New York University

NSF Awards · FY 2024 · 2024-12

The broader impact of this I-Corps project is the development of a motion capture technology to be used in filmmaking and animation. Currently, capturing intricate motions of finger joints has remained a significant challenge. Traditional motion capture technologies, such as optical tracking systems, primarily rely on cameras and reflective markers to track movements. This technology presents several drawbacks as it relies on controlled environments, such as specific lighting conditions, and inaccurate capturing of complex and rapid movements. The new technology addresses these issues by incorporating a wearable, self-powered sensor to capture motion. This application may improve the design of immersive experiences using Extended Reality (XR) technologies. The sensor also may have application in the field of rehabilitation, capturing real-time biomechanical motions. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a wearable motion capturing device. The aim is to offer users greater flexibility and adaptability in working in dynamic settings and environments, such as capturing the motion of the human hand. The new technology is based on a textile sensor composed of hundreds of miniatured sensors directly in contact with the human hand. This integration allows the motion data to be genuine and comprehensive and, combined with an algorithm for real-time signal processing, the sensor can track the motion of the user with seamless data transfer. In comparison with optical or Inertial Movement Unit (IMU)-based systems, this device does not require a specifically allocated workspace and is self-powered. This flexibility provides the wearers more freedom to carry on the hand movement tasks in any environment. 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 · 2024-12

Project Summary/Abstract This proposal aims to leverage advances in artificial intelligence (AI) to develop clinically impactful tools that can support progress in speech therapy for children with Speech Sound Disorder (SSD) while alleviating the pressing issue of overburdened speech language pathologist (SLP) caseloads [5]. SSD affects a significant portion of school-aged children [1, 13], leading to social and emotional challenges that can persist into adolescence and adulthood [14, 16, 17]. This project aligns with the NIH health-related mission by focusing on improving access to effective speech therapy, thereby enhancing the quality of life for individuals with SSD. Research suggests roughly 5,000 accurate speech productions are necessary for speech sound generalization [4], motivating the development of AI systems to supplement therapy. However, a critical challenge in automating therapy is providing accurate qualitative feedback, especially for complex sounds like the American English rhotic /ɹ/. Ultrasound biofeedback has shown promise in therapy [24], but cost and training barriers limit its adoption. The research design involves using state-of-the-art machine learning algorithms to analyze a multimodal corpus of children's speech collected in our team’s previous research. Acoustic and ultrasound data will be processed and segmented to extract relevant features, which will then be used to train and evaluate the performance of the machine learning models. The project will incorporate innovative techniques such as cross-modal embedding and retrieval [11] to integrate information from multiple modalities and improve the accuracy of pattern recognition. More specifically, the first aim focuses on training an acoustic classifier to identify tongue shapes within the class of perceptually accurate /ɹ/ productions using acoustic data. Ground truth labels were obtained from previous human coding of articulatory patterns [9] and will be supplemented with additional labeling. Supervised machine learning methods will be employed to differentiate between tongue shapes, aiming for high precision and recall. The second aim focuses on utilizing semi-supervised machine learning methods to distinguish clinically relevant acoustic-articulatory patterns within both accurate and inaccurate /ɹ/ productions. Ground truth labels provided by certified SLPs will inform the training of models, which will be designed with cross-modal embedding to recognize patterns with high precision and recall in a high-dimensional multimodal dataset comprising audio and ultrasound data [11]. The study anticipates developing classifiers suitable for differentiating tongue shapes within accurate and inaccurate /ɹ/ productions, with potential applications in enhancing clinician cueing and providing at-home practice for children with SSD. The long-term goal is to integrate these classifiers into freely available clinical speech software which will provide accurate customized articulatory feedback for /ɹ/ as a first step, with the potential to expand to other speech sounds in the future.

NSF Awards · FY 2024 · 2024-11

Of the many Earth system hazards that are expected to increase with climate change, urban flooding is one of the most dangerous and costly by negatively impacting public health, safety, infrastructure, and mobility. Multiple stakeholders, including the National Weather Service, city agencies, emergency management teams, community members, and Earth Science researchers, require real-time, quantitative, and accurate data on ongoing and past flood events. To address this need, low-cost water level sensors are being developed by the FloodNet project in New York City to collect, transmit, and provide data on flood depth to stakeholders. The ultimate goal is to make flood data and monitoring tools accessible and useful to stakeholders to ultimately advance flood risk knowledge and mitigation and build community flood resilience. However, there remain open questions related to flood data use by different stakeholders and strategies needed to clean, analyze, and distribute the data to meet desired use cases. The main goal of this planning grant is to develop collaborative partnerships with government agencies, the National Weather Service, and Earth Science researchers, and use the extensive dataset being produced by FloodNet to co-identify and refine research questions aimed at using flood sensor data to better understand and predict urban flooding, as well as implement community-level actions toward adaptation and mitigation. This project will be conducted through the following objectives: (1) develop and optimize data processing tools to prepare the flood dataset for actionable use; (2) assess desired use cases for flood data at real-time, intermediate, and long-term time scales, and needs for integrating data into existing information systems; and (3) share flood data with stakeholders to co-identify research questions related to flood risk mitigation and needs for design of new tools for data integration and sensemaking, to ultimately co-develop actionable tools and services to aid flood adaptation and mitigation. 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 · 2024-10

Graphs are one of the most natural ways to represent relationships between data and are used to model a wide variety of settings: social networks, the communication infrastructure, the interconnections of financial markets, metabolic processes, and the wiring of the human brain, to name a few. Processing such graphs has long been a cornerstone of computer science research, but the rise of big data poses unique computational challenges, as the scale of the graphs in these applications has far outpaced available computing power. The goal of this project is to develop a new toolkit for processing massive graphs. This project studies a novel set of research questions in the analysis of big data, and the new tools developed can be applied to foundational optimization problems such as shortest paths and matching that are central to applications in computer science, social networks, biology, and computational economics. The focus on foundational problems allows the project to bring together undergraduate and graduate students from a wide range of backgrounds. In additional to PhD mentoring and high-school outreach, the education plan includes research opportunities for undergraduates and the development of a new course in sublinear graph algorithms at Rutgers University. This project centers on three major challenges to processing massive graphs. The first is that such graphs are too large to fit in the memory of a computer, so the data must be compressed on the fly. The second is that it would take too long for a single computer to process the graph, so the computation is distributed over many machines. The third that is that in many applications the underlying graph is changing over time and it is necessary to respond to these changes locally. The project develops novel tools for tackling each individual challenge. At the same time, the project introduces general frameworks that connect the studies of these different challenges and lead to tools that can overcome a broad set of obstacles simultaneously. More specifically, what unifies the above challenges is the need to extrapolate global information about the entire graph from local information computed in small regions. For example, can one detect overloaded vertices from a small random sample of the graph? How can shortest paths in different regions be patched together to form a path from one end of the graph to another? How can a graph be compressed to only retain the most relevant edges? By answering these and related questions, the research will help extend the motivating applications to significantly larger scales and will lead to new mathematical insights into the structure of graphs. 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 · 2024-10

