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. ^

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. ^

Assessing the Environmental Protection Agency's guidelines for cumulative risk assessment: Are they suitable for state agencies and EPA regional offices?

In the field of health risk analysis, cumulative risk assessment (CRA) is a necessary, although undeniably more complex approach to understanding the mixture of stressors, whether chemical or psychosocial, that exist in our environment, in all the pathways through which the chemicals may evolve—air, soil, or water, as well as the accumulation of these exposures over time. Related, or attached to the developing awareness of scientists understanding this mix of combined health effects is the burgeoning of the environmental justice movement, in which educated community advocates and even affected community members have called attention to evidence of a higher pollution burden in minority and/or lower SES communities. The intention of this paper is to 1) examine the development and understanding of CRA, primarily by the U.S. Environmental Protection Agency; 2) to assess several states agencies and some EPA regional offices' interpretation of CRA, again based primarily on EPA guidance, and 3) to analyze how CRA might be refined in its implementation—giving some cues as to how the EPA may more effectively interact with communities interested in CRA.^

The response of Texas public health agencies and workers to the emergency preparedness initiatives after 9/11

The purpose of this research was to better understand the impact of the terrorist attacks in 2001 on public health, particularly for Texas public health. This study employed mixed methods to examine changes to public health culture within Texas local public health agencies, important attitudes of public health workers toward responding to a disaster, and the funding policies that might ensure our investment in public health emergency preparedness is protected. ^ A qualitative analysis of interviews conducted with a large sample of public health officials in Texas found that all the constituent parts of a peculiar culture for public health preparedness existed that spanned the state's local health departments regardless of size, or funding level. The new preparedness culture in Texas had the hallmarks necessary for a robust public health preparedness and emergency response system. ^ The willingness of public health workers, necessary to make these kinds of changes and mount a disaster response was examined in one of Texas' most experienced disaster response teams—the public health workers for the City of Houston. A hypothesized latent variable model showed that willingness mediated all other factors in the model (self-efficacy, knowledge, barriers, and risk perception) for self-reported likelihood of reporting to work for a disaster. The RMSEA for the final model was 0.042 with a confidence interval of 0.036—0.049 and the chi-squared difference test was P=0.08, indicating a well-fitted model that suggests willingness is an important factor for consideration by preparedness planners and researchers alike. ^ Finally, with disasters on the rise and federal funding for preparedness dwindling, a review of states' policies for the distribution of these funds and their advantages and disadvantages were examined through a review of current literature and public documents, and a survey of state-level public health officials, emergency management professionals and researchers. Although the base plus per-capita method is the most common, it is not necessarily perceived to be the most effective. No clear "optimal" method emerged from the study, but recommendations for a strategic combination of three methods were made that has the potential to maximize the benefits of each method, while minimizing the weaknesses.^