In the distal lung, alveolar epithelial type I cells (AT1s) comprise the vast majority of alveolar surface area and are uniquely flattened to allow the diffusion of oxygen into the capillaries. This structure along with a quiescent, terminally differentiated phenotype has made AT1s particularly challenging to isolate or maintain in cell culture. As a result, there is a lack of established models for the study of human AT1 biology, and in contrast to alveolar epithelial type II cells (AT2s), little is known about the mechanisms regulating their differentiation. Here we engineer a human in vitro AT1 model system through the directed differentiation of induced pluripotent stem cells (iPSC). We first define the global transcriptomes of primary adult human AT1s, suggesting gene-set benchmarks and pathways, such as Hippo-LATS-YAP/TAZ signaling, that are enriched in these cells. Next, we generate iPSC-derived AT2s (iAT2s) and find that activating nuclear YAP signaling is sufficient to promote a broad transcriptomic shift from AT2 to AT1 gene programs. The resulting cells express a molecular, morphologic, and functional phenotype reminiscent of human AT1 cells, including the capacity to form a flat epithelial barrier which produces characteristic extracellular matrix molecules and secreted ligands. Our results indicate a role for Hippo-LATS-YAP signaling in the differentiation of human AT1s and demonstrate the generation of viable AT1-like cells from iAT2s, providing an in vitro model of human alveolar epithelial differentiation and a potential source of human AT1s that until now have been challenging to viably obtain from patients.

The present study set out to evaluate the relationship between list-method directed forgetting and one’s individual differences. Previous research has found personality and emotion as having an influence in how well participants were able to intentionally forget stimuli. Participants were split into a remember group and a forget group of 22 each and tasked to memorize a list of 10 words. They were then given a free recall test and the results for individual differences such as Need for Cognition, Mini-IPIP personality test, and Beck’s Depression Inventory were analyzed. Our first hypothesis presumes that participants in the forget group will have impaired recall of words. The second hypothesis predicts that individual differences have an effect with how many words participants recall. Results in this study indicated that while individual measures proved not significant between both groups, overall recall for the first list was lower than recall for the second list. There were also indications of an interaction between amount recalled from lists and whether they were in the remember group or in the forget group. Analyses showed that remember group had a recall mean similar in lists 1 and list 2, while the forget group had a higher recall mean in list 2 and a lower recall mean in the list 1, indicating that directed forgetting had taken place in the forget group.

Advanced architecture processors provide features such as caches and branch prediction that result in improved, but variable, execution time of software. Hard real-time systems require tasks to complete within timing constraints. Consequently, hard real-time systems are typically designed conservatively through the use of tasks? worst-case execution times (WCET) in order to compute deterministic schedules that guarantee task?s execution within giving time constraints. This use of pessimistic execution time assumptions provides real-time guarantees at the cost of decreased performance and resource utilization. In soft real-time systems, however, meeting deadlines is not an absolute requirement (i.e., missing a few deadlines does not severely degrade system performance or cause catastrophic failure). In such systems, a guaranteed minimum probability of completing by the deadline is sufficient. Therefore, there is considerable latitude in such systems for improving resource utilization and performance as compared with hard real-time systems, through the use of more realistic execution time assumptions. Given probability distribution functions (PDFs) representing tasks? execution time requirements, and tasks? communication and precedence requirements, represented as a directed acyclic graph (DAG), this dissertation proposes and investigates algorithms for constructing non-preemptive stochastic schedules. New PDF manipulation operators developed in this dissertation are used to compute tasks? start and completion time PDFs during schedule construction. PDFs of the schedules? completion times are also computed and used to systematically trade the probability of meeting end-to-end deadlines for schedule length and jitter in task completion times. Because of the NP-hard nature of the non-preemptive DAG scheduling problem, the new stochastic scheduling algorithms extend traditional heuristic list scheduling and genetic list scheduling algorithms for DAGs by using PDFs instead of fixed time values for task execution requirements. The stochastic scheduling algorithms also account for delays caused by communication contention, typically ignored in prior DAG scheduling research. Extensive experimental results are used to demonstrate the efficacy of the new algorithms in constructing stochastic schedules. Results also show that through the use of the techniques developed in this dissertation, the probability of meeting deadlines can be usefully traded for performance and jitter in soft real-time systems.

A study was conducted to determine the effects of directed discovery-based teaching methods (hands-on learning) and weekly quizzes on short-term learning and long-term retention of course material in an introductory geosciences laboratory course. Assessment of learning was accomplished using percentages of correct responses to questions on two tests, using percentages from the first semester of the study as a baseline to which data from each subsequent semester were compared to determine the effects of the study variable that was introduced. Student evaluations of value, meaning, and enjoyment of the course were also investigated through the use of an essay question at the end of the second test. The study revealed that directed discovery-based methods were successful for the teaching of some subject material, but not for all, and that the method did not necessarily enhance learning of scientific vocabulary. Weekly quizzes resulted in improved learning in all subject areas. Simultaneous use of traditional and directed-discovery teaching methods as well as weekly quizzes is recommended.

This research presents a hybrid algorithm that combines List Scheduling (LS) with a Genetic Algorithm (GA) for constructing non-preemptive schedules for soft real-time parallel applications represented as directed acyclic graphs (DAGs). The execution time requirements of the applications' tasks are assumed to be stochastic and are represented as probability distribution functions. The performance in terms of schedule lengths for three different genetic representation schemes are evaluated and compared for a number of different DAGs. The approaches presented in this research produce shorter schedules than HLFET, a popular LS approach for all of the sample problems. Of the three genetic representation schemes investigated, PosCT, the technique that allows the GA to learn which tasks to delay in order to allow other tasks to complete produced the shortest schedules for a majority of the sample DAGs.

Bayesian network is a popular machine learning tool for modeling uncertain dependence relationships among the random factors of a domain. It represents the relations qualitatively by using a directed acyclic graph (DAG) and quantitatively by using a set of conditional probability distributions. Several exact algorithms for learning optimal Bayesian networks from data have been developed recently. However, these algorithms are still inefficient to some extent. This is not surprising because learning Bayesian network has been proven to be an NP-Hard problem. Based on a critique of these algorithms, this thesis introduces a new algorithm based on heuristic search for learning optimal Bayesian.

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