Ohio State University

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY/ABSTRACT Many emerging zoonotic viruses (animal viruses that transmit to humans) are highly pathogenic, having the potential to cause deadly epidemics or even global pandemics. The risks zoonotic viruses pose are highlighted by the emergence of the SARS/MERS coronaviruses, Ebola virus, and HIV-1, all of which are related to animal viruses that were unknown before they caused substantial cases of disease in humans. Given the risk animal viruses pose to humans, many researchers have turned to viral discovery—using genome sequencing tools and metagenomic analyses, researchers hope to identify novel animal viruses before they emerge in humans. We've developed a pipeline that integrates viral surveillance with molecular investigations in the laboratory to identify pre-emergent viruses with epidemic potential. Using this approach, we've provided compelling evidence suggesting that simian arteriviruses (SAVs)—understudied and neglected pathogens of African monkeys—are poised for spillover, posing a threat to human health. We demonstrate key biological features that poise SAVs for zoonosis, including: (1) compatibility with human receptors; (2) high titer propagation in human cells; and (3) potential for evasion of human innate immunity. Further interrogation of the biology of SAV infection is crucial for future epidemic preparedness efforts. The objective of this proposal is to uncover mechanisms of cell entry, immune evasion, and zoonotic potential for these highly concerning viral pathogens. In Aim 1, we employ a series of molecular, biochemical, structural, and functional approaches to define SAV-receptor interactions and establish proof-of-concept strategies for future therapeutics—an essential step in outbreak preparedness. In Aim 2, we will identify SAV proteins that antagonize the human innate immune response, with the goal of revealing vulnerabilities that may help develop safe and effective antiviral approaches. In Aim 3, we will thoroughly evaluate the zoonotic potential of diverse SAVs. This includes: (1) identifying novel SAVs through whole virome sequencing of wild African primate biomaterials; (2) the development and application of non-human primate induced-pluripotent stem cell (iPSC)-derived macrophages to isolate novel SAVs in cell targets from natural host species; and (3) detailed infection studies in human cells to evaluate human compatibility. Further, we will perform the first in-depth serosurvey for SAV exposure history using banked sera from a Ugandan case-control cohort. When taken together, this proposal will lead to a deeper understanding of the molecular biology and pathogenesis of these understudied viruses, as well as a greater appreciation for the zoonotic risk that they pose. It is imperative that we invest in characterizing the biology and pathogenesis of SAVs now so that we may begin to develop platform technologies (i.e., diagnostics, vaccines, therapeutics) in case they do emerge in the future.

NIH Research Projects · FY 2026 · 2026-02

Project Summary or Abstract: Gut barrier dysfunction leads to endotoxemia, characterized by increased levels of endotoxin/lipopolysaccha- ride (LPS) in the blood circulation, which affects multiple organs and is associated with liver diseases and sev- eral other LPS-associated diseases. LPS, a potent microbial ligand from gram negative bacterial membrane, induces intense systemic inflammation via TLR4 in immune cells. As a proactive host defense mechanism, the liver clears gut derived LPS from portal circulation. However, the cell types in the liver, receptors involved in LPS clearance, and inflammatory response of those cells is unknown. Identifying the innate immune cells, re- ceptor and the molecular mechanism involved in rapid clearance of circulating endotoxin by liver will provide critical insights to develop therapeutic options for endotoxemia. In this proposal, we present six novel findings. First, we found that liver sinusoidal endothelial cells (LSEC) eliminate a major portion of LPS from blood circu- lation very rapidly within a few minutes, and that clearance of LPS is facilitated by high density lipoprotein (HDL). Second, LSEC clear circulating LPS via Stabilin-1 (Stab1) and Stabilin-2 (Stab2) receptor mediated endocytosis and localize to lysosomes for degradation. Third, the lack of both Stab1 and Stab2 (double knock out mice) results in diminished LPS uptake, clearance, and endocytosis by LSEC, but escalated systemic in- flammation and early death. Fourth, Stab1, and to a lesser extent Stab2, participates in LPS clearance and host defense. Fifth, Stabilin and TLR4 are functionally opposite receptors for LPS mediated immune response. Six, Liquid chromatography-tandem mass spectrometry has identified novel serum and intracellular proteins as potential facilitators of Stabilin receptor-mediated LPS clearance. These results lead us to hypothesize that Stabilin receptors clear LPS through a distinct pathway involving serum and intracellular proteins, leading to enzymatic inactivation of LPS in human and mouse LSEC. Upregulating the clearance function of Stabilin re- ceptors will control TLR4-mediated systemic inflammation. This hypothesis will be tested with following aims: Aim 1: Determine the molecular mechanism of Stabilin-mediated LPS clearance. Aim 2: Determine the relative immune function of Stabilin receptors with TLR4. Aim 3: Determine how the enhancement of Stabilin receptor- mediated LPS clearance protects mice from endotoxemia. This project presents a new paradigm in which Sta- bilin receptors expressed by LSEC offer a host defense mechanism and protection against LPS -associated diseases, and points to novel targets to treat endotoxemia both prophylactically and therapeutically.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY The fungal pathogen Histoplasma capsulatum infects and thrives within phagocytes of the immune system. Results from a genetic screen indicate that peroxisome organelles play a critical role in facilitating Histoplasma proliferation in macrophages. Preliminary work demonstrated that the known peroxisomal functions of fatty acid β-oxidation, the glyoxylate cycle, and catalase destruction of reactive oxygen are all dispensible for Histoplasma pathogenesis indicating that peroxisomes play a novel role. This proposal will uncover new peroxisome functions by definition of the peroxisome proteome and metabolome in pathogenic-phase fungal cells. Candidate peroxisomal functions will be validated by functional tests to determine their contributions to Histoplasma’s intramacrophage proliferation. This work will uncover new peroxisomal functions and reveal novel mechanisms for fungal exploitation of the macrophage.

