Ohio State University

NIH Research Projects · FY 2025 · 2024-08

TITLE Biochemical properties and implications of NRAS mutant-specific BRAF interactions in melanoma ABSTRACT Extracellular growth factors promote cellular proliferation, motility, and survival through a complex network of signal transduction pathways. Thus, mutations in these pathways can cause inappropriate cellular proliferation and lead to diseases, such as cancer. RAS, an intracellular hub for multiple signaling pathways, is mutated in 20-30% of all human cancers. While the three RAS isoforms (H-, K-, and N-RAS) share a high degree of similarity, each RAS-driven cancer type is enriched for mutations in a specific RAS isoform, codon (12, 13, or 61), and amino acid. We do not fully understand the mechanism driving this observed selectivity, although each RAS mutant has distinct biochemical and functional properties. Elucidating the mechanisms underlying these mutational preferences could help identify the features of oncogenic RAS required to initiate cancer in different tissue types. To address this knowledge gap, we have focused on the selection of specific NRAS mutants in melanoma. Our work has shown that common melanoma-associated NRAS mutants (Q61R, K) promote MAPK signaling through increased activation of BRAF homo- and hetero-dimers. New molecular dynamics simulations suggest that conformational properties, specific to the NRAS mutants that drive melanoma, facilitate BRAF binding. Here, I will test the hypothesis that structural differences between NRAS mutants determine their ability to outcompete autoinhibitory BRAF interactions, drive enhanced MAPK>ERK activation, and alter the potency of RAF inhibitors. To test my hypothesis, I will use a variety of in vitro biosensors, cell-based signaling assays, and mouse models to define the mutant-specific features of NRAS that facilitate BRAF interactions (Aim 1) and how the structural determinants of different NRAS mutant-BRAF interactions influence BRAF inhibitor sensitivity (Aim 2). Successful completion of these studies will enhance my knowledge of structural biology, therapeutic development, and mouse models of cancer. I will also identify mutant-specific NRAS-BRAF interfaces to guide the design of novel therapeutic approaches for NRAS-mutant cancers and provide information relevant to the clinical implementation of next-generation RAF inhibitors.

NSF Awards · FY 2024 · 2024-08

The GENETIS and Nebulous projects aim to provide a nexus point for collaborations from astrophysics, other areas of science, and industry to design instruments for optimal scientific outcomes that may not be achievable with human engineering. Initially, they have focused on genetic algorithms and have already evolved antennas for neutrino astrophysics applications. This group travel award supports attendance by team members at a “Blue Sky Studies” workshop and follow-up GENETIS meeting to be held at CalTech in Pasadena, CA, August 12-14. The goal of this workshop is to develop a plan for broadening and streamlining the use of AI for the design of instruments, with a focus on applications in astrophysics. By standardizing tools developed by the GENETIS and Nebulous projects, the expectation is that the design phase of experiments will become more efficient, while also increasing scientific impact. The meeting will create a prioritized roadmap for developing AI for instrument design, understanding the needs of future missions and experiments, and the limitations of AI. GENETIS has an exemplary record as a launch pad for diverse undergraduate and graduate students, including those from minority-serving institutions. This interdisciplinary team adds new perspectives to every aspect of the work and provides rich learning opportunities for early career researchers. 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 · 2024-08

This project aims to serve the national interest by reforming college-level chemistry teaching through an instructional coaching program developed by and for graduate student instructors. Instructional coaching addresses the need for long-term instructor support that attends to the complexities of teaching. This collaboration between the University of Michigan (U-M) and The Ohio State University (OSU) will engage in Institutional and Community Transformation (Level 2) by changing departmental structures that guide, incentivize, and sustain reform-based instruction. The project is significant because of its potential to effect change in chemistry classroom interactions. By working with graduate student instructors and leveraging feedback from undergraduate students, this project will advance our understanding of how teaching and learning can be enhanced within the moment. Expected outcomes will include (1) robust pedagogical training models at both U-M and OSU, (2) the generation and dissemination of a video library that curates authentic chemistry teaching-learning moments, and (3) improved undergraduate learning outcomes and chemistry experiences. This project will study how an iterative research-based intervention supports pedagogical noticing and, as a result, influences undergraduate students' chemistry experiences. The scope of the project will improve how one observes, reasons, and responds throughout instruction via a coaching cycle that uses recorded sessions of graduate instructors' teaching. The project goal is to develop college instructors as reflective practitioner-researchers who can analyze and reinvent their own and their peers' pedagogy. Teacher growth, informed by the Interconnected Model of Teacher Professional Growth framework, will be determined using interviews, surveys, and recorded instruction in naturalistic settings with graduate student instructors and undergraduate students. To evaluate undergraduate students' learning, the project will leverage classroom observations, survey responses to understand undergraduate perceptions, interviews to explore undergraduate insights on research-based practices, and course grades to assess the outcomes of the project. This project will unpack the nuances of effective teaching that can significantly improve how college-level instructors learn pedagogy and undergraduate students learn chemistry. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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 · 2024-08

The rapid growth of autonomous driving systems (ADS) has resulted in two sets of new tasks for vehicle computing and control: 1) object detection and trajectory planning tasks that run machine learning algorithms mainly on on-board GPUs, 2) corresponding vehicle motion control tasks that run mainly on embedded Electronic Control Units (ECUs), such as path tracking. This project's novelty is an integrated ECU-GPU feedback real-time scheduling framework that helps ADS tasks meet their stringent deadlines, despite unexpected runtime execution time variations, while achieving 1) the maximum possible computing precision (and thus minimum tracking errors) for ECU-based motion control tasks, and 2) the highest possible recognition accuracy (and thus maximum safety) for GPU-based object detection and planning tasks. The project's broader significance and importance are its potential impacts on the designs of future autonomous vehicles, by substantially improving the timeliness of ECU and GPU-based ADS tasks, thus considerably enhancing the vehicle roadway safety and reducing the numbers of vehicle crashes, injuries, and fatalities. The new research challenges introduced by ADS cannot be properly handled by existing real-time scheduling solutions, because 1) ADS task execution times can vary significantly at runtime, 2) ADS heavily relies on GPUs that have more complex architectures than CPUs. To address those challenges, this project has several major research thrusts. First, it designs a two-tier ECU real-time scheduling algorithm that dynamically lowers the ADS execution times (with minimum precision degradation) within the allowed ranges for real-time guarantees. Second, it ensures the response times of GPU-based tasks are shorter than the desired deadlines, by adapting the GPU resources allocated to ADS tasks co-located on the same GPUs. Finally, this research jointly controls both ECU and GPU-based ADS tasks, as an integrated feedback real-time scheduling framework, to meet the end-to-end deadlines for all vehicle computing and control tasks. As autonomous vehicles are gradually becoming parts of our everyday lives, this timely project may produce findings that can offer effective approaches to substantially improving the real-time performance of ADS, thus enhancing the driving safety of future vehicles equipped with ADS. Dissemination and outreach are also planned to benefit industrial ADS 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 · 2024-08

