University Of Southern California

NIH Research Projects · FY 2025 · 2025-08

ABSTRACT Emerin is a largely disordered integral protein of the inner nuclear envelope (INE) with a short LEM-domain. It is a major contributor to the maintenance of the nuclear architecture and to mechanotransduction processes at the NE. When mutated, emerin causes Emery-Dreifuss muscular dystrophy (EDMD), an envelopathy whose underlying mechanisms and muscle specific effects are not fully understood. How emerin participates in molecular scaffolding at the INE and helps protect the nucleus against mechanical strains has remained largely elusive. Recently, we discovered that emerin monomers self- assemble into INE oligomeric nanodomains to coordinate NE mechanics. Compared to wild-type emerin, EDMD-inducing emerin mutants display either insufficient or excessive self-assemblies, both of which result in defective nuclear shape adaptations against mechanical challenges, as typically observed in EDMD patients. Defining the pathogenesis of EDMD therefore hinges on understanding how the oligomerization of emerin is regulated in response to forces exerted at the NE. Here, we propose innovative approaches that integrate quantitative proteomics, super-resolution microscopy in cells, nuclear biomechanics, and single-molecule biophysical studies: (i) to establish the proteomic environments and the mechanisms governing the self-assembly of emerin in muscle and non-muscle cells and (ii) to uncover the molecular steps and conformational changes of emerin allowing its oligomerization to promote adequate nuclear compliance against forces and prevent abnormal NE mechanics in the context of EDMD. Building on our previous work and preliminary data, we hypothesize that the nanoscale self-assembly of emerin is regulated by competitive interactions with a meshwork of INE-proximal proteins that modulate its structural conformations and the ability of the LEM-domain to bind the disordered region of other emerin for emerin:emerin contacts. We also hypothesize that tuning those emerin self-assemblies and the mechanics of the nucleus during myogenesis and responses to force involves adaptive changes in this interaction meshwork, which do not occur correctly in EDMD. These hypotheses will be tested in three aims. In Aim 1, we will employ in situ spatial proximity labeling and quantitative proteomics to establish changes in the local proteomic environments of emerin monomers or oligomers within the NE as a function of: (i) EDMD- inducing emerin mutations, (ii) cell myogenesis, and (iii) mechanical forces on nuclei. In Aim2, we will define how interactions of the emerin LEM-domain with the disordered region of other emerin, and competition from other LEM- domain proteins at the INE, allow for the adaptive formation of emerin oligomers in response to forces. This will be done using functional genomics, super-resolution imaging and single molecule tracking in cells and biomechanic assays. In Aim3, we will map conformational changes as the emerin LEM-domain makes in-cis or in-trans molecular contacts with various parts of the disordered region. We will also define how interacting partners of emerin modulate those conformations to promote or impede its self-assembly. These studies will be done by in vitro single molecule FRET on recombinant emerin. This work will provide novel mechanistic insights into emerin’s function and the general principles of NE mechanics, laying a groundwork to develop remedial strategies for EDMD and other diseases involving impaired nuclear responses to forces.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Nuclear RNA interference (RNAi) is a conserved gene regulatory pathway that is essential for transgenerational fertility and genome stability. Mediated by Argonaute proteins and small RNAs, nuclear RNAi ensures proper regulation of vital processes such as accurate gametogenesis, proper embryonic development, and transposon silencing. Given their critical biological importance, defective and dysregulated Argonaute proteins are associated with various reproductive and developmental disorders, as well as cancer formation and progression. Since the nuclear RNAi pathway and the Argonaute protein structure are highly conserved, we can use C. elegans to investigate these complex human disorders. Our long-term goal is to use C. elegans to characterize the nuclear RNAi pathway, which will provide insight into the molecular biology that drives these human disorders and how it affects fertility, development, and genome integrity. There remain critical outstanding questions regarding the nuclear RNAi pathway. Firstly, Argonaute nuclear import remains poorly characterized, even though it is imperative for nuclear RNAi. Secondly, the biological function of the N-terminal intrinsically disordered regions (IDRs) of Argonaute proteins remains elusive. Thirdly, post-translational modifications (PTMs) and their role in regulating nuclear Argonaute function are not well characterized. The objective of this proposal is to determine the regulation and molecular mechanism of Argonaute nuclear import that mediates transgenerational fertility. The proposal will address the central hypothesis that the N-terminal IDR of nuclear Argonaute protein HRDE-1, is essential for small RNA-loading and that PTMs regulate HRDE-1 function and nuclear import. Aim 1 will elucidate the biological function of the HRDE-1 N-terminal IDR, using innovative experiments such as CRISPR editing, small RNA-binding assays and sequencing, and fluorescence imaging. Importantly, we will directly determine the effect on transgenerational fertility and RNAi inheritance in these mutants with phenotypic assays. Aim 2 will characterize the role of PTMs in regulating HRDE-1 sRNA binding and nuclear import, utilizing the experiments listed in Aim 1 as well as the phenotypic assays. Aim 3 will determine the factors and pathways that mediate HRDE-1 nuclear import. Potential nuclear import factors have been identified and preliminary results have verified two candidates to be true HRDE-1 nuclear import factors. We will continue screening other candidates with RNAi gene knockdown and fluorescence imaging experiments. The physical interaction of HRDE-1 and the nuclear import factors will be determined with co-immunoprecipitations. The expected results of this proposal will determine the biological function of HRDE-1 N-terminal IDR, as well as identify the molecular mechanism and factors that mediate HRDE-1 nuclear import. The overall impact of this research is that it will provide valuable molecular insight into the conserved nuclear RNAi pathway, which will shed light on how dysregulation of the pathway can lead to devastating human disorders.

