Melanocortin receptors are a family of G-protein-coupled receptors (GPCRs) that regulate many important physiological functions. Specifically, melanocortin-1-receptor (MC1R) on skin melanocytes regulates skin pigmentation, which is a natural protection against UV-induced DNA damage, the major risk for melanoma. However, the lack of selectivity to MC1R and the requirements of needle injections of currently available peptide drugs greatly hinder the development of MC1R agonists as melanoma prevention medicines. This thesis mainly describes efforts to develop MC1R selective peptide agonists. Inspired by previous structure-activity studies and receptor mutagenesis studies, the peptide drug design enhances MC1R selectivity through removing strong ionic interactions between ligand and melanocortin receptors other than MC1R, introducing local structural constrains on the backbone (N-methylations) and sidechain (φ and ψ angle constrains) as well as global conformational constrains on cyclic peptides. The other emphasis of this thesis is to develop peptide drugs that are accessible to the target receptors and can be taken in a non-invasive way. To achieve this goal, different peptide drug design templates were exploited. Lead compounds with potentials for different applications include: 1. a long linear peptide suitable to be directly applied to the skin surface and induce skin pigmentation with less concerns on side effects; 2. tetrapeptides with great potential to be developed into oral administration drugs to induce skin pigmentation; 3. cyclic peptides suitable to target the MC3R or the MC4R in the brain; 4. an orally available cyclotide to activate the MC1R. This thesis also describes the opportunity to target MC1R-overexpressing melanoma cells with MC1R agonist for targeted drug delivery. Such approach can bring versatility to the cytotoxic drug selection, which is the key to solve the serious drug resistance problem of melanoma. Future opportunities exist to develop peptide drugs targeting different signaling pathways downstream of melanocortin receptors to regulate physiological functions governed by the same melanocortin receptor subtype.

Objective: Despite the limited evidence, a threshold of 80% proportion of days covered (PDC) is used to categorize medication adherence. The objective of this study was to assess the association of 1) antidiabetic, 2) renin-angiotensin system antagonists (RASA), and 3) statin medication adherence, at deciles of PDC, with disease-specific and all-cause economic outcomes (inpatient utilization and total healthcare costs) to identify optimal medication adherence thresholds using the law of diminishing returns. Methods: This retrospective cohort study included individuals from the Truven Health MarketScan® Commercial Claims and Encounters Research Databases (2010-2012) eligible for inclusion in the Pharmacy Quality Alliance diabetes, RASA, and statin medication adherence measures with non-capitated health plans. Generalized linear models (GLMs) with log link and gamma (costs) or negative binomial (utilization) distributions were used to assess the relationship of adherence with economic outcomes while adjusting for covariables (e.g., age, gender, Charlson comorbidity index). An alpha level of 0.01 was set a priori. Beta coefficients were used to compute use ratios and cost ratios and plotted to generate use and cost reduction functions. Marginal use and cost reduction curves were estimated and points of diminishing marginal returns and maximum returns were identified. Results: A total of 404,108 (diabetes), 1,329,576 (RASA), and 1,266,066 (statin) individuals were included in the study cohorts. Of the 120 GLMs that assessed the relationship between adherence and economic outcomes, 116 significant associations were identified (all p<0.0001). Of these, 98 models identified that adherence was associated with lower cost and utilization compared to nonadherence. Eighteen models showed adherence was associated with higher healthcare costs than nonadherence. The following adherence thresholds were identified as the optimal range of medication adherence thresholds (i.e., points of diminishing marginal and maximum returns): between 86% and 91% PDC for diabetes, 83% and 89% PDC for RASA, and 90% and 96% PDC for statin medications. Conclusions: The law of diminishing returns can be successfully applied to medication-taking behavior to derive optimal adherence thresholds in a commercially-insured patient population. Reliance on the 80% PDC adherence threshold should be re-evaluated to optimize benefits of diabetes, RASA, and statin medication adherence. The application of the law of diminishing returns in other patient populations and medication classes is warranted.

