University Of California Berkeley

NSF Awards · FY 2026 · 2026-09

Large language models (LLM) are increasingly used by the public to seek health information, but current LLM-based systems can still generate inaccurate information due to the well-known problem of LLM hallucinations, while expressing it with high confidence. The issue of confidently representing erroneous data creates risks in high-stakes settings. This project addresses that problem by developing artificial intelligence methods that reduce hallucinations and improve the reliability, transparency, uncertainty estimation, and information-seeking behavior of large language models. The project focuses on women’s health as an application area because it provides a testbed for a broad range of conditions, including breast cancer, osteoporosis, cardiovascular disease, autoimmune disorders, and mental health. By improving the ability of language models to reduce hallucinations, communicate uncertainty, and ask clarifications questions, the project aims to accelerate the adoption of AI technologies in high-risk domains that require stable LLM behavior such as the medical domain and law, among many others. This project develops new multilingual natural language processing methods for language models operating in high-stakes environments. First, it will create methods to curate and structure evidence from heterogeneous sources into an evidence-aligned, reliability-scored knowledge repository in English, Spanish, and French, together with dynamic benchmarks that test reasoning, attribution, abstention, and clarification under evolving conditions. Second, it will develop new model training and inference methods for long-form non-hallucinating generation, fine-grained attribution, calibrated uncertainty estimation, abstention when confidence is low, and proactive follow-up questioning when user queries are ambiguous or incomplete. Third, it will establish a staged validation framework for deployment in health applications, including retrospective evaluation, expert review, user pilot studies, and continuous monitoring. The resulting methods, datasets, benchmarks, and evaluation protocols will advance the science of stable behavior of language modeling and support safe deployment of language models in health and other high-stakes domains. 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 2026 · 2026-09

Polynomial equations, which are essentially made by adding and multiplying together variables, are among the most fundamental equations in mathematics and arise in many areas of science and engineering. The field of algebraic geometry seeks to classify the shapes defined by polynomial equations. The 1-dimensional shapes, called algebraic curves, have important applications in cryptography and string theory. The starting point for the classification of algebraic curves is Riemann's work in the 1850s, which introduced the concept of their moduli space -- a space in which each point corresponds to a different algebraic curve. Moreover, curves with certain geometric properties correspond to subsets of the moduli space. In order to understand how these different properties interact with each other, one must understand how different subsets of the moduli space intersect each other. The project's main goal is to develop novel tools in intersection theory to shed light on different aspects of the geometry of the moduli space of curves. This research will be complemented by educational activities for a range of students, including providing enrichment for elementary and middle school students at local math circles, mentoring undergraduate research projects, and organizing a summer school in algebraic geometry for graduate students. More precisely, the research will have three main directions. First, the PI will pioneer new approaches to the intersection theory of moduli spaces of curves of low genus, including connecting them to, and studying, other closely-related moduli spaces. Second, the PI will apply her expertise in intersection theory to study the cohomology and point counts of moduli spaces of curves over finite fields. Finally, a given algebraic curve can map to different spaces in different ways. The study of these different concrete realizations, known as Brill-Noether theory, is essential to understanding curves. While the Brill-Noether theory of general curves is well understood, the theory breaks down for special curves. Building upon the PI's earlier work for curves of low gonality, the third research direction is to further develop the Brill-Noether theory of special curves. 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 2026 · 2026-09

This Faculty Early Career Development Program (CAREER) project advances methods for strengthening infrastructure systems against rare but high-consequence disasters such as earthquakes, wildfires, and hurricanes. Modern communities depend on interconnected systems, including hospitals, transportation networks, and electric power, yet resilience planning often treats these systems separately or focuses only on individual components. Such approaches can miss the cascading effects that arise when disruptions propagate across sectors and delay emergency response and recovery. This research addresses that challenge by developing a computational framework for risk-informed resilience planning in interdependent infrastructure systems. The work serves the national interest by improving the reliability of critical infrastructure, advancing methods for disaster risk reduction, and helping communities recover more effectively from extreme events. Educational and public-engagement activities include citizen-science tools for reporting infrastructure disruptions after disasters, immersive learning modules on cascading failures, and open computational resources that help train the next generation of engineers in resilience planning. To enable next-generation resilience planning under rare events, this research develops methods that integrate uncertainty quantification, stochastic optimization, and computational surrogate modeling. The project (i) develops adaptive methods to identify high-impact failure scenarios and quantify their consequences for interdependent infrastructure systems, (ii) creates efficient surrogate models that accelerate risk analysis while preserving decision-relevant structure and uncertainty propagation, and (iii) links rare-event simulation with optimization to prioritize resilience investments across connected infrastructure systems subject to budget constraints. The framework is evaluated through representative applications involving healthcare, transportation, and power infrastructure under multiple hazards, including a primary California testbed and a comparative East Coast power-system application in hurricane settings. By linking rare-event analysis with infrastructure interdependencies and optimization-based decision-making, the project contributes new tools for resilience planning across hazards, infrastructure systems, and regional contexts. 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 2026 · 2026-09

Nucleons (protons and neutrons) make up atomic nuclei and are a common building block of all matter in the universe. Understanding their interactions provides insight into the nature of the universe from subatomic to the cosmological scales. To better understand atomic nuclei, as well as to assist current experiments to find new particle physics and to understand particles known as neutrinos, it is important to carry out calculations of nucleons interacting with each other as well as other particles in nature, such as electrons, muons, and mesons. This project develops and distributes software to carry out these challenging computations on the nation’s most advanced supercomputer systems. The physics of hadron-hadron interactions can be studied using Monte Carlo estimates of path integrals involving quark and gluon fields on a space-time lattice. Baryon-meson and baryon-baryon scattering phase shifts can be computed, yielding important information on hadron structure. Quantities known as form factors which involve the nucleon and the so-called Delta baryon are particularly important since they are crucial to interpreting results obtained in accelerator-based neutrino experiments, such as the Deep Underground Neutrino Experiment (DUNE). New computational techniques have made possible such computations in lattice quantum chromodynamics (LQCD). One goal of this work is to build on past efforts to develop highly optimized software to carry out such calculations on modern Graphics Processing Unit (GPU)-accelerated architectures. In particular, this work focuses on the evaluation of various important correlation functions which involve meson and baryon sources and sinks constructed using software developed in a prior award. The tensor contractions needed in these calculations require program executions having different wall times and numbers of computing processors. Effectively bundling such numerous runs together into a handful of batch jobs on supercomputer systems is crucial in the work flow of these computations. This work will also extend the development of software designed to efficiently carry out this bundling. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier in the Division of Physics within the Directorate for Mathematical and Physical 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 2026 · 2026-07

