University Of Rhode Island

NSF Awards · FY 2024 · 2024-09

This project seeks to use films that speak directly to anti-racism, science, and environmental justice in ways that support reflection, thoughtful dialogues, behavior change, and approaches to mend and develop relationships between informal STEM learning institutions and local communities of color. In this first phase, a Partnership Development and Planning project, the team will cultivate partnerships between community leaders and informal learning institutions in two cities along the Mississippi River (New Orleans, LA and St. Louis, MO). Each partnership includes multiple community leaders, based on an evolution of collaborations. In prior work, needs, interests, and blind spots emerged through in-depth interviews with informal STEM learning professionals and community leaders. Community leaders, who have worked with a variety of local groups, noted that collaborations with anchoring institutions, such as science museums and zoos, would be beneficial in supporting STEM identities and career pathways for local youth. The project will engage in and evaluate an ethical equitable partnership framework that forefronts community needs and values, as they work toward building partnerships between science museums and their communities. Together, partners will screen excerpts and consider the potential of film to engage their community in difficult conversations connected to local and complex racial dynamics and environmental justice issues. They will explore film’s potential to expand understanding of varied epistemologies, lived experiences, and perspectives that affect people’s sense of belonging in spaces intended for STEM learning. Partners will also consider how films can offer shared vocabularies to discuss values, principles, and decisions across various historically marginalized diverse communities. Ultimately, this partnership will work to identify a future AISL research and development project(s) that benefit all partners, co-determining the research focus, purpose, audience, timing, venue, and accompanying programming for films that serve as a catalyst for difficult conversations on around race, anti-racism, and inclusion in STEM. Throughout the project the team will employ and document an ethical equitable partnership framework, informed by cross-cultural engagement practices that forefront the community that has been marginalized. They will use dialogic theory to better understand the use of critical conversations to support individual’s and organization’s growth toward change that addresses injustices. Two principles, grounded in the project’s conceptualization of equity, belonging, and broadening participation, will guide decision-making throughout. Each partnership will be cultivated through conversations, convenings, and workshops with a team of difficult conversations facilitators, educators, and an evaluator with expertise in social justice and communication. The series of initial conversations will result in separate needs statements and rules of engagement for the community leaders and the informal STEM institutions. Convening meetings will bring the community partners and informal STEM learning institutions together; leading with the needs of the community, partners will work on building trust and deepening their relationships partly through screening film excerpts and engaging in critical dialogue. Convenings will, over time, turn to designing screenings and accompanying programming while the partners work through dialogic approaches. Near the end of the project, day-long retreats in each city will engage with the broader questions of future project research foci, desired outcomes and indicators, and consideration of methods. As part of knowledge building, these processes will be documented to allow the project team to become better able to articulate the rules of reciprocity and power redistribution for current and future partnership projects. Culturally responsive evaluation will be employed to investigate, understand, improve, and describe the ethical equitable partnership development processes in a report to be shared with partners, their communities, and the broader informal STEM learning field. This Partnership Development and Planning project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences. 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 Nearly 400,000 people with an opioid use disorder (OUD) receive OUD treatment each year (19.7% of all people with an OUD). Community re-entry following residential OUD treatment is a critically vulnerable time, as risk of return to use, overdose, and death are increased in the 30 days following discharge. Continuity of care during re-entry is vital in supporting recovery outcomes, but little is known about treatment utilization during this high-risk period. Individual and socio-structural factors can influence treatment utilization; however, there is a dearth of information regarding the factors that predict treatment utilization during re-entry. Characterizing treatment utilization and identifying predictors of treatment engagement is necessary to detect those who are at risk for not engaging with treatment during community re-entry and thus address critical gaps in the OUD treatment pipeline. By defining treatment broadly, inclusive of harm reduction strategies, and combining intensive longitudinal methods (ecological momentary assessment [EMA]) with innovative machine learning analyses, this study will both characterize treatment utilization with greater precision and improve prediction of treatment engagement during community re-entry. Our findings aim to identify (in residential OUD treatment) those at risk for not engaging with treatment during community re-entry (to inform preventative interventions) as well as to pinpoint proximal facilitators and barriers to treatment utilization during community re-entry. The proposed secondary data analysis aims to characterize treatment utilization and identify individual and structural facilitators and barriers to, and predictors of, OUD treatment during community re-entry. The target population is adults with OUD who discharged from residential OUD treatment (N=150). This study uses innovative methods and cutting-edge data analyses to maximize existing data from the Sponsor’s NIH-funded study (P20GM125507). Aim 1 uses EMA to accurately and reliably describe experiences with treatment utilization during community re-entry. Aim 2 applies innovative machine learning modeling approaches to 1) socio-structural and clinical data gathered during residential treatment to identify those at risk for not engaging with treatment during community re-entry, and 2) symptoms and behaviors gathered during community re-entry with EMA to identify facilitators and barriers to treatment utilization the re-entry period. Findings will inform continuity of care, evidence-based tools to prevent and/or delay return to opioid use, and reduce harms associated with community re-entry. This proposal addresses critical gaps in my training and knowledge and will be necessary to develop an independent, NIH-funded research program focused on opioid use and OUD treatment utilization during high-risk transitional periods, such as community re-entry.

NSF Awards · FY 2024 · 2024-09

Data sampled from real-world applications such as image inpainting (e.g. reconstruction of the area covered by the mask of a criminal) and recommender systems (e.g. suggesting products based on a user’s Amazon purchase history) often consists of partially observed or unobserved entries; thus, estimating those entries before any analysis is vital. The technique of recovering missing entries of a data set, specifically, a data matrix, is known as Matrix Completion (MC) in which the data matrix is decomposed into a low-rank component representing features of the data, and a sparse component representing anomalies and noise. However, conventional MC frameworks have limited transferability and robustness when applied in diverse domains since such methods do not consider the natural correlation of the data. Thus, the principal investigator (PI) develops a highly transferable and robust MC framework by harnessing the natural correlation of the data. The optimization scheme of the MC framework is numerically implemented using both an efficient algebraic approach and a high-precision Deep Neural Network (DNN) approach. The performance of this MC framework is validated using both a theoretical analysis, as well as synthetic and real-world benchmark datasets. Real-world data with natural correlation underlies low-rank nonlinear manifold representations; thus, robust MC methods should guarantee the manifold's primary characteristic of distance-preserving ability within the low-rank component of the data matrix, which results in meticulous sparse information retention ability within the sparse component. The PI will develop the MC model with a new mathematical foundation to assure the aforementioned characteristics within decomposed low-rank and sparse components of the data matrix. Especially, the method intakes training data as bounds in any distance of interest (e.g., geodesic, hamming, hop), which helps incorporate observed, unobserved, and fully observed data instances so that the method produces the recovered matrix in the same type of distance. The PI adopts the truncated nuclear norm convex relaxation on the low-rank component of the data matrix as a surrogate to the non-convex and discontinuous truncated rank minimization. The distance-preserving ability of the nonlinear manifold is attained by the adaptation of a special constraint into the optimization scheme that emphasizes the Gramian matrix of low-rank component is positive semi-definite. Anomalies in the real-world data are structured; thus, the PI extracts the sparse component by utilizing both square integrable and intergable norm minimization in contrast to the use of only integrable norm minimization. This MC model is numerically implemented by the algebraic approach Alternating Directional Methods of Multipliers and the DNN approach Hadamard Deep Autoencoders. This project is jointly funded by the Launching Early-Career Academic Pathways in the Mathematical and Physical Sciences Program and the Established Program to Stimulate Competitive Research (EPSCoR). 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

