Ohio State University

NSF Awards · FY 2025 · 2025-01

While rapid advances in digital technologies have revolutionized modern living, they have also increased the complexity and frequency of cyber threats. To address this ongoing challenge, the Buckeye SFS program at The Ohio State University will prepare a cohort of cybersecurity experts with advanced degrees to serve national security interests. By offering a comprehensive curriculum and hands-on learning opportunities, the project will train students to contribute to the federal cybersecurity workforce. The program will equip graduates with advanced knowledge and practical experience to secure critical systems, enable them to drive innovation, and lead the cybersecurity field. The project will recruit a wide range of students, including those from diverse backgrounds, to ensure a sustainable and robust cybersecurity workforce for the next generation. The Buckeye SFS program will emphasize advanced graduate training through the accelerated BS+MS, MS, and PhD programs. The program will offer an integrated curriculum encompassing both solid theories in cybersecurity and applied cybersecurity fields, such as network security, system security, cryptography, and secure autonomous vehicles. The project will provide extensive opportunities to engage students in a wide spectrum of cutting-edge cybersecurity research ranging from hardware to software, large cyber-physical systems to ubiquitous Internet of Things devices and large-scale networked distributed systems. It will also prepare doctoral graduates as researchers and educators in higher education, to build capacity for expanding the pipeline of scientific talent. Through outreach and community-service activities, the project will spotlight cybersecurity issues for the community, introduce educational opportunities, and stimulate public awareness with the long-term goal of changing people's security and privacy perceptions and improving their security behaviors in daily life. This project is supported by the CyberCorps® Scholarship for Service (SFS) program, which funds proposals establishing or continuing scholarship programs in cybersecurity and aligns with the U.S. National Cyber Strategy to develop a superior cybersecurity workforce. Following graduation, scholarship recipients are required to work in cybersecurity for a federal, state, local, or tribal Government organization for the same duration as their scholarship support.  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 · 2025-01

Modified Project Summary/Abstract Section Bisexual people are disproportionately affected by depression and suicidal ideation (SI) relative to both heterosexual and gay/lesbian people. Rates of depression and SI are highest during adolescence and young adulthood, coinciding with peaks in stressors. Despite evidence that bisexual people outnumber gay/lesbian people and experience a greater mental health burden, bisexual people remain underrepresented in research and there are major gaps in our understanding of their risk for depression and SI. In particular, the mental health disparities affecting bisexual people begin in adolescence, but little is known about developmental trajectories of depression and SI in this population. By examining the heterogeneous trajectories of depression and SI among bisexual adolescents and young adults, we can identify those at greatest risk and pinpoint critical periods when depression and SI peak and increase most rapidly to determine when and with whom to intervene. Further, most prior studies of bisexual people’s mental health have been cross-sectional studies of adults, limiting our understanding of developmental changes in depression and SI in this population, risk and protective factors related to such changes, and underlying mechanisms. Last, people of color (POC) and females are especially likely to identify as bisexual, and they experience unique risk and protective factors, but little is known about the unique influences on depression and SI in these subgroups. Given these gaps, to better understand and address the mental health disparities affecting bisexual people, the proposed R01 will use an accelerated longitudinal design to examine risk for depression and SI from adolescence to young adulthood among bisexual people. Compared to a traditional longitudinal design, which follows a single group for the entire age range of interest, an accelerated longitudinal design recruits multiple groups, each starting at a different age. It is ideal for examining developmental change because of its ability to span the age range of interest in less time than would be possible with a single group. We will recruit a cohort of 500 bisexual people with equal proportions of participants ages 14-23 (n = 50 per age; 50% male, 50% female; 25% White, 25% Black, 25% Hispanic, and 25% other POC). Data will be collected at 5 biannual assessments (baseline, 6-, 12-, 18-, 24-months) and used to accomplish three specific aims: (1) Identify heterogeneous trajectories of depression and SI from adolescence to young adulthood among bisexual people; (2) Examine risk factors for depression and SI and their underlying mechanisms across development; and (3) Examine protective factors as buffers of the associations between risk factors and depression and SI. The proposed R01 will provide essential insights into developmental changes in risk for depression and SI among bisexual people, which is critical for developing tailored interventions to reduce the mental health disparities affecting this population.

NSF Awards · FY 2024 · 2024-12

The emergence of practical applications for quantum information is being hailed as the second quantum revolution due to its potential to transform applications in computing, communications, and sensing. At the root of this revolution is the ability to harness the property of quantum entanglement between two or more quantum systems, or qubits, to push performance beyond the single-state quantum limit. This quantum testbed will work to realize this performance for solid-state quantum sensors capable of measuring molecular structure and dynamics at the single molecule level, validating quantum advantage in applications ranging from materials characterization, to catalysis, to drug discovery. The testbed will coordinate between the quantum creator and end user communities to develop a roadmap to quantum advantage, aligning research activity with critical needs in science and industry. Students participating in the testbed will be mentored within a highly interdisciplinary and convergent environment. The emerging roadmap will be used to inform the development and dissemination of curricular material that will lay the foundation for training the next generation of quantum scientists and engineers in partnership with the QuSTEAM Initiative. Specifically, the testbed is developing a platform for exploiting the entanglement of multi-qubit ensembles to achieve quantum advantage in the measurement of molecular and solid-state systems, including structural, electronic, and dynamic degrees of freedom. This platform allows for the modular deconstruction of the quantum sensor into three fundamental units: (1) a set of molecular targets, (2) a spin-relay layer that directly couples to both the target and readout, and (3) a readout qubit. The power of this modular approach can be seen in the fact that the spin-relay layer can be selectively driven into a metrologically relevant entangled state to enable sensing beyond the standard quantum limit (i.e., sensitivity scaling that surpasses sqrtN). Further, this modularity provides a framework for structuring collaboration and co-design between stakeholders including end users, system manufacturers and quantum researchers across academia, government, and industry. This project advances the objectives of Quantum Information Science and Engineering at NSF in response to the National Quantum Initiative Act for the continued leadership of the United States in QIS and its technology applications. This project is jointly funded by the NSF National Quantum Virtual Laboratory program and the Division of Molecular and Cellular Biosciences in the Directorate for Biological 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 2024 · 2024-12

