University Of California Los Angeles

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY [18F]FDG PET is a key imaging approach used regularly for the diagnosis, staging, and identification of recurrences of various cancer types including lung, colorectal, and esophageal cancers. Studies have also shown that decreases in tumor [18F]FDG accumulation can function as a biomarker of drug efficacy. [18F]FDG PET is well-studied at the level of bulk cell populations, but studies of [18F]FDG accumulation at the level of individual cells that could yield information on cell subpopulations are lacking. Studies of cancer cell genomes and transcriptomes at the level of individual cells demonstrate important heterogeneity and subpopulations in bulk cell populations. Similarly, the few available studies of [18F]FDG accumulation at the level of individual cells suggest potentially important heterogeneity but this remains largely unexplored. Identifying subpopulations of cells with differing levels of [18F]FDG accumulation could improve how we interpret pre-clinical and clinical [18F]FDG PET scans at baseline and following treatment by, for example, identifying which subpopulation is the main driver of [18F]FDG PET signal measured in bulk. We recently developed the BetaBox, a new technology platform for measuring PET radiotracer accumulation at the individual cell level. Our preliminary data using the BetaBox demonstrates heterogeneity in [18F]FDG accumulation in cancer cells as well as our ability to use the BetaBox to measure [18F]FDG accumulation at the level of individual cells. However, prior research by our group and others on [18F]FDG accumulation at the level of individual cells is limited to four cell lines and mostly involves untreated cells in culture. Additionally, the current BetaBox is limited in the number of cells that can be studied at any one time and has no mechanism for isolating and further studying potentially interesting cells. The goal of this proposal is to use the BetaBox to study [18F]FDG accumulation at the level of individual cells across model systems in culture and in vivo including cell lines, patient-derived sphere cultures, and patient-derived xenografts with and without drug treatment, and to use this information to identify subpopulations of cells that differ in their [18F]FDG accumulation for comparison across samples. An additional goal is to develop next generation BetaBox technology to enable hundreds of cells to be studied simultaneously and for interesting cells to be isolated for further functional characterization. We propose to accomplish this through the following three Specific Aims: (1) To develop the next generation BetaBox to increase throughput and enable isolation of individual cells for further analysis. (2) To characterize cell subpopulations based on [18F]FDG accumulation in individual cells across model systems in culture and in vivo. (3) To study based on [18F]FDG accumulation in individual cells how cell subpopulations change in response to targeted kinase inhibitors.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY Elucidating the genetic and environmental drivers of disease is critical to precision medicine. Advancements in in vitro human stem cell culture are accelerating disease and therapeutic research across individuals of di- verse genetic backgrounds. Cell villages are an e!cient platform, that pools populations of 10-100 genetically distinct, human stem cell lines, to study how genetic variation and environment influence a spectrum of molec- ular phenotypes. This experimental design allows researchers to apply selection and perturbation treatments to large, ancestrally diverse cell populations, while simultaneously measuring phenotype di↵erences between donors through molecular and omics assays. While villages show a lot of promise for uncovering Gene x Environment (GxE) interactions and the end e↵ect on disease, two main limitations need to be addressed: (i) experimental methods need to be refined to ensure reproducibility and adequate statistical power and (ii) statistical analysis tools need to be built to incorporate the full structure of these data. We aim to address these limitations by first developing standards for cell village experimental design through statistical modeling and simulation of village donor growth. Specifically, we will test how di↵erent experimental parameters a↵ect the amount of variation in donor growth rate estimates, the consistency of these predictions and whether experiments can maintain donor representation over the desired time frames. These factors will be important as we want to help researchers design experiments that minimize the variance in the growth rate estimates, while maintaining high genetic diversity throughout the entire experiment. Using this experimentally validated simulation framework, we will build a user- friendly tool that enables researchers to input their own preliminary data to project how di↵erent experimental design choices might impact the signals they are able to detect in the data and if donors might drop out or take over the population. In the second aim, we will build a hierarchical model of single cell RNA-seq (scRNA-seq) time series count data. The scRNA-seq pipeline will focus on two main applications: (i) a statistical tool that addresses the sparsity in single cell time series count data while accounting for natural variation in villages, and (ii) a tool that estimates the sequencing depth and village design needed to optimize statistical power. This work will help researchers conduct reproducible, high powered cell village experiments while fully leveraging the structure of the data. Improving cell village experimental and statistical methods will give researchers a powerful platform for investigating individual disease risk and potential for specific therapeutic interventions.

NIH Research Projects · FY 2026 · 2024-12

Project Summary Walking is one of the most essential forms of mammalian behavior, but there is still only an incomplete understanding of the neural mechanisms that control gait performance at the level of individual limbs and steps. Several movement disorders including Parkinson’s disease (PD) are associated with impaired gait, and current therapies are often only partially effective at alleviating these motor symptoms. The basal ganglia are a set of subcortical nuclei that are strongly implicated in Parkinsonian motor symptoms. However, most efforts to link motor deficits with altered basal ganglia activity in PD animal models have focused on relatively low spatial resolution measures of motion such as movement initiation, termination, and whole-body speed. This leaves a large unmet need to study the neurophysiological basis of impaired gait in PD animal models with single-limb and step resolution. This project will examine how the dorsolateral striatum and substantia nigra pars reticulata, a major input and output nucleus of the basal ganglia, encode gait information in two complementary mouse PD models. Experiments will use high speed video, automated behavioral tracking, single-unit electrophysiology from genetically identified cell types, and optogenetic manipulations to mimic or rescue motor impairments in the unilateral 6-hydroxydopamine (6OHDA) and alpha-synuclein preformed fibril models of PD. This proposal is motivated by novel electrophysiological data suggesting that a sizable fraction of neurons in the striatum and substantia nigra are normally coupled to the cycle of limb movements during walking. Furthermore, lesioning dopamine with unilateral 6OHDA injections disrupts the normal balance of gait phase coding between direct and indirect pathway neurons in the striatum. Aim 1 will build on these preliminary results by identifying the neural signatures of impaired gait and other, more commonly studied measures of motion (start/stop and whole-body speed), in 6OHDA lesioned mice on and off dopamine replacement medication. Aim 2 will track the progression of gait impairments and movement-related neural activity changes in the alpha-synuclein preformed fibril model. Finally, Aim 3 will determine whether manipulating certain basal ganglia cell types can impair single-limb gait performance in healthy animals, or rescue gait in PD models. Together, this project will significantly advance our understanding of the basal ganglia’s role in encoding and controlling gait in PD animal models.

