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.

Solving satisfiability, *implication, and equivalence problems in database systems

Satisfiability, implication and equivalence problems are important and widely-encountered database problems that need to be efficiently and effectively solved. We provide a comprehensive and systematic study of these problems. We consider three popular types of arithmetic inequalities, (X op C), (X op Y), and (X op Y + C), where X and Y are attributes, C is a constant of the domain of X, and op $\in\ \{{<},\ {\le},\ {=},\ {\not=},\ {>},\ {\ge}\}.$ These inequalities are most frequently used in a database system, since the first type of inequalities represents $\theta$-join, the second type represents selection, and the third type is popular in deductive databases. We study the problems under the integer domain and the real domain, as well as under two different operator sets.^ Our results show that solutions under different domains and/or different operator sets are quite different. In this dissertation, we either report the first necessary and sufficient conditions as well as their efficient algorithms with complexity analysis, or provide improved algorithms. ^

The effect of an enriched learning community on success and retention in chemistry courses

Since the mid-1990s, the United States has experienced a shortage of scientists and engineers, declining numbers of students choosing these fields as majors, and low student success and retention rates in these disciplines. Learning theorists, educational researchers, and practitioners believe that learning environments can be created so that an improvement in the numbers of students who complete courses successfully could be attained (Astin, 1993; Magolda & Terenzini, n.d.; O'Banion, 1997). Learning communities do this by providing high expectations, academic and social support, feedback during the entire educational process, and involvement with faculty, other students, and the institution (Ketcheson & Levine, 1999). ^ A program evaluation of an existing learning community of science, mathematics, and engineering majors was conducted to determine the extent to which the program met its goals and was effective from faculty and student perspectives. The program provided laptop computers, peer tutors, supplemental instruction with and without computer software, small class size, opportunities for contact with specialists in selected career fields, a resource library, and Peer-Led Team Learning. During the two years the project has existed, success, retention, and next-course continuation rates were higher than in traditional courses. Faculty and student interviews indicated there were many affective accomplishments as well. ^ Success and retention rates for one learning community class ( n = 27) and one traditional class (n = 61) in chemistry were collected and compared using Pearson chi square procedures ( p = .05). No statistically significant difference was found between the two groups. Data from an open-ended student survey about how specific elements of their course experiences contributed to success and persistence were analyzed by coding the responses and comparing the learning community and traditional classes. Substantial differences were found in their perceptions about the lecture, the lab, other supports used for the course, contact with other students, helping them reach their potential, and their recommendation about the course to others. Because of the limitation of small sample size, these differences are reported in descriptive terms. ^

Supplemental instruction, study habits, and the community college student

The purpose of this study was to demonstrate if the academic assistance program Supplemental Instruction (SI) facilitates the acquisition of effective study behaviors through strategies that transcend simple double-exposure to the course material. Its advocates claim it increases academic achievement using learner-centered knowledge and acquisition of effective study behaviors. SI sessions are specifically related to particular courses that students are taking. Sessions are facilitated by the SI leader who has taken the subject matter course in the past. Students review the content of the previous subject matter class using collaborative learning strategies coordinated by a SI leader. In addition, the SI leader models appropriate study behaviors in his or her interactions with the students. ^ An instructor at a large Florida community college who taught five classes of an Anatomy & Physiology I course (traditionally supported by SI) was identified. Two of the classes were randomly selected to participate in SI activities, and two classes were random chosen to participate in alternate, computer-based activities that dealt with the course content, but did not include work in developing students' study behaviors. These treatments were carried out over the course of an entire semester. Participation was mandatory. ^ Data were collected on two variables. Academic achievement in anatomy and physiology content was measured both pre- and post-treatment using an instructor developed examination. Student study behaviors were measured using pre- and post-treatment administration of the Study Behavior Inventory, a valid and reliable instrument that provides scores on three categories of study behaviors: (a) Academic self-efficacy, (b) Preparation for routine academic tasks, and (c) Preparation for long range academic tasks. Measures obtained at the end of the semester of treatment revealed no significant differences between the SI and alternative treatment groups in post-treatment achievement test score and the post-treatment scores on the three study behaviors categories when adjusted for pre-treatment scores. ^ These results suggest that the development of appropriate study behaviors requires more time than SI, as it is now implemented, can provide. In addition, results indicate that improved academic achievement may be attained through any number of means that include repeated exposure to course material. ^

