Using sulfur isotope fractionation to understand the atmospheric oxidation of SO 2

Sulfate aerosol plays an important but uncertain role in cloud formation and radiative forcing of the climate, and is also important for acid deposition and human health. The oxidation of SO2 to sulfate is a key reaction in determining the impact of sulfate in the environment through its effect on aerosol size distribution and composition. This thesis presents a laboratory investigation of sulfur isotope fractionation during SO2 oxidation by the most important gas-phase and heterogeneous pathways occurring in the atmosphere. The fractionation factors are then used to examine the role of sulfate formation in cloud processing of aerosol particles during the HCCT campaign in Thuringia, central Germany. The fractionation factor for the oxidation of SO2 by ·OH radicals was measured by reacting SO2 gas, with a known initial isotopic composition, with ·OH radicals generated from the photolysis of water at -25, 0, 19 and 40°C (Chapter 2). The product sulfate and the residual SO2 were collected as BaSO4 and the sulfur isotopic compositions measured with the Cameca NanoSIMS 50. The measured fractionation factor for 34S/32S during gas phase oxidation is αOH = (1.0089 ± 0.0007) − ((4 ± 5) × 10−5 )T (°C). Fractionation during oxidation by major aqueous pathways was measured by bubbling the SO2 gas through a solution of H2 O2

Frequency of Malaria and Glucose-6-phosphate Dehydrogenase Deficiency in Tajikistan

Background During the Soviet era, malaria was close to eradication in Tajikistan. Since the early 1990s, the disease has been on the rise and has become endemic in large areas of southern and western Tajikistan. The standard national treatment for Plasmodium vivax is based on primaquine. This entails the risk of severe haemolysis for patients with glucose-6-phosphate dehydrogenase (G6PD) deficiency. Seasonal and geographical distribution patterns as well as G6PD deficiency frequency were analysed with a view to improve understanding of the current malaria situation in Tajikistan. Methods Spatial and seasonal distribution was analysed, applying a risk model that included key environmental factors such as temperature and the availability of mosquito breeding sites. The frequency of G6PD deficiency was studied at the health service level, including a cross-sectional sample of 382 adult men. Results Analysis revealed high rates of malaria transmission in most districts of the southern province of Khatlon, as well as in some zones in the northern province of Sughd. Three categories of risk areas were identified: (i) zones at relatively high malaria risk with high current incidence rates, where malaria control and prevention measures should be taken at all stages of the transmission cycle; (ii) zones at relatively high malaria risk with low current incidence rates, where malaria prevention measures are recommended; and (iii) zones at intermediate or low malaria risk with low current incidence rates where no particular measures appear necessary. The average prevalence of G6PD deficiency was 2.1% with apparent differences between ethnic groups and geographical regions. Conclusion The study clearly indicates that malaria is a serious health issue in specific regions of Tajikistan. Transmission is mainly determined by temperature. Consequently, locations at lower altitude are more malaria-prone. G6PD deficiency frequency is too moderate to require fundamental changes in standard national treatment of cases of P. vivax.

Invasion patterns of emerald ash borer and European earthworms in forested ecosystems

Invasive and exotic species present a serious threat to the health and sustainability of natural ecosystems. These species often benefit from anthropogenic activities that aid their introduction and dispersal. This dissertation focuses on invasion dynamics of the emerald ash borer, native to Asia, and European earthworms. These species have shown detrimental impacts in invaded forest ecosystems across the Great Lakes region, and continue to spread via human-assisted long distance dispersal and by natural modes of dispersal into interior forests from areas of introduction. Successful forest management requires that the impact and effect of invasive species be considered and incorporated into management plans. Understanding patterns and constraints of introduction, establishment, and spread will aid in this effort. To assist in efforts to locate introduction points of emerald ash borer, a multicriteria risk model was developed to predict the highest risk areas. Important parameters in the model were road proximity, land cover type, and campground proximity. The model correctly predicted 85% of known emerald ash borer invasion sites to be at high risk. The model’s predictions across northern Michigan can be used to focus and guide future monitoring efforts. Similar modeling efforts were applied to the prediction of European earthworm invasion in northern Michigan forests. Field sampling provided a means to improve upon modeling efforts for earthworms to create current and future predictions of earthworm invasion. Those sites with high soil pH and high basal area of earthworm preferred overstory species (such as basswood and maples) had the highest likelihood of European earthworm invasion. Expanding beyond Michigan into the Upper Great Lakes region, earthworm populations were sampled across six National Wildlife Refuges to identify potential correlates and deduce specific drivers and constraints of earthworm invasion. Earthworm communities across all refuges were influenced by patterns of anthropogenic activity both within refuges and in surrounding ecoregions of study. Forest composition, soil pH, soil organic matter, anthropogenic cover, and agriculture proximity also proved to be important drivers of earthworm abundance and community composition. While there are few management options to remove either emerald ash borer or European earthworms from forests after they have become well established, prevention and early detection are important and can be beneficial. An improved understanding the factors controlling the distribution and invasion patterns of exotic species across the landscape will aid efforts to determine their consequences and generate appropriate forest management solutions to sustain ecosystem health in the presence of these invaders.

