University Of Wisconsin-Madison

NSF Awards · FY 2025 · 2025-09

Rural students pursuing STEM degrees often face unique challenges as they navigate academic and career pathways far from home, with fewer built-in opportunities for mentorship, peer connection, or institutional support. Yet, many bring with them strong social networks that may play a critical--but understudied--role in their persistence and success. This project seeks to examine these issues by studying the links between STEM career pathway persistence and social support networks among rural STEM university students. Prior research suggests that rural university students have distinct and resource-rich social support networks, which are important to STEM success. Little work, though, details these networks in universities or their possible connections to rural student STEM career persistence. Building from tools the research team has used in ongoing studies, this investigation will advance knowledge on the characteristics that make rural STEM student support networks unique, how networks associate with STEM career persistence, and how students perceive these issues playing out in their daily lives. Findings will also significantly contribute to knowledge on the role of social support networks in strengthening and broadening student STEM workforce pathways more generally, helping build new theories that support the STEM career development of students. Using social network analysis and social capital theory, the researchers are administering two online surveys, 18 months apart, to a panel of rural and nonrural STEM students across eight public universities in Wisconsin (initial n~1,000). At each of these two data collection points, the team is also conducting interviews with a subsample of rural and nonrural STEM survey respondents (initial n~50). Using descriptive statistics and regression models to analyze survey data and inductive and a priori coding to analyze interviews, the project will map rural student networks and trajectories between the two phases, examine links between networks and STEM career persistence, compare rural and nonrural student networks and STEM persistence, and capture the personal perspectives of rural university students on these issues. Results are being disseminated through presentations, reports, scholarly publications, and public media to ensure a wide array of stakeholders access the findings. In addition, STEM researchers will be able to build on the network data collection tools and findings to conduct further work on the impact of networks on STEM student populations. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Hydrogels have great potential for applications in healthcare, robotics, and more. Additive manufacturing of hydrogel enables building 3D objects with sophisticated structures. However, a key challenge is the lack of cost-efficient methodologies for designing the hydrogel synthesis and printing processes together to achieve desired product performance. This award enables research in creating hydrogels with customized features tailored for distinct applications with lower cost, reducing the cost of additive-manufactured hydrogel products. If successful, the outcomes of this research are expected to shorten the design cycle of new materials and processes and facilitate wide utilization and rapid scale-up to industry. This research aims to develop a unique analytical design framework, consisting of (a) interpretable and efficient uncertainty quantification models that adaptively accommodate the model complexity and (b) a unique decision algorithm for the multistage experiments that simultaneously decides the next experimental operation and the volume of material, in order to make diverse products achieving multiple targeted functionalities. Additionally, the experimental platform, dynamic-fluid-assisted micro-continuous liquid interface printing (DF-μCLIP), offers a dedicated “hardware in the loop” system that synergizes in-situ hydrogel synthesis and printing for implementing the design optimization procedure. The resulting integrated material discovery and manufacturing platform has broad potential impact across material and manufacturing systems. 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 2025 · 2025-09

With this award, the Chemistry of Life Processes (CLP) Program in the NSF Division of Chemistry is supporting Professor Helen Blackwell from the University of Wisconsin– Madison to investigate the chemicals that bacteria produce to communicate with each other. This communication pathway is important because many common bacteria use it to cause infections and disease in humans, animals, and plants. However, the structures of these signals and how they function remain poorly understood. The goals of this NSF project are to examine the chemical signals made by one group of bacteria, to investigate how they function in bacteria, to use chemistry to make signals that the bacteria cannot make themselves, and to apply these non-natural signals to activate and inhibit bacterial communication pathways on demand. This research approach, rooted in chemistry, allows for the signaling pathway to be explored in new ways and in important, biologically relevant environments, providing fundamental new insights into how it works. This project has additional broad impacts, which include providing ample opportunities for training students in modern scientific techniques and preparing them for advanced careers in science. In addition, immersive artwork and hands-on activities to communicate the presence of bacteria and their chemical signals in the environment, and their global importance, to the general public. This research project is motivated by the amazing ability of bacteria to act as a group at high cell number and initiate behaviors that can have devastating eQects on humanity. This process is called “quorum sensing” (QS). Gram-positive bacteria use accessory gene regulator (agr) type, two-component signaling systems for QS that are reliant on autoinducing peptide (AIP) signals. The dependence of common bacteria on a chemical language of small peptides places organic chemists and chemical biologists in a unique position to uncover the fundamental principles underlying this communication network and design new tools to modulate it at the molecular level. In prior studies, non-native peptides were developed that are capable of either blocking or activating agr-type QS in the Staphylococci. More recently, these approaches have been extended to other pathogens and have identified some of the first small molecule inhibitors of agr-type QS. Many questions remain about the mechanisms and potential utility of these non-native compounds. The current project leverages this strong foundation of research and expands to goals that: (1) apply chemical synthesis and biosynthesis to develop next-generation ligands to target agr-type QS with improved activity profiles and physical properties; (2) apply biochemistry to delineate the molecular mechanisms by which non-native ligands interact with agr QS systems; (3) apply chemical biology to investigate the role of agr QS in mixed bacterial communities with both spatial and temporal control. Together, the results of these goals will significantly advance the current understanding of QS. 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 · 2025-09

Project Summary Sensorimotor adaptation, learning from the mismatch between the predicted sensory feedback of our intended motor action and the actual sensory feedback we observe, is a fundamental skill that supports accurate movement control, such as speech. One common paradigm used to study the mechanisms of speech adaptation is examining how speakers adjust their speech production in response to an external perturbation applied to the auditory feedback of their speech acoustics in real time. While this paradigm has been proposed as a tool to assess the mechanisms of speech deficits in various neurogenic disorders, reduced speech adaptation is commonly found across various clinical populations, despite impairments in very different neural structures. This pattern is not well-predicted by the current models of speech motor control, potentially because speech adaptation may not involve a single learning process (as in current models) but, as suggested by extensive work in other (nonspeech) motor domains, two distinct processes – a fast process that learns quickly from error but retains less, and a slow process that learns slowly but retains more. Here, we test the hypothesis that adaptation in speech, like in other motor domains, involves separable fast and slow processes by translating a behavioral paradigm known to uncover multiple learning processes across motor domains to speech for the first time (Aim 1). Separately, we will test the neurocomputational basis of adaptation by combining this novel behavioral paradigm with continuous theta-burst transcranial magnetic stimulation to temporarily inhibit regions known to affect adaptation (Aim 2). By assessing behavioral correlates of both fast and slow processes after stimulation, we can separately assess which regions are involved in each process. We test this both in speech and, as a control, in reaching, which is known involved both the fast and slow processes. As the neural networks underlying these separate processes in reaching have not been well established this work additionally provides the first assessment of the neurocomputational basis of adaptation across multiple motor domains, allowing us to test the domain generality of the two-process model of adaptation. Even in the case that we only find a single adaptation process in speech, comparison of the neural findings in speech and those in reaching allows us to test whether the neural substrate underlying speech adaptation maps onto the analogous regions involved for the fast or slow process (or both) in reaching. This research will improve our understanding of the computational processes in speech adaptation and refine the neural mechanisms of sensorimotor adaptation across all motor domains. This work will provide a first step towards developing more sensitive measures that can differentiate control deficits through speech adaptation which may help the development of speech therapy that precisely targets the mechanisms of speech deficits across various clinical populations.

