University Of Wisconsin-Madison

NIH Research Projects · FY 2026 · 2025-08

Project Summary Age-related diseases are the major causes of morbidity and mortality in the US. Many elderly people suffer from multiple age-related diseases simultaneously; while the risk of almost every individual disease rises with age, they also interact, with age-related disease such as diabetes and obesity serving as additional risk factors for neurodegenerative diseases including AD. Targeting the aging process through interventions like calorie restriction (CR), which extends lifespan while delaying or preventing multiple age-related diseases simultaneously, is one plausible approach to lessen the burden of these diseases. However, reduced-calorie diets are notoriously difficult to sustain. Understanding identifying the physiological and molecular mechanisms by which CR influences metabolism, healthspan, and longevity will provide mechanistic insight into the regulation of healthy aging as well as potential therapies for AD. As typically implemented in the laboratory, CR-fed animals are subject to a period of prolonged daily fasting. We and others have found that fasting itself has beneficial effects on metabolic health and longevity, mimicking the effects of a CR diet at both the physiological and molecular levels. Here, we will use a series of distinct feeding regimens to rigorously identify the unique contributions of caloric intake and fasting to the effects of a CR diet on the lifespan, metabolic health, frailty and healthspan of wild-type mice. CR slows or prevents the development and progression of AD in mouse models, and our preliminary data suggests that fasting plays a key role in the beneficial effects of CR on cognition and AD pathology. Here, we will interrogate the ability of fasting to preserve cognition in mouse Aβ and Tau models of AD. Finally, we will use genetic mouse models with altered function of the mTORC1 protein kinase to gain insight into the role of this kinase in the metabolic response to CR. The proposed work will address long-standing questions regarding the physiological, metabolic, and molecular mechanisms by which a CR diet promotes healthy aging and slows or prevents age-related diseases, with a particular emphasis on understanding the potentially therapeutic role of fasting in AD. In the long term, this work will enable our laboratory and others to develop a mechanistic understanding of how when, how much, and what we eat regulates health and disease vulnerability, and to identify new targets for the pharmacological treatment of age-related diseases including AD, and to promote healthy aging.

NIH Research Projects · FY 2026 · 2025-08

Project Summary/Abstract From everyday clinical practice to fundamental research, the biomedical field is in constant need of accurate measurement technologies. Moreover, non-destructive manipulation of live biological systems has been the aspiration of biomedical scientists. Recent progress in quantum information science, especially in the development of quantum optics and sensing techniques, provides enticing opportunities for biomedical sciences. When quantum mechanical principles like superposition and entanglement are applied to light-matter interactions at extremely small length scales, on the order of single molecules and atomic bonds, unparalleled precision and accuracy can be introduced to biological systems. However, practical implementations of quantum metrology in the biomedical field are usually hindered by their low efficiency and demanding environmental conditions. Dr. Yesilkoy’s research focuses on the development of nanophotonic devices that can manipulate light at the nanoscale beyond the diffraction limit and enhance light-matter interactions. The lab’s approach is key to satisfying the demanding requirements of quantum metrology and enabling its practical applications in the biomedical field. Specifically, in this research program, Dr. Yesilkoy’s team will work on the following two thrusts: Thrust 1: Quantum biochemistry to manipulate intracellular processes: Noninvasive regulation of intracellular chemical processes, such as gene expression, protein, and metabolic pathway, is critical in biomedical sciences. Here, Dr. Yesilkoy’s lab will implement the powerful photonic cavities generated by our nanophotonic devices to achieve vibrational strong coupling, underpinned by the entanglement of infrared cavity resonance and biomolecules’ quantum stares. Her team will demonstrate regulation over kinase activities in live cells. Thrust 2: Quantum-enhanced nanophotonic biosensors: Biomolecular sensors capable of detecting biomarkers at the single molecule level can provide high accuracy, sensitivity, and real-time detection capabilities, which are urgently needed for early disease diagnosis, personalized medicine, drug development, and fundamental biomedical research. Here, Dr. Yesilkoy and her team will combine the precision of quantum metrology with powerful nanophotonic sensors to reach single molecule detection limits in complex biological samples. The proposed technologies are significant for biomedicine because 1) ultrasensitive label-free biomarker detection beyond the detection limits of commercially available assays (<10-12M) can enable early detection of cancer and make population screening initiatives more effective. 2) non-destructive control over intracellular processes can lead to groundbreaking opportunities in biomanufacturing of biological drugs and cell therapies.

NSF Awards · FY 2025 · 2025-08

Development, scale-up and commercialization of new fermentation processes typically requires investments in the range of $100MM - $1B. Reduced performance from seemingly small process deviations during scale-up or plant operation can significantly impact the financial viability of a biomanufacturing facility. While pilot and demonstration campaigns carry less financial risk ($1MM - $10MM), the relatively small number of batches (on the order of 10) in such a campaign represents a high technical risk, since a small number of failed batches represents a large proportion of the total product and data expected to inform commercial investment decisions. Predictable scale-up and consistently near-optimal performance is, therefore, critical for the success of the United States biomanufacturing enterprise. This project seeks to develop a machine learning model for rapid assessment of the technical and economic impact of process perturbations to inform real-time decision-making related to mitigation or termination of a batch. The team leverages recent advances in machine learning, insights from an industry partner and data collected across scales from benchtop to commercial processes to deliver the decision-making tool. The award also provides research experiences for undergraduate students and supports outreach science activities to K-12 students. The scale-up and commercialization of new fermentation processes relies on pilot and demonstration campaigns with high technical risk. Reduced performance from seemingly small process deviations during pilot operation significantly impact the data used to inform commercial investment decisions. Predictable scale-up and consistently near-optimal performance is, therefore, critical for building financially viable biomanufacturing facilities. The project develops a multiscale model to: (i) predict the impact of process perturbations on fermentation performance, (ii) quantify the economic impact of potential operator decisions on the fermentation and downstream processing, and (iii) propose the optimal response to maximize profit. More broadly, the proposed modeling framework can inform decision-making in both the design and operation of the complete process system. The approach integrates machine learning, metabolic modeling, and mechanistic process modeling to predict strain response to process perturbations and its economic impact. The resulting model is made of three hierarchical components: (1) a Machine Learning Genome Scale Metabolic Model calibrated through Bayesian design of experiments, (2) a digital twin fermentation model and (3) the decision-making model informed by techno-economic analysis. This integration bridges the gaps between biological, process, and economic modeling to allow for simultaneous prediction of performance at the strain, unit operation, and process system levels. This project is jointly supported by the NSF Division of Molecular and Cellular Biosciences and the BioMADE Manufacturing Innovation Institute. 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 · 2025-08

