University Of Missouri-Columbia

NSF Awards · FY 2026 · 2026-01

Statistical models — probabilistic descriptions of the processes that give rise to observed data — are an integral component of modern science across various disciplines, enabling researchers to learn from the data they collect and make predictions about future events. This research addresses the important challenges of model selection and model combination within the Bayesian statistical framework. Model selection involves choosing, from a collection of candidate models, the single model that best describes the data, while model combination involves constructing a hybrid model that outperforms any single model. The Bayesian framework has gained prominence in recent decades because it enables researchers to fit complex models to data, simultaneously account for multiple sources of uncertainty, and combine the information in newly observed data with prior scientific knowledge. This research will develop new, computationally efficient techniques for estimating a model’s prediction accuracy in the Bayesian framework and will apply these techniques to the problems of model selection and model combination. In the process, it will contribute to STEM education by training statisticians at the graduate level. It will also lead to publicly available software for researchers across a broad range of disciplines. This research will include several novel projects aimed at developing Stein’s unbiased risk estimate (SURE) as a practical and computationally efficient tool for Bayesian analysis. SURE has become an established tool for model selection and parameter tuning in frequentist settings. However, SURE requires the computation of a penalty term, sometimes referred to as the generalized degrees of freedom, which adds a significant computational burden for complex estimators. Consequently, SURE has been applied considerably less for more computationally demanding Bayesian and machine learning models. This research will: (1) develop a novel expression of SURE that is straightforward to compute via Markov chain Monte Carlo for Bayes estimators of a Gaussian mean resulting from essentially arbitrary prior distributions, along with extensions to unknown variances and continuous tuning parameters; (2) introduce methodology for fully Bayesian M-open inference for both Gaussian, improving upon existing model combination/selection techniques and allowing for fully Bayesian uncertainty quantification for machine learning models. 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 · 2025-12

Aging with co-morbid conditions (i.e., obesity, hypertension) increases the risk of developing heart failure with preserved ejection fraction (HFpEF), and related cardiovascular morbidity and mortality for which there are few approved treatments. Importantly, HFpEF has become the most common form of HF and is characterized, in part, by cardiac fibrosis and reduced capillary density (i.e., rarefaction). Capillary rarefaction is associated with cardiac fibrosis in HFpEF and cardiac fibrosis is independently predictive of cardiac mortality in adults >70 years of age. LARP6 (La Ribonucleoprotein 6, Translational Regulator), an RNA binding protein, is implicated in pathologic fibrosis via binding to a unique 5' stem loop (5'SL) structure in collagen mRNA resulting in mRNA stabilization and increased translation, and excess collagen production/deposition. Our preliminary data provide the first evidence of LARP6-dependent cardiac fibrosis and dysfunction in response to chronic cardiac stress. Specifically, disruption of the LARP6-collagen mRNA interaction in a genetic (unique knock-in mouse model where the 5'SL region is mutated to prevent LARP6 binding; 5'SL mutant mice) and an interventional (the use of C9, a small molecule inhibitor of LARP6-collagen mRNA interaction) model each prevented cardiac fibrosis and contractile dysfunction following chronic β-adrenergic stimulation. Moreover, disruption of LARP6- collagen interaction promotes pro-angiogenic LARP6 signaling exhibited by increased capillary density and vasculogenic gene signatures in the heart. Lastly, cardiac LARP6 expression is increased in aging. Based on these `proof of concept' findings, we hypothesize that LARP6 signaling is a druggable target for the treatment of co-morbid aging-associated HFpEF. To test this hypothesis, we will utilize a `three-hit' mouse model of co- morbid aging-associated HFpEF to examine the following Specific Aims: Aim 1 will delineate the therapeutic potential of targeting LARP6-collagen interaction in cardiac fibrosis and dysfunction in lean and HFpEF 5'SL mutant mice/littermate controls (genetic) as well as lean and HFpEF C57BL/6J mice treated with C9 or vehicle (interventional). Cardiac morphology, function, and fibrosis in vivo by magnetic resonance imaging coupled with ex vivo analysis of fibrosis by staining and atomic force microscopy and assessment of LARP6 signaling will serve as major endpoints. Utilizing the same genetic and interventional models (5'SL mutant mice and C9), Aim 2 will elucidate the therapeutic potential of LARP6-Vegfa manipulation on capillary rarefaction and cardiac vasculogenic signaling in co-morbid aging-associated HFpEF. Capillary density, cardiac vascularity, analysis of cardiac pro-angiogenic mediator expression, delineation of the cardiac myocyte and non-myocyte transcriptome, and capillary sprouting in a tissue culture model are major endpoints. Together, the proposed conceptually innovative and translationally significant studies will provide novel evidence that disruption of LARP6-collagen interaction is a viable therapeutic target to attenuate cardiac fibrosis and enhance vascularity in co-morbid aging-associated HFpEF for which there are currently few approved treatments.

