A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

Understanding the Multifaceted Role of Neutrophils in Cancer and Autoimmune Diseases

Neutrophils are one of the first immune cell types that are recruited to injury and infection site. As a vital component of the immune system, neutrophils are heterogeneous immune cells known to have phagocytic property and function in inflammation. Recent studies revealed that neutrophils play dual roles in tumor initiation, development, and progression. The multifunctional roles of neutrophils in diseases are mainly due to their production of different effector molecules under different conditions. N1 and N2 neutrophils or high density neutrophils (HDNs) and low density neutrophils (LDNs) have been used to distinguish neutrophils subpopulations with pro- vs. anti-tumor activity, respectively. Indeed, N1 and N2 neutrophils also represent immunostimulating and immunosuppressive subsets, respectively, in cancer. The emerging studies support their multifaceted roles in autoimmune diseases. Although such subsets are rarely identified in autoimmune diseases, some unique subsets of neutrophils, including low density granulocytes (LDGs) and CD177+ neutrophils, have been reported. Given the heterogeneity and functional plasticity of neutrophils, it is necessary to understand the phenotypical and functional features of neutrophils in disease status. In this article, we review the multifaceted activates of neutrophils in cancer and autoimmune diseases, which may support new classification of neutrophils to help understand their important functions in immune homeostasis and pathologies

KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions

Adverse drug reaction (ADR) detection is an essential task in the medical field, as ADRs have a gravely detrimental impact on patients' health and the healthcare system. Due to a large number of people sharing information on social media platforms, an increasing number of efforts focus on social media data to carry out effective ADR detection. Despite having achieved impressive performance, the existing methods of ADR detection still suffer from three main challenges. Firstly, researchers have consistently ignored the interaction between domain keywords and other words in the sentence. Secondly, social media datasets suffer from the challenges of low annotated data. Thirdly, the issue of sample imbalance is commonly observed in social media datasets. To solve these challenges, we propose the Knowledge Enhanced Shallow and Deep Transformer(KESDT) model for ADR detection. Specifically, to cope with the first issue, we incorporate the domain keywords into the Transformer model through a shallow fusion manner, which enables the model to fully exploit the interactive relationships between domain keywords and other words in the sentence. To overcome the low annotated data, we integrate the synonym sets into the Transformer model through a deep fusion manner, which expands the size of the samples. To mitigate the impact of sample imbalance, we replace the standard cross entropy loss function with the focal loss function for effective model training. We conduct extensive experiments on three public datasets including TwiMed, Twitter, and CADEC. The proposed KESDT outperforms state-of-the-art baselines on F1 values, with relative improvements of 4.87%, 47.83%, and 5.73% respectively, which demonstrates the effectiveness of our proposed KESDT

4-Benzyl-4-methyl­morpholinium hexa­fluoro­phosphate

In the title compound, C12H18NO+·PF6 −, the asymmetric unit consists of two cation–anion pairs. The six F atoms of one anion are disordered over two sets of sites in a 0.592 (6):0.408 (6) ratio. The morpholinium rings adopt chair conformations

SRSF2 Is Essential For Hematopoiesis and Its Mutations Dysregulate Alternative RNA Splicing In MDS

Abstract Myelodysplastic syndromes (MDS) are a group of neoplasms that are ineffective in generating multiple lineages of myeloid cells and have various risks to progress to acute myeloid leukemia. Recent genome-wide sequencing studies reveal that mutations in genes of splicing factors are commonly associated with MDS. However, the importance of these splicing factors in hematopoiesis has been unclear and the causal effect of their mutations on MDS development remains to be determined. One of these newly identified genes is SRSF2, and its mutations have been linked to poor survival among MDS patients. Interestingly, most of SRSF2 mutations occur at proline 95 and the majority of these mutations change this proline to histidine (P95H). Given that SRSF2 is a well-characterized splicing factor involved in both constitutive and regulated splicing, we hypothesize that SRSF2 plays an important role in normal hematopoiesis and the SRSF2 mutations induce specific changes in alternative splicing that favor disease progression. We first examined the role of SRSF2 in hematopoiesis by generating Srsf2 null mutation in mouse blood cells via crossing conditional Srsf2 knockout mice (Srsf2f/f) with blood cell-specific Cre transgenic mice (Vav-Cre). The mutant mice produced significantly fewer definitive blood cells (10% of wild type controls), exhibited increased apoptosis in the remaining blood cells, and died during embryonic development. Importantly, we detected no hematopoietic stem/progenitor cells (lineage-/cKit+) in E14 fetal livers of Vav-Cre/Srsf2f/f mice. These results indicate that SRSF2 is essential for hematopoiesis during embryonic development. We next examined the role of SRSF2 in adult hematopoiesis by injecting polyIC into mice that carry a polyIC inducible Cre expression unit. Unexpectedly, after multiple polyIC treatments, the Srsf2f/f mice stayed alive during several months of observation. Time course genotyping analyses of polyIC treated mice revealed an increased rate of incomplete Srsf2 deletion in peripheral blood cells. These observations suggest that Srsf2 ablation did not cause immediate cell lethality in differentiated blood cells, but the gene is indispensable for the function of blood stem/progenitor cells. Since mutations of splicing factors are generally heterozygous in MDS patients, we also examined mice with Srsf2+/- blood cells. No obvious defect of hematopoiesis was observed under normal conditions or in response to stress with 5-FU treatment and sublethal irradiation. To gain molecular insight into the splicing activity of MDS-associated mutant forms of SRSF2, we performed large-scale alternative splicing surveys by using RNA-mediated oligonucleotide annealing, selection, and ligation coupled with next-generation sequencing (RASL-seq) previously developed in our lab, which offers a robust and cost-effective platform for splicing profiling. Compared to vector transduction controls, we found that overexpression of both wild type and P95H SRSF2 induced many, but distinct changes in alternative splicing in lineage-negative bone marrow cells, and importantly, we noted several changes in genes with known roles in hematopoietic malignancies that were uniquely induced by the mutant SRSF2. To further link the mutations to altered splicing in MDS patients, we also applied RASL-seq to a large number of MDS patient samples with or without mutations in SRSF2 or other splicing regulators. The data revealed a specific set of alternative splicing events that are commonly linked to MDS with splicing factor mutations. These findings strongly suggest that many of these mutations in splicing regulators are gain-of-function mutations that are causal to MDS. In conclusion, we report that SRSF2 plays an essential role in hematopoietic stem/progenitor cells and that the MDS-associated mutations in SRSF2 have a dominant effect on RNA alternative splicing. These findings provide functional information and molecular basis of SRSF2 and its MDS-related mutations in hematopoiesis and related clinical disorders. Disclosures: No relevant conflicts of interest to declare