As the amount of data being generated is rapidly increasing and many technologies keep scaling up, more and more applications are relying on cloud servers for high-performance computing and data processing. The scalability offered by cloud computing allows users to meet performance goals without maintaining expensive infrastructures. In particular, the booming applications of artificial intelligence and increasingly complicated neural networks for higher precision make offering inference services and/or training neural networks a major application of cloud computing. In many cases, the user data contains private information, such as patient images for medical diagnosis, genome samples for sequencing, and financial data for analysis. User data can be protected against eavesdropping by cryptography schemes during the transmission to and from cloud servers. However, if traditional cryptography schemes are utilized, decryption must be carried out before any computation is possible and the cloud server will get access to user private data. Privacy-preserving cloud computing and machine learning are enabled by the new homomorphic encryption (HE) technique, which allows computations to be directly carried out on encrypted data. Using HE, the cloud server does not gain any information on the user data and the computation results are encrypted. However, the achievable speed of HE implementations is far from being practical despite previous research efforts. This project aims to speed up HE implementations by orders of magnitude, expanding the privacy-preserving capabilities of cloud providers. This project pursues scalability improvements by taking into account the specific computations involved in applications and integrating algorithmic reformulations with hardware architecture design. Such application-aware cross-layer design approaches can enable unprecedented complexity reduction. For the first time, new HE operators implementing combined computations with much reduced complexity will be investigated. In addition, the overall complexity of homomorphically encrypted neural networks will be further reduced by developing new techniques that enable the sharing of intermediate results and more efficient packing of data into ciphertexts. The algorithmic reformulation, new operator design, and computation optimization are carried out jointly with the corresponding hardware architecture design to truly speed up HE in real implementations. As a result of this project, HE hardware implementations achieving practical speed with low complexity will be developed for deep neural networks. The new designs can be also extended to other domains and will have significant impacts on privacy-preserving medical diagnoses, genome sequencing, data analytics, and many other applications. 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 · 2024-12

ABSTRACT Acute respiratory distress syndrome (ARDS) causes respiratory failure, affects more than 200,000 people annually, and has a mortality rate approaching forty percent. There are no molecularly targeted treatments for ARDS and clinicians utilize life-support with mechanical ventilation (MV) to give patients time to recover. Although lifesaving, MV can cause injury known as ventilator-induced lung injury (VILI). One mechanism by which MV induces VILI is through pulmonary surfactant dysfunction. We have identified a protein, annexin A2 (AnxA2), that is phosphorylated during VILI and is known to play a role in surfactant release. We hypothesize that injurious forces during VILI lead to phosphorylation of AnxA2 and impaired surfactant function by inhibiting actin bundling at the site of lamellar body fusion in alveolar type 2 (AT2) epithelial cells. Aim 1 will define the mechanism by which AnxA2 phosphorylation impairs surfactant function using a murine pre-clinical ARDS model. Aim 2 will determine how AnxA2 phosphorylation regulates surfactant release from AT2 cells using a novel, human ventilator-on-a-chip device. The scientific goal of this proposal is to identify novel molecular targets to treat or prevent surfactant dysfunction during ARDS and ameliorate the harmful effects of MV in critically ill patients. The research approach has been structured to support a training plan that will provide training for the principal investigator (Dr. Bentley) in several skills, including (1) becoming proficient in the fabrication and use of microscale models of the alveolar micro-environment and primary human lung cell isolation and culture, (2) the generation and use of cell-specific genetically modified mice, and (3) gaining expertise in murine models of lung injury. Additionally, Dr. Bentley will participate in didactic courses, workshops, and national conferences to build skills in grant writing and networking. The proposed work will also support the preparation and submission of 1-2 first-author manuscripts to build a publication portfolio. The training plan and mentorship committee assembled in this proposal will assist the principle investigator (Dr. Bentley) in attaining the skills and mentorship necessary to submit a competitive NIH career development (K-series) award at the end of the award period. Taken together, this proposal will assist the PI in attaining his goal of becoming an independent physician-scientist with expertise in acute lung injury.

NSF Awards · FY 2024 · 2024-12

X-lites network builds international collaboration at the frontiers of laser science and applications.The invention of the laser in 1960 enabled a wide range of scientific and technological advances, from communications to surgery to facial recognition to analysis of Martian soil. Laser science and technologies continue to advance, and a new generation of lasers are accessing frontiers of laser-matter interactions at the highest intensities, the fastest times, and the shortest distances. The Extreme Light in Intensity, Time, and Space (X-lites) network builds on these technological advances to organize and support scientists around the world as they research and apply this extreme light. X-lites is a new global network of networks that unites the work of scientists across disciplines and geographic borders to support the next generation of scientific advances, scientific workforce training, and skills development. X-lites accelerates discovery and invention through its support of team building, knowledge sharing, and education across disciplinary and national borders. X-lites applies the Science of Team Science (SciTS) to build on existing networks of researchers developing and applying extreme light. X-lites exploits prior US and European investments in research facilities that provide access to cutting edge extreme light capabilities. The X-lites network foundation is a coupled, two-layer network concept that has proven to be effective in forming impactful networks. One networked layer links 10 user facilities/collaborations, and the second layer links these facility's users. Users are a diverse community of researchers taking advantage of infrastructure investments to explore questions in physics, chemistry, biology, materials, and more. X-lites hosts virtual seminars and workshops, conducts courses on effective team science for early career researchers, funds the exchange of early career staff between facilities, and brings together researchers across disciplinary boundaries to explore scientific frontiers. These activities intentionally engage early career and diverse researchers (i.e., diverse demographics, scientific specialties, and geographic locations) who are the future of these fields and who will drive X-lites and extreme light science, technologies, and applications forward. 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 · 2024-12

Abstract Suicide is a leading cause of death in the United States. Patients who have recently been discharged from an inpatient psychiatric hospitalization are at particularly elevated risk for suicide. Although safety planning interventions (SPIs) are provided to many patients to alleviate risk, the success of these approaches relies on patients' capability to remember and apply these strategies amidst severe emotional distress. Unfortunately, a large proportion of patients do not use their plans in the post-discharge period, and many that do use their SPIs report that they do not find them to be helpful or go on to make a suicide attempt. The precise reasons for these findings are unclear. A critical knowledge gap lies in understanding the individual-specific triggers, contexts, and timing of suicide risk states that signify SPI use may be clinically indicated, and the conditions that can potentiate or inhibit the efficacy of SPIs. That is, a) whether patients are successful at identifying changes in suicide risk that would necessitate the use of SPIs or b) whether clinicians are recommending the use of these plans in a manner that supports suicide risk reduction, is unclear. To address these critical knowledge gaps, we will follow 240 patients at high-risk for suicide who have completed a SPIs in a naturalistic observational study. We will collect multimethod data from a combination of ecological momentary assessment, wearables, and periodic clinician ratings of risk together with advanced analytic approaches to intensively characterize the contexts within which patients are most likely to use and benefit from their SPIs. We will leverage these data in novel, Bayesian change point detection models to identify clinically dissociable suicide risk states that indicate worsening in momentary risk, even the absence of explicit information about suicidal ideation, to help clinicians refine warning signs to help patients recognize when SPI use is clinically indicated. Additional models will examine whether factors such as inflection points in risk, patient characteristics, and patterns of SPI usage impact the degree to which patients benefit from plan use. Our proposal aligns precisely with the NIH's focus on precision medicine, representing a pioneering application of technological assessment and computational modeling in suicide prevention research. In the short-term, our primary objective is to equip clinicians with the information needed to maximize the impact of SPIs. In the longer-term, our findings can inform the development of digital health strategies that use SPIs to effectively address risk in real-world settings, thereby contributing to a pragmatic, data-driven solution to the public health emergency of suicide prevention. The ramifications of this work are vast and meaningful: improving clinical practice, enhancing the delivery of SPIs, including eventually through digital health strategies, and ultimately, driving down suicide rates. This project bears the potential to effect monumental, life-preserving changes in the realm of suicide prevention, showcasing the transformative power of integrating cutting-edge technology into mental health care.

