Viral pathogens have plagued human civilizations since ancient times and continue to pose a serious and constant global threat to not only human health but all facets of life. To date, more than 200 viruses capable of infecting humans have been identified, and the combined efforts of the academic and pharmaceutical sectors have yielded both extensive understanding of the biology and pathology of the viral infections as well as breakthrough interventions against a number of devastating diseases such as those caused by HIV (human immunodeficiency virus) and HCV (hepatitis C virus). In late 2019, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the etiological agent of COVID-19 (coronavirus disease 2019), rapidly spread worldwide, leading to detrimental public health and socioeconomic crises. While the immediate response of the scientific community to the pandemic, which involved investigation of the disease and discovery of several therapeutic options at an unprecedented pace, has been impressive, this recent experience exposes the serious need to continuously fortify our fundamental knowledge of virology and equip our antiviral arsenal in preparation for future outbreaks. Moreover, given the scale of the challenge at hand, it highlights the value in the development and application of experimental approaches that accelerate the rate at which this information is obtained. In this dissertation, we utilize various techniques that allow high-throughput analysis of the SARS-CoV-2 3CL (3-chymotrypsin-like) protease to better understand its functional landscape as a favorable therapeutic target of the virus, and to investigate its response in developing resistance against the clinically used protease inhibitor, nirmatrelvir, at scale. We further expand our efforts to develop a platform for multiplexed drug screening that has the capacity to detect viral protease inhibitors for not only coronaviruses but also other targets across six additional virus families. Using this approach, we are able to rapidly identify broad-acting inhibitors, which are favorable for pandemic preparedness purposes where the exact nature of the future threat is difficult to predict a priori. To perform our studies, we make use of a variety of model systems, from a simple yeast-based system for detecting viral protease activity to the passaging of live virus within cultured human cells. Utilizing our yeast-based reporters, we comprehensively profile the activity landscape of all possible single mutants of the SARS-CoV-2 3CL protease via deep mutational scanning (DMS), uncovering its general malleability while also identifying several immutable regions within the enzyme that can serve as targets for the design of the next generation of protease inhibitors. Among the sites that show tolerance to changes, we predict E166 to be a residue that may confer nirmatrelvir resistance upon mutation based on available structural data which reveal its critical role in the binding of the drug to the active site. We prove this to be true by demonstrating a 265-fold loss in EC50 for the E166V mutant relative to the wild type protease within the recombinant virus. Recognizing that the plasticity of the enzyme could translate to a lower genetic barrier to resistance, we extend our investigation to study the whole virus response to nirmatrelvir at scale via in vitro passaging of SARS-CoV-2 in increasing concentrations of the drug. Upon examining 53 independent viral lineages to explore the ways by which resistance can be acquired, we identify a total of 23 mutations that arise in often non-overlapping combinations, with T21I, P252L, and T304I being the most common precursor mutations within all analyzed mutational trajectories. Validation of select single, double, and triple mutants based on the frequency of their appearance reveals that most single mutations, including the aforementioned founder mutations, confer low-level resistance (~5 – 6 fold) while greater resistance is acquired with the accumulation of additional mutations. Moreover, some mutations, such as T21I and L50F, appear to mediate, through a compensatory mechanism, the acquisition of secondary mutations such as E166V, which alone may confer much greater resistance but also cause significant loss in replicative fitness. Overall, the myriad of solutions that exist for the virus to escape the drug further corroborate the malleability of the SARS-CoV-2 3CL protease as established by our initial DMS study. These findings also establish a foundation for extended analysis of the mechanism of resistance and informed drug design. Lastly, by introducing additional viral proteases into our yeast cellular chassis and labelling each model with a set of unique DNA-barcodes, we develop a method of screening a pool of 40 unique protease targets simultaneously against small molecule libraries. Using this platform, we screen 2,480 structurally diverse compounds, and identify and orthogonally validate a series of broad-acting coronavirus 3CL protease inhibitors with a chromen-2-one structure. Together, the work described in this thesis underline the importance of innovative high-throughput approaches to investigating biology as demonstrated by their application to viral protease research.

The SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic has led to widespread socioeconomic and clinical damage. The coalescent response from the global scientific community has been unparalleled, both in speed and furor. Numerous efficacious interventions have been developed and deployed, including several vaccines, antibody therapies, and drugs. Yet, SARS-CoV-2 embodies the quintessential virological issue which threaten these achievements; rapid evolution in the face of selective pressure. This dissertation investigates such adaptations by SARS-CoV-2, and accordingly, modalities to combat this virus despite such evasive measures. To this end, we first studied the antigenic properties of several members of the B.1.1.529 or Omicron lineage of SARS-CoV-2. We observed that B.1.1.529.1 (BA.1), B.1.1.529.1.1 (BA.1.1), and B.1.1.529.2 (BA.2) are the most antibody resistant SARS-CoV-2 variants to-date, while being antigenically unique between each other. Consequently, we turned to explore modalities which may withstand such formidable resistance. We undertook some of the first explorations of a heterologous booster vaccination regimen, finding expanded breadth and potency against SARS-CoV-2, suggesting it may be one simple measure that could be utilized. We also sought to identify broadly neutralizing SARS-CoV-2 antibodies, isolating several with breadth against coronaviruses beyond that of SARS-CoV-2. One of these antibodies, 10-40, was determined to be the broadest receptor-binding domain-directed antibody reported to-date. Finally, we examined an alternative viral target, the 3CL protease. We discovered several SARS-CoV 3CL protease inhibitors that could be repurposed for inhibition of SARS-CoV-2 and determined their crystal structures, which could allow for their use as lead compounds. We further developed and conducted a deep mutational scan of the 3CL protease to examine the activity of all possible single point mutants, revealing that the enzyme had unexpected malleability, as well as several conserved sites that may be targeted by future inhibitors. The SARS-CoV-2 pandemic has been a remarkable trial, but has also served to demonstrate the good that science can do. We hope that this work has been a small contribution among such difficult times.

This Applied Research Project (ARP) utilized qualitative research, specifically hermeneutic phenomenology, to investigate the vital roles of chaplains in healthcare during the COVID-19 pandemic. Chaplains played a significant role in providing spiritual care to patients, their families, and staff during the pandemic. Since there were limited studies about the role of chaplains during the pandemic, this research project adds to the knowledge and resources of what is known about the role of chaplains amid the pandemic. In this qualitative study, I convened and interviewed five Board Certified Chaplains (BCC) to identify their role during the height of the COVID-19 pandemic. This research shows the different roles of chaplains, the interventions they used, the challenges they experienced, and the changes in their roles because of the pandemic. The result of this study tells us that the chaplain's role did not change; chaplains continue to provide spiritual care. What changed was the implementation of the care they provided and the shifting of their priorities.

This study aimed to i) investigate the prevalence of unhealthy attributes, common diseases, and inequities in social determinants of health across a large and representative sample of adults in New York City; and ii) identify common key predictors of COVID-19 seropositivity by comparing various regression models using a hierarchical regression method among a sample of New York City adults. The study will use the New York City Community Health Survey (NYC CHS) 2020 dataset for this analysis. An exploratory approach is used to data to understand the social, environmental, and individual determinants of health in the New York City population at the peak of the pandemic and their effects on COVID-19 seropositivity. The study also emphasizes on using a predictive modeling approach to develop and select an optimal ML model that accurately predicts COVID-19 seropositivity from various ML algorithms. Hierarchical logistic regression was carried out on a sample of 928 participants. It was found that age group 65-75, Black and Hispanic race and being born in the US were statistically significant factors in model 1 of the hierarchical regression where only socioeconomic factors were considered. With the inclusion of health behaviors, tobacco smoking behaviors, and physical activity were statistically significant. In the full model, BMI, asthma prevalence, and suicidal thoughts were statistically significantly correlated with COVID-19 seropositivity. The findings are consistent with public health literature highlighting the importance of healthy behaviors and public health efforts in maintaining overall health and immunity.

Small molecules are powerful tools to probe biological systems and cure diseases. In the scope of this dissertation, small molecules were applied to study three distinct disease models: cancer, Sedaghatian-type spondylometaphyseal dysplasia (SSMD), and COVID-19. First, encouraged by the recently reported vulnerability of drug-resistant, metastatic cancers to GPX4 (Glutathione Peroxidase 4) inhibition, we examined the basis for nanomolar potency of proof-of-concept GPX4 inhibitors, which revealed an unexpected allosteric binding site. Through hierarchical screening of a lead-optimized compound library, we identified novel small molecules binding to this allosteric site. Second, a homozygous point mutation in the GPX4 gene was identified in three living patients with SSMD. With a structure-based analysis and cell models of the patient-derived variant, we found that the missense variant significantly changed the protein structure and caused substantial loss of enzymatic function. Proposed proof-of-concept treatments were subsequentially validated in patient fibroblasts. Our further structural investigation into the origin of the reduced enzymatic activity revealed a key residue modulating GPX4 enzymatic function. We also found that the variant alters the degradation of GPX4, unveiling the native degradation mechanism of GPX4 protein. Third, driven by the recent urgent need for COVID-19 antiviral therapeutics, we utilized the conservation of 3CL protease substrate-binding pockets across coronaviruses to identify four structurally divergent lead compounds that inhibit SARS-CoV-2 3CL protease. With structure-based optimization, we ultimately identified drug-like compounds with < 10 nM potency for inhibiting the SARS-CoV-2 3CL protease and blocking SARS-CoV-2 replication in human cells.

