The discovery of gene regulatory network (GRN) using gene expression data is one of the promising directions for deciphering biological mechanisms, which underlie many basic aspects of scientific and medical advances. In this thesis, we focus on the reconstruction of GRN from time-series data using a Granger causality (GC) approach. As there is little existing research on combining data from multiple time-series experiments, we identify the need for developing a methodology with underlying theory to combine multiple experiments for statistical significant discovery. We derive a statistical theory for intersection of two discovered networks. Such a statistical framework is novel and intended for our GRN discovery problem. However, this theory is not limited to GRN or GC, and may be applied to other problems as long as one can take the intersection of discoveries obtained from multiple experiments (or datasets). We propose a number of novel methods for combining data from multiple experiments. Our single underlying model (SUM) method regresses data of multiple experiments in one go, enabling GC to fully utilize the information in the original data. Based on our statistical theory and SUM, we develop new meta-analysis methods, including union of pairwise common edges (UPCE) and leave-one-out hybrid of SUM and UPCE (LOOHSU). Applications on synthetic data and real data show that our new methods give discoveries of substantially higher precision than traditional meta-analysis. We also propose methods for estimating the precision of GC-discovered networks and thus fill in an important gap not considered in the literature. This allows us to assess how good a discovered network is in the case of unknown ground truth, which is typical in most biological applications. Our precision estimation by half-half splitting with combinations (HHSC) gives an estimate much closer to the true value compared with that computed from the Benjamini-Hochberg false discovery rate controlling procedure. Furthermore, using a network covering notion, we design a method that can identify a small number of links with high precision of around 0.8-0.9, which may relieve the burden of testing many hypothetical interactions of low precision in biological experiments. For the situation where the number of genes is much larger than the data length, in which case full-model GC cannot be applied, GC is often applied to the genes pairwisely. We analyze how spurious causalities (false discoveries) may arise. Consequently, we demonstrate that model validation can effectively remove spurious discoveries. With our proposed implementation that model orders are fixed by the Akaike information criterion and every model is subject to validation, we report a new observation that network hubs tend to act as sources rather than receivers of interactions. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

This research experimentally investigated the interactive effects of regulatory focus and brand personality on persuasion in advertising. Specifically, the study sought to know whether the interplay would increase a positive attitude toward the advertisement, the brand and purchase intention. The results of this present research showed that excitement brand personality was concerned with a promotion focus and when an exciting brand is advertised with a promotion focused message, the effectiveness of the advertisement was increased. In contrast, competence brand personality was concerned with prevention focus and when a competent brand is advertised with a prevention focused message, the effectiveness of the advertisement was increased. / text

No description available.

Multidrug resistance has become a major issue in the treatment of both microbial infections and cancers. While genetically encoded drug resistance is fairly well understood, it cannot explain all observed cases of resistance, namely the ability of a subset of disease cells to persist in an otherwise susceptible population. This non-genetic resistance requires the heterogeneous expression of a drug resistance phenotype, which can be produced by certain gene regulatory network architectures. Two particular network motifs, the coherent feedforward loop (CFFL) and the positive feedback loop (PFL), have functional properties that implicate them in the development of non-genetic heterogeneity and response to changing conditions. Motivated by the observation that CFFL and PFL motifs are involved in the transcriptional regulation of multiple pleiotropic drug resistance (PDR) genes in yeast, it has been hypothesized that CFFLs and PFLs could contribute to the development of drug resistance. This hypothesis was based on model simulations and has not been tested experimentally. In this thesis, it is demonstrated experimentally that the PDR5 gene is indeed expressed heterogeneously within an isogenic population of yeast cells, and that this cell-to-cell variability enables a subset of cells to persist drug treatment. While these observations agree with model predictions, it is also observed that the resistant phenotype occurs within a subset of cells that are morphologically distinct. This subpopulation has previously been linked to abnormal mitochondrial function, which cannot be ruled out as a likely cause of the observed drug resistance. To validate the hypothesis that CFFLs and PFLs contribute to drug resistance, the expression of the PDR5 gene was placed under the control of synthetic gene regulatory networks constructed to contain different combinations of direct activation, indirect activation, and positive feedback. These networks are used to show that direct activation can provide a selective advantage enabling rapid responses, while indirect activation and positive feedback can provide a selective advantage by maintaining favourable gene expression states. These results demonstrate that a gene regulatory network combining CFFLs and PFLs can contribute to the development of drug resistance, and highlight plausible means by which cells can exploit certain network features to gain a fitness advantage.

