EmsB, a tandem repeated multi-loci microsatellite, new tool to investigate the genetic diversity of Echinococcus multilocularis

In order to explore the genetic diversity within Echinococcus multilocularis (E. multilocularis), the cestode responsible for the alveolar echinococcosis (AE) in humans, a microsatellite, composed of (CA) and (GA) repeats and designated EmsB, was isolated and characterized in view of its nature and potential field application. PCR-amplification with specific primers exhibited a high degree of size polymorphism between E. multilocularis and Echinococcus granulosus sheep (G1) and camel (G6) strains. Fluorescent-PCR was subsequently performed on a panel of E. multilocularis isolates to assess intra-species polymorphism level. EmsB provided a multi-peak profile, characterized by tandemly repeated microsatellite sequences in the E. multilocularis genome. This "repetition of repeats" feature provided to EmsB a high discriminatory power in that eight clusters, supported by bootstrap p-values larger than 95%, could be defined among the tested E. multilocularis samples. We were able to differentiate not only the Alaskan from the European samples, but also to detect different European isolate clusters. In total, 25 genotypes were defined within 37 E. multilocularis samples. Despite its complexity, this tandem repeated multi-loci microsatellite possesses the three important features for a molecular marker, i.e. sensitivity, repetitiveness and discriminatory power. It will permit assessing the genetic polymorphism of E. multilocularis and to investigate its spatial distribution in detail.

Control algorithms for large scale adaptive optics

In this dissertation, the problem of creating effective large scale Adaptive Optics (AO) systems control algorithms for the new generation of giant optical telescopes is addressed. The effectiveness of AO control algorithms is evaluated in several respects, such as computational complexity, compensation error rejection and robustness, i.e. reasonable insensitivity to the system imperfections. The results of this research are summarized as follows: 1. Robustness study of Sparse Minimum Variance Pseudo Open Loop Controller (POLC) for multi-conjugate adaptive optics (MCAO). The AO system model that accounts for various system errors has been developed and applied to check the stability and performance of the POLC algorithm, which is one of the most promising approaches for the future AO systems control. It has been shown through numerous simulations that, despite the initial assumption that the exact system knowledge is necessary for the POLC algorithm to work, it is highly robust against various system errors. 2. Predictive Kalman Filter (KF) and Minimum Variance (MV) control algorithms for MCAO. The limiting performance of the non-dynamic Minimum Variance and dynamic KF-based phase estimation algorithms for MCAO has been evaluated by doing Monte-Carlo simulations. The validity of simple near-Markov autoregressive phase dynamics model has been tested and its adequate ability to predict the turbulence phase has been demonstrated both for single- and multiconjugate AO. It has also been shown that there is no performance improvement gained from the use of the more complicated KF approach in comparison to the much simpler MV algorithm in the case of MCAO. 3. Sparse predictive Minimum Variance control algorithm for MCAO. The temporal prediction stage has been added to the non-dynamic MV control algorithm in such a way that no additional computational burden is introduced. It has been confirmed through simulations that the use of phase prediction makes it possible to significantly reduce the system sampling rate and thus overall computational complexity while both maintaining the system stable and effectively compensating for the measurement and control latencies.

Compressed sensing based cyclic feature spectrum sensing for cognitive radios

Spectrum sensing is currently one of the most challenging design problems in cognitive radio. A robust spectrum sensing technique is important in allowing implementation of a practical dynamic spectrum access in noisy and interference uncertain environments. In addition, it is desired to minimize the sensing time, while meeting the stringent cognitive radio application requirements. To cope with this challenge, cyclic spectrum sensing techniques have been proposed. However, such techniques require very high sampling rates in the wideband regime and thus are costly in hardware implementation and power consumption. In this thesis the concept of compressed sensing is applied to circumvent this problem by utilizing the sparsity of the two-dimensional cyclic spectrum. Compressive sampling is used to reduce the sampling rate and a recovery method is developed for re- constructing the sparse cyclic spectrum from the compressed samples. The reconstruction solution used, exploits the sparsity structure in the two-dimensional cyclic spectrum do-main which is different from conventional compressed sensing techniques for vector-form sparse signals. The entire wideband cyclic spectrum is reconstructed from sub-Nyquist-rate samples for simultaneous detection of multiple signal sources. After the cyclic spectrum recovery two methods are proposed to make spectral occupancy decisions from the recovered cyclic spectrum: a band-by-band multi-cycle detector which works for all modulation schemes, and a fast and simple thresholding method that works for Binary Phase Shift Keying (BPSK) signals only. In addition a method for recovering the power spectrum of stationary signals is developed as a special case. Simulation results demonstrate that the proposed spectrum sensing algorithms can significantly reduce sampling rate without sacrifcing performance. The robustness of the algorithms to the noise uncertainty of the wireless channel is also shown.

