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Anti-BOB.1 antibody [ABT-BOB1]

Catalog No : V0009
  • Applications
    IHC
  • Host Species
    Mouse
  • Reactivity
    Human
  • Size
    50ul
  • Price
    $175.00
Details
  • Product Name
    Anti-BOB.1 antibody [ABT-BOB1]
  • Catalog No
    V0009
  • Description
    Mouse monoclonal [ABT-BOB1] antibody to BOB1
  • Applications
    IHC
  • Clonality
    Monoclonal
  • Host Species
    Mouse
  • Reactivity
    Human
  • ABT Clone ID
    ABT-BOB1
  • Antigen Retrieval
    Heat-induced epitope retrieval (HIER)
  • Cell Marque/Ventana Control
    B-cell lymphoma, tonsil
  • Concentration
    0.6mg/ml
  • Dilution
    1:50-200
  • Entrez Gene ID (NCBI)
    5450
  • IHC Recommended control
    Normal Tonsil
  • IHC Target Name
    BOB.1
  • Immunogen
    Synthetic peptide
  • Localization
    Nuclear, Cytoplasmic
  • OMIM
    601206
  • Purification
    Immunogen affinity purified
  • Research
    B-cell lymphomas: Follicular, CLL/SLL, Mantle cell, Marginal zone bcl, lymphoplasmacytic, diffuse large cell lymphoma, Hodgkin Lymphoma: nodular lymphocyte predominant, T-cell rich LBCL, Acute myeloid leukemia: promyelocytic M3, megakaryoblastic M7
  • Retrieval buffer
    Citrate buffer of pH6.0
  • Source/Ig Isotype
    Mouse IgG
  • Storage/Stablility
    -20°C/1 year
  • Synonyms
    B-cell-specific coactivator OBF-1, BOB1, OCA-B, OBF1
  • UniProt Gene Name
    POU2AF1
  • UniProtKB
    Q16633
  • Volume
    2ml
Application Images
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  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
For the process of attaching edges to nodes, it is straightforward to compute the likelihood using (2). However, because of the nature of weighted sampling without replacement, we have to, for each i, calculate the probability conditi onal on each of the Xi!Xi! permutations of the selected nodes and then aver age over all Xi!Xi! probabilities to arrive at the likelihood. As calculating the exact likelihood in this way is not computationally feasible because the factorial grows faster than the exponential function, we approximate the likelihood based on one permutation of weighted sampling without replacement instead. The contribution by the new edges brought by node i is
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