Although processes are available for participating in policy implementation, citizen participation is rare because of the complexity and obscurity of administrative procedures performed by federal agencies. This CAREER research and teaching agenda aims to strengthen the relationship between citizens and their governing institutions. The investigator will conduct a series of town hall events that will provide the foundation for deliberative, interactive discussion between legislators and citizens that educates and informs constituents and their representatives about how agencies are implementing policies, the impact of these policies on constituencies, and how administrative processes work. This project will achieve the following objectives: (1) examine how legislators interact with federal agencies to influence policy and represent their constituencies outside of their formal legislative functions, (2) conduct a series of congressional town hall events to ascertain how they might be used to strengthen constituents’ understanding of agency regulatory processes, their trust in government, and their participation in policy implementation, (3) determine whether legislators’ participation in the town halls facilitates their ability to intervene with agencies to advocate on behalf of their constituents on related issues. This study will integrate unique sources of observational data with large-scale field experiments to bridge the study of institutions and citizen behavior and trace the linkages between citizens, legislators, and agencies. 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 · 2024-08

NON-TECHNICAL SUMMARY This project aims to gain a fundamental understanding of the unique deformation behavior of metallic glasses (MGs). MGs possess excellent properties, such as extremely high strength, corrosion resistance, and unique magnetic characteristics, which can be harnessed for potential applications in various fields, including nanotechnology, electronics, and aerospace. Broader manufacturing and application of MGs require a better understanding of the atomic structure of MGs (i.e., how the atoms are arranged inside the material) and how this arrangement changes when force is applied, which defines the way MGs deform when in use, typically under various stress conditions. This project seeks to explore the details of MGs' atomic structure and gain critical insights into how to control this structure to obtain desired properties that can be utilized for many important applications in science and industry. By employing cutting-edge techniques such as time-resolved 4-dimensional scanning transmission electron microscopy (4D-STEM), machine learning-assisted data analysis, and computer simulations, the research team is mapping and monitoring the atomic structures within MGs and tracking how they change over time and under stress. A particular focus is given to understanding what types of atomic arrangements can lead to significant variations in the material's response to stress. The deformation process involves the softening of local volumes of material, a process that makes these volumes easier to deform as the deformation progresses. This research is investigating the detailed mechanism of this softening behavior and how it relates to the local atomic arrangements within the material. This work is providing crucial insights into why some MGs exhibit better ductility and resistance to failure than others, which will pave the way to harnessing this knowledge to design MGs with improved mechanical properties, making them more reliable for practical and industrial applications. The findings from this research are being integrated into undergraduate and graduate curricula, enhancing the educational experience for students. The project also includes outreach activities that are being conducted at local K-12 schools to inspire and educate young minds about materials science. Moreover, the project is offering internships to community college students from diverse backgrounds, providing them with hands-on research experience and encouraging their pursuit of STEM careers. TECHNICAL SUMMARY This project investigates structural heterogeneities and variations in shear transformation zone (STZ) properties to understand the softening behavior, autocatalysis, and strain localization in metallic glasses (MGs). The research integrates time-resolved 4-dimensional scanning transmission electron microscopy (4D-STEM), machine learning-assisted data analysis, atomistic simulations, and mesoscale STZ dynamics modeling. The core hypothesis is that the autocatalytic shear activities and resultant deformation localization in MGs are influenced by intrinsic structural heterogeneities and the softening behaviors of local atomic environments over time. To validate this hypothesis, the project is: 1) Performing 4D-STEM on MGs with slight compositional differences to identify dominant medium-range ordering (MRO) structures, their relaxation times, and evolution pathways. 2) Using machine learning to analyze the angular correlation functions from 4D-STEM data, determining the types, volume fractions, and spatial distributions of MROs. 3) Extending 4D-STEM to the time domain to track thermal relaxation of MRO symmetries and relate these changes to STZ activation energies. 4) Connecting experimental data to atomistic models to reveal atomic arrangements within MROs, using potential energy landscape analysis to determine how different MROs impose different barriers for STZ activation. 5) As well as integrating the activation energy information and other STZ properties into mesoscale simulations to investigate how heterogeneous distributions of local structures and STZ activation energies influence softening behavior and shear localization during deformation. This research is examining MGs that have undergone various thermomechanical treatments to understand how ageing and rejuvenation affect local structures and softening behaviors. The results are providing detailed insights into the complex interplay between atomic-scale structures and macroscopic mechanical properties in MGs, contributing to the development of MGs with improved ductility by mitigating autocatalysis and promoting dispersed shear band activities. This project is jointly funded by the Division of Materials Research’s Metals and Metallic Nanostructures (MMN) and Ceramics (CER) 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.

NIH Research Projects · FY 2026 · 2024-07

Project Summary/Abstract Suicide rates among Black youth have risen markedly in recent decades, now exceeding those among White youth by over 60%. Black youth also exhibit the highest rates of Emergency Department (ED) visits for suicidal ideation and self-harm compared to all other racial and ethnic groups. While the causes of these concerning trends are likely multifactorial, there is increasing interest in examining how broader structural and environmental factors may contribute to mental health risk among youth. One such factor is hyper-surveillance, defined as intensive monitoring and engagement by law enforcement, including frequent or intrusive police stops. Research has identified police contact as a potential social determinant of youth mental health, with evidence linking such contact to increased psychological distress, anxiety, depression, and trauma-related symptoms. However, there remains a significant gap in understanding the potential relationship between law enforcement practices and suicide-related outcomes. To address this gap in research, we propose to examine the relation between police stops and (i) ED visits for suicidal ideation/self-harm and (ii) suicide mortality among youth (outcomes). We propose a multi-state, county-level examination of the relation between police stops and suicide-related outcomes (ED visits for suicidal ideation/self-harm, suicides) across 10 US states (AZ, FL, KS, MA, MN, NC, NJ, NY, SC, WI), from 2006 to 2019 using national, high-quality datasets and advanced econometric methods. We will also leverage the unique case of the New York Police Department's (NYPD) stop, question, and frisk (SQF) policy as a natural experiment towards causal examination of the ecological relation between the rate and volume of police stops and suicide-related outcomes among youth in New York City (NYC). In 2013, a federal court ruled NYPD’s SQF policy unconstitutional (Floyd, et al. v. City of New York, et al., 2013), and the number of stop-and-frisks conducted by NYPD declined sharply. We will use this ruling as a natural experiment to examine whether reductions in hyper-surveillance are associated with changes in suicide-related outcomes among youth in New York City. We will retrieve data on police stops for 10 US states from the Stanford Open Policing Database, and New York City-specific police stops data from the New York Civil Liberties Union's stop-and-frisk database. Suicide mortality by region and sociodemographic factors will be obtained from the National Center for Health Statistics mortality database. We will acquire ED visits data (by ICD9/10 diagnosis code, county, month, year, sociodemographics) from the State Emergency Department Database (SEDD) and the State Inpatient Database (SID). We will formulate our exposures as the rate and volume of police stops (per 100,000 population). We will conduct empirical analyses using (1) two-way fixed effects, (2) time series, (3) difference-in-difference, and (4) synthetic control methods to examine the spatio-temporal relation between our outcomes and exposures. Our study will rigorously examine the ecological relation between police stops and suicide-related outcomes among youth. Findings from our study may inform policy-relevant interventions for suicide prevention.

