University Of Tennessee Knoxville

NSF Awards · FY 2024 · 2024-09

University of Tennessee-Knoxville (UTK) researchers from economics, business, and policy fields will collaborate with Launch Tennessee to uncover new insights about the timeline of innovation as well as the effect of public-private investments on innovation and innovators. The team will survey Tennessee innovators to assemble the Tennessee Longitudinal Survey of Ideas (TLSI). The TLSI will be different from other business surveys in one important way: A TLSI observation will be an idea rather than a firm or individual. This will allow researchers to focus on and track the process of innovation itself, along with characteristics of innovating firms. The TLSI will identify the amount of time that new ideas spend in different phases of innovation, such as conception, research and development, translation, patenting, marketization, or pivot/withdrawal. TLSI data and participants will support a second arm of the project, a mixed-methods evaluation of the public-private approach to investing in Tennessee innovators and communities. The starting point for the TLSI sample will be Launch Tennessee’s expansive network of over 3,300 startups from its ten-year history, supplemented with additional contacts from UTK surveys of business leaders and the state database of registered business entities. Surveyed firms will identify some of their recent product and process developments and—in broad, unidentifiable terms—describe the nature and goals for each innovation as well as start and end dates for several potentially overlapping phases of development and deployment. Firms will be surveyed retrospectively, about innovations that are complete or withdrawn, as well as prospectively, about innovations in progress. A subset of surveyed firms will participate in follow-up interviews and case studies to add depth and meaning to TLSI data. Interviews will also contribute to a mixed-methods analysis of Launch Tennessee’s public-private model of startup support. Comparisons of firms in and outside of the Launch Tennessee network will describe the startup experience with and without centralized, place-based economic development. These qualitative insights will be paired with quantitative analyses of the association between Launch Tennessee support and economic outcomes at the firm and county levels. These interrelated projects complement UTK’s NSF-funded Regional Innovation Engines and the overarching TEAM TN body of work. Specifically, the TLSI will quantify the practical timeline and success rate for innovations in transportation, manufacturing, supply chain, and biotech, focal areas for TEAM TN. The Launch Tennessee evaluation will inform TEAM TN about the financial side of innovation by, for example, showing whether and when economic development investments in innovating firms leads to capital events and patenting. Both the TLSI and Launch TN evaluation will bring new data to open questions about access to funding and other resources for innovators from all communities. 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

Pine trees are vital both commercially and ecologically, with many industries around the world relying on their lumber and pulp wood. In the United States, pine trees make up a large majority of the lumber output, playing a critical role in the forestry industry. Understanding how rainfall affects pine trees is crucial for industry and conserving habitats under a changing climate. This project explores how pine trees have adapted to manage water on their needles. Water on needle surfaces can block tiny pores needed for gas exchange, which is essential for photosynthesis, and make the trees more vulnerable to disease. By combining experiments, ecological data analysis, and predictive modeling, we will decipher the interplay between needle shape, surface properties, and elasticity in their ability to passively shed water. This research will enhance our understanding of how pine trees adapt to different environments and improve our knowledge of ecosystem resilience in the face of a changing climate. Ultimately, this research will revolutionize our understanding of pine needle function and shed light on the physics of fluid interaction with flexible biological structures. Pine needles represent an extreme end of the spectrum of global leaf form and function with highly elongated filament-like foliage. This project will experimentally decompose needle/drop interactions into their fundamental components: fiber elasticity, wettability, surface profile, impact geometry, and needle vibration. We will conduct focused laboratory experiments to define how Pinus traits are tuned against liquid mass retention. Using a phylogenetic comparative approach, we will examine the pertinent test variables to reveal how Pinus traits vary in response to environmental factors before exploring a greater morphological trait space with predictive modeling. In this way, empirical experimentation will provide informative priors for conducting phylogenetic comparative analyses, which will expand our taxonomic and phenotypic scope. Results from these analyses will then be used as inputs for predictive modeling of trait interactions, which will in turn refine our mechanistic hypotheses of trait-trait interactions permitting rigorous examination of trait evolution in response to environmental stressors. This novel approach creates a template for fusing experimental data, new physical insights, and phylogenetic comparisons with multivariable regression to explore optimums, trade-offs, and limitations in Pinus foliage. 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

Sedimentary basins adjacent to convergent margin mountain belts host important economic resources and contain key records of past climatic and tectonic events. As such, basin archives are often used to reconstruct long-term variability in mountain building and climatic processes. New quantitative approaches provide direct links between signals of erosion in mountain belts and depositional processes preserved in basins, which can improve understanding of how to interpret these critical sedimentary archives. This project applies novel methodologies to the Alberta basin, next to the Canadian Rocky Mountains, which provides an excellent testing ground due to abundant subsurface datasets and constraints on the geometries of deformation within the mountain belt. This grant supports training of undergraduate and graduate students, collaborations between US and Canadian geoscientists, and development of training modules for the technical methods utilized in this proposal which will be made publicly available to other researchers. This project couples bedrock low-temperature thermochronology and thermokinematic modeling within the fold-thrust belt, with detrital thermochronology, subsidence curves, and provenance analysis in the foreland basin to assess linkages between fold-thrust belt shortening in the Canadian Rocky Mountains and depositional pulses in the Alberta foreland basin system. Low-temperature thermochronology (zircon fission-track and zircon (U-Th)/He), applied in an orogen-perpendicular sampling transect across major thrust sheets, will constrain the timing and pathways of rock cooling. In the foreland basin, stratigraphic sections and maximum depositional ages from detrital zircon geochronology will facilitate construction of sediment accumulation curves. Detrital zircons dated for U-Pb geochronology will also be dated via low temperature thermochronology, which will allow calculation of lag times. Ultimately, constraints from the fold-thrust belt and subsidence histories from the foreland basin will be combined in a flexurally validated, thermokinematic model, which will provide sequential, temporally constrained reconstructions of shortening magnitude, thrust belt geometry, and size and location of the orogenic load. These datasets will resolve: (1) the timing and magnitude of shortening, and whether it was a protracted or pulsed process, and (2) the relationships between thrust loading, unconformity development, and coarse clastic deposition. 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

