This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Committee on the Ethics of Animal Experiments of the University of California Berkeley (Permit Number: R292-0507). All animals were anesthetized prior to infection with isoflurane, and all efforts were made to minimize suffering.  A modified MS2 bacteriophage plaque reduction assay for the rapid screening of antiviral plant extracts INTRODUCTION: Traditional methods of screening plant extracts and purified components for antiviral activity require up to a week to perform, prompting the need to develop more rapid quantitative methods to measure the ability of plant based preparations to block viral replication. We describe an adaption of an MS2 plaque reduction assay for use in S. aureus. RESULTS: MS2 bacteriophage was capable of infecting and replicating in B. cereus, S. aureus and F + E. coli but not F- E. coli. Indeed, both B. cereus and S. aureus were more sensitive to MS2 induced lysis than F+ E. coli. When MS2 bacteriophage was mixed with Camellia sinensis extract (1 mg/ml), Scaevola spinescens extract (1 mg/ml) or Aloe barbadensis juice and the mixtures inoculated into S. aureus, the formation of plaques was reduced to 8.9 ± 3.8%, 5.4 ± 2.4% and 72.7 ± 20.9% of the untreated MS2 control values respectively. CONCLUSIONS: The ability of the MS2 plaque reduction assay to detect antiviral activity in these known antiviral plant preparations indicates its suitability as an antiviral screening tool. An advantage of this assay compared with traditionally used cytopathic effect reduction assays and replicon based assays is the more rapid acquisition of results. Antiviral activity was detected within 24 h of the start of testing. The MS2 assay is also inexpensive and non-pathogenic to humans making it ideal for initial screening studies or as a simulant for pathogenic viruses.  Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure Controlling severe outbreaks remains the most important problem in infectious disease area. With time, this problem will only become more severe as population density in urban centers grows. Social interactions play a very important role in determining how infectious diseases spread, and organization of people along social lines gives rise to non-spatial networks in which the infections spread. Infection networks are different for diseases with different transmission modes, but are likely to be identical or highly similar for diseases that spread the same way. Hence, infection networks estimated from common infections can be useful to contain epidemics of a more severe disease with the same transmission mode. Here we present a proof-of-concept study demonstrating the effectiveness of epidemic mitigation based on such estimated infection networks. We first generate artificial social networks of different sizes and average degrees, but with roughly the same clustering characteristic. We then start SIR epidemics on these networks, censor the simulated incidences, and use them to reconstruct the infection network. We then efficiently fragment the estimated network by removing the smallest number of nodes identified by a graph partitioning algorithm. Finally, we demonstrate the effectiveness of this targeted strategy, by comparing it against traditional untargeted strategies, in slowing down and reducing the size of advancing epidemics. Understanding and containing the spread of an infectious disease has always attracted a lot of interest from the scientific community, and even more so after the recent SARS and H1N1 outbreaks. Besides their social and healthcare impacts, severe infectious disease outbreaks also present an important burden to the economy through the decrease in productivity and high cost of treatment. As denser populations promote faster spreading, this problem can only grow in severity and magnitude with the increasing world population density. Motivated by this, many works have been done to predict [1, 2] or even contain [3] [4] [5] [6] the spread of severe epidemics. In spite of these efforts, effective control of infectious disease outbreaks continues to elude us.