Wetlands of the Prairie Pothole Region of North America provide habitat for over 50% of the continent's breeding waterfowl, but most of the region's wetlands have been lost or degraded through intensive agricultural development. Despite widespread wetland losses in much of the Canadian prairies, there is little information about trends in degradation of remaining wetlands. Using habitat data collected for ~10,500 wetlands across the Canadian prairies during annual waterfowl surveys, 1985-2005, I employed multistate models in Program MARK to estimate rates of impact and recovery of wetlands resulting from agricultural activities. Then, I characterized the incidence of agricultural degradation to these wetlands. Rates of impact to wetland margins (natural vegetation around flooded basins) declined over time, likely due to a decreasing percentage of unaffected wetlands; recovery rates for margins were always lower than impact rates, suggesting increased cumulative degradation of wetlands over time. Unlike margins, impact and recovery rates for basins fluctuated with spring pond densities. Shallow ephemeral wetlands located in agricultural fields had the highest impact and lowest recovery rates. Multistate modeling could also be used to estimate rates associated with other landscape processes.<p><p> My second objective was to determine whether physical characteristics of prairie Canada wetlands could be used to predict breeding duck abundance. First, I sought to determine how pre-existing models developed in the Dakotas (USA) performed when predicting breeding duck abundances on Canadian prairie wetlands. I related duck pair abundance to pond area, and then compared observed to predicted duck abundance. The Dakota models performed reasonably well in predicting numbers of blue-winged teal (<i>Anas discors</i>), gadwall (<i>A. strepera</i>), and northern pintail (<i>A. acuta</i>), but predicted fewer mallards (<i>A. platyrhynchos</i>) and northern shovelers (<i>A. clypeata</i>) than were observed on wetlands. Pond area was an important predictor of duck abundance in all models, but results were less biased and more consistent in models developed specifically for Canadian wetlands. Spatiotemporal variation in the relationship of breeding duck abundance and wetland characteristics was also affected by regional duck and pond densities. Overall, the new applications and models developed and validated in this study will be useful for wetland and waterfowl management in the Canadian prairies.

This dissertation aims at developing and testing a new method that can better capture preferences for multistate health profiles. The motivation arose from the failure of the QALY (Quality-Adjusted Life Year) model in adequately capturing preferences in multistate health profiles. The current QALY-based technique captures preferences for multistate health profiles by evaluating each health state in the profile independently of other states. As the past literature showed, this additive independence condition does not hold in practice and hence such approach is inadequate. To address this issue, this study proposes a novel approach to measure preferences for multistate health profiles by looking at two consecutive health states at a time. It hypothesizes that an evaluation of the future health state is dependent or "conditioned" on the level of the preceding, or current, health state. Characteristics of the current health state that are suspected to impact the resulting conditional preference scores for future health state are systematically explored in a carefully designed empirical study. The interested factors include duration of the current health state, direction of change and amplitude of change between the current and future health states. A 2

