Northwestern University

NSF Awards · FY 2024 · 2024-09

The work of the NSF Engineering Research Center for Human AugmentatioN via Dexterity (HAND) will lead to versatile, dexterous robotic arms and hands that address a broad range of human, industry, and societal needs. The purpose is to create robot manipulators that are widely useful “out of the box.” Today, robot arms become useful only after an expensive integration process, making them inaccessible to many who might benefit, including most of the country’s quarter-million Small and Medium Enterprises (SMEs). To be useful out of the box, robots must have truly versatile end-effectors (“hands”), AI-powered dexterous skills, and intuitive interfaces that trained workers can use immediately. Training must be widely accessible and career paths must be available to learners from a young age. Firms of all sizes must be able to adopt these robots, and workers of all education levels, high school through postgraduate, must be able to use them. The breadth and structure of the ERC program will enable HAND to address these technical, workforce, and ecosystem challenges, ultimately democratizing access to robot dexterity. Robots will no longer be limited to high-volume, highly repeatable operations; they will find application in low-volume high-mix manufacturing, food processing, remote handling of precious or dangerous materials, assistance for individuals with motor impairments, and many other areas. Widespread access to robotic manipulation will be vitally important as the U.S. addresses labor shortages in fields such as manufacturing and caregiving, and as demographics inexorably change, leading to a shrinking pool of workers supporting an aging population. While some areas of robotics have seen dramatic advances in recent years, dexterous manipulation has proven to be a more challenging problem, requiring a very high level of convergence. HAND will provide this with a Convergent Research program organized into three thrusts: Hands (sensing, actuation, design), Intelligent Dexterity (simulation, AI, control), and Human Interface (multimodal interface, programming, social/legal/industrial studies). The Center will bring together experts in materials, manufacturing, manipulation, soft robotics, artificial intelligence, machine perception, modeling, haptics, human-robot interaction, participatory design and research, team science, education, law, and the social sciences to overcome fundamental barriers to dexterity. These include achieving large scale integration of actuators and sensors, building robust visuo-tactile-motor skills that are composable into complex behaviors, and low-code programming by non-roboticists. The result will be an engineered system — hands, skills, interface, and training materials — that dramatically advances robotic manipulation and its accessibility. HAND’s dexterous manipulators will be where AI learns about the physical world, and where AI is transformed to useful physical work. Additionally, through Engineering Workforce Development efforts, HAND will provide a novel education platform for introducing learners to AI and dexterity; an accelerator program to help SMEs succeed with robots; REU and RET programs that increase access to STEM; and undergraduate and graduate certificates built on a foundation of dexterity, social impacts of automation, and participatory research and design methods. HAND will use those methods to engage potential users and ensure that the benefits of dexterity are widely shared. HAND’s Innovation Ecosystem will support strong engagement with small, medium, and large manufacturers, robotics companies, national labs, civic organizations, and educators via testbeds, advisory boards, a robust process for technology transfer, and a public interest initiative. Together with this ecosystem, HAND will impact the future of work by democratizing access to human augmentation via dexterity and framing the associated social, economic, and ethical implications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Scientific teams are increasingly disrupting science by making pivotal discoveries and breakthroughs. These disruptive teams reshape established scientific paradigms and forge new ones, eclipsing established theories, methods, and research directions. Consequently, understanding the factors that foster disruptive scientific teams is essential for promoting new scientific paradigms, theories, methods, and avenues for future research. Previous research has documented the effects of team size, hierarchies, and distance among members on scientific disruption. However, the influence of gender composition on teams’ abilities to make disruptive discoveries and create new inventions remains underexplored. Drawing on previous research in gender composition and scientific disruption, the researchers aim to investigate the effects of gender composition on disruption and examine the causal mechanisms that could explain differences in its impact. This project encompasses three research goals. First, the project analyzes the impact of gender composition on disruption by examining more than 49 million papers and 4 million patents across different scientific fields over the last 50 years. The results yield empirical evidence of the impact of different gender compositions on scientific disruption. Second, the project conducts a laboratory experiment with 320 participants to understand the causal mechanisms that drive these effects. This experiment, which controls for gender composition, requires three-person teams to complete a disruption task that is designed for this experience and based on disruption research. Third, the project involves a massive survey and follow-up interviews with female scientists who have been part of disruptive teams to learn about their experiences and insights. This research promises to enhance understanding of the effects of different gender compositions on teams’ disruptiveness and contributions to science. The project highlights the theoretical and practical implications of specific team combinations in scientific research, giving institutions and funders information they can use as they reflect on the role of gender composition in scientific teams. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

In this proposal, we aim to address a critical challenge in lung transplantation: primary graft dysfunction (PGD), a severe complication impacting over half of all lung transplant recipients and have a particularly high incidence in those with pre-existing acute lung injuries (ALI) or acute respiratory distress syndrome (ARDS). Despite lung transplantation being a life-saving treatment for end-stage lung diseases, the high incidence of PGD significantly diminishes the long-term success and survival rates of these procedures. Our research is grounded in the discovery of the pivotal role played by intravascular nonclassical monocytes (NCM) in the donor lungs, which are activated by damage-associated molecular patterns (DAMPs), especially high mobility group box 1 (HMGB1), initiating the cascade leading to PGD. Building on this foundation, our proposal hypothesizes that receptor-interacting protein kinase 3 (RIPK3)- dependent necroptosis in both the recipient's native lungs and the donor lungs is a key driver of PGD. This hypothesis is supported by our preliminary data, which shows a sustained necroptotic process in the diseased lungs of recipients, particularly in cases of ALI and ARDS. We propose two specific aims to test this hypothesis: 1. Exploration of Autocrine Necroptosis in Recipient Lungs: The first aim focuses on the role of TNF-α induced autocrine necroptosis in monocyte-derived alveolar macrophages within the acutely injured native lungs. We plan to investigate the release of HMGB1 as a result of this necroptosis and how it contributes to the activation of donor-derived NCM during lung transplantation. This study will provide insights into the mechanisms through which pre-transplant lung conditions exacerbate the risk of PGD. 2. Investigation of Necroptosis in Donor Lungs: The second aim targets the necroptosis in donor lung tissue, specifically induced by mitochondrial reactive oxygen species (ROS) during the transplantation process. We aim to identify the cell populations in the lung responsible for mitochondrial ROS generation in response to ischemia-reperfusion injury and delineate their role in the necroptotic process within the graft. This understanding is crucial for developing targeted interventions to mitigate the risk of PGD arising from donor tissue conditions. The overarching goal of this research is to comprehensively understand and pharmacologically target the necroptotic pathways in both the donor and recipient lungs to reduce the incidence of PGD. This could positively impact the field of lung transplantation by significantly improving post-transplant outcomes. The successful completion of this research could lead to the development of novel therapeutic strategies and biomarkers for predicting and managing PGD, thereby enhancing patient survival and quality of life following lung transplantation.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Diabetic Retinopathy (DR) is the most common complication of diabetes and is the leading cause of vision loss in working-aged adults. DR is classified into non-proliferative DR and proliferative DR. In the non- proliferative stage, damage to retinal capillaries results in a lack of oxygen to the tissue. When capillary loss is significant, patients progress to the proliferative stage of disease, which is characterized by neovascularization, or growth of new blood vessels. These vessels do not function properly and cause bleeding into the retina and subsequent vision loss. While there are treatments for proliferative DR, there are none for non-proliferative DR, a stage at which vision loss could still be prevented for millions of patients. The lack of treatments is due to a lack of understanding of the underlying mechanisms that contribute to disease development. The literature demonstrates that inflammation may contribute to disease pathogenesis. C-chemokine ligand 2 (CCL2) is the most consistently elevated chemokine in intraocular patient samples from all stages of DR. Studies have also shown that leukostasis, or the firm attachment of leukocytes to the retinal vasculature, results in endothelial cell damage. However, there is a gap in knowledge in the field regarding how leukocytes promote capillary degeneration, which leukocytes are responsible, where this occurs, and which interactions between leukocytes and endothelial cells are necessary for DR pathogenesis. Investigating the inflammatory cascade beyond leukostasis could elucidate additional steps that can be targeted, reducing ischemia and thereby preventing vision loss from advanced disease. Based on the premise that static intravascular myeloid cells do not generally harm endothelial cells, but when they do (e.g. vasculitis) the damage is immediate and histologically obvious, our central hypothesis is CCR2-responsive myeloid cell transmigration is critical for DR progression, and blocking transmigration may prevent disease progression. We will test our hypothesis through two Specific Aims: (1) Determine whether and when blockade of CCL2-driven myeloid cell infiltration will halt the progression of inflammation in the mouse retina, and (2) Determine if blocking leukocyte-endothelial interactions early in the DR disease course can reduce DR progression. In Aim 1 we will use an acute model of inflammation induced by CCL2 intravitreal injections; in Aim 2 we will use a streptozotocin-induced diabetic mouse model. We will compare the effects of blocking leukocyte transmigration versus adhesion on DR progression, measured by markers of endothelial cell apoptosis, tight junction integrity, microglial activation, and leukostasis. We will adapt our intravital microscopy system to observe leukocyte dynamics in real time serially in the retinas of live mice with DR. The information obtained from this project will improve our understanding of the role of innate immunity on disease progression and aid in development of novel therapies for non-proliferative DR.