NIH Research Projects · FY 2026 · 2024-11

PROJECT SUMMARY Genetic and environmental factors determine individuals’ susceptibility to autoimmunity. Genome-wide association studies of human subjects provide valuable information; however, the interpretation of these results is complex due to intrinsic and extrinsic variations. This proposal aims to uncover the genetic basis underlying the intrinsic propensity of T cells towards autoimmunity using a hybrid mouse system with minimum extrinsic variations and to elucidate the molecular mechanism underlying these genetic differences. We performed a forward genetics screen using a hybrid mouse diversity panel of 107 common and recombinant inbred strains to examine T cell propensity. We have uncovered four significant genetic loci associated with expression of proinflammatory cytokines. A targeted screen using deletion of candidate genes within these loci uncovered a mitochondrial cristae morphology regulator, TMEM11, as a genetic determinant of the Th1 response. Tmem11- /- mice show normal development and homeostasis of T cells. However, while T cell differentiation was not affected, selective effector functions of Th1 cells, including expression of IFNg, were impaired due to the loss of TMEM11. This impairment decreased the severity of an animal model of autoimmunity, experimental autoimmune encephalomyelitis (EAE). Further analyses revealed that Tmem11-/- Th1 cells showed impairment in cristae integrity and mitochondrial respiration. Among these defects, excessive mitochondrial reactive oxygen species (mtROS) production was the underlying cause of impaired Th1 function since mitigating mtROS rescued the defect. We also found that excessive mtROS decreased histone acetylation and activation of the Ca2+-NFAT pathway, which are important for Ifng transcription. Based on these findings, we propose to 1) uncover the mechanism regulating mitochondrial cristae morphology in effector T cells using high-resolution imaging techniques and proteomics, 2) understand the relationship between mitochondrial cristae integrity and effector T cell responses by checking the role of excessive mtROS in histone modification and the NFAT signaling pathway, and 3) Determine the role of mitochondrial cristae integrity in T cell-mediated autoimmunity using active and passive EAE models. This study is innovative because our forward and reverse genetics approaches uncovered a unique tool to investigate the roles of cristae integrity and mtROS in effector T cells. Knockout of Tmem11 provides an ideal system to determine the physiological role of cristae morphology and integrity because TMEM11 is a regulatory protein but not a core subunit of the cristae junctional complex, and its deficiency, hence does not influence mitochondrial biogenesis or global cellular physiology. The outcome of this study will positively impact our understanding of the roles of mitochondrial cristae integrity and oxidative stress in effector T cell functions and autoimmunity, which need further investigations.

NSF Awards · FY 2024 · 2024-10

One of the most dramatic differences between artificial neural network (ANN) models, as currently used in machine learning, and biological neural networks pertains to how they process information about time. Specifically, the difference is in how the brain tells time and how it uses its inherent neural dynamics to process time-related information. Because of the importance of time and prediction in natural behaviors, the brain is an inherently time-based computational device. For example, during speech recognition people continuously and unconsciously extract information about the durations of syllables and the intervals between words in order to understand the meaning and emotional content of speech. Current experimental and theoretical results suggest that the processing of time-varying information, such as speech, relies on the inherent short-term dynamics of synapses on the scale of milliseconds to seconds. This phenomenon is referred to as short-term synaptic plasticity (STP). The standard view is that the temporal characteristics of STP are essentially a hard-wired property of specific classes of synapses. Inspired by recent neuroscientific findings, this project proposes that STP itself undergoes long-term plasticity in a manner that adapts to and optimizes the processing of the timing of information and experience. This project will develop a novel class of feedforward networks referred to as adaptive synaptic dynamics neural networks, which can process temporal information in the absence of recurrency, delay lines, or the spatialization of time. STP will be incorporated into feedforward ANNs and, in addition to standard training of ANN weights, the time constants that determine the temporal profile of STP will also be trained. Performance of networks with adaptive-STP will be contrasted to standard feedforward and recurrent ANNs on tasks including Morse Code and speech recognition. The primary hypothesis to be tested in this project extends a fundamental tenet of computational neuroscience—that information is stored in the population of weights of a neural network—into the temporal domain by suggesting that synapses may also learn to govern their short-term dynamics in order to optimize the processing of temporal information. Overall, this project will lead to novel biological principles being applied towards machine learning, and further advance the ability to emulate the brain’s computational strategies. 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

