University Of Notre Dame

NSF Awards · FY 2024 · 2024-10

Personalized healthcare that considers individual differences in genetics, lifestyle, and medical history is more effective than one-size-fits-all solutions. This project utilizes advanced wearable and portable devices with large language models (LLMs) to enhance personalized healthcare by addressing the patient variability often overlooked by current methods. It focuses on real-time healthcare personalization through fast and accurate data searches in ever-growing personal databases, using novel memory semiconductor devices, advanced circuits, and custom architecture. This enables quick, personalized interactions on devices, potentially saving lives with timely interventions for emergencies like suicide attempts and strokes, thus advancing precision medicine and national health. Additionally, the project will support activities to enhance healthcare education for K-12 students, and tech briefings on semiconductor technology for undergraduate students. The project addresses on-device LLM personalization through Retrieval Augmented Generation (RAG) for healthcare applications, aiming to significantly reduce latency and hardware overhead via algorithm-hardware co-design. It will define healthcare scenarios for LLM applications, generate user prompt input datasets for benchmarking, and create personalized healthcare datasets for LLM personalization. Efficient RAG-based personalization will be explored, focusing on unsupervised data selection and optimal embedding dimension/bit-width selection. To mitigate computation-storage data transfer bottlenecks, custom compute-in-memory architectures and data search frameworks using Ferroelectric Field-Effect Transistors will be investigated. This approach aims for a 1000-fold latency reduction and a 100-fold increase in energy efficiency compared to state-of-the-art edge LLM embedded systems, setting new benchmarks for edge computing performance and sustainability. Successful implementation will enhance personalized healthcare interventions and advance AI-assisted personalized healthcare. This project is jointly funded by the Software and Hardware Foundation (SHF) core research program and the Advancing Informal STEM Learning (AISL) program. 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 United States faces a severe shortage of affordable housing, particularly for communities with extremely low incomes. This crisis is compounded by the outdated infrastructure of existing housing, which results in high energy costs and inadequate living conditions. This project, BUILT2AFFORD, aims to address this dual challenge by leveraging advanced technology and strong community partnerships to enhance the energy efficiency of affordable housing. By focusing on low-cost passive design strategies, such as improved ventilation and shading, this project seeks to reduce the energy burden on low-income households and improve their living conditions. This project is significant because it tackles the pressing need for affordable, energy-efficient housing in the Midwest, particularly in South Bend, Indiana. By developing a framework to pre-identify housing units suitable for retrofits, our research will enable more targeted and effective interventions. The broader impact of this work includes reducing energy costs for low-income families, mitigating heat-related health risks, and contributing to the sustainability goals of local communities. The successful implementation of this project could serve as a model for other regions, demonstrating how affordable housing can be preserved and improved through innovative, data-driven approaches. The BUILT2AFFORD project aims to enhance the energy efficiency of affordable housing by developing, testing, and validating a tool that uses machine learning algorithms and Google Street View images. This tool will automate the identification of housing units suitable for low-cost passive retrofits. In Stage 1, we will collaborate with the City of South Bend and Near Northwest Neighborhood to conduct audits of 10-20 houses to create archetype layouts for thermal comfort simulations. We will develop computer vision algorithms to extract passive design indicators from Street View images, combining this with property data to build the BUILT2AFFORD model. In Stage 2, the model will be validated by retrofitting two testbed buildings with passive design strategies. Sensors will monitor energy usage and indoor environmental conditions over eight months. The data will refine and calibrate the model for accuracy and reliability. The project will produce the BUILT2AFFORD tool, a dashboard pre-identifying affordable housing units for retrofits. It will visualize data on design indicators, energy efficiency, and health risks, aiding homeowners, policymakers, and public health officials. This project supports energy efficiency, improved home comfort, and equitable health outcomes, contributing to broader climate resilience efforts. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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

Supporting the development of language, literacy, and STEM skills is essential for children’s future academic success. This project leverages the power of emerging AI technologies to develop a novel, low-cost solution for providing evidence-based instructional support to early education teachers. Specifically, the research team is collaborating with early childhood educators to co-design an AI-powered, app-based platform that delivers targeted instructional feedback to teachers and content-based professional development designed to support children’s development of language, literacy, and STEM skills. The context for the feedback and novel professional development platform is shared book reading in early education classrooms. Shared book reading is a common educational activity in pre-school and elementary school classrooms, and quality of teacher talk during shared book reading is predictive of children’s attainment of critical early learning skills. Current approaches to evaluating and providing feedback to teachers about their shared book reading practices necessitate observational measures and human coding, posing practical challenges to providing timely feedback to teachers at scale. This project represents a transformative approach to the provision of timely feedback to teachers making use of emerging AI technologies. Specifically, the research team first aims to bypass resource-intensive human coding by developing a Natural Language Processing (NLP) pipeline in combination with machine learning (ML) models to harness the power of a large pre-trained model – capable of understanding complex language patterns and contexts – and adapt it to the specific requirements of the educational domain. Next, the project aims to implement user-centered design in collaboration with early childhood educators to develop an AI-powered app-based platform to deliver timely instructional support, conduct usability testing, and iteratively improve the platform based on educator feedback. Creation of this novel instructional support system advances the knowledge base of how innovative technology solutions can deliver individualized, timely pedagogical support towards improving early learning outcomes. 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

