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 Open Door Mission: Measuring and predicting outcomes of one community-based substance abuse treatment program

The objectives of this study were to identify and measure the average outcomes of the Open Door Mission's nine-month community-based substance abuse treatment program, identify predictors of successful outcomes, and make recommendations to the Open Door Mission for improving its treatment program.^ The Mission's program is exclusive to adult men who have limited financial resources: most of which were homeless or dependent on parents or other family members for basic living needs. Many, but not all, of these men are either chemically dependent or have a history of substance abuse.^ This study tracked a cohort of the Mission's graduates throughout this one-year study and identified various indicators of success at short-term intervals, which may be predictive of longer-term outcomes. We tracked various levels of 12-step program involvement, as well as other social and spiritual activities, such as church affiliation and recovery support.^ Twenty-four of the 66 subjects, or 36% met the Mission's requirements for success. Specific to this success criteria; Fifty-four, or 82% reported affiliation with a home church; Twenty-six, or 39% reported full-time employment; Sixty-one, or 92% did not report or were not identified as having any post-treatment arrests or incarceration, and; Forty, or 61% reported continuous abstinence from both drugs and alcohol.^ Five research-based hypotheses were developed and tested. The primary analysis tool was the web-based non-parametric dependency modeling tool, B-Course, which revealed some strong associations with certain variables, and helped the researchers generate and test several data-driven hypotheses. Full-time employment is the greatest predictor of abstinence: 95% of those who reported full time employment also reported continuous post-treatment abstinence, while 50% of those working part-time were abstinent and 29% of those with no employment were abstinent. Working with a 12-step sponsor, attending aftercare, and service with others were identified as predictors of abstinence.^ This study demonstrates that associations with abstinence and the ODM success criteria are not simply based on one social or behavioral factor. Rather, these relationships are interdependent, and show that abstinence is achieved and maintained through a combination of several 12-step recovery activities. This study used a simple assessment methodology, which demonstrated strong associations across variables and outcomes, which have practical applicability to the Open Door Mission for improving its treatment program. By leveraging the predictive capability of the various success determination methodologies discussed and developed throughout this study, we can identify accurate outcomes with both validity and reliability. This assessment instrument can also be used as an intervention that, if operationalized to the Mission’s clients during the primary treatment program, may measurably improve the effectiveness and outcomes of the Open Door Mission.^

An assessment of post-disaster psychological stress in hazardous waste operations and emergency response (HAZWOPER) workers

Personnel involved in natural or man-made disaster response and recovery efforts may be exposed to a wide variety of physical and mental stressors that can exhibit long-lasting and detrimental psychopathological outcomes. In a disaster situation, huge numbers of "secondary" responders can be involved in contaminant clean-up and debris removal and can be at risk of developing stress-related mental health outcomes. The Occupational Safety and Health Administration (OSHA) worker training hierarchy typically required for response workers, known as "Hazardous Waste Operations and Emergency Response" (HAZWOPER), does not address the mental health and safety concerns of workers. This study focused on the prevalence of traumatic stress experienced by secondary responders that had received or expressed interest in receiving HAZWOPER training through the National Institute of Environmental Health Sciences Worker Education and Training Program (NIEHS WETP). ^ The study involved the modification of two preexisting and validated survey tools to assess secondary responder awareness of physical, mental, and traumatic stressors on mental health and sought to determine if a need existed to include traumatic stress-related mental health education in the current HAZWOPER training regimen. The study evaluated post-traumatic stress disorder (PTSD), resiliency, mental distress, and negative effects within a secondary responder population of 176 respondents. Elevated PTSD levels were seen in the study population as compared to a general responder population (32.9% positive vs. 8%-22.5% positive). Results indicated that HAZWOPER-trained disaster responders were likely to test positive for PTSD, whereas, untrained responders with no disaster experience and responders who possessed either training or disaster experience only were likely to test PTSD negative. A majority (68.75%) of the population tested below the mean resiliency to cope score (80.4) of the average worker population. Results indicated that those who were trained only or who possessed both training and disaster work experience were more likely to have lower resiliency scores than those with no training or experience. There were direct correlations between being PTSD positive and having worked at a disaster site and experiencing mental distress and negative effects. However, HAZWOPER training status does not significantly correlate with mental distress or negative effect. ^ The survey indicated clear support (91% of respondents) for mental health education. The development of a pre- and post-deployment training module is recommended. Such training could provide responders with the necessary knowledge and skills to recognize the symptomology of PTSD, mental stressors, and physical and traumatic stressors, thus empowering them to employ protective strategies or seek professional help if needed. It is further recommended that pre-deployment mental health education be included in the current HAZWOPER 24- and 40-hour course curriculums, as well as, consideration be given towards integrating a stand-alone post-deployment mental health education training course into the current HAZWOPER hierarchy.^