Ranking can be defined in many ways and has been applied in many areas. Over the past years, many research studies have been conducted on ranking methods for decision-making. The issue is that results pertaining to ranking entities are based only on one method, which can be subjective and prone to biases. As a result, results vary from method to method. To resolve this issue, a combined model approach is proposed, in which multiple methods are taken into account. The goals of the combined model are to rank entities more objectively and obtain more reliable results. The combined model has both qualitative and quantitative elements, where the qualitative element is the ranking and clustering, while the quantitative element is comprised of the scores. Since ranking is based on the scores of the entities, it takes into account the distribution of scores. In some scenarios, closely ranked entities can have similar scores, while in other scenarios, their scores can be relatively different even though they are ranked close to one another. The score distribution leads to clustering analysis, where entities are divided into clusters based on the spread of the scores. Hence, the combined model takes into account not only the ranks but also the scores of the entities. The proposed combined model is applied to three areas for this dissertation research. The first is identifying and ranking road hotspots and predicting the number of traffic crashes in road segments using the Empirical Bayesian (EB) enhanced by the Proportion Discordance Ratio (PDR) metric. The effectiveness of the Enhanced EB method is tested and demonstrated through a case study that is conducted in one of the major highways in Phoenix, Arizona. The second is ranking major US metropolitan areas in traffic congestion using unsupervised learning based on the Normalized Scoring Method (NSM), Principal Component Analysis (PCA), and the PDR similarity matrix. In 2015, TomTom ranked Tucson as the 21st most congested metropolitan area in the US, and the unsupervised learning combined model is applied to assess TomTom’s traffic congestion ranking of the metropolitan areas in order to determine if Tucson is highly congested based on the proposed model. The third is ranking and assessing the Hall of Fame (HOF) worthiness of retired Major League Baseball players based on their performance statistics through supervised learning based on Support Vector Machines (SVM) and Neural Networks (NN). Players are considered for the HOF through a voting procedure, where voters are comprised of members of the media, but there is a possibility of voting bias that can favor or go against certain players. Results from all three scenarios show that the proposed combined models are more reliable and can more objectively rank entities in order to correct biases based on previous methodologies.

Individuals are not identical. Organisms of the same species usually exhibit diverse phenotypes, a phenomenon that extends not only to physical characteristics but to behavior as well. Indeed, consistent differences in behavior across individuals are now well cited across the animal kingdom and have been shown to be an integral part of the ecology and evolution of an organism. Yet, the proximate, physiological mechanisms upon which natural selection might act to either favor or eliminate variation is often not known. Here, I use social insect division of labor – essentially an exaggerated form of behavioral variation in which nestmate workers specialize in different tasks – as a study system for exploring potential neural mechanisms of inter-individual behavioral variation. This investigation also led me to test and review one of the most widely accepted hypotheses for division of labor, the results of which provide new and important insights into how and why social insect workers choose different tasks. In my first chapter I explored variation among Temnothorax rugatulus ant workers in their peripheral sensory organs, a potentially important, but often overlooked, source of behavioral variation (or, in the context of social insects, task specialization). The idea for this study was based on the popular “response threshold” mechanism for division of labor, whereby workers become specialized in tasks because they vary in their response (often assumed to be ‘sensitivity’) to task-associated stimuli. Potentially, variation in worker sensory organs might influence a worker’s ability to detect such work-associated stimuli. Though workers showed considerable variation in the number of antennal sensory structures, this variation was not related to variation in worker behavior. This led to my second chapter, in which I directly tested the response threshold hypothesis in T. rugatulus ants. Here, I found no evidence for individual variation in response thresholds to three different putative task stimuli, calling into question the ubiquity of this mechanism as a general driver of social insect division of labor. To follow up on this idea, in my third chapter I performed a comprehensive review of the response threshold hypothesis in the social insect literature and found that the empirical evidence for it is not as strong as is commonly assumed. This chapter also provides guidelines for how to test response thresholds, with the goal of becoming a valuable reference work for those studying both social insects and self-organization in complex systems in general. My last chapter scaled up to the colony level. I explored plasticity and constraint in task reallocation by challenging colonies of T. rugatulus with different types of perturbations. By considering multiple types of tasks, this study provided novel insight into how the variety of tasks a colony performs are regulated quite differently. Here, I found that colonies were able to achieve considerable flexibility through the use of two different strategies. Which strategy the colony used was task-dependent. In addition, colonies appeared to have two types of reserve workers upon which to draw when task needs increased, dependent on task type.