NSF Awards · FY 2024 · 2024-12

This doctoral dissertation project uses archaeobotany, the study of ancient plant remans, to examine the relationship between agricultural production systems, the local environment, and socio-political organization. The project tests whether pre-existing models of hydraulic agriculture accurately represent small-scale agriculture in dry summer oases which necessitate irrigation to support agricultural production. The project tests whether these dry summer oases require complex hierarchies to organize irrigation by reconstructing agricultural production, environment, and socio-political organization using archaeological plant remains. The project further explores the relationship between agricultural production and the local environment. Archaeological plant remains are well suited to address the impact of local environmental change on agricultural production systems as well as social aspects of agricultural production systems such as harvesting schedules, labor organization, and grain storage. This research integrates multiple lines of data to address these questions, including ancient microscopic and macroscopic plant remains in comparison to environmental proxies and the relevant archaeological record. Dissemination of results will be accomplished through peer reviewed journals and an open access platform. The project fosters participation through undergraduate student training in archaeobotanical methods and presents opportunities for students to participate in firsthand archaeological research. The research team analyzes archaeological plant remains, including carbonized and mineralized plant remains (seeds and plant parts) and phytoliths (microscopic silica bodies that form within and between plant cells), and integrates environmental proxies including wood charcoal and pollen data with the archaeological record. These archaeological plant remains serve as proxies to reconstruct the ancient environment and to study past agricultural production systems during transitions between important past eras. This transition is marked by broad environmental change in multiple locations. The doctoral student seeks to answer the central question of how agricultural production systems were adapted to known periods of environmental change as along with socio-political organization. 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 2024 · 2024-12

Project Summary Each year, as many as 250,000 individuals tear their anterior cruciate ligament (ACL) in the United States, with over 75% undergoing an ACL reconstruction (ACLR). Despite completion of formal rehabilitation, a significant portion of young individuals demonstrate persistent functional deficits at the time of return to pre- injury sports participation and up to 30% of young, active individuals sustain a second ACL injury within months of returning to sports participation. Traditional biomechanical methodologies assess individual components of the movement system. However, the application of nonlinear methodology may uncover the underlying mechanisms between the components of movement faults in a way that has the potential to uncover new information about how the entire movement system interacts with its environment. There are many implications for nonlinear methodology in rehabilitation research. As physical therapists, we are movement experts and tailor our treatments to improve and enhance our patients’ abilities to move effectively. It is possible that the ability to responsively tailor movement to the demands of the context in which they are imbedded may prevent injury, whether that be an athlete sustaining a second ACL injury or an elderly individual sustaining a fracture due to a fall. The innovative research described in this proposal has the potential to advance current knowledge in ACL research and inform clinical care for athletes returning to sports after an ACL injury as well as in other clinical populations. My goals for the training period include refining skills in rigorous research study design, applications of nonlinear methodology and systems science theory, leadership and research dissemination and professional development. The proposed research will take place at Ohio State University at the Martha Morehouse Pavillion and the Jameson Crane Sports Medicine Institute. Both the Sports and Tactical Athlete Injury Resiliency Science Laboratory in the Martha Morehouse Pavillion and the Jameson Crane Sports Medicine Institute have dedicated faculty and staff members to ensure accurate and streamlined data acquisition with dedicated time for student research with a multidisciplinary environment housing surgical suites, physician/surgeon’s offices, a physical therapy clinic, surgical skills laboratory, and dedicated research office space with data analysis software to provide a productive environment for analysis, interpretation and manuscript development. The environment affords many collaborative interactions and ample resources to complete all data collections outlined in this fellowship application. My sponsorship team consists of four university faculty members (3 at Ohio State University and one at the University of Cincinnati). My sponsors have diverse areas of expertise that are directly in line with their defined areas of contribution to the research and specific roles and responsibilities over the course of the fellowship period. At minimum, the full mentorship team will meet monthly and individual or sub-group meetings will occur weekly and bi-weekly.

