Northeastern University

NSF Awards · FY 2025 · 2025-06

NONTECHNICAL SUMMARY This CAREER award supports research and education activities towards advancing our understanding of an unusual phase of matter known as the "fracton" phase. Fractons are unique particle-like responses in certain materials that have distinctive behavior: Unlike familiar particles like electrons or protons, they cannot move freely and often require binding in groups in order to move. This intriguing property is transforming our understanding of matter and sparking interest across diverse fields such as quantum materials, quantum field theory, and quantum information science. The restricted mobility of fractons also offers exciting potential for breakthroughs in emerging technologies, especially in developing quantum hardware and efficient quantum computers. The research will focus on three main objectives: 1) developing theoretical approaches to understand the collective behavior of fractons, 2) investigating fractons in open quantum systems, where interactions with the environment introduce challenges like noise and interference, and 3) designing algorithms that enable control of fracton phases in dynamic, out-of-equilibrium settings. Progress in these areas is key to leveraging fractons for quantum error correction and information processing technologies. This CAREER award also supports educational and outreach activities to train, mentor, energize, and retain students in STEM by providing immersive learning experiences. The PI will expand outreach to encourage female students and postdocs in STEM through the Women in Quantum Era seminar series and inspire future STEM aspirants through the Science Inspired by Art workshop, which will employ modular origami and decorative knots to interactively explore geometry and related scientific ideas. TECHNICAL SUMMARY This CAREER award supports theoretical research and education to understand the interplay between symmetry and decoherence in fracton and topological phases under both equilibrium and non-equilibrium conditions. The project aims to study fracton and topological states by constructing microscopic models and developing hybrid fracton field theories, allowing for the exploration of various correlated phases and phase transitions. A key component of this research involves open quantum systems, investigating whether fracton states can retain quantum coherence and entanglement in noisy environments, and exploring if decoherence can give rise to unique mixed ensembles absent in thermal equilibrium. Expected outcomes include advancements in quantum field theory frameworks with generalized symmetries and insights into dissipation and decoherence in dynamical phase transitions. More broadly, this research connects the fields of condensed matter and quantum information theory, with potential applications in scalable quantum simulators and robust quantum information processing. This CAREER award also supports educational and outreach activities to train, mentor, energize, and retain students in STEM by providing immersive learning experiences. The PI will expand outreach to encourage female students and postdocs in STEM through the Women in Quantum Era seminar series and inspire future STEM aspirants through the Science Inspired by Art workshop, which will employ modular origami and decorative knots to interactively explore geometry and related scientific ideas. 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 · 2025-06

This Faculty Early Career Development (CAREER) project will support research that investigates novel design strategies to enhance the adhesion and switching performance of soft sticky adhesives. These materials are made of rubbery viscoelastic polymer networks that form strong adhesion to various materials within seconds under gentle pressure through dense physical bonds at the interface. Their broad applications range from conventional load bearing tapes to novel adhesives for wound dressing, wearable sensors, and flexible displays. Their expanding application domain has revealed a critical need for enhanced fatigue resistance under prolonged cyclic loads, together with a new opportunity for controlled switching between adhering and non-adhering states. This award will support fundamental research to understand the mechanics of interfacial fracture, fatigue, and switchable adhesion in soft sticky adhesives through an integrated experimental and modeling approach. The outcome of the research will benefit the economy and society by enabling advanced fatigue-resistant and switchable soft sticky adhesives for load bearing, electronics, manufacturing, robotics, and healthcare applications. The research-integrated education will provide high school, undergraduate, and graduate students with multifaceted experiential learning through joint academia-industry workshops, summer camps, and curriculum enhancement. The objective of this CAREER project is to understand and harness the interplay between interfacial bonding, bulk dissipation, stimuli-responsiveness, and interfacial geometry (e.g., surface patterning) in soft sticky adhesives. To achieve this objective, model material systems will be fabricated and characterized to understand the underlying mechanisms. The investigation will focus on interfacial fracture, fatigue, and associated dissipation at multiple length scales by experimentally measuring key material properties, advancing theoretical models, and performing finite element simulations. In addition, a new switchable soft sticky adhesive based on thermally actuated liquid crystal elastomers (LCEs) will be designed by tuning the reversible phase transformation and viscoelastic dissipation in the material. Finally, the research will leverage the interaction between material properties and surface patterning to improve the adhesion and switching performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-05

Project Summary In Alzheimer's disease (AD), the most common form of dementia, aberrant and mis-processed proteins accumulate throughout the brain. AD-associated aberrant and mis-processed proteins may result from a variety of mechanisms, including protein-altering DNA sequence differences, dysregulated mRNA splicing, post- translational modifications, proteolytic cleavages, and protein misfolding. Although these mechanisms are known to affect a handful of proteins in AD, we have not been able to comprehensively characterize their impact on the proteome because of technological and methodological limitations. Thus, new approaches that can systematically profile aberrant and mis-processed proteins in AD are needed to improve our understanding of the disease's causes and identify actionable therapeutic targets. AD-associated protein dysfunctions also differentially affect distinct brain regions and cell types. To understand the factors responsible for this heterogeneity, we need approaches with sufficient scale to profile multiple brain regions and sufficient resolution to profile individual cells. Here, we pair state-of-the-art proteomic methods that we developed with proteogenomics, an approach that integrates genomic, transcriptomic, and proteomic data. The resulting proteogenomic pipeline will allow us to globally profile disease-associated proteins with amino acid substitutions, alternative protein isoforms, post-translationally modified proteins, and proteolytic cleavage products. In Aim 1, we apply our proteogenomic pipeline to multiple brain regions in a large AD cohort and multiple tauopathy mouse models. By profiling brain regions selectively vulnerable versus resilient to neurodegeneration in AD, the full spectrum of AD pathological stages, multiple disease subtypes, and multiple tauopathy mouse models using mass spectrometry methods that provide high proteome and individual protein sequence coverage, we will substantially expand our understanding of protein dysfunctions in AD. In Aim 2, we pair proteogenomics with the pioneering single-cell mass spectrometric proteomic methods we developed. We will profile individual neurons and non-neuronal cells extracted from human AD and tauopathy mouse brain tissues to provide needed insights into the cell type-specific factors that cause protein dysfunctions in AD. Across both aims, we apply rigorous experimental methods for functional validation and analytical approaches for causal inference to mechanistically characterize AD-associated aberrant and mis-processed proteins. Collectively, our aims will improve our understanding of AD's causes, identify new AD therapeutic targets, and provide new tools for studying protein dysfunctions in AD.

