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Bacteria play a major role in our health and wellbeing. The microbe-host interaction is often mediated by sugar polymers at the cell surface. An incredibly diverse amount of glycan variation exists throughout these structures, which makes identification of surface components difficult. The composition of the surface is unique to bacteria and acts as molecular fingerprint which can distinguish even subspecies apart. Better methods to decipher what those glycan identities are or how to reproduce them may help develop future advancements towards exploiting them as therapeutic targets. The major challenge addressed herein is to simplify the tools used to track the formation of these natural materials. To do this, we expand on the chemoenzymatic preparation of a tagged lipid substrate central to early stages of biosynthesis for many surface polysaccharides. Further, we identify conditions in which these unnatural substrates can be used in vitro. Lastly, we develop methodologies to detect BP and polysaccharide intermediates in live cells. These tools have facilitated robust detection and reconstruction of glycan assembly building blocks. This research may lay the ground work for future applications technologies towards novel therapeutics, such as glycoconjugates vaccines.
With the introduction of job embeddedness theory, turnover research shifted away from the precursors of why people leave and focused on why people stay. Distinct from similar constructs, such as job satisfaction and organizational commitment, job embeddedness includes community-related issues in addition to job-related issues. While existing literature has evaluated alternate ways to measure the job embeddedness construct, variable-centered approaches continue to be utilized. This approach implicitly assumes that being high or low on job embeddedness perceptions in the form of an overall composite indicates that individuals perceive each aspect and their attachment ties similarly. Thus, this study advances the job embeddedness theoretical framework by engaging in a person-centered approach. This study found that the presumed underlying factors used to measure job embeddedness were best represented by a six-factor model through confirmatory factor analysis. In addition, this study conducted a latent profile analysis to examine patterns in response indicators within the sample data and found that distinct job embeddedness profiles emerged. These profiles showed unique patterns of job-based and non-job-based experiences that relate to staying on the job. Lastly, this study examined each job embeddedness profile and compared them with respect to job satisfaction, organizational commitment, and turnover intentions.
The purpose of this study was to determine the effects of Morse v. Frederick on student speech in K-12 public schools. Cases meeting the research criteria were selected from federal court districts. Those cases were briefed and analyzed. The results of the research were used to develop findings that were placed into four categories: (1) the concurring opinion’s support for school safety, (2) political and social commentary, (3) harassment of school officials, and (4) speech concerning possession, distribution, and use of illegal drugs while at school. The findings led to the development of recommendations for school officials to consider regarding student speech and the development of a Four-Prong Speech Progression Test.
The Tax Cuts and Jobs Act of 2017 (TCJA) provides a novel context in which to examine the effects of U.S. taxation of foreign earnings on the behavior of multi-national corporations (MNCs). Prior to the TCJA, the U.S. levied taxes on an MNC’s worldwide earnings, deferred until firms repatriated the funds to U.S. The worldwide taxation and deferral until repatriation led to firms holding significant amounts of cash offshore. By 2017, there was an estimated $2.8 trillion of repatriatable funds “trapped” offshore. Prior legislation intended to encourage repatriation offered temporary “tax holiday” measures. The TCJA lowered corporate tax rates for all firms and eliminated future U.S. tax on repatriated earnings after payment of a one-time transition tax, creating a “permanent tax holiday” for foreign earnings. I examine the relationship between pre-TCJA foreign cash holdings disclosed by MNCs and their shareholder payout and investment behavior in the two years immediately following enactment of the TCJA. Similar to research into the effects of the temporary tax holidays in prior legislation, I find share repurchases in the post-TCJA period are associated with pre-TCJA foreign cash holdings. I further find that MNCs disclosing pre-TCJA foreign cash holdings increased research and development and capital expenditures in the second year following the TCJA. These findings indicate that the foreign earnings provisions of the TCJA may have had some longer-term effects in line with its legislative intent. This contrasts in some ways with the findings of prior research and should be of interest to policymakers, particularly as the current U.S. administration considers changes to the corporate tax regime, while also providing a basis for future research.
