Gilles de la Tourette syndrome (GTS) is a chronic neuropsychiatric disorder characterized by multiple motor and vocal tics, affecting approximately 1% of the population. The precise neuropathology of GTS has not yet been delineated, but current models implicate subcortical and cortical areas - the cortico-striato-thalamo-cortical (CSTC) circuit. The majority of studies in the literature have either dealt with GTS with comorbid conditions and/or children with GTS. As these factors are known to affect brain structure and function, it unknown what the neurobiological underpinnings of pure adult GTS are. The objective of this body of work was to use functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) to characterize differences in brain function and structure in pure adult GTS patients versus age- and sex-matched controls. I employed a series of three distinct analyses for this purpose, based upon current models of CSTC circuit-related dysfunction in GTS. In the first, GTS patients and control participants executed three finger-tapping paradigms that varied in both complexity and memory requirements. These finger-tapping tasks were modeled after previous studies that showed CSTC circuit-related activity in healthy individuals. Using a multivariate statistical technique to assess task-related patterns of activation across the whole brain, I found that, while there was much overlap in brain activation patterns between groups, sensorimotor cortical regions were differentially recruited by GTS patients compared to controls. In the second fMRI analysis, I measured low-frequency spontaneous fluctuations of the blood oxygen level dependent signal during rest, and found that GTS patients exhibited greater resting state functional connectivity with the left putamen compared to controls. In the final analysis, DTI was used to provide a whole-brain assessment of regional diffusion anisotropy in GTS patients and healthy volunteers and to investigate the fractional anisotropy in predetermined ROIs. This analysis found no differences between GTS patients and controls. Overall, my findings indicated that several CSTC-related regions shown to be atypical in GTS patients previously, are also atypical in pure adult GTS, and that sensorimotor cortical regions and the putamen may be regions of functional disturbance in pure adult GTS.

<p>This thesis presents a method for predicting radon (222Rn) levels in groundwater on a general scale, within an area of approximately 185 x 145 km2. The method applies to Swedish conditions, where 222Rn is the main contributor to natural radioactivity. Prediction of radon potential in groundwater is complex because there are many different factors affecting radon content, including geochemical and flow processes. The proposed method is based on univariate and multivariate statistical analyses and investigated the influence of different factors such as bedrock, soils, uranium distribution, altitude, distance to fractures and land use. A statistical variable based method (the RV method) was used to estimate risk values related to different radon concentrations. The method was calibrated and tested on more than 4400 drilled wells in Stockholm County. The weighted index (risk value) estimated by the RV method provided a fair prediction of radon potential in groundwater on a general scale. The RV method was successful in estimating the median radon concentration within 12 subregions (at a local scale, each of area 25 x 25 km2), based on weighted index values obtained from half of all wells tested. A high correlation between risk values and median radon concentrations was demonstrated. The factors bedrock, altitude, distance to fracture zone and distribution of uranium in bedrock were found to be significant in the prediction approach on a general scale. Visual data mining, which comprised analysis of 3D images, was a useful tool for data exploration but could not be used as an independent method for drawing conclusions regarding radon in groundwater. Results of a field study based on 38 drilled wells on the island of Ljusterö in the Stockholm archipelago showed that 222Rn concentrations in groundwater were weakly correlated to the parent elements (226Ra and 238U) in solution.</p>

This study focuses on reconstructing the dynamics within the Pliocene San Joaquin Basin (SJB) molluscan fauna. This was accomplished by 'binning' the data within a constrained chronostratigraphic framework into: 1) 484 individual stratigraphically-ordered locality collections; 2.) 116 stratigraphically-sequential compiled ten-meter sample intervals; 3.) 15 intervals compiled by 4th-order eustatic cycles; 4.) three formation-level compiled samples; and 5.) the Etchegoin group fauna (informal San Joaquin Basin nomenclature) overall. These datasets were analyzed by inferential, multivariate, and descriptive statistics to examine local and regional environmental controls on faunal composition, community associations and distributions; cross-scale faunal structure; and large-scale environmental controls on immigration, diversity, and extinction. Primary environmental controls on community composition and spatial distribution were substrate type and water paleo-depth. Consequently, the Pliocene SJB record is one of a temporal succession of complexly distributed habitats and species. Regional habitat patchiness controlled individual locality-level (a1) diversity and contributed 62% of regional sample-level (a2) diversity. Endemic species comprise 30% of the fauna but account for 42% of a2 diversity, indicative of their environmental sensitivity. Partitioning a2 diversity between non-endemic and endemic species reveals habitats segmented as shared or available solely to endemic species. At the level of 4th-order eustatic variations, diversity between temporal samples (b1) accounts for ~80% of total (y) diversity consistent with eustatic control of faunal structure. During eustatic fluctuations, endemic habitats expanded and contracted at rates greater than shared habitats. Invading species quickly filled shared habitat during transgression and displaced endemic species during regression. Therefore, climatic- and regression-driven hydrologic change and productivity collapse in the Pliocene SJB led to seven extinctions of >40% species. Peak faunal diversity corresponded to periods of highest sea-levels whereas low-diversity faunas characterized low to rising sea levels. Thus, speciation events following extinctions suggest diversification of surviving faunas into habitats newly-created by changed environmental conditions.The broader implication of this study is that during current global sea level rise depleted endemic faunas of shallow-coastal and ocean-marginal environments will be displaced into the shared-habitat with consequent extinction likely if adaptation does not keep pace with environmental change.

