Survivin a radiogenetic promoter for glioblastoma viral gene therapy independently from CArG motifs

BACKGROUND: Radiogenetic therapy is a novel approach in the treatment of cancer, which employs genetic modification to alter the sensitivity of tumor cells to the effect of applied radiation. AIM: To select a potent radiation inducible promoter in the context of brain tumors and to investigate if CArG radio responsive motifs or other elements in the promoter nucleotide sequences can correlate to its response to radiation. METHODS: To select initial candidates for promoter inducible elements, the levels of mRNA expression of six different promoters were assessed using Quantitative RTPCR in D54 MG cells before and after radiation exposure. Recombinant Ad/reporter genes driven by five different promoters; CMV, VEGF, FLT-1, DR5 and survivin were constructed. Glioma cell lines were infected with different multiplicity of infection of the (promoter) Ad or CMV Ad. Cells were then exposed to a range of radiation (0–12 Gy) at single fraction. Fluorescent microscopy, Luc assay and X-gal staining was used to detect the level of expression of related genes. Different glioma cell lines and normal astrocytes were infected with Ad survivin and exposed to radiation. The promoters were analyzed for presence of CArG radio-responsive motifs and CCAAT box consensus using NCBI blast bioinformatics software. RESULTS: Radiotherapy increases the expression of gene expression by 1.25–2.5 fold in different promoters other than survivin after 2 h of radiation. RNA analysis was done and has shown an increase in copy number of tenfold for survivin. Most importantly cells treated with RT and Ad Luc driven by survivin promoter showed a fivefold increase in expression after 2 Gy of radiation in comparison to non-irradiated cells. Presence or absence of CArG motifs did not correlate with promoter response to radiation. Survivin with the best response to radiation had the lowest number of CCAAT box. CONCLUSION: Survivin is a selective potent radiation inducible promoter for glioblastoma viral gene therapy and this response to radiation could be independent of CArG motifs

An Analysis of Performance Interference Effects on Energy-Efficiency of Virtualized Cloud Environments

Co-allocated workloads in a virtualized computing environment often have to compete for resources, thereby suffering from performance interference. While this phenomenon has a direct impact on the Quality of Service provided to customers, it also changes the patterns of resource utilization and reduces the amount of work per Watt consumed. Unfortunately, there has been only limited research into how performance interference affects energy-efficiency of servers in such environments. In reality, there is a highly dynamic and complicated correlation among resource utilization, performance interference and energy-efficiency. This paper presents a comprehensive analysis that quantifies the negative impact of performance interference on the energy-efficiency of virtualized servers. Our analysis methodology takes into account the heterogeneous workload characteristics identified from a real Cloud environment. In particular, we investigate the impact due to different workload type combinations and develop a method for approximating the levels of performance interference and energy-efficiency degradation. The proposed method is based on profiles of pair combinations of existing workload types and the patterns derived from the analysis. Our experimental results reveal a non-linear relationship between the increase in interference and the reduction in energy-efficiency as well as an average precision within +/-5% of error margin for the estimation of both parameters. These findings provide vital information for research into dynamic trade-offs between resource utilization, performance, and energy-efficiency of a data center

Cider: a Rapid Docker Container Deployment System through Sharing Network Storage

Container technology has been prevalent and widely-adopted in production environment considering the huge benefits to application packing, deploying and management. However, the deployment process is relatively slow by using conventional approaches. In large-scale concurrent deployments, resource contentions on the central image repository would aggravate such situation. In fact, it is observable that the image pulling operation is mainly responsible for the degraded performance. To this end, we propose Cider - a novel deployment system to enable rapid container deployment in a high concurrent and scalable manner at scale. Firstly, on-demand image data loading is proposed by altering the local Docker storage of worker nodes into all-nodes-sharing network storage. Also, the local copy-on-write layer for containers can ensure Cider to achieve the scalability whilst improving the cost-effectiveness during the holistic deployment. Experimental results reveal that Cider can shorten the overall deployment time by 85% and 62% on average when deploying one container and 100 concurrent containers respectively

Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement

Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments — along with management inefficiencies — often creates scenarios of high-competition for resources between running workloads, leading to performance degradation. This phenomenon is known as Performance Interference, and introduces a non-negligible overhead that affects both a datacenter's Quality of Service and its energy-efficiency. This paper introduces a novel approach to workload allocation that improves energy-efficiency in Cloud datacenters by taking into account their workload heterogeneity. We analyze the impact of performance interference on energy-efficiency using workload characteristics identified from a real Cloud environment, and develop a model that implements various decision-making techniques intelligently to select the best workload host according to its internal interference level. Our experimental results show reductions in interference by 27.5% and increased energy-efficiency up to 15% in contrast to current mechanisms for workload allocation

D^2PS: A Dependable Data Provisioning Service in Multi-tenant Cloud Environment

Software as a Service (SaaS) is a software delivery and business model widely used by Cloud computing. Instead of purchasing and maintaining a software suite permanently, customers only need to lease the software on-demand. The domain of high assurance distributed systems has focused greatly on the areas of fault tolerance and dependability. In a multi-tenant context, it is particularly important to store, manage and provision data services to customers in a highly efficient and dependable manner due to a large number of file operations involved in running such services. It is also desirable to allow a user group to share and cooperate (e.g., co-edit) on some specific data. In this paper we present a dependable data provisioning service in a multi-tenant Cloud environment. We describe a metadata management approach and leverage multiple replicated metadata caching to shorten the file access time, with the improved efficiency of data sharing. In order to reduce frequent data transmission and data access latency, we introduce a distributed cooperative disk cache mechanism that supports effective cache placement and pull-push cache synchronization. In addition, we use efficient component failover to enhance the service dependability whilst avoiding negative impact from system failures. Our experimental results show that our system can significantly reduce both unused data transmission and response latency. Specifically, over 50% network transmission and operational latency can be saved for random reads while 28.24% network traffic and 25% response latency can be reduced for random write operations. We believe that these findings are demonstrating positive results along the right direction of resolving storage-related challenges in a multi-tenant Cloud environment

