Purdue University

NSF Awards · FY 2024 · 2024-08

With this award, the Environmental Chemical Sciences Program in the Division of Chemistry funds Professor Alexander Laskin at Purdue University and Professor Jonas Baltrusaitis at Lehigh University and their graduate and undergraduate students. Struvite, a common wastewater valorization product, contains essential plant nutrients, phosphorus and nitrogen. Utilized as a fertilizer, its low solubility microcrystals exhibit slow-release properties, providing a gradual nutrient release in line with plant uptake rates. Because of the unique composition of struvite and typically present iron impurities, struvite can catalyze environmental chemistry reactions, facilitating the transformation and remediation of organic pollutants and dissolved organic matter (DOM). This project will advance fundamental environmental chemistry knowledge concerning the under-studied colloidal struvite-organic mixtures, which in turn will be transformative to advance broader research on the complex multi-phase environmental aquatic mixtures. It will lay the foundation for the fundamental understanding of processes underlying practical use of the environmentally friendly slow-release struvite fertilizers. The broader impact of the project extends to providing quantitative predictions of the composition and physical properties of struvite microcrystals, a common wastewater valorization product, and their its environmental impact when used as a fertilizer. Project results will inform decisions regarding engineering, process design, and management controls for practical utilization of environmentally friendly struvite fertilizers, enhancing their performance and value, while aligning with the needs of environmental protection and sustainability. The interdisciplinary nature of this project creates a unique educational opportunity for students, offering hands-on experience with advanced synthesis techniques and state-of-the-art analytical methodologies. This experimental project investigates the chemical composition, physical properties, and catalytic behavior of laboratory-synthesized struvite with varying and tailored iron content. Given struvite's potential to serve as a sustainable alternative to conventional fertilizers and mitigate environmental issues such as water eutrophication and tropospheric pollution, this project fills critical knowledge gaps necessary for a predictive understanding of struvite's environmental implications. The project will investigate the chemical transformations of representative laboratory proxies of DOM catalyzed by synthesized struvite colloids with varying iron content. These experiments will yield fundamental insights into the multiphase reaction chemistry of struvite microcrystals and their impact on DOM in aquatic environments. Ultimately, this research will delineate struvite’s role in the complex multi-phase chemistry of terrestrial and atmospheric water 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 2024 · 2024-08

This award supports an effort to develop a database of plasma photoionization processes that are of great practical importance for applications such as combustion and high-speed aerodynamics. Ionization of gas by means of intense laser radiation, a process known as photoionization, is important in the research fields of gaseous discharges and optics; and photoionization is broadly utilized in state-of-the-art optical diagnostics of combustion and high-speed aerodynamic flows, as well as in generation of laser filaments. However, numerous photoionization processes utilized daily in these research fields are weakly characterized, which can cause ambiguities. This project will utilize a novel technique to study a number of specific photoionization processes of particular relevance to the practical applications. The broader impacts of the project will also include engagement and mentorship of undergraduate and graduate students who will be involved in the project, and integration of the research results into several graduate-level courses. Wide general audiences will be reached via the production of a series of educational YouTube videos on plasma science topics. The project will use the recently developed method of Thomson in-phase coherent microwave scattering (TMS) to precisely characterize and understand photoionization processes that have great practical importance in multiple research fields. These fields include nonlinear optics and filamentation physics, as well as optical diagnostics of gaseous discharges, combustion, and high-speed aerodynamic flows. The diagnostics methods that rely on the knowledge of the photoionization processes include electric-field induced second harmonic generation, two-photon laser induced fluorescence, and femtosecond laser electronic excitation tagging velocimetry. Specific research goals of the project include the development of a vacuum testing facility equipped with the TMS system and with an anechoic environment ideal for isolating the scattered signal from background environmental reflections, and characterization of the photoionization rates for several processes in a broad range of intensities up to ~ 100 TW/cm^2. This includes non-resonant photoionization of air, N2, O2, H2O, Ar, CO2, Ne, He, Kr, Xe, H2, and CH4 at 800 nm, non-resonant photoionization of He and N2 at 1064 nm, and (2 + 1) resonance-enhanced multiphoton ionization of CO at 230.1 nm, NH3 at 305 nm, and H2O at 248.3 nm. 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 · 2024-07

The Baroclinic Annular Mode (BAM) dominates southern hemisphere midlatitude wind variance on subseasonal-to-seasonal time scales. Enhanced BAM activity has been linked to extreme weather events, such as heat waves and cold outbreaks, and an increase in the frequency of blocking events. The researcher has found that rainfall variability associated with the BAM is isolated to the south Pacific, suggesting the BAM may be a source of predictability for rainfall in this region, while its prominence in the hemispheric mean storm activity suggests it plays an important role in shaping the large-scale circulation and hydrological cycle of the southern hemisphere as a whole. The dynamical linkages between the BAM and extreme weather and blocking events are not well understood. The Intergovernmental Panel on Climate Change has also identified the future projections of large-scale coherent atmospheric modes like the BAM as highly uncertain. This project will investigate the gap in knowledge linking the BAM to extreme weather events and blocking, as well as a hypothesis for how BAM activity might be expected to change in a warming climate. The improved understanding of the BAM and its physical links to extreme weather and blocking will then be extended to other internal midlatitude modes of variability on subseasonal to seasonal time scales around the globe. This project has the potential to advance knowledge at the weather-climate interface in an area with high relevance to society. The strong focus on K-12 education is also a priority for increasing the future participation in STEM. The project will use a hierarchy of modeling studies, climate model output, and gridded data sets to carry out the research. Three hypotheses will be tested: 1) Certain BAM states are more conducive to blocking; 2) The BAM increases the frequency of blocking through synoptic eddy feedbacks; 3) Enhanced diabatic heating with global warming will enhance the BAM and blocking. Tasks include building a quantitative relationship between synoptic eddies, blocking patterns, and BAM states using a two-layer quasi-geostrophic model that can simulate key dynamical processes with limited interference from other atmospheric processes. The aim is to use two types of atmospheric general circulation models to investigate the role of climatological basic states and diabatic heating in determining the BAM and blocking. In a warming climate, the intensity of the BAM has been projected to increase. Given the dynamical connection identified in the PI’s preliminary results, in a warming climate, following an enhanced high BAM state, a more regular cluster of blocking events is expected, which has important implications for mitigating disasters for regions in the mid-latitudes. In combination, this project will enable the development of improved subseasonal-to-seasonal prediction and will also provide guidance to improve the ability of dynamical models to simulate extreme weather events. The researcher will support and mentor one undergraduate and one graduate student. The researcher will also work with Purdue’s College of Science K-12 outreach programs to create online resources for climate-based education in the local schools, working closely with educators in the public school system. 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 · 2024-07