The unique low gravity environment of the International Space Station (ISS) offers advantageous opportunities for gaining new insights into human health and disease. On Earth, diseases such as cancer are often studied in flat layers of growing cells that do not reproduce the complex, three-dimensional behavior of human physiology. This award supports research to grow three-dimensional models of the brain cancer on the ISS in order to better mimic how these tumors form within the brain, and to study interactions between cancer and immune cells. Opportunities will be provided for K-12 teachers to learn how to demonstrate simulated space experiments in their classrooms, for high school students to learn how to conduct these experiments themselves in a university laboratory setting, and for college students to learn in the classroom about how space impacts human physiology. This will lay important groundwork for training the next generation of space scientists and engineers. Unlike ground-based organoid cultures, which are subject to settling, sedimentation, disassociation, and heterogeneity due to Earth’s gravitational forces, organoids grown in microgravity conditions are more uniform, larger, complex, and consistently reproducible. Here, novel cancer-myeloid organoids (CMOs) will be generated on-ground vs. on-orbit to mimic and study the tumor microenvironment of glioblastoma (GBM), the deadliest primary brain tumor in adults. The GBM microenvironment is dominated by myeloid cells, such as resident brain microglia and infiltrating monocytes and macrophages, which often exist in a tumor-supporting (i.e., anti-inflammatory, or “M2-like”) phenotypic and functional state. GBM are further plagued by the effects of a tumor growth-induced mechanical force known as solid stress. It is hypothesized here that solid stress promotes tumor supporting behavior in myeloid cells. In turn, because myeloid cells comprise up to 50% of the GBM microenvironment, it is further posited that they directly contribute to tumor solid stress generation. By utilizing robust CMOs that are grown in the microgravity environment of the ISS, novel cellular and molecular mechanisms underlying these “immunomechanical” relationships will be revealed, which are not readily discoverable in limitation-plagued Earth-grown organoids. First, flight ready CMO seeds (i.e., micro-organoids) will be generated from different mixtures of GBM and myeloid cell types with varied functional polarization states spanning from tumor-supporting to tumor-combating (i.e., pro-inflammatory, or “M1-like”). The extracellular environment will also be tuned to mimic tumor confinement by the host-organ (i.e., the brain). Next, following a successful launch, an established passive containment system for organoid cultures on the ISS will be used to sustain and promote CMO growth for up to 30-45 days on-orbit. Finally, upon return after splashdown, the CMOs will be subjected to downstream analysis including histology, transcriptomics, proteomics, and mechanical force evaluations. These data will be integrated to derive novel immunomechanical signatures of the microgravity CMOs vs. ground controls, which advance fundamental knowledge of brain cancer for further studies and future interventions. The PI has developed plans for outreach and train the next generation of space scientists and engineers. 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 award funds a research project that will study the effects of governance and trust in determining autonomous growth of local economies. It compares variation in technology, finance, business structures governance, social identities, and trust among local people between and across two local economies. This research is designed to better understand technology, finance, existing Small, Medium, and Micro Enterprises (SMMEs), and the extent to which these entities are influenced by governance, social identities and networks, and localized trust. The project will use a three-stage research design to identify drivers of growth. We will draw a combination of secondary data, direct observation, key informant interviews, and a rapid household-level survey in two contrasting sub-localities in selected townships to build a profile of each, covering its history, land use, demography, and sectoral economic activity. The first stage is to conduct a baseline rapid survey and mapping of the major businesses, investors, or other major economic players in each township. The data aims to create a profile of the selected townships and document the resources available for effective economic interaction. In the second stage, using the township profiles constructed in the first stage, we will identify key groups for quota samples of open-ended qualitative interviews using an adapted version of the Qualitative Impact Assessment Protocol (QuIP). The QuIP is a methodology designed to facilitate narrative explanations of the drivers of change working backwards from perceived changes in selected domains of respondents' lives and livelihoods. We will aim to understand how various relationships and associations intermediate between economic activity, institutions and trust in other people and government and how adherence to formal and informal institutions of economic governance shape hope for the future. We will use the trust lens to understand respondent’s perceptions of tangible outcomes like income, employment, business activity and education looking both backwards and forwards. We seek to engage with various demographic groups in each township. In the final stage, these tools will be used in ‘sensemaking’ activities with selected stakeholders to get participatory feedback on causes identified by the participants. 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 Much of the youth resilience literature to date has focused on resilience to family adversity, with far less examining resilience to neighborhood adversity. This is a key gap in the literature because neighborhood disadvantage is a robust indicator of poor outcomes throughout the lifespan. The focus on only one type of resilience (psychiatric) to the exclusion of others (social, emotional, and academic) and the inconsistent evidence of stability in resilience across development further exacerbate this gap. The transitions from middle childhood to adolescence and adolescence to emerging adulthood are important developmental periods marked by agency, cognitive, and self-regulation gains. The proposed research aims to examine not only how resilience can change from middle childhood to adolescence and to later emerging adulthood, but whether these developmental changes vary by type of resilience (psychiatric, social, emotional, academic), and finally, how those changes might differ with protective factors (i.e., warm and involved parenting, structural neighborhood protective features). During the K99 phase, Specific Aim 1 will investigate trajectories of the multifaceted conceptualization of resilience from middle childhood to adolescence and Specific Aim 2 will examine how these resilience trajectories may change when considering the protective factor of warm and involved parenting. During the R00 phase, Specific Aim 3 will investigate how a comprehensive set of positive structural neighborhood features (previously linked to resident well-being) promote youth resilience during the critical transitions between middle childhood and emerging adulthood. Dr. Elizabeth Shewark’s background in developmental psychology, family systems, advanced methods, and neighborhood effects uniquely positions her to carry out this line of research. Dr. Shewark’s long-term career goal is to lead an interdisciplinary research team that investigates how and by what mechanism(s) the contexts in which youth are developing impact their resilience. To independently lead this future research team, the goal of this Pathway to Independence Award is to build expertise in youth resilience to neighborhood adversity, and gain further training in cutting edge longitudinal analytic techniques, the assessment of positive structural neighborhood features, and conducting research in vulnerable at-risk populations. The proposal includes a highly established group of researchers who will strengthen the interdisciplinary impact of Dr. Shewark’s work and her development into an independent researcher. The theoretical knowledge and methodological skills gained during this project will allow Dr. Shewark to produce a rich body of research on how warm and involved parenting and structural neighborhood protective factors shape the development of youth resilience, facilitating the identification of modifiable targets for intervention programs to promote healthy youth development.