Diesel spray mixing limited vaporization with non-ideal and multi-component fuel thermophysical property effects

Internal combustion engines are, and will continue to be, a primary mode of power generation for ground transportation. Challenges exist in meeting fuel consumption regulations and emission standards while upholding performance, as fuel prices rise, and resource depletion and environmental impacts are of increasing concern. Diesel engines are advantageous due to their inherent efficiency advantage over spark ignition engines; however, their NOx and soot emissions can be difficult to control and reduce due to an inherent tradeoff. Diesel combustion is spray and mixing controlled providing an intrinsic link between spray and emissions, motivating detailed, fundamental studies on spray, vaporization, mixing, and combustion characteristics under engine relevant conditions. An optical combustion vessel facility has been developed at Michigan Technological University for these studies, with detailed tests and analysis being conducted. In this combustion vessel facility a preburn procedure for thermodynamic state generation is used, and validated using chemical kinetics modeling both for the MTU vessel, and institutions comprising the Engine Combustion Network international collaborative research initiative. It is shown that minor species produced are representative of modern diesel engines running exhaust gas recirculation and do not impact the autoignition of n-heptane. Diesel spray testing of a high-pressure (2000 bar) multi-hole injector is undertaken including non-vaporizing, vaporizing, and combusting tests, with sprays characterized using Mie back scatter imaging diagnostics. Liquid phase spray parameter trends agree with literature. Fluctuations in liquid length about a quasi-steady value are quantified, along with plume to plume variations. Hypotheses are developed for their causes including fuel pressure fluctuations, nozzle cavitation, internal injector flow and geometry, chamber temperature gradients, and turbulence. These are explored using a mixing limited vaporization model with an equation of state approach for thermopyhysical properties. This model is also applied to single and multi-component surrogates. Results include the development of the combustion research facility and validated thermodynamic state generation procedure. The developed equation of state approach provides application for improving surrogate fuels, both single and multi-component, in terms of diesel spray liquid length, with knowledge of only critical fuel properties. Experimental studies are coupled with modeling incorporating improved thermodynamic non-ideal gas and fuel

Risk assessment for invasive exotic plants using multi-criteria risk models

Invasive exotic plants have altered natural ecosystems across much of North America. In the Midwest, the presence of invasive plants is increasing rapidly, causing changes in ecosystem patterns and processes. Early detection has become a key component in invasive plant management and in the detection of ecosystem change. Risk assessment through predictive modeling has been a useful resource for monitoring and assisting with treatment decisions for invasive plants. Predictive models were developed to assist with early detection of ten target invasive plants in the Great Lakes Network of the National Park Service and for garlic mustard throughout the Upper Peninsula of Michigan. These multi-criteria risk models utilize geographic information system (GIS) data to predict the areas at highest risk for three phases of invasion: introduction, establishment, and spread. An accuracy assessment of the models for the ten target plants in the Great Lakes Network showed an average overall accuracy of 86.3%. The model developed for garlic mustard in the Upper Peninsula resulted in an accuracy of 99.0%. Used as one of many resources, the risk maps created from the model outputs will assist with the detection of ecosystem change, the monitoring of plant invasions, and the management of invasive plants through prioritized control efforts.