The fruit fly eye has incredible vision in terms of speed and ability to adapt to different light intensities, which is critical for the survival of a small flying insect. The molecular signaling pathways that respond to light and enable vision have been extensively studied in flies and provide a key model for understanding this class of signaling pathways, which are critical for a wide range of biological processes in animals, including mammals. However, key questions remain as to how this signaling cascade can adapt, while retaining high temporal resolution during prolonged periods of bright light that are typical of sunlight. This project will examine the role of a specific type of chemical modification termed redox signaling that can be initiated in response to light and could act as a rheostat to lower activity of the signaling pathway in very bright light. Understanding how redox signaling regulates this type of pathway could also provide important insights into the potential regulation of related pathways involved in neuronal signaling, cell growth, and differentiation. This project will also enable training for graduate student researchers who will gain skills in biochemistry, genetics, and electrophysiology (measuring electrical current in neurons). The project will also continue a successful course-based research experience (CURE) that provides opportunities for undergraduates to participate in authentic research experiences directly related to this project. Drosophila visual transduction is one of the fastest and most adaptable G-protein-coupled receptor (GPCR) phosphoinositide signaling cascades, responding with exquisite temporal resolution and able to operate efficiently under the extremely wide range of ambient light intensities. Preliminary redox proteomic studies identified oxidation of specific cysteines in three phototransduction signaling proteins upon exposure to intense blue light. These redox changes were accompanied by a large reduction in the sensitivity to light and the frequency response to oscillating light, which implies that redox signaling could contribute to light adaptation and response termination – the two major remaining gaps in understanding how phototransduction is regulated. Based on the position of the oxidized cysteines in these phototransduction proteins, this project hypothesizes that these redox signaling events would reduce the sensitivity to light and enable light adaptation. This project further hypothesizes that blue light induces these redox signaling events via reduction of a blue light flavoprotein present in the rhabdomere of photoreceptors. In this model, the phototransduction cascade can be deactivated by light signaling independent of the GPCR Rhodopsin, enabling it to adapt to a wide light intensity range and quickly terminate in the presence of bright intense sunlight that contains abundant blue wavelengths. This collaborative US/Israel project is supported by the US National Science Foundation and the Israeli Binational Science Foundation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

With the rapid adoption of electric vehicles (EVs) on the road, there will be numerous new job opportunities for EV maintenance and repairs for the next several decades. However, there is a significant shortage of adequately trained automotive technicians in the United States who are well-prepared to maintain and repair EVs. The existing automotive technicians have limited career development opportunities due to the fact that they have full-time jobs and limited time and resources to acquire the knowledge and skills needed for maintaining and repairing EVs. In addition, EV maintenance and repair require professional knowledge from multiple domains, which makes it challenging for existing training methods to create immersive and effective learning experiences for automotive technicians. The broader impacts of this project include the development of a globally competitive EV workforce, broadening the full participation of minorities and underrepresented populations in the EV industry, promoting future EV adoption to achieve global sustainable goals, and enhancing the future designs of EVs through the partnerships between academia, industry, local communities, and public agencies. In this project, an interdisciplinary team of researchers will work with multiple industry and educational partners to explore and test the practical foundations of an experiential learning approach for helping existing automotive technicians upskill their knowledge and skills for future EV technologies. This project will: (1) Understand existing EV training workflow and identify automotive technicians’ training needs through co-design and survey; (2) Develop a multi-stage experiential learning pipeline for existing automotive technicians; (3) Propose an effective and scalable experiential learning curriculum for automotive technicians to identify and solve real-world EV problems via online learning, hands-on learning, factory visits, and co-op/internship. This project aligns with the NSF ExLENT Program as it seeks to support experiential learning opportunities which range from fundamental theory to hands-on applications of EV diagnosis, maintenance, and repair. These opportunities exist for individuals from diverse professional and educational backgrounds, and seek to increase their interest in, and their access to, career pathways in emerging EV technology fields. 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 · 2024-07

The basis for the onset and progression of Parkinson's disease (PD) and related dementias remains unknown, but the formation and spread of aggregates of the protein alpha-synuclein (aSyn) between neurons has been identified as a likely mechanism. Thus, understanding the molecular underpinnings of this prion-like spread is a key step that would set the stage for developing therapies to delay or alleviate PD-related motor dysfunction and dementia. However, to date, there has been no viable method to comprehensively investigate the under- lying phenomena of aggregation in live cells, and certainly not in vivo. The long-term goal of this research is to define the molecular mechanisms that contribute to neurodegeneration in people with PD and related de- mentias, in order to stimulate the development of new therapies. The overall objective of this application is to establish a role for aSyn-mediated membrane permeabilization in the spread of aSyn neuropathology in PD. The central hypothesis is that aSyn oligomers derived from internalized preformed fibrils (PFFs) facilitate the endocytic escape of aSyn seeds by permeabilizing the endocytic membrane from within. This hypothesis will be addressed with the following specific aims: (i) Define aSyn assembly states in different subcellular loca- tions of PFF-treated neurons; and (ii) Define aSyn assembly states at various stages of PFF-mediated aSyn propagation in vivo. The project entails fluorescence lifetime imaging microscopy (FLIM) studies with neurons, where lifetime depends on aggregation level, as well as steps to application in vivo, enabled by high-resolution three-dimensional fluorescence localization with point-wise lifetime information (and hence insight into the aSyn self-assembly state) at various positions in the brain over time. Notably, fluorescence quenching and hence lifetime reduction, similar to what occurs in fluorescence resonance energy transfer (FRET), has been shown by the group to occur when aSyn fused to a fluorescent protein (aSyn-FP) undergoes self-assembly to a beta- sheet-rich, aggregated state, with a lifetime that reduces with increasing aggregate size. This provides a means to study aSyn aggregate formation and spread using high-resolution FLIM (Aim 1). Drawing on the group's conceptualization and demonstration of a means to image fluorescence parameters in vivo and through heavy scatter, and also at high resolution using computational imaging with localization, fluorescence lifetime param- eters determined at a set of locations in the brain should yield aSyn aggregate and spread information (Aim 2). Upon completion of this project, live-cell studies will have allowed aSyn aggregation and spread to be character- ized, which should lead to understanding of the molecular process, and critical steps to optical sensing of aSyn aggregate spread in the whole brain of animals will have been achieved. This approach is innovative because it is focused on new technologies and research avenues related to the propagation of aSyn pathology in PD and related dementias. The research is significant because the new knowledge from this study would set the stage for developing therapeutic strategies to interfere with the spread of aSyn aggregates in the brains of patients.

