University Of Chicago

NSF Awards · FY 2025 · 2025-09

Several types of soil bacteria can actually glide across surfaces using specialized molecular machinery. This ability has evolved in multiple groups of microbes, and understanding how it works holds potential for advancing the bioeconomy, especially in agriculture. The research integrates work of specialists in genetics, biophysics, and in cryo-electron tomography (cryo-ET) to explore how multiple rotating motors on the bacterial surface coordinate to drive a protein-based conveyor belt on the bacterial cell surface, enabling cell movement analogous to a molecular snowmobile. By identifying the location , shape, and reactivity of the proteins involved, the project will uncover the fundamental structure and mechanical principles underlying bacterial gliding, providing insights for bio-inspired technological innovations and advances in soft material robotics. The investigators will also collaborate with the Arizona State 'Ask A Biologist' program to develop interactive online educational tools to enhance public understanding and student engagement in microbiology. This research specifically examines the molecular and mechanical intricacies of the bacterial gliding machinery, emphasizing its macromolecular assembly and torque-generation mechanism. Primary objectives include determining how multiple rotary motors cooperate to propel the conveyor belt and elucidating the distribution of tension across this belt. The project also aims to identify the polymerization mechanism of the conveyor belt and the molecular basis underlying its directional control. Employing a multidisciplinary approach, the research combines genetic manipulation to elucidate protein function, biophysical assays to characterize motor dynamics and conveyor belt properties, cryo-ET for high-resolution structural visualization in intact cells, and computational simulations to model molecular interactions and dynamics. Collectively, these methods will yield comprehensive insights into gliding motility at molecular and cellular scales, substantially advancing the understanding of biological nanomotors. This project is funded by the NSF/BIO/MCB Cell Dynamics & Function Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT The extracellular matrix (ECM) is crucial for maintaining the connective tissue in all organs, supporting tissue homeostasis and repair. When tissue homeostasis is disrupted by wounds or injury, fibroblasts become activated to help repair the damage and restore tissue structure by producing fibrillar collagens and other ECM molecules. If fibroblasts produce insufficient collagen, it could lead to nonhealing, chronic wounds. Conversely, excessive collagen synthesis can lead to fibrosis and impairment of organ function, a major healthcare challenge. How fibroblasts maintain the intricate balance between underproduction and overproduction of ECM remains an important yet unresolved question. A key challenge is understanding how fibroblasts regulate the production of ECM proteins, particularly collagen. An important but often overlooked aspect of ECM production is that fibroblasts must meet substantial metabolic demands to produce ECM biomass. These metabolic requirements differ from those for cellular biomass generation during proliferation, including a high demand for the non-essential amino acids glycine and proline for synthesizing collagens. We and others have demonstrated that activated fibroblasts upregulate nutrient uptake and metabolic flux into glycine and proline biosynthesis and that these pathways are required for collagen production. Despite these advances, little is known about how fibroblasts meet the metabolic demands of ECM synthesis physiologically during tissue repair. To address this gap, my research program aims to determine the physiological nutrients and metabolic pathways required for fibroblast ECM synthesis during tissue repair. The central question we will address over the next five years is: how do fibroblasts meet the nitrogen demands of collagen synthesis to drive tissue repair while tolerating toxicity from accumulating reduced nitrogen during this process? To answer this question, we have developed a novel experimental platform to trace nutrients directly into the ECM under physiological conditions. Leveraging this model, we will: (1) develop a long-term in vivo stable isotope tracing platform to determine the nitrogen sources for collagen synthesis during tissue repair; (2) understand how ECM synthesis associated metabolic rewiring allows fibroblasts to tolerate the accumulation of toxic metabolic waste products. Achieving these goals will provide fundamental insights into cellular metabolism and tissue repair and help lay the groundwork for strategies to modulate nutrients and their associated metabolic pathways to overcome healthcare challenges associated with insufficient or excessive ECM synthesis.

