University Of Delaware

NSF Awards · FY 2024 · 2024-10

Humans have a remarkable ability to seamlessly interact with synthetic agents (e.g., control brain machine interfaces, active prosthetics, and assistive exoskeletons) and other humans. Despite its ubiquity and importance, the mechanisms of co-adaptation that govern and lead to the emergence of these seamless interactions remain unknown. Understanding how humans interact with other adaptive entities is highly relevant across many facets of society, such as medical rehabilitation, human and artificial intelligence interactions, military, and economics. This award supports research towards the development of improved synthetic agents that co-adapt alongside humans, with the potential to enable development of more effective biologically-inspired robot-guided neurorehabilitation systems that can seamlessly interact with humans. The investigators leverage a state-space approach that has been successful in explaining how an individual learns, but has not been utilized to capture co-adaptation. Humans will be immersed in a state-of-the-art virtual reality and robotics suite that allows individuals to sense their synthetic agent partner or human partner during a learning task. In addition to healthy participants, the investigators also consider a stroke population to find the balance between assisting and resisting when attempting to improve long-term overall performance (human plus agent) or individual performance (human only). The blend of computational modeling and experimental work involving both healthy and clinical populations will allow for better understanding of the mechanisms that underpin emergent interactive behavior. The investigators also have a strong commitment to diversity, equity, and inclusion, and plan to include undergraduates and high school students in the research. 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 · 2024-10

Deep Neural Network (DNN) technology has achieved significant success in autonomous driving systems, particularly in environment sensing and perception tasks. However, ensuring the timing predictability of DNN decisions during operation of autonomous vehicles (AV) is crucial for safety. Substantial time variations persist in most DNN models within AV systems, and this project’s novelties are systematically controlling such variations for autonomous vehicles secure operation. The project's broader significance is enhancing the reliability and safety of autonomous vehicle perception systems, ultimately reducing accidents and improving road safety. This project consists of three research thrusts: (1) understanding the challenges of timing predictability in DNN inference for autonomous vehicles; (2) designing a framework for predictable DNN inference in multi-sensor and multi-task AV perception; and (3) integrating this framework into Autoware, a real AV pipeline. The project develops a configurable profiling framework to comprehensively understand the root causes of variability in the DNN inference pipeline. This framework allows fine-grained profiling of time variation issues, including data variability (sensor, weather, and traffic scenarios), model variability, and runtime system variability (communication middleware, operating system, and hardware architecture). To mitigate DNN inference time variations and ensure predictability, this project addresses single DNN inference variations through feature maps caching and fusion techniques. Additionally, multi-tenant DNN inference is optimized through co-scheduling across the application, middleware, operating system, and architectural layers. The team integrates the multi-tenant co-scheduling framework into Autoware, creating a lightweight message-wise timeline checkpoint with a feedback-based co-scheduler. Comprehensive evaluations are conducted using open AV datasets, an indoor connected and autonomous testbed (ICAT), and a 2018 Lincoln MKZ-based level-4 AV equipped with Autoware at the University of Delaware. 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 · 2024-10

This Engineering Research Initiation (ERI) award supports the evaluation of liquefaction effects within the context of seismic soil-system response through advanced field investigation and laboratory testing. Conventional liquefaction evaluation procedures overestimate the severity of ejecta at sites with stratified silty soil deposits and underestimate it at sites with thick, clean sand deposits, leading to overspending and economic losses, respectively. Nonlinear dynamic analyses suggest the in-situ soil-system response effects associated with pore water pressure redistribution and flow of water govern the formation of ejecta. However, those effects are not incorporated into conventional procedures. In addition, defects, such as voids and fissures, enhance the formation of ejecta yet are not captured by conventional procedures and numerical models. This research will generate new knowledge regarding liquefaction and its consequences and will significantly advance resilient engineering design and practice. It will deliver high-quality laboratory and field data to the worldwide research community to develop and improve empirical correlations regarding soil parameters, liquefaction triggering, and consequential effects. It will also improve the assessment and mapping of liquefaction effects by non-invasive cost-effective methodologies. Collaborations with US and New Zealand geotechnical engineers will result in the implementation of research findings in practice. The project will be complemented with educational materials that will expose students from diverse backgrounds to cutting-edge research in earthquake engineering. This research seeks to advance the state of knowledge and practice regarding the evaluation of liquefaction ejecta and its effects on structures at both stratified silty and thick, clean sand sites. The primary research objectives are: 1) document and characterize subsurface features (dikes, crust, liquefaction source layer, etc.) through ground penetrating radar and electrical resistivity imaging, unconventional trenching explorations using light detection and ranging and multispectral imaging techniques, detailed logging, and triaxial testing of “undisturbed” samples from a liquefaction source layer to identify mechanisms responsible for the formation or abatement of ejecta at sites where conventional procedures misestimated its severity; 2) use the subsurface data to validate the dynamic nonlinear effective-stress models for stratified silty deposits; 3) develop fundamental insights into the cyclic response of stratified silty deposits through advanced triaxial testing of reconstituted stratified silt-sand specimens; 4) develop recommendations for assessing the vulnerability of soil deposits to the formation of ejecta. This award will allow the PI to form the sound basis for the development of robust methods for evaluating ejecta and its effects on infrastructure and to establish an impactful research program and advance as a researcher and innovator in earthquake engineering. 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 · 2024-10