NSF Awards · FY 2025 · 2025-09

Many vehicles today feature automation to assist or replace human drivers, but fully autonomous systems requiring minimal human input are still years away. As a result, human drivers and automated vehicles (AVs) frequently interact, leading to driver interventions. While most research focuses on over-reliance on AVs, this project supports research that attempts to address a different issue: driver-initiated takeovers, which occur when AV behavior does not match driver preferences. Although these takeovers are rarely safety-critical, they reduce the benefits of AVs in improving traffic flow. This project aims to improve the interaction between human drivers, AVs, and traffic systems by exploring how human preferences shape driving behavior and traffic dynamics. The challenge is understanding how these preferences interact with AVs and the broader traffic system. If AVs align too closely with human preferences, it can lead to inefficiencies, while focusing solely on traffic flow may result in unnecessary interventions. The goal is to find a balance where AVs guide driver behavior to improve traffic outcomes without sacrificing individual needs. The project looks to introduce the concept of bidirectional preference shaping, where AVs influence driver behavior to foster safer and more efficient driving. This approach differs from traditional systems that merely replicate human habits, which can be inefficient and unsafe. The results could improve national mobility, safety, and economic competitiveness. This project looks to investigate bidirectional influence in sensorimotor interactions across human drivers, AVs, and traffic systems. Alignment translates micro-level human preferences into actions that scale up to macro-level traffic outcomes. Complete alignment with human preferences risks traffic inefficiency and instability, while optimizing solely for traffic flow may provoke unnecessary driver intervention. The hypothesis is that this strategic bidirectional alignment can be achieved by exploring which preferences are most malleable and consequential, and enabling AVs to shape human preferences for system-wide benefits. The proposed research comprises three research thrusts: (1) Human preference: understand and model human driver preferences and their dimensionality and malleability, (2) Alignment: align AV driving control and preferences through embodied models of human drivers, and (3) Collaborative consensus: map preferences to traffic outcomes to inform bidirectional alignment and formulate consensus to balance competing interests of human, AV, and traffic system. If successful, this project will advance the understanding of bidirectional interactions between human-automation behavior and traffic dynamics, offering broad applications in aviation, manufacturing, and other domains where individual decisions impact overall system performance. More broadly, it seeks to address the ethical and scientific challenge of aligning AI with human and societal values. 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 2025 · 2025-09

Frontier AI models have pushed the boundaries of machine learning and artificial intelligence research and sparked transformative technological innovation in many US industries. These large-scale AI models are able to process and generate text, image, audio, and video, and currently require massive amounts of data and computing. This Mathematical Foundations of Artificial Intelligence (MFAI) project aims to uncover the mathematical principles that explain when and why these highly advanced AI models are so effective, and to overcome the fundamental limits of brute-force scale presently employed to surpass human expert intelligence in benchmarks. The project will advance the capabilities of AI models to conduct inference in new situations in which there is no training data, and to perform complex reasoning and problem-solving tasks. This research will ensure that the US remains the global leader in AI, advancing economic prosperity, national security, and global competitiveness. This project aims to rigorously characterize the mathematical frontiers of generative AI models, including state-of-the-art large language models (LLMs), by developing new theoretical frameworks and modeling principles rooted in machine learning, probability theory, variational analysis, mathematical statistics, and information theory. The research will investigate how frontier AI models achieve remarkable performance despite fundamental theoretical barriers and will identify the key mathematical quantities that drive their generalization abilities. The project will develop new mathematical analyses of diffusion-based generative AI models, design novel data strategies for AI models used towards zero-shot inference, and discover scaling laws enabling models to achieve compute-optimal accuracy tradeoffs for inference and generation. This award is jointly funded by the Directorate for Mathematics and Physical Sciences, Division Of Mathematical Sciences; Directorate for Engineering, Division of Civil, Mechanical, & Manufacturing Innovation, and Directorate for Computer & Information Science & Engineering, Division of Computing and Communication Foundations and Division of Information & Intelligent Systems. 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 2025 · 2025-09

Computability theory emerged in the 1930’s from the need to understand what exactly one means by “algorithm”. Many problems in mathematics can be solved by a person following a set of rules, assuming they have an unlimited amount of time and scratch paper. For example, a person can divide polynomials or determine if a number is prime. Mathematicians have long had an intuitive understanding of what constitutes a valid set of computational rules, i.e., an algorithm. However, to prove that an algorithm cannot solve something, it is needed to formalize that intuition. This problem was addressed by the work of Gödel, Church, and Turing, who took different approaches, yet all managed to identify the same class of “computable functions”. (This class captures what can be solved by a programable computer, even though it would be 10 years before the first computer was built.) This early work showed that there are specific problems in elementary number theory that do not have computable solutions. Since then, non-computable problems have been identified in many different subfields of mathematics. Most famously, the solution sets to some Diophantine equations and the word problem for some finitely presented groups are not computable. In computability theory, the goal is not only to show which problems are solvable by an algorithm, but also to study the relative algorithmic complexity of such non-computable problems, which is often connected to the complexity of those problems as measured in other ways. In this project, the focus is on a framework used to measure relative complexity: the enumeration degrees. The motivation then is to study the enumeration degrees because of their nontrivial connections to effective mathematics, where other frameworks prove to be insufficient to measure the effective content of an object. The PIs approach this from various directions, including accumulating new methods, investigating combinatorial properties of the structure, and isolating special classes of degrees that determine the logical character of the structure. Enumeration reducibility captures relative enumerability between sets of natural numbers. The PIs call the induced partial order the enumeration degrees. They can be viewed as a proper extension of the (better studied) partial order of the Turing degrees and a wider context for relative computability. Miller, Soskova, and co-authors proved that the Turing degrees have a first-order definable copy within the enumeration degrees. They also provided many other examples of definable relations, suggesting that the long-standing problem of whether the partial order is rigid might be more approachable in this wider setting. The proposed in-depth study of the enumeration degrees explores a variety of approaches: accumulating an arsenal of identifiable classes of degrees that live within this structure; expanding our methodology; and gaining a better understanding of the algebraic properties of the structure. Effective mathematics has served as a source for identifying non-trivial classes of enumeration degrees. Miller established a connection between topological spaces and the enumeration degrees; other classes that are being investigated arise from symbolic dynamics, from computable structure theory, and from effective algebra. The PIs have many questions about the properties and structure of these classes as well as the interactions between them. Methods form hyperarithmetic theory and more sophisticated notions of forcing have emerged through this work and will be further be extended and analyzed within this project. 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 2025 · 2025-09