NSF Awards · FY 2024 · 2024-09

Ground based telescopes with adaptive optics (AO) have now provided direct imaging (DI) detections of about 20 extrasolar gas giant planets. Targets are usually selected based on system properties like age and distance, and these surveys have low yields, about 1%. These detections do not directly measure a planet’s mass and often poorly measure its orbit. This program takes a different approach to exoplanet imaging discovery and characterization, to remedy these aspects. DI survey targets are selected from among young, nearby stars based on their accelerations across the sky, indicating they are being gravitationally pulled by a dimmer companion. The search is expected to discover new exoplanets via two world-class ground-based AO systems located in Hawaii and – due to the selection criterion – measure their masses and constrain their orbits. This project will start a collaborative partnership with Maunakea Visitor’s Center, providing funding for new exhibits that highlight the knowledge about extrasolar planets revealed from Maunakea and the cultural and biological significance of Maunakea. It will support an astronomy-related internship to a student in Hawaii in the Akamai Workforce Initiative. Local to one PI’s institution, the project supports and expands the San Antonio Teachers Training Astronomy Academy, providing effective science education professional development for Texas high school teachers who predominately teach economically disadvantaged and underrepresented minority groups. The dynamical evidence for a companion from precision astrometry is contained in the Hipparcos-Gaia Catalogue of Accelerations (HGCA): i.e., stars showing an astrometric acceleration. The project uses the Subaru Coronagraphic Extreme Adaptive Optics Project (SCExAO) coupled with the CHARIS integral field spectrograph in the near-infrared (near-IR) to discover the perturbing bodies. For the brightest planets and brown dwarfs, analogues to jovian planets, it will also obtain follow-up thermal IR imaging with NIRC2 camera on the Keck II Telescope. The new dynamical code orvara simultaneously constrains the planet’s masses and orbits from these data. The planetary atmospheres are analyzed via empirical libraries and new atmospheric models. These discoveries anchor models of substellar formation and evolution from the largest brown dwarfs to jovian exoplanets. 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

Conventional thermal-based chemical separation technologies, such as distillation and absorption, are highly energy inefficient, consuming about 8 GJ per person per year globally. Additionally, these methods produce significant carbon emissions, raising serious environmental and health concerns. Polymer membranes are a more energy-efficient chemical separation technology that can improve energy efficiency by up to 90% when used alone or with traditional methods. These membranes separate substances based on their ability to move through the membrane material (permeability). Many new membrane materials capable of effectively separating desired gas molecules from mixtures (selectivity) have been developed. However, few of these materials have been adopted in industry due to issues with selectivity, durability, and robustness under real-world conditions. Challenges include plasticization from contaminants in the feed stream and physical aging, which reduces permeability over time. Improving membrane performance requires a deep understanding of the molecular mechanisms behind selectivity and long-term durability; developing such knowledge is the primary goal of this project. Fluorinated polymer membranes are known for their high permeability and resistance to aging. Still, they face problems with plasticization and low selectivity and are difficult and expensive to produce. This project will combine experimental and theoretical approaches to explore the potential use of alternative halogenated polymers, including chloro-polymers and bromo-polymers, as robust membrane materials. The study will investigate how chlorination and bromination affect polymer membranes' selectivity and long-term stability. This research will help scientists and engineers design more effective polymer gas separation membranes using halogenation to optimize selectivity and durability. Additionally, this project will engage students and the public in Oklahoma and Indiana through various STEM activities. These include educational and research opportunities for high school students, seminars for young researchers, the creation of online databases to standardize experimental protocols and improve reproducibility, and training a diverse group of future leaders in chemical engineering. The investigators hypothesize that carefully controlled degrees of polymer chlorination or bromination are sufficient to simultaneously enhance polymer gas membrane sorption-selectivity, diffusion-selectivity, and long-term stability, in contrast with fluorination, which is often ineffective in enhancing selectivity and plasticization stability. Moreover, there is evidence that the effects of chlorination and bromination on membrane hydrophilicity are profoundly different from those observed upon polymer fluorination. To test these hypotheses, the team will design, synthesize, and characterize a family of new halogenated polymer materials exhibiting systematically varied degrees of fluorination, chlorination, or bromination. The molecular structure of the newly synthesized halogenated polymers will be examined in detail and correlated with selectivity and long-term stability behaviors using advanced thermodynamic models in conjunction with state-of-the-art experiments, including permeability, plasticization, and physical aging measurements under complex gas mixtures and in the thin film composite membrane configuration. Successful execution of this research will unlock unique opportunities to design polymer membranes exhibiting previously unattainable selectivity and stability and de-bottleneck molecular separations in areas of strategic importance for the U.S. economy, welfare, and national safety. 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

Scientific teams are increasingly disrupting science by making pivotal discoveries and breakthroughs. These disruptive teams reshape established scientific paradigms and forge new ones, eclipsing established theories, methods, and research directions. Consequently, understanding the factors that foster disruptive scientific teams is essential for promoting new scientific paradigms, theories, methods, and avenues for future research. Previous research has documented the effects of team size, hierarchies, and distance among members on scientific disruption. However, the influence of gender composition on teams’ abilities to make disruptive discoveries and create new inventions remains underexplored. Drawing on previous research in gender composition and scientific disruption, the researchers aim to investigate the effects of gender composition on disruption and examine the causal mechanisms that could explain differences in its impact. This project encompasses three research goals. First, the project analyzes the impact of gender composition on disruption by examining more than 49 million papers and 4 million patents across different scientific fields over the last 50 years. The results yield empirical evidence of the impact of different gender compositions on scientific disruption. Second, the project conducts a laboratory experiment with 320 participants to understand the causal mechanisms that drive these effects. This experiment, which controls for gender composition, requires three-person teams to complete a disruption task that is designed for this experience and based on disruption research. Third, the project involves a massive survey and follow-up interviews with female scientists who have been part of disruptive teams to learn about their experiences and insights. This research promises to enhance understanding of the effects of different gender compositions on teams’ disruptiveness and contributions to science. The project highlights the theoretical and practical implications of specific team combinations in scientific research, giving institutions and funders information they can use as they reflect on the role of gender composition in scientific teams. 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