NSF Awards · FY 2024 · 2024-09

A major challenge in processing granular materials such as grains, pellets, beads, and powders in chemical, consumer product, and pharmaceutical manufacturing is that cohesive forces between particles affect their flow and mixing. The impact can be profound — poor mixing of active ingredients with fillers in the pharmaceutical industry can result in pills with too much active ingredient, risking overdose for the patient, or too little active ingredient to have the intended medical impact. The problem is that as granular materials flow, particles of different sizes tend to de-mix, or “segregate.” Although models to predict segregation and mixing are available for non-cohesive particles, the segregation behavior changes dramatically for “sticky” particles, which are very common in industry. The issue is further complicated because particle cohesion can be advantageous in some situations and problematic in others — the “stickiness” of cohesive particles can prevent unwanted segregation but also can reduce the flowability of powders or clog production equipment. This research will transform the understanding of the segregation and mixing of cohesive particles which will lead to physics-based models that can be used to design manufacturing processes that prevent segregation and promote mixing of cohesive granular materials in diverse areas ranging from pharmaceutical production to additive manufacturing. When granular materials flow, small particles tend to fall between larger ones such that particles of different sizes de-mix, or “segregate.” Physics-based models for segregation developed over the past decade work well for non-cohesive particles but do not apply to cohesive particles. The goals of this research are to gain a fundamental understanding of how cohesive particles segregate due to differences in size and to develop predictive approaches that can be used to ensure that particles remain mixed. Computer simulations and experiments will be used to characterize the segregation of flowing cohesive particle mixtures to determine the dependence of segregation on flow and particle parameters as well as to examine the underlying physics of cohesive particle segregation at the particle scale. The resulting understanding of mechanisms at both particle and flow levels will lead to a continuum model for segregation of cohesive particles analogous to that for segregation of non-cohesive particles. Not only will this research transform the understanding of segregation and mixing of cohesive particles, but it will also result in a transition from current ad hoc approaches for predicting cohesive particle segregation and mixing to physics-based 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.