Understanding how new biological traits arise is a fundamental question in evolutionary biology, with broad implications for explaining the origins of biodiversity, predicting how organisms respond to environmental change, and advancing biotechnology innovation. Most research into evolutionary novelty has focused on traits that arise through the gain of new genes or pathways; far less is known about how the loss or modification of existing pathways can generate novelty. This project investigates the process of kleptocnidy, which is the theft and storage of microscopic stinging structures (nematocysts) from cnidarian prey, a striking natural example of biological innovation that has evolved independently multiple times. The research will determine whether this unique process evolved in nudibranch sea slugs through specialization of phagocytosis, an ancient cellular process used across animals for immune defense and intracellular digestion. By generating high-quality genomic, transcriptomic, and single-cell data resources for marine invertebrates, the project produces strategic biological data assets useful to the broader scientific community for biotechnology applications and the development of fundamental knowledge across evolutionary biology, comparative immunology, and cell biology. This project will also support the training of graduate and undergraduate students and postdoctoral researchers in genomics and computational analysis through research experiences and will broaden participation in science through public engagement activities. Overall, these efforts will contribute substantially to the training of a competitive STEM workforce in molecular tools and biotechnology. This collaborative project tests whether kleptocnidy evolved primarily through specialization (loss of function) of conserved phagocytosis pathways or through the origin of new molecular machinery. Three objectives integrate complementary approaches across two laboratory-tractable nudibranch species, Berghia stephanieae and Hermissenda opalescens. Objective 1 characterizes the molecular processes underlying kleptocnidy in adult Hermissenda using RNA-sequencing of cerata tissues under cnidarian and non-cnidarian feeding regimes, paired with pharmacological inhibition assays targeting candidate phagocytosis pathways. Objective 2 reconstructs the regulatory networks involved in the development of the cnidophage, which is the specialized cell type that captures and stores nematocysts, using single-cell RNA-sequencing across early juvenile Berghia development, with in situ hybridization chain reaction validation, to test whether cnidophages develop through pathways conserved with generalist phagocytic cells. Objective 3 evaluates the role of lineage-specific (“novel”) genes through comparative differential expression and orthology analyses across approximately 20 cladobranch nudibranch species, including independent origins of nematocyst sequestration, collected from California and French Polynesia. Outputs will include a new reference genome, single-cell atlases, gene co-expression and regulatory networks, and a phylogenetically informed catalog of conserved versus lineage-specific genes underlying a charismatic case of evolutionary innovation, while providing foundational genomic resources for an emerging marine invertebrate model system. The results of this work will improve our understanding of the evolution of novelty. 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 2026 · 2026-07

