Johns Hopkins University

NSF Awards · FY 2025 · 2025-08

Min-max optimization underpins technologies ranging from generative Artificial Intelligence (AI) to large-scale reinforcement learning, yet today’s methods remain slow and unreliable for many real-world tasks. This suboptimality stems from the traditional approach of adapting minimization techniques to the min-max setup, which necessarily overlooks the unique complexities inherent in min-max problems. This project fundamentally revises this approach, developing specialized theoretical frameworks and efficient algorithms tailored explicitly to min-max optimization. By establishing a deeper understanding of these unique characteristics, the proposed research will significantly enhance the efficiency and robustness of min-max optimization, directly impacting practical applications in machine learning and artificial intelligence. Technically, this project will first explore core theoretical foundations under idealized convex-concave conditions, emphasizing accelerated convergence through anchor-type algorithms and enhanced stochastic methods with relaxed assumptions. Building upon this, the project will also develop practical algorithms that are robust to realistic, non-ideal conditions, including methods for nonconvex problems, efficient sampling strategies for stochastic settings, and adaptive update rules. Additionally, the research will investigate efficient alternating-update strategies, proximal gradient-type methods, and applications to training deep neural networks. These efforts are anticipated to greatly enrich the mathematical tools of min-max optimization and to lead to the discovery of more practically efficient algorithms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project belongs to the field of dynamical systems, which is the mathematical study of processes that evolve over time according to fixed rules. These processes often exhibit complicated and chaotic behavior yet display underlying patterns that can be described in terms of stationary measures, that is, certain probabilistic objects that remain stable as the underlying dynamics evolve. The focus here is on stationary measures that arise in nonlinear systems, with particular attention to their geometric and analytic structure. The goal is to understand when such measures are absolutely continuous, when their Fourier transform decays, and how these properties relate to the dynamics that generate them. The work will bring together researchers based in the United States and Israel and will involve the training of graduate students and postdoctoral fellows. A particular emphasis will be placed on maintaining strong collaborative ties between research groups working in dynamics, geometry, and analysis. The main technical goal of this project is to study the regularity and dimension of stationary measures arising from nonlinear actions, such as self-conformal systems and random matrix products. When the maps involved are real analytic and satisfy appropriate separation properties, one expects to be able to compute their dimension in simple terms such as entropy and Lyapunov exponents, and to determine whether they are absolutely continuous. The project aims to establish these properties by studying the behavior of the system under repeated iteration and by using tools that reveal how randomness and geometry interact at different scales. The project will develop via methods from hyperbolic dynamics, harmonic analysis, homogeneous dynamics, spectral theory of transfer operators, and additive combinatorics. These include the use of appropriate disintegrations of measures, spectral gaps for transfer operators under appropriate assumptions, and comparisons between different criteria for separation. The broader aim is to clarify how non-linearity results in stationary measures enjoying rich multiscale structures, which in turn governs their analytic and geometric properties, and to use this understanding to characterize rigidity and regularity phenomena in these systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The Super Dual Auroral Radar Network, or SuperDARN, is an international collaborative experiment for observations of plasma motions in Earth’s upper atmosphere. By observing ionospheric plasma motions, a multitude of geophysical processes are being studied. These processes range from fundamental plasma instabilities to the global-scale plasma response to changes in the solar-terrestrial environment. Each of these areas of study contributes to developing an understanding of the coupling of energy from the Sun into Earth’s upper atmosphere and its effects on humanity and technological systems. This project will support operations and maintenance of the U.S. SuperDARN radars in the northern hemisphere by the consortium of Penn State University, Virginia Tech, Dartmouth College, and the Johns Hopkins University Applied Physics Laboratory. The collaboration operates twelve radars that cover a vast region from Alaska to Iceland at high latitudes, and Oregon to Virginia at middle latitudes. In addition to operation and maintenance activities, the project will support a program of research that exploits new capabilities that have been developed over the last several years. This includes providing improved fidelity in measurements (plasma convection mapping and imaging), extending the area over which measurements are obtained (bistatic observations), and providing new types of measurements (sounding). SuperDARN has a long-standing commitment to including graduate students in all aspects of the program. The SuperDARN observations are also important for space weather applications since HF radio propagation is sensitive to perturbations in the bottomside ionosphere, e.g., solar flares. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The research team aims to address the fundamental challenge of designing quantum algorithms with inherent noise robustness. While industry, government, and academia are investing heavily in realizing quantum computing, current devices remain limited by noise, which undermines their potential advantages. The approach tackles this issue at the algorithmic level, embedding robustness into the structure and execution of the algorithm itself. This helps shift the burden of noise mitigation away from hardware and toward software. The researchers will focus on variational quantum algorithms (VQAs), a widely applicable class of algorithms spanning quantum chemistry, optimization, and machine learning. Success in this effort will advance the field of quantum computing and enable higher-performance quantum applications that drive scientific discovery. Moreover, this effort will aid in developing a quantum workforce that can immediately contribute to the challenges of today’s hardware while gaining knowledge relevant to future hardware. Focusing specifically on algorithms that possess inherent symmetry, the proposed work leverages concepts from quantum control and quantum error correction. The research team will exploit this symmetry to develop a theoretical framework based on dynamical Lie algebras to characterize the propagation of noise through a VQA. This framework will be leveraged to identify algorithmic motifs inspired by dynamical decoupling and noise-filtering protocols to suppress symmetry-preserving errors. These techniques will be complemented by quantum error-avoiding codes to address symmetry-breaking noise. The integration of control and quantum codes will lead to hybrid protocols that can be tuned based on hardware specifications. Ultimately, the objective is to maximize algorithmic performance while minimizing gate and qubit resources to maintain practicality for existing and near-future quantum processors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

SUMMARY The open-source community has been a major driver of innovation of novel MRI image reconstruction and processing methods. The data acquisition step, on the other hand, has traditionally required proprietary, vendor- specific software tools that are complex and non-portable. We developed Pypulseq to enable the MRI research community to design and share vendor-neutral MRI pulse sequences using open-source tools. Pypulseq is built on the Pulseq file format, which has quickly established itself as the standard for specifying and sharing MRI pulse sequences. Pulseq also comprises a suite of vendor-specific interpreters, allowing the same Pulseq file to be executed across a variety of hardware platforms. Pypulseq is Python-based and can be installed freely in any computing environment without license requirements or accessed online through Google Colab. However, the rapid growth of Pypulseq and the Pulseq file format has produced a variety of disjoint tools that can be difficult to use, and that do not always guarantee correct operation across vendor platforms. The work proposed here addresses these needs through three specific aims. Aim 1 focuses on integrating and accelerating pre-scan safety checks related to SAR and PNS, and enabling users to input custom electromagnetic models. Aim 2 will make multi-vendor scanning with Pulseq more robust and efficient by refining the sequence specification and implementing a multi-vendor pulse sequence validator. Aim 3 will promote best practices for validating and robustly sharing Pulseq sequences, and disseminate Pypulseq advances through tutorials and workshops. This project will make Pypulseq sequences safer and more robust, in support of more reproducible MRI sequences and a faster MRI development cycle.

