Saint Louis University

NSF Awards · FY 2026 · 2026-10

Underwater wireless communication and networking are critical for monitoring aquatic environments, improving maritime safety, supporting offshore exploration, and enhancing national security. However, research progress in this field has been slow compared with terrestrial wireless systems because conducting underwater wireless experiments is challenging. Natural underwater environments are uncontrollable, and indoor pools and tanks are static and small, which significantly limits research reproducibility, innovation, and public accessibility. To bridge this gap, this project implements a remotely accessible underwater communication and networking platform in a water tunnel that enables experimentation, dataset collection, and artificial intelligence model building under a range of controlled, reconfigurable, reproducible conditions. The testbed, datasets, and developed software enable new wireless communication technology development without the high cost and complications of natural underwater deployments. By sharing advanced experimental tools, datasets, and software with the research community, this project advances scientific discovery and strengthens national leadership in next-generation underwater communication and networking systems. In addition, this project integrates research with education and actively trains students in communication, networking, sensing, and artificial intelligence to support workforce development and address critical national needs. This project designs and deploys a hybrid underwater acoustic, magnetic, and visible light networking system that integrates a reconfigurable water-tunnel testbed, physics-informed multi-modal deep generative channel models, and a scalable digital twin for dynamic underwater networking simulation and optimal control. First, the remotely accessible, reconfigurable testbed instrument enables the collection of acoustic, magnetic, and visible light communication channel data under dynamic water flow and blockage conditions. Second, the collected datasets are used to train physics-informed deep generative channel models that extend beyond the physical testbed to enable large-scale, measurement-driven simulations. Last, the physical testbed and channel models are integrated to develop a networking digital twin, which allows researchers to evaluate multi-modal scheduling strategies, resource allocation schemes, and networking protocols under realistic dynamic underwater conditions. All software, datasets, models, and documentation will be publicly released through open repositories and public websites. By linking physical experimentation with scalable digital simulation, this project will provide sustainable cyberinfrastructure that accelerates data-driven and artificial intelligence-enabled innovation in underwater wireless communication and networking. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Over the past fifteen years, there has been an increase in the prevalence of pulmonary nontuberculous mycobacteria (NTM) in the US and other developed countries. Patients with underlying lung diseases such as chronic obstructive lung disease (COPD) and cystic fibrosis (CF) are at increased risk of developing pulmonary NTM infections. Mycobacterium avium complex (MAC) is the most common cause of pulmonary NTM. Management of pulmonary MAC is challenging. Despite multiple drugs given for at least 18 months, failure rates are in the range of 30% to 40%. Thus, there is a dire need to develop vaccine and therapeutics. Development of safe vaccines and therapeutics require the use of animals with structural lung diseases to imitate the structural lung diseases in humans which increase the risk of pulmonary MAC. Scnn1b-transgenic mouse lungs are characterized by enlarged alveoli, mucus accumulation in the bronchioles, and neutrophilic infiltration even in the absence of infection. This mouse model has made studies of pulmonary infections/colonization common in CF patients (e.g., early Pseudomonas colonization) possible. We have received scnn1b transgenic mice from the University of North Carolina and we now have an established protocol for breeding scnn1b- transgenic mice and confirming their background genetically. Therefore, this study is proposed with the following two aims: 1. Evaluate lung pathology, MAC growth and immunity following aerosol MAC infection of scnn1b-trangenic mice. 2. Test the effectiveness of DAR-901 and BCG as a vaccine for pulmonary MAC in scnn1b-trangenic mice. In aim1, we will compare scnn1b-transgenic mice with BALB/c and C57BL/6 mice for growth of MAC in lungs, histopathology and changes in mucosal immunity following aerosol MAC infection. Mucosal immunity will be measured using T cells from lungs for flowcytometry and BAL for cytokine/chemokine array. Changes in mucosal immunity following pulmonary MAC infection in the three strains of mice will be compared with changes in systemic immunity. Antibody responses in the blood will be measured by ELISA. In aim-2, using scnn1b-transgenic mice, we will evaluate the safety of BCG, test MAC-specific mucosal and systemic immunity induced by DAR-901 and BCG, and assess the ability of DAR- 901 and BCG to protect against pulmonary MAC-infection. The results from this study will help develop a mice model with structural lung disease for studies on pulmonary MAC and help advance two whole cell vaccines, DAR-901 and BCG, for further studies in animals and humans.

NIH Research Projects · FY 2026 · 2026-05

Hepadnaviruses are partially double-stranded DNA viruses that replicate by protein-primed reverse transcription. This family includes duck hepatitis B virus (DHBV) with which hepadnaviral reverse transcription was discovered and human hepatitis B virus (HBV) that kills 1,100,000 people annually. HBV therapy primarily employs nucleos(t)ide analog drugs that target the viral reverse transcriptase (RT) activity. Reverse transcription is catalyzed by the 4-domain viral polymerase (P) which has protein priming, RT, and ribonuclease H (RNase H) activities. The TP and spacer domains are unique to the hepadnaviruses. Reverse transcription starts with chaperone-mediated binding of P to the ε stem loop on the viral pregenomic RNA (pgRNA). Reverse transcription is primed by a tyrosine in P’s terminal protein domain (TP), templated by ε. The RT synthesizes the first strand of the viral DNA, and the RNase H destroys the pgRNA to permit synthesis of the second DNA strand. P is a monomer, and the covalent linkage between P and the DNA persists throughout reverse transcription. Hepadnaviral protein-primed reverse transcription differs greatly from retroviral reverse transcription, but its enzymology is poorly understood even though HBV P is a major drug target. This knowledge gap is in part due to the inability to determine the structure of P. We recently predicted the structure of P and validated the model. This revealed a novel fold in which the TP domain that primes reverse transcription is cupped over P’s catalytic core of P, with the priming tyrosine on a loop over the RT active site. This model makes mechanistic predictions regarding reverse transcription and provides guidance for how to test the hypotheses. Premise: The molecular model of P enables in-depth mechanistic analyses of P structure, nucleic acid binding, and DNA priming by the enzyme for the first time. Aim 1. What P sequences are needed for ε binding and priming? We will define the minimal active form(s) of P for RNA binding and DNA priming, and identify residues of P that contact ε and are essential for priming. Aim 2. What are the structural alterations to P associated with the shift from the priming-incompetent to priming-competent state? We will define how the TP domain binds to the catalytic core of P, explore P’s conformational shifts during priming, and determine how key RNA binding motifs are exposed during ε binding. Aim 3. How do conformational dynamics of P contribute to ε binding and priming? We will probe how P’s flexibility affects ε binding and DNA priming using molecular dynamics plus pharmacological and mutational analyses. We will test effects of mutations affecting RNA binding and DNA priming on viral replication in cells. This study will fill major gaps in our understanding of hepadnaviral reverse transcriptase enzymology by defining the interactions holding P in its novel conformation, how P binds to ε, and how enzyme flexibility contributes to the early phases of reverse transcription. It will also provide key information needed to develop non-active site inhibitors of HBV P to improve therapy for HBV patients.