NIH Research Projects · FY 2025 · 2025-08

ABSTRACT / PROJECT SUMMARY In breast cancer, interactions between suppressive components of the immune system contribute to the lack of efficacy of immunotherapies. This project will use mechanistic computational modeling to understand the tumor-immune ecosystem of breast tumors and guide strategies to improve immunotherapy. The main objective of this proposal is to quantitatively study and model the tumor-immune microenvironment in breast cancer. We will use an integrated approach combining computational modeling of the spatiotemporal organization of the breast tumors with quantitative experiments. The overarching hypothesis is that constructing a computational model whose long-term states match tumor characteristics identified in vivo can successfully predict the spatiotemporal behaviors and effects of immunotherapies for breast tumors. The outcomes of this project are a validated computational model of the tumor-immune ecosystem and new quantitative insights into how immunotherapies can be more strategically employed in breast cancer. This work builds on the PIs’ extensive experience in modeling and analysis of cancer cell behavior and tumor growth (Finley) and clinical oncology and immunotherapy (Roussos Torres). To establish a predictive framework of the breast tumor microenvironment (TME), we will construct an agent- based computational model that captures the tumor-immune ecosystem and cell-cell interactions (AIM 1). In parallel with development of the computational model, we will quantify MDSC immunosuppressive function, measure tumor growth dynamics, and profile the composition and spatial organization of advanced breast tumors using an established in vivo mouse model of breast-to-lung metastasis (AIM 2). These data will be used to calibrate the computational model using a novel approach to leverage tumor imaging data. We will apply the calibrated model to determine how individual cell-cell interactions and cellular properties affect tumor composition, spatial organization, and response to immunotherapies (AIM 3). We will investigate how cell-cell interactions affect tumor growth and simulate a range of immunotherapies to determine the cell properties and interactions that influence response. The effective immunotherapeutic strategies will be tested in vivo and validated using human tumor samples. This project leverages a physical sciences perspective to drive cancer research, leveraging experiment-based mechanistic modeling of the tumor-immune ecosystem. Upon completion of this project, we will: (1) quantitatively illuminate the impact of cell-cell interactions on metastatic breast tumor growth, (2) predict the effects of immunotherapy strategies for metastatic breast tumors, and (3) test the predicted strategies in vivo. We will pursue in silico, in vivo, and ex vivo studies, ultimately paving the way for clinical translation. Our work will provide quantitative understanding of the tumor-immune ecosystem in breast tumors and aid in the development of effective immunotherapy for this devastating form of cancer.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY The diversity of neuronal cell types within the mammalian brain is derived from neural stem cells that sequentially express specific transcriptional programs. Control of gene expression programs is critically important for proper neural stem cell differentiation and subtype specification, which consists of many regulatory processes, including transcriptional and post-transcriptional regulatory events that balance both RNA synthesis and degradation. Intriguingly, mutations in essential and ubiquitous post-transcriptional RNA-regulatory proteins cause neurodevelopmental disorders (NDDs) characterized by distinct cell type and/or brain-region-specific pathology, thus underscoring the importance of post-transcriptional regulation of gene expression to support proper brain development. This proposal will focus on an essential and ubiquitous RNA processing and decay machine, the RNA exosome, and its role during cell fate determination in neuronal cells. The RNA exosome is critically important for the precise processing and turnover of various RNAs, including rRNAs. Recessive missense mutations in the EXOSC3 gene, which encodes a structural subunit of the RNA exosome, cause Pontocerebellar Hypoplasia Type 1b (PCH1b). PCH1b is a severe NDD clinically characterized by atrophy of the brainstem and cerebellar structures. The severity of PCH1b pathology is influenced by EXOSC3 allelic heterogeneity, suggesting a genotype-phenotype correlation. Most RNA exosome-associated diseases include neurological phenotypes, suggesting tissue-specific function(s) for the RNA exosome. These observations reveal a vital role for the RNA exosome within the nervous system and provide a rationale to characterize the function of the complex within the brain. The central hypothesis underlying the proposed work is that the RNA exosome processes specific target RNAs essential for proper progenitor proliferation and neuronal differentiation within the brain. We have generated human induced pluripotent stem cells (hiPSCs) with two distinct PCH1b disease- causing EXOSC3 mutations linked to moderate or severe disease via CRISPR engineering to reveal the molecular and cellular basis of PCH1b. My preliminary data in hiPSC-derived cerebellar organoids modeling PCH1b mutations show cellular defects and increased steady-state levels of multiple functionally important neuronal transcripts, including synaptic regulator Arc, compared to controls. Utilizing this model, I aim to 1) define cell type-specific defects, including progenitor proliferation and differentiation and genotype-phenotype correlations, and 2) analyze gene expression dynamics in hiPSC-derived cerebellar organoids modeling PCH1b- linked EXOSC3 missense mutations compared to controls. Alongside the work proposed here, we will perform functional assays to test the neuronal function of mutant cerebellar organoids compared to controls. This project is designed to elucidate the role of the RNA exosome in cell fate determination within the cerebellum and establish whether defective differentiation is a critical component of PCH1b pathogenesis.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY In response to Notice of Special Interest (NOSI): Single-Cell Level Spatiotemporal Mapping of Dental and Craniofacial Embryogenesis (NOT-DE-22-003), we propose innovative new technology to comprehensively map the transcriptomes and lineage histories of all cell types in the vertebrate face, and how these are altered in congenital anomalies and adult repair. Current lineage tracing techniques lack the ability to define all cell relationships at single-cell resolution in complex vertebrate tissues such as the face. The cranial neural crest- derived mesenchyme generates the majority of skeletal and connective tissues of the face, yet we still have an incomplete picture of the diversity of mesenchymal progenitors, whether there is a hierarchy or continuum of cell fate decisions, and whether lineages vary across the face to generate diverse structures. To do so requires new ways to label every cell uniquely and permanently in the developing face, and then to read out shared lineage histories and cell type information in the context of native tissue. This proposal aims to achieve this through a cross-disciplinary collaboration between zebrafish (Gage Crump) and mouse (Amy Merrill) craniofacial experts at the University of Southern California and a systems biologist (Michael Elowitz) at the California Institute of Technology. As zebrafish and mouse are powerful and complementary models for craniofacial disease research, we will use seqFISH technology to map the transcriptomes of all cell types of the developing face in both species. We will then use a new type of MEMOIR lineage barcoding technique developed by PI Elowitz that allows lineage histories to be read out together with transcriptomes by seqFISH while preserving spatial context. Integration of seqFISH and MEMOIR will allow us to comprehensively define all craniofacial cell types and their lineage relatedness from the time of barcoding. Analysis of lineage histories of the normal fish and mouse face will reveal the types of lineage relationships, how they vary across the face, and the extent to which they are conserved across vertebrates. Next, we will apply MEMOIR to complementary zebrafish and mouse Nr5a2 mutant models, which we showed have altered ratios of skeletal and connective tissue types in the jaw. Global analysis of altered lineage relationships in mutants will reveal the types of lineage decisions controlled by the Nr5a2 nuclear receptor and the extent to which its role has evolved from fish to mammals. We will also use MEMOIR to understand the degree to which reprogramming of cell identity may underlie the ability of zebrafish to robustly regenerate bone and cartilage in the adult jaw. In particular, we will compare the extent to which different injuries induce distinct types of lineage plasticity to replace the correct missing cell types. In the future, stimulating lineage plasticity in mammals may represent a novel strategy to boost endogenous repair of non-healing injuries. More broadly, our project will serve as a paradigm for globally defining lineage relationships in diverse craniofacial tissues in the context of normal development, genetic disease, and adult repair.

