Rationale: Respiratory syncytial virus (RSV) and Streptococcus pneumoniae are major respiratory pathogens. Coinfection with RSV and S. pneumoniae is associated with severe and often fatal pneumonia but the molecular basis for this remains unclear.Objectives: To determine if interaction between RSV and pneumococci enhances pneumococcal virulence.Methods: We used confocal microscopy and Western blot to identify the receptors involved in direct binding of RSV and pneumococci, the effects of which were studied in both in vivo and in vitro models of infection. Human ciliated respiratory epithelial cell cultures were infected with RSV for 72 hours and then challenged with pneumococci. Pneumococci were collected after 2 hours exposure and changes in gene expression determined using quantitative real-time polymerase chain reaction.Measurements and Main Results: Following incubation with RSV or purified G protein, pneumococci demonstrated a significant increase in the inflammatory response and bacterial adherence to human ciliated epithelial cultures and markedly increased virulence in a pneumonia model in mice. This was associated with extensive changes in the pneumococcal transcriptome and significant up-regulation in the expression of key pneumococcal virulence genes, including the gene for the pneumococcal toxin, pneumolysin. We show that mechanistically this is caused by RSV G glycoprotein binding penicillin binding protein 1a.Conclusions: The direct interaction between a respiratory virus protein and the pneumococcus resulting in increased bacterial virulence and worsening disease outcome is a new paradigm in respiratory infection.Keywords: respiratory syncytial virus; pneumococcus; cilia; virulence; G protein Respiratory syncytial virus (RSV) and Streptococcus pneumoniae, also known as the pneumococcus, are major respiratory pathogens causing a huge global healthcare burden, predominantly in young children and the elderly (1). Global RSV disease burden is estimated at 64 million cases and 160,000 deaths every year (2). Pneumococcal pneumonia results in more than 1 million deaths each year worldwide with approximately half a million in children younger than 5 years (3). There is increasing evidence that RSV and the pneumococcus interact to increase the severity of respiratory disease (4-6). Seasonal increases in infections This article has an online supplement, which is accessible from this issue's table of contents at www.atsjournals.org This article has embedded videos, which play in place from both the online PDF or HTML versions. If you cannot view Flash videos on your device, please try playing the videos in the online PDF, or access the original uncompressed videos at http://www.atsjournals.org/doi/suppl/10.1164/rccm.201311-2110OC. To play, mouse over the image and click on the arrow that will appear in the center or on the lower left.

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This paper presents a risk management matrix for effective planning of industrial projects. A schematic view of the project environment has been suggested to systematically identify the effects of various project environments, and thereby take a holistic view of risks arising out of immediate and external environments. Various subfactors within each risk element are presented. A process to quantify the immediate and external risks for industrial projects using a weighted probability method is proposed. To derive a useful interpretation, a risk management matrix is also proposed. The results of empirical validation of the model in two recently concluded large industrial projects are also presented. The risk management matrix model can be used to identify a number of policy alternatives at the planning stage.

The Springer Series on Bio-and Neurosystems publishes fundamental principles and state-of-the-art research at the intersection of biology, neuroscience, information processing and the engineering sciences. The series covers general informatics methods and techniques, together with their use to answer biological or medical questions. Of interest are both basics and new developments on traditional methods such as machine learning, artificial neural networks, statistical methods, nonlinear dynamics, information processing methods, and image and signal processing. New findings in biology and neuroscience obtained through informatics and engineering methods, topics in systems biology, medicine, neuroscience and ecology, as well as engineering applications such as robotic rehabilitation, health information technologies, and many more, are also examined. The main target group includes informaticians and engineers interested in biology, neuroscience and medicine, as well as biologists and neuroscientists using computational and engineering tools. Volumes published in the series include monographs, edited volumes, and selected conference proceedings. Books purposely devoted to supporting education at the graduate and post-graduate levels in bio-and neuroinformatics, computational biology and neuroscience, systems biology, systems neuroscience and other related areas are of particular interest.

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