NSF Awards · FY 2024 · 2024-11

The mission of the Center for Industrial Metal Forming (CIMF), which is comprised of Ohio State University (OSU), Oakland University (OU), and University of New Hampshire (UNH), is to perform cutting-edge, pre-competitive fundamental research in metal forming science and engineering. Metal forming processes are widely used in automotive, aerospace, defense, electronics, appliances, and biomedical industries and play an essential role in generating significant economic impact and attaining global market competitiveness. Transportation, defense, aerospace, household and biomedical industries consume and process large quantities of metals in forgings, extrusions, and sheet metal components. New and future demands in metal forming will require new material processing methods, innovative tool designs, new lubricants, automation, AI, integration with computing resources, and intelligent sensors to improve quality, minimize variability, and reduce the amount of scrap for lightweight and high strength materials. Significant needs and challenges exist with respect to computational and materials modeling, developing innovative forming processes using state-of-the-art technologies, and creating equipment and die innovations to enhance the forming of metals. If addressed, these advancements would lead to considerable product performance, manufacturing, and societal benefits. CIMF activities will strengthen the US manufacturing industry and facilitate rapid development of new metal forming technologies by conducting industrially-relevant and challenging projects that couple fundamental and applied research. CIMF will collaborate closely with its industrial Members to prepare work-ready professionals for the metal forming industry through academic programs and industrial training to improve the knowledge and skill base for these critical industries. Advancement in material utilization and broader implementation of lightweighting alloys from CIMF research will help to protect the environment from CO2 emissions. Diversity with respect to underrepresented groups in CIMF research and educational efforts will be achieved through proven programs and outreach activities. OSU will focus on material suppliers and automotive industries, due to the concentration of companies in these areas in the Midwest U.S. CIMF will drive innovation and competitiveness in U.S. industry by conducting transformational research in novel forming processes, Integrated Computational Metal Forming, advanced equipment and die technologies, the application of sensors and the Industrial Internet of Things (IIoT) in metal forming, and artificial intelligence (AI)-assisted materials modeling. This will necessitate an interdisciplinary approach with experts from manufacturing engineering, electrical engineering, materials science, computational methods, AI and data analytics, and experimental mechanics. The industrial Members of CIMF include original equipment manufacturers, components suppliers, material suppliers, and machine builders. Vertical, fundamental advances will be achieved by employing innovative approaches in sheet metal/tube forming, forging, and extrusion, improving material formability, advancing methods for virtual process design and simulation, and employing new lubricants and metal forming equipment. Specifically, projects target process innovation, forming control based on IIoT, energy-efficient forming machines, etc. The results will be advancements in material utilization and weight reduction, final part performance, industry-friendly computational tools for process design, metal forming dies with extended life, and industrial metal forming processes of increased productivity, across a range of advanced material systems. OSU has unique facilities and research expertise with respect to process innovation and modeling, materials development and testing, constitutive modeling, and data analytics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Neutrinos are unique messenger particles that can carry information about some of the most energetic astrophysical sources in the Universe. The IceCube Neutrino Observatory (ICNO) has very successfully detected astrophysical neutrinos, and discovered sources that expand our knowledge of physics at very high energy scales. There is more to discover, but a larger detector is needed to discover neutrinos at even higher energies. Measuring both cosmic rays and neutrinos at extreme energies will answer questions about both particle physics and astrophysics. This is the purpose of the Radio Neutrino Observatory in Greenland (RNO-G) which looks for the flash of radio emission caused by an extremely energetic neutrino slamming into glacial ice. RNO-G’s full detector will cover dozens of square kilometers; at present, seven of 35 stations are taking data, and more are being built every summer. The greatest challenge is to classify and reject radio backgrounds, especially signals created by cosmic ray particles. This work involves critical simulation and classification development for RNO-G, efficiently separating out the neutrino signal. Building on knowledge from other fields, including machine learning and glaciology, the tools developed for this project will improve our ability to detect neutrinos with RNO-G. The included education and outreach efforts will make this research accessible to a broad range of scientists and to the general public. Over the course of the project, the team at Ohio State will compare leading time-domain classification techniques and build a library of analysis tools. This work will: (1) develop high-end simulated models including neutrino- and cosmic ray-induced radio emission, (2) classify the flux of cosmic rays into in-air and in-ice categories, likely detecting in-ice Askaryan emission for the first time, and (3) use machine learning to categorize backgrounds with much higher efficiency. The team will conduct both data-driven and simulation-driven studies of cosmic ray signals, comparing to neutrino simulations to optimize classifiers, and thus maximize the separation between neutrinos and backgrounds. Developed tools will enable a future successful neutrino search. This study will transform RNO-G into a true neutrino observatory for energies above 10 Pev, perhaps even above 1 Eev. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The proposed research aims to address critical challenges in fine-grained information flow control (IFC) within software systems. Traditional access control methods have been inadequate in preventing improper data flow, especially as systems become increasingly complex. This project seeks to make fine-grained IFC practical and widely deployable in today’s computing environments. The research promises to significantly improve the way software systems manage confidentiality and integrity, ultimately resulting in a much more secure cyberspace. The resulting IFC systems will be available as open source tools. Users of Rust will have the benefits of sound IFC analysis. The results will be folded into course and curriculum to enhance cybersecurity education. Specifically, the research focuses on the Rust programming language, leveraging its unique features to build a novel IFC system. First, a new static IFC library, tailored to Rust, will be designed to minimize programming model restrictions while effectively controlling side effects -- an elusive goal in mainstream languages. Second, an innovative hybrid static–dynamic IFC model will be introduced, harmonizing the strengths of both static and dynamic checks to achieve optimal performance. This innovative hybrid system will be interoperable with vanilla Linux, providing a seamless translation of OS-level permissions and policies. Finally, the project leverages advanced program analysis and synthesis techniques to automate the annotation process, thereby easing the developer burden and fostering a more secure coding environment. 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 2024 · 2024-10

Project Summary Sclerostin is a potent inhibitor of bone formation and has been shown to be a valuable drug target for treating osteoporosis. Mechanistically, sclerostin functions by binding to LRP5/6 on the osteoblast lineage cells to antagonize canonical Wnt signaling, thus negatively regulates bone formation. Presumably, after sclerostin is secreted by osteocytes, they reach the target osteoblasts at the bone surfaces by diffusion. However, to date it remains unknown how secreted sclerostin is regulated on the cell surface and in the extracellular matrix. To address this significant gap in sclerostin biology, we focus our study on sclerostin–heparan sulfate (HS) interaction. HS, a universal glycosaminoglycan found at the cell surface and in the extracellular matrix, is known to bind sclerostin and might regulate the bioactivity and diffusion of sclerostin. Our central hypothesis is that HS can regulate the biological functions of sclerostin in bone formation. To test this hypothesis, our overall objective here is to elucidate how HS interacts with sclerostin and how HS–sclerostin interaction regulates bone formation. We plan to pursue the following two specific aims: Aim 1. Determine the biological significance of HS–sclerostin interactions in vitro. We postulate that HS helps concentrate sclerostin on the osteoblast cell surface and facilitates the binding of sclerostin to its receptor LRP5/6 by forming ternary complex. We also hypothesize that HS serves a storage depot of sclerostin on the cell surface of osteocytes after it is secreted, and protects it from proteolytic degradation. We will manipulate HS–sclerostin interactions biochemically at the surface of both osteoblasts and osteocytes to determine the mechanisms by which HS regulates the function of sclerostin in these cellular contexts. Aim 2. Determine the role of HS–sclerostin interaction in bone formation in vivo. Our working hypothesis is that dampening HS–sclerostin interactions impairs the inhibitory potency of sclerostin towards LRP5/6, which leads to enhanced bone formation. Using a novel sclerostin knock-in mouse strain, we will examine the consequence of altering HS–sclerostin interactions in bone formation in vivo. Our contribution will be significant because we will identify multiple molecular mechanisms by which HS regulates sclerostin and elucidate how such interactions regulate bone formation. Results from the proposed experiments will substantially advance our understanding of the cellular regulation of sclerostin on both osteoblasts and osteocytes by elucidating the role of HS in the system. Importantly, these results are expected to have positive translational impact because by identifying how HS regulates the bioavailability of sclerostin, we may be able to provide new perspective for manipulating sclerostin in bone diseases.