NIH Research Projects · FY 2025 · 2025-08

Cranial bone is richly innervated with sensory nerves that play critical roles in fracture healing by signaling and regulating osteoprogenitor function. This is supported by our prior findings which suggest that small-molecule mediated (i.e. TrkA (tyrosine kinase A) agonists) stimulation of nerve sprouting into calvarial defects enhances bone formation while TrkA inhibition using a chemical-genetic approach abrogates bone repair. In contrast to normal fracture healing, critical-sized, craniofacial bone defects require therapeutic intervention such as the delivery of biomaterials and stem cells. In preliminary studies, we have observed that the delivery of exogenous stem/stromal cells to cranial bone injuries magnifies neural ingrowth associated with bone healing, suggesting that stem cells may regulate bone repair in part by impacting these early-stage neural interactions. To optimize the efficacy of these bioengineering strategies, we will define the neuro-immuno-skeletal signaling axis during bone repair across healing and non-healing cranial bone injuries and elucidate how biomaterial- and stem cell- delivery impacts this signaling axis. We will employ a quantitative light-sheet microscopy (QLSM) platform to visualize and quantitatively describe ?3-tubulin labeled nerve responses to 1-mm and 4-mm full-thickness injuries in the parietal bones of mice. We will combine this information with corresponding single cell RNA sequencing (scRNA-seq) data obtained from cells at the injury sites and the nerve bodies in the trigeminal ganglia which provides sensory innervation to the skull. We propose three Specific Aims: In Aim 1 We will create 1-mm or 4-mm injuries in the parietal bones of 12-week old Baf53b-tdTom mice, in which all peripheral nerves express a tdTomato reporter. We will image macrophages, axons, and osteoprogenitors at multiple time points following injury. Additionally, we will perform timelapse scRNA-seq on cells isolated from both the injury site and trigeminal ganglia and interrogate the reciprocal signaling interactome between early responding macrophages and recruited peripheral nerves in injuries and decipher how these impact osteoprogenitor recruitment and differentiation into osteoblasts. In Aim 2, we will Independently manipulate the magnitude and duration of TrkA+ nerve sprouting using agonists and inhibitors and evaluate the impacts on signaling and cranial bone healing. With either gain or loss of function approaches, changes to the neuro-immuno-skeletal signaling landscape and bone repair outcomes will be assessed using spatial and sequencing methods as described in Aim 1. Lastly, in Aim 3, we will design, fabricate, and test bi-functional scaffolds which provide spatially and temporally controlled release of neurotrophic factors and neural inhibitors to orchestrate the appropriate signaling cascade that maximizes bone healing in critical-sized calvarial injuries in mice. Overall, we will combine information on ligand- receptor signaling into an engineering strategy to maximize the therapeutic outcomes via the neuro-immuno- skeletal signaling that promotes therapeutic cranial bone healing.

NIH Research Projects · FY 2025 · 2025-08

ABSTRACT There are approximately 82 million incident Neisseria gonorrhoeae (Ng) infections globally each year and the highest per-capita burden is in Eastern and Southern Africa (ESA). Over half of global incident HIV infections also occur in ESA and people living with HIV are disproportionately at risk of acquiring Ng infection. Further, a global increase in multi- and extensively-drug resistant Ng threatens control efforts. Our understanding of Ng transmission dynamics within ESA, and in particular its interaction with the sexual networks on which HIV is also transmitting, is limited by a lack of surveillance data from the region, particularly among women and asymptomatic men. Ng surveillance efforts in ESA are hindered by the need for isolate culturing for antimicrobial susceptibility testing and whole genome sequencing (WGS). In this proposal, we will develop and validate direct from sample Ng WGS methods that alleviate the need for isolate culturing. These methods will be used to generate Ng WGS data from ~250 GC+ penile and vaginal swabs sampled from population-based STI trials (STIPS and IN-STEP) nested within one of the largest HIV population surveillance cohorts in ESA, the Rakai Community Cohort Study (RCCS, Uganda). Data generated as part of this study will be used to provide preliminary insights into the transmission of Ng into- and within-Uganda, the presence of circulating drug resistant lineages, and the overlap between Ng and HIV transmission networks. This work will support future expansion of genomic surveillance of Ng and other bacterial sexually transmitted infections (STIs) in the RCCS and other low-resources regions with high burdens of HIV and non-HIV STIs.

