The use of traditional tools for the discovery and characterization of biological systems has resulted in a wealth of biological knowledge. Unfortunately, only a small portion of the biological world is well-understood to date, and the study of the rest remains a daunting task. This work involves using time-varying stimuli in order to more rapidly interrogate and characterize signaling pathways. The time-dependent stimulation of a signaling pathway can be used in conjunction with a model of the pathway to efficiently evaluate and test hypotheses. We are developing this technology using the yeast pheromone signal transduction pathway as a model system. The time-varying stimuli will be applied to the yeast cells via a novel microfluidic device, and the pathway output will be measured via various fluorescent reporters. The output of the pathway can then be compared to the output from a computational model of the pathway in order to test hypotheses and constrain our knowledge of the pathway. Initial work shows that a computational model can be used to identify stimuli time-courses that increase the parameter sensitivity, meaning that corresponding experiments could potentially be much more informative. / Poster presented at the 2005 ICSB meeting, held at Harvard Medical School in Boston, MA.

In this thesis we study the impact of time in the analysis of social networks. To do that we represent a knowledge mobilization network, Knowledge-Net, both as a standard static graph and a time-varying graph and study both graphs to see their differences. For our study, we implemented some temporal metrics and added them to Gephi, an open source software for graph and network analysis which already contains some static metrics. Then we used that software to obtain our results. Knowledge-Net is a network built using the knowledge mobilization concept. In social science, knowledge mobilization is defined as the use of knowledge towards the achievement of goals. The networks which are built using the knowledge mobilization concept make more visible the relations among heterogeneous human and non-human individuals, organizational actors and non-human mobilization actors. A time-varying graph is a graph with nodes and edges appearing and disappearing over time. A journey in a time-varying graph is equivalent to a path in a static graph. The notion of shortest path in a static graph has three variations in a time-varying graph: the shortest journey is the journey with the least number of temporal hops, the fastest journey is the journey that takes the least amount of time and the foremost journey is the journey that arrives the soonest. Out of those three, we focus on the foremost journey for our analysis.

Growth mixture modeling (GMM) is a useful tool for examining both between- and within-persons change over time and uncovering unobserved heterogeneity in growth trajectories. Importantly, the correct extraction of latent classes and parameter recovery can be dependent upon the type of covariates used. Time-varying covariates (TVCs) can influence class membership but are scarcely included in GMMs as predictors. Other times, TVCs are incorrectly modeled as time-invariant covariates (TICs). Additionally, problematic results can occur with the use of maximum likelihood (ML) estimation in GMMs, including convergence issues and sub-optimal maxima. In such cases, Bayesian estimation may prove to be a useful solution. The present Monte Carlo simulation study aimed to assess class enumeration accuracy and parameter recovery of GMMs with a TVC, particularly when a TVC has been incorrectly specified as a TIC. Both ML estimation and Bayesian estimation were examined. Results indicated that class enumeration indices perform less favorably in the case of TVC misspecification, particularly absolute class enumeration indices. Additionally, in the case of TVC misspecification, parameter bias was found to be greater than the generally accepted cutoff of 10%, particularly for variance estimates. It is recommended that researchers continue to use a variety of class enumeration indices during class enumeration, particularly relative indices. Additionally, researchers should take caution when interpreting variance parameter estimates when the GMM contains a misspecified TVC.

Most of the methodologies dealing with viscoelastic damping focused exclusively on the frequency dependence behavior of the material. Only a few looked into the temperature dependence of the model, although none of them has taken a more serious investigation on the control design subjected to temperature disturbances. The general purpose of this work is to develop and investigate structures with damping modeled by means of internal variables. Thermodynamic principles are used to develop models, which are based on a generalized Maxwell element. Initially, studies are conducted to verify how the method of reduced variables can be applied to account for temperature dependence, as well as to evaluate the number of internal variables necessary for good accuracy of material properties representation. Lumped and finite element models are characterized and validated against other methods. A constrained layer damping model is experimentally validated for many temperatures. A control analysis is carried out on the models with the purpose to identify the role played by the internal variables on the control design. The results show that moving the internal poles is very expensive in terms of control energy. It is also shown that it is not always possible to eliminate the internal coordinates in the reduced order model if the system is highly damped. The problem of having the internal pole moved is solved by applying partial pole placement. This technique shows similar performance as compared to the linear quadratic Gaussian regulator. The control designs are implemented and it is shown that good regulation can be achieved for a fixed temperature. It is further shown that the controller will lose its performance when the model is subjected to temperature changes. To investigate the behavior of the model under different temperatures, a linear temperature-dependent model is developed, which clearly shows how the temperature affects the time response of the model. This model is used as a baseline to develop an adaptive and a time-varying controllers. With the aid of the shift factor, the eigenvalue variation with temperature is used as a time-varying function in the design. The results show that good track performance and regulation can be achieved with a control law that is capable of compensating for temperature variations. / Ph. D.

No / This paper employs the standard General Auto-regressive Conditional Heteroskedasticity (GARCH(1,1)) process to examine the impact of option listing on volatility the underlying stocks. It takes into consideration the time variation in the individual stock's variance and explicitly tests whether option listing causes any permanent volatility change. It also investigates the impact of option listing on the speed at which information is incorporated into the stock price. The study uses clean samples to avoid sample selection biases and control samples to account for the change in the volatility and/or information flows that may be caused by factors other than option listing.