The security of supply chains is critical to ensuring the safety of systems and infrastructure that society depends on. The autonomous vehicle industry is an important and complex example of a system that (a) has large implications for safety and (b) relies on a wide variety of hardware and software elements in both the cars themselves and the roads they drive on. This makes connected and autonomous vehicles (CAVs) an excellent testbed for studying supply chain security questions. This project will support the planning of a large-scale SaTC Frontier proposal to advance science, education, and workforce development around supply chain security for CAVs. The eventual proposal will include identifying key security and privacy risks around CAV supply chains, developing methods to address them, and creating new courses and educational materials to better train a workforce ready to deal with critical supply chain security issues in CAVs and beyond. The project's goal is to develop a fully realized proposal in terms of the scientific questions, team and partnerships, and social impacts to be addressed. To do this the research team plans a number of activities with industry and academic partners. A seminar series and visits between the host institution and potential partners will develop both a better understanding of the problems and the foundation for advisory and research partnerships in the eventual full proposal. A workshop focused on CAV supply chain security associated with a large international conference will bring a wider community together. There are also plans to work with nearby institutions to broaden and widen the team's research expertise, as well as gaining expertise in how to structure and manage large research teams from the leaders of existing SaTC Frontier awards. Through these efforts the team will develop the knowledge, resources, and people required to create a high-quality, large-scale proposal. 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 · 2024-10

Social media has had a profound impact on various aspects of human lives, influencing how people communicate, access information, and interact with the world. Their impact is mainly delivered via the vehicle of social influence diffusion, the spreading of information cascades through the network. Example applications of social influence diffusion includes deploying advertising campaigns with the goal of maximizing brand awareness or having youth generate excitement around posts encouraging the prevention of obesity. From a computational perspective, the core of influence diffusion tasks is a decision-making problem that aims at computing an effective decision in response to a query, where the underlying system is often not completely known. This project seeks to develop novel methods to understand influence diffusion. In line with our research agenda, educational efforts will be devoted to curriculum design and encouraging the participation of underrepresented groups as well as K-12 students. The overall goal of this project is to establish query-decision regression as a principled decision-making diagram to understand social influence diffusion. In direct contrast with the conventional learn-and-optimize pipeline that computes decisions based on models assembled with separately learned components, we neither assume either a priori knowledge of the diffusion rules nor a parametric family of the energy function via deep architectures. Instead, we explore the principle of learn-for-optimization, seeking to push the learning process toward generating direct predictions that can maximize the aimed quantities. The proposed framework is statistically principled, with its foundations built on function approximation, hypothesis realizability, and generalization bounds. Our algorithmic development targets several key components, including loss-augmented inference based on the submodular nature of the diffusion process, cyclic training schemes that can seamlessly connect the discrete and continuous modules, and simulation mechanisms for generating task-aware estimators. The theoretical parts will be supplemented by empirical studies over real-world datasets of social influence cascades, covering a great variety of management tasks such as targeted viral marketing, information source detection, outbreak detection, and active friending. 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 · 2024-10

This project supports students’ travel to the 2024 Association for Computing Machinery (ACM) International Conference on Mobile Computing and Networking (ACM MobiCom), which will be held in Washington, D.C., USA, between November 18th and 22nd, 2024. MobiCom is a premier international conference focusing on systems issues in the emerging area of mobile computing and wireless communications. The conference attendance supported by this grant will provide career development and learning opportunities for 25 US-based students, with priority given to students from underrepresented groups (women, minorities, and people with disabilities) or students who would typically find it challenging to attend as the result of having not having a paper accepted in the conference. This proposal will increase the dissemination of the conference's research results to a larger and more diverse audience. Moreover, MobiCom has a strong track record of giving preference in grant awards to women and minority students. To encourage researchers from under-represented groups in their research in the nationally critical area of mobile networking, in the selection process, under-represented groups or institutions will be given priority for about two thirds of the travel awards. Advertising to a wide range of colleges and universities, participants from a more diverse set of institutions should also be able to attend and benefit from the conference. 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 · 2024-10

Machine Learning (ML) has emerged as a transformative force in advancing numerous high-stake domains, particularly in the realm of autonomous driving. With the rise of computationally powerful ML techniques, autonomous vehicles (AVs) can understand their environment with high precision of perception, make real-time decisions, and operate reliably without human intervention. As AVs are safety-critical, the end-to-end safety of its learning system is essential. However, our research reveals that potential unsafety may stem from multiple aspects, including the model, the hardware, or the system. First, ML models trained predominantly on common driving scenarios may extrapolate inappropriately when encountering unique road obstructions or rare weather conditions, leading to incorrect driving decisions or unsafe vehicle control. Moreover, the hardware platforms executing the ML models are imperfect and suffer from various types of faults and errors. Last but not least, when multiple ML models are executed concurrently, the real-time operating system (RTOS) of AVs may not deliver the decisions in time, leading to catastrophic consequences. Developing a safe learning-enable system for AVs requires orchestration of the model, the hardware, and the system. Bringing together experts from machine learning, hardware fault tolerance, and autonomous driving, this project focuses on cross-layer optimizations to achieve end-to-end safety. Key innovations include developing rational ML models to produce accurate predictions based on valid rationales, integrating hardware reliability into ML model design to tolerate runtime faults, and designing a novel RTOS scheduler that ensures time predictability while considering model and hardware reliability. The project will implement these advancements on real autonomous driving platforms to validate their effectiveness. The success of this initiative will significantly enhance AV safety, promoting safer, cleaner, and more efficient transportation. Additionally, the project will advance education and workforce development in AI and autonomous driving, with a commitment to diversity and inclusion in STEM fields. Results and software will be disseminated through open-source platforms, educational programs, tutorials and workshops, fostering broader impacts on technology and society. 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 · 2024-10