NSF Awards · FY 2025 · 2025-05

2520694 (Eckelman). The 2025 Symposium on Industrial Ecology for Young Professionals (SIEYP), organized by the International Society for Industrial Ecology Student Chapter Board (ISIE‐SCB), focuses on translating industrial ecology research into tangible outcomes through policy development and implementation. The main goal of this symposium is to provide an opportunity for young US-based professionals who perform research in Industrial Ecology (IE) to use their research skills to identify and develop ways to translate environmental engineering and sustainability research concepts into action, discuss their novel research ideas and experiences, and receive constructive feedback. The symposium will take place just prior to the ISIE Biennial Conference taking place July 1-4, 2025 in Singapore. The ISIE student community consists of more than one hundred members. ISIE‐SCB is suited to achieve the above symposium goal due to its proven track record and robust event planning experience. There are several sub-goals that the SIEYP aims to deliver to participants through its programming, including: (1) learning how to identify and effectively communicate the intersectionality of research with pertinent and emerging policy initiatives; (2) building skills of argument framing to appeal to cross‐disciplinary audiences including policy makers; and (3) translating research and communication skills into effective research planning and grant writing habits. SIEYP will provide participants with fundamental skills that will assist in research and grant writing, oral presentation, developing effective research products and increasing the impact of their research. The symposium will also provide students and young researchers with the skills to connect their research interests to policy relevant questions. Moreover, this symposium will facilitate long‐lasting international relationships and collaboration among young IE professionals. These collaborations provide future opportunities for joint projects that may result in benefits to US science and society at large. The SIEYP will have immediate benefits for those US researchers who attend, and compounding benefits for domestic and international environmental sustainability efforts that stem from idea-sharing and collaborative projects over time. 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 · 2025-05

The impact of a droplet on a surface is common in nature and industry, ranging from raindrops landing on the ocean or soil to spray coating. In industrial applications, the droplets often contain particles, which can modify the fluid properties and lead to peculiar behavior upon impact. The dynamics of droplet impact have been studied extensively for simple liquid drops, but it has not been studied as extensively for droplets composed of complicated fluids such as a suspension of particles. Droplet impact for these systems is difficult to predict because it depends on the interplay of multiple effects such as surface tension, viscosity, and inertia. As a result, the flow fields in these systems can be complex, and particles inside the drop may not be evenly distributed throughout the drop. If the concentration of particles in the drop is high, the drop is opaque, which makes it difficult to visualize internal flows. This project will establish a framework that combines several state-of-the-art diagnostic methods to probe the impact dynamics of drops composed of complicated fluids. Data will provide critical insight into the impact dynamics of complex fluid droplets on surfaces. The project will support an educational plan for K-12, undergraduate, and graduate students to help build the STEM workforce pipeline and engage public interest in multiphase flow research through the art of science competitions. The goal of this award is to develop an experimental platform by synergistically combining various advanced diagnostic techniques to probe the detailed dynamics as complex fluids droplets impact on surfaces, and to generate systematic understanding of the coupling between the complex fluid properties and flow behavior. The spatially and temporally varying flow, stress, and particle concentration fields will be resolved and analyzed to determine their contributions to global and local dynamics, such as impact outcome, cavity dynamics, and jamming behavior. The insight will help build models to predict behavior and will provide pathways to optimize existing industrial practices. The experimental and analytical tools that will be developed for the proposed project could be applied to a broad range of complex fluid and multiphase systems beyond droplet impact. 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 · 2025-05

Graphs are mathematical structures used to model pairwise relationships among objects of various forms. They consist of a collection of "vertices" (the objects) and "edges" (the connections) which connect pairs of vertices. Graphs can model a wide range of applications such as connections in social networks, hyperlinks in the web, and the wiring of the brain. Many of these graphs are massive. The availability of such large-scale graphs has posed significant challenges in terms of storage, processing, and analysis. At their heart, these tasks require efficient algorithms for various problems defined over graphs. While designing such graph algorithms has been a cornerstone of computer science research for decades, traditional algorithms often fall short when scaling to massive data. For instance, algorithms designed for massive graphs cannot even afford to read the entire input -- an assumption that is crucial for most traditional algorithms. This project focuses instead on advancing "sublinear-time" graph algorithms, which are specifically designed to process massive inputs. These are algorithms that uncover meaningful information about their entire input by examining only a small portion of it. The research objectives of this project will be complemented by a comprehensive approach to broadening the outreach of sublinear time algorithms through exploring practical applications, educational initiatives, dissemination efforts, and expanding participation in algorithmic research. More concretely, this project focuses on three aspects of sublinear time graph algorithms: their foundations, their limitations, and the connections they have to other models of computation. Specifically, this project aims to develop more efficient and ideally optimal sublinear time algorithms for a variety of foundational graph problems such as maximum matching or various graph connectivity problems (such as minimum spanning trees). The investigator also aims to develop a better understanding of the limitations of sublinear-time graph algorithms by designing systematic approaches to prove (unconditional) query lower bounds for these problems. Finally, many of the recent developments in other models of computation such as dynamic, parallel, or streaming algorithms rely heavily on better sublinear time graph algorithms. This project aims to take concrete steps towards better understanding these connections and further developing them. 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 · 2025-05

This award provides student travel and subsistence for the 2025 International Programming Language Implementation Summer School (PLISS) to be held May 26 -- May 31, 2025, in Bertinoro, Italy. This summer school provides an important and valuable educational opportunity for students to study topics related to designs and implementations of programming languages. The award's broader significance and importance include building international community and enhancing education of US students, including students from historically underrepresented groups. The school also provides students exposure to and multiple opportunities to interact with leading-edge research and researchers. By supporting US-based students, the school thus imparts training to the next generation of researchers in design and implementation of programming languages in both industry and academia, as well as to future application developers. 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 · 2025-05