NSF Awards · FY 2025 · 2025-10

The broader impact/commercial potential of this Partnerships for Innovation – Mid Career Advancement (PFI-MCA) project lies in an innovative approach and platform technology – integrating 3D cell culture, sensing, and imaging. This system will further enable understanding of physiological processes and can be personalized for each expecting mother using their stem cells, leading to higher accuracy in testing for the transport rate of specific compounds and setting safe exposure levels. The successful completion of this project will lead to an economical and physiologically relevant tool that accommodates testing of a larger number of compounds, facilitates high-throughput screening and rapid data collection, and thus ensures market potential for this technology. The proposed project presents a paradigm shift from the approach currently used to study the transport of pharmaceutical drugs through the placental barrier and their effects on the developing placenta. This platform will replicate the physiological matrix mechanics, hemodynamics, and compound’s ability to permeate through the placental barrier, which will enable quantitative studies to be performed on a vast range of pharmaceuticals to understand their interactions, including transport through the vasculature, and translocation across the placental barrier to diffuse into the fetal blood stream. Furthermore, the system can ultimately be personalized for each expecting mother using their stem cells. This could lead to higher accuracy in testing the transport rate of certain compounds while reducing the cost and enabling real-time monitoring and rapid data collection. Collectively, the proposed technology enables the understanding of critical metabolic and inflammatory processes in the placenta, laying the foundation for its potential commercialization in the future. This project is jointly funded by the Partnerships for Innovation program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Biological cells utilize ions to signal and communicate with neighboring cells and their environment. Despite the critical role of ionic signaling in biological systems, our understanding of such phenomena remains limited. Ionic kinetics, crucial for cellular functions and signaling, are only understood qualitatively due to the lack of near-real-time monitoring technologies. Studying and manipulating these ionic signals not only helps in understanding how microorganisms communicate, evolve, and develop, but also enables the probing and controlling of cellular activities. This proposal aims to develop innovative soft ionic transistors to measure and analyze the ionic characteristics of biological cells and their environments. This project seeks to bridge the gap between rigid electronic devices and soft ionic biological systems by designing soft ionic transistors that use cellular ions to trigger their gating mechanism. This advancement would enable the study and could significantly enhance the understanding of cellular functions, stress responses, and the role of ions and ionic signals in biological systems, thereby providing deeper insights into biological mechanisms. The outcomes of this project are expected to have broad societal impacts, including significant effects on drug discovery and testing, personalized medicine, aging studies, and novel therapeutic strategies such as pain management and rehabilitation. Ionic kinetics and the resultant intracellular-extracellular ion concentration gradients control cellular functions and intercellular signaling and communications. These ion concentration gradients indicate the health and functionality of cells and organs. Despite its critical role, the current understanding of cellular ionic kinetics remains largely qualitative, and the capacity to evaluate cellular functions based on their ionic activities is very limited. These limitations stem from the absence of technologies that can monitor ionic activities at the cellular level in near-real-time and continuously. This proposal aims to advance the understanding of cellular ionic kinetics by developing soft ionic transistors capable of continuously monitoring ionic activities at the cellular level. These transistors feature a novel gating mechanism triggered by ions secreted during cellular activities, allowing for near-real-time, quantitative evaluation of cellular ionic functions and addressing the current technological gap in monitoring ionic activities in biological systems. The central hypothesis of this research is that ion concentration gradients can polarize the ionic environment inside a transistor, forming electron-conductive ionic double layers at the bioenvironment-transistor interface. This hypothesis will be tested through in-vitro cell studies facilitated by soft ionic transistors. The project will involve designing, fabricating, and characterizing soft ionic transistors, followed by in-vitro cell studies to test the central hypothesis. Machine learning algorithms will be employed to develop a comprehensive model of the ionic attributes of biological systems, establishing relationships involving multiple interrelated physical, chemical, and biological variables. The anticipated outcome is a significant enhancement in the quantitative evaluation of cellular ionic activities and understanding of the correlations between ionic kinetics and cellular functions, thus bridging a critical gap in current knowledge of cellular ionics and their role in biological systems. The project outcomes are expected to have broad scientific and societal impacts. The ability to chronically study developing and growing biological systems will significantly impact various scientific fields, including drug discovery, aging research, personalized medicine, and novel therapeutic strategies such as pain management, all of which have broad societal impacts. 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

Brain function has high energy demands, and when these needs are not met there is a loss of neural function. Most of the energy for brain function comes from burning sugar (glucose) using oxygen. In contrast, preliminary observations led to discovery of an alternate scenario whereby bullfrogs display an unusually large ability to abandon these conventional energy sources to promote survival. Like most vertebrates, brain activity in frogs typically requires energy from glucose metabolism. However, hibernation transforms the bullfrog brain to function for over two hours without glucose and oxygen, promoting survival when these resources are otherwise limited. This represents the largest improvement in neural activity during severe metabolic stress reported in the vertebrate brain. This project tests the hypothesis that this impressive feat involves the metabolism of by-products of fat breakdown (ketone bodies) without any ongoing glucose metabolism. While ketone bodies are well-known to fuel the brain during starvation and certain diets, neural function in most animals requires at least some glucose at the same time. As issues with glucose metabolism contribute to diverse neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and ALS, these studies have long-term implications for improving brain function in neurodegenerative diseases of metabolic origin. This project will also train graduate students, including introducing a pipeline for education training that incorporates international relations at the University of Missouri. During winter hibernation, bullfrogs adapt to conditions in which they experience low metabolic rates, starvation, and low oxygen and glucose blood levels. Emergence from these conditions in the spring requires neural circuits to become active in conditions that are atypical for vertebrates. Previously, these investigators found that neurons in respiratory brainstem circuits can shift to alternative fuel sources during hibernation. This project they will test the hypothesis that frogs switch from glucose to ketone metabolism. Circuit physiology approaches will be used to test that switching to ketones and other non-glucose fuels improves activity during hypoxia, in part through upregulation of ketone body transporters. Combining single-cell electrophysiology and RNA-sequencing, the researchers will then test the hypothesis that synaptic physiology and metabolism are preferentially modified to allow the exclusive use of non-glucose fuels. Finally, biochemical approaches and cutting-edge methods will be used to measure mitochondrial function and test the hypothesis that neurophysiological adjustments are matched at the mitochondria to maintain ATP homeostasis when running only on non-glucose fuels. Overall, this project will uncover plasticity that improves the brain’s capacity to run on alternative energy sources. The results are expected to reveal how an animal reconfigures the brain to function without glucose metabolism and to put forth a framework that guides strategies to boost the use of non-glucose energy sources. 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 Accurate speech production requires the precise control of multiple articulators in space and time. Ataxic dysarthria is a speech motor disease caused by damage to or degeneration of the cerebellum. Symptoms are varied, including both temporal and articulatory symptoms, and result in decreased speech naturalness and intelligibility, significantly impacting quality of life. In addition, there is wide variation across speakers with ataxic dysarthria, leading some to propose multiple subtypes of ataxic dysarthria. While the range of symptoms of ataxic dysarthria are well established, their underlying causes are poorly understood. As such, there are currently no evidence-based treatments that target the specific symptoms in this population. The experiments in this proposal test the hypothesis that predictive control is impaired in people with cerebellar disease. The studies test three components of predictive control in three aims. The first aim tests the hypothesis that speakers with ataxic dysarthria have impaired predictive state estimation. To generate the correct motor commands to reach a goal, it is necessary to know where the articulators start from; inaccurate estimates of the starting point could lead to inaccurate production. Variability will be measured in the final syllable of words when speakers are speaking quickly, necessitating predictive state estimation, and when speakers are speaking slowly, which allows the use of sensory feedback for state estimation. The second aim tests the hypothesis that speakers with ataxic dysarthria have impaired temporal predictions. As speech production relies on the coordination of multiple articulators, speakers must be able to accurately estimate how long each articulator will take to reach its next goal. This study examines the relationship between movement timing and articulatory distance in disyllabic words, building on work that indicates that typical speakers adjust the timing of vowel movement initiation depending on the distance to the next target. The third aim tests the hypothesis that speakers with ataxic dysarthria have a shortened planning horizon. To generate optimal motor trajectories, it is necessary to plan in advance. Non-speech work indicates that planning is impaired in individuals with cerebellar disease. This study measures anticipatory vowel-to-vowel coarticulation in multisyllabic utterances, which has been previously shown to reflect planning. The proposed studies will establish the status of three components of predictive control in speakers with ataxic dysarthria. Although it is predicted that speakers with ataxic dysarthria as a group will show deficits in all components of predictive control, it may be the case that individual speakers have different levels of impairment in each component. This work lays the groundwork for investigating the wide inter-speaker variability in symptoms in ataxic dysarthria, and is a critical first step towards the development of therapies that target the specific impairments that result in reduced naturalness and intelligibility in this population.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Asthma, the most common respiratory disease in the US, affects nearly 10% of the population and costs nearly $50 billion annually. While many asthma patients can control their asthma with inhaled medications, some patients cannot. Although asthma is a disease characterized by airway inflammation and impaired ventilation, recent evidence suggests that anomalies in pulmonary vasculature may also contribute to asthma presentation and severity. This is a difficult hypothesis to address however because it requires a test that can accurately assess pulmonary vasculature non-invasively with high spatial and temporal resolution. Furthermore, for diseases such as asthma which are known to be spatially heterogeneous within the lung, regional vascular function is necessary to understand the spatially heterogeneous nature of the disease – something which global measures of gas exchange function such as DLCO lack. Treatments for asthma often target one component of the underlying pathophysiology (airways/ventilation) while leaving anomalies in gas-exchange/perfusion unaddressed and unmanaged. Thus, the lack of a robust, sensitive, non-invasive biomarker for pulmonary vascular function presents an unmet need in understanding and managing individual asthma patients. Hyperpolarized 129Xe MRI is a technique in which the nuclear magnetic moment of 129Xe is greatly enhanced in situ such that the gas itself can be imaged by MRI. Subjects inhale a small quantity of xenon during an MR exam, and the gas is imaged as it is physiologically distributed within the lung airspaces. The xenon can also diffuse slightly into the tissue and blood whereupon it experiences an MR chemical shift. This allows for the xenon to be separately imaged in three distinct physiological environments: alveolar airspaces (gas), tissue and blood plasma (membrane), and in red blood cells (RBC). The RBC component further demonstrates oscillation due to cardiac activity – another marker of cardiopulmonary function. Thus, within a single breath-hold 129Xe MRI can be used to image three components of lung function: ventilation, perfusion, and gas exchange. Because hyperpolarized 129Xe MRI provides quantitative, regional measures of both perfusion and gas-exchange we hypothesize it is a technique which can reveal the precise nature of vascular involvement in asthma. In this work we hypothesize that in some patients with asthma, 129Xe MRI will depict abnormalities of lung perfusion spatially separate from ventilation abnormalities and that the abnormalities of lung ventilation, perfusion and gas exchange will respond to treatment with bronchodilator (albuterol). We will perform 129Xe MRI in mild, moderate, and severe asthma patients and healthy controls before and after administration of albuterol. We will quantify abnormalities in pulmonary vascular function, measure the percent of lung with impaired ventilation and perfusion in individual subjects, and evaluate regional responses of 129Xe MRI to bronchodilators and pulmonary vasodilators. We anticipate that there will be significant variability in the degree of pulmonary ventilation and perfusion impairments among asthmatics of the same severity.