NSF Awards · FY 2026 · 2026-05

This award provides partial travel support for students from U.S. institutions to attend the Doctoral Consortium at the 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), taking place in Denver, CO, from June 3-7, 2026. As the premier international conference in computer vision, CVPR attracts over 10,000 participants annually, showcasing advancements in artificial intelligence, machine learning, and image analysis. The Doctoral Consortium provides a dedicated platform for Ph.D. students to present their research, engage in deep discussions with senior scholars, and receive valuable mentorship to support their career development. By funding travel for approximately 25 students from a wide range of U.S. academic institutions, this award helps nurture the next generation of researchers and broadens participation in a leading technical community. Selected students will take part in structured consortium activities, including poster sessions, mentorship groups, and career-focused discussions. The travel awards will help cover expenses such as airfare, lodging, and local transportation, ensuring that talented students—especially those with limited financial resources—can attend. Recipients will be chosen by the 2026 CVPR Doctoral Consortium chairs based on research potential, alignment with the conference themes, and the impact of participation on their academic and professional growth. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

Exposures to chlorine (Cl2) and bromine gases (Br2) are public health threats. Cl2 and Br2 exposures occur due to industrial accidents as well as chemical warfare. Although cardiopulmonary failure caused mortality and morbidity is a major concern following exposures, the mechanisms underlying end organ injury after exposure to Cl2 or Br2 remain to be fully elucidated. Coagulopathy occurs following both Cl2 and Br2 exposures and likely leads to organ injury. We discovered that exposure of mice to Cl2 or Br2 gas results in robust levels of both 2- halofatty aldehyde (2-haloFALD) and their glutathione (GSH) adducts (FALD-GSH) in the lungs. Recent pilot data suggest a link between 2-haloFALD production, red blood cell (RBC) fragility and coagulopathy. These pilot data show: 1) FALD-GSH levels are elevated in RBCs following Cl2 and Br2 exposures to mice; 2) the 2- haloFALD, 2-ClFALD, modifies spectrin; 3) platelets metabolize 2-ClFALD to FALD-GSH; 4) FALD-GSH is an agonist for the cysteinyl leukotriene receptor, CysLT2; and 5) FALD-GSH causes CysLT2 receptor-dependent thrombus formation in blood. Since spectrin is responsible for RBC structural integrity and since CysLT2 receptor agonists cause platelet activation, we propose that halolipid metabolism is central in halogen gas toxicity and FALD-GSH is a major mediator of coagulation. We will investigate two mechanisms, direct FALD-GSH activation of platelets and indirect FALD-GSH activation of platelets following RBC hemolysis and release of stored FALD- GSH. We will then determine how FALD-GSH mediates platelet activation via the CysLT2 receptor. This hypothesis will test a unifying mechanism mediating both Cl2 and Br2 gas toxicity resulting in the production of novel agonists of platelet activation which may provide a common therapeutic target for a countermeasure against halogen exposure. Furthermore, while Cl2 and Br2 are similar, the 25-fold greater reactivity of 2-BrFALD reactivity with nucleophiles (glutathione and proteins) compared to that of 2-ClFALD may evoke differences in mechanisms by which each cause injury. There are two specific aims to test this hypothesis. Specific Aim 1 will identify FALD-GSH activation of the CysLT2 receptor as a mechanism responsible for coagulation and lung injury following Cl2 and Br2 exposure. Specific Aim 2 will identify the direct and indirect mechanisms by which FALD-GSH mediates platelet activation via the CysLT2 receptor in primary human cells. We will employ both mouse and rat models of Cl2 and Br2 gas exposure in both males and females. This, together with testing two distinct toxicants meet criteria for this RFA. Collectively, the proposed studies will delineate a common mechanism for Cl2 and Br2 toxicity mediated by 2-haloFALD modification of GSH and RBC spectrin leading to CysLT2 receptor-dependent platelet activation and organ failure. This mechanism could lead to a common treatment for these Chemical Countermeasures Research Program concerns in the future.

NIH Research Projects · FY 2026 · 2026-04

Diabetes mellitus (DM) is a group of metabolic diseases that affect how the body utilizes glucose and result in elevated levels of glucose in the blood and urine. DM increases the risk of many infections and their complications, but comorbidity with tuberculosis (TB) stands out due to its tremendous global impact. DM and TB each individually rank in the top ten global causes of death. In 2020, there were approximately 537 million adults with DM and 10 million new cases of active TB. TB patients with DM are twice as likely to die from TB and 1.5 times more likely to experience TB recurrence than non-DM patients. To better protect DM patients from TB, we need to understand how DM alters immune function to favor developing active TB disease. The impact of the metabolic perturbations in DM patients on immune cell function have largely been explored in the context of lymphocytes and macrophages. However, a systems biology comparison of DM patients, TB patients, comorbid patients (DM/TB), and healthy individuals was recently performed and identified neutrophilic inflammation as a central feature of DM/TB. Dysfunctional neutrophils have been implicated in DM vascular complications and impaired wound healing, but it is unclear how they contribute to disease outcomes in Mycobacterium tuberculosis (Mtb)-infected DM patients. Neutrophils are the most abundant and predominantly-infected cell type in the airways and lung tissue of active TB patients, but are unable to control Mtb replication. In addition, we have recently demonstrated that specific neutrophil effector functions can directly promote Mtb replication and pathogenesis. It is currently unclear how neutrophils contribute to disease outcomes in DM/TB patients, particularly because neutrophils are typically discarded from blood cell isolations and must be analyzed immediately without storing. We hypothesize that altered neutrophil function during DM contributes to increased susceptibility to Mtb infection. The proposed experiments will for the first time directly dissect the neutrophil responses associated with the DM/TB comorbidity and determine how neutrophils impact the outcome of Mtb infection in the context of DM. In Aim 1 we will define the neutrophil responses in DM patients that are associated with susceptibility to developing active TB disease. In Aim 2, we will determine how DM neutrophils promote Mtb pathogenesis. The results from the proposed studies will provide the critical foundation for future translational studies aimed at modulating neutrophil responses in DM/TB comorbid human patients as new strategies to treat TB in individuals with DM. Host directed therapies will work to target drug sensitive and resistant infections. Drugs already being developed that target NET release and other neutrophil effector functions, we can exploit these.