This dissertation consists of three experiments testing various aspects of sentence processing in non-native English speakers. Experiment 1 tested the effect of constraining sentence context on word recognition time (RT) in the first and second language. English language learners (L2) and English native speakers (L1) performed self-paced reading and listening tasks to see whether a semantically-rich preceding context would lead to the activation of a probable upcoming word prior to encountering it. The pre-access prediction model (e.g., Altmann & Kamide, 1999; McClelland & Rumelhart, 1981) posits that when the preceding context is semantically high-constraining, the perceptual system anticipates a probable upcoming word prior to encountering it. In contrast, the post-access model (e.g., Fodor, 1983; Forster, 1981; Traxler et al., 2000; Van Petten & Luka, 2012) suggests that it is only after a word is encountered that a subsequent process integrates it into the preceding context. The integration process is easier and faster when the word is more congruent with the preceding context. In line with these two models of visual word recognition, auditory word recognition is modeled by the Trace Model (McClelland & Elman, 1986) which suggests that spoken word is influenced by the preceding sentence context whereas models such as the Cohort Model (Marslen-Wilson, 1984) supports post-access integration process. Regarding L2 processing, some studies observed a facilitative effect of sentence context on L2 word recognition (e.g., Kamide, 2003) while others found no effect of sentence context in L2 (e.g., Ito et al., 2017; Martin et al., 2013). In the present experiment, the RTs of English native speakers (L1) and English non-native speakers (L2) were collected in both visual vs. auditory word recognition in semantically high-constraint sentences and semantically low-constraint sentences. A linear mixed effects model showed that both groups of participants were faster on a word when it was proceeded by a semantically high-constraining context. This result was observed in both the visual and the auditory modalities, lending some support for a mechanism facilitating access of target words based on sentence context in both L1 and L2. Experiment 2 also investigated the effect of a constraining sentence context on word recognition, but the target words have multiple meanings (e.g., bank: [1] a financial institution, [2] an edge of a river/lake). A self-paced reading task was performed by both English L1 and L2 participants, in which all sentences were biased for the secondary meaning (i.e., bank as an edge of a river/lake). The results showed that L1 participants were able to use the constraining context to activate the secondary meaning, but L2 participants had some difficulty in activating the secondary meaning even when the preceding context was biased for it. The results of the L1 participants are compatible with the Reordered Access Model (Duffy, Kambe, & Rayner, 2001; Duffy, Morris, & Rayner, 1988; Sheridan, Reingold, & Daneman, 2009) in that a preceding biasing context leads to an activation of the secondary meaning equal to that of the primary meaning. However, the results of the L2 participants are not compatible with this model’s explanation. Experiment 3 tested the recognition process of L2 words in formulaic sequences whose co-occurrence happens more often than by chance (e.g., The new job requires a teacher who is highly qualified) compared to a combination that sounds less natural (e.g., The new job requires a teacher who is hugely qualified) in a sentence context. Based on the Associative Relatedness Model (Postman & Keppel, 1970) that underlies the Spreading Activation theory (Collins & Loftus, 1975), a facilitative recognition of a target word occurs as a result of activation from an associated word. Thus, this experiment tested whether L2 speakers are sensitive to the less natural co-occurrence formulaic sequence in the same way as L1 speakers. Both language groups performed a self-paced reading task. The results showed that the RTs of L1 participants on the target word (e.g., qualified in the above example) is significantly longer in the less natural combination compared to the natural one. However, the RTs of L2 participants on both combination types were not significantly different from one another, although the L2 participants were able to detect the less natural combination in the post-experiment test. This result indicates a difference in processing between the declarative knowledge (i.e., the stored facts) and the procedural processing (i.e., putting the stored knowledge into action), which is more compatible with the declarative/procedural model proposed by Ullman (2001). The final part of this dissertation discusses some general aspects of L2 word processing when embedded in sentence contexts and some points to consider when conducting such tasks with non-native speakers.

