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Objective-The H2.0-like homeobox transcription factor (HLX) plays an essential role in visceral organogenesis in mice and has been shown to regulate angiogenic sprouting in vitro and in zebrafish embryos. We therefore examined the role of HLX in vascular development in mouse and avian embryos. Approach and Results-In situ hybridization showed that Hlx is expressed in a subset of sprouting blood vessels in postnatal mouse retinas and embryos. Hlx expression was conserved in quail embryos and upregulated in blood vessels at the onset of circulation. In vitro assays showed that Hlx is dynamically regulated by growth factors and shear stress alterations. Proangiogenic vascular endothelial growth factor induces Hlx expression in cultured endothelial cells, whereas signals that induce stalk cell identity lead to a reduction in Hlx expression. HLX was also downregulated in embryos in which flow was ablated, whereas injection of a starch solution, which increases blood viscosity and therefore shear stress, causes an upregulation in HLX. HLX knockdown in vitro resulted in a reduction in tip cell marker expression and in reduced angiogenic sprouting, but Hlx −/− embryos showed no defect in vascular sprouting at E8.5, E9.5, or E11.5 in vivo. Vascular remodeling of the capillary plexus was altered in Hlx −/− embryos, with a modestly enlarged venous plexus and reduction of the arterial plexus. Conclusions-Our findings indicate not only that Hlx regulates sprouting in vitro, but that its role in sprouting is nonessential in vivo. We find HLX is regulated by shear stress and a subtle defect in vascular remodeling is present in knockout embryos. (Arterioscler Thromb Vasc

The impact of hurricanes is so devastating throughout different levels of society that there is a pressing need to provide a range of users with accurate and timely information that can enable effective planning for and response to potential hurricane landfalls. The Weather Research and Forecasting (WRF) code is the latest numerical model that has been adopted by meteorological services worldwide. The current version of WRF has not been designed to scale out of a single organization's local computing resources. However, the high resource requirements of WRF for fine-resolution and ensemble forecasting demand a large number of computing nodes, which typically cannot be found within one organization. Therefore, there is a pressing need for the Grid-enablement of the WRF code such that it can utilize resources available in partner organizations. In this paper, we present our research on Grid enablement of WRF by leveraging our work in transparent shaping, GRID superscalar, profiling, code inspection, code modeling, meta-scheduling, and job flow management.

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Grid computing supports workload execution on computing resources that are shared across a set of collaborative organizations. At the core of workload management for Grid computing is a software component, called meta-scheduler or Grid resource broker, that provides a virtual layer on top of heterogeneous Grid middleware, schedulers, and resources. Meta-schedulers typically enable end-users and applications to compete over distributed shared resources through the use of one or more instances of the same meta-scheduler, in a centralized or distributed manner, respectively. We propose an approach to enabling autonomic meta-scheduling through the use of a new communication protocol that -if adopted by different meta-schedulers or by the applications using themcan improve the workload execution while avoiding potential chaos, which can be resulted from blind competition over resources. This can be made possible by allowing the metaschedulers and/or their applications to engage in a process to negotiate their roles (e.g., consumer, provider, or both), scheduling policies, service-level agreement, etc. To show the feasibility of our approach, we developed a prototype that enables some preliminary autonomic management among three different meta-schedulers, namely, GridWay, eNANOS, and TDWB.

Abstract. Low upfront costs, rapid deployment of infrastructure and flexible management of resources has resulted in the quick adoption of cloud computing. Nowadays, different types of applications in areas such as enterprise web, virtual labs and high-performance computing are already being deployed in private and public clouds. However, one of the remaining challenges is how to allow users to specify Quality of Service (QoS) requirements for composite groups of virtual machines and enforce them effectively across the deployed resources. In this paper, we propose an Infrastructure as a Service resource manager capable of allocating Distributed Ensembles of Virtual Appliances (DEVAs) in the Cloud. DEVAs are groups of virtual machines and their network connectivities instantiated on heterogeneous shared resources with QoS specifications for individual entities as well as their connections. We discuss the different stages in their lifecycle: declaration, scheduling, provisioning and dynamic management, and show how this approach can be used to maintain QoS for complex deployments of virtual resources.