Flexible enzymes are notoriously a bane to structure-based drug design and discovery efforts. This is because no single structure can accurately capture the vast array of conformations that exist in solution and many are subject to ligand-associated structural changes that are difficult to predict. Glutamate racemase (GR) – an antibiotic drug discovery target involved in cell wall biosynthesis – is one such enzyme that has eluded basic structure-based drug design and discovery efforts due to these flexibility issues. In this study, our focus is on overcoming the impediment of unpredictable ligand-associated structural changes in GR drug discovery campaigns. The flexibility of the GR active site is such that it is capable of accommodating ligands with very different structures. Though these ligands may bind to the same pocket, they may associate with quite dissimilar conformations where some are more favorable for complexation than others. Knowledge of these changes is invaluable in guiding drug discovery efforts, indicating which compounds selectively associate with more favorable conformations and are therefore better suited for optimization and providing starting structures to guide structure-based drug design optimization efforts. In this study, we develop a mutant GR possessing a genetically encoded non-natural fluorescent amino acid in a region remote from the active site whose movement has been previously observed to correlate with active site changes. With this mutant GR, we observe a differential fluorescence pattern upon binding of two structurally distinct competitive inhibitors known to associate with unique GR conformations – one to a favorable conformation with a smaller, less solvated active site and the other to an unfavorable conformation with a larger, more solvated active site. A concomitant computational study ascribes the source of this differential fluorescence pattern to ligand-associated conformational changes resulting in changes to the local environment of the fluorescent residue. Therefore, this mutant permits the elucidation of valuable structural information with relative ease by simply monitoring the fluorescence pattern resulting from ligand binding, which indicates whether the ligand has bound to a favorable or unfavorable conformation and offers insight into the general structure of this conformation.

Data containing large number of variables is becoming increasingly more common and sparsity inducing penalized regression methods, such the lasso, have become a popular analysis tool for these datasets due to their ability to naturally perform variable selection. However, quantifying the importance of the variables selected by these models is a difficult task. These difficulties are compounded by the tendency for the most predictive models, for example those which were chosen using procedures like cross-validation, to include substantial amounts of noise variables with no real relationship with the outcome. To address the task of performing inference on penalized regression models, this thesis proposes false discovery rate approaches for a broad class of penalized regression models. This work includes the development of an upper bound for the number of noise variables in a model, as well as local false discovery rate approaches that quantify the likelihood of each individual selection being a false discovery. These methods are applicable to a wide range of penalties, such as the lasso, elastic net, SCAD, and MCP; a wide range of models, including linear regression, generalized linear models, and Cox proportional hazards models; and are also extended to the group regression setting under the group lasso penalty. In addition to studying these methods using numerous simulation studies, the practical utility of these methods is demonstrated using real data from several high-dimensional genome wide association studies.

The primary research aim of this dissertation is to identify the strategies that best meet the information retrieval needs as expressed in the "e-discovery" scenario. This task calls for a high-recall system that, in response to a request for all available relevant documents to a legal complaint, effectively prioritizes documents from an enterprise document collection in order of likelihood of relevance. High recall information retrieval strategies, such as those employed for e-discovery and patent or medical literature searches, reflect high costs when relevant documents are missed, but they also carry high document review costs. Our approaches parallel the evaluation opportunities afforded by the TREC Legal Track. Within the ad hoc framework, we propose an approach that includes query field selection, techniques for mitigating OCR error, term weighting strategies, query language reduction, pseudo-relevance feedback using document metadata and terms extracted from documents, merging result sets, and biasing results to favor documents responsive to lawyer-negotiated queries. We conduct several experiments to identify effective parameters for each of these strategies. Within the relevance feedback framework, we use an active learning approach informed by signals from collected prior relevance judgments and ranking data. We train a classifier to prioritize the unjudged documents retrieved using different ad hoc information retrieval techniques applied to the same topic. We demonstrate significant improvements over heuristic rank aggregation strategies when choosing from a relatively small pool of documents. With a larger pool of documents, we validate the effectiveness of the merging strategy as a means to increase recall, but that sparseness of judgment data prevents effective ranking by the classifier-based ranker. We conclude our research by optimizing the classifier-based ranker and applying it to other high recall datasets. Our concluding experiments consider the potential benefits to be derived by modifying the merged runs using methods derived from social choice models. We find that this technique, Local Kemenization, is hampered by the large number of documents and the minimal number of contributing result sets to the ranked list. This two-stage approach to high-recall information retrieval tasks continues to offer a rich set of research questions for future research.

