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T Cell Receptor-MHC Class I Peptide Interactions : Affinity, Kinetics, and Specificity
Corr, Maripat,Slanetz, Alfred E.,Boyd, Lisa F.,Jelonek, Marie T.,Khilko, Sergei,Al-Ramadi, Basel K.,Kim, Young Sang,Maher, Stephen E.,Bothwell, Alfred L. M.,Margulies, David H. 충남대학교 생물공학연구소 1996 생물공학연구지 Vol.4 No.-
The critical discriminatory event in the activation of T lymphocytes bearing αβT cell receptors (TCRs) is their interaction with a molecular complex consisting of a peptide bound to a major histocompatibility complex (MHC)-encoded classⅠor class Ⅱ molecule on the surface of an antigen-presenting cell. The kinetics of binding were measured of a purified TCR to molecular complexes of a purified soluble analog of the murine MHC classⅠ molecule H-2L^d (sH-2L^d) and a synthetic octamer peptide p2CL in a direct, real-time assay based on surface plasmon resonance. The kinetic dissociation rate of the MHC-peptide complex from the TCR was rapid (2.6×10^-2) second^-1, corresponding to a half-time for dissociation of approximately 27 seconds), and the kinetic association rate was 2.1×10^ 5 M^-1 second^-1. The equilibrium constant for dissociation was approximately 10^-7M These values indicate that TCRs must interact with a multivalent array of MHC-peptide complexes to trigger T cell signaling.
Choi, Kihwan,Wang, Jing,Zhu, Lei,Suh, Tae-Suk,Boyd, Stephen,Xing, Lei Wiley (John WileySons) 2010 Medical physics Vol.37 No.9
<P>PURPOSE: This article considers the problem of reconstructing cone-beam computed tomography (CBCT) images from a set of undersampled and potentially noisy projection measurements. METHODS: The authors cast the reconstruction as a compressed sensing problem based on l1 norm minimization constrained by statistically weighted least-squares of CBCT projection data. For accurate modeling, the noise characteristics of the CBCT projection data are used to determine the relative importance of each projection measurement. To solve the compressed sensing problem, the authors employ a method minimizing total-variation norm, satisfying a prespecified level of measurement consistency using a first-order method developed by Nesterov. RESULTS: The method converges fast to the optimal solution without excessive memory requirement, thanks to the method of iterative forward and back-projections. The performance of the proposed algorithm is demonstrated through a series of digital and experimental phantom studies. It is found a that high quality CBCT image can be reconstructed from undersampled and potentially noisy projection data by using the proposed method. Both sparse sampling and decreasing x-ray tube current (i.e., noisy projection data) lead to the reduction of radiation dose in CBCT imaging. CONCLUSIONS: It is demonstrated that compressed sensing outperforms the traditional algorithm when dealing with sparse, and potentially noisy, CBCT projection views.</P>