This study examined the use of discovery learning in science and how it affects students' academic performance as well as their self-efficacy in science. It also used a diagnostic tool to identify students' misconceptions about processes in the water cycle and where the misconceptions originated. While the study showed that the treatment group had a statistically significant greater academic gain from the pre-test to the post- test than did the no-treatment comparison group, from a teachers view point the gain would not be enough to benefit a student's performance on high stakes tests. Because the study was able to identify eight common misconceptions, it suggests that the misconceptions that students possess are difficult to uproot even using teaching methods that have been proven successful. / CITE/Mathematics and Science Education

Molecular recognition is an important tool for developing tunable controlled release systems and fabricating biosensors with increased selectivity and sensitivity. The development of polymer-based materials that exploit molecular recognition events such as host-guest complexation, enzyme-substrate and enzyme-inhibitor interactions and nucleic acid hybridization was pursued in this thesis. Using polymers as an anchor for molecular recognition can enhance the affinity, selectivity, and the capacity for immobilization of recognition units, enabling the practical use of affinity-based systems in real applications. To introduce the potential for immobilization while preserving or enhancing the affinity of small molecule recognition units, the affinity of derivatized cyclodextrins for the hydrophobic drug, dexamethasone, was investigated. Cyclodextrins (CDs) are molecules that possess a hydrophilic exterior and a hydrophobic cavity capable of accommodating a wide range of small molecule guests. Analysis of the solubilization capacities, thermodynamic parameters and aggregative potentials of carboxymethyl and hydrazide derivatives of CDs established the dextran-conjugated βCD derivative as an ideal carrier of hydrophobic drugs and the hydrazide βCD derivative as an optimal solubilizer of lipophilic pharmaceuticals, both alone and when incorporated in a polymer-based drug delivery vehicle. To enable non-covalent immobilization and stabilization of biomacromolecular recognition units, a printed layer hydrogel was investigated as a selective diffusion barrier for analyte sensing and enzyme inhibitor recognition. A printable hydrogel platform was developed from an established injectable system composed of aldehyde- and hydrazide-functionalized poly(oligoethylene glycol methacrylate) polymers. The printed layer hydrogel effectively immobilized a wide range of enzymes and protected enzyme activity against time-dependent and protease-induced denaturation, while facilitating the diffusion of small molecules. Furthermore, to demonstrate the potential of the printed film hydrogel immobilization layer to enhance the selectivity of the target, the printable hydrogel platform was used to develop a microarray-based assay for the screening of inhibitors of the model enzyme, β-lactamase. The assay was able to accurately quantify dose-response relationships of a series of established inhibitors, while reducing the required reagent volumes in traditional drug screening campaigns by 95%. Most significantly, the assay demonstrated an ability to discriminate true inhibitors of β-lactamase from a class of non-specific inhibitors called promiscuous aggregating inhibitors. Finally, to enable non-covalent immobilization of DNA recognition units, the printable hydrogel-based microarray was tested for its ability to immobilize DNA recognition sites and promote the detection of DNA hybridization events. A long, concatameric DNA molecule was generated through rolling circle amplification and was used as a sensing material for the detection of a small, fluorophore labeled oligonucleotide. The printable hydrogel was able to effectively entrap the rolling circle amplification product. Properties of the printable hydrogel were investigated for their ability to support the detection of DNA hybridization events. / Thesis / Doctor of Philosophy (PhD) / This thesis describes the development of polymer-based materials that exploit molecular recognition events for drug delivery and biosensing applications. First, cyclodextrins (CDs) are molecules that are capable of binding a wide range of small molecules. A comprehensive analysis of the complexation properties of CD derivatives revealed critical insight regarding their application in polymer-based drug delivery vehicles. Second, a printable hydrogel platform was developed to support the immobilization and activity of biomolecules and establish a biosensing interface that facilitates the diffusion of small molecules but not molecular aggregates. A microarray-based assay was developed by employing the printed hydrogel interface for the screening of inhibitors of the model enzyme, β-lactamase, and the detection of DNA hybridization events.