This project investigates the fundamental mathematical principles governing how waves move and interact in complex environments. These phenomena are central to a wide range of physical systems, including the motion of water waves in the oceans, the propagation of electromagnetic waves, and the dynamics of gravitation throughout the universe. By developing new mathematical methods to understand how such waves evolve over very long time scales, this research offers deeper insight into the stability, structure, and long-term behavior of complex natural systems. This work serves the national interest by advancing the progress of science and contributing to national prosperity and public welfare. In particular, the mathematical insights developed through this research enhance our ability to model and predict the evolution of ocean surface waves and related dispersive phenomena, with potential applications to maritime safety, coastal resilience, environmental forecasting, and engineering design. More broadly, the development of rigorous analytical frameworks for complex wave dynamics strengthens the mathematical foundations underlying a variety of scientific and technological disciplines. Furthermore, the project is dedicated to the education and training of the next generation of American scientists. By integrating graduate and postdoctoral researchers into cutting-edge mathematical research and developing advanced university curricula, the project ensures a robust and technically skilled workforce capable of addressing complex challenges in science and technology. The project spans an array of topics in nonlinear partial differential equations. The problems investigated are all associated to the field of nonlinear dispersive equations, focusing on models derived from fluid dynamics, electromagnetism, and general relativity, for which wave propagation and interaction are the leading evolution mechanisms. This work also has deep connections to related areas such as geometry, harmonic analysis, complex analysis and microlocal analysis. The primary goal is to establish a rigorous mathematical framework for understanding nonlinear wave interactions across multiple temporal scales, with a particular emphasis on long-time global dynamics and scattering phenomena. The research focuses on four central objectives: the analysis of long-time dynamics in strongly nonlinear dispersive flows where nonlinear effects outweigh dispersion; the study of free boundary problems in fluid dynamics, specifically the evolution of water waves; the investigation of completely integrable systems and their associated inverse scattering theory; and the analysis of geometric nonlinear wave equations. The methodology draws upon and synthesizes advanced techniques from harmonic analysis, microlocal analysis, differential geometry, and complex analysis, with the goal of developing new analytical frameworks and mathematical tools of broad applicability across nonlinear partial differential equations and related areas of mathematical physics. The anticipated contributions of this research include progress toward the resolution of long-standing questions concerning the stability and singularity formation in nonlinear wave equations, thereby advancing the state of the art in the field of nonlinear analysis and deepening the mathematical understanding of complex phenomena arising in physical models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Obesity and diabetes are escalating global health crises, with insulin resistance serving as a key pathogenic feature of both obesity and type 2 diabetes mellitus. Adipose tissue is a central regulator of systemic insulin sensitivity, and its dysfunction is a hallmark of obesity-related metabolic diseases. Adipokines play critical roles in maintaining metabolic homeostasis through local effects and inter-organ communication; however, many remain unidentified or poorly characterized. The goal of this proposal is to investigate the physiological role and mechanisms of Neuritin 1 (NRN1), a novel adipokine we recently identified. NRN1 is a glycosylphosphatidylinositol (GPI)-anchored and secreted protein that is highly enriched in adipocytes. Its expression is significantly reduced in adipocytes from both obese mice and humans. Moreover, NRN1 secretion from epididymal white adipose tissue, as well as its circulating levels, are markedly diminished in obesity. Notably, genetic variation at the NRN1 locus is associated with metabolic traits in human populations. Our preliminary data suggest that NRN1 is a critical regulator of insulin sensitivity and glucose metabolism, acting primarily on adipose tissue. Unbiased mechanistic studies indicate that NRN1 may act through fibroblast growth factor receptor 1 (FGFR1), a receptor known to support adipocyte insulin sensitivity and systemic metabolic regulation. Aim 1 will define the physiological role of NRN1 in adipose tissue biology and systemic insulin sensitivity by characterizing its cell-autonomous functions and assessing metabolic consequences in mouse genetic models with adipocyte-specific gain- and loss-of-function. Aim 2 will elucidate the mechanisms by which NRN1 regulates insulin sensitivity, focusing on FGFR1-mediated effects on adipocyte differentiation, lipolysis, and inflammation. Aim 3 will evaluate whether systemic delivery of NRN1—via AAV-mediated expression or recombinant protein administration—can mitigate insulin resistance and glucose intolerance in diet-induced obese mice. Together, these studies will establish NRN1 as a previously unrecognized adipokine with critical roles in regulating insulin sensitivity and metabolic homeostasis. By linking adipocyte-intrinsic mechanisms to whole-body insulin action, this work may uncover novel biological pathways contributing to insulin resistance and identify new targets for therapeutic intervention in metabolic disease.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract Eukaryotic cells utilize organelles to compartmentalize biochemical processes, ensuring spatial control over complex metabolism. This project aims to re-engineer the peroxisome and vacuole organelles to be synthetic, specialized organelles for engineered metabolic pathways. Accordingly, this project has significance both in directly testing, and likely expanding, the current understanding of the biology of these organelles and in the engineering of platform strains that will enable the unicellular microbe Saccharomyces cerevisiae to mimic the tissue compartmentalization observed in many plants for the synthesis of many natural products used as therapeutics in the clinic. Specifically, the reprogramming of the following key organelle properties will be targeted: organelle biogenesis and morphology, membrane permeability to small molecules, rate and efficiency of soluble protein import, and membrane protein trafficking to gain selective small molecule import/export. These engineering efforts will establish design rules for these organelle functions as well as develop powerful tools for the metabolic engineering of complex, therapeutic molecules with the preferred fermentation host, Saccharomyces cerevisiae. Accordingly, this unicellular microbe can be a chassis capable of compartmentalizing parts of a pathway in the peroxisome, vacuole, and cytoplasm. In the proposed work, increased flux through the benzylisoquinoline alkaloid pathway and control over which products are biosynthesized will be achieved using the specialized organelles engineered in this work.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY/ABSTRACT (DESCRIPTION) Across the United States, hospital markets are becoming increasingly concentrated via organizational mergers, acquisitions, and closures. Hospital consolidation can reduce competitive incentives, potentially impacting organization behavior in ways that can either improve or worsen quality of care, but there is limited evidence about how hospital consolidation is differentially associated with quality of care for patients by race and ethnicity or payer coverage. This project aims to examine how increases in hospital consolidation influences quality of care disparities for racial/ethnic minority groups (compared to non-Hispanic White adults) and uninsured and low-income, publicly insured patients (vs commercially insured patients).

NSF Awards · FY 2026 · 2026-06

Scientific findings should come with error rates that mean what they say: among findings assigned a 5 percent chance of error, about 5 in 100 should turn out to be wrong. This standard, called calibration, underlies trusted probability claims from weather forecasting to machine learning (ML), but it is not yet a routine part of the statistical tools used in many large-scale scientific studies. The issue arises whenever researchers must triage long lists of possible discoveries, anomalies, or published claims. In metascience, the question is which findings in the literature will replicate; in artificial intelligence (AI) safety, which suspicious model inputs deserve greater scrutiny. Current methods control the average error rate across an entire list of discoveries, but they rarely provide individual findings with calibrated error probabilities. This award supports research on calibrated hypothesis testing, which will develop methods that distinguish strong evidence from borderline evidence with interpretable, rigorous guarantees. The work will support more reproducible science and safer data-driven AI/ML systems, while training graduate researchers, developing new instructional materials, and releasing open-source software. This project will develop theory and methodology for calibrated, large-scale inference. The framework draws upon probabilistic forecasting but addresses a distinct challenge: unlike forecasting, where labels are eventually observed, in multiple testing the ground truth is never revealed, so calibration must be assessed stochastically and established indirectly. The investigators will combine empirical Bayes estimation with frequentist finite-sample guarantees, extending local and boundary false discovery rates beyond settings with independent p-values. Variable selection will serve as the first setting, using knockoff and sign-symmetric statistics to construct local error assessments for selected variables. Conformal outlier detection will extend these ideas to discrete and dependent p-values produced by a shared calibration dataset. Online testing will build on both directions by treating sequential threshold choice as an online learning problem under distribution drift. Together, these three settings will demonstrate that calibrated local error rates constitute a fully functional statistical concept with broad applicability. 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 2026 · 2026-06