Recent advances in data science and statistics have revolutionized how researchers uncover cause-and-effect relationships from complex, real-world data. Many pressing questions—such as whether flu vaccination reduces infection rates, whether sanitation programs improve children’s health, or whether educational policies enhance student outcomes—cannot be answered through randomized experiments alone. Observational data, while abundant, often pose serious challenges due to hidden biases, unmeasured factors, or interconnected influences among individuals. For example, a person’s risk of flu depends not only on their own vaccination status but also on whether people around them are vaccinated, while unmeasured behaviors such as health-seeking habits can distort results. This project tackles these challenges by developing advanced statistical methodologies that improve the reliability of causal conclusions. In particular, it enhances a class of techniques known as distributional balancing methods, which create fair, comparable groups across the full range of observed variables. By extending these methods to account for complex data structures and unobserved confounding, the project will equip scientists and policymakers with more trustworthy evidence for decision-making. The research outcomes will impact healthcare, education, economics, and environmental policy, while also contributing to science through open-source software, user-friendly resources, and the training of students in cutting-edge statistical methods. Technically, the project focuses on two complementary innovations. First, it develops a novel framework for distributional balancing in settings where data exhibit dependency structures, such as patients treated within hospitals, students nested within schools, or individuals connected by social networks. The proposed methodology constructs balancing weights by aligning the joint distribution of covariates between treatment groups while explicitly accounting for clustering and network effects, which pose major challenges for current balancing methods. The approach includes diagnostic procedures for assessing covariate balance under dependence and robust sensitivity analysis for evaluating the stability of causal conclusions. Second, the project introduces a new integration of instrumental variable (IV) techniques with reproducing kernel Hilbert space (RKHS)-based distributional balancing. This extension allows researchers to address unmeasured confounding by leveraging valid instruments and estimating balancing weights with respect to flexible, nonparametric distributional distances. The resulting IV-balancing methods provide both theoretical guarantees and computational efficiency, expanding the toolkit of modern causal inference. Together, these methodological advances fill critical gaps in existing frameworks, enabling robust causal analysis in complex observational studies and yielding immediate applications in healthcare policy evaluation, biomedical research, and other domains where confounding and dependency are inherent. 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 2025 · 2025-09

Sodium metal anode-based battery chemistry is a promising candidate for next generation energy storage systems due to its high energy density, natural abundance, and low cost. These advantages make it well suited for large-scale or grid-scale applications such as electric grids and transportation. However, the practical use of sodium batteries is hindered by challenges in controlling the battery materials’ decay, which directly affects battery performance attributes such as power, shelf life, and cycle life. This project will develop new materials characterization tools to study the interior of the batteries’ materials under changing conditions similar to how it would operate to store and discharge energy. The resulting fundamental knowledge will help enable the rational design of next generation batteries and electrochemical systems. These insights will not only promote the progress of science but also support the development of alternative energy storage technologies beyond lithium-ion, contributing to enhanced national energy security and use of domestic critical materials. Additionally, the project will engage undergraduate and graduate students through hands-on research experiences, while expanding science outreach to K-12 students to promote scientific literacy and awareness of sustainable energy. These efforts will help expand the workforce in science and engineering and help cultivate a skilled workforce to address future energy challenges. This project will advance fundamental understanding of the interfacial solvation structure and dynamics at sodium metal–electrolyte interfaces in sodium-ion batteries, a critical yet underexplored area in battery science. The project will develop and apply advanced characterization techniques that integrate high resolution spectroscopy with electrochemical measurements. These tools will enable direct investigation of both static and dynamic molecular interactions at the metal–electrolyte interface, providing unprecedented spatial and temporal resolution. Specifically, the project will probe processes such as solid electrolyte interphase formation and ion transport, which are critical to the development of more stable and efficient sodium metal batteries. The insights generated through this work will inform the rational design of electrolytes and interfaces for sodium metal batteries and, more broadly, for emerging energy storage 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.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Cigarette smoking is the leading preventable cause of death and claims more than 480,000 lives in the US each year. Roughly 20 million adults in the US want to quit smoking tobacco. At least 7 million people access digital resources (e.g., Smokefree.gov) on this topic each year. Although evidence supports many tobacco treatment interventions, absolute quit rates remain low and relapse or continued smoking is the most common outcome of most quit attempts, even with treatment. Personalized treatment is a promising strategy that may enhance the effectiveness of smoking therapies and deliver the right support for a particular person at a particular time in the quitting process. Sophisticated, high-dimensional data analytic approaches are needed to account for the complexity and intersectionality of influences on smoking lapses and relapses during quit attempts, and to develop robust and effective personalized risk predictions and interventions. Identifying the dynamic patterns and key features of individuals, events, and contexts that predict smoking during quit attempts could inform treatment delivery in multiple ways (e.g., clinical decision support tools for treatment teams, smartphone quitting apps). This K23 career development proposal aims to enhance Dr. Kaye’s capacity to pursue the important public health goal of improving smoking cessation treatment personalization by teaching him how to apply powerful new machine learning methods to this effort. Specifically, he will apply machine learning methods to rich existing datasets from diverse samples of adults trying to quit smoking. Trials combine extensive assessment of individual differences (e.g., demographics, smoking history, comorbidities) and ecological momentary assessment of time-varying risk (e.g., craving, stress, anhedonia) and protective factors (e.g., quitting self-efficacy, medication adherence). The use of data from multiple clinical trials offering varying treatments (e.g., counseling, medications, smartphone apps) creates opportunities to assess the robustness of smoking risk prediction across treatment contexts. This project will conduct secondary data analysis of 4 smoking cessation clinical trials (total N=3023), using machine learning methods to develop, train, and validate: 1) a long term smoking relapse prediction model based on baseline (pre-treatment) data, and, 2) a smoking lapse risk prediction model based on baseline data and dynamic time-varying risk signals. Dr. Kaye will receive training and mentoring in tobacco treatment (Training Goal 1: Drs. McCarthy, Baker), feature engineering to generate predictors (Goal 2: Drs. McCarthy, Curtin, Bolt, Loh), multiple machine learning methods (Goal 3: Drs. Curtin, Bolt, Loh), and digital just-in-time adaptive interventions for substance use (Goal 4: Drs. Businelle, Curtin). The K23 will generate new knowledge about how to predict near- and long-term abstinence outcomes and inform hypotheses about when, how, and for whom to deliver personalized interventions to people trying to quit smoking. The K23 will also launch Dr. Kaye’s program of research to develop open-source, publicly-available risk prediction models that can be incorporated in future treatments.

