Testing the Efficacy of Panax ginseng Extract as a Novel Anti-Quorum Sensing Agent

Quorum sensing (QS) is the phenomenon by which microorganisms regulate gene expression in response to cell-population density. These microorganisms synthesize and secrete small molecules known as autoinducers that increase in concentration as a function of cell density. Once the cell detects the minimal threshold concentration of an autoinducer, the gene expression is altered accordingly. Although the cellular circuitry responding to QS is relatively conserved, the cellular processes regulated by QS, for example are conjugation, motility, sporulation, biofilm formation and production of virulence factors importamt for infection. Since many pathogens have developed resistance to available antibiotics, novel therapies are required to further treat these pathogens. Inhibitors of QS are potential therapeutic candidates since they would inhibit virulence without selecting for antibiotic resistant strains. Previous studies have shown that the Chinese herb Panax ginseng contains compounds that inhibit QS activity. To further characterize this activity, a highly sensitive quantitative liquid assay was developed using a Chromobacterium violaceum AHL mutant, CV026 as a biomonitor strain. After confirmation that P. ginseng aqueous extracts had anti-QS activity, the extract was fractionated on a reverse phase C18 Sep Pak column. Most anti-QS activity was present in the flow through, and compounds eluted with ten percent acetonitrile in water antibacterial activity. We thus conclude that P. ginseng has anti-QS activity and the active compound is water soluble. Compounds from P. ginseng, could be used as a lead structure to design compound with higher anti-QS activity for therapeutic treatments.

Characterization of the amp genes involved in the regulation of beta-lactamase expression in Pseudomonas aeruginosa

Antibiotic resistance, production of alginate and virulence factors, and altered host immune responses are the hallmarks of chronic Pseudomonas aeruginosa infection. Failure of antibiotic therapy has been attributed to the emergence of P. aeruginosa strains that produce β-lactamase constitutively. In Enterobacteriaceae, β-lactamase induction involves four genes with known functions: ampC, ampR, ampD, and ampG, encoding the enzyme, transcriptional regulator, amidase and permease, respectively. In addition to all these amp genes, P. aeruginosa possesses two ampG paralogs, designated ampG and ampP. In this study, P. aeruginosa ampC, ampR, ampG and ampP were analyzed. Inactivation of ampC in the prototypic PAO1 failed to abolish the β-lactamase activity leading to the discovery of P. aeruginosa oxacillinase PoxB. Cloning and expression of poxB in Escherichia coli confers β-lactam resistance. Both AmpC and PoxB contribute to P. aeruginosa resistance against a wide spectrum of β-lactam antibiotics. The expression of PoxB and AmpC is regulated by a LysR-type transcriptional regulator AmpR that up-regulates AmpC but down-regulates PoxB activities. Analyses of P. aeruginosa ampR mutant demonstrate that AmpR is a global regulator that modulates the expressions of Las and Rhl quorum sensing (QS) systems, and the production of pyocyanin, LasA protease and LasB elastase. Introduction of the ampR mutation into an alginate-producing strain reveals the presence of a complex co-regulatory network between antibiotic resistance, QS alginate and other virulence factor production. Using phoA and lacZ protein fusion analyses, AmpR, AmpG and AmpP were localized to the inner membrane with one, 16 and 10 transmembrane helices, respectively. AmpR has a cytoplasmic DNA-binding and a periplasmic substrate binding domains. AmpG and AmpP are essential for the maximal expression of β-lactamase. Analysis of the murein breakdown products suggests that AmpG exports UDP-N-acetylmuramyl-L-alanine-γ-D-glutamate-meso-diaminopimelic acid-D-alanine-D-alanine (UDP-MurNAc-pentapeptide), the corepressor of AmpR, whereas AmpP imports N-acetylglucosaminyl-beta-1,4-anhydro-N-acetylmuramic acid-Ala-γ-D-Glu-meso-diaminopimelic acid (GlcNAc-anhMurNAc-tripeptide) and GlcNAc-anhMurNAc-pentapeptide, the co-inducers of AmpR. This study reveals a complex interaction between the Amp proteins and murein breakdown products involved in P. aeruginosa β-lactamase induction. In summary, this dissertation takes us a little closer to understanding the P. aeruginosa complex co-regulatory mechanism in the development of β-lactam resistance and establishment of chronic infection. ^