NIH Research Projects · FY 2026 · 2024-07

Abstract: Wound healing after injury is accomplished by the coordinated action of several cell types that migrate to the injury region and participate in tissue repair. Interactions between invading cells and the surrounding microen- vironment are critical for the healing outcome. Current in vitro studies focus on comparing cell behavior in con- ditions mimicking healthy tissue or fully fibrosed scar and mostly ignore the complex and dynamic properties of the provisional matrix that plays a critical role in instructing cell processes during wound healing. The goal of my research program is to develop cell-instructive biomaterials to support endogenous tissue repair after criti- cal injury. My lab is at the forefront of designing injectable and microporous granular hydrogels with tunable properties to support endogenous cell infiltration and tissue repair. While this approach presents intriguing pos- sibilities for clinical translation, progress is severely hindered by a lack of knowledge on how endogenous cells process the complex biophysical and biochemical properties of the provisional wound matrix and the implanted hydrogels. A major aspect of our lab’s approach is the creation of in vitro platforms to model cellular interac- tions, which serve the dual purpose of improving fundamental understanding of cell behavior in wound-mimick- ing environments and rationalizing biomaterials design to elicit specific cellular behaviors. Over the next five years, our lab will develop or advance in vitro models of endothelial sprouting, mesenchymal stromal cell para- crine signaling, and fibroblast-driven matrix deposition, using synthetically designed modular hydrogels with tunable porosity, ligand density, mechanics, and degradability. These hydrogel platforms will enable us to ad- dress questions not easily answerable with traditional 2D coatings on plastic substrates or naturally derived materials such as Matrigel that cannot be modified to recapitulate wound properties. Outcomes of projects in these thematic areas will ultimately serve our overarching goal of leveraging the fundamental understanding of cellular processes to design therapeutic biomaterials for effective wound healing in diverse tissue and organ systems. Projects across all three themes have the potential to present several intriguing questions that can be addressed with new hypotheses, experimental designs, and increasingly sophisticated biomaterial platforms over the course of this funding period and beyond, making this proposal a perfect fit for the Maximizing Investi- gators Research Award mechanism.

NIH Research Projects · FY 2026 · 2024-07

SUMMARY The molecular basis by which a new function emerges in evolution sets the mechanisms by which it can break down – ultimately contributing to disease. Unfortunately, our understanding of the genetic, molecular, and evolutionary mechanisms responsible for the origins of new functions is limited because most functions evolved only once in the distant past and have been modified by subsequent evolutionary processes. In particular, changes in gene regulation are a common route by which new molecular functions occur, account for much of the variation in complex traits, and play important roles in human health and disease. However, fundamental questions about the evolution of gene regulation remain unknown. First, we often do not know the genes responsible for regulatory changes. This is in large part because we lack systems and tools where the genetic basis of regulatory differences can be determined between species. Second, while transcription factors play key roles in gene regulation and changes in them can be sources of disease, we lack approaches for determining whether they play key roles in the evolution of gene regulation. Furthermore, if transcription factors are important sources of regulatory variation, the molecular mechanisms by which pleiotropic consequences are avoided are poorly understood. Third, correct gene regulation is essential, yet differences in regulation are quite common. Despite this, the rate of regulatory change and the evolutionary mechanisms responsible are not known because we lack systems where these can be dissected into the individual molecular changes that occurred. Over the next five years, my research group will address these knowledge gaps using high- throughput approaches in the Saccharomyces yeast. Despite being as genetically distinct as humans and birds, species in the Saccharomyces genus naturally produce viable hybrids and can be genetically crossed after precise genome engineering. We will use this ability to develop a system for mapping the genetic basis of regulatory differences on long evolutionary timescales. This will reveal common genetic sources of regulatory divergence, as well as the broad molecular basis by which regulatory evolution occurs. Saccharomyces yeast also contain all common mechanisms of transcription factor binding found among eukaryotes. We will develop high- throughput and unbiased approaches for determining how important changes in these transcription factors have been in regulatory evolution. Finally, we will determine the rate of regulatory change and the molecular basis for compensatory changes in gene regulation. The long-term goal of this work is to improve our understanding of how gene regulation functions and evolves to improve our ability to predict the effects of regulatory variation in health and disease.

NSF Awards · FY 2024 · 2024-07

Solid-state lithium-ion batteries (LIBs) present a safer alternative to their commercially available counterparts that utilize liquid electrolytes, due to the substantial safety risks associated with solvent leakage and flammability. Such batteries could even support smaller and more powerful battery packs by diminishing the necessity for extra safety features. However, despite the benefits of these highly ionic conductive solid electrolytes, achieving comparable specific capacity, rate capability, and cycle life to that of liquid electrolyte LIBs remains challenging. Research indicates a major obstacle being inadequate interfacing between solid electrolytes and solid electrodes. Contrarily, the liquid-solid interface in liquid electrolytes permits full electrode infiltration, fostering comprehensive lithium-ion transport across the surface of active material particles. To leverage the benefits of the liquid-solid interface in solid-state batteries, the research team proposes a transition from the solid-solid interface to a liquid-solid interface. This could be achieved by replacing the solid electrode with a liquid metal electrode within solid-state LIBs. During the processes of lithium-ion insertion and removal, both the electrode-electrolyte interface and the interior of the liquid metal particle experience continuous alterations due to the shift between liquid and solid states. It is essential to understand these dynamic changes to fully investigate the potential of liquid metal solid electrolyte LIBs. The objective of this project is to gain a fundamental understanding of the interface dynamics between the liquid metal anode and the solid electrolyte during lithium-ion insertion and removal processes. The project will cultivate a diverse and inclusive team, encompassing graduate students, undergraduate students, precollege students, and K-12 teachers, with special emphasis on encouraging the participation of underrepresented populations. The project aims to provide interdisciplinary training to students, bridging the gap between theoretical understanding and experimentation. In this project, the team proposes an innovative approach to study a novel solid electrolyte LIB, the first to utilize a liquid metal electrode and a solid electrolyte, forming a liquid-solid interface at room temperature. Their objective is to understand how the transition between this liquid-solid interface and the solid-solid interface occurs during lithium-ion insertion and removal processes. Insight gained from this investigation will foster the development of new techniques that incorporate liquid metal electrodes in solid electrolyte batteries. The team will utilize in situ and operando focused ion beam-scanning electron microscopy (FIB-SEM) to analyze dynamic changes in morphology and monitor the advancement of the liquid-solid reaction front in liquid metal particles during cycling processes. Furthermore, the researchers aim to investigate the influence of dopants by studying dynamic phase changes obtained through operando X-ray diffraction (XRD). To understand the development of stress and strain, along with its effect on phases and morphologies within a liquid metal particle during lithium-ion insertion and removal, numerical simulations based on a phase field model, incorporating fluid-structure interaction, electrochemical reaction, species diffusion, and interfacial effects, will be conducted. 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 · 2024-07

