Design of experiments to study the impact of process parameters on droplet size and development of non-invasive imaging techniques in tablet coating

Atomisation of an aqueous solution for tablet film coating is a complex process with multiple factors determining droplet formation and properties. The importance of droplet size for an efficient process and a high quality final product has been noted in the literature, with smaller droplets reported to produce smoother, more homogenous coatings whilst simultaneously avoiding the risk of damage through over-wetting of the tablet core. In this work the effect of droplet size on tablet film coat characteristics was investigated using X-ray microcomputed tomography (XμCT) and confocal laser scanning microscopy (CLSM). A quality by design approach utilising design of experiments (DOE) was used to optimise the conditions necessary for production of droplets at a small (20 μm) and large (70 μm) droplet size. Droplet size distribution was measured using real-time laser diffraction and the volume median diameter taken as a response. DOE yielded information on the relationship three critical process parameters: pump rate, atomisation pressure and coating-polymer concentration, had upon droplet size. The model generated was robust, scoring highly for model fit (R2 = 0.977), predictability (Q2 = 0.837), validity and reproducibility. Modelling confirmed that all parameters had either a linear or quadratic effect on droplet size and revealed an interaction between pump rate and atomisation pressure. Fluidised bed coating of tablet cores was performed with either small or large droplets followed by CLSM and XμCT imaging. Addition of commonly used contrast materials to the coating solution improved visualisation of the coating by XμCT, showing the coat as a discrete section of the overall tablet. Imaging provided qualitative and quantitative evidence revealing that smaller droplets formed thinner, more uniform and less porous film coats.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Radiotherapy Treatment Assessment using DCE-MRI

Abstract

The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information.

This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.

I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising.

Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI.

II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response.

The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment.

In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.

Optical properties of chiral thin films and microparticles

In this work I study the optical properties of helical particles and chiral sculptured thin films, using computational modeling (discrete dipole approximation, Berreman calculus), and experimental techniques (glancing angle deposition, ellipsometry, scatterometry, and non-linear optical measurements). The first part of this work focuses on linear optics, namely light scattering from helical microparticles. I study the influence of structural parameters and orientation on the optical properties of particles: circular dichroism (CD) and optical rotation (OR), and show that as a consequence of random orientation, CD and OR can have the opposite sign, compared to that of the oriented particle, potentially resulting in ambiguity of measurement interpretation. Additionally, particles in random orientation scatter light with circular and elliptical polarization states, which implies that in order to study multiple scattering from randomly oriented chiral particles, the polarization state of light cannot be disregarded. To perform experiments and attempt to produce particles, a newly constructed multi stage thin film coating chamber is calibrated. It enables the simultaneous fabrication of multiple sculptured thin film coatings, each with different structure. With it I successfully produce helical thin film coatings with Ti and TiO_{2}. The second part of this work focuses on non-linear optics, with special emphasis on second-harmonic generation. The scientific literature shows extensive experimental and theoretical work on second harmonic generation from chiral thin films. Such films are expected to always show this non-linear effect, due to their lack of inversion symmetry. However no experimental studies report non-linear response of chiral sculptured thin films. In this work I grow films suitable for a second harmonic generation experiment, and report the first measurements of non-linear response.

Investigating Multiple Roles of Vocal Confidence in Persuasion

Although persuasion often occurs via oral communication, it remains a comparatively understudied area. This research tested the hypothesis that changes in three properties of voice influence perceptions of speaker confidence, which in turn differentially affects attitudes according to different underlying psychological processes that the Elaboration Likelihood Model (ELM, Petty & Cacioppo, 1984), suggests should emerge under different levels of thought. Experiment 1 was a 2 (Elaboration: high vs. low) x 2 (Vocal speed: increased speed vs. decreased speed) x 2 (Vocal intonation: falling intonation vs. rising intonation) between participants factorial design. Vocal speed and vocal intonation influenced perceptions of speaker confidence as predicted. In line with the ELM, under high elaboration, confidence biased thought favorability, which in turn influenced attitudes. Under low elaboration, confidence did not bias thoughts but rather directly influenced attitudes as a peripheral cue. Experiment 2 used a similar design as Experiment 1 but focused on vocal pitch. Results confirmed pitch influenced perceptions of confidence as predicted. Importantly, we also replicated the bias and cue processes found in Experiment 1. Experiment 3 investigated the process by which a broader spectrum of speech rate influenced persuasion under moderate elaboration. In a 2 (Argument quality: strong vs. weak) x 4 (Vocal speed: extremely slow vs. moderately slow vs. moderately fast vs. extremely fast) between participants factorial design, results confirmed the hypothesized non-linear relationship between speech rate and perceptions of confidence. In line with the ELM, speech rate influenced persuasion based on the amount of processing. Experiment 4 investigated the effects of a broader spectrum of vocal intonation on persuasion under moderate elaboration and used a similar design as Experiment 3. Results indicated a partial success of our vocal intonation manipulation. No evidence was found to support the hypothesized mechanism. These studies show that changes in several different properties of voice can influence the extent to which others perceive them as confident. Importantly, evidence suggests different vocal properties influence persuasion by the same bias and cue processes under high and low thought. Evidence also suggests that under moderate thought, speech rate influences persuasion based on the amount of processing.