Continuous software development is a modern software practice enabling automated software development and deployment. It has gained widespread adoption across many organizations, including technology companies and financial institutions. Unfortunately, continuous software development is vulnerable to various software supply chain attacks, which have posed significant risks to the United States and the global community. This project aims to investigate new emerging threats and develop effective mitigation strategies to secure the software build and development process. The novelty of this project is to comprehensively and systematically inspect the entire continuous software development pipeline with relevant stakeholders covered. It can improve the nation’s cybersecurity by enhancing software supply chain security. Additionally, educational efforts and outreach activities will be conducted to promote cybersecurity awareness. This project will develop a holistic framework combining online dynamic executions and offline static analysis to automatically analyze the continuous software development pipeline. The first task will focus on developing a comprehensive security testing system analyzing all critical components and their interactions with relevant stakeholders. The second task will incorporate additional modules to investigate security threats in popular mechanisms and add-on features, such as third-party plugins and different sandboxing techniques. The third task will design a novel threat detector for organizations and developers to thoroughly analyze their repositories and identify security smells. Ultimately, the project aims to develop both short-term remediations and long-term defense systems that can effectively mitigate potential security threats. The overall security risks will be evaluated by large-scale measurement studies on open source repositories. The defense strategies will be integrated into existing systems, and thoroughly evaluated in real-world scenarios to demonstrate their effectiveness. 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 · 2024-10

Nowadays, scientific discovery increasingly involves generating and analyzing large amounts of data. These data-intensive scientific applications pose significant challenges to the storage systems of high-performance computing (HPC) clusters, that are heterogeneous and extremely complex. Scientists who need high-speed data access often experience frustration in effectively using these heterogeneous storage options. There is need to build the long-missing automated HPC I/O (Input/Output) middleware to transparently help scientists achieve optimal data access performance without their manual efforts. Designing automated HPC I/O middleware for large-scale, heterogeneous, and shared HPC storage systems is an extremely challenging task. The researchers supported by this grant plan to leverage machine learning techniques to understand the requests and the current system status, intelligently and adaptively scheduling and coordinating I/O requests. The outcomes of this research are expected to work with existing storage components and minimize the impacts on both scientific applications and the HPC systems. This project plans to tackle this grand challenge by exploring practical reinforcement learning-based (RL) methods and building relevant software infrastructure in an HPC environment. There are two main focuses in the project: 1) RL-based data placement for high storage utilization, and 2) RL-based I/O coordination for shared storage. Both tasks depend on identifying effective reinforcement learning methods and integrating these methods effectively into HPC systems. To achieve this goal, a novel, system-centric reinforcement learning framework will be developed. Moreover, in each research focus, various RL algorithms, deep neural network designs, and reward shaping will be proposed, implemented, rigorously benchmarked, and compared with state-of-the-art solutions. 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 · 2024-10

Because light can be collected freely or produced efficiently, driving chemical transformations with light instead of heat can have significant advantages over traditional chemical production methods. However, challenges remain in bringing these mostly academic findings to the scale necessary for industrial adoption, limiting the benefits to society that would result from safer and milder chemical processing conditions. An important factor in restricting the scalability of photochemical processes is that while high light absorptivity of the feedstocks is desirable for reaction efficiency, it also limits light penetration depth into the reaction medium. Commonly used photocatalysts can also be prohibitively expensive, and residual catalyst impurities in the final products often leads to discoloration or degradation. To address these limitations, this CAREER project will study the use of photocatalyst-coated optical fibers to guide light into the reactor vessels. If successful, the proposed work will lay the scientific foundations to facilitate the implementation of modern photochemistry on an industrial scale and enhance the impact of academic photo-reaction engineering innovations. Immobilizing photocatalysts on optical fibers is anticipated to improve light-penetration and efficiency of the catalytic process. Because the catalysts are immobilized and will not be added continuously with the reactor feed, the proposed approach will improve the process economics and will provide a path to manufacturing both pristine small molecules and polymers free of catalyst impurities. Eliminating such impurities is of importance for synthesis of high purity chemicals in biomedical and electronic applications where trace metals can introduce toxicity or be detrimental to device performance. The research plans have the potential to accelerate the implementation of modern and mild photochemistries on large scales and benefit society by helping to bridge the academia-industry divide. Education and outreach activities will also benefit from the close academia-industry ties to be developed, connecting undergraduate and graduate students and potential employers through field trips and panel discussions with industry leadership. Further, this program will develop and distribute inexpensive polymer science laboratory kits that will benefit underserved middle and high school students by improving access to a quality STEM education experience. This CAREER project will provide the fundamental engineering knowledge needed to translate academic advances in modern photochemistry to large-scale industrial applications. The objectives of this research program are to identify critical chemical structure-property relationships for organic photoredox catalysts that will enable surface-grafting to immobilizing substrates without affecting catalytic activity. By investigating a range of approaches to control the optical fiber evanescent field, optical fiber surface-tethered catalysts will subsequently be tested as heterogeneous photocatalysts in both batch and continuous-flow reactor systems. Catalyst surface density will be controlled through a combination of surface monolayer grafting and the use of bottlebrush polymer tethers. Once an optimal fiber unit spacing and distribution is identified, process throughput and scalability will no longer limited by light absorption, but exclusively by the size of the reactor. By bringing light into the reactor, Beer-Lambert absorption limitations will be circumvented to provide a highly scalable continuous throughput methodology. Because the photocatalyst is immobilized within the reactor (and not continuously added), it can be recycled for multiple reactions; furthermore, the final chemical product will be free of catalyst impurities, a condition necessary in many pharmaceutical and electronics chemical products. From an educational and outreach perspective, this program will broadly impact students of all ages and backgrounds by forming a coalition between university entities, rural schools, and industrial partners. The principal investigator will increase interfaces between undergraduate and graduate students and potential employers through field trips and panel discussions with industry leadership. Further, this program will pilot and distribute inexpensive at-cost polymer science laboratory kits to secondary students to benefit underserved middle and high school students by improving their access to quality STEM education. Finally, targeted community outreach events will promote university enrollment of socioeconomically challenged students while communicating scientific principles and the importance of sustainability and plastic waste recycling to non-technical audiences. 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 · 2024-09