Cyberinfrastructure (CI) plays a vital role in today's scientific landscape. Investments in CI bring significant economic benefits across various sectors. However, the field faces three critical challenges: a lack of skilled professionals, especially in combining cybersecurity and machine learning; limited opportunities for underrepresented minorities; and education falling behind the fast pace of technological advancements like AI and large language models. Project ACE (Advancing Cyberinfrastructure Education) aims to address these challenges by providing comprehensive education and training to build a more robust CI workforce. This effort will enhance defenses against cyberattacks, drive technological progress, and support the resilience and advancement of digital economies and scientific research. Project ACE's innovative approaches are highlighted in three key areas. First, it integrates Machine Learning (ML) and Cybersecurity within Cyberinfrastructure (CI) frameworks, training students on secure design principles and advanced ML techniques to tackle modern cyber threats. Second, it incorporates advanced Artificial Intelligence (AI), particularly Large Language Models (LLM), and experiential learning to bridge the gap between theory and practice, preparing students to apply AI creatively in various CI contexts. Lastly, it lays the groundwork for future CI workforce development through a scalable and adaptable educational framework that meets current technological needs and adapts to future advancements. The project's success will enhance the security and resilience of Cyberinfrastructure (CI) across various sectors by democratizing access to CI resources and education, particularly for underrepresented groups, including those at Historically Black Colleges and Universities (HBCUs). Furthermore, a cooperative network of educators, industry experts, and policymakers will foster the growth of a dynamic and innovative CI workforce ecosystem. 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 · 2024-07

The performance and quality of semiconductor materials are critical to advanced technologies for a wide range of applications. A significant challenge in the production of these materials is the cooling process. During the production phase, semiconductor materials are prone to cracking as they cool. These cracks can lead to failures in the final products, decreased reliability, and higher manufacturing costs. This Engineering Research Initiation (ERI) award supports fundamental research aiming to prevent the formation of cracks during the semiconductor cooling process. The objective of this project is to develop a novel method that integrates machine learning techniques with fundamental principles of mechanics to predict crack formation. This research will enhance production of high-quality semiconductor materials. This project will also make significant contributions to the field of STEM education. A widely accessible Virtual Mechanical Testing Lab will be established, which will use interactive virtual tools to educate students about testing materials. Special efforts will also be made to engage students who have historically been underrepresented in STEM fields in this research. The goal of this project is to develop a mechanics-informed machine learning framework to predict and quantify interfacial cracking in semiconductor materials, specifically at silicon carbide/aluminum nitride (SiC/AlN) interfaces during the cooling process. Recognizing that interfacial defects and residual stresses are critical factors in cracking, the research aim is to use advanced machine learning and simulation techniques to identify the mechanisms of cracking and proactively prevent it. The machine learning model will be trained using atomistic simulations of cracking behaviors, providing innovative insights into the design of semiconductor materials. The potential contributions of this research are numerous, aiming not only to mitigate damage in semiconductor interfaces, thereby revolutionizing their design and production, but also to develop an integrated machine learning framework with uncertainty quantification, which will have broader applicability in predicting behaviors and properties of other materials. 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 · 2024-07

The goal of this project is to develop a new instrument to measure accurately the size distribution of nanoparticles sampled in a variety of environmental applications. The new instrument will enhance the sensitivity, selectivity, and detection speed of ultrafine particles up to 100 nanometers in size, which are referred to as PM0.1 particles. Particles of this small size arise from the same natural or human-made sources that produce larger particles, but PM0.1 particles may pose special health threats because they readily enter the body. Improved measurement techniques could help with the detection and control of PM0.1 particles in the environment. The instrument that will be developed is an Ion Mobility Spectrometer (IMS). The IMS will use an electric field that varies spatially to restrict diffusion of particles in the gas phase and therefore will enhance the resolution of measurements of the particle size distribution. In addition, the IMS system will be coupled to a mass spectrometer so that particle mobility can be correlated with particle mass. The use of this new instrument will improve our fundamental understanding of aerosol and nanoparticle characterization and transport in the gas phase. The research team will conduct activities that demonstrate principles of aerosol science to K-12 students, especially those from underrepresented groups, in summer camps. The team will also communicate the role of aerosols in climate change and pollution through K-12 teacher/mentor awareness symposia. Aerosols are generally classified by size obtained from their mobility in the gas phase. Most often, mobility-based size distribution functions of aerosol particles are measured with a scanning mobility particle sizer (SMPS). While the SMPS has been highly successful, it has several shortcomings that could be addressed by employing different techniques. For example, diffusional broadening leads to a degradation in resolution for most operating commercial devices. Furthermore, SMPSs typically require minutes to complete voltage scans. This duration limits the information that can be obtained when aerosol samples vary rapidly in time, which can occur when sampling near aircraft or roadways. These challenges are exacerbated for measurements of PM0.1 particles in the gas phase. Despite continued experimental and theoretical interest, there is still a knowledge gap in the theoretical understanding of momentum transfer of particles that lie in the free molecular regime (1-100nm). The proposed research is expected to impact the aerosol field through increases in instrument separation/resolution by restricting diffusion broadening of nanoparticles, classifications of small aerosols through a mass-mobility and size relationship and quick, low signal-to-noise scans to study rapidly varying aerosols (up to tens of milliseconds per scan for particles smaller than 10 nanometers). 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 · 2024-07

This project in classical harmonic analysis focuses on time-frequency analysis and its connections with other fields such as combinatorics, ergodic theory and fluid dynamics. Time-frequency analysis originates in signal processing and considers the properties of a signal in both the time and frequency domains simultaneously. Historically, the development of time-frequency analysis was motivated in quantum mechanics (e.g. the celebrated Heisenberg uncertainty principle and related work of Wigner and Gabor) and radar detection. Modern applications relying on time-frequency techniques include areas such as image sampling, satellite transmission/GPS location and biomedicine. The PI will continue the development of powerful analytical methods to advance time-frequency analysis from a theoretical perspective. The PI will organize a series of educational activities that include outreach and mentoring for high school and undergraduate students, Putnam exam preparation, reading-course offerings, supervision of graduate students and postdoctoral researchers, and co-organization of seminars, conferences, and summer schools. This project involves important problems in harmonic analysis with connections to additive combinatorics, ergodic theory and PDE. Relying on time-frequency analysis, the main focus is on the interplay between non-zero and zero curvature settings, with special attention paid to hybrid situations that encapsulate features from both extremes of the scale. The main themes include: (I) Multilinear maximal/singular/oscillatory operators: Building on a natural hierarchical structure that includes the Carleson operator and the bilinear Hilbert transform, this topic studies several relevant model problems in connection to two celebrated open questions: (a) the behavior of the pointwise convergence of bilinear Fourier series, and (b) the boundedness properties of the trilinear Hilbert transform. (II) Multidimensional maximal/singular/oscillatory operators along variable curves: This theme focuses on Carleson-Radon type behavior as well as on an array of problems related to Zygmund's differentiation conjecture. The crucial difficulty here lies in the multivariable dependence of the curves involved in the representation of the operators under study. This creates a series of new difficulties in part due to the existence of Kakeya/Besicovitch type phenomena. (III) Pointwise convergence of Fourier Series, end-point behavior: This topic revolves around the century-old problem regarding the behavior of Fourier Series and discusses some long-standing conjectures on which the investigator has made relevant progress. The main interest in this study relies on the new methods to be developed in order to exploit subtle connections with additive combinatorics. 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 · 2024-07