NSF Awards · FY 2025 · 2025-08

Recent advances in artificial intelligence and machine learning offer a unique opportunity to develop the next generation of autonomous systems for high-impact applications such as search and rescue missions, natural disaster prevention, and personalized robotics. However, because AI systems inevitably exhibit some degree of error, a major obstacle to their widespread deployment is ensuring they operate safely and reliably in real-world environments—while minimizing the risk of catastrophic failures that could compromise mission success. Existing approaches to these challenges are either bottlenecked by their computational requirements, or rely on heuristic methods that lack formal guarantees. This project addresses these challenges by designing algorithmic frameworks that offer rigorous risk quantification and control, without sacrificing computational tractability. The successful completion of this work will mark a significant step toward the safe and reliable deployment of AI-driven autonomous systems at scale. This project advances state-of-the-art control design and verification techniques for Cyber-Physical Systems (CPS) that make decisions based on partial information collected from high-dimensional sensor modalities, such as cameras and LiDAR—settings where conventional methods become intractable due to scalability limitations. To overcome computational barriers, this project develops algorithms that use representation learning to summarize the semantic information required for decision-making into compact latent representations. These representations are equipped with high-probability confidence sets using modern uncertainty quantification techniques. Crucially, this project also addresses the distribution shift challenges that inevitably arise when incorporating learned components into feedback loops. With calibrated uncertainty sets in place, formal methods are used to design controllers directly in the latent space, enabling safety and correctness guarantees to be transferred to the original CPS. The proposed approach is validated both in simulation and on real hardware across a wide range of benchmark tasks, including wildfire monitoring and intervention, robotic manipulation, and autonomous navigation in cluttered environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Neuromodulation implants such as spinal cord stimulators and vagus nerve stimulators have the potential to treat a wide range of conditions, including chronic pain, movement disorder, and organ dysfunction. These technologies can significantly improve quality of life for people with injuries or neurological diseases. However, the high cost and complexity of developing such devices greatly limits the development of new therapies, especially for patients with rare diseases. To help address these challenges, the Center for Autonomic Recording and Stimulation Systems (CARSS) was formed to develop and share open-source neurotechnology devices that can be used for research, clinical trials, and the commercialization of new therapies. CARSS resulted in the development of OpenNerve, a neuromodulation implant and a set of compatible sensors and stimulators, with designs released online under an open-source copyright license. Collaborations and outreach to researchers aim to test new therapies in preclinical trials. This effort seeks to formalize a community that works together to address serious diseases using neuromodulation device technology and eventually to advance these into clinical use. The goal of the Pathways to Enable Open-Source Ecosystems (POSE) Phase 1 project is to better understand and document the needs and opportunities in open-source neuromodulation through interviews with physicians, researchers, and entrepreneurs who are developing neurotechnology, as well as individuals with experience in open-source medical technology. This project engages with these communities through individual interviews and group workshops where potential users can interact with the resources CARSS is developing and give direct feedback. The results of the community engagement will guide the development of a sustainable governance structure for CARSS, including transparent governance and clear protocols for accepting design changes developed by outside contributors while still ensuring patient safety and device functionality. Ultimately, establishing a robust open-source ecosystem will accelerate innovation in neuromodulation devices and expand access to life-changing therapies for patients who currently have limited treatment options. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This Research Experiences for Undergraduates (REU) site award to University of Southern California Information Sciences Institute supports the training of students. The program immerses students in cutting-edge research in networking, cybersecurity, AI, and data science. Each student is partnered with a research mentor to help guide their research on a given topic. This REU project will help mentor and grow the next generation of researchers and scientists. The specific research directions that students will explore evolve with each year's mentors and their current research projects. Projects span a range of topics in networking, cybersecurity, AI, and data science: In networking, research topics include new methods to evaluate networking in interconnected regions of the world, new methods to measure wireless network reliability, and understanding DNS root traffic. In cybersecurity, research topics include innovative ways to detect attack preparation, improvements to privacy by data aggregation, and new approaches to build knowledge graphs for security analysis of cyberinfrastructure. In data science and AI, research topics include AI-driven simulations of human decision making, boosting simulation techniques of quantum, many-body physics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

