The effect of student self -described learning styles within two models of teaching in an introductory data mining course

This dissertation examines the roles of learning styles and teaching methodologies within a data mining educational program designed for non-Computer Science undergraduate college students. The experimental design is framed by a discussion of the history and development of data mining and education, as well as a vision for its future.;Data mining is a relatively new discipline which has grown out of the fields of database management and data warehousing, statistics, logic, and decision sciences. Over the course of its approximately 15 year history, data mining has emerged from its genesis within the academic and commercial research and development arenas to become a widely accepted and utilized method of exploratory data analysis for management, strategic planning and decision support. Over the first several years of its development, data mining remained the province of computer scientists and professional statisticians at large corporations and research universities around the world. Beginning in about 1989, these data mining pioneers developed many of data mining\u27s standards and methodologies on large datasets using mainframe computing systems. Throughout the 1990s, as both the hardware and software tools required for the realization of data mining have become increasingly accessible, powerful and affordable, the pool of potential data miners has expanded rapidly. Today, even individuals and small businesses can exploit the power of data mining using freely acquirable open source software packages capable of running on personal computers.;During the growth and development of data mining methodologies however, little research has been dedicated specifically to the pedagogical approaches used in teaching data mining. Educational programs that have evolved have largely remained within Computer Science departments and have often targeted graduate students as an audience. This dissertation seeks to examine the possibility of successful teaching data mining concepts and techniques to a non-Computer Science undergraduate audience. The study approached this research question by delivering a lesson on the data mining topic of Association Rules to 86 participants who are representative of the target audience. These participants were randomly assigned to receive the Association Rules lesson through either a Direct Instruction or a Concept Attainment teaching approach. The students completed Kolb\u27s Learning Styles Inventory, participated in the data mining lesson, and then completed a quiz on the concepts and techniques of Association Rules. A t-test was used to determine if significant differences existed between the scores generated under the two teaching models, and an ANOVA was conducted to identify significant differences between the four learning style groups from Kolb\u27s instrument. In addition to these two statistical tests, the data were also mined using Association Rules and Decision Tree methods.;In both statistical tests, we failed to reject the null hypothesis, finding no significant differences in quiz scores between the two teaching models or among the four learning style groups. Further investigation into the differences among learning styles within teaching models however did reveal that the Assimilator learning style students who received their instruction via Direct Instruction did score significantly higher on the quiz than did their learning style counterparts who received the lesson via Concept Attainment. This finding suggests that although we cannot rely solely on one instructional approach as consistently more effective than the other, there may be instances where the correct instructional choice will positively benefit some learners with certain learning styles. The results of the data mining activities also support this assertion. Association Rules mining yielded no strong relationships between teaching models, learning styles and quiz scores, but Decision Tree mining did reveal a similar pattern of higher scores earned by Assimilator learners within Direct Instruction.;The findings of this study show that effectively teaching data mining concepts to undergraduate non-Computer Science students will not be as simple as choosing one teaching methodology over another or targeting a specific learning style group. Rather, designing instructional activities using teaching methodologies which closely align with predominant learning styles in a classroom should prove more effective. Perhaps the most significant finding of the study is that elementary data mining concepts and techniques can be effectively taught to the target audience. Finally, we recommend that additional teaching methodologies and perhaps different learning style assessments could be tested in the same way as those selected for this study

Aligning ecology and markets in the forest carbon cycle

A forest carbon (C) offset is a quantifiable unit of C that is commonly developed at the local or regional project scale and is designed to counterbalance anthropogenic C emissions by sequestering C in trees. In cap-and-trade programs, forest offsets have market value if the sequestered C is additional (more than would have occurred in the absence of the project) and permanent (sequestered within the project boundary for a specified period of time). Local management and ecological context determine the rate of C sequestration, risk of loss, and hence the market value. An understanding of global C dynamics can inform policy but may not be able to effectively price an ecosystem service, such as C sequestration. Appropriate pricing requires the assistance of ecologists to assess C stock abundance and stability over spatial and temporal scales appropriate for the regional market. We use the risk that sequestered C will be emitted as a result of wildfire (reversal risk) to show how ecological context can influence market valuation in offset programs