Natural history collections are a record of the biodiversity of our planet. These specimens enable us to track the movement of organisms across space and time, which informs conservation efforts at regional, national, and international levels, thus supporting the maintenance of critical ecosystem services that are essential for human life. Despite their importance, natural history collections are being defunded at a rapid rate, resulting in these historic and irreplaceable specimens needing to be rescued from loss. The East Tennessee State University (ETSU) no longer has the capacity to house their plant collections (herbarium). This collection contains around 35,000 specimens and represents an important resource documenting a region of exceptional biological diversity, comprising eastern Tennessee and the southern Appalachian Mountains. This project includes moving the ETSU herbarium to the University of Tennessee – Knoxville (UTK), integrating the collections, and mobilizing ETSU specimen information online via public databases, such as the Southeast Regional Network of Expertise and Collections (www.sernecportal.org) and the Consortium of Bryophyte Herbaria (www.bryophyteportal.org). Transferring the collection will enable these specimens and their data to be accessed worldwide by researchers, government agencies, and members of the public into the future. Located at Tennessee’s flagship public university, the UTK herbarium is the largest botanical natural history collection in the state and the third largest in the southeastern United States. The UTK herbarium serves as a major hub for research focusing on the ecosystems of the southern Appalachian Mountains and thus is an ideal institution to house and care for the ETSU specimens. This project will include relocating ETSU’s 35,000 specimens from Johnson City to Knoxville. In order to accommodate the specimens, a mobile storage system will be purchased and installed, consisting of compactor carriages and additional herbarium cabinets. UTK staff and students will also barcode and digitize the ETSU specimens, as needed. Following specimen digitization, the physical specimens will be integrated into the UTK collection. Finally, the label information from the imaged specimens will be transcribed and the locality information georeferenced, so that the digital data for these specimens is complete and can be shared online. As part of this project the UTK herbarium will engage members of the public, who live in Tennessee, in the process of specimen curation by including the ETSU specimens in the WeDigBio online transcription events over the next year. During these events curators, faculty, and students will train members of the public in the process of specimen label transcription. 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 National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP 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 · 2024-08

The cities of Mendoza and San Juan, Argentina, have been repeatedly damaged or leveled by large-magnitude earthquakes generated by geological structures associated with Andean mountain building. Because active seismicity occurs along faults that can be expressed at or hidden below Earth’s surface, the nature and history of these enigmatic structures remains debated. Several competing geologic models have been proposed that link the seismicity at depth with faults and mountain ranges expressed at Earth’s surface. This project will address these debates through fieldwork to map fault relationships and measurement of associated sedimentary basin deposits that record the uplift and erosion history of actively growing Andean ranges. A variety of geochronologic and low-temperature thermochronologic analytical techniques will be employed to determine the timing and magnitude of deformation across the geological structures. Results will be integrated using computational models to help resolve the debated geologic history for these Andean ranges. New field and analytical records of long-term fault deformation will be integrated with geophysical observations and decades of earthquake data with the help of collaborating Argentinian scientists. This will lead to a better understanding of how tectonic forces are partitioned among deep and surficial geologic structures and which faults may generate large magnitude earthquakes, information that is critical for assessments of Andean earthquake hazards with the potential to impact human populations and infrastructure. In addition to the scientific goals of the research, the award supports the development of infrastructure to support the engagement of diverse and historically underrepresented high school, undergraduate, and PhD-level students in geoscience research and education. This will involve mentoring undergraduate and graduate students at the participating institutions and creating a place-based educational virtual field trip through the western USA and field area in Argentina. Tectonic stresses associated with flat slab subduction have driven deformation 800 km inboard across the broken foreland of west-central Argentina. Ongoing shortening accommodated within the overlapping thin-skinned and basement-involved structural provinces makes this broken foreland region one of the most seismically active places on Earth. However, there is no consensus on the structural or kinematic links between the thin-skinned Central Precordillera, the Sierras Pampeanas basement uplifts, or the enigmatic thrust front of the Eastern Precordillera structural domain. The principal investigators aim to resolve the temporal and kinematic relationships among structures by (1) determining the timing and magnitude of deformation in the Eastern Precordillera thrust front and (2) interrogating how spatial patterns in exhumational cooling and subsidence coincide with predictions based on the various structural geometries and kinematics proposed for the region. Their research plan will integrate geologic and structural mapping, basin analysis, geo- and thermochronology, and flexural thermokinematic modeling to discriminate between hypothesized structural models. Evaluating spatial-temporal relationships among the seismically active, structural domains in Argentina will inform models of subsurface structural geometries, how shortening transfers from lower to upper crustal levels, and the long-term interactions between frontal thrust structures and foreland-basin sedimentation. New results will quantify the effects of enhanced mechanical coupling between the subducting and overriding plate during flat slab subduction, which dictates the thermo-tectonic evolution of orogenesis and topographic growth and decay of mountain belts. 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

Project Summary/Abstract The burden of caring for persons with Alzheimer's disease and related dementias (PwADRD) often falls on unpaid caregivers, usually family members or friends. The mental, emotional, and physical stress associated with such caregiving can lead to adverse health outcomes for the caregivers themselves. Recognizing this, our project aims to develop and evaluate the Robot Interaction Scale of Engagement (RISE) system, a novel tool designed to support these caregivers. By leveraging advanced artificial intelligence, the social robot, Pepper, and the (Resources for Enhancing Alzheimer’s Caregivers Health) REACH Community and REACH VA content, the RISE system aims to provide caregivers with essential information on effective care strategies, self-care and coping, and stress management techniques. The goal is to enhance caregivers’ ability to provide care and improve their well-being. The specific objectives of the project are to: (1) tailor an AI-driven social robot to engage caregivers, (2) evaluate user acceptance of the RISE system and the caregivers' attitudes toward it, and (3) assess the accuracy and quality of the RISE system's interactions with caregivers. A pilot study is proposed to meet these objectives, involving 30 caregivers at the O'Connor Senior Center in Knoxville, an institution with a diverse visitor demographic. Participants will engage in full RISE sessions, interacting with the robot. Based on their needs and preferences, participants will select knowledge modules, consisting of a presentation, question and answer session, and knowledge quiz on behavioral topics such as bathing, wandering, or self-care and coping topics such as asking for health, followed by their choice of stress management activity guided by the robot. The sessions will then be evaluated using both quantitative and qualitative methods to assess user acceptance and the system's accuracy. The findings from this pilot study will contribute valuable insights into the use of AI and robotics in the caregiving context. This knowledge will inform the development of an optimized version of the RISE system and will serve as a solid foundation for a larger, more comprehensive study in the future.

