Measuring morphological features using light-scattering spectroscopy and Fourier-domain low-coherence interferometry.

We present measurements of morphological features in a thick turbid sample using light-scattering spectroscopy (LSS) and Fourier-domain low-coherence interferometry (fLCI) by processing with the dual-window (DW) method. A parallel frequency domain optical coherence tomography (OCT) system with a white-light source is used to image a two-layer phantom containing polystyrene beads of diameters 4.00 and 6.98 mum on the top and bottom layers, respectively. The DW method decomposes each OCT A-scan into a time-frequency distribution with simultaneously high spectral and spatial resolution. The spectral information from localized regions in the sample is used to determine scatterer structure. The results show that the two scatterer populations can be differentiated using LSS and fLCI.

Morphological and genomic characterization of Filobasidiella depauperata: a homothallic sibling species of the pathogenic cryptococcus species complex.

The fungal species Cryptococcus neoformans and Cryptococcus gattii cause respiratory and neurological disease in animals and humans following inhalation of basidiospores or desiccated yeast cells from the environment. Sexual reproduction in C. neoformans and C. gattii is controlled by a bipolar system in which a single mating type locus (MAT) specifies compatibility. These two species are dimorphic, growing as yeast in the asexual stage, and producing hyphae, basidia, and basidiospores during the sexual stage. In contrast, Filobasidiella depauperata, one of the closest related species, grows exclusively as hyphae and it is found in association with decaying insects. Examination of two available strains of F. depauperata showed that the life cycle of this fungal species shares features associated with the unisexual or same-sex mating cycle in C. neoformans. Therefore, F. depauperata may represent a homothallic and possibly an obligately sexual fungal species. RAPD genotyping of 39 randomly isolated progeny from isolate CBS7855 revealed a new genotype pattern in one of the isolated basidiospores progeny, therefore suggesting that the homothallic cycle in F. depauperata could lead to the emergence of new genotypes. Phylogenetic analyses of genes linked to MAT in C. neoformans indicated that two of these genes in F. depauperata, MYO2 and STE20, appear to form a monophyletic clade with the MATa alleles of C. neoformans and C. gattii, and thus these genes may have been recruited to the MAT locus before F. depauperata diverged. Furthermore, the ancestral MATa locus may have undergone accelerated evolution prior to the divergence of the pathogenic Cryptococcus species since several of the genes linked to the MATa locus appear to have a higher number of changes and substitutions than their MATalpha counterparts. Synteny analyses between C. neoformans and F. depauperata showed that genomic regions on other chromosomes displayed conserved gene order. In contrast, the genes linked to the MAT locus of C. neoformans showed a higher number of chromosomal translocations in the genome of F. depauperata. We therefore propose that chromosomal rearrangements appear to be a major force driving speciation and sexual divergence in these closely related pathogenic and saprobic species.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Morphological characteristics of urban water bodies: mechanisms of change and implications for ecosystem function.

The size, shape, and connectivity of water bodies (lakes, ponds, and wetlands) can have important effects on ecological communities and ecosystem processes, but how these characteristics are influenced by land use and land cover change over broad spatial scales is not known. Intensive alteration of water bodies during urban development, including construction, burial, drainage, and reshaping, may select for certain morphometric characteristics and influence the types of water bodies present in cities. We used a database of over one million water bodies in 100 cities across the conterminous United States to compare the size distributions, connectivity (as intersection with surface flow lines), and shape (as measured by shoreline development factor) of water bodies in different land cover classes. Water bodies in all urban land covers were dominated by lakes and ponds, while reservoirs and wetlands comprised only a small fraction of the sample. In urban land covers, as compared to surrounding undeveloped land, water body size distributions converged on moderate sizes, shapes toward less tortuous shorelines, and the number and area of water bodies that intersected surface flow lines (i.e., streams and rivers) decreased. Potential mechanisms responsible for changing the characteristics of urban water bodies include: preferential removal, physical reshaping or addition of water bodies, and selection of locations for development. The relative contributions of each mechanism likely change as cities grow. The larger size and reduced surface connectivity of urban water bodies may affect the role of internal dynamics and sensitivity to catchment processes. More broadly, these results illustrate the complex nature of urban watersheds and highlight the need to develop a conceptual framework for urban water bodies.