Nontechnical Description: The goal of this project is the development of a new type of microscope that can image materials with unprecedented spatial and temporal resolution. The new instrument is called an atomic resolution microwave microscope (ARMM) and is designed to have the capability of imaging fast moving electrons in electrical devices down to the atomic scale. Current state of the art microscopes can only visualize the atomic structure of devices when electrons are moving very slowly. This is inadequate for modern technology where there is need to not only visualize electrons at very small length scales (due to the relentless miniaturization of electrical devices), but also to visualize their behavior when they move very fast (due to the ever-increasing speed of modern technology). The new ARMM instrument is designed to image electrons when they move at speeds that allow them to orbit a device billions of times per second. These are called “microwave frequencies” and this frequency range is critical for many modern technological applications and quantum science discoveries. In order to develop more highly efficient electrical devices that can operate at these frequencies it is important to develop microscopes that can probe new materials in this regime. The ARMM instrument fulfills this need. Technical Description: The key capabilities of the new atomic-resolution microwave microscope (ARMM) include combined atomically resolved imaging and local microwave characterization of 2D devices. The new instrument is designed to operate at cryogenic temperatures in ultrahigh vacuum while integrating scanning tunneling microscopy, atomic force microscopy, microwave impedance microscopy (MIM), and electron spin resonance detection (STM ESR), all using the same probe tip. MIM and STM ESR will allow measurement of local high-frequency complex permittivity and spin resonance behavior. Major research projects involve the determination of how 2D Wigner crystals melt in inhomogeneous disordered environments, including spatial resolution of new solid/liquid electronic phases coexisting with atomic-scale defects. Other important research targets involve characterization of the quantum magnetism of 1D Wigner chains, 0D Wigner molecules, and topological boundary modes. Direct measurement of quantum coherence times for individual defects in 2D devices is designed to enable their evaluation as potential qubits. The goals and scope of this project include the design, testing, and fabrication of new radio frequency (rf) circuit components. This involves impedance matching to the tip assembly, installation of cryogenic microwave amplifier components, and the construction of a new rf compatible sample holder. Design and fabrication phases, as well as ARMM software development, precede final assembly and commissioning phases. 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 2026 · 2026-06

Cells rely on a highly organized transport system to move essential materials, such as proteins, organelles, and signaling molecules, to precise locations. This process is especially critical in neurons, where cargo must travel long distances to maintain proper function and survival. Failures in intracellular transport are linked to major neurological disorders, including Alzheimer’s and Parkinson’s diseases. Although molecular motors such as kinesin are responsible for carrying cargo along cellular “tracks” called microtubules, it remains unclear how these motors are selectively regulated to ensure accurate delivery. Emerging evidence suggests that proteins decorating microtubules, known as microtubule-associated proteins (MAPs), act as key regulators that determine which motors can access and move along these tracks. However, the mechanisms by which MAPs selectively activate or inhibit different motors are poorly understood. This project aims to uncover how MAPs control motor-driven transport at the molecular level. By revealing fundamental principles governing intracellular transport, this work will advance understanding of cellular organization and the molecular basis of neurological disease. The project also contributes to national priorities by supporting STEM education and workforce development, including the engagement of high school students in research activities and the integration of interdisciplinary training in physics and biology at the undergraduate level. This project will combine structural biology, biophysics, and protein engineering to determine how specific MAPs (tau, MAP7, MAP9, and doublecortin) regulate the activity of kinesin-1 and kinesin-3 motors. The research will pursue four main objectives. First, it will determine the full structural footprint of MAPs on microtubules using cryo-electron microscopy (cryo-EM) and newly developed labeling strategies that overcome current resolution limitations. Second, it will define how tau inhibits motor movement by testing whether MAPs and motors compete for overlapping binding sites on microtubules and by tracking motor stepping behavior at nanometer precision. Third, it will identify how MAP9 distinguishes between different kinesin motors by combining structural analysis with mutagenesis and single-molecule assays to pinpoint the molecular features responsible for selective regulation. Fourth, it will characterize how doublecortin promotes kinesin-3 motility through direct interactions with the motor while inhibiting other motors. Across all aims, the project will integrate cryo-EM, fluorescence imaging, molecular dynamics simulations, and crosslinking mass spectrometry to establish a comprehensive mechanistic framework. The expected outcomes include new structural and functional models describing how MAPs control motor access and movement on microtubules. These findings will provide fundamental insights into intracellular transport and establish broadly applicable experimental approaches for studying dynamic protein interactions on cytoskeletal filaments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

ABSTRACT A fundamental gap in our understanding is how mycobacteria, including the important pathogen M. tuberculosis, export lipids from the inner membrane across the aqueous periplasm to the outer membrane. The mycobacterial outer membrane contains a uniquely complex array of lipids that are essential for their resilience, antibiotic resistance, and virulence. In Gram-negative bacteria, lipid transport is accomplished by protein complexes that span the cell envelope, but no such export machinery has been identified in mycobacteria. This study introduces MELO (Mycobacterial Exporter of Lipids to the Outer membrane), a novel protein complex that mediates lipid export. We will implement new genetic and proteomic approaches to identify MELO and use functional assays in M. smegmatis and M. tuberculosis to test its role in lipid transport. We have discovered that MELO mutants are resistant to the phage Bxz1, providing a unique selective pressure enabling a novel genetic screen to search for MELO mutants in M. smegmatis. We will then map the MELO protein interaction network using an affinity purification-crosslinking mass spectrometry (AP-XL-MS) approach we have developed. Piloting this approach has already identified three candidate MELO components. Based on this preliminary data, we will functionally test the role of MELO components in lipid transport in the pathogenic M. tuberculosis. Successful completion of this project will identify MELO, a new system critical for outer membrane biogenesis in mycobacteria with broad implications for our understanding of bacterial cell envelope biology.