This project aims to enable automatic question answering systems to produce paragraph-level answers. Prior work on question answering has focused on simpler questions that can be answered with short phrases. Building systems to produce paragraph-level answers opens up exciting opportunities to answer complicated questions, and to offer more nuanced and comprehensive answers to simpler questions. This project will create comprehensive and reliable evaluation protocols for long form question answering (LFQA), pioneer multilingual studies to broaden information access to a wider population, and develop new algorithms that integrate web search with LFQA systems to provide verifiable long form answers paired with human-written evidence documents. This project focuses on three core dimensions of LFQA – datasets, evaluation, and modeling. Expanding the scope of prior English-centric LFQA, this research will investigate multilingual capabilities of large language models by constructing multilingual LFQA datasets and studying knowledge transfer across languages. In terms of modeling, it will propose a new framework that iteratively weaves together – in a transparent manner—knowledge retrieved from documents and memorized knowledge from a language model. Finally for evaluation, the project will engage domain experts who are familiar with the question topic to provide rationales for their evaluation of model generated answers. Such feedback will be used to derive a fine-grained annotation framework which localizes errors and unpack the weaknesses of generated answers. Together, the proposed work will bring significant progress to LFQA, an emerging topic for natural language processing and artificial intelligence 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 · 2024-10

This proposal will focus on rare and extreme climate events, such as heat waves and cold spells, which have major societal impacts. Rapid developments in AI are transforming scientific research, but are difficult to apply to rare events because too few of them occur in training sets. The proposed work will develop the essential mathematical tools to leverage AI methods to significantly improve the estimation of rare event statistics, both in climate and in other fields. The broadening participation aspect of this proposal is centered on making a positive impact on the lives and studies of veteran scholars of the United States military through college and graduate school admissions mentoring and research internships. This interdisciplinary project relies on an essential collaboration among AI, math, and climate to make transformational advances in knowledge that build on and enhance each field. This proposal will develop AI Dynamic Galerkin Approximation (AI-DGA) to extract long return periods from large-ensemble short-duration emulations. Then, it will leverage rare-event sampling in a novel hybrid and iterative use of numerical solvers and AI emulators (AI-RES) to develop additional estimates of return periods and generate more rare event data to re-train, and thus improve the emulator. The proposed work will deliver methods to improve 1) return period estimates for rare events and 2) the training of the AI emulators themselves. The proposal will focus on heat waves and cold snaps, but the methods developed will increase the usefulness of AI emulators across climate science, and geoscience broadly, by innovating new ways to apply them even on rare events they have never seen in their training set and even if the emulators are not reliable for long simulations. This award by the Division of Research, Innovation, Synergies, and Education within the Directorate for Geosciences is jointly supported by the National Discovery Cloud for Climate initiative of the Office of Advanced Cyberinfrastructure within the Directorate for Computer and Information Science and Engineering and by the Division of Mathematical Sciences within the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project funds an AI Summer Institute on Communities (AISIC), a collaboration between New York University and North Central College. This project addresses key challenges for undergraduate students entering the emerging artificial intelligence (AI) workforce in industry, government, or the nonprofit sector. These challenges include broadening participation in technology innovation, identifying and preventing potential technology harms in/on underrepresented communities, meaningfully and equitably engaging underrepresented communities in developing new technologies, and developing new methodologies for translating local knowledge for the purposes of equitable technology design and deployment. Through in-person workshops, experiential learning activities, collaborative projects, and engagement with industry and community leaders, AISIC will equip undergraduates entering the AI workforce with critical literacies in both the social and technical dimensions of AI and related technologies so they are able to develop strategies for the responsible design, development and deployment of technology. Through AISIC, the project aims to create a dynamic environment where students can gain the necessary skills to lead with empathy, ethics, and equity in the development of AI technologies, ensuring these advancements contribute positively to all segments of society, especially those historically and disproportionately harmed by computing technologies. A year-long planning process for the AISIC is critical to ensure its success, relevance, and impact. The project team will conduct the necessary planning to launch AISIC, which at full scale will be a six-week, residential program at North Central College (Naperville, Illinois). During the planning phase, the project will assess needs, identify stakeholders, develop key AISIC partners, build curriculum, address logistical and infrastructural needs, develop recruitment and marketing materials for selecting a diverse participant pool, and execute a limited pilot. The pilot will entail launching a set of exemplary workshops, soliciting participant feedback via pre- and post-test surveys and focus groups. Findings from the pilot will be used to reshape relevant program components in preparation for full implementation. 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 · 2024-10

