Inhibition of vascular smooth muscle cell growth by inhibition of fibronectin matrix assembly

The regulation of vascular smooth muscle cell (VSMC) proliferation by the fibronectin matrix was tested by treating human umbilical artery smooth muscle cells (HUASMCs) with a recombinant fragment of fibronectin (protein III1-C) that has previously been shown to modulate fibronectin matrix assembly. III1-C inhibited HUASMC proliferation by 75% to 90%. The inhibition of growth was time dependent; III1-C had no effect on DNA synthesis after 0 to 5 hours of treatment but did have an effect at 24 hours and beyond. III1-C did not stimulate apoptosis in these cells, indicating that the inhibition of proliferation was not due to an induction of programmed cell death. The effects of III1-C on cell growth were only specific for normal diploid smooth muscle cells. III1-C had no effect on the proliferation of IMR-90 fibroblasts, endothelial cells, NIH 3T3 cells, or the rat aortic smooth muscle cell line A7r5. However, III1-C did inhibit proliferation by primary rat aortic smooth muscle cells. An analysis of HUASMC fibronectin receptor (integrin alpha5beta1) distribution revealed that III1-C did not inhibit alpha5beta1 localization to focal contacts. Moreover, III1-C had no effect on the relative expression levels of seven different integrin subunits on HUASMCs. However, III1-C did inhibit fibronectin matrix assembly by rat aortic smooth muscle cells, HUASMCs, A7r5 cells, IMR-90 cells, and endothelial cells. An analysis of fibronectin synthesis indicated that the inhibition of fibronectin matrix assembly by III1-C was not due solely to a decrease in fibronectin synthesis. Finally, treatment of HUASMCs with anti-fibronectin monoclonal antibody L8 (which is known to inhibit fibronectin matrix assembly) also decreased the rate of HUASMC DNA synthesis. These results demonstrate that III1-C inhibits VSMC proliferation and suggest that this effect may be mediated by the inhibition of fibronectin matrix assembly.     

Predicting throughput in IEEE 802.11 based wireless networks using directional antenna

Wireless Networks - In IEEE 802.11 based wireless networks interference increases as more access points are added. A metric helping to quantize this interference seems to be of high interest. In...     

A behavioral reflective architecture for managing the integration of personal ubicomp systems: automatic SNMP-based discovery and management of behavior context in smart-spaces

Context-aware ubiquitous computing systems should be able to introspect the surrounding environment and adapt their behavior according to other existing systems and context changes. Although numerous ubiquitous computing systems have been developed that are aware of different types of context such as location, social situation, and available computational resources, few are aware of their computational behavior. Computational behavior introspection is common in reflective systems and can be used to improve the awareness and autonomy of ubicomp systems. In this paper, we propose a decentralized approach based on Simple Network Management Protocol (SNMP) and Universal Plug and Play (UPnP), and on state transition models to model and expose computational behavior. Typically, SNMP and UPnP are targeted to retrieve raw operational variables from managed network devices and consumer electronic devices, e.g., checking network interface bandwidth and automating device discovery and plug and play operations. We extend the use of these protocols by exposing the state of different ubicomp systems and associated state transitions statistics. This computational behavior may be collected locally or remotely from ubicomp systems that share a physical environment, and sent to a coordinator node or simply shared among ubicomp systems. We describe the implementation of this behavior awareness approach in a home health-care environment equipped with a VoIP Phone and a drug dispenser. We provide the means for exposing and using the behavior context in managing a simple home health-care setting. Our approach relies on a system state specification being provided by manufacturers. In the case where the specification is not provided, we show how it can be automatically discovered. We propose two machine learning approaches for automatic behavior discovery and evaluate them by determining the expected state graphs of our two systems (a VoIP Phone and a drug dispenser). These two approaches are also evaluated regarding the effectiveness of generated behavior graphs.     

In vitro activity of biapenem against beta-lactamase producing Enterobacteriaceae

The activity of biapenem was compared with that of imipenem and cefotaxime against 108 strains of beta-lactamase producing Enterobacteriaceae. Biapenem and imipenem were very active, inhibiting 90% of the strains at a concentration of 0.5 microgram/ml. Both carbapenems were very active against plasmidic beta-lactamase producers, with MIC90s below 1 microgram/ml. However, the MIC90 of biapenem for cephalosporinase producers was 1 microgram/ml. Against strains producing extended-spectrum beta-lactamases, biapenem exhibited better activity against TEM-type producers (MIC90 0.25 microgram/ml) than against SHV-type producers (MIC90 0.5 microgram/ml). Overall, the in vitro antibacterial activity of biapenem is similar to that of imipenem.     

Hidden Markov models on a self-organizing map for anomaly detection in 802.11 wireless networks

Neural Computing and Applications - The present work introduces a hybrid integration of the self-organizing map and the hidden Markov model (HMM) for anomaly detection in 802.11 wireless networks....     

Evaluating a location-based application: a hybrid test and simulation environment

Complex and expensive testing and evaluation can inhibit cost-effective development of ubiquitous computing applications. A new environment for testing and evaluating system- and network-related issues in location-based applications strives to evaluate these applications prior to deployment. We discuss a health-monitoring application and its simulation environment.     

Modeling 802.11 AP usage through daily keep-alive event counts

Wireless and in particular 802.11 is one of the major technologies for accessing the Internet at home, in coffee shops, enterprises, university campuses, and other public places. While most recent works on modeling wireless sites focuses on user mobility and user residing time, this paper presents and compares a number of models for characterizing access point (AP) usage including time-dependent models that considers week structure usage. Moreover, rather than looking at throughput we focus on daily counts of keep-alive events that mobile devices generate every 15 min while they are connected to the wireless network. We model both daily event counts and above–below AP event counts average binary indicator. Our models are trained and evaluated on data collected from Porto hotspot of Eduroam, the European academic wireless network. The models we present are generative, in the sense they can be used to generate synthetic daily event counts for a single AP or a collection of APs. We provide standard cross-validation comparison of models using the log-likelihood of the models on training and test data. We conclude that significant improvements in AP usage modeling capability can be observed by considering (1) simple time dependency (2) week-days/week-ends usage structure and (3) individual day’s usage; whereas extending the complexity of time dependency ordering of AP’s usage samples does not show significant improvements for daily event count models.