Development of a duplex real-time PCR for the detection of Rickettsia spp. and typhus group rickettsia in clinical samples.

Molecular diagnosis using real-time polymerase chain reaction (PCR) may allow earlier diagnosis of rickettsiosis. We developed a duplex real-time PCR that amplifies (1) DNA of any rickettsial species and (2) DNA of both typhus group rickettsia, that is, Rickettsia prowazekii and Rickettsia typhi. Primers and probes were selected to amplify a segment of the 16S rRNA gene of Rickettsia spp. for the pan-rickettsial PCR and the citrate synthase gene (gltA) for the typhus group rickettsia PCR. Analytical sensitivity was 10 copies of control plasmid DNA per reaction. No cross-amplification was observed when testing human DNA and 22 pathogens or skin commensals. Real-time PCR was applied to 16 clinical samples. Rickettsial DNA was detected in the skin biopsies of three patients. In one patient with severe murine typhus, the typhus group PCR was positive in a skin biopsy from a petechial lesion and seroconversion was later documented. The two other patients with negative typhus group PCR suffered from Mediterranean and African spotted fever, respectively; in both cases, skin biopsy was performed on the eschar. Our duplex real-time PCR showed a good analytical sensitivity and specificity, allowing early diagnosis of rickettsiosis among three patients, and recognition of typhus in one of them.

Discovery of a 29-gene panel in peripheral blood mononuclear cells for the detection of colorectal cancer and adenomas using high throughput real-time PCR.

Colorectal cancer (CRC) is the second leading cause of cancer-related death in developed countries. Early detection of CRC leads to decreased CRC mortality. A blood-based CRC screening test is highly desirable due to limited invasiveness and high acceptance rate among patients compared to currently used fecal occult blood testing and colonoscopy. Here we describe the discovery and validation of a 29-gene panel in peripheral blood mononuclear cells (PBMC) for the detection of CRC and adenomatous polyps (AP). Blood samples were prospectively collected from a multicenter, case-control clinical study. First, we profiled 93 samples with 667 candidate and 3 reference genes by high throughput real-time PCR (OpenArray system). After analysis, 160 genes were retained and tested again on 51 additional samples. Low expressed and unstable genes were discarded resulting in a final dataset of 144 samples profiled with 140 genes. To define which genes, alone or in combinations had the highest potential to discriminate AP and/or CRC from controls, data were analyzed by a combination of univariate and multivariate methods. A list of 29 potentially discriminant genes was compiled and evaluated for its predictive accuracy by penalized logistic regression and bootstrap. This method discriminated AP >1cm and CRC from controls with a sensitivity of 59% and 75%, respectively, with 91% specificity. The behavior of the 29-gene panel was validated with a LightCycler 480 real-time PCR platform, commonly adopted by clinical laboratories. In this work we identified a 29-gene panel expressed in PBMC that can be used for developing a novel minimally-invasive test for accurate detection of AP and CRC using a standard real-time PCR platform.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

Statistical Analysis and Numerics of Heat Exchanger Models

The identifiability of the parameters of a heat exchanger model without phase change was studied in this Master’s thesis using synthetically made data. A fast, two-step Markov chain Monte Carlo method (MCMC) was tested with a couple of case studies and a heat exchanger model. The two-step MCMC-method worked well and decreased the computation time compared to the traditional MCMC-method. The effect of measurement accuracy of certain control variables to the identifiability of parameters was also studied. The accuracy used did not seem to have a remarkable effect to the identifiability of parameters. The use of the posterior distribution of parameters in different heat exchanger geometries was studied. It would be computationally most efficient to use the same posterior distribution among different geometries in the optimisation of heat exchanger networks. According to the results, this was possible in the case when the frontal surface areas were the same among different geometries. In the other cases the same posterior distribution can be used for optimisation too, but that will give a wider predictive distribution as a result. For condensing surface heat exchangers the numerical stability of the simulation model was studied. As a result, a stable algorithm was developed.

