University Of Wisconsin-Madison

NSF Awards · FY 2024 · 2024-08

Semiconductors are materials with electrical conductivity that can be switched on and off and that have enabled modern electronics. However, most electronics rely on silicon and gallium arsenide, which have been the semiconductor materials of choice for more than fifty years. These semiconductors are being pushed to their limits and cannot meet the demands of next-generation electronic devices. Carbon nanotubes are atomically thin semiconducting wires that conduct more electricity per area and changes conductivity with less voltage, thus outperforming conventional semiconductors and enabling high performance circuits and devices. Aligned arrays of carbon nanotubes are poised to become the semiconductor of choice in logic microprocessors by substantially increasing speed and decreasing power consumption. For nanotubes to be most useful, they must be lined up in the same direction in a dense single layer, so that electricity can rapidly and efficiently travel through them. However, there are fundamental nanomanufacturing problems that need to be solved. This award supports research that aims to develop scientific understanding associated with using chemical and topographic cues on a substrate to create arrays of nanotubes that are not just aligned but have tailored packing density and consistent inter-nanotube spacing – critical for realizing their ideal electrical performance. Integrated with the research goals are outreach activities such as a YouTube tutorial about the basics of nanoelectronics for the public, research and mentoring opportunities for undergraduates, and dissemination through Engineering Open House and science fairs. The overarching goal of this project is to conduct fundamental research into the scalable nanomanufacturing of carbon nanotube arrays using chemical and topographical patterning approaches and achieve nanotube-nanotube alignment to ±1˚, nanotube packing density of 200-250 μm-1, and improved regularity in packing needed to drive transformative advances in future microelectronic technologies. The research uncovers the factors that affect the lateral diffusion and reordering of nanotubes in trenches, through chemical cues in the trenches, in conjunction with design of new polymer wrappers that are programmed to set the nanotube-nanotube spacing and pitch. The project advances the trench-based assembly of nanotubes to their current limits and assembles nanotubes in trenches that are only a single nanotube wide. The project uses high resolution and large-area morphological and electrical metrology tools for quality assurance. The use of MD simulations provides insights on the possible carbon nanotube self-assembly mechanisms and helps screen different polymer chemistries and surface functionalization strategies. High-performance field effect transistors are fabricated from aligned carbon nanotubes and benchmarked against conventional semiconductor materials and previous carbon nanotube-based transistor technologies. 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

In-space production application is a national initiative to ensure US leadership of in-space manufacturing in low Earth orbit by demonstrating the production of advanced materials and products for the terrestrial market. However, the zero-gravity environment impairs the product quality of many manufacturing systems that perform efficiently and reliably on-ground. Moreover, the data collection cost of in-space manufacturing is so expensive that commonly used quality control and uncertainty quantification strategies fail for such data-scarce systems. This EArly-concept Grant for Exploratory Research (EAGER) project addresses these fundamental issues by establishing a real-time heterogeneous transfer active learning framework. This framework leverages knowledge from well-studied, data-rich on-ground manufacturing systems to enhance experiment design, uncertainty quantification, and quality control in in-space manufacturing systems. Specifically, this project focuses on the in-space electrohydrodynamic inkjet printing and collaborates with the National Aeronautics and Space Administration to collect both on-ground and parabolic flight test data, develop transfer learning models, and validate their performance. The project also contributes to workforce training by promoting the interdisciplinary research of manufacturing, sensing, and data analytics and integrating the research as project topics into undergraduate/graduate courses and various outreach activities. This project leverages the state-of-the-art transfer learning strategy to resolve the urgent need for reliable in-space manufacturing. While transfer learning is effective for dealing with data scarcity, it faces unique challenges when adapted for integrating on-ground and in-space manufacturing systems: 1) The input values, dimensions, or even data types between the on-ground and in-space systems are different. Such heterogeneity requires not only the adaptation of inputs among systems, but also the identification of useful source systems. 2) The in-situ computational resource is limited. This limitation hampers most active learning methods, where the estimated or predicted uncertainty from training data must be recalculated from scratch whenever new experimental data (identified by active learning) is added. This project facilitates a real-time heterogeneous transfer active learning to conduct the heterogeneous transfer learning batch-by-batch within the context of active learning. This project features 1) a flexible and interpretable transfer learning framework to deal with heterogeneous inputs; 2) a Bayesian mechanism to update experiment design and predictions in real-time; and 3) a tailored experiment validation plan for on-ground and in-space manufacturing systems. The successful implementation of the project fills in the knowledge gaps and challenges when translating a manufacturing system into a different environment where there are unforeseen uncertainties. 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

PROJECT SUMMARY Lower-income Americans increasingly rely on a range of unsecured debt sources (e.g., credit cards, payday loans) to help manage expenses, likely contributing to frequent fluctuations in debt levels throughout the year. Frequent fluctuations in debt amounts, use of certain debt sources, or for particular reasons (e.g., managing unexpected expenses) may be adaptive, helping individuals to reduce stress and anxiety related to financial management. Yet, in some cases, these fluctuations may be harmful, increasing experiences of stress, anxiety, and depression. Due to existing survey limitations, we know little about experiences of within-year changes in debt and their potential role as health stressors or stress-reducers for lower-income individuals. The R21 project will systematically capture short-term changes in debt amounts and composition and evaluate associations with generalized stress, anxiety, and depression. The project has three specific aims: 1) Field a novel smartphone survey that collects biweekly self- reported health, income, and debt data from 410 lower-income adults for one year. The development of a biweekly survey on debt is supported by prior research that underscores the importance of capturing short-term fluctuations in income to understand economic insecurity of lower-income individuals and families. The proposed survey addresses the lack of short-term data on debt holdings across a range of unsecured and understudied sources. 2) Develop standard measures of within-year fluctuations in debt burdens across debt sources using data collected from the survey. In doing so, we introduce the concept and measurement of within- year debt fluctuations to the multidisciplinary literature on debt and health. 3) Analyze the data to provide the first insights on the scope of lower-income individuals’ experiences with short- term debt fluctuations and consequences for health, with attention to variation by race. The study is the first to collect high-frequency data on debt and health via mobile surveys, with implications for advancing data collection efforts in this area. Findings will inform economic and family policy, with insights on how addressing debt fluctuations in addition to overall debt burdens in policy design may have positive public health impacts. Data created through this project will be made publicly available to advance research across disciplines concerning family functioning, poverty, and health.