The 6th Geoscience Alliance (GA-6) conference builds on a successful series of national conferences aiming to broaden the participation of Native Americans in geoscience and environmental sciences. The first five Geoscience Alliance conferences brought together a total of more than 500 graduate, undergraduate, and K-12 students, educators, Elders, community members, and professionals representing 40 Tribes, Bands, and Native Villages. The GA-6 conference will take place in North Carolina and is expected to increase participation from the Southeast and Mid-Atlantic, regions that have been underrepresented at prior Geoscience Alliance conferences. The conference theme, Geoscience and Environmental Justice in Indigenous Communities, considers the distributions of environmental benefits and burdens along with the environmental policies, practices, and power dynamics that influence these distributions. Noting that Indigenous communities regularly shoulder disproportionately large environmental burdens from pollution, resource extraction, and climate change, conference participants will learn about, share, and discuss some of the ways that Indigenous and western knowledges can be used to address these problems and promote environmental justice. By increasing the involvement of Native American communities underrepresented in geoscience and environmental science, the conference will help enhance human capacity in these fields at a national level. Despite growing recognition that Indigenous knowledge systems have much to offer the geoscience and environmental sciences, Indigenous peoples themselves are among the most underrepresented groups in careers and degree programs in these fields. This underrepresentation has implications for scientific research, science education, management, and other areas. The 6th Geoscience Alliance (GA-6) conference will help address this issue by building on a successful series of national conferences aimed at broadening the participation of Native Americans in geoscience and environmental sciences. The conference theme engages with Indigenous environmental justice, an area of academic research and a social movement that is both relevant to public policy and linked to scientific issues related to air and water quality, natural resource management, and climate change. In particular, the GA-6 conference will elevate Indigenous perspectives in environmental justice research, education, and engagement to spur ideas, dialog, and collaboration among participants and their networks. The three-day conference will include activities proven successful in previous Geoscience Alliance conferences: discussion circles, workshops, poster sessions, and field trips. The main objectives of the GA-6 conference are learning; networking and career progress; understanding; making it memorable; and growing the community. The conference will fulfill each of these objectives under the established and effective Geoscience Alliance principle that everyone teaches and everyone learns. The GA-6 conference will also serve to disseminate information to participants about opportunities such as Research Experience for Undergraduate programs, internships, and academic degree programs. A focus on networking at the conference will support all participants in developing a strong network of peers and collaborators. 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

Particle interactions of importance to nuclear astrophysics are the priority of the CASPAR (Compact Accelerator System for Performing Astrophysical Research) research instruments. CASPAR is the first and only deep underground accelerator laboratory in the United States and only the third existing in the world. It is located nearly a mile underground at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. This award will support researchers using gamma and particle detection techniques, to focus on the study of the early stages of light elemental synthesis in primordial stars (the first stars formed after the Big Bang). The nuclear interactions involving light elements in stellar environments are the foundation for the production of all elements in the Universe. This work will provide data to explain the abundance of elements that are observed by astronomers in the oldest stars of our Universe. Additional investigations will study the source of neutrons that drive these processes and will provide a more complete picture of the nuclear burning in stars that facilitates the production of the heavier elements. Understanding these processes is crucial for our understanding of multi-messenger astrophysical sources and thus addresses goals of NSF's "Windows on the Universe: The Era of Multi-Messenger Astrophysics. Laboratory studies of nucleosynthesis in stars are difficult to perform near the surface of the earth due to an enormous background from cosmic ray interactions in the atmosphere that overwhelm the experiment. Therefore, this work will utilize a 1 Mega Volt electrostatic accelerator located 4850 ft underground at SURF to perform experiments that exploit the low cosmic ray background conditions at CASPAR. The acceleration and subsequent bombardment of a range of elements of interest (for example boron and neon) with proton and alpha ion beams, will result in the emission of neutrons and gamma rays to be analyzed with Sodium Iodide (NaI) summing detectors and deuterated liquid scintillator arrays. The facility is nearly unique in the world. It is comparable to the LUNA facility in Italy and JUNA in China. CASPAR is a collaboration between the nuclear astrophysics programs at the University of Notre Dame and the South Dakota School of Mines and Technology. The unique nature of the proposed research will support the education of graduate students working in Nuclear Physics and the training of the nation's future nuclear workforce. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Prof. Svetlana Neretina of the University of Notre Dame will establish new photochemical routes for the synthesis of ultrathin gold layers on glass surfaces offering crystalline perfection, atomically flatness, and enhanced photo- and chemical-stability. Through improvements in the understanding of the nascent stages of crystal growth, Prof. Neretina aims to overcome the challenge of low crystallinity. In doing so, she will establish a low-cost platform for prototypic high-performance crystalline devices. The project will also open up opportunities for Prof. Neretina and her team to engage undergraduates in research activities and instill an interest and passion for science in the K−12 age group through the mentorship of high-school science fair projects and demonstrations performed at the annual Science Alive event. Current methods for the photochemical synthesis of noble metal nanoplates directly on substrate surfaces are reliant on plasmon-mediated growth modes and seed-induced symmetry-breaking controls that compel an otherwise isotropic material to grow along a two-dimensional pathway. With such growth modes being self-limiting in terms of the maximum lateral dimension achievable and substrate-seed fabrication requiring a heteroepitaxial relationship with a crystalline substrate, the growth of large-area nanoplates on amorphous surfaces is unfeasible using existing methodologies. Prof. Neretina’s research team will overcome these hurdles by defining and providing a mechanistic understanding of a new modality for the photochemical synthesis of gold nanoplates that distinguishes itself from current methods in that (i) the illumination of the growth solution, as opposed to the emerging nanoplate, is the key requirement for growth, (ii) growth persists from nanometer to millimeter length scales, and (iii) growth is initiated, not by heteroepitaxially aligned seeds, but by topographical features formed on glass substrates. Experiments will be performed that track and model nanoplate nucleation and growth under wavelength-dependent illumination, monitor the chemical fate of species within the growth solution, and identify the topographical features most favorable for nanoplate nucleation. Taken together, these studies will provide the means for producing high-aspect-ratio gold nanoplates with near-arbitrary size and provide the foundational knowhow that it could act as a generic route for the synthesis of a broad range of substrate-based nanomaterials. 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