Adolescence is an important developmental period marked by increases in independence, changes in motivations, and new experiences. As adolescents engage in new behaviors, they may experience good or bad outcomes, learn from the experience, and use what is learned when faced with a new situation. The goal of this project is to understand the cognitive and brain mechanisms related to learning from experience, and how these change from childhood to adolescence and then to young adulthood. The project tests a relatively novel characterization of adolescence as a time when learning from experience is heavily weighted both at behavioral and brain level. In addition to the scientific work, the project includes STEM outreach in K-12 schools and internships for high school students. In more detail, learning from experience has been broadly described as motivated learning. Motivated learning is critical for behaving adaptively, but little is known about the neurocognitive development of motivated learning. The project uses behavioral, computational, and neuroimaging approaches to investigate age-related changes in learning and integration of different types of learning. In particular, the project focuses on striatal and medial-temporal learning systems in the brain, and how these change across development. Notably, the project also uses a novel brain imaging technique to make inferences about dopamine to better understand its role in learning over the course of development. The results of the proposed work have the potential to better understand critical changes in the adolescent brain, focusing not just on vulnerabilities but also on adaptive opportunities and strengths related to 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 · 2025-05

Formal verification has made considerable progress in ensuring the correctness of system designs, but verifying distributed systems remains a significant challenge. Existing research on verifying distributed systems suggests general verification strategies that have limited scalability or concentrate on specific systems without sufficient attention to modularity and proof reuse. These limitations become apparent when verifying large-scale distributed systems with multiple distributed subsystems. Verifying different subsystems requires redundant work, and insufficient modularity impedes the verification process as a small code change entails a significant amount of proof rewriting. This project's novelties are composable semantic models that capture a variety of weakly consistent distributed system semantics and their correctness proofs and methodologies that reuse the models to simplify and scale the verification of distributed systems. The project's impacts are improved reliability and assurance of small to large-scale distributed systems, such as individual distributed key-value stores and cloud services that rely on multiple distributed systems. Additional impacts include new courses on verifying distributed systems. This project focuses on modeling and verifying weakly consistent distributed systems, the most widely deployed distributed system family. The key approach is to first model and verify the distributed system semantics rather than a specific implementation for high utility. A semantic model modularly captures common behaviors and states of distributed systems that observe the same semantics as an object and hides node and network-level details of the system for simple reasoning. The model encompasses reusable proofs of safety and liveness properties that must be satisfied by the system implementation. The model's proofs are reused to verify different system implementations under the same semantics through refinement. Semantic models are designed to compose with each other. Composability facilitates the reuse of designs and proofs of weaker semantic models to create stronger semantic models. The semantic models simplify the verification of composite distributed systems. Individual subsystems can be verified using corresponding semantic models, and interactions of the subsystems can be reasoned and verified based on simple model-level behaviors rather than sophisticated implementation-level behaviors. The model-based reasoning of interactions and the model's compositionality allow for modularly defining and verifying existing and new distributed system semantics that span multiple distributed systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

As 5G wireless services expand and the 6G era approaches, the American society will soon be able to leverage groundbreaking technologies to revolutionize communication, education, research, and decision-making. However, unlocking this potential requires the development of new on-chip radiofrequency (RF) systems allowing wireless transceivers to transmit data at extremely high rates even in highly congested electromagnetic environments. Therefore, new technologies are needed to enhance these wireless transceivers' resilience against electromagnetic interference (EMI). In this context, the rise of artificial intelligence (AI) and machine learning (ML) has led to new computational resources that enable the rapid detection and characterization of EMI in wireless systems. These advancements have paved the way for radio receivers (RXs) capable of suppressing EMI by activating tunable and sharp notches within the passband of their embedded bandpass filters. Among the most widely used filters in RF wireless transceivers are Aluminum Nitride (AlN) and Aluminum Scandium Nitride (AlScN) bandpass filters. Their popularity stems from their low loss, high out-of-band rejection, and compatibility with manufacturing processes used for integrated circuits in consumer electronics. However, current AlN and AlScN filters cannot activate tunable and sharp notches within their passband without suffering severe performance degradation, strong signal distortion, and a significant reduction in spectral efficiency. Additionally, these filters are unable to achieve fractional bandwidths of 10% or higher, which is a major challenge for their application in future 5G and 6G wireless transceivers. This project will leverage our interdisciplinary expertise in micro- and nanofabrication, spintronics, and microwave acoustics to develop SpinPhonic -- the first AlScN microelectromechanical RF bandpass filter capable of activating widely tunable and sharp notches within its passband for effective EMI suppression. The new SpinPhonic technology will achieve the widest fractional bandwidth ever reported for AlScN filters by leveraging the unique acoustic properties of phononic crystals (PnCs). Additionally, it will harness novel dynamical interactions between magnetic and mechanical degrees of freedom to activate exceptionally sharp notches for EMI suppression. These notches will attenuate EMI by more than 40 dB through magnon-phonon coupling in a ferromagnetic film heterogeneously integrated with the AlScN layer. This attenuation will be highly frequency-selective, minimizing disruptions to other RF communication channels at unaffected frequencies. Furthermore, SpinPhonic's notches will be tunable through changes of an external magnetic field, offering a fractional tuning range of their center frequency exceeding 10% and an average magnetic tuning sensitivity two orders of magnitude higher than that of current state-of-the-art magnetically tunable counterparts. Integrating SpinPhonic into future RXs will enable reduction of bit-error rates by two orders of magnitude in EMI-affected RF wireless transceivers, allowing the use of higher data rates for transmitting larger volumes of information. The project team will collaborate with the STEM education and workforce development program at Northeastern University to organize on-campus education activities involving K-12 students, community colleges, and local schools, with a focus on enhancing STEM engagement. 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 · 2025-04

