University Of Delaware

NSF Awards · FY 2024 · 2024-09

The uptake of anthropogenic CO2 by the ocean has slowed down CO2 increase in the atmosphere and thus our planet’s warming potential. However, this CO2 uptake decreases ocean pH and carbonate mineral saturation state slowly, a process popularly known as ocean acidification, which has altered marine and estuarine biogeochemistry and may be detrimental to carbonate bearing marine organisms and the ecosystem. Therefore, the detection/sensing/quantification of these CO2 derivatives, such as CO32, has been a significant topic in environmental research, as well as in biochemical research and ocean chemistry. Potentiometric carbonate sensors based on ionophores have demonstrated advantages over other sensor types by being simple, light weight, and low power consumption while not requiring any sample pretreatment other than standard calibration solutions to provide the measured results directly and immediately. However, for long-term deployment, the desired lifetime should be at least eight weeks. Therefore, the overall objectives of this proposed research include: 1) to synthesize polymer ionophores by grafting organic functionalities onto polymer substrates, and further fabricate electrodes and sensor devices with a high selectivity of carbonate ion and a long life and a short response time (90 % response in seconds) for seawater sample measurements, 2) to establish STEM professional development pathways, achieve sustainable increases in research and education, provide training and mentoring opportunities for underrepresented groups, and finally benefit the economic development by addressing a wide range of issues including ocean acidification, coastal hazards, habitat protection, coastal development, water quality, coral reef conservation, energy facility siting, and ocean planning. This proposed research aims for carbonate sensing in coastal and seawater, with the following hypotheses to be tested: (1) The sensor devices made from the grafted polymers as ionophores, with an increased density of functional groups (group numbers per mass of the ionophore), will exhibit fast responses to carbonate with an improved high selectivity with minimum interference; (2) The stability of the sensor devices will be improved by using polymer ionophores with functional groups covalently grafted on the polymer backbone to avoid leaching under corrosive salty environments, enabling a stable and long-term carbonate detection and measurement in the ocean. For the approaches and methods, firstly functional groups will be grafted onto polymer backbones and the resultant polymer ionophores will be fabricated into ion selective membranes for sensor electrodes, and carbonate detection will be conducted to study the sensitivity, selectivity, and stability in mimic and real seawater samples. The research will bridge the knowledge gap between these important fields (materials chemistry, ocean chemistry, environmental remediation, and chemical engineering) and lead to long-lasting and fundamental impacts on environment protection and oceanic agriculture. The results also have potential applications for detecting and monitoring other environmentally important anions such as nitrate and phosphate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This NSF CAREER project presents a comprehensive research and education strategy aimed at creating a trustworthy optimization toolbox for geo-distributed scientific data analytics. The toolbox is designed to address a critical gap in current artificial intelligence (AI)/machine learning (ML) practices, where predictive models are typically trained on historical and/or regional data. This approach is inadequate for capturing the full spectrum of complex and evolving phenomena, such as extreme weather events and climate change. The project focuses on innovating optimization methods that improve the robustness of predictions, the reliability of explanations, and the scalability of privacy protections. These innovations are essential for increasing the trustworthiness of AI/ML systems when dealing with rare or previously unseen scenarios, thereby enabling decisions that can be trusted by domain experts. The success of this project is expected to establish a solid foundation for recognizing AI/ML as a legitimate scientific methodology for high-stake, safety-critical applications. The project holds substantial merit by pursuing three research aims. Aim-1 bridges the long-standing gap between data topology and robust optimization, resulting in a new topological robust optimization framework for out-of-distribution generalization. Aim-2 revolutionizes the use of explainable machine learning by interpreting model predictions at data, knowledge, and concept levels, facilitating interactive scientific knowledge discovery. Aim-3 addresses the pressing need for trustworthy collaborative learning in out-of-federation scenarios, ensuring scalable and explainable data protection. To validate these advancements, the project uses standard benchmarks alongside newly curated AI-ready datasets in three applications: flood water mapping, seafloor characterization, and global urbanization forecasting. To maximize societal impact, this project integrates research findings at all educational levels through interdisciplinary collaborations. Initiatives include promoting undergraduate AI4Sciences research, developing interdisciplinary curricula, supporting underrepresented groups in computer science, and enhancing outreach efforts at K-12 and community levels. This goal is to cultivate a diverse and inclusive STEM workforce, ensuring the benefits of this research reach broad society. This project is jointly funded by Information Integration and Informatics and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

To improve the behavior of micro aerial vehicles (MAVs), also known as drones, this project aims to develop ultra-fast, energy-efficient, high-accuracy sensors for detecting the position of the MAV in space. MAVs equipped with cameras and inertial measurement units hold immense potential across different industries but face challenges in achieving robust and efficient 3D perception within the constraints of size, weight, and power. To address these limitations, the project focuses on ultra-efficient 3D motion tracking and visual understanding by designing methods for position estimation that optimize data transfer and minimize power consumption, as well as gravity-aware perception inspired by the neural computation performed by the inner ear balancing system in people and animals. The project’s co-design of control algorithms and drone hardware aims to enhance energy efficiency and perception performance, which will be evaluated in collaboration with industry partners. With commercial MAV use growing rapidly in sectors like agriculture, construction, insurance, and law enforcement, this project will drive widespread deployment and economic growth. Moreover, it will offer research opportunities for diverse students at the University of Delaware and promote STEM education through hands-on programming activities for K-12 students, families, and teachers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The increasing reliance on Artificial Intelligence (AI) and Machine Learning (ML) in critical scientific domains requires models that both predict with high accuracy but also derive their conclusions from valid and justified rationales, particularly for data with deviations from known patterns. This project addresses the critical gap in current AI/ML practices, where models are often trained on historical data insufficient to capture the full spectrum of complex, evolving phenomena, such as extreme weather events. The project introduces a generic, trustworthy toolbox aimed at enhancing the validity, explainability, and scalability of existing predictive models. By focusing on both the accuracy of predictions and the validity of their underlying rationales, the toolbox ensures that AI/ML systems remain reliable in the face of rare or even unprecedented scenarios, resulting in decisions that domain experts can trust. Resources will be made publicly available, ensuring that the broader scientific and technology communities can access, leverage, and build upon the advances. This project develops a trustworthy toolbox for making accurate predictions that are backed by scientifically grounded rationales. This is an important step towards trustworthy AI, ensuring that models used in critical scenarios are both precise and rationally transparent. The project includes three major areas of research. The first focuses on making the best predictions possible, even when faced with data patterns that are very unusual. The second maximizes the reliability of the reasoning supporting the models. The third deals with improving the models’ ability to scale and address the growing volume of scientific data. Research outcomes will be translated into open-source software, workflows that can be made widely applicable, and AI-ready scientific datasets. The principal investigators (PIs) will integrate research findings at multiple educational levels by collaborating with both academic and non-academic researchers. Initiatives include developing Vertically Integrated Projects (VIP) to engage undergraduate research, organizing ML in science hackathon events, presenting at the University of Delaware's annual DARWIN symposium, and reaching out to K-12 teachers and students at local high schools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