Optimal game theoretic distributed control methodologies in small scale power systems

This dissertation presents the competitive control methodologies for small-scale power system (SSPS). A SSPS is a collection of sources and loads that shares a common network which can be isolated during terrestrial disturbances. Micro-grids, naval ship electric power systems (NSEPS), aircraft power systems and telecommunication system power systems are typical examples of SSPS. The analysis and development of control systems for small-scale power systems (SSPS) lacks a defined slack bus. In addition, a change of a load or source will influence the real time system parameters of the system. Therefore, the control system should provide the required flexibility, to ensure operation as a single aggregated system. In most of the cases of a SSPS the sources and loads must be equipped with power electronic interfaces which can be modeled as a dynamic controllable quantity. The mathematical formulation of the micro-grid is carried out with the help of game theory, optimal control and fundamental theory of electrical power systems. Then the micro-grid can be viewed as a dynamical multi-objective optimization problem with nonlinear objectives and variables. Basically detailed analysis was done with optimal solutions with regards to start up transient modeling, bus selection modeling and level of communication within the micro-grids. In each approach a detail mathematical model is formed to observe the system response. The differential game theoretic approach was also used for modeling and optimization of startup transients. The startup transient controller was implemented with open loop, PI and feedback control methodologies. Then the hardware implementation was carried out to validate the theoretical results. The proposed game theoretic controller shows higher performances over traditional the PI controller during startup. In addition, the optimal transient surface is necessary while implementing the feedback controller for startup transient. Further, the experimental results are in agreement with the theoretical simulation. The bus selection and team communication was modeled with discrete and continuous game theory models. Although players have multiple choices, this controller is capable of choosing the optimum bus. Next the team communication structures are able to optimize the players’ Nash equilibrium point. All mathematical models are based on the local information of the load or source. As a result, these models are the keys to developing accurate distributed controllers.

STOCHASTIC CHARGE TRANSPORT IN MULTI-ISLAND SINGLE-ELECTRON TUNNELING DEVICES

The physics of the operation of singe-electron tunneling devices (SEDs) and singe-electron tunneling transistors (SETs), especially of those with multiple nanometer-sized islands, has remained poorly understood in spite of some intensive experimental and theoretical research. This computational study examines the current-voltage (IV) characteristics of multi-island single-electron devices using a newly developed multi-island transport simulator (MITS) that is based on semi-classical tunneling theory and kinetic Monte Carlo simulation. The dependence of device characteristics on physical device parameters is explored, and the physical mechanisms that lead to the Coulomb blockade (CB) and Coulomb staircase (CS) characteristics are proposed. Simulations using MITS demonstrate that the overall IV characteristics in a device with a random distribution of islands are a result of a complex interplay among those factors that affect the tunneling rates that are fixed a priori (e.g. island sizes, island separations, temperature, gate bias, etc.), and the evolving charge state of the system, which changes as the source-drain bias (VSD) is changed. With increasing VSD, a multi-island device has to overcome multiple discrete energy barriers (up-steps) before it reaches the threshold voltage (Vth). Beyond Vth, current flow is rate-limited by slow junctions, which leads to the CS structures in the IV characteristic. Each step in the CS is characterized by a unique distribution of island charges with an associated distribution of tunneling probabilities. MITS simulation studies done on one-dimensional (1D) disordered chains show that longer chains are better suited for switching applications as Vth increases with increasing chain length. They are also able to retain CS structures at higher temperatures better than shorter chains. In sufficiently disordered 2D systems, we demonstrate that there may exist a dominant conducting path (DCP) for conduction, which makes the 2D device behave as a quasi-1D device. The existence of a DCP is sensitive to the device structure, but is robust with respect to changes in temperature, gate bias, and VSD. A side gate in 1D and 2D systems can effectively control Vth. We argue that devices with smaller island sizes and narrower junctions may be better suited for practical applications, especially at room temperature.