NIH Research Projects · FY 2025 · 2025-09

Our goal is to improve the assessment of cutaneous wound healing by quantitatively measuring biomarkers of vascular perfusion and hypoxia. Our research employs photoacoustic imaging (PAI), which is an outstanding imaging modality for interrogating the entire wound depth at 4-5 mm in pre-clinical models and eventually in patients. For comparison, optical imaging methods cannot reliably measure biomarkers deeper than 1-2 mm into tissues. To meet this goal, we have developed Dynamic Contrast Enhanced (DCE) PAI that can evaluate vascular perfusion in wounds. Our DCE PAI approach can quantitatively measure pharmacokinetics parameters of perfusion rates of the agent in the tissue. We have also adapted our Oxygen Sensitive (OS) PAI method for wound healing studies that can measure oxyhemoglobin (HbO2), deoxyhemoglobin (Hb), total hemoglobin (HbT) and oxygensaturation (%sO2). We have shown that OS PAI and DCE PAI both detect early-stage wound healing which precedes a significant change in wound area. In an excisional model, showing the strong impact that OS- DCE PAI can provide. We will further refine OS-DCE PAI for imaging wound healing models. We will improve our methodology by performing DCE PAI with our advanced analysis method that avoids complications from variable light absorbance and scattering in deep tissues. We will investigate multiparametric analyses to demonstrate that combined measurements of oxygenation and vascular perfusion can improve wound healing diagnoses relative to a single parameter. We will also evaluate our DCE PAI method that uses a single absorbance wavelength relative to multi-wavelength imaging, which will expand our method to perform multislice PAI that can cover an entire wound. Imaging the entire wound will allow us to investigate the diagnostic utility of evaluating voxel distributions of our imaging measurements, and the utility of regional analyses. Each of these improvements is designed to improve the clinical translation of OS-DCE PAI. To demonstrate the strong impact of our research, we will use our OS-DCE PAI methodology to evaluate our excisional “punch” wound model, ischemic “crush” wound model, and burn model with normal mice and diabetic mice. Importantly, we have established each of these wound models in our research program. We will test diabetic mice to support eventual clinical translation, because diabetic patients with foot & leg ulcers and other wounds is a major chronic problem in current health care. Our deliverable is a new OS-DCE PAI method that will position us at the doorstep of clinical translation of our OE-DCE PAI method to monitor patients with burns, diabetic foot ulcers, and other wounds. Our top-ranked Burn and Wound Center at the University of Wisconsin is highly motivated to support clinical translation. The three manufacturers of clinical PAI instruments have expressed strong interest in our research with wound healing that can expand the market for clinical PAI.

NSF Awards · FY 2025 · 2025-09

This grant provides funding to support students to participate in the Student Challenge at the 40th Annual Meeting of the American Society for Precision Engineering (ASPE) in San Diego, California, 3-7 November 2025. ASPE is the leading international conference on precision engineering, attracting researchers and practitioners from academia, industry, and government. Its focus includes the fundamentals and applications of precision engineering, which underpin critical advancements in manufacturing, defense, healthcare, and measurement science. A highlight of the conference is the ASPE Student Challenge, an annual team competition launched in 2014 to foster hands-on experience and deep understanding in precision design and control principles. By supporting student participation in the ASPE Annual Meeting and providing valuable experiential learning through the Student Challenge, this grant will help cultivate future engineering leaders in precision design, mechatronics, and precision control. ASPE is a flagship conference in the field of precision engineering, with technical coverage spanning mechanical, electrical, and optical metrology; flexure design; mechatronic design; precision control theory and practice; ultra-precision machining; and system integration. Conference activities include oral and poster presentations, expert-led tutorials, commercial presentations, an industrial exhibition, and a student challenge. In 2025, the ASPE Student Challenge will task teams with manufacturing a playable musical record from acrylic using an air-bearing spindle and a diamond cutting tool. Through this project, students will gain key technical knowledge and hands-on experience in precision machine design and control, surface metrology, toolpath generation, and high-precision motion systems. This grant will promote the next generation of precision engineers by (a) providing experiential learning in precision design, manufacturing, and metrology through the hands-on challenge; (b) enabling exposure to foundational knowledge via expert-led tutorials; and (c) offering insight into state-of-the-art research and industrial practices through technical sessions and networking opportunities. 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 · 2025-09

PROJECT SUMMARY Kaposi's sarcoma, caused by Kaposi's sarcoma-associated herpesvirus (KSHV), remains a major health challenge for people living with HIV and other immunocompromised patients. While research has historically focused on the latent cycle of KSHV infection, recent evidence demonstrates that viral reactivation to the lytic cycle is crucial for tumor development through growth factor secretion and maintenance of latently infected cells. This central role of the lytic cycle offers a new therapeutic opportunity. Therefore, there is a need to discover strategies to block KSHV reactivation and its associated pro-tumor activities, as a starting point to develop new targeted therapies. Type I interferons (IFNs) potently block KSHV replication at early stages of the lytic cycle. However, direct IFN administration causes severe systemic side effects in patients, limiting its use. We recently discovered that KSHV hijacks cellular caspase enzymes to suppress IFN responses. This finding suggests that caspase inhibition could restore antiviral IFN responses in infected cells and serve as an alternative approach to leveraging IFN response for therapy. However, we do not yet know whether manipulating caspase activity and type I IFNs can block the pro-tumor activities of lytic replication. This is because IFN responses have been studied in conventional 2D cell culture infection models that poorly mimic KSHV infection in tumors. To address this gap, we will use a recently developed 3D primary human endothelial cell organoid model. These organoids better recapitulate characteristics of Kaposi’s sarcoma tumors, including stable maintenance of the latently infected cell mass through spontaneous viral reactivation and changes in cell morphology and differentiation. We will use this new model to test the hypothesis that activation of the IFN pathway, either through direct IFN administration or caspase inhibition, disrupts two key tumorigenic processes due to lytic reactivation: growth factor secretion and maintenance of the latently infected cell population. We will first dissect type I IFN and caspase signaling during KSHV infection in organoids and compare it to IFN and caspase activation in Kaposi’s sarcoma tumor tissue (Aim 1). We will then test how inducing IFN responses through direct IFN administration or caspase inhibition affects growth factor secretion and maintenance of the latently infected cell population (Aim 2). This study will thus evaluate the potential for modulation of caspases and IFN responses in blocking KSHV tumorigenic activities and as therapeutic strategy against Kaposi’s sarcoma. Success could lead to new targeted treatments for Kaposi’s sarcoma while providing a foundation for future mechanistic studies of KSHV pathogenesis.