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 electrochemical production of net-zero fuels, powered by wind and solar energy is an attractive way to decarbonized liquid fuels. Substantial efforts have been made to develop efficient electrocatalytic materials that aid in transforming feedstock molecules, like CO2 or biomass, to fuels, but engineering of the electrochemical reactors that perform such transformations is not well-developed. The principal investigators and their team at Purdue University explore the underlying reasons why instabilities in electrochemical reactor arise, which reduces their efficiency and can potentially cause safety concerns. This work combines computational and experimental approaches to explicitly study this challenge to electrocatalytic production of net-zero fuels. The results of this work will provide an improved electrochemical engineering design framework to enable efficient production of low-carbon fuels. Additionally, the project provides research opportunities for students and the research outcomes are used to prepare students with in-depth knowledge about electrochemical reactor design, optimization, and the pressing need for decarbonization in modern chemical engineering. With funding from an EArly-concept Grants for Exploratory Research (EAGER) award through the NSF-wide Clean Energy Technology initiative, the principal investigators aim to bridge crucial knowledge gaps concerning the impact of multiple steady states (MSS) on electrochemical reactor performance for net-zero fuel production by developing a mathematical framework to describe the physical interplay between reaction, mass transport, and thermodynamics. Specifically, they explore the conditions under which MSS manifests using fundamental electroanalytical techniques, focusing specifically on two-electron two-proton reduction reactions relevant to decarbonized fuel production in order to deepen the understanding of the behavior of electrochemical MSS. Additionally, the research is focused on integrating these fundamental discoveries with practical reactor applications through the development of a hybrid modeling framework. This framework, capitalizing on physics-informed machine learning, promises a transformative approach to reactor design optimization. With the support of modern computational resources, the proposed research is poised to offer robust, optimized models and innovative solutions for enhancing electrochemical reactor efficiency and safety. 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 · 2024-07

Modern applications of statistics aim to solve complex scientific problems involving high-dimensional unknowns. One feature that these applications often share is that the high-dimensional unknown is believed to satisfy a complexity-limiting, low-dimensional structure. Specifics of the posited low-dimensional structure are mostly unknown, so a statistically interesting and scientifically relevant problem is structure learning, i.e., using data to learn the latent low-dimensional structure. Because structure learning problems are ubiquitous and reliable uncertainty quantification is imperative, results from this project will have an impact across the biomedical, physical, and social sciences. In addition, the project will offer multiple opportunities for career development of new generations of statisticians and data scientists. Frequentist methods focus on data-driven estimation or selection of a candidate structure, but currently there are no general strategies for reliable uncertainty quantification concerning the unknown structure. Bayesian methods produce a data-dependent probability distribution over the space of structures that can be used for uncertainty quantification, but it comes with no reliability guarantees. A barrier to progress in reliable uncertainty quantification is the oppositely extreme perspectives: frequentists' anathema of modeling structural/parametric uncertainty versus Bayesians' insistence that such uncertainty always be modeled precisely and probabilistically. Overcoming this barrier requires a new perspective falling between these two extremes, and this project will develop a new framework that features a more general and flexible perspective on probability, namely, imprecise probability. Most importantly, this framework will resolve the aforementioned issues by offering new and powerful methods boasting provably reliable uncertainty quantification in structure learning applications. 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 · 2024-07

With support from the Chemical Structure, Dynamics, and Mechanisms A (CSDM-A) program in the Division of Chemistry, Professor Jonathan Hood of Purdue University is investigating entangled states of organic molecules. Entanglement is a quantum mechanical property in which the behavior of two molecules is correlated, even when they are well-separated from each other. However, to entangle two molecules, they must have the same energy, which is difficult to achieve in molecular systems. Entanglement is also fragile, and once it is created, it can be lost through decoherence. Professor Hood and his students will employ laser-induced tuning to bring arrays of molecules into resonance (i.e., make their energies equal) and observe the decoherence of entangled states by measuring correlations of the emitted photons. Their discoveries could lead to advancements in quantum-based technologies for computing, encryption, and communication. Additionally, the project will contribute to developing a quantum-enabled workforce by providing research opportunities and outreach programs to a multi-disciplinary group of students. Currently, quantum light with many photons is inefficiently generated from single photons. A novel approach to addressing this issue involves utilizing multiple emitters to create many-photon entangled light. However, this strategy faces challenges when working with solid-state emitters, particularly in bringing lifetime-limited solid-state emitters into resonance. This research aims to tackle these challenges through two main objectives. The first goal is to integrate organic molecules into nanophotonic cavities and waveguides, thereby enhancing photon collection efficiency and the zero-phonon line. By doing so, the system's overall performance can be significantly improved. The second goal focuses on bringing multiple coupled molecules into resonance and generating entangled states between the emitters and photons. This step is crucial for creating high-fidelity quantum states of light that are essential for various applications. The outcomes of this work are expected to contribute to a deeper understanding of decoherence in collective quantum states. Furthermore, it aims to provide a scalable method for producing high-fidelity quantum states of light, which have significant potential in fields such as quantum-enhanced sensing, imaging, and secure communication. By advancing the knowledge and techniques in this area, this research paves the way for more efficient and practical quantum light sources. 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 · 2024-07