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No. of bitstreams: 1 DissRSC.pdf: 1649931 bytes, checksum: c3449a4367ea7de9e327fa7dc9110861 (MD5) Previous issue date: 2016-03-31 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Often intermediate events provide more detailed information about the disease process or recovery, for example, and allow greater accuracy in predicting the prognosis of patients. Such non-fatal events during the course of the disease can be seen as transitions from one state to another. The basic idea of a multistate models is that the person moves through a series of states in continuous time, it is possible to estimate the transition probabilities and intensities between them and the effect of covariates associated with each transition. Many studies include the grouping of survival times, for example, in multi-center studies, and is also of interest to study the evolution of patients over time, characterizing grouped multistate data. Because the data coming from different centers/groups, the failure times these individuals are grouped and the common risk factors not observed, it is interesting to consider the use of frailty so that we can capture the heterogeneity between the groups at risk for different types of transition, in addition to considering the dependence structure between transitions of individuals of the same group. In this work we present the methodology of multistate models, frailty models and then the integration of models with multi-state fragility models, dealing with the process of parametric and semi-parametric estimation. The conducted simulation study showed the importance of considering frailty in grouped multistate models, because without considering them, the estimates become biased. Furthermore, we find the frequentist properties of estimators of multistate model with nested frailty. Finally, as an application example to a set of real data, we use the process of bone marrow transplantation recovery of patients in four hospitals.We did a comparison of models through quality teasures setting AIC and BIC, coming to the conclusion that the model considers two random effects (one for the hospital and another for interaction transition-hospital) fits the data better. In addition to considering the heterogeneity between hospitals, such a model also considers the heterogeneity between hospitals in each transition. Thus, the values of the frailty estimated interaction transition-hospital reveal how fragile patients from each hospital are to experience certain type of event/transition. / Frequentemente eventos intermediários fornecem informações mais detalhadas sobre o processo da doença ou recuperação, por exemplo, e permitem uma maior precisão na previsão do prognóstico de pacientes. Tais eventos não fatais durante o curso da doença podem ser vistos como transições de um estado para outro. A ideia básica dos modelos multiestado é que o indivíduo se move através de uma série de estados em tempo contínuo, sendo possível estimar as probabilidades e intensidades de transição entre eles e o efeito das coivaráveis associadas a cada transição. Muitos estudos incluem o agrupamento dos tempos de sobrevivência como, por exemplo, em estudos multicêntricos, e também é de interesse estudar a evolução dos pacientes ao longo do tempo, caracterizando assim dados multiestado agrupados. Devido ao fato de os dados virem de diferentes centros/grupos, os tempos de falha desses indivíduos estarem agrupados e a fatores de risco comuns não observados, é interessante considerar o uso de fragilidades para que possamos capturar a heterogeneidade entre os grupos no risco para os diferentes tipos de transição, além de considerar a estrutura de dependência entre transições dos indivíduos de um mesmo grupo. Neste trabalho apresentamos a metodologia dos modelos multiestado, dos modelos de fragilidade e, em seguida, a integração dos modelos multiestado com modelos de fragilidade, tratando do seu processo de estimação paramétrica e semiparamétrica. O estudo de simulação realizado mostrou a importância de considerarmos fragilidades em modelos multiestado agrupados, pois sem consider´a-las, as estimativas tornam-se viesadas. Al´em disso, verificamos as propriedades frequentistas dos estimadores do modelo multiestado com fragilidades aninhadas. Por fim, como um exemplo de aplicação a um conjunto de dados reais, utilizamos o processo de recuperação de transplante de medula óssea de pacientes tratados em quatro hospitais. Fizemos uma comparação de modelos por meio das medidas de qualidade do ajuste AIC e BIC, chegando `a conclusão de que o modelo que considera dois efeitos aleatórios (uma para o hospital e outro para a interação transição-hospital) ajusta-se melhor aos dados. Além de considerar a heterogeneidade entre os hospitais, tal modelo também considera a heterogeneidade entre os hospitais em cada transição. Sendo assim, os valores das fragilidades estimadas da interação transição-hospital revelam o quão frágeis os pacientes de cada hospital são para experimentarem determinado tipo de evento/transição.

Traditional computational protein design (CPD) calculations model sequence perturbations and evaluate their stabilities using a single fixed protein backbone template in an approach referred to as single‐state design (SSD). However, certain design objectives require the explicit consideration of multiple conformational states. Cases where a multistate framework may be advantageous over the single‐state approach include the computer aided discovery of new enzyme substrates, the prediction of protein stabilities, and the design of protein dynamics. These design objectives can be tackled using multistate design (MSD). However, it is often the case that a design objective requires the consideration of a protein state having no available structure information. For such circumstances the multistate framework cannot be applied. In this thesis I present the development of two template and ensemble preparation methodologies and their application to three projects. The purpose of which is to demonstrate the necessary ensemble modeling strategies to overcome limitations in available structure information. Particular emphasis is placed on the ability to recapitulate experimental data to guide modelling of the design space. Specifically, the use of MSD allowed for the accurate prediction of a methyltransferase recognition motif and new substrates, the prediction of mutant sequence stabilities with quantitative accuracy, and the design of dynamics into the rigid Gβ1 scaffold producing a set of dynamic variants whose tryptophan residue exchanges between two conformations on the millisecond timescale. Implementation of both the ensemble, coordinate perturbation followed by energy minimization (PertMin), and template, rotamer optimization followed by energy minimization (ROM), generation protocols developed here allow for exploration and manipulation of the structure space enabling the success of these applications.