The tremendous growth of wireless data traffic over the past decades is expected to accelerate even more in future due to increasing demands for high-speed wireless connectivity, ubiquitous network access, and end-user experience. Sub-terahertz (THz) communications, defined as above 100 GHz, are envisioned as a key technology to enable the needed wireless terabit-per-second links by leveraging the hundreds of gigahertz bandwidths available at sub-THz bands. A major challenge in sub-THz bands, caused by higher propagation loss with increasing frequencies, is the limited communication distance. An emerging technology that promises to improve wireless coverage is the active reconfigurable intelligent surface (active-RIS) that consumes low power and provides efficient control of the reflected signals in both phases and amplification. Realizing this potential will require substantial research in hardware design and prototyping of wideband RIS operating above 100 GHz, as well as novel communication and network algorithms for active-RIS-aided wideband systems, together with experimental evaluation and validation of such unique sub-THz networks with active RIS. This project focuses on the 142 GHz frequency band as a front-runner for the first sixth-generation (6G) spectrum to be allocated above 100 GHz and a top choice for future Wi-Fi spectrum allocations in the years to come. The project consists of three intertwined thrusts. The first thrust is to design and prototype a wideband liquid crystal-based RIS with a wide angular range of tunable reflection operating at 142 GHz. Starting with a design for passive RIS as the proof-of-concept at this high frequency, an active RIS design will then be realized using amplifier-integrated LC-based substrate-integrated waveguide, enabling high tunability for each RIS element. The second thrust is to design robust and efficient algorithms for optimal control of the active RIS coefficients including frequency-dependent phase shift and amplitude amplification. Novel algorithms leveraging unsupervised graph neural networks and reinforcement learning will be used to capture the underlying network interaction and to provide strong scalability and generalizability. The third thrust is to perform extensive validation using the NSF-funded open-source ray-tracing simulation tool “NYURay” for active-RIS-aided sub-THz channel simulations. In addition, the prototyped passive and active RISs will be used to conduct on-site wireless propagation measurements utilizing the wideband sliding correlation channel sounder to create a site-specific hybrid channel model for RIS-aided communication. Through various education and outreach activities to broaden participation in computing, this project will foster knowledge sharing and contribute to industry and regulatory advancements in THz communications. 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 · 2024-10

Housing instability among youth is a significant issue across the U.S., with at least one in thirty adolescents aged 13-17 and one in ten youth aged 18-25 experiencing unstable housing annually. In cities like NYC, the lack of affordable housing and living-wage youth employment opportunities make it especially difficult for youth to find and remain in safe, stable housing. Accessing support services, such as mental health, legal, and financial assistance, is crucial for transitioning out of homelessness. However, resource availability and youth needs are often mismatched due to inadequate resources, changing economic conditions and other exogenous factors, and surges of young people requiring assistance. To increase the ability of runaway and homeless youth organizations to respond to youth needs and navigate a changing landscape, this project will develop and pilot a coordinated data platform. This platform will enable youth to provide up-to-date intake information to providers and specify their service needs, and enable multiple organizations to assess the population waiting for shelter and services. The project aims to better match youth with organizations that can increase their access to shelter and meet their unique set of needs. Through strategic partnerships with local service providers, governmental agencies, local coalitions, and with direct input from youth with lived experience, the project will develop the infrastructure for a coordinated data platform to improve data collection and management processes across varied youth service organizations. This platform will empower youth to convey their needs more effectively, enhancing service responsiveness and efficiency. During planning, the project team and partners will use a collaborative community-based approach to ensure that the proposed platform to be deployed in the pilot project meets the varied and unique needs of multiple stakeholders, increasing the likelihood of successful adoption and long-term viability. This project is in response to the Civic Innovation Challenge program’s Track B. Bridging the gap between essential resources and services & community needs and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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 · 2024-10

Mathematical problems involving matrices, such as systems of linear equations and eigenvalue problems, arise in a huge number of applications across machine learning, statistics, scientific computing, engineering, and beyond. More efficient algorithms for solving these problems can thus have widespread practical impact, significantly accelerating the pace of scientific discovery, decreasing the cost of data analysis, and facilitating simulations of large-scale, complex systems. Developing efficient algorithms for matrix problems is the central goal of the field of numerical linear algebra. Unfortunately, it can often be difficult to understand the limits of algorithmic research. How close are the current best algorithms to being optimal? Are there inherent barriers to developing faster algorithms? This project will tackle these questions by studying fast matrix algorithms through the lens of “query complexity”. Specifically, the goal is to develop algorithms that access, or “query”, the input matrix as few times as possible in order to solve a given problem. Algorithms with small query complexity are frequently efficient in practice, so focusing on query complexity will lead to the development of faster algorithms for applications. At the same time, unlike more general models of computation, it is often possible to prove impossibility results for query complexity, i.e., to show that no algorithm can solve a given problem with too small of query complexity. Thus, the project will improve understanding of the limits of algorithmic research and of the optimality or sub-optimality of existing methods. The work will produce general-purpose algorithmic tools and lower-bound techniques that can be applied to algorithms research at large. The project will also expand the dialogue between research communities, including theoretical computer science, numerical analysis, and quantum computing communities. Concretely, the research team will study linear algebraic computation through the lens of query complexity by 1) developing new, query-efficient algorithms for core matrix problems and 2) proving unconditional lower bounds on the number of queries required to solve these problems. The research will center on two important query models: entrywise matrix queries and matrix-vector product queries. Entrywise queries are perhaps the simplest query model for linear algebraic computation, although the complexity of many basic problems in the model, from eigenvalue approximation to matrix norm computation, remains unresolved. By studying the model, the project will explore broad themes, such as the importance of randomness and query adaptivity, and the power of “augmented” entrywise query models that rise in quantum-inspired numerical linear algebra, spectral graph theory, and beyond. The project’s second focus on matrix-vector product queries is motivated by the fact that matrix-vector products often dominate the runtime of linear algebraic methods in practice. As such, understanding the number of matrix-vector products required to solve central problems like structured matrix approximation, norm approximation, and spectral density estimation is a key goal. The matrix-vector product model generalizes the widely studied matrix sketching and Krylov subspace models, so proving lower bounds in this model will require new techniques. Overall, the project will strengthen the theoretical foundations of computational linear algebra, with the goal of establishing query complexity as a central framework for developing faster algorithms and computational lower bounds. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Alzheimer’s Disease (AD) is a highly heritable complex human disease, but only a limited number of its genetic risk factors have been identified. In this project, we propose to leverage identical-by-descent (IBD) segments, which are expected to better tag haplotype effects and provide complementary genetic information to additively coded genotypes that are commonly used in genome-wide association studies, in the genetic association mapping for AD and related brain imaging endophenotypes. In Aim 1, we will develop the statistical and computational framework for efficient genetic association mapping using IBD segments in large samples. In Aim 2, we will apply IBD mapping to identify novel genomic regions associated with brain imaging endophenotypes from the UK Biobank. In Aim 3, we will replicate these associations in the Alzheimer’s Disease Sequencing Project (ADSP), develop and apply IBD mapping methods to AD, and combine association evidence from diverse populations in the UK Biobank and ADSP. Our novel tools and findings from this project will be made publicly available to facilitate novel discoveries that improve our understanding on the genetic architecture of AD and related brain imaging data.

