University Of Arizona

NSF Awards · FY 2025 · 2025-10

With support from the Improving Undergraduate Education: Hispanic-Serving Institutions (HSI Program), this Implementation and Evaluation Project (IEP) Level 1 Track aims to improve the classroom experience and academic success of undergraduate students in introductory science, technology, engineering, and mathematics (STEM) courses. Too many students who enter college intending to pursue STEM careers leave STEM majors within the first few years, in part because they do not feel supported or successful in their early courses. The project will address this issue by training graduate teaching assistants, who lead many of these foundational STEM courses, in teaching strategies that recognize and build on students’ strengths and experiences. These strategies will support graduate educators in creating welcoming classrooms, connecting course material to students' experiences, and offering meaningful feedback that supports growth for all students. The project expects to reduce failure and withdrawal rates in foundational STEM courses, increase students’ confidence and sense of community, and ultimately improve persistence in STEM majors. This work will contribute to national efforts to improve undergraduate STEM education, thereby contributing to increases in the STEM workforce. Fifty graduate educators who teach foundational STEM courses will participate in a training institute over three years on using student-centered and asset-based teaching strategies, impacting 1,100 undergraduates. The institute will be followed by mentorship from experienced faculty and monthly community of practice meetings. The training will be grounded in prior successful initiatives for faculty and responds to institutional data showing that graduate educators play a key role in shaping student experiences in high-impact STEM courses. The research will use a mixed-methods approach to evaluate the impact of the intervention on graduate educators’ self-efficacy and use of these practices. Data will be collected through pre- and post-surveys, classroom observations, focus groups, and analysis of institutional data on course outcomes and student persistence in STEM majors. Results will be shared through academic conferences, peer-reviewed publications, institutional policy briefs, and professional networks that support Hispanic-Serving Institutions and other institutions of higher education. The broader impact includes developing a scalable model of educator training that improves STEM instruction, builds institutional capacity, and supports national efforts to develop a strong and prepared STEM workforce. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education and increase capacity to engage in the development and implementation of innovations to improve STEM teaching and learning at HSIs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This NSF CAREER project aims to develop foundational mathematical tools to address emerging challenges in distributed and uncertain systems, such as those in energy infrastructure, machine learning, and wireless communication. Despite recent advances in networked systems, current models and algorithms lack provable performance guarantees for a broad class of critical problems. These include (i) Generalized Nash games, where agents compete over shared resources; (ii) Bilevel optimization with constraints at both decision levels; and (iii) Saddle point problems with coupling constraints. Existing methods are not equipped to handle the complex, interdependent decision spaces or the uncertainty and decentralization that characterize these large-scale systems. This project addresses these gaps through a unified lens of Generalized Quasi-Variational Inequalities (GQVI), a powerful yet underdeveloped framework for capturing interdependent decisions under constraints. By improving solution methods for GQVI, the project will contribute to more reliable, efficient, and scalable decision-making tools for real-world applications. The educational plan includes engaging undergraduates in hands-on research experiences and training them to lead outreach activities in middle and high schools, featuring Python coding exercises and modules on optimization. Additionally, a virtual tour with faculty presentations and lab visits will simulate in-person field trips, aiming to inspire interest in STEM and improve engineering retention. The proposed research builds a unified theoretical and algorithmic framework for solving deterministic, stochastic, and distributed GQVI problems. The project is organized around three interconnected thrusts. First, it develops novel algorithms using operator relaxation techniques to manage the interdependencies that arise in constraint structures, providing provable convergence guarantees that overcome limitations of existing methods. Second, it reformulates nonlinear constrained GQVI problems as fixed-point problems and designs new parametric primal-dual algorithms tailored to this structure, enabling efficient and scalable solutions. Third, it addresses the complexities introduced by uncertainty and decentralization through the development of consensus-based and asynchronous methods, alongside advanced variance reduction strategies for solving large-scale stochastic and distributed settings. These innovations will deliver the first non-asymptotic convergence guarantees for GQVI problems, with broad impacts in game theory, bilevel programming, power systems, and machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This IUSE Level 1 project aims to serve the national interest by creating generative artificial intelligence (GenAI) learning assistants tailored to architecture, engineering, and construction (AEC) education, thereby helping students develop independent problem-solving skills. AEC is a unique, cross-disciplinary field in STEM entered on complex project-based learning, requiring deep information resources and personalized learning support. By focusing on the significance of providing real-time, course-specific feedback and personalized guidance, the GenAI learning assistants will help students understand complex concepts, boost their confidence, and foster more consistent independent learning. The project also aims to better understand how GenAI adoption strategies can be tailored to different AEC courses and academic levels. The project’s insights could be adapted to other STEM disciplines where project-based learning is integral. This proposed research involves the development and evaluation of three course-specific GenAI learning assistants: one for Construction Graphics (freshmen), one for Structural Systems (juniors), and one for Human-Building Interaction (seniors). The assistants will feature automated symbol identification in construction drawings, 3D visualization of load distributions, augmented onto real-world structures, and coding and hardware configuration support for developing intelligent building sensing systems. The core hypothesis is that adopting the GenAI learning assistants will enhance student learning and promote self-regulated learning (SRL). Each GenAI learning assistant is grounded in key SRL principles such as goal setting, self-monitoring, self-reflection, and strategic help-seeking—skills essential for preparing future AEC professionals. The immediate outcomes will transform AEC education by providing empirical evidence of the effectiveness of GenAI learning assistants. In addition, these results will help educators understand how GenAI-enhanced learning supports influence AEC student perceptions and use of AI after completing those courses. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Bilevel optimization is a foundational tool for modeling hierarchical decision-making problems, becoming increasingly prevalent in various fields including machine learning, transportation, energy systems, and robotics. However, existing research on bilevel optimization often focuses on simplified or unconstrained settings, limiting its applicability to practical scenarios that require safe, real-time decision-making under complex constraints. This project aims to bridge this gap by developing efficient and scalable algorithms for constrained, nonconvex bilevel problems, with particular emphasis on planning and control tasks in safety-critical domains. The outcomes of this research will provide general-purpose optimization tools that benefit a broad range of applications in robotics, learning, and autonomous systems. Software developed through this project will be released as open-source packages, enabling broad adoption across academia and industry. Furthermore, this project includes a comprehensive educational and outreach component to engage students at multiple academic levels, foster hands-on learning experiences, and broaden participation in STEM fields. This project advances the theory and practice of bilevel optimization by designing efficient, safe, and scalable algorithms tailored specifically to constrained, nonconvex bilevel problems in planning and control. The research comprises two main thrusts. In the first thrust, a novel control-theoretic framework will be developed to systematically design bilevel solvers. By modeling optimization algorithms as controlled dynamical systems, this approach will leverage techniques from control theory to establish algorithms with provable convergence guarantees and anytime safety. The foundational work in this thrust specifically targets scalability, non-unique lower-level solutions, and nonconvexities at both optimization levels. In the second thrust, this framework will be applied to two high-impact domains: (i) safe inverse optimal control and reinforcement learning, where the objective is to recover control policies from expert demonstrations under strict safety constraints; and (ii) safe interactive planning for navigation in crowded environments, integrating real-time decision-making with predictive models of human motion. By addressing fundamental challenges in hierarchical safety-critical decision-making, this research will advance the state of the art in optimization, control, and autonomous systems, benefiting both theoretical developments and practical 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 · 2025-10