Multi-parametric classification of Alzheimer's disease and mild cognitive impairment: the impact of quantitative magnetization transfer MR imaging

Multi-parametric and quantitative magnetic resonance imaging (MRI) techniques have come into the focus of interest, both as a research and diagnostic modality for the evaluation of patients suffering from mild cognitive decline and overt dementia. In this study we address the question, if disease related quantitative magnetization transfer effects (qMT) within the intra- and extracellular matrices of the hippocampus may aid in the differentiation between clinically diagnosed patients with Alzheimer disease (AD), patients with mild cognitive impairment (MCI) and healthy controls. We evaluated 22 patients with AD (n=12) and MCI (n=10) and 22 healthy elderly (n=12) and younger (n=10) controls with multi-parametric MRI. Neuropsychological testing was performed in patients and elderly controls (n=34). In order to quantify the qMT effects, the absorption spectrum was sampled at relevant off-resonance frequencies. The qMT-parameters were calculated according to a two-pool spin-bath model including the T1- and T2 relaxation parameters of the free pool, determined in separate experiments. Histograms (fixed bin-size) of the normalized qMT-parameter values (z-scores) within the anterior and posterior hippocampus (hippocampal head and body) were subjected to a fuzzy-c-means classification algorithm with downstreamed PCA projection. The within-cluster sums of point-to-centroid distances were used to examine the effects of qMT- and diffusion anisotropy parameters on the discrimination of healthy volunteers, patients with Alzheimer and MCIs. The qMT-parameters T2(r) (T2 of the restricted pool) and F (fractional pool size) differentiated between the three groups (control, MCI and AD) in the anterior hippocampus. In our cohort, the MT ratio, as proposed in previous reports, did not differentiate between MCI and AD or healthy controls and MCI, but between healthy controls and AD.

Control concept for autonomous changeable material flow systems

Today’s material flow systems for mass customization or dynamic productions are usually realized with manual transportation systems. However new concepts in the domain of material flow and device control like function-oriented modularization and intelligent multi-agent-systems offer the possibility to employ changeable and automated material flow systems in dynamic production structures. These systems need the ability to react on unplanned and unexpected events autonomously.

Registration of Sub-Sequence and Multi-Camera Reconstructions for Camera Motion Estimation

This paper presents different application scenarios for which the registration of sub-sequence reconstructions or multi-camera reconstructions is essential for successful camera motion estimation and 3D reconstruction from video. The registration is achieved by merging unconnected feature point tracks between the reconstructions. One application is drift removal for sequential camera motion estimation of long sequences. The state-of-the-art in drift removal is to apply a RANSAC approach to find unconnected feature point tracks. In this paper an alternative spectral algorithm for pairwise matching of unconnected feature point tracks is used. It is then shown that the algorithms can be combined and applied to novel scenarios where independent camera motion estimations must be registered into a common global coordinate system. In the first scenario multiple moving cameras, which capture the same scene simultaneously, are registered. A second new scenario occurs in situations where the tracking of feature points during sequential camera motion estimation fails completely, e.g., due to large occluding objects in the foreground, and the unconnected tracks of the independent reconstructions must be merged. In the third scenario image sequences of the same scene, which are captured under different illuminations, are registered. Several experiments with challenging real video sequences demonstrate that the presented techniques work in practice.

Fuzzy Multi-Objective Linear Programming and Simulation Approach to the Development of Valid and Realistic Master Production Schedule

Master production schedule (MPS) plays an important role in an integrated production planning system. It converts the strategic planning defined in a production plan into the tactical operation execution. The MPS is also known as a tool for top management to control over manufacture resources and becomes input of the downstream planning levels such as material requirement planning (MRP) and capacity requirement planning (CRP). Hence, inappropriate decision on the MPS development may lead to infeasible execution, which ultimately causes poor delivery performance. One must ensure that the proposed MPS is valid and realistic for implementation before it is released to real manufacturing system. In practice, where production environment is stochastic in nature, the development of MPS is no longer simple task. The varying processing time, random event such as machine failure is just some of the underlying causes of uncertainty that may be hardly addressed at planning stage so that in the end the valid and realistic MPS is tough to be realized. The MPS creation problem becomes even more sophisticated as decision makers try to consider multi-objectives; minimizing inventory, maximizing customer satisfaction, and maximizing resource utilization. This study attempts to propose a methodology for MPS creation which is able to deal with those obstacles. This approach takes into account uncertainty and makes trade off among conflicting multi-objectives at the same time. It incorporates fuzzy multi-objective linear programming (FMOLP) and discrete event simulation (DES) for MPS development.