NIH Research Projects · FY 2025 · 2024-09

PROJECT ABSTRACT Nager syndrome (OMIM#154400) is a rare craniofacial and limb disorder characterized by midface retrusion, micrognathia, absent thumbs, and radial hypoplasia. This disorder results from mutations in the SF3B4 (splicing factor 3b, subunit 4) gene, which encodes SAP49, a protein that is a component of the spliceosome. The spliceosome is a complex of RNA and proteins that function together to remove introns and join exons from transcribed pre-mRNA. While the spliceosome is present and functions in all cells of the body, many spliceosomopathies – including Nager syndrome – are often cell/tissue-specific in their pathology. In Nager syndrome patients it is the neural crest (NC)-derived craniofacial skeletal structures that are affected. The mechanisms underlying Nager syndrome pathology, as well as its tissue-specificity, are poorly understood. Interestingly, other craniofacial spliceosomopathies, such as craniofacial microsomia (SF3B2), mandibulofacial dysostosis Guion-Almeida type (EFTUD2), Burn-McKeown syndrome (TXNL4A), and Verheij syndrome (PUF60) share similar clinical features with Nager syndrome, however it is unclear if they are caused by the same underlying mechanisms. In this application, I will combine use of a Xenopus tropicalis sf3b4 mutant line and Nager syndrome patient-derived induced pluripotent stem cells (iPSCs) to tease apart the mechanisms underlying Nager syndrome. This combination of in vivo and in vitro approaches will provide novel insights into the mechanisms driving craniofacial defects in the context of Nager syndrome, which can then be translated to other craniofacial spliceosomopathies to determine if they share a common root cause. The proposed experiments will test the hypothesis that SF3B4 has NC-specific targets and/or binding partners, and upon mutation these interactions are disrupted or lost, leading to abnormal NC development and subsequent Nager syndrome-associated craniofacial defects. I have crafted three specific aims to test this possibility. Specific Aim 1: Using preliminary data from RNA-seq analyses performed on a Xenopus tropicalis sf3b4 mutant line, I propose to identify the biological pathways disrupted in Nager syndrome by testing candidate genes via gain- and loss-of-function experiments in Xenopus tropicalis. Specific Aim 2: In collaboration with the Columbia Stem Cell Core, I propose to generate and characterize a Nager syndrome patient-derived iPSC line, and use this line to characterize NC in the context of Nager syndrome. Specific Aim 3: In the R00 phase of the application, I plan to use the knowledge and experience from modeling Nager syndrome and develop in vivo and in vitro models of other craniofacial spliceosomopathies in order to determine their root cause. Altogether these studies will provide novel insights into the mechanisms underlying Nager syndrome craniofacial defects, which can be used to inform work on other craniofacial spliceosomopathies, establishing a unique view on understanding craniofacial development and disorders.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY. The proposed study responds to NOT-MD-23-008, which calls for community-engaged interventions to increase vaccine uptake among populations experiencing health disparities. We focus on COVID-19 and influenza vaccination, both of which now require annual vaccines. Among those at highest risk for morbidity, hospitalization, and mortality are African American/Black and Latino (ABBL) persons who are not up-to-date on these vaccinations. Only 20-28% of adult AABL persons are up-to-date on COVID-19 vaccination, compared to 31% of White persons, and only 30-40% of AABL persons receive the influenza vaccine annually compared to >55% among White persons. AABL experience serious impediments to COVID- 19 (and to a lesser extent, influenza) vaccination at individual- (e.g., distrust, insufficient knowledge, low perceived risk, cognitive biases), social- (e.g., peer norms), and structural-levels of influence (e.g., poor access). Taken together, these comprise multi-level vaccine hesitancy. Factors that promote vaccination include trusted AABL health educators (peers, nurses), tapping into altruism and collective responsibility, circumventing cognitive biases, and reducing structural barriers. Without efforts to address multi-level vaccine hesitancy, rates of COVID-19 and influenza vaccination will remain unacceptably low and racial/ethnic health disparities in infectious disease morbidity and mortality will persist. The proposed study is led by a collaborative team at New York University and the Northern Manhattan Improvement Corporation. It uses the multiphase optimization strategy (MOST), an engineering-inspired framework, to test effects of individual candidate intervention components in a factorial design and then optimize a multi-component intervention made up of the most cost-effective combination of components. Staying up-to-date with COVID-19 vaccination (confirmed with documentary evidence) is the primary outcome, and influenza vaccination is the secondary outcome. We have identified four promising candidate components, with an emphasis on brevity, low-touch, and future scalability: A) nurse-led shared decision making, B) a text message intervention, C) modest lottery prizes for vaccination, and D) peer navigation to vaccination appointments. Participants will be N=560 community-residing adult English and Spanish-speaking AABL persons who are not up-to-date on COVID-19 and influenza vaccinations but with at least one COVID-19 vaccine dose. Specific aims are: Aim 1) identify which of four components contribute meaningfully to improvement in the outcomes; Aim 2) identify mediators (e.g., altruism, norms) and moderators (e.g., sociodemographic characteristics, distrust) of the effects of each component; and Aim 3) build the most cost-effective intervention package(s). Participants will be randomly assigned to an experimental condition, and assessed at 3- and 6-months post-baseline; N=45 participants will engage in qualitative in-depth interviews. We will also uncover, describe, and plan for implementation issues so the optimized intervention can be rapidly scaled up by community-based and outpatient health organizations.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY/ABSTRACT Chemotherapy-induced painful peripheral neuropathy (CIPPN) significantly reduces quality of life. Patients often require tapering or cessation of chemotherapy, resulting in treatment failure and poor survival. Key mechanisms include mitochondria dysfunction, oxidative stress, and sustained neuroinflammation, which lead to neuronal hypersensitivities and chronic pain. There are no satisfactory treatment or preventative therapies available. The research on CIPPN has largely focused on sensory neurons, however, peripheral glia Schwann cells (SCs), emerge as an essential component of the functional unit with sensory neurons that regulate pain states. Yet, despite being highly susceptible to chemotherapy toxicity, mechanisms underlying SC contributions to CIPPN are largely unknown. We discovered a key reciprocal signaling system mediated by peroxisome proliferator activated receptor gamma (PPARγ) and low-density lipoprotein receptor related protein 1 (LRP1) that regulate SC survival, bioenergetics and inflammation. We hypothesize that PPARγ and LRP1 signaling governs the metabolic homeostasis in SCs, and boosting their activity is pro-survival, anti-inflammatory, and anti-oxidative, and thereby reduces neuronal mitochondrial dysfunction and suppresses neuronal hyperexcitability induced by chemotherapeutic agents. Our hypothesis is based on published data showing that PPARγ and LRP1 agonists prevent the development of pain-related behaviors after nerve damage and compelling new preliminary data showing that PPARγ and LRP1 regulate bioenergetics and mitochondrial dynamics in SCs in response to chemotherapeutics. In Aim 1, we will examine the role of PPARγ and LRP1 in cell survival, mitochondria dynamics and heterogeneities in primary mouse SCs. We will examine ultrastructural and bioenergetic changes in nerve mitochondria in a tumor bearing oral cancer model treated with chemotherapeutics. In Aim 2, we will examine the role of SC PPARγ-LRP1 in chemotherapy-induced oxidative stress and neuroinflammation using primary mouse SCs and tumor bearing models of CIPPN. In Aim 3, we will examine the role of SC PPARg and LRP1 signaling in chemotherapy-induced changes in nociceptive behaviors, sensory neuron metabolism and hyperexcitability. We will use two chemotherapeutics: paclitaxel and oxaliplatin for generalizability. All key findings from mouse studies will be validated in human cells and tissues including SCs, nerves collected from cancer patients, and novel SC-dorsal ganglion neuron cocultures. Abuse liabilities are determined by monitoring mouse behaviors. The potential effect of SC PPARγ-LRP1 signaling on tumor’s response to chemotherapeutics will be evaluated by monitoring tumor growth and histopathology. Proposed studies will validate the role of PPARγ in CIPPN, address new cellular and molecular mechanisms of PPARγ regulation of CIPPN, and identify LRP1 as a novel target for CIPPN. Clinical relevance is predicated on recent reports demonstrating that a selective PPARγ agonist is protective against nerve degeneration and inflammation in patients with rare neurological disorders. SP16, an innovative LRP1 agonist has shown safety and tolerability in clinical trials.