Bone mineral density (BMD) and computer tomographic measurements of the equine proximal phalanx in correlation with breaking strength

Despite the fact that bone mineral density (BMD) is an important fracture risk predictor in human medicine, studies in equine orthopedic research are still lacking. We hypothesized that BMD correlates with bone failure and fatigue fractures of this bone. Thus, the objectives of this study were to measure the structural and mechanical properties of the proximal phalanx with dual energy X-ray absorptiometry (DXA), to correlate the data obtained from DXA and computer tomography (CT) measurements to those obtained by loading pressure examination and to establish representative region of interest (ROI) for in vitro BMD measurements of the equine proximal phalanx for predicting bone failure force. DXA was used to measure the whole bone BMD and additional three ROI sites in 14 equine proximal phalanges. Following evaluation of the bone density, whole bone, cortical width and area in the mid-diaphyseal plane were measured on CT images. Bones were broken using a manually controlled universal bone crusher to measure bone failure force and reevaluated for the site of fractures on follow-up CT images. Compressive load was applied at a constant displacement rate of 2 mm/min until failure, defined as the first clear drop in the load measurement. The lowest BMD was measured at the trabecular region (mean +/- SD: 1.52 +/- 0.12 g/cm2; median: 1.48 g/cm2; range: 1.38-1.83 g/cm2). There was a significant positive linear correlation between trabelcular BMD and the breaking strength (P = 0.023, r = 0.62). The trabecular region of the proximal phalanx appears to be the only significant indicator of failure of strength in vitro. This finding should be reassessed to further reveal the prognostic value of trabecular BMD in an in vivo fracture risk model.

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Proton therapy versus intensity modulated x-ray therapy in the treatment of prostate cancer: Estimating secondary cancer risks

External beam radiation therapy is used to treat nearly half of the more than 200,000 new cases of prostate cancer diagnosed in the United States each year. During a radiation therapy treatment, healthy tissues in the path of the therapeutic beam are exposed to high doses. In addition, the whole body is exposed to a low-dose bath of unwanted scatter radiation from the pelvis and leakage radiation from the treatment unit. As a result, survivors of radiation therapy for prostate cancer face an elevated risk of developing a radiogenic second cancer. Recently, proton therapy has been shown to reduce the dose delivered by the therapeutic beam to normal tissues during treatment compared to intensity modulated x-ray therapy (IMXT, the current standard of care). However, the magnitude of stray radiation doses from proton therapy, and their impact on this incidence of radiogenic second cancers, was not known. ^ The risk of a radiogenic second cancer following proton therapy for prostate cancer relative to IMXT was determined for 3 patients of large, median, and small anatomical stature. Doses delivered to healthy tissues from the therapeutic beam were obtained from treatment planning system calculations. Stray doses from IMXT were taken from the literature, while stray doses from proton therapy were simulated using a Monte Carlo model of a passive scattering treatment unit and an anthropomorphic phantom. Baseline risk models were taken from the Biological Effects of Ionizing Radiation VII report. A sensitivity analysis was conducted to characterize the uncertainty of risk calculations to uncertainties in the risk model, the relative biological effectiveness (RBE) of neutrons for carcinogenesis, and inter-patient anatomical variations. ^ The risk projections revealed that proton therapy carries a lower risk for radiogenic second cancer incidence following prostate irradiation compared to IMXT. The sensitivity analysis revealed that the results of the risk analysis depended only weakly on uncertainties in the risk model and inter-patient variations. Second cancer risks were sensitive to changes in the RBE of neutrons. However, the findings of the study were qualitatively consistent for all patient sizes and risk models considered, and for all neutron RBE values less than 100. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Variation of PM2.5 concentrations in relation to street washing activities

Several studies conducted in urban areas have pointed out that road dust resuspension contributes significantly to PM concentration levels. Street washing is one of the methods proposed to reduce resuspended road dust contributions to ambient PM concentrations. As resuspended particles are mainly found in the coarse mode, published studies investigating the effects of street washing have focused on PM10 size fraction. As the PM2.5 mass fraction of particles originating from mechanical abrasion processes may still be significant we conducted a study in order to evaluate the effects of street washing on the mitigation of resuspension of fine particles. The PM2.5 mass concentration data were examined and integrated with the occurrence of street washing activities. In addition, the effect of the meteorological variability, traffic flow and street washing activities, on ambient PM2.5 levels was valuated by means of a multivariate regression model. The results revealed that traffic low is the most important factor that controls PM2.5 hourly concentrations while street washing activities did not influence fine particle mass levels.