In this dissertation, we present a novel approach for Web service mining. Web service mining is a new research discipline. It is different from conventional top down service composition approaches that are driven by specific search criteria. Web service mining starts with no such criteria and aims at the discovery of interesting and useful compositions of existing Web services. Web service mining requires the study of three main research topics: semantic description of Web services, efficient bottom up composition of composable services, and interestingness and usefulness evaluation of composed services. We first propose a Web service ontology to describe and organize the constructs of a Web service. We introduce the concept of Web service operation interface for the description of shared Web service capabilities and use Web service domains for grouping Web service capabilities based on these interfaces. We take clues from how Nature solves the problem of molecular composition and introduce the notion of Web service recognition to help devise efficient bottom up service composition strategies. We introduce several service recognition mechanisms that take advantage of the domain-based categorization of Web service capabilities and ontology-based description of operation semantics. We take clues from the drug discovery process and propose a Web service mining framework to group relevant mining activities into a progression of phases that would lead to the eventual discovery of useful compositions. Based on the composition strategies that are derived from recognition mechanisms, we propose a set of algorithms in the screening phase of the framework to automatically identify leads of service compositions. We propose objective interestingness and usefulness measures in the evaluation phase to narrow down the pool of composition leads for further exploration. To demonstrate the effectiveness of our framework and to address challenges faced by existing biological data representation methodologies, we have applied relevant techniques presented in this dissertation to the field of biological pathway discovery. / Ph. D.

The need for new antibiotics has been highlighted recently with the increasing pace of emergence of drug resistant pathogens (MRSA, XDR-TB, etc.). Modification of existing antibiotics with the additions of side chains or other chemical groups and genomics based drug targeting have been the preferred method of drug development at the corporate level in recent years. These approaches have yielded few viable antibiotics and natural products are once again becoming an area of interest for drug discovery. We examined the antimicrobial "Red Soils" of the Hashemite Kingdom of Jordan that have historically been used to prevent infection and cure rashes by the native peoples. Antimicrobial producing bacteria were present in these soils and found to be the reason for their antibiotic activity. After isolation, these bacteria were found to excrete their antimicrobials into the liquid culture media which we could then attempt to isolate for further study. Adsorbent resins were employed to capture the antimicrobial compounds and then elute them in a more concentrated solution. As part of a drug discovery program, we sought a way to quickly characterize other soils for potential antibiotic producing bacteria. The community level physiologic profile was examined to determine if this approach would allow for a rapid categorizing of soils based on their probability of containing antimicrobial producing microorganisms. This method proved to have a high level of variability that could not be overcome even after mixing using a commercial blender. The role of these antimicrobial producing bacteria within their natural microbial community has largely been confined to microbe-plant interactions. The role of antimicrobial-producing microorganisms in driving the diversity of their community has not been a focus of considerable study. The potential of an antimicrobial-producing bacterium to act as a driver of diversity was examined using an artificial microbial community based in a sand microcosm. The changes in the microbial assemblage indicate that antimicrobial-producing bacteria may act in an allelopathic manner rather than in a predatory role. / Ph. D.

<p dir="ltr"><i>Pseudomonas aeruginosa</i> (<i>P. aeruginosa</i>) is a Gram-negative bacterium responsible for many infections in immunocompromised humans. This multi-drug resistance human pathogen can form biofilms, which help protect it from not only clinical treatment but also from main homeostasis and metabolism. Understanding biofilm structures is critical to help combat biofilm formation and develop better ways to treat <i>P. aeruginosa</i> infections. A molecule that helps biofilm formation and virulence infections for <i>P. aeruginosa</i> is pyocyanin, which is believed to be correlated with the invasiveness of the bacteria and the stabilization of biofilms. To better understand the role of pyocyanin in assisting <i>P. aeruginosa</i> with survival, we applied optical imaging to study pyocyanin in biofilms and under blue light treatment. Using nonlinear optical imaging methods, we could successfully detect the aggregation of pyocyanin in biofilms. Furthermore, we discovered that pyocyanin protects <i>P. aeruginosa</i> from blue light inactivation. In addition, we found that blue light treatment alters the structure of pyocyanin, leading to irreversible changes that produce distinct spectra in UV-Vis and fluorescence signals. <i>These results provide new insights into how pyocyanin protects </i><i>P. aeruginosa</i> in blue light treatment. Further investigation would lead to better treatment strategies for more effective treatment of <i>P. aeruginosa</i> and biofilms for various infections.</p>