NIH Research Projects · FY 2026 · 2026-02

Project Summary/Abstract The objective of this project is to understand the role of the mechanistic target of rapamycin (MTOR) pathway in the association between energy imbalance and prognosis in patients with breast cancer and the potential benefits of targeting MTOR to inhibit its activation to improve clinical outcomes in these patients. Energy imbalance is an important factor affecting breast cancer prognosis. Although behavioral interventions leading to weight reduction have shown a potential to reduce breast cancer recurrence and mortality rates, the biological mechanisms between obesity and breast cancer outcomes are not entirely clear. It is crucial to identify mechanisms through which overall and central adiposity exert their effects. Lifestyle interventions may not be applicable or effective for all women with breast cancer; targeting the underlying biological mechanisms may open new opportunities to improve the prognosis for a greater number of patients. We propose that MTOR pathway signaling in breast tumors is a significant and targetable mechanism mediating energy imbalance and breast cancer prognosis. Our preliminary data show a positive linear association of body mass index and waist circumference with MTOR pathway activation, as indicated by phosphorylated MTOR expression levels, in patients with breast cancer overall and in estrogen receptor-negative (ER-) tumors. Also, from the expression of several phospho-proteins, higher vs. lower levels of MTOR pathway activities are associated with disease-free survival. The main weaknesses of these data are the lack of information on treatment, and the number of patients in subtypes is small. We will address these research gaps in our proposed Pathways Study, a prospective cohort study of 4,505 women who had received a diagnosis of incident primary invasive breast cancer. The cohort has ascertained 571 recurrences, 420 second primary cancers, and 880 deaths. We will assess MTOR pathway activities using a multiplex immunohistochemistry panel in tumor tissue samples. We will evaluate the association between body size (BMI, waist circumference, and waist-to-hip ratio) and MTOR pathway activation in breast tumors (Aim 1) and assess the association of MTOR pathway activation independently and jointly with body size on breast cancer outcomes (Aim 2). To further understand the role of the MTOR pathway in prevention, we will examine whether the status of non- obesity, exercise, and metformin use, as interventions of energy imbalance, affects patient outcomes through MTOR pathway regulation (Aim 3). Our proposal is innovative in employing a large panel of phospho-protein expression, comprehensive clinical and epidemiologic data, and adequate statistical power for ER- breast cancer subtype. The results will improve our understanding of the extent to which MTOR pathway activation, which is modifiable in early-stage breast cancer, may alleviate the influence of energy imbalance on breast cancer prognosis and shed light on the potential for promoting energy balance and using MTOR inhibitors as a combination strategy to improve clinical outcomes.

NIH Research Projects · FY 2026 · 2026-02

ABSTRACT A comprehensive and accurate assessment of kidney function is essential for managing renal diseases, but current diagnostic methods often provide an incomplete evaluation. Standard tests, such as glomerular filtration rate (GFR) measurements and proteinuria, primarily assess glomerular function and renal perfusion, offering limited insight into tubular health. Furthermore, compensatory increases in single-nephron filtration can sustain GFR even in the presence of nephron loss, often masking disease progression. These limitations are especially apparent in acute kidney injury (AKI), where GFR may return to normal even as tubular dysfunction persists, obscuring the transition to chronic kidney disease (CKD). Direct assessment of tubular function can provide valuable prognostic information by complementing existing diagnostic methods. To this end, we recently identified resazurin dye as a novel, non-invasive sensor for evaluating tubular function and nephron mass. Following intravenous injection, resazurin is selectively taken up by tubular cells, reduced to fluorescent metabolites, and excreted in urine, offering a GFR-independent measure of tubular secretion. In murine models of severe AKI and subclinical nephron loss (unilateral nephrectomy and genetic renal hypoplasia), urinary excretion of resazurin metabolites sensitively reflected both tubular dysfunction and nephron mass loss. Based on these findings, we propose that resazurin provides a rapid, sensitive, and GFR-independent method for quantifying tubular function, nephron mass, and renal reserve following kidney injury. This hypothesis is based on the premise that while nephron loss can lead to compensatory increases in single-nephron filtration to maintain GFR, tubular secretion is less adaptable, making it a more reliable indicator of tubular function and nephron mass. This proposal aims to investigate the tubular transport and metabolism of resazurin (Aim 1) as well as its effectiveness and sensitivity (Aim 2) in quantifying nephron mass loss in murine and porcine models of AKI. The results will provide proof-of-principle data to support clinical translation, facilitating earlier detection of nephron loss, enabling the identification of high-risk AKI patients, and improving monitoring of recovery from AKI. Ultimately, this work has the potential to transform the way we assess kidney function in both preclinical and clinical settings by providing a non-invasive and precise tool for detecting early tubular dysfunction and nephron loss.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY/ABSTRACT Periodontal diseases are among the most prevalent on earth. Cementum is a root-covering mineralized periodontal tissue, which is critical for tooth attachment. Disorders that disrupt cementum formation or cause its destruction can result in periodontal ligament (PDL) detachment, periodontal dysfunction, and tooth loss. However, gaps in knowledge about factors that regulate cementum during development have, to date, limited therapeutic advances for cementum repair and regeneration. Inorganic pyrophosphate (PPi) is a physiological regulator of mineralization that works to prevent hydroxyapatite (HA) mineral growth. Modulation of PPi levels is a powerful approach to regulate cementogenesis. Through a series of experiments on human disorders, genetically engineered mouse models, and in vitro cell work, we established that PPi is perhaps the most important molecular regulator of cementum formation. Local levels of PPi are controlled by a few key proteins. Ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) increases PPi levels, limiting mineralization. ENPP1 loss-of-function in development reduces PPi levels and dramatically increases cementum growth. Tissue-nonspecific alkaline phosphatase (TNAP) hydrolyzes and decreases PPi levels, promoting mineralization. Loss-of-function mutations in ALPL, which encodes TNAP, cause hypophosphatasia (HPP). HPP is a mineralization disorder that contributes to substantial dental mineralization defects, including loss of fully rooted deciduous and/or permanent teeth is pathognomic due to cementum defects. Limitations in the current HPP enzyme replacement therapy have prompted research into additional treatment strategies. Pharmacological targeting of ENPP1 function represents a promising alternative approach to reduce PPi in HPP and promote cementum growth, periodontal attachment, and tooth retention. Pilot studies using dietary administration of an ENPP1 inhibitor showed dramatic improvements in multiple skeletal defects in a mouse model of HPP. However, this therapeutic approach represents a much bigger opportunity where insights gained from studying a rare disease can more broadly impact oral health in the general population. The strategy to improve cementum in HPP can also be applied to periodontal disease, where cementum-PDL-alveolar bone structures must be regenerated to restore periodontal function. Our central hypothesis is that ENPP1 inhibition will improve cementum repair and periodontal function in models of HPP and periodontal disease. We will test this hypothesis by two aims: (1) Test pharmacological ENPP1 inhibition to ameliorate periodontal defects in a mouse model of HPP; (2) Define ability of pharmacological ENPP1 inhibition to re-establish cementum in a mouse model of periodontal regeneration. Expected outcomes of these proof-of-principle experiments include data to support future translational work employing ENPP1 inhibition to promote cementum repair, potentially scaling up to larger animal models and different models of periodontal disease.

