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. ^