NIH Research Projects · FY 2025 · 2025-08

Title: Quantitative Assessment of the DPP Syphilis TnT assay in infection and treatment response Abstract Summary The number of syphilis cases continues to rise in the United States. Current testing algorithms for syphilis, a sexually transmitted infection, are based on serological assays developed in the first half of the 20th century; two different serological tests for syphilis need to be positive to make a serological diagnosis. Serologic testing is used to diagnose most syphilis cases using a test algorithm of a lipoidal (non-treponemal) rapid plasma reagin (RPR) measured as a titer through serial dilution testing (e.g., 1:1, 1:2, 1:4, etc) which is time- consuming, labor-intensive, and requires moderate complexity lab testing, followed by a sensitive and specific anti-treponemal test for confirmation. RPR titers are also required to monitor treatment response and for evidence of reinfection. Lateral flow tests with excellent performance characteristics for anti-treponemal antibodies have been developed and can be useful in screening patients at risk of infection but never treated for syphilis. The lack of the RPR titer limits the use of lateral flow treponemal antibody tests in high-prevalence settings in the US where serial RPR titers and clinical history are needed for test interpretation in previously treated patients. The importance and need for syphilis diagnostic innovation is clear. The point-of-care (POC) Dual Path Platform (DPP) Syphilis TnT (treponemal and non-treponemal) assay is under FDA approval consideration for the full diagnosis of syphilis infection. The goal of this proposal is to assess the feasibility of using the assay for the longitudinal evaluation of treatment response and reinfection. We propose to use the quantitative results from the microreader (which are currently hidden from the user) and to correlate it with the concentration of antibody (IgM and IgG subtypes) using statistical models. The reader results might be exploited to allow clinicians to determine stage of infection and response to treatment at point-of-care in lieu of a centralized lab RPR titer. Because the DPP Syphilis TnT assay separates the antibody responses into IgM and IgG specific channels, it might also add specificity to the diagnosis of active syphilis infection. Because people living with HIV have different antibody responses than those without HIV, we will stratify our analyses by HIV status. Using de-identified, characterized, syphilis serum discards from the Johns Hopkins Immunology Lab, we will also assess the correlation of the reader values to the concentration of antibody measured in the centralized treponemal tests and the correlation of the non-treponemal results to the RPR titers. Simple, fast, inexpensive lateral flow assays combined with quantitative microreader results with the DPP TnT could revolutionize a move away from RPR titers and give clinicians actionable diagnostic information within a clinical encounter in the US and resource-limited settings.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY The use of extracorporeal membrane oxygenation (ECMO), a technique of extracorporeal life support used for severe refractory cardiopulmonary failure or cardiac arrest, has more than doubled in children over the last 15 years. While lifesaving, ECMO is associated with high mortality and significant morbidity, with nearly two-third of children developing acute kidney injury (AKI) during the ECMO course. AKI is also associated with increased risk of mortality and morbidity at hospital discharge with unknown long-term kidney outcomes in survivors in the U.S. AKI in pediatric ECMO has multifactorial etiology and data to support evidence-based AKI prevention and management strategies are sparse and based primarily on retrospective reviews of electronic health records. Consequently, there is high variability in clinical practice across ECMO centers, and lack of consensus or clinical practice guidelines on optimal fluid, diuretic, and kidney replacement therapy (KRT) strategies in this patient population. The overall goal of this research is to develop and refine an AKI multimarker panel for accurate kidney function monitoring at the bedside and early classification of morbidity and mortality outcomes that will allow timely interventions to optimize kidney health. We hypothesize that early detection of AKI using novel blood- and urine-based biomarkers with careful implementation of fluid, diuretic, and KRT strategies, have the potential to improve kidney function and patient outcomes in the high-risk pediatric ECMO population. For this project, we propose the secondary use of data and biospecimens of a recently completed prospective multicenter cohort of children on ECMO enrolled between 2020 and 2023 (n=200 children). In this proposal, we aim to determine if urine and plasma biomarkers measured serially during ECMO can predict AKI and classify major adverse kidney events at hospital discharge (Aim 1), and whether prevention of fluid overload through early initiation of diuretic therapy or KRT while avoiding hypovolemia is associated with AKI biomarker trajectory and with major adverse kidney events at hospital discharge (Aim 2). Urine biomarkers include indicators of subclinical AKI (neutrophil gelatinase-associated lipocalin [NGAL]), of persistence of severe AKI (C–C motif chemokine ligand 14 [CCL14]), and prediction and diagnosis of AKI and assessment of severity (tissue inhibitor of metalloproteinases-2 [TIMP2] and insulin-like growth factor binding protein 7 [IGFBP-7]). Urine biomarker measurements will be normalized to urinary creatinine concentration. Plasma biomarkers include inflammatory markers interleukin 8 (IL-8) and tumor necrosis factor alpha (TNF-α). Preliminary results through the study proposed will allow refinement of the AKI biomarker panel and the development of novel risk models for short- and long-term kidney-related outcomes in the pediatric ECMO population. This in turn will allow for the design of future prospective clinical cohorts and interventional trials aiming to optimize multimodal kidney protective strategies during critical illness with ECMO support, and to investigate post-discharge interventions to improve long-term kidney function.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Paclitaxel-induced peripheral neuropathy (PIPN) is a common painful treatment side effect that affects millions of cancer patients. Currently there are no adequate treatments for this debilitating neuropathy, thus studies are needed to understand the neuronal and molecular changes that lead to PIPN. Most PIPN studies utilize traditional cell culture systems and single unit electrophysiological recordings, methods that cannot capture the full dynamics of the intact peripheral nervous system. Our lab recently developed a novel whole dorsal root ganglia (DRG) in vivo imaging technology that can monitor activity of all DRG neurons simultaneously. Using this new technology, we found medium to large DRG neurons become hyperactive after the onset of PIPN. These neurons are significantly hypersensitive to mechanical and cold stimuli, but not heat. Our proposal combines in vivo imaging with genetic labeling strategies to explore the function of medium to large diameter neuron subtypes in paclitaxel induced pain (PIP). We hypothesize that paclitaxel selectively affects specific medium to large diameter neuronal populations, which leads to painful mechanical and cold hypersensitivity. Aim 1 will determine the neuronal populations and molecular mechanisms for mechanical and spontaneous pain in PIP. We will use cre-dependent mouse lines to specifically delete and activate neurons expressing low- threshold mechanoreceptors (TrkB+ Aδ- and TrkB+ Aβ RA1-LTMRs) and mechanoreceptors (Bmpr1b+ and Smr2+ A-MRs). Behavior and in vivo imaging approaches will determine the roles of these subpopulations in PIP. We will examine the role of gap junctions in paclitaxel treatment-induced mechanical hypersensitivity. Genetic and pharmacological approaches will study how blocking gap junction-mediated neuron coupling reduces mechanical pain and DRG neuronal activation. Our Pirt-Cre; R26-CAG-lsl-CaMPARI2 mouse studies will characterize the subpopulation of medium to large diameter DRG neurons involved in spontaneous pain after paclitaxel dosing. Aim 2 will identify novel cold-sensing neuronal populations hyperactivated in PIP. The one verified cold receptor TRPM8 is expressed on small diameter DRG neurons. Our preliminary data showed that in PIP, medium diameter neurons hyperactivated by cold do not respond to the canonical TRPM8 agonist menthol. We recently showed that a novel cold receptor, GluK2, is expressed in medium diameter DRG neurons. We hypothesize that GluK2 is involved in paclitaxel induced cold pain. We will utilize a new GluK2-cre reporter mouse to examine the function of this newly identified cold-sensing population. We will analyze the transcriptome and electrophysiological properties of these neurons to identify molecular targets suitable for pharmacology. Together, our proposed studies will dissect the crucial roles that novel medium to large diameter neuron subtypes play in transmitting mechanical pain from innocuous touch and noxious pinch, cold and spontaneous pain in PIP. These impactful studies could lead to discovery of novel pharmacological targets for specific populations of sensory neurons to treat PIP patients’ intolerable mechanical and cold hypersensitivities and spontaneous pain.

