WLC28-6: A Sphere Decoding Approach to Multiple Symbols Differential Detection for UWB Systems

2006 ◽  
Author(s):  
Vincenzo Lottici ◽  
Zhi Tian
Keyword(s):  
Sphere Decoding ◽  
Differential Detection

Differential Detection of Single-Chain and Two-Chain Urokinase-Type Plasminogen Activator by a New Immunoadsorbent-Amidolytic Assay (IAA)

1986 ◽  
Vol 56 (03) ◽  
pp. 407-410 ◽  
Author(s):  
Angelo Corti ◽  
Maria Luisa Nolli ◽  
Giovanni Cassani
Keyword(s):  
Plasminogen Activator ◽  
Carcinoma Cell ◽  
Differential Detection ◽  
Single Chain ◽  
Response Curves ◽  
Type Plasminogen Activator ◽  
Urokinase Type Plasminogen Activator ◽  
Before And After ◽  
Culture Supernatants ◽  
Human Epidermoid Carcinoma

SummaryA new immunoadsorbent-amidolytic assay (IAA) for the specific differential detection of two-chain urokinase-type plasminogen activator (tcu-PA) and its single-chain precursor (scu-PA) in cell culture supernatants has been developed. The assay combines the selectivity of immunoassays with the specificity of enzyme activity assays exploiting both the antigenic and enzymatic properties of the two proteins. tcu-PA and scu-PA are selectively immunoadsorbed on the wells of a microtiterplate coated with the monoclonal antibody 5B4 and tested for enzymatic activity before and after activation by plasmin treatment. Both proteins are determined with similar efficiency since overlapping dose-response curves were obtained in the range between 12.5-200 ng/ml. The assay has been used to determine tcu-PA and scu-PA in A431 human epidermoid carcinoma cell supernatants. The analytical recoveries for tcu-PA and scu-PA added to A431 cell supernatants were 95.2% and 96.9% respectively. The intra- and inter-assay variations (CV) were 5.5% and 9.0% for tcu-PA and 9.7% and 9.8% for scu-PA respectively.

scholarly journals Lossless Dimension Reduction for Integer Least Squares With Application to Sphere Decoding

2020 ◽  
Vol 68 ◽  
pp. 6547-6561
Author(s):  
Mohammad Neinavaie ◽  
Mostafa Derakhtian ◽  
Sergiy A. Vorobyov
Keyword(s):  
Dimension Reduction ◽  
Least Squares ◽  
Sphere Decoding ◽  
Integer Least Squares
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