NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

A trilocus sequence typing scheme for hospital epidemiology and subspecies differentiation of an important nosocomial pathogen, Enterococcus faecalis.

In this study, we present a trilocus sequence typing (TLST) scheme based on intragenic regions of two antigenic genes, ace and salA (encoding a collagen/laminin adhesin and a cell wall-associated antigen, respectively), and a gene associated with antibiotic resistance, lsa (encoding a putative ABC transporter), for subspecies differentiation of Enterococcus faecalis. Each of the alleles was analyzed using 50 E. faecalis isolates representing 42 diverse multilocus sequence types (ST(M); based on seven housekeeping genes) and four groups of clonally linked (by pulsed-field gel electrophoresis [PFGE]) isolates. The allelic profiles and/or concatenated sequences of the three genes agreed with multilocus sequence typing (MLST) results for typing of 49 of the 50 isolates; in addition to the one exception, two isolates were found to have identical TLST types but were single-locus variants (differing by a single nucleotide) by MLST and were therefore also classified as clonally related by MLST. TLST was also comparable to PFGE for establishing short-term epidemiological relationships, typing all isolates classified as clonally related by PFGE with the same type. TLST was then applied to representative isolates (of each PFGE subtype and isolation year) of a collection of 48 hospital isolates and demonstrated the same relationships between isolates of an outbreak strain as those found by MLST and PFGE. In conclusion, the TLST scheme described here was shown to be successful for investigating short-term epidemiology in a hospital setting and may provide an alternative to MLST for discriminating isolates.

A MANAGEMENT INFORMATION SYSTEM AND EVALUATION SCHEME FOR THE UNIVERSITY OF TEXAS EMPLOYEE ASSISTANCE PROGRAMS

Early Employee Assistance Programs (EAPs) had their origin in humanitarian motives, and there was little concern for their cost/benefit ratios; however, as some programs began accumulating data and analyzing it over time, even with single variables such as absenteeism, it became apparent that the humanitarian reasons for a program could be reinforced by cost savings particularly when the existence of the program was subject to justification.^ Today there is general agreement that cost/benefit analyses of EAPs are desirable, but the specific models for such analyses, particularly those making use of sophisticated but simple computer based data management systems, are few.^ The purpose of this research and development project was to develop a method, a design, and a prototype for gathering managing and presenting information about EAPS. This scheme provides information retrieval and analyses relevant to such aspects of EAP operations as: (1) EAP personnel activities, (2) Supervisory training effectiveness, (3) Client population demographics, (4) Assessment and Referral Effectiveness, (5) Treatment network efficacy, (6) Economic worth of the EAP.^ This scheme has been implemented and made operational at The University of Texas Employee Assistance Programs for more than three years.^ Application of the scheme in the various programs has defined certain variables which remained necessary in all programs. Depending on the degree of aggressiveness for data acquisition maintained by program personnel, other program specific variables are also defined. ^

Future pandemic influenza control (FPIC) related strategic management plan based on the 2009 H1N1 pandemic influenza review of health policy decisions, their practice and implications