Summary: Current therapies for glaucoma are often limited in their efficacy by poor patient compliance with frequent medications. There is a critical need for new glaucoma therapies that are less arduous for patients, particularly for children and very elderly people with glaucoma. By providing lasting effects from a single treatment, and directly addressing underlying pathophysiology of glaucoma, gene therapy represents an attractive strategy for the management of this chronic and blinding disease. High concentrations of TGFβ2 in the aqueous humor contribute to fibrogenic extracellular matrix and stiffening of the trabecular meshwork (TM) in glaucoma, which in turn reduces aqueous humor outflow and increases intraocular pressure (IOP). Our goal is to provide initial proof of concept for a novel therapeutic approach to glaucoma, that will target progressive TGFβ2-associated aqueous outflow pathway pathology in a feline model of early onset glaucoma due to LTBP2 mutation. This project builds on a previously reported strategy of CRISPR interference to suppress the pathologic TGFβ signaling that is a common feature in glaucomatous eyes. In a recently published study, lentiviral vector delivery of KRAB-dCAS9 and sgRNAs targeting the promoter region of TGFβ2 led to effective CRISPR interference of TGFβ2 expression in human TM cells cultured in vitro, and the same strategy showed IOP-lowering efficacy in a TGFβ2 over-expressing mouse model of ocular hypertension. In this proposal, in Aim 1, we will optimize viral vector mediated transduction of feline TM cells in vitro and in feline glaucomatous eyes in vivo. We will identify the lentivirus (Feline Immunodeficiency Virus, FIV) vector system and dose with optimal transduction, ocular tissue specificity for feline TM, and safety profile in vivo. In Aim 2, We will design and optimize a panel of sgRNAs targeting the promoter regions of TGFβ2 and TGFβ receptor 1 (TGFβR1) for KRAB-dCAS9 CRISPR interference in feline TM cells in vitro, to advance to evaluate safety and efficacy in pre-clinical pilot studies in vivo. Unlike conventional Cas9 which is used for genome editing, dCAS9 lacks endonuclease activity and instead serves as a “blocker” or repressor in the promoter region of the targeted gene. KRAB further enhances this inhibition by its histone deacetylase activity. We will use the top candidate sgRNAs to conduct a small scale proof of concept study for CRISPR interference of TGFβ2 and TGFβR1 in vivo to provide a foundation for future, larger scale pre-clinical studies. Outcomes will include quantitation of TGFβ2 and TGFβR1 by qPCR and immunoblotting and/or ELISA as appropriate, clinical evaluation of IOP and ocular toxicity, and screening for off- target effects. Accomplishing these aims will provide the validated and optimized tools necessary for future in vivo testing of this promising, mutation-agnostic gene therapy approach in our highly translationally relevant spontaneous large animal model of early onset glaucoma due to LTBP2 mutation.

NSF Awards · FY 2025 · 2025-08

Drinking water in the United States is usually disinfected using chlorine. This disinfection is important for inactivating pathogens like E. coli, which can make people sick. However, chlorine also reacts with chemicals that are naturally present in water to form disinfection byproducts (DBPs), some of which may be harmful to human health. There are potentially thousands of different DBPs, yet only a few DBPs are well studied and regulated in drinking water. The well studied DBPs include chemicals like chloroform that are very low in molecular weight. In contrast, there is very little known about the high molecular weight DBPs that form in chlorinated drinking water. Therefore, this project will use several state-of-the-art mass spectrometry techniques to study and identify high molecular weight DBPs for the first time. Natural waters (e.g., lakes and rivers) and engineered waters (e.g., drinking and wastewater) contain mixtures of thousands of organic chemicals. This dissolved organic matter (DOM) reacts with disinfectants during drinking and wastewater treatment to form toxic disinfection byproducts (DBPs). The complexity of DOM has prevented a full understanding of its composition and reactivity, even with advanced analytical techniques. Importantly, only a fraction of disinfected water toxicity can be explained by known low molecular weight DBPs, demonstrating that investigation of unknown higher molecular weight DBPs is warranted. In addition, there is a disconnect between different fields of research. Researchers who focus on DOM often use high-resolution mass spectrometry techniques to identify thousands of formulas in a sample, yet this approach lacks data validation and cannot be used to identify structures. In contrast, contaminant researchers spend significant effort to overcome the noise of “background” DOM to identify and quantify individual compounds. These siloed analytical approaches present an opportunity, which will be addressed by this project. The project will develop a transferrable method for data collection and analysis that can be used for characterizing DOM, which has the potential to transform the way scientists view and analyze complex mixtures. The research will combine advances in mass spectrometry, new computational tools, and techniques from non-target analysis of contaminants to more fully unravel DOM. During Objective 1, a wide range of formula assignment methods will first be systematically evaluated. In addition, formula validation approaches will be developed for both Fourier transform-ion cyclotron resonance and Orbitrap mass spectrometry with the goals of expanding the chemical space of analysis of DOM. During Objective 2, contaminant-focused methods, including suspect screening and semi-quantitation, will be applied to DOM for the first time. During Objective 3, these techniques will be applied to investigate the hundreds to thousands of high-molecular weight DBPs that are detected by high-resolution mass spectrometry when DOM reacts with chlorine. Throughout this project, data and methods will be made publicly available to environmental engineers and chemists. This project will improve our understanding of DOM and will provide critical knowledge about the composition and formation mechanisms of high-molecular weight DBPs. While chlorination of water produces potentially thousands of DBPs, only a fraction of these species have been identified and the known DBPs do not sufficiently explain the toxicity of chlorinated water. Therefore, the information generated by this project is important for informing future toxicity studies and developing strategies to limit the formation of toxic DBPs. 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 · 2025-08

Large language models (LLMs), such as GPT-4, also known as foundation models, represent a groundbreaking advancement in artificial intelligence, powering diverse applications such as chatbots, information retrieval engines, and scientific discovery tools. The success of LLMs is primarily attributed to their vast internal knowledge learned from massive unstructured texts. However, LLMs' internal knowledge is inherently constrained by static snapshots of closed-world texts used for training. This limitation presents two key challenges when deploying such a closed-world LLM in an open-world environment where knowledge (including unstructured texts and structured data) is rapidly evolving. First, closed-world LLMs have a limited and static view of open-world knowledge, often leading to unfaithful yet overconfident hallucinations. Second, since they primarily process unstructured text, they struggle with tasks that require reasoning over structured data, such as databases or scientific records. This project addresses key challenges in artificial intelligence by developing new algorithms, theorems, and systems to ensure the reliability of advanced foundation models. At its core, the research focuses on an open-world foundation model (OWFM), a powerful AI model designed to interact with ever-changing real-world information. This model is built on an open-world knowledge network (OKN), a flexible and expandable data structure that organizes semi-structured information from diverse sources. Moving beyond unstructured knowledge and closed-world LLMs, this project establishes a new paradigm of knowledge organization and foundation models in an open-world environment through three key thrusts. The first thrust creates a highly expandable OKN across domains and builds an OWFM with plug-and-play modular components. The second thrust develops adaptation methods that enable OWFMs to accurately answer questions on rapidly evolving topics. The third thrust deploys the OWFM in two knowledge-intensive, high-stakes applications: medical diagnostic reasoning and financial portfolio generation. Beyond advancing scientific research, this project also integrates its findings into educational activities, ensuring broad dissemination and real-world impact. 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 · 2025-08

