<p>RPL23a is one of the ~80 ribosomal proteins (r-proteins) of the cytoplasmic ribosome in the model plant <i>Arabidopsis thaliana</i>. The objectives of this research were to establish Arabidopsis RPL23a as a functional r-protein, characterize expression patterns for the two genes (RPL23aA and B) encoding RPL23a using reverse transcription PCR (RT-PCR), and identify regulatory elements controlling the expression of RPL23aA and B. Complementation of a yeast l25 mutant with AtRPL23aA demonstrated that AtRPL23aA can fulfill all the essential functions of L25 in vivo. A survey of various Arabidopsis tissue types showed that, while RPL23aA and B expression patterns both showed increased transcript abundance in mitotically active tissues, RPL23aB transcript levels were generally lower than those of RPL23aA and responded differently to abiotic stresses. In order to determine cis regulatory elements controlling RPL23aA and B expression, the 5 regulatory region (RR) of each gene was characterized via plants carrying a series of 5 RR deletion fragments upstream of a reporter. Transcript start sites and 5 untranslated regions (UTRs) for both RPL23aA and B were also characterized using primer extension, and transcripts from 5 deletion transgenics were amplified using RT-PCR. No correlation was observed between putative cis-acting elements and the expression patterns from the RPL23aA and B deletion transgenics, although a 102 bp sequence in the RPL23aB 5 RR was found to contain pollen-specific elements. 5 leader introns were found in each RPL23a gene, and amplification of transgene transcripts from deletion series plants indicated the importance of post-transcriptional and translational regulation in RPL23aA and B expression. This thesis work is the first demonstration of a plant RPL23a protein as a functional member of the L23/L25 (L23p) conserved r-protein family, and is one of the few in-depth studies of the regulation of r-protein genes in plants. While the majority of previous research on plant r-protein gene expression has focused solely on transcript levels, I show herein that post-transcriptional mechanisms have a critical role in regulating these genes, and thus plant r-protein genes more strongly resemble their mammalian counterparts than those of yeast in terms of structure and regulation.

Hematopoietic systems in vertebrates have been the focus of intense study. However immunocyte development is well characterized in very few invertebrate groups. The sea urchin is an attractive model for the study of immune cell development. Larval immunocytes, pigment cells and derivatives of the blastocoelar cells, emerge from a small population of precursors specified at blastula stage. Analyses from the genome reveal a complex system of immune receptors and effectors and a near complete set of homologues of vertebrate transcriptional regulators. Characterization of the expression profile and function of sea urchin homologues of key vertebrate hematopoietic transcription factors imply a conserved role in immunocyte development. SpGatac, an orthologue of the vertebrate Gata-1/2/3 transcription factors and SpScl, an orthologue of Scl/Tal-2/Lyl-1 transcription factors are both required for immune cell specification in the embryo. An important cis-regulatory mechanism that restricts SpGatac expression to the blastocoelar cells involves repression by SpGcm in the pigment cells. Characterization of the expression of several additional transcription factors, including SpE2A, an orthologue of vertebrate E2A/HEB/ITF2, SpId, an orthologue of the Class V bHLH factors that modulate E-protein function, and SpLmo2, an orthologue of the cofactor part of the transcriptional complex that includes Scl and Gata family members, suggests the existence of a conserved regulatory complex for hematopoiesis. Two isoforms of the SpE2A gene were identified. The shorter isoform shares genomic organization and sequence conservation with the mouse paralogue of E2A, HEBAlt. Expression of SpE2A and SpE2AAlt is consistent with a function in immunocyte development in the sea urchin embryo. Findings of the counterpart to a key vertebrate regulatory system functioning in the development of immunocytes in the simple sea urchin embryo lay the foundation for comparative immunocyte developmental gene regulatory network analyses. These will in turn lead to a greater understanding of the evolution of immune systems across phyla and will provide simple invertebrate model systems for detailed comparative investigations of regulatory function with direct relevance to vertebrates.