Machine learning is crucial for advancing artificial intelligence (AI) and big data analysis, focusing on training models by minimizing objectives with a key element, loss. Many losses are rank-based, playing a critical role in applications like information retrieval, search engines, and recommender systems. They are also valued for training robust models capable of dealing with label noise, outliers, and imbalanced data. Decomposability is a key feature of these losses, allowing them to be broken down into individual components for efficiency and distributed computing. As a bridge between training data and the model, rank-based decomposable loss plays a vital role in machine learning, highlighting the need for thorough study. The project will explore rank-based decomposable loss from two perspectives: aggregate and individual losses. The aggregate loss is the loss over all training data and is constructed from the individual loss of the model for each data sample. The project will concentrate on several key inquiries: What are the general abstract formulations of the rank-based decomposable aggregate and individual losses for machine learning? How to efficiently optimize learning objectives formed based upon them while ensuring convergence? How can these losses be customized or modified to suit different machine learning problems? And what are the statistical behaviors of machine learning algorithms using these losses? The outcomes of this project will unearth new insights into established robust machine learning techniques and give the loss a new twist to consider ranks and decomposability. This project will be conducted in two interrelated thrusts. The first thrust explores a novel and general rank-based aggregate loss for supervised learning. The focus will encompass efficient algorithms that can optimize this loss with guaranteed convergence, along with streamlined techniques to determine relevant hyperparameters. The developed loss will be connected with distributionally robust optimization to gain insights into its sample-level robustness and the development of new types of rank-based aggregate losses. Additionally, theoretical guarantees will be established for rank-based aggregate losses, including classification calibration, classification consistency, and generalization properties. The second thrust aims to study a general formulation of rank-based individual loss with theoretical analysis, bolstering label-level robustness in multi-class and multi-label learning scenarios. Furthermore, the use of rank-based individual loss will be expanded to tackle fairness learning challenges and investigate the resilience of models trained with this loss against adversarial threats, including verification and defense mechanisms. 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 · 2024-06

Project Summary Prevention of adolescent substance use (SU) and interventions to reduce harmful use has long been a goal of NIDA in order to improve the health of individuals and lighten the societal burden of substance misuse. Research from longitudinal observational studies is critical for establishing sufficient evidence that the target of intervention could be a causal factor prior to developing and for fine-tuning interventions. This R21 responds to a call (RFA-DA-22-038) to leverage data from the Adolescent Brain and Cognitive Development (ABCD) study to develop new and advanced statistical methods, in this case, to improve causal inference and inform intervention target selection. We will develop and test a new model: a Discordant Sibling Random-Intercept Cross-Lagged Panel Model (DS RI-CLPM) using data on several (child- and parent-driven) facets of parental monitoring, a likely causal environmental factor for which there is strong extant evidence of an inverse association with adolescent SU (aim 1). We will also (aim 2) explore the comparability of estimates from the DS RI-CLPM model to standard RI-CLPM and DS models and other models to understand how the DS RI- CLPM compares to current models, and (aim 3) conduct a series of simulation studies to explore power and the minimum sample sizes needed to establish meaningful effects under various circumstances (i.e., effect sizes, degree of sibling discordance). The initial products of this R21 will include: 1) openly available code to fit the DS RI-CLPM model in tutorial form, 2) publications explaining the interpretation and utility of the model, and 3) publications of simulation studies explicating power, sample size, and other (i.e., model specification) considerations of the model. The expected end-users are twofold: This model can be used by any researcher interested in testing a stricter model of causality for their focal associations using large-scale, longitudinal data that includes twins and/or siblings, many of which are publicly available (e.g., ABCD, Add Health, harmonized data from the NIH Environment in Child Health Outcomes initiative, Twins Early Development Sample). Second, we have formed an advisory board of intervention experts to help guide our analyses, sensitivity analyses, presentation, and dissemination of findings to aid in translation of these findings to ongoing work in the intervention and prevention of adolescent SU. Through discussions with our advisory board, we expect that scientists who develop and test interventions will find results from this model of use. By subjecting multiple aspects of key malleable constructs to this model, we will provide intervention scientists with more detailed information about the likelihood of causal links and key targets for future interventions. A longer-term product of this R21 will be an R01 grant submission exploring the generalizability of this model (and the standard RI- CLPM and standard DS design) to intervention samples (extant data provided by members of our advisory board). Combined with results from this R21, the planned R01 is expected to yield critical information about how and when nationally representative longitudinal data are more and less informative for interventions.

NIH Research Projects · FY 2026 · 2024-06

Project Summary Iron (Fe) is a redox-reactive metal that is essential for several critical physiological functions in the human body. It plays an integral role in oxygen transport, DNA repair and synthesis, mitochondrial energy production, the formation of myelin, the generation and metabolism of neurotransmitters, and the regulation of immune response and defense mechanisms. The body needs to strictly maintain Fe levels as both deficiencies and excess can result in severe health complications. A notable illustration of the consequences of Fe imbalance is ferroptosis, an Fe-dependent form of programmed cell death that has become a focal point in cancer therapy. Furthermore, Fe is acknowledged as a pivotal element in the progression of neurodegenerative diseases. Its accumulation has been consistently observed in the parietal cortex, motor cortex, and hippocampus of brains impacted by such disorders. Significant strides have been made in understanding the overall procedure of human Fe homeostasis, and the principal components involved are relatively well-understood. However, the specific molecular mechanisms governing Fe homeostasis remain obscure. This project will employ a multidisciplinary approach to unravel the structures and mechanisms of metalloproteins integral to human Fe homeostasis from the following perspectives: 1) Deciphering Fe transport through the membrane - Key components of the cellular Fe-regulation system include the only known Fe exporter ferroportin (Fpn) and an extracellular ferroxidase ceruloplasmin (Cp). Perturbation of the regulation is likely the direct cause of Fe accumulation in cells. This work will study the synergistic effects between Fpn and Cp structurally and spectroscopically and clarify the perturbation process; 2) Probing the interactome and transcriptome of iron regulatory proteins (IRPs) via proximity labelling – IRPs are intracellular proteins that detect Fe concentrations and modulate the expression/translation of Fe homeostasis-associated genes post-transcriptionally to maintain cellular iron balance. This project will leverage proximity labeling to discern the interactome and transcriptome of IRP; 3) Exploring the function of amyloid precursor protein (APP) - Amyloid plaques, aggregates of Aβ peptides, are pathological hallmarks of Alzheimer's disease, originating from APP through secretase cleavage. However, the biological role of APP remains enigmatic. Notably, APP mRNA contains an iron response element (IRE) in the 5'-UTR, hinting at a potential role in iron homeostasis as a ferroxidase, though definitive experimental evidence is still pending. This portion of the project will investigate the biological functions of APP in relation to iron homeostasis. In summary, the envisaged research is set to yield insights at the molecular level into Fe homeostasis. This is foundational not just for acquiring an understanding of pivotal physiological functions, but it also paves the way for pioneering therapeutic strategies. Such innovations have the potential to address a myriad of diseases, ranging from cancer to neurodegenerative conditions.