Mapping Transnational Crime in El Salvador: New Trends and Lessons from Colombia

Since El Salvador’s civil war formally ended in 1992 the small Central American nation has undergone profound social changes and significant reforms. However, few changes have been as important or as devastating as the nation’s emergence as a central hub in the transnational criminal “pipeline” or series of recombinant, overlapping chains of routes and actors that illicit organizations use to traffic in drugs, money weapons, human being, endangered animals and other products. The erasing of the once-clear ideological lines that drove the civil war and the ability of erstwhile enemies to join forces in criminal enterprises in the post-war period is an enduring and dangerous characteristic of El Salvador’s transnational criminal evolution. Trained, elite cadres from both sides, with few legitimate job opportunities, found their skills were marketable in the growing criminal structures. The groups moved from kidnapping and extortion to providing protection services to transnational criminal organizations to becoming integral parts of the organizations themselves. The demand for specialized military and transportation services in El Salvador have exploded as the Mexican DTOs consolidate their hold on the cocaine market and their relationships with the transportista networks, which is still in flux. The value of their services has risen dramatically also because of the fact that multiple Mexican DTOs, at war with each other in Mexico and seeking to physically control the geographic space of the lucrative pipeline routes in from Guatemala to Panama, are eager to increase their military capabilities and intelligence gathering capacities. The emergence of multiple non-state armed groups, often with significant ties to the formal political structure (state) through webs of judicial, legislative and administrative corruption, has some striking parallels to Colombia in the 1980s, where multiple types of violence ultimately challenged the sovereignty of state and left a lasting legacy of embedded corruption within the nation’s political structure. Organized crime in El Salvador is now transnational in nature and more integrated into stronger, more versatile global networks such as the Mexican DTOs. It is a hybrid of both local crime – with gangs vying for control off specific geographic space so they can extract payment for the safe passage of illicit products – and transnational groups that need to use that space to successfully move their products. These symbiotic relationships are both complex and generally transient in nature but growing more consolidated and dangerous.

Identification of Conserved Splicing Motifs in Mutually Exclusive Exons of 15 Insect Species

Background: During alternative splicing, the inclusion of an exon in the final mRNA molecule is determined by nuclear proteins that bind cis-regulatory sequences in a target pre-mRNA molecule. A recent study suggested that the regulatory codes of individual RNA-binding proteins may be nearly immutable between very diverse species such as mammals and insects. The model system Drosophila melanogaster therefore presents an excellent opportunity for the study of alternative splicing due to the availability of quality EST annotations in FlyBase. Methods: In this paper, we describe an in silico analysis pipeline to extract putative exonic splicing regulatory sequences from a multiple alignment of 15 species of insects. Our method, ESTs-to-ESRs (E2E), uses graph analysis of EST splicing graphs to identify mutually exclusive (ME) exons and combines phylogenetic measures, a sliding window approach along the multiple alignment and the Welch’s t statistic to extract conserved ESR motifs. Results: The most frequent 100% conserved word of length 5 bp in different insect exons was “ATGGA”. We identified 799 statistically significant “spike” hexamers, 218 motifs with either a left or right FDR corrected spike magnitude p-value < 0.05 and 83 with both left and right uncorrected p < 0.01. 11 genes were identified with highly significant motifs in one ME exon but not in the other, suggesting regulation of ME exon splicing through these highly conserved hexamers. The majority of these genes have been shown to have regulated spatiotemporal expression. 10 elements were found to match three mammalian splicing regulator databases. A putative ESR motif, GATGCAG, was identified in the ME-13b but not in the ME-13a of Drosophila N-Cadherin, a gene that has been shown to have a distinct spatiotemporal expression pattern of spliced isoforms in a recent study. Conclusions: Analysis of phylogenetic relationships and variability of sequence conservation as implemented in the E2E spikes method may lead to improved identification of ESRs. We found that approximately half of the putative ESRs in common between insects and mammals have a high statistical support (p < 0.01). Several Drosophila genes with spatiotemporal expression patterns were identified to contain putative ESRs located in one exon of the ME exon pairs but not in the other.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

The Construction and Optimization on an Ion Mobility Spectrometer for the Analysis of Explosives and Drugs

