Role of Pseudomonas aeruginosa mifSR Two-Component System in Antibiotic Resistance and Biofilm Formation

Pseudomonas aeruginosa is an opportunistic pathogen found in a wide variety of environments. It is one of the leading causes of morbidity and mortality in cystic fibrosis patients, and one of the main sources of nosocomial infections in the United States. One of the most prominent features of this pathogen is its wide resistance to antibiotics. P. aeruginosa employs a variety of mechanisms including efflux pumps and the expression of B-lactamases to overcome antibiotic treatment. Two chromosomally encoded lactamases, ampC and poxB, have been identified in P. aeruginosa. Sequence analyses have shown the presence of a two-component system (TCS) called MifSR (MifS-Sensor and MifR-Response Regulator), immediately upstream of the poxAB operon. It is hypothesized that the MifSR TCS is involved in B-lactam resistance via the regulation of poxB. Recently, the response regulator MifR has been reported to play a crucial role in biofilm formation, a major characteristic of chronic infections and increased antibiotic resistance. In this study, mifR and mifSR deletion mutants were constructed, and compared to the wild type parent strain PAOl for differences in growth and B-lactam sensitivity. Results obtained thus far indicate that mifR and mifSR are not essential for growth, and do not confer B-lactam resistance under the conditions tested. This study is significant because biofilm formation and antibiotic resistance are two hallmarks of P. aeruginosa infections, and finding a link between these two may lead to the development of improved treatment strategies.

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Information Discovery on Electronic Health Records Using Authority Flow Techniques

Background As the use of electronic health records (EHRs) becomes more widespread, so does the need to search and provide effective information discovery within them. Querying by keyword has emerged as one of the most effective paradigms for searching. Most work in this area is based on traditional Information Retrieval (IR) techniques, where each document is compared individually against the query. We compare the effectiveness of two fundamentally different techniques for keyword search of EHRs. Methods We built two ranking systems. The traditional BM25 system exploits the EHRs' content without regard to association among entities within. The Clinical ObjectRank (CO) system exploits the entities' associations in EHRs using an authority-flow algorithm to discover the most relevant entities. BM25 and CO were deployed on an EHR dataset of the cardiovascular division of Miami Children's Hospital. Using sequences of keywords as queries, sensitivity and specificity were measured by two physicians for a set of 11 queries related to congenital cardiac disease. Results Our pilot evaluation showed that CO outperforms BM25 in terms of sensitivity (65% vs. 38%) by 71% on average, while maintaining the specificity (64% vs. 61%). The evaluation was done by two physicians. Conclusions Authority-flow techniques can greatly improve the detection of relevant information in EHRs and hence deserve further study.