New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

The effectiveness of a promotora-led intervention for Mexican Americans with type 2 diabetes

Purpose. The purpose of this randomized control repeated measures trial was to determine the effectiveness of a self-management intervention led by community lay workers called promotoras on the health outcomes of Mexican Americans with type 2 diabetes living in a major city on the Texas - Mexico border. The specific aims of this study, in relation to the intervention group participants, were to: (1) decrease the glycosylated hemoglobin (A1c) blood levels at the six-month assessment, (2) increase diabetes knowledge at the three and six-month assessments, and (3) strengthen the participants' beliefs in their ability to manage diabetes at the three and six-month assessments.^ Methods. One hundred and fifty Mexican American participants were recruited at a Catholic faith-based clinic and randomized into an intervention group and a usual-care control group. Personal characteristics, acculturation and baseline A1c, diabetes knowledge and diabetes health beliefs were measured. The six-month, two-phase intervention was culturally specific and it was delivered entirely by promotoras. Phase One of the intervention consisted of sixteen hours of participative group education and bi-weekly telephone contact follow-up. Phase Two consisted of bi-weekly follow-up using inspirational faith-based health behavior change postcards. The A1c levels, diabetes knowledge and diabetes health beliefs were measured at baseline, and three and six months post-baseline. The mean changes between the groups were analyzed using analysis of covariance. ^ Results. The 80% female sample, with a mean age of 58 years, demonstrated very low: acculturation, income, education, health insurance coverage, and strong Catholicism. No significant changes were noted at the three-month assessment, but the mean change of the A1c levels (F (1, 148 = 10.28, p < .001) and the diabetes knowledge scores (F (1, 148 = 9.0, p < .002) of the intervention group improved significantly at six months, adjusting for health insurance coverage. The diabetes health belief scores decreased in both groups.^ Conclusions. This study demonstrated that an intervention led by promotoras could result in decreased A1c levels and increased diabetes knowledge in spite of the very low acculturation, educational level and insurance coverage of the intervention group participants. Clinical implications and recommendations for future research are suggested. ^

Investigator/IRB-led community communication in genetic research: A descriptive feasibility pilot study

This pilot study, conducted in the Houston, TX, area, established a structured dialogue among a university Institutional Review Board, its researchers, and its local community members (i.e. pool of potential research participants) for the purpose of further educating all three parties about genetic research and community concerns related to such research. An IRB-designed educational presentation aimed at assisting potential subjects in making an informed decision to participate in genetic research was provided to four community groups (n=54); this presentation also included a current example of genetic research being conducted in the community as explained by the researcher, and a question-and-answer session designed to assist the IRB and the researcher in understanding the community's concerns about genetic research. Comparisons of pre- and post- presentation community questionnaires indicate that the joint presentation was effective in increasing community knowledge about genetic research, most notably related to the risks and benefits of this research to the individual, as well as the understanding that protections are in place for research participants. While researchers are optimistic about the idea of a collaborative effort with the IRB and the community, the feasibility of such a program and the benefit to the participating researchers remain unclear; additional research is necessary to establish the most effective method of communication for all groups involved, as well as to obtain statistically significant results with regard to race/ethnicity, gender, and education levels of community participants. ^

Relationship between nurse leader emotional intelligence and registered nurse job satisfaction

Aim: To determine the relationship between nurse leader emotional intelligence and registered nurse job satisfaction. ^ Background: Nurse leaders influence the work environments of nurses working at the bedside. Nursing leadership plays an important role in fostering work environments that attract and retain nurses. ^ Methods: A non-experimental, predictive design study conducted in 5 hospitals evaluated relationships between 31 nurse leaders and 799 registered nurses. The nurse leaders were administered the MSCEIT and MBTI. The registered nurses participated in the 2010 NDNQI RN Job Satisfaction Survey. ^ Measurements and Results: The sample population completed two online instruments, the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) and the Myers Brigg Trait Inventory (MBTI). Nurse leader demographic data was collected consisting of age, sex, race, educational level, certification status and years in the profession of nursing. The relationships among characteristics of the nurse leader and staff nurses were examined using regression analysis and stepwise deletion. The results from the MBTI were obtained electronically from CPP. Inc. and the results of MSCEIT were obtained electronically from MHS, Inc. The nurse leader response rate was 46% and the NDNQI RN Job Satisfaction response rate was 62%. The sample of 31 nurse leaders were 65 percent female and 67.7% were White, 12.9% Black, and 19.4% Hispanic. The most prevalent MBTI type was ESTJ (19.35%), followed by ENFJ and ISFJ (9.68% each). The nurse leader sample was primarily extroverts (n=20), sensing (n=18), thinking (n=16) and judging (n=19). The nurse leaders' overall MSCEIT scores ranged from 69 to 111 (implying a range from those who should consider development to competent) with a mean score of 89.84 (consider improvement). The nurse leaders scored highest in the MSCEIT Facilitating subscale with scores ranging from 69 to 121 (consider development to strength) and a mean score of 95.19 (low average score). The overall mean MSCEIT mean scores for the entire sample ranged from 89.90 to 95.19 (consider emotional intelligence improvement to low average score) Overall, staff nurse participants in the NDNQI RN Job Satisfaction Survey were moderately satisfied with the nurse leaders as noted by a mean t score of 55.03 of 60 and this score was consistent with the comparison hospitals that participated in the 2010 NDNQI RN Job Satisfaction Survey (American Nurses Association, 2010). Staff nurses gave nurse leaders a mean score of 4.50 for patient assignments appropriate, and rated a mean score of 4.35 and moderately agreeing to recommend the hospital to a friend. ^ Conclusions: Future research is needed to determine if there is a relationship between nurse leader emotional intelligence ability and registered nurse job satisfaction. Additional research is also needed to determine what to measure in regards to nurse leader emotional intelligence, ability or behavior. Another issue that emerged in the examination of EI is the moderating relationship between the nurse leaders span of control and staff nurse satisfaction on the NDNQI. ^