NSF Awards · FY 2025 · 2025-09

The behavior of ice sheets during past warm periods can be used to help us better project how ice sheets and sea level will respond in the future under conditions of sustained warming. In turn, this knowledge can be leveraged to help build more resilient coastal communities and protect and defend our coastlines. Currently there is debate about the dynamics of the polar ice sheets during the most recent past warm period in the geological record, known as the Last Interglacial around 125,000 years ago. Some reconstructions for this time interval suggest a stable/slow rising sea level from ice sheets retreat, while others cite evidence consistent with more variable sea level from rapid ice sheet volume changes. While evidence for this problem remains controversial, a new methodology was recently applied, and firm evidence was found for a brief local fall in sea level at a single location. However, the timing and rate of sea level change are still poorly constrained, and the global extent of this feature remains unknown. This project seeks to build on this new approach and combine it with high-precision dating to refine our understanding of the rates and extent of Last Interglacial ice sheet retreat and sea level change. This project will provide professional development and mentoring opportunities for the fellow and develop a virtual field trip website using drone and other imagery to expand accessibility of sea level research. Shallow marine carbonates such as coral reefs can be used to help reconstruct the magnitude of past sea level changes. This project will leverage existing samples collected from fossil coral reefs around the globe that grew during the Last Interglacial. The focus will be on Last Interglacial coral reef deposits in Western Australia and the Seychelles that contain three distinct generations of reef growth separated by disconformities or transitional sedimentary facies, to test the question of sea-level variability. The main goals of the project are (1) to assess whether there is evidence of subaerial exposure associated with the sedimentary surfaces bounding the reef units and (2) define the time represented between the deposition of reef units. New cutting-edge, super resolution autofluorescence (SRAF) microscopy that can reveal fine-scale carbonate petrography will be combined with U-series coral geochronology within detailed stratigraphic frameworks to determine the timing and magnitude of potential subaerial exposure events and to correlate observed relative sea-level change between sites. This project will produce important proxy data to calibrate and improve ice sheet models to determine which ice sheets were most susceptible to past warming and how future ice sheet retreat will affect U.S. and global coastlines. 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 2025 · 2025-09

The end of the last ice age and its transformation of North American ecosystems provides a natural experiment for studying how far species and ecosystems can move as environments change and a naturally engaging way to teach students about the history of their local landscapes. This project will employ state-of-the-art open databases of fossils and Earth system models to map past biome distributions in North America and measure how far these biomes moved as ice sheets melted, temperatures rose, and rainfall patterns shifted. The project focuses on four major ecotones: Arctic treeline, the northern transition from temperate to cold-hardy trees, the eastern Great Plains, and the Great Plains transition between cool-season and warm-season grasses. These ecotones moved by hundreds to thousands of miles in the past; this project will more precisely estimate these movements and quantify the amount of movement per degree Celsius warming or mm rainfall change. This information is directly relevant to land managers and policymakers seeking to help species adapt to current changes and to mitigate risks associated with ecosystem transformations. The project will harness the rich datasets and visualizations produced by this project to develop a variety of data-powered and place-based educational materials for multiple student audiences and shared through multiple in-person and online venues. Land cover reconstructions for the last 21,000 years will be based on fossil pollen datasets drawn from the Neotoma Paleoecology Database and new compilations of carbon isotopic data. Land cover will be reconstructed using the REVEALS model and interpolated using a Bayesian hierarchical model with spatial dependence. Past temperature and rainfall patterns will be reconstructed using paleoclimate data assimilation (PDA), an ensemble of Earth system models, and over 600 proxy records from LiPDverse. A novel addition of statistical downscaling into the PDA workflow will enable high-resolution reconstructions and the modeling of local ecotone movements as a function of changes in temperature and rainfall. A hands-on, specimen-rich demonstration will be built for K-5 students and the general public that teaches about past ecosystem change and will be deployed via in-person science fairs, meetings of opportunity, and on-line educational portals. The project also will build new high-school to college-level curricular materials that draw upon these high-quality datasets and data visualizations, engaging students in scaffolded analysis and interpretation of data about ecosystem change at local to global scales. Curricular materials will be shared via the Teach the Earth website, which reaches >5 million visitors annually. This project also will engage with Tribal and agency land management professionals, train early-career researchers, and openly share these next-generation reconstructions of past environments and ecosystems. 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 · 2025-09