NIH Research Projects · FY 2025 · 2025-08

The cerebral cortex is a six-layered brain structure that is essential for sensory processing, motor skills, learning and memory and thought. The establishment of functional cortical connectivity requires newly born neurons to migrate into appropriate layers during embryogenesis and then elaborate complex and unique axon and dendrite branching patterns. This results in laminar-specific organization of interstitial axon branches and also the elaboration of select intra- and inter-areal cortical connections. Given the complexity of axon trajectories, precise regulation of collateral axon branch formation is vital for the generation of a functional brain connectome. However, the intracellular signaling pathways, receptors, cell surface molecules and extracellular ligands that regulate laminar-specific interstitial axon branching in the cortex are poorly understood. Thus, a central remaining question is how laminar-specific innervation is established in the cerebral cortex. In this proposal, we investigate interstitial axon branching in vivo using novel approaches for precise labeling of layer 2/3 callosal projection neurons (CPNs), allowing for quantitative analysis of axonal morphology at high acuity and also manipulation of gene expression in well-defined temporal windows. Our recent work identifies an intracellular signaling pathway that regulates interstitial axon branching in vivo in layer 2/3 CPNs – we show that GSK3β activates MAP1B to promote interstitial axon branching via increased tyrosination of tubulin. In the Aim 1 of the current proposal, we will investigate the role of this pathway in other classes of cortical excitatory projection neurons, and we will investigate the molecular mechanisms underlying how MAP1B and tubulin tyrosination lead to the formation of interstitial axon branches. Next, our preliminary analysis of single cell RNA sequencing (scRNA seq) data acquired from the developing somatosensory cortex identifies multiple cell surface molecules enriched in layer 2/3 CPNs, and a follow-up genetic survey of these molecules show that an adhesion synaptic protein LRRTM4 has strong potential to act as an interstitial axon branch-restricting factor. In the Aim 2, we will investigate the function of LRRTM4 and identify its ligands in cortical layer 4. In the Aim 3, we will broaden our analysis on the Auditory cortex and will also address if GSK3β/MAP1B signaling and LRRTM4 adhesion molecule regulate interstitial axon branching in the contralateral axons. In summary, our work will lead to the identification of molecular principles governing the development of cortical connectivity and will provide a robust foundation for future independent research program.

NIH Research Projects · FY 2025 · 2025-08

: Invasive fungal infections (IFIs) are a growing public health challenge, leading to over 1.5 million deaths each year worldwide. Contextual factors are believed to be critical components of IFI risk as evidenced by historical fungal outbreaks associated with specific geographic distributions and ecological disruptions. Certain communities are thought to be particularly vulnerable with greater exposure to high-risk contextual factors, leading to higher rates of and worse outcomes from IFIs. In addition, socioeconomic status may impact IFI outcomes, due to delays in diagnosis, access to care, and quality of care. However, prior studies of IFI epidemiology have focused primarily on host factors without adequately considering the uneven distribution of hazards and their spatiotemporal trends related to socioeconomic factors. The objective of this proposal is to assess the impact of social determinants on IFI risk by leveraging a national data source, the National COVID Cohort Collaborative, and a more granular hospital system electronic-health medical records data source. In Aim 1, we will determine neighborhood-level contextual and socioeconomic predictors of IFIs. In Aim 2, we will examine the association between individual socioeconomic status factors and clinical outcomes of IFIs. This work will advance our understanding of the spatial and social determinants of IFI risk and outcomes as well as provide a robust training platform for the award recipient, Dr. Lucy Li. Through both research and career development training, Dr. Li will acquire essential skills in large data analysis, including advanced methods in spatial science and social factors research, time series analyses, and risk prediction modeling. Dr. Li will then be well positioned to be an independent clinical investigator focused on IFIs in at-risk populations with an expertise in integrating spatial and social factors data into population health analyses.

