964 resultados para Classes of Analytic Functions


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A chapter based on a paper given at major conference, arguing that the civic and architectural contexts of many public libraries in the Roman world contributed strongly to their status as conspicuous 'public' buildings, and should inform the way we think of library functions in the Roman world

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A full assessment of para-­virtualization is important, because without knowledge about the various overheads, users can not understand whether using virtualization is a good idea or not. In this paper we are very interested in assessing the overheads of running various benchmarks on bare-­‐metal, as well as on para-­‐virtualization. The idea is to see what the overheads of para-­‐ virtualization are, as well as looking at the overheads of turning on monitoring and logging. The knowledge from assessing various benchmarks on these different systems will help a range of users understand the use of virtualization systems. In this paper we assess the overheads of using Xen, VMware, KVM and Citrix, see Table 1. These different virtualization systems are used extensively by cloud-­‐users. We are using various Netlib1 benchmarks, which have been developed by the University of Tennessee at Knoxville (UTK), and Oak Ridge National Laboratory (ORNL). In order to assess these virtualization systems, we run the benchmarks on bare-­‐metal, then on the para-­‐virtualization, and finally we turn on monitoring and logging. The later is important as users are interested in Service Level Agreements (SLAs) used by the Cloud providers, and the use of logging is a means of assessing the services bought and used from commercial providers. In this paper we assess the virtualization systems on three different systems. We use the Thamesblue supercomputer, the Hactar cluster and IBM JS20 blade server (see Table 2), which are all servers available at the University of Reading. A functional virtualization system is multi-­‐layered and is driven by the privileged components. Virtualization systems can host multiple guest operating systems, which run on its own domain, and the system schedules virtual CPUs and memory within each Virtual Machines (VM) to make the best use of the available resources. The guest-­‐operating system schedules each application accordingly. You can deploy virtualization as full virtualization or para-­‐virtualization. Full virtualization provides a total abstraction of the underlying physical system and creates a new virtual system, where the guest operating systems can run. No modifications are needed in the guest OS or application, e.g. the guest OS or application is not aware of the virtualized environment and runs normally. Para-­‐virualization requires user modification of the guest operating systems, which runs on the virtual machines, e.g. these guest operating systems are aware that they are running on a virtual machine, and provide near-­‐native performance. You can deploy both para-­‐virtualization and full virtualization across various virtualized systems. Para-­‐virtualization is an OS-­‐assisted virtualization; where some modifications are made in the guest operating system to enable better performance. In this kind of virtualization, the guest operating system is aware of the fact that it is running on the virtualized hardware and not on the bare hardware. In para-­‐virtualization, the device drivers in the guest operating system coordinate the device drivers of host operating system and reduce the performance overheads. The use of para-­‐virtualization [0] is intended to avoid the bottleneck associated with slow hardware interrupts that exist when full virtualization is employed. It has revealed [0] that para-­‐ virtualization does not impose significant performance overhead in high performance computing, and this in turn this has implications for the use of cloud computing for hosting HPC applications. The “apparent” improvement in virtualization has led us to formulate the hypothesis that certain classes of HPC applications should be able to execute in a cloud environment, with minimal performance degradation. In order to support this hypothesis, first it is necessary to define exactly what is meant by a “class” of application, and secondly it will be necessary to observe application performance, both within a virtual machine and when executing on bare hardware. A further potential complication is associated with the need for Cloud service providers to support Service Level Agreements (SLA), so that system utilisation can be audited.

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P-glycoproteins (p-gps) are ubiquitous membrane proteins from the ABC (ATP-binding cassette) family. They have been found in many animals, bacteria, plants and fungi and are extremely important in regulating a wide range of xenobiotics including pesticides. P-gps have been linked to xenobiotic resistance, most famously in resistance to cancer drug treatments. Their wide substrate range has led to what is known as "multidrug resistance", where resistance developed to one type of xenobiotic gives resistance to a different classes of xenobiotic. P-gps are a major contributor to drug resistance in mammalian tumours and infections of protozoan parasites such as Plasmodium and Leishmania. There is a growing body of literature suggesting that p-gps, and other ABC proteins, are important in regulating pesticide toxicity and represent potential control failure through the development of pesticide resistance, in both agricultural and medical pests. At the same time, aspects of their biochemistry offer new hope in pest control, in particular in furthering our understanding of toxicity and offering insights into how we can improve control without recourse to new chemical discovery. (c) 2008 Elsevier Inc. All rights reserved.

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Iron oxidation in the bacterial ferritin EcFtnA from Escherichia coli shows marked differences from its homologue human H-chain ferritin (HuHF). While the amino acid residues that constitute the dinuclear center in these proteins are highly conserved, EcFtnA has a third iron-binding site (C site) in close proximity to the dinuclear center that is seemingly responsible for these differences. Here, we describe the first thermodynamic study of Fe2+ binding to EcFtnA and its variants to determine the location of the primary ferrous ion-binding sites on the protein and to better understand the role of the third C site in iron binding. Isothermal titration calorimetric analyses of the wild-type protein reveal the presence of two main classes of binding sites in the pH range of 6.5-7.5, ascribed to Fe2+ binding, first at the A and then the B sites. Site-directed mutagenesis of ligands in the A, B, or C sites affects the apparent Fe2+-binding stoichiometries at the unaltered sites. The data imply some degree of inter- and intrasubunit negative cooperative interaction between sites. Unlike HuHF where only the A site initially binds Fe2+, both A and B sites in EcFtnA bind Fe2+, implying a role for the C site in influencing the binding of Fe2+ at the B site of the di-iron center of EcFtnA. The ITC equations describing a binding model for three classes of independent binding sites are reported here for the first time.