Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition

Thesis (Ph.D.)--University of Washington, 2016-06

Effect of early marine diagenesis on coral reconstructions of surface-ocean 13C/12C and carbonate saturation state

Recent research suggests that future decreases in the carbonate saturation state of surface seawater associated with the projected build-up of atmospheric CO2 could cause a global decline in coral reef-building capacity. Whether significant reductions in coral calcification are underway is a matter of considerable debate. Multicentury records of skeletal calcification extracted from massive corals have the potential to reconstruct the progressive effect of anthropogenic changes in carbonate saturation on coral reefs. However, early marine aragonite cements are commonly precipitated from pore waters in the basal portions of massive coral skeletons and, if undetected, could result in apparent nonlinear reductions in coral calcification toward the present. To address this issue, we present records of coral skeletal density, extension rate, calcification rate, δ13C, and δ18O for well preserved and diagenetically altered coral cores spanning ∼1830-1994 A.D. at Ningaloo Reef Marine Park, Western Australia. The record for the pristine coral shows no significant decrease in skeletal density or δ13C indicative of anthropogenic changes in carbonate saturation state or δ13C of surface seawater (oceanic Suess effect). In contrast, progressive addition of early marine inorganic aragonite toward the base of the altered coral produces an apparent ∼25% decrease in skeletal density toward the present, which misleadingly matches the nonlinear twentieth century decrease in coral calcification predicted by recent modeling and experimental studies. In addition, the diagenetic aragonite is enriched in 13C, relative to coral aragonite, resulting in a nonlinear decrease in δ13C toward the present that mimics the decrease in δ13C expected from the oceanic Suess effect. Taken together, these diagenetic changes in skeletal density and δ13C could be misinterpreted to reflect changes in surface-ocean carbonate saturation state driven by the twentieth century build-up of atmospheric CO2. Copyright 2004 by the American Geophysical Union.

Active site mutations and substrate inhibition in human sulfotransferase 1A1 and 1A3

Human SULT1A1 is primarily responsible for sulfonation of xenobiotics, including the activation of promutagens, and it has been implicated in several forms of cancer. Human SULT1A3 has been shown to be the major sulfotransferase that sulfonates dopamine. These two enzymes shares 93% amino acid sequence identity and have distinct but overlapping substrate preferences. The resolution of the crystal structures of these two enzymes has enabled us to elucidate the mechanisms controlling their substrate preferences and inhibition. The presence of two p-nitrophenol (pNP) molecules in the crystal structure of SULT1A1 was postulated to explain cooperativity at low and inhibition at high substrate concentrations, respectively. In SULT1A1, substrate inhibition occurs with pNP as the substrate but not with dopamine. For SULT1A3, substrate inhibition is found for dopamine but not with pNP. We investigated how substrate inhibition occurs in these two enzymes using molecular modeling, site-directed mutagenesis, and kinetic analysis. The results show that residue Phe-247 of SULT1A1, which interacts with both p-nitrophenol molecules in the active site, is important for substrate inhibition. Mutation of phenylalanine to leucine at this position in SULT1A1 results in substrate inhibition by dopamine. We also propose, based on modeling and kinetic studies, that substrate inhibition by dopamine in SULT1A3 is caused by binding of two dopamine molecules in the active site. © 2004 by The American Society for Biochemistry and Molecular Biology, Inc.

Length of Stay, Discharge Destination, and Functional Improvement: Utility of the Australian National Subacute and Nonacute Patient Casemix Classification

Background and Purpose - Although implemented in 1998, no research has examined how well the Australian National Subacute and Nonacute Patient (AN-SNAP) Casemix Classification predicts length of stay (LOS), discharge destination, and functional improvement in public hospital stroke rehabilitation units in Australia. Methods - 406 consecutive admissions to 3 stroke rehabilitation units in Queensland, Australia were studied. Sociode-mographic, clinical, and functional data were collected. General linear modeling and logistic regression were used to assess the ability of AN-SNAP to predict outcomes. Results - AN-SNAP significantly predicted each outcome. There were clear relationships between the outcomes of longer LOS, poorer functional improvement and discharge into care, and the AN-SNAP classes that reflected poorer functional ability and older age. Other predictors included living situation, acute LOS, comorbidity, and stroke type. Conclusions - AN-SNAP is a consistent predictor of LOS, functional change and discharge destination, and has utility in assisting clinicians to set rehabilitation goals and plan discharge.