Quantum technologies stand at the frontier of scientific innovation, offering transformative capabilities extending well beyond the reach of current technologies. Quantum sensors achieve unprecedented levels of sensitivity, quantum computing promises to tackle challenges deemed impossible for classical computers, and quantum communication introduces the potential for unbreakable security, heralding a new era of secure communication networks. A new legal and regulatory landscape is evolving in response to the advent of quantum technologies, but the state of scholarship on responsible quantum innovation remains nascent. There is a critical need for interdisciplinary research that bridges the gap between quantum advancements and their societal, ethical, and legal implications. This project aims to fill this gap by networking quantum researchers with experts across law and the social sciences to develop strategies to promote responsible innovation and early-stage intervention against potential societal harms. The network and research products flowing from this project will establish a research foundation to support regulators, legislators, and industry leaders in crafting and refining responsible innovation strategies. Additionally, it will create an ongoing hub for sustained cross-sectoral and interdisciplinary dialogue and collaboration among a network of researchers and public policy professionals, fostering continuous advancement in research, policy development, and ethical considerations in the quantum domain. The primary mechanism for developing the aforementioned outcomes is through a high-level convening of experts from the fields of law, humanities, public policy, business, physics, engineering, and computer science. The project integrates interdisciplinary expertise from faculty collaborators, along with partners from industry and civil society, to generate actionable guidelines to manage the social impacts of quantum technologies as they are developed and deployed. The main hub for developing this research and expertise will be through a workshop at UCLA, organized under the auspices of the UCLA Institute for Technology, Law & Policy (ITLP), a collaboration between the schools of law and engineering. However, the workshop will be bookended by extensive engagement and networking activities at both the frontend and backend, culminating in a basket of policy-focused deliverables including the launch of a new interdisciplinary Quantum Policy Research Group, a blog series on major quantum policy challenges, and a Special Issue of the UCLA Journal of Law & Technology dedicated to quantum research and the law. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The American Lung Association stated that air quality, in particular that in urban settings that contain large amounts of particulate matter, is a serious health problem for residents. In addition, for many parts of the U.S, government owned and maintained air monitoring equipment and data does not have the spatial resolution to provide communities, especially those in low income heavily polluted areas, with air quality data where they live, work, and play. This Civic Innovation Challenge (CIVIC) planning process brings together a science team with local community advocacy and faith-based organizations to co-design a science/research-based, implementable, scalable, and sustainable solution that addresses air quality, an important community resilience problem in many low income neighborhoods. This CIVIC planning activity supports infrastructure for the collection of local air quality data and provides community education and empowerment to improve air quality in a pilot program focused on a South Los Angeles low income community. The planning process will also plan, promote, and create programs to expose local youth and community residents to green jobs and careers that can further build climate resilience and provide good paying jobs. The University of California, Los Angeles and the Ezrach Brain Trust Association lead this effort and will partner with faith- and community-based organizations, educational institutions, and workforce centers to engage them in co-designing a community-based air monitoring network and green workforce development program for the South Los Angeles neighborhood of Leimert Park. Broader impacts include a community-based air monitoring network that will provide hyperlocal, real-time, air quality data to increase community education and awareness of air quality and its impacts on their health and lives as well as provide essential data to allow community advocacy for interventions and mitigation strategies; thus, improving community health and well-being. The green workforce training program will expose Leimert Park community youth and members to green career pathways, ensuring the community is part of the rapidly growing green economy. This will involve working with project partners and local industry, fostering economic development by educating and equipping residents and youth for green jobs and careers. The project involves building an air quality measurement and monitoring infrastructure to support a network of people and online accessible tools to collect community air quality data, share individual and collective narratives about local environmental issues, and support the community in helping them know how to critically analyze data to build a science and data-driven advocacy campaign for improved community air quality. The project will develop community training and education programs about air quality data, data analysis literacy, and how the data can be used for advocacy. Another objective is to design, with partners, and implement a community-engaged and participatory action approach to improving local air quality. Low-cost air monitoring deployment sites in Leimert Park will be informed via a community needs assessment and information from focus groups. An online data visualization platform will be developed to provide community members access to real-time air quality data. Data will be used to improve understanding, awareness of the impacts of compromised air quality to help individuals and the community advocate for action. Essential to the activity is the engagement of faith- and community-based organizations that have the trust and involvement of local community members. Also crucial is the involvement of local workforce centers and secondary schools to help develop the green workforce training initiative and establish a training pipeline. This planning process will improve the understanding of how community-based efforts can be designed to lead to policy changes. It will also foster and strengthen collaboration between researchers and community stakeholders, develop new collaborations and partnerships, refine the research vision to enable submission of a successful follow-on proposal that will implement the community vision, and provide data to address research questions and develop evaluation methods and measures for the follow-on project. Through this approach, the project team feels the activities and anticipated outcomes can be replicated in other similar urban communities facing similar challenges. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. The proposal was co-funded by the NSF Directorate for Geosciences and Directorate for Social, Behavioral, and Economic Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Chronic health conditions are financially and emotionally costly. Immediate action is needed to shift from current practice, in which frequently the patient is a passive recipient of care to empower patients to have a central active role in self-managing their health. The Center to Stream Healthcare In Place (C2SHIP) unites the best minds in academic medicine and bioengineering with leaders in biomedical industry to research, develop and promote in-place care technologies for fertilizing a patient engagement ecosystem. The University of Arizona serves as the C2SHIP Lead Site with Partner Sites being University of Southern California, Baylor College of Medicine, and California Institute of Technology. The Center will accelerate innovation through partnerships, multi-specialty collaborations, resource sharing, and preparing an educated workforce to promote wellness through self-care technologies. The Center’s trans-disciplinary team will pursue research and development in new material-based sensors, reconfigurable designs, system integration, intelligent data mining, and comprehensive data visualization. Through “Digital Health”, patient data can be streamed to medical professionals at remote locations, establishing a mobile hub for vulnerable patients in their own home, and personalizing care coordination. C2SHIP will focus on mitigating physiological, environmental, and psychological changes for timely management and intervention. Baylor College of Medicine (BCM) will focus on the clinical medicine aspect of the Center, accelerating translation of cutting-edge technologies to patients in-place, while training students and fellows in remote patient care. The Center will accelerate knowledge and intellectual property transfer between academia and industry through collaborative partnerships. This will promote rapid development of new technologies, and transform health care delivery by enhancing the quality of life of chronically-ill patients while reducing health care costs and preventing hospitalizations. Student engagement in the proposed research projects will create opportunities with Center companies and organizations, and provide multidisciplinary participation at C2SHIP conferences and workshops. BCM C2SHIP will recruit students and engage faculty in medicine, biomedical engineering, allied health. BCM promotes an inclusive environment, allowing the Center to engage underrepresented individuals from diverse backgrounds. Data produced from the projects will be housed in Center-wide servers at the C2SHIP Center using a password protected Box data sharing folder. Box is a cloud computing business which provides file-sharing, collaborating, and other tools for working with files that are uploaded to its servers. Data will be maintained for five years. BCM C2SHIP projects that collect patient data will be approved by BCM IRB prior to project onset. All data will be deidentified, with no personal identifiers recorded or retained in any 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 2024 · 2024-10

Human-centric cyber-physical systems (CPS) such as assistive driving and exoskeletons aim to augment human capabilities instead of replacing humans. Humans can collaborate with these machines to overcome corner cases and demonstrate the correct action under safety-critical situations. Such human collaboration enables the human-centric CPS to achieve a better outcome than either could achieve alone. In this project, investigators will develop an efficient human-in-the-loop learning framework for human-centric CPS. During training, the machine will learn to make decisions in an uncertain environment, while the human will oversee the machine and actively intervene when anomalous or unsafe behavior occurs. The human will then demonstrate the correct action to the machine. The project’s novelties are incorporating a human subject to guard the learning agent, where the human can actively intervene in unsafe situations and demonstrate the correct actions to the agent during training, and developing a reward-free learning approach that substantially encourages learning efficiency, safety, and AI alignment. The proposed human-in-the-loop learning framework is being instantiated in two human-centric CPS including assistive driving and exoskeleton. The project's impacts are facilitating harmonious human-machine collaborations and enabling CPS's efficient and safe autonomy. This research contributes to establishing best practices and standards applicable to various industries where it is essential to integrate humans in the operation of CPS, including automotive, package delivery, and rehabilitation. The team is creating research and training opportunities for high school, undergraduate, and graduate students in machine learning, robotics, control, and biomechanics. The project breaks away from the prevailing paradigms of model-based control and safe reinforcement learning through three research thrusts. 1) Development of a human-in-the-loop learning framework that incorporates a human subject to guard the learning agent, where the human can actively intervene in unsafe situations and demonstrate the correct actions to the agent during training. This approach is reward-free and encourages learning efficiency, safety, and AI alignment. 2) Creation of digital twins of task-specific human behaviors for evaluating the proposed learning method in each targeted CPS, with focus on developing a simulated environment of human behaviors in driving and exoskeleton. 3) Empirical evaluation and real-world experimentation of each targeted CPS to train and evaluate the proposed learning methods against various scenarios in simulation and real-world settings to validate their performance. 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