Interpreting many of the results from astronomical observatories requires numerical simulations, often run on supercomputers. These include dynamical radiative magnetohydrodynamic (MHD) simulations with algorithmic innovations, many of which are not currently in hand. Many of these innovations can be described as structure-preserving; in other words. there is physics in the numerical partial differential equations (PDEs) that has to be mimicked at a discrete level in codes that are optimized for spherical geometry. Our current capabilities for computational astrophysics are deficient because the relevant applied mathematics has not been developed for solving these problems. A research collaboration between the University of Notre Dame, Brown University and ETH Zürich in Switzerland will work together to overcome some of these deficiencies by making targeted advances in applied mathematics, which would be transformative in how they enable unprecedentedly novel astrophysical simulations that help in the interpretation of valuable observational data. The students and postdocs trained in this project will find many fertile career trajectories in astrophysics, applied math, and other fields such as plasma physics. The PI also runs an after-school remedial math program for students in the Gary, Indiana area who have fallen far behind in their math education. The applied mathematics challenges come in three parts: 1) We need high order divergence-preserving methods for capturing MHD turbulence that occurs in the vicinity of massive stellar winds. 2) We need multigroup radiation hydrodynamics 3) For long-term, high-fidelity, simulation of planetary atmospheres, we need well-balanced methods that are implicit in the radial direction and can preserve angular momentum. To overcome deficiencies in current astrophysical codes, the team will develop: 1) Discontinuous Galerkin (DG) methods with non-oscillatory design and a capability for preserving geometrical conservation laws (GCL) 2) Divergence-free DG methods that can operate on geometrically complex cube sphere meshes. 3) An efficient multi-group method for radiation hydrodynamics based on DG schemes. 4) Well-balanced DG schemes that are implicit in the radial direction so that very thin zones in the radial direction can be handled. 5) DG schemes that are angular momentum preserving. The MHD and radiation hydrodynamics innovations will also find engineering applications in fields as plasma-based space propulsion and in simulating the physics of tokamaks. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. 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

All organisms collect and store information. This holds true for single- and multi-celled organisms. The information is used to inform decision-making. This insight led to the notion of biocomputing. A promising biocomputing system could involve cardiac muscle cells. They naturally transmit electricity. This project is designed to explore the programming of arrays of cardiac muscle cells. If these arrays are programmable and can store information, they would offer an energy-efficient computational system. The project will also involve interdisciplinary student mentoring and workshops for undergraduate and high school students. One workshop will be dedicated to ethical, philosophical, and social science dimensions of the project. The goal of this project is to explore the potential of using heart muscle cells as the basis for a recurrent (Hopfield) neural network. The hypothesis is that Cardiac muscle cell-based Reprogrammable BioOscillator Neural Networks (CARBON) can implement scalable and high-capacity associative memory. The cardiac muscle cells will be connected by fibroblasts. A key component of the project is modifying the fibroblast cell lines with optogenetically tunable ion channels. These channels would be used to adjust their capacitive (C), resistive (R) and hybrid-RC filter characteristics. This enables their application as programmable weights in the Hopfield RNN. The research has ethical, legal, and social implications that will be studied by exploring questions that were never answered before. In a highly convergent endeavor, this project will aim to study questions pertaining to "systems capable of displaying aspects of intelligence", especially not in traditional “regulatory ethics” way, but around questions that explore the topic from a cultural and traditional standpoint to philosophy of mind and to metaphysical questions around “What properties does a “computing” device or system need to have to be called an “entity,” “living” or “intelligent?”, to History and Philosophy of Science questions such as “What is the history and future of biocomputing?” none of which has been asked before in the context of biocomputing using living cells. This project is jointly funded by the Emerging Frontiers in Research and Innovation Program (BEGIN OI), the Directorate for Biological Sciences, and the Directorate for Mathematical and Physical Sciences. 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 investigates how physical forces, specifically axonal tension or the pulling forces exerted by nerve fibers, shape the complex folded structure of the human brain. Understanding the mechanisms behind cortical folding is crucial because brain structure and function are closely related in neurological health and diseases. The primary hypothesis is that axonal tension helps determine the placement of cortical folds and ensures that the axonal connections of the brain are as efficient as possible. By developing advanced computational models, the research aims to test these hypotheses and understand better how axonal tension contributes to brain development. This study will advance our understanding of brain morphology, offering insights into conditions such as Autism Spectrum Disorder (ASD) that are associated with abnormal brain connectivity and folding. Additionally, the project will result in valuable computational tools and data for research into other complex biological tissues that contain fibers, and will provide training opportunities in interdisciplinary research at the intersection of mechanics, biology, neuroscience, and computation. The project aims to rigorously evaluate the axonal tension hypothesis by first developing a novel computational model of white matter, incorporating realistic axon orientations and densities (Aim 1). This model will then be used to analyze the impact of varying levels of axonal tension on the consistent placement of cortical folds (Aim 2) and on the global efficiency of white matter connectivity (Aim 3). The research will employ advanced finite element simulations to compare axonal tension with other known factors influencing cortical folding, such as thickness, stiffness, and growth. The expected outcomes include a quantitative understanding of how different perturbations affect fold placement and connectivity, contributing significantly to the field of brain development. This work could validate the axonal tension hypothesis and its role in cortical folding, offering new perspectives on neurological disorders associated with altered connectivity. 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 harnesses the power of multi-dimensional tensor data to improve predictive accuracy and insights across crucial scientific and societal sectors through the development of advanced tensor classification techniques. Specifically, these techniques will facilitate early Alzheimer's diagnosis via sophisticated fMRI tensor analysis and improve the detection of anomalies in complex financial transactions. Despite the richness of tensor data, a significant barrier exists due to the scarcity of labeled instances, which are essential for effective statistical learning. These labels are often costly and labor-intensive to produce, particularly given the complex nature of tensor data. To address this challenge, the methods developed in the project will be optimized to perform robustly even with limited labeled data. By improving diagnostic tools and financial monitoring systems through enhanced tensor classification techniques, the project will support national health, economic security, and overall societal well-being. Moreover, it will promote interdisciplinary collaboration and educational growth, enhancing diversity in STEM fields and broadening participation across scientific and technological sectors. This initiative will not only drive scientific innovation but also serve national interests by improving public health, economic stability, and educating future scientists. This project will create computationally efficient and statistically optimal methods for tensor classification amidst the challenge of the scarcity of labeled data. The approach encompasses three innovative strategies: (i) employing low-rank discriminant tensors for high-dimensional tensor classification, (ii) utilizing abundant unlabeled tensor data for semi-supervised tensor learning, and (iii) adjusting for distributional differences between labeled and unlabeled data. The research team brings a strong theoretical foundation in tensor classification, supported by preliminary studies and experimental results. Collaborations with experts in biology, medical science, economics, computer science, and social science will facilitate the application of these new methods to a variety of pressing issues in these fields. This integrated approach is expected to yield significant advancements in tensor-based data analysis techniques, enhancing the capabilities and understanding across multiple disciplines. 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 Even two decades after the complete sequencing of the human genome, a significant subpopulation (~10%) of the encoded proteins remains unknown. The questions surrounding this “dark” proteome are further compounded because there is not a one-to-one correspondence between genes and proteins and the function of expressed proteins can be changed by hundreds of different types of post-translational modifications. The resulting protein variants, or proteoforms, could be in the millions and may appear at concentrations well below the detection limit of current methods. Mass spectrometry (MS) is one of most powerful tools in the modern proteomics toolbox; however, MS only measures things that can be ionized and promoted to the gas phase, highlighting the importance of the ionization process. Today, measurement of high-abundance proteins is a trivial task for modern mass spectrometers, but closer examination reveals that low-abundance proteoforms comprising the “dark” proteome remain elusive. Therefore, new measurement capabilities are urgently needed that can increase the sensitivity and selectivity of the ionization process. While improvements in MS instrumentation are expected to continue, a parallel approach relying on chemical methods to increase sensitivity would provide a synergistic route to detect low abundance proteoforms. Here, we outline a high-risk but high- reward strategy for improving the ionization efficiency of these low abundance peptides and post-translationally modified peptides. Specifically, proof-of-concept experiments will demonstrate that N-heterocyclic carbene (NHC) decorated gold nanoparticles (AuNPs) are an excellent platform for the selective capture and fragment- free ionization of peptides, delivering at least two orders of magnitude improvement over state-of-the-art methods. Additionally, we will demonstrate that these NHC-AuNPs can improve the detection of post- translationally modified proteins and are compatible with pre-existing proteomics workflows employing bioorthogonal click chemistries. Lastly, while the proof-of-concept studies proposed here target bottom-up MS proteomics applications, the NHC mass tag platform is quite general and would have broader implications for MS applications ranging from tissue and single-cell imaging to disease biomarker identification and detection.