ABSTRACT Antibiotics are a cornerstone of modern medicine, but antibiotic resistance threatens the effectiveness of our existing antibacterial arsenal. This phenomenon poses an acute challenge with Mycobacterium tuberculosis, a global pathogen that causes greater mortality than any other bacterium. Nearly half a million drug-resistant tuberculosis infections occurred in 2021, driving an urgent clinical need to develop novel drugs against M. tuberculosis and to rigorously characterize new molecular targets. One promising class of targets are the mycobacterial Clp proteases, which carry out regulated degradation of cytosolic proteins. Although Clp proteases are known to be essential, their cellular roles and the reasons for their essentiality are poorly understood, which in turn constrains efforts to develop Clp-targeting therapeutics. This proposal investigates the connection between arginine phosphorylation and the mycobacterial ClpC1P1P2 Clp protease. In the distantly related species Bacillus subtilis, phosphoarginine (pArg) modifications occur on diverse proteins during proteotoxic stress and are recognized by the ClpCP protease as a degradation signal. We recently reported that pArg modifications also occur in mycobacteria, although the specific conditions that stimulate pArg installation appear to be different than in B. subtilis. Preliminary studies confirm that pArg-bearing proteins are proteolyzed by ClpC1P1P2. Additionally, we find that pArg binds ClpC2, a protein that contributes to antibiotic persistence. We hypothesize that mycobacteria install pArg in response to stress or starvation; that pArg directs ClpC1P1P2 activity and modulates its assembly state; and that ClpC2 is a pArg-sensitive regulator of dormancy and persistence. This proposal seeks to test this overarching hypothesis and interrogate key biochemical aspects of pArg recognition. In Aim 1, we use phosphoproteomics to identify stress conditions that elevate pArg, assess the requirement of pArg on mycobacterial stress tolerance, and identify mycobacterial arginine kinases and phosphatases. In Aim 2, we use in vitro biochemical approaches to test the influence of pArg number and substrate stability on proteolysis by ClpC1, and determine the influence of pArg binding on the ClpC1 activity state. In Aim 3 we assess how pArg-dependent changes in ClpC2 oligomerization influence its function. The outcomes of this proposal will include an expanded understanding of the roles of pArg in mycobacteria, new mechanistic details of how pArg-bearing proteins interact with Clp proteases, and elucidation of the role of ClpC2 in pArg-sensing and persistence. These studies will lay the groundwork for future efforts to test the connection between pArg and M. tuberculosis infection. Moreover, a detailed understanding of pArg-linked proteolytic pathways will improve our ability to screen for new ClpC1-targeting therapeutic leads.

NSF Awards · FY 2024 · 2024-08

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, the research groups of Dr. Karl Booksh at the University of Delaware and Dr. Barry Levine at Oklahoma State are developing new computational tools and methods for “soft sensing” in chemical industry applications. “Soft sensing” refers to the application of data science techniques to infer key process indicators (KPIs) and critical process parameters (CPPs) when these indicators and attributes cannot be directly or conveniently assessed by physical sensors. Advanced applications for soft sensing are being developed and explored through collaborations with industrial partners at Merck and Arkema. Merck is interested in developing new soft sensing technologies for understanding and optimizing the production process for biopharmaceuticals, targeting the recombinant expression of virus-like particles (VLPs) in yeast-based bioreactors for vaccines and the immobilization of biocatalytic enzymes on resin and polymer beads. Arkema is interested in the production process of polymers, using soft sensing to detect contamination in polymers assuring final product quality. The benefits of soft sensing are projected to extend beyond industrial applications. For example, soft sensing can be applied to environmental and health related monitoring to estimate KPIs that cannot be directly measured. The project addresses the persistent need for highly trained professionals in chemical data sciences. Graduate and undergraduate students will develop state of the art chemical data science and machine learning tools and apply them to chemical problems presented by the industrial partners. The approach being devenoped in the Booksh and Lavine labs combines advancement of multiway chemical data science methods – machine learning tools designed to exploit the structure of data cubes or higher order tensors – with industry-driven needs to maximize understanding and control of chemical processes. Multiway methods, under the broad category of Tucker models, promise to better extract the chemical information imbedded in the interactions among measurement modes. Specifically, the team is developing excitation-emission matrix fluorescence (EEMF) as an on-line or in-line soft sensor for KPIs and CPPs of VLP production processes, targeting significant improvement over currently available multivariate sensing options. They are also developing improved methodology for collection and analyses of hyperspectral images for soft sensing of polymer contaminants and biocatalytic enzymes immobilized on resins. Methods that determine statistically meaningful confidence limits, especially when the associated error matrix is structured and non-Gaussian, are being developed. Additionally, modified versions of the popular multivariate curve resolution approach (MCR) are being developed to better extract KPIs/CPPs associated with minor components from hyperspectral images. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The social and emotional aspects of a classroom climate are essential to creating a space conducive to learning. Research has shown that the emotions elementary school teachers and their students experience when engaging in mathematics activities play an important role in mathematics teaching and learning. If teachers or their students regularly experience negative emotions such as anxiety and anger when working on mathematics activities, then the quality of teaching and learner engagement in the mathematics may be negatively impacted. If teachers or their students engage in mindfulness and emotional regulation and can experience more positive emotions when engaging in mathematics activities, then the quality of teaching and learner engagement in the mathematics can be positively impacted. Yet, the field lacks mathematics-specific professional learning opportunities for elementary teachers that focus on the role of teachers' and learners' emotions in the way they experience mathematics in the classroom. Professional learning opportunities have also not focused on the unique needs and experiences of first-year educators. This project will address these gaps by developing and testing the Orienting Positive Emotions in New Teachers for Mathematics (OPEN for Math) professional learning program. OPEN for Math professional learning program will support beginning elementary educators' ability to create a positive mathematics classroom in which teachers leverage the emotions they and their students' experience when engaging in mathematics activities to facilitate students' positive mathematics engagement and achievement. This project will serve to increase the field's capacity to improve elementary mathematics teachers' effectiveness and retention via more emotionally responsive teacher professional learning. This teaching strand, design and development (early stage), level 2 project at the University of Delaware will be carried out in partnership with district and school leaders and educators from five public school districts in the state of Delaware. The research team will work in collaboration with instructional designers, school leaders, and in-service educators to develop an initial version of OPEN for Math content and a delivery plan that is responsive to the needs and experiences of the project's focal audiences. In the initial stages of this project, the research team will collaborate with district and school leaders to assess potential implementation barriers and supports to implementing the OPEN for Math professional learning program. In the latter stages of this project, the research team will conduct two consecutive year-long pilot trials of OPEN for Math with first-year educators in partnering districts, recruiting approximately 90 first year teachers. OPEN for Math content and delivery strategies will be revised based on detailed user feedback collected throughout each pilot trial. The research team will collect a combination of survey, interview, and classroom observation data from among pilot participants to perform mixed-methods analyses that will speak to the potential efficacy of OPEN for Math in improving target teacher-and classroom-level outcomes. The research team will analyze data to better understand which features of OPEN for Math teachers use when teaching and are responsive to the needs of beginning teachers. The work will also investigate the extent to which teachers' emotions when engaging with mathematical activities and their mathematical instructional practices change, how their personal characteristics influence their response to the program and how OPEN for Math could be successfully applied in different contexts. Lastly, a retroactive implementation evaluation will be carried out to inform future adaptation and scaling of the program. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The aim of the research is to develop a new strategy for metabolite sensing and metabolite-induced enzyme localization; this will contribute to fundamental cellular knowledge and also improve the efficiency of bioprocesses. Living cells interact with their surroundings and with each other by recognizing specific chemical signatures in the environment, followed by logical processing of information to elicit required responses. This adaptability is achieved by a dynamic metabolic network that tightly regulates the activity of cellular components to adjust to fluctuating chemical signatures in the environment. A detailed real-time analysis of these chemical signatures (e.g. metabolites) would provide a deeper understanding of their physiological roles in promoting and regulating cellular activities, enabling more robust paradigms for cellular reprogramming. To achieve this, the investigators will exploit the reversibility of ligand-responsive transcription factor (LRTF)-DNA interaction as a new transformative framework to enable real-time metabolite sensing in live cells. The idea is to create synthetic LRTF-based protein devices that can translate metabolite-induced DNA binding into reversible protein condensate formation. Rapid switching in condensate formation leads to reversible compartmentalization of a cluster of reporters for highly sensitive, real-time biosensing. The reversible nature of LRTF-DNA interaction will also be exploited to create dynamic metabolons for metabolite-responsive control of metabolism that is useful for a wide range of fundamental studies and synthetic biology applications. From educational and outreach perspectives, the research will span the core disciplines of biology, chemistry, and engineering, and will therefore provide ample opportunities for student training at all levels and in multiple areas. This project will also facilitate outreach activities to local high school teachers and students through existing programs available at the University of Delaware Real-time quantification of intracellular metabolites is essential for our ability to interrogate, understand, and engineer metabolism in a range of biological systems. This project will exploit the rapid and reversible binding of LRTFs to their cognate operator sequences as a transformative framework for real-time metabolite sensing. Inspired by the ease and efficiency of promoting condensate formation, the investigators will create a new metabolite-responsive strategy to reversibly sequester fluorescent protein reporters within the condensate compartment based on DNA binding for real-time biosensing. The reversible nature of assembly will also be exploited to create dynamic metabolons for metabolite-responsive control of metabolism that is useful for a wide range of fundamental studies and synthetic biology applications. This research will impact the field of synthetic biology by creating a new method for real-time metabolite sensing and for metabolite-mediated dynamic assembly of metabolons in many organisms of interest. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This is an age of data, much of which is in the form of large networks, such as social networks, biological networks, or neural cell networks. These real-life networks are very complex, and understanding their structure is crucial for developing robust and efficient algorithms on them. The Principal Investigator (PI) aims to develop a systematic mathematical approach for analyzing and visualizing large networks. By integrating two different areas of mathematics, namely Discrete Mathematics and Analysis, the PI plans to extract large-scale features of networks using mathematical limit theories. Potential practical applications of this research include diverse instances such as data analysis in sociology, psychology, and image processing. Additionally, the PI is dedicated to teaching and training graduate and undergraduate students. To support this, the PI will supervise student research on problems related to this topic. Moreover, the PI will organize two events at the University of Delaware: one for high school students and another for undergraduate students. A fundamental problem in the analysis of networks is to uncover their ‘hidden spatial layout’, i.e. to label their vertices according to the spatial features of the network. This is the well-known seriation problem, a challenging problem in machine learning. In recent years, various heuristics and algorithms for the approximate versions of the seriation problem have been developed, but there is little theoretical evidence for why these methods should perform successfully or how they can be extended to the multi-dimensional case. Here, we discuss several intriguing and challenging questions regarding robustness/consistency of spectral seriation and its generalizations to higher dimensions. Graphons offer a non-parametric approach to network modeling, that is highly valuable when studying stochastic networks. Employing the powerful theory of dense/sparse graph limits, the PI plans to answer questions about stochastic networks (large discrete structures that vary over time) by inspecting the associated graphons (fundamental limit). To address the multi-dimensional case, the PI will develop a novel robust parameter which measures the extent of n-dim spatiality of a network. The PI will then investigate how spatial graphons can be used to provide instance-independent graph signal processing methods for real-life networks. Moreover, the results of this research will lead to further interactions between graphon theory, functional analysis, and learning theory. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award is funded by the Major Research Instrumentation Program and the Chemistry Research Instrumentation Program. Professor Donald Watson from the University of Delaware (UD) along with Professors Mary Watson and Mark Blenner and Dr. Jessica Sampson, on behalf of 20 research groups from seven departments, is acquiring a high throughput enabled ultra-high pressure liquid chromatography mass spectrometer (UPLC-MS) system equipped with a diode array detector, evaporative light scattering detector, and fluorescence detector. UPLC-MS enables both the rapid separation and identification by exact mass of small molecules by reverse phase chromatography, essential for analysis and quantification of the complex mixtures present in samples generated in high throughput synthetic biology and synthetic chemistry experiments. This instrumentation enables high throughput research across the university and the nation and enables a diverse population of students of all levels to receive training in high throughput experimentation for synthetic chemistry and synthetic biology. This award is aimed at enhancing and enabling high throughput synthetic chemistry and synthetic biology research and education. The instrument, an ultra-high pressure liquid chromatography mass spectrometer (UPLC-MS), enables rapid separations of small molecules with detection by UV-Vis, mass spectrometry, fluorescence, and evaporative light scattering. It enables the collection of large, high-quality datasets for the synthesis of small molecules, which are essential in the development of machine learning models for chemistry and the optimization, development, and evaluation of new synthetic chemistry and synthetic biology methods. This instrument will enable research of 20 highly funded and productive research labs from 7 departments at UD, including Biological Sciences, Chemical & Biomolecular Engineering, Chemistry & Biochemistry, Earth Sciences, Materials Science & Engineering, Plant & Soil Sciences, and Physics & Astronomy, as well as for partner academic labs and collaborators at R1, R2, and PUI institutions both regionally and across the nation. It will be used to support a wide variety of exciting research projects, including the development of synthetic biology methods aimed at the production of monoterpenes, indole alkaloids, flavonoids, and fatty alcohols; the total synthesis of anti-cancer natural