PROJECT SUMMARY/ABSTRACT We propose to study the link between bat hibernation, metabolism and immunity in the setting of infection. Bats are uniquely vulnerable to Pseudogymnoascus destructans (Pd), a fungal invader that infects bat skin during hibernation. The fungal epidemic of white nose syndrome has killed millions of North American hibernating bats, highlighting the need to understand how temperature impacts immunometabolism and resistance to infection. Mechanistic links that underpin this relationship in bat innate immune cells in skin represent a knowledge gap. Keratinocytes sense skin infection and secrete cytokines to recruit other immune cells. Neutrophils detect the signals and migrate into the tissue, releasing reactive oxygen species and extracellular traps to phagocytose and kill invaders. Keratinocyte and neutrophil functions demand energy and metabolic rewiring to sustain energy demand. In preliminary data, we found that hibernation alters pathways regulated by PI3K/mTOR, HIF-1a, “temperature-sensing” TRPV3 and EGFR associated with metabolic rewiring. We hypothesize that hibernation temperatures impact the metabolism and function of bat keratinocytes and neutrophils, and that mTOR, HIF-1α, TRPV3 and EGFR underpin the interconnection between temperature, metabolism and immunity. We will test this hypothesis in a novel bat hibernation model and 3D organotypic platform in the setting of infection with Pd. Aim 1 defines how temperature affects keratinocyte metabolism & function. We will use our hibernation model to test how temperature changes affect keratinocytes: barrier integrity; redox potential: mitochondrial fitness; glycolysis and oxidative phosphorylation. We will also test how temperature changes affect keratinocyte immune functions such as alarmin response to pathogen associated molecular patterns (PAMP), particle uptake, and production of antimicrobial effectors e.g. ROS. We will use RNA silencing and pharmacological inhibitors to establish mechanistic links between metabolic regulators noted above and keratinocyte immune function. Aim 2 defines how temperature affects keratinocyte & neutrophil metabolism & response to Pd infection. We will use a 3-D organotypic model combined with non-destructive and real-time imaging to define how temperature change affects metabolism and response to Pd. We will test keratinocyte function as outlined above together with neutrophil function including migration, extravasation, phagocytosis of Pd, NETosis, and Pd killing. We will use RNA silencing and pharmacological inhibitors/agonists targeting metabolic pathways in keratinocytes to test how they affect, or rescue, keratinocyte and neutrophil immune response to Pd and clearance of infection. Our work will offer new insight on how temperatures during hibernation uniquely affects bat immunity to infection.

NSF Awards · FY 2025 · 2025-08

The Center for Interdisciplinary Research on Convective Storms (CIRCS) is an industry-university cooperative research center (IUCRC), focused on addressing the needs of the insurance and reinsurance industry that is being impacted by the increasing frequency and cost of convective storms which spin off tornados, damaging hail, high velocity wind gusts (derechos), and extreme rain events which damage property and endanger lives. This is a two-Site Center consisting of faculty from a variety of disciplines from Northern Illinois University and the University of Wisconsin at Madison. These cross-Site, interdisciplinary science teams work closely with members of a sector of the economy, in this case the insurance/reinsurance sector, to identify critical research thrusts that align with sector needs. Faculty teams then propose cutting-edge projects in these areas that respond to input from the Center's industrial advisory board, which is formed by representatives from companies and other interested parties that pay memberships to serve on the Center's Board. Members of the Board work, as a collective, to identify and use the pooled Center membership fees to fund faculty-proposed projects that align with the sector's high priority needs. CIRCS research is organized around six core thrust areas: Prediction, Modeling, Data Science, Climate Variability, Risk, and Societal Impacts. These reflect the multifaceted nature of convective storm hazards and the need for integrated research that spans basic science, emerging technologies, and decision-making applications. Broader impacts of the work include improving the ability of the insurance sector to assess present and future risks and financial consequences of convective storms so they can weather the rapidly changing extreme weather landscape, as well as the training of students involved in Center projects which provides a skilled and workplace-ready workforce able to integrate into jobs in the private sector. The Center for Interdisciplinary Research on Convective Storms (CIRCS) initiates projects that employ a wide array of cutting-edge modeling, mathematical, and data analysis methods including high-resolution numerical weather prediction, ensemble-based forecasting, remote sensing, machine learning, climatological synthesis, and impact-based modeling. Projects are conceived to address and align with high-priority interests of the insurance and reinsurance sector of the economy. Input from industry is obtained through a variety of meetings to discuss sector challenges and needs that reflect the overall sector, as opposed to those of a single company. New projects are proposed on an annual basis by Center-related faculty. Projects are then evaluated by the Center industrial advisory board which, as a group, recommends for funding projects reflecting the highest need of the collective. Through this interdisciplinary and use-inspired science approach, the Center addresses knowledge gaps in convective storm behavior and risk; accelerates innovation in weather-related technologies and analytics; and trains, through involvement of students and postdocs in the projects, the next generation of scientists and practitioners equipped to address the high-impact weather challenges of industry. Projects coming out of the Center include improving short- and long-term forecasts of hail, tornadoes, and damaging winds; understanding how convective storm characteristics shift in a warming climate; developing AI-based tools for rapid risk assessment; and enhancing communication strategies for high-stakes decision environments. Each project is developed through iterative engagement with industry members, ensuring alignment with stakeholder priorities and pathways for technology transition. 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 · 2025-08

The goal of the Molecular Biophysics Training Program (MBTP) at the University of Wisconsin-Madison is to provide predoctoral-level training in interdisciplinary quantitative research at the interface between biological and physical disciplines. The MBTP is tightly associated with the Biophysics Program (BP), which is an interdepartmental Ph.D. program consisting of 59 trainers from 14 departments and 5 colleges. The BP/MBTP faculty trainers share an interest in research at the interface between biological and physical disciplines and are engaged in a robust collaborative network that will provide an ideal ground for rigorous interdisciplinary graduate training in molecular biophysics. The mission of this training program is supported by access and technical training provided by state-of-the-art facilities in cryo-EM / cryo-ET, NMR spectroscopy, X-ray crystallography, mass spectrometry, light and super- resolution microscopy, high-throughput computing, and more. The on-site environment will provide trainees with the advanced skills and the rigorous conceptual training on the theoretical foundations of biophysics necessary to solve biological problems at the molecular level via quantitative approaches, and thus prepare them to successfully embrace careers in biomedical research. The program addresses challenges posed by the fast-paced evolution of modern biophysics and by the pressing need of combining expertise from multiple areas to solve complex biological problems. In response, the MBTP aims at recruiting cohorts of students from a variety of biological and physical majors, bringing together trainers and trainees with complementary experiences and interests and favoring communication, exchange of ideas, peer teaching/learning, sharing, and collaboration. The curriculum is designed to be flexible to support students from a wide range of educational and research backgrounds, yet it includes a fixed-core of foundational courses and educational activities. The training plan also includes important shared activities aimed at fostering participation. The plan includes robust measures to ensure the most favorable matches between students and faculty. The program will support progress and retention by monitoring students throughout their graduate careers and by providing targeted interventions to help them, whenever challenges arise. Special attention will be paid to attracting a highly talented cohort of trainees and on fostering their success via a highly collaborative and encouraging environment. The success of the training program will be monitored by assessing recruiting outcomes, scientific progress, personal growth, time to degree, number and quality of publications (including collaborative work) and career outcomes after graduation. Twelve slots to support trainees at early graduate-career stages are requested, based on the growing demand for PhD-level scientists with rigorous training in biophysics across the UW-Madison campus.