NSF Awards · FY 2024 · 2024-08

To maximize scientific contributions in the field of legislative studies, this project creates a new initiative with the mission to engage, support, and promote the study of legislative politics across gender and sub-disciplinary divides. The initiative hosts virtual events monthly throughout the year, a professional development seminar, a research seminar, and a writing group, an in-person annual conference. The project also maintains a website and listserve with over 550 members and promotes women’s research via social media. Additionally, the initiative collects/analyzes data on women in legislative studies. This project seeks to bring new research and perspectives to scholarship on legislative politics by promoting the study of legislative politics across gender and sub-disciplinary divides. The initiative focuses on the research being done by a diverse set of scholars studying legislatures around the world. One of the aims of the project is to bridge the gap across the study of individual legislatures and the study of legislatures in comparative perspective. Bringing together a diverse set of scholars of legislative politics will encourage intellectual contributions that bridge these subfields. The initiative hosts virtual events monthly throughout the year, a professional development seminar, a research seminar, and a writing group, an in-person annual conference. The project also maintains a website and listserve with over 550 members and promotes women’s research via social media. Additionally, the initiative collects/analyzes data on women in legislative studies. 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

Ordinary gases like the Earth's atmosphere are often characterized by their pressure and temperature, from which it's possible to make many useful predictions using the laws of thermodynamics. Astrophysical gases follow similar laws, but most astrophysical systems are plasmas: made up of energetic, electrically charged particles often with an embedded magnetic field. Under these conditions the simple thermodynamic descriptions do not apply because the laws which regulate the flow of energy between charged particles and the electromagnetic fields are so complex. Examples of such systems include accretion disks around black holes, the tenuous medium pervading clusters of galaxies, and the solar wind coming off of our own Sun. This project will use state of the art computer hardware and software to extract simple descriptions of these systems and use them to develop testable predictions for fundamental, observables such as the rate at which galaxy cluster plasmas emit x-rays and radio waves, whether plasma orbiting a black hole is swallowed up or launched into high energy jets, and what mechanisms power Nature's most energetic explosions, such as the mysterious bursts of radio waves and gamma rays detected throughout the cosmos. Hydrodynamics and magnetohydrodynamics are proven tools for elucidating the properties of diffuse astrophysical gas and have enabled great progress. Still, many unsolved problems remain, including heating and acceleration of particles at shocks and transport of momentum and heat in a turbulent plasma rich in instabilities driven by velocity space anisotropies. This collaborative award to Columbia University and the University of Wisconsin-Madison supports development of physics-based models of kinetic, non-equilibrium processes suitable for use in fluid codes. The project will focus on (1) particle heating and acceleration in shocks and (2) thermodynamics and transport driven by large scale turbulence. These problems will be investigated with numerical simulations, analytical theory, and spacecraft data. The project will engage undergraduate students who will receive mentorship and academic support throughout the project. The project will also develop an interactive website that will be used to expose local high-school students to the forefront of astrophysical research during day-long workshops in the schools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Some of the greatest earthquake hazard on the planet is located along the Alaska-Aleutian subduction zone, where the Pacific Plate is diving underneath North America. Understanding seismic hazard in this region is complicated by a phenomenon known as slow slip, in which tectonic plates slide past each other without causing damaging earthquakes. Changes in water content and in rock type along the subduction zone are thought to control whether slow slip or fast earthquakes happen, but these properties are not well understood. In addition, there are gaps in seismic data coverage that make it difficult to understand how water and rock type vary along the plate boundary. This study focuses on one of these gaps, in the Cook Inlet region of Alaska. The research team has deployed two types of seismic instruments to record local seismic events and use data from earthquakes around the world to understand the deep structure of the subduction zone. One is a traditional experiment using broadband instruments deployed on land in the Kenai Peninsula. These instruments are placed on the property of volunteers, including two K-12 schools, enabling community participation and outreach. The other experiment uses Distributed Acoustic Sensing (DAS) technology – using fiber optic cables as seismic sensors – along and across the Cook Inlet. The goal of this project is to combine both types of data to model the structure and speed of seismic waves in the Pacific plate and its interface with the North American plate. From this water content and rock type, and how these properties vary between regions where slow slip does and does not happen, can be understood. Ultimately, this work will help scientists understand the processes that control seismic hazard where multiple modes and rates of slip coexist. Broader Impacts from this work include showcasing project field practices, and facilitating K-12 school outreach and community participation in broadband data collection. Slow slip events and tectonic tremor have been linked to eclogitization in the subduction interface, but the mechanisms of how these properties vary along the subduction zone and how they govern seismicity style are not well understood. This project harnesses novel and cutting-edge seismic data to investigate the processes that govern these phenomena in the Cook Inlet region of the Alaska-Aleutian subduction zone. The study area encompasses two locations where the subduction interface undergoes recurrent, prolonged, Slow Slip Events (SSEs) as well as damaging megathrust earthquakes. Crucially, only one of the SSE-prone regions is associated with tectonic tremor, providing a natural laboratory for isolating tremor and slip mechanisms. In this project, seismic data from a traditional land-based broadband seismometer deployment will be combined with data from a Distributed Acoustic Sensing (DAS) experiment within the Cook Inlet to fill in a crucial resolution gap over a patch of the subduction zone that experiences slow slip but not tremor. A suite of seismic imaging products, including receiver functions, P-wave autocorrelations, and surface wave dispersion from ambient noise correlations, will be generated and jointly inverted to constrain P and S wavespeed within both plates in order to infer variations in fluid properties and eclogite content across slipping and locked sections of the subduction zone, and evaluate the relative importance of these processes. 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