NSF Awards · FY 2024 · 2024-10

With support from the NSF Division of Chemistry, the NSF National eXtreme Ultrafast Science facility (NeXUS) is a first-of-its-kind laser user facility that provides broad user access to cutting edge tools for studying ultrafast processes in molecules and materials. Scientific challenges that are being addressed by NeXUS include the ability to produce a molecular “movie” of a chemical reaction and the ability to master information transport on the atomic scale to create new quantum information technologies. As such, NeXUS represents a focal point of interdisciplinary collaboration among researchers spanning chemistry, physics, materials science, biology, and engineering. In the past, the lack of access to ultrafast research infrastructure has not only limited the capabilities of US science, but has presented a major challenge to development of the workforce that is needed to maintain the competitiveness of US research and education. By addressing these challenges, NeXUS fills a key strategic gap in the US research infrastructure. NeXUS directly responds to the community-identified grand challenges of observing and controlling energy transport on the scale of individual electrons and atoms. To accomplish this goal, the NeXUS facility allows direct observation of electron motion with attosecond to femtosecond time resolution, angstrom spatial resolution, and element-specific spectral resolution. At the heart of NeXUS is a kW-class ultrafast laser that produces XUV and soft x-ray light by high harmonic generation. The combination of attosecond pulses, soft x-ray photon energies, and high repetition rate enables measurements at NeXUS that can only be made at a handful of places worldwide. Combining this cutting-edge light source with state-of-the-art analysis end stations and a team of professional support staff allows NeXUS to support a dynamic, open-access user program that levels the scientific playing field by providing researchers from all career stages and institutions access to the most advanced characterization tools available worldwide for ultrafast science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Glacial erosion produces large quantities of sediment that can change the chemistry of surrounding land and ocean ecosystems. While the nutrients underneath glaciers are known to be important for nearby biological communities, comparatively less is known about the chemistry and importance of glacier surfaces and snowpack, which can trap dust - small particles of rock that are deposited by wind and with snowfall. The dust is darker than ice and snow and therefore can warm during sunny periods and melt the surrounding frozen water, generating small amounts of liquid water. During cloudy and cold periods, solar radiation can no longer heat the particles and the liquid water around the dust freezes again. These thawing and freezing cycles can break down the dust and release nutrients, such as iron, which can potentially be used by organisms in the ice or can be transported to streams, lakes, and/or the ocean during periods of high melt. This research will combine computer modeling and laboratory experiments to understand 1) what happens (chemically and physically) to glacier and snowpack dust during freezing and thawing and 2) how to model freezing and thawing of water and dust in glacier ice. Two traveling exhibits exploring the connections between science and art will result from this project, allowing for diverse audiences to connect with the Antarctic continent and understand how small-scale science influences large-scale systems. The results of this study will determine the geochemistry of glacial meltwater due to freezing and thawing, and whether the meltwater contains critical nutrients for surrounding ecosystems. Despite low temperatures and the relative scarcity of liquid water, glacial systems can be a major source of trace metals, nutrients and other weathering products to proglacial and marine systems. While the importance of weathering has been established in subglacial and proglacial environments, less is understood about weathering mechanisms or the composition of major and trace nutrients at the most upstream source: within snow and supraglacial ice. Wind deposits fine-grained sediment on ice surfaces, which can then melt or become incorporated into the ice profile and experience a range of thermal regimes and freeze-thaw conditions. Daily freeze-thaw cycling drives physical and chemical weathering of sediment grains, yet few studies have explicitly examined the frequency and intensity of freeze-thaw cycles and how they control major ion and trace metal release, alteration, and mobility. This interdisciplinary study will use geochemical and energy balance modeling, freeze-thaw experimentation, and scanning electron microscopy to advance knowledge of mineral weathering in ice and snow active layers. Existing samples collected from the McMurdo Dry Valleys of Antarctica, an ecosystem that relies on runoff derived from supraglacial ice and snow melt, will be utilized. Two traveling exhibits exploring the connections between science and art will result from this project, the first focused on connecting the macro-scale Antarctic continent to micro-scale microscopy images, and the second a contemporary art exhibit that will explore the Antarctic continent and our perceptions of scale. Findings from this research will contribute to knowledge of nutrient bioavailability and delivery to proglacial environments and polar oceans, watershed-scale weathering in glacial systems, and the conditions that create microsites for life on glaciers and other icy systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Non-Technical Abstract: This project aims to improve our understanding of East Antarctica's solid Earth, ice sheet dynamics, atmosphere, ionosphere, and sea level by enhancing real-time data retrieval using GPS and Iridium technology. By upgrading existing and deploying new GPS receivers across previously unmonitored regions, the project will enable daily data uploads to funded NSF data repositories, ensuring immediate access to crucial information. These efforts will significantly expand the availability of GNSS data, supporting studies on tectonic motion, glacial isostatic adjustment, and ice sheet mass changes. The project also addresses gaps in atmospheric and ionospheric research, impacting climate predictions and space weather studies. By making data freely accessible through NSF data repositories, this initiative promotes global collaboration and enhances our ability to monitor and understand Antarctica's environmental changes. Technical Abstract This proposal aims to enhance observational capabilities in East Antarctica through the deployment of Iridium communications systems and upgrade of Australian GNSS networks in Antarctica, facilitating near-real-time data retrieval and analysis at a fraction of the cost of US deployment of similar GNSS systems. Approximately 10 existing GNSS sites will be upgraded with new Iridium cards, while 20 new receivers will be deployed to expand coverage into previously unobserved regions. Daily data downloads to NSF funded data repositoris will provide immediate access to GNSS observations to US investigatiors, crucial for studying tectonic motion, glacial isostatic adjustment, and ice sheet mass changes. The project will validate geophysical models and improve estimates of ice sheet mass balance, particularly in regions underrepresented in current datasets. Furthermore, the initiative will advance atmospheric and ionospheric research, contributing to climate modeling and space weather predictions. Collaboration with international partners will ensure data interoperability and strengthen global scientific efforts in Antarctic research. The broader impacts include societal benefits through improved sea level rise predictions and enhanced resilience against climate change 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 2024 · 2024-10