When one uses a social media site or accesses services on a phone (e.g., to make an online purchase) one relies on the Internet to be available. The Internet works using software running on multiple servers and routers called protocol implementations. It includes implementations of famous protocols like Transmission Control Protocol/Internet Protocol (TCP/IP) and Domain Name System (DNS). The Internet is sometimes down because of hardware failures; however, many major outages have been caused by bugs in protocol implementations. This project uses mathematical methods and AI to create protocol implementations provably free of errors. This research uses three different approaches. First, the project plans to develop techniques to generate tests for protocol implementations that provably cover all the features/behaviors that the protocol claims to support. This is done by first building simple models of Internet protocols from their specifications and analyzing the models mathematically to identify behavior classes. Second, the project plans to build provably correct network protocol implementations by breaking up complex protocols into pieces (sublayers), which are mini-protocols in themselves, to make proofs easier. Third, the project plans to exploit advances in large language models (LLMs) to lower the large human cost today to develop reliable protocol implementations. LLMs may hallucinate and (by themselves) can, in fact, make the Internet less reliable. The key insight is that combining LLMs with progress in computer aided proofs can provide the best of both worlds: provably correct protocol implementations with greatly reduced human effort. This project will create a more reliable Internet by making one important part (implementations of popular protocols like TCP, DNS, QUIC and Border Gateway Protocol) of the Internet provably reliable in a mathematically precise sense. When social media sites are down for several hours, it greatly affects our social life. But beyond interaction, the Internet matters greatly as more commerce is electronic (19 trillion USD per year) and the dollar cost of Internet outages can be hundreds of millions of dollars per day. In addition to the techniques that the project will develop, it will also produce concrete artifacts for use by others, including new protocol implementations with provable guarantees. This project is part of a broader vision to build an industry for Network Design Automation (NDA), a set of tools for networks akin to those that led to the billion dollar chip industry in Electronic Design Automation (EDA). Publications, tools, data, and coursework related to this project will be stored at the NDA website and maintained indefinitely: https://www.georgevargheseucla.com/nda 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

Traditional data centers are organized with a cluster of computer servers. Like a personal computer, each computer server in a data center includes a set of computing hardware resources such as CPU, memory, and disks. In recent years, a new data-center architecture called resource disaggregation has arisen. With resource disaggregation, different types of hardware resources are divided into separate pools (e.g., a CPU pool and a memory pool), and an application can run with any available resources in a pool, thereby improving the resource utilization of a data center. Prior resource-disaggregation research has taken two main approaches: (1) asking application developers to port their software to a resource-disaggregation-specific model and (2) changing the operating system to add support for resource disaggregation. The former requires manual work, while the latter incurs significant performance overhead because of its generality. To solve these shortcomings, this project proposes to leverage application features and behaviors in building resource-disaggregation solutions. The project's novelties are a new direction in resource-disaggregation research, new computing layers explored when building resource-disaggregation systems, and the study of data-center applications from the perspective of resource disaggregation. The project's broader significance and importance is to render resource disaggregation a cost-efficient and performance-efficient option for production data-center and cloud environments that often host a range of applications with diverse behaviors and performance requirements launched by multiple users. More specifically, the project aims to extract and leverage application-inherent semantics, including static and dynamic semantics such as memory access patterns, data object ownership, type-based data structures, execution profiles, and language runtime activities. This project lays the groundwork for constructing programming languages, compilers, and system support that can integrate a comprehensive range of program semantics into diverse disaggregation choices. This project aims to enable disaggregation systems to customize, without manual intervention, these multiple choices based on application-specific behaviors rather than relying on generic decisions. 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

Deep learning techniques have achieved good success in the perception tasks such as image classification, where the correct model output can be obtained and annotated beforehand. For example, when a deep learning model is asked to identify dogs and is trained at the start with a series of images of dogs with annotations identifying dog features (e.g., tails, fur, muzzles etc.), these models do well. However, when the deep learning models are deployed to support decision making tasks such as medical diagnosis, autonomous driving, and conversational systems, only incomplete feedback for training is available. When the correct outputs in decision-making situations are not available to train a system, it is difficult to employ traditional deep learning techniques directly. Bandit learning methods are algorithms that have been developed to deal with incomplete feedback information for learning. This is important because previous work in the perception area, could afford to blindly optimize a model for the best response. For decision-making, this type of optimization may not be optimal, because small mistakes in perception can lead to huge losses in quality of decision making of which the model may not be aware. This project aims to bridge the gap by training the deep neural networks in their natural use context to directly optimize decision making. The goal is to develop a suite of neural bandit learning algorithms, which leverage the most recent advances in deep learning theory for provably efficient neural network model training with bandit feedback. The project consists of three research thrusts. Thrust one develops bandit learning methods in more advanced neural network architectures and studies new deep learning theory with bandit feedback. Thrust two investigates neural bandit learning in decentralized and distributed settings. Thrust three equips the learnt models with privacy and adversarial robustness guarantees. The team of researchers will develop an open-source neural bandit library and teaching materials to disseminate research outcomes and make them publicly available to the broader community to benefit research and education. The project provides unique training opportunities in machine learning and artificial intelligence for undergraduates and graduate students, especially students from underrepresented groups. The researchers will also engage K-12 students, fostering their interest in STEM by educating them on deep learning and AI techniques. 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

Laser technology is one of the most transformative inventions of the modern era, which has become an indispensable tool for scientific research and technological innovation - revolutionizing the semiconductor industry, telecommunications, healthcare, and defense. However, current laser design and manufacturing approaches remain stagnant, stymieing further breakthroughs. Developing novel integrated systems of laser architectures, components, and techniques leveraging digital twins (DT) is imperative to expand frontiers in intensity, wavelength regime, and high average power. This project will fill this gap using state-of-the-art predictive and generative artificial intelligence (AI) coupled with physical principles and high-fidelity, close-loop, rapid feedback between digital models and physical systems. Graduate students and postdoctoral researchers will also be integrated within the research team as part of the training of the next generation of scientists required to advance the field. This project will develop theoretical foundations for AI-assisted DTs to integrate scientific data, physical models, and machine learning for complex high-power laser science and engineering (HPLSE) to enable efficient design, failure and performance prediction, operational optimization, and emerging lasing conditions. Laser technologies are extremely complex to model because they rely on a cascaded set of mode-locked laser dynamics and a manifold of architectures and configurations of chirped pulse amplification, and nonlinear optical stages, such as parametric amplification. Their architectural complexity and multi-dimensional data far exceed current modeling and analysis tools. The project will address these challenges by (1) extracting reduced representation of scientific data from experiments or high-fidelity HPLSE simulation, (2) building data-efficient and physics-aware predictive machine learning surrogate models of laser fields with uncertainty quantification, and (3) developing generative model-based rapid closed-loop control between digital models and physical high-power laser systems. The project will be AI-focused, multi-disciplinary, and involve a diverse workforce of future scientists and engineers. The project will also include an education thrust to integrate the research results into interdisciplinary education. The project will bolster AI foundations and its application curricula at both UCLA and the University of Utah. More critically, it will forge a robust collaboration among mathematics, data science, and laser technologies. 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

