Propidium monoazide (PMA) and ethidium bromide monoazide(EMA) improve DNA array and high-throughput sequencing ofporcine reproductive and respiratory syndrome virus identification

Pan-viral DNA array (PVDA) and high-throughput sequencing (HTS) are useful tools to identify novel viruses of emerging diseases. However, both techniques have difficulties to identify viruses in clinical samples because of the host genomic nucleic acid content (hg/cont). Both propidium monoazide (PMA) and ethidium bromide monoazide (EMA) have the capacity to bind free DNA/RNA, but are cell membrane-impermeable. Thus, both are unable to bind protected nucleic acid such as viral genomes within intact virions. However, EMA/PMA modified genetic material cannot be amplified by enzymes. In order to assess the potential of EMA/PMA to lower the presence of amplifiable hg/cont in samples and improve virus detection, serum and lung tissue homogenates were spiked with porcine reproductive and respiratory virus (PRRSV) and were processed with EMA/PMA. In addition, PRRSV RT-qPCR positive clinical samples were also tested. EMA/PMA treatments significantly decreased amplifiable hg/cont and significantly increased the number of PVDA positive probes and their signal intensity compared to untreated spiked lung samples. EMA/PMA treatments also increased the sensitivity of HTS by increasing the number of specific PRRSV reads and the PRRSV percentage of coverage. Interestingly, EMA/PMA treatments significantly increased the sensitivity of PVDA and HTS in two out of three clinical tissue samples. Thus, EMA/PMA treatments offer a new approach to lower the amplifiable hg/cont in clinical samples and increase the success of PVDA and HTS to identify viruses.

Interrelated product design activities sequencing with efficient tabu search algorithms

This paper proposes and investigates a metaheuristic tabu search algorithm (TSA) that generates optimal or near optimal solutions sequences for the feedback length minimization problem (FLMP) associated to a design structure matrix (DSM). The FLMP is a non-linear combinatorial optimization problem, belonging to the NP-hard class, and therefore finding an exact optimal solution is very hard and time consuming, especially on medium and large problem instances. First, we introduce the subject and provide a review of the related literature and problem definitions. Using the tabu search method (TSM) paradigm, this paper presents a new tabu search algorithm that generates optimal or sub-optimal solutions for the feedback length minimization problem, using two different neighborhoods based on swaps of two activities and shifting an activity to a different position. Furthermore, this paper includes numerical results for analyzing the performance of the proposed TSA and for fixing the proper values of its parameters. Then we compare our results on benchmarked problems with those already published in the literature. We conclude that the proposed tabu search algorithm is very promising because it outperforms the existing methods, and because no other tabu search method for the FLMP is reported in the literature. The proposed tabu search algorithm applied to the process layer of the multidimensional design structure matrices proves to be a key optimization method for an optimal product development.