In an increasingly data-driven society, sensors and actuators are the bridge between the physical world and the world of "data." Electroacoustic transducers convert acoustic energy into electrical energy (or vice versa), so it can be interpreted as data. Piezoelectric materials are often used for transducer manufacturing, and recent advancements in additive manufacturing have enabled this material to take on complex geometric forms with micro-scale features. This work advances the additive manufacturing of piezoelectric materials by developing a model for predictive success of complex 3D printed geometries in Mask Image Projection-Stereolithography (MIP-SL) by accounting for mechanical wear on Polydimethylsiloxane (PDMS). This work proposes a framework for the rapid manufacture of 3D printed transducers, adaptable to a multitude of transducer element forms. Using the print model and transducer framework, latticed hydrophone elements are designed and tested, showing evidence of selectively tunable sensitivity, resonance, and directivity pattern. These technology advancements are extended to enable a workflow for users to input polar coordinates and receive an acoustic element of a continuously tuned directivity pattern. Investigation into customer problem spaces via tech-push methods are adapted from the NSF's Lean Launchpad to reveal insight to the problems faced in hydrophone applications and other neighboring problem spaces. / Master of Science / In an increasingly data-driven world, sensors are the bridge between the physical world and the world of "data." The better the sensor; the better the data. Electroacoustic transducers are sensors that convert acoustic sound energy into electrical energy or vice versa. These are observed in the world around us as microphones, speakers, ultrasound devices, and more. In the early 1900's, piezoelectric materials became one of the dominant methods for transducer creation, and recent advancements in additive manufacturing have enabled this material to take on highly complex geometric forms with micro-scale feature sizes. Further advancements to additive manufacturing of piezoelectric materials are contributed through development of a model for predicting the success of complex 3D printed geometries in an Mask Image Projection-Stereolithography (MIP-SL) by accounting for mechanical wear on the Polydimethylsiloxane (PDMS) print window. This work proposes a framework for the rapid manufacture of 3D printed transducers, adaptable to a multitude of element forms. Using the developed print model and transducer framework, latticed hydrophone elements are designed and tested, showing evidence of selectively tunable sensitivity, resonance and beampattern. The advancements in technology are extended to enable a workflow for users to input polar coordinates and receive an acoustic element of continuously tuned beampattern. Investigation into customer problem spaces via tech-push methods are adapted from NSF's Lean Launchpad and reveals great insight to the problems faced in hydrophone applications and other neighboring industry spaces.

Islet amyloid polypeptide (IAPP) is a 37-residue amyloidogenic hormone implicated in the progression of Type II Diabetes (T2D). T2D affects an estimated 422 million people yearly and is a co-morbidity with numerous diseases. IAPP forms toxic oligomers and amyloid fibrils that reduce pancreatic β-cell mass and exacerbate the T2D disease state. Toxic oligomer formation is attributed, in part, to the formation of inter-peptide β-strands comprised of residues 23-27 (FGAIL). Flavonoids, a class of polyphenolic natural products, have been found experimentally to inhibit IAPP aggregate formation. Many of these known IAPP aggregation attenuating small flavonoids differ structurally only slightly; the influence of functional group placement on inhibiting the aggregation of the IAPP(20-29) has yet to be explored. To probe the role of small-molecule structural features that impede IAPP aggregation, molecular dynamics (MD) simulations were performed on a model fragment of IAPP(20-29) in the presence of morin, quercetin, dihydroquercetin, epicatechin, and myricetin. Contacts between Phe23 residues are critical to oligomer formation, and small-molecule contacts with Phe23 are a key predictor of β-strand reduction. Structural properties influencing the ability of compounds to disrupt Phe23-Phe23 contacts include carbonyl and hydroxyl group placement. These structural features influence aromaticity and hydrophobicity, principally affecting ability to disrupt IAPP(20-29) oligomer formation. This work provides key information on design considerations for T2D therapeutics. / Master of Science in Life Sciences / Type II Diabetes (T2D) affects an estimated 422 million people worldwide, with the World Health Organization (WHO) reporting that approximately 1.5 million deaths were directly caused by T2D in 2019. The progression of T2D has been attributed to a protein, called islet amyloid polypeptide (IAPP, or amylin) that is co-secreted with insulin after individuals eat or consumes calories. IAPP has been discovered to form toxic aggregates or clumps of protein material that worsen the disease state and cause a loss of mass of pancreatic cells. There is a large market for therapeutics of T2D and more small molecule drugs are needed to slow progression and severity of T2D. Flavonoids, a class of natural molecules, have been found to inhibit the processes by which IAPP promotes T2D disease progression by stopping the aggregation of IAPP. The structures of these flavonoid compounds differ slightly but show difference in ability to slow IAPP aggregation. By understanding how those differences confer more or less protection against T2D and inhibit IAPP aggregation, we can design more potent and specific drugs to target IAPP. To probe the role of molecular structure in preventing IAPP aggregation, molecular dynamics (MD) simulations — a powerful computational technique — were performed on a model fragment of IAPP in the presence of molecules morin, quercetin, dihydroquercetin, epicatechin, and myricetin. MD simulations provide extremely detailed information about potential drug interactions with a given target, serving as an important tool in the development of new drugs. This work has identified key features and predictors of effective IAPP drugs, providing a framework for the further development of therapeutics against T2D and similar diseases.