Potencies of human immunodeficiency virus protease inhibitors in vitro against Plasmodium falciparum and in vivo against murine malaria

Parasite resistance to antimalarial drugs is a serious threat to human health, and novel agents that act on enzymes essential for parasite metabolism, such as proteases, are attractive targets for drug development. Recent studies have shown that clinically utilized human immunodeficiency virus (HIV) protease inhibitors can inhibit the in vitro growth of Plasmodium falciparum at or below concentrations found in human plasma after oral drug administration. The most potent in vitro antimalarial effects have been obtained for parasites treated with saquinavir, ritonavir, or lopinavir, findings confirmed in this study for a genetically distinct P. falciparum line (3D7). To investigate the potential in vivo activity of antiretroviral protease inhibitors (ARPIs) against malaria, we examined the effect of ARPI combinations in a murine model of malaria. In mice infected with Plasmodium chabaudi AS and treated orally with ritonavir-saquinavir or ritonavir-lopinavir, a delay in patency and a significant attenuation of parasitemia were observed. Using modeling and ligand docking studies we examined putative ligand binding sites of ARPIs in aspartyl proteases of P. falciparum (plasmepsins II and IV) and P. chabaudi (plasmepsin) and found that these in silico analyses support the antimalarial activity hypothesized to be mediated through inhibition of these enzymes. In addition, in vitro enzyme assays demonstrated that P. falciparum plasmepsins II and IV are both inhibited by the ARPIs saquinavir, ritonavir, and lopinavir. The combined results suggest that ARPIs have useful antimalarial activity that may be especially relevant in geographical regions where HIV and P. falciparum infections are both endemic.

Construction of an Intraplate Fault System Model of South Australia, and Simulation Tool for the iSERVO Institute Seed Project

To foster ongoing international cooperation beyond ACES (APEC Cooperation for Earthquake Simulation) on the simulation of solid earth phenomena, agreement was reached to work towards establishment of a frontier international research institute for simulating the solid earth: iSERVO = International Solid Earth Research Virtual Observatory institute (http://www.iservo.edu.au). This paper outlines a key Australian contribution towards the iSERVO institute seed project, this is the construction of: (1) a typical intraplate fault system model using practical fault system data of South Australia (i.e., SA interacting fault model), which includes data management and editing, geometrical modeling and mesh generation; and (2) a finite-element based software tool, which is built on our long-term and ongoing effort to develop the R-minimum strategy based finite-element computational algorithm and software tool for modelling three-dimensional nonlinear frictional contact behavior between multiple deformable bodies with the arbitrarily-shaped contact element strategy. A numerical simulation of the SA fault system is carried out using this software tool to demonstrate its capability and our efforts towards seeding the iSERVO Institute.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Load profiling and data mining techniques in electricity deregulated market

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.

Short-term electricity price forecasting using wavelet and SVM techniques

In deregulated electricity market, modeling and forecasting the spot price present a number of challenges. By applying wavelet and support vector machine techniques, a new time series model for short term electricity price forecasting has been developed in this paper. The model employs both historical price and other important information, such as load capacity and weather (temperature), to forecast the price of one or more time steps ahead. The developed model has been evaluated with the actual data from Australian National Electricity Market. The simulation results demonstrated that the forecast model is capable of forecasting the electricity price with a reasonable forecasting accuracy.

Words and intelligence I:selected papers by Yorick Wilks

Yorick Wilks is a central figure in the fields of Natural Language Processing and Artificial Intelligence. His influence extends to many areas and includes contributions to Machines Translation, word sense disambiguation, dialogue modeling and Information Extraction. This book celebrates the work of Yorick Wilks in the form of a selection of his papers which are intended to reflect the range and depth of his work. The volume accompanies a Festschrift which celebrates his contribution to the fields of Computational Linguistics and Artificial Intelligence. The papers include early work carried out at Cambridge University, descriptions of groundbreaking work on Machine Translation and Preference Semantics as well as more recent works on belief modeling and computational semantics. The selected papers reflect Yorick’s contribution to both practical and theoretical aspects of automatic language processing.

A study of business user based information systems development, and modelling success factors

This thesis describes research into business user involvement in the information systems application building process. The main interest of this research is in establishing and testing techniques to quantify the relationships between identified success factors and the outcome effectiveness of 'business user development' (BUD). The availability of a mechanism to measure the levels of the success factors, and quantifiably relate them to outcome effectiveness, is important in that it provides an organisation with the capability to predict and monitor effects on BUD outcome effectiveness. This is particularly important in an era where BUD levels have risen dramatically, user centred information systems development benefits are recognised as significant, and awareness of the risks of uncontrolled BUD activity is becoming more widespread. This research targets the measurement and prediction of BUD success factors and implementation effectiveness for particular business users. A questionnaire instrument and analysis technique has been tested and developed which constitutes a tool for predicting and monitoring BUD outcome effectiveness, and is based on the BUDES (Business User Development Effectiveness and Scope) research model - which is introduced and described in this thesis. The questionnaire instrument is designed for completion by 'business users' - the target community being more explicitly defined as 'people who primarily have a business role within an organisation'. The instrument, named BUD ESP (Business User Development Effectiveness and Scope Predictor), can readily be used with survey participants, and has been shown to give meaningful and representative results.

