Computational Techniques for Haplotype Inference and for Local Alignment Significance

This thesis which consists of an introduction and four peer-reviewed original publications studies the problems of haplotype inference (haplotyping) and local alignment significance. The problems studied here belong to the broad area of bioinformatics and computational biology. The presented solutions are computationally fast and accurate, which makes them practical in high-throughput sequence data analysis. Haplotype inference is a computational problem where the goal is to estimate haplotypes from a sample of genotypes as accurately as possible. This problem is important as the direct measurement of haplotypes is difficult, whereas the genotypes are easier to quantify. Haplotypes are the key-players when studying for example the genetic causes of diseases. In this thesis, three methods are presented for the haplotype inference problem referred to as HaploParser, HIT, and BACH. HaploParser is based on a combinatorial mosaic model and hierarchical parsing that together mimic recombinations and point-mutations in a biologically plausible way. In this mosaic model, the current population is assumed to be evolved from a small founder population. Thus, the haplotypes of the current population are recombinations of the (implicit) founder haplotypes with some point--mutations. HIT (Haplotype Inference Technique) uses a hidden Markov model for haplotypes and efficient algorithms are presented to learn this model from genotype data. The model structure of HIT is analogous to the mosaic model of HaploParser with founder haplotypes. Therefore, it can be seen as a probabilistic model of recombinations and point-mutations. BACH (Bayesian Context-based Haplotyping) utilizes a context tree weighting algorithm to efficiently sum over all variable-length Markov chains to evaluate the posterior probability of a haplotype configuration. Algorithms are presented that find haplotype configurations with high posterior probability. BACH is the most accurate method presented in this thesis and has comparable performance to the best available software for haplotype inference. Local alignment significance is a computational problem where one is interested in whether the local similarities in two sequences are due to the fact that the sequences are related or just by chance. Similarity of sequences is measured by their best local alignment score and from that, a p-value is computed. This p-value is the probability of picking two sequences from the null model that have as good or better best local alignment score. Local alignment significance is used routinely for example in homology searches. In this thesis, a general framework is sketched that allows one to compute a tight upper bound for the p-value of a local pairwise alignment score. Unlike the previous methods, the presented framework is not affeced by so-called edge-effects and can handle gaps (deletions and insertions) without troublesome sampling and curve fitting.

Harmonic Analysis of Advanced Bus-Clamping PWM Techniques

Special switching sequences can be employed in space-vector-based generation of pulsewidth-modulated (PWM) waveforms for voltage-source inverters. These sequences involve switching a phase twice, switching the second phase once, and clamping the third phase in a subcycle. Advanced bus-clamping PWM (ABCPWM) techniques have been proposed recently that employ such switching sequences. This letter studies the spectral properties of the waveforms produced by these PWM techniques. Further, analytical closed-form expressions are derived for the total rms harmonic distortion due to these techniques. It is shown that the ABCPWM techniques lead to lower distortion than conventional space vector PWM and discontinuous PWM at higher modulation indexes. The findings are validated on a 2.2-kW constant $V/f$ induction motor drive and also on a 100-kW motor drive.

Feature selection and the concept of immediate neighborhood in the context of clustering techniques

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

Review of continuum, finite element and hybrid techniques in the analysis of stress concentrations in structures

When a uniform flow of any nature is interrupted, the readjustment of the flow results in concentrations and rare-factions, so that the peak value of the flow parameter will be higher than that which an elementary computation would suggest. When stress flow in a structure is interrupted, there are stress concentrations. These are generally localized and often large, in relation to the values indicated by simple equilibrium calculations. With the advent of the industrial revolution, dynamic and repeated loading of materials had become commonplace in engine parts and fast moving vehicles of locomotion. This led to serious fatigue failures arising from stress concentrations. Also, many metal forming processes, fabrication techniques and weak-link type safety systems benefit substantially from the intelligent use or avoidance, as appropriate, of stress concentrations. As a result, in the last 80 years, the study and and evaluation of stress concentrations has been a primary objective in the study of solid mechanics. Exact mathematical analysis of stress concentrations in finite bodies presents considerable difficulty for all but a few problems of infinite fields, concentric annuli and the like, treated under the presumption of small deformation, linear elasticity. A whole series of techniques have been developed to deal with different classes of shapes and domains, causes and sources of concentration, material behaviour, phenomenological formulation, etc. These include real and complex functions, conformal mapping, transform techniques, integral equations, finite differences and relaxation, and, more recently, the finite element methods. With the advent of large high speed computers, development of finite element concepts and a good understanding of functional analysis, it is now, in principle, possible to obtain with economy satisfactory solutions to a whole range of concentration problems by intelligently combining theory and computer application. An example is the hybridization of continuum concepts with computer based finite element formulations. This new situation also makes possible a more direct approach to the problem of design which is the primary purpose of most engineering analyses. The trend would appear to be clear: the computer will shape the theory, analysis and design.