NSF Awards · FY 2025 · 2025-08

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Jason Bates of the University of Virginia and Professor Brandon Bukowski of Johns Hopkins University are studying new approaches to improve the productivity and durability of molecular catalysts through supporting them on nanostructured solids. These fundamental approaches will enable more efficient, flexible, and sustainable pharmaceutical manufacturing processes by developing catalysts that are not only more active, but also longer lasting and safer to use in continuous flow systems. These advances will help facilitate a transition to a distributed manufacturing model that can respond quickly to shifting or localized demand. Combined experimental and computational studies in this project will focus on asymmetric hydrogenation, which is an important catalytic reaction in pharmaceutical production, and computational methods and models will be made broadly accessible to the community. Educational and outreach efforts will train undergraduate and graduate students in cutting-edge experimental and machine learning techniques and engage high school students through hands-on science programs. The team will build strong inter-institutional collaborations through regular student exchanges, helping to prepare the next generation of scientists and engineers. With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Jason Bates of the University of Virginia and Professor Brandon Bukowski of Johns Hopkins University are studying the impact of solid support structure on heterogenized asymmetric hydrogenation catalysts. By integrating experimental and theoretical approaches, the project will establish a materials-driven approach to molecular catalyst design, leveraging the structural features of zeolites as tunable, non-coordinating solid supports for cationic complexes. Hierarchical and nanosheet zeolite structures will be targeted to exploit partial confinement at zeolite nanopore mouths while maintaining substrate accessibility. The team will combine synthesis, spectroscopic characterization, kinetic analysis, and ab initio microkinetic modeling to systematically explore how support structure influences catalytic behavior. These insights will inform the development of predictive computational tools to guide the design of zeolite–ligand pairs across a broad chemical space, to reshape reaction energy landscapes and suppress off-cycle deactivation pathways. Integrating advances from zeolite synthesis, molecular catalysis, and computational catalysis, this project offers a blueprint for discovering supported catalysts that outperform their homogeneous analogues. The experimental and computational approaches will be extensible beyond asymmetric hydrogenations, offering the potential to improve the reactivity of other key reactions facilitated by molecular catalysts, such as cross-couplings and selective oxidations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Müller glia (MG) cells function as injury responsive stem cells to enable retinal regeneration in zebrafish. Importantly, MG retain regenerative capacity in mice and potentially humans as well. Defining how MG regenerative potential is controlled could therefore lead to therapeutics for restoring visual function to patients. Many genes are known to regulate regeneration in the context of widespread retinal tissue damage. In contrast, genes controlling MG regenerative potential following limited retinal cell loss, as per degenerative disease, are unknown. Studying regeneration in the context of selective cell loss is nevertheless important as recent evidence suggests the nature of the retinal injury informs the regenerative process – i.e., MG-derived progenitor cell (MGPC) proliferation rates and fate decisions are correlated to the extent and specificity of cell loss. In a pilot screen of +100 genes for regulators of retinal ganglion cell (RGC) ablation in zebrafish, we identified 7 knockouts that inhibited and 11 that accelerated RGC regeneration kinetics. Moreover, disruption of 35 of 36 known/implicated regulators of retinal tissue regeneration either failed to impact (28 genes) or accelerated RGC regeneration kinetics (7 genes). Among the latter were proneural transcription factors, including olig2, neurog1, and ascl1a. Mechanistic analyses revealed disruption of ascl1a – a gene required for retinal tissue regeneration – accelerated RGC regeneration by increasing the propensity of MGPCs to produce RGCs; i.e., promoting RGC “fate bias”. These findings demonstrate plasticity in how MG can convert to stem cells (i.e., ascl1a-independent paths) and context specificity in how genes function to control tissue versus cellular regeneration in the retina. To rigorously test the hypothesis that the regenerative process actively adapts to the extent and specificity of cell loss, we will: 1) test ≥250 genes implicated as regulators of RGC, cone, bipolar, and/or hair cell regeneration for effects across each of these paradigms; 2) screen ~500 genes for effects on RGC replacement kinetics to define gene regulatory networks (GRNs) controlling RGC regeneration, and 3) determine if the MGPC fate bias extends to the level of RGC subtypes. Comparisons across cell regeneration paradigms – three retinal cell types, two RGC subtypes, and hair cells – will advance new knowledge of the mechanisms controlling MG regenerative potential. In particular, we will determine the degree to which paradigm-specific versus universal genetic programs govern cellular regeneration by testing how the specificity of cell loss informs the regenerative process. Our aims are designed to: 1) assign functional roles for 18 genes in RGC regeneration and define genes as paradigm/context-specific or universal per effects on RGC, cone, bipolar, and/or hair cell regeneration; 2) functionally validate entire GRNs that regulate RGC regeneration, and; 3) determine whether the propensity of MGPCs to give rise preferentially to lost cells extends to the level of RGC subtypes. We posit that defining how disease-relevant retinal cell loss parameters impact the regenerative process will support the development of disease-tailored regenerative therapeutics for restoring lost visual function to patients.

NSF Awards · FY 2025 · 2025-08

This project concerns the geometric calculus of variations, that is the study of the properties of objects which are optimal or nearly optimal in various geometric senses. These variational problems arise in various areas of pure and applied mathematics and also in many physical sciences. The project is focused on studying certain measures of complexity for submanifolds and their linkage to interesting geometric partial differential equations such as the minimal surface equation and mean curvature flow. Minimal surfaces are classical geometric objects that naturally arise in the study of surface tension and, in particular, model the shape of soap films. The mean curvature flow is a dynamic process that, roughly speaking, continuously deforms a surface in a manner that decreases its area as quickly as possible. It was first studied as a model of certain phenomena in materials science and has also found applications in computer graphics and image recognition. Finally, the project has several educational components and the organization of many workshops and seminars. The project will study several related geometric functionals defined on the space of submanifolds. These functionals share the feature of measuring complexity and are related with the theory of mean curvature. One focus is the Colding-Minicozzi entropy, which is closely linked with the mean curvature flow. Another is the Li-Yau conformal volume which has important applications to minimal surface theory and other geometric problems. The project will investigate these quantities and the ways that they interact with each other and with the theory of minimal submanifolds and mean curvature flow. A major area of emphasis will be on higher codimension submanifolds. Specifically, the PI proposes to study surfaces in certain symmetric four-manifolds. The stability operator is poorly understood when the codimension is more than one and this is a major source of difficulty blocking progress on such topics. The project will start with the simplest settings by systematically utilizing the various available techniques and by developing new ones. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Prostate cancer (PCa) is the most commonly diagnosed type of cancer and the second leading cause of cancer related deaths among US men. The proposed project aims to improve prostate image-guided interventions (IGI) with a novel ultrasound probe and robot developed by our team, ProBot. We propose a Phase 1 clinical trial to evaluate the safety and feasibility of the new device at biopsy. We also propose to improve the technology and expand it to focal therapy of PCa. ProBot is an entirely new concept including a novel ultrasound probe and robot kinematics specifically designed for prostate IGI. A novel feature is that it does not change the deformation of the prostate gland, allowing more accurate MRI-ultrasound co-registration and needle targeting. In addition to accurate MRI targeted biopsy (TB), at systematic biopsy (SB), instead of using the usual template plan, our innovative software optimizes the plan to ensure appropriate biopsy spacing and obtain diagnosis representative of whole gland histology. ProBot will also be uniquely capable of transrectal (TR) and transperineal (TP) biopsy and focal therapies. ProBot is ready for immediate clinical assessment as proposed. It is a refined prototype and has already attained approval by the FDA for clinical trial evaluation. We recently completed 2 TR biopsy cases with ProBot with IRB approval. We propose to extend the approval for TP biopsy and perform the trial for TR and TP biopsies. Focal therapy is a promising, minimally invasive treatment strategy to selectively treat localized PCa while minimizing the side effects associated with whole gland treatment options. Focal therapies aim to deliver ablative energy to PCa lesions sampled at biopsy. Repeatably targeting a lesion between biopsy and therapy may be improved if the same device, such as ProBot, is used to guide both procedures. As a research aim, we also propose to further develop ProBot for percutaneous interstitial ablative treatment, an innovative approach to be integrated with ablative technology and tested in a future trial. ProBot is a small, lightweight (1.3Kg ultrasound probe and robot), inexpensive to manufacture device that could ultimately provide a cost-effective solution for PCa care. The ProBot allows hands-free operation of its ultrasound probe at 3D image scanning and needle targeting. This device could reduce the level of physician training and skill currently needed while minimizing the variability in outcomes among physicians, and ultimately improve the accuracy of biopsy targeting and reliability in the results of biopsy. An early-stage clinical trial is required to evaluate the safety and feasibility of the new device and biopsy approaches.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Immunosuppressed persons (ISPs) have a nearly 40% risk of hospitalization if infected with respiratory syncytial virus (RSV). Recently, novel RSV vaccines based on the prefusion F protein of the virus were found to be effective at reducing RSV disease, but these were not tested in high-risk ISPs. Owing to immunosuppression, ISPs often develop attenuated antibody responses to vaccines and require either additional doses or different formulations to achieve protective levels of antibodies. Antibodies against prefusion F are associated with protection from RSV disease and thought to be central to the protection afforded by RSV vaccines. Our preliminary data indicate that, among ISPs, the antibody response to RSV vaccines is heterogeneous, with many ISPs demonstrating minimal or no response. However, the nature of this decreased response (i.e. unrecognized antibody epitopes and impact on neutralizing and non-neutralizing functions) remains unknown. Furthermore, the influence of specific immunosuppressive medications and the type of vaccine received (adjuvanted vs. unadjuvanted) is undetermined. To protect this high-risk group from this common and often devastating infection, a comprehensive understanding of the antibody response to these vaccines by ISPs is necessary and will improve vaccine recommendations, design, and medication management. The proposed research will address these uncertainties by systematically studying the antibody response to novel RSV vaccines in a national observational cohort of ISPs, comparing the responses to those of healthy participants (HPs). This national cohort of highly engaged ISPs was critical in our previous successful efforts to define the effects of immunosuppression on SARS-CoV-2 vaccination, and we will leverage this opportunity to discern the impact of immunosuppression on antibody responses to RSV vaccines. We will accomplish this by quantifying the prefusion F and neutralizing antibody response longitudinally in these groups and determining the impact of vaccine type (adjuvanted vs. unadjuvanted), type of immunosuppressive medication, and demographic factors (Aim 1). Additionally, we will study the entire viral antibody epitope landscape before and after vaccination using innovative DNA barcoded-protein technology to identify key gaps in epitope recognition, measure the impact of pre-existing immunity on vaccine responses, and correlate specific epitope responses with antibody function (Aim 2). Finally, the researchers will measure the effect of immunosuppression and vaccine adjuvant on antibody subtype, subclass, and non-neutralizing functions using a systems serology approach (Aim 3). The investigators’ established productive collaborative relationships, extensive experience studying antibody responses in high-risk and/or immunosuppressed populations, and this cohort of ISPs provide the ideal circumstances to provide critical knowledge that promises to be directly applicable to improving RSV vaccine responses in this high-risk group and inform future vaccine design for immunosuppressed persons.