No description available.

CMOS SRAM area scaling is slowing down due to several challenges faced by transistors at nanoscale such as increased leakage. This calls for new concepts and technologies to overcome CMOS scaling limitations. In this thesis, we propose a multi-state memory to store multiple bits in a single cell, enabled by graphene and graphene nanoribbon crossbar devices (xGNR). This could provide a new dimension for scaling. We present a new multi-state volatile memory fabric called Graphene Nanoribbon Tunneling Random Access Memory (GNTRAM) featuring a heterogeneous integration between graphene and CMOS. A latch based on the xGNR devices is used as the memory element which exhibits 3 stable states. We propose binary and ternary GNTRAM and compare them with respect to 16nm CMOS SRAM and 3T DRAM. Ternary GNTRAM (1.58 bits/cell) shows up to 1.77x density-per-bit benefit over CMOS SRAMs and 1.42x benefit over 3T DRAM in 16nm technology node. Ternary GNTRAM is also up to 1196x more power-efficient per bit against high-performance CMOS SRAMs during stand-by. To enable further scaling, we explore two approaches to increase the number of bits per cell. We propose quaternary GNTRAM (2 bits/cell) using these approaches and extensively benchmark these designs. The first uses additional xGNR devices in the latch to achieve 4 stable states and the quaternary memory shows up to 2.27x density benefit vs. 16nm CMOS SRAMs and 1.8x vs. 3T DRAM. It has comparable read performance in addition to being power-efficient, up to 1.32x during active period and 818x during stand-by against high performance SRAMs. However, the need for relatively high-voltage operation may ultimately limit this scaling approach. An alternative approach is also explored by increasing the stub length in the xGNR devices, which allows for storing 2 bits per cell without requiring an increased operating voltage. This approach for quaternary GNTRAM shows higher benefits in terms of power, specifically up to 4.67x in terms of active power and 3498x during stand-by against high-performance SRAMs. Multi-bit GNTRAM has the potential to realize high-density low-power nanoscale memories. Further improvements may be possible by using graphene more extensively, as graphene transistors become available in future.

By increasing the level of abstraction in the representation of regulatory network models, we can hope to allow modelers to create models that are beyond the threshold of what can currently be expressed reliably. As hundreds of reactions are difficult to understand, maintain, and extend, thousands of reactions become next to impossible without any automation or aid. Using the multistate-species concept we can reduce the number of reactions needed to represent certain systems and thus, lessen the cognitive load on modelers. A multistate species is an entity with a defined range for state variables, which refers to a group of different forms for a specific species. A multistate reaction involves one or more multistate species and compactly represents a group of similar single reactions. In this work, we have extended JCMB (the JigCell Model Builder) to comply with multistate species and reactions modeling and presented a proposal for enhancing SBML (the Systems Biology Markup Language) standards to support multistate models. / Master of Science

