Myocardial infarction (MI) occurs following acute thrombotic occlusion of a coronary artery, and triggers a robust inflammatory response. Within hours, neutrophils are recruited to the infarcted myocardium followed by the infiltration of pro-inflammatory Ly6Chi monocytes. Transition from the pro-inflammatory macrophage phenotype (M1) to an anti-inflammatory, pro-resolution phenotype (M2-like) is critical to successful infarct healing. Interventions that polarize macrophages towards an anti-inflammatory 'M2-like' phenotype improve infarct healing in the experimental MI mouse model and reduce subsequent adverse remodelling of the myocardium, but the endogenous mechanisms that regulate repair are not well understood. Furthermore, differences in the resolution of inflammation in C57BL/6 and BALB/c mice, which are two of the commonly used wild-type mouse strains in experimental MI have not been characterised. We previously found that low peripheral blood eosinophil count is associated with increased short-term risk of mortality in low-intermediate risk patients with ischaemic heart disease. This suggests that eosinophils may have a role in the successful remodelling and repair of the heart following myocardial infarction. Eosinophils express a number of immuno-modulating cytokines and lipid mediators implicated in the resolution of inflammation. Increasingly prominent is interleukin-4 (IL-4), a cytokine that has been found to maintain the anti-inflammatory M2-like phenotype in macrophages. We therefore hypothesised that IL-4Rα signalling and recruitment of eosinophils to the myocardium following infarction are key in regulating the subsequent inflammatory response and scar tissue formation during infarct repair and cardiac remodelling. Experimental MI was induced by permanent left anterior descending artery ligation in isofluorane anaesthetized 12-15 week-old male wild-type (WT) BALB/c, WT C57BL/6, IL4Rα-/-, IL-4Rαflox/-, IL-4Rαflox/-LysMCre mice and eosinophil-deficient ΔdblGATA mice. Cardiac function was characterised by high-resolution ultrasound and immune cell infiltration by flow cytometry of single cell infarct and remote zone tissue digests. Blood eosinophil count and 6-month all-cause mortality were assessed in 732 consecutive patients undergoing primary percutaneous coronary intervention for ST-segment elevation myocardial infarction (STEMI). The rate of mortality due to cardiac rupture was significantly higher in C57BL/6 mice in comparison with BALB/c mice at Day 7 post-MI. This was associated with a higher proportion of pro-inflammatory Ly-6Chi macrophages infiltrating the infarct zone tissue of C57BL/6 mice following MI. An accompanying reduction in the number of splenic Ly-6Chi monocytes post-MI, suggestive of splenic monocyte mobilisation, was seen in C57BL/6 mice but not found in BALB/c mice. Furthermore, C57BL/6 mice had a delayed transition in macrophage polarisation towards an anti-inflammatory phenotype. Disruption of IL4Rα signalling, in mice null for the IL4Rα gene, resulted in increased F4/80+ macrophage and pro-inflammatory Ly6Chi macrophage infiltration of the infarct zone and reduced expression of the anti-inflammatory macrophage marker CD206, compared to wild-type controls. Furthermore, expression of GATA3 and ST2, both associated with the immunosuppressive function of (CD4+ Foxp3+) regulatory T cells, was reduced in infarct zone regulatory T cells from IL4Rα-/- mice. These findings were associated with defective wound healing with impaired angiogenesis, increased scar size, disarrayed infarct zone collagen deposition, accompanied by modified expression of plod2 that encodes the collagen cross-linking enzyme lysyl hydroxylase 2. Resulting in greater left ventricular dilatation and loss of cardiac function, as well as a higher 7- day mortality due to cardiac rupture in IL4Rα-/- mice. This indicates that successful infarct repair requires the engagement of IL-4Rα signalling to facilitate the accumulation of anti-inflammatory macrophages and highly immunosuppressive ST2+ regulatory T cells in the heart following MI. Resident cardiac macrophages from naïve hearts of IL-4Rαflox/-LysMCre mice failed to undergo LysMCre-mediated deletion of the IL-4Rα gene, potentially because low or absent expression of Lyz2 (encoding lysozyme M). In both ST-elevation MI (STEMI) patients and mice after acute MI, there was a decline in peripheral blood eosinophil count, with activated eosinophils being recruited to the infarct zone and paracardial adipose tissue of mice. The transcription factors GATA-1 plays a role in the differentiation of eosinophils from eosinophil progenitor cells. Deletion of GATA-1 results in loss of the eosinophil lineage and has been exploited to develop the eosinophil-deficient ΔdblGATA mouse. ΔdblGATA mice were used to address the role of eosinophils in cardiac remodelling following MI. ΔdblGATA mice had increased left ventricular dilatation and reduced ejection fraction after induction of MI, relative to wild-type mice. ΔdblGATA mice had increased scar size with disarrayed infarct zone collagen deposition, accompanied by modified expression of the genes plod2 and lox, which are associated with collagen cross-linking. The proportion of CD206+ anti-inflammatory macrophages was less in the infarct zone of ΔdblGATA mice, but was restored by adoptive transfer of eosinophils from WT mice. Furthermore, adverse cardiac remodelling in eosinophil-deficient ΔdblGATA mice was rescued by provision of IL-4 complex following MI. In conclusion, an enhanced inflammatory response following MI underlies the increased risk of cardiac rupture seen with WT C57BL/6 mice in comparison to WT BALB/c mice. WT BALB/c mice are protected from cardiac rupture, which was associated with an absence of splenic monocyte mobilisation following ischaemic injury. The resolution of inflammation was found to be dependent on IL4Rα signalling which is crucial for cardiac repair and remodelling, through modulating inflammatory cell recruitment and phenotype, as well as scar formation. Eosinophils are recruited to the heart post-MI and are essential for regulating cardiac repair and remodelling, likely through provision of IL-4. Therefore, we were able to show that IL-4Rα signalling and recruitment of eosinophils to the myocardium following infarction are both key in regulating the subsequent inflammatory response and scar tissue formation during infarct healing and cardiac remodelling.