NIH Research Projects · FY 2026 · 2026-02

Project Summary/Abstract Tuberculosis (TB) remains a leading global health problem. Though one fourth of the world’s population is infected by Mycobacterium tuberculosis (Mtb), 90% of the people infected with Mtb remain asymptomatic as latent TB infection (LTBI). The reasons why 10% of the infected people progress to active TB are still elusive. Host genetic variations play an important role in TB susceptibility and LTBI reactivation. It has been reported that one single nucleotide polymorphism G/G (SNP-G) at -2518 in the promoter region of monocyte chemoattractant protein-1 (MCP-1) is strongly associated with active pulmonary TB (PTB). The underlying mechanisms of the - 2518 SNP-G-associated PTB susceptibility, however, remain unknown. MCP-1 is one of the key chemokines that regulate migration and infiltration of monocytes and macrophages to the sites of infection. Active TB patients show high levels of MCP-1 compared to people with LTBI and healthy volunteers. Higher levels of MCP-1 have also been used as a biomarker to distinguish active TB from LTBI. Flores-Villanueva, et al. first reported that people harboring the -2518 SNP-G had higher MCP-1 and lower IL-12 in sera and were more likely to develop PTB than individuals carrying the -2518 A/A (SNP-A) haplotype. Two meta-analyses analyzing a total of 5341 active TB cases and 6075 controls in 13 case-control studies report that the -2518 G/G homozygote carriers have a 67% increased risk of TB compared with the A allele carriers and the SNP-G homozygote is a risk factor for PTB. To test whether the SNP-G affected MCP-1 transcription, we cloned the MCP-1 promoter harboring the SNP G/G and SNP A/A into a luciferase vector and checked their luciferase activity in THP1 cells after mycobacterial infection. The SNP-G promoter showed a significant increase in luciferase activity compared to the SNP-A promoter. The MCP-1 transcripts in human macrophages carrying the homozygous SNP-G/G were also the highest than the cells harboring the SNP-A/A and the SNP-A/G after Mtb infection. We also found that BCG induced a strong nuclear binding specifically to the SNP-G and a small group of nuclear proteins called E- box binding protein bound to SNP-G. By reexamining the flanking sequence over the SNP, we found that the SNP-G but SNP-A constitutes the E-box motif (5’-CAGCTG-3’) in the MCP-1 promoter. The E-box-binding proteins are composed of a large superfamily basic helix-loop-helix (bHLH) proteins that transcriptionally regulate many functions in cancer, sex determination and development of the nervous system and muscles. How bHLH proteins regulate MCP-1 expression and affect mycobacterial growth are unknown and will be explored with the following two aims. Aim 1: Identify the E-box-binding proteins that bind to the SNP-G and regulate MCP-1 gene expression in Mtb-infected human macrophages. Aim 2: Explore the molecular mechanisms by which the SNP- G in MCP-1 promoter affects Mtb growth in human macrophages. The results will help us understand the molecular mechanisms of how -2518 G/G homozygote in the MCP-1 promoter contributes to TB susceptibility and reveal a novel role for the E-box binding proteins in control of host defense against Mtb infection.

NSF Awards · FY 2026 · 2026-01

Geospatial data has become a cornerstone of modern society, enabling critical applications in navigation, national security, transportation, emergency response, infrastructure management, environmental monitoring, precision agriculture, and more. The rapid proliferation of smartphones, drones, satellites, and connected sensors has resulted in an explosion of geospatial data. However, the true value of these datasets depends on one critical factor: accurate and reliable georeferencing. Without precise spatial positioning, data products become misaligned, leading to flawed analysis, compromised decision-making, and even threats to public safety. The Industry–University Cooperative Research Center (IUCRC) for Accurate Georeferencing of the Environment (CAGE) at Saint Louis University (SLU), in partnership with The Ohio State University (OSU) and Purdue University (PU), aims to tackle this foundational challenge. CAGE seeks to accelerate innovation and enhance the economic competitiveness of the U.S. geospatial industry and its users through industry-driven, convergent, precompetitive research in geospatial technologies. The center unites academic researchers, government agencies, and industry stakeholders to advance next-generation methods for georeferencing, spatial data fusion, quality assurance, and decision support. Through a collaborative, industry-focused model, CAGE ensures that innovations are closely aligned with real-world needs while supporting the development of a highly skilled geospatial workforce. CAGE’s proposed research portfolio is organized into three thrust areas: (1) Georeferencing and Navigation, (2) Geoinformation Extraction Assisted by Artificial Intelligence (AI) and Machine Learning (ML), and (3) Rigorous Quality Assurance and Quality Control Processes, which are built upon the outputs of the first two thrusts. The development of quality assurance and control processes is essential due to the vast amounts of data generated by geospatial sensors. These processes will enable greater technological advancement by ensuring compatibility and managing known variables. To achieve these goals, SLU CAGE leverages world-class computing and AI infrastructure, robotics, and various remote sensing platforms to advance research in GeoAI, precision and quantum agriculture, GPS and Positioning, Navigation and Timing (PNT), GPS alternatives, and food security. These innovations will enable scalable, cost-effective deployment across platforms ranging from satellites and UAVs to mobile phones and wearable sensors. As AI and machine learning become standard tools for extracting insights from geospatial big data, CAGE’s work helps ensure that these insights rest on a solid spatial foundation. CAGE directly serves the national interest by strengthening the geospatial innovation ecosystem, supporting national security, enabling data-driven public services, and training the next generation of geospatial professionals through inclusive outreach and education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-11