NIH Research Projects · FY 2025 · 2025-08

The goal of the proposed fellowship is to prepare the applicant, Valerie Ganetsky, for an independent research career focused on improving access to medication for opioid use disorder (MOUD) treatment for individuals with opioid use disorder (OUD), particularly for pregnant and parenting women. This fellowship will help Valerie prepare for a career as an independent investigator by providing opportunities for individualized training aimed at achieving four key goals: (1) to attain proficiency in analyzing large administrative claims datasets to investigate OUD health services, (2) to apply advanced quantitative statistical methods to examine critical issues within OUD health services, (3) to lead qualitative research to explore the perceptions of relevant stakeholders within the OUD treatment paradigm, and (4) to integrate her prior clinical expertise with advanced research training to disseminate scientific findings and inform evidence-based policy and practice interventions. To achieve these goals, Valerie will engage in activities such as mentored research, didactic and informal training through coursework and seminars, experiential learning through participation in national organizations and action groups, and dissemination of research findings through manuscript development, grant writing, and presenting at academic conferences. Throughout the training period, Valerie will benefit from a wealth of resources at the Johns Hopkins Bloomberg School of Public Health and a strong mentorship team with complementary expertise in substance use health services research, the use of administrative claims datasets, advanced causal inference methods, qualitative methods, and perinatal substance use policy issues. The mentored research will employ a multi-method approach to examine the association between buprenorphine dose trajectories and OUD-related outcomes among pregnant and postpartum women, particularly during an era when high-potency synthetic opioids dominate the illicit drug supply. This research aims to fill several gaps in the literature. First, the role of buprenorphine dosing as a potential strategy for improving OUD outcomes in pregnant and postpartum women has been notably understudied, highlighting the timeliness and significance of this research. Second, this proposal represents the first use of group-based trajectory modeling (GBTM) to examine buprenorphine dose changes during pregnancy and their impact on postpartum OUD-related outcomes. GBTMs will allow the applicant to identify distinct buprenorphine dose trajectories, identify subgroups more likely to follow certain trajectories, and examine how different trajectories influence outcomes. Third, this research will incorporate qualitative methods to explore previously unexamined attitudes, beliefs, and prescribing practices among buprenorphine treatment providers in Maryland. The proposed research is well-aligned with NIDA’s Strategic Plan Goal 4.2 to support research on developing and testing strategies for overcoming barriers to access and continuity of care.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY The pediatric brain tumor diffuse midline glioma (DMG) has a dismal prognosis and no curative therapies. Despite many clinical trials, DMG remains an incurable, uniformly fatal diagnosis with median survival under one year. A breakthrough in our understanding of DMG biology was the discovery of oncogenic histone H3 K27M mutation as the defining molecular driver of DMG. Genetic studies have confirmed that DMG cells are critically dependent on the mutant histone, such that deletion of H3-K27M abolishes the ability of these cells to form tumors. Further, the dominant negative effect of H3-K27M on gene regulation requires its deposition into chromatin. Therefore, drugs that interfere with H3-K27M mutant histone incorporation into chromatin could revolutionize DMG therapy by directly targeting the root cause of oncogenic transformation. Anthracycline drugs are cornerstones of chemotherapy due to their ability to intercalate into DNA, trap topoisomerases, and induce DNA damage. Recently, a novel mechanism of action for anthracycline derivatives was discovered. By intercalating into DNA, these drugs can 'evict' or displace histones from chromatin. N-alkylated derivatives (such as the natural product aclarubicin) have been shown to selectively induce histone eviction in regions marked by H3-K27 trimethylation at concentrations that do not induce DNA breaks. We found that aclarubicin is selectively toxic to H3-K27M DMG cells and causes specific gene expression changes at targets of H3K27 trimethylation that are dysregulated by H3-K27M mutation. We subsequently developed novel anthracycline derivatives, exemplified by lead compound JHU-5287, that act predominantly by the mechanism of histone eviction, rather than by induction of DNA damage. Importantly, JHU-5287 exhibits excellent brain penetration, in contrast to the poor brain distribution of current anthracycline drugs. To capitalize on this discovery, we will now focus on further characterization and optimization of the lead compound for desirable pharmacological properties (R61 Phase), thus enabling in vivo efficacy studies in mouse orthotopic xenograft models of DMG (R33 Phase). In vivo efficacy achieved at the R33 phase would allow us to fulfill entry criteria for the Blueprint Neurotherapeutics Network and advance to the translational phase. This proposal involves a collaborative effort with investigators of Johns Hopkins Drug Discovery (JHDD), who supply broad expertise in medicinal chemistry, drug metabolism and pharmacokinetics, and animal pharmacology.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT The goal of the proposed fellowship is to prepare Mr. Jirka Taylor for an independent research career utilizing qualitative and quantitative approaches to inform the development of drug policy, with a particular focus on challenges and opportunities located at the intersection of various policy systems. The proposed fellowship will help Mr. Taylor achieve this long-term career goal by enabling opportunities for individualized training: (1) to become proficient in quantitative analyses using medical claims data, (2) to build expertise in advanced statistical techniques, with a particular focus on causal inference, and (3) to gain in-depth understanding of the health policy environment related to substance use and illicit drugs and become skilled in using policy data for research purposes. To achieve these goals, Mr. Taylor will engage in activities including mentored research, structured training via Johns Hopkins University coursework and informal training via independent study programs, and attendance and presentations at seminars and conferences. Throughout the training period, Mr. Taylor will receive support from a wide array of resources at the Johns Hopkins Bloomberg School of Public Health and from a strong mentorship team with complementary expertise in policy research, large data set analysis, advanced statistical and causal inference methods, and issues related to drug policy and care for people who use drugs. The mentored research will consist of a mixed methods study focused on improving care for people experiencing nonfatal opioid overdoses. Non-fatal overdoses attended by emergency provide a chance to initiate the treatment cascade of care by facilitating patients’ linkage to treatment and other services to help address their needs. However, in practice, there are multiple points at which patients may disengage before receiving effective care or where the cascade of care is interrupted. The proposed research aims to address existing research gaps by analyzing individual, structural, and policy-related factors determining service use at key post-overdose points of care. It will use advanced causal inference methods to investigate the impact of Medicaid expansion on rates of non-transport by EMS after nonfatal overdoses. It will also analyze national Medicaid claims data to identify predictors of post-ED treatment service update, focusing on the characteristics of hospital-based care. Lastly, it will use qualitative interviews to describe the implementation of crisis stabilization centers as an alternative to emergency departments for post-overdose care. Taken together, the proposed research will strengthen the understanding of gaps in effective post-overdose linkage to care and identify potential policy and practice mechanisms to mitigate their impact. The proposed research is strongly aligned with NIDA’s Priority Scientific Area #4 to study the implementation of evidence-based strategies in real- world settings.