With support of the Chemical Mechanism, Function, and Properties Program and Chemical Catalysis Programs of the Division of Chemistry, Professor Smaranda C. Marinescu of the Department of Chemistry at the University of Southern California will study the development of biologically inspired catalytic systems for the electrochemical conversion of carbon dioxide into chemical fuels. The goal of the project is to develop a series of cobalt complexes with pendent amines and explore their electrochemical activity to understand the effect of proton relays on multi-electron, multi-proton reactions. The ligand framework allows for excellent control of electronic and positioning effects, as well as the number of proton relays incorporated in the second coordination sphere of the metal complex, which facilitate structure-activity studies. The project lies at the interface of synthetic inorganic and organic chemistry, as well as electrochemistry and catalysis, and is therefore well suited to provide the highest level of education and training for scientists at all levels. Previous studies in the Marinescu group have shown that a series of cobalt complexes with zero to four pendent secondary amines (NH) displays a linear correlation between the rate of CO2 reduction and the number of pendent NH moieties. Experiment and theory suggest that the pendent NH groups do not directly transfer protons to CO2, but instead bind acid molecules from solution, leading to the formation of a catalyst-acid adduct, held together through a hydrogen-bonding network, that enables direct proton transfer from acid to the activated CO2 substrate. The research goals of this proposal are to understand the factors that govern the catalytic properties of these complexes, in terms of activity and selectivity for CO2 reduction, by characterizing and changing the electronic environment (the primary and secondary coordination spheres) of these metal aminopyridine macrocycles. This project focuses on the synthesis and characterization of a variety of aminopyridine complexes with pendent hydrogen bond donors or cationic groups, to understand the effects of these moieties on the catalyst activity and selectivity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Autonomous agents like warehouse robots, sidewalk-operating delivery robots, drones, and robo-taxi fleets are being increasingly deployed in the real world. These agents incorporate artificial intelligence (AI)-driven sensing and estimation, and planning and control. The real-world performance of these agents, however, is far from satisfactory. It is not uncommon to see a delivery robot being confused by an obstacle in its path on the sidewalk, a drone failing to achieve its objectives, or a robo-car driving irratically. Despite significant advances in AI-based design for such agents, there remain critical challenges in making them robust for performance in the real world. Firstly, autonomous agents must obey operational rules (e.g., traffic rules) and maintain safety at all times. Secondly, these agents rely on various sensors, such as cameras, radar, and proximity sensors, which provide only a partial or imperfect observation of the state of the environment. Thirdly, since such agents rarely operate in isolation, they need to coordinate with other autonomous agents or humans. Finally, to ensure robust and resilient performance upon deployment, it is vital to develop methods for runtime monitoring and adaptation for these agents. This project aims to address these critical challenges for achieving robust performance by autonomous agents in the real world. The research conducted in this project can significantly impact the science of safety-assured autonomy with potential applications in autonomous transportation systems, robotics, and smart manufacturing systems. This project will bring together formal methods, reinforcement learning, and multi-agent control theory to develop a scalable framework for high-assurance design of safety-critical and mission-critical cooperative interacting agents. The research conducted in this project will aim to develop model-based and model-free algorithms for control synthesis using data generated in high-fidelity simulators to optimize a performance criterion subject to the satisfaction of specifications expressed in signal temporal logic. The logic will capture safety requirements, operational constraints, and complex environment behaviors. This project will also develop control algorithms for an autonomous agent operating in a mixed-agent environment where human agents may be present, and for teams of cooperative autonomous agents with system-wide objectives and specifications. It will investigate the design and analysis of robust offline and online monitoring algorithms for high-assurance design under uncertainty. It will also aim to design scalable, hierarchical verification algorithms that leverage data and a new formal model based on graphs of assume-guarantee specifications to reason about system correctness and safety both at design-time and runtime. The research in this project will contribute to the foundations of robust intelligence for multi-agent systems that can operate in complex, real-world environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

There is a growing shortage of cybersecurity professionals today, with the shortage estimated at 5.5 million in 2023. Cybersecurity professionals require education in data science to be able to properly collect, clean, correlate, store and analyze real system and network data, and make informed cybersecurity decisions. This project develops a large set of hands-on, virtual materials for teaching data science for cybersecurity. These materials will be hosted on the NSF-funded SPHERE research infrastructure and will be publicly and freely available to interested teachers and students. These materials will prepare new generations of cybersecurity professionals to tackle real-world problems collaboratively and at a large scale. This project builds learning materials that will enrich cybersecurity curricula at many schools and colleges, with practical, hands-on exercises that teach data science for cybersecurity. The materials will be useful as either homework assignments or class projects. The materials will include individual and group assignments. Individual materials will provide three difficulty levels - beginner, intermediate and advanced -- and they will support self-learning and self-assessment. Group assignments will engage groups of students on realistic, large scale cybersecurity tasks to promote teamwork, collaboration and communication. The materials will be publicly and freely available to all interested teachers and students, on the NSF-funded SPHERE research infrastructure. Teachers and students will also leverage SPHERE to collect, clean, store, manage and analyze cybersecurity data to gain practical data-science skills. This project is supported by the Data Science Corps program, which supports data science education and training to build a strong national data science infrastructure and 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 2025 · 2025-08

This CAREER project will apply machine learning tools to develop new computational methods to improve predictability in atmospheric chemistry. The project is focused on gaining a better understanding of the controlling factors behind the chemical composition of the atmosphere, especially those related to the plant biosphere. This effort is expected to transform the current understanding of biosphere-atmosphere interactions and provide an important base for numerous future atmospheric observational and modeling studies. This work supports an investigation into the impacts of the uncertainties in the biosphere-atmosphere interactions on atmospheric composition using a combination of traditional computational methods alongside novel machine learning techniques, all grounded in a suite of observations. The plant biosphere contributes many trace species to the atmosphere, as well as exerting secondary effects and feedbacks within the Earth System. Biogenic emissions are responsible for most reactive carbon in the atmosphere, and control much of the atmospheric reactivity worldwide. The science objectives of this project are to: (1) quantify uncertainty in modern understanding of the influence of the plant biosphere on atmospheric chemistry and composition; and (2) investigate the impact of canopy-scale processes on regional-to-global aerosol and reactive trace gas abundances. The project will support the participation of both graduate and undergraduate students in the research. It also includes an outreach program focused on the impact of the plant biosphere on air quality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Alzheimer’s disease (AD) affects around 6 million Americans and imposes a burden of $320 billion on the US economy. Since 2003, very few medications have been approved for AD. Neuroinflammation is one of the leading mechanisms for AD pathology affecting several brain cell types and is an active target for treatment. One of the promising targets for neuroinflammation is the calcium-dependent phospholipase A2 (cPLA2) which regulates neuronal membrane excitability, polyunsaturated fatty acid metabolism, and vascular integrity. Increased activation of cPLA2 in the brain is associated with eicosanoid inflammatory lipid profiles and is observed in AD, post-stroke, Parkinson, spinal cord injury, and traumatic brain animal models. Importantly, genetic or pharmacological reduction of cPLA2 activity ameliorates disease severity across these models. Our group has demonstrated that carrying the APOE e4 (APOE4) allele, the strongest genetic risk factor for AD, activates cPLA2 in the brain. In postmortem human brain tissues, we reported eicosanoid lipid activation profiles consistent with cPLA2 activation in APOE4 AD. Despite the strong preclinical signals, no brain penetrant cPLA2 inhibitors advanced to clinical trials. Our team has established a structure-based drug discovery platform and used it to identify new lead compounds that inhibit cPLA2, confirming their potency in vitro and in cells and brain penetrance. Our current lead has an affinity, safety, and blood- brain barrier penetration consistent with drug-like properties and initial SAR suggesting high optimization potential. Moreover, we developed a brain imaging tool based on the uptake of arachidonic acid in the brain to assess the efficacy of cPLA2 inhibition in vivo. Using these platforms, we plan to establish a comprehensive SAR for our main and backup lead series and develop a screening funnel. In close collaboration with consultants and contractors, this screening funnel will then be employed to optimize the lead potency, selectivity, ADMET, and PK properties relevant for cPLA2 inhibitors, including in vivo potency and minimal toxicity profiles. This would allow for the selection of clinical candidates for the development of IND- enabling studies and preparation of IND targeting AD neuroinflammation.

