Source/receptor reconciliation of ambient organic fine particulate matter in Palm Beach County

Airborne particulate matter (PM) is of environmental concern not only in urban but also rural areas that are easily inhalable and have been considered responsible, together with gaseous pollutants, for possible health effects. The objectives of this research study is to generate an extensive data set for ambient PM collected at Belle Glade and Delray Beach that ultimately was used together with published source profiles to predict the contributions of major sources to the overall airborne particle burden in Belle Glade and Delray Beach. ^ The size segregated particle sampling was conducted for one entire year. The samples collected during the months of January and May were further subjected to chemical analysis for organic compounds by Gas Chromatography-Mass Spectrometry. Additional, PM10 sampling was conducted simultaneously with size segregated particle sampling during January and May to analyze for trace elements using Instrumental Neutron Activation Analysis technique. Elements and organic marker compounds were used in Chemical Mass Balance modeling to determine the major source contribution to the ambient fine particle matter burden. ^ Size segregated particle distribution results show bimodal in both sampling sites. Sugarcane pre-harvest burning in the rural site elevated PM10 concentration by about 30% during the sugarcane harvest season compared to sugarcane growing season. Sea salt particles and Saharan dust particles accounted for the external sources. ^ The results of trace element analysis show that Al, Ca, Cs, Eu, Lu, Nd, Sc, Sm, Th, and Yb are more abundant at the rural sampling site. The trace elements Ba, Br, Ce, Cl, Cr, Fe, Gd, Hf, Na, Sb, Ta, V, and W show high abundance at the urban site due to anthropogenic activities except for Na and Cl, which are from sea salt spray. On the other hand, size segregated trace organic compounds measurements show that organic compounds mainly from combustion process were accumulated in PM0.95. ^ In conclusion, major particle sources were determined by the CMB8.2 software as follows: road dust, sugarcane leaf burning, diesel-powered and gasoline powered vehicle exhaust, leaf surface abrasion particles, and a very small fraction of meat cooking. ^

Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.