Explaining the effects of class size on faculty and students

This study explored the effects of class size on faculty and students. Specifically, it examined the relationship of class size and students' participation in class, faculty interactive styles, and academic environment and how these behaviors affected student achievement (percentage of students passing). The sample was composed of 629 students in 30 sections of Algebra I at a large, urban community college. A survey was administered to the students to solicit their perceptions on their participation in class, their faculty interaction style, and the academic environment in their classes. Selected classes were observed to triangulate the findings. The relationship of class size to student participation, faculty interactive styles, and academic environment was determined by using hierarchical linear modeling (HLM). A significant difference was found on the participation of students related to class size. Students in smaller classes participated more and were more engaged than students in larger classes. Regression analysis using the same variables in small and large classes showed that faculty interactive styles significantly predicted student achievement. Stepwise regression analyses of student and faculty background variables showed that (a) students' estimate of GPA was significantly related to their achievement (r = .63); (b) older students reported more participation than did younger ones, (c) students in classes taught by female, Hispanic faculty earned higher passing grades, and (d) students' participation was greater with adjunct professors. Class observations corroborated these findings. The analysis and observational data provided sufficient evidence to warrant the conclusion that small classes were not always most effective in promoting achievement. It was found that small classes may be an artifact of ineffectual teaching, actual or by reputation. While students in small classes participate and are more engaged than students in larger classes, the class-size effect is essentially due to what happens in instruction to promote learning. The interaction of the faculty with students significantly predicted students' achievement regardless of class size. Since college students select their own classes, students do not register for classes taught by faculty with poor teaching reputation, thereby leading to small classes. Further studies are suggested to determine reasons why classes differ in size.

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.

The influence of study and travel abroad on the personal and professional development of students in architecture design programs

International travel has significant implications on the study of architecture. This study analyzed ways in which undergraduate and graduate students benefited from the experience of international travel and study abroad. Taken from the perspective of 15 individuals who were currently or had been architecture students at the University of Miami and Florida International University or who were alumni of the University of Florida and Syracuse University, the research explored how international travel and study abroad enhanced their awareness and understanding of architecture, and how it complemented their architecture curricula. This study also addressed a more personal aspect of international travel in order to learn how the experience and exposure to foreign cultures had positively influenced the personal and professional development of the participants.^ Participants’ individual and two-person semi-structured interviews about study abroad experiences were electronically recorded and transcribed for analysis. A second interview was conducted with five of the participants to obtain feedback concerning the accuracy of the transcripts and the interpretation of the data. Sketch journals and design projects were also analyzed from five participants and used as data for the purposes of better understanding what these individuals learned and experienced as part of their study abroad.^ Findings indicated that study abroad experiences helped to broaden student understanding about architecture and urban development. These experiences also opened the possibilities of creative and professional expression. For many, this was the most important aspect of their education as architects because it heightened their interest in architecture. These individuals talked about how they had the opportunity to experience contemporary and ancient buildings that they had learned about in their history and design classes on their home campuses. In terms of personal and professional development, many of the participants remarked that they became more independent and self-reliant because of their study abroad experiences. They also displayed a sense of global awareness and were interested in the cultures of their host nations. The study abroad experiences also had a lasting influence on their professional development.^

Comparison of Belowground Biomass in C3- and C4-Dominated Mixed Communities in a Chesapeake Bay Brackish Marsh

Belowground biomass is a critical factor regulating ecosystem functions of coastal marshes, including soil organic matter (SOM) accumulation and the ability of these systems to keep pace with sea-level rise. Nevertheless, belowground biomass responses to environmental and vegetation changes have been given little emphasis marsh studies. Here we present a method using stable carbon isotopes and color to identify root and rhizomes of Schoenoplectus americanus (Pers.) Volk. ex Schinz and R. Keller (C3) and Spartina patens (Ait.) Muhl. (C4) occurring in C3− and C4-dominated communities in a Chesapeake Bay brackish marsh. The functional significance of the biomass classes we identified is underscored by differences in their chemistry, depth profiles, and variation in biomass and profiles relative to abiotic and biotic factors. C3 rhizomes had the lowest concentrations of cellulose (29.19%) and lignin (14.43%) and the lowest C:N (46.97) and lignin:N (0.16) ratios. We distinguished two types of C3 roots, and of these, the dark red C3 roots had anomalously high C:N (195.35) and lignin:N (1.14) ratios, compared with other root and rhizome classes examined here and with previously published values. The C4-dominated community had significantly greater belowground biomass (4119.1 g m−2) than the C3-dominated community (3256.9 g m−2), due to greater total root biomass and a 3.6-fold higher C3-root:rhizome ratio in the C4-dominated community. C3 rhizomes were distributed significantly shallower in the C4-dominated community, while C3 roots were significantly deeper. Variability in C3 rhizome depth distributions was explained primarily by C4 biomass, and C3 roots were explained primarily by water table height. Our results suggest that belowground biomass in this system is sensitive to slight variations in water table height (across an 8 cm range), and that the reduced overlap between C3 and C4 root profiles in the C4-dominated community may account for the greater total root biomass observed in that community. Given that future elevated atmospheric CO2 and accelerated sea-level rise are likely to increase C3 abundance in Atlantic and Gulf coast marshes, investigations that quantify how patterns of C3 and C4 belowground biomass respond to environmental and biological factors stand to improve our understanding of ecosystem-wide impacts of global changes on coastal wetlands.