This thesis explores Single-Photon Avalanche Diodes (SPADs), which are solid-state devices for photon timing and counting, and concentrates on SPADs integrated in nano-scale CMOS. The thesis focuses on: the search for new theory regarding Geiger-mode operation; proving the utility of calibrated Technology Computer- Aided Design (TCAD) tools for accurately simulating SPADs for the first time; the investigation of how manufacture influences device operation; and the integration of high performance SPADs into CMOS which rival discrete devices. The accepted theories of SPAD operation are revisited and it is discovered that previously neglected minority carriers have many significant roles such as determining: after-pulsing, Dark Count Rate (DCR), bipolar “SPAD latch-up,” nonequilibrium DCR, and “quenching”. The “quenching” process is revisited and it is concluded that it is the “probability time” of ≈100-200ps, and not the previously thought latching current that is important. SPADs are also found to have transient negative differential resistance. The new theories of SPADs are also supported by steady-state 1D, 2D and 3D TCAD simulations as well as novel transient simulations and videos. It is demonstrated as possible to simulate DCR, Photon Detection Efficiency (PDE), guard ring performance, breakdown voltage, breakdown voltage variation, “quenching,” and transient operation of SPADs with great accuracy. The manufacture of SPADs is studied focusing on the operation and optimisation of guard rings and it is found that ion implantation induced asymmetry from the tilt and rotation/twist is critical. Where symmetric, guard rings fail first along the <100> directions due to enhanced mobility. Process integration rules are outlined for obtaining high performance SPADs in CMOS while maintaining compatibility with transistors. The minimisation of tunnelling with lightly-doped junctions and the reduction of ion implantation induced defects by additional annealing are found essential for achieving low DCR. The thesis demonstrates that it is possible to realise high performance SPADs in CMOS through the innovation of a “Deep SPAD” which achieves record PDE of ≈72% at 560nm with >40% PDE from 410-760nm, combined with 18Hz DCR, <60ps FWHM timing resolution, and <4% after-pulsing which is demonstrated to have potential for significant further improvement. The findings suggest that CMOS SPAD-based micro-systems could outperform existing photon timing and counting solutions in the future.