Apicomplexans, which include the parasites that cause malaria, include the deadliest eukaryotic pathogens on the planet and have long been assumed to be a parasitic group of organisms that only live within host cells. The story has become more complex, however, with the discovery of apicomplexan lineages, Nephromycidae, which live inside their host without invading cells. Despite their lifestyle, Nephromyces still features the typical apicomplexan cellular invasion machinery—the apical complex. Nephromyces, therefore, provides an opportunity to understand the ancestral state that served as a platform for the development of major blood pathogens of humans and animals. The research will use a combination of cutting-edge microscopy and genomics to compare the invasion machinery between related virulent pathogens and mutualistic members of Apicomplexa to identify critical elements of pathogen cell invasion. Outreach to K-12 will be accomplished via a Research Experience for Teachers (RET) fellowship, that will build on previous success engaging high school marine biology teachers and students in Providence, Rhode Island. Public outreach will be through a partnership with Art League RI, in which will pair scientists and artists to create exhibits for a series of public engagement events. blood parasites, and reconstruct the ancestral infection-like processes and other pre-adaptations for parasitism in the Hematozoa. The research will expand taxonomic expertise, through recruitment, retention and mentoring of undergraduate and graduate students, who will be trained in both modern microscopy and genomic scale techniques. How parasites evolve from free-living organisms can provide clues as to key features that can be targeted in therapeutics. Here, a strategy has been devised to characterize the apical complex of the extracellular Nephromyces using single-cell transcriptomics, immuno TEM, and expansion microscopy. These data will be used to perform comparative genomics to reconstruct ancestral features of the intracellular hematozoan cellular invasion and parasitic machineries. Specifically, the investigators will identify the life cycle stage when the invasion machinery is expressed in Nephromyces extracellular symbionts and reconstruct how the different cell forms linked within its life cycle, determine the structural conservation of the apical complex in Nephromyces as compared to hematozoan blood parasites, and reconstruct the ancestral infection-like processes and other pre-adaptations for parasitism in the Hematozoa. 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

Parkinson’s disease (PD) is a progressive condition that affects both movement and cognitive abilities. A common symptom of PD is visual hallucinations, which can severely impact daily activities like planning movements and maintaining balance. Visual hallucinations in PD are believed to result from disrupted brain activities, leading to incorrect visual perceptions. There remains a substantial gap in understanding how different brain activities impact disturbances in visual perception and impair motor functions. This project will develop an engineering framework to integrate brain imaging data of electrical signals and blood flow with human motion data to identify complex connections between vision and movement in the brain. Virtual reality studies will be performed to understand the relationship between visual stimuli and motor function. This project unites a multidisciplinary team of researchers, including experts in neural signal processing, neurology, and deep learning. The research, alongside educational and outreach activities, will unite academic expertise with students to promote knowledge transfer, involve diverse student groups in cutting-edge research, and offer experiential student learning opportunities in the engineering field. This project pursues an innovative measurement and analysis framework called VisuoMotor multimodal framework within a controlled Virtual Reality (ViMoVR) setting. ViMoVR will leverage electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) measurements within a controlled virtual reality environment to investigate the interaction of electrocortical and vascular-hemodynamic activities associated with visual hallucinations and motor impairments in PD. Leveraging this framework, the team will quantify the hierarchical neural organizations of visual hallucinations within multiscale electro-vascular dynamics in PD using a temporally embedded canonical correlation analysis-general linear model (tCCA-GLM) pipeline, which identifies optimal time lags and spatial correlations across different modalities in a data-driven manner. A data fusion framework will be developed, based on multi-view and attention-based deep learning to integrate multimodal data with distinct spatiotemporal dynamics. The framework will be validated through rigorous model evaluation and visualization to determine the extent to which the identified multimodal neural signatures can enhance motor function predictions. The outcomes of this project will reveal previously unknown neural links between visuoperceptual and motor functions in PD, introduce a novel multimodal data fusion tool that creates dual spatiotemporal representations to enhance the comprehensive understanding of neural mechanisms, elucidate the interactions between slow hemodynamic and fast electrocortical oscillations, and provide unique insights through multimodal data recording and analysis of PD patients. 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

In January 2022, Hunga volcano in Tonga produced the largest underwater volcanic eruption recorded by modern instruments. This project will collect samples and new observations of volcanic sediment at this location to understand how underwater volcanic eruptions transport material. The research uses this recent eruption to learn about underwater volcanic landslides that can damage communication cables and other seafloor infrastructure. The project involves international collaboration with the Kingdom of Tonga and the training of graduate students. In 2022, Hunga Tonga volcano erupted explosively producing a widely studied stratospheric ash cloud and an estimated 10 cubic kilometers of much less well studied submarine volcaniclastic deposits. This project seeks to characterize the large-scale submarine volcaniclastic density currents produced by this eruption in order to better understand these understudied volcanic products globally. Systematic sampling and mapping in a field campaign using remotely operated submersible Jason, autonomous underwater vehicle Sentry, and ship-based gravity and coring will sample the eruption deposits at multiple spatial scales. These new data will enable computational modeling aimed at better understanding transport mechanisms, mobility, and rheology of submarine volcaniclastic flows. The project will include public outreach from the ship and through events hosted at the Smithsonian Institution. 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