NSF Awards · FY 2025 · 2025-09

This project aims to shed new light on some of the most fundamental constants in mathematics, such as pi, by uncovering their hidden arithmetic structure. In a major breakthrough, the principal investigator and collaborators recently proved that a certain Dirichlet L-value—a class of numbers that generalizes pi—is irrational, marking only the second such result since the 19th century. These numbers, known as periods, arise from definite integrals and appear throughout mathematics, from number theory to geometry and physics. By advancing the understanding of their irrationality, this research targets one of the deepest and most enduring mysteries in mathematics: what kinds of numbers naturally arise from geometry, and how well can they be approximated by rational numbers? At the same time, the project seeks to illuminate profound connections between geometry and analysis, by deepening the understanding of the Langlands program and its vision of a unified mathematical landscape. The project pursues two major directions. The first develops new methods to study the irrationality of periods, with the goal of proving landmark results such as the irrationality of Catalan’s constant and improving bounds on how closely pi can be approximated by rational numbers—mirroring famous theorems about algebraic numbers. The second builds on the principal investigator’s recent modularity theorem for a positive proportion of genus 2 curves over the rational numbers—the first of its kind—and aims to extend these results to all such curves, advancing a long-standing frontier of the Langlands program. Together, these efforts aim not only to resolve classical open problems, but also to bring new conceptual clarity to the structure of mathematics itself. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The origin of Ultra-High Energy Cosmic Rays (UHECR) is a long-standing mystery. These extraordinary particles, with energies up to ten-millions times higher than those achieved in particle accelerators, are so rare that their detection required the construction of the Pierre Auger Observatory, covering 3000 square kilometers in Argentina. The world’s largest cosmic ray detector has recently completed an upgrade, which will be exploited in this award by the University of Chicago group. Making progress on the puzzles of UHECR may radically change our understanding of the most extreme astrophysical objects in the Universe, of the magnetic fields which permeate it, or of particle physics. Immersive research experiences and engagement of the local community and partnerships will bring UHECR science to both formal and informal audiences. These activities include engaging high school students in a stimulating research environment, reaching out and communicating science to older adults, and partnering with Planetariums. Data from the Auger Observatory are publicly available to a broad community – comprising both professional and citizen scientists – encouraging exploration for educational and outreach purposes, fostering scientific literacy and collaboration. The large statistics collected to date by the Pierre Auger Observatory, along with new data from its upgrade, provides unique scientific opportunities and the world-leading precision measurements of the ultra-high energy (UHE) cosmic ray spectrum, composition, and arrival directions. The next operating period for the observatory is set to tackle a number of key questions including the origin of the observed features in the energy spectrum, the sources of UHE cosmic rays and the search for new physics at particle energies above 100 TeV. The observatory has recently completed an upgrade, AugerPrime, and combined with larger statistic data sets and planned improvements in the analysis methods, significant enhancement in the ability to address the above questions is anticipated. The research supported by this award will exploit these data through novel analysis methods – including Machine Learning - to advance composition reconstruction and further composition-assisted anisotropy studies. In addition, the installation and operation at the Auger Observatory of low-cost Fluorescence telescopes of new design will validate a concept for next-generation observatories and allow for cross-calibration of the energy scale of the measured UHECRs. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project builds the next-generation Python Simulations of Chemistry Framework (PySCF) software platform to make electronic structure simulations faster, more robust, and more accessible to computational scientists across many disciplines. The new cyberinfrastructure will enable researchers to better understand the behavior of complex molecules and materials, which plays a crucial role in advancing energy technologies, catalysis, drug discovery, and quantum materials. By harnessing modern computing architectures such as graphics processing units (GPUs) and developing advanced quantum chemistry algorithms, the project will significantly speed up large-scale quantum simulations while reducing computational cost. The project will also produce user-friendly interfaces, manuals, tutorials, and training materials to support education and workforce development in computational science. As an open-source and extensible platform, the PySCF software will catalyze innovation across a broad research community, including chemistry, physics, materials science, artificial intelligence (AI), and quantum information science. First-principles simulations play an essential role in chemistry and materials research, yet the user adoption of more robust electronic structure methods has been hindered by the lack of open-source, high-performance, and user-friendly software infrastructure. The sustained innovation of new quantum chemistry tools is also often hampered by high code complexity and limited extensibility of existing software implementations. This collaborative project addresses these fundamental challenges by advancing the PySCF framework to deliver high-efficiency electronic structure tools and an extensible method development platform. Specifically, this project will develop GPU-accelerated quantum chemistry infrastructure, a low-rank density fitting engine to exploit sparse tensor structures, and a quantum embedding library to enable simulation of complex systems. By incorporating automatic capabilities such as autodifferentiation and designing reusable and modular libraries, this project will substantially lower the barrier for developing quantum chemistry methods and incorporating electronic structure components into AI workflows. Furthermore, a wide selection of cutting-edge stochastic and multireference methods, such as auxiliary-field quantum Monte Carlo and complete active space perturbation theory, will be implemented and integrated with new acceleration techniques. Overall, this project will open new frontiers for accurate and scalable simulations of molecules and materials. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry in 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 2025 · 2025-09

Research using observational data and natural experiments relies on statistical analysis to provide reliable results. This project develops new methods to help data analysts test hypotheses about the causes of observed outcomes. The team improves statistical methods in a practical way that can be widely adopted by researchers, business analysts, policy analysts, and others who want to isolate the effects of changes in business and/or government methods, policies, and regulations. This award funds development of (a) computationally simple methods for sharp identification of causal parameters, (b) good estimators for the bounds on partially identified parameters, (c) computationally reliable methods to derive identifying restrictions, and (d) translational research through a publicly available code library that implements the methods and makes these advances available to the broad community that uses statistical tools to conduct program evaluation. The research advances knowledge by developing a unified framework for identification, counterfactual prediction, and specification analyses for potential outcome models through two subprojects. The first subproject uses a new approach, based on random set theory, to bound counterfactuals of interest in a class of potential outcome models. Crucially, this approach avoids computing the sharp identified set for the joint distribution of potential quantities, which is often intractable. The team obtains simple closed-form solutions in several well-studied settings where the bounds have previously been expressed through high dimensional linear programs or intractable optimization problems. The second subproject derives sharp testable implications of the modeling assumptions in a class of potential outcome models. So far, such testable implications have been studied case-by-case in a limited set of models. Using a novel graph-based representation of the model, the team provides a systematic way of deriving sharp testable implications of commonly used identifying assumptions. The research achieves broader impacts through those who conduct empirical research and program evaluation via a translational research component. The team provides practitioners with an accessible “guided tour” of the existing results, focusing on implementation. The guide discusses which of the available approaches (moment inequalities, support functions, linear programs) leads to the most tractable description of the identified set and provide guidance on estimation and inference procedures. Furthermore, the PIs develop a Python library associated with the guided-tour paper and the subprojects described above. The library is accompanied by “hands-on” tutorials hosted on a GitHub repository. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Updated Abstract Text: Perhaps the biggest challenge in designing and implementing a data management & sharing strategy for HEAL is its broad range of programs, projects and types of data being collected. While individual programs or projects are supported by Data Coordinating Centers (DCCs) and Data Management Centers (DMCs), broad sharing of HEAL data will require a platform that can link together the DMCs and extend their reach while working smoothly with them. The range of HEAL data types is extensive, spanning multiple measurement modalities (e.g., clinical data, bioassays, wearables and self-report data) as well as varying in size and complexity. In addition, the range of scientific disciplines represented not only among the HEAL investigators but among other researchers likely to use HEAL data, together with the range of scientific questions that might be pursued, also have implications for how data should be organized, documented and made accessible to maximize their scientific value. Specifically, a single monolithic system is likely to fail, or at least be sub-optimal. At the same time, asking all programs or projects to build their own systems conforming to a set of common requirements would be both expensive and unsustainable. We propose to build and maintain a HEAL Platform that will interoperate with the DMCs to enable discoverability and access of HEAL data stored in the DMCs or other repositories. The platform will allow search and query of the HEAL metadata, and selected data, within and across studies, and provide access to a secure, scalable computing environment to conduct independent analysis on HEAL data along with the user’s own data.

