Imunodeficiência Comum Variável (ICV) é uma imunodeficiência primária de igual distribuição entre os sexos e que afeta crianças e adultos, caracterizada por hipogamaglobulinemia com susceptibilidade aumentada a infecções e ampla variedade de complicações não infecciosas, como autoimunidade, malignidade, hiperplasia linfoide, doenças gastrointestinais, dentre outras. Os objetivos deste estudo foram: avaliar as manifestações clínicas, infecciosas e não infecciosas, mais frequentes em portadores de ICV (antes e após início da terapia com reposição de imunoglobulina humana) acompanhados em ambulatórios de imunologia do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), além dos níveis séricos de imunoglobulinas (IgG, IgA e IgM) ao diagnóstico, bem como as alterações quantitativas de células CD19+, CD3+CD4+, CD3+CD8+ e CD3-CD16+CD56+ desses pacientes. Neste estudo descritivo foram obtidas informações de pacientes com ICV acompanhados no HCFMRPUSP, através de registros de prontuários médicos. Foram avaliados 32 pacientes: 19 do sexo masculino e 13 do sexo feminino. A mediana da idade de início dos sintomas foi de 8,5 anos, com um pico de incidência precoce. O tempo médio de atraso para o diagnóstico foi de 7,7 anos. Todos os pacientes apresentaram infecções recorrentes, que levaram ao diagnóstico da ICV. As infecções mais frequentes foram as respiratórias, sendo que antes do diagnóstico as pneumonias foram as mais observadas (gerando, inclusive, grande número de internações) e durante o primeiro ano de uso regular da terapia de reposição com imunoglobulina humana as rinossinusites foram as que mais ocorreram. Houve redução na incidência de infecções após início do tratamento. Todos os pacientes apresentaram níveis séricos de IgG, IgA e IgM reduzidos ao diagnóstico, sendo que as medianas dos níveis séricos foram de 158 mg/dL, 10,15 mg/dL e 17 mg/dL, respectivamente. De 30 pacientes que haviam realizado imunofenotipagem, cerca de 73% apresentaram número absoluto reduzido de células CD19+ e 40% apresentaram número absoluto reduzido de linfócitos T CD4+. A relação CD4/CD8 foi invertida em aproximadamente 53% dos pacientes. Em 18 pacientes as células natural killer foram quantificadas e cerca de 56% deles apresentaram número absoluto reduzido. A maioria (97%) dos pacientes manifestou, no mínimo, uma comorbidade não infecciosa no tempo médio de seguimento de 8,2 anos, sendo que hiperplasia linfoide e doença pulmonar crônica foram as mais frequentes, cada uma ocorrendo em cerca de metade dos pacientes. O atraso para o diagnóstico da ICV foi importante, sugerindo que a presença de infecções recorrentes, especialmente do trato respiratório, deveria levar à investigação de deficiências de anticorpos, com dosagem de imunoglobulinas. Complicações não infecciosas foram extremamente comuns nesta casuística, ressaltando o amplo espectro clínico da doença. / Common variable immunodeficiency (CVID) is a primary immunodeficiency that is equally distributed between men and women and affects children and adults, characterized by hypogammaglobulinemia with increased susceptibility to infections and a wide variety of noninfectious complications such as autoimmunity, malignancy, lymphoid hyperplasia, gastrointestinal diseases, among others. The purposes of this study were to evaluate infectious and non-infectious clinical manifestations (before and after immunoglobulin replacement therapy) of CVID patients attended at immunology outpatient clinics of the Clinical Hospital of Ribeirão Preto Medical School - University of São Paulo (HCFMRP-USP), in addition to immunoglobulins (IgG, IgA and IgM) serum levels at diagnosis, as well as quantitative differences in CD19+, CD3+CD4+, CD3+CD8+ and CD3-CD16+CD56+ cells. In this descriptive study, data of CVID patients followed up at HCFMRP-USP were collected through medical records. Thirty-two patients were found: 19 males and 13 females. The median age of onset of symptoms was 8.5 years, with an early peak of incidence. The mean delay for diagnosis was 7.7 years. All patients had recurrent infections, which led to the diagnosis of CVID. The most frequent infections were respiratory tract infections. Pneumonias were more observed before the diagnosis (generating a large number of hospitalizations) and rhinosinusitis were more frequent during the first year under regular use of immunoglobulin replacement therapy. There was a reduction in the incidence of infections after initiation of treatment. All patients had low IgG, IgA and IgM serum levels (lower than the 3th percentile for age) at diagnosis and the median of serum levels were 158 mg/dL, 10.15 mg/dL and 17 mg/dL, respectively. Among 30 patients that had been immunophenotyped, approximately 73% had a reduced absolute number of CD19+ cells and 40% had a reduced absolute number of T CD4+ lymphocytes. The CD4/CD8 ratio was inverted in approximately 53% of the patients. Natural killer cells were quantified in 18 patients and about 56% of them had reduced absolute number. The majority (97%) of patients manifested at least one noninfectious comorbidity at a mean follow-up time of 8.2 years, with lymphoid hyperplasia and chronic lung disease being the most common, each occurring in about half of the patients. The delay for the diagnosis of CVID was important, suggesting that the presence of recurrent infections, especially of the respiratory tract, should lead to the investigation of antibody deficiencies with dosage of immunoglobulins. Noninfectious complications were extremely common in this series, highlighting the broad clinical spectrum of the disease.