Novel force control methods in a teleoperation system of a hydraulic slider

The objective of the this research project is to develop a novel force control scheme for the teleoperation of a hydraulically driven manipulator, and to implement an ideal transparent mapping between human and machine interaction, and machine and task environment interaction. This master‘s thesis provides a preparatory study for the present research project. The research is limited into a single degree of freedom hydraulic slider with 6-DOF Phantom haptic device. The key contribution of the thesis is to set up the experimental rig including electromechanical haptic device, hydraulic servo and 6-DOF force sensor. The slider is firstly tested as a position servo by using previously developed intelligent switching control algorithm. Subsequently the teleoperated system is set up and the preliminary experiments are carried out. In addition to development of the single DOF experimental set up, methods such as passivity control in teleoperation are reviewed. The thesis also contains review of modeling of the servo slider in particular reference to the servo valve. Markov Chain Monte Carlo method is utilized in developing the robustness of the model in presence of noise.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

DYNAMICS AND PREDICTION OF DIAMETRIC STRUCTURE IN TWO ATLANTIC FOREST FRAGMENTS IN NORTHEASTERN BRAZIL

ABSTRACT Monitoring analyses aim to understand the processes that drive changes in forest structure and, along with prediction studies, may assist in the management planning and conservation of forest remnants. The objective of this study was to analyze the forest dynamics in two Atlantic rainforest fragments in Pernambuco, Brazil, and to predict their future forest diameter structure using the Markov chain model. We used continuous forest inventory data from three surveys in two forest fragments of 87 ha (F1) and 388 ha (F2). We calculated the annual rates of mortality and recruitment, the mean annual increment, and the basal area for each of the 3-year periods. Data from the first and second surveys were used to project the third inventory measurements, which were compared to the observed values in the permanent plots using chi-squared tests (a = 0.05). In F1, a decrease in the number of individuals was observed due to mortality rates being higher than recruitment rates; however, there was an increase in the basal area. In this fragment, the fit to the Markov model was adequate. In F2, there was an increase in both the basal area and the number of individuals during the 6-year period due to the recruitment rate exceeding the mortality rate. For this fragment, the fit of the model was unacceptable. Hence, for the studied fragments, the demographic rates influenced the stem density more than the floristic composition. Yet, even with these intense dynamics, both fragments showed active growth.

Development of a Real-time PCR test for porcine group A rotavirus diagnosis

Group A Rotavirus (RVA) is one of the most common causes of diarrhea in humans and several animal species. A SYBR-Green Real-Time polymerase chain reaction (PCR) was developed to diagnose RVA from porcine fecal samples, targeting amplification of a 137-bp fragment of nonstructural protein 5 (NSP5) gene using mRNA of bovine NADH-desidrogenase-5 as exogenous internal control. Sixty-five samples were tested (25 tested positive for conventional PCR and genetic sequencing). The overall agreement (kappa) was 0.843, indicating 'very good' concordance between tests, presenting 100% of relative sensitivity (25+ Real Time PCR/25+ Conventional PCR) and 87.5% of relative sensitivity (35- Real Time PCR/40- Conventional PCR). The results also demonstrated high intra- and inter-assay reproducibility (coefficient of variation ≤1.42%); thus, this method proved to be a fast and sensitive approach for the diagnosis of RVA in pigs.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Global Spare Parts Supply Chain Analysis

In today’s global industrial service business, markets are dynamic and finding new ways of value creation towards customers has become more and more challenging. Customer orientation is needed because of the demanding after-sales business which is both quickly changing and stochastic in nature. In after-sales business customers require fast and reliable service for their spare part needs. This thesis objective is to clarify this challenging after-sales business environment and find ways to increase customer satisfaction via balanced measurement system which will help to find possible targets to reduce order cycle times in a large metal and mineral company Outotec (Filters)’ Spare Part Supply business line. In case study, internal documents and data and numerical calculations together with qualitative interviews with different persons in key roles of Spare Part Supply organizations are used to analyze the performance of different processes from the spare parts delivery function. The chosen performance measurement tool is Balanced Scorecard which is slightly modified to suit the lead time study from customer’s perspective better. Findings show that many different processes in spare parts supply are facing different kind of challenges in achieving the lead time levels wanted and that these processes’ problems seem to accumulate. Findings also show that putting effort in supply side challenges and information flows visibility should give the best results.