Human beings by nature have a predisposition towards learning and the exploration of the natural world. We are intrinsically intellectual and social beings knitted with adaptive cognitive architectures. As Foot (2014) succinctly sums it up: “humans act collectively, learn by doing, and communicate in and via their actions” and they “… make, employ, and adapt tools of all kinds to learn and communicate” and “community is central to the process of making and interpreting meaning—and thus to all forms of learning, communicating, and acting” (p.3). Education remains pivotal in the transmission of social values including language, knowledge, science, technology, and an avalanche of others. Indeed, Science, Technology, Engineering, and Mathematics (STEM) have been significant to the advancement of social cultures transcending every epoch to contemporary times. As Jasanoff (2004) poignantly observed, “the ways in which we know and represent the world (both nature and society) are inseparable from the ways in which we choose to live in it. […] Scientific knowledge [..] both embeds and is embedded in social practices, identities, norms, conventions, discourses, instruments, and institutions” (p.2-3). In essence, science remains both a tacit and an explicit cultural activity through which human beings explore their own world, discover nature, create knowledge and technology towards their progress and existence. This has been possible through the interaction and applications of artifacts, tools, and technologies within the purviews of their environments. The applications of technologies are found across almost every luster of organizational learning especially teacher education, STEM, architecture, manufacturing, and a flurry of others. Thus, human evolution and development are inexplicably linked with education either formally or informally. The 21st century has however seen a surge in the use of artificial intelligence (AI) and digital technologies in education. The proliferation of artificial intelligence and associated technologies are creating new overtures of digital multiculturalism with distinct worldviews of significance to education. For example, learners are demonstrating digital literacy skills and are knowledgeable about AI technologies across every specter of their lives (Bennett et al., 2008). It is also opening new artesian well-springs of educational opportunities and pedagogical applications. This includes mapping new methodological pathways, content creation and curriculum design, career preparations and indeed a seemingly new paradigm shift in teaching STEM. There is growing scholarly evidence about the use and diffusion of these technologies in K-12 and higher education (Bonk & Graham, 2012; Hew & Brush, 2007; Langer, 2018; Mishra & Koehler, 2006). Some of these include the Sphero robots, Micro Bit, Jill Watson, BrickPi3 Classroom kit, Engino STEM Mechanic, Lego Education WeDo Core Set and Spike. Both educators and learners are using these in STEM programs as well as other education related activities. Just as human activities and interactions with artifacts and tools shaped and redefined the scientific-technological feat of previous generations, so the contemporary digital technological era seems to be on a similar trajectory. However, there is sparsity of empirical scholarship on the pedagogical prospects and effectiveness of artificial intelligence in STEM classrooms. Also, it should be noted that scholarship on how AI impacts pedagogical content knowledge of STEM educators and how learners perceive these technologies are just emerging. In addition, the recent COVID-19 pandemic (Ghandhi et al., 2020; Rasmussen et al., 2020) has unexpectedly created a renewed synergy towards the applications of digital technologies in teaching STEM. In the context of this force majeure (COVID-19), the traditional brick and mortar educational spaces metamorphosed into digital spaces with the applications of many artificial intelligent technologies and resources in the arena of education. This doctoral dissertation study examined these enigmas including how educators use these technologies in STEM classrooms. The study is informed by activity theory or cultural-historical activity theory (Engeström et al., 2007; Hasan et al., 2014; Krinski & Barker, 2009; Oers, 2010; Vygotsky,1987). The study participants will be selected from educators currently integrating artificial intelligent systems and digital technologies in their respective STEM classrooms. Pre-data survey inquiry has shown that many educators were incorporating some forms of AIS into their STEM classrooms. In view of these, I have explored Sphero educational robots to interrogate the research topic. The Sphero Edu described as a “…STEAM-based toolset that weaves hardware, software, and community engagement to promote 21st century skills. While these skills are absolutely crucial, our edu program goes beyond code by nurturing students’ creativity and ingenuity like no other education program can” (Sphero, April 2020). The Sphero robots also have features and applications for designing and teaching STEM topics such as nature, space science, geometry, and other activities of pedagogical significance. Users could also design and write advanced engineering programs in JavaScript during STEM educational activities formally and outside of the classrooms. In essence, educators and students can learn designing, programming, engineering, mathematics, computational thinking, and hands-on skills reflective of the 21st century. In brief, the dissertation study research has explored artificial intelligence and emerging technologies and how these could transform and advance teaching and learning of STEM hence the research topic: Exploring the use of Artificial Intelligent Systems in STEM Classrooms. Methodologically, this is a qualitative study through the theoretical frameworks of activity theory as applicable to STEM education. The main research questions are: 1) Given that artificial intelligent systems and digital technologies have been applied in STEM educational domains (content, pedagogy, student learning, assessment). How does the application of AIS and digital technologies impact pedagogy in STEM educational activities? 2) Given that digital technology is transforming contemporary society in every facet. How/What does AIS tell us about how digital technology impacts STEM pedagogy? Data was collected from the study participants, archival sources, and others for analyses. It is hoped that the findings will inform and address theories of learning and teaching, policy and praxis in science education, teacher preparatory and professional development programs as it relates to STEM classrooms

