Throughout this study, I investigate the interaction that occurs between a parent and her child when creating a collaborative drawing. The purpose of this study is to find ways in which to change common images of children and their capabilities in forming and making decisions, problem solving and communication skills, and imaginative story telling abilities. This research seeks to answer some of the following questions: In what ways are children and adults influenced by the child taking ownership of the artmaking experience and how does giving the child ownership and control over the experience change the experience for the adult? I observe two mothers as they collaborate with their young sons (ages 3 and 4) to create a drawing, discuss their experience with them and analyze their final images. Utilizing these methods, I uncover common themes and ideas about the view that adults have of children and ways of shifting these ideas of power and control over to children. I provide my recommendations and implications for the field of early childhood art education and offer a guide for parents when working with their young children.

One goal of persistent homology is to recover meaningful information from point-cloud data by examining long-lived topological features of filtered simplicial complexes built over the point-cloud. Motivated by real-world applications, the classic setting for this approach has been on finite metric spaces where many suitable complexes can be defined, and a natural filtration exists via sublevel sets of the metric. We consider the extension of persistent homology to dissimilarity networks equipped with a relaxed metric that does not assume symmetry nor the triangle inequality, by computing persistent homology on the directed clique complex defined over weighted directed graphs induced from a dissimilarity network and filtered by an adapted Rips filtration. We characterize digraph maps that induce maps on homology, describe a procedure to lift any digraph map to one that does induce maps on homology, and present a homotopy classification that provides a condition for two such digraph maps to induce the same map at the homology level. We also prove functoriality of directed clique homology and describe filtrations of digraphs induced by digraph maps. We then prove stability of persistent directed clique homology by showing that the persistence modules of a digraph and that of an admissible perturbation are interleaved. These admissible perturbations include perturbing dissimilarity measures in the network that either preserve the digraph structure or collapse series of arrows. We also explore similar constructions for maps between digraphs that allow reversal of arrows and show that while such maps, in general, produce unstable persistence barcodes, one can recover stability by inducing a reverse filtration and truncating at an appropriate threshold. Finally, we present an application of persistent directed clique homology to trace patterns and shapes embedded in migration and remittance networks.

Problem-based learning is a student-centered, inquiry-based approach that builds problem-solving skills. Reviews of problem-based learning, as compared to traditional lecture-based learning, report modest positive gains in cognitive outcomes. Many metaanalyses have been conducted to analyze the effectiveness of problem-based learning, but none have examined self-directed learning in the context of problem-based learning. The purpose of this study was to conduct a meta-analysis across all disciplines examining the extent to which problem-based learning engenders self-directed learning compared to a lecture-based approach. This study used a random effects model meta-analysis using 75 outcomes from 38 studies. Results indicated a statistically significant, z(74) = 7.11, p = 0.01, overall medium effect size (g = 0.45) favoring problem-based learning. A test of heterogeneity indicated genuine variance across outcomes (Q = 559.57, df = 74, p < 0.01). Subgroup analyses indicate positive effect sizes for the four components of self-directed learning with two being statistically significant: personal autonomy, g = 0.51, z(47) = 6.4, p = 0.01, and independent pursuit of learning, g = 0.66, z(2) = 3.49, p = 0.01. Two emergent subgroups were also examined. From the 23 subgroup components, 12 reported statistically significant effect size estimates above 0. Findings and conclusions provided the first synthesis of conative and affective outcomes in problem-based learning by specifically analyzing self-directed learning. From this synthesis, practitioners learn that problem-based learning promotes conative and affective skills in self-directed learning.

The bottom-up assembly of functional devices requires novel building blocks to facilitate the incorporation of functional and structural hierarchy. Anisotropic building blocks can substantially broaden the creation of self-assembled devices with unique properties because of their morphological and/or chemical asymmetry. In this regard, we have created microspheres with one hemispherical face exposing silica and the other exposing gold. These microspheres were formed by the shadow deposition of gold onto silica microspheres. The two chemical surfaces allowed use of different surface reactions—silane chemistry for the silica side and thiol chemistry for the gold side—for immobilizing different oligonucleotide sequences on each of the two faces. These dual-functionalized microspheres were used in the selective orthogonal assembly of fluorophore-tagged target oligonucleotides. The DNA-directed assembly was confirmed by confocal microscopy of the microspheres. In essence, employing DNA as the linker molecule, these “Janus” particles can be assembled into various novel 1-D, 2-D, and 3-D structures, which are difficult to realize using symmetric building blocks. / Singapore-MIT Alliance (SMA)

In this paper, we describe the facial structure of the steiner problem in a directed graph by formulating it as a set covering problem. We first characterize trivial facets and derive a necessary condition for nontrivial facets. We also introduce a class of valid inequalities with 0-1 coefficients and show when such inequalities define facets.