In a regression context, the aim is to analyse a response variable of interest conditional to a set of covariates. In many applications the response variable is discrete. Examples include the event of surviving a heart attack, the number of hospitalisation days, the number of times that individuals benefit of a health service, and so on. This thesis advances the methodology and the application of regression models with discrete response. First, we present a difference-in-differences approach to model a binary response in a health policy evaluation framework. In particular, generalized linear mixed methods are employed to model multiple dependent outcomes in order to quantify the effect of an adopted pay-for-performance program while accounting for the heterogeneity of the data at the multiple nested levels. The results show how the policy had a positive effect on the hospitals' quality in terms of those outcomes that can be more influenced by a managerial activity. Next, we focus on regression models for count response variables. In a parametric framework, Poisson regression is the simplest model for count data though it is often found not adequate in real applications, particularly in the presence of excessive zeros and in the case of dispersion, i.e. when the conditional mean is different to the conditional variance. Negative Binomial regression is the standard model for over-dispersed data, but it fails in the presence of under-dispersion. Poisson-Inverse Gaussian regression can be used in the case of over-dispersed data, Generalised-Poisson regression can be employed in the case of under-dispersed data, and Conway-Maxwell Poisson regression can be employed in both cases of over- or under-dispersed data, though the interpretability of these models is ot straightforward and they are often found computationally demanding. While Jittering is the default non-parametric approach for count data, inference has to be made for each individual quantile, separate quantiles may cross and the underlying uniform random sampling can generate instability in the estimation. These features motivate the development of a novel parametric regression model for counts via a Discrete Weibull distribution. This distribution is able to adapt to different types of dispersion relative to Poisson, and it also has the advantage of having a closed form expression for the quantiles. As well as the standard regression model, generalized linear mixed models and generalized additive models are presented via this distribution. Simulated and real data applications with different type of dispersion show a good performance of Discrete Weibull-based regression models compared with existing regression approaches for count data.

Submitted by Erika Demachki (erikademachki@gmail.com) on 2014-08-28T17:57:43Z No. of bitstreams: 2 FINALTCC_Karina 2.pdf: 867852 bytes, checksum: 5d14f3376d96066f3e7ef54c93d60d6b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-08-28T17:57:43Z (GMT). No. of bitstreams: 2 FINALTCC_Karina 2.pdf: 867852 bytes, checksum: 5d14f3376d96066f3e7ef54c93d60d6b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2013-03-01 / The objective of this work is to address the Combinatorial Analysis as a theme that can be treated in the classroom, without the utilization of mathematical formulas in problem solving. For this it will presented a method of reasoning that, basing on logic and languages, underscores the importance of: To read, interpret and analyze texts that are presented in the mother tongue; Formulate conjectures, make choices and seek strategies of count; and Make the correct translation between languages maternal and mathematics. Expected to show from this approach that the Combinatorial Analysis, besides its importance in various elds of knowledge, may also be seen by students and teachers as a challenging and stimulating content. / O objetivo deste trabalho é abordar a Análise Combinatória como um tema que pode ser tratado, em sala de aula, sem a utilização de fórmulas matemáticas na resolução de problemas. Será apresentado um método de raciocínio que, baseando-se em Lógica e Linguagens, destaca a importância de: Ler, interpretar e analisar textos que se apresentam em língua materna; Formular conjecturas, fazer escolhas e buscar estratégias de contagem; e Fazer a tradução correta entre as linguagens materna e matemática. Espera-se mostrar, a partir desta abordagem, que a Análise Combinatória, além da sua importância em vários campos do conhecimento, também pode ser encarada, por estudantes e professores, como um conteúdo desa ador e estimulante.