NSF Awards · FY 2024 · 2024-09

NON-TECHNICAL SUMMARY: Proteins perform a myriad of functions essential to life, making them subjects of extensive study across the physical and life sciences. Protein organization, either through natural or artificial means, is a way to realize materials with structural and functional diversity. Leveraging their versatility, scientists have shown that proteins can be engineered for specific tasks, including binding events, energy transformations, and more. However, controlling protein-protein interactions remains challenging, partly due to the competing forces between the molecular units that exist in these systems. The goal of this proposed research is to overcome challenges associated with protein-protein interactions by synthesizing designer protein structures with strategically designed modifications. Specifically, this work will combine protein modifications with DNA, an easily tailored molecular unit that can be programmed and synthesized in the lab. This proposal will leverage the distinct attributes of DNA, including its specific bonding, tunable length, inherent flexibility, and tailorable interaction strength, to prepare protein-DNA hybrid structures. By investigating the interplay between proteins and DNA at the molecular level, this research seeks to unravel underlying principles governing biomolecular interactions and assembly processes. Through a systematic approach of exploration and experimentation, the project will elucidate fundamental mechanisms driving the formation of protein-DNA hybrid structures across different dimensions (i.e., one-, two-, or three-dimensions). This pursuit of knowledge will not only enhance our understanding of biological systems but also lay the foundation for developing generalizable design principles applicable to a diverse set of biomaterials. Consequently, the outcomes from this project are expected to expand the boundaries of scientific knowledge and provide insights that could spur innovations in various fields, ranging from biotechnology and medicine to materials engineering. Finally, the project encompasses a comprehensive plan to nurture scientific talent within the United States, providing graduate and undergraduate students and postdoctoral trainees with opportunities for professional skill development, technical expertise, and the expansion of fundamental scientific knowledge through collaborations within Northwestern University and other institutions. TECHNICAL SUMMARY: The proposed research aims to deepen our understanding of how protein organization can be controlled using DNA, leading to the design of novel functional materials such as the formation of larger complexes and assemblies that display a wealth of structural diversity. Although chemically engineered proteins have shown promise in building designer protein architectures, protein-protein interactions are generally complex and difficult to control synthetically, and therefore the design of materials that leverage the inherent functions of natural protein building blocks is challenging. Therefore, the exploration of biochemically disrupting, controlling, and directing protein interactions is worthwhile and could lead to the engineering of materials possessing properties and functions that rival or exceed those observed in nature. This proposal seeks to advance protein-based materials into a new era where designer structures can be made by controlling the linkage of structures into one, two, and three dimensions in an effort to control cooperative function. We hypothesize that new properties, like dynamic or responsive actuation, can be accessed within hybrid DNA-protein supramolecular structure. By realizing such materials, we will gain a greater fundamental understanding of chemical and biological design principles essential to these systems. Therefore, this proposal will address a major challenge pertaining to the realization of dynamic functional biomaterials by developing generalizable bioconjugation strategies and DNA design rules to disrupt and override complex protein-protein interactions. Moreover, these strategies are expected to enable precise structural and functional control with hybrid protein-DNA supramolecular structures. To achieve this, the project is organized into three distinct and synergistic objectives: (1) designing generalizable site-specific DNA functionalization to control proteins in 1D arrays; (2) designing dynamic, DNA-driven, 2D protein lattices; and (3) utilizing DNA interactions to facilitate protein crystallization in 3D. In doing so, this work seeks to advance our overall understanding of protein-based biomaterials and pave the way for accessing new materials with defined cooperative functions. This proposal builds upon knowledge gained from prior work where our research team established routes to direct protein crystallization outcomes via programmable DNA interactions. Broadly, the knowledge gained from these studies will contribute to fundamental knowledge at the intersection of biochemistry, molecular biology, and materials science by investigating hybrid protein-nucleic acid structures to reprogram protein interactions. Finally, the project will provide the expansion of fundamental scientific knowledge to the broader community and will provide graduate, undergraduate, and postdoctoral trainees with opportunities for professional skill development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Super-resolution microscopy has revolutionized the study of biology, but crucial technical barriers remain to image cells in 3D at the extremely small length scales needed to understand the fundamental structure and function of the genome, such as double-strand DNA. Current imaging techniques require the use of fluorescent labels which can disrupt natural cellular processes, presenting a challenge for understanding how the molecular machinery underpinning cellular behavior and many disease processes operates. This project will develop a new label-free nanoimaging platform to image the genome of intact fixed cells in 3D, with 2 nanometer resolution while providing chemical and molecular information, along with comprehensive structural and functional information. This may lead to a deeper understanding of the 3D structure of cells in their natural state and eventually to better understanding of multiple significant disease processes at the fundamental molecular level. In addition, the project will provide training and educational opportunities to a diverse audience through public symposia and workshops, mentorship, outreach to K-12 students, and by recruiting undergraduate students from minority-serving institutions in the Chicago region who may not have the opportunity to gain exposure or access to cutting-edge scientific research projects. This project will develop a new label-free nanoimaging platform, 3D DNA spectroscopic photon-localization intrinsic-contrast nanoscopy (3D DNA-SPIN), to image chromatin in 3D in intact fixed cells with 2 nm resolution while providing chemical and molecular information. Bringing the spatial resolution below 10 nm would enable imaging of chromatin at the nucleosomal scale. Enhancing resolution to ~2 nm would allow thus far unattainable imaging of chromatin at its most fundamental level, the double-strand DNA. If developed, this technique may answer the long-standing question of the 3D conformation of the chromatin polymer in its native state. The ability to record stochastic excitation-emission spectra will increase the spatial resolution of DNA localization and provide chemico-functional information about emitting DNA such as nucleotide sequences. Finally, 3D DNA-SPIN will be co-registered with existing single molecule localization microscopy (SMLM) modalities for superresolution molecular imaging of histone states, polymerases, locations of specific genes, and other molecular events, together providing comprehensive structural, functional, and molecular information, which in the longer term may help elucidate the interplay between chromatin, epigenetics, and phenotype. This project aims to 1) develop 3D DNA-SPIN for 3D imaging of cells with spatial resolution approaching 2 nm, 2) characterize photophysical properties of label-free, endogenous DNA photoswitching, and 3) develop and validate algorithms for molecular recognition using 3D DNA-SPIN. Based on the photochemical characterization in the second aim, machine/deep learning algorithms will be developed to use DNA-SPIN emission-excitation data to distinguish nucleotide sequences, including AT- versus CG-rich parts of the genome, which are mostly associated with repressed vs gene-rich parts of the genome. The project will also explore the feasibility of generating other functional data including DNA and nucleosomal conformations, volume concentration, and surrounding ionic environment. Cross-validation experiments on fixed cells will be performed to verify the accuracy, reliability, and robustness of the algorithm. In addition, the project will provide training and educational opportunities to a diverse audience through public symposia and workshops, mentorship, outreach to K-12 students, and by recruiting undergraduate students from minority-serving institutions in the Chicago region who may not have the opportunity to have exposure or access to cutting-edge scientific research projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Since the beginning of COVID-19 (SARS-CoV-2) pandemic, we have seen a number of prominent variants, and two of them are very recent. COVID-19 continues to affect millions of people: over 771 million people were affected and over 6.9 million deaths occurred (as of October 2023). A significant portion of patients who suffer from severe COVID-19 disease are at risk for developing long-term Post-Acute Sequelae (PASC) with pulmonary fibrosis. Identifying patients who are most at risk for post COVID-19 pulmonary fibrosis could allow early intervention with anti-fibrotic drugs and other potential treatment strategies. Given the extent of people infected with COVID-19, post COVID-19 pulmonary fibrosis is a significant cause of mortality and morbidity, yet there is no strategy developed to address this unmet critical clinical need. Hence, the overall goal of this proposal is to address this need for predicting post COVID-19 pulmonary fibrosis at an early stage by developing novel explainable deep learning (DL) algorithms on multimodal data (combined imaging and electronic health record (EHR) data) from multiple centers. Main Hypothesis: The proposed explainable DL algorithms will identify patients at risk for development of post COVID-19 related pulmonary fibrosis at early phases by multimodal data with high accuracy. In Aim 1, we will identify imaging and EHR characteristics associated with post COVID-19 fibrosis from patient data gathered from Northwestern, Columbia, NIH, and UPenn. We will automatically extract imaging (CT scan) features in two ways: (i) pulmonary analysis (PA) and (ii) body composition analysis (BCA). For each patient, we will collect EHR data consisting of demographic, clinical (including vital, medication, vaccination, and comorbidity) and laboratory information. We expect to retrospectively collect a balanced dataset of 1,150 initial CT scans and associated EHR data. In Aim 2, we will develop Transformer embedded explainable capsule networks (X-TCaps) for prediction of post COVID-19 pulmonary fibrosis. We will integrate our newly established visual explanation algorithm (called IBA) into the machine-generated results in addition to radiographical explanations, and PA and BCA features captured by X-TCaps. In Aim 3, we will employ our established optimal biomarker (OBM) method to determine the most potent features (biomarkers) from EHR and imaging data to predict post COVID-19 pulmonary fibrosis at the highest accuracy possible. PASC is a massive emergency and very little is known about it. Once accomplished, our proposed framework will provide early prediction of post COVID-19 pulmonary fibrosis and determine biomarkers to understand pulmonary fibrosis better. Our study is innovative as no previous study has investigated post COVID-19 pulmonary fibrosis, which is paramount to developing a robust knowledge database and informing clinical practice in this area. With this project, we will provide mechanistic understanding of post COVID-19 pulmonary fibrosis, which is central in determining therapeutic options and will ultimately play a role in lung transplant considerations in the long term.