NSF Awards · FY 2026 · 2026-01

Geospatial data has become a cornerstone of modern society, enabling critical applications in navigation, national security, transportation, emergency response, infrastructure management, environmental monitoring, precision agriculture, and more. The rapid proliferation of smartphones, drones, satellites, and connected sensors has resulted in an explosion of geospatial data. However, the utility of this data depends on one fundamental requirement: accurate and reliable georeferencing. Without precise spatial positioning, data products become misaligned, leading to flawed analysis, compromised decision-making, and even threats to public safety and national security. The Industry University Cooperative Research Center (IUCRC) for Accurate Georeferencing of the Environment (CAGE) led by The Ohio State University with partner sites at Saint Louis University (SLU), in partnership and Purdue University (PU), is dedicated to advancing methods for accurate and reliable geo-referencing of diverse geospatial data sources. The center serves as a collaborative research hub uniting academic researchers, government agencies, and industry partners to address the technical and operational challenges in geo-referencing, sensor calibration, spatial data fusion, data quality assessment, and data-driven decision making. CAGE unites leading experts from The Ohio State University, Purdue University, and Saint Louis University to tackle three interconnected thrust areas: (1) Advanced Georeferencing providing resilient alternatives to GPS for navigation in denied environments—from Arctic expeditions to space missions, (2) Rigorous Quality Assurance and Control for industries like transportation and precision agriculture, and (3) Transformative Data Analytics by harnessing AI, machine learning for applications in smart farming, infrastructure maintenance, and autonomous systems. CAGE works closely with industry and government partners providing geospatial services and technologies across a broad range of industries. Within the geospatial community, CAGE addresses the urgent need for alternative positioning and georeferencing technologies that can serve as reliable backups in the event of GPS unavailability, exploiting expertise in promising new areas including signals of opportunity and environment-based solutions. CAGE also emphasizes research assessing and assuring the validity, accuracy, and reliability of geospatial data and derived products. CAGE addresses the entire geospatial data lifecycle, from data acquisition and curation to advanced processing, analysis, and application to ensure the highest level of accuracy throughout the entire pipeline and across different application areas. Importantly, all projects involve students and industry mentors ensuring pipeline development to address critical national workforce needs in the geospatial sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Fungi are a diverse Kingdom, with an estimated 2-3 million species, playing essential roles in ecosystem functioning and economic enterprises. Despite the importance of fungi to ecosystems and society, fungal conservation is underdeveloped compared to that for plants and animals, and is challenged by limited data on fungal biodiversity, distributions, and ecological roles. This project will study oak savannas, which are ecosystems that are being lost in the Midwest due to land-use changes and fire suppression. These land use changes have caused a process called "mesophication," where shade-tolerant tree species replace oaks. The loss of oak ecosystems also results in the loss of fungal species that live in these ecosystems. These fungi are critical for keeping ecosystems healthy and balanced because of the role they play in nutrient cycling, litter decomposition, and plant-soil feedbacks. This project will compare the effects of different management practices on fungal biodiversity. Project activities include fungal biodiversity assessments, population genomics, and the development of fungal conservation and restoration strategies. This project will also include undergraduate education in mycology and citizen-scientists in the collection of data. This research is a collaborative effort involving universities, conservation organizations, and citizen scientists aimed to insert fungal conservation into management practices for oak savannas, an endangered and culturally significant ecosystem. This research will use many indicators of fungal diversity including sporocarp surveys, metabarcoding from soil and air samples, and population genomics of key fungal taxa associate with oak savanna ecosystems. Citizen scientists and truffle dogs will contribute to collections, enhancing the project’s reach and impact. Restoration experiments will include mycorrhizal inoculations to assess their role in oak establishment and litter decay microcosms to study fungal contributions to decomposition and soil properties. Data analysis will integrate fungal biodiversity into ecological integrity indices assisted by machine learning to identify fungal indicator species and predict ecosystem health. Through this project we will develop a Fungal Quality Index (FUNQ) to incorporate fungi into ecological assessments and indices, providing actionable recommendations for fungal conservation and oak savanna restoration. By addressing these urgent needs, the project will advance fungal conservation and contribute to the restoration of oak savanna ecosystems. This research will further train the next generation of conservation scientists through hands-on experiences with conservation organizations and research, and will culminate in science-based recommendations and an online repository of data for land managers and policymakers, enhancing evidence-based fungal conservation practices. 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 2026 · 2026-01

Composite materials, created by closely bonding two or more distinct substances, are common in both natural and engineered systems. Many of these materials display significant spatial variation in properties such as electrical conductivity and dielectric permeability, and are known as high-contrast composites. Examples include filled polymers, porous media, and biological tissues, with applications in bioengineering, medical imaging, and electronics. The Principal Investigator (PI) focuses on quantitatively analyzing field concentration -- a ubiquitous phenomenon in material science that arises when material properties change drastically over a very small length scale. Gaining a deeper understanding of this effect is crucial for accurately determining the effective properties of such materials and for designing them more efficiently to enhance performance. The PI will also perform undergraduate and graduate mentorship. Mathematically, high-contrast composites are modeled by partial differential equations (PDEs) with highly oscillatory coefficients. These equations are challenging to analyze, as classical analytical techniques often fail to apply. This project aims to develop new mathematical methods to address several open problems in this area. The first focus is on the buildup of the electric field between insulators, with particular attention to the asymptotic behavior of solutions and equations involving the p-Laplacian and the Lamé system. The second objective is to study composites composed of perfect or mixed conductors, emphasizing nonlinear governing equations and systems. Finally, the project will investigate models in which the conductors are imperfectly bonded. These involve more complicated transmission conditions and boundary conditions, including Robin-type boundary conditions. Such models are also of practical importance in biology, as they serve as approximations of membrane structures in biological systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-01