The nature of the edge-on eclipsing binary Epsilon Aurigae remains perplexing, despite notable progress since the recent 2009-2011 eclipse. The binary involves an early F supergiant with a still unknown companion enshrouded in a disk. Although the eclipse geometry produces a significant broad band polarization signature, semiregular pulsations of the F supergiant are also a source of variable polarization, with an amplitude that is commensurate with the effect of the eclipse. This fact makes use of the polarization for studying the disk of the companion far more challenging. In an effort to better understand the pulsation nature of the supergiant, we explore a simple model for the stellar contribution to the polarization signal. The model does reasonably well in characterizing the gross properties of the time-variable polarization.

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More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as Escherichia coli are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict E. coli impairment and F+-somatic bacteriophage detections. Models were validated using a bootstrapping cross-validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial E. coli model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. Escherichia coli impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation.

Randomized clinical trials (RCTs) are considered to be the "gold standard" in order to demonstrate a causal relationship between a treatment and an outcome because complete randomization ensures that the only difference between the two units being compared is the treatment. The intention-to-treat (ITT) comparison has long been regarded as the preferred analytic approach for RCTs. However, if there exists an “intermediate” variable between the treatment and outcome, and the analysis conditions on this intermediate, randomization will break down, and the ITT approach does not account properly for the intermediate. In this dissertation, we explore the principal stratification approach for dealing with intermediate variables, illustrate its applications in two different clinical trial settings, and extend the existing analytic approaches with respect to specific challenges in these settings. The first part of our work focuses on clinical endpoint bioequivalence (BE) studies with noncompliance and missing data. In clinical endpoint BE studies, the primary analysis for assessing equivalence between a generic and an innovator product is usually based on the observed per-protocol (PP) population (usually completers and compliers). The FDA Missing Data Working Group recently recommended using “causal estimands of primary interest.” This PP analysis, however, is not generally causal because the observed PP is post-treatment, and conditioning on it may introduce selection bias. To date, no causal estimand has been proposed for equivalence assessment. We propose co-primary causal estimands to test equivalence by applying the principal stratification approach. We discuss and verify by simulation the causal assumptions under which the current PP estimator is unbiased for the primary principal stratum causal estimand – the "Survivor Average Causal Effect" (SACE). We also propose tipping point sensitivity analysis methods to assess the robustness of the current PP estimator from the SACE estimand when these causal assumptions are not met. Data from a clinical endpoint BE study is used to illustrate the proposed co-primary causal estimands and sensitivity analysis methods. Our work introduces a causal framework for equivalence assessment in clinical endpoint BE studies with noncompliance and missing data. The second part of this dissertation targets the use of principal stratification analysis approaches in a pragmatic randomized clinical trial -- the Patient Activation after DXA Result Notification (PAADRN) study. PAADRN is a multi-center, pragmatic randomized clinical trial that was designed to improve bone health. Participants were randomly assigned to either intervention group with usual care augmented by a tailored patient-activation Dual-energy X-ray absorptiometry (DXA) results letter accompanied by an educational brochure, or control group with usual care only. The primary analyses followed the standard ITT principle, which provided a valid estimate for the intervention assignment. However, findings might underestimate the effect of intervention because PAADRN might not have an effect if the patient did not read, remember and act on the letter. We apply principal stratification to evaluate the effectiveness of PAADRN for subgroups, defined by patient's recall of having received a DXA result letter, which is an intermediate outcome that's post-treatment. We perform simulation studies to compare the principal score weighting methods with the instrumental variable (IV) methods. We examine principal strata causal effects on three outcome measures regarding pharmacological treatment and bone health behaviors. Finally, we conduct sensitivity analyses to assess the effect of potential violations of relevant causal assumptions. Our work is an important addition to the primary findings based on ITT. It provides a profound understanding of why the PAADRN intervention does (or does not) work for patients with different letter recall statuses, and sheds light on the improvement of the intervention.