Neutrinos are weakly interacting electric charge neutral particles that are produced in copious amounts in the stars, supernova explosions, and neutron-star mergers. As such they easily transport excess energy and entropy away from those objects. The PI and their graduate students will study the interaction of neutrinos with their background particles as well as with each other in these astrophysical environments using both conventional techniques of neutrino many-body theory and tools from quantum information science. Theoretical research in those areas has a close coupling with ongoing state-of-the-art experimental and observational programs in the United States and abroad. Previously the PI and their students considered neutrino transport in astrophysical object within the two-flavor approximation, mapping two neutrino mass eigenstates onto up and down states of a qubit. Since neutrinos come in three flavors this approximation is inadequate and one needs to map three mass eigenstates onto qutrits. The team will develop the description of neutrino collective oscillations using qutrits. Since neutrinos control the value of the electron fraction, one can expect that different treatments of neutrino transport would result in different nucleosynthesis scenarios. Preliminary work showed that neutrino oscillations amplify the shift from proton-rich to neutron-rich nucleosynthesis and can result in a full intermediate neutron capture process. The team will explore the consequences of this “nu-i process” in detail. The PI with their collaborators will explore how neutrino spin-flavor precession gives feedback to the core-collapse supernova dynamics. The team will apply the statistical data assimilation technique, an inference procedure wherein a dynamical system is assumed to underlie any measured quantities, to the neutrino transport in the Sun (where much data are available) and core-collapse supernovae (using data from modeling). This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. 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

PROJECT SUMMARY Diabetes mellitus affects 37 million US adults and is the leading cause of vision loss among adults aged 18-64 years. Countries such as the UK that have robust eye screening and treatment programs successfully prevent blindness from diabetes. In the US, where screening programs have been less successful, usual-care screening involves a primary care provider referring patients with diabetes to an eye care provider for a dilated eye exam. Barriers to usual-care screening include transportation, cost, and the time required for patients to make and attend this separate eye care appointment. Racial/ethnic minorities and socioeconomically disadvantaged communities, such as individuals on Medicaid, are more affected by these challenges, resulting in lower screening rates and higher rates of preventable vision loss. Thus, an urgent need exists for a program that equitably improves eye screening and follow-up eye care rates for patients with diabetes. The overall objective of this proposal is to investigate an FDA-approved artificial intelligence (AI)-based system that allows primary care providers to identify diabetic eye disease at the primary care clinic without the need for oversight by an eye care provider. The novel intervention we are testing, AI-BRIDGE (Artificial Intelligence-Based point of caRe, Incorporating Diagnosis, SchedulinG, and Education), is an autonomous AI-based protocol that provides screening for diabetic eye disease at primary care visits, as well as culturally adapted patient education on diabetic eye disease and, if a patient screens positive, assistance with scheduling an in-person, follow-up eye care visit. To achieve our objective, we will carry out 2 specific aims: (1) Determine whether, relative to usual- care screening, AI-BRIDGE improves eye screening and follow-up care rates across races/ethnicities and reduces racial/ethnic disparities in screening rates. To do so, we will work with stakeholders to adapt AI-BRIDGE to an underserved primary care setting and then conduct a stepped-wedge, cluster randomized controlled trial of the adapted intervention in partnership with 10 clinics that are Federally Qualified Healthcare Centers, providing primary care to medically underserved communities. (2) Identify determinants of, and strategies to promote, effective and equitable implementation of AI-BRIDGE using a mixed methods approach. We hypothesize that factors such as organizational leadership’s commitment to the intervention, competing demands on the clinic, and patient and provider perceptions of AI will contribute to adoption of AI-BRIDGE. To test this hypothesis, we will conduct semi-structured interviews of patients, clinic leadership, and providers to identify barriers and facilitators, and then work with stakeholders to identify strategies to address the barriers identified. This work is innovative because it is the first-ever randomized controlled trial that (1) evaluates whether AI can improve equity in eye screening and follow-up care and (2) identifies barriers, facilitators, and strategies to successfully implement this screening strategy in an underserved setting. It is significant because a scalable, AI- based eye screening and follow-up program could reduce disparities in vision loss and ensure that, in the future, diabetic eye disease is no longer the leading cause of blindness in the US.

NIH Research Projects · FY 2025 · 2024-08

Project summary: The evolutionary and genomic drivers of mutation spectra. Patterns of spontaneous mutation vary widely, but there is currently no consensus as to how selection and drift shape this variation. Our goal is to explain diverse mutation patterns, connecting evolutionary causes and consequences with molecular mechanisms. Mutation rates are predicted to be higher in cases where selection had less opportunity to act over evolutionary time, but there is no consensus as to whether this hypothesis is supported by existing evidence. We will test this idea using a novel framework that leverages variation within and among yeast species. Yeast can grow asexually as either haploids or diploids, but species differ in which cell type predominates. We predict that mutation rates will be higher in cells of the rare type in any given species, because selection has had little opportunity to act on mutator alleles specific to that cell type. We will test for ploidy-specific mutation patterns in multiple yeast species that have either haploid- or diploid-dominant life cycles, and test candidate molecular mechanisms for these differences. This project is expected to confirm a controversial hypothesis for the evolution of mutation rates while avoiding previous limitations. Sex is a major dimension of biological variation that can affect mutation patterns, both with regards to differences between males and females as well as the presence or absence of recombination. There is evidence that spontaneous mutations are more likely to arise in the germline of males than females, but the reasons for this bias are still debated. Many existing measures of sex biased mutation come with important limitations, and many organisms also have a sex bias in recombination, potentially confounding the interpretation of sex differences. To disentangle the influence of sex versus recombination we will use a fruit fly strain that lacks meiotic DNA double strand breaks to manipulate the presence of recombination. We will also use crossing techniques to maintain chromosomes with male- or female-exclusive transmission. Allowing mutations to accumulate in these strains and then sequencing their genomes we will provide a unique and powerful perspective on the respective roles of parental sex and recombination in the mutation process. We will further explore the interaction between sex and mutation by measuring how mutations affect reproductive traits. In fruit flies, we will explore the impact of aneuploidy on males and females to understand levels of observed karyotype variation under mutation-selection balance. Yeast are largely asexual, but there is evidence that sexual selection can operate in this system. We will characterize genetic variation for mating- type specific reproductive performance, using genomic data to identify causal alleles. By converting mutant strains between mating types and ploidy states we will be able to infer genetic trade-offs between sexual and asexual performance, informing models for the evolution of sex and anisogamy. These projects will deepen our understanding of how sex and recombination co-evolve with the mutation process.