Project Summary/Abstract Selective neuronal and regional vulnerability to tau protein aggregation is found in many neurodegenerative diseases including Alzheimer’s disease (AD). Understanding the molecular origins of this selective vulnerability and mechanisms underlying tau degradation and clearance are therefore of fundamental importance to elucidate the pathogenesis of AD. Previous studies from our group and others have shown that excitatory (EX) neurons expressing wolframin (WFS1) in the entorhinal cortex (EC) layer-II are a major cell type preferentially vulnerable to tau pathology and cell death compared to other layers/regions in human AD and tau mouse models. WFS1 is an integral ER-membrane glycoprotein that regulates ER stress and autophagy, two processes that interconnect and are implicated in the clearance of pathological tau. It is not clear, however, how WFS1 promotes pathological tau clearance, why WFS1 level is reduced in AD, and why reduced WFS1 makes EX neurons in EC layer-II vulnerable to tau pathology and neurodegeneration. We have recently reported that knockout (KO) of WFS1 increases tau pathology and reduces the expression of a master regulator (i.e., Transcription Factor EB, TFEB) of the autophagy-lysosome pathway (ALP) in the EC of PS19 tau mice. Overexpression of WFS1 increases TFEB and reverses the inhibition of autophagy flux. 14-3-3 proteins bind to phospho-TFEB and inhibit its nuclear translocation and the activation of ALP. Our pilot results reveal that 14-3-3 proteins interact with WFS1, suggesting WFS1 may interact with 14-3-3 proteins and release its inhibition of TFEB nuclear translocation, resulting in ALP activation. Smad ubiquitination regulatory factor 1 (Smurf1), which degrades WFS1 protein, is increased in tangle-bearing neurons in human AD, indicating increased Smurf1 may reduce WFS1 and inhibit ALP induction. Pathological tau and Smurf1 have been found to induce cell death mainly via necroptosis and correlate well with memory loss in AD. We also find that WFS1 KO increases necroptosis (also found in tangle-bearing neurons in human AD) and reduces long-term potentiation in htau knock-in mice. In addition, single nucleus RNA-seq analysis shows WFS1-high EX neurons are intrinsically enriched with genes associated with tau protein homeostasis, which can tip the proteostasis towards tau aggregation under pathological conditions with reduced WFS1. Thus, we hypothesize that both intrinsic factors (e.g. WFS1- expressing cell properties) and extrinsic factors (e.g. epigenetics and environment)-triggered disruption in the Smurf1-WFS1-14-3-3-TFEB axis contribute to selective vulnerability of WFS1-expressing EX neurons in the EC in early AD. We aim to (1) define molecular signatures underlying selective vulnerability of WFS1-expressing EX neurons in the EC; (2) investigate why WFS1 level is reduced in EX neurons and why WFS1 deficiency induces cell death and cognitive deficits in AD; and (3) determine the mechanistic role of WFS1- 14-3-3-TFEB axis in tau pathology. The proposed studies will provide significant insights into the role of WFS1 in AD, the molecular mechanisms underlying selective vulnerability in early AD, and novel drug targets for AD.

NSF Awards · FY 2026 · 2026-01

This project aims to serve the national interest by improving and testing curricula in economics education. Addressing student persistence in quantitative STEM disciplines and the limited number of rigorous randomized control trials (RCTs) investigating instructional practices in economics, this project will examine the effectiveness of a low-touch instructional intervention. A vital part of this project is the partnership among three institutions, Miami University, Texas Tech University, and Ohio State University. Building on a successful pilot study, the project aims to reshape introductory economics courses to emphasize social context. The project team intends to design an instructional intervention of curricular modules and to rigorously test their effectiveness by conducting a randomized control trial study across six public institutions in Ohio. This intervention applies core economics concepts and principles to real world contexts, as an instructional approach to relate economics to students' lives. Economics is well suited for this effort, as the economics curriculum is similar across institutions, which includes a two-semester introductory economics course sequence comprised of a Principles of Microeconomics course and a Principles of Macroeconomics course. These courses are required for students intending to major and minor in economics and are popular among non-majors seeking to fulfill distributional requirements. Courses are structured around a common teaching structure, in which core concepts and principles are taught and then illustrated through examples. Based on context-based learning theories, the instructional intervention will consist of curriculum modules that cover core concepts/principles and examples, which are relevant to the varied lives and interests of students. Each experimental module includes non-technical general news articles, class discussion questions, exam questions, and teaching resources. To support implementation, faculty professional development activities will be scheduled throughout the project, which will include the development, testing, and review of the modules. Based on its design and planned implementation, the project has the potential to significantly understand complex relationships that link instructional interventions with expected outcomes. The goals of the project are to scale-up the approach and assess the effectiveness of this promising curricular intervention, which exposes students in introductory economics courses to the principles and core concepts of economics through relatable, context-based material that connects course content to social relevance. In addition, the project intends to examine how exposure affects students' perceptions of economics and future academic and career choices, paying close attention to heterogeneous effects across population subgroups. Using survey and administrative data, the team will implement a robust plan that compares the outcomes of students randomly assigned to treatment course sections to students randomly assigned to control course sections. Additionally, by linking the state's Higher Education Information dataset with records from the Department of Jobs and Family Services, the team intends to estimate the predicted effect of the intervention not only on students' major choice, but also on their potential future income levels. The IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the project supports the creation, exploration, and implementation of promising practices and tools. 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 · 2026-01

PROJECT SUMMARY/ABSTRACT Post-transplant lymphoproliferative disorder (PTLD) is the second most common cancer and a leading cause of death in solid-organ transplant recipients. Most PTLD cases are associated with Epstein-Barr virus (EBV), a highly prevalent oncogenic virus. Our limited understanding of PTLD risk, inadequate monitoring strategies, and lack of therapeutic options undermine our ability to address PTLD-associated morbidity and mortality. While we have identified a limited number of PTLD risk factors, these factors are insufficient to predict which patients are at greatest risk. Understanding the specific factors driving PTLD risk and development remains an unmet need required to monitor high risk patients and prevent PTLD development. We hypothesize that disruptions in the balance between host T follicular helper, T regulatory (Treg), cytotoxic T lymphocytes and T follicular cytotoxic lymphocyte subsets, and their associated cytokines, IFN-γ and TGF-β, create a permissive environment for EBV lytic reactivation and B-cell transformation and that these disruptions can be leveraged as predictive biomarkers and therapeutic targets for patients with PTLD. To test this, human samples and a chimeric human-mouse model of EBV driven lymphoproliferative disorder (hu-PBL-SCID) will be utilized. Blood samples from patients who developed PTLD and samples from patients who failed to develop PTLD will be analyzed with a custom spectral flow cytometry panel allowing for the investigation of the indicated immune cell subsets and their relationship to PTLD development. Additionally, a novel and rapid EBV DNA methylation assay will be used to characterize the EBV epigenetic profile in cell-free DNA in the plasma of patients with PTLD and those without PTLD. This assay can distinguish lytic (infectious) from various latent viral states and offers valuable diagnostic information to identify PTLD early. Methylation profiles from plasma and, when available, PTLD tumor biopsies, will be compared to clinical state and contribute toward biomarker development. All epigenetic and immunophenotypic data will be correlated with clinical outcomes to identify biomarkers that predict PTLD risk. Finally, based on preliminary data showing the importance of Tregs in preclinical and clinical studies of EBV driven lymphoproliferative disease, the hu-PBL-SCID model will be treated with a novel monoclonal antibody able to reduce Treg quantity and levels of the Treg associated cytokine, TGF-β. Spectral flow cytometry and methylation studies will be performed on murine samples and survival will be analyzed to explore if treatment is able to diminish PTLD driving subsets and prevent disease. This project holds potential to shed light on PTLD pathogenesis and identify risk factors and therapeutic targets.

