CYP2D6: a global analysis of phenotypic and genotypic variation in search of radical cure of Plasmodium vivax malaria

Thesis (Master's)--University of Washington, 2016-06

Optimization and Machine Learning Frameworks for Complex Network Analysis

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

SVMtm: Support vector machines to predict transmembrane segments

A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of similar to99%. This method can be used to complement current transmembrane helix prediction methods and can be Used for consensus analysis of entire proteomes . The predictor is located at http://genet.imb.uq.edu.au/predictors/ SVMtm. (C) 2004 Wiley Periodicals, Inc.

Ross River virus disease in Australia, 1886-1998, with analysis of risk factors associated with outbreaks

Ross River virus (RE) is a mosquito-borne arbovirus responsible for outbreaks of polyarthritic disease throughout Australia. To better understand human and environmental factors driving such events, 57 historical reports oil RR Outbreaks between 1896 and 1998 were examined collectively. The magnitude, regularity, seasonality, and locality of outbreaks were found to be wide ranging; however, analysis of climatic and tidal data highlighted that environmental conditions let differently ill tropical, arid, and temperate regions. Overall, rainfall seems to be the single most important risk factor, with over 90% of major outbreak locations receiving higher than average rainfall in preceding mouths. Many temperatures were close to average, particularly in tropical populations; however, in arid regions, below average maximum temperatures predominated, and ill southeast temperate regions, above average minimum temperatures predominated. High spring tides preceded coastal Outbreaks, both in the presence and absence of rainfall, and the relationship between rainfall and the Southern Oscillation Index and Lit Nina episodes suggest they may be useful predictive tools, but only ill southeast temperate regions. Such heterogeneity predisposing outbreaks supports the notion that there are different RE epidemiologies throughout Australia but also Suggests that generic parameters for the prediction and control of outbreaks are of limited use at a local level.

Eph/Ephrin memhrane proteins: A mammalian expression vector pTIg-BOS-Fc allowing rapid protein purification

There is an urgent need for high purity, single chain, fully functional Eph/ephrin membrane proteins. This report outlines the pTIg-BOS-Fc vector and purification approach resulting in rapid increased production of fully functional single chain extracellular proteins that were isolated with high purity and used in structure-function analysis and pre-clinical studies.

Molecular systematics of the Philippine malaria vector Anopheles flavirostris

Allozyme and molecular sequence data from the malaria vector Anopheles flavirostris (Ludlow) (Diptera: Culicidae) were analysed from 34 sites throughout the Philippines, including the type locality, to test the hypothesis that this taxon is a single panmictic species. A finer-scaled allozyme study, of mainly Luzon samples, revealed no fixed genetic differences in sympatric sites and only low levels of variation. We obtained data from partial sequences for the internal transcribed spacer 2 (ITS2) (483 bp), the third domain (D3) (330 bp) of the 28S ribosomal DNA subunit and cytochrome c oxidase subunit I (COI) of mitochondrial DNA (261 bp). No sequence variation was observed for ITS2, only a one base pair difference was observed between Philippine and Indonesian D3 sequences and An. flavirostris sequences were unique, confirming their diagnostic value for this taxon. Sixteen COI haplotypes were identified, giving 25 parsimony informative sites. Neighbour-Joining, Maximum Parsimony, Maximum Likelihood and Bayesian phylogenetic analysis of COI sequences for An. flavirostris and outgroup taxa revealed strong branch support for the monophyly of An. flavirostris, thus confirming that Philippine populations of this taxon comprise a single separate species within the Minimus Subgroup of the Funestus Group. Variation in the behaviour of An. flavirostris is likely to be intraspecific rather than interspecific in origin. © 2006 The Royal Entomological Society.

Stability for parametric implicit vector equilibrium problems

In this paper, we consider a class of parametric implicit vector equilibrium problems in Hausdorff topological vector spaces where a mapping f and a set K are perturbed by parameters is an element of and lambda respectively. We establish sufficient conditions for the upper semicontinuity and lower semicontinuity of the solution set mapping S : Lambda(1) x A(2) -> 2(X) for such parametric implicit vector equilibrium problems. (c) 2005 Elsevier Ltd. All rights reserved.

Motion vector encryption in multimedia streaming

Streaming video application requires high security as well as high computational performance. In video encryption, traditional selective algorithms have been used to partially encrypt the relatively important data in order to satisfy the streaming performance requirement. Most video selective encryption algorithms are inherited from still image encryption algorithms, the encryption on motion vector data is not considered. The assumption is that motion vector data are not as important as pixel image data. Unfortunately, in some cases, motion vector itself may be sufficient enough to leak out useful video information. Normally motion vector data consume over half of the whole video stream bandwidth, neglecting their security may be unwise. In this paper, we target this security problem and illustrate attacks at two different levels that can restore useful video information using motion vectors only. Further, an information analysis is made and a motion vector information model is built. Based on this model, we describe a new motion vector encryption algorithm called MVEA. We show the experimental results of MVEA. The security strength and performance of the algorithm are also evaluated.