NIH Research Projects · FY 2026 · 2024-08

Abstract: Over the course of an animal’s lifetime, cell-fate decisions are continually being made that allow for normal development and growth as well as the health of the adult organism. Cell- fate decisions require precisely controlled temporal and spatial expression of particular proteins. In early vertebrate development and certain adult cell types, such as those of the nervous system, this regulated protein expression relies heavily on post-transcriptional mechanisms, particularly translational control. This proposal focuses on a conserved RNA binding protein named Bicaudal- C (Bicc1) that functions in translational regulation and is essential for normal vertebrate development. While it is established that Bicc1 is an RNA binding protein required for the normal development and health of vertebrates, the cellular and molecular mechanisms by which Bicc1 performs these roles are largely unknown and thus represent a major gap in knowledge. The long- term research goal is to define the molecular mechanisms by which developmentally important RNA binding proteins select their target mRNAs and control mRNA expression to effect specific cell-fate decisions, and to understand how defects in these processes contribute to cell dysfunction and organismal disease. The central hypothesis is that Bicc1 selects particular target mRNAs through a complex RNA binding domain with multiple independent RNA binding surfaces and regulates translation via additional distinct regions yet to be defined. This hypothesis is based on extensive research from the lab focused on defining how Bicc1 directs the earliest, maternal stages of vertebrate development in the model organism Xenopus laevis. This work has established Bicc1 as a paradigm for understanding how RNA binding proteins control mRNA translation to direct complex cell-fate decisions. Building on extensive conceptual and technical progress over the past decade, the Specific Aims will address the central hypothesis by: 1. Defining how Bicc1 selects its target mRNAs; 2. Defining how Bicc1 represses translation; and 3. Determining the role of Bicc1 RNA binding and translational repression activities in Xenopus cell- fate specification. The research employs a rigorous and multidisciplinary strategy incorporating RNA-protein biochemistry, unique translation-reporter assays, genome-enabled approaches, reverse molecular genetics, and embryology to define the molecular mechanisms by which the conserved and disease-relevant RNA binding protein Bicc1 directs the earliest cell-fate decisions essential for vertebrate development.

NIH Research Projects · FY 2026 · 2024-07

Project Summary Exposure to one flavivirus can elicit immune responses that cross-react with genetically related viruses, in complex relationships with a variety of impacts on subsequent flavivirus infections. The best-characterized example of this is within the dengue virus (DENV) serocomplex. Pre-existing immunity to one of the 4 DENV serotypes, DENV-1, for instance, can increase the risk of severe disease upon infection with a different sero- type in what is termed antibody-dependent enhancement (ADE). Interestingly, emerging evidence suggests that immunological cross-reactivity among flaviviruses is not always reciprocal—that is, pre-existing immunity to virus A may protect against disease associated with virus B, while pre-existing immunity to virus B may increase the risk of disease upon infection with virus A. For example, we and others have shown in hu- man cohorts that pre-existing immunity to DENV reduces the risk of disease associated with Zika virus (ZIKV) infection, while immunity to ZIKV enhances the risk of disease associated with certain DENV serotypes. Thus, the degree to which pre-existing flavivirus immunity is cross-protective, enhancing, or neutral may depend on the order in which the host has encountered different flaviviruses previously. We hypothesize that differences in the antigenic sites recognized by antibodies and/or antibody func- tional “quality” are major determinants of non-reciprocal flavivirus immunity. To address this hypothesis, we have assembled a unique team of investigators with expertise in virology, immunology, epidemiology, and nonhuman primate models, with access to samples and data from our 19-year Pediatric Dengue Cohort Study in Nicaragua, the longest continuous such study in the arbovirus field. We will integrate studies of flavivirus immunological cross-reactivity in nonhuman primate models and human cohorts to evaluate the impact of flavivirus exposure on virus replication dynamics, antibody repertoire diversi- ty, neutralization titer, and Fc effector function. We will also leverage our unique cohort of children in Nicaragua with known flavivirus infection histories to examine how the order of exposure to DENV serotypes and to ZIKV shapes cross-reactive antibody profiles, specifically, the capacity to respond to Spondweni virus (SPONV), a model emerging flavivirus and the closest known relative to ZIKV. We include SPONV here due to our prelimi- nary data indicating that cross-reactive immunity between ZIKV and SPONV in macaques is strikingly non-re- ciprocal. Macaques with ZIKV immunity were completely protected against SPONV challenge, while SPONV immunity provided no protection against ZIKV. This study will provide tractable model systems in which to identify how the order of flavivirus exposure impacts immune responses and infection outcomes. Our findings will have broad implications for how we assess the risk of emerging viruses and disease in flavivirus-exposed populations and design next-generation flavivirus vaccines.

NIH Research Projects · FY 2026 · 2024-07

ABSTRACT All eukaryotic cells face the critical problem of regulating the subcellular distribution of lipids. The lipid composition of organelle membranes is distinct, with each membrane containing a specific subset of lipids that defines organelle identity and regulates organelle function. These membrane lipids are also critical for intracellular signaling pathways and for proper control of lipid metabolism. Not surprisingly, defects in the subcellular distribution or metabolism of these macromolecules underlie many devastating human diseases. We recently identified and unified a novel family of proteins: the bridge-like lipid transfer proteins (BLTPs). BLTPs are very large, rod-shaped proteins with uninterrupted inner hydrophobic channels that serve as lipid superhighways between apposing membranes at organelle contact sites. Although mutations in BLTPs are associated with human disease, BLTPs are not required for single cell viability, a fact that has limited efforts to understand how disruption of BLTP function contributes to disease. Our goal in this proposal is to use an animal context to tackle this gap and, in doing so, uncover the biological significance of BLTPs—and bulk lipid transport—to physiology and disease. We identified a Drosophila model system where disruption of a single BLTP, BLTP2, results in dramatic physiological and cellular phenotypes, leaving us uniquely poised to systematically dissect the molecular and cellular functions of this protein and understand how BLTP dysfunction leads to disease. We will pursue the following two specific aims: 1) Define the essential molecular properties of BLTP2. 2) How is BLTP2 function at membrane contact sites regulated? Our long-term goal is to understand BLTP function and how regulation of bulk lipid transport impacts animal physiology. We expect our studies to reveal essential insights into the role of BLTPs in human disease which, in turn, we hope will lead to novel therapies to treat disorders caused by BLTP dysfunction.