products; new biorthogonal ligation reactions for chemical biology discoveries; non-canonical amino acid biosynthesis; boron-based materials synthesis; polyolefin degradation; biomass valorization; and new methods for the chemical synthesis of valuable small molecules. This instrument will also support efforts to rapidly generate high-quality data that can be used for machine learning projects. It will also enable the creation of a new undergraduate laboratory experience, allowing college students to collect data sets that can then be used to teach them machine learning principles and strategies. Overall, this instrument will have a tremendous impact the synthetic biology and synthetic chemistry research at the University of Delaware and beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The behavior and function of microbial communities can be driven by the spatial arrangement of their members. However, few molecular strategies exist to direct the self-assembly of some community members and not others. This project seeks to repurpose innovative tools, recently generated in the field of biological containment, for the study and control of the structure of microbial communities. The benefit will be greater understanding of the principles by which natural and synthetic microbial communities arrange themselves for improved function in settings such as soil, which in turn aids the health of ecosystems. These modified microbial communities would enhance applications in biotechnology as well as the containment of environmental toxic waste. In addition, scientists in the UK and US will conduct local outreach activities to strengthen the pipeline of students interested in STEM and public awareness of microbial containment techniques. This project engineers certain microbes whose persistence rely on molecules that do not occur in nature as well as other microbes. This creates a new metabolic interaction that is required for survival of the dependent microbe and is exclusive to this pair of microbes. The project investigates whether such interactions can create predictable spatial variation, including within a larger synthetic microbial community. The project also investigates innovative advances in physical containment techniques to understand how the encapsulation of one of the engineered microbes affects the entire spatial arrangement or maintain distinct conditions. Together, the application of physical and biological containment techniques holds promise for understanding the rules of spatial association between microbes in a microbial community. This collaborative US/UK project is supported by the US National Science Foundation (NSF) and the UK Biotechnology and Biological Sciences Research Council (BBSRC), where NSF funds the US investigator and BBSRC funds the partners in the UK. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Abstract The treatment and care of Alzheimer's disease and related dementias (ADRD) place a huge burden on individuals, families, and healthcare systems. The estimated lifetime cost of care for an individual living with ADRD is nearly $400,000. Caring for a loved one with ADRD can put family members in untenable financial positions, such as having to decide between providing appropriate medical care for a loved one or paying for other essential living costs. These hardships are exponentially worse for lower-income and otherwise marginalized families, who often have inadequate insurance or resources for their loved ones with dementia. Thus, there is a need to investigate and quantify the financial hardship that is experienced with ADRD, using the National Institute on Aging Health Disparities Framework, with the ultimate goal of ameliorating this financial hardship, particularly among families in socioeconomically diverse or disadvantaged groups. Due to the lack of standardized, valid measurement tools, the true financial hardships from ADRD are unknown, and continue to go unaddressed. Current measures of financial stress and strain are general and grossly underestimate the burdens because they do not measure unique ADRD care-related challenges and costs. The impact on spouses, partners, children, and even grandchildren are overlooked, leading to the intergenerational costs of ADRD being ignored, with devastating impact. Therefore, we propose to develop and validate novel item banks using best-in-the-field, psychometrically advanced, mixed-method approaches to quantify financial hardship across families, including individuals living with ADRD and their families, partners, and caregivers. These measures of financial hardship in ADRD will be publicly available to inform researchers and policymakers about the extent of the burden and any disparities in who is most impacted. Ultimately, new measurement tools of financial hardship are required to address these disparities and improve quality of life for individuals living with ADRD, their families, partners, and caregivers, to provide early identification and proper interventions. Four aims of this study include: 1) identify the experiences of financial hardship for individuals living with ADRD and their family members, partners, and caregivers using qualitative methods, and develop pools of items on financial hardship responsive to these experiences; 2) calibrate the item banks using Item Response Theory and develop efficient, user-friendly measurement tools for use with individuals living with ADRD, their families, partners, and caregivers; 3) establish reliability and evaluate construct validity of the developed measures in individuals living with ADRD, their families, partners, and caregivers; and 4) devise an efficient screening approach from the newly developed measures that identifies people most at risk of financial hardship and validate it against a well-established set of demographic and socioeconomic variables.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Selenoproteins are prominently engaged in the integrated cellular response to stress. Here we focus on the ER- bound membrane proteins selenoprotein K (selenok) and selenoprotein S (selenos), which are mostly known for their connection to ER stress resolution. However, they also contribute to other cellular processes, such as protein quality control, protein palmitoylation, cytokine secretion, immune response, calcium signaling, protein trafficking, and differentiation. Consequently, their genetic variations are associated with heightened risks for - among others- cardiovascular diseases, diabetes, and cancer, while their expression levels are tied to cancer prognosis. However, there is no unifying and comprehensive view that plausibly spans their involvement in multiple protein complexes and connects their seemingly disparate cellular roles. Motivated by our work, we propose that selenok and selenos are putative ER-bound transcription factors. Upon stimulation, they are processed and migrate to the nucleus to affect gene expression. To test selenok’s and selenos’s direct involvement in gene transcription, we will determine their nuclear forms and evaluate their interactions with nucleotides and transcription factors. We also suggest that in the ER membrane, selenok and selenos contribute to protein quality control by acting as sensors of misfolded and misassembled proteins. There are indications that the two selenoproteins act as accessory proteins of derlins, which are part of the ER- associated degradation (ERAD) pore-forming complex. In this capacity, selenok and selenos could identify “client” proteins for extraction through the ERAD’s pore, delivering them to derlins and thus preventing harmful accumulation. Based on preliminary experiments, selenok and selenos appear able to recognize certain protein surfaces, such as hydrophobic leucine zippers, positive patches on armadillo domains, and b-propeller folds of proteins. These ‘recognition’ surfaces are usually hidden when proteins are properly folded, and complexes are well-assembled. However, when these systems fail to take on their intended forms, these surfaces get exposed. Using a structural biology approach, we will characterize the complex of selenok, selenos, and derlin. To substantiate this sensing function, we will investigate how the presence and absence of selenos influence the interactions between representative degradable ‘clients’ and derlins using in vivo photo-crosslinking of unnatural amino acids at site-specific locations. In addition, we will study the interactome of the less characterized selenok and identify its directly bound protein partners and enzymatic substrates. These dual roles of selenok and selenos in gene transcription and ERAD sensing would explain how they can influence numerous signaling pathways, engage with multiple protein complexes, and respond to cellular stress. Altogether, this proposed research will provide fundamental insights into the rich diversity of selenium-based redox biology and the specifics of cellular roles of selenok and selenos under normal and stressed conditions, thus providing a molecular-level explanation of why they exert such a significant influence on human health.