The Use of Almost Linear Phase IIR filters In DFT Modulated Filter Banks for Communication Systems

This paper is on the use and performance of M-path polyphase Infinite Impulse Response (IIR) filters for channelisation, conventionally where Finite Impulse Response (FIR) filters are preferred. This paper specifically focuses on the Discrete Fourier Transform (DFT) modulated filter banks, which are known to be an efficient choice for channelisation in communication systems. In this paper, the low-pass prototype filter for the DFT filter bank has been implemented using an M-path polyphase IIR filter and we show that the spikes present at the stopband can be avoided by making use of the guardbands between narrowband channels. It will be shown that the channelisation performance will not be affected when polyphase IIR filters are employed instead of their counterparts derived from FIR prototype filters. Detailed complexity and performance analysis of the proposed use will be given in this article.

Model reduction for nonlinear aerodynamics and aeroelasticity using a Discrete Empirical Interpolation Method

A novel surrogate model is proposed in lieu of Computational Fluid Dynamics (CFD) solvers, for fast nonlinear aerodynamic and aeroelastic modeling. A nonlinear function is identified on selected interpolation points by
a discrete empirical interpolation method (DEIM). The flow field is then reconstructed using a least square approximation of the flow modes extracted
by proper orthogonal decomposition (POD). The aeroelastic reduce order
model (ROM) is completed by introducing a nonlinear mapping function
between displacements and the DEIM points. The proposed model is investigated to predict the aerodynamic forces due to forced motions using
a N ACA 0012 airfoil undergoing a prescribed pitching oscillation. To investigate aeroelastic problems at transonic conditions, a pitch/plunge airfoil
and a cropped delta wing aeroelastic models are built using linear structural models. The presence of shock-waves triggers the appearance of limit
cycle oscillations (LCO), which the model is able to predict. For all cases
tested, the new ROM shows the ability to replicate the nonlinear aerodynamic forces, structural displacements and reconstruct the complete flow
field with sufficient accuracy at a fraction of the cost of full order CFD
model.

Secure Transmission in Cooperative Relaying Networks with Multiple Antennas

We investigate the secrecy performance of dualhop amplify-and-forward (AF) multi-antenna relaying systems over Rayleigh fading channels, by taking into account the direct link between the source and destination. In order to exploit the available direct link and the multiple antennas for secrecy improvement, different linear processing schemes at the relay and different diversity combining techniques at the destination are proposed, namely, 1) Zero-forcing/Maximal ratio combining (ZF/MRC), 2) ZF/Selection combining (ZF/SC), 3) Maximal ratio transmission/MRC (MRT/MRC) and 4) MRT/Selection combining (MRT/SC). For all these schemes, we present new closed-form approximations for the secrecy outage probability. Moreover, we investigate a benchmark scheme, i.e., cooperative jamming/ZF (CJ/ZF), where the secrecy outage probability is obtained in exact closed-form. In addition, we present asymptotic secrecy outage expressions for all the proposed schemes in the high signal-to-noise ratio (SNR) regime, in order to characterize key design parameters, such as secrecy diversity order and secrecy array gain. The outcomes of this paper can be summarized as follows: a) MRT/MRC and MRT/SC achieve a full diversity order of M + 1, ZF/MRC and ZF/SC achieve a diversity order of M, while CJ/ZF only achieves unit diversity order, where M is the number of antennas at the relay. b) ZF/MRC (ZF/SC) outperforms the corresponding MRT/MRC (MRT/SC) in the low SNR regime, while becomes inferior to the corresponding MRT/MRC (MRT/SC) in the high SNR. c) All of the proposed schemes tend to outperform the CJ/ZF with moderate number of antennas, and linear processing schemes with MRC attain better performance than those with SC.