NIH Research Projects · FY 2025 · 2025-09

Abstract/Project Summary Alcohol consumption, contexts, and consequences across the transition to legal drinking: A longitudinal EMA burst design Heavy alcohol use accounts for approximately 178,000 deaths each year in the U.S., where the annual economic cost of excessive drinking is estimated to be over $249 billion. There is considerable evidence that excessive alcohol use patterns often begin in late adolescence or early adulthood. A crucial event in young adulthood is the transition to the legal drinking, which occurs at 21 years of age in all U.S. States. The transition to legal drinking is associated with changes in patterns of alcohol use and can introduce substantial and abrupt changes in the context in which consumption occurs and the consequences associated with excessive consumption. We propose an intensive longitudinal study of 20-year-olds, using a population-based sample, and employing a longitudinal burst design covering the 6 months before and after each participant’s 21st birthday. Specifically, participants will be sampled from a Driver’s License data-base of those born and resident in Missouri rural or small metro areas (see Recruitment and Retention for maps), with dates of birth 2005-2008. Participants (50% woman-identifying, 12% Black American) will complete five repeated 2-week EMA bursts at roughly 3-month intervals, over 12 months. We will use ecological momentary assessment (EMA), including self-report, smartphone breathalyzers, and smartphone sensors, to study alcohol use, contexts, and correlates. We focus on four core addiction constructs: cognitive control/executive functioning; reward; negative affect/valence; and response to alcohol. These constructs are central to current theoretical frameworks for understanding alcohol addiction. Each is hypothesized to influence substance use, in part, through in-the- moment processes (e.g., cue-elicited craving, alcohol-induced increases in impulsivity), and are therefore ideal foci for EMA investigations and potential therapeutic targets. The proposed project has three major Aims: Aim 1. Characterize changes in drinking context (e.g., where, with whom, what others are drinking, and how much) across the transition to legal drinking. Aim 2. Characterize changes in drinking topography (frequency of drinking, speed of drinking, type of alcohol, timing of drinking) and in frequency of alcohol consequences across the transition. Aim 3. Test person × environment interactions in the prediction of event-level drinking topography and consequences. This aim will examine how drinking contexts, and changes in contexts across the transition to legal drinking, constrain or exacerbate the influence of core person-level constructs on drinking topography and consequences. The proposed work can provide insights into risk for significant alcohol consequences and the development alcohol use disorder in young adults that cannot be achieved using traditional approaches. We can examine drinking patterns across time, within bursts, and within participants and can inform intervention approaches that prevent or target maladaptive drinking patterns.

NSF Awards · FY 2025 · 2025-09

This IRES project prepares U.S. students to address future supply chain challenges in manufacturing and service systems by exploring how humans and autonomous mobile robots can collaborate effectively in shared workspaces. In response to increasing automation across industries, students engage in hands-on research to design, optimize, and manage collaborative systems for real-world tasks such as hospital intralogistics, semiconductor manufacturing, and warehouse order picking. Germany, a global leader in Industry 4.0, ranks among the highest in industrial robot adoption, making it an ideal setting for research on collaborative automation. The University of Passau in Germany, serving as the host institution, offers a premier research environment with strengths in decision science, human-robot interaction, and optimization, supported by strong academic programs and industry partnerships. By working directly with German faculty and collaborators, students gain international experience while contributing to innovations essential for next-generation smart supply chains. Through cross-cultural engagement, industry site visits, and sustained mentorship, participants develop a global outlook, strengthen their analytical and technical skills, cultivate a passion for advanced careers, and contribute to building a globally competitive STEM workforce in areas of national importance. This project addresses both fundamental and applied research challenges in Collaborative Human-Mobile Robot Operations (CHROps), a critical area for advancing human-centered automation in next-generation supply chains. Students engage with use-inspired problems across strategic, tactical, and operational levels, including the coordination of human–robot teams in dynamic environments, cost-effective fleet sizing under uncertainty, and facility layout redesign for safe and efficient collaboration. The program prepares students to (i) develop mathematical models and AI-powered algorithms, (ii) implement and evaluate solutions using digital twin platforms, and (iii) formulate interdisciplinary frameworks for CHROps design, operations and management. Research activities are organized into three phases: pre-departure technical and cultural preparation, immersive research at the University of Passau in Germany, and post-research synthesis and dissemination in the United States. The project advances theories, algorithms, and modeling frameworks for CHROps and promotes interdisciplinary approaches to drive future innovations in collaborative automation across critical sectors. A structured evaluation process assesses student development and program impact across all phases, informing continuous refinement of the training experience. 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

