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. ^

Taxonomy development and knowledge representation of nurses' personal cognitive artifacts.

Nurses prepare knowledge representations, or summaries of patient clinical data, each shift. These knowledge representations serve multiple purposes, including support of working memory, workload organization and prioritization, critical thinking, and reflection. This summary is integral to internal knowledge representations, working memory, and decision-making. Study of this nurse knowledge representation resulted in development of a taxonomy of knowledge representations necessary to nursing practice.This paper describes the methods used to elicit the knowledge representations and structures necessary for the work of clinical nurses, described the development of a taxonomy of this knowledge representation, and discusses translation of this methodology to the cognitive artifacts of other disciplines. Understanding the development and purpose of practitioner's knowledge representations provides important direction to informaticists seeking to create information technology alternatives. The outcome of this paper is to suggest a process template for transition of cognitive artifacts to an information system.

Contraceptive attitudes and knowledge of female adolescents

Despite the recent decline in adolescent pregnancy rates, adolescent pregnancy continues to be a significant public health issue in the United States. The United States consistently reports the highest rate of adolescent pregnancy among developed countries. Adolescent mothers are more likely to have multiple pregnancies, to access welfare and other social services, and to be unmarried. Teen mothers are less likely to complete high school, enter college, and typically command much less earning power throughout their lifetime as compared to women who delay childbirth until later. Moreover, the United States spends approximately $9.1 billion annually on teen pregnancies. ^ Additionally disconcerting is recent data which demonstrates that the decline in teen pregnancy rates is leveling off and that the rate of adolescent pregnancy has increased for the first time since 1993. Contraceptive use is a key component to the prevention of adolescent pregnancy. Contraceptive nonuse and failure result in unintended pregnancies among adolescents. This review sought to assess the levels of knowledge and attitudes toward contraception among adolescent females.^ Levels of knowledge of contraception among adolescents are tolerable; however, there is substantial room for improvement. Misperceptions about the side effects and mechanisms of action of contraception are pervasive among this population. Adolescents who have low levels of knowledge regarding contraception tend to discontinue usage or use inconsistently. Attitudes toward contraception are greatly influenced by levels of knowledge. As a result, adolescents tend to develop more positive attitudes as misperceptions are abated. Moreover, clear disparities persist among adolescents with minority and young adolescents being at increased risk of pregnancy, poor contraceptive use, and insufficient knowledge about contraception.^ Understanding the level of knowledge of and attitudes toward contraceptives among adolescents is essential to the development of effective pregnancy prevention programs. In order to effectively reduce adolescent pregnancy, prevention initiatives must target the vulnerable populations and incorporate the necessary cultural components.^