The aim of this thesis is to measure changes in dependencies among returns on equity indices for European countries in tranquil periods against crisis periods and to investigate their asymmetries in the lower and upper tail of their distributions. The approach is based on a conditional probability that a random variable is lower than a given quantile while other random variables are also lower than their corresponding quantiles. Time-varying conditional quantiles are modeled by the Conditional Autoregressive Value at Risk via Regression Quantiles (CAViaR) method. In addition to the univariate conditional autoregressive models, the vector autoregressive extension is considered. In the second step, the conditional probability is estimated through the OLS regression. Moreover, the model which allows the distribution of returns in one country to lead or to lag the distribution of returns in another country, is defined and applied on European equity returns. Finally, the model measuring dependencies among more than two return series is derived and the relating dimensionality problems are discussed. The results document a significant increase in European equity return comovements in bear markets during the crisis in 1990s and 2000s. The explicit controlling for the high volatility days does not appear to have an impact on the main findings. For the comparison purposes, the results for Latin American countries are reported as well.

The application of multivariable Mendelian randomisation (MVMR) to analyse time-varying data with multiple measurements of both an exposure and an outcome is unclear. The purpose of this thesis is to develop and examine the properties of a potential model to extend MVMR to handle two measurements of both an outcome and an exposure. The exposure effect at Time 1 is estimated using univariable Mendelian randomisation (MR), while the exposure effects at Time 2 are estimated using MVMR by using a set of single nucleotide polymorphisms (SNPs) exclusive to the first outcome measurement. Simulations examining the properties of the causal effect estimates in the model under different scenarios were undertaken. The scenarios included different sampling schemes (1, 2, or 4 samples) for summary statistics. Confidence intervals were too wide, over-coverage was present when following the one-sample scheme, while slight under-coverage in both the two-sample and four-sample schemes was observed. Parameter estimators appeared to be mainly unaffected by increasing instrument strength. Increasing the number of SNPs pertaining to each exposure led to increased biases for the parameters affecting the second outcome measurement. Lastly, parameter estimates maintained acceptable coverage and small biases for different scenarios of overlapping SNPs. The inclusion of SNPs pertaining to the first outcome measurement in a time-varying MVMR model with two exposure and two outcome measurements allows for the estimation of exposure effects at both time points. However, the apparent drop in performance when the number of SNPs increases is of concern. / Thesis / Master of Science (MSc)

In this paper, focus is on the global vector autoregressive (GVAR) model. Its attractiveness stems from an ability to incorporate global interdependencies when modeling local economies. The model is based on a collection of local models, which in general are estimated as regular VAR models. This paper examines alternative specifications of the local models by estimating them as regime-switching VAR models, where transition probabilities between different states are studied using both constant and time-varying settings. The results show that regime-switching models are appealing as they yield inferences about the states of the economy, but these inferences are not guaranteed to be reasonable from an economic point of view. Furthermore, the global solution of the model is in some cases non-stationary when local models are regime-switching. The conclusion is that the regime-switching alternatives, while theoretically reasonable, are sensitive to the exact specification used. At the same time, the issue of specifying the regime-switching models in such a way that they perform adequately speaks in favor of the simpler, yet functional, basic GVAR model.

"Renal autoregulation stabilizes renal blood flow to protect the glomerular capillaries and maintain glomerular filtration rates through two mechanisms: tubuloglomerular feedback (TGF) and the myogenic response (MR). It is considered that the feedback mechanisms operate independently in each nephron (the functional unit of the kidney) within a kidney, but renal autoregulation dynamics can be coupled between vascular connected nephrons. It has also been shown that the mechanisms are time-varying and interact with each other. Understanding of the significance of such complex behavior has been limited by absence of techniques capable of monitoring renal flow signals among more than 2 or 3 nephrons simultaneously. The purpose of this thesis was to develop approaches to allow the identification and characterization of spatial and temporal properties of renal autoregulation dynamics. We present evidence that laser speckle perfusion imaging (LSPI) effectively captures renal autoregulation dynamics in perfusion signals across the renal cortex of anaesthetized rats and that spatial heterogeneity of the dynamics is present and can be investigated using LSPI. Next, we present a novel approach to segment LSPI of the renal surface into phase synchronized clusters representing areas with coupled renal autoregulation dynamics. Results are shown for the MR and demonstrate that when a signal is present phase synchronized regions can be identified. We then describe an approach to identify quadratic phase coupling between the TGF and MR mechanisms in time and space. Using this approach we can identify locations across the renal surface where both mechanisms are operating cooperatively. Finally, we show how synchronization between nephrons can be investigated in relation to renal autoregulation effectiveness by comparing phase synchronization estimates from LSPI with renal autoregulation system properties estimated from renal blood flow and blood pressure measurements. Overall, we have developed approaches to 1) capture renal autoregulation dynamics across the renal surface, 2) identify regions with phase synchronized renal autoregulation dynamics, 3) quantify the presence of the TGF-MR interaction across the renal surface, and 4) determine how the above vary over time. The described tools allow for investigations of the significance and mechanisms behind the complex spatial interactions and time-varying properties of renal autoregulation dynamics. "

We employ some of the modern tools of economic theory and time series econometrics to consider a number of economic problems. The communication and coordination problems we study are relevant in accounting, business, economics and finance. The thesis begins by examining the behaviour of people and organisations, who are supposed to share a common goal. Then it considers the equilibriating mechanisms of behaviour by groups of economic agents, who usually have conflicting interests. We apply the tools of non-cooperative game theory, which constitutes a large part of modern economic theory. In the sequel, we address the question of why people behave the way they do in their economic a↵airs. Peoples' economic behaviour is mirrored in the aggregates of macroeconomics. We propose a Time Varying Autoregressive model to study the movements in the five main macroeconomic variables. The methods come from standard Time Series Analysis, but we do introduce some innovative time series techniques. Finally, we conduct an empirical investigation of the movements in one of the five main macroeconomic variables, the rate of inflation. Among the econometric tools employed are standard Autoregressive models (AR), Autoregressive Distributed Lag models (ADL) and the more recent Generalised Autoregressive Conditional Heteroskedasticity (GARCH) methodology.