. Modern pharmaceutical synthesis faces significant challenges in the production of complex, fluorinated, and highly functional molecules that are essential for ensuring the safety, potency, and stability of life-saving medications. This research program aims to address these challenges by developing safer and more efficient chemical methods that develop and leverage water-based reactions, cost-effective reagents, recyclable materials, and resource-efficient catalysts. The Handa Lab is set to lead innovations such as electromicellar catalysis in water, resource-efficient fluorination and trifluoromethylation of (hetero)aromatics, C-N cross-couplings of difficult molecules, and selective catalysis involving transient Pd(III) species, thereby enabling the synthesis of functionally enriched compounds, nitrogen-rich atropisomers, and other intricate drug components under mild conditions. Key research areas encompass: (1) electromicellar catalysis for the formation of fluorinated heterocycles, allenes, and reactive intermediates to access trifluoromethylated (hetero)aromatics using in situ derived reagents from safer sources; (2) enantioselective cross-couplings to access challenging atropisomers through the use of recyclable catalysts and in-house developed photoactive polymers that facilitate Dexter energy transfer; and (3) room-temperature cross-couplings of unreactive (hetero)aryl fluorides and chlorides, driven by surface-faceting or transient hot catalytic species, along with in situ regeneration of inactive catalysts to highly active forms. These strategies aim to enhance reaction selectivity, reduce costs, and improve safety, thereby making pharmaceutical manufacturing more efficient and accessible. Additionally, the program emphasizes the importance of training students in cutting-edge chemistry relevant to pharmaceuticals and fostering strong partnerships with the domestic pharmaceutical industry to translate discoveries into real-world health solutions.

NSF Awards · FY 2025 · 2025-09

Nobel laureate Barbara McClintock discovered a “tiny fragment” chromosome, which she postulated contained an “X component” that could rapidly reorganize the genome. This fragment chromosome rearranges itself as well as other regions of the genome. The regions that would participate in these rearrangements are now known to contain highly repetitive sequences. They present a challenge for the DNA copying mechanism that is often stalled in repetitive sequences and needs to be reinitiated. If this reinitiation is defective, DNA breaks occur at these sites and could form the basis of the rearrangements produced by the X component if the breaks in various places in the genome occur simultaneously and are eventually repaired with swapped partners. The types of rearrangements produced are similar to those that commonly occur during karyotype evolution, so an understanding of the X component will reveal a novel genomic activity that can have a profound effect on chromosomal changes. An educational program is proposed to digitize microscope slides of chromosomal aberrations and their behavior from an historical collection and an instructional video on the technique of whole chromosome painting in maize will be produced and uploaded to the laboratory Youtube channel. The proposed experiments will test the hypothesis that dramatic changes in transmission frequency of the tiny fragment chromosome are the result of genomic rearrangements catalyzed by the X component carried on the minichromosome and define their structure with whole chromosome paints and DNA sequencing. Experiments will test the hypothesis that silencing is operating versus fragment loss by comparing mRNA to genomic DNA via droplet digital PCR from total nucleic acid isolations from different sectors on mosaic plants with or without Bronze1 expression. RNAseq experiments will assess the silencing versus activity of all the genes on tiny fragment in these sectors. Based on previous examples, cases of mosaic patterns of Bronze1 will be tested for heritability. The gene WSS1, a DNA-dependent metalloprotease, is present on tiny fragment and its normal function is to facilitate the reinitiation of replication of highly repetitive sequences. With under or overexpression of this gene in other species, DNA double strand breaks occur, making this gene a strong candidate as the X component. CRISPR-Cas9 editing of this gene in the endogenous chromosome will test if chromosomal rearrangements analogous to those produced by the X component result. Transformation of this gene to produce overexpression will test the same. 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 Archaeometry Laboratory at the University of Missouri Research Reactor (MURR) specializes in the provenance and compositional analysis of archaeological materials including ceramics, chert, obsidian, pigments, and metals. Such chemical analyses are essential pieces of evidence used by archaeologists to investigate the technology, economics, social and political organization and identity practices in ancient societies. Understanding past human behavior can inform modern efforts to address issues such as population movement, resource exploitation, social networks, and land-use patterns. The laboratory supports a wide variety of projects, trains students, graduate interns, researchers, and visiting scholars from US institutions every year, and contributes to knowledge diffusion through close scientific collaborations between MURR staff and outside researchers, student education, and professional publications. Broader impacts of the Archaeometry Laboratory at MURR include: (1) availability of affordable chemical and isotopic analyses for students and faculty from academic departments and non-profit research organizations in the US; (2) training of undergraduate and graduate students in the selection and use of laboratory methods of analysis; (3) advice in project design, statistical analysis, and interpretation of the chemical and isotopic data; and (4) public access by archaeologists, geologists, and scientists from many disciplines to the compositional database. The long-term success of the Archaeometry Laboratory has resulted in an unparalleled database of artifact and raw material chemical compositions that enhance the explanatory power of future analytical studies, as well as provide the raw material for large-scale research projects using existing data. These databases are increasingly relevant for the emerging use of Artificial Intelligence to interpret large and complex datasets. The Archaeometry Laboratory plays a leading role in developing novel applications in AI-enhanced analysis of archaeometric datasets. The MURR Archaeometry Laboratory focuses on elemental and isotopic analysis in order to examine the production, exchange, and movement of a broad variety of artifacts using neutron activation analysis (NAA), X-ray fluorescence (XRF), Raman spectroscopy (RS), and inductively coupled plasma mass spectrometry (ICP-MS) including a laser ablation system and a multi-collector. The laboratory also supports geochemical research on the petrogenesis of various igneous, metamorphic, and sedimentary rocks. 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 Conference on Topological Data Analysis: Recent Developments and Applications, University of Missouri-Columbia, November 22-24, 2025, will bring together leading researchers from academia and industry as well as graduate students to discuss and explore the latest theoretical developments and applications of Topological Data Analysis (TDA). TDA has emerged as a powerful framework for capturing the intrinsic shape of data and now informs work in areas such as biology, machine learning, and materials science. Because the field is evolving rapidly across theory, algorithms, and real-world use cases, the program aims to balance cutting-edge theory with demonstrations of practical impact across disciplines. A central goal is to train the next generation of TDA researchers by featuring lectures and presentations from internationally recognized experts, as well as offering oral and poster presentation opportunities for students and postdoctoral researchers—an arrangement that accelerates knowledge transfer, broadens participation, and provides early-career mathematicians and scientists with valuable professional experience and networking opportunities. The three-day agenda features keynote and invited talks, short parallel oral sessions, and poster presentations around various themes of TDA such as (1) multiparameter persistence; (2) Mobius inversion techniques in TDA; (3) spectral methods in TDA; and (4) applications across disciplines. By gathering leading theorists with practitioners who apply these ideas to real data, the conference will both advance fundamental understanding of topological data analysis and accelerate its practical use across disciplines, ensuring that mathematical insight continues to fuel innovation. Further details, including the program and speaker information, will be available on the conference webpage at: https://zhengchaow.github.io/tda-conference-2025/ 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 Arterial stiffening and microvascular inward remodeling are early and independent predictive risk factors of cardiovascular disease (CVD) and mortality. In obesity, these risk factors occur early, preceding and contributing to the development of hypertension and organ dysfunction. However, despite CVD being the primary cause of mortality in individuals with obesity, no treatments are currently available for obesity- associated vascular stiffening and remodeling. This is due, in part, to an unclear understanding of the underlying mechanisms for these common conditions. Rigorous published research and preliminary studies indicate that vascular smooth muscle cell (SMC) tissue transglutaminase (TG2) is increased in obesity and that TG2 activity contributes to arterial stiffening and microvascular inward remodeling. However, the mechanisms by which obesity increases TG2 activity, how TG2 promotes arterial remodeling and contributes to hypertension development, and whether TG2 could be targeted therapeutically to reduce CVD in obese patients remain unclear. This proposal addresses those significant gaps in knowledge. The central hypothesis of this proposal is that obesity-associated SMC TG2 upregulation and mechanosensation increase arterial collagen crosslinking, actin polymerization, and myogenic tone, contributing to vascular stiffening and hypertension. A corollary to this hypothesis is that treatment with the novel TG2-specific inhibitor, ZED3641, will attenuate arterial stiffening and blood pressure in obese-hypertensive pigs. This innovative hypothesis will be tested with gain- and loss-of-function genetic manipulation and pharmacological experiments in cultured vascular SMC and isolated arteries, in animal models of SMC-specific MR and TG2 deletion, and in obese- hypertensive pigs. Specifically, studies in Aim 1 will determine the mechanism of SMC mineralocorticoid- dependent regulation of TG2 in obesity. In Aim 2, the role of TG2 mechanosensation in arterial stiffening, myogenic vasoconstriction, and remodeling in obesity will be determined. Finally, studies in Aim 3 will determine the arterial de-stiffening effects of TG2 inhibition in isolated arteries from obese patients and in a swine model of obesity and hypertension. It is posited that targeting TG2 activity holds extraordinary promise for correcting arterial stiffening, remodeling, and hypertension in obese patients, thus reducing their CVD burden.