Desarrollo de un modelo de riesgo de colisión en espacios aéreos de área terminal

El transporte aéreo es un sector estratégico para el crecimiento económico de cualquier país. La estabilidad y el desarrollo de este modo de transporte tienen un pilar fundamental en una operación segura, especialmente cuando las previsiones indican escenarios de crecimiento continuo del tráfico aéreo. La estimación del riesgo y, por tanto, del nivel de seguridad de un entorno operativo se ha basado en métodos indirectos como puede ser la cuantificación y análisis de los reportes voluntarios de incidentes o el uso de modelos de riesgo de colisión enfocados a escenarios operativos parciales, como puede ser un espacio aéreo oceánico. La operación en un área terminal de maniobra es compleja, con distintos flujos de tráfico de arribada y salida a uno o varios aeropuertos, con cambios frecuentes en el rumbo y velocidad de las aeronaves y con instrucciones tácticas del control de tráfico aéreo para secuenciar y separar las aeronaves El objetivo de la presente Tesis es complementar los actuales métodos de monitorización de la seguridad que presentan sus limitaciones, con el desarrollo de un modelo de riesgo de colisión para áreas terminales de alta densidad que se base en datos objetivos como son las trazar radar de las aeronaves y que tenga en cuenta la complejidad de la operación en un área terminal. Para evaluar el modelo desarrollado se ha implementado una herramienta prototipo en MATLAB© que permite procesar un número masivo de trazar radar para un escenario de área terminal y calcular un valor del riesgo de colisión para el escenario analizado. El prototipo ha sido utilizado para estimar la probabilidad de colisión para distintos escenarios del área terminal de Madrid. El uso de trazas radar permite monitorizar el nivel de riesgo de escenarios reales de manera periódica estableciendo niveles de alerta temprana si se detecta que el valor de riesgo se desvía en exceso, pero también permite evaluar el nivel de riesgo de diseños de espacio aéreo o de nuevos modos de operación a partir de las trazas radar obtenidas en las simulaciones en tiempo real o acelerado y actuar en fases tempranas de los proyectos. ABSTRACT The air transport is a strategic sector for the economic growth of any country. The stability and development of the transport mode have a fundamental pillar in a safe operation, especially when long-term forecasts show scenarios of continuous growth in air traffic. Risk estimation and therefore the level of safety in an operational airspace has been based on indirect methods such as the quantification and analysis of voluntary reports of safety incidents or use of collision risk models focused on partial or simple operational scenarios such as an oceanic airspace. The operation on a terminal maneuvering area is complex, with different traffic flows of arrival and departure at one or more airports, with frequent changes in direction and speed of aircraft and tactical instructions of air traffic control to sequence and separate aircraft. The objective of this Thesis is to complement existing methods of monitoring safety that have their limitations, with the development of a collision risk model for high-density terminal areas that is based on objective data such as aircraft radar tracks and taking into account the complexity of the operation in a terminal area. To evaluate the developed model a prototype tool was implemented with MATLAB© that can process massive numbers of radar tracks for a terminal area scenario and computing a collision risk value for that scenario. The prototype has been used to estimate the probability of collision for different scenarios of the terminal area of Madrid. The use of radar tracks allows to monitor the level of risk of real scenarios periodically establishing levels of early warning when the risk value deviates too much, but also to assess the risk level of airspace designs or modes of operations from the radar tracks obtained in real or fast time simulations and act in the early stages of projects.

Avaliação do impacto do horizonte temporal nas estimativas de EAD de linhas de crédito com limites explícitos a PME

Tese de mestrado em Matemática Aplicada à Economia e Gestão, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2016

On the Poisson Process of Order k Space

2010 Mathematics Subject Classification: 60E05, 62P05.

Essays on financial crises

This dissertation analyzes recent financial crises in developed and developing countries. The research emphasizes the effects of institutional factors on the international banking and currency crises and their output losses. ^ Chapter two examines the roles of regulation, supervision, and countries' institutional environment in determining the probability of banking crises for a panel of fifteen developed countries from 1975 to 1998. The results from a multivariate logit model indicated that countries with greater government involvement, less capital standard requirements, and lower lending limits on a single borrower are associated with a higher probability of banking crises. ^ Chapter three studies whether output loss in banking crisis differs in market-based or bank-based financial systems. Using existing banking crisis data for sixty-nine countries during 1970–1999, we investigate whether the underlying financial system affects the output loss. The results show that output losses are more serious in market-based economies than those in bank-based economies. Longer crisis duration tends to increase the output losses in banking crises. Finally, countries with deposit insurance and strict law enforcement have less output losses. ^ Chapter four uses macroeconomic and institutional measures to explain the extent of exchange rate depreciation and the decline in stock prices for emerging countries affected by the Mexican currency crisis of 1994–95. The results show that countries with more government budget deficits, and worse reserve adequacies tend to experience large exchange rate depreciation. The institutional measures do not explain much the extent of both the exchange rate depreciation and the decline in stock prices. ^