Floating, single-celled algae, or phytoplankton, form the base of marine food webs. When phytoplankton have sufficient nutrients to grow quickly and generate dense populations, known as blooms, they influence productivity of the entire food web, including rich coastal fisheries. The present research explores how the environment (nutrients) as well as physical and chemical interactions between individual cells in a phytoplankton community and their associated bacteria act to control the timing of bloom events in a dynamic coastal ecosystem. The work reveals key biomolecules within the base of the food web that can inform food web functioning (including fisheries) and be used in global computational models that forecast the impacts of phytoplankton activities on global carbon cycling. A unique set of samples and data collected in 2021 and 2022 that captured phytoplankton and bacterial communities before, during, and after phytoplankton blooms, is analyzed using genomic methods and the results are used to interrogate these communities for biomolecules associated with blooms stages. The team mentors undergraduates, graduate students, and postdoctoral researchers in the fields of biochemical oceanography, genome sciences, and time-series multivariate statistics. University of Washington organized hackathons develop publicly accessible portals for the simplified interrogation and visualization of ‘omics data by high schoolers and undergraduates and are implemented in investigator-led undergraduate teaching modules and the University of Rhode Island Ocean Classroom. The research team also returns to Orcas Island, WA, where the field sampling takes place, to host a series of annual Science Weekends to foster scientific engagement with the local community. Phytoplankton blooms, from initiation to decline, play vital roles in biogeochemical cycling by fueling primary production, influencing nutrient availability, impacting carbon sequestration in aquatic ecosystems, and supporting secondary production. In addition to environmental conditions, the physical and chemical interactions between individual phytoplankton can significantly modulate blooms, influencing the growth, maintenance, and senescence of phytoplankton. Recent work in steady-state open ocean ecosystems has shown that important chemicals are transferred amongst plankton on time-dependent metabolic schedules that are related to diel cycles. It is unknown how these metabolic schedules operate in dynamic coastal environments that experience perturbations, such as phytoplankton blooms. Here, the investigators are examining metabolic scheduling using long-term, diel sample sets to reveal how chemical and biological signals associated with the initiation, maintenance, and cessation of phytoplankton blooms are modulated on both short (hrs) and long (days-weeks) time scales. Findings are advancing the ability to predict and manage phytoplankton dynamics, providing crucial insights into ecological stability and future oceanographic sampling strategies. Additionally, outcomes of this study are providing a new foundational understanding of the succession of microbial communities and their chemical interactions across a range of timescales. In the long term, this research has the potential to identify predictors of the timing of phytoplankton blooms, optimize fisheries management, and guide future research on carbon sequestration. 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

Bolstered by thirteen Institutions of Higher Education, Rhode Island (RI) has long been an engine for scientific and technological innovation, from catalyzing the Industrial Revolution in 1790 to developing the first offshore wind farm in the U.S. in 2015. Continuing these institutions’ mission to enable a thriving, informed citizenry, this project will strengthen research infrastructure and capacity in RI. Led by the University of Rhode Island, Rhode Island College, Roger Williams University, Brown University, and the Rhode Island School of Design, the project will focus initially on building capacity in sciences related to RI’s blue economy. Once established, this network will further support life science and public health, energy, advanced materials, and food innovation and technology, aligning with the five research and workforce development themes in RI’s Science and Technology Plan. With research and educational facilities in close proximity, RI is emerging as an economic development leader in these areas. This project will position the state’s institutions, the Narragansett Indian Tribe, and government, community, and industry partners to sustain use-inspired research, and economic growth into the future. The project has four main goals: 1) Strengthen workforce development by broadening research and education capacity; 2) Catalyze partnerships by seeding varied, use-inspired research collaborations; 3) Strengthen science translation by implementing science communication; and 4) Provide for a robust administration of coordinated E-CORE and broader science and technology activity in the state. The Rhode Island (RI) Inclusive Network for Excellence in Science and Technology (RII-NEST) project will enable RI and the Narragansett Indian Tribe (NIT) and its people to develop and maintain a sustainable, and competitive research ecosystem that supports use-inspired science & technology and workforce development. Project goals will be met through implementation of four research infrastructure cores: Administration, Workforce Development, Partnership, and Science Communications. These cores will develop and sustain a competitive research ecosystem that supports use-inspired S&T and workforce development. Led by the University of Rhode Island, in collaboration with Brown University, Rhode Island College, Roger Williams University, and the Rhode Island School of Design, RII-NEST aligns with the strategic plans of participating institutions as well as state and national priorities. The multi-institutional RII-NEST leadership team will collaborate with the RI Science and Technology Advisory Council (RI STAC), also serving as the RI EPSCoR Jurisdictional Steering Committee, to reinvigorate and grow the RI Research Alliance. RII-NEST will develop capacity, programming, platforms, and partnerships that sustain and grow over time. To reach this goal, RII-NEST will: 1) Institutionalize research infrastructure support programs that serve the whole jurisdiction; 2) Implement seed and planning grants that lead to the submission of collaborative proposals that strengthen and grow the S&T ecosystem; 3) Generate institutional and partner commitments that sustain key RII-NEST activities; and 4) Enhance the leadership, expertise, and benefit of RII-NEST programs and approaches across the jurisdiction by engaging a broad range of institutions. This project is funded by the NSF EPSCoR Collaborations for Optimizing Research Ecosystems (E-CORE) RII Program. The E-CORE RII program supports jurisdictions in building capacity in one or more targeted research infrastructure cores that underlie the jurisdiction’s research 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 · 2024-09

With the support of the Chemical Mechanism, Function, and Properties Program in the Division of Chemistry, Professor Dugan Hayes at the University of Rhode Island will explore the scope of novel solution-phase thermal chemistry which is driven by visible light. Photochemical reactions generally occur when a molecule resides in a reactive excited state which is generated by absorbing the energy of a photon. The goal with this project is instead to convert the energy of the excited state quickly into heat using organic dyes and thereby drive thermal reactions in the ground state. This approach is expected to enable challenging reactions which normally only at high temperatures, to be performed under ambient conditions using diffuse light from conventional LED lamps. This concept will also be applied to supramolecular catalysis, wherein the binding of a small “guest” inside a larger “host” accelerates the rate of a particular reaction of the guest. By again converted the energy of a photon into localized heat, dyes tethered to the host will modulate the strength of the host-guest interaction and thereby facilitate the catalytic process. These research efforts will be carried out by a diverse team of graduate and undergraduate students who will gain experience in organic synthesis, laser spectroscopy, and X-ray scattering experiments at world-class synchrotron radiation facilities. The results of this project, as well as discoveries by other researchers working in related fields, will be discussed on the Goeppert Mayer Gauge, a monthly podcast discussion about light-matter interactions and the scientists who study them, which is co-hosted by the PI. This award will support a research program that integrates synthetic organic chemistry, steady-state and ultrafast optical spectroscopies, computational chemistry and modeling, time-resolved X-ray scattering, and advanced NMR techniques to understand the mechanism and scope of photoinduced molecular heating for driving ground-state chemistry. Rather than exploiting optical transitions to access excited state potential energy surfaces with low activation energy barriers, this approach relies on rapid internal conversion of organic chromophores following photoexcitation to impulsively deposit several eV of thermal energy into covalently tethered, thermally reactive moieties using incoherent light. This concept will first be applied to photoinduced non-radical aromatic Claisen rearrangements and 2-aza-Cope rearrangements to demonstrate proof of principle, and ultimately it will be used to modulate and observe intermolecular interactions in supramolecular host-guest complexes on the ultrafast timescale. This work will provide unique insights into molecular reactivity under extreme conditions in the condensed phase, as these reactions are believed to proceed through short-lived, exceptionally hot ground states that are otherwise unachievable through steady-state heating or infrared multiphoton absorption. Because of the strongly non-equilibrium nature of the reactant environment, the regioselectivity of such reactions can be entirely distinct from that observed under conventional thermal conditions, providing routes to previously inaccessible synthetic targets and substantially broadening the scope of well-known rearrangement reactions. 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