PROJECT SUMMARY/ABSTRACT UniProt is one of the foundational resources for understanding the molecular details of the inner life of the cell. As a hub for knowledge of protein sequence and function, UniProt organizes knowledge from biomedical literature, integrates datasets with curated knowledge, and serves as an exemplar FAIR and TRUST resource reused by hundreds of other data resources. The Common Fund (CF) program brings together diverse collections of valuable biomedical data, yet still needs to be connected with the major data repositories ecosystem to uncover its full potential for discovery and innovation. In this application we will strengthen the protein-centric connectivity of UniProt to multiple CF Data Coordinating Centers (DCCs) and the Data Resource Center (DRC) in the Common Fund Data Ecosystem (CFDE), and will collaborate with two CF DCCs, the Knockout Mouse Phenotyping Program (KOMP2) and the Cell Maps for AI (CM4AI) Project of the Bridge to Artificial Intelligence (Bridge2AI) Program. The collaboration will support CFDE data integration and reuse, while building new capabilities to interrogate and understand complex biological systems and cell networks for deeper understanding of responses to perturbations at the systems level. To foster biomedical discovery through the re- use of CF data and enable novel scientific research that was not possible before, we will increase the connectivity and data integration between UniProt and CF datasets. We will create a new form-based mechanism to link with CF data resources, starting with several already identified for targeted integration. We will extend the UniProt mapping service, allowing researchers to identify data of interest through protein-centric search and promote interoperability at the protein level across the CF ecosystem. We will develop new tools, APIs and workflows with easy access and navigation for researchers to interrogate and understand complex biological systems and cell networks. It will allow seamless bidirectional navigation of data from whole subcellular systems to functional pathways and interacting proteins and vice versa. We will hold annual workshops to broaden collaborations with the CFDE and engagement with the research community. We will incorporate user feedback throughout the tool development lifecycle and implement an integration plan with the CFDE for dissemination and long-term sustainability. We aim to understand systems-level responses to perturbations through genotype-phenotype mapping and metabolic and signaling network discovery. We will integrate relevant datasets, tools, and literature and extend our knowledge graph learning algorithm for link prediction in a drug discovery use case. We will build customized knowledge graphs with various use scenarios, incorporating scientific questions from the demonstration project and user engagement. This UniProt-CF partnership will support functional genomics towards fundamental biological systems understanding and the development of new diagnostics and therapies.

NSF Awards · FY 2024 · 2024-09

This award funds a research project that tests an economic theory about the unintended consequences of public policy. Policies that aim to increase female economic empowerment can improve their economic inclusion, bargaining power in the home, and psychological well-being. However, these policies can have unintended consequences, with parents reallocating resources away from daughters’ education, dowry, and lower quality marital spouses. This research project will study the effect of a new system of land records that digitizes and centralizes records on female land inheritance, human capital, and marriage outcomes. The researchers will study the overall welfare effects of strengthening women’s’ inheritance and other investments, including education. This research contributes to knowledge by considering the full extent of household decision-making by linking inheritance decisions as closely tied to the other human capital investments. The results of this research will provide inputs into policies to improve women’s inheritance rights around the world. The results will also provide guidance on how to craft more efficient policies that will minimize their unintended consequences. This award funds a research project that studies the unintended consequences of policy reforms. The project exploits the staggered implementation of digitizing land records as well as the timing of household head deaths among agricultural landowning landlords to answer three questions: (i) whether the intervention works as it was intended, (ii) what are the direct impacts of the reform on education and marriage outcomes for women who receive inherited land, and (iii) what are the effects of the reform on resource allocation within landowning households who have not directly experienced an inheritance but may respond to the reform in anticipation of future inheritances. Data collection is through a phone survey. The research results will help policy makers craft better land reform policies that reduces the unintended consequences of the reform; the lessons can also be applied to other policies. The results of this research will also help establish the US a global leader in women’s rights. 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 project is designed to broaden our understanding of myogenesis (muscle formation) in birds and mammals. Myogenic processes play fundamental roles in decoding genetic instructions in developing (embryonic) and adult muscles. This project will use a novel approach for analyzing gene expression pattern to advance our understanding of such developmental mechanisms. The primary focus of the project is on the chicken, a species of immense importance to U.S. and global food security. This project will support the advancement of genome science and muscle stem cell biology through graduate and undergraduate mentorship, K-12 educator training, and Extension and outreach. Curricula for high school biology classes and the 4-H Youth animal program will be created to convey applied concepts of how proper muscle stem cell function ensures muscle development. Teacher Training Workshops will be offered on genome sequencing and computational analysis of resulting data. Educators will be supported as they incorporate these concepts and methods into lesson plans for their students. These workshops will provide teachers with hands-on experiences in state-of-the-science laboratories to learn applicable skills and scalable genetics projects for use in high school settings, thus enhancing science education and fostering a deeper understanding of genetics and muscle biology among high school students. Variation in gene regulation plays a fundamental role in shaping phenotypic diversity and is crucial for adaptation, ecosystem functioning, agriculture, and medicine. However, the mechanisms by which dynamic changes in gene regulation during development influence phenotypic variation remains poorly understood. This project will utilize recent progress in genomics, high-resolution tissue imaging, metabolomics and single-cell-resolution spatial transcriptomics to reveal genetic effects undergirding gene expression and phenotype, and guide future research into the genetic basis of phenotypic variation in plants and animals. Using chickens, an integrated analysis of gene and allelic expression, along with metabolite levels, will be conducted to identify gene and metabolic regulatory networks critical to fundamental cellular processes and regulation during myogenesis. Our research will identify alleles that play a key role in reprograming or acquiring specialized cellular metabolic state, or supporting anabolic growth. We will then examine the precise roles of a subset of these alleles using primary muscle cell cultures from multiple species and a muscle cell line to validate interspecies generalizability of results. The chicken (Gallus gallus) is an outstanding system for developmental biology and studying the genetic basis of complex traits due to in ovo (egg) embryonic development and extensive diversity among domestic chickens, as well as the availability of existing genetic and high-quality genomic resources. Moreover, our prior research found that genomic imprinting has not evolved in chickens as it has in non-oviparous (non-egg laying) animal species, making poultry an ideal system to reduce confounding factors for genetic analysis. 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 · 2024-09