Self-image (autoidolon) techniques for the realisation of optical computing type operations

Abstract is not available.

Assimilation of remote sensing and hydrological data using adaptive filtering techniques for watershed modelling

The knowledge of hydrological variables (e. g. soil moisture, evapotranspiration) are of pronounced importance in various applications including flood control, agricultural production and effective water resources management. These applications require the accurate prediction of hydrological variables spatially and temporally in watershed/basin. Though hydrological models can simulate these variables at desired resolution (spatial and temporal), often they are validated against the variables, which are either sparse in resolution (e. g. soil moisture) or averaged over large regions (e. g. runoff). A combination of the distributed hydrological model (DHM) and remote sensing (RS) has the potential to improve resolution. Data assimilation schemes can optimally combine DHM and RS. Retrieval of hydrological variables (e. g. soil moisture) from remote sensing and assimilating it in hydrological model requires validation of algorithms using field studies. Here we present a review of methodologies developed to assimilate RS in DHM and demonstrate the application for soil moisture in a small experimental watershed in south India.

Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

Scheduling techniques to optimise sugarcane rail transport systems

In Australia, railway systems play a vital role in transporting the sugarcane crop from farms to mills. In this paper, a novel job shop approach is proposed to create a more efficient integrated harvesting and sugarcane transport scheduling system to reduce the cost of sugarcane transport. There are several benefits that can be attained by treating the train scheduling problem as a job shop problem. Job shop is generic and suitable for all trains scheduling problems. Job shop technique prevents operating two trains on one section at the same time because it considers that the section or the machine is unique. This technique is more promising to find better solutions in reasonable computation times.

Deploying speaker verification techniques to vision-based RPA detect and avoid

Detect and Avoid (DAA) technology is widely acknowledged as a critical enabler for unsegregated Remote Piloted Aircraft (RPA) operations, particularly Beyond Visual Line of Sight (BVLOS). Image-based DAA, in the visible spectrum, is a promising technological option for addressing the challenges DAA presents. Two impediments to progress for this approach are the scarcity of available video footage to train and test algorithms, in conjunction with testing regimes and specifications which facilitate repeatable, statistically valid, performance assessment. This paper includes three key contributions undertaken to address these impediments. In the first instance, we detail our progress towards the creation of a large hybrid collision and near-collision encounter database. Second, we explore the suitability of techniques employed by the biometric research community (Speaker Verification and Language Identification), for DAA performance optimisation and assessment. These techniques include Detection Error Trade-off (DET) curves, Equal Error Rates (EER), and the Detection Cost Function (DCF). Finally, the hybrid database and the speech-based techniques are combined and employed in the assessment of a contemporary, image based DAA system. This system includes stabilisation, morphological filtering and a Hidden Markov Model (HMM) temporal filter.

Aircraft trajectory clustering techniques using circular statistics

This paper presents a statistical aircraft trajectory clustering approach aimed at discriminating between typical manned and expected unmanned traffic patterns. First, a resampled version of each trajectory is modelled using a mixture of Von Mises distributions (circular statistics). Second, the remodelled trajectories are globally aligned using tools from bioinformatics. Third, the alignment scores are used to cluster the trajectories using an iterative k-medoids approach and an appropriate distance function. The approach is then evaluated using synthetically generated unmanned aircraft flights combined with real air traffic position reports taken over a sector of Northern Queensland, Australia. Results suggest that the technique is useful in distinguishing between expected unmanned and manned aircraft traffic behaviour, as well as identifying some common conventional air traffic patterns.

Detection of microorganisms using biosensors-a smarter way towards detection techniques.