Prevalence and risk factors for carriage of multi-drug resistant Staphylococci in healthy cats and dogs

We investigated the distribution of commensal staphylococcal species and determined the prevalence of multi-drug resistance in healthy cats and dogs. Risk factors associated with the carriage of multi-drug resistant strains were explored. Isolates from 256 dogs and 277 cats were identified at the species level using matrix-assisted laser desorption ionisation-time of flight mass spectrometry. The diversity of coagulase-negative Staphylococci (CNS) was high, with 22 species in dogs and 24 in cats. Multi-drug resistance was frequent (17%) and not always associated with the presence of the mecA gene. A stay in a veterinary clinic in the last year was associated with an increased risk of colonisation by multi-drug resistant Staphylococci (OR = 2.4, 95% CI: 1.1˜5.2, p value LRT = 0.04). When identifying efficient control strategies against antibiotic resistance, the presence of mechanisms other than methicillin resistance and the possible role of CNS in the spread of resistance determinants should be considered.

Enhanced antibiotic multi-resistance in nasal and faecal bacteria after agricultural use of streptomycin

Streptomycin is used in arboriculture to control fire blight. Using sheep as a model, multidrug-resistant bacteria in mammals were found to be selected after the intentional release of streptomycin into the environment. Escherichia coli and Staphylococcus spp. were isolated from the faeces and nasal cavities, respectively, of sheep grazing on a field sprayed with streptomycin at concentrations used in orchards (test group) and on a field without streptomycin (control group). Before the application of streptomycin, the percentage of streptomycin-resistant E. coli isolates in faeces was 15.8% in the control group and 14.7% in the test group. After the application of streptomycin, the overall number of streptomycin-resistant E. coli isolates was significantly higher in the test group (39.9%) than in the control group (22.3%). Streptomycin-resistant Staphylococcus isolates were only detected after the application of streptomycin. Streptomycin resistance was frequently associated with resistance to sulfamethoxazole, ampicillin, tetracycline and chloramphenicol and less frequently to cefotaxime in E. coli, and to tetracycline, fusidic acid and tiamulin in Staphylococcus spp. This study shows that the application of low concentrations of streptomycin on grass, as occurs during the spraying of orchards, selects for multidrug-resistant nasal and enteric bacterial flora, including extended-spectrum beta-lactamase-producing E. coli.

MULTIPLE POSTTRANSCRIPTIONAL REGULATORY FEATURES CONTROL EXPRESSION OF ETHANOLAMINE UTILIZATION GENES IN ENTEROCOCCUS FAECALIS

Enterococcus faecalis is a Gram-positive bacterium that lives as a commensal organism in the mammalian gastrointestinal tract, but can behave as an opportunistic pathogen. Our lab discovered that mutation of the eutK gene attenuates virulence of E. faecalis in the C. elegans model host. eutK is part of the ethanolamine metabolic pathway which was previously unknown in E. faecalis. I discovered the presence of two unique posttranscriptional regulatory features that control expression of eut locus genes. The first feature I found is an AdoCBL riboswitch, a cis-acting RNA regulatory element that acts as a positive regulator of gene expression. The second feature I discovered is a unique two-component system, EutVW. The EutV response regulator contains an ANTAR family domain, which binds RNA to trigger transcriptional antitermination. I determined that induction of expression of several genes in the eut locus is dependent on ethanolamine, AdoCBL and the two-component system. AdoCBL and ethanolamine are both required for induction of eut locus gene expression. Additionally, I discovered eutG is regulated by a unique mechanism of antitermination. Both the AdoCBL riboswitch and EutV response regulator control the expression of the downstream gene eutG. EutV potentially acts through a novel antitermination mechanism in which a dimer of EutV binds to a pair of mRNA stem loops forming an antitermination complex. My data show a unique mechanism by which two environmental signals are integrated by two different posttranscriptional regulators to regulate a single locus.