Online teacher professional development (oTPD) has gained momentum globally as a mode of teacher professional development (Dede et al., 2009; Lieberman & Mace, 2010), appealing to teachers who prefer the convenience of online learning and/or the autonomy of self-paced learning. With oTPD gaining traction, especially in this climate of COVID-19 pandemic where many face-to-face interactions have shifted to an online space, there is insufficient research done on teachers’ learning experiences and the type of reflective thinking observed during teachers’ participation in oTPD activities. This is compounded by the ubiquitous but poorly defined use of reflection in literature pertaining to learning and professional development (Finlay, 2008; Roessger, 2014). In Singapore where teachers have access to a range of oTPD opportunities, this problem is similarly observed. Very few studies have been undertaken in Singapore to understand teachers’ learning experiences and how teachers reflect when they engage in TPD or oTPD. In light of the growing popularity of oTPD as a means for Singapore teachers to learn and improve their classroom practice, this exploratory case study sought to contribute to TPD research by studying the oTPD experiences of Singapore teachers. Specifically, this study explored factors that facilitated and/or impeded teacher learning in oTPD and the level of reflective thinking observed in teachers’ oTPD participation. The motivation for this study stems from an appreciation of the complexity of classroom practice and the recognition that what teachers do in their respective classrooms is pivotal to student learning. This study recognizes the crucial need to support teacher learning through oTPD. Findings from this study may inform the design and implementation of oTPD in Singapore and address the paucity of research in this area by providing qualitative case study data on the understudied area of oTPD and teacher learning. Recommendations pertaining to the design and implementation of oTPD may benefit professional development providers and the teachers they serve, as well as teacher leaders hoping to support teacher learning. This study and the recommendations it proposes will also be of interest to researchers in educational research who seek to understand the phenomenon of oTPD.