NSF Awards · FY 2024 · 2024-09

The biggest stars can produce bright explosions like supernova or collapse into black holes. One important question is whether fresh fuel from the outer part of the star can get mixed into the center of the star, where it could burn, extending the life of the star. Observations of these stars suggest they live longer than might be expected, so some mixing must be occurring in the star. This work will model mixing in massive stars using computer simulations. The simulations will determine what fraction of the star gets mixed. These results will be used to make new predictions of the lifetimes of such stars, and they will be compared to observations. The results will also be used to predict how many supernova and black holes we expect to see. Each summer, this project will support high school students to complete independent research projects supervised by the project team. This award will support the Research Experience in Astronomy at CIERA for High school students program at Northwestern. This is a highly interactive three-week program that provides high school students experience with astronomy research in an atmosphere of team-style learning, hands-on training, and mentorship from professional scientists. Although rare, massive stars are disproportionately important in astrophysics. They are progenitors of neutron stars and black holes, and they chemically enrich their environments through winds and/or mass loss. Accurate stellar and population synthesis models of intermediate- and high-mass stars are required to robustly predict properties of stellar remnants and nucleosynthetic yields. The lives and deaths of these stars are intrinsically linked to mixing that occurs at the boundary of their convective cores. If fresh fuel can mix into the core, it can extend the star’s main-sequence lifetime and alter its subsequent evolution. Constraining convective boundary mixing is essential for accurate and robust neutron star and black hole population synthesis modeling. This investigator will derive convective boundary mixing parameterizations from multi-dimensional numerical simulations. The team will run a suite of three-dimensional global spherical numerical simulations to measure convective penetration. They will determine how convective penetration varies with stellar mass, age, and rotation rate. The parameterization of convective penetration will be implemented in the MESA code, and we will validate it by comparing to asteroseismic observations. Finally, they will use the new parameterization in the population synthesis code POSYDON to determine how our new parameterization affects compact object binary merger rates. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Functional Magnetic Resonance Imaging (fMRI) has become a powerful tool for studying the underlying functional architecture of brain networks by tracking temporal correlations in the activity of different brain regions; a technique called resting-state functional connectivity (rs-FC). Recently, individualized methods of rs-FC have revealed reliable differences in functional organization between individuals and group averages that have been implicated with differences in behavior. These precision fMRI methods involve collecting extended amounts of rs-FC data across multiple sessions for each subject and the use of advanced denoising techniques to improve the quality of the data. Individual-specific networks have not been examined in older adults yet, even though group-average studies suggest that brain networks change systematically over the course of the lifespan and as a function of disease, both in cortical and cerebellar regions. I propose the use of a dataset of highly sampled older adults, ages 60-75 (N = 38), and younger adults, ages 18-30 (N = 38), to create individual-specific parcellations and network representations using high-quality anatomical and rs-FC data. In Aim 1, I will examine whether the properties of individualized networks in older adults differ compared to young adults. In Aim 2, I will examine whether the networks affected in Alzheimer’s Disease (AD) differ from those affected in healthy aging, particularly in the cerebellum. Preliminary data suggests that, with sufficient high-quality data, cortical networks in young adults are stable across days (r > 0.85), supporting their endophenotypic nature and potential for use as biomarkers. This study will be the first to use individualized measures in older adults to provide a better understanding of neurodevelopmental changes to rs-FC that may be relevant to behavior. Individualized network topology has previously been found to be predictive of behavioral and cognitive measurements, suggesting that it may be a promising avenue to search for biomarkers of cognitive decline. My pre-doctoral work (Aim 1) will set the benchmark for using precision fMRI with an older population to study the relationship between brain network variability and cognitive decline. My post-doctoral goal (Aim 2) is to apply these methodologies to the study of AD-related changes to the functional architecture of the brain and how these changes drive hallmark cognitive symptoms. This project will also provide ample opportunities for additional scientific and professional training. My training goals will focus on gaining theoretical and practical knowledge of defining individualized functional networks and brain parcellations, ensuring the quality of anatomical images using FreeSurfer, and applying special considerations to obtain reliable signal from cerebellar data. Professional development goals will center on mentoring practices and science communication. These skills will be key to my future career as an independent researcher dedicated to elucidating the relationship between individual differences in networks and cognitive changes in healthy and pathological aging and fostering diversity and inclusion in academia.

NSF Awards · FY 2024 · 2024-09

This EArly-concept Grants for Exploratory Research (EAGER) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. The future of renewable energy resorts to energy storage solutions to cope with the intermittency of most renewable energy sources. In this context, if the walls and slabs of existing and new building basements, parking garages, and metro stations could be turned into geothermal heat exchangers by integrating piping networks within or onto them, it would open up a tremendous capacity for energy storage options. This novel use of underground structures, by using a heat-carrier fluid to source renewable geothermal energy and store general thermal energy in the ground, is potentially disruptive because virtually all structures in contact with the ground worldwide could be turned into thermal batteries, hence decarbonizing the environment, decreasing grid dependence, and democratizing access to renewables. However, knowledge on the performance (energy, geotechnical, and structural) and impacts (social, economic, and environmental) of underground structures used as thermal batteries currently remains limited. This project sheds light on the performance and impacts of geothermal structures used for underground thermal energy storage. The researchers at Northwestern University create experimental and theoretical knowledge via full-scale field experiments, computational analyses, and community surveys. Additionally, education, professional training, community engagement, and outreach activities are offered to educate a broad audience, including underserved communities, about opportunities associated with carbon-neutrality and a clean energy transition. The project will investigate (1) fundamentals that characterize the heat transfer, mass transfer, and deformation of geothermal structures used for thermal storage thought the proposed retrofit strategy, and (2) uncovering the impacts of a prototype installation and use on the living, economic, and environmental conditions characterizing underserved and advantaged communities. Geothermal panels will be installed on an existing underground wall in a garage in collaboration with a company, Millennium Garages LLC. Continuous monitoring of the performance of the panels and the structure will provide information on the thermal and structural behavior to short-term and long-term temperature changes. The data will also generate data to assess different configurations of the panel fluid distribution network and physical-thermal coupling between the panel, the wall and the soil beyond. Data will be used to validate finite element models, which will enable further parametric studies on effects of site and local ground conditions, structure geometries, operative features, and energy loads. Additional simulations will address building energy performance and retrofit potential of different building types and life-cycle assessment and overall environmental and societal benefits. Potential technological and societal broader impacts include developing knowledge to design a new class of renewable energy technologies that can boost decarbonization of buildings and infrastructure through retrofit strategies in urban areas. Furthermore, the research efforts, in collaboration with the non-profit Civic Infrastructure Collaborative, have the potential to advance social justice and equity by creating competence that can foster the deployment at scale of technologies capable of reducing energy poverty, democratizing access to clean energy sources, limiting grid dependence issues, and turning communities into energy owners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

University research labs play an important role in strengthening U.S. innovation, global competitiveness, and economic growth by making transformative scientific discoveries and training the scientific and engineering workforce. In addition to research and training, academic research labs are also involved in activities found in the typical business organization, from hiring workers and delegating tasks to making capital investments and creating shared goals. These activities are recorded in the form of administrative expenditures and other organizational archives that have the potential to advance understanding of how the organization of academic research labs relates to scientific productivity, innovation, and impact. This project uses expenditure and research records to better understand how data that are localized and readily available to universities can be used to study academic research labs and develop improved metrics for benchmarking, policy development, and communication to public constituents. This project applies theories of organizational formation and performance to academic research labs and uses a multi-level research design at the individual, lab, and university levels to investigate the organizational character of scientific productivity in three interrelated stages. First, this project combines large-scale administrative archives with machine learning and inferential statistics to develop a novel computational approach for characterizing the organizational models that underlie the operation of academic research labs. In this step, the project demonstrates a strategy and means of leveraging administrative records to construct theory-driven and empirically instantiated organizational models expressed across a variety of academic research labs. Second, this project extends its focus to the longitudinal operation of academic research labs to examine the extent to which lab models persist or change over time and evaluate whether and how academic research labs differ from organizations in the private sector. Third, much like studies of scientific production based on individuals and collaborative teams, this project links labs to their research products to operationalize the same ideas of scientific productivity, impact, and innovation in an organizational context. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