Heterogeneous hardware architectures (including graphics processing units, field programmable gate arrays, and application-specific integrated circuits) are shaping the future of computing and artificial intelligence (AI) acceleration. However, the use of such extraordinary computing power from heterogeneity is restricted to a limited pool of software developers with deep microprocessor expertise. Although high-level synthesis (HLS) compilers have been developed to convert computation logic written in high-level programming languages to low-level register transfer level based kernels, this process requires significant code rewriting to meet synthesizability and performance requirements. To improve developer productivity in this emerging domain, this project develops new automated code transpilation, testing, and debugging technologies to lower the barriers of developing heterogeneous applications, thereby making emerging hardware accessible to software engineers with different levels of hardware expertise. This project will also train the technology workforce with interdisciplinary computing backgrounds in software engineering, hardware design, heterogeneous architecture, and compilers. Specifically, this research has three innovative components. First, it will design efficient program transformation and interactive design exploration methods for porting classical software that targets central processing units (CPUs) to its heterogeneous version with behavior preservation and optimized performance. Second, it will design new automated testing methods for heterogeneous applications that obtain increased visibility with new hardware-level probes and adapt existing tests to various platforms. Third, it will design automated debugging methods for source code tracing and pinpointing the root causes of failures throughout a multi-phased hardware compilation process. In summary, this project will produce a suite of advanced open-source debugging and testing tools as a key enabler for harnessing the potential of hardware heterogeneity. 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

Proposal Number: 2043803 Principal Investigator: Elaheh Ahmadi, Title: CAREER: A novel Gallium Oxide based transistor for low-waste power conversion applications Institution: University of Michigan Nontechnical Abstract There is an urgent need for new device technologies to efficiently manage and distribute electrical power in the 2000V-20000V voltage range. The current technology, however, can no longer meet the efficiency and reliability requirements for these high-power electronic applications. The proposed ultra-high voltage switch will enable efficient high-power switches in the 2000V-20000V voltage range, which is required in many systems, including distributed grid systems, industrial automation, electric vehicles, and electrical mass transit including high-speed trains. The integrated education plan aims to motivate young students, especially female students and those from the underrepresented groups, to pursue STEM studies and careers by direct participation in the proposed research activities. The tutorials prepared for high school students will be made widely available on PI’s research website and YouTube. The scientific results will be disseminated in the form of articles in technical journals, conference presentations, and university seminars. Technical Abstract The goal of this program is to demonstrate E-mode β-Ga2O3 fin field-effect transistors (FinFETs) with breakdown voltages beyond 3kV, specific on-resistance lower than 1 milli-ohm-cm-square switching efficiency higher than 98% at 15kHz frequency. The scientific goals of this CAREER plan are (i) A thorough investigation and experimentation into the impact of slanted-sidewall and N implantation in the inter-fin areas on electric fields to enhance breakdown voltage. (ii) Fabrication and characterization of FinFETs on both metal-organic chemical vapor deposition (MOCVD)-grown and halide vapor phase epitaxy (HVPE)-grown epi-structure to analyze and compare the impact of growth technique, and material quality on device performance. This study assists in selecting the appropriate growth technique for each process module in the future. (iii) A systematic study of the substrate thickness impact on device characteristics such as Ron. (iv) Development of β-(Al,Ga)2O3 regrowth on the fin sidewalls by plasma-assisted molecular beam epitaxy (PAMBE) and a complete investigation of the impact of regrowth conditions on electron mobility in the channel, interface trap density, and FinFET characteristics. This will be the first demonstration of sidewall regrowth in this material system. (v) Development of robust and reliable Hf(Si)O2 dielectrics by PAMBE as well as investigation and analysis of deposition conditions on dielectric quality (breakdown, dielectric constant, leakage, etc.). This will be the first demonstration of in-situ dielectric deposition in this material system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This research addresses the challenge of making terahertz semiconductor laser sources that emit electromagnetic waves with frequencies between 2 and 5 THz (i.e. wavelengths between 60 and 150 microns). More specifically, the goal is to develop new types of high-performance terahertz frequency combs – a type of laser source which emits many colors of light simultaneously, where each “color” has a frequency precisely spaced by a fixed amount from the other frequencies. This is an international collaboration between the UCLA group, which has expertise in a unique type of external-cavity terahertz quantum-cascade (QC) laser, and a Swiss group at ETH Zürich, which has expertise in terahertz frequency comb physics and measurement. If successful, this research would result in a new terahertz source for applications in the fields of astrophysics, atmospheric science, biological and medical sciences, security screening, illicit material detection, combustion science, antiquities, waste-sorting, next-generation wireless communications, and non-destructive evaluation. The broader impacts of the project include training graduate and undergraduate students (including international scientific exchange and visits between the two partners), as well as support recruitment and retention of a broad range of students to engineering through participation in a research project course. The objective of this research is to demonstrate terahertz quantum-cascade (QC) metasurface laser frequency combs (FC), characterize these combs in the frequency and time-domain using novel coherent photonic techniques, and to explore the physics of comb states. This will include frequency-modulated quantum-walk combs as well as amplitude-modulated combs producing ultrafast mode-locked pulses. In the past several years, the UCLA group has pioneered a novel configuration for terahertz QC-lasers in the vertical-external-cavity surface-emitting-laser architecture (VECSEL). The ETH Zurich group brings expertise in the development, physics, and coherent characterization of waveguide-based THz QC-laser frequency combs. The complementary expertise of both groups will be leveraged in the collaborative development of novel metasurfaces, microfabrication process, and dispersion compensation elements, as well as coherent characterization of the resulting combs for the first time, and exploration of novel frequency comb physics. The intellectual merit lies in the unique region of parameter space made accessible by the QC-VECSEL for the study of frequency comb states. Specifically, (a) QC-laser gain material generally has a fast picosecond gain recovery time (compared to the cavity round-trip time), (b) the amplifying metasurface does not exhibit the spatial hole burning ordinarily found in a Fabry-Pérot cavity, (c) and the external cavity allows adjustment of the cavity round trip time over a large range – both shorter and longer than the gain recovery time. Collectively, these features will allow investigation of both frequency modulated and amplitude modulated comb states, including novel comb states such as the quantum-walk comb. Practically, the QC-VECSEL exhibits many desirable features for applications, including large scalable powers, broad gain bandwidths, excellent near-diffraction limited beam patterns, the ability to readily modulate the cavity length (and thus comb tooth spacing) for tuning and stabilization, and the ability to incorporate additional cavity elements for dispersion compensation. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. 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