Next Generation (NextG) networks aim to create an immersive multi-sensory user experience by supporting new technologies such as integrated precision sensing, artificial intelligence/machine learning (AI/ML) optimizations for automated network management, and a virtualizable architecture running on commodity hardware. Prominent in NextG networks is a new service-centric model, in which infrastructure providers virtualize the network into logically isolated network slices, running services with different demands. This flexible architecture is driven by the deployment of powerful ML algorithms that manage resources at fine and longer timescales. However, the security of decision-making algorithms is under-explored. This project aims to fill this research gap by investigating the security and verifiability of resource allocation for NextG networks. The project’s novelties are in exploring new threats emerging from the automated nature of decision-making and devising robust, secure, and verifiable resource management solutions. The project's broader significance and importance are in improving the availability and self-healing capabilities of the nation's wireless infrastructure and the safety-critical applications it supports. Moreover, the project strengthens the US workforce by providing training opportunities in the critical areas of cybersecurity, AI/ML, and communications. The research agenda is organized in three interrelated thrusts. The first thrust establishes a comprehensive threat model against reinforcement learning-based methods, which are commonly employed for resource allocation, and studies the impact of attacks. Guided by the insights gained from exploring the attack surface in Thrust 1, the second thrust designs robust resource allocation methods for operating in uncertain and maliciously distorted radio environments. A multi-pronged defense, which limits information leakage from the wireless medium while developing attack-resilient allocation methods through adversarial training and robust reward function design, is investigated. The third thrust focuses on verifying that allocation policies adhere to the service-level agreements between stakeholders by building proofs of service to validate the resource transactions that take place over the air. 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 Despite being associated with increased survival and decreased drug use, emergency department (ED) initiation and prescription of medications for opioid use disorder (MOUD), including buprenorphine, for opioid overdose and untreated opioid use disorder (OUD), is not yet commonplace in the United States, most likely due to provider/healthcare system factors (e.g., lack of training, uncodified guidelines, and regulatory barriers) and patient/family factors (e.g., stigma). Thus, our highly experienced, multidisciplinary research team proposes a mixed-method quality improvement study, based on the California (CA) Bridge model, in a high volume ED (where we estimate that buprenorphine was initiated with only 20% of likely patients with OUD in 2024) to increase such care, along with establishing a national coordinating center (CC) that will produce an evidence- based guideline and provide training, technical assistance, and resources to all collaborating centers. The University of Arizona’s Prevention Research Center will partner with Banner Health and the Comprehensive Center for Pain and Addiction to complete our specific aims. Aim 1. (Research Project) Implement a SUN (substance use navigator) to increase ED OUD screening, MOUD initiation/prescribing, and linkages to care. Using electronic health data, we will examine percentage change over time in (a) screening/diagnosis (ICD-10 codes), (b) MOUD initiation and prescribing, and (c) linkage to post-discharge treatment within 7 days to increase ED provider knowledge for identifying and treating patients with OUD as measured by increases in rates over time. Aim 2. (Research Project) Investigate various structural factors that influence implementation, scalability, and sustainability (e.g., precise documentation of the ED champion’s types and categories of activities, an annual survey with ED personnel regarding the utility of the ED champion’s activities, and an examination of reimbursement data and support for appropriate documentation) to produce an effective, sustainable and replicable protocol. Aim 3. (CC) Evaluate the ability of the respective healthcare systems of the network-affiliated collaborators to provide MOUD administration directly to patients until their outpatient intake appointment or for 7 days (e.g., conduct a focus group with all collaborating centers to identify barriers, create actionable strategies to overcome those barriers, invite the collaborating centers to implement one strategy, and evaluate their progress in making changes) to create an evidence-based guideline for healthcare systems to provide patients with MOUD at discharge. Aim 4. (CC) Among network-affiliated collaborating centers, evaluate the provider-focused implementation strategies (e.g., SUNs, ED Champion, or other similar strategies) that facilitate ED clinician prescribing and linkage to care within 7-days. Through measures such as the documentation of each collaborating center’s implementation plans, quarterly updates of the types and number of implementation strategies, and network-wide feedback, we expect to increase the number of ED clinicians who initiate medication.