NSF Awards · FY 2025 · 2025-12

An offspring’s traits, such as appearance or behavior, are shaped by both its genes and its environment. Scientists now know that parents' experiences, well before offspring exist, also influence offspring traits. This transgenerational parental impact allows parents to help offspring prepare for survival in risky or stressful environments, even if the parents and offspring never physically meet, by altering how genes are expressed (turned on or off) in their offspring. Most research has focused on how mothers pass along information or cues about their environment to their young, but in reality, both mothers’ and fathers’ experiences are important. For example, if both parents experienced similar environments, their combined cues to offspring might make transgenerational plasticity more beneficial compared to the mother’s cue alone. On the other hand, if mothers and fathers experience different environments, the information they provide their offspring might be contradictory and not beneficial. This research uses both theoretical models and laboratory experiments to ask how offspring respond to maternal and paternal information that differs and whether parents develop behaviors to avoid or manage these mismatches, including choosing mates with similar experiences or changing how they care for their young. By exploring how both parents’ experiences collectively affect their offspring, this work will help us predict when offspring respond to parental cues and when they ignore them. The project will also train undergraduate and graduate students by offering long-term internships that provide students with independent research opportunities, coding workshops to train students in mathematical modeling, and seminars to prepare students for careers in science after they graduate. Biologists are particularly interested in understanding why plasticity occurs, when it is adaptive, and how it influences biological patterns. While transgenerational plasticity (TGP) can benefit offspring beyond what is possible for within-generational plasticity alone, mismatches in parental experiences (e.g., maternal cues of low predation and paternal cues of high predation) can result in traits that are maladaptive for the offspring. We hypothesize that parents can gain and respond to environmental information from mates in ways that may rescue offspring from the detrimental effects of mismatching. We will use mathematical models to understand whether TGP is more likely to arise when maternal and paternal cues match and when differential allocation of care in response to mismatching cues is possible. We will then use experiments with threespined sticklebacks (Gasterosteus aculeatus) to evaluate whether females use mate choice to reduce the frequency of mismatching maternal and paternal cues. Finally, we will empirically test whether males differentially allocate paternal care in response to maternal experience, if this differential allocation reduces the fitness costs of mismatching maternal and paternal experiences, and if it alters the ways in which TGP persists across generations. We predict that mate choice and differential allocation in response to parental cues may allow for the evolution of TGP in environments that would otherwise not select for TGP. This could explain the ubiquity of transgenerational plasticity across taxonomic groups, despite existing theory predicting it should evolve only under limited conditions. 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 · 2025-11

Viruses are the most abundant biological entities on Earth, infecting all major categories of life. To study viruses, the viral genomics community has generated enormous amounts of scientific data through advanced gene sequencing and other techniques. Exploring and extracting scientific insights from data of this size is challenging and requires accessible training that can teach the research community how to use the advanced computational tools and infrastructure needed for research. At the same time, the rapid development of novel algorithms and Artificial Intelligence-driven methods for analyzing this data outpaces current training opportunities, both for scientists who use computational infrastructure and for the research computing professionals who design and maintain these systems. A critical gap exists in equipping both groups with practical skills to leverage shared, high-performance computing systems and integrate reproducible scientific processes into the nation's advanced research computing frameworks. By helping scientists use cutting-edge, AI-driven tools to understand viruses better, this project promotes the progress of scientific understanding of viruses, their interactions with their hosts, and their effects on biological systems. The broader impacts of this research support public health, environmental understanding, and national preparedness. This work also builds a stronger research community by making training accessible to a broad range of scientists and students. The iVirus Cyberinfrastructure (CI) Training Initiative will develop six modular, self-paced, online training resources to enable effective use and development of scalable pipelines in NSF-supported CI ecosystems. The training modules will be designed around the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) software, emphasizing interactive learning, reproducibility, evolvability, and sustainable design. The modules will be open-source, portable across CI platforms, and designed to meet the diverse needs of researchers and developers working in viral ecology. Training content will be developed through a unified pipeline that includes (1) interactive instructional design, (2) modular training components, (3) expert input and curated test datasets, and (4) community engagement and dissemination. A key feature of this project is the integration of hands-on viral genomics (viromics) CI training into the annual Ohio State University Viromics Workshop, where materials will be piloted and refined through participant feedback. Together, these resources will help close the skills and knowledge gaps in viral ecology CI training, enabling a broad range of researchers and developers to accelerate discovery and innovation using NSF-supported computational resources. 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 · 2025-11

With the support of the Chemical Measurement and Imaging Program in the Division of Chemistry, and partial co-funding from the Ceramics Program in the Division of Materials Research, Professor Philip Grandinetti and his group at Ohio State University are developing new computational methods that integrate advanced nuclear magnetic resonance (NMR) techniques to reveal the atomic-level structure of non-crystalline materials such as glass. These materials, which lack the regular atomic arrangement of crystals, are difficult to characterize but are critical to technologies ranging from electronics to optics and biomedical devices. This project will create tools that allow scientists to identify and quantify the diverse atomic environments within these disordered materials, enabling the design of stronger, more durable glasses. As part of this work, the team will train undergraduate and graduate students in areas such as chemistry, materials science, and data-driven analysis, thereby contributing to workforce development in critical STEM fields. They will also release open-source, fully documented software tools (MRSimulator and MRInversion) that follow FAIR data principles—ensuring they are findable, accessible, interoperable, and reusable. These resources will expand access to state-of-the-art NMR analysis methods across disciplines. Educational outreach will include the development of online tutorials and video content to engage students and the public, promoting careers in science and improving public understanding of chemistry and materials science. Scientifically, the project will integrate two-dimensional solid-state NMR experiments with model-free inverse analysis algorithms, supported by machine learning, to extract distributions of NMR parameters such as chemical shifts and quadrupolar couplings. These distributions will be interpreted through quantum-chemical calculations that map NMR observables to local atomic motifs, allowing structural features to be identified without assuming a predefined structural model. To resolve overlapping NMR signals in complex glasses, the team will apply curve-resolution techniques to separate and quantify distinct structural contributions. One key application will be to study how phosphorus modifies the atomic structure of aluminosilicate glasses—a process used to chemically strengthen display glass. This spectrum-inversion approach represents a novel and more direct alternative to conventional structure-forward modeling and could set a new standard for characterizing atomic disorder in amorphous solids. The resulting insights may deepen understanding of how local structure governs properties like ionic transport and mechanical strength and could inform improved models for designing next-generation glassy materials. 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 · 2025-11