NSF Awards · FY 2025 · 2025-09

Research into ceramic production and exchange in past societies has led to wide-ranging understanding of social, economic, and political organization in these ancient systems as well as implications for contemporary societies. Much of this work has utilized chemical analysis of ceramics using neutron activation analysis (NAA) and the American Southwest has been one area of intensive research. Existing NAA data represent significant research investment, but the combined power of this investment remains unrealized largely due to two impediments: 1) existing databases require synthesis and standardization; and 2) analytical methods that can systematically compare compositional data at these macroregional scales needs more development. This project produces a standardized, expanded, and broadly accessible database of more than 30,000 ceramic compositional analyses that will allow new big-data approaches (AI and machine learning) to large-scale interactions. The resulting databases are updated and readily available for a broad range of future research. Recent developments in artificial intelligence and machine learning allow exploration of large-scale multivariate data at scales that facilitate the exploration of regional-scale interaction. This project develops new methods for analyzing NAA data using social network analysis (SNA) tools that allow one to evaluate models and expectations derived from social network theory using findings of past networks based on typological similarities. Shifting Southwestern NAA studies from narrowly focused individual projects to regionally informed large-scale investigations allows researchers to more effectively sample sites or regions for NAA, making small samples considerably more valuable while not duplicating existing data. The methods developed in this project are directly applicable to other regions with comparable NAA databases. 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 Budget cuts are common during times of fiscal austerity, and a frequent target for spending reductions are welfare programs such as income support for poor families. Although its withdrawal has been shown to have negative shorter-run effects on poverty, education, health, and criminal activity, we often lack the data to study impacts in the longer run and across generations. However, the introduction of the New Poor Law of 1834 in England and Wales, precipitating the largest decline in welfare spending in British history, and the wealth of data available offer a unique opportunity to examine the effects of income support (“poor relief”) across multiple generations. The law centralized the administration of poor relief in these countries, dramatically restricting eligibility and reducing payments so that only the very neediest would receive them. Because each parish had previously determined the generosity of poor relief within its borders, there was wide geographic heterogeneity in how much income support declined after 1834. Therefore, we will use a difference-in-differences design to study the impact of the withdrawal of income support on a range of health and socioeconomic outcomes, including mortality, fertility, occupational skill levels, and education. By collecting novel and detailed data on parish-level poor relief spending and linking males across detailed versions of the 1861 and 1891 censuses of England and Wales, we will study the effects on individuals who were children in 1834 and on two generations of their descendants. As a preliminary validation of our project, we used coarser publicly available versions of these data and a naïve record-linking strategy to examine some outcomes of interest across these 6 decades. In the very short run, we find evidence of increased mortality in counties where poor relief declined the most between 1833 and 1834. Additionally, we show that in 1861, surviving cohorts more exposed to these declines as children were less likely to hold high-skilled jobs as adults and their children were less likely to be in school. Despite well-known biases toward statistical insignificance due to our naïve linking strategy, we nonetheless find that as adults in 1891, these male descendants had more offspring. Consistent with greater fertility being a sign of poverty due to the tradeoff between having more children and investing more in their human capital, we find that fewer of their female children – the granddaughters of those exposed to poor-relief declines as children – were in school. This proposal therefore focuses on collecting and gaining access to more detailed data and refining the record-linking strategy used in our preliminary study. Our aim is to more accurately and precisely estimate the long-run and intergenerational impacts of income support in childhood, as more convincing evidence is needed to improve our understanding of the mechanisms and critical periods shaping health and well-being across the life course and into subsequent generations. We will also help to inform policymakers about multi-generational considerations in cost-benefit analyses of welfare spending, as failing to incorporate these effects could result in long-run societal costs that greatly outweigh the short-run savings from cutting such programs.