NSF Awards · FY 2024 · 2024-08

The incidence of extreme heat events has increased in frequency and intensity in the last century as global temperatures have risen, driven by anthropogenic greenhouse gas forcing. When extreme heat occurs at the same time as drought, the impacts are exacerbated. These "hot drought" events have complex consequences for communities across North America, including altered water resource availability and fire regimes, as well as the magnitude of the uptake of carbon dioxide by forests. This project will compile new and previously collected temperature reconstruction data from tree cores from across North America into a "North American Temperature Atlas," which will allow for the analysis of relationship of heat and drought at a range of time and spatial scales. The goals of this project are to make new blue intensity measurements on previously collected tree cores from North America, compile existing blue intensity and maximum latewood density tree ring chronologies from North America, and combine the new and existing datasets together to create the “North American Temperature Atlas” (NATA), a gridded reconstruction of warm season surface air temperature. The NATA will be compared to a gridded North American drought atlas and a gridded North American seasonal precipitation atlas to determine the contribution of temperature to past droughts, evaluate the temperature-drought relationship, and place the modern occurrence of drought in the context of the last several centuries. The Broader Impacts are to create a web interface for public access to the NATA, support for graduate students at University of Tennessee, Knoxville, and University of Idaho, development of outreach to water and natural resource managers, creation of K-12 STEM activities for middle school students, tours of tree ring lab for K-12 students, mentoring high school and undergraduate students underrepresented in STEM on projects related to this work. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project is focused on developing the mathematical foundations for information theory and specifically with information-theoretic criteria relevant for tackling heavily contaminated data. Such criteria are widely applied to machine learning tasks arising from real-world applications such as medical imaging, face recognition, and weather forecasting. Their theoretical understanding is lagging, and many fundamental problems remain open. The project will deepen the understanding of information-theoretic criteria, explore their cutting-edge applications, and help advance research in robust machine learning. This project is integrated with educational and outreach activities. This research involves three dedicated components towards information-theoretic criteria based learning; these are theoretical assessments, computational methodologies, and application explorations. Theoretical assessments aim at unveiling the mechanisms of learning in the presence of non-Gaussian noise. Computational methodologies integrate information-theoretic criteria and the involved non-convex optimization with modern machine learning techniques such as distributed learning and deep learning. The application component is dedicated to the exploration of new application domains such as biological spectral imaging. Theoretical and computational foundations of information-theoretic criteria based learning and new applications will enrich and broaden the current understanding of non-Gaussian data analysis. By introducing advanced learning techniques into this area, this project has the potential to realize more powerful and robust machine learning systems that are broadly applicable to a variety of modern applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project will use novel diagnostics and machine learning to better understand laser-induced formation of plasma filaments in air. Laser-induced filamentation occurs when an intense laser beam travels through a medium like air and forms a self-guided, stable channel of plasma known as a filament. This happens because of a dynamical balance between the self-focusing of the laser beam, known as the Kerr effect, and the defocusing of the laser beam by the plasma. The filament plasma is a type of nonequilibrium plasma, where the electron temperature can reach tens of thousands of kelvins, while the ion and neutral temperatures are only a few hundred kelvins. Better understanding of laser-induced filament plasmas can enable many applications, including improving combustion performance with laser ignition sources, creating new light sources for Laser-Induced Breakdown Spectroscopy, remote stand-off detection, air lasers for remote detection, and hypersonic flow control. The research project focuses on understanding the plasma dynamics of laser-induced nonequilibrium filament plasmas by various novel ultrafast diagnostic methods, including Resonantly Ionized Photoemission Thermometry (RIPT) and MUltiplexed Structure Imaging and Capture (MUSIC). Plasma kinetic models with physics informed neural network will be calibrated and validated by the experimental measurements. The goals of the project are to understand and quantify the nonequilibrium states of laser-induced filament plasmas; to broaden student participation in plasma physics research; and to integrate research with teaching to enhance students' learning experience. An associated comprehensive educational plan is poised to significantly impact the education of the next generation workforce in plasma science and engineering for both undergraduate and graduate students. 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

Core collapse supernova explosions are the death throes of massive stars. They are directly or indirectly responsible for the lion’s share of the elements in the Periodic Table – i.e., the building blocks of matter in the Universe, and of life – making them one of the Universe’s most important phenomena. Such an explosive event involves strong gravity, as described by Einstein (i.e., general relativity); the turbulent fluid flow of the star; the production, transport, and interaction with the star of elementary particles known as neutrinos; and magnetic fields. The neutrinos, aided by the turbulent fluid flow and the magnetic fields, power the explosion. Given such complexity, to understand core collapse supernovae we perform simulations on the world’s leading supercomputers, such as Frontier at the Oak Ridge National Laboratory. This project has two focuses: (1) Incorporating magnetic fields into our world-leading core collapse supernova simulations. (2) Predicting the gravitational wave emission from core collapse supernovae based on data from these simulations. Core collapse supernovae are one of three sources, the only one not yet detected, of gravitational wave emission for which NSF’s premier facility, the Laser Interferometer Gravitational Observatory, the centerpiece of NSF’s Gravitational Physics Program, was designed and built. Core collapse supernovae are the deaths of massive stars and are directly or indirectly responsible for the lion’s share of the elements in the Periodic Table, making them one of the Universe’s most important phenomena. They are multi-physics events involving general relativistic gravity, magnetohydrodynamics, and neutrino transport. The lion’s share of core collapse supernovae is powered by neutrinos, with assistance from instabilities of the stellar core fluid, such as turbulent convection, and stellar core magnetic fields. That magnetic fields aid in powering these explosions has been established. Their inclusion in core collapse supernova simulations is necessary. We focus on their inclusion in our next-generation core collapse supernova simulation framework, thornado, which currently includes modules for general relativistic gravity and hydrodynamics, and will soon include a module for general relativistic neutrino transport as well. thornado is based on numerical methods ideally suited to this application. We also focus on the computation of gravitational wave emission based on simulation data from our current production core collapse supernova simulation framework, Chimera. Core collapse supernovae are among the three sources of gravitational waves for which the Laser Interferometer Gravitational Observatory, NSF’s premier facility, was designed and built, but the only source not yet detected. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. 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