NIH Research Projects · FY 2026 · 2024-09

Modified Project Summary section Obesity has been increasing in children and adolescents for decades. The reasons for this increase are not fully understood, despite research into factors such as diet, physical activity, and socioeconomic position. Psychosocial stress has been implicated as a social determinant for obesity. While psychosocial stress is understood as a multidimensional construct that operates at multiple levels and in different social contexts, the association between multiple forms of psychosocial stress and obesity among youth remains understudied, as well as the underlying mechanisms by which psychosocial stress may affect obesity. In this study, psychosocial stress is defined as exposure to neighborhood conditions characterized by limited educational, health/environmental, and social/economic resources (neighborhood stress), as well as adverse interactions with peers, teachers, and other adults (interpersonal stress). This study aims to define the relationship between multiple forms of psychosocial stress and obesity using longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study, in order to increase the efficacy of prevention efforts and treatment to reduce obesity. The study will assess the association between different forms of psychosocial stress (neighborhood and interpersonal stress) and the growth trajectory of anthropometric outcomes (i.e., BMI and waist circumference), examine the role of psychosocial stress in explaining subgroup differences in anthropometric outcomes, investigate the extent to which obesity-related health behaviors mediates the relationship between psychosocial stress and adiposity, and evaluate the potential buffering effect of psychosocial resources (e.g., family support and positive school environment). The study will also leverage the embedded twin sample within ABCD to strengthen causal inference by accounting for shared genetic and familial factors in the association between interpersonal stress and adiposity. Overall, this study has the potential to inform the optimization of existing clinical and place-based interventions aimed at reducing obesity by highlighting the importance of addressing psychosocial stress and identifying the context and most at-risk groups that can benefit from these interventions to reduce obesity.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Physical organization of biomolecules within the cell is critical for controlling key life processes in health and disease. It has recently been realized that in addition to membrane bound compartments such as the nucleus or mitochondria, membraneless compartments may play a pivotal role in organizing biomolecules in the cell. Called biomolecular condensates, these assemblies form by weak multivalent interactions and are known for their spherical shapes and viscoelastic behavior. A biomolecular condensate can reversibly localize or delocalize biomolecules in response to environmental cues. They can also interact with numerous biomolecules in a non- stoichiometric manner. There is growing evidence suggesting that certain proteins in bacteria may function as condensates. While they provide a means of compartmentalization for cells lacking traditional organelles, their most impactful role could be as rapid response sensors. However, studying condensates in bacteria is particularly challenging. Most evidence comes from laboratory-based reconstitution experiments that fail to accurately replicate the true cellular environment, including context-dependent interactions. Due to the lack of suitable tools, understanding the effects of condensates on phenotypes and inhibiting bacterial growth through this novel mechanism have proven difficult. Current methods to assess condensate formation rely on fluorescence microscopy, which is limited by the diffraction limit of light and cannot resolve structures smaller than approximately 250 nm. Alternatively, proximity ligation-based assays followed by mass spectrometry are often biased towards capturing strong interactions, thus neglecting dynamic interactions. In this proposal, we aim to develop a novel approach that combines modular fluorophores and correlative super-resolution microscopy to investigate the structure, dynamics, and interactome of condensates using genetically encoded tags. Our goal is to apply this opto-proteomic toolbox to understand the role of biomolecular condensates in various aspects of the bacterial cell cycle, surface colonization, and evolution within both free and host environments. This approach will be readily applicable for the study of condensates in other bacteria and beyond, while shedding light on the nanoscale mechanisms that impact macroscopic phenotypes.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Opioids remain the most efficacious, but insufficient, analgesic regimen for treating patients with oral cancer pain. Oral cancer pain is generated at the primary cancer site and is associated with high levels of proteases released in the cancer microenvironment. These proteases cleave distinct regions of the protease-activated receptor 2 (PAR2) to activate PAR2 and mediate cleavage site-specific intracellular trafficking and signaling of the receptor. Mice lacking PAR2 do not develop oral cancer pain, highlighting a central role for neuronal PAR2 in oral cancer pain. The impact of oral cancer on PAR2 signaling and trafficking in trigeminal (TG) neurons is not known. The long-term goal is to improve management of oral cancer patients by identifying components of the PAR2 signaling cascade that are viable targets for development of location-selective drug delivery systems to alleviate oral cancer pain. The overall objectives for this application are to augment my expertise in signaling and nanoparticle drug development by gaining training in oral cancer pain and research with complex biological systems, as well as undertaking career development activities to foster transition to an independent investigator. The central hypothesis for the proposed research is that oral cancers release proteases that activate PAR2 and induce sustained pain via PAR2 signaling from multiple cellular compartments in neurons. The rationale for this project is that identification of cancer-induced PAR2 signaling and trafficking affords the opportunity to develop drug delivery systems that specifically target these signaling pathways to treat cancer pain. The central hypothesis will be tested by three Aims: K99, Determine oral cancer-induced changes in the 1) trafficking and 2) signaling of PAR2 in TG neurons, and 3) R00, Develop nanoparticles to target PAR2 located at the plasma membrane, endosomes and Golgi to alleviate oral cancer pain. In Aim 1, cancer induced changes in PAR2 trafficking will be investigated by confocal microscopy using TG neuron cultures from PAR2-muGFP mice bearing oral cancers and controls. Aim 2 will evaluate the signaling of neuronal PAR2 by measuring cAMP and Ca2+ in TG neuron cultures from cancer bearing and naïve mice expressing PAR2-muGFP in sensory (Nav1.8) neurons. For aims 1 and 2, TG cultures will be challenged with (a) four oral cancer proteases, (b) a mixture of these proteases, and (c) conditioned media (CM) from human oral cancers that contain pain mediators (proteases) released by the cancers. The focus of the R00 phase will be on the development and in vitro and in vivo evaluation of location-selective nanoparticles targeting PAR2 at the plasma membrane, endosomes and Golgi. The proposed studies are innovative because they are motivated by previously unappreciated oral cancer- induced PAR2 trafficking and signaling, and the drug targeting and delivery approach. The work is significant because these studies will lay the foundation for repurposing drugs to target PAR2 at specific intracellular sites.