NSF Awards · FY 2025 · 2025-09

Many major crops use hybrids between different lines to produce more robust and higher yielding plants in the field. This phenomenon is referred to as hybrid vigor or heterosis. The genetic and molecular basis of this phenomenon has been debated for over a century. This project will address the hypothesis that heterosis results from a generalized overall stimulation of gene expression in hybrids compared to their respective parents. The overall stimulation of gene expression results in larger and more cells in the hybrid than in the parents, thus producing progeny that have greater biomass. A genetic and molecular understanding of heterosis will provide the potential to improve crop production in predictable and expedited ways. If a unifying principle of heterosis can be established, it should become possible to edit plant genomes for better crop cultivation. The amount of arable land worldwide has been decreasing for several decades but the need for increased agricultural output is only increasing. The foundations established in this project will hopefully contribute to this need by providing for and advancing agricultural biotechnology through basic research. Several lines of evidence indicate that there is a transcriptome size increase in heterotic genotypes compared to their inbred parents. Generally, the expression of regulatory components is additive in hybrids and in a more stoichiometric register with each other than in their parental inbreds while the overall expression of target genes in hybrids is elevated. This project will test the hypothesis that more balanced regulatory components result in greater target gene expression. One goal will test whether RNA polymerase II has increased occupancy in hybrids as the basis of the observed increased transcriptome size. A second goal is to test whether producing unbalanced genomes in inbred and hybrid backgrounds will block or otherwise modify the heterotic response and the transcriptome size. Aneuploids for multiple regions of the maize genome will be analyzed in inbred and hybrid backgrounds for the transcriptome size and for biomass of the adult plants. The hypothesis is that imbalanced genomes will have reduced magnitude of heterosis compared to normal balanced genomes when comparing inbreds and hybrids. 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

This project will reimagine engineering education by creating innovative tools and strategies that support the success of all students, particularly in the critical first two years of the undergraduate experience. Grounded in the concept of neurodiversity as the natural variation in how individuals think, learn, and process information, the project embraces the idea that every student brings a unique cognitive profile to the classroom. The project will use artificial intelligence (AI) to build learning environments that flex to meet a wide range of student strengths, needs, and preferences. Through redesigned courses, professional development for faculty, and AI-powered tutoring and academic coaching, the project will help students develop essential academic behaviors such as time management, self-regulation, and metacognitive awareness. The interventions will be designed to be accessible to all students and will be embedded in existing instructional and advising structures. By improving student engagement and persistence in engineering pathways, this work will contribute to a more diverse, capable, and innovative engineering workforce. It will advance the national interest by expanding access to high-quality STEM education and supporting a broader spectrum of learners whose talents may not be fully realized in traditional academic settings. This project will implement a coordinated institutional change strategy at the University of Missouri and the University of Connecticut, focused on transforming gateway courses in engineering and mathematics using universal design for learning principles and inclusive pedagogy. Faculty will participate in a structured professional development sequence that includes the Neuroinclusive Teaching Institute and interdisciplinary I-teams to support course redesign and the integration of AI-powered tools. A virtual academic coach, built on large language models, will be deployed in tutoring, peer mentoring, and advising contexts to guide students through personalized learning strategies outside the classroom. All tools and redesigned instructional practices will be made openly available to all students. The project will advance fundamental knowledge in two distinct areas: first, in engineering education, it will examine how the deployment of AI tools in instructional and support environments affects engagement, self-regulated learning, and engineering identity formation across cognitively diverse learners. Second, it will provide insight into the policies and institutional practices that promote a culture that values the strengths of all students. Based on prior experience, these cultural shifts are essential for both the transformation and long-term sustainability of educational change. In addition to advancing research, the project will generate actionable tools and structures to support adoption of AI-enhanced, neuroadaptive practices across institutions. These will include a faculty teaching workshop with adaptable materials for engineering and math courses; AI-coaching tools with companion training for advisors and peer mentors; and a roadmap for onboarding faculty into neuroadaptive, technology-enabled teaching models. Research activities will assess how these innovations influence students, faculty, and departments, contributing new knowledge to the national conversation about AI in education and the professional formation of engineers. Together, these efforts will create a replicable model for building more responsive learning environments in undergraduate engineering education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Immunotherapy with immune checkpoint inhibitors (ICIs) has been a significant breakthrough in lung cancer (LC) treatment, yet many patients do not fully benefit from ICIs. Abnormal antigen presentation, the immune- regulatory tumor microenvironment (TME), plus other factors contribute to primary or secondary resistance to ICIs. Therefore, there is an evident clinical unmet need to develop approaches to overcome LC resistance to ICIs. The long-term goal is to accelerate development of transductional targeting OAdSA-4-1BBL, an oncolytic adenovirus targeting preferentially transferrin receptor (TfR) in LC cells and that expresses a novel co-stimulatory molecule streptavidin-4-1BBL to treat non-small cell lung cancer (NSCLC) patients unresponsive to ICIs. The overall objective for this proposal is to evaluate the effectiveness of OAdSA-4-1BBL alone or combined with anti- PD1 blockade in an immunocompetent orthotopic lung cancer mouse model and develop a novel OAd expressing SA-human4-1BBL that preferentially infects and kill LC cells by targeting transferrin receptor (TfR). The central hypothesis is that OAdSA-4-1BBL-mediated immunomodulation of the TME promotes durable protective antitumor immunity to overcome resistance to ICIs in NSCLC, and that combined therapy OAdSA-4- 1BBL plus ICI will synergistically and permanently eradicate NSCLC tumors. The rationale for the proposed study is that rigorous scientific evidence supports an immunotherapeutic role for OAdSA-4-1BBL in an immunocompetent orthotopic LC host in settings of ICIs. Further, selectivity of TfR/OAdSA-h4-1BBL in patient- derived organoids (PDOs) likely provides a strong foundation for the development of clinical approaches testing TfR/OAdSA-h4-1BBL as a novel strategy to boost the effects of ICIs in NSCLC patients. Guided by strong preliminary data, the following specific aims are proposed: 1) Evaluate the immunotherapeutic efficacy of OAdSA-4-1BBL alone or in combination with anti-PD-1 blockade in an orthotopic LC mouse model and 2) Development and characterization of transductional targeting-(TfR) OAdSA-h4-1BBL in a PDOs. For Aim 1, syngeneic orthotopic lung tumor models expressing luciferase will be used to monitor antitumor efficacy of OAdSA-4-1BBL alone or in combination with anti-PD-1 blockade, OAdSA-4-1BBL-mediated immunogenic cell death will be evaluated, flow cytometry for immune cell phenotyping from tumors, lymph nodes, and spleen will be used to evaluate OAdSA-4-1BBL capacity to reprogram the TME. In Aim 2, an improved transductional targeting (TfR) OAd expressing SA-human-4-1BBL will be developed and tested in clinically relevant PDOs. The proposed research is novel because it combines a cancer selective tumoricidal agent with a novel, chimeric, and soluble co-stimulatory molecule SA-4-1BBL in one single agent that could overcome LC resistance to ICIs. The proposed research is significant because the results are expected to provide strong justification for continued development and future clinical trials using OAdSA-4-1BBL in combination with ICIs. Apart from advancing the field of oncolytic immunotherapy in NSCLC, this new generation of immunotherapies is expected to benefit patients with other cancer types that are also resistant to ICIs.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Focal ischemic ischemia (FCI) is a leading cause of human death, however, functional recovery after FCI has been observed and can be remarkable in both patients and animal models. Due to limited therapeutic choices for clinical treatment, studying its mechanisms has been an important topic and could provide therapeutic insights. Astrocytes in normal adult brains referred to as resting astrocytes (RestAs) are quiescent, but they undergo rapid proliferation, display a `hypertrophic' morphology, exhibit an altered transcriptional profile, and eventually form glial scars in the peri-infarct region (PIR) after FCI. These phenomena are known as reactive astrogliosis, and the activated astrocytes are called reactive astrocytes (ReAs). The phenomena of reactive astrogliosis indicate that the microenvironment constraining the proliferation capacity of astrocytes is removed and ReAs must adapt to a more metabolically active phenotype to meet the increased biosynthetic and bioenergetic demands. Accordingly, the current application will study the metabolic reprogramming of ReAs and its effects on brain repair after FCI. Our preliminary studies discovered that nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in the NAD+ salvage pathway, is undetectable in RestAs but is highly upregulated in ReAs after photothrombosis (PT)-induced FCI and oxygen-glucose deprivation (OGD) of cell cultures. Metabolomic analysis indicated increased metabolites in major metabolic pathways including glycolysis and pentose phosphate pathway (PPP) in ReAs. Furthermore, we found that hypoxia inducible factor 1 (HIF1) is an upstream regulator of NAMPT and conditional deletion of NAMPT in astrocytes caused increased brain damage, neuronal death, and motor function deficits after PT. Because NAD+ and its derivative NADP+ are important determinants of glycolysis and PPP, we hypothesize that NAMPT upregulation is necessary and sufficient for metabolic reprogramming of ReAs after FCI, thereby mediating reactive astrogliosis, promoting brain repair and improve stroke outcomes after FCI. Our overall project goal is to conduct an in-depth study on the metabolic reprogramming of ReAs and its effect on post-stroke brain repair after FCI. To accomplish our goals, we will use integrated technologies including astrocyte-specific inducible and conditional NAMPT knockout and overexpression mice and HIF1 knockout mice, stable isotope labeling (SIL), LC-MS analysis of metabolomic profile, RNA-seq, in vivo two-photon imaging of metabolism, histology and behavioral tests. We propose three specific aims. Aim 1 will characterize metabolic phenotype and determine the effect of NAMPT on metabolic reprogramming of ReAs after FCI. Aim 2 will investigate the regulatory mechanism of NAMPT induction by HIF1 and other potential transcriptional factors (TFs) in ReAs. Aim 3 will study the effect and mechanisms by which ReAs promote brain repair and functional recovery through metabolic reprogramming after FCI. We expect our work will elucidate novel mechanisms of ReAs in post-stroke brain repair in the context of glia-neuron interactions and provide potential therapeutic insights for FCI.