PROJECT SUMMARY Normal aging is associated with a gradual decline in cognitive abilities across the lifespan, which is accelerated by midlife vascular risk factors, including stiffening of the large elastic arteries. The purpose of this project is to determine whether arterial stiffness is an early contributor to cognitive aging through the transmission of damaging pulsatile energy into the brain. Magnetic resonance elastography (MRE) is a non-invasive imaging modality that detects the viscoelastic properties of tissue and serves as a sensitive biomarker of brain tissue health and microstructural integrity. MRE has shown that brain tissue softening occurs with normal aging and likely precedes gross tissue atrophy. This 3-year longitudinal study seeks to determine the underlying mechanisms linking midlife vascular risk factors with the early loss of brain tissue structure and function. We will (1) determine the early mechanisms leading to age-related increases in cerebrovascular pulsatility, (2) determine whether changes in cerebrovascular pulsatility and brain tissue viscoelasticity are spatially and temporally correlated, and (3) determine whether cerebrovascular pulsatility mediates the loss of cognitive performance through the disruption of specific neuronal substrates important for memory performance (i.e., the hippocampus). Ultimately, this project will identify the earliest underlying mechanisms by which vascular aging contributes to the loss of brain tissue integrity and cognitive performance leading to new targets for interventions aimed at preventing age-related cognitive impairment.

NSF Awards · FY 2024 · 2024-09

The U.S. Bureau of Labor Statistics 2019-29 employment projections show that occupations in STEM fields are expected to grow 8.0 percent by 2029, compared with 3.7 percent for all occupations. Computing occupations as a group are projected to grow about 3 times as fast as the average between 2019 and 2029 at 11.5 percent resulting in slightly more than half a million new computing jobs over the 10-year period. Despite efforts to increase learner performance in introductory computing courses, studies have shown only a slight decline in failure rates. The goal of this project is to explore the use of generative AI to reduce instructor workload and to improve student learning in introductory computing courses by providing real-time, personalized feedback for students. The LAPIS (Learner-Adaptive, Pedagogical Interactive Solutions) system will use generative AI to provide real-time, personalized feedback for students spending too much time mastering a topic. For instructors, it will offer critical insights through visual dashboards, allowing them to manage introductory computing courses at scale. The project will focus on optimizing intervention data representation, determining critical student information for personalized feedback, and understanding the impact of feedback variations on student outcomes and benefits to instructors and course staff. Research conducted in this project will focus on (1) representing introductory computing course data for intervention opportunities, (2) determining necessary student information for personalized feedback, and (3) understanding how feedback variation influences student outcomes. The evaluation of LAPIS will utilize persona creation, rubric revision, A/B testing, variation in LAPIS implementation, surveys, interviews, and think-aloud sessions. A development panel of educators and CS professionals will serve as the initial users of LAPIS, ensuring LAPIS design aligns with various user abilities and motivations. An evaluation advisory board, consisting of experts in related fields, will assess project progress, methods, effectiveness and feasibility. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering. Stormwater runoff is a major source of water pollution and flooding in cities, and up to $150 billion is needed to manage stormwater in the U.S. over the next 20 years. To manage stormwater cost-effectively, many communities, including those within the Chesapeake Bay watershed, have been investing in "green stormwater infrastructure" (GSI) practices. These include rain gardens, which reduce and treat stormwater runoff by mimicking natural hydrologic processes. GSI can provide environmental and societal benefits beyond stormwater management and may help address environmental injustices while reducing economic costs for cities. However, these practices must be maintained to continue providing ecologic, hydrologic, and social benefits; without maintenance, these practices may become a burden to communities instead. GSI practices are a relatively new stormwater management tool, but some of the oldest practices have been installed for a decade or more, particularly in the Chesapeake Bay region. In light of this, a pressing question is: to what extent are these practices a continued "success" ecologically, hydrologically, and socially 10+ years after installation? Furthermore, to what extent can we predict the "success" of aging GSI practices? This project analyzes the ecologic, hydrologic, and social performance of residential rain gardens that are 10+ years old within the District of Columbia's Department of Environment & Energy (DOEE) RiverSmart Homes program. First, a database of visual inspection reports that have been performed on these practices since 2021 is analyzed to determine ecological success. Next, a survey of the residents with rain gardens on their property is conducted to evaluate to what extent they see these practices as a success, how they maintain their rain gardens and at what frequency, and what factors influence their willingness to do this maintenance; this will determine social success and the role of maintenance in this success. The project also evaluates how quickly water drains through rain garden soils and running hydrologic models of each inspected rain garden which will be used to assess hydrologic success. Lastly, survey responses and publicly available data are used to model residents' decision-making with respect to maintenance and to evaluate where "successful" 10+ year old residential rain gardens are likely to occur. This will yield helpful insights to other stormwater managers in the Chesapeake Bay and beyond regarding how site design and maintenance behaviors interact, and how to best support residents in maintenance behavior so that aging GSI practices maintain their ecological, hydrologic, and social benefits. This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences and the Directorate for Geosciences. 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 · 2024-09