When a massive star runs out of fuel, its core collapses under its own weight. The core gets so dense that it can form a black hole that feasts on the rest of the star. Such objects are called collapsars. Scientists have long suspected that collapsars can produce heavy elements, including gold and platinum. However, how much of the heavy elements, with which we interact in our day-to-day life, comes from collapsars has remained a mystery. This is because black holes are fussy eaters and burp away most of the star instead of swallowing it. This makes it hard for the scientists to answer the crucial question: What role do collapsars play in creating the heavy elements, which enrich our everyday lives? A team led by Northwestern University will use a three-year award to investigate these questions. The investigators will involve under-represented minority undergraduate and graduate students in their research. They will inspire with their research undergraduate students by teaching classes as part the Northwestern Prison Education Program. They will give guest lectures at local high schools. They will reach the broader public by sharing the collapsar and guest lecture videos in planetaria, via social media and press releases. Because collapsars produce multimessenger emission – gravitational waves and a wide range of electromagnetic counterparts – and leave behind black holes whose mergers can later produce gravitational waves, they are prime targets of NSF flagship facilities, such as LIGO-Virgo-KAGRA and Vera Rubin observatories. However, no models directly connecting the pre-collapse progenitor star to the newly formed black hole and to the gravitational waves and electromagnetic counterparts currently exist. The investigators will combine neutrino transport numerical relativity simulations with 3D general relativistic magnetohydrodynamic simulations that start with the pre-collapse stellar structure, describe the stellar core collapse and formation of the black hole, model the subsequent explosion for a duration of tens of seconds post-collapse, and then follow the expanding ejecta for 100 s, until it reaches homology. The main objectives are to constrain the origin of heavy elements, mechanisms of jet-powered hypernova explosions, the nature of multimessenger emission, and the properties of black hole remnants. The numerical schemes and atlas of multimessenger light curves developed here will be made public, directly connecting the pre-collapse stellar structure to multimessenger observables. The proposed work responds to the “Windows on the Universe: Multimessenger Astrophysics” theme. 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 2024 · 2024-09

Project Summary/ Abstract The aim of the Robert H. Lurie Comprehensive Cancer Center Flow Cytometry Core Facility is to provide analysis and cell sorting to enhance scientific interaction and productivity within Northwestern University’s research community. This is accomplished by allowing access to quality controlled highly specialized technology, education, and technical assistance in a cost-effective manner. All sorting demands for the entire campus are accomplished on six BD instruments (four high-end and two mid- level sorters) and one Miltenyi MACSQuant Tyto. Our high-end sorters are heavily utilized and exceed over 4900 hours, even though our total practical capacity is 4572 hours. Additionally, three of our high-end sorters are also nearing their end-of-life (over 10 years and showing increased downtimes resulting from age and repairs. This, together with the increased user demand, leads to scheduling wait times of 2-4 weeks. Since much of our user base utilizes human patient and other time-sensitive specimens, which tend to come in at short notice, we are no longer able to accommodate these sorting requests in a timely manner. Should a sorter be down due to major repairs or must be decommissioned due to extensiveness of repairs; not having a replacement sorter to provide uninterrupted service, further hinders investigators’ research. Additionally, with installation of a 5-laser 50-parameter BD FACSymphony S6 cell sorter in 2021, many of the users have transitioned or are in the process of transitioning to more complex 18-33 color assays for spectral based-cell sorting. However, the current FACSAria’s lack the ability to translate these panels directly into cell sorting for further downstream assays, thus limiting research. This proposal is for purchase of a 5-laser 78- fluorescent detector system (86-parameters total) BD FACSDiscover S8 cell sorter with CellView Image Technology, integrated in a biosafety cabinet. This is the first high-dimensional sorter combining spectral flow cytometry with real-time spatial and morphological insights, thus empowering scientists to address previously impossible-to-answer questions. The CellView Image Technology will further enhance and allow for assays including sort QC, label-free sorting, fluorescent localization, cell-cycle analysis and cell-cell interactions. The instrument, in addition to providing the acutely needed additional capacity, will accommodate the high parameter cell sorting demands and allow for better detection and efficient sorting of rarer events. The sorter will be permanently integrated in a Baker Class II Type A2 biosafety hood. This will provide state-of-the-art cell sorter instrumentation that can keep pace with the technological advances and growing needs of future. This sorter will enable safe single cell high- parameter image-capable spectral cell sorting of human and other animal specimen to meet the increasingly complex needs of our user base and advancing research in a timely manner.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Cancer remains one of the leading causes of death worldwide despite advances in diagnoses and treatments. Immunomodulatory therapeutics like anti-PD-L1 and anti-PD1 enhance the host immune response against the tumor and have saved countless lives. However, these treatments only work a small fraction of the time. Immunosuppressive T-regulatory cells (Tregs) have come under scrutiny as the ratio of Tregs in the tumor microenvironment can dictate both clinical prognoses and whether a patient responds to immunotherapy treatment. Furthermore, current therapeutics to target Treg suppressive function, directly or indirectly, remain unspecific and transient with an extremely low efficacy. Therefore, new therapeutics need to be developed against Tregs and Treg suppressive function. Forkhead box protein 3 (FoxP3) is a Treg and Treg suppressive function specific target. FoxP3 is not only a lineage defining marker in Tregs, but also the master regulator of Treg differentiation and function. Given the role of FoxP3 as a transcription factor though, FoxP3 is known to be “notoriously undruggable” due to both its nuclear localization and lack of inhibitable binding pocket. Fortunately, our lab and others have shown chemically targeting the ubiquitin proteasome system (UPS) can regulate FoxP3 protein levels, and in turn, modulate Treg suppressive function. Unfortunately, UPS enzymes have wide substrate diversity and broad cell type expression, leading to off-target effects. However, through using small molecule compounds known as “molecular glues,” we can specifically glue FoxP3 with E3 ligases to promote its targeted proteasomal degradation. My preliminary data identifies and characterizes one lead small molecule compound as a FoxP3 degrader. First, using a FoxP3-GFP reporter system, 81 potential hits were identified from 640 small molecule library, which reduced FoxP3. Following a secondary dose-response screen, an additional screen by flow cytometry in human Treg-like MT-2 cells, and resynthesis of four potential lead compounds, one lead compound, termed MG03, was identified. MG03 was found to decrease FoxP3 in MT-2 cells as well as mouse primary Tregs, and my preliminary data suggests that MG03 promotes proteasomal degradation of FoxP3. Thus, the long-term goal of this proposed project is to further develop this lead compound to target Treg suppressive function in the context of cancer. The central hypothesis for this project is that Tregs can be targeted through FoxP3 by small molecular degraders to partially diminish Treg suppressive function and enhance the anti-tumor response. To test this hypothesis, we will address the following aims: Aim 1 will determine efficacy of the lead compound, MG03, in inducing FoxP3 degradation in vitro; Aim 2 will investigate the underlying mechanisms of MG03; and Aim 3 will evaluate efficacy of MG03 on Treg suppressive function in vitro and in vivo. This study will identify the first FoxP3-specific molecule glue to partially diminish Treg suppressive functions for cancer treatment.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Cytokine therapies have the potential to revolutionize treatment for immunologic diseases but are limited by their poor pharmacokinetic profiles, off-target effects, and pleiotropic nature. Engineered cytokine platforms, on the other hand, have the potential to target specific tissue environments and cell types to provide local immunomodulation with minimal side effects. Since macrophages play a central role in many immune-mediated diseases and can be polarized toward anti-inflammatory or pro-inflammatory phenotypes, they are promising targets for cytokine therapies. In my preliminary work, I developed a platform technology to target metabolically dysfunctional macrophages in the context of atherosclerosis, a paradigm chronic inflammatory disease with high prevalence. Since macrophages that comprise atherosclerotic plaques engulf large amounts of low-density lipoprotein (LDL) and become pro-inflammatory lipid-laden “foam cells,” I engineered a protein fusion in which one side is an antibody fragment (Fab) that binds to LDL and the other side is the anti-inflammatory cytokine IL- 10. I have shown that Fab-IL-10 constructs attach to LDL upon i.v. injection in hypercholesterolemic mice, hitchhike a ride to inflamed regions, preferentially target macrophages, and successfully reduce inflammation. This proposal aims to elucidate the molecular mechanisms of action whereby inflammation is locally suppressed in atherosclerosis (Aim 1), engineer additional functionalities into the construct (Aim 2), and determine its generalizability to other cytokine payloads (Aim 3). In Aim 1, we will primarily use in vitro models to dissect the roles of different scavenger receptors involved in Fab-IL-10 binding and uptake and characterize the resulting phenotype and transcriptome of Fab-IL-10-treated lipid-laden macrophages. We will also perform single cell RNA sequencing on plaque-resident macrophages in an experimental mouse model of atherosclerosis to determine the effects of treatment with Fab-IL-10 in vivo. In Aim 2, we will engineer and evaluate an LDL-binding full antibody-IL-10 construct with enhanced avidity due to multiple binding regions, extended half-life due to neonatal Fc receptor-mediated recycling, and higher potency due to an extra copy of IL-10 per construct. In Aim 3, we will evaluate the generalizability of this platform to other payloads while also uncovering biological insights on the effects of less well-studied cytokines in atherosclerosis (i.e., IL-19 and IL-33). With data generated from this proposal, we will apply to multiple R01-level grants to expand this platform to target additional disease models that are partially regulated by lipid-laden macrophages including non-alcoholic fatty liver disease and certain solid cancers. This research proposal combined with my individualized career development plan will enable me to expand my scientific and professional skillsets and will enable my seamless transition to research independence as a future tenure-track assistant professor.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Swallowing difficulties are extremely common and result in substantial morbidity, reduction in the quality of life, and mortality related to malnutrition and complications from regurgitation and aspiration. Unfortunately, our understanding regarding the pathophysiology of dysphagia and GERD has been hampered by focusing predominantly on circular muscle activity and ignoring the essential biomechanical properties of the esophageal wall that promote normal emptying. Our initial work explored the relationship between intrabolus pressure (IBP) and esophagogastric junction (EGJ) compliance as a metric for outflow resistance. This work highlighted the direct relationship between IBP and EGJ opening and was the foundation for the development of the classification scheme utilized around the world to diagnose esophageal motor disorders: “the Chicago Classification” (CC). Despite this improved understanding focused on bolus transit dynamics, there are still significant gaps in our scientific understanding centered on the lack of a true correlate for symptoms, reliable predictive models and effective treatments for Functional dysphagia, IEM and EGJOO. Given these limitations, we have developed novel approaches that combine assessments of primary and secondary peristalsis (a NeuroMyogenic Model of esophageal function). These will leverage our recent findings supporting the importance of the esophageal response to distension in bolus clearance, noting that this response of the esophageal wall to bolus retention or reflux is one of the most essential functions of the esophagus in preventing complications of aspiration, or reflux injury. We will also include an assessment of esophageal geometry and wall biomechanics (elasticity/dilatation) as these carry essential interactions with esophageal function that are overlooked in the current diagnostic paradigms. In order to test our hypothesis that wall mechanics are a major determinant of esophageal diseases, we had to develop new approaches and new technology to directly measure mechanical wall state, descending inhibition and LES opening. Using impedance techniques combined with manometry, we are now capable of assessing IBP and diameter changes across a space-time continuum (4D HRM). We also developed physics- based hybrid diagnostics that include a FLIP technique to assess esophageal work and power during volumetric distention (FLIP-MECH) and a fluoroscopy approach that simultaneously assesses esophageal diameter- pressure relationships (Fluoro-MECH). We also developed a new approach, Interactive FLIP Panometry, which facilitates an assessment of descending inhibition and the mechanism behind impaired LES opening. These tools will allow us to expand our models to combine an assessment of neuromyogenic function simultaneously with geometry. Our overarching goal will be to study well-defined patient populations (Functional Dysphagia, IEM/GERD, EGJOO and Achalasia) before and after targeted interventions to test the NeuroMyogenic and MechanoGeometric Model. This work will build upon the previous success of the CC and help advance the evolution of the CC by defining new, relevant biomechanical physiomarkers of disease activity that can identify new targets for therapeutic intervention and facilitate prediction of clinical outcomes.