PROJECT SUMMARY/ABSTRACT Kaposi’s Sarcoma-associated herpesvirus (KSHV), also known as Human Herpesvirus 8 (HHV8), is the etiological agent of the human malignancies Kaposi’s sarcoma (KS), primary effusion lymphoma (PEL), and multicentric Castleman’s disease (MCD). KS is the most common AIDS-defining malignancy and represents the leading cause of cancer in sub-Saharan African men. Despite KSHV associated malignancies having a unifying variable (KSHV), no effective viral specific biomarker exists. Current viral monitoring commonly relies on crude viral PCR load levels, is not informative of viral activity, and is not currently recommended for monitoring of disease response within National Comprehensive Cancer Network (NCCN) guidelines. KSHV establishes lifelong infection and generally exists in either a latent or lytic state. Latency is the default state and is characterized by viral expression of a very limited subset of genes. On the other hand, lytic is full expression of the viral genome with production of infectious progeny. KSHV derived malignancy is thought to depend on gene products from both latent and lytic states. Therefore, understanding regulation of KSHV latent/lytic balance has long been a highly sought after goal. Epigenetic regulation of viral genes is a well-known mechanism for gene control; however, this remains an incompletely explored area within KSHV, particularly in regards to viral DNA methylation. Previous work has shown variation in KSHV DNA methylation patterns globally in cell lines and at the lytic origin of replication in primary samples. We hypothesize that KSHV DNA methylation is dynamic during latent/lytic shift and that overall patterns will be predictive of viral activity in vivo which may represent a novel biomarker. To explore KSHV DNA methylation, we have created a novel approach for high throughput multi-locus methylation investigation within KSHV samples. We propose to characterize KSHV methylation dynamics in vitro during latent/lytic states utilizing this approach. Further, we aim to validate overall KSHV methylation pattern as a novel biomarker for KSHV associated disease by analyzing primary patient samples from an internal biorepository and from a clinical trial . Collectively, the proposed studies will fill a distinct gap in our basic understanding of KSHV virology; DNA methylation dynamics during the crucial latent/lytic shift and in differing disease contexts. Additionally, we aim to generate a novel biomarker in KSHV associated disease to be utilized as a potential proxy of viral activity, disease response, and prognosis.

NSF Awards · FY 2025 · 2025-10

Continual learning (CL), also known as lifelong learning, seeks to enable artificial intelligence (AI) systems to learn new skills over time without forgetting what they already know—much like how humans continuously refine and integrate knowledge. However, a major challenge that needs to be overcome is “catastrophic forgetting,” where an AI system loses competence in old tasks when learning new ones. This project will advance fundamental understanding of continual learning by developing theoretical tools to explain and predict how knowledge is transferred and forgotten across tasks. Such insights will inform the design of new algorithms that better preserve and share knowledge over time. The broader significance lies in building more adaptive, reliable AI systems for a wide variety of applications ranging from robotics and personal assistants to scientific discovery. By strengthening the theoretical foundations of CL, the project will help ensure that AI systems can adapt in dynamic, real-world environments while remaining robust and trustworthy. The project will also help train the next generation of researchers, while broadening public understanding of AI through workshops, teaching, and outreach to K–12 audiences. This project aims to establish a rigorous theoretical framework for understanding forgetting, generalization, and knowledge transfer in CL. It has three main thrusts: (i) developing and optimizing a new class of sequential replay algorithms designed to outperform existing concurrent replay methods—especially for dissimilar tasks—and creating hybrid strategies that combine both approaches effectively; (ii) analyzing catastrophic forgetting and generalization performance in transformer-based models tackling time-varying classification tasks, with a focus on how evolving attention mechanisms govern knowledge transfer and retention; and (iii) investigating how transformers’ in-context learning ability behaves under shifts in input distributions, revealing how such shifts impact training dynamics and forgetting. Methodologies that will be developed will combine mathematical analysis, algorithm design, and empirical validation using synthetic data, real-world datasets, and large-scale models such as deep neural networks and large language models (LLMs). By delivering both provable theoretical insights and practical algorithmic solutions, the project will advance the state of the art in CL and enable the deployment of AI systems that can continually adapt, learn safely in changing environments, and maintain strong performance across diverse and evolving tasks. 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

This project develops artificial intelligence and spatial modeling methods for the extraction of architectural and geospatial data from historical records. In particular, the project automates the process of extracting data from maps of over 10,000 American municipalities that were made by The Sanborn Map Company during the nineteenth and twentieth centuries for fire insurance companies to assess potential liability. These historical maps have value for urban and regional planning, but the use of these maps has previously been hindered by the lack of tools for efficiently extracting the data. Also, the methods developed in this project facilitate the creation of longitudinal datasets that permit further scientific research on the development of cities in the United States. The project also provides training opportunities for students and researchers. Expanding on previous methods, this project develops refined techniques for extracting data from the Sanborn maps, including building footprints, construction materials, building use (e.g., residential, commercial, industrial), and the numbers of stories. A key innovation is the advancement of methods for inferring the three-dimensional architecture and structure of buildings. This approach requires extracting additional building geometry and orientation from the Sanborn maps and ancillary high resolution orthoimagery data for extant buildings, then converting this information into graph representations for analytical processing. Using this graph representation, this project clusters buildings based on morphology to identify common building templates and uses the parameters of these templates to accelerate the reconstruction of buildings with detailed geometry. This project also advances and evaluates the use of generative artificial intelligence to further automate these steps, the validity of which can be assessed through comparisons to still existing historical buildings and structured qualitative analysis by local experts. Key outputs are disseminated in accessible formats to urban and regional planners. 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