This cross-sectional study is based on the qualitative and quantitative research design to review health policy decisions, their practice and implications during 2009 H1N1 influenza pandemic in the United States and globally. The “Future Pandemic Influenza Control (FPIC) related Strategic Management Plan” was developed based on the incorporation of the “National Strategy for Pandemic Influenza (2005)” for the United States from the U.S. Homeland Security Council and “The Canadian Pandemic Influenza Plan for the Health Sector (2006)” from the Canadian Pandemic Influenza Committee for use by the public health agencies in the United States as well as globally. The “global influenza experts’ survey” was primarily designed and administered via email through the “Survey Monkey” system to the 2009 H1N1 influenza pandemic experts as the study respondents. The effectiveness of this plan was confirmed and the approach of the study questionnaire was validated to be convenient and the excellent quality of the questions provided an efficient opportunity to the study respondents to evaluate the effectiveness of predefined strategies/interventions for future pandemic influenza control.^ The quantitative analysis of the responses to the Likert-scale based questions in the survey about predefined strategies/interventions, addressing five strategic issues to control future pandemic influenza. The effectiveness of strategies defined as pertinent interventions in this plan was evaluated by targeting five strategic issues regarding pandemic influenza control. For the first strategic issue pertaining influenza prevention and pre pandemic planning; the confirmed effectiveness (agreement) for strategy (1a) 87.5%, strategy (1b) 91.7% and strategy (1c) 83.3%. The assessment of the priority level for strategies to address the strategic issue no. (1); (1b (High Priority) > 1a (Medium Priority) > 1c (Low Priority) based on the available resources of the developing and developed countries. For the second Strategic Issue encompassing the preparedness and communication regarding pandemic influenza control; the confirmed effectiveness (agreement) for the strategy (2a) 95.6%, strategy (2b) 82.6%, strategy (2c) 91.3% and Strategy (2d) 87.0%. The assessment of the priority level for these strategies to address the strategic issue no. (2); (2a (highest priority) > 2c (high priority) >2d (medium priority) > 2b (low priority). For the third strategic issue encompassing the surveillance and detection of pandemic influenza; the confirmed effectiveness (agreement) for the strategy (3a) 90.9% and strategy (3b) 77.3%. The assessment of the priority level for theses strategies to address the strategic Issue No. (3) (3a (high priority) > 3b (medium/low priority). For the fourth strategic issue pertaining the response and containment of pandemic influenza; the confirmed effectiveness (agreement) for the strategy (4a) 63.6%, strategy (4b) 81.8%, strategy (4c) 86.3%, and strategy (4d) 86.4%. The assessment of the priority level for these strategies to address the strategic issue no. (4); (4d (highest priority) > 4c (high priority) > 4b (medium priority) > 4a (low priority). The fifth strategic issue about recovery from influenza and post pandemic planning; the confirmed effectiveness (agreement) for the strategy (5a) 68.2%, strategy (5b) 36.3% and strategy (5c) 40.9%. The assessment of the priority level for strategies to address the strategic issue no. (5); (5a (high priority) > 5c (medium priority) > 5b (low priority).^ The qualitative analysis of responses to the open-ended questions in the study questionnaire was performed by means of thematic content analysis. The following recurrent or common “themes” were determined for the future implementation of various predefined strategies to address five strategic issues from the “FPIC related Strategic Management Plan” to control future influenza pandemics. (1) Pre Pandemic Influenza Prevention, (2) Seasonal Influenza Control, (3) Cost Effectiveness of Non Pharmaceutical Interventions (NPI), (4) Raising Global Public Awareness, (5) Global Influenza Vaccination Campaigns, (6)Priority for High Risk Population, (7) Prompt Accessibility and Distribution of Influenza Vaccines and Antiviral Drugs, (8) The Vital Role of Private Sector, (9) School Based Influenza Containment, (10) Efficient Global Risk Communication, (11) Global Research Collaboration, (12) The Critical Role of Global Public Health Organizations, (13) Global Syndromic Surveillance and Surge Capacity and (14) Post Pandemic Recovery and Lessons Learned. The future implementation of these strategies with confirmed effectiveness to primarily “reduce the overall response time’ in the process of ‘early detection’, ‘strategies (interventions) formulation’ and their ‘implementation’ to eventually ensure the following health outcomes: (a) reduced influenza transmission, (b) prompt and effective influenza treatment and control, (c) reduced influenza related morbidity and mortality.^

COMPREHENSIVE CALCULATION-BASED IMRT QA USING R&V DATA, TREATMENT RECORDS, AND A SECOND TREATMENT PLANNING SYSTEM

Purpose: Traditional patient-specific IMRT QA measurements are labor intensive and consume machine time. Calculation-based IMRT QA methods typically are not comprehensive. We have developed a comprehensive calculation-based IMRT QA method to detect uncertainties introduced by the initial dose calculation, the data transfer through the Record-and-Verify (R&V) system, and various aspects of the physical delivery. Methods: We recomputed the treatment plans in the patient geometry for 48 cases using data from the R&V, and from the delivery unit to calculate the “as-transferred” and “as-delivered” doses respectively. These data were sent to the original TPS to verify transfer and delivery or to a second TPS to verify the original calculation. For each dataset we examined the dose computed from the R&V record (RV) and from the delivery records (Tx), and the dose computed with a second verification TPS (vTPS). Each verification dose was compared to the clinical dose distribution using 3D gamma analysis and by comparison of mean dose and ROI-specific dose levels to target volumes. Plans were also compared to IMRT QA absolute and relative dose measurements. Results: The average 3D gamma passing percentages using 3%-3mm, 2%-2mm, and 1%-1mm criteria for the RV plan were 100.0 (σ=0.0), 100.0 (σ=0.0), and 100.0 (σ=0.1); for the Tx plan they were 100.0 (σ=0.0), 100.0 (σ=0.0), and 99.0 (σ=1.4); and for the vTPS plan they were 99.3 (σ=0.6), 97.2 (σ=1.5), and 79.0 (σ=8.6). When comparing target volume doses in the RV, Tx, and vTPS plans to the clinical plans, the average ratios of ROI mean doses were 0.999 (σ=0.001), 1.001 (σ=0.002), and 0.990 (σ=0.009) and ROI-specific dose levels were 0.999 (σ=0.001), 1.001 (σ=0.002), and 0.980 (σ=0.043), respectively. Comparing the clinical, RV, TR, and vTPS calculated doses to the IMRT QA measurements for all 48 patients, the average ratios for absolute doses were 0.999 (σ=0.013), 0.998 (σ=0.013), 0.999 σ=0.015), and 0.990 (σ=0.012), respectively, and the average 2D gamma(5%-3mm) passing percentages for relative doses for 9 patients was were 99.36 (σ=0.68), 99.50 (σ=0.49), 99.13 (σ=0.84), and 98.76 (σ=1.66), respectively. Conclusions: Together with mechanical and dosimetric QA, our calculation-based IMRT QA method promises to minimize the need for patient-specific QA measurements by identifying outliers in need of further review.