Ultra-deep RNA sequencing has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We develop MATS (Multivariate Analysis of Transcript Splicing), a Bayesian statistical framework for flexible hypothesis testing of differential alternative splicing patterns on RNA-Seq data. MATS uses a multivariate uniform prior to model the between-sample correlation in exon splicing patterns, and a Markov chain Monte Carlo (MCMC) method coupled with a simulation-based adaptive sampling procedure to calculate the P value and false discovery rate (FDR) of differential alternative splicing. Importantly, the MATS approach is applicable to almost any type of null hypotheses of interest, providing the flexibility to identify differential alternative splicing events that match a given user-defined pattern. We evaluated the performance of MATS using simulated and real RNA-Seq data sets. In the RNA-Seq analysis of alternative splicing events regulated by the epithelial-specific splicing factor ESRP1, we obtained a high RT-PCR validation rate of 86% for differential alternative splicing events with a MATS FDR of < 10%. Additionally, over the full list of RT-PCR tested exons, the MATS FDR estimates matched well with the experimental validation rate. Our results demonstrate that MATS is an effective and flexible approach for detecting differential alternative splicing from RNA-Seq data.

The initial aim of this project was to uncover novel cellulolytic organisms or enzymes from the diverse microbial source, activated sludge. Two isolation methods were used; either directly inoculating the sludge material onto filter paper as a carbon source, or using the Evolver� technology as an enrichment device. In both cases, as expected, cellulase activity was evident, however attributing this activity to one species was difficult in either case. This highlighted the complex interrelationships that existed between the many microorganisms present as the cellulosic carbon sources were degraded. In one instance, a Cellvibrio sp. was isolated. This genus of bacteria is known to possess both types of cellulase activity (exo- and endo- acting) and was therefore likely to contribute to the degradation of the cellulose. However, the isolate, once purified, did not display significant cellulolytic ability as compared to the unpurified consortium of microorganisms. Therefore, in each case, microorganisms responsible for the cellulolytic activity were not uncovered. It was suspected that the microorganisms responsible for some of the cellulolytic activity were protists. During the isolation of microorganisms, an Aeromonas sp. bearing the novel phenotype (for this genus) of CMCase activity was isolated. This activity was at first suspected to contribute to the degradation of the filter paper that was seen during isolation. However, tests with the pure isolate suggested that the Aeromonas sp. CMCase was not used for cellulose catabolism. Ironically, the enzyme may instead function in the production of a cellulose-like exopolysaccharide by the bacterium. Part of a cellulose synthase operon was found in the genome of the Aeromonas sp. isolate, including a gene coding for an endoglucanase that gives a predicted molecular weight enzyme similar to the 39 kDa CMCase purified from the bacterium. The CMCase enzyme, operating as part of of a synthetic operon is expected to be important in terms of the biofilm forming ability of this Aeromonas strain. Such capabilities of the bacterium were investigated here, including observing motility behaviour of the organism on agar surfaces. Studying the biofilm forming ability of this genus in general will be important in understanding how the fish and human pathogens persist in aquatic environments