Solving society’s biggest problems, will require input from engineers and non-engineers, who have different backgrounds and ways of thinking. A good engineer must know how technology works but even more importantly, how technology might affect people and society. This means that engineering faculty need to prepare their students for working well with others and to respect their ideas. In order for engineering faculty to know how to best help their students, this project will conduct this research study to explore what engineering students believe about non-engineers when they are working together to solve problems in a community. This will be accomplished by 1) investigating different ways of determining what engineering students believe about those who are not engineers, 2) grouping students’ beliefs into categories that allow exploration of trends, and 3) working together with a team of students, community partners, and teachers to brainstorm ideas on how students can learn more about themselves. This will help make future professional engineers better, by showing students the importance of treating those who are not engineers with respect and valuing their ideas. The goal of this research study is to make sure that members of society do not experience negative impacts because of what engineers think or believe. The intent is to ensure that the work engineers do to help society is not unintentionally hurting communities or treating people who are different from them unfairly. This research will help engineering faculty learn more about what their students believe about non-engineers, which can help them teach in a way that truly makes engineering work benefit all of society by improving the way engineers solve problems. Since the professional socialization of engineering students commonly fosters the belief that engineers’ scientific approaches to problem solving are superior to other ways of thinking, it is important that engineering education provides students with explicit opportunities to reflect on, and learn to be critical of, such beliefs, a process known as reflexivity. This research will produce new knowledge on qualitative methods for effectively accessing implicit beliefs in engineering education. Service-learning in engineering provides an educational context to investigate this phenomenon as it explicitly positions students to engage with others in socio-technical contexts. With an overarching goal of fostering egalitarian beliefs about the value of diverse perspectives in all engineering students, this project will investigate the context of service-learning. Within this context, this project will produce knowledge that enables engineering educators and other researchers, both in and out of service-learning contexts, to access important constructs of professional formation through the contribution of a nuanced characterization of the beliefs held by engineering students. Our analysis will reveal the beliefs for engineering students that are both 1) commonly held, and 2) varied, enabling the identification of beliefs that exist at a broad, cultural level in engineering as well as beliefs that can be understood as implicit outcomes of students’ lived experiences. Lastly, the collaborative inquiry component of this project will produce recommendations for how these implicit belief patterns can be used as inputs for enhancing service-learning. This is a meaningful contribution in that it can inform the programmatic implementation of service-learning experiences and serve as evidence for the development of instructional tools that support enhancing service-learning curricula through promoting reflexivity in engineering students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This Transitions to Excellence in Molecular and Cellular Biosciences research grant will support implementation of cutting-edge molecular techniques into the research and teaching program of a mid-career scientist allowing her to adopt empowering technologies that are not accessible in her current research environment. Rapid advances in molecular techniques have facilitated widespread taxonomic characterization of microbial communities. Current research is quickly moving beyond just characterizing diversity (who is there?), towards a focus on linking individual species with their functional properties (what do they do?). However, the study of some microbial communities remain relatively unexplored with respect to diversity or function. This is particularly true for benthic biofilms (i.e., assemblage of algae, bacteria, and fungi) in freshwater ecosystems. The overarching goal of this research is to use molecular techniques to bridge the gap between microbial community composition and functioning within natural environments. Findings from this research will make significant contributions to an understanding of, and ability to, link microbial diversity with ecosystem function. By providing data from natural microbial communities to publically available repositories, this project will facilitate improved taxonomic resolution and genomic library construction. These data may also be useful beyond their ecological application given that identification of novel microbial associations in natural environments could facilitate the design of synthetic microbial communities as a tool for industrial cultivation and biotechnology. In addition to charting new conceptual ground in molecular ecology, this project will provide research experiences for graduate and undergraduate students from under-represented groups. Many ecosystem processes (e.g., carbon cycling) are mediated by microorganisms and understanding how microbial functions scale up to the ecosystem level is an important goal in ecology. Boreal peatlands provide a model ecosystem to examine relationships between microbial structure and function owing to their role in global carbon storage. In this project, researchers will use molecular techniques to examine both the eukaryotic and prokaryotic diversity of the biofilm microbiome and identify functional traits along a hydrologic gradient and relate changes in microbiome structure and function to peatland carbon dioxide (CO2) emissions. Biofilm composition and metabolism are strongly influenced by differences in hydrologically mediated environmental conditions with consequences for net CO2 emissions. Conditions that promote a higher proportion of autotrophic (algae) biofilm results in greater CO2 uptake from the atmosphere, whereas a biofilm dominated by heterotrophic microorganisms (bacteria and fungi) promotes greater CO2 emissions. The composition of autotrophic and heterotrophic components of the biofilm are intricately linked and perturbations to one portion of the biofilm community can cascade through the rest. Therefore, it is anticipated that changes in gene expression that control metabolic functions within the autotrophic component of the biofilm will be reflected in the make-up and functioning of the heterotrophic component of the biofilm and vice versa. This project is expected to reveal the influence of environmental conditions on gene expression within the autotrophic and heterotrophic components of the biofilm. Further, this research is likely to facilitate the discovery of correlated patterns of abundance between certain eukaryotic and prokaryotic microbes, and link trait-mediated metabolic functions at the community level. Using metagenomic approaches to evaluate how abiotic and biotic interactions shape microbial communities and microbial-mediated biogeochemical processes addresses a critical knowledge gap in the field of aquatic microbiology and will provide a better understanding of how aquatic microbes participate in biogeochemical cycling within peatlands. Methodological procedures will be integrated into college curriculum, providing opportunities for a diversity of students to gain exposure to ecological molecular techniques and applications that will prepare them for the increasing use of applied microbiology in industry, environmental monitoring, and management careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Employers across the United States are facing a crisis of labor supply, driven in part by changes in the working conditions employees desire and the wages they perceive as fair. Simultaneously, prospective homebuyers are faced with rising costs and fierce competition for housing – with aging adults, veterans, minorities, and low-income families hit hardest by rising home prices. This project seeks to understand how people assign value in these types of transactions (wages, homebuying), focusing on how individuals respond to competition and market changes. The project team is partnering with financial education programs on money management and first-time homebuyers to understand how differences between people – such as their tolerance for risks or willingness to delay purchases until a future date – predict their success on job and housing markets. The findings will allow us to better understand the psychology of value, create educational resources to help people adopt better strategies for finding jobs or housing, and identify public policy interventions that can alleviate housing and labor shortages. Across a series of experiments, this project tests computational models of pricing in multi-alternative, multi-attribute settings. These experiments manipulate simulated market competitiveness, delays, risks, and attributes of the choice alternatives in order to examine the dynamics of price-setting among individuals and test the cognitive mechanisms of pricing models. To improve the accessibility and efficiency of pricing model fitting and comparison, the project embeds them in neural networks trained to map observed pricing data onto generative model parameters. These will be added to online tools, teaching materials, and workshops to make them widely available. The pricing models will be used to estimate risk and delay aversion, bias, and pricing dynamics in multi-alternative choice experiments. The model parameters will also be used to predict the outcomes of job and home searches among participants in financial education programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Although crucial for advanced Artificial Intelligence (AI) applications due to their language understanding and generation capabilities, Large Language Models (LLMs) are energy intensive. This project’s goals and novelty are to enhance the efficiency of training and inference associated with LLMs by leveraging emerging high-speed networks and computing architecture. The project’s broader significance and importance are to (1) enable a broad range of LLMs to efficiently operate, advancing AI applications at a low energy cost; (2) strengthen international research collaboration between U.S. and India researchers; and (3) provide educational opportunities for graduate students. This project addresses the energy efficiency challenges of LLMs by optimizing their energy consumption in heterogeneous Compute Express Link (CXL)-enabled hardware environments. By leveraging High-Performance Computing (HPC) middleware and the high-bandwidth, low-latency features of CXL, the project aims to ensure sustainable and efficient AI operations. This project seeks to find solutions to the following set of fundamental issues in training and using LLMs at scale: 1) identifying and characterizing idleness in the LLM workloads; 2) using the knowledge of long idleness to insert low-overhead Dynamic Voltage and Frequency Scaling (DVFS) control and undervolting to save static energy consumption; 3) designing CXL-aware and energy-efficient Message Passing Interface (MPI)-based communication runtime for LLM training and inferencing; and 4) studying the overall impact of the integrated systems on the energy consumption of LLM training and inference. The results are disseminated to collaborating organizations to impact their HPC/AI software applications and hardware chip designs, promoting broader societal advancement through improved technological capabilities. 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 2024 · 2024-09