NSF Awards · FY 2025 · 2025-09

Phytoplankton are tiny organisms that form the base of food webs in lakes, rivers, and oceans, and sometimes cause harmful algal blooms. Understanding how phytoplankton respond to changing temperatures is crucial, but we currently lack the knowledge to predict their future state. Our project investigates a type of phytoplankton called cyanobacteria that thrive in hot springs. We will analyze their genetic adaptations and responses to temperature changes, both over short timescales and across long-term laboratory evolution. This will help us uncover how they survive extreme heat. We will then test how well our findings apply to cyanobacteria in freshwater sources across the U.S. This research will help predict which phytoplankton are most vulnerable to warming and explore ways to engineer heat-resilient cyanobacteria that produce supplements, biofuels, and other valuable products. We will also create educational programs to train future scientists in cutting-edge biological data analysis and engage the public in how microbiology can inform our understanding of life on earth. Phytoplankton responses to warming are mechanistically poorly understood, limiting our ability to predict their future fitness, forecast harmful algal blooms, or cultivate them effectively for bioproducts. This project aims to elucidate thermal adaptation mechanisms in cyanobacteria by integrating heat stress responses with eco-evolutionary processes. We will leverage thermophilic cyanobacteria that evolved across natural temperature gradients approaching the upper thermal limit for oxygenic photosynthesis, likely leaving strong genomic signatures of thermal adaptation. First, we will identify genomic features—including amino acid frequencies and gene content—that predict optimal growth temperature (OGT) in cyanobacteria. We will then test these models in mesophilic cyanobacteria and metagenomic data from freshwater samples, including NSF’s NEON data, to identify thermally maladapted species. Second, we will distinguish adaptive from maladaptive heat stress responses. This involves analyzing transcriptomic and metabolomic responses of isolates with varying heat-stress survival, using sparse canonical correlation analysis to link gene expression patterns with metabolite profiles and thermal tolerance. Third, we will investigate the role of horizontal gene transfer (HGT) in rapid thermotolerance evolution through laboratory selection experiments introducing thermotolerant donor DNA to maladapted strains and comparing these findings to the contribution of HGT in natural populations. This research will generate novel, testable insights into cyanobacterial thermal adaptation, providing frameworks for predicting phytoplankton traits from genomes, engineering thermotolerance in industrial strains, and utilizing HGT-facilitated artificial evolution. Furthermore, we will develop workshops to train students in advanced biological data analyses and engage in outreach to inform the public about the importance of algae and cyanobacteria. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary During embryogenesis, retinal ganglia cells (RGCs) from both eyes grow axons towards the brain to establish proper vision. Disruptions in this process can lead to permanent visual defects. Despite its importance, we lack comprehensive models describing how 100,000’s of RGC axons faithfully grow in the proper direction. Most current approaches sample few (<1%) of RGCs and little of the surrounding cellular milieu. We propose to address this gap by leveraging advances in large volume serial electron microscopy (vEM) connectomics and genetic labeling techniques to produce nanoscale 3D maps of how every individual RGC axon develops embryonically from optic nerve to tract during and their resulting adult organization. We will label functional subgroups of RGCs to test hypotheses functionally similar axons travel and develop together. This project will generate unprecedented data on the formation of an critical component of the early visual system, laying the groundwork for targeted interventions aimed at preventing or reversing developmental circuit abnormalities or damage.