This project engages stakeholders to drive research and workforce development on equitable design and implementation of nature-based solutions (NbS). The research will be conducted around three living hubs in New Hampshire (NH), Rhode Island (RI), and Kentucky (KY). The project will make major contributions to decision-making about NbS over the next decade. Given the project's location, it will have a direct impact on disproportionately affected populations in the three living hubs. The project's community-engaged, transdisciplinary approach will empower community members as change agents for increased climate resilience. The project has the potential to change the national and international paradigms for designing and implementing socially equitable NbS. The research will contribute to improved decisions about NbS in the three jurisdictions and help to address the urgent global need for improved decision tools for climate resilience. This research will also build human and social capital through transdisciplinary knowledge transfer, as well as training and mentoring of undergraduate and graduate students, post-docs, and early career faculty. A recruitment program for underrepresented students and mentorship plans for all early career personnel will significantly develop capacity for climate resilience research across the three jurisdictions while developing leadership skills and collective efficacy. The overarching goal of the Equitable Nature-based Climate Solutions (ENACTS) project is to understand and quantify the influence of NbS on social equity and to conceive design principles and best practices that promote equity, alongside sustainability, resilience, and practicality. ENACTS builds capacity through new research infrastructure to identify novel methods to center equity in NbS for climate resilience. The research, supported by local knowledge, coupled with integrated social and natural sciences, engineering, art, and design, will create an ecosystem of academia, government, and communities inclusive of underserved and Indigenous groups to support more informed and equitable NbS decisions. ENACTS will make transformative advances in our understanding and capacity in designing and implementing socially equitable NbS, leading to increased community resilience against the ongoing climate crisis. This work will generate new co-produced knowledge in 1) the risk-scapes and the NbS environmental, social, and economic effect-scapes across the three living hubs and their influence on distributional equity; 2) the level of process-based equity in past and current NbS projects and its influence on NbS decision-making; 3) the preferred NbS landscape designs for different social groups; 4) the optimal siting, sizing, and timing of NbS implementations in the three living hubs; and 5) the effectiveness of various behavioral interventions, including visual aids (e.g., augmented reality tours), scientific knowledge about the optimum solution (e.g., optimization model), understanding of the cultural background of the marginalized communities (e.g., Indigenous stories, photovoice, GIS story maps), and consensus building exercises (e.g., concept mapping, COPEWELL, serious gaming). 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

Volcanic rift zones are key features of shield volcanoes such as the Hawaiian Islands. They accommodate the growth and spreading of volcanic edifices and, as areas of weakness, allow magma to travel laterally within the volcanoes and erupt far away from the summit, posing threats or causing damage to local communities and infrastructure. On May 3, 2018, one such eruption occurred in the Lower East Rift Zone of Kilauea. The eruption lasted for about 4 months, destroying over 700 buildings, and resulting in an estimated $800-million-dollar recovery cost. Four years later, Mauna Loa erupted in late 2022, for the first time since 1984. Outpouring lava flows from its Northeast Rift Zone almost reached the Daniel K. Inouye Highway, threatening to cut off the only highway that connects directly the east and northwest coasts of the Island of Hawaii. These two eruptions highlighted the knowledge gaps in our understanding of volcanic rift zones. This project aims to obtain detailed subsurface imaging of the rift zones and their spatial and temporal variations, which are essential for volcanic hazard mitigation. The project will collect seismic data including from a similar survey to one completed after the 2018 eruption. The project will also coordinate with collaborators from Switzerland who will be deploying seismic instruments around Kilauea in 2024. In collaboration with the USGS Hawaiian Volcano Observatory and Hawaii Volcanoes National Park Service, the results from this project will enable the development of new volcano monitoring capabilities and enhance societal preparedness against volcanic hazards. Four linear arrays consisting of 460 nodal geophones will be deployed in the first year of the project: One across the Kilauea Lower East Rift Zone (KLERZ), one across the Kilauea Middle East Rift Zone near Pu‘u‘ō‘ō, two across the Mauna Loa Northeast Rift Zone (MLNRZ) up-rift and down-rift of the 2022 eruption site. In the second year, the KLERZ line and the MLNRZ up-rift line will be re-deployed for constraints on temporal variations at the two locations. The main scientific goals of this project are to understand (1) the fine structures of the Mauna Loa and Kilauea rift zones and the differences between the two, (2) the relation between the fine structures and their volcano-tectonic settings, as well as the relation between the fine structures and the frequency and intensity of rift zone eruptions, (3) the post eruption changes and constraints on rift zone healing and cooling of the magma plumbing system, and (4) the long-term evolution of the Kilauea East Rift Zone. This project is funded by the Geophysics Program and Cross-cutting fund in the Division of Earth 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 · 2024-08

Scientists recently detected gravitational waves from black holes and neutron stars orbiting each other. These waves, which were first predicted by Albert Einstein, are like ripples in space-time created by the movement of massive objects in distant galaxies. Current models of these gravitational waves focus on two-body systems, like a pair of black holes or neutron stars orbiting each other, without considering the effects of their astrophysical environments. The main scientific challenge is incorporating these complex environmental interactions into the models. This award will fund an interdisciplinary team from multiple institutions to use new machine learning advancements to tackle these challenges. The resulting models and machine learning techniques will allow researchers to study powerful collisions of binary black holes in extreme environments. Moreover, this research project will engage the public through outreach activities and train diverse students with strong backgrounds in science, technology, engineering, and math, preparing them for careers requiring technical and computational skills. This research team's previous work introduced gravitational waveform inversion (GWI), a machine-learning technique for discovering orbital models from gravitational waveform data without environmental effects. The current project aims to advance GWI by incorporating environmental effects to discover new, detailed physical models. To this end, the team will develop specific models for dark matter halos and disc-embedded extreme mass ratio inspiral systems. The team will also focus on connecting their models to observations by interfacing with – and contributing to – open-source projects such as PyCBC and the Black Hole Perturbation Toolkit. This new approach could unlock the full potential of upcoming gravitational wave detectors, such as LISA, revealing precise information about binary black hole systems and their host environments. This award advances the goals of the NSF Windows on the Universe Big Idea through research in Multi-Messenger Astrophysics. 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

The climatic and anthropogenic controls of fire in the eastern United States (US) are understudied relative to other areas of the US. Despite recent fire events and suggestions that anthropogenic climate change will drive longer, more intense fire seasons, the understanding of long-term fire-climate relationships in eastern US forests remains limited. The project will inform broader scientific understanding of the future fire risks of the region. Wildfire events and seasons in the eastern US are increasingly affecting people, ecology, and land management, so this project will provide direct societal benefits by generating new science to inform wildfire-related climate adaptation efforts. The project will work with US Geological Survey Climate Adaptation Science Centers to communicate findings to regional scientists, stakeholders and policymakers. The project also includes education and training of graduate and undergraduate students, as well as educational outreach with middle and high school students. The goal of this project is to better resolve regional fire-climate relationships through the development and analysis of Holocene (last ~12,000 years) paleofire records and to better define controls on current and projected fire potential. By analyzing particulate and molecular by-products of wildfires preserved in sediment records (e.g., lakes, wetlands) spanning the Holocene, the project will compare fire and climate histories prior to the onset of human impacts to landscapes as a means of understanding baseline fire-climate relationships in the region. In addition to collecting new empirical data, the project will also leverage existing paleoclimate datasets to better resolve regional fire-climate relationships and better define controls on future fire potential. Empirical data will be compared to transient paleoclimate model simulations to enable quantitative characterization of region-specific fire activity and relationships to past and future climate changes based on trend analysis, statistical modeling, and application of select fire indices. 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