An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks with Interference Constraints

We consider a linear precoder design for an underlay cognitive radio multiple-input multiple-output broadcast channel, where the secondary system consisting of a secondary base-station (BS) and a group of secondary users (SUs) is allowed to share the same spectrum with the primary system. All the transceivers are equipped with multiple antennas, each of which has its own maximum power constraint. Assuming zero-forcing method to eliminate the multiuser interference, we study the sum rate maximization problem for the secondary system subject to both per-antenna power constraints at the secondary BS and the interference power constraints at the primary users. The problem of interest differs from the ones studied previously that often assumed a sum power constraint and/or single antenna employed at either both the primary and secondary receivers or the primary receivers. To develop an efficient numerical algorithm, we first invoke the rank relaxation method to transform the considered problem into a convex-concave problem based on a downlink-uplink result. We then propose a barrier interior-point method to solve the resulting saddle point problem. In particular, in each iteration of the proposed method we find the Newton step by solving a system of discrete-time Sylvester equations, which help reduce the complexity significantly, compared to the conventional method. Simulation results are provided to demonstrate fast convergence and effectiveness of the proposed algorithm. 

Model reduction for nonlinear aeroelasticity using the Discrete Empirical Interpolation Method

A novel surrogate model is proposed in lieu of computational fluid dynamic (CFD) code for fast nonlinear aerodynamic modeling. First, a nonlinear function is identified on selected interpolation points defined by discrete empirical interpolation method (DEIM). The flow field is then reconstructed by a least square approximation of flow modes extracted by proper orthogonal decomposition (POD). The proposed model is applied in the prediction of limit cycle oscillation for a plunge/pitch airfoil and a delta wing with linear structural model, results are validate against a time accurate CFD-FEM code. The results show the model is able to replicate the aerodynamic forces and flow fields with sufficient accuracy while requiring a fraction of CFD cost.

Molecular characterization of extended-spectrum cephalosporin-resistance in Escherichia coli in pigs on-farm and from clinical cases throughout Quebec, Canada during 16 years

