Spatial and temporal distributions of epiphytic diatoms growing on Thalassia testudinum Banks ex König: relationships to water quality

The spatial and temporal distributions of the epiphytic diatom flora on Thalassia testudinum was described within the Florida Bay estuary and at one Atlantic site east of the Florida Keys over a 1-year period. Species of the genus Mastogloia dominated the epiphytic diatom flora (82 out of 332 total species). Nonmetric Multidimensional Scaling (NMDS) and Analysis of Similarity (ANOSIM) revealed four distinct spatial assemblages and two temporal assemblages. Eastern and western Florida Bay assemblages were identified within the estuary. The eastern diatom assemblage was characterized by high relative abundances of Brachysira aponina and Nitzschia liebetruthii, while the western assemblage was characterized by the abundance of Reimerothrix floridensis, particularly during summer. Two diverse and distinct marine assemblages, one located in the Gulf of Mexico along the western edge of Florida Bay and the other behind the Florida reef tract in the Atlantic Ocean, were also identified. Analysis of the spatial distribution of diatoms and water quality characteristics within Florida Bay suggest that these assemblages may be structured by salinity and nutrient availability, particularly P. The Gulf of Mexico and the western Florida Bay assemblages were associated with higher water column salinities and TP concentrations and lower DIN concentrations and TN:TP ratios relative to the eastern Florida Bay assemblage. The temporal variation in diatom assemblages was associated with water temperature, though temporal indicator species were few relative to the number of spatial indicators.

Trajectories of Vegetation Response to Water Management in Taylor Slough, Everglades National Park, Florida

Ecosystem management practices that modify the major drivers and stressors of an ecosystem often lead to changes in plant community composition. This paper examines how closely the trajectory of vegetation change in seasonally-flooded wetlands tracks management-induced alterations in hydrology and soil characteristics. We used trajectory analysis, a multivariate method designed to test hypotheses about rates and directions of community change, to examine vegetation shifts in response to changes in water management practices within the Taylor Slough basin of Everglades National Park. We summarized vegetation data by non-metric multidimensional scaling ordination, and examined the time trajectory of each site along environmental vectors representing hydrology and soil phosphorus gradients. In the Taylor Slough basin, vegetation change trajectories closely followed the hydrologic changes caused by the operation of water pumps and detention ponds adjacent to the canals. We also observed a shift in vegetation composition along a vector of increasing soil phosphorus, which suggests the need for implementing measures to avoid P-enrichment in southern Everglades marl prairies. This study indicates that shifts in vegetation composition in response to changes in hydrologic conditions and associated parameters may be detected through trajectory analysis, thereby providing feedback for adaptive management of wetland ecosystems.

Multidimensional classification and diagnosis of leukemia in flow cytometry

This research is to establish new optimization methods for pattern recognition and classification of different white blood cells in actual patient data to enhance the process of diagnosis. Beckman-Coulter Corporation supplied flow cytometry data of numerous patients that are used as training sets to exploit the different physiological characteristics of the different samples provided. The methods of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used as promising pattern classification techniques to identify different white blood cell samples and provide information to medical doctors in the form of diagnostic references for the specific disease states, leukemia. The obtained results prove that when a neural network classifier is well configured and trained with cross-validation, it can perform better than support vector classifiers alone for this type of data. Furthermore, a new unsupervised learning algorithm---Density based Adaptive Window Clustering algorithm (DAWC) was designed to process large volumes of data for finding location of high data cluster in real-time. It reduces the computational load to ∼O(N) number of computations, and thus making the algorithm more attractive and faster than current hierarchical algorithms.