The time evolution of many physical, biological, and engineering systems is described by functional differential equations, where the future state of the system is not only determined by its present state, but also by the state of the system at some prior time(s). Examples can be found in the study of epidemic and ecological models, multi-agent models in financial systems, neural network models, and other areas in statistics, data science, and engineering. Among the various modeling approaches in existence, stochastic functional differential equations (SFDE) and McKean-Vlasov stochastic functional differential equations (MVSFDE) play a crucial role in modeling complex systems across science and engineering. Despite extensive research, many questions about these systems remain unresolved due to their challenging past-dependent nature. At the same time, a growing interest in functional stochastic approximation algorithms (FSAA) has emerged from new problems in optimization, data science, and machine learning. This project aims to systematically investigate these systems to establish their critical properties, broaden current applications, and discover new applications in science, machine learning, and engineering. In addition, this project will provide research opportunities for graduate students, engage high school students through math tournaments, and work towards creating a network of academia, students, and industry representatives to enhance career opportunities for students and increase public awareness of the role of mathematics in real-world applications. This project aims to (i) explore long-term properties, such as ergodicity and stability, of SFDE; (ii) formulate a new approach for MVSFDE to systematically examine their fundamental properties and long-term behaviors; and (iii) propose a framework for FSAA dealing with discontinuous operators, establish convergence conditions and rates, and provide implementation methods. The project will apply these theories to address specific problems in ecology, infectious diseases, control engineering, networked systems, neutral network models, game theory, and cell biology, as well as emerging problems in statistics, data science, and engineering. To achieve these goals, the research will integrate Dupire's functional Itô's formula, inventive concepts of generalized coupling, and will bridge stochastic calculus and non-smooth analysis in infinite-dimensional spaces, in addition to employing other advanced techniques. 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 · 2024-08

Program Director/Principal Investigator (Last, First, Middle): Liebermann, Erica Project Summary/Abstract Human papillomavirus (HPV) is the most common sexually transmitted infection in the US; there were 43 million HPV infections in the US in 2018 and most of those infections were in teens and young adults. Though many infections may resolve on their own, persistent infection with HPV can cause cervical cancer and other types of cancers. The HPV vaccine is a critical tool for preventing HPV infection but has been underutilized in the US to date. HPV Vaccine completion remains well below the Healthy People 2030 targets of 80% vaccine completion for adolescents. The Centers for Disease Control and Prevention (CDC)'s Advisory Committee on Immunization Practices (ACIP) recommends vaccinating children ages 11-12 with two doses of HPV vaccine 6-12 months apart; the ACIP also recommends catch-up vaccination for unvaccinated adolescents and adults up to age 26, with three doses of vaccine. The college student population represents an important group for catch-up vaccination as many students have insurance coverage, have access to student health services, and are beginning to make their own health decisions. However, recent surveys indicate that only about half of college students report they have completed HPV vaccination. College health centers are an ideal setting in which to identify teens and young adults who have not yet completed HPV vaccination. Offering HPV vaccine to unvaccinated or under-vaccinated students is an important public health intervention to reduce the burden of HPV infection and its future health consequences. The aim of the proposed mixed methods study is to better understand college healthcare providers' attitudes, beliefs and practices related to HPV vaccination and identify their barriers and facilitators to vaccinating college students. There is limited information about college healthcare provider practices related to HPV vaccine (assessing HPV vaccination history, recommending and offering vaccine). We propose a sequential explanatory mixed methods study that will examine healthcare provider attitudes and beliefs and the multi-level influences of healthcare provider practices related to HPV vaccine. The study utilizes quantitative data regarding college healthcare providers' HPV vaccine-related attitudes and behaviors/practices from a national study of college healthcare providers from 480 colleges across the US. Guided by the Consolidated Framework for Implementation Research (CFIR), we will conduct follow-up interviews with a subsample of college healthcare providers who participated in the national survey to explore individual provider-level (e.g., knowledge, attitudes, beliefs) and multisystem-level factors (e.g., college type, state policies, organizational climate, relative priority, change capacity) influencing HPV vaccine uptake in the college health setting. The long-term goal of this research is to generate new knowledge regarding factors affecting HPV vaccine uptake in college health settings and to inform future interventions to improve HPV vaccination rates in the college student population. OMB No. 0925-0001/0002 (Rev. 03/2020 Approved Through 02/28/2023) Page Continuation Format Page