This dissertation examines three important issues in energy markets: price dynamics, information flow, and structural change. We discuss each issue in detail, building empirical time series models, analyzing the results, and interpreting the findings. First, we examine the contemporaneous interdependencies and information flows among crude oil, natural gas, and electricity prices in the United States (US) through the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, Directed Acyclic Graph (DAG) for contemporaneous causal structures and Bernanke factorization for price dynamic processes. Test results show that the DAG from residuals of out-of-sample-forecast is consistent with the DAG from residuals of within-sample-fit. The result supports innovation accounting analysis based on DAGs using residuals of out-of-sample-forecast. Second, we look at the effects of the federal fund rate and/or WTI crude oil price shock on US macroeconomic and financial indicators by using a Factor Augmented Vector Autoregression (FAVAR) model and a graphical model without any deductive assumption. The results show that, in contemporaneous time, the federal fund rate shock is exogenous as the identifying assumption in the Vector Autoregression (VAR) framework of the monetary shock transmission mechanism, whereas the WTI crude oil price return is not exogenous. Third, we examine price dynamics and contemporaneous causality among the price returns of WTI crude oil, gasoline, corn, and the S&P 500. We look for structural break points and then build an econometric model to find the consistent sub-periods having stable parameters in a given VAR framework and to explain recent movements and interdependency among returns. We found strong evidence of two structural breaks and contemporaneous causal relationships among the residuals, but also significant differences between contemporaneous causal structures for each sub-period.

Gene therapy holds the unprecedented potential to treat disease by manipulating the underlying genetic blueprints of phenotypic behavior. Targeting of gene delivery is essential to achieve specificity for the intended tissue, which is especially critical in cancer gene therapy to avoid destruction of healthy tissue. Adeno-associated virus (AAV) is considered the safest viral vector and, compared to non-viral vectors, offers several advantages: higher efficiency, genetic modification, combinatorial panning, and high monodispersity. Classic viral targeting has focused on engineering ligand-receptor interactions, but many cell surface targets do not support post-binding transduction events. Furthermore, many potential target tissues – such as triple negative breast cancer – may not display a single, unique identifying surface receptor, so new methods of targeting are needed. Alternatively, many pathological states, including most cancers, exhibit upregulation of proteolytic enzymes in the extracellular milieu. The present work describes the development of an AAV platform that has been engineered to activate in response to disease-related proteases. The specificity and sensitivity of these protease-activatable viruses (PAVs) can be tuned to meet the demands of various clinical scenarios, giving the platform some therapeutic versatility. This work represents the first demonstration of a protease-controlled, non-enveloped virus for genetic therapy. These results extend the therapeutic value of AAV, the safest gene vector currently being explored in 73 clinical trials worldwide.

High globalization of economy has led companies into a business environment in which the changes are increasingly complex and faster. In order to survive even succeed over the long term, the modern enterprises must confront reality in real time and predict possibility. In the 1990s, the theory of dynamic capability arose for responding the rapid changes in market conditions. Many scholars¡¦ researches found that the learning of organization, management models and organizational ambidexterity are the factors to affect the dynamic capability of enterprise. And the organizational ambidexterity can enhance organizational strength of controlling new knowledge and informational diversity. The purpose of this research is to explore the relationships among self-directed learning, organizational ambidexterity and dynamic capability of enterprise. Research is conducted using survey data collection. Samples are from engineers, administrators, specialists, first-line and midlevel managers in enterprises. Via electronic and paper questionnaires, the numbers of distributed questionnaire is 557, effective questionnaire is 230, and the rate of effective questionnaire is 41.3%. The analysis results show that there are significantly parallel relationships among self-directed learning, organizational ambidexterity and dynamic capability of enterprise. For enterprise management, strengthening employees¡¦ self-directed learning and building up a high organizational ambidexterity are useful to enhance the dynamic capability of enterprise.