Quantum technologies are poised to usher in new capabilities for secure data communication, advanced computing, and improved sensing. In these applications, photons play a crucial role in transferring quantum information. Thus, developing sources of quantum light, single and entangled photons, is essential for advancing these applications and generating societal impact through new technologies/capabilities. Yet many of the existing techniques for producing quantum light are limited by their random production of photons in time or poor rate of photon production, and, they largely operate in free space. This work seeks to realize a device that is able to rapidly assemble and improve the rate of single photon production on-chip. It will provide new information into the manipulation and enhancement of quantum optical emitters across multiple length scales, realize a prototype device for single photon production on-chip, and develop resources for training a new generation of quantum optical scientists through the creation of virtual laboratory exercises and simulations. This project intends to realize ‘nanoscale emitter dock’ to simultaneously overcome two outstanding challenges for quasi-atom non-classical light sources - rapid and precise integration of an emitter alongside emission enhancement (trap and enhance) at room temperature. We accomplish this by engineering thermal and optical spatial distributions through non-resonant plasmonic structures paired with a standard low-loss photonic backbone (Si/SiN) for excitation and routing. Doing so enables a ‘multi-scale funnel’, synergistically combining electrothermoplasmonic (mm), negative thermophoretic (μm), and optical gradient forces (nm), to dock a single emitter with an electromagnetic hot-spot where strong enhancement to emission (Purcell effect) improves both the emission rate and stability. Through this we (A) deterministically route, capture, and ultimately print single quantum emitters (~20 nm) to a nanoscale hot-spot within seconds with sub-10 nm precision, (B) enhance the emission rate up to 1000× to achieve GHz-rates, (C) excite, capture, and guide light on-chip with dB/mm-scale loss. The proposed effort will culminate in the demonstration of a scalable and versatile platform for integrated on-demand GHz-rate single photon sources at room temperature, that will accelerate the expansion of compact quantum key distribution systems and quantum simulators. Moreover, the synergistic integration of optical gradient force, attractive negative thermophoretic force, and long-range electrothermoplasmonic flow for emitter transport and placement at plasmonic cavity hotspots have not been explored, and would provide a powerful means for long-range, precise, and strong optical manipulation on-chip. This manipulation (and the overall proposed device structure) is also general, and not dependent upon the properties of any emitter, solving existing heterogeneous integration challenges. It can also be completed in parallel, allowing an entire wafer to be loaded simultaneously, opening a route to scale source construction. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

There are a growing number of underwater applications, including climate change monitoring, marine biology research, oil rigs exploration, unmanned operations, search and rescue, underwater navigation, and scuba diving. Most of these applications demand reliable, flexible, and high-speed underwater sensing and communication systems. Despite significant advancements in terrestrial and space communication, high-speed underwater wireless communication remains in its infancy due to the harsh environmental conditions, unique signal propagation challenges, and a lack of infrastructure. The most widely used underwater communication methods - acoustic, radio frequency (RF), and optical waves - each face trade-offs. Acoustic signals can travel long distances but suffer from low data rates and high latency. RF signals offer higher data rates but are significantly attenuated in water, limiting their effective range to just a few meters. Optical communication holds great promise for delivering high-speed data transmission, however, it remains underutilized in underwater systems due to issues such as light scattering, absorption, misalignment, and sensitivity to environmental disturbances. To address these limitations, this project aims to develop an AI-defined high-speed underwater optical networking system that integrates sensing and communication into a unified architecture. In such a system, sensing and communication mutually enhance each other: real-time sensing informs more effective communication decisions, while efficient communication ensures timely delivery of sensing data. This synergy enables low-latency, high-reliability, intelligent, and adaptive data exchange in complex underwater environments. The research is organized into four main thrusts. In Thrust 1, we will design novel environment-aware underwater optical sensing techniques by discovering unique received signal patterns. Then, we will develop an innovative efficient framework for multi-agent underwater optical communication in Thrust 2. These two key thrusts will lay the foundation for our efforts to design advanced seamless underwater sensing and communication integration techniques in Thrust 3. Finally, in Thrust 4, we will design and implement an AI-driven software-defined underwater optical wireless networking testbed that natively supports real-time AI/ML-based optimization and decision-making in optical underwater communication, and experimentally validate and further optimize the techniques developed in Thrusts 1-3. Beyond its technical contribution, this project will promote education and workforce development by involving students in cutting-edge STEM research and conducting outreach to K–12 learners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Many of the efficient photosensitizers utilized today are monometallic and consist of noble metals, such as ruthenium and iridium. These noble metal photosensitizers utilize high-energy radiation which leads to substrate degradation & unwanted side products in organic synthesis. Low permeability of high-energy radiation also limits photodynamic therapy applications only to the skin. These noble metals are also high in toxicity and create adverse side effects. The proposed research focuses on achieving complementary photoreactivity using inexpensive, environmentally benign and cheap earth-abundant metals by switching to a multimetallic paradigm. The overall goal is to invent new photosensitizers using earth-abundant metals to complement traditional noble metal reactivity and develop sustainable organic reactions and photodynamic therapies. Metal-metal bonding in the proposed photosensitizers will also facilitate a red-shift in the operational wavelength eliminating all the drawbacks of high-energy radiation in the above applications. Upon successful completion, aim 1 will yield a novel class of multimetallic earth-abundant metal photosensitizers which can perform photoredox catalysis. These photosensitizers will feature high excited state lifetimes and perform organic transformations in target substrates identified utilizing their redox potentials. Successful completion of aim 2 will yield a class of multimetallic carbon-monoxide-releasing-molecules that feature metal-metal bonds which facilitate red-shifted absorptions and photo-dissociation of CO in wavelengths in the therapeutic window laying out the groundwork for photodynamic therapies in internal tissues. This project will also yield working predictive computational models for optimization of the photosensitizers. The proposed research is significant because it applies an underdeveloped photosensitizer paradigm— metal–metal cooperativity—to develop photosensitizers that overcome the limitations of traditional noble metal monometallic photosensitizers in organic synthesis and photodynamic therapies. The proposed research will also provide the intellectual foundation for potential long-term applications in LEDs and solar cells using earth- abundant metal photosensitizers, thereby advancing knowledge across different fields. The rationale for pursing the proposed studies is derived from the careful analysis of the independent literature on precious-metal bimetallic photosensitizers, which show a relationship between the metal–metal cooperativity and photosensitizer parameters, such as absorption intensity and wavelength. Preliminary experimental results in our lab also support our hypothesis. All the resources and the expertise required to pursue the proposed research are available at Saint Louis University or in nearby institutions with which the PI has already established relevant connections.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Maintenance and completion of a healthy pregnancy requires profound physiological adaptations of maternal physiology. In particular, the maternal plasma volume in a singleton pregnancy must expand by ~50% by 40 weeks of gestation to ensure proper placental perfusion to accommodate and support the growing offspring. In the absence of appropriate plasma volume expansion (PVE), the pregnancy, and health of the mother, can be compromised. For example, failure of PVE in pregnancy can lead to intra-uterine growth restriction or fetuses that are small for gestational age. Central mechanisms underlying pregnancy-associated PVE include alterations to drinking behavior and neuroendocrine regulation of vascular and renal function. In particular, the osmotic thresholds for thirst and the release of arginine vasopressin (AVP) from the posterior pituitary are reduced in pregnancy. We reported the discovery of a hypothalamic peptide, phoenixin (PNX), and the identification of its receptor, GPR173, that appears to play important roles in the regulation of thirst and drinking behavior as well as AVP secretion. In pregnant rats, PNX expression increases as pregnancy progresses, and correlates significantly with AVP levels. Our preliminary data also suggest that knockdown of GPR173 in hypothalamus of rats leads to fetuses that are significantly smaller than that of controls, consistent with reduced PVE in GPR173 siRNA-treated rats. We therefore hypothesize that PNX/GPR173 signaling contributes to pregnancy-associated PVE through the regulation of AVP secretion and thirst. This hypothesis will be tested in two Specific Aims. In the first Aim, we will use our established model of siRNA-mediated knockdown of PNX or GPR173 in hypothalamus to evaluate the impact of loss of hypothalamic PNX signaling on basal and compensatory water drinking and ingestive patterning in pregnant rats. We have previously shown that PNX stimulates water drinking in non-pregnant rats via an interaction with the central angiotensin system. We therefore propose downstream activation of angiotensin signaling as a potential mechanism by which PNX impacts drinking in the setting of pregnancy. This will be assessed using pharmacological and molecular tools. In the second Aim, we will investigate how loss of PNX signaling impacts the increase in AVP secretion during normal pregnancy. In addition, using the method of Lindheimer and colleagues, we will investigate the role of hypothalamic PNX in the resetting of the osmotic threshold for AVP release in pregnant animals. Lastly, we will investigate the molecular mechanism underlying PNX’s actions in hypothalamus in the setting of pregnancy using biased (pharmacologic and immunohistochemical) and unbiased (spatial transcriptomic, i.e., spatial molecular imaging) methods. The successful completion of these proposed studies will offer insights into the basic biological mechanisms underlying changes in plasma volume associated with pregnancy. This new knowledge will serve as the basis of future experiments designed to investigate how changes in PVE in pregnancy lead to disease, as well as offer potential therapeutic targets for common pregnancy complications.