NSF Awards · FY 2025 · 2025-09

This project explores the origins of our Milky Way galaxy by identifying and analyzing stars in its “stellar halo”, which is composed of remnants of small dwarf galaxies that were torn apart by the Milky Way’s gravity long ago. A team of scientists at the University of Chicago will use data from the Sloan Digital Sky Survey (SDSS) to study the chemical fingerprints of these stars. The goal is to reconstruct the Milky Way’s early assembly and search for signatures of the first supernovae in the universe. Undergraduate students will carry out significant portions of this research as part of an astronomy field course at the University of Chicago. The project also includes a collaboration with a non-R1 institution to engage students in studying pulsating halo stars. Educational materials from both efforts will be publicly shared through the SDSS Education and Public Outreach platform. The halo stars will be selected from the SDSS-V halo star survey, which uses low-resolution spectroscopy of photometrically metal-poor stars to measure iron and alpha-element abundances. Most halo stars come from a few massive accretion events, which are already well-characterized. This project instead focuses on the frontier of lower-mass accreted galaxies, traced by low-alpha, metal-poor stars (LAMPS). Approximately 400–500 LAMPS will be followed up with high-resolution optical spectroscopy, enabling the measurement of abundances for 20+ elements per star. These data will support studies of r-process nucleosynthesis, Type Ia supernova origins, and potentially remnants of Population III explosions such as pair-instability supernovae. Undergraduate students will be fully integrated into the research process, from target selection to observing and abundance analysis. The PI will also collaborate with a Faculty and Student Team at California State University San Bernardino to measure the Milky Way’s mass using RR Lyrae stars and develop tutorials for accessing public SDSS-V data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The PI will explore new structures in higher algebra, in collaboration with Dr. Shachar Carmeli at the Weizmann Institute of Science, Israel. This is an emerging field that combines ideas from classical algebra and modern homotopy theory. Classical algebra studies systems like the real numbers, with operations such as addition and multiplication that satisfy several rules (such as associative and distributive laws). Higher algebra studies structures in which these equalities are replaced by coherent witnesses, called homotopies. Over the past several decades, mathematicians have discovered that many important algebraic structures can be refined in this way, leading to many applications to other disciplines, such as mathematical physics and foundations of computer science. The project will study such higher structures in the subfield of stable homotopy theory. Moreover, the project will support the training and development of junior mathematicians in the field. The project aims to use methods from algebraic K-theory and power operations to study chromatic homotopy theory. Chromatic homotopy theory studies questions in stable homotopy theory (e.g., stable homotopy groups of spheres) via tools arising from the algebraic geometry of formal groups. Recently, categorical and K-theoretic techniques play an increasing role in the subject. The PI and collaborator intend to study the chromatic localizations of K-theory and other invariants of ring spectra, and relate them with recent advances in p-adic geometry such as the new theory of prismatization. This collaborative US/Israel project is supported by the Division of Mathematical Sciences of the US National Science Foundation and by the Israeli Binational Science Foundation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT A critical function for all living organisms is the ability to move when needed. These movements–intracellular trafficking, cell division, muscle contraction, and cell motility–are driven by molecular machines that exert an amazing amount of force considering that they are only a few nanometers across. Given the variety of motor proteins in the cell, a key question is how motors cooperate and compete while moving cargoes and applying forces. An emerging paradigm is the notion of specialized motors, or motors that are fine-tuned to perform a specific function. Despite the importance of these motor proteins, relatively little is known about their individual adaptations and how these relate to the motility patterns found in the cell. This work focuses on myosin-10 and its unique ability to navigate to a specific location in the cell. Myosin-10 delivers essential cargoes such as integrins, cadherins and netrin receptors to filopodia at the leading edge of the cell. This transport function plays a pivotal role in migrating cells, both in normal developmental biology and in metastasizing tumor cells. To navigate the cell, myosin-10 walks along multiple filaments in the fascin-actin bundle found at the core of the filopodium, and effectively ignores other actin filaments in the rest of the cell. The proposed work will study how ligand binding, myosin quaternary structure, and actin filament architecture all tune myosin-10 motility. The approach will integrate structural biology techniques applied to full-length myosin-10, coupled with a comprehensive and systematic investigation of the ligands that lead to full activation and processive motility. This proposal will test the hypothesis that myosin-10 is regulated by head-to-tail autoinhibitory interactions that are relieved by phosphorylation and, potentially, cargo binding. Completion of this study will yield a molecular mechanism for cytoskeletal motor protein navigation in the cell. It will further define the general principles that determine how all types of cytoskeletal motors engage cargo, activate, and navigate. Motor protein activation and navigation is a process of fundamental biological importance but is poorly understood. This work will direct future efforts to understand and control motility in multiple contexts.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Epigenetic modifications of DNA, including DNA methylation (DNAm), play a central role in determining cell identity and function, and epigenetic perturbations contribute to aging and disease. Characterizing variability in human epigenomes and the causes of such perturbations is critical for understanding disease mechanisms. “Epigenome-wide” studies of DNAm have demonstrated that effects of genetic variants on DNAm are pervasive in the human genome (i.e., methylation quantitative trait loci, mQTLs), suggesting epigenetic mechanisms of disease susceptibility. These studies have also identified loci where DNAm is associated with age, exposures, and disease, enabling the development of “epigenetic clock” algorithms, exposure signatures, and potential biomarkers of disease. However, these prior studies have (1) focused on a limited number of tissue/cell types and (2) captured a small fraction of the ~28 million CpGs in the human genome. Thus, we currently lack a truly whole-genome resource to study DNAm in large numbers of non-blood human tissue samples. Large studies of DNAm in diverse tissue types are needed to improve our understanding of variability in human epigenomes and its determinants. In response to this need, we propose to create a multi-tissue DNAm data resource using samples obtained from tissue donors by the Genotype-Tissue Expression (GTEx) Project. The GTEx data resource currently provides genome-wide data on genetic variation and gene expression for >15,000 tissue samples from >900 donors (>50 tissue types), data which has provided an unprecedented view of tissue-specific gene expression and regulation in humans. Complementary data on DNAm would provide an integrated view of genetic effects on both transcription (eQTLs) and the epigenetic background on which transcription occurs (mQTLs). A multi-tissue DNAm data resource would also support many research questions that rely on comparable DNAm data across tissue types, including research on aging, environmental epigenetics, and genome structure and function. Our first aim is to provide genome-wide data on DNAm for ~2,100 GTEx tissue samples (~210 samples for each of 10 unique tissue types) to the scientific community, using whole-genome bisulfite sequencing. Our second aim is to create and disseminate a comprehensive catalog of genetic effects on DNAm in human tissues (mQTLs). We will present these results on the GTEx portal, leveraging existing data visualization tools. Our third aim is to promote the use of this DNAm data resource. We will provide online tutorials on data access and GTEx portal features, host workshops at scientific meetings that will train data users, monitor use of the resource, and respond to the needs of data users. The unique multi-tissue DNAm data resource we propose to create will be of broad interest to the genomics community and relevant to many diseases of public health importance. We anticipate this resource will be highly complementary to existing GTEx data and will greatly enhance the long-term impact of the GTEx project on biomedical science and human health.