NSF Awards · FY 2024 · 2024-06

Baccalaureate Engineering student Success and Transition (BEST) programs are holistic student success programs at four-year undergraduate universities that provide evidence-based, well-structured activities to enhance engineering students' academic and professional success in the college transition and to increase their retention in the engineering major. These programs often struggle to attract diverse students, including women, students from underrepresented minority groups, first-generation students, low-income and rural students. This project aims to examine this issue by finding the most effective methods for reaching underserved students and recruiting them to BEST programs. The project seeks to identify strategies that are successful, impactful, and sustainable. This will strengthen BEST programs, allowing them to fulfill their goals and expand further. The project will contribute to our national understanding of efficient recruitment methods and effective communication channels. Additionally, it will promote diversity in engineering education and the workforce. By recruiting more engineering students from diverse backgrounds, we can address the nation's need for a diverse engineering workforce, which is critical for the advancement of society. By implementing strategic recruitment efforts, we can uplift underserved students, their families, and their communities, and these efforts have the potential to enhance the economic growth of rural areas. The goal of this project is to build STEM education research skills in the PI through a project that broadens participation in engineering in the U.S. via assessing different strategies to recruit underserved students (women, minorities, first-generation, low-income, and rural students) into BEST programs at 4-year universities. The study is based on the Diffusion of Innovations framework and will examine students’ awareness of BEST programs and decision-making processes about whether or not to apply to these programs through surveys and interviews. Responses will be analyzed to study the effectiveness and alignment of different recruitment methods in use by BEST programs from the perspectives of both program organizers and students. The project is national in scope and aims to include 100 BEST programs across the U.S. and more than 2000 students via surveys and interviews with program leaders, and BEST program participants and prospective participants. Knowledge generated on effective recruitment strategies from this project will contribute to the literature base, broadening its impact and helping to tackle the challenges of low enrollment of underserved students in BEST programs, while supporting the creation and continuation of student success programs. This project also trains new STEM education researchers (PI and supported students) to enhance their capacity to conduct rigorous research in STEM education. The project is supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education research. 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 · 2024-06

Cyber-Physical Systems (CPS) are typically composed of interconnected hardware and software components, which individually may not be inherently highly reliable or secure. However, several CPS applications demand a high degree of safety, security, and reliability. Thus, the fundamental problem is constructing highly dependable CPS applications from building blocks that are, in themselves, not inherently reliable. There has been enormous progress made in understanding and patching various classes of vulnerabilities in large-scale distributed CPS. However, these efforts at designing and operating resilient CPS have often been stymied by the lack of understanding of the impact of any perturbation to the overall system, under the economic and policy constraints involved in any realistic real-world CPS. We define perturbations as failures due to: (1) unintended errors in hardware/software, (2) security attacks, (3) unexpected interactions among cyber-physical and human elements including natural disasters, and (4) incomplete cooperation among stakeholders. In this project, we address these shortcomings to challenges to create resilient, large-scale CPS through our CHORUS Frontier award. Chorus will develop rigorous, scientific mechanisms to enable CPS resilience against a large universe of perturbations. Our application domain is Connected and Autonomous Transportation Systems (CATS) and thus, the benefits of CHORUS will be demonstrated through improvements in safety and security in this domain. We will achieve goals of CHORUS through three interacting intellectually challenging thrusts in the project. Thrust 1 is on Modeling which will create executable specifications of cyber, physical, and human assets, their interconnections, and the economic and policy constraints. The models will capture various stakeholders in the system and their degree of information sharing and cooperation in defense of the target CPS. Thrust 2 is centered on Proactive planning and deployment. We will develop rigorous game-theoretic formulations to model the spread of perturbations (natural and man-made), their effects, and the appropriate resource allocations that can be applied for resilience at the planning stage, i.e., prior to system deployment. We will also consider which defensive investments are feasible under a rational versus a bounded rational behavioral model of the stakeholders. Thrust 3 focuses on Runtime distributed detection and response. We will determine, at runtime, the security state of the system, through novel uses of existing sensors in the system even though they are imperfect. This will then trigger the response mechanisms, which will be proven to be approximately optimal, through analysis and experimentation. In terms of broader impact, the greatest impact will be that CPS owners will gain a higher degree of trust in the operation of the CPS and policy-makers will understand what level of cooperation among multiple stakeholders in a CPS to incentivize. We will create compelling demonstrations of CHORUS on a connected vehicle testbed distributed between our academic institutions and our industrial partner GM. We will also organize an annual student security competition and develop two MOOCS, both having foundational material on resilient CPS and one focusing more on the CATS application domain. 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 · 2024-06

Almost 25% of U.S. households experience high energy burdens, referring to the percentage of gross household income spent on energy costs. As a result, more than 20% fall behind on their energy bills and suffer from utility service disconnection. To empower affordable housing communities with long-term energy access, a drastically different paradigm is required—one driven by new Smart and Connected technology that achieves energy security and improved well-being to all US families in need. Our goal is to realize a new paradigm for smart energy assistants that monitor energy use and energy needs and create customized community-based interventions that have the largest potential to mitigate energy-related disparities experienced by low to moderate income households. To achieve this goal, we will bring together community residents and diverse stakeholders (state and regional housing authorities, utility service providers, municipal administrations, community action groups, housing developers/landlords) to co-design an interactive cross-platform for smart energy assistants that: 1) Collect and organize community sociotechnical data and create a community-shared knowledge platform; 2) Integrate a counterfactual engine developed using a theory-informed, hierarchical Bayesian approach that reveals causal links between socioeconomic disparities and system determinants; 3) Generate customized policies for interventions that are tailored to the unique needs of each community; 4) Leverage an AI layer empowered by Large Language Models (LLMs) to interface with residents and stakeholders and the developed tools and databases. Also, the AI layer facilitates effective communication among residents in need and enables stronger connections between community residents and stakeholders, lowering the barriers to residents understanding and accessing energy programs. Our sociotechnical research will have direct positive impacts in five pilot communities in Indiana with ultimate goal to extend these benefits to 4.5 million affordable housing units nationwide. Our foundational contributions of Smart and Connected Communities sociotechnical research will utilize AI powered by LLM as the basis for smart energy assistants that will integrate energy use and energy needs and create customized community-based interventions that have significant potential to reduce energy-related disparities experienced by low to moderate income households. Our research approach will impact diverse application domains, including energy, transportation, water, urban planning. This will also lead into ground-breaking insights on AI-supported decision-making pertaining to measures of accuracy, trust, and social influence across dimensions of the technical and social system. 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 · 2024-06