NSF Awards · FY 2024 · 2024-08

Hurricanes can have devastating impacts on coastal areas. This research will evaluate the ecological and evolutionary effects of hurricanes on island ecosystems. Climate change caused by humans has increased the strength and frequency of hurricanes in the North Atlantic over the past 20 years. Using decades of data on the lizards, spiders, insects, and plants living on islands in a hurricane-prone region, this project will determine how storm intensity is related to natural selection, extinction risk, and species interactions. This research seeks to establish a better understanding of the ecological and evolutionary consequences of climate-change driven increases in the frequency and magnitude of hurricanes. A better understanding of the biological impacts of extreme weather events on ecosystems will increase the accuracy of predictions needed to protect and manage biodiversity. The publications and synthesized data sets from this project will be useful to a broad range of stakeholders, including basic and applied scientists, resource managers, and policy makers. This research will capitalize on decades of annual measurements of trait variation in a lizard, spider community composition, and food-web interactions across three trophic levels from dozens of small islands in the Bahamas. During this time, 17 hurricanes and tropical storms passed close to the study site, generating variation in hurricane impacts across time and space. This retrospective analysis will integrate long-term biological data with meteorological models that generate island-specific estimates of wind speed, storm surge, and wave energy during storms. Tests of synthetic hypotheses aim to 1) reveal thresholds for natural selection and extinction risk for lizards related to variation in hurricane intensity, 2) determine recovery trajectories for spider communities related to the interactive effects of hurricane intensity, dispersal, and species interactions, and 3) elucidate the roles of top-down and bottom-up pathways in hurricane-driven increases in herbivory. The simultaneous consideration of many storms and the integration of detailed estimates of hurricane-specific damage adds substantial value to long-term data on lizards, spiders, herbivores, and plants in these island ecosystems. The analytical approach will serve as an example for integrating biological and meteorological data sets to address the effects of other increasingly common and strong extreme weather events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Coastal communities face growing and compounding risks that are exacerbated by the effects of climate change and sea-level rise. With nearly twice the global rates of sea-level rise, the U.S. Atlantic seaboard is particularly vulnerable and some communities are disproportionately affected. Advancing our scientific understanding of the physical risks and vulnerabilities to coastal hazards, such as flooding and salinization, is essential for identifying vulnerable communities and assessing how threats are likely to impact the wellbeing of people in these areas. The Risks, Impacts, & Strategies for Coastal Communities (RISCC) project brings together researchers and community stakeholders from three EPSCoR jurisdictions representing the lowest-lying states in the country: Delaware, Rhode Island, and South Carolina. The overarching goal of the project is to empower disproportionately affected communities to make effective and inclusive adaptation decisions that support long-term climate resilience to threats of flooding and salinization. To accomplish this goal, the RISCC team will build convergent and translational research and workforce development infrastructure that integrates behavioral and natural sciences, engineering, economics, public policy, planning, education, and outreach. The team will advance the assessment of risks and vulnerabilities to compounding hazards, identify effective adaptation strategies that are supported by coastal residents and decision makers, develop decision support system innovations based on iterative feedback from users in our partner communities, and create novel education and outreach materials that will enhance the capacity of disproportionately affected communities to increase resilience to climate change threats through evidence-based planning and adaptation. This project is a collaboration among the University of Delaware, University of Rhode Island, College of Charleston, University of South Carolina, South Carolina Sea Grant Consortium, Delaware Technical Community College, and The Citadel, in partnership with community organizations representing the interests of Little Creek and Delaware Bay beaches, Delaware; the city of Warren, Rhode Island; and Edisto Island, South Carolina. The project will advance scientific knowledge on how disproportionately affected coastal communities experience and effectively adapt to flooding and salinization. The research team will accomplish this goal with seven objectives: (1) co-develop solutions with partner communities to support sustainable adaptation decisions, (2) develop comprehensive geospatial datasets to advance the assessment of flood vulnerability and mitigation suitability, (3) model and map groundwater flooding and salinization risks, (4) quantify the economic impacts of flooding and salinization, and analyze different community preferences for adaptation strategies, (5) understand how local decision makers assess and plan for climate hazards, (6) integrate research outcomes from natural and social sciences and engineering to develop decision support systems that our disproportionately impacted partner communities (and communities like them) can use to determine which adaptation strategies will likely be effective now and in the future, and (7) prepare diverse researchers and decision makers to understand the science and implementation of coastal adaptation through education, outreach, and workforce development. Faculty and researchers from diverse institutions will co-create education, outreach, and workforce development materials, which will be widely communicated among racially and culturally diverse students and stakeholders. Outreach activities will include the development of a documentary film for national distribution via the PBS network to increase public scientific literacy and promote public engagement with science on climate change hazards and adaptation. This project is funded by the EPSCoR Research Infrastructure Improvement-Focused EPSCoR Collaborations (RII-FEC) program. The RII-FEC program builds inter-jurisdictional collaborative teams of EPSCoR investigators in focus areas consistent with the NSF Strategic Plan. RII-FEC projects include researchers from at least two EPSCoR eligible jurisdictions with complementary expertise and resources necessary to address challenges, which neither party could address as well or as rapidly independently. 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

With the rising cost associated with chip manufacturing, domestic chip companies have chosen to delegate costly fabrication processes to foreign foundries. While these third-party fabrication services can significantly alleviate the design burden and reduce the expenses associated with maintaining expensive foundries, they also introduce security risks. Malicious actors can potentially exploit this situation, leading to the manipulation and degradation of hardware components at any point within the entire chip supply chain. For the past decade, logic-locking research, which introduces additional key gates or states in both combinational and sequential circuits, offers potential opportunities for integrating hardware security measures into the commercial chip design process. While previous work on logic-locking has explored different aspects of developing countermeasures, the absence of a systematic analysis and associated overhead associated with locking methods have had a notable impact on the practicality of employing logic-locking techniques. Taking these observations into consideration, this project seeks to establish a new infrastructure that heavily relies on graph neural networks to investigate the characteristics of different logic locking techniques. This method will streamline the advancement of logic-locking techniques, thereby enhancing their feasibility within contemporary integrated circuit (IC) design workflows. This project can ultimately shift logic-locking from in-lab research to a recognized industry standard for safeguarding intellectual property. Furthermore, this project aims to disseminate insights and breakthroughs through the publication of scholarly articles, as well as the release of open-source software, demonstration resources, and datasets to the wider community. This project will also have significant impact on education by incorporating research findings into university curriculum and providing unique learning opportunities for students, including those from underrepresented groups. Through these efforts, the project will enhance our comprehension and capabilities in hardware security and will also cultivate a new generation of students and engineers equipped with the necessary knowledge and tools to address future challenges in this rapidly evolving field. The goal of this project is to advance the field of hardware security techniques and foundational elements by harnessing advanced graph neural networks. To achieve this goal, this project promotes the development of an infrastructure that utilizes graph neural networks to learn features of various locking techniques, build models based on logic locking principles, generate novel locking methods through transfer learning, and deploy generated locking strategies to hardware implementations. The project will be structured around three tightly intertwined research thrusts: 1) developing graph neural network models centered around sequential logic locking methods; 2) advancing transfer learning methodologies applied to graph neural network models for the automated generation of locking techniques; and 3) implementing the proposed design schemes in hardware, spanning both legacy and mixed modes. Collectively, this project will lead to a new design paradigm and novel infrastructure supported by graph neural networks, aiming to advance research in logic locking. 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

As the global microplastic pollution intensifies and related health concerns grow, monitoring microplastics in seawater through effective sampling and identification has become imperative. Traditional methods of sampling are often time-consuming and ineffective at isolating tiny microplastics (<100 micrometers), which in fact constitute the majority of microplastics in the oceans. Moreover, while vibration spectroscopy has proven effective in microplastic identification, interpreting results through traditional library matching heavily relies on expert knowledge due to significant spectral variations caused by plastic degradation. Given the pressing need to enhance microplastic monitoring down to sizes as small as 1 micrometer in both sampling and identification aspects, this project aims to develop new methodologies for effectively monitoring small-sized microplastics in seawater in a label-free and reliable manner. The outcomes of this research have significant implications for environmental and ecological monitoring, including abundance assessments, source tracking, and analysis of microplastic degradation in the seas. These findings will also drive participation from researchers, engineers, and other stakeholders, heightening public awareness about these global issues. Ultimately, the data from microplastic monitoring will contribute to restoring the safety of our waters, allowing aquatic ecosystems to thrive again. To meet these goals, this project aims to develop a microfluidic microplastic monitoring tool that leverages both acoustofluidic particle manipulation and machine learning-assisted Raman spectral identification. The microfluidic device will provide a streamlined monitoring process capable of sampling, concentrating, and sorting microplastics in seawater down to 1 micrometer by adjusting the frequency and layout of surface acoustic waves, while also identifying seawater microplastics regardless of their degradation status through machine learning classification coupled with Raman spectroscopy. Specifically, this project will explore the use of both traveling and standing surface acoustic waves for sampling and sorting microplastics in microfluidic chips. The samples will include pristine microplastics, artificially degraded variants, and environmental microplastics, with a focus on those sourced from surface seawater in Rhode Island. A microfluidic particle trapping component will also be developed to trap microplastics at predetermined locations in a microfluidic chip, enhancing the reliability of Raman spectra acquisition by facilitating an automated process. Lastly, machine learning classification will be employed to interpret the Raman spectral data of seawater microplastics, even when they are agglomerated. The results from this project will contribute fundamental knowledge towards addressing the global challenge of plastic pollution, strengthening collaboration among academia, industry, and fisheries. 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 · 2024-07