NSF Awards · FY 2025 · 2025-09

The Douglas-fir tussock moth and the spongy moth are insect pests that defoliate forests in North America, causing millions of dollars of damage every year, but damage would be far worse if not for the mortality caused by insect-killing viruses. Models that could predict how and when insect viruses will protect forests from defoliating insects would be invaluable for protecting forests. The creation of accurate models is hampered by the computational difficulties of using data to create realistic models, and by the logistic difficulties of collecting sufficient data to determine the best models. The investigators have recently developed a new class of interpretable machine learning algorithms that can discover the best mathematical models directly from data, even if the data are sparse and noisy, as ecological data usually are. In this project, the investigators will advance these methods to work with insect host-pathogen data. The ultimate goal is to rapidly provide robust, evidence-based models for guiding the management of pests of American forests. This project will foster a variety of inter-disciplinary mathematical biology and quantitative ecology research experiences for graduate and undergraduate students. Students in high school and university communities will be trained through the project outreach activities. The goal of this work is to advance Weak form Scientific Machine Learning (WSciML) theory and methodology, expanding its capabilities in model discovery and parameter inference to answer critical questions in disease ecology, with applications in the use of pathogens to control pest insects. The central premise of the research is that faster and more robust parameter estimation algorithms and automated model discovery methods will dramatically enhance the usefulness of general models of host-pathogen dynamics for guiding the microbial control of forest pests, as well as enabling accelerated scientific discovery more broadly. This project builds on a close collaboration between the investigators, whose collective research expertise spans applied mathematics, computational statistics, disease ecology, and forestry. The project aims to transform parameter inference and equation discovery from forward-solver discretizations, which take months of computing time, to data-driven weak form computations, which take seconds to minutes of computing time. Modern weak-form methods are superior in accuracy, robustness, and computational efficiency, but have not been sufficiently developed to be of practical use in ecology. The WSciML methods that are developed will be tested by using them to understand how host and pathogen variation drive the spread of insect pathogens, thereby testing whether WSciML can handle the sparse observations, non-Gaussian errors, and other problems that have prevented the effective use of insect pathogens for protecting forest health. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Dark matter is a mysterious substance that makes up most of the matter in the Universe, but it has never been seen directly. To uncover this cosmic enigma, scientists are conducting the XENONnT experiment. This experiment uses a detector filled with nearly nine tons of ultra-pure liquid xenon to search for extremely rare interactions that could help us understand what dark matter is composed of. XENONnT is the last experiment in the international XENON Dark Matter project, which has received support from the National Science Foundation since it began. This project creates a rich environment for educating students and researchers in the U.S. and around the world, with more than twenty institutions collaborating globally. The scientists working on this project are trained in advanced science and technology that cover multiple disciplines. The specialized tools and techniques they use, along with advanced data analysis and statistical methods, are not only important for understanding dark matter but also have significant applications in fields like medicine, nuclear safety, and data science. Candidates for the dark matter which dominates the matter content of the Universe span decades in mass and interaction cross-section with normal matter. The class of Weakly Interacting Massive Particles (WIMPs) has been the most studied theoretically and experimentally with indirect and direct searches as well as at the Large Hadron Collider. The sensitivity for WIMPs direct detection has increased by many orders of magnitude in the past twenty years thanks to experiments using liquid xenon in dual-phase time projection chambers with increasing target mass and decreasing background. The phased XENON Dark Matter project has led the direct detection field with its XENON10, XENON100 and XENON1T experiments and has paved the way to the current generation of multi-tonne scale liquid xenon detectors, including the largest of the XENON detectors, XENONnT with 6 tonnes of active target. The unprecedented ultra-low background achieved by XENONnT, the lowest among all direct searches, has enabled a sensitive search not just for WIMPs but also other rare interactions, such as the recent first observation of coherent elastic neutrino-nucleus scattering from solar B-8 neutrinos. This award will enable the XENON US groups to continue to contribute to the operation of the experiment at the Italian Gran Sasso Underground Laboratory (LNGS) and to continue to lead several science analyses using the data acquired to-date with the XENONnT detector. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY We work at the interface of chemistry and biology. Our research aims to understand how protein post- translational modifications (PTMs) and the enzymes involved contribute to cancer signaling and immune regulation, and how these PTMs, enzymes, enzymatic reactions, and related cellular processes can be exploited for targeted therapy. One of the PTMs that we are specifically focusing on involves protein crosslinking mediated by Lysyl Oxidase (LOX) family enzymes. LOXs, including LOX and four LOX-like proteins (LOXL1-4), are copper- and lysine tyrosylquinone (LTQ)-dependent amine oxidases. They catalyze the oxidative deamination of lysine residues on proteins, leading to the production of highly reactive aldehydes. These aldehydes form cross- linkages essential for the structural integrity of collagens and elastin within the extracellular matrix (ECM). While these processes are vital for tissue development, excessive LOX activity is linked to fibrotic and musculoskeletal diseases and is implicated in nearly all cancer types. LOXs are believed to promote malignant transformation by increasing ECM component secretion, stabilization, and crosslinking, thereby inhibiting drug and nutrient diffusion, and effector immune cell infiltration. High LOX expression in pre-malignant tissues and primary tumors correlates with increased tumor incidence, invasiveness, and poor patient outcomes. LOXs are significantly upregulated in a subset of solid tumors resistant to chemotherapy, radiotherapy, and immunotherapy, highlighting their crucial role in cancer pathophysiology and treatment resistance. Despite their importance, significant research gaps remain: 1) the identification of LOX substrates is limited due to a lack of technologies for profiling LOX substrate specificity and real-time activity in vivo; 2) there is minimal exploration of LOX functions in cancer progression beyond ECM remodeling; and 3) there is a scarcity of research on developing selective and effective LOX-targeting strategies, crucial for improving therapeutic efficacy and safety in cancer treatment. Our research aims to address these gaps by developing enabling technologies and novel inhibitors or probes to thoroughly investigate LOXs, exploring their biological functions and assessing their therapeutic potential in combination therapy. This initiative, spearheading advancements in technology development, biological study, and therapeutic intervention, is poised to establish a robust foundation for our laboratory's research endeavors for the foreseeable future.