PROJECT SUMMARY/ABSTRACT Rates of unemployment and underemployment among autistic adults remain high and are expected to rise over the next decade, with more and more autistic adolescents transitioning into adulthood. There is a critical need to investigate mechanisms that lead to better vocational outcomes for autistic youth. Parent-child relationship quality is one modifiable factor that theory and prior research suggests is important to vocational success. Career construction theory highlights the importance of the early parent-child relationship in a child’s sense of security, mental health, and self-determination, shaping their later career development. This process is virtually unknown in autistic populations even though autistic individuals rely heavily on their parents as a primary source of support across the lifespan. To improve later vocational outcomes, it is critical to first investigate modifiable factors which can improve pre-vocational skills and activities. Pre-vocational skills and activities, defined as work-related functional skills (e.g., self-direction, social competence, behavior) and experiential learning opportunities (e.g., job exploration, shadowing) are particularly important to investigate because of their ties to vocational outcomes in adulthood. We will leverage two existing longitudinal datasets of autistic youth to understand the impact of parent-child relationship quality in childhood and adolescence on pre-vocational skills and activities during the transition out of high school. Our central hypothesis is that parent-child relationship quality will predict pre-vocational skills and activities, mediated by self-determination and mental health. Aim 1a will use data from the Family Outcomes in Autism Spectrum Disorder (ASD) study involving 176 triads of autistic children (mean age 8 years at baseline, followed for 5 years) and their mothers and fathers. In this aim, we will examine longitudinal profiles of parent-child relationship quality across five data points during childhood as a predictor of pre- vocational skills (i.e., work-related functional skills). Aim 1b will use data from the Center on Secondary Education for Students with Autism Spectrum Disorders (CSESA) study involving 547 diverse autistic adolescents (mean age 16 years at baseline, followed for 7 years) and their parents. In this aim, we will similarly examine longitudinal associations between parent-reported relationship quality and pre-vocational skills and activities. Aim 2 will investigate youth self-determination and mental health, reported by autistic youth and their parents, as mediators of the pathways between parent-child relationship quality and pre- vocational skills and activities in the CSESA dataset. In an exploratory Aim 3, we will investigate whether parent-child relationship quality and pre-vocational skills and activities in high school predicts job status and satisfaction after high school exit in a subset of the CSESA sample for which post high school data is available (n=211). The proposed secondary data analysis will provide new information about the role of parent-child relationship quality in pre-vocational skills and activities, contributing to theory and practice.

NSF Awards · FY 2025 · 2025-09

NON-TECHNICAL SUMMARY: Alloys and metals and play a central and evolving role in the progress of science, national prosperity, and national defense. While metals are best known for their traditional use as structural materials, well-defined combinations of them with fascinating and transformational properties continue to be discovered. Examples include the ability to conduct electricity without resistance, interconvert heat and electric energy, exert powerful magnetic forces, catalyze chemical reactions, and manifest the quantum nature of matter. A central factor in the emergence of such behaviors is the formation of intermetallic compounds, in which mixtures of atoms of different metals adopt specific geometries that are often unrecognizable compared to those of the pure elements. However, how these geometrical configurations determine the properties of a metallic material and how they can, in turn, be controlled remain critical questions. With this project, funded by the Solid State and Materials Chemistry program and the Condensed Matter and Materials program, both in NSF’s Division of Materials Research, Prof. Fredrickson and his research group is developing (1) principles that connect the properties of a material to patterns formed by the forces between its atoms, and (2) guidelines for how these forces can be harnessed in materials discovery. The implications of the devised principles are explored with the synthesis and characterization of new solid state compounds, as well as theoretical calculations. This project also contributes to the strengthening of the national STEM workforce through the training of new scientists and educational outreach. Content for the online resource “Interactive Solid State Chemistry” is being created, which merges comics and interactive web modules to teach concepts in solid state and materials chemistry. In addition, the project is enabling a traveling exhibit, “From Crystals to the Molecular World”, that uses the theme of crystallography to bridge hands-on experiences with rocks and minerals to the atomic-level structures of materials. TECHNICAL SUMMARY: Intermetallic phases represent a seemingly limitless source of potential structure-properties relationships, but the realization of this prospect is a daunting challenge. The diverse members of this class of solid state compounds encompass an incredible range in both structure and behavior. However, that very diversity—along with the intricate nature of their bonding and electronic structures—creates great difficulty in relating geometry and properties through either theoretical or empirical means. Net-based descriptions of structures have long formed a shared language for describing these two aspects of intermetallics: nets, or their 3D generalization as polyhedra, form a convenient way of communicating complex geometrical arrangements, while certain nets or patterns of atoms are sought after for predicted properties. In most cases, though, it remains challenging to divide a crystal structure into nets or other motifs from which materials behavior can be predicted. This project, with support from the Solid State and Materials Chemistry program and the Condensed Matter and Materials program, both in NSF’s Division of Materials Research, develops the chemical pressure (CP) scaffolding concept as a framework for directly associating specific periodic geometric patterns with materials properties. Networks of overly compressed interatomic interactions (positive CP) are hypothesized to form scaffolds for various types of soft atomic motions that underlie structural transformations or materials properties. This concept is being developed through the creation, testing, and refinement of (1) tools to compare CP scaffolds among crystal structures and connect them to properties, (2) synthetic strategies to modify the CP scaffolds within structures, and (3) approaches to investigate the interaction between CP scaffolds and electronic driving forces in materials behavior. The pursuit of these objectives tightly integrates theory and experiment, as well as use of graph-theory approaches to generalize the results into broad themes for intermetallic structures. 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 · 2025-09

Project Summary/Abstract Hematopoietic stem and progenitor cells (HSPCs) reside in a microenvironment that regulates their behavior by interactions with niche support cells. During development of the mammalian embryo HSPCs are born in the dorsal aorta and migrate to the fetal liver, where they expand and differentiate. Fetal liver HSPCs are proliferative and when transplanted have a greater capacity for reconstituting the blood system compared to quiescent adult HSPCs. However, the genetic networks regulating fetal liver HSPCs, and their maturation, are poorly understood. To dissect these networks, we are characterizing a viable integrin α4 (itga4) mutant zebrafish model with perturbed interaction between HSPCs and the mammalian fetal liver equivalent—the caudal hematopoietic tissue (CHT). Preliminary analysis of HSPCs at 5 days post fertilization (dpf), after migration from the CHT to the presumptive adult kidney marrow niche, detected transcriptomic and epigenomic differences between wild-type (WT) and itga4 mutant HSPCs, indicating reprogramming of HSPCs after interaction with the CHT niche. Gene Set Enrichment Analysis (GSEA) of differentially expressed genes between WT and itga4 mutant HSPCs showed enrichment of inflammatory signaling in itga4 mutant HSPCs which did not lodge in the CHT niche. Motif analysis of accessible chromatin regions unique to itga4 mutant HSPCs revealed potential regulatory factors of HSPC reprogramming, such as ets1 and AP-1 factors. We hypothesize that bypass of the CHT niche prevents correct HSPC programming and transition to quiescence, causing itga4 mutant HSPCs to remain in an immature state. In Aim 1, we will analyze the proliferative capacity, inflammatory profile, and stem cell capacity of HSPCs that did (WT) or did not lodge in the CHT niche (itga4 mutant) to characterize the programming event initiated by itga4-mediated interactions between HSPCs and the CHT niche. In Aim 2, we will map the epigenomic landscape and perform multiomic analysis of WT and itga4 mutant HSPCs to determine the gene regulatory networks underlying the developmental switch of HSPCs from proliferation to quiescence. We will utilize our unique zebrafish system and multiomic approaches to dissect the developmental networks of HSPC regulation. By understanding the transitions between proliferative and quiescent HSPC states, our findings could translate into novel approaches for stem cell expansion and improved stem cell therapy.