NSF Awards · FY 2024 · 2024-07

This project will determine when and how quickly the Cordilleran Ice Sheet in western Canada disappeared since the end of the last ice age, approximately 20 to 10 thousand years ago. It will create a 3-D reconstruction of the ice sheet’s collapse through geologic dating of rock samples collected from mountains across the region that record the lowering ice sheet surface and use computer models of the ice sheet shape to help ‘connect the dots’ between these datapoints. Because the Cordilleran Ice Sheet shared many similarities with the present-day Greenland Ice Sheet, this reconstruction will provide a key test for models used to simulate Greenland’s future decay – one of the largest and most uncertain sources of sea level rise. The project will build an international collaboration from the U.S., U.K., and Canada and train the next generation of scientists in holistic approaches to better understanding how ice sheets collapse. The team will contribute content to a popular website on glaciers targeted at the general public and teachers/students. A climate journalist will take part in fieldwork and potentially write a story. Projections of future sea level rise rely on ice sheet models that are highly tuned to the present day, limiting confidence in their ability to simulate the future. Recent advances in cosmogenic dating as well as ice sheet modeling and uncertainty quantification now make it feasible to use reconstructions of past ice sheet changes to test and improve coupled climate-ice sheet models. The deglaciation of the Cordilleran Ice Sheet is poorly constrained, yet this ice sheet offers great potential to constrain models due to its similarities to the southern Greenland Ice Sheet: mountainous, high mass accumulation, strong precipitation gradients, and marine/land terminating. The Cordilleran Ice Sheet is also thought to have played a key role in rapid sea level and climate changes during the last deglaciation, but evidence of this is limited. This project will produce the first 3-D reconstruction of Cordilleran Ice Sheet collapse. It will apply the "glacial dipstick" approach, generating 135 10Be ages along ~15 vertical transects. Using a Bayesian Uncertainty Quantification approach, these field data will be combined with Cordilleran Ice Sheet simulations from a complex yet efficient coupled climate-ice sheet model used for future projections. This will produce an ensemble of plausible reconstructions for the deglaciation of the Cordilleran Ice Sheet. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The International School of Cosmic Ray Astrophysics (ISCRA) is a biannual summer school designed to provide an overview of the field to graduate students and young postdoctoral scholars through both lectures and informal discussions with Particle Astrophysics experts. This is the only regular Particle Astrophysics summer school and it has an excellent history of training future leaders. This award offsets the total cost per participant and allows around 12 graduate students and young postdocs from the US to attend. The ISCRA represents a major contribution to the education of these attendees. Proceedings are also published for broad dissemination. The 23rd ISCRA course will take place in Erice, Italy, from the 20th through the 28th of July 2024. The theme of the course is “Multi-Messenger Astroparticle Physics”. Discussions will include neutrino and gravitational wave astronomy, the highest energy particles, acceleration and interactions of high-energy radiation, balloon, satellite, and ground-based measurements, the propagation of high-energy radiation through the galaxy, and future space- or ground-based experiments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Scientists increasingly recognize that macro-organisms harbor complex communities of microbes that deeply influence their biology. The Ninth Conference on Beneficial Microbes will provide a forum for the exchange of the latest conceptual and technological developments in the field of host-microbe interactions. The conference will promote inclusion of early career scientists, especially those that have been historically excluded from science. Networking events and interactive poster sessions will be designed with the specific goal of facilitating networking opportunities for early career scientists. Biologists increasingly recognize that macro-organisms (animals, plants and macro-fungi) are multi-organismal. These associations with microorganisms can have profound implications, such that the phenotype and fitness of animal/plant/fungal hosts can only be understood fully in the context of the microbiome. The study of beneficial microbes is a rapidly advancing field requiring exchange of ideas and methods among scientists working across diverse fields. The Ninth Conference on Beneficial Microbes meeting will provide an overview of the current state of research and future directions of inquiry in the field of host-microbe interactions. The meeting will bring together US and international researchers from multiple disciplines, including microbiology, evolutionary biology, ecology, genomics, developmental biology, immunology, engineering, nutrition and systems biology, who study host-microbe associations across a diversity of systems. The structure and venue of the meeting facilitates networking across scientists and trainee levels, which will spur new ideas and collaborations. Results of the meeting will be disseminated through social media and a published report. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

