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ABSTRACT Microbes can be programmed to record participation in gene transfer by coding biological-recording devices into mobile DNA. Upon DNA uptake, these devices transcribe a catalytic RNA (cat-RNA) that binds to conserved sequences within ribosomal RNA (rRNA) and perform a trans-splicing reaction that adds a barcode to the rRNA. Existing cat-RNA designs were generated to be broad-host range, providing no control over the organisms that were barcoded. To achieve control over the organisms barcoded by cat-RNA, we created a program called Ribodesigner that uses input sets of rRNA sequences to create designs with varying specificities. We show how this algorithm can be used to identify designs that enable kingdom-wide barcoding, or selective barcoding of specific taxonomic groups within a kingdom. We use Ribodesigner to create cat-RNA designs that target Pseudomonadales while avoiding Enterobacterales, and we compare the performance of one design to a cat-RNA that was previously found to be broad host range. When conjugated into a mixture ofEscherichia coliandPseudomonas putida, the new design presents increased selectivity compared to a broad host range cat-RNA. Ribodesigner is expected to aid in developing cat-RNA that store information within user-defined sets of microbes in environmental communities for gene transfer studies. GRAPHICAL ABSTRACT 

Premature ventricular contraction (PVC) can cause great harm to human health. Both invasive and non-invasive techniques for detecting electrical activity of PVC or locating ectopic pacemakers are used in clinical diagnosis. Among them, the electrocardiographic imaging is a popular method for non-invasive reconstruction of cardiac electrophysiology through body-surface potential. In this paper, we propose a novel framework based on low-rank and sparse decomposition (LSD) + total variation (TV) to solve the ill-posedness of the spatiotemporal ECG-inverse problem to reconstruct the cardiac electrical activity of PVC. The proposed framework considers the spatiotemporal distribution of multi-frame cardiac potential as a whole. TV is used to filter out relatively smooth candidates from countless inverse solutions. In addition, LSD utilizes the low-rank characteristic of the potential background and the sparseness of the potential outliers to avoid the loss of potential details and improve the accuracy of potential reconstruction. This improves the quality of electrical activity retrieval and the accuracy of locating the PVC origin. Simulation experiments of ventricular pacing reconstruction and diagnostic experiments of real PVC patients prove that the proposed framework is superior to the conventional quadratic methods (Tikhonov-0, Tikhonov-2) and the non-quadratic method TV. 

In this study, we explore the use of low rank and sparse constraints for the noninvasive estimation of epicardial and endocardial extracellular potentials from body-surface electrocardiographic data to locate the focus of premature ventricular contractions (PVCs). The proposed strategy formulates the dynamic spatiotemporal distribution of cardiac potentials by means of low rank and sparse decomposition, where the low rank term represents the smooth background and the anomalous potentials are extracted in the sparse matrix. Compared to the most previous potential-based approaches, the proposed low rank and sparse constraints are batch spatiotemporal constraints that capture the underlying relationship of dynamic potentials. The resulting optimization problem is solved using alternating direction method of multipliers . Three sets of simulation experiments with eight different ventricular pacing sites demonstrate that the proposed model outperforms the existing Tikhonov regularization (zero-order, second-order) and L1-norm based method at accurately reconstructing the potentials and locating the ventricular pacing sites. Experiments on a total of 39 cases of real PVC data also validate the ability of the proposed method to correctly locate ectopic pacing sites.