156 research outputs found
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Neutralization of chemokines RANTES and MIG increases virus antigen expression and spinal cord pathology during Theiler's virus infection.
The role of chemokines during some viral infections is unpredictable because the inflammatory response regulated by these molecules can have two, contrasting effects-viral immunity and immunopathologic injury to host tissues. Using Theiler's virus infection of SJL mice as a model of this type of disease, we have investigated the roles of two chemokines-regulated on activation, normal T cell-expressed and secreted (RANTES) chemokine and monokine induced by IFN-gamma (MIG)-by treating mice with antisera that block lymphocyte migration. Control, infected mice showed virus persistence, mild inflammation and a small degree of demyelination in the white matter of the spinal cord at 6 weeks post-infection. Treatment of mice with RANTES antiserum starting at 2 weeks post-infection increased both viral antigen expression and the severity of inflammatory demyelination at 6 weeks post-infection. MIG antiserum increased the spread of virus and the proportion of spinal cord white matter with demyelination. Overall, viral antigen levels correlated strongly with the extent of pathology. At the RNA level, high virus expression was associated with low IL-2 and high IL-10 levels, and RANTES antiserum decreased the IL-2/IL-10 ratio. Our results suggest that RANTES and MIG participate in an immune response that attempts to restrict viral expression while limiting immunopathology and that anti-chemokine treatment poses the risk of exacerbating both conditions in the long term
Continuum modeling of size-segregation and flow in dense, bidisperse granular media: Accounting for segregation driven by both pressure gradients and shear-strain-rate gradients
Dense mixtures of particles of varying size tend to segregate based on size
during flow. Granular size-segregation plays an important role in many
industrial and geophysical processes, but the development of coupled, continuum
models capable of predicting the evolution of segregation dynamics and flow
fields in dense granular media across different geometries has remained a
longstanding challenge. One reason is because size-segregation stems from two
driving forces: (1) pressure gradients and (2) shear-strain-rate gradients.
Another reason is due to the challenge of integrating segregation models with
rheological constitutive equations for dense granular flow. In this paper, we
build upon our prior work, which combined a model for
shear-strain-rate-gradient-driven segregation with a nonlocal continuum model
for dense granular flow rheology, and append a model for
pressure-gradient-driven segregation. We perform discrete element method (DEM)
simulations of dense flow of bidisperse granular systems in two flow
geometries, in which both segregation driving forces are present: namely,
inclined plane flow and planar shear flow with gravity. Steady-state DEM data
from inclined plane flow is used to determine the dimensionless material
parameters in the pressure-gradient-driven segregation model for both spheres
and disks. Then, predictions of the coupled, continuum model accounting for
both driving forces are tested against DEM simulation results across different
cases of both inclined plane flow and planar shear flow with gravity, while
varying parameters such as the size of the flow geometry, the driving
conditions of flow, and the initial conditions. Overall, we find that it is
crucial to account for both driving forces to capture segregation dynamics in
dense, bidisperse granular media across both flow geometries with a single set
of parameters.Comment: 25 pages with 9 figure
OCSO: Off-the-cloud service optimization for green efficient service resource utilization
Many efforts have been made in optimizing cloud service resource management for efficient service provision and delivery, yet little research addresses how to consume the provisioned service resources efficiently. Meanwhile, typical existing resource scaling management approaches often rest on single monitor category statistics and are driven by certain threshold algorithms, they usually fail to function effectively in case of dealing with complicated and unpredictable workload patterns. Fundamentally, this is due to the inflexibility of using static monitor, threshold and scaling parameters. This paper presents Off-the-Cloud Service Optimization (OCSO), a novel user-side optimization solution which specifically deals with service resource consumption efficiency from the service consumer perspective. OCSO rests on an intelligent resource scaling algorithm which relies on multiple service monitor metrics plus dynamic threshold and scaling parameters. It can achieve proactive and continuous service optimizations for both real-world IaaS and PaaS services, through OCSO cloud service API. From the two series of experiments conducted over Amazon EC2 and ElasticBeanstalk using OCSO prototype, it is demonstrated that the proposed approach can make significant improvement over Amazon native automated service provision and scaling options, regardless of scaling up/down or in/out
Launching Return-Oriented Programming Attacks against Randomized Relocatable Executables
Abstract—Since the day it was proposed, return-oriented programming has shown to be an effective and powerful attack technique against the write or execute only (W ⊕ X) protection. However, a general belief in the previous research is, systems deployed with address space randomization where the executables are also randomized at run-time are able to defend against return-oriented programming, as the addresses of all instructions are randomized. In this paper, we show that due to the weakness of current address space randomization technique, there are still ways of launching return-oriented programming attacks against those well-protected systems efficiently. We demonstrate and evaluate our attacks with existing typical web server applications and discuss possible methods of mitigating such threats. Keywords-return-oriented programming; address space randomization; position independent executable; I