NSF Awards · FY 2025 · 2025-08

Proteins are essential for many biological and engineering functions, and their behavior is determined by how their three-dimensional structures change over time. Current computational methods for studying these dynamics, like molecular dynamics (MD) simulations, are limited by their inability to capture long-duration events crucial for understanding processes like protein folding or aggregation. To address this, this project will develop deep-learning models, specifically consistency models, to simulate protein dynamics more efficiently and over longer time scales. These models are expected to predict molecular changes without the tiny time steps required by traditional methods, significantly speeding up the process while maintaining accuracy. The success of this project will provide approaches that scientists can use to unlock insights into protein behavior that are currently inaccessible, paving the way for advancements in medicine, biotechnology, and materials science. This project also combines artificial intelligence with molecular engineering to train the next generation of researchers, fostering a skilled workforce. If successful, it could significantly accelerate our understanding the dynamic properties of proteins. This project addresses the limitations of molecular dynamics (MD) simulations in capturing the long-time-scale dynamics of protein structures, focusing on the integration of deep learning-based consistency models. MD simulations rely on Newtonian equations with small time intervals (1-2 femtoseconds), limiting their applications from studying milliseconds or longer processes. Consistency models, a recent advancement in generative modeling, offer an alternative by predicting probabilistic distributions of molecular states with significantly larger time steps. The central hypothesis is that well-trained consistency models can replace traditional force fields in MD simulations, enabling long-stride simulations without compromising physical accuracy. The research consists of three primary tasks: (1) developing prototype consistency models trained on MD trajectories of simplified systems like polyalanine peptides, (2) optimizing model architecture and encoding methods to balance efficiency and fidelity, and (3) benchmarking these models on complex systems such as the Pin1 WW domain and Aβ-42 peptide aggregation. This approach is expected to overcome current MD simulation barriers, laying a computational foundation for studying protein folding, aggregation, and other time-dependent processes with high precision and computational efficiency. 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

Powerful generative artificial intelligence (AI) models have emerged in recent years, with applications extending beyond language and images to fields such as drug design and beyond. One fast-growing branch of generative AI models, called diffusion models, is an especially efficient and effective mathematical framework for generative AI. However, despite the strong empirical performance of diffusion models, the fundamental mechanisms that let them generate novel samples remain poorly understood. This Mathematical Foundations of Artificial Intelligence (MFAI) award enables research that looks to develop new theoretical tools to both elucidate how flow-based generative models—a broad framework that includes diffusion models—produce novel outputs and enhance that capability, while also extending these models to handle complex data such as graphs and sets. The resulting theory seeks to strengthen the mathematical foundations of AI and help make the technology safer for real-world use by reducing risks, such as unintentionally copying private training data into public outputs. The project will also nurture the next generation of researchers through student training at the intersection of mathematics and AI. Research enabled by this award investigates two central challenges in flow-based generative modeling: (1) how to achieve controlled generalization to produce diverse and novel in-distribution samples, and (2) how to extend these models to complex data types beyond the Euclidean setting, such as graphs, point clouds, or sets. The first thrust focuses on understanding why trained flow models often generalize better than the theoretically optimal solution suggests, using tools from geometry, ODE, manifold learning, and deep learning theory. The second thrust takes a metric space perspective, formulating a general-purpose meta framework for generative modeling of structured data via geometric tools and optimal transport. New scientific findings are expected to lead to both theoretical insights and new modeling strategies, potentially improving the safety and applicability of generative AI. 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