NIH Research Projects · FY 2025 · 2025-09

The long-term goal of this research is to understand the function and mechanism of DNA N6-adenine methylation (6mA) and its demethylases in gene regulation in eukaryotes. Epigenetics has attracted many research efforts in the last two decades due to its importance in regulating many processes in cells, as well as the growth and development of plants and mammals. DNA methylation on the fifth position of cytosine (5mC) is an abundant and essential epigenetic mark in both plants and mammals. However, DNA 6mA is relatively rare in eukaryotes, and the existence and functions of 6mA in eukaryotes remain controversial. With recent progress in next-generation sequencing technology and mass spectrometry, it has been shown that 6mA plays a critical role in regulating gene expression in eukaryotes. Furthermore, little is currently known about 6mA demethylases in eukaryotes, which remove the methyl group from 6mA residues. We have found an E. coli alkylation B homolog 4 (ALKBH4) gene in Arabidopsis (AtALKBH4) that can serve as a 6mA demethylase in vivo. Here, I propose to 1) Measure DNA 6mA levels in the Atalkbh4 mutant and AtALKBH4 overexpression transgenic plants, as well as map the genomic locations of DNA 6mA in plants. I propose to use an ultra-high-performance liquid chromatography-triple-quadrupole mass spectrometer- mass spectrometer (UHPLC-QQQ-MS/MS) to measure 6mA levels in the Atalkbh4 mutant and AtALKBH4 overexpression transgenic plants. We will also use Single Molecule, Real-Time (SMRT) sequencing technology to determine the genomic locations of 6mA in Arabidopsis. 2) Analyze the enzymatic activities of the putative 6mA demethylase AtALKBH4 in vivo and in vitro. We will mutate the conserved residues of AtALKBH4 and transform the mutated constructs into the Atalkbh4 mutant plant to examine AtALKBH4 activities in vivo. We will also express and purify AtALKBH4 proteins and incubate them with putative DNA substrates to determine AtALKBH4 demethylase activities in vitro. 3) Determine the function of the AtALKBH4 gene in Arabidopsis. We will examine vegetative and reproductive phenotypes of the Atalkbh4 mutant and AtALKBH4 overexpression transgenic plants under normal and stress conditions in comparison to the wild-type plants. This study is significant because it responds to recent calls for the reassessment of the existence of 6mA in eukaryotes, and will determine the function and enzyme activities of a putative 6mA demethylase in plants. This research is innovative because it employs novel approaches to overcome previous limitations in addressing 6mA existence and functionality in eukaryotes. These approaches include using plants grown on sterile mediums without soil bacterial contamination, UHPLC-QQQ-MS/MS, and SMRT sequencing technology. To achieve the above specific aims, this research will employ genetic, molecular, biochemical, and genomic approaches, revealing the enzymatic activities and functions of the DNA 6mA demethylase AtALKBH4 in plants.

NIH Research Projects · FY 2025 · 2025-09

Abstract Pyruvate is one of the most important carbon inputs for mitochondria, not only for energy production but also as an anaplerotic and biosynthetic carbon source. Since pyruvate is formed in the cytosol from either glycolysis or lactate and the pyruvate metabolizing enzymes (i.e., pyruvate dehydrogenase and pyruvate carboxylase) reside in the mitochondrial matrix, the transport of pyruvate across the inner mitochondrial membrane represents a critical step in intermediary metabolism. The McCommis laboratory uses a wide range of model systems from in vitro biophysical techniques to genetic mouse models to investigate the importance of mitochondrial metabolism in pathophysiology. Our primary focus has been the mitochondrial pyruvate carrier (MPC). We and others have previously described the importance of the MPC in a variety of metabolic tissues with the use of tissue-specific knockout mice. In some instances, deletion or inhibition of the MPC is beneficial. For example, inhibition of the MPC in the liver can improve diabetes by decreasing hepatic glucose production, enhancing fatty acid and amino acid catabolism, and activating cellular nutrient sensors. Despite this importance of the MPC, there is no experimental knowledge of the protein structure or conformational dynamics during pyruvate transport or inhibition. Additionally, beyond the identification of several synthetic inhibitors, almost nothing is known about factors that regulate MPC activity. The proposed studies will use DEER spectroscopy as a powerful Pulse EPR technique to uncover the conformational changes of the MPC during transport or inhibition. Additionally, using a combination of DEER spectroscopy, biochemical techniques, and bioenergetics measures, we will determine endogenous factor(s) that regulate MPC activity. The branched chain ketoacid of leucine, a-ketoisocaproate (KIC), has been shown to inhibit pyruvate oxidation without affecting pyruvate dehydrogenase activity. We present data herein that indeed KIC inhibits mitochondrial pyruvate oxidation, however this effect is completely lost in PPM1K-/- mitochondria that have reduced oxidation of branched chain ketoacids. KIC also has little-to-no effect in preliminary DEER analyses. This suggests that a downstream metabolite of KIC produces the inhibition of pyruvate metabolism. We will also evaluate how altered cardiolipin composition and content, as commonly observed in cardiometabolic diseases, dysregulates MPC transport function. This mechanistic information will shed light on MPC function and how it is regulated, which is a critical process for nearly all cell types under various physiologic contexts. Ultimately, the overarching goal of our research program is to employ this multidisciplinary approach to mechanistically study orphan mitochondrial transporters with potential pathophysiological significance, aiming to determine the functional dynamics of their regulation by potential substrates and modulators.