Interval censoring arises frequently in life history data, as individuals are often only observed at a sequence of assessment times. This leads to a situation where we do not know when an event of interest occurs, only that it occurred somewhere between two assessment times. Here, the focus will be on methods of estimation for recurrent event data, current status data, and multistate data, subject to interval censoring. With recurrent event data, the focus is often on estimating the rate and mean functions. Nonparametric estimates are readily available, but are not smooth. Methods based on local likelihood and the assumption of a Poisson process are developed to obtain smooth estimates of the rate and mean functions without specifying a parametric form. Covariates and extra-Poisson variation are accommodated by using a pseudo-profile local likelihood. The methods are assessed by simulations and applied to a number of datasets, including data from a psoriatic arthritis clinic. Current status data is an extreme form of interval censoring that occurs when each individual is observed at only one assessment time. If current status data arise in clusters, this must be taken into account in order to obtain valid conclusions. Copulas offer a convenient framework for modelling the association separately from the margins. Estimating equations are developed for estimating marginal parameters as well as association parameters. Efficiency and robustness to the choice of copula are examined for first and second order estimating equations. The methods are applied to data from an orthopedic surgery study as well as data on joint damage in psoriatic arthritis. Multistate models can be used to characterize the progression of a disease as individuals move through different states. Considerable attention is given to a three-state model to characterize the development of a back condition known as spondylitis in psoriatic arthritis, along with the associated risk of mortality. Robust estimates of the state occupancy probabilities are derived based on a difference in distribution functions of the entry times. A five-state model which differentiates between left-side and right-side spondylitis is also considered, which allows us to characterize what effect spondylitis on one side of the body has on the development of spondylitis on the other side. Covariate effects are considered through multiplicative time homogeneous Markov models. The robust state occupancy probabilities are also applied to data on CMV infection in patients with HIV.

Interval censoring arises frequently in life history data, as individuals are often only observed at a sequence of assessment times. This leads to a situation where we do not know when an event of interest occurs, only that it occurred somewhere between two assessment times. Here, the focus will be on methods of estimation for recurrent event data, current status data, and multistate data, subject to interval censoring. With recurrent event data, the focus is often on estimating the rate and mean functions. Nonparametric estimates are readily available, but are not smooth. Methods based on local likelihood and the assumption of a Poisson process are developed to obtain smooth estimates of the rate and mean functions without specifying a parametric form. Covariates and extra-Poisson variation are accommodated by using a pseudo-profile local likelihood. The methods are assessed by simulations and applied to a number of datasets, including data from a psoriatic arthritis clinic. Current status data is an extreme form of interval censoring that occurs when each individual is observed at only one assessment time. If current status data arise in clusters, this must be taken into account in order to obtain valid conclusions. Copulas offer a convenient framework for modelling the association separately from the margins. Estimating equations are developed for estimating marginal parameters as well as association parameters. Efficiency and robustness to the choice of copula are examined for first and second order estimating equations. The methods are applied to data from an orthopedic surgery study as well as data on joint damage in psoriatic arthritis. Multistate models can be used to characterize the progression of a disease as individuals move through different states. Considerable attention is given to a three-state model to characterize the development of a back condition known as spondylitis in psoriatic arthritis, along with the associated risk of mortality. Robust estimates of the state occupancy probabilities are derived based on a difference in distribution functions of the entry times. A five-state model which differentiates between left-side and right-side spondylitis is also considered, which allows us to characterize what effect spondylitis on one side of the body has on the development of spondylitis on the other side. Covariate effects are considered through multiplicative time homogeneous Markov models. The robust state occupancy probabilities are also applied to data on CMV infection in patients with HIV.

The main aim of this thesis is to analyse the development of the processes of family formation and dissolution in the Czech Republic between 1993 and 2008 using multistate demography. By this method, we analyze probability of transition between individual marriage states and events, which characterise these transitions. The first part of the analysis deals with the population as a whole, while the second part analyses only the part of population in fertile ages. LIPRO program was used to create a model of marital status based on data for the Czech Republic as well as for producing a forecast of population divided by marital status for the years of 2019?2023. Other parts of this thesis support the main aim of the thesis. The level of marriage and divorce rate were analysed between 1993 and 2008 as well as potential factors that could influence the level of above mentioned processes. Rising number of cohabitations could be one of those factors and therefore a special attention is paid to the development of this type of coexistence. Socioeconomic development and family and social policy are mentioned as well, because they could also have significant impact on the processes of formation and dissolution of families. The above mentioned factors and their relationship to the marriage rate are analysed using the...