NIH Research Projects · FY 2026 · 2025-08

SUMMARY DSBs occur in every cell because of exposure to environmental mutagens, such ionizing radiation, chemotherapeutic drugs, UV light, toxic pollutants, and smoking, as well as in response to normal cell metabolism, such as DNA replication. Repairing DSBs is particularly challenging in pericentromeric heterochromatin, a poorly characterized region of the genome where the abundance of repeated sequences can trigger aberrant recombination and widespread genomic instability. And yet, heterochromatin repair mechanisms remain poorly understood. We discovered a specialized pathway that promotes faithful homologous recombination (HR) repair in heterochromatin while preventing aberrant recombination. HR starts inside the heterochromatin “domain” with resection, but it continues only after a striking relocalization of repair sites to the nuclear periphery. Relocalization likely prevents aberrant recombination by isolating DSBs and their repair templates away from ectopic sequences before strand invasion. We have recently discovered that the movement occurs in two phases. Repair sites initially diffuse from the center to the periphery of the heterochromatin domain, and this requires Nup98 and its phase separation properties. Next, nuclear actin filaments (F-actin) and myosins drive the directed motion of repair sites to the nuclear periphery. How condensates contribute to these dynamics is not clear. The mechanisms targeting condensate formation and F-actin assembly specifically to heterochromatic DSBs are also unknown. Dysregulation of heterochromatin repair is likely one of the most underestimated and powerful sources of tumorigenesis and identifying the mechanisms involved is essential for understanding cancer etiology. Our central hypotheses are that immiscibility between Nup98 and HP1 condensates generate capillary forces that mobilize repair sites inside the heterochromatin domain, while DNA damage response proteins work in concert with HP1 to target Nup98 and actin nucleators specifically to heterochromatic DSBs. We will combine a wealth of imaging, genetic and biochemical approaches to investigate the molecular mechanisms involved in this process. Expected positive outcomes of this research include the systematic identification of the molecular machinery that protects heterochromatin from massive genome rearrangements, enabling successful completion of HR repair. These studies are also expected to illuminate missing links between nuclear architecture and dynamics, phase separation, repair progression, and the stability of repeated DNA sequences. These results will have an important positive impact by identifying crucial safeguard mechanisms used by normal cells to protect their genome from a variety of environmental threats. Dysregulation of these pathways result in genome instability and tumorigenesis. Thus, we expect that the proposed studies and future research will trigger exciting advancements in the prevention, early detection, and treatment of cancer and other genome-instability disorders.

NSF Awards · FY 2025 · 2025-08

This project explores exciting new interactions between two central areas of mathematics - algebra and geometry - and their unexpected connections through physics. Algebra and geometry are foundational tools in mathematics, widely used in numerous scientific and engineering applications, such as computer science, data analysis, robotics, and theoretical physics. Historically, the interplay between algebraic equations and geometric shapes has led to powerful methods and profound insights, shaping much of modern mathematics and technology. In recent decades, researchers discovered surprising connections linking algebraic geometry, which studies shapes defined by polynomial equations, to symplectic geometry, an area crucial to physics and engineering. This project leverages these emerging connections to develop new mathematical tools that bridge algebra and geometry. Broader impacts of this research include significant training and mentoring activities. The project supports early-career researchers and graduate students, providing extensive professional development through workshops, virtual seminars, public lectures, and the creation of publicly available computational tools. On the technical side, the project aims to advance understanding in multigraded commutative algebra, toric geometry, and symplectic geometry. It addresses long-standing gaps and open questions in commutative algebra and toric geometry by introducing methods inspired by recent advances in homological mirror symmetry into purely algebraic contexts. The P.I.’s will explore new approaches to studying multigraded polynomial rings, aiming to uncover deeper structural properties that parallel classical results for standard graded polynomial rings. The project will develop algebraic analogues of effective symplectic geometry techniques, such as "stop manipulation," adapting these symplectic methods to algebraic settings. The project will also extend foundational results, including Orlov’s Theorem, to multigraded and toric settings, construct novel categorical structures that unify algebraic and geometric perspectives, explore applications to virtual resolutions and other questions involving shortest resolutions, and investigate extensions to broader classes of geometric objects through toric degenerations and natural generalizations from toric varieties. Furthermore, by establishing explicit links between algebraic constructions and Fukaya categories, the project will introduce new computational tools and theoretical approaches in symplectic geometry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Human milk is recommended as the exclusive mode of feeding for infants, but the factors influencing the hundreds of nutritive and bioactive factors in milk and their functions in the infant remain mostly unexplored. A genomics approach enables investigation of human milk as a biological system and can identify the most important factors connecting human milk composition with infant health. One such approach is milk metabolomics, which reflects the biology of the lactating mammary gland and the bioactive and nutritive components of milk that shape its function in the infant. This proposal leverages metabolomics and other cutting edge technologies to comprehensively profile the composition of human milk and its impacts on infant development. The results will provide foundational data for advancing milk research and help advance our knowledge of human nutrition during the critical first 1000 days of life. Aim 1 will identify genetic variants influencing specific milk metabolites and the genes underlying these associations, and investigate connections between the milk metabolome and infant gut microbiome. In Aim 2, the effect of human cytomegalovirus (HCMV) reactivation in the mammary gland on the milk metabolome will be assessed. HCMV is a highly prevalent virus, and postnatal exposure to HCMV via breast milk often has serious clinical consequences for preterm infants. This aim will also test for impacts of mammary HCMV reactivation and the milk metabolome on infant serum markers of inflammation. The first two aims will occur primarily during the K99 period, and will be accompanied by training in virology, immunology, and computational methods for integration of multimodal datasets. Aim 3, during the R00 phase, will investigate the impacts of human milk composition on the systems-level development of the preterm infant immune system and gut microbiome, utilizing a new cohort of preterm infants. Longitudinal sample collection will enable disentangling of the connections across these dynamic biological systems. Training in the K99 period will take place at the University of Minnesota – Twin Cities, a major research institution, with state-of-the-art core facilities for genomics and clinical/translational research. The PI will receive training from an interdisciplinary team of mentors in the departments of Pediatrics, Epidemiology & Community Health, and Genetics, Cell Biology and Development. This training plan will result in the trainee acquiring the skills and a scientific foundation to launch an independent academic career.