Understanding and Demonstration of Current-driven Ultrafast Dynamics and Devices in Antiferromagnets Abstract The goal of this project is to achieve an in-depth understanding of the ultrafast dynamics and devices based on new classes of antiferromagnetic (AFM) materials. These materials hold the potential to overcome the bottlenecks in speed and energy efficiency present in current technologies. The proposed research aims to set a significant milestone in next-generation information and communication technologies by investigating the unique properties of AFMs. This research is closely aligned with industrial needs, offering new directions that may spur further innovation and socio-economic growth. The impact is transformative, targeting the discovery of novel functional nanomaterials and new physical phenomena for high-density, high-speed, and ultra-efficient devices. Furthermore, this interdisciplinary research will have a significant educational impact, as the insights gained from spin-orbit torque engineering will offer valuable learning opportunities for students. Individuals at various educational stages, from high school to undergraduate and graduate levels, as well as postdoctoral researchers (including women and minorities), will receive training in the interconnected and evolving fields of physical science, engineering, and computer science. This will be facilitated through the PI's involvement in outreach programs for high school and freshman students at UCLA. Such training will help develop a diverse pool of talent skilled in scientific methodologies and practical applications. The educational benefits will be further enhanced by existing outreach initiatives at the California NanoSystems Institute and the prior NSF Engineering Research Center for Translational Applications of Nanoscale Multiferroic Systems. Although AFM devices have the potential for ultra-high speed (picoseconds), there are major challenges in the demonstration of practical devices. Among them are lack of the understanding of switching dynamics of antiferromagnets, particularly in electrical transport measurements. Another challenge is the small readout signal due to the lack of net magnetic moment. The proposal addresses these challenges and proposes the use of two special classes of AFM material prototypes, altermagnets and noncollinear AFM to achieve high readout for a proposed SOT device. The dynamics of the AFM materials will be studied with a time-resolved quadratic magneto-optical Kerr effect (TR-QMOKE) since AFM usually does not have a noticeable readout in linear MOKE. To explore the antiferromagnetic dynamics excited by SOT, the combination of the state-of-the-art TR-QMOKE measurements with the innovative electrical readout for speed beyond 100 GHz will be used. In the later stage, the prototype SOT devices will be studied further with coplanar circuits integrated with high-speed CMOS. The expected outcomes of this research are the comprehensive understanding of the ultrafast current-driven dynamics in antiferromagnets and the demonstration of novel antiferromagnetic material/highly efficient spin-orbit coupling (SOC) device structures for next-generation ultra-fast (>100 GHz) antiferromagnetic spintronic applications. Detailed proposed tasks for realizing the objective are included. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Cigarette smoking is a leading global health problem, and limited success of even the most successful treatments for Tobacco Use Disorder (TUD) leaves a pressing need for novel treatments. Elucidating the neural circuitry linked to smoking and the genetic influences on it can facilitate development of such advances, possibly using brain-based and precision medicine approaches. Animal research has identified the habenula (Hb) and its circuitry as essential to the effects of nicotine, including aversion and withdrawal. The Hb is part of the brain’s “anti-reward” system, containing neurons that respond to aversive stimuli, representing negative emotional value through inhibitory actions on dopaminergic nuclei. These effects can contribute to nicotine avoidance and nicotine-induced anxiety, and to reinstatement of nicotine self-administration. The Hb also is functionally linked to the Salience Network, a large-scale brain network related to withdrawal and nicotine dependence. Yet, study of human Hb function with respect to TUD and other psychiatric disorders has been severely limited because available neuroimaging methodologies have precluded precise localization of this small epithalamic structure and separating it from neighboring tissue. Recent advances in higher-resolution scan protocols combined with analytic strategies to optimize localization allow more precise measurement of Hb function. Large, publicly available datasets, such as the UK Biobank, can provide robust neuroimaging results, offering unprecedented opportunity to perform functional neuroimaging studies without the problems of replicability associated with small sample size studies. Inclusion of genomic data in the data repository also allows examination of the effects of genomic variation on neuroimaging phenotypes. This study will use the UK Biobank neuroimaging dataset to examine Hb functional connectivity with the Salience Network and ventral tegmental area, and the influence of genomic variations on this connectivity. We will compare Hb functional connectivity in the resting state between participants who smoke regularly and those who never did, examining the relationship of functional connectivity with heaviness of smoking and lifetime exposure to smoking among people who smoke regularly. Given the importance of negative reinforcement in maintaining TUD and the association between Hb and negative emotional states, we will also study relationships between Hb functional connectivity with self-reported negative affect and amygdala activation during a negative affect-induction task. Finally, we will conduct a genome-wide association study of Hb function to create polygenic risk scores (PRSs) to identify novel relationships between Hb function and TUD (and other psychiatric disorders), and for hypothesis-testing in non-imaging datasets. PRSs can also be used to provide causal evidence using Mendelian randomization and associations with other phenotypes using phenome-wide association studies. The results can have wide-ranging implications beyond the scope of TUD, given the role of the Hb in other psychiatric conditions, including Major Depressive Disorder and Anxiety Disorders.

NIH Research Projects · FY 2025 · 2024-09

Project Summary / Abstract Cognitive rigidity is the inability to adapt behavior in a context-dependent manner. A hallmark of neurodevelopmental disorders such as autism spectrum disorders and schizophrenia, cognitive rigidity is a debilitating symptom with limited therapeutic tools available for promoting proper cognitive flexibility1,2. Currently, the field’s understanding of the neural mechanisms underlying cognitive flexibility are limited to several implicated brain regions with little insight into the how the microcircuitry of these areas contributes to context-dependent rule switching. Multiple studies point to the medial prefrontal cortex (mPFC) as a critical hub for cognitive flexibility, specifically for its encoding of response and outcome information1,2,3. The proposed work aims to parse apart how information is encoded in different neuron types, how disrupting the activity of these neuronal types impacts context-dependent decision making, and how distinct neurotransmitter signaling onto these cells impacts behavioral flexibility. The overarching goal is to understand the contributions of multiple cell-types in mPFC to the updating of response and outcome task variables in response to changing reward contingencies. Given the advent of genetic tools to target and manipulate genetically-defined neuronal types in mPFC in mice, the goal of this project is to leverage tools such as calcium imaging through cortical microprisms, optogenetic silencing of functionally-distinct neuronal types, and cell-type-specific knockout of key receptors using CRISPR-Cas9 to explore this microcircuitry. I have chosen to pursue this project at UCLA given its vast resources and highly collaborative environment. Furthermore, the Golshani lab is an optimal setting for completing the aims in this proposal due to its long history of using various calcium imaging and optogenetic techniques. With existing access to mentors who are capable in these techniques, as well as the tools themselves, I will quickly proceed through my proposed training plan and become a capable systems neuroscientist. With both the support from experienced lab members as well as the broader UCLA community, I believe that I am well equipped to succeed and that the additional training and resources facilitated by this award will help me to fulfill my goal of becoming an independent career scientist with my own lab at an R1 research university.