NSF Awards · FY 2024 · 2024-08

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-08

Ubiquitination as a reversible post-translational modification is essential to a broad range of cellular processes in humans. In addition to its function in proteasome-mediated protein degradation, ubiquitination is crucial for many non-proteolytic cellular processes. These cellular processes are regulated by a diverse array of polyubiquitin chains of different linkages and topologies, including recently discovered branched polyubiquitin chains. Deubiquitinases (DUBs) modulate the ubiquitin chain linkage and structure and are considered promising new targets for therapeutic intervention. The goals of our work are to investigate the recognition and cleavage of atypical polyubiquitin chains and ubiquitinated proteins by DUBs; identify and characterize reader proteins of branched ubiquitin chains and understand the roles of branched ubiquitin chains in mitophagy and receptor endocytosis. These projects are interconnected and complementary to each other, building on our success in chemical protein ubiquitination and DUB probe construction using semisynthetic approaches. The new chemical biology tools developed, particularly novel activity-based DUB probes, will serve to unveil new biological targets and functions in the ubiquitin system and provide new ways of identifying the reader and eraser proteins of ubiquitin signals. The mechanistic and structural insights obtained will in turn propel drug discovery efforts exploiting the ubiquitin system and lead to new opportunities and means for treating human diseases including neurodegeneration and cancer.

NSF Awards · FY 2024 · 2024-08

Coastal communities face growing and compounding risks that are exacerbated by the effects of climate change and sea-level rise. With nearly twice the global rates of sea-level rise, the U.S. Atlantic seaboard is particularly vulnerable and some communities are disproportionately affected. Advancing our scientific understanding of the physical risks and vulnerabilities to coastal hazards, such as flooding and salinization, is essential for identifying vulnerable communities and assessing how threats are likely to impact the wellbeing of people in these areas. The Risks, Impacts, & Strategies for Coastal Communities (RISCC) project brings together researchers and community stakeholders from three EPSCoR jurisdictions representing the lowest-lying states in the country: Delaware, Rhode Island, and South Carolina. The overarching goal of the project is to empower disproportionately affected communities to make effective and inclusive adaptation decisions that support long-term climate resilience to threats of flooding and salinization. To accomplish this goal, the RISCC team will build convergent and translational research and workforce development infrastructure that integrates behavioral and natural sciences, engineering, economics, public policy, planning, education, and outreach. The team will advance the assessment of risks and vulnerabilities to compounding hazards, identify effective adaptation strategies that are supported by coastal residents and decision makers, develop decision support system innovations based on iterative feedback from users in our partner communities, and create novel education and outreach materials that will enhance the capacity of disproportionately affected communities to increase resilience to climate change threats through evidence-based planning and adaptation. This project is a collaboration among the University of Delaware, University of Rhode Island, College of Charleston, University of South Carolina, South Carolina Sea Grant Consortium, Delaware Technical Community College, and The Citadel, in partnership with community organizations representing the interests of Little Creek and Delaware Bay beaches, Delaware; the city of Warren, Rhode Island; and Edisto Island, South Carolina. The project will advance scientific knowledge on how disproportionately affected coastal communities experience and effectively adapt to flooding and salinization. The research team will accomplish this goal with seven objectives: (1) co-develop solutions with partner communities to support sustainable adaptation decisions, (2) develop comprehensive geospatial datasets to advance the assessment of flood vulnerability and mitigation suitability, (3) model and map groundwater flooding and salinization risks, (4) quantify the economic impacts of flooding and salinization, and analyze different community preferences for adaptation strategies, (5) understand how local decision makers assess and plan for climate hazards, (6) integrate research outcomes from natural and social sciences and engineering to develop decision support systems that our disproportionately impacted partner communities (and communities like them) can use to determine which adaptation strategies will likely be effective now and in the future, and (7) prepare diverse researchers and decision makers to understand the science and implementation of coastal adaptation through education, outreach, and workforce development. Faculty and researchers from diverse institutions will co-create education, outreach, and workforce development materials, which will be widely communicated among racially and culturally diverse students and stakeholders. Outreach activities will include the development of a documentary film for national distribution via the PBS network to increase public scientific literacy and promote public engagement with science on climate change hazards and adaptation. This project is funded by the EPSCoR Research Infrastructure Improvement-Focused EPSCoR Collaborations (RII-FEC) program. The RII-FEC program builds inter-jurisdictional collaborative teams of EPSCoR investigators in focus areas consistent with the NSF Strategic Plan. RII-FEC projects include researchers from at least two EPSCoR eligible jurisdictions with complementary expertise and resources necessary to address challenges, which neither party could address as well or as rapidly independently. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Constructing approximations of complicated functions using simpler ones is a problem of fundamental importance in many fields of sciences and engineering. Such approximations are used, for example, to simplify calculations and make computer algorithms faster and more efficient. This is particularly important for real-time systems (flight control systems, medical devices, smartphones, sensors, etc.) where numerical calculations need to be performed as quickly as possible. Polynomials play a crucial role in constructing such approximations. They are used across many fields due to their versatility in simplifying complex functions and providing accurate estimations. In many instances, however, building explicit polynomial approximation schemes remains an open problem. In this project, the investigator and his colleagues study new methods to construct polynomial approximations for functions belonging to well-known spaces of functions. The research advances our knowledge of how to efficiently build such approximations and also connects the problem to other fields in mathematics such as matrix analysis and operator theory. The project also supports education by training one PhD student to become a new expert in the field. The PI will also engage in undergraduate and high school student mentoring and outreach activities. Function approximation constitutes a significant branch of analysis, involving the approximation of general functions by various families of simpler ones. This concept holds far-reaching applications across diverse mathematical disciplines and scientific domains. This project tackles challenging questions in complex analysis, focusing on polynomial approximation in spaces of analytic functions. While its history is extensive, a comprehensive understanding of polynomial approximation in many function spaces has been attained only recently, while others remain very active areas of research. The first part of the project explores constructive polynomial approximation schemes in weighted Dirichlet and in de Branges--Rovnyak spaces by re-framing the approximation problem as concrete matrix and operator theory problems. The second part of the project focuses on determining general conditions guaranteeing well-known approximation schemes (Cesaro, Abel, etc.) converge in general Banach holomorphic function spaces. The classical approximation property (AP) of Banach spaces plays a crucial role in this problem. Of particular interest is how strengthened versions of the AP can be used to characterize the existence of specific approximation schemes. Properties of the kernel of reproducing kernel Hilbert spaces of functions also have the potential to reveal when specific approximation schemes are valid. The techniques developed in the project are applicable to several fundamental spaces of analytic functions and therefore contribute to the broad problem of understanding constructive polynomial approximation in function spaces. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Students’ motivation to learn mathematics in school tends to decline as students move from elementary school through middle school, and changes in how mathematics is taught influence their motivation and interest. When students are more interested in mathematics, they are more likely to continue to want to learn mathematics as they advance from secondary school to college and the workplace. Rough draft mathematics is an engaging and motivating set of teaching practices that can reduce students’ fears so that they participate and experience positive emotions while learning mathematics. The teaching approach gives students the opportunity to revise their thinking so they can receive support for making new connections between mathematics ideas and concepts. The study will explore how teachers' use rough draft mathematics approaches and the influence on students' engagement with mathematics. Three different school districts in Delaware, Washington, and Virginia will participate in the study to understand how teachers use different teaching practices. The project will (1) identify the presence and degree of mathematics teaching practices that promote engaging in rough drafting and revising in 24-32 middle school classrooms, (2) study middle school students’ engagement (participation and emotions) in the moment of a lesson, and (3) examine changes in students’ motivation over time (self-efficacy, mastery goals, growth mindset, and sense of belonging in math) in these instructional contexts. A major goal of this study is to capture how variations in teachers’ enactments of rough drafting and revising have differential impacts on students’ engagement and motivation. Data will come from classroom videos, an observation rubric, and teachers' self-report data in the form of surveys and teaching diaries. The teacher data will gather their plans for teaching and reflections after the class. From students, the data gathered will include experience sampling surveys to capture their engagement during the lesson and surveys about their motivations, goals, and self-efficacy. Students may also participate in focus group interviews about their experience in mathematics class. Findings from the study will help inform how specific mathematics teaching practices might engage students and influence their motivation to learn mathematics. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Part 1: NON-TECHNICAL DESCRIPTION The need for improved energy storage materials for rechargeable batteries cannot be overstated. This is particularly true for sodium-ion batteries, which are an emergent technology following up on lithium-ion batteries. Owing to the higher natural abundance of sodium in comparison to lithium, especially in the U.S., it is conceivable that sodium-ion batteries can replace lithium-ion batteries for mid-to-large scale applications in the near future. However, identifying suitable materials with higher charge storage capacities and cycle life for sodium-ion batteries remains a major challenge, which could be overcome with more fundamental research aimed at understanding how the structure of the material affects ion migration and how the structure of the material is affected by repeated electrochemical cycling. Through this collaborative project, supported by the Solid State and Materials Chemistry program in the Division of Materials Research at NSF, researchers at Arizona State University and the University of Delaware jointly identify structural features of open framework materials, based on the elements silicon, germanium and tin, which can promote fast, sodium-ion diffusion. Of particular interest are a class of compounds known as clathrates, which exhibit cage-like structures that can host a variety of metal guest atoms, including lithium and sodium. The team also develops new approaches to synthesize such materials. Thereby, the gathered new knowledge helps establish connections between the structural aspects to the physical, electrochemical, and materials chemistry properties, which can lead to new materials for improved battery technologies. The fundamental science gained from these studies could also have far reaching impacts in other fields where these materials have potential applications, such as superconductors, thermoelectrics, optoelectronics, magnets, and photovoltaics. Additionally, this collaboration between two universities and three different departments (materials science, chemistry, and physics) engages students in multidisciplinary research. Outreach and educational activities also provide students with interdisciplinary training and immerse them into areas outside their immediate field of expertise. Part 2: TECHNICAL DESCRIPTION This collaborative project, supported by the Solid State and Materials Chemistry program in the Division of Materials Research at NSF, identifies structural features that lead to fast ion diffusion and aims to obtain better understanding of electrochemically driven phase transformations in Li-Tetrel (Tt) and Na-Tt systems, particularly for clathrates and other open framework structures. The specific objectives of the research are to: (1) Understand the structural subtleties for Tt (Tt = Si, Ge, Sn) clathrate and related materials that promote high ionic mobility; (2) Understand ionic transport within this phases; (3) Re-evaluate phase equilibria within the Na-Tt systems using novel synthetic strategies and isostructural model compounds; and (4) Use electrochemistry to inform solid-state synthesis and vice versa, to enable new synthetic approaches for energy-related bulk materials. Through a concerted approach combining the synthetic, structural and electrochemical characterization, and theoretical expertise of the PIs, this work furthers understanding the electrochemical behavior, leading to new insights on structural features that result in fast diffusion pathways, low ion migration barriers, and phase stability. Novel synthetic approaches combining high temperature coulometric titration and low temperature flux methods are used to trap kinetic/metastable phases and controllably synthesize high quality single-crystalline materials. Isostructural compounds containing key Li local environments are employed as model compounds to understand the ion (de)insertion processes in Li-Tt and Na-Tt binary (and ternary/quaternary) compounds. By means of a unique feedback loop connecting electrochemistry and synthesis, information about phases formed during electrochemical lithiation/sodiation is used to design novel precursors for synthesis, and solid-state reactions using chemical oxidation are adapted to develop electrochemical synthesis methods with finer control over composition. Synchrotron X-ray studies are used to characterize the local and crystalline structures and phase evolution during electrochemical reaction and/or synthesis. In all cases, density functional theory calculations support experimental findings and guide materials design, particularly by identifying formation energies and ionic transport mechanisms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Both the amplitude and recurrence frequency of Earth’s glacial-interglacial cycles have evolved over the past two million years. Prior to 1.2 Myr, glaciers were small and appeared every 41 thousand years. Larger glaciers that appeared every 100 thousand years became dominant after 0.7 million years ago. The period between 1.2-0.7 million years ago is known as the Mid-Pleistocene Transition (MPT). During the MPT, Earth did not experience any significant changes in its orbit to support the observed changes in ice age patterns. Why the Earth’s glacial rhythm changed during the MPT has been a topic of debate for the last several decades. Changes in deep ocean circulation during the MPT have been proposed to have a major role in perturbing the Earth’s carbon cycle and ushering the 100 thousand-year world. The overarching goal of this project is to reconstruct the changes in deep ocean circulation and how the water mass geometry evolved over time, with a special emphasis on