NIH Research Projects · FY 2026 · 2025-08

PROJECT SUMMARY: The overall goal of this application is to design technologies that improve the value of MRI, and specifically mitigate many of the complications the negatively impact this value. The scope of MRI value is vast, but can be expressed by efficiency (high patient throughput and consistent image quality), accessibility (better scope of use and equity of availability), safety, and user experience for the patient, technologist, and interpreting physician. The wide reach of this goal reflects a strategy to improve these expressions of value broadly, if not completely. The motive for a broad strategy grows from observations that shared complications such as scanning inefficiencies, patient anxiety, inconsistent image quality, and technologist distraction from the patient reduce many of these value expressions, which in turn results in a cascade of negative impacts on each other. Thus, our two-fold aim is to improve efficiencies directly with optimized scanning, and also to mitigate complications that impede value at several levels. This application creates and brings together a broad array of technologies and work to achieve these aims with design goals of (1) scanning efficiency, (2), operational simplicity, (3) operational clarity, and (4) a strong patient focus. Specific outcomes from this application will include (i) faster scanning for most common clinical sequences, (ii) consistently higher image quality and consistent signal contrast, (iii) fewer operational and protocoling errors, (iv) faster scanner operation (time between scans), (v) scans which are quieter and have less rf power deposition (for patients with active implants), and (vi) less distraction of the technologist from an ideally patient-centered role. This drive towards simplicity and consistency, along with improved experience of the technologist and interpreting physician, should improve access, and is urgently needed given the dire shortages of both technologists and radiologists along with expected increases in the need for diagnostic imaging given population trends. Specific technologies introduced include (1) a portfolio of spiral-based, SNR-optimal sequences designed to be well-matched with modern, AI based reconstruction, (2) supporting infrastructure to automate and clarify a wide array of operational tasks in order to remove technologist distraction, prevent errors, and improve consistency, and (3) introduction of a highly novel approach to protocoling and operating the scanner that is designed for the user and employs a physics-free lexicon. We will test the performance of these technologies for their speed, image quality, and clarity, and deploy them in a clinical venue for evaluation.

NIH Research Projects · FY 2025 · 2025-08

Probiotic bacteria offer great potential for use as therapeutic delivery vehicles due to their health-promoting phenotypes, a long history of safe consumption, and the ability to survive passage through the mammalian gastrointestinal tract. To express recombinant proteins, genes can be under the control of a constitutive promoter on a plasmid. However, especially for proteins that are less tolerated, this places a metabolic burden on the cell which hampers cell yield. One solution is controlled gene expression, in gram-positive probiotics accomplished by two-component regulatory systems. However, these systems require an inducer peptide to induce gene expression, which complicates the application in the gut ecosystem due to unpredictable pharmacokinetics of the induction peptide during gastrointestinal transit. Moreover, from a safety perspective, plasmids harboring genes conferring antibiotic resistance should be avoided. Thus, there is a critical need to develop technological innovations with an inducer for plasmid-independent recombinant protein production that are independent of induction peptides and antibiotic markers. Our long-term goal is to develop the probiotic human gut symbiont Limosilactobacillus reuteri (Lr) as a therapeutic delivery vehicle for use in humans. Our group has pioneered the approach of exploiting prophage-mediated lysis to release and deliver recombinant proteins, which has proven functional in multiple preclinical models. The overall objective of this application is to develop the innovative concept of plasmid-independent and carbohydrate-regulated production of recombinant proteins. Our central hypothesis is that carbohydrate-regulated promoters fused to recombinant interleukin-22 (IL-22) will reduce liver triglycerides in a diet-dependent manner. Our hypothesis has been formulated based on our preliminary data, demonstrating the identification of tightly controlled sugar-inducible promoters. Also, published work from our laboratory demonstrated that Lr engineered to release IL-22 ameliorates liver triglycerides, which means the chosen preclinical model is a viable approach to test our hypothesis. The rationale for the proposed research is that its successful completion will create exciting opportunities to expand the repertoire of dietary sugars and their combinations in the application of tailored gene expression in the gut, which can be expanded to other microbes and disease models. We plan to accomplish the overall objective by addressing the following specific aims: (1) determine the therapeutic efficacy of dietary sugar-regulated recombinant protein production; (2) determine the therapeutic efficacy following in vitro priming of recombinant protein production. The expected outcomes are to have established that dietary sugars can function as an inducing agent for therapeutic production when relying on in vivo or in vitro carbohydrate metabolism. Thus, a positive impact is expected as our work will create previously unexplored opportunities to control (recombinant) protein production in the gut with uses in basic biology, industry, and medicine.

