Vegetative health was measured both before and after Hurricane Harvey using remotely sensed vegetation indices on the coastal marshland surrounding Galveston Island's West Bay. Data were recorded on a monthly basis following the hurricane from September of 2005 until September of 2019 in order to document the vegetation response to this significant disturbance event. Both initial impact and recovery were found to be dependent on a variety of factors, including elevation zone, spatial proximity to the bay, the season during which recovery took place, as well as the amount of time since the hurricane. Slope was also tested as a potential variable using a LiDAR-derived slope raster, and while unable to significantly explain variations in vegetative health immediately following the hurricane, it was able to explain some degree of variability among spatially close data points. Among environmental factors, elevation zone appeared to be the most key in determining the degree of vegetation impact, suggesting that the different plant assemblages that make up different portions of the marsh react differently to the severe flooding that took place during Harvey.

It is well established in the literature that children with developmental coordination disorder (DCD) are more likely to be physically inactive, have unhealthy weight, and report lower perceptions of self-worth than typically developing (TD) children. Physical inactivity, overweight/obesity and low self-worth are important risk factors for many physical and psychological health conditions. The interrelationships among these factors, however, have yet to be explored in children with DCD. There is limited information on change in body composition measures and self-worth over time in children with DCD, the effect of physical activity (PA) on body composition, and whether the combined negative influence of having both DCD and obesity result in poorer conceptions of self-worth. In this dissertation, I present a series of studies that explore the connections among these factors using longitudinal, population-based data on a large cohort of children with and without poor motor coordination. The first study, presented in Chapter 2, describes the results of change in BMI and waist circumference (WC) in children with probable DCD (pDCD) and TD children over a five-year time period, and the effects of sex and PA on this relationship. Chapter 3 describes the results of the relationship between body fat, pDCD, and PA after addressing the measurement- related limitations of the study reported in Chapter 2. Chapter 4 describes the results of self-worth in children with pDCD and overweight/obesity, only pDCD, only overweight/obesity, and the control group at baseline as well as change over time. Collectively, the results show that children with pDCD have a consistently higher BMI, WC, and body fat than TD children. BMI and WC increases over time in children with pDCD; specifically boys with pDCD show a much accelerated increase in these measures. Scores of body composition measures increase with decrease in self-reported and objectively measured PA, but participation in PA does not explain why children with pDCD are more likely to have excess weight gain. Finally, children with both pDCD and overweight/obesity and children with either of these conditions alone report lower self- worth than the control group, and the change in self-worth between groups remains constant over time. / Dissertation / Doctor of Philosophy (PhD)

<p dir="ltr">Crohn’s disease and ulcerative colitis are chronic inflammatory bowel diseases (IBD) with a rising global prevalence, influenced by clinical and demographics factors. The pathogenesis of IBD involves complex interactions between gut microbiome dysbiosis, epithelial cell barrier disruption, and immune hyperactivity, which are poorly understood. This necessitates the development of novel approaches to integrate and model multiple clinical and molecular data modalities from patients, animal models, and <i>in-vitro</i> systems to discover effective biomarkers for disease progression and drug response. As sequencing technologies advance, the amount of molecular and compositional data from paired measurements of host and microbiome systems is exploding. While it is become routine to generate such rich, deep datasets, tools for their interpretation lag behind. Here, I present a computational framework for integrative modeling of microbiome multi-omics data titled: Latent Interacting Variable Effects (LIVE) modeling. LIVE combines various types of microbiome multi-omics data using single-omic latent variables (LV) into a structured meta-model to determine the most predictive combinations of multi-omics features predicting an outcome, patient group, or phenotype. I implemented and tested LIVE using publicly available metagenomic and metabolomics data set from Crohn’s Disease (CD) and ulcerative colitis (UC) status patients in the PRISM and LLDeep cohorts. The findings show that LIVE reduced the number of features interactions from the original datasets for CD to tractable numbers and facilitated prioritization of biological associations between microbes, metabolites, enzymes, clinical variables, and a disease status outcome. LIVE modeling makes a distinct and complementary contribution to the current methods to integrate microbiome data to predict IBD status because of its flexibility to adapt to different types of microbiome multi-omics data, scalability for large and small cohort studies via reliance on latent variables and dimensionality reduction, and the intuitive interpretability of the meta-model integrating -omic data types.</p><p dir="ltr">A novel application of LIVE modeling framework was associated with sex-based differences in UC. Men are 20% more likely to develop this condition and 60% more likely to progress to colitis-associated cancer compared to women. A possible explanation for this observation is differences in estrogen signaling among men and women in which estrogen signaling may be protective against UC. Extracting causal insights into how gut microbes and metabolites regulate host estrogen receptor β (ERβ) signaling can facilitate the study of the gut microbiome’s effects on ERβ’s protective role against UC. Supervised LIVE models<b> </b>ERβ signaling using high-dimensional gut microbiome data by controlling clinical covariates such as: sex and disease status. LIVE models predicted an inhibitory effect on ER-UP and ER-DOWN signaling activities by pairs of gut microbiome features, generating a novel of catalog of metabolites, microbial species and their interactions, capable of modulating ER. Two strongly positively correlated gut microbiome features: <i>Ruminoccocus gnavus</i><i> </i>with acesulfame and <i>Eubacterium rectale</i><i> </i>with 4-Methylcatechol were prioritized as suppressors ER-UP and ER-DOWN signaling activities. An <i>in-vitro</i> experimental validation roadmap is proposed to study the synergistic relationships between metabolites and microbiota suppressors of ERβ signaling in the context of UC. Two i<i>n-vitro</i> systems, HT-29 female colon cancer cell and female epithelial gut organoids are described to evaluate the effect of gut microbiome on ERβ signaling. A detailed experimentation is described per each system including the selection of doses, treatments, metrics, potential interpretations and limitations. This experimental roadmap attempts to compare experimental conditions to study the inhibitory effects of gut microbiome on ERβ signaling and how it could elevate or reduce the risk of developing UC. The intuitive interpretability of the meta-model integrating -omic data types in conjunction with the presented experimental validation roadmap aim to transform an artificial intelligence-generated big data hypothesis into testable experimental predictions.</p>