NIH Research Projects · FY 2024 · 2024-09

Project summary Down syndrome (DS) is caused by trisomy of all or part of human chromosome 21 (Hsa21) and is the most common genetic cause of intellectual disability. DS predisposes affected individuals to a wide range of comorbidities that shorten their life expectancy and lower quality of life. People with DS are strongly predisposed to develop autoimmune disorders, show consistent activation of interferon (IFN) responses, and produce significantly less saliva than healthy controls. These last set of features are shared with another syndrome, Sjogren’s Disease (SjD), an autoimmune disease affecting salivary (and lacrimal) glands resulting in reduced saliva, elevated autoantibodies (SSA, SSB) in serum, and lymphocytic infiltration of the glands. In DS, there is a triplication of the interferon receptor (IFNR) gene cluster resulting in chronic interferon hyperactivity and inflammation. In SjD, patients exbibit not only salivary gland inflammation and elevated levels of type 1 IFN. Because of the shorter life expectancy associated with DS and given that SjD predominantly affects middle-aged persons, this may have biased findings linking both conditions, although some case reports have described SjD in DS. Our preliminary data, obtained in our studies part of the parent grant, show that Dp16 mice hyposalivate, indicating abnormal salivary gland function. The aim of this supplement is to address if DS is a condition that predisposes individuals to develop Sjogren’s-like disease. To address this, we will challenge the Dp16 mice, a well-known DS mouse model, with agonists of both toll-like receptors (TLR), particularly TLR7 and TLR8, which we and others have been reported in salivary glands and in SjD, and interferon stimulation (e.g. DMXAA), and to test the ability of common blockers of IFN signaling, such as JAK inhibitors, to ameliorate inflammation and improve salivation. These studies enhance the goals of the parent award by assessing whether the salivary glands in Dp16 mice are prone to developing an inflammatory response typical of SjD.

