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Bacterial non-enzymatic Mn(II) oxidation involving reactive oxygen species (ROS) (i.e. indirect oxidation), initially discovered from a marine alpha-proteobacterium, is believed to be of importance in controlling biogeochemical cycles. For soil-borne bacteria, however, evidence of indirect Mn(II) oxidation remains unclear. In this study, the indirect Mn(II) oxidation was evidenced in a soil-borne bacterium, Providencia sp. LLDRA6. First, with and without 50 mM of Mn(II) exposure for LLDRA6, 300 differentially expressed genes were found to be linked to Mn(II) exposure via transcriptome sequencing. Among them, an operon, responsible for phenylacetic acid catabolism, was sharply upregulated in transcription, drawing us a special attention since its transcriptional upregulation has recently shown to be important for withstanding ROS. Next, a uorometric probe, 2′,7′-Dichloro uorescin diacetate (DCFDA), was used to qualitatively detect ROS from cells, showing a distinct increase in uorescence intensities of ROS during Mn(II) exposure. Further, concentrations of superoxide and hydrogen peroxide from cells were detected respectively with and without Mn(II) exposure, exhibiting that when Mn(II) oxidation occurred, superoxide concentration signi cantly increased but hydrogen peroxide concentration signi cantly decreased. Particularly, superoxide produced by LLDRA6 was proven to be the oxidant for Mn(II) in the formation of Mn oxides. Finally, we predicted links between phenylacetic acid metabolism pathway and ROS during Mn(II) exposure, proposing that the excessive ROS, generated in response to Mn(II) exposure, transcriptionally activate phenylacetic acid catabolism presumably by increasing concentrations of highly reactive oxepins.

The networking scale and traffic have exploded. At the same time, the rapid development of virtualization and cloud computing technologies not only poses a considerable challenge to the endurance of the network, but also causes more and more problems to the traditional network architecture with IP as the core. Cloud computing is a supercomputing model based on the Internet. With the rapid growth of network access and data traffic, the processing power and computing intensity will also increase, and a single server cannot afford the increase in business. In order to reduce network pressure and improve computing efficiency, load balancing for network computing is particularly important. This paper uses ant colony algorithm to design cloud computing load balance. The ant colony algorithm runs in the controller. According to the real-time network load situation provided by the controller, it calculates the link with the smallest load and provides a dynamic data stream forwarding strategy. The result of the experiments shows that the load-balanced ACO optimized technique can significantly provide an improved computational response. In the ACO algorithm, the average response time is about 30% lower than that in other algorithms. This shows that the use of the ant colony algorithm achieves a good optimization effect.

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