NSF Awards · FY 2025 · 2025-08

This project addresses the increasing demand for reliable and interpretable reinforcement learning (RL) systems that can function effectively in complex, data-limited environments. RL has demonstrated potential in fields such as healthcare and public policy, where decisions often need to be made with limited and noisy data. However, ensuring that these systems are statistically robust, interpretable, and socially responsible remains a challenge. This research aims to develop tools that improve decision quality, support valid statistical inference, and enhance interpretability in RL algorithms, ultimately building trust and accountability for real-world applications. The broader impacts include advancing the science of machine learning, improving decision-making in resource-constrained settings, and training future data scientists through interdisciplinary mentorship and open educational resources. The research focuses on developing theoretical foundations and methods for reliable inference and decision-making in RL when data is scarce and models are misspecified. It consists of three main components: First, developing inference and decision tools for contextual bandits with misspecified reward models, where standard methods may fail. Second, constructing an inference framework for adaptive RL algorithms deployed across a population, utilizing a state-statistics decomposition to enhance interpretability and support principled personalization. Third, leveraging auxiliary offline data and structural assumptions to enable robust decision-making in nonstationary environments. This project will produce novel estimation methods and inference procedures with both finite-sample and asymptotic guarantees, along with publicly available software tools. Applications will include adaptive experimentation across various scientific domains, supported by interdisciplinary training in statistics, data science, and engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Project Summary / Abstract The United States is experiencing a mental health crisis and most individuals who could benefit from treatment do not receive it. Mobile health (mHealth) technology has the potential to reduce the public health burden of depression and anxiety, two extremely common mental illnesses. Meditation apps are by far the most widely used mental health apps, but they are plagued by low rates of engagement and modest effect sizes relative to human-delivered interventions. The addition of small amounts of instructor support and digital prompts may increase the effectiveness of meditation apps while retaining the efficiency and scalability of the mHealth delivery format. There is a need for careful, programmatic research examining the effects of these components and identifying for whom and when they may be most beneficial. The proposed study will begin addressing this gap through a hybrid factorial and micro-randomized trial (MRT) to optimize augmentation of the Healthy Minds Program (HMP), a widely-used meditation app with promising empirical support. Participants downloading HMP with clinically elevated symptoms of depression and/or anxiety (n=688) will be randomly assigned via a 2X2X2 factorial design to one of eight conditions crossing introductory coaching (yes/no), on-demand email-based coaching (yes/no), and digital prompts (yes/no). Those in the digital prompts condition will be further micro-randomized daily via a MRT to receive or not receive a digital prompt encouraging them to engage with HMP. Psychological distress (composite of depression and anxiety) will be assessed at baseline, over the 4-week intervention period, and at 6-month follow-up. Key candidate mechanisms linking instructor support and digital prompts with outcomes will also be assessed. Aim 1a will evaluate the effect of introductory coaching, email-based coaching, and digital prompts on month 6 psychological distress (primary outcome) and candidate mechanisms (therapeutic alliance, HMP engagement). Aim 1b will identify baseline characteristics that moderate the effects of these components. Aim 2a will investigate whether receiving (vs. not receiving) a digital prompt on a given day reduces proximal (same day) psychological distress and daily HMP engagement. Aim 2b will investigate whether the proximal effects of receiving (vs. not receiving) a daily digital prompt are moderated by baseline and time-varying information. Aim 3 will examine whether therapeutic alliance and HMP engagement mediate the effects of introductory coaching, email-based coaching, and digital prompts on month 6 psychological distress. The proposed research will result in an optimized version of HMP that can be implemented at scale. Results will also clarify who is most likely to benefit from these components and under what conditions just-in-time prompting is most beneficial.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY CT urography is a three-phase CT technique used clinically in the evaluation of blood in the urine, known as hematuria. Each of the three phases in CT urography—the non-contrast, nephrographic, and urographic phases—provide unique information that aid in diagnosing the various causes of hematuria. Unfortunately, three- phase CT urography requires approximately ~2x the examination time and 3x the radiation dose of a standard single-phase CT. Previously developed methods for improving the CT urography technique have lower diagnostic accuracy, are more challenging to interpret, and/or have no savings in examination time. Our group has developed an innovative and novel technique termed SNICT (synthetic nephrographic phase images in CT). The SNICT technique uses image processing to reconstruct the nephrographic phase images from the non-contrast and urographic phases. Our preliminary results with a transformer-based deep learning SNICT technique have shown high fidelity in synthesizing these nephrographic phase images. As a result, the nephrographic phase acquisition could be eliminated—effectively reducing the CT urography acquisition from a three-phase to a two-phase study and reducing radiation dose by 33%. In this R01 project, we seek to further improve and validate the SNICT technique. We will expand upon our SNICT technique by developing a diffusion-based deep learning model (Aim 1A), which will provide further improvements in image quality. Furthermore, we will develop a SNICT technique with dual-energy (DE) CT urography studies that can provide additional benefits, including a 2x reduction in overall examination time and a radiation dose reduction of 66% (Aim 1B). Moreover, we will perform a rigorous clinical validation of the SNICT technique (Aim 2), in which the diagnostic accuracy of the reconstructed images will be assessed and the savings in examination time will be quantified. Our criteria for success will be to show that synthesized nephrographic phase images are (a) of equivalent image quality compared to ground truth images, (b) are of equivalent diagnostic quality compared to ground truth images, and (c) provide significantly reduced examination time compared to the standard image acquisition. These criteria will ensure that the SNICT technique is clinically actionable as a robust and reliable image reconstruction technique in CT urography examinations that ultimately provides up to a 66% reduction in radiation dose and 2x reduction in examination time.

NIH Research Projects · FY 2025 · 2025-07

Advances in radiological sciences that respond to the complexity of cancer continue to expand exponentially with the integration of groundbreaking technologies such as (i) artificial intelligence (AI) throughout all cancer care, (ii) advanced imaging systems at the single quantum detection level, (iii) an explosion of combination therapies and radiopharmaceuticals as both imaging and therapeutic agents, and (iv) precision medicine for individualized treatment. To respond to this rapidly changing landscape, graduate programs in medical physics face significant challenges to train cancer researchers with scientific and technological skills to harness the potential of these emerging innovations. To address this critical need, we established the Academic Summer Student Undergraduate Research Experience (ASSURE) in Advanced Radiological Sciences at the University of Wisconsin-Madison (UW). ASSURE’s long-term objective is to equip future cancer researchers with the necessary skills to launch a successful scientific career through comprehensive undergraduate research experiences. Moreover, ASSURE addresses current barriers that limit the recruitment and retention of a diverse cancer research workforce, thereby enabling us to propel cancer research forward to redefine the technological landscape of what is possible in cancer diagnosis and treatment. To achieve this, we propose the following specific aims: Aim 1) Establish an immersive summer research experience in the radiological sciences. Through proactive outreach, we will recruit a diverse cohort of 17 undergraduate students that will participate in cancer- focused scientific training, including didactic lectures, mentored research experiences within laboratories, and interactive laboratory tours, culminating with a trainee symposium to showcase research accomplishments and provide community engagement. Aim 2) Implement a professional educational curriculum for mentees and selected mentors as facilitators, via structured training that complements the research experience. We have partnered with the Wisconsin Institute for Science Education and Community Engagement (WISCIENCE) at UW to i) provide ASSURE participants with a sustainable framework for professional development, ii) train ASSURE faculty in effective mentoring strategies, and iii) collect structured feedback and assessments of our program’s longitudinal improvement and successes. Aim 3) Quantify the sustained and measurable impact of ASSURE. We will assess the program at multiple endpoints, starting with intake and outcomes surveys while leveraging multi-layer assessment via faculty and an advisory board. We will establish an outcomes database to track career trajectories of ASSURE alumni and maintain long-term engagement activities for iterative program improvement. Overall, ASSURE incorporates state-of-the-art research experiences with unparalleled resources and complementary professional education to achieve our mission: to prepare the next generation of technologically- versed researchers while responding to the need of a diverse workforce to innovate and create new approaches to cancer diagnosis and treatment, with the overarching goal of improving outcomes in cancer patients.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY In the United States today, women are delaying having children. This is a critical issue because as a woman ages, her reproductive capability decreases as the ovarian reserve diminishes. Additionally, approximately 1% of women in the United States are diagnosed with primary ovarian insufficiency, by which the quantity and/or quality of the ovarian reserve is reduced. The ovarian reserve is formed between weeks 9 to 13 in gestation in humans and at the time of birth in mice; once it’s depleted, ovulation and conception can no longer occur. The ovarian reserve is comprised of primordial follicles, a singular oocyte surrounded by supporting pre-granulosa cells. The establishment of ovarian reserve is the result of germline cyst breakdown. During this process, connections between sister oocytes breakdown and singular oocyte primordial follicles are formed. The long- term goal of the Jorgensen lab is to understand cell-cell interactions and signals that direct germline cyst breakdown and primordial follicle formation. The objective of this proposal is to determine the role Rho GTPases, CDC42 and RAC1, play in modulating cell-to-cell connections during the transition from oocyte- oocyte to oocyte-pre-granulosa cells when new primordial follicles form. Aim 1 will test the idea that CDC42 and RAC1 are present in the ovary during germline cyst breakdown and primordial follicle formation and interact with CDH1 in the ovary. It has been suggested that CDC42, RAC1, and CDH1 are essential for proper primordial follicle formation but the connection between these three proteins has yet to be explored. Preliminary data suggest that CDC42 and CDH1 are both oocyte-specific throughout the processes of germline cyst breakdown and primordial follicle formation. Aim 2 will test the hypothesis that CDC42 and RAC1 are essential for proper primordial follicle formation. Using conditional knockout mice, I will characterize the phenotype and functionality of the ovary absent of CDC42 and/or RAC1 prior to germline cyst breakdown. Additionally, I will employ live imaging of germline cyst breakdown and primordial follicle formation in wild-type and conditional knockout animals to understand how the loss of CDC42 and/or RAC1 affects germline cyst breakdown and primordial follicle formation in real-time. In total, these studies will uncover the role of CDC42 and RAC1 in germline cyst breakdown and primordial follicle formation which will provide insight into early ovarian development and ultimately fertility.