Coastal ecosystems provide numerous benefits to people. While much research has focused on the coastal water column, less attention has been given to the importance of the seafloor or sediments of coastal systems. The sediments, however, can recycle nutrients to the water column and support the growth of primary producers such as phytoplankton. In turn these phytoplankton provide food for nutritionally and commercially important shellfish and finfish. The sediments also store carbon and filter excess nutrients from the water column, thereby improving water quality. This project is focused on bringing together a variety of data sources - from observations to modeling results for the Northeast United States. This region was chosen because it is relatively data rich compared to other areas and plays an important role in the US blue economy. A literature review and synthesis will be conducted to gather relevant data, and these data will be evaluated to find patterns of variation. Ocean models will then be used to assess changes across this region that could happen on the short and long-term. Sediment data will also be compared to water column data to see how they are connected, and if their connection is changing over time. This study will engage the scientific community to develop best practice guidelines for sediment data collection and develop community driven priorities for future sediment research studies. The project will provide training for a graduate student and a postdoctoral researcher and support workshops for both scientists and stakeholders. The seafloor plays a major role in influencing atmospheric carbon dioxide and oxygen concentrations, low-oxygen zones in the ocean, and ocean acidity, and represents the only geologic-scale storage of oceanic carbon. The sediments in coastal areas are particularly important as it is estimated that they account for ~70% of ocean carbon burial. Coastal sediments also recycle nutrients to the water column, fueling future water column primary production, and they can improve water quality via nutrient removal. Despite their importance, coastal sediments are poorly sampled relative to the water column, with large spatiotemporal gaps in datasets of nutrients and biogenic gas fluxes. The paucity of coastal sediment flux data leads to incomplete estimates for carbon, oxygen, and nutrient budgets in the ocean. Synthesizing disparate datasets of benthic variables therefore addresses an urgent need to improve the understanding of the role of sediments in the carbon and nutrient cycling in coastal regions at multiple timescales. This effort would further the understanding of mechanisms and environmental conditions influencing benthic dynamics, and consequently the role they play in driving pelagic biogeochemical cycles. The project seeks to do this by focusing on the Northeast US (NE-US), which is a relatively data-rich region. Specifically, existing long-term sediment and water column datasets from the NE-US, model output, and re-analyses will be combined to evaluate the role of different environmental conditions on benthic fluxes of carbon, oxygen, and nutrients. The project will characterize flux patterns in this region, help interpret the larger context these fluxes were observed in, co-locate different benthic fluxes and related variables in space and time to evaluate their relationship to changing environmental conditions, and develop community driven guidelines for data use and future observations. This project will support an early career scientist, one Ph.D. student, and one postdoctoral researcher. Results from this study will be disseminated through publications, presentations at scientific conferences, workshops with stakeholders and public outreach events. 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 · 2025-04

PROJECT SUMMARY This proposal aims to improve the rigor and reproducibility of research on plasticity in human executive functions (EFs) in older adults, many of which are at risk for developing Alzheimer’s Disease and Related Dementias (ADRD). We address a critical gap between research and practice that is characterized by a growing commercial space marketing “brain training” approaches (with EFs being one of the most common targets), many of which are particularly catering to older adult populations promising to mitigate age-related cognitive decline or even prevent ADRD. However, despite the growing literature, the current evidence is mixed, with both methods and findings varying vastly across studies. Here, we address these significant gaps that pose obstacles to understanding interventions’ reliability and validity by collecting a large-scale open dataset that compares different training approaches on a common set of outcome measures. This is addressed through 4 independent aims, all based upon a large-scale study involving new data collection from 1,250 older adults (aged 60+). Aim 1 focuses on determining ingredients that promote adherence and effective engagement with EF training by examining a set of commonly used ingredients in EF training studies (single vs multicomponent training, standard vs gamified training, and standard vs. added coaching). We will examine how these ingredients differentially impact various dimensions of adherence including initiation, implementation, persistence, and effective engagement. Aim 2 investigates how those ingredients mediate training outcomes, specifically, we test the differential impact of training conditions on EFs, broader cognition, everyday functions, quality of life, and belief structures. Aim 3 seeks to uncover individual characteristics that moderate training outcomes. Here, we examine how adherence and cognitive training outcomes may differ across individuals, and the extent to which this variability may explain differential outcomes across studies. Aim 4 highlights our goal of promoting open science through sharing of software tools and data. We will freely share the cross-platform training and assessment app, research portal, and dataset, that together will promote replication and provide access to other groups using common outcomes and individual difference variables. Our project goals extend our systematic and programmatic approach that builds upon successful work in younger adult populations using a citizen science approach involving thousands of participants, as well as an R21 that has focused on testing the impact of gamification and the extent to which participant characteristics (i.e., inhibitory control skills) interact with the implementation of such motivational structures. Our unique and novel large-scale dataset will lead to a robust and representative understanding of factors that mediate and moderate EF training that will be impactful whether or not one hypothesizes benefits from EF training. Project outcomes will advance our scientific understanding of factors impacting effectiveness of cognitive training and help empower older adults with evidence-based and freely available tools that are effective and genuinely enhance their cognitive resilience and quality of life.

NSF Awards · FY 2025 · 2025-04

Plastics are accumulating throughout the Earth system including in the ocean’s subtropical gyres. Studies suggest that plastics are now a major source of dissolved organic material in these regions of the ocean. Therefore, it is important to consider plastic-derived organic material as part of the ocean carbon cycle and understand its effects on ocean biology. This project will test the idea that bacteria in ocean surface waters consume plastic-derived organic matter, and in doing so they compete with phytoplankton for key nutrients like nitrogen and phosphorus. The investigators will utilize a combination of laboratory experiments, field sampling, and numerical modeling to explore this hypothesis. The project will support two graduate students and an undergraduate student researcher. The investigators will share their research with policy makers and with the general public, with a focus on middle and high school aged students. The core hypothesis of this project is that “nutrient-theft” by bacteria when consuming plastic-derived dissolved organic carbon (pDOC) results in sufficient drawdown of nitrogen and phosphorus in surface waters to reduce new production and the strength of the biological carbon pump, especially in the subtropical gyres where microplastic abundance is greatest and nutrients are at a minimum. The team will test this hypothesis using an ecosystem-biogeochemistry model (CESM-MARBL) augmented to include explicit representation of marine bacterial growth, nutrient use, and pDOC all parameterized using novel empirical knowledge gained through shipboard fieldwork, photochemical studies to improve estimates of pDOC photoproduction rates, and bioassays to determine the kinetic parameters of bacterial nitrogen and phosphorus uptake in response to pDOC fertilization. Global ocean model runs will simulate spatiotemporal change in net primary productivity, nitrogen- and phosphorus-based new production, and the strength of the biological carbon pump. Model simulations will be run with no added pDOC, 1948-present pDOC fertilization rates, and with predicted future pDOC fertilization based on business-as-usual increases in surface ocean plastics until 2100. These model simulations will test the core hypothesis by assessing how nutrient theft due to pDOC fertilization is altering ocean biogeochemistry and reducing the strength of the biological carbon pump today and through the 21st century. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-03