PROJECT SUMMARY/ABSTRACT The goal of this proposed study is to examine the impact of retail policies on disparities in tobacco outcomes and allow me to establish research independence as an early-stage investigator. The tobacco retail environment greatly influences tobacco use disparities through the presence of advertisements, promotions, and the availability of tobacco products. Communities with predominantly lower socioeconomic and minority population groups have more tobacco retail stores and higher tobacco prevalence, hence perpetuating tobacco use disparities. To curb the effect of the retail environment on tobacco use, localities are actively adopting policies that limit tobacco product availability. Some policies include restrictions on the sale of flavored tobacco products (FTP) or tobacco retailer licensing (TRL) policies to reduce the presence of tobacco retail stores. However, the impact of these policies on selected tobacco outcomes or related disparities remains understudied and unclear. This policy evaluation study will use advanced statistical methods to achieve three main goals: 1) describe and examine the impact of local TRL laws and fees on sociodemographic differences in retailer density by area- level race/ethnicity, income, and education; 2) explain and measure the impact of local FTP restrictions on sociodemographic differences in product sales within the communities by area- level race/ethnicity, income, and education; and 3) visualize and investigate the interactive effect of both policies on sociodemographic differences in individual-level exclusive and dual youth and adult cigarette and e-cigarette use. This K01 Mentored Career Development Award will provide the necessary foundation for my career as a principal investigator, skilled in analyzing tobacco policies with advanced analytic skills to understand their effect on tobacco outcomes and health disparities within the communities. These study findings will help policymakers and researchers understand the need to revise existing or adopt new retail policies to address the tobacco use epidemic in their communities.

NIH Research Projects · FY 2026 · 2024-06

Project Summary Exposure to per- and polyfluoroalkyl substances (PFAS) is highly prevalent in the US population and has been associated with neurodevelopmental and neurodegenerative diseases particularly via changes in dopaminergic (DA) neurons. Conventional PFAS chemicals are being rapidly replaced by novel chemicals with unknown long- term neurotoxicity necessitating the comparison of neurotoxicity of various PFAS. The goal of the proposed research is to evaluate and compare the impact of a developmental exposure to selected PFAS chemicals including the legacy PFAS, PFOA, and its PFAS replacements, PFBA and GenX, at low doses on the plasticity of neuronal compartments; and subsequently characterize their vulnerability to established neurotoxins promoting DA neuron degeneration. There are limited studies comparing developmental neurotoxicity of PFAS and the lasting impacts on the central nervous system, especially replacement PFAS due to the scarcity of longitudinal epidemiological studies. Our studies using the zebrafish model suggest although lethality decreases in PFAS with shorter carbon chain length and addition of side chains common in replacement PFAS, perturbations on development, behavior, and dopamine (DA) concentrations occur at lower dose exposures in PFAS replacements (e.g., PFBA and GenX). These findings were further corroborated using dopaminergic-like cells differentiated from SH-SY5Y and floor plate progenitor cells derived for human induced pluripotent stem cells (hiPSCs). Collectively, PFOA seems to have a distinctive neurotoxic mechanism compared to PFBA and GenX, while all three PFAS can result in persistent alterations at various sub-cellular compartments and perturb calcium (Ca) homeostasis. We will thus test our CENTRAL HYPOTHESIS that low dose PFAS exposure disrupts communication between different cellular compartments via altered intracellular Ca concentrations, leading to systematic disruptions in multiple cellular compartments, interrupting the formation of the neuronal network, and increasing risk of neuron damage and degeneration. We will use a combination of the zebrafish animal model and DA neurons derived from hiPSC to evaluate immediate impact of developmental PFAS exposure (SA1) and latent long-term neurotoxicity (SA2) emphasizing the dopaminergic pathway. Throughout SA1 and SA2, we will determine changes in neuronal vulnerability to established neurotoxins for altered viability and accumulation of degenerative markers, such as synuclein aggregates. Sub-cellular and network Ca activity will be recorded and correlated to reveal driving pathogenic mechanisms for abnormal neuronal activity and neurodegeneration induced by developmental PFAS exposure, while exploring normalization options (SA3). Collectively, we will determine unique and shared neurotoxicity associated with selected PFAS; reveal sub-cellular compartments most compromised and conferring to the neurodegenerative-like phenotype; and explore the feasibility of restoring neuronal plasticity via targeted interventions.

NIH Research Projects · FY 2023 · 2024-06

Project Summary. This Short-Term Mentored Career Enhancement Award in Dental, Oral, and Craniofacial (DOC) Research for Mid-Career and Senior Investigators (K18) will provide the candidate with the protected time to acquire the skills and knowledge to augment her research program to include DOC concepts. The Pl is an established investigator who studies the genetic, epigenetic, and socialenvironmental determinants of depression, with a focus on the role of childhood adversity in early life. The Pl has not yet received any formal training in dental development nor any DOC concept. She seeks short-term training to learn concepts and methods to measure tooth development and dental hard tissue phenotypes, which she will then use to achieve her career goal of studying the connections between tooth and brain development. Training will be overseen by mentor Dr. Mary L. Marazita, an internationally recognized expert in the genetics of caries and other DOC features, along with three senior advisors. Training will consist of coursework, on-line seminars, guided readings, conference attendance, and lab experiences at two sites (University of Kent and Calgary), which will provide training distinct from the Pl's and mentor's home institutions. Training will also include a secondary analysis of a study from the Center for Oral Health Research in Appalachia (COHRA). Under one of COHRA's projects (R01-DE014899 PD/Pis Marazita, McNeil, Foxman, Shaffer) the COHRA2 longitudinal birth cohort was built and followed 1000 European-ancestry and 250 African American pregnant women from northern Appalachia. The Pl will analyze existing data from COHRA2 to investigate the extent to which genetic factors and children's exposure to maternal distress, a common type of childhood adversity, associate with dental caries (tooth decay) and age at first tooth eruption. In Aim 1, the Pl will use bioinformatics data on brain structures and disorders to calculate genetic risk scores capturing the aggregate effect of multiple genes (i.e., polygenic risk scores; PRS) and then examine their role on both dental caries risk and age at first tooth emergence. In Aim 2, the Pl will use an analytic technique called the structured lifecourse modeling approach (SLCMA) to assess with repeated-measures data how the developmental timing of children's exposure to maternal distress (e.g., global and parenting stress; depressive symptoms) associates with number of dental caries and primary tooth eruption timing. Findings from this K18 may lead to the identification of new genes associated with dental caries and tooth formation timing, and increase knowledge on the role of maternal distress on these dental outcomes, which could then guide targeted preventive interventions. The proposed training experiences will ensure the Pl learns concepts and measurement approaches to study tooth formation so she can more deeply integrate these concepts into her research. This K18 will also serve as the basis for an R01 proposal that will replicate and extend findings investigated here.