Proton-proton fusion is a critical reaction in the sun in which two protons react to form a deuteron under the emission of a positron and a neutrino. This rate is very small so it cannot be measured on Earth, and theoretical calculations are necessary. These calculations provide predictions for the rate that are important for solar simulations that impact, for example, the total amount of radiated neutrinos. Different approaches are currently being used to calculate this rate, each with its own uncertainties. In the past, combining the predictions and uncertainties obtained with different approaches has been challenging as differences between different methods are hard to compare. With this project, the PI and their team will study the application of the Bayesian model averaging technique to combining predictions for the proton-proton fusion rate into a single result with a reliably quantified uncertainty. Proton-proton fusion is the first process to be considered, as the PI and their team will apply this modern statistical technique to other processes of interest in nuclear physics, such as the nuclear structure induced Lamb and hyperfine shifts in atomic deuterium. Various observables in nuclear physics are calculated using different models for nuclear interactions. In particular, two approaches are used in the few-body sector: Pionless effective field theory and chiral effective field theory. These effective field theories are low-energy expansions in two different parameters reflecting the scale separations present in nuclear systems. These expansions are used to obtain results up to a desired order, where the largest omitted order provides an estimate of the theory uncertainty. To obtain a single result for an observable and reliable uncertainty estimates, the team will use Bayesian model averaging to obtain predictions for the pp-fusion rate with Bayesian uncertainties using chiral and pionless effective field theories. The team will then consolidate the Bayesian model averaging predictions, providing an accurate framework for theoretical calculations in nuclear physics. This approach will be extended to other observables that can be calculated using effective field theories, such as nuclear structure induced line shifts in atom deuterium. 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 behavior of neutrinos in stellar supernova explosions and neutron star mergers determine the amount and composition of the matter that form subsequent generations of stars and planets, but it is yet unclear whether these events are able to explain the origin of all of the heaviest elements in the universe. Supernovae and mergers generate all three flavors (i.e., types) of neutrinos, but the ability of neutrinos to change their type is a fundamentally quantum mechanical process that has a strong impact on the synthesis of heavy elements. The complexity of quantum mechanics applied to a large number of entangled neutrinos is vastly too expensive to be included in even supercomputer simulations of astrophysical explosions. So a robust connection between the basic quantum mechanics and efficient modeling approximations needs to be drawn. The PI will perform the first multidimensional simulations of rapid neutrino flavor transformation processes under both levels of approximation. This apples-to-apples comparison will anchor our understanding of our origins in basic quantum mechanics. The PI will mentor undergraduate and graduate students engaged in the research and will host an international summer workshop week to make the state-of-the-art methods developed during the project globally accessible. The project seeks to establish a robust link between exact many-body and approximate mean-field calculations of the dominant neutrino flavor instabilities in core-collapse supernova and neutron star merger environments. These instabilities are inherently anisotropic and inhomogeneous, and the state-of-the-art simulations of mean-field flavor instabilities have no direct basis for comparison with many-body theory. The project involves developing a many-body neutrino simulation framework using tensor network methods that treats neutrinos as localized particles moving in three-dimensional space. The mean-field and many-body limits will be compared by tuning a single parameter (i.e., the bond dimension of the tensor network) that determines the amount of quantum entanglement that can build between neutrinos. This will enable the first comparison of 1D and 2D simulations of the Fast Flavor Instability under exactly identical assumptions in both limits, except for the amount of entanglement allowed. In situations where mean-field and many-body results agree, effective treatments of flavor transformation in dynamical supernova and merger simulations based on local simulations of flavor transformation will be justified. Enhancements to effective flavor transformation treatments will be proposed for situations where results diverge. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. 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

Nanoparticles ranging from one to a hundred nanometers are one of the most important building blocks for advanced functional materials and have demonstrated intriguing optical, electronic, and mechanical properties. This project aims to fabricate ordered nanoparticle-polymer superstructures by co-crystallizing linear crystalline polymers and inorganic nanoparticles. Polymers are soft and flexible, while inorganic nanoparticles are stiff and rigid. They are unlikely to co-exist in a regular crystalline structure. However, recent work shows that they co-crystallize into unusual structures, such as hollow spheres, by controlling the chemistry and crystallization conditions. This project will systematically investigate how to fabricate and control these novel co-assembled structures. It is anticipated that a library of unprecedented nanoparticle-polymer superstructures will be formed, and these structures could find applications in bioimaging and drug delivery. The educational component of the proposal includes (1) mentoring graduate and undergraduate students, exposing them to polymer/nanoparticle synthesis, crystallization, and advanced characterization; (2) developing modules on nanoparticle assembly for graduate courses; (3) involving high school students and teachers, particularly under-represented populations, in the proposed research activities. Crystalline polymers and nanoparticles are dissimilar motifs that can co-assemble to form superstructures. Nanoparticles have slower relaxation, more sluggish diffusion, and stronger interparticle interactions than polymers. Because of the contrasting symmetry demands for dense packing and the distinct structure formation kinetics, the co-crystallization of these dissimilar motifs can produce unconventional hybrid structures, such as hollow spherical crystals with a bilayer shell composed of nanoparticles and crystalline polymer, denoted as nanoparticle crystalsomes. This project aims to investigate the co-crystallization of tailor-designed nanoparticles and polymers, focusing on novel structures arising from the incommensurability of the two structural motifs. Accordingly, the specific aims of the projects are: (1) understanding the co-crystallization process of polymer and nanoparticles; (2) controlling the nanoparticle crystalsome structure via co-crystallization; (3) tuning nanoparticle crystalsomes by varying the shape and dynamics of the constituent motifs. Significant intellectual merits include: (1) A novel scientific approach for co-crystallizing dynamically and topologically asymmetric motifs will be established. (2) The kinetics aspect of the approach will elucidate the complex interplay between the ligand exchange and polymer crystallization kinetics in the nanoparticle crystalsome formation process. 3) The structural aspect of the approach will be established based on a packing mismatch theory. 4) A library of unprecedented nanoparticle-polymer superstructures will be formed. These structures will have controlled characteristics such as size, opening, and curvature, which would otherwise be unattainable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Alcohol use is a prevalent problem among college students. College students report disproportionately high rates of drinking and heavy episodic drinking (HED; i.e., consuming 5+ drinks among men, consuming 4+ drinks among women in 2 hours) relative to non-college enrolled young adults. College students endure unique stressors in relational, academic, and adjustment-related domains. Stress sensitization theory posits that Individuals with trauma histories may be prone to heightened stress responses to life stressors and experiencing stress can provoke and exacerbate PTSD symptoms. Prior research supports the relationship between PTSD symptoms and alcohol use and between stress and alcohol use; however, there is limited understanding into whether these factors synergistically increase the risk for alcohol use among college students. The self-medication model suggests that individuals with PTSD symptoms are more likely to drink alcohol to manage distress. An integrated stress sensitization and self-medication model would suggest that the risk of alcohol use may be highest among those who experience co-occurring PTSD symptoms and stress. The overall objective of the proposed study is to elucidate the proximal associations between stress, PTSD symptoms, and alcohol use among trauma-exposed college students. The study will identify the interacting effects between daily perceived stress and daily PTSD symptoms utilizing an ecological momentary assessment (EMA) design. No prior research has examined the relationship between college students’ perceived stress, PTSD symptoms, and alcohol use and whether these risk factors interact to predict increased drinking. Guided by stress sensitization theory and the self-medication model, the proposed study will advance the understanding and prevention of alcohol misuse. The specific aims of this proposed study are to: (1) evaluate whether PTSD symptoms and perceived stress temporally relate to alcohol use (any, HED, and number of drinks) and (2) evaluate whether PTSD symptoms moderate the temporal association between perceived stress and drinking. The proposed study will examine these factors in 60 college students with trauma histories utilizing an innovative EMA design. The proposed study is significant because it will identify specific risk factors to address in the prevention and intervention of alcohol use among trauma-exposed college students.