NIH Research Projects · FY 2025 · 2025-09

Recent evidence suggests that neuropsychological testing is sensitive to the accumulation of key Alzheimer’s disease (AD) pathologies in older adults without dementia. Semantic memory, meaning factual knowledge about the world, has emerged as a potentially sensitive cognitive domain to early AD pathology. Yet, barriers limit our understanding of semantic memory’s promise for detecting AD-related cognitive decline. Notably, efforts to examine semantic memory’s sensitivity to amyloid and tau have not capitalized on key cognitive mechanisms - identified through advancements in cognitive neuroscience - that may maximize sensitivity to AD pathology. Additionally, there remains uncertainty as to how semantic memory maps to hippocampal/medial temporal lobe (MTL) integrity, despite being relevant to revealing mechanisms and profiling AD pathology. Guided by contemporary cognitive neuroscience theories, the present proposal aims to ascertain, among older adults without dementia, the sensitivity of several new semantic memory tasks to: 1) a promising plasma biomarker of AD-specific amyloid/tau pathology and 2) hippocampal/medial temporal lobe atrophy. Led by PI Dr. Grilli, our team combines expertise in the cognitive neuroscience of semantic memory, hippocampal/medial temporal lobe functioning, and the neuropsychological detection of early AD. Together, we will undertake an innovative project integrating novel cognitive assessment, comprehensive neuropsychological profiling, structural MRI, and plasma assays. We will test, among 225 older adults spanning from cognitively unimpaired to those with mild cognitive impairment, the relationships between AD biomarkers and several mechanisms of semantic memory functioning. In Aim 1, we will investigate whether adding precision to semantic memory enhances this cognitive construct’s sensitivity to AD biomarkers, as measured by plasma and hippocampal/medial temporal lobe atrophy. Aim 2 will explore our hypothesis that experience-nearness, another property of semantic memory, is sensitive to the same AD biomarkers. Finally, Aim 3 will assess our hypothesis that generative processing, as a third semantic memory property, can detect early signs of AD through our target biomarkers. All three aims are not only informed by contemporary theory but also supported by extensive preliminary data from our team. The aims also are aligned with the NIH’s Notice of Special Interest: Novel Approaches to Diagnosing and Studying Clinical Alzheimer's Disease and Related Dementias (NOT-AG-21-036). This project, to our knowledge, will be the first to evaluate the sensitivity of these three semantic memory properties to critical, early AD biomarkers. Ultimately, the results of this project may lead to the development of more sensitive and specific cognitive tools for early tracking of AD-related pathologies, with broad implications for scientists conducting clinical trials, as well as clinicians on the frontline of healthcare treatment. The knowledge gained also may significantly advance our understanding of hippocampal/medial temporal lobe functioning by clarifying the mechanistic role of these neural structures in semantic memory.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY In the event of a radiological emergency, human skin tissues will suffer significant damage by ionization resulting in disrupted cellular repair processes and delayed wound healing. There has been a growing interest in utilizing gaseous molecules (H2, CO, NO, Xe) to treat cutaneous radiation injury as they exhibit promising abilities to regulate oxidative stress, reduce inflammation, provide cellular protection, and limit cellular senescence markers. However, delivery in the medical gas therapy domain has predominantly centered around inhalation-based methods requiring gas cylinders, which are impractical for radiological emergency scenarios. As a result, many gasotransmitters, noble gases, or traditional gases have not yet been tested for their ability to mitigate radiation- induced injuries. Recently, gas marble technology has emerged as a stable formulation by which to capture and contain gaseous species. The technology is made possible by the addition of partially wetted micro/nanoparticles to the air-liquid interface of liquid-entrapped gas. As this fortification strengthens resistance to mechanical stress and provides remarkable stability it is called a gas marble. These robust preparations could serve as an effective means of controlled delivery, encapsulating therapeutic gases to function as medical countermeasures against radiation exposure. The proposed study is for the investigation of medical gases (Xenon) as MCMs to mitigate and/or treat injury to normal skin cells arising from exposure to ionizing radiation in human tissue in vitro models. In parallel, computational modeling simulations will identify novel gas-protein targets. Additionally, the gas marble delivery system will be tested for the first time in a small animal model for its ability to deliver a known radiomitigating gas (H2) to ameliorate cutaneous radiation injury. Our Aims propose: (1) To investigate by computational and in vitro skin modeling, xenon gas's ability to bind to molecular targets and function as a specific drug that can mitigate ionizing radiation-induced biological responses; and (2) To demonstrate the in vivo feasibility of the gas marble technology for the skin delivery of established radiomitigating H2 gas against cutaneous radiation injury.

NIH Research Projects · FY 2025 · 2025-09

Vet-LIRN Network Capacity-Building Project and Equipment Grants (U18) PAR-23-202 Increased Extraction Capacity for Arizona Veterinary Diagnostic Laboratory Molecular Section Project Summary/Abstract- The Arizona Veterinary Diagnostic Laboratory (AZVDL) is seeking funding to purchase an Indical IndiMag2 to enhance its nucleic acid extraction capability and capacity, thereby strengthening its emergency surge testing for significant animal food/feed emergency events. Veterinary diagnostic laboratories play a critical role in protecting both animal and public health, especially during large-scale foodborne disease outbreaks or contamination incidents. Funding through the Vet-LIRN Network Capacity-Building Project and Equipment Grants (U18) is essential to expand AZVDL’s ability to respond effectively to such emergencies. This support will enable AZVDL to improve its diagnostic capabilities by acquiring advanced equipment. The COVID-19 pandemic has demonstrated that veterinary laboratories are uniquely positioned to provide high-throughput testing during public health crises, as they routinely handle large- scale diagnostic procedures for zoonotic and animal diseases. By enhancing its capacity, AZVDL will be better equipped to provide real-time detection of pathogens or contaminants in animal food and feed, helping to prevent outbreaks that threaten food safety, public health, and economic stability. This funding initiative also aligns with the goals of the Food Safety Modernization Act (FSMA), which emphasizes proactive measures to detect and prevent foodborne illnesses. Upgrading AZVDL’s infrastructure will not only improve its response to emerging threats but also contribute to the broader national network of veterinary laboratories dedicated to safeguarding both animal and human populations.