NIH Research Projects · FY 2026 · 2024-07

Early prediction and timely decision-making of acute diseases are critical to enabling early intervention and improving clinical outcomes (for example, a sepsis patient may benefit from a 4% higher chance of survival if diagnosed 1 hour earlier). Developing machine learning (ML) models for clinical decision-making on Electronic Health Records (EHRs) presents several significant challenges: 1) existing models are trained mostly on EHR data from intensive care units (ICUs), which are not generalizable for sepsis onsets in emergency rooms and hospital wards; 2) most existing tools simply output the prediction result as a risk score, without sufficient explanation or confidence interval for it, which is not trustworthy for physicians; 3) existing systems often ignore the human workflow by neither providing actionable insights to physicians nor enabling interactive explorations from physicians, which limits their clinical usages. To address these challenges, we propose a Human- Centered Artificial Intelligence (HCAI) system to collaborate with human domain experts in the high-stake and high-uncertainty decision-making process. Specifically, we 1) create a deidentified database with complete visits and long-term EHR history for patients with sepsis risk; 2) develop early sepsis risk prediction models with uncertainty quantification and active sensing; 3) design and implement a physician-centered AI prediction module and user interface for early sepsis human-AI decision making; and 4) design and conduct controlled usability evaluations to quantitatively and qualitatively measure the clinical outcome and user satisfaction. This project integrates human-AI collaboration design, novel ML algorithms, and data visualization tools for improving early prediction and decision-making for sepsis, which holds great promise for leading new insights into human-AI systems for clinical decision support.

NSF Awards · FY 2024 · 2024-07

This project will provide interdisciplinary research internships to community college and university students to gain valuable experience in an advanced battery manufacturing environment that will prepare them for careers in the electric vehicle (EV) industry. This initiative will reach a diverse and inclusive STEM workforce, promote collaboration between organizations in the EV industry, and align closely with the rapidly evolving energy storage workforce needs of the United States. By providing these opportunities, it will promote the development of a diverse, globally competitive workforce by offering skill development and knowledge acquisition to students from different backgrounds, including underrepresented groups. It will also foster partnerships between academia and industry, engaging students in real-world challenges and market assessments. This will prepare participants for various career pathways and promote interdisciplinary learning and skill integration, enhancing their technical skills and problem-solving abilities. This project will expand access to career-enhancing experiential learning opportunities to reach a broader and more diverse population, targeting students in Associate and early Bachelor of Science programs. Through collaboration with the EV automotive industry, it will support the technology shift to electrification and domestic energy storage technology manufacturing. With a focus on advanced battery manufacturing skills, this project will support the training, reskilling, and upskilling of a diverse workforce for our industry partners in the region, directly contributing to the United States’ advanced manufacturing future. This project will employ various strategies, including implementing Project-Based Learning (PBL) cultivating an Entrepreneurial Mindset (EM), offering summer research internships, providing mentorship and networking opportunities, and integrating interdisciplinary concepts. Using both training and project learning activities, students will be immersed in interdisciplinary, applied experiences in a clean-tech manufacturing learning setting where they will have opportunity for career and self-exploration while improving their skills, knowledge, professional competencies, and networks to pursue careers in the field of advanced battery manufacturing. The project’s overall evaluation plan contains both formative (processes-based) and summative (outcomes-based) elements embedded with a longitudinal evaluation design with comparison groups to assess the extent to which exposure to and participation in the project will help students achieve the knowledge, attitudes, and skills necessary for successful entry into the advanced battery manufacturing workforce. 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 · 2024-07

This project focuses on research connecting multiple fields of mathematics, namely, analysis, combinatorics, and model theory. Analysis and combinatorics can be seen as two different approaches toward using mathematics to study the physical world. In analysis, which evolved from calculus, the approach is based on continuous and dynamical methods of an infinite nature. By contrast, combinatorics seeks to understand complicated and subtle patterns in discrete (and often finite) systems. The proposed research centers on using model theory, a branch of mathematical logic, to bridge these two different perspectives. Model theory is the abstract study of mathematical objects using properties that can be described with formal language and semantics. The leverage provided by model theory stems from the fact that two mathematical objects can appear substantially different in nature, but share enough semantic properties so that an understanding of one object leads to understanding of the other. This approach has led to significant breakthroughs in mathematical research, which will be further developed in this project. The interdisciplinary nature of this project will also allow for collaboration between researchers and students from a variety of mathematical backgrounds and levels. The broad theme of the research proposed is the use of continuous logic (model theory for metric structures) to develop a stronger foundation for the interaction between analysis and combinatorics described above, with a focus on arithmetic combinatorics in noncommutative groups. There are two main goals. The first is to prove a fully general arithmetic regularity lemma valid for arbitrary groups using a Radon-Nikodym-type strategy (similar to the nonstandard proof of Szemeredi's regularity lemma for graphs). Previous attempts toward such a theorem have been impeded by fundamental drawbacks of classical (discrete) logic, and this project proposes a new strategy based on continuous logic. This theorem is envisioned as a necessary step in the ongoing development of a model-theoretic framework for arithmetic combinatorics. The second main goal is based on recent work on the structure of stable functions on groups, which establishes a connection to existing results in arithmetic combinatorics (e.g., on approximate groups). A majority of these results are only currently understood at a qualitative level, and thus a quantitative understanding of stable functions on groups should lead to quantitative breakthroughs in these other areas. Moreover, model theoretic ideas were previously successful in obtaining a quantitative analysis of stable sets in groups. This project will pursue an analogous quantitative analysis of stable functions, motivated by applications to arithmetic combinatorics. 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 · 2024-07