The purpose of this qualitative study was to explore professional development from the perspective of instructional leaders to identify if the assumptions of Knowles’s (1990) Adult Learning Theory were present in the planning and implementation of continuing education. A qualitative case study research design was utilized, and the research setting was dependent on the participants and the locations in which they were contracted to conduct continuing education sessions with teachers. The instructional leaders were committed to plan and present professional development at three different suburban schools surrounding a city in the Southeastern United States. The participants in the study were instructional leaders and educational consultants with at least 10 years of experience who work across school districts with multiple elementary, middle, and high school sites in suburban and urban districts. Data sources included two rounds of interviews, observations of planned and implemented professional development, and document analysis of staff development materials. The data was analyzed using thematic analysis that included within-case and cross-case investigation.
This dissertation explores the cybersecurity risk disclosure and the information an organization signals via disclosure contents. Extant literature acknowledges the ability of the cybersecurity risk disclosure to predict subsequent related outcome (i.e., realization of breach incident). However, little research has addressed whether the disclosure signals important information about the IT Risk Culture governing the organization. To fill this gap, I examine cybersecurity risk disclosures using textual analysis and clustering techniques to analyze the IT Risk Culture of a sample of organizations between the years 2011 – 2019. Three classifications of IT Risk Culture are identified. I find that a certain IT Risk Culture, evidenced by the vulnerability and the propensity for risk transfer (i.e. cybersecurity insurance) expressed in the cybersecurity risk disclosure, is associated with subsequent cybersecurity breach. Additionally, the disclosure of Corporate Social Responsibility activity is found to be associated with a second classification of IT Risk Culture, one in which there is no significant association with subsequent cybersecurity breach. This dissertation contributes to holistic risk management literature by employing a systems perspective of IT Risk Culture to analyze related disclosures. Findings contribute greatly to the understanding of IT Risk Culture classification, predominant risk response behavior and the likelihood of subsequent related outcomes.
Deep neural networks (DNN) are proved to be effective and improve the performance dramatically in various kinds of computer vision tasks. The end-to-end learning manner in training DNN consistently shows the powerful modeling ability and consequently mitigates the dedicated efforts for expert feature engineering. On the other hand, it raises the issue that how to improve the black-box network with better representation (feature) learning especially when the learned representations and classifiers are tied together in the manner of supervised learning. In this work, representation learning is studied in four perspectives of different fields, i.e. diversity in ensemble learning, aspect ratio in image aesthetics assessment, invariance in identification task, and composition in color attribute recognition.
In light of analyzing the bottleneck of black-box network and designing better representation learning for target tasks, we introduce that: (a) Ensemble learning relies on the diversity of the complementary neural networks, in both feature representations and classifier representations. A diverse representation learning method, namely learning-difficulty-aware embedding, is proposed to adaptively reconcile learning attentions for different categories by training a series of networks with diversified representations sequentially; (b) Widely-adopted data augmentation method in image recognition deteriorates aspect ratios, which is an important factor in image aesthetics assessment. An aspect ratio representation learning method, namely adaptive fractional dilated convolution, is proposed to explicitly preserve the learning representation related to aspect ratios by adjusting the receptive fields adaptively and natively; (c) Identification tasks, e.g. person re-identification, aim at learning representations that are robust to interfering variances, e.g. lighting variances, view variances, pose variances. An invariance representation learning method, namely anchor loss, is proposed to train a robust feature extractor, which distills the identity-related representations while disentangling and removing interfering variances by global supervision under local mini-batch training; (d) Color recognition is entangled with compositional representation in both visual perception and language attentions. A compositional learning module with attention to key colors is proposed to learn better color representations. Besides, another compositional learning method, namely classifier as descriptor, is proposed for long-tail color recognition by incorporating the rich knowledge in classifier representations to remove the bias from bias-trained
model.
Through extensive experiments and thorough analysis, we demonstrate some novel insights about the impacts of four factors, i.e. diversity, receptive field, invariance, and composition. Several methods are proposed to learn better representations for those factors, achieving state-of-the-art results in different tasks.
