Role of the ArcAB two-component system in the resistance of Escherichia coli to reactive oxygen stress

<p>Abstract</p> <p>Background</p> <p>The global regulatory system ArcAB controls the anaerobic growth of <it>E. coli</it>, however, its role in aerobic conditions is not well characterized. We have previously reported that ArcA was necessary for <it>Salmonella </it>to resist reactive oxygen species (ROS) in aerobic conditions.</p> <p>Results</p> <p>To investigate the mechanism of ROS resistance mediated by ArcAB, we generated deletion mutants of ArcA and ArcB in <it>E. coli</it>. Our results demonstrated that both ArcA and ArcB were necessary for resistance to hydrogen peroxide (H₂O₂), a type of ROS, and their function in this resistance was independent from H₂O₂scavenge. Mutagenesis analysis of ArcA indicated that ROS resistance was mediated through a distinct signaling pathway from that used in anaerobic conditions. An abundant protein flagellin was elevated at both the protein and mRNA levels in the <it>ΔarcA </it>mutant as compared to the wild type <it>E. coli</it>, and deletion of flagellin restored the resistance of the <it>ΔarcA </it>mutant to H₂O₂. The resistance of the <it>ΔarcA </it>mutant <it>E. coli </it>to H₂O₂can also be restored by amino acid supplementation, suggesting that a deficiency in amino acid and/or protein synthesis in the mutant contributed to its susceptibility to H₂O₂, which is consistent with the notion that protein synthesis is necessary for ROS resistance.</p> <p>Conclusion</p> <p>Our results suggest that in addition to its role as a global regulator for anaerobic growth of bacteria, ArcAB system is also important for bacterial resistance to ROS in aerobic conditions, possibly through its influence on bacterial metabolism, especially amino acid and/or protein assimilation and synthesis.</p

Consumer Video Understanding: A Benchmark Database and An Evaluation of Human and Machine Performance

Recognizing visual content in unconstrained videos has become a very important problem for many applications. Existingcorpora for video analysis lack scale and/or content diversity,and thus limited the needed progress in this critical area. In this paper, we describe and release a new database called CCV, containing 9,317 web videos over 20 semantic categories, including events like “baseball” and “parade”, scenes like “beach”, and objects like “cat”. The database was collected with extra care to ensure relevance to consumer interest and originality of video content without post-editing. Such videos typically have very little textual annotation and thus can benefit from the development of automatic content analysis techniques. We used Amazon MTurk platform to perform manual annotation, and studied the behaviors and performance of human annotators on MTurk.We also compared the abilities in understanding consumer video content by humans and machines. For the latter, we implemented automatic classifiers using state-of-the-art multi-modal approach that achieved top performance in recent TRECVID multimedia event detection task. Results confirmed classifiers fusing audio and video features significantly outperform single-modality solutions. We also found that humans are much better at understanding categories of nonrigid objects such as “cat”, while current automatic techniques are relatively close to humans in recognizing categories that have distinctive background scenes or audio patterns