NSF Awards · FY 2025 · 2025-09

This REU program addresses critical national challenges related to the aging highway infrastructure and limited maintenance funding by preparing undergraduate students to apply digital technologies to infrastructure engineering. Focusing on digital twins—virtual models that mirror physical road assets—the program equips students with the knowledge, skills, and tools to improve infrastructure monitoring, decision-making, and long-term resilience. It supports NSF’s mission by advancing science, promoting national welfare, and developing a skilled STEM workforce capable of leading digital innovation in infrastructure. Through hands-on research, mentorship, and international collaboration, students gain interdisciplinary experience that blends engineering, computing, and data science. The program also broadens access to emerging research areas and prepares participants for graduate study and future careers in infrastructure systems. The objective of this REU site is to engage U.S. undergraduate students in interdisciplinary research on digital twins for road infrastructure. Over three summers, 24 students from West Virginia University, the University of Wisconsin–Madison, and nearby institutions will participate in a 10-week program—eight weeks at U.S. host institutions, followed by two weeks at the University of Cambridge’s Laing O’Rourke Center. Students will conduct research on data acquisition, modeling, simulation, and decision-support tools for digital replicas of road assets. Activities will address challenges such as creating scalable models, integrating sensor data, and validating digital twin outputs for infrastructure monitoring and maintenance planning. The program combines civil engineering, computing, and data science to expose students to real-world infrastructure systems and emerging digital technologies, while fostering transatlantic collaboration and broadening their academic and professional perspectives. This project is jointly funded by the Division of Engineering Education and Centers (EEC) and the Division of Civil, Mechanical and Manufacturing Innovation (CMMI). 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 2025 · 2025-09

The goal of the project is to develop novel advanced materials integrated with real-time process feedback, assisted by a machine learning algorithm, to enable scalable, autonomous in-situ manufacturing of electronics. The technology will provide capabilities for on-demand fabrication, adaptive repair, and dynamic reconfiguration of circuits, functions that are particularly critical for long-duration space missions where resupply is difficult. These enhanced materials and manufacturing processes will support future space exploration initiatives. Beyond space applications, the methods developed here may also transform multiple technology sectors including flexible hybrid electronics for wearable devices, neuromorphic computing systems that mimic brain functions, and distributed manufacturing solutions for remote or resource-limited environments. The research incorporates workforce development initiatives to train students in cutting-edge techniques spanning materials science, artificial intelligence, and advanced manufacturing. Participants will gain hands-on experience in functional materials synthesis, intelligent process control systems, and semiconductor device fabrication, skills directly aligned with emerging needs in the advanced manufacturing sector. The project specifically addresses national workforce development priorities in critical technology areas including additive manufacturing, semiconductor processing, and autonomous production systems. This project develops a new method to manufacture electronics in space using 2D materials like molybdenum disulfide (MoS₂). These ultra-thin materials are ideal for space applications because they are lightweight, radiation-resistant, and energy-efficient. The key innovation combines three critical components: (1) specially designed chemical inks that transform into functional electronics at relatively low temperatures, (2) an artificial intelligence (AI)-controlled printing system that adjusts in real-time to produce perfectly aligned layers, and (3) precision laser processing that fine tune the material's properties after printing. First, new ink materials and formulations will be created, where the molecular structure determines how well the material performs in final functional semiconductor devices. Then AI systems will be implemented to monitor and optimize the printing process, catching and correcting any defects in real-time. Finally, laser sintering will be utilized to control and enhance the material's electrical properties, enabling complete electronic device processing onsite. This integrated approach solves a major challenge in space manufacturing by eliminating the need for complex equipment or high temperature processing. The methods could enable in space manufacturing of electronics during long missions without relying on Earth-based supplies. The same technology may also improve manufacturing of flexible electronics and advanced computing systems on Earth. 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 2025 · 2025-09

This project focuses on complex analysis, a branch of mathematics that investigates the theory of calculus over the complex. Complex analysis serves as an important tool in numerous applications: It plays a crucial role in physics (e.g., modeling airflow over airfoils and analyzing dispersion relations in optics), engineering (e.g., signal processing and control theory), and computer science (e.g., image processing and quantum computation). The theory of complex analysis in one variable is classical and well understood, but when additional variables are introduced, many mysteries remain. In this project, the investigator will further the theoretical understanding of complex analysis of several variables. The proposed activities also involve collaboration with and mentoring of junior researchers at the undergraduate, graduate, and postdoctoral level. The investigator will study the Gromov hyperbolicity of the Kobayashi metric; regularity properties of biholomorphic mappings between families of domains in complex Euclidean spaces; quantitative versions of the Hartogs’ extension theorem and analytic continuation; and proper holomorphic maps between unit balls. These topics involve a range of mathematical fields, including differential geometry, metric geometry, geometric group theory, Lie theory, and dynamical systems. Thus, the project will not only contribute to the field of several complex variables but also strengthen its ties with these other areas. 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 · 2025-09

PROJECT SUMMARY/ABSTRACT Benign prostatic hyperplasia (BPH) and associated lower urinary tract symptoms (LUTS) is a prevalent condition among aging men, affecting ninety percent of all men above the age of eighty years. The causes of BPH/LUTS are considered multifactorial although it is known that aging is the number one risk factor for development and progression of disease. Despite initial effectiveness, current medical management strategies that target smooth muscle dysfunction or prostatic proliferations often fail to effectively manage disease progression, leading patients to seek out invasive surgical interventions. A key factor hypothesized to contribute to BPH/LUTS progression is prostatic fibrosis, which current therapies do not address. Age mediated cellular senescence, defined as a stable and irreversible cell-cycle arrest, has been implicated in the dysregulation of prostate tissue homeostasis. I hypothesize that age-mediated cellular senescence within the prostate drives prostatic fibrosis and therefore contributes to the development of lower urinary tract dysfunction (LUTD) and BPH. Our study aims to determine the role of senescent cells in BPH/LUTS pathogenesis and evaluate the therapeutic potential of senescent cell elimination. I aim to evaluate this hypothesis using clinical human prostate tissues and mouse models for BPH/LUTS. Specifically, I will investigate whether the degree of lower urinary tract senescence correlates with dysfunction and if targeting p16-positive senescent cells can decrease prostate fibrosis and improve urinary health. Additionally, I will examine the relationship between senescent cells and prostatic fibrosis in human BPH tissues and assess the impact of pharmacologically eliminating senescent cells using a clinically translatable senolytic dasatinib and quercetin (D+Q) in aged mice. This research will provide insights into the role of cellular senescence in BPH/LUTS and explore novel therapeutic approaches to improve patient outcomes. The extensive resources at the University of Wisconsin School of Medicine and Public Health create an optimal setting for the successful execution of this proposed work. Completing this study will foster the development of crucial experimental, mentorship, communication, and clinical skills, facilitating a smooth transition to a career as an independent researcher and surgeon-scientist.