Project Summary Safe and effective treatment of neuropathic pain remains a challenging medical problem. First- and second-line treatments show modest, variable efficacies in treating neuropathic pain and carry serious liabilities. Orthosteric CB1R agonists like delta-9-tetrahydrocannabinol show (pre)clinical efficacy in suppressing neuropathic nociception but also produce unwanted side-effects (e.g., tolerance, physical dependence, psychoactivity). CB2R agonists (GW842166X, S-777469, and JBT-101) have not shown efficacy in clinical trials in other indications and have not been tested for efficacy in people in therapeutic indications supported by the preclinical literature. We have successfully identified novel “dualsteric modulators” having a unique pharmacological phenotype characterized by activity as both a non-psychoactive cannabinoid CB1R allosteric agonist-positive allosteric modulator (ago-PAM) and a CB2R agonist. Our central hypothesis is that such dualsteric ligands leverage the therapeutic advantages of allosteric GPCR regulation of both cannabinoid receptors and provide effective broad-spectrum analgesia without abuse liability and other CB1R-mediated side effects. We will unite the complementary and non-overlapping expertise of five different laboratories to conduct experiments proposed under three Specific Aims. Aim1 will design and synthesize a series of novel N-arylindole and 2-cycyloalkyllindole analogs as CB1R/CB2R ago-PAMs with improved pharmacological profiles compared to our lead compounds, GAT1102 and GAT588. Aim 2 will perform in vitro pharmacological characterization and target-engagement studies and in vivo ADME/PK profiling of key CB1R/CB2R ago-PAMs to eliminate potential liabilities and identify compounds with suitable drug-like properties for in vivo studies. Aim 3 will perform in vivo evaluation of optimized CB1R/CB2R ago-PAMs with suitable drug-like properties for suppressing nociception in mechanistically distinct neuropathic pain states. We postulate that our dualsteric ligands will suppress both sensory and negative affective dimensions associated with neuropathic nociception without unwanted side effects of orthosteric CB1 agonists. Successful validation of our innovative first-in-class therapeutic strategy offers the potential to change the face of pain management.

NSF Awards · FY 2025 · 2025-03

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering. This project examines the management and restoration of watersheds in remote, mountainous regions. Management efforts can reduce the risk of severe wildfires in these regions. Among local residents and stakeholders, however, there may be misunderstandings about the ecological processes that lead management efforts to reduce the risks of fires. As seen more generally in studies of risk perception, the resulting uncertainties can result in indecision and inaction. In this study, the researchers examine how uncertainties in evaluating risks lead people to prioritize different management opportunities. Using experimental methods, the study presents participants with varying degrees of uncertainty about anticipated outcomes of restoration efforts to determine how this variation affects decisions to allocate resources toward management. The project contributes to goals of forest management by identifying the information that stakeholders need to make decisions about restoration efforts. The project also provides training opportunities for a graduate student and a postdoctoral scholar. This study addresses the effects of uncertain outcomes on the perceived benefits of restoration efforts in remote, mountainous watersheds. Drawing on methods and theory from cognitive psychology, the researchers experimentally pose scenarios to participants to determine how varying uncertainty leads individuals to evaluate the benefits of different management options. This work focuses on three distinct types of uncertainty, namely direct, indirect, and perceived uncertainty. Direct uncertainty assumes that the probabilities of events are known completely whereas indirect uncertainty arises when the respective probabilities are known only incompletely. Perceived uncertainty refers to subjective feelings of uncertainty, which are commonly influential in decision-making. This project disentangles the respective effects of the different types of uncertainty on assessments of risk and subsequent decisions. An additional objective is to assess the extent to which visualization techniques can reshape conceptualizations of watershed-restoration uncertainties. This study tests the hypothesis that modern uncertainty-visualization techniques can reduce the complexity of watershed restoration uncertainties by intuitively communicating the uncertainties and key aspects of relevant ecological processes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-03

Project Summary: Chronic inflammatory and neuropathic pain represents a substantial public health challenge, affecting an estimated 20.9% of U.S. adults, with conventional treatments often limited by efficacy, tolerance, and addiction risks. Recognizing the urgent need for innovative therapeutic strategies, this proposal focuses on the exploration of nicotinic acetylcholine receptors containing the α9 subunit (α9*-nAChR) as novel targets for pain management. Recent advances have highlighted the potential of α9*-nAChR selective modulation to offer significant relief in neuropathic pain conditions through immune cell function regulation, presenting a promising avenue for drug development. Our proposed research aims to address the critical gap in effective pain management by developing and optimizing novel small-molecule probes targeting α9*-nAChRs. These probes are anticipated to demonstrate superior anti-inflammatory and analgesic properties, devoid of the central nervous system side effects associated with opioids. The project is structured around four specific aims, designed to advance our understanding and therapeutic exploitation of α9*-nAChR systematically. Aim 1: We will develop a series of small-molecule agonists and antagonists, utilizing structure-activity relationship (SAR) studies to enhance drug- like attributes, including efficacy, potency, selectivity, solubility, and stability. Computational analyses will guide the optimization process. Aim 2: Novel compounds will undergo rigorous testing to confirm their selectivity and functional activity at α9*-nAChR, employing two-electrode voltage clamp recordings and a comprehensive screening against other human and mouse nAChR subtypes to ensure specificity and minimize side-effect liabilities. Leads that exhibit high potency and selectivity for α9*-nAChR vs. other human and mouse nAChR subtypes will be tested for their inhibitory effect on ATP-mediated release of IL-1β by human mononuclear phagocytes. After passing a Psychoactive Drug Screening Program (PDSP), compounds exhibiting potent anti- inflammatory activities will be profiled pharmacologically (ADME/PK studies). Aim 3: Selected compounds will be evaluated for their absorption, distribution, metabolism, excretion, and pharmacokinetic properties to identify candidates with optimal drug-like characteristics for in vivo efficacy testing. Aim 4: The most promising α9*- nAChR probes will be tested in mouse models of chronic inflammatory and chemotherapy-induced neuropathic pain, aiming to establish their therapeutic potential, side-effect profile, and the sustainability of their analgesic effects upon repeated administration. This interdisciplinary and collaborative project leverages cutting-edge pharmacological, computational, and in vivo methodologies to unveil the therapeutic potential of α9*-nAChR modulation in chronic inflammatory and neuropathic pain treatment. By elucidating the roles of α9*-nAChR in pain pathways and developing novel agonists and antagonists, our research promises to pave the way for the creation of non-addictive, effective pain relief options, thereby addressing a critical unmet medical need and making a significant contribution to pain management and neuropharmacology.