Não consta resumo na publicação. / Abstract not available.

In the first part, this thesis performed a study to compile and compare current procedures or methodologies for the estimation of traffic volumes on the roads where traffic counts are not easily available. In the second part, linear regression was practiced as an AADT estimation process, which was primarily based on known or accepted AADT values on the neighboring state and local roadways, population densities and other social/economic data. To develop AADT prediction models for estimating AADT values, two different types of database were created, including a social economic database and a roadway characteristics database. Ten years social economic data, from 1995 to 2005 were collected for each of the 67 counties in the state of Florida, and a social economic database was created by manually imputing data obtained from different resources into the social economic database. The roadway characteristics database was created by joining different GIS data layers to the Tele Atlas base map provided by Florida Department of Transportation (FDOT). Stepwise regression method was used to select variables that will be included into the final models. All selected independent variables in the models are statistically significant with a 90% level of confidence. In total, six linear regression models were built. The adjusted R2 values of the AADT prediction models vary from 0.166 to 0.418. Model validation results show that the MAPE values of the AADT prediction models vary from 31.99% to 159.49%. The model with the lowest MAPE value is found to be the minor state/county highway model for rural area. The model with the highest MAPE value is found to be the local street model for large metropolitan area. In general, minor state/county highway models provide more reasonable AADT estimates as compared to the local street model in terms of the lower MAPE values.

A practical problem often encountered with observed count data is the presence of excess zeros. Zero-inflation in count data can easily be handled by zero-inflated models, which is a two-component mixture of a point mass at zero and a discrete distribution for the count data. In the presence of predictors, zero-inflated Poisson (ZIP) regression models are, perhaps, the most commonly used. However, the fully parametric ZIP regression model could sometimes be restrictive, especially with respect to the mixing proportions. Taking inspiration from some of the recent literature on semiparametric mixtures of regressions models for flexible mixture modeling, we propose a semiparametric ZIP regression model. We present an "EM-like" algorithm for estimation and a summary of asymptotic properties of the estimators. The proposed semiparametric models are then applied to a data set involving clandestine methamphetamine laboratories and Alzheimer's disease.

According to the USDA Economic Research service, farm-level prices are on the decline. This decline in prices particularly hurts smaller scale operators with many needing to rely on off-farm income in order to ensure they remain in operation. This thesis studies two problems of key interest to the Southeast region and the State of Kentucky by investigating dairy management practices and the environmental benefits of hemp production. As dairy prices have been on the decline and dairy co-ops have tightened their restrictions on somatic cell count (SCC) levels, dairy farmers and farm managers must decide the best course of action for maintaining milk quality in order to maintain their contract and profitability. Maintenance decisions as well as factors like sanitation and animal living conditions can all contribute to bulk tank SCC and depending on the type of incentives or penalties instituted by the co-op they can have an impact on net farm income. The objective of the dairy study is to determine which dairy management practices have the largest impact on SCC levels. Industrial hemp is produced worldwide. Historically, the major producers of hemp have been China, Europe, and Russia. In 2014, the passage of the Farm Bill opened the door to the production of Industrial hemp through the development of state pilot programs. Then the 2018 Farm Bill removed industrial hemp from the Scheduled Drug list. This has further expanded the opportunities and excitement for this crop. The plant’s versatility and the variety of products that can be made from it are coming to light. Sustainability is one of the key attributes touted concerning industrial hemp. Specifically, in the state of Kentucky, it is expected to be a replacement for tobacco and other traditional crops. However, how does the crop compare to tobacco production in terms of sustainability? The objective of the hemp study is to develop a life cycle analysis on the planting and harvesting of hemp and compare its impacts to more traditional crops.