NSF Awards · FY 2025 · 2025-09

The understanding of the origin of ultra high-energy particles and light is one of the central challenges in contemporary astronomy and astrophysics. It is a study that will lead us to a better understanding of the dynamics of the cosmos. The High-Altitude Water Cherenkov Observatory (HAWC), a technologically advanced gamma-ray and cosmic-ray detector located on the slopes of Sierra Negra volcano in Mexico, is among the most sensitive gamma-ray observatories in the world. Since 2015, HAWC has accumulated high-quality data sets of gamma rays and cosmic rays from the Northern sky. This project will study high energy gamma rays and cosmic rays recorded by HAWC, with the goal to uncover the nature of extreme astrophysical objects that emit light at energies of trillions of electron volts (TeV). These studies will contribute to our understanding of particle acceleration and its propagation in space. In parallel to advancing scientific knowledge, this project prepares students across educational levels to join the national science and technology workforce. The project will bring the excitement of this research to the public through outreach activities. Taking advantage of HAWC's new data pass and by implementing advanced analysis algorithms, these gamma-ray studies will tackle important questions of gamma-ray astrophysics. A central focus is the investigation of TeV halos, which are extended gamma-ray structures surrounding middle-aged pulsars. The research will involve identifying new TeV halos, characterizing their morphology and spectral properties, and interpreting these findings in the context of local particle injection and diffusion in pulsar environments. Additionally, the team will analyze HAWC's cosmic-ray data to measure and interpret anisotropies in the arrival direction distribution of Galactic cosmic rays. These complementary studies aim to improve our understanding of particle acceleration mechanisms, transport processes, and their role in shaping the high-energy universe. 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.

NSF Awards · FY 2025 · 2025-09

Oxygen is crucial for macroscopic life, yet the causes and repercussions of its accumulation in the atmosphere are poorly understood. A key question for resolving the trajectory of planetary habitability is if chemical shifts recorded in ~2.4-2.0 billion-year-old rocks reflect global-scale oxygen changes or regional-local conditions. To answer this question, rock cores from Gabon, which hosts the best-preserved sedimentary archive across this interval, will be analyzed for possible chemical imprints of oxygenation. This project serves the national interest by promoting the progress of fundamental science that identifies how Earth became habitable. Synergistic outreach objectives include initiatives such as community tables at farmers’ markets from Northeast-Midwest USA to enhance public scientific literacy and undergraduate curriculum development to support an American STEM workforce that is globally competitive through improved education. This interdisciplinary project applies stratigraphy, paleomagnetism, geochemistry, and geochronology to assess whether extreme geochemical shifts in the wake of the Great Oxidation Event (GOE) reflect global, regional, or local-diagenetic conditions. Laterally correlative drill cores across shallow-to-deep paleoenvironments in the Francevillian sub-basins of Gabon will be applied to test the hypothesis that, in the Paleoproterozoic, there was a prolonged overshoot in O₂ coeval with a widespread perturbation of the carbon cycle. The objectives are: 1) Create a detailed stratigraphic framework and isolate primary magnetizations to examine facies and latitudinal climate-belt controls; 2) Assess a variety of isotopic and geochemical criteria in carbonates to determine if a primary “oxygen overshoot” is preserved; and 3) Apply U-Pb zircon geochronology/geochemistry and carbonate Rb-Sr isotope compositions to evaluate if geochemical shifts are of global relevance. The results will facilitate detailed tracking of the dynamics of oxygenation and concurrent environmental changes in the wake of the GOE—including extraordinary disturbances to the carbon cycle—that are key for deciphering this tipping point in the trajectory of planetary habitability. 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 · 2025-09

Project Summary In the heart, perturbation in the balance of channels that produce the ventricular action potential (AP) can lead to dangerous arrhythmias and sudden cardiac death. How excitable cells regulate the precise the bal- ance of ion channels mediating electrical signaling is poorly understood. Previous studies in the Robertson lab have shown that ion channels are co-regulated at the translational level, via pairs of interacting mRNA during biosynthesis. My project aims to investigate how and where this cotranslational regulation occurs. Previous studies found that mRNAs of functionally related ion channels – including hERG1a, Cav1.2, Nav1.5, and KCNQ1- critical for the AP co-purify with nascent proteins via antibody pull-down. These associations were also identified using single-molecule fluorescence in situ hybridization (smFISH) combined with immu- nofluorescence. Similarly, alternative transcripts hERG1a and 1b associate during biosynthesis of heter- omeric channels producing the repolarizing current IKr. However, technical obstacles have limited the charac- terization of hERG1b within the regulatory framework of functionally related but distinct ion channels pairs. My project leverages RNAscope, a smFISH technique capable of detecting hERG1b to probe several key questions, and for which I provide preliminary data supporting its implementation. In Aim 1, I will determine whether hERG1a channels co-synthesized with functionally related channels are homotetramers, or whether they are heterotetramers with hERG1b. In Aim 2, I will determine whether these channel pairs are synthesized in the perinuclear ER, or near their functional domains. Using engineered micropatterned iPSC-CMs, and mature human myocardium I will apply RNAscope to test the hypothesis that a subset of translational com- plexes localize near insertion sites on the T-tubule and intercalated disc. This fellowship training plan is intended to prepare me for a career as tenure-track faculty and will include weekly mentoring sessions with Dr. Robertson to discuss progress toward IDP goals, participation in lab meetings, participation and presentation in journal clubs, managing collaboration with Dr. Lee Eckhardt’s lab, the cardiac tissue bank and the Translational Research Initiatives in Pathology lab, and mentoring students in a richly diverse laboratory. I will gain more knowledge in RNA biology, biogenesis, single-molecule micros- copy, advanced analysis techniques, and new human cellular and tissue cardiac models. I plan to broaden my network and present my work at local symposia and international conferences, with the goal of becoming a versatile and collaborative scientist prepared for the next steps in my career.