The overarching goal of the Ohio State University (OSU) Center for Clinical and Translational Science affiliated K12 program is to develop a multi-disciplinary cadre of well-trained, early-stage faculty investigators through individualized training to engage fully, succeed, and lead in clinical translational research and science (CTR/CTS). The program is open to eligible early career faculty members at the Ohio State University (OSU) and Nationwide Children’s Hospital (NCH). To accomplish this, we propose the following Specific Objectives (S.O.): S.O.1. Provide training to foster the development and long-term success of early career faculty engaged in clinical and translational research. S.O.2. Provide mentorship training and monitor progress for scholars, mentors, and mentor mentee dyads. S.O.3. Support the development of skills for scholars to lead in translational science. S.O.4. Conduct ongoing evaluation of program and scholar outcomes for continuous improvement. Highlights of programmatic training elements include: development of monitored individual training plans (IDP); structured communication and grant writing preparation; leadership skill development; understanding and thriving in a team science environment; training in responsible conduct of research; training in research ethics; training in working with research teams and participants; research design and data interpretation; and training in rigor and reproducibility in the conduct of research. Program elements will be opened to other institutional and individual K scholars at OSU and NCH. Four early career K12 scholars will be trained with two-year appointments (2 per year) supported by the K12, followed by institutional support to continue a third year of K12 training. Scholar success will be defined based on research productivity (particularly independent research funding in CTR/CTS) and progression in the characteristics of a translational scientist. Scholars will be monitored during the K12 based on their IDPs, and after the K12 to evaluate career progression and participation and leadership in clinical and translational research and clinical and translational science. A RE-AIM and Logic Model framework will be used for programmatic evaluation and programmatic adaptation. The OSU campus research environment and participating communities, and the ability to look at questions across the lifespan through our partnership with NCH provides a range of opportunities to train future leaders in clinical and translational research and science.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY The survival of every multicellular organism relies on effective cell-cell communication. Despite the diversity of signals that cells need to communicate to their neighbors, evolution appears to have settled on only a few physical mechanisms for transferring information across membranes. One such mechanism is the oligomerization, or clustering, of membrane-spanning receptor proteins, wherein the receipt of a signal on one side of the membrane is converted into a biochemical response on the other side. The mechanisms and regulation of oligomeric transitions remain poorly understood for most families of receptor proteins due to the inherent experimental difficulty of detecting and probing these subtle transitions. My lab is interested in harnessing the latest developments in optogenetics, single-molecule microscopy, genome editing, and high- throughput sequencing to dissect the receptor clustering events that allow cells to “talk” to each other in a systematic and quantitative way. The proposed research program will tackle the activation and regulation of Eph receptors in human cells. Eph receptors constitute the largest known family of receptor tyrosine kinases (RTK’s) and are linked to a broad range of biological processes in both health and disease, ranging from tissue patterning and embryonic development to neurodegeneration and cancer. The mechanistic details behind Eph receptor activation remain obscure, in large part because of the large number of receptor types that are often expressed in a single cell and their apparent ability to from both homo- and hetero-oligomers of varying stoichiometries. Over the next five years, we will develop a combined approach for precisely manipulating and detecting homo- and hetero-oligomerization of Eph receptors, allowing us to directly determine the effects of cluster size and composition on the downstream signaling outcomes. This approach will also allow us to reveal the cryptic roles of heterodimers between Eph receptors of different types, including the two catalytically inactive members of the family. Light-inducible optogenetic clustering in living cells will be cross-validated with in vitro enzymology and direct observation of clustering in single-molecule tracking experiments, ensuring that we are faithfully recapitulating the biologically relevant oligomeric transitions. In addition to its immediate significance to the Eph receptor field, the proposed work will develop a biophysical framework for analyzing the signaling of other receptor families at the cell-cell interface. Finally, the spatially defined and non-invasive nature of light means that development of optically controlled receptors will serve as a powerful tool for the study of cell-cell signaling at the organoid or organismal level.

NSF Awards · FY 2024 · 2024-07

The resurgence of deep neural networks has led to revolutionary success across almost all areas of engineering and science. Despite recent endeavors, current theoretical understandings of deep networks remain fragmented and only pertain to idealized and over-simplified network models. There is a significant lack of a systemic and unified approach for designing and explaining deep networks. Therefore, the underlying principles behind the success of deep learning still largely remain a mystery, which hinders its further development and adoption to broader applications. Nevertheless, the blessings of dimensionality imply that real-world data often reside in low-dimensional structures, and ample empirical evidence implies that there is a strong connection between deep learning and low-dimensional modeling. This connection implicitly appears in many different forms, in terms of learned representations, network architectures, and optimization strategies. However, these connections are far from being elucidated nor are they fully exploited. Based on the theory of data compression and optimal coding for learning from low-dimensional structures, this project aims to bridge the gap between the theory and practice of deep learning by developing a principled and unified mathematical framework. To develop this framework requires two steps. First, this project will design white-box deep networks by unrolled optimization schemes for maximizing the information gain of the resulting representation, which can be measured precisely by the coding rates of the representation. Second, the project will guarantee correctness through rigorous mathematical analysis of the optimization objective for learned representations. Third, this project will ensure consistency of the learned representations through a self-correcting closed-loop transcription framework that integrates encoding and decoding into a complete autonomous learning system. This new framework naturally unifies representation learning for all purposes: discriminative, generative, and auto-encoding, and is generalizable to all settings: supervised, unsupervised, self-supervised, and continuous learning. 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 · 2024-07

Project Summary Every living organism possesses a defense system crucial for distinguishing self from potentially harmful foreign invaders. This defense system is essential for survival and is finely tuned to protect against threats that may compromise the organism's integrity. The long-term goal of this proposal is to unravel the immune surveillance mechanisms employed by a new class of immune GTPases, the 65~120kDa Guanylate binding protein-like (GBPL) proteins, in the model plant species Arabidopsis thaliana. Preliminary evidence suggests that this new three-member GBPL protein family bridges both sensing and sterilizing activities in plants via mechanisms distinct from well-established plant defense pathways. We are focused in part on how GBPL1, a pseudoGTPase, fine-tunes immune homeostasis as a negative regulator of defense. Here a combination of targeted mutagenesis, structural modeling and organismal genetics will be used to determine the sequence- enzyme relationships governing GBPL1's functions and, in turn, understand its implications in host defense. We are also testing the importance of GBPL2 to act as a broad-spectrum danger sensor under physiological conditions to elicit sterilizing activities. This will be examined using powerful host-pathogen genetics, biochemistry and advanced imaging strategies to unveil the intricate molecular processes that enable GBPL2 to sense external cues and trigger effective immune responses. Lastly, we are exploring the role of GBPL3 in enhancing defense responses and influencing gene expression through the realm of biological phase separation. We are employing facile protein organelle biochemistry, proteomics, imaging and reconstitution techniques to probe how GBPL3 condensates are regulated in vivo in the context of infection. Collectively, this research addresses fundamental questions in plant host defense and offers insights into immune mechanisms applicable to diverse organisms including humans.

