An Exploration of Connection, Engagement, and Belonging in Environmental Education

Addressing environmental challenges—such as global warming, climate change, and environmental justice—requires diverse perspectives and active participation across multicultural communities. Despite this, environmental organizations often struggle to attract and retain a diverse and engaged membership base. This study examined the challenges faced by the Pennsylvania Association of Environmental Educators (PAEE) in engaging its members and addressing declining participation. Guided by my theory of improvement aimed at understanding a sense of belonging among members, the study employed a mixed-methods approach, including semi-structured interviews with eight participants and a survey of 341 members, yielding a 24% response rate. Findings revealed that members valued connection and communication through in-person events but identified a need for targeted outreach to specific groups, such as formal educators and young people. The study highlights the importance of cultivating a sense of belonging to enhance meaningful engagement, build stronger community connections, and increase membership participation and retention. These insights provide a framework for environmental educational organizations to build inclusive and active networks

Environmental and Genetic Influences on Low-density Lipoprotein Cholesterol Levels and Cholesterol-lowering Medication Use in Asian Americans:Findings from the All of Us Research Program

Cardiovascular disease (CVD) remains the leading cause of death globally and in the US. Around 7% of Asian Americans have CVD, posing a significant health burden. One of the major risk factors for CVD is elevated Low-density lipoprotein (LDL) cholesterol. Although extensive research has examined LDL cholesterol and its environmental and genetic determinants in the general population, studies focused specifically on Asian Americans are limited. Statins, essential for reducing LDL cholesterol and preventing CVD, are often underused in the US, with about 40% of eligible individuals not receiving treatment. Research on racial disparities in statin use has primarily highlighted Black/African American and Hispanic American populations, leaving gaps in understanding for Asian Americans. This study aims to identify environmental and genetic factors affecting LDL cholesterol levels and assess statin use and barriers among Asian Americans. We analyzed data from self-reported Asian American adults aged 18 and older in the All of Us Research Program. Environmental factors were examined using surveys, physical measurements, and lab data. We also conducted a genome-wide association study (GWAS) to test the association between genetic markers and LDL cholesterol levels. Statin use was assessed based on the 2013 ACC/AHA (American College of Cardiology/American Heart Association) Blood Cholesterol Guideline. Our findings indicate that Asian Americans have higher LDL cholesterol levels compared to other racial/ethnic groups, with no significant lifestyle factors linked to high LDL cholesterol. GWAS analysis identified six suggestive SNPs associated with decreasing the risk of high LDL cholesterol. Among them, rs5748554 and rs6485549 were located near ZDHHC8 and DKK3, which are indirectly related to lipid metabolism. Despite eligibility, only two-thirds of Asian Americans received cholesterol-lowering medications, and statin use was 17% lower in women than in men with ASCVD (atherosclerotic cardiovascular disease), indicating a notable gender disparity. The public health significance of this study is identifying novel genetic and non-genetic risk factors for high LDL cholesterol and underutilizing lipid-lowering medication. These findings will help to understand population-specific factors affecting LDL cholesterol and inform tailored interventions for its management by improving medication adherence, ultimately leading to improved cardiovascular health in this population

Predicting Atomic Structure of Multi-metallic Nanoparticles with Physics-based Machine Learning

Metal nanoparticles (NPs) find tremendous application in various fields, including catalysis, biomedicine, and electronics, due to their unique physicochemical properties arising from their morphology (i.e., size and shape) and composition. The chemical ordering of NPs, consisting of more than one metal, is crucial for optimizing their application performance, including stability. Traditionally, Density Functional Theory (DFT) is used to investigate NP stability, but it is computationally expensive, limited to small systems and cannot be applied to multi-metallic NPs where the materials space is enormous. To address this, recent efforts coupled a physics-based model (Bond-Centric Model, BCM) with a developed genetic algorithm (GA) to optimize the chemical ordering of NPs leading to minimum (most exothermic) cohesive energies (CEs). Central to this approach is the calculation of weighting factors that scale the monometallic bond strength to describe that of the bimetallic bond. Herein, we perform a critical analysis and set some rules on how to apply these methods for rapid and accurate nanomaterials predictions. Specifically, we optimized the chemical ordering of 2869-atom cuboctahedron NPs across 15 different bimetallic combinations. In comparison with both experimental and computational results, our findings indicate that the use of small metal dimers for the calculation of the weighting factors leads to accurate and computationally efficient chemical ordering and stability predictions for a wide range of NP compositions. We further extended our investigation to 6 trimetallic NPs with a tremendously large materials space, testing our model’s capability to predict chemical ordering patterns in multi-metallic systems and demonstrating its power as a rapid and accurate computational method. This methodology can facilitate the design of thermodynamically stable multi-metallic NPs and predict the distribution of different metal atoms from the core to the surface, which is central to any nanotechnological application

Towards Rebalancing the Nitrogen Cycle: Development and Examination of Tools to Reduce Nitrogen Fertilizer Emissions from Crop Agriculture