NSF Awards · FY 2024 · 2024-08

Strongly correlated quantum materials realize a wide range of novel phases of matter and functional properties that result from the quantum many-body interactions between their constitute electrons and atoms. While these materials have tremendous transformative potential across nearly all science and technology sectors, they are incredibly challenging to model using conventional theoretical approaches. Progress in understanding and predictive capabilities for many of these materials has thus primarily been achieved with powerful numerical methods capable of simulating their complex quantum many-body dynamics. Efforts in this area have focused mainly on cyberinfrastructure for materials with strong electron-electron interactions. However, interactions between electrons and the motion of material atoms (the so-called electron-phonon interaction) also play a crucial role in several families of quantum materials, while the cyberinfrastructure for simulating such systems has remained underdeveloped. This project fills this gap by developing two new packages for performing state-of-the-art quantum Monte Carlo simulations of diverse models with realistic electron-phonon interactions. These packages leverage recent advances in sampling methods to enable simulations of physically realistic models for the first time. These packages are being developed and maintained as part of the SmoQySuite organization (https://github.com/SmoQySuite), prioritizing the creation of flexible and easy-to-use many-body codes while maintaining excellent performance. By providing new and flexible tools for simulating generalized electron-phonon coupled models, this project will enable new research into the role this interaction plays across a wide range of quantum materials. In addition to providing new scientific capabilities, this cyberinfrastructure will reduce the barrier to entry into computational materials research and promote reproducibility in quantum materials research. Finally, the SmoQySuite organization will host workshops and create tutorials for these codes, helping to grow a community of users and developers to disseminate our codes more broadly. Understanding and predicting the properties of quantum materials remains at the forefront of condensed matter physics and materials science research. The strong electron-electron and electron-phonon interactions in these materials can produce a highly correlated electron liquid that often defies conventional single-particle descriptions. Therefore, modeling for these materials frequently relies on nonperturbative numerical methods, which has motivated the development of many open-source codes for simulating correlated models for quantum materials. However, many of these tools cannot treat electron-phonon interactions, or if they can, they are limited to unphysical parameter regimes where the phonon’s energy is comparable to or larger than the nearest-neighbor electronic hopping. This project is closing this critical cyber infrastructure gap by developing SmoQyElPhQMC.jl and SmoQyDCA.jl, two open-source and user-friendly software packages for performing state-of-the-art quantum Monte Carlo (QMC) simulations of electron-phonon coupled systems. These packages leverage recent advances in hybrid Monte Carlo (HMC) sampling methods. This approach significantly reduces autocorrelation times and overall computational cost, enabling simulations of a broad general class of electron-phonon interactions with physically realistic low-energy optical and even acoustic phonon branches. The SmoQyElPhQMC.jl package targets uncorrelated electron-phonon Hamiltonians and combines HMC with several other algorithmic advances to perform scalable nonperturbative sign-problem-free QMC simulations with a computational complexity that is approximately linear in both system size and inverse temperature. Conversely, SmoQyDCA.jl package targets correlated models, where the fermion sign problem is mitigated by self-consistently embedding a cluster in a mean field within the dynamical cluster approximation. In this case, SmoQyDCA.jl will use determinant quantum Monte Carlo with HMC sampling of the phonon degrees of freedom as the cluster solver to achieve efficient scaling while retaining the ability to treat realistic electron-phonon interactions. These packages are being developed as part of the SmoQySuite organization (https://github.com/SmoQySuite). This platform prioritizes creating many-body codes that are flexible and easy-to-use while retaining high performance levels. In addition to providing new scientific capabilities, developing this cyberinfrastructure will help reduce the barrier to entry into computational materials research for new and established researchers. The SmoQySuite organization will host workshops and create tutorials for these codes, helping to grow a community of users and developers to disseminate our codes more broadly. This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials 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 · 2024-08