NSF Awards · FY 2025 · 2025-03

The manufacture of ammonia (NH3) – a key chemical used in fertilizer production - is one of the world’s largest chemical processes. Presently, almost all of the world’s NH3 production is based on the Haber-Bosch (H-B) process. The H-B process utilizes thermal energy derived from fossil fuel resources and produces carbon dioxide emissions. The project investigates a sustainable, alternative electrochemical approach to NH3 manufacture from nitrogen (N2) gas and hydrogen gas or water. The process is facilitated by a lithium-based electrolyte supported on copper (Cu). Key mechanistic aspects occur in a layer that forms on the copper. The reactions occur on a short time scale in a complex chemical environment. It is difficult to study this reaction. The project focuses on first-principles calculations and artificial intelligence (AI) to identify key steps in the reaction. Integration of the research and educational/outreach activities will be achieved through a Computation and Catalysis (ComCatalysis) program, which includes a 5-hour workshop and multiple summer research internship opportunities for high school students in the Greater Boston area. The project focuses on the lithium-mediated nitrogen reduction reaction (Li-NRR) in nonaqueous electrolytes to advance ammonia electrosynthesis using multi-faceted electronic structure theory simulations, including density functional theory (DFT), embedded correlated wavefunction theory (ECW), ab initio molecular dynamics (AIMD), and active machine learning. Specifically, the study will advance Li-NRR by 1) computationally characterizing microenvironments of the SEI, formed from reductive electrolyte decomposition on the cathode to provide active sites for nitrogen activation and reduction, 2) elucidating reaction mechanisms of Li-NRR in the nonaqueous electrolyte via rigorous kinetics prediction using ECW, and 3) understanding synergy between the electrolyte and electrode in controlling Li-NRR activity towards optimizing ammonia electrosynthesis performance. The emerging theoretical predictions will be validated through collaboration with an experimental partner at the California Institute of Technology. Expected outcomes include: 1) rationalization of morphology, composition, and dynamic formation/evolution of the SEI in Li-NRR, 2) identification of the reaction pathway, active sites, and the rate-limiting step of Li-NRR, and 3) optimization strategies to improve Li-NRR efficiency through tuning electrolytes and electrodes. The computational modeling protocols developed in the project have potential to advance theory development in heterogeneous catalysis and can be extended to understand fundamentals of many electrocatalytic systems, including CO2 mitigation and conversion, water electrolysis, and hydrogen fuel generation. 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 · 2025-02

Quantum computers leverage quantum phenomena such as superposition and entanglement in quantum bits (qubits), enabling them to solve certain computational problems exponentially faster than classical computers. The successful realization of quantum computers has the potential to transform diverse fields such as drug discovery, quantum chemistry, biology, cryptography, image processing, optimization, and machine learning by addressing computational challenges that are infeasible for classical systems. Estimates suggest that general-purpose quantum computers capable of solving real-world problems will require 10⁴–10⁵ physical qubits. A significant obstacle to scaling quantum computers to this level is the hardware infrastructure, which currently relies on room-temperature rack electronics for qubit control and readout, along with bulky, connectorized microwave components—such as circulators and amplifiers—inside dilution refrigerators. This project aims to address these limitations by developing energy-efficient, low-cost, and compact cryogenic chips that enable scaling quantum systems to support thousands of qubits. The research focuses on advancing cryogenic Complementary Metal-Oxide-Semiconductor (CMOS) integrated circuits (ICs) for qubit control pulse generation and superconducting chip technology for on-chip circulators. These innovations are expected to accelerate breakthroughs in quantum computing while also benefiting related fields such as satellite communication, space-based telescopes, and cryogenic electronics. Furthermore, the project seeks to foster seamless integration between circuit design and quantum physics, laying the foundation for a diverse and skilled workforce in this multidisciplinary research domain. To achieve this, the project will implement a range of educational and outreach initiatives, including online courses, undergraduate research opportunities, career development workshops for K-12 students, and the creation of open-source infrastructure. The research activities are organized into three thrusts: cryogenic (4K) CMOS IC development, superconducting chip development, and system integration with superconducting qubits operating at 10-100mK. First, a fully analog, low-power, and scalable qubit control scheme will be demonstrated using CMOS ICs operating at 4K, eliminating the need for room-temperature rack electronics. Unlike current digital-intensive control schemes, this project explores low-power microwave pulse generation using analog filter synthesis, enabling significant power savings compared to state-of-the-art digital qubit control circuits. Analog multiplexing schemes will be explored to reduce the cabling overheard between the 4K to 10mK stages. Second, time-modulated Josephson Junction-based non-reciprocal devices will be developed to replace the bulky and costly ferrite-based circulators and isolators currently used in dilution refrigerators. These on-chip, superconducting circulators are expected to offer drastically reduced size and cost when compared to their ferrite counterparts. To aid easier integration with the qubits, these superconducting circulators will be designed to achieve to low intermodulation power while achieving low-loss transmission and high isolation at the input frequency. Finally, the cryogenic-CMOS ICs and superconducting circulators will be integrated with superconducting qubits to demonstrate a fully integrated closed-loop system for qubit control and readout. 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 · 2025-02