NSF Awards · FY 2024 · 2024-07

Ammonia is the key chemical for the mass production of fertilizers to feed the World’s growing population. The current conventional route for manufacturing ammonia (NH3) is the Haber-Bosch (HB) process, which involves converting nitrogen and hydrogen through a thermo-catalytic reaction path at high temperatures and pressures. Being the exclusive route for producing one of the most crucial chemicals worldwide, HB consumes almost 2% of the World’s total energy supply and releases significant amounts of the primary greenhouse gas, carbon dioxide (CO2). Therefore, for a sustainable future, alternative NH3 production processes need to be developed. To that end, the project investigates an alternative electrochemical process for scalable and on-site ammonia production from readily available nitrogen (N2) and water (H2O), thus providing a less energy-intensive, more economically feasible, and environmentally friendlier path compared to the conventional Haber-Bosch process. Beyond the technical aspects, the project includes STEM related activities at the high-school and undergraduate levels. The project investigates double perovskite oxynitride-type materials as electrocatalysts for potential application in solid oxide nitrogen reduction reaction (e-NRR) electrochemical cells (SOECs) operating at atmospheric pressure and an intermediate-temperature range (450-600°C). These materials show a large diversity of features that can be tuned using strategies such as doping and optimizing nitridation parameters. The cathode development efforts will concentrate on maximizing lattice nitrogen amount and maintaining the co-presence of nitrogen and oxygen vacancies while preserving their crystallinity and cationic/anionic ordering. Although the double perovskite oxynitrides are potentially very promising materials to be used in electrocatalytic nitrogen fixation/activation, they have been scarcely investigated and, to the best of the investigators’ knowledge, the study is the first to consider these materials as an alternative cathode catalyst in an e-NRR process. The project will undertake a systematic design and ex-situ/in-situ/operando characterization of novel iron-based double perovskite oxynitride-type electrocatalytic materials. The study fundamentally investigates properties of the double perovskite oxynitrides at the electronic, atomic, and molecular levels. The carefully formulated and engineered double perovskite oxynitrides will be evaluated in the e-NRR process to elucidate a relationship between the structure/(electro)-chemical/physical properties of iron-based double perovskite oxynitrides and their e-NRR activity. Considering the fact that even the reaction mechanism of the thermal ammonolysis - which is the main route to synthesize oxynitride materials - is not yet fully understood, the present study can be considered as an important first step to gain insights into (i) the engineering and synthesis of oxynitride-type electrocatalysts, (ii) the effect of ammonolysis parameters on their catalytic performance, (iii) anionic order and O/N distribution in the crystalline structure, (iv) preferential sites for the nitride ions, and (v) maintaining the co-presence of nitrogen and oxygen vacancies. The ultimate goal is to understand how these properties affect the electrocatalytic activity of the oxynitrides towards nitrogen fixation/activation using H2O as the H2 (proton) source. The project benefits from access to state-of-the-art characterization tools within the PIs’ laboratories as well as those at the Oak Ridge and Brookhaven National Laboratories and the Stanford Synchrotron Radiation Lightsource. 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 · 2024-07

The purpose of this travel grant is to broaden and diversify the student participation in the 44th International Conference on Distributed Computing Systems (ICDCS 2024), sponsored by the Institute of Electrical and Electronics Engineers (IEEE), a premier annual international forum for the presentation of research results in distributed computing. It seeks to increase student participation in the conference and the field. The requested funding would support the travel of eligible students from US universities and institutions to the conference. This travel grant encourages the involvement of students in the field who are not well funded and those who are just beginning their participation in the field or are interested in entering it. A special effort is made to reach out to women, people from underrepresented populations in computing, students from Minority-Serving Institutions, and students from institutions in jurisdictions eligible to NSF’s Established Program to Stimulate Competitive Research (EPSCoR). Preference is also given to students who will present their research at ICDCS. 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 · 2024-07

Fundamentals of stellar astrophysics rest on our understanding of the Sun. New solar abundances that are up to 40% lower for common volatile elements C, N and O, lead to serious disagreement with stellar models and helioseismological parameters such as the radiative- convection zone boundary, sound speed, and helium abundance. This is commonly referred to as the “solar problem”. The resolution of these discrepancies depends on computing accurate opacities for key atomic elements that dominate the physics of the solar interior. This project will compute accurate opacities that can be applied to the solution of the solar problem, as well emerging areas such as asteroseismology and exoplanets. The project will provide a research opportunity for a graduate student, who will be trained in computational atomic-plasma physics and in the next generation of opacity codes that will be developed during this project. This project will extend known plasma broadening schemes: thermal Doppler effect, Stark broadening due to manifold overlap and ionization due to ion microfields, and collisional electron impact broadening that is the dominant mechanism at high densities. These effects lead to broadening and dissolution of resonances, continuum lowering below atomic levels, and raising the continuum opacity. The proposed R-matrix method accounts for resonance broadening in an a priori and ab initio manner. The project entails considerable extension of previous methods and codes in a new computational package for R-matrix opacities, and calculations will encompass other Fe-group elements such as Cr and Ni ions. Monochromatic opacities will be computed at an order-of-magnitude higher resolution than hitherto, at 100,000 photon frequencies, and yield opacity tables for stellar interior models. The extensive atomic data will be available freely via the dedicated electronic database NORAD at the Ohio State University. 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 · 2024-07