BACKGROUND: Bullying victimization is correlated with depressive symptoms in adolescents. While the literature is extensive, there has been little focus on racial/ethnic minorities, specifically Latinx youth. In the United States, there is some evidence that Latinx adolescents experience bullying victimization and depressive symptoms at higher rates than their non-Hispanic white (NHW) peers. OBJECTIVES: This dissertation is composed of three studies that work together to identify factors of influence in the development of depressive symptoms within Latinx adolescents that experience peer violence: 1) a synthesis of the bullying/depression literature to evaluate Latinx representation and Latinx-specific factors; 2) identification of the interaction between racial/ethnic discrimination and bullying victimization on depressive symptoms; and 3) examination of the role of family and social support as a protective factor in the relationship between bullying victimization and depressive symptoms three years after victimization. METHODS: A systematic review was conducted for research aim one following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For research aims two and three, secondary data analysis was conducted utilizing the Healthy Passages national and longitudinal dataset with permission from the publication committee. Hierarchal regression analyses were conducted for research aims two and three. RESULTS: For research aim one, of 957 studies identified, 17 included a Latinx population of 25% or more. They all identified a relationship between bullying and depression, with nine examining factors related to race/ethnicity or unique to the Latinx population. For aims two and three, a sample of 1,666 Latinx adolescents (grade 7) reported bullying victimization rates of 60% within the previous year. For aim two specifically, 15.7% reported racial/ethnic discrimination and 14.4% reported bullying and discrimination victimization in the previous year. All forms of victimization were found to be significantly related to depressive symptoms, including the interaction between bullying and discrimination victimization (p<.001 for immediate effect, p<.05 three years later). For research aim three, parent/child connectedness had a moderation effect (p<.001, b=-.061; p=.011, b=.006), reducing the likelihood of depressive symptoms three years after victimization. Social support reduced the relationship to depressive symptoms (p<.001, b=.025) but was not a moderator, and global parental monitoring had no significant effect. CONCLUSIONS: The Latinx community is the fastest growing racial/ethnic minority population in the United States, but they are underrepresented in the bullying literature. Studies that included variables unique to Latinx communities such as acculturation and unique family factors found a stronger relationship between bullying victimization and depression. In a Latinx sample, this dissertation identified an increased likelihood of depressive symptoms when bullying and racial/ethnic discrimination were experienced. Additionally, this work found that strong family relationships and strong social support reduced the likelihood of depressive symptoms of bullying victimization even three years after victimization occurred. These two findings identify potential future directions for bullying research and practice. At a minimum, they illustrate a need for an expanded social-ecological lens when measuring victimization as well as the inclusion of family and the development of strong family relationships in bullying interventions.