PROJECT SUMMARY / ABSTRACT Mitochondrial dysfunction (MD) plays a central role in the pathophysiology of many human diseases commonly referred as non-communicable diseases (NCDs). NCDs are non-infectious, non-transmissible disorders characterized by low-grade chronic inflammation, among which the most common are obesity, insulin resistance, diabetes, forms of cancer, cardiovascular diseases, and nonalcoholic fatty liver disease. NCDs affect an estimated 41 million people each year. Additionally, MD has also been implicated in aging and age-related diseases, including neurological disorders such as Alzheimer's disease and related dementias, which represent the sixth-leading cause of death in the United States, with cost for long-term and hospice care calculated to be around 200 billion dollars per year. One cause of MD is an increase in mitochondrial DNA (mtDNA) mutations resulting in impaired oxidative phosphorylation that leads to loss of bioenergetic homeostasis, increased cell apoptosis, and senescence. To address the role of mtDNA mutations on tissue/organ homeostasis, two independent groups developed a knock-in mouse expressing a proofreading-deficient version of the nucleus- encoded catalytic subunit of mtDNA polymerase-γ (PolgA). The mtDNA mutator mouse model demonstrates a cause-and-effect relationship between slowly increasing somatic mtDNA mutation levels and several phenotypes associated with aging that manifest much earlier in life, including reduced lifespan, weight loss, alopecia, anemia, kyphosis, osteoporosis, sarcopenia, and loss of subcutaneous fat. Although this mouse model has been key to understand the effect of mtDNA mutations driven MD on organismal health, the global MD results in overall tissue/organ dysfunction and makes it difficult to dissect how different tissues are affected and compensate for MD, since it has not possible to separate single organ dysfunction from other organs equally affected by MD. Moreover, due to the overall marked and severe progeroid phenotypes that affect the auditory, visual, and ambulatory systems in this model, it has not been possible to test how MD affects brain health and cognition. To overcome this limitation, we have generated a novel knock-in inducible mtDNA mutator mouse (RJA- PolgACDS/CDS) with both spatial and temporal regulation capabilities that will allow us to study the effect of mtDNA mutation-induced MD in single tissues at different time-points. The overall goal of this proposal is to validate this new mouse model and demonstrate its utility in studying the role of MD in a broad range of human diseases. We will test its temporal and spatial inducibility by crossing it with a whole-body expressing Cre mouse (Aim 1) as well as inducible (CreERT2) and muscle-specific (ACTA1-Cre) Cre-expressing mouse lines (Aim 2). The proposed work is highly responsive in addressing important knowledge gaps in understanding the role of MD on organ homeostasis, including its role on onset and progression of many human diseases. Successful completion of this work will provide a novel and unique animal model to investigate the relationship between MD and several diseases and will provide a novel platform to investigate therapeutic strategies to target these pathologies.

NSF Awards · FY 2024 · 2024-07

This project will study the turbulence in a stratified layer at the air-water interface, as caused by waves and wind. The study will carry out simulations with laboratory experiments and with computer models. Simulations will test the hypothesis that to represent the deepening of a surface layer reliably, it is necessary to couple currents and waves. Simulations will also test a parametrization of turbulence related to waves (Craik-Leibovich parametrization), and the results of combining a couple of parametrizations. For broader impacts, the project could improve the reliability in representing the ocean surface boundary layer of Earth-system models. Moreover, the PIs would produce educational materials to be used at their home institutions. In addition to supporting 4 PIs, this effort would fund one postdoctoral scholar and one graduate student. The proposed study seeks to advance understanding of wind-driven and wave-driven near-surface turbulence in a stratified surface boundary layer. This goal would be pursued with a combination of a) controlled laboratory experiments of stratified turbulent mixing under the influence of surface waves in the surface boundary layer, and b) state-of-the-art (Large-Eddy Simulations) numerical experiments. Laboratory and numerical simulations will test the hypothesis that the coupling between waves and wind-driven currents is necessary to reliably represent the surface mixed-layer deepening. The comparison between lab measurements and numerical simulations would seek to i) assess the reliability of LES (Craik-Leibovich) simulations in representing lab observations of turbulent mixing beneath horizontally heterogenous surface waves, and (ii) determine the effects of combining coarser grids in LES simulations with a turbulence closure. As broader impacts, the project could potentially improve the accuracy of Earth-system numerical simulations of the ocean surface boundary layer. Moreover, the PIs would produce educational materials to be used at their home institutions. In addition to supporting 4 PIs, this effort would fund one postdoctoral scholar and one graduate student. 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

PROJECT SUMMARY The evolutionarily conserved SWI/SNF chromatin remodeler, exists as distinct polymorphic complexes, BAF, PBAF and ncBAF, each with differing subunit composition. Though these complexes share a common biological function of altering chromatin architecture, it is the unique subunits within each form of SWI/SNF that help define their genomic roles. With the identification of SWI/SNF as a tumor suppressor, a lot of attention has been focused on how loss of subunits leverage the remaining complexes for transcriptional output. However, focusing attention on a normal cell that needs to constantly adjust transcriptional output to respond to a variety of external stimuli, an interesting question remains: How do cells dynamically alter the usage of the various SWI/SNF complexes for gene expression and do they achieve this dynamic usage by changing the amounts of each form of the complex within cells. We have leveraged hypoxia as an environmental stimulus to address this very question. Our preliminary studies show that hypoxic cells retain BAF and ncBAF proteins at levels similar to those in normoxic cells, but decrease levels of PBAF members, while displaying a dependence on BAF for expression of hypoxic genes. The overall goal of this proposal is to gain mechanistic understanding of the dynamic regulation and usage of various forms of SWI/SNF as a response to environmental cues, specifically hypoxic exposure. In AIM 1, we will define the composition of the various forms of SWI/SNF, during hypoxic response, with emphasis on how varying levels of oxygen concentration affects SWI/SNF complex levels, using both 2D and 3D cell culture models. In AIM 2, we ask how cells use the altered stoichiometry of SWI/SNF forms for regulation of hypoxic gene expression. In AIM 3, we seek to define the molecular mechanisms of downregulation of PBAF during hypoxia. Our studies, will cumulatively provide a mechanistic understanding of how cells dynamically utilize the various forms of SWI/SNF to respond to changing environmental signals. Given that many chromatin modifiers exist in a plurality of forms, our studies will help identify mechanisms that may commonly be in play to regulate functions and usage of these important enzyme complexes.