NIH Research Projects · FY 2026 · 2024-09

7. Project Summary/Abstract This K23 Mentored Patient-Oriented Research Career Development Award involves complementary research and training plans to develop and then pilot a modified motivational enhancement therapy and cognitive behavioral therapy (MET-CBT) intervention tailored to target alcohol use in patients with alcohol use disorder (AUD) in opioid agonist treatment (OAT). Alcohol use is an under-recognized contributor to the opioid crisis, greatly increasing the risk of overdose when used together with opioids. Further, alcohol use and related problems are prevalent among patients in OAT and significantly increase the risk of opioid relapse and treatment dropout. Office-based buprenorphine treatment, a fast-growing form of OAT, is effective at treating opioid use disorder and decreasing risk of opioid overdose, but relapse rates are high in the first year of treatment. With nearly 130 individuals dying each day from an opioid overdose and evidence of recent increases in overdoses during the COVID-19 pandemic, there is an urgent need to increase treatment retention. Reducing alcohol use and use-related problems in patients receiving buprenorphine may have a significant indirect effect on improving buprenorphine outcomes. However, past randomized clinical trials (RCTs) have found no condition effect for brief alcohol-focused intervention for patients in OAT, despite considerable evidence that these interventions are generally effective at reducing alcohol use. Critically, past work examined standard alcohol interventions that were not tailored to individuals in OAT, suggesting that there are unique and significant challenges to alcohol intervention in patients receiving buprenorphine. This K23 project will first qualitatively interview patients with AUD in their first year of office-based buprenorphine treatment and buprenorphine providers to directly inform modifications to an existing MET-CBT protocol, tailoring the intervention to fit the needs and challenges of buprenorphine treatment. Following treatment development and refinement, 60 participants will be randomized to receive two MET-CBT sessions or treatment as usual in a proof-of-concept RCT. Key RCT outcomes will be the feasibility and acceptability of the modified MET-CBT intervention. Sustained benefit will also be evaluated at 1- and 3-month follow-ups in exploratory analyses. Through addressing AUD in people receiving OAT, this proposal is closely aligns with national priorities to improve OAT-related outcomes and to respond to the opioid overdose crisis.

NSF Awards · FY 2024 · 2024-08

This doctoral dissertation project investigates how smaller scale, largely non-commercial economies become integrated into larger, regional, and commercializing economies. One of the ways to examine the processes by which this occurs is through the exchange items that circulate within these systems. As both people and ideas travel along with trade routes social and cultural structures shape and are shaped by these interactions. Anthropological archaeology allows for the opportunity to explore the dynamic of these relationships across cultures and throughout time, which permits understanding not only novel arrangements that exist in particular places and times but also, more importantly, common patterns that emerge in seemingly diverse circumstances. These commonalities, in turn, reflect the range of human behavioral patterns in the face of culture contact and economic relationships. This insight has relevance for understanding both the past arrangements that produced current circumstances and the present processes in a globalized economy. Specifically, this project examines the social organization of a Medieval crafting economy in a region which existed on the periphery of much larger economic systems. This project explores how the economic relationship affected the organization of social and economic systems. Glass is the central focus of the project for several reasons. Relevant glass chemistry has been well-studied in adjacent regions, with discrete glass recipes and associated chronologies identified. This project uses chemical analysis, by means of portable x-ray spectrometry and laser ablation – inductively coupled plasma – mass spectrometry, to identify the composition of relevant glass to allow for a comparison with the known compositional groups established through previous research. Glass objects are common finds on relevant sites associated with different social groups and each of these site types have produced evidence for glass working. This permits identification of long-distance and local exchange networks, and potential differences in approaches by different social actors. Placing the glass evidence generated by this project within the broader context of crafting permits a more refined picture of the organization of this early economy and its relationship to larger economic systems is produced. This, in turn, contributes to the larger anthropological goal of understanding the cross-cultural development of early and modern economies. 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