Many important scientific questions can not be studied properly using a single measurement as a response. For example, many phenotypes of interest in recent clinical research may be difficult to characterize due to their inherent complexity. It may be difficult to determine the presence or absence of disease based on a single measurement, or even a few measurements, or the phenotype may only be defined based on a series of symptoms. Similarly, a set of related phenotypes or measurements may be studied together in order to detect a shared etiology. In this work, we propose methods for studying complex phenotypes of these types, where the phenotype may be characterized either longitudinally or by a diverse set of continuous, discrete, or not fully observed components. In chapter 1, we seek to identify predictors that are related to multiple components of diverse outcomes. We take up specifically the question of identifying a multiple regulator, where we seek a genetic marker that is associated with multiple biomarkers for autoimmune disease. To do this, we propose sparse multiple regulation testing (SMRT) both to estimate the relationship between a set of predictors and diverse outcomes and to provide a testing framework in which to identify which predictors are associated with multiple elements of the outcomes, while controlling error rates. In chapter 2, we seek to identify risk profiles or risk scores for diverse outcomes, where a risk profile is a linear combination of predictors. The risk profiles will be chosen to be highly correlated to latent traits underlying the outcomes. To do this, we propose semiparametric canonical correlation analysis (sCCA), an updated version of the classical canonical correlation analysis. In chapter 3, the scientific question of interest pertains directly to the progression of disease over time. We provide a testing framework in which to detect the association between a set of genetic markers and the progression of disease in the context of a GWAS. To test for this association while allowing for highly nonlinear longitudinal progression of disease, we propose functional principal variance component (FPVC) testing.

Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2017-06-09T17:17:51Z No. of bitstreams: 2 lucas_cunha_ximenes.pdf: 2578435 bytes, checksum: 674c742b7551f13345ff22f59b8220c5 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2017-06-22T15:04:07Z (GMT) No. of bitstreams: 2 lucas_cunha_ximenes.pdf: 2578435 bytes, checksum: 674c742b7551f13345ff22f59b8220c5 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-06-22T15:04:07Z (GMT). No. of bitstreams: 2 lucas_cunha_ximenes.pdf: 2578435 bytes, checksum: 674c742b7551f13345ff22f59b8220c5 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / O estudo teve como objetivo definir a melhor combina??o de m?todo de agrupamento com medida de similaridade para a classificar a capacidade produtiva de um trecho na Floresta Nacional do Tapaj?s. O invent?rio florestal amostral foi realizado no ano de 2012 e para a loca??o das parcelas foram abertas 12 faixas de, aproximadamente, 1,5 m de largura, equidistantes 4,0 km, na dire??o leste-oeste, e com comprimento variando de 4 km a 13,75 km. A instala??o das parcelas, com dimens?es de 30 x 250 m, distribu?das sistematicamente por 500 m em cada linha. Foi levado em considera??o para a defini??o das classes de tamanho (CT): CT 1 (classe de regenera??o) - 10 cm ? DAP < 25 cm nos primeiros 50 m da parcela (30 m x 50 m); CT 2 (classe de crescimento) - 25 cm ? DAP < 50 cm nos primeiros 100 m (30 m x 100 m); e CT 3 (classe de colheita) - DAP ? 50 cm em toda a parcela (30 m x 250 m). Para a classifica??o da capacidade produtiva, realizou-se um filtro no banco de dados original por classe de tamanho, no qual foram selecionados os indiv?duos com qualidade de fuste 1 (fuste reto) e 2 (fuste com pequenas tortuosidades) e que t?m valor no mercado regional. As 204 parcelas foram agrupadas em grupos homog?neos, no qual foram produzidos 40 dendrogramas do tipo vertical para cada uma das 3 classes de tamanho (totalizando 120 dendrogramas), baseados na combina??o de 5 medidas de dist?ncia (Euclidiana Simples, Euclidiana Quadrada, Manhattan, Canberra e Mahalanobis), com 8 m?todos de agrupamento hier?rquicos, sendo: Ward1, Ward2 Liga??o Simples, Liga??o Completa, UPGMA, WPGMA, Mediana e Centroide. Com o intuito de verificar a valida??o dos m?todos de agrupamento testados, foram confeccionadas 120 tabelas de an?lise discriminante linear de Fisher, sendo 40 para cada classe de tamanho, contendo as probabilidades para cada classe de estoque, bem como a porcentagem de classifica??o das combina??es testadas na an?lise de agrupamento. As an?lises de agrupamento e discriminante possibilitaram estratificar as parcelas heterog?neas de uma floresta inequi?nea em ?reas com parcelas homog?neas em termos de volume, densidade b?sica da madeira e grupo de comercializa??o. A combina??o entre medida de dist?ncia de Manhattan e m?todo de Ward2 mostrou-se ser a mais eficiente para estratificar florestas inequi?neas em classes de estoque volum?trico. / Disserta??o (Mestrado) ? Programa de P?s-Gradua??o em Ci?ncia Florestal, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2016. / The study aimed to determine the best combination of clustering method with similarity measure to classify the productive capacity of a stretch at Tapaj?s National Forest. The sample forest inventory was carried out in 2012 and for plot allocations we opened 12 tracks of approximately 1.5 m wide, 4.0 km equidistant in east-west direction, and length ranging from 4 km to 13.75 km. The plot installation, with dimensions of 30 x 250 m, systematically distributed in 500 m in each row. We took into account for the definition of size classes (CT): CT 1 (regeneration class) - 10 cm ? DBH <25 cm in the first 50 m of the plot (30 m x 50 m); CT 2 (growth class) - 25 cm ? DBH <50 cm in the first 100 m (30 m x 100 m); and CT 3 (harvesting class) - DBH ? 50 cm in the whole plot (30 m x 250 m). For the classification of productive capacity, there was a filter in the original database by size class, in which individuals were selected with bole quality 1 (straight bole) and 2 (bole with small tortuosities) and which have value in the regional market. The 204 plots were grouped into homogeneous groups, which were produced 40 dendrograms of the vertical type for each of the three size classes (totaling 120 dendrograms), based on the combination of five measures of distance (Euclidean Simple, Squared Euclidean, Manhattan, Canberra and Mahalanobis) with 8 hierarchical clustering methods, namely: Ward1, Ward2 Simple Link, Complete Link, UPGMA, WPGMA, Median and Centroid. In order to check the validation of the tested clustering methods, we produced 120 Fisher linear discriminant analysis tables, with 40 for each size class containing the probabilities for each stock class as well as the percentage of the combinations tested in the cluster analysis. The cluster and discriminant analysis made it possible to stratify the heterogeneous plots of a native forest in areas with homogeneous portions in terms of volume, wood density and commercialization group. The combination of measure distance of Manhattan and Ward2 method proved to be the most efficient to stratify uneven-aged stands in forest stock volume classes.

Wet pine savannas are among the most diverse ecosystems in North America and provide critical habitat for many species but have seen a dramatic decline in size over the past century due to urbanization, logging, and fire suppression. Coastal pine savannas are also vulnerable to anticipated effects of global climate change. Models of climate change predict rapid sea-level rise along the northern Gulf of Mexico and more intense hurricanes. Restoration of these fragile wetland ecosystems is needed, but the effects of climate change on restored, as well as remnant communities, are unknown. This research aimed to compare resiliency of remnant and restored plant communities to simulated hurricane disturbance. I hypothesized that species composition within both site types will be altered following experimental storm surge, and restored plots will follow a different compositional trajectory due to site conditions including invasion by non-target species and disturbed soils. I compared community composition and soil properties between remnant and restored sites experiencing experimental storm surge. Non-metric multidimensional scaling (NMDS) ordinations and a cluster analysis was used to visualize dissimilarities in composition and permutational analysis of similarity (PERMANOVA) was used to compare composition among treatment, site, and time. Repeated measure analysis of variance (ANOVA) was used to compare soil water conductivity and available ammonium over the course of the study. Results from compositional surveys suggested no significant effect of treatment on community composition, but there were significant vectors for soil moisture and ammonium resulting in different compositional trends and an apparent degree of divergence over time between the two site types. Soil characteristics (texture and bulk density) and pressure from neighboring plants within the restored site are also likely contributing to differences between the two site types. As climate change continues to alter disturbance regimes that shape coastal ecosystems, it will be necessary to assess structure and function of remnant and potentially novel plant communities and their capacity for adaptation.