NIH Research Projects · FY 2025 · 2024-06

Cerebral amyloid angiopathy (CAA), a disease with no available treatments, is a prevalent Alzheimer’s disease related disorder (ADRD), occurring as a comorbidity in > 80% of Alzheimer’s disease (AD) cases, but also sporadically in > 50% of people over the age of 80 years. Arising from fibrillar amyloid β (Aβ) deposition in cortical arteries and arterioles and brain capillaries, CAA is marked clinically by cerebral infarction, microbleeds and intracerebral hemorrhages (ICH), pronounced perivascular neuroinflammation, and is a prominent contributor to vascular cognitive impairment and dementia. CAA severity strongly correlates with cognitive decline in sporadic CAA and AD. Despite the severe clinical burden, mechanisms linking vascular Aβ deposition to microthrombus, vascular damage, and inflammation are poorly understood, and there are no available treatments for CAA. Investigation of these mechanisms in reliable animal models is critical, and the rTg-DI rat, that faithfully recapitulates human CAA pathologies, including progressive vascular Aβ deposition, pronounced perivascular neuroinflammation, and thrombotic events/microbleeds, is such a model. Neutrophil mediated inflammatory mechanisms, such as neutrophil extracellular traps (NETs) have been reported in cardiovascular disease, and various chronic inflammatory disorders, including AD patient brains. NET formation often leads to thrombin generation, thrombotic events, and vascular damage like that seen in CAA and may be an important component of Aβ related vascular damage and neuroinflammation. Recently we demonstrated the presence of NET markers in brain regions containing microhemorrhages and thrombotic events in the rTg-DI rat model of CAA and defined a proteomic signature of NET formation in early and late disease stages. Based on this strong evidence, our central hypothesis is that NETs are directly induced by vascular Aβ fibril deposits and NETs forming either luminally or abluminally may contribute to vascular degeneration prior to microbleed occurrence in CAA. Here we will investigate CAA-specific fibrillar Aβ’s ability to induce NET formation in-vitro, including NET visualization, quantitation, and molecular characterization by proteomic and transcriptomic analysis. We will also chronologize neutrophil and NET involvement in CAA progression by investigation of neutrophil and NET presence in cerebral blood vessels and surrounding tissue, in early and late disease stages, using cerebral vessel and brain regional isolation, proteomics and targeted transcriptomics, and immunohistological approaches. This study will provide important insight to currently unknown mechanisms of CAA progression and investigate the potential of finding targets within the NET pathway for therapeutic intervention in CAA.

NSF Awards · FY 2024 · 2024-06

Stroke is the second leading cause of death and disability globally and poses a significant challenge to the affected individuals as well as society at-large due to its extensive socioeconomic burden. In response, this project (RE-GAIN) is developed as an international collaboration between the United States and India to transform the landscape of stroke rehabilitation for young adult survivors. RE-GAIN aims to intertwine cutting-edge medical Cyber-Physical System (mCPS) technology to help restore motor functions and facilitate the reintegration of stroke survivors into the workforce and society. Central to RE-GAIN's mission is to harness the synergy of smart gloves, Virtual/Augmented Reality (VR/AR), and Artificial Intelligence (AI). This integrated mCPS system promises a personalized and human-centered rehabilitation journey, where the technology can engage stroke survivors for improved engagement, adherence, and outcomes. As it addresses both physical and socio-economic challenges of stroke, RE-GAIN aligns with healthcare-centered priorities of both the US and India by enhancing the multidisciplinary research capacities, high-skilled workforce development, and international knowledge exchange to revolutionize rehabilitation practices. RE-GAIN will design, develop, and integrate medical Cyber-Physical System (mCPS) for stroke rehabilitation with three primary aims. Aim 1 will involve the project team from the University of Rhode Island (URI) and will advance smart textile gloves embedded with multimodal sensors for monitoring and biofeedback, enhancing the rehabilitation experience through interactive visual and haptic cues. Aim 2 will involve institutions in India, including the Indian Institute of Technology (Banaras Hindu University) Varanasi (IIT-BHU), Indian Institute of Technology-Gandhinagar (IITGN), and Institute of Medical Science, Varanasi (IMS-BHU), who will jointly create an engaging virtual and augmented reality (VR/AR) platform tailored to individual patients, integrating advanced technologies such as electroencephalography (EEG) and eye-tracking. Aim 3 will facilitate a collaborative effort between the US and Indian research teams and will integrate the developed technologies into a cohesive mCPS system, culminating in a pilot deployment on stroke survivors in clinical settings across both nations. Additionally, the project will train the next-generation STEM workforce, engaging underrepresented groups through well-designed curriculum, hands-on workshops, and hack-a-thons. This project is jointly funded by the Cyber-Physical Systems program and the Established Program to Stimulate Competitive Research (EPSCoR). 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 · 2024-05

Project Summary/Abstract Simultaneous use of alcohol and cannabis, or co-use (i.e., the use of both substances so that effects overlap), is an increasingly significant public health concern among young adults between the ages of 18 and 25 in the U.S. Compared to single substance use occasions of alcohol and cannabis, co-use occasions are associated with greater negative consequences, including driving under the influence and blacking out. Given the steady increase in the prevalence of co-use among young adults, there is a pressing need to understand factors related to co-use that could inform intervention efforts in this population. Protective behavioral strategies (PBS; i.e., tactics used to modify or limit substance use related harm) have been robustly linked to fewer consequences (e.g., lower use quantity, frequency, and consequences) for alcohol- and cannabis-only occasions. However, research has not examined PBS in the context of co-use occasions, despite a burgeoning literature supporting that co-use occasions are associated with greater harms. Consequently, the goal of this proposal is to examine the extent that alcohol and cannabis PBS are used and associated with alcohol and cannabis use outcomes on co-use occasions relative to alcohol- and cannabis-only occasions. The proposed research will conduct secondary data analyses of a NIH-funded project using ecological momentary assessment (EMA) data collected from 123 college students over four consecutive weekends (Thursday- Sunday; 16 total days). The proposed research has three specific aims: 1) To assess whether alcohol and cannabis PBS are used more frequently on alcohol- or cannabis-only occasions relative to co-use occasions; (2) To determine whether alcohol and cannabis PBS are associated with levels of alcohol use, cannabis use, and consequences experienced on alcohol- and cannabis-only occasions relative to co-use occasions; and (3) To identify daily factors (environmental context, social setting) related to alcohol and cannabis PBS on alcohol- and cannabis-only occasions relative to co-use occasions. Findings from the proposal will inform our understanding of alcohol and cannabis PBS during co-use occasions, including proximal factors (i.e., social setting and environment) that impact the use of PBS. In alignment with the NIAAA strategic plan, findings will help develop and improve strategies to reduce and prevent harms associated with co-use, particularly those that may be delivered in-the-moment. Additionally, the proposed research would provide the applicant with training that will inform her developing program of research on the association between co-use and PBS among young adults through providing content training on alcohol and co-use behaviors among young adults, ecological research methods, intensive longitudinal data analyses, and professional development activities.