This project aims to bring to light hidden similarities between objects that are studied in the fields of commutative algebra, algebraic geometry and arithmetic geometry. These are some of the oldest fields that have been continuously studied in mathematics, and they have broad ranging applications such as in cryptography and in physical, social and biological sciences. At a fundamental level, these fields aim to understand the behavior of shapes that can be described using polynomial equations. The behavior of the shapes are, in turn, influenced by algebraic properties of the functions on them. The broad goal of this project is to establish results that exhibit unexpected uniform behavior among these algebraic objects. A particular focus is on using techniques that arise from modular, or “clock”, arithmetic. This project also includes outreach to the community, opportunities for students, and conference organization. The discovery of unexpected uniform behavior in Noetherian rings has been a central theme in commutative algebra over the past fifty years. Among all Noetherian rings, there is an important class consisting of those considered to be the most geometric, known as excellent rings. Several uniformity results such as uniform Artin-Rees and uniform Briançon-Skoda remain open for the class of excellent rings in general, and in the mixed characteristic setting even for finite type algebras over a discrete valuation ring. The principal investigator will use techniques from prime characteristic commutative algebra, non-Archimedean functional analysis and recent advances in mixed characteristic singularity theory to tackle these open problems for new classes of excellent rings, especially those arising in an arithmetic setting. With the recent prolific use of arithmetic techniques in commutative algebra and algebraic geometry, the study of such rings is becoming increasingly important. In addition, these rings have also helped clarify how some fundamental notions of singularities defined via the Frobenius map behave outside the settings of finite type algebras over a field, and rings with finite Frobenius. 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

In mammalian females, quiescent primary oocytes serve as the ovarian reserve supporting mature oocyte production and ovarian function. Primary oocytes differentiate in fetal ovaries and remain quiescent for years in adult ovaries, where a cohort of primary oocytes activates periodically to develop into mature oocytes. Quiescent primary oocytes undergo cell death continuously, leading to diminished ovarian function in adult females. Here, we propose to continue our study on how primary oocytes are determined among fetal germ cells during oocyte differentiation; and mechanisms underlying primary oocytes quiescence and loss in adult ovaries. Our previous study characterized roles of organelle dynamics in mouse oocyte differentiation and quiescence. We found that primary oocytes form via organelle transport within germline cysts, in which germ cells derived from the same progenitor are connected via intercellular bridges. ~20% of the germ cells collect organelles (Golgi, centrosomes, and mitochondria) from sister cyst cells to become primary oocytes, whereas the rest donate their organelles and undergo cell death. In the quiescent primary oocyte, enriched organelles organize into a Balbiani body (B- body), represented by a spherical configuration of Golgi complexes. The B-body is maintained by microtubules and actin, and regulates oocyte quiescence via RNA storage. These studies have laid a foundation for us to continue investigating oocyte determination and quiescence with a focus on organelle transport and organization in the next five years. We will continue our study on how the cell fate of collecting vs. donating organelles is determined among fetal germ cells. Our recent study suggests that organelle transport in cysts is conducted in a directional manner and facilitated by intercellular polarity between cyst germ cells. We will define the intercellular polarity by characterizing patterns of organelle (Golgi and mitochondria) transport, and the interactions between organelles, actin and microtubules in mouse and human germline cysts using advanced imaging approaches. We will identify functions of MACF1 (microtubule actin crosslinking factor 1) and microtubule minus-end-targeting protein CAMSAP3 (Calmodulin-regulated spectrin-associated proteins 3) in intercellular polarity, organelle transport and oocyte determination using inducible germ cell-specific conditional knockout mouse models. To further our knowledge of the structure and function of the B-body in mammals, we will unveil protein and organelle compositions of mouse and human B-bodies by protein profiling and imaging. We will conduct mechanistic study on B-body-mediated quiescence-senescence-cell death transitions in primary oocytes by investigating roles of the p53/TAp63 pathway in this process using in vitro ovary culture and mutant mouse models. We will also test the feasibility of protecting primary oocytes from chemotherapy drug-induced cell death by preserving the B-body function. Successful completion of the proposed study will advance our understanding of cell and developmental biology in mammalian oogenesis and have a positive impact on women’s health.

NSF Awards · FY 2025 · 2025-08

Roughly 550 million years ago, life on Earth underwent a major transformation. During this time at the end of the Ediacaran Period the first animals began to form hard shells, move across the sea floor, and interact in new ways, including hunting and burrowing. These innovations laid the foundation for the “Cambrian Explosion,” a burst of evolutionary change that produced nearly all major animal groups. However, the fossil record from this critical time remains incomplete, and major questions persist about how and why these changes occurred. This project investigates ancient rocks at the Nevada National Security Site (NNSS), a region that has been closed to paleontological study for over 70 years. By uncovering and analyzing fossils preserved in these rocks, this research will provide new insights into the earliest animals and how they shaped Earth’s ecosystems. In addition to advancing scientific discovery, the project supports student training, public science outreach, and national collaboration, helping connect society with its deep evolutionary past. This project will be the first to systematically investigate Ediacaran- to Cambrian-aged strata within the Nevada National Security Site (NNSS), with the goal of understanding biotic and environmental dynamics during the terminal Ediacaran Period. The study focuses on fossiliferous carbonate and siliciclastic units that may preserve cloudinomorph-grade taxa and associated trace fossil assemblages, with particular attention to characterizing evidence of early biomineralization, ecological interactions (e.g., predation), and taphonomic pathways. The research team will employ high-resolution imaging (including scanning electron microscopy and X-ray tomographic microscopy), geochemical analyses (e.g., elemental mapping, stable isotope chemostratigraphy), and stratigraphic correlation to document fossil morphology, reconstruct paleoecological settings, and assess facies changes and preservational controls. The project also aims to provide data relevant to defining the proposed Terminal Ediacaran Stage of the geologic time scale and will contribute to international stratigraphic efforts through collaboration with the Ediacaran Subcommission of the International Commission on Stratigraphy. Outcomes will include taxonomic revisions, refined geochronologic and paleoenvironmental interpretations, and increased understanding of early animal evolution. 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.