NIH Research Projects · FY 2024 · 2024-09

Project Summary/Abstract Recent genetic and functional studies suggest microglia play a significant role in tau-associated neurodegeneration. However, key questions persist regarding microglia-neuron interactions in tau pathology and how diseased neurons affect microglial function. Recent research indicates neuronal electrophysiological defects, including alterations in oscillatory properties, can influence microglial transitions in Alzheimer's models. Our findings support widespread microglial changes early in tau pathology, including markers of synaptic regulation. We hypothesize that bidirectional changes between microglia and neurons in early tauopathy facilitate disease progression. To investigate, we propose a new model using microglial co-cultures with electrocompetent hippocampal assembloids derived from patient iPSC lines with MAPT mutations and controls, providing a foundation for exploring the maladaptive feedforward loop between microglia and early neuronal dysfunction in tau-related pathology.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY/ABSTRACT Overall, the world population is aging and in the era of anti-retroviral therapy (ART) people living with HIV (PLWH) live longer. ART has converted HIV infection into a survivable chronic infection; however, comorbidities are a mounting clinical reality, likely related to accelerated immunosenescence. It is well documented that HIV infection leads to accelerated aging, and HIV-1 chronic infection is considered a good model for studying immunosenescence. The impact of aging on increased susceptibility to infections and reduced responsiveness to vaccines is a well-known clinical problem. Antibodies are one of the first and most important responses against pathogens and the reason for this dampened immune response with aging may be due to age-related reduced humoral immunity (B cell immunosenescence). Humoral immunity has been understudied in humans, particularly in lymphoid tissue, due to the difficulty of sample availability. It is not clear how accurately changes observed in blood reflect immunosenescence processes in tissues, especially the gastrointestinal mucosa which contains most of the body’s lymphocytes (~60%) and it is a region of high antigen exposure. Therefore, gut-associated lymphoid tissue (GALT) represents a good model to study lymphoid tissue in humans that is highly exposed to antigen stimulation. A growing body of literature has begun to reveal the pathophysiological processes driving immune dysfunction of T cells, including work by our team, which has studied the human mucosal immune compartment. It is not well understood how aging depresses humoral immunity and there are little or no studies on humoral immunity in the mucosal immune compartment. Compared to blood, gut mucosa contains fewer naïve cells, more activated memory cell, and more antibody secreting cells, consistent with the high level of antigen exposure. Therefore, in this study, we propose to analyze the individual and combined impact of age and HIV-1 on immunosenescence of humoral immunity, especially in the highly immune active gastrointestinal tract, by quantifying B-cell immune aging in GALT and peripheral blood in PLWH, as a model of accelerated aging, and in people without HIV (PWOH). Our hypothesis is that natural aging and HIV-1-induced-accelerated aging diminishes the capacity of the immune system to produce efficient antibody responses in the periphery and GALT; this will be a result of inappropriate accumulation of memory-effector T-cells fueled by chronic persistent viral infections, causing ineffective follicular helper T-cells, inefficient recall of cytotoxic responses, and a generalized, persistent low-level inflammatory state. The Specific Aims to test this hypothesis are: Aim 1: To determine age-related B cell lymphocyte senescence in the GALT versus peripheral blood compartments of PWOH and PLWH ranging in age from 18 to ≥60-65 years. We will also study the functional aspects of B cells in terms of antibody production and B cell receptor repertoire. Aim 2: To determine if the accelerated B lymphocyte senescence seen in GALT affects humoral responses, using SARS-CoV-2 as a neoantigen challenge model.

NIH Research Projects · FY 2025 · 2024-09

Project summary Our understanding of biology is being transformed by the discovery and characterization of organelles arising from the spontaneous phase separation of proteins and RNA, in the absence of a lipid membrane. These membraneless organelles, also called condensates, occur at different cellular locations and can appear as solids, gels, or liquids. Dozens of distinct biomolecular condensates are associated with pathways that regulate genes, stress response, and, and their function depends on the types of molecules they recruit from the cellular environment. This has spurred the interest toward developing means to harness condensation by building artificial condensates, that could be used for separating molecules in vitro and as organelles inside living cells. Most efforts in this direction rely on engineered proteins that include disordered domains: this approach however is hampered by difficulties in building proteins presenting well-defined interactions, minimal promiscuity, and limited side effects. These challenges can be addressed by adopting engineered RNA, rather than proteins, as a building block for artificial condensates, because specific RNA-RNA interactions are easy to program, and RNA is a molecule easily portable across organisms presenting low toxicity. This project aims to develop a new class of artificial condensates by taking advantage of nanostructured RNA. We will build RNA condensates capitalizing on our recent discovery that star-shaped RNA motifs (nanostars), comprising a single molecule of RNA, can produce dense RNA droplets in cell-free samples and in living cells. By bridging concepts in phase separation science and RNA nanotechnology, our project will establish RNA nanostars as a platform to build customizable RNA organelles through different research focus areas aimed at: (1) developing methods for sequence and structure optimization, leading to condensates with desired thermodynamic and biophysical properties, and with specific affinity for separating guest molecules; (2) gaining control over the location, kinetics, and composition of RNA organelles forming inside cells; (3) establishing means to build RNA condensates that can sense and respond to molecular signals, and explore their usefulness as sensing and imaging tools. By providing a tunable platform to control the spatial and temporal distribution of target molecules within living cells, our synthetic organelles will serve as a powerful tool toward achieving control of gene expression and biosensing.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT: HEART AND LUNG OUTCOMES POST-TUBERCULOSIS (HALO Post-TB) The global prevalence of post-tuberculosis lung disease (PTLD) is estimated to be ~50%, but our understanding of PTLD is incomplete as studies have not performed full pulmonary function testing (PFTs). Furthermore, the prevalence of cardiovascular sequelae of TB is largely unknown as few post-TB studies have included assessments of cardiac structure and function. HIV is a risk factor for TB. We and others have shown that HIV is associated with cardiopulmonary markers associated with worse mortality including decreased diffusion capacity for carbon monoxide (DLco) on PFTs, elevated pulmonary artery systolic pressure (PASP) on echocardiography, and reduced cardiorespiratory fitness (CRF) on cardiopulmonary exercise testing (CPET). These US-based studies have included few, if any, individuals with prior TB. To our knowledge, no prior study has examined pulmonary and cardiac outcomes after TB and the role of HIV as an effect modifier for the cardiopulmonary phenotypes seen. This Heart and Lung Outcomes Post-TB (HALO Post-TB) study will fill these gaps. Our central hypothesis is that TB is associated with a higher prevalence of cardiopulmonary disease and that HIV coinfection modifies cardiopulmonary phenotypes post TB. The overall objectives of this proposal are to elucidate prevalence, trajectory, and mechanisms of cardiopulmonary abnormalities post-TB in a high TB and HIV burden country. HALO Post-TB will be a longitudinal cohort study that will enroll people with and without HIV in Kampala, Uganda including 480 individuals at completion of treatment for pulmonary TB and 120 healthy comparators who have never had pulmonary TB disease. Specific Aim 1 will perform full PFTs (spirometry, DLco, and lung volumes), echocardiography, 6-minute walk test, and validated questionnaires to determine the impact of HIV coinfection on cardiopulmonary function at baseline (completion of TB treatment) and the persistence of these findings at 2 years. Aim 2 will perform CPET at baseline (completion of TB treatment) in a random subset of 50% of participants to determine the impact of HIV coinfection on mechanisms underlying cardiorespiratory fitness. Finally, Aim 3 will examine a panel of 11 plasma biomarkers and use an unbiased machine learning approach to identify phenotypic clusters and determine whether there are differences in biomarkers that may identify mechanistic pathways that explain the different phenotypes. The proposed research will include specimen banking and will serve as the foundational study of the effect of HIV on cardiopulmonary involvement in post-TB disease, a necessary precursor to identifying mechanisms and potential treatment. The expected outcome to develop a well-characterized post-TB cohort that includes comprehensive cardiopulmonary measures to identify distinct cardiopulmonary phenotypes and whether HIV is an effect-modifier on these phenotypes to enable future mechanistic studies, targeted interventions, and improvements in clinical care.