NIH Research Projects · FY 2025 · 2025-08

SUMMARY Lesions, breaks, and errors in DNA are drivers of genomic instability. Double-stranded DNA breaks (DSBs) are a particularly severe form of damage and are repaired by the homologous recombination (HR) pathway. On average, ~10 to 50 DSBs occur per cell per day and thus pose the highest risk to genomic integrity. Unrepaired DSBs lead to gross chromosomal rearrangements and are key drivers of cancers. HR is initiated when the DSB is resected to generate ssDNA. Rad51 is the recombinase that drives HR and forms a helical nucleoprotein filament on the resected ssDNA. This step, termed ‘pre-synaptic nucleoprotein filament formation’ commits to repairing the DSB through the HR pathway. Not surprisingly, this step is tightly regulated by mediator proteins. Pro-HR mediators such as Rad52 (yeast) and BRCA2 (humans) promote HR by aiding in the formation and stabilization of the Rad51 filament. In contrast, anti-HR mediators antagonize this step by displacing Rad51 from ssDNA. In addition, Rad51-paralogs further promote Rad51 filament formation by capping and stabilizing the filaments. The mechanisms of how mediators and Rad51-paralogs work to facilitate Rad51 filaments are poorly understood. Our recent discoveries on Rad52 revealed how two Rad51 binding modes are utilized to facilitate nucleoprotein filament formation. Mode-1 sorts Rad51 oligomers into monomeric units and Mode-2 stacks Rad51 at a defined position within the complex. We also uncovered that the Rad51-Rad52 complex recognizes the ss-dsDNA junction on an RPA-coated ssDNA, thus initiating filament formation from a single defined position on DNA. We propose a Sort, Stack, Extend, and Protect (SSEP) model for mediator and Rad51 paralogs in formation of the pre-synaptic Rad51 nucleoprotein filament. In this proposal, we seek to decipher a) how the junction is recognized through RPA-Rad52-Rad51 interactions; b) the mechanisms of how Rad51 paralogs (Rad55-Rad57 and Csm2-Psy3-Shu1-Shu2) promote Rad51 filament formation; and c) establish how the sorting and stacking properties are enacted in the human BRCA2 protein. Ensemble and single-molecule Förster resonance energy transfer (FRET) fluorescence microscopy, structural mass-spectrometry, and C-trap analysis are utilized to establish how double-strand DNA breaks are repaired through homologous recombination.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Bacterial protein toxins and effectors play a pivotal role in organismal conflicts, contributing significantly to self/non-self recognition, bacteria-phage interactions, and the pathogenesis of bacterial diseases in human and non-human animals, as well as commercially important plants impacting food security. Propelled by the constant evolution of an arms race, these proteins and their associated components typically evolve rapidly and display enormous diversity in terms of protein sequence, structure, domain architecture, and the organization of their genomic loci (often referred to as virulence islands). The intricate complexity across various levels has posed a formidable challenge for the identification and characterization of novel toxin/effector systems and their biochemical mechanisms through conventional experimental and bioinformatic approaches. Over the past decade, my research has made significant contribution in unraveling the organizational and diversification principles governing several toxin/effector systems, encompassing both protein domain architecture and genomic loci. Through the integration of these principles into a unique domain- centric "guilt-by-association" analysis pipeline, I have successfully identified, unified, and categorized an extensive array of known and novel toxin systems into a realm of polymorphic toxin and effector systems (PTSs). This approach has enabled the dissection of hundreds of domains and the prediction of diverse functions and activities for toxins/effectors, immunity proteins, and other associated components. Despite these advancements, the ever-expanding genomic data highlights the presence of many undiscovered systems, emphasizing the need for more sophisticated approaches to fully comprehend these complex molecular mechanisms. In this proposal, my aim is to develop innovative machine learning (ML)-based computational pipelines and resource for the systematic exploration and analysis of complex toxin/effector systems. Additionally, I will leverage our unique analysis strategies to uncover novel toxin systems linked to specific pathogens responsible for severe human and plant diseases, as well as crucial bacterial functions like antiphage mechanisms and species competition.

NSF Awards · FY 2025 · 2025-08

High-performance computing drives groundbreaking advancements in diverse domains by enabling researchers to analyze massive datasets and perform complex simulations with unparalleled speed and precision. SEAM supports these advances by introducing an adaptive mixed-precision framework to optimize performance further for scientific and artificial intelligence workloads across various heterogeneous systems. This approach fosters breakthroughs in fields ranging from climate science to healthcare and stands at the intersection of technological innovation, computational excellence, and practical application. By pushing the boundaries of computational capabilities, SEAM has the potential to benefit society broadly, from accelerating scientific discoveries to enhancing the tools available for national defense and economic competitiveness. The high-performance computing landscape is significantly evolving in supporting energy-efficient, low-precision computations. This shift has prompted researchers to reassess traditional numerical algorithms, identifying areas where reduced precision can be employed without compromising the overall solution quality. The synergy between hardware advancements and algorithmic optimization is pivotal in addressing complex scientific challenges, particularly on heterogeneous GPU architectures that leverage Tensor Cores. SEAM develops a scalable and efficient adaptive mixed-precision framework tailored for scientific and artificial intelligence workloads on GPUs. This framework applies to fundamental operations like General Matrix Multiplication, Cholesky Decomposition, LU Decomposition, etc. In addition, SEAM is built on top of task-based runtime systems to ensure efficiency, scalability, and portability. It also features Julia interfaces for ease of use and is applied in various domains, including geospatial modeling, genome-wide association studies, and transformer-based foundation models on multiple GPU platforms. SEAM has significant implications for future research and development in high-performance computing, highlighting the transformative potential of mixed-precision arithmetic on GPUs and making progress toward a more sustainable, efficient, and accurate future for scientific and artificial intelligence 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.