Due to increasingly stringent regulations, concerns about disinfection byproduct formation, and the need for improved control of distribution system water quality, there has been a shift towards the use of alternative disinfectants and oxidants in the production of drinking water. Technologies that modify water chemistry, such as hydrogen peroxide, UV irradiation, chlorine and/or chloramines may result in the generation of mineral and organic precipitates. Turbidity provides an indirect measure of the presence of particles by evaluating the light scattering properties of water. Turbidity levels are currently not monitored or regulated in treated groundwater. An important water quality parameter that influences groundwater quality is hydrogen sulfide. The control of sulfides in groundwater is of importance because its presence can cause odor and taste complaints, corrosion of pipes and other plumbing fixtures, and black-water problems in distribution systems (Levine et. al, 2004). In addition, sulfides can impose a significant oxidant demand and possibly interfere with disinfection treatments. Characteristics of particles from untreated and treated groundwater were tested as part of a field study to evaluate alternative wellhead treatment approaches for controlling hydrogen sulfide. A 1 gallon per minute (gpm) pilot-plant was used to test several groundwater treatment scenarios. The chemicals tested included chlorine, monochloramine, and hydrogen peroxide either alone or in tandem. Photochemical oxidation was evaluated using UV and advanced oxidation was evaluated using hydrogen peroxide coupled with UV. Testing was conducted either on water pumped directly from the well at ambient (7.0-7.5), or pretreated with caustic soda to evaluate the impact of elevated pH (8.2) conditions. The formation of particles was quantified using turbidity, solids (total, dissolved and suspended), and particle counts before and after oxidation. The particulate matter was characterized using a particle size analyzer in conjunction with scanning electron microscopy coupled with energy dispersive spectroscopy (SEM/EDS). Treatment systems that rely on in-line treatment lack mechanisms for particle removal, therefore particles generated through treatment are introduced into the distribution system. It is evident from this project that treatment systems should be optimized to prevent particle formation.

Laboratory tests are a primary resource for diagnosing patient diseases. However, physicians often make decisions based on a single laboratory result and have a limited perspective of the role of commonly-measured parameters in enhancing the diagnostic process. By providing a dynamic patient profile, the diagnosis could be more accurate and timely, allowing physicians to anticipate changes in the recovery trajectory and intervene more effectively. The assessment and monitoring of the circulatory system is essential for patients in intensive care units (ICU). One component of this system is the platelet count, which is used in assessing blood clotting. However, platelet counts represent a dynamic equilibrium of many simultaneous processes, including altered capillary permeability, inflammatory cascades (sepsis), and the coagulation process. To characterize the value of dynamic changes in platelet count, analytical methods are applied to datasets of critically-ill patients in (1) a homogeneous population of ICU cardiac surgery patients and (2) a heterogeneous group of ICU patients with different conditions and several hospital admissions. The objective of this study was to develop a methodology to anticipate adverse events using metrics that capture dynamic changes of platelet counts in a homogeneous population, then redefine the methodology for a more heterogeneous and complex dataset. The methodology was extended to analyze other important physiological parameters of the circulatory system (i.e., calcium, albumin, anion gap, and total carbon dioxide). Finally, the methodology was applied to simultaneously analyze some parameters enhancing the predictive power of various models. This methodology assesses dynamic changes of clinical parameters for a heterogeneous population of ICU patients, defining rates of change determined by multiple point regression and by the simpler fixed time parameter value ratios at specific time intervals. Both metrics provide prognostic information, differentiating survivors from non-survivors and have demonstrated being more predictive than complex metrics and risk assessment scores with greater dimensionality. The goal was to determine a minimal set of biomarkers that would better assist care providers in assessing the risk of complications, allowing them alterations in the management of patients. These metrics should be simple and their implementation would be feasible in any environment and under uncertain conditions of the specific diagnosis and the onset of an acute event that causes a patient's admission to the ICU. The results provide evidence of the different behaviors of physiologic parameters during the recovery processes for survivors and non-survivors. These differences were observed during the first 8 to 10 days after a patient's admission to the ICU. The application of the presented methodology could enhance physicians' ability to diagnose more accurately, anticipate changes in recovery trajectories, and prescribe effective treatment, leading to more personalized care and reduced mortality rates.