Chemical mass transport between fluid fine tailings and the overlying water cover of an oil sands end pit lake

NSERC Grant No. CRDPJ 476388; NSERC Grant No. IRCPJ 428588–11Peer ReviewedFluid fine tailings (FFT) are a principal by-product of the bitumen extraction process at oil sands mines. Base Mine Lake (BML)—the first full-scale demonstration oil sands end pit lake (EPL)—contains approximately 1.9 3 108 m^3 of FFT stored under a water cover within a decommissioned mine pit. Chemical mass transfer from the FFT to the water cover can occur via two key processes: (1) advection-dispersion driven by tailings settlement; and (2) FFT disturbance due to fluid movement in the water cover. Dissolved chloride (Cl) was used to evaluate the water cover mass balance and to track mass transport within the underlying FFT based on field sampling and numerical modeling. Results indicated that FFT was the dominant Cl source to the water cover and that the FFT is exhibiting a transient advection-dispersion mass transport regime with intermittent disturbance near the FFT-water interface. The advective pore water flux was estimated by the mass balance to be 0.002 m^3 m^-2 d^-1, which represents 0.73 m of FFT settlement per year. However, the FFT pore water Cl concentrations and corresponding mass transport simulations indicated that advection rates and disturbance depths vary between sample locations. The disturbance depth was estimated to vary with location between 0.75 and 0.95 m. This investigation provides valuable insight for assessing the geochemical evolution of the water cover and performance of EPLs as an oil sands reclamation strategy

Phosphorylation of histone H3(T118) alters nucleosome dynamics and remodeling

Nucleosomes, the fundamental units of chromatin structure, are regulators and barriers to transcription, replication and repair. Post-translational modifications (PTMs) of the histone proteins within nucleosomes regulate these DNA processes. Histone H3(T118) is a site of phosphorylation [H3(T118ph)] and is implicated in regulation of transcription and DNA repair. We prepared H3(T118ph) by expressed protein ligation and determined its influence on nucleosome dynamics. We find H3(T118ph) reduces DNA–histone binding by 2 kcal/mol, increases nucleosome mobility by 28-fold and increases DNA accessibility near the dyad region by 6-fold. Moreover, H3(T118ph) increases the rate of hMSH2–hMSH6 nucleosome disassembly and enables nucleosome disassembly by the SWI/SNF chromatin remodeler. These studies suggest that H3(T118ph) directly enhances and may reprogram chromatin remodeling reactions

Structural basis for high-affinity binding of LEDGF PWWP to mononucleosomes

Lens epithelium-derived growth factor (LEDGF/p75) tethers lentiviral preintegration complexes (PICs) to chromatin and is essential for effective HIV-1 replication. LEDGF/p75 interactions with lentiviral integrases are well characterized, but the structural basis for how LEDGF/p75 engages chromatin is unknown. We demonstrate that cellular LEDGF/p75 is tightly bound to mononucleosomes (MNs). Our proteomic experiments indicate that this interaction is direct and not mediated by other cellular factors. We determined the solution structure of LEDGF PWWP and monitored binding to the histone H3 tail containing trimethylated Lys36 (H3K36me3) and DNA by NMR. Results reveal two distinct functional interfaces of LEDGF PWWP: a well-defined hydrophobic cavity, which selectively interacts with the H3K36me3 peptide and adjacent basic surface, which non-specifically binds DNA. LEDGF PWWP exhibits nanomolar binding affinity to purified native MNs, but displays markedly lower affinities for the isolated H3K36me3 peptide and DNA. Furthermore, we show that LEDGF PWWP preferentially and tightly binds to in vitro reconstituted MNs containing a tri-methyl-lysine analogue at position 36 of H3 and not to their unmodified counterparts. We conclude that cooperative binding of the hydrophobic cavity and basic surface to the cognate histone peptide and DNA wrapped in MNs is essential for high-affinity binding to chromatin