NIH Research Projects · FY 2025 · 2025-09

Project Summary: Over 16,0000 children in the US are hospitalized for treatments of their spinal deformities. The current surgical standard of care for these conditions is an instrumented posterior spinal fusion. These procedures are expensive (>$100,000) and as they “fuse” the spine they result in loss of spinal growth and mobility. Vertebral Body Tethering i(VBT) is a promising vertebral growth modulating surgical technique that attempts to harness the body’s own growth potential to correct spinal deformities. Despite VBT’s promising benefits, predicting which patient might benefit most from the procedure has been challenging. This project seeks to further refine a computational based surgical decision tool, using novel surgical devices and large animal model. The objective of the current proposal is to incorporate differences in growth potential (skeletal maturity) and vertebral segment mobility (“curve stiffness”) elucidated in our animal model, into our computational model so that the computational model can be used pre-operatively or intra-operatively to inform surgical decision-making using patient specific data. The long-term goal of this project is to provide personalized, vertebral-level by level strategies in correcting spinal deformities that maximize vertebral growth modulation and minimize the use of spinal fusion. We will accomplish this through the following aims: Aim 1: To characterize the maturity-related changes in the hyperkyphotic porcine vertebral physis and their effect on the sensitivity to vertebral tether load. Hypothesis: The vertebral physis will thin and stiffen with age, becoming less sensitive to mechanical load. Approach: Porcine vertebral phases of different maturity will be characterized and growth modulation response to VBT will be measured. Aim 2: To incorporate skeletal-maturity related data into our computational model and then test its ability to account for independent differences in vertebral segment mobility. Hypothesis: Growth modulation following VBT is directly related to vertebral segment mobility independent of skeletal maturity. Approach: Computational modeling throughout clinically relevant pre-operative and post-operative disc geometries will be performed; a subset of scenarios will be surgically reproduced to assess the computational model’s predictions and further refine parameters if necessary Aim 3: To translate our computational model into a clinically predictive tool to guide operative intervention. Challenge: To obtain pre-operative and intra-operative data for our computational model to allow clinical predictions of vertebral endplate stress to guide treatment. Together these aims will further elucidate the effects that skeletal maturity and vertebral segmental mobility have on vertebral growth modulation and allow translation of our research-based computational model into a predictive clinical tool. This will allow better patient and vertebral level selection, minimize unnecessary revision surgery, and limit the use of growth- and motion- eliminating spinal fusions.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY This research aims to revolutionize the understanding of nutrition by investigating the concept of "nutritional memory," which posits that early-life dietary experiences create lasting impressions on gut neuropod cells that influence lifelong food choices and consumption patterns. This paradigm shift challenges the traditional calorie-centric view and proposes that the gut acts as a sensory organ, communicating vital nutrient information to the brain. The research will be conducted in three parts: 1) Characterization of the sensory landscape of gut neuropod cells, utilizing calcium imaging and electrophysiology to identify and profile nutrient receptors; 2) Elucidation of the role of long-lived neuropod cells in shaping food preferences, analyzing how early-life dietary exposure influences their transcriptome, epigenome, and proteome; and 3) Establishment of causality and exploration of therapeutic potential by ablating long-lived neuropod cells and assessing the impact on food preferences and metabolic health. The potential outcomes of this research are transformative, including: 1) identification of the molecular mechanisms underlying nutritional memory, 2) development of innovative interventions to reprogram unhealthy food preferences and combat diet-related diseases, and 3) establishment of new dietary guidelines for pregnant women and infants to reduce health disparities. The investigator is uniquely positioned to undertake this ambitious project, with a strong technical and conceptual foundation, a team of world-renowned experts, and a collaborative approach to unravel the mysteries of gut sensory memory and create a healthier future for generations to come.

NIH Research Projects · FY 2025 · 2025-09

Abstract Ionizing radiation (IR) exposure from accidents or warfare can cause severe damage to radiosensitive tissues, such as bone marrow, intestines, and blood vessels, leading to acute radiation syndrome (ARS) and delayed effects of acute radiation exposure (DEARE) with high mortality and morbidity. These effects are mainly due to DNA double-strand breaks (DSBs) induced by IR, which require efficient repair to maintain genomic integrity and cell survival. We have developed small-molecule protein ligand interface stabilizers (SPLINTS), referred to as MH analogs, that target the binding pocket of the BRCA2-RAD51 complex, a key component of homologous recombination (HR), the most accurate and efficient pathway for DSB repair in S/G2 phases of the cell cycle. Our preliminary results show that MH01, the most promising SPLINTS, can be administered hours after IR exposure and mitigates cytotoxicity in a RAD51- and BRCA2-dependent manner, and enhances the repair kinetics of radiation-induced DSBs in cell culture models. However, the precise mechanism by which MH01 stabilizes the BRCA2-RAD51 complex and enhances its functions during DSB repair and break-induced replication is still elusive. Remarkably, we found that MH01 treatment 24 hours after lethal whole-body irradiation (WBI) rescues 80% of mice from IR-induced death and reverses intestinal toxicity. However, the optimal dose, pharmacokinetics, biodistribution, pharmacodynamics, and mechanism of action of MH01 are still unknown. We hypothesize that MH01 is a potent and safe SPLINTS that mitigates tissue damage caused by IR exposure by enhancing HR-mediated DSB repair in the S/G2 phases of the cell cycle through BRCA2-RAD51 complex stabilization. Our specific aims are: 1. Optimize the MH01 regimen for mitigating radiation toxicity by integrating pharmacokinetics, pharmacodynamics, and biophysical assays; 2. Elucidate the mechanism underlying MH01- mediated enhancement of the BRCA2-RAD51 complex's functioning during DSB repair following IR; and 3. Evaluate MH01 efficacy in mitigating radiation gastrointestinal tract and cardiac toxicities. Our approach on strengthening the stability of the BRCA2-RAD51 complex using SPLINTS at the peak of BRCA2-RAD51 activity during radiation-induced DSB repair, signifies a paradigm shift in the modulation of DNA DSB repair. This novel strategy presents a therapeutic avenue for mitigating radiation injuries in normal tissues with cells capable of undergoing division. Our overarching objective is to establish SPLINTS as a new and potentially significant inclusion in the current array of medical countermeasures designed to address accidental radiation exposure.