NIH Research Projects · FY 2024 · 2024-08

Abstract Dementia with Lewy bodies (DLB), Parkinson’s disease (PD), and PD-Dementia (PDD) are all classified as synucleinopathies due to the accumulation and aggregation of a-synuclein (a-syn) the nervous system. The pathological mechanisms leading to neurodegeneration are not completely understood, however the assembly of a-syn into insoluble fibrils is thought to play key role. This is supported by the discovery that aggregation-promoting mutations in a-syn lead to early onset PD with Dementia, including the A53T mutation and triplication of the SNCA genomic locus. Furthermore, recent GWAS studies indicate that variants in protein trafficking and lysosomal machinery confer increased risk for developing DLB and PD. Since a-syn is normally degraded by lysosomes, disruption of this pathway is expected to increase a-syn levels and promote conditions for aggregation. The relationship between dysfunctional lysosomes and synucleinopathies is best described by the discovery that loss of function mutations in lysosomal GBA1, that encodes b-glucocerebrosidase (GCase) represent the strongest genetic risk factor for both PD and DLB with odds ratios of 5.43 and 8.28 respectively. GCase degrades glycosphingolipids (GSLs) in the lysosome, and our previous work showed that GSLs interact and convert a-syn into toxic aggregates in patient-derived iPSC neurons. Furthermore, our group and others have shown that a-syn inhibits protein maturation of lysosomal hydrolases between the endoplasmic reticulum (ER) and the Golgi, leading to depletion of hydrolases and lysosomal dysfunction. In turn, hydrolases accumulate in the ER and overwhelm the quality control machinery, causing their aggregation into insoluble species. Our proposal is focused on synergistic improvement of ER folding machinery, protein trafficking, and lysosomal activity. We hypothesize that improving two or all three of these pathways simultaneously will enhance therapeutic benefit compared to each one alone. During the R61 phase, we will use established small molecules to enhance ER chaperones and pharmacological chaperones that directly bind and stabilize GCase in the ER. These will be combined with trafficking enhancers that promote ER-Golgi SNARE assembly and hydrolase maturation into lysosomes. Finally, we will test the effect of direct GCase allosteric activators combined with both ER proteostasis and trafficking enhancers. We use a combination of patient-derived iPSC-neuron models and mouse models to test our hypotheses in vivo. Our assay readouts include protein folding in the ER, hydrolase maturation, lysosomal activity, a-syn / tau aggregation, cognitive functions, and neurotoxicity. Our go / no go decisions will be based on whether the combinatorial treatments synergistically improve lysosomal activity, reduction in protein pathology and improve cognition. The R33 phase will be focused on establishing thresholds and duration of activation that are required to activate lysosomes and reduce a-syn, development of blood biomarkers that accurately reflect brain target engagement, and safety assessments for escalating doses in vivo. Our studies may provide the groundwork for future combination therapies for DLB and PD.