NSF Awards · FY 2025 · 2025-09

RNO-G is a research project that looks for radio signals created by neutrinos when they interact with polar ice. It is the first ultra-high energy neutrino observatory that can observe the Northern sky. This grant will allow the project to expand by adding new modular stations, with the aim of doubling its current level of sensitivity to these particles. The funding will support the building of antennas and systems to collect data. To set up each new station, researchers will drill three deep holes and install equipment that can work on its own. Enhancements to the experiment will ensure it can achieve its intended sensitivity and function for ten years after the array is finished. During this time, it will gather reliable data that will be shared with the wider community studying multimessenger astrophysics. Neutrinos can probe extreme conditions in astrophysical objects throughout the universe. This award will expand the currently operating Radio Neutrino Observatory in Greenland (RNO-G) which can observe neutrinos in a new energy scale. When combined with observations from other messengers like photons, cosmic rays, and gravitational waves, observations of neutrinos made with RNO-G can further advance our understanding of the most powerful cosmic ray accelerators and explosive events in the universe. This award will introduce the general public and students to particle astrophysics through workshops, research opportunities, and outreach events and provide infrastructure and engineering opportunities in Greenland. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Actin-binding proteins (ABPs) direct and meticulously regulate several actin-related cellular processes. Actin- related protein (Arp)2/3 complex is an ABP that nucleates “daughter” branches from existing “mother” filaments which push against the plasma membrane to facilitate endocytosis and motility. Dysfunction is implicated in cell invasion and metastasis in which Arp2/3 complex and branch stabilizers such as cortactin are overexpressed. In filamentous (F-)actin, the bound nucleotide state and rates of transition between states function as a biological clock to mark the age of the filament. Like F-actin, the Arp2 and Arp3 subunits of Arp2/3 complex bind ATP which is necessary for the nucleation of the daughter filament. However, the role of ATP hydrolysis and subsequent phosphate release in both Arp subunits on the aging and dissociation of actin filament branches is still being explored. In this work, I propose using novel computer simulations to obtain a mechanistic understanding of three key processes in the aging of actin branch filaments, focusing on the role of the nucleotide state bound to Arp2 and Arp3 on branch aging and dissociation. ATP hydrolysis is important for branch dissociation, but not necessary for branch formation. Differences in the propensities for ATP hydrolysis in Arp2 and Arp3 based on the protein source, the presence of the daughter and mother filaments, and Arp2/3 complex activation method indicate that multiple factors may influence hydrolysis rates. Importantly, the outlined factors consist of large protein complexes which preclude conventional quantum mechanics/molecular mechanics (QM/MM) methods. I will develop a multiresolution computational framework to obtain thermodynamics, kinetic, and mechanistic insights into ATP hydrolysis in the Arp2 and Arp3 subunits, respectively, in the context of the coarse-grained protein environment. I will focus on the role of the mother and daughter filament in facilitating the rearrangement of key amino acids in the active sites of Arp2 and Arp3. Phosphate release in Arp3 substantially decreases the mechanical stability of branches under force. The release process itself is very slow with lifetime estimates ranging from <1 minute to >80 minutes depending on the source. I will employ novel enhanced sampling methods with all-atom molecular dynamics (MD) simulations to investigate the kinetics and mechanism of release in Arp2 and Arp3. Differences in amino acids of the exit channel and backdoor gate of F-actin, Arp2, and Arp3 may influence the relative rates of this process. Finally, debranching under force will be modeled with MD simulations to obtain a molecular-level understanding of the mechanism and its dependence on the nucleotide state of Arp2 and Arp3. This is important to understand because it has direct implications for the fate of the branch and its ability to regenerate. Cortactin will be incorporated into the model to determine how branch stabilizers may influence the debranching mechanism. The outcome of this work will provide a cohesive mechanistic understanding of ATP hydrolysis, phosphate release, and branch dissociation which is critical to evaluate the role of Arp2 and Arp3 in growth and turnover of branches.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT This application is in response to PA-20-272: Administrative Supplements to Existing NIH Grants and Cooperative Agreements (Parent Admin Supp Clinical Trial Optional). Summary of parent-award Specific Aims. The Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) was assembled in 2012 to increase the understanding of lower urinary tract symptoms (LUTS). Related to this goal was the improvement of patient-centered assessment of LUTS. This work involves improving the self-report measurement of LUTS using new, high-quality items to be used in questionnaire assessments. These items were created with qualitative input from patients, community participants, internists, urologists, urogynecologists, and clinical researchers. This set of items is referred to as the Comprehensive Assessment of Self-Reported Urinary Symptoms (CASUS). The original aims of the grant were Aim 1: To refine and expand a clinically-relevant cluster model across a cohort of patients with LUTS. Relative to LURN I, participants will have a wider range of symptom severity and be characterized using novel measures of bladder and urethral function; Aim 2: To identify protein biomarker signatures contained within plasma of specific subgroups of men and women with LUTS; Aim 3: To determine phenotypic characteristics of women with LUTS by measuring the functional components of the lower urinary tract; Aim 4 To validate comprehensive outcome tools for men and women with LUTS; and Aim 5: To determine influences of stress and mental health on LUTS. In this supplement, we will focus on an orderly closeout of the project, which includes completion of manuscripts as well as curation of data consistent with open science.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Membraneless organelles (MLOs) are prevalent in eukaryotic cells, with a typical size of 1-200 nm to several microns. Through phase separation, MLOs can be assembled under native condition or upon external stimuli, based on either scaffold proteins or scaffold RNAs. “Client” biomolecules, including both RNAs and proteins are further recruited to these MLOs to form localized proteome and transcriptome. MLOs spatially organize RNA- protein interactions and coordinate biochemical reactions. They are widely involved in processing essential cellular RNAs, assembling ribonucleoprotein complexes, regulating RNA metabolism, and responding to cellular stress. Changes in the morphology or the residing RNA and protein components of MLOs are often found to be associated with aging, infection and various human diseases. Despite the essential biological functions of MLOs and potentials of targeting MLOs for therapeutics, adaptable and sensitive methods for efficient transcriptomic characterization of MLOs, in both native and pathogenic samples, are still lacking. Many MLOs contain internal sun-domains or display layered structures. However, the functions of intra-MLO organization in most MLOs are unclear, presenting a gap in our understanding of MLO biology. Do transcripts differentially occupy different subdomains in MLO? Does localization to different subdomains differentially impact the RNA metabolism? These are outstanding questions that have never been addressed, due to the lack of tools with the capability of transcriptomic characterization at subdomain resolution. This proposal aims to fill the critical technical gaps by providing a new platform for transcriptomic mapping of MLOs and intro-MLO organization. A key innovation of our methods is the targeted in situ reverse transcription, with an engineered reverse transcriptase, which can be localized to a specific MLO through a protein targeting module (via protein-antibody interaction), or an RNA targeting module (via RNA-MS2 coat protein interaction). Our new methods have several key advantages: (1) with enhanced and controllable spatial resolution to allow subdomain characterization; (2) highly modular to allow broad characterization of MLOs with either marker proteins or RNAs, and of different physical dimensions; (3) highly adaptable to different sample types by avoiding any requirement of genetic manipulation; and (4) highly efficient for the potential application to tissue samples including clinical samples, and even potentially to the single-cell level. Taking advantage of the new methods, together with super-resolution imaging, we will for the first time perform sub-organelle transcriptomic characterization of nuclear speckles and nucleoli, two prominent nucleus-localized MLOs. These results will reveal differential intra-organelle localization of transcripts and provide critical functional insights for internal organization of these MLOs.