Malaria continues to be a global health burden that affects millions of people worldwide each year. Increasing demand for malaria control and eradication has led research to focus on sexual development of the malaria parasite. Sexual development is initiated when pre-destined intraerythrocytic ring stage parasites leave asexual reproduction and develop into gametocytes. A mosquito vector will ingest mature gametocytes during a blood meal. Sexual reproduction will occur in the midgut, leading to the production of sporozoites that will migrate to the salivary gland. The sporozoites will be injected to another human host during the next blood meal consequently, transmitting malaria. Due to decreased drug susceptibility of mature gametocytes, more investigation of the biology and metabolic requirements of malaria parasites during gametocytogenesis, as well as during the mosquito stages, are urgently needed to reveal novel targets for development of transmission-blocking agents. Furthermore, increasing drug resistance of the parasites to current antimalarials, including slowed clearance rates to artemisinin, requires the discovery of innovative drugs against asexual intraerythrocytic stages with novel mechanisms of action. Here, we have investigated the role of the apicoplast during Plasmodium falciparum gametocytogenesis. In addition, we describe drug-screening studies that have elucidated a novel mode of action of one compound from the Malaria Box, as well as identified new natural product compounds that may be serve as starting molecules for antimalarial development. / Ph. D.

Bacterial antibiotic resistance is a globally recognized problem that has prompted extensive research into novel antimicrobial compounds. This dissertation describes research focusing on two types of potential antimicrobial molecules, organometallic compounds (OMC) and peptide nucleic acids (PNA). Organometallic compounds show promise as antimicrobial drugs because of their structural difference from conventional antibiotics and antimicrobials, and because of the ability to "tune" their chemical and biological properties by varying ligand attachments. Peptide nucleic acids, when linked to a cell-penetrating peptide (CPP), can suppress bacterial gene expression by an antisense mechanism and are attractive candidates for antimicrobial drugs because they bind strongly to target nucleic acids and are resistant to nucleases. Chapters 1 and 2 of the dissertation provide an introduction and broad literature review to frame the experimental questions addressed. Chapter 3 describes work to test the cytotoxicity and cellular penetration capabilities of novel OMCs by evaluating their effects on J774A.1 murine macrophage-like cells that were either uninfected or were infected with Mycobacterium bovis BCG. Results indicate that OMCs with an iridium (Ir) metal center and an amino acid ligand show minimal cytotoxicity against eukaryotic cells but likely do not penetrate the intracellular compartment in significant amounts. Chapter 4 presents research into in vitro effects of CPP-PNAs targeting the tetA and tetR antibiotic resistance genes (CPP-anti-tetA PNA and CPP-anti-tetR PNA, respectively) in tetracycline-resistant Salmonella enterica ssp. enterica serovar Typhimurium DT104 (DT104). Through the use of modified minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays it was shown that both the CPP-anti-tetA PNA and CPP-anti-tetR PNA increase tetracycline susceptibility in DT104. Chapter 5 explores the molecular mechanism of the CPP-anti-tetA PNA and CPP-anti-tetR PNA through the use of reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Results indicate good specificity of the CPP-anti-tetA PNA for its nucleic acid target as evidenced by suppression of tetA mRNA expression in DT104 cultures treated with a combination of tetracycline and the PNA. Chapter 6 describes the development of a mouse model of DT104 infection using BALB/c mice, followed by implementation of that model to test in vivo antimicrobial effects of the CPP-anti-tetA PNA and the CPP-Sal-tsf PNA, which targets expression of the essential tsf gene. An optimal dose of DT104 was identified that causes clinical illness within 2-4 days. At the doses tested, concurrent treatment of infected mice with tetracycline and the CPP-anti-tetA PNA or with the CPP-Sal-tsf PNA alone did not have a protective effect. Final conclusions are 1) that further research with the OMCs should focus on compounds with an Ir center and an amino acid ligand, and should explore ways to enhance intracellular penetration, 2) that the in vitro