NIH Research Projects · FY 2025 · 2024-08

Abstract: The gut microbiota is increasingly recognized as an important contributor to a range of human physiological processes and is attracting increasing attention as a dynamic area for the development of therapeutics. To realize the potential of the gut microbiome in a therapeutic context, animal models are necessary to generate causal, mechanistic data describing host-microbe interactions. While mice and fish have been critical in generating foundational microbiome data toward this goal, they have key genetic, physiological, and behavioral differences from humans that can interfere with the translation of findings. Non-human primates share many genetic, physiological, and behavioral traits with humans, increasing their translational potential. Although non-human primates are used for research across the NIH, they are currently underutilized in microbiome research and have not been systematically validated as models in this context. Given the significant investment required for non-human primate studies, it is especially important to characterize which models are best suited for particular questions as we seek to examine how microbiomes impact human health and disease. Here we propose to develop non-human primates as valuable model organisms for functional studies of the human microbiota. Specifically, we aim to identify the best uses for different non-human primate species in microbiome research. We will generate baseline microbiome (shotgun metagenomics, analysis of SCFA concentrations, untargeted metabolomics) and physiological data (metabolic panels, immune cell populations, serum metabolomics) from humans and five non-human primate species commonly used as biomedical models (marmosets, owl monkeys, squirrel monkeys, baboons, and macaques). These data will allow us to determine which non-human primate species model different taxonomic and functional features of the human gut microbiome and to measure the consistency of host-microbiome interactions across human and non-human primate species. Our efforts hold potential to unlock critical insight about the utility of longstanding non-human primate biomedical models for different aspects of microbiome research and will ultimately help researchers accelerate the translation of microbiome discoveries into clinical settings. By identifying how well different non-human primate species model humans in a subclinical context, the proposed project will lay the groundwork for future R01 proposals that use non-human primate models to interrogate host-microbe interactions in a disease context. Overall, this line of inquiry will facilitate future studies targeting key questions about host-microbe interactions with a high potential for translational science and medical benefit.

NSF Awards · FY 2024 · 2024-08

New York University, Northwestern University, and the University of Pennsylvania form the Critical STEM Faculty Alliance (C-STEM). Leveraging their combined strengths, they aim to develop an infrastructural technology system that provides more opportunities and lowers systemic risks for historically underrepresented groups. C-STEM will examine college and university functions, to understand how to train effective technology researchers and teachers from these groups. NSF emphasizes creating opportunities everywhere. Accordingly, C-STEM seeks to help new researchers from underrepresented backgrounds build strong professional networks, establish stable pathways that advance careers, and collaborate with other experts in academia, industry, and government. Also, C-STEM aims to help researchers build new projects and design innovative educational tools to improve people's lives. Its goal is to ensure that technology serves the public interest, especially those who have been most negatively affected by technology. C-STEM aims to design and implement institutional self-assessments at the three C-STEM Alliance institutions. The alliance will prioritize collecting and analyzing data to identify inequities affecting underrepresented minority (URM) doctoral students, postdoctoral scholars and early career faculty in STEM fields. To assess the need for the C-STEM Alliance, the project will collect data on the demographic representation (race, ethnicity, national origin, sex, gender, first-generation status) of doctoral students and faculty in STEM and related fields. The project will also conduct curriculum surveys to understand demographic and socio-technical content representation in STEM courses, and review research production by minority and non-minority STEM students and faculty. Surveys will also evaluate existing mentorship and support structures, and collect data on minority STEM doctoral student outcomes, such as degree completion and post-degree hiring. Additionally, the alliance will gather qualitative data from minority STEM students and faculty about their experiences. This data will help identify institutional challenges and justify the alliance's activities, demonstrating how they address specific needs. To assess institutional readiness, the project will collect data that include reviews of diversity commitments by university leaders and progress towards diversity, equity, and inclusion goals. This will demonstrate C-STEM institutions’ commitment to increasing the representation, resilience, and success of minority doctoral students and faculty in STEM. The alliance intends for this work to help research communities better understand the incentives and affordances institutional leaders’ encounter in their efforts to create, continue, or expand key structures, such as postdoctoral programs and frameworks for transitioning postdoctoral scholars to tenure-track positions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

It has been nearly a decade since the first direct measurement of a “ripple in spacetime” known as a gravitational wave. Since then, over 100 gravitational-wave candidates have been detected, all arising from the violent collisions of black holes and neutron stars millions to billions of light years away. These extreme events offer a new look into fundamental physics, from the nature of gravity to the properties of dense nuclear matter, as well as into the astrophysical properties of black holes and neutron stars. Extracting these insights, however, requires major computing resources, especially given the ever-increasing number of gravitational-wave detections. This award supports the purchase of a high-performance computing cluster at Northwestern University for use in analyzing and understanding gravitational waves. The cluster will enable discoveries from large catalogs of gravitational waves and provide a training ground for the next generation of gravitational-wave scientists. With this new computing cluster, the PI's team will develop libraries of simulations of gravitational-wave signals and use advanced statistical techniques to infer the individual and population properties of their sources. The cluster will also allow the group to advance simulations of complex systems of stars, which could be factories of sources of gravitational waves. By understanding how different astrophysical environments affect these sources' physical parameters and comparing them to gravitational-wave data, the PI and her team will answer fundamental questions about the origins of black holes and neutron stars. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Low back pain affects nearly half of all Americans per year and accounts for nearly three million annual emergency department (ED) visits. In nearly two thirds of these visits, an opioid medication is administered or prescribed, making low back pain the most common reason for opioid prescribing in the emergency care setting and in the general population. Despite this medication-based treatment strategy, many patients with low back pain continue to do poorly after an ED visit: nearly half of all patients report persistent functional impairment at three months, and one in five patients report continued opioid use. Clearly there is a need for an alternative treatment approach that improves low back pain symptoms and reduces the need to use opioid medications. Multiple randomized trials have demonstrated that early referral to physical therapy in the outpatient setting is efficacious for low back pain, but it is unknown whether these same benefits can be extended to patients evaluated by physical therapists in ED settings – which differ from clinic-based settings due to the acuity/severity of pain necessitating an emergency visit and accompanying psychosocial stressors. We previously developed, pilot-tested, and refined an “embedded” ED physical therapy intervention protocol for low back pain, in which a dedicated physical therapist is placed on the primary ED treatment team to evaluate and treat patients with low back pain early in their overall treatment course. The ED physical therapist uses a diagnosis-driven treatment protocol to deliver an integrated mind and body intervention grounded in a biopsychosocial model of pain. In our preliminary work at a single site, ED patients that received ED physical therapy, as compared to usual care, reported greater improvements in pain-related functioning and less use of opioid medications over three months of follow-up. We now seek to evaluate whether this embedded ED physical therapy intervention can be feasibly delivered with high fidelity at multiple other sites and demonstrate that we can consistently collect electronic health record and patient-reported outcomes of interest. We will then use the findings from this multi-site feasibility trial to justify and inform a full-scale multi-site cluster-randomized clinical trial of an embedded ED physical therapy care model for low back pain.

