Arsenic can be biomethylated to form a variety of organic arsenicals differing in toxicity and environmental mobility. Trivalent methylarsenite (MAs(III)) produced in the methylation process is more toxic than inorganic arsenite (As(III)). MAs(III) also serves as a primitive antibiotic and, consequently, some environmental microorganisms have evolved mechanisms to detoxify MAs(III). However, the mechanisms of MAs(III) detoxification are not well understood. In this study, we identified an arsenic resistance (ars) operon consisting of three genes, arsRVK, that contribute to MAs(III) resistance in Ensifer adhaerens ST2. ArsV is annotated as an NADPH-dependent flavin monooxygenase with unknown function. Expression of arsV in the arsenic hypersensitive Escherichia coli strain AW3110Δars conferred resistance to MAs(III) and the ability to oxidize MAs(III) to MAs(V). In the presence of NADPH and either FAD or FMN, purified ArsV protein was able to oxidize both MAs(III) to MAs(V) and Sb(III) to Sb(V). Genes with arsV-like sequences are widely present in soils and environmental bacteria. Metagenomic analysis of five paddy soils showed the abundance of arsV-like sequences of 0.12–0.25 ppm. These results demonstrate that ArsV is a novel enzyme for the detoxification of MAs(III) and Sb(III) and the genes encoding ArsV are widely present in soil bacteria. 相似文献
As the services provided by cloud vendors are providing better performance, achieving auto-scaling, load-balancing, and optimized performance along with low infrastructure maintenance, more and more companies migrate their services to the cloud. Since the cloud workload is dynamic and complex, scheduling the jobs submitted by users in an effective way is proving to be a challenging task. Although a lot of advanced job scheduling approaches have been proposed in the past years, almost all of them are designed to handle batch jobs rather than real-time workloads, such as that user requests are submitted at any time with any amount of numbers. In this work, we have proposed a Deep Reinforcement Learning (DRL) based job scheduler that dispatches the jobs in real time to tackle this problem. Specifically, we focus on scheduling user requests in such a way as to provide the quality of service (QoS) to the end-user along with a significant reduction of the cost spent on the execution of jobs on the virtual instances. We have implemented our method by Deep Q-learning Network (DQN) model, and our experimental results demonstrate that our approach can significantly outperform the commonly used real-time scheduling algorithms.
In this study, we present a facile and low-cost approach for detecting protein kinase A (PKA) by assembling a purpose-designed carboxyfluorescein (FAM)-labelled peptide with carboxylic carbon nanoparticles (CNPs). Fluorescence of the FAM-labelled peptide gradually decreases to a low background signal as a result of the electron transfer from CNPs to FAM-labelled peptide via the peptide, which acts as a bridge. The reaction in the sensor in the presence of adenosine 5′-triphosphate and PKA phosphorylates the substrate peptide and disrupts the electrostatic repulsive force between the CNPs and the peptide, therefore altering the spectroscopic signal of the system. The change in fluorescence signal was directly proportional to the PKA concentration in the range 0–1.8 U/ml with a detection limit of 0.04 U/ml. These results suggest that PKA activity can be effectively measured using the developed PKA biosensor. Moreover, the fluorescence biosensor was successfully used in the investigation of PKA in spiked human embryonic kidney (HEK) 293 cells lysates, indicating its potential applications in protein kinase-related biochemical fundamental research. 相似文献