NSF Awards · FY 2024 · 2024-09

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. Amidst the global shift toward green and sustainable urban transportation systems, substantial investments have been made in infrastructure to facilitate the adoption of electric vehicles (EVs) in the United States. The placement of an EV charging station (EVCS) potentially has significant broader impacts on peoples' mobility, activity patterns, and visitation to nearby businesses during charging sessions. This provides an opportunity for policy makers to support local businesses (e.g., cafes, restaurants, grocery stores), particularly small and medium-sized enterprises, which play a pivotal role in maintaining community health, especially in vulnerable communities. This SAI project tackles the question of how and where to best place EV charging stations to ensure they not only meet the needs of drivers but also boost the economic resilience of small businesses and promote social equity. The project integrates theory and methods from computational social science, urban resilience, behavioral science, and complex systems to address a pressing societal need -- the equitable, resilient, and sustainable deployment of EVCSs. This project leverages large-scale datasets including mobile phone GPS, charging station usage data, and real-world intervention experiments to understand the broader social and economic impacts of EVCS placement on mobility, social dynamics, and the resilience of businesses. This complex systems approach introduces a new paradigm of infrastructure development and management that significantly extends the scope from individual behavior to social and economic community-wide effects, offering a more comprehensive understanding of the EVCS ecosystem. The optimization and visualization platform will enable agencies and businesses to evaluate hypothetical deployment scenarios, promoting a multi-dimensional approach to infrastructure design. The open-source and public-facing platform ensures that its benefits are not confined to the academic realm but are extended to diverse community stakeholders, reinforcing the project's commitment to inclusive and comprehensive urban development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Non-Technical Description: Fibrous biomaterials are very helpful in medicine as they support cell growth, aid tissue function, and deliver drugs to specific parts of the body. Recent advancements have made it possible to add fluorine, a special chemical, to these materials, which helps with imaging. This project aims to enhance the design of these fluorinated biomaterials for medical use. The team has three main goals: 1) to create fibers that can release drugs in a controlled manner; 2) to develop gels that change with temperature; and 3) to use imaging techniques to view these materials inside the body without surgery. Their approach allows them to precisely control how these materials interact with other molecules, form gels, and produce imaging signals. This is promising for developing new treatments that can be tracked inside the body. The project aims to create innovative medical materials that can both treat and allow doctors to see inside the body. It also helps train future scientists and engineers by involving students from various fields such as chemical engineering, materials chemistry, electrical engineering, and biomedical engineering. Additionally, as part of their outreach, the team will engage with the Urban Assembly Institute for Young Women, a school with many underrepresented students. They plan to offer educational modules that include lessons on fluorine, proteins, and virtual lab experiences, aiming to inspire and educate students in grades 6-12 about science and technology, promoting diversity in these fields. Technical description: Fibrous biomaterials offer valuable advantages in creating scaffolds for cell growth, supporting tissue function, and maintaining composition and localization for drug delivery. With advancements in synthetic and chemical biology, it is possible to incorporate non-canonical amino acids (NCAAs) bearing fluorine for imaging purposes. This proposal aims to enhance the design of fluorinated protein fibers and hydrogels through three key objectives: 1) generating coiled-coil fluorinated fibers for drug encapsulation; 2) developing fluorinated coiled-coil upper critical solution temperature hydrogels; and 3) assessing the potential of coiled-coil fluorinated fibers and hydrogels as 19F Magnetic Resonance Spectroscopy (MRS)/(MRI) Imaging agents. Recently the principal investigator’s group in collaboration with key collaborators demonstrated the incorporation of trifluoroleucine (TFL) in a designed coiled-coil protein that exhibited enhanced thermostability and small molecule binding in protein fibers. It was hypothesized that a 19F nuclei-dense coiled-coil could serve as a sensitive 19F MRS/MRI Imaging theranostic agent, with tunable supramolecular assemblies achievable through NCAA incorporation of TFL. This interdisciplinary approach allows precise control over small molecule binding behavior, gelation, and MRS/MRI signal, holding significant promise for theranostic applications where both therapeutic delivery and non-invasive imaging are crucial. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Young adults experiencing homelessness (YAEH) have high prevalence of mental disorders, however, they face unique access barriers to mental health services combined with low recognition of mental health symptoms, leading to low rates of mental health service utilization. The point of transition from homelessness to housing presents a period of opportunity to identify mental health symptoms and connect and engage YAEH into mental health services to reduce symptoms and promote housing stability. The COVID-19 pandemic has heightened mental health symptoms and increased the need for services among YAEH, but it has also resulted in recovery funds to increase housing supports in many communities. There is a critical need to develop interventions that can support YAEH with mental health challenges as they make the transition from homelessness to housing as more supported housing becomes available. Critical Time Intervention (CTI), a structured, time limited case management intervention has demonstrated success with adults with serious mental illness in multiple randomized controlled trials but it has not been tested in YAEH. The goal of this study is to adapt CTI for the context of transition from homelessness to supported housing within the rapid rehousing program, integrating mental health specific content from a young adult treatment model, C4, to develop CTI-YAMH (young adult mental health), and then test the new intervention in a feasibility pilot. Specifically, we aim to: Aim 1: Refine the draft CTI- YAMH intervention (treatment, training and assessment protocols) to ensure the target mechanisms are adequately addressed for stabilizing housing and mental health, utilizing an iterative stakeholder feedback process to finalize the manuals for pilot testing, then Aim 2: Conduct an open trial of the adapted CTI- YAMH intervention to assess the feasibility of randomization procedures, refine outcome measures, assess acceptability, and examine the preliminary signal of impact of the intervention. This innovative study targets a critical point of intervention, the transition from homelessness to housing, for an extremely marginalized group (YAEH), utilizing an innovative adaptation framework, ADAPT-ITT, to systematically adapt CTI in partnership with youth with lived expertise and community providers. The CTI-YAMH intervention aims to support a population with high unmet need for mental health services through a model that can be paired with rapid rehousing, a supportive housing model widely used in communities across the U.S. Results from this R34 study lay the foundation for a fully powered RCT of the CTI-YAMH intervention in a future R01 study.

NSF Awards · FY 2024 · 2024-09

Many physical simulation applications can be viewed as building “digital twins” of real systems, i.e., computer models that enable studying physical phenomena computationally, avoiding the costs and risks associated with physical experiments. Differentiable simulation allows automation of two critical aspects of digital twin creation and use, improving the quality of the result and democratizing digital twin use: integration of real-world data, and in the case of engineering systems, optimization of system parameters to achieve a particular goal. Examples include identifying realistic material parameters of a patient-specific biomechanical digital twin or discovering the optimal shape of a shoe sole for uniform load distribution. This project will develop open-source software for differentiable simulation for systems involving elastic deformations with contact. These tools will be evaluated in three major application areas: (computational fabrication, biomechanics, and robotics). The deliverables of this project will be open-source software packages accessible to a broad user base. The project plans to utilize dPolyFEM, a modular software framework for design, control, system parameter inference, and learning problems for physical phenomena in material design, biomechanics, and robotics, based on differentiable simulation. The focus is on developing robust, efficient, and scalable software blocks for differentiable simulation that can handle input data satisfying only weak assumptions (e.g., on mesh quality, shape, or boundary conditions) and require no parameter tuning while providing users sufficient control over performance-accuracy trade-offs. The project will support the most common class of physical problems in the target domains: elastodynamic problems involving complex geometry, large deformations, contact, and friction. For scalability, dPolyFEM will provide shared-memory parallelization. This system will consist of several modules that can be used independently or in an integrated way, enabling easy integration of its components into existing general-purpose and domain-specific software. From a technical standpoint, this system will build on three innovations: (1) considering differentiable simulation as a single end-to-end problem including meshing, FE solution, and adjoint formulation, (2) casting the time-integration of physical systems as an energy minimization, for which robust solvers can be developed, and (3) systematically testing the system on large-scale benchmarks The resulting open-source differentiable simulation framework will enable applications in many fields of interest to NSF. The project team includes computer scientists (CISE), applied mathematicians (DMS), and engineers (ENG), and it is expected that the contributions will have an impact on all three communities. Individual modules can and will be integrated into major open-source projects, likely benefitting tens of thousands of users. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Civil Mechanical and Manufacturing Innovation within the Directorate for Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-09