NSF Awards · FY 2026 · 2026-05

The increased use of lithium-ion batteries has increased the risk of large-scale battery fires in storage facilities. Recently, a fire occurred at a lithium-ion battery storage facility in coastal California, damaging more than half of the facility's batteries. This fire may have released metals and fluorinated chemicals into the environment. This project will conduct rapid-response field sampling of soil, sediment and shallow groundwater to assess the presence of contaminants. The project will help to implement remediation activities to reduce effects of the fire. The project will train doctoral and undergraduate students in field environmental chemistry and advanced analytical techniques. This research will inform for future national safety policy on storage facilities for lithium-ion batteries. This research will quantify per- and polyfluoroalkyl substances, including novel lithium-battery-associated fluorinated compounds, as well as metals and metalloids in soils, sediments, estuarine waters, and shallow groundwater that may have affected by the recent lithium-ion battery storage facility fire in the Central Coast of California. The research team hypothesizes that differential per- and polyfluoroalkyl substance transport based on perfluoroalkyl chain-length will be modulated by salinity gradients, tidal dynamics, and bank filtration processes characteristic of the Elkhorn Slough coastal system. Approximately 50 primary sampling locations have been identified within 15 kilometers of the fire site to measure contaminant distributions prior to hydrologic redistribution following precipitation. Per- and polyfluoroalkyl substances will be quantified using liquid chromatography-tandem mass spectrometry standard methods (United States Environmental Protection Agency Method 1633) and nontargeted approaches. Metals and metalloids will be analyzed using inductively coupled plasma-based techniques. Spatial distributions will be mapped using geographic information systems and interpreted in relation to soil properties, groundwater gradients, and estuarine salinity regimes. This research will generate the first field-based dataset characterizing early-stage fate and transport of lithium-ion battery contaminants due to fire in a coastal setting. The research will also provide scientific evidence for exposure assessment, emergency response planning, and long-term remediation 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.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT Directly genome editing cells inside the body could treat numerous genetic diseases, including sickle cell disease. However, genome editing of most cell types outside of the liver, such as hematopoietic stem cells, is limited by inefficient delivery. Only a small subset of cells inside of a tissue are accessible to delivery vehicles and editing enzymes. To overcome this delivery problem, my central objective is to enable cells initially receiving delivery vehicles to transiently produce and distribute genome editing enzymes to neighboring cells in vesicles. This allows editing activity to spread beyond initial delivery. My proposal builds on my postdoctoral research studying the delivery mechanisms of Enveloped Delivery Vehicles (EDVs). EDVs are lentivirus-derived lipid vesicles engineered to package CRISPR-Cas9 ribonucleoproteins that can be targeted to specific cell types using surface-displayed fusogens and antibodies. I hypothesize that transient, local production of EDVs in vivo will amplify genome editing efficiency by increasing the concentration of genome editing enzymes and enabling their spread across cells. This hypothesis will be tested through three specific aims: (1) develop single nucleic acid molecules encoding EDVs, (2) establish methods to target EDV production and uptake to specific cell types, and (3) deliver EDV-encoding plasmids to amplify editing in vivo. In preliminary work for this proposal, I showed that hydrodynamic injection of EDV-encoding plasmids into mice amplified genome editing efficiencies compared to Cas9 only plasmid controls. Successful completion of this proposal will generate fundamental insights into propagating genome editing effects beyond cells initially reached by delivery vehicles starting with hematopoietic stem cells as a model therapeutic cell type. This approach could broadly transform biological therapy delivery by overcoming low delivery efficiencies through localized amplification and spread of therapeutic macromolecules. The training acquired through this proposal in primary cell culture, bioinformatics, and next-generation sequencing will bolster my readiness to lead an independent research program. I will be mentored by Dr. Jennifer Doudna, a global leader in genome editing technology, and leading experts and clinicians in primary cell engineering, hematopoietic stem cell biology and virology in the California scientific community. During the mentored phase of this project, I will hone my scientific and professional skills to become a scientific leader. I will engage in structured professional development activities and actively present my research at leading conferences to facilitate a successful transition to an independent academic research position. The vibrant and collaborative environment provided by UC Berkeley and the Innovative Genomics Institute offers an outstanding environment to complete my training and start my independent scientific career.

NSF Awards · FY 2026 · 2026-05

This is a renewal of a 10-week summer REU (Research Experiences for Undergraduates) Site serving 8 student interns per year. This site is hosted by the University of California Berkeley Search for Extra-Terrestrial Intelligence (SETI) Research Center (BSRC). The renewal of this successful NSF REU Site will provide transformative research experiences and long-term mentorship to support students’ growth as scientists, technologists, and leaders. The Breakthrough Listen initiative at BSRC offers an unparalleled opportunity for students to contribute to big-data, big-question science. The SETI field is small but growing, and BSRC is recognized as a world leader. Breakthrough Listen generates massive datasets, providing students with real-world opportunities to develop open-source tools, data pipelines, visualizations, and documentation that are actively used by the scientific community. The fundamental question we explore: Are we alone in the Universe? - drives advances in instrumentation, algorithm development, and data infrastructure that benefit both SETI and astronomy more broadly. Undergraduate students will work with BSRC researchers to develop scientific approaches to constrain the prevalence of technosignatures from surveys that generate petabytes of data; design new instruments and digital backends for radio telescopes; write algorithms to quantify radio frequency interference and to identify good candidate signals, including with machine learning (ML) approaches and citizen science, and work on making data useful and accessible to the astronomical community and to the general public. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

Five distinct prostaglandins are essential lipid mediators and the non-selective inhibition of their formation is the mechanism of action for all NSAIDs. NSAIDs are a routine treatment for ocular allergies, pain, inflammation and macular edema. In sharp contrast, amplification of prostaglandin signaling in in the trabecular meshwork is the primary treatment for lowering intraocular pressure. Clearly, prostaglandins are important therapeutic targets in the eye. It is striking that our knowledge of prostaglandin generation and function in retinal physiology or pathophysiology is extremely limited. We discovered that PGD2 is a highly abundant and constitutive prostaglandin in the healthy mouse and rat retina, optic nerve, and non-human primate optic nerve. PGD2 levels are very low in other tissues and release of PGD2 from activated mast cells is a key feature of vascular disease and allergic responses. Using a complementary approach of single cell and bulk RNAseq, in situ hybridization, Western blot, and immunohistochemistry, we established that the complete PGD2 pathway, including enzymes and receptors, is expressed in the retina. The PGD2 receptor DP1 is broadly expressed throughout the retina while DP2 is more selectively expressed in the ganglion cell and inner nuclear layers. These findings are of great interest since PGD2 and a DP1 agonist potently rescues hippocampal neurons from glutamate toxicity. In addition, our preliminary data establishes that a DP2 agonist provides complete protection against glutamate induced cell death in neuronal cells. However, no studies have investigated the physiological or neuroprotective role of PGD2 in the retina. To explore the unknown role of PGD2 in the retina, we used an ocular NSAID to inhibit the high basal production of PGD2 in an established model of OHT-induced RGC degeneration. Inhibition of PGD2 in the healthy retina resulted in selective upregulation of RGC genes for function and maintenance, which indicates a key role for constitutive PGD2 in maintaining RGC. OHT caused a marked downregulation of retinal PGD2 levels and the entire pathway including enzymes and receptors DP1 and DP2. More importantly, inhibition of PGD2 formation caused increased neurodegeneration in response to OHT. The increased loss of optic nerve axons and RGC correlated with upregulation of astrocyte and microglia genes for inflammatory reactivity. We hypothesize that retinal PGD2-DP pathways, which are down-regulated during OHT, are essential for homeostasis and that therapeutic amplification of PGD2-DP signaling is a potential neuroprotective strategy for RGC degeneration. Three specific aims will 1) Define cell-specific expression and functional activity of the PGD2 pathway in healthy retina and investigate how OHT compromises this constitutive pathway, 2) Investigate the role and homeostatic mechanisms of DP1 and DP2 in the retina, RGC and macroglia and 3) Investigate wheth3er amplification of DP1 and/or DP2 is protective against OHT-induced RGC degeneration.