Nuclear protein KLIP1 cooperates with myeloid leukemia factor 1 (MLF1) to inhibit the programmed cell death resulting in tumor formation. It also inhibits the activity of thymidine kinase promoter of Kaposi¡¦s sarcoma-associated Herpes Virus. KLIP1 functions as a centromere protein, hence acquires its name as CENP-U or CENP-50, to regulate the separation of sister-chromatids during mitosis. These results indicate that KLIP1 plays important roles in regulation of transcription and cell cycle. In this study, six potential SUMO modification sites, K33, K63, K126, K127, K185 and K210, were identified bioinformatically using SUMOplot. Many reports address that SUMO modification alters the transcriptional activity, protein-protein interaction, the subcellular localization and stability of its target protein. Recent data suggest that SUMO is required for centromere binding protein to mediate proper mitotic spindle attachment to the kinetochore, and previous research suggest that there has a SUMO-interaction motif (SIM) in KLIP1 protein sequence. To reveal the interaction between KLIP1 and SUMO-1, and study its effects on KLIP1 function, we co-express GFP-KLIP1 and His-tagged SUMO-1 in HEK 293 cells. After affinity purification of SUMOylated proteins from transfected cells using nickel conjugated beads and subsequent western blotted with anti-GFP. The results indicated the interaction between KLIP1 and SUMO-1 in co-transfected cells. Our confocal microscopy imaging also found colocalization of GFP-KLIP1 with RFP-SUMO-1 nuclear foci. In addition, we failed to detect the interaction between SUMO-1 and mutant KLIP1-M6 ,whose six potential SUMO modified lysine residues were mutated to arginine. Furthermore, we found a distinct nuclear localization of GFP-KLIP1-M6 as compared to the image of wildtype GFP-KLIP1, which show a significant higher frequency of colocalization with RFP-SUMO-1 foci. Taken together, our data suggest the interaction between KLIP1 and SUMO-1 may be related to these six potential lysine residues, which upon mutation blocks its colocalization with SUMO-1 in nuclear foci. The biological significance of their interaction are awaits for further investigation.

Many researchers would be interested in one question: If a change of X is made, will Y be influenced in response? However, while a lot of statistical methods are developed to analyze association between variables, how to find a causal relationship among variables is relatively neglected. The PC algorithm, developed on the basis of Pearl, Sprites, Glymour, and Scheines‟s studies, is used to find the causal pattern of the real-world observed data. However, PC in Tetrad produces a class of directed acyclic graphs (DAGs) that are statistically equivalent under a normal distribution, and therefore such a distributional assumption causes a series of unidentifiable DAGs because of the same joint probability. In 2006 Shimizu, Hoyer, Hyvärinen, and Kerminen developed the Linear Independent Non-Gaussian Model (LiNGAM) to do a causal search based on the independently non-Gaussian distributed disturbances by applying higher-order moment structures. The research objective of this dissertation is to examine whether the LiNGAM is helpful relative to the PC algorithm, to detect the causal relation of non-normal data. The LiNGAM algorithm is implemented by first doing independent component analysis (ICA) estimation and then discovering the correct ordering of variables. Thus, the procedures of ICA estimation and the process of finding the correct causal orderings in LiNGAM are illustrated. Next, we do a causal search on the retail-level scanner data to investigate the pricing interaction between the manufacturer and the retailer by applying these two algorithms. While PC generates the set of indistinguishable DAGs, LiNGAM gives more exact causal patterns. This work demonstrates the algorithm based on the non-normal distribution assumption makes causal associations clearer. In Chapter IV, we apply a classical structural demand model to investigate the consumer purchase behavior in the carbonated soft drink market. Unfortunately, when further restrictions are imposed, we cannot get reasonable results as most researchers require. LiNGAM is applied to prove the existence of endogeneity for the brand‟s retail price and verify that the brand‟s wholesale price is not a proper instrument for its retail price. Therefore, consistent estimates cannot be derived as the theories suggest. These results imply that economic theory is not always found in restriction applied to observational data.