NSF Awards · FY 2025 · 2025-08

This project addresses two aspects of topology as it is conventionally understood, through the study of particular cases that seem ripe for attack: The first is that it tends to be a qualitative subject, which typically produces statements that certain kinds of objects or deformations exist. It does not tell us how complicated such an object is, nor how much of some resource (think energy) is necessary to expend in producing the deformation. The second aspect is that for many problems, topology progresses by reduction to algebra -- and the algebraic problems are themselves extremely difficult. In some cases, this reduction can better be thought of as a reformulation of the problem in very different terms, but not necessarily easier ones. The PI will work with and mentor younger researchers on these projects, present his findings at conferences, and work on building bridges to other disciplines. The central attack envisioned in this project is to use analytic methods that are already known to connect to ring theoretic constructions in some cases (square integrable cohomology, and the Betti numbers defined for them using von Neumann algebras, pioneered by Atiyah) to study problems related to the number of handles necessary for manifold representatives of homology classes and how that contrasts with earlier work of the PI on the number of simplices (or volume). This will involve, in the case of lattices, representation theory of semisimple Lie groups. Within pure topology, the PI aims to apply this work in various directions, such as knot theory, open book decompositions and group actions on manifolds. Given the wide use of square integrable cohomology, the range of applications will be much wider. On the applied side, to the extent that this project also advances understanding of quantitative aspects of topology, it will have applications in several other scientific fields, as this is one of the bottlenecks to applications of topology. 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 develops methods for the qualitative and quantitative study of nonlinear first- and second-order partial differential equations (PDE) set in finite and/or infinite dimensional state spaces arising in science and engineering, and some of their applications. The emphasis is on (i) the theory of mean field games (MFG) and the techniques associated to them including the well-posedeness of nonlinear PDE in infinite dimensional spaces and applications to mean field limits, large deviations of random matrices, filtering, information acquisition, deep learning and economics; and (ii) viscosity solutions techniques to study traffic models and goal-based stochastic control of portfolio selection with time inconsistency and mental accounting. The project aims at creating a multifaceted platform for theoretical advances across the areas of mean field games, PDE set in infinite dimensional spaces, large deviations of random matrices, mean field limits of interacting particle systems with singular interactions, deep learning, filtering and control of partial information, robust control, time-inconsistent stochastic optimization, and the modeling for applications in asset pricing, traffic management, information acquisition, personalized goal-setting optimization and mental accounting. The analysis of the proposed applications will first require a deeper theoretical understanding that will prompt the formulation of new mathematical questions and their study. It will also generate substantial synergies among distinct areas, bringing together expertise from different areas of analysis and probability. The project cultivates various collaborations with both established and early-career researchers, including graduate students. The project is concerned with the development pf the mathematical methodologies and innovation and the modeling required to study many applications of current interest in several diverse fields like filtering and control of partial information, assessment of risk and model ambiguity in economics, information acquisition, asset allocation with time inconsistency and mental accounting, random matrices, traffic modeling, and, finally, deep learning. Most of the applications considered here are described using either deterministic and stochastic control in finite and infinite spaces (mean field games of control) or mean field games. These are very active areas of research with many open mathematical problems and questions. Some of the mathematical difficulties that this project focus on are the development of the methodology to study the well-posedness of solutions to PDE set in spaces of probability measures and to systems of backward-forward equations, the understanding of mean field limits for models with singular dynamics, the study of large deviations of random matrices, and the rigorous mathematical description of some of the algorithms of deep learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The meandering variability of upper-tropospheric jet streams have a strong influence on the surface weather of the middle latitudes, and very large meanders of the jet streams lead to extreme surface weather including heat waves, cold air outbreaks, and heavy precipitation. Classical linear wave theory can account for jet meanders but the theory assumes that the meanders have small amplitude, an unfortunate limitation since the meanders that matter for extreme weather are quite large. In earlier NSF-funded work the Principal Investigator (PI) pioneered a theory of wave dynamics which overcomes the small-amplitude constraint of linear theory and is thus better suited to real-world weather. The theory is based on a conserved quantity called Local Wave Activity (LWA), a measure of the waviness of the atmospheric circulation that can be used to identify the dynamical mechanisms responsible for important forms of wave behavior in the jet streams. Work performed here uses the LWA framework to address three longstanding problems in middle-latitude atmospheric circulation dynamics, the first of which is the suppression of cyclonic weather activity in the storm zones of the North Pacific and North Atlantic, which is typically weaker in midwinter than in October and March despite seemingly more favorable conditions for cyclogenesis. The work is guided by the hypothesis that the key issue is the exchange of LWA between weather systems and lower-frequency flow variations. The second problem is the unexplained dynamics that leads to Sudden Stratospheric Warmings (SSWs), in which the stratosphere over the North Pole warms dramatically as the stratospheric circulation flowing around it breaks down. The work focuses on the role of wave resonance, with different resonant modes responsible for different forms of SSWs (splitting versus displacement). The third problem is explaining the duration of blocking events, in which a high-pressure center forms in the upper troposphere and persists for many days as the air flow moves around it. The LWA framework is used to understand the lifecycle of the block and the various factors, including condensational heating in clouds, that could contribute to its persistence. The work is of practical as well as scientific interest given the potential for all of the above phenomena to create extreme weather. In addition, the work contributes to the efforts of the Model Diagnostics Task force of the National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory. The project also has educational value through its support of graduate students and a postdoctoral research fellow. 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