NIH Research Projects · FY 2026 · 2024-09

Abstract Intrauterine infection/inflammation (IUI) is a major contributor to preterm labor and fetal inflammation leading to injury responses in fetal organs such as the brain, lung and the GI tract. However, the mechanisms and precise therapeutic approaches remain elusive largely because of lack of relevant animal models. We have developed a powerful new model of intrauterine infection in preterm Rhesus macaques: Intraamniotic (IA) injection of live E. coli followed 24h later with antibiotics. This model results in persistent IUI. Importantly, the maternal and fetal inflammation persists despite clearance of E. coli bacteremia, resulting in preterm labor (PTL), fetal immune aberrations and fetal neuroinflammation. After an expert FDA panel recommended withdrawal, the only drug approved drug for preterm labor, 17-hydroxy progesterone caproate (17-HPC) was withdrawn from the market by its manufacturer in 2023. Our data suggests that a major reason for failure of 17-HPC is that progesterone is inactivated intracellularly by an enzyme encoded by the gene AKR1C1. We further demonstrated that IUI induces AKR1C1 expression both in the Rhesus macaque uterus and choriodecidua. We therefore propose using a novel synthetic progestin R5020 that is resistant to AKR1C1 mediated inactivation. R5020 is already in clinical use in some European countries for menopausal therapy and certain gynecological disorders. The grant is based on the premise that drug repurposing of a novel progestin with favorable pharmacological properties of enhanced affinity for the progesterone receptor and resistance to AKR1C1 inactivation will be an effective therapeutic strategy. We propose to test the hypothesis that R5020 will reduce the residual maternal and fetal inflammation in infectious models that closely simulate IUI in pregnant women with two Aims. In Aim 1, we will test if R5020 will decrease IUI induced inflammation and preterm labor. We will use state-of-the-art single-nucleus transcriptomic approach and inflammatory marker discovery science to unravel cellular and molecular mechanisms of inflammation at the maternal-fetal interface, and define labor associated pathways of IUI. Using multi-parameter flow cytometry, we will identify mechanisms of neutrophil recruitment and activation in the chorio-decidua. In Aim 2, We will identify systemic fetal immune perturbations resulting from IUI. We will determine if R5020 can reduce fetal systemic inflammation. We will uncover mechanisms of neuroinflammation resulting from IUI by single nucleus RNA seq and multi-parameter immunohistology. These studies will develop the critical knowledge base for future studies aimed at repurposing of R5020 as a novel preterm labor preventative therapy for human IUI. A collaborative multi-disciplinary team will use high-resolution immunology, genomics/proteomics, neuro-science, and translational approaches in modeling IUI and fetal inflammation in an animal model that closely mimics the human pathology.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT/SUMMARY Prenatal alcohol exposure (PAE) is a severe, life-long neurodevelopmental disorder. It afflicts hundreds of thousands of children in the US and worldwide. The highly distressing, disruptive, and disabling behavioral symptoms of PAE include inattention, hyperactivity, and executive dysfunction, symptoms that overlap with those of attention deficit hyperactivity disorder (ADHD). In ADHD, these symptoms are treated routinely with stimulant medications. But children with PAE often fail to respond to stimulants, or to any therapy. There has long been, therefore, a pressing need for new treatments for PAE. This R61/R33 proposal is about one prospective way to meet that need. Recently, it was demonstrated at UCLA that trigeminal nerve stimulation (TNS) is safe and effective in treating pediatric ADHD. TNS is a low-current neurostimulatory therapy that is noninvasive, minimal-risk, and now FDA-cleared. Given the similarities PAE has with ADHD, there is a very good (though not 100%) chance that PAE would respond to TNS as well. But, to our knowledge, TNS has never been tested systematically in children with PAE. Here we propose the first clinical trial of TNS for PAE. The R61 (pilot) phase will determine whether TNS is feasible for PAE: Do children with PAE comply with the TNS procedure? Does TNS have any any serious side effects in these children? Is there an indication that TNS relieves symptoms of PAE? If the pilot shows feasibility, as we expect it will, in the ensuing R33 phase we will conduct a formal double-blind sham-controlled randomized clinical trial to determine rigorously whether TNS is truly efficacious for PAE. If it is, the impact could be enormous. TNS could bring much needed relief to children with PAE and their families everywhere. Our proposal will furthermore address the need to understand the brain mechanisms by which TNS works, which are underexplored and largely unknown. The few studies done to date point to three major candidate brain regions where TNS may exercise its therapeutic effects. All three are regions where we, in prior work, detected differences between children with PAE and typically developing children in tissue properties (“endpoints”) measured using multiple different varieties (“modalities”) of brain MRI. These findings raise our confidence that TNS will be effective for PAE, i.e., if the brain regions that produce PAE symptoms are the same as those acted upon by TNS, maybe TNS will quell those symptoms. In both phases of this clinical trial we will measure the same endpoints with the same modalities of brain MRI in children with PAE before and after TNS treatment. We will determine whether these endpoints are changed by TNS (target engagement) and/or predict TNS clinical response (prognostic marker) in individual patients in the three candidate regions or elsewhere in the brain. If successful, this proposal will help meet a long-standing urgent need for novel treatments for PAE and will help clarify the currently mysterious brain mechanisms of an emerging pediatric neurostimulatory therapy.