MOTIVATION: Unique marker sequences are highly sought after in molecular diagnostics. Nevertheless, there are only few programs available to search for marker sequences, compared to the many programs for similarity search. We therefore wrote the program Fur for Finding Unique genomic Regions. RESULTS: Fur takes as input a sample of target sequences and a sample of closely related neighbors. It returns the regions present in all targets and absent from all neighbors. The recently published program genmap can also be used for this purpose and we compared it to fur. When analyzing a sample of 33 genomes representing the major phylogroups of E.coli, fur was 40 times faster than genmap but used three times more memory. On the other hand, genmap yielded three times more markers, but they were less accurate when tested in silico on a sample of 237 E.coli genomes. We also designed phylogroup-specific PCR primers based on the markers proposed by genmap and fur, and tested them by analyzing their virtual amplicons in GenBank. Finally, we used fur to design primers specific to a Lactobacillus species, and found excellent sensitivity and specificity in vitro. AVAILABILITY AND IMPLEMENTATION: Fur sources and documentation are available from https://github.com/evolbioinf/fur. The compiled software is posted as a docker container at https://hub.docker.com/r/haubold/fox. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Digital beamforming (DBF) has long been heralded as the next frontier in phased array technology, and not without reason. The digitization of transmit and receive signals at the element level opens the door to new processing and beamforming schemes and promises to deliver maximum flexibility and unprecedented dynamic range in large systems. However, it is not without inherent technological risks and practical challenges associated with the amount of data to process and the use of less sophisticated transceivers. This paper provides broad overviews of several interrelated aspects of the resulting DBF trade spaces for these systems. In particular, emerging concepts are highlighted for the roles and interconnection of distributed beamforming/processing for fixed and adaptive beamforming. These are then related to digital array calibration mechanisms that can potentially reduce the need for data- and processing-intensive beamforming algorithms.

The l2-l1sparse signal minimization problem can be solved effi ciently by gradient projection. In many applications, the signal to be estimated is known to lie in some range of values. With these additional constraints on the estimate, the resultingconstrained min imization problem is more challenging to solve. In previous work, we proposed a gradient projection approach for solving this type of minimization problem with nonnegativity constraints. In this paper, we generalize this approach to solve any bound-constrained l2-l1minimization problem. Our method is based on solving the Lagrangian dual problem, and we show that by constraining the solution to known a priori bounds within the optimization method, we can obtain a more accurate estimate than simply thresholding the solution from the unconstrained minimization problem. Numerical results are presented to demonstrate the effectiveness of this approach.

This paper concerns the reconstruction of a temporally-varying scene from a video sequence of noisy linear projections. Assuming that each video frame is sparse or compressible in some basis, this inverse problem can be formulated as an ℓ2-ℓ1minimization problem, which can be solved efficiently using gradient projection. Since the signal of interest corresponds to nonnegative pixel intensities, additional nonnegativity constraints are included in the minimization problem, rendering the optimization problem more difficult to solve but with a greater potential for more accurate reconstructions. In this paper, we propose a method for reconstructing a video sequence that incorporates nonnegativity constraints and exploits inter-frame correlations to improve upon the naive approach of solving each frame independently. We present numerical experiments to demonstrate the effectiveness of this approach.

The ℓ2-ℓ1compressed sensing minimization problem can be solved efficiently by gradient projection. In imaging applications, the signal of interest corresponds to nonnegative pixel intensities; thus, with additional nonnegativity constraints on the reconstruction, the resulting constrained minimization problem becomes more challenging to solve. In this paper, we propose a gradient projection approach for sparse signal recovery where the reconstruction is subject to nonnegativity constraints. Numerical results are presented to demonstrate the effectiveness of this approach.