NSF Awards · FY 2025 · 2025-09

This I-Corps project is based on the development of supercomputers for real-time, interactive problem solving and data analysis. Currently, there are significant problems in creating robust, complex, open-ended learning environments that are personalized, engaging, adaptive and scalable. Yet, such environments have great potential for the development of valuable real-world skills (e.g., creativity, leadership, and collaboration). Preliminary implementations of this type of technology exist in medical simulation and military training, and it is emerging in K-12 and higher education. In addition, supercomputers are used in on-the-job training and life-long learning experiences. This technology addresses the challenges by providing well-integrated, immersive, collaborative, and virtual/physical extended reality environments. These environments provide unparalleled tools for research collaborations, intellectual exploration, education, and creative output. They incorporate visual, audio, and physical components; artificial intelligence (AI) technologies to enhance human-human, human-agent, and human-robot social interactions; rapid prototyping and fabrication tools; and tightly coupled interactive visual, audio, and physical experiences. This solution may be applied to next-generation healthcare simulators, advanced cyberlearning environments, and smart homes for well-being and entertainment. The technology provides deep integration of virtual and physical settings and has the potential to transform the fundamental understanding of collaboration, learning, creativity, discovery and innovation. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of experiential supercomputing. The core technology is the well-integrated fusion of interaction devices and artificial intelligence (AI) into a transformative, cohesive, and scalable instrument. The components include visual (headsets, projectors), audio (headsets, speakers, binaural), physical (spatial computing, collaborative social robots, 3D fabrication), human dynamic (wearable sensors, eye tracking), and social dynamic (co-located and remote dyadic and small team interaction) capabilities. The technology immerses users within customizable mixed-reality simulation environments by combining high-resolution visual, high-fidelity audio environments, and haptic feedback along with artificial intelligence (AI)-enabled collaborative agents and robots. The system architecture is comprised of three conceptual levels: node/field kit systems (local and distributed multimodal interaction), network systems software (latency, reliability, bandwidth), and computation (cluster/storage, data, analytic systems, services). In addition, this solution incorporates best practices from multimodal interaction research, human dynamics and social collaboration. Through AI-enabled extended reality environments that focus on the synergy of human-human, human-agent, and human-robot social interaction, experiential supercomputing may advance simulation and rapid prototyping for collocated, distributed, and collaborative teams. 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