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professors Alexei Sokolov, Stephen J. Paddison, and Ivan Popov of the University of Tennessee are developing a fundamental molecular-level understanding of the effect of ionic correlations on conductivity of polymerized ionic liquids and organic ionic plastic crystals. Dramatic enhancement in conductivity is observed upon the addition of salts to these systems, but the mechanisms responsible for this increase are not well understood. This project investigates the influence of molecular parameters on ionic correlations and conductivity, with the goal of enhancing charge transport. The team will utilize a unique combination of various spectroscopies complemented with atomistic molecular dynamic simulations to study the structure and dynamics of these ionic systems across a wide range of time and length scales. Their studies will deepen fundamental understanding of collective ion dynamics in complex ionic systems and will be instrumental in the rational design of novel highly conductive electrolytes for various energy storage and conversion technologies. Students and a postdoctoral researcher involved in this project will acquire valuable experience in advanced experimental and computational research, and the PIs will also be engaged in various outreach activities targeted K-12 audiences. The project focuses on unraveling fundamental mechanisms controlling ion transport in doped polymerized ionic liquids and organic ionic plastic crystals. Experimental work will include unique combinations of various spectroscopic techniques including: Broadband Dielectric Spectroscopy, Light Scattering, Rheology, Quasielastic Neutron Scattering, and Nuclear Magnetic Resonance (NMR) spectroscopy over a very broad range in frequency. They will be combined with ab initio and classical atomistic molecular dynamics (MD) simulations and electronic structure calculations. The results of the proposed research will unravel molecular-level mechanisms that lead to an increase in ionic conductivity in doped polymerized ionic liquids and organic ionic plastic crystals, thereby enhancing fundamental understanding of strong correlations in the dynamics of concentrated ionic systems. The proposal aims to provide comprehensive insight into the role of the size, mass, mobility, and electrostatic interactions of ions in conductivity and ionic correlations in complex ionic systems. The findings will facilitate the design of novel electrolytes for various critical electrochemical technologies, including solid-state batteries and long-duration energy storage. The graduate students and a postdoctoral researcher engaged in project will acquire a thorough and fundamental understanding that will provide opportunities for subsequent involvement and/or employment in high technology professions in green and renewable 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 · 2024-08

Ecological communities function as a network of interactions among individual organisms. The most widespread of these interactions is that among plants and mycorrhizal fungi in the soil. In these interactions, mycorrhizal fungi provide water and nutrients for plants, increasing plant growth by up to 50%. However, interactions among plants and mycorrhizal fungi are changing as global change alters the spatial distribution of plants and fungi as well as the times of the year in which they are active. This research will investigate how much plant and mycorrhizal fungi interactions vary over time using current and historical records of mycorrhizal fungal locations. The research will also mimic the effects of global change on mycorrhizal fungal distributions by observing how northern plants grow with fungi from southern locations. Finally, undergraduate students will create predictive models of how forests across the eastern United States may be affected by disrupted interactions among plants and mycorrhizal fungi. This CAREER proposal is highly integrated with an educational program where undergraduate students will receive hands on experience in designing, implementing, analyzing, and modeling research at all stages of the project, preparing a cohort of undergraduate students for the scientific workforce. The most widespread and biogeochemically impactful of symbiosis on Earth is the nutritional and protective symbioses between plants and mycorrhizal fungi. Yet, the stability of plant-mycorrhizal fungal interactions in the Anthropocene is far from certain. If plant and mycorrhizal fungal symbiont partners react divergently to shifting climates, previously beneficial interactions may be decoupled in space and across time creating no-analog biological communities with unknown functions. This research will address the potential for plant-mycorrhizal fungal symbiosis decoupling by examining the spatial variation in mycorrhizal fungal associations across ten foundational tree species ranges at 20 sites across the eastern United States, and short and long-term temporal variation in plant-mycorrhizal fungal associations at each of those sites and at continental scales. Through common gardens and newly developed, inquiry driven, course-based undergraduate research experiences (CURES), undergraduate students will test the outcome of symbiosis when plants are grown with mycorrhizal fungi from the leading, center, or trailing edges of their ranges. The project will then create predictive ecological forecasting models of the biogeography and function of plant-mycorrhizal fungal associations under current and future climate scenarios. Undergraduate training is at the heart of this project, with highly integrative course-based, field-based, and laboratory-based research as well as pedagogical training experiences for a cohort of students. 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

Monitoring the health of living plants holds critical significance across various domains, such as precision agriculture, horticulture, and environmental conservation. Effective plant monitoring aids decision-making in agriculture related to irrigation, fertilization, pest control, and harvesting. In urban settings like parks and gardens, it improves residents’ quality of life as healthy plants improve air quality, provide shade, and contribute to aesthetics and well-being. In forestry, it allows early detection of tree stress or disease, helping prevent large-scale die-offs and promoting forest health. However, existing solutions are bulky and high maintenance and often fail to capture essential health signals like nutrient and water levels. The project’s novelties are the development of zero-maintenance, intelligent, and robust computer systems that use biocompatible sensor arrays implanted in the plant’s xylem to continuously monitor and wirelessly report water and nutrient uptake in real-time, enhancing water management and irrigation practices based on plant needs and environmental conditions. The project's broader significance and importance are demonstrated through its commitment to publicly sharing research materials online via open-source hardware and software libraries, tutorials, talks, publications, and datasets, along with the integration of sustainable computing into curriculum development, mentoring for graduate students, research experiences for undergraduates, and a summer event focused on a wind-based, battery-free coding competition. This project seeks to develop a swarm of ultra-long-lasting and zero-maintenance intelligent devices to monitor the full life cycle of a plant and provide insights into critical biological aspects such as timing and coordination of nutrient uptake and metabolism. The developed system provides real-time, highly synchronized data from which robust calibration learning models can be developed to predict water and nutrient levels to guide the water’s application, fertilizers, and chemicals. This project creates a biocompatible, ion-sensitive sensor array and installation method, develops energy-harvesting techniques for remote data transmission, and builds AI-powered calibration models to enhance sensor accuracy. The project involves designing, implementing, and testing these innovations through both in-lab and in-the-field experiments to improve plant health monitoring and inform practical applications in agriculture and environmental management. 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