Nitrogen is a limiting nutrient in the growth of agricultural crops. To enhance yields and meet food demands, nitrogen fertilizers are added in excessive quantities during crop production. However, the use of these fertilizers is inefficient. An estimated 50% nitrogen fertilizer that is applied is not assimilated by crops and creates severe economic and environmental consequences. In order to develop effective interventions to this problem, the scientific community must first aim to quantify emissions with greater granularity and clarify the driving factors of inefficient nitrogen fertilizer use. Towards this aim, two separate modeling tools were developed to increase nitrogen emissions data availability and improve understanding of how practices and environmental conditions affect nitrogen use efficiency. In addition, a novel nitrogen fertilizer carrier was evaluated for its ability to aid in nitrogen emissions reduction in soil. First, to improve emissions data availability, a statistical model framework was developed to accurately predict daily nitrate loads (mean Kling-Gupta Efficiency of 0.74) in agricultural streams using streamflow and geographical data. Geographical variables related to nitrogen inputs (i.e., corn acreage density and livestock density) and water resources vulnerability (i.e., tile drainage density and water table depth) were highly correlated with nitrate loading concentrations. Next, a field-scale, process-based model was created to simulate nitrogen fertilizer dynamics during corn cultivation. Scenario modeling indicated that optimizing nitrogen application rates and delaying fertilizer application can reduce emissions to water and the atmosphere. Delayed application versus baseline aligns nitrogen availability in the root zone with peak crop demands, improving recovery rates. Based on these findings similar results in the literature, we hypothesized that nitrogen fertilizer inefficiency and emissions stem from rapid nitrogen transport and mismatched timing with crop needs. To avoid these dynamics, we explored using liposomes—micro-scale lipid carriers—to enhance nutrient retention and reduce transport in soil. Soil column experiments were conducted to analyze the fate and transport of these liposomes. Together, the research in this dissertation presents and evaluates new tools, both computational and technological, that fill research gaps and that could aid in the challenge of inefficient nitrogen fertilizer use

Wireless strategies for infectious disease control: Contact tracing and hand hygiene monitoring.

The healthcare sector increasingly faces disruptions that demand advanced strategies, robust systems, and innovative solutions. One significant disruption has been the COVID-19 pandemic, which has claimed over 7 million lives globally. Its rapid spread and the emergence of new variants highlight the importance of preventive and screening measures such as vaccination and contact tracing (CT). CT identifies individuals exposed to infected persons, allowing timely interventions like isolation. A common method of digital contact tracing (DCT) involves using smartphones with Bluetooth to broadcast and register close contacts (phone-phone CT). However, these approaches suffer from low accuracy due to limited control over range. Also, most DCT efforts focus on direct contact, such as touching or talking, while neglecting indirect contact via contaminated surfaces or respiratory particles. Another critical issue in healthcare is healthcare-associated infections (HAIs), which, according to the World Health Organization, are a leading cause of mortality in healthcare settings. One major contributor to HAIs is the failure of healthcare workers (doctors, nurses, etc.) to consistently adhere to hand hygiene protocols. This factor also contributes to the transmission of infections like COVID-19 within hospitals. Ensuring proper hand hygiene compliance (HHC) can significantly reduce the incidence of HAIs. This dissertation addresses both of these challenges. First, it aims to enhance the accuracy of DCT while safeguarding user privacy. We use the deployment of Bluetooth-based IoT devices in public gathering spaces, such as restaurants, hospitals, and schools, to detect direct and indirect contacts. We create a simulation to study the improvements of this method over common phone-phone-based approaches and efficient strategies for placing beacons. Additionally, we extend this approach to support bidirectional tracing, identifying additional contacts arising from asymptomatic carriers. We observe that the proposed bidirectional CT outperforms existing DCT works in averting possible infections. To address the second challenge, we propose a deep learning-based system that utilizes WiFi channel state information (CSI) to monitor hand hygiene compliance. We observe that the proposed model outperforms existing time series models on an existing HHC dataset in accuracy and training time

Dataset for Excess dietary sodium restores electrolyte and water homeostasis caused by loss of the endoplasmic reticulum molecular chaperone, GRP170, in the mouse nephron.

The maintenance of fluid and electrolyte homeostasis by the kidney requires proper folding and trafficking of ion channels and transporters in kidney epithelia. Each of these processes requires a specific subset of a diverse class of proteins termed molecular chaperones. One such chaperone is GRP170, which is an Hsp70-like, endoplasmic reticulum (ER)-localized chaperone that plays roles in protein quality control and protein folding in the ER. We previously determined that loss of GRP170 in the mouse nephron leads to hypovolemia, electrolyte imbalance, and rapid weight loss. In addition, GRP170-deficient mice develop an acute kidney injury (AKI)-like phenotype, typified by tubular injury, elevation of kidney injury markers, and induction of the unfolded protein response (UPR). By using an inducible GRP170 knockout cellular model, we confirmed that GRP170 depletion induces the UPR, triggers apoptosis, and disrupts protein homeostasis. Based on these data, we hypothesized that UPR induction underlies hyponatremia and volume depletion in these rodents and that these and other phenotypes might be rectified by sodium supplementation. To test this hypothesis, control and GRP170 tubule-specific knockout mice were provided a diet containing 8% sodium chloride. We discovered that sodium supplementation improved electrolyte imbalance and kidney injury markers in a sex-specific manner but was unable to restore weight or tubule integrity. These results are consistent with UPR induction contributing to the kidney injury phenotype in the nephron-specific GR170 knockout model and indicate that GRP170 function in kidney epithelia is essential to both maintain electrolyte balance and ER homeostasis