NIH Research Projects · FY 2025 · 2025-07

The goal of this proposal is to understand the dynamics of phage – bacterium – mammalian host interaction shaping Staphylococcus aureus fitness and adaptation to new environments. S. aureus is a leading cause of life-threatening diseases like pneumonia, infective endocarditis (IE), and septicemia. b-toxin is a sphingomye- linase known to increase the severity of these infections. However, the b-toxin hlb gene is disrupted by the ϕSa3 family of prophages in up to 96% of S. aureus human isolates. Yet, b-toxin-producing variants often arise during infections and are more virulent in experimental IE and pneumonia in rabbits. Little is known about the underlying mechanisms driving ϕSa3 mobilization and b-toxin production. Our recent studies demonstrated that b-toxin production is controlled by phage excision, where ϕSa3 functions as a phage-regulatory switch (phage-RS) that allows hlb expression in response to host signals. Here, we addressed the uncharacterized role of S. aureus prophages as molecular regulatory switches and b-toxin’s novel role in dysregulation of vascular regeneration. In turn, these studies will provide critical insight into S. aureus use of prophages for host adaptation and fitness, redefining the phage-bacterium interaction and b-toxin’s role in pathogenesis. ϕSa3 excision and integration or cell lysis are mediated by the timing and expression levels of phage genes, that in turn dictate phage dynamics. Yet, S. aureus sensing and responding to specific environments can differentially define the ϕSa3 dynamics as a result of the regulatory networks of the specific clonal lineage. Aim 1 will seek to elucidate the temporal ϕSa3 dynamics driving ‘active lysogeny’ and b-toxin production in S. aureus lineages of medical importance and under conditions encountered in the mammalian host. Several phage-RSs have now been described in various microbial species. Although incompletely understood, in all these examples phage excision leads to the alteration or regulation of critical processes. However, the mechanism controlling phage-RSs and their interaction with the native regulatory system of the microbial host have not been elucidated. Aim 2 will directly address this and will determine whether regulators of hlb expression trigger ϕSa3 excision to allow the timely production of b-toxin and/or alter phage dynamics. β-toxin is a sphingomyelinase (SMase). SMases hydrolyze sphingomyelin in eukaryotic membranes into bioactive sphingolipids widely recognized as essential signaling molecules that promote health or disease. Yet, the vascular responses specific to β-toxin SMase activity and its link to β-toxin’s anti-regenerative effects remain speculative. Aim 3 will elucidate the underlying molecular responses driving β-toxin’s vascular effects. In particular, the physiological context and sphingolipid metabolites associated with those responses.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Prostate cancer is the second leading cause of cancer-related death among men in the U.S. With a median life expectancy of less than 3 years for patients with metastatic castration-resistant prostate cancer, additional life prolonging therapies are needed. Currently approved therapies for this late-stage prostate cancer include immunotherapy and radiopharmaceutical therapy (RPT). Therapeutic cancer vaccines activate tumor-specific CD8+ T cells to infiltrate and kill tumor cells. Yet, tumors have evolved resistance mechanisms against the vaccine, in particular, the recruitment of immunosuppressive CD4+ regulatory cells (Tregs) and myeloid-derived suppressor cells (MDSCs). A recently approved RPT targeting prostate-specific membrane antigen (PSMA) can deliver radiation specifically to prostate cancer cells (177Lu-PSMA-617, also known as Pluvicto). As the radioisotope decays, additional tumor-infiltrating cells, such as Tregs and MDSCs, are irradiated as well. The physical properties of the radioisotope can largely affect the magnitude of radiation-induced cell killing as well as type I interferon in surviving cells. Therefore, selecting radioisotopes that effectively deplete Tregs and MDSCs while enhancing CD8+ T cells recruitment, might be advantageous in combination with targeted immunotherapy. We hypothesize the initial priming of tumor-specific CD8+ T cells using an anti-tumor vaccine, followed by depletion of Tregs and MDSCs by RPT, will enhance the infiltration of tumor-specific CD8+ T cells with memory phenotypes, enhancing anti-tumor efficacy. We have recently generated a novel immunocompetent transgenic mouse strain tolerant of human PSMA (hPSMA) in which hPSMA is expressed in the native mouse prostate. These mice will be implanted with syngeneic murine prostate cancer cell lines expressing human PSMA (TRAMP-C1-hPSMA or RM-1-hPSMA). Aim 1 will evaluate the depletion and functional suppression of tumor-infiltrating Tregs and MDSCs induced by the radionuclides of varying therapeutic emissions (α-emitting Actinium-225, β/Auger electron emitting Terbium- 161, and β-emitting Lutetium-177) linked to a PSMA-targeting moiety. Aim 2 will determine the anti-tumor efficacy of combining tumor-specific vaccination with PSMA-targeting RPT and characterize tumor-infiltrating CD8+ T cells. The immunomodulatory effects of PSMA-targeting RPT have been mostly unexplored due to the lack of an appropriate preclinical model. Our novel mouse model provides the opportunity to study the timing and mechanism of combining PSMA-targeting RPT with T-cell activating therapy. The translatable findings from this proposed work have the potential to be developed as a novel therapeutic approach to prolong the lives of patients with prostate cancer.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY/ABSTRACT At present, the diagnosis of Parkinson disease (PD) relies on clinical manifestation of motor signs which appear only after substantial loss of brain neurons. This diagnostic delay limits the opportunity for early intervention strategies and hampers the ability to study early PD pathophysiology. The proposed research aims to shift this paradigm by identifying and refining novel retinal biomarkers of PD that present in the prodromal stage, years before hallmark motor dysfunction. By focusing on translationally relevant, non-invasive retinal structure and function assays, and established motor assays, this study uses a longitudinal approach to define the onset and progression of PD-associated retinal disease in relation to PD-associated brain disease. The central hypothesis of this work is that quantifiable retinal pathology exists during prodromal-stage disease in a PD mouse model, which is recapitulated in the prodrome of human PD. To test this hypothesis, this project has two specific aims. In Aim 1, this work will identify features of retinal pathology in prodromal PD in a mouse model of PD using in vivo assays of retinal structure and function: adaptive optics scanning laser ophthalmoscopy (AOSLO), optical coherence tomography (OCT), electroretinography (ERG), optomotor reflex (OMR), combined with assays of motor function (pole and cylinder tests). In vivo assays will be compared with retinal and brain tissue pathology using immunohistochemistry, cell death assays, and electron microscopy. This innovative approach utilizes phenotypic definitions of retinal prodrome and motor clinical PD, rather than relying on fixed timepoints which accounts for disease progression heterogeneity in individuals, enhancing the translatability of findings to humans. In Aim 2, this work will identify in vivo retinal biomarkers of prodromal PD in humans, applying survival analysis of human subjects with OCT images from the UK Biobank who later are diagnosed with PD and comparing both inner and outer retina layer thicknesses measured via OCT with healthy matched control subjects. This fellowship includes a comprehensive training plan at the University of Wisconsin-Madison with complementary sponsor Dr. Freya Mowat (veterinary ophthalmologist clinician- scientist with expertise in animal models of retinal neurodegeneration) and cosponsor Dr. Michelle Ciucci (speech-language pathologist clinician-scientist with expertise in PD), with supportive collaborations within the Wisconsin Advanced Imaging of Visual Systems (WAIVS) lab and the Wisconsin Reading Center. The proposal and training plan is designed to enhance the physician fellow’s ophthalmology and neuroscience research skills through applied research and education in advanced imaging, medical statistics, clinical study design, and research ethics. This comprehensive training and research endeavor aims to equip the fellow with the necessary skills to emerge as an independent clinician-scientist investigator in vision research, with a project that promises significant public health impact through the potential for early, non-invasive PD detection.