A thorough treatment is feasible for the classical linear problems in the numerical approximation of partial differential equations. The continuous problem is well-posed. The numerical schemes are well-posed, parameter-robust, and convergent. It is even possible to prove convergence rates. However, the situation is more precarious for modern, complex systems of equations. Oftentimes, the uniqueness of solutions is not known. Even when there is uniqueness, the theory is far from complete, and so besides (weak) convergence of numerical solutions, little can be said about their behavior. In these scenarios, one must settle for simpler yet still relevant goals. An important goal in this front is that of structure preservation. The study of structure preservation in numerical methods is not new. Geometric numerical integration, many methods for electromagnetism, the finite element exterior calculus, and some novel approaches to hyperbolic systems of conservation laws, have this goal in mind: geometric, algebraic, or differential constraints must be preserved. This project does not focus on the problems mentioned above. Instead, it studies structure preservation in some evolution problems that have, possibly degenerate, diffusive behavior. This class of problems remains a largely unexplored topic when it comes to numerical discretizations. Bridging this gap will enhance modeling and prediction capabilities since diffusive models can be found in every aspect of scientific inquiry. This project is focused on a class of diffusive problems in which stability of the solution cannot be obtained by standard energy arguments, in other words, by testing the equation with the solution to assert that certain space-time norms are under control. Norms are always convex. Structure preservation may then be a generalization of the approach given above. Instead of norms being under control, a (family of) convex functional(s) evaluated at the solution behave predictably during the evolution. The project aims to develop numerical schemes that mimic this in the discrete setting. While this is a largely unexplored topic, at the same time, many of the problems under consideration can be used to describe a wide range of phenomena. In particular, the project will develop new numerical schemes for an emerging theory of non-equilibrium thermodynamics, active scalar equations, and a class of problems in hyperbolic geometry. These models have a very rich intrinsic structure and a wide range of applications, and the developments of this project will serve as a stepping stone to bring these tools to the numerical treatment of more general problems. The students involved in the project will be trained in exciting, mathematically and computationally challenging, and practically relevant areas of 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 · 2024-08

Although there is a push to integrate artificial intelligence (AI) in K-12 education, the novelty of AI means that little is known about what schools, teachers, students, and parents know, need and expect regarding AI in classrooms. Lack of formal AI education and professional development training poses a significant challenge for educators, parents, and students across the nation. The lack of access to AI knowledge and training is especially significant in rural high-needs communities where schools are under-resourced. This year-long partnership development project has three key goals : (1) strengthen and expand existing research-practice partnerships (RPPs) with East Tennessee teachers and school leaders, (2) develop new RPPs with parents and students enrolled in East Tennessee middle and high schools, and (3) co-construct a shared vision for AI that aligns with the needs and assets of the partner community. The project’s work will be guided by an RPP leadership team comprising three educational researchers, two secondary school teachers, two school leaders, four parents, and six middle and high school students. This leadership team is critical for accurately capturing the community's needs and assets and facilitating the co-construction of a shared vision for AI education in East Tennessee. This project is supported by the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.  RPPs seek to cultivate long-term collaborations that are designed to bring together individuals of varying expertise to co-construct knowledge to transform and improve education. This partnership development project will focus on the following: (1) establishing trust and teamwork through community learning activities, (2) developing an agreements document that unites the needs and interests of all partners (teachers, school leaders, parents, students, and researchers), and 3) building a model for change that leads to research and development efforts. Community building will involve a Value Mapping activity with the RPP leadership team to make values, experiences, and perspectives about AI more explicit. Results from the Value Mapping activity, along with the other structured and semi-structured activities such as interviews, focus groups, and surveys, will inform the construction of the shared vision for AI education in East Tennessee. To ensure alignment between project activities and project goals, an external evaluator, using a culturally responsive and comprehensive evaluation plan, will provide formative and summative feedback throughout the project. All involved partners will receive a summary of the deidentified data collected from the project. Final decisions about the shared vision statement will be made jointly by the RPP leadership team based on information gathered from (1) interview transcripts, (2) focus group notes, observations, and transcripts, (3) survey responses, (4) summary documents from community building activities and meetings with the RPP leadership team, and (5) feedback from the external evaluator. At the conclusion of this project, the deliverables encompass a collectively crafted vision of AI in Education in East Tennessee, alongside a model offering a blueprint for STEM education initiatives centered on community collaboration and research-practice partnerships. 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 · 2024-07

Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. This grant proposal represents a first competitive renewal (Cycle 2) of a highly productive, NINR-funded research program investigating a health policy intervention for children and adolescents with serious illness. The Cycle 1 research question was whether pediatric concurrent care (simultaneous hospice and medical care) affected pediatric and family outcomes. A significant increase in fragmented care transitions and a high risk of adverse outcomes was identified, along with a cohort of rural children and adolescents significantly affected by this care environment. In response, Medicaid programs in several states ushered in transitional care regulations (TCR) for pediatric concurrent care that mandated collaborative team efforts from nurses, physicians, pharmacists, child life specialists, and other health care professionals to support and coordinate pediatric end-of-life care across settings during concurrent care. In Cycle 2, the proposed policy effectiveness project focuses on advancing pediatric health policy by examining this policy modification within a rural cohort. The project’s specific aims are to: (1) estimate the effect of the TCR intervention on rural children’s end-of-life health (i.e., functioning) and health care (i.e., resource use, spending); (2) identify a subgroup of at-risk rural children using innovative machine learning methods; (3) examine potential mechanisms and pathways that may inform further policy improvement strategies important to rural children and families generally and the at-risk subgroup in particular. These aims will be achieved by conducting a multi-level natural experiment with a difference-in-difference analysis, supervised and unsupervised machine learning, and mediation and moderation analysis. The project team has the expertise in pediatric concurrent care (Lindley), data science (Keim-Malpass, Cao) and statistical analysis (Cozad, Svynarenko). Practice partner includes the National Network of Pediatric Palliative Care Coalitions. The project is innovative because it advances the understudied area of policy-focused effectiveness science. The project is significant and will have an impact because it focuses on a major policy modification and because it will generate knowledge to inform rural health care for children.