The relationship between scaffolding metacognitive strategies identified through dialogue journals and second graders' reading comprehension, science achievement, and metacognition using expository text

Poor informational reading and writing skills in early grades and the need to provide students more experience with informational text have been identified by research as areas of concern. Wilkinson and Son (2011) support future research in dialogic approaches to investigate the impact dialogic teaching has on comprehension. This study (N = 39) examined the gains in reading comprehension, science achievement, and metacognitive functioning of individual second grade students interacting with instructors using dialogue journals alongside their textbook. The 38 week study consisted of two instructional phases, and three assessment points. After a period of oral metacognitive strategies, one class formed the treatment group (n=17), consisting of two teachers following the co-teaching method, and two classes formed the comparison group ( n=22). The dialogue journal intervention for the treatment group embraced the transactional theory of instruction through the use of dialogic interaction between teachers and students. Students took notes on the assigned lesson after an oral discussion. Teachers responded to students' entries with scaffolding using reading strategies (prior knowledge, skim, slow down, mental integration, and diagrams) modeled after Schraw's (1998) strategy evaluation matrix, to enhance students' comprehension. The comparison group utilized text-based, teacher-led whole group discussion. Data were collected using different measures: (a) Florida Assessments for Instruction in Reading (FAIR) Broad Diagnostic Inventory; (b) Scott Foresman end of chapter tests; (c) Metacomprehension Strategy Index (Schmitt, 1990); and (d) researcher-made metacognitive scaffolding rubric. Statistical analyses were performed using paired sample t-tests, regression analysis of covariance, and two way analysis of covariance. Findings from the study revealed that experimental participants performed significantly better on the linear combination of reading comprehension, science achievement, and metacognitive function, than their comparison group counterparts while controlling for pretest scores. Overall, results from the study established that teacher scaffolding using metacognitive strategies can potentially develop students' reading comprehension, science achievement, and metacognitive awareness. This suggests that early childhood students gain from the integration of reading and writing when using authentic materials (science textbooks) in science classrooms. A replication of this study with more students across more schools, and different grade levels would improve the generalizability of these results.

Marl Prairie/Slough Gradients; Patterns and Trends in Shark Slough and Adjacent Marl Prairies (CERP monitoring activity 3.1.3.5), First Annual Report (2005)

The work on CERP monitoring item 3.1.3.5 (Marl prairie/slough gradients) is being conducted by Florida International University (Dr Michael Ross, Project Leader), with Everglades National Park (Dr. Craig Smith) providing administrative support and technical consultation. As of January 2006 the funds transferred by ACOE to ENP, and subsequently to FIU, have been entirely expended or encumbered in salaries or wages. The project work for 2005 started rather late in the fiscal year, but ultimately accomplished the Year 1 goals of securing a permit to conduct the research in Everglades National Park, finalizing a detailed scope of work, and sampling marsh sites which are most easily accessed during the wet season. 46 plots were sampled in detail, and a preliminary vegetation classification distinguished three groups among these sites (Sawgrass marsh, sawgrass and other, and slough) which may be arranged roughly along a hydrologic gradient from least to most persistently inundated . We also made coarser observations of vegetation type at 5-m intervals along 2 transects totaling ~ 5 km. When these data were compared with similar observations made in 1998-99, it appeared that vegetation in the western portion of Northeast Shark Slough (immediately east of the L-67 extension) had shifted toward a more hydric type during the last 6 years, while vegetation further east was unchanged in this respect. Because this classification and trend analysis is based on a small fraction of the data set that will be available after the first cycle of sampling (3 years from now), the results should not be interpreted too expansively. However, they do demonstrate the potential for gaining a more comprehensive view of marsh vegetation structure and dynamics in the Everglades, and will provide a sound basis for adaptive management.

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.