The importance of antibodies in preventing and controlling human disease cannot be underestimated. Many vaccines depend on neutralizing antibodies for protection. In addition, neutralizing antibodies are highly useful when administered therapeutically. The ongoing pandemic has brought to light that few therapeutic antibodies can stand the test of time as new viral variants emerge. The promise of broadly neutralizing antibodies, which recognize conserved epitopes shared across entire groups of related pathogens, is that they are evolution-proof and remain protective in the face of pathogen evolution. Yet, as evidenced by clinical data, there are few examples where such antibodies cannot be escaped. This led me to wonder, what properties truly define what it is to be broadly neutralizing? Irrespective of the pathogen, broadly neutralizing antibodies are almost always very rare. In any given study, one might screen thousands of antigen-specific B cells to find a single broadly neutralizing antibody. Therefore, this rarity may explain some crucial underlying biology. Perhaps, these neutralizing antibodies are only broad because they are rare. In other words, pathogens are under little selective pressure to escape antibodies that are infrequently made. This leads to my central hypothesis that most broadly neutralizing antibodies are broad because they are rare, not because they target conserved epitopes that cannot mutate without a fitness cost to the pathogen. Using SARS-CoV-2 as a model pathogen, I will test these concepts. In Aim 1, I will determine whether infrequency is a defining feature of broadly neutralizing antibodies. In Aim 2, I will determine whether some epitopes, are more difficult to evolve away from than others. The results of this work will provide a deeper understanding of the relationship between antibody epitopes and resistance to viral escape. The methods developed in this project have applications far beyond SARS-CoV-2. In addition, this approach may provide a more efficient way for researchers to screen large amounts of sequencing data to identify potentially broadly neutralizing antibodies.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Project Summary: Alcohol-associated liver disease (ALD) is the most prevalent type of chronic liver disease worldwide. ALD represents a spectrum of liver disorders resulting from alcohol use, ranging from alcohol- associated hepatic steatosis (i.e., alcoholic fatty liver) to more advanced forms, including alcohol-associated steatohepatitis (ASH)/hepatitis (AH) and cirrhosis (AC). Alcohol-associated hepatic steatosis is the result of the earliest response of the liver to alcohol abuse. So far, the mechanism underlying the pathogenesis of ALD remains to be fully understood. There are also limited options for the treatment of ALD. Our preliminary study shows that hepatic forkhead box A3 (FOXA3), a member of the winged-helix transcription factors, is reduced in patients or mice with ALD. In this project, we will investigate the role of hepatic FOXA3 in the development and progression of ALD. We will also characterize the underlying mechanisms. Gain- and loss-of-function approaches and state-of-the-art techniques will be used to investigate whether hepatic FOXA3 is a key player in the pathogenesis of ALD.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY My research as a physician-scientist has focused on the biology of bladder cancer (BC). I have translated this knowledge into diagnostic and prognostic markers and novel therapeutic targets with the goal of improving patient outcomes. My contributions include: identification of new growth and metastasis genes; description of BC heterogeneity using single cell and spatial transcriptomics that provided clinically relevant stratification; development of the “Molecular Twin”, an artificial intelligence (AI) platform that led to prognostic biomarkers superior to those in standard of care; discovery of Ral GTPase inhibitors, targeting a protein considered “undruggable”; and an in vivo functional genomic screen that discovered therapeutic targets enhancing effectiveness of immune checkpoint blockade (ICB). My most recent discovery was made while investigating why men have a higher incidence of BC than women, even though the latter experience more aggressive disease. We found that aggressive forms of BC in men lose the Y chromosome and that this Loss Of chromosome Y (LOY) drives BC growth while inducing T-cell exhaustion, which fortunately makes BC patients with LOY more sensitive to ICB. Interestingly, LOY in peripheral blood mononuclear cells (PBMCs) is the most common somatic genetic alteration in healthy men. This is associated with increased risk of BC and other epithelial tumor types. We found a high proportion of LOY in CD4 and CD8 T-cells in the microenvironment of human and murine BC tumors, and this independently correlated with poor outcomes. This was the first demonstration that concomitant presence of LOY in immune cells and LOY cancer cells in tumor samples adversely impacted prognosis. This led us to the hypothesis that urothelial and T-cell LOY conspire to promote carcinogenesis and tumor progression while inducing tumor vulnerabilities. With BC as a clinically relevant model to study LOY, we will use pioneering models and approaches we developed including novel genetically engineered mice (GEM) with tissue-specific and inducible LOY to evaluate the impact of LOY in urothelial and T-cells on carcinogenesis and progression. The impact of GEM-derived LOY T-cells and of human T-cells generated from unique differentiated LOY-induced pluripotent stem cells (iPSCs) on in vivo growth of Y+/LOY murine and human BC cells will be assessed, complementing GEM studies. Murine and human BC cell lines coupled with functional genomic and high throughput small molecule screens will identify novel therapeutic vulnerabilities of LOY cancer cells and LOY tumors. To validate results from models, we will evaluate LOY in PBMCs, tissue, and urine, in diverse BC patient cohorts to understand the impact of LOY on bladder carcinogenesis and patient outcomes and whether the impact is dependent on other patient characteristics such as race, ethnicity, and treatment. While a nascent field, the clinical relevance of LOY offers new and untapped opportunities to understand cancer. Our objective is to provide scientific insights into LOY biology that serve as foundations for prevention, early detection, and therapeutic approaches in bladder and other cancer types.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT A large volume of literature indicates an association of oxidative stress with the causes or progression of heart failure. Heart failure is difficult to treat and costly to manage, yet carries a high mortality rate. Among the most common causes of heart failure is myocardial ischemia or infarction. Lack of endogenous oxidant removal or damage repair systems may explain the progression of heart failure. At the cellular level, low to mild levels of oxidative stress activate Nrf2 transcription factor to turn on the expression of antioxidant and detoxification genes. Works from our laboratory has demonstrated that de novo Nrf2 protein translation serves as an important mechanism for Nrf2 activation under oxidative stress. It is well established that stress causes a general inhibition of protein translation. How certain proteins, such as Nrf2, can escape the general control and be selectively translated when cardiac cells encounter oxidative stress is largely unknown. When investigating the translation machinery using quantitative LC-MS/MS based proteomics, we have discovered that YTHD2, a YTH domain containing N6 methyladenosine (m6A) binding protein, increased association with the ribosomes during oxidative stress. We have found that YTHD2 increases the binding to Nrf2 mRNA and have detected an increase of m6A in Nrf2 mRNA due to oxidative stress. Knocking out YTHD2 blocked oxidants from inducing Nrf2 protein. These data lead us to hypothesize that YTHD2 reading of m6A in Nrf2 mRNA serves as a passport for de novo Nrf2 protein translation under oxidative stress. The loss of stress associated protein translation mechanisms contributes to the progression from cardiac injury to heart failure. Aim 1 will test whether YTHD2 causes recruitment of a translation initiation complex for de novo protein translation under oxidative stress. Aim 2 will test that oxidative stress causes methylation at specific sites of Nrf2 mRNA and m6A landscape change due to activation of METTL3 methylase. Aim 3 will demonstrate the loss of stress associated translation machinery during the progression from ischemic injury to heart failure. Our study will advance the knowledge pertaining to a novel discovery involving RNA methylation for de novo protein translation in oxidative stress response. This project will utilize animal models of heart failure, and transcriptomic data from human heart failure patients to validate mechanistic findings for implications in real life human disease.

NIH Research Projects · FY 2025 · 2025-09

Project Summary In the last decade, the role of the commensal microbiota in host health has become well-established. For instance, gut microbiota are intimately involved in nutrient extraction, energy efficiency, and host metabolism, as well as the initial priming and maintenance of the host immune system. Dysbiosis, in which gut microbiota become imbalanced or disrupted, has been linked to the onset and progression of disease in nearly every human organ system. Emerging cross-disciplinary research suggests that the host physiological stress response may be a powerful but relatively understudied contributor to microbial dysbiosis, with wide-reaching implications for host health and quality of life. Yet, how gut microbiota respond to activation of the host stress response–and the molecular mechanisms underlie these responses–remain poorly understood. Uncovering the biological processes that transduce signals of host stress to the commensal gut microbiota is thus of paramount importance to advancing our understanding of complex disease. This early-stage investigator-led team utilizes a novel and tractable non-model rodent system, free-living North American red squirrels (Tamiasciurus hudsonicus), to identify the molecular underpinnings of microbial responses to host stress and determine the specificity and scope of these responses. As rodents, red squirrels offer high translational value for humans with physiological responses to stress that are evolutionarily conserved. In the wild, red squirrels are easily trapped, tracked, and manipulated. This facilitates investigations of host-microbial ecology in subjects with intact and ecologically-relevant rather than artificially-engineered microbiota, illuminating microbial responses and underlying mechanisms favored by natural selection. During the course of this five- year award, the team will use a series of controlled experimental manipulations to induce physiological stress, and subsequently 1) identify the precise microbial traits that respond to activation of the host stress response, and 2) interrogate top-down molecular mechanisms related to gut barrier function that underlie these responses. The team will take an integrative approach that combines amplicon sequencing, metagenomics, metabolomics, immunoglobulin sequencing and physiological surveys to interrogate microbial responses across multiple scales of the microbiota (e.g., genetic, phenotypic, ecological) and capture stress-induced change in gut barrier function (e.g., mucosal immunity, barrier integrity). The proposed work will contribute to the team’s overall vision of integrating the commensal microbiota into a broader understanding of the physiological cascades associated with the host stress response. By establishing a free-living rodent model of the gut-brain axis, this proposal will generate unprecedented insight into the potential adaptive value of the biological processes that govern multidimensional microbial responses to host stress. Findings generated by this work may substantially inform clinical and therapeutic approaches aimed at preventing or reversing stress- induced dysbiosis in the human microbiota by increasing the precision of microbial targets.