NIH Research Projects · FY 2025 · 2025-08

The ongoing, indeed once again increasing, burden of malaria is well-recognized, as is the steadfast threat of drug resistance that drives discovery efforts for new antimalarials. Drug combinations to enhance efficacy and to thwart the emergence of resistance are now considered a requirement for antimalarial therapy. Elegant genetic and inhibitor studies have revealed the sequential multistep process of merozoite invasion into erythrocytes, and have identified as essential the linkage of highly conserved reticulocyte-like binding protein 5 (Rh5) to basigin, its red cell membrane protein receptor. This proposal explores the consequences of combining human anti-Rh5 monoclonal antibodies with small molecule inhibitors, against asexual falciparum malaria parasites in vitro. Selected monoclonals will target Rh5 epitope communities that are known to inhibit parasite growth. Small molecule choices will include representative inhibitors for every phase of merozoite invasion. Combinations will be evaluated for additivity, synergy or antagonism. Findings from these experiments will guide further studies.

NIH Research Projects · FY 2025 · 2025-08

Project Abstract/Summary Insects are guided by their chemosensory systems of smell and tase. A multitude of behaviors, such as foraging, mating, navigation, and oviposition rely on smell and taste. For disease vectors such as mosquitoes, their chemosensory systems guide host-seeking (attraction to humans) and biting. As such, the chemosensory systems of insects are excellent targets for behavioral control, and strategies that target insect smell and taste could have significant and widespread benefits from reducing mosquito biting to preventing crop destruction by invasive pests. The chemosensory systems of insects rely on the expression of 3 different receptor family members: the Odorant Receptors (ORs), the chemosensory Ionotropic Receptors (IRs) and the Gustatory Receptors (GRs). Of these three, IRs represent one of the most ancient and abundant chemosensory receptors on the planet. IRs likely diverged ~600 million years ago from an ancestral ionotropic glutamate receptor (iGluRs) to take on new roles as a multi-functional chemosensory receptor family. Chemosensory insect IRs are a complex between an IR co-receptor and a ‘tuning’ IR that binds to a chemical ligand. The IR co-receptors Ir8a and Ir25a share protein homology to iGluRs which contain a large amino- terminal domain. In contrast, the tuning IRs lack this domain. The structure of mammalian iGluR complexes has been solved by the Twomey group using cryo-EM. The structure of chemosensory IRs remains unknown. A key limitation has been harvesting functional chemosensory IR-complexes for cryo-EM analyses. The expression of IR complexes in cell culture systems does not lead to functional complexes, likely reflecting defects in IR complex formation or cellular trafficking. To address these limitations, we assembled a team of insect sensory biologists and structural biologists. Using transgenic Drosophila that express an N-terminal tagged functional Ir8a co- receptor (EGFP:Ir8a), we will: Specific Aim 1, develop methods to harvest functional Ir8a receptor complexes directly from ~10,000,000 Drosophila antenna, and Specific Aim 2, purify EGFP:Ir8a complexes from Drosophila tissues using nanobodies to EGFP followed by cryo-EM analyses on the complexes as previously done for iGluR complexes. These experiments have the potential to establish Drosophila as a viable in vivo tissue source for chemosensory receptor purification. This work will also reveal the cryo-EM structure of chemosensory IRs, their stoichiometry, and provide clues as to how this receptor complex evolved from iGluRs to be gated by a variety of chemicals. This could lead to innovative new strategies to target these receptor complexes to control insect behaviors.