This research examines the viability of a need-based approach that models the age-trajectories of healthcare utilization. We propose a fundamentally different way of treating age in modeling healthcare use. Rather than treating age as a need indicator, we refocus modeling efforts to predicting the age-trajectories of healthcare use. Using inpatient hospital utilization data from the Discharge Abstract Database, first, we model the age-trajectories of the rate of hospital use employing a common functional form. Second, we assess variation in these age-trajectories using growth curve modeling. Third, we explain variation in these age-trajectories using census variables. Our analysis shows that the regional variation in the age-trajectories of the rate of inpatient hospital use is sufficient to justify this method, and could be partially explained using census variables. This indicates that modeling age-trajectories of healthcare use is advantageous, and the current need-based approach may benefit from this new modeling strategy.

Hlavním cílem této práce je obohatit hudební signály charakteristikami lidské řeči. Práce zahrnuje tvorbu audioefektu inspirovaného efektem talk-box: analýzu hlasového ústrojí vhodným algoritmem jako je lineární predikce, a aplikaci odhadnutého filtru na hudební audio-signál. Důraz je kladen na dokonalou kvalitu výstupu, malou latenci a nízkou výpočetní náročnost pro použití v reálném čase. Výstupem práce je softwarový plugin využitelný v profesionálních aplikacích pro úpravu audia a při využití vhodné hardwarové platformy také pro živé hraní. Plugin emuluje reálné zařízení typu talk-box a poskytuje podobnou kvalitu výstupu s unikátním zvukem.

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Emergency departments are fundamental for providing high-quality care, and their operations directly impact the logistics of the hospitals in their entirety. Poor emergency department performance leads to delays, prolonged hospitalization, and improper allocation of resources, reducing the quality of the provided care and increasing costs. Describing the variability embedded in real clinical data in a useful way is essential for improving the organization of hospitals in the near future. However, it is a challenging task due to clinical complexity and the lack of an established bridge between logistic systems and the clinical insights of the hospital. Therefore, this work aims to design and implement a simplified process model describing patient flow within an emergency department, which could allow the evaluation of the clinical impact of complex patient characteristics on the system's logistics. To achieve this, a novel nonlinear mixed-effects approach with hospital medical records was applied to design patient flow within the emergency department in the form of a multi-state Markov process. Four independent training data samples were extracted from the main dataset. For each of them, the set of covariates that could lead to the most significant improvement in the values of the employed likelihood indicators was selected. Through statistical tests, analysis of the outputs, and a validation process carried out on a fifth and independent dataset, it was possible to obtain a final model containing the most relevant and significant covariates for describing each of the modeled state transitions and confirming their clinical meaningfulness and relevance. The results achieved in this thesis can lead to future improvement of the healthcare logistics systems by extending the use of nonlinear mixed-effects approaches to the estimation of the covariate impact on emergency department flows. / Akutmottagningar är centrala för att tillhandahålla högkvalitativ vård. Deras verksamhet har en direkt inverkan på sjukhusens logistik i helhet. Undermålig prestation i en akutmottagning leder till förseningar, förlängd sjukhusvistelse för patienter och olämpliga resursfördelningar, som i sin tur försämrar kvaliteten på den erbjudna vården, samt ökar kostnader. Därför är det viktigt att beskriva den variabilitet som är inbäddad i kliniskt data för att kunna förbättra strukturen av sjukhus i den närmaste framtiden. Emellertid är det ett utmanande uppdrag på grund av den kliniska komplexiteten och bristen på en etablerad bro mellan logistiska system och insikter om den kliniska situationen på sjukhuset. Detta examensarbete ämnar därför designa och implementera en förenklad processmodel som beskriver patientflödet inom en akutmottagning, vilket skulle kunna tillåta evaluering av vad för klinisk inverkan patienters komplexa egenskaper har på systemets logistik. För att uppnå detta tillämpades ett nytt icke-linjärt tillvägagångssätt för blandade effekter med patientjournaler, med syfte att designa patientflöde inom akutmottagningen i form av en Markovprocess i flera tillstånd. Fyra oberoende urvalsgrupper med övningsdata extraherades från huvuddatasetet och för var och en av dem valdes den uppsättning kovariat som hade möjlighet att leda till den största förbättringen i de applicerade sannolikhetsindikatorerna. Genom statistiska test, analys av uteffekten och en valideringsprocess utförd på en femte oberoende urvalsgrupp, möjliggjordes framtagandet av en slutgiltig modell innehållande de mest relevanta och signifikanta kovariat för att beskriva var och en av de modellerade tillståndsövergångarna, och bekräfta dess kliniska betydelse och relevans. De resultat som uppnåddes i det här examensarbetet har potential att i framtiden leda till förbättring av sjukvårdens logistiksystem, genom att utvidga användningen av icke-linjära blandade effektmodeller för att uppskatta kovariatinverkan på akutmottagningsflöden.