Single-cell profiling of peripheral neuroblastic tumors identifies an aggressive transitional state that bridges an adrenergic-mesenchymal trajectory.

Peripheral neuroblastic tumors (PNTs) represent a spectrum of neural-crest-derived tumors, including neuroblastoma, ganglioneuroblastoma, and ganglioneuroma. Malignant cells in PNTs are theorized to interconvert between adrenergic/noradrenergic and mesenchymal/neural crest cell states. Here, single-cell RNA-sequencing analysis of 10 PNTs demonstrates extensive transcriptomic heterogeneity. Trajectory modeling suggests that malignant neuroblasts move between adrenergic and mesenchymal cell states via an intermediate state that we term "transitional." Transitional cells express programs linked to a sympathoadrenal development and aggressive tumor phenotypes such as rapid proliferation and tumor dissemination. Among primary bulk tumor patient cohorts, high expression of the transitional gene signature is predictive of poor prognosis compared with adrenergic and mesenchymal expression patterns. High transitional gene expression in neuroblastoma cell lines identifies a similar transitional H3K27-acetylation super-enhancer landscape. Collectively, our study supports the concept that PNTs have phenotypic plasticity and uncovers potential biomarkers and therapeutic targets.

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

A location selection policy of live virtual machine migration for power saving and load balancing.

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.