Abstract It is well appreciated that the spatial organization of the biophysical and biochemical cues in the extracellular matrix (ECM) in concert with the stromal cell organization along the cortical-medullary axis of the developing kidney is vitally important to nephron development. These insights have been obtained from animal and organoid models, however, the exact mechanisms behind the tightly regulated spatial and temporal differentiation and cell specification have not been fully established. Although reductionistic, an in vitro microphysiological system (MPS) offers a platform that allows recapitulation and control of the developing kidney's critical structural and functional components. Here, we develop an easy-to-use modular 3D model of the developing nephron in which the biophysical and biochemical cues of the ECM, soluble factors, and stromal cell identity can be spatially patterned along the length of an epithelialized tube (Aim 1). In Aim 2, we will create a framework for MPS fabrication and model establishment to enable wide distribution and technology transfer to other labs that are established experts in investigating different aspects of nephrogenesis and renal pathology but non-experts in using microfluidic in vitro models. This easy-to-use system will provide researchers without MPS expertise with a tool to investigate the role and mechanisms of biochemical, cell-cell, and cell-ECM interactions during nephrogenesis. As such, this model will be designed to be quickly and easily disseminated to the research community to rapidly increase mechanistic investigation of nephrogenesis for biological understanding, pharmacological screening, and tissue engineering applications.

NSF Awards · FY 2024 · 2024-09

With rapid advances in hardware architecture and software ecosystem, there is a crucial need to modernize applications and leverage the advancements in High Performance Computing (HPC), Artificial Intelligence (AI), Machine Learning (ML) and Data Science. These advancements can only be achieved by creating partnerships between domain scientists and computational experts. Such collaborations will help build a trained and skilled workforce capable of harnessing the benefits of modern hardware and software in a manner that aligns with domain science perspectives. This project addresses this need by creating a team of Cyberinfrastructure Professionals (also called Research Software Engineers (RSEs)) to support computational and data-intensive research in the Mid-Atlantic region. This team focuses on improving RSE skills and support to advance (i) social, behavioral, and economic (SBE) sciences and (ii) coastal sciences and infrastructure (CSI). The University of Delaware (UD) will implement the proposed work in close collaboration with Howard University, Delaware State University, and Lincoln University–three Minority Serving Institutions (MSIs). The project addresses the challenge of building and sustaining RSE teams by offering training, education, and certification, as well as career development programs. The goal is to ensure the recognition, recruitment, and retention of RSEs, making the team a role model and a steady source of future RSEs. Via an inter-institutional initiative, this project is (a) identifying and creating a sustainable and scalable talent pipeline of RSEs to accelerate and enable domain sciences, especially the SBE and CSI domain areas traditionally underserved by RSEs, (b) connecting RSEs from other initiatives such as ACCESS, SCIPE, and US-RSE to foster a broader network, (c) establishing a graduate level course and RSE pilot certificate, and (d) studying the novel applicability of data-driven ML/AI methods and HPC on the domain science problems. 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

Biotechnology and biomanufacturing show great promise for expanding beyond human health applications, to address climate and energy goals, improve food security, secure the domestic supply chain, promote rural economic development, and grow the nation’s economy. These capabilities are enabled by the ability of scientists and engineers to design and build biological systems to perform new tasks with high efficiency, such as converting renewable domestically produced feedstocks into chemicals, fuels, and materials, or fixing nitrogen to reduce fertilizer use in food production. However, the scale at which these biological systems are currently designed, built, and tested, suffer from long cycle times, limited scale, high cost, and frequent unexpected behavior. Centralized BioFoundries that leverage automation to scale up biodesign building and testing are becoming more common but are very expensive to operate and maintain and have a limited user base. To start to increase the scale at which biodesign, building, and testing is done at a fraction of the cost, this project brings together a team to establish The Center for Robust, Equitable and Accessible Technology and Education (CREATE) for Next Generation BioFoundries The mission of CREATE is to democratize and decentralize the BioFoundry, and ultimately empower individual scientists and engineers to leverage scale to accelerate scientific discovery and biotechnology translation. CREATE will empower the community through accessible and user-friendly technology development that makes biofoundry operations faster, higher throughput, and less resource intensive. CREATE will also develop educational and workforce development tools to prepare the community for this new way of scaling biotechnology. Support from this award will advance three technological approaches that facilitate designing biological systems at scale, at a lower cost than conventional methods. Large genetic system libraries will be built by massively parallel genetic system assembly from low-cost oligo pools, combined with the design of very large synthetic metabolic pathways that systematically vary enzymes and their expression levels, and massively parallel development for cellular sensors for metabolites. These methods will be applied to pilot projects involving protein material production, bacteriophage engineering, and microbial cell factories. Specific challenges addressed in these projects include low cost gene synthesis for enzyme discovery and phage assembly, for pathway optimization, for lignocellulosic inhibitor detoxification, and for discovery of novel metabolite biosensors for metabolites. Education and workforce development efforts will produce a draft of a curriculum for “High Throughput Thinking in Biotechnology“, a course to train biotechnologists not to be limited by throughput and scale. A virtual design-build-test tool will also be adapted for undergraduate and graduate education. CREATE will engage with the community to prioritize sensor development and identify future users of CREATE resources. This award is jointly funded by the Directorates of Biological Sciences (BIO), Engineering (ENG) and Mathematical and Physical Sciences (MPS), 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