NIH Research Projects · FY 2026 · 2024-08

PROJECT SUMMARY/ABSTRACT Dr. Minjee Kim is a practicing neurologist with expertise in sleep research and clinical assessment of cognitive impairment (CI). Her long-term goal is to become an independent investigator studying: (1) the role of sleep as a potentially modifiable determinant of CI, Alzheimer’s disease and related dementias (ADRD); (2) the design of health system interventions for the early detection and treatment of sleep disturbance (SD) to mitigate CI/ADRD risk. The career development plan proposes to fill critical gaps in her skills through formal and informal training in aging research, data science, and health system interventions. This award will ensure she has the knowledge, skills, and experience to conduct in-depth investigations, lead pragmatic clinical trials, and translate research findings into routine clinical care. Dr. Kim has convened an outstanding mentoring team with complementary expertise in sleep, cognitive aging, health system interventions, pragmatic trials, data science, biostatistics, and primary care. Northwestern University offers Dr. Kim an exceptional environment, with dedicated resources from the Department of Neurology, Center for Circadian and Sleep Medicine, Center for Applied Health Research on Aging, Claude D. Pepper Older Americans Independence Center, and Clinical and Translational Sciences Institute to support her research, career development, and transition to independent clinical investigation. SD refers to manifestations of poor sleep health, including inadequate duration, inappropriate timing, irregular pattern, low efficiency, unsatisfactory quality, and daytime sleepiness. Many forms of SD can be effectively addressed in primary care, yet SD remains largely undetected in clinical settings. SD is common in later life and has been linked to CI/ADRD risk. Yet less is known about SD in middle age (MA). As clinically meaningful cognitive decline is believed to present during MA, more research is needed that examines midlife determinants of cognitive decline that could be targets of interventions. If identified early, SD in midlife may be modifiable with immediate benefits on physical and mental health, and might possibly reduce later life risk of CI/ADRD. To address this, Dr. Kim has added new sleep measures (actigraphy, sleep diary, questionnaires) to a new NIA cohort study investigating cognitive function among middle-aged adults (‘MidCog’; PI: Wolf) explicitly for this proposal. Leveraging the MidCog study and linked electronic health records (EHR) data, Dr. Kim will investigate associations between SD, cognitive function, self-care capacity, and health status (Aim 1), and then apply machine learning to develop SD prediction models from EHR data (Aim 2). In Aim 3, Dr. Kim will adapt and pilot test an EHR-embedded primary care strategy for routine detection of SD, guided by the SD prediction model. Fidelity and preliminary efficacy outcomes will be captured via the EHR, providing preliminary data for a next-step implementation and evaluation of a scalable, primary care strategy for the timely detection and management of SD. This K23 award will be the foundation for Dr. Kim’s future investigations focused on multi- site pragmatic trials testing health system interventions targeting SD to mitigate later life CI/ADRD risk.

NIH Research Projects · FY 2026 · 2024-08

PROJECT SUMMARY Alzheimer's disease and related dementias (ADRD) disproportionately affect Black and Hispanic adults, attributable, in part, to disparities in socioeconomic status and age-related health conditions. ADRDs are among the costliest diseases to treat, posing a profound burden for people already experiencing health disparities. Incarceration, which affects more people of low socioeconomic status, may play a key role. We propose to extend our current 28-year longitudinal study, the Northwestern Juvenile Project (NJP), to conduct the first comprehensive, prospective study of how the dose of incarceration—frequency and duration of stays, type of facility (juvenile detention, jail, prison), age(s), and recency—affects health, age-related conditions, and risk factors for ADRD. Leveraging our original sample (n=1829 (now 1492), who will be ages 39-49 in the proposed study), we will use a mixed-methods approach, focusing on modifiable, midlife risk factors for ADRD as noted in the Lancet Commission 2020 report on ADRD prevention and other risk indexes: hypertension, smoking, obesity, depression, exercise, diabetes, social con-tact, and alcohol abuse. Middle adulthood (40+ years) is a critical developmental period for targeting ADRD risk factors. We have already conducted a pilot study (n=65) that demonstrates the feasibility of the proposed methods and the need to study health, age-related conditions, and risk factors for ADRD in people who have been incarcerated. We will: (1) Interview participants to assess physical and mental health, psychological well-being, and cognition using the NIH Toolbox (crystallized and fluid intelligence) and the Uniform Data Set (memory); (2) Collect blood-based biomarkers of health (glucose metabolism, lipid ratios, kidney function, inflammation, and biological aging); (3) Collect physiological measurements of health (height, weight, waist circumference, blood pressure, resting heart rate); and (4) Use detailed data on dose of incarceration and the framework of dose-response models to assess how incarceration affects health, age-related conditions, and risk factors for ADRD. We have 3 specific aims: (1) To assess health, age-related conditions, and ADRD risk factors among participants at median age 45 and compare them to participants in the Add Health study, an NIA-funded study with a similarly aged sample; (2) To examine the relationship between the dose of incarceration and health, age-related conditions, and risk factors for ADRD; and (3) To identify risk and protective factors—including geocoded addresses to determine environmental risks—that moderate the relationship between dose of incarceration and health, age-related conditions, and ADRD risk factors. We hypothesize, for example: that our participants will have worse health than those in Add Health; that persons who cycle in and out of jail will demonstrate poorer health and greater risk for ADRD than persons with long prison stays, even after controlling for days incarcerated; and that limited exercise in corrections and unstable housing upon release will exacerbate incarceration’s consequences. By elucidating how the dose of incarceration and subsequent reentry experiences influence risk for ADRD, the proposed study will establish the empirical foundation needed to reduce disparities in healthy aging and mitigate risk for ADRD in persons who have been incarcerated.

NIH Research Projects · FY 2025 · 2024-08

Mutations on the LRRK2 gene, which increase the encoded protein's kinase activity, are common genetic causes of familial Parkinson's disease (PD). Noncoding variants at the LRRK2 locus have also been linked to an increased risk of sporadic PD. Thus, LRRK2 is a promising therapeutic target in familial and sporadic PD. While small molecule LRRK2 kinase inhibitors are currently tested in clinical trials, the precise pathological mechanisms of LRRK2 mutations remain unknown. Several studies have supported the idea of non-cell autonomous mechanisms leading to DA neuron death, a pathological PD hallmark. We observed elevated astrogliosis in the caudate/putamen of postmortem LRRK2G2019S carriers and LRRK2G2019S knockin mice. Together with a well-accepted role of LRRK2 in synapse and inflammation, this suggests that aberrant LRRK2 kinase activity increases the inflammatory burden in the nigrostriatal synapse by involving astrocytic activation. A limited understanding of the astrocyte signaling pathways relevant to PD hampered progress in establishing astrocyte dysfunction with PD pathophysiology. As our findings show a role of LRRK2 in the dopamine D2 receptor (D2R) signaling in the striatal astrocytes, the overarching goal of this study is to link astrocytic LRRK2- mediated D2R signaling impairments to neuroinflammation in these mice representing prodromal PD. Overall, we aim to explore if LRRK2-mediated striatal astrocyte D2R signaling perturbations exacerbate early synaptic inflammatory processes that lead to PD. We will utilize innovative genetic models and viral approaches to manipulate D2R signaling in identified astrocytes, allowing us to dissect D2R signaling-mediated mechanisms with high specificity in a specific cell type manner. Aim 1 will mechanistically link impaired D2R signaling and inflammation in striatal LRRK2G2019S astrocytes. In Aim 2, we will manipulate D2R signaling specific in striatal astrocytes and assess how this influences inflammation, neurotoxicity, and behavior in vivo. In light of the clinical testing of small molecule inhibitors against LRRK2 kinase and D2R agonists in the clinical setting, our work is crucially placed to set the framework for developing disease-modifying strategies targeting astrocyte dopamine signaling in the striatum.