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4-1BB-R (Human)

Catalog No : h41BBR
  • Reactivity
    Human
  • Assay Type
    Sandwich ELISA
  • Size
    1 unit
  • Price
    Inquire
Details
  • Product Name
    4-1BB-R (Human)
  • Catalog No
    h41BBR
  • Detection Method
    Colorimetric 450 nm
  • Dynamic Range
    16-2000 pg/ml
  • Storage/Stability
    4°C/6 Months
  • Reactivity
    Human
  • Assay Type
    Sandwich ELISA
  • Database Links
    Gene ID: 3604, UniProt ID: Q07011
  • Format
    12 x 8-Well Microstrips
  • Manual (PDF)
  • NCBI Gene Symbol
    TNFRSF9
  • Specificity
    The Human 4-1BBR ELISA is capable of recognizing both recombinant and naturally produced Human 4-1BBR proteins. The antigens listed below were tested at 50 ng/ml and did not exhibit significant cross reactivity or interference. •, Human: 4-1BBL, AITRL, BAFF, BAFF Receptor, BCMA, CD40 Ligand/TRAP, Fas Receptor, LIGHT, OPG, sOX40L, sRANK (Receptor), sRANKL, TACI, TL-1A, TNF-alpha, TNF-beta, sTNF-Receptor Type I, sTNF-Receptor Type II, sTRAIL/Apo2L, sTRAIL Receptor-1, sTRAIL Receptor-2, TWEAK, TWEAK Receptor
  • Sub Type
    None
  • Synonyms
    Tumor necrosis factor receptor superfamily member 9, 4-1BB ligand receptor T-cell, antigen 4-1BB homolog, T-cell antigen ILA, CD137 antigen, CDw137, ILA, 4-1BB, MGC2172, 4-1BBR, TNFRSF9
  • TargetName
    Human 4-1BB-R
Application Images
Image 1
  • 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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