This thesis presents three related essays on the dynamic relationship between different types of investment flow and prices in the equity market. These studies attempt to provide greater insight into the evolution of prices by investigating not ???what moves prices??? but ???who moves prices??? by utilising a unique database from the Korean Stock Exchange. The first essay investigates the trading behaviour and performance of online equity investors in comparison to other investors on the Korean stock market. Whilst the usage of online resources for trading is becoming more and more prevalent in financial markets, the literature on the role of online investors and their impact on prices is limited. The main finding arising from this essay supports the claim that online investors are noise traders at an aggregate level. Whereas foreigners show distinct trading patterns as a group in terms of consensus on the direction of market movements, online investors do not show such distinct trading patterns. The essay concludes that online investors do not trade on clear information signals and introduce noise into the market. Direct performance and market timing ability measures further show that online investors are the worst performers and market timers whereas foreign investors consistently show outstanding performance and market timing ability. Domestic mutual funds in Korea have not been extensively researched. The second essay analyses mutual fund activity and relations between stock market returns and mutual fund flows in Korea. Although regulatory authorities have been cautious about introducing competing funds, contractual-type mutual funds have not been cannibalized by the US-style corporate mutual funds that started trading in 1998. Negative feedback trading activity is observed between stock market returns and mutual fund flows, measured as net trading volumes using stock purchases and sales volume. It is predominantly returns that drive flows, although stock purchases contain information about returns, partially supporting the price pressure hypothesis. After controlling for declining markets, the results suggest Korean equity fund managers tend to swing indiscriminately between increasing purchases and increasing sales in times of rising market volatility, possibly viewing volatility as an opportunity to profit and defying the mean-variance framework that predicts investors should retract from the market as volatility increases. Mutual funds respond indifferently to wide dispersions in investor beliefs. The third essay focuses on the conflicting issue of home bias by looking at the impact on domestic prices of foreign trades relative to locals using high frequency data from the Korean Stock Exchange (KSE). This essay extends the work of Choe, Kho and Stulz (2004) (CKS) in three ways. First, it analyses the post-Asian financial crisis period, whereas CKS (2004) analyse the crisis (1996-98) period. Second, this essay adopts a modified version of the CKS method to better capture the aggregate behaviour of each investor-type by utilising the participation ratio in comparison to the CKS method. Third, this essay does not limit investigation to intra-day analysis but extends to daily analysis up to 50 days to observe the effect of intensive trading activity in a longer horizon than the CKS study. In contrast to the CKS findings, this paper finds that foreigners have a short-lived private information advantage over locals and trades by foreigners have a larger impact on prices using intra-day data. However, assuming investors buy-hold for up to 50 days, the local individuals provide a greater impact and more profitable returns than foreigners. Superior performance is documented for buys rather than sells.

本研究探討台灣加權股價指數以及本土指數期貨間的「價格發現」關係。研究期間乃自民國87年9月1日至88年12月31日止，選取各交易日內期貨與現貨每5分鐘的資料作為觀察值。在研究方法的採用上包括：ADF單根檢定、共整合檢定、錯誤更正模型（ECM）以及衝擊反應分析與變異數分解等。進而，本研究亦依照相同之分析流程，將資料進一步區分為週一至週六等6個交易日，以探討各交易日的結果是否不同。本研究得出以下之結論： 1. 在ADF單根檢定之下，我們發現不論期貨或現貨，兩數列均為I(1)之數列。 2. 根據共整合的檢定結果，發現台股指數期貨與現貨間存在「共整合關係」，即兩者存在一長期均衡關係，且此一情形亦適用於所有資料與各交易日。 3. 將共整合關係考慮進ECM分析中則可發現，對全體資料而言，不論是期貨或現貨，兩者均會對前期均衡誤差作調整，但是期貨的調整速度較現貨為快，也較為顯著。但對於單一交易日而言，可發現不同之結果：期貨仍會往均衡方向作移動，但現貨除星期五外，並沒有往均衡移動之情形。 4. 在「領先-落後」關係上：就全部資料來看（落後4期），期貨會領先現貨約15分鐘左右，而現貨領先期貨亦為20分鐘，兩者並非單一方向之因果關係。而在週一至週六的結果上，回饋關係亦存在，且領先落後的時間也約為15至20分鐘，唯獨「星期一」期貨似乎未有領先現貨之情形。 5. 在衝擊反應分析與變異數分解方面，不論期貨或現貨，大部分的波動來源，仍是來自於自身的變異程度。但相對上，期貨對現貨預測誤差變異數的解釋程度會高於現貨對期貨預測誤差變異數的解釋程度。同時，由衝擊反應函數來看，亦可得出相類似的結果：即相對而言，期貨對現貨之衝擊較大，且衝擊時間約為15至20分鐘。

