Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor function. Pathological mechanisms and clinical measures vary extensively from patient to patient, creating a spectrum of disease phenotypes with a poorly understood influence on individual outcomes like disease duration. The inability to ascertain patient phenotype has hindered clinical trial design and the development of more personalized and effective therapeutics. Wholistic analytical methods (‘-omics’) have provided unprecedented molecular resolution into cellular and system level disease processes and offer a foundation to better understand ALS disease variability. Building off initiatives by the New York Genome Center ALS Consortium and Target ALS groups, the goal of this work was to stratify a large patient cohort utilizing a range of bioinformatic strategies and bulk tissue gene expression (transcriptomes) from the brain and spinal cord. Central Hypothesis: Variability in the onset and progression of ALS is partially captured by molecular subgroups (subtypes) with distinct gene expression profiles and implicated pathologies. Work presented in this dissertation addresses the following: (Chapter 2): The use of unsupervised clustering and gene enrichment methods for the identification and characterization of patient subtypes in the postmortem cortex and spinal cord. Results obtained from this Chapter establish three ALS subtypes, identify uniquely dysregulated pathways, and examine intra-patient concordance between regions of the central nervous system. (Chapter 3): Patient subtypes from Chapter 2 are considered in the context of clinical outcomes, leveraging multiple survival models and gene co-expression analyses. Results from this Chapter establish a weak association between ALS subtype and clinical outcomes including disease duration and age at symptom onset. (Chapter 4): Utilizing differential expression analysis, ‘marker’ genes are defined and leveraged with supervised classification (“machine learning”) methods to develop a suite of classifiers design to stratify patients by subtype. Results from this Chapter provide postmortem marker genes for two of the three ALS subtypes and offer a foundation for clinical stratification. Significance: Knowledge gained from this research provides a foundation to stratify patients in the clinic and prior to enrollment in clinical trials, offering a path toward improved therapies.

Thirty percent of engineering students suffer from extremely severe stress, which is associated with poor academic performance, decreased motivation, and poor mental health. As a result, new, effective techniques must be developed to improve student outcomes. A potential technique that could be valuable in the classroom is persuasion techniques. There are six primary persuasion techniques: reciprocity, liking, social proof, scarcity, commitment, and authority (coercive and expert). Persuasion has been studied exhaustively with respect to altering behavior (e.g., sales, compliance), but has only briefly been studied in education. Studies show that positive student-teacher relationships can improve grades, positive peer relationships can improve mental health, and coercive power can increase stress. No studies have examined all persuasion techniques with respect to student outcomes, and this study aims to fill that gap. The objective of this study is to evaluate the use of persuasion techniques in the classroom to improve mental health and enhance academic outcomes. I hypothesized that methods that enhance community and improve sense of belonging (reciprocity, commitment, liking, social proof) will lead to better academic and mental health outcomes, and methods associated with negative professor attitudes (coercive authority) will lead to poor academic and mental health outcomes. To evaluate these hypotheses, a sample of 336 university students were surveyed to see which persuasion techniques they perceived their professors to use and examine the effects of these on academic outcomes (grades, attendance, assignments) and mental health outcomes (engagement, positive impact, stress, well-being, executive function). The data partially supports the hypotheses, with various student academic and mental health outcomes significantly improving with higher use of liking, social proof, commitment, and expert authority, and worsening with higher use of coercive authority. In conclusion, by teaching professors to use liking, social proof, expert authority, and commitment in their classrooms while decreasing coercive techniques, professors can effectively improve student grades and mental health.