In this R01, we intend to test the impact of basic income guarantee on the lives of young people living with HIV. The proposal will build off prior basic income guarantee programs in the city of Chicago such as the Chicago Resilient Communities Pilot. Our trial is among the first basic income guarantee interventions to test the impact of a basic income on clinic engagement and viral suppression. Other programs for people living with HIV have focused on conditional cash transfers, or fiscal remuneration for viral suppression or clinic engagement. Basic Income Guarantee Chicago uses a dignity-affirming approach which assumes that participants know best how to manage their income allotment and represents an unconditional cash transfer program. Young people living with HIV have the highest rates of HIV in the United States, and are the least to be retained in services for treatment and prevention of HIV, both of which are key components to Ending the HIV Epidemic. Multiple methodologies for engagement of young people living with HIV in care for HIV prevention and treatment success are required. Use of a dignity-affirming framework to support unconditional cash transfers over 18 months for young people living with HIV is a novel and innovative strategy for the improvement of social determinants of health. Therefore, this proposal will: 1) disperse basic income guarantee in $500 allotments using a randomized controlled trial compared to a treatment as usual group; 2) test the effect of basic income guarantee on viral suppression and clinic engagement, income volatility, and food insecurity; 3) examine whether housing, food security, transportation, employment, and mental health improvement moderate viral suppression; 4) explore the acceptability, feasibility, appropriateness, and potential impact of basic income guarantee on quality of life, education progress, and employment using in-depth-interviews; 5) measure potential spill-over effects within participants’ social networks; and 6) conduct a cost effectiveness analysis of basic income guarantee as a sustainable intervention for young people living with HIV. The body of work generated from this proposal will have major implications for engaging a key population in Getting to Zero efforts and has the potential to have important public health impacts for reducing rates of HIV in the United States. Without the prioritization of young people living with HIV, the United States will not end the HIV epidemic. If this basic income guarantee intervention is successful in improving clinic engagement, viral suppression, and/or quality of life metrics (mental health, employment, spill-over effects) for young people living with HIV, this work may have major policy implications beyond this proposal for provision of basic income guarantee to people living with HIV to mitigate the effects of social determinants of health for vulnerable populations who might benefit from an increased social safety net. Our multidisciplinary team of HIV preventionists, clinical psychologists, network epidemiologists, social interventionists, health economists, and cost-effectiveness experts is well positioned for the successful roll out of this trial which aims to provide basic income and learn how basic income guarantee augments the lives of young people living with HIV.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT: The exocrine (e.g., acinar cell) and endocrine (e.g., islet b-cell) compartments of the pancreas have long been considered functionally distinct. However, there is now overwhelming evidence for exocrine-endocrine crosstalk in the development, physiology, and dysfunction of the pancreas. For example, pancreas size (99% of which is exocrine tissue) is significantly reduced at type 1 diabetes (T1D) onset, in islet autoantibody-positive donors without diabetes, and in first degree relatives of T1D patients elements for exocrine-specific digestive enzymes pathogenesis, but mechanisms remain unknown. In response to RFA-DK-23-007, our Team brings deep . Multiple T1D risk variants map to cis- regulatory . These findings implicate the exocrine pancreas in T1D expertise in the biology, immunology and imaging of the exocrine/endocrine pancreas to unravel the molecular crosstalk among these cellular compartments in b-cell health and immunity. Our Team recently discovered that unregulated pancreatic elastase activity from acinar cells has detrimental effects on b-cell biology and immunity. Mechanistically, we have evidence that elevated pancreatic elastase proteolytically inactivates a cell surface growth receptor on b-cells to trigger a signaling cascade that halts proliferation, increases inflammatory cytokines, and leads to cell death. Furthermore, our Team discovered a missense mutation in pancreatic elastase that increases its expression and is linked to an inherited syndrome of pancreatitis, diabetes and pancreatic ductal adenocarcinoma. In parallel, we have identified small molecules that inhibit pancreatic elastase expression or activity, increase regulatory T-cell numbers in vivo, and promote murine and human b-cell health to prevent diabetes in mice. Taken together, our preliminary data support the over-arching hypothesis that elevated pancreatic elastase activity is directly pathogenic to islet b-cells in part through impairing growth signaling to induce secretion of inflammatory cytokines, autoimmunity and cell death; and that attenuating elastase activity will promote b-cell health and protect against T1D. Building upon these results, the goals of this project are to determine the human T1D relevance of the pancreatic elastase signaling circuit by defining how it: (1) predicts T1D risk in patients, (2) triggers inflammation and autoimmunity, and (3) can be effectively targeted to prevent and/or reverse T1D. These studies will provide insights into the mechanisms responsible for pancreatic exocrine-endocrine crosstalk in T1D by understanding how pancreatic elastase impacts human b-cell health and autoimmunity, and test new pharmacologic approaches to limit elastase activity as a novel therapeutic strategy for T1D.