End-linked polymer networks are macromolecules that can be highly stretchable, tough, yet accumulate minimal damage under cyclic loads, making them promising next-generation load-bearing soft materials. Their excellent mechanical properties rely on the potential for large and reversible strain-induced crystallization, which is regulated by defects in the material and with temperature. However, the coupling between strain-induced crystallization, topological defects, temperature, and the corresponding mechanical properties of end-linked polymer networks is not well understood. This award will support fundamental research to combine experimental and modeling approaches in understanding mechanisms and developing new end-linked polymer networks with desired mechanical properties. The fundamental understanding developed from this project will benefit the society by enabling novel strong and tough soft materials that can maintain their excellent properties under cyclic loads, hence facilitating emerging applications such as soft robotics, medical devices, and wearable electronics. In addition, the project will introduce students to emerging industrial needs through a new university-industry workshop. The outcomes of the research will be integrated into core undergraduate courses and multiple well-organized outreach activities such as the high school Building Bridges program and the Research Experiences for Teachers Summer Institute, with an expectation to engage a diverse group of students. The objective of this research is to investigate the fundamental role of topological defects in regulating the strain-induced crystallization in end-linked polymer networks at the microscale, as well as their stress-strain behaviors, fracture, and fatigue properties at the macroscale. To achieve this goal, the project will study a model material system and focus on topological defects of dangling chains and cyclic loops with quantitative tunability. The research will combine experiment and modeling at two length scales, including mechanical characterization at the macroscale and in-situ X-ray scattering characterization at the microscale. The two length scales will be linked by a continuum thermodynamic model, a microscopic polymer fracture model, and a numerical finite element model. The collaborative research will investigate stress-strain responses across a wide range of temperatures, as well as fracture and fatigue behaviors of the model end-linked polymer network. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Traumatic events experienced early in life can shift the behavioral repertoire in adolescence and adulthood, causing increases in risk seeking behaviors. These risky behavioral changes can lead to consequences such as motor vehicle accidents, increased sexual behaviors, and drug and alcohol abuse. The likelihood of making a risky decision is highest in adolescence, when unfortunately, the consequences may be the most severe and life altering. Understanding the ways in which early life adversity (ELA) increases adolescent propensity for risk taking is essential to preventing this maladaptive response, which can lead to harmful outcomes. ELA alters cellular function and activity in areas responsible for control of risky behaviors, including the basolateral amygdala (BLA), nucleus accumbens (NAc), and prefrontal cortex (PFC). The connectivity between these areas is responsible for assessing threat, responding to novel stimuli, and assessing valence, all of which are essential for safety related behaviors. The BLA is particularly poised to control risky behaviors and is also vulnerable to ELA related changes, including increases in neuronal excitability, making the BLA an incredibly valuable region to study when investigating ELA induced risk taking. Thus, examining activity changes to the BLA during ELA and manipulating ELA-impacted BLA ensemble activity during a risky decision-making task (RDT) will uncover a targetable neural mechanism underpinning adolescent maladaptive behavior. The RDT is a suitable task to assess risk taking because it measures an animal’s probability to choose a risky reward under variable probabilities of known risk, which is a shared core feature of risky decision making across species and therefore provides translational insight. In my previous work in the Brenhouse lab, I use a maternal separation (MS) model to assess adolescent changes in anxiety-like behaviors following chronic BLA inhibition. I found that rats exposed to MS exhibited decreased anxiety behaviors in adolescence as evidenced by increased exploratory behavior, which was contrary to my original hypothesis. This led me to design the current proposal, as I hypothesize that MS can drive increased risk taking in adolescence by recruiting BLA neuronal populations to be hyperexcitable. In Aim 1, I will test for ELA-driven changes to BLA cellular activity profiles by using RNAscope to examine increased immediate early gene expression in glutamatergic and GABAergic cells within the BLA following various time points of MS or control rearing. In Aim 2 I will tag cells with an activity dependent viral vector during MS, and then optogenetically excite or inhibit these cells during the RDT in order to test the ability of MS-affected cells to drive changes to risky behaviors. Ultimately the resulting discoveries from this project will contribute to the understanding of how ELA alters risk taking behaviors in adolescence and inform strategies to develop treatments for disordered behaviors involving increased risk seeking. The training I will receive through this F31 award will aid in my goals of becoming a well-rounded independent researcher focused on studying animal models of disease following ELA.

NSF Awards · FY 2025 · 2025-01

Many currently unresolved combinatorial optimization problems can be mapped into large-scale Quadratic Unconstrained Binary Optimization (QUBO) problems. Solving these problems can lead to breakthroughs in various disciplines including medicine, finance, and engineering, among others. Unfortunately, traditional computing systems based on von Neumann architectures struggle to accurately solve QUBO problems involving more than a few tens of variables. This limitation has spurred academia and industry to seek alternative methods for addressing large-scale QUBO problems. Recently, this effort has driven considerable interest in analog computing systems known as Oscillator Ising Machines (OIMs). OIMs use oscillating physical devices as artificial binary spins and offer high parallelization during the computation. However, current OIMs consume significant power per spin, and their accuracy in solving QUBO problems decays sharply as the problem size grows, which is primarily due to amplitude heterogeneity (AH), a phenomenon that disrupts the dynamics of large analog spin networks. In practice, AH limits the ability of OIMs to find correct solutions for QUBO problems with more than hundred variables. This project will create “n-SPHERE” (multidimeNSional comPlementary metal-oxide-semiconductor HypERspin programmablE circuits), a new solver for combinatorial optimization problems manufacturable in CMOS technology. The project team will collaborate with the STEM education and workforce development program at Northeastern University to organize on-campus activities involving K-12 students, community colleges, and local schools, focusing on enhancing STEM engagement, particularly among underrepresented groups. This research will form a new foundation for analog computing by generating a chip-scale computing engine that successfully solves QUBO problems with thousands of variables while consuming power levels in the microwatt range. n-SPHERE will surpass previous OIMs by utilizing novel spin-network dynamics and advanced nonlinear circuit designs. It will prevent performance degradation due to AH by employing CMOS circuits that mimic the behavior of multidimensional hyperspins and by implementing a new annealing technique, called dimensional annealing, during the computation. When addressing QUBO problems with thousands of variables, n-SPHERE is expected to achieve a probability of success and time-to-solution two orders of magnitude better than current OIMs. In addition, n-SPHERE will consume over ten times less power per spin compared to the existing QUBO solvers. This capability will produce new computational resources for exploring and leveraging emerging phenomena in a variety of disciplines. 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 · 2025-01