As data size and complexity increases, it is necessary to find ways to characterize features in the data for scientific studies. This research project concerns the development of statistical methodology in the field of topological data analysis (TDA). TDA offers a framework for quantifying shape-related features of data, such as holes with persistent homology (PH). The topological descriptors from PH (i.e., persistence diagrams) give a description of the different holes identified in data, which may be used in an analysis. This research project will advance the statistical foundations of PH and address major statistical challenges in astronomy. The research is motivated by two astronomical areas related to the large-scale structure (LSS) of the Universe and the detection of planets outside our Solar System. While PH gives informative descriptions of complex data, the computations are intensive, and the persistence diagrams are difficult objects to work with statistically. The project aims to improve the computational aspects of the PH algorithm and to develop a statistically sound framework for the analysis of complex data using PH. This research develops novel methodologies that contribute to the progress of science, especially astronomy. The project provides research training opportunities for graduate students, and the methodology, software, and materials produced will be available for researchers, instructors, and the broader public. This research project will advance the statistical foundations of PH for broad applicability, including addressing statistical challenges in astronomy. More specifically, both LSS and exoplanet data may be characterized by holes: LSS is a complex web of matter that includes clusters of matter, loops of filaments, and voids, while exoplanets induce periodic signals in starlight that may be construed as loops in certain embedding spaces. The three main objectives of this research are: (1) To develop TDA tools for big data with statistical guarantees. The project team will develop new algorithms for computing PH summaries to make them computable for large datasets, such as large cosmological simulations. (2) To build sound representations of holes for visualization and scientific discovery. The aim is to develop methods for estimating robust representations of homology group generators and assess their statistical properties. These representations identified in LSS have the potential to constrain the cosmological model. (3) To establish TDA methods for realistic time-series data. This will include state space reconstruction for time series data that are not uniformly spaced with noise, with an application to the detection of exoplanets in the presence of stellar variability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This project will undertake a comprehensive molecular analysis to understand how different plant cells within leaves mobilize nutrients through a cellular process called autophagy (“self-eating”) and what compensatory mechanisms are activated when autophagy fails. With such knowledge, it should be possible to re-engineer crops that more efficiently use nutrients, that better remobilize these nutrients to areas of new growth and storage, and that provide better yields under normal and stressful conditions. The participating investigators will provide training to students and postdoctoral fellows in modern biological approaches to study crop physiology. At UW Madison, high school students will be trained through the “Wisconsin Youth Apprentices” program, a state-certified program designed to provide high school students with experiences in industry and academic research. At MSU, data generated in this project will be used in an undergraduate course to enhance students’ analytical skills. The four labs will design and implement both virtual and in-person workshops during scientific meetings. Autophagy is a central regulator of the cellular, developmental, and physiological responses that underlie key agricultural traits, such as fertilizer-use efficiency and remobilization, carbon allocation, seed quality and germination, yield, and tolerance to environmental stress. However, current understanding of the organization, regulation, and cell-type specificity of autophagy in crops is limited. This project has four aims: 1. Analyze how autophagy impacts cellular recycling of photosynthetic maize leaf cell types using pooled cell-type omics approaches (proteomics, transcriptomics, and metabolomics). 2. Analyze the mechanisms of chloroplast remodeling and turnover by different autophagy-related pathways at the single-chloroplast type level. 3. Identify gene regulatory networks controlling cellular recycling in maize leaf cell types by integrating omics data at the cell-type, single-cell, and chloroplast-type levels. 4. Experimentally test predicted metabolites and transcription factors as potential regulators of autophagy and nutrient mobilization in maize. Collectively, this work will provide the first analysis of cellular and nutrient recycling in different leaf cell types in a crop plant. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Although there have been improvements in modeling Arctic processes, individual computer models still disagree on projected changes in the Arctic Ocean, where trends in temperature, salinity, stratification, and volume transports are highly model dependent. Furthermore, it has been shown that the variability in the interior layers (internal variability) of the Arctic plays a central role in its processes. However, to date, there is no comprehensive comparison of this variability in the Arctic Ocean for the most recent Coupled Model Intercomparison Project (CMIP). This project will examine Arctic Ocean variability in CMIP6 models. The results will serve as a baseline for future studies of Arctic Ocean internal variability. Key results could also contribute to determining when and where future observational campaigns will be most beneficial for detecting and monitoring Arctic Ocean change. The proposed work aims to better understand present and future Arctic Ocean internal variability by comparing CMIP6 models with multiple ensemble members. The project will answer the following questions: How does internal variability in Arctic-wide upper ocean temperature, salinity, and stratification, as well as volume transports through the Arctic gateways compare across CMIP6 models? Do trends in these same variables shift and emerge during the 21st century in CMIP6 models, and if so, do the models agree on the timing? Are the timing of shift and emergence dependent on the future forcing scenario? A combination of observations, models, and an ocean state estimate (ECCO) will be used. Major outcomes will include an improved understanding of Arctic Ocean internal variability, the first assessment of this variability across the most recent suite of state-of-the-art climate models participating in CMIP6, and demonstration of the utility of the synthetic ensemble technique for Arctic Ocean variables in models, observations, and ECCO. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

Project Summary Dietary protein is a powerful determinant of the biological state and represents a vital element in a comprehensive geroprotective therapy. Several groups have demonstrated that the manipulation of dietary protein levels can directly influence the aging process. Specifically, the reduction of protein intake (PR) robustly enhances metabolic health, promotes healthspan, and extends lifespan. Unpublished data from the Lamming Lab further demonstrates in an early onset model of Alzheimer’s disease (3xTg-AD) that PR improves glucose tolerance, reduces tau hyperphosphorylation, and ameliorates cognitive deficits. However, a recent conflicting report suggests that a high protein diet can reduce the accumulation of Aβ in the brains of Alzheimer’s disease (AD) patients. This indicates that further understanding of PR’s molecular mechanisms is necessary to appreciate the true contribution of dietary protein in the aging process. In investigating the components of a PR diet, the Lamming Lab has reported that isoleucine restriction (IleR) alone can effectively recapitulate PR’s benefits and is required for a significant portion of PR’s effects. To further understand the role of IleR as a geroprotective therapy, young C57BL/6J male mice were fed an IleR diet and concurrently treated with rapamycin, which is a well-studied life-extending drug with proven benefits in AD mouse models. Surprisingly, rapamycin largely overtook the short-term effects of the diet, blocking IleR’s benefits in body composition, glucose tolerance, and energy expenditure. At the molecular level, rapamycin specifically prevented the induction of lipolytic programs and not of thermogenesis or lipogenesis in the inguinal white adipose tissue. Lipolysis and lipid regulation plays a critical role in the systemic state of metabolism. While the effect of PR on brain lipid regulation is unknown, recent publications have found that dysfunctions in lipolysis is associated with AD progression. As such, this proposal investigates the overarching hypothesis that lipolysis regulation is a critical mechanism of action in the physiological role of dietary protein, with a focus on healthspan, lifespan, and cognitive health. Aim 1 will define the metabolic and molecular interactions between rapamycin and various dietary restrictions and investigate the role of dietary protein concentrations on the life-extending effects of rapamycin. Aim 2 will leverage transgenic mouse lines with genetic ablation of putative rapamycin targets in order to dissect the molecular mechanisms responsible for the effects of PR. Aim 3 will determine the role of dietary protein on the development of AD symptoms and on the effects of rapamycin in the early onset 3xTg-AD and the late onset hAβ-AD mouse models. We will further characterize changes of the lipidomic profile in the serum and the brain. In summary, this research program seeks to provide significant advancements in our mechanistic understanding of the role of dietary protein in the biology of aging as well as in the manifestation of AD pathogenesis.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY In addition to intellectual disability, individuals with Down syndrome (DS, Trisomy 21, Ts21) have an increased risk for many other health conditions, including Alzheimer’s disease (AD). DS is the leading genetic risk factor for AD, and nearly 90% of individuals with DS develop AD (DS-AD). Beta-amyloid (Aβ) plaques and tau tangles, characteristic AD pathology, begin accumulating in DS brains earlier than in the general population, and most individuals with DS are diagnosed with DS-AD in their early to mid-50s. Basal forebrain cholinergic neurons (BFCNs), involved in arousal, learning, attention, and memory, are a susceptible population prone to degeneration in DS-AD as well as other neurodegenerative diseases, including AD, Parkinson’s disease (PD), and Dementia with Lewy bodies (DLB). Little is known about BFCN development and molecular mechanisms underlying vulnerability. Limited analysis of human DS tissue suggests fewer BFCNs populate the basal forebrain, however, it remains unclear whether the reduction is due to developmental deficits, accelerated degeneration, or a combination of both. The goals of the F99 pre-doctoral proposal are to 1) determine whether fewer BFCNs and AD pathology are present in the early postnatal DS basal forebrain, 2) identify underlying molecular signatures that may contribute to the susceptibility of BFCNs later in life, and 3) model BFCN development in vitro using isogenic of Ts21 and control induced pluripotent stem cells. The goals of the K00 post-doctoral proposal are to 1) analyze tissue across the lifespan at different disease stages to determine the temporal order of AD pathology accumulation in the DS basal forebrain, and 2) sequence basal forebrain tissue from DS-AD, AD, PD, and DLB to determine if shared mechanisms underlie BFCN vulnerability across several neurodegenerative diseases. This F99 proposal will be the first study of human DS post-mortem tissue to reveal early deficits in DS BFCNs, suggesting deficits in DS BDCN development. Elucidating early post-natal deficits in DS BFCNs may inform early interventions and improve BFCN health across the lifespan. The K00 proposal will be the first cross-sectional study of human DS post-mortem tissue to reveal early emerging pathology and molecular mechanisms contributing to the susceptibility of BFCNs in the basal forebrain and elucidate shared mechanisms underlying BFCN vulnerability in several neurodegenerative diseases. Results may provide novel targets for therapeutics impactful for many neurodegenerative diseases. The results from this project will meet the goals of the NIH INCLUDE project by establishing scientific data to improve the health and neurodevelopment of individuals with Down syndrome and have a broader impact on the health of individuals at risk for other neurodegenerative diseases characterized by the loss of BFCNs, including DS-AD, AD, PD, and DLB.