Today, over 15,000 Ion Mobility Spectrometry (IMS) analyzers are employed at worldwide security checkpoints to detect explosives and illicit drugs. Current portal IMS instruments and other electronic nose technologies detect explosives and drugs by analyzing samples containing the headspace air and loose particles residing on a surface. Canines can outperform these systems at sampling and detecting the low vapor pressure explosives and drugs, such as RDX, PETN, cocaine, and MDMA, because these biological detectors target the volatile signature compounds available in the headspace rather than the non-volatile parent compounds of explosives and drugs. In this dissertation research volatile signature compounds available in the headspace over explosive and drug samples were detected using SPME as a headspace sampling tool coupled to an IMS analyzer. A Genetic Algorithm (GA) technique was developed to optimize the operating conditions of a commercial IMS (GE Itemizer 2), leading to the successful detection of plastic explosives (Detasheet, Semtex H, and C-4) and illicit drugs (cocaine, MDMA, and marijuana). Short sampling times (between 10 sec to 5 min) were adequate to extract and preconcentrate sufficient analytes (> 20 ng) representing the volatile signatures in the headspace of a 15 mL glass vial or a quart-sized can containing ≤ 1 g of the bulk explosive or drug. Furthermore, a research grade IMS with flexibility for changing operating conditions and physical configurations was designed and fabricated to accommodate future research into different analytes or physical configurations. The design and construction of the FIU-IMS were facilitated by computer modeling and simulation of ion’s behavior within an IMS. The simulation method developed uses SIMION/SDS and was evaluated with experimental data collected using a commercial IMS (PCP Phemto Chem 110). The FIU-IMS instrument has comparable performance to the GE Itemizer 2 (average resolving power of 14, resolution of 3 between two drugs and two explosives, and LODs range from 0.7 to 9 ng). The results from this dissertation further advance the concept of targeting volatile components to presumptively detect the presence of concealed bulk explosives and drugs by SPME-IMS, and the new FIU-IMS provides a flexible platform for future IMS research projects.

Headspace Analysis of Smokeless Powders: Development of Mass Calibration Methods using Microdrop Printing for Chromatographic and Ion Mobility Spectrometric Detection

Smokeless powder additives are usually detected by their extraction from post-blast residues or unburned powder particles followed by analysis using chromatographic techniques. This work presents the first comprehensive study of the detection of the volatile and semi-volatile additives of smokeless powders using solid phase microextraction (SPME) as a sampling and pre-concentration technique. Seventy smokeless powders were studied using laboratory based chromatography techniques and a field deployable ion mobility spectrometer (IMS). The detection of diphenylamine, ethyl and methyl centralite, 2,4-dinitrotoluene, diethyl and dibutyl phthalate by IMS to associate the presence of these compounds to smokeless powders is also reported for the first time. A previously reported SPME-IMS analytical approach facilitates rapid sub-nanogram detection of the vapor phase components of smokeless powders. A mass calibration procedure for the analytical techniques used in this study was developed. Precise and accurate mass delivery of analytes in picoliter volumes was achieved using a drop-on-demand inkjet printing method. Absolute mass detection limits determined using this method for the various analytes of interest ranged between 0.03 - 0.8 ng for the GC-MS and between 0.03 - 2 ng for the IMS. Mass response graphs generated for different detection techniques help in the determination of mass extracted from the headspace of each smokeless powder. The analyte mass present in the vapor phase was sufficient for a SPME fiber to extract most analytes at amounts above the detection limits of both chromatographic techniques and the ion mobility spectrometer. Analysis of the large number of smokeless powders revealed that diphenylamine was present in the headspace of 96% of the powders. Ethyl centralite was detected in 47% of the powders and 8% of the powders had methyl centralite available for detection from the headspace sampling of the powders by SPME. Nitroglycerin was the dominant peak present in the headspace of the double-based powders. 2,4-dinitrotoluene which is another important headspace component was detected in 44% of the powders. The powders therefore have more than one headspace component and the detection of a combination of these compounds is achievable by SPME-IMS leading to an association to the presence of smokeless powders.