Kalman filter for computational market dynamics

Kalman filter is a recursive mathematical power tool that plays an increasingly vital role in innumerable fields of study. The filter has been put to service in a multitude of studies involving both time series modelling and financial time series modelling. Modelling time series data in Computational Market Dynamics (CMD) can be accomplished using the Jablonska-Capasso-Morale (JCM) model. Maximum likelihood approach has always been utilised to estimate the parameters of the JCM model. The purpose of this study is to discover if the Kalman filter can be effectively utilized in CMD. Ensemble Kalman filter (EnKF), with 50 ensemble members, applied to US sugar prices spanning the period of January, 1960 to February, 2012 was employed for this work. The real data and Kalman filter trajectories showed no significant discrepancies, hence indicating satisfactory performance of the technique. Since only US sugar prices were utilized, it would be interesting to discover the nature of results if other data sets are employed.

Novel control, haptic and calibration methods for teleoperated electrohydraulic servo systems

The aim of this thesis is to propose a novel control method for teleoperated electrohydraulic servo systems that implements a reliable haptic sense between the human and manipulator interaction, and an ideal position control between the manipulator and the task environment interaction. The proposed method has the characteristics of a universal technique independent of the actual control algorithm and it can be applied with other suitable control methods as a real-time control strategy. The motivation to develop this control method is the necessity for a reliable real-time controller for teleoperated electrohydraulic servo systems that provides highly accurate position control based on joystick inputs with haptic capabilities. The contribution of the research is that the proposed control method combines a directed random search method and a real-time simulation to develop an intelligent controller in which each generation of parameters is tested on-line by the real-time simulator before being applied to the real process. The controller was evaluated on a hydraulic position servo system. The simulator of the hydraulic system was built based on Markov chain Monte Carlo (MCMC) method. A Particle Swarm Optimization algorithm combined with the foraging behavior of E. coli bacteria was utilized as the directed random search engine. The control strategy allows the operator to be plugged into the work environment dynamically and kinetically. This helps to ensure the system has haptic sense with high stability, without abstracting away the dynamics of the hydraulic system. The new control algorithm provides asymptotically exact tracking of both, the position and the contact force. In addition, this research proposes a novel method for re-calibration of multi-axis force/torque sensors. The method makes several improvements to traditional methods. It can be used without dismantling the sensor from its application and it requires smaller number of standard loads for calibration. It is also more cost efficient and faster in comparison to traditional calibration methods. The proposed method was developed in response to re-calibration issues with the force sensors utilized in teleoperated systems. The new approach aimed to avoid dismantling of the sensors from their applications for applying calibration. A major complication with many manipulators is the difficulty accessing them when they operate inside a non-accessible environment; especially if those environments are harsh; such as in radioactive areas. The proposed technique is based on design of experiment methodology. It has been successfully applied to different force/torque sensors and this research presents experimental validation of use of the calibration method with one of the force sensors which method has been applied to.

Parameter estimation for Chaotic or Stochastic Dynamics

Since its discovery, chaos has been a very interesting and challenging topic of research. Many great minds spent their entire lives trying to give some rules to it. Nowadays, thanks to the research of last century and the advent of computers, it is possible to predict chaotic phenomena of nature for a certain limited amount of time. The aim of this study is to present a recently discovered method for the parameter estimation of the chaotic dynamical system models via the correlation integral likelihood, and give some hints for a more optimized use of it, together with a possible application to the industry. The main part of our study concerned two chaotic attractors whose general behaviour is diff erent, in order to capture eventual di fferences in the results. In the various simulations that we performed, the initial conditions have been changed in a quite exhaustive way. The results obtained show that, under certain conditions, this method works very well in all the case. In particular, it came out that the most important aspect is to be very careful while creating the training set and the empirical likelihood, since a lack of information in this part of the procedure leads to low quality results.

Explaining the Transition Between Exchange Rate Regimes

This paper studies the transition between exchange rate regimes using a Markov chain model with time-varying transition probabilities. The probabilities are parameterized as nonlinear functions of variables suggested by the currency crisis and optimal currency area literature. Results using annual data indicate that inflation, and to a lesser extent, output growth and trade openness help explain the exchange rate regime transition dynamics.

Efficient estimation using the characteristic function : theory and applications with high frequency data

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