NSF Awards · FY 2026 · 2026-05

This research will advance a form of zero-field nuclear magnetic resonance (ZF NMR) detection spectroscopy for high-information chemical analysis. While conventional, high-field NMR spectroscopy typically relies on expensive, homogenous, superconducting magnets, in this approach, samples would be briefly polarized and then measured in a shielded, near-zero-field environment using compact atomic quantum sensors, enabling a path toward low-cost instruments that could operate in parallel rather than one sample at a time. By overcoming sensitivity and analysis bottlenecks, the project would help unlock distributed chemical fingerprinting for applications such as faster reaction screening and quality control, and it could simplify measurements in water-rich samples where conventional NMR often requires complex suppression methods. The effort would also contribute to workforce development by training undergraduate and graduate researchers in research instrumentation, quantum sensing, and quantum information methods, and by engaging local high school and community college students through lab visits and hands-on exposure to modern chemical measurement science. Technically, the research will focus on four complementary thrusts: (1) sensitivity improvements intrinsic to the laboratory ZF NMR instrument; (2) development of machine-learning tools for prediction of J-couplings and zero-field NMR spectra; (3) quantum-computing-assisted algorithms for scalable spectral inference (Hamiltonian learning); and (4) zero-field-compatible optical hyperpolarization. In the first thrust, the team will pursue a set of instrumentation advances to improve sensitivity, including increasing sample–sensor coupling, deploying small arrays of optically pumped magnetometers (OPMs) to expand the effective detection volume and enable common-mode noise rejection, reducing environmental magnetic noise through improved shielding and low-noise electronics, and implementing compact high-field prepolarizers together with faster sample shuttling to minimize polarization loss. These improvements will be quantitatively benchmarked across representative small molecules using standardized sensitivity metrics. To enable scalable spectral interpretation, the project will build an experimental library of zero-field spectra for a broad range of small organic molecules and use these data to develop machine-learning models capable of predicting J-couplings and full ZF NMR spectra directly from molecular structure. In parallel, spectral analysis will be formulated as a parameter-estimation problem in which a candidate J-coupling Hamiltonian is iteratively refined to reproduce experimentally measured time-domain signals or spectra. Hybrid quantum–classical algorithms will be developed to accelerate the simulation and observable estimation required for this refinement as the size of coupled spin networks grows beyond the regime where exact classical simulation is practical. Finally, the team will develop reproducible triplet-based optical polarization sources and engineer a rapid crush-and-dissolve transfer workflow that delivers enhanced nuclear polarization into liquid analytes. Integration of this workflow with ZF NMR detection will allow systematic characterization of polarization enhancement factors, polarization lifetimes, and their effects on spectral resolution and repeatability across a wide panel of analytes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT Globally, there were 1.3 million new HIV infections in 2023, despite expanded access to biomedical HIV prevention products with high efficacy. Implementation strategies are needed to expand the reach of HIV risk screening and to facilitate the use of biomedical prevention among persons with risk. These implementation strategies are often delivered at the group-level or induce changes at the group-level (e.g., health clinics or health systems). Cluster randomized trials (CRTs) are integral to evaluating and optimizing strategies deployed at the group-level. CRTs provide an exciting opportunity to evaluate strategies aiming to both improve reach into the target population and health outcomes among persons reached. However, these CRTs create a complex missing data problem: the strategy improves outcomes directly and indirectly; yet, outcomes are only measured among persons reached. While machine learning can facilitate adjustment for missing data in simpler CRT settings, new methods are needed to minimize bias arising from this common CRT setting. CRTs also provide an exciting opportunity for intervention optimization by evaluating for whom and in what context the strategy works best. However, existing methods to evaluate effect heterogeneity in CRTs are prone to false conclusions (i.e., Type-I and Type-II errors). While machine learning can facilitate data-driven evaluation of effect modification in individually randomized trials, CRTs present distinct challenges due to their small effective sample sizes. In this proposal, we will address these crucial gaps in the analysis of CRTs. To do so, we will develop, apply, and disseminate new Targeted Machine Learning Estimators (TMLEs) to minimize bias due to missing data and to facilitate data-driven evaluation of effect modification. TMLE combines formal causal modeling, statistical theory, and machine learning to improve the accuracy, precision, and relevance of our findings. This proposal has the following aims. We will develop new TMLEs to minimize bias due to missing data and robustly evaluate overall effectiveness in CRTs of strategies that aim to improve both reach and health outcomes (Aim 1A). We will combine these TMLEs with novel sample-splitting and multiple testing procedures to data-adaptively identify and evaluate effect heterogeneity at multiple levels (Aim 1B). In secondary data analyses of two CRTs, we apply the proposed methods to generate new insights about the effectiveness and implementation of an HIV prevention strategy when offered at scale and when adapted to a new context (Aim 2). We will disseminate the proposed methods through a user-friendly and interactive website – facilitating the rigorous and reproducible use of our new methods (Aim 3). This work is timely and significant given the role of CRTs in evaluating and optimizing strategies to prevent HIV and other chronic conditions, such as hypertension, diabetes, and cardiovascular disease.