Collaborative Research: Designing Intelligent Industrial Robots for STEM Inclusion by Leveraging Self-Determination Theory to Foster Autistic Talent in Manufacturing Work Workforce diversity is crucial in today's rapidly changing world. Autistic adults, with their unique perspectives and skills, can significantly contribute to workplace diversity. However, compared to similarly qualified peers, they often struggle to find and retain jobs, including in STEM fields where the U.S. faces an increasing skills gap. Autistic adults comprise at least 2% of the U.S. population, so increasing their employment rate could meaningfully expand and enhance the U.S. manufacturing and STEM workforce. This project aims to address this issue by developing smart industrial robots that provide personalized support for autistic employees in manufacturing and STEM work environments. By creating more supportive and inclusive workplaces, we seek to improve job retention, income, and independence for autistic employees. Furthermore, this initiative will help bridge the skills gap in manufacturing and boost economic growth. The advancements from this project will also enhance educational opportunities and improve employment prospects for autistic adults, fostering more neurodiverse and productive work environments that drive innovation in the U.S. manufacturing sector. This project focuses on developing smart industrial robots that offer personalized support for autistic employees in STEM and manufacturing jobs. Our approach combines the co-design framework of mutual shaping with the principles of Self-Determination Theory (SDT). We will engage key stakeholders, including autistic adults and industry experts, throughout all development cycles in an iterative design process to advance industrial robot intelligence. The primary objectives of this project are twofold: (1) to co-create support approaches based on SDT that address fundamental psychological needs (i.e., autonomy, competence, and relatedness) through interviews, focus groups, and human-in-the-loop simulations, and (2) to enhance robot intelligence for accurately identifying and meeting workers' psychological needs in manufacturing settings, resulting in adaptive and personalized support. By integrating SDT-based support into industrial robot design, we anticipate increased motivation, work quality, and job satisfaction for all employees. This neuro-affirming work environment will, in turn, promote inclusion, productivity, and innovation in the STEM workforce. This award has been made in response to the NSF solicitation “Workplace Equity for Persons with Disabilities in STEM and STEM Education” (NSF 23-593). This project is funded by the Advancing Informal STEM Learning (AISL) Program in the Division of Research on Learning in Formal and Informal Settings (DRL) in the Directorate for STEM Education. 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

One goal of mathematics curricular and instructional reforms in the United States is to help students build “mature number sense,” which involves making sense of numbers and operations, using reasoning to notice patterns, and flexibly selecting the most effective and efficient problem-solving strategies. In support of this goal, mathematics educators have developed a variety of instructional practices designed to move students beyond seeing mathematics as a set of disconnected procedures and facts to appreciating it as a coherent set of ideas and tools. These practices have been growing in popularity across mathematics classrooms, but it is unclear whether they are effective. Moreover, there is limited evidence on the typical progression of students’ mature number sense across elementary and middle school. How does students’ mature number sense change across a school year? Does that pattern of change differ by grade level? Lack of such knowledge is a critical problem because, without it, researchers cannot explain, predict, and study mature number sense, and teachers are left to rely on their own intuitions about whether and how to focus on it in their classrooms. In this project, researchers will advance fundamental knowledge of mathematical cognition through a yearlong study of the progression of 3rd-8th grade students’ mature number sense. They will determine how malleable mature number sense is and how any such changes in mature number sense relate to students’ grade-level mathematics content learning. This project is funded by the ECR program which supports fundamental research that generates foundational knowledge that advances the research literatures in STEM learning and learning environments, broadening participation in STEM, and STEM workforce development. The central hypothesis of this project is that mature number sense is a distinct and malleable characteristic of mathematical cognition that predicts students’ learning of grade-level mathematics content. The research team will work with a diverse sample of students in grades 3-8 to determine the malleability of mature number sense and how changes in mature number sense relate to students’ grade-level mathematics learning. They will start by studying the progression of 3rd-8th grade students’ mature number sense both within a school year and cross-sectionally across grade levels. They will collect student scores on a measure of mature number sense at three timepoints over a year, analyzing how students grow within a grade level as well as comparing how students grow across grade levels. The research team will then interview a representative subsample of students, balanced across grade levels, at two timepoints in the year to gain an in-depth understanding of their mature number sense. The researchers will pair student scores from the assessments with their cognitive interview reports to examine what has changed when students grow in mature number sense. In addition, the research team will examine if growth in mature number sense predicts students’ grade-level achievement at the end of the year, controlling for initial achievement, multiplication fluency, and grade level. By the end of the project, there will be significant evidence on the typical progression of students’ mature number sense in grades 3-8. The project will advance the understanding of a foundational construct in mathematical cognition and a core proficiency with longstanding importance in mathematics education. 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

Dengue is a human disease caused by the dengue virus (DENV) and transmitted by Aedes aegypti and Aedes albopictus mosquitoes that afflicts hundreds of millions of humans, causes 20,000 confirmed human deaths annually, and puts >3.6 billion people at risk. Temperature varies profoundly spatially and temporally across transmission regions, and transmission-linked traits such as mosquito survival, reproduction, biting, and vectorial capacity are highly sensitive to temperature. Therefore, the R0, an important proxy of the population growth rate of pathogens and thus transmission, of dengue also varies profoundly with temperature. In recent years, my sponsor, co-sponsor, and colleagues have developed temperature-dependent, trait-based transmission models for mosquito-borne diseases (including dengue), using a generalized R0 equation derived from the classic Ross-Macdonald model. This predictive equation, however, is limited by the fact that temperature is the sole abiotic factor considered, despite other widespread abiotic factors, such as insecticides, being well known to impact traits of mosquitoes that affect transmission. My objective for this application is to develop and parameterize this model for DENV transmission by Ae. aegypti with commonly deployed insecticides with an overall goal of reducing DENV transmission. My central hypothesis is that insecticides and temperature interact synergistically or antagonistically, rather than additively, to affect the R0 of DENV. To test this hypothesis, I will conduct response surface experiments crossing 5 insecticide doses of both the larvicide temephos and the adulticide deltamethrin and 7 temperatures on juvenile and adult Ae. aegypti and DENV, and I will measure all eight temperature-dependent parameters in the generalized R0 equation. Using Bayesian inference, I will fit thermal performance curves to each trait across insecticide doses and implement these into the R0 equation. Once these aims have been completed, I will have developed the first fully parameterized insecticide- and temperaturedependent R0 model for dengue. The aim of this R0 model is to more accurately predict disease incidence, identify the extent to which temperature impacts the efficacy of common insecticides, determine the ideal seasonal conditions to deploy insecticides in, and determine minimum insecticide concentrations to prevent dengue transmission across thermally variable landscapes. Public Health Statement Temperature-dependent, trait-based transmission models are essential to informing control of dengue virus (DENV) transmission by Aedes aegpyti and Aedes albopictus mosquitoes. They are limited, however, by temperature being the sole abiotic factor considered, despite other widespread abiotic factors, such as insecticides, being well known to impact transmission-linked mosquito and pathogen traits. Through experiments crossing common insecticides with temperature treatments, this application will develop and parameterize an insecticide- and temperature-dependent, trait-based transmission model for DENV. This is expected to predict dengue incidence more accurately than current temperature-alone models, identify the extent to which temperature impacts the efficacy of common insecticides, and determine the most effective environmental conditions to deploy insecticides in to reduce transmission.