Non-tuberculous mycobacteria (NTM) cause opportunistic lung disease though environmental exposure pathways. Among the United States, Hawaii has a significantly higher infection rate. Preliminary studies have shown certain environmental factors, such as phosphorus and other select soil geochemical characteristics, to be statistically significant to NTM occurrence. However, a model to predict NTM occurrence based on soil geochemistry had yet to be attempted. A selection of 40 NTM positive and 40 NTM negative soils from Oahu were selected for a geochemical analysis to search for possible correlations to mineralogy and elemental abundances that may promote, or inhibit, NTM growth in the environment. Oahu results were compared with soil chemistry from the Kilauea and Kohala areas of Hawaii Island. Parameters included mineralogy, total organic carbon (TOC), pH, major elements, and trace elements. Statistical analyses performed include: student's t-test, multidimensional hypothesis tests, principal components analysis (PCA), and multidimensional statistical analysis. The data for Oahu showed NTM presence correlated with 1:1 clays and NTM absence correlated with goethite, 2:1 clays, Nb, Ce, La, Ba, and Rb. Kohala soil data showed NTM presence correlated with Al2O3, Ce, Sc, and Sm and NTM absence correlated with rainfall, Cr, Pb, and S. Kilauea data showed NTM presence correlated with TOC, pH, P, mafic silicate minerals, and Pb and NTM absence correlated with transition metals and oxides such as TiO2, Zr, and Nb. The results of the multidimensional statistical analysis were used to build a predictive model of NTM occurrence. The best model for Oahu had an accuracy of 65.9%, while Kohala had an accuracy of 71.7% and Kilauea 79.9%. A selection of five Hawaiian rock samples consisting of basalt, volcanic glass, and saprolite were used to culture clinically significant M. abscessus and visualize the effect mineralogy has on NTM growth. NTM cells were found on all mineral surfaces. However, volcanic glass was shown to visibly increase NTM growth and survival. As time passes, the ability to predict soil features that enhance NTM predictability decreases from 79.9% in modern Kilauea soils, to 71.7% in 0.17 Ma Kohala soils, to 65.9% in ~2 Ma Oahu soils. With age, it appears that distinct properties that enhance NTM survival are either erased or weathered to a more uniform state. Nonetheless, the NTM risk remains high in Hawaii due to environmental factors.

Degraded surface water quality is a growing public health concern. While indicator organisms are frequently used as a surrogate measure of pathogen contamination, poor correlation is often observed between indicators and pathogens. Because of adverse health effects associated with poor water quality, an assessment of the factors influencing the fate and transport of fecal pollution is necessary to identify sources and effectively design and implement Best Management Practices (BMPs) to protect and restore surface water quality. Sinking Creek is listed on the State of Tennessee’s 303D list as impaired due to pathogen contamination. The need to address the listing of this and other water bodies on the 303D list through the Total Maximum Daily Load (TMDL) process has resulted in increased research to find methods that effectively and universally identify sources of fecal pollution. The main objective of this research is to better understand how microbial, chemical, and physical factors influence pathogen fate and transport in Sinking Creek. This increased understanding can be used to improve source identification and remediation. To accomplish this objective, physical, chemical, and microbial water quality parameters were measured and the data were analyzed using multivariate statistical methods to identify those parameters influencing pathogen fate and transport. Physical, chemical, and microbial water and soil properties were also characterized along Sinking Creek to determine their influences on the introduction of fecal pollution to surface water. Results indicate that the 30-day geometric mean of fecal indicator organisms is not representative of true watershed dynamics and that their presence does not correlate with the presence of bacterial, protozoan, or viral pathogens in Sinking Creek. The use of multivariate statistical analyses coupled with a targeted water quality-monitoring program has demonstrated that nonpoint sources of fecal pollution vary spatially and temporally and are related to land use patterns. It is suggested that this data analysis approach can be used to effectively identify nonpoint sources of fecal pollution in surface water.

This thesis presents a method for predicting radon (222Rn) levels in groundwater on a general scale, within an area of approximately 185 x 145 km2. The method applies to Swedish conditions, where 222Rn is the main contributor to natural radioactivity. Prediction of radon potential in groundwater is complex because there are many different factors affecting radon content, including geochemical and flow processes. The proposed method is based on univariate and multivariate statistical analyses and investigated the influence of different factors such as bedrock, soils, uranium distribution, altitude, distance to fractures and land use. A statistical variable based method (the RV method) was used to estimate risk values related to different radon concentrations. The method was calibrated and tested on more than 4400 drilled wells in Stockholm County. The weighted index (risk value) estimated by the RV method provided a fair prediction of radon potential in groundwater on a general scale. The RV method was successful in estimating the median radon concentration within 12 subregions (at a local scale, each of area 25 x 25 km2), based on weighted index values obtained from half of all wells tested. A high correlation between risk values and median radon concentrations was demonstrated. The factors bedrock, altitude, distance to fracture zone and distribution of uranium in bedrock were found to be significant in the prediction approach on a general scale. Visual data mining, which comprised analysis of 3D images, was a useful tool for data exploration but could not be used as an independent method for drawing conclusions regarding radon in groundwater. Results of a field study based on 38 drilled wells on the island of Ljusterö in the Stockholm archipelago showed that 222Rn concentrations in groundwater were weakly correlated to the parent elements (226Ra and 238U) in solution. / QC 20101221

No description available.