Rhinovirus (RV) and influenza virus infections are the leading causes of airway tract problems that lead to cold and flu and exacerbated asthma, chronic obstructive pulmonary disease (COPD), and other respiratory illnesses. This dissertational study investigated the effects of host factors in these viral infections. Chapter 2 and chapter 3 are related research about rhinovirus, and chapter 4 is research about influenza virus. Human rhinovirus (RV) is the major cause of common cold and it also plays a significant role in asthma and asthma exacerbation. Airway epithelium is the primary site of RV infection and production. In contrast, monocytic cells (e.g., monocytes and macrophages) are believed to be non-permissive for RV replication. Instead, RV has been shown to modulate inflammatory gene expressions in these cells via a replication-independent mechanism. However, Johnston et al. firstly reported a low-grade productive replication of RV9 in monocytes and also showed the RV16 replication in monocytes in following studies. In the present study, we generated the epithelial cell- monocyte coculture system. RV16 (a major-group RV) replication was found to be significantly enhanced in monocytes when co-cultivated with airway epithelial cells. This effect appeared to be mediated by secretory components from epithelial cells, which stimulated RV16 replication and significantly elevated the expression of a number of proinflammatory cytokines. The lack of such effect with RV1A, a minor-group RV that enters the cell by a different receptor, suggests that ICAM1, the receptor for major-group RVs, may be involved. Conditioned media from epithelial cells significantly increased ICAM1 expression in monocytes. Consistently, ICAM1 overexpression and ICAM1 knockdown enhanced and blocked RV production, confirming the role of ICAM1 in this process. In addition, we isolated secretory components from the epithelial conditioned medium and further determined their function. Alpha-Heremans Schmid Glycoprotein (AHSG) is the top matched secretory protein to induce the ICAM1 expression. High AHSG expression is closely related to asthma in mice models and human patients. Thus, we demonstrated that airway epithelial cells direct significant RV16 replication in monocytic cells via an ICAM1-dependent mechanism. This is the first time we demonstrated that AHSG expression is related to asthma. Influenza virus is the major cause of influenza (or the flu). Pandemic flu killed tens of millions of people and seasonal flu yearly outbreak also caused severe illness and hundreds of thousands of deaths. Current options for preventing or treating influenza are either limited (e.g., vaccine) or becoming ineffective due to the emergence of drug resistant strains (e.g., M2 blockers). Thus, new treatments for influenza viral infection are urgently needed. Host targets are relatively stable, so the drugs have high barrier when they are targeting on the host factors. To develop drugs targeting the host, it is important to understand the influenza related pathway in the host cells. In the present study, we generated an influenza infectious model on human airway epithelial cells, which are primary sites for the virus’ infections. We serendipitously found that a small-molecule inhibitor (AG1478), previously used for epidermal growth factor receptor (EGFR) inhibition, demonstrated a potent and broad-spectrum activity against influenza. Surprisingly, the antiviral effect of AG1478 was not mediated by its EGFR inhibitory activity, as influenza was insensitive to EGFR blockade by other EGFR inhibitors or by the knockdown using a small interference RNA against EGFR. Additionally, interferons are the major anti-viral proteins in the cells, its effect has to be considered when we research on the anti-viral activity. A knockout approach using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) further demonstrated that this antiviral activity was also interferon independent. AG1478 was found to target on Golgi-specific brefeldin A-resistance guanine nucleotide exchange factor 1- ADP ribosylation factor 1 (GBF1-ARF1). AG1478 reversibly inhibit GBF1 activity and disrupted its Golgi-cytoplasmic trafficking. Compared to the two existing GBF1 inhibitors, AG1478 demonstrated lower cellular toxicity and better preservation of Golgi structure, suggesting its antiviral activity may not originate from a non-specific disruption of intracellular vesicle trafficking regulated by GBF1. GBF1 was found to interact with a specific set of viral proteins including M1, NP and PA. AG1478-elicited spatial alternation of GBF1 distribution disrupted these interactions. Because host factors are more genetically stable than viral proteins, host-targeting antivirals might have a higher genetic barrier to drug resistance than direct-acting antivirals. In conclusion, the host factors are crucial for these respiratory viral illnesses, and drugs targeting host factors are a new direction for treating infectious illnesses. 1) For rhinovirus, we demonstrate that epithelial secretions, especially AHSG, direct robust RV replication in monocytes via significantly increased ICAM1. This new information will advance our understanding of the interaction between airway epithelium and inflammatory cells in the context of RV infection and RV-induced disease exacerbation. Blocking ICAM1 or decreasing AHSG expression will be a new direction in treating rhinovirus infections. This finding will open a new venue for the study of RV infection in airway disease and its exacerbation. 2) For influenza virus, through a serendipitous finding, we have discovered a potent and broad-spectrum anti-influenza drug candidate-AG1478. Its antiviral activity is mediated by targeting GBF1.The treatment of AG1478 disrupted this interaction and potentially impaired vRNP transport leading to markedly decreased IAV production. It is a new direction to develop the drugs targeting GBF1. Further development on this candidate target will lead to novel anti-influenza therapy.