NSF Awards · FY 2024 · 2024-08

The Notre Dame Site of the Center for Bioanalytic Metrology (CBM) works in collaboration with partner Sites at Indiana University-Bloomington and Purdue University to address two objectives: (1) to deliver best-in-class analytic metrology tools and expertise enabling the development of powerful new precompetitive technologies across the pharmaceutical, biotechnology, food/nutrition/agriculture, and energy sectors; and (2) to test applications of new instrumentation to cutting-edge chemical and biochemical problems. These objectives contribute to the national welfare by supporting the development of advanced industrial technologies across all four sectors. In addition, CBM provides compelling opportunities to invigorate human resources through access to hires of Center-trained students and opportunities for continuing education of existing staff. CBM operates under a unified site model, in which projects, independent of location, are associated with any member company with a significant interest in them. In addition, CBM operates by identifying the most timely and important problems of interest to its members and devising projects to address them. This approach is accomplished in a yearly cycle that starts with member companies identifying their most pressing Gaps, Needs, and Opportunities (GNO). These GNOs are used to solicit proposals, and the most timely and responsive proposals are selected for funding. CBM’s research groups projects into five thematic areas - (1) overcoming performance limits, (2) point-of-use technologies, (3) ML/AI data science & automation, (4) chemical imaging, and (5) enabling research technologies. The grouping of project themes recognizes a natural organization of the research carried out within CBM that reflects the strengths of the individual sites, but each theme contains projects of interest to members in all four industry sectors. The Notre Dame site contributes most substantially to the point-of-use technologies and enabling research technologies themes. This mapping reflects the strength of Notre Dame scientists in protein biochemistry, molecular separations, chemical sensors, biomimetic materials, optical spectroscopy, and molecular recognition. CBM research provides longer-term, larger-scale, and more cost-effective solutions to pre-competitive industry measurement science problems than those that can be achieved in-house or through contract research organizations. In addition, CBM provides industry members with compelling opportunities to invigorate human resources through access to hires of Center-trained students and opportunities for continuing education of existing staff. Broader impacts of the CBM include: increasing US economic competitiveness, increasing the number of partnerships between academia and industry, and contributing directly to the development of a globally competitive STEM workforce. This award is co-funded by the following Programs: Industry University Cooperative Research Centers Program in the Division of Engineering Education and Centers - in the Directorate for Engineering, and the Chemical Measurement and Imaging (CMI) Program in the Division of Chemistry, Directorate for Mathematical and Physical Sciences. 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 · 2024-08

ABSTRACT Clostridioides difficile is an anaerobic Gram-positive bacterium that colonizes the gut of patients treated with broad-spectrum antibiotics. A challenge in treating C. difficile infection (CDI) is the production of spores that germinate to active vegetative cells in response to host bile acids. The normal gut microflora typically prevents colonization of C. difficile. Gut microflora dysbiosis as a consequence of treatment with broad-spectrum antibiotics causes recurrence of CDI in 25% of patients, for which spore germination is an instigator. There are no antibiotics for the treatment of multiple recurrent CDI, leading to 11,500 annual deaths in the United States. CDI is the deadliest of the five bacterial urgent threats. Understanding the process of spore germination is key to elucidating the basis for recurrent CDI. If spore germination could be prevented, the nefarious cycles of CDI can be interrupted. We disclose the discovery of the oxadiazoles with bactericidal activity against C. difficile vegetative cells, of which certain oxadiazoles also inhibit spore germination. This grant proposal is outlined in three Specific Aims. We disclose our efforts in elucidation of the details of the spore-germination pathway (Specific Aim 1). We have proposed experiments to demonstrate that inhibition of spore germination is at the root of recurrence of CDI (Specific Aim 2). Furthermore, we outline our efforts to intervene pharmacologically in recurrent CDI by inhibition of spore germination (Specific Aim 3).

NSF Awards · FY 2024 · 2024-08

The Standard Model of particle physics describes nearly everything we can observe in the universe, but we know that it must be incomplete, because it does not explain gravitation, the dark matter that makes up 85% of the mass of the universe, or why there is more matter than anti-matter. Proposed theories that extend the Standard Model to explain these effects typically predict the existence of new particles. In principle, these new particles can be directly searched for with particle colliders. An alternative is to perform very precise measurements to find signatures of the new physics encoded in tiny deviations from the predictions of the Standard Model. These experiments can often be performed on a table-top device. The challenge is that, beyond exquisite experimental control, these searches require very precise predictions of the Standard Model. Often this requires calculating properties of the atomic nucleus, which is a strongly-interacting quantum mechanical many-body system. Historically, such a problem was intractable. But recent developments in nuclear theory have placed precise calculations within reach. This project aims to improve the nuclear theory to the precision needed for searches of new physics. The goal of ab initio nuclear theory is to begin with the force between protons and neutrons and directly solve the quantum many-body problem. Exact solutions are not possible, and so the approach is to formulate an approximation scheme in which tractable calculations systematically approach the exact solution. A crucial feature is the ability to estimate the effect of what has been left out. This project will focus on a many-body method called the in-medium similarity renormalization group (IMSRG). The systematic approximation employed in the IMSRG is that interactions between pairs of nucleons are retained while effects acting on three or more particles are neglected. Including three-particle interactions while neglecting four-particle interactions would yield an improved approximation at greater cost which is just beyond the capabilities of current supercomputers. The goal of this project is to develop a tractable approximation to keeping three-particle interactions, and a framework for estimating the size of what is left out. This will enable the precise calculations with theoretical error bars needed for precision searches for new physics. 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.