NSF Awards · FY 2026 · 2026-05

This project supports the participation of US-based researchers in the conference A85: algebra and arithmetic from model theory to be held at the International Centre for the Mathematical Sciences in Edinburgh, Scotland from May 11th to May 15 of 2026. The conference will bring together specialists from the algebraic, arithmetic and the pure model theory communities to exposit the latest results on the connections between model theory, arithmetic, and algebra. Participation by American researchers and scientists is essential for maintaining our leading position within the field of model theory and its applications to other parts of mathematics through the knowledge to be acquired, potential new collaborations, and the education and training afforded to doctoral students and early career researchers. More technically, the conference and attendant discussions focus on the following intertwined aspects of model theory and its applications: Algebra from classification theory, Measurable structures, Fields with operators, Definability in natural rings and fields, O-minimality and Ax-Schanuel-type problems, Peano arithmetic, and Model theory and categorical logic. For example, it has been known since the early 1970s that classification theoretic hypotheses such as superstability have strong consequences on the structure of groups and fields. Contemporary research extends the classification theoretic tameness / algebraic simplicity correspondence to much more sophisticated classes. In another direction, functional transcendence theorems in the style of Ax's Schanuel conjecture have been proven using methods from the model theory of differential fields and from o-minimality. The workshop website is at https://icms.ac.uk/activities/workshop/a85/ 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 2026 · 2026-04

Optimization serves as the mathematical engine powering modern artificial intelligence and complex decision-making systems. Many real-world challenges, however, ranging from managing energy grids to training machine learning models, involve mathematical landscapes that are jagged, unpredictable, and obscured by data noise. These irregularities often trap existing technologies in suboptimal or inefficient solutions. This project pursues a new generation of rigorous mathematical tools and stable algorithms designed to navigate these difficult landscapes with precision and speed. The research will be translated into open-source software to ensure these high-performance tools are accessible to both academic researchers and industry practitioners. Furthermore, educational initiatives will span from K-12 outreach to community college teacher training and specialized graduate instruction, ensuring the next generation of scholars is equipped to tackle the new wave of global scientific and engineering challenges. The research supported by this award is to establish a rigorous theoretical and algorithmic framework for nonconvex and nonsmooth optimization problems, focusing on both the efficient computation of local solutions and the effective certification of global optimality. The research addresses fundamental challenges in problems that lack standard structural assumptions. The technical approach includes: establishing new variational characterizations for irregular objectives to enable principled function approximations; developing stable algorithms capable of escaping irregular saddle points; leveraging implicit dimension reduction induced by nonsmooth maps to create scalable second-order methods; and implementing a novel homotopic sketching framework to provide global optimality certificates via progressively refined relaxations. The project aims to deliver convergence guarantees and numerical algorithms for broad classes of optimization problems that currently lack effective solution methods, thereby advancing the theoretical and algorithmic frontiers of the field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

Abstract There are 118 known elements. Nearly all of them have nuclear magnetic resonance (NMR) active isotopes and at least 39 different nuclei from 33 elements have been used in biological and biomedical NMR studies. Despite the availability of dozens of NMR active isotopes (2H, 7Li, 13C, 17O, 23Na, 31P, 35Cl, 39K, etc.), most of today’s MRI is based on one nucleus – 1H. Since its inception in the 1970s, MRI technology has made immense gains in SNR with hyperpolarization, high and ultra-high field magnets, anatomy-conforming receiver coils, improved reconstruction, and other techniques. With these SNR gains, the imaging of nuclei other than 1H, or X- nuclei, has become more clinically feasible, inspiring a variety of studies capitalizing on the essentially perfect nuclear specificity of NMR/MRI to gain information not possible with 1H alone. Notably, hyperpolarized media and deuterium imaging have made significant gains recently. These and further studies, however, are still held back by technical challenges and the low availability and high cost of the necessary tools. To overcome these bottlenecks, we aim to develop an RF system, called the ADAPT PRO system, that can be digitally programmed on the fly to image any nucleus of interest independently or simultaneously. The system will bring out the full potential of all NMR active nuclei, significantly enhancing disease knowledge, diagnoses, and treatment evaluations. X-nuclei benefits have already been shown for cancer, osteoarthritis, Alzheimer’s, and many more. The system can be mass manufactured on assembly lines without the need of highly trained coil engineers. As such, it can be produced at orders-of-magnitude lower cost, thus facilitating the clinical translation and democratization of X-nuclei spectroscopy and MRI in general. Our innovative approaches have independent transmit and receive components. The transmit side integrates high-frequency, high-power switches into the coil structure, merging the RF amplifier and coil into a single programmable device that converts DC power to any RF frequency of interest. The receive side uses high-frequency, low-noise variable capacitors (varactors) driven to convert received MRI signals from an untuned coil to the ~500 MHz range, which are then amplified by a resonant ~500 MHz circuit. These advances promise to bring MRI coils to the digital age, enabling vastly more capabilities via programmability. Any-nucleus imaging is one new capability, and more potential capabilities include magnetic field shimming for undistorted data, improving quantification by reducing coil loading effects by the patient, and being reused between scanners of different field strengths, including emerging low-field portable scanners. Our proposed work has the potential to solve a wide range of important problems all at once.

