20125179">https://doi.org/10.1101/2020.06.08.20125179</a>).</p> <p><strong>Funding:</strong> This work received funding from the European Union's Horizon 2020 research and innovation program - project EpiPose (No 101003688), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement 682540 TransMID), the Flemish Research Fund (FWO 1150017N) and from The Antwerp University Fund; which is a community of donors who contribute to research and education with their personal commitment through a donation, gift, bequest or through academic chairs. The funders had no role in study design, data collection, data analysis, data interpretation, writing or submitting of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.</p>
The rapid and highly parallel identification of antibodies with defined biological activities by SLISY
<p>This is the sequencing data that accompanies the manuscript published in Nature Communications titled "The rapid and highly parallel identification of antibodies with defined biological activities by SLISY".</p>
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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.