the MPT. It will also investigate the link between deep Southern Ocean stratification, associated changes in the carbon cycle, and subsequent changes in glacial rhythm. This project will use Nd isotopes preserved in fossil fish teeth and Fe-Mn coatings on foraminifera as a water mass proxy to reconstruct past changes in deep ocean circulation. These analyses will be performed at two sites drilled in the central South Pacific during the International Ocean Discovery Program Expedition 383. This project will support a graduate student and create research opportunities for undergraduate students from a Minority-Serving Institution. The deep ocean is a critical component of the climate system because it can act as a carbon reservoir and, consequently, influence glacial cycle behavior. Very recently, a few Neodymium (Nd) isotope studies from the Atlantic Ocean have reported that changes in deep ocean circulation during the MPT could have ushered/supported the late Pleistocene 100 kyr glacial periodicity. Currently, there is limited knowledge about the state of deep ocean circulation in the deep South Pacific and its relationship to concurrent climate changes. This project will use Nd isotopes preserved in fossil fish teeth and Fe-Mn coatings on foraminifera as a water mass proxy to reconstruct past changes in deep ocean circulation at two sites drilled in the central South Pacific during the International Ocean Discovery Program Expedition 383. The South Pacific represents the largest volume fraction of the deep Southern Ocean and could have acted as a carbon reservoir storing and releasing carbon on a glacial-interglacial time scale. By integrating the results from the South Pacific with findings from the Atlantic Ocean, this project will generate a more comprehensive understanding of the role of deep ocean circulation in driving climate changes during the MPT. Mechanisms that changed oceanic carbon cycling in the past are of particular importance to understanding present and future climate change. This project will also support a graduate student and create research opportunities for undergraduate students from a Minority-Serving Institution. The researchers will participate in local community outreach activities to communicate the importance of scientific ocean drilling, and this work in general will serve the US scientific ocean drilling community during the transition period post-JOIDES Resolution. This project is jointly funded by the Marine Geology and Geophysics Program in the Division of Ocean Sciences and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project addresses the critical challenge of improving gait rehabilitation for stroke survivors, who often face long-term disabilities that significantly impact their quality of life. Current rehabilitation methods, which are typically repetitive and generic, offer limited effectiveness. This research introduces an innovative robotic intervention using the Variable Stiffness Treadmill 2 (VST 2), which can adjust the walking surface's stiffness to enhance balance, propulsion, and symmetry in gait. By developing a comprehensive and personalized gait model, the project aims to tailor rehabilitation strategies to individual characteristics, potentially revolutionizing gait therapy with long-lasting benefits. The project's broader impacts include advancing our understanding of motor adaptation and learning in human gait, which can be applied to various fields such as orthotics, prosthetics, and robotic walkers. Additionally, the research will establish practical guidelines for clinical settings to improve stroke rehabilitation outcomes. The project also emphasizes education and training for underrepresented high school and college students through annual design competitions and summer internships, fostering diversity in the field. All generated materials and models will be openly shared with the scientific community, promoting reproducibility and further research. The primary goal of this project is to enhance post-stroke gait recovery by improving balance, propulsion, and symmetry. The scope of the project includes utilizing the Variable Stiffness Treadmill 2 (VST 2), which introduces unilateral and bilateral perturbations to the walking surface's vertical stiffness. Using a Nonlinear Model Predictive Control (NR-MPC) approach, a multi-layer neuromuscular model that captures long-term motor adaptations in human gait will be developed. This model will guide the creation of personalized intervention protocols tailored to individual patient characteristics. Methodologically, the project will involve testing the VST 2 and the neuromuscular model on both healthy subjects and hemiplegic stroke survivors. For stroke patients, the model will be customized to generate optimized perturbations aimed at promoting specific long-term rehabilitation outcomes. These interventions will be evaluated on stroke subjects, focusing on metrics such as increased propulsion, improved balance, and enhanced gait symmetry. The potential contributions of this project to science and engineering include a deeper understanding of motor adaptation and learning in human gait, advancements in personalized rehabilitation techniques, and the development of innovative technologies applicable to orthotics, prosthetics, and robotic assistance devices. This research has the potential to significantly impact clinical practices and improve the quality of life for individuals with gait impairments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

PROJECT ABSTRACT/SUMMARY Despite decades of traditional interventions targeting diet and physical activity, childhood obesity remains a pervasive and intractable public health problem. It is therefore imperative to prioritize discovery of novel, modifiable risk factors to prevent obesity beginning in early life. Environmental exposures during critical developmental windows, especially the prenatal period, may contribute to obesity development by altering infant physiology and metabolism. Among these potential environmental obesogens, phthalates are one class of endocrine-disrupting chemicals with ubiquitous exposure in the population. Phthalates may promote obesity through mechanisms involving oxidative stress, alteration of adipocyte formation and function, and interference with metabolic pathways. Prenatal exposure to phthalates has been associated with greater childhood body mass index (BMI) in some studies, but others have reported null or inverse associations. Importantly, methodological limitations, small sample sizes, and homogenous study populations of these prior investigations make it difficult to reconcile apparent discrepancies, limit our capacity to determine the most potent metabolites, and preclude identification of critical windows of vulnerability. Furthermore, no strategies have been identified to mitigate potential obesogenic effects of prenatal phthalate exposure. Oxidative stress and associated inflammation are proposed pathways linking phthalates to obesity. Therefore, dietary patterns that reduce oxidative stress, such as those rich in antioxidants and omega-3 fatty acids, may be an impactful intervention target during pregnancy. The proposed research will utilize the rich longitudinal data from the Environmental influences on Child Health Outcomes (ECHO) Program, a consortium of 69 pediatric cohorts from across the United States. The proposed project aims to examine the association between prenatal phthalate exposure and BMI z-score and overweight/obesity risk in children 2-5 years of age (Aim 1a) and explore prenatal critical windows of phthalate exposure on childhood BMI z-score (Aim 1b). As a solution- oriented approach, we will also evaluate how diets closely matching the Dietary Guidelines for Americans (Aim 2a) and those rich in fish consumption (Aim 2b) may modify the association of prenatal phthalate exposure with early childhood overweight/obesity. Our team's combined expertise extends across multiple domains critical to the success of this project. Dissertation committee members serving as key personnel bring substantial expertise in statistical methods, ECHO structure and organization, nutrition, childhood growth, and the effects of phthalate exposure on childhood health. Successful completion of this research will help set the stage for the development of strategies to mitigate the potential obesogenic effects of phthalate exposure in vulnerable populations. Given that childhood obesity tracks into adulthood, utilizing ECHO to address these aims will enhance the health of children for generations to come.