NSF Awards · FY 2026 · 2026-04

This award is jointly supported by the Major Research Instrumentation and the Chemistry Research Instrumentation programs. The University of California, Berkeley, is developing a next-generation tabletop ultrafast soft x-ray spectroscopy instrument to support the research of Professor Michael Zuerch and colleagues Stephen Leone and Daniel Neumark, as well as a broad community of campus and external users. By establishing a campus-based hub for ultrafast soft x-ray science, the project increases access to advanced x-ray capabilities for chemists, physicists, materials scientists, and engineers. The instrument produces high-flux, tunable soft x-ray pulses with durations reaching the few-femtosecond and attosecond regime and measures element-specific absorption changes as chemical and material systems evolve in real time. The design enables systematic, comparative studies across phases of matter and significantly expands the capabilities of existing laboratory x-ray tools. The system will be operated as a shared-use resource with structured access for internal and external collaborators. The project also incorporates a coordinated education and workforce development program that provides undergraduate and graduate students with hands-on training in high-power lasers, vacuum technology, x-ray optics, and ultrafast spectroscopy. The award addresses the development of an instrument which integrates an optical parametric chirped pulse amplification laser platform with modular sample environments spanning gases, liquids (including ultrathin liquid jets), and solids within a single shared-use environment. By extending laboratory soft x-ray spectroscopy to photon energies up to approximately 650 eV, the system enables direct observation of electron motion, charge redistribution, and bond rearrangements with sensitivity to carbon, nitrogen, oxygen, sulfur, and first-row transition metals. The instrument provides element-, site-, and oxidation-state-specific sensitivity through core-to-valence transitions and supports soft x-ray transient absorption and attosecond pump–probe measurements. Enabled research includes molecular charge migration, excited-state dynamics in solution-phase organic systems, water radiolysis at the oxygen K-edge, and ultrafast spin and charge dynamics in magnetic and photoactive materials. By enabling element- and site-specific tracking of charge, spin, and orbital dynamics on their natural time scales, the instrument provides direct access to decoherence pathways and nonequilibrium control mechanisms in quantum materials relevant to quantum information science. These capabilities support studies of ultrafast spin and valley dynamics, correlated electron phases, and light-induced symmetry control in solid-state platforms, informing the design of materials and control strategies for future quantum devices. Through research participation, workshops, and mentored projects, students gain technical expertise directly aligned with national needs in x-ray science, future quantum information science platforms, advanced manufacturing, microelectronics, and energy 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.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY The mammalian brain has up to 100 trillion synapses, representing an immense potential reservoir for information storage. Most theories of memory storage in the brain assume that memories are stored by changes in synaptic strength but, despite decades of work on synaptic plasticity and neuromodulation, and tantalizing progress the best understood mechanisms that change synaptic strength have yet been shown to underlie memory storage in the intact brain. A key reason is the challenge of measuring and manipulating synaptic strength at identified synapses at the population scale during learning. These challenges can now be addressed by new technologies for imaging and manipulating synaptic function at scale during behavior. We propose here to make a major advance in synaptic manipulation. Optogenetics has revolutionized neural circuit analysis by enabling stimulation or inhibition of action potential firing in select neurons. Chemical optogenetics has extended optical control to the synapse by enabling light- activation and ilght-block of the receptors that mediate synaptic transmission, plasticity and neuromodulation. Synthetic photoswitches have been developed to control ionotropic receptors for fast signaling and G protein coupled receptors for neuromodulation. The number of receptors has expanded greatly in the past 5 years, and there has been great success in using these in the brain of awake behaving animals from flies to fish to mouse. We propose to make a quantum leap in the precision of synapse control through new schemes for targeting optical control of receptors to specific synaptic compartments and specific classes of synaptic connections. Each neurotransmitter has multiple receptors, creating great complexity. The difficulty for analysis is increased by the fact the same receptor may be found on multiple cells in a circuit and, in fact, in more than one location in a particular cell, with distinct function at each location. Our method enables us to selectively control receptors in a genetically selected manner. We now add the ability to restrict control to one compartment in the cell: say the presynaptic site, where transmitter release is regulated, or the postsynaptic site, where the response to transmitter is regulated. We add to this, methods for enhancing penetration of control light through brain tissue— a key step to reduce invasiveness of implanted fiber optics and to ease the transition of the application to larger brains. The project is made possible by an inter-disciplinary collaboration between molecular and cell biologist Isacoff and synthetic chemist Trauner, who co-developed chemical optogenetics have collaborated extensively since, physical chemist Cohen, a pioneer in upconverting nanoparticles that turn IR light into visible light, and circuit neuroscientist Lammel, an expert in optogenetic and behavioral analysis.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY The objective of this study is to determine epigenetic mechanisms that impact genomic imprinting upon in utero exposure of mice to human-relevant levels of lead (Pb). Pb is an infamous environmental exposure to human populations in the US and around the world, due in part to its neurotoxic effects. Pb exposure during early development has been linked to adverse health outcomes later in life. Preliminary data generated for this grant indicates that in utero and perinatal Pb exposure increases placenta/embryo size, and alters the DNA methylation of imprinted genes, respectively. However, the molecular mechanisms by which Pb exposure reprograms genomic imprinting during early gestation remain largely unknown. Imprinted genes are epigenetically regulated in a parent-of-origin specific manner with their mono-allelic expression driving critical periods of development. Known mechanisms of genomic imprinting include the 1) long non-coding RNA (lncRNA) and 2) insulator models, each of which program allele-specific regulation of imprinting control regions. Although dysfunctional genomic imprinting is implicated in several human diseases, the mechanisms leading to toxicant-induced imprinting dysregulation by the two models remain poorly understood. Using an established Pb exposure mouse model, this study seeks to determine in utero mechanisms that impact genomic imprinting and health effects from altered epigenetic reprogramming. Thus, female animals exposed to Pb two weeks prior to mating through 13-14 days post-conception will be used in the following Aims: 1) Determine fetal sex-specific imprinting dysregulation associated with in utero Pb exposure in mouse placenta, 2) Assess allele- and sex-specific mechanisms of in utero Pb exposure regulating genomic imprinting in the brain. Pb-exposed animals will be compared against controls to investigate genomic imprinting mechanisms in the lncRNA and insulator models by characterizing sex-, tissue-, and developmental stage-specific imprinted gene dysregulation via phenotypic, gene expression, DNA methylation, and immunohistochemical analyses. This study will reveal Pb-associated lncRNA mechanisms that inform the current epigenetic reprogramming by fetal sex. The University of Michigan provides an ideal environment to conduct the proposed research in collaboration with multiple core facilities outlined herein. The candidate will receive mentorship from a multidisciplinary team of experts to: 1) Gain proficiency in computational and statistical skills required for data analysis; 2) Acquire expertise in developmental toxicological research and mechanistic investigation; and 3) Build skills critical for leadership, teaching and mentoring, laboratory management, and grantsmanship. The proposed study will address fundamental knowledge gaps of genomic imprinting in the field to inform potential Pb-induced disease interventions. The training and research goals established in this K01 proposal constitute an exceptional foundation to ensure the candidate success in obtaining research independence.