New cassettes for single-step drug-resistance and prototrophic marker switching in fission yeast

Copyright © 2015 John Wiley & Sons, Ltd. Funded by College of Life Science and Medicine, University of Aberdeen, UK This work was funded by a start-up grant from the College of Life Science and Medicine, University of Aberdeen, UK. I am grateful to J. Bähler, E. Hartsuiker, F. Klein, J. Kohli, K. Nasmyth, M. C. Whitby, the Leibniz Institute – German Collection of Microorganisms and Cell Cultures (DMSZ) and the National BioResource Project Japan (NBRP) for providing materials used in this study. I thank Alistair J. P. Brown and Takashi Kubota for critically reading this manuscript.

New cassettes for single-step drug-resistance and prototrophic marker switching in fission yeast

Copyright © 2015 John Wiley & Sons, Ltd. Funded by College of Life Science and Medicine, University of Aberdeen, UK This work was funded by a start-up grant from the College of Life Science and Medicine, University of Aberdeen, UK. I am grateful to J. Bähler, E. Hartsuiker, F. Klein, J. Kohli, K. Nasmyth, M. C. Whitby, the Leibniz Institute – German Collection of Microorganisms and Cell Cultures (DMSZ) and the National BioResource Project Japan (NBRP) for providing materials used in this study. I thank Alistair J. P. Brown and Takashi Kubota for critically reading this manuscript.

Political Sophistication and Vote Intention Switching

This article investigates the link between political sophistication and electoral volatility. Showing that there is disagreement in the literature on whether switching party preferences is related to low or high levels of political sophistication, it is then argued that the effect of sophistication on vote switching might differ depending on when switching is measured. The effect of timing on volatility is investigated by means of the Short-term panel of the 2009 German Longitudinal Election Study. Results indicate that timing indeed matters, while sophistication increases the probability of switching parties before the campaign, the effect of political sophistication becomes more negative as Election Day draws near.

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.

Magnetic field-induced ferroelectric switching in multiferroic aurivillius phase thin films at room temperature

Single-phase multiferroic materials are of considerable interest for future memory and sensing applications. Thin films of Aurivillius phase Bi 7Ti3Fe3O21 and Bi6Ti 2.8Fe1.52Mn0.68O18 (possessing six and five perovskite units per half-cell, respectively) have been prepared by chemical solution deposition on c-plane sapphire. Superconducting quantum interference device magnetometry reveal Bi7Ti3Fe 3O21 to be antiferromagnetic (TN = 190 K) and weakly ferromagnetic below 35 K, however, Bi6Ti2.8Fe 1.52Mn0.68O18 gives a distinct room-temperature in-plane ferromagnetic signature (Ms = 0.74 emu/g, μ0Hc =7 mT). Microstructural analysis, coupled with the use of a statistical analysis of the data, allows us to conclude that ferromagnetism does not originate from second phase inclusions, with a confidence level of 99.5%. Piezoresponse force microscopy (PFM) demonstrates room-temperature ferroelectricity in both films, whereas PFM observations on Bi6Ti2.8Fe1.52Mn0.68O18 show Aurivillius grains undergo ferroelectric domain polarization switching induced by an applied magnetic field. Here, we show for the first time that Bi6Ti2.8Fe1.52Mn0.68O18 thin films are both ferroelectric and ferromagnetic and, demonstrate magnetic field-induced switching of ferroelectric polarization in individual Aurivillius phase grains at room temperature.

Resonance Energy Transfer-Based Molecular Switch Designed Using a Systematic Design Process Based on Monte Carlo Methods and Markov Chains

A RET network consists of a network of photo-active molecules called chromophores that can participate in inter-molecular energy transfer called resonance energy transfer (RET). RET networks are used in a variety of applications including cryptographic devices, storage systems, light harvesting complexes, biological sensors, and molecular rulers. In this dissertation, we focus on creating a RET device called closed-diffusive exciton valve (C-DEV) in which the input to output transfer function is controlled by an external energy source, similar to a semiconductor transistor like the MOSFET. Due to their biocompatibility, molecular devices like the C-DEVs can be used to introduce computing power in biological, organic, and aqueous environments such as living cells. Furthermore, the underlying physics in RET devices are stochastic in nature, making them suitable for stochastic computing in which true random distribution generation is critical.