Wetlands provide vital ecosystem services despite covering only a small area of the Earth’s land surface. Earth System Models are increasingly used to inform decisions on land use and water quality conservation. However, wetlands are poorly represented because of their small size and complex interactions with surrounding rivers and landscapes. This project develops novel artificial intelligence (AI) tools that simulate wetland processes in Earth System Models. Three AI tools will be developed to simulate different aspects of wetlands for integration in Earth System Models. A transformer-based AI model will model nutrient and pollutant transport. A graph neural network will simulate wetland flow connectivity. A contrastive-learning model will predict wetland distribution from remote sensing data. The project will also support interdisciplinary education and training in geoscience and AI. It will provide new learning opportunities for students and advance public understanding of wetlands. Wetlands play a vital role in maintaining ecosystem health by storing organic material, cycling nutrients, and protecting water quality. Despite covering a small area of the Earth’s land surface, they are a critical buffer for waterborne pollutants. However, because of their small size and complex interactions with surrounding rivers and landscapes, wetlands are poorly represented in large-scale Earth System Models used to predict atmospheric circulation and manage water resources. This project will create a unified, physics-guided AI framework to improve models of wetland hydrology and biogeochemical modeling across sub-grid to watershed scales. The project will develop three AI models to represent wetlands within Earth System Models. First, a two-level transformer-based model will integrate mass balance and biochemical kinetics to predict wetland inundation and nutrient transport. Second, hydrology-aware graph neural network will use flow-conditioned attention and memory mechanisms to model wetland connectivity. Third, a contrastive learning-based model will predict wetlands in unmonitored regions using remote sensing data. All components will be integrated into a feedback system to enhance prediction accuracy, scalability, and generalizability. The models will be tested using data from the Western Lake Erie Basin and applied in other U.S. wetland regions. By embedding physical constraints into advanced AI architectures, this work bridges a key gap in Earth system modeling and enables robust, interpretable predictions of wetland function under broad environmental conditions. The project will also support interdisciplinary education and training at the intersection of environmental science and AI. Societal benefits include new learning opportunities for students and advancing public understanding of wetlands. All models and data will be openly shared to ensure accessibility and reuse. 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

Physics-Informed Neural Networks (PINNs) are an emerging class of Artificial Intelligence (AI) models that incorporate physical laws directly into their architecture, enabling fast and accurate simulations even with limited or noisy data. They show significant promise for electromagnetic (EM) simulations, particularly in managing parameter variations in real time. However, ensuring both accuracy and stability in PINN training remains a major challenge, often requiring large datasets and exhibiting sensitivity to minor input changes. To address these limitations, researchers from Stevens Institute of Technology (SIT) and The Ohio State University (OSU) are developing an Open-Source AI-Driven Electronic Design Automation (EDA) Tool for Real-Time Synthesis of Short-Distance Wireless Interconnects on Silicon (OASIS), the first open-source, AI-powered EDA tool for real-time parametric EM simulation. OASIS will explore scalable strategies for training large-scale PINNs efficiently and robustly. This research will focus on the design of short-range (~10 mm) wireless interconnects on silicon for two cutting-edge applications: (1) contactless connectors that leverage spatial multiplexing to minimize interference and enhance data throughput, and (2) batteryless brain-machine interfaces (BMIs) that depend on real-time signal cancellation and sensitivity optimization. By replacing traditional slow solvers with a faster, AI-driven alternative, OASIS aims to transform next-generation EM design. To achieve the project’s objectives, the investigators will pursue six key research directions. First, the team of researchers will develop a graph-based importance sampling framework to accelerate the training and convergence of physics-informed neural networks (PINNs) on large-scale point clouds. Second, they will implement a stability-guided training approach to enable robust and efficient parametric EM simulations using PINNs. Third, the team will design a novel proximity communication method capable of multi-gigabit data transfer in dense, low-power environments where traditional EM solvers are ineffective. Fourth, they will investigate spatial multiplexing techniques to scale interconnect bandwidth. Fifth, the project will explore a new class of wireless, batteryless brain implants that utilize signal backscattering and AI-driven leakage cancellation to improve sensitivity. Sixth, the researchers will introduce real-time adaptive specifications for brain-machine interfaces (BMIs) to accommodate dynamic environmental conditions. To broaden the project’s impact, the investigators at SIT and OSU will also develop new courses that integrate advanced machine learning concepts into software-hardware co-design education. Collectively, this research aims to advance the frontiers of millimeter-wave and RF integrated circuit design, computer-aided design (CAD), machine learning, scientific computing, and biomedical 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.

NSF Awards · FY 2025 · 2025-10

Recent years have witnessed a surge in human–machine interfaces (HMIs) due to their potential in medical, civil, and military applications. However, most current HMI systems primarily focus on biophysical input and output signals. In contrast, HMIs involving chemical cues (e.g., taste and smell) are often overlooked in the research community. As a critical component of human perception, taste significantly impacts quality of life and overall well-being. This study aims to develop a gustatory interface integrated with an Internet of Things (IoT) framework to support remote human-human interaction. The goal is to explore the design, integration, and characterization of sensors and actuators for remote sharing and transmission of taste information between users. The researchers plan to demonstrate the concept in a medical care context, where remotely transmitting taste information can provide insights into patients’ taste profiles and identify potential distortions related to diseases or treatments. Due to the interdisciplinary nature of this project, the integrated education plan will focus on fostering systems thinking skills. By collaborating with local high schools, the research team will conduct a two-semester K-12 outreach activity, the “I-CORE” program, to promote awareness between engineering, food science, and health science. The project will also introduce an undergraduate curriculum offering that bridges science, society, economics, and ethics. The project builds on the team’s experience in bio-integrated electronics and HMIs. Its three aims are: (1) Uncover the design principles of a flexible gustatory interface for taste simulation, (2) build and characterize a multiplexed electrochemical sensor chip for tastant information capture and remote control, and (3) examine and validate the performance of the IoT framework for sensor-actuator coupling and evaluate user perception through field testing. As a case study, an integrated IoT system will be progressively developed as an assistive tool to connect medical professionals with patients for remote assessment, monitoring, and intervention. The outcomes of this project will address a series of fundamental scientific questions, such as the development of flexible actuators, electrochemical sensing strategies, circuit design of IoT chips, and the integration of human factors in system performance. 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

This BCSER Individual Investigator Development (IID) project will investigate course, department and institutional variables that influence teaching assistants (TA) instructional behavior in undergraduate chemistry laboratory courses. The PI will conduct think-aloud interviews with chemistry laboratory TAs that would be used to design a survey to unpack systems that affect TA instructional behavior. The survey will be implemented nationally across various institutional types. The findings from the surveys can provide insights into TA interactions in chemistry laboratory courses, thereby supporting TA training reform and positively impact undergraduate students enrolled in these courses. The findings will be shared through the facilitation of workshops, presentations at chemistry specific conferences that will impact practitioners and researchers, and journal publications. This project will build the Principal Investigator's capacity to carry out high-quality STEM education research, with the goal of improving the experiences of students in general chemistry laboratory courses. By exploring how TAs’ noticing opportunities for meaningful learning, specifically moments that invite all students to engage with chemistry in terms of the cognitive, affective, and psychomotor dimensions, the project will allow the PI to develop foundational skills and gain practical experience in designing and implementing cutting edge STEM education research using innovative methods and tools. The PI will develop new expertise in survey development and analysis, factor analysis and Hierarchical Linear Modeling. The PI will work with a mentor in quantitative methods and survey design and an advisory board. The PI will apply these quantitative research methods to design a survey instrument to assess the interactions of TAs in the chemistry laboratory. The success of this project will be assessed through the PI's interaction with an external advisory board. The project is supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education 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 · 2025-10