This study examines how the Securities and Exchange Commission balances competing regulatory objectives in its decisions to approve requests to withhold proprietary information from firms' financial reports. The confidential treatment process requires the SEC to balance the public interest in protecting proprietary information with the public interest in promoting disclosures to investors. I draw upon the economic and political science literatures on regulatory decision-making to test the strength of these interests on three aspects of the SEC's decisions to grant confidential treatment: the duration of time required to approve the request, the duration of time the SEC agrees to protect proprietary information from disclosure, and whether the firm is successful in securing confidential treatment for all redacted information. I find that the public interest in promoting disclosure and protecting proprietary information influence different aspects of the SEC's decisions to grant regulatory exemptions for confidential treatment. Firms requiring greater monitoring by the SEC receive greater scrutiny and have lower odds of successful redaction. High proprietary costs are associated with significantly longer protection periods but proprietary costs generally are not associated with duration to approval or the success of the application. Finally, I find that the SEC applies greater scrutiny to firms exhibiting objective and salient measures of low financial reporting quality although these firms have higher odds of success. These findings are consistent with the SEC reviewing CTRs to reduce the risk of legislative oversight. This study contributes to the literature on disclosure regulation by providing evidence as to how securities regulators balance competing interests when reviewing requests for disclosure exemptions. These findings also contribute to the role of political influence on disclosure policy, as the SEC's exemption decisions are consistent with avoiding the threat of legislative oversight. Second, these findings contribute to the literature on the SEC's regulatory decisions by demonstrating that the SEC staff appears to allocate resources and apply scrutiny to applications for disclosure exemptions using aspects of registered firms' financial reporting quality. Third, these findings contribute to the literature on redaction as a disclosure choice by providing evidence suggesting that firms with low financial reporting quality are more likely to redact, and I provide evidence on the success of this disclosure choice. Overall, these findings suggest that the public interest in promoting disclosure, as well as the threat of legislative oversight, influence the SECs decisions when granting regulatory exemptions to protect proprietary information.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

Genetic regulatory networks are of great importance in terms of scientific interests and practical medical importance. Since a number of high-throughput measurement devices are available, such as microarrays and sequencing techniques, regulatory networks have been intensively studied over the last decade. Based on these high-throughput data sets, statistical interpretations of these billions of bits are crucial for biologist to extract meaningful results. In this thesis, we compare a variety of existing regression models and apply them to construct regulatory networks which span trancription factors and microRNAs. We also propose an extended algorithm to address the local optimum issue in finding the Maximum A Posterjorj estimator. An E. coli mRNA expression microarray data set with known bona fide interactions is used to evaluate our models and we show that our regression networks with a properly chosen prior can perform comparably to the state-of-the-art regulatory network construction algorithm. Finally, we apply our models on a p53-related data set, NCI-60 data. By further incorporating available prior structural information from sequencing data, we identify several significantly enriched interactions with cell proliferation function. In both of the two data sets, we select specific examples to show that many regulatory interactions can be confirmed by previous studies or functional enrichment analysis. Through comparing statistical models, we conclude from the project that combining different models with over-representation analysis and prior structural information can improve the quality of prediction and facilitate biological interpretation. Keywords: regulatory network, variable selection, penalized maximum likelihood estimation, optimization, functional enrichment analysis.

In order to facilitate trade between EU members, the European Committee has created regulations that will govern supervision of organic products. Every regulatory agency shall, according to the European Committees regulations, carry out a risk classification of each organic producer they regulate. This study for The National Food Administration compares regulation of Swedish organic products with other countries, and aims to produce a simplified model based on risk that can be the beginning of the risk classification model that Sweden in the current situation don’t have. The thesis is based on three questions: 1) What criteria should we use for assigning organic food producers? 2) How do selected European countries and frontrunners rate organic producers and what can Sweden learn from them? 3) How might a national classification model of organic production look in order to ensure an equivalent level of regulation and prevent competition among private inspection bodies? A review of information gathered from the different countries gave differing results. Few real classification models were provided, and instead only guidance and manuals were received. The simplified classification model that has been recommended as a model for the Swedish risk classification is largely taken from the Norwegian control body Debio, which in the current situation seems to have one of the most developed risk classification models in Europe. The conclusion of this study is that