In order to determine a valid configuration of chromophores for the C-DEV, we developed a systematic process based on user-guided design space pruning techniques and built-in simulation tools. We show that our C-DEV is 15x better than C-DEVs designed using ad hoc methods that rely on limited data from prior experiments. We also show ways in which the C-DEV can be improved further and how different varieties of C-DEVs can be combined to form more complex logic circuits. Moreover, the systematic design process can be used to search for valid chromophore network configurations for a variety of RET applications.

We also describe a feasibility study for a technique used to control the orientation of chromophores attached to DNA. Being able to control the orientation can expand the design space for RET networks because it provides another parameter to tune their collective behavior. While results showed limited control over orientation, the analysis required the development of a mathematical model that can be used to determine the distribution of dipoles in a given sample of chromophore constructs. The model can be used to evaluate the feasibility of other potential orientation control techniques.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Scale-dependent to scale-free: daily behavioural switching and optimized searching in a marine predator

Highlights • We study diel behavioural differences in activity patterns in bigeye tuna. • Daytime activity patterns showed scale free movements consistent with searching. • Night-time activity showed simpler movements indicative of rich patch exploitation. • The results confirm predictions of the Lévy foraging hypothesis.

Scale-dependent to scale-free: daily behavioural switching and optimized searching in a marine predator

Highlights • We study diel behavioural differences in activity patterns in bigeye tuna. • Daytime activity patterns showed scale free movements consistent with searching. • Night-time activity showed simpler movements indicative of rich patch exploitation. • The results confirm predictions of the Lévy foraging hypothesis.

Deterministic Switching in Bismuth Ferrite Nanoislands

We report deterministic selection of polarization variant in bismuth BiFeO3 nanoislands via a two-step scanning probe microscopy procedure. The polarization orientation in a nanoisland is toggled to the desired variant after a reset operation by scanning a conductive atomic force probe in contact over the surface while a bias is applied. The final polarization variant is determined by the direction of the inhomogeneous in-plane trailing field associated with the moving probe tip. This work provides the framework for better control of switching in rhombohedral ferroelectrics and for a deeper under- standing of exchange coupling in multiferroic nanoscale hetero- structures toward the realization of magnetoelectric devices.

Redox-dependent conformational switching of diphenylacetylenes

Herein we describe the design and synthesis of a redox-dependent single-molecule switch. Appending a ferrocene unit to a diphenylacetylene scaffold gives a redox-sensitive handle, which undergoes reversible one-electron oxidation, as demonstrated by cyclic voltammetry analysis. ¹H-NMR spectroscopy of the partially oxidized switch and control compounds suggests that oxidation to the ferrocenium cation induces a change in hydrogen bonding interactions that results in a conformational switch. 

A new method of current switching for linear wide-range measurement systems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

BETA-GA2O3: A TRANSPARENT CONDUCTIVE OXIDE FOR POTENTIAL RESISTIVE SWITCHING APPLICATIONS

Thesis (Ph.D.)--University of Washington, 2016-08

Modèles de Markov cachés à haute précision dynamique

La reconnaissance vocale est une technologie sujette à amélioration. Malgré 40 ans de travaux, de nombreuses applications restent néanmoins hors de portée en raison d'une trop faible efficacité. De façon à pallier à ce problème, l'auteur propose une amélioration au cadre conceptuel classique. Plus précisément, une nouvelle méthode d'entraînement des modèles markoviens cachés est exposée de manière à augmenter la précision dynamique des classificateurs. Le présent document décrit en détail le résultat de trois ans de recherche et les contributions scientifiques qui en sont le produit. L'aboutissement final de cet effort est la production d'un article de journal proposant une nouvelle tentative d'approche à la communauté scientifique internationale. Dans cet article, les auteurs proposent que des topologies finement adaptées de modèles markoviens cachés (HMMs) soient essentielles à une modélisation temporelle de haute précision. Un cadre conceptuel pour l'apprentissage efficace de topologies par élagage de modèles génériques complexes est donc soumis. Des modèles HMM à topologie gauche-à-droite sont d'abord entraînés de façon classique. Des modèles complexes à topologie générique sont ensuite obtenus par écrasement des modèles gauche-à-droite. Finalement, un enchaînement successif d'élagages et d'entraînements Baum-Welch est fait de manière à augmenter la précision temporelle des modèles.