NIH Research Projects · FY 2025 · 2025-07

Project Summary / Abstract The Cyclotron Research Group in the School of Medicine and Public Health (SMPH) Department of Medical Physics stewards a GE PETtrace 16 MeV proton / 8 MeV deuteron cyclotron in the Wisconsin Institutes for Research and Discovery whose capacity and capabilities have become insufficient to meet growing demand for theranostic radionuclides. The group has a four-decade history of achievement in research, training, and revenue-neutral service provision as an institutional and national resource in radionuclide production. Building on a successful C06 award in 2023, funding is requested to purchase a 30XP multiparticle cyclotron manufactured by Ion Beam Applications (IBA) of Louvain la Neuve, Belgium. This machine accelerates extractable negative ion proton and deuteron beams with maximum beam intensities of 400 and 50 µA, respectively, and can be configured in positive ion mode to accelerate alpha beams up to 50 µA. The machine has variable position stripping foil mounts to feed each of two extraction ports, creating the option for nominal variable energy for the proton (15-30 MeV) and deuteron (8-15 MeV) beams. The machine will be configured with three primary target end stations, each servedby a combination of switching magnets and multiple-quadrupole focusing elements. A five-port switching magnet on the first extraction port of the vacuum tank will send beam to one of five individual targets at a time, enabling rapid switching between targets for 18F, 11C, a dummy for accelerator developing and beam tuning, and two oblique, coin-type solid target stations that can each produce a wide range of isotopes (64/67Cu, 89Zr, 52g/51Mn, 86Y, 55Co, 117/119Sb, 45Ti, 43/44/47Sc, 203Pb, 201Tl, and many others) needed by biomedical researchers. The second extraction port’s beam will be split by a selection magnet in the main vault and directed to an independently-accessed secondary vault with two high-current (>100 µA) end stations homing glancing incidence solid targets. Each beam line is equipped with dual quadrupole magnets and beam “wobblers” to distribute the intense thermal power these target stations can tolerate. Wire scanning and phosphorescent screen beam profile monitors will be equipped on each beamline to ensure quantitative intercept of the beam’s transverse profile by the desired target material. All target stations will be equipped with remote transport solutions that feed radiochemical hot cells in adjacent laboratories. The variable extraction system will also be capable of extracting user-selectable fractions of the beam, thereby allowing simultaneous beam delivery at a single energy to both extraction ports, dramatically increasing the flexibility of production scenarios. The combination of infrastructure described will more than quadruple available quantities of standard positron- and single photon-emitting radionuclides for imaging and make new therapeutic radionuclides (especially radiometals from solid target irradiations). These materials are needed by over 40 current awards distributed between local UW and national components of the user base and will also support the education and training mission of the Group and multiple T32 awards.

NSF Awards · FY 2025 · 2025-07

Algebra is a fundamental mathematics topic. A key part of algebra is understanding mathematical equations (e.g., 2+5=7). This project will explore how middle school learners make sense of mathematical expressions (e.g., 2+5) within an equation. More specifically, the project will explore how children think about the ways those expressions can be transformed into an alternate, but equivalent form (e.g., 7-2). This study has potential to inform mathematics teaching and learning as well as psychology about humans' developmental learning about the equal sign. A result from this study will be a deeper understanding of students' work with such mathematical transformations, which has capacity to support students' success in algebra. The equal sign takes on many different interpretations depending on how it is used in mathematical equations. A goal of this study of grade 6-8 students is to investigate relations among students' conceptions of the equal sign, the foundational understandings that support these conceptions, and students' algebraic reasoning. The project team seeks to answer the following research question during the three-year project: How are students' conceptions of the equal sign related to their representational flexibility and algebraic reasoning? Over 210 grades 6-8 students will be recruited and asked to participate in task-based interviews to provide survey responses for the first part of this study. Quantitative data from those interviewed will be analyzed using structural equation modeling. In the second part, approximately 60 students will be interviewed and complete task-based interviews and surveys. Qualitative data from those interviews will be transcribed and qualitatively coded. One outcome from this project is better understanding relationships among equal sign conceptions, other foundational skills, and algebraic reasoning. Mathematical equivalence is a cornerstone 'big idea' of algebra and outcomes from this project have potential advance understanding of learners' conceptions of the equal sign and mathematical equivalence. 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 · 2025-07