Anticipating how stream water quality will respond to change, such as increased pollution or water diversions, requires knowledge of the main mechanisms controlling water and chemical constituent movement and a reasonable representation of those mechanisms. By deriving mathematical models to represent a stream system and collecting supporting field-based measurements, water quality response can be predicted. However, because each stream is unique and the movement of water and constituents is spatially and temporally complex, assessing whether the stream is appropriately represented and whether predictions are trustworthy is still a challenge within the scientific and management communities. Building on decades of stream research, this dissertation provides a step towards better representing some of the complexities found within streams and rivers to better predict water quality responses over long stream distances. First, a method is presented to assess which mechanisms are considered most important in chemical constituent predictions. Next, the number of measurements necessary to represent the general complexities of water, mass, and heat movement in streams was determined. The advancements developed in this dissertation provide a foundation to more efficiently and accurately inform water resource management.

Variable-rate technologies for sprayer applications stand to increase efficacy by ensuring the right amount of chemical is applied at the right location. However, external environmental factors such as droplet drift caused by variable ambient condition, are not yet integrated into modern sprayer systems. Real-time wind velocity measurements can be used to control droplet spectra for reducing spray drift by actuating a variable-orifice nozzle. This work aimed to develop data processing methods needed to filter noise and remove vehicle speed from wind velocity measurements collected with an ultrasonic anemometer aboard a moving platform. Using a global navigation satellite system (GNSS), vehicle speed was calculated in the field and subtracted from apparent wind velocity for comparison to static measurements. Experiments under stationary and dynamic sensor deployments were used to develop an algorithm to provide instantaneous local wind velocity and to better understand the local spatiotemporal variability of wind under field conditions.

Carbon Fiber Reinforced Polymers (CFRP) are a class of advanced materials widely used in versatile applications including aerospace and automotive industries due to their exceptional physical and mechanical properties. Owing to the heterogenous nature of the composites, it is often a challenging task to machine them unlike metals. Drilling in particular, the most commonly used process for component assembly is critical especially in the aerospace sector which demands parts of highest quality and surface integrity. Conventionally, all composites are machined under dry conditions. While there are drawbacks related to dry drilling, for example, poor surface roughness, there is a need to develop processes which yield good quality parts. This thesis investigates the machining performance when drilling CFRP under cryogenic, MQL and hybrid (CryoMQL) modes and comparing with dry drilling in terms of the machining forces, delamination, diameter error and surface integrity assessment including surface roughness, hardness and sub-surface damage analysis. Additionally, the effect of varying the feed rate on the machining performance is examined. From the study, it is concluded that drilling using coolant/ lubricant outperforms dry drilling by producing better quality parts. Also, varying the feed rate proved to be advantageous over drilling at constant feed.

As power electronic based controllers and loads become more prevalent in power systems, there is a growing concern about how the harmonics generated by these controllers and loads affect the power quality of the system. One widely used power electronic based load is the Variable Frequency Drive (VFDs) used to vary the speed of an induction motor; whereas a common example of a power electronic based controller used in power systems is the Static VAR Compensator (SVC) for improving a system’s power factor. In this thesis, the harmonic content and overall performance of a system including both a VFD and a SVC will be studied and analyzed. Specifically, the cases of no compensation, static capacitor compensation, and power electronic based static VAR compensation are examined. A small-scale model of a system for study was constructed in lab. Several cases were then performed and tested to simulate a system which contained both fixed and power electronic based harmonic generating loads. The performance of each case was determined by total harmonic current and voltage distortions, true power factor, and RMS current levels at different points in the system.

In fields such as statistics, economics and biology, heterogeneity is an important topic concerning validity of data inference and discovery of hidden patterns. This thesis focuses on penalized methods for regression analysis with the presence of heterogeneity in a potentially high-dimensional setting. Two possible strategies to deal with heterogeneity are: robust regression methods that provide heterogeneity-resistant coefficient estimation, and direct detection of heterogeneity while estimating coefficients accurately in the meantime. We consider the first strategy for two robust regression methods, Huber loss regression and quantile regression with Lasso or Elastic-Net penalties, which have been studied theoretically but lack efficient algorithms. We propose a new algorithm Semismooth Newton Coordinate Descent to solve them. The algorithm is a novel combination of Semismooth Newton Algorithm and Coordinate Descent that applies to penalized optimization problems with both nonsmooth loss and nonsmooth penalty. We prove its convergence properties, and show its computational efficiency through numerical studies. We also propose a nonconvex penalized regression method, Heterogeneity Discovery Regression (HDR) , as a realization of the second idea. We establish theoretical results that guarantees statistical precision for any local optimum of the objective function with high probability. We also compare the numerical performances of HDR with competitors including Huber loss regression, quantile regression and least squares through simulation studies and a real data example. In these experiments, HDR methods are able to detect heterogeneity accurately, and also largely outperform the competitors in terms of coefficient estimation and variable selection.