NIH Research Projects · FY 2026 · 2024-07

PROJECT ABSTRACT The interplay between metabolic and inflammatory processes is critical to maintaining overall health where a delicate balance exists between pathogen clearance and limiting host tissue damage. Severe bacterial infections upset this equilibrium and lead to a critical condition known as septicemia, where metabolic and immunologic dysregulation rapidly overwhelms the body’s defenses and poses a life-threatening challenge. Neutrophils are crucial in establishing innate immunity and undergo an enigmatic cell death process termed neutrophil extracellular trap (NET) formation (NETosis) that elicits antimicrobial activities but also induces collateral damage to host tissues during sepsis. We aim to investigate the intricate mechanisms underlying NETosis and how shifts in metabolic homeostasis during septicemia antagonize NET formation thereby causing aberrant inflammation. We previously demonstrated that mitochondrial superoxide production promotes downstream NETosis, and preliminary data indicate a critical role for lactate in this cascade. My research program aims to determine (i) how excess lactate during bacterial septicemia antagonizes NETosis and the impact this has on disease outcome. This study reframes mitochondria as ‘sensory organelles’ within neutrophils that respond to perturbations in the metabolic environment and dictate downstream inflammatory responses rather than acting as the ‘powerhouse’ of the cell. These metabolic shifts during sepsis may play a direct role in modulating innate immunity and (ii) we aim to understand how hyperglycemia and increased lactate availability during septicemia influence lactylation of histones and downstream gene expression in neutrophils. These studies are aided by our discovery of a biomarker that transiently accumulates on the surface of neutrophils that accurately predicts neutrophils that will undergo NETosis 2-3 hr later, which will allow us to decipher transcriptional changes related to NETosis and contrast transcriptomes across different biologically and clinically relevant bacterial inducers of sepsis in various tissues during septicemia. While much of this proposal focuses on the impacts of glucose and lactate and inflammation, the complex metabolic environment during sepsis likely has diverse effects on innate immunity in differing tissues. Therefore, (iii) we will employ chimeric immune cell editing (CHIME) using small CRISPR-based libraries to target 40-50 metabolic genes so that each neutrophil in a mouse has a single gene disrupted. This technology will allow us to decipher the metabolic dependencies driving neutrophil recruitment and inflammatory processes across differing tissues or in response to common bacterial pathogens associated with sepsis. These studies are significant as they begin to unravel the mechanistic links between metabolic and transcriptional dysregulation during sepsis and the impact this has in skewing innate immunity of neutrophils to combat the invading pathogen or in drive tissue damaging inflammation. In addition, the fundamental insights resolved from this proposal will establish a scientific and technological foundation to expand into novel metabolic pathways, other innate immune cells, and/or alternative inflammatory processes and diseases.

NSF Awards · FY 2024 · 2024-07

The soybean seed delivers proteins, oils, and carbohydrates for a wide variety of agricultural and industrial uses. However, the protein and amino acid composition that has evolved to be most adaptive to the soybean itself is less than ideal for the nutrition of humans and their livestock. For example, most soybean seed storage proteins are poor in the essential, sulfur-containing amino acids, methionine and cysteine. Attempts to modify the amino acid and protein content of the soybean seed by means of agricultural biotechnology have encountered a phenomenon termed 'rebalancing', where the plant compensates for the forced reduction of one type of seed storage protein by increasing expression of other proteins, such that the overall protein content and amino acid composition is maintained within tight margins. Rebalancing has been observed in many crop species including pulses, oilseeds, and cereals. This team of four investigators seeks to better understand how the plant genome guides the protein and amino acid composition of its seeds. To this end the gene expression will be measured over the time course of soybean seed development with a panel of complementary techniques. The data will be analyzed using a mathematical representation and machine learning techniques. The success of this project may allow molecular biologists and breeders to better predict which genetic changes can deliver the desired effects on the nutritional quality of seed crops. The regulatory mechanism that underlies the rebalancing of the seed proteome is not well understood. The project is guided by the hypothesis that rebalancing is due to homeostatic constraints at multiple levels, including transcription and amino acid supply, but experimental data as well as theoretical considerations suggest that translational control deserves special attention. Using CRISPR gene editing, the Stacey lab recently engineered a new, genetically robust model system for rebalancing, with modifications in both a major soybean seed storage protein (beta-conglycinin) and amino acid synthesis (methionine). The project will collect multi-omic datasets over the developmental time course of soybean seed development. In Aim 1, data from the transcriptome, translatome, proteome, and metabolome will be compared between wild type and rebalanced seeds. Data will be integrated by mathematical modeling to identify potential drivers of rebalancing and their developmental timing. Aim 2 implements experiments and machine learning to pinpoint potential, nonintuitive mechanisms of rebalancing. This includes a novel test of the intellectually intriguing but controversial 'specialized ribosome' hypothesis, which states that translational control is exerted by biochemically distinct types of ribosomes. The broader impacts of this project include training of researchers in multi-omic data integration, including undergraduates participating in a program for deaf and hard-of-hearing students, a population underrepresented in science. The US is one of the two largest producers of soybeans in the world, and a deeper mechanistic understanding of seed storage protein production may increase its nutritional quality, sustainability of production, and diversity of uses. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The Monitoring Infrastructure for Network and Computing Environment Research (MINCER) project provides simple ways to monitor and collect data that helps scientists understand how computer and network systems work. This information allows them to see the important details about how well these systems perform, especially as technology becomes more advanced. With this clear and precise information, researchers can make smart decisions to improve computer speed, data transfer, security, and energy use. This approach helps scientists to study new hardware designs and find ways to improve their functionality, leading to better research and education in computer and network systems. MINCER aims to provide comprehensive insights into distributed heterogeneous computing environments, advancing research and education in platform reliability, efficiency, and security, through three main thrusts: (1) Enhanced performance and power monitoring capabilities for AI architectures to support research aimed at improving AI system performance and utilization, thus enabling informed decisions on optimization and system configurations. (2) Unique network-related measurements to support research in network anomaly detection, resilience, and energy efficiency. (3) Integration of the MINCER monitoring infrastructure with renowned research platforms like Chameleon and the Open Science Grid to enhance the understanding of cloud and edge computing dynamics, as well as the relationship between data transfer and computation in large-scale distributed computing. The MINCER initiative offers open-source software to bolster the research community’s capabilities in computer and network systems, providing an opportunity for broader community engagement in nationally significant systems-related research. Integration of the MINCER monitoring infrastructure with research platforms like ACCESS, Chameleon and the Open Science Grid will significantly enhance the utility of these platforms for advancing systems research and associated educational programs for a growing community of users. Through collaborations with minority-serving institutions and by offering mentoring and hands-on activities, the project will introduce researchers and their students to the capabilities of MINCER, thereby broadening participation and fostering a more inclusive research environment. The MINCER project will be hosted on the following website: https://icl.utk.edu/research/mincer. Here, users will be able to download the MINCER monitoring infrastructure, as well as access documentation, code examples, and tutorial and workshop materials to help them understand and use MINCER. This website will be maintained and updated throughout the duration of the project and will remain available after the completion of this project. 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.