NSF Awards · FY 2024 · 2024-07

This project pursues the contemporary problem of statistical network integration facing scientists, practitioners, and theoreticians. The study of networks and graph-structured data has received growing attention in recent years, motivated by investigations of complex systems throughout the biological and social sciences. Models and methods have been developed to analyze network data objects, often focused on single networks or homogeneous data settings, yet modern available data are increasingly heterogeneous, multi-sample, and multi-modal. Consequently, there is a growing need to leverage data arising from different sources that result in multiple network observations with attributes. This project will develop statistically principled data integration methodologies for neuroimaging studies, which routinely collect multiple subject data across different groups (strains, conditions, edge groups), modalities (functional and diffusion MRI), and brain covariate information (phenotypes, healthy status, gene expression data from brain tissue). The investigators will offer interdisciplinary mentoring opportunities to students participating in the research project and co-teach a workshop based on the proposed research. The goals of this project are to establish flexible, parsimonious latent space models for network integration and to develop efficient, theoretically justified inference procedures for such models. More specifically, this project will develop latent space models to disentangle common and individual local and global latent features in samples of networks, propose efficient spectral matrix-based methods for data integration, provide high-dimensional structured penalties for dimensionality reduction and regularization in network data, and develop cross-validation methods for multiple network data integration. New theoretical developments spanning concentration inequalities, eigenvector perturbation analysis, and distributional asymptotic results will elucidate the advantages and limitations of these methods in terms of signal aggregation, heterogeneity, and flexibility. Applications of these methodologies to the analysis of multi-subject brain network data will be studied. Emphasis will be on interpretability, computation, and theoretical justification. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

This proposal outlines a comprehensive plan for graduate education in chemistry & biology at UW–Madison via the Chemistry-Biology Interface training (CBIT) program. We seek to provide cross-disciplinary research training to our students, so that chemists and biologists not only appreciate, but also use, the tools and techniques developed by each other. These types of fresh approaches can lead to meaningful and truly impactful breakthroughs in science. The overall objective of CBIT is to educate trainees so they understand and can articulate scientific problems that span the chemistry–biology interface, have the technical skills to realize an independent research project at this interface, and can effectively communicate their discoveries to both scientific and general audiences. CBIT’s specific objective is to maximize Ph.D. completion rates within our 5 core departments/programs and measure longitudinal student outcomes (as per degree completion and job placement in the biomedical workforce) that will advance best practices in biomedical graduate training overall. CBIT’s overall & specific objectives are shaped by our mission to cultivate cross-disciplinary scholars capable of strong communication and teamwork. To realize these objectives, we request funds to support the CBIT program at the level of 10 trainees/year, with each trainee funded for 2 years. Key features of our proposed program are underscored below: ● The CBIT program will provide an integrated set of coursework (foundational and area specific, with dedicated courses in ethics and research rigor), research experiences that span the frontiers of the chemical biology field, mentorship training, focused training in communication (with mentors, other scientists, and the public), substantive career development opportunities (internships, annual workshops, and IDPs), and team-based experiences (via courses, research, and outreach). ● We will provide trainees a palette of four professional skill sets aligned with different biomedical careers (academic, industry, government, and legal/non-profit), guided by the needs of current employers within the biomedical workforce (via consultation with our new External Advisory Board), and composed of different activities that are specifically tailored for success in these four career spaces. ● We will evaluate our ability to achieve these results through quantitative assessments of the outcomes of the CBIT program, including gains in science identity, science self-efficacy, and core graduate school competencies such as broad knowledge of a discipline, experimental skills, and critical thinking skills. Our CBIT program fills a unique niche at UW–Madison as the only T32 program centered in the chemical sciences. This interfacial program has significantly impacted student outcomes—notably, of our 57 CBIT Ph.D. graduates since 2008, 55 (96%) are in careers that directly impact human health. We will build on this strong history and continue to innovate in graduate education over the next 5 years.