Statistical Inference Methods to Identify Tumor Microenvironment Heterogeneity

The tumor microenvironment (TME) is a dynamic ecosystem that is continually tested and shaped by the tumor cells. It protects the tumor cells from being attacked by the host immune system and facilitates tumor growth. Therefore, it is of significant interest to understand the cell- cell interplays within the TME to help demystify the mechanisms of immune evasion. However, current computational methods fail to reveal the whole picture of the cell-cell interactions since the activated cell functions in the cell are not fully captured. Several text mining models and factorization analysis methods have been developed to solve this problem; nevertheless, they require strong hidden assumptions that are not met in the context of molecular biology. The goal of this work is to explore a way to quantify and estimate the utility of upregulated functions (specific biological activities that a cell would perform) for cell differentiation and specialization, through which to understand and interpret the communications between cells in a causal relationship. To this end, I developed three aims and address them step by step: 1) scGEM is a nonparametric Bayesian model based on a Dirichlet process to identify correlated gene co- expressing modules within the cell subtypes. The gene modules identified by scGEM are expected to follow cell differentiation path and reflect cellular functions at a higher resolution than the current methods; 2) CRCAtlas uses scGEM to investigate gene modules of colorectal cancer and then employs a pharmacological computational method to locate the ligand receptor pairs that explain such correlations; 3) IOhub is introduced as one of the largest publicly curated databases for immuno-oncology research. By incorporating the gene modules from single cells, IOhub has the potential to discover new biomarkers to predict clinical response to immune checkpoint blockade. Overall, this dissertation contributes to the understanding of cancer immunology and the advancement of precision medicine

Distinct transmission sites within a synapse for strengthening and homeostasis.

At synapses, miniature synaptic transmission forms the basic unit of evoked transmission, thought to use one canonical transmission site. Two general types of synaptic plasticity, associative plasticity to change synaptic weights and homeostatic plasticity to maintain an excitatory balance, are so far thought to be expressed at individual canonical sites in principal neurons of the cortex. Here, we report two separate types of transmission sites, termed silenceable and idle-able, each participating distinctly in evoked or miniature transmission in the mouse visual cortex. Both sites operated with a postsynaptic binary mode with different unitary sizes and mechanisms. During postnatal development, silenceable sites were unsilenced by associative plasticity with α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA)-receptor incorporation, increasing evoked transmission. Concurrently, miniature transmission remained constant, where AMPA-receptor state changes balanced unsilencing with increased idling at idle-able sites. Thus, individual cortical spine synapses mediated two parallel, interacting types of transmission, which predominantly contributed to either associative or homeostatic plasticity

On the Ground with Community Partners, Researchers and Residents: Learning from One Another to Foster Community Resilience.

This workshop will focus on the experiences of community partners, residents and interdisciplinary researchers working together on a community-centered violence prevention project (ReCAST-Resiliency in Communities after Stress and Trauma). The intervention uses CBPR methods to engage youth and adults to build collective efficacy and community resilience to prevent youth and community violence and improve community wellbeing. It includes a training program for youth and adults, and a community-based project to address issues identified by young people in the training. Participants learn relationship building strategies, restorative approaches to intervene to prevent violence, and ways to advocate for community resources to support community resilience and safety. In this session, researchers along with partners, facilitators and residents from our Sto-Rox community will discuss what we have learned from one another about ways to foster authentic community engagement, build on existing efforts, integrate residents’ lived experiences, focus on community strengths, and elevate youth voice

Characterizing Replication Stress in Glioblastoma

This dissertation investigated glioblastoma multiforme (GBM), a highly aggressive brain malignancy with poor outcomes. The disease’s heterogeneity promotes treatment resistance and recurrence, with limited biomarkers available o guide therapy. MGMT promoter methylation, the sole clinical biomarker for temozolomide response, has limited predictive utility, underscoring the need for novel approaches. This dissertation examined replication stress (RS), a key feature of GBM linked to DNA damage and genetic instability. Using advanced tissued-based multiplexed imaging technologies, RS was characterized at single-cell resolution in tumor specimens. High-dimensional biomarkers, including the Multidimensional Replication Stress Index (MRSI), were developed to quantify RS and identify therapeutic vulnerabilities. The researched aimed to (1) identify and validate a panel of RS markers using multiplexed imaging, (2) develop the MRSI metric for quantifying RS data, (3) characterize RS profiles in primary GBM, and (4) assess RS modulation following drug treatment in a clinical trial for recurrent GBM. These efforts integrated imaging data with computational modeling to uncover novel biomarkers and stratify patients for personalized therapy. This dissertation’s innovative use of multiplexed imaging enabled detailed visualization of protein dynamics and RS at multiple scales. Findings advanced biomarker development for GBM and provided a framework for addressing RS-driven vulnerabilities in other cancers, with potential implications for improving precision oncology