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.
Prostate cancer is the most common cancer in males worldwide. Mass spectrometry-based targeted proteomics has demonstrated great potential in quantifying proteins from formalin-fixed paraffin-embedded (FFPE) and (fresh) frozen biopsy tissues. Here we provide a comprehensive tissue-specific spectral library for targeted proteomic analysis of prostate tissue samples. Benign and malignant FFPE prostate tissue samples were processed into peptide samples by pressure cycling technology (PCT)-assisted sample preparation, and fractionated with high-pH reversed phase liquid chromatography (RPLC). Based on data-dependent acquisition (DDA) MS analysis using a TripleTOF 6600, we built a library containing 108,533 precursors, 84,198 peptides and 9384 unique proteins (1% FDR). The applicability of the library was demonstrated in prostate specimens. 相似文献
Plasma Physics Reports - The interactions among geodesic acoustic modes (GAMs), mean flow, and mean flow shear were investigated using Langmuir probe arrays under periodic supersonic molecular beam... 相似文献
Bocaviruses are associated with many human infectious diseases, such as respiratory tract infections, gastroenteritis, and hepatitis. Rats are known to be reservoirs of bocaviruses, including rodent bocavirus and rat bocavirus. Recently, ungulate bocaparvovirus 4, a known porcine bocavirus, has also been found in rats. Thus, investigating bocaviruses in rats is important for determining the origin of the viruses and preventing and controlling their transmission. To the best of our knowledge, no study to date has investigated bocaviruses in the livers of rats. In this report, a total of 624 rats were trapped in southern China between 2014 and 2017. Liver and serum samples from rats were tested for the prevalence of bocaviruses using PCR. Sequences related to ungulate bocaparvovirus 4 and rodent bocavirus were detected in both liver and serum samples. Interestingly, the prevalence of ungulate bocaparvovirus 4 (reference strain:KJ622366.1) was higher than that of rodent bocavirus (reference strain:KY927868.1) in both liver (2.24% and 0.64%, respectively) and serum samples (2.19% and 0.44%, respectively). The NS1 regions of ungulate bocaparvovirus 4 and rodent bocavirus related sequences displayed over 84% and 88% identity at the nucleic acid and amino acid levels, respectively. Furthermore, these sequences had similar genomic structure, genomic features, and codon usage bias, and shared a common ancestor. These viruses also displayed greater adaptability to rats than pigs. Our results suggested that ungulate bocaparvovirus 4 and rodent bocavirus may originate from rats and may be different genotypes of the same bocavirus species. 相似文献