The sample (N=188) was 100% Black (N=188), 83.5% female N=(157), with mean age of 43.16 (min=18, max=72, SD 12.567). Some 81.4% (n=153) were working full- or part-time during the pandemic with low-moderate work-related risks for COVID-19 transmission (mean =2.56, min=0, max=7, SD= 2.102). The sample had a moderate household size (mean=2.64, min=1, max=6, SD= 1.363) with low levels of multi-generational living, as 10.6% (n=20) had seniors living with them. For risk of COVID-19 transmission within the home, there was a low-moderate risk (mean=1.668; min=1, max= 4, SD=.699). Only 19.1% (n=36) had COVID-19 in the past year. Both physical health status and mental/emotional health status declined during the pandemic. They had moderate mental distress (mean= 1.94; min=0; max=3, SD=1.066) in the past year—with depression (70.7%, n=133), anxiety (78.2%, n=147), and trauma (45.2%, n=85), while 43% (N=81) sought counselling. They had moderately good social support (mean = 2.71; min=0, max=4, SD=1.172), and a good quality of life (mean=4.05, min=1, max=6, SD=1.073). They reported moderately high medical mistrust (mean=3.273; min=1.50, max=5.00, SD=.7615), and 58.7% (N=127) did or would vaccinate. Scores on the new Our COVID-19 Knowledge Test (OCKT-44) produced a mean of 40.34 (min=23, max=44, SD=3.092) for excellent knowledge, and 83.8% (N=155) of would recommend the OCKT-44 to others. Using paired t-tests, the experience of taking the OCKT-44 (with all true answers) demonstrated a positive impact on both COVID-19 Knowledge and COVID-19 Prevention Self-Efficacy. Using backward stepwise regression, controlling for social desirability, the significant predictors of a high Level of COVID-19 Knowledge Based on Our Covid-19 Knowledge Test (OCKT-44) Score were: if had COVID-19—yes (b =1.026, SEB=.431, p = .018); and, if has been/will be vaccinated—yes (b=.912 SEB=.405, p=.026)—with this model (R 2=.0.060, Adj R 2 =0.044) only explaining 4.4% of the variance. Second, significant predictors, controlling for social desirability, for the a high Level of Self-Efficacy for COVID-19 Risk Reduction Post-OCKT-44-Test-Taking were: gender—female (b =-.363, SEB=.157, p = .022); born in the U.S.—No (b=-.253 SEB=.117, p=.032); children—No (b = -.216, SEB=.052, p= .045); and, higher Quality of Life (b=.127 SEB=.052, p=.016)—with this model explaining (R 2=0.330, and the Adj R 2 =0.083) only 8.3 % of the variance. Qualitative data amplified and expanded upon the quantitative data findings.

Telehealth is needed urgently nationwide, given the COVID-19 pandemic. It isespecially urgent in rural and less populated areas where healthcare access is limited. Currently, because there are no pediatric handwriting assessments validated for telehealth use, the TeleWrite assessment would fill an unmet service need and expand the use of telehealth-based occupational therapy (OT) assessment in pediatric practice. This dissertation explored the preliminary psychometric properties of TeleWrite, a handwriting assessment tool designed to measure the legibility and fluency of handwriting for children in first through third grade administered via telehealth. A series of studies were completed to determine initial interrater reliability, content validity, and clinical utility using classical test theory. The Rasch model of measurement was used to determine the preliminary psychometric properties of TeleWrite using Winsteps® (v. 4.7.0). The quantitative Rasch analysis of TeleWrite included administration of the tool to 148 children from first to third grade. This study tested the initial construct validity (internal validity) and test reliability of TeleWrite using the Rasch model of measurement. The Partial Credit Model (PCM) was used for rating scale analysis because TeleWrite is composed of three distinct scales (handwriting rate, accuracy, and fluency) that differs per task (near point or far point) and per grade level. The Rasch analysis showed a generally good fit with the Rasch unidimensional model, indicating strong construct and internal validity and moderate ability to separate abilities of students reliably in terms of handwriting skills. However, following the Rasch model, a larger sample is necessary to obtain improved calibration, reliability, and validity measures. This study and supported by the literature described the need for a new handwriting evaluation tool validated for telehealth use. The findings of the current research contribute to the literature and OT practice as the first handwriting assessment specifically designed and validated for telehealth use that assesses all pertinent variables of handwriting associated with handwriting difficulties.

This study explores the challenges that elementary science educators face when teaching science in a time of crisis, as well as how to best provide elementary teachers with ongoing support for their science teaching during the novel COVID-19 pandemic. Using a phenomenological approach, this research focuses on elementary science teachers, educators, and formal and informal leaders to understand their experiences during the pandemic and how to best support them during remote and in-person science teaching. Using data collected from questionnaires, semi-structured interviews, and focus group discussions, findings discuss the specific experiences and challenges faced by elementary science first-year teachers, early career teachers, and leaders. Following the transactional model of stress and coping (Lazarus & Folkman, 1984) and the buffering effect of social support (Cohen & McKay, 1984), first-year and early career elementary science teachers used multiple coping mechanisms to handle the stress of science teaching during the pandemic, including problem solving and collaborating with other educators. From a distributed leadership perspective (Spillane, Halverson, & Diamond, 2001b), district-level elementary science curriculum specialists and coaches act as leaders in science education. When faced with constraints and challenges due to the pandemic, these district-level leaders used this opportunity to reimagine what their leadership work could look like, including rethinking what supports they can offer classroom teachers when they cannot easily access classrooms, how to design effective science curricula for remote teaching, and how to collaborate with other educators in new ways.