NIH Research Projects · FY 2025 · 2025-07

Vietnam Research Training Program on Sexually Transmitted Infections and Antimicrobial Resistance (V-STAR): Summary/Abstract The proposed training program will train PhD and postdoctoral fellows in sexually transmitted infections and antimicrobial resistance. Sexually transmitted diseases remain a serious public health issue in Vietnam in particular syphilis, chlamydia, and gonorrhea. Untreated sexually transmitted infections may result in severe health consequences including chronic pelvic pain, ectopic pregnancy, pelvic inflammatory disease, infertility, adverse pregnancy outcomes, and increased acquisition and spread of human immunodeficiency virus (HIV) infection. Increases in sexually transmitted infections over the past decade have been associated with changes in behaviors associated with increased transmission including a greater number of sexual contacts, decreased condom use, and increased sexual concurrency associated with Vietnam’s rapid socioeconomic transition and globalization. Additionally, the introduction of HIV pre-exposure prophylaxis (PrEP) has resulted in similar trends in groups using PrEP, such as men who have sex with men and sex workers. Furthermore, the continued and rapid emergence of antimicrobial resistance is a severe threat to population and public health and the ability to treat and prevent infections. The training program will support the integration of research on sexually transmitted infections, antimicrobial resistance and behavioral science. In this proposal, we will train local early-career scientists in three focus areas (epidemiology, microbiology, and behavioral sciences) to foster the next generation of researchers and policy makers to control and prevent sexually transmitted infections and antimicrobial resistance. The overall goals of the proposed V-STAR program are to: 1) Build and maintain an integrated research training program in applied infectious disease epidemiology, microbiology, and behavioral sciences with multiple academic and research institutions in Vietnam in partnership with the University of Southern California. 2) Support and conduct pilot research studies to enable successful future grant applications for independent investigators in the areas of sexually transmitted infections and antimicrobial resistance

NIH Research Projects · FY 2026 · 2025-07

Cigarette smoking and obesity are the two leading preventable causes of morbidity and mortality in the United States. Despite historical declines in smoking rates, cigarette smoking continues to account for an estimated 480,000 U.S. deaths per year, and available pharmacotherapies for smoking cessation are only moderately effective. Obesity rates continue to rise dramatically: nearly one-half of U.S. adults will live with obesity by 2030. The emergence of highly effective incretin-based therapies for diabetes and obesity, including long-acting glucagon-like-peptide 1 (GLP-1) receptor agonists and dual GLP-1/GIP (gastric inhibitory peptide) receptor agonists, is leading to rapid advances in the clinical management of obesity and other cardiometabolic disorders. Importantly, preclinical and emerging clinical findings indicate that GLP-1 receptor agonists reduce the intake of addictive drugs, including nicotine. Data from early studies with people who smoke suggest that GLP-1RA may increase cigarette abstinence and prevent post-cessation weight gain. Consistent with this evidence, our Phase II clinical trial data indicate that low-dose treatment with a long-acting GLP-1 receptor agonist leads to prospective reductions in cigarette craving and cigarettes per day in non-treatment-seeking people who smoke. Larger randomized trials are now needed to determine the efficacy of incretin-based therapies for smoking cessation in treatment-seeking samples. Tirzepatide, the first dual GLP-1/GIP receptor agonist, shows superior efficacy for weight loss compared to semaglutide. Evidence further indicates that tirzepatide has protective effects on cardiovascular risk outcomes. Therefore, tirzepatide is a promising candidate therapy with potential applications for cessation, reduction of cardiovascular risks, and prevention of post-cessation weight gain. This multi-site, 24-week clinical trial seeks to expedite data on the efficacy of tirzepatide for smoking cessation and associated outcomes (e.g., cigarettes per day, post-cessation weight gain, cardiovascular risk factors) in treatment-seeking people who smoke. Expediting Phase II clinical data on the efficacy of tirzepatide in treatment-seeking people who smoke will help to inform dose selection and populations of focus for pivotal Phase III clinical trials.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Stroke is a leading cause of long-term disability worldwide, leaving many with restricted mobility that contributes to progressive functional decline, reduces quality of life, and increases the risk of recurrent stroke. Rehabilitation interventions are designed to enhance mobility post-stroke by reducing motor impairments and walking capacity limitations. However, post-stroke mobility is a multifactorial construct that is only partially explained by motor impairments, and motor-centered treatment approaches have a limited impact on walking performance in daily life. This suggests a more comprehensive understanding of the non-motor sequelae of stroke that influence mobility is necessary. Here, we posit that post-stroke apathy is an important factor to examine. Apathy, affecting over one-third of stroke survivors, is rarely studied in neurorehabilitation. Defined as a psychiatric syndrome characterized by a loss of motivation and reduced self-driven, goal-directed activity, apathy impacts effort-based decision-making, reducing willingness to exert effort for rewards due to altered sensitivity to rewards or effort. This provides a theoretical basis for a potential negative association between apathy and mobility, which remains untested. Additionally, the neural mechanism underlying post-stroke apathy is not well understood. Emerging research suggests that lower functional connectivity of brain areas involved in reward and salience processing are neural signatures of apathy in older adults. Yet, functional connectivity correlates of post-stroke apathy are poorly understood. The overall objective of this Katz Early-Stage Investigator Research Project Grant proposal is to determine the association between post-stroke apathy mobility and identify the functional connectivity signatures of post-stroke apathy. To accomplish this, we will conduct a cross-sectional observational study with three aims in a single cohort of 125 participants with chronic stroke. We will study two aspects of mobility post- stroke– habitual gait speed and community mobility. Aim 1 is designed to determine the relationship between post-stroke apathy and gait speed reserve, where gait speed reserve is the difference between one’s habitual gait speed and the speed that would minimize the total effort to move a given distance. Aim 2 will determine how post-stroke apathy is related to community mobility. Community mobility will be operationalized as a comprehensive combined metric of three commonly used measures – steps per day, GPS-derived activity space, and life-space mobility. Finally, Aim 3 will examine the association between post-stroke apathy and functional connectivity signatures of apathy using resting state-functional MRI. People with stroke who have a range of apathy severity will complete the procedures necessary for these aims over two sessions that take place one week apart. Successful completion of these aims will 1) lead to a more comprehensive understanding of post- stroke mobility determinants, catalyzing a paradigm shift in neurorehabilitation to consider neuropsychiatric symptoms as necessary treatment targets to improve mobility, and 2) delineate a potential neural mechanism of post-stroke apathy, essential for developing effective interventions to prevent or mitigate apathy after stroke.