Anti-Quorum Sensing Agents from South Florida Medicinal Plants and their Attenuation of Pseudomonas Aeruginosa Pathogenicity

With the difficulty in treating recalcitrant infections and the growing resistance to antibiotics, new therapeutic modalities are becoming increasingly necessary. The interruption of bacterial quorum sensing (QS), or cell-cell communication is known to attenuate virulence, while limiting selective pressure toward resistance. This study initiates an ethnobotanically-directed search for QS inhibiting agents in south Florida medicinal plants. Fifty plants were screened for anti-QS activity using two biomonitor strains, Chromobacterium violaceum and Agrobacterium tumefaciens. Of these plants, six showed QS inhibition: Conocarpus erectus L. (Combretaceae), Chamaecyce hypericifolia (L.) Millsp. (Euphorbiaceae), Callistemon viminalis (Sol.ex Gaertn.) G. Don (Myrtaceae), Bucida burceras L. (Combretaceae), Tetrazygia bicolor (Mill.) Cogn. (Melastomataceae), and Quercus virginiana Mill. (Fagaceae). These plants were further examined for their effects on the QS system and virulence of Pseudomonas aeruginosa, an intractable opportunistic pathogen responsible for morbidity and mortality in the immunocompromised patient. C. erectus, B. buceras, and C. viminalis were found to significantly inhibit multiple virulence factors and biofilm formation in this organism. Each plant presented a distinct profile of effect on QS genes and signaling molecules, suggesting varying modes of action. Virulence attenuation was observed with marginal reduction of bacterial growth, suggesting quorum quenching mechanisms unrelated to static or cidal effects. Extracts of these plants were also investigated for their effects on P. aeruginosa killing of the nematode Caenorhabditis elegans. Results were evaluated in both toxin-based and infection-based assays with P. aeruginosa strains PA01 and PA14. Overall nematode mortality was reduced 50-90%. There was no indication of host toxicity, suggesting the potential for further development as anti-infectives. Using low-pressure chromatography and HPLC, two stereoisomeric ellagitannins, vescalagin and castalagin were isolated from an aqueous extract of C. erectus. Structures were confirmed via mass spectrometry and NMR spectroscopy. Both ellagitannins were shown to decrease signal production, QS gene expression, and virulence factor production in P. aeruginosa. This study introduces a potentially new therapeutic direction for the treatment of bacterial infections. In addition, this is the first report of vescalagin and castalagin being isolated from C. erectus, and the first report of ellagitannin activity on the QS system.

Investigating the origin of PM2.5 in Baltimore using highly time-resolved organic molecular markers measured during the Baltimore PM-supersite

The detailed organic composition of atmospheric fine particles with an aerodynamic diameter smaller than or equal to 2.5 micrometers (PM 2.5) is an integral part of the knowledge needed in order to fully characterize its sources and transformation in the environment. For the study presented here, samples were collected at 3-hour intervals. This high time resolution allows gaining unique insights on the influence of short- and long-range transport phenomena, and dynamic atmospheric processes. A specially designed sequential sampler was deployed at the 2002-2003 Baltimore PM Supersite to collect PM2.5 samples at a 3-hourly resolution for extended periods of consecutive days, during both summer and winter seasons. Established solvent-extraction and GC-MS techniques were used to extract and analyze the organic compounds in 119 samples from each season. Over 100 individual compounds were quantified in each sample. For primary organics, averaging the diurnal ambient concentrations over the sampled periods revealed ambient patterns that relate to diurnal emission patterns of major source classes. Several short-term releases of pollutants from local sources were detected, and local meteorological data was used to pinpoint possible source regions. Biogenic secondary organic compounds were detected as well, and possible mechanisms of formation were evaluated. The relationships between the observed continuous variations of the concentrations of selected organic markers and both the on-site meteorological measurements conducted parallel to the PM2.5 sampling, and the synoptic patterns of weather and wind conditions were also examined. Several one-to-two days episodes were identified from the sequential variation of the concentration observed for specific marker compounds and markers ratios. The influence of the meteorological events on the concentrations of the organic compounds during selected episodes was discussed. It was observed that during the summer, under conditions of pervasive influence of air masses originated from the west/northwest, some organic species displayed characteristics consistent with the measured PM2.5 being strongly influenced by the aged nature of these long-traveling background parcels. During the winter, intrusions from more regional air masses originating from the south and the southwest were more important.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.