<p>Ubiquitous computing (Ubicomp) increases in proliferation. Multiple and ever growing in numbers, computational devices are now at the users' disposal throughout the physical environment, while simultaneously being effectively invisible. Consequently, a significant challenge is service discovery. Services may for instance be physical, such as printing a document, or virtual, such as communicating information. The existing solutions, such as Bluetooth and UPnP, address part of the issue, specifically low-level physical interconnectivity. Still absent are solutions for high-level challenges, such as connecting users with appropriate services. In order to provide appropriate service offerings, service discovery in Ubicomp must take the users' context, tasks, goals, intentions, and available resources into consideration. It is possible to divide the high-level service-discovery issue into two parts; inadequate service models, and insufficient common-sense models of human activities.</p><p>This thesis contributes to service discovery in Ubicomp, by arguing that in order to meet these high-level challenges, a new layer is required. Furthermore, the thesis presents a prototype implementation of this new service-discovery architecture and model. The architecture consists of hardware, ontology-layer, and common-sense-layer. This work addresses the ontology and common-sense layers. Subsequently, implementation is divided into two parts; Oden and Magubi. Oden addresses the issue of inadequate service models through a combination of service-ontologies in concert with logical reasoning engines, and Magubi addresses the issue of insufficient common-sense models of human activities, by using common sense models in combination with rule engines. The synthesis of these two stages enables the system to reason about services, devices, and user expectations, as well as to make suitable connections to satisfy the users' overall goal.</p><p>Designing common-sense models and service ontologies for a Ubicomp environment is a non-trivial task. Despite this, we believe that if correctly done, it might be possible to reuse at least part of the knowledge in different situations. With the ability to reason about services and human activities it is possible to decide if, how, and where to present the services to the users. The solution is intended to off-load users in diverse Ubicomp environments as well as provide a more relevant service discovery.</p> / Report code: LiU-Tek-Lic-2007:14.

<p>The main purpose of this study is to analyse how the main themes of alienation and the search for identity is portrayed by Swedish author Inger Edelfeldt. I have applied the theories of Rita Felski concerning feminist novels of self-discovery, in which the development of the female identity is the main question. As Edelfeldt’s writing consists of literature for the young as well as adults, I have included material from both of these genres. I have also taken use of Ulla Lundqvists theories about Swedish juvenile books when examining aspects of the main character’s feelings of alienation and identity searching.</p><p>The results of my analysis show that the reading of my material as feminist novels of self-discovery has revealed pervading charachteristics of alienation, love, friendship and psychological development. The genre-crossing tendency of Edelfeldt’s writing shows in that the theme of identity crisis and the search for identity is an important issue in all of her novels, despite the protagonist’s age. Finally, I show in my study, that by rejecting the heterosexual love-story narrative, Edelfeldts novels put the woman’s own psychological development in focus.</p>

This dissertation addresses a number of inter-related and fundamental problems in computer vision. Specifically, we address object discovery, recognition, segmentation, and 3D pose estimation in images, as well as 3D scene reconstruction and scene interpretation. The key ideas behind our approaches include using shape as a basic object feature, and using structured prediction modeling paradigms for representing objects and scenes. In this work, we make a number of new contributions both in computer vision and machine learning. We address the vision problems of shape matching, shape-based mining of objects in arbitrary image collections, context-aware object recognition, monocular estimation of 3D object poses, and monocular 3D scene reconstruction using shape from texture. Our work on shape-based object discovery is the first to show that meaningful objects can be extracted from a collection of arbitrary images, without any human supervision, by shape matching. We also show that a spatial repetition of objects in images (e.g., windows on a building facade, or cars lined up along a street) can be used for 3D scene reconstruction from a single image. The aforementioned topics have never been addressed in the literature. The dissertation also presents new algorithms and object representations for the aforementioned vision problems. We fuse two traditionally different modeling paradigms Conditional Random Fields (CRF) and Random Forests (RF) into a unified framework, referred to as (RF)^2. We also derive theoretical error bounds of estimating distribution ratios by a two-class RF, which is then used to derive the theoretical performance bounds of a two-class (RF)^2. Thorough experimental evaluation of individual aspects of all our approaches is presented. In general, the experiments demonstrate that we outperform the state of the art on the benchmark datasets, without increasing complexity and supervision in training. / Graduation date: 2011 / Access restricted to the OSU Community at author's request from May 12, 2011 - May 12, 2012