<p>RPL23a is one of the ~80 ribosomal proteins (r-proteins) of the cytoplasmic ribosome in the model plant <i>Arabidopsis thaliana</i>. The objectives of this research were to establish Arabidopsis RPL23a as a functional r-protein, characterize expression patterns for the two genes (RPL23aA and B) encoding RPL23a using reverse transcription PCR (RT-PCR), and identify regulatory elements controlling the expression of RPL23aA and B. Complementation of a yeast l25 mutant with AtRPL23aA demonstrated that AtRPL23aA can fulfill all the essential functions of L25 in vivo. A survey of various Arabidopsis tissue types showed that, while RPL23aA and B expression patterns both showed increased transcript abundance in mitotically active tissues, RPL23aB transcript levels were generally lower than those of RPL23aA and responded differently to abiotic stresses. In order to determine cis regulatory elements controlling RPL23aA and B expression, the 5 regulatory region (RR) of each gene was characterized via plants carrying a series of 5 RR deletion fragments upstream of a reporter. Transcript start sites and 5 untranslated regions (UTRs) for both RPL23aA and B were also characterized using primer extension, and transcripts from 5 deletion transgenics were amplified using RT-PCR. No correlation was observed between putative cis-acting elements and the expression patterns from the RPL23aA and B deletion transgenics, although a 102 bp sequence in the RPL23aB 5 RR was found to contain pollen-specific elements. 5 leader introns were found in each RPL23a gene, and amplification of transgene transcripts from deletion series plants indicated the importance of post-transcriptional and translational regulation in RPL23aA and B expression. This thesis work is the first demonstration of a plant RPL23a protein as a functional member of the L23/L25 (L23p) conserved r-protein family, and is one of the few in-depth studies of the regulation of r-protein genes in plants. While the majority of previous research on plant r-protein gene expression has focused solely on transcript levels, I show herein that post-transcriptional mechanisms have a critical role in regulating these genes, and thus plant r-protein genes more strongly resemble their mammalian counterparts than those of yeast in terms of structure and regulation.

<p>Naturally occurring CD4+CD25+Foxp3+ regulatory T cells (TReg) are a cell lineage that develops in the thymus and exits to the periphery, where they represent 5-10% of the peripheral CD4+ T cell population. Phenotypically, they are characterized by the expression of the cell surface markers CD25, as known as the IL-2 receptor alpha chain, glucocorticoid-induced tumor necrosis factor receptor (GITR), and cytotoxic T-lymphocyte antigen-4 (CTLA-4), as well as forkhead box P3 (Foxp3), a transcription factor considered to be the most specific TReg marker. Functionally, TReg cells are defined by their ability to suppress the activation of multiple cell types including CD4+ and CD8+ T cells, B cells, natural killer (NK) cells, and dendritic cells (DCs). Suppression can be achieved by the production of immunosuppressive cytokines or direct cell-to-cell contact, with these mechanisms directly affecting suppressed cells or indirectly affecting them by modulating antigen presenting cells (APCs). The suppressive abilities of TReg cells are crucial in maintaining dominant tolerance--the active, trans-acting suppression of the immune system for the prevention of autoimmune diseases. In addition to preventing autoimmune diseases, studies have also demonstrated critical roles for TReg cells in down-modulating anti-tumor immunity, suppressing allergic diseases, such as asthma, and achieving transplant tolerance. Recent studies have also demonstrated roles for TReg cells during pathogen infection, which will be the focus of this thesis.</p><p>Studies examining TReg cells during infection have largely focused on chronic infection models. These studies have shown that TReg cells can affect responses to pathogens in various ways that can be beneficial or detrimental for either the host or the invading pathogen. In some infections, TReg cells downregulation effector responses, which can lead to pathogen persistence and, in some cases, concomitant immunity. TReg cell-mediated suppression can also reduce immunopathology at sites of infection, which can occur as a result of a vigorous anti-pathogen immune response. </p><p>In contrast to chronic infection, how TReg cells behave and function following acute infections remains largely unknown as, to date, very few studies have been conducted. Current work with acute infection models has indicated that TReg cells affect immune responses in some acute infection models, but not in all. Furthermore, the results of these studies have implicated that current approaches to examine TReg cells during acute infection by depleting the total TReg cell repertoire, as opposed to targeting pathogen-specific TReg cells, may not be ideal. Finally, it is unclear what happens to activated TReg cells following the resolution of infection. </p><p>Due to the lack of knowledge about the role of pathogen-specific TReg cells during acute infection, we sought to employ a different approach to address some of the outstanding questions in the field. Here, we utilized CD4+ non-TReg and TReg cells from T cell receptor (TCR) transgenic mice that recognize a pathogen-specific epitope found in three different models of acute viral infection: recombinant vaccinia virus, recombinant adenovirus, and influenza. Using this model system, we were able to track pathogen-specific TReg cells following acute viral infection to determine their kinetics during the course of infection, as well as their influence on CD4+ non-TReg cells during different times after infection. We also employed major histocompatibility complex (MHC) Class II tetramer technology to track the fate of endogenous pathogen-specific TReg cells following infection with influenza. </p><p>Using these models systems, we show that pathogen-specific TReg cells can be activated and expand upon acute viral infections in vivo. The activated TReg cells then contract to form a "memory" pool after resolution of the infection. These "memory" TReg cells expand rapidly upon a secondary challenge, secrete large amounts of IL-10, and suppress excessive immunopathology, which is elicited by the expansion of non-TReg cells, via an IL-10-dependent mechanism. The work presented in this thesis reveals a previously unknown "memory" TReg cell population that develops after acute viral infections and may help design effective strategies to circumvent excessive immunopathology.</p> / Dissertation

