In HIV infection, some closely associated human leukocyte antigen (HLA) alleles are correlated with distinct clinical outcomes although presenting the same HIV epitopes. The mechanism that underpins this observation is still unknown, but may be due to the essential features of HLA alleles or T cell receptors (TCR). In this study, we investigate how T18A TCR, which is beneficial for a long-term control of HIV in clinic, recognizes immunodominant Gag epitope TL9 (TPQDLTML180-188) from HIV in the context of the antigen presenting molecule HLA-B*81:01. We found that T18A TCR exhibits differential recognition for TL9 restricted by HLA-B*81:01. Furthermore, via structural and biophysical approaches, we observed that TL9 complexes with HLA-B*81:01 undergoes no conformational change after TCR engagement. Remarkably, the CDR3ß in T18A complexes does not contact with TL9 at all but with intensive contacts to HLA-B*81:01. The binding kinetic data of T18A TCR revealed that this TCR can recognize TL9 epitope and several mutant versions, which might explain the correlation of T18A TCR with better clinic outcomes despite the relative high mutation rate of HIV. Collectively, we provided a portrait of how CD8+ T cells engage in HIV-mediated T cell response.
HIV-1/metabolism , HLA-B Antigens/immunology , Mutation/genetics , Receptors, Antigen, T-Cell/metabolism , gag Gene Products, Human Immunodeficiency Virus/genetics , CD8-Positive T-Lymphocytes/immunology , Humans , Immunodominant Epitopes/immunology , gag Gene Products, Human Immunodeficiency Virus/chemistry
The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis function, to effectively match the different characteristic information of the welding crack AE signal. Taking into account the adaptive decomposition characteristics of Empirical Mode Decomposition (EMD), a novel wavelet packet noise reduction method for welding AE signal was proposed. The welding AE signal was adaptively decomposed into several Intrinsic Mode Functions (IMFs) by the EMD. The effective IMFs were selected by the frequency distribution characteristics of the welding crack AE signal. A wavelet packet, with a specific basis function, was subsequently performed on the effective IMFs, which were reconstructed to be the welding crack AE signal. The simulated and experimental results indicated that the proposed method can effectively achieve noise reduction of the welding crack AE signal, which provided a mean for structure crack detection in the welding process.