NIH Research Projects · FY 2026 · 2024-07

ABSTRACT The cardiac and pulmonary systems are inherently linked through the pulmonary vascular system which leads to secondary pulmonary disease in cases of cardiac pathology. This is especially the case in congenital heart disease where the pulmonary blood supply is often substantially altered by abnormal outflow tracts and ventricular formation. Cross-sectional imaging has proven to be invaluable for assessing pediatric diseases, including congenital heart disease; however, current cardiopulmonary evaluations typically require multiple exams (SPECT, echo, MRI, and CT) to evaluate the cardiovascular and pulmonary systems. Each exam adds risk to already fragile patients, introduces complex logistics of performing multiple exams, and can delay care of patients who may require urgent management. Often, only a subset of exams are performed, and clinical management is based on incomplete information and disregards the strong potential for cardiopulmonary coupling. In this project, we aim to develop MRI methods that can simultaneously and efficiently evaluate both anatomy and function in pediatric cardiopulmonary diseases. MRI is theoretically well suited for quantitatively imaging both the cardiac and respiratory systems but is traditionally challenged by its slow imaging speed and sensitivity to artifacts. Recently, our group has proposed methods for dramatically more robust lung imaging using the combination of ultrashort echo time MRI with advanced motion corrected reconstruction strategies. In this proposal, we extend these techniques and introduce novel methods to provide improved and comprehensive diagnostics of the entire cardiopulmonary system. First, we introduce a free-running approach to cardiopulmonary imaging to provide anatomical imaging and the quantifications of ventilation, perfusion, cardiac function, and respiratory resolved cardiac flow dynamics. We specifically aim to image continuously with T1 weighted and velocity encoded sequences, and subsequently reconstruct this data with a high-dimensional deep learning approach. The reconstructions use novel motion corrected methods to directly estimate images and apply deep learning in a highly compressed space. Secondly, we aim to develop next-generation motion management using an RF navigator technique, Beat Pilot Tone, that can be applied during any pulse sequence to measure bulk, respiratory and cardiac motion. Beat Pilot Tone provides a basis for motion tracking that enables improved imaging efficiency, a simplified setup without cardiac leads or respiratory belts, and much better measures of bulk motion. These techniques will be evaluated in normal control participants and pediatric subjects with congenital heart disease, each with comparisons to state-of-the-art imaging. The impact of this project is to shift the paradigm for clinical management of cardiopulmonary diseases to a single-scan comprehensive imaging study and supporting an integrated assessment of interaction between the pulmonary and cardiac systems in disease. While this is targeted at pediatric cardiopulmonary diseases, the innovations can be applied broadly to MRI studies throughout age ranges and to other studies that suffer from motion artifacts throughout the body.

NIH Research Projects · FY 2026 · 2024-07

Project Summary/Abstract Telomeres are specialized nucleoprotein structures that protect chromosome ends and confer genome stability in eukaryotic cells. Without telomeres, chromosomes undergo misguided end-to-end fusions, leading to chromosomal aberrations and eventual genome instability. Hence, deregulation of telomere maintenance results in human diseases such as cancer and dyskeratosis congenita. The human telomeric DNA comprises thousands of tandem repeats of the conserved hexameric sequence, TTAGGG. The telomeric DNA has a unique structure, a several kilobases long double-stranded DNA (dsDNA) region that ends with a 3' single- stranded DNA (ssDNA) overhang. The cornerstone of telomere maintenance is the two-part process of telomeric DNA synthesis. In the first step, the ribonucleoprotein telomerase extends the telomere G-rich 3' overhang. Telomerase adds telomeric repeats to the ssDNA overhang. Next, telomeric ssDNA-binding protein complex, CTC1-STN1-TEN1 (CST), coordinates with DNA polymerase alpha-primase (Polα-primase) to fill in the newly-synthesized telomeric G-overhang by de novo C-strand synthesis. While the mechanism of the extension of telomeric G-overhang by telomerase is an area of intense study since the discovery of telomerase almost four decades ago, that for the telomeric C-strand fill-in by CST-polα-primase is much less understood. Telomere C-strand fill-in is equally essential in telomere maintenance as the G-overhang extension process. As such, this proposal aims to study the molecular mechanism of the human telomere C-strand fill-in machinery by providing biochemical and structure-function relationship understandings of template-bound CST-Polα- primase at key catalytic steps. Multiple novel models and hypotheses from this proposal are based on the premises of our recent cryogenic-electron microscopy (cryo-EM) structures of the human template-bound CST- Polα-primase preinitiation complex (PIC). For the first aim, we will establish an unprecedented consensus template sequence for CST-Polα-primase assembly and C-strand synthesis initiation. For the second aim, we will use cryo-EM single-particle analysis to determine the structural basis of how CST-Polα-primase initiates de novo RNA primer synthesis after PIC assembly at telomeric overhangs. For the third aim, we will elucidate the molecular mechanism of how CST-Polα-primase “counts” the RNA primer length during synthesis and promptly terminate the “matured” RNA primer for the intramolecular primer handover to the DNA polymerase domain. The findings from this proposed work will provide a timely advancement to our understanding of the human telomere C-strand fill-in mechanism and mammalian telomeric DNA synthesis in general. The proposal structure-function studies provide a missing platform to connect CST and Polα-primase human disease mutations to mechanistic understandings. From a broader perspective, telomere C-strand fill-in is an excellent model for studying lagging-strand synthesis by Polα-primase in replisomes.

NSF Awards · FY 2024 · 2024-07

The broader impact of this I-Corps project is the development of infrastructure to promote robust, automated face recognition systems. To date, face recognition systems have broadly proliferated across various industries, including commercial and governmental domains. Automated face recognition enables many applications including identifying individuals on social media, locating missing persons, assisting law enforcement and surveillance activities, and authenticating personal identities. Unfortunately, there are still significant concerns which prevent automated face recognition by smaller organizations. This technology makes face recognition systems both auditable and finely tunable. These properties can potentially mitigate many of the concerns that have prevented widespread deployment. Consequently, face recognition deployments, if used in conjunction with this technology, will become more acceptable while increasing efficacy and improving fairness properties. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a system to generate synthetic face data, to be used to audit and tune face recognition systems. This technology is based on generating synthetic face data though novel systematization of text-to-image generative image architectures. Users can synthesize high-quality faces for different text-specified facial semantics. These generated faces may be subsequently used to assess face recognition model performance or to tune under-performing systems. Synthetically generated faces have a high degree of utility when natural face images are too expensive or are otherwise impossible to collect. The recent literature shows that face recognition systems, in practice, exhibit hard-to-detect conditional failure modes. These failure modes imply that face recognition systems are not robust to changes in inputs and have demographic disparities. This solution debugs failures in current face recognition systems through well-curated synthetic data. The approach to face recognition validation and tuning was preliminary verified by a human study. 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.