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Anti-cadherin pan antibody [ABT-MCDH]

Catalog No : V0011
  • Applications
    IHC, WB
  • Host Species
    Mouse
  • Reactivity
    Human
  • Size
    100ul
  • Price
    $275.00
Details
  • Product Name
    Anti-cadherin pan antibody [ABT-MCDH]
  • Catalog No
    V0011
  • Description
    Mouse monoclonal [ABT-MCDH] antibody to MCDH
  • Applications
    IHC, WB
  • Clonality
    Monoclonal
  • Host Species
    Mouse
  • Reactivity
    Human
  • ABT Clone ID
    ABT-MCDH
  • Antigen Retrieval
    Heat-induced epitope retrieval (HIER)
  • Dilution
    1:50-200
  • IHC Recommended control
    Colon
  • IHC Target Name
    cadherin pan
  • Immunogen
    Synthetic peptide
  • Localization
    Membranous
  • Purification
    Immunogen affinity purified
  • Retrieval buffer
    Citrate buffer of pH6.0
  • Source/Ig Isotype
    Mouse IgG
  • Storage/Stablility
    -20°C/1 year
  • UniProtKB
    MANY
Application Images
Image 1 Immunohistochemistry analysis of Formalin-fixed, paraffin-embedded Human Breast carcinoma using cadherin-pan antibody.
Image 2 Immunohistochemistry analysis of Formalin-fixed, paraffin-embedded Human Breast carcinoma using cadherin-pan antibody.
Image 3 Immunohistochemistry analysis of Formalin-fixed, paraffin-embedded Human Colon carcinoma using cadherin-pan antibody.
  • 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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