The overdose and substance use disorder epidemic has had devasting consequences for families since parental substance use disorders put children at elevated risk of abuse and neglect To address this dual crisis, 53 Ohio county child welfare agencies are implementing Sobriety Treatment and Recovery Teams (START), an effective intervention for improving parents’ access to substance use disorder treatments (including Medication Assisted Treatment), sobriety, and reunification with their children. However, 1/3 of Ohio START teams (mostly rural or Appalachian) stopped serving families because of high turnover among case workers and family peer mentors. Family peer mentors are peer supporters with lived substance use disorder recovery and child welfare histories who help parents navigate across systems, but often leave their position after feeling unsupported and stigmatized within child welfare agencies. Supervision coaching has the potential to improve supportive supervision practices in child welfare agencies, and create healthy work environments that support staff retention, and continued implementation which is needed to address the overdose crisis among families. This project will test the effectiveness of a supervision coaching strategy to promote workforce stability and START implementation while also creating conditions for strategy sustainment in Ohio’s child welfare system. This Hybrid Type III implementation-effectiveness study will test the impact of CrOss System Technical Assistance for Retaining Staff (COSTARS), a supervision coaching strategy for child welfare supervisors focused on supportive supervision practices, destigmatizing substance use disorder, and improving collaboration between child welfare and substance use disorder treatment systems. Building on our earlier research and partnerships, the goal of the R61 phase is to refine and build the COSTARS model. R61 Aim 1: We will tailor COSTARS to local supervisor, family peer mentor and caseworker needs by convening a community workgroup, conducting a supervision needs assessment, and developing a manual and fidelity measure for COSTARS. R61 Aim 2: Through a series of in-person and virtual didactic training, coaching, and fidelity feedback sessions, we will train COSTARS coaches (a peer coach paired with an expert supervision coach) to competency. The goal of the R33 phase is to investigate the real-world effects of COSTARS. Using a stepped wedge cluster-randomized implementation trial design, COSTARS will be delivered to 40 Ohio START teams in 4 waves. R33 Aim 1: Drawing on annual survey data from all staff members, we will examine the effects of COSTARS on perceptions of their work environment, and retention. R33 Aim 2: We will analyze program records from 1300 parents expected to participate in START to examine the effects of COSTARS on service timeliness, implementation fidelity, and parental outcomes. The proposed study is significant because we will build child welfare system capacity to address the opioid epidemic and address treatment inequities in rural and Appalachian communities, where overdoses and child maltreatment are especially high. This study is part of the NIH’s Helping to End Addiction Long-term (HEAL) initiative to speed scientific solutions to the national opioid public health crisis. The NIH HEAL Initiative bolsters research across NIH to improve treatment for opioid misuse and addiction.

NIH Research Projects · FY 2025 · 2024-09

The overdose and substance use disorder epidemic has had devasting consequences for families since parental substance use disorders put children at elevated risk of abuse and neglect To address this dual crisis, 53 Ohio county child welfare agencies are implementing Sobriety Treatment and Recovery Teams (START), an effective intervention for improving parents’ access to substance use disorder treatments (including Medication Assisted Treatment), sobriety, and reunification with their children. However, 1/3 of Ohio START teams (mostly rural or Appalachian) stopped serving families because of high turnover among case workers and family peer mentors. Family peer mentors are peer supporters with lived substance use disorder recovery and child welfare histories who help parents navigate across systems, but often leave their position after feeling unsupported and stigmatized within child welfare agencies. Supervision coaching has the potential to improve supportive supervision practices in child welfare agencies, and create healthy work environments that support staff retention, and continued implementation which is needed to address the overdose crisis among families. This project will test the effectiveness of a supervision coaching strategy to promote workforce stability and START implementation while also creating conditions for strategy sustainment in Ohio’s child welfare system. This Hybrid Type III implementation-effectiveness study will test the impact of CrOss System Technical Assistance for Retaining Staff (COSTARS), a supervision coaching strategy for child welfare supervisors focused on supportive supervision practices, destigmatizing substance use disorder, and improving collaboration between child welfare and substance use disorder treatment systems. Building on our earlier research and partnerships, the goal of the R61 phase is to refine and build the COSTARS model. R61 Aim 1: We will tailor COSTARS to local supervisor, family peer mentor and caseworker needs by convening a community workgroup, conducting a supervision needs assessment, and developing a manual and fidelity measure for COSTARS. R61 Aim 2: Through a series of in-person and virtual didactic training, coaching, and fidelity feedback sessions, we will train COSTARS coaches (a peer coach paired with an expert supervision coach) to competency. The goal of the R33 phase is to investigate the real-world effects of COSTARS. Using a stepped wedge cluster-randomized implementation trial design, COSTARS will be delivered to 40 Ohio START teams in 4 waves. R33 Aim 1: Drawing on annual survey data from all staff members, we will examine the effects of COSTARS on perceptions of their work environment, and retention. R33 Aim 2: We will analyze program records from 1300 parents expected to participate in START to examine the effects of COSTARS on service timeliness, implementation fidelity, and parental outcomes. The proposed study is significant because we will build child welfare system capacity to address the opioid epidemic and address treatment inequities in rural and Appalachian communities, where overdoses and child maltreatment are especially high. This study is part of the NIH’s Helping to End Addiction Long-term (HEAL) initiative to speed scientific solutions to the national opioid public health crisis. The NIH HEAL Initiative bolsters research across NIH to improve treatment for opioid misuse and addiction.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Idiopathic inflammatory myopathies (IIM) are a group of inflammatory disorders characterized by muscle weakness and are associated with significant morbidity and mortality. Inflammation and muscle injury are the central histology features observed in IIM muscle. Both adaptive and innate immune responses are involved in the pathogenesis of IIM but the pathogenic mechanisms are not yet well defined. Current treatment options for myositis are limited and focus largely on the use of broad-spectrum immunosuppressive drugs that often lead to significant complications. Previous studies with synaptotagmin VII null (SytVII-/-) mice displayed impaired membrane repair capacity and developed mild myositis at two months of age, suggesting that antigen presentation of internal skeletal muscle proteins may play a role in initiating or exacerbating myositis. We generated a more robust model of inflammatory myositis by combining the SytVII-/- model with scurfy mice that have a regulatory T cell deficiency (FoxP3-/Y/SytVII-/-). Adoptive transfer of lymph node cells from FoxP3-/Y/SytVII- /- mice into Rag-1-/- mice lacking both T- and B-cells results in significant muscle inflammation. This finding also links the progression of myositis with defects in plasma membrane repair. The plasma membrane repair response is a conserved cellular response necessary to restore membrane integrity in myocytes and other cells as part of normal cellular physiology. Defects in membrane repair are linked to a variety of muscle diseases. Our previous work helped identify specific intracellular proteins as critical components of the membrane repair process. This application builds on our recently published work and new preliminary studies that identified novel autoantibodies in myositis patients against multiple proteins that are essential for the membrane repair. We also established that these autoantibodies can alter the membrane repair capacity of skeletal muscle. We hypothesize that compromised membrane repair leads to exposure of these membrane repair proteins to the extracellular space and that the autoantibodies produced against these proteins further compromise membrane repair and exacerbate the inflammatory response in myositis. Recent results support this hypothesis as increasing the levels of the antibodies in a myositis mouse model does elevate the pathologic hallmarks of myositis. We will test this hypothesis with three specific aims. Aim 1 will define the mechanistic role of autoantibodies against membrane repair proteins in myositis. Aim 2 will determine if patient autoantibodies directed against membrane repair proteins are sufficient to induce myositis. Aim 3 will test the efficacy of increasing membrane integrity of skeletal muscle as a novel therapeutic strategy to treat myositis. Our findings that compromised membrane repair contributes to the development and/or progression of myositis suggests a potential therapeutic approach for IIM.