The maintenance of genome integrity and fidelity is essential for the proper function and survival of all organisms. Recent studies have revealed that APE2 is required for the activation of an ATR-Chk1 DNA damage response (DDR) pathway in response to oxidative stress and a defined DNA single-strand break (SSB) in Xenopus laevis egg extracts. However, it remains unclear whether APE2 is a general regulator of DDR pathway and what the biological significance of APE2 is in mammalian cells. Here, I provide evidence using mammalian cultured cell lines including human pancreatic cancer cells that APE2 is important for ATR DDR pathway activation in response to different stressful conditions including oxidative stress, DNA replication stress, and DNA double-strand breaks. Fluorescence microscopy analysis shows that APE2-knock-down (KD) leads to enhanced γH2AX foci and increased micronuclei formation. In addition, a small molecule compound is identified as APE2 inhibitor that specifically compromises the binding of APE2 to ssDNA, its 3′-5′ exonuclease activity, and the defined SSB-induced ATR Chk1 DDR pathway in Xenopus egg extracts. Notably, cell viability assays demonstrate that APE2-KD or APE2 inhibitor sensitizes pancreatic cancer cells to chemotherapy drugs. Overall, APE2 is proposed as a general regulator for DDR pathway in genome integrity maintenance in mammalian cells.
As employers leverage recruitment activities to compete for active job seekers, they may also seize opportunities to recruit those who are not actively searching for jobs (i.e., passive job seekers). The literature currently focuses on active job seekers and has created a false dichotomy between active and passive job seekers. This study aims to change the conversation on passive job seekers and emphasize that all individuals fall on a continuum of job seeking behavior frequency. There is currently a lack of theoretical insight into the cognitive processes involved in the recruitment of active and passive job seekers, and misalignment between theoretically and practically relevant constructs and the measures currently being used. This study aims to identify the factors and mechanisms that attract talent across the job seeking behavior frequency continuum and establish a more thorough understanding of the factors that influence candidates’ actual job choices. The first contribution of this study to the field of recruitment is the reconceptualization of active and passive job seeking as different levels of job seeking behavior frequency on a continuum. The second is the extension of expectancy theory to the recruitment of job seekers across the continuum. The third contribution of this research is that it moves the needle to more closely approximate a measure of actual job choice decisions and provides a better understanding of how candidates make job choice decisions. This research may also inform the tailoring of organizational policies and practices to best attract job seekers on the passive end of the continuum, which could lead to advantageous recruitment outcomes.
A unifying theme of this thesis is the implementation and characterization of point probes for surface metrology. The implementations include two optical non-contact profiling methods; fiber-based Fabry-Perot interferometry and Confocal microscopy.
Even though Fiber-based Fabry-Perot interferometers are suitable for measuring surface texture in confined space, literature describing its implementation and limitations of this technique are scarce. To explore these knowledge gaps an experimental facility has been built and the feasibility for surface height measurements is validated by measuring two sinusoidal reference surfaces with heights of 1 μm and 1.5 μm and wavelengths of 100 μm and 50 μm respectively.
A second part of this thesis is to implement a flexure-based vertical scanning of an objective lens used for Confocal microscopy to increase the bandwidth of height detection and, therefore, surface scanning speeds. A sinusoidal reference sample is designed and manufactured, which is later measured using a confocal microscope prototype built using a 60X objective lens. A flexure to house the lens stack to provide a surface height scanning range of 10 μm is also designed and fabricated.
A third part of this thesis involves characterizing a stylus-based contact profiler for measuring areal form of freeform optics. Typically, for non-contact optical probes, the probe axis must be orthogonal to the curvature of the part being measured. This is not required for a stylus profiler. Reference objects such as optical flats, prisms, and spheres are measured using the stylus profiler and these measurements are compared with results from a Fizeau interferometer. From these measurements, vertical error of the X scanning carriage, side loading on the stylus probe due to the surface slope of the part being measured are estimated. A geometrical model of the profiler has been developed and used in a Monte Carlo simulation that predicts an uncertainty in the areal form measurements of less than 100 nm PV for a 100 mm measurement aperture.