Résoudre une tâche nous met régulièrement au défit d'adopter les stratégies les plus adaptées au contexte et à la nature de celle-ci. En se basant sur la Théorie du Régulatory Focus, la présente étude propose d'utiliser certaines caractéristiques visuelles, formes et couleurs, afin d'amorcer une régulation cognitive susceptible de correspondre à des tâches demandant soit de la précision, soit de la créativité. Ainsi, une première étude identifie certaines formes et couleurs comme ayant la capacité d'activer une cognition soit de type « prevention », soit de type « promotion ». Ces résultats ont servi de base pour concevoir des interfaces graphiques amorçant de manière ciblée l'une ou l'autre de ces orientations cognitives. Il est suggéré que la concordance entre le type d'amorce (e.g., induisant une cognition « prevention »)et le type de tâche (e.g., demandant de la précision) influence positivement performance ainsi que vécu subjectif. Les résultats supportent ces hypothèses : Les participants exécutent plus correctement les tâches lorsque le type de celles-ci concorde avec l'orientation stratégique amorcée par les caractéristiques visuelles de l'interface graphique. De plus, les participants rapportent un vécu plus positif dans ces mêmes conditions. De manière générale, les résultats soulignent les avantages à utiliser le potentiel inhérent des caractéristiques visuelles afin d'optimiser une situation de résolution de tâche. Ces résultats sont considérés comme développant autant la recherche que les domaines plus appliqués. / Task-solving situations are often challenging our ability to approach them in the most adapted state of mind. Building on regulatory focus theory, we propose to use specific color and form characteristics to prime cognitive regulations that are likely to match either creative or accuracy framed tasks. In that line, a first study identified color and form characteristics as being involved in activating either a vigilant prevention cognition or a flexible promotion cognition. These findings were then used to visually design creative or accuracy oriented tasks. It was expected that a fit between the strategic orientations induced by these designs and tasks accuracy or creativity framing, would to positively influence both performance and overall subjective experience. Experimental results supported these predictions: Participants performed better when the visual design was composed of colors and forms inducing a regulatory orientation matching tasks' natures. Regulatory fit had also a global positive impact on subjective variables like fun and satisfaction. Overall, the results point out the advantage of taking benefit from visual characteristics' inherent priming potential. Our research can be related both to research domains (RFT, user experience research, color research) and to concrete applications in actual design processes.

Magister Pharmaceuticae - MPharm / The objective of the study was to perform a comparative review of pathways, timelines and improvements of countries with markets that the South African Health Products Authority (SAHPRA) benchmark themselves against. Furthermore, this study intends to identify the factors that improved and accelerated submissions and approval process in investigated countries and potential introduction of these strategies into the South African market.

Submitted to the faculty of the informatics Graduate Program in partial fulfillment of the requirements for the degree Master of Science in Bioinformatics in the School of Informatics, Indiana University, May 2006 / The publication of the draft human genome consisting of 30,000 genes is merely the beginning of genome biology. A new way to understand the complexity and richness of molecular and cellular function of proteins in biological processes is through understanding of biological networks. These networks include protein-protein interaction networks, gene regulatory networks, and metabolic networks. In this thesis, we focus on human protein-protein interaction networks using informatics techniques. First, we performed a thorough literature survey to document different experimental methods to detect and collect protein interactions, current public databases that store these interactions, computational software to predict, validate and interpret protein networks. Then, we developed the Human Annotated Protein-Protein Interaction (HAPPI) database to manage a wealth of integrated information related to protein functions, protein-protein functional links, and protein-protein interactions. Approximately 12900 proteins from Swissprot, 57900 proteins from Trembl, 52186 protein-domains from Swisspfam, 4084 gene-pathways from KEGG, 2403190 interactions from STRING and 51207 interactions from OPHID public databases were integrated into a single relational database platform using Oracle 10g on an IU Supercomputing grid. We further assigned a confidence score to each protein interaction pair to help assess the quality and reliability of protein-protein interaction. We hosted the database on the Discovery Informatics and Computing web site, which is now publicly accessible. HAPPI database differs from other protein interaction databases in these following aspects: 1) It focuses on human protein interactions and contains approximately 860000 high-confidence protein interaction records—one of the most complete and reliable sources of human protein interaction today; 2) It includes thorough protein domain, gene and pathway information of interacting proteins, therefore providing a whole view of protein functional information; 3) It contains a consistent ranking score that can be used to gauge the confidence of protein interactions. To show the benefits of HAPPI database, we performed a case study using Insulin Signaling pathway in collaboration with a biology team on campus. We began by taking two sets of proteins that were previously well studied as separate processes, set A and set B. We queried these proteins against the HAPPI database, and derived high-confidence protein interaction data sets annotated with known KEGG pathways. We then organized these protein interactions on a network diagram. The end result shows many novel hub proteins that connect set A or B proteins. Some hub proteins are even novel members outside of any annotated pathway, making them interesting targets to validate for subsequent biological studies.

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The Centers for Medicare and Medicaid Services (CMS) overhauled the reimbursement system for skilled nursing facilities (SNFs) with the intent to improve payments by ensuring therapy services were focused on meeting the needs and preferences for beneficiaries, rather than the volume of services provided. The Patient-Driven Payment Model (PDPM) went into effect on 10/1/19. Because the payment model employs a new methodology for reimbursement, there are multiple concerns that some SNFs may inappropriately manipulate therapy services to increase profits, such as by mandating therapists to maximize the use of group and concurrent therapy regardless of the resident’s needs. The doctoral program addresses PDPM from a regulatory oversight perspective. Surveyors will receive updated investigatory pathways and training regarding how to investigate therapy services to address the impact PDPM may be having on the provision of therapy, functional performance, and falls. Surveyors will have the knowledge and skills to ensure SNF beneficiaries across the nation receive quality, individualized rehabilitation services to satisfy the intent behind PDPM.

No description available.