Interactions of the tumor necrosis factor superfamily members B lymphocyte stimulator (BLyS) and a proliferation-inducing ligand (APRIL) with their receptors-transmembrane activator and CAML interactor (TACI) and B cell maturation molecule (BCMA)-on B cells play an important role in the humoral immune response. Whereas BCMA is restricted to B cells, TACI is also expressed on activated T cells; we show here that TACI-Fc blocks the activation of T cells in vitro and inhibits antigen-specific T cell activation and priming in vivo. In a mouse model for rheumatoid arthritis (RA), an autoimmune disease that involves both B and T cell components, TACI-Fc treatment substantially inhibited inflammation, bone and cartilage destruction and disease development. Thus, BLyS and/or APRIL are important not only for B cell function but for T cell-mediated immune responses. Inhibition of these ligands might have therapeutic benefits for autoimmune diseases, such as RA, that involve both B and T cells.

The reconstruction of seismic data with missing traces has been a long-standing issue in seismic data processing; deep learning methods have attracted significant attention in seismic data reconstruction. One barrier associated with these deep-learning based reconstruction methods is the need for large training datasets, which are difficult to acquire owing to physical or financial constraints in practice. A novel method for the recovery of incomplete seismic data without the need of training datasets was developed. Seismic prior is implicitly captured based on the particular CNN structure choice, referred to as the “deep-seismic-prior-based”. The learned network weights are the parameters that represent seismic data, and as the convolutional filter weights are shared for spatial invariance, the CNN structure can function as a regularizer to guide the network learning. The reconstruction is realized during the iterative process by minimizing the mean square error (MSE) between the network output and the original corrupted seismic data. Our method could handle both irregular and regular seismic data, and testing its performance using both synthetic and field data showed it was more advantageous compared with the singular spectrum analysis (SSA) and de-aliased Cadzow methods employed in the reconstruction of irregular and regular data, respectively. The experimental results showed that the proposed method provided better reconstruction performance than the SSA and Cadzow methods.

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Separation of potential field data forms the basis of inversion and interpretation. The low-rank matrix theory is used for the separation of potential field data. A theoretical analysis led to the approximate equation that demonstrates the relation between the amplitudes of the wavenumber components of potential field data and the singular values of the trajectory matrix embedded from the potential field data matrix. Therefore, the low-rank feature of the trajectory matrix of regional field data and the sparse feature of the trajectory matrix of residual field data can be obtained based on the features of the power spectrum of the potential field data. Based on this, we have developed a low-rank matrix decomposition model for the separation of the trajectory matrix of the potential field data. Minimizing the rank of the trajectory matrix of the regional field data and the [Formula: see text]-norm of the trajectory matrix of the residual field data is a double-objective optimization task, and this optimization task can be solved by the inexact augmented Lagrange multiplier algorithm. The weighting parameter is robust and easy to set. Numerical experiment results indicate that our method is robust, and the separation errors of the method are smaller compared to the matched filtering and wavelet analysis methods. In addition, our method can be easily implemented and has clear geophysical significance. Finally, our method is applied on real data sets in the Daye area, Hubei Province, China. The separated gravity and magnetic fields coincide well with target geologic sources.