NIH Research Projects · FY 2025 · 2025-07

Cannabis is the most used illicit substance during pregnancy. Rates of self-medicating with cannabis escalated during the COVID-19 pandemic. The scientific objective of this proposal is to investigate the mechanisms contributing to preconceptions about those who use cannabis, especially during pregnancy. The central hypothesis is that preconceptions about those who use cannabis result in negative interactions between patients and clinicians that reduce the quality of healthcare and result in poor outcomes. This innovative project will be the first to: (a) leverage natural language processing/artificial intelligence (NLP/AI) techniques to investigate preconceptions about cannabis use in clinical notes, and (b) investigate associations between cannabis use and prenatal care quality. Research aims will: (Aim 1) Investigate preconceptions about those who use cannabis during pregnancy using a mixed methods approach that integrates NLP/AI and qualitative interviews; (Aim 2) Investigate associations between cannabis use and prenatal care quality among different population groups, such as differences in socioeconomic status and education levels; and (Aim 3) Develop, adapt, and test the feasibility and usability of a clinician training on quality health care practices for those who use cannabis during pregnancy using a multistage modified Delphi process, survey, and qualitative focus groups. This research is complemented by a training plan that builds upon Dr. Rachel Carmen Ceasar’s background in mixed qualitative-quantitative methods and substance use research. The training plan includes using NLP/AI approaches, advanced survey methods in reproductive epidemiology, and implementation science. Together, this research and training will prepare Dr. Ceasar to advance as an independent investigator conducting research on health and substance use among those who are pregnant across the lifespan. The proposed project will improve clinicians’ care of those who use cannabis during pregnancy, providing evidence to inform the development of interventions designed to reduce cannabis-use-related notions in prenatal care.

NIH Research Projects · FY 2025 · 2025-07

Project Summary: Accurate stem cell differentiation requires precise gene regulation at both the transcriptional and post- transcriptional levels. While the transcriptional activation networks governing cellular differentiation are well characterized, the mechanisms by which post-transcriptional control of gene expression alters the transcriptome to confer cellular identity remain poorly understood. The RNA exosome, an essential and ubiquitous post- transcriptional ribonuclease complex, plays a significant role in cellular differentiation by modulating steady-state level of RNA to maintain the pluripotent potential of stem cell and progenitor populations. Depletion of a single RNA exosome subunit results in precocious differentiation in several cell types, and deletion of any subunit is embryonically lethal. Although the RNA exosome is conserved from yeast to humans and plays essential roles in all cells, recessive mutations in RNA exosome subunit genes disproportionately affect neuronal tissues and give rise to severe neurodevelopmental disorders (NDDs). For instance, mutations in the EXOSC3 gene, encoding a structural cap subunit of the RNA exosome, cause Pontocerebellar Hypoplasia Type 1b (PCH1b), a prenatal onset NDD associated with severe spinal motor neuron defects that emerge during embryonic development. Similarly, a mutation in the EXOSC4 gene, which encodes a core subunit of the RNA exosome, was recently linked to a distinct NDD with a comparable spinal motor neuropathy phenotype. These findings suggest that spinal motor neurons are uniquely susceptible to RNA exosome-mediated post-transcriptional regulation defects. However, the RNA exosome has not been studied in human neurons or during neuronal differentiation. To investigate the function of the RNA exosome during neuronal differentiation, I will utilize human induced pluripotent stem cells (hiPSCs), engineered by my lab via CRISPR technology, to model RNA exosome- linked motor neuron diseases, including severe PCH1b (EXOSC3-G31A), mild PCH1b (EXOSC3-G191C), and a novel NDD linked to a mutation in EXOSC4 (EXOSC4-L187P). To study the RNA exosome in a disease- relevant context, I will investigate the impact of pathogenic EXOSC3/4 mutations on human spinal motor neuron differentiation using a 2D cell culture method. I hypothesize that disease-linked mutations in EXOSC3/4 alter the function of the RNA exosome during motor neuron differentiation, leading to an accumulation of pathogenic RNA species that cause spinal motor neuropathy. This research will provide a comprehensive analysis of how pathogenic mutations in RNA exosome subunit genes impact post-transcriptional regulation during human neuronal differentiation and provide insight into pathological mechanisms underlying RNA-mediated motor neuropathy.

NIH Research Projects · FY 2025 · 2025-07

The latest Artificial Intelligence (A.I.) smartphone apps have been a game changer for many persons with visual impairment. Yet, the current workflow of these apps remains cumbersome: snap a picture, ask a question, wait for a few seconds, listen to an (often verbose) answer; repeat. Could a workflow that is more natural, goal-driven, and more synergistic with a visually-impaired person be created? The basic tenet of this proposal is to leverage A.I. in a new way: Instead of being front and center, the A.I. will operate mostly in the background. Instead of only answering user queries, it will also be informed by a collection of machine vision algorithms that collect rich real-time visual information, from 4 cameras mounted on smart eyeglasses with an ultra-wide 270o field of view. The machine vision algorithms will run 100x faster than the A.I. can, to handle the dynamics of the world and of the moving user. The role for the A.I., then, will be to leverage its world knowledge to combine, unbeknownst to the user, contextual situation awareness from real-time vision with high-level tasks and goals specified verbally by the user. Our central working hypothesis is that the A.I. will be able to better assist its user by proactively integrating what the glasses have seen with what the user actually wants. Our team combines expertise in a) computational neuroscience, machine vision, and A.I. (PI Itti), b) micro- electronics and machine vision hardware (co-PI Narayanan), c) human factors, lived experience focus groups, and participant recruiting (GetBraille consultants), d) wearable electronics design and fabrication (Siliconscapes consultant), and e) lived experience partners and participants at the National Federation of the Blind and several other local chapters and communities. In the R61 phase, we aim to: 1) engage our lived experience partners in selecting the most useful algorithms for visual attention, object detection, 3D depth estimation, scene recognition, spatial navigation and mapping, and hand/face/body tracking algorithms. These will provide real-time situation awareness that runs in the glasses (no cloud servers), and is non-intrusive to the user; 2) develop visual-assistive large language models (LLMs) to digest and leverage the machine vision results and help the user achieve an unbounded range of tasks; 3) implement and accelerate the real-time machine vision algorithms onto Field-Programmable Gate Array (FPGA) custom hardware processors to achieve high energy efficiency in a small form-factor. In the R33 phase, we will collaborate with our lived experience partners to create and test affordable, intuitive, and useful smart glasses that can be broadly disseminated ($399; compare to $4,250 Orcam MyEye 3 Pro). We aim to 1) develop miniature, low-power, low-cost implementations of the glasses, 2) engage our lived experience partners in developing a user interface that works best for them, 3) conduct a user study with 5 groups of 50 PVI participants, each R33 year, covering a range of tasks from grocery shopping to cooking to working in a team, 4) address issues of privacy, security, social acceptance, and liability, through focus-groups with experts, the PVI community, and students.