West Nile virus (WNV) is the most prevalent arbovirus in the US and seriously threatens the health of livestock, wildlife (especially birds) and humans. WNV transmission shows clear annual cycles, with high levels of human and animal cases during the summer, and few or no cases during the late fall, winter, and early spring. Furthermore, WNV transmission is higher in urban areas relative to rural areas. Yet, we do not fully understand what is driving seasonal and spatial differences in WNV transmission, or how cycles of WNV transmission are able to reinitiate each year. The investigators work from the central hypothesis that mosquitoes and birds are affected by differences in rural and urban landscapes that lead to predictable, seasonal changes in WNV transmission. The overall objective of this proposal is to develop predictive models that incorporate critical drivers of WNV transmission in rural and urban areas, including seasonal changes in mosquito and avian abundance and community composition. The research team has complementary expertise in mosquito overwintering, mathematical modeling, and avian disease ecology. This proposal combines sophisticated mathematical models with high-resolution, field data from mosquitoes and birds in rural and urban sites in central and northwestern Ohio. The first goal of the proposal is to uncover how WNV transmission reinitiates each spring. Mosquito and avian community composition, WNV infection, mosquito host-use, WNV phylogenetics and measures of urbanization will be incorporated into predictive models and used to test the hypotheses that WNV-infected urban mosquitoes terminate their overwintering dormancy in early spring, while uninfected rural mosquitoes acquire WNV from migratory birds. The second goal is to characterize factors that drive WNV transmission during the peak of the epidemic. Abiotic factors and data from birds and mosquitoes will be incorporated into models to determine why WNV transmission starts earlier, persists longer and is higher in cities relative to rural areas. The third goal is to determine how WNV persists during fall and winter. Continuous collections of rural and urban mosquitoes and birds, WNV phylogenetics and environmental data will be modeled to test the hypotheses that urbanization postpones mosquito overwintering dormancy, increases WNV in birds, and allows more urban mosquitoes to overwinter infected with WNV. Finally, the models will be re-coded to be adaptable to multiple locations using freely available, open-source weather, bird, and mosquito data. Thus, at the conclusion of this work the PIs will have developed, parameterized, and validated the first multispecies, multiannual, and adaptable models to predict WNV transmission across time and space.

NIH Research Projects · FY 2026 · 2024-07

Project Summary: While elevations of circulating pro-inflammatory modulators correlate directly with variables of aggression, and direct application of cytokines to specific cortico-limbic regions in animals elicit aggressive responding, no studies have tested the hypothesis that acute increases in pro-inflammatory modulators can/will increase aggressive behavior in humans. We aim to demonstrate a causal relationship between pro- inflammatory cytokines and aggression in human subjects by showing that an acute pro-inflammatory state, via endotoxin challenge, will increase aggressive responding, anger ratings, and hostile social cognition, to a greater degree in “aggressive” (n = 45), compared with “non-aggressive” (n = 45), individuals with mood/anxiety/ stress- related and/or personality disorders. The proposed study is a double-blind comparison of endotoxin/placebo challenge in the same individuals (within-subject) as a function of aggression status. Aggressive individuals will have high lifetime aggression (> 12 on Life History of Aggression: LHA) and be positive for an average of two anger attacks per week and/or three anger attacks per year that include physical assault of another person and/or or non-trivial destruction of property. “Non-Aggressive” individuals will be similar diagnostically but will have low lifetime aggression. “Aggressive responding” will be assessed using the Taylor Aggression Paradigm (TAP), “Anger” will be assessed by self-reported assessments (POMS), and “Hostile Social Cognition” will be assessed by the Video-SEIP (V-SEIP) paradigm. The primary plasma pro-inflammatory outcome measures will be a composite of CRP, IL-6, IL-8, and TNF-α (as in our previous studies). MRI scans will including task-based scans involving “explicit” and “ambiguous” social threat. We hypothesize that aggressive responding, anger ratings, and hostile social cognition (Primary Outcomes) and Composite Plasma Pro-Inflammatory Marker levels (Secondary Outcome), will be greater after endotoxin, compared with placebo, and will be greater in “aggressive” than “non-aggressive” study participants. We also hypothesize that these variables will correlate with dimensional measures of aggression. In addition, we hypothesize that amygdala responses to explicit social threat (anger faces) will be enhanced (greater BOLD fMRI signal response) after Endotoxin, and that cortico- limbic responses to ambiguous social threat (V-SEIP) will be reduced (i.e., lesser BOLD fMRI signal response) in all study participants but reduced to a greater extent in “aggressive”, compared with “non-aggressive”, study participants. Finally, we hypothesize that the pro-inflammatory effects of the endotoxin challenge will result in reduced connectivity between the functional edges supporting higher aggressive behavior and that endotoxin challenge will facilitate stronger connections among nodes associated with low aggressive behavior. If supported, this study will provide a strong rationale for clinical trials of anti-inflammatory agents in impulsive aggressive individuals.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY Sarcoidosis is a pulmonary and systemic granulomatous disease of unknown cause. To better understand disease mechanisms, we have recently established a novel ex vivo human granuloma model that shares many structural and molecular features of the disease in human tissues, yielding novel insights into mechanisms regulating early granuloma formation. In keeping with prior investigations and clinical experience linking sarcoidosis to elevated levels of angiotensin converting enzyme (ACE) in sarcoidosis tissues, molecular characterization of the sarcoidosis granuloma model indicates that macrophages participating in sarcoidosis granuloma formation are regulated by the renin-angiotensin-aldosterone system (RAAS). Our strong preliminary data shows that sarcoidosis macrophages produce aldosterone, a hormone that promotes inflammation through the activation of mineralocorticoid receptors (MRs); and we further show that inhibition of MRs attenuates granuloma formation. We hypothesize that RAAS promotes pathological granuloma formation and fibrosis in patients with sarcoidosis through activation of MRs and related production of reactive oxidant species (ROS). In the spirit of the R21 funding mechanisms, this project is highly innovative and has important beneficial implications for advancing our understanding of sarcoidosis disease mechanisms and for providing novel therapeutic targets and disease biomarkers. Aim 1 will determine if RAAS/MR activation promotes sarcoidosis granuloma formation through redox signaling to promote NRF2/heme oxygenase-1 (HO-1) pathway activation. We posit that MR activation promotes mitochondrial ROS production to trigger NRF2/HO-1 signaling resulting in alternative macrophage (M2) polarization favoring granuloma formation. Aim 2 will determine if RAAS/MR activation promotes collagen and pro-fibrotic molecule formation by sarcoidosis granulomas through induction of TGFβ and inflammasome signaling. These studies will determine if sustained MR activation (by RAAS) promotes pro-fibrotic induction through pathways independent from NRF2/HO-1; dependent on direct ROS-dependent activation of TGFβ and induction of inflammasome mediated IL-33 release. The short-term goals of this project are to advance basic understanding of sarcoidosis disease mechanisms regulating distinct disease manifestations of (1) granuloma formation induced by MR induced ROS production and downstream pathways regulating granuloma formation (NRF2/HO-1) and (2) tissue fibrosis relating to TGFβ and inflammasome/IL-33 regulated pro-fibrotic responses. Long-term aspirations of this project are to address current deficiencies in the field of sarcoidosis as relates to identifying novel disease-specific therapies targeting granuloma formation and tissue fibrosis, including repurposing of safe, widely available RAAS modulating drugs (e.g., ACE and MR inhibitors) or antioxidants, and emerging therapies targeting NRF2, HO-1 and IL-33.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY Targeted nanotechnologies have shown great promise in overcoming roadblocks in cancer therapeutics. Tumor metastasis has a limited response or resistance to chemotherapeutics and a moderate response to new antibody therapies. Our RNA nanotechnology has shown great promise in targeting metastatic disease to deliver both gene silencing and chemical drug therapies. Our long goal is to overcome colon cancer, the second most common cause of cancer death primarily due to the mutations to KRAS and subsequent lung metastasis with expected survival of months. KRAS, the gene that codes for the K-Ras protein, is considered “undruggable”. KRAS mutations are found in up to 45% of colorectal cancers. We have designed an epigenetic repressor to silence mutant K-Ras through epigenome editing. We created a fusion protein consisting of nuclease-inactive dCas9 and the histone deacetylase HDAC1 and targeted dCas9-HDAC1 to the promoter of mutant KRAS. We can load the recombinant dCas9-HDAC1- gRNA ribonucleoprotein (RNP) complex into exosomes and silence K-Ras; we designed RNA nanoparticles carrying mutant K-Ras siRNA to inhibit KRAS mutant lung cancer; we also successfully constructed RNA 4WJ carrying SN-38 to inhibit colon cancer lung metastasis. The goal of this proposal is to identify mechanisms that govern the high delivery platform to silence K-Ras in colon cancer. We intend to deactivate mutant KRAS via RNA-ligand displaying exosomes loaded with dCas9- HDAC1-gRNA ribonucleoprotein, siRNAs, and chemical drugs individually or in combination in colorectal cancer primary and metastasis tumors, using orthotopic and PDX xenograft models. We will investigate the mechanism of action in K-Ras inhibition, including the conditions for the integration and assembly of dCas9-HDAC1 and gRNA or crRNA such as sequence and length requirement for silencing mutant KRAS and suppressing colon cancer cells. We will apply RNA nanoparticle orientation to display targeting ligands on the surface of exosomes. Instead of delivering dCas9 plasmids, we will deliver a ribonucleoprotein complex of dCas9-recombinant protein and gRNA. Exosomes will display RNA nanoparticles with an aptamer to bind colon cancer cells specifically. We will engineer RNA nanoparticles and increase the surface display density of the negatively charged RNA ligands to enhance the negative zeta potential of exosomes for preventing binding to the vital organs and healthy cells that normally have negatively charged lipid membranes. We will try to enhance therapeutic efficacy and reduce toxicity by overcoming endosome trapping and non-specific cell entry through RNA ligand manipulation. Zonal and density gradient ultracentrifugation or size exclusion columns will select exosomes smaller than 100 nm to escape macrophage engulfment and improve biodistribution. This project with a multidisciplinary approach will build a strong foundation from which researchers can deploy large protein complexes to treat cancer by tumor- specific delivery and effectively targeting those previously difficult targets like K-Ras.