This project involves several topics in Number Theory, and more specifically the Kudla Program. The Kudla Program is an important part of research in modern Number Theory. It seeks to understand the deep connections between arithmetic and analysis on certain geometric objects called Shimura Varieties. Number theory focuses on the properties of whole numbers, including the primes, which have proved essential, resulting in far-reaching and unexpected connections to other fields such as electronic communications and cryptography, among others. The Principal Investigator is actively involved in the training of future mathematicians, including several PhD students who work on related projects. The Principal Investigator and his collaborators will continue fundamental research on the Kudla Program, including CM values of higher Green functions, and the construction of Green currents for Kudla special cycles of co-dimension larger than one and proof of their modularity. The relative trace formula and (arithmetic) Gan-Gross-Prasad theory is another very important direction in which the Principal Investigator will work, as well as the interactions between these two programs, resulting in a new line of fundamental research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The cells that make up the brain called neurons contain internal structures that have to be positioned at precise locations within the cell. To ensure that these structures are transported to the right site, cells use molecular machines called motor proteins to move them to the right location. Such transport is essential for the formation and function of the nervous system. Defects in motor protein-based movement of specific structures in neurons have been linked to neurodegenerative and neurodevelopmental disorders. Despite the importance of this process, we still do not understand how these motor machines bind the right structures at the right time and how they “know” where to deliver them inside the cell. The proposed work will use in imaging, genetic manipulations, and biochemistry to determine how one of these motors, called dynein, is activated at the right time and place to bind specific structures to initiate their transport. A focus of this research is on the transport of the cellular structure called the autophagosome which is essential for the breakdown of cellular building blocks, as well as pathological aggregates in diseases such as Alzheimer’s, Huntington’s and Parkinson’s. Graduate and undergraduate students will participate in the proposed research and will be mentored by post-graduate trainees. This research will define fundamental aspects of dynein function, and form the foundation for future studies to target dynein within a therapeutic context. Dynein-dependent cargo transport is essential to move organelles, proteins, cytoskeletal elements, and signaling molecules from the axon terminal to the cell body in neurons. This process is highly regulated. Dynein accumulates in the axon terminal in an autoinhibited conformation prior to activation, cargo engagement, and transport initiation. How dynein is conditionally activated and cargo transport initiated is unknown. Given the ubiquitous localization of the motor, its activators, cargos, and cargo adaptors, conditional mechanisms must exist to trigger dynein-mediated transport for discreet cargos. This project will use in vivo imaging, biochemistry, and genetics to define the mechanisms of dynein motor activation and cargo transport initiation in the axon terminal of neurons. The results of the proposed work will delineate regulatory mechanisms for these essential steps to trigger dynein-dependent cargo transport initiation in the axon terminal. 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 · 2025-07

With recent advances in artificial intelligence, organizations increasingly recognize data as a vital resource for advancing scientific, economic, and societal progress. Many modern machine learning systems rely on large and complex datasets to learn patterns, generate insights, create new content, and support intelligent decision-making. However, the data needed to develop these models is often scattered across different organizations, with each holding only a portion of the necessary information. By sharing data with each other, instead of relying only on their own data, organizations can unlock new opportunities for discovery and innovation. Despite this potential, organizations often hesitate to share data unless there are clear incentives to do so. This raises important questions: Is it always beneficial for an organization to share data? Do participants receive benefits that reflect their contributions? What mechanisms can encourage data sharing while preventing participants from benefiting without contributing? When several parties collaborate to train a shared model, how should the benefits be allocated? This project addresses these questions by investigating the incentives that influence participation in data sharing and collaborative machine learning. The project aims to create environments where data sharing supports collaboration and innovation, ultimately enabling breakthroughs in areas such as health care, public policy, and education. This project will develop new theoretical foundations for the design of protocols that govern data sharing and collaborative machine learning, focusing on two central incentive-related challenges: the balanced allocation of responsibilities and benefits, and reducing the potential for strategic behavior. The research will design protocols that assign data collection responsibilities in a balanced way, allocate shared data in line with contributions, and ensure that exchanges among competing participants are mutually beneficial. These protocols will also be designed to prevent strategic behaviors to exploit the system, such as avoiding data collection, withholding contributions, or providing incorrect data. The project will further explore multi-round collaborations, where participants exchange data over time, adapt their strategies, and operate under uncertainty about the value of others’ data. The proposed methods will be evaluated through simulations and partnerships with Alzheimer’s disease research consortia. The project is expected to contribute broadly to the fields of machine learning and game theory. Education efforts will focus on the development of new courses, workshops, and research opportunities for undergraduate and high-school students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-07

Over half (56%) of U.S. nurses are burned out, and their burnout is associated with adverse patient care consequences, including mortality. Burnout has additional negative consequences for nurse health and well-being, including depression and suicide, and is the primary driver of turnover reported by 31.5% of nurses leaving their positions. Burnout occurs when there is an imbalance between job demands and resources in the work system. Prior efforts to address burnout among nurses have included individual-level or organizational interventions, but few have been rigorously tested. Individual-level interventions often seek to increase nurse resilience, fail to address root causes contributing to burnout, and put the onus on the nurse to fix their own burnout. Organizational interventions are generally not tailored to address unique unit-context-specific drivers of burnout, do not engage nurses in their development and implementation, and are not scalable to resource-limited settings. To address these gaps and in alignment with NINR’s NOSI to address organizational factors to prevent or mitigate nurse burnout, we propose adapting, evaluating, and exploring the scalability of the REducing nurse burnout through SysTems analysis and Organizational REdesign (RESTORE) intervention. Our scientific premise is that hospital nursing staff burnout will be reduced by: 1) identifying and addressing context-specific job demands that act as drivers of burnout, and 2) optimizing job resources for nursing staff through ownership over the design and implementation of unit-level solutions targeting burnout drivers. In our preliminary work, we conducted a system analysis of drivers of burnout for 6,000 nurses at Banner Health; however, nurses did not know how to develop solutions to address these drivers. RESTORE adapts a human-centered design approach we have previously used to develop nurse-driven unit-level solutions to address burnout. Specific aims are to: Aim 1. Adapt the RESTORE intervention to engage nursing staff in system analysis and the design, implementation, and evaluation of unit-level burnout system redesign solutions. Aim 2. Evaluate the effectiveness of RESTORE on reducing nursing staff job demands, increasing job resources, and reducing burnout. Aim 3. Identify ongoing challenges and facilitators that impact the scalability of RESTORE to engage nursing staff in system redesign and address burnout across hospital units and organizations and develop strategies for dissemination. The outcomes of this project will be a rigorously tested RESTORE intervention that can be implemented to address nursing staff burnout in hospital settings. This work addresses critical gaps in current knowledge related to how organizations can effectively use a systems approach to address nursing staff burnout. The proposed study will advance the science and contribute new knowledge related to unit-level drivers of nursing staff burnout that may be amenable to organizational interventions and system redesign, and an effective approach to engaging nurses in systems redesign to reduce burnout.