NSF Awards · FY 2025 · 2025-07

The use of modern computing and data infrastructure is critical to harnessing the full potential of instruments, data, and tools offered by state-of-the-art laboratory facilities, but many scientists do not have the necessary knowledge of data management, scientific programming skills, or the ability to use computing resources to bring them to bear for data analysis, leading to new discoveries. This project - CITEAM - addresses the gap by developing an innovative training program targeting the materials science research community that relies on advanced microscopes for research and needs to process and manage large data volumes to make fundamental advances in materials science. CITEAM provides training for microscope data processing, the use of Artificial Intelligence methods in data analysis, and effective data management, thereby reducing time-to-science. The project helps researchers in overcoming challenges in handling large-scale datasets and utilizing novel computing methods and resources. The project increases computing skills, awareness, and literacy for researchers with limited computing expertise, thereby accelerating the scientific innovations in materials science. The CITEAM project brings together a team of researchers with expertise in cyberinfrastructure (CI) as well as in imaging-enabled materials science to develop an innovative training program targeting the materials science community that relies on advanced microscopes (e.g. Transmission Electron Microscopes (TEM)) for research. This project aims at optimizing return on a state-of-the-art investment in physical infrastructure - a new aberration-corrected Transmission/Scanning Electron Microscope (AC TEM/STEM) at UMD. The training program covers several relevant thematic areas - TEM instrument software, image analysis, scientific computing, application of AI in TEM image and data analysis, diffraction and spectroscopy data analysis, distributed computing for microscope data processing, data curation, and FAIR principles. The training program includes an additional element of "training the trainers" by exposing the research facilitators and laboratory staff scientists to advanced CI topics, empowering them to guide others and innovate in the use of CI for materials science. Training is offered for both users and trainers in a multitude of modalities to promote efficient learning - self-paced modules, video lectures, templates and catalogs, office hours, training sessions at annual CITEAM Users' workshop, and tutorials at domain science conferences. CITEAM promotes community building by developing a coordination network comprising similar imaging laboratories, different domain science communities that use advanced microscopes, and experts from national CI resource providers. The CITEAM coordination network helps in adapting and disseminating training materials beyond the participating institutions, ensuring both scalability and sustainability of the program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This grant fosters excellence and promotes student engagement in the fields of Dynamics, Vibration, and Acoustics by supporting their participation in the 37th Conference on Mechanical Vibration and Noise (Technical Track VIB), part of the 2025 ASME International Design Engineering Technical Conferences (IDETC 2025). VIB is a leading technical conference that covers a wide spectrum of experimental, analytical, and computational research in dynamics, vibration, and sound/acoustics engineering. Key topics include dynamics and control of smart structures, contact dynamics, data-driven and machine learning techniques in dynamics, biomechanics, biomimetics, industrial applications, and energy harvesting. The conference aims to foster the exchange of ideas and information among engineers and researchers within the vibration and sound community. The primary objective of this grant is to facilitate the participation of a diverse group of undergraduate and graduate students, particularly from underrepresented backgrounds, enhancing their academic and professional development at VIB 2025. This will be achieved by supporting student-oriented activities designed to maximize the academic and professional benefits of the conference. Three specific activities will be initiated under this grant. First, travel support for students and postdoctoral scholars will encourage participation from young talents, building a diverse community and fostering collaborative networks. A panel will select at least 15 participants to expand conference access. Second, the grant supports the Best Student Paper Awards, recognizing outstanding student achievements in dynamics and acoustics, and encouraging high-quality research. Lastly, an Industry Panel will guide graduates and postdocs into the workforce, providing insights from experts and linking academic research with practical applications, aiming to empower students for successful 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 2025 · 2025-07

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Richard Brutchey of the University of Southern California, in collaboration with Professors Elad Gross and Uri Banin of the Hebrew University of Jerusalem, will investigate new strategies to enhance the stability and performance of colloidal gold nanoparticles, which are widely used in medical diagnostics, cancer therapies, and chemical catalysis. Although gold nanoparticles have been employed for decades—such as in immunoassay-based diagnostic tests—their surfaces are typically coated with sulfur-containing organic molecules called thiols, which are prone to degradation when exposed to heat, light, or air. This project will explore a more durable class of organic molecules known as N-heterocyclic carbenes (NHCs), aiming to understand how these molecules bind to gold surfaces and contribute to the development of more robust, stable, and customizable nanoparticles. Beyond laboratory research, the team will conduct immersive nanochemistry workshops for community college students at Cerritos College in Los Angeles County, fostering interest and retention in STEM fields while promoting international collaboration and increasing transfer rates to four-year institutions. This research effort will focus on elucidating the binding behavior of NHCs on gold nanoparticle surfaces in comparison with traditional thiol-based ligands, particularly in terms of binding strength and crystal facet specificity. The team will experimentally quantify the thermodynamics of ligand binding—measuring enthalpy, entropy, and free energy—using variable-temperature solution nuclear magnetic resonance spectroscopy and isothermal titration calorimetry. Additionally, high-resolution infrared nanospectroscopy will be employed to investigate how NHCs and thiols competitively interact with distinct gold nanoparticle facets at the atomic scale. These findings will provide crucial insights into the mechanisms of NHC–gold surface binding, helping enable the rational design of more stable gold nanoparticle systems for targeted 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.