NIH Research Projects · FY 2025 · 2024-09

Project Summary: One in three adults over the age of 65 years has hearing loss, which has significant negative communicative, cognitive, and social consequences. Although cochlear implants (Cls) restore access to sound, enormous individual differences in effective speech communication - a hearing health behavior typically assessed clinically with measures of speech recognition and hearing-related quality of life (HR-Qol) - are observed in adult Cl users. Further, older adults are at greater risk for poor communication outcomes. Our long-term goal is to better understand social networks and speech communication in new adult Cl users following rapid restoration of hearing through Cls, and the social, cognitive, and linguistic processes that support effective speech communication, in order to better understand underlying mechanisms and to guide clinical interventions. New adult Cl users improve their communication ability largely through everyday interactions and relationships with communication partners within their social networks. We propose that social networks represent important and potentially malleable factors that contribute to the communication ability of adult Cl users. Broadly, adult Cl users present a unique opportunity to study social parameters following rapid restoration of hearing. However, we do not have a good understanding of how Cl use impacts social networks, both in terms of social network structure and the interpersonal processes that occur within them, as well as how these social factors relate to communication ability. Therefore, the objectives of the proposed research are to characterize the effects of Cl use on social network structure and interpersonal processes in middle-aged and older adult Cl users following implantation, and to assess in what ways and why individual differences in social networks relate to communication outcomes. Our central hypothesis is that Cl use impacts both social network structure and interpersonal processes, and that social networks and communication ability have a bi-directional relationship over time. Aim 1 will characterize social network structure and processes among middle-aged and older adult Cl users both prior to implantation and after 12 months of Cl use. We will also compare Cl users' social networks to those of normal-hearing (NH) peers and evaluate the influence of age on social networks. Aim 2 will determine the degree to which social network structure and processes explain long-term clinical communication outcomes (i.e., speech recognition and HR-Qol) at 12 months of Cl use. Aim 3 will investigate cognitive compensation as a potential mechanism underlying the longitudinal and bi-directional relationship between social networks and speech recognition, over the first 12 months of Cl use. We will specifically evaluate the use of predictive sentence context in sentence recognition and its relationship to social network structure across individual Cl users. The proposed research will provide unique insights into the interplay between hearing loss, speech communication, and social parameters, and will provide a scientific basis for the development of novel social network interventions to optimize speech communication in older adults with hearing loss and Cls.

NIH Research Projects · FY 2025 · 2024-09

Project Abstract: Breast cancer (BC) is the second most frequently diagnosed cancer among women (second only to skin cancer)[11,12] and up to 80% of survivors experience chemotherapy-induced neuropathy (neuropathy)[14] which causes pain[15,16], falls[17–19], difficulty walking[18,20,21] and diminished quality of life in survivorship [16,20]. To restore mobility and sensation, interventions that address motor control, patient- reported symptoms, and motivation to participate - in combination - are most likely to succeed. Thus, we propose to test the effect of partnered Adapted Argentine Tango (Tango) as an ideal physical activity intervention to simultaneously target restoration of mobility and alleviation of symptoms through musically-entrained movement. Tango is moderate intensity social dance, adapted for individuals with mobility deficits, that delivers sensorimotor and cardiovascular training to rhythmic music. Tango promotes survivor participation through creative engagement and caregiver inclusion. Previous research by the investigators established Tango as feasible for aging survivors (up to 82 years) to engage in with high satisfaction and improvements to CIN-related neurosensorimotor deficits. Therefore, we propose to conduct a multi-center, Phase II, randomized clinical trial investigating Tango in 140 BC survivors with CIN. Our overarching hypothesis is that Tango is associated with improved patient-reported sensation and clinically-measured dual task performance (primary) as well as improved neuromotor control, falls incidence, and patient-reported outcomes (PROs) (secondary) relative to standard of care (SOC) due to response of mediators including inflammation, neurotoxicity, and cognitive load (exploratory) Our rationale: Tango combines the effects of rhythmic auditory stimulation on neuromotor stability; physical activity practice on sensory, functional, and cardiovascular fitness; with social motivation to participate in treatment. Our long-term goal is to optimize prevention and treatment of neuropathy utilizing non- invasive, patient-centered treatment to improve sensory relief and function more definitively and more quickly for more people. Toward this goal, BC survivors with CIN will be randomly assigned to Tango, twice per week, for an 8-week period versus SOC for 4wks followed by one-way crossover to Tango for 8wks. Using an intent-to- treat approach, this innovative pilot study will determine impact of Tango on sensory symptoms (primary AIM1), dual task function (“walking and calculating”; primary AIM2); and explore treatment effects on biomarkers of neurotoxicity and inflammation as well as cognitive load(AIM3). This project will expand our pilot work (R21- AG068831) into a multicenter clinical trial designed to investigate the effect of social dance on sensory relief, functional restoration, and key mechanisms of action among a diverse cohort of BC survivors suffering from chronic CIN-related deficits.