Coastal margins along the Atlantic seaboard are experiencing significant changes due to sea level rise, causing the intrusion of saltwater into freshwater systems. This inundation and groundwater salinization has led to the emergence of “ghost forests,” characterized by stressed and dead trees, which are altering the ecology and hydrology of coastal ecosystems. This research will study the impact of sea level rise on the health of coastal forests through changes in hydrological processes, with a particular emphasis on stemflow (precipitation intercepted by trees and channeled down the trunks to the soil). Stemflow plays a crucial role in transporting nutrients and organic matter to the forest floor, as well as soil moisture recharge in near-trunk soils. The research will provide new insights into coastal forest resilience and inform strategies for mitigating the effects of sea level rise. Further, the project will develop publicly available training modules to disseminate knowledge about the flow and dynamics of water in coastal forests and provide interdisciplinary training to undergraduate and graduate students. The project will investigate the impacts of sea level rise on stemflow dynamics and associated hydrological and biogeochemical processes in coastal forests along a transect from healthy to ghost forests. Field measurements, laboratory analyses, and statistical modeling approaches will be used to understand stemflow's role in the ecohydrological responses of coastal ecosystems to changing environmental conditions. Results will inform efforts to enhance ecosystem resilience and adaptation to sea level rise by elucidating how coastal forests respond to stressors such as soil salinity, vegetation changes, and hydrological dynamics. 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

High-quality early educational experiences, particularly in mathematics, are crucial for students’ success in K-12 schooling. To create these foundational experiences for young children, early childhood educators need opportunities to enhance their mathematics teaching through job-embedded, sustained professional learning. This partnership development project establishes the Research Practice Partnership for Professional Learning in Early Mathematics (RPP-PLEM), a collaboration among early childhood mathematics educators, school and district leaders, the state department of education, and university faculty in Delaware. These partners aim to enhance children’s early mathematics learning by collaboratively designing support systems for strengthening their teachers’ professional learning. Partnership development activities include having the university researcher collaborate with early childhood mathematics educators and leaders to learn more about their professional learning needs and to collect local evidence that reflects the voices of those most impacted by this work. To establish the RPP-PLEM, the team will engage in development activities. These activities aim to build trust, conduct rigorous research, support partner organizations, produce knowledge for educational improvement, and build the capacity of researchers and practitioners to engage in partnership work. The project will collect evidence using a research-practice partnership framework and use related tools to assess the formation of the partnership and to guide its work. As an outcome of this work the partnership will be positioned to begin responding to their collaboratively developed set of research and policy questions. These questions will be related to providing early childhood educators with support that deepens their knowledge of and ability to engage young children in learning mathematics. Evaluation findings will guide improvements, aid other researchers in developing similar partnerships, and will mobilize knowledge related to understanding the lived experiences of early childhood educators in relation to their ongoing professional identities as teachers of mathematics. This project is supported by the Discovery Research preK-12 program (DRK-12) which 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.