NSF Awards · FY 2025 · 2025-09

Drought conditions in the Colorado River basin have persisted since the year 2000, and this has resulted in decreased Colorado River streamflow, challenging water management for communities, agriculture and ecosystems. According to existing tree-ring based drought reconstructions, this current drought is comparable to the most severe drought of the last 1200 years, which occurred in the twelfth century. However, there is limited evidence that suggests a more severe and sustained drought occurred in the 2nd century, which if it happened today would have severe impacts. This project will extend tree ring reconstruction of drought back to 2000 years ago and characterize the 2nd century drought. These data will be combined with other geological records of drought in order to assess how drought severity and duration has changed through time, the contributions of changes in rainfall and temperature on droughts and the streamflow of the Colorado River, and inform understanding of drought risk in the future. The project includes development of K-12 homeschool educational materials, and opportunities for a postdoc and undergraduate students to participate in the research. The goal of this project is to use existing and new tree ring samples to reconstruct drought in the Colorado River basin during the past 2000 years. The tree-ring data will be combined with other proxy records and hydrological modeling to investigate the intensity and persistence of the second century drought in this basin, determine how long-term trends may have amplified droughts through time, and evaluate the impacts of warming versus drying on streamflow. The project includes collaboration with water resource managers to explore how to apply these data to management, development of K-12 homeschool educational materials, and opportunities for a postdoc and undergraduate students to participate in the research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award supports participation in the Southwest Center for Arithmetic Geometry’s 2026 Arizona Winter School (AWS) and the 2025 Preliminary Arizona Winter School (PAWS). AWS 2026 will be held March 7-11, 2026 at the University of Arizona in Tucson, and PAWS 2025 will be held online September 22-November 21, 2025. AWS 2026 will be organized around the central topic “Computational aspects of arithmetic geometry and cryptography,” and will feature a set of courses and accompanying research projects carefully designed and delivered by leading and emerging experts. The event will be a fusion of traditional mathematics conference and intensive research workshop: the speakers will each design courses of 4-5 lectures and propose research projects for graduate students. Nightly working sessions on these projects, and separate problem sets, will be run by the speakers and postdoctoral fellow assistants, and on the last day, students will present their findings. PAWS 2025, a virtual program aimed at advanced undergraduates and junior graduate students, will feature two six-week-long courses organized around the same topic as the AWS. Participants will engage in weekly problem sessions run by advanced graduate students, and participate in community-building and mentorship activities. Goals of both AWS 2026 and PAWS 2025 are to foster connections among peers, and mentoring relationships between students and senior researchers. Students will likely make concrete strides toward becoming research mathematicians, postdoctoral assistants will gain valuable mentoring experience in their academic careers, and faculty will develop new interests and see new connections. The Southwest Center website shares content from both AWS and PAWS, including lecture notes, project descriptions, and audio and video of lectures. More information about AWS 2026 and PAWS 2025 can be found on the website: http://swc.math.arizona.edu/ Both PAWS 2025 and the AWS 2026 center on the topic “Computational aspects of arithmetic geometry and cryptography.” With ever-more sophisticated computational infrastructure, opportunities arise to develop new mathematics with both intrinsic value and real societal impact. Both PAWS 2025 and AWS 2026 will have a wide scope, covering the fundamentals of analysis of algorithms in number theory and algebraic geometry, while also showcasing recent advances in computational number theory and post-quantum cryptography. Both programs have rosters of speakers who are leading experts: In PAWS 2025, Sabrina Kunzweiler and Juanita Duque-Rosero will give introductions to cryptography and analysis of the algorithms. In AWS 2026, David Harvey and John Voight will take on the algorithmic number theory aspects of the school, while Kirsten Eisentraeger and Chloe Martindale will take on the post-quantum cryptographic aspects of the school. Topics for the lecture series are plentiful, including algorithms for fundamental arithmetic, e.g., Harvey and van der Hoeven's polynomial multiplication over finite fields algorithm in time O(n log n); computing zeta functions of varieties over finite fields; algorithms for quaternion algebras; algorithms for lattice computations, lattice-based cryptography and attacks; quantum algorithms for number theory; the future of isogeny-based cryptography in light of the recent onslaught of successful attacks. 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 Over 17,000 new cases of spinal cord injuries (SCIs) are experienced annually in the U.S. due to motor vehicle collisions, sports injuries, and other traumatic events. In addition to the loss of mobility, numerous complications including urinary tract infections, pressure sores, and chronic central neuropathic pain (CNP) significantly impact the quality of life and contribute to morbidity and mortality. However, no effective treatments exist for individuals with SCIs. A mechanism- based approach that based on individual spinal learning to promote motor recovery and re- balance the pain circuitry would be the key to developing personalized SCI treatment. In the New Innovator Award program, our team will develop novel response-contingent neuromodulation devices to electrically modulate neural circuit plasticity. This individualized treatment strategy will be based on establishing a close-loop electrical stimulation paradigm via novel design of neuromodulation to self-train spinal circuitry. Our team will assess if the motor- contingent electrical stimulation can promote motor recovery and pain sensitization and investigate the underlying molecular mechanisms associated with spinal plasticity using the rat SCI model. The proposed work will determine the biology behind adaptive plasticity associated with NP and lay down the foundation to develop a close-loop electrical modulation technology for individualized neurotrauma treatment in the future.