NSF Awards · FY 2024 · 2024-07

This project will study the turbulence in a stratified layer at the air-water interface, as caused by waves and wind. The study will carry out simulations with laboratory experiments and with computer models. Simulations will test the hypothesis that to represent the deepening of a surface layer reliably, it is necessary to couple currents and waves. Simulations will also test a parametrization of turbulence related to waves (Craik-Leibovich parametrization), and the results of combining a couple of parametrizations. For broader impacts, the project could improve the reliability in representing the ocean surface boundary layer of Earth-system models. Moreover, the PIs would produce educational materials to be used at their home institutions. In addition to supporting 4 PIs, this effort would fund one postdoctoral scholar and one graduate student. The proposed study seeks to advance understanding of wind-driven and wave-driven near-surface turbulence in a stratified surface boundary layer. This goal would be pursued with a combination of a) controlled laboratory experiments of stratified turbulent mixing under the influence of surface waves in the surface boundary layer, and b) state-of-the-art (Large-Eddy Simulations) numerical experiments. Laboratory and numerical simulations will test the hypothesis that the coupling between waves and wind-driven currents is necessary to reliably represent the surface mixed-layer deepening. The comparison between lab measurements and numerical simulations would seek to i) assess the reliability of LES (Craik-Leibovich) simulations in representing lab observations of turbulent mixing beneath horizontally heterogenous surface waves, and (ii) determine the effects of combining coarser grids in LES simulations with a turbulence closure. As broader impacts, the project could potentially improve the accuracy of Earth-system numerical simulations of the ocean surface boundary layer. Moreover, the PIs would produce educational materials to be used at their home institutions. In addition to supporting 4 PIs, this effort would fund one postdoctoral scholar and one graduate student. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Data with survival outcomes are commonly encountered in real-world applications to capture the time duration until a specific event of interest occurs. Nonparametric learning for high dimensional survival data offers promising avenues in practice because of its ability to capture complex relationships and provide comprehensive insights for diverse problems in medical and business services, where vast covariates and individual metrics are prevalent. This project will significantly advance the methods and theory for nonparametric learning in high-dimensional survival data analysis, with a specific focus on causal inference and sequential decision making problems. The study will be of interest to practitioners in various fields, particularly providing useful methods for medical researchers to discover relevant risk factors, assess causal treatment effects, and utilize personalized treatment strategies in contemporary health sciences. It will also provide useful analytics tools beneficial to financial and related institutions for assessing user credit risks and facilitating informed decisions through personalized services. The theoretical and empirical studies to incorporate complex nonparametric structures in high-dimensional survival analysis, together with their interdisciplinary applications, will create valuable training and research opportunities for graduate and undergraduate students, including those from underrepresented minority groups. Under flexible nonparametric learning frameworks, new embedding methods and learning algorithms will be developed for high dimensional survival analysis. First, the investigators will develop supervised doubly robust linear embedding and supervised nonlinear manifold learning method for supervised dimension reduction of high dimensional survival data, without imposing stringent model or distributional assumptions. Second, a robust nonparametric learning framework will be established for estimating causal treatment effect for high dimensional survival data that allows the covariate dimension to grow much faster than the sample size. Third, motivated by applications in personalized service, the investigators will develop a new nonparametric multi-stage algorithm for high dimensional censored bandit problems that allows flexibility with potential non-linear decision boundaries with optimal regret guarantees. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Team-based projects are widely used in undergraduate engineering courses—from first-year introductory engineering courses to senior-level capstone projects. Assigning students into teams to collaborate on a design problem helps them to develop technical skills as well as essential professional skills. In this process, engineering students often struggle with conflicts in their teams. Typically, introductory engineering courses have large enrollments making it hard for a single faculty instructor to effectively monitor all the teams and intervene on team conflicts quickly. To mitigate this problem, many large-enrollment engineering courses employ undergraduate students who have previously taken the course to mentor student teams. It is important to coach undergraduate student mentors to identify and respond to team conflicts. This work will first document the common reasons behind student team conflicts in a large introductory engineering course with approximately 650 students. It will then use a simulated classroom environment to understand how undergraduate student mentors respond to various team conflict scenarios. Through this, we will identify the essential features of a coaching program that enables undergraduate students to become effective mentors of student teams and help them better navigate such conflicts. By developing strategies to promote persistence and professional skills for teamwork in the first year, we will empower all engineering students, including student mentors, to successfully navigate future team experiences in both future courses and professional life. Many students in engineering programs are assigned their first team-based design project in first-semester introductory engineering courses. Interpersonal conflict with teammates is a common challenge for students. Responding to team conflict in a timely manner is a logistical challenge in introductory engineering courses that typically have large enrollments. We are focused on developing scalable strategies for enabling first-year engineering students to navigate team conflicts successfully with the help of near-peer mentors (NPMs)—undergraduate students who have previously completed the course. This project seeks to develop a coaching program for NPMs to promote equity-oriented strategies for identifying and responding to conflicts that arise during team-based design projects. This project will focus on three key research questions: (1) What are the root causes and common characteristics of engagement-related team conflicts in introductory engineering courses? (2) Given a mixed-reality simulation of engagement-related team conflict, how do NPMs facilitate discussions that aim to diagnose and intervene in instances of team conflicts? (3) What features of a coaching program are essential to improve the efficacy of NPMs in responding to reports of disengaged team members with strategies that promote persistence and develop professional habits for all team members? This study will produce knowledge about the underlying causes of these team conflicts and how NPMs use pedagogical strategies to identify and remedy them. The research will be conducted using a mixed methods approach to collecting, analyzing, and integrating both qualitative and quantitative data sources. We will use a novel mixed-reality simulation environment to collect data from NPMs. The simulation scenarios will be informed by interdisciplinary research in education; diversity, equity, inclusion, and justice (DEIJ); and organizational behavior. It will be a first step toward a capacity-building strategy for addressing the problem in large-enrollment introductory engineering courses. Overall, this study promotes the professional formation of engineers through the development of professional skills necessary for equitable and effective collaboration on teams early in the undergraduate engineering curriculum. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.