Performance analysis of multi-radio AODV in hybrid wireless mesh networks

Wireless Mesh Networks (WMNs), based on commodity hardware, present a promising technology for a wide range of applications due to their self-configuring and self-healing capabilities, as well as their low equipment and deployment costs. One of the key challenges that WMN technology faces is the limited capacity and scalability due to co-channel interference, which is typical for multi-hop wireless networks. A simple and relatively low-cost approach to address this problem is the use of multiple wireless network interfaces (radios) per node. Operating the radios on distinct orthogonal channels permits effective use of the frequency spectrum, thereby, reducing interference and contention. In this paper, we evaluate the performance of the multi-radio Ad-hoc On-demand Distance Vector (AODV) routing protocol with a specific focus on hybrid WMNs. Our simulation results show that under high mobility and traffic load conditions, multi-radio AODV offers superior performance as compared to its single-radio counterpart. We believe that multi-radio AODV is a promising candidate for WMNs, which need to service a large number of mobile clients with low latency and high bandwidth requirements.

Modeling equity market integration using smooth transition analysis:a study of Eastern European stock markets

This paper assesses the extent to which the equity markets of Hungary, Poland the Czech Republic and Russia have become less segmented. Using a variety of tests it is shown there has been a consistent increase in the co-movement of some Eastern European markets and developed markets. Using the variance decompositions from a vector autoregressive representation of returns it is shown that for Poland and Hungary global factors are having an increasing influence on equity returns, suggestive of increased equity market integration. In this paper we model a system of bivariate equity market correlations as a smooth transition logistic trend model in order to establish how rapidly the countries of Eastern Europe are moving away from market segmentation. We find that Hungary is the country which is becoming integrated the most quickly. © 2005 ELsevier Ltd. All rights reserved.

Orbit and altimetric corrections for the ERS satellites through analysis of single and dual satellite crossovers

Due to the failure of PRARE the orbital accuracy of ERS-1 is typically 10-15 cm radially as compared to 3-4cm for TOPEX/Poseidon. To gain the most from these simultaneous datasets it is necessary to improve the orbital accuracy of ERS-1 so that it is commensurate with that of TOPEX/Poseidon. For the integration of these two datasets it is also necessary to determine the altimeter and sea state biases for each of the satellites. Several models for the sea state bias of ERS-1 are considered by analysis of the ERS-1 single satellite crossovers. The model adopted consists of the sea state bias as a percentage of the significant wave height, namely 5.95%. The removal of ERS-1 orbit error and recovery of an ERS-1 - TOPEX/Poseidon relative bias are both achieved by analysis of dual crossover residuals. The gravitational field based radial orbit error is modelled by a finite Fourier expansion series with the dominant frequencies determined by analysis of the JGM-2 co-variance matrix. Periodic and secular terms to model the errors due to atmospheric density, solar radiation pressure and initial state vector mis-modelling are also solved for. Validation of the dataset unification consists of comparing the mean sea surface topographies and annual variabilities derived from both the corrected and uncorrected ERS-1 orbits with those derived from TOPEX/Poseidon. The global and regional geographically fixed/variable orbit errors are also analysed pre and post correction, and a significant reduction is noted. Finally the use of dual/single satellite crossovers and repeat pass data, for the calibration of ERS-2 with respect to ERS-1 and TOPEX/Poseidon is shown by calculating the ERS-1/2 sea state and relative biases.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Leontief Model Analysis with Fuzzy Parameters by Basic Matrixes Method

The basic matrixes method is suggested for the Leontief model analysis (LM) with some of its components indistinctly given. LM can be construed as a forecast task of product’s expenses-output on the basis of the known statistic information at indistinctly given several elements’ meanings of technological matrix, restriction vector and variables’ limits. Elements of technological matrix, right parts of restriction vector LM can occur as functions of some arguments. In this case the task’s dynamic analog occurs. LM essential complication lies in inclusion of variables restriction and criterion function in it.

Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis

In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.

Derivation of Context-free Stochastic L-Grammar Rules for Promoter Sequence Modeling Using Support Vector Machine

Formal grammars can used for describing complex repeatable structures such as DNA sequences. In this paper, we describe the structural composition of DNA sequences using a context-free stochastic L-grammar. L-grammars are a special class of parallel grammars that can model the growth of living organisms, e.g. plant development, and model the morphology of a variety of organisms. We believe that parallel grammars also can be used for modeling genetic mechanisms and sequences such as promoters. Promoters are short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. Promoters can be recognized by certain patterns that are conserved within a species, but there are many exceptions which makes the promoter recognition a complex problem. We replace the problem of promoter recognition by induction of context-free stochastic L-grammar rules, which are later used for the structural analysis of promoter sequences. L-grammar rules are derived automatically from the drosophila and vertebrate promoter datasets using a genetic programming technique and their fitness is evaluated using a Support Vector Machine (SVM) classifier. The artificial promoter sequences generated using the derived L- grammar rules are analyzed and compared with natural promoter sequences.