Buyer-supplier relationships in advanced manufacturing technology acquisition and implementation : a study in Malaysia

This thesis reports the results of research into the connections between transaction attributes and buyer-supplier relationships (BSR) in advanced manufacturing technology (AMT) acquisitions and implementation. It also examines the impact of the different patterns of BSR on performance. Specifically, it addresses the issues of how the three transaction attributes; namely level of complexity, level of asset specificity, and level of uncertainty, can affect the relationships between the technology buyer and suppler in AMT acquisition and implementation, and then to see the impact of different patterns of BSR on the two aspect of performance; namely technology and implementation performance. In understanding the pohenomena, the study mainly draws on and integrates the literature of transaction cost economics theory,buyer-supplier relationships and advanced manufacturing technology as a basis of theoretical framework and hypotheses development.data were gathered through a questionnaire survey with 147 responses and seven semi-structured interviews of manufacturing firms in Malaysia. Quantitative data were analysed mainly using the AMOS (Analysis of Moment Structure) package for structural equation modeling and SPSS (Statistical Package for Social Science) for analysis of variance (ANOVA). Data from interview sessions were used to develop a case study with the intention of providing a richer and deeper understanding on the subject under investigation and to offer triangulation in the research process. he results of the questionnaire survey indicate that the higher the level of technological specificity and uncertainty, the more firms are likely to engage in a closer relationship with technology suppliers.However, the complexity of the technology being implemented is associated with BSR only because it is associated with the level of uncertainty that has direct impact upon BSR.The analysis also provides strong support for the premise that developing strong BSR could lead to an improved performance. However, with high levels of transaction attribute, implementation performance suffers more when firms have weak relationships with technology suppliers than with moderate and low levels of transaction attributes. The implications of the study are offered for both the academic and practitioner audience. The thesis closes with reports on its limitations and suggestions for further research that would address some of these limitations.

Advances in predicting and manipulating the immunogenicity of biotherapeutics and vaccines

Therapeutic proteins are vital to the future of human health provision and the survival and profitability of the global pharmaceutical industry. Returns from protein therapeutics are experiencing unprecedented growth: both their number and their economic dividend have increased by an order of magnitude in the last 10 years. The potential immunogenicity of protein therapeutics raises many clinical and safety concerns. Many poorly understood factors relating to both product and host affect immune responses. Available laboratory measurement of immunogenicity is of little utility for predicting the clinical properties of biotherapeutics. Coupled with assay variability and standardization issues, this precludes adequate prediction of the biological or clinical responses of therapeutic proteins, arguing for the utilization of informatic strategies in the analysis and prediction of protein immunogenicity. Currently, many unresolved issues must be addressed and thus circumvented before effective prediction can become routine.

In silico identified CCR4 antagonists target regulatory T cells and exert adjuvant activity in vaccination

Adjuvants are substances that enhance immune responses and thus improve the efficacy of vaccination. Few adjuvants are available for use in humans, and the one that is most commonly used (alum) often induces suboptimal immunity for protection against many pathogens. There is thus an obvious need to develop new and improved adjuvants. We have therefore taken an approach to adjuvant discovery that uses in silico modeling and structure-based drug-design. As proof-of-principle we chose to target the interaction of the chemokines CCL22 and CCL17 with their receptor CCR4. CCR4 was posited as an adjuvant target based on its expression on CD4(+)CD25(+) regulatory T cells (Tregs), which negatively regulate immune responses induced by dendritic cells (DC), whereas CCL17 and CCL22 are chemotactic agents produced by DC, which are crucial in promoting contact between DC and CCR4(+) T cells. Molecules identified by virtual screening and molecular docking as CCR4 antagonists were able to block CCL22- and CCL17-mediated recruitment of human Tregs and Th2 cells. Furthermore, CCR4 antagonists enhanced DC-mediated human CD4(+) T cell proliferation in an in vitro immune response model and amplified cellular and humoral immune responses in vivo in experimental models when injected in combination with either Modified Vaccinia Ankara expressing Ag85A from Mycobacterium tuberculosis (MVA85A) or recombinant hepatitis B virus surface antigen (rHBsAg) vaccines. The significant adjuvant activity observed provides good evidence supporting our hypothesis that CCR4 is a viable target for rational adjuvant design.

Loss fluctuations and temporal correlations in network queues

We consider data losses in a single node of a packet- switched Internet-like network. We employ two distinct models, one with discrete and the other with continuous one-dimensional random walks, representing the state of a queue in a router. Both models have a built-in critical behavior with a sharp transition from exponentially small to finite losses. It turns out that the finite capacity of a buffer and the packet-dropping procedure give rise to specific boundary conditions which lead to strong loss rate fluctuations at the critical point even in the absence of such fluctuations in the data arrival process.