Le développement de la multirésistance chez Escherichia coli est un problème important en médecine animale et humaine. En outre, l’émergence et la diffusion des déterminants de résistance aux céphalosporines à larges spectres de troisième génération (ESCs) parmi les isolats, incluant des céphalosporines essentielles en médecine humaine (ex. ceftriaxone et ceftiofur), est un problème majeur de santé publique. Cette thèse visait trois objectifs. D’abord étudier la dynamique de la résistance aux antimicrobiens (AMR) ainsi que la virulence et les profils génétiques de la AMR des E. coli isolées de porcs recevant une nourriture post-sevrage supplémentée avec de la chlortétracycline et de la pénicilline G, et, accessoirement, évaluer les effets d'additifs alimentaires sur cette dynamique en prenant pour exemple d'étude un minéral argileux, la clinoptilolite, étant donné son possible lien avec le gène blaCMY-2 qui confère la résistance au ceftiofur. L'objectif suivant était d'investiguer les mécanismes menant à une augmentation de la prévalence du gène blaCMY-2 chez les porcs qui reçoivent de la nourriture médicamentée et qui n'ont pas été exposés au ceftiofur Ici encore,nous avons examiné les effets d’un supplément alimentaire avec un minéral argileux sur ce phénomène. Enfin, notre dernier objectif était d’étudier, dans le temps, les génotypes des isolats cliniques d'E. coli résistant au ceftiofur, isolés de porcs malades au Québec à partir du moment où la résistance au ceftiofur a été rapportée, soit de 1997 jusqu'à 2012. Dans l'étude initiale, la prévalence de la résistance à 10 agents antimicrobiens, incluant le ceftiofur, s’accroît avec le temps chez les E.coli isolées de porcelets sevrés. Une augmentation tardive de la fréquence du gène blaCMY-2, encodant pour la résistance au ceftiofur, et la présence des gènes de virulence iucD et tsh a été observée chez les isolats. La nourriture supplémentée avec de la clinoptilolite a été associée à une augmentation rapide mais, par la suite, à une diminution de la fréquence des gènes blaCMY-2 dans les isolats. En parallèle, une augmentation tardive dans la fréquence des gènes blaCMY-2 et des gènes de virulence iucD et tsh a été observée dans les isolats des porcs contrôles, étant significativement plus élevé que dans les porcs ayant reçu l'additif au jour 28. La diversité, au sein des E. coli positives pour blaCMY-2 , a été observée au regard des profils AMR. Certaines lignées clonales d'E.coli sont devenues prédominantes avec le temps. La lignée clonale du phylotype A prédominait dans le groupe supplémenté, alors que les lignées clonales du phylotype B1, qui possèdent souvent le gène de virulence iucD associé aux ExPEC, prédominaient dans le groupe contrôle. Les plasmides d'incompatibilité (Inc) des groupes, I1, A/C, et ColE, porteurs de blaCMY-2, ont été observés dans les transformants. Parmi les souches cliniques d'E.coli ESC-résistantes, isolées de porcs malades au Québec de 1997 à 2012, blaCMY-2 était le gène codant pour une β-lactamase le plus fréquemment détecté; suivi par blaTEM et blaCTX-M,. De plus, les analyses clonales montrent une grande diversité génétique. Par contre, des isolats d'E. coli avec des profils PFGE identiques ont été retrouvés dans de multiples fermes la même année mais aussi dans des années différentes. La résistance à la gentamicine, kanamycine, chloramphenicol, et la fréquence de blaTEM et de IncA/C diminuent significativement au cour de la période étudiée, alors que la fréquence de IncI1 et de la multirésistance à sept catégories d'agents antimicrobiens augmente significativement avec le temps. L'émergence d'isolats d'E. coli positifs pour blaCTX-M, une β-lactamase à large spectre et produisant des ESBL, a été observée en 2011 et 2012 à partir de lignées clonales distinctes et chez de nombreuses fermes. Ces résultats, mis ensemble, apportent des précisions sur la dissémination de la résistance au ceftiofur dans les E. coli isolées de porcs. Au sein des échantillons prélevés chez les porcs sevrés recevant l'alimentation médicamentée sur une ferme, et pour laquelle une augmentation de la résistance au ceftiofur a été observée, les données révèlent que les souches d'E. coli positives pour blaCMY-2 et résistantes aux ESCs appartenaient à plusieurs lignées clonales différentes arborant divers profils AMR. Le gène blaCMY-2 se répand à la fois horizontalement et clonalement chez ces E. coli. L'ajout de clinoptilotite à la nourriture et le temps après le sevrage influencent la clonalité et la prévalence du gène blaCMY-2 dans les E. coli. Durant les 16 années d'étude, plusieurs lignées clonales différentes ont été observées parmi les souches d'E. coli résistantes au ceftiofur isolées de porc malades de fermes québécoises, bien qu’aucune lignée n'était persistante ou prédominante pendant l'étude. Les résultats suggèrent aussi que le gène blaCMY-2 s'est répandu à la fois horizontalement et clonalement au sein des fermes. De plus, blaCMY-2 est le gène majeur des β-lactamases chez ces isolats. À partir de 2011, nous rapportons l'émergence du gène blaCTX-M dans des lignées génétiques distinctes.

Utility Analysis for Multiple Selection Devices and Multiple Outcomes

Traditional utility analysis only calculates the value of a given selection procedure over random selection. This assumption is not only an inaccurate representation of staffing policy but also leads to overestimates of a device’s value. This paper presents a more accurate method for computing the validity of a selection battery for when there are multiple selection devices and multiple criteria. Application of the method is illustrated using previous utility analysis work and an actual case of administrative assistants with eight predictors and nine criteria. A final example also is provided that includes these advancements as well as other researchers’ advances in a combined utility model. Results reveal that accounting for multiple criteria and outcomes dramatically reduces the utility estimates of implementing new selection devices.

Stepped Wedge Design for Multiple Interventions

Thesis (Master's)--University of Washington, 2016-08

Discrete-time positive periodic systems with state and control constraints

The aim of this paper is to provide an efficient control design technique for discrete-time positive periodic systems. In particular, stability, positivity and periodic invariance of such systems are studied. Moreover, the concept of periodic invariance with respect to a collection of boxes is introduced and investigated with connection to stability. It is shown how such concept can be used for deriving a stabilizing state-feedback control that maintains the positivity of the closed-loop system and respects states and control signals constraints. In addition, all the proposed results can be efficiently solved in terms of linear programming.

Random Access over Multi-Packet Reception Channels: A Mean-Field Approach

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