Mutation mapping and identification by whole-genome sequencing

Genetic mapping of mutations in model systems has facilitated the identification of genes contributing to fundamental biological processes including human diseases. However, this approach has historically required the prior characterization of informative markers. Here we report a fast and cost-effective method for genetic mapping using next-generation sequencing that combines single nucleotide polymorphism discovery, mutation localization, and potential identification of causal sequence variants. In contrast to prior approaches, we have developed a hidden Markov model to narrowly define the mutation area by inferring recombination breakpoints of chromosomes in the mutant pool. In addition, we created an interactive online software resource to facilitate automated analysis of sequencing data and demonstrate its utility in the zebrafish and mouse models. Our novel methodology and online tools will make next-generation sequencing an easily applicable resource for mutation mapping in all model systems.Harvard Stem Cell Institute (Junior Faculty Grant)National Institutes of Health (U.S.) (Grant 1R01DK090311)National Institutes of Health (U.S.) (Grant 5R01MH084676

Association of Cardiometabolic Genes with Arsenic Metabolism Biomarkers in American Indian Communities: The Strong Heart Family Study (SHFS)

BACKGROUND: Metabolism of inorganic arsenic (iAs) is subject to inter-individual variability, which is explained partly by genetic determinants. OBJECTIVES: We investigated the association of genetic variants with arsenic species and principal components of arsenic species in the Strong Heart Family Study (SHFS). METHODS: We examined variants previously associated with cardiometabolic traits (~ 200,000 from Illumina Cardio MetaboChip) or arsenic metabolism and toxicity (670) among 2,428 American Indian participants in the SHFS. Urine arsenic species were measured by high performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS), and percent arsenic species [iAs, monomethylarsonate (MMA), and dimethylarsinate (DMA), divided by their sum × 100] were logit transformed. We created two orthogonal principal components that summarized iAs, MMA, and DMA and were also phenotypes for genetic analyses. Linear regression was performed for each phenotype, dependent on allele dosage of the variant. Models accounted for familial relatedness and were adjusted for age, sex, total arsenic levels, and population stratification. Single nucleotide polymorphism (SNP) associations were stratified by study site and were meta-analyzed. Bonferroni correction was used to account for multiple testing. RESULTS: Variants at 10q24 were statistically significant for all percent arsenic species and principal components of arsenic species. The index SNP for iAs%, MMA%, and DMA% (rs12768205) and for the principal components (rs3740394, rs3740393) were located near AS3MT, whose gene product catalyzes methylation of iAs to MMA and DMA. Among the candidate arsenic variant associations, functional SNPs in AS3MT and 10q24 were most significant (p < 9.33 × 10-5). CONCLUSIONS: This hypothesis-driven association study supports the role of common variants in arsenic metabolism, particularly AS3MT and 10q24. Citation: Balakrishnan P, Vaidya D, Franceschini N, Voruganti VS, Gribble MO, Haack K, Laston S, Umans JG, Francesconi KA, Goessler W, North KE, Lee E, Yracheta J, Best LG, MacCluer JW, Kent J Jr., Cole SA, Navas-Acien A. 2017. Association of cardiometabolic genes with arsenic metabolism biomarkers in American Indian communities: the Strong Heart Family Study (SHFS). Environ Health Perspect 125:15-22; http://dx.doi.org/10.1289/EHP251

Структурно-семантичний аналіз еврісемантів української мови (на матеріалі лексико-семантичного поля "річ")

В статье рассматриваются лексико-семантические особенности эврисемантов в украинском языке, осуществляется их семантическая классификация, методом компонентного анализа проводится структурный анализ. Представлен фрагмент иерархично упорядоченной парадигмы широкозначных имен существительных, состоящий из ЛСГ "Предмет" и "Дело".У статті розглядаються лексико-семантичні особливості еврісемантів української мови, здійснюється їх семантична класифікація, за допомогою компонентного аналізу проводиться структурний аналіз. Подається фрагмент ієрархічно впорядкованої парадигми широкозначних іменників, представлений ЛСГ "Предмет" та "Справа".In this article lexica-semantic peculiarities of everysemantical nouns in Ukrainian are considered. It was made semantic distinguishing and structural analysis of those elements. The everysemants of a lexica-semantic field "Thing", represented by two groups "Subject" and "Work", are disposed in specific hierarchy