Sparse Bayesian Learning Approach for Discrete Signal Reconstruction

This study addresses the problem of discrete signal reconstruction from the perspective of sparse Bayesian learning (SBL). Generally, it is intractable to perform the Bayesian inference with the ideal discretization prior under the SBL framework. To overcome this challenge, we introduce a novel discretization enforcing prior to exploit the knowledge of the discrete nature of the signal-of-interest. By integrating the discretization enforcing prior into the SBL framework and applying the variational Bayesian inference (VBI) methodology, we devise an alternating update algorithm to jointly characterize the finite alphabet feature and reconstruct the unknown signal. When the measurement matrix is i.i.d. Gaussian per component, we further embed the generalized approximate message passing (GAMP) into the VBI-based method, so as to directly adopt the ideal prior and significantly reduce the computational burden. Simulation results demonstrate substantial performance improvement of the two proposed methods over existing schemes. Moreover, the GAMP-based variant outperforms the VBI-based method with an i.i.d. Gaussian measurement matrix but it fails to work for non i.i.d. Gaussian matrices.Comment: 13 pages, 7 figure

Separate DOD and DOA Estimation for Bistatic MIMO Radar

A novel MUSIC-type algorithm is derived in this paper for the direction of departure (DOD) and direction of arrival (DOA) estimation in a bistatic MIMO radar. Through rearranging the received signal matrix, we illustrate that the DOD and the DOA can be separately estimated. Compared with conventional MUSIC-type algorithms, the proposed separate MUSIC algorithm can avoid the interference between DOD and DOA estimations effectively. Therefore, it is expected to give a better angle estimation performance and have a much lower computational complexity. Meanwhile, we demonstrate that our method is also effective for coherent targets in MIMO radar. Simulation results verify the efficiency of the proposed method, particularly when the signal-to-noise ratio (SNR) is low and/or the number of snapshots is small

Diversity and Biosynthetic Potential of Culturable Actinomycetes Associated with Marine Sponges in the China Seas

The diversity and secondary metabolite potential of culturable actinomycetes associated with eight different marine sponges collected from the South China Sea and the Yellow sea were investigated. A total of 327 strains were isolated and 108 representative isolates were selected for phylogenetic analysis. Ten families and 13 genera of Actinomycetales were detected, among which five genera represent first records isolated from marine sponges. Oligotrophic medium M5 (water agar) proved to be efficient for selective isolation, and “Micromonospora–Streptomyces” was proposed as the major distribution group of sponge-associated actinomycetes from the China Seas. Ten isolates are likely to represent novel species. Sponge Hymeniacidon perleve was found to contain the highest genus diversity (seven genera) of actinomycetes. Housekeeping gene phylogenetic analyses of the isolates indicated one ubiquitous Micromonospora species, one unique Streptomyces species and one unique Verrucosispora phylogroup. Of the isolates, 27.5% displayed antimicrobial activity, and 91% contained polyketide synthase and/or nonribosomal peptide synthetase genes, indicating that these isolates had a high potential to produce secondary metabolites. The isolates from sponge Axinella sp. contained the highest presence of both antimicrobial activity and NRPS genes, while those from isolation medium DNBA showed the highest presence of antimicrobial activity and PKS I genes

Sparse Bayesian Learning for DOA Estimation with Mutual Coupling

Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise

A Role for Auxin Response Factor 19 in Auxin and Ethylene Signaling in Arabidopsis

Although auxin response factors (ARFs) are the first well-characterized proteins that bind to the auxin response elements, elucidation of the roles of each ARF gene in auxin responses and plant development has been challenging. Here we show that ARF19 and ARF7 not only participate in auxin signaling, but also play a critical role in ethylene responses in Arabidopsis (Arabidopsis thaliana) roots, indicating that the ARFs serve as a cross talk point between the two hormones. Both arf19 and arf7 mutants isolated from our forward genetic screens are auxin resistant and the arf19arf7 double mutant had stronger auxin resistance than the single mutants and displayed phenotypes not seen in the single mutants. Furthermore, we show that a genomic fragment of ARF19 not only complements arf19, but also rescues arf7. We conclude that ARF19 complements ARF7 at the protein level and that the ARF7 target sequences are also recognized by ARF19. Therefore, it is the differences in expression level/pattern and not the differences in protein sequences between the two ARFs that determines the relative contribution of the two ARFs in auxin signaling and plant development. In addition to being auxin resistant, arf19 has also ethylene-insensitive roots and ARF19 expression is induced by ethylene treatment. This work provides a sensitive genetic screen for uncovering auxin-resistant mutants including the described arf mutants. This study also provides a likely mechanism for coordination and integration of hormonal signals to regulate plant growth and development