NIH Research Projects · FY 2025 · 2025-08

Surgeons need expert feedback to improve their skill throughout their career. But expert feedback is not easily available to surgeons after they complete training. Expert feedback typically takes the form of natural language, i.e., surgeons learn from experts’ verbal teachings. Recent advances in artificial intelligence (AI) have created a tremendous potential for technology that can provide surgeons with expert-like language-based (i.e., narrative) feedback on any surgery they perform. This project aims to leverage current AI models and develop new ones that analyze videos of surgical procedures and generate expert-like narrative feedback. The AI models will include those that analyze language (i.e., large language models) and videos plus language (vision language models). This project will focus on cataract surgery as a prototype procedure to develop the AI models and evaluate them in additional procedures including surgery of the sinuses around the nose and surgery to remove a lobe in the lung. The overall goal for this project is to develop AI models that provide an expert analysis of a surgical video that includes description, interpretation, and reasoning about what is observed in the surgery and prediction of how the procedure evolves over time. To achieve this goal, this project consists of the following specific aims: (1) To develop a unified framework of vision language and large language AI models to generate expert analyses of surgical videos; (2) To develop methods for the AI models to continuously learn from techniques such as data augmentation, pretraining, and incorporating expert feedback; (3) To develop methods for synthesizing surgical videos from expert analyses to address the challenges in creating sufficiently large datasets needed to train the AI models; and (4) To create a dataset that enables this research. The expected impact of this work is to allow surgeons to learn more skill quickly and reduce variation in patient outcomes resulting from different skill among surgeons.

NIH Research Projects · FY 2025 · 2025-08

We use our tongue to shape the air and generate sounds in order to communicate, and we use our tongue to evaluate food morsels and transport them through the oral cavity in order to eat. These are skillful acts that involve activation of over 100 muscles 1, producing movements that are fundamental to our existence. Damage to the cerebellum profoundly disrupts the ability to control our tongue, resulting in abnormal muscle activation patterns 2 that resemblance ataxia of the arm 3. But unlike the arm, control of tongue movements by the cerebellum has been difficult to study because of the limited access that we have for kinematic measurements. As a result, from a behavioral perspective, we have no standard task to measure learning of tongue movements, and from a neurophysiological perspective, despite the fact that dysarthria is a core feature of cerebellar disease 4, there are very few studies that have quantified lingual control by the cerebellum in non-human primates 5,6. Here, we propose to develop the marmoset model for the study of the cerebellum in lingual control. We think that these animals can significantly contribute to the study of tongue control because they have an exceptionally long tongue 7, and can skillfully use it to make target-directed movements, during which we can measure kinematics using standard marker-less tracking tools 8,9. Moreover, because marmosets are skilled in bending their tongue so to burrow into small holes, we can develop behavioral paradigms that involve precise endpoint control as well as error-dependent learning. Thus, we propose to develop novel behavioral paradigms in marmosets and combine them with neurophysiological studies of their cerebellum. We have two scientific questions: 1) Do phylogenetically newer parts of the cerebellum differ in their contributions to control of the tongue than the older regions? To answer this question, we will employ suppression of Purkinje cells during target-directed tongue movements and measure how this alters the tongue’s trajectory. We will compare these effects in the vermis, vs. the paravermis regions. We predict that whereas in the vermis, P-cell suppression affects simple protraction-retraction dimension of the tongue’s trajectory, disrupting the ability to stop at the target, in the paravermal regions a similar suppression will produce medial-lateral deviations, disrupting the ability to bend the tongue at oblique angles. Next, we ask: 2) How does the cerebellum contribute to learning of tongue movements? To answer this question, we will develop methods to induce errors for target-directed tongue movements, then record how Purkinje cells encode those errors and learn from them. We will build a robotic system that displaces the target of the tongue, inducing endpoint errors. We hypothesize that the spatial parameters of the error will be reported via climbing fiber inputs to the P-cells, inducing error-dependent plasticity. Thus, we will develop a task in which the tongue experiences errors, then quantify the encoding of those errors in the climbing fibers, as well as the error induced trial-to-trial changes in the simple spikes of the P-cells. Overall, our goal is to help expand the field of lingual neuroscience by developing a new NHP model in marmosets.

NIH Research Projects · FY 2025 · 2025-08

While antiretroviral therapy (ART) has significantly reduced HIV-related mortality and increased life expectancy for people living with HIV (PWH), a range of comorbidities (e.g., kidney disease, mental health conditions, cognitive impairment) remain highly prevalent. Depression, the most common mental health comorbidity in PWH, affects 20% to 60% of this population, posing a major challenge to long-term HIV management. Modern combination ART (cART) regimens typically consist of three or more drugs from multiple classes with different mechanisms. Since PWH must remain on cART indefinitely once initiated, and its effects on depression vary across individuals, designing individualized, optimally effective cART regimens with minimal risk of worsening depression is critical in the emerging field of precision medicine for HIV. The availability of large-scale, longitudinal HIV cohort data, such as the MACS/WIHS Combined Cohort Study (MWCCS), spanning over 35 years, presents an unprecedented opportunity to investigate the effects of cART on both viral suppression and depression at an individual level. However, significant scientific challenges remain, including the need to accurately predict individuals' depression and other health outcomes, account for complex drug-drug interactions in estimating cART effects, and develop strategies for planning long-term, patient-tailored regimens that adapt to evolving health conditions. This proposal aims to develop novel statistical and machine learning methods to address these challenges, advancing NIAID's mission by leveraging real-world cohort data and innovative data science approaches to drive precision medicine for people with HIV. Specifically, we propose three aims: (1) Develop novel causal structural discovery models and robust prediction tools to effectively handle distribution shifts resulting from interventions in HIV-related health outcomes; (2) Develop a Bayesian model-based reinforcement learning (RL) framework to optimize personalized cART regimens and improve long-term mental health outcomes in PWH; and (3) Encapsulate the proposed statistical methods and computational algorithms into R and Python packages and develop a web interface for practical application and dissemination. RELEVANCE (See instructions): While antiretroviral therapy (ART) has reduced HIV-related mortality and increased life expectancy for people with HIV, mental health comorbidities, including depression, remain prevalent. Our proposed Bayesian causal discovery and reinforcement learning methods aim to accurately predict depression and other health outcomes while optimizing personalized ART. These advancements have the potential to reduce HIV transmission risk and assist physicians in making personalized treatment decisions.