NSF Awards · FY 2025 · 2025-06

This award provides partial travel support for students from U.S. institutions to attend the Doctoral Consortium at the 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), taking place in Nashville, Tennessee, from June 11 to June 15, 2025. As the premier international conference in computer vision, CVPR attracts over 10,000 participants annually, showcasing advancements in artificial intelligence, machine learning, and image analysis. The Doctoral Consortium provides a dedicated platform for Ph.D. students to present their research, engage in deep discussions with senior scholars, and receive valuable mentorship to support their career development. By funding travel for approximately 25 students from a wide range of U.S. academic institutions, this award helps nurture the next generation of researchers and broadens participation in a leading technical community. Selected students will take part in structured consortium activities, including poster sessions, mentorship groups, and career-focused discussions. The travel awards will help cover expenses such as airfare, lodging, and local transportation, ensuring that talented students—especially those with limited financial resources—can attend. Recipients will be chosen by the 2025 CVPR Doctoral Consortium chairs based on research potential, alignment with the conference themes, and the impact of participation on their academic and professional growth. 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 · 2025-06

Fine-grained visual categorization involves identifying subtle differences between highly similar visual categories, such as distinguishing between two closely related bird species or recognizing different models of a car. While these capabilities are critical in a variety of fields, including biodiversity research, forensic investigations, and e-commerce, the task is challenging because differences between categories can be small, while variations within a category can be large. For example, two different bird species may look very similar, while male and female birds of the same species may look very different. Visual categorization in fine-grained domains is often treated as an image retrieval problem, where the label for a query image is determined based on the labels of the most visually similar images. An image retrieval approach typically performs better than standard classification approaches on fine-grained visual categorization tasks, especially in domains with very large numbers of classes. However, image-retrieval approaches often fail because a retrieved image that is visually similar is not necessarily from the same class, and potential images from the same class may not be retrieved due to low visual similarity. Moreover, the features learned by standard image retrieval models are often biased towards overall visual similarity rather than task-specific or domain-specific notions of importance. This limitation can hinder analysts and domain experts who may want to prioritize specific visual features due to their own expertise or intuitions. This project aims to improve image retrieval systems for fine-grained domains by aligning them more closely with how human experts perceive and prioritize differences, and enabling users to focus on the features most important to their task. While these innovations will be evaluated across a variety of fine-grained domains, they will studied through their integration into an existing real-world image retrieval system developed by the investigator that is used by analysts at the National Center for Missing and Exploited Children to recognize the hotels where victims of child sexual abuse and human trafficking are photographed. This project will address the limitations of traditional image retrieval in fine-grained domains by developing systems that better align with human notions of visual similarity while empowering users to dynamically guide retrieval processes. Ensembles of specialized models, each focused on a single visual notion, offer improved alignment with human judgments but are computationally impractical for real-time use, particularly in resource-constrained settings. To address this, the project will explore the use of knowledge distillation to integrate the varied knowledge of the ensemble models into a single model, while preserving flexibility for users to prioritize specific visual notions learned by individual models in the ensemble at query time. The project will also develop additional mechanisms for a user to dynamically refine search results. This line of work will have two directions: one where users specify their refinement by identifying visual features to prioritize, and one where the refinement is expressed in natural language. The project will also investigate the utility of subspace projection techniques as a pre-processing step for building task-specific indices from the embedding space of pre-trained vision language models. These innovations will enable both image-based and text-based retrieval systems where users can articulate preferences through natural language or visual cues, creating intuitive and scalable tools for specific fine-grained domains. Complementing these technical advancements, the project’s educational and outreach initiatives will focus on integrating machine learning competitions into undergraduate and graduate curricula to foster hands-on learning and critical thinking about real-world applications. Workshops will be organized to share best practices for using machine learning competitions as an educational tool, engaging educators, researchers, and students from a variety of backgrounds. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-06

Project Summary The overarching goals of my lab are to (i) develop molecular tools to control and map how subcellular signaling influences physiology and pathology and (ii) learn from and use biological photon reception to advance molecular pharmacology. Due to their unprecedented physiological and pathological footprint in many diseases, including cardiovascular, metabolic, neurological, and oncological, we focus on G protein-coupled receptors (GPCRs). Although GPCRs in individual cells in the body are controlled by spatially and temporally fluctuating ligands with various cell membrane permeabilities, limited tools to interrogate their signaling with native fidelity have been an obstacle to understanding their signaling regulations in health and disease. With the R01 funding support, promising published and preliminary data we generated show the feasibility of optical control of endogenous GPCRs and G proteins to overcome this challenge. This MIRA proposal will deliver innovative optical tools to control endogenous GPCRs in unmodified cells and in vivo (Goal 1) and GPCRs and endogenous G proteins at exclusive subcellular locations such as Golgi and nuclear membranes (Goal 2). It also focuses on finding molecular links between environmental light conditions, cell signaling, and animal behavior mediated by the melanopsin photopigment (Goal 3). Given the immense challenges in controlling deep-tissue GPCRs to understand diseases such as neurodegeneration, the likely pathological outcomes of GPCR-G protein signaling from endomembranes, including heart failure and addiction, and the significance of the link between environmental light conditions with mood and behavior disorders, such as seasonal affective disorders and depression, we anticipate that the project deliverables will be significant in deciphering pathological mechanisms and finding disease intervention points.

