Do Agglomeration Economies Exist in the Hospital Services Industry

Given the importance of knowledge flows and the continued emphasis on face-to-face encounters especially for medical care, close proximity of hospitals may be essential for the efficient delivery of medical care. That is, hospital productivity might be greater where hospitals cluster and allow knowledge to more easily and quickly disperse among personnel in the various organizations. To add to the understanding about agglomeration economies in the hospital services sector, this study analyzes how the clustering of hospitals in the various metropolitan areas of the US affects industry wide productivity. The multiple regression analysis is conducted on a cross-sectional basis for both 1993 and 1999 and by using first differencing of the data between the two years. The observed productivity improvements resulting from the clustering of hospitals provides yet another justification for encouraging a larger number of hospitals in metropolitan areas.

Fibbing in action: On-demand load-balancing for better video delivery

Video streaming, in conjunction with social networks, have given birth to a new traffic pattern over the Internet: transient, localized traffic surges, known as flash crowds. Traditional traffic-engineering methods can hardly cope with these surges, as they are unpredictable by nature. Consequently, networks either have to be overprovisioned, which is expensive and wastes resources, or risk to periodically incur congestion, which infuriates customers. This demonstration shows how Fibbing can improve network performance and preserve users' quality of experience when accessing video streams, by implementing a fine-grained load-balancing service. This service leverages two unique features of Fibbing: programming per destination load-balancing and implementing uneven splitting ratios

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case $R^2=0.86$) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.Comment: 12 page

Traffic engineering with traditional IP routing protocols

peer reviewedTraffic engineering involves adapting the routing of traffic to network conditions, with the joint goals of good user performance and efficient use of network resources. We describe an approach to intradomain traffic engineering that works within the existing deployed base of interior gateway protocols, such as Open Shortest Path First and Intermediate System-Intermediate System. We explain how to adapt the configuration of link weights, based on a networkwide view of the traffic and topology within a domain. In addition, we summarize the results of several studies of techniques for optimizing OSPF/IS-IS weights to the prevailing traffic. The article argues that traditional shortest path routing protocols are surprisingly effective for engineering the flow of traffic in large IP networks

Frenetic: A High-Level Language for OpenFlow Networks

Network administrators must configure network devices to simultaneously provide several interrelated services such as routing, load balancing, traffic monitoring, and access control. Unfortunately, most interfaces for programming networks are defined at the low level of abstraction supported by the underlying hardware, leading to complicated programs with subtle bugs. We present Frenetic, a high-level language for OpenFlow networks that enables writing programs in a declarative and compositional style, with a simple "program like you see every packet" abstraction. Building on ideas from functional programming, Frenetic offers a rich pattern algebra for classifying packets into traffic streams and a suite of operators for transforming streams. The run-time system efficiently manages the low-level details of (un)installing packet-processing rules in the switches. We describe the design of Frenetic, an implementation on top of OpenFlow, and experiments and example programs that validate our design choices.Office of Naval Research grant N00014-09-1-0770 "Networks Opposing Botnets

Increased mTOR activity and metabolic efficiency in mouse and human cells containing the African-centric tumor-predisposing p53 variant Pro47Ser

The Pro47Ser variant of p53 (S47) exists in African-descent populations and is associated with increased cancer risk in humans and mice. Due to impaired repression of the cystine importe

The ICCAM platform study: An experimental medicine platform for evaluating new drugs for relapse prevention in addiction. Part B: fMRI description.

OBJECTIVES: We aimed to set up a robust multi-centre clinical fMRI and neuropsychological platform to investigate the neuropharmacology of brain processes relevant to addiction - reward, impulsivity and emotional reactivity. Here we provide an overview of the fMRI battery, carried out across three centres, characterizing neuronal response to the tasks, along with exploring inter-centre differences in healthy participants. EXPERIMENTAL DESIGN: Three fMRI tasks were used: monetary incentive delay to probe reward sensitivity, go/no-go to probe impulsivity and an evocative images task to probe emotional reactivity. A coordinate-based activation likelihood estimation (ALE) meta-analysis was carried out for the reward and impulsivity tasks to help establish region of interest (ROI) placement. A group of healthy participants was recruited from across three centres (total n=43) to investigate inter-centre differences. Principle observations: The pattern of response observed for each of the three tasks was consistent with previous studies using similar paradigms. At the whole brain level, significant differences were not observed between centres for any task. CONCLUSIONS: In developing this platform we successfully integrated neuroimaging data from three centres, adapted validated tasks and applied whole brain and ROI approaches to explore and demonstrate their consistency across centres.Medical Research Council (Grant ID: G1000018), GlaxoSmithKlineThis is the author accepted manuscript. The final version is available from SAGE Publications via http://dx.doi.org/10.1177/026988111666859

Squalamine: An Appropriate Strategy against the Emergence of Multidrug Resistant Gram-Negative Bacteria?

We reported that squalamine is a membrane-active molecule that targets the membrane integrity as demonstrated by the ATP release and dye entry. In this context, its activity may depend on the membrane lipid composition. This molecule shows a preserved activity against bacterial pathogens presenting a noticeable multi-resistance phenotype against antibiotics such as polymyxin B. In this context and because of its structure, action and its relative insensitivity to efflux resistance mechanisms, we have demonstrated that squalamine appears as an alternate way to combat MDR pathogens and by pass the gap regarding the failure of new active antibacterial molecules