Essays in Macro Finance

I study the link between capital markets and sources of macroeconomic risk. In chapter 1 I show that expected inflation risk is priced in the cross section of stock returns even after controlling for cash flow growth and volatility risks. Motivated by this evidence I study a long run risk model with a built-in inflation non-neutrality channel that allows me to decompose the real stochastic discount factor into news about current and expected cash flow growth, news about expected inflation and news about volatility. The model can successfully price a broad menu of assets and provides a setting for analyzing cross sectional variation in expected inflation risk premium. For industries like retail and durable goods inflation risk can account for nearly a third of the overall risk premium while the energy industry and a broad commodity index act like inflation hedges. Nominal bonds are exposed to expected inflation risk and have inflation premiums that increase with bond maturity. The price of expected inflation risk was very high during the 70's and 80's, but has come down a lot since being very close to zero over the past decade. On average, the expected inflation price of risk is negative, consistent with the view that periods of high inflation represent a "bad" state of the world and are associated with low economic growth and poor stock market performance. In chapter 2 I look at the way capital markets react to predetermined macroeconomic announcements. I document significantly higher excess returns on the US stock market on macro release dates as compared to days when no macroeconomic news hit the market. Almost the entire equity premium since 1997 is being realized on days when macroeconomic news are released. At high frequency, there is a pattern of returns increasing in the hours prior to the pre-determined announcement time, peaking around the time of the announcement and dropping thereafter.

Climate change adaptation strategies of maize producers of the Central Rift Valley of Ethiopia

The impacts of climate change are considered to be strong in countries located in tropical Africa that depend on agriculture for their food, income and livelihood. Therefore, a better understanding of the local dimensions of adaptation strategies is essential to develop appropriate measures that will mitigate adverse consequences. Hence, this study was conducted to identify the most commonly used adaptation strategies that farm households practice among a set of options to withstand the effects of climate change and to identify factors that affect the choice of climate change adaptation strategies in the Central Rift Valley of Ethiopia. To address this objective, Multivariate Probit model was used. The results of the model indicated that the likelihood of households to adapt improved varieties of crops, adjust planting date, crop diversification and soil conservation practices were 58.73%, 57.72%, 35.61% and 41.15%, respectively. The Simulated Maximum Likelihood estimation of the Multivariate Probit model results suggested that there was positive and significant interdependence between household decisions to adapt crop diversification and using improved varieties of crops; and between adjusting planting date and using improved varieties of crops. The results also showed that there was a negative and significant relationship between household decisions to adapt crop diversification and soil conservation practices. The paper also recommended household, socioeconomic, institutional and plot characteristics that facilitate and impede the probability of choosing those adaptation strategies.

Parental reports of the oral health-related quality of life of children with cerebral palsy

BACKGROUND The severity of physical and mental impairments and oral problems, as well as socioeconomic factors, may have an impact on quality of life of children with cerebral palsy (CP). The aim of this research was to assess the impact of impairments and oral health conditions, adjusted by socioeconomic factors, on the Oral Health-Related Quality of Life (OHRQoL) of children with CP using their parents as proxies. METHODS Sixty children, between 6-14 years of age were selected. Their parents answered a children's OHRQoL instrument (5 domains) which combines the Parental-Caregivers Perception Questionnaire (P-CPQ) and Family Impact Scale (FIS). The severity of dental caries, type of CP, communication ability, gross motor function, seizures and socioeconomic conditions were assessed. RESULTS Considering the total score of the OHRQoL instrument, only the reduction of communication ability and dental caries severity had a negative impact on the OHRQoL (p < 0.05). Considering each domain of the instrument, the severity of the type of CP and its reduction of communication ability showed a negative impact on oral symptoms and functional limitations domains (p < 0.05). Seizures have a negative impact on oral symptoms domain (p = 0.006). The multivariate fitted model showed that the severity of dental caries, communication ability and low family income were negatively associated with the impact on OHRQoL (p = 0.001). CONCLUSIONS The severity of dental caries, communication ability, and family income are conditions strongly associated with a negative impact on OHRQoL of children with CP.