NIH Research Projects · FY 2025 · 2024-07

Project Summary: Acute retinal ganglion cell (RGC) and optic nerve injury due to trauma, autoimmune inflammation, and ischemia reperfusion results in permanent disability due to eventual neuronal death. Traditionally, inflammation after acute neuronal injury has been considered a major driver of secondary damage, leading to neuronal loss. The innate immune response of infiltrating neutrophils and monocytes (myeloid cells) after neuronal injury results in production of matrix metalloproteases, proinflammatory cytokines, and reactive oxygen species that are directly neurotoxic, and contribute glial cell modulation that prevent neurological recovery. However, animal model studies have demonstrated that certain immune cell subsets can have neuroprotective and reparative effects including in the eye. We have identified that this immune mediated regeneration after optic nerve crush is initiated by a novel immature alternatively activated neutrophil characterized as Ly6Glow CD14+ CD101-. These alternatively activated neutrophils recruit arginase+ (Arg+) monocytes that stay at the site of injury and continue to stimulate axon regeneration. Additionally, alternatively activated neutrophils modulate microglia that likely contribute to the neuro-reparative environment after RGC injury. The presence of Arg+ monocytes in the central nervous system after injury has been associated with inflammation resolution and neurorepair, however the mechanism in which Arg+ monocytes do this is not known. In addition to understanding the beneficial effects of alternatively activated neutrophils on recruited monocytes and microglia, we have also identified a human monocyte population that shows neuroprotective effects and stimulates axon regeneration after ONC. The overall goals of this work are based on the hypothesis that monocytes can be polarized in situ towards the unique neuroregenerative phenotype to improve neuronal recovery after ONC. This hypothesis will be interrogated in three aims. In aim 1 we will determine the interactions between neutrophils and monocytes that result in monocyte polarization towards an Arg+ reparative phenotype. In aim 2, we will focus on the role of retinal microglia in contributing to modulation of the ocular inflammatory environment and polarization of immune cells towards a neuro-reparative phenotype. Aim 3 will determine the translational capacity of these mechanistic mouse experiments by exploring the neuroregenerative capacity of polarizing human monocytes to stimulate axon regeneration after ONC. The outcomes of these studies will be to understand the underlying cytokine, growth factor and metabolic signals that influence monocytes to take on a neuro-reparative role after optic nerve injury. To date, the role of Arg+ myeloid cells on neuroprotection has been correlative. This work will help us gain a mechanistic understanding of the factors responsible for driving myeloid cells towards the neuro-regenerative phenotype and could lead to immune based therapies to drive neuron recovery after acute injury.