Spatial variation in boundary conditions can govern selection and location of eyespots in butterfly wings

Despite being the subject of widespread study, many aspects of the development of eyespot patterns in butterfly wings remain poorly understood. In this work, we examine, through numerical simulations, a mathematical model for eyespot focus point formation in which a reaction-diffusion system is assumed to play the role of the patterning mechanism. In the model, changes in the boundary conditions at the veins at the proximal boundary alone are capable of determining whether or not an eyespot focus forms in a given wing cell and the eventual position of focus points within the wing cell. Furthermore, an auxiliary surface reaction diffusion system posed along the entire proximal boundary of the wing cells is proposed as the mechanism that generates the necessary changes in the proximal boundary profiles. In order to illustrate the robustness of the model, we perform simulations on a curved wing geometry that is somewhat closer to a biological realistic domain than the rectangular wing cells previously considered, and we also illustrate the ability of the model to reproduce experimental results on artificial selection of eyespots.Publisher PD

Intra- and interspecific polymorphisms ofLeishmania donovani andL. tropica minicircle DNA

A pair of degenerate polymerase chain reaction (PCR) primers (LEI-1, TCG GAT CC[C,T] [G,C]TG GGT AGG GGC GT; LEI-2, ACG GAT CC[G,C] [G,C][A,C]C TAT [A,T]TT ACA CC) defining a 0.15-kb segment ofLeishmania minicircle DNA was constructed. These primers amplified not only inter- but also intraspecifically polymorphic sequences. Individual sequences revealed a higher intraspecific than interspecific divergence. It is concluded that individual sequences are of limited relevance for species determination. In contrast, when a data base of 19 different sequences was analyzed in a dendrographic plot, an accurate species differentiation was feasible

Perphon: a ML-based Agent for Workload Co-location via Performance Prediction and Resource Inference

Cluster administrators are facing great pressures to improve cluster utilization through workload co-location. Guaranteeing performance of long-running applications (LRAs), however, is far from settled as unpredictable interference across applications is catastrophic to QoS [2]. Current solutions such as [1] usually employ sandboxed and offline profiling for different workload combinations and leverage them to predict incoming interference. However, the time complexity restricts the applicability to complex co-locations. Hence, this issue entails a new framework to harness runtime performance and mitigate the time cost with machine intelligence: i) It is desirable to explore a quantitative relationship between allocated resource and consequent workload performance, not relying on analyzing interference derived from different workload combinations. The majority of works, however, depend on offline profiling and training which may lead to model aging problem. Moreover, multi-resource dimensions (e.g., LLC contention) that are not completely included by existing works but have impact on performance interference need to be considered [3]. ii) Workload co-location also necessitates fine-grained isolation and access control mechanism. Once performance degradation is detected, dynamic resource adjustment will be enforced and application will be assigned an access to specific slices of each resources. Inferring a "just enough" amount of resource adjustment ensures the application performance can be secured whilst improving cluster utilization. We present Perphon, a runtime agent on a per node basis, that decouples ML-based performance prediction and resource inference from centralized scheduler. Figure 1 outlines the proposed architecture. We initially exploit sensitivity of applications to multi-resources to establish performance prediction. To achieve this, Metric Monitor aggregates application fingerprint and system-level performance metrics including CPU, memory, Last Level Cache (LLC), memory bandwidth (MBW) and number of running threads, etc. They are enabled by Intel-RDT and precisely obtained from resource group manager. Perphon employs an Online Gradient Boost Regression Tree (OGBRT) approach to resolve model aging problem. Res-Perf Model warms up via offline learning that merely relies on a small volume of profiling in the early stage, but evolves with arrival of workloads. Consequently, parameters will be automatically updated and synchronized among agents. Anomaly Detector can timely pinpoint a performance degradation via LSTM time-series analysis and determine when and which application need to be re-allocated resources. Once abnormal performance counter or load is detected, Resource Inferer conducts a gradient ascend based inference to work out a proper slice of resources, towards dynamically recovering targeted performance. Upon receiving an updated re-allocation, Access Controller re-assigns a specific portion of the node resources to the affected application. Eventually, Isolation Executor enforces resource manipulation and ensures performance isolation across applications. Specifically, we use cgroup cpuset and memory subsystem to control usage of CPU and memory while leveraging Intel-RDT technology to underpin the manipulation of LLC and MBW. For fine-granularity management, we create different groups for LRA and batch jobs when the agent starts. Our prototype integration with Node Manager of Apache YARN shows that throughput of Kafka data-streaming application in Perphon is 2.0x and 1.82x times that of isolation execution schemes in native YARN and pure cgroup cpu subsystem

In vivo terahertz imaging to evaluate scar treatment strategies : silicone gel sheeting

Silicone gel sheeting (SGS) is widely used for scar treatment; however, studies showing its interaction with skin and efficacy of scar treatment are still lacking. THz light is non-ionizing and highly sensitive to changes in water content and thus skin hydration. In this work, we use in-vivo THz imaging to monitor how SGS affects the THz response of human skin during occlusion, and the associated THz reflectivity and refractive index changes are presented. We find that SGS effectively hydrates the skin beneath it, with minimal lateral effects beyond the sheeting. Our work demonstrates that THz imaging is able to detect the subtle hydration changes on the surface of human skin caused by SGS, and it has the potential to be used to evaluate different scar treatment strategies