In the field of computational linguistics, computational modeling of linguistic behavior has been motivated not only by the creation of practical language-related tools such as machine translation, automatic speech recognition, speaker identification, and natural language search, but also by a desire to deepen our understanding of how language works, either in an abstract mathematical sense or in the more literal sense of describing human behavior at various levels of analysis. These models take various forms, some derived from mathematical models of electronic transmission of information (Shannon, 1948), others from abstract models of neural behavior (McCulloch and Pitts, 1943; Rosenblatt, 1958). In computational neuroscience, computer models are developed to mimic the behavior of brains, with a greater degree of biological realism. These models focus on neural behavior ranging from single neurons to large-scale networks of neurons. Typically, the behavior of interest is the relative activation of groups of neurons, the emergence of synchronized or otherwise patterned activation, and the propagation of signals across networks. The elaboration of relatively high-level cognitive behavior is, at best, secondary to the exploration of low-level physical and electrical interaction (Zednik, 2018). The growing field of computational cognitive neuroscience has as a goal the development of computational models that are biologically plausible and that exhibit cognitive behavior of interest (Ashby, 2011). Linguistic models of this type are intended to exhibit the kind of linguistic behavior that is observed in humans, but with an underlying structure and behavior that closely parallels the human brain. Marr (1982) offers a three-level framework for analyzing models of the brain that has become a standard in neuroscience (Bechtel, 2014). Applying this method of analysis to broad classes of computational models yields insights into the strengths and weaknesses of each. While the ideas in this dissertation may ultimately find broader relevance, it is presently concerned primarily with the modeling of phonemic acquisition in infants. Application of Marr’s analysis to the actual system being modeled – the human infant – suggests an approach to the development of acquisition models that departs significantly from traditional computational linguistics models and computational cognitive neuroscience models.

Madrid, es la imagen icónica del cine español. La ciudad, como figuración emblemática, se va construyendo a partir de sus múltiples representaciones cinematográficas. Las circunstancias socioculturales, políticas y los cambios estéticos que se advierten en el cine español en las décadas del 80 y 90 determinan otra forma de apropiación de la ciudad, haciéndola trascender de espacio real a espacio narrativo. Esta tesis quiere demostrar la incorporación del espacio urbano como parte integrante de la imagen cinematográfica y elemento esencial de la narración fílmica en activa interacción con el contexto sociocultural de la película; fundamentos estos que sitúan al espacio citadino como una categoría estética y un componente más del lenguaje cinematográfico. PALABRAS CLAVE: espacio urbano, lenguaje cinematográfico, espacio-tiempo-contexto.

Atomic and organic semiconductors interfaces with inorganic semiconductors are promising materials for use in next-generation electronic and optoelectronic devices. A predictive-level understanding of the interfaces is lacking which hinders rational design of such devices. This is attributed to the key processes, e.g. charge generation, separation, delocalization and extraction, which are heavily interfacial in nature and difficult to simulate. Thus, it is imperative to understand the fundamental processes at individual interfaces to enable design and optimization of next-generation technologies. This work aims to understand these interfaces by studying the interfacial processes using optical photoemission spectroscopy techniques. In this dissertation, I focus on alkali metal and molecular interfaces with layered materials to understand and control the dynamics and electronic and optical properties of the interface. Initially, I aim to characterize the nature of layered materials (SnS2 and MoS2) that exhibit unusual thickness-dependent properties, which already affords tunable materials properties. I demonstrate the anisotropy of the many-layered crystal at ultrafast timescales not yet realized. Pairing the layered material with thin films (K, CuPc and PTCDA) demonstrates layer decoupling and quantum confinement effects that show an enhancement of carrier delocalization at the interface. Furthermore, studies show a systematic tuning of the dielectric environment of the layered material dependent upon adsorbate thin film thickness. Overall, this work elucidates key interfacial interactions of select atoms and molecules on layered materials that can be used to understand this class of materials and their implementation in nanodevices.