results of the PNA studies suggest that PNAs targeting expression of antibiotic resistance genes could allow for repurposing of antibiotics to which bacteria are resistant, and 3) additional study of the behavior of PNAs in vivo is advised. / Doctor of Philosophy / Antibiotic-resistant bacteria are increasingly recognized as a threat to global health, and new antibacterial drugs are urgently needed. Before a chemical compound can advance far in the journey to becoming a new drug it must be tested for toxicity against mammalian cells. A portion of this dissertation research involved testing the toxicity of several organometallic compounds (OMCs) previously shown to have antibacterial potential. Mouse-derived mammalian cells were treated with several of the OMCs, and initial results indicated that one of the OMCs is non-toxic and is likely a safe option for additional analysis. This OMC was further tested to see if it could inhibit mycobacterial growth inside of the mammalian cells. It did not effectively clear bacteria from inside of the mammalian cells, likely because of poor penetration of the cell membrane. Further research with this compound should focus on ways to effectively transport the OMC inside infected mammalian cells so that it can reach the bacteria it is meant to target. A second portion of this research involved using a peptide nucleic acid (PNA) to try and reverse tetracycline antibiotic resistance in the bacterial strain Salmonella enterica ssp. enterica serovar Typhimurium DT104 (DT104). Peptide nucleic acids are short linear molecules that can bind strongly to complementary DNA and RNA sequences and thus be used to interfere with expression of specific genes. A PNA was designed to inhibit expression of the bacterial tetA gene that codes for a protein called the TetA tetracycline efflux pump, which imparts resistances to tetracycline. Treating the bacteria with the PNA resulted in a lower dose of tetracycline needed to inhibit bacterial growth, indicating a successful increase in tetracycline susceptibility. By using a molecular analysis technique called reversetranscriptase quantitative polymerase chain reaction (RT-qPCR), it was possible to measure the amount of tetA messenger RNA (mRNA) in cultures of DT104 treated only with tetracycline or with a combination of tetracycline and the PNA. As expected, bacteria treated with both the antibiotic and the PNA had less tetA mRNA than the cultures treated only with tetracycline, supporting the hypothesis that the PNA prevents the bacteria from effectively expressing the tetA gene. The PNA was next used in conjunction with tetracycline as an experimental treatment for mice infected with DT104. The PNA did not provide the expected protective effect under these circumstances. The overall conclusion for this part of the research is that PNAs offer an exciting potential avenue for counteracting antibiotic resistance, but additional experimentation is needed. Future research should focus on investigating more effective ways to get the PNAs inside the bacteria and on understanding more about how the PNAs behave in live animals. Several other PNAs targeting different genes involved in antibiotic resistance or essential bacterial functions were also tested against DT104 with variable success.

Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community. / Ph. D. / High-throughput technique such as transcriptomics, proteomics and metabolomics is widely used to generate ‘big’ data for cancer biomarker discovery. Typically, differential expression analysis is performed to identify cancer biomarkers. However, discrepancies from independent studies for the same clinical types of samples using differential expression analysis are observed. This may be attributed to that biomolecules such as genes, proteins and metabolites are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. In this dissertation, we propose to identify cancer biomarker candidates using network-based approaches. We start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. Then we propose a novel method (i.e., wgLASSO) to incorporate prior biological knowledge from public available databases into purely data-driven network model to build biologically relevant networks. In addition, a novel differential network analysis method (i.e., dwgLASSO) is proposed to identify cancer biomarkers. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, and metabolomics) to identify cancer biomarkers from a systems biology perspective. Matlab and R packages for the proposed methods are developed and shared with the research community.