Along with useful microorganisms, there are some that cause potential damage to the animals and plants. Detection and identification of these harmful organisms in a cost and time effective way is a challenge for the researchers. The future of detection methods for microorganisms shall be guided by biosensor, which has already contributed enormously in sensing and detection technology. Here, we aim to review the use of various biosensors, developed by integrating the biological and physicochemical/mechanical properties (of tranducers), which can have enormous implication in healthcare, food, agriculture and biodefence. We have also highlighted the ways to improve the functioning of the biosensor.

Clinical Studies on Epidural and Spinal Postoperative Analgesia : With Special Reference to Continuous Techniques of Administration and Adjuvant Drugs

Continuous epidural analgesia (CEA) and continuous spinal postoperative analgesia (CSPA) provided by a mixture of local anaesthetic and opioid are widely used for postoperative pain relief. E.g., with the introduction of so-called microcatheters, CSPA found its way particularly in orthopaedic surgery. These techniques, however, may be associated with dose-dependent side-effects as hypotension, weakness in the legs, and nausea and vomiting. At times, they may fail to offer sufficient analgesia, e.g., because of a misplaced catheter. The correct position of an epidural catheter might be confirmed by the supposedly easy and reliable epidural stimulation test (EST). The aims of this thesis were to determine a) whether the efficacy, tolerability, and reliability of CEA might be improved by adding the α2-adrenergic agonists adrenaline and clonidine to CEA, and by the repeated use of EST during CEA; and, b) the feasibility of CSPA given through a microcatheter after vascular surgery. Studies I IV were double-blinded, randomized, and controlled trials; Study V was of a diagnostic, prospective nature. Patients underwent arterial bypass surgery of the legs (I, n=50; IV, n=46), total knee arthroplasty (II, n=70; III, n=72), and abdominal surgery or thoracotomy (V, n=30). Postoperative lumbar CEA consisted of regular mixtures of ropivacaine and fentanyl either without or with adrenaline (2 µg/ml (I) and 4 µg/ml (II)) and clonidine (2 µg/ml (III)). CSPA (IV) was given through a microcatheter (28G) and contained either ropivacaine (max. 2 mg/h) or a mixture of ropivacaine (max. 1 mg/h) and morphine (max. 8 µg/h). Epidural catheter tip position (V) was evaluated both by EST at the moment of catheter placement and several times during CEA, and by epidurography as reference diagnostic test. CEA and CSPA were administered for 24 or 48 h. Study parameters included pain scores assessed with a visual analogue scale, requirements of rescue pain medication, vital signs, and side-effects. Adrenaline (I and II) had no beneficial influence as regards the efficacy or tolerability of CEA. The total amounts of epidurally-infused drugs were even increased in the adrenaline group in Study II (p=0.02, RM ANOVA). Clonidine (III) augmented pain relief with lowered amounts of epidurally infused drugs (p=0.01, RM ANOVA) and reduced need for rescue oxycodone given i.m. (p=0.027, MW-U; median difference 3 mg (95% CI 0 7 mg)). Clonidine did not contribute to sedation and its influence on haemodynamics was minimal. CSPA (IV) provided satisfactory pain relief with only limited blockade of the legs (no inter-group differences). EST (V) was often related to technical problems and difficulties of interpretation, e.g., it failed to identify the four patients whose catheters were outside the spinal canal already at the time of catheter placement. As adjuvants to lumbar CEA, clonidine only slightly improved pain relief, while adrenaline did not provide any benefit. The role of EST applied at the time of epidural catheter placement or repeatedly during CEA remains open. The microcatheter CSPA technique appeared effective and reliable, but needs to be compared to routine CEA after peripheral arterial bypass surgery.

Performance Analysis of Reconstruction Techniques for Frequency-Domain Optical-Coherence Tomography

We address the issue of noise robustness of reconstruction techniques for frequency-domain optical-coherence tomography (FDOCT). We consider three reconstruction techniques: Fourier, iterative phase recovery, and cepstral techniques. We characterize the reconstructions in terms of their statistical bias and variance and obtain approximate analytical expressions under the assumption of small noise. We also perform Monte Carlo analyses and show that the experimental results are in agreement with the theoretical predictions. It turns out that the iterative and cepstral techniques yield reconstructions with a smaller bias than the Fourier method. The three techniques, however, have identical variance profiles, and their consistency increases linearly as a function of the signal-to-noise ratio.