NSF Awards · FY 2025 · 2025-06

The 2025 ACM SIGCOMM Conference will be held in Coimbra, Portugal on September 8 - 11, 2025. This will be the 39th edition of the conference series. Each year, the conference attracts hundreds of submissions, with acceptance rates typically in the ranging of 10-20%, underscoring its reputation as one of the most competitive venues in the field. The program committee, composed of leading experts from academia and industry, ensures that only the most significant and impactful works are presented. This project supports students from US universities to attend 2025 ACM SIGCOMM in person. Students will have the opportunity to present their work and be exposed to state-of-the-art developments in the field. They will also have the opportunity to interact with peers from institutions worldwide, meet with senior researchers, and participate in discussions that are likely to shape the future of the field. This grant will target graduate students who will substantially benefit from attending this conference but have limited travel funds. Priority will be given to first-time attendees. 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 · 2025-05

The conference "Operators on analytic function spaces" will take place at the Centre International de Rencontres Mathematiques (CIRM) in Marseille, France from December 2 - 6, 2024. The goal is to create a diverse group of mathematicians poised to solve an important set of problems in function and operator theory, and to allow attendees to develop new directions and partnerships. Funding will be used for US participant support, with priority going to members of underrepresented groups and early career researchers. CIRM provides facilities and equipment as well as an excellent library and serves as a place for collaborative work. The focus of the conference is on recent progress on Hilbert and Banach spaces of holomorphic functions and the operators acting on them. During the week at CIRM participants will discuss important open questions in function theory and operator theory, including operators on model spaces, Toeplitz and Hankel operators, cyclic vectors, sampling, frames, interpolation and reproducing kernels, and the Crouzeix conjecture. In addition to the talks, the conference will offer activities for attendees to interact and discuss future directions for research. More information may be found at the conference webpage, https://conferences.cirm-math.fr/3085.html 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 · 2024-11

PROJECT SUMMARY: Gain- and loss-of function of architectural nuclear proteins lamin- A/C, -B1, -B2 are linked to both physiological aging and aging comorbidities such as cardiovascular disease, neurodegeneration, and cancer. Mutations in the LMNA gene cause devastating premature aging diseases: Hutchinson Gilford Progeria Syndrome (HGPS), Restrictive Dermopathy, and Atypical Werner Syndrome (AWS). Degradation of lamin B1 protein plays a major role in Alzheimer’s disease (AD). Accordingly, much emphasis is placed on identifying sorely needed therapeutic strategies to reduce the toxicity caused by lamins’ dysfunction. Here, pilot studies in HGPS patients-derived fibroblasts identified an unexpected mechanism to reduce the toxicity of progerin (lamin A mutant protein that causes HGPS): we found that treating these progerin-expressing cells with chloroquine robustly ameliorates aging hallmarks of progeria via upregulation and nuclear localization of the protease cathepsin L (CTSL). We provide evidence that CTSL modifies the C-terminal toxic tail of progerin, yet the mechanism remains to be elucidated. This discovery is highly significant because our studies in last decade have recurrently found a similar upregulation of nuclear CTSL under different contexts of nuclear damage and cellular stress, including lamin A/C loss, BRCA loss, oncogenic Ras expression, and starvation. In all those contexts, CTSL directly cleaves proteins that play key roles in DNA repair, cell cycle regulation, chromatin structure, and gene transcription. Altogether, these data suggest that activation of nuclear CTSL and CTSL-mediated remodeling of the nuclear proteome is a key event in the cellular response to stresses of different etiologies. In some contexts, nuclear CTSL may promote restoration of the damaged genome/lamina, reduction of nuclear defects, and improvement of cellular health; while in others, it may lead to elimination of the damaged cell by apoptosis or its growth arrest by senescence. Given the association of laminopathies with over twenty-five different degenerative disorders, cancer, and accelerated aging, understanding how nuclear CTSL modulates lamin/progerin processing (aim 1) and functional interactions (aim 2), and understanding nuclear CSTL substrate degradome under different stress conditions (aim 3) will have a profound and sustained impact across disciplines by advancing our understanding of cellular responses to nuclear damage.

NSF Awards · FY 2024 · 2024-11

Saint Louis University (SLU) is modernizing its campus cyberinfrastructure to support artificial intelligence (AI)-enhanced research and education. The existing high-performance computing (HPC) system is outdated, limiting the ability of researchers to conduct data-intensive AI research. ModernCARE addresses these challenges by establishing a new HPC cluster with advanced CPU and GPU nodes. This infrastructure supports nine key science drivers, allowing researchers to efficiently solve complex problems in biology, chemistry, engineering, genomics, molecular structure prediction, solid mechanics, trustworthy computation, and more. The system enhances collaboration across the university campuses and reduces data management complexities, creating a more efficient research environment. It also offers students hands-on experience with cutting-edge technology in AI and HPC, strengthening STEM education at the university. The infrastructure includes high-end GPU nodes equipped with NVIDIA H100 tensor-core GPUs and mid-range GPU nodes designed for diverse AI applications. Intel Software Guard Extension (SGX)-enabled CPUs provide a trusted environment for secure AI research. ModernCARE connects the university to the national Open Science Grid, with 20 percent of the system resources allocated for off-campus research. This infrastructure is expected to have a significant impact by advancing research capabilities, encouraging interdisciplinary collaboration, and preparing students for careers in AI and high-performance computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Nontechnical Abstract: Quantum information science and engineering (QISE) has far-reaching implications for scientific advancement, technological innovation, and national priorities. Research into advanced quantum materials will help QISE realize these goals. By bringing together expertise in microwave resonator characterization, carbon nanotube synthesis, and superconducting qubit technology, the research team seeks to develop novel quantum materials and devices with enhanced performance. This project seeks to advance QISE through collaborative research and education initiatives in the St. Louis area. The project also focuses on expanding the quantum workforce by creating new educational pathways and research opportunities for students from diverse backgrounds, including those from historically underrepresented groups. Through partnerships between Saint Louis University, Washington University in St. Louis, Harris-Stowe State University, and St. Louis Community College, the project aims to broaden participation in QISE and create a robust pipeline of skilled quantum scientists and engineers. Technical Abstract: This research aims to develop materials and devices that can better preserve quantum coherence, enhance light-matter interactions, and achieve strong confinement of charge, thereby enabling novel approaches to computation, sensing, and communication that surpass classical limitations. The research component of this project addresses fundamental challenges in quantum materials and devices through three main goals. First, the team will investigate advanced superconducting quantum materials by measuring superconducting resonator quality factors. The team will study new materials and apply surface treatment techniques to mitigate losses. Second, the project develops methods to embed semiconducting carbon nanotubes in superconducting resonators, leveraging their unique properties for quantum sensing and information processing. Third, the team will use these devices’ design, production, and measurement as a workforce training tool, integrating students at various levels into cutting-edge QISE research. The project will employ state-of-the-art fabrication techniques, including flip-chip integration of carbon nanotubes with superconducting circuits, and utilize microwave resonator measurements to characterize and optimize device performance. This work aims to advance our understanding of quantum decoherence mechanisms and develop new platforms for quantum sensing and computation. 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.