Droughts and wildfires generate increasing amounts of dust and soot. Dust in the atmosphere has doubled since 1850 and soot has doubled in the last decade. Dust, soot, and other atmospheric pollutants are the biggest source of uncertainty in climate models. Long-term observations are especially lacking in the Arctic region. Ice cores from Arctic glaciers are the best way to fill gaps in climate data for the Arctic. This project will reconstruct a detailed 800-year history of fires, dust, sea ice, and Arctic glaciers. It will use the only existing ice core ever recovered from Franz Joseph Land, Russia. This comprehensive project will constitute a baseline for future observations of pollutants entering the Arctic atmosphere. This project will train the next generation of Arctic scientists and will engage local community partners to deploy air samplers in Ohio to provide air chemistry data, highly valuable to health and urban planners, and to the public. The intensifying wildfire activity worldwide and the emission of black carbon (BC) aerosols have negative consequences. This project will investigate a unique ice core archive from Franz Joseph Land (FJL) in the western Russian High Arctic. It will test the hypothesis that environmental changes have favored the transport of aerosols towards the Barents-Kara region of the Arctic ocean. The project will reconstruct 800-year high resolution records of aerosol deposition by analyzing the concentration and deposition fluxes of BC and trace elements (TEs), distinguishing different sources. The team will also trace the provenance of the FJL ice core dust by determining its mineralogical and geochemical composition. They will resolve the statistical relationship between these new “paleo-aerosol” time series and the Barents-sea-ice proxy records previously extracted from the FJL ice core. This study will yield a highly comprehensive “paleo-aerosol” dataset essential for glacier energy balance models and for paleofire danger models. The FJL BC record will help constrain the inventory of Arctic methane, while the detailed dust geochemical and mineralogical data will help calibrate radiative forcing models. All time series (i.e., BC, TEs, dust) will serve as fundamental references for Arctic ice core microbiological 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 · 2025-10

The Center on Responsible AI and Governance (CRAIG) brings together leading academics, businesses, and government agencies to develop the knowledge and workforce required to pursue artificial intelligence that is safe, accurate, impartial, and accountable. Expert faculty from four universities (Ohio State, Baylor, Northeastern, and Rutgers) and disciplines ranging from computer science to law, business, and the social sciences, work with industry and government partners to identify the most pressing responsible AI challenges and develop scalable solutions and the workforce required to implement them. By advancing trustworthy and responsible AI, CRAIG increases the acceptance and adoption of AI, thereby promoting competitiveness and national security. CRAIG will generate practice-informed research and forward-looking workforce development. CRAIG project teams investigate core questions such as how to design more accurate and safe generative AI systems; which AI governance technologies and strategies best achieve social and business goals; how to audit deployed AI models; how to provide privacy and transparency in smart, sensed environments; and how to engage constructively with ethical challenges pertaining to AI. The research teams’ multiple disciplinary lenses enable them to perceive how technology, law, ethics, and business management interact with and impact one another, and so to generate fresh and practical insights and translational solutions. Ohio State University leverages its deep expertise in AI auditing, explainability, law, and governance to strengthen CRAIGs research-based solutions. In combination with its research efforts, CRAIG educates and trains the next generation of responsible AI professionals. The Center provides students with research experiences, internships, “real world” mentors, and curricular innovations, and current employees with continuing and executive education, in the new and growing field of responsible AI. Ohio State leverages its strength in curricular development and experiential and executive education to help realize these educational aims. CRAIG’s research and educational pillars provide businesses and governments with the knowledge and workforce development approaches required to achieve safer and more trustworthy AI. Thus, the project contributes to the building of a more sustainable, prosperous, and secure AI economy. 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 There is a critical need to intervene with individuals affected by substance use disorder (SUD) earlier in their trajectory, even before they present to a location where screening or care could be possible. Rapid diagnosis and linkage to treatment (secondary prevention) is a cornerstone of any epidemic response but often very delayed for SUD. For communicable infectious diseases (ID), health departments employ disease intervention specialists (DIS) skilled in outreach to underserved and disproportionately affected populations identified via social contacts, who once located, disclose their own contacts, yielding “snowball” chain-referral for the most direct, rapid, and proactive approach to identify affected, but undetected, populations. SUD also exists within social networks and substance exposure spreads like contagion, but the DIS paradigm has not been adapted to the problem of SUD. With support from Funding Option B (develop, implement, and rigorously evaluate strategies), we will (AIM 1): Develop and implement promising operational models for adapting the conventional ID DIS model to provide secondary SUD prevention: In close collaboration with health department partners, we will create three service models: 1) Overlay of SUD intervention on current ID DIS: systematically screen clients (already being identified by ID diagnosis or exposure) for concurrent SUD; 2) SUD DIS: SUD specific DIS elicit social contacts with potential SUD from index clients with SUD who are identified in collaboration with other local service agencies; and 3) SUD DIS with peer-support: Peers embedded with the SUD DIS team. Model 1 will be operated separately from the SUD DIS program, with randomization by day to Model 2 or 3. In all 3 models, DIS will provide secondary prevention to those SUD screen+ (i.e., high risk score on NIDA-Modified ASSIST) and not already in care. (AIM 2): Rigorously evaluate models for incorporating SUD prevention into DIS programs. We will compare program-level outcomes using (i) data from DIS records, (ii) summary intake counts from regional SUD treatment centers, (iii) time and motion observations of DIS activities, and (iv) qualitative interviews with stakeholders (clients and staff) about the DIS program. We will also prospectively observe clients (n=400 each DIS program, for total n=1200) receiving DIS intervention who consent to research to enable comparison of the effectiveness of SUD secondary prevention provided by the different DIS program models. Assessment: Self-report at baseline (0),1, and 6 months, complemented by review of health records (via release of information) and vital statistics (i.e., mortality). Primary Outcome: severity of drug use at 6 months post initial DIS interview. Key Secondary Outcomes: participant characteristics; healthcare/service utilization; alcohol use; and overdose. This innovative, high-impact investigation will be the first to use this well-established public health approach, capitalizing on social networks, to offer early and more specifically targeted secondary prevention for SUD. Results will vastly improve public health practice, program planning, policy, and future intervention.