NIH Research Projects · FY 2024 · 2024-09

Summary Individuals with chronic stroke are characterized by low levels of walking activity, which negatively impacts post-stroke health and the risk for recurrent stroke. Although walking speed and endurance are logical targets for improving walking activity, improving them alone rarely translates to more walking activity in the free-living environment in this group. Individuals with chronic stroke also have low falls efficacy, which includes their confidence in performing daily activities without falling. Evidence suggests that, if the low falls efficacy of those with chronic stroke is not addressed, the benefits of improved walking capacity on walking activity won’t be realized. To date, falls efficacy has been quantified as an aggregate score from self-reported questionnaires, such as the Activities-specific Balance Confidence (ABC) scale. A challenge to addressing falls efficacy after stroke is that it is likely comprised of distinct balance domains not reflected by an aggregate score. These balance domains, including anticipatory control, walking balance, and reactive balance, can be considered independent targets for rehabilitation. The ABC scale contains questions relevant to each of these domains. By aggregating the score, however, the ABC scale does not inform the specific balance intervention targets that could be used to address falls efficacy and, subsequently, walking activity. Our study aims are to 1) demonstrate that the construct of falls efficacy in those with chronic stroke is comprised of multiple factors representing distinct balance domains, and 2) demonstrate that falls efficacy has specific, factor-based relationships with walking activity in those with chronic stroke. These aims will be addressed using secondary analyses of ABC scale data within the University of Delaware Stroke Studies Registry, as well as baseline ABC scale and sensor-based walking data from a multi-site clinical trial in those with chronic stroke (PI: Reisman). To address the first aim, we will conduct a factor analysis of ABC scale questions to objectively identify unique domains within the falls efficacy construct. To address the second aim, we will predict walking activity volume, frequency, intensity, and sedentary behavior from factor-specific ABC scale data. The results of this study will advance the specificity and utility of the ABC scale in characterizing falls efficacy, as well as informing domain- specific balance targets for improving falls efficacy and walking activity.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract Mental health disorders are associated with a myriad of negative health outcomes, including a reduced life expectancy. Recent work has begun to investigate factors that contribute to the increased mortality rate among those with psychopathology. Emerging data implicate a link between psychopathology and the aging process (e.g., premature cognitive decline). These findings are in line with the psychological scar theory, which posits that psychopathology degrades cognitive abilities over time due to behavioral and emotional alterations that can disrupt the neural correlates of cognition. At the same time, cognitive vulnerability theory suggests that a reduced cognitive capacity may be a risk factor for the emergence of psychopathology by compromising effective, yet cognitively demanding, coping strategies. It is thought that these cognitive vulnerabilities are also instantiated at the neurobiological level, but relatively little work has examined this model using neural indicators of cognition. The two competing theoretical models suggest that reduced cognitive functions may be both a risk factor and consequence of psychopathology. However, relatively few studies have examined whether there is a bidirectional relationship between psychopathology and cognition that exacerbates psychological and cognitive change in middle adulthood. Investigating these hypothesized causal pathways in midlife has the potential to identify early risk markers of cognitive decline and future psychopathology prior to the onset of neurodegeneration. The objective of this application is to investigate the bidirectional effects of psychopathological and cognitive processes over time in midlife. The proposed research will reassess 100 community adults across a spectrum of psychological severity who previously completed a large multi-method study using measures of cognitive functioning and psychopathology symptomatology. Participants will be reassessed at least 12 months after the previously collected baseline assessment and then again, every 6 months for a year (4 total assessments spanning at least 2 years). I hypothesize baseline psychopathology severity will show a negative relationship with cognitive functions over time consistent with the psychological scar theory (Aim 1). In addition, I expect baseline cognitive functions measured behaviorally and neurally to show a negative relationship with psychopathology symptoms over time consistent with the cognitive vulnerability theory (Aim 2). Finally, I anticipate bidirectional effects, such that decrements in cognitive functions will predict worsening of psychopathology symptoms, and worsening psychopathology symptoms are expected to predict declines in cognitive functions over time (Aim 3). Investigation of these aims will provide training opportunities in three critical areas: i) the conceptualization of bidirectional models of psychopathology and cognition over time, ii) the application of advanced data analysis techniques to longitudinal data, and iii) the enhancement of knowledge on best practices in research reproducibility, rigor, and transparency.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Human Papillomavirus (HPV) related head and neck cancer (HNC) has increased in incidence by nearly 225% since the 1980s and is expected to rise over the next forty years. Patients with HPV positive HNC typically have lower rates of substance use and a more favorable prognosis than HPV negative HNC. Nevertheless, patients with HPV positive HNC often experience significant side effects from extensive treatment regiments and psychosocial distress secondary to facial disfigurement, symptom burden, and concerns about prognosis. It remains unclear how this distress may contribute to health behaviors that have a direct bearing on relapse, morbidity, and mortality in individuals with HNC. Despite the lower rates of behavioral risk factors associated with HPV negative HNC (e.g., tobacco and alcohol use), patients with HPV positive HNC who engage in these risk factors (≥ 50% of patients) are at an increased risk for recurrence or secondary malignancy. Among cancer survivors generally, including HPV negative HNC, psychosocial distress (i.e., anxiety, depression, and fear of cancer recurrence) has been associated with higher rates of tobacco and alcohol use. However, this relationship has been understudied in the context of HPV positive HNC. This study involves a secondary analysis of the Head and Neck 5000 study, a large longitudinal clinical cohort of HNC patients in the United Kingdom. Through the parent study, patients with HNC were recruited shortly after diagnosis and prior to cancer treatment, provided blood samples at baseline for HPV status, and completed self-report surveys at baseline (diagnosis), 4-months, 12-months, and 5-years post-diagnosis. The current study aims to 1) evaluate the trajectories of psychosocial variables (i.e., psychological distress, fear of cancer recurrence, and physical symptom burden) in patients with HPV positive HNC from diagnosis to one year following diagnosis, 2) examine the relationship between psychosocial variables and change in tobacco and alcohol use in patients with HPV positive HNC during the first year after diagnosis, and 3) assess the role of health behavior change on 5-year health outcomes (recurrence-free survival). This study will be the first to examine psychosocial variables in association to health behavior change and long-term health outcomes in a large longitudinal clinical cohort of patients with HPV positive HNC. Results will inform the need for resources in clinical care and contribute to psychosocial and behavioral intervention development for patients with HPV positive HNC. This Fellowship will provide the applicant with the training needed to launch an independent and productive research program examining the psychosocial and behavioral processes affecting the quality of life of patients with cancer. The proposed research and training plan will fill gaps in her discipline-specific knowledge in health psychology, head and neck oncology, and HPV-infection. As well as enhance her skills in longitudinal quantitative analysis and prepare her for a career as an independent investigator in academia.

NSF Awards · FY 2024 · 2024-09

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Professors Christopher J. Kloxin and Darrin J. Pochan at the University of Delaware will synthesize innovative polymeric materials using biologically inspired nanoparticles. Traditional block copolymers, composed of two or more chemically bonded polymers, have significantly impacted polymer science and are used in a wide range of applications, from adhesives to biomedical implants. This research uses biologically derived nanoparticles called bundlemers to initiate and guide polymer synthesis, creating a new class of block copolymers. The templated display of polymers can not only mimic traditional block copolymer materials but also allows for the creation of entirely new materials through programmed materials assembly. The modular nature of bundlemer-templated block copolymers allows for the simple creation of more sophisticated and advanced materials. These research activities will offer undergraduate and graduate students invaluable educational experiences in polymer and materials chemistry. Additionally, the research team will further enhance their impact by engaging with K-12 students and the broader community through various outreach activities and programs. The research team will synthesize bundlemer-templated block copolymers, leveraging the unique feature of bundlemers to precisely position molecular species and enabling new complex morphologies. The bundlemer-forming peptides will be synthesized with atom-transfer radical polymerization (ATRP) initiator sites that will ultimately reside at predetermined locations along the bundlemer periphery. After surface-initiated ATRP, unique polymer interactions will be displayed in specific directions from the bundlemer surface. Polymers will be initiated at designated sites to study the effect of initiator site location. Characterization of polymer grafts will be achieved through selective cleavage. Site locations and polymer types will be examined to assess their impact on the polymerization kinetics and molar mass distribution. Using ‘click’ chemical conjugation, the modular nature of the bundlemer-templated copolymers will be exploited to create new bottlebrush polymer-like structures. Modulating polymer interactions along the rod-like polybundlemer structure enables unique patterned interactions between these rod-like assemblies. A range of polymerization conditions and polymer types will be explored, and structural verification conducted using light scattering, film-cast and cryogenic transmission electron microscopy, and small angle neutron and x-ray scattering. 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.