This project approaches the question of higher performance and better energy efficiency in electronic chip design with two key insights from biological systems: non-Boolean information encoding (analog processing in brain), and co-localized memory and computation (as in brain synapses). The specific objective of this project is to create a design framework for efficient information processing with intrinsic non-binary representations and in-memory memory and computation. If successful, this project can shed light on the fundamental role of information encoding and its physical implementation in determining system energy efficiency, as well as provide practical design automation methodology to infuse computation and learning into the analog/mixed-signal (AMS) domain before the digitalization step. Apart from its technological impacts, the integrated educational plan of this project is to empower students from all backgrounds with interdisciplinary experience and to cultivate a community of lifelong learners with social awareness. The project will enable joint optimization of circuit, architecture, and algorithm in a seamless manner across wide-range of applications including in-memory computing (IMC) and near-sensor processing (NSP), and consists of three major research thrusts: (1) to advance AMS design automation, novel neural network-inspired model abstraction, and hardware substrate will be developed to enable a streamlined design flow that uses AMS circuits as building blocks for information processing; (2) to support flexible and efficient in-memory computing architecture, this project will build intelligent and malleable peripheral interfaces and compilation framework by leveraging the AMS design methodology developed earlier; (3) to address the energy efficiency challenge in resource-constrained sensor systems, it will explore a context-aware analog-to-information frontend design by developing efficient near-sensor processing with multiple signal channels and multiple sensing modalities. These will serve as building blocks towards understanding the holistic interactions and design trade-offs of performance, efficiency, safety, and security in heterogeneous systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This project is co-developing Radio Frequency (RF) architectures, analog computing algorithms, and compute-in-memory architectures for code-domain, Multiple-Input / Multiple-Output (MIMO) radar systems. This cross-layer project reimagines the boundary between analog and digital signal processing by moving code-domain radar processing operations closer to the RF frontend. This enables low-power and ADC-free architectures, supporting scaling to large MIMO arrays. The code-domain radar signals will be processed at Gigahertz rates in the analog domain using cross-correlators that operate using a margin-propagation paradigm, resulting in lower power and compute latency. A compute-in-memory (CIM) architecture reduces the data transfer between the high-speed memory and the correlator sub-systems. The proposed approach is expected to achieve a 10-100x reduction in power consumption, and 5x lower compute-time per frame compared to conventional radar systems. The project also aims to prototype a low-power, high-performance radar system-on-chip using commercial Complementary Metal-Oxide-Semiconductor (CMOS) technologies to demonstrate both the cost-effectiveness and the scalability of the proposed approach. The advancements in MIMO radar technology can significantly enhance detection range and target discrimination, improving safety and efficiency in various applications, such as autonomous vehicles, drone navigation, aviation, and security systems. The reduction in power consumption makes these systems more environmentally friendly, reduces the cost of thermal management, and enables their deployment in power and cost-constrained environments, such as in situ sensing and portable devices. Furthermore, the project’s success in demonstrating highly efficient cross-correlations can pave the way for broader adoption of analog computing in edge devices, addressing critical power and latency constraints in real-time applications. Ultimately, this research promises to make technology more accessible, reliable, and sustainable, contributing to public safety advancements and environmental monitoring. The education and workforce development activities within the project will focus on widespread dissemination, broadening the STEM workforce and deepening cross-domain expertise in software-hardware codesign. 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 · 2025-01

Cell motility through tissues is a key aspect of both healthy developmental processes and the spread of cancer to metastatic sites. Hence, understanding the key ingredients behind this process in a quantitatively predictive manner is of tremendous interest both for fundamental biophysics and practical applications. This award focuses on a joint experiment-theory project to further this understanding. The methodology will rely on constructing a detailed computational model taking into account features of the motility process that have heretofore not been included in any such approach. These features include how the cell utilizes “blebbing” (pressure induced protrusions of the cell membrane) to move forward and how it deforms its nucleus to allow passage through tight spaces. These aspects will be investigated experimentally by studying cells moving through carefully designed microstructures and measuring their morphology and mechanics during the motility process; these experiments, carried out by French collaborators from the Ecole Normale in Paris, will provide critical data for both developing and testing the computational model. Coupled to the research aspects of this award will be the training of students to address highly interdisciplinary research areas and also the furthering of scientific exchanges between teams in the US and France, who have historically approached this subject using complementary perspectives. In this award the investigators propose to develop a new generation of phase-field-based models for studying eukaryotic cell motility, especially when it takes place in complex geometries. Previous efforts have neglected important features such as cortex contractility, the role of the nucleus, and the ability of cells to switch motility phenotypes. Now, recent advances in both available experimental information and computational techniques coupled with raw computational power will enable this critical advance to tackle a fundamental aspect of cell physiology. The creation of such a new generation model will yield results that have wide implications for both developmental processes and disease states such as metastatic cancer. This project brings together principal investigators who have extensive experience in the nonlinear dynamics underlying cytoskeletal dynamics and cell motility (Levine), and in the development of advanced analytic and computational techniques that have transformed the phase-field idea into a powerful and quantitatively accurate tool for free surface problems in non-living systems (Karma). The proposal also will also involve collaborators from the Ecole Normale who will design and conduct specific experiments to provide needed data to further model development and validation. This collaborative US/France project is supported by the Physics of Living Systems program in the Division of Physics at the US National Science Foundation and the French Agence Nationale de la Recherche, where NSF funds the US investigator and ANR funds the partners in France. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

Project Summary By age 49, nearly 85% of women in the US will have given birth at least once. The pregnancy and postpartum periods are marked by extreme hormonal fluctuations that can induce long-lasting changes in the brain, but mechanistic insight into how the unique hormonal milieu that characterizes pregnancy produces stable alterations in neural activity and behavior is lacking. The medial prefrontal cortex (mPFC)—a brain region critical to higher-order processes like executive function, emotion regulation, and decision-making—has been especially under-studied in parous animals, despite reports of alterations in these functions in parous women. Our lab has collected intriguing preliminary data in rats suggesting that during late pregnancy, neural activity in the mPFC is markedly suppressed compared to that of nulliparous (NP) animals. After weaning, however, we found that mPFC activity swings well past that of NP, suggesting that the brain undergoes an over-compensatory rebound from the pregnancy after parturition. The main goal of this proposal is to investigate the specific role that the hormone allopregnanolone (AP)—whose levels are notably high during late pregnancy—plays in producing this effect. AP acts as a positive allosteric modulator at the GABAA receptor, making it a potential driver of the neural inhibition we observe in late pregnancy. Here we will test the hypothesis that allopregnanolone-mediated GABA signaling is both necessary and sufficient for producing the long-term alterations in mPFC function observed in primiparous animals. The Aims that comprise this proposal integrate pharmacological, chemogenetic, and CRISPR/Cas9-based manipulations to either block or mimic AP-related processes during late pregnancy. We will then use fiber photometry to measure mPFC activity during recall of conditioned fear (which relies on optimal mPFC function) at post-weaning timepoints. Together, these experiments are able to establish a causal, mechanistic link between the pregnant and primiparous brain.