NSF Awards · FY 2025 · 2025-09

Galaxies are made of many different components, not only stars, and some of their parts are difficult to detect because they do not emit visible light. Dust is one of these dark, yet ubiquitous, components of galaxies. Due to its nature, dust is very difficult to detect and quantify. This program will measure the amount of dust in the halos that surround galaxies by measuring the dimming of light sources in the background of 25 galaxies. The dimming of background sources will provide an estimate of the amount of dust surrounding these galaxies. Mapping the distribution of dust around galaxies will also help us understand the nature of dark matter. As part of this program, the PI will expand an established series of public talks given to the local community in the Tucson area. This program will also support the training of a graduate student in the field of astronomy with an emphasis on machine learning. This program aims to quantify the properties of circumgalactic dust by mapping the reddening of the many thousands of faint background sources projected near local galaxies. This work will be done with a set of 25 high-quality, extremely deep images of local galaxies to make the first measurements of halo dust in individual galaxies and demonstrate what will eventually be done with the full Rubin/LSST dataset. There is almost no work on tracing halo dust beyond the mere confirmation of its existence. Despite constituting a relatively minor component of the circumgalactic medium (CGM), this dust provides an alternative avenue for tracing chemically enriched halo material and determining the dark matter halo density profile, which in turn constrains the physical nature of the dark matter particle. 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 · 2025-09

Many naturally occurring microorganisms can produce soap-like substances, called biosurfactants, that alter how fluids and other substances in the liquids move through porous spaces such as soils and biological tissues. However, scientists still do not know how far and how fast these biosurfactants spread or how they redirect substances ranging from nutrients and pollutants to disease-causing bacteria. Yet, these microbial agents play important roles in the health of soils, plant roots, and even human lungs. This project tackles this knowledge gap by using laboratory models that mimic real soils to observe the microbes and biosurfactants in action and quantify their influence on the transport of fluids and dissolved chemicals. Outcomes from the project could support improved soil remediation strategies, more sustainable agricultural practices, and new methods to manage the spread of harmful bacteria. The project also promotes national goals in science and education by training undergraduate and graduate students, supporting interdisciplinary collaboration, and conducting outreach activities to help K-12 students appreciate fluid mechanics and microbiology and their relevance to daily life. This project integrates multiscale experiments and physics-based modeling to investigate how biosurfactants and the microbes that secrete them alter mass transport in unsaturated porous media. The goals are to: (1) quantify how time-dependent biosurfactant production alters transport behavior in two-dimensional porous systems; (2) characterize feedback loops between biosurfactant-induced flow, solute transport, and bacterial migration; and (3) measure and model biosurfactant-driven transport processes in three-dimensional porous media. Innovative visualization experiments paired with advanced multiscale models will reveal the intertwined dynamics of bacteria, biosurfactants, and transported substances. Model-data comparisons will guide the development of predictive tools that can be applied to environmental systems ranging from contaminated soils to biological tissues. The findings will provide a mechanistic framework for understanding biosurfactant-mediated mass transport, improving our ability to forecast chemical and microbial movement in complex, unsaturated porous media. 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 · 2025-09

This program builds a visible-light extension for interferometric imaging (LIVE) on the Large Binocular Telescope (LBT). The LBT, featuring two 8.4-m mirrors on a common mount that form a 22.8-m effective aperture, can be considered the first of the Extremely Large Telescopes (ELTs). LIVE leverages existing LBT infrastructure with targeted, low-risk upgrades to improve its visible-light performance, and adds a fast, sensitive, visible-light camera. This project will enable groundbreaking science at unprecedented resolution, probing structures and planet formation in protoplanetary disks, mapping Solar system moons, imaging dynamical processes and feedback in active galactic nuclei, and imaging the outflows and binary interaction of massive and evolved stars. Early-stage researchers and students will benefit from access to instrument development and deployment for hands-on research experience. As a pathfinder, LIVE generates valuable optical design expertise and trains the next generation of scientists in Adaptive Optics techniques for interferometry and fringe tracking. The LBT Interferometer Visible Extension (LIVE) is a pioneering instrument for ELT-scale visible imaging. LIVE enables imaging at the 4-5 mas scale. The unprecedented high resolution will drive advances in both engineering and imaging, serving as a science and instrumentation pathfinder for the US-ELT program. As such, LIVE addresses numerous open questions in astronomy across an enormous range in physical scales by imaging (i) the surfaces of stars to measure their activity and evolution; (ii) Solar System bodies, such Io and Europa, to monitor surface changes; (iii) emission from highly ionized, outflowing material which traces the impact of the supermassive black hole on the host galaxy; and (iv) star-formation sites in external galaxies to pinpoint the driving processes. LIVE also supports the search for habitable exoplanets and our understanding of how planets form. 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 · 2025-09

With the support of the Chemical Synthesis Program of the Division of Chemistry, Professor Jon Njardarson of the University of Arizona is developing new chemical reaction cascades in that leverage readily available feedstock materials in concert with negatively charged reaction partners to trigger sequences of reactions that produce higher-value fine chemical products. These new reaction processes will have broader scientific impacts by enabling researchers in industry and academia to construct and manufacture target architectures more efficiently, and the fundamental studies of reaction mechanisms will support the design of other new reactions. Broader impacts of this project also include workforce development and the continued dissemination of publicly available and popular educational work products from the PI and team. This project is focused on making significant advances on the asymmetric dienolate cascade reaction platform that the PI’s lab has been developing. Specific advances include in situ trapping of lithium enamides at carbon or nitrogen to expand the type of products that can be assembled in one pot, and realizing the formation of products containing all carbon quaternary centers. Detailed mechanistic investigations have opened and will continue to open new directions of reaction development, including the proposed routes to chiral lactams and atropisomers. This project is also focused on efficient assembly of chiral complex aromatic nitrogen heterocycles and fused ring structures via pericyclic and radical-metal-mediated cascades respectively, with the goal of increasing the impact of the chiral auxiliary. The suite of reactions being developed is expected to impact chemical synthesis and its applications in medicine, materials, and other areas. 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.