An approach to unified cloud service access, manipulation and dynamic orchestration via semantic cloud service operation specification framework
Cloud computing offers various computational resources via convenient on-demand service provision. Currently, heterogeneous services and cloud resources are usually utilized and managed through diverse service portals. This significantly limits the effectiveness and efficiency for tasks implementation. Fundamentally, it is due to the lack of adequate specifications for service concepts, operations and interfaces from diverse cloud service models and types. This paper proposes a service management operation semantic description framework for comprehensive cloud service operation specification. Relying on ontological modelling techniques, cloud service operations are specified via entity classification, attribute assertion, relationship assertion and annotation assertion. Further, the proposed framework benefits from operation reasoning application. It enables intelligent assistances for multiple operation preparation and remote execution tasks. Based on the approach, a cloud service operation ontology and a unified service access and manipulation system prototype are implemented. Extensive experiments are conducted over different cloud service providers and for distinct service models. Obtained results demonstrate that the approach outperforms existing practices by facilitating reliable and effective service access, manipulation and interaction tasks
An agility-oriented and fuzziness-embedded semantic model for collaborative cloud service search, retrieval and recommendation
Cloud computing enables a revolutionary paradigm of consuming ICT services. However, due to the inadequately described service information, users often feel confused while trying to find the optimal services. Although some approaches are proposed to deal with cloud service semantic modelling and recommendation issues, they would only work for certain restricted scenarios in dealing with basic service specifications. Indeed, the missing extent is that most cloud services are "agile" whilst there are many vague service terms and descriptions. This paper proposes an agility-oriented and fuzziness-embedded ontology model, which adopts agility-centric design along with OWL2 (Web Ontology Language) fuzzy extensions. The captured cloud service specifications are maintained in an open and collaborative manner, as the fuzziness in the model accepts rating updates from users on the fly. The model enables comprehensive service specification by capturing cloud concept details and their interactions, even across multiple service categories and abstraction levels. Utilizing the model as a knowledge base, a service recommendation system prototype is developed. Case studies demonstrate that the approach can outperform existing practices by achieving effective service search, retrieval and recommendation outcomes
A Classification and Comparison Framework for Cloud Service Brokerage Architectures
Cloud service brokerage and related management and marketplace concepts have been identified as key concerns for future cloud technology development and research. Cloud service management is an important building block of cloud architectures that can be extended to act as a broker service layer between consumers and providers, and even to form marketplace services. We present a 3-pronged classification and comparison framework for broker platforms and applications. A range of specific broker development concerns like architecture, programming and quality are investigated. Based on this framework, selected management, brokerage and marketplace solutions will be compared, not only to demonstrate the utility of the framework, but also to identify challenges and wider research objectives based on an identification of cloud broker architecture concerns and technical requirements for service brokerage solutions. Cloud architecture concerns such as commoditisation and federation of integrated, vertical cloud stacks emerge
Numerical investigation on shock train control and applications in a scramjet engine
Different factors which help to control the shock train in the scramjet isolator and combustor were analyzed via numerical investigations, and were applied to a whole scramjet engine in the working environment. A streamline traced Busemann inlet is proposed and simulated along with an isolator. During the combustor design, the influence of boundary layer thickness, slot bleeding, cavity and hydrogen injection position on the basic combustor performance with uniform inlet flow condition are investigated, and it was found that the boundary layer bleeding could prevent the shock train from moving upstream, and the cavity could further enhance the combustion efficiency. By arranging hydrogen injections at certain intervals, it could reduce the combustion back pressure. An improved basic model by integrating the aforementioned advantages is then numerically studied. The results have shown that the improved combustor model contained a section of shock train which can reduce the loads on the isolator. Another model with bleeding slots in the isolator is also found able to raise the maximum chemical equivalence ratio from 0.7 to 1, but unfortunately it comes with undesirable combustion efficiency decrease
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