The construction of Gene Regulatory Networks (GRNs) is one of the most important issues in systems biology. To infer a large-scale GRN with a nonlinear mathematical model, researchers need to encounter the time-consuming problem due to the large number of network parameters involved. In recent years, the cloud computing technique has been widely used to solve large-scale problems. Among others, Hadoop is currently the most well-known and reliable cloud computing framework, which allows users to analyze large amount of data in a distributed environment (i.e., MapReduce). It also supports data backup and data recovery mechanisms. This study proposes an Island-based GAPSO algorithm under the Hadoop cloud computing environment to infer large-scale GRNs. GAPSO exploited the position and velocity functions of PSO, and integrated the operations of Genetic Algorithm. This approach is often used to derive the optimal solution in nonlinear mathematical models. Several sets of experiments have been conducted, in which the number of network nodes varied from 50 to 125. The experiments were executed in the Hadoop distributed environment with 10, 20, and 26 computers, respectively. In the experiments of inferring the network with 125 gene nodes on the largest Hadoop cluster (i.e. 26 computers), the proposed framework performed up to 9.7 times faster than the stand-alone computer. It means that our work can successfully reduce 90% of the computation time in a single experimental run.

This dissertation explores the socio-cognitive system of collective influences on consumers’ evaluation and decision processes, which have not been discussed fully in consumer literature, by examining how people resolve a conflict between group orientation and trait regulatory focus. It is proposed that, depending on the interaction between group commitment and collective efficacy, consumers implement one of three conflict resolution processes (i.e., depersonalization, compliance, and self-preservation) to determine the outcome of their regulatory shift. The impact of these three conflict resolution processes on regulatory shift and following message evaluations are tested with a series of six experiments. The results of these studies showed that people shift their regulatory orientation from trait regulatory focus to group orientation if the group identity is strong enough (experiment 1 and 2), the impact of group orientation on message evaluation is stronger when group members have higher group commitment (i.e., depersonalization; experiment 3 and 4) or experience higher collective efficacy (i.e., compliance; experiment 5), and people experience regulatory non-fit when they follow compliance process and generated less favorable message evaluations than when they follow depersonalization process (experiment 6). These findings highlight the importance of understanding group influence on a consumer’s mindset that consequently affects his/her various psychological processes and consumption behaviors.