NIH Research Projects · FY 2025 · 2024-07

Triple-negative breast cancer (TNBC) disproportionately affects African American (AA) women, with mortality rates 65% higher than Caucasian (CA) women. The mechanisms underlying the heightened aggressiveness and metastasis in AA TNBC remain elusive. This study investigates the immune suppressive tumor microenvironment (TME) as a driving factor in AA TNBC progression. Specifically, it delves into the novel role of the S100A7 and its interplay with intrinsic IFNγ signaling, elucidating their impact on TNBC aggressiveness and metastasis in AA women. Our recent findings reveal elevated S100A7 expression in AA TNBC patient samples and cell lines relative to CA counterparts. Moreover, higher S100A7 expression correlates with increased tumor burden in various pre-clinical models, including AA TNBC patient-derived xenografts (PDX). We also noted that S100A7 knockout (KO) mouse models (generated in our lab) exhibit reduced tumor burden, while treatment with a novel S100A7-neutralizing antibody (nAb) shows promising efficacy in inhibiting TNBC growth and metastasis. Mechanistically, S100A7 is demonstrated to enhance cPLA2/PGE2/IFNGR1 signaling in AA TNBC cells, modulating intrinsic IFNγ signaling. This process generates an immune suppressive TME by upregulating PD-L1 and downregulating Fas on tumor cells. Additionally, AA TNBC tumor tissues manifest heightened immunosuppression, characterized by increased PD-L1 expression and infiltration of FoxP3+ Treg cells. Our proposed research aims to meticulously uncover how S100A7 orchestrates IFNγ responsive genes (PD-L1 and Fas) to generate an immunosuppressive TME in AA TNBC. This investigation leverages diverse AA TNBC cell lines, humanized PDX models, and genetically engineered mouse models (GEMMs) overexpressing mS100a7, mS100a7 KO, and the S100A7 nAb. The study's overarching hypothesis posits that S100A7 contributes to AA TNBC aggressiveness by fostering an immunosuppressive and immune evasive TME via regulating intrinsic IFNγ signaling, resulting in PD-L1 upregulation and Fas downregulation. The research strategy encompasses three key aims: Aim 1 will elucidate how S100A7 signaling regulates IFNγ responsive genes in AA TNBC using AA TNBC cells and mS100a7 GEMMs. Aim 2 will determine the novel role of S100A7 in regulating macrophage plasticity, T cell function, and Fas-mediated immune evasion in AA TNBC. Aim 3 will evaluate the therapeutic efficacy of S100A7 nAb in combination with chemo or immunotherapy using AA TNBC PDOs and PDX mouse models. This aim will also determine the prognostic significance of S100A7 and its downstream signaling molecules in AA TNBC. In addition, this aim will establish the association of S100A7 and various immune cells in TNBC racial disparity. The insights gained are poised to identify novel S100A7-mediated downstream signaling pathways and determine the clinical relevance of S100A7 in AA TNBC. This study holds the immense potential to inform the design of innovative therapeutic strategies tailored for AA TNBC, thereby contributing to improved outcomes in this high-risk population.

NIH Research Projects · FY 2025 · 2024-07

ABSTRACT Cancer-associated RAS mutants were once considered oncogenic equivalents. However, it is increasingly clear that mutants of the same RAS isoform (H-, K- or NRAS) and amino acid (codon 12, 13) can exhibit distinct GTPase, effector binding, signal transduction, and tumorigenic properties. These distinctions may contribute to disparities in therapeutic response as well as the enrichment of specific RAS isoforms and amino acid substitutions in each cancer type. Thus, a deeper understanding of how each amino acid substitution influences RAS structure, biochemistry, and function could yield meaningful biological and clinical advances. KRAS mutants are well-studied, but whether the biochemical consequences of an amino acid change in one RAS isoform can be extended to another is unclear. In particular, research into the mutant-specific functions of NRAS, the dominant RAS oncoprotein in thyroid cancer, acute myeloid leukemia, and melanoma, is limited. Here, we examine the ability of eight different oncogenic NRAS oncoproteins to induce spontaneous melanoma in mouse models. We find that oncogenic substitutions, even in the same amino acid of NRAS, have distinct melanomagenic potential. Our preliminary data link the greater melanomagenic potential of these mutants to structural features that enhance BRAF affinity, activation, and Mitogen-activated protein kinase (MAPK) signaling. These observations are consistent with studies linking increasing MAPK activity to melanoma progression in human nevi. Furthermore, we provide evidence that current knowledge of KRAS mutants cannot be translated to NRAS. Based on these data, we hypothesize that melanoma formation depends on substitution- and isoform-specific structural differences in NRAS that enhance BRAF dimerization and MAPK>ERK signaling. In Aim 1, we propose integrated X-ray, NMR, computational, and biochemical approaches to determine the structural ensembles and biochemical properties of NRAS oncoproteins of varying melanomagenic potential and compare these profiles to similar KRAS mutants. In Aim 2, we describe a combination of structural, biochemical, and live-cell reporter experiments to determine how NRAS mutants differentially recognize and promote BRAF activation. In Aim 3, we will test the concept that a transient pharmacological approach could prevent melanoma by eliminating NRAS-mutant cancer precursors with elevated MAPK>ERK signaling from the skin. If successful, these studies will uncover distinguishing structural and biochemical features of the NRAS oncoproteins that may lead to the identification of new interfaces for drug targeting, determine the molecular basis for NRAS-driven melanomagenesis, elucidate RAS isoform-specific mechanisms of RAF activation, and test a potential prevention strategy for NRAS-mutant melanoma.

NIH Research Projects · FY 2025 · 2024-07

Project Summary Mobile CT scanners are routinely used in the neuro intensive care unit (ICU) for critically ill patients to avoid morbidity and adverse events associated with patient transport. The image quality of mobile CT is inferior to a fixed MDCT in terms of image noise, spatial resolution, soft tissue contrast, and susceptibility to artifacts from beam hardening, motion, metallic implants, and truncation. Reduced image quality may compromise care and necessitate transport to a fixed scanner. For example, on a mobile CT scanner, it is difficult to diagnose small infarcts or hemorrhage, or to differentiate between intracranial hemorrhage and contrast extravasation after endovascular processes. In this project, we will leverage the benefits from an FDA-approved mobile photon counting CT (PCCT), as well as deep learning-based image reconstruction algorithms to improve the image quality of the mobile PCCT to or beyond that of a fixed scanner. The multi-spectral and high-resolution features of the mobile PCCT will be explored and combined with deep learning algorithms for noise and artifacts reduction. In this project, the following aims will be investigated to achieve our goal to match the image quality of a mobile PCCT to a fixed scanner: (1) We will develop low-dose, high-resolution deep learning-based reconstruction algorithms to reduce the noise and improve gray-white matter contrast in the mobile PCCT; (2) We will develop methods for material decomposition with reduced noise amplification and spectral optimization to overcome beam hardening artifacts and achieve discrimination between calcium/contrast/hemorrhage; (3) We will develop deep learning-based algorithms for image artifacts correction to tackle artifacts that are more frequent on a mobile CT, including motion, truncation, and metal artifacts; (4) To validate the methods, the optimized mobile PCCT images will be compared with fixed CT images by trained radiologists.