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.

A Serological and Disc Electrophoretic Study of North American Typha

Pollen and seed proteins of seven selected North American and Puerto Rican Typha populations were compared using two serological methods and disc electrophoresis. These methods were capable of discriminating among all taxa studied: Typha latifolia, T. angustifolia, T. X glauca, and T. domingensis. The two hybrid populations were found to contain proteins not found in either parent. Typha domingensis was serologically the most distinct of the four taxa. The diagnostic morphological characteristics for Typha species were studied in all populations, and statistical comparisons are presented. Data from the morphological observations agreed with the information obtained from the chemosystematic research. All data indicate that the three taxa should be maintained as separate species. The hybrid nature of the putative T. X glauca is verified by both the biochemical and morphological data. Observed morphological and biochemical differences support taxonomic treatments in which T. domingensis is designated as a separate species.

Trace elemental analysis of glass by inductively coupled plasma-mass spectrometry (ICP-MS) and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS)

This work evaluated the capabilities of inductively coupled plasma mass spectrometry (ICP-MS) for elemental analysis of trace evidence. A method was developed and validated for the analysis of glass by ICP-MS. A database of ∼700 glass samples was analyzed for elemental composition by external calibration with internal standardization (EC) ICP-MS and refractive index (RI). Additional methods were developed during the course of this work using two well-known techniques, isotope dilution (ID) and laser ablation (LA). These methods were then applied to analyze subsets of this database. ICP-MS data from 161 containers, 45 headlamps, and 458 float glasses (among them at least 143 vehicle windows) are presented and summarized. Data from the analysis of ∼190 glass samples collected from a single glass manufacturing facility over a period of 53 months at different intervals, including 97 samples collected in a 24 hour period are presented. Data from the analysis of 125 glass samples representing 36 manufacturing plants in the U.S. are also presented. ^ The three methods used, ICP-MS, ID-ICP-MS and LA-ICP-MS, were shown to be excellent methods for distinguishing between different glass samples. The database provided information about the variability of refractive index and elemental composition in glasses from diverse population types. Using the proposed methods, the database supports the hypothesis that different glass samples have different elemental profiles and a comparison between fragments from the same source results in indistinguishable profiles. ^

Mechanisms of Arsenic Detoxification and Resistance

Arsenic is a ubiquitous environmental toxic substance. As a consequence of continual exposure to arsenic, nearly every organism, from Escherichia coli to humans have evolved arsenic detoxification pathways. One of the pathways is extrusion of arsenic from inside the cells, thereby conferring resistance. The R773 arsRDABC operon in E. coli encodes an ArsAB efflux pump that confers resistance to arsenite. ArsA is the catalytic subunit of the pump, while ArsB forms the oxyanion conducting pathway. ArsD is an arsenite metallochaperone that binds arsenite and transfers it to ArsA. The interaction of ArsA and ArsD allows for resistance to As(III) at environmental concentrations. The interaction between ArsA ATPase and ArsD metallochaperone was examined. A quadruple mutant in the arsD gene encoding a K2A/K37A/K62A/K104A ArsD is unable to interact with ArsA. An error-prone mutagenesis approach was used to generate random mutations in the arsA gene that restored interaction with the quadruple arsD mutant in yeast two-hybrid assays. Three such mutants encoding Q56R, F120I and D137V ArsA were able to restore interaction with the quadruple ArsD mutant. Structural models generated by in silico docking suggest that an electrostatic interface favors reversible interaction between ArsA and ArsD. Mutations in ArsA that propagate changes in hydrogen bonding and salt bridges to the ArsA-ArsD interface also affect their interactions. The second objective was to examine the mechanism of arsenite resistance through methylation and subsequent volatilization. Microbial ArsM (As(III) S-adenosylmethyltransferase) catalyzes the formation of trimethylarsine as the volatile end product. The net result is loss of arsenic from cells. The gene for CrArsM from the eukaryotic green alga Chlamydomonas reinhardtii was chemically synthesized and expressed in E. coli. The purified protein catalyzed the methylation of arsenite into methyl-, dimethyl- and trimethyl products. Synthetic purified CrArsM was crystallized in an unliganded form. Biochemical and biophysical studies conducted on CrArsM sheds new light on the pathways of biomethylation. While in microbes ArsM detoxifies arsenic, the human homolog, hAS3MT, converts inorganic arsenic into more toxic and carcinogenic forms. An understanding of the enzymatic mechanism of ArsM will be critical in deciphering its parallel roles in arsenic detoxification and carcinogenesis.