The overarching objective of this proposal is to increase the quantitative and data management capabilities of graduate students, postdoctoral fellows, and early- to mid-career faculty by providing training in quantitative research methods and hands-on analysis and programming experience with the All of Us Researcher Workbench. Our team has extensive experience engaging and training researchers to work with the All of Us Research Program. Building on this foundation, we will utilize the Social-Ecological Researcher Engagement Framework to expand our proven engagement strategies while implementing novel approaches to train graduate students, postdoctoral fellows, and early- to mid-career faculty on advanced quantitative and programming skills using the All of Us Research Program. This proposal incorporates four evidence-based approaches for increasing access to high-quality, large-scale data and promoting quantitative and data management capabilities through research training and hands-on experience with the All of Us Researcher Workbench. These approaches include collaboration with institutions across the United States to integrate knowledge, resources, and training as well as increase awareness of the All of Us Researcher Workbench; training in highly relevant and deployable quantitative and data science skills for graduate students and postdoctoral fellows; strengthening the quantitative expertise of postdoctoral fellows and early- to mid-career faculty; and provision of ongoing technical assistance, including statistical guidance, webinars, and individual project support to collaborative institutions to facilitate accessibility and usability of the All of Us Researcher Workbench. This program is critical to the development of a quantitatively skilled public health workforce at multiple levels, from graduate training through mid-career faculty. These engagement activities have the potential to foster long-term change in the landscape of public health scholarship by cultivating a more diverse and equitable workforce that spearheads important innovations in the field.

NIH Research Projects · FY 2024 · 2024-09

Project Summary This is a feasibility trial testing music listening as an intervention for mental health recovery after stroke. Stroke disproportionally affects older individuals and is the second largest cause of death and the third leading cause of disability world-wide, with 75% of survivors suffering from motor impairments and more than 30% suffering from mental health problems (Lawrence et al., 2001; Hacket and Pickels, 2014). While worse mental health after stroke is related with higher risk of long-term disability and higher mortality rate (Jorgensen et al., 2016; Medeiros et al., 2020; Blöchl et al. 2019; Shi et al., 2017), there is a lack of affordable, easy to deliver, and accessible interventions with minimal side effects that improve mental health in the chronic stage of stroke (Allida et al., 2020). Intentional music listening (IML) refers to the active practice of listening to music (i.e., paying attention to the music without engaging in other activities). IML is one of the most popular leisure activities that people use to upregulate their mood (Dingle et al., 2021; Linnemann et al., 2015). In stroke, preliminary studies show that IML has the potential to reduce depression and anxiety at the acute stage of stroke (Särkämö et al., 2008, 2014; Le Danseur et al., 2019; Baylan et al., 2016). However, these preliminary studies suffer from multiple limitations, including small sample sizes and the lack of an objective measure of treatment dose (e.g., time engaged in IML, acoustic properties of the music listened to). Therefore, research has yet to provide a neuromechanistic account of whether and how IML improves mental health outcomes in stroke. Moreover, there are no studies assessing the effects of IML at the chronic stage of stroke, when long-term mental health ailments keep increasing the risk of disability and mortality (Jorgensen et al., 2016). Here we propose a feasibility trial testing a remote IML intervention in chronic stroke that is guided by the NIH Music-based Intervention Toolkit and that includes an objective measure of treatment dose. Our neuromechanistic framework builds connections between clinical and basic research and borrows the notion of “enriched environment” from animal models of stroke rehabilitation: increased motor, sensory, cognitive, and social contexts promote brain plasticity and recovery after brain injury (Matsumori et al., 2006; Söderström et al., 2009; Johansson and Belichenko, 2002). This feasibility trial will pave the way for future large-scale clinical trials testing whether and how IML provides an optimal enriched environment for mental health recovery in chronic stroke. Across two aims, we develop and test the feasibility of the proposed IML intervention for chronic stroke. The studies will help to validate the NIH Music-based Intervention Toolkit’s guiding principles and will generate pilot data to design future large-scale clinical trials that use IML as an intervention for brain disorders of aging.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT. Military-connected mothers (MCM; mothers who are either service members or veterans, or spouses of a service member or veteran) experience significant trauma exposure associated with post-traumatic stress symptoms and ultimately deficits in parental functioning. Maternal trauma and its mental health and functional sequelae can have significant consequences for children, including adverse mental health outcomes. Existing evidence-based treatments (EBT) available to MCM to address trauma have not been adapted for military culture and do not fully resolve symptoms or address the bi-directional relationship between symptoms and parental functioning. To address this gap, in this R21, we propose to adapt and collect preliminary data on an innovative intervention, Parenting-STAIR (PSTAIR), which addresses trauma symptoms and parenting among MCM. PSTAIR is a novel intervention, combining two existing EBTs: Skills Training in Affective and Interpersonal Regulation Narrative Therapy (STAIR), targeting maternal emotion dysregulation and mental health symptoms, and dyadic Parent- Child Care (PC-CARE), targeting parental functioning. In an open pilot with child-welfare involved mothers, 23 session PSTAIR dramatically reduced symptoms, improved parenting skills, and prevented maltreatment. However, non-trivial treatment dropout and nonresponse consistent with other trauma- and parenting-focused EBTs were also observed; results suggest shortening and individualizing treatment may offer solutions to address dropout and nonresponse. Guided by a heuristic framework for cultural adaptation of behavioral interventions, the present study will proceed in three phases. In phase 1, we conduct qualitative interviews and focus groups with key informants including MCM to guide adaptation of PSTAIR in Phase 2. Adaptation will be guided by qualitative findings and informed by novel approaches to shorten and individualize EBTs to address dropout and nonresponse, including modular and adaptive design elements and shared decision-making between participants and clinicians. We anticipate the outcome of Phase 2 will be a 10-15 session intervention (PSTAIR-M), involving a compact version of PSTAIR in Module 1 and tailored options for Module 2, focusing on mental health (Module 2a) and parental functioning (Module 2b), implemented based on response to Module 1. Phase 3 will involve a pilot randomized controlled trial conducted in a community mental health setting, in which we will randomly assign N=120 trauma-exposed MCM who screen positive for PSTD with/without comorbid depression and one identified child (ages 2-10) to PSTAIR-M or treatment as usual. In Phase 3, we will also collect critical data on mechanisms which may account for observed effects of PSTAIR. Pilot data collection will set the stage for a future R01 in which we will conduct a full-fledged mediator-moderator clinical trial. Successful treatment with an efficient, personalized intervention has the potential to prevent adverse outcomes for MCM and their children and to interrupt the intergenerational cycle of trauma in military family systems.